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Causal Story Behind Hotel Booking Cancellations.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb @@ -33,10 +33,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:07.523557Z", - "iopub.status.busy": "2024-10-19T23:27:07.523375Z", - "iopub.status.idle": "2024-10-19T23:27:09.592500Z", - "shell.execute_reply": "2024-10-19T23:27:09.591865Z" + "iopub.execute_input": "2024-10-22T00:52:45.353096Z", + "iopub.status.busy": "2024-10-22T00:52:45.352896Z", + "iopub.status.idle": "2024-10-22T00:52:47.516693Z", + "shell.execute_reply": "2024-10-22T00:52:47.515970Z" } }, "outputs": [], @@ -62,10 +62,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:09.594928Z", - "iopub.status.busy": "2024-10-19T23:27:09.594640Z", - "iopub.status.idle": "2024-10-19T23:27:10.160715Z", - "shell.execute_reply": "2024-10-19T23:27:10.160104Z" + "iopub.execute_input": "2024-10-22T00:52:47.519151Z", + "iopub.status.busy": "2024-10-22T00:52:47.518803Z", + "iopub.status.idle": "2024-10-22T00:52:48.368059Z", + "shell.execute_reply": "2024-10-22T00:52:48.367396Z" } }, "outputs": [ @@ -300,10 +300,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.189529Z", - "iopub.status.busy": "2024-10-19T23:27:10.188960Z", - "iopub.status.idle": "2024-10-19T23:27:10.193265Z", - "shell.execute_reply": "2024-10-19T23:27:10.192771Z" + "iopub.execute_input": "2024-10-22T00:52:48.400522Z", + "iopub.status.busy": "2024-10-22T00:52:48.399866Z", + "iopub.status.idle": "2024-10-22T00:52:48.406918Z", + "shell.execute_reply": "2024-10-22T00:52:48.406331Z" } }, "outputs": [ @@ -350,10 +350,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.195226Z", - "iopub.status.busy": "2024-10-19T23:27:10.194859Z", - "iopub.status.idle": "2024-10-19T23:27:10.217189Z", - "shell.execute_reply": "2024-10-19T23:27:10.216540Z" + "iopub.execute_input": "2024-10-22T00:52:48.408795Z", + "iopub.status.busy": "2024-10-22T00:52:48.408512Z", + "iopub.status.idle": "2024-10-22T00:52:48.431474Z", + "shell.execute_reply": "2024-10-22T00:52:48.430850Z" } }, "outputs": [ @@ -404,10 +404,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.219315Z", - "iopub.status.busy": "2024-10-19T23:27:10.218957Z", - "iopub.status.idle": "2024-10-19T23:27:10.348170Z", - "shell.execute_reply": "2024-10-19T23:27:10.347580Z" + "iopub.execute_input": "2024-10-22T00:52:48.433522Z", + "iopub.status.busy": "2024-10-22T00:52:48.433202Z", + "iopub.status.idle": "2024-10-22T00:52:48.565680Z", + "shell.execute_reply": "2024-10-22T00:52:48.565089Z" } }, "outputs": [], @@ -423,10 +423,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.350380Z", - "iopub.status.busy": "2024-10-19T23:27:10.350002Z", - "iopub.status.idle": "2024-10-19T23:27:10.374056Z", - "shell.execute_reply": "2024-10-19T23:27:10.373469Z" + "iopub.execute_input": "2024-10-22T00:52:48.567838Z", + "iopub.status.busy": "2024-10-22T00:52:48.567630Z", + "iopub.status.idle": "2024-10-22T00:52:48.591195Z", + "shell.execute_reply": "2024-10-22T00:52:48.590631Z" } }, "outputs": [], @@ -441,10 +441,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.376199Z", - "iopub.status.busy": "2024-10-19T23:27:10.375897Z", - "iopub.status.idle": "2024-10-19T23:27:10.526408Z", - "shell.execute_reply": "2024-10-19T23:27:10.525785Z" + "iopub.execute_input": "2024-10-22T00:52:48.593298Z", + "iopub.status.busy": "2024-10-22T00:52:48.593104Z", + "iopub.status.idle": "2024-10-22T00:52:48.746974Z", + "shell.execute_reply": "2024-10-22T00:52:48.746333Z" } }, "outputs": [ @@ -781,10 +781,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.528360Z", - "iopub.status.busy": "2024-10-19T23:27:10.528068Z", - "iopub.status.idle": "2024-10-19T23:27:10.615627Z", - "shell.execute_reply": "2024-10-19T23:27:10.614980Z" + "iopub.execute_input": "2024-10-22T00:52:48.748997Z", + "iopub.status.busy": "2024-10-22T00:52:48.748807Z", + "iopub.status.idle": "2024-10-22T00:52:48.839426Z", + "shell.execute_reply": "2024-10-22T00:52:48.838773Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.617590Z", - "iopub.status.busy": "2024-10-19T23:27:10.617258Z", - "iopub.status.idle": "2024-10-19T23:27:10.624873Z", - "shell.execute_reply": "2024-10-19T23:27:10.624248Z" + "iopub.execute_input": "2024-10-22T00:52:48.841533Z", + "iopub.status.busy": "2024-10-22T00:52:48.841044Z", + "iopub.status.idle": "2024-10-22T00:52:48.849218Z", + "shell.execute_reply": "2024-10-22T00:52:48.848564Z" } }, "outputs": [], @@ -991,21 +991,21 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.626987Z", - "iopub.status.busy": "2024-10-19T23:27:10.626625Z", - "iopub.status.idle": "2024-10-19T23:27:32.037779Z", - "shell.execute_reply": "2024-10-19T23:27:32.037239Z" + "iopub.execute_input": "2024-10-22T00:52:48.851537Z", + "iopub.status.busy": "2024-10-22T00:52:48.851100Z", + "iopub.status.idle": "2024-10-22T00:53:11.499971Z", + "shell.execute_reply": "2024-10-22T00:53:11.499385Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/latex": [ - "$\\displaystyle 588.2833$" + "$\\displaystyle 588.7483$" ], "text/plain": [ - "588.2833" + "588.7483" ] }, "execution_count": 10, @@ -1035,21 +1035,21 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:32.039632Z", - "iopub.status.busy": "2024-10-19T23:27:32.039453Z", - "iopub.status.idle": "2024-10-19T23:28:47.277321Z", - "shell.execute_reply": "2024-10-19T23:28:47.276771Z" + "iopub.execute_input": "2024-10-22T00:53:11.502130Z", + "iopub.status.busy": "2024-10-22T00:53:11.501710Z", + "iopub.status.idle": "2024-10-22T00:54:30.427249Z", + "shell.execute_reply": "2024-10-22T00:54:30.426549Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": "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", "text/latex": [ - "$\\displaystyle 572.9239$" + "$\\displaystyle 573.138$" ], "text/plain": [ - "572.9239" + "573.138" ] }, "execution_count": 11, @@ -1080,21 +1080,21 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:28:47.279357Z", - "iopub.status.busy": "2024-10-19T23:28:47.278972Z", - "iopub.status.idle": "2024-10-19T23:29:12.342320Z", - "shell.execute_reply": "2024-10-19T23:29:12.341657Z" + "iopub.execute_input": "2024-10-22T00:54:30.429682Z", + "iopub.status.busy": "2024-10-22T00:54:30.429269Z", + "iopub.status.idle": "2024-10-22T00:54:57.498056Z", + "shell.execute_reply": "2024-10-22T00:54:57.497490Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": "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", "text/latex": [ - "$\\displaystyle 666.0656$" + "$\\displaystyle 665.8655$" ], "text/plain": [ - "666.0656" + "665.8655" ] }, "execution_count": 12, @@ -1152,10 +1152,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:12.344525Z", - "iopub.status.busy": "2024-10-19T23:29:12.344091Z", - "iopub.status.idle": "2024-10-19T23:29:12.350224Z", - "shell.execute_reply": "2024-10-19T23:29:12.349762Z" + "iopub.execute_input": "2024-10-22T00:54:57.500270Z", + "iopub.status.busy": "2024-10-22T00:54:57.499904Z", + "iopub.status.idle": "2024-10-22T00:54:57.506300Z", + "shell.execute_reply": "2024-10-22T00:54:57.505842Z" } }, "outputs": [], @@ -1214,10 +1214,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:12.352081Z", - "iopub.status.busy": "2024-10-19T23:29:12.351692Z", - "iopub.status.idle": "2024-10-19T23:29:12.688190Z", - "shell.execute_reply": "2024-10-19T23:29:12.687560Z" + "iopub.execute_input": "2024-10-22T00:54:57.508010Z", + "iopub.status.busy": "2024-10-22T00:54:57.507822Z", + "iopub.status.idle": "2024-10-22T00:54:57.828704Z", + "shell.execute_reply": "2024-10-22T00:54:57.828057Z" } }, "outputs": [ @@ -1275,10 +1275,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:12.691268Z", - "iopub.status.busy": "2024-10-19T23:29:12.690319Z", - "iopub.status.idle": "2024-10-19T23:29:14.837989Z", - "shell.execute_reply": "2024-10-19T23:29:14.837385Z" + "iopub.execute_input": "2024-10-22T00:54:57.831329Z", + "iopub.status.busy": "2024-10-22T00:54:57.831090Z", + "iopub.status.idle": "2024-10-22T00:55:00.008153Z", + "shell.execute_reply": "2024-10-22T00:55:00.007514Z" } }, "outputs": [ @@ -1292,17 +1292,17 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "──────────────────────────(E[is_canceled|lead_time,guests,is_repeated_guest,to ↪\n", + "──────────────────────────(E[is_canceled|total_of_special_requests,total_stay, ↪\n", "d[different_room_assigned] ↪\n", "\n", "↪ ↪\n", - "↪ tal_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_change ↪\n", + "↪ hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_par ↪\n", "↪ ↪\n", "\n", "↪ \n", - "↪ s,required_car_parking_spaces])\n", + "↪ king_spaces,is_repeated_guest])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces,U) = P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces)\n", + "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest,U) = P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -1333,10 +1333,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:14.839920Z", - "iopub.status.busy": "2024-10-19T23:29:14.839691Z", - "iopub.status.idle": "2024-10-19T23:29:15.387693Z", - "shell.execute_reply": "2024-10-19T23:29:15.386861Z" + "iopub.execute_input": "2024-10-22T00:55:00.010304Z", + "iopub.status.busy": "2024-10-22T00:55:00.009985Z", + "iopub.status.idle": "2024-10-22T00:55:00.576470Z", + "shell.execute_reply": "2024-10-22T00:55:00.575873Z" } }, "outputs": [ @@ -1353,20 +1353,20 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "──────────────────────────(E[is_canceled|lead_time,guests,is_repeated_guest,to ↪\n", + "──────────────────────────(E[is_canceled|total_of_special_requests,total_stay, ↪\n", "d[different_room_assigned] ↪\n", "\n", "↪ ↪\n", - "↪ tal_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_change ↪\n", + "↪ hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_par ↪\n", "↪ ↪\n", "\n", "↪ \n", - "↪ s,required_car_parking_spaces])\n", + "↪ king_spaces,is_repeated_guest])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces,U) = P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces)\n", + "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest,U) = P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest)\n", "\n", "## Realized estimand\n", - "b: is_canceled~different_room_assigned+lead_time+guests+is_repeated_guest+total_of_special_requests+total_stay+hotel+days_in_waiting_list+booking_changes+required_car_parking_spaces\n", + "b: is_canceled~different_room_assigned+total_of_special_requests+total_stay+hotel+guests+days_in_waiting_list+lead_time+booking_changes+required_car_parking_spaces+is_repeated_guest\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -1435,10 +1435,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:15.390158Z", - "iopub.status.busy": "2024-10-19T23:29:15.389933Z", - "iopub.status.idle": "2024-10-19T23:29:25.319577Z", - "shell.execute_reply": "2024-10-19T23:29:25.318958Z" + "iopub.execute_input": "2024-10-22T00:55:00.580828Z", + "iopub.status.busy": "2024-10-22T00:55:00.580094Z", + "iopub.status.idle": "2024-10-22T00:55:11.018269Z", + "shell.execute_reply": "2024-10-22T00:55:11.017622Z" } }, "outputs": [ @@ -1473,10 +1473,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:25.321804Z", - "iopub.status.busy": "2024-10-19T23:29:25.321328Z", - "iopub.status.idle": "2024-10-19T23:29:35.461617Z", - "shell.execute_reply": "2024-10-19T23:29:35.461040Z" + "iopub.execute_input": "2024-10-22T00:55:11.020519Z", + "iopub.status.busy": "2024-10-22T00:55:11.020092Z", + "iopub.status.idle": "2024-10-22T00:55:21.553769Z", + "shell.execute_reply": "2024-10-22T00:55:21.553128Z" } }, "outputs": [ @@ -1486,7 +1486,7 @@ "text": [ "Refute: Use a Placebo Treatment\n", "Estimated effect:-0.2622285649999514\n", - "New effect:0.05464854856195345\n", + "New effect:0.05497954255454352\n", "p value:0.0\n", "\n" ] @@ -1511,10 +1511,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:35.463774Z", - "iopub.status.busy": "2024-10-19T23:29:35.463261Z", - "iopub.status.idle": "2024-10-19T23:29:44.584141Z", - "shell.execute_reply": "2024-10-19T23:29:44.583542Z" + "iopub.execute_input": "2024-10-22T00:55:21.555659Z", + "iopub.status.busy": "2024-10-22T00:55:21.555466Z", + "iopub.status.idle": "2024-10-22T00:55:31.028906Z", + "shell.execute_reply": "2024-10-22T00:55:31.028247Z" } }, "outputs": [ @@ -1524,8 +1524,8 @@ "text": [ "Refute: Use a subset of data\n", "Estimated effect:-0.2622285649999514\n", - "New effect:-0.2623624531466946\n", - "p value:0.92\n", + "New effect:-0.2622036470791978\n", + "p value:0.8\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb b/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb index 6e33dc393..28465d01a 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/counterfactual_fairness_dowhy.ipynb @@ -6,10 +6,10 @@ "id": "ef2e40dc", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:48.069838Z", - "iopub.status.busy": "2024-10-19T23:29:48.069634Z", - "iopub.status.idle": "2024-10-19T23:29:48.072998Z", - "shell.execute_reply": "2024-10-19T23:29:48.072364Z" + "iopub.execute_input": "2024-10-22T00:55:34.510070Z", + "iopub.status.busy": "2024-10-22T00:55:34.509890Z", + "iopub.status.idle": "2024-10-22T00:55:34.513099Z", + "shell.execute_reply": "2024-10-22T00:55:34.512627Z" } }, "outputs": [], @@ -63,10 +63,10 @@ "id": "46026b9b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:48.075006Z", - "iopub.status.busy": "2024-10-19T23:29:48.074711Z", - "iopub.status.idle": "2024-10-19T23:29:49.591653Z", - "shell.execute_reply": "2024-10-19T23:29:49.591027Z" + "iopub.execute_input": "2024-10-22T00:55:34.515007Z", + "iopub.status.busy": "2024-10-22T00:55:34.514836Z", + "iopub.status.idle": "2024-10-22T00:55:36.185568Z", + "shell.execute_reply": "2024-10-22T00:55:36.184877Z" } }, "outputs": [], @@ -296,10 +296,10 @@ "id": "37212ae1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:49.594126Z", - "iopub.status.busy": "2024-10-19T23:29:49.593659Z", - "iopub.status.idle": "2024-10-19T23:29:49.657237Z", - "shell.execute_reply": "2024-10-19T23:29:49.656595Z" + "iopub.execute_input": "2024-10-22T00:55:36.188184Z", + "iopub.status.busy": "2024-10-22T00:55:36.187860Z", + "iopub.status.idle": "2024-10-22T00:55:36.239323Z", + "shell.execute_reply": "2024-10-22T00:55:36.238657Z" } }, "outputs": [ @@ -334,43 +334,43 @@ " \n", " \n", " 0\n", - " 0.0\n", - " 0.0\n", - " 3.0\n", - " 38.0\n", - " -0.45\n", - " \n", - " \n", - " 1\n", " 1.0\n", " 1.0\n", - " 2.4\n", + " 2.6\n", " 25.5\n", - " -0.80\n", + " 0.21\n", + " \n", + " \n", + " 1\n", + " 0.0\n", + " 0.0\n", + " 3.5\n", + " 38.0\n", + " 0.42\n", " \n", " \n", " 2\n", " 0.0\n", " 0.0\n", - " 3.4\n", - " 41.0\n", - " 0.83\n", + " 3.1\n", + " 34.0\n", + " 0.20\n", " \n", " \n", " 3\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 3.1\n", - " 38.0\n", - " 0.53\n", + " 26.0\n", + " 0.10\n", " \n", " \n", " 4\n", " 0.0\n", " 1.0\n", - " 3.7\n", - " 39.0\n", - " 0.48\n", + " 3.4\n", + " 38.0\n", + " 0.68\n", " \n", " \n", "\n", @@ -378,11 +378,11 @@ ], "text/plain": [ " Race Gender GPA LSAT avg_grade\n", - "0 0.0 0.0 3.0 38.0 -0.45\n", - "1 1.0 1.0 2.4 25.5 -0.80\n", - "2 0.0 0.0 3.4 41.0 0.83\n", - "3 0.0 0.0 3.1 38.0 0.53\n", - "4 0.0 1.0 3.7 39.0 0.48" + "0 1.0 1.0 2.6 25.5 0.21\n", + "1 0.0 0.0 3.5 38.0 0.42\n", + "2 0.0 0.0 3.1 34.0 0.20\n", + "3 0.0 1.0 3.1 26.0 0.10\n", + "4 0.0 1.0 3.4 38.0 0.68" ] }, "execution_count": 3, @@ -466,10 +466,10 @@ "id": "2280c2df", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:49.659189Z", - "iopub.status.busy": "2024-10-19T23:29:49.658903Z", - "iopub.status.idle": "2024-10-19T23:29:49.661986Z", - "shell.execute_reply": "2024-10-19T23:29:49.661504Z" + "iopub.execute_input": "2024-10-22T00:55:36.241573Z", + "iopub.status.busy": "2024-10-22T00:55:36.241192Z", + "iopub.status.idle": "2024-10-22T00:55:36.244561Z", + "shell.execute_reply": "2024-10-22T00:55:36.244055Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "d70d0187", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:49.663768Z", - "iopub.status.busy": "2024-10-19T23:29:49.663466Z", - "iopub.status.idle": "2024-10-19T23:29:49.666213Z", - "shell.execute_reply": "2024-10-19T23:29:49.665744Z" + "iopub.execute_input": "2024-10-22T00:55:36.246471Z", + "iopub.status.busy": "2024-10-22T00:55:36.246100Z", + "iopub.status.idle": "2024-10-22T00:55:36.249003Z", + "shell.execute_reply": "2024-10-22T00:55:36.248528Z" } }, "outputs": [], @@ -536,10 +536,10 @@ "id": "6fdaa7be", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:49.668065Z", - "iopub.status.busy": "2024-10-19T23:29:49.667637Z", - "iopub.status.idle": "2024-10-19T23:30:44.485071Z", - "shell.execute_reply": "2024-10-19T23:30:44.484505Z" + "iopub.execute_input": "2024-10-22T00:55:36.250890Z", + "iopub.status.busy": "2024-10-22T00:55:36.250475Z", + "iopub.status.idle": "2024-10-22T00:56:32.454539Z", + "shell.execute_reply": "2024-10-22T00:56:32.453294Z" } }, "outputs": [ @@ -596,7 +596,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 297.80it/s]" + "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 240.47it/s]" ] }, { @@ -608,12 +608,12 @@ }, { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle -0.524687380683375$" + "$\\displaystyle -0.517771471371438$" ], "text/plain": [ - "-0.524687380683375" + "-0.517771471371438" ] }, "execution_count": 6, @@ -651,10 +651,10 @@ "id": "79c2a233", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:30:44.488032Z", - "iopub.status.busy": "2024-10-19T23:30:44.487555Z", - "iopub.status.idle": "2024-10-19T23:30:44.498981Z", - "shell.execute_reply": "2024-10-19T23:30:44.497982Z" + "iopub.execute_input": "2024-10-22T00:56:32.457461Z", + "iopub.status.busy": "2024-10-22T00:56:32.457195Z", + "iopub.status.idle": "2024-10-22T00:56:32.468560Z", + "shell.execute_reply": "2024-10-22T00:56:32.467974Z" } }, "outputs": [ @@ -690,48 +690,48 @@ " \n", " \n", " 0\n", - " 0.0\n", - " 0.0\n", - " 3.0\n", - " 38.0\n", - " -0.45\n", - " 0.133481\n", - " \n", - " \n", - " 1\n", " 1.0\n", " 1.0\n", - " 2.4\n", + " 2.6\n", " 25.5\n", - " -0.80\n", - " -0.571618\n", + " 0.21\n", + " -0.509516\n", + " \n", + " \n", + " 1\n", + " 0.0\n", + " 0.0\n", + " 3.5\n", + " 38.0\n", + " 0.42\n", + " 0.251650\n", " \n", " \n", " 2\n", " 0.0\n", " 0.0\n", - " 3.4\n", - " 41.0\n", - " 0.83\n", - " 0.358154\n", + " 3.1\n", + " 34.0\n", + " 0.20\n", + " -0.021055\n", " \n", " \n", " 3\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 3.1\n", - " 38.0\n", - " 0.53\n", - " 0.155141\n", + " 26.0\n", + " 0.10\n", + " -0.358381\n", " \n", " \n", " 4\n", " 0.0\n", " 1.0\n", - " 3.7\n", - " 39.0\n", - " 0.48\n", - " 0.331112\n", + " 3.4\n", + " 38.0\n", + " 0.68\n", + " 0.225640\n", " \n", " \n", "\n", @@ -739,11 +739,11 @@ ], "text/plain": [ " Race Gender GPA LSAT avg_grade preds\n", - "0 0.0 0.0 3.0 38.0 -0.45 0.133481\n", - "1 1.0 1.0 2.4 25.5 -0.80 -0.571618\n", - "2 0.0 0.0 3.4 41.0 0.83 0.358154\n", - "3 0.0 0.0 3.1 38.0 0.53 0.155141\n", - "4 0.0 1.0 3.7 39.0 0.48 0.331112" + "0 1.0 1.0 2.6 25.5 0.21 -0.509516\n", + "1 0.0 0.0 3.5 38.0 0.42 0.251650\n", + "2 0.0 0.0 3.1 34.0 0.20 -0.021055\n", + "3 0.0 1.0 3.1 26.0 0.10 -0.358381\n", + "4 0.0 1.0 3.4 38.0 0.68 0.225640" ] }, "execution_count": 7, @@ -769,10 +769,10 @@ "id": "56d88825", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:30:44.501072Z", - "iopub.status.busy": "2024-10-19T23:30:44.500870Z", - "iopub.status.idle": "2024-10-19T23:30:44.511295Z", - "shell.execute_reply": "2024-10-19T23:30:44.510750Z" + "iopub.execute_input": "2024-10-22T00:56:32.470822Z", + "iopub.status.busy": "2024-10-22T00:56:32.470586Z", + "iopub.status.idle": "2024-10-22T00:56:32.480722Z", + "shell.execute_reply": "2024-10-22T00:56:32.480197Z" } }, "outputs": [ @@ -808,60 +808,60 @@ " \n", " \n", " 0\n", - " 1.0\n", " 0.0\n", - " 2.607567\n", - " 29.986959\n", - " -1.505042\n", - " 0.131699\n", + " 1.0\n", + " 2.975501\n", + " 33.82517\n", + " 1.035202\n", + " 0.184346\n", " \n", " \n", " 1\n", - " 0.0\n", " 1.0\n", - " 2.792433\n", - " 33.513041\n", - " -0.198171\n", - " 0.181322\n", + " 0.0\n", + " 3.124499\n", + " 29.67483\n", + " -0.875016\n", + " 0.159886\n", " \n", " \n", " 2\n", " 1.0\n", " 0.0\n", - " 3.007567\n", - " 32.986959\n", - " -0.483705\n", - " 0.178554\n", + " 2.724499\n", + " 25.67483\n", + " -0.870791\n", + " 0.091847\n", " \n", " \n", " 3\n", " 1.0\n", - " 0.0\n", - " 2.707567\n", - " 29.986959\n", - " -0.570016\n", - " 0.133609\n", + " 1.0\n", + " 2.724499\n", + " 17.67483\n", + " -0.664738\n", + " 0.021207\n", " \n", " \n", " 4\n", " 1.0\n", " 1.0\n", - " 3.307567\n", - " 30.986959\n", - " -0.674063\n", - " 0.158139\n", + " 3.024499\n", + " 29.67483\n", + " -0.382152\n", + " 0.151706\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Race Gender GPA LSAT avg_grade preds_cf\n", - "0 1.0 0.0 2.607567 29.986959 -1.505042 0.131699\n", - "1 0.0 1.0 2.792433 33.513041 -0.198171 0.181322\n", - "2 1.0 0.0 3.007567 32.986959 -0.483705 0.178554\n", - "3 1.0 0.0 2.707567 29.986959 -0.570016 0.133609\n", - "4 1.0 1.0 3.307567 30.986959 -0.674063 0.158139" + " Race Gender GPA LSAT avg_grade preds_cf\n", + "0 0.0 1.0 2.975501 33.82517 1.035202 0.184346\n", + "1 1.0 0.0 3.124499 29.67483 -0.875016 0.159886\n", + "2 1.0 0.0 2.724499 25.67483 -0.870791 0.091847\n", + "3 1.0 1.0 2.724499 17.67483 -0.664738 0.021207\n", + "4 1.0 1.0 3.024499 29.67483 -0.382152 0.151706" ] }, "execution_count": 8, @@ -879,16 +879,16 @@ "id": "a1176177", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:30:44.514307Z", - "iopub.status.busy": "2024-10-19T23:30:44.513530Z", - "iopub.status.idle": "2024-10-19T23:30:44.803104Z", - "shell.execute_reply": "2024-10-19T23:30:44.802439Z" + "iopub.execute_input": "2024-10-22T00:56:32.482575Z", + "iopub.status.busy": "2024-10-22T00:56:32.482370Z", + "iopub.status.idle": "2024-10-22T00:56:32.817664Z", + "shell.execute_reply": "2024-10-22T00:56:32.817002Z" } }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -928,10 +928,10 @@ "id": "725bf447", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:30:44.805315Z", - "iopub.status.busy": "2024-10-19T23:30:44.804934Z", - "iopub.status.idle": "2024-10-19T23:31:36.660967Z", - "shell.execute_reply": "2024-10-19T23:31:36.660439Z" + "iopub.execute_input": "2024-10-22T00:56:32.819616Z", + "iopub.status.busy": "2024-10-22T00:56:32.819418Z", + "iopub.status.idle": "2024-10-22T00:57:28.624969Z", + "shell.execute_reply": "2024-10-22T00:57:28.624132Z" } }, "outputs": [ @@ -988,7 +988,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 373.92it/s]" + "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 338.04it/s]" ] }, { @@ -1000,12 +1000,12 @@ }, { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle -0.52911930891247$" + "$\\displaystyle -0.503963256935917$" ], "text/plain": [ - "-0.5291193089124704" + "-0.5039632569359168" ] }, "execution_count": 10, @@ -1037,16 +1037,16 @@ "id": "ad7671e5", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:31:36.663535Z", - "iopub.status.busy": "2024-10-19T23:31:36.663320Z", - "iopub.status.idle": "2024-10-19T23:31:37.020548Z", - "shell.execute_reply": "2024-10-19T23:31:37.020012Z" + "iopub.execute_input": "2024-10-22T00:57:28.628054Z", + "iopub.status.busy": "2024-10-22T00:57:28.627105Z", + "iopub.status.idle": "2024-10-22T00:57:28.946656Z", + "shell.execute_reply": "2024-10-22T00:57:28.946039Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1114,10 +1114,10 @@ "id": "c6baef0b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:31:37.022712Z", - "iopub.status.busy": "2024-10-19T23:31:37.022340Z", - "iopub.status.idle": "2024-10-19T23:32:29.248204Z", - "shell.execute_reply": "2024-10-19T23:32:29.247560Z" + "iopub.execute_input": "2024-10-22T00:57:28.948748Z", + "iopub.status.busy": "2024-10-22T00:57:28.948376Z", + "iopub.status.idle": "2024-10-22T00:58:24.673079Z", + "shell.execute_reply": "2024-10-22T00:58:24.672404Z" } }, "outputs": [ @@ -1174,7 +1174,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 373.34it/s]" + "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 377.93it/s]" ] }, { @@ -1186,12 +1186,12 @@ }, { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 0.0$" + "$\\displaystyle -1.11022302462516 \\cdot 10^{-15}$" ], "text/plain": [ - "0.0" + "-1.1102230246251565e-15" ] }, "execution_count": 12, @@ -1223,16 +1223,16 @@ "id": "84b1c218", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:29.250470Z", - "iopub.status.busy": "2024-10-19T23:32:29.250221Z", - "iopub.status.idle": "2024-10-19T23:32:29.584226Z", - "shell.execute_reply": "2024-10-19T23:32:29.583554Z" + "iopub.execute_input": "2024-10-22T00:58:24.675513Z", + "iopub.status.busy": "2024-10-22T00:58:24.675229Z", + "iopub.status.idle": "2024-10-22T00:58:25.031102Z", + "shell.execute_reply": "2024-10-22T00:58:25.030463Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb b/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb index 3d6c26c01..a00e6cac4 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/do_sampler_demo.ipynb @@ -64,10 +64,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:34.199327Z", - "iopub.status.busy": "2024-10-19T23:32:34.198907Z", - "iopub.status.idle": "2024-10-19T23:32:34.204748Z", - "shell.execute_reply": "2024-10-19T23:32:34.204296Z" + "iopub.execute_input": "2024-10-22T00:58:29.613604Z", + "iopub.status.busy": "2024-10-22T00:58:29.613422Z", + "iopub.status.idle": "2024-10-22T00:58:29.619427Z", + "shell.execute_reply": "2024-10-22T00:58:29.618941Z" } }, "outputs": [], @@ -81,10 +81,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:34.206481Z", - "iopub.status.busy": "2024-10-19T23:32:34.206148Z", - "iopub.status.idle": "2024-10-19T23:32:35.628044Z", - "shell.execute_reply": "2024-10-19T23:32:35.627490Z" + "iopub.execute_input": "2024-10-22T00:58:29.621239Z", + "iopub.status.busy": "2024-10-22T00:58:29.620905Z", + "iopub.status.idle": "2024-10-22T00:58:31.050382Z", + "shell.execute_reply": "2024-10-22T00:58:31.049739Z" }, "scrolled": true }, @@ -100,10 +100,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.630348Z", - "iopub.status.busy": "2024-10-19T23:32:35.630066Z", - "iopub.status.idle": "2024-10-19T23:32:35.634944Z", - "shell.execute_reply": "2024-10-19T23:32:35.634434Z" + "iopub.execute_input": "2024-10-22T00:58:31.052509Z", + "iopub.status.busy": "2024-10-22T00:58:31.052203Z", + "iopub.status.idle": "2024-10-22T00:58:31.057207Z", + "shell.execute_reply": "2024-10-22T00:58:31.056753Z" } }, "outputs": [], @@ -122,21 +122,21 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.636643Z", - "iopub.status.busy": "2024-10-19T23:32:35.636318Z", - "iopub.status.idle": "2024-10-19T23:32:35.691980Z", - "shell.execute_reply": "2024-10-19T23:32:35.691355Z" + "iopub.execute_input": "2024-10-22T00:58:31.059009Z", + "iopub.status.busy": "2024-10-22T00:58:31.058656Z", + "iopub.status.idle": "2024-10-22T00:58:31.114422Z", + "shell.execute_reply": "2024-10-22T00:58:31.113762Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle 1.62585044692902$" + "$\\displaystyle 1.63689405412668$" ], "text/plain": [ - "1.6258504469290211" + "1.636894054126685" ] }, "execution_count": 4, @@ -160,10 +160,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.694086Z", - "iopub.status.busy": "2024-10-19T23:32:35.693624Z", - "iopub.status.idle": "2024-10-19T23:32:35.697174Z", - "shell.execute_reply": "2024-10-19T23:32:35.696572Z" + "iopub.execute_input": "2024-10-22T00:58:31.116581Z", + "iopub.status.busy": "2024-10-22T00:58:31.116197Z", + "iopub.status.idle": "2024-10-22T00:58:31.119536Z", + "shell.execute_reply": "2024-10-22T00:58:31.119048Z" } }, "outputs": [], @@ -193,10 +193,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.699141Z", - "iopub.status.busy": "2024-10-19T23:32:35.698696Z", - "iopub.status.idle": "2024-10-19T23:32:35.703753Z", - "shell.execute_reply": "2024-10-19T23:32:35.703153Z" + "iopub.execute_input": "2024-10-22T00:58:31.121158Z", + "iopub.status.busy": "2024-10-22T00:58:31.120988Z", + "iopub.status.idle": "2024-10-22T00:58:31.125670Z", + "shell.execute_reply": "2024-10-22T00:58:31.125165Z" } }, "outputs": [], @@ -216,10 +216,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.705770Z", - "iopub.status.busy": "2024-10-19T23:32:35.705328Z", - "iopub.status.idle": "2024-10-19T23:32:35.712307Z", - "shell.execute_reply": "2024-10-19T23:32:35.711846Z" + "iopub.execute_input": "2024-10-22T00:58:31.127561Z", + "iopub.status.busy": "2024-10-22T00:58:31.127198Z", + "iopub.status.idle": "2024-10-22T00:58:31.134467Z", + "shell.execute_reply": "2024-10-22T00:58:31.133841Z" } }, "outputs": [], @@ -251,10 +251,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.714081Z", - "iopub.status.busy": "2024-10-19T23:32:35.713766Z", - "iopub.status.idle": "2024-10-19T23:32:35.725595Z", - "shell.execute_reply": "2024-10-19T23:32:35.725034Z" + "iopub.execute_input": "2024-10-22T00:58:31.136415Z", + "iopub.status.busy": "2024-10-22T00:58:31.136061Z", + "iopub.status.idle": "2024-10-22T00:58:31.147676Z", + "shell.execute_reply": "2024-10-22T00:58:31.147182Z" } }, "outputs": [], @@ -267,21 +267,21 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.727684Z", - "iopub.status.busy": "2024-10-19T23:32:35.727245Z", - "iopub.status.idle": "2024-10-19T23:32:35.745208Z", - "shell.execute_reply": "2024-10-19T23:32:35.744593Z" + "iopub.execute_input": "2024-10-22T00:58:31.149432Z", + "iopub.status.busy": "2024-10-22T00:58:31.149132Z", + "iopub.status.idle": "2024-10-22T00:58:31.166657Z", + "shell.execute_reply": "2024-10-22T00:58:31.166138Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle 0.959364599217468$" + "$\\displaystyle 1.1185969100772$" ], "text/plain": [ - "0.9593645992174682" + "1.1185969100771995" ] }, "execution_count": 9, diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb index f180f1662..75a2a5b34 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy-conditional-treatment-effects.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:37.575619Z", - "iopub.status.busy": "2024-10-19T23:32:37.575203Z", - "iopub.status.idle": "2024-10-19T23:32:37.595235Z", - "shell.execute_reply": "2024-10-19T23:32:37.594765Z" + "iopub.execute_input": "2024-10-22T00:58:33.209986Z", + "iopub.status.busy": "2024-10-22T00:58:33.209569Z", + "iopub.status.idle": "2024-10-22T00:58:33.227514Z", + "shell.execute_reply": "2024-10-22T00:58:33.227046Z" } }, "outputs": [], @@ -36,10 +36,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:37.597203Z", - "iopub.status.busy": "2024-10-19T23:32:37.597032Z", - "iopub.status.idle": "2024-10-19T23:32:39.059365Z", - "shell.execute_reply": "2024-10-19T23:32:39.058764Z" + "iopub.execute_input": "2024-10-22T00:58:33.229447Z", + "iopub.status.busy": "2024-10-22T00:58:33.229084Z", + "iopub.status.idle": "2024-10-22T00:58:34.706365Z", + "shell.execute_reply": "2024-10-22T00:58:34.705658Z" } }, "outputs": [], @@ -64,10 +64,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.061517Z", - "iopub.status.busy": "2024-10-19T23:32:39.061237Z", - "iopub.status.idle": "2024-10-19T23:32:39.116213Z", - "shell.execute_reply": "2024-10-19T23:32:39.115616Z" + "iopub.execute_input": "2024-10-22T00:58:34.708824Z", + "iopub.status.busy": "2024-10-22T00:58:34.708521Z", + "iopub.status.idle": "2024-10-22T00:58:34.765280Z", + "shell.execute_reply": "2024-10-22T00:58:34.764164Z" } }, "outputs": [ @@ -76,19 +76,19 @@ "output_type": "stream", "text": [ " X0 X1 Z0 Z1 W0 W1 W2 W3 v0 \\\n", - "0 1.491701 -1.737984 1.0 0.300386 0.563925 -1.215789 3 0 14.891185 \n", - "1 0.692461 -1.791338 0.0 0.458973 0.056990 -0.571325 3 1 8.496209 \n", - "2 0.872723 -0.166527 1.0 0.844340 0.023435 -0.135442 3 2 22.756933 \n", - "3 0.655365 0.379222 1.0 0.940465 1.431118 -0.576851 2 2 27.321689 \n", - "4 0.485370 -1.121584 0.0 0.534082 0.448856 -1.454139 3 0 6.868651 \n", + "0 2.023455 1.562361 1.0 0.158316 -2.917928 -0.905594 0 2 1.906373 \n", + "1 1.480928 0.525396 1.0 0.883311 1.098866 0.322358 1 3 38.704043 \n", + "2 -0.786564 0.115644 1.0 0.207903 0.034024 -1.382215 0 3 18.700457 \n", + "3 1.635684 0.822219 1.0 0.322280 0.366915 0.257753 3 0 19.561754 \n", + "4 2.261918 -0.308412 1.0 0.602596 -1.818019 -0.114524 0 0 8.478407 \n", "\n", " y \n", - "0 137.397772 \n", - "1 58.552502 \n", - "2 301.496443 \n", - "3 394.580374 \n", - "4 57.178667 \n", - "True causal estimate is 9.90297950800993\n" + "0 28.438381 \n", + "1 572.167489 \n", + "2 171.981562 \n", + "3 313.928792 \n", + "4 100.054705 \n", + "True causal estimate is 12.326444409564749\n" ] } ], @@ -110,10 +110,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.118928Z", - "iopub.status.busy": "2024-10-19T23:32:39.118403Z", - "iopub.status.idle": "2024-10-19T23:32:39.160885Z", - "shell.execute_reply": "2024-10-19T23:32:39.160344Z" + "iopub.execute_input": "2024-10-22T00:58:34.768104Z", + "iopub.status.busy": "2024-10-22T00:58:34.767554Z", + "iopub.status.idle": "2024-10-22T00:58:34.810529Z", + "shell.execute_reply": "2024-10-22T00:58:34.809987Z" } }, "outputs": [], @@ -128,10 +128,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.163523Z", - "iopub.status.busy": "2024-10-19T23:32:39.163035Z", - "iopub.status.idle": "2024-10-19T23:32:39.336605Z", - "shell.execute_reply": "2024-10-19T23:32:39.336010Z" + "iopub.execute_input": "2024-10-22T00:58:34.812615Z", + "iopub.status.busy": "2024-10-22T00:58:34.812408Z", + "iopub.status.idle": "2024-10-22T00:58:34.984595Z", + "shell.execute_reply": "2024-10-22T00:58:34.983957Z" } }, "outputs": [ @@ -167,10 +167,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.338577Z", - "iopub.status.busy": "2024-10-19T23:32:39.338385Z", - "iopub.status.idle": "2024-10-19T23:32:39.673800Z", - "shell.execute_reply": "2024-10-19T23:32:39.673231Z" + "iopub.execute_input": "2024-10-22T00:58:34.986614Z", + "iopub.status.busy": "2024-10-22T00:58:34.986416Z", + "iopub.status.idle": "2024-10-22T00:58:35.329877Z", + "shell.execute_reply": "2024-10-22T00:58:35.329247Z" }, "scrolled": true }, @@ -185,9 +185,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -226,10 +226,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.675835Z", - "iopub.status.busy": "2024-10-19T23:32:39.675443Z", - "iopub.status.idle": "2024-10-19T23:32:40.017093Z", - "shell.execute_reply": "2024-10-19T23:32:40.016520Z" + "iopub.execute_input": "2024-10-22T00:58:35.331987Z", + "iopub.status.busy": "2024-10-22T00:58:35.331643Z", + "iopub.status.idle": "2024-10-22T00:58:35.681869Z", + "shell.execute_reply": "2024-10-22T00:58:35.681210Z" } }, "outputs": [ @@ -246,43 +246,43 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2+v0*X1+v0*X0\n", + "b: y~v0+W3+W1+W2+W0+v0*X0+v0*X1\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: 9.903186220972193\n", + "Mean value: 12.326487748766223\n", "### Conditional Estimates\n", - "__categorical__X1 __categorical__X0 \n", - "(-4.517, -1.479] (-2.7729999999999997, -0.206] -0.854567\n", - " (-0.206, 0.392] 2.729455\n", - " (0.392, 0.893] 4.593359\n", - " (0.893, 1.491] 6.473340\n", - " (1.491, 4.942] 9.569572\n", - "(-1.479, -0.9] (-2.7729999999999997, -0.206] 2.647748\n", - " (-0.206, 0.392] 5.898428\n", - " (0.392, 0.893] 7.793650\n", - " (0.893, 1.491] 9.867785\n", - " (1.491, 4.942] 12.992648\n", - "(-0.9, -0.382] (-2.7729999999999997, -0.206] 4.765016\n", - " (-0.206, 0.392] 7.855087\n", - " (0.392, 0.893] 9.846105\n", - " (0.893, 1.491] 11.863300\n", - " (1.491, 4.942] 15.080355\n", - "(-0.382, 0.202] (-2.7729999999999997, -0.206] 6.856693\n", - " (-0.206, 0.392] 9.979435\n", - " (0.392, 0.893] 11.944220\n", - " (0.893, 1.491] 13.891133\n", - " (1.491, 4.942] 17.220567\n", - "(0.202, 3.39] (-2.7729999999999997, -0.206] 10.099547\n", - " (-0.206, 0.392] 13.267852\n", - " (0.392, 0.893] 15.331551\n", - " (0.893, 1.491] 17.310951\n", - " (1.491, 4.942] 20.560836\n", + "__categorical__X0 __categorical__X1 \n", + "(-3.0589999999999997, -0.0333] (-3.3049999999999997, -0.58] 4.640146\n", + " (-0.58, 0.00381] 7.961317\n", + " (0.00381, 0.517] 10.003873\n", + " (0.517, 1.107] 12.066093\n", + " (1.107, 4.088] 15.330083\n", + "(-0.0333, 0.555] (-3.3049999999999997, -0.58] 6.137589\n", + " (-0.58, 0.00381] 9.427405\n", + " (0.00381, 0.517] 11.380003\n", + " (0.517, 1.107] 13.484874\n", + " (1.107, 4.088] 16.902725\n", + "(0.555, 1.057] (-3.3049999999999997, -0.58] 6.981416\n", + " (-0.58, 0.00381] 10.295854\n", + " (0.00381, 0.517] 12.303682\n", + " (0.517, 1.107] 14.439904\n", + " (1.107, 4.088] 17.655446\n", + "(1.057, 1.657] (-3.3049999999999997, -0.58] 7.649759\n", + " (-0.58, 0.00381] 11.126852\n", + " (0.00381, 0.517] 13.170195\n", + " (0.517, 1.107] 15.298160\n", + " (1.107, 4.088] 18.680237\n", + "(1.657, 4.502] (-3.3049999999999997, -0.58] 9.274378\n", + " (-0.58, 0.00381] 12.532155\n", + " (0.00381, 0.517] 14.667815\n", + " (0.517, 1.107] 16.736425\n", + " (1.107, 4.088] 20.003727\n", "dtype: float64\n" ] } @@ -314,10 +314,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:40.019218Z", - "iopub.status.busy": "2024-10-19T23:32:40.018833Z", - "iopub.status.idle": "2024-10-19T23:32:44.756035Z", - "shell.execute_reply": "2024-10-19T23:32:44.754779Z" + "iopub.execute_input": "2024-10-22T00:58:35.684113Z", + "iopub.status.busy": "2024-10-22T00:58:35.683718Z", + "iopub.status.idle": "2024-10-22T00:58:40.552555Z", + "shell.execute_reply": "2024-10-22T00:58:40.551317Z" } }, "outputs": [ @@ -334,23 +334,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: Data subset defined by a function\n", "\n", "## Estimate\n", - "Mean value: 13.753748963935088\n", - "Effect estimates: [[ 8.73122277]\n", - " [15.23876743]\n", - " [17.29460755]\n", + "Mean value: 13.93194215968301\n", + "Effect estimates: [[19.58502909]\n", + " [14.50763963]\n", + " [15.96006866]\n", " ...\n", - " [15.87908319]\n", - " [11.24352305]\n", - " [25.84231277]]\n", + " [12.86547484]\n", + " [17.1101046 ]\n", + " [16.39475864]]\n", "\n" ] } @@ -377,10 +377,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:44.760753Z", - "iopub.status.busy": "2024-10-19T23:32:44.760509Z", - "iopub.status.idle": "2024-10-19T23:32:44.839846Z", - "shell.execute_reply": "2024-10-19T23:32:44.839313Z" + "iopub.execute_input": "2024-10-22T00:58:40.556361Z", + "iopub.status.busy": "2024-10-22T00:58:40.556074Z", + "iopub.status.idle": "2024-10-22T00:58:40.620240Z", + "shell.execute_reply": "2024-10-22T00:58:40.619129Z" } }, "outputs": [ @@ -388,7 +388,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "True causal estimate is 9.90297950800993\n" + "True causal estimate is 12.326444409564749\n" ] } ], @@ -401,10 +401,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:44.841527Z", - "iopub.status.busy": "2024-10-19T23:32:44.841356Z", - "iopub.status.idle": "2024-10-19T23:32:48.505837Z", - "shell.execute_reply": "2024-10-19T23:32:48.504469Z" + "iopub.execute_input": "2024-10-22T00:58:40.622945Z", + "iopub.status.busy": "2024-10-22T00:58:40.622705Z", + "iopub.status.idle": "2024-10-22T00:58:44.269453Z", + "shell.execute_reply": "2024-10-22T00:58:44.267707Z" } }, "outputs": [ @@ -421,23 +421,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: 9.919879528997198\n", - "Effect estimates: [[ 8.81651721]\n", - " [ 5.69962658]\n", - " [12.56157938]\n", + "Mean value: 12.232711770496296\n", + "Effect estimates: [[19.50191592]\n", + " [14.48628432]\n", + " [ 8.77201276]\n", " ...\n", - " [ 5.72285068]\n", - " [ 9.26534355]\n", - " [ 1.82141633]]\n", + " [ 9.31526092]\n", + " [ 1.20345234]\n", + " [12.42482179]]\n", "\n" ] } @@ -469,10 +469,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:48.509952Z", - "iopub.status.busy": "2024-10-19T23:32:48.509178Z", - "iopub.status.idle": "2024-10-19T23:34:57.171270Z", - "shell.execute_reply": "2024-10-19T23:34:57.170680Z" + "iopub.execute_input": "2024-10-22T00:58:44.274163Z", + "iopub.status.busy": "2024-10-22T00:58:44.273918Z", + "iopub.status.idle": "2024-10-22T01:01:00.858452Z", + "shell.execute_reply": "2024-10-22T01:01:00.857821Z" } }, "outputs": [ @@ -489,28 +489,28 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.866981805100442\n", - "Effect estimates: [[ 8.76788198]\n", - " [ 5.63768763]\n", - " [12.51641217]\n", + "Mean value: 12.314628691878262\n", + "Effect estimates: [[19.5655154 ]\n", + " [14.51963354]\n", + " [ 9.01097059]\n", " ...\n", - " [ 5.65328576]\n", - " [ 9.21045244]\n", - " [ 1.74479777]]\n", - "95.0% confidence interval: [[[ 8.73665267 5.56645407 12.56084318 ... 5.58889059 9.2246464\n", - " 1.54243643]]\n", + " [ 9.31578975]\n", + " [ 1.17294938]\n", + " [12.49848647]]\n", + "95.0% confidence interval: [[[19.81494888 14.70998842 8.93032579 ... 9.27559761 0.54856956\n", + " 12.65799309]]\n", "\n", - " [[ 8.96365154 5.72125451 12.73561168 ... 5.70241494 9.32302885\n", - " 1.76524244]]]\n", + " [[20.45154685 15.03025232 9.35074604 ... 9.58882303 1.34584941\n", + " 12.86393751]]]\n", "\n" ] } @@ -547,10 +547,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:34:57.174413Z", - "iopub.status.busy": "2024-10-19T23:34:57.173448Z", - "iopub.status.idle": "2024-10-19T23:35:00.817084Z", - "shell.execute_reply": "2024-10-19T23:35:00.815772Z" + "iopub.execute_input": "2024-10-22T01:01:00.861113Z", + "iopub.status.busy": "2024-10-22T01:01:00.860869Z", + "iopub.status.idle": "2024-10-22T01:01:04.600661Z", + "shell.execute_reply": "2024-10-22T01:01:04.599554Z" } }, "outputs": [ @@ -558,16 +558,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[15.34215019]\n", - " [14.90055945]\n", - " [14.15823019]\n", - " [14.77471051]\n", - " [14.90021874]\n", - " [14.7392562 ]\n", - " [14.83142581]\n", - " [16.29700014]\n", - " [14.56724122]\n", - " [15.94164365]]\n" + "[[13.65743569]\n", + " [11.25520921]\n", + " [13.51849661]\n", + " [14.75180693]\n", + " [13.57443238]\n", + " [14.32896054]\n", + " [12.8873053 ]\n", + " [12.01943554]\n", + " [10.65435693]\n", + " [10.15937714]]\n" ] } ], @@ -600,10 +600,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:00.820120Z", - "iopub.status.busy": "2024-10-19T23:35:00.819899Z", - "iopub.status.idle": "2024-10-19T23:35:00.917684Z", - "shell.execute_reply": "2024-10-19T23:35:00.917031Z" + "iopub.execute_input": "2024-10-22T01:01:04.604482Z", + "iopub.status.busy": "2024-10-22T01:01:04.603467Z", + "iopub.status.idle": "2024-10-22T01:01:04.704127Z", + "shell.execute_reply": "2024-10-22T01:01:04.703611Z" } }, "outputs": [ @@ -611,7 +611,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -639,10 +639,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:00.919667Z", - "iopub.status.busy": "2024-10-19T23:35:00.919469Z", - "iopub.status.idle": "2024-10-19T23:35:01.895868Z", - "shell.execute_reply": "2024-10-19T23:35:01.895296Z" + "iopub.execute_input": "2024-10-22T01:01:04.706215Z", + "iopub.status.busy": "2024-10-22T01:01:04.705736Z", + "iopub.status.idle": "2024-10-22T01:01:05.684616Z", + "shell.execute_reply": "2024-10-22T01:01:05.684046Z" } }, "outputs": [ @@ -651,17 +651,17 @@ "output_type": "stream", "text": [ " X0 X1 Z0 W0 W1 W2 W3 v0 y\n", - "0 0.737371 -0.988791 0.0 1.315618 -0.459543 1.092568 1.518797 1 1\n", - "1 0.721527 0.564647 0.0 2.142769 0.395111 1.847626 0.161921 1 1\n", - "2 3.244662 -1.704488 1.0 1.258298 -0.041071 2.464063 0.140888 1 1\n", - "3 1.373530 -1.057219 0.0 -0.091845 -1.011054 1.744316 0.501808 0 1\n", - "4 0.039205 0.064237 1.0 -2.113835 -1.106553 2.352720 -1.011937 0 0\n", + "0 1.187002 2.129281 1.0 0.225979 1.520578 -0.462146 1.104043 1 1\n", + "1 0.319704 -1.603687 0.0 -1.050686 1.183747 -0.434124 1.927326 1 1\n", + "2 0.736122 -1.360670 1.0 -0.383549 -0.925797 -1.607262 -0.784307 0 0\n", + "3 1.053287 -1.567965 0.0 -0.911918 -0.927349 -0.626209 0.647853 0 1\n", + "4 0.391219 1.150939 0.0 0.014391 3.230884 0.387733 0.988914 1 1\n", "... ... ... ... ... ... ... ... .. ..\n", - "9995 1.441554 -0.454320 1.0 -0.455541 0.105561 -0.139760 -0.901243 1 1\n", - "9996 2.459208 -0.186497 0.0 0.406960 -1.386559 0.612979 -0.141619 0 1\n", - "9997 2.364170 -1.539729 0.0 0.085340 -0.113041 0.280497 -1.187246 1 1\n", - "9998 0.358942 0.023838 1.0 1.088847 -1.587456 1.154165 1.042078 1 1\n", - "9999 0.201153 0.665577 1.0 1.740281 0.240991 1.508345 -1.355040 1 1\n", + "9995 0.400726 -1.221735 0.0 0.046251 0.605087 -1.082564 -0.150353 0 0\n", + "9996 -1.029194 -0.604113 1.0 0.901130 0.324673 -1.156698 1.271759 1 1\n", + "9997 -1.184801 -1.120879 1.0 -0.607204 0.970222 -0.631891 0.605802 1 1\n", + "9998 1.181210 -1.491519 0.0 0.708097 0.720793 -0.993977 -0.766604 0 0\n", + "9999 -0.354285 -0.105086 0.0 0.172579 0.497326 0.166035 0.258179 1 1\n", "\n", "[10000 rows x 9 columns]\n" ] @@ -694,10 +694,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:01.897833Z", - "iopub.status.busy": "2024-10-19T23:35:01.897596Z", - "iopub.status.idle": "2024-10-19T23:35:10.833455Z", - "shell.execute_reply": "2024-10-19T23:35:10.832879Z" + "iopub.execute_input": "2024-10-22T01:01:05.686826Z", + "iopub.status.busy": "2024-10-22T01:01:05.686386Z", + "iopub.status.idle": "2024-10-22T01:01:15.006908Z", + "shell.execute_reply": "2024-10-22T01:01:15.006279Z" } }, "outputs": [ @@ -714,25 +714,25 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 0.29243078341186574\n", - "Effect estimates: [[0.28844194]\n", - " [0.28652766]\n", - " [0.34885284]\n", + "Mean value: 0.3758135379923914\n", + "Effect estimates: [[0.43224547]\n", + " [0.36184538]\n", + " [0.37576416]\n", " ...\n", - " [0.32772384]\n", - " [0.27842901]\n", - " [0.27403682]]\n", + " [0.32902103]\n", + " [0.38562862]\n", + " [0.36359694]]\n", "\n", - "True causal estimate is 0.2696\n" + "True causal estimate is 0.3519\n" ] } ], @@ -763,10 +763,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:10.836176Z", - "iopub.status.busy": "2024-10-19T23:35:10.835949Z", - "iopub.status.idle": "2024-10-19T23:35:39.881809Z", - "shell.execute_reply": "2024-10-19T23:35:39.880859Z" + "iopub.execute_input": "2024-10-22T01:01:15.009705Z", + "iopub.status.busy": "2024-10-22T01:01:15.009452Z", + "iopub.status.idle": "2024-10-22T01:01:42.721527Z", + "shell.execute_reply": "2024-10-22T01:01:42.720148Z" }, "scrolled": true }, @@ -791,18 +791,18 @@ "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: Data subset defined by a function\n", "\n", "## Estimate\n", - "Mean value: 10.32704802755174\n", - "Effect estimates: [[ 8.79935732]\n", - " [ 5.64897801]\n", - " [12.56897615]\n", + "Mean value: 12.498979396226137\n", + "Effect estimates: [[19.35420994]\n", + " [14.47770887]\n", + " [ 9.20311738]\n", " ...\n", - " [ 5.66277359]\n", - " [ 9.24276854]\n", - " [ 1.73144656]]\n", + " [ 9.448214 ]\n", + " [ 1.58237698]\n", + " [12.53520843]]\n", "\n" ] } @@ -832,10 +832,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:39.885756Z", - "iopub.status.busy": "2024-10-19T23:35:39.885529Z", - "iopub.status.idle": "2024-10-19T23:35:40.463398Z", - "shell.execute_reply": "2024-10-19T23:35:40.462772Z" + "iopub.execute_input": "2024-10-22T01:01:42.726197Z", + "iopub.status.busy": "2024-10-22T01:01:42.725055Z", + "iopub.status.idle": "2024-10-22T01:01:43.335284Z", + "shell.execute_reply": "2024-10-22T01:01:43.334719Z" } }, "outputs": [ @@ -844,30 +844,30 @@ "output_type": "stream", "text": [ " X0 X1 X2 X3 X4 Z0 Z1 \\\n", - "0 1.194694 0.806424 3.378051 1.066913 -1.762461 0.0 0.042603 \n", - "1 0.210889 -0.866121 -0.036220 2.189422 -0.133892 1.0 0.255470 \n", - "2 0.994021 0.356651 1.642013 0.319378 1.683821 0.0 0.574598 \n", - "3 0.974397 -1.042993 1.734511 -0.220498 -0.545196 1.0 0.228140 \n", - "4 0.723457 -0.935144 1.068031 0.576495 0.740326 0.0 0.078338 \n", + "0 -0.086925 -0.100078 0.571303 -0.828653 1.344718 1.0 0.389962 \n", + "1 0.280182 0.470249 -0.375436 -2.764916 -1.072220 1.0 0.079642 \n", + "2 0.123467 1.309425 0.476237 -0.106417 -0.895539 0.0 0.733598 \n", + "3 2.834800 0.230924 -0.666959 -0.828474 -2.011372 0.0 0.447600 \n", + "4 1.963431 1.069789 -0.394695 -0.459135 -0.751069 0.0 0.744901 \n", "... ... ... ... ... ... ... ... \n", - "9995 0.238972 1.086033 0.053144 0.610416 -1.248808 0.0 0.344754 \n", - "9996 0.894745 -1.155823 0.090509 0.859903 0.165150 0.0 0.163243 \n", - "9997 0.143863 -2.810947 -0.974355 0.820165 -1.555686 0.0 0.614141 \n", - "9998 -1.351503 -1.443587 2.677787 0.725252 -0.779037 1.0 0.206247 \n", - "9999 0.533301 0.557858 1.470046 1.655571 -0.140108 0.0 0.601939 \n", + "9995 0.097454 0.801626 -1.719491 -1.689063 -2.283606 1.0 0.009369 \n", + "9996 0.963483 1.062666 -0.515928 -0.444270 -1.900877 0.0 0.802029 \n", + "9997 2.137832 -0.146762 0.527316 -0.725324 0.121707 0.0 0.712036 \n", + "9998 -0.080499 -0.660111 0.237723 -0.246514 -2.791567 0.0 0.401258 \n", + "9999 -0.788721 1.639797 -1.126375 -1.565304 -0.038917 0.0 0.995005 \n", "\n", " W0 W1 W2 W3 W4 v0 y \n", - "0 1.442070 0.181076 -1.064983 1.689094 0.055662 1 26.705185 \n", - "1 -2.113033 -0.435568 -0.524926 0.086432 -0.242206 1 3.194405 \n", - "2 -0.027573 -0.570038 -0.923142 4.924895 1.241415 1 24.708508 \n", - "3 0.124236 1.725932 0.494322 0.869966 0.520273 1 22.239015 \n", - "4 0.021272 -0.616530 0.169321 0.429555 1.769569 1 20.260806 \n", + "0 -0.244303 0.266005 -0.060768 -1.208970 0.657691 1 14.009190 \n", + "1 -0.259919 1.107581 1.013343 -0.576170 0.785727 1 9.425114 \n", + "2 1.172103 -2.312375 1.224012 0.729851 0.266905 1 20.818196 \n", + "3 -1.500939 0.032816 1.983530 0.401027 2.369935 1 23.159152 \n", + "4 -1.728847 1.182353 -0.503303 0.137425 1.013635 1 15.171032 \n", "... ... ... ... ... ... .. ... \n", - "9995 -0.039791 0.527084 0.293256 2.452390 -0.205648 1 22.632414 \n", - "9996 -0.061491 0.964071 -0.595591 -0.367892 1.588571 0 9.045665 \n", - "9997 0.459640 1.143104 -0.054694 0.328811 0.083701 1 10.594588 \n", - "9998 -0.902153 0.840342 1.524773 0.998653 -1.077402 1 5.035180 \n", - "9999 -0.745454 0.777239 1.253353 1.271281 -1.389755 1 21.136632 \n", + "9995 0.611338 0.775651 0.447902 0.201046 -0.223241 1 -0.536255 \n", + "9996 -1.367978 -1.128647 1.219540 0.502938 -0.306668 1 3.482780 \n", + "9997 0.845712 0.613335 1.259556 0.220525 0.967669 1 34.807461 \n", + "9998 -1.174897 -0.269773 0.150152 -0.064002 0.298968 0 -3.497418 \n", + "9999 -1.950860 -0.652349 0.677681 1.184015 1.058340 1 11.260721 \n", "\n", "[10000 rows x 14 columns]\n" ] @@ -891,10 +891,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:40.465428Z", - "iopub.status.busy": "2024-10-19T23:35:40.465238Z", - "iopub.status.idle": "2024-10-19T23:35:48.817290Z", - "shell.execute_reply": "2024-10-19T23:35:48.816650Z" + "iopub.execute_input": "2024-10-22T01:01:43.337648Z", + "iopub.status.busy": "2024-10-22T01:01:43.337248Z", + "iopub.status.idle": "2024-10-22T01:01:51.504236Z", + "shell.execute_reply": "2024-10-22T01:01:51.503495Z" } }, "outputs": [ @@ -911,25 +911,25 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W4,W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W4,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W0,W1,W2,U) = P(y|v0,W4,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W4,W0,U) = P(y|v0,W3,W1,W2,W4,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+X1+X4+X2+X0+X3+W4+W3+W0+W1+W2\n", + "b: y~v0+X0+X2+X4+X3+X1+W3+W1+W2+W4+W0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 17.090990931755716\n", - "Effect estimates: [[23.15233028]\n", - " [18.7117749 ]\n", - " [20.73704997]\n", + "Mean value: 14.22779046611777\n", + "Effect estimates: [[18.51798921]\n", + " [ 7.55906167]\n", + " [21.53200484]\n", " ...\n", - " [ 8.88761776]\n", - " [14.08657328]\n", - " [30.4898313 ]]\n", + " [30.12004075]\n", + " [-2.74523381]\n", + " [13.90682249]]\n", "\n", - "True causal estimate is 11.867334844408562\n" + "True causal estimate is 8.001323825995941\n" ] } ], @@ -966,10 +966,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:48.819269Z", - "iopub.status.busy": "2024-10-19T23:35:48.819080Z", - "iopub.status.idle": "2024-10-19T23:35:48.868227Z", - "shell.execute_reply": "2024-10-19T23:35:48.867585Z" + "iopub.execute_input": "2024-10-22T01:01:51.506595Z", + "iopub.status.busy": "2024-10-22T01:01:51.506192Z", + "iopub.status.idle": "2024-10-22T01:01:51.557535Z", + "shell.execute_reply": "2024-10-22T01:01:51.556938Z" } }, "outputs": [ @@ -986,23 +986,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W4,W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W4,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W0,W1,W2,U) = P(y|v0,W4,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W4,W0,U) = P(y|v0,W3,W1,W2,W4,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+X1+X4+X2+X0+X3+W4+W3+W0+W1+W2\n", + "b: y~v0+X0+X2+X4+X3+X1+W3+W1+W2+W4+W0\n", "Target units: Data subset provided as a data frame\n", "\n", "## Estimate\n", - "Mean value: 17.44536912162681\n", - "Effect estimates: [[21.85051043]\n", - " [11.91231284]\n", - " [ 8.88761776]\n", - " [14.08657328]\n", - " [30.4898313 ]]\n", + "Mean value: 10.425956082465873\n", + "Effect estimates: [[ 1.30116632]\n", + " [ 9.54698467]\n", + " [30.12004075]\n", + " [-2.74523381]\n", + " [13.90682249]]\n", "\n", - "True causal estimate is 11.867334844408562\n" + "True causal estimate is 8.001323825995941\n" ] } ], @@ -1037,10 +1037,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:48.870098Z", - "iopub.status.busy": "2024-10-19T23:35:48.869911Z", - "iopub.status.idle": "2024-10-19T23:41:54.369614Z", - "shell.execute_reply": "2024-10-19T23:41:54.368475Z" + "iopub.execute_input": "2024-10-22T01:01:51.559574Z", + "iopub.status.busy": "2024-10-22T01:01:51.559374Z", + "iopub.status.idle": "2024-10-22T01:08:03.541530Z", + "shell.execute_reply": "2024-10-22T01:08:03.540517Z" } }, "outputs": [ @@ -1049,9 +1049,9 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:15.045243609692054\n", - "New effect:15.040517118845221\n", - "p value:0.86\n", + "Estimated effect:12.680681626503253\n", + "New effect:12.695571695534525\n", + "p value:0.8400000000000001\n", "\n" ] } @@ -1073,10 +1073,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:41:54.372988Z", - "iopub.status.busy": "2024-10-19T23:41:54.372769Z", - "iopub.status.idle": "2024-10-19T23:41:58.089380Z", - "shell.execute_reply": "2024-10-19T23:41:58.088008Z" + "iopub.execute_input": "2024-10-22T01:08:03.546925Z", + "iopub.status.busy": "2024-10-22T01:08:03.546643Z", + "iopub.status.idle": "2024-10-22T01:08:07.276258Z", + "shell.execute_reply": "2024-10-22T01:08:07.275304Z" } }, "outputs": [ @@ -1085,8 +1085,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:15.045243609692054\n", - "New effect:15.079754922662948\n", + "Estimated effect:12.680681626503253\n", + "New effect:12.78054614324095\n", "\n" ] } @@ -1110,10 +1110,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:41:58.093678Z", - "iopub.status.busy": "2024-10-19T23:41:58.093458Z", - "iopub.status.idle": "2024-10-19T23:42:34.804857Z", - "shell.execute_reply": "2024-10-19T23:42:34.803535Z" + "iopub.execute_input": "2024-10-22T01:08:07.280459Z", + "iopub.status.busy": "2024-10-22T01:08:07.279307Z", + "iopub.status.idle": "2024-10-22T01:08:44.152544Z", + "shell.execute_reply": "2024-10-22T01:08:44.151589Z" } }, "outputs": [ @@ -1122,9 +1122,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:15.045243609692054\n", - "New effect:-0.021354476275475112\n", - "p value:0.4443630005685122\n", + "Estimated effect:12.680681626503253\n", + "New effect:-0.027534656014193824\n", + "p value:0.3319205721239611\n", "\n" ] } @@ -1149,10 +1149,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:42:34.809143Z", - "iopub.status.busy": "2024-10-19T23:42:34.808247Z", - "iopub.status.idle": "2024-10-19T23:43:04.361698Z", - "shell.execute_reply": "2024-10-19T23:43:04.360767Z" + "iopub.execute_input": "2024-10-22T01:08:44.157646Z", + "iopub.status.busy": "2024-10-22T01:08:44.156647Z", + "iopub.status.idle": "2024-10-22T01:09:13.951379Z", + "shell.execute_reply": "2024-10-22T01:09:13.950699Z" } }, "outputs": [ @@ -1161,9 +1161,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:15.045243609692054\n", - "New effect:15.031111389585709\n", - "p value:0.40967889817265557\n", + "Estimated effect:12.680681626503253\n", + "New effect:12.71535645089526\n", + "p value:0.24567680945783077\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb index e2e600b1b..6f22b189e 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy-simple-iv-example.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:07.777808Z", - "iopub.status.busy": "2024-10-19T23:43:07.777363Z", - "iopub.status.idle": "2024-10-19T23:43:07.793487Z", - "shell.execute_reply": "2024-10-19T23:43:07.793040Z" + "iopub.execute_input": "2024-10-22T01:09:17.374210Z", + "iopub.status.busy": "2024-10-22T01:09:17.373760Z", + "iopub.status.idle": "2024-10-22T01:09:17.389787Z", + "shell.execute_reply": "2024-10-22T01:09:17.389120Z" } }, "outputs": [], @@ -29,10 +29,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:07.795271Z", - "iopub.status.busy": "2024-10-19T23:43:07.794940Z", - "iopub.status.idle": "2024-10-19T23:43:09.232379Z", - "shell.execute_reply": "2024-10-19T23:43:09.231755Z" + "iopub.execute_input": "2024-10-22T01:09:17.391971Z", + "iopub.status.busy": "2024-10-22T01:09:17.391777Z", + "iopub.status.idle": "2024-10-22T01:09:18.972429Z", + "shell.execute_reply": "2024-10-22T01:09:18.971756Z" } }, "outputs": [], @@ -60,10 +60,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.234802Z", - "iopub.status.busy": "2024-10-19T23:43:09.234321Z", - "iopub.status.idle": "2024-10-19T23:43:09.266001Z", - "shell.execute_reply": "2024-10-19T23:43:09.265450Z" + "iopub.execute_input": "2024-10-22T01:09:18.974977Z", + "iopub.status.busy": "2024-10-22T01:09:18.974495Z", + "iopub.status.idle": "2024-10-22T01:09:19.008836Z", + "shell.execute_reply": "2024-10-22T01:09:19.008258Z" } }, "outputs": [], @@ -115,10 +115,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.267708Z", - "iopub.status.busy": "2024-10-19T23:43:09.267533Z", - "iopub.status.idle": "2024-10-19T23:43:09.429333Z", - "shell.execute_reply": "2024-10-19T23:43:09.428708Z" + "iopub.execute_input": "2024-10-22T01:09:19.011249Z", + "iopub.status.busy": "2024-10-22T01:09:19.010867Z", + "iopub.status.idle": "2024-10-22T01:09:19.183916Z", + "shell.execute_reply": "2024-10-22T01:09:19.183300Z" } }, "outputs": [ @@ -170,10 +170,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.431588Z", - "iopub.status.busy": "2024-10-19T23:43:09.431365Z", - "iopub.status.idle": "2024-10-19T23:43:09.676994Z", - "shell.execute_reply": "2024-10-19T23:43:09.676345Z" + "iopub.execute_input": "2024-10-22T01:09:19.186291Z", + "iopub.status.busy": "2024-10-22T01:09:19.185856Z", + "iopub.status.idle": "2024-10-22T01:09:19.465176Z", + "shell.execute_reply": "2024-10-22T01:09:19.464594Z" } }, "outputs": [ @@ -215,10 +215,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.679113Z", - "iopub.status.busy": "2024-10-19T23:43:09.678645Z", - "iopub.status.idle": "2024-10-19T23:43:12.573679Z", - "shell.execute_reply": "2024-10-19T23:43:12.573058Z" + "iopub.execute_input": "2024-10-22T01:09:19.467292Z", + "iopub.status.busy": "2024-10-22T01:09:19.466901Z", + "iopub.status.idle": "2024-10-22T01:09:22.392442Z", + "shell.execute_reply": "2024-10-22T01:09:22.391683Z" } }, "outputs": [ @@ -260,7 +260,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 4.130869232919477\n", + "Mean value: 4.15323575009908\n", "p-value: [0, 0.001]\n", "\n" ] @@ -289,10 +289,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:12.575552Z", - "iopub.status.busy": "2024-10-19T23:43:12.575363Z", - "iopub.status.idle": "2024-10-19T23:43:13.017205Z", - "shell.execute_reply": "2024-10-19T23:43:13.016653Z" + "iopub.execute_input": "2024-10-22T01:09:22.394557Z", + "iopub.status.busy": "2024-10-22T01:09:22.394104Z", + "iopub.status.idle": "2024-10-22T01:09:22.845322Z", + "shell.execute_reply": "2024-10-22T01:09:22.844724Z" } }, "outputs": [ @@ -301,9 +301,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:4.130869232919477\n", - "New effect:0.019853343491346664\n", - "p value:0.94\n", + "Estimated effect:4.15323575009908\n", + "New effect:0.05746995723853913\n", + "p value:0.8200000000000001\n", "\n" ] } @@ -335,10 +335,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:13.019276Z", - "iopub.status.busy": "2024-10-19T23:43:13.018914Z", - "iopub.status.idle": "2024-10-19T23:43:13.070342Z", - "shell.execute_reply": "2024-10-19T23:43:13.069778Z" + "iopub.execute_input": "2024-10-22T01:09:22.847500Z", + "iopub.status.busy": "2024-10-22T01:09:22.847012Z", + "iopub.status.idle": "2024-10-22T01:09:22.896720Z", + "shell.execute_reply": "2024-10-22T01:09:22.896053Z" } }, "outputs": [ @@ -348,22 +348,22 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -380,24 +380,24 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
IV2SLS Regression Results
Dep. Variable: income R-squared: 0.904Dep. Variable: income R-squared: 0.903
Model: IV2SLS Adj. R-squared: 0.904Model: IV2SLS Adj. R-squared: 0.902
Method: Two Stage F-statistic: 1815.Method: Two Stage F-statistic: 1853.
Least Squares Prob (F-statistic): 8.27e-227 Least Squares Prob (F-statistic): 9.90e-230
Date: Sat, 19 Oct 2024 Date: Tue, 22 Oct 2024
Time: 23:43:13 Time: 01:09:22
No. Observations: 1000 coef std err t P>|t| [0.025 0.975]
Intercept 8.7593 0.893 9.811 0.000 7.007 10.511Intercept 8.8450 0.899 9.836 0.000 7.080 10.610
education 4.1309 0.097 42.604 0.000 3.941 4.321education 4.1532 0.096 43.050 0.000 3.964 4.343
\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Omnibus: 3.915 Durbin-Watson: 2.128Omnibus: 0.661 Durbin-Watson: 1.971
Prob(Omnibus): 0.141 Jarque-Bera (JB): 4.009Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.575
Skew: 0.096 Prob(JB): 0.135Skew: -0.054 Prob(JB): 0.750
Kurtosis: 3.243 Cond. No. 25.2Kurtosis: 3.048 Cond. No. 25.9
" ], @@ -405,12 +405,12 @@ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", - "\\textbf{Dep. Variable:} & income & \\textbf{ R-squared: } & 0.904 \\\\\n", - "\\textbf{Model:} & IV2SLS & \\textbf{ Adj. R-squared: } & 0.904 \\\\\n", - "\\textbf{Method:} & Two Stage & \\textbf{ F-statistic: } & 1815. \\\\\n", - "\\textbf{} & Least Squares & \\textbf{ Prob (F-statistic):} & 8.27e-227 \\\\\n", - "\\textbf{Date:} & Sat, 19 Oct 2024 & \\textbf{ } & \\\\\n", - "\\textbf{Time:} & 23:43:13 & \\textbf{ } & \\\\\n", + "\\textbf{Dep. Variable:} & income & \\textbf{ R-squared: } & 0.903 \\\\\n", + "\\textbf{Model:} & IV2SLS & \\textbf{ Adj. R-squared: } & 0.902 \\\\\n", + "\\textbf{Method:} & Two Stage & \\textbf{ F-statistic: } & 1853. \\\\\n", + "\\textbf{} & Least Squares & \\textbf{ Prob (F-statistic):} & 9.90e-230 \\\\\n", + "\\textbf{Date:} & Tue, 22 Oct 2024 & \\textbf{ } & \\\\\n", + "\\textbf{Time:} & 01:09:22 & \\textbf{ } & \\\\\n", "\\textbf{No. Observations:} & 1000 & \\textbf{ } & \\\\\n", "\\textbf{Df Residuals:} & 998 & \\textbf{ } & \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -419,15 +419,15 @@ "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", - "\\textbf{Intercept} & 8.7593 & 0.893 & 9.811 & 0.000 & 7.007 & 10.511 \\\\\n", - "\\textbf{education} & 4.1309 & 0.097 & 42.604 & 0.000 & 3.941 & 4.321 \\\\\n", + "\\textbf{Intercept} & 8.8450 & 0.899 & 9.836 & 0.000 & 7.080 & 10.610 \\\\\n", + "\\textbf{education} & 4.1532 & 0.096 & 43.050 & 0.000 & 3.964 & 4.343 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 3.915 & \\textbf{ Durbin-Watson: } & 2.128 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.141 & \\textbf{ Jarque-Bera (JB): } & 4.009 \\\\\n", - "\\textbf{Skew:} & 0.096 & \\textbf{ Prob(JB): } & 0.135 \\\\\n", - "\\textbf{Kurtosis:} & 3.243 & \\textbf{ Cond. No. } & 25.2 \\\\\n", + "\\textbf{Omnibus:} & 0.661 & \\textbf{ Durbin-Watson: } & 1.971 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.719 & \\textbf{ Jarque-Bera (JB): } & 0.575 \\\\\n", + "\\textbf{Skew:} & -0.054 & \\textbf{ Prob(JB): } & 0.750 \\\\\n", + "\\textbf{Kurtosis:} & 3.048 & \\textbf{ Cond. No. } & 25.9 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{IV2SLS Regression Results}\n", @@ -438,25 +438,25 @@ "\"\"\"\n", " IV2SLS Regression Results \n", "==============================================================================\n", - "Dep. Variable: income R-squared: 0.904\n", - "Model: IV2SLS Adj. R-squared: 0.904\n", - "Method: Two Stage F-statistic: 1815.\n", - " Least Squares Prob (F-statistic): 8.27e-227\n", - "Date: Sat, 19 Oct 2024 \n", - "Time: 23:43:13 \n", + "Dep. Variable: income R-squared: 0.903\n", + "Model: IV2SLS Adj. R-squared: 0.902\n", + "Method: Two Stage F-statistic: 1853.\n", + " Least Squares Prob (F-statistic): 9.90e-230\n", + "Date: Tue, 22 Oct 2024 \n", + "Time: 01:09:22 \n", "No. Observations: 1000 \n", "Df Residuals: 998 \n", "Df Model: 1 \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "Intercept 8.7593 0.893 9.811 0.000 7.007 10.511\n", - "education 4.1309 0.097 42.604 0.000 3.941 4.321\n", + "Intercept 8.8450 0.899 9.836 0.000 7.080 10.610\n", + "education 4.1532 0.096 43.050 0.000 3.964 4.343\n", "==============================================================================\n", - "Omnibus: 3.915 Durbin-Watson: 2.128\n", - "Prob(Omnibus): 0.141 Jarque-Bera (JB): 4.009\n", - "Skew: 0.096 Prob(JB): 0.135\n", - "Kurtosis: 3.243 Cond. No. 25.2\n", + "Omnibus: 0.661 Durbin-Watson: 1.971\n", + "Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.575\n", + "Skew: -0.054 Prob(JB): 0.750\n", + "Kurtosis: 3.048 Cond. No. 25.9\n", "==============================================================================\n", "\"\"\"" ] diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb index 34bbbc96c..b4739cdaf 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_api.ipynb @@ -13,10 +13,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:14.815124Z", - "iopub.status.busy": "2024-10-19T23:43:14.814950Z", - "iopub.status.idle": "2024-10-19T23:43:16.221720Z", - "shell.execute_reply": "2024-10-19T23:43:16.221067Z" + "iopub.execute_input": "2024-10-22T01:09:24.919407Z", + "iopub.status.busy": "2024-10-22T01:09:24.918967Z", + "iopub.status.idle": "2024-10-22T01:09:26.468497Z", + "shell.execute_reply": "2024-10-22T01:09:26.467766Z" } }, "outputs": [], @@ -36,10 +36,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.224066Z", - "iopub.status.busy": "2024-10-19T23:43:16.223760Z", - "iopub.status.idle": "2024-10-19T23:43:16.287489Z", - "shell.execute_reply": "2024-10-19T23:43:16.286909Z" + "iopub.execute_input": "2024-10-22T01:09:26.470980Z", + "iopub.status.busy": "2024-10-22T01:09:26.470670Z", + "iopub.status.idle": "2024-10-22T01:09:26.521690Z", + "shell.execute_reply": "2024-10-22T01:09:26.521072Z" } }, "outputs": [ @@ -72,33 +72,33 @@ " \n", " \n", " 0\n", - " 2.189678\n", + " 1.043443\n", " False\n", - " 5.600430\n", + " 3.288370\n", " \n", " \n", " 1\n", - " 1.388721\n", - " False\n", - " 2.093958\n", + " -0.719086\n", + " True\n", + " 3.827861\n", " \n", " \n", " 2\n", - " 0.310346\n", - " True\n", - " 8.289784\n", + " 0.967549\n", + " False\n", + " 2.757559\n", " \n", " \n", " 3\n", - " 1.913677\n", + " -0.676069\n", " False\n", - " 4.320300\n", + " -2.196697\n", " \n", " \n", " 4\n", - " 1.694236\n", - " False\n", - " 3.616701\n", + " 0.240388\n", + " True\n", + " 6.114017\n", " \n", " \n", " ...\n", @@ -108,33 +108,33 @@ " \n", " \n", " 995\n", - " -0.417768\n", - " False\n", - " -0.910359\n", + " -1.173342\n", + " True\n", + " 3.233798\n", " \n", " \n", " 996\n", - " 0.822609\n", + " 1.061021\n", " False\n", - " 0.387237\n", + " 0.766653\n", " \n", " \n", " 997\n", - " -1.059541\n", + " 1.812507\n", " True\n", - " 2.176587\n", + " 8.387034\n", " \n", " \n", " 998\n", - " 2.330745\n", + " 1.389300\n", " False\n", - " 4.708835\n", + " 1.748483\n", " \n", " \n", " 999\n", - " 0.382361\n", + " 1.041059\n", " True\n", - " 6.688796\n", + " 8.247229\n", " \n", " \n", "\n", @@ -143,17 +143,17 @@ ], "text/plain": [ " W0 v0 y\n", - "0 2.189678 False 5.600430\n", - "1 1.388721 False 2.093958\n", - "2 0.310346 True 8.289784\n", - "3 1.913677 False 4.320300\n", - "4 1.694236 False 3.616701\n", + "0 1.043443 False 3.288370\n", + "1 -0.719086 True 3.827861\n", + "2 0.967549 False 2.757559\n", + "3 -0.676069 False -2.196697\n", + "4 0.240388 True 6.114017\n", ".. ... ... ...\n", - "995 -0.417768 False -0.910359\n", - "996 0.822609 False 0.387237\n", - "997 -1.059541 True 2.176587\n", - "998 2.330745 False 4.708835\n", - "999 0.382361 True 6.688796\n", + "995 -1.173342 True 3.233798\n", + "996 1.061021 False 0.766653\n", + "997 1.812507 True 8.387034\n", + "998 1.389300 False 1.748483\n", + "999 1.041059 True 8.247229\n", "\n", "[1000 rows x 3 columns]" ] @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.289451Z", - "iopub.status.busy": "2024-10-19T23:43:16.289091Z", - "iopub.status.idle": "2024-10-19T23:43:16.449650Z", - "shell.execute_reply": "2024-10-19T23:43:16.448998Z" + "iopub.execute_input": "2024-10-22T01:09:26.523796Z", + "iopub.status.busy": "2024-10-22T01:09:26.523412Z", + "iopub.status.idle": "2024-10-22T01:09:26.718046Z", + "shell.execute_reply": "2024-10-22T01:09:26.717483Z" }, "scrolled": true }, @@ -204,7 +204,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -227,10 +227,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.451574Z", - "iopub.status.busy": "2024-10-19T23:43:16.451383Z", - "iopub.status.idle": "2024-10-19T23:43:16.566580Z", - "shell.execute_reply": "2024-10-19T23:43:16.565965Z" + "iopub.execute_input": "2024-10-22T01:09:26.720112Z", + "iopub.status.busy": "2024-10-22T01:09:26.719729Z", + "iopub.status.idle": "2024-10-22T01:09:26.862788Z", + "shell.execute_reply": "2024-10-22T01:09:26.862198Z" } }, "outputs": [ @@ -246,7 +246,7 @@ }, { "data": { - "image/png": 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gRowYEatWrYoTTzwx6WAAQPUpKiz+r7a2toiI2GeffXa6Tz6fj3w+37mey+V255QAQA9W8sObHR0dceWVV8akSZNiwoQJO92vqakpstls51JfX1/qKQGAHq7ksJg1a1asWbMmHnjggV3u19jYGG1tbZ1La2trqacEAHq4km6F/OhHP4o///nP8cwzz8TYsWN3uW8mk4lMJlPScABAdSkqLAqFQlx22WXx8MMPR3NzcxxwwAHlmgsAqEJFhcWsWbNi0aJF8ac//SkGDx4cGzZsiIiIbDYbAwcOLMuAAED1KOoZi7lz50ZbW1ucfPLJMXr06M7lwQcfLNd8AEAVKfpWCADAzvisEAAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEim6LB45pln4owzzogxY8ZETU1NPPLII2UYCwCoRkWHxbZt2+KII46I22+/vRzzAABVrG+xB0ybNi2mTZtWjlkAgCrnGQsAIJmir1gUK5/PRz6f71zP5XLlPiUAUCFlv2LR1NQU2Wy2c6mvry/3KQGACil7WDQ2NkZbW1vn0traWu5TAgAVUvZbIZlMJjKZTLlPAwD0AEWHxdatW6OlpaVzfe3atbF69erYZ599Yr/99ks6HABQXYoOi5UrV8bkyZM712fPnh0RETNnzowFCxYkGwwAqD5Fh8XJJ58chUKhHLMAAFXOeywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACRTUljcfvvtsf/++8eAAQPihBNOiH/84x+p5wIAqlDRYfHggw/G7Nmz47rrrosXX3wxjjjiiDjttNNi06ZN5ZgPAKgiRYfFrbfeGhdffHFceOGFMX78+Ljzzjtj0KBBce+995ZjPgCgivQtZuf29vZYtWpVNDY2dm7r06dPTJkyJZYtW/a5x+Tz+cjn853rbW1tERGRy+VKmZcq1JHfXukRgDLx//I9x6f/rQuFwi73Kyos/v3vf8eOHTti5MiRXbaPHDkyXnvttc89pqmpKW644YbPbK+vry/m1AD0QNnbKj0B3W3Lli2RzWZ3+vWiwqIUjY2NMXv27M71jo6O+Oijj2LYsGFRU1NT7tMD3SiXy0V9fX20trZGXV1dpccBEioUCrFly5YYM2bMLvcrKiz23XffqK2tjY0bN3bZvnHjxhg1atTnHpPJZCKTyXTZNmTIkGJOC1SZuro6YQG90K6uVHyqqIc3+/fvH8ccc0w89dRTnds6OjriqaeeiokTJxY/IQDQqxR9K2T27Nkxc+bMOPbYY+P444+P2267LbZt2xYXXnhhOeYDAKpI0WHx3e9+Nz744IP42c9+Fhs2bIgjjzwylixZ8pkHOoE9TyaTieuuu+4ztz+BPUdN4Yv+bgQA4EvyWSEAQDLCAgBIRlgAAMkICwAgGWEBACQjLIDd8uyzz8b5558fEydOjHXr1kVExG9/+9t47rnnKjwZUAnCAijZQw89FKeddloMHDgw/vnPf3Z+knFbW1vcfPPNFZ4OqARhAZTs5z//edx5550xb9686NevX+f2SZMmxYsvvljByYBKERZAyV5//fU48cQTP7M9m83G5s2bu38goOKEBVCyUaNGRUtLy2e2P/fcc3HggQdWYCKg0oQFULKLL744rrjiinjhhReipqYm3n///Vi4cGFcffXVcemll1Z6PKACiv4QMoBPXXPNNdHR0RGnnHJKbN++PU488cTIZDJx9dVXx2WXXVbp8YAK8CFkwG5rb2+PlpaW2Lp1a4wfPz723nvvSo8EVIiwAACScSsEKNnkyZOjpqZmp1//61//2o3TAD2BsABKduSRR3ZZ/+STT2L16tWxZs2amDlzZmWGAipKWAAl++Uvf/m526+//vrYunVrN08D9ASesQCSa2lpieOPPz4++uijSo8CdDPvsQCSW7ZsWQwYMKDSYwAV4FYIULKzzz67y3qhUIj169fHypUr49prr63QVEAlCQugZNlstst6nz59Yty4cXHjjTfG1KlTKzQVUEmesQBKsmPHjvj73/8ehx12WAwdOrTS4wA9hLAASjZgwIB49dVX44ADDqj0KEAP4eFNoGQTJkyIt956q9JjAD2IKxZAyZYsWRKNjY1x0003xTHHHBN77bVXl6/X1dVVaDKgUoQFULQbb7wxfvzjH8fgwYM7t/3vV3sXCoWoqamJHTt2VGI8oIKEBVC02traWL9+fbz66qu73O+kk07qpomAnkJYAEXr06dPbNiwIUaMGFHpUYAexsObQEl29ammwJ7LFQugaH369IlsNvuFceGzQmDP482bQEluuOGGz7x5E8AVC6BonrEAdsYzFkDRPF8B7IywAIrmQiewM26FAADJuGIBACQjLACAZIQFAJCMsAAAkhEWQHLNzc1x9NFHRyaTiYMOOigWLFhQ6ZGAbiIsgKTWrl0b06dPj8mTJ8fq1avjyiuvjIsuuiiWLl1a6dGAbuDPTYGi3H333XH99dfHe++9F336/M+/Tc4888wYNmxYDB8+PBYvXhxr1qzp/No555wTmzdvjiVLllRiZKAbuWIBFGXGjBnx4Ycfxt/+9rfObR999FEsWbIkGhoaYtmyZTFlypQux5x22mmxbNmy7h4VqABhARRl6NChMW3atFi0aFHntj/+8Y+x7777xuTJk2PDhg0xcuTILseMHDkycrlc/Oc//+nucYFuJiyAojU0NMRDDz0U+Xw+IiIWLlwY55xzTpdbI8Ceyf8FgKKdccYZUSgUYvHixdHa2hrPPvtsNDQ0RETEqFGjYuPGjV3237hxY9TV1cXAgQMrMS7QjfpWegCg+gwYMCDOPvvsWLhwYbS0tMS4cePi6KOPjoiIiRMnxl/+8pcu+z/xxBMxceLESowKdDNXLICSNDQ0xOLFi+Pee+/tvFoREXHJJZfEW2+9FT/5yU/itddeizvuuCN+//vfx1VXXVXBaYHu4s9NgZJ0dHTE2LFjY/369fHmm2/GgQce2Pm15ubmuOqqq+KVV16JsWPHxrXXXhsXXHBB5YYFuo2wAACScSsEAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACTz/wC3AFUAgO0RRQAAAABJRU5ErkJggg==", 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" ] @@ -269,10 +269,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.568494Z", - "iopub.status.busy": "2024-10-19T23:43:16.568308Z", - "iopub.status.idle": "2024-10-19T23:43:16.589229Z", - "shell.execute_reply": "2024-10-19T23:43:16.588612Z" + "iopub.execute_input": "2024-10-22T01:09:26.864886Z", + "iopub.status.busy": "2024-10-22T01:09:26.864502Z", + "iopub.status.idle": "2024-10-22T01:09:26.886262Z", + "shell.execute_reply": "2024-10-22T01:09:26.885688Z" } }, "outputs": [], @@ -295,10 +295,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.591018Z", - "iopub.status.busy": "2024-10-19T23:43:16.590835Z", - "iopub.status.idle": "2024-10-19T23:43:16.599571Z", - "shell.execute_reply": "2024-10-19T23:43:16.599094Z" + "iopub.execute_input": "2024-10-22T01:09:26.888572Z", + "iopub.status.busy": "2024-10-22T01:09:26.888180Z", + "iopub.status.idle": "2024-10-22T01:09:26.897261Z", + "shell.execute_reply": "2024-10-22T01:09:26.896761Z" }, "scrolled": true }, @@ -334,43 +334,43 @@ " \n", " \n", " 0\n", - " -1.019356\n", + " 1.251503\n", " False\n", - " -2.347354\n", - " 0.500862\n", - " 1.996559\n", + " 4.648686\n", + " 0.445524\n", + " 2.244549\n", " \n", " \n", " 1\n", - " 0.721223\n", + " 2.575259\n", " False\n", - " 1.808889\n", - " 0.483995\n", - " 2.066135\n", + " 6.555148\n", + " 0.396166\n", + " 2.524197\n", " \n", " \n", " 2\n", - " 2.625687\n", + " 0.840849\n", " False\n", - " 5.881100\n", - " 0.465584\n", - " 2.147840\n", + " 1.729413\n", + " 0.461105\n", + " 2.168702\n", " \n", " \n", " 3\n", - " 1.135806\n", + " 2.991209\n", " False\n", - " 1.664204\n", - " 0.479982\n", - " 2.083411\n", + " 5.811982\n", + " 0.381035\n", + " 2.624432\n", " \n", " \n", " 4\n", - " 0.240817\n", + " 1.165415\n", " False\n", - " 0.878289\n", - " 0.488649\n", - " 2.046460\n", + " 1.450720\n", + " 0.448782\n", + " 2.228251\n", " \n", " \n", " ...\n", @@ -382,43 +382,43 @@ " \n", " \n", " 995\n", - " 1.062827\n", + " 1.095525\n", " False\n", - " 2.114951\n", - " 0.480688\n", - " 2.080350\n", + " 2.874825\n", + " 0.451431\n", + " 2.215177\n", " \n", " \n", " 996\n", - " 1.936814\n", + " 1.061021\n", " False\n", - " 4.098610\n", - " 0.472236\n", - " 2.117587\n", + " 0.766653\n", + " 0.452740\n", + " 2.208774\n", " \n", " \n", " 997\n", - " 0.820606\n", + " 1.095525\n", " False\n", - " 1.303303\n", - " 0.483033\n", - " 2.070251\n", + " 2.874825\n", + " 0.451431\n", + " 2.215177\n", " \n", " \n", " 998\n", - " -0.382583\n", + " -0.010993\n", " False\n", - " -2.777227\n", - " 0.494690\n", - " 2.021470\n", + " 0.013939\n", + " 0.493636\n", + " 2.025785\n", " \n", " \n", " 999\n", - " 0.093856\n", + " 0.379926\n", " False\n", - " -0.035757\n", - " 0.490072\n", - " 2.040515\n", + " 0.652875\n", + " 0.478684\n", + " 2.089062\n", " \n", " \n", "\n", @@ -427,17 +427,17 @@ ], "text/plain": [ " W0 v0 y propensity_score weight\n", - "0 -1.019356 False -2.347354 0.500862 1.996559\n", - "1 0.721223 False 1.808889 0.483995 2.066135\n", - "2 2.625687 False 5.881100 0.465584 2.147840\n", - "3 1.135806 False 1.664204 0.479982 2.083411\n", - "4 0.240817 False 0.878289 0.488649 2.046460\n", + "0 1.251503 False 4.648686 0.445524 2.244549\n", + "1 2.575259 False 6.555148 0.396166 2.524197\n", + "2 0.840849 False 1.729413 0.461105 2.168702\n", + "3 2.991209 False 5.811982 0.381035 2.624432\n", + "4 1.165415 False 1.450720 0.448782 2.228251\n", ".. ... ... ... ... ...\n", - "995 1.062827 False 2.114951 0.480688 2.080350\n", - "996 1.936814 False 4.098610 0.472236 2.117587\n", - "997 0.820606 False 1.303303 0.483033 2.070251\n", - "998 -0.382583 False -2.777227 0.494690 2.021470\n", - "999 0.093856 False -0.035757 0.490072 2.040515\n", + "995 1.095525 False 2.874825 0.451431 2.215177\n", + "996 1.061021 False 0.766653 0.452740 2.208774\n", + "997 1.095525 False 2.874825 0.451431 2.215177\n", + "998 -0.010993 False 0.013939 0.493636 2.025785\n", + "999 0.379926 False 0.652875 0.478684 2.089062\n", "\n", "[1000 rows x 5 columns]" ] @@ -456,10 +456,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.601442Z", - "iopub.status.busy": "2024-10-19T23:43:16.601057Z", - "iopub.status.idle": "2024-10-19T23:43:16.609814Z", - "shell.execute_reply": "2024-10-19T23:43:16.609209Z" + "iopub.execute_input": "2024-10-22T01:09:26.899268Z", + "iopub.status.busy": "2024-10-22T01:09:26.898893Z", + "iopub.status.idle": "2024-10-22T01:09:26.907993Z", + "shell.execute_reply": "2024-10-22T01:09:26.907484Z" } }, "outputs": [ @@ -494,43 +494,43 @@ " \n", " \n", " 0\n", - " 0.179666\n", + " 0.212877\n", " True\n", - " 7.359646\n", - " 0.510759\n", - " 1.957871\n", + " 3.122988\n", + " 0.514930\n", + " 1.942011\n", " \n", " \n", " 1\n", - " -1.304697\n", + " 0.791773\n", " True\n", - " 2.206922\n", - " 0.496372\n", - " 2.014616\n", + " 9.022931\n", + " 0.537027\n", + " 1.862104\n", " \n", " \n", " 2\n", - " 2.146927\n", + " 0.626781\n", " True\n", - " 11.696896\n", - " 0.529794\n", - " 1.887525\n", + " 6.875759\n", + " 0.530740\n", + " 1.884162\n", " \n", " \n", " 3\n", - " 0.931729\n", + " 0.119495\n", " True\n", - " 7.280872\n", - " 0.518043\n", - " 1.930343\n", + " 6.846478\n", + " 0.511358\n", + " 1.955578\n", " \n", " \n", " 4\n", - " 0.256639\n", + " -0.397422\n", " True\n", - " 5.648205\n", - " 0.511505\n", - " 1.955016\n", + " 4.782325\n", + " 0.491573\n", + " 2.034286\n", " \n", " \n", " ...\n", @@ -542,43 +542,43 @@ " \n", " \n", " 995\n", - " -1.304697\n", + " 0.592020\n", " True\n", - " 2.206922\n", - " 0.496372\n", - " 2.014616\n", + " 7.671610\n", + " 0.529414\n", + " 1.888880\n", " \n", " \n", " 996\n", - " -0.799042\n", + " -0.193414\n", " True\n", - " 4.638932\n", - " 0.501274\n", - " 1.994918\n", + " 4.251908\n", + " 0.499382\n", + " 2.002477\n", " \n", " \n", " 997\n", - " 0.387640\n", + " -0.764507\n", " True\n", - " 7.164544\n", - " 0.512774\n", - " 1.950178\n", + " 5.038481\n", + " 0.477535\n", + " 2.094087\n", " \n", " \n", " 998\n", - " 0.036980\n", + " 0.669331\n", " True\n", - " 5.724190\n", - " 0.509376\n", - " 1.963185\n", + " 5.780822\n", + " 0.532362\n", + " 1.878420\n", " \n", " \n", " 999\n", - " 2.513577\n", + " 0.322424\n", " True\n", - " 10.959737\n", - " 0.533334\n", - " 1.874997\n", + " 5.712695\n", + " 0.519119\n", + " 1.926342\n", " \n", " \n", "\n", @@ -586,18 +586,18 @@ "" ], "text/plain": [ - " W0 v0 y propensity_score weight\n", - "0 0.179666 True 7.359646 0.510759 1.957871\n", - "1 -1.304697 True 2.206922 0.496372 2.014616\n", - "2 2.146927 True 11.696896 0.529794 1.887525\n", - "3 0.931729 True 7.280872 0.518043 1.930343\n", - "4 0.256639 True 5.648205 0.511505 1.955016\n", - ".. ... ... ... ... ...\n", - "995 -1.304697 True 2.206922 0.496372 2.014616\n", - "996 -0.799042 True 4.638932 0.501274 1.994918\n", - "997 0.387640 True 7.164544 0.512774 1.950178\n", - "998 0.036980 True 5.724190 0.509376 1.963185\n", - "999 2.513577 True 10.959737 0.533334 1.874997\n", + " W0 v0 y propensity_score weight\n", + "0 0.212877 True 3.122988 0.514930 1.942011\n", + "1 0.791773 True 9.022931 0.537027 1.862104\n", + "2 0.626781 True 6.875759 0.530740 1.884162\n", + "3 0.119495 True 6.846478 0.511358 1.955578\n", + "4 -0.397422 True 4.782325 0.491573 2.034286\n", + ".. ... ... ... ... ...\n", + "995 0.592020 True 7.671610 0.529414 1.888880\n", + "996 -0.193414 True 4.251908 0.499382 2.002477\n", + "997 -0.764507 True 5.038481 0.477535 2.094087\n", + "998 0.669331 True 5.780822 0.532362 1.878420\n", + "999 0.322424 True 5.712695 0.519119 1.926342\n", "\n", "[1000 rows x 5 columns]" ] @@ -624,21 +624,21 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.611767Z", - "iopub.status.busy": "2024-10-19T23:43:16.611334Z", - "iopub.status.idle": "2024-10-19T23:43:16.657554Z", - "shell.execute_reply": "2024-10-19T23:43:16.656955Z" + "iopub.execute_input": "2024-10-22T01:09:26.909808Z", + "iopub.status.busy": "2024-10-22T01:09:26.909611Z", + "iopub.status.idle": "2024-10-22T01:09:26.958108Z", + "shell.execute_reply": "2024-10-22T01:09:26.957524Z" } }, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 4.97996296363016$" + "$\\displaystyle 4.8534431379023$" ], "text/plain": [ - "4.979962963630156" + "4.8534431379023" ] }, "execution_count": 8, @@ -655,21 +655,21 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.659442Z", - "iopub.status.busy": "2024-10-19T23:43:16.659091Z", - "iopub.status.idle": "2024-10-19T23:43:16.674816Z", - "shell.execute_reply": "2024-10-19T23:43:16.674338Z" + "iopub.execute_input": "2024-10-22T01:09:26.960001Z", + "iopub.status.busy": "2024-10-22T01:09:26.959769Z", + "iopub.status.idle": "2024-10-22T01:09:26.977067Z", + "shell.execute_reply": "2024-10-22T01:09:26.976461Z" } }, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 0.232608819702524$" + "$\\displaystyle 0.21798855853523$" ], "text/plain": [ - "0.23260881970252437" + "0.21798855853522991" ] }, "execution_count": 9, @@ -693,10 +693,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.676669Z", - "iopub.status.busy": "2024-10-19T23:43:16.676326Z", - "iopub.status.idle": "2024-10-19T23:43:16.769138Z", - "shell.execute_reply": "2024-10-19T23:43:16.768581Z" + "iopub.execute_input": "2024-10-22T01:09:26.979075Z", + "iopub.status.busy": "2024-10-22T01:09:26.978875Z", + "iopub.status.idle": "2024-10-22T01:09:26.997555Z", + "shell.execute_reply": "2024-10-22T01:09:26.996869Z" } }, "outputs": [ @@ -706,19 +706,19 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -738,24 +738,24 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.970Dep. Variable: y R-squared (uncentered): 0.963
Model: OLS Adj. R-squared (uncentered): 0.970Model: OLS Adj. R-squared (uncentered): 0.963
Method: Least Squares F-statistic: 1.608e+04Method: Least Squares F-statistic: 1.301e+04
Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00
Time: 23:43:16 Log-Likelihood: -1420.5Time: 01:09:26 Log-Likelihood: -1420.6
No. Observations: 1000 AIC: 2845. coef std err t P>|t| [0.025 0.975]
x1 2.3400 0.028 84.977 0.000 2.286 2.394x1 2.3089 0.031 74.423 0.000 2.248 2.370
x2 4.9894 0.050 100.109 0.000 4.892 5.087x2 5.0562 0.047 108.194 0.000 4.964 5.148
\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Omnibus: 0.410 Durbin-Watson: 2.037Omnibus: 0.524 Durbin-Watson: 2.068
Prob(Omnibus): 0.815 Jarque-Bera (JB): 0.454Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.565
Skew: -0.047 Prob(JB): 0.797Skew: 0.054 Prob(JB): 0.754
Kurtosis: 2.955 Cond. No. 2.22Kurtosis: 2.957 Cond. No. 1.74


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], @@ -763,11 +763,11 @@ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", - "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.970 \\\\\n", - "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.970 \\\\\n", - "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 1.608e+04 \\\\\n", - "\\textbf{Date:} & Sat, 19 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", - "\\textbf{Time:} & 23:43:16 & \\textbf{ Log-Likelihood: } & -1420.5 \\\\\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.963 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.963 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 1.301e+04 \\\\\n", + "\\textbf{Date:} & Tue, 22 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 01:09:26 & \\textbf{ Log-Likelihood: } & -1420.6 \\\\\n", "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 2845. \\\\\n", "\\textbf{Df Residuals:} & 998 & \\textbf{ BIC: } & 2855. \\\\\n", "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n", @@ -777,15 +777,15 @@ "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", - "\\textbf{x1} & 2.3400 & 0.028 & 84.977 & 0.000 & 2.286 & 2.394 \\\\\n", - "\\textbf{x2} & 4.9894 & 0.050 & 100.109 & 0.000 & 4.892 & 5.087 \\\\\n", + "\\textbf{x1} & 2.3089 & 0.031 & 74.423 & 0.000 & 2.248 & 2.370 \\\\\n", + "\\textbf{x2} & 5.0562 & 0.047 & 108.194 & 0.000 & 4.964 & 5.148 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 0.410 & \\textbf{ Durbin-Watson: } & 2.037 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.815 & \\textbf{ Jarque-Bera (JB): } & 0.454 \\\\\n", - "\\textbf{Skew:} & -0.047 & \\textbf{ Prob(JB): } & 0.797 \\\\\n", - "\\textbf{Kurtosis:} & 2.955 & \\textbf{ Cond. No. } & 2.22 \\\\\n", + "\\textbf{Omnibus:} & 0.524 & \\textbf{ Durbin-Watson: } & 2.068 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.770 & \\textbf{ Jarque-Bera (JB): } & 0.565 \\\\\n", + "\\textbf{Skew:} & 0.054 & \\textbf{ Prob(JB): } & 0.754 \\\\\n", + "\\textbf{Kurtosis:} & 2.957 & \\textbf{ Cond. No. } & 1.74 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{OLS Regression Results}\n", @@ -800,11 +800,11 @@ "\"\"\"\n", " OLS Regression Results \n", "=======================================================================================\n", - "Dep. Variable: y R-squared (uncentered): 0.970\n", - "Model: OLS Adj. R-squared (uncentered): 0.970\n", - "Method: Least Squares F-statistic: 1.608e+04\n", - "Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00\n", - "Time: 23:43:16 Log-Likelihood: -1420.5\n", + "Dep. Variable: y R-squared (uncentered): 0.963\n", + "Model: OLS Adj. R-squared (uncentered): 0.963\n", + "Method: Least Squares F-statistic: 1.301e+04\n", + "Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 01:09:26 Log-Likelihood: -1420.6\n", "No. Observations: 1000 AIC: 2845.\n", "Df Residuals: 998 BIC: 2855.\n", "Df Model: 2 \n", @@ -812,13 +812,13 @@ "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "x1 2.3400 0.028 84.977 0.000 2.286 2.394\n", - "x2 4.9894 0.050 100.109 0.000 4.892 5.087\n", + "x1 2.3089 0.031 74.423 0.000 2.248 2.370\n", + "x2 5.0562 0.047 108.194 0.000 4.964 5.148\n", "==============================================================================\n", - "Omnibus: 0.410 Durbin-Watson: 2.037\n", - "Prob(Omnibus): 0.815 Jarque-Bera (JB): 0.454\n", - "Skew: -0.047 Prob(JB): 0.797\n", - "Kurtosis: 2.955 Cond. No. 2.22\n", + "Omnibus: 0.524 Durbin-Watson: 2.068\n", + "Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.565\n", + "Skew: 0.054 Prob(JB): 0.754\n", + "Kurtosis: 2.957 Cond. No. 1.74\n", "==============================================================================\n", "\n", "Notes:\n", diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb index 1848548e3..d226a977b 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_causal_discovery_example.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:18.347869Z", - "iopub.status.busy": "2024-10-19T23:43:18.347667Z", - "iopub.status.idle": "2024-10-19T23:43:19.776909Z", - "shell.execute_reply": "2024-10-19T23:43:19.776292Z" + "iopub.execute_input": "2024-10-22T01:09:28.857104Z", + "iopub.status.busy": "2024-10-22T01:09:28.856908Z", + "iopub.status.idle": "2024-10-22T01:09:30.487298Z", + "shell.execute_reply": "2024-10-22T01:09:30.486670Z" } }, "outputs": [], @@ -47,10 +47,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:19.779448Z", - "iopub.status.busy": "2024-10-19T23:43:19.778907Z", - "iopub.status.idle": "2024-10-19T23:43:19.784107Z", - "shell.execute_reply": "2024-10-19T23:43:19.783474Z" + "iopub.execute_input": "2024-10-22T01:09:30.489881Z", + "iopub.status.busy": "2024-10-22T01:09:30.489379Z", + "iopub.status.idle": "2024-10-22T01:09:30.494723Z", + "shell.execute_reply": "2024-10-22T01:09:30.494223Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:19.786095Z", - "iopub.status.busy": "2024-10-19T23:43:19.785639Z", - "iopub.status.idle": "2024-10-19T23:43:19.798955Z", - "shell.execute_reply": "2024-10-19T23:43:19.798476Z" + "iopub.execute_input": "2024-10-22T01:09:30.496770Z", + "iopub.status.busy": "2024-10-22T01:09:30.496362Z", + "iopub.status.idle": 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"model_name": "LayoutModel", diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb index 8c9f32c40..cdc3b8b94 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_confounder_example.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:14.683729Z", - "iopub.status.busy": "2024-10-19T23:48:14.683547Z", - "iopub.status.idle": "2024-10-19T23:48:16.122205Z", - "shell.execute_reply": "2024-10-19T23:48:16.121531Z" + "iopub.execute_input": "2024-10-22T01:14:25.010514Z", + "iopub.status.busy": "2024-10-22T01:14:25.010307Z", + "iopub.status.idle": "2024-10-22T01:14:26.605358Z", + "shell.execute_reply": "2024-10-22T01:14:26.604652Z" } }, "outputs": [], @@ -61,10 +61,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.124553Z", - "iopub.status.busy": "2024-10-19T23:48:16.124256Z", - "iopub.status.idle": "2024-10-19T23:48:16.132491Z", - "shell.execute_reply": "2024-10-19T23:48:16.132002Z" + "iopub.execute_input": "2024-10-22T01:14:26.607987Z", + "iopub.status.busy": "2024-10-22T01:14:26.607451Z", + "iopub.status.idle": "2024-10-22T01:14:26.615962Z", + "shell.execute_reply": "2024-10-22T01:14:26.615398Z" } }, "outputs": [ @@ -73,11 +73,11 @@ "output_type": "stream", "text": [ " Treatment Outcome w0\n", - "0 6.176718 12.620508 0.327005\n", - "1 8.459975 16.672067 2.160446\n", - "2 1.045561 2.423261 -4.774266\n", - "3 6.610747 13.749108 1.021957\n", - "4 5.539514 10.965522 -0.768080\n" + "0 2.429087 4.844044 -3.608025\n", + "1 5.686840 11.918230 -0.140240\n", + "2 9.558291 19.287474 3.546276\n", + "3 9.146309 18.691248 3.308028\n", + "4 2.140224 4.728417 -3.769274\n" ] } ], @@ -96,16 +96,16 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.134416Z", - "iopub.status.busy": "2024-10-19T23:48:16.133997Z", - "iopub.status.idle": "2024-10-19T23:48:16.496685Z", - "shell.execute_reply": "2024-10-19T23:48:16.496045Z" + "iopub.execute_input": "2024-10-22T01:14:26.617737Z", + "iopub.status.busy": "2024-10-22T01:14:26.617556Z", + "iopub.status.idle": "2024-10-22T01:14:26.990732Z", + "shell.execute_reply": "2024-10-22T01:14:26.989804Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -133,10 +133,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.498832Z", - "iopub.status.busy": "2024-10-19T23:48:16.498482Z", - "iopub.status.idle": "2024-10-19T23:48:16.624218Z", - "shell.execute_reply": "2024-10-19T23:48:16.623648Z" + "iopub.execute_input": "2024-10-22T01:14:26.993209Z", + "iopub.status.busy": "2024-10-22T01:14:26.992686Z", + "iopub.status.idle": "2024-10-22T01:14:27.125229Z", + "shell.execute_reply": "2024-10-22T01:14:27.124625Z" } }, "outputs": [ @@ -173,10 +173,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.626347Z", - "iopub.status.busy": "2024-10-19T23:48:16.626127Z", - "iopub.status.idle": "2024-10-19T23:48:16.631961Z", - "shell.execute_reply": "2024-10-19T23:48:16.631398Z" + "iopub.execute_input": "2024-10-22T01:14:27.127797Z", + "iopub.status.busy": "2024-10-22T01:14:27.127445Z", + "iopub.status.idle": "2024-10-22T01:14:27.132263Z", + "shell.execute_reply": "2024-10-22T01:14:27.131784Z" } }, "outputs": [ @@ -209,10 +209,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.634023Z", - "iopub.status.busy": "2024-10-19T23:48:16.633808Z", - "iopub.status.idle": "2024-10-19T23:48:16.641632Z", - "shell.execute_reply": "2024-10-19T23:48:16.641029Z" + "iopub.execute_input": "2024-10-22T01:14:27.134465Z", + "iopub.status.busy": "2024-10-22T01:14:27.134068Z", + "iopub.status.idle": "2024-10-22T01:14:27.141795Z", + "shell.execute_reply": "2024-10-22T01:14:27.141255Z" } }, "outputs": [ @@ -262,10 +262,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.643917Z", - "iopub.status.busy": "2024-10-19T23:48:16.643686Z", - "iopub.status.idle": "2024-10-19T23:48:17.055305Z", - "shell.execute_reply": "2024-10-19T23:48:17.054636Z" + "iopub.execute_input": "2024-10-22T01:14:27.143767Z", + "iopub.status.busy": "2024-10-22T01:14:27.143380Z", + "iopub.status.idle": "2024-10-22T01:14:27.548052Z", + "shell.execute_reply": "2024-10-22T01:14:27.547496Z" } }, "outputs": [ @@ -273,12 +273,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 0.9829405717720379\n" + "Causal Estimate is 0.005268900430303702\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -308,10 +308,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:17.057465Z", - "iopub.status.busy": "2024-10-19T23:48:17.057096Z", - "iopub.status.idle": "2024-10-19T23:48:17.060111Z", - "shell.execute_reply": "2024-10-19T23:48:17.059577Z" + "iopub.execute_input": "2024-10-22T01:14:27.550098Z", + "iopub.status.busy": "2024-10-22T01:14:27.549724Z", + "iopub.status.idle": "2024-10-22T01:14:27.552794Z", + "shell.execute_reply": "2024-10-22T01:14:27.552286Z" } }, "outputs": [ @@ -319,8 +319,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "DoWhy estimate is 0.9829405717720379\n", - "Actual true causal effect was 1\n" + "DoWhy estimate is 0.005268900430303702\n", + "Actual true causal effect was 0\n" ] } ], @@ -350,10 +350,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:17.062009Z", - "iopub.status.busy": "2024-10-19T23:48:17.061645Z", - "iopub.status.idle": "2024-10-19T23:48:18.791934Z", - "shell.execute_reply": "2024-10-19T23:48:18.790937Z" + "iopub.execute_input": "2024-10-22T01:14:27.554439Z", + "iopub.status.busy": "2024-10-22T01:14:27.554256Z", + "iopub.status.idle": "2024-10-22T01:14:29.321078Z", + "shell.execute_reply": "2024-10-22T01:14:29.320384Z" } }, "outputs": [ @@ -362,9 +362,9 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:0.9829405717720379\n", - "New effect:0.9829458586586253\n", - "p value:0.9199999999999999\n", + "Estimated effect:0.005268900430303702\n", + "New effect:0.005277215320695472\n", + "p value:0.92\n", "\n" ] } @@ -386,10 +386,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:18.795093Z", - "iopub.status.busy": "2024-10-19T23:48:18.794862Z", - "iopub.status.idle": "2024-10-19T23:48:20.892051Z", - "shell.execute_reply": "2024-10-19T23:48:20.891473Z" + "iopub.execute_input": "2024-10-22T01:14:29.323363Z", + "iopub.status.busy": "2024-10-22T01:14:29.323137Z", + "iopub.status.idle": "2024-10-22T01:14:31.473157Z", + "shell.execute_reply": "2024-10-22T01:14:31.472585Z" } }, "outputs": [ @@ -398,9 +398,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:0.9829405717720379\n", - "New effect:6.157230339285392e-05\n", - "p value:1.0\n", + "Estimated effect:0.005268900430303702\n", + "New effect:-7.902808168136487e-06\n", + "p value:0.96\n", "\n" ] } @@ -423,10 +423,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:20.894955Z", - "iopub.status.busy": "2024-10-19T23:48:20.894572Z", - "iopub.status.idle": "2024-10-19T23:48:22.816763Z", - "shell.execute_reply": "2024-10-19T23:48:22.815939Z" + "iopub.execute_input": "2024-10-22T01:14:31.476564Z", + "iopub.status.busy": "2024-10-22T01:14:31.475763Z", + "iopub.status.idle": "2024-10-22T01:14:33.409987Z", + "shell.execute_reply": "2024-10-22T01:14:33.409428Z" } }, "outputs": [ @@ -435,9 +435,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:0.9829405717720379\n", - "New effect:0.9825105635614834\n", - "p value:0.96\n", + "Estimated effect:0.005268900430303702\n", + "New effect:0.004281457098964161\n", + "p value:0.72\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb index 8cb517d03..2b9355b48 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb @@ -20,10 +20,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:25.233283Z", - "iopub.status.busy": "2024-10-19T23:48:25.232817Z", - "iopub.status.idle": "2024-10-19T23:48:26.663448Z", - "shell.execute_reply": "2024-10-19T23:48:26.662824Z" + "iopub.execute_input": "2024-10-22T01:14:35.598225Z", + "iopub.status.busy": "2024-10-22T01:14:35.598043Z", + "iopub.status.idle": "2024-10-22T01:14:37.056273Z", + "shell.execute_reply": "2024-10-22T01:14:37.055667Z" } }, "outputs": [], @@ -70,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.665807Z", - "iopub.status.busy": "2024-10-19T23:48:26.665486Z", - "iopub.status.idle": "2024-10-19T23:48:26.715605Z", - "shell.execute_reply": "2024-10-19T23:48:26.714517Z" + "iopub.execute_input": "2024-10-22T01:14:37.058757Z", + "iopub.status.busy": "2024-10-22T01:14:37.058446Z", + "iopub.status.idle": "2024-10-22T01:14:37.108668Z", + "shell.execute_reply": "2024-10-22T01:14:37.107822Z" } }, "outputs": [ @@ -108,55 +108,55 @@ " \n", " \n", " 0\n", - " 1.0\n", - " -0.406629\n", - " 0.657206\n", - " 13.601381\n", - " 135.071109\n", + " 0.0\n", + " -1.076630\n", + " 0.649139\n", + " -1.653619\n", + " -18.700173\n", " \n", " \n", " 1\n", - " 1.0\n", - " -0.339279\n", - " 0.137269\n", - " 9.438659\n", - " 93.251089\n", + " 0.0\n", + " -0.904553\n", + " 1.344766\n", + " 1.596628\n", + " 17.062082\n", " \n", " \n", " 2\n", - " 1.0\n", - " 1.975973\n", - " 1.463278\n", - " 22.909722\n", - " 237.632926\n", + " 0.0\n", + " -0.203244\n", + " -0.922881\n", + " -2.108656\n", + " -25.281342\n", " \n", " \n", " 3\n", - " 1.0\n", - " 2.179468\n", - " 2.888625\n", - " 27.138411\n", - " 281.855087\n", + " 0.0\n", + " 0.402820\n", + " 1.054545\n", + " 0.785004\n", + " 13.328140\n", " \n", " \n", " 4\n", - " 1.0\n", - " -0.503646\n", - " 0.315278\n", - " 11.930401\n", - " 117.719475\n", + " 0.0\n", + " -0.584076\n", + " 0.160731\n", + " 0.221487\n", + " 0.354862\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Z0 W0 W1 v0 y\n", - "0 1.0 -0.406629 0.657206 13.601381 135.071109\n", - "1 1.0 -0.339279 0.137269 9.438659 93.251089\n", - "2 1.0 1.975973 1.463278 22.909722 237.632926\n", - "3 1.0 2.179468 2.888625 27.138411 281.855087\n", - "4 1.0 -0.503646 0.315278 11.930401 117.719475" + " Z0 W0 W1 v0 y\n", + "0 0.0 -1.076630 0.649139 -1.653619 -18.700173\n", + "1 0.0 -0.904553 1.344766 1.596628 17.062082\n", + "2 0.0 -0.203244 -0.922881 -2.108656 -25.281342\n", + "3 0.0 0.402820 1.054545 0.785004 13.328140\n", + "4 0.0 -0.584076 0.160731 0.221487 0.354862" ] }, "execution_count": 2, @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.717936Z", - "iopub.status.busy": "2024-10-19T23:48:26.717696Z", - "iopub.status.idle": "2024-10-19T23:48:26.724036Z", - "shell.execute_reply": "2024-10-19T23:48:26.723465Z" + "iopub.execute_input": "2024-10-22T01:14:37.111261Z", + "iopub.status.busy": "2024-10-22T01:14:37.110779Z", + "iopub.status.idle": "2024-10-22T01:14:37.117207Z", + "shell.execute_reply": "2024-10-22T01:14:37.116683Z" } }, "outputs": [], @@ -217,10 +217,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.727080Z", - "iopub.status.busy": "2024-10-19T23:48:26.726291Z", - "iopub.status.idle": "2024-10-19T23:48:26.891726Z", - "shell.execute_reply": "2024-10-19T23:48:26.891091Z" + "iopub.execute_input": "2024-10-22T01:14:37.119586Z", + "iopub.status.busy": "2024-10-22T01:14:37.119110Z", + "iopub.status.idle": "2024-10-22T01:14:37.269856Z", + "shell.execute_reply": "2024-10-22T01:14:37.269172Z" } }, "outputs": [ @@ -260,10 +260,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.893975Z", - "iopub.status.busy": "2024-10-19T23:48:26.893752Z", - "iopub.status.idle": "2024-10-19T23:48:27.037212Z", - "shell.execute_reply": "2024-10-19T23:48:27.036607Z" + "iopub.execute_input": "2024-10-22T01:14:37.272091Z", + "iopub.status.busy": "2024-10-22T01:14:37.271860Z", + "iopub.status.idle": "2024-10-22T01:14:37.427998Z", + "shell.execute_reply": "2024-10-22T01:14:37.427308Z" } }, "outputs": [ @@ -315,10 +315,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:27.039321Z", - "iopub.status.busy": "2024-10-19T23:48:27.038949Z", - "iopub.status.idle": "2024-10-19T23:48:27.052683Z", - "shell.execute_reply": "2024-10-19T23:48:27.052196Z" + "iopub.execute_input": "2024-10-22T01:14:37.429896Z", + "iopub.status.busy": "2024-10-22T01:14:37.429701Z", + "iopub.status.idle": "2024-10-22T01:14:37.444297Z", + "shell.execute_reply": "2024-10-22T01:14:37.443665Z" }, "scrolled": true }, @@ -361,7 +361,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.997204381724089\n", + "Mean value: 10.005290320123438\n", "\n" ] } @@ -393,10 +393,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:27.054639Z", - "iopub.status.busy": "2024-10-19T23:48:27.054277Z", - "iopub.status.idle": "2024-10-19T23:48:28.321053Z", - "shell.execute_reply": "2024-10-19T23:48:28.320405Z" + "iopub.execute_input": "2024-10-22T01:14:37.446334Z", + "iopub.status.busy": "2024-10-22T01:14:37.446141Z", + "iopub.status.idle": "2024-10-22T01:14:38.839019Z", + "shell.execute_reply": "2024-10-22T01:14:38.838376Z" } }, "outputs": [ @@ -406,8 +406,8 @@ "text": [ "Refute: Use a Dummy Outcome\n", "Estimated effect:0\n", - "New effect:-8.325535477770815e-05\n", - "p value:0.94\n", + "New effect:-8.194888015547049e-05\n", + "p value:0.92\n", "\n" ] } @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:28.323207Z", - "iopub.status.busy": "2024-10-19T23:48:28.322845Z", - "iopub.status.idle": "2024-10-19T23:48:28.326154Z", - "shell.execute_reply": "2024-10-19T23:48:28.325545Z" + "iopub.execute_input": "2024-10-22T01:14:38.841006Z", + "iopub.status.busy": "2024-10-22T01:14:38.840807Z", + "iopub.status.idle": "2024-10-22T01:14:38.844219Z", + "shell.execute_reply": "2024-10-22T01:14:38.843753Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:28.328066Z", - "iopub.status.busy": "2024-10-19T23:48:28.327695Z", - "iopub.status.idle": "2024-10-19T23:48:29.536261Z", - "shell.execute_reply": "2024-10-19T23:48:29.535626Z" + "iopub.execute_input": "2024-10-22T01:14:38.846078Z", + "iopub.status.busy": "2024-10-22T01:14:38.845748Z", + "iopub.status.idle": "2024-10-22T01:14:40.082710Z", + "shell.execute_reply": "2024-10-22T01:14:40.082113Z" } }, "outputs": [ @@ -490,7 +490,7 @@ "text": [ "Refute: Use a Dummy Outcome\n", "Estimated effect:0\n", - "New effect:-0.00011766505684934831\n", + "New effect:-4.858144270064502e-05\n", "p value:0.98\n", "\n" ] diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb index a3cfbc2f4..291c10272 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_efficient_backdoor_example.ipynb @@ -60,10 +60,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:31.852576Z", - "iopub.status.busy": "2024-10-19T23:48:31.852403Z", - "iopub.status.idle": "2024-10-19T23:48:33.282706Z", - "shell.execute_reply": "2024-10-19T23:48:33.282096Z" + "iopub.execute_input": "2024-10-22T01:14:42.661189Z", + "iopub.status.busy": "2024-10-22T01:14:42.660779Z", + "iopub.status.idle": "2024-10-22T01:14:44.102280Z", + "shell.execute_reply": "2024-10-22T01:14:44.101679Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.285215Z", - "iopub.status.busy": "2024-10-19T23:48:33.284716Z", - "iopub.status.idle": "2024-10-19T23:48:33.291478Z", - "shell.execute_reply": "2024-10-19T23:48:33.290991Z" + "iopub.execute_input": "2024-10-22T01:14:44.104867Z", + "iopub.status.busy": "2024-10-22T01:14:44.104311Z", + "iopub.status.idle": "2024-10-22T01:14:44.110957Z", + "shell.execute_reply": "2024-10-22T01:14:44.110394Z" } }, "outputs": [], @@ -166,10 +166,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.293258Z", - "iopub.status.busy": "2024-10-19T23:48:33.292914Z", - "iopub.status.idle": "2024-10-19T23:48:33.477551Z", - "shell.execute_reply": "2024-10-19T23:48:33.476923Z" + "iopub.execute_input": "2024-10-22T01:14:44.112777Z", + "iopub.status.busy": "2024-10-22T01:14:44.112461Z", + "iopub.status.idle": "2024-10-22T01:14:44.276811Z", + "shell.execute_reply": "2024-10-22T01:14:44.276132Z" } }, "outputs": [ @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.480060Z", - "iopub.status.busy": "2024-10-19T23:48:33.479527Z", - "iopub.status.idle": "2024-10-19T23:48:33.483149Z", - "shell.execute_reply": "2024-10-19T23:48:33.482569Z" + "iopub.execute_input": "2024-10-22T01:14:44.278835Z", + "iopub.status.busy": "2024-10-22T01:14:44.278644Z", + "iopub.status.idle": "2024-10-22T01:14:44.281655Z", + "shell.execute_reply": "2024-10-22T01:14:44.281055Z" } }, "outputs": [], @@ -216,10 +216,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.485192Z", - "iopub.status.busy": "2024-10-19T23:48:33.484987Z", - "iopub.status.idle": "2024-10-19T23:48:33.574285Z", - "shell.execute_reply": "2024-10-19T23:48:33.573757Z" + "iopub.execute_input": "2024-10-22T01:14:44.283368Z", + "iopub.status.busy": "2024-10-22T01:14:44.283168Z", + "iopub.status.idle": "2024-10-22T01:14:44.356306Z", + "shell.execute_reply": "2024-10-22T01:14:44.355806Z" } }, "outputs": [ @@ -233,13 +233,13 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "──────────(E[injury|previous injury,contact sport,neuromusc fatigue,team motiv ↪\n", + "──────────(E[injury|neuromusc fatigue,team motivation,previous injury,tissue d ↪\n", "d[warm-up] ↪\n", "\n", "↪ \n", - "↪ ation,tissue disorder])\n", + "↪ isorder,contact sport])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,contact sport,neuromusc fatigue,team motivation,tissue disorder,U) = P(injury|warm-up,previous injury,contact sport,neuromusc fatigue,team motivation,tissue disorder)\n", + "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,neuromusc fatigue,team motivation,previous injury,tissue disorder,contact sport,U) = P(injury|warm-up,neuromusc fatigue,team motivation,previous injury,tissue disorder,contact sport)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -287,10 +287,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.576189Z", - "iopub.status.busy": "2024-10-19T23:48:33.575836Z", - "iopub.status.idle": "2024-10-19T23:48:33.647789Z", - "shell.execute_reply": "2024-10-19T23:48:33.647278Z" + "iopub.execute_input": "2024-10-22T01:14:44.358137Z", + "iopub.status.busy": "2024-10-22T01:14:44.357854Z", + "iopub.status.idle": "2024-10-22T01:14:44.430157Z", + "shell.execute_reply": "2024-10-22T01:14:44.429550Z" } }, "outputs": [ @@ -304,13 +304,13 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "──────────(E[injury|previous injury,neuromusc fatigue,team motivation,tissue d ↪\n", + "──────────(E[injury|previous injury,tissue disorder,team motivation,neuromusc ↪\n", "d[warm-up] ↪\n", "\n", "↪ \n", - "↪ isorder])\n", + "↪ fatigue])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,neuromusc fatigue,team motivation,tissue disorder,U) = P(injury|warm-up,previous injury,neuromusc fatigue,team motivation,tissue disorder)\n", + "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,tissue disorder,team motivation,neuromusc fatigue,U) = P(injury|warm-up,previous injury,tissue disorder,team motivation,neuromusc fatigue)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -351,10 +351,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.649551Z", - "iopub.status.busy": "2024-10-19T23:48:33.649220Z", - "iopub.status.idle": "2024-10-19T23:48:33.789228Z", - "shell.execute_reply": "2024-10-19T23:48:33.788625Z" + "iopub.execute_input": "2024-10-22T01:14:44.432237Z", + "iopub.status.busy": "2024-10-22T01:14:44.431878Z", + "iopub.status.idle": "2024-10-22T01:14:44.572096Z", + "shell.execute_reply": "2024-10-22T01:14:44.571559Z" } }, "outputs": [ @@ -368,9 +368,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "──────────(E[injury|previous injury,team motivation,fitness])\n", + "──────────(E[injury|previous injury,fitness,team motivation])\n", "d[warm-up] \n", - "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,team motivation,fitness,U) = P(injury|warm-up,previous injury,team motivation,fitness)\n", + "Estimand assumption 1, Unconfoundedness: If U→{warm-up} and U→injury then P(injury|warm-up,previous injury,fitness,team motivation,U) = P(injury|warm-up,previous injury,fitness,team motivation)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -433,10 +433,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.791226Z", - "iopub.status.busy": "2024-10-19T23:48:33.790890Z", - "iopub.status.idle": "2024-10-19T23:48:33.794558Z", - "shell.execute_reply": "2024-10-19T23:48:33.794078Z" + "iopub.execute_input": "2024-10-22T01:14:44.574215Z", + "iopub.status.busy": "2024-10-22T01:14:44.573913Z", + "iopub.status.idle": "2024-10-22T01:14:44.577683Z", + "shell.execute_reply": "2024-10-22T01:14:44.577078Z" } }, "outputs": [], @@ -471,10 +471,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.796191Z", - "iopub.status.busy": "2024-10-19T23:48:33.796011Z", - "iopub.status.idle": "2024-10-19T23:48:33.800057Z", - "shell.execute_reply": "2024-10-19T23:48:33.799553Z" + "iopub.execute_input": "2024-10-22T01:14:44.579507Z", + "iopub.status.busy": "2024-10-22T01:14:44.579179Z", + "iopub.status.idle": "2024-10-22T01:14:44.582872Z", + "shell.execute_reply": "2024-10-22T01:14:44.582405Z" } }, "outputs": [ @@ -505,10 +505,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.801824Z", - "iopub.status.busy": "2024-10-19T23:48:33.801461Z", - "iopub.status.idle": "2024-10-19T23:48:33.927058Z", - "shell.execute_reply": "2024-10-19T23:48:33.926458Z" + "iopub.execute_input": "2024-10-22T01:14:44.584615Z", + "iopub.status.busy": "2024-10-22T01:14:44.584275Z", + "iopub.status.idle": "2024-10-22T01:14:44.710761Z", + "shell.execute_reply": "2024-10-22T01:14:44.710110Z" } }, "outputs": [ @@ -563,10 +563,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.928978Z", - "iopub.status.busy": "2024-10-19T23:48:33.928592Z", - "iopub.status.idle": "2024-10-19T23:48:33.935817Z", - "shell.execute_reply": "2024-10-19T23:48:33.935313Z" + "iopub.execute_input": "2024-10-22T01:14:44.712715Z", + "iopub.status.busy": "2024-10-22T01:14:44.712372Z", + "iopub.status.idle": "2024-10-22T01:14:44.720014Z", + "shell.execute_reply": "2024-10-22T01:14:44.719420Z" } }, "outputs": [ @@ -635,10 +635,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.937574Z", - "iopub.status.busy": "2024-10-19T23:48:33.937285Z", - "iopub.status.idle": "2024-10-19T23:48:33.940717Z", - "shell.execute_reply": "2024-10-19T23:48:33.940226Z" + "iopub.execute_input": "2024-10-22T01:14:44.721910Z", + "iopub.status.busy": "2024-10-22T01:14:44.721562Z", + "iopub.status.idle": "2024-10-22T01:14:44.724694Z", + "shell.execute_reply": "2024-10-22T01:14:44.724203Z" } }, "outputs": [], @@ -662,10 +662,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.942294Z", - "iopub.status.busy": "2024-10-19T23:48:33.942118Z", - "iopub.status.idle": "2024-10-19T23:48:33.946163Z", - "shell.execute_reply": "2024-10-19T23:48:33.945662Z" + "iopub.execute_input": "2024-10-22T01:14:44.726423Z", + "iopub.status.busy": "2024-10-22T01:14:44.726078Z", + "iopub.status.idle": "2024-10-22T01:14:44.730048Z", + "shell.execute_reply": "2024-10-22T01:14:44.729489Z" } }, "outputs": [ @@ -712,10 +712,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.947898Z", - "iopub.status.busy": "2024-10-19T23:48:33.947690Z", - "iopub.status.idle": "2024-10-19T23:48:33.952679Z", - "shell.execute_reply": "2024-10-19T23:48:33.952063Z" + "iopub.execute_input": "2024-10-22T01:14:44.731777Z", + "iopub.status.busy": "2024-10-22T01:14:44.731597Z", + "iopub.status.idle": "2024-10-22T01:14:44.736488Z", + "shell.execute_reply": "2024-10-22T01:14:44.735893Z" }, "pycharm": { "name": "#%%\n" @@ -773,10 +773,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:33.954605Z", - "iopub.status.busy": "2024-10-19T23:48:33.954265Z", - "iopub.status.idle": "2024-10-19T23:48:34.154123Z", - "shell.execute_reply": "2024-10-19T23:48:34.153433Z" + "iopub.execute_input": "2024-10-22T01:14:44.738244Z", + "iopub.status.busy": "2024-10-22T01:14:44.738060Z", + "iopub.status.idle": "2024-10-22T01:14:44.939271Z", + "shell.execute_reply": "2024-10-22T01:14:44.938726Z" }, "pycharm": { "name": "#%%\n" @@ -849,10 +849,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:34.156222Z", - "iopub.status.busy": "2024-10-19T23:48:34.155802Z", - "iopub.status.idle": "2024-10-19T23:48:34.258695Z", - "shell.execute_reply": "2024-10-19T23:48:34.258066Z" + "iopub.execute_input": "2024-10-22T01:14:44.941153Z", + "iopub.status.busy": "2024-10-22T01:14:44.940959Z", + "iopub.status.idle": "2024-10-22T01:14:45.042334Z", + "shell.execute_reply": "2024-10-22T01:14:45.041793Z" } }, "outputs": [ @@ -866,9 +866,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────(E[Y|L,R,T])\n", + "────(E[Y|R,T,L])\n", "d[X] \n", - "Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,L,R,T,U) = P(Y|X,L,R,T)\n", + "Estimand assumption 1, Unconfoundedness: If U→{X} and U→Y then P(Y|X,R,T,L,U) = P(Y|X,R,T,L)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb index 22ae98fd8..1e9384c1f 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_estimation_methods.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:37.327543Z", - "iopub.status.busy": "2024-10-19T23:48:37.327355Z", - "iopub.status.idle": "2024-10-19T23:48:37.343042Z", - "shell.execute_reply": "2024-10-19T23:48:37.342414Z" + "iopub.execute_input": "2024-10-22T01:14:48.116288Z", + "iopub.status.busy": "2024-10-22T01:14:48.116096Z", + "iopub.status.idle": "2024-10-22T01:14:48.132953Z", + "shell.execute_reply": "2024-10-22T01:14:48.132285Z" } }, "outputs": [], @@ -35,10 +35,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:37.344961Z", - "iopub.status.busy": "2024-10-19T23:48:37.344595Z", - "iopub.status.idle": "2024-10-19T23:48:38.796218Z", - "shell.execute_reply": "2024-10-19T23:48:38.795517Z" + "iopub.execute_input": "2024-10-22T01:14:48.135194Z", + "iopub.status.busy": "2024-10-22T01:14:48.134774Z", + "iopub.status.idle": "2024-10-22T01:14:49.616028Z", + "shell.execute_reply": "2024-10-22T01:14:49.615311Z" } }, "outputs": [], @@ -66,10 +66,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:38.798563Z", - "iopub.status.busy": "2024-10-19T23:48:38.798242Z", - "iopub.status.idle": "2024-10-19T23:48:39.082393Z", - "shell.execute_reply": "2024-10-19T23:48:39.081766Z" + "iopub.execute_input": "2024-10-22T01:14:49.619008Z", + "iopub.status.busy": "2024-10-22T01:14:49.618490Z", + "iopub.status.idle": "2024-10-22T01:14:49.909951Z", + "shell.execute_reply": "2024-10-22T01:14:49.909296Z" } }, "outputs": [ @@ -109,62 +109,62 @@ " \n", " 0\n", " 0.0\n", - " 0.700924\n", - " 2.003474\n", - " -1.384330\n", - " -1.077363\n", - " 1.578925\n", - " -0.015912\n", + " 0.396511\n", + " 2.284501\n", + " 1.046547\n", + " -0.186356\n", + " 0.535768\n", + " -0.387440\n", " True\n", - " 6.892037\n", + " 18.171403\n", " \n", " \n", " 1\n", " 0.0\n", - " 0.069260\n", - " 0.352670\n", - " -0.725968\n", - " -0.308867\n", - " -1.251763\n", - " 0.279309\n", - " False\n", - " -5.471840\n", + " 0.553483\n", + " 1.504512\n", + " 0.146214\n", + " -0.940229\n", + " -1.702890\n", + " 0.413664\n", + " True\n", + " 10.745640\n", " \n", " \n", " 2\n", " 0.0\n", - " 0.681990\n", - " -0.396417\n", - " -0.213663\n", - " -1.934848\n", - " 1.269992\n", - " 0.853344\n", + " 0.828142\n", + " 1.488155\n", + " -1.509005\n", + " -0.563991\n", + " -0.620357\n", + " -0.310603\n", " False\n", - " -5.707764\n", + " -6.365163\n", " \n", " \n", " 3\n", - " 1.0\n", - " 0.464012\n", - " -0.798440\n", - " -0.502134\n", - " -1.255451\n", - " -1.032031\n", - " 0.936978\n", - " True\n", - " 0.598095\n", + " 0.0\n", + " 0.849105\n", + " -0.372715\n", + " 0.372227\n", + " -0.418977\n", + " 0.210340\n", + " 0.749899\n", + " False\n", + " 3.566787\n", " \n", " \n", " 4\n", - " 1.0\n", - " 0.242159\n", - " -1.118668\n", - " 0.142072\n", - " -0.788882\n", - " 1.235869\n", - " -0.847403\n", + " 0.0\n", + " 0.905093\n", + " 1.525682\n", + " -0.156306\n", + " -1.610560\n", + " -0.407204\n", + " 0.159589\n", " True\n", - " 3.251620\n", + " 9.840732\n", " \n", " \n", " ...\n", @@ -181,62 +181,62 @@ " \n", " 9995\n", " 0.0\n", - " 0.480650\n", - " -2.274500\n", - " -1.333670\n", - " -0.846298\n", - " -0.679329\n", - " 0.319555\n", + " 0.197731\n", + " 0.557255\n", + " 0.325835\n", + " -1.212832\n", + " -1.164383\n", + " -1.807983\n", " False\n", - " -17.951348\n", + " -9.234784\n", " \n", " \n", " 9996\n", - " 0.0\n", - " 0.153102\n", - " 0.333702\n", - " -0.345303\n", - " 0.553626\n", - " -0.964486\n", - " 1.010486\n", + " 1.0\n", + " 0.510984\n", + " 0.796696\n", + " 2.164684\n", + " -2.138929\n", + " -0.917557\n", + " 0.373379\n", " True\n", - " 13.289393\n", + " 16.813862\n", " \n", " \n", " 9997\n", " 0.0\n", - " 0.315476\n", - " 0.192321\n", - " -0.560291\n", - " -0.133407\n", - " 0.101016\n", - " 1.491129\n", - " True\n", - " 12.239693\n", + " 0.440900\n", + " 0.927370\n", + " 0.256726\n", + " -2.347163\n", + " -0.547954\n", + " -0.784016\n", + " False\n", + " -5.154270\n", " \n", " \n", " 9998\n", - " 0.0\n", - " 0.729459\n", - " 0.781247\n", - " -1.315457\n", - " -0.489871\n", - " -0.292444\n", - " 1.243067\n", + " 1.0\n", + " 0.024808\n", + " -0.324998\n", + " -0.876410\n", + " 0.281398\n", + " -0.965584\n", + " 0.601558\n", " True\n", - " 6.919374\n", + " 6.902531\n", " \n", " \n", " 9999\n", " 0.0\n", - " 0.304672\n", - " 1.213176\n", - " -1.136650\n", - " -1.339962\n", - " -0.014332\n", - " -0.644697\n", - " True\n", - " -0.400405\n", + " 0.619958\n", + " -0.400779\n", + " 0.895544\n", + " -1.862085\n", + " -0.293696\n", + " 0.244533\n", + " False\n", + " 0.190840\n", " \n", " \n", "\n", @@ -245,30 +245,30 @@ ], "text/plain": [ " Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 0.0 0.700924 2.003474 -1.384330 -1.077363 1.578925 -0.015912 True \n", - "1 0.0 0.069260 0.352670 -0.725968 -0.308867 -1.251763 0.279309 False \n", - "2 0.0 0.681990 -0.396417 -0.213663 -1.934848 1.269992 0.853344 False \n", - "3 1.0 0.464012 -0.798440 -0.502134 -1.255451 -1.032031 0.936978 True \n", - "4 1.0 0.242159 -1.118668 0.142072 -0.788882 1.235869 -0.847403 True \n", + "0 0.0 0.396511 2.284501 1.046547 -0.186356 0.535768 -0.387440 True \n", + "1 0.0 0.553483 1.504512 0.146214 -0.940229 -1.702890 0.413664 True \n", + "2 0.0 0.828142 1.488155 -1.509005 -0.563991 -0.620357 -0.310603 False \n", + "3 0.0 0.849105 -0.372715 0.372227 -0.418977 0.210340 0.749899 False \n", + "4 0.0 0.905093 1.525682 -0.156306 -1.610560 -0.407204 0.159589 True \n", "... ... ... ... ... ... ... ... ... \n", - "9995 0.0 0.480650 -2.274500 -1.333670 -0.846298 -0.679329 0.319555 False \n", - "9996 0.0 0.153102 0.333702 -0.345303 0.553626 -0.964486 1.010486 True \n", - "9997 0.0 0.315476 0.192321 -0.560291 -0.133407 0.101016 1.491129 True \n", - "9998 0.0 0.729459 0.781247 -1.315457 -0.489871 -0.292444 1.243067 True \n", - "9999 0.0 0.304672 1.213176 -1.136650 -1.339962 -0.014332 -0.644697 True \n", + "9995 0.0 0.197731 0.557255 0.325835 -1.212832 -1.164383 -1.807983 False \n", + "9996 1.0 0.510984 0.796696 2.164684 -2.138929 -0.917557 0.373379 True \n", + "9997 0.0 0.440900 0.927370 0.256726 -2.347163 -0.547954 -0.784016 False \n", + "9998 1.0 0.024808 -0.324998 -0.876410 0.281398 -0.965584 0.601558 True \n", + "9999 0.0 0.619958 -0.400779 0.895544 -1.862085 -0.293696 0.244533 False \n", "\n", " y \n", - "0 6.892037 \n", - "1 -5.471840 \n", - "2 -5.707764 \n", - "3 0.598095 \n", - "4 3.251620 \n", + "0 18.171403 \n", + "1 10.745640 \n", + "2 -6.365163 \n", + "3 3.566787 \n", + "4 9.840732 \n", "... ... \n", - "9995 -17.951348 \n", - "9996 13.289393 \n", - "9997 12.239693 \n", - "9998 6.919374 \n", - "9999 -0.400405 \n", + "9995 -9.234784 \n", + "9996 16.813862 \n", + "9997 -5.154270 \n", + "9998 6.902531 \n", + "9999 0.190840 \n", "\n", "[10000 rows x 9 columns]" ] @@ -317,10 +317,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.085287Z", - "iopub.status.busy": "2024-10-19T23:48:39.084705Z", - "iopub.status.idle": "2024-10-19T23:48:39.127764Z", - "shell.execute_reply": "2024-10-19T23:48:39.127175Z" + "iopub.execute_input": "2024-10-22T01:14:49.912782Z", + "iopub.status.busy": "2024-10-22T01:14:49.912241Z", + "iopub.status.idle": "2024-10-22T01:14:49.955139Z", + "shell.execute_reply": "2024-10-22T01:14:49.954555Z" } }, "outputs": [], @@ -340,10 +340,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.130316Z", - "iopub.status.busy": "2024-10-19T23:48:39.130103Z", - "iopub.status.idle": "2024-10-19T23:48:39.294682Z", - "shell.execute_reply": "2024-10-19T23:48:39.294174Z" + "iopub.execute_input": "2024-10-22T01:14:49.957711Z", + "iopub.status.busy": "2024-10-22T01:14:49.957220Z", + "iopub.status.idle": "2024-10-22T01:14:50.135748Z", + "shell.execute_reply": "2024-10-22T01:14:50.135191Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.297096Z", - "iopub.status.busy": "2024-10-19T23:48:39.296723Z", - "iopub.status.idle": "2024-10-19T23:48:39.338994Z", - "shell.execute_reply": "2024-10-19T23:48:39.338370Z" + "iopub.execute_input": "2024-10-22T01:14:50.137813Z", + "iopub.status.busy": "2024-10-22T01:14:50.137589Z", + "iopub.status.idle": "2024-10-22T01:14:50.180652Z", + "shell.execute_reply": "2024-10-22T01:14:50.180043Z" } }, "outputs": [ @@ -402,10 +402,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.341184Z", - "iopub.status.busy": "2024-10-19T23:48:39.340968Z", - "iopub.status.idle": "2024-10-19T23:48:39.601145Z", - "shell.execute_reply": "2024-10-19T23:48:39.600463Z" + "iopub.execute_input": "2024-10-22T01:14:50.182850Z", + "iopub.status.busy": "2024-10-22T01:14:50.182639Z", + "iopub.status.idle": "2024-10-22T01:14:50.440132Z", + "shell.execute_reply": "2024-10-22T01:14:50.439457Z" } }, "outputs": [ @@ -419,9 +419,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -429,9 +429,9 @@ " ⎡ -1⎤\n", " ⎢ d ⎛ d ⎞ ⎥\n", "E⎢─────────(y)⋅⎜─────────([v₀])⎟ ⎥\n", - " ⎣d[Z₀ Z₁] ⎝d[Z₀ Z₁] ⎠ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + " ⎣d[Z₁ Z₀] ⎝d[Z₁ Z₀] ⎠ ⎦\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", @@ -459,10 +459,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.603286Z", - "iopub.status.busy": "2024-10-19T23:48:39.602916Z", - "iopub.status.idle": "2024-10-19T23:48:39.654629Z", - "shell.execute_reply": "2024-10-19T23:48:39.653784Z" + "iopub.execute_input": "2024-10-22T01:14:50.442172Z", + "iopub.status.busy": "2024-10-22T01:14:50.441833Z", + "iopub.status.idle": "2024-10-22T01:14:50.493265Z", + "shell.execute_reply": "2024-10-22T01:14:50.492684Z" } }, "outputs": [ @@ -479,19 +479,19 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.99993066743126\n", + "Mean value: 9.999992741562023\n", "p-value: [0.]\n", "\n", - "Causal Estimate is 9.99993066743126\n" + "Causal Estimate is 9.999992741562023\n" ] } ], @@ -517,10 +517,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.657279Z", - "iopub.status.busy": "2024-10-19T23:48:39.657001Z", - "iopub.status.idle": "2024-10-19T23:48:41.672000Z", - "shell.execute_reply": "2024-10-19T23:48:41.671414Z" + "iopub.execute_input": "2024-10-22T01:14:50.495753Z", + "iopub.status.busy": "2024-10-22T01:14:50.495542Z", + "iopub.status.idle": "2024-10-22T01:14:52.408904Z", + "shell.execute_reply": "2024-10-22T01:14:52.408214Z" } }, "outputs": [ @@ -537,18 +537,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: att\n", "\n", "## Estimate\n", - "Mean value: 10.37814721851544\n", + "Mean value: 10.921410307145099\n", "\n", - "Causal Estimate is 10.37814721851544\n" + "Causal Estimate is 10.921410307145099\n" ] } ], @@ -575,10 +575,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:41.674119Z", - "iopub.status.busy": "2024-10-19T23:48:41.673729Z", - "iopub.status.idle": "2024-10-19T23:48:42.102517Z", - "shell.execute_reply": "2024-10-19T23:48:42.101977Z" + "iopub.execute_input": "2024-10-22T01:14:52.411087Z", + "iopub.status.busy": "2024-10-22T01:14:52.410712Z", + "iopub.status.idle": "2024-10-22T01:14:52.907120Z", + "shell.execute_reply": "2024-10-22T01:14:52.906543Z" } }, "outputs": [ @@ -595,18 +595,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: att\n", "\n", "## Estimate\n", - "Mean value: 10.043191997278369\n", + "Mean value: 9.92168802722978\n", "\n", - "Causal Estimate is 10.043191997278369\n" + "Causal Estimate is 9.92168802722978\n" ] } ], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.104497Z", - "iopub.status.busy": "2024-10-19T23:48:42.104138Z", - "iopub.status.idle": "2024-10-19T23:48:42.161189Z", - "shell.execute_reply": "2024-10-19T23:48:42.160645Z" + "iopub.execute_input": "2024-10-22T01:14:52.909073Z", + "iopub.status.busy": "2024-10-22T01:14:52.908733Z", + "iopub.status.idle": "2024-10-22T01:14:52.956643Z", + "shell.execute_reply": "2024-10-22T01:14:52.955974Z" } }, "outputs": [ @@ -652,18 +652,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 9.863997269458439\n", + "Mean value: 10.085510450492208\n", "\n", - "Causal Estimate is 9.863997269458439\n" + "Causal Estimate is 10.085510450492208\n" ] } ], @@ -692,10 +692,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.163178Z", - "iopub.status.busy": "2024-10-19T23:48:42.162799Z", - "iopub.status.idle": "2024-10-19T23:48:42.209723Z", - "shell.execute_reply": "2024-10-19T23:48:42.209217Z" + "iopub.execute_input": "2024-10-22T01:14:52.958731Z", + "iopub.status.busy": "2024-10-22T01:14:52.958426Z", + "iopub.status.idle": "2024-10-22T01:14:53.006986Z", + "shell.execute_reply": "2024-10-22T01:14:53.006406Z" } }, "outputs": [ @@ -712,18 +712,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 10.118877157276382\n", + "Mean value: 10.69830260335515\n", "\n", - "Causal Estimate is 10.118877157276382\n" + "Causal Estimate is 10.69830260335515\n" ] } ], @@ -750,10 +750,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.211631Z", - "iopub.status.busy": "2024-10-19T23:48:42.211272Z", - "iopub.status.idle": "2024-10-19T23:48:42.249673Z", - "shell.execute_reply": "2024-10-19T23:48:42.249072Z" + "iopub.execute_input": "2024-10-22T01:14:53.008844Z", + "iopub.status.busy": "2024-10-22T01:14:53.008650Z", + "iopub.status.idle": "2024-10-22T01:14:53.049021Z", + "shell.execute_reply": "2024-10-22T01:14:53.048454Z" }, "scrolled": true }, @@ -773,9 +773,9 @@ " ⎡ -1⎤\n", " ⎢ d ⎛ d ⎞ ⎥\n", "E⎢─────────(y)⋅⎜─────────([v₀])⎟ ⎥\n", - " ⎣d[Z₀ Z₁] ⎝d[Z₀ Z₁] ⎠ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + " ⎣d[Z₁ Z₀] ⎝d[Z₁ Z₀] ⎠ ⎦\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "## Realized estimand\n", "Realized estimand: Wald Estimator\n", @@ -788,17 +788,17 @@ " ⎡ d ⎤\n", "E⎢───(v₀)⎥\n", " ⎣dZ₀ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n", "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n", "\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.419142541349753\n", + "Mean value: 10.208659706642896\n", "\n", - "Causal Estimate is 9.419142541349753\n" + "Causal Estimate is 10.208659706642896\n" ] } ], @@ -823,10 +823,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.251664Z", - "iopub.status.busy": "2024-10-19T23:48:42.251325Z", - "iopub.status.idle": "2024-10-19T23:48:42.289549Z", - "shell.execute_reply": "2024-10-19T23:48:42.288900Z" + "iopub.execute_input": "2024-10-22T01:14:53.050907Z", + "iopub.status.busy": "2024-10-22T01:14:53.050713Z", + "iopub.status.idle": "2024-10-22T01:14:53.091060Z", + "shell.execute_reply": "2024-10-22T01:14:53.090479Z" } }, "outputs": [ @@ -845,9 +845,9 @@ " ⎡ -1⎤\n", " ⎢ d ⎛ d ⎞ ⎥\n", "E⎢─────────(y)⋅⎜─────────([v₀])⎟ ⎥\n", - " ⎣d[Z₀ Z₁] ⎝d[Z₀ Z₁] ⎠ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + " ⎣d[Z₁ Z₀] ⎝d[Z₁ Z₀] ⎠ ⎦\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "## Realized estimand\n", "Realized estimand: Wald Estimator\n", @@ -860,17 +860,17 @@ " ⎡ d ⎤\n", "E⎢──────────────────(v₀)⎥\n", " ⎣dlocal_rd_variable ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n", "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n", "\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 10.018266612768816\n", + "Mean value: 4.036255557227603\n", "\n", - "Causal Estimate is 10.018266612768816\n" + "Causal Estimate is 4.036255557227603\n" ] } ], diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb index 8e8206128..4ecd01d59 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_functional_api.ipynb @@ -40,10 +40,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:45.575771Z", - "iopub.status.busy": "2024-10-19T23:48:45.575575Z", - "iopub.status.idle": "2024-10-19T23:48:47.006924Z", - "shell.execute_reply": "2024-10-19T23:48:47.006280Z" + "iopub.execute_input": "2024-10-22T01:14:56.532606Z", + "iopub.status.busy": "2024-10-22T01:14:56.532417Z", + "iopub.status.idle": "2024-10-22T01:14:58.091702Z", + "shell.execute_reply": "2024-10-22T01:14:58.091041Z" } }, "outputs": [], @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:47.009357Z", - "iopub.status.busy": "2024-10-19T23:48:47.008883Z", - "iopub.status.idle": "2024-10-19T23:48:47.039243Z", - "shell.execute_reply": "2024-10-19T23:48:47.038674Z" + "iopub.execute_input": "2024-10-22T01:14:58.094464Z", + "iopub.status.busy": "2024-10-22T01:14:58.093852Z", + "iopub.status.idle": "2024-10-22T01:14:58.126932Z", + "shell.execute_reply": "2024-10-22T01:14:58.126204Z" } }, "outputs": [ @@ -178,10 +178,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:47.041280Z", - "iopub.status.busy": "2024-10-19T23:48:47.040916Z", - "iopub.status.idle": "2024-10-19T23:48:47.060004Z", - "shell.execute_reply": "2024-10-19T23:48:47.059490Z" + "iopub.execute_input": "2024-10-22T01:14:58.129060Z", + "iopub.status.busy": "2024-10-22T01:14:58.128684Z", + "iopub.status.idle": "2024-10-22T01:14:58.149220Z", + "shell.execute_reply": "2024-10-22T01:14:58.148560Z" } }, "outputs": [ @@ -195,9 +195,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W1,W2])\n", + "─────(E[y|W1,W0,W2])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,W2,U) = P(y|v0,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -244,10 +244,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:47.062031Z", - "iopub.status.busy": "2024-10-19T23:48:47.061568Z", - "iopub.status.idle": "2024-10-19T23:48:47.080214Z", - "shell.execute_reply": "2024-10-19T23:48:47.079711Z" + "iopub.execute_input": "2024-10-22T01:14:58.151265Z", + "iopub.status.busy": "2024-10-22T01:14:58.150894Z", + "iopub.status.idle": "2024-10-22T01:14:58.171542Z", + "shell.execute_reply": "2024-10-22T01:14:58.170797Z" } }, "outputs": [ @@ -261,7 +261,7 @@ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W1+W2\n", + "b: y~v0+W1+W0+W2\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -274,7 +274,7 @@ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W1+W2\n", + "b: y~v0+W1+W0+W2\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -320,10 +320,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:47.082221Z", - "iopub.status.busy": "2024-10-19T23:48:47.081835Z", - "iopub.status.idle": "2024-10-19T23:48:48.456004Z", - "shell.execute_reply": "2024-10-19T23:48:48.455351Z" + "iopub.execute_input": "2024-10-22T01:14:58.173722Z", + "iopub.status.busy": "2024-10-22T01:14:58.173495Z", + "iopub.status.idle": "2024-10-22T01:14:59.585023Z", + "shell.execute_reply": "2024-10-22T01:14:59.584371Z" } }, "outputs": [ @@ -337,7 +337,7 @@ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W1+W2\n", + "b: y~v0+W1+W0+W2\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -389,10 +389,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:48.458055Z", - "iopub.status.busy": "2024-10-19T23:48:48.457757Z", - "iopub.status.idle": "2024-10-19T23:48:50.577005Z", - "shell.execute_reply": "2024-10-19T23:48:50.576350Z" + "iopub.execute_input": "2024-10-22T01:14:59.587243Z", + "iopub.status.busy": "2024-10-22T01:14:59.586874Z", + "iopub.status.idle": "2024-10-22T01:15:01.696671Z", + "shell.execute_reply": "2024-10-22T01:15:01.695956Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:50.579166Z", - "iopub.status.busy": "2024-10-19T23:48:50.578800Z", - "iopub.status.idle": "2024-10-19T23:48:50.583380Z", - "shell.execute_reply": "2024-10-19T23:48:50.582919Z" + "iopub.execute_input": "2024-10-22T01:15:01.698783Z", + "iopub.status.busy": "2024-10-22T01:15:01.698419Z", + "iopub.status.idle": "2024-10-22T01:15:01.703280Z", + "shell.execute_reply": "2024-10-22T01:15:01.702673Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:50.585142Z", - "iopub.status.busy": "2024-10-19T23:48:50.584795Z", - "iopub.status.idle": "2024-10-19T23:48:50.721390Z", - "shell.execute_reply": "2024-10-19T23:48:50.720727Z" + "iopub.execute_input": "2024-10-22T01:15:01.705020Z", + "iopub.status.busy": "2024-10-22T01:15:01.704832Z", + "iopub.status.idle": "2024-10-22T01:15:01.844137Z", + "shell.execute_reply": "2024-10-22T01:15:01.843467Z" } }, "outputs": [ @@ -515,9 +515,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W1,W2])\n", + "─────(E[y|W1,W0,W2])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,W2,U) = P(y|v0,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -556,10 +556,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:50.723554Z", - "iopub.status.busy": "2024-10-19T23:48:50.723174Z", - "iopub.status.idle": "2024-10-19T23:48:50.732190Z", - "shell.execute_reply": "2024-10-19T23:48:50.731573Z" + "iopub.execute_input": "2024-10-22T01:15:01.846426Z", + "iopub.status.busy": "2024-10-22T01:15:01.845945Z", + "iopub.status.idle": "2024-10-22T01:15:01.855860Z", + "shell.execute_reply": "2024-10-22T01:15:01.855198Z" } }, "outputs": [ @@ -576,12 +576,12 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W1,W2])\n", + "─────(E[y|W1,W0,W2])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,W2,U) = P(y|v0,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W1+W2\n", + "b: y~v0+W1+W0+W2\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -610,10 +610,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:50.734132Z", - "iopub.status.busy": "2024-10-19T23:48:50.733781Z", - "iopub.status.idle": "2024-10-19T23:48:51.931321Z", - "shell.execute_reply": "2024-10-19T23:48:51.930724Z" + "iopub.execute_input": "2024-10-22T01:15:01.858170Z", + "iopub.status.busy": "2024-10-22T01:15:01.857957Z", + "iopub.status.idle": "2024-10-22T01:15:03.053744Z", + "shell.execute_reply": "2024-10-22T01:15:03.053142Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb index c9038a7f6..d1403ba13 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_ihdp_data_example.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:54.340432Z", - "iopub.status.busy": "2024-10-19T23:48:54.340225Z", - "iopub.status.idle": "2024-10-19T23:48:55.766638Z", - "shell.execute_reply": "2024-10-19T23:48:55.766040Z" + "iopub.execute_input": "2024-10-22T01:15:05.708445Z", + "iopub.status.busy": "2024-10-22T01:15:05.708002Z", + "iopub.status.idle": "2024-10-22T01:15:07.163619Z", + "shell.execute_reply": "2024-10-22T01:15:07.162914Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:55.769104Z", - "iopub.status.busy": "2024-10-19T23:48:55.768601Z", - "iopub.status.idle": "2024-10-19T23:48:55.871588Z", - "shell.execute_reply": "2024-10-19T23:48:55.871056Z" + "iopub.execute_input": "2024-10-22T01:15:07.166105Z", + "iopub.status.busy": "2024-10-22T01:15:07.165670Z", + "iopub.status.idle": "2024-10-22T01:15:07.341949Z", + "shell.execute_reply": "2024-10-22T01:15:07.341394Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:55.873513Z", - "iopub.status.busy": "2024-10-19T23:48:55.873219Z", - "iopub.status.idle": "2024-10-19T23:48:56.115454Z", - "shell.execute_reply": "2024-10-19T23:48:56.114836Z" + "iopub.execute_input": "2024-10-22T01:15:07.343914Z", + "iopub.status.busy": "2024-10-22T01:15:07.343553Z", + "iopub.status.idle": "2024-10-22T01:15:07.587098Z", + "shell.execute_reply": "2024-10-22T01:15:07.586538Z" } }, "outputs": [ @@ -321,10 +321,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.117710Z", - "iopub.status.busy": "2024-10-19T23:48:56.117323Z", - "iopub.status.idle": "2024-10-19T23:48:56.136300Z", - "shell.execute_reply": "2024-10-19T23:48:56.135654Z" + "iopub.execute_input": "2024-10-22T01:15:07.589556Z", + "iopub.status.busy": "2024-10-22T01:15:07.589192Z", + "iopub.status.idle": "2024-10-22T01:15:07.614840Z", + "shell.execute_reply": "2024-10-22T01:15:07.614274Z" } }, "outputs": [ @@ -338,13 +338,13 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "────────────(E[y_factual|x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13 ↪\n", + "────────────(E[y_factual|x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x ↪\n", "d[treatment] ↪\n", "\n", "↪ \n", - "↪ ,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16])\n", + "↪ 20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16,U) = P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7,U) = P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -382,10 +382,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.138347Z", - "iopub.status.busy": "2024-10-19T23:48:56.138150Z", - "iopub.status.idle": "2024-10-19T23:48:56.164780Z", - "shell.execute_reply": "2024-10-19T23:48:56.163683Z" + "iopub.execute_input": "2024-10-22T01:15:07.616963Z", + "iopub.status.busy": "2024-10-22T01:15:07.616754Z", + "iopub.status.idle": "2024-10-22T01:15:07.643872Z", + "shell.execute_reply": "2024-10-22T01:15:07.643363Z" } }, "outputs": [ @@ -402,23 +402,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "────────────(E[y_factual|x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13 ↪\n", + "────────────(E[y_factual|x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x ↪\n", "d[treatment] ↪\n", "\n", "↪ \n", - "↪ ,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16])\n", + "↪ 20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16,U) = P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7,U) = P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7)\n", "\n", "## Realized estimand\n", - "b: y_factual~treatment+x5+x15+x22+x9+x7+x14+x4+x17+x2+x19+x18+x25+x1+x24+x13+x11+x20+x8+x23+x3+x21+x12+x10+x6+x16\n", + "b: y_factual~treatment+x3+x1+x15+x22+x9+x17+x4+x12+x10+x13+x14+x18+x19+x21+x20+x24+x5+x8+x2+x6+x11+x23+x25+x16+x7\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 3.9286717508727147\n", + "Mean value: 3.928671750872714\n", "p-value: [1.58915682e-156]\n", "\n", - "Causal Estimate is 3.9286717508727147\n", + "Causal Estimate is 3.928671750872714\n", "ATE 4.021121012430829\n" ] } @@ -450,10 +450,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.167172Z", - "iopub.status.busy": "2024-10-19T23:48:56.166962Z", - "iopub.status.idle": "2024-10-19T23:48:56.191612Z", - "shell.execute_reply": "2024-10-19T23:48:56.191066Z" + "iopub.execute_input": "2024-10-22T01:15:07.646453Z", + "iopub.status.busy": "2024-10-22T01:15:07.645987Z", + "iopub.status.idle": "2024-10-22T01:15:07.670692Z", + "shell.execute_reply": "2024-10-22T01:15:07.670143Z" } }, "outputs": [ @@ -488,10 +488,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.194643Z", - "iopub.status.busy": "2024-10-19T23:48:56.193923Z", - "iopub.status.idle": "2024-10-19T23:48:56.242752Z", - "shell.execute_reply": "2024-10-19T23:48:56.242206Z" + "iopub.execute_input": "2024-10-22T01:15:07.673232Z", + "iopub.status.busy": "2024-10-22T01:15:07.672680Z", + "iopub.status.idle": "2024-10-22T01:15:07.721444Z", + "shell.execute_reply": "2024-10-22T01:15:07.720871Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.245315Z", - "iopub.status.busy": "2024-10-19T23:48:56.245106Z", - "iopub.status.idle": "2024-10-19T23:48:56.262634Z", - "shell.execute_reply": "2024-10-19T23:48:56.262102Z" + "iopub.execute_input": "2024-10-22T01:15:07.723970Z", + "iopub.status.busy": "2024-10-22T01:15:07.723417Z", + "iopub.status.idle": "2024-10-22T01:15:07.741585Z", + "shell.execute_reply": "2024-10-22T01:15:07.741025Z" } }, "outputs": [ @@ -537,7 +537,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 4.0287482183892624\n", + "Causal Estimate is 4.028748218389719\n", "ATE 4.021121012430829\n" ] } @@ -566,10 +566,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.265649Z", - "iopub.status.busy": "2024-10-19T23:48:56.264895Z", - "iopub.status.idle": "2024-10-19T23:48:57.183003Z", - "shell.execute_reply": "2024-10-19T23:48:57.182378Z" + "iopub.execute_input": "2024-10-22T01:15:07.744007Z", + "iopub.status.busy": "2024-10-22T01:15:07.743467Z", + "iopub.status.idle": "2024-10-22T01:15:08.701617Z", + "shell.execute_reply": "2024-10-22T01:15:08.700931Z" } }, "outputs": [ @@ -578,8 +578,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:4.0287482183892624\n", - "New effect:4.028748218389261\n", + "Estimated effect:4.028748218389719\n", + "New effect:4.028748218389719\n", "p value:1.0\n", "\n" ] @@ -603,10 +603,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:57.184896Z", - "iopub.status.busy": "2024-10-19T23:48:57.184677Z", - "iopub.status.idle": "2024-10-19T23:48:58.132004Z", - "shell.execute_reply": "2024-10-19T23:48:58.131428Z" + "iopub.execute_input": "2024-10-22T01:15:08.703811Z", + "iopub.status.busy": "2024-10-22T01:15:08.703379Z", + "iopub.status.idle": "2024-10-22T01:15:09.661150Z", + "shell.execute_reply": "2024-10-22T01:15:09.660551Z" } }, "outputs": [ @@ -615,8 +615,8 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:4.0287482183892624\n", - "New effect:-0.474358716591812\n", + "Estimated effect:4.028748218389719\n", + "New effect:-0.43873349315010624\n", "p value:0.04\n", "\n" ] @@ -640,10 +640,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:58.133925Z", - "iopub.status.busy": "2024-10-19T23:48:58.133709Z", - "iopub.status.idle": "2024-10-19T23:48:59.009657Z", - "shell.execute_reply": "2024-10-19T23:48:59.009072Z" + "iopub.execute_input": "2024-10-22T01:15:09.663065Z", + "iopub.status.busy": "2024-10-22T01:15:09.662707Z", + "iopub.status.idle": "2024-10-22T01:15:10.527657Z", + "shell.execute_reply": "2024-10-22T01:15:10.527034Z" } }, "outputs": [ @@ -652,9 +652,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:4.0287482183892624\n", - "New effect:4.021811738587015\n", - "p value:0.82\n", + "Estimated effect:4.028748218389719\n", + "New effect:4.026465486389576\n", + "p value:0.88\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb index b421846ad..3b66f9852 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_interpreter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "83ffbcdf", + "id": "e0d169dc", "metadata": {}, "source": [ "# DoWhy: Interpreters for Causal Estimators\n", @@ -16,13 +16,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "a49c29c2", + "id": "e9cfbba3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:01.364992Z", - "iopub.status.busy": "2024-10-19T23:49:01.364815Z", - "iopub.status.idle": "2024-10-19T23:49:01.379907Z", - "shell.execute_reply": "2024-10-19T23:49:01.379407Z" + "iopub.execute_input": "2024-10-22T01:15:12.721690Z", + "iopub.status.busy": "2024-10-22T01:15:12.721508Z", + "iopub.status.idle": "2024-10-22T01:15:12.737959Z", + "shell.execute_reply": "2024-10-22T01:15:12.737385Z" } }, "outputs": [], @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "b2c531c3", + "id": "81d1f768", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:01.381556Z", - "iopub.status.busy": "2024-10-19T23:49:01.381380Z", - "iopub.status.idle": "2024-10-19T23:49:02.838234Z", - "shell.execute_reply": "2024-10-19T23:49:02.837614Z" + "iopub.execute_input": "2024-10-22T01:15:12.739760Z", + "iopub.status.busy": "2024-10-22T01:15:12.739596Z", + "iopub.status.idle": "2024-10-22T01:15:14.317164Z", + "shell.execute_reply": "2024-10-22T01:15:14.316509Z" } }, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "e5f1c131", + "id": "e3b85430", "metadata": {}, "source": [ "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome.\n", @@ -67,13 +67,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "16416f60", + "id": "98f3cf81", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:02.840599Z", - "iopub.status.busy": "2024-10-19T23:49:02.840121Z", - "iopub.status.idle": "2024-10-19T23:49:03.127574Z", - "shell.execute_reply": "2024-10-19T23:49:03.126992Z" + "iopub.execute_input": "2024-10-22T01:15:14.319668Z", + "iopub.status.busy": "2024-10-22T01:15:14.319293Z", + "iopub.status.idle": "2024-10-22T01:15:14.607647Z", + "shell.execute_reply": "2024-10-22T01:15:14.606864Z" } }, "outputs": [ @@ -81,7 +81,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "7633\n" + "7514\n" ] }, { @@ -120,62 +120,62 @@ " \n", " 0\n", " 0.0\n", - " 0.025228\n", - " 1.100087\n", - " 0.251319\n", - " 0.967396\n", - " -0.070943\n", - " 3\n", + " 0.686451\n", + " -1.880858\n", + " 0.312247\n", + " 0.506022\n", + " -0.432920\n", + " 1\n", " True\n", - " 2.441736\n", + " 0.667578\n", " \n", " \n", " 1\n", " 0.0\n", - " 0.050578\n", - " -1.059087\n", - " -1.024157\n", - " -0.931269\n", - " -0.008544\n", - " 2\n", + " 0.812954\n", + " 0.194572\n", + " 0.112574\n", + " -0.866071\n", + " -1.220483\n", + " 0\n", " False\n", - " -1.351059\n", + " -0.450874\n", " \n", " \n", " 2\n", " 0.0\n", - " 0.983814\n", - " -0.265495\n", - " 0.138566\n", - " -0.201379\n", - " -0.793082\n", + " 0.060108\n", + " -1.404344\n", + " 1.963471\n", + " 0.351305\n", + " 0.376100\n", " 1\n", " True\n", - " 0.703998\n", + " 2.343394\n", " \n", " \n", " 3\n", " 0.0\n", - " 0.058638\n", - " -0.389303\n", - " -0.224825\n", - " 0.529911\n", - " -1.109725\n", - " 1\n", - " False\n", - " 0.053730\n", + " 0.416385\n", + " -0.356534\n", + " -0.529168\n", + " -1.218528\n", + " -1.951671\n", + " 3\n", + " True\n", + " 0.617386\n", " \n", " \n", " 4\n", - " 0.0\n", - " 0.835845\n", - " 0.042306\n", - " 0.345074\n", - " 0.125246\n", - " -1.127950\n", - " 1\n", - " False\n", - " 0.046581\n", + " 1.0\n", + " 0.939757\n", + " -0.878371\n", + " 0.922175\n", + " -0.788306\n", + " -0.424262\n", + " 2\n", + " True\n", + " 1.445012\n", " \n", " \n", " ...\n", @@ -191,63 +191,63 @@ " \n", " \n", " 9995\n", - " 0.0\n", - " 0.601954\n", - " 0.295718\n", - " -1.746804\n", - " -0.417831\n", - " 0.891260\n", + " 1.0\n", + " 0.777358\n", + " -1.824224\n", + " -1.168284\n", + " -1.137629\n", + " -1.102965\n", " 1\n", " True\n", - " 0.074727\n", + " -1.712360\n", " \n", " \n", " 9996\n", " 0.0\n", - " 0.844898\n", - " 1.390792\n", - " -1.198053\n", - " -0.444348\n", - " 0.175100\n", - " 3\n", + " 0.626805\n", + " 0.442451\n", + " 0.021703\n", + " 0.527621\n", + " 0.276867\n", + " 1\n", " True\n", - " 0.458489\n", + " 2.129588\n", " \n", " \n", " 9997\n", - " 0.0\n", - " 0.194669\n", - " -0.483547\n", - " -2.524919\n", - " -1.561724\n", - " -0.043256\n", + " 1.0\n", + " 0.634063\n", + " -2.355253\n", + " -0.180250\n", + " -0.651788\n", + " -0.054815\n", " 3\n", " True\n", - " -1.613163\n", + " 0.016879\n", " \n", " \n", " 9998\n", " 0.0\n", - " 0.984473\n", - " 0.324328\n", - " -0.450699\n", - " 0.346091\n", - " -0.452536\n", - " 3\n", - " True\n", - " 1.252557\n", + " 0.066714\n", + " -0.069945\n", + " -0.068083\n", + " 1.255475\n", + " -0.428094\n", + " 2\n", + " False\n", + " 1.572778\n", " \n", " \n", " 9999\n", - " 1.0\n", - " 0.329549\n", - " 0.553322\n", - " -1.821996\n", - " 0.470235\n", - " -1.458865\n", - " 3\n", - " True\n", - " 0.333653\n", + " 0.0\n", + " 0.690870\n", + " -1.695679\n", + " 0.045482\n", + " -1.015905\n", + " 0.701215\n", + " 1\n", + " False\n", + " -1.382399\n", " \n", " \n", "\n", @@ -256,30 +256,30 @@ ], "text/plain": [ " Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 0.0 0.025228 1.100087 0.251319 0.967396 -0.070943 3 True \n", - "1 0.0 0.050578 -1.059087 -1.024157 -0.931269 -0.008544 2 False \n", - "2 0.0 0.983814 -0.265495 0.138566 -0.201379 -0.793082 1 True \n", - "3 0.0 0.058638 -0.389303 -0.224825 0.529911 -1.109725 1 False \n", - "4 0.0 0.835845 0.042306 0.345074 0.125246 -1.127950 1 False \n", + "0 0.0 0.686451 -1.880858 0.312247 0.506022 -0.432920 1 True \n", + "1 0.0 0.812954 0.194572 0.112574 -0.866071 -1.220483 0 False \n", + "2 0.0 0.060108 -1.404344 1.963471 0.351305 0.376100 1 True \n", + "3 0.0 0.416385 -0.356534 -0.529168 -1.218528 -1.951671 3 True \n", + "4 1.0 0.939757 -0.878371 0.922175 -0.788306 -0.424262 2 True \n", "... ... ... ... ... ... ... .. ... \n", - "9995 0.0 0.601954 0.295718 -1.746804 -0.417831 0.891260 1 True \n", - "9996 0.0 0.844898 1.390792 -1.198053 -0.444348 0.175100 3 True \n", - "9997 0.0 0.194669 -0.483547 -2.524919 -1.561724 -0.043256 3 True \n", - "9998 0.0 0.984473 0.324328 -0.450699 0.346091 -0.452536 3 True \n", - "9999 1.0 0.329549 0.553322 -1.821996 0.470235 -1.458865 3 True \n", + "9995 1.0 0.777358 -1.824224 -1.168284 -1.137629 -1.102965 1 True \n", + "9996 0.0 0.626805 0.442451 0.021703 0.527621 0.276867 1 True \n", + "9997 1.0 0.634063 -2.355253 -0.180250 -0.651788 -0.054815 3 True \n", + "9998 0.0 0.066714 -0.069945 -0.068083 1.255475 -0.428094 2 False \n", + "9999 0.0 0.690870 -1.695679 0.045482 -1.015905 0.701215 1 False \n", "\n", " y \n", - "0 2.441736 \n", - "1 -1.351059 \n", - "2 0.703998 \n", - "3 0.053730 \n", - "4 0.046581 \n", + "0 0.667578 \n", + "1 -0.450874 \n", + "2 2.343394 \n", + "3 0.617386 \n", + "4 1.445012 \n", "... ... \n", - "9995 0.074727 \n", - "9996 0.458489 \n", - "9997 -1.613163 \n", - "9998 1.252557 \n", - "9999 0.333653 \n", + "9995 -1.712360 \n", + "9996 2.129588 \n", + "9997 0.016879 \n", + "9998 1.572778 \n", + "9999 -1.382399 \n", "\n", "[10000 rows x 9 columns]" ] @@ -305,7 +305,7 @@ }, { "cell_type": "markdown", - "id": "2ea3292a", + "id": "091bc2cd", "metadata": {}, "source": [ "Note that we are using a pandas dataframe to load the data." @@ -313,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "2022753b", + "id": "893396dd", "metadata": {}, "source": [ "## Identifying the causal estimand" @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "93732956", + "id": "a0be0d1a", "metadata": {}, "source": [ "We now input a causal graph in the GML graph format." @@ -330,13 +330,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "fe9f13d2", + "id": "175662b2", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.129959Z", - "iopub.status.busy": "2024-10-19T23:49:03.129542Z", - "iopub.status.idle": "2024-10-19T23:49:03.171982Z", - "shell.execute_reply": "2024-10-19T23:49:03.171417Z" + "iopub.execute_input": "2024-10-22T01:15:14.610616Z", + "iopub.status.busy": "2024-10-22T01:15:14.609867Z", + "iopub.status.idle": "2024-10-22T01:15:14.653716Z", + "shell.execute_reply": "2024-10-22T01:15:14.653115Z" } }, "outputs": [], @@ -354,13 +354,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "728b131a", + "id": "40a97c28", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.174205Z", - "iopub.status.busy": "2024-10-19T23:49:03.173750Z", - "iopub.status.idle": "2024-10-19T23:49:03.353135Z", - "shell.execute_reply": "2024-10-19T23:49:03.352567Z" + "iopub.execute_input": "2024-10-22T01:15:14.656148Z", + "iopub.status.busy": "2024-10-22T01:15:14.655943Z", + "iopub.status.idle": "2024-10-22T01:15:14.838423Z", + "shell.execute_reply": "2024-10-22T01:15:14.837772Z" } }, "outputs": [ @@ -382,13 +382,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "90d90f25", + "id": "ccae709c", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.355562Z", - "iopub.status.busy": "2024-10-19T23:49:03.355349Z", - "iopub.status.idle": "2024-10-19T23:49:03.396296Z", - "shell.execute_reply": "2024-10-19T23:49:03.395767Z" + "iopub.execute_input": "2024-10-22T01:15:14.840730Z", + "iopub.status.busy": "2024-10-22T01:15:14.840292Z", + "iopub.status.idle": "2024-10-22T01:15:14.882910Z", + "shell.execute_reply": "2024-10-22T01:15:14.882297Z" } }, "outputs": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "5bf54323", + "id": "9ad7e25f", "metadata": {}, "source": [ "We get a causal graph. Now identification and estimation is done." @@ -419,13 +419,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "ce514903", + "id": "2a08c3a6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.398428Z", - "iopub.status.busy": "2024-10-19T23:49:03.398061Z", - "iopub.status.idle": "2024-10-19T23:49:03.655264Z", - "shell.execute_reply": "2024-10-19T23:49:03.654589Z" + "iopub.execute_input": "2024-10-22T01:15:14.885309Z", + "iopub.status.busy": "2024-10-22T01:15:14.884966Z", + "iopub.status.idle": "2024-10-22T01:15:15.150286Z", + "shell.execute_reply": "2024-10-22T01:15:15.149642Z" } }, "outputs": [ @@ -439,9 +439,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -467,7 +467,7 @@ }, { "cell_type": "markdown", - "id": "2ca12db4", + "id": "1d306793", "metadata": {}, "source": [ "## Method 1: Propensity Score Stratification\n", @@ -478,13 +478,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "c6112302", + "id": "8de65e8f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.657227Z", - "iopub.status.busy": "2024-10-19T23:49:03.657030Z", - "iopub.status.idle": "2024-10-19T23:49:04.203596Z", - "shell.execute_reply": "2024-10-19T23:49:04.202988Z" + "iopub.execute_input": "2024-10-22T01:15:15.152644Z", + "iopub.status.busy": "2024-10-22T01:15:15.152169Z", + "iopub.status.idle": "2024-10-22T01:15:15.729264Z", + "shell.execute_reply": "2024-10-22T01:15:15.728643Z" } }, "outputs": [ @@ -501,18 +501,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W4+W2+W3+W1\n", + "b: y~v0+W2+W3+W1+W0+W4\n", "Target units: att\n", "\n", "## Estimate\n", - "Mean value: 0.9899959475311039\n", + "Mean value: 0.9940453251583979\n", "\n", - "Causal Estimate is 0.9899959475311039\n" + "Causal Estimate is 0.9940453251583979\n" ] } ], @@ -526,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "5e64153a", + "id": "f7ac113b", "metadata": {}, "source": [ "### Textual Interpreter\n", @@ -537,13 +537,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "dd999510", + "id": "c9bea51f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.205749Z", - "iopub.status.busy": "2024-10-19T23:49:04.205360Z", - "iopub.status.idle": "2024-10-19T23:49:04.237761Z", - "shell.execute_reply": "2024-10-19T23:49:04.237260Z" + "iopub.execute_input": "2024-10-22T01:15:15.731378Z", + "iopub.status.busy": "2024-10-22T01:15:15.730965Z", + "iopub.status.idle": "2024-10-22T01:15:15.765371Z", + "shell.execute_reply": "2024-10-22T01:15:15.764760Z" } }, "outputs": [ @@ -551,7 +551,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9899959475311039 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" + "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9940453251583979 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" ] } ], @@ -562,7 +562,7 @@ }, { "cell_type": "markdown", - "id": "d78ba452", + "id": "7d968dcb", "metadata": {}, "source": [ "### Visual Interpreter\n", @@ -578,19 +578,19 @@ { "cell_type": "code", "execution_count": 10, - "id": "d0b99494", + "id": "b4a2cbb7", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.239468Z", - "iopub.status.busy": "2024-10-19T23:49:04.239278Z", - "iopub.status.idle": "2024-10-19T23:49:04.934119Z", - "shell.execute_reply": "2024-10-19T23:49:04.933360Z" + "iopub.execute_input": "2024-10-22T01:15:15.767548Z", + "iopub.status.busy": "2024-10-22T01:15:15.767110Z", + "iopub.status.idle": "2024-10-22T01:15:16.490140Z", + "shell.execute_reply": "2024-10-22T01:15:16.489559Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -606,7 +606,7 @@ }, { "cell_type": "markdown", - "id": "92214fb0", + "id": "6ee814c4", "metadata": {}, "source": [ "This plot shows how the SMD decreases from the unadjusted to the stratified units. " @@ -614,7 +614,7 @@ }, { "cell_type": "markdown", - "id": "433052d5", + "id": "510813b1", "metadata": {}, "source": [ "## Method 2: Propensity Score Matching\n", @@ -625,13 +625,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "92c3c44d", + "id": "e561313d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.936630Z", - "iopub.status.busy": "2024-10-19T23:49:04.936219Z", - "iopub.status.idle": "2024-10-19T23:49:04.995859Z", - "shell.execute_reply": "2024-10-19T23:49:04.995285Z" + "iopub.execute_input": "2024-10-22T01:15:16.492282Z", + "iopub.status.busy": "2024-10-22T01:15:16.491907Z", + "iopub.status.idle": "2024-10-22T01:15:16.552614Z", + "shell.execute_reply": "2024-10-22T01:15:16.552045Z" } }, "outputs": [ @@ -648,18 +648,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W4+W2+W3+W1\n", + "b: y~v0+W2+W3+W1+W0+W4\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 0.9969548315933395\n", + "Mean value: 1.0031657778165648\n", "\n", - "Causal Estimate is 0.9969548315933395\n" + "Causal Estimate is 1.0031657778165648\n" ] } ], @@ -674,13 +674,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "62608e0d", + "id": "6b6bed00", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.997953Z", - "iopub.status.busy": "2024-10-19T23:49:04.997495Z", - "iopub.status.idle": "2024-10-19T23:49:05.029628Z", - "shell.execute_reply": "2024-10-19T23:49:05.029123Z" + "iopub.execute_input": "2024-10-22T01:15:16.554654Z", + "iopub.status.busy": "2024-10-22T01:15:16.554263Z", + "iopub.status.idle": "2024-10-22T01:15:16.586510Z", + "shell.execute_reply": "2024-10-22T01:15:16.585981Z" } }, "outputs": [ @@ -688,7 +688,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9969548315933395 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" + "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0031657778165648 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" ] } ], @@ -699,7 +699,7 @@ }, { "cell_type": "markdown", - "id": "e1c2635c", + "id": "e63272ee", "metadata": {}, "source": [ "Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator." @@ -707,7 +707,7 @@ }, { "cell_type": "markdown", - "id": "bcd9f15e", + "id": "aa01387e", "metadata": {}, "source": [ "## Method 3: Weighting\n", @@ -721,13 +721,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "cad2287b", + "id": "ae674424", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:05.031479Z", - "iopub.status.busy": "2024-10-19T23:49:05.031113Z", - "iopub.status.idle": "2024-10-19T23:49:05.080436Z", - "shell.execute_reply": "2024-10-19T23:49:05.079883Z" + "iopub.execute_input": "2024-10-22T01:15:16.588442Z", + "iopub.status.busy": "2024-10-22T01:15:16.588082Z", + "iopub.status.idle": "2024-10-22T01:15:16.637085Z", + "shell.execute_reply": "2024-10-22T01:15:16.636459Z" } }, "outputs": [ @@ -744,18 +744,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W4+W2+W3+W1\n", + "b: y~v0+W2+W3+W1+W0+W4\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 1.0480577208389077\n", + "Mean value: 1.0234846748653175\n", "\n", - "Causal Estimate is 1.0480577208389077\n" + "Causal Estimate is 1.0234846748653175\n" ] } ], @@ -771,13 +771,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "54e13a2c", + "id": "6c050a3f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:05.082399Z", - "iopub.status.busy": "2024-10-19T23:49:05.081974Z", - "iopub.status.idle": "2024-10-19T23:49:05.115636Z", - "shell.execute_reply": "2024-10-19T23:49:05.115057Z" + "iopub.execute_input": "2024-10-22T01:15:16.639067Z", + "iopub.status.busy": "2024-10-22T01:15:16.638721Z", + "iopub.status.idle": "2024-10-22T01:15:16.672307Z", + "shell.execute_reply": "2024-10-22T01:15:16.671773Z" } }, "outputs": [ @@ -785,7 +785,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0480577208389077 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" + "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0234846748653175 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" ] } ], @@ -797,19 +797,19 @@ { "cell_type": "code", "execution_count": 15, - "id": "0115afed", + "id": "18a66e9b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:05.117413Z", - "iopub.status.busy": "2024-10-19T23:49:05.117225Z", - "iopub.status.idle": "2024-10-19T23:49:05.466453Z", - "shell.execute_reply": "2024-10-19T23:49:05.465819Z" + "iopub.execute_input": "2024-10-22T01:15:16.674113Z", + "iopub.status.busy": "2024-10-22T01:15:16.673919Z", + "iopub.status.idle": "2024-10-22T01:15:17.022930Z", + "shell.execute_reply": "2024-10-22T01:15:17.022368Z" } }, "outputs": [ { "data": { - "image/png": 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RUFCQatasqXHjxlnOcmSnbt262rhxo4YOHaqqVavqoYce0rPPPqvLly9n+tSlVq1aGjBggKpXr67WrVvrySef1I4dO6w+pbh8+bKmTZum2rVry9/fX6NHj852G+3YsaNKlSpldVAUExOjo0ePqkePHtn2+cyZM3rzzTfl4+MjLy8vSyD7+++/JUmff/65ypUrp9dee021atVSmzZtFBYWZvWp3q0cHR3l7Oxs+dvcfLlHcnKypk2bJk9PTwUEBKh79+6Ki4uz7AMiIiJUuXJlzZo1S7Vq1ZKfn59mz56tpKQkffbZZ1nOr1mzZjp//ryOHz8u6cZNoQ8++KCaNWtmdb3vnj17LAc1ixYtUsOGDTV+/HhVr15dQUFBevPNN3X8+HF98803Wc7n888/18MPP6ynn35aNWrUUL9+/Syfzt3q0Ucf1X//+195eHho0KBBcnJyspx1yuo9GR0drfr16yswMFAPPvigWrRooeXLl+uZZ57Jdj0XZ9SO4l077mZfW758+Ux/51v3S6mpqXrvvff02GOP6fnnn1e1atXUokULjRs3Tnv27LHab589e1ZjxoyRl5dXtpewVaxYUevXr7fsV6pVq6ahQ4cqLS3Naj1krNfRo0froYceUuPGjTV48GAdO3ZMR44csYxz4cIFvf766/Lx8VGdOnU0efJkpaSkZDoTIt3Ydj08PKzOov7777/avn37bWvAnbafjBvhZ86cqTp16qhJkyaaNWvWbcPr7bbx8+fPa9q0aapbt67q1Kmjvn376vLly5YzDMuWLZOtra3lQwAvLy+98cYbKlWqlJYtW5bl/Jo1a6a0tDTL3ysmJkb//vuvunbtahUudu/erZCQEEk5qzWff/656tatq7CwMD300EPq0qWLunXrluW4jRs3Vo8ePeTh4aEePXqoZs2alhrg7u4uW1tbOTo6qnz58rK3t1d0dLS8vLzUtGlTPfjggwoKCtKSJUsIFzlx/fp1Scpyp9WsWTOlp6fr8ccf1yeffKK//vpLTk5Oqlevnuzt7W87XV9f3zvO29fX1+pGTS8vL0myHKAY4dChQzKbzQoKCrIaHhgYKElWO38nJyd5enpafs94M2Z3EJeUlKQTJ05kmna5cuVUtWrV2xaWrDRv3lwJCQmW1/30009q0qSJJKlp06b6888/deHCBaWlpennn3+2+uQpJiZG7777riZPnpyja9QbNGhg9buXl5fl73DgwAGVLFlSjRo1shqnadOmOnHiRJaf4ks3rvmvVq2aKlSoYDU8MDBQJ0+etLpc5tbt5eDBg6pWrZqqVatmNbxJkyaWSyuy4uTkpP379+upp55ScHCwAgMDLadqTSaT1bi3/t3q1q0rs9ls9Umsn5+f1TZat25dScryUysnJyd17txZ69atsxT5TZs2ycPDQ40bN862zx4eHlY7/jJlykj6v+0uNjZW3t7eKlHi/24VCwkJyXS99N3y8fGxev9mzDvj73jw4EE1adLEap9Qrlw51a5dO9tt2t/fXy4uLvrll18k3SggQUFBatiwoeWSjBMnTujMmTMKCQlRUlKSjh8/nunSFW9vb7m5uWU5n2vXruncuXOZtpWWLVtm2aebP4G1tbWVm5tbttuqdOOT+M8++0yvvPKKtmzZon///VfVqlXLsxsvCxtqR/GuHfeyr72dmJgYJSUlZdoXZPT/5nXh4OBgtZ6z4uDgoGPHjmnQoEEKCQlRYGCgZVq39qtBgwaysbGx/J7V/r1atWpW1+VXrVpVpUuXzrIG2NjYqGfPntq8ebNlX/Ptt9+qZMmSeuSRR7Lt8522n5MnT6patWpWZ7+9vLz0n//857brIju31pysakC9evWs7ndycHBQ/fr1s62/FSpUkKenp1UNqFevnoKDgy3DLl++rKioKMv2l5NaExsbm+m+zuxqQMZ79eblvFMN2L59u+VM5YULF1SpUiXL/uVuFKsbum/n77//lo2NTZYbqY+PjyIjI/X+++/rnXfe0eTJk1WrVi299NJLatOmzW2nezc34WWk6gwZN5zd7antu5FxAHtrfzI+Pbl5Q7v5hreb3Xr95K3TzpjWrdO/3UaclWbNmsnGxkY//fSTXFxcdOrUKct1x/Xr11fJkiW1a9cuValSRUlJSZY3aHp6usaPH6+OHTtm+ynundz6t3jggQcsl+UkJSUpNTU1UwDJOLiIj4/PsiglJSVluR3cvO4zfr51/klJSYqNjc20c0hNTVVqaqpSUlKyPEhZtmyZpk+frt69e2vcuHFydXXV2bNns7wu9da+Zfz9bz4guJtxbtarVy99/PHH2r17t5o2bapNmzbpf//7n1UBu9Wt213GuBnbnclkynQ5hL29fY5vdH7ggQduO7+kpCStW7dOX375pdV4165dy/bA0M7OTk2bNtXevXvVs2dP7dmzR3369FGDBg00fvx4JScna/fu3SpXrpy8vb0tl00sWLAg05moK1eu6Ny5c5nmkXFgcOtyZ3cj8a03AdrY2GT7Xpakl156STVr1tSaNWs0YsQISTeetDJhwgTDb/orCqgdxbt23Mu+9nYy1sWECRM0adKkTO03nxm7m23j22+/1bBhw/Tf//5Xb731lsqVKycbGxu1a9cu07g5qQEZ42VXA7p166a33npLX331lbp3764vv/xSHTt2zHYbuXm+t7q5BmS1v8/4IOpe3U0N+P333zPV35SUlNs+uCEkJMTyYdLu3bvVqFEj+fv7KzExUTExMfrnn39kNpst22ZOak1W68KoGtCrVy9VrFhRH3/8sV555RWlpKSoSZMmGj9+fLaXtN2KcPH/ffPNN6pbt262fxwvLy/NmDFDZrNZUVFRWrJkiV588UVt2rTpvj/Ru3UHmlEYMjacrDaEey0eGUUo41rCDBm/31qk7kVGYbj5E/gMSUlJ2T7dIzvu7u6qW7eu9u3bp7Jly6pUqVLy9/eXdONTg8DAQP3888+Ki4uTg4OD5UzC6dOn9dtvv+ngwYOWa8olKS0tTTY2NtqwYYOWLVuW6czDzZKTk612OJcvX7b8HVxcXOTo6Gi5v+JWWV0HnPG6rE7bZqz7rArrza+tWrWqlixZkmX7zZ/i32zDhg0KCAjQ5MmTLcOyu3bz1u0v4/ebPx26m3Fu5uXlpcDAQG3cuFFlypTRyZMnsz1le7fs7e0z3VScmpp6zwcgd8vFxUUhISF68cUXs+xLdpo1a6ZFixbp0qVLOnLkiBo1aqTKlSurXLly2rdvn/bs2WM5CMoo2P369cvycoGsim3GmZpb18W9fEp6OzY2NurSpYu6dOmi5ORk/fDDD5o1a5ZeeuklrVy50pB5FCXUjuJdO+5lX3s7GfvSl19+OcuAc69PC9uwYYMqVqyouXPnWm5Mz+rDCilnNSBjWHZ/f3d3d7Vv314bN25U69attXv3bn366af3tAy3yqoGSNmHjvvl4uKiSpUqZfn9Kbe72b9Zs2b66KOPdOXKFf3888/q06ePHBwc5OfnZ9n+AgICrD5UvNdak9W6MKoGSDc+UMp4ZPLOnTs1e/ZsPf/88/ruu+9u+yFhBi6L0o3vJYiOjtYLL7yQZfu+fft04MABSTd21v7+/po6darS0tL0xx9/WMa7XRK8nYMHD1ptJBmn22rXri3pxk4lMTHR8gm5JEt/bpVdH3x9fWVra2v1tAxJlutl7+exqc7OzqpVq1amaZ87dy7LU3d3o3nz5vrtt9+0f/9+NWrUyOogumnTptq3b59+++03NWzY0JLKK1SooC+++ELr1q2z+pfxFIiMn2/n5msizWazDh8+bPk7BAQE6OrVq7py5Yo8PDws/xwdHeXi4pLtTsDf31+xsbGZbqTet2+fatasedudYkBAgE6fPi1nZ2eredrZ2als2bLZ7uBSU1MzfZqTcQ/ErdvIrTfdHTp0SCVLllTVqlUtw7LbRmvUqJFt33v16qVvv/1Wa9asUWhoaKZn098rDw8PRUdHKy0tzTJs69atd/04x3t9fwYEBCgmJsZqvXt4eOj69eu3XZZmzZrpn3/+0eeff64HH3zQcoAUFBSkn3/+WXv37rV8YlqqVCl5enrqr7/+yjSflJQUlS1bNtP03d3d5erqmulpXdndn3E3MtbNlStX9OWXX1o+jSxVqpQeffRRPf3001bXX+MGage14172tVnJGKdGjRpycXFRbGys1X6gSpUqun79+j0/4jg1NVWurq5WNSK7ft26/jO2o4ceesgy7O+//7aqYX///beSkpKsxrlVr1699PPPP2vFihWWewnuh4eHh06cOKFLly5Zhh06dCjLS7OykpMa8Ndff+nBBx+0+puYzeZMlznfrGHDhrKxsVFkZKTlEcrS/9WAX375xeqSvJzUGg8Pj1ypAenp6dq8ebPlA1F7e3u1bNlSw4YNU1xcnNW6v51iFS7S09MVHx+v+Ph4nT17Vr/99psmTpyoadOmaeDAgWrbtm2Wr9u2bZsGDx6szZs3Ky4uTsePH9eiRYvk6OhoebO4uLjoxIkTioqKuu3NRVlxdHTU+PHj9ccff+jgwYOaNm2aKlasqODgYEk3Dk5TU1O1aNEixcbGasuWLZkeN5nxCcNPP/2kw4cPZ3oTlS9fXl27drU8rSg2Nlbfffedpk+frsaNG1s+3cmp5557Ttu3b9f8+fN14sQJ7d+/X8OHD5ebm1umx7LdjZCQEF26dEnffPON5TrRDE2bNtXx48czvUFLliwpT0/PTP+cnJzk4uJi+fl2li9frh07duivv/7SjBkzFBcXp65du0q6keQ9PT01atQo7dy5U3Fxcfrhhx/01FNPaeLEidlOs1u3bnJzc1NYWJgOHjyov/76S++8845+/PFHPf/887ftT7du3eTq6qphw4Zp3759OnXqlDZt2qQePXrc9ovPAgICtGfPHu3cuVN///23Zs2apfT0dNnZ2engwYNWn6z98ccfioiI0IkTJ7R161Z9/PHHevjhh60+KbO3t7faRmfOnKkKFSrc9lu5H3nkEdnY2Ojjjz++7U18d+uRRx5RfHy8Zs6cqb/++kvbtm3TkiVL7viJXsZ744cffrA83vduPPvss/r99981efJkHT16VCdOnFBERIQ6deqU7SMYJalKlSqqXr26li1bZnWPSVBQkDZt2qTz589brbeBAwfqu+++07x58xQTE6Njx45pxowZ6tq1a7bX2/73v//Vd999p9WrV+vvv//WihUrrG6svVv29vZydHTU/v37dfToUV25ckUzZ87U6NGjdfDgQZ0+fVq//vqrNmzYcNszfkUdtYPakV3tuJd97c1u3S+VKFFCzz77rD755BMtX75cJ06c0JEjR/TKK6+oR48emT6cupOAgAAdO3ZMmzZtUmxsrN577z0dOHBADz74oA4fPmx1FiMxMVHTp09XTEyM9uzZo3fffVf+/v6W7+TI6O+4ceMUHR2to0ePasqUKXJ0dLztPRQNGzZUjRo1FBERYVgNSE1N1ZQpU3Ts2DH9/PPPmjRp0h3PcN1pG89O3759lZycrJEjRyoqKkqxsbH67LPP1KVLF0VGRmb7OgcHBwUFBWnZsmVW91cFBQVp7969io6OttzMLeWs1jzyyCOKiorS0qVL9ffff2v9+vVZ3lx/N1xcXHT48GEdOXJEFy9e1NKlSzVixAj98ssvOn36tKKjo/Xpp5/K09Pzrr8Dp1hdFnXx4kXLH9TGxkaurq6qV6+eli5davWHvtXw4cNlZ2enGTNm6Ny5c3JycpK3t7eWLFliuRTmmWee0ejRo/XEE0/opZdeko+Pz133KyQkRJ6ennruued04cIFeXt7a9GiRZYb9R599FHt379fH3/8sZYuXarAwEC9/vrr6tChg2Uafn5+atOmjT744AOtWbNG27dvzzSfyZMny93dXeHh4YqPj1eZMmXUtm3bbB9heS+6dOmi9PR0ffDBB5bi2ahRI02bNu2eP3GRbuwYS5curUuXLlmuS8zg5+cnJycn/fvvv5meUX4/7Ozs9Oqrr2ry5Mk6cuSI3Nzc9Morr6hVq1aSbhyILVu2TOHh4Ro5cqQuXbqkcuXKqUOHDpm+2+Bm7u7uWrFihWbOnKlnnnlG165d00MPPaQZM2aoS5cut+2Tm5ubPv74Y4WHh+uFF17Q5cuX9eCDD+rpp5/Wc889l+3rRowYofj4eA0dOlQODg7q3LmzJk2aJCcnJ33yySeysbHRkCFDJN14vOPx48fVs2dPpaSkqFmzZnr11VetptewYUP5+flp4MCBio+PtzwbPbvLsqQbO9iMZ2jn9B6Ym3Xs2FEnT57UypUr9emnn8rf31/Tp09Xnz59bnuZUmhoqOrXr68333xTnp6emQ6ushMUFKSlS5dq3rx5evzxx5Weni4vLy/NnTv3jtfLh4SE6KOPPrI6IA8KCtLkyZPl6+tr9Z7o2LGjbG1ttWTJEi1evFglSpSQn5+fli5dmu2npRmP6H3jjTdkZ2enli1b6tVXX1Xv3r3v6Vu8bWxsNHjwYC1atEhPPvmkli5dqmXLlmnmzJl67rnnlJycrPLly6t58+Y5evpaUUHtoHZk5272tVk9xjmr/dLAgQNVqlQprVy5UjNnzpS9vb0aNmyolStX3vP9Tn379tXx48c1adIk2djYqFWrVpo5c6ZWrVqlt956S6NGjbI8Cr1z584qUaKE+vbtq8TERAUGBmratGlW06tataq6du2ql156SXFxcfLw8NCCBQvueL/DI488oqVLl6pz58731P+sBAYGaurUqVq4cKG6deum2rVr65VXXtH06dNvWwPuZhvPioeHh1asWKG5c+eqb9++Sk1NVfXq1TVmzBj17t37tq8NCQnRTz/9ZPlyTenGfT8XL16Uq6ur5aZ5KWe1ZsCAAYqPj1dERITeffddNWrUSG+88YYeeeSRe6oB0o0PuKZNm6bevXtr+vTpWrBggWbMmKHhw4fr0qVLKlOmjBo1aqTXXnvt7idqBlAsxcbGmj09Pc0ff/zxbcdr1aqVecSIEfc8/eTkZHOzZs3MS5cuzWkXraSnp5vPnj1rTk9PtwwzmUxmT09P85IlSwyZR2GRkpJiPn/+vNWwb7/91uzp6Wk+cOBAPvUKQGHj6elpnjVr1m3Heeqpp8w9evS452mnpaWZO3fubH7ttddy2r1MLly4YE5JSbH8npqaam7UqJF58uTJhs2jMLh+/br53LlzVsMOHz5s9vT0NG/atCmfevV/itVlUQByX1JSko4dO6bhw4fLycnJ8k2092vXrl1q3ry55syZo5MnT+ro0aN65ZVX5OTkpI4dOxoyj8Ji/vz5atWqldavX6+4uDj9/PPPmjNnjurWrXtXjzAFgNxy+fJlnTx5UuPHj9eZM2c0aNAgQ6YbExOj5s2b69VXX1VMTIxiYmI0ZcoUJSYmWp0hKA7Wrl2rkJAQffjhhzp16pQOHjyoKVOmqFKlSoZcKXC/itVlUQBy34oVK7RgwQIFBgYqIiIi0+P+cio4OFizZs3SBx98oI8++kj29vby9vbWsmXLbvtFY0XRiy++aPlyp7Nnz8rd3V2NGjXSqFGjbvsUEwDIbVu2bNHYsWNVp04dLVmy5L4f5pGhZs2aWrRokRYsWKAePXrI1tZWtWrV0uLFi60uMyoOevTooeTkZEVGRmrOnDkqXbq0/P39NW3atFx5cta9sjGbc/iYCgAAAAC4CR9xAQAAADAE4QIAAACAIQgXAAAAAAzBDd268YzhlJQUw246AoDCJj4+Xvb29vrll1/yuysFCvUBQHF3r/WBcCHp2rVrSktLy+9uAEC+uX79+l1/c21xQn0AUNzda30gXEiqUKGCJOm7777L554AQP6407eOF1fUBwDF3b3WB+65AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwgUKrZ8+emjdvXn53AwAAoEDLy2OmEnkylyKo+tgv83R+J97scE/jt27dWs8995x69+5tNfzHH3/Uc889p99///2O01i9erVat24td3f3e5r33dq8ebO8vLzk4eGRK9MHAAD5j2Om+1eYjpk4c4EspaWl6c0331RCQkKuzeOdd97R33//nWvTBwAAyG0cM1kjXBRjrVu31qpVq/T8888rMDBQDz/8sHbs2CFJatSokf7991899thjmj9/vvbs2aPAwEAtW7ZM9evX12+//SZJ+uijj/TII4+oXr166tChg7Zs2WKZ/sWLFzVs2DA1bdpUQUFBeu6553T69GlJUufOnfXnn39q8ODBeuWVVyRJR48e1dNPP62goCA1adJEU6dOVWpqqmV6CxYsUEhIiBo3bqwFCxbk1WoCAADFHMdMd49wUcy99957Gjp0qPbs2aNGjRrpjTfekCStX7/e8v/QoUMlSampqfr777+1c+dOBQQEaPPmzZo/f75mzZqlffv2afjw4RoxYoT++ecfSdKsWbOUnJys7777Tj/88IMkWaa/YcMGSdK7776r6dOn68qVK3r22WcVHBysnTt3atWqVdqzZ4/ee+89SdKOHTsUERGht99+Wz/++KPMZrP++OOPvFtRAACgWOOY6e4QLoq5Vq1ayd/fX/b29mrfvr1OnDih9PT0LMdNTU3VE088IUdHR9nY2Gj16tXq3r27fH19VaJECbVr104NGjTQxo0bJUmvvfaa5s2bJycnJ5UqVUoPP/ywDh06lOW0v//+e5nNZg0cOFD29vaqWrWqBgwYYHnDfvvtt2rRooUaNGggBwcHy3gAAAB5gWOmu8MN3cVclSpVLD87OjoqLS3N6rTarf7zn/9Yfj558qR++uknffjhh5ZhZrNZtWrVkiT9/fffevPNN3Xw4EFdvXpV6enpcnNzy3K6sbGxunDhgvz8/KymlfFmOHv2rGrUqGFpK1mypFXfAQAAchPHTHeHcFFElSxZUlevXs00PCkpSQ4ODpbfbW3v7eRViRL/t8k4Ojpq5MiR6t+/f6bx0tPTNXDgQDVo0EDffPON3N3dtWrVKr311ltZTtfBwUG1a9fWF198kWV7SkqKrl+/nmkeAAAA94NjJmNxWVQRVaNGDUVHR2ca/ttvv8nT09OQeVSrVi3T49n++ecfmc1mnT9/XnFxcerTp4/lsWyHDx++7bRiY2OVnJxsGZaQkKCkpCRJUoUKFXTmzBlLW0pKimJjYw1ZDgAAUHxxzGQswkUR9eyzz+rrr7/Wp59+qsuXL+vy5ctas2aNIiMjNXbs2Du+3tHRUZJ04sQJy8Z6q8cff1ybNm3S999/r+vXr2v37t3q2LGjDhw4IHd3dzk5OWn//v26du2avvjiCx05ckRJSUmWN4ODg4P+/vtvJSUlKSQkRO7u7poxY4aSkpIUHx+v4cOHKzw8XJLUokUL7dixw3K6cP78+Zy5AAAA941jJmMRLoqooKAgrVixQps3b1arVq3UqlUrrV27VvPmzVNQUNAdX1+uXDm1b99ew4cPz/a0XLNmzTRmzBhNmTJF9evX15QpUzR58mQFBASoRIkSmjx5siIiIhQcHKy9e/dq3rx5qlSpktq1aydJ6tWrl2bOnKmXX35ZJUuW1Lvvvqvjx4+rWbNm6tKli6pXr64xY8ZIkh555BH17dtXL7zwgkJDQ2Vvb6+AgACjVhcAACimOGYylo3ZbDbn2dwKqDZt2kiSvvvuu3zuCQDkD/aDWWO9ACju7nU/yJkLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiiR3x0AwsPDdeDAAa1YsSK/uwIYb7JrHs/vUt7OD8gl1cd+mWfzOvFmhzybF3A/CsMxE+EipwrwAUP//v21d+9eSVJaWprS09NVsmRJS/vXX3+typUrG9a16OhoXbp0ScHBwYZNEwAAFBEcM1kUh2MmwkUR9P7771t+njdvnrZv367PPvss1+a3Zs0aOTk5Fek3CgAAKHo4ZjIe91wUQ15eXlq2bJlCQkIUEREhSdq1a5cef/xxBQYGqnnz5lqwYIFlfLPZrPDwcIWGhiowMFBdu3a1pPzXX39dH3/8sd5//321bdtWkmQymTRq1CiFhIQoMDBQgwYN0tmzZy3T27p1q9q3b6/AwECNGDFCV69ezcOlBwAAuDscM907zlwUU1u2bNG6detUtmxZnTlzRoMHD9akSZPUqVMnHTt2TM8++6yqVaumTp06af369Vq3bp1Wr16t8uXLa+HChRo2bJh27NihiRMn6o8//lC9evU0atQoSdLYsWNVokQJffnll7Kzs9OkSZP0yiuv6P3331diYqLCwsI0atQoPf7449q1a5dGjhwpb2/vfF4jAGA87hsACj+Ome4NZy6KqUceeUTlypWTjY2NNm7cqNq1a6tLly6ys7OTl5eXevXqpfXr10uSOnXqpK+++kqVKlWSnZ2dOnTooIsXL+qff/7JNN0LFy5o27ZtCgsLk6urq5ydnTVq1Cj99NNPio+P144dO+Tk5KQnn3xS9vb2Cg0NVVBQUF4vPgAAwF3hmOnecOaimPrPf/5j+fnkyZOKioqSn5+fZZjZbFaNGjUkSVeuXNEbb7yhH3/8UZcu/d9NUikpKZmmGxsbK0nq0qWL1XA7OzudPn1aZ86c0YMPPihb2//LtdWrV1d0dLQhywUAAGAkjpnuDeGimLKzs7P87OjoqNDQUC1atCjLcV977TX9/vvvWrlypTw8PBQbG2u5VvBWjo6OkqQff/xRZcqUydS+c+dOpaWlWQ1LT0/P6WIAKMLi4uL0xhtv6JdffpGdnZ1atGihcePGKTExUW3atJG9vb3V+CNGjNCAAQMkSZs2bdLChQt16tQp1ahRQy+99JJCQkIk3djnvP3229q4caMSExPl7++vyZMnq2rVqnm+jAAKPo6Z7g3hAqpWrZq2bNkis9ksGxsbSVJ8fLxcXV1lb2+vgwcPqkePHqpevbok3TYxV65cWba2tvr999/VpEkTSVJqaqouXryoihUrqkKFCjp79qzVvGJiYnJ3AQEUSi+88IJ8fX21detW/fvvvxoyZIhmzJihQYMGSZKioqKyfN2RI0c0ZswYzZ8/X02aNNE333yjoUOH6uuvv1alSpW0cuVKffHFF1qyZIkqVqyouXPnasiQIVq/fr1lv4RipgA/KhUFC8dMd8Y9F1CHDh1kMpn07rvv6urVq4qNjVX//v314YcfSpKqVKmiqKgopaSkaP/+/fryyxs3KJ47d06S5ODgoFOnTunSpUsqXbq0Hn30UYWHh+vMmTO6evWq5syZo/79+8tsNis4OFhJSUn69NNPlZKSoi1btujAgQP5tuwACqbExET5+vpq5MiRKlWqlCpVqqSuXbvql19+ueNrV61apdDQUIWGhsrBwUGdO3eWp6enNmzYIEmKjIxUv379VLNmTTk7OyssLEwxMTHsiwDcEcdMd8aZC6hMmTJ69913NXPmTC1atEju7u567LHH1L9/f0nSyJEjNXr0aDVq1Ej16tXTzJkzJUmDBw/WRx99pG7dumnChAlq166ddu7cqYkTJ+r1119Xhw4dZGtrq4CAAL377ruysbFRpUqVNHv2bIWHh2vGjBlq0aKFnnjiCf3222/5uQoAFDAuLi6aPn261bDTp0+rQoUKlt9Hjx6tnTt36vr16+rRo4eGDRumkiVLKjo6WqGhoVav9fHxUVRUlK5evapjx47Jx8fH0ubs7CwPDw9FRUUpICAgV5crV/HpO5DrDD9msn9Prye6qMPDzWUrKaBcqt5tkCib19xUSdLsxg4KD5+gGa+/qhb/SdETVa/rtxMXcvZ+z6P3LOEipwrJTvXFF1/Uiy++aDXs999/zzRekyZNtHbt2iyn4ePjo40bN1oNmz9/vuXnunXr6tFHH7X87ubmptmzZ2fbp/bt26t9+/Z31X8AkG5cAvXRRx9p4cKFsre3V2BgoNq2batp06bpyJEjevHFF1WiRAkNHz5cJpNJrq7WhdfV1VXHjh3TpUuXZDabs2xPSEjIy0UCig+OmSw/ZzpmcjBrdnD266d91WtqX/XaXfW/oCBcAAAKtH379mnQoEEaOXKk5VttP/30U0u7v7+/Bg4cqMWLF2v48OGSbjy95Xbu1A7gHuTlWbNCElSKs3y95yIuLk5DhgxR48aNFRwcrLFjxyoxMVHSjRvynnrqKTVo0EDt2rWz+np26caTQDp16qTAwEB169ZNO3bssLSlp6dr7ty5atOmjRo2bKgBAwZYHvcFACg8tm7dqueff17jxo1T3759sx2vcuXKOn/+vMxms8qUKSOTyWTVbjKZ5O7uLjc3N9na2mbZXrZs2VxYAgAoXvI1XLzwwgtycXHR1q1btXbtWv3555+aMWOGrl69qoEDB6pJkybavn275s6dq8WLF2vz5s2S/u9JIKNGjdLu3bvVr18/DR06VGfOnJEky5NAIiIitG3bNlWvXl1DhgzhkyoAKER+/fVXjRkzRm+//bbVc+B37dqlhQsXWo17/PhxVa5cWTY2NvL19dWhQ4es2qOiolSvXj05ODiodu3aVk9wSUxM1MmTJ+Xv75+rywMAxUG+hYvbPQnk+++/V2pqqgYNGiQnJyfVrVtXPXr0UGRkpCSeBAIARd3169c1YcIEjRo1yvL9FBlKly6tBQsWaP369UpNTVVUVJTee+899e7dW5LUs2dP7dy5U99//72uXbum1atX68SJE+rcubMkqXfv3lq+fLliYmKUlJSk8PBweXt7W30pFgAgZ/LtnovbPQkkOjpaXl5eVl9a4uPjo1WrVklS8X0SCAAUE/v371dMTIymTp2qqVOnWrV9/fXXmjt3rubPn69XX31VpUuXVp8+ffT0009Lkjw9PRUeHq7p06crLi5OtWrV0uLFi1W+fHlJUq9evRQfH68+ffooOTlZjRs3trrhEgCQcwXmhu6bnwTy1VdfycXFxardzc1NJpNJ6enpPAkEyGs84hJ5LCgoKMuntGSoXLlytt96K0nt2rVTu3btsmyzsbHRsGHDNGzYsPvuJwDAWoH4Er19+/ZpwIABVk8CycrN35zKk0AAAACAgiXfw0VWTwJxd3fPdJbBZDJZnvLBk0AAAACAgidfw0V2TwLx9fXV77//ruvXr1uGZTzpI6OdJ4EAAAAABUu+hYvbPQkkNDRUzs7OWrhwoa5cuaIDBw5o9erVPAkEAAAAKMDy7YbuOz0JZNGiRZo0aZIiIiJUrlw5hYWFqWXLlpJ4EggAAABQEOVbuLjTk0Ak6ZNPPsm2jSeBAAAAAAVLvt/QDQAAAKBoIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwRIn87gAAAABQEFQf+2Wezu+EY57OLk9w5gIAAACAIQgXAAAAAAxBuAAAAABgCO65AAAAKEK4bwD5iTMXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBA8LQrFx2TXPJ7fpbydHwAAQD7jzAUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiiRH53YPv27RozZowaN26suXPnWoZPmDBB69evtxo3LS1Njz32mKZPn66xY8dqw4YNsrOzs7Q7ODjol19+kSSZTCZNnjxZP//8s2xtbRUaGqqJEyfK0dExbxYMAAAAKGbyNVwsWbJEq1evloeHR6a2qVOnaurUqZbfr1+/ri5duui///2vZdigQYP04osvZjntiRMnKiUlRRs3blRqaqqGDx+u8PBwTZgwwfgFAQAAAJC/l0U5ODhkGy5u9eGHH+o///mPQkND7zju+fPntWXLFoWFhcnd3V0VK1bU4MGDtWbNGqWmphrRdQAAAAC3yNdw0bdvX5UuXfqO4yUmJmrRokV6+eWXrYbv3r1bXbp0UWBgoLp3765Dhw5Jko4cOSI7Ozt5eXlZxq1bt64uX76s48ePG7sQAAAAACQVkhu6P/roIzVs2FC1a9e2DKtatao8PDy0ePFibd++XUFBQerfv78SEhJkMpnk7OwsGxsby/iurq6SpISEhDzvPwAAAFAc5PsN3XeSlpamlStXavbs2VbDhwwZYvX7yy+/rI0bN2rLli1ydHSU2WzOy24CAAAAxV6BP3Oxd+9epaSkKCgo6Lbj2dnZ6cEHH9S5c+fk7u6upKQkpaWlWdpNJpMkqWzZsrnZXQAAAKDYKvDh4rvvvlOTJk1UosT/nWQxm82aPn26jh49ahmWkpKikydPqmrVqvL29pbZbLZqj4qKkouLi2rUqJGn/QcAAACKiwIfLo4cOaIqVapYDbOxsdGpU6f02muv6ezZs0pOTlZ4eLhKliyphx9+WO7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3q5ACAAAAwDj5eqTt5+cn6cZ3WEjSli1bJN04y5AhPj5e5cqVy/TaadOmacaMGerWrZuSkpLk7++vDz/8UE5OTpKkKVOmaNKkSWrTpo1Kliypjh07KiwsLLcXCQAAACi28jVc3BwisvPNN99kOdzNzU3Tp0/P9nWlS5fWnDlzctw3AAAAAPeGa4QKm8mueTy/S3k7PwAAABRaBf6eCwAAAACFA+ECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAUCDFxcVpyJAhaty4sYKDgzV27FglJiZKko4cOaKnnnpKDRo0ULt27fT+++9bvXbTpk3q1KmTAgMD1a1bN+3YscPSlp6errlz56pNmzZq2LChBgwYoNjY2DxdNgAoqggXAIAC6YUXXpCLi4u2bt2qtWvX6s8//9SMGTN09epVDRw4UE2aNNH27ds1d+5cLV68WJs3b5Z0I3iMGTNGo0aN0u7du9WvXz8NHTpUZ86ckSStXLlSX3zxhSIiIrRt2zZVr15dQ4YMkdlszs/FBYAigXABAChwEhMT5evrq5EjR6pUqVKqVKmSunbtql9++UXff/+9UlNTNWjQIDk5Oalu3brq0aOHIiMjJUmrVq1SaGioQkND5eDgoM6dO8vT01MbNmyQJEVGRqpfv36qWbOmnJ2dFRYWppiYGB04cCA/FxkAigTCBQCgwHFxcdH06dNVrlw5y7DTp0+rQoUKio6OlpeXl+zs7CxtPj4+OnTokCQpOjpaPj4+VtPz8fFRVFSUrl69qmPHjlm1Ozs7y8PDQ1FRUbm8VABQ9BEuAAAFXlRUlD766CMNGjRIJpNJLi4uVu1ubm4ymUxKT0+XyWSSq6urVburq6sSEhJ06dIlmc3mbNsBAPeHcAEAKND27dunAQMGaOTIkQoODs52PBsbG8vPd7p/gvsrACB3EC4AAAXW1q1b9fzzz2vcuHHq27evJMnd3T3TWQaTySQ3NzfZ2tqqTJkyMplMmdrd3d0t42TVXrZs2dxcFAAoFggXAIAC6ddff9WYMWP09ttvq0uXLpbhvr6++v3333X9+nXLsKioKNWrV8/SnnH/xa3tDg4Oql27tqKjoy1tiYmJOnnypPz9/XN3gQCgGCBcAAAKnOvXr2vChAkaNWqUQkJCrNpCQ0Pl7OyshQsX6sqVKzpw4IBWr16t3r17S5J69uypnTt36vvvv9e1a9e0evVqnThxQp07d5Yk9e7dW8uXL1dMTIySkpIUHh4ub29v+fn55flyAkBRUyK/OwAAwK3279+vmJgYTZ06VVOnTrVq+/rrr7Vo0SJNmjRJERERKleunMLCwtSyZUtJkqenp8LDwzV9+nTFxcWpVq1aWrx4scqXLy9J6tWrl+Lj49WnTx8lJyercePGmj9/fl4vIgAUSYQLAECBExQUpN9///2243zyySfZtrVr107t2rXLss3GxkbDhg3TsGHD7quPAIDMuCwKAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYIh8Dxfbt29XcHCwwsLCrIavXbtWderUkZ+fn9W/gwcPSpLS09M1d+5ctWnTRg0bNtSAAQMUGxtreb3JZNKIESMUHByskJAQjR8/XlevXs3TZQMAAACKk3wNF0uWLNHUqVPl4eGRZXvDhg0VFRVl9c/f31+StHLlSn3xxReKiIjQtm3bVL16dQ0ZMkRms1mSNHHiRF25ckUbN27UmjVrFBMTo/Dw8DxbNgAAAKC4yddw4eDgoNWrV2cbLm4nMjJS/fr1U82aNeXs7KywsDDFxMTowIEDOn/+vLZs2aKwsDC5u7urYsWKGjx4sNasWaPU1NRcWBIAAAAA+Rou+vbtq9KlS2fbfvr0aT3zzDNq2LCh2rRpo/Xr10uSrl69qmPHjsnHx8cyrrOzszw8PBQVFaUjR47Izs5OXl5elva6devq8uXLOn78eO4tEAAAAFCMlcjvDmTH3d1d1atX10svvaRatWrp22+/1ejRo1WhQgU99NBDMpvNcnV1tXqNq6urEhIS5ObmJmdnZ9nY2Fi1SVJCQkKeLgcAAABQXOT7Dd3ZadmypZYuXSofHx/Z29urQ4cOatu2rdauXWsZJ+P+iqzcrg0AAACA8QpsuMhK5cqVde7cObm5ucnW1lYmk8mq3WQyqWzZsnJ3d1dSUpLS0tKs2iSpbNmyedhjAAAAoPgosOHik08+0aZNm6yGxcTEqGrVqnJwcFDt2rUVHR1taUtMTNTJkyfl7+8vb29vmc1mHT161NIeFRUlFxcX1ahRI8+WAQAAAChOCmy4SElJ0euvv66oqCilpqZq48aN+vHHH9WrVy9JUu/evbV8+XLFxMQoKSlJ4eHh8vb2lp+fn9zd3dW+fXu99dZbunjxos6cOaMFCxaoe/fuKlGiwN5mAgAAABRq+Xqk7efnJ0m6fv26JGnLli2Sbpxl6Nu3r5KTkzV8+HDFx8erSpUqWrBggXx9fSVJvXr1Unx8vPr06aPk5GQ1btxY8+fPt0x7ypQpmjRpktq0aaOSJUuqY8eOmb6oDwAAAIBx8jVcREVFZdtmY2OjwYMHa/Dgwdm2Dxs2TMOGDcuyvXTp0pozZ44h/QQAAABwZwX2sigAAAAAhQvhAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBD5Hi62b9+u4OBghYWFZWrbvHmzOnfurMDAQLVv316fffaZpW3evHny9vaWn5+f1b/z589Lkq5du6ZXX31VLVq0UOPGjTVs2DAlJCTk2XIBAAAAxU2+hoslS5Zo6tSp8vDwyNR28OBBjRo1SsOGDdPevXs1btw4TZkyRb/88otlnMcee0xRUVFW/8qVKydJmjt3rqKjoxUZGalvvvlGZrNZr7zySp4tGwAAAFDc5Gu4cHBw0OrVq7MMFyaTSQMHDtTDDz+sEiVKKDQ0VJ6enlbhIjvXr1/X6tWrNXjwYD344INyc3PTiBEj9P333+vs2bO5sSgAAABAsZev4aJv374qXbp0lm0tWrTQkCFDLL9fv35d8fHxqlixomXY77//rl69eql+/frq0KGDduzYIUk6efKk/v33X9WtW9cybs2aNeXo6Kjo6OhcWhoAAACgeMv3ey7uVnh4uJycnPToo49KkipVqqSqVatqxowZ+umnn9SjRw+98MILOn78uEwmkyTJxcXFahouLi7cdwEAAADkkgIfLsxms2bNmqWNGzdq4cKFcnBwkCT16NFD77zzjjw8PPTAAw+oX79+8vb21oYNG6xeCwAAACBvlMjvDtxOenq6XnnlFR08eFCffPKJqlatetvxK1eurHPnzsnd3V3Sjfs2SpUqZWm/dOmSypYtm6t9BgAAAIqrAn3m4o033tCff/6ZZbB49913tWvXLqthMTExqlq1qqpWrSpXV1er+yv++OMPpaSkyNfXN0/6DgAAABQ3BTZc7Nu3Txs2bFBERITc3NwytZtMJr322ms6fvy4rl27pvfff18nT55U165dZWdnp549e2rRokU6ffq0EhISNGfOHLVt29byqFoAAAAAxsrXy6L8/Pwk3XgSlCRt2bJFkhQVFaU1a9bo33//VatWraxe07BhQ73//vsaOXKkJKlfv34ymUyqVauWli1bpkqVKkmShg0bpuTkZD322GO6fv26WrVqpcmTJ+fRkgEAAADFT76Gi6ioqGzb3njjDb3xxhvZtjs4OGjcuHEaN25clu329vaaNGmSJk2adN/9BAAAAHBnBfayKAAAAACFC+ECAAAAgCEIFwCAAmn79u0KDg5WWFiY1fC1a9eqTp068vPzs/p38OBBSTceYz537ly1adNGDRs21IABAxQbG2t5vclk0ogRIxQcHKyQkBCNHz9eV69ezdNlA4CiinABAChwlixZoqlTp8rDwyPL9oYNGyoqKsrqn7+/vyRp5cqV+uKLLxQREaFt27apevXqGjJkiOWLVSdOnKgrV65o48aNWrNmjWJiYhQeHp5nywYARRnhAgBQ4Dg4OGj16tXZhovbiYyMVL9+/VSzZk05OzsrLCxMMTExOnDggM6fP68tW7YoLCxM7u7uqlixogYPHqw1a9YoNTU1F5YEAIoXwgUAoMDp27evSpcunW376dOn9cwzz6hhw4Zq06aN1q9fL0m6evWqjh07Jh8fH8u4zs7O8vDwUFRUlI4cOSI7Ozt5eXlZ2uvWravLly/r+PHjubdAAFBM5OujaAEAuFfu7u6qXr26XnrpJdWqVUvffvutRo8erQoVKuihhx6S2WyWq6ur1WtcXV2VkJAgNzc3OTs7y8bGxqpNkhISEvJ0OQCgKOLMBQCgUGnZsqWWLl0qHx8f2dvbq0OHDmrbtq3Wrl1rGSfj/oqs3K4NAHB/CBcAgEKvcuXKOnfunNzc3GRrayuTyWTVbjKZVLZsWbm7uyspKUlpaWlWbZJUtmzZPOwxABRNhAsAQKHyySefaNOmTVbDYmJiVLVqVTk4OKh27dqKjo62tCUmJurkyZPy9/eXt7e3zGazjh49ammPioqSi4uLatSokWfLAABFFeECAFCopKSk6PXXX1dUVJRSU1O1ceNG/fjjj+rVq5ckqXfv3lq+fLliYmKUlJSk8PBweXt7y8/PT+7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3lSjBbYgAcL/YkwIAChw/Pz9J0vXr1yVJW7ZskXTjLEPfvn2VnJys4cOHKz4+XlWqVNGCBQvk6+srSerVq5fi4+PVp08fJScnq3Hjxpo/f75l2lOmTNGkSZPUpk0blSxZUh07dsz0RX0AgJwhXAAACpyoqKhs22xsbDR48GANHjw42/Zhw4Zp2LBhWbaXLl1ac+bMMaSfAABrXBYFAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxheLgwm81GTxIAUARQHwCg6MtRuGjfvn2WwxMTExUcHHxfHQIAFF7UBwAo3krcy8i7du3Szp07FRcXpzlz5mRqP3XqlFJSUgzrHACgcKA+AACkewwXrq6uunz5stLS0vTbb79land0dNTUqVMN6xwAoHCgPgAApHsMFz4+PvLx8ZGNjY0mTJiQW30CABQy1AcAgHSP4SLDhAkTFBUVpZiYGF27di1T++OPP37fHQMAFD7UBwAo3nIULt544w0tX75c7u7ucnR0tGqzsbGheABAMUV9AIDiLUfhYt26dfrggw/UtGlTo/sDACjEqA8AULzl6FG09vb2CgoKMrovAIBCjvoAAMVbjsJFv3799P777xvdFwBAIUd9AIDiLUeXRf3666/69ddftWLFCv3nP/+Rra11Rvn0008N6RwAoHChPgBA8ZajcJHxyEEAAG5GfQCA4i1H4WLo0KFG9wMAUARQHwCgeMtRuJg/f/5t2ykuAFA8UR8AoHjLUbjYvn271e9paWmKi4uT2WxW/fr1DekYAKDwoT4AQPGWo3ARGRmZaVh6eroWLVoke3v7++4UAKBwoj4AQPGWo0fRZjkhW1s999xzPIIQAGCF+gAAxYdh4UKS9u7dq+vXrxs5SQBAEUB9AIDiIUeXRYWEhGQadvXqVSUnJ6tfv3732ycAQCFFfQCA4i1H4WLkyJGZhjk4OMjDw0N169a9704BAAon6gMAFG85Chddu3aVJKWmpurcuXOysbFRxYoVZWdnZ2jnAACFC/UBAIq3HIWLxMRETZo0SVu2bLFcQ+vg4KCOHTtq4sSJcnBwMLSTAIDCgfoAAMVbjsLF5MmTFR8fr/nz58vDw0OSFBMTo0WLFik8PFzjx483tJMAgMKB+gAAxVuOv0Tvm2++kbu7u2VY9erV5evrq169elE8AKCYoj4AQPGWo0fR2tnZ6YEHHsg03MXFRZcvX77vTgEACifqAwAUbzkKF/Xr19eUKVN08eJFy7CLFy/q9ddfl5+fn2GdAwAULtQHACjecnRZ1KRJkzRo0CA1a9ZMLi4ukqRLly6pZs2aWrhwoaEdBAAUHtQHACje7jlcpKSkKCUlRWvXrtXRo0d16tQppaSkqEKFCgoMDORxgwBQTFEfAAD3FC4uXbqkJ554QvXq1dMbb7yhOnXqqE6dOpKkbt26qVSpUnrvvfdkb2+fK50FABRM1AcAgHSP91zMnz9f7u7umjBhQqa2lStXymw2a8mSJYZ1DgBQOFAfAADSPYaLbdu2afz48XJycsrU9sADD2j8+PHauHGjYZ0DABQO1AcAgHSP4eLChQvy8vLKtr1OnTo6c+bMfXcKAFC4UB8AANI9hgsnJyclJCRk237u3Lksn28OACjaqA8AAOkew0XTpk21bNmybNtnzpypJk2a3G+fAACFDPUBACDd49OihgwZou7duys2NlZPPvmkatSoobS0NB07dkzvv/++Dhw4oM8++yy3+goAKKCoDwAA6R7DRY0aNfTRRx/p9ddf11NPPSUbGxtJktlsVqNGjfTRRx+pRo0audJRAEDBRX0AAEg5+BI9b29vffzxx7p48aJiY2NlY2OjatWqyc3NLRe6BwAoLKgPAIB7DhcZ3N3d5e7ubmRfAABFAPUBAIqve7qhOzds375dwcHBCgsLy9S2adMmderUSYGBgerWrZt27NhhaUtPT9fcuXPVpk0bNWzYUAMGDFBsbKyl3WQyacSIEQoODlZISIjGjx+vq1ev5skyAQAAAMVRvoaLJUuWaOrUqfLw8MjUduTIEY0ZM0ajRo3S7t271a9fPw0dOtTynPSVK1fqiy++UEREhLZt26bq1atryJAhMpvNkqSJEyfqypUr2rhxo9asWaOYmBiFh4fn6fIBAAAAxUm+hgsHBwetXr06y3CxatUqhYaGKjQ0VA4ODurcubM8PT21YcMGSVJkZKT69eunmjVrytnZWWFhYYqJidGBAwd0/vx5bdmyRWFhYXJ3d1fFihU1ePBgrVmzRqmpqXm9mAAAAECxkK/hom/fvipdunSWbdHR0fLx8bEa5uPjo6ioKF29elXHjh2zand2dpaHh4eioqJ05MgR2dnZWX1bbN26dXX58mUdP348dxYGAAAAKOby/Z6L7JhMJrm6uloNc3V1VUJCgi5duiSz2Zxtu8lkkrOzs+VRiBltkm77DbIAAAAAcq7AhgtJlvsnctJ+p9cCAAAAMFaBDRdlypSRyWSyGmYymeTu7i43NzfZ2tpm2V62bFm5u7srKSlJaWlpVm2SVLZs2VzuOQAAAFA8Fdhw4evrq0OHDlkNi4qKUr169eTg4KDatWsrOjra0paYmKiTJ0/K399f3t7eMpvNOnr0qNVrXVxc+IZYAAAAIJcU2HDRs2dP7dy5U99//72uXbum1atX68SJE+rcubMkqXfv3lq+fLliYmKUlJSk8PBweXt7y8/PT+7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3lSiR4+8NBAAAAHAb+Xqk7efnJ0m6fv26JGnLli2Sbpxl8PT0VHh4uKZPn664uDjVqlVLixcvVvny5SVJvXr1Unx8vPr06aPk5GQ1btxY8+fPt0x7ypQpmjRpktq0aaOSJUuqY8eOWX5RHwAAAABj5Gu4iIqKum17u3bt1K5duyzbbGxsNGzYMA0bNizL9tKlS2vOnDn33UcAAAAAd6fAXhYFAAAAoHAhXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAABRI27dvV3BwsMLCwjK1bdq0SZ06dVJgYKC6deumHTt2WNrS09M1d+5ctWnTRg0bNtSAAQMUGxtraTeZTBoxYoSCg4MVEhKi8ePH6+rVq3myTABQ1BEuAAAFzpIlSzR16lR5eHhkajty5IjGjBmjUaNGaffu3erXr5+GDh2qM2fOSJJWrlypL774QhEREdq2bZuqV6+uIUOGyGw2S5ImTpyoK1euaOPGjVqzZo1iYmIUHh6ep8sHAEUV4QIAUOA4ODho9erVWYaLVatWKTQ0VKGhoXJwcFDnzp3l6empDRs2SJIiIyPVr18/1axZU87OzgoLC1NMTIwOHDig8+fPa8uWLQoLC5O7u7sqVqyowYMHa82aNUpNTc3rxQSAIodwAQAocPr27avSpUtn2RYdHS0fHx+rYT4+PoqKitLVq1d17Ngxq3ZnZ2d5eHgoKipKR44ckZ2dnby8vCztdevW1eXLl3X8+PHcWRgAKEYIFwCAQsVkMsnV1dVqmKurqxISEnTp0iWZzeZs200mk5ydnWVjY2PVJkkJCQm533kAKOIIFwCAQifj/omctN/ptQCAnCNcAAAKlTJlyshkMlkNM5lMcnd3l5ubm2xtbbNsL1u2rNzd3ZWUlKS0tDSrNkkqW7ZsLvccAIo+wgUAoFDx9fXVoUOHrIZFRUWpXr16cnBwUO3atRUdHW1pS0xM1MmTJ+Xv7y9vb2+ZzWYdPXrU6rUuLi6qUaNGni0DABRVhAsAQKHSs2dP7dy5U99//72uXbum1atX68SJE+rcubMkqXfv3lq+fLliYmKUlJSk8PBweXt7y8/PT+7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3lShRIp+XDAAKP/akAIACx8/PT5J0/fp1SdKWLVsk3TjL4OnpqfDwcE2fPl1xcXGqVauWFi9erPLly0uSevXqpfj4ePXp00fJyclq3Lix5s+fb5n2lClTNGnSJLVp00YlS5ZUx44ds/yiPgDAvSNcAAAKnKioqNu2t2vXTu3atcuyzcbGRsOGDdOwYcOybC9durTmzJlz330EAGTGZVEAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBN9zcZ+qj/0yT+d3wjFPZwcAAADcNc5cAAAAADBEgT1zsXfvXvXv399qmNlsVmpqqpYvX66+ffvK3t7eqn3mzJl65JFHJEnLly/XypUrFR8fLy8vL40fP16+vr551n8AAACguCmw4aJhw4aKioqyGrZo0SIdPXpUklS5cmVt3bo1y9du3bpV8+bN09KlS+Xl5aXly5frhRde0ObNm+Xk5JTrfQcAAACKo0JzWdQ///yjDz74QKNHj77juJGRkerWrZvq1asnR0dHPfvss5Kkbdu25XY3AQAAgGKr0ISLt99+W//73//0n//8R5KUnJysIUOGqHHjxmrevLk++OADmc1mSVJ0dLR8fHwsr7W1tZW3t3emMyEAAAAAjFNgL4u62alTp7R582Zt3rxZkuTs7CxPT089/fTTmjt3rn7++WcNHz5cpUuXVvfu3WUymeTq6mo1DVdXVyUkJORH9wEAAIBioVCcuVi5cqXatWun8uXLS5Lq1q2rFStWqFGjRrK3t1dISIh69eqltWvXWl6TcRYDAAAAQN4oFOHim2++UevWrW87TuXKlXXu3DlJUpkyZWQymazaTSaT3N3dc6uLAAAAQLFX4MPFkSNHFBcXp2bNmlmGffXVV/r444+txjt+/LiqVq0qSfL19VV0dLSlLS0tTYcPH1a9evXyptMAAABAMVTgw8Xhw4fl5uYmZ2dny7CSJUtqxowZ2rFjh1JTU/XTTz9pzZo16t27tySpd+/eWrdunfbv368rV65o4cKFsre3V8uWLfNpKQAAAICir8Df0H3+/HnLvRYZHn74YY0bN06vv/66Tp8+rXLlymncuHFq166dJKlFixZ66aWXNGLECF24cEF+fn6KiIiQo6NjfiwCAAAAUCwU+HAxcOBADRw4MNPwxx9/XI8//ni2r3viiSf0xBNP5GbXAAAAANykwF8WBQAAAKBwIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGKJHfHUDxVX3sl3k6vxOOeTo7AACAYoczFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEAU6XHh5ecnX11d+fn6Wf6+//rokadeuXerevbvq16+vDh06aMOGDVavXb58udq3b6/69eurd+/eOnToUH4sAgAAAFBslMjvDtzJ119/rSpVqlgNO3funAYPHqzx48erU6dO2rdvnwYNGqQaNWrIz89PW7du1bx587R06VJ5eXlp+fLleuGFF7R582Y5OTnl05IAAAAARVuBPnORnS+++ELVq1dX9+7d5eDgoODgYLVu3VqrVq2SJEVGRqpbt26qV6+eHB0d9eyzz0qStm3blp/dBgAAAIq0Ah8uZs+erZYtWyooKEgTJ05UcnKyoqOj5ePjYzWej4+P5dKnW9ttbW3l7e2tqKioPO07AAAAUJwU6HAREBCg4OBgbd68WZGRkdq/f79ee+01mUwmubi4WI3r5uamhIQESZLJZJKrq6tVu6urq6UdAAAAgPEK9D0XkZGRlp9r1qypUaNGadCgQWrQoMEdX2s2m3Oza0C+qj72yzyd3wnHPJ0dAAAopAr0mYtbValSRWlpabK1tZXJZLJqS0hIkLu7uySpTJkymdpNJpOlHQAAAIDxCmy4OHz4sN58802rYTExMbK3t1doaGimR8seOnRI9erVkyT5+voqOjra0paWlqbDhw9b2gEAAAAYr8CGi7JlyyoyMlIRERFKSUnRX3/9pbfffluPP/64HnvsMcXFxWnVqlW6du2afvjhB/3www/q2bOnJKl3795at26d9u/frytXrmjhwoWyt7dXy5Yt83ehAAAAgCKswN5zUbFiRUVERGj27NmWcNC1a1eFhYXJwcFBixcv1tSpU/Xaa6+pcuXKmjVrlurUqSNJatGihV566SWNGDFCFy5ckJ+fnyIiIuToyIXjAAAAQG4psOFCkho2bKhPP/0027b169dn+9onnnhCTzzxRG51DQAAAMAtCuxlUQAAAAAKF8IFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiiRH53AADyUvWxX+bp/E445unsAADIV5y5AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQCg0PHy8pKvr6/8/Pws/15//XVJ0q5du9S9e3fVr19fHTp00IYNG6xeu3z5crVv317169dX7969dejQofxYBAAokkrkdwcAAMiJr7/+WlWqVLEadu7cOQ0ePFjjx49Xp06dtG/fPg0aNEg1atSQn5+ftm7dqnnz5mnp0qXy8vLS8uXL9cILL2jz5s1ycnLKpyUBgKKDMxcAgCLjiy++UPXq1dW9e3c5ODgoODhYrVu31qpVqyRJkZGR6tatm+rVqydHR0c9++yzkqRt27blZ7cBoMggXAAACqXZs2erZcuWCgoK0sSJE5WcnKzo6Gj5+PhYjefj42O59OnWdltbW3l7eysqKipP+w4ARRXhAgBQ6AQEBCg4OFibN29WZGSk9u/fr9dee00mk0kuLi5W47q5uSkhIUGSZDKZ5OrqatXu6upqaQcA3B/uuQAAFDqRkZGWn2vWrKlRo0Zp0KBBatCgwR1fazabc7NrAFCsceYCAFDoValSRWlpabK1tZXJZLJqS0hIkLu7uySpTJkymdpNJpOlHQBwfwgXAIBC5fDhw3rzzTethsXExMje3l6hoaGZHi176NAh1atXT5Lk6+ur6OhoS1taWpoOHz5saQcA3B/CBQCgUClbtqwiIyMVERGhlJQU/fXXX3r77bf1+OOP67HHHlNcXJxWrVqla9eu6YcfftAPP/ygnj17SpJ69+6tdevWaf/+/bpy5YoWLlwoe3t7tWzZMn8XCgCKCO65AAAUKhUrVlRERIRmz55tCQddu3ZVWFiYHBwctHjxYk2dOlWvvfaaKleurFmzZqlOnTqSpBYtWuill17SiBEjdOHCBfn5+SkiIkKOjo75vFQAUDQQLgAAhU7Dhg316aefZtu2fv36bF/7xBNP6IknnsitrgFAscZlUQAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCnS4iIuL05AhQ9S4cWMFBwdr7NixSkxM1KlTp+Tl5SU/Pz+rf++9957ltZs2bVKnTp0UGBiobt26aceOHfm4JAAAAEDRVyK/O3A7L7zwgnx9fbV161b9+++/GjJkiGbMmKFBgwZJkqKiorJ83ZEjRzRmzBjNnz9fTZo00TfffKOhQ4fq66+/VqVKlfJyEQAAAIBio8CeuUhMTJSvr69GjhypUqVKqVKlSuratat++eWXO7521apVCg0NVWhoqBwcHNS5c2d5enpqw4YNedBzAAAAoHgqsOHCxcVF06dPV7ly5SzDTp8+rQoVKlh+Hz16tEJCQtSkSRPNnj1bqampkqTo6Gj5+PhYTc/HxyfbMx0AAAAA7l+BDRe3ioqK0kcffaRBgwbJ3t5egYGBatu2rbZt26aIiAht2LBB7777riTJZDLJ1dXV6vWurq5KSEjIj64DAAAAxUKhCBf79u3TgAEDNHLkSAUHB6tChQr69NNP1bZtW5UsWVL+/v4aOHCg1q5da3mN2WzOxx4DAAAAxU+BDxdbt27V888/r3Hjxqlv377Zjle5cmWdP39eZrNZZcqUkclksmo3mUxyd3fP5d4CAAAAxVeBDhe//vqrxowZo7fffltdunSxDN+1a5cWLlxoNe7x48dVuXJl2djYyNfXV4cOHbJqj4qKUr169fKi2wAAAECxVGDDxfXr1zVhwgSNGjVKISEhVm2lS5fWggULtH79eqWmpioqKkrvvfeeevfuLUnq2bOndu7cqe+//17Xrl3T6tWrdeLECXXu3Dk/FgUAAAAoFgrs91zs379fMTExmjp1qqZOnWrV9vXXX2vu3LmaP3++Xn31VZUuXVp9+vTR008/LUny9PRUeHi4pk+frri4ONWqVUuLFy9W+fLl82NRAAAAgGKhwIaLoKAg/f7779m2V65cWW3bts22vV27dmrXrl1udA0AAABAFgrsZVEAAAAAChfCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCGKbLiIi4vT888/r8aNG6tVq1aaNWuW0tPT87tbAIACgBoBALmjRH53ILe8+OKLqlu3rrZs2aILFy5o4MCBKleunJ555pn87hoAIJ9RIwAgdxTJMxdRUVE6evSoRo0apdKlS6t69erq16+fIiMj87trAIB8Ro0AgNxTJMNFdHS0KleuLFdXV8uwunXr6q+//lJSUlI+9gwAkN+oEQCQe4rkZVEmk0kuLi5WwzKKSEJCgpydna3azp07p7S0NLVp0+ae52V/8XLOO5oDbWzK5en8tP3e18ndYt3lHOsu51h3WTt9+rTs7OwM7kzBdC814n7qg5S321th2dbuFusu54r0fo51l3N5VB+KZLiQJLPZfNfjOjg4KCUlJUfzqerulKPX5Vz1PJ5f7mHd5RzrLudYd1krUaKE7O3t87sbeeZua8T91Acpr7e36nk4r9zHuss59nM5x7rL7F7rQ5EMF+7u7jKZTFbDTCaTbGxs5O7unmn8X375JY96BgDIb/dSI6gPAHBviuQ9F76+vjp9+rQuXrxoGRYVFaVatWqpVKlS+dgzAEB+o0YAQO4pkuHCx8dHfn5+mj17tpKSkhQTE6MPPvhAvXv3zu+uAQDyGTUCAHKPjflebk4oRM6cOaOJEyfq559/lrOzs3r16qWhQ4fKxsYmv7sGAMhn1AgAyB1F8syFJFWqVElLlizRgQMH9NNPP+nFF18sFEXjXr41dvny5Wrfvr3q16+v3r1769ChQ3nc24Jl+/btCg4OVlhY2G3HS09P19y5c9WmTRs1bNhQAwYMUGxsbB71smCKi4vTkCFD1LhxYwUHB2vs2LFKTEzMctxNmzapU6dOCgwMVLdu3bRjx4487m3BcvToUT399NNq0KCBgoODNWLECMXHx2c5Lu/ZgqMw1gjqw/2hRuQcNSLnimWNMKNA6dq1q3nChAnmxMRE819//WVu166d+f3338803nfffWcOCgoy79+/33zlyhXz4sWLzc2aNTMnJyfnQ6/zX0REhLldu3bmXr16mUeMGHHbcZcvX25u1aqV+dixY+Z///3XPGXKFHOnTp3M6enpedTbgqdjx47msWPHmpOSksynT582d+vWzTxu3LhM4x0+fNjs6+tr/v77781Xr141r1+/3lyvXj3z6dOn86HX+e/atWvmpk2bmufPn2++du2a+cKFC+annnrKPHjw4Ezj8p7F/aI+5Bw14v5QI3KmuNaIInvmojC6l2+NjYyMVLdu3VSvXj05Ojrq2WeflSRt27Ytr7tdIDg4OGj16tXy8PC447iRkZHq16+fatasKWdnZ4WFhSkmJkYHDhzIg54WPImJifL19dXIkSNVqlQpVapUSV27ds3yKTmrVq1SaGioQkND5eDgoM6dO8vT01MbNmzIh57nvytXrigsLEwDBw6Uvb293N3d1bZtW/3555+ZxuU9i/tBfbg/1Iico0bkXHGtEYSLAuRevjU2OjpaPj4+lt9tbW3l7e2tqKioPOtvQdK3b1+VLl36juNdvXpVx44ds1p3zs7O8vDwKLbrzsXFRdOnT1e5cv/3RT6nT59WhQoVMo1763Yn3bg5triuO1dXV/Xo0UMlStx4qvfx48f1+eef65FHHsk0Lu9Z3A/qw/2hRuQcNSLnimuNIFwUIHf61thbx725yGSMe+t4sHbp0iWZzWbW3W1ERUXpo48+0qBBgzK1sd1lLS4uTr6+vnr00Ufl5+enYcOGZRqHdYf7QX3IG9SIO6NG3LviViMIFwWM+R4e3nUv48Ia6y5r+/bt04ABAzRy5EgFBwdnOQ7rLrPKlSsrKipKX3/9tU6cOKHRo0dnOR7rDveD+pB3WH9Zo0bkTHGrEYSLAuRevjW2TJkyWY6b1TeQ4/+4ubnJ1tY2y3VXtmzZ/OlUAbF161Y9//zzGjdunPr27ZvlOGx32bOxsVH16tUVFhamjRs3Wn1Bm8S6w/2hPuQNakT2qBH3pzjVCMJFAXIv3xrr6+ur6Ohoy+9paWk6fPiw6tWrl2f9LYwcHBxUu3Ztq3WXmJiokydPyt/fPx97lr9+/fVXjRkzRm+//ba6dOmS7Xi+vr6ZHo0XFRVVbLe7Xbt2qX379laPA7W1vbFbLVmypNW4vGdxP6gPeYMakTVqRM4U1xpBuChA7vStsf/9738tT2fo3bu31q1bp/379+vKlStauHCh7O3t1bJly3xcgoLp7Nmz+u9//2t5Tnnv3r21fPlyxcTEKCkpSeHh4fL29pafn18+9zR/XL9+XRMmTNCoUaMUEhKSqf3pp5/Wpk2bJEk9e/bUzp079f333+vatWtavXq1Tpw4oc6dO+d1twsEX19fJSUladasWbpy5YouXryoefPmKSgoSKVLl+Y9C8NQH3IPNeL2qBE5V1xrRIn87gCsvfPOO5o4caKaNWtm+dbYJ554QpL0119/6fLly5KkFi1a6KWXXtKIESN04cIF+fn5KSIiQo6OjvnZ/XyTsdO/fv26JGnLli2Sbnxikpqaqr/++kspKSmSpF69eik+Pl59+vRRcnKyGjdurPnz5+dPxwuA/fv3KyYmRlOnTtXUqVOt2r7++mvFxsbq0qVLkiRPT0+Fh4dr+vTpiouLU61atbR48WKVL18+P7qe70qXLq33339fU6dOVZMmTeTk5KQmTZpo2rRpknjPwljUh5yjRuQcNSLnimuNsDEXlbtHAAAAAOQrLosCAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFkIu6deummTNnWg2Ljo6Wl5eXNm/ebDV8+fLlCgkJkdlstgz78MMP5eXlpVOnTuVJfwEAeYcagaKIcAHkoubNm2vnzp1Ww3766Sc5OTllGr5z506FhITIxsZGknT27Fm9//77edZXAEDeokagKCJcALmoefPmOnr0qC5evGgZtmvXLnXt2lW7du2yDLt+/br27t2r5s2bW4ZNmzZNvXr1ytP+AgDyDjUCRRHhAshFAQEBcnZ2tnwClZKSol9//VV9+/bVmTNn9M8//0iSDh48qMuXL6tZs2aSpB9++EG///67BgwYkG99BwDkLmoEiiLCBZCLSpQooeDgYP3000+SpH379qlixYqqXr26AgICLAVl586d8vPzk5ubm65evarXX39dr776quzt7fOz+wCAXESNQFFEuABy2c3X1O7cuVNNmjSRJDVt2tRy2nvXrl2W090LFy6Ur6+v5RMqAEDRRY1AUUO4AHJZ8+bNdebMGcXExGj37t1q2rSpJKlJkybavXu3Ll++rAMHDqh58+aKiYnRZ599pldeeSWfew0AyAvUCBQ1hAsgl1WqVEm1a9fWjz/+qCNHjqhx48aSJF9fX125ckVr165VqVKl5O/vr6+++kr//vuvOnfurMaNG1vG7datm5YsWZKfiwEAyAXUCBQ1NuabH5gMIFfMmDFD3377rZydnbVu3TrL8IEDB+r48ePy9fXV3LlzlZSUpKSkJKvXhoaGKjIyUrVq1ZKzs3Me9xwAkNuoEShKOHMB5IHmzZsrNjbWci1thqZNm+rkyZOWa2mdnZ1VqVIlq3+SVK5cOYoGABRR1AgUJZy5AAAAAGAIzlwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYIj/B9oTecIMgDdmAAAAAElFTkSuQmCC", + "image/png": 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"text/plain": [ "
" ] @@ -825,7 +825,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f75ffffc", + "id": "242ea0ef", "metadata": {}, "outputs": [], "source": [] diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb index a478c5fcd..12f12c02d 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_lalonde_example.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:07.930708Z", - "iopub.status.busy": "2024-10-19T23:49:07.930277Z", - "iopub.status.idle": "2024-10-19T23:49:10.277784Z", - "shell.execute_reply": "2024-10-19T23:49:10.277155Z" + "iopub.execute_input": "2024-10-22T01:15:19.383913Z", + "iopub.status.busy": "2024-10-22T01:15:19.383732Z", + "iopub.status.idle": "2024-10-22T01:15:22.029032Z", + "shell.execute_reply": "2024-10-22T01:15:22.028367Z" } }, "outputs": [], @@ -46,10 +46,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:10.280097Z", - "iopub.status.busy": "2024-10-19T23:49:10.279697Z", - "iopub.status.idle": "2024-10-19T23:49:10.332866Z", - "shell.execute_reply": "2024-10-19T23:49:10.332238Z" + "iopub.execute_input": "2024-10-22T01:15:22.031289Z", + "iopub.status.busy": "2024-10-22T01:15:22.030923Z", + "iopub.status.idle": "2024-10-22T01:15:22.084804Z", + "shell.execute_reply": "2024-10-22T01:15:22.084242Z" } }, "outputs": [ @@ -57,7 +57,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 1639.8323003718579\n" + "Causal Estimate is 1639.8298498687827\n" ] }, { @@ -75,10 +75,10 @@ " Method: Least Squares F-statistic: 6.743\n", "\n", "\n", - " Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00972\n", + " Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00972\n", "\n", "\n", - " Time: 23:49:10 Log-Likelihood: -4544.7\n", + " Time: 01:15:22 Log-Likelihood: -4544.7\n", "\n", "\n", " No. Observations: 445 AIC: 9093.\n", @@ -98,10 +98,10 @@ " coef std err t P>|t| [0.025 0.975] \n", "\n", "\n", - " Intercept 4555.0707 406.706 11.200 0.000 3755.759 5354.383\n", + " Intercept 4555.0711 406.705 11.200 0.000 3755.759 5354.383\n", "\n", "\n", - " treat[T.True] 1639.8323 631.497 2.597 0.010 398.731 2880.934\n", + " treat[T.True] 1639.8298 631.497 2.597 0.010 398.729 2880.931\n", "\n", "\n", "\n", @@ -109,7 +109,7 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -126,8 +126,8 @@ "\\textbf{Dep. Variable:} & re78 & \\textbf{ R-squared: } & 0.015 \\\\\n", "\\textbf{Model:} & WLS & \\textbf{ Adj. R-squared: } & 0.013 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 6.743 \\\\\n", - "\\textbf{Date:} & Sat, 19 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00972 \\\\\n", - "\\textbf{Time:} & 23:49:10 & \\textbf{ Log-Likelihood: } & -4544.7 \\\\\n", + "\\textbf{Date:} & Tue, 22 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00972 \\\\\n", + "\\textbf{Time:} & 01:15:22 & \\textbf{ Log-Likelihood: } & -4544.7 \\\\\n", "\\textbf{No. Observations:} & 445 & \\textbf{ AIC: } & 9093. \\\\\n", "\\textbf{Df Residuals:} & 443 & \\textbf{ BIC: } & 9102. \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -137,13 +137,13 @@ "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", - "\\textbf{Intercept} & 4555.0707 & 406.706 & 11.200 & 0.000 & 3755.759 & 5354.383 \\\\\n", - "\\textbf{treat[T.True]} & 1639.8323 & 631.497 & 2.597 & 0.010 & 398.731 & 2880.934 \\\\\n", + "\\textbf{Intercept} & 4555.0711 & 406.705 & 11.200 & 0.000 & 3755.759 & 5354.383 \\\\\n", + "\\textbf{treat[T.True]} & 1639.8298 & 631.497 & 2.597 & 0.010 & 398.729 & 2880.931 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", "\\textbf{Omnibus:} & 303.265 & \\textbf{ Durbin-Watson: } & 2.085 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 4770.760 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 4770.748 \\\\\n", "\\textbf{Skew:} & 2.709 & \\textbf{ Prob(JB): } & 0.00 \\\\\n", "\\textbf{Kurtosis:} & 18.098 & \\textbf{ Cond. No. } & 2.47 \\\\\n", "\\bottomrule\n", @@ -162,8 +162,8 @@ "Dep. Variable: re78 R-squared: 0.015\n", "Model: WLS Adj. R-squared: 0.013\n", "Method: Least Squares F-statistic: 6.743\n", - "Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00972\n", - "Time: 23:49:10 Log-Likelihood: -4544.7\n", + "Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00972\n", + "Time: 01:15:22 Log-Likelihood: -4544.7\n", "No. Observations: 445 AIC: 9093.\n", "Df Residuals: 443 BIC: 9102.\n", "Df Model: 1 \n", @@ -171,11 +171,11 @@ "=================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "---------------------------------------------------------------------------------\n", - "Intercept 4555.0707 406.706 11.200 0.000 3755.759 5354.383\n", - "treat[T.True] 1639.8323 631.497 2.597 0.010 398.731 2880.934\n", + "Intercept 4555.0711 406.705 11.200 0.000 3755.759 5354.383\n", + "treat[T.True] 1639.8298 631.497 2.597 0.010 398.729 2880.931\n", "==============================================================================\n", "Omnibus: 303.265 Durbin-Watson: 2.085\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.760\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.748\n", "Skew: 2.709 Prob(JB): 0.00\n", "Kurtosis: 18.098 Cond. No. 2.47\n", "==============================================================================\n", @@ -226,10 +226,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:10.334867Z", - "iopub.status.busy": "2024-10-19T23:49:10.334490Z", - "iopub.status.idle": "2024-10-19T23:49:10.633170Z", - "shell.execute_reply": "2024-10-19T23:49:10.632554Z" + "iopub.execute_input": "2024-10-22T01:15:22.086793Z", + "iopub.status.busy": "2024-10-22T01:15:22.086489Z", + "iopub.status.idle": "2024-10-22T01:15:22.384040Z", + "shell.execute_reply": "2024-10-22T01:15:22.383384Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:10.635279Z", - "iopub.status.busy": "2024-10-19T23:49:10.634922Z", - "iopub.status.idle": "2024-10-19T23:49:10.640311Z", - "shell.execute_reply": "2024-10-19T23:49:10.639761Z" + "iopub.execute_input": "2024-10-22T01:15:22.385971Z", + "iopub.status.busy": "2024-10-22T01:15:22.385669Z", + "iopub.status.idle": "2024-10-22T01:15:22.390952Z", + "shell.execute_reply": "2024-10-22T01:15:22.390497Z" } }, "outputs": [ @@ -272,7 +272,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 1639.8323003718551\n" + "Causal Estimate is 1639.8298498687882\n" ] } ], diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb index 521745ebe..f44feb937 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_mediation_analysis.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:12.337167Z", - "iopub.status.busy": "2024-10-19T23:49:12.336989Z", - "iopub.status.idle": "2024-10-19T23:49:13.771559Z", - "shell.execute_reply": "2024-10-19T23:49:13.770959Z" + "iopub.execute_input": "2024-10-22T01:15:24.068018Z", + "iopub.status.busy": "2024-10-22T01:15:24.067591Z", + "iopub.status.idle": "2024-10-22T01:15:25.523197Z", + "shell.execute_reply": "2024-10-22T01:15:25.522595Z" } }, "outputs": [], @@ -43,10 +43,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.773833Z", - "iopub.status.busy": "2024-10-19T23:49:13.773501Z", - "iopub.status.idle": "2024-10-19T23:49:13.783396Z", - "shell.execute_reply": "2024-10-19T23:49:13.782805Z" + "iopub.execute_input": "2024-10-22T01:15:25.525406Z", + "iopub.status.busy": "2024-10-22T01:15:25.525091Z", + "iopub.status.idle": "2024-10-22T01:15:25.534817Z", + "shell.execute_reply": "2024-10-22T01:15:25.534322Z" } }, "outputs": [ @@ -54,12 +54,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " FD0 W0 v0 y\n", - "0 6.489292 0.733013 1.220239 34.601677\n", - "1 -5.289214 0.068313 -1.238072 -24.993585\n", - "2 2.263775 0.210433 0.165696 11.842802\n", - "3 0.094201 -0.544390 0.277355 -2.182567\n", - "4 -18.464862 -1.645708 -4.082562 -96.327466\n" + " FD0 W0 v0 y\n", + "0 3.787914 -0.227960 1.412614 4.343411\n", + "1 4.690292 0.815757 2.508414 10.676013\n", + "2 -0.028364 -0.311694 -0.166239 -1.553875\n", + "3 -0.963158 -0.309174 -0.739863 -2.860223\n", + "4 -5.910934 -0.533363 -3.582746 -11.060230\n" ] } ], @@ -88,10 +88,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.785235Z", - "iopub.status.busy": "2024-10-19T23:49:13.785051Z", - "iopub.status.idle": "2024-10-19T23:49:13.896181Z", - "shell.execute_reply": "2024-10-19T23:49:13.895570Z" + "iopub.execute_input": "2024-10-22T01:15:25.536630Z", + "iopub.status.busy": "2024-10-22T01:15:25.536265Z", + "iopub.status.idle": "2024-10-22T01:15:25.649977Z", + "shell.execute_reply": "2024-10-22T01:15:25.649448Z" } }, "outputs": [ @@ -144,10 +144,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.898273Z", - "iopub.status.busy": "2024-10-19T23:49:13.898060Z", - "iopub.status.idle": "2024-10-19T23:49:13.912625Z", - "shell.execute_reply": "2024-10-19T23:49:13.912056Z" + "iopub.execute_input": "2024-10-22T01:15:25.651961Z", + "iopub.status.busy": "2024-10-22T01:15:25.651750Z", + "iopub.status.idle": "2024-10-22T01:15:25.665950Z", + "shell.execute_reply": "2024-10-22T01:15:25.665381Z" } }, "outputs": [ @@ -182,10 +182,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.914674Z", - "iopub.status.busy": "2024-10-19T23:49:13.914463Z", - "iopub.status.idle": "2024-10-19T23:49:14.140529Z", - "shell.execute_reply": "2024-10-19T23:49:14.139972Z" + "iopub.execute_input": "2024-10-22T01:15:25.667980Z", + "iopub.status.busy": "2024-10-22T01:15:25.667775Z", + "iopub.status.idle": "2024-10-22T01:15:25.893819Z", + "shell.execute_reply": "2024-10-22T01:15:25.893257Z" } }, "outputs": [ @@ -233,10 +233,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:14.142362Z", - "iopub.status.busy": "2024-10-19T23:49:14.142188Z", - "iopub.status.idle": "2024-10-19T23:49:14.190538Z", - "shell.execute_reply": "2024-10-19T23:49:14.189381Z" + "iopub.execute_input": "2024-10-22T01:15:25.895823Z", + "iopub.status.busy": "2024-10-22T01:15:25.895458Z", + "iopub.status.idle": "2024-10-22T01:15:25.943166Z", + "shell.execute_reply": "2024-10-22T01:15:25.942596Z" } }, "outputs": [ @@ -264,7 +264,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 19.47167530678116\n", + "Mean value: 2.594882809806675\n", "\n" ] } @@ -295,10 +295,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:14.193098Z", - "iopub.status.busy": "2024-10-19T23:49:14.192567Z", - "iopub.status.idle": "2024-10-19T23:49:14.196799Z", - "shell.execute_reply": "2024-10-19T23:49:14.196263Z" + "iopub.execute_input": "2024-10-22T01:15:25.945974Z", + "iopub.status.busy": "2024-10-22T01:15:25.945465Z", + "iopub.status.idle": "2024-10-22T01:15:25.949757Z", + "shell.execute_reply": "2024-10-22T01:15:25.949284Z" } }, "outputs": [ @@ -306,7 +306,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "19.47167530678116 19.518019667969362\n" + "2.594882809806675 2.6008692268961195\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:14.199161Z", - "iopub.status.busy": "2024-10-19T23:49:14.198690Z", - "iopub.status.idle": "2024-10-19T23:49:14.264863Z", - "shell.execute_reply": "2024-10-19T23:49:14.264285Z" + "iopub.execute_input": "2024-10-22T01:15:25.952297Z", + "iopub.status.busy": "2024-10-22T01:15:25.951850Z", + "iopub.status.idle": "2024-10-22T01:15:26.019230Z", + "shell.execute_reply": "2024-10-22T01:15:26.018668Z" } }, "outputs": [ @@ -360,7 +360,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 3.5485295992288e-05\n", + "Mean value: 2.27800892300678e-05\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb index 08b471bad..7e2b599a0 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_multiple_treatments.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:16.211981Z", - "iopub.status.busy": "2024-10-19T23:49:16.211794Z", - "iopub.status.idle": "2024-10-19T23:49:17.643927Z", - "shell.execute_reply": "2024-10-19T23:49:17.643351Z" + "iopub.execute_input": "2024-10-22T01:15:27.997083Z", + "iopub.status.busy": "2024-10-22T01:15:27.996905Z", + "iopub.status.idle": "2024-10-22T01:15:29.486005Z", + "shell.execute_reply": "2024-10-22T01:15:29.485384Z" } }, "outputs": [], @@ -32,10 +32,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.646141Z", - "iopub.status.busy": "2024-10-19T23:49:17.645837Z", - "iopub.status.idle": "2024-10-19T23:49:17.673221Z", - "shell.execute_reply": "2024-10-19T23:49:17.672630Z" + "iopub.execute_input": "2024-10-22T01:15:29.488651Z", + "iopub.status.busy": "2024-10-22T01:15:29.488116Z", + "iopub.status.idle": "2024-10-22T01:15:29.517836Z", + "shell.execute_reply": "2024-10-22T01:15:29.516963Z" } }, "outputs": [ @@ -74,63 +74,63 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - 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Omnibus: 303.265 Durbin-Watson: 2.085
Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.760Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.748
Skew: 2.709 Prob(JB): 0.00
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41.044167-0.849258-0.6694860.624146000.888019-0.6957460.529529-0.7298950.7011852.4242680.4233963233.44794423.229281-668.944083
\n", @@ -138,18 +138,18 @@ ], "text/plain": [ " X0 X1 W0 W1 W2 W3 v0 v1 \\\n", - "0 1.355903 -0.387930 -2.327660 -1.735969 0 0 -5.521305 -15.649951 \n", - "1 -0.320515 -0.417196 -1.171597 -2.574418 2 3 2.823160 -3.830411 \n", - "2 0.060713 -0.593707 -0.206585 0.384868 2 3 10.669174 13.004237 \n", - "3 1.541170 0.797725 -0.218512 0.356313 3 1 10.094043 13.653444 \n", - "4 1.044167 -0.849258 -0.669486 0.624146 0 0 0.888019 -0.695746 \n", + "0 -0.349212 -0.009528 -0.294154 -1.326155 3 2 12.125456 8.374340 \n", + "1 0.772577 -2.104355 0.948946 0.778592 3 0 21.513650 7.646989 \n", + "2 -0.396874 0.159136 -0.832059 0.168977 3 1 13.246901 1.618905 \n", + "3 -0.740952 -2.030404 -0.026773 -0.054814 1 3 15.690822 10.933237 \n", + "4 -0.729895 0.701185 2.424268 0.423396 3 2 33.447944 23.229281 \n", "\n", " y \n", - "0 -155.383932 \n", - "1 9.204169 \n", - "2 211.838941 \n", - "3 473.957283 \n", - "4 0.529529 " + "0 68.072214 \n", + "1 170.307903 \n", + "2 125.310203 \n", + "3 -855.902869 \n", + "4 -668.944083 " ] }, "execution_count": 2, @@ -174,10 +174,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.675471Z", - "iopub.status.busy": "2024-10-19T23:49:17.675250Z", - "iopub.status.idle": "2024-10-19T23:49:17.681318Z", - "shell.execute_reply": "2024-10-19T23:49:17.680778Z" + "iopub.execute_input": "2024-10-22T01:15:29.520680Z", + "iopub.status.busy": "2024-10-22T01:15:29.520127Z", + "iopub.status.idle": "2024-10-22T01:15:29.527036Z", + "shell.execute_reply": "2024-10-22T01:15:29.526475Z" } }, "outputs": [], @@ -192,10 +192,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.683561Z", - "iopub.status.busy": "2024-10-19T23:49:17.683169Z", - "iopub.status.idle": "2024-10-19T23:49:17.852337Z", - "shell.execute_reply": "2024-10-19T23:49:17.851673Z" + "iopub.execute_input": "2024-10-22T01:15:29.529777Z", + "iopub.status.busy": "2024-10-22T01:15:29.529515Z", + "iopub.status.idle": "2024-10-22T01:15:29.692315Z", + "shell.execute_reply": "2024-10-22T01:15:29.691677Z" } }, "outputs": [ @@ -231,10 +231,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.854343Z", - "iopub.status.busy": "2024-10-19T23:49:17.854124Z", - "iopub.status.idle": "2024-10-19T23:49:17.875059Z", - "shell.execute_reply": "2024-10-19T23:49:17.874541Z" + "iopub.execute_input": "2024-10-22T01:15:29.694490Z", + "iopub.status.busy": "2024-10-22T01:15:29.694144Z", + "iopub.status.idle": "2024-10-22T01:15:29.711124Z", + "shell.execute_reply": "2024-10-22T01:15:29.710464Z" } }, "outputs": [ @@ -248,9 +248,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────────(E[y|W3,W1,W2,W0])\n", + "─────────(E[y|W1,W3,W0,W2])\n", "d[v₀ v₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W1,W2,W0,U) = P(y|v0,v1,W3,W1,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W1,W3,W0,W2,U) = P(y|v0,v1,W1,W3,W0,W2)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -284,10 +284,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.877012Z", - "iopub.status.busy": "2024-10-19T23:49:17.876806Z", - "iopub.status.idle": "2024-10-19T23:49:17.939568Z", - "shell.execute_reply": "2024-10-19T23:49:17.939000Z" + "iopub.execute_input": "2024-10-22T01:15:29.713367Z", + "iopub.status.busy": "2024-10-22T01:15:29.712886Z", + "iopub.status.idle": "2024-10-22T01:15:29.768955Z", + "shell.execute_reply": "2024-10-22T01:15:29.768316Z" } }, "outputs": [ @@ -304,16 +304,16 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────────(E[y|W3,W1,W2,W0])\n", + "─────────(E[y|W1,W3,W0,W2])\n", "d[v₀ v₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W1,W2,W0,U) = P(y|v0,v1,W3,W1,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W1,W3,W0,W2,U) = P(y|v0,v1,W1,W3,W0,W2)\n", "\n", "## Realized estimand\n", - "b: y~v0+v1+W3+W1+W2+W0+v0*X0+v0*X1+v1*X0+v1*X1\n", + "b: y~v0+v1+W1+W3+W0+W2+v0*X1+v0*X0+v1*X1+v1*X0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 21.052209166265527\n", + "Mean value: 64.93760915623568\n", "\n" ] } @@ -339,10 +339,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.942029Z", - "iopub.status.busy": "2024-10-19T23:49:17.941800Z", - "iopub.status.idle": "2024-10-19T23:49:18.294726Z", - "shell.execute_reply": "2024-10-19T23:49:18.294055Z" + "iopub.execute_input": "2024-10-22T01:15:29.771479Z", + "iopub.status.busy": "2024-10-22T01:15:29.771238Z", + "iopub.status.idle": "2024-10-22T01:15:30.130811Z", + "shell.execute_reply": "2024-10-22T01:15:30.130094Z" } }, "outputs": [ @@ -359,43 +359,43 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────────(E[y|W3,W1,W2,W0])\n", + "─────────(E[y|W1,W3,W0,W2])\n", "d[v₀ v₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W1,W2,W0,U) = P(y|v0,v1,W3,W1,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W1,W3,W0,W2,U) = P(y|v0,v1,W1,W3,W0,W2)\n", "\n", "## Realized estimand\n", - "b: y~v0+v1+W3+W1+W2+W0+v0*X0+v0*X1+v1*X0+v1*X1\n", + "b: y~v0+v1+W1+W3+W0+W2+v0*X1+v0*X0+v1*X1+v1*X0\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: 21.052209166265527\n", + "Mean value: 64.93760915623568\n", "### Conditional Estimates\n", - "__categorical__X0 __categorical__X1 \n", - "(-2.799, 0.0662] (-4.933000000000001, -1.667] 5.125910\n", - " (-1.667, -1.093] 9.297402\n", - " (-1.093, -0.58] 12.095977\n", - " (-0.58, 0.0346] 14.987859\n", - " (0.0346, 3.349] 19.223786\n", - "(0.0662, 0.654] (-4.933000000000001, -1.667] 10.174763\n", - " (-1.667, -1.093] 14.694029\n", - " (-1.093, -0.58] 17.647011\n", - " (-0.58, 0.0346] 20.455978\n", - " (0.0346, 3.349] 25.069082\n", - "(0.654, 1.171] (-4.933000000000001, -1.667] 13.549221\n", - " (-1.667, -1.093] 18.195581\n", - " (-1.093, -0.58] 20.914473\n", - " (-0.58, 0.0346] 23.862252\n", - " (0.0346, 3.349] 28.547426\n", - "(1.171, 1.753] (-4.933000000000001, -1.667] 16.987343\n", - " (-1.667, -1.093] 21.747473\n", - " (-1.093, -0.58] 24.462509\n", - " (-0.58, 0.0346] 27.240189\n", - " (0.0346, 3.349] 31.896187\n", - "(1.753, 4.583] (-4.933000000000001, -1.667] 22.703744\n", - " (-1.667, -1.093] 27.237801\n", - " (-1.093, -0.58] 29.801650\n", - " (-0.58, 0.0346] 32.810708\n", - " (0.0346, 3.349] 37.503156\n", + "__categorical__X1 __categorical__X0\n", + "(-4.646, -1.595] (-2.444, 0.1] -83.223076\n", + " (0.1, 0.722] -21.272362\n", + " (0.722, 1.212] 19.736681\n", + " (1.212, 1.786] 57.274426\n", + " (1.786, 5.117] 121.408808\n", + "(-1.595, -1.003] (-2.444, 0.1] -55.416964\n", + " (0.1, 0.722] 7.615118\n", + " (0.722, 1.212] 47.655290\n", + " (1.212, 1.786] 84.543374\n", + " (1.786, 5.117] 146.107955\n", + "(-1.003, -0.484] (-2.444, 0.1] -36.333046\n", + " (0.1, 0.722] 26.080394\n", + " (0.722, 1.212] 66.385902\n", + " (1.212, 1.786] 102.107474\n", + " (1.786, 5.117] 165.431383\n", + "(-0.484, 0.0986] (-2.444, 0.1] -18.863482\n", + " (0.1, 0.722] 44.673243\n", + " (0.722, 1.212] 83.519976\n", + " (1.212, 1.786] 122.690563\n", + " (1.786, 5.117] 182.880352\n", + "(0.0986, 2.92] (-2.444, 0.1] 10.171146\n", + " (0.1, 0.722] 73.169405\n", + " (0.722, 1.212] 113.105707\n", + " (1.212, 1.786] 150.266148\n", + " (1.786, 5.117] 213.857166\n", "dtype: float64\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb index 12906ab65..8f5993c06 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_optimize_backdoor_example.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:19.848769Z", - "iopub.status.busy": "2024-10-19T23:49:19.848573Z", - "iopub.status.idle": "2024-10-19T23:49:21.265860Z", - "shell.execute_reply": "2024-10-19T23:49:21.265230Z" + "iopub.execute_input": "2024-10-22T01:15:32.047687Z", + "iopub.status.busy": "2024-10-22T01:15:32.047506Z", + "iopub.status.idle": "2024-10-22T01:15:33.561774Z", + "shell.execute_reply": "2024-10-22T01:15:33.561114Z" } }, "outputs": [], @@ -48,10 +48,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:21.268143Z", - "iopub.status.busy": "2024-10-19T23:49:21.267844Z", - "iopub.status.idle": "2024-10-19T23:49:21.285472Z", - "shell.execute_reply": "2024-10-19T23:49:21.284965Z" + "iopub.execute_input": "2024-10-22T01:15:33.564216Z", + "iopub.status.busy": "2024-10-22T01:15:33.563733Z", + "iopub.status.idle": "2024-10-22T01:15:33.581274Z", + "shell.execute_reply": "2024-10-22T01:15:33.580741Z" } }, "outputs": [ @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:21.287366Z", - "iopub.status.busy": "2024-10-19T23:49:21.287014Z", - "iopub.status.idle": "2024-10-19T23:49:21.296025Z", - "shell.execute_reply": "2024-10-19T23:49:21.295499Z" + "iopub.execute_input": "2024-10-22T01:15:33.583236Z", + "iopub.status.busy": "2024-10-22T01:15:33.582882Z", + "iopub.status.idle": "2024-10-22T01:15:33.620182Z", + "shell.execute_reply": "2024-10-22T01:15:33.619619Z" } }, "outputs": [ @@ -107,9 +107,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time taken for initializing model = 0.004712104797363281\n", - "Time taken for vanilla identification = 0.0002238750457763672\n", - "Time taken for optimized backdoor identification = 0.00014019012451171875\n" + "Time taken for initializing model = 0.004521846771240234\n", + "Time taken for vanilla identification = 0.01575326919555664\n", + "Time taken for optimized backdoor identification = 0.012814521789550781\n" ] } ], diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb index 4ea136963..eba0ab132 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_refutation_testing.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:22.925856Z", - "iopub.status.busy": "2024-10-19T23:49:22.925510Z", - "iopub.status.idle": "2024-10-19T23:49:24.345759Z", - "shell.execute_reply": "2024-10-19T23:49:24.345154Z" + "iopub.execute_input": "2024-10-22T01:15:35.365742Z", + "iopub.status.busy": "2024-10-22T01:15:35.365563Z", + "iopub.status.idle": "2024-10-22T01:15:36.819888Z", + "shell.execute_reply": "2024-10-22T01:15:36.819209Z" } }, "outputs": [], @@ -80,10 +80,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:24.348066Z", - "iopub.status.busy": "2024-10-19T23:49:24.347785Z", - "iopub.status.idle": "2024-10-19T23:49:24.405661Z", - "shell.execute_reply": "2024-10-19T23:49:24.405143Z" + "iopub.execute_input": "2024-10-22T01:15:36.822397Z", + "iopub.status.busy": "2024-10-22T01:15:36.821901Z", + "iopub.status.idle": "2024-10-22T01:15:36.856382Z", + "shell.execute_reply": "2024-10-22T01:15:36.855818Z" } }, "outputs": [ @@ -346,10 +346,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:24.407622Z", - "iopub.status.busy": "2024-10-19T23:49:24.407257Z", - "iopub.status.idle": "2024-10-19T23:49:25.073611Z", - "shell.execute_reply": "2024-10-19T23:49:25.073049Z" + "iopub.execute_input": "2024-10-22T01:15:36.858236Z", + "iopub.status.busy": "2024-10-22T01:15:36.858056Z", + "iopub.status.idle": "2024-10-22T01:15:38.004874Z", + "shell.execute_reply": "2024-10-22T01:15:38.004328Z" } }, "outputs": [ @@ -516,10 +516,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:25.075647Z", - "iopub.status.busy": "2024-10-19T23:49:25.075288Z", - "iopub.status.idle": "2024-10-19T23:49:25.319265Z", - "shell.execute_reply": "2024-10-19T23:49:25.318778Z" + "iopub.execute_input": "2024-10-22T01:15:38.006944Z", + "iopub.status.busy": "2024-10-22T01:15:38.006620Z", + "iopub.status.idle": "2024-10-22T01:15:38.250648Z", + "shell.execute_reply": "2024-10-22T01:15:38.250121Z" } }, "outputs": [ @@ -575,10 +575,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:25.321904Z", - "iopub.status.busy": "2024-10-19T23:49:25.321474Z", - "iopub.status.idle": "2024-10-19T23:49:25.467992Z", - "shell.execute_reply": "2024-10-19T23:49:25.467398Z" + "iopub.execute_input": "2024-10-22T01:15:38.252760Z", + "iopub.status.busy": "2024-10-22T01:15:38.252544Z", + "iopub.status.idle": "2024-10-22T01:15:38.405751Z", + "shell.execute_reply": "2024-10-22T01:15:38.404523Z" } }, "outputs": [ @@ -634,10 +634,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:25.470354Z", - "iopub.status.busy": "2024-10-19T23:49:25.469944Z", - "iopub.status.idle": "2024-10-19T23:50:20.487755Z", - "shell.execute_reply": "2024-10-19T23:50:20.487059Z" + "iopub.execute_input": "2024-10-22T01:15:38.408117Z", + "iopub.status.busy": "2024-10-22T01:15:38.407876Z", + "iopub.status.idle": "2024-10-22T01:16:33.420762Z", + "shell.execute_reply": "2024-10-22T01:16:33.420118Z" } }, "outputs": [ @@ -651,13 +651,13 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "────────────(E[y_factual|x11,x19,x1,x18,x3,x2,x17,x24,x5,x12,x20,x25,x9,x14,x2 ↪\n", + "────────────(E[y_factual|x12,x7,x21,x25,x8,x24,x13,x20,x23,x6,x22,x1,x11,x2,x1 ↪\n", "d[treatment] ↪\n", "\n", "↪ \n", - "↪ 1,x15,x22,x7,x10,x23,x8,x13,x4,x16,x6])\n", + "↪ 4,x18,x4,x5,x15,x3,x16,x10,x17,x9,x19])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x11,x19,x1,x18,x3,x2,x17,x24,x5,x12,x20,x25,x9,x14,x21,x15,x22,x7,x10,x23,x8,x13,x4,x16,x6,U) = P(y_factual|treatment,x11,x19,x1,x18,x3,x2,x17,x24,x5,x12,x20,x25,x9,x14,x21,x15,x22,x7,x10,x23,x8,x13,x4,x16,x6)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x12,x7,x21,x25,x8,x24,x13,x20,x23,x6,x22,x1,x11,x2,x14,x18,x4,x5,x15,x3,x16,x10,x17,x9,x19,U) = P(y_factual|treatment,x12,x7,x21,x25,x8,x24,x13,x20,x23,x6,x22,x1,x11,x2,x14,x18,x4,x5,x15,x3,x16,x10,x17,x9,x19)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -688,10 +688,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.489809Z", - "iopub.status.busy": "2024-10-19T23:50:20.489581Z", - "iopub.status.idle": "2024-10-19T23:50:20.503010Z", - "shell.execute_reply": "2024-10-19T23:50:20.502505Z" + "iopub.execute_input": "2024-10-22T01:16:33.422835Z", + "iopub.status.busy": "2024-10-22T01:16:33.422464Z", + "iopub.status.idle": "2024-10-22T01:16:33.435975Z", + "shell.execute_reply": "2024-10-22T01:16:33.435462Z" } }, "outputs": [ @@ -705,9 +705,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────────(E[re78|hisp,black,nodegr,educ,married,age])\n", + "────────(E[re78|hisp,black,age,married,educ,nodegr])\n", "d[treat] \n", - "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,black,nodegr,educ,married,age,U) = P(re78|treat,hisp,black,nodegr,educ,married,age)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,black,age,married,educ,nodegr,U) = P(re78|treat,hisp,black,age,married,educ,nodegr)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -745,10 +745,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.504764Z", - "iopub.status.busy": "2024-10-19T23:50:20.504559Z", - "iopub.status.idle": "2024-10-19T23:50:20.535009Z", - "shell.execute_reply": "2024-10-19T23:50:20.534270Z" + "iopub.execute_input": "2024-10-22T01:16:33.438074Z", + "iopub.status.busy": "2024-10-22T01:16:33.437699Z", + "iopub.status.idle": "2024-10-22T01:16:33.469315Z", + "shell.execute_reply": "2024-10-22T01:16:33.468468Z" } }, "outputs": [ @@ -756,7 +756,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The Causal Estimate is 4.028748218389782\n" + "The Causal Estimate is 4.0287482183899\n" ] } ], @@ -781,10 +781,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.537983Z", - "iopub.status.busy": "2024-10-19T23:50:20.536999Z", - "iopub.status.idle": "2024-10-19T23:50:20.571075Z", - "shell.execute_reply": "2024-10-19T23:50:20.570504Z" + "iopub.execute_input": "2024-10-22T01:16:33.471901Z", + "iopub.status.busy": "2024-10-22T01:16:33.471420Z", + "iopub.status.idle": "2024-10-22T01:16:33.504664Z", + "shell.execute_reply": "2024-10-22T01:16:33.504088Z" } }, "outputs": [ @@ -792,7 +792,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The Causal Estimate is 1639.796346804399\n" + "The Causal Estimate is 1639.7917591321402\n" ] } ], @@ -831,10 +831,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.574234Z", - "iopub.status.busy": "2024-10-19T23:50:20.573449Z", - "iopub.status.idle": "2024-10-19T23:50:21.625440Z", - "shell.execute_reply": "2024-10-19T23:50:21.624889Z" + "iopub.execute_input": "2024-10-22T01:16:33.507892Z", + "iopub.status.busy": "2024-10-22T01:16:33.506958Z", + "iopub.status.idle": "2024-10-22T01:16:34.436698Z", + "shell.execute_reply": "2024-10-22T01:16:34.436051Z" } }, "outputs": [ @@ -843,8 +843,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:4.028748218389782\n", - "New effect:4.028748218389782\n", + "Estimated effect:4.0287482183899\n", + "New effect:4.028748218389901\n", "p value:1.0\n", "\n" ] @@ -872,10 +872,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:21.627389Z", - "iopub.status.busy": "2024-10-19T23:50:21.627025Z", - "iopub.status.idle": "2024-10-19T23:50:22.578447Z", - "shell.execute_reply": "2024-10-19T23:50:22.577874Z" + "iopub.execute_input": "2024-10-22T01:16:34.438680Z", + "iopub.status.busy": "2024-10-22T01:16:34.438494Z", + "iopub.status.idle": "2024-10-22T01:16:35.368453Z", + "shell.execute_reply": "2024-10-22T01:16:35.367847Z" } }, "outputs": [ @@ -884,9 +884,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:4.028748218389782\n", - "New effect:-0.47708408750457004\n", - "p value:0.02\n", + "Estimated effect:4.0287482183899\n", + "New effect:-0.47856889604057196\n", + "p value:0.0\n", "\n" ] } @@ -914,10 +914,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:22.580424Z", - "iopub.status.busy": "2024-10-19T23:50:22.580054Z", - "iopub.status.idle": "2024-10-19T23:50:23.445116Z", - "shell.execute_reply": "2024-10-19T23:50:23.444461Z" + "iopub.execute_input": "2024-10-22T01:16:35.370727Z", + "iopub.status.busy": "2024-10-22T01:16:35.370240Z", + "iopub.status.idle": "2024-10-22T01:16:36.238020Z", + "shell.execute_reply": "2024-10-22T01:16:36.237315Z" } }, "outputs": [ @@ -926,9 +926,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:4.028748218389782\n", - "New effect:4.030673559907819\n", - "p value:0.98\n", + "Estimated effect:4.0287482183899\n", + "New effect:4.021788149954388\n", + "p value:0.92\n", "\n" ] } @@ -962,10 +962,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:23.447330Z", - "iopub.status.busy": "2024-10-19T23:50:23.446958Z", - "iopub.status.idle": "2024-10-19T23:50:24.333411Z", - "shell.execute_reply": "2024-10-19T23:50:24.332776Z" + "iopub.execute_input": "2024-10-22T01:16:36.240012Z", + "iopub.status.busy": "2024-10-22T01:16:36.239794Z", + "iopub.status.idle": "2024-10-22T01:16:37.154916Z", + "shell.execute_reply": "2024-10-22T01:16:37.154283Z" } }, "outputs": [ @@ -974,8 +974,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:1639.796346804399\n", - "New effect:1639.7963468043993\n", + "Estimated effect:1639.7917591321402\n", + "New effect:1639.7917591321404\n", "p value:1.0\n", "\n" ] @@ -1003,10 +1003,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:24.335563Z", - "iopub.status.busy": "2024-10-19T23:50:24.335137Z", - "iopub.status.idle": "2024-10-19T23:50:25.285665Z", - "shell.execute_reply": "2024-10-19T23:50:25.285040Z" + "iopub.execute_input": "2024-10-22T01:16:37.156994Z", + "iopub.status.busy": "2024-10-22T01:16:37.156756Z", + "iopub.status.idle": "2024-10-22T01:16:38.112721Z", + "shell.execute_reply": "2024-10-22T01:16:38.112071Z" } }, "outputs": [ @@ -1015,9 +1015,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:1639.796346804399\n", - "New effect:-220.33816894361186\n", - "p value:0.66\n", + "Estimated effect:1639.7917591321402\n", + "New effect:-130.77125442290446\n", + "p value:0.94\n", "\n" ] } @@ -1045,10 +1045,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:25.287665Z", - "iopub.status.busy": "2024-10-19T23:50:25.287373Z", - "iopub.status.idle": "2024-10-19T23:50:26.141226Z", - "shell.execute_reply": "2024-10-19T23:50:26.140516Z" + "iopub.execute_input": "2024-10-22T01:16:38.114809Z", + "iopub.status.busy": "2024-10-22T01:16:38.114595Z", + "iopub.status.idle": "2024-10-22T01:16:39.015522Z", + "shell.execute_reply": "2024-10-22T01:16:39.014944Z" } }, "outputs": [ @@ -1057,9 +1057,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:1639.796346804399\n", - "New effect:1621.9710142661302\n", - "p value:0.94\n", + "Estimated effect:1639.7917591321402\n", + "New effect:1636.6887995233171\n", + "p value:1.0\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb index c84286a3b..0440f5227 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/dowhy_simple_example.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:32.702691Z", - "iopub.status.busy": "2024-10-19T23:50:32.702277Z", - "iopub.status.idle": "2024-10-19T23:50:34.143525Z", - "shell.execute_reply": "2024-10-19T23:50:34.142922Z" + "iopub.execute_input": "2024-10-22T01:16:45.939252Z", + "iopub.status.busy": "2024-10-22T01:16:45.939081Z", + "iopub.status.idle": "2024-10-22T01:16:47.512380Z", + "shell.execute_reply": "2024-10-22T01:16:47.511810Z" } }, "outputs": [], @@ -44,10 +44,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.146010Z", - "iopub.status.busy": "2024-10-19T23:50:34.145491Z", - "iopub.status.idle": "2024-10-19T23:50:34.292757Z", - "shell.execute_reply": "2024-10-19T23:50:34.292124Z" + "iopub.execute_input": "2024-10-22T01:16:47.514954Z", + "iopub.status.busy": "2024-10-22T01:16:47.514622Z", + "iopub.status.idle": "2024-10-22T01:16:47.663628Z", + "shell.execute_reply": "2024-10-22T01:16:47.662980Z" } }, "outputs": [], @@ -68,10 +68,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.295636Z", - "iopub.status.busy": "2024-10-19T23:50:34.295178Z", - "iopub.status.idle": "2024-10-19T23:50:34.308728Z", - "shell.execute_reply": "2024-10-19T23:50:34.308261Z" + "iopub.execute_input": "2024-10-22T01:16:47.666172Z", + "iopub.status.busy": "2024-10-22T01:16:47.665959Z", + "iopub.status.idle": "2024-10-22T01:16:47.682055Z", + "shell.execute_reply": "2024-10-22T01:16:47.681461Z" } }, "outputs": [ @@ -111,68 +111,68 @@ " \n", " \n", " 0\n", - " 2.433025\n", - " 1.0\n", - " 0.446315\n", - " 0.565050\n", - " 1.584307\n", - " 0.044203\n", - " 0.998964\n", - " 0\n", + " -1.480699\n", + " 0.0\n", + " 0.682122\n", + " 1.092432\n", + " -0.403973\n", + " -0.545784\n", + " 1.560930\n", + " 3\n", " True\n", - " 29.326757\n", + " 14.906206\n", " \n", " \n", " 1\n", - " -0.615400\n", + " -0.085327\n", " 0.0\n", - " 0.686974\n", - " 1.094997\n", - " 0.789523\n", - " 0.220281\n", - " -0.269505\n", - " 2\n", - " False\n", - " 4.139483\n", + " 0.023824\n", + " 1.109181\n", + " 0.085558\n", + " 1.882593\n", + " -0.660220\n", + " 0\n", + " True\n", + " 13.612012\n", " \n", " \n", " 2\n", - " 0.464745\n", - " 0.0\n", - " 0.694356\n", - " -0.211672\n", - " -0.423395\n", - " 1.916099\n", - " 0.775553\n", - " 2\n", + " -0.111724\n", + " 1.0\n", + " 0.355073\n", + " 1.655963\n", + " -0.580056\n", + " 1.430628\n", + " -0.566377\n", + " 3\n", " True\n", - " 20.520108\n", + " 20.258636\n", " \n", " \n", " 3\n", - " 1.419616\n", - " 1.0\n", - " 0.964398\n", - " 0.086734\n", - " 0.598756\n", - " 0.791396\n", - " 0.424756\n", + " -0.116541\n", + " 0.0\n", + " 0.777078\n", + " 0.500918\n", + " 1.786132\n", + " -1.350500\n", + " -1.842522\n", " 2\n", - " True\n", - " 22.203880\n", + " False\n", + " -3.794496\n", " \n", " \n", " 4\n", - " 0.117763\n", + " -1.012609\n", " 0.0\n", - " 0.301013\n", - " 0.463781\n", - " 2.771895\n", - " -0.385546\n", - " -0.401188\n", - " 1\n", + " 0.458027\n", + " 0.694989\n", + " 1.747985\n", + " 1.535833\n", + " 0.911330\n", + " 0\n", " True\n", - " 18.342432\n", + " 13.092393\n", " \n", " \n", "\n", @@ -180,18 +180,18 @@ ], "text/plain": [ " X0 Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 2.433025 1.0 0.446315 0.565050 1.584307 0.044203 0.998964 0 True \n", - "1 -0.615400 0.0 0.686974 1.094997 0.789523 0.220281 -0.269505 2 False \n", - "2 0.464745 0.0 0.694356 -0.211672 -0.423395 1.916099 0.775553 2 True \n", - "3 1.419616 1.0 0.964398 0.086734 0.598756 0.791396 0.424756 2 True \n", - "4 0.117763 0.0 0.301013 0.463781 2.771895 -0.385546 -0.401188 1 True \n", + "0 -1.480699 0.0 0.682122 1.092432 -0.403973 -0.545784 1.560930 3 True \n", + "1 -0.085327 0.0 0.023824 1.109181 0.085558 1.882593 -0.660220 0 True \n", + "2 -0.111724 1.0 0.355073 1.655963 -0.580056 1.430628 -0.566377 3 True \n", + "3 -0.116541 0.0 0.777078 0.500918 1.786132 -1.350500 -1.842522 2 False \n", + "4 -1.012609 0.0 0.458027 0.694989 1.747985 1.535833 0.911330 0 True \n", "\n", " y \n", - "0 29.326757 \n", - "1 4.139483 \n", - "2 20.520108 \n", - "3 22.203880 \n", - "4 18.342432 " + "0 14.906206 \n", + "1 13.612012 \n", + "2 20.258636 \n", + "3 -3.794496 \n", + "4 13.092393 " ] }, "execution_count": 3, @@ -231,10 +231,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.311422Z", - "iopub.status.busy": "2024-10-19T23:50:34.310975Z", - "iopub.status.idle": "2024-10-19T23:50:34.317127Z", - "shell.execute_reply": "2024-10-19T23:50:34.316604Z" + "iopub.execute_input": "2024-10-22T01:16:47.684661Z", + "iopub.status.busy": "2024-10-22T01:16:47.684429Z", + "iopub.status.idle": "2024-10-22T01:16:47.691943Z", + "shell.execute_reply": "2024-10-22T01:16:47.691359Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.318919Z", - "iopub.status.busy": "2024-10-19T23:50:34.318697Z", - "iopub.status.idle": "2024-10-19T23:50:34.483757Z", - "shell.execute_reply": "2024-10-19T23:50:34.483177Z" + "iopub.execute_input": "2024-10-22T01:16:47.694711Z", + "iopub.status.busy": "2024-10-22T01:16:47.694330Z", + "iopub.status.idle": "2024-10-22T01:16:47.863465Z", + "shell.execute_reply": "2024-10-22T01:16:47.862869Z" } }, "outputs": [ @@ -280,10 +280,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.485996Z", - "iopub.status.busy": "2024-10-19T23:50:34.485621Z", - "iopub.status.idle": "2024-10-19T23:50:34.492025Z", - "shell.execute_reply": "2024-10-19T23:50:34.491476Z" + "iopub.execute_input": "2024-10-22T01:16:47.865840Z", + "iopub.status.busy": "2024-10-22T01:16:47.865301Z", + "iopub.status.idle": "2024-10-22T01:16:47.872759Z", + "shell.execute_reply": "2024-10-22T01:16:47.872151Z" }, "scrolled": true }, @@ -329,10 +329,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.494153Z", - "iopub.status.busy": "2024-10-19T23:50:34.493759Z", - "iopub.status.idle": "2024-10-19T23:50:34.664237Z", - "shell.execute_reply": "2024-10-19T23:50:34.663557Z" + "iopub.execute_input": "2024-10-22T01:16:47.874894Z", + "iopub.status.busy": "2024-10-22T01:16:47.874682Z", + "iopub.status.idle": "2024-10-22T01:16:48.050495Z", + "shell.execute_reply": "2024-10-22T01:16:48.049879Z" } }, "outputs": [ @@ -346,9 +346,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -391,10 +391,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.666451Z", - "iopub.status.busy": "2024-10-19T23:50:34.666012Z", - "iopub.status.idle": "2024-10-19T23:50:35.003677Z", - "shell.execute_reply": "2024-10-19T23:50:35.003082Z" + "iopub.execute_input": "2024-10-22T01:16:48.052633Z", + "iopub.status.busy": "2024-10-22T01:16:48.052223Z", + "iopub.status.idle": "2024-10-22T01:16:48.366769Z", + "shell.execute_reply": "2024-10-22T01:16:48.366128Z" }, "scrolled": true }, @@ -412,16 +412,16 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W4+W3+W0\n", + "b: y~v0+W4+W2+W3+W0+W1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 12.1455097789681\n", + "Mean value: 10.32668021161025\n", "\n" ] } @@ -444,10 +444,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.005772Z", - "iopub.status.busy": "2024-10-19T23:50:35.005379Z", - "iopub.status.idle": "2024-10-19T23:50:35.288112Z", - "shell.execute_reply": "2024-10-19T23:50:35.287460Z" + "iopub.execute_input": "2024-10-22T01:16:48.368942Z", + "iopub.status.busy": "2024-10-22T01:16:48.368552Z", + "iopub.status.idle": "2024-10-22T01:16:48.630220Z", + "shell.execute_reply": "2024-10-22T01:16:48.629529Z" } }, "outputs": [ @@ -464,18 +464,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W4+W3+W0\n", + "b: y~v0+W4+W2+W3+W0+W1\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 11.868218360802024\n", + "Mean value: 10.42646085358387\n", "\n", - "Causal Estimate is 11.868218360802024\n" + "Causal Estimate is 10.42646085358387\n" ] } ], @@ -500,10 +500,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.290148Z", - "iopub.status.busy": "2024-10-19T23:50:35.289851Z", - "iopub.status.idle": "2024-10-19T23:50:35.293087Z", - "shell.execute_reply": "2024-10-19T23:50:35.292589Z" + "iopub.execute_input": "2024-10-22T01:16:48.632549Z", + "iopub.status.busy": "2024-10-22T01:16:48.632145Z", + "iopub.status.idle": "2024-10-22T01:16:48.635704Z", + "shell.execute_reply": "2024-10-22T01:16:48.635150Z" }, "scrolled": true }, @@ -523,10 +523,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.294955Z", - "iopub.status.busy": "2024-10-19T23:50:35.294575Z", - "iopub.status.idle": "2024-10-19T23:50:35.418793Z", - "shell.execute_reply": "2024-10-19T23:50:35.418113Z" + "iopub.execute_input": "2024-10-22T01:16:48.637709Z", + "iopub.status.busy": "2024-10-22T01:16:48.637334Z", + "iopub.status.idle": "2024-10-22T01:16:48.783111Z", + "shell.execute_reply": "2024-10-22T01:16:48.782448Z" } }, "outputs": [ @@ -550,10 +550,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.421057Z", - "iopub.status.busy": "2024-10-19T23:50:35.420638Z", - "iopub.status.idle": "2024-10-19T23:50:35.425528Z", - "shell.execute_reply": "2024-10-19T23:50:35.424913Z" + "iopub.execute_input": "2024-10-22T01:16:48.785428Z", + "iopub.status.busy": "2024-10-22T01:16:48.785171Z", + "iopub.status.idle": "2024-10-22T01:16:48.791415Z", + "shell.execute_reply": "2024-10-22T01:16:48.790796Z" } }, "outputs": [ @@ -587,10 +587,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.427340Z", - "iopub.status.busy": "2024-10-19T23:50:35.427153Z", - "iopub.status.idle": "2024-10-19T23:50:35.527840Z", - "shell.execute_reply": "2024-10-19T23:50:35.527280Z" + "iopub.execute_input": "2024-10-22T01:16:48.793633Z", + "iopub.status.busy": "2024-10-22T01:16:48.793420Z", + "iopub.status.idle": "2024-10-22T01:16:48.906498Z", + "shell.execute_reply": "2024-10-22T01:16:48.905906Z" } }, "outputs": [], @@ -610,10 +610,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.529626Z", - "iopub.status.busy": "2024-10-19T23:50:35.529432Z", - "iopub.status.idle": "2024-10-19T23:50:35.812018Z", - "shell.execute_reply": "2024-10-19T23:50:35.811389Z" + "iopub.execute_input": "2024-10-22T01:16:48.908462Z", + "iopub.status.busy": "2024-10-22T01:16:48.908083Z", + "iopub.status.idle": "2024-10-22T01:16:49.166593Z", + "shell.execute_reply": "2024-10-22T01:16:49.165985Z" } }, "outputs": [ @@ -630,18 +630,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W4+W3+W0\n", + "b: y~v0+W4+W2+W3+W0+W1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 12.1455097789681\n", + "Mean value: 10.32668021161025\n", "\n", - "Causal Estimate is 12.1455097789681\n" + "Causal Estimate is 10.32668021161025\n" ] } ], @@ -685,17 +685,17 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.814043Z", - "iopub.status.busy": "2024-10-19T23:50:35.813856Z", - "iopub.status.idle": "2024-10-19T23:51:04.311699Z", - "shell.execute_reply": "2024-10-19T23:51:04.311018Z" + "iopub.execute_input": "2024-10-22T01:16:49.168700Z", + "iopub.status.busy": "2024-10-22T01:16:49.168277Z", + "iopub.status.idle": "2024-10-22T01:17:14.411254Z", + "shell.execute_reply": "2024-10-22T01:17:14.410647Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c359e565e6ae4d3a8320924f3092a413", + "model_id": "f7665810416e4f69a627a0e32c80fc07", "version_major": 2, "version_minor": 0 }, @@ -711,8 +711,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:12.145509778968098\n", + "Estimated effect:10.32668021161025\n", + "New effect:10.32668021161025\n", "p value:1.0\n", "\n" ] @@ -735,17 +735,17 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:51:04.313589Z", - "iopub.status.busy": "2024-10-19T23:51:04.313386Z", - "iopub.status.idle": "2024-10-19T23:51:30.751914Z", - "shell.execute_reply": "2024-10-19T23:51:30.751224Z" + "iopub.execute_input": "2024-10-22T01:17:14.413283Z", + "iopub.status.busy": "2024-10-22T01:17:14.412888Z", + "iopub.status.idle": "2024-10-22T01:17:39.582841Z", + "shell.execute_reply": "2024-10-22T01:17:39.582302Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "521fef3fa630438399a9c99ce9fa21d9", + "model_id": "a289d04f173d48df994c1e73591f26aa", "version_major": 2, "version_minor": 0 }, @@ -761,9 +761,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:12.1455097789681\n", - "New effect:0.055367443227466485\n", - "p value:0.8799999999999999\n", + "Estimated effect:10.32668021161025\n", + "New effect:0.012367961362952769\n", + "p value:0.98\n", "\n" ] } @@ -786,17 +786,17 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:51:30.753860Z", - "iopub.status.busy": "2024-10-19T23:51:30.753658Z", - "iopub.status.idle": "2024-10-19T23:51:56.640854Z", - "shell.execute_reply": "2024-10-19T23:51:56.640223Z" + "iopub.execute_input": "2024-10-22T01:17:39.585024Z", + "iopub.status.busy": "2024-10-22T01:17:39.584695Z", + "iopub.status.idle": "2024-10-22T01:18:02.497611Z", + "shell.execute_reply": "2024-10-22T01:18:02.497006Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "49166b74edc243cd997ed2a91258d69d", + "model_id": "8991d6af3bab4fd6889e6ab0041f81f2", "version_major": 2, "version_minor": 0 }, @@ -812,9 +812,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:12.1455097789681\n", - "New effect:11.96555527788205\n", - "p value:0.36\n", + "Estimated effect:10.32668021161025\n", + "New effect:10.297025170147867\n", + "p value:0.56\n", "\n" ] } @@ -841,17 +841,17 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:51:56.642984Z", - "iopub.status.busy": "2024-10-19T23:51:56.642577Z", - "iopub.status.idle": "2024-10-19T23:52:09.655444Z", - "shell.execute_reply": "2024-10-19T23:52:09.654693Z" + "iopub.execute_input": "2024-10-22T01:18:02.499736Z", + "iopub.status.busy": "2024-10-22T01:18:02.499362Z", + "iopub.status.idle": "2024-10-22T01:18:14.426891Z", + "shell.execute_reply": "2024-10-22T01:18:14.425395Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b137779c5e844e68418c223c9b563ba", + "model_id": "4e074034b0314ac88de0e3234827a52f", "version_major": 2, "version_minor": 0 }, @@ -887,56 +887,56 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 4.4s\n" + "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 4.2s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 24 tasks | elapsed: 4.9s\n" + "[Parallel(n_jobs=-1)]: Done 24 tasks | elapsed: 4.8s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 6.1s\n" + "[Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 5.8s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 7.0s\n" + "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 6.6s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 53 tasks | elapsed: 8.2s\n" + "[Parallel(n_jobs=-1)]: Done 53 tasks | elapsed: 7.6s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 64 tasks | elapsed: 9.2s\n" + "[Parallel(n_jobs=-1)]: Done 64 tasks | elapsed: 8.5s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 77 tasks | elapsed: 10.7s\n" + "[Parallel(n_jobs=-1)]: Done 77 tasks | elapsed: 9.9s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 90 tasks | elapsed: 12.0s\n" + "[Parallel(n_jobs=-1)]: Done 90 tasks | elapsed: 11.0s\n" ] }, { @@ -944,9 +944,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:12.1455097789681\n", - "New effect:11.953778728959206\n", - "p value:0.34\n", + "Estimated effect:10.32668021161025\n", + "New effect:10.293059888977698\n", + "p value:0.5\n", "\n" ] }, @@ -954,7 +954,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.0s finished\n" + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 11.9s finished\n" ] } ], @@ -984,10 +984,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:09.657394Z", - "iopub.status.busy": "2024-10-19T23:52:09.657177Z", - "iopub.status.idle": "2024-10-19T23:52:09.958038Z", - "shell.execute_reply": "2024-10-19T23:52:09.957342Z" + "iopub.execute_input": "2024-10-22T01:18:14.431075Z", + "iopub.status.busy": "2024-10-22T01:18:14.430748Z", + "iopub.status.idle": "2024-10-22T01:18:14.705541Z", + "shell.execute_reply": "2024-10-22T01:18:14.704799Z" } }, "outputs": [ @@ -996,8 +996,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:10.382153142787415\n", + "Estimated effect:10.32668021161025\n", + "New effect:9.989729726705876\n", "\n" ] } @@ -1021,16 +1021,16 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:09.960114Z", - "iopub.status.busy": "2024-10-19T23:52:09.959726Z", - "iopub.status.idle": "2024-10-19T23:52:11.278904Z", - "shell.execute_reply": "2024-10-19T23:52:11.278243Z" + "iopub.execute_input": "2024-10-22T01:18:14.707710Z", + "iopub.status.busy": "2024-10-22T01:18:14.707499Z", + "iopub.status.idle": "2024-10-22T01:18:15.900519Z", + "shell.execute_reply": "2024-10-22T01:18:15.899882Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1043,8 +1043,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:(8.382168111045187, 12.004268127514308)\n", + "Estimated effect:10.32668021161025\n", + "New effect:(9.026401102591986, 10.283703220838348)\n", "\n" ] } @@ -1070,16 +1070,16 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:11.280919Z", - "iopub.status.busy": "2024-10-19T23:52:11.280562Z", - "iopub.status.idle": "2024-10-19T23:52:15.861961Z", - "shell.execute_reply": "2024-10-19T23:52:15.861346Z" + "iopub.execute_input": "2024-10-22T01:18:15.902699Z", + "iopub.status.busy": "2024-10-22T01:18:15.902302Z", + "iopub.status.idle": "2024-10-22T01:18:19.982846Z", + "shell.execute_reply": "2024-10-22T01:18:19.982011Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1092,8 +1092,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:(4.164924356241952, 12.037138430567376)\n", + "Estimated effect:10.32668021161025\n", + "New effect:(6.9550266796339475, 10.294311909679474)\n", "\n" ] } @@ -1118,10 +1118,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:15.864399Z", - "iopub.status.busy": "2024-10-19T23:52:15.863850Z", - "iopub.status.idle": "2024-10-19T23:52:42.457268Z", - "shell.execute_reply": "2024-10-19T23:52:42.456524Z" + "iopub.execute_input": "2024-10-22T01:18:19.985242Z", + "iopub.status.busy": "2024-10-22T01:18:19.984891Z", + "iopub.status.idle": "2024-10-22T01:18:42.595021Z", + "shell.execute_reply": "2024-10-22T01:18:42.594291Z" } }, "outputs": [ @@ -1135,7 +1135,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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"iopub.status.busy": "2024-10-19T23:52:47.696481Z", - "iopub.status.idle": "2024-10-19T23:52:47.946510Z", - "shell.execute_reply": "2024-10-19T23:52:47.945939Z" + "iopub.execute_input": "2024-10-22T01:18:47.838819Z", + "iopub.status.busy": "2024-10-22T01:18:47.838627Z", + "iopub.status.idle": "2024-10-22T01:18:48.115189Z", + "shell.execute_reply": "2024-10-22T01:18:48.114481Z" } }, "outputs": [], @@ -81,10 +81,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:47.948821Z", - "iopub.status.busy": "2024-10-19T23:52:47.948422Z", - "iopub.status.idle": "2024-10-19T23:52:47.965599Z", - "shell.execute_reply": "2024-10-19T23:52:47.965069Z" + "iopub.execute_input": "2024-10-22T01:18:48.117639Z", + "iopub.status.busy": "2024-10-22T01:18:48.117217Z", + "iopub.status.idle": "2024-10-22T01:18:48.135124Z", + "shell.execute_reply": "2024-10-22T01:18:48.134498Z" } }, "outputs": [ @@ -299,10 +299,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:47.967502Z", - "iopub.status.busy": "2024-10-19T23:52:47.967303Z", - "iopub.status.idle": "2024-10-19T23:52:49.173676Z", - "shell.execute_reply": "2024-10-19T23:52:49.173074Z" + "iopub.execute_input": "2024-10-22T01:18:48.137274Z", + "iopub.status.busy": "2024-10-22T01:18:48.136883Z", + "iopub.status.idle": "2024-10-22T01:18:49.444529Z", + "shell.execute_reply": "2024-10-22T01:18:49.443807Z" } }, "outputs": [], @@ -331,10 +331,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:49.175950Z", - "iopub.status.busy": "2024-10-19T23:52:49.175587Z", - "iopub.status.idle": "2024-10-19T23:52:49.387522Z", - "shell.execute_reply": "2024-10-19T23:52:49.386980Z" + "iopub.execute_input": "2024-10-22T01:18:49.446958Z", + "iopub.status.busy": "2024-10-22T01:18:49.446643Z", + "iopub.status.idle": "2024-10-22T01:18:49.665622Z", + "shell.execute_reply": "2024-10-22T01:18:49.665001Z" } }, "outputs": [ @@ -369,10 +369,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:49.390130Z", - "iopub.status.busy": "2024-10-19T23:52:49.389703Z", - "iopub.status.idle": "2024-10-19T23:52:50.835809Z", - "shell.execute_reply": "2024-10-19T23:52:50.835153Z" + "iopub.execute_input": "2024-10-22T01:18:49.668360Z", + "iopub.status.busy": "2024-10-22T01:18:49.667779Z", + "iopub.status.idle": "2024-10-22T01:18:51.211985Z", + "shell.execute_reply": "2024-10-22T01:18:51.211298Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:50.837705Z", - "iopub.status.busy": "2024-10-19T23:52:50.837515Z", - "iopub.status.idle": "2024-10-19T23:52:50.841060Z", - "shell.execute_reply": "2024-10-19T23:52:50.840569Z" + "iopub.execute_input": "2024-10-22T01:18:51.214310Z", + "iopub.status.busy": "2024-10-22T01:18:51.213866Z", + "iopub.status.idle": "2024-10-22T01:18:51.218094Z", + "shell.execute_reply": "2024-10-22T01:18:51.217548Z" } }, "outputs": [], @@ -453,10 +453,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:50.842649Z", - "iopub.status.busy": "2024-10-19T23:52:50.842470Z", - "iopub.status.idle": "2024-10-19T23:52:57.662535Z", - "shell.execute_reply": "2024-10-19T23:52:57.662032Z" + "iopub.execute_input": "2024-10-22T01:18:51.219963Z", + "iopub.status.busy": "2024-10-22T01:18:51.219678Z", + "iopub.status.idle": "2024-10-22T01:18:58.040257Z", + "shell.execute_reply": "2024-10-22T01:18:58.039730Z" } }, "outputs": [ @@ -481,7 +481,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node e401: 6%|▌ | 1/18 [00:01<00:21, 1.28s/it]" + "Fitting causal mechanism of node e401: 6%|▌ | 1/18 [00:01<00:21, 1.25s/it]" ] }, { @@ -489,7 +489,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node net_tfa: 6%|▌ | 1/18 [00:01<00:21, 1.28s/it]" + "Fitting causal mechanism of node net_tfa: 6%|▌ | 1/18 [00:01<00:21, 1.25s/it]" ] }, { @@ -660,10 +660,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:57.664515Z", - "iopub.status.busy": "2024-10-19T23:52:57.664193Z", - "iopub.status.idle": "2024-10-19T23:52:57.668513Z", - "shell.execute_reply": "2024-10-19T23:52:57.668059Z" + "iopub.execute_input": "2024-10-22T01:18:58.042085Z", + "iopub.status.busy": "2024-10-22T01:18:58.041895Z", + "iopub.status.idle": "2024-10-22T01:18:58.046369Z", + "shell.execute_reply": "2024-10-22T01:18:58.045936Z" } }, "outputs": [], @@ -691,10 +691,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:57.670247Z", - "iopub.status.busy": "2024-10-19T23:52:57.669906Z", - "iopub.status.idle": "2024-10-19T23:53:41.044103Z", - "shell.execute_reply": "2024-10-19T23:53:41.043530Z" + "iopub.execute_input": "2024-10-22T01:18:58.047990Z", + "iopub.status.busy": "2024-10-22T01:18:58.047811Z", + "iopub.status.idle": "2024-10-22T01:19:42.054793Z", + "shell.execute_reply": "2024-10-22T01:19:42.054225Z" } }, "outputs": [ @@ -711,7 +711,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 1%| | 1/100 [00:00<00:43, 2.28it/s]" + "Estimating bootstrap interval...: 1%| | 1/100 [00:00<00:43, 2.26it/s]" ] }, { @@ -719,7 +719,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 2%|▏ | 2/100 [00:00<00:42, 2.30it/s]" + "Estimating bootstrap interval...: 2%|▏ | 2/100 [00:00<00:42, 2.29it/s]" ] }, { @@ -727,7 +727,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 3%|▎ | 3/100 [00:01<00:42, 2.30it/s]" + "Estimating bootstrap interval...: 3%|▎ | 3/100 [00:01<00:42, 2.29it/s]" ] }, { @@ -735,7 +735,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 4%|▍ | 4/100 [00:01<00:41, 2.31it/s]" + "Estimating bootstrap interval...: 4%|▍ | 4/100 [00:01<00:41, 2.30it/s]" ] }, { @@ -743,7 +743,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 5%|▌ | 5/100 [00:02<00:41, 2.31it/s]" + "Estimating bootstrap interval...: 5%|▌ | 5/100 [00:02<00:41, 2.30it/s]" ] }, { @@ -751,7 +751,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 6%|▌ | 6/100 [00:02<00:40, 2.31it/s]" + "Estimating bootstrap interval...: 6%|▌ | 6/100 [00:02<00:40, 2.30it/s]" ] }, { @@ -759,7 +759,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 7%|▋ | 7/100 [00:03<00:40, 2.31it/s]" + "Estimating bootstrap interval...: 7%|▋ | 7/100 [00:03<00:40, 2.30it/s]" ] }, { @@ -767,7 +767,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 8%|▊ | 8/100 [00:03<00:39, 2.31it/s]" + "Estimating bootstrap interval...: 8%|▊ | 8/100 [00:03<00:40, 2.30it/s]" ] }, { @@ -775,7 +775,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 9%|▉ | 9/100 [00:03<00:39, 2.31it/s]" + "Estimating bootstrap interval...: 9%|▉ | 9/100 [00:03<00:40, 2.27it/s]" ] }, { @@ -783,7 +783,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 10%|█ | 10/100 [00:04<00:38, 2.31it/s]" + "Estimating bootstrap interval...: 10%|█ | 10/100 [00:04<00:39, 2.27it/s]" ] }, { @@ -791,7 +791,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 11%|█ | 11/100 [00:04<00:38, 2.31it/s]" + "Estimating bootstrap interval...: 11%|█ | 11/100 [00:04<00:38, 2.28it/s]" ] }, { @@ -799,7 +799,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 12%|█▏ | 12/100 [00:05<00:38, 2.31it/s]" + "Estimating bootstrap interval...: 12%|█▏ | 12/100 [00:05<00:38, 2.27it/s]" ] }, { @@ -807,7 +807,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 13%|█▎ | 13/100 [00:05<00:37, 2.31it/s]" + "Estimating bootstrap interval...: 13%|█▎ | 13/100 [00:05<00:38, 2.26it/s]" ] }, { @@ -815,7 +815,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 14%|█▍ | 14/100 [00:06<00:37, 2.31it/s]" + "Estimating bootstrap interval...: 14%|█▍ | 14/100 [00:06<00:38, 2.26it/s]" ] }, { @@ -823,7 +823,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 15%|█▌ | 15/100 [00:06<00:36, 2.31it/s]" + "Estimating bootstrap interval...: 15%|█▌ | 15/100 [00:06<00:37, 2.24it/s]" ] }, { @@ -831,7 +831,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 16%|█▌ | 16/100 [00:06<00:36, 2.31it/s]" + "Estimating bootstrap interval...: 16%|█▌ | 16/100 [00:07<00:37, 2.26it/s]" ] }, { @@ -839,7 +839,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 17%|█▋ | 17/100 [00:07<00:35, 2.31it/s]" + "Estimating bootstrap interval...: 17%|█▋ | 17/100 [00:07<00:36, 2.27it/s]" ] }, { @@ -847,7 +847,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 18%|█▊ | 18/100 [00:07<00:35, 2.31it/s]" + "Estimating bootstrap interval...: 18%|█▊ | 18/100 [00:07<00:35, 2.28it/s]" ] }, { @@ -855,7 +855,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 19%|█▉ | 19/100 [00:08<00:35, 2.31it/s]" + "Estimating bootstrap interval...: 19%|█▉ | 19/100 [00:08<00:35, 2.29it/s]" ] }, { @@ -863,7 +863,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 20%|██ | 20/100 [00:08<00:34, 2.30it/s]" + "Estimating bootstrap interval...: 20%|██ | 20/100 [00:08<00:34, 2.29it/s]" ] }, { @@ -871,7 +871,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 21%|██ | 21/100 [00:09<00:34, 2.30it/s]" + "Estimating bootstrap interval...: 21%|██ | 21/100 [00:09<00:34, 2.29it/s]" ] }, { @@ -879,7 +879,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 22%|██▏ | 22/100 [00:09<00:33, 2.31it/s]" + "Estimating bootstrap interval...: 22%|██▏ | 22/100 [00:09<00:34, 2.28it/s]" ] }, { @@ -887,7 +887,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 23%|██▎ | 23/100 [00:09<00:33, 2.31it/s]" + "Estimating bootstrap interval...: 23%|██▎ | 23/100 [00:10<00:33, 2.29it/s]" ] }, { @@ -895,7 +895,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 24%|██▍ | 24/100 [00:10<00:32, 2.31it/s]" + "Estimating bootstrap interval...: 24%|██▍ | 24/100 [00:10<00:33, 2.29it/s]" ] }, { @@ -903,7 +903,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 25%|██▌ | 25/100 [00:10<00:32, 2.31it/s]" + "Estimating bootstrap interval...: 25%|██▌ | 25/100 [00:10<00:32, 2.29it/s]" ] }, { @@ -911,7 +911,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 26%|██▌ | 26/100 [00:11<00:32, 2.31it/s]" + "Estimating bootstrap interval...: 26%|██▌ | 26/100 [00:11<00:33, 2.22it/s]" ] }, { @@ -919,7 +919,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 27%|██▋ | 27/100 [00:11<00:31, 2.31it/s]" + "Estimating bootstrap interval...: 27%|██▋ | 27/100 [00:11<00:32, 2.22it/s]" ] }, { @@ -927,7 +927,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 28%|██▊ | 28/100 [00:12<00:31, 2.30it/s]" + "Estimating bootstrap interval...: 28%|██▊ | 28/100 [00:12<00:32, 2.22it/s]" ] }, { @@ -935,7 +935,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 29%|██▉ | 29/100 [00:12<00:31, 2.29it/s]" + "Estimating bootstrap interval...: 29%|██▉ | 29/100 [00:12<00:32, 2.20it/s]" ] }, { @@ -943,7 +943,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 30%|███ | 30/100 [00:13<00:30, 2.30it/s]" + "Estimating bootstrap interval...: 30%|███ | 30/100 [00:13<00:31, 2.22it/s]" ] }, { @@ -951,7 +951,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 31%|███ | 31/100 [00:13<00:30, 2.27it/s]" + "Estimating bootstrap interval...: 31%|███ | 31/100 [00:13<00:30, 2.24it/s]" ] }, { @@ -959,7 +959,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 32%|███▏ | 32/100 [00:13<00:29, 2.28it/s]" + "Estimating bootstrap interval...: 32%|███▏ | 32/100 [00:14<00:30, 2.26it/s]" ] }, { @@ -975,7 +975,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 34%|███▍ | 34/100 [00:14<00:28, 2.28it/s]" + "Estimating bootstrap interval...: 34%|███▍ | 34/100 [00:14<00:28, 2.29it/s]" ] }, { @@ -1007,7 +1007,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 38%|███▊ | 38/100 [00:16<00:26, 2.31it/s]" + "Estimating bootstrap interval...: 38%|███▊ | 38/100 [00:16<00:27, 2.29it/s]" ] }, { @@ -1015,7 +1015,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 39%|███▉ | 39/100 [00:16<00:26, 2.31it/s]" + "Estimating bootstrap interval...: 39%|███▉ | 39/100 [00:17<00:26, 2.29it/s]" ] }, { @@ -1023,7 +1023,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 40%|████ | 40/100 [00:17<00:26, 2.31it/s]" + "Estimating bootstrap interval...: 40%|████ | 40/100 [00:17<00:26, 2.29it/s]" ] }, { @@ -1031,7 +1031,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 41%|████ | 41/100 [00:17<00:25, 2.31it/s]" + "Estimating bootstrap interval...: 41%|████ | 41/100 [00:18<00:25, 2.29it/s]" ] }, { @@ -1039,7 +1039,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 42%|████▏ | 42/100 [00:18<00:25, 2.31it/s]" + "Estimating bootstrap interval...: 42%|████▏ | 42/100 [00:18<00:25, 2.26it/s]" ] }, { @@ -1047,7 +1047,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 43%|████▎ | 43/100 [00:18<00:24, 2.31it/s]" + "Estimating bootstrap interval...: 43%|████▎ | 43/100 [00:18<00:25, 2.25it/s]" ] }, { @@ -1055,7 +1055,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 44%|████▍ | 44/100 [00:19<00:24, 2.31it/s]" + "Estimating bootstrap interval...: 44%|████▍ | 44/100 [00:19<00:24, 2.27it/s]" ] }, { @@ -1063,7 +1063,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 45%|████▌ | 45/100 [00:19<00:23, 2.31it/s]" + "Estimating bootstrap interval...: 45%|████▌ | 45/100 [00:19<00:24, 2.28it/s]" ] }, { @@ -1071,7 +1071,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 46%|████▌ | 46/100 [00:19<00:23, 2.31it/s]" + "Estimating bootstrap interval...: 46%|████▌ | 46/100 [00:20<00:23, 2.29it/s]" ] }, { @@ -1079,7 +1079,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 47%|████▋ | 47/100 [00:20<00:22, 2.31it/s]" + "Estimating bootstrap interval...: 47%|████▋ | 47/100 [00:20<00:23, 2.26it/s]" ] }, { @@ -1087,7 +1087,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 48%|████▊ | 48/100 [00:20<00:22, 2.31it/s]" + "Estimating bootstrap interval...: 48%|████▊ | 48/100 [00:21<00:22, 2.27it/s]" ] }, { @@ -1095,7 +1095,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 49%|████▉ | 49/100 [00:21<00:22, 2.30it/s]" + "Estimating bootstrap interval...: 49%|████▉ | 49/100 [00:21<00:22, 2.28it/s]" ] }, { @@ -1103,7 +1103,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 50%|█████ | 50/100 [00:21<00:21, 2.31it/s]" + "Estimating bootstrap interval...: 50%|█████ | 50/100 [00:22<00:21, 2.28it/s]" ] }, { @@ -1111,7 +1111,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 51%|█████ | 51/100 [00:22<00:21, 2.31it/s]" + "Estimating bootstrap interval...: 51%|█████ | 51/100 [00:22<00:21, 2.29it/s]" ] }, { @@ -1119,7 +1119,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 52%|█████▏ | 52/100 [00:22<00:20, 2.31it/s]" + "Estimating bootstrap interval...: 52%|█████▏ | 52/100 [00:22<00:20, 2.29it/s]" ] }, { @@ -1127,7 +1127,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 53%|█████▎ | 53/100 [00:22<00:20, 2.31it/s]" + "Estimating bootstrap interval...: 53%|█████▎ | 53/100 [00:23<00:20, 2.29it/s]" ] }, { @@ -1135,7 +1135,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 54%|█████▍ | 54/100 [00:23<00:19, 2.31it/s]" + "Estimating bootstrap interval...: 54%|█████▍ | 54/100 [00:23<00:20, 2.29it/s]" ] }, { @@ -1143,7 +1143,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 55%|█████▌ | 55/100 [00:23<00:19, 2.31it/s]" + "Estimating bootstrap interval...: 55%|█████▌ | 55/100 [00:24<00:19, 2.29it/s]" ] }, { @@ -1151,7 +1151,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 56%|█████▌ | 56/100 [00:24<00:19, 2.31it/s]" + "Estimating bootstrap interval...: 56%|█████▌ | 56/100 [00:24<00:19, 2.29it/s]" ] }, { @@ -1159,7 +1159,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 57%|█████▋ | 57/100 [00:24<00:18, 2.31it/s]" + "Estimating bootstrap interval...: 57%|█████▋ | 57/100 [00:25<00:18, 2.29it/s]" ] }, { @@ -1167,7 +1167,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 58%|█████▊ | 58/100 [00:25<00:18, 2.31it/s]" + "Estimating bootstrap interval...: 58%|█████▊ | 58/100 [00:25<00:18, 2.29it/s]" ] }, { @@ -1175,7 +1175,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 59%|█████▉ | 59/100 [00:25<00:17, 2.31it/s]" + "Estimating bootstrap interval...: 59%|█████▉ | 59/100 [00:25<00:17, 2.29it/s]" ] }, { @@ -1183,7 +1183,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 60%|██████ | 60/100 [00:26<00:17, 2.31it/s]" + "Estimating bootstrap interval...: 60%|██████ | 60/100 [00:26<00:17, 2.30it/s]" ] }, { @@ -1191,7 +1191,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 61%|██████ | 61/100 [00:26<00:16, 2.31it/s]" + "Estimating bootstrap interval...: 61%|██████ | 61/100 [00:26<00:16, 2.30it/s]" ] }, { @@ -1199,7 +1199,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 62%|██████▏ | 62/100 [00:26<00:16, 2.31it/s]" + "Estimating bootstrap interval...: 62%|██████▏ | 62/100 [00:27<00:16, 2.30it/s]" ] }, { @@ -1207,7 +1207,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 63%|██████▎ | 63/100 [00:27<00:16, 2.31it/s]" + "Estimating bootstrap interval...: 63%|██████▎ | 63/100 [00:27<00:16, 2.30it/s]" ] }, { @@ -1215,7 +1215,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 64%|██████▍ | 64/100 [00:27<00:15, 2.31it/s]" + "Estimating bootstrap interval...: 64%|██████▍ | 64/100 [00:28<00:15, 2.30it/s]" ] }, { @@ -1223,7 +1223,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 65%|██████▌ | 65/100 [00:28<00:15, 2.31it/s]" + "Estimating bootstrap interval...: 65%|██████▌ | 65/100 [00:28<00:15, 2.30it/s]" ] }, { @@ -1231,7 +1231,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 66%|██████▌ | 66/100 [00:28<00:14, 2.31it/s]" + "Estimating bootstrap interval...: 66%|██████▌ | 66/100 [00:28<00:14, 2.29it/s]" ] }, { @@ -1239,7 +1239,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 67%|██████▋ | 67/100 [00:29<00:14, 2.31it/s]" + "Estimating bootstrap interval...: 67%|██████▋ | 67/100 [00:29<00:14, 2.27it/s]" ] }, { @@ -1247,7 +1247,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 68%|██████▊ | 68/100 [00:29<00:13, 2.31it/s]" + "Estimating bootstrap interval...: 68%|██████▊ | 68/100 [00:29<00:14, 2.18it/s]" ] }, { @@ -1255,7 +1255,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 69%|██████▉ | 69/100 [00:29<00:13, 2.31it/s]" + "Estimating bootstrap interval...: 69%|██████▉ | 69/100 [00:30<00:13, 2.22it/s]" ] }, { @@ -1263,7 +1263,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 70%|███████ | 70/100 [00:30<00:12, 2.31it/s]" + "Estimating bootstrap interval...: 70%|███████ | 70/100 [00:30<00:13, 2.24it/s]" ] }, { @@ -1271,7 +1271,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 71%|███████ | 71/100 [00:30<00:12, 2.31it/s]" + "Estimating bootstrap interval...: 71%|███████ | 71/100 [00:31<00:12, 2.26it/s]" ] }, { @@ -1279,7 +1279,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 72%|███████▏ | 72/100 [00:31<00:12, 2.31it/s]" + "Estimating bootstrap interval...: 72%|███████▏ | 72/100 [00:31<00:12, 2.27it/s]" ] }, { @@ -1287,7 +1287,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 73%|███████▎ | 73/100 [00:31<00:11, 2.31it/s]" + "Estimating bootstrap interval...: 73%|███████▎ | 73/100 [00:32<00:11, 2.27it/s]" ] }, { @@ -1295,7 +1295,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 74%|███████▍ | 74/100 [00:32<00:11, 2.31it/s]" + "Estimating bootstrap interval...: 74%|███████▍ | 74/100 [00:32<00:11, 2.28it/s]" ] }, { @@ -1303,7 +1303,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 75%|███████▌ | 75/100 [00:32<00:10, 2.29it/s]" + "Estimating bootstrap interval...: 75%|███████▌ | 75/100 [00:32<00:10, 2.28it/s]" ] }, { @@ -1311,7 +1311,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 76%|███████▌ | 76/100 [00:32<00:10, 2.30it/s]" + "Estimating bootstrap interval...: 76%|███████▌ | 76/100 [00:33<00:10, 2.26it/s]" ] }, { @@ -1319,7 +1319,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 77%|███████▋ | 77/100 [00:33<00:10, 2.30it/s]" + "Estimating bootstrap interval...: 77%|███████▋ | 77/100 [00:33<00:10, 2.27it/s]" ] }, { @@ -1327,7 +1327,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 78%|███████▊ | 78/100 [00:33<00:09, 2.30it/s]" + "Estimating bootstrap interval...: 78%|███████▊ | 78/100 [00:34<00:09, 2.27it/s]" ] }, { @@ -1335,7 +1335,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 79%|███████▉ | 79/100 [00:34<00:09, 2.30it/s]" + "Estimating bootstrap interval...: 79%|███████▉ | 79/100 [00:34<00:09, 2.28it/s]" ] }, { @@ -1343,7 +1343,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 80%|████████ | 80/100 [00:34<00:08, 2.31it/s]" + "Estimating bootstrap interval...: 80%|████████ | 80/100 [00:35<00:08, 2.27it/s]" ] }, { @@ -1351,7 +1351,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 81%|████████ | 81/100 [00:35<00:08, 2.31it/s]" + "Estimating bootstrap interval...: 81%|████████ | 81/100 [00:35<00:08, 2.28it/s]" ] }, { @@ -1359,7 +1359,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 82%|████████▏ | 82/100 [00:35<00:07, 2.31it/s]" + "Estimating bootstrap interval...: 82%|████████▏ | 82/100 [00:36<00:07, 2.28it/s]" ] }, { @@ -1367,7 +1367,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 83%|████████▎ | 83/100 [00:35<00:07, 2.31it/s]" + "Estimating bootstrap interval...: 83%|████████▎ | 83/100 [00:36<00:07, 2.29it/s]" ] }, { @@ -1383,7 +1383,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 85%|████████▌ | 85/100 [00:36<00:06, 2.31it/s]" + "Estimating bootstrap interval...: 85%|████████▌ | 85/100 [00:37<00:06, 2.29it/s]" ] }, { @@ -1391,7 +1391,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 86%|████████▌ | 86/100 [00:37<00:06, 2.31it/s]" + "Estimating bootstrap interval...: 86%|████████▌ | 86/100 [00:37<00:06, 2.30it/s]" ] }, { @@ -1399,7 +1399,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 87%|████████▋ | 87/100 [00:37<00:05, 2.31it/s]" + "Estimating bootstrap interval...: 87%|████████▋ | 87/100 [00:38<00:05, 2.30it/s]" ] }, { @@ -1407,7 +1407,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 88%|████████▊ | 88/100 [00:38<00:05, 2.31it/s]" + "Estimating bootstrap interval...: 88%|████████▊ | 88/100 [00:38<00:05, 2.30it/s]" ] }, { @@ -1415,7 +1415,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 89%|████████▉ | 89/100 [00:38<00:04, 2.31it/s]" + "Estimating bootstrap interval...: 89%|████████▉ | 89/100 [00:39<00:04, 2.30it/s]" ] }, { @@ -1423,7 +1423,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 90%|█████████ | 90/100 [00:39<00:04, 2.31it/s]" + "Estimating bootstrap interval...: 90%|█████████ | 90/100 [00:39<00:04, 2.27it/s]" ] }, { @@ -1431,7 +1431,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 91%|█████████ | 91/100 [00:39<00:03, 2.31it/s]" + "Estimating bootstrap interval...: 91%|█████████ | 91/100 [00:40<00:04, 2.18it/s]" ] }, { @@ -1439,7 +1439,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 92%|█████████▏| 92/100 [00:39<00:03, 2.31it/s]" + "Estimating bootstrap interval...: 92%|█████████▏| 92/100 [00:40<00:03, 2.22it/s]" ] }, { @@ -1447,7 +1447,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 93%|█████████▎| 93/100 [00:40<00:03, 2.31it/s]" + "Estimating bootstrap interval...: 93%|█████████▎| 93/100 [00:40<00:03, 2.25it/s]" ] }, { @@ -1455,7 +1455,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 94%|█████████▍| 94/100 [00:40<00:02, 2.31it/s]" + "Estimating bootstrap interval...: 94%|█████████▍| 94/100 [00:41<00:02, 2.26it/s]" ] }, { @@ -1463,7 +1463,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 95%|█████████▌| 95/100 [00:41<00:02, 2.31it/s]" + "Estimating bootstrap interval...: 95%|█████████▌| 95/100 [00:41<00:02, 2.24it/s]" ] }, { @@ -1471,7 +1471,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 96%|█████████▌| 96/100 [00:41<00:01, 2.31it/s]" + "Estimating bootstrap interval...: 96%|█████████▌| 96/100 [00:42<00:01, 2.26it/s]" ] }, { @@ -1479,7 +1479,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 97%|█████████▋| 97/100 [00:42<00:01, 2.31it/s]" + "Estimating bootstrap interval...: 97%|█████████▋| 97/100 [00:42<00:01, 2.28it/s]" ] }, { @@ -1487,7 +1487,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 98%|█████████▊| 98/100 [00:42<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 98%|█████████▊| 98/100 [00:43<00:00, 2.29it/s]" ] }, { @@ -1495,7 +1495,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 99%|█████████▉| 99/100 [00:42<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 99%|█████████▉| 99/100 [00:43<00:00, 2.29it/s]" ] }, { @@ -1503,7 +1503,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.27it/s]" ] }, { @@ -1511,15 +1511,15 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.27it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'ate': 6625.920676709266, '0%-20%': 4189.075458725501, '20%-40%': 3377.758863663636, '40%-60%': 5708.485661944268, '60%-80%': 7650.805116771037, '80%-100%': 12053.7668997243}\n", - "{'ate': array([5068.14408455, 8530.33139243]), '0%-20%': array([2852.5444978 , 5813.86194937]), '20%-40%': array([1542.59819043, 5847.81952526]), '40%-60%': array([3555.12606816, 8280.83280268]), '60%-80%': array([ 4452.12672782, 10727.92604392]), '80%-100%': array([ 5246.02924007, 19210.44069844])}\n" + "{'ate': 6754.262821634007, '0%-20%': 4311.355389405208, '20%-40%': 3326.9069591859184, '40%-60%': 5818.682773291764, '60%-80%': 7765.007274775296, '80%-100%': 12318.072193752065}\n", + "{'ate': array([4960.09827088, 8710.01062402]), '0%-20%': array([3045.2342499, 5818.661168 ]), '20%-40%': array([1572.75217838, 5499.96751887]), '40%-60%': array([3575.11437279, 8306.54464987]), '60%-80%': array([ 4659.97458355, 11206.3565352 ]), '80%-100%': array([ 5589.58571445, 19310.90794145])}\n" ] }, { @@ -1569,10 +1569,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:41.046119Z", - "iopub.status.busy": "2024-10-19T23:53:41.045779Z", - "iopub.status.idle": "2024-10-19T23:53:41.166599Z", - "shell.execute_reply": "2024-10-19T23:53:41.166065Z" + "iopub.execute_input": "2024-10-22T01:19:42.056910Z", + "iopub.status.busy": "2024-10-22T01:19:42.056454Z", + "iopub.status.idle": "2024-10-22T01:19:42.156465Z", + "shell.execute_reply": "2024-10-22T01:19:42.155796Z" } }, "outputs": [ @@ -1580,13 +1580,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_4015/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n", + "/tmp/ipykernel_4021/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n", " plt.plot((x, x), (ci[0], ci[1]), 'ro-', color='orange')\n" ] }, { "data": { - "image/png": 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bAICnpye+/PJLZGZmonz58gCA0NBQ1K1bF5aWllJMWFiY2g2LoaGh8PT01PmxEBEREVHpoFEhbWFhUSg7T01NxY0bN6T1+Ph4xMTEwMrKCk5OTgBUQya2bduG+fPn53p/REQETp8+jXbt2sHMzAwRERGYMGECPv30U6lI7tevH2bMmIGhQ4ciODgYsbGxWLx4MRYuXChtZ/z48WjTpg3mz58PX19fbN68GefOneMjz4mIiIgoX+90+rujR4+iXbt2udoHDRqEtWvXAgBWrFiBwMBAJCYm5iroo6KiMGbMGFy9ehUZGRmoXr06BgwYgKCgILVhFxcuXIC/vz/Onj0La2trjBs3DsHBwWrb2rZtG6ZOnYrbt2+jdu3amDt3Ljp37qzxsXD6OyIiIiItlILp74rNPNIlHQtpIiIiIi2UgkK6QDcbbt++HVu3bsXdu3fx8uVLtb6oqCidJEZEREREVJxp/UCW77//Hp999hlsbW0RHR2NZs2aoVKlSrh16xY6depUGDkSERERERU7WhfSP/74I1asWIEffvgBBgYGmDx5MkJDQxEQEICUlJTCyJGIiIiIqNjRupC+e/cu3n//fQCAsbExnj17BgAYMGAANm3apNvsiIiIiIiKKa0LaTs7Ozx58gQA4OTkhL/++guAauo63rdIRERERGWF1oX0hx9+iN27dwMAPvvsM0yYMAEdOnRAnz590L17d50nSERERERUHGk9/Z1SqYRSqUS5cqoJPzZv3ozw8HDUrl0bI0eOhIGBQaEkWtxx+jsiIiIiLZSC6e84j7SOsJAmIiIi0kIpKKQ1mkf6woULqF+/PvT09HDhwoU3xjZo0EAniRERERERFWcaFdINGzaEXC6HjY0NGjZsCJlMlueNhTKZDNnZ2TpPkoiIiIiouNGokI6Pj0flypWl10REREREZZ1GhXS1atXyfE1EREREVFZpPf3d7NmzsXr16lztq1evxnfffaeTpIiIiIiIijutC+mffvoJLi4uudrfe+89LF++XCdJEREREREVd1oX0nK5HPb29rnaK1eujMTERJ0kRURERERU3GldSDs6OuLUqVO52k+dOgUHBwedJEVEREREVNxpdLPhq4YPH47AwEBkZmbiww8/BACEhYVh8uTJmDhxos4TJCIiIiIqjrQupCdNmoTHjx9jzJgxePnyJQDAyMgIwcHBmDJlis4TJCIiIiIqjgr8iPDU1FRcuXIFxsbGqF27NgwNDXWdW4nCR4QTERERaaGsPCI8L6ampmjatKkucyEiIiIiKjG0LqTT0tIwZ84chIWF4eHDh1AqlWr9t27d0llyRERERETFldaF9LBhw3Ds2DEMGDAA9vb2kMlkhZEXEREREVGxpnUh/ccff2Dfvn1o2bJlYeRDRERERFQiaD2PtKWlJaysrAojFyIiIiKiEkPrQnrmzJkICQlBenp6YeRDRERERFQiaD20Y/78+bh58yZsbW3h7OyM8uXLq/VHRUXpLDkiIiIiouJK60Laz8+vENIgIiIiIipZtC6kv/rqq8LIg4iIiIioRNF6jLQuHT9+HF26dIGDgwNkMhl27dql1j948GDIZDK1pWPHjmoxT548Qf/+/WFubo6KFSti6NChSE1NVYu5cOECWrVqBSMjIzg6OmLu3Lm5ctm2bRtcXFxgZGQENzc37N+/X+fHS0RERESlh9aFdHZ2Nv7v//4PzZo1g52dHaysrNQWbaSlpcHd3R1Lly7NN6Zjx45ITEyUlk2bNqn19+/fH5cuXUJoaCj27t2L48ePY8SIEVK/QqGAt7c3qlWrhsjISMybNw/Tp0/HihUrpJjw8HD07dsXQ4cORXR0NPz8/ODn54fY2FitjoeIiIiIyg6ZEEJo84aQkBCsXLkSEydOxNSpU/Hll1/i9u3b2LVrF0JCQhAQEFCwRGQy7Ny5U20M9uDBg5GcnJzrSnWOK1euwNXVFWfPnkWTJk0AAAcOHEDnzp1x7949ODg4YNmyZfjyyy8hl8thYGAAAPjiiy+wa9cuXL16FQDQp08fpKWlYe/evdK2W7RogYYNG2L58uUa5a9QKGBhYYGUlBSYm5sX4BMgIiIiKkOy0oCtpqrXvVOBchXebT4FoPUV6Q0bNuDnn3/GxIkTUa5cOfTt2xcrV65ESEgI/vrrL50nePToUdjY2KBu3boYPXo0Hj9+LPVFRESgYsWKUhENAF5eXtDT08Pp06elmNatW0tFNAD4+PggLi4OT58+lWK8vLzU9uvj44OIiIh888rIyIBCoVBbiIiIiKjs0LqQlsvlcHNzAwCYmpoiJSUFAPDRRx9h3759Ok2uY8eOWL9+PcLCwvDdd9/h2LFj6NSpE7Kzs6VcbGxs1N5Trlw5WFlZQS6XSzG2trZqMTnr/xWT05+X2bNnw8LCQlocHR3f7mCJiIiIqETRupCuWrUqEhMTAQA1a9bEn3/+CQA4e/YsDA0NdZrcJ598gq5du8LNzQ1+fn7Yu3cvzp49i6NHj+p0PwUxZcoUpKSkSEtCQsK7TomIiIiIipDWhXT37t0RFhYGABg3bhymTZuG2rVrY+DAgRgyZIjOE3xVjRo1YG1tjRs3bgAA7Ozs8PDhQ7WYrKwsPHnyBHZ2dlJMUlKSWkzO+n/F5PTnxdDQEObm5moLEREREZUdWs8jPWfOHOl1nz594OTkhIiICNSuXRtdunTRaXKvu3fvHh4/fgx7e3sAgKenJ5KTkxEZGQkPDw8AwOHDh6FUKtG8eXMp5ssvv0RmZqb0FMbQ0FDUrVsXlpaWUkxYWBgCAwOlfYWGhsLT07NQj4eIiIiISi6tZ+3QpdTUVOnqcqNGjbBgwQK0a9dOmkpvxowZ6NmzJ+zs7HDz5k1MnjwZz549w8WLF6VhJJ06dUJSUhKWL1+OzMxMfPbZZ2jSpAk2btwIAEhJSUHdunXh7e2N4OBgxMbGYsiQIVi4cKE0TV54eDjatGmDOXPmwNfXF5s3b8a3336LqKgo1K9fX6Nj4awdRERERFooBbN2FKiQvn79Oo4cOYKHDx9CqVSq9YWEhGi8naNHj6Jdu3a52gcNGoRly5bBz88P0dHRSE5OhoODA7y9vTFz5ky1GwOfPHmCsWPHYs+ePdDT00PPnj3x/fffw9TUVIq5cOEC/P39cfbsWVhbW2PcuHEIDg5W2+e2bdswdepU3L59G7Vr18bcuXPRuXNnjY+FhTQRERGRFspiIf3zzz9j9OjRsLa2hp2dHWQy2b8bk8kQFRWl8yRLAhbSRERERFooBYW01mOkv/nmG8yaNSvXFV0iIiIiorJE61k7nj59il69ehVGLkREREREJYbWhXSvXr2kuaOJiIiIiMoqrYd21KpVC9OmTcNff/0FNzc3aUq5HAEBATpLjoiIiIiouNL6ZsPq1avnvzGZDLdu3XrrpEoi3mxIREREpIWyeLNhfHx8YeRBRERERFSiaD1GmoiIiIiINLwiHRQUhJkzZ6JChQoICgp6Y+yCBQt0khgRERERUXGmUSEdHR2NzMxMAEBUVJTaQ1helV87EREREVFpo1EhvXjxYukGuqNHjxZmPkREREREJYJGY6QbNWqEv//+GwBQo0YNPH78uFCTIiIiIiIq7jQqpCtWrCjN1nH79m0olcpCTYqIiIiIqLjTaGhHz5490aZNG9jb20Mmk6FJkybQ19fPM7asziNNRERERGWLRoX0ihUr0KNHD9y4cQMBAQEYPnw4zMzMCjs3IiIiIqJiS+MHsnTs2BEAEBkZifHjx7OQJiIiIqIyTesnG65Zs6Yw8iAiIiIiKlH4ZEMiIiIiogJgIU1EREREVAAspImIiIiICkDrQvr48ePIysrK1Z6VlYXjx4/rJCkiIiIiouJO60K6Xbt2ePLkSa72lJQUtGvXTidJEREREREVd1oX0kIIyGSyXO2PHz9GhQoVdJIUEREREVFxp/H0dz169AAAyGQyDB48GIaGhlJfdnY2Lly4gPfff1/3GRIRERERFUMaF9IWFhYAVFekzczMYGxsLPUZGBigRYsWGD58uO4zJCIiIiIqhjQupHMexOLs7IzPP/+cwziIiIiIqEzTeoz0V199BUNDQxw6dAg//fQTnj17BgB48OABUlNTdZ4gEREREVFxpPUjwu/cuYOOHTvi7t27yMjIQIcOHWBmZobvvvsOGRkZWL58eWHkSURERERUrGh9RXr8+PFo0qQJnj59qjZOunv37ggLC9NpckRERERExZXWV6RPnDiB8PBwGBgYqLU7Ozvj/v37OkuMiIiIiKg40/qKtFKpRHZ2dq72e/fuwczMTKttHT9+HF26dIGDgwNkMhl27dol9WVmZiI4OBhubm6oUKECHBwcMHDgQDx48EBtG87OzpDJZGrLnDlz1GIuXLiAVq1awcjICI6Ojpg7d26uXLZt2wYXFxcYGRnBzc0N+/fv1+pYiIiIiKhs0bqQ9vb2xqJFi6R1mUyG1NRUfPXVV+jcubNW20pLS4O7uzuWLl2aqy89PR1RUVGYNm0aoqKisGPHDsTFxaFr1665Yr/++mskJiZKy7hx46Q+hUIBb29vVKtWDZGRkZg3bx6mT5+OFStWSDHh4eHo27cvhg4diujoaPj5+cHPzw+xsbFaHQ8RERERlR0yIYTQ5g337t2Dj48PhBC4fv06mjRpguvXr8Pa2hrHjx+HjY1NwRKRybBz5074+fnlG3P27Fk0a9YMd+7cgZOTEwDVFenAwEAEBgbm+Z5ly5bhyy+/hFwul4ajfPHFF9i1axeuXr0KAOjTpw/S0tKwd+9e6X0tWrRAw4YNNb55UqFQwMLCAikpKTA3N9foPURERERlVlYasNVU9bp3KlCu5E2trPUV6apVq+L8+fP43//+hwkTJqBRo0aYM2cOoqOjC1xEayolJQUymQwVK1ZUa58zZw4qVaqERo0aYd68ecjKypL6IiIi0Lp1a7Ux3T4+PoiLi8PTp0+lGC8vL7Vt+vj4ICIiIt9cMjIyoFAo1BYiIiIiKju0vtkQAMqVK4dPP/1U17m80YsXLxAcHIy+ffuqXfENCAhA48aNYWVlhfDwcEyZMgWJiYlYsGABAEAul6N69epq27K1tZX6LC0tIZfLpbZXY+Ryeb75zJ49GzNmzNDV4RERERFRCVOgQvr69es4cuQIHj58CKVSqdYXEhKik8RelZmZid69e0MIgWXLlqn1BQUFSa8bNGgAAwMDjBw5ErNnz4ahoaHOc8kxZcoUtX0rFAo4OjoW2v6IiIiIqHjRupD++eefMXr0aFhbW8POzg4ymUzqk8lkOi+kc4roO3fu4PDhw/85/rh58+bIysrC7du3UbduXdjZ2SEpKUktJmfdzs5O+plXTE5/XgwNDQu1UCciIiKi4k3rMdLffPMNZs2aBblcjpiYGERHR0tLVFSUTpPLKaKvX7+OQ4cOoVKlSv/5npiYGOjp6UnjtT09PXH8+HFkZmZKMaGhoahbty4sLS2lmNcfJhMaGgpPT08dHg0RERERlSZaX5F++vQpevXqpZOdp6am4saNG9J6fHw8YmJiYGVlBXt7e3z88ceIiorC3r17kZ2dLY1ZtrKygoGBASIiInD69Gm0a9cOZmZmiIiIwIQJE/Dpp59KRXK/fv0wY8YMDB06FMHBwYiNjcXixYuxcOFCab/jx49HmzZtMH/+fPj6+mLz5s04d+6c2hR5RERERESv0nr6u6FDh6Jp06YYNWrUW+/86NGjaNeuXa72QYMGYfr06bluEsxx5MgRtG3bFlFRURgzZgyuXr2KjIwMVK9eHQMGDEBQUJDasIsLFy7A398fZ8+ehbW1NcaNG4fg4GC1bW7btg1Tp07F7du3Ubt2bcydO1erebE5/R0RERGRFkrB9HdaF9KzZ8/GggUL4OvrCzc3N5QvX16tPyAgQKcJlhQspImIiIi0UBYL6fyuEgOqmw1v3br11kmVRCykiYiIiLRQCgpprcdIx8fHF0YeREREREQlitazdhAREREREQtpIiIiIqICYSFNRERERFQALKSJiIiIiAqAhTQRERERUQFoPWsHACQnJ+PMmTN4+PAhlEqlWt/AgQN1khgRERERUXGmdSG9Z88e9O/fH6mpqTA3N4dMJpP6ZDIZC2kiIiIiKhO0HtoxceJEDBkyBKmpqUhOTsbTp0+l5cmTJ4WRIxERERFRsaN1IX3//n0EBATAxMSkMPIhIiIiIioRtC6kfXx8cO7cucLIhYiIiIioxNB6jLSvry8mTZqEy5cvw83NDeXLl1fr79q1q86SIyIiIiIqrmRCCKHNG/T08r+ILZPJkJ2d/dZJlUQKhQIWFhZISUmBubn5u06HiIiIqHjLSgO2mqpe904FylV4t/kUgNZXpF+f7o6IiIiIqCziA1mIiIiIiAqgQIX0sWPH0KVLF9SqVQu1atVC165dceLECV3nRkRERCWFMhtIOgrc3qT6qSybQz2pbNG6kP7111/h5eUFExMTBAQEICAgAMbGxmjfvj02btxYGDkSERFRcZawA9jtDIS1A8L7qX7udla1E5ViWt9sWK9ePYwYMQITJkxQa1+wYAF+/vlnXLlyRacJlhS82ZCIiMqkhB3AiY8BvF5O/PPk41bbAcceRZ0VlQSl4GZDra9I37p1C126dMnV3rVrV8THx+skKSIiIioBlNlA5HjkLqLxb1tkIId5UKmldSHt6OiIsLCwXO2HDh2Co6OjTpIiIiKiEuDRCSD93hsCBJCeoIojKoW0nv5u4sSJCAgIQExMDN5//30AwKlTp7B27VosXrxY5wkSERFRMfU8UbdxRCWM1oX06NGjYWdnh/nz52Pr1q0AVOOmt2zZgm7duuk8QSIiIiqmjO11G0dUwmh9syHljTcbEhFRmaPMVs3OkX4feY+TlgEmVYGu8YCefhEnR8VeWbzZkIiIiAiAqjj2yBnWKXut8591j0UsoqnUYiFNREREBefYQzXFnbGDertJVU59R6We1mOkiYiIiNQ49gBsvYDtFqr1tvsBO29eiaZSj1ekiYiI6O29WjTbtGYRTWWC1oX0kSNHdLbz48ePo0uXLnBwcIBMJsOuXbvU+oUQCAkJgb29PYyNjeHl5YXr16+rxTx58gT9+/eHubk5KlasiKFDhyI1NVUt5sKFC2jVqhWMjIzg6OiIuXPn5spl27ZtcHFxgZGREdzc3LB//36dHScRERERlT5aF9IdO3ZEzZo18c033yAhIeGtdp6WlgZ3d3csXbo0z/65c+fi+++/x/Lly3H69GlUqFABPj4+ePHihRTTv39/XLp0CaGhodi7dy+OHz+OESNGSP0KhQLe3t6oVq0aIiMjMW/ePEyfPh0rVqyQYsLDw9G3b18MHToU0dHR8PPzg5+fH2JjY9/q+IiIiIioFBNaevTokViwYIFwd3cX5cqVE97e3mLLli0iIyND202pASB27twprSuVSmFnZyfmzZsntSUnJwtDQ0OxadMmIYQQly9fFgDE2bNnpZg//vhDyGQycf/+fSGEED/++KOwtLRUyy84OFjUrVtXWu/du7fw9fVVy6d58+Zi5MiRGuefkpIiAIiUlBSN30NERFRqZKYKsQGqJTP1XWdDJUEpOGe0viJtbW2NCRMmICYmBqdPn0adOnUwZswYODg4ICAgAOfPn9dJgR8fHw+5XA4vLy+pzcLCAs2bN0dERAQAICIiAhUrVkSTJk2kGC8vL+jp6eH06dNSTOvWrWFgYCDF+Pj4IC4uDk+fPpViXt1PTkzOfoiIiIiIXvdWNxs2btwYU6ZMwdixY5GamorVq1fDw8MDrVq1wqVLl94qMblcDgCwtbVVa7e1tZX65HI5bGxs1PrLlSsHKysrtZi8tvHqPvKLyenPS0ZGBhQKhdpCRERERGVHgQrpzMxMbN++HZ07d0a1atVw8OBBLFmyBElJSbhx4waqVauGXr166TrXYmX27NmwsLCQFkdHx3edEhEREREVIa0L6XHjxsHe3h4jR45EnTp1EB0djYiICAwbNgwVKlSAs7Mz/u///g9Xr159q8Ts7OwAAElJSWrtSUlJUp+dnR0ePnyo1p+VlYUnT56oxeS1jVf3kV9MTn9epkyZgpSUFGl52xsviYiIiKhk0bqQvnz5Mn744Qc8ePAAixYtQv369XPFWFtbv/U0edWrV4ednR3CwsKkNoVCgdOnT8PT0xMA4OnpieTkZERGRkoxhw8fhlKpRPPmzaWY48ePIzMzU4oJDQ1F3bp1YWlpKcW8up+cmJz95MXQ0BDm5uZqCxERERGVHVoX0l999RV69eoFQ0NDtfasrCwcP34cgGqccps2bf5zW6mpqYiJiUFMTAwA1Q2GMTExuHv3LmQyGQIDA/HNN99g9+7duHjxIgYOHAgHBwf4+fkBAOrVq4eOHTti+PDhOHPmDE6dOoWxY8fik08+gYOD6lGl/fr1g4GBAYYOHYpLly5hy5YtWLx4MYKCgqQ8xo8fjwMHDmD+/Pm4evUqpk+fjnPnzmHs2LHafjxEREREVFZoPc2Hnp5ISkrK1f73338LPT09rbZ15MgRASDXMmjQICGEagq8adOmCVtbW2FoaCjat28v4uLi1Lbx+PFj0bdvX2FqairMzc3FZ599Jp49e6YWc/78efHBBx8IQ0NDUaVKFTFnzpxcuWzdulXUqVNHGBgYiPfee0/s27dPq2Ph9HdERFSmlYKpzKiIlYJzRiaEENoU3np6ekhKSkLlypXV2q9du4YmTZqU2dkrFAoFLCwskJKSwmEeRERU9mSlAVtNVa97pwLlKrzbfKj4KwXnTDlNA3v06AEAkMlkGDx4sNrQjuzsbFy4cAHvv/++7jMkIiIiIiqGNC6kLSwsAABCCJiZmcHY2FjqMzAwQIsWLTB8+HDdZ0hEREREVAxpXEivWbMGAODs7IzPP/8cFSqUvMvvRERERES6onEhneOrr74qjDyIiIiIiEoUjQrpxo0bIywsDJaWlmjUqBFkMlm+sVFRUTpLjoiIiIiouNKokO7WrZt0c2HOHM5ERERERGWZ1tPfUd44/R0REZVppWAqMypipeCc0frJhkREREREpOHQDktLyzeOi37VkydP3iohIiIiIqKSQKNCetGiRYWcBhERERFRyaJRIT1o0KDCzoOIiIiIqETRqJBWKBTSDXQKheKNsbzRjoiIiIjKAo3HSCcmJsLGxgYVK1bMc7y0EAIymQzZ2dk6T5KIiIiIqLjRqJA+fPgwrKysAABHjhwp1ISIiIiIiEoCjQrpNm3a5PmaiIiIiKis0qiQft3Tp0+xatUqXLlyBQDg6uqKzz77TLpqTURERERU2mn9QJbjx4/D2dkZ33//PZ4+fYqnT5/i+++/R/Xq1XH8+PHCyJGIiIqaMhtIOgrc3qT6qeT9L0REr9P6irS/vz/69OmDZcuWQV9fHwCQnZ2NMWPGwN/fHxcvXtR5kkREVIQSdgCR44H0e/+2mVQFPBYDjj3eXV5ERMWM1lekb9y4gYkTJ0pFNADo6+sjKCgIN27c0GlyRERUxBJ2ACc+Vi+iASD9vqo9Yce7yYuIqBjSupBu3LixNDb6VVeuXIG7u7tOkiIiondAma26Eg2RR+c/bZGBHOZBRPQPjYZ2XLhwQXodEBCA8ePH48aNG2jRogUA4K+//sLSpUsxZ86cwsmSiIgK36MTua9EqxFAeoIqzrZtUWVFRFRsaVRIN2zYEDKZDEL8e5Vi8uTJueL69euHPn366C47IiIqOs8TdRtHRFTKaVRIx8fHF3YeRET0rhnb6zaOiKiU06iQrlatWmHnQURE71rlVqrZOdLvI+9x0jJVf+VWRZ0ZEVGxpFEhvXv3bnTq1Anly5fH7t273xjbtWtXnSRGRERFTE9fNcXdiY8ByKBeTMtUPzwWqeKIiEizQtrPzw9yuRw2Njbw8/PLN04mkyE7m3dzExGVWI49gFbbgXMBwPP7/7abVFUV0ZxHmohIolEhrVQq83xNRESlkGMPwNYL2G6hWm+7H7Dz5pVoIqLXaD2PNBERlQGvFs02rVlEExHlQetHhH///fd5tstkMhgZGaFWrVpo3bq12pMPiYiIiIhKG60L6YULF+LRo0dIT0+HpaUlAODp06cwMTGBqakpHj58iBo1auDIkSNwdHR86wSdnZ1x586dXO1jxozB0qVL0bZtWxw7dkytb+TIkVi+fLm0fvfuXYwePRpHjhyBqakpBg0ahNmzZ6NcuX8P/+jRowgKCsKlS5fg6OiIqVOnYvDgwW+dPxERERGVTloP7fj222/RtGlTXL9+HY8fP8bjx49x7do1NG/eHIsXL8bdu3dhZ2eHCRMm6CTBs2fPIjExUVpCQ0MBAL169ZJihg8frhYzd+5cqS87Oxu+vr54+fIlwsPDsW7dOqxduxYhISFSTHx8PHx9fdGuXTvExMQgMDAQw4YNw8GDB3VyDERERERU+sjEq48r1EDNmjXx22+/oWHDhmrt0dHR6NmzJ27duoXw8HD07NkTiYm6f/pVYGAg9u7di+vXr0Mmk6Ft27Zo2LAhFi1alGf8H3/8gY8++ggPHjyAra0tAGD58uUIDg7Go0ePYGBggODgYOzbtw+xsbHS+z755BMkJyfjwIEDGuWlUChgYWGBlJQUmJubv/VxEhG9U1lpwFZT1eveqUC5Cu82Hyr+eM6QtkrBOaP1FenExERkZWXlas/KyoJcLgcAODg44NmzZ2+f3WtevnyJX3/9FUOGDIFMJpPaN2zYAGtra9SvXx9TpkxBenq61BcREQE3NzepiAYAHx8fKBQKXLp0SYrx8vJS25ePjw8iIiLyzSUjIwMKhUJtISIiIqKyQ+tCul27dhg5ciSio6OltujoaIwePRoffvghAODixYuoXr267rL8x65du5CcnKw2drlfv3749ddfceTIEUyZMgW//PILPv30U6lfLperFdEApPWcwj+/GIVCgefPn+eZy+zZs2FhYSEtuhgPTkREREQlh9Y3G65atQoDBgyAh4cHypcvD0B1Nbp9+/ZYtWoVAMDU1BTz58/Xbab/7LtTp05wcHCQ2kaMGCG9dnNzg729Pdq3b4+bN2+iZs2aOs8hx5QpUxAUFCStKxQKFtNEREREZYjWhbSdnR1CQ0Nx9epVXLt2DQBQt25d1K1bV4pp166d7jL8x507d3Do0CHs2LHjjXHNmzcHANy4cQM1a9aEnZ0dzpw5oxaTlJQEQHUsOT9z2l6NMTc3h7GxcZ77MTQ0hKGhYYGOhYiIiIhKPq0L6RwuLi5wcXHRZS5vtGbNGtjY2MDX1/eNcTExMQAAe3t7AICnpydmzZqFhw8fwsbGBgAQGhoKc3NzuLq6SjH79+9X205oaCg8PT11fBREREREVFpoVEgHBQVh5syZqFChgtpwhrwsWLBAJ4m9SqlUYs2aNRg0aJDa3M83b97Exo0b0blzZ1SqVAkXLlzAhAkT0Lp1azRo0AAA4O3tDVdXVwwYMABz586FXC7H1KlT4e/vL11RHjVqFJYsWYLJkydjyJAhOHz4MLZu3Yp9+/bp/FiIiIiIqHTQqJCOjo5GZmam9Do/r86koUuHDh3C3bt3MWTIELV2AwMDHDp0CIsWLUJaWhocHR3Rs2dPTJ06VYrR19fH3r17MXr0aHh6eqJChQoYNGgQvv76aymmevXq2LdvHyZMmIDFixejatWqWLlyJXx8fArleIiIiIio5NN6HmnKG+eRJqJSpRTM70pFjOcMaasUnDNaT39HREREREQaDu3o0aOHxhv8r1k1iIiIiIhKA40KaQsLi8LOg4iIiIioRNGokF6zZk1h50FEREREVKIUaIx0VlYWDh06hJ9++gnPnj0DADx48ACpqak6TY6IiIiIqLjS+oEsd+7cQceOHXH37l1kZGSgQ4cOMDMzw3fffYeMjAwsX768MPIkIiIiIipWtL4iPX78eDRp0gRPnz5Ve3x29+7dERYWptPkiIiIiIiKK62vSJ84cQLh4eEwMDBQa3d2dsb9+/d1lhgRERERUXGm9RVppVKJ7OzsXO337t2DmZmZTpIiIiIiIirutC6kvb29sWjRImldJpMhNTUVX331FTp37qzL3IiIiIiIii2th3bMnz8fPj4+cHV1xYsXL9CvXz9cv34d1tbW2LRpU2HkSERERERU7GhdSFetWhXnz5/Hli1bcP78eaSmpmLo0KHo37+/2s2HRERERESlmdaFNACUK1cO/fv3R//+/XWdDxERERFRiVCgB7IQEREREZV1BboiTUQljDIbeHQCeJ4IGNsDlVsBevrvOisiIqISjYV0ScSiiLSRsAOIHA+k3/u3zaQq4LEYcOzx7vIiIiIq4VhIlzQsikgbCTuAEx8DEOrt6fdV7a2287whIiIqoLcaI52RkaGrPEgTOUXRq0U08G9RlLDj3eRFxZMyW/VH1+tFNPBvW2SgKo6IiIi0plUh/ccff2DQoEGoUaMGypcvDxMTE5ibm6NNmzaYNWsWHjx4UFh5Eosi0tajE7n/6FIjgPQEVRwRERFpTaNCeufOnahTpw6GDBmCcuXKITg4GDt27MDBgwexcuVKtGnTBocOHUKNGjUwatQoPHr0qLDzLntYFJG2nifqNo6IiIjUaDRGeu7cuVi4cCE6deoEPb3ctXfv3r0BAPfv38cPP/yAX3/9FRMmTNBtpmUdiyLSlrG9buOIiIhIjUaFdEREhEYbq1KlCubMmfNWCVE+WBSRtiq3Ut2Imn4feQ8Jkqn6K7cq6syIiIhKBa1vNoyNjc23b9euXW+TC71JTlEEWT4BMsDEkUUR/UtPXzWbC4Dc580/6x6LOHUiERFRAWldSPv4+CA+Pj5X+2+//cZHhhcmFkVUEI49VFPcGTuot5tU5dR3REREb0nrQnrYsGHw8vKCXC6X2rZs2YKBAwdi7dq1usyNXseiiArCsQfge/nf9bb7ga7xPF+IiOjdenWmsYfHS+TMY1o/kGXGjBl48uQJvLy8cPz4cRw4cADDhg3DL7/8gp49exZGjvQqxx6ArRew3UK13nY/YOfNK9H0Zq+eHzateb4QEdG7lbADOBfw7/rRziXyAXMFeiDLDz/8AHd3d7Ro0QLDhw/Hpk2bWEQXJRZFREREVFLlPGDu+X319hL4gDmNrkjv3r07V1uPHj1w4sQJ9O3bFzKZTIrp2rWrbjMkIiIiotLhPx8wJ1M9YK5KtxJxoVCjQtrPzy/fvtWrV2P16tUAAJlMhuzskje+hYiIiIiKgDYPmLNtW1RZFZhGQzuUSqVGi66L6OnTp0Mmk6ktLi4uUv+LFy/g7++PSpUqwdTUFD179kRSUpLaNu7evQtfX1+YmJjAxsYGkyZNQlZWllrM0aNH0bhxYxgaGqJWrVq8aZKIiIioMJSyB8wVaIx0UXrvvfeQmJgoLSdPnpT6JkyYgD179mDbtm04duwYHjx4gB49/h2gnp2dDV9fX7x8+RLh4eFYt24d1q5di5CQECkmPj4evr6+aNeuHWJiYhAYGIhhw4bh4MGDRXqcRERERKVeKXvAnEaF9ObNmzXeYEJCAk6dOlXghF5Xrlw52NnZSYu1tTUAICUlBatWrcKCBQvw4YcfwsPDA2vWrEF4eDj++usvAMCff/6Jy5cv49dff0XDhg3RqVMnzJw5E0uXLsXLly8BAMuXL0f16tUxf/581KtXD2PHjsXHH3+MhQsX6uwYiIiIiAil7gFzGhXSy5YtQ7169TB37lxcuXIlV39KSgr279+Pfv36oXHjxnj8+LHOErx+/TocHBxQo0YN9O/fH3fv3gUAREZGIjMzE15eXlKsi4sLnJycpEeaR0REwM3NDba2tlKMj48PFAoFLl26JMW8uo2cmP96LHpGRgYUCoXaQkRERERvUMoeMKdRIX3s2DF89913CA0NRf369WFubo7atWvDzc0NVatWRaVKlTBkyBA4OTkhNjZWZzN3NG/eHGvXrsWBAwewbNkyxMfHo1WrVnj27BnkcjkMDAxQsWJFtffY2tpKD4uRy+VqRXROf07fm2IUCgWeP3+eb26zZ8+GhYWFtDg6Or7t4RIRERGVfjkPmDOpot5eAh8wp/EDWbp27YquXbvi77//xsmTJ3Hnzh08f/4c1tbWaNSoERo1agQ9Pd0Oue7UqZP0ukGDBmjevDmqVauGrVu3wtjYWKf70taUKVMQFBQkrSsUChbTRERERJpw7KGa4u7RCdWNhcb2quEcJeRKdA6tn2xobW39xunwClPFihVRp04d3LhxAx06dMDLly+RnJysdlU6KSkJdnZ2AAA7OzucOXNGbRs5s3q8GvP6TB9JSUkwNzd/Y7FuaGgIQ0NDXRwWERERUdmjp18iprh7k2I/a8erUlNTcfPmTdjb28PDwwPly5dHWFiY1B8XF4e7d+/C09MTAODp6YmLFy/i4cOHUkxoaCjMzc3h6uoqxby6jZyYnG0QEREREeVFZ4X0+fPnoa+v28vxn3/+OY4dO4bbt28jPDwc3bt3h76+Pvr27QsLCwsMHToUQUFBOHLkCCIjI/HZZ5/B09MTLVq0AAB4e3vD1dUVAwYMwPnz53Hw4EFMnToV/v7+0tXkUaNG4datW5g8eTKuXr2KH3/8EVu3bsWECRN0eixERESlmvKVZ0k8PK6+TlRKaT20402EyOtxjwV379499O3bF48fP0blypXxwQcf4K+//kLlypUBAAsXLoSenh569uyJjIwM+Pj44Mcff5Ter6+vj71792L06NHw9PREhQoVMGjQIHz99ddSTPXq1bFv3z5MmDABixcvRtWqVbFy5Ur4+Pjo9FiIiIhKrYQdwLmAf9ePdlbdOOaxuETdOEakLZnQsPp99UEneUlJScHRo0fL7CPCFQoFLCwskJKSAnNz88LdWVYasNVU9bp3KlCuQuHuj0o+njOkLZ4zpKmEHcCJjwG8Xk78M5VZCZuFgUgbGg/t2LNnD168eKE25duri6mpaWHmSURERMWNMhuIHI/cRTT+bYsM5DAPKrU0HtpRr1499OzZE0OHDs2zPyYmBnv37tVZYkRERFTMPToBpN97Q4AA0hNUcSV8dgaivGh8RdrDwwNRUVH59hsaGsLJyUknSREREVEJ8DxRt3FEJYzGV6SXL1/+xvHP9erVQ3x8vE6SIiIiohLA2F63cUQljMZXpA0NDWFiYlKYuRAREVFJUrmVanaOnBsLc5EBJo6qOKJSSOvp7+RyOU6fPg25XA5A9WTA5s2bS08KJCIiojJCT181xd2Jj6Eqpl+96fCf4tpjUYl77DORpjQupNPS0jBy5Ehs3rwZMpkMVlZWAIAnT55ACIG+ffvip59+4lVrIiKissSxh2qKu8jx6jcemlRVFdGc+o5KMY2HdowfPx5nzpzBvn378OLFCyQlJSEpKQkvXrzA/v37cebMGYwfP74wcyUiIqLiyLEH0PU20P4I8P5G1c+u8SyiqdTT+IEslpaW2LdvH95///08+0+dOoWPPvoIT58+1WmCJQUfyELFGs8Z0hbPGSKi/6TxFWmlUgkDA4N8+w0MDKBUKnWSFBERERFRcadxIf3RRx9hxIgRiI6OztUXHR2N0aNHo0uXLjpNjoiIiIiouNK4kF6yZAlsbW3h4eGBSpUqoV69eqhXrx4qVaqEJk2awMbGBkuWLCnMXImIqKi8+kjnh8f5iGciojxoPGuHpaUl/vjjD1y9ehURERFq0995enrCxcWl0JIkIqIilLADOBfw7/rRzv/MwLCYN48REb1C63mkXVxcWDQTEZVWCTv+mRP4tfvQ0++r2lttZzFNRPQPjYd25KdGjRq4fv26LnIhIqJ3SZmtmgv49SIa+LctMpDDPIiI/qHxFenvv/8+z/a7d+9izZo10pMNAwIC8owjIqJi7tEJ9Qdq5CKA9ARVnG3bosqKiKjY0riQDgwMRJUqVVCunPpblEol1q9fj/Lly0Mmk7GQJiIqqZ4n6jaOiKiU07iQHjFiBE6fPo2NGzeiXr16Unv58uXx559/wtXVtVASJCKiImJsr9s4IqJSTuMx0suXL0dISAh8fHw4zR0RUWlUuZVqdg7I8gmQASaOqjgiItLuZsPu3bsjIiICO3fuRKdOnaQp8IiIqBTQ01dNcQcgdzH9z7rHIlUcERFpP2tHlSpVcOjQIbRu3RqNGjWCEHnd3U1ERCWSYw/VFHcmVdTbTapy6jsiotdoPY80AMhkMkyZMgXe3t44efIk7O05Xo6IqNRw7AFU6aaaneN5ompMdOVWvBJNRPSaAhXSOTw8PODh4aGrXEhTrz+6186bX3BEpFt6+pzijojoP7z1A1moiCXsAPa9MkPK0c7AbmdVOxEREREVGRbSJUnOo3uf31dvz3l0L4tpIiIioiLDQrqk4KN7iYiIiIoVjQrpHj16QKFQAADWr1+PjIyMQk2K8qDNo3uJiIiIqNBpVEjv3bsXaWlpAIDPPvsMKSkphZoU5YGP7iUiIiIqVjQqpF1cXDBlyhSsW7cOQghs3boV69evz3PRpdmzZ6Np06YwMzODjY0N/Pz8EBcXpxbTtm1byGQytWXUqFFqMXfv3oWvry9MTExgY2ODSZMmISsrSy3m6NGjaNy4MQwNDVGrVi2sXbtWp8fy1vjoXiIiIqJiRaPp75YvX46goCDs27cPMpkMU6dOhUyW+xGyMpkMAwcO1Flyx44dg7+/P5o2bYqsrCz873//g7e3Ny5fvowKFSpIccOHD8fXX38trZuYmEivs7Oz4evrCzs7O4SHhyMxMREDBw5E+fLl8e233wIA4uPj4evri1GjRmHDhg0ICwvDsGHDYG9vDx8fH50dz1vJeXRv+n3kPU5apurno3uJiIiIioRMaPloQj09PcjlctjY2BRWTvl69OgRbGxscOzYMbRu3RqA6op0w4YNsWjRojzf88cff+Cjjz7CgwcPYGtrC0D1h0FwcDAePXoEAwMDBAcHY9++fYiNjZXe98knnyA5ORkHDhzQKDeFQgELCwukpKTA3Nz87Q40PzmzdgBQL6b/+aOGTx2j/GSlAVtNVa97pwLlKrw5noiIiP6T1rN2xMfHo3LlyoWRy3/KGZttZWWl1r5hwwZYW1ujfv36mDJlCtLT06W+iIgIuLm5SUU0APj4+EChUODSpUtSjJeXl9o2fXx8EBERUViHUjB8dC8RERFRsaH1kw2rVauG5ORkrFq1CleuXAEAuLq6YujQobCwsNB5gjmUSiUCAwPRsmVL1K9fX2rv168fqlWrBgcHB1y4cAHBwcGIi4vDjh2qOZXlcrlaEQ1AWpfL5W+MUSgUeP78OYyNjXPlk5GRoTZ7Sc6sJoWOj+4lIiIiKha0LqTPnTsHHx8fGBsbo1mzZgCAhQsX4ttvv8Wff/6Jxo0b6zxJAPD390dsbCxOnjyp1j5ixAjptZubG+zt7dG+fXvcvHkTNWvWLJRcANWNkDNmzCi07b8RH91LRERE9M5pPbRjwoQJ6Nq1K27fvo0dO3Zgx44diI+Px0cffYTAwMBCSBEYO3Ys9u7diyNHjqBq1apvjG3evDkA4MaNGwAAOzs7JCUlqcXkrNvZ2b0xxtzcPM+r0QAwZcoUpKSkSEtCQoL2B0ZEREREJZbWhfS5c+cQHByMcuX+vZhdrlw5TJ48GefOndNpckIIjB07Fjt37sThw4dRvXr1/3xPTEwMAMDeXjUNnKenJy5evIiHDx9KMaGhoTA3N4erq6sUExYWprad0NBQeHp65rsfQ0NDmJubqy1EREREVHZoXUibm5vj7t27udoTEhJgZmamk6Ry+Pv749dff8XGjRthZmYGuVwOuVyO58+fAwBu3ryJmTNnIjIyErdv38bu3bsxcOBAtG7dGg0aNAAAeHt7w9XVFQMGDMD58+dx8OBBTJ06Ff7+/jA0NAQAjBo1Crdu3cLkyZNx9epV/Pjjj9i6dSsmTJig0+MhIiIiotJD60K6T58+GDp0KLZs2YKEhAQkJCRg8+bNGDZsGPr27avT5JYtW4aUlBS0bdsW9vb20rJlyxYAgIGBAQ4dOgRvb2+4uLhg4sSJ6NmzJ/bs2SNtQ19fH3v37oW+vj48PT3x6aefYuDAgWrzTlevXh379u1DaGgo3N3dMX/+fKxcubL4zCFNRERERMWO1vNIv3z5EpMmTcLy5culpwOWL18eo0ePxpw5c6SrvGVNkcwjTVRQnEeaiIhI57QupHOkp6fj5s2bAICaNWuqPU2wLGIhTcUaC2kiIiKd03r6uxwmJiZwc3PTZS5ERERERCWG1mOkiYiIiIiIhTQRERERUYGwkCYqC5TZ/75+eFx9nYiIiAqEhTRRaZewA9jn+u/60c7AbmdVOxERERWYxoX0mDFjkJqaKq1v2rQJaWlp0npycjI6d+6s2+yI6O0k7ABOfAw8v6/enn5f1c5imoiIqMA0nv5OX18fiYmJsLGxAaB6wmFMTAxq1KgBAEhKSoKDgwOys8vm/zLm9HdU7CizVVee0+/lEyADTKoCXeMBPf2izIyIiKhU0PiK9Ov1dgGnnyaiovLoxBuKaAAQQHqCKo6IiIi0xjHSRKXV80TdxhEREZEaFtJEpZWxvW7jiIiISI1WTzYMCQmRHgX+8uVLzJo1CxYWFgBUjwwnomKkcivVGOj0+wDyGor1zxjpyq2KOjMiIqJSQeObDdu2bQuZTPafcUeOHHnrpEoi3mxIxVLOrB0A1Ivpf/5bbrUdcOxR1FkRERGVChoX0vRmLKSp2ErYAUSOV7/x0MQR8FjEIpqIiOgtaFxI16hRA2fPnkWlSpUKO6cSiYU0FWvKbNXsHM8TVWOiK7filHdERERvSeMx0rdv3y6zc0QTlXh6+oBt23edBRERUanCWTuIiIiIiApAq1k7Dh48KM3SkZ+uXbu+VUJERERERCWBxmOk9fT+++K1TCYrs8M/OEaaiIiIqGzRamiHXC6HUqnMdymrRTQRERERlT0aF9KazCFNRERERFRWaFxIazICJDY29q2SISIiIiIqKTS+2XDQoEEwNjbO1f7s2TNs2rQJK1euRGRkZJkd3pHzh4ZCoXjHmRARERHRm5iZmelktEWBn2x4/PhxrFq1Cr/99hscHBzQo0cP9OzZE02bNn3rpEqie/fuwdHR8V2nQURERET/QVeTQ2g1/Z1cLsfatWuxatUqKBQK9O7dGxkZGdi1axdcXV3fOpmSzMHBAQkJCTr7C+e/KBQKODo6IiEhgbOEkEZ4zpC2eM6QtnjOkLbe1TljZmamk+1oXEh36dIFx48fh6+vLxYtWoSOHTtCX18fy5cv10kiJZ2enh6qVq1a5Ps1NzfnP1akFZ4zpC2eM6QtnjOkrZJ6zmhcSP/xxx8ICAjA6NGjUbt27cLMiYiIiIio2NN41o6TJ0/i2bNn8PDwQPPmzbFkyRL8/fffhZkbEREREVGxpXEh3aJFC/z8889ITEzEyJEjsXnzZjg4OECpVCI0NBTPnj0rzDzpNYaGhvjqq69gaGj4rlOhEoLnDGmL5wxpi+cMaauknzMFnrUDAOLi4rBq1Sr88ssvSE5ORocOHbB7925d5kdEREREVCy9VSGdIzs7G3v27MHq1atZSBMRERFRmaCTQpqIiIiIqKzReIw0ERERERH9i4U0EREREVEBsJAuJEuXLoWzszOMjIzQvHlznDlzRuoLCgqClZUVHB0dsWHDBrX3bdu2DV26dPnP7T958gTjxo1D3bp1YWxsDCcnJwQEBCAlJUUt7u7du/D19YWJiQlsbGwwadIkZGVlSf3R0dFo1KgRTE1N0aVLFzx58kTqy8rKgoeHh1ru9HZmz56Npk2bwszMDDY2NvDz80NcXJxazIsXL+Dv749KlSrB1NQUPXv2RFJSktT/5MkTdOnSBaampmjUqBGio6PV3u/v74/58+drldfjx49RtWpVyGQyJCcnq/UdPXoUjRs3hqGhIWrVqoW1a9eq9W/YsAGOjo6wtLREUFCQWt/t27dRp04dKBQKrfKh/M2ZMwcymQyBgYFSW1GfM2vXrkWDBg1gZGQEGxsb+Pv7q/VfuHABrVq1gpGRERwdHTF37ly1/tDQUNSpUwfm5uYYMGAAXr58KfWlpKSgTp06uHPnjsb5UG7379/Hp59+ikqVKsHY2Bhubm44d+6c1C+EQEhICOzt7WFsbAwvLy9cv35d6s/IyMCAAQNgbm6OOnXq4NChQ2rbnzdvHsaNG6dRLteuXUO3bt1gbW0Nc3NzfPDBBzhy5IhaDL+rik52djamTZuG6tWrw9jYGDVr1sTMmTPx6kjfojw/Zs2ahffffx8mJiaoWLFinjH/dX4A7/C7SpDObd68WRgYGIjVq1eLS5cuieHDh4uKFSuKpKQksXv3bmFrayvOnj0rNm7cKIyMjMSjR4+EEEIkJyeL2rVrizt37vznPi5evCh69Oghdu/eLW7cuCHCwsJE7dq1Rc+ePaWYrKwsUb9+feHl5SWio6PF/v37hbW1tZgyZYoU07hxYxEUFCTi4uJEq1atxMSJE6W+OXPmiHHjxunwkyEfHx+xZs0aERsbK2JiYkTnzp2Fk5OTSE1NlWJGjRolHB0dRVhYmDh37pxo0aKFeP/996X+oKAg0aZNGxEXFycCAwOFh4eH1BcRESE8PDxEVlaWVnl169ZNdOrUSQAQT58+ldpv3bolTExMRFBQkLh8+bL44YcfhL6+vjhw4IAQQohHjx4JIyMjsXnzZnHmzBlRuXJlsWfPHun9nTp1Er/99pu2HxPl48yZM8LZ2Vk0aNBAjB8/XmovynNm/vz5wsHBQWzYsEHcuHFDnD9/Xvz+++9Sf0pKirC1tRX9+/cXsbGxYtOmTcLY2Fj89NNPQgghsrOzhbW1tZg/f76IjY0VLi4u4ocfflA7lvnz5xf0IyIhxJMnT0S1atXE4MGDxenTp8WtW7fEwYMHxY0bN6SYOXPmCAsLC7Fr1y5x/vx50bVrV1G9enXx/PlzIYQQ33//vahXr56IjY0V8+bNE5UrVxZKpVIIofp3oXbt2iIlJUWjfGrXri06d+4szp8/L65duybGjBkjTExMRGJiohCC31VFbdasWaJSpUpi7969Ij4+Xmzbtk2YmpqKxYsXSzFFeX6EhISIBQsWiKCgIGFhYZGrX5Pz411+V7GQLgTNmjUT/v7+0np2drZwcHAQs2fPFt99953o06eP1GdjYyPOnDkjhBBixIgRYsGCBQXe79atW4WBgYHIzMwUQgixf/9+oaenJ+RyuRSzbNkyYW5uLjIyMoQQQhgbG4srV64IIYT48ccfRefOnYUQQty8eVPUrl1bKBSKAudD/+3hw4cCgDh27JgQQvXHVPny5cW2bdukmCtXrggAIiIiQgih+g9+2bJlQgghLl++LExMTIQQQrx8+VK4u7uLs2fPapXDjz/+KNq0aSPCwsJyFdKTJ08W7733nlp8nz59hI+PjxBCiNOnTwtbW1upr3fv3mLu3LlCCCE2btwounbtqlUulL9nz56J2rVri9DQUNGmTRupkC7Kc+bJkyfC2NhYHDp0KN+YH3/8UVhaWkr/xgghRHBwsKhbt64QQoikpCQBQPpCnjx5shgzZowQQohTp04V6A9BUhccHCw++OCDfPuVSqWws7MT8+bNk9qSk5OFoaGh2LRpkxBCiNGjR4vg4GAhhBDp6ekCgHj48KEQQnVBYMeOHRrl8ujRIwFAHD9+XGpTKBQCgAgNDRVC8LuqqPn6+oohQ4aotfXo0UP0799fCFG058er1qxZk2chrcn58S6/qzi0Q8devnyJyMhIeHl5SW16enrw8vJCREQE3N3dce7cOTx9+hSRkZF4/vw5atWqhZMnTyIqKgoBAQEF3ndKSgrMzc1Rrpzqye8RERFwc3ODra2tFOPj4wOFQoFLly4BANzd3REaGoqsrCyEhYWhQYMGAIBRo0Zh7ty5MDMzK3A+9N9yhuJYWVkBACIjI5GZmal2/ri4uMDJyQkREREAVL+zw4cPIysrCwcPHpR+Z3PnzkXbtm3RpEkTjfd/+fJlfP3111i/fj309HL/cxAREaGWC6A6h3JyqV27NtLT0xEdHY0nT57g7NmzaNCgAZ4+fYpp06ZhyZIlWnwa9Cb+/v7w9fXN9fsoynMmNDQUSqUS9+/fR7169VC1alX07t0bCQkJUkxERARat24NAwMDqc3HxwdxcXF4+vQpKleuDHt7e/z5559IT0/HiRMn0KBBA2RmZmL06NH46aefoK+vX+DPiYDdu3ejSZMm6NWrF2xsbNCoUSP8/PPPUn98fDzkcrnaOWNhYYHmzZurnTMnT57E8+fPcfDgQdjb28Pa2hobNmyAkZERunfvrlEulSpVQt26dbF+/XqkpaUhKysLP/30E2xsbODh4QGA31VF7f3330dYWBiuXbsGADh//jxOnjyJTp06ASja80MTmpwf7/K7ioW0jv3999/Izs5W+4UDgK2tLeRyOXx8fPDpp5+iadOmGDx4MNatW4cKFSpg9OjRWL58OZYtW4a6deuiZcuW0gmi6X5nzpyJESNGSG1yuTzPPHL6AGDlypXYvn07atasCQMDA0yZMgW//PILTExM0LRpU/j4+KBWrVqYOnVqQT8SyodSqURgYCBatmyJ+vXrA1D9XgwMDHKNE8s5fwDgiy++QLly5VCzZk3s3LkTq1atwvXr17Fu3TpMmzYNo0aNQo0aNdC7d+9cY+ZflZGRgb59+2LevHlwcnLKMya/c0ihUOD58+ewtLTEunXrMHDgQDRr1gwDBw6Ej48PPv/8c4wdOxbx8fFo1KgR6tevj+3bt7/Fp1W2bd68GVFRUZg9e3auvqI8Z27dugWlUolvv/0WixYtwvbt2/HkyRN06NBBGuf8X//uyGQybN26FTNnzsR7772HRo0aYciQIZgzZw7atWsHIyMjtGzZEnXr1uUfYgV069YtLFu2DLVr18bBgwcxevRoBAQEYN26dQD+/fc/v+8pABgyZAjc3d3h6uqKWbNmYevWrXj69ClCQkLwww8/YOrUqahVqxZ8fHxw//79fHORyWQ4dOgQoqOjYWZmBiMjIyxYsAAHDhyApaWllA+/q4rOF198gU8++QQuLi4oX748GjVqhMDAQPTv3x9A0Z4fmtDk/Hin31UFvpZNebp//74AIMLDw9XaJ02aJJo1a5bne6ZPny4CAwPF+fPnha2trXj48KFYvXq1aNy4sRBCNZ6pQoUK0vL6GOqUlBTRrFkz0bFjR/Hy5Uupffjw4cLb21stNi0tTQAQ+/fvzzOXv//+W1SvXl0kJCSI7t27i+nTp4vU1FRRr149sXv3bq0/D8rfqFGjRLVq1URCQoLUtmHDBmFgYJArtmnTpmLy5Mn5bqtdu3Zi165dYvHixaJDhw7i5cuXYtCgQSIoKEgIIUTHjh2l88fV1VUIIcSECRPUhhkdOXIk19CO2rVri2+//VZtX/v27RMARHp6ep65HD16VDRp0kSkpaUJe3t7cfToUXH16lVhbm4ukpKS/vuDITV3794VNjY24vz581Lbq0M7ivKcmTVrlgAgDh48KG3n4cOHQk9PTxqL2KFDBzFixAi1fV26dEkAEJcvX84zl7i4OFGrVi3x7Nkz0ahRI7F27VqRlJQkKleurHbcpJny5csLT09PtbZx48aJFi1aCCFUQ2gAiAcPHqjF9OrVS/Tu3Tvf7Q4ePFgsWrRI/P777+K9994TqampIiQkRPTo0UMIIcTIkSPVvquEUA0T6Nq1q+jUqZM4efKkiIyMFKNHjxZVqlSR9s/vqqK1adMmUbVqVbFp0yZx4cIFsX79emFlZSXWrl0rhCja8+NV+Q3t0OT8eJffVSykdSwjI0Po6+uLnTt3qrUPHDgwzzE4V65ckb5AFi9eLHr16iWEECI1NVUAEAqFQjx+/Fhcv35dWnLGQAuhGmvm6ekp2rdvL405zDFt2jTh7u6u1nbr1i0BQERFReWZ/8CBA6UbDiwtLcWlS5eEEEJ8/vnn0hcsvT1/f39RtWpVcevWLbX2vMYpCyGEk5NTvuPnV69eLbp37y6EEKJ79+5i6dKlQggh9u7dK/0xdu/ePen8uX37thBCCHd3d6Gnpyf09fWFvr6+0NPTEwCEvr6+CAkJEUII0apVK7Wb2nL2Z25unmcuL168EK6uriIyMlKcP39eVK5cWepr0qQJv+AKYOfOndLvJWcBIGQymdDX1xeHDh0qsnNm9erVAoDaH39CqO71WLFihRBCiAEDBohu3bqp9R8+fFgAEE+ePMkzn7Zt24rff/9dpKSkCAAiLS1NCCHExx9/LL7//nsNPynK4eTkJIYOHarW9uOPPwoHBwchhGpcMQARHR2tFtO6dWsREBCQ5zYPHz4smjZtKrKyssSECRPEpEmThBBCxMbGCisrKyGEavz7q99VQghx6NAhoaenl+vGs1q1aonZs2cLIfhdVdSqVq0qlixZotY2c+ZM6T6Gojw/XpVfIa3J+fEuv6s4tEPHDAwM4OHhgbCwMKlNqVQiLCwMnp6earFCCIwcORILFiyAqakpsrOzkZmZCQDSz+zsbFhZWaFWrVrSkjMGWqFQwNvbGwYGBti9ezeMjIzUtu/p6YmLFy/i4cOHUltoaCjMzc3h6uqaK/ewsDBcuXIFY8eOlfb9aj7Z2dlv+/GUeUIIjB07Fjt37sThw4dRvXp1tX4PDw+UL19e7fyJi4vD3bt3c50/APDo0SN8/fXX+OGHHwDk/zurUqWKdP5Uq1YNAPDbb7/h/PnziImJQUxMDFauXAkAOHHihDSdmaenp1ougOocyisXAPjmm2/QsWNHNG7cGNnZ2WrTE/EcKpj27dvj4sWL0u8pJiYGTZo0Qf/+/aXXRXXOtGzZUtp+jidPnuDvv/+WYjw9PXH8+HFpm4DqnKlbt670v/JftWrVKlhZWaFr167Svvnvzttp2bJlrmk1r127Jv2OqlevDjs7O7VzRqFQ4PTp03meMznTK+aMX8/vnLGxsVH7rgKA9PR0AMh1D4aenh6USiUAflcVtfT09Fy/D319fen3UZTnhyY0OT/e6XeVxiU3aWzz5s3C0NBQrF27Vly+fFmMGDFCVKxYUe2OUyGEWLFihdp0dadPnxbm5uYiIiJChISESP87NS8pKSmiefPmws3NTdy4cUMkJiZKS84d7zlTxnh7e4uYmBhx4MABUblyZbUpY3I8f/5cuLi4qP0F2qlTJzF8+HARExMjqlatKrZu3fqWnwyNHj1aWFhYiKNHj6r9zl79X0+jRo0STk5O4vDhw+LcuXPC09Mz1/+mzdGvXz+1qcO+++474eHhIS5fviw6deokzYagibyGduRMKTRp0iRx5coVsXTpUrUphV516dIlUbt2bWkqv/T0dFGpUiWxcuVKsXfvXmFoaCju3buncT6Uv1eHdghRtOdMt27dxHvvvSdOnTolLl68KD766CPh6uoqDStLTk4Wtra2YsCAASI2NlZs3rxZmJiYSNPfvSopKUk4OzuL+/fvS2316tUT06dPF+Hh4cLU1FSa1Yg0d+bMGVGuXDkxa9Yscf36dbFhwwZhYmIifv31Vylmzpw5omLFiuL3338XFy5cEN26dVOb3uxV//vf/9Smm9uyZYtwcnIS58+fF0OHDpVm0MjLo0ePRKVKlUSPHj1ETEyMiIuLE59//rkoX768iImJEULwu6qoDRo0SFSpUkWa/m7Hjh3C2tpabShYUZ0fQghx584dER0dLWbMmCFMTU1FdHS0iI6OFs+ePRNCaHZ+vMvvKhbSheSHH34QTk5OwsDAQDRr1kz89ddfav1yuVxUq1ZN7QtECCFmzJghrKyshIuLizh9+nS+288pevJa4uPjpbjbt2+LTp06CWNjY2FtbS0mTpyoNjQkxxdffKH2H4IQQly/fl00bdpUmJubi9GjR4vs7OwCfBL0qvx+Z2vWrJFinj9/LsaMGSMsLS2FiYmJ6N69uzTf6qsOHDggmjVrpvZ7SUtLE7169RJmZmaiffv2Wo3zyquQzmlv2LChMDAwEDVq1FDLNYdSqRQtW7ZUm5dTCCH27NkjnJychK2trfj55581zoXe7PVCuijPmZSUFDFkyBBRsWJFYWVlJbp37y7u3r2rFnP+/HnxwQcfCENDQ1GlShUxZ86cPLf1ySefqBX1QqguKLi4uAgrKysxY8aM//ooKB979uwR9evXF4aGhsLFxUUaepNDqVSKadOmCVtbW2FoaCjat28v4uLicm3n4sWLolatWmpz3WdnZ4vRo0cLc3Nz0bRp0zz/N/2rzp49K7y9vYWVlZUwMzMTLVq0yDX2md9VRUehUIjx48cLJycnYWRkJGrUqCG+/PJLtSkri/L8GDRoUJ7fi0eOHJFiNDk/3tV3lUyIVx5lQ0REREREGuEYaSIiIiKiAmAhTURERERUACykiYiIiIgKgIU0EREREVEBsJAmIiIiIioAFtJERERERAXAQpqIiIiIqABYSBMRERERFQALaSKiQjZ48GD4+fm96zSIiEjHWEgTEVGRevny5btOgYhIJ1hIExEVsbZt2yIgIACTJ0+GlZUV7OzsMH36dLWY5ORkjBw5Era2tjAyMkL9+vWxd+9eqf+3337De++9B0NDQzg7O2P+/Plq73d2dsY333yDgQMHwtTUFNWqVcPu3bvx6NEjdOvWDaampmjQoAHOnTun9r6TJ0+iVatWMDY2hqOjIwICApCWlvbG4/nmm29gY2MDMzMzDBs2DF988QUaNmwo9edckZ81axYcHBxQt25dAMDFixfx4YcfwtjYGJUqVcKIESOQmpqq9jkFBgaq7cvPzw+DBw9WO86ZM2eib9++qFChAqpUqYKlS5e+MV8iIl1hIU1E9A6sW7cOFSpUwOnTpzF37lx8/fXXCA0NBQAolUp06tQJp06dwq+//orLly9jzpw50NfXBwBERkaid+/e+OSTT3Dx4kVMnz4d06ZNw9q1a9X2sXDhQrRs2RLR0dHw9fXFgAEDMHDgQHz66aeIiopCzZo1MXDgQAghAAA3b95Ex44d0bNnT1y4cAFbtmzByZMnMXbs2HyPY8OGDZg1axa+++47REZGwsnJCcuWLcsVFxYWhri4OISGhmLv3r1IS0uDj48PLC0tcfbsWWzbtg2HDh16477yM2/ePLi7uyM6OhpffPEFxo8fL32WRESFShARUaEaNGiQ6Natm7Tepk0b8cEHH6jFNG3aVAQHBwshhDh48KDQ09MTcXFxeW6vX79+okOHDmptkyZNEq6urtJ6tWrVxKeffiqtJyYmCgBi2rRpUltERIQAIBITE4UQQgwdOlSMGDFCbbsnTpwQenp64vnz53nm0rx5c+Hv76/W1rJlS+Hu7q52/La2tiIjI0NqW7FihbC0tBSpqalS2759+4Senp6Qy+VCCNXnNH78eLVtd+vWTQwaNEjtODt27KgW06dPH9GpU6c88yUi0iVekSYiegcaNGigtm5vb4+HDx8CAGJiYlC1alXUqVMnz/deuXIFLVu2VGtr2bIlrl+/juzs7Dz3YWtrCwBwc3PL1Zaz3/Pnz2Pt2rUwNTWVFh8fHyiVSsTHx+eZS1xcHJo1a6bW9vp6zn4NDAzUjsHd3R0VKlRQOwalUom4uLg895UfT0/PXOtXrlzRahtERAVR7l0nQERUFpUvX15tXSaTQalUAgCMjY11vg+ZTJZvW85+U1NTMXLkSAQEBOTalpOT01vl8mrBrCk9PT1p2EmOzMzMt8qDiEiXeEWaiKiYadCgAe7du4dr167l2V+vXj2cOnVKre3UqVOoU6eONI66IBo3bozLly+jVq1auZZXrya/qm7dujh79qxa2+vr+R3D+fPn1W5kPHXqFPT09KSbEStXrozExESpPzs7G7Gxsbm29ddff+Var1ev3n/mQET0tlhIExEVM23atEHr1q3Rs2dPhIaGIj4+Hn/88QcOHDgAAJg4cSLCwsIwc+ZMXLt2DevWrcOSJUvw+eefv9V+g4ODER4ejrFjxyImJgbXr1/H77///sYbAMeNG4dVq1Zh3bp1uH79Or755htcuHBButqdn/79+8PIyAiDBg1CbGwsjhw5gnHjxmHAgAHSkJMPP/wQ+/btw759+3D16lWMHj0aycnJubZ16tQpzJ07F9euXcPSpUuxbds2jB8//q0+CyIiTbCQJiIqhn777Tc0bdoUffv2haurKyZPniyNf27cuDG2bt2KzZs3o379+ggJCcHXX3+tNi1cQTRo0ADHjh3DtWvX0KpVKzRq1AghISFwcHDI9z39+/fHlClT8Pnnn6Nx48aIj4/H4MGDYWRk9MZ9mZiY4ODBg3jy5AmaNm2Kjz/+GO3bt8eSJUukmCFDhmDQoEEYOHAg2rRpgxo1aqBdu3a5tjVx4kScO3cOjRo1wjfffIMFCxbAx8en4B8EEZGGZOL1AWhERERvoUOHDrCzs8Mvv/xS6PtydnZGYGBgrvmmiYiKAm82JCKiAktPT8fy5cvh4+MDfX19bNq0CYcOHeI8zkRUJrCQJiKiApPJZNi/fz9mzZqFFy9eoG7duvjtt9/g5eX1rlMjIip0HNpBRERERFQAvNmQiIiIiKgAWEgTERERERUAC2kiIiIiogJgIU1EREREVAAspImIiIiICoCFNBERERFRAbCQJiIiIiIqABbSREREREQFwEKaiIiIiKgA/h9oZz+ML05aXQAAAABJRU5ErkJggg==", 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", 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" ] diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb index d4b526a7d..3be09e247 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_basic_example.ipynb @@ -25,10 +25,10 @@ "id": "f22337ab", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:43.592579Z", - "iopub.status.busy": "2024-10-19T23:53:43.592401Z", - "iopub.status.idle": "2024-10-19T23:53:43.874546Z", - "shell.execute_reply": "2024-10-19T23:53:43.874015Z" + "iopub.execute_input": "2024-10-22T01:19:45.171789Z", + "iopub.status.busy": "2024-10-22T01:19:45.171319Z", + "iopub.status.idle": "2024-10-22T01:19:45.488531Z", + "shell.execute_reply": "2024-10-22T01:19:45.487836Z" } }, "outputs": [], @@ -51,10 +51,10 @@ "id": "0367caeb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:43.876832Z", - "iopub.status.busy": "2024-10-19T23:53:43.876548Z", - "iopub.status.idle": "2024-10-19T23:53:45.018934Z", - "shell.execute_reply": "2024-10-19T23:53:45.018303Z" + "iopub.execute_input": "2024-10-22T01:19:45.491180Z", + "iopub.status.busy": "2024-10-22T01:19:45.490687Z", + "iopub.status.idle": "2024-10-22T01:19:46.712407Z", + "shell.execute_reply": "2024-10-22T01:19:46.711690Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:53:45.021187Z", - "iopub.status.busy": "2024-10-19T23:53:45.020894Z", - "iopub.status.idle": "2024-10-19T23:53:45.031251Z", - "shell.execute_reply": "2024-10-19T23:53:45.030673Z" + "iopub.execute_input": "2024-10-22T01:19:46.715110Z", + "iopub.status.busy": "2024-10-22T01:19:46.714525Z", + "iopub.status.idle": "2024-10-22T01:19:46.724991Z", + "shell.execute_reply": "2024-10-22T01:19:46.724502Z" }, "jupyter": { "outputs_hidden": false @@ -129,45 +129,45 @@ " \n", " \n", " 0\n", - " 0.743675\n", - " 3.381843\n", - " 9.769149\n", + " 2.028617\n", + " 3.339915\n", + " 11.424105\n", " \n", " \n", " 1\n", - " -0.371102\n", - " -0.376468\n", - " -2.062196\n", + " 0.929315\n", + " 1.362639\n", + " 2.781130\n", " \n", " \n", " 2\n", - " -0.750238\n", - " -3.046208\n", - " -8.310544\n", + " -0.203330\n", + " -1.045402\n", + " -5.305729\n", " \n", " \n", " 3\n", - " -0.018300\n", - " 1.331412\n", - " 3.343701\n", + " 0.814279\n", + " 1.467193\n", + " 4.137201\n", " \n", " \n", " 4\n", - " 0.292231\n", - " 1.661945\n", - " 6.149433\n", + " 0.269044\n", + " -0.659678\n", + " -1.931055\n", " \n", " \n", "\n", "" ], "text/plain": [ - " X Y Z\n", - "0 0.743675 3.381843 9.769149\n", - "1 -0.371102 -0.376468 -2.062196\n", - "2 -0.750238 -3.046208 -8.310544\n", - "3 -0.018300 1.331412 3.343701\n", - "4 0.292231 1.661945 6.149433" + " X Y Z\n", + "0 2.028617 3.339915 11.424105\n", + "1 0.929315 1.362639 2.781130\n", + "2 -0.203330 -1.045402 -5.305729\n", + "3 0.814279 1.467193 4.137201\n", + "4 0.269044 -0.659678 -1.931055" ] }, "execution_count": 3, @@ -214,10 +214,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:53:45.033288Z", - "iopub.status.busy": "2024-10-19T23:53:45.032857Z", - "iopub.status.idle": "2024-10-19T23:53:48.738883Z", - "shell.execute_reply": "2024-10-19T23:53:48.737831Z" + "iopub.execute_input": "2024-10-22T01:19:46.726934Z", + "iopub.status.busy": "2024-10-22T01:19:46.726504Z", + "iopub.status.idle": "2024-10-22T01:19:49.869944Z", + "shell.execute_reply": "2024-10-22T01:19:49.868845Z" }, "jupyter": { "outputs_hidden": false @@ -245,10 +245,10 @@ "id": "4f1493d8-9a0b-4ed5-97be-7b5f39cbd7d4", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.742312Z", - "iopub.status.busy": "2024-10-19T23:53:48.741781Z", - "iopub.status.idle": "2024-10-19T23:53:48.751262Z", - "shell.execute_reply": "2024-10-19T23:53:48.750637Z" + "iopub.execute_input": "2024-10-22T01:19:49.872927Z", + "iopub.status.busy": "2024-10-22T01:19:49.872701Z", + "iopub.status.idle": "2024-10-22T01:19:49.880831Z", + "shell.execute_reply": "2024-10-22T01:19:49.880337Z" } }, "outputs": [ @@ -282,19 +282,19 @@ "Node Y is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Y := f(X) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 1.0776648471428856\n", + "LinearRegression: 1.1002813947388796\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 1.0779355716960863\n", - "HistGradientBoostingRegressor: 1.2198942819273426\n", + " ('linearregression', LinearRegression)]): 1.1008911568888382\n", + "HistGradientBoostingRegressor: 1.2658372486275384\n", "\n", "--- Node: Z\n", - "Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", + "Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n", "This represents the causal relationship as Z := f(Y) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 0.9814349659546397\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 0.9834430345330534\n", - "HistGradientBoostingRegressor: 1.4602664103257639\n", + " ('linearregression', LinearRegression)]): 0.9322685457630433\n", + "LinearRegression: 0.934716145618161\n", + "HistGradientBoostingRegressor: 1.4779386097949847\n", "\n", "===Note===\n", "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n", @@ -322,10 +322,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.753443Z", - "iopub.status.busy": "2024-10-19T23:53:48.753025Z", - "iopub.status.idle": "2024-10-19T23:53:48.756863Z", - "shell.execute_reply": "2024-10-19T23:53:48.756286Z" + "iopub.execute_input": "2024-10-22T01:19:49.882770Z", + "iopub.status.busy": "2024-10-22T01:19:49.882432Z", + "iopub.status.idle": "2024-10-22T01:19:49.885758Z", + "shell.execute_reply": "2024-10-22T01:19:49.885190Z" }, "jupyter": { "outputs_hidden": false @@ -369,10 +369,10 @@ "id": "fe5f99f0", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.758973Z", - "iopub.status.busy": "2024-10-19T23:53:48.758557Z", - "iopub.status.idle": "2024-10-19T23:53:48.772149Z", - "shell.execute_reply": "2024-10-19T23:53:48.771541Z" + "iopub.execute_input": "2024-10-22T01:19:49.887672Z", + "iopub.status.busy": "2024-10-22T01:19:49.887298Z", + "iopub.status.idle": "2024-10-22T01:19:49.899165Z", + "shell.execute_reply": "2024-10-22T01:19:49.898570Z" } }, "outputs": [ @@ -413,7 +413,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 414.63it/s]" + "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 505.32it/s]" ] }, { @@ -450,10 +450,10 @@ "id": "671cd8d8-75b0-4019-b503-8aa83ad91615", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.774425Z", - "iopub.status.busy": "2024-10-19T23:53:48.773985Z", - "iopub.status.idle": "2024-10-19T23:53:49.841482Z", - "shell.execute_reply": "2024-10-19T23:53:49.840861Z" + "iopub.execute_input": "2024-10-22T01:19:49.900885Z", + "iopub.status.busy": "2024-10-22T01:19:49.900707Z", + "iopub.status.idle": "2024-10-22T01:19:50.957297Z", + "shell.execute_reply": "2024-10-22T01:19:50.956722Z" } }, "outputs": [ @@ -470,7 +470,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3744.91it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 4291.58it/s]" ] }, { @@ -493,7 +493,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 12.23it/s]" + "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 13.03it/s]" ] }, { @@ -501,7 +501,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.65it/s]" + "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.99it/s]" ] }, { @@ -509,7 +509,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.71it/s]" + "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 19.30it/s]" ] }, { @@ -521,7 +521,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -547,21 +547,21 @@ "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n", "\n", "--- Node X\n", - "- The KL divergence between generated and observed distribution is 0.1083365946213318.\n", + "- The KL divergence between generated and observed distribution is 0.07624922073720022.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Y\n", - "- The MSE is 1.0790193944800426.\n", - "- The NMSE is 0.45097097135917863.\n", - "- The R2 coefficient is 0.7964795543107239.\n", - "- The normalized CRPS is 0.25672986729669534.\n", + "- The MSE is 1.097888062196601.\n", + "- The NMSE is 0.49439332888932563.\n", + "- The R2 coefficient is 0.754451960243354.\n", + "- The normalized CRPS is 0.2843681015505487.\n", "The estimated CRPS indicates a good model performance.\n", "\n", "--- Node Z\n", - "- The MSE is 0.9654404823689962.\n", - "- The NMSE is 0.1414731008457315.\n", - "- The R2 coefficient is 0.9799289646454417.\n", - "- The normalized CRPS is 0.0809725428180018.\n", + "- The MSE is 0.9317194069647587.\n", + "- The NMSE is 0.15106453639402395.\n", + "- The R2 coefficient is 0.97699833188681.\n", + "- The normalized CRPS is 0.08688513089875502.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "==== Evaluation of Invertible Functional Causal Model Assumption ====\n", @@ -569,13 +569,13 @@ "--- The model assumption for node Y is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", - "--- The model assumption for node Z is not rejected with a p-value of 0.49802363047370024 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Z is not rejected with a p-value of 0.6816413250088438 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 0.014318906648073039\n", + "The overall average KL divergence between the generated and observed distribution is 0.0036988615988169265\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", @@ -637,10 +637,10 @@ "id": "52452496", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:49.843639Z", - "iopub.status.busy": "2024-10-19T23:53:49.843250Z", - "iopub.status.idle": "2024-10-19T23:53:49.855543Z", - "shell.execute_reply": "2024-10-19T23:53:49.854931Z" + "iopub.execute_input": "2024-10-22T01:19:50.959474Z", + "iopub.status.busy": "2024-10-22T01:19:50.959092Z", + "iopub.status.idle": "2024-10-22T01:19:50.971486Z", + "shell.execute_reply": "2024-10-22T01:19:50.970833Z" } }, "outputs": [ @@ -673,33 +673,33 @@ " \n", " \n", " 0\n", - " 1.641232\n", + " 1.593606\n", " 2.34\n", - " 7.099762\n", + " 6.023067\n", " \n", " \n", " 1\n", - " -1.637951\n", + " -0.940958\n", " 2.34\n", - " 7.350658\n", + " 6.497881\n", " \n", " \n", " 2\n", - " 0.327314\n", + " 0.225591\n", " 2.34\n", - " 8.630947\n", + " 8.664585\n", " \n", " \n", " 3\n", - " -0.056939\n", + " 0.003608\n", " 2.34\n", - " 7.843168\n", + " 7.991808\n", " \n", " \n", " 4\n", - " 1.069523\n", + " -0.329552\n", " 2.34\n", - " 6.189711\n", + " 8.322043\n", " \n", " \n", "\n", @@ -707,11 +707,11 @@ ], "text/plain": [ " X Y Z\n", - "0 1.641232 2.34 7.099762\n", - "1 -1.637951 2.34 7.350658\n", - "2 0.327314 2.34 8.630947\n", - "3 -0.056939 2.34 7.843168\n", - "4 1.069523 2.34 6.189711" + "0 1.593606 2.34 6.023067\n", + "1 -0.940958 2.34 6.497881\n", + "2 0.225591 2.34 8.664585\n", + "3 0.003608 2.34 7.991808\n", + "4 -0.329552 2.34 8.322043" ] }, "execution_count": 9, diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb index 493f7e3d1..aea6bf708 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb @@ -34,10 +34,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:54.598000Z", - "iopub.status.busy": "2024-10-19T23:53:54.597820Z", - "iopub.status.idle": "2024-10-19T23:53:54.834392Z", - "shell.execute_reply": "2024-10-19T23:53:54.833818Z" + "iopub.execute_input": "2024-10-22T01:19:55.671251Z", + "iopub.status.busy": "2024-10-22T01:19:55.671061Z", + "iopub.status.idle": "2024-10-22T01:19:55.911778Z", + "shell.execute_reply": "2024-10-22T01:19:55.911152Z" } }, "outputs": [ @@ -128,10 +128,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:54.836529Z", - "iopub.status.busy": "2024-10-19T23:53:54.836157Z", - "iopub.status.idle": "2024-10-19T23:53:55.377698Z", - "shell.execute_reply": "2024-10-19T23:53:55.376996Z" + "iopub.execute_input": "2024-10-22T01:19:55.913882Z", + "iopub.status.busy": "2024-10-22T01:19:55.913523Z", + "iopub.status.idle": "2024-10-22T01:19:56.457003Z", + "shell.execute_reply": "2024-10-22T01:19:56.456368Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:55.379905Z", - "iopub.status.busy": "2024-10-19T23:53:55.379543Z", - "iopub.status.idle": "2024-10-19T23:53:58.718575Z", - "shell.execute_reply": "2024-10-19T23:53:58.717823Z" + "iopub.execute_input": "2024-10-22T01:19:56.459077Z", + "iopub.status.busy": "2024-10-22T01:19:56.458715Z", + "iopub.status.idle": "2024-10-22T01:19:59.774175Z", + "shell.execute_reply": "2024-10-22T01:19:59.773438Z" } }, "outputs": [ @@ -227,7 +227,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Vision: 67%|██████▋ | 2/3 [00:00<00:00, 19.92it/s]" + "Fitting causal mechanism of node Vision: 67%|██████▋ | 2/3 [00:00<00:00, 19.63it/s]" ] }, { @@ -235,7 +235,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Condition: 67%|██████▋ | 2/3 [00:00<00:00, 19.92it/s]" + "Fitting causal mechanism of node Condition: 67%|██████▋ | 2/3 [00:00<00:00, 19.63it/s]" ] }, { @@ -243,7 +243,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 29.27it/s]" + "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 28.70it/s]" ] }, { @@ -279,10 +279,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:58.721217Z", - "iopub.status.busy": "2024-10-19T23:53:58.720562Z", - "iopub.status.idle": "2024-10-19T23:54:12.687356Z", - "shell.execute_reply": "2024-10-19T23:54:12.686665Z" + "iopub.execute_input": "2024-10-22T01:19:59.776815Z", + "iopub.status.busy": "2024-10-22T01:19:59.776166Z", + "iopub.status.idle": "2024-10-22T01:20:13.752958Z", + "shell.execute_reply": "2024-10-22T01:20:13.752307Z" } }, "outputs": [ @@ -299,7 +299,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3573.68it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3630.38it/s]" ] }, { @@ -322,7 +322,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.62it/s]" + "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.40it/s]" ] }, { @@ -334,7 +334,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -368,15 +368,15 @@ "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Vision\n", - "- The MSE is 0.003261792953176713.\n", - "- The NMSE is 0.18251379349564886.\n", - "- The R2 coefficient is 0.9666840172193771.\n", - "- The normalized CRPS is 0.10629621558204352.\n", + "- The MSE is 0.0032629985147496613.\n", + "- The NMSE is 0.18256886571617365.\n", + "- The R2 coefficient is 0.9666647696233858.\n", + "- The normalized CRPS is 0.10663383830931623.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "==== Evaluation of Invertible Functional Causal Model Assumption ====\n", "\n", - "--- The model assumption for node Vision is not rejected with a p-value of 0.7411494488201407 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Vision is not rejected with a p-value of 0.8870605896160012 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n", @@ -391,7 +391,7 @@ "+-------------------------------------------------------------------------------------------------------+\n", "| The given DAG is not informative because 2 / 6 of the permutations lie in the Markov |\n", "| equivalence class of the given DAG (p-value: 0.33). |\n", - "| The given DAG violates 0/2 LMCs and is better than 33.3% of the permuted DAGs (p-value: 0.67). |\n", + "| The given DAG violates 0/2 LMCs and is better than 66.7% of the permuted DAGs (p-value: 0.33). |\n", "| Based on the provided significance level (0.2) and because the DAG is not informative, |\n", "| we do not reject the DAG. |\n", "+-------------------------------------------------------------------------------------------------------+\n", @@ -420,10 +420,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.689456Z", - "iopub.status.busy": "2024-10-19T23:54:12.689224Z", - "iopub.status.idle": "2024-10-19T23:54:12.698614Z", - "shell.execute_reply": "2024-10-19T23:54:12.698073Z" + "iopub.execute_input": "2024-10-22T01:20:13.755167Z", + "iopub.status.busy": "2024-10-22T01:20:13.754772Z", + "iopub.status.idle": "2024-10-22T01:20:13.763000Z", + "shell.execute_reply": "2024-10-22T01:20:13.762374Z" } }, "outputs": [ @@ -506,17 +506,17 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.700819Z", - "iopub.status.busy": "2024-10-19T23:54:12.700406Z", - "iopub.status.idle": "2024-10-19T23:54:12.886077Z", - "shell.execute_reply": "2024-10-19T23:54:12.885520Z" + "iopub.execute_input": "2024-10-22T01:20:13.765051Z", + "iopub.status.busy": "2024-10-22T01:20:13.764700Z", + "iopub.status.idle": "2024-10-22T01:20:13.949169Z", + "shell.execute_reply": "2024-10-22T01:20:13.948619Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -620,10 +620,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.888295Z", - "iopub.status.busy": "2024-10-19T23:54:12.887911Z", - "iopub.status.idle": "2024-10-19T23:54:12.900345Z", - "shell.execute_reply": "2024-10-19T23:54:12.899851Z" + "iopub.execute_input": "2024-10-22T01:20:13.951264Z", + "iopub.status.busy": "2024-10-22T01:20:13.951050Z", + "iopub.status.idle": "2024-10-22T01:20:13.964087Z", + "shell.execute_reply": "2024-10-22T01:20:13.963625Z" } }, "outputs": [], @@ -663,10 +663,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.902051Z", - "iopub.status.busy": "2024-10-19T23:54:12.901866Z", - "iopub.status.idle": "2024-10-19T23:54:12.906041Z", - "shell.execute_reply": "2024-10-19T23:54:12.905545Z" + "iopub.execute_input": "2024-10-22T01:20:13.965929Z", + "iopub.status.busy": "2024-10-22T01:20:13.965576Z", + "iopub.status.idle": "2024-10-22T01:20:13.969572Z", + "shell.execute_reply": "2024-10-22T01:20:13.969082Z" } }, "outputs": [], diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb index 6eb5a17df..fc173525d 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_cps2015_dist_change_robust.ipynb @@ -27,10 +27,10 @@ "id": "6e926b16-37e2-4921-9afb-1c99db9c9feb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:17.509624Z", - "iopub.status.busy": "2024-10-19T23:54:17.509200Z", - "iopub.status.idle": "2024-10-19T23:54:17.838633Z", - "shell.execute_reply": "2024-10-19T23:54:17.838090Z" + "iopub.execute_input": "2024-10-22T01:20:18.478777Z", + "iopub.status.busy": "2024-10-22T01:20:18.478601Z", + "iopub.status.idle": "2024-10-22T01:20:18.807637Z", + "shell.execute_reply": "2024-10-22T01:20:18.807054Z" }, "tags": [] }, @@ -78,10 +78,10 @@ "id": "2ca5daf9-be47-4c12-bd02-bb5b01014e0c", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:17.841079Z", - "iopub.status.busy": "2024-10-19T23:54:17.840677Z", - "iopub.status.idle": "2024-10-19T23:54:19.036765Z", - "shell.execute_reply": "2024-10-19T23:54:19.036201Z" + "iopub.execute_input": "2024-10-22T01:20:18.810107Z", + "iopub.status.busy": "2024-10-22T01:20:18.809661Z", + "iopub.status.idle": "2024-10-22T01:20:20.010162Z", + "shell.execute_reply": "2024-10-22T01:20:20.009446Z" } }, "outputs": [], @@ -117,10 +117,10 @@ "id": "39dcb294-25df-4358-81cb-3ee5a41e0419", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:19.039023Z", - "iopub.status.busy": "2024-10-19T23:54:19.038697Z", - "iopub.status.idle": "2024-10-19T23:54:22.598855Z", - "shell.execute_reply": "2024-10-19T23:54:22.598195Z" + "iopub.execute_input": "2024-10-22T01:20:20.012630Z", + "iopub.status.busy": "2024-10-22T01:20:20.012290Z", + "iopub.status.idle": "2024-10-22T01:20:23.695495Z", + "shell.execute_reply": "2024-10-22T01:20:23.694878Z" }, "tags": [] }, @@ -146,7 +146,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00, 3.72it/s]" + "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00, 3.71it/s]" ] }, { @@ -216,10 +216,10 @@ "id": "f7febdc3-8c1e-46cf-816b-4a62251d641b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:22.600858Z", - "iopub.status.busy": "2024-10-19T23:54:22.600644Z", - "iopub.status.idle": "2024-10-19T23:54:22.615644Z", - "shell.execute_reply": "2024-10-19T23:54:22.615149Z" + "iopub.execute_input": "2024-10-22T01:20:23.697638Z", + "iopub.status.busy": "2024-10-22T01:20:23.697233Z", + "iopub.status.idle": "2024-10-22T01:20:23.711765Z", + "shell.execute_reply": "2024-10-22T01:20:23.711272Z" }, "tags": [] }, @@ -250,10 +250,10 @@ "id": "5f9e1bad-ae6e-432d-bbb8-1f65ab7bbab6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:22.617710Z", - "iopub.status.busy": "2024-10-19T23:54:22.617273Z", - "iopub.status.idle": "2024-10-19T23:54:24.915512Z", - "shell.execute_reply": "2024-10-19T23:54:24.914778Z" + "iopub.execute_input": "2024-10-22T01:20:23.713705Z", + "iopub.status.busy": "2024-10-22T01:20:23.713358Z", + "iopub.status.idle": "2024-10-22T01:20:26.008798Z", + "shell.execute_reply": "2024-10-22T01:20:26.008175Z" }, "tags": [] }, @@ -344,10 +344,10 @@ "id": "e6d0b582-20c6-476b-a07c-9d336ccda3fa", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:24.917824Z", - "iopub.status.busy": "2024-10-19T23:54:24.917339Z", - "iopub.status.idle": "2024-10-19T23:54:29.487039Z", - "shell.execute_reply": "2024-10-19T23:54:29.486334Z" + "iopub.execute_input": "2024-10-22T01:20:26.011016Z", + "iopub.status.busy": "2024-10-22T01:20:26.010622Z", + "iopub.status.idle": "2024-10-22T01:20:30.556328Z", + "shell.execute_reply": "2024-10-22T01:20:30.555781Z" }, "tags": [] }, @@ -396,10 +396,10 @@ "id": "95ec0312-84c2-4405-a823-740c98482090", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:29.489217Z", - "iopub.status.busy": "2024-10-19T23:54:29.488828Z", - "iopub.status.idle": "2024-10-19T23:54:29.641186Z", - "shell.execute_reply": "2024-10-19T23:54:29.640534Z" + "iopub.execute_input": "2024-10-22T01:20:30.558437Z", + "iopub.status.busy": "2024-10-22T01:20:30.558035Z", + "iopub.status.idle": "2024-10-22T01:20:30.707566Z", + "shell.execute_reply": "2024-10-22T01:20:30.706970Z" }, "tags": [] }, @@ -491,10 +491,10 @@ "id": "a5b5dbf8-046b-4d9d-848c-694eb742aafb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:29.643707Z", - "iopub.status.busy": "2024-10-19T23:54:29.643412Z", - "iopub.status.idle": "2024-10-19T23:54:29.807622Z", - "shell.execute_reply": "2024-10-19T23:54:29.807085Z" + "iopub.execute_input": "2024-10-22T01:20:30.709828Z", + "iopub.status.busy": "2024-10-22T01:20:30.709481Z", + "iopub.status.idle": "2024-10-22T01:20:30.872318Z", + "shell.execute_reply": "2024-10-22T01:20:30.871689Z" }, "tags": [] }, @@ -571,10 +571,10 @@ "id": "8b977dca-89e4-406c-80a9-6d24dd8a3aca", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:29.809760Z", - "iopub.status.busy": "2024-10-19T23:54:29.809345Z", - "iopub.status.idle": "2024-10-19T23:54:30.093594Z", - "shell.execute_reply": "2024-10-19T23:54:30.093064Z" + "iopub.execute_input": "2024-10-22T01:20:30.874373Z", + "iopub.status.busy": "2024-10-22T01:20:30.874156Z", + "iopub.status.idle": "2024-10-22T01:20:31.046767Z", + "shell.execute_reply": "2024-10-22T01:20:31.046115Z" }, "tags": [] }, diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb index 0f9b29e8b..61e44ef97 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_draw_samples.ipynb @@ -29,10 +29,10 @@ "id": "f22337ab", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:34.547301Z", - "iopub.status.busy": "2024-10-19T23:54:34.547118Z", - "iopub.status.idle": "2024-10-19T23:54:34.780840Z", - "shell.execute_reply": "2024-10-19T23:54:34.780191Z" + "iopub.execute_input": "2024-10-22T01:20:35.349320Z", + "iopub.status.busy": "2024-10-22T01:20:35.348909Z", + "iopub.status.idle": "2024-10-22T01:20:35.585932Z", + "shell.execute_reply": "2024-10-22T01:20:35.585307Z" } }, "outputs": [ @@ -65,33 +65,33 @@ " \n", " \n", " 0\n", - " 0.052003\n", - " 1.483941\n", - " 4.916561\n", + " 0.625450\n", + " 0.820544\n", + " 1.621421\n", " \n", " \n", " 1\n", - " -1.182643\n", - " -1.100917\n", - " -3.034945\n", + " -0.921440\n", + " -2.291946\n", + " -5.542924\n", " \n", " \n", " 2\n", - " 1.160655\n", - " 1.535785\n", - " 5.887016\n", + " -0.578712\n", + " -1.317664\n", + " -5.610970\n", " \n", " \n", " 3\n", - " -0.717647\n", - " -0.051851\n", - " 0.529103\n", + " 2.235428\n", + " 3.111105\n", + " 8.982018\n", " \n", " \n", " 4\n", - " 0.285487\n", - " 1.114898\n", - " 3.832859\n", + " -0.556047\n", + " 1.068834\n", + " 2.799001\n", " \n", " \n", "\n", @@ -99,11 +99,11 @@ ], "text/plain": [ " X Y Z\n", - "0 0.052003 1.483941 4.916561\n", - "1 -1.182643 -1.100917 -3.034945\n", - "2 1.160655 1.535785 5.887016\n", - "3 -0.717647 -0.051851 0.529103\n", - "4 0.285487 1.114898 3.832859" + "0 0.625450 0.820544 1.621421\n", + "1 -0.921440 -2.291946 -5.542924\n", + "2 -0.578712 -1.317664 -5.610970\n", + "3 2.235428 3.111105 8.982018\n", + "4 -0.556047 1.068834 2.799001" ] }, "execution_count": 1, @@ -136,10 +136,10 @@ "id": "0367caeb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:34.783077Z", - "iopub.status.busy": "2024-10-19T23:54:34.782516Z", - "iopub.status.idle": "2024-10-19T23:54:39.714403Z", - "shell.execute_reply": "2024-10-19T23:54:39.713726Z" + "iopub.execute_input": "2024-10-22T01:20:35.588094Z", + "iopub.status.busy": "2024-10-22T01:20:35.587672Z", + "iopub.status.idle": "2024-10-22T01:20:40.669265Z", + "shell.execute_reply": "2024-10-22T01:20:40.668581Z" } }, "outputs": [ @@ -180,7 +180,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 490.70it/s]" + "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 456.83it/s]" ] }, { @@ -220,10 +220,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:54:39.716664Z", - "iopub.status.busy": "2024-10-19T23:54:39.716233Z", - "iopub.status.idle": "2024-10-19T23:54:39.726624Z", - "shell.execute_reply": "2024-10-19T23:54:39.726016Z" + "iopub.execute_input": "2024-10-22T01:20:40.671681Z", + "iopub.status.busy": "2024-10-22T01:20:40.671255Z", + "iopub.status.idle": "2024-10-22T01:20:40.682752Z", + "shell.execute_reply": "2024-10-22T01:20:40.682135Z" }, "jupyter": { "outputs_hidden": false @@ -262,33 +262,33 @@ " \n", " \n", " 0\n", - " -0.713625\n", - " -1.676018\n", - " -3.091644\n", + " -0.058468\n", + " 0.518959\n", + " 2.993328\n", " \n", " \n", " 1\n", - " -0.861692\n", - " -1.707183\n", - " -3.505719\n", + " 0.744540\n", + " 2.547959\n", + " 7.710327\n", " \n", " \n", " 2\n", - " 0.704378\n", - " 2.330441\n", - " 7.048587\n", + " 0.752173\n", + " -0.243187\n", + " -0.805433\n", " \n", " \n", " 3\n", - " -1.681803\n", - " -2.035157\n", - " -5.782651\n", + " 0.426648\n", + " 0.242184\n", + " -0.267344\n", " \n", " \n", " 4\n", - " -0.233174\n", - " 1.122552\n", - " 2.060792\n", + " 0.025863\n", + " -0.826618\n", + " -3.154461\n", " \n", " \n", "\n", @@ -296,11 +296,11 @@ ], "text/plain": [ " X Y Z\n", - "0 -0.713625 -1.676018 -3.091644\n", - "1 -0.861692 -1.707183 -3.505719\n", - "2 0.704378 2.330441 7.048587\n", - "3 -1.681803 -2.035157 -5.782651\n", - "4 -0.233174 1.122552 2.060792" + "0 -0.058468 0.518959 2.993328\n", + "1 0.744540 2.547959 7.710327\n", + "2 0.752173 -0.243187 -0.805433\n", + "3 0.426648 0.242184 -0.267344\n", + "4 0.025863 -0.826618 -3.154461" ] }, "execution_count": 3, @@ -332,10 +332,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:54:39.728613Z", - "iopub.status.busy": "2024-10-19T23:54:39.728420Z", - "iopub.status.idle": "2024-10-19T23:54:40.456847Z", - "shell.execute_reply": "2024-10-19T23:54:40.456257Z" + "iopub.execute_input": "2024-10-22T01:20:40.684960Z", + "iopub.status.busy": "2024-10-22T01:20:40.684554Z", + "iopub.status.idle": "2024-10-22T01:20:41.400001Z", + "shell.execute_reply": "2024-10-22T01:20:41.399334Z" }, "jupyter": { "outputs_hidden": false @@ -358,7 +358,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 12.23it/s]" + "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 13.03it/s]" ] }, { @@ -366,7 +366,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.18it/s]" + "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.50it/s]" ] }, { @@ -374,7 +374,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.12it/s]" + "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.69it/s]" ] }, { @@ -386,7 +386,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -401,7 +401,7 @@ "Evaluated and the overall average KL divergence between generated and observed distribution and the graph structure. The results are as follows:\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 0.016457558998081354\n", + "The overall average KL divergence between the generated and observed distribution is 0.04850074524068953\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb index a7b6c1798..3ef0af6d2 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_falsify_dag.ipynb @@ -18,10 +18,10 @@ "id": "13a5f7f6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:44.631754Z", - "iopub.status.busy": "2024-10-19T23:54:44.631559Z", - "iopub.status.idle": "2024-10-19T23:54:46.053017Z", - "shell.execute_reply": "2024-10-19T23:54:46.052280Z" + "iopub.execute_input": "2024-10-22T01:20:45.366172Z", + "iopub.status.busy": "2024-10-22T01:20:45.365987Z", + "iopub.status.idle": "2024-10-22T01:20:46.817464Z", + "shell.execute_reply": "2024-10-22T01:20:46.816831Z" } }, "outputs": [], @@ -56,10 +56,10 @@ "id": "08d67fd1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:46.055493Z", - "iopub.status.busy": "2024-10-19T23:54:46.055187Z", - "iopub.status.idle": "2024-10-19T23:54:46.165216Z", - "shell.execute_reply": "2024-10-19T23:54:46.164656Z" + "iopub.execute_input": "2024-10-22T01:20:46.819840Z", + "iopub.status.busy": "2024-10-22T01:20:46.819371Z", + "iopub.status.idle": "2024-10-22T01:20:46.929964Z", + "shell.execute_reply": "2024-10-22T01:20:46.929325Z" } }, "outputs": [ @@ -105,10 +105,10 @@ "id": "953753d0", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:46.167789Z", - "iopub.status.busy": "2024-10-19T23:54:46.167423Z", - "iopub.status.idle": "2024-10-19T23:55:06.901968Z", - "shell.execute_reply": "2024-10-19T23:55:06.901318Z" + "iopub.execute_input": "2024-10-22T01:20:46.932300Z", + "iopub.status.busy": "2024-10-22T01:20:46.932090Z", + "iopub.status.idle": "2024-10-22T01:21:07.460714Z", + "shell.execute_reply": "2024-10-22T01:21:07.460026Z" } }, "outputs": [ @@ -125,7 +125,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 1/20 [00:01<00:26, 1.40s/it]" + "Test permutations of given graph: 5%|▌ | 1/20 [00:01<00:24, 1.31s/it]" ] }, { @@ -133,7 +133,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 2/20 [00:02<00:24, 1.35s/it]" + "Test permutations of given graph: 10%|█ | 2/20 [00:02<00:23, 1.29s/it]" ] }, { @@ -141,7 +141,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 3/20 [00:04<00:22, 1.34s/it]" + "Test permutations of given graph: 15%|█▌ | 3/20 [00:03<00:21, 1.28s/it]" ] }, { @@ -149,7 +149,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 4/20 [00:04<00:18, 1.16s/it]" + "Test permutations of given graph: 20%|██ | 4/20 [00:04<00:17, 1.11s/it]" ] }, { @@ -157,7 +157,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 5/20 [00:05<00:15, 1.07s/it]" + "Test permutations of given graph: 25%|██▌ | 5/20 [00:05<00:15, 1.03s/it]" ] }, { @@ -165,7 +165,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 6/20 [00:06<00:14, 1.00s/it]" + "Test permutations of given graph: 30%|███ | 6/20 [00:06<00:13, 1.02it/s]" ] }, { @@ -173,7 +173,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 7/20 [00:08<00:14, 1.10s/it]" + "Test permutations of given graph: 35%|███▌ | 7/20 [00:07<00:14, 1.10s/it]" ] }, { @@ -189,7 +189,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 45%|████▌ | 9/20 [00:10<00:12, 1.11s/it]" + "Test permutations of given graph: 45%|████▌ | 9/20 [00:10<00:12, 1.12s/it]" ] }, { @@ -197,7 +197,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 10/20 [00:11<00:11, 1.16s/it]" + "Test permutations of given graph: 50%|█████ | 10/20 [00:11<00:11, 1.17s/it]" ] }, { @@ -205,7 +205,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 12/20 [00:12<00:06, 1.21it/s]" + "Test permutations of given graph: 60%|██████ | 12/20 [00:12<00:06, 1.22it/s]" ] }, { @@ -213,7 +213,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 13/20 [00:13<00:05, 1.19it/s]" + "Test permutations of given graph: 65%|██████▌ | 13/20 [00:12<00:05, 1.21it/s]" ] }, { @@ -221,7 +221,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 14/20 [00:14<00:05, 1.16it/s]" + "Test permutations of given graph: 70%|███████ | 14/20 [00:13<00:04, 1.20it/s]" ] }, { @@ -229,7 +229,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 85%|████████▌ | 17/20 [00:15<00:01, 1.77it/s]" + "Test permutations of given graph: 85%|████████▌ | 17/20 [00:14<00:01, 1.82it/s]" ] }, { @@ -237,7 +237,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 18/20 [00:15<00:01, 1.58it/s]" + "Test permutations of given graph: 90%|█████████ | 18/20 [00:15<00:01, 1.63it/s]" ] }, { @@ -245,7 +245,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 95%|█████████▌| 19/20 [00:16<00:00, 1.44it/s]" + "Test permutations of given graph: 95%|█████████▌| 19/20 [00:16<00:00, 1.47it/s]" ] }, { @@ -253,7 +253,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00, 1.19it/s]" + "Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00, 1.21it/s]" ] }, { @@ -317,10 +317,10 @@ "id": "529de3f5", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:06.904060Z", - "iopub.status.busy": "2024-10-19T23:55:06.903666Z", - "iopub.status.idle": "2024-10-19T23:55:06.907175Z", - "shell.execute_reply": "2024-10-19T23:55:06.906575Z" + "iopub.execute_input": "2024-10-22T01:21:07.462772Z", + "iopub.status.busy": "2024-10-22T01:21:07.462557Z", + "iopub.status.idle": "2024-10-22T01:21:07.465711Z", + "shell.execute_reply": "2024-10-22T01:21:07.465209Z" } }, "outputs": [ @@ -350,10 +350,10 @@ "id": "4623dbc3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:06.909101Z", - "iopub.status.busy": "2024-10-19T23:55:06.908753Z", - "iopub.status.idle": "2024-10-19T23:55:07.002287Z", - "shell.execute_reply": "2024-10-19T23:55:07.001615Z" + "iopub.execute_input": "2024-10-22T01:21:07.467482Z", + "iopub.status.busy": "2024-10-22T01:21:07.467118Z", + "iopub.status.idle": "2024-10-22T01:21:07.561099Z", + "shell.execute_reply": "2024-10-22T01:21:07.560487Z" } }, "outputs": [ @@ -382,10 +382,10 @@ "id": "fcd847fa", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:07.004435Z", - "iopub.status.busy": "2024-10-19T23:55:07.004217Z", - "iopub.status.idle": "2024-10-19T23:55:27.816916Z", - "shell.execute_reply": "2024-10-19T23:55:27.816291Z" + "iopub.execute_input": "2024-10-22T01:21:07.563339Z", + "iopub.status.busy": "2024-10-22T01:21:07.563125Z", + "iopub.status.idle": "2024-10-22T01:21:28.372558Z", + "shell.execute_reply": "2024-10-22T01:21:28.371899Z" } }, "outputs": [ @@ -402,7 +402,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:46, 2.47s/it]" + "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:47, 2.51s/it]" ] }, { @@ -410,7 +410,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 2/20 [00:04<00:44, 2.46s/it]" + "Test permutations of given graph: 10%|█ | 2/20 [00:04<00:44, 2.49s/it]" ] }, { @@ -418,7 +418,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:35, 2.07s/it]" + "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:35, 2.09s/it]" ] }, { @@ -426,7 +426,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 4/20 [00:08<00:30, 1.90s/it]" + "Test permutations of given graph: 20%|██ | 4/20 [00:08<00:30, 1.93s/it]" ] }, { @@ -434,7 +434,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 5/20 [00:09<00:26, 1.80s/it]" + "Test permutations of given graph: 25%|██▌ | 5/20 [00:09<00:27, 1.82s/it]" ] }, { @@ -442,7 +442,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:22, 1.62s/it]" + "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:22, 1.63s/it]" ] }, { @@ -450,7 +450,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 7/20 [00:12<00:19, 1.52s/it]" + "Test permutations of given graph: 35%|███▌ | 7/20 [00:12<00:19, 1.51s/it]" ] }, { @@ -458,7 +458,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:17, 1.45s/it]" + "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:17, 1.43s/it]" ] }, { @@ -466,7 +466,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 45%|████▌ | 9/20 [00:14<00:15, 1.40s/it]" + "Test permutations of given graph: 45%|████▌ | 9/20 [00:14<00:15, 1.38s/it]" ] }, { @@ -482,7 +482,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 13/20 [00:17<00:05, 1.18it/s]" + "Test permutations of given graph: 65%|██████▌ | 13/20 [00:17<00:05, 1.19it/s]" ] }, { @@ -490,7 +490,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.45it/s]" + "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.46it/s]" ] }, { @@ -498,7 +498,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 20/20 [00:17<00:00, 1.11it/s]" + "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00, 1.11it/s]" ] }, { @@ -557,10 +557,10 @@ "id": "3fe68e25", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:27.818943Z", - "iopub.status.busy": "2024-10-19T23:55:27.818573Z", - "iopub.status.idle": "2024-10-19T23:55:27.913167Z", - "shell.execute_reply": "2024-10-19T23:55:27.912532Z" + "iopub.execute_input": "2024-10-22T01:21:28.374617Z", + "iopub.status.busy": "2024-10-22T01:21:28.374263Z", + "iopub.status.idle": "2024-10-22T01:21:28.457490Z", + "shell.execute_reply": "2024-10-22T01:21:28.456879Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "id": "03e8ffa3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:27.915514Z", - "iopub.status.busy": "2024-10-19T23:55:27.915291Z", - "iopub.status.idle": "2024-10-19T23:55:28.017291Z", - "shell.execute_reply": "2024-10-19T23:55:28.016725Z" + "iopub.execute_input": "2024-10-22T01:21:28.460161Z", + "iopub.status.busy": "2024-10-22T01:21:28.459892Z", + "iopub.status.idle": "2024-10-22T01:21:29.040059Z", + "shell.execute_reply": "2024-10-22T01:21:29.039468Z" } }, "outputs": [], @@ -624,10 +624,10 @@ "id": "3d30a01b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:28.019698Z", - "iopub.status.busy": "2024-10-19T23:55:28.019203Z", - "iopub.status.idle": "2024-10-19T23:55:28.133439Z", - "shell.execute_reply": "2024-10-19T23:55:28.132822Z" + "iopub.execute_input": "2024-10-22T01:21:29.042241Z", + "iopub.status.busy": "2024-10-22T01:21:29.041898Z", + "iopub.status.idle": "2024-10-22T01:21:29.168495Z", + "shell.execute_reply": "2024-10-22T01:21:29.167921Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "072544c1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:28.135593Z", - "iopub.status.busy": "2024-10-19T23:55:28.135274Z", - "iopub.status.idle": "2024-10-19T23:55:28.138828Z", - "shell.execute_reply": "2024-10-19T23:55:28.138216Z" + "iopub.execute_input": "2024-10-22T01:21:29.171119Z", + "iopub.status.busy": "2024-10-22T01:21:29.170689Z", + "iopub.status.idle": "2024-10-22T01:21:29.174566Z", + "shell.execute_reply": "2024-10-22T01:21:29.174044Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "id": "bbfe658d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:28.140675Z", - "iopub.status.busy": "2024-10-19T23:55:28.140488Z", - "iopub.status.idle": "2024-10-20T00:07:43.760554Z", - "shell.execute_reply": "2024-10-20T00:07:43.759945Z" + "iopub.execute_input": "2024-10-22T01:21:29.176450Z", + "iopub.status.busy": "2024-10-22T01:21:29.176227Z", + "iopub.status.idle": "2024-10-22T01:34:01.630770Z", + "shell.execute_reply": "2024-10-22T01:34:01.630104Z" } }, "outputs": [ @@ -712,7 +712,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 1%| | 1/100 [00:10<17:54, 10.86s/it]" + "Test permutations of given graph: 1%| | 1/100 [00:10<17:48, 10.79s/it]" ] }, { @@ -720,7 +720,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 2%|▏ | 2/100 [00:21<17:44, 10.86s/it]" + "Test permutations of given graph: 2%|▏ | 2/100 [00:21<17:33, 10.75s/it]" ] }, { @@ -728,7 +728,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 3%|▎ | 3/100 [00:32<17:16, 10.68s/it]" + "Test permutations of given graph: 3%|▎ | 3/100 [00:32<17:15, 10.68s/it]" ] }, { @@ -736,7 +736,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 4%|▍ | 4/100 [00:43<17:11, 10.74s/it]" + "Test permutations of given graph: 4%|▍ | 4/100 [00:43<17:13, 10.77s/it]" ] }, { @@ -744,7 +744,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 5/100 [00:54<17:13, 10.88s/it]" + "Test permutations of given graph: 5%|▌ | 5/100 [00:54<17:16, 10.91s/it]" ] }, { @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 6%|▌ | 6/100 [01:03<16:21, 10.44s/it]" + "Test permutations of given graph: 6%|▌ | 6/100 [01:03<16:25, 10.48s/it]" ] }, { @@ -760,7 +760,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 7%|▋ | 7/100 [01:11<14:49, 9.56s/it]" + "Test permutations of given graph: 7%|▋ | 7/100 [01:11<14:58, 9.66s/it]" ] }, { @@ -768,7 +768,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 8%|▊ | 8/100 [01:22<15:21, 10.02s/it]" + "Test permutations of given graph: 8%|▊ | 8/100 [01:22<15:29, 10.10s/it]" ] }, { @@ -776,7 +776,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 9%|▉ | 9/100 [01:33<15:42, 10.35s/it]" + "Test permutations of given graph: 9%|▉ | 9/100 [01:34<15:53, 10.47s/it]" ] }, { @@ -784,7 +784,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 10/100 [01:44<15:35, 10.40s/it]" + "Test permutations of given graph: 10%|█ | 10/100 [01:44<15:48, 10.54s/it]" ] }, { @@ -792,7 +792,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 11%|█ | 11/100 [01:54<15:27, 10.43s/it]" + "Test permutations of given graph: 11%|█ | 11/100 [01:55<15:42, 10.60s/it]" ] }, { @@ -800,7 +800,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 12%|█▏ | 12/100 [02:04<15:17, 10.42s/it]" + "Test permutations of given graph: 12%|█▏ | 12/100 [02:06<15:32, 10.60s/it]" ] }, { @@ -808,7 +808,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 13%|█▎ | 13/100 [02:14<14:35, 10.06s/it]" + "Test permutations of given graph: 13%|█▎ | 13/100 [02:15<14:47, 10.20s/it]" ] }, { @@ -816,7 +816,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 14%|█▍ | 14/100 [02:22<13:28, 9.40s/it]" + "Test permutations of given graph: 14%|█▍ | 14/100 [02:23<13:38, 9.52s/it]" ] }, { @@ -824,7 +824,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 15/100 [02:32<13:48, 9.75s/it]" + "Test permutations of given graph: 15%|█▌ | 15/100 [02:34<13:59, 9.88s/it]" ] }, { @@ -832,7 +832,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 16%|█▌ | 16/100 [02:43<13:58, 9.98s/it]" + "Test permutations of given graph: 16%|█▌ | 16/100 [02:44<14:10, 10.13s/it]" ] }, { @@ -840,7 +840,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 17%|█▋ | 17/100 [02:50<12:39, 9.15s/it]" + "Test permutations of given graph: 17%|█▋ | 17/100 [02:52<12:49, 9.27s/it]" ] }, { @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 18%|█▊ | 18/100 [03:00<12:58, 9.50s/it]" + "Test permutations of given graph: 18%|█▊ | 18/100 [03:02<13:09, 9.63s/it]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 19%|█▉ | 19/100 [03:11<13:09, 9.75s/it]" + "Test permutations of given graph: 19%|█▉ | 19/100 [03:12<13:20, 9.88s/it]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 20/100 [03:18<12:15, 9.19s/it]" + "Test permutations of given graph: 20%|██ | 20/100 [03:20<12:23, 9.30s/it]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 21%|██ | 21/100 [03:29<12:32, 9.52s/it]" + "Test permutations of given graph: 21%|██ | 21/100 [03:31<12:43, 9.66s/it]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 22%|██▏ | 22/100 [03:39<12:41, 9.76s/it]" + "Test permutations of given graph: 22%|██▏ | 22/100 [03:41<12:53, 9.92s/it]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 23%|██▎ | 23/100 [03:46<11:18, 8.81s/it]" + "Test permutations of given graph: 23%|██▎ | 23/100 [03:48<11:32, 8.99s/it]" ] }, { @@ -896,7 +896,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 24%|██▍ | 24/100 [03:54<10:59, 8.68s/it]" + "Test permutations of given graph: 24%|██▍ | 24/100 [03:57<11:15, 8.89s/it]" ] }, { @@ -904,7 +904,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 25/100 [04:04<11:24, 9.13s/it]" + "Test permutations of given graph: 25%|██▌ | 25/100 [04:07<11:43, 9.38s/it]" ] }, { @@ -912,7 +912,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 26%|██▌ | 26/100 [04:13<11:05, 8.99s/it]" + "Test permutations of given graph: 26%|██▌ | 26/100 [04:17<11:26, 9.28s/it]" ] }, { @@ -920,7 +920,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 27%|██▋ | 27/100 [04:21<10:34, 8.69s/it]" + "Test permutations of given graph: 27%|██▋ | 27/100 [04:25<10:50, 8.91s/it]" ] }, { @@ -928,7 +928,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 28%|██▊ | 28/100 [04:30<10:43, 8.94s/it]" + "Test permutations of given graph: 28%|██▊ | 28/100 [04:34<10:54, 9.09s/it]" ] }, { @@ -936,7 +936,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 29%|██▉ | 29/100 [04:41<11:01, 9.31s/it]" + "Test permutations of given graph: 29%|██▉ | 29/100 [04:45<11:13, 9.49s/it]" ] }, { @@ -944,7 +944,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 30/100 [04:48<10:04, 8.63s/it]" + "Test permutations of given graph: 30%|███ | 30/100 [04:52<10:16, 8.80s/it]" ] }, { @@ -952,7 +952,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 31%|███ | 31/100 [04:55<09:30, 8.27s/it]" + "Test permutations of given graph: 31%|███ | 31/100 [04:59<09:41, 8.43s/it]" ] }, { @@ -960,7 +960,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 32%|███▏ | 32/100 [05:03<09:22, 8.28s/it]" + "Test permutations of given graph: 32%|███▏ | 32/100 [05:08<09:36, 8.48s/it]" ] }, { @@ -968,7 +968,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 33%|███▎ | 33/100 [05:11<09:05, 8.15s/it]" + "Test permutations of given graph: 33%|███▎ | 33/100 [05:16<09:20, 8.37s/it]" ] }, { @@ -976,7 +976,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 34%|███▍ | 34/100 [05:19<08:44, 7.95s/it]" + "Test permutations of given graph: 34%|███▍ | 34/100 [05:24<08:57, 8.14s/it]" ] }, { @@ -984,7 +984,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 35/100 [05:28<08:59, 8.30s/it]" + "Test permutations of given graph: 35%|███▌ | 35/100 [05:33<09:16, 8.57s/it]" ] }, { @@ -992,7 +992,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 36%|███▌ | 36/100 [05:37<09:00, 8.44s/it]" + "Test permutations of given graph: 36%|███▌ | 36/100 [05:42<09:18, 8.72s/it]" ] }, { @@ -1000,7 +1000,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 37%|███▋ | 37/100 [05:44<08:36, 8.20s/it]" + "Test permutations of given graph: 37%|███▋ | 37/100 [05:50<08:54, 8.48s/it]" ] }, { @@ -1008,7 +1008,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 38%|███▊ | 38/100 [05:50<07:37, 7.38s/it]" + "Test permutations of given graph: 38%|███▊ | 38/100 [05:56<07:51, 7.61s/it]" ] }, { @@ -1016,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 39%|███▉ | 39/100 [06:00<08:23, 8.26s/it]" + "Test permutations of given graph: 39%|███▉ | 39/100 [06:06<08:40, 8.53s/it]" ] }, { @@ -1024,7 +1024,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 40/100 [06:08<08:12, 8.21s/it]" + "Test permutations of given graph: 40%|████ | 40/100 [06:15<08:27, 8.46s/it]" ] }, { @@ -1032,7 +1032,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 41%|████ | 41/100 [06:14<07:26, 7.57s/it]" + "Test permutations of given graph: 41%|████ | 41/100 [06:21<07:39, 7.79s/it]" ] }, { @@ -1040,7 +1040,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 42%|████▏ | 42/100 [06:23<07:50, 8.11s/it]" + "Test permutations of given graph: 42%|████▏ | 42/100 [06:31<08:05, 8.37s/it]" ] }, { @@ -1048,7 +1048,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 43%|████▎ | 43/100 [06:29<06:56, 7.31s/it]" + "Test permutations of given graph: 43%|████▎ | 43/100 [06:36<07:10, 7.56s/it]" ] }, { @@ -1056,7 +1056,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 44%|████▍ | 44/100 [06:36<06:50, 7.33s/it]" + "Test permutations of given graph: 44%|████▍ | 44/100 [06:44<07:05, 7.59s/it]" ] }, { @@ -1064,7 +1064,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 45%|████▌ | 45/100 [06:44<06:55, 7.55s/it]" + "Test permutations of given graph: 45%|████▌ | 45/100 [06:52<07:09, 7.81s/it]" ] }, { @@ -1072,7 +1072,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 46%|████▌ | 46/100 [06:47<05:33, 6.18s/it]" + "Test permutations of given graph: 46%|████▌ | 46/100 [06:55<05:44, 6.39s/it]" ] }, { @@ -1080,7 +1080,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 47%|████▋ | 47/100 [06:55<05:57, 6.75s/it]" + "Test permutations of given graph: 47%|████▋ | 47/100 [07:04<06:07, 6.94s/it]" ] }, { @@ -1088,7 +1088,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 48%|████▊ | 48/100 [07:02<05:45, 6.64s/it]" + "Test permutations of given graph: 48%|████▊ | 48/100 [07:10<05:54, 6.82s/it]" ] }, { @@ -1096,7 +1096,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 49%|████▉ | 49/100 [07:11<06:17, 7.41s/it]" + "Test permutations of given graph: 49%|████▉ | 49/100 [07:20<06:28, 7.61s/it]" ] }, { @@ -1104,7 +1104,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 50/100 [07:16<05:32, 6.66s/it]" + "Test permutations of given graph: 50%|█████ | 50/100 [07:24<05:39, 6.80s/it]" ] }, { @@ -1112,7 +1112,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 51%|█████ | 51/100 [07:24<05:51, 7.18s/it]" + "Test permutations of given graph: 51%|█████ | 51/100 [07:33<06:01, 7.38s/it]" ] }, { @@ -1120,7 +1120,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 52%|█████▏ | 52/100 [07:33<06:13, 7.78s/it]" + "Test permutations of given graph: 52%|█████▏ | 52/100 [07:43<06:23, 7.98s/it]" ] }, { @@ -1128,7 +1128,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 53%|█████▎ | 53/100 [07:39<05:33, 7.09s/it]" + "Test permutations of given graph: 53%|█████▎ | 53/100 [07:48<05:40, 7.25s/it]" ] }, { @@ -1136,7 +1136,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 54%|█████▍ | 54/100 [07:46<05:27, 7.11s/it]" + "Test permutations of given graph: 54%|█████▍ | 54/100 [07:55<05:34, 7.27s/it]" ] }, { @@ -1144,7 +1144,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 55%|█████▌ | 55/100 [07:54<05:36, 7.48s/it]" + "Test permutations of given graph: 55%|█████▌ | 55/100 [08:04<05:44, 7.66s/it]" ] }, { @@ -1152,7 +1152,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 56%|█████▌ | 56/100 [07:59<04:51, 6.61s/it]" + "Test permutations of given graph: 56%|█████▌ | 56/100 [08:09<04:59, 6.81s/it]" ] }, { @@ -1160,7 +1160,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 57%|█████▋ | 57/100 [08:06<04:44, 6.63s/it]" + "Test permutations of given graph: 57%|█████▋ | 57/100 [08:16<04:53, 6.83s/it]" ] }, { @@ -1168,7 +1168,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 58%|█████▊ | 58/100 [08:13<04:42, 6.73s/it]" + "Test permutations of given graph: 58%|█████▊ | 58/100 [08:23<04:52, 6.97s/it]" ] }, { @@ -1176,7 +1176,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 59%|█████▉ | 59/100 [08:17<04:07, 6.03s/it]" + "Test permutations of given graph: 59%|█████▉ | 59/100 [08:28<04:15, 6.23s/it]" ] }, { @@ -1184,7 +1184,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 60/100 [08:25<04:18, 6.45s/it]" + "Test permutations of given graph: 60%|██████ | 60/100 [08:35<04:25, 6.63s/it]" ] }, { @@ -1192,7 +1192,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 61%|██████ | 61/100 [08:29<03:49, 5.89s/it]" + "Test permutations of given graph: 61%|██████ | 61/100 [08:40<03:56, 6.06s/it]" ] }, { @@ -1200,7 +1200,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 62%|██████▏ | 62/100 [08:35<03:42, 5.86s/it]" + "Test permutations of given graph: 62%|██████▏ | 62/100 [08:46<03:49, 6.03s/it]" ] }, { @@ -1208,7 +1208,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 63%|██████▎ | 63/100 [08:41<03:37, 5.88s/it]" + "Test permutations of given graph: 63%|██████▎ | 63/100 [08:52<03:44, 6.07s/it]" ] }, { @@ -1216,7 +1216,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 64%|██████▍ | 64/100 [08:48<03:45, 6.28s/it]" + "Test permutations of given graph: 64%|██████▍ | 64/100 [08:59<03:52, 6.46s/it]" ] }, { @@ -1224,7 +1224,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 65/100 [08:54<03:33, 6.09s/it]" + "Test permutations of given graph: 65%|██████▌ | 65/100 [09:05<03:39, 6.27s/it]" ] }, { @@ -1232,7 +1232,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 66%|██████▌ | 66/100 [08:59<03:15, 5.74s/it]" + "Test permutations of given graph: 66%|██████▌ | 66/100 [09:10<03:19, 5.88s/it]" ] }, { @@ -1240,7 +1240,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 67%|██████▋ | 67/100 [09:05<03:13, 5.86s/it]" + "Test permutations of given graph: 67%|██████▋ | 67/100 [09:16<03:18, 6.01s/it]" ] }, { @@ -1248,7 +1248,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 68%|██████▊ | 68/100 [09:09<02:52, 5.39s/it]" + "Test permutations of given graph: 68%|██████▊ | 68/100 [09:21<02:56, 5.52s/it]" ] }, { @@ -1256,7 +1256,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 69%|██████▉ | 69/100 [09:16<03:05, 6.00s/it]" + "Test permutations of given graph: 69%|██████▉ | 69/100 [09:28<03:10, 6.14s/it]" ] }, { @@ -1264,7 +1264,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 70/100 [09:26<03:28, 6.94s/it]" + "Test permutations of given graph: 70%|███████ | 70/100 [09:38<03:33, 7.11s/it]" ] }, { @@ -1272,7 +1272,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 71%|███████ | 71/100 [09:31<03:08, 6.49s/it]" + "Test permutations of given graph: 71%|███████ | 71/100 [09:43<03:13, 6.66s/it]" ] }, { @@ -1280,7 +1280,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 72%|███████▏ | 72/100 [09:38<03:05, 6.61s/it]" + "Test permutations of given graph: 72%|███████▏ | 72/100 [09:50<03:10, 6.79s/it]" ] }, { @@ -1288,7 +1288,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 73%|███████▎ | 73/100 [09:42<02:34, 5.73s/it]" + "Test permutations of given graph: 73%|███████▎ | 73/100 [09:54<02:38, 5.88s/it]" ] }, { @@ -1296,7 +1296,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 74%|███████▍ | 74/100 [09:48<02:34, 5.95s/it]" + "Test permutations of given graph: 74%|███████▍ | 74/100 [10:01<02:38, 6.08s/it]" ] }, { @@ -1304,7 +1304,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 75%|███████▌ | 75/100 [09:53<02:18, 5.55s/it]" + "Test permutations of given graph: 75%|███████▌ | 75/100 [10:06<02:22, 5.70s/it]" ] }, { @@ -1312,7 +1312,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 76%|███████▌ | 76/100 [09:57<02:06, 5.27s/it]" + "Test permutations of given graph: 76%|███████▌ | 76/100 [10:10<02:09, 5.41s/it]" ] }, { @@ -1320,7 +1320,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 77%|███████▋ | 77/100 [10:03<02:01, 5.28s/it]" + "Test permutations of given graph: 77%|███████▋ | 77/100 [10:16<02:05, 5.45s/it]" ] }, { @@ -1328,7 +1328,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 78%|███████▊ | 78/100 [10:08<01:58, 5.37s/it]" + "Test permutations of given graph: 78%|███████▊ | 78/100 [10:22<02:02, 5.56s/it]" ] }, { @@ -1336,7 +1336,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 79%|███████▉ | 79/100 [10:13<01:48, 5.15s/it]" + "Test permutations of given graph: 79%|███████▉ | 79/100 [10:27<01:52, 5.34s/it]" ] }, { @@ -1344,7 +1344,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 80%|████████ | 80/100 [10:19<01:52, 5.60s/it]" + "Test permutations of given graph: 80%|████████ | 80/100 [10:33<01:56, 5.82s/it]" ] }, { @@ -1352,7 +1352,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 81%|████████ | 81/100 [10:24<01:41, 5.33s/it]" + "Test permutations of given graph: 81%|████████ | 81/100 [10:38<01:44, 5.51s/it]" ] }, { @@ -1360,7 +1360,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 82%|████████▏ | 82/100 [10:29<01:32, 5.16s/it]" + "Test permutations of given graph: 82%|████████▏ | 82/100 [10:43<01:35, 5.33s/it]" ] }, { @@ -1368,7 +1368,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 83%|████████▎ | 83/100 [10:33<01:24, 4.97s/it]" + "Test permutations of given graph: 83%|████████▎ | 83/100 [10:48<01:27, 5.13s/it]" ] }, { @@ -1376,7 +1376,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 84%|████████▍ | 84/100 [10:41<01:32, 5.76s/it]" + "Test permutations of given graph: 84%|████████▍ | 84/100 [10:56<01:35, 5.96s/it]" ] }, { @@ -1384,7 +1384,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 85%|████████▌ | 85/100 [10:46<01:23, 5.55s/it]" + "Test permutations of given graph: 85%|████████▌ | 85/100 [11:01<01:26, 5.76s/it]" ] }, { @@ -1392,7 +1392,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 86%|████████▌ | 86/100 [10:54<01:26, 6.14s/it]" + "Test permutations of given graph: 86%|████████▌ | 86/100 [11:09<01:28, 6.35s/it]" ] }, { @@ -1400,7 +1400,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 87%|████████▋ | 87/100 [11:00<01:21, 6.24s/it]" + "Test permutations of given graph: 87%|████████▋ | 87/100 [11:15<01:23, 6.45s/it]" ] }, { @@ -1408,7 +1408,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 88%|████████▊ | 88/100 [11:07<01:18, 6.53s/it]" + "Test permutations of given graph: 88%|████████▊ | 88/100 [11:23<01:20, 6.72s/it]" ] }, { @@ -1416,7 +1416,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 89%|████████▉ | 89/100 [11:11<01:02, 5.72s/it]" + "Test permutations of given graph: 89%|████████▉ | 89/100 [11:27<01:04, 5.87s/it]" ] }, { @@ -1424,7 +1424,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 90/100 [11:17<00:58, 5.80s/it]" + "Test permutations of given graph: 90%|█████████ | 90/100 [11:33<00:59, 5.95s/it]" ] }, { @@ -1432,7 +1432,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 91%|█████████ | 91/100 [11:20<00:45, 5.07s/it]" + "Test permutations of given graph: 91%|█████████ | 91/100 [11:36<00:46, 5.18s/it]" ] }, { @@ -1440,7 +1440,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 92%|█████████▏| 92/100 [11:26<00:41, 5.22s/it]" + "Test permutations of given graph: 92%|█████████▏| 92/100 [11:42<00:42, 5.35s/it]" ] }, { @@ -1448,7 +1448,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 93%|█████████▎| 93/100 [11:32<00:38, 5.55s/it]" + "Test permutations of given graph: 93%|█████████▎| 93/100 [11:48<00:39, 5.69s/it]" ] }, { @@ -1456,7 +1456,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 94%|█████████▍| 94/100 [11:36<00:29, 4.87s/it]" + "Test permutations of given graph: 94%|█████████▍| 94/100 [11:52<00:29, 5.00s/it]" ] }, { @@ -1464,7 +1464,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 95%|█████████▌| 95/100 [11:43<00:28, 5.69s/it]" + "Test permutations of given graph: 95%|█████████▌| 95/100 [11:59<00:29, 5.81s/it]" ] }, { @@ -1472,7 +1472,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 96%|█████████▌| 96/100 [11:48<00:21, 5.38s/it]" + "Test permutations of given graph: 96%|█████████▌| 96/100 [12:04<00:22, 5.51s/it]" ] }, { @@ -1480,7 +1480,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 97%|█████████▋| 97/100 [11:53<00:16, 5.42s/it]" + "Test permutations of given graph: 97%|█████████▋| 97/100 [12:10<00:16, 5.56s/it]" ] }, { @@ -1488,7 +1488,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 98%|█████████▊| 98/100 [11:55<00:08, 4.40s/it]" + "Test permutations of given graph: 98%|█████████▊| 98/100 [12:12<00:09, 4.52s/it]" ] }, { @@ -1496,7 +1496,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 99%|█████████▉| 99/100 [12:02<00:05, 5.00s/it]" + "Test permutations of given graph: 99%|█████████▉| 99/100 [12:19<00:05, 5.15s/it]" ] }, { @@ -1504,7 +1504,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 100/100 [12:04<00:00, 4.11s/it]" + "Test permutations of given graph: 100%|██████████| 100/100 [12:21<00:00, 4.23s/it]" ] }, { @@ -1512,7 +1512,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 100/100 [12:04<00:00, 7.24s/it]" + "Test permutations of given graph: 100%|██████████| 100/100 [12:21<00:00, 7.41s/it]" ] }, { @@ -1580,10 +1580,10 @@ "id": "58650ea8", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:07:43.762570Z", - "iopub.status.busy": "2024-10-20T00:07:43.762365Z", - "iopub.status.idle": "2024-10-20T00:08:06.376500Z", - "shell.execute_reply": "2024-10-20T00:08:06.375801Z" + "iopub.execute_input": "2024-10-22T01:34:01.633030Z", + "iopub.status.busy": "2024-10-22T01:34:01.632608Z", + "iopub.status.idle": "2024-10-22T01:34:24.594983Z", + "shell.execute_reply": "2024-10-22T01:34:24.594243Z" } }, "outputs": [ @@ -1600,7 +1600,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:46, 2.44s/it]" + "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:48, 2.53s/it]" ] }, { @@ -1608,7 +1608,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 2/20 [00:04<00:44, 2.47s/it]" + "Test permutations of given graph: 10%|█ | 2/20 [00:05<00:45, 2.53s/it]" ] }, { @@ -1616,7 +1616,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:35, 2.10s/it]" + "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:36, 2.15s/it]" ] }, { @@ -1624,7 +1624,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 4/20 [00:07<00:28, 1.78s/it]" + "Test permutations of given graph: 20%|██ | 4/20 [00:08<00:28, 1.81s/it]" ] }, { @@ -1632,7 +1632,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 5/20 [00:10<00:30, 2.04s/it]" + "Test permutations of given graph: 25%|██▌ | 5/20 [00:10<00:30, 2.05s/it]" ] }, { @@ -1640,7 +1640,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:23, 1.65s/it]" + "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:23, 1.66s/it]" ] }, { @@ -1648,7 +1648,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 7/20 [00:12<00:21, 1.64s/it]" + "Test permutations of given graph: 35%|███▌ | 7/20 [00:13<00:21, 1.66s/it]" ] }, { @@ -1656,7 +1656,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:16, 1.39s/it]" + "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:16, 1.41s/it]" ] }, { @@ -1664,7 +1664,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 55%|█████▌ | 11/20 [00:14<00:06, 1.32it/s]" + "Test permutations of given graph: 55%|█████▌ | 11/20 [00:14<00:06, 1.31it/s]" ] }, { @@ -1672,7 +1672,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 13/20 [00:15<00:04, 1.51it/s]" + "Test permutations of given graph: 65%|██████▌ | 13/20 [00:15<00:04, 1.49it/s]" ] }, { @@ -1680,7 +1680,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 14/20 [00:16<00:04, 1.41it/s]" + "Test permutations of given graph: 70%|███████ | 14/20 [00:16<00:04, 1.40it/s]" ] }, { @@ -1688,7 +1688,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.33it/s]" + "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.32it/s]" ] }, { @@ -1704,7 +1704,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 20/20 [00:19<00:00, 1.05it/s]" + "Test permutations of given graph: 100%|██████████| 20/20 [00:19<00:00, 1.04it/s]" ] }, { @@ -1763,10 +1763,10 @@ "id": "ab8ef360", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:06.378479Z", - "iopub.status.busy": "2024-10-20T00:08:06.378280Z", - "iopub.status.idle": "2024-10-20T00:08:06.466401Z", - "shell.execute_reply": "2024-10-20T00:08:06.465871Z" + "iopub.execute_input": "2024-10-22T01:34:24.597090Z", + "iopub.status.busy": "2024-10-22T01:34:24.596877Z", + "iopub.status.idle": "2024-10-22T01:34:24.678447Z", + "shell.execute_reply": "2024-10-22T01:34:24.677784Z" } }, "outputs": [ @@ -1800,10 +1800,10 @@ "id": "073a54c4", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:06.468822Z", - "iopub.status.busy": "2024-10-20T00:08:06.468571Z", - "iopub.status.idle": "2024-10-20T00:08:06.569473Z", - "shell.execute_reply": "2024-10-20T00:08:06.568903Z" + "iopub.execute_input": "2024-10-22T01:34:24.680461Z", + "iopub.status.busy": "2024-10-22T01:34:24.680247Z", + "iopub.status.idle": "2024-10-22T01:34:24.757000Z", + "shell.execute_reply": "2024-10-22T01:34:24.756324Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb index 79bdf5517..50bd4ff48 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_icc.ipynb @@ -46,10 +46,10 @@ "id": "6bfa09f9-67e2-4c65-a27c-903c2742fe0f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:11.135897Z", - "iopub.status.busy": "2024-10-20T00:08:11.135689Z", - "iopub.status.idle": "2024-10-20T00:08:12.666159Z", - "shell.execute_reply": "2024-10-20T00:08:12.665475Z" + "iopub.execute_input": "2024-10-22T01:34:29.409966Z", + "iopub.status.busy": "2024-10-22T01:34:29.409772Z", + "iopub.status.idle": "2024-10-22T01:34:31.162535Z", + "shell.execute_reply": "2024-10-22T01:34:31.161847Z" } }, "outputs": [ @@ -101,10 +101,10 @@ "id": "04f25d22-df49-45c8-9e4d-6fe05bc30e88", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:12.668164Z", - "iopub.status.busy": "2024-10-20T00:08:12.667973Z", - "iopub.status.idle": "2024-10-20T00:08:16.737382Z", - "shell.execute_reply": "2024-10-20T00:08:16.736717Z" + "iopub.execute_input": "2024-10-22T01:34:31.165004Z", + "iopub.status.busy": "2024-10-22T01:34:31.164743Z", + "iopub.status.idle": "2024-10-22T01:34:35.679961Z", + "shell.execute_reply": "2024-10-22T01:34:35.679211Z" } }, "outputs": [ @@ -153,7 +153,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node horsepower: 80%|████████ | 4/5 [00:00<00:00, 24.55it/s]" + "Fitting causal mechanism of node horsepower: 80%|████████ | 4/5 [00:00<00:00, 24.12it/s]" ] }, { @@ -161,7 +161,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node mpg: 80%|████████ | 4/5 [00:00<00:00, 24.55it/s] " + "Fitting causal mechanism of node mpg: 80%|████████ | 4/5 [00:00<00:00, 24.12it/s] " ] }, { @@ -169,7 +169,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 29.97it/s]" + "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 29.35it/s]" ] }, { @@ -200,10 +200,10 @@ "id": "ec3eadc5-e305-4291-864a-fab105c27443", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:16.739452Z", - "iopub.status.busy": "2024-10-20T00:08:16.739249Z", - "iopub.status.idle": "2024-10-20T00:08:17.822782Z", - "shell.execute_reply": "2024-10-20T00:08:17.822203Z" + "iopub.execute_input": "2024-10-22T01:34:35.682023Z", + "iopub.status.busy": "2024-10-22T01:34:35.681816Z", + "iopub.status.idle": "2024-10-22T01:34:36.890186Z", + "shell.execute_reply": "2024-10-22T01:34:36.889611Z" } }, "outputs": [ @@ -220,7 +220,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 4523.62it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 3311.99it/s]" ] }, { @@ -252,36 +252,36 @@ "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n", "\n", "--- Node displacement\n", - "- The MSE is 1046.1386127285114.\n", - "- The NMSE is 0.3122627933469142.\n", - "- The R2 coefficient is 0.9023228951821402.\n", - "- The normalized CRPS is 0.17491878800736213.\n", + "- The MSE is 1041.6789617242168.\n", + "- The NMSE is 0.31110594400261765.\n", + "- The R2 coefficient is 0.9030196864757061.\n", + "- The normalized CRPS is 0.17389510458463725.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "--- Node weight\n", - "- The MSE is 78405.98159688411.\n", - "- The NMSE is 0.3305052973475223.\n", - "- The R2 coefficient is 0.8907286116580121.\n", - "- The normalized CRPS is 0.1812969474437766.\n", + "- The MSE is 79878.78338201882.\n", + "- The NMSE is 0.3339593114124556.\n", + "- The R2 coefficient is 0.8875230464804005.\n", + "- The normalized CRPS is 0.18324396514993632.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "--- Node horsepower\n", - "- The MSE is 213.07861084063614.\n", - "- The NMSE is 0.38752088273071295.\n", - "- The R2 coefficient is 0.8454044157528504.\n", - "- The normalized CRPS is 0.20674889754173442.\n", + "- The MSE is 212.60668614086336.\n", + "- The NMSE is 0.3824548766720538.\n", + "- The R2 coefficient is 0.8510773450667953.\n", + "- The normalized CRPS is 0.20279112012331843.\n", "The estimated CRPS indicates a good model performance.\n", "\n", "--- Node mpg\n", - "- The MSE is 16.060486823938973.\n", - "- The NMSE is 0.5203406299058366.\n", - "- The R2 coefficient is 0.727082619378933.\n", - "- The normalized CRPS is 0.28605114114490204.\n", + "- The MSE is 15.748779043326902.\n", + "- The NMSE is 0.5079018424532107.\n", + "- The R2 coefficient is 0.7403548726484821.\n", + "- The normalized CRPS is 0.2775001467867037.\n", "The estimated CRPS indicates a good model performance.\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 1.0618178820046136\n", - "The estimated KL divergence indicates some mismatches between the distributions.\n", + "The overall average KL divergence between the generated and observed distribution is 0.9807451423111999\n", + "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n", "\n", "==== NOTE ====\n", "Always double check the made model assumptions with respect to the graph structure and choice of causal mechanisms.\n", @@ -307,16 +307,16 @@ "id": "6257a9e2-a1f4-4c16-b629-b5a13bbf32dc", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:17.825015Z", - "iopub.status.busy": "2024-10-20T00:08:17.824492Z", - "iopub.status.idle": "2024-10-20T00:08:18.445225Z", - "shell.execute_reply": "2024-10-20T00:08:18.444664Z" + "iopub.execute_input": "2024-10-22T01:34:36.892464Z", + "iopub.status.busy": "2024-10-22T01:34:36.892055Z", + "iopub.status.idle": "2024-10-22T01:34:37.528911Z", + "shell.execute_reply": "2024-10-22T01:34:37.528224Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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m5uYO24eHh6OiogJyuZzxXDkcDq5fv44FCxbg008//dUPtY0bN+Lpp59GfX09uFwuQkJC8PLLL2PVqlUoLy9HVlZWp3/vn6OtrQ25ublwcnJCaGholx6bYluouHYSI0eOhIODA06cOAGNRtMlx3zmmWcwYMAAfPPNN9ixYweqq6shl8vx4YcfIj09HZMnT4avry+AH4Rq6tSpCA0Nxfbt2yESiRAfH9+hhIytmTJlCkaPHo2jR49iw4YNKCwsRGVlJb788kucPXu2gxc9fvx4zJw5ExcuXMBnn32Ge/fuoaWlBWlpaVi9ejVYLBYmTJjwq2OlFosF586dw9dff43KykqoVCpcuXIFp06dgpeXFyIjI23xtX+S2tpa3Lx5E0lJSV3+W1BsjH2TFXoPFouFPPPMMyQxMZFJDeqKYzY1NZGPPvqIJCcnE2dnZ+Ll5UUmTpxIjh07RlQqVTs7LBYL+fTTT4mvry/58MMPSWNjY7v97d+/n/Tp04ew2WwCgDg4OJA5c+aQLVu2kGHDhjHr2Ww2GT58ONm2bRuZPHlyu/XTpk0jN2/eJCtXriQODg7M+oSEBHLkyBFisVhITU0N+eCDD0hsbCxxcnIiYWFh5M033ySvvvoqCQgIIGw2mwwcOJAcO3aMWCwWolQqyXfffUcmTZpEPDw8iKurK0lISCBvv/02yc/PJxaLhVRVVZHnn3++3TE5HA559dVX233Huro6snXrVjJ16lQSGBhIXF1dSVhYGHnyySfJuXPniNlstvnvZkWhUJB169aRPn36kAsXLnTZcSldA51ysBMpLi7G+PHjMX78ePzjH//oktLL5H9j/u9vFlvTkh40Xv/48eNYt24d/u///g+DBw9u97nFYoHZbG4X1rB2jP34GL91/f1pWw+y2ep5Wju/fry99X8eZBuLxWI+//FEKNZOtfvP14P29XPnzBaYTCZcunQJr7zyCoYOHYrPP/+8yzMVKLaFpmJ1IiEhIfjoo4/w7LPPom/fvpg/f77Nm3pWEfpxtsBPcfToUfTv3x/BwcEdRMQqVg+is9b/ks0PWmcVvIfd52/dV1eQn5+P1atXw9vbG2+88QYdUtwLoeLaibBYLEyfPh3Xr1/HO++8A0dHR0yfPt2upT2OHj2KgwcPYvny5aitrUVubi5mzJhB8yntBCEE+fn5eOedd1BTU4Ovv/4anp6eVFx7IVRcOxk2m413330XJpMJL7/8MlpaWrB48WKIxWK72EMIwaVLl7B161YEBATgjTfeQL9+/ejNbAcIIcjOzsabb76JmpoafPTRR0hOTqa/RS+FxlxtACEEJpMJ7733Hr766ivMnz8fb7zxBjw8PLr8Rrp/DtafGsZKsT1arRZnz57F3/72N/D5fHzyySdISUmxe3iCYjuouNoIQgjMZjN27NiBt99+G1KpFKtXr8bAgQO7bO4BSvegpqYGa9euxZYtWzB06FC88847iIiIoMLay6HiamPMZjOKi4vxyiuv4OrVq1i0aBH++Mc/wsfHh95cvRyVSoUzZ87g448/hlwux0svvYTly5czo9QovRsqrl0AIQQ6nQ5btmzBZ599BoPBgBdeeAHLly9vN+EKpXeg1+tx48YNfPLJJ0hLS8OoUaPw6quvIjExEQ4ODvT3fkSg4tpFWHMrGxoasGbNGmzcuBGurq5Yvnw5Fi5cCHd3d+rJ9nA0Gg1u3LiBdevW4cyZM4iNjcUf//hHjB07Flwul/6+jxhUXLsYa2dXSUkJ1q9fjx07doDNZmP+/PlYsWIFQkJCwOHQJI6eRGtrK06dOoUNGzbgxo0biI2NxapVqzBlyhQ4OjrSDsRHFCqudsI6mqihoQE7duzAhg0bUFdXh/79+2POnDmYPn06pFIpvSm7KQaDAQUFBfjuu+9w4MAB1NTUYMiQIVi6dCnGjBkDoVBIRfURh4qrnbFmFSiVShw/fhw7duzApUuXIBKJMHnyZMyZMweDBg2inSDdAJPJhIqKChw+fBh79uzBnTt34OvrixkzZmDmzJmIj4+nqW4UBiqu3QRrTNZkMqGqqgoHDhzA7t27kZubC2dnZ4waNQqTJ0/G6NGj4ebmRuN3XYRWq0VOTg6OHj2KEydOIDs7G05OThg7dixmz56NQYMGwdHRsV25GwoFoOLaLbGGDMxmMwoKCnDq1CkcP34cN2/eBACkpKRg+PDhGDZsGBITEyEWi6mn1EkYjUZUVFQgLS0NFy9eRHp6OioqKuDj44PRo0djwoQJGDhwIFxdXamXSvlZqLh2c+4X2rq6Oly4cAHnzp1DWloa5HI5RCIREhMTMWzYMPTv3x8JCQlwc3P71RO5POqo1WqUlJTg5s2buHLlCtLT01FaWsrMdzt8+HCMHj0affr0gUAgAIfDoYJK+VVQce1BWOOz1qWkpATp6elIS0tDeno6ampqQAhBYGAg+vbti8TERCQmJiI2NhYymeyRz0JQKpUoLS1FVlYWbt++jVu3biEvLw+tra1wcnJCfHw8Bg8ejCFDhiAhIQEuLi6Md0oFlfJboeLaQ7HOS2o2mxnvVi6XIzs7G5mZmbhz5w4yMzPR0NAAi8UCd3d3hIWFISoqCpGRkYiMjERISAh8fX27tLCirTGZTGhubkZZWRmKioqQl5fHLNXV1dBoNBCLxQgLC0Pfvn2ZJSwsDCKRiJm2kIop5fdCxbUXYZ2A2rqYTCbU1taioKAA+fn5KCwsREFBAQoLC9HU1ASLxQIulwt3d3f4+/sjICAAAQEB8PPzg6enJ9zd3eHu7g4PDw9IJBK7hxq0Wi2amprQ0NCAhoYG1NfXo6amBlVVVaioqGCq2lrrmIlEIgQEBCAiIgLh4eGIiIhAVFQUgoKC2nVCUc+UYguouPZirN7t/TP/W5empiZUVFQwolRVVYWqqipUVlaipqYGCoUCZrOZmVxaKBRCKpVCKpXC2dkZYrEYYrEYjo6OcHJygkQiAZ/PB5fLhVAoZEIQXC4XAoGAEWZr1VbrZafVaqHX62EwGKBWq6FWq6FUKqFQKKBWq6FSqdDa2orGxkbGJut3EgqF8PT0hI+PD/z8/ODv788sgYGB8PLyYvJNfyyiVEgptoaK6yPK/aVOHvSq1+vR0tKC+vp6NDQ0MB5ja2srFAoFVCpVOzFUKpUwGAzQ6/XQ6/VMuRXrOmtJFx6PBz6fz6Qt8fl88Pl88Hg8CIVCiMViSCQSSCQSODo6wtHRES4uLnBzc4Obmxs8PDwgk8kgk8kYb/r+Ei0/fqUiSrEXVFwpD+R+r/enlvu3s76///+t3L59G++88w5SU1OxYsWKdsexit+DXn/8/kGiScWT0l15tLuPKT9JZ3p9IpEIHA4HAoEAEomkU/ZJoXR36JASCoVCsQFUXCkUCsUGUHGlUCgUG0DFlUKhUGwAFVcKhUKxAVRcKRQKxQZQcaVQKBQbQMWVQqFQbAAVVwqFQrEBVFwpFArFBlBxpVAoFBtAxZVCoVBsABVXCoVCsQFUXCkUCsUGUHGlUCgUG0DFlUKhUGwAFVcKhUKxAVRcKRQKxQZQcaVQKBQbQMWVQqFQbAAVVwqFQrEBVFwpFArFBlBxpVAoFBtAxZVCoVBsABVXCoVCsQFUXCkUCsUGUHGlUCgUG0DFlUKhUGwAFVcKhUKxAVRcKRQKxQZQcaVQKBQbQMWVQqFQbADH3gZQehcqlQq3bt1CdXU1s66kpARyuRw3btzAjh07mPU8Hg+JiYkICQmxh6kUik1hEUKIvY2g9B4MBgNef/117NixAxaLBSwWCyaTCSqVCnw+H0KhEABgsVgQHx+P119/HWPGjLGz1RRK50M9V0qnwuVyERYWBrFYjOLiYlgsFuYznU6HtrY25u+AgAAEBATYw0wKxebQmCulU2GxWBg4cCCCgoLaCeuP4fP56NOnDwIDA7vQOgql66DiSul0YmJiEB4eDj6f/8DP2Ww2EhMTERMTAy6X28XWUShdAxVXSqfD4/GQkpKCkJAQsNkdLzGLxYLExERERkaCxWLZwUIKxfZQcaV0OiwWC/3790dwcPADQwNisRhxcXHw8fGxg3UUStdAxZViE8LCwhAZGQmRSNRuPZvNRlJSEmJiYsDh0P5USu+FiivFJvB4PPTv3x+hoaHtmv4WiwVJSUmIiIigIQFKr4aKK8VmDBgwAKGhobg/ldrV1RUxMTHw9PS0o2UUiu2h4kqxGf7+/oiMjISTkxOAH0ICycnJiI6OhoODg52to1BsCxVXis3gcDjo378/wsLCwGKxYLFY0K9fP4SFhdnbNArF5lBxpdgUa9yVEAIvLy/ExsbCzc3N3mZRKDaHiivFpnh5eSEmJgZSqRTJycmIioqiIQHKIwGduOURRqPRQKlUQqFQQKlUdniv0+mgVquh1+uh0+mYRavVwmAwQKPRwGg0wmKxQKfTwWQytdu/0WiEXq9HXV0dGhsb4ejoCA8PDzg5OYHP57fLFmCxWBAIBOByuXBwcGAmeeHz+RAIBMxiXScUCiEWi+Hk5ASJRNJucXJygqOjIx39RbErVFx7ESaTCa2trWhoaEBTU9MDl+bmZjQ3N6OtrQ0GgwFms7ndwuFwIBKJIBKJwOPxIBQKweVywefzwePxmPdcLhcCgYDJVbV+dj9sNhs8Hg8mkwlXr16Fh4cHoqOjodfrOwix2WyG0Whk7LAKs/XVYDC0W6wir9FoYDAYAAAODg7MwuFw4OjoCGdnZ7i5uUEmk0EqlXZ4b13EYnHX/EiURwYqrj0IjUaDuro6yOVy1NTUoLa2FrW1tZDL5ZDL5aivr2e8SZPJBC6XCycnJzg5OcHZ2RnOzs7Me7FY/ECPTyAQMALFZrOZhcViPfBvq/d5/3sr1m0IIaioqIBIJIK7uzsIIQ8cuWWxWJi0LYvFwvz9oPcWiwVmsxkWiwUGgwFqtbqd161UKqFSqaBUKtHW1obW1lYoFAq0tbWhra0NWq0WLBYLHA4HfD4fLi4u8Pb2hpeXF7y9vZnF+rdUKqXhDMpvgoprN8NkMqGurg7l5eXtlrKyMtTW1jJNcj6fD6lU2s4bc3Nzg4eHB9zd3RlvjMfjgcPhMAuXywWHw2nn5d2/sFgsmyT3G41GsNlsmwgUIaSDB37/Yn3YWBej0QidToeWlhY0NjaioaEBDQ0NaGxsRHNzM+PlK5VKsFgsRnz9/f0RGBiIoKAgBAYGIjAwEH5+fkyqGYVyP1Rc7UhTUxOKi4tRUFDALOXl5VAoFNDpdEyM0svLC56envD19YWvry+8vb3h7OzMNM95PB7TLLe+cjicB3qTlB+wer5GoxFGoxEGg6HDq1qtRmNjI2pqalBVVcW0FGpra9HS0gIAEIlE8PDwQFhYGCIiIhAZGYmwsDD4+/v/5KxglEcDKq5dRGNjI+7evYu7d+8iPz8fBQUFqK2thVqthlgshq+vL/z9/eHn58dMIi2VSpkOnPsXa6cPFU7bYrFYGKHV6/XtFpVKhZqaGpSXl6OqqgqVlZWorKxEY2MjWCwWXFxcEBQUhMjISERHRyM+Ph6hoaFUcB8hqLjaAJPJhKKiIty9exdZWVm4c+cOysvLoVar4e7ujoCAAPj5+SE4OBgRERHw8PCAo6MjhEIh0yvO5/OpgHZjrLFeayaFVquFVqtFW1sbysrKUFxcjPLyclRWVqK6uhpGoxEymQxRUVFISEhAnz59EB0dDXd3d3t/FYqNoOLaCRBCUFpaioyMDFy7dg03b95EQ0MD2Gw2/P39ERISgujoaMTGxsLd3Z0RUqFQCJFIBA6HQ0W0l2A2m6HX6xmx1Wg0aGtrQ2lpKe7du4eioiKUlpaiqakJYrEYoaGhGDBgAPr3748+ffrA2dnZ3l+B0klQcX1IWltbcf36dVy9ehXXrl1DRUUFCCEIDQ1FTEwM+vTpg/DwcDg5OUEsFsPR0RGOjo7UG30EsVgs0Ov1UKvVUKlUUKlUqKurw71795CdnY38/HzU19fD1dUVcXFxGDhwIAYMGICoqCg6LWMPhorrb0ChUODq1as4c+YMLl26hMbGRri5uSEyMhL9+/dHv379IJPJGEEVCAQ2632n9FwIITCZTFCr1UyqWHV1NbKysnD37l0UFhZCqVQiODgYI0eOxKhRoxAXF0cHRfQwqLj+Amq1GmfPnsWpU6eQkZEBhUKB8PBw9OvXD0OGDIGHhweTQyqRSKiYUn4zhBAYjUaoVCooFAo0NTUhJycHly9fRnZ2NhQKBUJDQzFy5EiMGTMGcXFxDyyfQ+leUHH9CfLz83Hw4EEcP34cdXV1CA8PR3JyMgYPHgxfX19IpVK4uLjQeCml07FYLNBqtWhubkZ9fT3y8vKQlpaG7OxsqFQq9O3bF9OnT8fIkSNpjLYbQ8X1PkwmE06fPo29e/fi+vXrEAqFGDJkCMaPH4+goCBGUGnclNJVWIW2paUFdXV1uHXrFk6dOoW8vDx4eHhg4sSJmDZtGp3GsRtCxRU/9PCeOXMGmzdvRnZ2NiIiIvDYY49h0KBB8Pb2hkwmox4qxe5YLBamM6yoqAinT5/G1atXYbFYkJqainnz5iEkJMTeZlL+xyMvrufPn8fmzZtx8+ZNREVFYe7cuejTpw+8vb0hkUhobIvS7bDGaBsbG1FWVoZTp07hxIkTYLPZmDp1KubPnw8/Pz97m/nI88iKa2NjI1avXo2DBw8iNDQUc+fORXJyMvz9/Zlefgqlu2MymdDS0oLCwkIcPXoUp06dgouLC5555hlMmTKFTjZjRx5Jcb148SI++OADVFZWYunSpZgwYQKCgoIgFAq7XFSvXLmCjz76CPX19eByudiyZYtdmnYXLlzAN998g9LSUjg7O+Ovf/0rBg4c2OV2UB4Ok8mEpqYm5ObmYvfu3UhPT8fo0aPx4osvIjAw0N7mPZI8Um1es9mML7/8Ei+88AL4fD5Wr16NJUuWIDo6GiKRyC7ealRUFN58801IpVJkZmZCo9Ewn+l0OvzpT3/Cyy+/jKqqKpvaERcXh1deeQV+fn64d+8eWltbbXo8SufC4XDg4eGBIUOG4JVXXsHTTz+N9PR0PP/887h69aq9zXskeWTE1Ww2491338WXX36JWbNm4b333sOIESPg6upq1xCAVCpFSkoKPD09OzThzGYz7t27h6ysLOh0Opva4ebmhn79+sHHx4eOCnpILl++jOnTp2Pt2rV2OT6LxQKXy0VwcDCeeOIJvPPOO7BYLHj99ddx+fJlu9j0KPPI3EVfffUVtm/fjpdffhlz5syBh4dHt++sEggE+PDDD2GxWODr62tvcyi/QFNTEzIyMhAVFWVXO9hsNpydnTFixAiIxWJ89tlneOedd7B69WrEx8fb1bZHie6tLp3E5cuX8cUXX2DJkiWYN28ePD09u72wAj+ULYmLi0OfPn0gFArtbQ6lB2Gd5Ds5ORl/+MMfAAAffPABlEqlnS17dOj1nqvJZMJHH32EiIgILF26FG5ubnYNA9TU1OA///kP0tPToVarERQUhAULFkCv17fb7tatW/jwww8hl8sBAGvXrkV0dDTYbDaMRiOOHj2KEydOoKysDAAQFhaGsWPHYtiwYXB2dsbZs2exceNGVFdXw8XFBStXrkReXh7S09OhVCrh7++PWbNmYejQob84k741PHHixAncuXMHjY2NcHJywpAhQzBt2jQEBga2O6darRYXLlzA8ePHUVhYCBaLBW9vb6SkpGDMmDEICgqCg4MDNBoNLl++jKNHj6KwsBBmsxmBgYGYMGECRowYAalUirKyMmzevBnnz58HAMyZMwdBQUE4cOAAampq4O/vj5kzZyI5ORlXrlzB/v37UV9fj8DAQEyfPh0jRowAj8djbNPr9bh9+zYOHDiAnJwc6PV6eHt7Y/To0ZgwYQI8PDxQW1uLXbt2Ye/evQCA4cOHY9y4cdi9ezdKS0vh4uKC0aNHY8aMGZBKpaisrMS2bdvw7bfforGxEd9++y2uXLkCFouFwYMH4/333/+9l81Dw+Vy0a9fPzz//PN48803ceDAASxatMhu9jxK9HpxvXXrFtLS0rB9+3Z4e3vb1WOtra3Fq6++iqysLDz++ONISkqCyWTCgQMHkJubC6PRyGwbHByMP/zhD/j0009x7NgxKBQK5rMdO3Zg06ZNGDlyJCZOnAg2m40rV67ggw8+gFqtRmpqKuLj4/Hyyy/j3XffRXp6OoxGIyZOnIhly5ahpaUFe/bswV//+le8+OKLmDZt2s8KbFFRET755BMQQjBu3Di4ubkhPz8f33//PYqLi/Hcc88hMjISAKBSqbBhwwbs27cPcXFxWLRoEZydnZGXl4e9e/ciPz8fq1atgo+PD/79739j586diIyMxMKFC8Hn83Hx4kV8/vnnKC4uxuLFi+Hu7o65c+fCYrFg27Zt2LlzJ+bMmYMZM2agrq4O+/btw0cffYS+ffvCz88PM2fORG1tLQ4ePIg1a9aAz+dj+PDhAH4Q/cOHD2Pt2rWQyWSYNWsWnJ2dcevWLWzevBn37t3Diy++CDc3N0yaNAkWiwVr167F/v374ejoiLFjx8JkMuHUqVNYs2YNdDodnnvuObi5uWHmzJkAgNWrV2Po0KFYuXIlAEAmk3X6dfRbsFbVTUlJwYABA7Bz507Mnz+fpmh1Ab1eXC9cuAAPDw/079/f7hfU9u3bcerUKSxevBhPPvkk/Pz8YDQa4enpiZMnT7ariOrq6oqBAwfCz8+vQwfT6dOn0dbWhnHjxiElJQUA0KdPH9y6dQttbW0wmUzw8PCAh4cHvL29YTAYkJKSgtTUVOaYPj4+ePXVV7F+/XrExsaiX79+P2m3SqWCxWLBxIkTMWXKFAiFQgwYMACtra04ePAgBg8ejNDQUHA4HJw9exa7du2Cn58f5s+fj6SkJHC5XCQnJzP1qdRqNS5evIidO3fC2dkZ8+bNw8CBA+Hg4ICoqCi899572LFjB8LDwzFt2jTExsYyGR1ubm5ISUlBYmIijEYj6urq8OWXXzIi3KdPHxgMBjQ2NuLf//43MjMzGXHNysrC9u3bodfr8cQTT2Ds2LHg8Xjo06cPtFotk/O8cuVKhIeHIyEhAWKxGHw+H/369cPQoUPh4OAALpeLq1evIi0tDUuXLoVIJEJ0dDTi4uLA5/MREBCAkSNHdv4F9JCwWCy4urpi7NixePvtt1FeXk5HcnUB3T/w+DspLS1FYGCg3VKt7ufIkSNobW3F5MmT4e/vDx6PB0dHR2bwwq8Vf39/f7S0tGDv3r24ceMGDAYD/Pz88PHHH2PKlClwdHRstz2Hw8GAAQMQEBDAHHPQoEEICQlBdnY2SkpKmPLUDyI8PBxvvfUWJk2aBCcnJ3C5XMhkMiQkJAAAiouLGc86LS0NOTk5SE5ORt++fSESicDlcuHu7o6nn34af/jDHxAaGoqMjAzcvXsXiYmJSE5OhqOjIwQCASIiIhAfHw+5XI6srKx2qWnADx59ZGQkBAIBJBIJfHx8wOfz4enpiejoaAgEAjg5OcHX1xeEEDQ0NDD/m5eXh6tXryI0NBTDhw+HRCIBn89HYGAgEhMToVarcf369Q5xSS8vLyQkJDA2ent7w8PDAy0tLUwtre4On89HWFgYtFotqqur7W3OI0Gv91w5HA7MZrO9zYDRaIRcLgeLxYKXl1c7b5TH48HDw+NXz9e5YsUKSKVSXLt2Da+//jr4fD4iIyMxduxY+Pv7d9iPo6MjJBJJu2MKhULIZDJYLBY0NjZCr9e3i03eD4/HQ2VlJbZs2YKSkhIoFAqYTCY0NjaipKQEKpUKBoMBJpMJ9fX10Ol0kMlkHUTeOiTTZDKhoaEBbW1t2LFjBy5fvtwuXFNZWYmmpibI5XK0tLRAJBIxn4nFYojFYuZvLpcLLpfLTEZuxToXhDXUYjab0dLSgqamJpw5cwaPP/54u4dZfX09GhoaUF9fj/r6ekgkknbnysXFpd2+BQIB9Hp9u1BOd8ZaIddaTpxie3r9WY6MjMSBAweg0WjsMgLLioODA3g8HlP0jhDSzhaDwYBfO1guMDAQixcvxrhx49DY2Ii8vDycP38e7777LubPn49Fixa1q82k0+lgNps7HNO63lqv66f473//i02bNiEoKAgTJkyAr68vOBwOMjIysGXLFlgsFlgsFrDZbEagjUYjs+7HWLfjcrkYO3YsZs6c+cDfxTq144//98f7tM6he//6H++PzWYz1XFTUlLw7LPPPtA2mUwGLy+vDvv/8b5ZLBYIIe1+M3u3jH4OnU6He/fuQSgUIigoyN7mPBL0+rDAmDFjoNVqcfTo0XYxza6GzWYjMTERPB4PmZmZ0Gq1zGdKpRIVFRU/2zS/nz/96U/Izc1FREQERowYgfnz5+P111+H0WjE4cOH2zWFgR9urLKysnZN7NraWlRXV8PV1RW+vr4/WZXUYrHg0qVLyMvLw+jRozF79myMHTuWmbT5fi+SzWYjKioKnp6eKCoqQl1dXbt97dixA3//+9+Rk5ODiIgI+Pr6QiKRoG/fvnjssceYxd3dHZcuXUJ+fn6nVUtlsVjw9/dHaGgohEIhYmJi2h0zODgYt27dYqaafBjYbHa7ltLt27fxxhtvICMjo1O+w8NCCEFrayuOHDmCfv360aKIXUSvF9eQkBBMnToVX3zxBSorK2GxWOxmy1NPPQV/f3+sW7cOly5dQmtrK6qqqvDll1+ivr7+V+/n5s2b+PLLL5GRkQGNRgMWi4WKigpotVp4eHh0ECQ+n48DBw7g0qVLaG5uRkVFBb7++mvk5eVh1qxZiIqK+knPlcViwd3dHQ4ODsjNzYVcLoder0dZWRnOnDmDysrKdttPnjwZw4YNw9mzZ7Fv3z5UVVWhpaUFR48exY4dO2A0GiGVSjF27FiMHj0aFy5cwM6dO1FRUQGlUok7d+7gm2++QVFREdzd3TvVG0xOTsb06dORm5uLTZs2oaioCCqVCvn5+di6dSvS0tLg4+Pz0MeUSCSQSqWorq5GVVUVrl+/juvXr7d7ANkDlUqFI0eOIDc3F0uWLKFhgS7ikZi4JTc3F/PmzUNcXBw+//xzu+W6arVaHD9+HNu2bUNTUxNEIhE8PDzQt29fnDhxAmlpaUhISMD48eMxaNAgbN++HRcvXkR1dTX69euHxx57DM8//zyuX7+OEydOoKWlBRqNhnlg+Pr6YsGCBUhOTma8r6eeegonT57EqlWroNVqkZeXh+bmZuj1egwdOhQLFixAZGQkrl+/jq1bt+LkyZNoaGhAdHQ0pk+fjkWLFkGpVGLz5s3IzMwEi8WCRCJBQEAATCYTTp48CYvFgv79++MPf/gDkpKScPfuXezbtw/Z2dnQ6XRMTDQhIQFTp05l0rZyc3Nx8OBB3L59G2q1GhwOB2KxGAEBARg3bhwGDhwIjUaDbdu2Ydu2bSguLoaXlxemTZuGSZMmIScnBxs3bkRxcTE8PT0xa9YsTJgwgVlfVFQEDw8PTJ06FQsXLkS/fv1QVlaGQ4cOIS0tDSqVCmw2G46OjvD29saoUaMwatQoEEJw4MABrFmzBvn5+XB0dERqaioWLVoEs9mMN998E0VFRbBYLBg6dCieeOIJPPHEE6ipqcH27duxf/9+uLi4wMnJCSNHjsSyZcsgEAi6/HqzXnNnz57Fu+++i2HDhuGdd96xu9g/KjwS4mo2m3H06FH8+c9/xpAhQ/C3v/0NXl5edsl51Wg0KCkpQUNDA0wmE0QiEYKCgtDQ0ICGhgaYzWZ4enrCw8MDRUVF7cIHMpkMMTExIISgpqYGjY2N0Gg0IIRAKBTCy8sLfn5+4PF4zMPjqaeewpkzZ/DNN9/Az88PdXV1MBqNEIlECAwMhKenJ7hcLurq6lBYWAiVSsUcz9PTE2FhYRAKhaiurkZNTQ3UajVYLBacnZ3h6OiIpqYmqFQqCIVCREdHw8PDAyaTCXV1daiuroZCoQCLxYJYLIa/vz/c3d2ZDjez2Yz6+npUVVVBoVC0+x4+Pj4QCoXQarUoKipq18Pt6urKZEzc7zlLpVL4+fl1WO/i4oLQ0FC4u7szHVuVlZVoaWmB2WyGQCCAh4cHfH19IRaLodfrUVFRgeLiYmYfYrEYYWFhIITgzp07zHpr6lVoaCjTUVdSUgKdTgeJRILg4GC7NcO1Wi1Onz6NTz75BH5+fvjnP/9J461dyCMhrsAPccdDhw7hvffeQ2hoKP70pz8hKSnJ7rmvtsYqrtu2bcOwYcPsbQ6lC7DGWL/99lv897//RXh4OP70pz8hJiamW3e69TYemeCLQCDA5MmTIZFI8K9//QsvvfQS5s2bhyVLljBVWymUno7BYMDdu3fx9ddfIysrC2PGjMGSJUsQHh5Or/Eu5pHxXK0YDAbcu3cP3333HY4dO4Y+ffpg+fLlGDx4cK8K9F+8eBFbt27FqVOn0NjYiISEBMycORMLFiyAj4+Pvc2jdDIWiwX19fXYt28f9u7dCw6Hg8WLF2P48OG05IudeOTE1UpNTQ3S0tKwbds21NXVYcCAAZg/fz5SUlJ6hcjW1NQgPz+/XfqVj48PIiIiOiT3U3ou1kEghw4dwvfff4+GhgYMGjQIc+fORVxcXLsBGJSu5ZEVV+CHOGxubi6uXLmCo0ePoqmpCYMGDcKMGTMwYMCAdh1DFEp3wmw2Qy6X48SJEzh69Cjkcjn69u2LiRMnIiEhAf7+/vTatTOPtLhaaWtrQ2FhIS5fvoxTp06hqakJ4eHhGDt2LMaOHdtj5n+l9H60Wi3u3LmDEydO4OrVq1AqlYiLi8OECRMQHx+PgICAXz2MmmJbqLjeR2trKwoLC5GVlYVLly6hoKAATk5OGDRoEEaMGMFMMEI9AkpXYjQaUVVVhcuXL+PChQsoKCiAWCxGYmIiBg8ejMjISGZSHkr3gYrrA9BoNKioqEBJSQkuX76MO3fuoLm5GZ6enkhOTsbgwYORnJxMswwoNsNgMKCiogLp6em4evUq8vPzYTAYEBISgoEDByIhIYHJU+7t6YQ9FSquP4PZbEZdXR3Ky8tRXFyMmzdvoqioCI2NjfD09ERiYiL69u3LTNRML3LKw0IIgUqlQl5eHjIzM3H79m0UFBTAaDQiMDAQMTExjKD6+/u3m7WL0j2h4vor0ev1zGQnpaWluHnzJkpLS1FfX8+MsoqPj0dCQgLi4+Ph4uJC47SUn8U6R0NWVhbu3LmDvLw8NDQ0gMfjwc/PD9HR0YiPj4efnx98fHzg6upKr6keBBXXh8BgMKC2thZyuRx1dXUoLi5GcXExampq0NDQAIlEAj8/P0RERCA6OhpRUVEICAhg5hilPHoQQqBWq1FcXIz8/HwUFRUhPz8flZWVMBgMkEqlCAgIQExMDDOzmLe3N31I92CouP5OCCFQKBTMJMvl5eXIyclBeXk5Ghsb0dbWBpFIBC8vL4SHhyMiIgLBwcEICgpiZpui9C6sQlpVVYWysjIUFxejoKAAFRUVaGtrA5vNhru7O9zd3REcHIywsDC4u7vDw8MD7u7utMnfS6Di2skYjUY0NTWhsbGReS0pKUFZWRmzTq/XQywWw93dHYGBge0WPz8/u07qTfltmEwmNDU1oby8vN1SU1OD1tZWmEwmODk5QSaTwdvbGyEhIfD19YWbmxvc3Nwgk8ng5OREf+9eCBVXG2OxWKBQKNDU1ISmpiamSF9lZSVqa2uhUCjQ0tKC5uZmcLlciMViSKVS+Pr6wsvLC56envD19YWfnx88PDzowAY7YLFYoNVq0djYCLlcjqqqKlRXV6O+vh5yuRy1tbVQqVTMbGGurq6QSqXw9/dHUFAQI6RSqRSurq4PPRk3pWdBxdUOGI1GKJVKtLW1MeLa1NSE6upqVFdXo7m5GSqVCgqFgpmKTywWw8XFhWlOymQyyGQyuLm5MX+7ublR8X0IzGYzlEolGhsb0dDQwLQ4rH83NjaipaUFarUaJpMJfD4fYrEYTk5OcHJyYh6G7u7ucHZ2ZuZydXFxgUQioTHTRxQqrt0Ei8UCtVoNhUIBpVLZbmlubkZtbS3q6uqgUqmg0Wig0Wig1Wqh1Wrh4OAAkUgER0dHuLi4wNXVFc7OzswNfv/NbhUEa8HC3irEZrMZOp0OSqUSra2tzIPsx++ti0KhgFqthsFgYM6nUChkzqurqyu8vb0ZAbWKq0QiYZbOKklD6R1Qce0BGAwGRnjVajWzWEW2paWFieeqVCrodDro9foOC4fDAZ/PZxaBQABHR0dGQO5/LxKJwOfzIRQKweVywefzwePxwOPxwOfzmXVcLhcsFoupwnq/WFsLEf54IpwHVU01mUxMUUPre71eD4PBAIPBwPyPdZ1er4dGo2l3Lu5/r9VqodPpYDAYoNVqYTQawWazme/N4/EgEAiY7+jq6gqZTAapVAqJRNLunDg6OkIsFkMikUAoFFJPlPKroOLawyGEMEJz/6LX66HT6dotSqUSCoUCbW1tUKvVjGD9eFGr1WhrawOPx4ODgwMsFgtTPdbBwQEcDgcODg7MYq2Oan1vhcVidVgH/OBV/riWmdlsZgr7WY9nMpmY9dZKshwOB2w2m7HVGi7hcDiMwFurvAqFQkgkEsaLF4lEEAgEHRahUMiIqEAg6BWzolHsD72KejgsFosRiR+Xob4fQgjj8Vk9OutiNBqZ9w0NDVi7di1UKhXmz5+P8PDwdt7k/f+j0+lgMpmYfVvfWzGbzTAYDB2E9EFeroODA7hcLiPYP/aQrUJvFfbS0lLs27cPRqMREydORExMDONZW8XVug+rh0rT3ihdCRXXRwQWi8WEA5ycnB64jVarxYYNG6BSqbBo0SIsWrQInp6ejAj+2KO0vgLtPU8r93u892MV0Pu53/O1vrcKqfX1fpqbmyESibBnzx6UlJTgscceo5NCU7oVVFwpAH7IYNi9ezf27NmD0aNHY86cOfDw8OgQQ2Wz2d1iSjupVIrp06dDo9Hg2LFjEIvFWLp0qd2KAVIoP4aKKwWEEBw5cgRbt25FcnIy5s+fDz8/v27fcePp6Yk5c+ZArVbj5MmTcHR0xIIFC+Di4mJv0ygUKq4U4Pz581i/fj2Cg4OxcOFChISE9Jj4pI+PDxYsWACNRoODBw/C0dERs2fPpqVsKHaHiusjzs2bN7FmzRpIJBI8+eSTiImJ6RbN/l8Li8VCYGAgFi9eDK1Wix07dkAkEmHq1KkQCAT2No/yCNO9230Um1JcXIy1a9fCYDBg8eLFSE5O7pGCxGazERYWhsWLFyMwMBBbt27F6dOnYTKZ7G0a5RGGiusjSm1tLdavX4/S0lLMnz8fw4YN69FNaQcHB8TGxmLZsmWQyWTYsmULLl261CENjELpKqi4PoK0trZi48aNuHr1KmbPno1x48b9ZHpWT4LL5SIxMRFLliwBj8fDN998g2vXrnVIBaNQugIqro8YWq0W27dvx/Hjx5Gamorp06fD1dXV3mZ1Gnw+HwMGDMCCBQug1WqxadMmZGdn29ssyiMIFddHCJPJhO+++w67d+/GqFGjMHfu3A65rL0BkUiE4cOHY968eaivr8emTZtQUFBgb7MojxhUXB8RCCE4fvw4tmzZgsTERCxYsKBH5LI+LE5OThg3bhymT5+OvLw8bN26FRUVFfY2i/IIQVOxHhEuXryIdevWITg4GIsXL+5RuawPi6urK1JTU6FSqXD48GE4Ojpi2bJl8PLysrdplEcAKq6PAFlZWVizZg2EQiEWL16M2NhY8Hg8e5vVJXh4eGDWrFlQqVQ4c+YMHB0d8eSTT/aqODOle0LFtZdTWlqKNWvWQKPRYNWqVT02l/X34OPjg3nz5kGtVuPIkSOQSCSYO3duj049o3R/qLj2Yurq6rB27VoUFhZixYoVGD58+CMpKNZRXE8++SS0Wi127doFoVCIGTNmPHIPGkrX0Tt7Myhoa2vDxo0bkZ6ejjlz5mD8+PG9Ipf1YbGO4lqyZAlCQ0Pxn//8B6dOnepQEYFC6SyouPZCdDod/vOf/+DIkSOYMmVKr8tlfVgcHBwQExODpUuXwtPTE5s3b8aFCxc6zENLoXQGVFx7GWazGfv378e3336LkSNH9tpc1oeFw+GgT58+WLx4MQQCAR3FRbEZVFx7EYQQnDx5Ehs3bkTfvn2xaNEi+Pr69tpc1oeFz+cjJSUFTz75JCwWCzZs2ICsrCx7m0XpZdC7rheRlpaGNWvWwM/PD8uWLUNoaCgttvcTiEQiDB06FPPmzUNzczM2btyI/Px8e5tF6UVQce0l3L17F1988QX4fD6WLVv2SOWyPiwSiQSjR4/GzJkzUVRUhM2bN6OsrMzeZlF6CVRcewFlZWX46quvoFQqsXTp0kcyl/VhcXV1xaRJkzB58mTcunUL27dvR21trb3NovQCaJuxh1NfX49169YhLy8PK1eufGRzWX8P7u7umDlzJtRqNU6fPs1UZfi5UuUUyi9BxbUHo1AosGnTJpw/fx5PPvkkJkyY8Ejnsv4evL29MW/ePGi1Whw+fBgikQhPPPEEPZ+Uh4aKaw9Fr9djx44dOHjwIFJTUzFz5kyay/o7YLFY8Pf3x4IFC6BSqbB79244Ojpi1qxZNMRCeSiouPZAzGYzDh48iO3bt2P48OFYuHAhzWXtBFgsFkJCQphih//9738hkUgwceLEHlW0kdI9oB1aPQxCCE6fPo1169YhPj4eS5cu7dXzsnY1Dg4OiI6OxtKlS+Ht7c2EXegoLspvhd6RPYyrV6/iyy+/hI+PD5566imEhYXRXNZOhsPhMA8uiUSCb775Bunp6XQUF+U3QcW1B5GTk4MvvvgCHA4HK1asQGxsLG2u2gg+n4+kpCQsXrwYALB+/XrcunXLzlZRehJUXHsI5eXl+Oqrr9DY2Ijly5cjJSWFdrTYGKFQiEGDBmH+/PlobW3Fhg0b6Cguyq+GimsPoKmpCd988w3u3LmDxYsXY8SIERCJRPY265FALBZj1KhReOKJJ1BZWYkNGzagtLTU3mZRegBUXLs5KpUK//73v3HmzBnMmTMHkyZNgkQisbdZjxQuLi6YMGECpk6diszMTGzbtg1yudzeZlG6ObQnpBtjMBiwe/du7N69G5MnT8bcuXPh6upKU67sgJubG6ZOnQqlUoljx45BLBZj8eLFkMlk9jaN0k2h4tpNMZvNOHToEDZt2oThw4djyZIlNJfVznh5eWHu3LlQq9XMKK4FCxbQUVyUB0LF1Y6YzWaw2ewOgkkIwblz57BmzRrExsbiqaeeovOydgNYLBb8/PywYMECqNVq7N27FxKJBLNmzYJQKGS2MxqNaG5uhk6nQ2BgoB0tptgTerfakUOHDuHGjRvQ6XTt1mdkZOBf//oXPD09sWrVKoSEhMDBwcFOVlLuxzqKa+nSpYiOjsb27dtx/PhxGAwGAD8MS87MzMTq1auxf/9+WCwWO1tMsRdUXO1Ea2srvvrqK7z77rs4ceIE1Go1ACA3NxerV6+GxWLBqlWraC5rN4TNZiM8PBzLli1DQEAANm7ciHPnzkGlUuHatWv4+OOPsXbtWpw8eRJVVVX2NpdiJ2hYwE5cuXIF+fn5qKqqQlNTE1pbW9G/f3+sWbMGtbW1eOmll5CcnAw+n29vUykPgMPhIDY2FsuWLcNXX32Fr7/+Gjk5OTh79ixTVbaoqAhnz57FkiVL7G0uxQ6wCB3T1+WYzWY89dRT2Lt3L5RKJQAgLi4O/fr1Q35+Pp599lnMmDGDplz1ALRaLU6ePIn3338fFRUVaGhogNlsBiEEIpEIM2bMwFdffQUXFxd7m0rpYmhYwA4UFhbi6tWrUKlUzLp79+5h165dIISgb9++dJBAD0Gr1aKxsRFKpRK1tbUwmUzMHAQajQY5OTm4efOmna2k2AMqrnbg4MGDaGhoaDcRCCEEBoMBxcXF2LRpE/Lz82E0Gu1oJeXnIISgrq4Ou3fvxtq1a5GTk/PAiV0qKipw9OhR+ls+glBx7WJaW1tx7NgxJhxwP4QQtLS0YPPmzfj444+RlZXF9EJTuhe1tbXYsWMHvvjii5/1TJubm5GRkYHCwsIutI7SHaDi2sVcuXIFRUVFPymaFosFWq0Wu3fvxrlz55gsAkr3wmAwoK2tDUaj8YG5ylYIISgrK8Px48fplIWPGFRcuxCz2Yzdu3ejra3tgZ9bb9CQkBDMnz8f0dHRNA2rm+Lr64vFixfjtddew9y5cxEUFAQWi/VAka2rq8OFCxdQX19vB0sp9oJmC3Qh+fn5mDp1KgoLCx/oxXh7e2PIkCEYO3YsJk+eDC8vLzp4oJtjMBhQVlaGkydP4tSpU7h+/foDS3NHR0fj3XffxeOPP24HKyn2gOa5diEHDhxAY2NjB2GVSCRISUnBuHHjMG3aNISGhlKPtYfA4/EQERGBgIAADBkyBEePHsWZM2dw69atdi2U6upqnDhxAhMnTqSlzx8RqLh2Ea2trThy5AiTfsViscDhcNC3b1+MHj0aqampSEpKajdGndJzEAgESExMRHh4OIYNG4YjR47g/PnzyMrKgk6ng0KhQGZmJrKzszFw4EB7m0vpAh4ZcbVYLNBoNFCr1dDr9dDpdO1ezWYzjEYj41Xq9XrmfzkcDtM8Z7PZ4HK54PF44PP5EAgE4PP54PP5EAqFEIvF4HK5HWJv6enpKCoqgslkAovFQmxsLIYOHYoJEyZg1KhRkEgkdMarXoBYLMawYcMQHx+PoUOH4tixY7h8+TJyc3NRUVGBw4cPIyYmBkajEQaDAQaDAWazGQaDASaTCSaTiXlvNpthsVg6dH5a0/bux/qwvn9yH+s6BwcHsNls8Hg8cDgccDgc8Pl8ODg4MNcyl8tlrmVak61z6FVnUaPRoKWlBc3NzWhpaYFCoYBarYZSqYRKpUJbWxsUCgW0Wi00Gg0sFgtzAf8459Q64YZV8KydFYQQsNnsdguPx4NIJIKjoyOcnZ0hkUggFouZVycnJ2zduhVtbW3w9fXF4MGDMXnyZEyYMAFubm50tqseCiEEZrOZeWirVCqo1Wqo1WpoNBoQQhATE4OKigqUl5ejubkZBw4cgIODA/R6PbNYhZYQwlx71vfW5f4Hr/W491831m1+vA74oaItIQQODg7MdfxjJ8F6DYtEIgiFQsZxuH+xXuNisRgikYh5pf0CD6ZHdmhZLBa0tLSgvr4edXV1qK+vR3NzMxobG1FXV4fW1lYYjUbm6W8VUOvFY71YhEIhszg4OEAgEDAX8f3v9Xo9I7bW/ep0Omi1Wmi1Wsb71Wq1UKlUzIVv9RosFgvOnj2LlpYWhIeHY968eUhKSoK3tzc8PT3h4eEBPp9PPdduhtVDVCgUUCgUaGtrYx7Q1ge2SqViPlcqlcy1Yr3mrK9Wj1Sn08FisSAkJIQRNwcHB/B4vHYepNXDZLPZHa4NFosFHo/XQUh/PLuaxWKBXq9nxFiv18NgMMBoNEKn0zGtNetyfyvOKsRsNpvxfB0cHBhbnZycGEdCIpHA0dEREokEzs7OcHZ2Zj53cnJ6ZPsPeoS4ms1m1NbWoqqqClVVVaitrUV1dTVqamqg0WiYC4bP58PV1RVubm7w8PCAm5sbnJ2d4erqCicnJ7i4uMDJyYkR2c4SM6uwqtVqtLW1oaWlBW1tbWhtbUVLSwvu3r0Ls9nM3IjWm4PH48HNzQ1+fn7w9vaGt7c3/P394evrC7FYTD3aLsL62zQ3N6OpqYlp+Vh/S+vfGo2GebhaH9wsFou5nqytFRcXF4jFYjg6OjKfiUQi8Pl8RlxjY2MZ75DD4YDH43XpdyaEMIJv9aA1Gk2HxeqFazSadg8ZjUYDlUoFrVYL4AfvmMPhgMvlgsPhQCQSwc3NDW5ubnBxcYGzszOkUilzf0ql0l4/d0a3Fde2tjaUlJSgoqIClZWVKCoqQm1tLTQaDUwmE5ycnODp6Ql/f3/4+/vD29sbHh4e8PT0hIuLS7fyAmtqaiAWi9HW1sZ421VVVaisrIRcLmcm++Dz+XB2dkZQUBCCgoLg7++PoKAg+Pr6PjCOS/ltEEKg1+vR1NSE+vp6NDY2MoJq/V0UCgXTVLd6cGKxmHlIWwVDKpUyXprVQ5NIJL3eS7OKslVkFQoFWltboVAo0NTUxCzWsJxCoQAhhPHSeTwenJ2d4enpCU9PT0ilUri5uUEmk8HT0xNubm69JubbrcS1sbERxcXFKCkpQV5eHgoKChjP1MPDA0FBQYiIiEBQUBACAgLg7e3NNF96Kq2traiqqkJFRQWKiopQVFSEqqoqGAwG8Hg8eHt7IyoqCmFhYQgJCUFAQEC7kAXlpzEYDGhubmZCR/X19ZDL5aiurkZjYyN0Oh3ToSQUCuHm5gZ3d3d4eXnBy8sLMpmMEVNXV1dayvxXcv9DzLo0NDSgrq6OcSaam5uh1WrB5XKZDmFPT0/4+fnBy8sLHh4ejLPUU/sl7C6uer0eRUVFuHfvHjIzM1FUVITW1lZwuVxER0cjNjYWERERiIiIgEwm6/WiotPpUF5ejoKCAuTm5qKwsBB1dXXgcDiQyWSIjo5GVFQU4uLi4OXl1es9pd+CwWBAS0sLampqUF1djYqKCpSWlqKurg4qlQo6nQ4sFgtSqRS+vr7w8/ODj48PvLy84O3tDXd3dzg6Ovb6a8yeEEKgVqshl8tRW1sLuVwOuVyOyspKpiXB4XAgFArB4/Hg5eWFwMBA+Pj4MOEzDw+PHtGJZjdxbWhowJ07d3Dv3j3cvn0bcrkcjo6OiIqKQmxsLOLj4xETE9PjPdPfS0tLC/Ly8pCZmYnMzEyUlpaCxWIhOjoaSUlJ6NOnD8LDwyEUCh+582Q2m9HS0sKEjqyCKpfLoVQqYTabIRaL4e3tDT8/PwQEBMDf3x8BAQG9qvnZGyCEoLW1FRUVFcxSUlKC6upqNDU1gc/nw8nJCT4+PggPD2fCgUFBQd02dtvl4lpVVYXbt28jPT0dd+7cgcFgQHh4OAYMGIAhQ4YgJCTkJ8doP+q0tbUx5+727dtoa2uDp6cnBgwYgOTkZMTExEAsFvfqc6fX61FfX4/y8nKUlpaioKAAZWVlaG1thcVigYuLC4KCghAWFoaIiAiEhoZCJpNRIe1hWFPSamtrUVBQgIKCAhQWFqKyshItLS0QCoVwdnZGZGQkwsPDERgYiKCgoG5VIbnLxLW2thZXrlzBhQsXkJOTAz6fj0GDBmHEiBFISkqiccTfgHWmpWvXruHixYsoKiqCs7MzBg4ciCFDhiAuLq5XNW+NRiNqa2tRVFSE/Px8pjyOWq2GSCRCSEgIoqOjERkZyYhpT2g2Un491nxfuVzOiO29e/dQWloKo9EIZ2dnBAYGIiYmBhEREQgPD4e7u7tdbba5uCqVSly/fh0nT55EWloaPDw8MGzYMIwfPx7R0dG9RgDsRUtLC65cuYIzZ84gKysLUqkUo0aNwogRIxAaGtpja3BZLBY0NTUxnZvZ2dkoLi6GTqeDm5sbIiMjER8fj/j4ePj5+fXY70l5OKxZC4WFhcjKykJ2djYKCgrQ1NQEV1dXREdHIyEhAeHh4QgPD7fLfA42E1dCCPLy8nD06FFmLsuJEydi1qxZCAwMpKLayajVaqSlpeHQoUPIy8tDUFAQxo4dixEjRsDd3b3H9LYajUZUV1fj7t27uHHjBnJzc9HS0gJXV1ckJCSgf//+iI+P71HfiWJ7CCGoqanBrVu3cPXqVeTk5ECj0cDd3R0JCQno06cP+vbtC09Pzy6zySbiqtFocOHCBezcuRMFBQUYNWoUZs6ciaSkJCqqNqa5uRlnz57FgQMHUFVVhcceewzTpk1DREREt/buNBoNysrKcOfOHVy/fh35+fngcrmIiopCSkoKkpOT4efnR2OnlF+EEILy8nKkp6fj6tWruHv3LgCgf//+GDx4MPr06YOAgACba1Gni2tzczN27dqFbdu2wcPDA0uXLkVqauoj3+vf1RQXF2PHjh04c+YMQkNDMXv2bAwePLjb9axa50O9cuUKLl++jNLSUshkMgwZMgSjRo3q9g8FSvfFGjq4efMmM9eu0WhEREQERo0ahYEDB8Lb29tmx+9Uca2srMT27duxY8cOPPbYY3jmmWcQHh5ORdVOaDQaHD16FNu2bQMhBIsWLcK4cePg7Oxs99/EYrGguroa169fx9mzZ1FUVARvb28MGzYMI0eORFBQEO2UonQahBAUFhbi9OnTOHPmDGprazFs2DCMGzcOKSkpNnE6Ok1cKyoq8Pnnn+P06dN48skn8dxzz9EMgG7CtWvXsHbtWsjlcixYsABTpkyBq6ur3exRqVS4ffs2jhw5gtu3b8PNzQ2TJk3CmDFj4OXlRa8Zis0ghKC6uhq7d+/G8ePHYbFYMHHiREyaNAnR0dGdeqxOEdfGxka8//77OHbsGF555RUsXrz4kQ0DVFdX4+LFi8wMRTNmzOgWnmJubi6++uor5OXl4emnn8bEiRO7PERACEFtbS3Onj2Lffv2QafT4bHHHkNqaipCQkI63VNVKBTIyspiKq9GRUVhwIABtCOMAkIIsrKysGPHDpw9exbR0dFYsmQJhgwZ0nmT6JDfiVarJR988AEJCQkhmzZtIhaLhVgslt+7W7tgMBjIoUOHyMmTJ4ler3+ofWRlZZHnn3+ehIeHEwcHB3Lv3j1iNps72dKHo7CwkCxdupSkpqaSkydPEo1G02XHNpvNJC8vj7z33ntk7NixZMWKFeTSpUtEp9PZ7Ji1tbVkw4YNZPTo0cTFxYU8++yzxGAw2Ox4lJ6FxWIhRqOR7N+/n0ycOJEMHz6cbN++nbS1tXXK/n+3uB47doz4+PiQ999/n5hMph4rrIQQolQqSb9+/cj48eNJa2vr79rXihUriEgk+tXieujQIfL9998ThULxu477S9y8eZNMmTKFLF26tMuE32KxkLt375IXX3yRjBkzhvzzn/8kxcXFXXatHDx4kMTFxVFxpTwQi8VCcnJyyMqVK0lMTAz58ssvO0Vgf1f7qL6+Hu+99x4GDBiAl1566Wfrt/cEOBwOJk2ahNGjR3f5hCh/+9vf8M4776ChocGmx+nXrx+WLVuG/Px8nD179ifLfHcmpaWl2LBhA3Jzc7FgwQKsWrWKGeZModgbFouFqKgofPTRR5g+fTr+9a9/YefOnR1K6fxWflfS4Pfff4/8/Hx88sknvaLzSiAQ4O9//7u9zbA548aNw4ULF3Ds2DH0798fycnJNotDtrS0YMeOHbh37x7mzZuHmTNnwtnZ2SbHolAeFhaLBScnJ7z55pswGAz44IMPEBoaijFjxjz0Ph9aXPV6PTZt2oRJkyYhJSXFJsJ68eJFVFVVMbWAZDIZxo4dCzabjdraWpw7dw5DhgyBv78/WCwWbt26hfz8fKYky+zZs8HlcqHX65GTk4PKykpoNBpwuVzIZDLExcVBKpWCzWajuroaly9fhslkAvBDR9T9M00plUpkZ2ejpqaGmaw7JiYG5eXlkMvlMJvNCAwMREpKSru8TL1ejxs3bqCqqgpmsxlSqRR9+vRh5qgsLi5GZmYmmpqaQAjBwYMHmcknJk6caJNefZFIhNmzZ+PVV19FVlYWM+FLZ0MIQVpaGi5duoQRI0Zg8uTJdhVWs9mM8vJyFBcXQ6FQQCgUIjg4GKGhoe3majWbzWhoaEBhYSHq6+thNBohEong6+uL0NBQuLi4QKPRIC8vD7m5uQAAb29vhIeHIz8/H01NTTCZTPD29sbQoUOh0WiQn58PuVwOvV4PgUAAb29vBAcHt5tG01q+KC8vD3V1dTAYDBAKhfD19UVERAScnJyg1Wpx79495Ofng81mQyaTISoqCjk5OWhtbYWDgwO8vLwQExMDqVTKfCeLxQKVSoXc3FzI5XJotVrw+Xx4eHggKioKMpkMWq0W6enpkMvlAH4oxunr64vhw4fDbDajqqoK169fx9ChQ+Ht7Q2j0Yhbt26hqKgIAMDn8zFz5kywWCxoNBrcu3cP1dXV0Ol0zHytMTExcHV1hcViQUVFBdLS0gD8UMlgwoQJyMrKYr67g4MDZsyY0WV5ziwWC0KhEG+88Qby8vLw97//Hf369Xvoe/ChxbWoqAi3b9/GP/7xj4fdxS9y69Yt7NixAxkZGRg4cCDGjh2LMWPGgM1m49ixY1i5ciU+/PBDvPjii+BwOMjLy8PGjRuRl5eH4cOHY8aMGTCbzThw4AD27NnDlFchhECpVGLQoEFYsmQJvL290dDQgJMnT+Ls2bNMQTlfX18AP6QO7dixA9999x24XC5cXV3h6uqK7OxsHD58GBkZGZg4cSJGjx6NhISEdhfDlStX0NrairKyMjQ1NaG2thZz587FkiVL4OzsjKqqKpw5cwZtbW0ghOD8+fNMiZchQ4bYLGUqMTERISEhuHXrFkaNGmUTcW1ra8OxY8cglUoxceLELh16+CAqKytx5swZRugaGxvh7++PFStWYNCgQQDAjFfft28fbt26xdSxMhqNEAqFGDlyJCZPngwej4e8vDxs3boVN2/eREhICJYuXYqSkhLI5XLk5ubCw8MDISEhuHz5Mg4fPszsixDClFVfuHAh3NzcYDabUV1djT179uDixYtMpVaTyQQHBweMGzcOM2bMAADk5ORg9+7duHLlCvz9/ZnjWsvRmEwmpKamYv78+XBzc4PFYkFjYyP27duHkydPMrXdrPXl+vfvjyeeeAIuLi5IS0vDnj17kJ+fjyFDhiA1NRXDhw+HTqfD7t278Y9//AOfffYZli9fDpPJhMzMTGzbtg2VlZWYNGkSpk+fDpVKhT179uDQoUNMXTCLxQKtVouRI0di0aJFcHV1RU1NDQ4dOoRLly6hqakJ69atQ0FBAWpra1FZWYm0tDQ89thjXTqIhMViwcXFBW+99RYmTJiAM2fO4PHHH3+4nT1ssHb9+vVEKpUSjUZj046JI0eOEC6XS/72t78Rk8lECCHEaDSSiRMnEicnJzJ48GCiUqkYG7744guyYsUKphf65MmTxNfXl4wdO5akp6cTlUpFqqqqyF//+lfi5OREPvzwQ6LVapnjLVy4kPB4PFJZWcns89ChQ8Tb25uMHz+epKWlEaVSSaqrq8kHH3xA/P39iUAgIBUVFe3stnZoLVu2jGRkZBClUkny8/PJqFGjSGhoKMnJyWnXmZScnEz69etHiouLbXYuf8yHH35IpkyZQq5du2aT/V+5coVMnDiRrF69mqhUKpsc49dg7dBKTEwkmzdvJjU1NaS2tpZ88sknJCIigrz22mtMR1dlZSV5/fXXSXR0NHnjjTdIUVERaW1tJWlpaWTRokUkJiaGrF+/nskmuXz5MklJSSEhISHk448/JmVlZaSlpYUcOnSIfPDBB+T8+fNkxIgRZMaMGaS4uJhoNBpSWFhI3nzzTTJ06FBy+/ZtQgghjY2N5LPPPiNBQUHkmWeeIXl5eUShUJCMjAyyePFiEhERQXbv3s18p+zsbBIREUF8fX3Ju+++S0pLS4lCoSDp6elk9uzZxNfXl/z73/8mhPzQUbtp0ybi7+9PFi1aRLKzs5nr8fXXXyceHh7kzTffJDqdjqjVarJ582bi6upKvvjiC0LIDx0+tbW1ZPjw4cTV1ZVMnTqVOV8qlYp89NFH5OWXXyaEEKLX68nu3buJl5cXmTlzJrlz5w5Rq9WkqKiI/PGPfyTOzs5k3bp1zPfQaDRk8uTJhMPhkBdeeIHk5OQQrVZLcnJyyJIlS2zewfsgrBlPkyZNIlOmTHno/Ty0uL722mukX79+Nu/xVSqVJDQ0lCQkJBCFQkEsFgspLCwk0dHRZPz48UQgEJBbt24Rs9lM9Ho9GT16NDl27BgjxKmpqcTBwYEcOXKEGI1GZr+tra0kNDSUBAUFkZqaGuZ7PEhcp0yZQrhcLjl48GC7FC2FQkESEhJ+Vlx3797dTrzfeOMN4uTkRM6ePduu59oe4vrtt9+SUaNGkXPnztlk/9u3bydTpkwhBw4csMn+fy1WcZ09e3a783v27FkyePBgMn/+fNLU1EQIIeTo0aMkOjqaTJgwgeTm5jLbms1m8v3335Pg4GCSmppKSktLCSH/X1xHjRpF8vPzOxz78uXLJCkpicyePZtkZ2eT1tZWYrFYSF5eHiPGhBBy+/Zt0q9fP9KnTx9y69Yt5v+NRiM5c+YM8fLyItOmTWOuJau4JiQkkOrqamZ7vV5PTp48ScRiMRkzZgzRarWkrKyMDBo0iPj7+5Pr168z21osFlJSUkJiY2NJUFAQkz2Sn59PgoODyfjx44lWqyVGo5Gkp6eTyMhIMmrUKOLj40Py8/OJxWIhcrmcjBs3jly8eJEQQkhLSwsZPnw4cXJyIpcuXWp3/srLy4mHhweJj48narWaEPL/xVUoFJKMjIyH+4FtgMViIatXrybu7u4PnVHz0GGBpqYmuLm5Pey//2rEYjHTg3fnzh0MHjwY+/fvx8iRIzF69GicO3cO+/btQ1xcHDODkjVR3Gw249q1a7BYLCgvL8eePXva7ZvH4yE/Px/l5eU/WTrCbDYjMzMTFosFffr0aZdFIJFIEBAQgOLi4p+0PzQ0tN3/ODs7w8HBAW1tbTCbzXYt0+Lk5MTUkbIFSqUSAoHALtO9PQhXV9d2oQmrbXq9Hmq1Gq6urpDL5SgpKUH//v0REhLCbMtms5k6bnV1dSgqKkJQUBDzuUQiYcJI9+Pv74/Jkyfj0qVL+PLLLxEdHY2goCD4+Phg8eLFcHd3Z2bhv3fvHoKCgpCVlYX8/HxmH+Xl5WCxWCgtLYVcLkdwcDBjk4uLC3x8fJhteTwe/P394ezsjIqKCjQ2NjIj4oKCghAfH89sy2Kx4OjoiNjYWJw9exZ37txBTEwMZDIZRo0ahWPHjiE3NxehoaE4ffo0JkyYgLi4OPzxj3/EoUOH8OKLLyIvLw8ajQYpKSlM7ayMjAw4ODigsLAQVVVVzPF0Oh0EAgFKS0tRWVmJyMjIdraEh4c/5C9rG3x9fdHU1ASz2fxQHb4PLa5CoRBqtfph//03MXv2bKxduxY7d+5EYmIivv/+e7zzzjvo168ffH198f333+OVV17Bzp07MXnyZGaiaGu9eAA4efJkhwqq1vlAORwO02n2Y6z7YLPZD5yR6ZdmafpxFoX1/U8dryvR6/XgcDg2m2mKz+fDaDTaTLx/Kw8qYX3/b0MIgdlshsVigYODQ4eHLZvNBpfLhdlshtFofOBnP8bf3x8vvvgiEhMTcfXqVaSlpeHo0aOQSCQYOnQoHn/8cfj6+jLxz+bmZhw5cqRDB/GwYcPg6+vb7rphsVhwcHAAIaTDNWbtWzAYDLBYLEwl2x//1iwWC3w+nxFGAHB0dMSUKVNw4MAB7N+/HytWrMDFixfxzjvvQCaTwdnZGUeOHMGTTz6JI0eOIDU1FQKBgKkeYDKZYLFYcPTo0Q6iNHDgQPD5fKbT+cd2dCfa2togEokeOpPmoe+qwMBA7Nu3DyaTCRwOx6ZpWElJSYiOjsaRI0cwdepUqFQq9O/fH2KxGJMnT8b69euRlpaGkydPYt26dcwFxOVyERYWhtu3b+Pjjz9GSEhIuxNVUFCA4uJixMfH/+TQSy6Xi8DAQNTX16OyshJeXl7M/nU6HWpqamA2m3/3d7R2MFhvnrS0NEilUoSEhNjsoqupqYFEIoFQKLTJ/v38/KDX6yGXy5lqtt0ZNpsNV1dXyGQytLa2oqGhAV5eXgB+EF6FQoG6ujq4ubm18xZ/jrq6OhQWFmLcuHGYNm0a2trakJGRge3bt+Pzzz+HSCTC008/DYlEwpSIX7duXbuefq1Wi/z8fKjV6nbestlsZspaW7MwrOtaW1sRFhYGmUwGi8WCgIAAaDQaVFZWMp6vVVDLysogEAgYT53H4yEpKQm+vr44fPgw+vbtC5PJhOTkZGg0GowaNQpHjx7F5cuXkZ6ejvXr1wP4/wIZGhqKhoYGrFmzBh4eHu3szc/P7+C1djes9+C1a9cQGxv70MOyHzq5MTExkUlXsTUcDgdz585FZWUlPvjgA0ydOpVJk5ozZw64XC4+/fRTCAQCxMXFtRP6xYsXQyKRYPfu3aiqqoJer4fBYEBpaSk+/fRTfP3118w8AD/F7Nmz4eTkhN27d6OiogIGgwEKhQIXLlxAXV1dh6fww2BNhVGr1WhubsZf/vIXnDp1Clqt9nfv+0EYjUbk5eUxnogtsKYDZWZmorKy0ibH6GwiIyMxatQolJeX48SJE2huboZOp0NFRQWT+dG/f3+EhYX9qv1lZmbitddeQ1paGtRqNcRiMfr164fBgwdDr9ejsbERwA+pXFOmTEFraysOHz6MxsZGGAwGppLHxx9/jL1793Zo8bS2tuLUqVNoaWmBXq9HRUUFjh8/DhaLhbFjx8LJyQmurq6YPXs2DAYDdu3axaQ6NTU1IS0tDQUFBejfvz/69u0L4P/nfE6aNAk5OTnYtGkTUlNTwePxIBQKMWPGDGi1Wnz11VdwcXFpN+GJQCDAwoULwWaz2x1Lp9MhPz8fH3zwATZt2tQtWm4/R2VlJU6cOIFZs2Y99D4e2nPt378/fHx8sGvXLrzzzjsPbcCvZdq0aXjvvfdw9epVfPrpp4wHOmDAAISHh+P8+fP47LPPOnjRCxcuRG5uLk6cOAGTyYSgoCBwuVwm1vp///d/cHR0RG1tLa5du4aKigqmSRMfH4/k5GQsXLgQ2dnZOHfuHIRCIWJiYmCxWJCdnQ1XV9d2o6qsZVdKS0thMplw6tQp6HQ6xMbG4ty5c8jOzobRaER6ejokEgkGDx4MkUiE0aNHo6KiAkeOHIGjoyMcHByQmJgIkUhkk/NZXl6Oe/fuYdKkSTZLkbLmSO7ZswcXLlyAp6enTVK+fgqrx3flyhW0tbWhpKQE586dQ3x8PDQaDc6dO4e6ujooFAqcP38egwYNQkREBBYvXox169bh4MGDaGlpgYuLC4qLi3Hjxg1MmjQJ8+fPB4fDQW5uLs6fP4/m5mawWCzs378fAQEBTFoXALi5ucHNzQ07d+5EVVUVRCIR6uvrkZ6ejiFDhmDYsGEAfhDXxYsXo7GxEXv27GG8Zr1ej+zsbPB4PCxZsqSdF2UNCdy+fRtKpRJcLhd5eXk4deoUJk2ahKVLlwIAXFxcsGTJElRWVjLC6+Pjg/r6epw/fx4DBw7EK6+8AicnJ2bfIpEIEydOxFdffYV79+7hs88+A/CDV2u99zMyMvDFF1+0aw0KBAIsXboUxcXF2L9/P9RqNfz8/AD8kEJmdRxYLBbq6+tx+vRpyOVymEwm7N+/H0KhENOnT7fZNfFLEEKg0+mwdu1acDgczJkz56H39btmxXr99ddx8OBBnDx5Er6+vjYfofXSSy9Bp9Phq6++aieia9euxZkzZ/Dhhx8iODi4Q4xEpVLhwIEDuHr1KhoaGiAUChEUFITU1FT07dsXHA4HN2/exGeffdbOUxw0aBCee+45iEQi1NbWYteuXbh9+zb0ej38/f0xZ84cvP3228jIyMDt27fh5+eHkpIS/POf/0RLSwuznwkTJmDevHl47bXXmARtAIiNjcVLL70EmUyGuro6/Pe//8WdO3fA5XIxduxYTJw4sd0F31kYDAasWbMGBw4cwF//+leMHDnSZiO0Kioq8N5776GpqQlPP/00hg4d2i5h35Y0Nzdj//79OHLkCLMuLCwMs2fPRlNTE7755htmvUwmw/Tp0zFp0iTGq7d26Gi1WkilUqSkpGDMmDEICAiAQqHAyZMn8e2337Y75oABA/DnP/+Z+duaTH/hwgUUFRVBoVDA0dER4eHhGDNmDPr168dcxyaTCZWVlTh06BCys7OhVCrh4uKCuLg4TJgwoZ23fPfuXcydOxdeXl747LPPsHv3bpSWloLL5aJPnz6YMWNGuw45s9mM2tpaHDx4EJmZmWhra2M6s1JTUx/YTLcKoVgsxgcffMDYqVarsW7dOmRlZeH999/vECIhhDDn/saNG2huboZEIkFYWBimTJmCuLg4mM1m3Lt3r4NjJhAIOpzTroIQAqPRiO+//x5/+MMf8Oqrr+Kll176XTt8aPLy8khQUBB56623iMFg6NGTtvwcWq2WnDhxokNOr1qtJklJSaRv375ELpfb0cLfxq1bt8iYMWPIn//85w4pZLbg4sWLZMaMGeTJJ58kFy5cYNJwKA9PdnY2iYmJIePHj7e3Kb0Ci8VCtFotOXToEImNjSXLli373bnZv8tdiYyMxPPPP48tW7bg/PnzTPnb3oZCocDKlStx4cIFKJVKmEwmtLa2Mk2aiRMn2qz53tnU1NRg48aN4PF4mDZt2q/umPk9DBkyBE899RSam5uxfv16nD17Fs3Nzb3yWqH0PAghaGhowL59+/Dmm2+iT58++Oc///m7Uwh/dw7OU089hcuXL+P111/HunXrkJSU1CE1pKfD4/EQGxuLrVu3ora2Fo6Ojqivr8eRI0eQnJyMRYsWdZtczp+jubkZ27dvx507d7Bs2TJER0d3SSkVNpuN8ePHg8vlYuPGjfj6669RVVWFiRMnwtfXlxYd/A3odDoUFhbi/PnzUCqVqK+vx/HjxxEREdEuDED5ZazOYEFBAfbs2YOdO3di0KBB+Pvf/85kifweOqUSQXFxMZYuXQqhUIj3338fffv2BYvF6lUCe+/ePRw/fpypjS4UChEbG4vZs2f3iHpPDQ0N2L9/P7Zu3YrU1FQsXbq0Uy6g38rdu3exZcsWZr6I1NRUxMbGwtXVlVYI+BU0Nzdj7969OHToELPOxcUFc+fOxeTJk+1oWc+CEIK6ujpkZWVh27ZtuHv3LhYsWMD0sXQGnVZD6/r16/jDH/4AR0dH/OUvf8HAgQNtnv9K+XVUVVXhu+++w759+zBkyBA8++yzCAgIsJs9DQ0N2Lt3L/bv3w8ul4sxY8Zg5MiRCAsLg1gsptcMxWaQ/+Ur5+Tk4OjRozh58iTc3NzwwgsvYOLEiZ16rE6t/nrjxg288cYbUCgUeOuttzB48GC4urrSm8VOGI1GFBYW4r///S/OnTuHcePGYcWKFfDx8bH7b2KdUem7777D9evX4e3tzfSeBwUFwdnZmXqylE6D/C+Dobi4GNeuXcPevXthNBoxdepULFmyxCbpiJ0qrsAPUxFa50N88sknkZqaioiIiF4XJujuNDc34+bNm9i6dSvKy8sxd+5czJ07F+7u7vY2rR06nQ5Xr17Fzp07cffuXXh4eGDEiBEYMGAAQkJCIJVKaUyW8lCQ/w3HlcvlKC0tRXp6Os6dO4empiYMGjQIK1euRGxsrM2O3+niCvyQSP+vf/0L33//PaKiorB48WLExsZ2SS7so45Wq0VZWRlOnjyJvXv3QiqVYsWKFRgxYkSXJvD/VlpbW5Geno4jR47g7t27kEgkGDhwIBITExEYGAgfHx/qzVJ+FYQQtLW1obq6GqWlpTh//jzS0tLAZrMxYsQIzJgxA8nJyTbXIpuIq5UzZ87g008/RU1NDcaMGYPZs2cjKiqqW5Sa7m0YDAZUVVXhxo0bOHLkCIqLi9G/f3+sXLkSYWFhPcb702q1uHv3Lo4cOcIMGQ0MDGSE1t/fH+7u7r9rQg1K74P8bwL82tpa1NTU4M6dO8jIyEBhYSEkEgmGDRuGuXPnIioqqstssqm4Aj8UMdyyZQsOHjwIDoeDmTNnYvDgwR1KXFAeDp1Oh6qqKuTm5uLo0aPIzMxESEgIFi5ciGHDhnVrb/XnMJlMKCsrw6VLl3Dp0iUUFBRAIBAgOjoaiYmJCA8Ph6enJ9zd3SGRSHrMw4PSOVib/M3NzWhoaEB9fT1ycnJw48YN5ObmwsHBAX369MFjjz2GkSNHtpsIp6uwubhayc3NxYYNG3D58mVIJBKMGjUKo0aNQkBAADPTFBXaX4e12VNTU4O7d+/i1KlTTLxy5syZSE1NhVQq7TXnU6fTIS8vD+fOnUN6ejqqqqogEAiY+Umjo6Ph7e0NNzc3uLq6QigUUq+2l2GVKZVKhebmZqZk0u3bt5GTk4PS0lIAQHBwMEaOHIkxY8bYPe+3y8TVSmZmJnbv3o3z58/DwcEBKSkpGDlyJIKDg+Hr68vUjOotwtCZ6HQ61NXVobq6Gjdv3kR6ejqKi4sREBCA1NRUTJw4ETKZrFcLi0qlQkFBAa5du4aMjAwUFRXBbDbD29sbISEhiIqKQnBwMNzc3CCVSuHi4kJDCD0Qa4K/SqVCS0sLmpub0dzcjMLCQuTk5KCiogKVlZUQi8WIjY3FgAEDMHDgQISHh3eb37rLxdVKUVER9u7di/Pnz6Ourg6hoaFMXM3d3R2enp5M2OBRFlqdTofm5mZmXtArV67g1q1bMBgMiI+Px8SJEzFq1Ci4uLg8cufJaDSioqICN27cwM2bN5Gfn4/6+npwOBz4+PggKCgI4eHhCAwMhKurK5ycnCCRSODk5ES9224G+d/csgqFAgqFAkqlEk1NTSgqKsK9e/dQXl6OmpoasNls+Pj4oE+fPkhJSUH//v3tXvjyp7CbuFpRq9W4cOECTp06hWvXrkGv1yMgIACJiYkYOHAgvLy8IJVK4erqCpFI1OsFxGg0QqFQME2f8vJy3LlzBzk5OZDL5fDy8sKgQYMwfvx4REdHd9kMUz0BrVaLiooK3LlzB1lZWcjNzUV1dTUIIXBzc4OnpydTHjsgIAAuLi4Qi8VwdHRklh9Xq6B0LuR/1RHUajXUajVUKhXUajUUCgVqa2tRXFwMuVyOuro6VFVVwcHBAe7u7oiOjka/fv0QGxuLyMjIHhFjt7u43k9ZWRkuX76My5cv4+7du9BoNAgODkZwcDDi4+OZvEdnZ2dIJBJIJJIe79nqdDrmad3a2ora2lrk5OSgsLAQxcXFUKlU8Pb2Rv/+/TFkyBAkJSXRUUy/EqvY5ufnIz8/HwUFBSgvL0drayvYbDZkMhlkMhk8PDzg4+PDZCKIxWKIRCKIRCIIhUIIhULw+Xy71jvrSRBCYDKZoNPpoNVqodVqodFooNFooFQq0djYiLKyMtTU1KChoQFNTU2oq6uDg4MDXF1dERYWhoiICERGRiIyMhKBgYE9spXRrcT1fuRyOdLS0nDjxg1kZWUxzb3AwED4+fkhNDQUoaGhkEqlkEgkjOchEonA5/O7lfio1WpwuVymEJ71IlMqlaiqqkJRUREqKipQXl6OxsZGiEQihIWFoW/fvhg4cCDi4+OZyguUh8cawysvL0dBQQGKiopQVlaGqqoqNDU1QafTgcvlQiaTMTFbNzc3eHl5wdPTEy4uLhAKhRAIBBAKheDxeODz+eDz+eDxeODxeN1+jonfi7WX3mAwQK/XM4u12oBVTK2OglwuR1NTE1paWtDY2IjGxkaYTCaIxWJIpVIEBgYy4Rvr5DO95SHWbcX1fkwmE/Ly8nD79m3cuXMHeXl5aGxshFarhUwmg7e3Nzw9PeHl5cXUIRKJRBAIBODz+RAIBODxeMwrh8NhfsDOECyTyQSTyQSj0djugrPGkAoKCuDo6IjGxkZUVlaioaGBycczm82QSCQICgpCbGwsEhMTkZCQAKlU2utv1O6C0WhEc3MzysrKUF5ejvLyclRUVKCmpgbNzc1Qq9UwGAzgcrlwdXWFq6sr03pydnZmRNjV1RWOjo6M2HK5XOZau//VikQiAZvNfmAxRFtjFUlrQUGz2cxcw9bXH783GAxMk76pqQlNTU1obm5GW1sblEol2tra0NLSgtbWVhiNRggEAohEIri7u8PX1xf+/v4ICgpCUFAQAgICIJPJuvQ7dzU9Qlx/jMViQVVVFQoKCto195qamqBWq0EIYXqKnZycmI4Md3d3pmNDLBYzQsvhcJiL3Cq297+/v3Cg9b1VUE0mE+OJtra2Mk9phUIBuVyOrKwsVFdXw2QyITw8HP7+/vD390dISAjT9AkPD6c92t0QQgg0Gg3q6+shl8vbLXV1dWhqakJbWxt0Oh30ej1MJhO4XC7TkrKGFH68WK+r8PDwdg7A/UJrfQ/8MGXj/dcGi8UCm81+YOXa+7GKp7Xn3Vph1iqoVo9TpVIxjoBSqYRGo2E80Pub9Wq1GkqlEkajsZ3XLpVKIZVK4enpCW9vb3h7e8PHxwfe3t7w8vLq9oUpbUWPFNefoq2tDZWVlUyaRl1dHZNg3NTUBI1GA4PBwDyRrd6steeYz+czFzGPx2MuYJ1Ox4irtVSxtQmk0+mYsttcLpcR7Pr6etTV1aGlpQUWiwUsFguvvvoqXnnlFXh4eNAmfi/BYDAwqUKNjY3Mq7Uqq1WwFAoFGhsbUVhYiKqqKgBgYozu7u4PvIasnWsODg7tmsosFgtcLpcRX6sXqtfr25Vuv78E+P0tK7PZDIPB0E7MrWW3uVwuHB0dmawK66uTkxPTsWx1Utzd3eHi4tIjOpfsQa8S15/DYrEwN4E13qlSqZhXk8nEeL3AD3FSa1VXgUDADHKwdmw4OjpCLBZDLBZDIpFALBbD2dkZ7u7uyMnJwaJFi5CXl9euMqxMJsOGDRswfvx4m5WzpnRPzGYzcnNz8f777zM1ohwdHTFmzBg888wz4HK5TNzS6gTc/yC/3ys1m83Q6XTtri02m90hvYzNZjOTuHO5XMZz5vF4TN8En8+HWCwGn89nOol7S8zT3jwyjxw2m81U4rQ1Pj4+CAwMRFFREQwGA7O+sbER7733Hvz9/ZGQkECf+I8IhBDU19dj79692Lt3L7PewcEBnp6e6N+/v12GZ1JsCw3y2QAvLy+89dZbDxwtkpGRgXXr1qG6urqd50HpvahUKpw6dQrr16+HXq8HAPD5fAwePBgvvPACFdZeChVXGzF48GCsXLkSnp6eHeKrW7duxYEDB6BUKmmRvl6OXq/HtWvX8NVXXzFl1R0cHBATE4NnnnkG8fHxdraQYiuouNqQlStXYvLkyR2KFxqNRnz88ce4du1au7ABpXdhNptRWFiIrVu3IiMjA8APnVE+Pj544oknaM2rXg4VVxsiEAjw+uuvo1+/fh06Caqrq/Hxxx+joKAAJpPJThZSbIW1AN53332H7777jlnv5OSESZMm4amnnqJ5zL0cKq42JiQkBK+++iqCg4M7xF9Pnz6NzZs3o76+nsZfexlKpRLHjh3DunXrmDirQCDAsGHD8NJLL9E46yMAFdcuIDU1FU8++eQDb6hvvvkGJ06caJcGRunZ6PV6pKenY+3ataivrwfwQ5w1Li4OTz/9NKKjo+1sIaUroOLaBbBYLDz//PMYM2ZMh/xWjUaDDz/8EJmZmUzCN6XnYh2qvXnzZty8eRPAD7+/n58f5s+f3+nlmyndFyquXYSzszP+8pe/IC4urkN+a35+Pj7//HOUlpZ2GMJI6TncH2fdv38/s97Z2RmTJ0/G0qVLaZz1EYKKaxcSGxuLF198EX5+fh3Ss/bv34+dO3eiubmZhgd6KEqlEsePH8eGDRuYVog1zvr888/DxcXFvgZSuhQqrl3MvHnz8Pjjj3e40Qgh+Ne//oXz589Dq9VSge1h6PV6XLlypUOctU+fPli1ahWNsz6CUHHtYhwcHPDnP/8ZQ4cOBZ/Pb/dZS0sL3nvvPdy9e5emZ/UgzGYzcnJysGnTpnZxVn9/fzzxxBOYMGGCnS2k2AMqrnZAJpPh9ddfR0RERIcYXGZmJtavX4+qqiqantUDIIRALpfju+++w8GDB5n1Li4uSE1NxbJly+hUko8o9Fe3E4MGDcLKlSvh5eXVIf66bds2HDhwAK2trTQ80M2x5rNu3LixXZx1xIgReP755+Hs7GxnCyn2goqrHVmxYgUmTZoEsVjcbr3JZMJHH32EK1euMAnolO6HXq/H5cuXsW7dOjQ0NAD4IeyTkJCAp59+GpGRkXa2kGJPqLjaET6fjzfffBPJyckdhsfW1tbis88+Q15eHo2/dkPMZjPu3buHzZs349atWwDax1nHjRtnZwsp9oaKq50JDAzEq6++itDQ0A6xuXPnzmHLli2ora2l8dduxM/FWadOnYqlS5fSOCuFimt3YNKkSVi0aBHc3d07fLZhwwacOHECKpWKxl+7CUqlEkeOHMGmTZuYVoVAIMDIkSPx7LPP0jgrBQAV127Diy++iMceewwikajdeq1Wi/fffx83b96kw2O7AXq9HhcvXsT69evbxVkTExOxatUqGmelMFBx7SaIxWK88cYb6NOnT4fhscXFxfjiiy9QVFREh8faEbPZjOzsbGzatAm3b98G8EOcNSAgAPPmzcNjjz1mZwsp3Qkqrt2ImJgYvPjiiwgMDOwQszt48CB27dqFhoYGGh6wA4QQ1NTUYM+ePTh8+DCz3hpnXbx4MY2zUtpBr4Zuxty5czFnzhy4urq2W28dHnv27FloNBoqsF2MQqHA4cOHsXnz5nZx1lGjRuHZZ5+Fk5OTnS2kdDeouHYz2Gw2/vjHP2Lo0KEQCATtPmtra8NHH32ErKwsmp7Vhej1epw/fx5ff/11uzhrv379sGrVKkRERNjZQkp3hNZ27obIZDK89dZbqKysxJ07d9rFWe/cuYO1a9fiL3/5ywPTt34rWq0WjY2NUKvV8PX1hUQi+cltCSFQqVRobW1lJpfhcrlwcnKCs7MzOBxOh9Fm92OxWKDRaKBQKKDRaGAymcBms8Hj8eDs7AyJRNIu3kwIgdFohEqlglKphE6nAyEEHA4HYrEYLi4u4PP5P3vM34vJZEJmZiY2b96Mu3fvAvghzhoUFIR58+ZhzJgxHbZvbW1Fc3MzhEIh/P39O+yTEAKDwQCFQgGlUsnUUePxeHB0dISrqyt4PN5vstNsNkOhUKCxsRFsNhuhoaE/u73FYoFCoUBDQwNYLBbCwsJ+0/EovwwV125KcnIyVqxYgffffx+VlZXtwgDffvst4uLisGzZMri5uT2UuBiNRrS0tOD69ev48ssvceXKFXz77beYMmXKA7e3WCyoq6vD8ePHcebMGZSUlMBoNMLJyQkDBgzAvHnzEBMT87PzlTY0NOD06dM4e/YsioqKoFAowOPxIJVKMWzYMEybNg3h4eGMsFg7kI4dO4Zbt26huroaBoMBIpEI0dHRePzxxzF06NAOI9w6C0IIqqqqsGvXLhw5coRZL5VKMXPmTCxZsoR5uFksFqjVahQWFuK7777Dli1bMHDgQBw4cKDDfjUaDTIyMnDo0CHcvXuX8YZdXV0RERGBuXPnon///h0yRx6ExWKBTqdDWVkZvv/+e3z11VeQyWTIzMz8ye+k1WpRWVmJI0eO4PPPP4dAIEBBQcFvP0GUn4dQui16vZ48++yzxNnZmQBot3h6epKDBw8SjUbzUPvOzc0lf/rTn8j06dNJSEgI4fF45Pvvv//J7evq6siLL75IxowZQzZv3kzkcjlpaGgg69atI1FRUeQf//gHaW5u/tljbt++nSQnJ5MVK1aQjIwM0tbWRkpKSsg777xDAgMDyezZs0lmZma7Y65cuZIkJiaSjRs3ktraWtLU1ER2795NhgwZQhITE8m+ffuIxWJ5qHPwc1gsFtLS0kL+9a9/EalUypx3gUBA5syZQ4qLi9tt39jYSLZv305mzZpFBg8eTBwdHcm0adMeuO+7d++SKVOmkIEDB5LDhw+TpqYm0tDQQHbt2kWSkpJIZGQkOXjw4K+yU6VSkYMHD5LHH3+cjB49mkgkEpKQkPCT22s0GnL06FHy+OOPk7FjxxInJycSHh7+a08L5TdAY67dGB6Ph9deew0pKSkdmol1dXX49NNPkZOT81Dx14sXL8LLywurV6/G4MGDO6R/3Y/ZbMZ///tfXLx4EUuXLsX8+fPh5eUFmUyGpUuXYurUqXB3d//FEIWTkxMmTJiA5cuXIzk5GU5OTggODsaqVaswatQonD59GlevXmW8dA6HA29vbyxZsgQLFiyAp6cn4zVOmzYNxcXFuHnzpk3izwaDAefOncM333yD5uZmxp6UlBQ8/fTTCAkJabd9VVUVCgsLsWLFCrz11lsd4uX3w+Px0KdPHzz99NOYPHkypFIpZDIZUlNT8fLLL6O8vBzbt2//VZ2WLS0tuHr1Kp544gmsXr26QxmhH6NUKnHhwgXMmjULX3/99S9uT/kd2FvdKb/MiRMnSGxsLGGz2R082Oeff56UlZURs9n8m/ap1+uJyWQihBCyaNEiIhKJftJzlcvlZNKkSSQ1NZXcunXrd3+fH2OxWMirr75KBAIB+fTTT3/REzWbzeSrr74i3t7e5PXXXyc6na5T7TEajSQ9PZ2kpqYy55nFYpHw8HCydu3aB55rk8lE9Ho9IYSQ48ePEzc3t5/0XH8Ki8VCLly4QPh8PhkzZsyv8sjNZjPz/e/evUs8PDx+1nO9f/uSkhLi6elJPVcbQT3XHsC4ceOwZMmSB05PuGHDBhw9ehQKheI3pWfxeLxfXc/p9u3bqKmpQUhICLhcLiorK1FUVITi4mLU1NT87tQwpVIJpVIJX19f+Pj4/GQM2WKxwGAwoLa2FiUlJXB1dUVUVFSHScd/D+S+OOuxY8eY9VKpFDNmzMDChQsf6KE7ODj85k6oH2M0GtHc3AyBQICYmJhfFUtns9m/6fv/1u0pDw/t0OohPPPMM8jOzsa+ffugUqmY9Xq9Hh9//DHCw8MxfPjw332DP4jy8nK0tbXBaDRiz549uHHjBioqKgAAwcHBzCxQUqn0N3euWSwW3LhxA6WlpRg/fjz69+//wO00Gg1qa2tRXFyM48ePIyMjA4sXL8akSZN+9/ezQghBW1sbDhw4gK1btzJZGgKBAOPGjcOqVats2nlWU1ODM2fOICoqCvPnz7fJcShdB/VcewiOjo546623kJiY2CE+Wlpain/961/Iz8+3yfDYtrY26HQ67NixA3fv3sVLL72Es2fPYv369WCz2XjttdewZ88eqNXq37Rfi8WC6upqHDhwAI6OjnjiiSc6xDKtZGdn4+2338bKlStx/vx5jBgxAuPHj/9VPeq/FoPBgNOnT2PDhg1oaWkB8INHOmDAAKxcuRLBwcGddqz7IYRAoVDg0qVLuHbtGpYvX46BAwfa5FiUroOKaw8iPDwcL7zwAkJCQjp4iIcPH8a3336L2traTh+9ZTabQQiBWCzGkiVLMGbMGMhkMgwaNAhPP/00PDw8cODAAeTm5v7qfRJCUFtbi3Xr1kEul+O5557DkCFDfnL7AQMGYOvWrbh+/TreeOMNXLt2DS+88AJOnz7dKRPamM1m3Lx5E1u2bEFOTg6AH/JZQ0NDsWDBAowYMeJ3H+NBkP/lDp88eRJbt27FggULsHz5cpsci9K1UHHtYcyaNQtz586FTCbr8NmaNWtw5syZTp+eUCgUgsPhICoqCl5eXu1ijnFxcfD29kZeXh7kcvmv2h8hBI2NjVi/fj2Kiorw7LPPYtSoUb+YbeDg4AB3d3fMmjULzzzzDNra2vDf//4XRUVFv+v7EUJQUVGBnTt34sSJE8x6qVSKWbNmYf78+TYZqED+l3N67tw5bNiwAdOnT8cLL7xA5yjoJdBfsYdhHR47YsSIDmk0SqUSH374IW7dutWp6UleXl4Qi8WMyN6PSCQCj8eDRqP51R6kQqHA1q1bUVBQgOXLl2PEiBFgs9loaWlBc3Pzr7I9ODgYoaGhKCkpQXFx8UN9L+D/x1n379+P7du3t4uzjh8/HitXroSjo+ND7//nMBgMSE9Px+bNmzF27Fg888wzYLPZ0Ol0kMvlzMgtSs+EimsPxMXFBa+99hri4uI69Pjn5ORg/fr1KC4u7rTqBdHR0fD09GSGyd7vFTc3N0OtVsPNze1XiZBGo8GuXbtw+/ZtLFiwAGPGjGE8tXXr1mH16tWor69ntrUe88dYLBZYLBaYTKbfFRYwGAw4deoUNm7ciNbWVgA/eMjWkEdQUNBD7/vnMBqNuH37NrZs2YKkpCS8/PLLzG+ZlZWF5cuXo7Cw0CbHpnQNVFx7KElJSVi5ciUCAgI6NFl37dqFffv24f+1d+dxUdX7H8dfwwAyCATIjgsioKBJappCXcNwKUsT8ZZbaKK2WD3SFLSytMxMM1O7eH2EmQoqYfeaqQkqmldNKTdiX8IAiR1kG4aZOb8/jPlJqGmByMz3+Xj4eNDhcPieaXz7nc/5LmVlZa1SHujbty8DBw4kPz+f1NRUqqur0Wg01NfXc/r0aQoLC3nooYd0QaTRaCgpKSE3N5eKigpdb1CpVLJ371727t3Lgw8+SO/evcnLyyM3N1d37vX/ICQkJLBw4UK+/vpr6urq0Gg0aDQaamtrSUpKIjs7Gzc3N7p37/6X7kuj0ZCYmEhkZKSuXtw0z37KlCk88sgjf++Fuwm1Wk1ycjIREREATJo0iYKCAt3rUFhY2OzBpCRJ1NbWkpubS0FBgVg0vYMQQ7E6sJCQEJKSkti+fbvu6TZc69WtXbsWT09Pxo4de8Mn6g0NDZSVlaFSqaipqUGr1VJcXExubi6Wlpa6hVgATExMmDVrFr/++qtuiNKgQYPIyclh+/btWFhY8Oyzz+Lp6QlcmzX06quvsmvXLt566y1ee+017OzsOHPmDJs2beL777/nwIEDzJ8/v0W75s2bp/taoVDw22+/ER0djSRJ+Pr6otVqSUxMJDo6GplMxvjx4+nfv/8dv3aSJHH58mV27dpFfHy87niXLl0IDg5m8uTJd1RnVavVVFdXU1VVRVFRkW6RmtzcXExMTLC1tdWVca5cuUJkZCTbtm0DICoqqsX1fH19dV83NDRw6NAhgoOD8fHxITY2lj59+gDX/l/X1NRQXl5OQUEBGo0GlUpFbm4ucrkcW1vbZp8omtZAKCsrIz8/H41Gg1qtJjc3FyMjo9v+BCL8ORGuHZiJiQnh4eFkZWVx+PDhZjW6srIyPv74Y7p3786gQYNa1EpTUlJ4++23SUpKAsDBwYHly5ezfPlyQkJCmDNnDl27dtWd37dvXz766COio6OJiorik08+wdramocffpjnnnuOvn376j7WGhkZYWdnh5ubG7a2trrf/euvv6JWq2/Z07SxsdFdx9/fHysrKw4cOEBMTAxr166lvr4eOzs7hgwZwqRJkxg8eHCLnXP/TFOddc+ePURFRel6y2ZmZjz++OPMnj37jgOmvLycrVu38tlnnwFgaWlJeno6w4cPp0+fPixZskQ34uDq1avk5eXd8nVwdnbW3ZdMJsPc3JwePXrg6urabGptTU0NsbGxLFu2DLg2ZK+2tpbhw4fj6OjIu+++22wscH19Pd988w1LliwBrtXMNRoNw4cPx9bWluXLl9908R7hDt3V+WBCmzhy5Ijk6+t7w+mxL7zwgpSdnX3H02P1mVKplHbv3i316dNH9zrJ5XIpMDBQOnHiRHs3T9ATouaqB0aMGEFISMgNp45+8cUX7N+/n6qqKrF7AdfqrGfOnOHzzz8nLS0NuNYz9PT0ZMqUKbccaysId0KEq5544YUXGDNmTIvFrhsaGvjoo484efKkwQ/t0Wq1ujrrkSNHdMe7dOnCpEmTeOaZZ9p04W3BsIhw1RMKhYLw8PAb1lfz8/PZuHEjKSkpBrt7rPR7nXX37t3s2LFDV2dVKBQ89dRTzJkzp1Wn0gqCCFc90qtXL1599VW8vLxazPI5dOgQ0dHRXLlyxSDLAyqViv3797Nlyxaqq6uBa+NZH3nkEWbPnt3s4Z0gtAYRrnpm/PjxTJ48GQcHhxbfi4iI4NChQ1RXVxtUwGo0Gk6dOsUXX3yhmyork8no06cP06dPZ9iwYe3cQkEfiXDVMzKZjFdeeYWAgIAWH3Nra2tZs2YNiYmJBrN7rCRJ5OTkEBUVRUJCgu64vb09zzzzDMHBwe3YOkGfiXDVQ/fddx+LFi3igQceaDE9Nj09nU2bNpGent5q02PvVZIkUVFRwe7du4mJidH11pvqrM8///wtt2MRhL9DhKueeuCBB3R7Pf3xCfiePXuIiYmhqKhIr8sDKpWKb7/9lq1btzarsw4fPpzZs2fj6urazi0U9JkIVz02depUnn76aWxtbZsdlySJzz77jISEBOrr69updW1Lo9Fw8uRJvvjiC92qWTKZDG9vb6ZPn85DDz3Uzi0U9J0IVz0ml8uZP38+/v7+LfZNKi8v55NPPuHcuXN6NzxLkiSys7PZsWMHx48f1x23t7fn2WefJSgoqB1bJxgKEa56zsnJibCwMPr3799ieNaPP/7Ili1bbrk8oUaj6VC12aY6686dO/nqq6+a1VknTJjAzJkzRZ1VuCtEuBoAPz8/nnvuObp169biezt27OCbb76hoqKiWf1V+n2Zu/T09GYrbt0LmtZyvRGVSsXevXvZtm2bbiNHuVzOiBEjmDVrFi4uLnezqYIBE+FqIEJDQ3niiSe47777mh1vbGxk3bp1nDhxQjc9Vq1WU1RUxKpVq5g1axaJiYn31IOv0tJSCgoKUCqVzdql0Wg4ceIEW7duJScnB7hWZ/Xx8WHatGkMHjy4vZosGCARrgbCzMyMsLAwhg4d2mKJvoKCAjZs2MDFixepr6/n/PnzzJ07lzVr1vDDDz+QnZ19xzu7thWtVst//vMfnn/+ebZv305lZSVarRZJksjKymLHjh2cOHFCd76DgwNTpkzh6aefbr9GCwZJrOdqQHr06MErr7xCYWEhSUlJzXp9R48e5csvv8TNzY1t27aRnJys+/65c+cYPXo0Hh4e7dV0naqqKtLS0jh8+DA//fQTqampvPTSS1hZWREdHc2ePXta1FlDQkJEnVW460S4GpgnnniCpKQkysrKKCgoaPa9TZs2IUlSixLA+fPnKSwspFevXu2+alRqaqpu6+uKigo2bNhAWloaPXr04LvvvmtWZw0MDCQ0NBRnZ+f2bLJgoES4GhiZTMZLL71EWloasbGxzT7u3+whUUpKCvn5+ajV6jte9b81SZJEWloa6enpumNqtZqDBw82O08mk9GvXz+mTZvGoEGD7nYzBQEQNVeDZGVlxYIFC/D19b2tnmhDQwMXL16kpKTkLrTu5mpqakhNTSUvL++W59nb24s6q9DuRLgaIJVKhVwux8rK6rZ7oufOnaOwsLCNW3Zr6enppKam/um4Ww8PD3x8fG5Y4hCEu0WEqwGRJIm6ujq++eYbpk6dSlxc3G3vTnDhwoV2XQtWkiRSU1OblQRu5tSpUyxZsoSdO3fqdrYVhLtNhKuB0Gq1lJSU8N577/Hyyy9z4cKFOwqdkpIS0tPTqaqqasNW3pxSqSQ1NZXc3NzbOj8pKYmFCxeyfPly3RbSgnA3iQdaBqKwsJA33niDffv2/eUxq+fOnePKlStYW1u3buNuQ0ZGBmlpaXe0Dm1paSn/+te/qKys5M0338TNza3tGigIfyB6rgbCxcWFGTNmMGDAABQKxV+6xvnz59ulNCBJEikpKbrdWm+HTCajc+fOBAQEMHXqVLp3796GLRSElkTP1UDIZDJGjx5Nv379WLVqFbGxsRQVFd1RaSAzM5PLly+jUqlarLLVlhobG0lNTdUtHfhnjI2N6dGjB7NmzWLWrFk33PJGENqa6LkaGFdXV9atW8fmzZsZMmTIHYWkRqPhwoULd33UQHZ2NmlpaX/68K2pt/r4448TGRlJWFiYCFah3YhwNUBGRkY8+eSTxMTEMGvWLBwdHVssR3gzd7s0IEkSycnJfzpKQC6X4+7uzltvvUVkZCTDhw+/7XsShLYgygIGrFu3bqxfv57AwEA+/PBDLly48Ke9w0uXLpGfn48kSchkMt1YUkmSdAuoXP8HaBbE109akMlkyGQyjIyMWnzdRKPRkJqaSmZm5g3bI5PJUCgUBAYG8sYbb+Dv7y9CVbgniHA1cHK5nAkTJtC/f39WrlzJ3r17KS8vv2kttrq6mp9//plhw4Zha2tLY2OjbgnA4uJiSkpKKC0t5erVq9TW1qJWq1EqlTQ0NCCXyzEzM8PY2BiFQoGlpSW2trbY2dlhZ2eHi4sLLi4udO7cGSMjI4yMjMjNzSU1NfWG29HI5XLc3NyYM2cOM2fOxN7evq1fLkG4bTJJTGERfqdSqYiJiWHNmjWkpKTQ2Nh4w/M8PDxwd3fn6tWr5Obm6jb/k8lkWFpaYmdnh7W1NRYWFhgbG2NmZkanTp3QaDQolUrUajX19fVUVlZSVlZGZWUlKpUKSZIwNTXFyckJd3d3vLy8UCqVHDlypFnP9fre6sKFC/Hz8xO9VeGeI8JVaEar1ZKSksLy5cs5cODADcfEmpiY4O/vz5AhQ/D09KRXr1706NEDBwcHzM3N7zjoVCoVFRUVFBQUkJOTQ3Z2NllZWfz888+kpqa2mLjg7OzMnDlzCA0NxcnJCblc3u6rdQnCH4mygIAkSajVatRqNZcvX+bo0aOUlZVhZmaGVqulsbGx2eD9xsZGgoKCeP755+ncufPf/v2mpqY4Ojri6OjIwIEDdccvX77M0qVL2bZtGzKZDBMTE0xMTKipqSEmJobS0lLGjx/PwIEDdb3kP9ZsBaG9iHA1YFqtFrVaTVVVFYcOHWLXrl2cPXsWMzMzAgICmD17Nk5OTmzZsoVvv/2WyspK3cOpprUGPD0926x9TaMEmmqrL774IsHBwRQWFhIXF8fBgweJioqia9eujBs3jqCgIHx8fDAxMRG9WaH9SYLB0Wg0UkNDg5SWliYtX75c8vb2luzt7aWJEydKu3btkkpKSiStVqs7v66uTtq0aZPk4+MjGRsbS4A0cOBAKSEhodl5rd3G1atXS66urtKECROkU6dOSRqNptk5SqVS+vHHH6UlS5ZI999/v+Tk5CRNnDhR2rdvn1ReXi41Nja2WfsE4c+ImqsBkX7/+F9QUMCOHTvYunUrAOPGjWPGjBl4e3vfcgnCS5cusXLlSg4ePEhDQwObN29m8uTJGBu3/gegoqIiIiMjMTc3Z/r06XTp0uWW51dUVHD8+HG2bt3KmTNn6NevH3PnzmXkyJFYWFiIcoFw14lwNRAajYby8nI+//xzPv/8c4yNjQkNDWXatGk4OTnddvDU1tYSHR3Np59+yrhx45g3b16bbFd99epVAF0w3q6GhgZOnjzJhg0b+OGHHxg2bBivvvoqfn5+mJiYiIAV7hoRrnpOkiQaGxs5fPgwK1euJC8vjxkzZhAaGoqrq+tfDpuLFy+SmJiIn58fPj4+rdzqv6+uro5jx46xbt06MjMzmTp1KrNnz6Zr166iFyvcFSJc9ZhWq6W8vJyVK1eya9cuhg0bxpIlS+jfv3+rfJRXq9W6wf73qpKSEqKiooiIiMDV1ZVFixbx2GOPYWxsLAJWaFMiXPWURqMhOTmZxYsXk5aWRnh4OJMmTWqXtVjbm1ar5aeffmLVqlUkJyfz4osvMnPmTCwsLETACm1GhKse0mg0nDhxggULFmBmZsbKlSsZOnQopqam7d20dlVQUEBERAS7d+8mKCiIRYsWYWtrKwJWaBMiXPWMRqMhLi6O8PBwPD09ef/99+ndu7cIkN/V1NSwY8cONmzYwMiRIwkPD8fR0VG8PkKrE5MI9IhWq+X48eOEhYXh6+vLsmXL6NmzpwiO61hYWBASEoJCoeDjjz8G4J133sHa2lq8TkKrEj1XPSFJEmlpaYSGhuLo6MiHH36Il5dXezfrnqVUKomKimLjxo0EBwfzxhtv3NXdFQT9J3queqK6uprw8HCMjY15++238fDwaO8m3dPMzMwICgqitLSUPXv20L17d6ZOnXpPj3wQOhbxTtIDWq2Wf//732RlZbF48WL69u0rQuI22NjYMHHiRAYMGEBsbCyZmZl3ffNFQX+Jv4F6IDU1lejoaIKDgxk4cKDBjwq4Ex4eHkyYMIGqqiq2b9+ORqNp7yYJekKEawcnSRJRUVFYWVkRFBQkNuT7C4YNG8aoUaM4e/Ys58+fb+/mCHpChGsHV1BQQHx8PKNGjcLV1bW9m9Mh3XfffQwZMgS5XE58fLwoDQitQjzQ6uASEhJQq9U89thjf7py1O3SarVoNBpdyMjlct1x6feNCZvWS9VqtbrjTVNhmzYbvJ3rXP8zTaTfNzv843k3akNr1Za9vb3x9vbm0qVLlJWVYWdn1yrXFQyX6Ll2YJIkkZiYiJeXF126dGm1cZqJiYk8+uijKBQKOnXqREREBB9++CH9+/fHxcWF0aNHExcXR15eHitWrOD++++nW7dujB07lsOHD+v23jpy5AgBAQEoFAocHR157733eOedd+jXrx/29vb4+vqyevVq3W6yTfdUVFTEunXrGDRoEA4ODnh7e7N48WLeeustvLy8UCgU+Pn5sW/fvla5XwAnJyd8fHwoLS0lJSWl1a4rGC4Rrh2YJElcunQJDw8PrKysWu26Dz30ECdPnmTFihU4OzsTFRWFm5sbR48eJTY2FqVSSVhYGPPmzaNnz54kJCQQFRVFTU0Na9as4dy5cwCMHDmS//3vf7z77rsoFAp27dqFjY0N8fHxnD9/nrFjx/Lpp5+yatUq8vLyACgtLWXFihWsXr2awMBAzpw5w7Fjx3B0dGT//v2UlZWxbNkyEhMTGT9+fKvds1wux8XFBQsLi5tu4y0Id0KEawdWXV1NbW0tDg4ObToAPiAggBEjRuDs7Myjjz7Kww8/zOXLl3nggQcYNWoUzs7OBAYG4u/vzy+//EJ+fn6La6hUKoKCgggJCaF79+64u7vz+uuvExgYyNdff82xY8eQJImDBw8SFxdHYGAgc+fOpXfv3nTt2pUXXniBJ5988qY70rYGS0tLFAoFlZWVbfY7BMMhwrUDq6iooLGxERsbm1vuIPB3ubu7Y2Njo/tva2trFAoFPXv2bFbntba2RqVSUVdX1+IapqamuLm5Natluri46LbozsnJoba2loyMDH755Re8vLzo2bOn7tzOnTvTvXv3Nq2FmpmZYWxsrFuoWxD+DhGuHVjTg6O2frptbm7eLLyNjIyQy+V06tSp2bqwt3q41FS//WNbLSwsMDc3p6amhurqaurq6pAkCYVC0eIfDDMzM8zMzFrprm5OrDEgtAYRrh1YU4+1qQd7L6utraW+vr7ZMUmSdIFqaWmJpaUlnTt3xsjIiLq6OlQqVbNza2trqa2tbbM21tfXo1arW7V+LRguEa4dmKWlJRYWFhQXF9PQ0NDezbkltVpNdnY2RUVFSJKEJEkUFBSQk5ODtbU1vXr1wsLCgj59+uDu7k56ejo5OTm6c6urq8nNzaW8vLxN2nd90BviguJC6xPjXDswmUxG//79yczMpKqqCgcHh1b5SHv9OFO4tkasVqvVlSCaxpo2jWO9/jj8/zjZ68evKhQK4uLicHFx4emnn6axsZGIiAiOHj3KpEmTePTRRwEYM2YMP/30Ezt37sTe3p65c+dibm5ObGwsx44dw8LC4m/f341otVquXLlCXV2dWE1MaBWi59rBDR48mKysLMrKylqt9pqRkUFgYCBLly6luLiYmTNnMmXKFPbt28fYsWNZunQpRUVFzJgxg2nTpvHf//6XcePGsXTpUgoKCggNDWXixIm6IVkAnTp1YsqUKdTV1fHEE0/w4IMPEhcXx/z58wkLC6Nr164AdOnShcWLF7N48WKOHz+um5paW1vL2LFjsbGxaZOaaGFhIcnJydjZ2eHt7d3q1xcMj+i5dnAjRoxg/fr1xMfH4+Hhgb29/d++Zu/evUlISLjh9242tjQoKOhPr+vk5ERISAhvvvnmTc+RJIkuXbowb948XnnlFV2QSpLEhg0b0Gq1mJub38Zd3D5JkkhOTiY1NZWAgIBWm+kmGDbRc+3gnJ2dGTVqFPHx8RQUFHT4efHV1dWEhYWxdu1a3dRZSZKoqKggJycHGxsb3N3dW/V3VlZWcvbsWQBGjx7dqtcWDJcIVz0wbdo0amtriY2Npbi4+J4J2Ovrs39cL+BmZDIZSqWS3bt3s3v3bkpKSsjJyWHdunXs37+fUaNG8Y9//KNV23jq1Cni4+MZNmwYvr6+rXZtwcBJgl745JNPpD59+kj79++XGhoaJK1W295Nkg4dOiQNHTpUMjIy0v0ZM2aMdPr06Vv+XGZmpvT+++9LQ4cOlezs7CRra2vJ399fioyMlMrKylqtfVqtVkpPT5dmzZoljR8/XsrMzGy1awuC2ENLT9TV1TF9+nR+++031q9fz4ABA8RuBLcgSRLl5eVERESwd+9e5s+fz+TJk9u7WYIeEX/79IS5uTkff/wxxsbGLFu2jIyMDN3HcaE5SZJQKpXExsYSExPDxIkTCQ4Obu9mCXpGhKsecXNzY8WKFVy+fJl33nmHrKwsABGw15Ekifr6eqKioli/fj2jR4/m5ZdfbtO1GQTDJMJVzzz88MOsXbuWtLQ0FixYQEpKiujB/k76fRZWZGQkq1ev5vHHHyc8PBxLS8v2bpqgh0TNVU+dPn1aN0501apV+Pv7Y2pqapCLkjS9xfPz89m4cSNfffUVU6ZMYdGiRWIdAaHNiHDVYxkZGSxcuJDz588TFhbG5MmTdUsHGkrINvXaz5w5wwcffEBmZiavv/46ISEhd2WFLcFwiXDVc9XV1XzwwQds2bKFwYMHs3TpUgYMGKBbKlBfQ7bpbV1cXMyXX35JREQE7u7uvP3227p1DAShLYlwNRBHjx7VjSKYOXMmc+fOpVu3brpw1ZeQbXo719XVcfjwYdauXcsvv/xCaGgoc+fOxdHRsZ1bKBgKEa4GpLq6ms2bN7Nx40Y0Gg1z5sxhxowZuLi4dPiQbXobK5VKvv/+e9atW8fZs2cJCAhgwYIFDB06tMPem9AxiXA1QEVFRWzdupWIiAhUKhUTJkwgNDSUvn37NhuSdK+H0fVv3fLycuLi4ti8eTPnz59n8ODBvPbaawQGBoraqtAuRLgasPz8fHbu3ElkZCRXrlzB39+fZ599ljFjxmBvb98sXO+VoL3+7apUKrl48SJfffUVe/fupaKiQrexoZ+fnwhVoV2JcBWora3lu+++Y9u2bRw/fhwjIyMCAgJ46qmnGDFiBC4uLsjl8mY/c7fC9o9vz+rqai5cuMCBAwfYv38/mZmZeHh4MGnSJP75z3/Su3dvMe1XuCeIcBWaKSgo4MCBA+zZs4fTp09TX19Pv379GD58OH5+fjz44IO4urreckbTnQbvrd6ClZWVJCcnc/bsWb7//ntOnz5NeXk5vXr1YuzYsQQHBzNgwADRSxXuOSJchZsqLy/nhx9+IC4ujmPHjpGRkYFSqcTFxYW+ffvSu3dvvLy88PT0xM3NDWdnZ90Gg3dCpVJRVlZGXl4e2dnZZGRkkJGRwc8//0x2djb19fU4OTkxZMgQRo4cyYgRI/Dw8BBTVoV7mghX4baVl5eTnJxMYmIiSUlJpKWlkZWVRXl5uW7/rM6dO2Nvb4+1tTVWVlaYmprSqVMnzMzMUKvVKJVKVCoV9fX1VFZWUlJSQlVVFWq1Gri211a3bt3w9PSkX79+DBw4kEGDBtGtWzdMTU3b8/YF4Y6IcBX+Fq1WS2lpKfn5+fz2228UFxdTVFREVVUV1dXVuiBtaGjA2NgYMzMzTE1NUSgUWFtbY2dnh729PQ4ODnTt2hVnZ+dW38ZFENqDCFdBEIQ2IB6rCoIgtAERroIgCG1AhKsgCEIbEOEqCILQBkS4CoIgtAERroIgCG1AhKsgCEIbEOEqCILQBkS4CoIgtAERroIgCG1AhKsgCEIbEOEqCILQBkS4CoIgtIH/A9LP2aRAqTR4AAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -352,10 +352,10 @@ "id": "9cbc1002-57e4-4b79-8b8b-cab7dff25d4a", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:18.447508Z", - "iopub.status.busy": "2024-10-20T00:08:18.447149Z", - "iopub.status.idle": "2024-10-20T00:08:41.584791Z", - "shell.execute_reply": "2024-10-20T00:08:41.584195Z" + "iopub.execute_input": "2024-10-22T01:34:37.531075Z", + "iopub.status.busy": "2024-10-22T01:34:37.530858Z", + "iopub.status.idle": "2024-10-22T01:35:00.982462Z", + "shell.execute_reply": "2024-10-22T01:35:00.981858Z" } }, "outputs": [ @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 50%|█████ | 16/32 [00:02<00:02, 6.31it/s]" + "Evaluating set functions...: 50%|█████ | 16/32 [00:02<00:02, 6.27it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 75%|███████▌ | 24/32 [00:07<00:02, 2.84it/s]" + "Evaluating set functions...: 75%|███████▌ | 24/32 [00:07<00:02, 2.82it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 88%|████████▊ | 28/32 [00:12<00:02, 1.82it/s]" + "Evaluating set functions...: 88%|████████▊ | 28/32 [00:12<00:02, 1.80it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00, 2.14it/s]" + "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00, 2.13it/s]" ] }, { @@ -433,10 +433,10 @@ "id": "885195c7-f9d7-449e-a96f-894ce09562d9", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:41.587117Z", - "iopub.status.busy": "2024-10-20T00:08:41.586716Z", - "iopub.status.idle": "2024-10-20T00:08:41.590211Z", - "shell.execute_reply": "2024-10-20T00:08:41.589707Z" + "iopub.execute_input": "2024-10-22T01:35:00.984886Z", + "iopub.status.busy": "2024-10-22T01:35:00.984536Z", + "iopub.status.idle": "2024-10-22T01:35:00.988224Z", + "shell.execute_reply": "2024-10-22T01:35:00.987611Z" } }, "outputs": [], @@ -452,16 +452,16 @@ "id": "a80ca005-23aa-46b0-889d-4de7144adb33", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:41.592016Z", - "iopub.status.busy": "2024-10-20T00:08:41.591642Z", - "iopub.status.idle": "2024-10-20T00:08:41.685289Z", - "shell.execute_reply": "2024-10-20T00:08:41.684669Z" + "iopub.execute_input": "2024-10-22T01:35:00.990174Z", + "iopub.status.busy": "2024-10-22T01:35:00.989812Z", + "iopub.status.idle": "2024-10-22T01:35:01.097576Z", + "shell.execute_reply": "2024-10-22T01:35:01.096884Z" } }, "outputs": [ { "data": { - "image/png": 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Pjw4fPnzb90ZFRbklGAAAwF/JU5GpV6+ekpOTFR4ernr16slisSi3iRyLxaKsrCy3hwQAAMhNnorMmTNnFBYW5vgzAABAQZCnIlO+fHnHn8+ePasmTZrI19f5W2/evKmdO3c6vRcAAMCTXL5rqWXLlrlufJeWlqaWLVu6JRQAAEBeuFxk7Ha7LBZLjvGff/5ZxYsXd0soAACAvMjzhngxMTGSflvQ26NHDwUEBDiOZWVl6fDhw2rSpIn7EwIAANxCnouM1WqV9NuMTGBgoIoVK+Y45u/vr8aNG6tv377uTwgAAHALeS4yixYtkiRVqFBBw4cP5zISAAAwnMvPWhozZowncgAAALjM5SJTsWLFXBf7/u706dN3FAgAACCvXC4yQ4YMcfo6MzNTBw4c0Nq1a/X3v//dXbkAAAD+kstFZvDgwbmOz549W3v37r3jQAAAAHnl8j4yt9K+fXt99tln7jodAADAX3JbkVm+fLlKlizprtMBAAD8JZcvLdWvX99psa/dbldycrJ+/PFHzZkzx63hAAAAbsflItO5c2enr318fBQWFqZHHnlE1atXd1cuAACAv8Q+MgAAwLRcLjLSb89WWrFihY4fPy5Jqlmzpjp16iRf33ydDgAAIF9cbh7fffedOnTooJSUFFWrVk2S9PbbbyssLEyrV69W7dq13R4SAAAgNy7ftdSnTx/Vrl1b586d0/79+7V//34lJSUpKipK/fr180RGAACAXLk8I3Pw4EHt3btXISEhjrGQkBC99dZbevDBB90aDgAA4HZcnpGpWrWqUlJScoynpqaqcuXKbgkFAACQF3kqMjabzfGaNGmSBg0apOXLl+vcuXM6d+6cli9friFDhujtt9/2dF4AAAAHi91ut//Vm3x8fHJsgifJMfbHr7OysjyRM99sNpusVqvS0tIUFBTk3pMHPe3e83k720dGJwAAeJk8rZHZvHmzp3MAAAC4LE9FpkWLFp7OAQAA4LI8FZnDhw+rdu3a8vHx0eHDh2/73qioKLcEAwAA+Ct5KjL16tVTcnKywsPDVa9ePVksFuW2tKYgrpEBAADeK09F5syZMwoLC3P8GQAAoCDIU5EpX768JCkzM1Pjxo3TqFGjVLFiRY8GAwAA+CsubYjn5+enzz77zFNZAAAAXOLyzr6dO3fWypUrPRAFAADANS4/a6lKlSoaP368duzYoQYNGqh48eJOxwcNGuS2cAAAALeTp519/+h2a2MsFotOnz59x6HciZ19CxB29gUAuJnLMzLctQQAAAoKl9fIjB8/XteuXcsx/uuvv2r8+PFuCQUAAJAXLl9aKlKkiC5evKjw8HCn8Z9//lnh4eEFbkM8Li0VIFxaAgC4mcszMna73elJ2L87dOiQSpYs6ZZQAAAAeZHnNTIhISGyWCyyWCyqWrWqU5nJyspSenq6+vfv75GQAAAAuclzkZkxY4bsdrt69eqlcePGyWq1Oo75+/urQoUKio6OdumHz507V3PnztX//u//SpJq1aql0aNHq3379pKk69eva9iwYfroo4+UkZGhtm3bas6cOYqIiHDp5wAAAO/k8hqZ+Ph4NWnSRH5+fnf8w1evXq0iRYqoSpUqstvtWrJkif71r3/pwIEDqlWrll5++WWtWbNGixcvltVq1auvviofHx/t2LEjzz+DNTIFCGtkAABu5nKRkaTs7GydOnVKqampys7OdjrWvHnzOwpUsmRJ/etf/9JTTz2lsLAwLV26VE899ZQk6fvvv1eNGjW0a9cuNW7cOE/no8gUIBQZAICbubyPzO7du/Xss8/q7Nmz+nMHslgs+b5rKSsrS59++qmuXr2q6Oho7du3T5mZmWrdurXjPdWrV1e5cuVuW2QyMjKUkZHh+Npms+UrDwAAKPhcvmupf//+atiwoY4ePapLly7pl19+cbwuXbrkcoAjR46oRIkSCggIUP/+/bVixQrVrFlTycnJ8vf3V3BwsNP7IyIilJycfMvzTZo0SVar1fGKjIx0ORMAADAHl2dkTp48qeXLl6ty5cpuCVCtWjUdPHhQaWlpWr58uV566SXFx8fn+3wjR45UbGys42ubzUaZAQDAS7lcZBo1aqRTp065rcj4+/s7ztWgQQPt2bNH77zzjrp3764bN27o8uXLTrMyKSkpKlWq1C3PFxAQoICAALdkAwAABZvLRWbgwIEaNmyYkpOTVadOnRx3L0VFRd1RoOzsbGVkZKhBgwby8/NTXFycunTpIkk6ceKEEhMTXb7NGwAAeCeXi8zvpaJXr16OMYvF4tjx15XFviNHjlT79u1Vrlw5XblyRUuXLtWWLVu0bt06Wa1W9e7dW7GxsSpZsqSCgoI0cOBARUdH5/mOJQAA4N0Mffp1amqqXnzxRV28eFFWq1VRUVFat26dHnvsMUnS9OnT5ePjoy5dujhtiAcAACDlcx8ZM2EfmQKEfWQAAG7m8oyMJCUkJGjGjBk6fvy4JKlmzZoaPHiwKlWq5NZwAAAAt+PyPjLr1q1TzZo19e233yoqKkpRUVH65ptvVKtWLW3YsMETGQEAAHLl8qWl+vXrq23btpo8ebLT+IgRI7R+/Xrt37/frQHvFJeWChAuLQEA3MzlGZnjx4+rd+/eOcZ79eqlY8eOuSUUAABAXrhcZMLCwnTw4MEc4wcPHlR4eLg7MgEAAOSJy4t9+/btq379+un06dNq0qSJJGnHjh16++23nR4NAAAA4Gkur5Gx2+2aMWOGpk6dqgsXLkiSypQpo7///e8aNGiQLBaLR4LmF2tkChDWyAAA3OyO9pG5cuWKJCkwMNBtgdyNIlOAUGQAAG6Wr519b968qSpVqjgVmJMnT8rPz08VKlRwZz4AAIBbcnmxb48ePbRz584c499884169OjhjkwAAAB54nKROXDggJo2bZpjvHHjxrnezQQAAOApLhcZi8XiWBvzR2lpaS49+RoAAOBOuVxkmjdvrkmTJjmVlqysLE2aNEkPP/ywW8MBAADcjsuLfd9++201b95c1apVU7NmzSRJ27Ztk81m06ZNm9weEAAA4FZcnpGpWbOmDh8+rG7duik1NVVXrlzRiy++qO+//161a9f2REYAAIBc3dE+MmbAPjIFCPvIAADczOUZGQAAgILC5TUyAAB4PWbc887g2XZmZAAAgGlRZAAAgGnlq8jcvHlTGzdu1LvvvuvYHO/ChQtKT093azgAAIDbcXmNzNmzZ9WuXTslJiYqIyNDjz32mAIDA/X2228rIyND8+bN80ROAACAHFyekRk8eLAaNmyoX375RcWKFXOMP/nkk4qLi3NrOAAAgNtxeUZm27Zt2rlzp/z9/Z3GK1SooPPnz7stGAAAwF9xeUYmOzs714dDnjt3ToGBgW4JBQAAkBcuF5k2bdpoxowZjq8tFovS09M1ZswY/e1vf3NnNgAAgNty+dLS1KlT1bZtW9WsWVPXr1/Xs88+q5MnT+ree+/VsmXLPJERAAAgVy4XmbJly+rQoUP6+OOPdejQIaWnp6t379567rnnnBb/AgAAeFq+HlHg6+ur5557Ts8995y78wAAAOSZy2tkJk2apIULF+YYX7hwod5++223hAIAAMgLl4vMu+++q+rVq+cYr1WrFpvhAQCAu8rlIpOcnKzSpUvnGA8LC9PFixfdEgoAACAvXC4ykZGR2rFjR47xHTt2qEyZMm4JBQAAkBcuL/bt27evhgwZoszMTLVq1UqSFBcXp9dee03Dhg1ze0AAAIBbcbnI/P3vf9fPP/+sV155RTdu3JAkFS1aVK+//rpGjhzp9oAAAAC3YrHb7fb8fGN6erqOHz+uYsWKqUqVKgoICHB3Nrew2WyyWq1KS0tTUFCQe08e9LR7z+ftbB8ZnQAA8obf73ln8O/2fO0jI0klSpTQgw8+6M4sAAAALnG5yFy9elWTJ09WXFycUlNTlZ2d7XT89OnTbgsHAABwOy4XmT59+ig+Pl4vvPCCSpcuLYvF4olcAAAAf8nlIvP1119rzZo1atq0qSfyAAAA5JnL+8iEhISoZMmSnsgCAADgEpeLzD//+U+NHj1a165d80QeAACAPHP50tLUqVOVkJCgiIgIVahQQX5+fk7H9+/f77ZwAAAAt+NykencubMHYgAAALjO5SIzZswYT+QAAABwmctrZAAAAAoKl2dksrKyNH36dH3yySdKTEx0PG/pd5cuXXJbOAAAgNtxeUZm3LhxmjZtmrp37660tDTFxsYqJiZGPj4+Gjt2rAciAgAA5M7lIvPhhx/qvffe07Bhw+Tr66tnnnlG77//vkaPHq3du3d7IiMAAECuXC4yycnJqlOnjqTfHhyZlpYmSXriiSe0Zs0al841adIkPfjggwoMDFR4eLg6d+6sEydOOL3n+vXrGjBggEJDQ1WiRAl16dJFKSkprsYGAABeyOUiU7ZsWV28eFGSVKlSJa1fv16StGfPHgUEBLh0rvj4eA0YMEC7d+/Whg0blJmZqTZt2ujq1auO9wwdOlSrV6/Wp59+qvj4eF24cEExMTGuxgYAAF7I5cW+Tz75pOLi4tSoUSMNHDhQzz//vBYsWKDExEQNHTrUpXOtXbvW6evFixcrPDxc+/btU/PmzZWWlqYFCxZo6dKlatWqlSRp0aJFqlGjhnbv3q3GjRvnOGdGRoYyMjIcX9tsNlf/EQEAgEm4XGQmT57s+HP37t1Vrlw57dq1S1WqVFGHDh3uKMzvl6l+f5bTvn37lJmZqdatWzveU716dcfPzK3ITJo0SePGjbujHAAAwBxcLjJ/Fh0drejo6DsOkp2drSFDhqhp06aqXbu2pN/W4/j7+ys4ONjpvREREUpOTs71PCNHjlRsbKzja5vNpsjIyDvOBwAACp48FZlVq1apffv28vPz06pVq2773o4dO+YryIABA3T06FFt3749X9//u4CAAJfX6gAAAHPKU5Hp3LmzkpOTHXcW3YrFYlFWVpbLIV599VV9+eWX2rp1q8qWLesYL1WqlG7cuKHLly87zcqkpKSoVKlSLv8cAADgXfJ011J2drbCw8Mdf77Vy9USY7fb9eqrr2rFihXatGmTKlas6HS8QYMG8vPzU1xcnGPsxIkTSkxMdMvlLAAAYG4u3X6dmZmpRx99VCdPnnTLDx8wYID++9//aunSpQoMDFRycrKSk5P166+/SpKsVqt69+6t2NhYbd68Wfv27VPPnj0VHR2d60JfAABQuLi02NfPz0+HDx922w+fO3euJOmRRx5xGl+0aJF69OghSZo+fbp8fHzUpUsXZWRkqG3btpozZ47bMgAAAPOy2O12uyvfMHToUAUEBDjdhl2Q2Ww2Wa1WpaWlKSgoyL0nD3ravefzdraPjE4AAHnD7/e8M/h3u8u3X9+8eVMLFy7Uxo0b1aBBAxUvXtzp+LRp09wWDgAA4HZcLjJHjx7VAw88IEn64YcfnI5ZLBb3pAIAAMgDl4vM5s2bPZEDAADAZS4/NBIAAKCgyNcjCvbu3atPPvlEiYmJunHjhtOxzz//3C3BAAAA/orLMzIfffSRmjRpouPHj2vFihXKzMzUd999p02bNslqtXoiIwAAQK5cLjITJ07U9OnTtXr1avn7++udd97R999/r27duqlcuXKeyAgAAJArl4tMQkKCHn/8cUmSv7+/rl69KovFoqFDh2r+/PluDwgAAHArLheZkJAQXblyRZJ033336ejRo5Kky5cv69q1a+5NBwAAcBsuL/Zt3ry5NmzYoDp16qhr164aPHiwNm3apA0bNujRRx/1REYAAIBc5bnIHD16VLVr19asWbN0/fp1SdIbb7whPz8/7dy5U126dNGbb77psaAAAAB/luciExUVpQcffFB9+vTR00//9gwKHx8fjRgxwmPhAAAAbifPa2Ti4+NVq1YtDRs2TKVLl9ZLL72kbdu2eTIbAADAbeW5yDRr1kwLFy7UxYsX9e9//1v/+7//qxYtWqhq1ap6++23lZyc7MmcAAAAObh811Lx4sXVs2dPxcfH64cfflDXrl01e/ZslStXTh07dvRERgAAgFzd0bOWKleurH/84x968803FRgYqDVr1rgrFwAAwF/K17OWJGnr1q1auHChPvvsM/n4+Khbt27q3bu3O7MBAADclktF5sKFC1q8eLEWL16sU6dOqUmTJpo5c6a6deum4sWLeyojAABArvJcZNq3b6+NGzfq3nvv1YsvvqhevXqpWrVqnswGAABwW3kuMn5+flq+fLmeeOIJFSlSxJOZAAAA8iTPRWbVqlWezAEAAOCyO7prCQAAwEgUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFqGFpmtW7eqQ4cOKlOmjCwWi1auXOl03G63a/To0SpdurSKFSum1q1b6+TJk8aEBQAABY6vkT/86tWrqlu3rnr16qWYmJgcx6dMmaKZM2dqyZIlqlixokaNGqW2bdvq2LFjKlq0qAGJUSAEPW10AnOxfWR0AgDwGEOLTPv27dW+fftcj9ntds2YMUNvvvmmOnXqJEn64IMPFBERoZUrV+rpp/mPGQAAhV2BXSNz5swZJScnq3Xr1o4xq9WqRo0aadeuXbf8voyMDNlsNqcXAADwTgW2yCQnJ0uSIiIinMYjIiIcx3IzadIkWa1WxysyMtKjOQEAgHEKbJHJr5EjRyotLc3xSkpKMjoSAADwkAJbZEqVKiVJSklJcRpPSUlxHMtNQECAgoKCnF4AAMA7FdgiU7FiRZUqVUpxcXGOMZvNpm+++UbR0dEGJgMAAAWFoXctpaen69SpU46vz5w5o4MHD6pkyZIqV66chgwZogkTJqhKlSqO26/LlCmjzp07GxcaAAAUGIYWmb1796ply5aOr2NjYyVJL730khYvXqzXXntNV69eVb9+/XT58mU9/PDDWrt2LXvIAAAASZLFbrfbjQ7hSTabTVarVWlpae5fL8PGbK5x18ZsfO6uYUM8wHX8nsk7g3/HFNg1MgAAAH+FIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEzL1+gAAIDbCHra6ATmYfvI6AQwADMyAADAtCgyAADAtCgyAADAtCgyAADAtCgyAADAtLhrCUDecPdM3nH3DHDXMCMDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMyxRFZvbs2apQoYKKFi2qRo0a6dtvvzU6EgAAKAAKfJH5+OOPFRsbqzFjxmj//v2qW7eu2rZtq9TUVKOjAQAAgxX4IjNt2jT17dtXPXv2VM2aNTVv3jzdc889WrhwodHRAACAwQr0zr43btzQvn37NHLkSMeYj4+PWrdurV27duX6PRkZGcrIyHB8nZaWJkmy2WzuD2jPdP85vZm7/h3wubuGz/3uc+fvGz73vONzN4Yn/vv6B4GBgbJYLLc8XqCLzE8//aSsrCxFREQ4jUdEROj777/P9XsmTZqkcePG5RiPjIz0SEa4wPq50QkKJz73u4/P3Bh87sbw8OeelpamoKCgWx4v0EUmP0aOHKnY2FjH19nZ2bp06ZJCQ0Nv2+i8hc1mU2RkpJKSkm77Lx7uxeduDD53Y/C5G6Owfu6BgYG3PV6gi8y9996rIkWKKCUlxWk8JSVFpUqVyvV7AgICFBAQ4DQWHBzsqYgFVlBQUKH6i15Q8Lkbg8/dGHzuxuBzd1agF/v6+/urQYMGiouLc4xlZ2crLi5O0dHRBiYDAAAFQYGekZGk2NhYvfTSS2rYsKEeeughzZgxQ1evXlXPnj2NjgYAAAxW4ItM9+7d9eOPP2r06NFKTk5WvXr1tHbt2hwLgPGbgIAAjRkzJsflNXgWn7sx+NyNweduDD733Fnsdrvd6BAAAAD5UaDXyAAAANwORQYAAJgWRQYAAJgWRQYAAJgWRQYAAJgWRQbIp61bt+rmzZs5xm/evKmtW7cakMj7jR8/XteuXcsx/uuvv2r8+PEGJAJgNIqMF1iyZInWrFnj+Pq1115TcHCwmjRporNnzxqYzLu1bNlSly5dyjGelpamli1bGpDI+40bN07p6ek5xq9du5brw2LhHpmZmerVq5fOnDljdBQgB4qMF5g4caKKFSsmSdq1a5dmz56tKVOm6N5779XQoUMNTue97HZ7rg8i/fnnn1W8eHEDEnm/W33mhw4dUsmSJQ1IVDj4+fnps88+MzoGkKsCv7Mv/lpSUpIqV64sSVq5cqW6dOmifv36qWnTpnrkkUeMDeeFYmJiJEkWi0U9evRw2mUzKytLhw8fVpMmTYyK55VCQkJksVhksVhUtWpVpzKTlZWl9PR09e/f38CE3q9z585auXIl/3N0l/3+d//PLBaLihYtqsqVK6tHjx6F+rE9FBkvUKJECf38888qV66c1q9fr9jYWElS0aJF9euvvxqczvtYrVZJv80OBAYGOmbDpN8edNq4cWP17dvXqHheacaMGbLb7erVq5fGjRvn+Hcg/faZV6hQgQfJeliVKlU0fvx47dixQw0aNMgx6zho0CCDknm30aNH66233lL79u310EMPSZK+/fZbrV27VgMGDNCZM2f08ssv6+bNm4X29w6PKPACzz33nL7//nvVr19fy5YtU2JiokJDQ7Vq1Sr94x//0NGjR42O6JXGjRun4cOHcxnpLoqPj1eTJk3k5+dndJRCp2LFirc8ZrFYdPr06buYpvDo0qWLHnvssRwzju+++67Wr1+vzz77TP/+9781f/58HTlyxKCUxqLIeIHLly9r1KhRSkxM1Msvv6x27dpJksaMGSN/f3+98cYbBicE3Cc7O1unTp1SamqqsrOznY41b97coFSAZ5QoUUIHDx50LB/43alTp1SvXj2lp6crISFBUVFRunr1qkEpjcWlJZO7efOmZs6cqddff11ly5Z1OsZdHJ6VkpKi4cOHKy4uTqmpqfrz/xNkZWUZlMx77d69W88++6zOnj2b4/O2WCx85nfBjRs3dObMGVWqVEm+vvwnxNNKliyp1atX51ibtHr1ascC96tXryowMNCIeAUCfwtNztfXV1OmTNGLL75odJRCp0ePHkpMTNSoUaNUunTpXBfkwb369++vhg0bas2aNXzmd9m1a9c0cOBALVmyRJL0ww8/6P7779fAgQN13333acSIEQYn9E6jRo3Syy+/rM2bNzvWyOzZs0dfffWV5s2bJ0nasGGDWrRoYWRMQ3FpyQt06tRJMTExeumll4yOUqgEBgZq27ZtqlevntFRCo3ixYvr0KFDOabZ4XmDBw/Wjh07NGPGDLVr106HDx/W/fffry+++EJjx47VgQMHjI7otXbs2KFZs2bpxIkTkqRq1app4MCB3B35f5iR8QLt27fXiBEjdOTIkVzvJujYsaNBybxbZGRkjssb8KxGjRrp1KlTFBkDrFy5Uh9//LEaN27sNBNWq1YtJSQkGJjM+zVt2lRNmzY1OkaBRZHxAq+88ookadq0aTmOsW7Ac2bMmKERI0bo3XffVYUKFYyO47UOHz7s+PPAgQM1bNgwJScnq06dOjnuXoqKirrb8QqNH3/8UeHh4TnGr169yiU+D8vKytKKFSt0/PhxSVLNmjXVqVMn1ij9Hy4tAfkUEhKia9eu6ebNm7rnnnty/Ec1t8cXwHU+Pj6yWCy3nP36/Ril3bOaN2+url27auDAgQoMDNThw4dVsWJFDRw4UCdPntTatWuNjuiVvvvuO3Xs2FHJycmqVq2apN/WJ4WFhWn16tWqXbu2wQmNR53zMtevX1fRokWNjlEozJgxw+gIhQLP9ykYJk6cqPbt2+vYsWO6efOm3nnnHR07dkw7d+5UfHy80fG8Vp8+fVSrVi3t3btXISEhkqRffvlFPXr0UL9+/bRz506DExqPGRkvkJWVpYkTJ2revHlKSUlx3E0watQoVahQQb179zY6IgAvkJCQoMmTJ+vQoUNKT0/XAw88oNdff1116tQxOprXKlasmPbu3atatWo5jR89elQPPvggu7eLGRmv8NZbb2nJkiWaMmWK0xbVtWvX1owZMygyHpSQkKBFixYpISFB77zzjsLDw/X111+rXLlyOX7x4M6tWrUq1/E/PnfmdjvQ4s5UqlRJ7733ntExCpWqVasqJSUlx++T1NRUFr3/zg7Tq1Spkn3jxo12u91uL1GihD0hIcFut9vtx48ftwcHBxsZzatt2bLFXqxYMXvr1q3t/v7+js990qRJ9i5duhiczjtZLBa7j4+P3WKxOL1+H/Px8bE3b97cfunSJaOjep0XXnjBvnDhQsffc9wda9assdeqVcv+6aef2pOSkuxJSUn2Tz/91F6nTh37mjVr7GlpaY5XYeVjdJHCnTt//nyuzTw7O1uZmZkGJCocRowYoQkTJmjDhg3y9/d3jLdq1Uq7d+82MJn32rBhgx588EFt2LBBaWlpSktL04YNG9SoUSN9+eWX2rp1q37++WcNHz7c6Khex9/fX5MmTVLlypUVGRmp559/Xu+//75OnjxpdDSv9sQTT+jYsWPq1q2bypcvr/Lly6tbt246evSoOnTooJCQEAUHBzvWzxRGXFryAjVr1tS2bdtUvnx5p/Hly5erfv36BqXyfkeOHNHSpUtzjIeHh+unn34yIJH3Gzx4sObPn++0Edijjz6qokWLql+/fvruu+80Y8YM9erVy8CU3un999+X9Nv/OG3dulXx8fGaOnWq/t//+38qXbq0zp07Z3BC77R582ajIxR4FBkvMHr0aL300ks6f/68srOz9fnnn+vEiRP64IMP9OWXXxodz2sFBwfr4sWLOdZkHDhwQPfdd59BqbxbQkKCgoKCcowHBQU5nr5cpUoViqQHhYSEKDQ01DET4Ovrq7CwMKNjea0WLVro+vXrOnz4cK4PSmXDU7FGxlts3brV3rp1a3tYWJi9WLFi9qZNm9rXrVtndCyvNmzYMPvDDz9sv3jxoj0wMNB+8uRJ+/bt2+3333+/fezYsUbH80pNmza1t2vXzp6amuoYS01Ntbdr187erFkzu91ut2/YsMFetWpVoyJ6rZEjR9qjo6PtRYsWtdevX98+ZMgQ+8qVK1mP5GFff/21PSwsLMe6sN/XhMFu5/ZrIJ9u3LihAQMGaPHixcrKypKvr6+ysrL07LPPavHixSpSpIjREb3OiRMn1KlTJ505c0aRkZGSpKSkJMczf6pWraqVK1fqypUreuGFFwxO6118fHwUFhamoUOHKiYmRlWrVjU6UqFQpUoVtWnTRqNHj1ZERITRcQokigxwhxITE3X06FGlp6erfv36qlKlitGRvFp2drbWr1+vH374QdJvD9B77LHH5OPDvQuedOjQIcXHx2vLli3atm2b/P391aJFCz3yyCN65JFHKDYeEhQUpAMHDqhSpUpGRymwKDImFRISkufnm7BVPgB3O3TokKZPn64PP/xQ2dnZPB7CQ3r16qWmTZuyH9htsNjXpP64Pf7PP/+sCRMmqG3btoqOjpYk7dq1S+vWrdOoUaMMSuj97Ha7li9frs2bN+e6CO/zzz83KJl3mTlzpvr166eiRYtq5syZt33voEGD7lKqwsdut+vAgQPasmWLtmzZou3bt8tmsykqKkotWrQwOp7XmjVrlrp27apt27bl+qBU/s4zI+MVunTpopYtW+rVV191Gp81a5Y2btyolStXGhPMyw0ePFjvvvuuWrZsqYiIiBwzZIsWLTIomXepWLGi9u7dq9DQ0Nvu2muxWBx3LsH9QkJClJ6errp16zouKTVr1kzBwcFGR/NqCxYsUP/+/VW0aFGFhoY6/Z7h7/xvKDJeoESJEjp48GCOTfFOnTqlevXqKT093aBk3q1kyZL673//q7/97W9GRwE8bs2aNWrWrFmut7/Dc0qVKqVBgwZpxIgRrAO7BT4VLxAaGqovvvgix/gXX3yh0NBQAxIVDlarVffff7/RMQqlGzdu6MSJE7p586bRUQqNxx9/3FFizp07xwZ4d8mNGzfUvXt3Ssxt8Ml4gXHjxun1119Xhw4dNGHCBE2YMEEdOnTQiBEjNG7cOKPjea2xY8dq3LhxPH32Lrp27Zp69+6te+65R7Vq1VJiYqIkaeDAgZo8ebLB6bxbdna2xo8fL6vV6tgqPzg4WP/85z9zrA+D+7z00kv6+OOPjY5RoLHY1wv06NFDNWrU0MyZMx0LTGvUqKHt27erUaNGBqfzXt26ddOyZcsUHh6uChUq5FiEt3//foOSea+RI0fq0KFD2rJli9q1a+cYb926tcaOHasRI0YYmM67vfHGG1qwYIEmT56spk2bSpK2b9+usWPH6vr163rrrbcMTuidsrKyNGXKFK1bt05RUVE5fs9MmzbNoGQFB2tkgHzq1q2bNm/erKeeeirXxb5jxowxKJn3Kl++vD7++GM1btxYgYGBOnTokO6//36dOnVKDzzwgGw2m9ERvVaZMmU0b968HFvif/HFF3rllVd0/vx5g5J5t5YtW97ymMVi0aZNm+5imoKJGRkvkZ2drVOnTuV6G3Dz5s0NSuXd1qxZo3Xr1unhhx82Okqh8eOPPyo8PDzH+NWrV/O8rxLy59KlS6pevXqO8erVq7NXlQfx0Mi/RpHxArt379azzz6rs2fP6s8TbBaLhY2qPCQyMpI7OO6yhg0bas2aNRo4cKAkOcrL+++/79hDCZ5Rt25dzZo1K8dePrNmzVLdunUNSgVQZLxC//79Hb/gS5cuzf+Z3iVTp07Va6+9pnnz5qlChQpGxykUJk6cqPbt2+vYsWO6efOm3nnnHR07dkw7d+5UfHy80fG82pQpU/T4449r48aNThtvJiUl6auvvjI4HQoz1sh4geLFi+vQoUM59pGBZ4WEhOjatWu6efOm7rnnnhyL8Jhu94zTp09r0qRJOnTokNLT0/XAAw/o9ddfV506dYyO5vUuXLig2bNn6/vvv5f0200Fr7zyisqUKWNwMhRmzMh4gUaNGunUqVMUmbvsj4+JwN3x4osvqmXLlhoxYgQP0TNAmTJluDsJBQ4zMl5gxYoVevPNN/X3v/8912dxREVFGZQMcK8+ffpo69atSkhIUJkyZRxb5bdo0YKnjt8Fv/zyixYsWKDjx49LkmrWrKmePXuqZMmSBidDYUaR8QK57fhosVhkt9tZ7OthCQkJWrRokRISEvTOO+8oPDxcX3/9tcqVK6datWoZHc9rnT9/Xlu3blV8fLzi4+P1ww8/qHTp0uw260Fbt25Vhw4dZLVa1bBhQ0nSvn37dPnyZa1evZq7I2EYLi15gTNnzhgdoVCKj49X+/bt1bRpU23dulVvvfWWwsPDdejQIS1YsEDLly83OqLXCgkJUWhoqEJCQhQcHCxfX1+FhYUZHcurDRgwQN27d9fcuXNVpEgRSb9t1vbKK69owIABOnLkiMEJUVgxIwPkU3R0tLp27arY2Finzdm+/fZbxcTEMDvgAf/4xz+0ZcsWHThwQDVq1HBcWmrevLlCQkKMjufVihUrpoMHD6patWpO4ydOnFC9evV4VAcMw4yMSa1atUrt27eXn5+fVq1addv3/nknTrjHkSNHtHTp0hzj4eHh+umnnwxI5P0mT56ssLAwjRkzRjExMapatarRkQqNBx54QMePH89RZI4fP84+MjAURcakOnfurOTkZIWHh6tz5863fB9rZDwnODhYFy9eVMWKFZ3GDxw4oPvuu8+gVN7twIEDio+P15YtWzR16lT5+/s7ZmUeeeQRio0HDRo0SIMHD9apU6fUuHFjSb9txjl79mxNnjxZhw8fdryXGwxwN3FpCcin4cOH65tvvtGnn36qqlWrav/+/UpJSdGLL76oF198kWct3QWHDh3S9OnT9eGHHyo7O5vS7kG53VTwR9xgAKMwIwPk08SJEzVgwABFRkYqKytLNWvWVFZWlp599lm9+eabRsfzSna7XQcOHNCWLVu0ZcsWbd++XTabTVFRUWrRooXR8bwaNxWgoGJGxqT+/LyT2xk0aJAHkyApKUlHjhxRenq66tevz34mHhQSEqL09HTVrVvXcUmpWbNmCg4ONjoaAINQZEzqz+sybsVisej06dMeTgPcHWvWrFGzZs14WKdB/vOf/2jevHk6c+aMdu3apfLly2vGjBmqWLGiOnXqZHQ8FFJcWjIppnmN16VLFz300EN6/fXXncanTJmiPXv26NNPPzUomfd6/PHHjY5QaM2dO1ejR4/WkCFD9NZbbznWwQQHB2vGjBkUGRjm9qu3YAqbN282OkKhtHXrVv3tb3/LMd6+fXtt3brVgESA5/z73//We++9pzfeeMOxIZ4kNWzYkM3wYCiKjBdo166dKlWqpAkTJigpKcnoOIVGenq6/P39c4z7+fnJZrMZkAjwnDNnzqh+/fo5xgMCAnT16lUDEgG/och4gfPnz+vVV1/V8uXLdf/996tt27b65JNPdOPGDaOjebU6dero448/zjH+0UcfqWbNmgYkAjynYsWKOnjwYI7xtWvXqkaNGnc/EPB/WCPjBe69914NHTpUQ4cO1f79+7Vo0SK98soreuWVV/Tss8+qd+/e7LzpAaNGjVJMTIwSEhLUqlUrSVJcXJyWLVvG+hh4ndjYWA0YMEDXr1+X3W7Xt99+q2XLlmnSpEl6//33jY6HQoy7lrzQhQsXNH/+fE2ePFm+vr66fv26oqOjNW/ePJ7I7GZr1qzRxIkTdfDgQRUrVkxRUVEaM2YMe5rAK3344YcaO3asEhISJEllypTRuHHj1Lt3b4OToTCjyHiJzMxMffHFF1q4cKE2bNighg0bqnfv3nrmmWf0448/6s0339T+/ft17Ngxo6MCMLlr164pPT1d4eHhRkcBKDLeYODAgVq2bJnsdrteeOEF9enTR7Vr13Z6T3JyssqUKaPs7GyDUgIws19//VV2u1333HOPJOns2bNasWKFatasqTZt2hicDoUZa2S8wLFjxzRr1iw9+eSTCggIyPU99957L7dpu1lWVpamT5+uTz75RImJiTkWV1+6dMmgZID7derUSTExMerfv78uX76shx56SP7+/vrpp580bdo0vfzyy0ZHRCHFXUte4NFHH9W1a9dylJiFCxfq7bffliT5+vqybsPNxo0bp2nTpql79+5KS0tTbGysYmJi5OPjo7FjxxodD3Cr/fv3q1mzZpKk5cuXq1SpUjp79qw++OADlx6ZArgbRcYLzJ8/X9WrV88xXqtWLc2bN8+ARIXDhx9+qPfee0/Dhg2Tr6+vnnnmGb3//vsaPXq0du/ebXQ8wK2uXbumwMBASdL69esdpb1x48Y6e/aswelQmFFkvEBycrJKly6dYzwsLEwXL140IFHhkJycrDp16kiSSpQoobS0NEnSE088oTVr1hgZDXC7ypUra+XKlUpKStK6desc62JSU1N59hUMRZHxApGRkdqxY0eO8R07dqhMmTIGJCocypYt6yiKlSpV0vr16yVJe/bsueVaJcCsRo8ereHDh6tChQpq1KiRoqOjJf02O5Pbjr/A3cJiXy/Qt29fDRkyRJmZmU4bs7322msaNmyYwem815NPPqm4uDg1atRIAwcO1PPPP68FCxYoMTFRQ4cONToe4FZPPfWUHn74YV28eNFpg81HH31UTz75pIHJUNhx+7UXsNvtGjFihGbOnOm4c6Zo0aJ6/fXXNXr0aIPTFR67du3Srl27VKVKFXXo0MHoOIDbZGZmqlixYjp48GCOrR0Ao1FkvEh6erqOHz+uYsWKqUqVKlzeAOA2999/v1asWMHjTlDgUGQAF6xatSrP7+3YsaMHkwB314IFC/T555/rP//5j0qWLGl0HMCBIgO4wMcnb+vjLRaLsrKyPJwGuHvq16+vU6dOKTMzU+XLl1fx4sWdju/fv9+gZCjsWOwLuIBHPKCw6ty5s9ERgFwxIwMAAEyLGRngDsTFxWn69Ok6fvy4JKlGjRoaMmSIWrdubXAywDP27dvn+Pteq1Yt9pCB4ZiRAfJpzpw5Gjx4sJ566inH5mC7d+/W8uXLNX36dA0YMMDghID7pKam6umnn9aWLVsUHBwsSbp8+bJatmypjz76SGFhYcYGRKFFkQHyqWzZshoxYoReffVVp/HZs2dr4sSJOn/+vEHJAPfr3r27Tp8+rQ8++EA1atSQJB07dkwvvfSSKleurGXLlhmcEIUVRQbIpxIlSujgwYOqXLmy0/jJkydVv359paenG5QMcD+r1aqNGzfqwQcfdBr/9ttv1aZNG12+fNmYYCj0eNYSkE8dO3bUihUrcox/8cUXeuKJJwxIBHhOdna2/Pz8coz7+flxNx8MxYwMkE8TJkzQ//zP/6hp06ZOa2R27NihYcOGOT0ReNCgQUbFBNyiU6dOunz5spYtW+Z4GO358+f13HPPKSQkJNdSD9wNFBkgnypWrJin91ksFp0+fdrDaQDPSkpKUseOHfXdd98pMjJSkpSYmKg6depo1apVKlu2rMEJUVhRZAAAeWK32xUXF+e03QBbDcBoFBnATbKysnTkyBGVL19eISEhRscB3C4uLk5xcXFKTU3NsS5m4cKFBqVCYcdiXyCfhgwZogULFkj6rcQ0b95cDzzwgCIjI7VlyxZjwwFuNm7cOLVp00ZxcXH66aef9Msvvzi9AKMwIwPkU9myZbVy5Uo1bNhQK1eu1IABA7R582b95z//0aZNm7Rjxw6jIwJuU7p0aU2ZMkUvvPCC0VEAJ8zIAPn0008/qVSpUpKkr776Sl27dlXVqlXVq1cvHTlyxOB0gHvduHFDTZo0MToGkANFBsiniIgIHTt2TFlZWVq7dq0ee+wxSdK1a9dUpEgRg9MB7tWnTx8tXbrU6BhADjw0Esinnj17qlu3bipdurQsFovj7o1vvvlG1atXNzgdcOdiY2Mdf87Oztb8+fO1ceNGRUVF5dgcb9q0aXc7HiCJNTLAHVm+fLmSkpLUtWtXxz4aS5YsUXBwsDp16mRwOuDOtGzZMk/vs1gs2rRpk4fTALmjyAAAANPi0hLggpkzZ6pfv34qWrSoZs6cedv38lgCAPA8ZmQAF1SsWFF79+5VaGjobR9RwGMJAODuoMgAAADT4tIS4II/3sVxOxaLRVOnTvVwGgAARQZwwYEDB5y+3r9/v27evKlq1apJkn744QcVKVJEDRo0MCIeABQ6FBnABZs3b3b8edq0aQoMDNSSJUscD4n85Zdf1LNnTzVr1syoiABQqLBGBsin++67T+vXr1etWrWcxo8ePao2bdrowoULBiUDgMKDRxQA+WSz2fTjjz/mGP/xxx915coVAxIBQOFDkQHy6cknn1TPnj31+eef69y5czp37pw+++wz9e7dWzExMUbHA4BCgUtLQD5du3ZNw4cP18KFC5WZmSlJ8vX1Ve/evfWvf/1LxYsXNzghAHg/igxwh65evaqEhARJUqVKlSgwAHAXUWQAAIBpsUYGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACY1v8HxzdYGSBzaBoAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -530,10 +530,10 @@ "id": "c3cb5016-edca-4314-ae34-dc64e3686ce5", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:41.687698Z", - "iopub.status.busy": "2024-10-20T00:08:41.687185Z", - "iopub.status.idle": "2024-10-20T00:08:41.766286Z", - "shell.execute_reply": "2024-10-20T00:08:41.765655Z" + "iopub.execute_input": "2024-10-22T01:35:01.100103Z", + "iopub.status.busy": "2024-10-22T01:35:01.099599Z", + "iopub.status.idle": "2024-10-22T01:35:01.181018Z", + "shell.execute_reply": "2024-10-22T01:35:01.180388Z" } }, "outputs": [ @@ -587,10 +587,10 @@ "id": "5c5a439d-4b43-4a38-bb78-975b0694a2c6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:41.768392Z", - "iopub.status.busy": "2024-10-20T00:08:41.768198Z", - "iopub.status.idle": "2024-10-20T00:08:46.817186Z", - "shell.execute_reply": "2024-10-20T00:08:46.816613Z" + "iopub.execute_input": "2024-10-22T01:35:01.183445Z", + "iopub.status.busy": "2024-10-22T01:35:01.182938Z", + "iopub.status.idle": "2024-10-22T01:35:06.243556Z", + "shell.execute_reply": "2024-10-22T01:35:06.242900Z" } }, "outputs": [ @@ -647,7 +647,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 325.76it/s]" + "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 298.61it/s]" ] }, { @@ -670,7 +670,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 75%|███████▌ | 24/32 [00:00<00:00, 216.42it/s]" + "Evaluating set functions...: 75%|███████▌ | 24/32 [00:00<00:00, 199.45it/s]" ] }, { @@ -678,7 +678,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 270.95it/s]" + "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 259.49it/s]" ] }, { @@ -690,7 +690,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb index 0e10fa91f..09e39fc59 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_online_shop.ipynb @@ -53,10 +53,10 @@ "id": "3e2cc4f9-539c-415d-98a5-3a35ebe226a8", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:51.696193Z", - "iopub.status.busy": "2024-10-20T00:08:51.695764Z", - "iopub.status.idle": "2024-10-20T00:08:51.706794Z", - "shell.execute_reply": "2024-10-20T00:08:51.706193Z" + "iopub.execute_input": "2024-10-22T01:35:11.443902Z", + "iopub.status.busy": "2024-10-22T01:35:11.443715Z", + "iopub.status.idle": "2024-10-22T01:35:11.454875Z", + "shell.execute_reply": "2024-10-22T01:35:11.454191Z" } }, "outputs": [ @@ -136,10 +136,10 @@ "id": "49665408-6239-4d17-ab3f-fca7a8bfbbd3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:51.708710Z", - "iopub.status.busy": "2024-10-20T00:08:51.708371Z", - "iopub.status.idle": "2024-10-20T00:08:51.989604Z", - "shell.execute_reply": "2024-10-20T00:08:51.989072Z" + "iopub.execute_input": "2024-10-22T01:35:11.457150Z", + "iopub.status.busy": "2024-10-22T01:35:11.456774Z", + "iopub.status.idle": "2024-10-22T01:35:11.763961Z", + "shell.execute_reply": "2024-10-22T01:35:11.763368Z" } }, "outputs": [], @@ -175,10 +175,10 @@ "id": "47243f27-c8ef-40e2-9d88-ac9418a96393", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:51.991836Z", - "iopub.status.busy": "2024-10-20T00:08:51.991545Z", - "iopub.status.idle": "2024-10-20T00:08:53.273065Z", - "shell.execute_reply": "2024-10-20T00:08:53.272429Z" + "iopub.execute_input": "2024-10-22T01:35:11.766387Z", + "iopub.status.busy": "2024-10-22T01:35:11.766048Z", + "iopub.status.idle": "2024-10-22T01:35:13.151255Z", + "shell.execute_reply": "2024-10-22T01:35:13.150681Z" } }, "outputs": [ @@ -213,10 +213,10 @@ "id": "9d4f5efd-5873-46ed-9c3a-d5b644380f84", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:53.275509Z", - "iopub.status.busy": "2024-10-20T00:08:53.274991Z", - "iopub.status.idle": "2024-10-20T00:08:53.291098Z", - "shell.execute_reply": "2024-10-20T00:08:53.290560Z" + "iopub.execute_input": "2024-10-22T01:35:13.153899Z", + "iopub.status.busy": "2024-10-22T01:35:13.153403Z", + "iopub.status.idle": "2024-10-22T01:35:13.170119Z", + "shell.execute_reply": "2024-10-22T01:35:13.169488Z" } }, "outputs": [ @@ -371,10 +371,10 @@ "id": "ffe4eb1d-3bf5-486b-947d-5d25115b5ab3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:53.293326Z", - "iopub.status.busy": "2024-10-20T00:08:53.293111Z", - "iopub.status.idle": "2024-10-20T00:08:58.109402Z", - "shell.execute_reply": "2024-10-20T00:08:58.108683Z" + "iopub.execute_input": "2024-10-22T01:35:13.172431Z", + "iopub.status.busy": "2024-10-22T01:35:13.172193Z", + "iopub.status.idle": "2024-10-22T01:35:18.345754Z", + "shell.execute_reply": "2024-10-22T01:35:18.344981Z" } }, "outputs": [], @@ -404,10 +404,10 @@ "id": "941a24d8-cb6c-442e-81d9-0d64bb5ec933", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:58.111655Z", - "iopub.status.busy": "2024-10-20T00:08:58.111454Z", - "iopub.status.idle": "2024-10-20T00:08:58.122268Z", - "shell.execute_reply": "2024-10-20T00:08:58.121751Z" + "iopub.execute_input": "2024-10-22T01:35:18.348478Z", + "iopub.status.busy": "2024-10-22T01:35:18.347965Z", + "iopub.status.idle": "2024-10-22T01:35:18.359627Z", + "shell.execute_reply": "2024-10-22T01:35:18.358977Z" } }, "outputs": [ @@ -441,64 +441,64 @@ "Node Unit Price is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Unit Price := f(Shopping Event?) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 170.2896958076838\n", + "LinearRegression: 140.17291970017308\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 170.31426299250154\n", - "HistGradientBoostingRegressor: 425.84284365749255\n", + " ('linearregression', LinearRegression)]): 140.18220715240867\n", + "HistGradientBoostingRegressor: 423.52475724553994\n", "\n", "--- Node: Ad Spend\n", "Node Ad Spend is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Ad Spend := f(Shopping Event?) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 16319.371837554645\n", + "LinearRegression: 16319.015589914266\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 16609.292691721013\n", - "HistGradientBoostingRegressor: 80383.83457077148\n", + " ('linearregression', LinearRegression)]): 16347.986303481035\n", + "HistGradientBoostingRegressor: 80624.32478842209\n", "\n", "--- Node: Page Views\n", "Node Page Views is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the discrete causal relationship as Page Views := f(Ad Spend,Shopping Event?) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 89536.99249298338\n", + "LinearRegression: 80794.31766180889\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 111056.20714304021\n", - "HistGradientBoostingRegressor: 1382067.8968955562\n", + " ('linearregression', LinearRegression)]): 100308.0900693651\n", + "HistGradientBoostingRegressor: 1494674.9787405445\n", "\n", "--- Node: Sold Units\n", "Node Sold Units is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the discrete causal relationship as Sold Units := f(Page Views,Shopping Event?,Unit Price) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 9816.125538225946\n", + "LinearRegression: 11037.833647061916\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 23017.09648916649\n", - "HistGradientBoostingRegressor: 279953.3697878738\n", + " ('linearregression', LinearRegression)]): 23452.767029787286\n", + "HistGradientBoostingRegressor: 252818.86004050882\n", "\n", "--- Node: Revenue\n", "Node Revenue is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n", "This represents the causal relationship as Revenue := f(Sold Units,Unit Price) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 3.683316969539796e-19\n", - "LinearRegression: 72775370.9536945\n", - "HistGradientBoostingRegressor: 126628611605.11624\n", + " ('linearregression', LinearRegression)]): 6.814136393648623e-19\n", + "LinearRegression: 52994220.20337007\n", + "HistGradientBoostingRegressor: 155123248537.05835\n", "\n", "--- Node: Operational Cost\n", - "Node Operational Cost is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n", + "Node Operational Cost is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Operational Cost := f(Ad Spend,Sold Units) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", + "LinearRegression: 38.81943962208829\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 38.473104806220924\n", - "LinearRegression: 38.76570032466681\n", - "HistGradientBoostingRegressor: 16048870912.075459\n", + " ('linearregression', LinearRegression)]): 39.103417791382334\n", + "HistGradientBoostingRegressor: 16297592562.688583\n", "\n", "--- Node: Profit\n", "Node Profit is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Profit := f(Operational Cost,Revenue) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 1.8493444385269016e-18\n", + "LinearRegression: 1.1263449624091704e-18\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 4.304225709266537e-06\n", - "HistGradientBoostingRegressor: 25975916143.442066\n", + " ('linearregression', LinearRegression)]): 1.1126361777340421e-06\n", + "HistGradientBoostingRegressor: 27847798723.59491\n", "\n", "===Note===\n", "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n", @@ -540,10 +540,10 @@ "id": "2b032fa2-9348-418c-b62b-9ae77f49c342", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:58.124143Z", - "iopub.status.busy": "2024-10-20T00:08:58.123799Z", - "iopub.status.idle": "2024-10-20T00:08:58.148676Z", - "shell.execute_reply": "2024-10-20T00:08:58.148165Z" + "iopub.execute_input": "2024-10-22T01:35:18.361730Z", + "iopub.status.busy": "2024-10-22T01:35:18.361510Z", + "iopub.status.idle": "2024-10-22T01:35:18.388403Z", + "shell.execute_reply": "2024-10-22T01:35:18.387674Z" } }, "outputs": [ @@ -624,7 +624,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 411.28it/s]" + "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 400.09it/s]" ] }, { @@ -653,10 +653,10 @@ "id": "ab315928-4d60-4008-abb9-a9030e4cad44", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:58.150588Z", - "iopub.status.busy": "2024-10-20T00:08:58.150228Z", - "iopub.status.idle": "2024-10-20T00:09:31.448673Z", - "shell.execute_reply": "2024-10-20T00:09:31.448124Z" + "iopub.execute_input": "2024-10-22T01:35:18.390448Z", + "iopub.status.busy": "2024-10-22T01:35:18.390206Z", + "iopub.status.idle": "2024-10-22T01:35:52.145807Z", + "shell.execute_reply": "2024-10-22T01:35:52.145166Z" } }, "outputs": [ @@ -673,7 +673,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 855.76it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 809.14it/s]" ] }, { @@ -696,7 +696,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 2%|▏ | 1/50 [00:00<00:28, 1.75it/s]" + "Test permutations of given graph: 2%|▏ | 1/50 [00:00<00:24, 1.98it/s]" ] }, { @@ -704,7 +704,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 4%|▍ | 2/50 [00:01<00:25, 1.85it/s]" + "Test permutations of given graph: 4%|▍ | 2/50 [00:00<00:21, 2.22it/s]" ] }, { @@ -712,7 +712,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 6%|▌ | 3/50 [00:01<00:23, 2.04it/s]" + "Test permutations of given graph: 6%|▌ | 3/50 [00:01<00:21, 2.23it/s]" ] }, { @@ -720,7 +720,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 8%|▊ | 4/50 [00:01<00:21, 2.11it/s]" + "Test permutations of given graph: 8%|▊ | 4/50 [00:01<00:14, 3.11it/s]" ] }, { @@ -728,7 +728,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 5/50 [00:02<00:22, 2.04it/s]" + "Test permutations of given graph: 10%|█ | 5/50 [00:02<00:17, 2.51it/s]" ] }, { @@ -736,7 +736,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 12%|█▏ | 6/50 [00:02<00:21, 2.02it/s]" + "Test permutations of given graph: 12%|█▏ | 6/50 [00:02<00:19, 2.30it/s]" ] }, { @@ -744,7 +744,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 14%|█▍ | 7/50 [00:03<00:20, 2.11it/s]" + "Test permutations of given graph: 14%|█▍ | 7/50 [00:02<00:19, 2.26it/s]" ] }, { @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 16%|█▌ | 8/50 [00:03<00:19, 2.14it/s]" + "Test permutations of given graph: 16%|█▌ | 8/50 [00:03<00:19, 2.15it/s]" ] }, { @@ -760,7 +760,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 18%|█▊ | 9/50 [00:04<00:18, 2.26it/s]" + "Test permutations of given graph: 18%|█▊ | 9/50 [00:04<00:19, 2.09it/s]" ] }, { @@ -768,7 +768,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 10/50 [00:04<00:16, 2.36it/s]" + "Test permutations of given graph: 20%|██ | 10/50 [00:04<00:19, 2.09it/s]" ] }, { @@ -776,7 +776,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 22%|██▏ | 11/50 [00:05<00:16, 2.32it/s]" + "Test permutations of given graph: 22%|██▏ | 11/50 [00:04<00:18, 2.12it/s]" ] }, { @@ -784,7 +784,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 24%|██▍ | 12/50 [00:05<00:15, 2.38it/s]" + "Test permutations of given graph: 24%|██▍ | 12/50 [00:05<00:18, 2.11it/s]" ] }, { @@ -792,7 +792,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 26%|██▌ | 13/50 [00:05<00:16, 2.28it/s]" + "Test permutations of given graph: 26%|██▌ | 13/50 [00:05<00:15, 2.43it/s]" ] }, { @@ -800,7 +800,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 28%|██▊ | 14/50 [00:06<00:14, 2.41it/s]" + "Test permutations of given graph: 28%|██▊ | 14/50 [00:05<00:13, 2.75it/s]" ] }, { @@ -808,7 +808,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 15/50 [00:06<00:12, 2.81it/s]" + "Test permutations of given graph: 30%|███ | 15/50 [00:06<00:13, 2.60it/s]" ] }, { @@ -816,7 +816,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 32%|███▏ | 16/50 [00:06<00:12, 2.70it/s]" + "Test permutations of given graph: 32%|███▏ | 16/50 [00:06<00:13, 2.50it/s]" ] }, { @@ -824,7 +824,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 34%|███▍ | 17/50 [00:07<00:12, 2.67it/s]" + "Test permutations of given graph: 34%|███▍ | 17/50 [00:07<00:13, 2.40it/s]" ] }, { @@ -832,7 +832,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 36%|███▌ | 18/50 [00:07<00:13, 2.46it/s]" + "Test permutations of given graph: 36%|███▌ | 18/50 [00:07<00:11, 2.68it/s]" ] }, { @@ -840,7 +840,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 38%|███▊ | 19/50 [00:08<00:11, 2.61it/s]" + "Test permutations of given graph: 38%|███▊ | 19/50 [00:07<00:11, 2.66it/s]" ] }, { @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 20/50 [00:08<00:11, 2.70it/s]" + "Test permutations of given graph: 40%|████ | 20/50 [00:08<00:10, 2.77it/s]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 42%|████▏ | 21/50 [00:08<00:11, 2.46it/s]" + "Test permutations of given graph: 42%|████▏ | 21/50 [00:08<00:09, 3.06it/s]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 44%|████▍ | 22/50 [00:09<00:10, 2.76it/s]" + "Test permutations of given graph: 44%|████▍ | 22/50 [00:08<00:08, 3.22it/s]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 46%|████▌ | 23/50 [00:09<00:09, 2.99it/s]" + "Test permutations of given graph: 46%|████▌ | 23/50 [00:09<00:08, 3.05it/s]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 48%|████▊ | 24/50 [00:09<00:08, 3.18it/s]" + "Test permutations of given graph: 48%|████▊ | 24/50 [00:09<00:09, 2.73it/s]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 25/50 [00:10<00:08, 3.10it/s]" + "Test permutations of given graph: 50%|█████ | 25/50 [00:09<00:07, 3.33it/s]" ] }, { @@ -896,7 +896,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 52%|█████▏ | 26/50 [00:10<00:07, 3.11it/s]" + "Test permutations of given graph: 52%|█████▏ | 26/50 [00:10<00:07, 3.13it/s]" ] }, { @@ -904,7 +904,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 54%|█████▍ | 27/50 [00:10<00:07, 2.92it/s]" + "Test permutations of given graph: 54%|█████▍ | 27/50 [00:10<00:06, 3.49it/s]" ] }, { @@ -912,7 +912,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 56%|█████▌ | 28/50 [00:11<00:07, 3.11it/s]" + "Test permutations of given graph: 56%|█████▌ | 28/50 [00:10<00:06, 3.34it/s]" ] }, { @@ -920,7 +920,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 58%|█████▊ | 29/50 [00:11<00:06, 3.44it/s]" + "Test permutations of given graph: 58%|█████▊ | 29/50 [00:10<00:05, 3.67it/s]" ] }, { @@ -928,7 +928,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 30/50 [00:11<00:06, 3.30it/s]" + "Test permutations of given graph: 60%|██████ | 30/50 [00:11<00:06, 3.25it/s]" ] }, { @@ -936,7 +936,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 64%|██████▍ | 32/50 [00:12<00:04, 4.09it/s]" + "Test permutations of given graph: 62%|██████▏ | 31/50 [00:11<00:05, 3.38it/s]" ] }, { @@ -944,7 +944,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 66%|██████▌ | 33/50 [00:12<00:04, 4.11it/s]" + "Test permutations of given graph: 64%|██████▍ | 32/50 [00:11<00:05, 3.54it/s]" ] }, { @@ -952,7 +952,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 68%|██████▊ | 34/50 [00:12<00:03, 4.64it/s]" + "Test permutations of given graph: 66%|██████▌ | 33/50 [00:12<00:04, 3.41it/s]" ] }, { @@ -960,7 +960,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 35/50 [00:12<00:03, 4.40it/s]" + "Test permutations of given graph: 68%|██████▊ | 34/50 [00:12<00:04, 3.47it/s]" ] }, { @@ -968,7 +968,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 72%|███████▏ | 36/50 [00:12<00:02, 4.88it/s]" + "Test permutations of given graph: 70%|███████ | 35/50 [00:12<00:04, 3.74it/s]" ] }, { @@ -976,7 +976,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 74%|███████▍ | 37/50 [00:12<00:02, 5.40it/s]" + "Test permutations of given graph: 72%|███████▏ | 36/50 [00:12<00:03, 4.43it/s]" ] }, { @@ -984,7 +984,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 76%|███████▌ | 38/50 [00:13<00:02, 5.85it/s]" + "Test permutations of given graph: 74%|███████▍ | 37/50 [00:12<00:03, 4.19it/s]" ] }, { @@ -992,7 +992,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 78%|███████▊ | 39/50 [00:13<00:02, 5.16it/s]" + "Test permutations of given graph: 76%|███████▌ | 38/50 [00:13<00:03, 3.78it/s]" ] }, { @@ -1000,7 +1000,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 80%|████████ | 40/50 [00:13<00:02, 4.75it/s]" + "Test permutations of given graph: 78%|███████▊ | 39/50 [00:13<00:02, 3.85it/s]" ] }, { @@ -1008,7 +1008,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 82%|████████▏ | 41/50 [00:13<00:01, 4.50it/s]" + "Test permutations of given graph: 80%|████████ | 40/50 [00:13<00:02, 4.55it/s]" ] }, { @@ -1016,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 84%|████████▍ | 42/50 [00:14<00:01, 4.34it/s]" + "Test permutations of given graph: 82%|████████▏ | 41/50 [00:13<00:02, 4.50it/s]" ] }, { @@ -1024,7 +1024,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 88%|████████▊ | 44/50 [00:14<00:01, 5.78it/s]" + "Test permutations of given graph: 84%|████████▍ | 42/50 [00:14<00:01, 4.65it/s]" ] }, { @@ -1032,7 +1032,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 45/50 [00:14<00:00, 5.43it/s]" + "Test permutations of given graph: 86%|████████▌ | 43/50 [00:14<00:01, 4.76it/s]" ] }, { @@ -1040,7 +1040,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 92%|█████████▏| 46/50 [00:14<00:00, 5.17it/s]" + "Test permutations of given graph: 88%|████████▊ | 44/50 [00:14<00:01, 4.63it/s]" ] }, { @@ -1048,7 +1048,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 94%|█████████▍| 47/50 [00:14<00:00, 5.70it/s]" + "Test permutations of given graph: 90%|█████████ | 45/50 [00:14<00:01, 4.30it/s]" ] }, { @@ -1056,7 +1056,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 96%|█████████▌| 48/50 [00:15<00:00, 5.42it/s]" + "Test permutations of given graph: 94%|█████████▍| 47/50 [00:15<00:00, 5.59it/s]" ] }, { @@ -1064,7 +1064,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00, 5.88it/s]" + "Test permutations of given graph: 96%|█████████▌| 48/50 [00:15<00:00, 5.44it/s]" ] }, { @@ -1072,7 +1072,15 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00, 3.26it/s]" + "Test permutations of given graph: 98%|█████████▊| 49/50 [00:15<00:00, 5.23it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00, 3.22it/s]" ] }, { @@ -1084,7 +1092,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1110,67 +1118,67 @@ "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n", "\n", "--- Node Shopping Event?\n", - "- The KL divergence between generated and observed distribution is 0.027990336402803684.\n", + "- The KL divergence between generated and observed distribution is 0.008904394006508259.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Unit Price\n", - "- The MSE is 152.00707808895962.\n", - "- The NMSE is 0.720411285360626.\n", - "- The R2 coefficient is 0.16175071478019634.\n", - "- The normalized CRPS is 0.1407033490352388.\n", - "The estimated CRPS indicates a very good model performance.\n", - "The mechanism is better or equally good than all 7 baseline mechanisms.\n", + "- The MSE is 148.11430498673633.\n", + "- The NMSE is 1342.263881817742.\n", + "- The R2 coefficient is -9002278.035382235.\n", + "- The normalized CRPS is 163.24112659743213.\n", + "The estimated CRPS indicates a very bad model performance. Consider trying alternative causal mechanism types.\n", + "6 of 7 baseline mechanisms performed significantly better. The best baseline mechanism is: AdditiveNoiseModel using HistGradientBoostingRegressor with a CRPS of 24.342782551962898. Seeing this, consider changing the mechanism to a AdditiveNoiseModel using HistGradientBoostingRegressor or, if you are using an auto assigment, consider a better assignment quality level such as AssignmentQuality.BETTER.\n", "\n", "--- Node Ad Spend\n", - "- The MSE is 16195.430869243308.\n", - "- The NMSE is 0.48896423364213043.\n", - "- The R2 coefficient is 0.7443950435230015.\n", - "- The normalized CRPS is 0.280859718724849.\n", + "- The MSE is 15800.804127125453.\n", + "- The NMSE is 0.49216705827116514.\n", + "- The R2 coefficient is 0.7462501575346435.\n", + "- The normalized CRPS is 0.2852065536532278.\n", "The estimated CRPS indicates a good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Page Views\n", - "- The MSE is 84221.1506849315.\n", - "- The NMSE is 0.15291392779783552.\n", - "- The R2 coefficient is 0.9754630013432213.\n", - "- The normalized CRPS is 0.06611624028756846.\n", + "- The MSE is 76340.72328767124.\n", + "- The NMSE is 0.1686117400922747.\n", + "- The R2 coefficient is 0.9693701076006261.\n", + "- The normalized CRPS is 0.06765478644055958.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Sold Units\n", - "- The MSE is 9025.345205479452.\n", - "- The NMSE is 0.28032677135492834.\n", - "- The R2 coefficient is 0.8096081043090505.\n", - "- The normalized CRPS is 0.11314055052761643.\n", + "- The MSE is 8566.380821917808.\n", + "- The NMSE is 0.12831895732544357.\n", + "- The R2 coefficient is 0.9823127200152071.\n", + "- The normalized CRPS is 0.05706410449649901.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Revenue\n", - "- The MSE is 3.261517768189271e-19.\n", - "- The NMSE is 8.375167717432583e-16.\n", + "- The MSE is 5.655673798390138e-19.\n", + "- The NMSE is 1.1956498945603868e-15.\n", "- The R2 coefficient is 1.0.\n", - "- The normalized CRPS is 2.0778033783637062e-16.\n", + "- The normalized CRPS is 1.9089910778478803e-16.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Operational Cost\n", - "- The MSE is 39.44346210470364.\n", - "- The NMSE is 1.7996147680273706e-05.\n", - "- The R2 coefficient is 0.9999999996466071.\n", - "- The normalized CRPS is 1.010554646963904e-05.\n", + "- The MSE is 38.51417507707491.\n", + "- The NMSE is 1.754174369432478e-05.\n", + "- The R2 coefficient is 0.9999999996840231.\n", + "- The normalized CRPS is 9.745765566210271e-06.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Profit\n", - "- The MSE is 1.6909098265407491e-18.\n", - "- The NMSE is 4.409180107629312e-15.\n", + "- The MSE is 1.1088195026347474e-18.\n", + "- The NMSE is 4.631086458497623e-15.\n", "- The R2 coefficient is 1.0.\n", - "- The normalized CRPS is 3.152869746103322e-16.\n", + "- The normalized CRPS is 3.900711108759761e-16.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 1.1642537304338478\n", + "The overall average KL divergence between the generated and observed distribution is 1.1503944967087358\n", "The estimated KL divergence indicates some mismatches between the distributions.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", @@ -1179,9 +1187,9 @@ "+-------------------------------------------------------------------------------------------------------+\n", "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov |\n", "| equivalence class of the given DAG (p-value: 0.00). |\n", - "| The given DAG violates 6/18 LMCs and is better than 88.0% of the permuted DAGs (p-value: 0.12). |\n", + "| The given DAG violates 6/18 LMCs and is better than 74.0% of the permuted DAGs (p-value: 0.26). |\n", "| Based on the provided significance level (0.2) and because the DAG is informative, |\n", - "| we do not reject the DAG. |\n", + "| we reject the DAG. |\n", "+-------------------------------------------------------------------------------------------------------+\n", "\n", "==== NOTE ====\n", @@ -1227,10 +1235,10 @@ "id": "74ed17eb-45ec-444d-9364-2aa850911a05", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.450693Z", - "iopub.status.busy": "2024-10-20T00:09:31.450396Z", - "iopub.status.idle": "2024-10-20T00:09:31.467505Z", - "shell.execute_reply": "2024-10-20T00:09:31.466926Z" + "iopub.execute_input": "2024-10-22T01:35:52.147942Z", + "iopub.status.busy": "2024-10-22T01:35:52.147547Z", + "iopub.status.idle": "2024-10-22T01:35:52.164771Z", + "shell.execute_reply": "2024-10-22T01:35:52.164105Z" } }, "outputs": [ @@ -1269,112 +1277,112 @@ " \n", " 0\n", " False\n", - " $999.00\n", - " $1,293.03\n", - " 11583\n", - " 2350\n", - " $2,347,650.00\n", - " $1,676,296.14\n", - " $671,353.86\n", + " $1,046.68\n", + " $1,171.92\n", + " 11595\n", + " 1992\n", + " $2,084,991.02\n", + " $1,497,179.83\n", + " $587,811.19\n", " \n", " \n", " 1\n", " False\n", " $999.00\n", - " $1,383.61\n", - " 11897\n", - " 2410\n", - " $2,407,590.00\n", - " $1,706,391.01\n", - " $701,198.99\n", + " $1,221.67\n", + " 11657\n", + " 2309\n", + " $2,306,691.00\n", + " $1,655,733.37\n", + " $650,957.63\n", " \n", " \n", " 2\n", " False\n", " $999.00\n", - " $1,309.75\n", - " 11690\n", - " 2317\n", - " $2,314,683.00\n", - " $1,659,828.40\n", - " $654,854.60\n", + " $1,215.97\n", + " 11841\n", + " 2328\n", + " $2,325,672.00\n", + " $1,665,220.65\n", + " $660,451.35\n", " \n", " \n", " 3\n", " False\n", " $999.00\n", - " $1,123.75\n", - " 11701\n", - " 2412\n", - " $2,409,588.00\n", - " $1,707,126.26\n", - " $702,461.74\n", + " $1,450.41\n", + " 11645\n", + " 2339\n", + " $2,336,661.00\n", + " $1,670,958.88\n", + " $665,702.12\n", " \n", " \n", " 4\n", - " True\n", - " $913.46\n", - " $2,764.01\n", - " 20618\n", - " 6035\n", - " $5,512,713.18\n", - " $3,520,266.23\n", - " $1,992,446.95\n", + " False\n", + " $999.00\n", + " $1,228.42\n", + " 11771\n", + " 2376\n", + " $2,373,624.00\n", + " $1,689,231.49\n", + " $684,392.51\n", " \n", " \n", " 5\n", " False\n", " $999.00\n", - " $1,128.02\n", - " 11753\n", - " 2466\n", - " $2,463,534.00\n", - " $1,734,142.88\n", - " $729,391.12\n", + " $1,132.55\n", + " 11742\n", + " 2332\n", + " $2,329,668.00\n", + " $1,667,136.70\n", + " $662,531.30\n", " \n", " \n", " 6\n", " False\n", " $999.00\n", - " $1,308.80\n", - " 11731\n", - " 2319\n", - " $2,316,681.00\n", - " $1,660,817.37\n", - " $655,863.63\n", + " $1,199.07\n", + " 11552\n", + " 2343\n", + " $2,340,657.00\n", + " $1,672,710.83\n", + " $667,946.17\n", " \n", " \n", " 7\n", " False\n", " $999.00\n", - " $1,428.01\n", - " 11750\n", - " 2309\n", - " $2,306,691.00\n", - " $1,655,943.41\n", - " $650,747.59\n", + " $1,306.93\n", + " 11892\n", + " 2389\n", + " $2,386,611.00\n", + " $1,695,817.89\n", + " $690,793.11\n", " \n", " \n", " 8\n", " False\n", " $999.00\n", - " $1,178.90\n", - " 11501\n", - " 2301\n", - " $2,298,699.00\n", - " $1,651,689.56\n", - " $647,009.44\n", + " $1,143.05\n", + " 11664\n", + " 2295\n", + " $2,292,705.00\n", + " $1,648,652.94\n", + " $644,052.06\n", " \n", " \n", " 9\n", " False\n", " $999.00\n", - " $1,279.35\n", - " 11677\n", - " 2274\n", - " $2,271,726.00\n", - " $1,638,281.46\n", - " $633,444.54\n", + " $1,412.59\n", + " 11782\n", + " 2370\n", + " $2,367,630.00\n", + " $1,686,422.20\n", + " $681,207.80\n", " \n", " \n", "\n", @@ -1382,28 +1390,28 @@ ], "text/plain": [ " Shopping Event? Unit Price Ad Spend Page Views Sold Units \\\n", - "0 False $999.00 $1,293.03 11583 2350 \n", - "1 False $999.00 $1,383.61 11897 2410 \n", - "2 False $999.00 $1,309.75 11690 2317 \n", - "3 False $999.00 $1,123.75 11701 2412 \n", - "4 True $913.46 $2,764.01 20618 6035 \n", - "5 False $999.00 $1,128.02 11753 2466 \n", - "6 False $999.00 $1,308.80 11731 2319 \n", - "7 False $999.00 $1,428.01 11750 2309 \n", - "8 False $999.00 $1,178.90 11501 2301 \n", - "9 False $999.00 $1,279.35 11677 2274 \n", + "0 False $1,046.68 $1,171.92 11595 1992 \n", + "1 False $999.00 $1,221.67 11657 2309 \n", + "2 False $999.00 $1,215.97 11841 2328 \n", + "3 False $999.00 $1,450.41 11645 2339 \n", + "4 False $999.00 $1,228.42 11771 2376 \n", + "5 False $999.00 $1,132.55 11742 2332 \n", + "6 False $999.00 $1,199.07 11552 2343 \n", + "7 False $999.00 $1,306.93 11892 2389 \n", + "8 False $999.00 $1,143.05 11664 2295 \n", + "9 False $999.00 $1,412.59 11782 2370 \n", "\n", - " Revenue Operational Cost Profit \n", - "0 $2,347,650.00 $1,676,296.14 $671,353.86 \n", - "1 $2,407,590.00 $1,706,391.01 $701,198.99 \n", - "2 $2,314,683.00 $1,659,828.40 $654,854.60 \n", - "3 $2,409,588.00 $1,707,126.26 $702,461.74 \n", - "4 $5,512,713.18 $3,520,266.23 $1,992,446.95 \n", - "5 $2,463,534.00 $1,734,142.88 $729,391.12 \n", - "6 $2,316,681.00 $1,660,817.37 $655,863.63 \n", - "7 $2,306,691.00 $1,655,943.41 $650,747.59 \n", - "8 $2,298,699.00 $1,651,689.56 $647,009.44 \n", - "9 $2,271,726.00 $1,638,281.46 $633,444.54 " + " Revenue Operational Cost Profit \n", + "0 $2,084,991.02 $1,497,179.83 $587,811.19 \n", + "1 $2,306,691.00 $1,655,733.37 $650,957.63 \n", + "2 $2,325,672.00 $1,665,220.65 $660,451.35 \n", + "3 $2,336,661.00 $1,670,958.88 $665,702.12 \n", + "4 $2,373,624.00 $1,689,231.49 $684,392.51 \n", + "5 $2,329,668.00 $1,667,136.70 $662,531.30 \n", + "6 $2,340,657.00 $1,672,710.83 $667,946.17 \n", + "7 $2,386,611.00 $1,695,817.89 $690,793.11 \n", + "8 $2,292,705.00 $1,648,652.94 $644,052.06 \n", + "9 $2,367,630.00 $1,686,422.20 $681,207.80 " ] }, "execution_count": 9, @@ -1445,10 +1453,10 @@ "id": "efe77b22-e99c-43e9-876f-4f7198d5b112", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.469498Z", - "iopub.status.busy": "2024-10-20T00:09:31.469172Z", - "iopub.status.idle": "2024-10-20T00:09:31.746480Z", - "shell.execute_reply": "2024-10-20T00:09:31.745875Z" + "iopub.execute_input": "2024-10-22T01:35:52.166750Z", + "iopub.status.busy": "2024-10-22T01:35:52.166547Z", + "iopub.status.idle": "2024-10-22T01:35:52.440722Z", + "shell.execute_reply": "2024-10-22T01:35:52.440023Z" } }, "outputs": [ @@ -1491,10 +1499,10 @@ "id": "b3ff0966-ddaf-4b19-b9ba-1fe854092f43", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.748588Z", - "iopub.status.busy": "2024-10-20T00:09:31.748106Z", - "iopub.status.idle": "2024-10-20T00:09:31.794711Z", - "shell.execute_reply": "2024-10-20T00:09:31.794197Z" + "iopub.execute_input": "2024-10-22T01:35:52.442692Z", + "iopub.status.busy": "2024-10-22T01:35:52.442489Z", + "iopub.status.idle": "2024-10-22T01:35:52.491495Z", + "shell.execute_reply": "2024-10-22T01:35:52.490873Z" } }, "outputs": [ @@ -1531,16 +1539,16 @@ "id": "ecef5abd-f551-444b-8ec4-53e224f6d529", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.796831Z", - "iopub.status.busy": "2024-10-20T00:09:31.796439Z", - "iopub.status.idle": "2024-10-20T00:09:32.108325Z", - "shell.execute_reply": "2024-10-20T00:09:32.107676Z" + "iopub.execute_input": "2024-10-22T01:35:52.493614Z", + "iopub.status.busy": "2024-10-22T01:35:52.493201Z", + "iopub.status.idle": "2024-10-22T01:35:52.810006Z", + "shell.execute_reply": "2024-10-22T01:35:52.809244Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1585,10 +1593,10 @@ "id": "43e49972-b137-4cae-9ee7-5c5879ff01ed", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:32.110711Z", - "iopub.status.busy": "2024-10-20T00:09:32.110395Z", - "iopub.status.idle": "2024-10-20T00:12:23.629257Z", - "shell.execute_reply": "2024-10-20T00:12:23.628502Z" + "iopub.execute_input": "2024-10-22T01:35:52.812224Z", + "iopub.status.busy": "2024-10-22T01:35:52.812000Z", + "iopub.status.idle": "2024-10-22T01:38:49.679394Z", + "shell.execute_reply": "2024-10-22T01:38:49.678665Z" } }, "outputs": [ @@ -1597,7 +1605,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 0%| | 0/116 [00:00" ] @@ -1888,17 +1896,17 @@ "id": "a3853007-fb0c-4a1d-bcca-f7bdd4dcef13", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.766450Z", - "iopub.status.busy": "2024-10-20T00:12:23.766234Z", - "iopub.status.idle": "2024-10-20T00:12:23.956558Z", - "shell.execute_reply": "2024-10-20T00:12:23.956016Z" + "iopub.execute_input": "2024-10-22T01:38:49.817141Z", + "iopub.status.busy": "2024-10-22T01:38:49.816499Z", + "iopub.status.idle": "2024-10-22T01:38:50.014603Z", + "shell.execute_reply": "2024-10-22T01:38:50.013876Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -1953,10 +1961,10 @@ "id": "c9be4254-9ac2-4abc-a3f3-906925dac53d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.958556Z", - "iopub.status.busy": "2024-10-20T00:12:23.958258Z", - "iopub.status.idle": "2024-10-20T00:12:23.976597Z", - "shell.execute_reply": "2024-10-20T00:12:23.976125Z" + "iopub.execute_input": "2024-10-22T01:38:50.016981Z", + "iopub.status.busy": "2024-10-22T01:38:50.016586Z", + "iopub.status.idle": "2024-10-22T01:38:50.037280Z", + "shell.execute_reply": "2024-10-22T01:38:50.036599Z" } }, "outputs": [ @@ -1994,10 +2002,10 @@ "id": "16215ca0-00c9-411c-b293-675a4cb595c2", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.978678Z", - "iopub.status.busy": "2024-10-20T00:12:23.978230Z", - "iopub.status.idle": "2024-10-20T00:12:23.993340Z", - "shell.execute_reply": "2024-10-20T00:12:23.992874Z" + "iopub.execute_input": "2024-10-22T01:38:50.039431Z", + "iopub.status.busy": "2024-10-22T01:38:50.039192Z", + "iopub.status.idle": "2024-10-22T01:38:50.056559Z", + "shell.execute_reply": "2024-10-22T01:38:50.055846Z" } }, "outputs": [ @@ -2036,10 +2044,10 @@ "id": "00c6b5bf-e6b7-456f-84f6-91e2cbda62d6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.995162Z", - "iopub.status.busy": "2024-10-20T00:12:23.994847Z", - "iopub.status.idle": "2024-10-20T00:12:27.924356Z", - "shell.execute_reply": "2024-10-20T00:12:27.923788Z" + "iopub.execute_input": "2024-10-22T01:38:50.058663Z", + "iopub.status.busy": "2024-10-22T01:38:50.058440Z", + "iopub.status.idle": "2024-10-22T01:38:53.858973Z", + "shell.execute_reply": "2024-10-22T01:38:53.858213Z" } }, "outputs": [ @@ -2048,15 +2056,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 0%| | 0/122 [00:00" ] @@ -2219,10 +2219,10 @@ "id": "e48b4b77-7ee6-4669-be82-05d0d15f4065", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:27.926881Z", - "iopub.status.busy": "2024-10-20T00:12:27.926499Z", - "iopub.status.idle": "2024-10-20T00:13:04.831361Z", - "shell.execute_reply": "2024-10-20T00:13:04.830809Z" + "iopub.execute_input": "2024-10-22T01:38:53.861073Z", + "iopub.status.busy": "2024-10-22T01:38:53.860863Z", + "iopub.status.idle": "2024-10-22T01:39:31.896840Z", + "shell.execute_reply": "2024-10-22T01:39:31.896087Z" } }, "outputs": [], @@ -2244,16 +2244,16 @@ "id": "4c7b083f-ef7f-47e0-b72f-2d0d1b9501e1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:04.833607Z", - "iopub.status.busy": "2024-10-20T00:13:04.833242Z", - "iopub.status.idle": "2024-10-20T00:13:04.958825Z", - "shell.execute_reply": "2024-10-20T00:13:04.958289Z" + "iopub.execute_input": "2024-10-22T01:39:31.899275Z", + "iopub.status.busy": "2024-10-22T01:39:31.898900Z", + "iopub.status.idle": "2024-10-22T01:39:32.034933Z", + "shell.execute_reply": "2024-10-22T01:39:32.034313Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2304,10 +2304,10 @@ "id": "d70a7edb-9374-4fce-b8d3-e9ef9f53f328", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:04.961173Z", - "iopub.status.busy": "2024-10-20T00:13:04.960820Z", - "iopub.status.idle": "2024-10-20T00:13:04.984942Z", - "shell.execute_reply": "2024-10-20T00:13:04.984451Z" + "iopub.execute_input": "2024-10-22T01:39:32.037190Z", + "iopub.status.busy": "2024-10-22T01:39:32.036957Z", + "iopub.status.idle": "2024-10-22T01:39:32.060721Z", + "shell.execute_reply": "2024-10-22T01:39:32.060078Z" } }, "outputs": [ @@ -2347,10 +2347,10 @@ "id": "7f4a5065-e436-46cd-be5e-e6c1274fc3b7", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:04.986665Z", - "iopub.status.busy": "2024-10-20T00:13:04.986490Z", - "iopub.status.idle": "2024-10-20T00:13:20.422247Z", - "shell.execute_reply": "2024-10-20T00:13:20.421537Z" + "iopub.execute_input": "2024-10-22T01:39:32.063000Z", + "iopub.status.busy": "2024-10-22T01:39:32.062610Z", + "iopub.status.idle": "2024-10-22T01:39:48.130700Z", + "shell.execute_reply": "2024-10-22T01:39:48.130072Z" } }, "outputs": [], @@ -2371,16 +2371,16 @@ "id": "fc2467b0-eb84-4f72-b895-954f5a8634e6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:20.424503Z", - "iopub.status.busy": "2024-10-20T00:13:20.424117Z", - "iopub.status.idle": "2024-10-20T00:13:20.553787Z", - "shell.execute_reply": "2024-10-20T00:13:20.552798Z" + "iopub.execute_input": "2024-10-22T01:39:48.133037Z", + "iopub.status.busy": "2024-10-22T01:39:48.132718Z", + "iopub.status.idle": "2024-10-22T01:39:48.247067Z", + "shell.execute_reply": "2024-10-22T01:39:48.246495Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -2407,10 +2407,10 @@ "id": "34b11c19-1256-4ae0-9067-2eb374ce5147", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:20.556956Z", - "iopub.status.busy": "2024-10-20T00:13:20.556131Z", - "iopub.status.idle": "2024-10-20T00:13:20.573046Z", - "shell.execute_reply": "2024-10-20T00:13:20.572570Z" + "iopub.execute_input": "2024-10-22T01:39:48.249269Z", + "iopub.status.busy": "2024-10-22T01:39:48.249042Z", + "iopub.status.idle": "2024-10-22T01:39:48.269824Z", + "shell.execute_reply": "2024-10-22T01:39:48.269253Z" } }, "outputs": [ @@ -2463,10 +2463,10 @@ "id": "1c4785f9-5ec1-4340-8837-6d0cdaf4c406", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:20.574899Z", - "iopub.status.busy": "2024-10-20T00:13:20.574708Z", - "iopub.status.idle": "2024-10-20T00:13:20.664123Z", - "shell.execute_reply": "2024-10-20T00:13:20.663570Z" + "iopub.execute_input": "2024-10-22T01:39:48.272380Z", + "iopub.status.busy": "2024-10-22T01:39:48.271973Z", + "iopub.status.idle": "2024-10-22T01:39:48.367635Z", + "shell.execute_reply": "2024-10-22T01:39:48.367041Z" } }, "outputs": [], diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb index 707e13e59..83c963e1b 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_rca_microservice_architecture.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:27.136435Z", - "iopub.status.busy": "2024-10-20T00:13:27.136252Z", - "iopub.status.idle": "2024-10-20T00:13:27.393960Z", - "shell.execute_reply": "2024-10-20T00:13:27.393298Z" + "iopub.execute_input": "2024-10-22T01:39:55.169686Z", + "iopub.status.busy": "2024-10-22T01:39:55.169477Z", + "iopub.status.idle": "2024-10-22T01:39:55.439061Z", + "shell.execute_reply": "2024-10-22T01:39:55.438470Z" } }, "outputs": [ @@ -194,10 +194,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:27.396190Z", - "iopub.status.busy": "2024-10-20T00:13:27.395712Z", - "iopub.status.idle": "2024-10-20T00:13:37.966003Z", - "shell.execute_reply": "2024-10-20T00:13:37.965356Z" + "iopub.execute_input": "2024-10-22T01:39:55.440944Z", + "iopub.status.busy": "2024-10-22T01:39:55.440753Z", + "iopub.status.idle": "2024-10-22T01:40:06.333976Z", + "shell.execute_reply": "2024-10-22T01:40:06.333322Z" } }, "outputs": [ @@ -244,10 +244,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:37.969373Z", - "iopub.status.busy": "2024-10-20T00:13:37.968953Z", - "iopub.status.idle": "2024-10-20T00:13:38.931903Z", - "shell.execute_reply": "2024-10-20T00:13:38.931280Z" + "iopub.execute_input": "2024-10-22T01:40:06.337371Z", + "iopub.status.busy": "2024-10-22T01:40:06.336806Z", + "iopub.status.idle": "2024-10-22T01:40:07.421915Z", + "shell.execute_reply": "2024-10-22T01:40:07.421150Z" } }, "outputs": [], @@ -276,10 +276,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:38.934339Z", - "iopub.status.busy": "2024-10-20T00:13:38.933785Z", - "iopub.status.idle": "2024-10-20T00:13:39.180165Z", - "shell.execute_reply": "2024-10-20T00:13:39.179623Z" + "iopub.execute_input": "2024-10-22T01:40:07.424830Z", + "iopub.status.busy": "2024-10-22T01:40:07.424059Z", + "iopub.status.idle": "2024-10-22T01:40:07.677524Z", + "shell.execute_reply": "2024-10-22T01:40:07.676850Z" } }, "outputs": [ @@ -317,10 +317,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:39.182305Z", - "iopub.status.busy": "2024-10-20T00:13:39.181942Z", - "iopub.status.idle": "2024-10-20T00:13:39.185452Z", - "shell.execute_reply": "2024-10-20T00:13:39.184987Z" + "iopub.execute_input": "2024-10-22T01:40:07.679924Z", + "iopub.status.busy": "2024-10-22T01:40:07.679446Z", + "iopub.status.idle": "2024-10-22T01:40:07.683488Z", + "shell.execute_reply": "2024-10-22T01:40:07.682867Z" } }, "outputs": [], @@ -358,10 +358,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:39.187273Z", - "iopub.status.busy": "2024-10-20T00:13:39.186925Z", - "iopub.status.idle": "2024-10-20T00:15:08.888366Z", - "shell.execute_reply": "2024-10-20T00:15:08.887670Z" + "iopub.execute_input": "2024-10-22T01:40:07.685727Z", + "iopub.status.busy": "2024-10-22T01:40:07.685263Z", + "iopub.status.idle": "2024-10-22T01:41:40.194966Z", + "shell.execute_reply": "2024-10-22T01:41:40.194240Z" } }, "outputs": [ @@ -458,7 +458,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Product DB: 91%|█████████ | 10/11 [00:00<00:00, 85.03it/s]" + "Fitting causal mechanism of node Product DB: 91%|█████████ | 10/11 [00:00<00:00, 83.24it/s]" ] }, { @@ -466,7 +466,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Order DB: 91%|█████████ | 10/11 [00:00<00:00, 85.03it/s] " + "Fitting causal mechanism of node Order DB: 91%|█████████ | 10/11 [00:00<00:00, 83.24it/s] " ] }, { @@ -474,7 +474,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 75.92it/s]" + "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 74.18it/s]" ] }, { @@ -497,7 +497,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 73%|███████▎ | 8/11 [00:02<00:00, 3.39it/s]" + "Evaluating causal mechanisms...: 73%|███████▎ | 8/11 [00:02<00:00, 3.15it/s]" ] }, { @@ -505,7 +505,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00, 4.65it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00, 4.32it/s]" ] }, { @@ -528,7 +528,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 2%|▏ | 1/50 [00:02<02:06, 2.59s/it]" + "Test permutations of given graph: 2%|▏ | 1/50 [00:02<02:08, 2.63s/it]" ] }, { @@ -536,7 +536,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 4%|▍ | 2/50 [00:05<02:02, 2.56s/it]" + "Test permutations of given graph: 4%|▍ | 2/50 [00:04<01:48, 2.26s/it]" ] }, { @@ -544,7 +544,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 6%|▌ | 3/50 [00:07<01:57, 2.50s/it]" + "Test permutations of given graph: 6%|▌ | 3/50 [00:06<01:42, 2.17s/it]" ] }, { @@ -552,7 +552,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 8%|▊ | 4/50 [00:09<01:51, 2.42s/it]" + "Test permutations of given graph: 8%|▊ | 4/50 [00:09<01:45, 2.30s/it]" ] }, { @@ -560,7 +560,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 5/50 [00:12<01:46, 2.36s/it]" + "Test permutations of given graph: 10%|█ | 5/50 [00:11<01:40, 2.23s/it]" ] }, { @@ -568,7 +568,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 12%|█▏ | 6/50 [00:14<01:43, 2.36s/it]" + "Test permutations of given graph: 12%|█▏ | 6/50 [00:13<01:37, 2.21s/it]" ] }, { @@ -576,7 +576,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 14%|█▍ | 7/50 [00:16<01:35, 2.21s/it]" + "Test permutations of given graph: 14%|█▍ | 7/50 [00:15<01:37, 2.28s/it]" ] }, { @@ -584,7 +584,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 16%|█▌ | 8/50 [00:18<01:25, 2.04s/it]" + "Test permutations of given graph: 16%|█▌ | 8/50 [00:17<01:31, 2.18s/it]" ] }, { @@ -592,7 +592,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 18%|█▊ | 9/50 [00:19<01:18, 1.92s/it]" + "Test permutations of given graph: 18%|█▊ | 9/50 [00:19<01:28, 2.17s/it]" ] }, { @@ -600,7 +600,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 10/50 [00:21<01:18, 1.97s/it]" + "Test permutations of given graph: 20%|██ | 10/50 [00:22<01:26, 2.15s/it]" ] }, { @@ -608,7 +608,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 22%|██▏ | 11/50 [00:23<01:18, 2.03s/it]" + "Test permutations of given graph: 22%|██▏ | 11/50 [00:23<01:20, 2.06s/it]" ] }, { @@ -616,7 +616,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 24%|██▍ | 12/50 [00:26<01:19, 2.08s/it]" + "Test permutations of given graph: 24%|██▍ | 12/50 [00:25<01:14, 1.95s/it]" ] }, { @@ -624,7 +624,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 26%|██▌ | 13/50 [00:27<01:05, 1.76s/it]" + "Test permutations of given graph: 26%|██▌ | 13/50 [00:27<01:08, 1.86s/it]" ] }, { @@ -632,7 +632,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 28%|██▊ | 14/50 [00:28<01:03, 1.76s/it]" + "Test permutations of given graph: 28%|██▊ | 14/50 [00:29<01:04, 1.80s/it]" ] }, { @@ -640,7 +640,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 15/50 [00:30<00:58, 1.66s/it]" + "Test permutations of given graph: 30%|███ | 15/50 [00:30<01:02, 1.78s/it]" ] }, { @@ -648,7 +648,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 32%|███▏ | 16/50 [00:31<00:52, 1.54s/it]" + "Test permutations of given graph: 32%|███▏ | 16/50 [00:32<00:57, 1.69s/it]" ] }, { @@ -656,7 +656,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 34%|███▍ | 17/50 [00:33<00:53, 1.62s/it]" + "Test permutations of given graph: 34%|███▍ | 17/50 [00:33<00:55, 1.68s/it]" ] }, { @@ -664,7 +664,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 36%|███▌ | 18/50 [00:35<00:52, 1.63s/it]" + "Test permutations of given graph: 36%|███▌ | 18/50 [00:35<00:55, 1.75s/it]" ] }, { @@ -672,7 +672,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 38%|███▊ | 19/50 [00:36<00:48, 1.56s/it]" + "Test permutations of given graph: 38%|███▊ | 19/50 [00:37<00:51, 1.65s/it]" ] }, { @@ -680,7 +680,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 20/50 [00:37<00:46, 1.54s/it]" + "Test permutations of given graph: 40%|████ | 20/50 [00:38<00:48, 1.62s/it]" ] }, { @@ -688,7 +688,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 42%|████▏ | 21/50 [00:39<00:45, 1.58s/it]" + "Test permutations of given graph: 42%|████▏ | 21/50 [00:40<00:47, 1.64s/it]" ] }, { @@ -696,7 +696,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 44%|████▍ | 22/50 [00:40<00:39, 1.42s/it]" + "Test permutations of given graph: 44%|████▍ | 22/50 [00:42<00:46, 1.64s/it]" ] }, { @@ -704,7 +704,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 46%|████▌ | 23/50 [00:41<00:36, 1.35s/it]" + "Test permutations of given graph: 46%|████▌ | 23/50 [00:43<00:46, 1.72s/it]" ] }, { @@ -712,7 +712,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 48%|████▊ | 24/50 [00:43<00:37, 1.44s/it]" + "Test permutations of given graph: 48%|████▊ | 24/50 [00:45<00:42, 1.65s/it]" ] }, { @@ -720,7 +720,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 25/50 [00:45<00:38, 1.53s/it]" + "Test permutations of given graph: 50%|█████ | 25/50 [00:46<00:36, 1.46s/it]" ] }, { @@ -728,7 +728,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 52%|█████▏ | 26/50 [00:46<00:34, 1.43s/it]" + "Test permutations of given graph: 52%|█████▏ | 26/50 [00:47<00:33, 1.38s/it]" ] }, { @@ -736,7 +736,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 54%|█████▍ | 27/50 [00:47<00:32, 1.42s/it]" + "Test permutations of given graph: 54%|█████▍ | 27/50 [00:48<00:31, 1.35s/it]" ] }, { @@ -744,7 +744,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 56%|█████▌ | 28/50 [00:49<00:30, 1.37s/it]" + "Test permutations of given graph: 56%|█████▌ | 28/50 [00:50<00:28, 1.29s/it]" ] }, { @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 58%|█████▊ | 29/50 [00:50<00:26, 1.27s/it]" + "Test permutations of given graph: 58%|█████▊ | 29/50 [00:51<00:26, 1.26s/it]" ] }, { @@ -760,7 +760,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 30/50 [00:51<00:28, 1.42s/it]" + "Test permutations of given graph: 60%|██████ | 30/50 [00:52<00:24, 1.23s/it]" ] }, { @@ -768,7 +768,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 62%|██████▏ | 31/50 [00:52<00:24, 1.31s/it]" + "Test permutations of given graph: 62%|██████▏ | 31/50 [00:53<00:23, 1.22s/it]" ] }, { @@ -776,7 +776,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 64%|██████▍ | 32/50 [00:53<00:20, 1.15s/it]" + "Test permutations of given graph: 64%|██████▍ | 32/50 [00:54<00:21, 1.17s/it]" ] }, { @@ -784,7 +784,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 66%|██████▌ | 33/50 [00:55<00:20, 1.23s/it]" + "Test permutations of given graph: 66%|██████▌ | 33/50 [00:55<00:19, 1.13s/it]" ] }, { @@ -792,7 +792,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 68%|██████▊ | 34/50 [00:56<00:18, 1.18s/it]" + "Test permutations of given graph: 68%|██████▊ | 34/50 [00:56<00:17, 1.11s/it]" ] }, { @@ -800,7 +800,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 35/50 [00:57<00:17, 1.18s/it]" + "Test permutations of given graph: 70%|███████ | 35/50 [00:58<00:17, 1.17s/it]" ] }, { @@ -808,7 +808,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 72%|███████▏ | 36/50 [00:58<00:16, 1.17s/it]" + "Test permutations of given graph: 72%|███████▏ | 36/50 [00:58<00:14, 1.06s/it]" ] }, { @@ -816,7 +816,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 74%|███████▍ | 37/50 [00:59<00:15, 1.16s/it]" + "Test permutations of given graph: 74%|███████▍ | 37/50 [01:00<00:15, 1.17s/it]" ] }, { @@ -824,7 +824,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 76%|███████▌ | 38/50 [01:00<00:12, 1.05s/it]" + "Test permutations of given graph: 76%|███████▌ | 38/50 [01:01<00:14, 1.17s/it]" ] }, { @@ -832,7 +832,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 78%|███████▊ | 39/50 [01:01<00:13, 1.19s/it]" + "Test permutations of given graph: 78%|███████▊ | 39/50 [01:02<00:13, 1.25s/it]" ] }, { @@ -840,7 +840,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 80%|████████ | 40/50 [01:03<00:11, 1.16s/it]" + "Test permutations of given graph: 80%|████████ | 40/50 [01:03<00:11, 1.18s/it]" ] }, { @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 82%|████████▏ | 41/50 [01:04<00:10, 1.18s/it]" + "Test permutations of given graph: 82%|████████▏ | 41/50 [01:05<00:11, 1.24s/it]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 84%|████████▍ | 42/50 [01:05<00:09, 1.18s/it]" + "Test permutations of given graph: 84%|████████▍ | 42/50 [01:06<00:09, 1.20s/it]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 86%|████████▌ | 43/50 [01:06<00:07, 1.13s/it]" + "Test permutations of given graph: 86%|████████▌ | 43/50 [01:07<00:08, 1.23s/it]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 88%|████████▊ | 44/50 [01:07<00:06, 1.14s/it]" + "Test permutations of given graph: 88%|████████▊ | 44/50 [01:09<00:07, 1.31s/it]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 45/50 [01:08<00:04, 1.09it/s]" + "Test permutations of given graph: 90%|█████████ | 45/50 [01:10<00:06, 1.25s/it]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 92%|█████████▏| 46/50 [01:09<00:04, 1.03s/it]" + "Test permutations of given graph: 92%|█████████▏| 46/50 [01:11<00:04, 1.17s/it]" ] }, { @@ -896,7 +896,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 94%|█████████▍| 47/50 [01:10<00:03, 1.04s/it]" + "Test permutations of given graph: 94%|█████████▍| 47/50 [01:12<00:03, 1.24s/it]" ] }, { @@ -904,7 +904,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 96%|█████████▌| 48/50 [01:11<00:02, 1.03s/it]" + "Test permutations of given graph: 96%|█████████▌| 48/50 [01:13<00:02, 1.22s/it]" ] }, { @@ -912,7 +912,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 98%|█████████▊| 49/50 [01:12<00:01, 1.02s/it]" + "Test permutations of given graph: 98%|█████████▊| 49/50 [01:14<00:01, 1.17s/it]" ] }, { @@ -920,7 +920,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 50/50 [01:13<00:00, 1.03s/it]" + "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00, 1.07s/it]" ] }, { @@ -928,7 +928,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 50/50 [01:13<00:00, 1.47s/it]" + "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00, 1.52s/it]" ] }, { @@ -940,7 +940,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -966,68 +966,68 @@ "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n", "\n", "--- Node Customer DB\n", - "- The KL divergence between generated and observed distribution is 0.04053244561112437.\n", + "- The KL divergence between generated and observed distribution is 0.04027380777121141.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Shipping Cost Service\n", - "- The KL divergence between generated and observed distribution is 0.00516151353196099.\n", + "- The KL divergence between generated and observed distribution is 0.05228673903078925.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Product DB\n", - "- The KL divergence between generated and observed distribution is 0.07907442198078539.\n", + "- The KL divergence between generated and observed distribution is 0.04453439493387481.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Order DB\n", - "- The KL divergence between generated and observed distribution is 0.07950995311601591.\n", + "- The KL divergence between generated and observed distribution is 0.02945564832908269.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Auth Service\n", - "- The MSE is 0.014409310390136789.\n", - "- The NMSE is 0.5462705245729054.\n", - "- The R2 coefficient is 0.7014376755538484.\n", - "- The normalized CRPS is 0.3027521836699161.\n", + "- The MSE is 0.014414661073635477.\n", + "- The NMSE is 0.5468392311614381.\n", + "- The R2 coefficient is 0.7008581544246362.\n", + "- The normalized CRPS is 0.3027241612157213.\n", "The estimated CRPS indicates a good model performance.\n", "\n", "--- Node Caching Service\n", - "- The MSE is 0.023158343257168923.\n", - "- The NMSE is 0.8427188063479955.\n", - "- The R2 coefficient is 0.28958072725807565.\n", - "- The normalized CRPS is 0.46295292151719725.\n", + "- The MSE is 0.02313891078313382.\n", + "- The NMSE is 0.8424878275526769.\n", + "- The R2 coefficient is 0.2901027347604022.\n", + "- The normalized CRPS is 0.46256528437410704.\n", "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n", "\n", "--- Node Order Service\n", - "- The MSE is 0.014397735388957358.\n", - "- The NMSE is 0.5489274923469949.\n", - "- The R2 coefficient is 0.6986121921831244.\n", - "- The normalized CRPS is 0.304260526459383.\n", + "- The MSE is 0.014401944309636233.\n", + "- The NMSE is 0.5486734295896927.\n", + "- The R2 coefficient is 0.6988465430408508.\n", + "- The normalized CRPS is 0.3034588760610286.\n", "The estimated CRPS indicates a good model performance.\n", "\n", "--- Node Product Service\n", - "- The MSE is 0.020441689979414278.\n", - "- The NMSE is 0.6493818414893976.\n", - "- The R2 coefficient is 0.5781215893815632.\n", - "- The normalized CRPS is 0.3639297409683348.\n", + "- The MSE is 0.02044996767396589.\n", + "- The NMSE is 0.6494615124205818.\n", + "- The R2 coefficient is 0.5780071956796627.\n", + "- The normalized CRPS is 0.3639706645045675.\n", "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n", "\n", "--- Node API\n", - "- The MSE is 0.0144834135564434.\n", - "- The NMSE is 0.20978740048124056.\n", - "- The R2 coefficient is 0.9559825162420468.\n", - "- The normalized CRPS is 0.11587627273143704.\n", + "- The MSE is 0.014487845771135594.\n", + "- The NMSE is 0.20979051156046288.\n", + "- The R2 coefficient is 0.9559736211348916.\n", + "- The normalized CRPS is 0.11621315648767647.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "--- Node www\n", - "- The MSE is 0.011043981547566816.\n", - "- The NMSE is 0.13770141930364738.\n", - "- The R2 coefficient is 0.9810206381989447.\n", - "- The normalized CRPS is 0.07781201996784066.\n", + "- The MSE is 0.011042719234612093.\n", + "- The NMSE is 0.13763928951285248.\n", + "- The R2 coefficient is 0.9810497649925918.\n", + "- The normalized CRPS is 0.07784582906798679.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "--- Node Website\n", - "- The MSE is 0.020771984383852027.\n", - "- The NMSE is 0.18531485117535818.\n", - "- The R2 coefficient is 0.9656314099183605.\n", - "- The normalized CRPS is 0.1022386286114898.\n", + "- The MSE is 0.02078048115561805.\n", + "- The NMSE is 0.18515791235912968.\n", + "- The R2 coefficient is 0.9656876357594084.\n", + "- The normalized CRPS is 0.10198831290029271.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "==== Evaluation of Invertible Functional Causal Model Assumption ====\n", @@ -1035,10 +1035,10 @@ "--- The model assumption for node www is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", - "--- The model assumption for node Website is not rejected with a p-value of 0.2889508066141974 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Website is not rejected with a p-value of 0.6537764264060258 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", - "--- The model assumption for node Auth Service is not rejected with a p-value of 0.5622900739464742 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Auth Service is not rejected with a p-value of 0.46869441012563545 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", "--- The model assumption for node API is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n", @@ -1047,7 +1047,7 @@ "--- The model assumption for node Product Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might not be valid. This is, the relationship cannot be represent with this type of mechanism or there is a hidden confounder between the node and its parents.\n", "\n", - "--- The model assumption for node Order Service is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Order Service is not rejected with a p-value of 0.7488000687727592 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", "--- The model assumption for node Caching Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.\n", @@ -1056,7 +1056,7 @@ "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 0.10478608550501169\n", + "The overall average KL divergence between the generated and observed distribution is 0.11130986058049971\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", @@ -1065,7 +1065,7 @@ "+-------------------------------------------------------------------------------------------------------+\n", "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov |\n", "| equivalence class of the given DAG (p-value: 0.00). |\n", - "| The given DAG violates 4/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00). |\n", + "| The given DAG violates 2/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00). |\n", "| Based on the provided significance level (0.2) and because the DAG is informative, |\n", "| we do not reject the DAG. |\n", "+-------------------------------------------------------------------------------------------------------+\n", @@ -1113,10 +1113,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:15:08.890634Z", - "iopub.status.busy": "2024-10-20T00:15:08.890244Z", - "iopub.status.idle": "2024-10-20T00:15:08.902032Z", - "shell.execute_reply": "2024-10-20T00:15:08.901538Z" + "iopub.execute_input": "2024-10-22T01:41:40.197119Z", + "iopub.status.busy": "2024-10-22T01:41:40.196914Z", + "iopub.status.idle": "2024-10-22T01:41:40.208825Z", + "shell.execute_reply": "2024-10-22T01:41:40.208179Z" }, "jupyter": { "outputs_hidden": false @@ -1207,10 +1207,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:15:08.903876Z", - "iopub.status.busy": "2024-10-20T00:15:08.903496Z", - "iopub.status.idle": "2024-10-20T00:15:08.951444Z", - "shell.execute_reply": "2024-10-20T00:15:08.950819Z" + "iopub.execute_input": "2024-10-22T01:41:40.211060Z", + "iopub.status.busy": "2024-10-22T01:41:40.210600Z", + "iopub.status.idle": "2024-10-22T01:41:40.261554Z", + "shell.execute_reply": "2024-10-22T01:41:40.260884Z" }, "jupyter": { "outputs_hidden": false @@ -1258,10 +1258,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:15:08.953608Z", - "iopub.status.busy": "2024-10-20T00:15:08.953250Z", - "iopub.status.idle": "2024-10-20T00:16:02.083392Z", - "shell.execute_reply": "2024-10-20T00:16:02.082622Z" + "iopub.execute_input": "2024-10-22T01:41:40.263721Z", + "iopub.status.busy": "2024-10-22T01:41:40.263361Z", + "iopub.status.idle": "2024-10-22T01:42:34.713972Z", + "shell.execute_reply": "2024-10-22T01:42:34.713386Z" }, "jupyter": { "outputs_hidden": false @@ -1301,10 +1301,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": 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", + "image/png": 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EREYhz6Xm/v37+OSTT1C2bFmYmprCxMRE60FEREQkQ64n33upT58+uHnzJiZMmIAyZcpApVIVRq4sHTx4EPPmzcOpU6dw7949bN68GV27di2yz09ERET6K8+lJiwsDKGhoahVq1YhxMlZcnIyatasiX79+qFbt25F/vmJiIhIf+W51Li6ukLWfH3t27dH+/btpXxuIiIi0m95HlMTGBiIgIAAXL9+vRDiFKyUlBRoNBqtBxERERmnPJ+p+eCDD/D06VNUrFgR1tbWMDMz09ofFxdXYOH+q1mzZmHKlCmyYxAREVERyHOpCQwMLIQYhWPcuHHw9/dXnms0Gri6ukpMRERERIUlz6Wmd+/ehZGjUFhYWMDCwkJ2DCIiIioCeS41AJCeno4tW7bg4sWLAIBq1aqhc+fOnKeGiIzff1n92swcGDM78+MyfYC01Pz9PVz9mihLeS410dHR6NChA+7cuYPKlSsDyBy74urqiu3bt6NixYoFHvKlpKQkREdHK89jYmJw5swZODk5oXz58oX2eYmIiEj/5fnup+HDh6NixYq4desWwsPDER4ejps3b8Ld3R3Dhw8vjIyKkydPonbt2qhduzYAwN/fH7Vr18bEiRML9fMSERGR/svzmZqQkBAcPXoUTk5OyrbixYtj9uzZaNy4cYGG+zdfX19pc+QQERGRfsvzmRoLCwskJibqbE9KSoK5uXmBhCIiIiLKqzyXmnfeeQcDBw7EsWPHIISAEAJHjx7FoEGD0Llz58LISERERPRaeS41CxcuRMWKFdGwYUNYWlrC0tISjRs3hqenJ7777rvCyEhERET0WnkeU+Pg4ICtW7fiypUriIqKAgBUrVoVnp6eBR6OiIiIKLfyNU8NAHh5ecHLy6sgsxARERHlW65Kjb+/P6ZNmwYbGxutZQeysmDBggIJRkRERJQXuSo1p0+fRlpamvIxERERkb7JVak5cOBAlh8TEVHuWael4saMnM92E1H+5fnup379+mU5T01ycjL69etXIKGIiIiI8irPpSYoKAjPnj3T2f7s2TOsXr26QEIRERER5VWu737SaDTKZHuJiYmwtLRU9qWnp2PHjh0oWbJkoYQkIiIiep1clxoHBweoVCqoVCpUqlRJZ79KpcKUKVMKNBwRERFRbuW61Bw4cABCCLRs2RJ//PGH1oKW5ubmcHNzQ9myZQslJBEREdHr5LrUNG/eHAAQExOD8uXLQ6VSFVooIiIiorzK84zCN27cwI0bN7Ld36xZs/8UiIiIiCg/8lxqfH19dba9etYmPT39PwUiIiIiyo8839L95MkTrceDBw+wc+dOvP3229i9e3dhZCQiIiJ6rTyfqbG3t9fZ1qZNG5ibm8Pf3x+nTp0qkGBE+fU0NQNVv40FAFz8sjSszfPc3YmIyAAV2Hf7UqVK4dKlSwX11xERERHlSZ7P1Jw9e1bruRAC9+7dw+zZs1GrVq2CykVERESUJ3kuNbVq1YJKpYIQQmt7gwYNsHLlygILRm84ux75/7Nm5sCY2Zkfl+kDpKXm7+/RrM9/BiIiKnJ5LjUxMTFaz9VqNZydnbWWTSAiIiIqankuNW5uboWRg4iIiOg/yddA4X379uGdd95BxYoVUbFiRbzzzjvYu3dvQWcjIiIiyrU8l5offvgBfn5+sLW1xYgRIzBixAjY2dmhQ4cOWLx4cWFkJMoT67RU3Jjhjxsz/GGd3/E0RERkcPJ8+WnmzJn49ttvMXToUGXb8OHD0bhxY8ycORNDhgwp0IBEREREuZHnMzXx8fHw8/PT2d62bVskJCQUSCgiIiKivMpzqencuTM2b96ss33r1q145513CiQUERERUV7l6vLTwoULlY+9vb0xY8YMBAcHo2HDhgCAo0eP4tChQxg5cmThpCQiKbjkBBEZklyVmm+//VbruaOjIyIjIxEZGalsc3BwwMqVKzF+/PiCTUhERESUC7kqNf+ecI+IiIhI3/BcMhERERmFXJ2p8ff3x7Rp02BjYwN/f/8cX7tgwYICCZadxYsXY968eYiNjUXNmjWxaNEi1KtXr1A/JxEREem/XJWa06dPIy0tDQAQHh4OlUqV5euy215QNmzYAH9/fyxZsgT169dHYGAg2rVrh0uXLqFkyZKF+rmJiIjyioPti1auSs2BAweUj4ODgwsry2stWLAAAwYMQN++fQEAS5Yswfbt27Fy5UoEBATovD4lJQUpKSnKc41GU2RZiYiIqGjlaUbhtLQ0WFlZ4cyZM6hevXphZcpSamoqTp06hXHjxinb1Go1WrdujSNHjmT5Z2bNmoUpU6YUTUC7HkXzeV5Hsz7n/fqQ83UZc/safWAI7+d/yWhmDoyZnflxmT7Af1l24nU5DeXfnDkLjj4cP4BhHEO5+ffUh/dT8tddnkqNmZkZypcvj/T09MLKk61Hjx4hPT0dpUqV0tpeqlQpREVFZflnxo0bpzUGSKPRwNXVtVBzEumd//JNJjUD+P9T57i3CuCpcyLSY3n+DvX111/jq6++QlxcXGHkKVAWFhaws7PTelD+PTUzh9vXC+D29QI8NTOXHYeIiEhLnhe0/P777xEdHY2yZcvCzc0NNjY2WvvDw8MLLNyrSpQoARMTE9y/f19r+/3791G6dOlC+ZxEheWpmTmq/v8p6YtzA7iaOJE+49lOg5HnUtOlS5dCv8spK+bm5qhTpw727duHrl27AgAyMjKwb98+rRXDiYiI6M2U51IzefLkQoiRO/7+/ujduzfq1q2LevXqITAwEMnJycrdUJQL/I2DiIiMVJ5LjYeHB06cOIHixYtrbY+Pj8dbb72Fa9euFVi4f/vggw/w8OFDTJw4EbGxsahVqxZ27typM3iYiIiI3jx5LjXXr1/P8u6nlJQU3L59u0BC5WTo0KG83ERERAbB2lyNG2PLyo7xxsh1qfnzzz+Vj3ft2gV7e3vleXp6Ovbt2wd3d/eCTUd6hQdnFvJ7OY+X8oiIClyuS83LwbkA0Lt3b619ZmZmqFChAr755psCC0ZERJQd3kFYsIzl/cx1qcnIyAAAuLu748SJEyhRokShhSIiIiLKqzyf854yZQpsbW11tqempmL16tUFEoqIiIgor/Jcavr27YuEhASd7YmJiby1moiIiKTJ891PQogsJ9+7ffu21uBhIjJ8HBxORIYk16Wmdu3aUKlUUKlUaNWqFUxN//mj6enpiImJgZ+fX6GEJCIiI8TJQKmA5fnupzNnzqBdu3YoVqyYss/c3BwVKlRA9+7dCzwgERER5QJLYu5LzaRJkwAAFSpUwAcffABLS0ud15w/fx7Vq1cvuHREREREuZTnKta7d2+tQpOYmIhly5ahXr16qFmzZoGGIyIiIsqtfJ9fOnjwIHr37o0yZcpg/vz5aNmyJY4ePVqQ2YiIiIhyLU93P8XGxmLVqlVYsWIFNBoN3n//faSkpGDLli3w9vYurIxEREREr5XrMzWdOnVC5cqVcfbsWQQGBuLu3btYtGhRYWYjIiLK0svpBm6MLQtrAx3USgUv12dq/v77bwwfPhyff/45vLy8CjMTERERFSFjmZMq1/U2LCwMiYmJqFOnDurXr4/vv/8ejx49KsxsREaLv2USERW8XH83bdCgAX766Sfcu3cPn332GdavX4+yZcsiIyMDe/bsQWJiYmHmJCIiIsqRSggh8vuHL126hBUrVuCXX35BfHw82rRpgz///LMg8xUojUYDe3t7JCQkwM7OrmD/crseBfv35dd/mXyJiIjIgP2n896VK1fG3Llzcfv2baxbt66gMhERERHl2X86U2NoeKaGiIjIeHGEIhERERkFlhoiIiIyCiw1REREZBRYaoiIiMgosNQQERGRUWCpISIiIqPAUkNERERGgaWGiIiIjAJLDRERERkFlhoiIiIyCiw1REREZBRYaoiIiMgoGEypmTFjBho1agRra2s4ODjIjkNERER6xmBKTWpqKt577z18/vnnsqMQERGRHjKVHSC3pkyZAgBYtWqV3CBERESklwym1ORHSkoKUlJSlOcajUZiGiIiIipMBnP5KT9mzZoFe3t75eHq6io7EhERERUSqaUmICAAKpUqx0dUVFS+//5x48YhISFBedy6dasA0xMREZE+kXr5aeTIkejTp0+Or/Hw8Mj3329hYQELC4t8/3kiIiIyHFJLjbOzM5ydnWVGICIiIiNhMAOFb968ibi4ONy8eRPp6ek4c+YMAMDT0xPFihWTG46IiIikM5hSM3HiRAQFBSnPa9euDQA4cOAAfH19JaUiIiIifaESQgjZIYqKRqOBvb09EhISYGdnV7B/uV2Pgv378kuzXnYCIiIiKYz6lm4iIiJ6c7DUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUDKLUXL9+Hf3794e7uzusrKxQsWJFTJo0CampqbKjERERkZ4wlR0gN6KiopCRkYGlS5fC09MT58+fx4ABA5CcnIz58+fLjkdERER6QCWEELJD5Me8efPw448/4tq1a7n+MxqNBvb29khISICdnV3BBrLrUbB/X35p1stOQEREJIVBnKnJSkJCApycnHJ8TUpKClJSUpTnGo2msGMRERGRJAYxpubfoqOjsWjRInz22Wc5vm7WrFmwt7dXHq6urkWUkIiIiIqa1FITEBAAlUqV4yMqKkrrz9y5cwd+fn547733MGDAgBz//nHjxiEhIUF53Lp1qzD/d4iIiEgiqWNqHj58iMePH+f4Gg8PD5ibmwMA7t69C19fXzRo0ACrVq2CWp23TsYxNURERMZL6pgaZ2dnODs75+q1d+7cQYsWLVCnTh38/PPPeS40REREZNwMYqDwnTt34OvrCzc3N8yfPx8PHz5U9pUuXVpiMiIiItIXBlFq9uzZg+joaERHR6NcuXJa+wz0jnQiIiIqYAZxDadPnz4QQmT5MAZPzczh9vUCuH29AE/NzGXHISIiMkgGUWqIiIiIXoelhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVEwlR3AaGjW5//PpmYA38ZmfnxvFWDOrklERJRX/OlJRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjYDClpnPnzihfvjwsLS1RpkwZfPLJJ7h7967sWERERKQnDKbUtGjRAr/99hsuXbqEP/74A1evXsX//vc/2bGIiIhITxjMMglffvml8rGbmxsCAgLQtWtXpKWlwczMTGIyIiIi0gcGU2peFRcXhzVr1qBRo0Y5FpqUlBSkpKQozzUaTVHEIyIiIgkM5vITAIwdOxY2NjYoXrw4bt68ia1bt+b4+lmzZsHe3l55uLq6FlFSIiIiKmpSS01AQABUKlWOj6ioKOX1o0ePxunTp7F7926YmJigV69eEEJk+/ePGzcOCQkJyuPWrVtF8b9FREREEqhETq2gkD18+BCPHz/O8TUeHh4wNzfX2X779m24urri8OHDaNiwYa4+n0ajgb29PRISEmBnZ5evzIXhaWoGqn4bCwC4+GVpWJsb1Ak0IiIivSB1TI2zszOcnZ3z9WczMjIAQGvMDBEREb25DGKg8LFjx3DixAk0adIEjo6OuHr1KiZMmICKFSvm+iwNERERGTepl59y69y5cxgxYgQiIiKQnJyMMmXKwM/PD+PHj4eLi0uu/x59vfxERERE/51BlJqCwlJDRERkvDgilYiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERoGlhoiIiIwCSw0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVEwlR2gKAkhAAAajUZyEiIiIsorW1tbqFSqbPe/UaUmMTERAODq6io5CREREeVVQkIC7Ozsst2vEi9PX7wBMjIycPfu3dc2PRk0Gg1cXV1x69atHP/BZGPOgmMIGQHmLGiGkNMQMgLMWdAMISfP1LxCrVajXLlysmPkyM7OTm+/mF7FnAXHEDICzFnQDCGnIWQEmLOgGUrOrHCgMBERERkFlhoiIiIyCiw1esLCwgKTJk2ChYWF7Cg5Ys6CYwgZAeYsaIaQ0xAyAsxZ0AwlZ07eqIHCREREZLx4poaIiIiMAksNERERGQWWGiIiIjIKLDVERERkFFhq9ERqaiqSkpJkxyACALx48QJ79+7F0qVLleVF7t69q5dfo6mpqbh06RJevHghO0q2DOn9JDJkLDUS/Pzzzxg2bBjWrFkDABg3bhxsbW1hb2+PNm3a4PHjx5ITagsNDcXHH3+Mhg0b4s6dOwCAX375BWFhYZKT/ePV9+zWrVuYOHEiRo8ejdDQUImpcnbt2jVcuHABGRkZsqNouXHjBmrUqIEuXbpgyJAhePjwIQBgzpw5GDVqlOR0/3j69Cn69+8Pa2trVKtWDTdv3gQADBs2DLNnz5ac7h+G8n4awnFuKFauXImYmBjZMd5MgorU9OnThZWVlWjdurVwcnISgwYNEqVLlxazZ88Wc+fOFeXKlRODBg2SHVOxceNGYWVlJT799FNhYWEhrl69KoQQYtGiRaJ9+/aS0wlx9uxZ4ebmJtRqtahcubI4ffq0KFWqlChWrJiws7MTJiYmYvPmzVIzpqamiokTJ4p33nlHTJ8+Xbx48UL06NFDqNVqoVarRdWqVUVMTIzUjK/q0qWL+Pjjj0VKSoooVqyY8m9+4MAB4enpKTndP4YPHy7q1KkjQkNDhY2NjZJzy5YtolatWpLT/cMQ3k99P86//PLLXD30haenp1Cr1cLV1VV8/PHH4qeffhJXrlyRHStL0dHR4uuvvxY9evQQ9+/fF0IIsWPHDnH+/HnJyfKHpaaIeXp6irVr1wohhDhx4oRQq9Vi48aNyv4dO3aI8uXLy4qno1atWiIoKEgIIbS+IYeHh4tSpUrJjCaEEMLPz0+88847IiwsTHz22WfCxcVF9OvXT6Snp4v09HQxePBgUb9+fakZ/f39hbOzs/j000+Fh4eH6Ny5s6hcubJYv369+O2330SNGjXERx99JDXjq5ycnERUVJQQQvvfPCYmRlhZWcmMpqV8+fLiyJEjQgjtnFeuXBG2trYyo2kxhPdT349zX1/f1z5atGghO6aW27dvi19//VUMHDhQVK5cWajVauHi4iJ69uwpO5oiODhY+SXb3Nxc+XefNWuW6N69u+R0+cNSU8TMzc3FzZs3tZ6//IYnROaBYGZmJiNalqysrJSzCK9+s7t69aqwsLCQmCxT8eLFRUREhBBCiMTERKFSqcTJkyeV/RcvXhT29vaS0mUqX7682L59uxBCiEuXLgmVSiV27Nih7A8ODhYuLi6y4ulwcHAQFy5cEEJo/5uHhoaKkiVLyoymxcrKSsn2as4zZ84IOzs7mdG0GML7qe/HuSFLTk4WO3fuFL179xampqbCxMREdiRFgwYNxDfffCOE0P53P3bsmF59T8oLjqkpYmlpaVpTUJubm8PMzEx5bmpqivT0dBnRslS6dGlER0frbA8LC4OHh4eERNri4uJQunRpAECxYsVgY2MDR0dHZb+jo6MyMFOWu3fvombNmgCASpUqwcLCAp6ensr+SpUqITY2VlY8HW3btkVgYKDyXKVSISkpCZMmTUKHDh3kBfuXunXrYvv27cpzlUoFAFi+fDkaNmwoK5YOQ3g/9f04BwCNRoM9e/Zg+/btyrgkfbV792589dVXaNSoEYoXL45x48bB0dERGzdu1Kvs586dw7vvvquzvWTJknj06JGERP+dqewAb6LIyEjlh5gQAlFRUcpdEPr2hTRgwACMGDECK1euhEqlwt27d3HkyBGMGjUKEyZMkB0PwD8/zLJ7Llt6erpOcTUxMVGeq9VqCD1areSbb75Bu3bt4O3tjefPn+Ojjz7ClStXUKJECaxbt052PMXMmTPRvn17REZG4sWLF/juu+8QGRmJw4cPIyQkRHY8hSG8n/p+nJ85cwYdOnRQvm/a2trit99+Q7t27SQny5qfnx+cnZ0xcuRI7NixAw4ODrIjZcnBwQH37t2Du7u71vbTp0/DxcVFUqr/hms/FTG1Wg2VSpXlD7GX21Uqld6crRFCYObMmZg1axaePn0KIHPRs1GjRmHatGmS02W+n+3bt1fOfm3btg0tW7aEjY0NACAlJQU7d+6U+n6q1WoEBQXB3t4eAPDhhx8iMDAQpUqVAgDEx8ejb9++evNvDmTegrxhwwZEREQgKSkJb731Fnr27AkrKyvZ0bRcvXoVs2fP1so5duxY1KhRQ3Y0Lfr+fur7cd6uXTskJSVh/vz5sLS0xLRp03Du3DlcuXJFdrQsBQYG4uDBgzh48CAsLCzQvHlz+Pr6wtfXF5UqVZIdTzFq1CgcO3YMv//+OypVqoTw8HDcv38fvXr1Qq9evTBp0iTZEfOMpaaI3bhxI1evc3NzK+QkeZOamoro6GgkJSXB29sbxYoVkx0JANC3b99cve7nn38u5CTZU6tff5VXn4osvbn09TgvUaIEdu/ejbfeegtA5i8CTk5OiI+Ph52dneR0OTt37hxCQkKwf/9+/PXXXyhZsiRu374tOxaAzH/vIUOGYNWqVUhPT1eGP3z00UdYtWqV1hllQ8FSQzlKSEhAeno6nJyctLbHxcXB1NRU77+hUN7NmjULpUqVQr9+/bS2r1y5Eg8fPsTYsWMlJdO2Y8cOmJiY6FyC2LVrFzIyMtC+fXtJybQZwvup78e5Wq1GbGwsSpYsqWyztbXF2bNndS6d6AshBE6fPo3g4GAcOHAAYWFhSExMRI0aNXD69GnZ8bTcunUL586dQ1JSEmrXrg0vLy/ZkfKNA4UluXLlCubPn4+hQ4di2LBhWLBgAa5duyY7lo4ePXpg/fr1Ott/++039OjRQ0Ki7Akh8OjRI72bvNDQLF26FFWqVNHZXq1aNSxZskRCoqwFBARkeXZLCIGAgAAJibJmCO+nIRznkZGROHv2rPIQQuDixYta2/RFp06dULx4cdSrVw9r1qxBpUqVEBQUhEePHulVoZk6dSqePn0KV1dXdOjQAe+//z68vLzw7NkzTJ06VXa8/Cn6G65o5syZwtTUVKjValG6dGlRqlQpoVarhZmZmZg3b57seFocHR1FZGSkzvaLFy8KJycnCYl03bt3T3zyySfC3t5emdDOwcFB9O3bV8TGxsqOp9i3b58YMmSI6Nixo3jnnXfEsGHDREhIiOxYOiwsLMS1a9d0tuvb7b2WlpZZTloYExMjrK2tiz5QNgzh/dT341ylUgm1Wi1UKlW2D7VaLTumYtSoUWLbtm0iPj5edpQcqdVqZcK9Vz169Eiv3s+84N1PRezAgQMYP348JkyYgBEjRii3H8fFxSEwMBABAQGoV68emjVrJjlpppSUlCzX1ElLS8OzZ88kJNKm0WjQqFEjJCUloW/fvqhSpQqEEIiMjMS6desQFhaG8PBw6WMDBg0ahGXLlsHR0RGVKlWCEAKHDx/G4sWLMXjwYCxatEhqvle5urri0KFDOqf1Dx06hLJly0pKpcve3h7Xrl1DhQoVtLZHR0crA8X1gSG8n/p+nOdmyQHZUze8atq0abC0tJQd47XE/9+Y8m8RERE6lyINhuRS9cZ5//33xcCBA7PdP2DAANGjR48iTJQzX19fMXToUJ3tgwcPFk2aNJGQSNvUqVOFp6enePDggc6++/fvC09PTzFjxgwJyf6xadMmYW5uLn7++WeRkZGhbE9PTxcrVqwQ5ubmYuvWrRITapszZ44oXry4WLlypbh+/bq4fv26WLFihShevLiYOXOm7HiKgQMHiho1aojo6Ghl25UrV4SPj4/o37+/xGTaDOH91PfjPDsajUYsXbpU1KtXT6/OLFhYWIimTZuK8ePHi71794qnT5/KjqTFwcFBODo6Kme1HR0dlYednZ1Qq9Vi8ODBsmPmC0tNEatQoYIIDQ3Ndv/BgwdFhQoVijBRzsLCwoSlpaVo2rSpmDx5spg8ebJo2rSpsLS0FAcPHpQdT9SvX1+sXLky2/0rVqwQDRo0KMJEujp16iQCAgKy3T9mzBjRuXPnIkyUs4yMDDFmzBhhaWmpXM6ztrYWU6ZMkR1NS3x8vGjQoIEwNTUVFSpUEBUqVBCmpqaiRYsW4smTJ7LjKQzh/dT34/zfQkJCRK9evYSNjY3w8vISY8eOFcePH5cdSxEaGipmzJgh2rRpI2xsbISFhYVo3Lix+Oqrr8Tu3btlxxOrVq0SP//8s1CpVOK7774Tq1atUh5r164Vhw8flh0x33j3UxGztrbG5cuXUa5cuSz33759WxmopS/OnDmDefPm4cyZM7CysoKPjw/GjRunFyPknZyccOTIEVSuXDnL/VFRUWjUqBHi4uKKONk/ypUrh02bNqFevXpZ7j927Bi6d++uN7d5vpSUlISLFy/CysoKXl5eWjNh6wshBPbs2YOIiAjla1NfLt3+m76/n/p8nANAbGwsVq1ahRUrVkCj0eD999/HkiVLEBERAW9vb9nxsvXixQucOHECS5cuxZo1a5CRkaE30zeEhISgUaNGWpODGjqWmiKW1a2Jr7p//z7Kli2rN1/0+s7U1BR37txRJrL7t9jYWJQrVy7L8QJFxdLSEteuXct2/MSdO3fg6empV0WWSJ906tQJBw8eRMeOHdGzZ0/4+fnBxMQEZmZmeltqLl++jODgYOWRkpKCZs2awdfXFyNGjJCWS6PRKLfoazSaHF8r+1b+/OBAYQmWL1+e7cBVfRjsZkhf9EKIHCe3y2725qKUmpqa429CpqamSE1NLcJEurp164ZVq1bBzs4O3bp1y/G1mzZtKqJUuhYuXIiBAwfC0tISCxcuzPG1w4cPL6JUugzh/TSk4/zvv//G8OHD8fnnn+vNmaOcuLi44NmzZ8oswmPHjoWPj49eLOHi6OiIe/fuoWTJknBwcMgyk9Czme3zgqWmiJUvXx4//fTTa18jkyF90QshUKlSpWy/WcguNC9NmDAB1tbWWe57OS29TPb29sp7+HI5B3307bffomfPnrC0tMS3336b7etUKpXUUmMI76chHedhYWFYsWIF6tSpg6pVq+KTTz7Rm/lzsuLs7IyoqCjExsYiNjYW9+/fx7Nnz7L9HlCU9u/fr9zZdODAAclpCh4vP5GOkJAQNG7cGKampggODs7xt4vmzZsXYTJdQUFBuXpd7969CzlJ9nx9fXP1G5oxfoMh/WVIx/lLycnJ2LBhA1auXInjx48jPT0dCxYsQL9+/WBrays7npb4+HgcPHgQISEhCAkJQWRkJGrVqoUWLVpgxowZsuMZLZYaItIyffp09OzZU2+nn38pLCwMTZo0kR3jtQzl/TQ0ly5dwooVK/DLL78gPj4ebdq0wZ9//ik7lo7Hjx8jODgYW7duxbp16/RqoPDOnTtRrFgx5ThavHgxfvrpJ3h7e2Px4sXKPGqGhMskUI68vLwwefJkvV0Nlwre77//Dk9PTzRq1Ag//PADHj16JDtSllq2bAl3d3d89dVXuHDhguw42TKE99MQj/PKlStj7ty5uH37NtatWyc7jpZNmzZh+PDh8PHxQalSpfD5558jKSkJ33zzDcLDw2XHU4wePVoZT3Xu3Dn4+/ujQ4cOiImJgb+/v+R0+VT0d5GTIVmwYIGoW7euUKvVom7duiIwMFDcu3dPdiwqZOfPnxfjxo0T7u7uwszMTHTo0EGsWbNGJCcny46mePjwoVi0aJFo1KiRUKlUombNmmLu3Lni1q1bsqPp0Pf3k8d5wXJ2dhb/+9//xKJFi8TZs2dlx8mWjY2NstTIpEmTRPfu3YUQQpw6dUqUKlVKYrL84+UnypXLly9jzZo1WLduHWJiYtCiRQt8/PHH6NWrl+xoVMgOHTqEtWvX4vfff8fz589fe6eMDDExMVi7di3WrVuHqKgoNGvWDPv375cdK0v6/H7yOC8YH3/8MVq2bInmzZujYsWKsuNky8nJCWFhYfD29kaTJk3Qq1cvDBw4ENevX4e3t7de3MSQV7z8JMGLFy+wevVq3L9/X3aUXKtUqRKmTJmCy5cvIzQ0FA8fPkTfvn1lx6IiYGNjAysrK5ibmyMtLU12nCy5u7sjICAAs2fPRo0aNRASEiI7Urb0+f3kcV4wrKysMHv2bFSqVAmurq74+OOPsXz5cr27vNekSRP4+/tj2rRpOH78ODp27AgAOU4Qq+9YaiQwNTXFoEGD8Pz5c9lR8uT48eP44osv8O677+Ly5ct47733ZEdSTJ06NcvfKp49e4apU6dKSKTtxYsXmDp1qt7NGpydmJgYzJgxA9WqVUPdunVx+vRpTJkyBbGxsbKj6Th06BAGDx6MMmXK4KOPPkL16tWxfft22bG0GNL7qc/HuaH46aefcPnyZdy8eRNz585FsWLF8M0336BKlSp6VRa+//57mJqaYuPGjfjxxx/h4uICIHNeID8/P8np8kn29a83VfPmzcWWLVtkx3itS5cuiYkTJwovLy9hamoq2rZtK4KCgkRiYqLsaFrUarW4f/++zvZHjx7pzUJ3xYoVU65f67P69esLtVotatWqJebNmydu374tO1KWxo4dKypUqCDMzc1Fx44dxdq1a/VmjMqrDOH9NJTj3NAkJyeLXbt2iYCAANGgQQNhbm4uatWqJTuWUePke5IMHjwY/v7+uHXrFurUqQMbGxut/T4+PpKSaatSpQrefvttDBkyBD169Mh2OQLZxP9PEvZvERERykRTsrVs2RIhISGoUKGC7Cg5atWqFVauXKmXU8+/KjQ0FKNHj8b777+PEiVKyI6TLUN4Pw3lODcUX331FYKDg3H69GlUrVoVzZs3R0BAAJo1a6Z3t0mnp6dj8+bNuHjxIgCgatWq6Nq1K0xNDbMecKCwJFlN7f9ySn99mMETyPxiX7lyJf73v//p3YH4kqOjI1QqFRISEmBnZ6dVbNLT05GUlIRBgwZh8eLFElNmWrJkCaZMmYKePXtmWWQ7d+4sKdk/0tLSUKVKFfz111+oWrWq7DjZSktLw2effYYJEybo9fwvhvB+GsJxbmjUajWcnZ3x5Zdfolu3bqhUqZLsSFm6cOECOnXqhPv37yuLAl++fBnOzs7Ytm0bqlevLjlh3rHUSHLjxo0c97u5uRVRkpxZWlri4sWLevuDIygoCEII9OvXD4GBgVpT0pubm6NChQpo2LChxIT/eN0aVfpQZIHMdWv27t2rtz+EX7K3t8eZM2f09mvzJUN4P/X9ODc0ERERCAkJQXBwMEJDQ2Fubo7mzZsra0HpS8lp2LAhnJ2dERQUpBTaJ0+eoE+fPnj48CEOHz4sOWHesdRQjurWrYs5c+agVatWsqPk6NUp3+m/mTlzJi5fvozly5fr9fvZu3dv1KpVC19++aXsKDkyhPfTUI5zQxUREYFvv/0Wa9as0asZha2srHDy5ElUq1ZNa/v58+fx9ttv49mzZ5KS5Z9+HmFviF9++QVLlixBTEwMjhw5Ajc3NwQGBsLd3R1dunSRHQ9A5hTvo0aNwrRp07K8ZCJ79d6XkpOTsW/fPrRr105r+65du5CRkYH27dtLSpa158+fw9LSUnaMLJ04cQL79u3D7t27UaNGDZ1/c5mrdL/Ky8sLU6dOxaFDh7L82pS5oOWrDOH9NJTj3FAIIXD69GkEBwcjODgYYWFh0Gg08PHx0Zt1tIDMW/jv37+vU2oePHgAT09PSan+G56pkeTHH3/ExIkT8cUXX2DGjBk4f/48PDw8sGrVKgQFBenN4oavXjJ5dbyKPo39ATIHVs+ePRsdOnTQ2r5z506MHTsWERERkpL9Iz09HTNnzsSSJUtw//59XL58GR4eHpgwYQIqVKiA/v37y44IAK+dl+Tnn38uoiQ5y+lSiUqlwrVr14owTfYM4f00lOPcUDg6OiIpKQk1a9ZULjs1bdoUDg4OsqNpTfYYFhaGMWPGYPLkyWjQoAEA4OjRo5g6dWqW308NAUuNJN7e3pg5cya6du0KW1tbREREwMPDA+fPn4evr6/erA/zuknM9OW3DisrK1y8eFHnzqLr16+jWrVqSE5OlhPsFVOnTkVQUBCmTp2KAQMGKEV2w4YNCAwMxJEjR2RHpDeUoRznhmL79u1o2rSpXp7hUqvVOsUV+KfMvvrcEMssLz9JEhMTg9q1a+tst7Cw0IsfwC8Zyjcze3t7XLt2TafUREdH65xKl2X16tVYtmwZWrVqhUGDBinba9asiaioKInJdL148QLBwcG4evUqPvroI9ja2uLu3buws7NDsWLFZMfTkpqaipiYGFSsWFFvx6zo+/tpKMe5oXg5M68+0perAIVFP78DvAHc3d1x5swZnbucdu7cqXd3SYSGhmLp0qW4du0afv/9d7i4uOCXX36Bu7u7smS9bF26dMEXX3yBzZs3K2utREdHY+TIkXpxqzQA3LlzJ8vr1BkZGXo1Xf6NGzfg5+eHmzdvIiUlBW3atIGtrS3mzJmDlJQULFmyRHZEAMDTp08xbNgwBAUFAYByOW/YsGFwcXFBQECA5ISZDOX9NITjnP47Yy+wXCZBEn9/fwwZMgQbNmyAEALHjx/HjBkzMG7cOIwZM0Z2PMUff/yBdu3awcrKCuHh4UhJSQEAJCQkYObMmZLT/WPu3LmwsbFBlSpV4O7uDnd3d1StWhXFixfH/PnzZccDkHnJMTQ0VGf7xo0bszxrJ8uIESNQt25dPHnyBFZWVsr2d999F/v27ZOYTNu4ceMQERGB4OBgrUHXrVu3xoYNGyQm02YI76ehHOdU8EJDQ/Hxxx+jUaNGuHPnDoDMm1jCwsIkJ8unIp7BmF7x66+/Ck9PT6FSqYRKpRIuLi5i+fLlsmNpqVWrlggKChJCZE7zf/XqVSGEEOHh4Xq3NH1GRobYtWuXmDt3rli0aJEICQmRHUnLli1bhL29vZg9e7awtrYW8+bNE59++qkwNzcXu3fvlh1P4eTkJKKiooQQ2v/mMTExwsrKSmY0LeXLlxdHjhwRQmjnvHLlirC1tZUZTYshvJ+GdJxTwdm4caOwsrISn376qbCwsFD+3RctWiTat28vOV3+8PKTRD179kTPnj3x9OlTJCUloWTJkrIj6bh06RKaNWums93e3h7x8fFFHygHKpUKbdu2Rdu2bWVHyVKXLl2wbds2TJ06FTY2Npg4cSLeeustbNu2DW3atJEdT5HdPBq3b9+Gra2thERZe/jwYZbHTHJycpZLZshiCO+nIR3nVHCmT5+OJUuWoFevXli/fr2yvXHjxpg+fbrEZPnHUqMHrK2tYW1tLTtGlkqXLo3o6GidAbhhYWHw8PCQEyoLr1uJe+LEiUWUJGdNmzbFnj17ZMfIUdu2bREYGIhly5YByCyLSUlJmDRpkl7d4lm3bl1s374dw4YNA/DP3RvLly/Xm1mkAcN4Pw3lOKeCZYxllqVGksePH2PixIk4cOAAHjx4gIyMDK39cXFxkpJpGzBgAEaMGIGVK1dCpVLh7t27OHLkCEaNGoUJEybIjqfYvHmz1vO0tDTExMTA1NQUFStW1JtSYwi++eYbtGvXDt7e3nj+/Dk++ugjXLlyBSVKlMC6detkx1PMnDkT7du3R2RkJF68eIHvvvsOkZGROHz48GtvUS5KhvB+GspxTgXLGMssS40kn3zyCaKjo9G/f3+UKlVKr06XvyogIAAZGRlo1aoVnj59imbNmsHCwgKjRo1SfkPWB6dPn9bZptFo0KdPH7z77rsSEmV6ueBmbuhLkS1XrhwiIiKwYcMGREREICkpCf3790fPnj21BrrK1qRJE5w5cwazZ89GjRo1sHv3brz11ls4cuQIatSoITuewhDeT0M5zqlgGWOZ5eR7ktja2iIsLAw1a9aUHSVXUlNTER0djaSkJHh7e+vF3Bq5ce7cOXTq1AnXr1+X8vlf3m4MZJ6dmz59Otq1a6dcHjly5Ah27dqFCRMm6P0aRmT8DPU4p7yJiYmBu7s7hBCYOXMmZs2ahadPnwKAUmanTZsmOWX+sNRI8vbbb2PRokXK1NSG4saNG0hOTkaVKlVyXHVaX4SFhaFTp0548uSJ7Cjo3r07WrRogaFDh2pt//7777F3715s2bJFTrD/d/nyZcTHx6NevXrKtn379mH69OlITk5G165d8dVXX0lMmOnFixdIT0+HhYWFsu3+/ftYsmQJkpOT0blzZ72YV8VQ3s+sGNpxTnmjVqvh5uaGFi1aoEWLFvD19UViYqJRlFmWGklOnDiBgIAATJw4EdWrV4eZmZnWftnTa69cuRLx8fHw9/dXtg0cOBArVqwAAFSuXBm7du2Cq6urrIhaFi5cqPVcCIF79+7hl19+QfPmzbF27VpJyf5RrFgxnDlzRmcCvujoaNSqVQtJSUmSkmV69913UaNGDWXQdUxMDKpVq4amTZuiSpUqWLlyJaZNm4YvvvhCas6+ffvC3NwcS5cuBQAkJiaiWrVqeP78OcqUKYPIyEhs3bpV+iBcQ3g/De04p4LxcqHN4OBgHDt2DKmpqfDw8EDLli3RsmVL+Pr6olSpUrJj5o+8u8nfbJcvXxZ169YVarVa66FSqYRarZYdT9SvX1+sXLlSef73338LU1NT8euvv4pTp06Jhg0biv79+0tMqK1ChQpaDw8PD1G/fn0xbtw4odFoZMcTQmTOqzJ//nyd7fPnzxfly5eXkEhbuXLlxOHDh5Xn06ZNEzVr1lSeL1++XOu5LF5eXmLXrl3K8++//16ULVtWxMfHCyGEGDNmjPD19ZUVT2EI76ehHedU8J49eyb27dsnJkyYIJo2bSosLCyEWq0W3t7esqPlCwcKS9KzZ0+YmZlh7dq1ejlQ+MqVK6hbt67yfOvWrejSpQt69uwJIPPOk9etPlyUYmJiZEd4rSlTpuDTTz9FcHAw6tevDwA4duwYdu7ciZ9++klyOuDRo0coV66c8vzAgQPo1KmT8tzX1xcjR46UEU3LnTt34OXlpTzft28funfvDnt7ewBA79699WLla0N4Pw3tOKeCZ2lpiZYtW6JJkyZo0aIF/v77byxdulTv1qPLLZYaSc6fP4/Tp0+jcuXKsqNk6dmzZ1qXwA4fPoz+/fsrzz08PBAbGysjmsHq06cPqlatioULF2LTpk0AgKpVqyIsLEwpOTI5OTnh3r17cHV1RUZGBk6ePKl1WSI1NVVZwVcmS0tLPHv2THl+9OhRzJs3T2u/7Et5gGG8nzzO31ypqak4evQoDhw4oFyGcnV1RbNmzfD9998b7BpRLDWS1K1bF7du3dLbUuPm5oZTp07Bzc0Njx49woULF9C4cWNlf2xsrPKbsSzdunXL9WtflghZ0tLS8Nlnn2HChAlYs2aN1CzZ8fX1xbRp0/DDDz/g999/R0ZGBnx9fZX9kZGROvNZyFCrVi388ssvmDVrFkJDQ3H//n20bNlS2X/16lWULVtWYsJMhvB+GsJxTgWvZcuWOHbsGNzd3dG8eXN89tlnWLt2LcqUKSM72n/GUiPJsGHDMGLECIwePRo1atTQGSjs4+MjKVmm3r17Y8iQIbhw4QL279+PKlWqoE6dOsr+w4cPo3r16hITQuubrRACmzdvhr29vXI6/dSpU4iPj89T+SksZmZm+OOPP/R67ocZM2agTZs2cHNzg4mJCRYuXAgbGxtl/y+//KJVHmSZOHEi2rdvj99++w337t1Dnz59tL4Zb968WesHsyyG8H4awnFOBS80NBRlypRRBgU3b94cxYsXlx2rQPDuJ0myuk1SpVJBCAGVSpXlWjFFKSMjA5MnT8a2bdtQunRpLFiwAFWrVlX2v/fee/Dz89M6VS3T2LFjERcXhyVLlsDExAQAkJ6ejsGDB8POzk7r8oQsvXv3Rq1atfR6PpoXL17gwoULcHZ21jnbERERgXLlyunFN7+LFy9i9+7dKF26NN577z2t42nZsmWoV68eatWqJS/g/9P399PQjnMqGMnJyQgNDUVwcDAOHDiAM2fOoFKlSmjevLlScpydnWXHzBeWGklu3LiR4343N7ciSmIcnJ2dERYWpnM579KlS2jUqBEeP34sKdk/pk+fjm+++QatWrVCnTp1tH5rB4Dhw4dLSkZEb7LExESEhYUp42siIiLg5eWF8+fPy46WZ7z8JAlLS8F68eIFoqKidEpNVFSUzrpasqxYsQIODg44deoUTp06pbVPpVKx1BCRFDY2NnBycoKTkxMcHR1hamqKixcvyo6VLyw1El29ehWBgYHKF4+3tzdGjBiBihUrSk5mePr27Yv+/fvj6tWrygyux44dw+zZs/XmllRDuO2ciIzfy7vxXl5+OnToEJKTk+Hi4oIWLVpg8eLFaNGiheyY+cLLT5Ls2rULnTt3Rq1atZRBjYcOHUJERAS2bduGNm3aSE5oWDIyMjB//nx89913uHfvHgCgTJkyGDFiBEaOHKmMs9EHjx49AgCUKFFCchIiehPZ2dkhOTkZpUuX1loqwRh+oWapkaR27dpo164dZs+erbU9ICAAu3fvRnh4uKRkhk+j0QCQv9TEq+Lj4/H1119jw4YNyjpUjo6O6NGjB6ZPnw4HBwe5AalQvHjxAjNnzkS/fv20JuIjkmnp0qVo0aIFKlWqJDtKgWOpkcTS0hLnzp3TmhkVyFwEz8fHB8+fP5eUTNvUqVMxatQoWFtba21/9uwZ5s2bh4kTJ0pKlrWHDx/i0qVLAIAqVaroxdmQuLg4NGzYEHfu3EHPnj2Vu0siIyOxdu1auLq64vDhw3B0dJSc9B/x8fE4fvw4Hjx4oDMmqVevXpJSGSZbW1ucO3dO+pw0OTG045woWzLWZqDMdWF+++03ne0bNmwQrq6uEhJlTa1Wi/v37+tsf/TokV6sUfVSUlKS6Nu3rzAxMREqlUqoVCphamoq+vXrJ5KTk6VmGzFihKhevbqIjY3V2Xfv3j1Ro0YN8cUXX0hIlrU///xT2NraCpVKJezt7YWDg4PycHR0lB1P8TLPvx9OTk6ibNmyolmzZlrrGsnSuXNnsWrVKtkxcmQoxznR63CgsCQDBgzAwIEDce3aNTRq1AhA5piaOXPmaE2lLpv4/3lz/i0iIgJOTk4SEmXN398fISEh2LZtmzJGKSwsDMOHD8fIkSPx448/Ssu2ZcsWLF26NMtVb0uXLo25c+di0KBB+PbbbyWk0zVy5Ej069cPM2fO1PnNXZ9MnDgRM2bMQPv27ZXB4cePH8fOnTsxZMgQxMTE4PPPP8eLFy8wYMAAaTnbt2+PgIAAnDt3Lstb+Tt37iwp2T8M5Tgnei3ZrepNlZGRIRYsWCBcXFyUMwsuLi4iMDBQZGRkyI6n/BasVqt1fiO2s7MTarVaDB48WHZMRfHixcWBAwd0tu/fv1+UKFGi6AO9wtzcXNy6dSvb/bdu3RIWFhZFmChn1tbW4urVq7JjvFa3bt3Ejz/+qLN9yZIlolu3bkIIIRYuXCiqV69e1NG0vDy+s3rIPgtiaMc50etwTI0eSExMBJB57V1fBAUFQQiBfv36ITAwUGtJAnNzc1SoUAENGzaUmFCbtbU1Tp06pTUbKgBcuHAB9erVQ3JysqRkgIuLCzZs2IAmTZpkuT80NBQffPAB7t69W8TJstatWzf06NED77//vuwoOSpWrBjOnDkDT09Pre3R0dGoVasWkpKScPXqVfj4+Ej999dnhnacE70OLz9J0rJlS2zatAkODg5aZUaj0aBr167Yv3+/xHSZU/oDgLu7Oxo3bgxTU/3+UmnYsCEmTZqE1atXw9LSEkDmIMcpU6ZI/6bcrl07fP3119izZw/Mzc219qWkpGDChAnw8/OTlC7Tn3/+qXzcsWNHjB49GpGRkVmuS6YPl0uAzFWwt23bprPsxLZt25RLJsnJyXr1y8Lz58+Vr099YGjHOdHr8EyNJGq1GrGxsShZsqTW9gcPHsDFxQVpaWmSkmkLDw+HmZkZatSoAQDYunUrfv75Z3h7e2Py5Mk6P6RlOXfuHPz8/JCSkoKaNWsCyBwPYGlpiV27dqFatWrSst2+fRt169aFhYUFhgwZgipVqkAIgYsXL+KHH35ASkoKTp48CVdXV2kZs1qLLCv6sC7ZSz/99BM+//xzdOjQQRlTc+LECezYsQNLlixB//798c033+D48ePYsGGDtJzp6emYOXMmlixZgvv37+Py5cvw8PDAhAkTUKFCBb1YV2nHjh0wMTFBu3bttLbv2rULGRkZaN++vaRkRHkk8dLXGykiIkJEREQIlUolDhw4oDyPiIgQ4eHhYubMmcLNzU12TEXdunXFxo0bhRBCXL16VVhYWIgPP/xQeHp6ihEjRsgN9y/Jycli2bJlwt/fX/j7+4uffvpJPH36VHYsIYQQ165dE35+fkKtVmuNp2jXrp24cuWK7HgGKywsTPTo0UPUrl1b1K5dW/To0UMcOnRIdiwtU6ZMER4eHuLXX38VVlZWynil9evXiwYNGkhOl6lGjRpi+/btOtv//vtv4ePjIyERUf6w1BSxlz/MXv3h9urD2tparFixQnZMhZ2dnYiOjhZCCDF79mzRtm1bIUTmD5Ny5crJjKZITU0VHh4eIjIyUnaU14qLixPHjh0Tx44dE48fP5YdJ0tBQUHi+fPnOttTUlJEUFCQhESGrWLFimLv3r1CCCGKFSumlJqLFy8KBwcHmdEUlpaWIiYmRmd7TEyMsLa2LvpARPnEC6hFLCYmBkIIeHh44Pjx41rLu5ubm6NkyZJ6NaW/EEKZfG3v3r145513AACurq7KdP+ymZmZ6c1kha/j6OioXCrRV3379oWfn5/OpdHExET07dtXrybfy8jIQHR0dJaTBDZr1kxSKm137tzRGcwMZGbXl8vM9vb2uHbtms4EgdHR0Tq3oBPpM5aaIvZydW59WTn6derWrYvp06ejdevWCAkJUeZ7iYmJyXLeFVmGDBmCOXPmYPny5Rzs+B+JbOYsuX37ttbdMbIdPXoUH330EW7cuAHxr6GB+jT2x9vbG6Ghocqx/9LGjRtRu3ZtSam0denSBV988QU2b96srP8THR2NkSNH6s3AcKLc4Hd/SYKCglCiRAl07NgRADBmzBgsW7YM3t7eWLdunc43QFkCAwPRs2dPbNmyBV9//bXyG+fGjRuVSQP1wYkTJ7Bv3z7s3r0bNWrU0PntctOmTZKSGY7atWtDpVJBpVKhVatWWuUwPT0dMTEx0u/SetWgQYNQt25dbN++HWXKlMmyiOmDiRMnonfv3rhz5w4yMjKwadMmXLp0CatXr8Zff/0lOx4AYO7cufDz80OVKlWUNapu376Npk2bYv78+ZLTEeUe736SpHLlyvjxxx/RsmVLHDlyBK1atUJgYCD++usvmJqa6v0P4efPn8PExETndl9Z+vbtm+P+n3/+uYiSGK4pU6Yo/x05ciSKFSum7Hs5Z0n37t315o43GxsbREREZHlpR9+EhoZi6tSpiIiIQFJSEt566y1MnDgRbdu2lR1NIYTAnj17EBERASsrK/j4+OjNJTyi3GKpkcTa2hpRUVEoX748xo4di3v37mH16tW4cOECfH198fDhQ9kRtZw6dQoXL14EkHk6/a233pKciApLUFAQPvjgA72aTyUrLVu2xJgxY/Tq7BERycXLT5IUK1YMjx8/Rvny5bF7925lvSdLS0s8e/ZMcrp/PHjwAB988AFCQkLg4OAAIHMF5xYtWmD9+vVaA51lyMjIwLx58/Dnn38iNTUVrVq1wqRJk2BlZSU1lyF7OSGbvhs2bBhGjhyJ2NjYLCcJ9PHxkZTM8EydOjXH/VylmwwFz9RI0rNnT0RFRaF27dpYt24dbt68ieLFi+PPP//EV199hfPnz8uOCAD44IMPcO3aNaxevVpZgiAyMhK9e/eGp6cn1q1bJzXftGnTMHnyZLRu3RpWVlbYtWsXPvzwQ6xcuVJqLkOmVqtzHJ+iLwNws5owUKVSKQOdZeZ0dHTM9RifuLi4Qk7zev8esJyWloaYmBiYmpqiYsWKCA8Pl5SMKG94pkaSxYsXY/z48bh16xb++OMPFC9eHEDmZZ4PP/xQcrp/7Ny5E3v37tVaU8nb2xuLFy/Wi/EAq1evxg8//IDPPvsMQOZt5x07dsTy5ctzPUsuadu0aZPWD+S0tDScPn0aQUFByrgbfRATEyM7QrYCAwOVjx8/fozp06ejXbt2ypIdR44cwa5duzBhwgRJCbWdPn1aZ5tGo0GfPn3w7rvvSkhElD88U0M5srW1RWhoKGrVqqW1/fTp02jevDk0Go2cYP/PwsIC0dHRWksMWFpaIjo6WrmLgwrG2rVrsWHDBmzdulV2FIPSvXt3tGjRAkOHDtXa/v3332Pv3r3YsmWLnGC5cO7cOXTq1AnXr1+XHYUoV1hqJDl48GCO+/XlroMuXbogPj4e69atQ9myZQFkTibWs2dPODo6YvPmzVLzmZiYIDY2Vmtsj62tLc6ePQt3d3eJyYzPtWvX4OPjg6SkJGkZ/vzzT7Rv3x5mZmZai3BmRV/mV8nNauL6KiwsDJ06dcKTJ09kRyHKFV5+ksTX11dn26un/PVl3ML333+Pzp07o0KFCsrZkFu3bqF69er49ddfJafLvA21T58+sLCwULY9f/4cgwYN0pqrRt9vkdd3z549w8KFC+Hi4iI1R9euXZWFYLt27Zrt62SPqXlV8eLFsXXrVowcOVJr+9atW5XLzrItXLhQ67kQAvfu3cMvv/zCxSzJoLDUSPLv33xejluYMGECZsyYISmVLldXV4SHh2Pv3r2IiooCAFStWhWtW7eWnCxTVnfqfPzxxxKSGI9/D3IVQiAxMRHW1tbSi+yrM3EbyqzcU6ZMwaefforg4GDUr18fAHDs2DHs3LkTP/30k+R0mb799lut52q1Gs7OzujduzfGjRsnKRVR3vHyk54JCQmBv78/Tp06JTsKvaGCgoK0nr/8AVe/fn04OjpKSmXYjh07hoULFypzPVWtWhXDhw9XSg4RFQyWGj0TFRWFunXrSr/Ovn//fgwdOhRHjx6FnZ2d1r6EhAQ0atQIS5YsQdOmTSUlJBnOnz+P6tWry46h2LdvH/bt25flgpb6cFt/WloaPvvsM0yYMIFjvIiKAEuNJGfPntV6/vIa9uzZs/HixQuEhYVJSpapc+fOaNGiBb788sss9y9cuBAHDhyQPlCYCl9iYiLWrVuH5cuX49SpU3ozVmXKlCmYOnUq6tatm+XaT/rytWlvb48zZ87oXanp1q1brl/LMWlkKDimRpJatWopE4W9qkGDBnrxG2ZERATmzJmT7f62bdtyoTsjd/DgQaxYsQJ//PEHypYti27dumHx4sWyYymWLFmCVatW4ZNPPpEdJUddu3bFli1bsv0FQZZXV1wXQmDz5s2wt7dH3bp1AWTOmRUfH5+n8kMkG0uNJP+eOOzluAV9WW/n/v37OS5WaWpqqnfrU9F/Fxsbi1WrVmHFihXQaDR4//33kZKSgi1btsDb21t2PC2pqal6tVJ8dry8vDB16lQcOnQIderU0VlBfvjw4VJyvbrI69ixY/H+++9jyZIlMDExAZB5B+bgwYN1Lj8T6TNefqIsVaxYEd988022t81u2rQJo0aNwrVr14o2GBWaTp064eDBg+jYsSN69uwJPz8/ZSX2iIgIvSs1Y8eORbFixfRmVt7s5HTZSaVS6cUx5OzsjLCwMFSuXFlr+6VLl9CoUSM8fvxYUjKivOGZmiJmKANwO3TogAkTJsDPz0/n7NGzZ88wadIkvPPOO5LSUWH4+++/MXz4cHz++efw8vKSHSdLLxd+BTJv6V62bBn27t0LHx8fnTOLCxYsKOp4WdLn5RxeevHiBaKionRKTVRUlMHcOk8EsNQUucDAQAwYMCDLU7r29vb47LPPsGDBAumlZvz48di0aRMqVaqEoUOHKt/soqKisHjxYqSnp+Prr7+WmpEKVlhYGFasWIE6deqgatWq+OSTT9CjRw/ZsbT8e42il8t3/HsB2NwuJlmUHj16BAAoUaKE5CS6+vbti/79++Pq1auoV68egMzb0GfPno2+fftKTkeUB4KKVPny5UVkZGS2+y9evChcXV2LMFH2rl+/Ltq3by/UarVQqVRCpVIJtVot2rdvL65duyY7HhWSpKQksWLFCtG4cWNhZmYm1Gq1CAwMFBqNRnY0g/PkyRMxePBgUbx4caFWq4VarRbFixcXQ4YMEU+ePJEdT5Geni7mzJkjypYtqxzrZcuWFXPmzBEvXryQHY8o1zimpohZWlri/PnzOuvAvBQdHY0aNWrg2bNnRZwse0+ePEF0dDSEEPDy8uIEbG+QS5cuYcWKFfjll18QHx+PNm3avHbNpaKSkJCA9PR0ODk5aW2Pi4uDqamp9AGucXFxaNiwobJW2suV7iMjI7F27Vq4urri8OHDenc8vVykVvb7R5QfatkB3jQuLi46p8pfdfbsWZQpU6YIE72eo6Mj3n77bdSrV0/vvgFT4apcuTLmzp2L27dvY926dbLjaOnRowfWr1+vs/23337Ti8tmU6dOhbm5Oa5evYqlS5fiiy++wBdffIFly5YhOjoaZmZmmDp1quyYWh4+fIizZ8/i7NmzyuUyIkPCMzVFbNiwYQgODsaJEyeyHIBbr149tGjRQmeBOSLS5uTkhEOHDilnQF6KiopC48aNpd+xU6FCBSxduhTt2rXLcv/OnTsxaNAgXL9+vWiDZSE5ORnDhg3D6tWrlYHBJiYm6NWrFxYtWgRra2vJCYlyh2dqitj48eMRFxeHSpUqYe7cudi6dSu2bt2KOXPmoHLlyoiLi+MAXKJcSElJwYsXL3S2p6Wl6cXl23v37qFatWrZ7q9evTpiY2OLMFH2/P39ERISgm3btiE+Ph7x8fHYunUrQkJCdFYXJ9JnPFMjwY0bN/D5559j165dyozCKpUK7dq1w+LFi/VuOnUifdSiRQtUr14dixYt0to+ZMgQnD17FqGhoZKSZXJxccGGDRvQpEmTLPeHhobigw8+wN27d4s4ma4SJUpg48aN8PX11dp+4MABvP/++5xokwwGS41EHIBLlH+HDh1C69at8fbbb6NVq1YAMhe4PHHiBHbv3i19WoR+/frh6tWr2LNnD8zNzbX2paSkoF27dvDw8NCLZVGsra1x6tQpnUt5Fy5cQL169ZCcnCwpGVHesNRQjrK700WlUsHS0hKenp48s0TSnDlzBvPmzcOZM2dgZWUFHx8fjBs3Ti8mD7x9+zbq1q0LCwsLDBkyBFWqVIEQAhcvXsQPP/yAlJQUnDx5Eq6urrKjolWrVihevDhWr16tjPV79uwZevfujbi4OOzdu1dyQqLcYamhHKnV6iwX3ny5TaVSoUmTJtiyZQvPNBH9S0xMDAYPHozdu3drXWpu06YNvv/++2yndihq586dg5+fH1JSUlCzZk0AmYvaWlpaYteuXTmODSLSJyw1lKN9+/bh66+/xowZM5SZRo8fP44JEyZg/PjxyizI9evXx4oVKySnpTfV8+fPkZqaqrVNn+ZZefLkCa5cuQIA8PT01JlbRx88ffoUa9asQVRUFACgatWq6NmzJ6ysrCQnI8o9lhrKUfXq1bFs2TKd1ZAPHTqEgQMH4sKFC9i7dy/69euHmzdvSkpJb6KnT59izJgx+O2337K8fTs9PV1CKsOTlpaGKlWq4K+//tIZU0NkaHhLN+Xo6tWrWf7Ga2dnp6wu7OXlxYm6qMiNHj0a+/fvx48//ggLCwssX74cU6ZMQdmyZbF69WrZ8QyGmZkZnj9/LjsGUYFgqaEc1alTB6NHj9a6pfPhw4cYM2YM3n77bQDAlStX9GKwI71Ztm3bhh9++AHdu3eHqakpmjZtivHjx2PmzJlYs2aN7HgGZciQIZgzZ06W8/4QGRKu0k05WrFiBbp06YJy5copxeXWrVvw8PDA1q1bAQBJSUkYP368zJj0BoqLi4OHhweAzDOHcXFxAIAmTZrg888/lxnN4Jw4cQL79u3D7t27UaNGDdjY2Gjt37Rpk6RkRHnDUkM5qly5MiIjI7F7925cvnxZ2damTRuo1Zkn+rp27SoxIb2pPDw8EBMTg/Lly6NKlSr47bffUK9ePWzbtg0ODg6y4xkUBwcHdO/eXXYMov+MA4WJyCB9++23MDExwfDhw7F371506tQJQgikpaVhwYIFGDFihOyIRFTEWGrotfbt24d9+/bhwYMHymJ3L+nDbKhEQObyI6dOnYKnpyd8fHxkxzEIGRkZmDdvHv7880+kpqaiVatWmDRpEm/jJoPFy0+UoylTpmDq1KmoW7cuypQpA5VKJTsSUZbc3Nzg5uYmO4ZBmTFjBiZPnozWrVvDysoK3333HR48eMBfVshg8UwN5ahMmTKYO3cuPvnkE9lRiAAA+/fvx9ChQ3H06FGd6QYSEhLQqFEjLFmyRPraT4bAy8sLo0aNwmeffQYA2Lt3Lzp27Ihnz54pY+aIDAm/ailHqampOhPvEckUGBiIAQMGZDl/0ssZrhcsWCAhmeG5efMmOnTooDxv3bo1VCqVXqwcTpQfLDWUo08//RRr166VHYNIERERAT8/v2z3t23bFqdOnSrCRIbrxYsXygKWL5mZmSEtLU1SIqL/hmNqKEfPnz/HsmXLsHfvXvj4+MDMzExrP38jpqJ2//59na/DV5mammpNFknZE0KgT58+sLCwULY9f/4cgwYN0pqrhvPUkKFgqaEcnT17FrVq1QIAnD9/XmsfBw2TDC4uLjh//ny2K1yfPXsWZcqUKeJUhql379462z7++GMJSYgKBgcKE5FBGTZsGIKDg3HixAmdSyfPnj1DvXr10KJFCyxcuFBSQiKShaWGiAzK/fv38dZbb8HExARDhw5F5cqVAQBRUVFYvHgx0tPTER4ejlKlSklOSkRFjaWGdHTr1g2rVq2CnZ0dunXrluNrea2dZLhx4wY+//xz7Nq1Cy+/halUKrRr1w6LFy+Gu7u75IREJAPH1JAOe3t7ZbyMvb295DREutzc3LBjxw48efIE0dHREELAy8sLjo6OsqMRkUQ8U0NERERGgWdqKFcePHiAS5cuAchcpbtkyZKSExEREWnj5HuUI41Gg08++QQuLi5o3rw5mjdvDhcXF3z88cdISEiQHY+IiEjBUkM5GjBgAI4dO4a//voL8fHxiI+Px19//YWTJ08q68UQERHpA46poRzZ2Nhg165daNKkidb20NBQ+Pn5ITk5WVIyIiIibTxTQzkqXrx4lndA2dvb804TIiLSKyw1lKPx48fD398fsbGxyrbY2FiMHj0aEyZMkJiMiIhIGy8/UY5q166N6OhopKSkoHz58gCAmzdvwsLCAl5eXlqvDQ8PlxGRiIgIAG/pptfo2rWr7AhERES5wjM1REREZBR4poZy5eTJk7h48SIAwNvbG3Xq1JGciIiISBtLDeXo9u3b+PDDD3Ho0CE4ODgAAOLj49GoUSOsX78e5cqVkxuQiIjo//HuJ8rRp59+irS0NFy8eBFxcXGIi4vDxYsXkZGRgU8//VR2PCIiIgXH1FCOrKyscPjwYdSuXVtr+6lTp9C0aVM8ffpUUjIiIiJtPFNDOXJ1dUVaWprO9vT0dJQtW1ZCIiIioqyx1FCO5s2bh2HDhuHkyZPKtpMnT2LEiBGYP3++xGRERETaePmJcuTo6IinT5/ixYsXMDXNHFf+8mMbGxut18bFxcmISEREBIB3P9FrBAYGyo5ARESUKzxTQ0REREaBZ2pIh0ajgZ2dnfJxTl6+joiISDaeqSEdJiYmuHfvHkqWLAm1Wg2VSqXzGiEEVCoV0tPTJSQkIiLSxTM1pGP//v1wcnICABw4cEByGiIiotzhmRoiIiIyCjxTQ68VHx+P48eP48GDB8jIyNDa16tXL0mpiIiItPFMDeVo27Zt6NmzJ5KSkmBnZ6c1vkalUnFuGiIi0hssNZSjSpUqoUOHDpg5cyasra1lxyEiIsoWSw3lyMbGBufOnYOHh4fsKERERDni2k+Uo3bt2mmt+0RERKSvOFCYdPz555/Kxx07dsTo0aMRGRmJGjVqwMzMTOu1nTt3Lup4REREWeLlJ9KhVufuBB4n3yMiIn3CUkNERERGgWNqiIiIyCiw1FCWjhw5gr/++ktr2+rVq+Hu7o6SJUti4MCBSElJkZSOiIhIF0sNZWnq1Km4cOGC8vzcuXPo378/WrdujYCAAGzbtg2zZs2SmJCIiEgbx9RQlsqUKYNt27ahbt26AICvv/4aISEhCAsLAwD8/vvvmDRpEiIjI2XGJCIiUvBMDWXpyZMnKFWqlPI8JCQE7du3V56//fbbuHXrloxoREREWWKpoSyVKlUKMTExAIDU1FSEh4ejQYMGyv7ExESdOWuIiIhkYqmhLHXo0AEBAQEIDQ3FuHHjYG1tjaZNmyr7z549i4oVK0pMSEREpI0zClOWpk2bhm7duqF58+YoVqwYgoKCYG5uruxfuXIl2rZtKzEhERGRNg4UphwlJCSgWLFiMDEx0doeFxeHYsWKaRUdIiIimVhqiIiIyChwTA0REREZBZYaIiIiMgosNURERGQUWGqIiIjIKLDUEBERkVFgqSEiIiKjwFJDRERERuH/AFiTD1ElMs/VAAAAAElFTkSuQmCC", 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" ] @@ -1349,10 +1349,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:16:02.475170Z", - "iopub.status.busy": "2024-10-20T00:16:02.474789Z", - "iopub.status.idle": "2024-10-20T00:16:02.488659Z", - "shell.execute_reply": "2024-10-20T00:16:02.488054Z" + "iopub.execute_input": "2024-10-22T01:42:34.878024Z", + "iopub.status.busy": "2024-10-22T01:42:34.877824Z", + "iopub.status.idle": "2024-10-22T01:42:34.892174Z", + "shell.execute_reply": "2024-10-22T01:42:34.891544Z" } }, "outputs": [ @@ -1503,10 +1503,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:16:02.490679Z", - "iopub.status.busy": "2024-10-20T00:16:02.490319Z", - "iopub.status.idle": "2024-10-20T00:16:02.505938Z", - "shell.execute_reply": "2024-10-20T00:16:02.505443Z" + "iopub.execute_input": "2024-10-22T01:42:34.894438Z", + "iopub.status.busy": "2024-10-22T01:42:34.894191Z", + "iopub.status.idle": "2024-10-22T01:42:34.912181Z", + "shell.execute_reply": "2024-10-22T01:42:34.911621Z" } }, "outputs": [ @@ -1550,16 +1550,16 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:16:02.507842Z", - "iopub.status.busy": "2024-10-20T00:16:02.507468Z", - "iopub.status.idle": "2024-10-20T00:17:55.847934Z", - "shell.execute_reply": "2024-10-20T00:17:55.847183Z" + "iopub.execute_input": "2024-10-22T01:42:34.914410Z", + "iopub.status.busy": "2024-10-22T01:42:34.914010Z", + "iopub.status.idle": "2024-10-22T01:44:26.789938Z", + "shell.execute_reply": "2024-10-22T01:44:26.789285Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1607,10 +1607,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:17:55.850082Z", - "iopub.status.busy": "2024-10-20T00:17:55.849888Z", - "iopub.status.idle": "2024-10-20T00:17:56.397818Z", - "shell.execute_reply": "2024-10-20T00:17:56.397196Z" + "iopub.execute_input": "2024-10-22T01:44:26.791929Z", + "iopub.status.busy": "2024-10-22T01:44:26.791720Z", + "iopub.status.idle": "2024-10-22T01:44:27.363751Z", + "shell.execute_reply": "2024-10-22T01:44:27.363038Z" } }, "outputs": [], @@ -1639,16 +1639,16 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:17:56.400136Z", - "iopub.status.busy": "2024-10-20T00:17:56.399692Z", - "iopub.status.idle": "2024-10-20T00:17:56.461262Z", - "shell.execute_reply": "2024-10-20T00:17:56.460612Z" + "iopub.execute_input": "2024-10-22T01:44:27.366439Z", + "iopub.status.busy": "2024-10-22T01:44:27.365954Z", + "iopub.status.idle": "2024-10-22T01:44:27.430206Z", + "shell.execute_reply": "2024-10-22T01:44:27.429474Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1689,10 +1689,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:17:56.463413Z", - "iopub.status.busy": "2024-10-20T00:17:56.463217Z", - "iopub.status.idle": "2024-10-20T00:17:56.475616Z", - "shell.execute_reply": "2024-10-20T00:17:56.475005Z" + "iopub.execute_input": "2024-10-22T01:44:27.432709Z", + "iopub.status.busy": "2024-10-22T01:44:27.432231Z", + "iopub.status.idle": "2024-10-22T01:44:27.445009Z", + "shell.execute_reply": "2024-10-22T01:44:27.444315Z" } }, "outputs": [], @@ -1780,10 +1780,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:17:56.477556Z", - "iopub.status.busy": "2024-10-20T00:17:56.477369Z", - "iopub.status.idle": "2024-10-20T00:17:56.483347Z", - "shell.execute_reply": "2024-10-20T00:17:56.482880Z" + "iopub.execute_input": "2024-10-22T01:44:27.447199Z", + "iopub.status.busy": "2024-10-22T01:44:27.446991Z", + "iopub.status.idle": "2024-10-22T01:44:27.454492Z", + "shell.execute_reply": "2024-10-22T01:44:27.453835Z" } }, "outputs": [], diff --git a/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb b/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb index 7a5166fe9..5d459a2ec 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/gcm_supply_chain_dist_change.ipynb @@ -27,10 +27,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.398032Z", - "iopub.status.busy": "2024-10-20T00:18:02.397596Z", - "iopub.status.idle": "2024-10-20T00:18:02.624842Z", - "shell.execute_reply": "2024-10-20T00:18:02.624300Z" + "iopub.execute_input": "2024-10-22T01:44:33.505294Z", + "iopub.status.busy": "2024-10-22T01:44:33.505105Z", + "iopub.status.idle": "2024-10-22T01:44:33.756627Z", + "shell.execute_reply": "2024-10-22T01:44:33.756015Z" } }, "outputs": [], @@ -46,10 +46,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.626902Z", - "iopub.status.busy": "2024-10-20T00:18:02.626708Z", - "iopub.status.idle": "2024-10-20T00:18:02.636384Z", - "shell.execute_reply": "2024-10-20T00:18:02.635866Z" + "iopub.execute_input": "2024-10-22T01:44:33.758857Z", + "iopub.status.busy": "2024-10-22T01:44:33.758647Z", + "iopub.status.idle": "2024-10-22T01:44:33.769200Z", + "shell.execute_reply": "2024-10-22T01:44:33.768605Z" }, "jupyter": { "outputs_hidden": false @@ -143,10 +143,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.638271Z", - "iopub.status.busy": "2024-10-20T00:18:02.637923Z", - "iopub.status.idle": "2024-10-20T00:18:02.644515Z", - "shell.execute_reply": "2024-10-20T00:18:02.644034Z" + "iopub.execute_input": "2024-10-22T01:44:33.771084Z", + "iopub.status.busy": "2024-10-22T01:44:33.770881Z", + "iopub.status.idle": "2024-10-22T01:44:33.778467Z", + "shell.execute_reply": "2024-10-22T01:44:33.777909Z" }, "jupyter": { "outputs_hidden": false @@ -250,10 +250,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.646323Z", - "iopub.status.busy": "2024-10-20T00:18:02.645989Z", - "iopub.status.idle": "2024-10-20T00:18:03.088970Z", - "shell.execute_reply": "2024-10-20T00:18:03.088290Z" + "iopub.execute_input": "2024-10-22T01:44:33.780453Z", + "iopub.status.busy": "2024-10-22T01:44:33.780212Z", + "iopub.status.idle": "2024-10-22T01:44:34.230769Z", + "shell.execute_reply": "2024-10-22T01:44:34.230190Z" }, "jupyter": { "outputs_hidden": false @@ -280,10 +280,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:03.091032Z", - "iopub.status.busy": "2024-10-20T00:18:03.090585Z", - "iopub.status.idle": "2024-10-20T00:18:03.095182Z", - "shell.execute_reply": "2024-10-20T00:18:03.094573Z" + "iopub.execute_input": "2024-10-22T01:44:34.232994Z", + "iopub.status.busy": "2024-10-22T01:44:34.232439Z", + "iopub.status.idle": "2024-10-22T01:44:34.237587Z", + "shell.execute_reply": "2024-10-22T01:44:34.237052Z" } }, "outputs": [ @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:03.097186Z", - "iopub.status.busy": "2024-10-20T00:18:03.096769Z", - "iopub.status.idle": "2024-10-20T00:18:03.276184Z", - "shell.execute_reply": "2024-10-20T00:18:03.275633Z" + "iopub.execute_input": "2024-10-22T01:44:34.239693Z", + "iopub.status.busy": "2024-10-22T01:44:34.239377Z", + "iopub.status.idle": "2024-10-22T01:44:34.451937Z", + "shell.execute_reply": "2024-10-22T01:44:34.451372Z" } }, "outputs": [ @@ -388,10 +388,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:03.278205Z", - "iopub.status.busy": "2024-10-20T00:18:03.278020Z", - "iopub.status.idle": "2024-10-20T00:18:04.339675Z", - "shell.execute_reply": "2024-10-20T00:18:04.339073Z" + "iopub.execute_input": "2024-10-22T01:44:34.454138Z", + "iopub.status.busy": "2024-10-22T01:44:34.453744Z", + "iopub.status.idle": "2024-10-22T01:44:35.560419Z", + "shell.execute_reply": "2024-10-22T01:44:35.559810Z" }, "jupyter": { "outputs_hidden": false @@ -433,10 +433,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:04.342317Z", - "iopub.status.busy": "2024-10-20T00:18:04.341800Z", - "iopub.status.idle": "2024-10-20T00:18:07.994614Z", - "shell.execute_reply": "2024-10-20T00:18:07.993934Z" + "iopub.execute_input": "2024-10-22T01:44:35.563319Z", + "iopub.status.busy": "2024-10-22T01:44:35.562744Z", + "iopub.status.idle": "2024-10-22T01:44:39.786524Z", + "shell.execute_reply": "2024-10-22T01:44:39.785819Z" } }, "outputs": [], @@ -468,10 +468,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:07.996946Z", - "iopub.status.busy": "2024-10-20T00:18:07.996713Z", - "iopub.status.idle": "2024-10-20T00:18:08.004294Z", - "shell.execute_reply": "2024-10-20T00:18:08.003804Z" + "iopub.execute_input": "2024-10-22T01:44:39.788968Z", + "iopub.status.busy": "2024-10-22T01:44:39.788636Z", + "iopub.status.idle": "2024-10-22T01:44:39.796449Z", + "shell.execute_reply": "2024-10-22T01:44:39.795907Z" } }, "outputs": [ @@ -553,16 +553,16 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:08.006003Z", - "iopub.status.busy": "2024-10-20T00:18:08.005815Z", - "iopub.status.idle": "2024-10-20T00:18:23.984179Z", - "shell.execute_reply": "2024-10-20T00:18:23.983551Z" + "iopub.execute_input": "2024-10-22T01:44:39.798457Z", + "iopub.status.busy": "2024-10-22T01:44:39.798056Z", + "iopub.status.idle": "2024-10-22T01:44:55.805640Z", + "shell.execute_reply": "2024-10-22T01:44:55.805109Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -619,10 +619,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:23.986310Z", - "iopub.status.busy": "2024-10-20T00:18:23.985939Z", - "iopub.status.idle": "2024-10-20T00:18:42.418061Z", - "shell.execute_reply": "2024-10-20T00:18:42.417375Z" + "iopub.execute_input": "2024-10-22T01:44:55.807827Z", + "iopub.status.busy": "2024-10-22T01:44:55.807453Z", + "iopub.status.idle": "2024-10-22T01:45:14.523970Z", + "shell.execute_reply": "2024-10-22T01:45:14.523385Z" } }, "outputs": [], @@ -641,16 +641,16 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:42.420309Z", - "iopub.status.busy": "2024-10-20T00:18:42.419937Z", - "iopub.status.idle": "2024-10-20T00:18:42.532671Z", - "shell.execute_reply": "2024-10-20T00:18:42.531840Z" + "iopub.execute_input": "2024-10-22T01:45:14.526328Z", + "iopub.status.busy": "2024-10-22T01:45:14.525970Z", + "iopub.status.idle": "2024-10-22T01:45:14.644761Z", + "shell.execute_reply": "2024-10-22T01:45:14.644138Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -676,10 +676,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:42.534789Z", - "iopub.status.busy": "2024-10-20T00:18:42.534548Z", - "iopub.status.idle": "2024-10-20T00:19:38.908429Z", - "shell.execute_reply": "2024-10-20T00:19:38.907688Z" + "iopub.execute_input": "2024-10-22T01:45:14.647419Z", + "iopub.status.busy": "2024-10-22T01:45:14.646983Z", + "iopub.status.idle": "2024-10-22T01:46:13.832454Z", + "shell.execute_reply": "2024-10-22T01:46:13.831717Z" } }, "outputs": [ @@ -689,8 +689,8 @@ "text": [ "\r", "Evaluating set functions...: 0%| | 0/32 [00:00" ] @@ -802,10 +808,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:19:39.133912Z", - "iopub.status.busy": "2024-10-20T00:19:39.133640Z", - "iopub.status.idle": "2024-10-20T00:19:39.145929Z", - "shell.execute_reply": "2024-10-20T00:19:39.145290Z" + "iopub.execute_input": "2024-10-22T01:46:14.073385Z", + "iopub.status.busy": "2024-10-22T01:46:14.073116Z", + "iopub.status.idle": "2024-10-22T01:46:14.086714Z", + "shell.execute_reply": "2024-10-22T01:46:14.086041Z" }, "jupyter": { "outputs_hidden": false diff --git a/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb b/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb index 9ef39c871..1fb77b1ab 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/graph_conditional_independence_refuter.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:44.100309Z", - "iopub.status.busy": "2024-10-20T00:19:44.099888Z", - "iopub.status.idle": "2024-10-20T00:19:44.116104Z", - "shell.execute_reply": "2024-10-20T00:19:44.115612Z" + "iopub.execute_input": "2024-10-22T01:46:19.068158Z", + "iopub.status.busy": "2024-10-22T01:46:19.067966Z", + "iopub.status.idle": "2024-10-22T01:46:19.083798Z", + "shell.execute_reply": "2024-10-22T01:46:19.083263Z" } }, "outputs": [], @@ -33,10 +33,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:44.118124Z", - "iopub.status.busy": "2024-10-20T00:19:44.117727Z", - "iopub.status.idle": "2024-10-20T00:19:45.572491Z", - "shell.execute_reply": "2024-10-20T00:19:45.571891Z" + "iopub.execute_input": "2024-10-22T01:46:19.085901Z", + "iopub.status.busy": "2024-10-22T01:46:19.085704Z", + "iopub.status.idle": "2024-10-22T01:46:20.667141Z", + "shell.execute_reply": "2024-10-22T01:46:20.666521Z" } }, "outputs": [], @@ -69,10 +69,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.574865Z", - "iopub.status.busy": "2024-10-20T00:19:45.574365Z", - "iopub.status.idle": "2024-10-20T00:19:45.854316Z", - "shell.execute_reply": "2024-10-20T00:19:45.853553Z" + "iopub.execute_input": "2024-10-22T01:46:20.669667Z", + "iopub.status.busy": "2024-10-22T01:46:20.669147Z", + "iopub.status.idle": "2024-10-22T01:46:20.952402Z", + "shell.execute_reply": "2024-10-22T01:46:20.951203Z" } }, "outputs": [ @@ -128,10 +128,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.857114Z", - "iopub.status.busy": "2024-10-20T00:19:45.856550Z", - "iopub.status.idle": "2024-10-20T00:19:45.896364Z", - "shell.execute_reply": "2024-10-20T00:19:45.895847Z" + "iopub.execute_input": "2024-10-22T01:46:20.955237Z", + "iopub.status.busy": "2024-10-22T01:46:20.954947Z", + "iopub.status.idle": "2024-10-22T01:46:20.997660Z", + "shell.execute_reply": "2024-10-22T01:46:20.996997Z" } }, "outputs": [], @@ -243,10 +243,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.898487Z", - "iopub.status.busy": "2024-10-20T00:19:45.898281Z", - "iopub.status.idle": "2024-10-20T00:19:45.939282Z", - "shell.execute_reply": "2024-10-20T00:19:45.938727Z" + "iopub.execute_input": "2024-10-22T01:46:21.001472Z", + "iopub.status.busy": "2024-10-22T01:46:21.000668Z", + "iopub.status.idle": "2024-10-22T01:46:21.045485Z", + "shell.execute_reply": "2024-10-22T01:46:21.044810Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.941683Z", - "iopub.status.busy": "2024-10-20T00:19:45.941287Z", - "iopub.status.idle": "2024-10-20T00:19:46.106486Z", - "shell.execute_reply": "2024-10-20T00:19:46.105883Z" + "iopub.execute_input": "2024-10-22T01:46:21.048402Z", + "iopub.status.busy": "2024-10-22T01:46:21.048140Z", + "iopub.status.idle": "2024-10-22T01:46:21.221513Z", + "shell.execute_reply": "2024-10-22T01:46:21.220845Z" } }, "outputs": [ @@ -291,10 +291,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.108807Z", - "iopub.status.busy": "2024-10-20T00:19:46.108433Z", - "iopub.status.idle": "2024-10-20T00:19:46.150685Z", - "shell.execute_reply": "2024-10-20T00:19:46.150098Z" + "iopub.execute_input": "2024-10-22T01:46:21.223974Z", + "iopub.status.busy": "2024-10-22T01:46:21.223731Z", + "iopub.status.idle": "2024-10-22T01:46:21.266997Z", + "shell.execute_reply": "2024-10-22T01:46:21.266394Z" }, "scrolled": true }, @@ -336,10 +336,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.153086Z", - "iopub.status.busy": "2024-10-20T00:19:46.152663Z", - "iopub.status.idle": "2024-10-20T00:19:46.389788Z", - "shell.execute_reply": "2024-10-20T00:19:46.389179Z" + "iopub.execute_input": "2024-10-22T01:46:21.269430Z", + "iopub.status.busy": "2024-10-22T01:46:21.268987Z", + "iopub.status.idle": "2024-10-22T01:46:21.525152Z", + "shell.execute_reply": "2024-10-22T01:46:21.524613Z" }, "scrolled": false }, @@ -353,10 +353,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.392032Z", - "iopub.status.busy": "2024-10-20T00:19:46.391701Z", - "iopub.status.idle": "2024-10-20T00:19:46.422253Z", - "shell.execute_reply": "2024-10-20T00:19:46.421658Z" + "iopub.execute_input": "2024-10-22T01:46:21.527422Z", + "iopub.status.busy": "2024-10-22T01:46:21.527018Z", + "iopub.status.idle": "2024-10-22T01:46:21.558040Z", + "shell.execute_reply": "2024-10-22T01:46:21.557521Z" } }, "outputs": [ @@ -396,10 +396,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.424167Z", - "iopub.status.busy": "2024-10-20T00:19:46.423802Z", - "iopub.status.idle": "2024-10-20T00:19:46.472213Z", - "shell.execute_reply": "2024-10-20T00:19:46.471536Z" + "iopub.execute_input": "2024-10-22T01:46:21.560021Z", + "iopub.status.busy": "2024-10-22T01:46:21.559673Z", + "iopub.status.idle": "2024-10-22T01:46:21.610876Z", + "shell.execute_reply": "2024-10-22T01:46:21.610164Z" } }, "outputs": [], @@ -420,10 +420,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.474268Z", - "iopub.status.busy": "2024-10-20T00:19:46.473902Z", - "iopub.status.idle": "2024-10-20T00:19:46.503588Z", - "shell.execute_reply": "2024-10-20T00:19:46.502978Z" + "iopub.execute_input": "2024-10-22T01:46:21.613296Z", + "iopub.status.busy": "2024-10-22T01:46:21.612906Z", + "iopub.status.idle": "2024-10-22T01:46:21.644434Z", + "shell.execute_reply": "2024-10-22T01:46:21.643880Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.505633Z", - "iopub.status.busy": "2024-10-20T00:19:46.505269Z", - "iopub.status.idle": "2024-10-20T00:19:46.534106Z", - "shell.execute_reply": "2024-10-20T00:19:46.533500Z" + "iopub.execute_input": "2024-10-22T01:46:21.646572Z", + "iopub.status.busy": "2024-10-22T01:46:21.646218Z", + "iopub.status.idle": "2024-10-22T01:46:21.677641Z", + "shell.execute_reply": "2024-10-22T01:46:21.677032Z" } }, "outputs": [], @@ -563,10 +563,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.535853Z", - "iopub.status.busy": "2024-10-20T00:19:46.535654Z", - "iopub.status.idle": "2024-10-20T00:19:46.564750Z", - "shell.execute_reply": "2024-10-20T00:19:46.564137Z" + "iopub.execute_input": "2024-10-22T01:46:21.679692Z", + "iopub.status.busy": "2024-10-22T01:46:21.679494Z", + "iopub.status.idle": "2024-10-22T01:46:21.711432Z", + "shell.execute_reply": "2024-10-22T01:46:21.710762Z" } }, "outputs": [], @@ -584,10 +584,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.566825Z", - "iopub.status.busy": "2024-10-20T00:19:46.566340Z", - "iopub.status.idle": "2024-10-20T00:19:46.716031Z", - "shell.execute_reply": "2024-10-20T00:19:46.715383Z" + "iopub.execute_input": "2024-10-22T01:46:21.713690Z", + "iopub.status.busy": "2024-10-22T01:46:21.713318Z", + "iopub.status.idle": "2024-10-22T01:46:21.871444Z", + "shell.execute_reply": "2024-10-22T01:46:21.870764Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.718061Z", - "iopub.status.busy": "2024-10-20T00:19:46.717861Z", - "iopub.status.idle": "2024-10-20T00:19:46.750474Z", - "shell.execute_reply": "2024-10-20T00:19:46.749867Z" + "iopub.execute_input": "2024-10-22T01:46:21.873900Z", + "iopub.status.busy": "2024-10-22T01:46:21.873475Z", + "iopub.status.idle": "2024-10-22T01:46:21.917795Z", + "shell.execute_reply": "2024-10-22T01:46:21.917144Z" } }, "outputs": [ @@ -639,10 +639,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.752370Z", - "iopub.status.busy": "2024-10-20T00:19:46.752177Z", - "iopub.status.idle": "2024-10-20T00:19:47.830059Z", - "shell.execute_reply": "2024-10-20T00:19:47.829366Z" + "iopub.execute_input": "2024-10-22T01:46:21.920254Z", + "iopub.status.busy": "2024-10-22T01:46:21.919888Z", + "iopub.status.idle": "2024-10-22T01:46:23.069075Z", + "shell.execute_reply": "2024-10-22T01:46:23.068410Z" } }, "outputs": [], @@ -662,10 +662,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:47.832529Z", - "iopub.status.busy": "2024-10-20T00:19:47.832082Z", - "iopub.status.idle": "2024-10-20T00:19:47.862810Z", - "shell.execute_reply": "2024-10-20T00:19:47.862167Z" + "iopub.execute_input": "2024-10-22T01:46:23.071403Z", + "iopub.status.busy": "2024-10-22T01:46:23.071173Z", + "iopub.status.idle": "2024-10-22T01:46:23.103531Z", + "shell.execute_reply": "2024-10-22T01:46:23.102889Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb b/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb index 1f546537b..46dbd6543 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/identifying_effects_using_id_algorithm.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:50.080103Z", - "iopub.status.busy": "2024-10-20T00:19:50.079663Z", - "iopub.status.idle": "2024-10-20T00:19:51.521426Z", - "shell.execute_reply": "2024-10-20T00:19:51.520769Z" + "iopub.execute_input": "2024-10-22T01:46:25.714117Z", + "iopub.status.busy": "2024-10-22T01:46:25.713918Z", + "iopub.status.idle": "2024-10-22T01:46:27.343798Z", + "shell.execute_reply": "2024-10-22T01:46:27.343125Z" } }, "outputs": [], @@ -55,10 +55,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.523928Z", - "iopub.status.busy": "2024-10-20T00:19:51.523454Z", - "iopub.status.idle": "2024-10-20T00:19:51.628681Z", - "shell.execute_reply": "2024-10-20T00:19:51.628146Z" + "iopub.execute_input": "2024-10-22T01:46:27.346479Z", + "iopub.status.busy": "2024-10-22T01:46:27.345951Z", + "iopub.status.idle": "2024-10-22T01:46:27.467642Z", + "shell.execute_reply": "2024-10-22T01:46:27.467065Z" } }, "outputs": [ @@ -136,10 +136,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.630866Z", - "iopub.status.busy": "2024-10-20T00:19:51.630631Z", - "iopub.status.idle": "2024-10-20T00:19:51.741067Z", - "shell.execute_reply": "2024-10-20T00:19:51.740485Z" + "iopub.execute_input": "2024-10-22T01:46:27.470470Z", + "iopub.status.busy": "2024-10-22T01:46:27.469849Z", + "iopub.status.idle": "2024-10-22T01:46:27.585791Z", + "shell.execute_reply": "2024-10-22T01:46:27.585192Z" } }, "outputs": [ @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.743513Z", - "iopub.status.busy": "2024-10-20T00:19:51.743294Z", - "iopub.status.idle": "2024-10-20T00:19:51.845437Z", - "shell.execute_reply": "2024-10-20T00:19:51.844834Z" + "iopub.execute_input": "2024-10-22T01:46:27.588381Z", + "iopub.status.busy": "2024-10-22T01:46:27.587815Z", + "iopub.status.idle": "2024-10-22T01:46:27.696657Z", + "shell.execute_reply": "2024-10-22T01:46:27.695970Z" } }, "outputs": [ @@ -302,10 +302,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.847605Z", - "iopub.status.busy": "2024-10-20T00:19:51.847389Z", - "iopub.status.idle": "2024-10-20T00:19:51.962810Z", - "shell.execute_reply": "2024-10-20T00:19:51.962198Z" + "iopub.execute_input": "2024-10-22T01:46:27.698990Z", + "iopub.status.busy": "2024-10-22T01:46:27.698752Z", + "iopub.status.idle": "2024-10-22T01:46:27.821761Z", + "shell.execute_reply": "2024-10-22T01:46:27.821079Z" } }, "outputs": [ @@ -386,10 +386,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.965270Z", - "iopub.status.busy": "2024-10-20T00:19:51.964687Z", - "iopub.status.idle": "2024-10-20T00:19:52.099902Z", - "shell.execute_reply": "2024-10-20T00:19:52.099262Z" + "iopub.execute_input": "2024-10-22T01:46:27.824587Z", + "iopub.status.busy": "2024-10-22T01:46:27.824128Z", + "iopub.status.idle": "2024-10-22T01:46:27.967761Z", + "shell.execute_reply": "2024-10-22T01:46:27.967093Z" } }, "outputs": [ @@ -470,10 +470,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:52.102606Z", - "iopub.status.busy": "2024-10-20T00:19:52.102201Z", - "iopub.status.idle": "2024-10-20T00:19:52.245123Z", - "shell.execute_reply": "2024-10-20T00:19:52.244528Z" + "iopub.execute_input": "2024-10-22T01:46:27.972768Z", + "iopub.status.busy": "2024-10-22T01:46:27.970832Z", + "iopub.status.idle": "2024-10-22T01:46:28.121773Z", + "shell.execute_reply": "2024-10-22T01:46:28.121151Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb b/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb index 64626cb29..e0c8f085b 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/lalonde_pandas_api.ipynb @@ -22,10 +22,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:54.319110Z", - "iopub.status.busy": "2024-10-20T00:19:54.318667Z", - "iopub.status.idle": "2024-10-20T00:19:54.324563Z", - "shell.execute_reply": "2024-10-20T00:19:54.324009Z" + "iopub.execute_input": "2024-10-22T01:46:30.430422Z", + "iopub.status.busy": "2024-10-22T01:46:30.430199Z", + "iopub.status.idle": "2024-10-22T01:46:30.436439Z", + "shell.execute_reply": "2024-10-22T01:46:30.435813Z" } }, "outputs": [], @@ -48,10 +48,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:54.326494Z", - "iopub.status.busy": "2024-10-20T00:19:54.326179Z", - "iopub.status.idle": "2024-10-20T00:19:56.722671Z", - "shell.execute_reply": "2024-10-20T00:19:56.722077Z" + "iopub.execute_input": "2024-10-22T01:46:30.438577Z", + "iopub.status.busy": "2024-10-22T01:46:30.438092Z", + "iopub.status.idle": "2024-10-22T01:46:33.256655Z", + "shell.execute_reply": "2024-10-22T01:46:33.256000Z" } }, "outputs": [], @@ -80,10 +80,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.724881Z", - "iopub.status.busy": "2024-10-20T00:19:56.724505Z", - "iopub.status.idle": "2024-10-20T00:19:56.728171Z", - "shell.execute_reply": "2024-10-20T00:19:56.727687Z" + "iopub.execute_input": "2024-10-22T01:46:33.259090Z", + "iopub.status.busy": "2024-10-22T01:46:33.258773Z", + "iopub.status.idle": "2024-10-22T01:46:33.262938Z", + "shell.execute_reply": "2024-10-22T01:46:33.262300Z" } }, "outputs": [], @@ -107,10 +107,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.729917Z", - "iopub.status.busy": "2024-10-20T00:19:56.729579Z", - "iopub.status.idle": "2024-10-20T00:19:56.768392Z", - "shell.execute_reply": "2024-10-20T00:19:56.767915Z" + "iopub.execute_input": "2024-10-22T01:46:33.264800Z", + "iopub.status.busy": "2024-10-22T01:46:33.264605Z", + "iopub.status.idle": "2024-10-22T01:46:33.305589Z", + "shell.execute_reply": "2024-10-22T01:46:33.304981Z" }, "scrolled": true }, @@ -138,10 +138,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.770381Z", - "iopub.status.busy": "2024-10-20T00:19:56.770036Z", - "iopub.status.idle": "2024-10-20T00:19:56.782990Z", - "shell.execute_reply": "2024-10-20T00:19:56.782475Z" + "iopub.execute_input": "2024-10-22T01:46:33.308064Z", + "iopub.status.busy": "2024-10-22T01:46:33.307638Z", + "iopub.status.idle": "2024-10-22T01:46:33.321678Z", + "shell.execute_reply": "2024-10-22T01:46:33.320975Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.785077Z", - "iopub.status.busy": "2024-10-20T00:19:56.784497Z", - "iopub.status.idle": "2024-10-20T00:19:56.796578Z", - "shell.execute_reply": "2024-10-20T00:19:56.796117Z" + "iopub.execute_input": "2024-10-22T01:46:33.323987Z", + "iopub.status.busy": "2024-10-22T01:46:33.323508Z", + "iopub.status.idle": "2024-10-22T01:46:33.336600Z", + "shell.execute_reply": "2024-10-22T01:46:33.335843Z" }, "scrolled": true }, @@ -339,106 +339,106 @@ " \n", " 0\n", " False\n", - " 31.0\n", - " 10.0\n", + " 17.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", + " 4080.730\n", + " 3796.029\n", + " 0.0000\n", " 0.0\n", - " 0.00\n", - " 17014.590\n", - " 1.0\n", - " 1.0\n", - " 0.616379\n", - " 1.622378\n", + " 0.0\n", + " 0.649916\n", + " 1.538661\n", " \n", " \n", " 1\n", - " True\n", + " False\n", " 23.0\n", - " 8.0\n", + " 11.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", + " 0.000\n", + " 0.000\n", + " 0.0000\n", " 1.0\n", - " 0.0\n", - " 1713.15\n", - " 4232.309\n", " 1.0\n", - " 0.0\n", - " 0.486703\n", - " 2.054643\n", + " 0.640709\n", + " 1.560772\n", " \n", " \n", " 2\n", " False\n", - " 22.0\n", - " 9.0\n", + " 19.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", - " 0.00\n", - " 12898.380\n", + " 0.000\n", + " 0.000\n", + " 0.0000\n", " 1.0\n", " 1.0\n", - " 0.618674\n", - " 1.616361\n", + " 0.646859\n", + " 1.545933\n", " \n", " \n", " 3\n", - " False\n", - " 32.0\n", - " 12.0\n", - " 0.0\n", - " 1.0\n", + " True\n", + " 27.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 0.00\n", - " 6735.320\n", + " 1.0\n", + " 0.000\n", + " 0.000\n", + " 549.2984\n", " 1.0\n", " 1.0\n", - " 0.518670\n", - " 1.928009\n", + " 0.365488\n", + " 2.736067\n", " \n", " \n", " 4\n", " False\n", - " 19.0\n", - " 10.0\n", - " 1.0\n", - " 0.0\n", + " 28.0\n", + " 11.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", - " 0.00\n", - " 4309.878\n", " 1.0\n", " 1.0\n", - " 0.635214\n", - " 1.574272\n", + " 3472.948\n", + " 0.000\n", + " 6771.6220\n", + " 0.0\n", + " 1.0\n", + " 0.694635\n", + " 1.439605\n", " \n", " \n", "\n", "" ], "text/plain": [ - " treat age educ black hisp married nodegr re74 re75 re78 \\\n", - "0 False 31.0 10.0 1.0 0.0 0.0 1.0 0.0 0.00 17014.590 \n", - "1 True 23.0 8.0 0.0 0.0 1.0 1.0 0.0 1713.15 4232.309 \n", - "2 False 22.0 9.0 1.0 0.0 0.0 1.0 0.0 0.00 12898.380 \n", - "3 False 32.0 12.0 0.0 1.0 1.0 0.0 0.0 0.00 6735.320 \n", - "4 False 19.0 10.0 1.0 0.0 0.0 1.0 0.0 0.00 4309.878 \n", + " treat age educ black hisp married nodegr re74 re75 \\\n", + "0 False 17.0 11.0 1.0 0.0 0.0 1.0 4080.730 3796.029 \n", + "1 False 23.0 11.0 1.0 0.0 0.0 1.0 0.000 0.000 \n", + "2 False 19.0 11.0 1.0 0.0 0.0 1.0 0.000 0.000 \n", + "3 True 27.0 11.0 1.0 0.0 0.0 1.0 0.000 0.000 \n", + "4 False 28.0 11.0 0.0 1.0 1.0 1.0 3472.948 0.000 \n", "\n", - " u74 u75 propensity_score weight \n", - "0 1.0 1.0 0.616379 1.622378 \n", - "1 1.0 0.0 0.486703 2.054643 \n", - "2 1.0 1.0 0.618674 1.616361 \n", - "3 1.0 1.0 0.518670 1.928009 \n", - "4 1.0 1.0 0.635214 1.574272 " + " re78 u74 u75 propensity_score weight \n", + "0 0.0000 0.0 0.0 0.649916 1.538661 \n", + "1 0.0000 1.0 1.0 0.640709 1.560772 \n", + "2 0.0000 1.0 1.0 0.646859 1.545933 \n", + "3 549.2984 1.0 1.0 0.365488 2.736067 \n", + "4 6771.6220 0.0 1.0 0.694635 1.439605 " ] }, "execution_count": 6, @@ -464,10 +464,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.798360Z", - "iopub.status.busy": "2024-10-20T00:19:56.798016Z", - "iopub.status.idle": "2024-10-20T00:19:56.850927Z", - "shell.execute_reply": "2024-10-20T00:19:56.850417Z" + "iopub.execute_input": "2024-10-22T01:46:33.338888Z", + "iopub.status.busy": "2024-10-22T01:46:33.338417Z", + "iopub.status.idle": "2024-10-22T01:46:33.395006Z", + "shell.execute_reply": "2024-10-22T01:46:33.394462Z" } }, "outputs": [ @@ -502,21 +502,21 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.852854Z", - "iopub.status.busy": "2024-10-20T00:19:56.852476Z", - "iopub.status.idle": "2024-10-20T00:19:56.869245Z", - "shell.execute_reply": "2024-10-20T00:19:56.868642Z" + "iopub.execute_input": "2024-10-22T01:46:33.397217Z", + "iopub.status.busy": "2024-10-22T01:46:33.396760Z", + "iopub.status.idle": "2024-10-22T01:46:33.415562Z", + "shell.execute_reply": "2024-10-22T01:46:33.414840Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle 1380.0630450237$" + "$\\displaystyle 1087.95177287071$" ], "text/plain": [ - "1380.0630450236968" + "1087.951772870707" ] }, "execution_count": 8, @@ -540,21 +540,21 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.871240Z", - "iopub.status.busy": "2024-10-20T00:19:56.870819Z", - "iopub.status.idle": "2024-10-20T00:19:56.889221Z", - "shell.execute_reply": "2024-10-20T00:19:56.888615Z" + "iopub.execute_input": "2024-10-22T01:46:33.417637Z", + "iopub.status.busy": "2024-10-22T01:46:33.417410Z", + "iopub.status.idle": "2024-10-22T01:46:33.437263Z", + "shell.execute_reply": "2024-10-22T01:46:33.436714Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": "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", "text/latex": [ - "$\\displaystyle 1176.21721690883$" + "$\\displaystyle 1331.19037172705$" ], "text/plain": [ - "1176.2172169088303" + "1331.1903717270516" ] }, "execution_count": 9, @@ -587,10 +587,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.891289Z", - "iopub.status.busy": "2024-10-20T00:19:56.890865Z", - "iopub.status.idle": "2024-10-20T00:19:56.897111Z", - "shell.execute_reply": "2024-10-20T00:19:56.896516Z" + "iopub.execute_input": "2024-10-22T01:46:33.439400Z", + "iopub.status.busy": "2024-10-22T01:46:33.439032Z", + "iopub.status.idle": "2024-10-22T01:46:33.445777Z", + "shell.execute_reply": "2024-10-22T01:46:33.445157Z" } }, "outputs": [ @@ -598,13 +598,13 @@ "data": { "text/plain": [ "count 445.000000\n", - "mean 5160.692072\n", - "std 6301.457031\n", + "mean 5621.276648\n", + "std 7162.567400\n", "min 0.000000\n", "25% 0.000000\n", - "50% 3194.010000\n", - "75% 7693.400000\n", - "max 39483.530000\n", + "50% 3708.719000\n", + "75% 8472.158000\n", + "max 60307.930000\n", "Name: re78, dtype: float64" ] }, @@ -622,10 +622,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.898835Z", - "iopub.status.busy": "2024-10-20T00:19:56.898633Z", - "iopub.status.idle": "2024-10-20T00:19:56.904605Z", - "shell.execute_reply": "2024-10-20T00:19:56.904121Z" + "iopub.execute_input": "2024-10-22T01:46:33.447804Z", + "iopub.status.busy": "2024-10-22T01:46:33.447491Z", + "iopub.status.idle": "2024-10-22T01:46:33.456137Z", + "shell.execute_reply": "2024-10-22T01:46:33.454465Z" } }, "outputs": [ @@ -664,10 +664,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.906470Z", - "iopub.status.busy": "2024-10-20T00:19:56.906117Z", - "iopub.status.idle": "2024-10-20T00:19:56.910902Z", - "shell.execute_reply": "2024-10-20T00:19:56.910391Z" + "iopub.execute_input": "2024-10-22T01:46:33.458819Z", + "iopub.status.busy": "2024-10-22T01:46:33.458417Z", + "iopub.status.idle": "2024-10-22T01:46:33.463848Z", + "shell.execute_reply": "2024-10-22T01:46:33.463294Z" } }, "outputs": [], @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.912556Z", - "iopub.status.busy": "2024-10-20T00:19:56.912370Z", - "iopub.status.idle": "2024-10-20T00:19:57.175422Z", - "shell.execute_reply": "2024-10-20T00:19:57.174783Z" + "iopub.execute_input": "2024-10-22T01:46:33.465801Z", + "iopub.status.busy": "2024-10-22T01:46:33.465372Z", + "iopub.status.idle": "2024-10-22T01:46:33.750737Z", + "shell.execute_reply": "2024-10-22T01:46:33.750157Z" } }, "outputs": [ @@ -699,7 +699,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -719,10 +719,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.177676Z", - "iopub.status.busy": "2024-10-20T00:19:57.177312Z", - "iopub.status.idle": "2024-10-20T00:19:57.342585Z", - "shell.execute_reply": "2024-10-20T00:19:57.341974Z" + "iopub.execute_input": "2024-10-22T01:46:33.752948Z", + "iopub.status.busy": "2024-10-22T01:46:33.752483Z", + "iopub.status.idle": "2024-10-22T01:46:33.925779Z", + "shell.execute_reply": "2024-10-22T01:46:33.925093Z" } }, "outputs": [ @@ -738,7 +738,7 @@ }, { "data": { - "image/png": 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RP//5T9199906cuSIWrRood69e+uDDz5QixYtJElz5sxRQECAkpKSVFVVpYSEBC1YsMB6fWBgoNasWaNx48bJ4/EoLCxMI0aM0IwZM6yamJgYZWVlKTU1VXPnzlXr1q316quv/uhH7gEAgHkcPp/PZ3cTDZ3X65Xb7VZ5eblcLpfd7aAOVVRUaNCgQdb+qlWrFB4ebmNHuFTETVxidwtAg5Q/a3idzX0+f78b1DNEAAAAdiAQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMF2d0A/r+4iUvsbsF4jlPVcp+x3/fRt+QLCrGtH/xb/qzhdrcA4BLHFSIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGazCB6Omnn5bD4VBKSop17MSJE0pOTlbz5s0VHh6upKQklZaW+r2uuLhYiYmJaty4sVq2bKmJEyfq1KlTfjWbNm1S9+7d5XQ61b59e2VmZtbDigAAwMWiQQSinTt36qWXXlLXrl39jqempmr16tVauXKlNm/erEOHDmnIkCHW+OnTp5WYmKjq6mpt375dixcvVmZmpqZNm2bVHDhwQImJierXr58KCgqUkpKiUaNGKScnp97WBwAAGjbbA1FFRYWGDRumV155RZdddpl1vLy8XK+99pqee+453XjjjYqLi9OiRYu0fft2ffDBB5KkdevW6dNPP9Wbb76pq6++WgMHDtQTTzyh+fPnq7q6WpKUkZGhmJgYzZ49W507d9b48eN1++23a86cObasFwAANDy2B6Lk5GQlJiYqPj7e73h+fr5Onjzpd7xTp05q06aN8vLyJEl5eXnq0qWLIiIirJqEhAR5vV4VFhZaNT+cOyEhwZrjXKqqquT1ev02AABw6Qqy8+RvvfWWPvroI+3cufOssZKSEoWEhKhp06Z+xyMiIlRSUmLVnBmGasdrx36qxuv16vjx4woNDT3r3Onp6Xr88cd/9boAAMDFxbYrRAcPHtT//M//aOnSpWrUqJFdbZzTlClTVF5ebm0HDx60uyUAAFCHbAtE+fn5Onz4sLp3766goCAFBQVp8+bNmjdvnoKCghQREaHq6mqVlZX5va60tFSRkZGSpMjIyLM+dVa7/3M1LpfrnFeHJMnpdMrlcvltAADg0mVbILrpppu0e/duFRQUWFuPHj00bNgw69/BwcHasGGD9ZqioiIVFxfL4/FIkjwej3bv3q3Dhw9bNbm5uXK5XIqNjbVqzpyjtqZ2DgAAANueIWrSpImuuuoqv2NhYWFq3ry5dXzkyJFKS0tTs2bN5HK59NBDD8nj8ahXr16SpP79+ys2Nlb33XefZs6cqZKSEk2dOlXJyclyOp2SpLFjx+rFF1/UpEmT9OCDD2rjxo1asWKFsrKy6nfBAACgwbL1oeqfM2fOHAUEBCgpKUlVVVVKSEjQggULrPHAwECtWbNG48aNk8fjUVhYmEaMGKEZM2ZYNTExMcrKylJqaqrmzp2r1q1b69VXX1VCQoIdSwIAAA1QgwpEmzZt8ttv1KiR5s+fr/nz5//oa9q2bau//e1vPzlv37599Y9//ONCtAgAAC5Btn8PEQAAgN0IRAAAwHjnfcvs448/Vn5+vvr27at27dqpsLBQ8+fPV01NjW677TaezQEAABed87pC9PbbbysuLk6TJk1St27dtH79evXu3Vv79u3TV199pcTERC1btqyuegUAAKgT5xWI/vKXv+jxxx/Xv/71L73yyiu64447lJaWptzcXGVnZ+uZZ57RrFmz6qpXAACAOnFegaioqEjDhg2TJN11112qrKzU4MGDrfHbbrtN+/fvv6ANAvXJFxis8q53W5svMNjulgAA9eC8AlGTJk105MgRSVJZWZlOnTpl7UvSkSNHFB4efmE7BOqTwyFfUIi1yeGwuyMAQD04r0AUHx+v5ORkLV26VCNGjFD//v01ZcoU7d27V0VFRZo4caJ69+5dV70CAADUifMKRM8++6xcLpfGjh2r6upqLV++XD169FBsbKxiY2N16NAhPf3003XVKwAAQJ04r4/dR0REaN26dX7HXnjhBaWmpur7779Xp06dFBTUoL78GgAA4GddkPTSrl27CzENAACALc7rltlDDz2kv//973XVCwAAgC3OKxDNnz9fffv2VYcOHfTMM8+opKSkrvoCAACoN+f9W2br1q3TLbfcomeffVZt2rTRoEGDtGbNGtXU1NRFfwAAAHXuvANRly5d9Pzzz+vQoUN68803VVVVpcGDBys6Olp//vOf+WJGAABw0fnVv3YfHBysO++8U9nZ2fryyy81evRoLV26VB07dryQ/QEAANS5Xx2IztSmTRtNnz5dBw4cUHZ29oWYEgAAoN6cVyBq27atAgMDf3Tc4XDo5ptv/s1NAQAA1Kfz+h6iAwcO1FUfAAAAtvlNt8z279+vnJwcHT9+XJLk8/kuSFMAAAD16VcFoiNHjig+Pl4dOnTQLbfcom+++UaSNHLkSP3pT3+6oA0CAADUtV8ViFJTUxUUFKTi4mI1btzYOn7XXXfxUDUAALjo/KrfMlu3bp1ycnLUunVrv+NXXnmlvv766wvSGAAAQH35VVeIKisr/a4M1Tp69KicTudvbgoAAKA+/apA1KdPHy1ZssTadzgcqqmp0cyZM9WvX78L1hwAAEB9+FW3zGbNmqUbb7xRu3btUnV1tSZNmqTCwkIdPXpU27Ztu9A9AgAA1KnzDkQnT57Uww8/rNWrVys3N1dNmjRRRUWFhgwZouTkZLVq1aou+gQAAKgz5x2IgoOD9cknn+iyyy7Tn//857roCQAAoF79qmeI7r33Xr322msXuhcAAABb/KpniE6dOqXXX39d69evV1xcnMLCwvzGn3vuuQvSHAAAQH34VYFoz5496t69uyTp888/9xtzOBy/vSsAAIB69KsC0fvvv3+h+wAAALDNb/pxVwAAgEsBgQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDxbA9HChQvVtWtXuVwuuVwueTwerV271ho/ceKEkpOT1bx5c4WHhyspKUmlpaV+cxQXFysxMVGNGzdWy5YtNXHiRJ06dcqvZtOmTerevbucTqfat2+vzMzM+lgeAAC4SNgaiFq3bq2nn35a+fn52rVrl2688UYNGjRIhYWFkqTU1FStXr1aK1eu1ObNm3Xo0CENGTLEev3p06eVmJio6upqbd++XYsXL1ZmZqamTZtm1Rw4cECJiYnq16+fCgoKlJKSolGjRiknJ6fe1wsAABomh8/n89ndxJmaNWumWbNm6fbbb1eLFi20bNky3X777ZKkvXv3qnPnzsrLy1OvXr20du1a3XrrrTp06JAiIiIkSRkZGZo8ebK+/fZbhYSEaPLkycrKytKePXuscwwdOlRlZWXKzs7+RT15vV653W6Vl5fL5XJd+EX/P3ETl9TZ3MDFLH/WcLtb+M14fwPnVpfv7/P5+91gniE6ffq03nrrLVVWVsrj8Sg/P18nT55UfHy8VdOpUye1adNGeXl5kqS8vDx16dLFCkOSlJCQIK/Xa11lysvL85ujtqZ2jnOpqqqS1+v12wAAwKXL9kC0e/duhYeHy+l0auzYsXrnnXcUGxurkpIShYSEqGnTpn71ERERKikpkSSVlJT4haHa8dqxn6rxer06fvz4OXtKT0+X2+22tujo6AuxVAAA0EDZHog6duyogoIC7dixQ+PGjdOIESP06aef2trTlClTVF5ebm0HDx60tR8AAFC3guxuICQkRO3bt5ckxcXFaefOnZo7d67uuusuVVdXq6yszO8qUWlpqSIjIyVJkZGR+vDDD/3mq/0U2pk1P/xkWmlpqVwul0JDQ8/Zk9PplNPpvCDrAwAADZ/tV4h+qKamRlVVVYqLi1NwcLA2bNhgjRUVFam4uFgej0eS5PF4tHv3bh0+fNiqyc3NlcvlUmxsrFVz5hy1NbVzAAAA2HqFaMqUKRo4cKDatGmjY8eOadmyZdq0aZNycnLkdrs1cuRIpaWlqVmzZnK5XHrooYfk8XjUq1cvSVL//v0VGxur++67TzNnzlRJSYmmTp2q5ORk6wrP2LFj9eKLL2rSpEl68MEHtXHjRq1YsUJZWVl2Lh0AADQgtgaiw4cPa/jw4frmm2/kdrvVtWtX5eTk6Oabb5YkzZkzRwEBAUpKSlJVVZUSEhK0YMEC6/WBgYFas2aNxo0bJ4/Ho7CwMI0YMUIzZsywamJiYpSVlaXU1FTNnTtXrVu31quvvqqEhIR6Xy8AAGiYGtz3EDVEfA8RYC++hwi4dPE9RAAAAA0EgQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwnq2BKD09Xddee62aNGmili1bavDgwSoqKvKrOXHihJKTk9W8eXOFh4crKSlJpaWlfjXFxcVKTExU48aN1bJlS02cOFGnTp3yq9m0aZO6d+8up9Op9u3bKzMzs66XBwAALhK2BqLNmzcrOTlZH3zwgXJzc3Xy5En1799flZWVVk1qaqpWr16tlStXavPmzTp06JCGDBlijZ8+fVqJiYmqrq7W9u3btXjxYmVmZmratGlWzYEDB5SYmKh+/fqpoKBAKSkpGjVqlHJycup1vQAAoGFy+Hw+n91N1Pr222/VsmVLbd68Wddff73Ky8vVokULLVu2TLfffrskae/evercubPy8vLUq1cvrV27VrfeeqsOHTqkiIgISVJGRoYmT56sb7/9ViEhIZo8ebKysrK0Z88e61xDhw5VWVmZsrOzz+qjqqpKVVVV1r7X61V0dLTKy8vlcrnqbP1xE5fU2dzAxSx/1nC7W/jNeH8D51aX72+v1yu32/2L/n43qGeIysvLJUnNmjWTJOXn5+vkyZOKj4+3ajp16qQ2bdooLy9PkpSXl6cuXbpYYUiSEhIS5PV6VVhYaNWcOUdtTe0cP5Seni63221t0dHRF26RAACgwWkwgaimpkYpKSm67rrrdNVVV0mSSkpKFBISoqZNm/rVRkREqKSkxKo5MwzVjteO/VSN1+vV8ePHz+plypQpKi8vt7aDBw9ekDUCAICGKcjuBmolJydrz5492rp1q92tyOl0yul02t0GAACoJw3iCtH48eO1Zs0avf/++2rdurV1PDIyUtXV1SorK/OrLy0tVWRkpFXzw0+d1e7/XI3L5VJoaOiFXg4AALjI2BqIfD6fxo8fr3feeUcbN25UTEyM33hcXJyCg4O1YcMG61hRUZGKi4vl8XgkSR6PR7t379bhw4etmtzcXLlcLsXGxlo1Z85RW1M7BwAAMJutt8ySk5O1bNkyrVq1Sk2aNLGe+XG73QoNDZXb7dbIkSOVlpamZs2ayeVy6aGHHpLH41GvXr0kSf3791dsbKzuu+8+zZw5UyUlJZo6daqSk5Ot215jx47Viy++qEmTJunBBx/Uxo0btWLFCmVlZdm2dgAA0HDYeoVo4cKFKi8vV9++fdWqVStrW758uVUzZ84c3XrrrUpKStL111+vyMhIvf3229Z4YGCg1qxZo8DAQHk8Ht17770aPny4ZsyYYdXExMQoKytLubm56tatm2bPnq1XX31VCQkJ9bpeAADQMNl6heiXfAVSo0aNNH/+fM2fP/9Ha9q2bau//e1vPzlP37599Y9//OO8ewQAAJe+BvFQNQAAgJ0IRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADj2RqItmzZov/+7/9WVFSUHA6H3n33Xb9xn8+nadOmqVWrVgoNDVV8fLz27dvnV3P06FENGzZMLpdLTZs21ciRI1VRUeFX88knn6hPnz5q1KiRoqOjNXPmzLpeGgAAuIjYGogqKyvVrVs3zZ8//5zjM2fO1Lx585SRkaEdO3YoLCxMCQkJOnHihFUzbNgwFRYWKjc3V2vWrNGWLVs0ZswYa9zr9ap///5q27at8vPzNWvWLE2fPl0vv/xyna8PAABcHILsPPnAgQM1cODAc475fD49//zzmjp1qgYNGiRJWrJkiSIiIvTuu+9q6NCh+uyzz5Sdna2dO3eqR48ekqQXXnhBt9xyi5599llFRUVp6dKlqq6u1uuvv66QkBD9/ve/V0FBgZ577jm/4HSmqqoqVVVVWfter/cCrxwAADQkDfYZogMHDqikpETx8fHWMbfbrZ49eyovL0+SlJeXp6ZNm1phSJLi4+MVEBCgHTt2WDXXX3+9QkJCrJqEhAQVFRXpu+++O+e509PT5Xa7rS06OroulggAABqIBhuISkpKJEkRERF+xyMiIqyxkpIStWzZ0m88KChIzZo186s51xxnnuOHpkyZovLycms7ePDgb18QAABosGy9ZdZQOZ1OOZ1Ou9sAAAD1pMFeIYqMjJQklZaW+h0vLS21xiIjI3X48GG/8VOnTuno0aN+Neea48xzAAAAszXYQBQTE6PIyEht2LDBOub1erVjxw55PB5JksfjUVlZmfLz862ajRs3qqamRj179rRqtmzZopMnT1o1ubm56tixoy677LJ6Wg0AAGjIbA1EFRUVKigoUEFBgaR/P0hdUFCg4uJiORwOpaSk6Mknn9R7772n3bt3a/jw4YqKitLgwYMlSZ07d9aAAQM0evRoffjhh9q2bZvGjx+voUOHKioqSpJ0zz33KCQkRCNHjlRhYaGWL1+uuXPnKi0tzaZVAwCAhsbWZ4h27dqlfv36Wfu1IWXEiBHKzMzUpEmTVFlZqTFjxqisrEy9e/dWdna2GjVqZL1m6dKlGj9+vG666SYFBAQoKSlJ8+bNs8bdbrfWrVun5ORkxcXF6fLLL9e0adN+9CP3AADAPA6fz+ezu4mGzuv1yu12q7y8XC6Xq87OEzdxSZ3NDVzM8mcNt7uF34z3N3Budfn+Pp+/3w32GSIAAID6QiACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGM+oQDR//nxdccUVatSokXr27KkPP/zQ7pYAAEADYEwgWr58udLS0vTYY4/po48+Urdu3ZSQkKDDhw/b3RoAALCZMYHoueee0+jRo/XAAw8oNjZWGRkZaty4sV5//XW7WwMAADYLsruB+lBdXa38/HxNmTLFOhYQEKD4+Hjl5eWdVV9VVaWqqiprv7y8XJLk9XrrtM/TVcfrdH7gYlXX7736wPsbOLe6fH/Xzu3z+X621ohA9K9//UunT59WRESE3/GIiAjt3bv3rPr09HQ9/vjjZx2Pjo6usx4B/Dj3C2PtbgFAHamP9/exY8fkdrt/ssaIQHS+pkyZorS0NGu/pqZGR48eVfPmzeVwOGzsDPXB6/UqOjpaBw8elMvlsrsdABcQ72+z+Hw+HTt2TFFRUT9ba0QguvzyyxUYGKjS0lK/46WlpYqMjDyr3ul0yul0+h1r2rRpXbaIBsjlcvE/TOASxfvbHD93ZaiWEQ9Vh4SEKC4uThs2bLCO1dTUaMOGDfJ4PDZ2BgAAGgIjrhBJUlpamkaMGKEePXroP//zP/X888+rsrJSDzzwgN2tAQAAmxkTiO666y59++23mjZtmkpKSnT11VcrOzv7rAetAafTqccee+ys26YALn68v/FjHL5f8lk0AACAS5gRzxABAAD8FAIRAAAwHoEIAAAYj0AEnCEzM5PvnAIAAxGIcEm6//775XA4ztr2799vd2sALpBzvcfP3KZPn253i7iIGPOxe5hnwIABWrRokd+xFi1a2NQNgAvtm2++sf69fPlyTZs2TUVFRdax8PBw698+n0+nT59WUBB/9nBuXCHCJcvpdCoyMtJvmzt3rrp06aKwsDBFR0frj3/8oyoqKn50jo8//lj9+vVTkyZN5HK5FBcXp127dlnjW7duVZ8+fRQaGqro6Gg9/PDDqqysrI/lAcY7873tdrvlcDis/b1796pJkyZau3at4uLi5HQ6tXXrVt1///0aPHiw3zwpKSnq27evtV9TU6P09HTFxMQoNDRU3bp101//+tf6XRzqHYEIRgkICNC8efNUWFioxYsXa+PGjZo0adKP1g8bNkytW7fWzp07lZ+fr0ceeUTBwcGSpC+++EIDBgxQUlKSPvnkEy1fvlxbt27V+PHj62s5AH7GI488oqefflqfffaZunbt+otek56eriVLligjI0OFhYVKTU3Vvffeq82bN9dxt7AT1w5xyVqzZo3fJfOBAwdq5cqV1v4VV1yhJ598UmPHjtWCBQvOOUdxcbEmTpyoTp06SZKuvPJKayw9PV3Dhg1TSkqKNTZv3jzdcMMNWrhwoRo1alQHqwJwPmbMmKGbb775F9dXVVXpqaee0vr1663fumzXrp22bt2ql156STfccENdtQqbEYhwyerXr58WLlxo7YeFhWn9+vVKT0/X3r175fV6derUKZ04cULff/+9GjdufNYcaWlpGjVqlN544w3Fx8frjjvu0H/8x39I+vfttE8++URLly616n0+n2pqanTgwAF17ty57hcJ4Cf16NHjvOr379+v77///qwQVV1drWuuueZCtoYGhkCES1ZYWJjat29v7X/11Ve69dZbNW7cOP3lL39Rs2bNtHXrVo0cOVLV1dXnDETTp0/XPffco6ysLK1du1aPPfaY3nrrLd12222qqKjQH/7wBz388MNnva5NmzZ1ujYAv0xYWJjffkBAgH74i1UnT560/l37TGFWVpZ+97vf+dXx+2eXNgIRjJGfn6+amhrNnj1bAQH/fnxuxYoVP/u6Dh06qEOHDkpNTdXdd9+tRYsW6bbbblP37t316aef+oUuAA1bixYttGfPHr9jBQUF1rOBsbGxcjqdKi4u5vaYYXioGsZo3769Tp48qRdeeEFffvml3njjDWVkZPxo/fHjxzV+/Hht2rRJX3/9tbZt26adO3dat8ImT56s7du3a/z48SooKNC+ffu0atUqHqoGGrAbb7xRu3bt0pIlS7Rv3z499thjfgGpSZMmmjBhglJTU7V48WJ98cUX+uijj/TCCy9o8eLFNnaOukYggjG6deum5557Ts8884yuuuoqLV26VOnp6T9aHxgYqCNHjmj48OHq0KGD7rzzTg0cOFCPP/64JKlr167avHmzPv/8c/Xp00fXXHONpk2bpqioqPpaEoDzlJCQoEcffVSTJk3Stddeq2PHjmn48OF+NU888YQeffRRpaenq3PnzhowYICysrIUExNjU9eoDw7fD2+mAgAAGIYrRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAJeEvn37KiUlpV7Odf/992vw4MH1ci4A9YNABMAIPp9Pp06dsrsNAA0UgQjARe/+++/X5s2bNXfuXDkcDjkcDmVmZsrhcGjt2rWKi4uT0+nU1q1bVVNTo/T0dMXExCg0NFTdunXTX//6V2uu06dPa+TIkdZ4x44dNXfuXGt8+vTpWrx4sVatWmWda9OmTTasGsCFFGR3AwDwW82dO1eff/65rrrqKs2YMUOSVFhYKEl65JFH9Oyzz6pdu3a67LLLlJ6erjfffFMZGRm68sortWXLFt17771q0aKFbrjhBtXU1Kh169ZauXKlmjdvru3bt2vMmDFq1aqV7rzzTk2YMEGfffaZvF6vFi1aJElq1qyZbWsHcGEQiABc9Nxut0JCQtS4cWNFRkZKkvbu3StJmjFjhm6++WZJUlVVlZ566imtX79eHo9HktSuXTtt3bpVL730km644QYFBwfr8ccft+aOiYlRXl6eVqxYoTvvvFPh4eEKDQ1VVVWVdS4AFz8CEYBLWo8ePax/79+/X99//70VkGpVV1frmmuusfbnz5+v119/XcXFxTp+/Liqq6t19dVX11fLAGxAIAJwSQsLC7P+XVFRIUnKysrS7373O786p9MpSXrrrbc0YcIEzZ49Wx6PR02aNNGsWbO0Y8eO+msaQL0jEAG4JISEhOj06dM/WRMbGyun06ni4mLdcMMN56zZtm2b/uu//kt//OMfrWNffPHFeZ8LwMWFQATgknDFFVdox44d+uqrrxQeHq6ampqzapo0aaIJEyYoNTVVNTU16t27t8rLy7Vt2za5XC6NGDFCV155pZYsWaKcnBzFxMTojTfe0M6dOxUTE+N3rpycHBUVFal58+Zyu90KDg6uz+UCuMD42D2AS8KECRMUGBio2NhYtWjRQsXFxeese+KJJ/Too48qPT1dnTt31oABA5SVlWUFnj/84Q8aMmSI7rrrLvXs2VNHjhzxu1okSaNHj1bHjh3Vo0cPtWjRQtu2bavz9QGoWw6fz+ezuwkAAAA7cYUIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMb7v5auPn2MF0UaAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -765,10 +765,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.344774Z", - "iopub.status.busy": "2024-10-20T00:19:57.344535Z", - "iopub.status.idle": "2024-10-20T00:19:57.384005Z", - "shell.execute_reply": "2024-10-20T00:19:57.383462Z" + "iopub.execute_input": "2024-10-22T01:46:33.928075Z", + "iopub.status.busy": "2024-10-22T01:46:33.927689Z", + "iopub.status.idle": "2024-10-22T01:46:33.969381Z", + "shell.execute_reply": "2024-10-22T01:46:33.968686Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.386028Z", - "iopub.status.busy": "2024-10-20T00:19:57.385542Z", - "iopub.status.idle": "2024-10-20T00:19:57.397802Z", - "shell.execute_reply": "2024-10-20T00:19:57.397311Z" + "iopub.execute_input": "2024-10-22T01:46:33.971812Z", + "iopub.status.busy": "2024-10-22T01:46:33.971409Z", + "iopub.status.idle": "2024-10-22T01:46:33.984130Z", + "shell.execute_reply": "2024-10-22T01:46:33.983576Z" } }, "outputs": [ @@ -834,106 +834,106 @@ " \n", " 0\n", " True\n", - " 19.0\n", - " 9.0\n", + " 20.0\n", + " 10.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.00\n", - " 0.000\n", - " 8173.908\n", - " 1.0\n", - " 1.0\n", - " 0.376593\n", - " 2.655385\n", + " 5005.7310\n", + " 2777.3550\n", + " 5615.1890\n", + " 0.0\n", + " 0.0\n", + " 0.366342\n", + " 2.729693\n", " \n", " \n", " 1\n", " True\n", - " 27.0\n", - " 11.0\n", + " 21.0\n", + " 9.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.00\n", - " 0.000\n", - " 7506.146\n", - " 1.0\n", - " 1.0\n", - " 0.365484\n", - " 2.736099\n", + " 6416.4700\n", + " 5749.3310\n", + " 743.6666\n", + " 0.0\n", + " 0.0\n", + " 0.379746\n", + " 2.633342\n", " \n", " \n", " 2\n", " True\n", - " 38.0\n", - " 12.0\n", - " 0.0\n", - " 0.0\n", + " 40.0\n", + " 11.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", - " 0.00\n", - " 0.000\n", - " 4941.849\n", " 1.0\n", + " 0.0000\n", + " 0.0000\n", + " 23005.6000\n", " 1.0\n", - " 0.600062\n", - " 1.666494\n", + " 1.0\n", + " 0.385928\n", + " 2.591158\n", " \n", " \n", " 3\n", " True\n", - " 20.0\n", - " 9.0\n", - " 0.0\n", + " 18.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", - " 12260.78\n", - " 5875.049\n", - " 1358.643\n", + " 858.2543\n", + " 214.5636\n", + " 929.8839\n", " 0.0\n", " 0.0\n", - " 0.272011\n", - " 3.676321\n", + " 0.351611\n", + " 2.844049\n", " \n", " \n", " 4\n", " True\n", - " 31.0\n", - " 11.0\n", + " 21.0\n", + " 13.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0000\n", + " 0.0000\n", + " 17094.6400\n", " 1.0\n", " 1.0\n", - " 0.00\n", - " 0.000\n", - " 14509.930\n", - " 1.0\n", - " 1.0\n", - " 0.421973\n", - " 2.369819\n", + " 0.519464\n", + " 1.925062\n", " \n", " \n", "\n", "" ], "text/plain": [ - " treat age educ black hisp married nodegr re74 re75 \\\n", - "0 True 19.0 9.0 1.0 0.0 0.0 1.0 0.00 0.000 \n", - "1 True 27.0 11.0 1.0 0.0 0.0 1.0 0.00 0.000 \n", - "2 True 38.0 12.0 0.0 0.0 0.0 0.0 0.00 0.000 \n", - "3 True 20.0 9.0 0.0 1.0 0.0 1.0 12260.78 5875.049 \n", - "4 True 31.0 11.0 1.0 0.0 1.0 1.0 0.00 0.000 \n", + " treat age educ black hisp married nodegr re74 re75 \\\n", + "0 True 20.0 10.0 1.0 0.0 0.0 1.0 5005.7310 2777.3550 \n", + "1 True 21.0 9.0 1.0 0.0 0.0 1.0 6416.4700 5749.3310 \n", + "2 True 40.0 11.0 1.0 0.0 0.0 1.0 0.0000 0.0000 \n", + "3 True 18.0 11.0 1.0 0.0 0.0 1.0 858.2543 214.5636 \n", + "4 True 21.0 13.0 1.0 0.0 0.0 0.0 0.0000 0.0000 \n", "\n", - " re78 u74 u75 propensity_score weight \n", - "0 8173.908 1.0 1.0 0.376593 2.655385 \n", - "1 7506.146 1.0 1.0 0.365484 2.736099 \n", - "2 4941.849 1.0 1.0 0.600062 1.666494 \n", - "3 1358.643 0.0 0.0 0.272011 3.676321 \n", - "4 14509.930 1.0 1.0 0.421973 2.369819 " + " re78 u74 u75 propensity_score weight \n", + "0 5615.1890 0.0 0.0 0.366342 2.729693 \n", + "1 743.6666 0.0 0.0 0.379746 2.633342 \n", + "2 23005.6000 1.0 1.0 0.385928 2.591158 \n", + "3 929.8839 0.0 0.0 0.351611 2.844049 \n", + "4 17094.6400 1.0 1.0 0.519464 1.925062 " ] }, "execution_count": 16, @@ -964,10 +964,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.399495Z", - "iopub.status.busy": "2024-10-20T00:19:57.399309Z", - "iopub.status.idle": "2024-10-20T00:19:57.402472Z", - "shell.execute_reply": "2024-10-20T00:19:57.402002Z" + "iopub.execute_input": "2024-10-22T01:46:33.986272Z", + "iopub.status.busy": "2024-10-22T01:46:33.986070Z", + "iopub.status.idle": "2024-10-22T01:46:33.989786Z", + "shell.execute_reply": "2024-10-22T01:46:33.989267Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb b/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb index c9604c05d..a97254471 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/load_graph_example.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:00.028113Z", - "iopub.status.busy": "2024-10-20T00:20:00.027936Z", - "iopub.status.idle": "2024-10-20T00:20:00.033839Z", - "shell.execute_reply": "2024-10-20T00:20:00.033263Z" + "iopub.execute_input": "2024-10-22T01:46:36.872568Z", + "iopub.status.busy": "2024-10-22T01:46:36.872390Z", + "iopub.status.idle": "2024-10-22T01:46:36.878217Z", + "shell.execute_reply": "2024-10-22T01:46:36.877755Z" } }, "outputs": [], @@ -36,10 +36,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:00.035676Z", - "iopub.status.busy": "2024-10-20T00:20:00.035364Z", - "iopub.status.idle": "2024-10-20T00:20:01.453766Z", - "shell.execute_reply": "2024-10-20T00:20:01.453154Z" + "iopub.execute_input": "2024-10-22T01:46:36.879960Z", + "iopub.status.busy": "2024-10-22T01:46:36.879619Z", + "iopub.status.idle": "2024-10-22T01:46:38.335243Z", + "shell.execute_reply": "2024-10-22T01:46:38.334563Z" } }, "outputs": [], @@ -65,10 +65,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.456050Z", - "iopub.status.busy": "2024-10-20T00:20:01.455727Z", - "iopub.status.idle": "2024-10-20T00:20:01.464769Z", - "shell.execute_reply": "2024-10-20T00:20:01.464129Z" + "iopub.execute_input": "2024-10-22T01:46:38.337689Z", + "iopub.status.busy": "2024-10-22T01:46:38.337370Z", + "iopub.status.idle": "2024-10-22T01:46:38.346204Z", + "shell.execute_reply": "2024-10-22T01:46:38.345635Z" } }, "outputs": [ @@ -101,55 +101,55 @@ " \n", " \n", " 0\n", - " 0\n", + " 6\n", " 0\n", " 0\n", " \n", " \n", " 1\n", - " 2\n", + " 1\n", " 1\n", " 10\n", " \n", " \n", " 2\n", - " 9\n", + " 0\n", " 2\n", " 20\n", " \n", " \n", " 3\n", - " 6\n", + " 2\n", " 3\n", " 30\n", " \n", " \n", " 4\n", - " 8\n", + " 5\n", " 4\n", " 40\n", " \n", " \n", " 5\n", - " 4\n", + " 8\n", " 5\n", " 50\n", " \n", " \n", " 6\n", - " 1\n", + " 9\n", " 6\n", " 60\n", " \n", " \n", " 7\n", - " 3\n", + " 4\n", " 7\n", " 70\n", " \n", " \n", " 8\n", - " 5\n", + " 3\n", " 8\n", " 80\n", " \n", @@ -165,15 +165,15 @@ ], "text/plain": [ " Z X Y\n", - "0 0 0 0\n", - "1 2 1 10\n", - "2 9 2 20\n", - "3 6 3 30\n", - "4 8 4 40\n", - "5 4 5 50\n", - "6 1 6 60\n", - "7 3 7 70\n", - "8 5 8 80\n", + "0 6 0 0\n", + "1 1 1 10\n", + "2 0 2 20\n", + "3 2 3 30\n", + "4 5 4 40\n", + "5 8 5 50\n", + "6 9 6 60\n", + "7 4 7 70\n", + "8 3 8 80\n", "9 7 9 90" ] }, @@ -202,10 +202,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.466980Z", - "iopub.status.busy": "2024-10-20T00:20:01.466483Z", - "iopub.status.idle": "2024-10-20T00:20:01.572713Z", - "shell.execute_reply": "2024-10-20T00:20:01.572160Z" + "iopub.execute_input": "2024-10-22T01:46:38.348043Z", + "iopub.status.busy": "2024-10-22T01:46:38.347855Z", + "iopub.status.idle": "2024-10-22T01:46:38.445036Z", + "shell.execute_reply": "2024-10-22T01:46:38.444360Z" } }, "outputs": [ @@ -255,10 +255,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.575104Z", - "iopub.status.busy": "2024-10-20T00:20:01.574886Z", - "iopub.status.idle": "2024-10-20T00:20:01.687203Z", - "shell.execute_reply": "2024-10-20T00:20:01.686564Z" + "iopub.execute_input": "2024-10-22T01:46:38.447341Z", + "iopub.status.busy": "2024-10-22T01:46:38.446858Z", + "iopub.status.idle": "2024-10-22T01:46:38.530871Z", + "shell.execute_reply": "2024-10-22T01:46:38.530232Z" } }, "outputs": [ @@ -309,10 +309,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.689460Z", - "iopub.status.busy": "2024-10-20T00:20:01.689113Z", - "iopub.status.idle": "2024-10-20T00:20:01.800333Z", - "shell.execute_reply": "2024-10-20T00:20:01.799758Z" + "iopub.execute_input": "2024-10-22T01:46:38.532956Z", + "iopub.status.busy": "2024-10-22T01:46:38.532758Z", + "iopub.status.idle": "2024-10-22T01:46:38.624676Z", + "shell.execute_reply": "2024-10-22T01:46:38.624187Z" } }, "outputs": [ @@ -356,10 +356,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.802653Z", - "iopub.status.busy": "2024-10-20T00:20:01.802117Z", - "iopub.status.idle": "2024-10-20T00:20:01.916719Z", - "shell.execute_reply": "2024-10-20T00:20:01.916127Z" + "iopub.execute_input": "2024-10-22T01:46:38.626805Z", + "iopub.status.busy": "2024-10-22T01:46:38.626433Z", + "iopub.status.idle": "2024-10-22T01:46:38.711917Z", + "shell.execute_reply": "2024-10-22T01:46:38.711236Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb b/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb index 64632dae9..a5ff8c846 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb @@ -90,10 +90,10 @@ "id": "8c441b37", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:03.681902Z", - "iopub.status.busy": "2024-10-20T00:20:03.681701Z", - "iopub.status.idle": "2024-10-20T00:20:05.318343Z", - "shell.execute_reply": "2024-10-20T00:20:05.317715Z" + "iopub.execute_input": "2024-10-22T01:46:43.592980Z", + "iopub.status.busy": "2024-10-22T01:46:43.592800Z", + "iopub.status.idle": "2024-10-22T01:46:45.276498Z", + "shell.execute_reply": "2024-10-22T01:46:45.275777Z" }, "scrolled": true }, @@ -117,10 +117,10 @@ "id": "23c5c320", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:05.320697Z", - "iopub.status.busy": "2024-10-20T00:20:05.320431Z", - "iopub.status.idle": "2024-10-20T00:20:08.047413Z", - "shell.execute_reply": "2024-10-20T00:20:08.046768Z" + "iopub.execute_input": "2024-10-22T01:46:45.279145Z", + "iopub.status.busy": "2024-10-22T01:46:45.278833Z", + "iopub.status.idle": "2024-10-22T01:46:51.930557Z", + "shell.execute_reply": "2024-10-22T01:46:51.929857Z" } }, "outputs": [ @@ -128,7 +128,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Failed to download (trying next):\n", "\n", "\n", @@ -145,7 +151,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1ffbb62b3d94ac7a34f5002f869a663", + "model_id": "283eefa5e0cb46e5b3e1161b9f7ae3c3", "version_major": 2, "version_minor": 0 }, @@ -168,7 +174,13 @@ "output_type": "stream", "text": [ "\n", - "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Failed to download (trying next):\n", "\n", "\n", @@ -185,7 +197,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1fe87d8c4e443f8b34a38b2b1bc2d43", + "model_id": "b32fb2e6e5d742b19d0c3e5f83bb5ef2", "version_major": 2, "version_minor": 0 }, @@ -225,7 +237,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f44f1dc26974e3a9cee0837bacbb6bf", + "model_id": "a956dfa80b12412795cfa1bc7e13afdc", "version_major": 2, "version_minor": 0 }, @@ -240,26 +252,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n" + "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Failed to download (trying next):\n", "\n", "\n", - "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ef95b193d7a417db298378090b2ba68", + "model_id": "ea72732ce8cf48668ab2170cb4aa409e", "version_major": 2, "version_minor": 0 }, @@ -309,10 +327,10 @@ "id": "64dd7f3c", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:08.049781Z", - "iopub.status.busy": "2024-10-20T00:20:08.049200Z", - "iopub.status.idle": "2024-10-20T00:20:08.053378Z", - "shell.execute_reply": "2024-10-20T00:20:08.052784Z" + "iopub.execute_input": "2024-10-22T01:46:51.932946Z", + "iopub.status.busy": "2024-10-22T01:46:51.932423Z", + "iopub.status.idle": "2024-10-22T01:46:51.936551Z", + "shell.execute_reply": "2024-10-22T01:46:51.936052Z" } }, "outputs": [], @@ -335,10 +353,10 @@ "id": "a75b74fa", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:08.055062Z", - "iopub.status.busy": "2024-10-20T00:20:08.054883Z", - "iopub.status.idle": "2024-10-20T00:20:08.530833Z", - "shell.execute_reply": "2024-10-20T00:20:08.529376Z" + "iopub.execute_input": "2024-10-22T01:46:51.938437Z", + "iopub.status.busy": "2024-10-22T01:46:51.938129Z", + "iopub.status.idle": "2024-10-22T01:46:52.492948Z", + "shell.execute_reply": "2024-10-22T01:46:52.491588Z" } }, "outputs": [ @@ -372,10 +390,10 @@ "id": "5201cd08", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:08.534978Z", - "iopub.status.busy": "2024-10-20T00:20:08.534274Z", - "iopub.status.idle": "2024-10-20T00:20:14.299204Z", - "shell.execute_reply": "2024-10-20T00:20:14.298063Z" + "iopub.execute_input": "2024-10-22T01:46:52.496388Z", + "iopub.status.busy": "2024-10-22T01:46:52.495985Z", + "iopub.status.idle": "2024-10-22T01:46:53.354055Z", + "shell.execute_reply": "2024-10-22T01:46:53.352930Z" } }, "outputs": [], @@ -398,10 +416,10 @@ "id": "afbd74ef", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.302248Z", - "iopub.status.busy": "2024-10-20T00:20:14.301926Z", - "iopub.status.idle": "2024-10-20T00:20:14.307427Z", - "shell.execute_reply": "2024-10-20T00:20:14.306799Z" + "iopub.execute_input": "2024-10-22T01:46:53.356897Z", + "iopub.status.busy": "2024-10-22T01:46:53.356568Z", + "iopub.status.idle": "2024-10-22T01:46:53.362553Z", + "shell.execute_reply": "2024-10-22T01:46:53.362048Z" } }, "outputs": [], @@ -432,10 +450,10 @@ "id": "5b977c7f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.309236Z", - "iopub.status.busy": "2024-10-20T00:20:14.308902Z", - "iopub.status.idle": "2024-10-20T00:20:14.313526Z", - "shell.execute_reply": "2024-10-20T00:20:14.312932Z" + "iopub.execute_input": "2024-10-22T01:46:53.364276Z", + "iopub.status.busy": "2024-10-22T01:46:53.364101Z", + "iopub.status.idle": "2024-10-22T01:46:53.368775Z", + "shell.execute_reply": "2024-10-22T01:46:53.368272Z" } }, "outputs": [], @@ -457,10 +475,10 @@ "id": "9a663fe7", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.315526Z", - "iopub.status.busy": "2024-10-20T00:20:14.315190Z", - "iopub.status.idle": "2024-10-20T00:20:14.323187Z", - "shell.execute_reply": "2024-10-20T00:20:14.322679Z" + "iopub.execute_input": "2024-10-22T01:46:53.370647Z", + "iopub.status.busy": "2024-10-22T01:46:53.370277Z", + "iopub.status.idle": "2024-10-22T01:46:53.378768Z", + "shell.execute_reply": "2024-10-22T01:46:53.378040Z" } }, "outputs": [], @@ -489,10 +507,10 @@ "id": "51fadf20", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.324889Z", - "iopub.status.busy": "2024-10-20T00:20:14.324685Z", - "iopub.status.idle": "2024-10-20T00:20:14.328352Z", - "shell.execute_reply": "2024-10-20T00:20:14.327879Z" + "iopub.execute_input": "2024-10-22T01:46:53.381048Z", + "iopub.status.busy": "2024-10-22T01:46:53.380690Z", + "iopub.status.idle": "2024-10-22T01:46:53.384819Z", + "shell.execute_reply": "2024-10-22T01:46:53.384201Z" } }, "outputs": [], @@ -506,10 +524,10 @@ "id": "633da7eb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.329917Z", - "iopub.status.busy": "2024-10-20T00:20:14.329717Z", - "iopub.status.idle": "2024-10-20T00:20:14.332933Z", - "shell.execute_reply": "2024-10-20T00:20:14.332438Z" + "iopub.execute_input": "2024-10-22T01:46:53.387057Z", + "iopub.status.busy": "2024-10-22T01:46:53.386625Z", + "iopub.status.idle": "2024-10-22T01:46:53.389930Z", + "shell.execute_reply": "2024-10-22T01:46:53.389453Z" } }, "outputs": [], @@ -533,10 +551,10 @@ "id": "27f43e54", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.334623Z", - "iopub.status.busy": "2024-10-20T00:20:14.334444Z", - "iopub.status.idle": "2024-10-20T00:20:37.621333Z", - "shell.execute_reply": "2024-10-20T00:20:37.620753Z" + "iopub.execute_input": "2024-10-22T01:46:53.391854Z", + "iopub.status.busy": "2024-10-22T01:46:53.391502Z", + "iopub.status.idle": "2024-10-22T01:47:17.799886Z", + "shell.execute_reply": "2024-10-22T01:47:17.799250Z" } }, "outputs": [ @@ -595,7 +613,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29351c6347174fd6a69148c6d330b3e4", + "model_id": "f1746bc39d84478ba2ec663d2c538169", "version_major": 2, "version_minor": 0 }, @@ -609,7 +627,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d58a9ec4c87d4ec08f3f984d9b3044c4", + "model_id": "82e5de3a9ef9494b884b7d4c3dac5f06", "version_major": 2, "version_minor": 0 }, @@ -623,7 +641,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cddc6b0a652e4f71a54ff60c8cee06b5", + "model_id": "afcea8aad865470aa4d32ba989701ef8", "version_major": 2, "version_minor": 0 }, @@ -637,7 +655,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "208d8b6981a3496589181fd2ae1a81ca", + "model_id": "e4b99fbacca4474abc1a55319f0b3aee", "version_major": 2, "version_minor": 0 }, @@ -651,7 +669,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8cfd3d075b1d4c57910e4617cc93aec9", + "model_id": "484d2165bd0b48e8b138c35f88adb34b", "version_major": 2, "version_minor": 0 }, @@ -665,7 +683,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c915594898449948b467b4ed6aeb7b6", + "model_id": "d5f178b0083d41ffbdc2d78ea652ad3b", "version_major": 2, "version_minor": 0 }, @@ -679,7 +697,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8cc5b734a784414b8eeb5bfa8a00dfc", + "model_id": "ee8e735949434438a4960b449b6c474b", "version_major": 2, "version_minor": 0 }, @@ -723,10 +741,10 @@ "id": "4f382191", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:37.623387Z", - "iopub.status.busy": "2024-10-20T00:20:37.622921Z", - "iopub.status.idle": "2024-10-20T00:20:38.350456Z", - "shell.execute_reply": "2024-10-20T00:20:38.349903Z" + "iopub.execute_input": "2024-10-22T01:47:17.801828Z", + "iopub.status.busy": "2024-10-22T01:47:17.801635Z", + "iopub.status.idle": "2024-10-22T01:47:18.562363Z", + "shell.execute_reply": "2024-10-22T01:47:18.561722Z" }, "scrolled": true }, @@ -748,7 +766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64a715023f2f460d9f91ea3a33e843d5", + "model_id": "be50f052ceeb42bea431f336c3f259f7", "version_major": 2, "version_minor": 0 }, @@ -766,8 +784,8 @@ "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", " Test metric DataLoader 0\n", "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_acc 0.16920000314712524\n", - " test_loss 1.8549073934555054\n", + " test_acc 0.17059999704360962\n", + " test_loss 1.7162753343582153\n", "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" ] } @@ -793,10 +811,10 @@ "id": "08881e8d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:38.352509Z", - "iopub.status.busy": "2024-10-20T00:20:38.352149Z", - "iopub.status.idle": "2024-10-20T00:20:38.356041Z", - "shell.execute_reply": "2024-10-20T00:20:38.355436Z" + "iopub.execute_input": "2024-10-22T01:47:18.564418Z", + "iopub.status.busy": "2024-10-22T01:47:18.564039Z", + "iopub.status.idle": "2024-10-22T01:47:18.568129Z", + "shell.execute_reply": "2024-10-22T01:47:18.567512Z" } }, "outputs": [], @@ -810,10 +828,10 @@ "id": "48c87949", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:38.357837Z", - "iopub.status.busy": "2024-10-20T00:20:38.357474Z", - "iopub.status.idle": "2024-10-20T00:20:38.360286Z", - "shell.execute_reply": "2024-10-20T00:20:38.359817Z" + "iopub.execute_input": "2024-10-22T01:47:18.570156Z", + "iopub.status.busy": "2024-10-22T01:47:18.569805Z", + "iopub.status.idle": "2024-10-22T01:47:18.572740Z", + "shell.execute_reply": "2024-10-22T01:47:18.572239Z" } }, "outputs": [], @@ -828,10 +846,10 @@ "id": "2a9b1e8a", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:38.361973Z", - "iopub.status.busy": "2024-10-20T00:20:38.361678Z", - "iopub.status.idle": "2024-10-20T00:21:10.369539Z", - "shell.execute_reply": "2024-10-20T00:21:10.369023Z" + "iopub.execute_input": "2024-10-22T01:47:18.574555Z", + "iopub.status.busy": "2024-10-22T01:47:18.574196Z", + "iopub.status.idle": "2024-10-22T01:47:51.879819Z", + "shell.execute_reply": "2024-10-22T01:47:51.879247Z" } }, "outputs": [ @@ -881,7 +899,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20cc2c46920340ec868faa7f861a5fff", + "model_id": "e6aefba72bd6419aa1ece8461ff9c185", "version_major": 2, "version_minor": 0 }, @@ -895,7 +913,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ac5cc4168c4462781f5fcfc3a23fa29", + "model_id": "37cd226c16b040388a4f19c93770987a", "version_major": 2, "version_minor": 0 }, @@ -909,7 +927,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e96258f822d4e6d9f5bb032bbd8f365", + "model_id": "d95e362a14034a909b6f6845679b397d", "version_major": 2, "version_minor": 0 }, @@ -923,7 +941,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2b5ae131173482a9573426211f2d37e", + "model_id": "a96231384054499bac249cca49982b1e", "version_major": 2, "version_minor": 0 }, @@ -937,7 +955,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c2b32805ae04f7e8a598f3e5f7d5fd3", + "model_id": "fe04b2f3b04f4df292688f2d95029298", "version_major": 2, "version_minor": 0 }, @@ -951,7 +969,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aced5188e32a49d7b0a3406bc829d517", + "model_id": "9287361d87814bc7a609350b4aa993c6", "version_major": 2, "version_minor": 0 }, @@ -965,7 +983,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "69e0d570592d4cc88ea429c0aef65ffd", + "model_id": "a3bceb7cd6b749ecbf66082aae3f69a3", "version_major": 2, "version_minor": 0 }, @@ -996,10 +1014,10 @@ "id": "cf1640b9", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:10.371364Z", - "iopub.status.busy": "2024-10-20T00:21:10.371173Z", - "iopub.status.idle": "2024-10-20T00:21:11.118030Z", - "shell.execute_reply": "2024-10-20T00:21:11.117460Z" + "iopub.execute_input": "2024-10-22T01:47:51.882035Z", + "iopub.status.busy": "2024-10-22T01:47:51.881636Z", + "iopub.status.idle": "2024-10-22T01:47:52.695225Z", + "shell.execute_reply": "2024-10-22T01:47:52.694665Z" } }, "outputs": [ @@ -1020,7 +1038,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "46e5573e7333472eb84dc453c2d95de5", + "model_id": "9ef7373917584f22a2d0a14b8c46cba8", "version_major": 2, "version_minor": 0 }, @@ -1038,8 +1056,8 @@ "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", " Test metric DataLoader 0\n", "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_acc 0.6682000160217285\n", - " test_loss 0.6799301505088806\n", + " test_acc 0.6761000156402588\n", + " test_loss 0.6758360266685486\n", "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" ] } @@ -1081,10 +1099,10 @@ "id": "8c5cd482", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:11.120029Z", - "iopub.status.busy": "2024-10-20T00:21:11.119655Z", - "iopub.status.idle": "2024-10-20T00:21:21.350397Z", - "shell.execute_reply": "2024-10-20T00:21:21.349775Z" + "iopub.execute_input": "2024-10-22T01:47:52.697309Z", + "iopub.status.busy": "2024-10-22T01:47:52.696926Z", + "iopub.status.idle": "2024-10-22T01:48:03.181734Z", + "shell.execute_reply": "2024-10-22T01:48:03.181027Z" } }, "outputs": [], @@ -1101,10 +1119,10 @@ "id": "4b28707a", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:21.352597Z", - "iopub.status.busy": "2024-10-20T00:21:21.352212Z", - "iopub.status.idle": "2024-10-20T00:21:21.355325Z", - "shell.execute_reply": "2024-10-20T00:21:21.354873Z" + "iopub.execute_input": "2024-10-22T01:48:03.184072Z", + "iopub.status.busy": "2024-10-22T01:48:03.183852Z", + "iopub.status.idle": "2024-10-22T01:48:03.187069Z", + "shell.execute_reply": "2024-10-22T01:48:03.186581Z" } }, "outputs": [], @@ -1126,10 +1144,10 @@ "id": "17b446be", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:21.357254Z", - "iopub.status.busy": "2024-10-20T00:21:21.356801Z", - "iopub.status.idle": "2024-10-20T00:21:31.536519Z", - "shell.execute_reply": "2024-10-20T00:21:31.535849Z" + "iopub.execute_input": "2024-10-22T01:48:03.188800Z", + "iopub.status.busy": "2024-10-22T01:48:03.188619Z", + "iopub.status.idle": "2024-10-22T01:48:13.664642Z", + "shell.execute_reply": "2024-10-22T01:48:13.663855Z" } }, "outputs": [], @@ -1146,10 +1164,10 @@ "id": "9448cc87", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:31.538890Z", - "iopub.status.busy": "2024-10-20T00:21:31.538636Z", - "iopub.status.idle": "2024-10-20T00:21:31.541998Z", - "shell.execute_reply": "2024-10-20T00:21:31.541393Z" + "iopub.execute_input": "2024-10-22T01:48:13.667487Z", + "iopub.status.busy": "2024-10-22T01:48:13.667020Z", + "iopub.status.idle": "2024-10-22T01:48:13.670549Z", + "shell.execute_reply": "2024-10-22T01:48:13.670030Z" } }, "outputs": [], @@ -1209,104 +1227,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "017147f2b008413a85cd6e6934ddff58": { - "model_module": 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"2024-10-20T00:21:38.728960Z", - "iopub.status.idle": "2024-10-20T00:21:38.825404Z", - "shell.execute_reply": "2024-10-20T00:21:38.824765Z" + "iopub.execute_input": "2024-10-22T01:48:21.140841Z", + "iopub.status.busy": "2024-10-22T01:48:21.140265Z", + "iopub.status.idle": "2024-10-22T01:48:21.236844Z", + "shell.execute_reply": "2024-10-22T01:48:21.236234Z" } }, "outputs": [ @@ -183,10 +183,10 @@ "id": "75882711", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:38.828173Z", - "iopub.status.busy": "2024-10-20T00:21:38.827598Z", - "iopub.status.idle": "2024-10-20T00:21:38.933992Z", - "shell.execute_reply": "2024-10-20T00:21:38.933493Z" + "iopub.execute_input": "2024-10-22T01:48:21.239189Z", + "iopub.status.busy": "2024-10-22T01:48:21.238954Z", + "iopub.status.idle": "2024-10-22T01:48:21.346054Z", + "shell.execute_reply": "2024-10-22T01:48:21.345568Z" } }, "outputs": [ @@ -223,10 +223,10 @@ "id": "636b6a25", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:38.935780Z", - "iopub.status.busy": "2024-10-20T00:21:38.935579Z", - "iopub.status.idle": "2024-10-20T00:21:38.975123Z", - "shell.execute_reply": "2024-10-20T00:21:38.974620Z" + "iopub.execute_input": "2024-10-22T01:48:21.348223Z", + "iopub.status.busy": "2024-10-22T01:48:21.348014Z", + "iopub.status.idle": "2024-10-22T01:48:21.389306Z", + "shell.execute_reply": "2024-10-22T01:48:21.388747Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "id": "207034e5", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:38.977033Z", - "iopub.status.busy": "2024-10-20T00:21:38.976662Z", - "iopub.status.idle": "2024-10-20T00:21:39.132091Z", - "shell.execute_reply": "2024-10-20T00:21:39.131445Z" + "iopub.execute_input": "2024-10-22T01:48:21.391199Z", + "iopub.status.busy": "2024-10-22T01:48:21.391008Z", + "iopub.status.idle": "2024-10-22T01:48:21.564589Z", + "shell.execute_reply": "2024-10-22T01:48:21.563956Z" } }, "outputs": [ @@ -498,10 +498,10 @@ "id": "4eaec5dc", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:39.134092Z", - "iopub.status.busy": "2024-10-20T00:21:39.133897Z", - "iopub.status.idle": "2024-10-20T00:21:39.176314Z", - "shell.execute_reply": "2024-10-20T00:21:39.175641Z" + "iopub.execute_input": "2024-10-22T01:48:21.566962Z", + "iopub.status.busy": "2024-10-22T01:48:21.566533Z", + "iopub.status.idle": "2024-10-22T01:48:21.624397Z", + "shell.execute_reply": "2024-10-22T01:48:21.623757Z" }, "scrolled": true }, @@ -516,9 +516,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W1,W6,W2,W4,W5,W3])\n", + "─────(E[y|W5,W3,W6,W2,W1,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W6,W2,W4,W5,W3,U) = P(y|v0,W1,W6,W2,W4,W5,W3)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W2,W1,W4)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -543,10 +543,10 @@ "id": "56889b39", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:39.178266Z", - "iopub.status.busy": "2024-10-20T00:21:39.178075Z", - "iopub.status.idle": "2024-10-20T00:21:40.787312Z", - "shell.execute_reply": "2024-10-20T00:21:40.786754Z" + "iopub.execute_input": "2024-10-22T01:48:21.626915Z", + "iopub.status.busy": "2024-10-22T01:48:21.626507Z", + "iopub.status.idle": "2024-10-22T01:48:23.250374Z", + "shell.execute_reply": "2024-10-22T01:48:23.249689Z" } }, "outputs": [ @@ -577,17 +577,17 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W1,W6,W2,W4,W5,W3])\n", + "─────(E[y|W5,W3,W6,W2,W1,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W6,W2,W4,W5,W3,U) = P(y|v0,W1,W6,W2,W4,W5,W3)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W2,W1,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W1+W6+W2+W4+W5+W3 | \n", + "b: y~v0+W5+W3+W6+W2+W1+W4 | \n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 11.679948861035195\n", - "Effect estimates: [[11.67994886]]\n", + "Mean value: 11.738281416879683\n", + "Effect estimates: [[11.73828142]]\n", "\n" ] } @@ -631,16 +631,16 @@ "id": "2488ccbb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:40.789338Z", - "iopub.status.busy": "2024-10-20T00:21:40.788961Z", - "iopub.status.idle": "2024-10-20T00:21:41.845551Z", - "shell.execute_reply": "2024-10-20T00:21:41.845013Z" + "iopub.execute_input": "2024-10-22T01:48:23.252460Z", + "iopub.status.busy": "2024-10-22T01:48:23.252223Z", + "iopub.status.idle": "2024-10-22T01:48:24.366491Z", + "shell.execute_reply": "2024-10-22T01:48:24.365905Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -654,13 +654,13 @@ "text": [ "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n", "\n", - "Original Effect Estimate : 11.679948861035195\n", - "Robustness Value : 0.44\n", + "Original Effect Estimate : 11.738281416879683\n", + "Robustness Value : 0.45\n", "\n", "Robustness Value (alpha=0.05) : 0.41000000000000003\n", "\n", "Interpretation of results :\n", - "Any confounder explaining less than 44.0% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n", + "Any confounder explaining less than 45.0% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n", "\n", "For a significance level of 5.0%, any confounder explaining more than 41.0% percent of the residual variance of both the treatment and the outcome would be strong enough to make the estimated effect not 'statistically significant'\n", "\n", @@ -694,21 +694,21 @@ "id": "52b2e904", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:41.847458Z", - "iopub.status.busy": "2024-10-20T00:21:41.847133Z", - "iopub.status.idle": "2024-10-20T00:21:41.892523Z", - "shell.execute_reply": "2024-10-20T00:21:41.892026Z" + "iopub.execute_input": "2024-10-22T01:48:24.368589Z", + "iopub.status.busy": "2024-10-22T01:48:24.368252Z", + "iopub.status.idle": "2024-10-22T01:48:24.416424Z", + "shell.execute_reply": "2024-10-22T01:48:24.415732Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": 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"text/latex": [ - "$\\displaystyle 0.44$" + "$\\displaystyle 0.45$" ], "text/plain": [ - "0.44" + "0.45" ] }, "execution_count": 10, @@ -754,16 +754,16 @@ "id": "85eefe08", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:41.894473Z", - "iopub.status.busy": "2024-10-20T00:21:41.894287Z", - "iopub.status.idle": "2024-10-20T00:21:57.328659Z", - "shell.execute_reply": "2024-10-20T00:21:57.328048Z" + "iopub.execute_input": "2024-10-22T01:48:24.418789Z", + "iopub.status.busy": "2024-10-22T01:48:24.418380Z", + "iopub.status.idle": "2024-10-22T01:48:39.843839Z", + "shell.execute_reply": "2024-10-22T01:48:39.843116Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -808,16 +808,16 @@ "id": "df23a49b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:57.331092Z", - "iopub.status.busy": "2024-10-20T00:21:57.330533Z", - "iopub.status.idle": "2024-10-20T00:22:12.324500Z", - "shell.execute_reply": "2024-10-20T00:22:12.323862Z" + "iopub.execute_input": "2024-10-22T01:48:39.846313Z", + "iopub.status.busy": "2024-10-22T01:48:39.845918Z", + "iopub.status.idle": "2024-10-22T01:48:56.230065Z", + "shell.execute_reply": "2024-10-22T01:48:56.229431Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -855,10 +855,10 @@ "id": "934eef00", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:12.326659Z", - "iopub.status.busy": "2024-10-20T00:22:12.326230Z", - "iopub.status.idle": "2024-10-20T00:22:12.370559Z", - "shell.execute_reply": "2024-10-20T00:22:12.370043Z" + "iopub.execute_input": "2024-10-22T01:48:56.232155Z", + "iopub.status.busy": "2024-10-22T01:48:56.231738Z", + "iopub.status.idle": "2024-10-22T01:48:56.275974Z", + "shell.execute_reply": "2024-10-22T01:48:56.275430Z" } }, "outputs": [ @@ -896,14 +896,14 @@ " \n", " \n", " 0\n", - " 0.048629\n", - " 0.052047\n", - " 11.679949\n", - " 1.025023\n", - " 10.654926\n", - " 12.704971\n", - " 9.508112\n", - " 13.825284\n", + " 0.048682\n", + " 0.055065\n", + " 11.738281\n", + " 1.058551\n", + " 10.67973\n", + " 12.796833\n", + " 9.528129\n", + " 13.9211\n", " \n", " \n", "\n", @@ -911,10 +911,10 @@ ], "text/plain": [ " r2tu_w r2yu_tw short estimate bias lower_ate_bound \\\n", - "0 0.048629 0.052047 11.679949 1.025023 10.654926 \n", + "0 0.048682 0.055065 11.738281 1.058551 10.67973 \n", "\n", " upper_ate_bound lower_confidence_bound upper_confidence_bound \n", - "0 12.704971 9.508112 13.825284 " + "0 12.796833 9.528129 13.9211 " ] }, "execution_count": 13, @@ -941,10 +941,10 @@ "id": "7cff3837", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:12.372540Z", - "iopub.status.busy": "2024-10-20T00:22:12.372350Z", - "iopub.status.idle": "2024-10-20T00:22:12.962666Z", - "shell.execute_reply": "2024-10-20T00:22:12.962041Z" + "iopub.execute_input": "2024-10-22T01:48:56.278095Z", + "iopub.status.busy": "2024-10-22T01:48:56.277725Z", + "iopub.status.idle": "2024-10-22T01:48:56.871617Z", + "shell.execute_reply": "2024-10-22T01:48:56.871061Z" } }, "outputs": [ @@ -975,17 +975,17 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W1,W6,W2,W4,W5,W3])\n", + "─────(E[y|W5,W3,W6,W2,W1,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W6,W2,W4,W5,W3,U) = P(y|v0,W1,W6,W2,W4,W5,W3)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W2,W1,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W1+W6+W2+W4+W5+W3 | \n", + "b: y~v0+W5+W3+W6+W2+W1+W4 | \n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 11.815195660841939\n", - "Effect estimates: [[11.81519566]]\n", + "Mean value: 11.80537621178161\n", + "Effect estimates: [[11.80537621]]\n", "\n" ] } @@ -1017,17 +1017,17 @@ "id": "946e1237", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:12.964673Z", - "iopub.status.busy": "2024-10-20T00:22:12.964378Z", - "iopub.status.idle": "2024-10-20T00:22:27.998838Z", - "shell.execute_reply": "2024-10-20T00:22:27.998195Z" + "iopub.execute_input": "2024-10-22T01:48:56.873608Z", + "iopub.status.busy": "2024-10-22T01:48:56.873218Z", + "iopub.status.idle": "2024-10-22T01:49:12.034724Z", + "shell.execute_reply": "2024-10-22T01:49:12.034044Z" }, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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c+fLl49NPP00w9+NNtWjRgsePHxsTzqioKDZu3GicQ/S6z+nq6sqSJUsAiIiIYP369bRp0+alzxl/khJ/Ep2YO3fu8OjRIwoUKJDgvkKFChEXF5dgbsmLCV/8CdLzxzopV69eTXJf8fe/jmzZsplVwMHBwcEk0QLInz8/QJJl81/nWCVH/GtO6nnjT9zf1Mv2U7BgQZNjX6lSJW7evMnff//N3r17MRgMlC9f3iR5++OPP3jnnXeMyYC531lz3zPQ3oPIyMiXfqbjX6s5n7M3+Uwntu+8efMm+G6+GM+FCxcAaN++fYLjNXfuXJ48eUJERIRZcV68eBEHB4eXJh/x+61evXqC/W7duvW1Cjjly5fP5GeDwUDevHmN3yVzPnvmetUxiX/uF2NMKh4hhLAVUt1R6MbFxYWyZctStmxZ8ufPT8eOHVmxYgXDhg0jLi4Og8HApk2bEq226OnpafJzUhUZ1XOFIAoVKsT58+dZv349mzdvZtWqVcyYMYOhQ4cyYsSIFHlNb7/9NoGBgSxfvpzWrVvz66+/8vjx4wTFFcyRPn163nvvPZYsWcLQoUNZuXIlT548Mek5tKbkHOs3lVTyGV945UXu7u4ptm+BsQdq9+7dXLp0iVKlSuHh4UGlSpWYMmUKUVFRHD16lNGjRxsfY+531pbeM2t8pl8U30v27bffJrnsxuv8nkvufhctWoS/v3+C+52c9D0tMBgMib6epL77erx3QghhDZKkCZsQP3Tp5s2bAOTJkwelFLly5TL2cKQEDw8PWrRoQYsWLYiJiaFJkyaMHj2aAQMGJFlO39wesObNmzN58mQiIyNZtmwZgYGBvP322y99zKv20a5dOxo2bMjBgwdZsmQJJUuWpEiRIi99THxP0alTp5JskylTJtKlS8f58+cT3Hfu3DkcHBzInj37S/eTmKReT86cOZPcV/z98P9Xw8PDw03avckV9+fFxcVx6dIlk8/WX3/9BZDkumbmHCtzPjPxrzmp582YMSMeHh7Jfr7k7Kd69eom950/f95kUewcOXKQI0cO/vjjDy5dumQcQla5cmX69u3LihUriI2NpXLlysbHWOo7+7xMmTLh7e390s80JP9zZgk5c+bk1KlTKKVMPgcvxpMnTx4AvL29CQkJSZF958mTh7i4OM6cOZNk4he/38yZM6fYfuN75+Ippfj7778pXrw4YN5nL3369IkOM33d7378c78YY/y+hRDCVslwR2FVO3fuTPQKZ/zchfjhJ02aNMHR0ZERI0YkaK+U4r///jN73y8+xsXFhcKFC6OU4unTp0k+zsPDI0Gy8DItWrTgyZMnLFy4kM2bN9O8efNXPib+JDyp/dSpU4eMGTMyduxYfv/992T1omXKlInKlSszb948rl27ZnJf/DF1dHSkZs2a/PLLLybD/G7dusVPP/1ExYoV8fb2fuW+Ens9ib2WunXrcuDAAUJDQ43bHj58yOzZswkMDDQO04o/kdy9e7exXWxsLLNnzzY7lqRMmzbN+H+lFNOmTcPZ2ZkaNWok2t6cY/Wq9/N5WbNmJSgoiIULF5q0P3XqFFu3bqVu3brmv7hElClThsyZMzNr1iyTcvCbNm3i7Nmz1KtXz6R9pUqV+O233zhw4IAxSQsKCsLLy4sxY8bg7u5O6dKlje0t8Z19kYODA40aNeLXX3/l0KFDCe6P329yP2eWULduXW7cuMHKlSuN2x49epTgs1u6dGny5MnD+PHjTeawxkts2Y1XadSoEQ4ODowcOTLBfLb4Y1OrVi28vb35+uuvE/299zr7/fHHH3nw4IHx55UrV3Lz5k3q1KkDmPfZy5MnD+fOnTOJ4/jx4689NP3579fzw0e3bdv22nNghRDCGqQnTVhVjx49ePToEY0bN6ZgwYLExMSwd+9eY49TfCGBPHnyMGrUKAYMGMCVK1do1KgRXl5eXL58mTVr1tCtWzc+++wzs/Zds2ZN/P39eeedd8iSJQtnz55l2rRp1KtXz2TS+4tKly7NzJkzGTVqFHnz5iVz5swJrgY/r1SpUuTNm5dBgwbx5MmTZA11zJMnD76+vsyaNQsvLy88PDwIDg4mV65cADg7O9OyZUumTZuGo6MjrVq1StZrnjJlChUrVqRUqVJ069aNXLlyceXKFTZs2MCxY8cAGDVqFNu2baNixYp88sknODk58f333/PkyZNE15pLjqSOWf/+/fn555+pU6cOPXv2xM/Pj4ULF3L58mVWrVplnN9UpEgR3n77bQYMGMC9e/fw8/Nj6dKlPHv27LXieZGbmxubN2+mffv2BAcHs2nTJjZs2MDAgQPJlClTko9L7rEKCgrC0dGRsWPHEhERgaurK9WrV090LUDQhrzVqVOH8uXL07lzZx4/fszUqVPx8fExrsf1ppydnRk7diwdO3akSpUqtGrVilu3bjF58mQCAwPp06ePSftKlSqxZMkSDAaDcfijo6MjFSpUYMuWLVStWtVkTpklvrOJ+frrr9m6dStVqlShW7duFCpUiJs3b7JixQr+/PNPfH19k/05s4SuXbsybdo02rVrx+HDh8maNSuLFi1KsFC3g4MDc+fOpU6dOhQpUoSOHTuSLVs2rl+/zs6dO/H29ubXX381a9/xv3e++uorKlWqRJMmTXB1deXgwYMEBATwzTff4O3tzcyZM2nbti2lSpWiZcuWZMqUiWvXrrFhwwbeeecdkwsYyeHn50fFihXp2LEjt27dYtKkSeTNm5euXbsC5n32OnXqxMSJE6lVqxadO3fm9u3bzJo1iyJFirxWYR6Ab775hnr16lGxYkU6derEvXv3jGtmJpYgCyGETbBmKUkhNm3apDp16qQKFiyoPD09lYuLi8qbN6/q0aOHunXrVoL2q1atUhUrVlQeHh7Kw8NDFSxYUH366afq/PnzxjZVqlRJtLT+iyWbv//+e1W5cmWVIUMG5erqqvLkyaM+//xzFRERYWyTWOn3sLAwVa9ePeXl5aUAY3noxMrBxxs0aJACVN68eRM9DomVmf7ll19U4cKFlZOTU6Ll+A8cOKAAVbNmzUSfMymnTp1SjRs3Vr6+vsrNzU0VKFAgwXpgR44cUbVq1VKenp4qXbp0qlq1amrv3r0mbeKPzYulzxM7DkkdM6W0dcTef/99YzzlypVT69evTxD3xYsXVUhIiHJ1dVVZsmRRAwcOVNu2bUu0BP+rllZ4Xvv27ZWHh4e6ePGiqlmzpkqXLp3KkiWLGjZsWIKy5bxQgj+5x0oppebMmaNy586tHB0dk1WOf/v27eqdd95R7u7uytvbW9WvX1+dOXPGpM2blOCPt2zZMlWyZEnl6uqq/Pz8VJs2bdS///6b4PGnT582lqd/3qhRoxJdUy7em3xnk+vq1auqXbt2KlOmTMrV1VXlzp1bffrppyZl9JPzOUvqeMaXd3/+O5jcEvzx8TVo0EClS5dOZcyYUfXq1Utt3rw50ffj6NGjqkmTJsbfSzlz5lTNmzdXO3bseOW+k1qqYt68ecb3OH369KpKlSpq27ZtCV57rVq1lI+Pj3Jzc1N58uRRHTp0UIcOHUpwvJMSf/x+/vlnNWDAAJU5c2bl7u6u6tWrl2DZD6WS/9lbvHixyp07t3JxcVFBQUFqy5YtSZbg//bbbxM8PrHv7apVq1ShQoWUq6urKly4sFq9enWC5xRCCFtiUEpm1wqRGhw/fpygoCB+/PFH2rZtq3c4qVaHDh1YuXKlXEEXQgghhM2SOWlCpBJz5szB09OTJk2a6B2KEEIIIYSwIJmTJoSN+/XXXzlz5gyzZ8+me/fuKVLpTwhbExUV9crezUyZMiVZcl2knJiYGO7du/fSNj4+Pja1jIIQQqQ1kqQJYeN69OjBrVu3qFu3boqt5yaErRk/fvwrP9+XL19OcokEkXL27t1LtWrVXtpm/vz5dOjQwToBCSGEHZI5aUIIIXR36dKlRNfHel7FihWTXM9QpJz79+9z+PDhl7YpUqQIWbNmtVJEQghhfyRJE0IIIYQQQggbIoVDhBBCCCGEEMKGyJw0HcXFxXHjxg28vLwwGAx6hyOEEEKINE4pxYMHDwgICLDowu5CiDcjSZqObty4Qfbs2fUOQwghhBB25p9//uGtt97SOwwhRBIkSdORl5cXoP2i9Pb21jkaIYQQQqR1kZGRZM+e3XgOIoSwTZKk6Sh+iKO3t7ckaUIIIYSwGplmIYRtk8HIQgghhBBCCGFDJEkTQgghhBBCCBsiSZoQQgghhBBC2BCZkyaEEEIIIXShlOLZs2fExsbqHYoQFufo6IiTk1Oy5oRKkiaEEEIIIawuJiaGmzdv8ujRI71DEcJq0qVLR9asWXFxcXlpO0nShBBCCCGEVcXFxXH58mUcHR0JCAjAxcVFKk6KNE0pRUxMDHfu3OHy5cvky5fvpQvKS5ImhBBCCCGsKiYmhri4OLJnz066dOn0DkcIq3B3d8fZ2ZmrV68SExODm5tbkm2lcIgQQgghhNDFy3oShEiLkvuZl2+GEEIIIYQQQtgQSdKEEEIIIYQQwoZIkiaEEEIIIVK1p0+f6h2CVezatQuDwUB4eHiiP9uS4cOHExQUZLItJiaGvHnzsnfvXn2CMtPmzZsJCgoiLi7O6vuWJE0IIYQQQqRa8+bNw9PTk3nz5lllf1WrVqV3794Jti9YsABfX1+rxBCvQoUK3Lx5Ex8fnxR5vsQSq5Q0a9YscuXKRYUKFYzbRo8eTYUKFUiXLl2Sx69nz56ULl0aV1fXZMcXFhZG27Zt8ff3x8PDg1KlSrFq1Srj/VeuXKFz587kypULd3d38uTJw7Bhw4iJiTG2qV27Ns7OzixZsuS1Xu+bkCRNCCGEEEKkSvPmzaNLly7ExMTQpUsXqyVqtsLFxQV/f/9UsXyBUopp06bRuXNnk+0xMTE0a9aMjz/++KWP79SpEy1atEj2/tq1a8f58+dZt24dJ0+epEmTJjRv3pyjR48CcO7cOeLi4vj+++85ffo03333HbNmzWLgwIEmz9OhQwemTJmS7P2mFEnS7NitW9otJZw9C6lppMHTp3DmjN5R2Bal4MQJ7V9hHXfvwtWrekchUkJUFPzv774QSdq5Ex4/1juKtCM+QVP/+8OllLKZRK1Dhw40atSI8ePHkzVrVjJkyMCnn35qMixz0aJFlClTBi8vL/z9/WndujW3b982eZ6NGzeSP39+3N3dqVatGleuXDG5/8Xhjon1hE2aNInAwECTx5QrVw4PDw98fX155513uHr1KgsWLGDEiBEcP34cg8GAwWBgwYIFAISHh9OlSxcyZcqEt7c31atX5/jx4yb7GTNmDFmyZMHLy4vOnTsTHR1tcv/hw4e5ePEi9erVM9k+YsQI+vTpQ7FixZI8nlOmTOHTTz8ld+7cSbZ50d69e+nRowflypUjd+7cDB48GF9fXw4fPgxovWTz58+nZs2a5M6dmwYNGvDZZ5+xevVqk+epX78+hw4d4uLFi8ned0qQJM2OTZoEU6emzHO98w7cvJkyz2UNDx5AUBA8e6Z3JLYjLg6qVYO//tI7EvsxZw706aN3FCIl7NsHdeqk3IUvkfYoBdOnQ8OGkqilhBcTtHi2lKjt3LmTixcvsnPnThYuXMiCBQuMSQ9o8+i++uorjh8/ztq1a7ly5QodOnQw3v/PP//QpEkT6tevz7Fjx+jSpQv9+/d/o5iePXtGo0aNqFKlCidOnCA0NJRu3bphMBho0aIF/fr1o0iRIty8eZObN28ae66aNWvG7du32bRpE4cPH6ZUqVLUqFGDe/fuAbB8+XKGDx/O119/zaFDh8iaNSszZsww2fcff/xB/vz58fLyeqPXkFwVKlRg2bJl3Lt3j7i4OJYuXUp0dDRVq1ZN8jERERH4+fmZbMuRIwdZsmThjz/+sHDEpmQxazvm4JByvSZeXlrik1r4+YGPj9aLkSeP3tHYBkdHqF0bNmyAAgX0jsY+fPwxjBun9eoWLqx3NOJNhIRA8+bQsaP2HUoFI4+ElRkM8NNP0LQpNGoEv/wCL1nHVrxEUglavPhEDbQhcnpJnz4906ZNw9HRkYIFC1KvXj127NhB165dE8SWO3dupkyZQtmyZYmKisLT05OZM2eSJ08eJkyYAECBAgU4efIkY8eOfe2YIiMjiYiI4L333iPP/06AChUqZLzf09MTJycn/P39jdv+/PNPDhw4wO3bt3F1dQVg/PjxrF27lpUrV9KtWzcmTZpE586djUMZR40axfbt2016065evUpAQMBrx26u5cuX06JFCzJkyICTkxPp0qVjzZo15M2bN9H2f//9N1OnTmX8+PEJ7gsICOCqlYe+SE+aHTMYtN6TlJDakjSA/Pnh/Hm9o7At9eppJ5jCOnx9tUTtDf7eChsybhz880/KjVAQaY+LC6xcCU5O0LgxvDAaTCTDqxK0eLbQo1akSBEcHR2NP2fNmtVkOOPhw4epX78+OXLkwMvLiypVqgBw7do1AM6ePUtwcLDJc5YvX/6NYvLz86NDhw7UqlWL+vXrM3nyZG6+YijU8ePHiYqKIkOGDHh6ehpvly9fNg4BTE6sjx8/xs2KVyaGDBlCeHg427dv59ChQ/Tt25fmzZtz8uTJBG2vX79O7dq1adasmTGJfp67uzuPHj2yRthGkqTZMXvuSQOtt0iSNFO1a8PevRAZqXck9qN3b1izBl6YZiBSITc3+PlnGDoUEjkHEAIAV1dYtUq7UNq0KTx5ondEqcfTp0/5+OOPX5mgxVNK8fHHH6d4eX5vb28iIiISbA8PDzepsujs7Gxyv8FgMJZyf/jwIbVq1cLb25slS5Zw8OBB1qxZA2BSXdBcDg4OCY7Pi69//vz5hIaGGocD5s+fn3379iX5nFFRUWTNmpVjx46Z3M6fP8/nn3+e7NgyZszI/fv3zXtBr+nixYtMmzaNefPmUaNGDUqUKMGwYcMoU6YM06dPN2l748YNqlWrRoUKFZg9e3aiz3fv3j0yZcpkjdCNJEmzY9KTJvOvXuTnB6VLw7ZtekdiPzJnhvbt4dtv9Y5EpISiReGrr6BVK5l3JJLm5garV0NsrCRq5nB2dmbmzJnJrmRoMBiYOXNmgmTpTRUoUIAjR44k2H7kyBHy58+frOc4d+4c//33H2PGjKFSpUoULFgwQdGQQoUKceDAAZNtL0umADJlykRYWJhJonbs2LEE7UqWLMmAAQPYu3cvRYsW5aeffgK0apGxsbEmbUuVKkVYWBhOTk7kzZvX5JYxY0ZjrPv3739prCVLluTcuXPJTrLfRHyvl4ODaarj6OhosubZ9evXqVq1KqVLl2b+/PkJ2gNER0dz8eJFSpYsadmgXyBJmh1zcEjZJC0qKmWey1qkJy1xMuTR+j77DBYtgrAwvSMRKaF7d8iZE774Qu9IhC1zc4O1ayEmBpo10/4Vr9apUyfmzp37ykTNYDAwd+5ci8xJ+/jjj/nrr7/o2bMnJ06c4Pz580ycOJGff/6Zfv36Jes5cuTIgYuLC1OnTuXSpUusW7eOr776yqTNRx99xIULF/j88885f/48P/30k0nhkcRUrVqVO3fuMG7cOC5evMj06dPZtGmT8f7Lly8zYMAAQkNDuXr1Klu3buXChQvGeWmBgYFcvnyZY8eOcffuXZ48eUJISAjly5enUaNGbN26lStXrrB3714GDRrEoUOHAOjVqxfz5s1j/vz5/PXXXwwbNozTp0+bxFatWjWioqISbL927RrHjh3j2rVrxMbGGnvqop47sfz77785duwYYWFhPH782Ngmvtfx+vXrFCxY0JjUFixYkLx58/Lhhx9y4MABLl68yIQJE9i2bRuNGjUyPqZq1arkyJGD8ePHc+fOHcLCwgh74Y/xvn37cHV1feOhpmZTQjcREREKUBEREbrsf8QIpfr2TZnnat9eqWnTUua5rOX0aaWyZdM7Cttz/LhSWbIoFRurdyT2pX17pb78Uu8oREq5dUv7Hq1fr3ckwtY9eqRUSIhSDRsq9eSJ5fen97lHvMePH6szZ86ox48fv9bjf/jhB2UwGBSQ4GYwGNQPP/yQwhGbOnDggHr33XdVpkyZlI+PjwoODlZr1qwx3t++fXvVsGFDk8f06tVLValSxfjzTz/9pAIDA5Wrq6sqX768WrdunQLU0aNHjW1+/fVXlTdvXuXq6qoqVaqk5s2bpwB1//59pZRSO3fuNPlZKaVmzpypsmfPrjw8PFS7du3U6NGjVc6cOZVSSoWFhalGjRqprFmzKhcXF5UzZ041dOhQFfu/P/rR0dGqadOmytfXVwFq/vz5SimlIiMjVY8ePVRAQIBydnZW2bNnV23atFHXrl0z7nf06NEqY8aMytPTU7Vv31598cUXqkSJEibHoHnz5qp///4m29q3b5/o+7hz505jmypVqiTa5vLly0oppS5fvpzgMX/99Zdq0qSJypw5s0qXLp0qXry4+vHHH433z58/P9HnfDE96tatm/rwww9VSknuZ9+glKyKpJfIyEh8fHyIiIjA29vb6vsfNQr++w++++7Nn6tHD8iWDd6wMqxVPXkCHh4QHg6ennpHYzuUghw5tHlSZcroHY39OHsWypeHy5chfXq9oxEpYdMm6NABjh+H5wqlCZHAo0dQv75WdXjZMkjh0Xkm9D73iBcdHc3ly5fJlSvXaxeTSKyIiCV70GzRli1bqFOnDtHR0bi4uOgdziudOHGCd999l4sXL+KZCk6+7t69S4ECBTh06BC5cuVKkedM7mdfhjvaMYPBvguHuLpqQ5JkXpopg0GGPOqhUCGtjPsL85lFKlanDrRsqZXlT6mh5SJtSpcOfv1Vu2jYqhWkcJ2LNOvFoY/2lqDdunWLX375hXz58qWKBA2gePHijB07lsuXL+sdSrJcuXKFGTNmpFiCZg7dk7Tp06cTGBiIm5sbwcHBCSZIvmjFihUULFgQNzc3ihUrxsaNG03uV0oxdOhQsmbNiru7OyEhIVy4cMGkzb1792jTpg3e3t74+vrSuXNnk3Gv0dHRdOjQgWLFiuHk5GQcu5qUPXv24OTklGCFd1uX0nPSUluSBtq8NEnSEpIkTR8DBsDkyfDwod6RiJQydixcvw7TpukdibB18Yna3bvQpg08e6Z3RKlDfKLm4uJiVwkaQN26ddm+fXuCaoW2Lv4cOzUoU6aMcUFva9M1SVu2bBl9+/Zl2LBhHDlyhBIlSlCrVq0E1W3i7d27l1atWtG5c2eOHj1Ko0aNaNSoEadOnTK2GTduHFOmTGHWrFns378fDw8PatWqZbKYXps2bTh9+jTbtm1j/fr17N69m27duhnvj42Nxd3dnZ49exISEvLS1xAeHk67du2oUaPGGx4N60vJ6o6enqkzSZO10hJXvTqcOgW3bukdiX0pXRpKlYK5c/WORKQUNzdtAeNhw6Qsv3g1Dw9Yv14rIvTBB5KoJVenTp2IioqyqwQNtHXW/vrrr1eeq4rUSdckbeLEiXTt2pWOHTtSuHBhZs2aRbp06ZJceHDy5MnUrl2bzz//nEKFCvHVV19RqlQppv3vEqVSikmTJjF48GAaNmxI8eLF+fHHH7lx4wZr164FtMX2Nm/ezNy5cwkODqZixYpMnTqVpUuXcuPGDQA8PDyYOXMmXbt2NVlxPTEfffQRrVu3tn7FlxRg7+ukgVR4TIqHB1Spos2pEdY1cCCMHy+V3tISKcsvzOHpCRs3aj2w7dpJopZcKV1mXwi96ZakxcTEcPjwYZPs38HBgZCQEEJDQxN9TGhoaIKrBbVq1TK2v3z5MmFhYSZtfHx8CA4ONrYJDQ3F19eXMs9VRAgJCcHBwSHB+g6vMn/+fC5dusSwYcOS1f7JkydERkaa3PQkwx1lrbSXkSGP+qhcWSvcsnix3pGIlPTppxAYKGX5RfLEJ2pXr2rFZ15YtkoIYQd0S9Lu3r1LbGwsWbJkMdmeJUuWBOsTxAsLC3tp+/h/X9Umc+bMJvc7OTnh5+eX5H4Tc+HCBfr378/ixYtxcnJK1mO++eYbfHx8jLfs2bMne3+WYO+FQ+D/e9KkxmlC9erB1q0ygd3aDAZtbtqYMXJilpYYDDBvHqxcKRc/RPJ4eWmjGS5d0orPyO8DIeyL7oVDUqPY2Fhat27NiBEjkr2yPMCAAQOIiIgw3v755x8LRvlq0pOmLRsQFwc3b+odie3JlUs7Pn/+qXck9qdePXB3h1Wr9I5EpKTMmWH+fOjUSRYuF8nj7a0lan/9BZ07S6ImhD3RLUnLmDEjjo6O3HqhMsGtW7eSnAfm7+//0vbx/76qzYuFSZ49e8a9e/deOf8s3oMHDzh06BDdu3fHyckJJycnRo4cyfHjx3FycuK3335L9HGurq54e3ub3PQkPWnaMZAhj0mTIY/6iO9N++Yb6eVNa2rX1uamSVl+kVw+PrBli7aWYo8eekcjhLAW3ZI0FxcXSpcuzY4dO4zb4uLi2LFjR5JFOMqXL2/SHmDbtm3G9rly5cLf39+kTWRkJPv37ze2KV++POHh4Rw+fNjY5rfffiMuLo7g4OBkxe7t7c3Jkyc5duyY8fbRRx9RoEABjh07luzn0ZuDQ8pdlUutSRpI8ZCXkSRNP82aQVSUFG9Ji8aMgRs3YMoUvSMRqUV8otamjd6RCCGsRdfhjn379mXOnDksXLiQs2fP8vHHH/Pw4UM6duwIQLt27RgwYICxfa9evdi8eTMTJkzg3LlzDB8+3NijBdoihr1792bUqFGsW7eOkydP0q5dOwICAoxrnRUqVIjatWvTtWtXDhw4wJ49e+jevTstW7YkICDAuK8zZ85w7Ngx7t27R0REhDEZA63ASdGiRU1umTNnxs3NjaJFi+Lh4WGdA/iGpCdNU7AgnDundxS26Z13tGFZLyw1KKzA0RH699eqAkpvWtri5gY//wwjRsChQ3pHI1ILX1/td7IQL9OhQwcMBgMGg8FY2Ty1xHP+/Hn8/f15kEpOKFu2bMmECRMs9vy6JmktWrRg/PjxDB06lKCgII4dO8bmzZuNhT+uXbvGzecmC1WoUIGffvqJ2bNnU6JECVauXMnatWspWrSosc0XX3xBjx496NatG2XLliUqKorNmzfj5uZmbLNkyRIKFixIjRo1qFu3LhUrVmT27NkmsdWtW5eSJUvy66+/smvXLkqWLEnJkiUtfESsK6VL8D99Ck+epMzzWVPBgtowEpGQszPUrQu//KJ3JPapXTttvuS2bXpHIlJa4cIwYQK0aAEREXpHI4Qwx/Tp0wkMDMTNzY3g4GAOHDjw0vZz5syhUqVKpE+fnvTp0xMSEvLKx3z55ZcEBgYmSFjq169P5cqViXvJeOnatWtz8+ZN6tSpY9xmMBi4cuUKAFmzZmXMmDEmj+nfvz8Gg4Fdu3aZbK9atSpt27YF4ObNm7Ru3Zr8+fPj4OBA7969E+x7+PDhdOjQwfjz5MmTTc7lX2bAgAH06NEDLy8vAKKjo40LXzs5ORk7XJ6XnJhedPz4cVq1akX27Nlxd3enUKFCTJ482aTNrl27jMnl87fniwwOHjyY0aNHE2GhX+K6Fw7p3r07V69e5cmTJ+zfv99kqOCuXbtYsGCBSftmzZpx/vx5njx5wqlTp6hbt67J/QaDgZEjRxIWFkZ0dDTbt29PUNzDz8+Pn376iQcPHhAREcG8efPw9PQ0aXPlyhWUUgluSRk+fLixpy21SMnFrJ2dtUIHOq8q8FoKFZKetJdp2FCSNL04O2vrpo0YIb1paVHHjlChAnTrJu+vEG9EKTh40CpfpGXLltG3b1+GDRvGkSNHKFGiBLVq1UpQ7+B5u3btolWrVuzcuZPQ0FCyZ89OzZo1uX79epKPGTlyJJ6envTt29e4bd68eezcuZP58+fj4JD0Kbyrqyv+/v64uromen/VqlUTJGM7d+4ke/bsJtujo6PZt28f1atXB7SlpDJlysTgwYMpUaJEkvt/no+PT7JqPly7do3169ebJHixsbG4u7vTs2fPJBfsfp2YDh8+TObMmVm8eDGnT59m0KBBDBgwwLju8vPOnz/PzZs3jbfnK8QXLVqUPHnysNhCa+bonqQJ/aRkdUdIvUMe8+eHf/+Fhw/1jsQ21a6tDcm6c0fvSOxThw7a5/OF6bgiDTAYYMYMOHYMvv9e72iESMUWL4Zy5WDJEovvauLEiXTt2pWOHTtSuHBhZs2aRbp06Zg3b16Sj1myZAmffPIJQUFBFCxYkLlz5xrrMCTF1dWVhQsXsnDhQjZv3sy1a9fo06cP48aNI0+ePG/0GqpVq8aePXt49r+V0h88eMDRo0f58ssvTZK00NBQnjx5QrVq1QAIDAxk8uTJtGvXDh8fnzeK4UXLly+nRIkSZMuWzbjNw8ODmTNn0rVr1yQTvdeJqVOnTkyePJkqVaqQO3duPvjgAzp27Mjq1asTtM2cOTP+/v7G24vJcf369Vm6dKkZrzT5JEmzYyk53BG0UsGpsSfN3R1y5pTiIUnx9oaqVWH9er0jsU8uLlqlx+HDpbclLfLyguXLtfc4lQ3GEMI2PHsGw4Zp/x82TPvZQmJiYjh8+LBJr46DgwMhISGEhoYm+3kePXrE06dP8fPze2m70qVLM2DAALp06ULbtm0pV64cH3/88WvHH69atWpERUVx8OBBAP744w/y589P06ZN2b9/P9HR0YDWuxYYGEhgYOAb7/NV/vjjD8qUKWPx/SQlIiIi0fcjKCiIrFmz8u6777Jnz54E95crV44DBw7wxALzfSRJs2MpOdwRUm+SBlI85FVkyKO+OnWC69dh82a9IxGWUKIEjBsHTZvC/ft6RyNEKvPzz3D5svb/S5fAQr0aAHfv3iU2NtZYOyFelixZTOYqvcqXX35JQEBAkkP4njd48GAcHBzYv38/P/zwAwaDwey4AZRSxmQrX758ZMuWzdhrtmvXLqpUqYK/vz85cuQwJpy7du0y9qIl1/DhwxNMVUqOq1evmhTws6a9e/eybNkyunXrZtyWNWtWZs2axapVq1i1ahXZs2enatWqHDlyxOSxAQEBxMTEmPX+J5ckaXYspXvSUutwR9DmpUnxkKQ1aADbt8OjR3pHYp9cXLSetEGDZG2ttKpLF6hcGdq3l/dYiGSL70WLT1wcHCzem/amxowZw9KlS1mzZo1JUbukbNu2jbCwMOLi4ow9Xynh+Xlpu3btomrVqgBUqVKFXbt28fjxY/bv3292kva6Hj9+nKzjkdJOnTpFw4YNGTZsGDVr1jRuL1CgAB9++CGlS5emQoUKzJs3jwoVKvDdd9+ZPN7d3R3QekdTmiRpdiyl56Sl5p40SdJeLiBAq0a3fbvekdivDz6A6GhIZMi8SAMMBpg+Ha5d09ZREzbOioUqxEvE96LFvw9xcRbtTcuYMSOOjo7cunXLZPutW7eSVRxj/PjxjBkzhq1bt1K8ePFXtr9//z5du3Zl8ODBDBo0iE8++YS7d+++dvzPi5+X9t9//3H06FGqVKkCaEnazp072bt3LzExMcaiIZaWMWNG7lt5KMGZM2eoUaMG3bp1Y/Dgwa9sX65cOf7++2+Tbffu3QMgU6ZMKR6fJGl2LKWHO3p5SZKWlsmQR305OsLIkTBkSMotQi9sS7p0sGoVTJwoF0RsnhULVYgkvNiLFs+CvWkuLi6ULl3apOBHfAGQ8uXLv/Sx48aN46uvvmLz5s3JnnvVo0cP/P39GThwIIMGDSJbtmx8+umnb/Qa4lWrVo2HDx8yceJE8uXLZ6xaWLlyZQ4cOMCmTZuMwyKtoWTJkpw5c8Yq+wI4ffo01apVo3379owePTpZjzl27BhZs2Y12Xbq1CneeustMmbMmOIxSpJmxyxROCS1DncsWFBbsNmGR0jormFD+PVXSRD01KSJdiJvoWq/wgbkyQPz50ObNvDPP3pHIxJlxUIV4iVe7EWLZ+HetL59+zJnzhwWLlzI2bNn+fjjj3n48CEdO3Y0tmnXrh0DBgww/jx27FiGDBnCvHnzCAwMJCwsjLCwMKKiopLcz5o1a1ixYgULFy7EyckJJycnFi5cyNq1a1m1atUbv47cuXOTI0cOpk6dauxFA8iePTsBAQHMnj070aGOx44d49ixY0RFRXHnzh2OHTuWIslVrVq1CA0NJfaFk4wzZ85w7Ngx7t27R0REhHH/5sS0Zs0aChYsaPz51KlTVKtWjZo1a9K3b1/j+3HnuTLWkyZN4pdffuHvv//m1KlT9O7dm99++y1BkvzHH3+YDJNMUUroJiIiQgEqIiJCl/0vWqRUo0Yp93xffKHUkCEp93zWljmzUufP6x2F7YqLUyp3bqX+/FPvSOzbpk1K5cypVHS03pEISxo4UKngYKWePNE7EpHAjz8qpaUG2m3RIr0jMove5x7xHj9+rM6cOaMeP35s/oOfPlUqVy6lDAbT9yL+5uCg/cF6+jTlA1dKTZ06VeXIkUO5uLiocuXKqX379pncX6VKFdW+fXvjzzlz5lRAgtuwYcMSff47d+6ozJkzq9GjRye4b/To0Spz5szqzp07iT62ffv2qmHDhsl6He3bt1eAWrp0qcn2Dh06KED9/PPPCR6T2OvImTPnK/cFqDVr1iR5/9OnT1VAQIDavHmzyfakjp05Mc2fP9/kMcOGDXvlY8aOHavy5Mmj3NzclJ+fn6patar67bffTPb7+PFj5ePjo0JDQ1/5+l98XHI++4b/vTihg8jISHx8fIiIiMDb29vq+1+yRCv9nFJD2EaNgv/+gxfmVKYaVatCnz5aj5FIXN++4OSkVaIT+lBKKzDRsiWk0KgXYYNiY6FWLa2XP5H1VYVenj3TFte8ckX7Mjo4QGCgtoaLk5Pe0SWL3uce8aKjo7l8+TK5cuUyv2DErl2QnIIWO3dqf9ztSIcOHQgPD2ft2rV6h2LCYDCwZs0aGjVqlGSb6dOns27dOrZs2WK9wN7AzJkzWbNmDVu3bjXrccn97KeO3yjCIiwx3DG+Cm5qFD8vTZK0pDVsCN26SZKmJ4MBvv4amjeHjh214Y8i7XF01EZzlSoF5ctrwx+FDXi+3DuYDq374AP94rI35ctrV5lftjaVq6vWzg6tX78eT09Pli5dynvvvadrLB999BGLkzlG/8MPPyQ8PJwHDx7g5eVl4cjenLOzM1OnTrXY80tPmo70vpq1dKk2tyWlFimePx82boQVK1Lm+axtyhQ4cgReY3kPu/HsGWTJAnv2aFf4hX5q14bq1eGLL/SORFjS/v3ae717NxQrpnc0du7FXrR4qaw3Te9zj3hv1JMmknT79m0i/1fFLWvWrHh4eEg8Nia5n30pHGLHLFGCP7UWDgEt6ZAKjy/n5ATvvSdVHm3B6NFaj2ZEhN6RCEsKDtbe66ZN5b3WnU6FKoQwR+bMmcmbNy958+a1iYTI1uJJTSRJs2OWGO6YWkvww/8Pd5S+5ZeTUvy2oXRpbarFxIl6RyIs7eOPtWrvHTvK7yfdJFXuPV4qWERZCJG6SJJmx2SdNFNvvaWdAN24oXcktq1WLTh+HMLC9I5EjBwJkyfDc1WDRRpkMMD332vLhHz7rd7R2Kk//0y8Fy1efG/an39aN640QGbdCHuT3M+87Q+eFhZjieGOqTlJMxi03rQzZ8BKazemSh4eUKOG1pv24Yd6R2PfCheGxo21yqqTJ+sdjbAkDw9YvRrefhuCgsBSy/KIJEihihTn7OwMwKNHj3B3d9c5GiGs59GjR8D/fweSIkmaHUvp4Y4+Pqk7SQMoUgROn4Z339U7Etv2/vuwaJEkabZg5EgoWhR69IC8efWORlhSvnza9651a62gSJ48ekdkR1xdoVkzvaNIUxwdHfH19eX27dsApEuXDkNSw0mFSAOUUjx69Ijbt2/j6+uLo6PjS9tLkmbHLNWTplTSw/ZtXXySJl6ufn346CNtXbwMGfSOxr5lzw6ffAIDB2oX+kXaVrcufPaZNjc0NFQbZi5EauXv7w9gTNSEsAe+vr7Gz/7LSJJmx1J6Tpqnp/ZvVFTqPXEoUkQbUiReLn16qFJFG/LYqZPe0Yj+/bVetH37tOFwIm378ks4dgzat4eVK7ULbkKkRgaDgaxZs5I5c2aePn2qdzhCWJyzs/Mre9DiSZJmx1J6uKPB8P+9aak5STt9OnX3BlrL++/DqlWSpNkCHx8YOhQ+/1xbT0s+u2mbwQA//ADvvKPNRxw6VO+IhHgzjo6OyT5xFcJeyPU3O5bSwx1BS9JS81o+2bNrCdr163pHYvsaNoSdOyE8XO9IBGjzA8PCZHkEe+HhAWvXwrRp8p4LIURaJEmaHbNEkpbai4cYDFrFPJmX9moZM0KFCvDrr3pHIgBcXOCbb7ShcDJqyD4EBmrzEDt21KrSCiGESDskSbNjKT0nDVJ/TxpI8RBzNG2qDXkUtqFpU/Dzg7lz9Y5EWEvVqjBihNazff++3tEIIYRIKZKk2bGUnpMGqb8nDSRJM0fjxrBtGzx4oHckArQLL99+C8OHy3tiT7p3h0qVtNL8sbF6RyOEECIlSJJmxyw1J02SNPvh7w9lysCGDXpHIuJVrKgNQ/32W70jEdZiMMDMmdr80P799Y5GCCFESpAkzY5Zak5aWhnumNK9jGmVDHm0PWPGwOTJcOOG3pEIa3F11ZYPWbpUq/wohBAidZMkzY5Zak5aau9Jy5ZNS2CvXdM7ktShSRPYtAkePdI7EhGvQAH44AMYNkzvSIQ1Zc2qFfL5/HPYtUvvaIQQQrwJSdLsmCXmpKWFwiEGgwx5NMdbb0GxYrB5s96RiOcNHaotdCyfY/sSFAQLFkCzZnDhgt7RCCGEeF2SpNkxKcGfNEnSzPP++1pCIGxHlizQr59Wkl/YlwYNtPf9vfek4qMQQqRWkqTZMVnMOmmSpJmnaVNYvx6io/WORDyvTx84ckRbdFzYl379tIqPzZrJunlCCJEaSZJmxywxJ0160uxTYCDkz6+V4xe2w8MDvvoKPvss5b/rwrYZDDBjBjx7ppXol0JIQgiRukiSZsdkTlrSihaFM2fkxNYcMuTRNnXoAE+ewM8/6x2JsDYXF63y6m+/waRJekcjhBDCHJKk2TEHh5Rf+DQtlOAHbf0vNze4fFnvSFKP99+HX37REgJhOxwdtTXTBgyAx4/1jkZYW4YM2lDkr7+GNWv0jkYIIURySZJmxyzRk5ZWkjSDQatYePKk3pGkHnnzQp48sGWL3pGIF9Wpow3hHT9e70iEHgoU0NZQ69QJQkP1jkYIIURySJJmx2Qx65crXlySNHO1bKktpitsz8SJMGEC/POP3pEIPVSqBN9/Dw0bSml+IYRIDSRJs2OWStIeP04b1cSKF4cTJ/SOInVp1QrWrUsbxWPSmkKFoEsX6NtX70iEXpo310rz16kDt2/rHY0QQoiXkSTNjlliuKOrqzaXKy30phUrJkmaud56C8qX14oVCNszbBjs3Qtbt+odidBL375Qt662ltqjR3pHI4QQIimSpNkxS5TgB603LTw85Z/X2ooUgUuX5ETGXO3awY8/6h2FSIyXlzbksXt3KfBirwwG+O47CAiA1q1TvniUEEKIlCFJmh2zxHBHSDvz0jw9tfW/zpzRO5LUpXFjOHwYrl7VOxKRmBYtIHt2LVkT9snRERYvhlu3oHdvWUNNCCFskSRpdsxSSZqvb9pI0kCGPL4OT08tUVuyRO9IRGIMBpg2DcaNgytX9I5G6CVdOm3+6ObNkrALIYQtkiTNjklP2qtJhcfXEz/kUa7Q26ZCheDDD6FPH70jEXrKlAk2bdLW0Vu2TO9ohBBCPE+SNDtmySQtLcxJA6nw+LqqVoWoKDh4UO9IRFKGDIFDh2DjRr0jEXrKm1dbhP6jj2D3br2jEUIIEU+SNDtmieqOkLZ60uKHO0qPkHkcHeGDD6SAiC3z9NQKSPTsCdHRekcj9PT22zB/PjRpAmfP6h2NEEIIkCTNrsmctFfLnVur7njrlt6RpD5t28LPP0NMjN6RiKQ0bap9xseN0zsSobdGjWD4cKhfX76zQghhCyRJs2OWLMGfVpI0R0coWlSGPL6OIkUgVy4ZTmfLDAaYOlUrHHHpkt7RCL117w6//gouLnpHIoQQQpI0OyZz0pKnWDEpHvK62rWDRYv0jkK8TIEC8OmnWil2IQoVenWb69evs3LlSssHI4QQdkySNDsm1R2TR4qHvL6WLWHLFrh3T+9IxMsMGgTHj2u9KEK8zNOnT9m3bx8zZ86kbdu2eocjhBBpliRpdsxShUPS0pw0kCTtTWTODNWqSXlvW+fhAZMmQa9e8Pix3tEIW6WUwtnZmQYNGlC/fn1WrlzJ999/r3dYQgiRJkmSZsekJy15ihXTKp49e6Z3JKlT/JppwrY1aqQNfRwzRu9IhC1SSmEwGAD47bffOHz4MA0aNKBGjRo6RyaEEGmTJGl2zMEBYmNT/nl9feH+/ZR/Xr1kyAAZM8K5c3pHkjrVrw/nz8vxs3XxRUQmT9beLyHiPZ+gbdq0iQULFhATE8OoUaPImzevztEJIUTaJEmaHZPhjskXFATHjukdRerk5gatW8MPP+gdiXiVvHnhiy+gWzfL9LKL1CcqKsqYoG3YsIEff/yR2NhYRo0aRb58+Yzt1q1bx8yZM/UKUwgh0hxJ0uyYJddJCw9PWwtAS5L2Zrp00YY8Pn2qdyTiVT77TCv0Mm+e3pEIvT19+pRevXoxadIkDh06xOLFi3n27JkxQVP/+yUfGRnJo0ePWLJkCS1bttQ5aiGESBteO0n7+++/2bJlC4//N8tcpaUzcjthqSTN21sbRvnoUco/t15KlpQk7U0EBcFbb8H69XpHIl7FxQXmzIEvv4SwML2jEXpydnbmk08+oV+/frRq1Qp3d3dGjx5N/vz5jUMgY2Nj8fb2pmnTprz99tvs2LGD9fJFF0KIN2Z2kvbff/8REhJC/vz5qVu3Ljdv3gSgc+fO9OvXL8UDFJZjqSTNwUFL1NLSWmlBQXD0aNrqHbS2zp1h7ly9oxDJ8fbb2hDVXr30jkTorXTp0pw4cYI7d+7g7e1N/vz5iYuLw2AwEBcXh6OjIwBTp07lwoUL9OjRg3feeUfnqIUQIvUzO0nr06cPTk5OXLt2jXTp0hm3t2jRgs2bN6docMKyHP737ltqXlpaKh6SK5dW3fHff/WOJPVq3Rp275ZjmFp8/TXs3Su9nwKKFCnCzp07mTdvHhs2bMDBwYG4uDgc/vdHZOLEiezYsYNSpUrRo0cP0qdPL6NrhBDiDZmdpG3dupWxY8fy1ltvmWzPly8fV69eTbHAhOX9by64RXrT0qdPWz1pDg5QooTWmyZej6+vVuZ9wQKdAxHJ4uUF06fDJ5/Agwd6RyP0VrJkSU6ePElISAiXL182JmgTJkxgx44dlCtXjp49exoTtPhiI0IIIV6P2Unaw4cPTXrQ4t27dw9XV9cUCUpYR3xPmiWLh6QlMi/tzXXpohWkkMqBqUODBhAcDIMH6x2JsAU5c+bk6dOntGnThjFjxjB//nx27NhBmTJlTHrQJEETQog3Z3aSVqlSJX58bmXa+HHp48aNo1q1aikanLAsSdLMEz8vTby+ypXB0RF27dI7EpFcU6bA4sWwf7/ekQhb4OnpyfTp0xk5ciSDBw+mZs2a9OrVCz8/P0nQhBAiBZmdpI0bN47Zs2dTp04dYmJi+OKLLyhatCi7d+9m7NixlohRWIgkaeaRnrQ3ZzBAp05SQCQ1yZoVvvkGunaVJRSEpmTJkhw+fJjIyEhu3779ygTt2bNn/PXXX1L1UQghzGB2kla0aFH++usvKlasSMOGDXn48CFNmjTh6NGj5MmTx+wApk+fTmBgIG5ubgQHB3PgwIGXtl+xYgUFCxbEzc2NYsWKsXHjRpP7lVIMHTqUrFmz4u7uTkhICBcuXDBpc+/ePdq0aYO3tze+vr507tyZqKgo4/3R0dF06NCBYsWK4eTkRKNGjRLEsXr1at59910yZcqEt7c35cuXZ8uWLWa/fj1Zck5aWiscAlC4MNy4kfZel7W1bw/r1mlrcYnUoUsX7Ts9frzekQhbUahQIX7//XfWrl3Lnj17XtqDFhMTw4EDB/j2229p3ry5FaMUQojU67XWSfPx8WHQoEEsX76cjRs3MmrUKLJmzWr28yxbtoy+ffsybNgwjhw5QokSJahVqxa3b99OtP3evXtp1aoVnTt35ujRozRq1IhGjRpx6tQpY5tx48YxZcoUZs2axf79+/Hw8KBWrVpER0cb27Rp04bTp0+zbds21q9fz+7du+nWrZvx/tjYWNzd3enZsychISGJxrJ7927effddNm7cyOHDh6lWrRr169fnaCoaD2cwaDcpHJI8Li5aonb8uN6RpG4BAVC9OixZonckIrkcHGD2bBg7Fl645iXsWKlSpdi1axflypV7abt06dLRtGlTNmzYwN27d2nRooWVIhRCiNTLoF6jTm50dDQnTpzg9u3bxL1wht+gQYNkP09wcDBly5Zl2rRpAMTFxZE9e3Z69OhB//79E7Rv0aIFDx8+NBky8fbbbxMUFMSsWbNQShEQEEC/fv347LPPAIiIiCBLliwsWLCAli1bcvbsWQoXLszBgwcpU6YMAJs3b6Zu3br8+++/BAQEmOyzQ4cOhIeHs3bt2le+niJFitCiRQuGDh2arNcfGRmJj48PEREReHt7J+sxKc3JCf77D3x8UvZ5p0zRkpkffkjZ59Vbx45QvDj06aN3JKnbL7/AsGHaHD+ZwpJ6jBwJv/8O27fL+yZMrVu3jrt379KpU6cE98WfZhgMBv777z+aNWvG/PnzyZkzp7XDFNjGuYcQ4tXM7knbvHkzOXLk4O2336ZBgwbG3qxGjRrRuHHjZD9PTEwMhw8fNumpcnBwICQkhNDQ0EQfExoamqBnq1atWsb2ly9fJiwszKSNj48PwcHBxjahoaH4+voaEzSAkJAQHBwc2P8GM+Pj4uJ48OABfn5+SbZ58uQJkZGRJje9WWpB67Q4Jw1kXlpKqVsXbt2CI0f0jkSYo39/CAtLexdfxJvLly8fV65cMZk6EM9gMBiHQ4aFhXHgwAEiIiKsHaIQQqQqTuY+oEePHjRr1oyhQ4eSJUuW197x3bt3iY2NTfAcWbJk4dy5c4k+JiwsLNH2YWFhxvvjt72sTebMmU3ud3Jyws/Pz9jmdYwfP56oqKiXjrf/5ptvGDFixGvvwxIkSTNPUJAUvUgJzs7a3LS5c6F0ab2jEcnl4gLz50Pt2lCzJuTIoXdEwlYUKlSIYcOG4eDgwN9//825c+fw9PREKcWDBw+MFymHDh1K/fr1KV68uN4hCyGETTM7Sbt16xZ9+/Z9owQtrfnpp58YMWIEv/zyS4IE8HkDBgygb9++xp8jIyPJnj27NUJMkiWTtLRYYKNECTh3DqKjwc1N72hSt06doFw5mDABEll6UdiocuXgww+1YiJbtsiwR/H/HB0defDgAR9++CE7d+6kefPmXL9+ndjYWJycnMicOTOdO3emZ8+exsfMnDmTHTt2sHLlSh0jF0II22N2kvb++++za9eu16rk+LyMGTPi6OjIrVu3TLbfunULf3//RB/j7+//0vbx/966dcukkMmtW7cICgoytnmxMMmzZ8+4d+9ekvt9maVLl9KlSxdWrFiRZJGReK6urja34Lf0pJnHxweyZ4czZ6BUKb2jSd3y59eS3pUroV07vaMR5hg+XPv8z5kDz9VcEgIvLy+mT59OSEgIpUqVYunSpQCJluifPn06vXv3xtvbm/r16/Prr7/qEbIQQtgks+ekTZs2jdWrV9OhQwcmTJjAlClTTG7J5eLiQunSpdmxY4dxW1xcHDt27KB8+fKJPqZ8+fIm7QG2bdtmbJ8rVy78/f1N2kRGRrJ//35jm/LlyxMeHs7hw4eNbX777Tfi4uIIDg5OdvwAP//8Mx07duTnn3+mXr16Zj3WVjg4QGxsyj9v+vRpsycNtHlpMpcqZXTpop3oi9TF1RUWLIAvv4RLl/SORtiaggULsmXLFsaNG8eYMWOM22Of+2OzcuVKevTowaZNm/jvv/8IDw+nTp06eoQrhBC2SZlp7ty5ysnJSXl6eqqcOXOqwMBA4y1XrlxmPdfSpUuVq6urWrBggTpz5ozq1q2b8vX1VWFhYUoppdq2bav69+9vbL9nzx7l5OSkxo8fr86ePauGDRumnJ2d1cmTJ41txowZo3x9fdUvv/yiTpw4oRo2bKhy5cqlHj9+bGxTu3ZtVbJkSbV//371559/qnz58qlWrVqZxHb69Gl19OhRVb9+fVW1alV19OhRdfToUeP9S5YsUU5OTmr69Onq5s2bxlt4eHiyX39ERIQCVEREhFnHLSX5+Cj1778p/7wPHihlMCgVG5vyz623r79W6qOP9I4ibXj0SCk/P6VOnNA7EvE6hg9XqkIFpZ4+1TsSYYtOnDih3N3d1S+//GKy/cmTJ2rp0qUqR44casSIEcbtlSpVUtOnT7d2mHbHFs49hBCvZnaSliVLFjV69GgVm0Jn31OnTlU5cuRQLi4uqly5cmrfvn3G+6pUqaLat29v0n758uUqf/78ysXFRRUpUkRt2LDB5P64uDg1ZMgQlSVLFuXq6qpq1Kihzp8/b9Lmv//+U61atVKenp7K29tbdezYUT148MCkTc6cORWQ4PZ8bInd/2K8L2MLvyjTp1fq2rWUf964OKWcnJS6dy/ln1tvW7YoVbas3lGkHf36KfXJJ3pHIV7H06dKBQcrNWqU3pEIW3X16lV18OBBpZRSd+/eNbnvzp07KleuXGrOnDl6hGa3bOHcQwjxamavk+bn58fBgwffeE6asI21SjJmhMOHwRLL1WTODPv2Qe7cKf/cevrvP8iWDSIjtWp34s1cuABlysC//4KXl97RCHNduABly2prpz23sokQJm7fvs2GDRto1qwZnp6eREVF4enpyZgxY4iKimLUqFGANu3BwcHsmRjCDLZw7iGEeDWzfxO2b9+eZcuWWSIWoQNLFQ6BtDsvLUMG8PeH06f1jiRtyJcPgoNhyRK9IxGvI18+GDcOPvgAHj3SOxphqxwdHZkwYQITJ04EwNPTE4Ddu3dz6tQpYzsHBwcuXrzI2rVr2bhxoy6xCiGELTC7umNsbCzjxo1jy5YtFC9eHGdnZ5P7438Bi9TBkkmanx/cu2eZ59ZbmTJaD2TJknpHkjZ8/LFWMfDDD6Wke2rUtSusXw+ffw7Tp+sdjbBFGTJkYNmyZVStWhWDwUCdOnVYvXo1kZGRxvVDz5w5w9q1axk7dixFixYlKiqKSZMmsXXrVp2jF0II6zO7J+3kyZOULFkSBwcHTp06xdGjR423Y8eOWSBEYUnSk/Z6SpfWkjSRMurX14aRhobqHYl4HQaDtjD5qlUgnR8iKUWKFOHPP//kjz/+oFevXvz666906tSJ8uXLc/78eb7//ns2btzI9OnT2bNnD8ePHycyMpIZM2boHboQQlid2T1pO3futEQcQieSpL2e0qVh9Wq9o0g7nJy09bZmzoQKFfSORryOzJm1RK1zZzhxAjJl0jsiYYsKFCjAL7/8QkxMjHHtUIPBwOLFizlx4gRffvkl9evXN85Ny58/P3fu3NE7bCGEsLo3mp3777//8u+//6ZULEIHjo6SpL2O0qXh5EmIidE7krSjSxdYswbu3tU7EvG63nsPGjbUEm7zSlIJe+Lu7o6Pjw9ubm4YDAYOHz7MzJkz+fDDD6lfvz6gzU07evQox48fJygoSN+AhRBCB2YnaXFxcYwcORIfHx9y5sxJzpw58fX15auvviLOUmf7wmJkTtrrkeIhKS8gAGrXhvnz9Y5EvIkJE7TvhbyPIrl+/fVXypYtS8uWLYkvOH3p0iUWL15M7ty5KVq0qM4RCiGE9ZmdpA0aNIhp06YxZswY41y0r7/+mqlTpzJkyBBLxCgsSIY7vr744iEi5Xz8McyaZbnPpLA8Dw9YvBj69YOLF/WORqQG6dOnx8fHBwCDwcCpU6eYPXs2+/bto3PnzuTJk4cLFy7Qrl07evXqxWeffaZzxEIIYXlmJ2kLFy5k7ty5fPzxxxQvXpzixYvzySefMGfOHBYsWGCBEIUlSZL2+kqXhkOH9I4ibaleHZydQYq5pW7lykGfPtC2LTx7pnc0wtY1aNCAbdu28eWXX9K/f3/69OljnJ/23nvvMWvWLJo2bcqlS5fIkycPe/fuJSQkRO+whRDCosxO0u7du0fBggUTbC9YsCD30urYtjRMkrTXV6YMHDigdxRpi8EAH32kFRARqdvAgdq8tDFj9I5E2LpcuXIRGhrKgwcPOHv2LM2aNWP06NE0aNCAadOmsWzZMurXr8+ff/5Jz5492bt3L9HR0SbrqwkhRFpjdnXHEiVKMG3aNKZMmWKyfdq0aZQoUSLFAhPWIXPSXl9wMJw6BVFR8L91WUUKaN8ehg2DS5cgd269oxGvy8kJFi2CsmWhVi3tXyGSkj9/fqZPn45SCgcH7frxoUOHWLBgAe+99x7Dhw83tv3rr784fvw4j2T1dCFEGmZ2kjZu3Djq1avH9u3bKV++PAChoaH8888/bJQFclId6Ul7fd7eUKgQHDwI1arpHU3akT69NkxuyhSYNEnvaMSbyJsXvv0WPvgAjhzR5qsJkRSDwYDhudXshwwZgr+/v0mCFhERwcKFC6lZsyalSpXSIUohhLAOs4c7VqlShfPnz9O4cWPCw8MJDw+nSZMmnD9/nkqVKlkiRmFBkqS9mQoVZAFmS+jVS6sOGBGhdyTiTXXuDAULwuef6x2JSE2UUjg6OvLJJ58Yt0VGRrJixQoOHDhA5cqVcXIy+zqzEEKkGq/1Gy5btmyMHj06pWMROrB0khYZCbGx2npsaVGFCrBsmd5RpD358kGVKvDDD9C3r97RiDdhMMCcOVC8uLaOWt26ekckUoOnT58SExPD7t27qVu3Ljdv3mTevHns2rWL4sWL06tXL0BL5p7vfRNCiLTC7J60+fPns2LFigTbV6xYwcKFC1MkKGE9Dg5aEmUJ7u7g6pq2e9MqVIC9e6VkvCX06QOTJ8PTp3pHIt5U5sxawt2pE4SF6R2NSA1cXFyYNm0ac+fOpWnTphQpUoQDBw5QvXp1JkyYAEiCJoRI28xO0r755hsyZsyYYHvmzJn5+uuvUyQoYT2OjpZLMAyGtF88JHduLRE9c0bvSNKeqlUhUyZYvlzvSERKqFcPWrSAdu3kooZInvz583PixAm6dOnCnDlz+OGHHxgwYAAgCZoQIu0zO0m7du0auXLlSrA9Z86cXLt2LUWCEtZjyeGOkPaTNIMBKlWCP/7QO5K0x2CAL7+EceO0Uu4i9Rs3Du7e1YqJCJEcAQEB1KlTh6ZNmxovEEuCJoSwB2YnaZkzZ+bEiRMJth8/fpwMGTKkSFDCeiydpGXIAP/9Z7nntwWSpFlOkybw8CFs2aJ3JCIluLrC0qXa2mlScEe8LknQhBD2wOwkrVWrVvTs2ZOdO3cSGxtLbGwsv/32G7169aJly5aWiFFYkCXnpEHa70kDqFgR/vxT7yjSJkdH+OwzGDtW70hESsmfH6ZNg+bN4c4dvaMRqd3AgbB/v95RCCFEyjM7Sfvqq68IDg6mRo0auLu74+7uTs2aNalevbpUfEyFrNGTltaTtOLFtVLxV6/qHUna1L69NufvwAG9IxEppU0baNAAWre27EUikfYVLw61a8P69XpHIoQQKcvsJM3FxYVly5Zx/vx5lixZwurVq7l48SLz5s3D1dXVEjEKC7Jk4RDQetLS+nBHR0etyqMMebQMd3fo2VObzyTSjokTtSU6RozQOxKRmrVsCatXaxdz5szROxohhEg5ZidpI0eO5NGjR+TLl49mzZrx3nvvkTNnTh4/fszIkSMtEaOwICkckjJkXpplffIJbNsGFy7oHYlIKa6usGIFzJoFGzboHY1IzapVg507tYR/+HApNCSESBvMTtJGjBhBVFRUgu2PHj1ihFwSTXWkcEjKkCTNstKnh86dYfx4vSMRKSlHDli8WOsFuXJF72hEala8uLZm5YoV0LUrPHumd0RCCPFmzE7Skip9e/z4cfz8/FIkKGE9UjgkZZQtC5cva+XFhWX06QM//SSLIac1NWtCr17w/vsQHa13NCI1y5FDK+L011/QsKFWGVYIIVKrZCdp6dOnx8/PD4PBQP78+fHz8zPefHx8ePfdd2nevLklYxUWYOk5afZQOATAzQ3KlJEqj5aUPbtWkn/KFL0jESlt0CDInFlL1oR4E+nTw9atkC6dNgzy9m29IxJCiNfjlNyGkyZNQilFp06dGDFiBD4+Psb7XFxcCAwMpHz58hYJUliONeak2cNwR9CGPP75JzRqpHckadcXX2hLHvTvD97eekcjUoqDgzbssVQpWLAAOnTQOyKRmrm5aevx9e2rFXXavBny5tU7KiGEME+yk7T27dsDkCtXLipUqICzs7PFghLWIyX4U07FilKpztKKFNGO85w50K+f3tGIlOTnBytXwrvvQsmSUKKE3hGJ1MzRESZN0nrgK1TQKkBWrKh3VEIIkXzJTtLi5cqVi5s3byZ5f44cOd4oIGFd1uhJi4jQJnE7mf1pS10qVIBjx7R5EB4eekeTdn3xBbRqBT16gIuL3tGIlFSmjLZwedOmcOgQ+PrqHZFIzQwG+OwzyJ0b6tfXFlFv00bvqIQQInnMPm0ODAxMtHBIvFhZmTRVsXThEHd3bejJ/fuQKZPl9mMLfH2hUCHYtw9q1NA7mrSrYkWtQMBPP8mwuLSoa1etSl+HDrBmjXaiLcSbaNJE61Fr0AD+/huGDpXPlRDC9pld3fHo0aMcOXLEeNu/fz+zZs0if/78rFixwhIxCguydOEQsJ8y/CCl+K3BYNB608aNs/xnV1ifwQAzZmjVUr/9Vu9oRFpRtqx2AW3lSmjbFp480TsiIYR4ObOTtBIlSpjcypQpQ9euXRk/fjxTpOxaqmPp4Y5gP2X4QZI0a2nQQPvcrl+vdyTCEtKlg1WrtKGPu3bpHY1IK3LmhD17tKVSQkJkyRQhhG0zO0lLSoECBTh48GBKPZ2wEksPdwT760nbtw9iYvSOJG1zcIDPP4cxY0ApvaMRlpA3L8ybBy1bwj//6B2NSCu8vbWLO8WKwdtvw5kzekckhBCJMztJi4yMNLlFRERw7tw5Bg8eTL58+SwRo7Aga/Sk2VOSljUrvPUWyPUKy2vbFq5fhx079I5EWErDhtoctcaN4fFjvaMRaYWTE0yfDr17a3NcpUdeCGGLzE7SfH19SZ8+vfHm5+dH4cKFCQ0NZebMmZaIUViQNeakZcxoX8NKqlWDnTv1jiLtc3GBAQO0ZQ+kNy3tGjECsmXTkjV5n0VKMRige3dYsQI6dpReeSGE7TG7uuPOF84+HRwcyJQpE3nz5sUprddYT4OsMdwxY0b76UkDLUmbPRsGD9Y7krSvY0cYPVqbt1Stmt7RCEtwcIBFi6B8eRg/XhvmKkRKqVFDG6LeoAGcOAE//KBVJRZCCL2ZnVVVqVLFEnEInVhruKM9jfuvWlVLHqKjteUHhOW4ukL//jBypCRpaZm3N/zyizaHqFgxqF1b74hEWpInD4SGwgcfaPOK167Vhq0LIYSeXqtwyMWLF+nRowchISGEhITQs2dPLl68mNKxCSuQ4Y4pL0sWbfHUffv0jsQ+dO4Mf/0Fu3frHYmwpLx5tbXx2rSB8+f1jkakNd7eWnJWqxaUKye/v4UQ+jM7SduyZQuFCxfmwIEDFC9enOLFi7N//36KFCnCtm3bLBGjsCAZ7mgZMi/NetzctN60ESP0jkRYWs2aMGiQVlAkIkLvaERa4+CgDZ+eOBHq1IGFC/WOSAhhz8xO0vr370+fPn3Yv38/EydOZOLEiezfv5/evXvz5ZdfWiJGYUHWGO5obz1pIEmatXXpAmfPwp9/6h2JsLQ+fbSejjZtLH+BSdinli1h+3ZtXnG/fvDsmd4RCSHskdlJ2tmzZ+ncuXOC7Z06deKMPU08SiMcHa2zTpq9JWlVqsCBA/Dokd6R2Ad3d/jiC21umkjbDAb4/nu4fRsGDtQ7GpFWlS6tLaUSGgrvvQfh4XpHJISwN2YnaZkyZeLYsWMJth87dozMmTOnREzCiqzVk3bvnuX3Y0syZICCBWHvXr0jsR/dumnV2UJD9Y5EWJq7uzZ/6KeftAWvhbAEf39tRERAAJQtC6dO6R2REMKemF3dsWvXrnTr1o1Lly5RoUIFAPbs2cPYsWPp27dvigcoLMsaSZqXl7afiAhIn96y+7Il1avDb79BSIjekdiHdOm08uwjR8KmTXpHIywtIAB+/VX7nuXMqZVSFyKlubpqZflnztQqP86aBS1a6B2VEMIemJ2kDRkyBC8vLyZMmMCAAQMACAgIYPjw4fTs2TPFAxSWZY3hjgbD/89Ls6ckrVo1+PprvaOwLx99BGPHakNNy5XTOxphaUFB2hpqrVrBhQvg46N3RCItMhjgk0+0z1uzZrB/v/Z7xtlZ78iEEGmZ2cMdDQYDffr04d9//yUiIoKIiAj+/fdfevXqhcFgsESMwoKs0ZMG9jkvrXJlOHoUHjzQOxL74eEBn30mc9PsSb162twhSdCEpVWoAIcPw6FD8O67cOuW3hEJIdKy11onLZ6XlxdeXl4pFYvQgTVK8IN9luH38dEW3pWKg9b1ySfale5Dh/SORFhLzpwvv//x48f89ttv1glGpGn+/rBjh9arVrq0rKcmhLCcN0rSROpnjcWswT7L8IOU4teDp6dWNvurr/SORNiCuLg4tmzZwnfffUePHj30DkekAc7OMGkSjBunrac2axYopXdUQoi0RpI0O2et4Y72mqTFFw8R1vXpp1plzYMH9Y5E6M3BwYH69etTt25dZs2axfTp0/UOSaQRrVvD77/D+PHQqRM8fqx3REKItESSNDtnreGOGTLY33BHgIoVtbLNssaOdXl5wZdfyjpaQnPs2DHOnj1LtWrVyJMnj97hiDSkeHHtYtCdO9rv+ytX9I5ICJFWSJJm56xR3RG0nrQ7dyy/H1vj6QmlSmlXW4V1ffopnD0L27frHYnQU2hoKN9//z1///03X375JbVr19Y7JJHGpE8P69ZBw4baemqyBIgQIiWYXYIfYMeOHezYsYPbt28T98JYuXmysmiqYq3hjpky2edwR9CqgG3frv0BF9bj7q5VefzsM60im6Oj3hEJa3j06BHp0qUD4Pfff+fHH3/k1q1bfPnll1SpUsXY7vjx49y7d49q1arpFapIQxwcYOhQbemPdu2gSxft94/Ta51lCSHEa/SkjRgxgpo1a7Jjxw7u3r3L/fv3TW4idbFW4ZBMmeD2bcvvxxbVrAlbt+odhX1q316b0L9okd6RCGt49uwZvXv3Zu7cuZw+fZp58+Zx+/ZtBg0aZEzQlFKEh4eze/duBg4cyKRJk/QNWqQptWtrF4V+/12bk3z9ut4RCSFSK7Ov8cyaNYsFCxbQtm1bS8QjrMxac9IyZbLP4Y6gXVkNC9PmKgQG6h2NfXF0hAkTtGStWTNtHTWRdjk5OdG+fXsqVapEsWLFKF68OEOHDqVs2bLGNkopfH19+fDDD/nrr7/o378/ZcqUoWLFijpGLtKS7Nlh1y4YNEgb7r5okXaxTgghzGF2T1pMTAwVKlSwRCxCB9aak2bPSZqzs3ZFdds2vSOxTyEhUKIETJyodyTCGt555x3+/PNPzp49S758+ShbtqxxWH5cXBwODtqfvdWrVxMWFkbbtm3JmDGjniGLNMjZWSvRP3euVgVy6FDr/K0VQqQdZidpXbp04aeffrJELEIH1pyTFhEBMTGW35ctkiGP+vr2W61H7eZNvSMR1lChQgXWrFnDN998w/79+3FwcDBJ0BYuXMiSJUsICAjg888/p2DBgjpHLNKq+vW14Y9bt2oXjGT4oxAiuQxKvXoJxr59+xr/HxcXx8KFCylevDjFixfH2dnZpO1EuVydbJGRkfj4+BAREYG3t7cuMUycCOfOwezZlt+Xt7e2r4AAy+/L1ly8CGXKaMVTpICFPj76SLsgYY3PurANBw8epEiRIjx+/JgMGTIAMHfuXNauXUv+/Pnp3r07uXPn1jlKYQ9iYrThjwsWwA8/QIMG+sViC+ceQohXS9actKNHj5r8HBQUBMCpU6dSPCBhXdYqHAKQObNWPMQek7Q8ecDPDw4dguBgvaOxTyNGQIEC0LMnFC2qdzTCGsqWLcutW7do3LgxY8eO5caNG6xevZoiRYrQvXt3cubMqXeIwk64uGg9+u++q82R3bpV+9ndXe/IhBC2KllJ2s6dOy0dh9CJtQqHgH3PS4P/H/IoSZo+smSBzz/XSvJv3qx3NMJasmTJQrt27ahSpQqlSpWiadOmtG7dWhI0oYuaNeH4cejYUSsqtXQpFCmid1RCCFtk9py0Tp068eDBgwTbHz58SKdOnVIkKGE91uxJkyRN5qXprU8fOHUKtmzROxJhTR999BHz5s3jyJEjlCxZkpw5cxL7iqtTkZGRsqyMsIjMmWH9em0ttXfegVmztKVChBDieWYnaQsXLuTx48cJtj9+/Jgff/wxRYIS1iM9adZTrRocPAiRkXpHYr/SpYOvv9Z61KTSmn3p0KEDo0aNol+/fty6dQvHl0wOvXXrFj/99BNt2rThyJEjVoxS2AuDAXr10kr1T5oETZrAf//pHZUQwpYkO0mLjIwkIiICpRQPHjwgMjLSeLt//z4bN24kc+bMZgcwffp0AgMDcXNzIzg4mAMHDry0/YoVKyhYsCBubm4UK1aMjRs3mtyvlGLo0KFkzZoVd3d3QkJCuHDhgkmbe/fu0aZNG7y9vfH19aVz585ERUUZ74+OjqZDhw4UK1YMJycnGjVqlGgsu3btolSpUri6upI3b14WLFhg9uvXm7VK8IMkab6+ULo0yOhhfX3wATg5wfz5ekcirG3gwIHMmDHDpFhCYj1qWbJkITg4mMqVK1OzZk1OnjxpzTCFHQkK0qo/ZsyoLRWya5feEQkhbEWykzRfX1/8/PwwGAzkz5+f9OnTG28ZM2akU6dOfPrpp2btfNmyZfTt25dhw4Zx5MgRSpQoQa1atbh9+3ai7ffu3UurVq3o3LkzR48epVGjRjRq1MikgMm4ceOYMmUKs2bNYv/+/Xh4eFCrVi2io6ONbdq0acPp06fZtm0b69evZ/fu3XTr1s14f2xsLO7u7vTs2ZOQkJBEY7l8+TL16tWjWrVqHDt2jN69e9OlSxe2pLJxVNYqwQ9akpbEW2s3ZMij/hwctHL8Q4bAc9dmhJ2oUqUK7u7u7Nixg/v37yfZo1ayZEn69+/PF198Qe/evRMdQSJESvDwgDlztB6199+HwYPh6VO9oxJC6C1ZJfgBfv/9d5RSVK9enVWrVuHn52e8z8XFhZw5cxJgZtm+4OBgypYty7Rp0wCtvH/27Nnp0aMH/fv3T9C+RYsWPHz4kPXr1xu3vf322wQFBTFr1iyUUgQEBNCvXz8+++wzACIiIsiSJQsLFiygZcuWnD17lsKFC3Pw4EHKlCkDwObNm6lbty7//vtvgtfQoUMHwsPDWbt2rcn2L7/8kg0bNpgkiC1btiQ8PJzNyaxKYAtlcBcs0MbGr1xp+X39+COsWaPd7FVoKLRrBy907godNGgAJUtqVR+F/fnqq6+Ii4tjyJAhxnXUABwcHIiNjSU2NhYXFxcOHjzI559/zoYNG/Dw8NA5apHWXbsGbdpoI1x++gkCA1N+H7Zw7iGEeLVkVXcE7eojaD1IOXLkwGAwvNGOY2JiOHz4MAMGDDBuc3BwICQkhNDQ0EQfExoaarJmG0CtWrWMCdTly5cJCwsz6f3y8fEhODiY0NBQWrZsSWhoKL6+vsYEDSAkJAQHBwf2799P48aNkxV/aGhogl62WrVq0bt37yQf8+TJE548eWL8OdIGJidZuwS/PQ93BChbVjsGly6BLM+kr3HjtEqb3bpBtmx6RyOsbciQIURERBgXuH7w4AEODg54eXnh4OBAZGQkT58+NV4w9PDwMFkQWwhLyJFDGxI/erQ2PH76dGjZUu+ohBB6SFaSduLECYoWLYqDgwMREREvHZ9fvHjxZO347t27xMbGkiVLFpPtWbJk4dy5c4k+JiwsLNH2YWFhxvvjt72szYtz55ycnPDz8zO2SY6kYomMjOTx48e4J7L4yTfffMMIG7tsL4VDrMvJCWrUgG3b4MMP9Y7GvhUsqM1PGzIE5s3TOxqhBx8fH44fP06HDh149OgRGTNmNCZr8f/6+/sbR3s4ODhw+fJlMmTIID0QwmKcnGDYMO1vRevWWjXaqVPB01PvyIQQ1pSsJC0oKMiY3AQFBWEwGEhslKTBYHhlWWN7NmDAAJOewMjISLJnz65jRNYvwW/vc9Lg/+elSZKmv+HDIX9+bYHroCC9oxF6KFGiBKVKlWLZsmWsXr0aFxcX7t27h5ubGwaDweTC4759+6hQoQJZsmThr7/+wsvLS8fIRVpXsaK2plq3blCqlLamWqlSekclhLCWZCVply9fJlOmTMb/p4SMGTPi6OjIrVu3TLbfunULf3//RB/j7+//0vbx/966dYusWbOatAn63xmYv79/gsIkz5494969e0nu15xYvL29E+1FA3B1dcXV1TXZ+7AGa/ekhYdrE6Kdna2zT1tUsyZ8+SU8e6ZdMRX6yZRJey/69YPt27Wy2ML+/PDDD0RERNCnTx+2JlHZZ/fu3dSsWZOvv/6aixcvUq5cOY4dO2Zzv9NF2pI+PSxfDj/8oPWsjRoFZtZoE0KkUskaXJ8zZ07jHLScOXO+9JZcLi4ulC5dmh07dhi3xcXFsWPHDsqXL5/oY8qXL2/SHmDbtm3G9rly5cLf39+kTWRkJPv37ze2KV++POHh4Rw+fNjY5rfffiMuLo7g4OBkx/+qWFILa5bgd3cHb294Ibe1O7lyQZYsWhERob/eveHKFVi9Wu9IhJ5WrlyJg4NDon8HwsPDGThwIN7e3vTv3585c+ZQtmxZSpcurUOkwt4YDNrC16Gh2t8PIYR9MHsGdI4cOWjXrh0//PADFy9efKOd9+3blzlz5rBw4ULOnj3Lxx9/zMOHD+nYsSMA7dq1Myks0qtXLzZv3syECRM4d+4cw4cP59ChQ3Tv3h3Qhlv27t2bUaNGsW7dOk6ePEm7du0ICAgwrnVWqFAhateuTdeuXTlw4AB79uyhe/futGzZ0qSy45kzZzh27Bj37t0jIiKCY8eOcezYMeP9H330EZcuXeKLL77g3LlzzJgxg+XLl9OnT583OibWZs3hjqBNiv7nH+vtz1bVqwcbNugdhQBwc4PJk6FPH3j0SO9ohJ42b95MjRo1OHPmDICx4qOnpyerVq2idOnStG7dGoAff/yRli1bcu/ePd3iFfalYEGoW1fvKIQQVqPMtGjRItW1a1eVL18+ZTAY1FtvvaXatGmjZs+erf766y9zn05NnTpV5ciRQ7m4uKhy5cqpffv2Ge+rUqWKat++vUn75cuXq/z58ysXFxdVpEgRtWHDBpP74+Li1JAhQ1SWLFmUq6urqlGjhjp//rxJm//++0+1atVKeXp6Km9vb9WxY0f14MEDkzY5c+ZUQILb83bu3KmCgoKUi4uLyp07t5o/f75Zrz0iIkIBKiIiwqzHpaRfflGqWjXr7a9uXaWWLrXe/mzVtm1KFS2qdxTiefXqKTV4sN5RCFvxzz//qFOnTimllHr27JlSSqnw8HBVtmxZtXPnTpO2MTEx1g5PiNdmC+ceQohXS/Y6aYm5efMmv//+O+vXr2fZsmXExcVJ4RAz2MJaJRs2aKXIf//dOvv76CPIkwc+/9w6+7NVMTGQIQOcPq31Lgr9/f23VvL68GHIm1fvaITeFi1axKpVq0zWyIyLi6NEiRJ8/fXX1K9fH9DW4ty3bx/Xrl2ja9euOkUrRPLZwrmHEOLVXmvBl0ePHrF161amTp3K5MmTWblyJUWLFqVnz54pHZ+wMGvOSQMZ7hjPxQXefRc2btQ7EhEvb16tyuNLljoUdqRt27ZcuXKFLl26GLf9+OOPPHnyhIIFCxq3OTo6Eh4ezuLFi43D6oUQQog3ZXaSVqFCBTJkyED//v2Jjo6mf//+3Lx5k6NHj/Ldd99ZIkZhQXrMSbt2zXr7s2V168q8NFszYACcPAnr1+sdibAF+/fv5/jx49SvX5+goCCmTZtGx44dyfbc6ueenp40b96c33//nfDwcD766CMdIxZCCJFWmJ2knTt3Dg8PDwoWLEjBggUpVKgQ6dOnt0RswgqsWYIfIHt2SdLi1a0Lv/0G0dF6RyLipUsH330HvXrJ+yK0ZVN2795N586dadmyJbNnz6Zbt26kS5fO2Ob5Yf5jx47l33//5ZFUoBFCCPGGzE7S/vvvP3777TfefvtttmzZwjvvvEO2bNlo3bo1c+bMsUSMwoL0GO4oSZomIEBbSHnXLr0jEc9r3FibN/ntt3pHImyBu7s7jRo1on///pQqVYoMGTKY3O/g4IDT/xY8PHHiBMePH+fx48e8wXRvIYQQgjcqHKKU4vDhw0ybNo0lS5ZI4RAz2cLk3T/+0ObhHD1qnf3FxGjrpT14oPVa2LvBgyEiAqZO1TsS8bxz5yA4GI4fh8BAvaMRtuL8+fNs376dK1eu4OzsjJeXF48fPyYiIoInT54we/Zsxo8fT9++fQHtb6RBVkgXNsYWzj2EEK9mdk/akSNHmDhxIg0aNCBDhgyUL1+eEydO0KNHD1bLarCpjrXnpLm4aAs5S/EQTfx6aXLR3bYULKhVIu3XT+9IhC3JlCkTU6ZMYfLkyfj6+nL79m3CwsJ4/Pgxb731Fhs3bjQmaHFxcXTr1o2BAwfqHLUQQojUyMncB5QrV46SJUtSpUoVunbtSuXKlfHx8bFEbMIKrD0nDf5/yGOBAtbdry0qVw4ePtSKVRQvrnc04nmDB2vJ2ubNULu23tEIW+Dn58eGDRuoUqUKGTJk4Isvvki03dOnT2nbti0bN27EyckJg8HA6NGjrRytEEKI1MzsJO3evXvSPZ6GWHtOGsi8tOc5OmpzoFaulCTN1nh5wZQp8PHHcOoUeHjoHZGwBXnz5mXDhg2EhITw4MEDev9vzYa4uDgcHBx48uQJvXv3Zvny5cTFxREVFUWePHlwdXVl6NCh+gYvhBAi1TB7uKMkaGmLHkla9uwy3PF5TZvCqlV6RyES06QJlCgBw4bpHYmwJUFBQfzyyy+MHTuWv/76C9AKiAA8e/aMEiVKkDVrVnbs2IGnpyd79+5l3bp1HDt2TMeohRBCpCavtZi1SDusPScNpCftRVWrQliYVqxC2BaDAaZNgx9+gMOH9Y5G2JJ33nmHo0ePkitXLqKjo7lx4wYAHh4efPTRRyxYsIAuXbpw6tQp8uTJw44dOwgKCtI3aCGEEKmGJGl2Ts85aULj7AwNG0pvmq166y0YNQq6doVnz/SORtgSf39/nJ2d2bFjBxv+tzJ9bGws0dHRhISEUKhQIe7duwdgnLsdZ+2rYkIIIVIlSdLsnMxJsw1Nm2rz0oRt+vhjcHODSZP0jkTYoowZM/LFF1+wbds2HB0dcXNzIzo6mj179hAWFmbSNn5YpBBCCPEyZv21ePr0KXny5OHs2bOWikdYmZ5z0qTs/P8LCYFLl+DiRb0jEYlxcIDZs7UetUuX9I5G2Jrg4GBmzJhBy5Yt+f7771myZAnVqlWjcuXKNG7cWO/whBBCpEJmVXd0dnYmOjraUrEIHeiRpGXKpM31uXMHMme27r5tlaurNuRx+XIYMEDvaERiihaF3r2hY0fYuVNL3ISI16pVK1xdXVm+fDmnT58mODiYXr164ezsrHdoQgghUiGzTzM+/fRTxo4dyzOZnJEm6JGkGQyQMydcvmzd/dq6Vq3g55/1jkK8zKBB8OCBVppfiBc1adKERYsWsXfvXubOnUuxYsX0DkkIIUQqZfY6aQcPHmTHjh1s3bqVYsWK4fHC4kGrV69OseCE5emRpAHkygVXrkBwsPX3batCQuDGDTh9GooU0TsakRhnZ1i4ECpVgjp1ZEF2kZCzs7Ox90wphcFg0DkiIYQQqZHZSZqvry9Nmza1RCxCB3olaYGBWpIm/p+zM7z/PixdCl99pXc0IinFikH//tChA/zxBziZ/VtU2IvEErSYGO27LrmbEEKIlzEoJeUb9BIZGYmPjw8RERG6LRIeFgZ580JUlHX3O26cNtxx5kzr7tfW/f47dO4MFy7ISZwte/YMKlaERo20hE2I5ProI20O6nffybxGoQ9bOPcQQrzaa/2JePbsGdu3b+f777/nwYMHANy4cYMoa5/pizcmPWm2pVIlePxYFk62dU5O2rDHMWPg5Em9oxGpyciRsGcPtGmj9aoJIYQQiTE7Sbt69SrFihWjYcOGfPrpp9y5cweAsWPH8tlnn6V4gMKyJEmzLQ4O0KKFFBBJDQoUgOHDoX17ePpU72hEapE5s1Yd9O5dqFdPK0QjhBBCvMjsJK1Xr16UKVOG+/fv4+7ubtzeuHFjduzYkaLBCcvTu3CIDLZNqFUrWLYM4uL0jkS8Ss+e4OUFo0frHYlITby8YMMGyJgRqleH27f1jkgIIYStMTtJ++OPPxg8eDAuLi4m2wMDA7l+/XqKBSasw8FBSwasnSxlzKjt+9Yt6+43NShTBtzctKIUwrY5OMD8+TB5sgxRFeZxcYElS6B8eW1+oyxJIoQQ4nlmJ2lxcXHEJtL18u+//+Ll5ZUiQQnrcXTU/rV2kmYwyJDHpBgMWm/a0qV6RyKSI3du+OYbbdjjkyd6RyNSEwcHLcHv2BHeeQeOHdM7IiGEELbC7CStZs2aTJo0yfizwWAgKiqKYcOGUbdu3ZSMTVhBfJIm89JsS8uWsGKFzHVKLT78EAICYNgwvSMRqY3BAAMGaENmq1eHjRv1jkgIIYQtMDtJmzBhAnv27KFw4cJER0fTunVr41DHsWPHWiJGYUGSpNmmIkW0k/7t2/WORCSHwQA//ABz5kBoqN7RiNSoY0dYvhzatoUZM/SORgghhN7MXob1rbfe4vjx4yxdupQTJ04QFRVF586dadOmjUkhEZE66Jmk5coFf/9t/f2mFvFDHuvU0TsSkRzZs8OECdqwx2PHIF06vSMSqU1ICOzeDXXrwqVL2nqSspaaEELYJ1nMWke2sqCkgwOEh4O1Q1i5EubOhc2brbvf1OLyZQgK0hYcl+sfqYNS0KAB5MkDz40KF8IsYWFQvz7kyAGLFknCL1KWrZx7CCFeLlk9aevWrUv2EzZo0OC1gxH60HOtNKlolrRcuaBQIW2OStOmekcjksNggNmzoXhxaNQIqlbVOyKRGvn7w65d2oLX1arBunWQJYveUQkhhLCmZCVpjRo1MvnZYDDwYgecwWAASLTyo7BteiZpV69qSwDIkJ7EtWqlLWwtSVrqkTUrTJ2qzTE6cUJbE0sIc3l4wKpV8Nln8Pbb2sWaQoX0jkoIIYS1JOvUOC4uznjbunUrQUFBbNq0ifDwcMLDw9m0aROlSpVis4xbS5X0StIyZAAnJ21oj0hc8+awaRNERuodiTBHixZQtqx2gi3E63J0hO++g379oFIl+O03vSMSQghhLWb3X/Tu3ZvJkydTq1YtvL298fb2platWkycOJGePXtaIkZhYU5O8OyZ9fdrMGhD+mTIY9KyZoUKFbT5eyL1MBi0Cn2//qoNVRPiTXTvDgsWwPvva58rmUkuhBBpn9lJ2sWLF/H19U2w3cfHhytSTz1V0qsnDbQCCxcv6rPv1KJDB+0ETaQuGTPCwoXQpQvcuKF3NCK1e+89+OMPrYLoxx9DTIzeEQkhhLAks5O0smXL0rdvX27dumXcduvWLT7//HPKlSuXosEJ65AkzbY1bqzNbZLlClKfd9+Fdu2gdWt9eqtF2lKkCBw4AH/9pX227tzROyIhhBCWYnaSNm/ePG7evEmOHDnImzcvefPmJUeOHFy/fp0ffvjBEjEKC5MkzbalS6fNTVu4UO9IxOv45ht4+hSGDNE7EpEWZMgAW7ZoFUTLldMu4AghhEh7zF7MOm/evJw4cYJt27Zx7tw5AAoVKkRISIixwqNIXZyc9E3SFi3SZ9+pSceOWjGK4cP/fwFykTo4O8Py5VCqlDa/sH59vSMSqZ2zs1ZBtFgxbZmHH37QetyFEEKkHWYnaaCV269ZsyY1a9ZM6XiEDqQnzfa9/ba2oPXOnRASonc0wlzZssGSJdCyJRw8qBXMEeJNdesGBQtCs2Zw6hQMHqwVrRFCCJH6vVaStmPHDnbs2MHt27eJi4szuW/evHkpEpiwHkdH/ebL5MwJ9+7BgweyntTLGAxaAZH58yVJS61CQqBXL61C35494Oamd0QiLahcGfbvh4YN4eRJ7XeEh4feUQkhhHhTZs9JGzFiBDVr1mTHjh3cvXuX+/fvm9xE6qNnT5qzM+TIIb1pydGuHfzyC0RE6B2JeF2DBkGWLNC7t96RiLQkMFBL/OPioHx5KTIkhBBpgdk9abNmzWLBggW0bdvWEvEIHeg5Jw3+f8hjUJB+MaQG2bJBxYqwbJk2zEmkPg4O2hzMUqW0f+XXqEgpnp6wYgWMG6cNj54/X+Y/CiFEamZ2T1pMTAwVKlSwRCxCJ3r2pIHMSzNHx46yZlpqlyGDdjLds6c2PE2IlGIwwJdfaoVqunTR5qjp+btdCCHE6zM7SevSpQs//fSTJWIROtE7ScudGy5d0m//qUnDhnD2LPyvsKpIpcqVg1GjtPlpkZF6RyPSmurV4dAh2L4d6tSBu3f1jkgIIYS5zB7uGB0dzezZs9m+fTvFixfH2dnZ5P6JEyemWHDCOpyc9F1oN08e2LZNv/2nJm5u0KqVtmbaN9/oHY14E598An/+qfV4LFsmVflEysqeHX7/Hfr2hdKlYeVKKFtW76iEEEIkl9lJ2okTJwj63+ShU6dOmdwn66SlTnr3pMlwR/N06KCtiTRqlKyZlpoZDDBnjtarNnWqNvxRiJTk6grTp2tz1GrW1OardekiFwSEECI1MDtJ27lzpyXiEDqyhSTt2jV4+lSr9ihermxZ8PHReh9r19Y7GvEmPD21Ho533tHe1/Ll9Y5IpEVt20Lx4tCkCYSGaombu7veUQkhhHgZs+ekibRH7yTNywv8/ODqVf1iSE0MBq2AyPz5ekciUkLhwtpJc/PmcOeO3tGItKpECW2e2p072kWBy5f1jkgIIcTLmN2TVq1atZcOa/ztt9/eKCBhfXrPSYP/H/KYN6++caQWH3wAw4fD/fuQPr3e0Yg31bq1ts5VmzawaZMMYxWWkT69ttbi119rPbeLFmmFRYQQQtges3vSgoKCKFGihPFWuHBhYmJiOHLkCMWKFbNEjMLC9O5JA5mXZq6sWaFaNfj5Z70jESll4kQID4eRI/WORKRlDg5aaf4lS6BdO+1ij96//4UQQiRkdk/ad999l+j24cOHExUV9cYBCeuzhSQtb164cEHfGFKbTp204iEffyyFANICV1dt/bSyZbXFrhs21DsikZbVqqUNf2zWTKsyungx+PvrHZUQQoh4KTYn7YMPPmDevHkp9XTCipyc9E/S8ueXJM1c9evDzZvaiZZIG3Lm1BYi7thRFroWlpczp5aglSgBQUGyFIoQQtiSFEvSQkNDcXNzS6mnE1bk6Kj/nLQCBeCvv/SNIbVxdobOneH77/WORKSkqlW1OUMNG8oixMLyXFxgwgSYO1ebGzlwoP5/D4QQQrzGcMcmTZqY/KyU4ubNmxw6dIghQ4akWGDCemyhcEi+fFq1sZgY7aRBJE/XrlCsmHaS5eOjdzQipXz0EZw4oQ1F27pVlqYQlvfee3DkiJaoVa6sDX/MnVvvqIQQwn6Z3ZPm4+NjcvPz86Nq1aps3LiRYcOGWSJGYWG2MCfN2xsyZpSy0ObKmRMqVdJOqETaMnmy9m+vXvrGIexH9uywc6e2/mKZMrBwISild1RCCGGfzO5Jmy+LM6U5ttCTBtq8tL/+0oY+iuT78EOtWtsnn0gBkbTE2fn/C4nMnKkViBHC0pycYOhQqFlTW+pj/XptSLWfn96RCSGEfXntOWmHDx9m8eLFLF68mKNHj6ZkTMLKbKEnDf4/SRPmqVtXWy9tzx69IxEpLWNGWLdOmye0a5fe0Qh78vbbcOyYNoy6eHHYvl3viIQQwr6YnaTdvn2b6tWrU7ZsWXr27EnPnj0pXbo0NWrU4M6dO5aIUViYrfSkSfGQ1+PkBD16QBKrY4hUrlgxmD9fm592/rze0Qh74umpFRSZOlWbq9avH0RH6x2VEELYB7OTtB49evDgwQNOnz7NvXv3uHfvHqdOnSIyMpKePXtaIkZhYbbUkyYnoa+na1ftSrcsCJ42NWoEgwZBnTpw65be0Qh707ix1qt26hSUK6f9K4QQwrLMTtI2b97MjBkzKFSokHFb4cKFmT59Ops2bUrR4IR12EpPmgx3fH3p00P79v9fbEKkPb17Q4MG2vp4Dx/qHY2wNwEBsGkTdOmiFSuaPBni4vSOSggh0i6zk7S4uDicE6kH7ezsTJz8xk6VbGGdNNDKPd++DQ8e6B1J6tS7t1aN7f59vSMRljJhArz1ljb0zBZ6v4V9cXCAnj21BbDnzdOqQN64oXdUQgiRNpmdpFWvXp1evXpx47nfzNevX6dPnz7UqFHD7ACmT59OYGAgbm5uBAcHc+DAgZe2X7FiBQULFsTNzY1ixYqxceNGk/uVUgwdOpSsWbPi7u5OSEgIFy5cMGlz79492rRpg7e3N76+vnTu3JmoqCiTNidOnKBSpUq4ubmRPXt2xo0blyCWSZMmUaBAAdzd3cmePTt9+vQhOhUO2Hdyso0TPhcXyJULXni7RDLlzg0hITB7tt6RCEtxdNSWW7h9W3pNhX6KFIEDB6BECe22erXeEQkhRNpjdpI2bdo0IiMjCQwMJE+ePOTJk4dcuXIRGRnJ1KlTzXquZcuW0bdvX4YNG8aRI0coUaIEtWrV4vbt24m237t3L61ataJz584cPXqURo0a0ahRI049N0B+3LhxTJkyhVmzZrF//348PDyoVauWSfLUpk0bTp8+zbZt21i/fj27d++mW7duxvsjIyOpWbMmOXPm5PDhw3z77bcMHz6c2c+d/f7000/079+fYcOGcfbsWX744QeWLVvGwIEDzToGtsBWetJAhjy+qX79YMoUbVFwkTalS6eVRf/oI70jEfbM1RW+/RaWLdN61zp3llEQQgiRkgxKmb9UpVKK7du3c+7cOQAKFSpESEiI2TsPDg6mbNmyTJs2DdCGUmbPnp0ePXrQv3//BO1btGjBw4cPWb9+vXHb22+/TVBQELNmzUIpRUBAAP369eOzzz4DICIigixZsrBgwQJatmzJ2bNnKVy4MAcPHqRMmTKANs+ubt26/PvvvwQEBDBz5kwGDRpEWFgYLi4uAPTv35+1a9caX3P37t05e/YsO3bsMMbSr18/9u/fz59//pms1x8ZGYmPjw8RERF4e3ubffxSymefgZcX2MJa5H36aPOrhg7VO5LUSSkoXx66d9fWOBJCCEu7d0+7aHDkiNbT+/bbekckXsZWzj2EEC+XrJ40Pz8/7t69C0CnTp2Iiori3XffpUePHvTo0eO1ErSYmBgOHz5s8lgHBwdCQkIIDQ1N9DGhoaEJ9lWrVi1j+8uXLxMWFmbSxsfHh+DgYGOb0NBQfH19jQkaQEhICA4ODuzfv9/YpnLlysYELX4/58+f5/7/JvxUqFCBw4cPG4dnXrp0iY0bN1K3bt0kX/OTJ0+IjIw0udkCW+tJkwqPr89ggL59YeJELWET9uvkyZMsXLhQ7zCEHfDz03rUhg7V1m0cPtx2/qYIIURqlawkLSYmxphQLFy4MEXmXd29e5fY2FiyZMlisj1LliyEhYUl+piwsLCXto//91VtMmfObHK/k5MTfn5+Jm0Se47n99G6dWtGjhxJxYoVcXZ2Jk+ePFStWvWlwx2/+eYbfHx8jLfs2bMn2daabGVOGshwx5TQpIl2Zfv33/WOROglOjqav//+m7lz5/L+++/rHY6wAwYDtGsHhw/Dtm1QsaL8LhdCiDfhlJxG5cuXp1GjRpQuXRqlFD179sTd3T3RtvPmzUvRAG3Vrl27+Prrr5kxYwbBwcH8/fff9OrVi6+++oohQ4Yk+pgBAwbQt29f48+RkZE2kag5OcHjx3pHoYlf0Fop7Y++MJ+TE/TqpVUCrFpV72iEtSmlcHNzo3bt2ly4cIFRo0axYMECOnTooHdowg7kyqVdIPr2W21NteHDtTlrDmbPgBdCCPuWrF+bixcvpm7dukRFRWEwGIiIiOD+/fuJ3pIrY8aMODo6cuuFlVlv3bqFv79/oo/x9/d/afv4f1/V5sXCJM+ePePevXsmbRJ7juf3MWTIENq2bUuXLl0oVqwYjRs35uuvv+abb75JcikCV1dXvL29TW62wJaGOwYEaLEk0ZkqkqlzZ9izRxadtTdKKQz/u7rx66+/cuDAAVq1akWjRo30DUzYFScnGDAA/vgDfvxR61U7fVrvqIQQInVJVpKWJUsWxowZw4oVK8iRIweLFi1izZo1id6Sy8XFhdKlS5sU3oiLi2PHjh2UL18+0ceUL1/epD3Atm3bjO1z5cqFv7+/SZvIyEj2799vbFO+fHnCw8M5fPiwsc1vv/1GXFwcwcHBxja7d+/m6dOnJvspUKAA6dOnB+DRo0c4vHBp0NHREdBOlFITWxru6OAAhQrB2bN6R5K6eXtrxUO+/lrvSIS1PJ+grVy5kp9//hkfHx9Gjx6Nr6+vvsEJu1SsmFaqv3FjeOcdrVftyRO9oxJCiNTB7AEIly9fJkOGDCmy8759+zJnzhwWLlzI2bNn+fjjj3n48CEdO3YEoF27dgwYMMDYvlevXmzevJkJEyZw7tw5hg8fzqFDh+jevTsABoOB3r17M2rUKNatW8fJkydp164dAQEBxivJhQoVonbt2nTt2pUDBw6wZ88eunfvTsuWLQkICAC0+WYuLi507tyZ06dPs2zZMiZPnmwyVLF+/frMnDmTpUuXcvnyZbZt28aQIUOoX7++MVlLLZycbKcnDaBwYThzRu8oUr9evWDDBpkXYg8iIyONCdqqVauMCdrXX39NxowZjReOFixYkORwbCEswckJPv8cDh3SetZKlYK9e/WOSgghbF+y5qS9aMeOHezYsYPbt28nGNpnzpy0Fi1acOfOHYYOHUpYWBhBQUFs3rzZWKTj2rVrJr1VFSpU4KeffmLw4MEMHDiQfPnysXbtWooWLWps88UXX/Dw4UO6detGeHg4FStWZPPmzbi5uRnbLFmyhO7du1OjRg0cHBxo2rQpU6ZMMd7v4+PD1q1b+fTTTyldujQZM2Zk6NChJmupDR48GIPBwODBg7l+/TqZMmWifv36jB49OvkH0kZIkpY2ZcgAH34IY8aAnUwVtUtPnz6lZ8+eFCpUiIoVK7J06VLc3d35+uuvyZIlC3FxcTg4OPDw4UOyZcvGokWLCA4OZu/evanugpJIvfLmhe3bYcECaNAAWraEb77Rln8RQgiRkNnrpI0YMYKRI0dSpkwZsmbNarx6G8+cIY/2zlbWKpkyBY4fhx9+0C0EE+vWaSXkd+3SO5LU79YtyJcPTpyAwEC9oxGWcuLECcqVK0eWLFmoVasWQ4YMIXv27MYELTY2FkdHR6KiohgyZAjr169nwoQJNGjQQO/QhR0KC9N6+kNDYcYMeO89vSOyL7Zy7iGEeDmze9JmzZrFggULaNu2rSXiETqQnrS0K0sW6NgRxo6FmTP1jkZYSvHixTl9+jTvvPMOcXFxxqqxDg4OxMXF4ejoSHR0NOPGjePixYv07NnzpWs6CmFJ/v7aumrr1mmLYFesCJMna7+vhBBCaMyekxYTE0OFChUsEYvQia0lablyQWQk3LmjdyRpw+efw5IlcP263pEIS8qTJw979uzh/9i78/iYrvcP4J+ZLJNNFiEbQWyx7xVRu5CWtqjWWj9bG1TttbU0VO1aWy21a6t2RS1VgtoiiNiJLfbEksiiyDbn98f5ZhgSMiS5M5PP+/W6r0nuPZk8h2RynznnPOevv/7CmjVrAMhiImq1GqmpqZg8eTLCw8PRuHFj9OrVC5aWlllWoiXKCx99JN+Qc3UFKlaUUyFNrO4WEVGuMThJ+/zzz/HHH3/kRiykEGOq7gjILQHKlWOFx5xStCjQsSMwbZrSkVBuK1WqFM6fP482bdrgzJkzuunokyZNwpEjR9CoUSP06dMH1tbWugSOSEmOjsCcOcDGjXLEv3lz4OpVpaMiIlKewdMdnz59igULFmDXrl2oUqUKrKys9K7/9NNPORYc5Q1jG0kDnk15bNBA6UjMw/DhQLVqcu8iNzelo6HcVLBgQaSkpGDAgAHw8fFBtWrVcOTIEdSrVw99+/aFjY2NXrl+ImNQrx5w4oTcNqRGDWDUKGDgQPn3iYgoPzL45e/UqVOoVq0aAODMCzvl8o++aTLmJI1yRsmScq+in36S1R7JvFlbW2PNmjWoVasWVq9ejR9++EFvBO1Vr9UZxUaI8ppGA4wdC3z6KfD558CqVcAvvwA1ayodGRFR3jM4SduzZ09uxEEKsrAwziSN1R1z1jffAH5+wLBhQMGCSkdDuc3V1RWnT5/GO++8g927d2PAgAEAsn4zLTk5GREREQgJCUGXLl1QrFixvAyXSKdSJeDgQVnsqFkzoFMn4IcfAO7JTkT5yVu9XXrr1i3cunUrp2IhhXAkLX/w9QXee09uuUD5g4ODA8LDw5GQkIBNmza9sq1Go8GjR48QFRWFevXqvTRTgigvWVgAX30l/w4kJMh1yr/9xsIiRJR/GJykabVafP/993ByckLx4sVRvHhxODs7Y9y4cawUZqKMMUkrVQp48AB4+FDpSMzLN9/IJC0xUelIKK/Y2dlh9+7d+OCFzagy2yIzICAAixYtwtChQ9GmTRtER0fnVZhEmfLwkMnZypVy8+tGjYCzZ5WOiogo9xmcpH377bf4+eefMWnSJERERCAiIgITJkzA7NmzMXr06NyIkXKZMSZpVlZA2bKs8JjTqlSRxVjmzFE6EspLarUaFhYW2LFjB3r16gXg5WmPaWlp0Gq1SE9PR79+/eDr68skjYxG48aysEiLFsC778piSI8eKR0VEVHuMThJW758ORYtWoQ+ffqgSpUqqFKlCr788kssXLgQy5Yty4UQKbcZWwn+DJzymDtGjZIFRP77T+lIKK8FBgaiSpUqiI+Pf+mapaWlLpk7duwYtm3bhv+e+yHJbOSNKC9ZW8vk7NQp4NIloHx5YMMGToEkIvNkcOGQuLg4lCtX7qXz5cqVQ1xcXI4ERXnLGEfSACZpuaVWLXksWAAMGqR0NJTX+vbtC61Wi/379+Pff/+Fi4sLoqOj8eTJE6hUKty9exerV6/GV199hXfffVf3dXfu3EGRIkUUjJxIKlZMJmdbtwL9+gGLFgGzZ8tp8kRE5sLgkbSqVavi559/fun8zz//jKpVq+ZIUJS3mKTlP6NGAVOnAk+fKh0JKUGtVmPDhg347rvvcOrUKSQnJ+PRo0dIT09H5cqVsXr1asyaNUtXir9fv37w9vbGjh07FI6c6JmWLeX6tFq1ZJn+sWP5mkZE5sPgkbQpU6agZcuW2LVrF/z9/QEAoaGhuHnzJrZt25bjAVLus7QEUlOVjuJlFSoALDCXO959V1ZLW7AA6N9f6WhICdOnT8edO3dw8eLFV26tMnToUKxfvx5Dhw5Fx44dsXTpUrRq1SoPIyXKmq0t8P33QJcushpkhQrAjz8CrVsD3LqViEyZwSNpDRs2RGRkJNq0aYP4+HjEx8fj448/RmRkJOrXr58bMVIuM9aRNF9f4P59gLNoc8cPPwDjx3PxfX62evVq2Nvbo+ZzuwU/X6V3y5Yt+PHHHzF16lRMnjwZS5cuxWeffYYNGzYoES5RlsqUAf7+G5g+HRg6FGjaVK5dIyIyVSrB1eCKSUxMhJOTExISEuDo6KhYHOHhQOfOwIULioWQpWrVgBkzZNllynkffgjUqQN8+63SkZCSWrZsiUqVKmHy5Ml6569du4Z58+bhr7/+wj///IOiRYvin3/+wfnz53WbYxMZm+RkYOZMWbK/Qwc50la4sNJRGQ9jufcgolczeCRt6dKlWLt27Uvn165di+XLl+dIUJS3rKyMcyQNAKpW5buhuWn8eDk1iKOV+dvWrVsRFBQEALhx4waePHkCAChRogQmT56MZs2aYciQIXj69CmaN2/OBI2MmkYDDBsmt3BJSZGzMiZNAv73Y01EZBIMTtImTpyIQoUKvXTezc0NEyZMyJGgKG8Z63RHQO7rxSQt91SpArz3HjBlitKRkNJKlSqFpKQkLFu2DKf+90uXlJQEAGjevDnu37+va5v+vz07du/ejdWrV+d9sETZ4OEBLF4M7NkjD19fuTH2czN6iYiMlsFJ2o0bN+Dj4/PS+eLFi+PGjRs5EhTlLWMtHALIJOLkSaWjMG/ffw/Mmwdw32IqUKAAHj16hM6dOyM5ORkFChQAAPz1119IS0uDjY0NtFotLCwssGvXLgQEBKBjx46YO3euwpETZa1qVWDHDmDhQlnVtlYtYPdupaMiIno1g5M0Nzc33buszzt58iRcXV1zJCjKW8Y8kla1qqzwaIybbZuL0qWBjh2BceOUjoSMwZQpU9CsWTNUq1YN06dPR2BgICIjI/Hll18CkOX7w8LC0Lx5cyxduhRHjx7F6NGj8eOPPyocOdGrBQYCERGyCmSXLsAHH3CbFyIyXgYnaR07dkT//v2xZ88epKenIz09Hbt378aAAQPQoUOH3IiRcpkxr0lzcwOcnIBLl5SOxLyNHg2sWAFcuaJ0JGQM5s2bh379+iE6Ohr29vYIDg5GixYtAADJycnYuHEjAKB06dKoWbMm/v33X0yYMAF//vmnglETvZ6FBdCjB3DxIuDnB9StCwQFcSYBERkfg6s7pqSkoEuXLli7di0sLeU2a1qtFv/3f/+H+fPnw9raOlcCNUfGUmEpOlrO1U9MVCyEV2reHPj8c6BdO6UjMW8jRsgkLZO6QETQarW6za0BYNWqVRg0aBC2bNmCmjVrIiYmBi4uLtBoNApGSWSYmBhgzBhg1Spg0CDg668Be3ulo8pdxnLvQUSvZvBImrW1NVavXo3IyEisWLECGzZswJUrV7BkyRImaCbKmKc7AqzwmFe++QbYvx/Yu1fpSMjYxMbG4tChQ0hJSYEQAunp6ejQoQM+/fRThISEQAgBDw8P/g0gk+PhAcyfDxw6BBw7JvdbW7SIU+yJSHkGJ2kZypQpg08//RQffPABihcvnpMxUR4z9iSNxUPyhqOj3FdowADj/nmgvHf//n0MHToUR44cgUqlgkqlAgDcuXMH4eHhunYqlQrJyck4d+4czp49q1S4RAarUAH46y857XvePLlH5/btAHeSJSKlvHGSRubDykpWdzTWP0YcScs7XbsC1tbynWSiDOXKlUObNm3w8ccf4/jx49Bqtdi8eTOuXbuGTz/9FCqVCnfv3sXKlStRrlw5dOjQAW3atOF+amRyGjcGjh6V07979waaNQNOnFA6KiLKjyyVDoCU97+lhdBq5aJqY1OunFw3Fx8PODsrHY15U6uBWbOAjz4C2rcHXFyUjoiMxbBhw2BjY4POnTvDxcUFV65cweeff44GDRrg7t27WLhwIbZs2YKOHTti+PDhSEhIQMOGDVGjRg107dpV6fCJsk2tBjp3Btq2la+HTZrISpBjxwKZ7EBERJQrDC4cQjnHWBbvpqXJ0bSnTwFjXfNfpQrw889AgwZKR5I/dOkCFCwIzJypdCRkbM6cOYPExEQ4ODigdOnSsLOzw9y5c7F27Vq0bdsWX331la5tv379UKRIEYwYMULBiInezoMHcir4woXA//0fMGqUXMtmqozl3oOIXo3THUk3emasG1oDnPKY1yZNApYv5x5C9LJKlSqhbt26qFKlCuzs7HD37l3MmDEDjRo10kvQbty4gZCQEBQuXFjBaIneXqFCwI8/AmfPAsnJshryN9/I2R1ERLklW9MdM9u8OitVqlR542BIGSqVaRQPYZKWd4oUAYYOBQYOBHbskD8jRJlZuHAhNBoNgoODdediY2OxfPlylCpVCnXq1FEwOqKc4+0tR9O+/hr47jugVCn5Otm/P2Bnp3R0RGRuspWkVatWDSqVCkIIXVWvrKSzbq1JMoUkbd06paPIX4YMkQVE/vpLrlEjyoyXlxdq1aql+/z+/ftYsWIFQkJC8NFHH6FixYoKRkeU83x9gdWrgePH5YjazJkyaevZUxZeIiLKCdma7hgVFYWrV68iKioK69evh4+PD+bOnYuIiAhERERg7ty5KFWqFNavX5/b8VIusbQ0/umOp09z75q8ZGMD/PSTHE17/FjpaMhYlS1bFuvWrcPKlSuxZcsWDBkyBNu2bUNAQAAGDx6sdHhEuaZGDeDvv+VG2L/9JpO3xYuN+28pEZkOgwuH1K5dG2PGjEGLFi30zm/btg2jR4/W2zOHXs2YFu8WLCjLDBcrpmgYr+TlBezcCfCN+bwjhBxFq1ABmDxZ6WjIWG3evBnTpk1DXFwc6tWrh2bNmqFt27YAkK0ZGESmTgg5NXzMGODuXVlc5P/+TxblMjbGdO9BRFkzOEmztbXF8ePHUb58eb3z58+fR40aNfDkyZMcDdCcGdMLpbs7EBoKlCypaBiv9OGHwKefyj98lHdu3pTTTXfvBqpXVzoaMlb//fcf0tLS4OTkpDvHBI3yGyGAf/4BgoNlsvbtt3L/SWNK1ozp3oOIsmZwdcfy5ctj4sSJSElJ0Z1LSUnBxIkTX0rcyHQY+3RHAKhZE+BAbd7z9pb7A33xhXGvWyRl2dvb6yVoAPQStGPHgMTEvI6KKG+pVEBgoHzTc/58Of2xbFm5vtfY/8YSkXExOEmbP38+duzYgaJFiyIgIAABAQEoWrQoduzYgfnz5+dGjJQHrKyM/w8IkzTl9O0rt2qYPVvpSMhULVsG1KkDXL6sdCREuS8jWTt06FmyVqYMMHeu3JOUiOh13mgz6//++w8rVqzAhQsXAMjRtU6dOsHe3j7HAzRnxjTloEwZYM0a457OdueOjDMx8dnebpR3Tp2Sm4mfOAGUKKF0NGRqhACmTAGmTZOV8Zo0UToiorwjBLBrFzBhAnDhAjB4MNC7N1CgQN7HYkz3HkSUtTdK0ihnGNMLZYUKcvPid95RNIzX8vQEQkJkvJT3vvkGiIgAtm3j3mn0Zv76S67RCQ6W+0vx54jym0OHZLIWGgr06yd/DwoWzLvvb0z3HkSUNYOnOwLAb7/9hnr16sHLywvXr18HAEyfPh2bNm3K0eAo75jCdEeAUx6VNnq0nK62apXSkZCp+vBD4MABOQWsQwcgKUnpiIjyVt26wJYt8g3HCxcAHx+5KXZ0tNKREZExMThJmzdvHgYPHoz3338fDx8+1G1e7eLighkzZuR0fJRHTKFwCMAkTWm2tsAvv8i902JjlY6GTFWFCsCRI3IU7Z13gLNnlY6IKO9Vqybf8Dp6FIiLk/usffklcO2a0pERkTEwOEmbPXs2Fi5ciG+//RaWlpa687Vq1cLp06dzNDjKOxxJo+xq0gRo2VK+80v0pgoUAFaulEVp6tUD/vhD6YiIlFG2rCwscuaMfMO0ShU5Jfj8eaUjIyIlGZykRUVFoXom1SU0Gg3++++/HAmK8p6VlWmUV69ZU66J+t8ALilk6lRg61a5dxrRm1Kp5JqcbduA4cNlwpacrHRURMooVgyYNQu4dAnw8gL8/YFPPgGOH1c6MiJSgsFJmo+PD06cOPHS+b///pv7pJkwUxlJ8/ICHByAixeVjiR/c3UFZswAgoIA7l9Pb8vfX96IXrwoK4jeuKF0RETKcXcHJk6U0x6rVZOl/N9/H9i/X+nIiCgvGZykDR48GH379sXq1ashhMCRI0cwfvx4jBw5EsOGDcuNGCkPmEqSplIBtWsDYWFKR0IdOsgtEcaNUzoSMgeFCwN//y1vSGvVAnbsUDoiImU5OwOjRslkrXlz+ZrL2yyi/OONSvCvWLECY8aMwZUrVwAAXl5eGDt2LHr27JnjAZozYyqD27KlnAPfrp2iYWTLtGmy0MDSpUpHQhnv9O7bJ9dREOWE7duB//s/4KuvZEVR9RvVISYyL8nJwIMHQJEib/c8xnTvQURZe6M/fZ07d8alS5fw6NEjxMTE4NatW0zQTJypjKQBQMOGwL//Kh0FAXJT6+Bg4PPPuU6Qcs7778uKd1u2AC1ayBtTovxOo3n7BI2ITIfBSdoPP/yAqKgoAICdnR3c3NxyPCjKe6aUpFWvDty/D9y8qXQkBMjCD1otMGeO0pGQOSlRQu6n5uMjCwYdOaJ0RERERHnH4CRt7dq1KF26NOrWrYu5c+fiAd/iNAumlKRZWgLvviun2JHyLC2BhQvliBoLPlBO0miAefOA8ePlWrW5cwHDJ+gTERGZHoOTtJMnT+LUqVNo1KgRpk2bBi8vL7Rs2RJ//PEHHj9+nBsxUh4wpSQNkFMe9+5VOgrKUL068MUXQO/evImmnPfZZ7Ky3cyZQMeOQHy80hERERHlrjdak1axYkVMmDABV69exZ49e1CiRAkMHDgQHh4eOR0f5RFTS9KaNgV27WJCYEzGjgWiooAFC5SOhMxRpUrAsWNydK1aNeDgQaUjIiIiyj1vXTPL3t4etra2sLa2Rqop3eWTHlNL0mrWBBISgP8VGCUjYGsLrFghNyU+dUrpaMgcFSgALF8OTJgAfPihnGJrSq9bRERE2fVGSVpUVBTGjx+PihUrolatWoiIiMDYsWMRExOT0/FRHrG2Nq2bHQsLoEkTOZpGxqNGDXkD/emnQFKS0tGQuerUCQgPl1Oe69QBzpxROiIiIqKcZXCSVqdOHZQuXRrr1q1D9+7dcf36dYSEhKBnz55wcnLKjRgpD1hZASkpSkdhmIAAYOdOpaOgF/XpI9eoBQVxOirlHh8fYM8eoEsXoF49YPJkbgNBRETmw+AkrWnTpjh9+jQiIiLw9ddfowg37TALpjbdEQCaNQN27+aNmbFRqeS6tPBw4JdflI6GzJlaDQwcCBw+DGzYANSvD1y8qHRUREREb8+gJC01NRWrVq2CSqXKrXhIIdbWpjeSVqoU4OIikwEyLo6OwNq1wMiRwPHjSkdD5q5cOVlI5IMPgNq1gdmz5d59REREpsqgJM3KygpPnz7NrVhIQaY4kgZwyqMxq1oVmDpVrk9LSFA6GjJ3lpbAN98A//4LLF4sXxuuXVM6KiIiojdj8HTHvn37YvLkyUhLS8uNeEghplY4JEOzZiweYsx69gTq1pWPXJ9GeaFqVeDIEbnhfY0awKJF/NkjIiLToxLCsD9fbdq0QUhICBwcHFC5cmXY29vrXd+wYUOOBmjOEhMT4eTkhISEBDg6Oioay/TpwPnzprfHVWwsULQo8OAB8MKPIhmJR4/kFLTevYH+/ZWOhvKTo0eB//s/WWRk4UKAS6iJjOveg4iyZvBImrOzM9q2bYvAwEB4eXnByclJ7yDTZKrTHV1dgYoVgX37lI6EsuLgINenBQfLEQ6ivPLOO3JNZIUKcoRtxQqOqhERkWmwNPQLli5dmhtxkMJMdboj8GzK4/vvKx0JZaViRWDGDKBdO3nTXLCg0hFRfmFrC0ybBrRqBXTrJqtAzpsHuLkpHRkREVHW3mgz67S0NOzatQu//PILkv63Y+2dO3fw6NEjg59rzpw5KFGiBGxsbODn54cjr3mrfe3atShXrhxsbGxQuXJlbNu2Te+6EALfffcdPD09YWtri4CAAFy6dEmvTVxcHDp37gxHR0c4OzujZ8+eL8V+6tQp1K9fHzY2NvD29saUKVNeiiU+Ph59+/aFp6cnNBoNypYt+1I8psIU90nL0KwZi4eYgq5dgaZN5Y0yRzMor9WvD5w8Cbi7A5Ury2SNiIjIWBmcpF2/fh2VK1dGq1at0LdvX9y/fx8AMHnyZHz99dcGPdfq1asxePBgBAcH4/jx46hatSoCAwNx7969TNsfOnQIHTt2RM+ePREREYHWrVujdevWOHPmjK7NlClTMGvWLMyfPx9hYWGwt7dHYGCgXlXKzp074+zZs9i5cye2bNmCffv2ISgoSHc9MTERzZs3R/HixREeHo6pU6dizJgxWPDcgq2UlBQ0a9YM165dw7p16xAZGYmFCxea7L5xpjrdEZCFKa5cAWJilI6EXmf2bFlx78cflY6E8iMHB2DuXOD334EBA4DPPgMePlQ6KiIiokwIA7Vq1Up89tlnIjk5WTg4OIgrV64IIYTYs2ePKF26tEHPVbt2bdG3b1/d5+np6cLLy0tMnDgx0/bt2rUTLVu21Dvn5+cnevXqJYQQQqvVCg8PDzF16lTd9fj4eKHRaMTKlSuFEEKcO3dOABBHjx7Vtdm+fbtQqVTi9u3bQggh5s6dK1xcXERycrKuzfDhw4Wvr6/u83nz5omSJUuKlJQUg/r8vISEBAFAJCQkvPFz5JTVq4Vo0ULpKN5c8+ZC/P670lFQdly4IISLixAHDigdCeVnDx8K0a2bEJ6eQmzYoHQ0RHnHmO49iChrBo+k7d+/H6NGjYK1tbXe+RIlSuD27dvZfp6UlBSEh4cjICBAd06tViMgIAChoaGZfk1oaKheewAIDAzUtY+KikJMTIxeGycnJ/j5+enahIaGwtnZGbVq1dK1CQgIgFqtRlhYmK5NgwYN9PoYGBiIyMhIPPzf266bN2+Gv78/+vbtC3d3d1SqVAkTJkxAenp6ln1OTk5GYmKi3mEsTHkkDeCUR1Pi6wvMmQN06CCrchIpwdkZWLpU7qk2cCDw8cfAnTtKR0VERCQZnKRptdpME5Fbt26hQIEC2X6eBw8eID09He7u7nrn3d3dEZPFvLWYmJhXts94fF0btxdWjFtaWqJgwYJ6bTJ7jue/x9WrV7Fu3Tqkp6dj27ZtGD16NH788Uf88MMPWfZ54sSJepUwvb29s2yb10y5cAjwbFNrrnUyDR07Ah98AHTpAmi1SkdD+dn77wNnzwLFiwOVKsltSPgzSURESjM4SWvevDlmzJih+1ylUuHRo0cIDg5GixYtcjI2o6bVauHm5oYFCxagZs2aaN++Pb799lvMnz8/y68ZOXIkEhISdMfNmzfzMOJXM+XCIQBQpYpMMs+dUzoSyq7p04G7d4Hx45WOhPI7Bwf58/j338DPPwMNGwKnTikdFRER5WcGJ2k//vgjDh48iAoVKuDp06fo1KmTbqrj5MmTs/08hQoVgoWFBe7evat3/u7du/Dw8Mj0azw8PF7ZPuPxdW1eLEySlpaGuLg4vTaZPcfz38PT0xNly5aFhYWFrk358uURExODlCyyHY1GA0dHR73DWJj6SJpaDbRoAWzdqnQklF02NsD69fKmmJX2yBjUrg2EhwMffSQTtQEDgPh4paMiIqL8yOAkrWjRojh58iS+/fZbDBo0CNWrV8ekSZMQERHx0jTCV7G2tkbNmjUREhKiO6fVahESEgJ/f/9Mv8bf31+vPQDs3LlT197HxwceHh56bRITExEWFqZr4+/vj/j4eISHh+va7N69G1qtFn5+fro2+/btQ+pzWcvOnTvh6+sLFxcXAMC7776Ly5cvQ/vcvJiLFy/C09PzpfV6psDa2rRH0gDgww+BzZuVjoIM4eMDrFsHzJ/PqapkHKysgKFDgTNngPv3gXLlgOXLOQWSiIjymJJVS1atWiU0Go1YtmyZOHfunAgKChLOzs4iJiZGCCFEly5dxIgRI3TtDx48KCwtLcW0adPE+fPnRXBwsLCyshKnT5/WtZk0aZJwdnYWmzZtEqdOnRKtWrUSPj4+4smTJ7o27733nqhevboICwsTBw4cEGXKlBEdO3bUXY+Pjxfu7u6iS5cu4syZM2LVqlXCzs5O/PLLL7o2N27cEAUKFBBfffWViIyMFFu2bBFubm7ihx9+yHb/janC0tGjQpQvr3QUbycxUQhbWyHu3VM6EjKUVqt0BESZ27NHiIoVhahbV4iICKWjIXp7xnTvQURZMzhJW7ZsmdiyZYvu86FDhwonJyfh7+8vrl27ZnAAs2fPFsWKFRPW1taidu3a4vDhw7prDRs2FF27dtVrv2bNGlG2bFlhbW0tKlasKLZu3ap3XavVitGjRwt3d3eh0WhE06ZNRWRkpF6b2NhY0bFjR+Hg4CAcHR1F9+7dRVJSkl6bkydPinr16gmNRiOKFCkiJk2a9FLshw4dEn5+fkKj0YiSJUuK8ePHi7S0tGz33ZheKE+eFKJkSaWjeHvvvSfE0qVKR0FE5iQlRYiffhLC2VmIvn2FiItTOiKiN2dM9x5ElDWVEIZNMvL19cW8efPQpEkThIaGomnTppgxYwa2bNkCS0tLbODikmxLTEyEk5MTEhISFF+fduGCLGNvRLVM3si8ebLKI38MzcuhQ4dw+vRp9OrVS+lQKB+LjgaGDQN27AAmTgS6d5frYYlMiTHdexBR1gz+83Lz5k2ULl0aALBx40Z88sknCAoKwsSJE7F///4cD5DyhkZj+mvSALkubedO4OlTpSOhnPL48WPcv38fy5Ytw0cffaR0OJSPeXoCv/0mC97MnAnUrSsLjRAREeU0g5M0BwcHxMbGAgD++ecfNGvWDABgY2ODJ0+e5Gx0lGfMoXAIABQtCpQtC+zZo3QklBOEELCzs0PTpk3xwQcf4MCBA/jjjz+UDovyufr1gePHgU6d5AyE3r1lkREiIqKcYnCS1qxZM3z++ef4/PPPcfHiRd3eaGfPnkWJEiVyOj7KI+aSpAGs8mguhBBQqVRIS0vD+vXrceTIEXTv3h2ffvqp0qERwdIS6N8fOH9ebl/i6wtMnsxRfCIiyhkGJ2lz5syBv78/7t+/j/Xr18PV1RUAEB4ejo4dO+Z4gJQ3zClJ++gjmaSxpLvpykjQhBBYvXo11q1bB29vb/zwww+wsrICIPc3TE9PVzhSyu/c3YHFi+Xo/a5dsmT/ypV8/SEiordjcOEQyjnGtHj3yRPAzg5ITzf9hfBCAN7ewKZNQM2aSkdDhkpISICTkxMAYNWqVVi5ciU8PDwwdepUODo6QsiqtJg3bx7OnTuHCRMm6NoTKUkIYPt24OuvgQIFgB9/BOrVUzoqIn3GdO9BRFmzfJMvevjwIRYvXozz588DAMqXL48ePXqgYMGCORoc5Z2M/bdTU2UREVOmUsnRtL/+YpJmalJTU9G/f3+ULl0a9evXx9q1a1GwYEFMmDABjo6O0Gq1UKvVSElJQY0aNbB161bUrFkTR48e1W00T6QUlQpo0QJo3hxYtAho21YmaZMnA/+rt0VERJQtBo+Z7Nu3DyVKlMCsWbPw8OFDPHz4ELNnz4aPjw/27duXGzFSHrCwkCNo5jblkUyLlZUVhg8fjkmTJqFTp05wc3PDlClT4OrqqkvQ0tPTodFoULZsWZQuXRoFChTAwYMHlQ6dSMfSUhYTuXRJrlWrWRMYNAiIi1M6MiIiMhUGJ2l9+/ZF+/btERUVhQ0bNmDDhg24evUqOnTogL59++ZGjJRHzGldWuPG8gbJ1Pd9y48qVKiAyMhIWFpaIjExEYULFwYAqNVqaLVaWFhYICEhAZMmTcK1a9fQr18/fPDBBwpHTfQyR0dgwgTg9GlZ/bFMGWD6dPN5nSUiotxjcJJ2+fJlDBkyBBYWFrpzFhYWGDx4MC5fvpyjwVHeMqckTaORU47++kvpSOhNFC1aFEeOHMH+/fvx+++/686r1Wo8fvwYkydPxoULF9CiRQv06NEDAKDVapUKl+iVihUDfv8d+Ptv4M8/gQoV5F5rXBFORERZMThJq1Gjhm4t2vPOnz+PqlWr5khQpAxzStIATnk0dR4eHoiMjESHDh1w7Ngx3fmJEyfi1KlTaN68OYKCggBANxWSyJi98w7w77/A1KnAiBFAgwbAkSNKR0VERMYoW4VDTp06pfu4f//+GDBgAC5fvow6deoAAA4fPow5c+Zg0qRJuRMl5QlrayA5Wekock6LFkCfPkBSkqy0RqbH1tYWQgiMHTsWQgg0bNgQp06dQsOGDdGrVy/dFEgmaGQqVCqgTRugZUtg3rxnhUYmTgSKF1c6OiIiMhbZKsGvVqt1exa98slUKu5bZABjK4Pr4yNHnipXVjqSnFO/PjBgAPDJJ0pHQm9Dq9XCz88PJ06cwOTJkzF48GDd+awSNK1WiydPnsDe3j4vQyUyyMOHwA8/yGqQn38ODB0KeHgoHRWZM2O79yCizGXr7eeoqChcvXoVUVFRrzyuXr2a2/FSLtJozGu6IyBLYK9erXQU9LbUajWOHj2Kd999F3/88Yfe+cw8ffoU//77L4YMGYJt27blVZhEBnNxkfupRUTIhK1sWWDwYCAmRunIiIhISdlK0ooXL57tg0yXRmNe0x0BoF07ublsYqLSkVBO2Lt3L0qVKoUVK1YAgG5j6xfZ2NjAyckJpUqVQrdu3bB48eK8DpXIICVLAkuWACdOyNersmWBgQOB6GilIyMiIiW80UKOK1euoF+/fggICEBAQAD69++PK1eu5HRslMfMbU0aAHh5AbVrA5s2KR0J5ZTVq1ejU6dOAOQU66ymYteoUQNDhw7Fli1bMG7cOL3iI0TGqmRJOfXx5Engv//kPmv9+wO3bysdGRER5SWDk7QdO3agQoUKOHLkCKpUqYIqVaogLCwMFStWxM6dO3MjRsoj5jiSBgCdOgHPzZAjM7F//36899570Gq1eomaVqtFamoqACA5ORm1a9dG5cqVERsbq2S4RAbx8QEWLgROnZKvy+XLA/36MVkjIsovDE7SRowYgUGDBiEsLAw//fQTfvrpJ4SFhWHgwIEYPnx4bsRIecRck7S2bYF9++RmsmQeVCoV6tevjx49eiA+Pl53DpDr1KysrAAAGo0GISEh2LFjBzQajVLhEr2xEiWAX36RG2KnpclkrW9f4OZNpSMjIqLclK3qjs+zsbHB6dOnUaZMGb3zFy9eRJUqVfD06dMcDdCcGVuFpRYtgG7d5Douc/PRR8D778uS/GRetFottm/fjj///BOFCxfGpUuXkJqaCrVajQcPHiA0NBTffvstRo4cCRsbGwDAtm3b0KJFC4UjJzLczZvApEnAb78Bn30m91srVkzpqMiUGNu9BxFlzuCRtMKFC+PEiRMvnT9x4gTc3NxyIiZSiLmOpAFAx47AypVKR0G5Qa1W48aNG1iyZAliYmLQqlUr1K9fH82bN8fXX3+N0NBQjB07VpegtW/fHh988AH69euncOREhvP2BubMAc6dA9RqoFIloHdv4Pp1pSMjIqKclK3NrJ/3xRdfICgoCFevXkXdunUBAAcPHtTbu4hMkzknaR99BAQFATdu8F1nc9SnTx8kJSVh1qxZmDZtGlxdXTNt161bN5w4cQJ79uzBl19+iW7dumHZsmV5GyxRDihaFPj5Z2DkSGDyZLm/ZYcO8nMfH6WjIyKit2XwSNro0aPx3XffYfbs2WjYsCEaNmyIn3/+GWPGjMGoUaNyI0bKI+acpNnbAx9+yD3TzNmwYcPQu3dvlCxZUjfa//xs7n379uHXX3/FJ598goYNGyIiIgLHjh1D9+7dFYqY6O0VKQLMmgVcuADY2QFVq8piSZlMeCEiIhNicJKmUqkwaNAg3Lp1CwkJCUhISMCtW7cwYMAA3cJ9Mk3mnKQBnPKYH4waNQqjR4/GzJkzkZycrPeaVLNmTYSEhOD333/HokWLYG1tjTNnzuDjjz9WMGKinOHlBcyYAVy5IvdYCwgAAgOB3bsBw1aeExGRMTC4cAjlHGNbvNuvn/xDP3Kk0pHkjpQUwMMDOHQIKFdO6WgoNz148ACFChXClStX4OTkhEKFCkGr1UKtVmPTpk2YNWsWVq5cqbeO9tKlSy8VRCIyVf/9ByxeDPz4I+DmBgwfDrRpA1hYKB0ZKc3Y7j2IKHMGj6TdvXsXXbp0gZeXFywtLWFhYaF3kOky95E0a2tZjp+jaeavUKFCSE5OxvLly7Fq1SoA0O2dVrRoUVy6dOmlSrSbN29GnTp18jxWotxgby83wb58GRgwAPj+e/nm1C+/ACzCTERk/AwuHNKtWzfcuHEDo0ePhqenJ6c4mhFzT9IAuVajVy9gzBiAP7rmTaPR4N1330Xbtm1RrFgxfPTRRwCAFStWoHTp0vD29tZrP2TIEOzZswd+fn4IDQ2FWm3we1hERsfKSpbq79wZ+PtvWWQkOFgmbn36AM7OSkdIRESZMThJO3DgAPbv349q1arlQjikJI0G+N++wGarQQPg0SPg+HGgZk2lo6HcFhgYiPXr16N79+7YtGkT9u/fj6JFi6Jbt2567dLT02FhYYEtW7agS5cuqFmzJg4fPswNsMlsqFRyr8j33wfCwmSyNnky8MUXwMCBsgAJEREZD4PfKvb29gaXsZmn/DCSZmEBtG8P/PGH0pFQXgkMDMQ///yDRo0a4bPPPsP8+fPRrl07qFQqaLVaANCNmqWnp+Onn37CyZMn0b59e90USSJz4ucHbNggk7WHD+U0yB49gPPnlY6MiIgyGJykzZgxAyNGjMC1a9dyIRxSUn5I0gBZ5XH1auB/9+eUD1SqVAldunTBd999h7Jly8LGxgZCCF1ylpCQgPDwcAQEBKBVq1aoXbs2unbtioSEBIUjJ8o9vr7AokVAZCRQuDDg7w+0bi2LKxERkbIMnu7Yvn17PH78GKVKlYKdnR2srKz0rsfFxeVYcJS38kuS9s47gI0NsG8f0KiR0tFQXsuY2pixnnbFihXYuHEj/v33XzRt2hTt27dH69atde3v3LkDCwsLuLu7KxQxUe7y8pJTH7/5Bpg/H/jkE6BYMVl4pG1b+beBiIjylsFJ2owZM3IhDDIGNjb5I0lTqYCuXWWVMyZp+U9GFdoRI0bgxo0bWLduHQYPHozPPvsMrVq10mt769YtlChRAhYWFjh9+jTKli2rRMhEecLJSZbqHzQIWLMGmD1brlf7/HMgKAgoUULpCImI8g+Dk7SuXbvmRhxkBGxs8k9p5t69gVKlgGvXeOORHyUmJuLo0aOoUqUKDh8+jBo1auiuZYy0xcbG4p133sGXX34JV1dX1K1bFzt37kT16tUVjJwo91lby4qQn30GREQA8+YBVaoADRsCX34pN8lm8VMiotxlcJJ248aNV14vVqzYGwdDyspPSVrhwsD//R8wfTowc6bS0VBec3R0xD///IO0tDRoNBrdRteAHGnTarUYOnQo7t69i/Hjx6NAgQJwcnJCgwYNEBoaikqVKincA6K8Ub06sGABMGUK8NtvwODBcsZF796y2EihQkpHSERknlTCwFKNarX6lXujpaenv3VQ+UViYiKcnJyQkJAAR0dHpcPB9u3AxIlyrVZ+cPUqUK0aEBUFuLoqHQ0Zk/T0dERHR2PMmDE4c+YMDh48CAsLC2zevBnVq1d/aY81ovxCCGDvXmDuXLnvWps2cnTNz497T5oKY7v3IKLMGTxhISIiAsePH9cdYWFhmD9/PsqWLYu1a9fmRoyUR/LTSBoAlCwp9wyaO1fpSMgYPH36FJcuXYJWq4WFhQWKFi2KRYsWoVChQvj5558BAB999BG8vb11pfuJ8huVCmjcGFi7VlaFLF1aFhepUQNYuBD47z+lIyQiMg8GJ2lVq1bVO2rVqoUvvvgC06ZNw6xZs3IjRsoj+S1JA4ChQ+Xi+CdPlI6ElHblyhVMmzYNsbGxAID//ne36e7ujkuXLum1VXNBDhG8vIDvvgOuX5ePa9YARYvKqpDcc42I6O3k2J2Gr68vjh49mlNPRwqwtc1/yUqtWkDlysCyZUpHQkqrWLEibty4gfbt2wMA7O3tcevWLYSHh6NcuXIKR0dkvCwt5bTHnTvlBtmWlsC77wL16snXVo6uEREZzuAkLTExUe9ISEjAhQsXMGrUKJQpUyY3YqQ8kh9H0gBg2DDgxx8BLqek7du3w97eHv7+/ggKCkKjRo1QtWpVNOJeDUTZUrYs8NNPwO3bcq3ar7/KEbdevYAjR+SaNiIier0cKRwihIC3tzdWrVoFf3//HA3QnBnb4t1r1+RGz/fvKx1J3hJCFhAZPVpu4kq0aNEi3L9/Hw4ODvjggw/g4+OjdEhEJuvKFWDJEjmq5uoK9Owpy/uzYJMyjO3eg4gyZ3CS9u+//+p9rlarUbhwYZQuXRqWlgZX9M/XjO2FMiYGKFMGSEpSOpK89/vvwKxZcqoOK5RRdjx6BDg4KB0FkelIS5MVIRctAnbtAj74QG6U3aQJ913LS8Z270FEmTM4SaOcY2wvlPHxcv+w1FSlI8l7qalyc+tffwU4s41eJy4OqFhR7h3VpYvS0RCZnpgYYPlyYPFi+frbowfQrRvA3S1yn7HdexBR5vjeFenY2Mh3OtPSlI4k71lZyU1ap0xROhIyBQULAhs3AqNGyZGA/FZwh+hteXgAw4fLMv7LlwOXLwMVKgAtWgAbNgApKUpHSESkLCZppKPRyMfkZGXjUMrnnwOHDwOnTysdCZkCPz8gIgK4exeoUwe4eFHpiIhMj0oFNGggE7Vbt4CPPgImTJCl/L/+Gjh7VukIiYiUwSSNdFQqmajlxwqPgFxf1KcPMG2a0pGQqShYENi0SRZBqFMHWL1a6YiITJeTE9C7N3DsGPDPP/INw4YNgZo15Zrh/FbUiojyt2wlabNmzcLT/92537hxA1zGZr5sbfNvkgYA/frJqTY3byodCZkKtVpuiv7XX8CQIbLseH7+HSLKCdWqAbNnA3fuyMq7e/cCJUoArVoB69fn3xkfRJR/ZCtJGzx4MBITEwEAPj4+uM+3s8xWft0rLYOHB9CpEzBjhtKRkKl59105/fHqVfnxlStKR0Rk+qytgdat5Ztn168DzZvLtcOenvINkbAw7r1GROYpW0mal5cX1q9fj+vXr0MIgVu3buHGjRuZHmTa8nuSBsjRkEWLgIcPlY6ETE3hwsC2bUDbtkDt2sC6dUpHRGQ+ChUC+vaVidnBg3J6ZNu2QPnywPjxfGOEiMxLtkrwL1iwAP369UPaK8r+CSGgUqmQnp6eowGaM2Msg1u+vCxD/847SkeirPbtgZIlgYkTlY6ETNW//8ry/A0bymlbzs5KR0RkftLT5VTIX3+VFVd9feXrd7t2LOefFWO89yCil2V7n7SkpCRcv34dVapUwa5du+Dq6pppu6pVq+ZogObMGF8oa9YEpk+X1bbys8uX5b/F2bOyyhjRm0hIAAYMkEUQZs8GPv6Ym6UT5ZanT4EdO4BVq4AtW4CqVYEOHeRom6en0tEZD2O89yCilxm8mfXy5cvRoUMHaDLqtdMbM8YXyvr15d5PgYFKR6K8r76S+18tXqx0JGTq/vlHVq2rXBn4+We+w0+U2/77D9i6VSZsO3YANWoAn3wi3yjJ779/xnjvQUQvM7gEf9euXaHRaBAeHo7ff/8dv//+O44fP54bsZECbG2Bx4+VjsI4fPedXKzOfXrobTVvLvff8/WV7+7Pni2naRFR7rC3l1MeN2yQexn26wccOiQ3zPb3l1utREUpHSURUdYMHkm7d+8eOnTogL1798L5f4ss4uPj0bhxY6xatQqFCxfOjTjNkjG+m9W6tfzD1qmT0pEYh3HjgCNHZHl1opwQEQF88QVgYQEsXAhUqaJ0RET5x+PHcmRt3To5JbJMGTkd8pNP5Mf5gTHeexDRywweSevXrx+SkpJw9uxZxMXFIS4uDmfOnEFiYiL69++fGzFSHuJImr7Bg+XGqvv2KR0JmYvq1YHDh4GOHWVRkW++kdNqiSj32dkBbdoAK1bIEbYxY4DISFmNtWpV4PvvgXPnlI6SiOgNkrS///4bc+fORfny5XXnKlSogDlz5mD79u05GhzlPTs7JmnPs7eXf8SHDeNePJRzLC2BgQOBEyfkUbkyEBKicFBE+YyNDfDBB8CyZTJhmzIFuHlTFs6qUEFOeT95kq/9RKQMg5M0rVYLKyurl85bWVlBq9XmSFCkHFtbvqv/op49gfh4YP16pSMhc1O8uCxuMH480Lkz0K0bEBurdFRE+Y+1tSyYtXAhEBMj140+eCDPlS0LDB8OhIYCvM0horxicJLWpEkTDBgwAHfu3NGdu337NgYNGoSmTZvmaHCU9ziS9jJLS7lf2siRQGqq0tGQuVGp5L5O587Jn7Xy5eVULL57T6QMS0ugaVNg7lzg9m1Z4TclRb6R4uUl15Ru3SpL/hMR5RaDk7Sff/4ZiYmJKFGiBEqVKoVSpUrBx8cHiYmJmD17dm7ESHnIzo4jaZlp3RpwcwMWLFA6EjJXBQsCixYBa9bIdTHvvQdcvap0VET5m4WFnP44fTpw5YrcTqNYMSA4GChcWBYd+fVXjoATUc4zuLojAAghsGvXLly4cAEAUL58eQQEBOR4cObOGCssTZ4s5+T//LPSkRifgwflHjuXLwMFCigdDZmzp0/lFMjZs4FvvwUGDZLv7hOR8bh5E9i8Gdi0CThwAPDzA1q1koePj9LRZc0Y7z2I6GVvlKRRzjDGF8rZs2WJ8CVLlI7EOLVuLSuAjR2rdCSUH5w5AwQFydHthQuBWrWUjoiIMpOQAGzfDmzcKB+LF5d/L1q1khtpq1RKR/iMMd57ENHLDJ7umBvmzJmDEiVKwMbGBn5+fjhy5Mgr269duxblypWDjY0NKleujG3btuldF0Lgu+++g6enJ2xtbREQEIBLly7ptYmLi0Pnzp3h6OgIZ2dn9OzZE48ePdJrc+rUKdSvXx82Njbw9vbGlClTsoxp1apVUKlUaN26tWGdNzIsHPJqEycCM2bIheVEua1SJfkO/RdfyA2xP/9cVqEjIuPi5AR06ACsWgXcvy83y46NlUlasWJA375yqmRystKREpGpUDxJW716NQYPHozg4GAcP34cVatWRWBgIO7du5dp+0OHDqFjx47o2bMnIiIi0Lp1a7Ru3RpnzpzRtZkyZQpmzZqF+fPnIywsDPb29ggMDMTT51b5du7cGWfPnsXOnTuxZcsW7Nu3D0FBQbrriYmJaN68OYoXL47w8HBMnToVY8aMwYJMFiVdu3YNX3/9NerXr5+D/zLKYOGQVytfXhZ54Ega5RW1GvjyS7mXk4WF/Bn88UdZyICIjI+1tXxTZc4cOSXyzz/lmtOhQ4FCheQ+bYsXA9HRSkdKREZNKKx27dqib9++us/T09OFl5eXmDhxYqbt27VrJ1q2bKl3zs/PT/Tq1UsIIYRWqxUeHh5i6tSpuuvx8fFCo9GIlStXCiGEOHfunAAgjh49qmuzfft2oVKpxO3bt4UQQsydO1e4uLiI5ORkXZvhw4cLX19fve+dlpYm6tatKxYtWiS6du0qWrVqle2+JyQkCAAiISEh21+T2/78U4hmzZSOwrjdvi2Eo6MQFy4oHQnlRxERQjRoIETZskJs3ap0NERkiBs3hJg/X4gPPhDCzk6IGjWE+O47IcLChEhPz5sYjPHeg4hepuhIWkpKCsLDw/WKjqjVagQEBCA0NDTTrwkNDX2pSElgYKCufVRUFGJiYvTaODk5wc/PT9cmNDQUzs7OqPXcAo+AgACo1WqEhYXp2jRo0ADW1tZ63ycyMhIPHz7Unfv+++/h5uaGnj17vra/ycnJSExM1DuMja0tR9Jex8sL6NcP+OYbpSOh/KhaNWDvXmDcOKB3b6BFC+B/NZyIyMh5ewO9egF//SX3YRs3Tj5++ing6Ql07w6sWwcY4e0BEeUxRZO0Bw8eID09He7u7nrn3d3dEZPFop+YmJhXts94fF0bNzc3veuWlpYoWLCgXpvMnuP573HgwAEsXrwYCxcuzFZ/J06cCCcnJ93h7e2dra/LS5zumD3DhgH798vNTYnymkoFtGsnk7M6deQxZAjw3PtHRGTkbG3lmyxz5gDXrgEhIUC5csDMmYC7u9yr7aef5B6KLPFGlP9kO0lLTU3FsGHDULp0adSuXRtLXij/d/fuXVhYWOR4gMYqKSkJXbp0wcKFC1GoUKFsfc3IkSORkJCgO27evJnLURqOSVr2ODoCY8bItUJcCE5KsbMDvvsOOHVKFrMpVQqYMAH47z+lIyMiQ6hUslDQ8OHyDcDbt4GePYHwcKBRIzkC1707sHu30pESUV7JdpI2fvx4/Prrr+jduzeaN2+OwYMHo1evXnpthIFv9RQqVAgWFha4+0K5srt378LDwyPTr/Hw8Hhl+4zH17V5sTBJWloa4uLi9Npk9hwZ165cuYJr167hww8/hKWlJSwtLfHrr79i8+bNsLS0xJUrV16KXaPRwNHRUe8wNvb2vMHLrt695SLwkSOVjoTyu2LFgBUrgD17gEOHZLI2ezbfQCAyVQULAp06yd/rmBg5PbJ8eSCLmmpEZIaynaStWLECixYtwtdff40ffvgBx44dw+7du9G9e3ddcqYycCMQa2tr1KxZEyEhIbpzWq0WISEh8Pf3z/Rr/P399doDwM6dO3XtfXx84OHhodcmMTERYWFhujb+/v6Ij49HeHi4rs3u3buh1Wrh5+ena7Nv3z6kpqbqfR9fX1+4uLigXLlyOH36NE6cOKE7PvroIzRu3BgnTpwwyqmM2eHgALywEwFlQa0Gfv1V/hF9YRcIIkVUrQps2QKsXy/Xtfj6AsuXA+npSkdGRG9KrQaqV5fT7Dt0UDoaIsoz2a0wYmtrK6KiovTO3bp1S5QtW1Z07txZ3L59W6jVaoMrl6xatUpoNBqxbNkyce7cOREUFCScnZ1FTEyMEEKILl26iBEjRujaHzx4UFhaWopp06aJ8+fPi+DgYGFlZSVOnz6tazNp0iTh7OwsNm3aJE6dOiVatWolfHx8xJMnT3Rt3nvvPVG9enURFhYmDhw4IMqUKSM6duyoux4fHy/c3d1Fly5dxJkzZ8SqVauEnZ2d+OWXX7LsizlUd3z4UAhLS6WjMC1btwrh5ibEnTtKR0L0jFYrxPbtQlSvLkT58kKsXy/PEVH+Zoz3HkT0smwnaT4+PmLXrl0vnb99+7YoW7asaNas2RslaUIIMXv2bFGsWDFhbW0tateuLQ4fPqy71rBhQ9G1a1e99mvWrBFly5YV1tbWomLFimLrC3WotVqtGD16tHB3dxcajUY0bdpUREZG6rWJjY0VHTt2FA4ODsLR0VF0795dJCUl6bU5efKkqFevntBoNKJIkSJi0qRJr+yHOSRpKSlCAEI8t/MAZcPAgUIEBORdCWWi7EpPF2LNGiF8fYWoVUuITZuYrBHlZ8Z470FEL1MJkb2FZJ9//jmEEFi8ePFL127fvo1GjRrh6tWrSOe8mmxLTEyEk5MTEhISjGp9mkYjN9ksWFDpSExHcrKssNepk9ywlMjYpKXJqbkTJgA2NsC33wJt28oNsoko/zDWew8i0pftJO369eu4cOECAgMDM71+584d7Ny5E127ds3RAM2Zsb5QFiwInDwpq0lR9kVGykTtn3+Ad95ROhqizKWny/Vq48cDKSlyv7+OHQErK6UjI6K8YKz3HkSkL9tJGuU8Y32hLFYM2LFDVpIiwyxdKm9+jx+XZfqJjJVWK4uMjBsHxMYCI0YAXbvKkXQiMl/Geu9BRPoM3syaOZ35Yxn+N9etG1CrFtC3r9KREL2aWg189BFw5Agwfz7w22+ydP+sWdwrkYiISGkGJWkpKSn49NNPcysWMhIsw//mVCp5w3vggLzpJTJ2KhXQvLncQPePP+Tomo8PMHkykJSkdHRERET5U7aTtEePHuH9999HWlpabsZDRoBJ2ttxdpY3uwMGAJcvKx0NUfY1aCDXVG7eDBw8CJQoAYwdCzx8qHRkRERE+Uu2krQHDx6gYcOGsLCwwNq1a3M7JlIYpzu+PX9/4OuvZUGGlBSloyEyjJ+fTNRCQoCzZ+XI2siRwL17SkdGRESUP2QrSatXrx7s7e2xceNGWLEEmNnjSFrOGD4cKFAAGDVK6UiI3ky1asCaNUBoKHD7NlC6NDBwIHDjhtKRERERmbdsJWlXrlzBe++9Bzs7u9yOh4wAR9JyhoWFXJe2dKmcQkZkqsqXB379FThxAnjyBKhYUY4SHz2qdGRERETmKVtJ2po1a/DDDz9g4cKFuR0PGQGOpOWcIkWAxYtlaXNOFSNTV7Ik8MsvwNWrMnH74AO5jm3jRrn/GhEREeWMbCVpbdq0wdatWzFs2DD88ccfuR0TKYwjaTnro4+ATz6RiZpWq3Q0RG+vcGHgu++A69flz/W33wLlygFz5vC1g4iIKCdku7pj48aNsWvXLgwdOjQ34yEjwJG0nDd1qlzTM2OG0pEQ5RwbG6BnT+DMGWD2bGDTJqBYMeCbb+TPOxEREb0Zg/ZJq1mzJvbs2ZNbsZCRcHDg/kg5zcYGWLUK+OEHWTGPyJyoVMB778m1l3v2ANHRgK8v0Lo18PffHEEmIiIylEFJGgCULVs2N+IgI+LoCCQmKh2F+alQQRYSad8eOH9e6WiIckeVKrJYzvXrQMOGshpkqVLAhAlATIzS0REREZkGg5O0V1m3bl1OPh0pxMmJSVpuadkSCA6WjywkQubM1RUYNEi+IbF0KXD6tCzh/+mnwK5dHF0jIiJ6FYOStLS0NJw5cwYXL17UO79p0yZUrVoVnTt3ztHgSBkcSctd/frJJK11a+DpU6WjIcpdKhXQqBGwciUQFQXUrg306SMTtrFj5TkiIiLSl+0k7cyZMyhdujSqVq2K8uXL4+OPP8bdu3fRsGFD9OjRA++//z6uXLmSm7FSHnF0BBISlI7CvE2fDri4AN26cUSB8o/ChYGhQ4HISGDZMjklsmpVoEkTuQ8bK0MSERFJ2U7Shg8fjtKlS2PTpk3o0KEDNm7ciEaNGuHDDz/ErVu3MGnSJBQtWjQ3Y6U8wumOuc/SUhYSuXBBljInyk/Uarm/2pIlwJ078s2KpUsBT0+gRw9g3z5ACKWjJCIiUo5KiOz9KXRzc8M///yDatWqISEhAS4uLli+fDm6dOmS2zGarcTERDg5OSEhIQGOjo5Kh6MTEwOUKcMKj3nh1i2gbl2gb19g+HCloyFSVlSUHFFbtkwmct26Af/3f0Dx4kpHRmQ+jPXeg4j0ZXsk7cGDB/Dy8gIAODk5wd7eHnXq1Mm1wEg5jo5yn7T0dKUjMX9FiwJ798pNgCdNUjoaImX5+MjCOleuAIsXy8dKlYCmTWVlVE6HJCKi/CLbSZpKpUJSUhISExORkJAAlUqFJ0+eIDExUe8g02drK6fjcSQtb5QsKRO1uXOZqBEBchStUSM5onbnDtCli0zavLzk5tkHDnA6JBERmbdsT3dUq9VQqVS6z4UQmX6ezuGXbDPmKQeurkBEBFCsmNKR5B9XrwKNGwO9ewMjRyodDZHxuXoVWL5cHpaWcipkhw4At+8kyj5jvvcgomcss9twz549uRkHGRmW4c97GSNqjRrJUYJvvlE6IiLjUrKkLNsfHCx/V1asAPz85PmOHYF27fjGEhERmYdsJ2kNGzbMzTjIyDg5sQy/Enx8niVqABM1osyo1bJsf5Mmcprwjh1yH7YxY4Bq1WTC9umngJub0pESERG9GYM2s6b8gyNpyslI1BYsAMaPVzoaIuOm0QAffSSTtLt35WbxO3fK36PmzWVp//h4paMkIiIyDJM0yhSTNGVlJGoLFwI//KB0NESmwd4eaN8e2LgRuH1bjqitXCkLjrRqJfcmZIVIIiIyBUzSKFOc7qi8EiVkorZ4MRM1IkM5OwPduwP//ANcuwYEBsqtLjw8ZPK2eTOQnKx0lERERJljkkaZ4kiacShRAtizR44AXL+udDREpsnNDfjyS2D/fuDcOaBmTeD772XC1qMHsG0bEzYiIjIuTNIoU0zSjEeJEsDJk0Dx4lm3SeKmdkTZ4u0NfP01cOwYEBYmK0N++y3g7i73Y9u4EXjyROkoiYgov8tWdcePP/4420+4YcOGNw6GjIeTExATo3QUlMHCIutrT58+xaRJkxAdHY1Ro0ahZMmSeRcYkQkrWxYYNUoeV64A69cDEycCnTvLCqstWwItWsg3SoiIiPJStkbSnJycsn2QeShYEIiLUzoKyo6kpCQ0btwYly9fRq1atXDgwAGlQyIyOaVKAcOGydG1y5eBTz4Bdu8GqlQBKlaU1/buBVJTlY6UiIjyg2yNpC1dujS34yAj4+rKJM0UCCFQuHBhVK1aFQ0aNIBGo0GRIkWUDovIpHl6yqIj3bsDKSnAgQNy3VqfPkB0tCzt37Il8N57cpokERFRTuOaNMpUwYJAbKzSUdDrqFQqAMCOHTtw5MgRdOzYET4+Prrr//77L9LS0pQKj8jkWVvLTbOnTQPOnweOHwcaNJDFfHx8gNq1gbFjgcOHAf6qERFRTnmjJG3dunVo164d6tSpgxo1augdZB44kmb8tFotAODkyZPYsWMHvL290aNHD931w4cPY8mSJahatSrWrVunVJhEZqVkSeCrr4Dt24H794HRo+X63c8+AwoXBtq2BX75BYiKUjpSIiIyZQYnabNmzUL37t3h7u6OiIgI1K5dG66urrh69Sref//93IiRFMCRNOOnVquRmJiIv/76C3fv3kWfPn0APEveqlSpguDgYLi7u2P06NE4deqUkuESmR17e+DDD4F58+Q6tmPHgGbNgB07gGrVgDJlgL59gU2bWC2XiIgMY3CSNnfuXCxYsACzZ8+GtbU1hg0bhp07d6J///5I4O7HZqNgQSA+HkhPVzoSepEQAvv27QMARERE4NChQ2jatClq1aoFQCZv6enpsLOzw/Xr1+Hi4oJOnTqhcuXKSoZNZPZKlQJ69wY2bJBvci1fLvdomzJFPtarJ/dn49RIIiJ6HYOTtBs3bqBu3boAAFtbW93+TF26dMHKlStzNjpSjL09YGUlEzUyLk+fPsXMmTPh6OiIiRMnwtPTE4MHDwYgR9GEELCwsEBCQgL++OMP2NnZ4dNPP4VKpdKNshFR7rK0BOrWBYKDgYMH5ZTIIUNk4RFOjSQiotcxOEnz8PBA3P8WKxUrVgyHDx8GAERFRUEIkbPRkWJUKpbhN1a2trZYv349xo0bh0OHDuHcuXOwsrICIEfRMqxYsQLR0dF4//33Ua5cuZeuE1HecXYG2rR59dTIXr1kQZK7d5WOloiIlGbwHVuTJk2wefNmAED37t0xaNAgNGvWDO3bt0ebNm1yPEBSjqsr16UZswEDBuDatWuwtbVFQEAAUlJSAMiKj8eOHcPOnTtRoUIFtG7dGgCyfBPl6dOnuHnzZl6FTUTIfGqktzewcKGsGlmhglzPtm4d8OCB0tESEVFeUwkDh7+0Wi20Wi0sLeUWa6tWrcKhQ4dQpkwZ9OrVC9bW1rkSqDlKTEyEk5MTEhIS4OjoqHQ4L2nQABgxAmjRQulI6HWio6NhaWmJiRMnYsCAAVi6dCnOnTuHgQMHom7duhBC6Mr1P+/JkyfYvXs3Zs+eDWdnZ8yePRuFCxdWoAdElCE5Wa5b27NHHmFhgK8v0LixPBo0AFxclI6STJWx33sQkWRwkkY5x9hfKNu0AT7+GOjSRelIKLt69eqFhQsXwt3dHePHj9cryZ+VS5cu4d69e1i1ahXWrFmDxYsX44MPPsiDaIkoO548AQ4depa0hYcDFSs+S9rq1weM8E8IGSljv/cgIilbSdqpU6dQqVIlqNXq15bxrlKlSo4FZ+6M/YWyZ0+gShVgwAClIyFDnDhxAj169MC9e/ewd+9elC5dOttf+/PPPyMyMhKzZ8/OxQiJ6G08eiSLkWQkbSdPAlWrAo0ayaNuXcDJSekoyVgZ+70HEUnZStLUajViYmLg5uYGtVoNlUqV6foWlUqFdNZszzZjf6EcNgywsZElo8n0TJw4EY8fP8a4ceNe2U6r1SI1NRUajQbz5s3DuHHjcPPmTVhYWECr1bLYCJGRS0wE9u+XCdu+fTJpK19ejrBlHJ6eSkdJxsLY7z2ISLLMTqOoqCjdOpUo1grONwoWBG7dUjoKelMjR47UvWmybt06uLq6onHjxgCgl3yp1Wqo1WpERERg7Nix6NmzJywsLABAt47t9u3bKFKkiAK9IKLXcXQEWraUByBH2sLCZOL2yy9A9+4ySatX71nSVqaMrOJLRETGKVtJWvHixXUfX79+HXXr1tUVDsmQlpaGQ4cO6bUl0+bqKt+RJdOVkWxZW1vj33//RZ06dWBra4v4+HjExsZi586d2Lt3r24LjQ8++ADjx48HACQkJGDgwIF48OAB7t+/Dy8vLyxduhROnEdFZNQcHICmTeUBAKmpQESETNq2bJEFodRq/aStalW5txsRERkHgwuHWFhYIDo6Gm5ubnrnY2Nj4ebmxumOBjD2KQfr1wMLFsh9fMh8/P777xg0aBAqV64Mb29vFClSBP7+/qhfvz4cHR2hVqtx8OBBzJw5Ezdu3MCcOXPg7u6Ob7/9Fv/88w927NjBtadEJkwI4MIFmbQdOCAfHzyQa9nq1ZNH7dqAvb3SkVJuMPZ7DyKSDH7fLKtS3rGxsbDnK7pZcXXl/jzmqEaNGihYsCCuXr2KlStXwt3dXe/6/fv3sXLlShw5cgSffvopEhISULNmTSxfvhwbN258qT0RmRaVSq5ZK18eCAqS527depawDRwInD8PlCsH1Kwpj1q15Gibra2ioRMR5RvZTtI+/vhjAHKNSrdu3aDRaHTX0tPTcerUKdStWzfnIyTFeHgAMTFKR0E5rUKFCoiMjMSoUaNQrlw5fPbZZ3rVHE+cOIHly5ejUqVKcHNzQ1BQEGrVqoUlS5boleZPTk7Go0eP4OrqqkQ3iCgHFS0KdOggDwBISgJOnJDl/g8fBubMAa5ckYlbrVrPkjcmbkREuSPbZducnJzg5OQEIQQKFCig+9zJyQkeHh4ICgrC77//npuxUh7z9ATu3gU4g9U8/fDDDzh69CiOHTuGOXPmIDk5GQCwfft2FC1aFKGhoRg6dCguX76MW7du4eTJk7q1qOfOnUPjxo3Rvn17lC1bFrt371ayK0SUwwoUkGvVBg4Efv9djqzFxgI//wxUriz3bevWTZb6r1pVbtkyd64sWPL0qdLRExGZvmyPpC1dulRXdn/27NlwcHDItaDIODg6AtbWcsojZ7iZp9KlSyM0NBQPHz7UjY4fOXIEn332GQBZBfLmzZt4+PAh7t27BwBYu3YtFi9ejMKFC+PHH3/Ezp07ERQUhO3bt6NMmTKK9YWIcleBAkCDBvLIkJgoi5KEh8vpkjNnAlFRQIUKQPXqQLVqzw7WHCIiyj6D1qQJIbBixQp88803vBnLB1QqOZoWHc0kzdy5uLjoPm7Tpg3Onj0LQJbnDwsLg5eXFzw9PREZGYl169ahZMmS+Pnnn2FtbY3SpUtj3bp1OHjwIF8XiPIZR0egYUN5ZEhIkInbiRPycelS4Nw5OaWyWjX95K1oUW4FQESUGYOSNLVajTJlyiA2NpY3Y/lERpJWrZrSkVBeCQwMxLx589C0aVMUKVIEp0+fRvPmzVG7dm388MMP0Gq16NGjB6ytrQEAT58+xZUrV15am5aenq7bAoCI8g8nJ6BRI3lkSE6WiVpG4jZ1qvzY2vpZwpaRvPn6cjsAIiKDXwYnTZqEoUOHYt68eahUqVJuxERGJCNJo/yjUqVKuHz5MmbMmAEhBIKCglC5cmWkp6fjyJEjqFKlCurUqaNrv3TpUpQuXRpFixYFANy5cwdTp07FnTt3UKlSJYwePVqprhCRkdBoZBJWvbrcXBsAtFo5NfLECXmsXAkMHy7XvlWqJNtWqSKPypWB5wb8iYjMnsH7pLm4uODx48dIS0uDtbU1bF8o6xQXF5ejAZozU9irpH9/OdXx22+VjoSUFh0dDR8fHxw+fBjV/je0eurUKUyYMAGFCxfGzJkzsW3bNkyaNAlarRa9e/fG9OnT4e3tjbVr1+pVhCUiysr9+89G3E6fBk6dkvu6ubnJZC0jaatSRY66/W9Qn7LJFO49iOgNRtJmzJiRC2GQsfL0BG7fVjoKMga2trZo0KAB1OpnRWF//PFHJCcn44svvsCFCxewaNEilClTBkuXLgUAtGjRAh9//DHu3LkDHx8fpUInIhNSuDDQrJk8MqSmApGRz5K2lSuBkSPlNjHlyj1L2jISuCJFuNaNiEybwUla165dcyMOMlKensCxY0pHQcbAyckJFSpUQJMmTdCvXz9s374dABAcHIwqVapg8ODB0Gg0GDBggO5rkpKScOzYMaRzHwciegtWVnIKZKVKQMeOz84/fAicOSMTt9OngU2b5KOV1bPErVIloGJFeXDKJBGZirdamvv06VOkpKTonePQuXnhmjTKoFKpMGPGDDRq1Ah///032rVrh88++wxubm6IjIzEmTNn0KJFC91USABYsWIFmjRpAjs7O+UCJyKz5eIi93OrX//ZOa0WuH79WeK2Z4/c3y0yEihU6FnC9vzB7QGIyNgYnKT9999/GD58ONasWYPY2NiXrvMdc/PCJI1e1Lp1a7Ru3Vrv3IMHDxAREYHVq1frzh07dgwnT55ElSpV4ObmlsdRElF+pVYDPj7yaNXq2fnUVODyZeDsWXns3AnMmAFcuiTXu72YuFWoILcYICJSgsFJ2rBhw7Bnzx7MmzcPXbp0wZw5c3D79m388ssvmDRpUm7ESAry9ATu3AGE4Px+ytqTJ09QtmxZ3X5rQgjMnz8fQgi0adMGlpaWEEJAxR8iIlKIlRVQvrw8Pvnk2fmUFJmoZSRv27cD06bJhM7T81nClvG15csDBQsq1w8iyh/Ur2+i76+//sLcuXPRtm1bWFpaon79+hg1ahQmTJiAFStWvFEQc+bMQYkSJWBjYwM/Pz8cOXLkle3Xrl2LcuXKwcbGBpUrV8a2bdv0rgsh8N1338HT0xO2trYICAjApUuX9NrExcWhc+fOcHR0hLOzM3r27IlHjx7ptTl16hTq168PGxsbeHt7Y8qUKXrXFy5ciPr168PFxQUuLi4ICAh4beymxtVVJmgPHyodCRmzypUrIz4+Hr169cKRI0cQGBiIq1evon379njnnXcAgAkaERkla2uZiLVrB4wdC6xbB5w/Dzx6BGzdCnTtCtjbAzt2AL17y8TNzU1u4N27txyN27FDTrHUapXuDRGZC4OTtLi4OJQsWRKAXH+WUXK/Xr162Ldvn8EBrF69GoMHD0ZwcDCOHz+OqlWrIjAwEPfu3cu0/aFDh9CxY0f07NkTERERuqlXZ86c0bWZMmUKZs2ahfnz5yMsLAz29vYIDAzE06dPdW06d+6Ms2fPYufOndiyZQv27duHoKAg3fXExEQ0b94cxYsXR3h4OKZOnYoxY8ZgwYIFujZ79+5Fx44dsWfPHoSGhsLb2xvNmzfHbTMqh6hWAx4enPJIr+bu7o5du3bh2rVrGDNmDOzs7LBo0SK0bds20/arVskboqNH8zhQIqJs0mhk8ZEOHYDvvwfWrpVr3P77Dzh4EBgyBChZEjh5EggOlkVKChQAatYEOncGfvgBWL9ejs69sHyfiOj1hIEqV64s9u7dK4QQomnTpmLIkCFCCCFmzpwpihQpYujTidq1a4u+ffvqPk9PTxdeXl5i4sSJmbZv166daNmypd45Pz8/0atXLyGEEFqtVnh4eIipU6fqrsfHxwuNRiNWrlwphBDi3LlzAoA4evSors327duFSqUSt2/fFkIIMXfuXOHi4iKSk5N1bYYPHy58fX2z7EtaWpooUKCAWL58ebb6npCQIACIhISEbLVXSu3aQuzcqXQUZCoePXqk+zg9PT3TNg8eCDFunBCFCwvRsKEQW7YIkUVTIiKToNUKceeOECEhQsyZI8RXXwnRtKkQXl5CWFoKUaaMEB98IMSQIUIsWCDE3r1CREfLr8tLpnLvQZTfGTyS1r17d5w8eRIAMGLECMyZMwc2NjYYNGgQhg4datBzpaSkIDw8HAEBAbpzarUaAQEBCA0NzfRrQkND9doDQGBgoK59VFQUYmJi9No4OTnBz89P1yY0NBTOzs6oVauWrk1AQADUajXCwsJ0bRo0aADr53bJDAwMRGRkJB5mMffv8ePHSE1NRcEsJqsnJycjMTFR7zAFLB5ChrC3t9d9/Pyeas9zdQVGjZLTgzp2BAYNku9CL1vGd5yJyDSpVPLvZZMmwJdfArNnA7t2yb1GY2OB338H2rcHbG1l0ZIBA+RInLMzULs20KULMG4csGaNHJ17/FjpHhGRkgwuHDJo0CDdxwEBAbhw4QLCw8NRunRpVKlSxaDnevDgAdLT0+Hu7q533t3dHRcuXMj0a2JiYjJtHxMTo7uece5VbV6sNmdpaYmCBQvqtXlx892M54yJidEVSHje8OHD4eXl9VISmWHixIkYO3ZspteMmZeXLB5ClNNsbYFevYDPPwc2bwamTpUb1HbtCnTqJKcacSkbEZk6R0eZiNWurX9eqwVu3ZLbA1y8KB8PHJAf37wpk76SJeXh4yMTwHr1lOkDEeWtbCdpWq0WU6dOxebNm5GSkoKmTZsiODgYxYsXR/HixXMzRpMwadIkrFq1Cnv37oWNjU2mbUaOHInBgwfrPk9MTIS3t3dehfjGSpQAoqKUjoLMmYUF0KaNPI4eBZYvBwIC5J5GHTvKNSFlyigdJRFRzlKrgWLF5NGsmf61J0+Aq1fl39+rV+Vx/TqTNKL8IttJ2vjx4zFmzBgEBATA1tYWM2fOxL1797BkyZI3/uaFChWChYUF7t69q3f+7t278PDwyPRrPDw8Xtk+4/Hu3bvw9PTUa5Oxya6Hh8dLhUnS0tIQFxen9zyZfZ/nv0eGadOmYdKkSdi1a9crRxM1Gg00Gk2W141VyZJyygZRXnjnHXnMmAHs3i2LjLzzDlC6tEzY2rcHihZVOkoiotxla/tszzYiyn+yvSbt119/xdy5c7Fjxw5s3LgRf/31F1asWAHtW9Sbtba2Rs2aNRESEqI7p9VqERISAn9//0y/xt/fX689AOzcuVPX3sfHBx4eHnptEhMTERYWpmvj7++P+Ph4hIeH69rs3r0bWq0Wfn5+ujb79u1Damqq3vfx9fXVm+o4ZcoUjBs3Dn///bfeGjdzUqqUfAePKC9ZWgLNmwNLlgB37wKjR8tRtnLlgAYNgHnzgPv3lY6SiIiIKBdkt8KItbW1uHHjht45jUYjbt68+VaVS1atWiU0Go1YtmyZOHfunAgKChLOzs4iJiZGCCFEly5dxIgRI3TtDx48KCwtLcW0adPE+fPnRXBwsLCyshKnT5/WtZk0aZJwdnYWmzZtEqdOnRKtWrUSPj4+4smTJ7o27733nqhevboICwsTBw4cEGXKlBEdO3bUXY+Pjxfu7u6iS5cu4syZM2LVqlXCzs5O/PLLL3rfx9raWqxbt05ER0frjqSkpGz13VQqLMXHy8pUqalKR0IkRFKSEH/8IcSHHwphaytEYKAQS5fKn1MiIno1U7n3IMrvsp2kqdVqce/ePb1zDg4O4urVq28dxOzZs0WxYsWEtbW1qF27tjh8+LDuWsOGDUXXrl312q9Zs0aULVtWWFtbi4oVK4qtW7fqXddqtWL06NHC3d1daDQa0bRpUxEZGanXJjY2VnTs2FE4ODgIR0dH0b1795eSq5MnT4p69eoJjUYjihQpIiZNmqR3vXjx4gLAS0dwcHC2+m1KL5SurkLkwH81UY6KixNi0SIhAgKEsLMTok0bIVavFuK//5SOjIjIOJnSvQdRfqYSQojsjLip1Wq8//77emuq/vrrLzRp0kSv5PaGDRtybJTP3CUmJsLJyQkJCQlwdHRUOpxXql0bmDBBFnMgMkYxMXKz2VWrgFOngI8+Alq3BgIDZWU1IiIyrXsPovws24VDunbt+tK5zz77LEeDIePFdWlk7Dw8gH795HHtmkzYZs8G/u//ZDW0jz4CPvxQVislIiIiMmbZHkmjnGdK72Z9+y2QlgZMnqx0JESGefAA2L4d+Osv4O+/geLFnyVstWvLEthERPmFKd17EOVnvD2hbOFIGpmqQoWALl2ANWtkwvbTT0BSktx7zdMT6NkT2LgR+O8/pSMlIiIikpikUbaULAlcuqR0FERvx9pabhg7a5bcIHbXLvkGxKRJQOHCQMuWwPz5wK1bSkdKRERE+RmnOyrIlKYcPHoEeHkBp0/L6WJE5ubuXWDrVmDzZpm8lS0rp0W+9x5Qq5bct42IyNSZ0r0HUX7GkTTKFgcHoG1b4NdflY6EKHe4uwM9esipj/fvAz/8IBO3zp0BV1dZKXLOHCAyEuBbW0RERJSbOJKmIFN7N+vff+VN7OXLgEqldDREeefqVSAkRI6whYQAtrZyO4pmzYCmTWWCR0RkCkzt3oMov+JIGmVb/fpyBOHAAaUjIcpbJUsCX3wBrF4N3LsHbNoElC8PLFsG+PgAVaoAgwcD27bJqcFEREREb4MjaQoyxXezxo4FbtwAFi9WOhIi4/D0KXDwoBxl27VLrtv085MjbQEBwDvvcD0bERkPU7z3IMqPmKQpyBRfKKOigGrVgDt3AHt7paMhMj6xscCePc+Stvv3gQYNgIYN5VG9OpM2IlKOKd57EOVHTNIUZKovlI0bA506yelfRPRqUVHA3r3Avn1yXef9+3KkrWbNZ0fJklznSUR5w1TvPYjyGyZpCjLVF8qQELk5cGQkUKCA0tEQmZabN4HDh4HwcOD4cfmYng7UqKGfuJUqBai5apiIcpip3nsQ5TdM0hRkyi+UH38MlCsHTJigdCREpk0I4Pp1maw9n7ilpMipkRlJW/XqQJkynCpJRG/HlO89iPITJmkKMuUXyqtX5U3j8ePyHX8iyjlCyBG355O248eBhASgYkVZTfL5o1AhpSMmIlNhyvceRPkJkzQFmfoL5bffAufOAX/+qXQkRPnDvXuyeuSpU8+Os2eBggWfJWxVq8pHX1/A2lrpiInI2Jj6vQdRfsEkTUGm/kL56JG8EVy+XJYaJ6K8l5YGXLoEnDypn7zFxMi93J4fcatcGfD0ZJESovzM1O89iPILJmkKMocXyt9/ByZNAk6c4FoZImPy8OHLo26nT8vRtfLlgQoV5JHxsbc3C5UQ5QfmcO9BlB8wSVOQObxQarXAu+8CnTsDX32ldDRE9CparVzrdu6cPM6ff/ZxWposBvRi8layJGBhoXTkRJRTzOHegyg/YJKmIHN5oTxyBGjRQpbkd3VVOhoiMpQQcnpkZslbYiJQtqx+4ubrC5QuDdjZKR05ERnKXO49iMwdkzQFmdMLZffugL098PPPSkdCRDnpwYNnSVvG48WLckSuSBGZwL14lCjB6c9Exsqc7j2IzBmTNAWZ0wtldLR8h33fPlmcgIjM25MnwJUrMmF78Xj4UE6TzCyB8/Ji4RIiJZnTvQeROWOSpiBze6GcPBnYsQMICeFNGFF+Fh8vK05mlsAJITflLl1a7rFYsqR8LFVKFi/h+jei3GVu9x5E5opJmoLM7YUyOVlutDt1KtCmjdLREJGxyVj7dvEicPkycPWqHI3LOB49klMln0/cMg4fHzmlmojejrndexCZKyZpCjLHF8rNm4GBA+W6FRsbpaMhIlPy8OHLiVvG57duAW5uz5K2jESuRAl5eHpyFI4oO8zx3oPIHDFJU5A5vlAKAQQGAo0aAd98o3Q0RGQukpOBa9f0k7irV4Hr14GoKLlGrlgxmbAVL/4secs4vLyYxBEB5nnvQWSOmKQpyFxfKM+dA+rWBc6eldXfiIhyW3y8TOIyO6KigMePnyVxzx/Fi8vzRYoAVlYKBU+Uh8z13oPI3DBJU5A5v1AOGADExQG//aZ0JEREr07ibt6U1z09ZcLm7S0fM46Mz11dWRSJTJ8533sQmRMmaQoy5xfKhw9lue0VK4DmzZWOhojo1R49ksnazZvAjRvPjozPb94E1OpnSZunJ+DuLtfJubkBhQvrHyxyQsbKnO89iMwJtxulXOHiAvz6K9ChA7BuHdCkidIRERFlzcEBKF9eHpnRaoH7958lbTExwL17cirl4cPyWsYRFycLJ72YuL3qcHTkKB0RET3DkTQF5Yd3s7ZtAzp3lola06ZKR0NElPvS0mSi9nzilnHcu/fyuQcPAEtLoFAhOaUys8fMzhUowMSODJcf7j2IzAFH0ihXtWgBrFwJfPIJsHYtEBCgdERERLnL0vLZNMjs0GrlFPEHD4DY2Jcfz5x5+VxsrKxWmVlSV7Dgs8cXPy5YENBocrf/RET09jiSpqD89G7Wjh1y6uPq1VyjRkT0trRaICFBJm0vJnBxcfLI7ONHj+R6udclci4u8nj+Y3t7jtyZg/x070FkypikKSi/vVD+8w/Qvj3Qrx8wfDgX1hMR5bWUlGeJW1aJXFycHNnLeHz4UCaEFhbPErYXE7isDmdneXBqpvHIb/ceRKaK0x0pzzRvDhw5AgwZAvj6ApMmAZ06yYppRESU+6ytAQ8PeRgiY+QuI2l7MYl7+BC4cEH/WkKC3NogPl4+R0bCltnxfEL3/OHkJA8HB/6tIKL8hSNpCsrP72bt3AkMGiT/8M6YAdSpo3RERESUG4QAkpKeJWwvHg8fZn0+IQFITJTP4+j4LGl7/ng+mcvqvKOjnL3BRC9/33sQmRKOpJEimjUDTpwAFiwAPvwQ6NUL+OEHpaMiIqKcplLJJMnRUe4zZyitVq6lS0jQP+Lj9T+/du3lc/HxMslLSpLPlRFHRsJnyMeOjnLaJpM9IsoLHElTEN/Nkh4+BG7fBipVevna+vXrcfPmTZQvXx716tWDPReyERGRgTISvcREeWSM0BnycVKSfA5AJmsFCjxL3J5P4jJ7zPj4+cPBQZlpnLz3IDINHEkjxWUsMH9RSkoKLl++jF27dmHYsGHo06cPZs6cCQBIS0vDtWvX4O7ujgIFCuRxxEREZErU6mfJ0tvQaoH//ns2Oveqx5s39T9PTJRJXlLSs0MIOTL3YgL3fCL3/Od+fkC9ejnzb0JExo1JGhktS0tL9OvXDw0aNMCMGTNQunRp3bUrV65gzJgxCAkJQeHChTFr1iw05W7ZRESUi9TqZwnT2xICePxYP2l7MYl7/oiOBry83v77EpFpYJJGRkutVsPOzg7x8fF4+PAhypcvr7tma2uLESNGwM/PDxs2bMDTp08BAFqtFupM5o4IIaBi/WciIjISKpUcRbO3N7zaJhGZPy59JaOVsVzy+vXrAABfX1/d+WLFiqFq1aoQQsDd3R0lS5YEgEwTscePH2P+/Plo1KgRWrZsienTp+Pu3bt51AsiIiIiIsNwJI2MlkqlQnp6Om7fvo0CBQrA29tbdz5jxOzatWtwdHRE0aJFdddeFBcXB41Gg6CgICQlJeHPP//EzZs3MW7cONjb2yM9PR0WFhZIS0uDpSV/JYiIiIhIWbwjJaMWExOD27dv65KwjGmLarUajx49wt27d1G6dOlXFg8pWrQoevToofv8008/RdGiRdG6dWs0aNAAFhYWuHbtGn777TesWbMGjx49gr+/P4KDg3Wjd0REREREeYVJGhk1tVqNx48fo27dugCgG/VSqVSIiorCo0ePdCNsr1t3JoSAEAIFCxaERqNBeno6ADkdsm3btqhcuTJWrVqFhIQEbNiwAYn/20H1+PHj+O2335CQkIDatWujZcuWuu9JRERERJTTmKSRUTpy5Ai+/vprJCUlITY2FoGBgQCgNx3x5s2buvVpwKuTtIxrCQkJGDlyJDw8PNC4cWMkJydjyZIliI2NxcKFC2FlZQUAqFu3rq4Yyfnz5+Hl5QVbW1ts2rQJK1euxLJly+Dj4wMAePToERITE+HFsltERERElANYOISMUrVq1TBs2DA0b94c5cqVQ9++feHr64v4+Hjs3LkTGzZswO7du+Hg4AB/f38A0KvqqNVqATwrPqJSqbB//360b98eN27cwIoVKwAAt27dwp49e1CgQAF06tQJBQoUQNeuXXHnzh3Y2NgAADp06IDBgwdjwoQJ2L59O3x8fDBkyBDd9/r3339RtGhRODk5oXLlyujevTsOHDiQJ/9ORERERGR+mKSRUbK2tsYHH3yAyZMn459//sHjx49x+PBhODs7IzY2FmPHjsXChQuxdu1a1KxZE7NmzUJycrLu6zMStoyRtREjRqBXr16oXbs2fvvtN9SoUQMAkJCQgGPHjsHFxQXjxo1DdHQ0kpKS8PPPPwOQSZ6FhQUsLCx0iZ+vry+ioqIAyE21L168iFq1aiEhIQHff/893N3dcf78+Tz7tyIiIiIi88LpjmQyXFxcAMiRrQ4dOgAAoqOjsXfvXqSnp0Oj0UAIgVq1amHMmDH48MMPcePGDXzyyScoWrQo/vnnH10BkgyWlpa4d+8e1q9fj3LlygEA6tWrh127duHGjRsoVqyYbi2bWq3Gxo0bsW3bNrRt2xYA8OTJExw9elT3vG3atEGbNm3y6p+EiIiIiMwQkzQyaZ6enujYsSOAZ+vONm/erFu7FhISgpMnT+Ls2bMICgpCxYoV0bx5czRu3BiWlpa6dWXPr3Vzd3dHSkoKrK2tdefUajVmzpyJ+fPno2vXrujfvz8A4OnTp7Czs8Nff/2FMmXKoH79+hg7duwrC4scPAjs2AEULPjscHICHB31j/8tjyMiIiKifEYlMhbtUJ5LTEyEk5MTEhIS4OjoqHQ4Zi0qKgohISHYvXs3oqOjMW7cONSrVw8pKSno378/tFotfv75Z9y8eRNt2rTBhx9+iPHjxwOQ2wB06dIFsbGxmDJlCgICAnTP+/TpUzx58gQuLi64fPkyBg8ejPLly2PcuHF6Sd7zDh8G/vwTiI0F4uLkkZgoj4QEeaSmAra2+klbZonc8+ft7QEHh8wfbW2BVxS+JCKifIL3HkSmgUmagvhCaRyuXr2KoKAghIaGomLFimjYsCG+++47FChQACtWrMC0adPQpUsXDB48+LXPFRoaimbNmiEqKgqFCxd+45iSk/UTt4yPM/s849yjR8B//8kj4+NHj4CnT2WCZm//6kQu47C1Bezsnj0+//GrHjUaJoJERMaO9x5EpoHTHSnfK1myJHbt2gUAuH37NgoVKgSNRoMjR45g0qRJOHv2LIoWLQpra2tUq1YNdevWhVqtxtOnT/H06VM4OzvrPVdKSoquyMib0miAwoXl8bbS019O3l5M5DIeHz8GnjwBYmLkY8bnGY+ZnXv8WI78qVQyWbO1BWxs5PH8x4YcGk3Wh7X1q69bWLz9vxkRERGRkjiSpiC+m2W8nt9z7fTp09i1axf27duHXbt2oXfv3pg6dSpOnz6NzZs3o169emjYsCEuX76MgQMHIiEhAfv371e4B3krLU0/iUtOliN4b3MkJ2d9pKS8fC41VcZiYaGf0GX3yKq9ldWz4/nPDbn24mFp+fI5NWvtElEe4L0HkWngSBpRJp7fFLty5cqoXLkyBg0aBODZ3msuLi54+PAhOnfujMePH6N48eKoXbs2Ro8erWuX1eba5sbSEihQQB5K0WpfTt5SU+W5F4+MRO91R8bzPHr07LlSU7P+OKtzaWnPzr/4cQaV6tWJXMbnlpbPjjf53MJC/1zGYcj5jHNZPWanjYXFsyPjcyaqREREEkfSFMR3s8xHcnIyrl27huLFi+s2wSZ6HSHkdNSsErgXP05Ly/7HmV1LTZXfLz1dv13GkZ3zLz5Hdh5fPPf8+Re9mLhllszl5KFW59znGR+/+Piqay+2ef78m3ycnc9fdS6fvK+Ur/Heg8g0cCQtn0hNTYUVa7rnGo1GA19fX6XDIBOjUj0bebK1VToaZWi1+knbi8lddj5/20Orff3Hz3+ekpJ12zd9TE9/lrQ/fy2rj193LrOvffFcVl5M3l6V2L2qrUqV/fZqdfbav6pNVtdePP82n2d8nNm5N7menWvPtyleHChbNu9+P4lIOUzS8oElS5agT58+mDdvHnr06KF0OEREOmq1XMNHeU+IrBPB569llexldTyfdL74PK87Xowps69/1XNmFveLcWT18eueK+Pz7D7Xi9dejOVNHtu3Z5JGlF8wSTNzS5Ysweeffw4hBD7//HMAYKJGRERQqZ5NteRECyIi42IUy7TnzJmDEiVKwMbGBn5+fjhy5Mgr269duxblypWDjY0NKleujG3btuldF0Lgu+++g6enJ2xtbREQEIBLly7ptYmLi0Pnzp3h6OgIZ2dn9OzZE48ePdJrc+rUKdSvXx82Njbw9vbGlClTDI5FSc8naAB0idqSJUsUjoyIiIiIiLKieJK2evVqDB48GMHBwTh+/DiqVq2KwMBA3Lt3L9P2hw4dQseOHdGzZ09ERESgdevWaN26Nc6cOaNrM2XKFMyaNQvz589HWFgY7O3tERgYiKdPn+radO7cGWfPnsXOnTuxZcsW7Nu3D0FBQbrriYmJaN68OYoXL47w8HBMnToVY8aMwYIFCwyKRSkvJmgZmKgRERERERk5obDatWuLvn376j5PT08XXl5eYuLEiZm2b9eunWjZsqXeTW7zLAAALS5JREFUOT8/P9GrVy8hhBBarVZ4eHiIqVOn6q7Hx8cLjUYjVq5cKYQQ4ty5cwKAOHr0qK7N9u3bhUqlErdv3xZCCDF37lzh4uIikpOTdW2GDx8ufH19sx3L6yQkJAgAIiEhIVvts2vx4sVCpVIJAFkeKpVKLF68OEe/LxERERm33Lr3IKKcpehIWkpKCsLDwxEQEKA7p1arERAQgNDQ0Ey/JjQ0VK89AAQGBuraR0VFISYmRq+Nk5MT/Pz8dG1CQ0Ph7OyMWrVq6doEBARArVYjLCxM16ZBgwawfm5Fe2BgICIjI/Hw4cNsxfKi5ORkJCYm6h05LasRtBcJjqgRERERERklRZO0Bw8eID09He7u7nrn3d3dERMTk+nXxMTEvLJ9xuPr2ri5ueldt7S0RMGCBfXaZPYcz3+P18XyookTJ8LJyUl3eHt7Z9ruTaWmpqJPnz6vTdAyCCHQp08fpKam5mgcRERERET05hRfk5afjBw5EgkJCbrj5s2bOfr8VlZWmDdvHlTZ3I1UpVJh3rx53D+NiIiIiMiIKJqkFSpUCBYWFrh7967e+bt378LDwyPTr/Hw8Hhl+4zH17V5sTBJWloa4uLi9Npk9hzPf4/XxfIijUYDR0dHvSOn9ejRA4sWLXptoqZSqbBo0SKW4yciIiIiMjKKJmnW1taoWbMmQkJCdOe0Wi1CQkLg7++f6df4+/vrtQeAnTt36tr7+PjAw8NDr01iYiLCwsJ0bfz9/REfH4/w8HBdm927d0Or1cLPz0/XZt++fXpTAXfu3AlfX1+4uLhkKxalvC5RY4JGRERERGTElK1bIsSqVauERqMRy5YtE+fOnRNBQUHC2dlZxMTECCGE6NKlixgxYoSu/cGDB4WlpaWYNm2aOH/+vAgODhZWVlbi9OnTujaTJk0Szs7OYtOmTeLUqVOiVatWwsfHRzx58kTX5r333hPVq1cXYWFh4sCBA6JMmTKiY8eOuuvx8fHC3d1ddOnSRZw5c0asWrVK2NnZiV9++cWgWF4ltyssZVblkVUdiYiI8i9WdyQyDYonaUIIMXv2bFGsWDFhbW0tateuLQ4fPqy71rBhQ9G1a1e99mvWrBFly5YV1tbWomLFimLr1q1617VarRg9erRwd3cXGo1GNG3aVERGRuq1iY2NFR07dhQODg7C0dFRdO/eXSQlJem1OXnypKhXr57QaDSiSJEiYtKkSS/F/rpYXiUvXiifT9SYoBEREeVvTNKITINKiGyWAqQcl5iYCCcnJyQkJOTK+rQMS5YsQZ8+fTBv3jxOcSQiIsrH8ureg4jeDpM0BeXlC2VqaiqrOBIREeVzTNKITANL8OcTTNCIiIiIiEwDkzQiIiIiIiIjwiSNiIiIiIjIiDBJIyIiIiIiMiJM0oiIiIiIiIwIkzQiIiIiIiIjwiSNiIiIiIjIiDBJIyIiIiIiMiJM0oiIiIiIiIwIkzQiIiIiIiIjwiSNiIiIiIjIiDBJIyIiIiIiMiJM0oiIiIiIiIwIkzQiIiIiIiIjwiSNiIiIiIjIiDBJIyIiIiIiMiJM0oiIiIiIiIyIpdIB5GdCCABAYmKiwpEQERFRfpBxz5FxD0JExolJmoKSkpIAAN7e3gpHQkRERPlJUlISnJyclA6DiLKgEnwrRTFarRZ37txBgQIFoFKpcu37JCYmwtvbGzdv3oSjo2OufR9jxL6z7+x7/sG+s+/s++sJIZCUlAQvLy+o1Vz1QmSsOJKmILVajaJFi+bZ93N0dMx3f8AysO/se37DvrPv+Q37nv2+cwSNyPjxLRQiIiIiIiIjwiSNiIiIiIjIiDBJywc0Gg2Cg4Oh0WiUDiXPse/se37DvrPv+Q37nj/7TmTuWDiEiIiIiIjIiHAkjYiIiIiIyIgwSSMiIiIiIjIiTNKIiIiIiIiMCJM0IiIiIiIiI8IkzQTMmTMHJUqUgI2NDfz8/HDkyJFXtl+7di3KlSsHGxsbVK5cGdu2bdO7LoTAd999B09PT9ja2iIgIACXLl3SaxMXF4fOnTvD0dERzs7O6NmzJx49epTjfXudvO77tWvX0LNnT/j4+MDW1halSpVCcHAwUlJScqV/r6LE/3uG5ORkVKtWDSqVCidOnMipLmWbUn3funUr/Pz8YGtrCxcXF7Ru3Tonu5UtSvT94sWLaNWqFQoVKgRHR0fUq1cPe/bsyfG+vU5O933Dhg1o3rw5XF1ds/xZfvr0Kfr27QtXV1c4ODigbdu2uHv3bk52K1vyuu9xcXHo168ffH19YWtri2LFiqF///5ISEjI6a69lhL/7xmEEHj//fehUqmwcePGHOiNYZTqe2hoKJo0aQJ7e3s4OjqiQYMGePLkSU51i4hygiCjtmrVKmFtbS2WLFkizp49K7744gvh7Ows7t69m2n7gwcPCgsLCzFlyhRx7tw5MWrUKGFlZSVOnz6tazNp0iTh5OQkNm7cKE6ePCk++ugj4ePjI548eaJr895774mqVauKw4cPi/3794vSpUuLjh075np/n6dE37dv3y66desmduzYIa5cuSI2bdok3NzcxJAhQ/KkzxmU+n/P0L9/f/H+++8LACIiIiK3upkppfq+bt064eLiIubNmyciIyPF2bNnxerVq3O9v89Tqu9lypQRLVq0ECdPnhQXL14UX375pbCzsxPR0dG53ucMudH3X3/9VYwdO1YsXLgwy5/l3r17C29vbxESEiKOHTsm6tSpI+rWrZtb3cyUEn0/ffq0+Pjjj8XmzZvF5cuXRUhIiChTpoxo27Ztbnb1JUr9v2f46aefdK91f/75Zw737tWU6vuhQ4eEo6OjmDhxojhz5oy4cOGCWL16tXj69GludZWI3gCTNCNXu3Zt0bdvX93n6enpwsvLS0ycODHT9u3atRMtW7bUO+fn5yd69eolhBBCq9UKDw8PMXXqVN31+Ph4odFoxMqVK4UQQpw7d04AEEePHtW12b59u1CpVOL27ds51rfXUaLvmZkyZYrw8fF5m64YTMm+b9u2TZQrV06cPXtWkSRNib6npqaKIkWKiEWLFuV0dwyiRN/v378vAIh9+/bp2iQmJgoAYufOnTnWt9fJ6b4/LyoqKtOf5fj4eGFlZSXWrl2rO3f+/HkBQISGhr5FbwyjRN8zs2bNGmFtbS1SU1MN68BbULLvERERokiRIiI6OlqRJE2pvvv5+YlRo0a9XfBElOs43dGIpaSkIDw8HAEBAbpzarUaAQEBCA0NzfRrQkND9doDQGBgoK59VFQUYmJi9No4OTnBz89P1yY0NBTOzs6oVauWrk1AQADUajXCwsJyrH+volTfM5OQkICCBQu+TXcMomTf7969iy+++AK//fYb7OzscrJb2aJU348fP47bt29DrVajevXq8PT0xPvvv48zZ87kdBezpFTfXV1d4evri19//RX//fcf0tLS8Msvv8DNzQ01a9bM6W5mKjf6nh3h4eFITU3Ve55y5cqhWLFiBj3P21Cq75lJSEiAo6MjLC0t3+p5skvJvj9+/BidOnXCnDlz4OHhYXjwb0mpvt+7dw9hYWFwc3ND3bp14e7ujoYNG+LAgQNv1hEiyjVM0ozYgwcPkJ6eDnd3d73z7u7uiImJyfRrYmJiXtk+4/F1bdzc3PSuW1paomDBgll+35ymVN9fdPnyZcyePRu9evV6o368CaX6LoRAt27d0Lt3b70EPS8p1ferV68CAMaMGYNRo0Zhy5YtcHFxQaNGjRAXF/f2HcsGpfquUqmwa9cuREREoECBArCxscFPP/2Ev//+Gy4uLjnSt9fJjb5nR0xMDKytreHs7PxWz/M2lOp7ZnGMGzcOQUFBb/wcb/I9ler7oEGDULduXbRq1cqwoHOIUn1//rXuiy++wN9//40aNWqgadOmWa5RJiJlMEkjysLt27fx3nvv4dNPP8UXX3yhdDi5bvbs2UhKSsLIkSOVDiXPabVaAMC3336Ltm3bombNmli6dClUKhXWrl2rcHS5SwiBvn37ws3NDfv378eRI0fQunVrfPjhh4iOjlY6PMoDiYmJaNmyJSpUqIAxY8YoHU6u27x5M3bv3o0ZM2YoHUqey3it69WrF7p3747q1atj+vTp8PX1xZIlSxSOjoiexyTNiBUqVAgWFhYvVRq7e/dultMzPDw8Xtk+4/F1be7du6d3PS0tDXFxcXk2LUSpvme4c+cOGjdujLp162LBggVv1RdDKdX33bt3IzQ0FBqNBpaWlihdujQAoFatWujatevbdywblOq7p6cnAKBChQq66xqNBiVLlsSNGzfeokfZp+T/+5YtW7Bq1Sq8++67qFGjBubOnQtbW1ssX748R/r2OrnR9+zw8PBASkoK4uPj3+p53oZSfc+QlJSE9957DwUKFMCff/4JKysrg5/jTSnV9927d+PKlStwdnaGpaWlbnpn27Zt0ahRI8M68YaU6ntmr3UAUL58+Tx7rSOi7GGSZsSsra1Rs2ZNhISE6M5ptVqEhITA398/06/x9/fXaw8AO3fu1LX38fGBh4eHXpvExESEhYXp2vj7+yM+Ph7h4eG6Nrt374ZWq4Wfn1+O9e9VlOo7IEfQGjVqpBtNUavz9tdEqb7PmjULJ0+exIkTJ3DixAldaefVq1dj/PjxOdrHrCjV95o1a0Kj0SAyMlLXJjU1FdeuXUPx4sVzrH+volTfHz9+DAAv/Zyr1Wrdu+65LTf6nh01a9aElZWV3vNERkbixo0bBj3P21Cq74D8WWjevDmsra2xefNm2NjYGN6Bt6BU30eMGIFTp07pXusyytRPnz4dS5cuNbwjb0CpvpcoUQJeXl56r3WA3IYjr17riCiblK5cQq+2atUqodFoxLJly8S5c+dEUFCQcHZ2FjExMUIIIbp06SJGjBiha3/w4EFhaWkppk2bJs6fPy+Cg4MzLcnt7OwsNm3aJE6dOiVatWqVaQn+6tWri7CwMHHgwAFRpkwZRUrw53Xfb926JUqXLi2aNm0qbt26JaKjo3WHuff9RYZUhctJSvV9wIABokiRImLHjh3iwoULomfPnsLNzU3ExcWZdd/v378vXF1dxccffyxOnDghIiMjxddffy2srKzEiRMnTLrvsbGxIiIiQmzdulUAEKtWrRIRERF6v8+9e/cWxYoVE7t37xbHjh0T/v7+wt/fP8/6LYQyfU9ISBB+fn6icuXK4vLly3qvdWlpaWbd98xAoRL8SvR9+vTpwtHRUaxdu1ZcunRJjBo1StjY2IjLly/nXeeJ6LWYpJmA2bNni2LFiglra2tRu3ZtcfjwYd21hg0biq5du+q1X7NmjShbtqywtrYWFStWFFu3btW7rtVqxejRo4W7u7vQaDSiadOmIjIyUq9NbGys6Nixo3BwcBCOjo6ie/fuIikpKdf6mJW87vvSpUsFgEyPvKbE//vzlErShFCm7ykpKWLIkCHCzc1NFChQQAQEBIgzZ87kWh+zokTfjx49Kpo3by4KFiwoChQoIOrUqSO2bduWa33MSk73Pavf5+DgYF2bJ0+eiC+//FK4uLgIOzs70aZNmzx/U0aIvO/7nj17snyti4qKyuXe6lPi//1FSiRpQijX94kTJ4qiRYsKOzs74e/vL/bv359bXSSiN6QSQojcG6cjIiIiIiIiQ3BNGhERERERkRFhkkZERERERGREmKQREREREREZESZpRERERERERoRJGhERERERkRFhkkZERERERGREmKQREREREREZESZpRERERERERoRJGmHv3r1QqVSIj49XOhQcPHgQlStXhpWVFVq3bp3tr1u2bBmcnZ1zLa6cdu3aNahUKpw4cULpUF7JmP5dS5QogRkzZuTKc1+4cAF16tSBjY0NqlWrlivfI7vGjBmjeAw5pVu3bgb9Hufl74VKpcLGjRuzvN6oUSMMHDgw1+PIDcb0mp4TjKU/xvQ6YSp/Q4jozTFJU1C3bt2gUqleOi5fvpxr3zOzG4+6desiOjoaTk5OufZ9s2vw4MGoVq0aoqKisGzZskzb5ObNel7x9vZGdHQ0KlWqpHQor9S+fXtcvHjxrZ4j4wYr4yhcuDBatGiB06dPG/Q8R48eRVBQ0FvFkpXg4GDY29sjMjISISEhufI9MpNZovD111/naQzGxFR+Lyh/Uup1gojyJyZpCnvvvfcQHR2td/j4+LzULiUlJddisLa2hoeHB1QqVa59j+y6cuUKmjRpgqJFixrNCE5OS0lJgYWFBTw8PGBpaal0OK9ka2sLNze3HHmuyMhIREdHY8eOHUhOTkbLli0N+rkuXLgw7OzsciSWF125cgX16tVD8eLF4erqmivfI7scHBwUj0EppvJ7QS/Lzb9RxsKYXidyQn74PyMyZUzSFKbRaODh4aF3WFhYoFGjRvjqq68wcOBAFCpUCIGBgQCAn376CZUrV4a9vT28vb3x5Zdf4tGjR3rPefDgQTRq1Ah2dnZwcXFBYGAgHj58iG7duuHff//FzJkzdaMa165dy3Qqyfr161GxYkVoNBqUKFECP/74o973KFGiBCZMmIAePXqgQIECKFasGBYsWPDKviYnJ6N///5wc3ODjY0N6tWrh6NHjwJ4NnUjNjYWPXr0gEqlynQkrVGjRrh+/ToGDRqk68PzduzYgfLly8PBwUGXAD9v0aJFKF++PGxsbFCuXDnMnTs3y3gXLFgALy8vaLVavfOtWrVCjx49AMg/2q1atYK7uzscHBzwzjvvYNeuXS/9W40bNw7/93//B0dHRwQFBb00VSU9PR09e/aEj48PbG1t4evri5kzZ+o9T8bUsWnTpsHT0xOurq7o27cvUlNT9f6Nhw8fDm9vb2g0GpQuXRqLFy/WXT9z5gzef/99ODg4wN3dHV26dMGDBw+y/Dd4cbpjxlS83377DSVKlICTkxM6dOiApKSkLJ8jg5ubGzw8PFCjRg0MHDgQN2/exIULF3TXDxw4gPr168PW1hbe3t7o378//vvvP71/x4wRVCEExowZg2LFikGj0cDLywv9+/cH8PLIXcbRrVu3TONSqVQIDw/H999/D5VKhTFjxmT6O3HixAnd78zz/zav+5lbsmSJ7nfJ09MTX331la4/ANCmTRuoVCrd5y9Od9Rqtfj+++9RtGhRaDQaVKtWDX///bfuesbP0oYNG9C4cWPY2dmhatWqCA0NfeX/R3x8PD7//HMULlwYjo6OaNKkCU6ePAkAuH//Pjw8PDBhwgRd+0OHDsHa2lo3gpAR5y+//AJvb2/Y2dmhXbt2SEhIyPJ7/v3336hXrx6cnZ3h6uqKDz74AFeuXHmpLxm/Fxn/DyEhIahVqxbs7OxQt25dREZG6j3vpk2bUKNGDdjY2KBkyZIYO3Ys0tLSdNcvXbqEBg0awMbGBhUqVMDOnTtf+W+TIS0tDV999RWcnJxQqFAhjB49GkIIAMD333+f6YhftWrVMHr06EyfL7Ppwxs3btR7HcvO79irXkufd/DgQVSpUgU2NjaoU6cOzpw5o7t2/fp1fPjhh3BxcYG9vT0qVqyIbdu26a6/7rUis79RnTp1Qvv27fViSE1NRaFChfDrr78CkD/PEydO1L3WVa1aFevWrdP7mm3btqFs2bKwtbVF48aNdb9zrxIfH49evXrB3d0dNjY2qFSpErZs2aK7/rZ/0zJ7nQCA06dPo0mTJrC1tYWrqyuCgoL0/iZnNnuldevWeq9H2fl7euTIEVSvXh02NjaoVasWIiIiXvo3eJP/MyIyYoIU07VrV9GqVatMrzVs2FA4ODiIoUOHigsXLogLFy4IIYSYPn262L17t4iKihIhISHC19dX9OnTR/d1ERERQqPRiD59+ogTJ06IM2fOiNmzZ4v79++L+Ph44e/vL7744gsRHR0toqOjRVpamtizZ48AIB4+fCiEEOLYsWNCrVaL77//XkRGRoqlS5cKW1tbsXTpUt33KV68uChYsKCYM2eOuHTpkpg4caJQq9W6ODPTv39/4eXlJbZt2ybOnj0runbtKlxcXERsbKxIS0sT0dHRwtHRUcyYMUNER0eLx48fv/QcsbGxomjRouL777/X9UEIIZYuXSqsrKxEQECAOHr0qAgPDxfly5cXnTp10n3t77//Ljw9PcX69evF1atXxfr160XBggXFsmXLMo03Li5OWFtbi127dul9/+fPnThxQsyfP1+cPn1aXLx4UYwaNUrY2NiI69ev6/1bOTo6imnTponLly+Ly5cvi6ioKAFARERECCGESElJEd999504evSouHr1qvj999+FnZ2dWL16te55unbtKhwdHUXv3r3F+fPnxV9//SXs7OzEggULdG3atWsnvL29xYYNG8SVK1fErl27xKpVq4QQQjx8+FAULlxYjBw5Upw/f14cP35cNGvWTDRu3DjL/7OlS5cKJycn3efBwcHCwcFBfPzxx+L06dNi3759wsPDQ3zzzTdZPseLP1/x8fGiU6dOAoA4f/68EEKIy5cvC3t7ezF9+nRx8eJFcfDgQVG9enXRrVs3vX/H6dOnCyGEWLt2rXB0dBTbtm0T169fF2FhYbp/h+TkZN3PRnR0tNi9e7ewsbERixcvzjS+6OhoUbFiRTFkyBARHR0tkpKSXopZCPm7BUBERUXp/m1e9zM3d+5cYWNjI2bMmCEiIyPFkSNHdH24d++eACCWLl0qoqOjxb1793T/xlWrVtU9x08//SQcHR3FypUrxYULF8SwYcOElZWVuHjxohBC6H6WypUrJ7Zs2SIiIyPFJ598IooXLy5SU1Oz/H8JCAgQH374oTh69Ki4ePGiGDJkiHB1dRWxsbFCCCG2bt0qrKysxNGjR0ViYqIoWbKkGDRokO7rg4ODhb29vWjSpImIiIgQ//77ryhdurRe/198jVu3bp1Yv369uHTpkoiIiBAffvihqFy5skhPT9frS8bvRcb/g5+fn9i7d684e/asqF+/vqhbt67uOfft2yccHR3FsmXLxJUrV8Q///wjSpQoIcaMGSOEECI9PV1UqlRJNG3aVJw4cUL8+++/onr16gKA+PPPP7P898l4DR4wYIC4cOGC7ncy4+fs5s3/b+/Og6I4+j6Af1kuF4SggAgGQY6FVRcLDCpSyqOomETiAYqAigpEBPFEjSHxAoNEUQs1qJiAIoJaBBVXBcQLl8KzuHRdYIXgRXlWFNGA0M8fvDsvs7usi0dCnupPFVXMTG9Pz9E985vpmblLOBwOuXLlCvObGzduEA0NDSKVSpXmKV+fCCEkOzubtD8Uq1PHVLWl7dcbn88neXl5pKysjEyYMIFYW1uTpqYmQgghX3/9NRk7diwpKysjUqmU5OTkkAsXLhBC1GsrlB2jTpw4QbhcLnn58iWTLicnh3C5XPLixQtCCCGxsbHE0dGRnD59mkilUpKSkkJ0dXXJ+fPnCSGE1NXVEV1dXbJ06VJmvZuZmSnUx/ZaWlrIsGHDyIABA0heXh6zPCdPniSEfJxjmrJ2oqGhgZibmzPbqqCggPTr148EBQWx1tOiRYtY5Z04cSIrzbvm/fLlS2JqakoCAgJIRUUFycnJITY2Nqy68r7bjKKorosGaf+goKAgoqmpSfT19Zk/X19fQkhbY+rs7PzOPI4cOUKMjY2ZYX9/f+Lu7t5hemUHDPkT0oCAADJ27FhWmuXLl5P+/fszw1ZWVmTGjBnMcGtrK+nVqxdJSkpSOt+Ghgaira1N0tPTmXFNTU3EwsKC/Pzzz8y4zz77jHXgVKb9ybpMSkoKAUCqq6uZcTt37iRmZmbMsK2tLTl48CDrdzExMcTNza3DeU2cOJHMnTuXGd69ezexsLBgTiqVGTBgANm+fTurvJMmTWKlkT8ZVSYiIoL4+Pgww0FBQcTKyoq8ffuWGTd16lTi5+dHCCFEIpEQACQ/P19pfjExMWTcuHGscXfv3iUAiEQiUfobZUGanp4ec8JFSNu+MXTo0A6XQ7Z/yfZxAAQA+eabb5g0wcHB5Ntvv2X9rrCwkHA4HPL69WtCCHu7JyQkEB6Px5xwduTJkyfExsaGhIeHq0w3aNAgsmbNGoUyvytIe9c+Z2FhQaKjozucr7JAQT5Is7CwIBs2bGClcXV1ZZZJti/t3buXmX7z5k1WECyvsLCQGBoakjdv3rDG29rakt27dzPD4eHhhMfjkYCAACIQCFjp16xZQzQ1Ncm9e/eYcadOnSIcDoe5eKLqQhQhhDx+/JgAIOXl5axlkQ/S2l8oEQqFBACzX3h6epKffvqJlW9aWhoxNzcnhBCSm5tLtLS0yP3791nlVCdI4/P5pLW1lRm3cuVKwufzmeEvv/ySdZEsMjKS/Oc//+kwT3WDNFV1TJ22VLbeZBdoCGm7wMTlcpkLPwKBgAlk5anTVig7RjU3NxMTExOyf/9+Zpy/vz/TRr1584bo6emRoqIi1u+Cg4OJv78/IYSQVatWsY41hLStd1VBWm5uLuFwOB22Yx/rmCbfTuzZs4f06NGDNDQ0MOOEQiHhcDikvr6eEKJ+kKZq3rt37ybGxsbMPk8IIUlJSay68r7bjKKorot2d/yHjRo1CiUlJcxfYmIiM23w4MEK6c+cOQNPT0/06dMHBgYGmDlzJp4+fYrGxkYAbV2yPD09P6hMYrEY7u7urHHu7u6oqqpCS0sLM87JyYn5X0NDA71798ajR4+U5imVStHc3MzKV1tbG0OGDIFYLP6g8sro6enB1taWGTY3N2fK8+rVK0ilUgQHB6N79+7MX2xsLKu7lbzAwEBkZWXhr7/+AgCkp6dj+vTp4HDaqk5DQwOioqLA5/NhZGSE7t27QywWo66ujpXPF1988c7y79y5E4MHD4apqSm6d++OPXv2KOQzYMAAaGpqKl3GkpISaGpqwsPDQ2n+paWlOHfuHGv5HR0dAUDlOpBnbW0NAwMDpWVQpbCwENevX0dqaip4PB527drFKltqaiqrbF5eXmhtbUVNTY1CXlOnTsXr169hY2OD0NBQZGdns7q3AW3drHx8fGBlZaXQdfRjUbXPPXr0CA8ePPig+vjixQs8ePBAaX2Urzft66O5uTlTBmVKS0vR0NAAY2Nj1jqvqalh7QubN2/G27dvceTIEaSnp0NXV5eVT9++fdGnTx9m2M3NDa2trQrdEWWqqqrg7+8PGxsbGBoaMl085fdzeaqWrbS0FOvXr2ctR2hoKB4+fIjGxkaIxWJYWlrCwsKCVU51DBs2jNUV0c3NjdUOhoaGIiMjA2/evEFTUxMOHjzIdIX+EKrqWGfa0vbL2bNnTzg4ODBpFi5ciNjYWLi7u2PNmjUoKytj0qrbVsgfo7S0tDBt2jSkp6cDaGt3jx07hsDAQABAdXU1GhsbMXbsWFbe+/fvZ/IVi8UYOnRoh8uhTElJCT7//HPweDyl0z/FMU2W76BBg6Cvr8/KV1Ud6IiqeYvFYqbbqoz8OnnfbUZRVNdFn87+h+nr68POzq7Dae3V1tZiwoQJmD9/PjZs2ICePXvi0qVLCA4ORlNTE/T09MDlcv+OYgNoOzFoT0NDQ+H5rb+TsvKQ/3t+RPaMQHJyssIJQPugR563tzcIIRAKhXB1dUVhYSG2bt3KTI+KikJ+fj42b94MOzs7cLlc+Pr6KjyQLb8t5WVmZiIqKgoJCQlwc3ODgYEBNm3ahMuXL79zGWXr/F3bvqGhAd7e3oiPj1eYJjvxVcf7bvd+/frByMgIDg4OePToEfz8/HDx4kWmbPPmzWOeK2uvb9++CuMsLS0hkUhw5swZ5OfnIzw8HJs2bcKFCxeY8s2fPx93797FlStXOv0iClkQLtt/ALCe/ZNRtc/9nXVRviyywKKj7dLQ0ABzc3OcP39eYVr7Z6akUikePHiA1tZW1NbWQiAQfFAZvb29YWVlheTkZOZ5z4EDB77zBQaqlq2hoQHr1q3DlClTFH7X/qT2U/D29oauri6ys7Oho6OD5uZm+Pr6dpiew+Gw9ilA/f3qY7etISEh8PLyglAoRF5eHuLi4pCQkIDIyEi12wpl7VpgYCA8PDzw6NEj5Ofng8vlYvz48QD+vx0WCoWs4B6AwgWAzvhYde1TrPe/a5t/yDajKKprokHav8j169fR2tqKhIQE5iTy8OHDrDROTk4oKCjAunXrlOaho6PDunKoDJ/Ph0gkYo0TiUTg8XgqAxpVbG1toaOjA5FIBCsrKwBtB6qrV692+ltE6iyDPDMzM1hYWODOnTvMVV11dOvWDVOmTEF6ejqqq6vh4OAAFxcXZrpIJMLs2bMxefJkAG0HSnUecpcnEokwfPhwhIeHM+M6c3cLAAQCAVpbW3HhwgWMGTNGYbqLiwuysrJgbW39j789LyIiAnFxccjOzsbkyZPh4uKCW7dudXjBQhkulwtvb294e3sjIiICjo6OKC8vh4uLC7Zs2YLDhw+jqKjovd7CZmpqCgB4+PAhevToAQCd/h6RgYEBrK2tUVBQgFGjRilNo62trXJfNjQ0hIWFBUQiEesOqUgkwpAhQzpVnvZcXFxQX18PLS0t5m6WvKamJsyYMQN+fn5wcHBASEgIysvLWW/7rKurw4MHD5i7VMXFxeBwOHBwcFDI7+nTp5BIJEhOTsaIESMAtL0s5kO5uLhAIpF0uO/w+XzcvXsXDx8+ZE5Wi4uL1cpb/iJJcXEx7O3tmXZQS0sLQUFBSElJgY6ODqZPn64yYDA1NcXLly/x6tUr5mS5s/tVZ9rS4uJi5iLH8+fPUVlZCT6fz0y3tLREWFgYwsLCsGrVKiQnJyMyMvKD2orhw4fD0tIShw4dwqlTpzB16lQmAOnfvz90dXVRV1fX4R1/Pp+P48ePKyyHKk5OTrh37x4qKyuV3k37FMc0Wb6pqams7SkSiVh1wNTUlPUyoZaWFlRUVHTYJnQ0n7S0NLx584a58CC/TrpS+05R1MdBuzv+i9jZ2aG5uRnbt2/HnTt3kJaWxuoyBgCrVq3C1atXER4ejrKyMty+fRtJSUnMG56sra1x+fJl1NbW4smTJ0qv1C1btgwFBQWIiYlBZWUl9u3bhx07diAqKuq9y66vr4/58+dj+fLlOH36NG7duoXQ0FA0NjYiODi4U3lZW1vj4sWLuH//vso3E8pbt24d4uLikJiYiMrKSpSXlyMlJQVbtmxR+bvAwEAIhUL89ttvCgGevb09fv/9d5SUlKC0tBQBAQHvdeXV3t4e165dQ25uLiorK/Hjjz8qfVubKtbW1ggKCsLcuXNx9OhR1NTU4Pz580wgHxERgWfPnsHf3x9Xr16FVCpFbm4u5syZ0+mg90Pp6ekhNDQUa9asASEEK1euRFFRERYsWICSkhJUVVXh2LFjzJsQ5aWmpuLXX39FRUUF7ty5gwMHDoDL5cLKygpnzpzBihUrsGnTJpiYmKC+vh719fUq3zooz87ODpaWlli7di2qqqogFAoV3ganjrVr1yIhIQGJiYmoqqrCjRs3sH37dma6LIirr6/H8+fPleaxfPlyxMfH49ChQ5BIJPjuu+9QUlKCRYsWdbo8MmPGjIGbmxsmTZqEvLw81NbWoqioCNHR0bh27RoAIDo6Gn/++ScSExOxcuVK8Hg8ha583bp1Q1BQEEpLS1FYWIiFCxdi2rRp6N27t8I8e/ToAWNjY+zZswfV1dU4e/Ysli5d+t7LILN69Wrs378f69atw82bNyEWi5GZmYkffviBWVYej8cqZ3R0tFp519XVYenSpZBIJMjIyMD27dsV1ntISAjOnj2L06dPv7Or49ChQ6Gnp4fvv/8eUqkUBw8e7PB7kB3pTFu6fv16FBQUoKKiArNnz4aJiQnzcfHFixcjNzcXNTU1uHHjBs6dO8cEcB/aVgQEBGDXrl3Iz89ntZkGBgaIiorCkiVLsG/fPkilUqZO7Nu3DwAQFhaGqqoqLF++HBKJRK115OHhgZEjR8LHxwf5+fmoqanBqVOnmLegfopjGtB2bJDVgYqKCpw7dw6RkZGYOXMmzMzMAACjR4+GUCiEUCjE7du3MX/+/E5/lDsgIAAaGhoIDQ3FrVu3cPLkSWzevJmVpiu17xRFfRw0SPsXGTRoELZs2YL4+HgMHDgQ6enpiIuLY6Xh8XjIy8tDaWkphgwZAjc3Nxw7doy5shYVFQVNTU30798fpqamSp8FcXFxweHDh5GZmYmBAwdi9erVWL9+fYevMFfXxo0b4ePjg5kzZ8LFxQXV1dXIzc1l7lSoa/369aitrYWtrS1zx0MdISEh2Lt3L1JSUiAQCODh4YHU1FSl36Vrb/To0ejZsyckEgkCAgJY07Zs2YIePXpg+PDh8Pb2hpeXF+tOm7rmzZuHKVOmwM/PD0OHDsXTp09Zd9XUlZSUBF9fX4SHh8PR0RGhoaHMa+xld2RaWlowbtw4CAQCLF68GEZGRsyd2b/TggULIBaLceTIETg5OeHChQuorKzEiBEj4OzsjNWrV7OeI2rPyMgIycnJcHd3h5OTE86cOYOcnBwYGxvj0qVLaGlpQVhYGMzNzZm/zgQ12trayMjIwO3bt+Hk5IT4+HjExsZ2ehmDgoKwbds2/PLLLxgwYAAmTJiAqqoqZnpCQgLy8/NhaWkJZ2dnpXksXLgQS5cuxbJlyyAQCHD69GkcP34c9vb2nS6PjIaGBk6ePImRI0dizpw54PF4mD59Ov744w+YmZnh/Pnz2LZtG9LS0mBoaAgOh4O0tDQUFhYiKSmJycfOzg5TpkzBV199hXHjxsHJyanDz1pwOBxkZmbi+vXrGDhwIJYsWYJNmza99zLIeHl54cSJE8jLy4OrqyuGDRuGrVu3MneZOBwOsrOz8fr1awwZMgQhISHYsGGDWnnPmjWL+V1ERAQWLVqk8EF1e3t7DB8+HI6OjgpdqeX17NkTBw4cwMmTJyEQCJCRkcG8yr0z1G1LN27ciEWLFmHw4MGor69HTk4OdHR0ALTd0YmIiACfz8f48ePB4/GYbfehbUVgYCBu3bqFPn36KDwLFhMTgx9//BFxcXHMvIVCIdMO9+3bF1lZWTh69CgGDRqEXbt2sT4F0ZGsrCy4urrC398f/fv3x4oVK5jg5FMd0/T09JCbm4tnz57B1dUVvr6+8PT0xI4dO5g0c+fORVBQEGbNmgUPDw/Y2Nh06i4a0PbtxJycHJSXl8PZ2RnR0dEK3Rq7WvtOUdSH0yDynaUpiqIo6h3Wrl2Lo0ePdrq73v8aQgjs7e0RHh7+Ue4MUhRFURRAn0mjKIqiqPfy+PFjZGZmor6+HnPmzPmni0NRFEX9D6FBGkVRFEW9h169esHExAR79uzpdLdtiqIoilKFdnekKIqiKIqiKIrqQujTpBRFURRFURRFUV0IDdIoiqIoiqIoiqK6EBqkURRFURRFURRFdSE0SKMoiqIoiqIoiupCaJBGURRFURRFURTVhdAgjaIoiqIoiqIoqguhQRpFURRFURRFUVQXQoM0iqIoiqIoiqKoLuS/F9Q39A11ASEAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -1041,7 +1041,7 @@ "text": [ "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n", "\n", - "Original Effect Estimate : 11.815195660841939\n", + "Original Effect Estimate : 11.80537621178161\n", "Robustness Value : 0.66\n", "\n", "Robustness Value (alpha=0.05) : 0.64\n", diff --git a/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb b/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb index 82f2fffa0..54b8dd839 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/sensitivity_analysis_testing.ipynb @@ -34,10 +34,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:32.906159Z", - "iopub.status.busy": "2024-10-20T00:22:32.905665Z", - "iopub.status.idle": "2024-10-20T00:22:34.342142Z", - "shell.execute_reply": "2024-10-20T00:22:34.341530Z" + "iopub.execute_input": "2024-10-22T01:49:17.285794Z", + "iopub.status.busy": "2024-10-22T01:49:17.285613Z", + "iopub.status.idle": "2024-10-22T01:49:18.798503Z", + "shell.execute_reply": "2024-10-22T01:49:18.797849Z" } }, "outputs": [], @@ -82,10 +82,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:34.344525Z", - "iopub.status.busy": "2024-10-20T00:22:34.344002Z", - "iopub.status.idle": "2024-10-20T00:22:34.360838Z", - "shell.execute_reply": "2024-10-20T00:22:34.360234Z" + "iopub.execute_input": "2024-10-22T01:49:18.801016Z", + "iopub.status.busy": "2024-10-22T01:49:18.800502Z", + "iopub.status.idle": "2024-10-22T01:49:18.817596Z", + "shell.execute_reply": "2024-10-22T01:49:18.817051Z" } }, "outputs": [], @@ -105,10 +105,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:34.362813Z", - "iopub.status.busy": "2024-10-20T00:22:34.362438Z", - "iopub.status.idle": "2024-10-20T00:22:34.519800Z", - "shell.execute_reply": "2024-10-20T00:22:34.519241Z" + "iopub.execute_input": "2024-10-22T01:49:18.819794Z", + "iopub.status.busy": "2024-10-22T01:49:18.819392Z", + "iopub.status.idle": "2024-10-22T01:49:18.982923Z", + "shell.execute_reply": "2024-10-22T01:49:18.982310Z" } }, "outputs": [ @@ -277,10 +277,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:34.521903Z", - "iopub.status.busy": "2024-10-20T00:22:34.521668Z", - "iopub.status.idle": "2024-10-20T00:22:34.666560Z", - "shell.execute_reply": "2024-10-20T00:22:34.665921Z" + "iopub.execute_input": "2024-10-22T01:49:18.985277Z", + "iopub.status.busy": "2024-10-22T01:49:18.984908Z", + "iopub.status.idle": "2024-10-22T01:49:19.150369Z", + "shell.execute_reply": "2024-10-22T01:49:19.149737Z" } }, "outputs": [ @@ -444,10 +444,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:34.668515Z", - "iopub.status.busy": "2024-10-20T00:22:34.668324Z", - "iopub.status.idle": "2024-10-20T00:22:34.683703Z", - "shell.execute_reply": "2024-10-20T00:22:34.683111Z" + "iopub.execute_input": "2024-10-22T01:49:19.153006Z", + "iopub.status.busy": "2024-10-22T01:49:19.152471Z", + "iopub.status.idle": "2024-10-22T01:49:19.172636Z", + "shell.execute_reply": "2024-10-22T01:49:19.172031Z" } }, "outputs": [ @@ -461,9 +461,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W5,W6,W1,W3,W2,W0])\n", + "─────(E[y|W1,W3,W0,W2,W6,W5])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W3,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W0,W2,W6,W5)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -494,10 +494,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:34.685523Z", - "iopub.status.busy": "2024-10-20T00:22:34.685339Z", - "iopub.status.idle": "2024-10-20T00:22:34.702098Z", - "shell.execute_reply": "2024-10-20T00:22:34.701480Z" + "iopub.execute_input": "2024-10-22T01:49:19.174964Z", + "iopub.status.busy": "2024-10-22T01:49:19.174552Z", + "iopub.status.idle": "2024-10-22T01:49:19.195621Z", + "shell.execute_reply": "2024-10-22T01:49:19.194957Z" } }, "outputs": [ @@ -514,12 +514,12 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W5,W6,W1,W3,W2,W0])\n", + "─────(E[y|W1,W3,W0,W2,W6,W5])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W3,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W0,W2,W6,W5)\n", "\n", "## Realized estimand\n", - "b: y~v0+W5+W6+W1+W3+W2+W0\n", + "b: y~v0+W1+W3+W0+W2+W6+W5\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -557,10 +557,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:34.703979Z", - "iopub.status.busy": "2024-10-20T00:22:34.703793Z", - "iopub.status.idle": "2024-10-20T00:22:39.042465Z", - "shell.execute_reply": "2024-10-20T00:22:39.041917Z" + "iopub.execute_input": "2024-10-22T01:49:19.197908Z", + "iopub.status.busy": "2024-10-22T01:49:19.197694Z", + "iopub.status.idle": "2024-10-22T01:49:23.561626Z", + "shell.execute_reply": "2024-10-22T01:49:23.561038Z" }, "scrolled": false }, @@ -599,10 +599,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:39.044477Z", - "iopub.status.busy": "2024-10-20T00:22:39.044087Z", - "iopub.status.idle": "2024-10-20T00:22:39.048304Z", - "shell.execute_reply": "2024-10-20T00:22:39.047675Z" + "iopub.execute_input": "2024-10-22T01:49:23.563472Z", + "iopub.status.busy": "2024-10-22T01:49:23.563279Z", + "iopub.status.idle": "2024-10-22T01:49:23.567575Z", + "shell.execute_reply": "2024-10-22T01:49:23.566967Z" } }, "outputs": [ @@ -610,13 +610,13 @@ "data": { "text/plain": [ "{'estimate': 10.697677486880917,\n", - " 'standard_error': 0.5938735661282945,\n", + " 'standard_error': 0.5938735661282948,\n", " 'degree of freedom': 492,\n", - " 't_statistic': 18.013392238727626,\n", - " 'r2yt_w': 0.3974149837266682,\n", - " 'partial_f2': 0.6595168698094567,\n", - " 'robustness_value': 0.5467445572181009,\n", - " 'robustness_value_alpha': 0.5076289101030926}" + " 't_statistic': 18.013392238727615,\n", + " 'r2yt_w': 0.39741498372666795,\n", + " 'partial_f2': 0.6595168698094559,\n", + " 'robustness_value': 0.5467445572181008,\n", + " 'robustness_value_alpha': 0.5076289101030925}" ] }, "execution_count": 8, @@ -633,10 +633,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:39.050094Z", - "iopub.status.busy": "2024-10-20T00:22:39.049780Z", - "iopub.status.idle": "2024-10-20T00:22:39.057855Z", - "shell.execute_reply": "2024-10-20T00:22:39.057234Z" + "iopub.execute_input": "2024-10-22T01:49:23.569420Z", + "iopub.status.busy": "2024-10-22T01:49:23.569048Z", + "iopub.status.idle": "2024-10-22T01:49:23.576576Z", + "shell.execute_reply": "2024-10-22T01:49:23.576099Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:39.059975Z", - "iopub.status.busy": "2024-10-20T00:22:39.059559Z", - "iopub.status.idle": "2024-10-20T00:22:43.562103Z", - "shell.execute_reply": "2024-10-20T00:22:43.561470Z" + "iopub.execute_input": "2024-10-22T01:49:23.578305Z", + "iopub.status.busy": "2024-10-22T01:49:23.577978Z", + "iopub.status.idle": "2024-10-22T01:49:28.129916Z", + "shell.execute_reply": "2024-10-22T01:49:28.129269Z" } }, "outputs": [ @@ -792,10 +792,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.564285Z", - "iopub.status.busy": "2024-10-20T00:22:43.563933Z", - "iopub.status.idle": "2024-10-20T00:22:43.567326Z", - "shell.execute_reply": "2024-10-20T00:22:43.566839Z" + "iopub.execute_input": "2024-10-22T01:49:28.132073Z", + "iopub.status.busy": "2024-10-22T01:49:28.131715Z", + "iopub.status.idle": "2024-10-22T01:49:28.135223Z", + "shell.execute_reply": "2024-10-22T01:49:28.134704Z" }, "scrolled": true }, @@ -809,13 +809,13 @@ "Unadjusted Estimates of Treatment ['v0'] :\n", "Coefficient Estimate : 10.697677486880917\n", "Degree of Freedom : 492\n", - "Standard Error : 0.5938735661282945\n", - "t-value : 18.013392238727626\n", - "F^2 value : 0.6595168698094567\n", + "Standard Error : 0.5938735661282948\n", + "t-value : 18.013392238727615\n", + "F^2 value : 0.6595168698094559\n", "\n", "Sensitivity Statistics : \n", - "Partial R2 of treatment with outcome : 0.3974149837266682\n", - "Robustness Value : 0.5467445572181009\n", + "Partial R2 of treatment with outcome : 0.39741498372666795\n", + "Robustness Value : 0.5467445572181008\n", "\n", "Interpretation of results :\n", "Any confounder explaining less than 54.67% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n", @@ -850,10 +850,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.568951Z", - "iopub.status.busy": "2024-10-20T00:22:43.568767Z", - "iopub.status.idle": "2024-10-20T00:22:43.876714Z", - "shell.execute_reply": "2024-10-20T00:22:43.876062Z" + "iopub.execute_input": "2024-10-22T01:49:28.137325Z", + "iopub.status.busy": "2024-10-22T01:49:28.137023Z", + "iopub.status.idle": "2024-10-22T01:49:28.499281Z", + "shell.execute_reply": "2024-10-22T01:49:28.498686Z" } }, "outputs": [ @@ -890,10 +890,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.878664Z", - "iopub.status.busy": "2024-10-20T00:22:43.878460Z", - "iopub.status.idle": "2024-10-20T00:22:43.882580Z", - "shell.execute_reply": "2024-10-20T00:22:43.881993Z" + "iopub.execute_input": "2024-10-22T01:49:28.501439Z", + "iopub.status.busy": "2024-10-22T01:49:28.501017Z", + "iopub.status.idle": "2024-10-22T01:49:28.505168Z", + "shell.execute_reply": "2024-10-22T01:49:28.504679Z" } }, "outputs": [ @@ -901,10 +901,10 @@ "data": { "text/plain": [ "{'converted_estimate': 2.457447065735422,\n", - " 'converted_lower_ci': 2.228856727004314,\n", - " 'converted_upper_ci': 2.709481505797994,\n", + " 'converted_lower_ci': 2.2288567270043136,\n", + " 'converted_upper_ci': 2.7094815057979944,\n", " 'evalue_estimate': 4.349958364289862,\n", - " 'evalue_lower_ci': 3.883832733630413,\n", + " 'evalue_lower_ci': 3.883832733630412,\n", " 'evalue_upper_ci': None}" ] }, @@ -922,10 +922,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.884211Z", - "iopub.status.busy": "2024-10-20T00:22:43.884025Z", - "iopub.status.idle": "2024-10-20T00:22:43.890955Z", - "shell.execute_reply": "2024-10-20T00:22:43.890374Z" + "iopub.execute_input": "2024-10-22T01:49:28.506957Z", + "iopub.status.busy": "2024-10-22T01:49:28.506765Z", + "iopub.status.idle": "2024-10-22T01:49:28.514223Z", + "shell.execute_reply": "2024-10-22T01:49:28.513599Z" } }, "outputs": [ @@ -1044,10 +1044,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.892838Z", - "iopub.status.busy": "2024-10-20T00:22:43.892474Z", - "iopub.status.idle": "2024-10-20T00:22:43.895374Z", - "shell.execute_reply": "2024-10-20T00:22:43.894911Z" + "iopub.execute_input": "2024-10-22T01:49:28.516204Z", + "iopub.status.busy": "2024-10-22T01:49:28.515778Z", + "iopub.status.idle": "2024-10-22T01:49:28.518934Z", + "shell.execute_reply": "2024-10-22T01:49:28.518428Z" } }, "outputs": [ @@ -1059,14 +1059,14 @@ "\n", "Unadjusted Estimates of Treatment: v0\n", "Estimate (converted to risk ratio scale): 2.457447065735422\n", - "Lower 95% CI (converted to risk ratio scale): 2.228856727004314\n", - "Upper 95% CI (converted to risk ratio scale): 2.709481505797994\n", + "Lower 95% CI (converted to risk ratio scale): 2.2288567270043136\n", + "Upper 95% CI (converted to risk ratio scale): 2.7094815057979944\n", "\n", "Sensitivity Statistics: \n", "E-value for point estimate: 4.349958364289862\n", - "E-value for lower 95% CI: 3.883832733630413\n", + "E-value for lower 95% CI: 3.883832733630412\n", "E-value for upper 95% CI: None\n", - "Largest Observed Covariate E-value: 1.6811401167656788 (W1)\n", + "Largest Observed Covariate E-value: 1.6811401167656792 (W1)\n", "\n", "Interpretation of results:\n", "Unmeasured confounder(s) would have to be associated with a 4.35-fold increase in the risk of y, and must be 4.35 times more prevalent in v0, to explain away the observed point estimate.\n", @@ -1092,10 +1092,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.897047Z", - "iopub.status.busy": "2024-10-20T00:22:43.896770Z", - "iopub.status.idle": "2024-10-20T00:22:43.912309Z", - "shell.execute_reply": "2024-10-20T00:22:43.911799Z" + "iopub.execute_input": "2024-10-22T01:49:28.520671Z", + "iopub.status.busy": "2024-10-22T01:49:28.520483Z", + "iopub.status.idle": "2024-10-22T01:49:28.536532Z", + "shell.execute_reply": "2024-10-22T01:49:28.535925Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:43.914233Z", - "iopub.status.busy": "2024-10-20T00:22:43.913869Z", - "iopub.status.idle": "2024-10-20T00:22:44.045954Z", - "shell.execute_reply": "2024-10-20T00:22:44.045273Z" + "iopub.execute_input": "2024-10-22T01:49:28.538696Z", + "iopub.status.busy": "2024-10-22T01:49:28.538483Z", + "iopub.status.idle": "2024-10-22T01:49:28.677507Z", + "shell.execute_reply": "2024-10-22T01:49:28.676924Z" } }, "outputs": [ @@ -1279,10 +1279,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:44.047903Z", - "iopub.status.busy": "2024-10-20T00:22:44.047687Z", - "iopub.status.idle": "2024-10-20T00:22:44.071970Z", - "shell.execute_reply": "2024-10-20T00:22:44.071353Z" + "iopub.execute_input": "2024-10-22T01:49:28.679866Z", + "iopub.status.busy": "2024-10-22T01:49:28.679642Z", + "iopub.status.idle": "2024-10-22T01:49:28.711994Z", + "shell.execute_reply": "2024-10-22T01:49:28.711400Z" } }, "outputs": [ @@ -1296,9 +1296,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W5,W6,W1,W4,W3,W2,W0])\n", + "─────(E[y|W1,W3,W4,W0,W2,W6,W5])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W4,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W4,W3,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W4,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W4,W0,W2,W6,W5)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -1321,10 +1321,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:44.073813Z", - "iopub.status.busy": "2024-10-20T00:22:44.073601Z", - "iopub.status.idle": "2024-10-20T00:22:44.089601Z", - "shell.execute_reply": "2024-10-20T00:22:44.088985Z" + "iopub.execute_input": "2024-10-22T01:49:28.714137Z", + "iopub.status.busy": "2024-10-22T01:49:28.713913Z", + "iopub.status.idle": "2024-10-22T01:49:28.734904Z", + "shell.execute_reply": "2024-10-22T01:49:28.734232Z" } }, "outputs": [ @@ -1341,16 +1341,16 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W5,W6,W1,W4,W3,W2,W0])\n", + "─────(E[y|W1,W3,W4,W0,W2,W6,W5])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W4,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W4,W3,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W4,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W4,W0,W2,W6,W5)\n", "\n", "## Realized estimand\n", - "b: y~v0+W5+W6+W1+W4+W3+W2+W0\n", + "b: y~v0+W1+W3+W4+W0+W2+W6+W5\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 10.081924375588304\n", + "Mean value: 10.081924375588297\n", "\n" ] } @@ -1365,10 +1365,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:44.091415Z", - "iopub.status.busy": "2024-10-20T00:22:44.091230Z", - "iopub.status.idle": "2024-10-20T00:22:48.579565Z", - "shell.execute_reply": "2024-10-20T00:22:48.579023Z" + "iopub.execute_input": "2024-10-22T01:49:28.737302Z", + "iopub.status.busy": "2024-10-22T01:49:28.736934Z", + "iopub.status.idle": "2024-10-22T01:49:33.236416Z", + "shell.execute_reply": "2024-10-22T01:49:33.235809Z" } }, "outputs": [ @@ -1406,10 +1406,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:48.581405Z", - "iopub.status.busy": "2024-10-20T00:22:48.581201Z", - "iopub.status.idle": "2024-10-20T00:22:52.975105Z", - "shell.execute_reply": "2024-10-20T00:22:52.974431Z" + "iopub.execute_input": "2024-10-22T01:49:33.238267Z", + "iopub.status.busy": "2024-10-22T01:49:33.238077Z", + "iopub.status.idle": "2024-10-22T01:49:37.679150Z", + "shell.execute_reply": "2024-10-22T01:49:37.678543Z" } }, "outputs": [ @@ -1433,10 +1433,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:52.977117Z", - "iopub.status.busy": "2024-10-20T00:22:52.976923Z", - "iopub.status.idle": "2024-10-20T00:22:52.980286Z", - "shell.execute_reply": "2024-10-20T00:22:52.979701Z" + "iopub.execute_input": "2024-10-22T01:49:37.680973Z", + "iopub.status.busy": "2024-10-22T01:49:37.680784Z", + "iopub.status.idle": "2024-10-22T01:49:37.684303Z", + "shell.execute_reply": "2024-10-22T01:49:37.683828Z" }, "scrolled": true }, @@ -1448,14 +1448,14 @@ "Sensitivity Analysis to Unobserved Confounding using R^2 paramterization\n", "\n", "Unadjusted Estimates of Treatment ['v0'] :\n", - "Coefficient Estimate : 10.081924375588304\n", + "Coefficient Estimate : 10.081924375588297\n", "Degree of Freedom : 491\n", - "Standard Error : 0.10446229543424757\n", - "t-value : 96.51256784735541\n", - "F^2 value : 18.970826379817478\n", + "Standard Error : 0.10446229543424743\n", + "t-value : 96.51256784735548\n", + "F^2 value : 18.970826379817506\n", "\n", "Sensitivity Statistics : \n", - "Partial R2 of treatment with outcome : 0.9499269594066172\n", + "Partial R2 of treatment with outcome : 0.9499269594066173\n", "Robustness Value : 0.9522057801012398\n", "\n", "Interpretation of results :\n", @@ -1477,10 +1477,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:52.982188Z", - "iopub.status.busy": "2024-10-20T00:22:52.981755Z", - "iopub.status.idle": "2024-10-20T00:22:52.985815Z", - "shell.execute_reply": "2024-10-20T00:22:52.985234Z" + "iopub.execute_input": "2024-10-22T01:49:37.686033Z", + "iopub.status.busy": "2024-10-22T01:49:37.685683Z", + "iopub.status.idle": "2024-10-22T01:49:37.689727Z", + "shell.execute_reply": "2024-10-22T01:49:37.689146Z" }, "scrolled": true }, @@ -1488,12 +1488,12 @@ { "data": { "text/plain": [ - "{'estimate': 10.081924375588304,\n", - " 'standard_error': 0.10446229543424757,\n", + "{'estimate': 10.081924375588297,\n", + " 'standard_error': 0.10446229543424743,\n", " 'degree of freedom': 491,\n", - " 't_statistic': 96.51256784735541,\n", - " 'r2yt_w': 0.9499269594066172,\n", - " 'partial_f2': 18.970826379817478,\n", + " 't_statistic': 96.51256784735548,\n", + " 'r2yt_w': 0.9499269594066173,\n", + " 'partial_f2': 18.970826379817506,\n", " 'robustness_value': 0.9522057801012398,\n", " 'robustness_value_alpha': 0.950386691319526}" ] @@ -1512,10 +1512,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:52.987660Z", - "iopub.status.busy": "2024-10-20T00:22:52.987255Z", - "iopub.status.idle": "2024-10-20T00:22:52.994932Z", - "shell.execute_reply": "2024-10-20T00:22:52.994429Z" + "iopub.execute_input": "2024-10-22T01:49:37.691504Z", + "iopub.status.busy": "2024-10-22T01:49:37.691203Z", + "iopub.status.idle": "2024-10-22T01:49:37.699179Z", + "shell.execute_reply": "2024-10-22T01:49:37.698589Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb b/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb index 979c383a3..ff8975a3e 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:56.069874Z", - "iopub.status.busy": "2024-10-20T00:22:56.069472Z", - "iopub.status.idle": "2024-10-20T00:22:57.493071Z", - "shell.execute_reply": "2024-10-20T00:22:57.492516Z" + "iopub.execute_input": "2024-10-22T01:49:40.333747Z", + "iopub.status.busy": "2024-10-22T01:49:40.333569Z", + "iopub.status.idle": "2024-10-22T01:49:41.848211Z", + "shell.execute_reply": "2024-10-22T01:49:41.847596Z" } }, "outputs": [], @@ -40,10 +40,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.495452Z", - "iopub.status.busy": "2024-10-20T00:22:57.495030Z", - "iopub.status.idle": "2024-10-20T00:22:57.499979Z", - "shell.execute_reply": "2024-10-20T00:22:57.499499Z" + "iopub.execute_input": "2024-10-22T01:49:41.850754Z", + "iopub.status.busy": "2024-10-22T01:49:41.850234Z", + "iopub.status.idle": "2024-10-22T01:49:41.855699Z", + "shell.execute_reply": "2024-10-22T01:49:41.855166Z" } }, "outputs": [], @@ -67,10 +67,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.501772Z", - "iopub.status.busy": "2024-10-20T00:22:57.501476Z", - "iopub.status.idle": "2024-10-20T00:22:57.613416Z", - "shell.execute_reply": "2024-10-20T00:22:57.612838Z" + "iopub.execute_input": "2024-10-22T01:49:41.857503Z", + "iopub.status.busy": "2024-10-22T01:49:41.857280Z", + "iopub.status.idle": "2024-10-22T01:49:41.958747Z", + "shell.execute_reply": "2024-10-22T01:49:41.958018Z" } }, "outputs": [ @@ -116,10 +116,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.615650Z", - "iopub.status.busy": "2024-10-20T00:22:57.615258Z", - "iopub.status.idle": "2024-10-20T00:22:57.733312Z", - "shell.execute_reply": "2024-10-20T00:22:57.732768Z" + "iopub.execute_input": "2024-10-22T01:49:41.960962Z", + "iopub.status.busy": "2024-10-22T01:49:41.960599Z", + "iopub.status.idle": "2024-10-22T01:49:42.063822Z", + "shell.execute_reply": "2024-10-22T01:49:42.063326Z" } }, "outputs": [ @@ -159,10 +159,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.735606Z", - "iopub.status.busy": "2024-10-20T00:22:57.735384Z", - "iopub.status.idle": "2024-10-20T00:22:57.740195Z", - "shell.execute_reply": "2024-10-20T00:22:57.739605Z" + "iopub.execute_input": "2024-10-22T01:49:42.065833Z", + "iopub.status.busy": "2024-10-22T01:49:42.065655Z", + "iopub.status.idle": "2024-10-22T01:49:42.070000Z", + "shell.execute_reply": "2024-10-22T01:49:42.069570Z" } }, "outputs": [], @@ -175,10 +175,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.742599Z", - "iopub.status.busy": "2024-10-20T00:22:57.742173Z", - "iopub.status.idle": "2024-10-20T00:22:57.842694Z", - "shell.execute_reply": "2024-10-20T00:22:57.842093Z" + "iopub.execute_input": "2024-10-22T01:49:42.071785Z", + "iopub.status.busy": "2024-10-22T01:49:42.071595Z", + "iopub.status.idle": "2024-10-22T01:49:42.150376Z", + "shell.execute_reply": "2024-10-22T01:49:42.149737Z" } }, "outputs": [ @@ -205,10 +205,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.844924Z", - "iopub.status.busy": "2024-10-20T00:22:57.844682Z", - "iopub.status.idle": "2024-10-20T00:22:57.858149Z", - "shell.execute_reply": "2024-10-20T00:22:57.857578Z" + "iopub.execute_input": "2024-10-22T01:49:42.152531Z", + "iopub.status.busy": "2024-10-22T01:49:42.152144Z", + "iopub.status.idle": "2024-10-22T01:49:42.163708Z", + "shell.execute_reply": "2024-10-22T01:49:42.163070Z" } }, "outputs": [ @@ -330,10 +330,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.860158Z", - "iopub.status.busy": "2024-10-20T00:22:57.859940Z", - "iopub.status.idle": "2024-10-20T00:22:57.864373Z", - "shell.execute_reply": "2024-10-20T00:22:57.863888Z" + "iopub.execute_input": "2024-10-22T01:49:42.165653Z", + "iopub.status.busy": "2024-10-22T01:49:42.165447Z", + "iopub.status.idle": "2024-10-22T01:49:42.169883Z", + "shell.execute_reply": "2024-10-22T01:49:42.169258Z" } }, "outputs": [ @@ -361,10 +361,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.866436Z", - "iopub.status.busy": "2024-10-20T00:22:57.866231Z", - "iopub.status.idle": "2024-10-20T00:22:57.963937Z", - "shell.execute_reply": "2024-10-20T00:22:57.963318Z" + "iopub.execute_input": "2024-10-22T01:49:42.171708Z", + "iopub.status.busy": "2024-10-22T01:49:42.171517Z", + "iopub.status.idle": "2024-10-22T01:49:42.258872Z", + "shell.execute_reply": "2024-10-22T01:49:42.258186Z" } }, "outputs": [ @@ -381,28 +381,28 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────────(E[V_6_0|V4_-7])\n", + "────────(E[V_6_0|V4_-3])\n", "d[V₅ ₋₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-7,U) = P(V6_0|V5_-1,V4_-7)\n", + "Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-3,U) = P(V6_0|V5_-1,V4_-3)\n", "\n", "## Realized estimand\n", - "b: V6_0~V5_-1+V4_-7+V5_-1*V7_-3+V5_-1*V7_-5\n", + "b: V6_0~V5_-1+V4_-3+V5_-1*V7_-3+V5_-1*V7_-5\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: -0.12612138021763197\n", - "p-value: [0.75401158]\n", + "Mean value: -0.008318651389864762\n", + "p-value: [0.75812695]\n", "### Conditional Estimates\n", "__categorical__V7_-3 __categorical__V7_-5\n", - "(-0.001, 0.6] (-0.001, 5.4] 0.111915\n", - "(0.6, 5.4] (7.8, 9.0] -0.257861\n", - " (9.0, 10.0] -0.314176\n", - "(5.4, 7.8] (-0.001, 5.4] 0.073537\n", - " (7.8, 9.0] -0.285273\n", - "(7.8, 9.0] (-0.001, 5.4] -0.034356\n", - " (5.4, 7.8] -0.216503\n", - " (7.8, 9.0] -0.296238\n", - "(9.0, 10.0] (7.8, 9.0] -0.261853\n", + "(-0.001, 0.6] (-0.001, 5.4] 0.101279\n", + "(0.6, 5.4] (7.8, 9.0] -0.167641\n", + " (9.0, 10.0] -0.167956\n", + "(5.4, 7.8] (-0.001, 5.4] 0.175782\n", + " (7.8, 9.0] -0.114425\n", + "(7.8, 9.0] (-0.001, 5.4] 0.111134\n", + " (5.4, 7.8] -0.028648\n", + " (7.8, 9.0] -0.093138\n", + "(9.0, 10.0] (7.8, 9.0] -0.050250\n", "dtype: float64\n" ] }, @@ -451,10 +451,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:22:57.965879Z", - "iopub.status.busy": "2024-10-20T00:22:57.965499Z", - "iopub.status.idle": "2024-10-20T00:23:00.583315Z", - "shell.execute_reply": "2024-10-20T00:23:00.582644Z" + "iopub.execute_input": "2024-10-22T01:49:42.261077Z", + "iopub.status.busy": "2024-10-22T01:49:42.260691Z", + "iopub.status.idle": "2024-10-22T01:49:44.948980Z", + "shell.execute_reply": "2024-10-22T01:49:44.948275Z" } }, "outputs": [ @@ -471,15 +471,8 @@ "text": [ " Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)\r\n", "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/296.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", - "\u001b[?25h" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)\r\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m47.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)\r\n", "Requirement already satisfied: scipy>=1.10.0 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.10.1)\r\n", "Requirement already satisfied: six in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.16.0)\r\n" ] @@ -517,10 +510,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:00.585790Z", - "iopub.status.busy": "2024-10-20T00:23:00.585360Z", - "iopub.status.idle": "2024-10-20T00:23:00.593575Z", - "shell.execute_reply": "2024-10-20T00:23:00.593107Z" + "iopub.execute_input": "2024-10-22T01:49:44.951399Z", + "iopub.status.busy": "2024-10-22T01:49:44.951041Z", + "iopub.status.idle": "2024-10-22T01:49:44.959727Z", + "shell.execute_reply": "2024-10-22T01:49:44.959203Z" } }, "outputs": [], @@ -541,10 +534,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:00.595461Z", - "iopub.status.busy": "2024-10-20T00:23:00.595099Z", - "iopub.status.idle": "2024-10-20T00:23:01.489338Z", - "shell.execute_reply": "2024-10-20T00:23:01.488770Z" + "iopub.execute_input": "2024-10-22T01:49:44.961822Z", + "iopub.status.busy": "2024-10-22T01:49:44.961321Z", + "iopub.status.idle": "2024-10-22T01:49:45.880191Z", + "shell.execute_reply": "2024-10-22T01:49:45.879575Z" } }, "outputs": [ @@ -569,10 +562,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:01.491700Z", - "iopub.status.busy": "2024-10-20T00:23:01.491307Z", - "iopub.status.idle": "2024-10-20T00:23:01.496692Z", - "shell.execute_reply": "2024-10-20T00:23:01.496229Z" + "iopub.execute_input": "2024-10-22T01:49:45.882290Z", + "iopub.status.busy": "2024-10-22T01:49:45.881862Z", + "iopub.status.idle": "2024-10-22T01:49:45.887551Z", + "shell.execute_reply": "2024-10-22T01:49:45.886934Z" } }, "outputs": [], @@ -592,10 +585,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:01.498450Z", - "iopub.status.busy": "2024-10-20T00:23:01.498102Z", - "iopub.status.idle": "2024-10-20T00:23:01.678192Z", - "shell.execute_reply": "2024-10-20T00:23:01.677549Z" + "iopub.execute_input": "2024-10-22T01:49:45.889385Z", + "iopub.status.busy": "2024-10-22T01:49:45.889034Z", + "iopub.status.idle": "2024-10-22T01:49:46.072993Z", + "shell.execute_reply": "2024-10-22T01:49:46.072391Z" } }, "outputs": [ @@ -626,10 +619,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:01.680169Z", - "iopub.status.busy": "2024-10-20T00:23:01.679826Z", - "iopub.status.idle": "2024-10-20T00:23:02.069006Z", - "shell.execute_reply": "2024-10-20T00:23:02.068356Z" + "iopub.execute_input": "2024-10-22T01:49:46.075133Z", + "iopub.status.busy": "2024-10-22T01:49:46.074755Z", + "iopub.status.idle": "2024-10-22T01:49:46.458815Z", + "shell.execute_reply": "2024-10-22T01:49:46.458181Z" } }, "outputs": [ @@ -767,13 +760,7 @@ "\n", "Testing condition sets of dimension 0:\n", "\n", - " Link (V1 -1) -?> V2 (1/21):\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " Link (V1 -1) -?> V2 (1/21):\n", " Subset 0: () gives pval = 0.12317 / val = -0.591\n", " Non-significance detected.\n", "\n", @@ -781,7 +768,13 @@ " Subset 0: () gives pval = 0.87304 / val = 0.068\n", " Non-significance detected.\n", "\n", - " Link (V1 -3) -?> V2 (3/21):\n", + " Link (V1 -3) -?> V2 (3/21):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Subset 0: () gives pval = 0.40658 / val = 0.342\n", " Non-significance detected.\n", "\n", @@ -2019,7 +2012,13 @@ " Subset 0: () gives pval = 0.73304 / val = 0.144\n", " Non-significance detected.\n", "\n", - " Link (V6 -3) -?> V3 (18/21):\n", + " Link (V6 -3) -?> V3 (18/21):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Subset 0: () gives pval = 0.71062 / val = 0.157\n", " Non-significance detected.\n", "\n", @@ -2181,13 +2180,7 @@ " Subset 0: () gives pval = 0.42170 / val = 0.332\n", " Non-significance detected.\n", "\n", - " Link (V3 -2) -?> V5 (8/21):\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " Link (V3 -2) -?> V5 (8/21):\n", " Subset 0: () gives pval = 0.70342 / val = -0.161\n", " Non-significance detected.\n", "\n", @@ -2594,7 +2587,13 @@ " Link (V3 0) o?o V4 (16/44):\n", " Iterate through 1 subset(s) of conditions: \n", " with conds_y = [ ]\n", - " with conds_x = [ ]\n", + " with conds_x = [ ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Subset 0: () gives pval = 0.30341 / val = 0.417\n", " Non-significance detected.\n", "\n", @@ -2671,13 +2670,7 @@ " Link (V5 0) o?o V7 (31/44):\n", " Iterate through 1 subset(s) of conditions: \n", " with conds_y = [ ]\n", - " with conds_x = [ ]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " with conds_x = [ ]\n", " Subset 0: () gives pval = 0.32066 / val = -0.404\n", " Non-significance detected.\n", "\n", @@ -6109,10 +6102,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:02.071502Z", - "iopub.status.busy": "2024-10-20T00:23:02.071312Z", - "iopub.status.idle": "2024-10-20T00:23:02.140404Z", - "shell.execute_reply": "2024-10-20T00:23:02.139787Z" + "iopub.execute_input": "2024-10-22T01:49:46.460772Z", + "iopub.status.busy": "2024-10-22T01:49:46.460575Z", + "iopub.status.idle": "2024-10-22T01:49:46.530397Z", + "shell.execute_reply": "2024-10-22T01:49:46.529750Z" } }, "outputs": [ diff --git a/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb b/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb index 9b47a678a..e2d091bc9 100644 --- a/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb +++ b/main/.doctrees/nbsphinx/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb @@ -104,10 +104,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:04.354094Z", - "iopub.status.busy": "2024-10-20T00:23:04.353654Z", - "iopub.status.idle": "2024-10-20T00:23:06.026530Z", - "shell.execute_reply": "2024-10-20T00:23:06.025819Z" + "iopub.execute_input": "2024-10-22T01:49:48.766649Z", + "iopub.status.busy": "2024-10-22T01:49:48.766463Z", + "iopub.status.idle": "2024-10-22T01:49:50.559322Z", + "shell.execute_reply": "2024-10-22T01:49:50.558613Z" } }, "outputs": [], @@ -152,10 +152,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:06.029540Z", - "iopub.status.busy": "2024-10-20T00:23:06.029155Z", - "iopub.status.idle": "2024-10-20T00:23:06.033205Z", - "shell.execute_reply": "2024-10-20T00:23:06.032460Z" + "iopub.execute_input": "2024-10-22T01:49:50.562535Z", + "iopub.status.busy": "2024-10-22T01:49:50.562000Z", + "iopub.status.idle": "2024-10-22T01:49:50.566782Z", + "shell.execute_reply": "2024-10-22T01:49:50.566249Z" } }, "outputs": [], @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:06.035548Z", - "iopub.status.busy": "2024-10-20T00:23:06.035177Z", - "iopub.status.idle": "2024-10-20T00:23:06.204608Z", - "shell.execute_reply": "2024-10-20T00:23:06.204051Z" + "iopub.execute_input": "2024-10-22T01:49:50.569446Z", + "iopub.status.busy": "2024-10-22T01:49:50.569212Z", + "iopub.status.idle": "2024-10-22T01:49:50.755120Z", + "shell.execute_reply": "2024-10-22T01:49:50.754404Z" } }, "outputs": [ @@ -227,10 +227,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:06.206678Z", - "iopub.status.busy": "2024-10-20T00:23:06.206471Z", - "iopub.status.idle": "2024-10-20T00:23:06.356151Z", - "shell.execute_reply": "2024-10-20T00:23:06.355571Z" + "iopub.execute_input": "2024-10-22T01:49:50.758557Z", + "iopub.status.busy": "2024-10-22T01:49:50.757966Z", + "iopub.status.idle": "2024-10-22T01:49:50.916025Z", + "shell.execute_reply": "2024-10-22T01:49:50.915381Z" } }, "outputs": [ @@ -278,10 +278,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:06.358647Z", - "iopub.status.busy": "2024-10-20T00:23:06.358248Z", - "iopub.status.idle": "2024-10-20T00:23:06.608367Z", - "shell.execute_reply": "2024-10-20T00:23:06.607822Z" + "iopub.execute_input": "2024-10-22T01:49:50.918290Z", + "iopub.status.busy": "2024-10-22T01:49:50.918059Z", + "iopub.status.idle": "2024-10-22T01:49:51.084865Z", + "shell.execute_reply": "2024-10-22T01:49:51.084238Z" } }, "outputs": [ @@ -295,9 +295,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W2,W1,W0,W4])\n", + "─────(E[y|W1,W0,W3,W2,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W2,W1,W0,W4,U) = P(y|v0,W3,W2,W1,W0,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W3,W2,W4,U) = P(y|v0,W1,W0,W3,W2,W4)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -338,10 +338,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:06.610351Z", - "iopub.status.busy": "2024-10-20T00:23:06.609995Z", - "iopub.status.idle": "2024-10-20T00:23:07.092906Z", - "shell.execute_reply": "2024-10-20T00:23:07.092236Z" + "iopub.execute_input": "2024-10-22T01:49:51.086929Z", + "iopub.status.busy": "2024-10-22T01:49:51.086610Z", + "iopub.status.idle": "2024-10-22T01:49:51.663101Z", + "shell.execute_reply": "2024-10-22T01:49:51.662501Z" } }, "outputs": [ @@ -358,16 +358,16 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W2,W1,W0,W4])\n", + "─────(E[y|W1,W0,W3,W2,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W2,W1,W0,W4,U) = P(y|v0,W3,W2,W1,W0,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W3,W2,W4,U) = P(y|v0,W1,W0,W3,W2,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W2+W1+W0+W4\n", + "b: y~v0+W1+W0+W3+W2+W4\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 10.065477684628421\n", + "Mean value: 10.021268689755129\n", "\n" ] } @@ -385,10 +385,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:07.094904Z", - "iopub.status.busy": "2024-10-20T00:23:07.094582Z", - "iopub.status.idle": "2024-10-20T00:23:12.167769Z", - "shell.execute_reply": "2024-10-20T00:23:12.166775Z" + "iopub.execute_input": "2024-10-22T01:49:51.665273Z", + "iopub.status.busy": "2024-10-22T01:49:51.664809Z", + "iopub.status.idle": "2024-10-22T01:49:56.772367Z", + "shell.execute_reply": "2024-10-22T01:49:56.771524Z" } }, "outputs": [ @@ -405,17 +405,17 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W2,W1,W0,W4])\n", + "─────(E[y|W1,W0,W3,W2,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W2,W1,W0,W4,U) = P(y|v0,W3,W2,W1,W0,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W3,W2,W4,U) = P(y|v0,W1,W0,W3,W2,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W2+W1+W0+W4 | \n", + "b: y~v0+W1+W0+W3+W2+W4 | \n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.948342659047578\n", - "Effect estimates: [[9.94834266]]\n", + "Mean value: 9.984673963903383\n", + "Effect estimates: [[9.98467396]]\n", "\n" ] } @@ -450,10 +450,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:12.170790Z", - "iopub.status.busy": "2024-10-20T00:23:12.169975Z", - "iopub.status.idle": "2024-10-20T00:23:47.768027Z", - "shell.execute_reply": "2024-10-20T00:23:47.767361Z" + "iopub.execute_input": "2024-10-22T01:49:56.775184Z", + "iopub.status.busy": "2024-10-22T01:49:56.774686Z", + "iopub.status.idle": "2024-10-22T01:50:32.061786Z", + "shell.execute_reply": "2024-10-22T01:50:32.061095Z" } }, "outputs": [ @@ -462,9 +462,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:10.065477684628421\n", - "New effect:0.018320036935283782\n", - "p value:0.8400000000000001\n", + "Estimated effect:10.021268689755129\n", + "New effect:0.021116924365320297\n", + "p value:0.6799999999999999\n", "\n" ] } @@ -503,10 +503,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:47.769937Z", - "iopub.status.busy": "2024-10-20T00:23:47.769707Z", - "iopub.status.idle": "2024-10-20T00:23:47.773195Z", - "shell.execute_reply": "2024-10-20T00:23:47.772725Z" + "iopub.execute_input": "2024-10-22T01:50:32.064093Z", + "iopub.status.busy": "2024-10-22T01:50:32.063647Z", + "iopub.status.idle": "2024-10-22T01:50:32.067283Z", + "shell.execute_reply": "2024-10-22T01:50:32.066696Z" } }, "outputs": [], @@ -530,10 +530,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:47.774838Z", - "iopub.status.busy": "2024-10-20T00:23:47.774604Z", - "iopub.status.idle": "2024-10-20T00:23:48.105209Z", - "shell.execute_reply": "2024-10-20T00:23:48.104544Z" + "iopub.execute_input": "2024-10-22T01:50:32.069391Z", + "iopub.status.busy": "2024-10-22T01:50:32.068950Z", + "iopub.status.idle": "2024-10-22T01:50:32.433757Z", + "shell.execute_reply": "2024-10-22T01:50:32.433053Z" } }, "outputs": [ @@ -542,16 +542,16 @@ "output_type": "stream", "text": [ " Treatment Outcome w0 s w1\n", - "0 9.283407 18.635052 -1.656601 0.621639 1.292029\n", - "1 20.387799 40.985144 -3.792526 7.455493 -0.185157\n", - "2 9.351217 17.920173 -1.572169 8.558150 0.983524\n", - "3 16.044158 31.827899 -3.058927 7.970089 1.207837\n", - "4 21.913201 43.921734 3.983981 2.873435 0.176288\n" + "0 16.062299 31.926540 3.158977 9.675626 -0.715888\n", + "1 6.438101 12.599912 -0.728012 8.808864 -1.557705\n", + "2 8.451761 16.537359 -1.569776 0.639963 -0.800780\n", + "3 24.371373 47.793866 -4.250825 0.127575 -0.277237\n", + "4 12.892423 26.003995 2.648309 1.837378 0.357678\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -586,10 +586,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:48.107318Z", - "iopub.status.busy": "2024-10-20T00:23:48.106929Z", - "iopub.status.idle": "2024-10-20T00:23:48.228228Z", - "shell.execute_reply": "2024-10-20T00:23:48.227659Z" + "iopub.execute_input": "2024-10-22T01:50:32.435896Z", + "iopub.status.busy": "2024-10-22T01:50:32.435646Z", + "iopub.status.idle": "2024-10-22T01:50:32.542527Z", + "shell.execute_reply": "2024-10-22T01:50:32.541791Z" } }, "outputs": [ @@ -626,10 +626,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:48.230383Z", - "iopub.status.busy": "2024-10-20T00:23:48.230161Z", - "iopub.status.idle": "2024-10-20T00:23:48.238805Z", - "shell.execute_reply": "2024-10-20T00:23:48.238193Z" + "iopub.execute_input": "2024-10-22T01:50:32.544636Z", + "iopub.status.busy": "2024-10-22T01:50:32.544443Z", + "iopub.status.idle": "2024-10-22T01:50:32.550742Z", + "shell.execute_reply": "2024-10-22T01:50:32.550050Z" } }, "outputs": [ @@ -643,9 +643,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────────────(E[Outcome|w0,w1])\n", + "────────────(E[Outcome|w1,w0])\n", "d[Treatment] \n", - "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -683,10 +683,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:48.241177Z", - "iopub.status.busy": "2024-10-20T00:23:48.240787Z", - "iopub.status.idle": "2024-10-20T00:23:48.642105Z", - "shell.execute_reply": "2024-10-20T00:23:48.641515Z" + "iopub.execute_input": "2024-10-22T01:50:32.552657Z", + "iopub.status.busy": "2024-10-22T01:50:32.552472Z", + "iopub.status.idle": "2024-10-22T01:50:32.944449Z", + "shell.execute_reply": "2024-10-22T01:50:32.943852Z" }, "scrolled": true }, @@ -704,23 +704,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────────────(E[Outcome|w0,w1])\n", + "────────────(E[Outcome|w1,w0])\n", "d[Treatment] \n", - "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n", "\n", "## Realized estimand\n", - "b: Outcome~Treatment+w0+w1\n", + "b: Outcome~Treatment+w1+w0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 1.998434097264643\n", + "Mean value: 1.9993789424751534\n", "\n", - "Causal Estimate is 1.998434097264643\n" + "Causal Estimate is 1.9993789424751534\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -755,10 +755,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:48.644302Z", - "iopub.status.busy": "2024-10-20T00:23:48.643904Z", - "iopub.status.idle": "2024-10-20T00:23:50.477642Z", - "shell.execute_reply": "2024-10-20T00:23:50.477050Z" + "iopub.execute_input": "2024-10-22T01:50:32.946620Z", + "iopub.status.busy": "2024-10-22T01:50:32.946134Z", + "iopub.status.idle": "2024-10-22T01:50:34.791585Z", + "shell.execute_reply": "2024-10-22T01:50:34.790680Z" } }, "outputs": [ @@ -775,17 +775,17 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────────────(E[Outcome|w0,w1])\n", + "────────────(E[Outcome|w1,w0])\n", "d[Treatment] \n", - "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n", "\n", "## Realized estimand\n", - "b: Outcome~Treatment+w0+w1 | \n", + "b: Outcome~Treatment+w1+w0 | \n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 1.1610439951723444\n", - "Effect estimates: [[1.161044]]\n", + "Mean value: 0.9894564817819103\n", + "Effect estimates: [[0.98945648]]\n", "\n" ] } @@ -825,10 +825,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:23:50.480543Z", - "iopub.status.busy": "2024-10-20T00:23:50.480122Z", - "iopub.status.idle": "2024-10-20T00:28:12.017965Z", - "shell.execute_reply": "2024-10-20T00:28:12.017082Z" + "iopub.execute_input": "2024-10-22T01:50:34.794376Z", + "iopub.status.busy": "2024-10-22T01:50:34.793997Z", + "iopub.status.idle": "2024-10-22T01:54:58.039304Z", + "shell.execute_reply": "2024-10-22T01:54:58.038532Z" } }, "outputs": [ @@ -837,9 +837,9 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:1.1610439951723444\n", - "New effect:1.131708565840576\n", - "p value:0.24\n", + "Estimated effect:0.9894564817819103\n", + "New effect:0.9788569428730344\n", + "p value:0.72\n", "\n" ] } @@ -854,10 +854,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:28:12.020921Z", - "iopub.status.busy": "2024-10-20T00:28:12.020099Z", - "iopub.status.idle": "2024-10-20T00:28:49.213038Z", - "shell.execute_reply": "2024-10-20T00:28:49.212259Z" + "iopub.execute_input": "2024-10-22T01:54:58.042038Z", + "iopub.status.busy": "2024-10-22T01:54:58.041487Z", + "iopub.status.idle": "2024-10-22T01:55:35.273295Z", + "shell.execute_reply": "2024-10-22T01:55:35.272497Z" } }, "outputs": [ @@ -866,9 +866,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:1.1610439951723444\n", - "New effect:-2.4894090262337903e-05\n", - "p value:0.4671139184878982\n", + "Estimated effect:0.9894564817819103\n", + "New effect:0.00013356798350309588\n", + "p value:0.4157368947724852\n", "\n" ] } diff --git a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_15_0.png b/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_15_0.png index 3d2090fb3..21eb3a596 100644 Binary files a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_15_0.png and b/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_15_0.png differ diff --git a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_17_0.png b/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_17_0.png index 20a5ce135..9c12e4b5d 100644 Binary files a/main/.doctrees/nbsphinx/example_notebooks_DoWhy-The_Causal_Story_Behind_Hotel_Booking_Cancellations_17_0.png and 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Booking Cancellations.html index b1389700d..a52e8299c 100644 --- a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.html +++ b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.html @@ -1291,7 +1291,7 @@

Calculating Expected Counts
-$\displaystyle 588.2833$
+$\displaystyle 588.7483$

We now consider the scenario when there were no booking changes and recalculate the expected count.

@@ -1315,7 +1315,7 @@

Calculating Expected Counts
-$\displaystyle 572.9239$

+$\displaystyle 573.138$

In the 2nd case, we take the scenario when there were booking changes(>0) and recalculate the expected count.

@@ -1339,7 +1339,7 @@

Calculating Expected Counts
-$\displaystyle 666.0656$

+$\displaystyle 665.8655$

There is definitely some change happening when the number of booking changes are non-zero. So it gives us a hint that Booking Changes may be affecting room cancellation.

But is Booking Changes the only confounding variable? What if there were some unobserved confounders, regarding which we have no information(feature) present in our dataset. Would we still be able to make the same claims as before?

@@ -1468,17 +1468,17 @@

Step-2. Identify the Causal Effect
 Refute: Use a Placebo Treatment
 Estimated effect:-0.2622285649999514
-New effect:0.05464854856195345
+New effect:0.05497954255454352
 p value:0.0
@@ -1636,8 +1636,8 @@

Method-3
 Refute: Use a subset of data
 Estimated effect:-0.2622285649999514
-New effect:-0.2623624531466946
-p value:0.92
+New effect:-0.2622036470791978
+p value:0.8
diff --git a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb index a2abcd675..aa1abb72e 100644 --- a/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb +++ b/main/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb @@ -33,10 +33,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:07.523557Z", - "iopub.status.busy": "2024-10-19T23:27:07.523375Z", - "iopub.status.idle": "2024-10-19T23:27:09.592500Z", - "shell.execute_reply": "2024-10-19T23:27:09.591865Z" + "iopub.execute_input": "2024-10-22T00:52:45.353096Z", + "iopub.status.busy": "2024-10-22T00:52:45.352896Z", + "iopub.status.idle": "2024-10-22T00:52:47.516693Z", + "shell.execute_reply": "2024-10-22T00:52:47.515970Z" } }, "outputs": [], @@ -62,10 +62,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:09.594928Z", - "iopub.status.busy": "2024-10-19T23:27:09.594640Z", - "iopub.status.idle": "2024-10-19T23:27:10.160715Z", - "shell.execute_reply": "2024-10-19T23:27:10.160104Z" + "iopub.execute_input": "2024-10-22T00:52:47.519151Z", + "iopub.status.busy": "2024-10-22T00:52:47.518803Z", + "iopub.status.idle": "2024-10-22T00:52:48.368059Z", + "shell.execute_reply": "2024-10-22T00:52:48.367396Z" } }, "outputs": [ @@ -300,10 +300,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.189529Z", - "iopub.status.busy": "2024-10-19T23:27:10.188960Z", - "iopub.status.idle": "2024-10-19T23:27:10.193265Z", - "shell.execute_reply": "2024-10-19T23:27:10.192771Z" + "iopub.execute_input": "2024-10-22T00:52:48.400522Z", + "iopub.status.busy": "2024-10-22T00:52:48.399866Z", + "iopub.status.idle": "2024-10-22T00:52:48.406918Z", + "shell.execute_reply": "2024-10-22T00:52:48.406331Z" } }, "outputs": [ @@ -350,10 +350,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.195226Z", - "iopub.status.busy": "2024-10-19T23:27:10.194859Z", - "iopub.status.idle": "2024-10-19T23:27:10.217189Z", - "shell.execute_reply": "2024-10-19T23:27:10.216540Z" + "iopub.execute_input": "2024-10-22T00:52:48.408795Z", + "iopub.status.busy": "2024-10-22T00:52:48.408512Z", + "iopub.status.idle": "2024-10-22T00:52:48.431474Z", + "shell.execute_reply": "2024-10-22T00:52:48.430850Z" } }, "outputs": [ @@ -404,10 +404,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.219315Z", - "iopub.status.busy": "2024-10-19T23:27:10.218957Z", - "iopub.status.idle": "2024-10-19T23:27:10.348170Z", - "shell.execute_reply": "2024-10-19T23:27:10.347580Z" + "iopub.execute_input": "2024-10-22T00:52:48.433522Z", + "iopub.status.busy": "2024-10-22T00:52:48.433202Z", + "iopub.status.idle": "2024-10-22T00:52:48.565680Z", + "shell.execute_reply": "2024-10-22T00:52:48.565089Z" } }, "outputs": [], @@ -423,10 +423,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.350380Z", - "iopub.status.busy": "2024-10-19T23:27:10.350002Z", - "iopub.status.idle": "2024-10-19T23:27:10.374056Z", - "shell.execute_reply": "2024-10-19T23:27:10.373469Z" + "iopub.execute_input": "2024-10-22T00:52:48.567838Z", + "iopub.status.busy": "2024-10-22T00:52:48.567630Z", + "iopub.status.idle": "2024-10-22T00:52:48.591195Z", + "shell.execute_reply": "2024-10-22T00:52:48.590631Z" } }, "outputs": [], @@ -441,10 +441,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.376199Z", - "iopub.status.busy": "2024-10-19T23:27:10.375897Z", - "iopub.status.idle": "2024-10-19T23:27:10.526408Z", - "shell.execute_reply": "2024-10-19T23:27:10.525785Z" + "iopub.execute_input": "2024-10-22T00:52:48.593298Z", + "iopub.status.busy": "2024-10-22T00:52:48.593104Z", + "iopub.status.idle": "2024-10-22T00:52:48.746974Z", + "shell.execute_reply": "2024-10-22T00:52:48.746333Z" } }, "outputs": [ @@ -781,10 +781,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.528360Z", - "iopub.status.busy": "2024-10-19T23:27:10.528068Z", - "iopub.status.idle": "2024-10-19T23:27:10.615627Z", - "shell.execute_reply": "2024-10-19T23:27:10.614980Z" + "iopub.execute_input": "2024-10-22T00:52:48.748997Z", + "iopub.status.busy": "2024-10-22T00:52:48.748807Z", + "iopub.status.idle": "2024-10-22T00:52:48.839426Z", + "shell.execute_reply": "2024-10-22T00:52:48.838773Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.617590Z", - "iopub.status.busy": "2024-10-19T23:27:10.617258Z", - "iopub.status.idle": "2024-10-19T23:27:10.624873Z", - "shell.execute_reply": "2024-10-19T23:27:10.624248Z" + "iopub.execute_input": "2024-10-22T00:52:48.841533Z", + "iopub.status.busy": "2024-10-22T00:52:48.841044Z", + "iopub.status.idle": "2024-10-22T00:52:48.849218Z", + "shell.execute_reply": "2024-10-22T00:52:48.848564Z" } }, "outputs": [], @@ -991,21 +991,21 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:10.626987Z", - "iopub.status.busy": "2024-10-19T23:27:10.626625Z", - "iopub.status.idle": "2024-10-19T23:27:32.037779Z", - "shell.execute_reply": "2024-10-19T23:27:32.037239Z" + "iopub.execute_input": "2024-10-22T00:52:48.851537Z", + "iopub.status.busy": "2024-10-22T00:52:48.851100Z", + "iopub.status.idle": "2024-10-22T00:53:11.499971Z", + "shell.execute_reply": "2024-10-22T00:53:11.499385Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/latex": [ - "$\\displaystyle 588.2833$" + "$\\displaystyle 588.7483$" ], "text/plain": [ - "588.2833" + "588.7483" ] }, "execution_count": 10, @@ -1035,21 +1035,21 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:27:32.039632Z", - "iopub.status.busy": "2024-10-19T23:27:32.039453Z", - "iopub.status.idle": "2024-10-19T23:28:47.277321Z", - "shell.execute_reply": "2024-10-19T23:28:47.276771Z" + "iopub.execute_input": "2024-10-22T00:53:11.502130Z", + "iopub.status.busy": "2024-10-22T00:53:11.501710Z", + "iopub.status.idle": "2024-10-22T00:54:30.427249Z", + "shell.execute_reply": "2024-10-22T00:54:30.426549Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/latex": [ - "$\\displaystyle 572.9239$" + "$\\displaystyle 573.138$" ], "text/plain": [ - "572.9239" + "573.138" ] }, "execution_count": 11, @@ -1080,21 +1080,21 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:28:47.279357Z", - "iopub.status.busy": "2024-10-19T23:28:47.278972Z", - "iopub.status.idle": "2024-10-19T23:29:12.342320Z", - "shell.execute_reply": "2024-10-19T23:29:12.341657Z" + "iopub.execute_input": "2024-10-22T00:54:30.429682Z", + "iopub.status.busy": "2024-10-22T00:54:30.429269Z", + "iopub.status.idle": "2024-10-22T00:54:57.498056Z", + "shell.execute_reply": "2024-10-22T00:54:57.497490Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/latex": [ - "$\\displaystyle 666.0656$" + "$\\displaystyle 665.8655$" ], "text/plain": [ - "666.0656" + "665.8655" ] }, "execution_count": 12, @@ -1152,10 +1152,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:12.344525Z", - "iopub.status.busy": "2024-10-19T23:29:12.344091Z", - "iopub.status.idle": "2024-10-19T23:29:12.350224Z", - "shell.execute_reply": "2024-10-19T23:29:12.349762Z" + "iopub.execute_input": "2024-10-22T00:54:57.500270Z", + "iopub.status.busy": "2024-10-22T00:54:57.499904Z", + "iopub.status.idle": "2024-10-22T00:54:57.506300Z", + "shell.execute_reply": "2024-10-22T00:54:57.505842Z" } }, "outputs": [], @@ -1214,10 +1214,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:12.352081Z", - "iopub.status.busy": "2024-10-19T23:29:12.351692Z", - "iopub.status.idle": "2024-10-19T23:29:12.688190Z", - "shell.execute_reply": "2024-10-19T23:29:12.687560Z" + "iopub.execute_input": "2024-10-22T00:54:57.508010Z", + "iopub.status.busy": "2024-10-22T00:54:57.507822Z", + "iopub.status.idle": "2024-10-22T00:54:57.828704Z", + "shell.execute_reply": "2024-10-22T00:54:57.828057Z" } }, "outputs": [ @@ -1275,10 +1275,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:12.691268Z", - "iopub.status.busy": "2024-10-19T23:29:12.690319Z", - "iopub.status.idle": "2024-10-19T23:29:14.837989Z", - "shell.execute_reply": "2024-10-19T23:29:14.837385Z" + "iopub.execute_input": "2024-10-22T00:54:57.831329Z", + "iopub.status.busy": "2024-10-22T00:54:57.831090Z", + "iopub.status.idle": "2024-10-22T00:55:00.008153Z", + "shell.execute_reply": "2024-10-22T00:55:00.007514Z" } }, "outputs": [ @@ -1292,17 +1292,17 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "──────────────────────────(E[is_canceled|lead_time,guests,is_repeated_guest,to ↪\n", + "──────────────────────────(E[is_canceled|total_of_special_requests,total_stay, ↪\n", "d[different_room_assigned] ↪\n", "\n", "↪ ↪\n", - "↪ tal_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_change ↪\n", + "↪ hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_par ↪\n", "↪ ↪\n", "\n", "↪ \n", - "↪ s,required_car_parking_spaces])\n", + "↪ king_spaces,is_repeated_guest])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces,U) = P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces)\n", + "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest,U) = P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -1333,10 +1333,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:14.839920Z", - "iopub.status.busy": "2024-10-19T23:29:14.839691Z", - "iopub.status.idle": "2024-10-19T23:29:15.387693Z", - "shell.execute_reply": "2024-10-19T23:29:15.386861Z" + "iopub.execute_input": "2024-10-22T00:55:00.010304Z", + "iopub.status.busy": "2024-10-22T00:55:00.009985Z", + "iopub.status.idle": "2024-10-22T00:55:00.576470Z", + "shell.execute_reply": "2024-10-22T00:55:00.575873Z" } }, "outputs": [ @@ -1353,20 +1353,20 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "──────────────────────────(E[is_canceled|lead_time,guests,is_repeated_guest,to ↪\n", + "──────────────────────────(E[is_canceled|total_of_special_requests,total_stay, ↪\n", "d[different_room_assigned] ↪\n", "\n", "↪ ↪\n", - "↪ tal_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_change ↪\n", + "↪ hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_par ↪\n", "↪ ↪\n", "\n", "↪ \n", - "↪ s,required_car_parking_spaces])\n", + "↪ king_spaces,is_repeated_guest])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces,U) = P(is_canceled|different_room_assigned,lead_time,guests,is_repeated_guest,total_of_special_requests,total_stay,hotel,days_in_waiting_list,booking_changes,required_car_parking_spaces)\n", + "Estimand assumption 1, Unconfoundedness: If U→{different_room_assigned} and U→is_canceled then P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest,U) = P(is_canceled|different_room_assigned,total_of_special_requests,total_stay,hotel,guests,days_in_waiting_list,lead_time,booking_changes,required_car_parking_spaces,is_repeated_guest)\n", "\n", "## Realized estimand\n", - "b: is_canceled~different_room_assigned+lead_time+guests+is_repeated_guest+total_of_special_requests+total_stay+hotel+days_in_waiting_list+booking_changes+required_car_parking_spaces\n", + "b: is_canceled~different_room_assigned+total_of_special_requests+total_stay+hotel+guests+days_in_waiting_list+lead_time+booking_changes+required_car_parking_spaces+is_repeated_guest\n", "Target units: ate\n", "\n", "## Estimate\n", @@ -1435,10 +1435,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:15.390158Z", - "iopub.status.busy": "2024-10-19T23:29:15.389933Z", - "iopub.status.idle": "2024-10-19T23:29:25.319577Z", - "shell.execute_reply": "2024-10-19T23:29:25.318958Z" + "iopub.execute_input": "2024-10-22T00:55:00.580828Z", + "iopub.status.busy": "2024-10-22T00:55:00.580094Z", + "iopub.status.idle": "2024-10-22T00:55:11.018269Z", + "shell.execute_reply": "2024-10-22T00:55:11.017622Z" } }, "outputs": [ @@ -1473,10 +1473,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:25.321804Z", - "iopub.status.busy": "2024-10-19T23:29:25.321328Z", - "iopub.status.idle": "2024-10-19T23:29:35.461617Z", - "shell.execute_reply": "2024-10-19T23:29:35.461040Z" + "iopub.execute_input": "2024-10-22T00:55:11.020519Z", + "iopub.status.busy": "2024-10-22T00:55:11.020092Z", + "iopub.status.idle": "2024-10-22T00:55:21.553769Z", + "shell.execute_reply": "2024-10-22T00:55:21.553128Z" } }, "outputs": [ @@ -1486,7 +1486,7 @@ "text": [ "Refute: Use a Placebo Treatment\n", "Estimated effect:-0.2622285649999514\n", - "New effect:0.05464854856195345\n", + "New effect:0.05497954255454352\n", "p value:0.0\n", "\n" ] @@ -1511,10 +1511,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:29:35.463774Z", - "iopub.status.busy": "2024-10-19T23:29:35.463261Z", - "iopub.status.idle": "2024-10-19T23:29:44.584141Z", - "shell.execute_reply": "2024-10-19T23:29:44.583542Z" + "iopub.execute_input": "2024-10-22T00:55:21.555659Z", + "iopub.status.busy": "2024-10-22T00:55:21.555466Z", + "iopub.status.idle": "2024-10-22T00:55:31.028906Z", + "shell.execute_reply": "2024-10-22T00:55:31.028247Z" } }, "outputs": [ @@ -1524,8 +1524,8 @@ "text": [ "Refute: Use a subset of data\n", "Estimated effect:-0.2622285649999514\n", - "New effect:-0.2623624531466946\n", - "p value:0.92\n", + "New effect:-0.2622036470791978\n", + "p value:0.8\n", "\n" ] } diff --git a/main/example_notebooks/counterfactual_fairness_dowhy.html b/main/example_notebooks/counterfactual_fairness_dowhy.html index 6b550ba8a..8a9bf5989 100644 --- a/main/example_notebooks/counterfactual_fairness_dowhy.html +++ b/main/example_notebooks/counterfactual_fairness_dowhy.html @@ -873,43 +873,43 @@

1. Load and Clean the Dataset 
-Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 297.80it/s]
+Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 240.47it/s]
@@ -1017,7 +1017,7 @@

3.1 Univariate Analysis
-$\displaystyle -0.524687380683375$
+$\displaystyle -0.517771471371438$

df_obs contains the predicted values for each individual given the observed value of their protected attribute in the real world.

@@ -1062,48 +1062,48 @@

3.1 Univariate Analysis 0 - 0.0 - 0.0 - 3.0 - 38.0 - -0.45 - 0.133481 - - - 1 1.0 1.0 - 2.4 + 2.6 25.5 - -0.80 - -0.571618 + 0.21 + -0.509516 + + + 1 + 0.0 + 0.0 + 3.5 + 38.0 + 0.42 + 0.251650 2 0.0 0.0 - 3.4 - 41.0 - 0.83 - 0.358154 + 3.1 + 34.0 + 0.20 + -0.021055 3 0.0 - 0.0 + 1.0 3.1 - 38.0 - 0.53 - 0.155141 + 26.0 + 0.10 + -0.358381 4 0.0 1.0 - 3.7 - 39.0 - 0.48 - 0.331112 + 3.4 + 38.0 + 0.68 + 0.225640 @@ -1152,48 +1152,48 @@

3.1 Univariate Analysis 0 - 1.0 0.0 - 2.607567 - 29.986959 - -1.505042 - 0.131699 + 1.0 + 2.975501 + 33.82517 + 1.035202 + 0.184346 1 - 0.0 1.0 - 2.792433 - 33.513041 - -0.198171 - 0.181322 + 0.0 + 3.124499 + 29.67483 + -0.875016 + 0.159886 2 1.0 0.0 - 3.007567 - 32.986959 - -0.483705 - 0.178554 + 2.724499 + 25.67483 + -0.870791 + 0.091847 3 1.0 - 0.0 - 2.707567 - 29.986959 - -0.570016 - 0.133609 + 1.0 + 2.724499 + 17.67483 + -0.664738 + 0.021207 4 1.0 1.0 - 3.307567 - 30.986959 - -0.674063 - 0.158139 + 3.024499 + 29.67483 + -0.382152 + 0.151706 @@ -1255,7 +1255,7 @@

3.2 Intersectional Analysis 
-Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 373.92it/s]
+Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 338.04it/s]

@@ -1264,7 +1264,7 @@

3.2 Intersectional Analysis
-$\displaystyle -0.52911930891247$

+$\displaystyle -0.503963256935917$ 
[11]:
@@ -1348,7 +1348,7 @@ 
Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unf
 
-Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 373.34it/s]
+Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 377.93it/s]
@@ -1357,7 +1357,7 @@

Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unf

-$\displaystyle 0.0$
+$\displaystyle -1.11022302462516 \cdot 10^{-15}$
[13]:
diff --git a/main/example_notebooks/counterfactual_fairness_dowhy.ipynb b/main/example_notebooks/counterfactual_fairness_dowhy.ipynb
index 6e33dc393..28465d01a 100644
--- a/main/example_notebooks/counterfactual_fairness_dowhy.ipynb
+++ b/main/example_notebooks/counterfactual_fairness_dowhy.ipynb
@@ -6,10 +6,10 @@
    "id": "ef2e40dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:29:48.069838Z",
-     "iopub.status.busy": "2024-10-19T23:29:48.069634Z",
-     "iopub.status.idle": "2024-10-19T23:29:48.072998Z",
-     "shell.execute_reply": "2024-10-19T23:29:48.072364Z"
+     "iopub.execute_input": "2024-10-22T00:55:34.510070Z",
+     "iopub.status.busy": "2024-10-22T00:55:34.509890Z",
+     "iopub.status.idle": "2024-10-22T00:55:34.513099Z",
+     "shell.execute_reply": "2024-10-22T00:55:34.512627Z"
     }
    },
    "outputs": [],
@@ -63,10 +63,10 @@
    "id": "46026b9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:29:48.075006Z",
-     "iopub.status.busy": "2024-10-19T23:29:48.074711Z",
-     "iopub.status.idle": "2024-10-19T23:29:49.591653Z",
-     "shell.execute_reply": "2024-10-19T23:29:49.591027Z"
+     "iopub.execute_input": "2024-10-22T00:55:34.515007Z",
+     "iopub.status.busy": "2024-10-22T00:55:34.514836Z",
+     "iopub.status.idle": "2024-10-22T00:55:36.185568Z",
+     "shell.execute_reply": "2024-10-22T00:55:36.184877Z"
     }
    },
    "outputs": [],
@@ -296,10 +296,10 @@
    "id": "37212ae1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:29:49.594126Z",
-     "iopub.status.busy": "2024-10-19T23:29:49.593659Z",
-     "iopub.status.idle": "2024-10-19T23:29:49.657237Z",
-     "shell.execute_reply": "2024-10-19T23:29:49.656595Z"
+     "iopub.execute_input": "2024-10-22T00:55:36.188184Z",
+     "iopub.status.busy": "2024-10-22T00:55:36.187860Z",
+     "iopub.status.idle": "2024-10-22T00:55:36.239323Z",
+     "shell.execute_reply": "2024-10-22T00:55:36.238657Z"
     }
    },
    "outputs": [
@@ -334,43 +334,43 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.0\n",
-       "      0.0\n",
-       "      3.0\n",
-       "      38.0\n",
-       "      -0.45\n",
-       "    \n",
-       "    \n",
-       "      1\n",
        "      1.0\n",
        "      1.0\n",
-       "      2.4\n",
+       "      2.6\n",
        "      25.5\n",
-       "      -0.80\n",
+       "      0.21\n",
+       "    \n",
+       "    \n",
+       "      1\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      3.5\n",
+       "      38.0\n",
+       "      0.42\n",
        "    \n",
        "    \n",
        "      2\n",
        "      0.0\n",
        "      0.0\n",
-       "      3.4\n",
-       "      41.0\n",
-       "      0.83\n",
+       "      3.1\n",
+       "      34.0\n",
+       "      0.20\n",
        "    \n",
        "    \n",
        "      3\n",
        "      0.0\n",
-       "      0.0\n",
+       "      1.0\n",
        "      3.1\n",
-       "      38.0\n",
-       "      0.53\n",
+       "      26.0\n",
+       "      0.10\n",
        "    \n",
        "    \n",
        "      4\n",
        "      0.0\n",
        "      1.0\n",
-       "      3.7\n",
-       "      39.0\n",
-       "      0.48\n",
+       "      3.4\n",
+       "      38.0\n",
+       "      0.68\n",
        "    \n",
        "  \n",
        "\n",
@@ -378,11 +378,11 @@
       ],
       "text/plain": [
        "   Race  Gender  GPA  LSAT  avg_grade\n",
-       "0   0.0     0.0  3.0  38.0      -0.45\n",
-       "1   1.0     1.0  2.4  25.5      -0.80\n",
-       "2   0.0     0.0  3.4  41.0       0.83\n",
-       "3   0.0     0.0  3.1  38.0       0.53\n",
-       "4   0.0     1.0  3.7  39.0       0.48"
+       "0   1.0     1.0  2.6  25.5       0.21\n",
+       "1   0.0     0.0  3.5  38.0       0.42\n",
+       "2   0.0     0.0  3.1  34.0       0.20\n",
+       "3   0.0     1.0  3.1  26.0       0.10\n",
+       "4   0.0     1.0  3.4  38.0       0.68"
       ]
      },
      "execution_count": 3,
@@ -466,10 +466,10 @@
    "id": "2280c2df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:29:49.659189Z",
-     "iopub.status.busy": "2024-10-19T23:29:49.658903Z",
-     "iopub.status.idle": "2024-10-19T23:29:49.661986Z",
-     "shell.execute_reply": "2024-10-19T23:29:49.661504Z"
+     "iopub.execute_input": "2024-10-22T00:55:36.241573Z",
+     "iopub.status.busy": "2024-10-22T00:55:36.241192Z",
+     "iopub.status.idle": "2024-10-22T00:55:36.244561Z",
+     "shell.execute_reply": "2024-10-22T00:55:36.244055Z"
     }
    },
    "outputs": [],
@@ -506,10 +506,10 @@
    "id": "d70d0187",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:29:49.663768Z",
-     "iopub.status.busy": "2024-10-19T23:29:49.663466Z",
-     "iopub.status.idle": "2024-10-19T23:29:49.666213Z",
-     "shell.execute_reply": "2024-10-19T23:29:49.665744Z"
+     "iopub.execute_input": "2024-10-22T00:55:36.246471Z",
+     "iopub.status.busy": "2024-10-22T00:55:36.246100Z",
+     "iopub.status.idle": "2024-10-22T00:55:36.249003Z",
+     "shell.execute_reply": "2024-10-22T00:55:36.248528Z"
     }
    },
    "outputs": [],
@@ -536,10 +536,10 @@
    "id": "6fdaa7be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:29:49.668065Z",
-     "iopub.status.busy": "2024-10-19T23:29:49.667637Z",
-     "iopub.status.idle": "2024-10-19T23:30:44.485071Z",
-     "shell.execute_reply": "2024-10-19T23:30:44.484505Z"
+     "iopub.execute_input": "2024-10-22T00:55:36.250890Z",
+     "iopub.status.busy": "2024-10-22T00:55:36.250475Z",
+     "iopub.status.idle": "2024-10-22T00:56:32.454539Z",
+     "shell.execute_reply": "2024-10-22T00:56:32.453294Z"
     }
    },
    "outputs": [
@@ -596,7 +596,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 297.80it/s]"
+      "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 240.47it/s]"
      ]
     },
     {
@@ -608,12 +608,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle -0.524687380683375$"
+       "$\\displaystyle -0.517771471371438$"
       ],
       "text/plain": [
-       "-0.524687380683375"
+       "-0.517771471371438"
       ]
      },
      "execution_count": 6,
@@ -651,10 +651,10 @@
    "id": "79c2a233",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:30:44.488032Z",
-     "iopub.status.busy": "2024-10-19T23:30:44.487555Z",
-     "iopub.status.idle": "2024-10-19T23:30:44.498981Z",
-     "shell.execute_reply": "2024-10-19T23:30:44.497982Z"
+     "iopub.execute_input": "2024-10-22T00:56:32.457461Z",
+     "iopub.status.busy": "2024-10-22T00:56:32.457195Z",
+     "iopub.status.idle": "2024-10-22T00:56:32.468560Z",
+     "shell.execute_reply": "2024-10-22T00:56:32.467974Z"
     }
    },
    "outputs": [
@@ -690,48 +690,48 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.0\n",
-       "      0.0\n",
-       "      3.0\n",
-       "      38.0\n",
-       "      -0.45\n",
-       "      0.133481\n",
-       "    \n",
-       "    \n",
-       "      1\n",
        "      1.0\n",
        "      1.0\n",
-       "      2.4\n",
+       "      2.6\n",
        "      25.5\n",
-       "      -0.80\n",
-       "      -0.571618\n",
+       "      0.21\n",
+       "      -0.509516\n",
+       "    \n",
+       "    \n",
+       "      1\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      3.5\n",
+       "      38.0\n",
+       "      0.42\n",
+       "      0.251650\n",
        "    \n",
        "    \n",
        "      2\n",
        "      0.0\n",
        "      0.0\n",
-       "      3.4\n",
-       "      41.0\n",
-       "      0.83\n",
-       "      0.358154\n",
+       "      3.1\n",
+       "      34.0\n",
+       "      0.20\n",
+       "      -0.021055\n",
        "    \n",
        "    \n",
        "      3\n",
        "      0.0\n",
-       "      0.0\n",
+       "      1.0\n",
        "      3.1\n",
-       "      38.0\n",
-       "      0.53\n",
-       "      0.155141\n",
+       "      26.0\n",
+       "      0.10\n",
+       "      -0.358381\n",
        "    \n",
        "    \n",
        "      4\n",
        "      0.0\n",
        "      1.0\n",
-       "      3.7\n",
-       "      39.0\n",
-       "      0.48\n",
-       "      0.331112\n",
+       "      3.4\n",
+       "      38.0\n",
+       "      0.68\n",
+       "      0.225640\n",
        "    \n",
        "  \n",
        "\n",
@@ -739,11 +739,11 @@
       ],
       "text/plain": [
        "   Race  Gender  GPA  LSAT  avg_grade     preds\n",
-       "0   0.0     0.0  3.0  38.0      -0.45  0.133481\n",
-       "1   1.0     1.0  2.4  25.5      -0.80 -0.571618\n",
-       "2   0.0     0.0  3.4  41.0       0.83  0.358154\n",
-       "3   0.0     0.0  3.1  38.0       0.53  0.155141\n",
-       "4   0.0     1.0  3.7  39.0       0.48  0.331112"
+       "0   1.0     1.0  2.6  25.5       0.21 -0.509516\n",
+       "1   0.0     0.0  3.5  38.0       0.42  0.251650\n",
+       "2   0.0     0.0  3.1  34.0       0.20 -0.021055\n",
+       "3   0.0     1.0  3.1  26.0       0.10 -0.358381\n",
+       "4   0.0     1.0  3.4  38.0       0.68  0.225640"
       ]
      },
      "execution_count": 7,
@@ -769,10 +769,10 @@
    "id": "56d88825",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-19T23:30:44.501072Z",
-     "iopub.status.busy": "2024-10-19T23:30:44.500870Z",
-     "iopub.status.idle": "2024-10-19T23:30:44.511295Z",
-     "shell.execute_reply": "2024-10-19T23:30:44.510750Z"
+     "iopub.execute_input": "2024-10-22T00:56:32.470822Z",
+     "iopub.status.busy": "2024-10-22T00:56:32.470586Z",
+     "iopub.status.idle": "2024-10-22T00:56:32.480722Z",
+     "shell.execute_reply": "2024-10-22T00:56:32.480197Z"
     }
    },
    "outputs": [
@@ -808,60 +808,60 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      1.0\n",
        "      0.0\n",
-       "      2.607567\n",
-       "      29.986959\n",
-       "      -1.505042\n",
-       "      0.131699\n",
+       "      1.0\n",
+       "      2.975501\n",
+       "      33.82517\n",
+       "      1.035202\n",
+       "      0.184346\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.0\n",
        "      1.0\n",
-       "      2.792433\n",
-       "      33.513041\n",
-       "      -0.198171\n",
-       "      0.181322\n",
+       "      0.0\n",
+       "      3.124499\n",
+       "      29.67483\n",
+       "      -0.875016\n",
+       "      0.159886\n",
        "    \n",
        "    \n",
        "      2\n",
        "      1.0\n",
        "      0.0\n",
-       "      3.007567\n",
-       "      32.986959\n",
-       "      -0.483705\n",
-       "      0.178554\n",
+       "      2.724499\n",
+       "      25.67483\n",
+       "      -0.870791\n",
+       "      0.091847\n",
        "    \n",
        "    \n",
        "      3\n",
        "      1.0\n",
-       "      0.0\n",
-       "      2.707567\n",
-       "      29.986959\n",
-       "      -0.570016\n",
-       "      0.133609\n",
+       "      1.0\n",
+       "      2.724499\n",
+       "      17.67483\n",
+       "      -0.664738\n",
+       "      0.021207\n",
        "    \n",
        "    \n",
        "      4\n",
        "      1.0\n",
        "      1.0\n",
-       "      3.307567\n",
-       "      30.986959\n",
-       "      -0.674063\n",
-       "      0.158139\n",
+       "      3.024499\n",
+       "      29.67483\n",
+       "      -0.382152\n",
+       "      0.151706\n",
        "    \n",
        "  \n",
        "\n",
        "
" ], "text/plain": [ - " Race Gender GPA LSAT avg_grade preds_cf\n", - "0 1.0 0.0 2.607567 29.986959 -1.505042 0.131699\n", - "1 0.0 1.0 2.792433 33.513041 -0.198171 0.181322\n", - "2 1.0 0.0 3.007567 32.986959 -0.483705 0.178554\n", - "3 1.0 0.0 2.707567 29.986959 -0.570016 0.133609\n", - "4 1.0 1.0 3.307567 30.986959 -0.674063 0.158139" + " Race Gender GPA LSAT avg_grade preds_cf\n", + "0 0.0 1.0 2.975501 33.82517 1.035202 0.184346\n", + "1 1.0 0.0 3.124499 29.67483 -0.875016 0.159886\n", + "2 1.0 0.0 2.724499 25.67483 -0.870791 0.091847\n", + "3 1.0 1.0 2.724499 17.67483 -0.664738 0.021207\n", + "4 1.0 1.0 3.024499 29.67483 -0.382152 0.151706" ] }, "execution_count": 8, @@ -879,16 +879,16 @@ "id": "a1176177", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:30:44.514307Z", - "iopub.status.busy": "2024-10-19T23:30:44.513530Z", - "iopub.status.idle": "2024-10-19T23:30:44.803104Z", - "shell.execute_reply": "2024-10-19T23:30:44.802439Z" + "iopub.execute_input": "2024-10-22T00:56:32.482575Z", + "iopub.status.busy": "2024-10-22T00:56:32.482370Z", + "iopub.status.idle": "2024-10-22T00:56:32.817664Z", + "shell.execute_reply": "2024-10-22T00:56:32.817002Z" } }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -928,10 +928,10 @@ "id": "725bf447", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:30:44.805315Z", - "iopub.status.busy": "2024-10-19T23:30:44.804934Z", - "iopub.status.idle": "2024-10-19T23:31:36.660967Z", - "shell.execute_reply": "2024-10-19T23:31:36.660439Z" + "iopub.execute_input": "2024-10-22T00:56:32.819616Z", + "iopub.status.busy": "2024-10-22T00:56:32.819418Z", + "iopub.status.idle": "2024-10-22T00:57:28.624969Z", + "shell.execute_reply": "2024-10-22T00:57:28.624132Z" } }, "outputs": [ @@ -988,7 +988,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 373.92it/s]" + "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 338.04it/s]" ] }, { @@ -1000,12 +1000,12 @@ }, { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle -0.52911930891247$" + "$\\displaystyle -0.503963256935917$" ], "text/plain": [ - "-0.5291193089124704" + "-0.5039632569359168" ] }, "execution_count": 10, @@ -1037,16 +1037,16 @@ "id": "ad7671e5", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:31:36.663535Z", - "iopub.status.busy": "2024-10-19T23:31:36.663320Z", - "iopub.status.idle": "2024-10-19T23:31:37.020548Z", - "shell.execute_reply": "2024-10-19T23:31:37.020012Z" + "iopub.execute_input": "2024-10-22T00:57:28.628054Z", + "iopub.status.busy": "2024-10-22T00:57:28.627105Z", + "iopub.status.idle": "2024-10-22T00:57:28.946656Z", + "shell.execute_reply": "2024-10-22T00:57:28.946039Z" } }, "outputs": [ { "data": { - "image/png": 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", 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K7WR+rzP88ccfCAsLU/rVdycnJ9SqVUvl87N69WooFAr07t0bANCuXTvs3r1b5VWpUiU0btwYu3fvhqenZ47bSUSUFa9YEFGB2NvbY/PmzejXrx9q166t9Mvbp0+fxvbt26XfJvjoo48wePBg/Pjjj3j+/DlcXV1x7tw5bNiwAd27d0fbtm2l9Q4fPhyffvopevXqhQ4dOuDKlSs4dOiQ0ln+gmrUqBFWr16NOXPmwMHBAZUrV0a7du3w1VdfYd++fejatSuGDBmCRo0a4dWrV7h27Rp27NiBuLi4QrfbtWtXzJo1C0OHDoWLiwuuXbuGTZs2oXr16kr1fHx88Msvv2DChAk4d+4cWrdujVevXuHo0aP4/PPP4eXlBT09PdSpUwfbtm2Do6MjKlSogLp166Ju3br45JNPsGTJEnh4eGDYsGGIj4/HDz/8ACcnJ+khe+Bd8rZkyRJ06tQJAwcORHx8PFauXAkHBwelZxPUpbjby2rQoEH47bff8OmnnyIoKAgtW7ZEWloa/v77b/z22284dOgQGjdujFmzZuHEiRPo0qULbGxsEB8fj1WrVqFatWrSkK8dO3aEubk5WrZsiSpVqiAiIgLff/89unTpku8BDDKrVq0axo0bh0WLFiElJQVNmjTBnj17cPLkSWzatEnpVq+pU6diw4YNiI2NlX6XxMXFBQ0aNEDjxo1Rvnx5XLx4EevWrYOVlZXK728sWrQI3bp1Q8eOHdG/f39cv34d33//PYYPHy5dPbS2tlZ5VgkAxo0bhypVqqB79+4F3kYi+sCV3IBURFSWRUVFiREjRghbW1uhra0tjIyMRMuWLcWKFSukYSuFECIlJUUEBAQIOzs7Ua5cOWFlZSWmTp2qVEcIIdLS0sTkyZNFxYoVhb6+vvDw8BA3b97McUjS8+fPKy0fFBQkAIigoCCp7MGDB6JLly7CyMhIAFAanvXly5di6tSpwsHBQWhra4uKFSsKFxcX8e2334q3b98KIf4bbnbRokXZ9gFyGG72yy+/FBYWFkJPT0+0bNlSnDlzJtvhYV+/fi2mTZsm9Y25ubno3bu3iImJkeqcPn1aNGrUSGhra6sMPfvrr7+K6tWrC21tbVG/fn1x6NChbIeb/fnnn0WNGjWEjo6OqFWrlggMDBT+/v4i61dAQYabzbrd6mgvp/dWiHfDzWY39Gt22/v27VuxYMEC4eTkJHR0dISpqalo1KiRCAgIEAkJCUIIIY4dOya8vLyEpaWl0NbWFpaWlmLAgAFKQxCvWbNGtGnTRpiZmQkdHR1hb28vvvrqK2kdhZGWlibmzZsnbGxshLa2tnBychK//vprttsFQMTGxkpl06ZNE/Xr1xfly5cX5cqVE9bW1uKzzz4TDx48yLat3bt3i/r16wsdHR1RrVo1MX36dGnfzg2HmyWiwlIIkY8nHYmIiIiIiHLBZyyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQkVotXLgQtWrVQnp6eqGWHzJkCGxtbdUbVCa2trbo2rVrka2/LIiLi4NCocD69esLvOzBgwdhaGiIR48eqT8wmeTue9kZMmQIDA0N81VXoVBg5syZamu7tJk5cyYUCkWhlu3fvz/69u2r5oiIqLRhYkFEavPixQssWLAAkydPhobGf/9eFAqF0svAwAB16tTBnDlz8Pr16xKMWL7169erbF/Ga8qUKSUdntp16tQJDg4OmD9/fkmHoiTrvpeWlgZjY2N4eXmp1F26dCkUCgUGDx6sMm/GjBlQKBSIioqSHdPp06cxc+ZMPH/+XPa68iNjvxs+fHi286dNmybVefz4cbHElGHy5MnYuXMnrly5UqztElHx0irpAIjo/bFu3TqkpqZiwIABKvM6dOgAHx8fAEBiYiJOnjwJPz8/XLlyBdu3by/uUNVu1qxZsLOzUyqrW7duCUVTtEaNGoWJEyciICAARkZGJR0OANV9T1NTE82bN8fp06dV6oaEhEBLSwshISHZzqtcuTIcHR0LHMObN2+gpfXf1+rp06cREBCAIUOGwMTEpMDrKwxdXV3s3LkTq1atgra2ttK8LVu2QFdXF0lJScUSS2YNGjRA48aNsXjxYvzyyy/F3j4RFQ9esSAitQkMDES3bt2gq6urMs/R0REff/wxPv74Y3z66afYtGkTevfujV27dpXIgY66de7cWdq+jFf9+vVLOqwi0atXLyQnJ8tKCGNiYtR6tSq7fa9Vq1Z4/PgxIiIilOqGhISgb9++iImJwYMHD6Ty1NRUhIaGomXLloWKQVdXVymxKAmdOnXCixcv8OeffyqVnz59GrGxsejSpUsJRQb07dsXu3btQmJiYonFQERFi4kFEalFbGwsrl69Cnd393wvY25uDoVCkefB2LfffgsXFxeYmZlBT08PjRo1wo4dO7Kt++uvv6Jp06bQ19eHqakp2rRpg8OHD+e6/g0bNkBLSwtfffVVvmMvqD///BOtW7eGgYEBjIyM0KVLF9y4cUOpTsb9/Ldv30bXrl1haGiIqlWrYuXKlQCAa9euoV27djAwMICNjQ02b96stPzTp08xceJEODs7w9DQEMbGxujcuXO+bz/5+++/0bt3b1SoUAG6urpo3Lgx9u3bp1KvcuXKqFevHvbu3VvI3gA2btwICwsLfPrppzh//nyh1wPkvO+1atUKAJSuTNy6dQsPHjzA6NGjoaurqzTv8uXLePXqlbRcZnfv3kX37t1haGiISpUqYeLEiUhLS1Oqk/kZi5kzZ0r7k52dnXQLUlxcnFT/119/RaNGjaCnp4cKFSqgf//+uHPnjqy+qFq1Ktq0aaOyb2zatAnOzs7ZXkU7efIk+vTpA2tra+jo6MDKygrjx4/Hmzdv8tVmfrejQ4cOePXqFY4cOVK4jSOiUo+JBRGpRcYtJw0bNsx2flJSEh4/fozHjx/jn3/+webNm7FhwwYMHDgwz8Ri+fLlaNCgAWbNmoV58+ZBS0sLffr0wYEDB5TqBQQEYNCgQShXrhxmzZqFgIAAWFlZ4fjx4zmu+8cff8TQoUMxZcoULFq0qIBb/Z+EhARp+zJeGTZu3IguXbrA0NAQCxYsgJ+fH8LDw9GqVSulA00ASEtLQ+fOnWFlZYWFCxfC1tYWo0ePxvr169GpUyc0btwYCxYsgJGREXx8fBAbGyste+vWLezZswddu3bFkiVL8NVXX+HatWtwdXXFvXv3co3/xo0baN68OSIiIjBlyhQsXrwYBgYG6N69O3bv3q1Sv1GjRtneZpRfAwcOhLe3N7Zt24amTZuiXr16WL58OZ48eVLgdeW07zVv3hxaWlo4deqUVBYSEgIDAwM0adIEjRs3VkosMv7OmlikpaXBw8MDZmZm+Pbbb+Hq6orFixfjxx9/zDGmnj17SrdlLV26FBs3bsTGjRtRqVIlAMDcuXPh4+ODGjVqYMmSJRg3bhyOHTuGNm3ayH4mY+DAgdi/f790ZSA1NRXbt2/HwIEDs62/fft2vH79Gp999hlWrFgBDw8PrFixQrp1MTcF2Y46depAT08v21vQiOg9IYiI1GD69OkCgHj58qXKPADZvrp37y6SkpKU6g4ePFjY2Ngolb1+/Vpp+u3bt6Ju3bqiXbt2Ull0dLTQ0NAQPXr0EGlpaUr109PTpb9tbGxEly5dhBBCLF++XCgUCjF79uxCbbMQQgQGBua4fUII8fLlS2FiYiJGjBihtNyDBw9E+fLllcoHDx4sAIh58+ZJZc+ePRN6enpCoVCIrVu3SuV///23ACD8/f2lsqSkJJVtj42NFTo6OmLWrFlKZQBEYGCgVNa+fXvh7Oys9H6kp6cLFxcXUaNGDZXtnjdvngAgHj58mM+eyt6bN2/Epk2bRPv27YVCoRA6OjqiX79+4vDhwyrbkpPc9r0mTZoIe3t7aXrUqFGibdu2QgghJk2aJJo0aSLN6927t9DX1xcpKSlSWcZ7krn/hBCiQYMGolGjRkplWd+PRYsWCQAiNjZWqV5cXJzQ1NQUc+fOVSq/du2a0NLSUinPLwDC19dXPH36VGhra4uNGzcKIYQ4cOCAUCgUIi4uTvj7+wsA4tGjR9JyWT9fQggxf/58oVAoxD///COVZSwrZzscHR1F586dC7V9RFT68YoFEanFkydPoKWllePQnF5eXjhy5AiOHDmCvXv3YurUqTh48CAGDhwIIUSu69bT05P+fvbsGRISEtC6dWtcvHhRKt+zZw/S09MxY8YMpRGpAGQ7RObChQsxduxYLFiwANOnTy/IpmZr5cqV0vZlvADgyJEjeP78OQYMGKB0NUNTUxPNmjVDUFCQyroyj+pjYmKCmjVrwsDAQGm4zpo1a8LExAS3bt2SynR0dKRtT0tLw5MnT2BoaIiaNWsq9VVWT58+xfHjx9G3b1+8fPlSivHJkyfw8PBAdHQ07t69q7SMqakpAMgeXUhXVxcDBw7E0aNHERsbi6lTpyI0NBQdO3ZE9erV8zX6VG77XqtWrZSepQgJCYGLiwsAoGXLlrh06ZL0rEdISAiaNWuW7RW0Tz/9VGm6devWSn1fELt27UJ6ejr69u2rtE+Ym5ujRo0a2e4TBWFqaopOnTphy5YtAIDNmzfDxcUFNjY22dbP/Pl69eoVHj9+DBcXFwghcOnSJbVuh6mpabGPSEVExYejQhFRsahWrZrSPfDdunWDmZkZJk6ciN9//x2enp45Lvv7779jzpw5uHz5MpKTk6XyzAlDTEwMNDQ0UKdOnTxj+euvv3DgwAFMnjxZbc9VNG3aFI0bN1Ypj46OBgC0a9cu2+WMjY2VpnV1daXbZTKUL18e1apVU0mQypcvj2fPnknT6enpWL58OVatWoXY2FilZwDMzMxyjP3mzZsQQsDPzw9+fn7Z1omPj0fVqlWl6YxkMLffNXjz5g0SEhKUyszNzXOsb2NjA39/f3z66acYMWIE9u/fjwULFmDq1Kk5LpOXVq1aYenSpQgJCUH79u1x48YNLFy4EADg4uKC1NRUnDt3DjY2Nrh//362Q7Vm956Ympoq9X1BREdHQwiBGjVqZDu/XLlyhVpvZgMHDsSgQYNw+/Zt7NmzR9rm7Ny+fRszZszAvn37VLYp6/uXWWG2QwhR6N/CIKLSj4kFEamFmZkZUlNT8fLly3wPQdq+fXsAwIkTJ3JMLE6ePIlu3bqhTZs2WLVqFSwsLFCuXDkEBgaqPKCaX05OTnj+/Dk2btyIUaNGqQwTq04ZP9a2cePGbA+qs54d19TUzHY9OZVnvtozb948+Pn54ZNPPsHs2bNRoUIFaGhoYNy4cbn+aFzGvIkTJ8LDwyPbOg4ODkrTGQegFStWzHG927Ztw9ChQ3OMN7PU1FT88ccfCAwMxIEDByCEQPfu3TFixIgc158ht30v43mJU6dOQV9fHwDQokULKfYaNWrg1KlT0sPG2T24nVPfF1Z6ejoUCgX+/PPPbNed3x/ky023bt2go6ODwYMHIzk5Occfp0tLS0OHDh3w9OlTTJ48GbVq1YKBgQHu3r2LIUOG5LnfFHQ7nj17lmMiQkRlHxMLIlKLWrVqAXg3Qk+9evXytUxqaioA5Dr85M6dO6Grq4tDhw5BR0dHKg8MDFSqZ29vj/T0dISHh+c5zGvFihWxY8cOtGrVCu3bt8epU6dgaWmZr5gLyt7eHsC7kZQKMmJWYezYsQNt27bFzz//rFT+/PnzXBOA6tWrA3h3hjm/McbGxqJixYoqZ/Iz8/DwyHMEoPDwcAQGBmLjxo14+PAhHB0dMXv2bAwZMgRVqlTJVyy57XuVK1eWkoeMH2bM/JsSLi4uCAkJwb///gtNTU0p6VCHnM7M29vbQwgBOzu7Qv1eRn7o6emhe/fu+PXXX9G5c+cc3/9r164hKioKGzZsUHpYOz8jNxV0O1JTU3Hnzh1069Yt/xtCRGUKn7EgIrXIOCC7cOFCvpfZv38/AOCjjz7KsY6mpiYUCoXSbT1xcXHYs2ePUr3u3btDQ0MDs2bNUjnLmt1Z8mrVquHo0aN48+YNOnToUKjRiPLDw8MDxsbGmDdvHlJSUlTmP3r0SG1taWpqqmzr9u3bVZ6PyKpy5cpwc3PDmjVrcP/+/XzFGBYWludBuIWFBdzd3ZVeGYKDg9G8eXM4OTlh5cqV6NixI/766y9ERkZi8uTJ+U4qgLz3vVatWuHy5cs4fPiw9HxFBhcXF5w5cwYnT55EvXr11PqDfwYGBgCgMjpSz549oampiYCAAJX3Swihtn1x4sSJ8Pf3z/H2NuC/qzGZ4xBCYPny5Xmuv6DbER4ejqSkJJX3gIjeH7xiQURqUb16ddStWxdHjx7FJ598ojI/KioKv/76KwDg9evXOHv2LDZs2AAHBwcMGjQox/V26dIFS5YsQadOnTBw4EDEx8dj5cqVcHBwwNWrV6V6Dg4OmDZtGmbPno3WrVujZ8+e0NHRwfnz52FpaZntQ8AODg44fPgw3Nzc4OHhgePHj0vPPAwZMgQbNmxAbGwsbG1tC90vxsbGWL16NQYNGoSGDRuif//+qFSpEm7fvo0DBw6gZcuW+P777wu9/sy6du2KWbNmYejQoXBxccG1a9ewadMm6YpEblauXIlWrVrB2dkZI0aMQPXq1fHw4UOcOXMG//77r9JvYcTHx+Pq1avw9fUtdKx//fUXUlJSsGrVKgwcOBDly5cv9Lry2vdatWqFwMBAnD9/XiVmFxcXJCQkICEhAV988UWhY8hOo0aNAADTpk1D//79Ua5cOXh6esLe3h5z5szB1KlTERcXh+7du8PIyAixsbHYvXs3Ro4ciYkTJwJ4l4C1bdsW/v7+0m9k5NdHH32Ua9IOvLvaY29vj4kTJ+Lu3bswNjbGzp078/X8SEG2A3h3FURfXx8dOnQo0HYQURlSvINQEdH7bMmSJcLQ0FBl+EpkGYZVU1NTVKtWTYwcOVJluNLshpv9+eefRY0aNYSOjo6oVauWCAwMVBn6MsO6detEgwYNhI6OjjA1NRWurq7iyJEj0vzMw81mCA0NFUZGRqJNmzZS7L169RJ6enri2bNnuW5zxnCz58+fz7VeUFCQ8PDwEOXLlxe6urrC3t5eDBkyRFy4cEFp2w0MDFSWdXV1FU5OTirlWbclKSlJfPnll8LCwkLo6emJli1bijNnzghXV1fh6uoq1ctuuFkhhIiJiRE+Pj7C3NxclCtXTlStWlV07dpV7NixQ6ne6tWrhb6+vnjx4kWu25ybxMTEQi+bnZz2PSGEiIyMlPa9qKgopXnp6enCxMREABDbtm1TWTan9yS7/Q9ZhpsVQojZs2eLqlWrCg0NDZWhZ3fu3ClatWolDAwMhIGBgahVq5bw9fUVkZGRUp39+/cLAOKHH37Isw/w/8PN5ia74WbDw8OFu7u7MDQ0FBUrVhQjRowQV65cUdlHcvrM5Wc7hBCiWbNm4uOPP85zO4io7FIIkcc4j0RE+ZSQkIDq1atj4cKFGDZsWEmHI0uVKlXg4+Mj60fz3lcNGjSAm5sbli5dWtKhSN6nfS+zSZMmYcuWLbh586bSM0ZlzeXLl9GwYUNcvHgxz2egiKjsYmJBRGq1YMECBAYGIjw8XOX3JMqKGzduoEWLFrh161auDz1/iA4ePIjevXvj1q1bqFy5ckmHo+R92PeyatKkCUaMGIGRI0eWdCiy9O/fH+np6fjtt99KOhQiKkJMLIiIiIiISLb345QOERERERGVKCYWREREREQkGxMLIiIiIiKSjYkFERERERHJVup+IC89PR337t2DkZERFApFSYdDRERERPTBEkLg5cuXsLS0zHPEvVKXWNy7dw9WVlYlHQYREREREf2/O3fuoFq1arnWKXWJhZGREYB3wRsbG5dwNEREREREH64XL17AyspKOkbPTalLLDJufzI2NmZiQURERERUCuTnEQU+vE1ERERERLIxsSAiIiIiItmYWBARERERkWyl7hmL/EpLS0NKSkpJh0FERB84bW3tPIdgJCL6EJS5xEIIgQcPHuD58+clHQoRERE0NDRgZ2cHbW3tkg6FiKhElbnEIiOpqFy5MvT19fkjekREVGIyftT1/v37sLa25ncSEX3QylRikZaWJiUVZmZmJR0OERERKlWqhHv37iE1NRXlypUr6XCIiEpMmbopNOOZCn19/RKOhIiI6J2MW6DS0tJKOBIiopJVphKLDLzUTEREpQW/k4iI3imTiQUREREREZUuTCxKIVtbWyxbtqykw1Cbsr49xRH/sWPHULt27Q/iVoq4uDgoFApcvnxZbetUKBTYs2eP2tZXEoqiX8qymTNnon79+vmu//jxY1SuXBn//vtv0QVFRES5KnBiceLECXh6esLS0lLlyzwlJQWTJ0+Gs7MzDAwMYGlpCR8fH9y7d0+dMWfL07N4X4Vx584dfPLJJ7C0tIS2tjZsbGwwduxYPHnyRL2dUca8fv0aU6dOhb29PXR1dVGpUiW4urpi7969JR1asZk0aRKmT58OTU1NAMD69euhUCigUCigoaEBCwsL9OvXD7dv3y7hSHM3ZMgQKW6FQgEzMzN06tQJV69eLenQ8iUoKAhdu3ZFpUqVoKurC3t7e/Tr1w8nTpwo6dCKTHBwsNJ7lvGaPn16SYdWIBUrVoSPjw/8/f1LOhQiog9WgROLV69e4aOPPsLKlStV5r1+/RoXL16En58fLl68iF27diEyMhLdunVTS7Bl2a1bt9C4cWNER0djy5YtuHnzJn744QccO3YMLVq0wNOnT0sstrS0NKSnp5dY+59++il27dqFFStW4O+//8bBgwfRu3fvDybhOnXqFGJiYtCrVy+lcmNjY9y/fx93797Fzp07ERkZiT59+pRQlPnXqVMn3L9/H/fv38exY8egpaWFrl27lnRYeVq1ahXat28PMzMzbNu2DZGRkdi9ezdcXFwwfvz4kg4vX96+fVvoZSMjI6X37f79+5gyZYoaIyseQ4cOxaZNm0r0/ykR0YeswIlF586dMWfOHPTo0UNlXvny5XHkyBH07dsXNWvWRPPmzfH9998jLCys1J9pLWq+vr7Q1tbG4cOH4erqCmtra3Tu3BlHjx7F3bt3MW3aNKX6L1++xIABA2BgYICqVasqJXJCCMycORPW1tbQ0dGBpaUlxowZI81PTk7GxIkTUbVqVRgYGKBZs2YIDg6W5q9fvx4mJibYt28f6tSpAx0dHfz000/Q1dVV+eHBsWPHol27dtL0qVOn0Lp1a+jp6cHKygpjxozBq1evpPnx8fHw9PSEnp4e7OzssGnTpjz7Zt++ffj666/xv//9D7a2tmjUqBG++OILfPLJJ1KdjRs3onHjxjAyMoK5uTkGDhyI+Ph4aX7GWddDhw6hQYMG0NPTQ7t27RAfH48///wTtWvXhrGxMQYOHIjXr19Ly7m5uWH06NEYPXo0ypcvj4oVK8LPzw9CiBzjff78OYYPH45KlSrB2NgY7dq1w5UrV6T5V65cQdu2bWFkZARjY2M0atQIFy5cyHF9W7duRYcOHaCrq6tUrlAoYG5uDgsLC7i4uGDYsGE4d+4cXrx4IdWZPHkyHB0doa+vj+rVq8PPz0/lF+n379+PJk2aQFdXFxUrVlT67Oa1rxSGjo4OzM3NYW5ujvr162PKlCm4c+cOHj16lG39tLQ0DBs2DHZ2dtDT00PNmjWxfPlylXrr1q2Dk5MTdHR0YGFhgdGjR+cYg7+/PywsLPJ9peT27dsYN24cxo0bhw0bNqBdu3awsbFBvXr1MHbsWJX3L6/Pga2tLebNm4dPPvkERkZGsLa2xo8//qi0jnPnzqFBgwbQ1dVF48aNcenSJZW4rl+/js6dO8PQ0BBVqlTBoEGD8PjxY2l+xv47btw4VKxYER4eHvna3uxUrlxZet/Mzc1haGgI4N2V1r59+8LExAQVKlSAl5cX4uLipOWGDBmC7t27Y968eahSpQpMTEwwa9YspKam4quvvkKFChVQrVo1BAYGKrWXn303q59++gm1a9eGrq4uatWqhVWrVinNd3JygqWlJXbv3l3ofiAiosIr8mcsEhISoFAoYGJiku385ORkvHjxQun1vnn69CkOHTqEzz//HHp6ekrzzM3N4e3tjW3btikdzC5atAgfffQRLl26hClTpmDs2LE4cuQIAGDnzp1YunQp1qxZg+joaOzZswfOzs7SsqNHj8aZM2ewdetWXL16FX369EGnTp0QHR0t1Xn9+jUWLFiAn376CTdu3IC3tzdMTEywc+dOqU5aWhq2bdsGb29vAEBMTAw6deqEXr164erVq9i2bRtOnTqldIA3ZMgQ3LlzB0FBQdixYwdWrVqllABkx9zcHH/88QdevnyZY52UlBTMnj0bV65cwZ49exAXF4chQ4ao1Js5cya+//57nD59WjogWrZsGTZv3owDBw7g8OHDWLFihdIyGzZsgJaWFs6dO4fly5djyZIl+Omnn3KMpU+fPlLCEhYWhoYNG6J9+/bSWVJvb29Uq1YN58+fR1hYGKZMmZLr2PYnT55E48aNc+2j+Ph47N69G5qamtLtUgBgZGSE9evXIzw8HMuXL8fatWuxdOlSaf6BAwfQo0cP/O9//8OlS5dw7NgxNG3aVJqf175y+/ZtGBoa5vqaN29ejnEnJibi119/hYODQ46/PZOeno5q1aph+/btCA8Px4wZM/D111/jt99+k+qsXr0avr6+GDlyJK5du4Z9+/bBwcFBZV1CCHzxxRf45ZdfcPLkSdSrVy/Xfs2wc+dOpKSkYNKkSdnOzzzqT34+BwCwePFiKWH4/PPP8dlnnyEyMlLql65du6JOnToICwvDzJkzMXHiRKXlnz9/jnbt2qFBgwa4cOECDh48iIcPH6Jv375K9TZs2ABtbW2EhITghx9+AAApGcnp5eTklK9+SUlJgYeHB4yMjHDy5EmEhITA0NAQnTp1Uro6cvz4cdy7dw8nTpzAkiVL4O/vj65du8LU1BShoaH49NNPMWrUKKXnH/Lad7PatGkTZsyYgblz5yIiIgLz5s2Dn58fNmzYoFSvadOmOHnyZL62j4iI1EzIAEDs3r07x/lv3rwRDRs2FAMHDsyxjr+/vwCg8kpISMh2feHh4eLNmzcq87p2Ld5XQZw9ezbXvlqyZIkAIB4+fCiEEMLGxkZ06tRJqU6/fv1E586dhRBCLF68WDg6Ooq3b9+qrOuff/4Rmpqa4u7du0rl7du3F1OnThVCCBEYGCgAiMuXLyvVGTt2rGjXrp00fejQIaGjoyOePXsmhBBi2LBhYuTIkUrLnDx5UmhoaIg3b96IyMhIAUCcO3dOmh8RESEAiKVLl+bQO0L89ddfolq1aqJcuXKicePGYty4ceLUqVM51hdCiPPnzwsA4uXLl0IIIYKCggQAcfToUanO/PnzBQARExMjlY0aNUp4eHhI066urqJ27doiPT1dKps8ebKoXbu2NG1jYyPFf/LkSWFsbCySkpKU4rG3txdr1qwRQghhZGQk1q9fn2v8mZUvX1788ssvSmUZ75GBgYHQ19eXPhdjxozJdV2LFi0SjRo1kqZbtGghvL29s62bn30lJSVFREdH5/p68uSJtOzgwYOFpqamMDAwEAYGBgKAsLCwEGFhYVKd2NhYAUBcunQpx+3w9fUVvXr1kqYtLS3FtGnTcqwPQGzfvl0MHDhQ1K5dW/z777851s3Op59+KoyNjZXKduzYIW2HgYGBuHr1qhAi78+BEO/2mY8//lian56eLipXrixWr14thBBizZo1wszMTOl/2erVq5X6Zfbs2aJjx45K7dy5c0cAEJGRkUKId/tvgwYNVLbn33//zfU9i4uLk+pmfHYyb6uBgYF4/Pix2Lhxo6hZs6bS5yM5OVno6emJQ4cOCSHevec2NjYiLS1NqlOzZk3RunVraTo1NVUYGBiILVu2ZNv/Qijvu1FRQowe7S9q1fpIREW9m7a2thdLlmyWpqOihBg3brZo0KCFNC2EEOPHjxdubm45tlMUcvtuIiqUoK7/vcr6OqjMS0hIyPHYPKsi++XtlJQU9O3bF0IIrF69Osd6U6dOxYQJE6TpFy9ewMrKqqjCKlEil9trsmrRooXKdMbIRH369MGyZctQvXp1dOrUCf/73//g6ekJLS0tXLt2DWlpaXB0dFRaPjk5WemMsba2tsrZXG9vbzRv3hz37t2DpaUlNm3ahC5dukhXm65cuYKrV68q3d4khEB6ejpiY2MRFRUFLS0tNGrUSJpfq1atHK9WZWjTpg1u3bqFs2fP4vTp0zh27BiWL1+OgIAA+Pn5AYB0VvfKlSt49uyZ9EzI7du3UadOHWldmbepSpUq0m0WmcvOnTun1H7z5s2Vzki3aNECixcvRlpamtLVgYw+SExMVDn7/ubNG8TExAAAJkyYgOHDh2Pjxo1wd3dHnz59YG9vn+P2v3nzRuU2KODdGd2LFy8iJSUFf/75JzZt2oS5c+cq1dm2bRu+++47xMTEIDExEampqTA2NpbmX758GSNGjMi23fzsK1paWtleGchN27Ztpc/8s2fPsGrVKnTu3Bnnzp2DjY1NtsusXLkS69atw+3bt/HmzRu8fftWGhEoPj4e9+7dQ/v27XNtd/z48dDR0cHZs2dRsWLFAsUMqP4WgYeHBy5fvoy7d+/Czc1NGrErr89B7dq1ASjvixm3tWVcvYuIiEC9evWU3vesn/krV64gKChIuiUps5iYGOl9y/x5y1C1atUCbTvw7sqZkZGRNG1qaoorV67g5s2bSuUAkJSUJO3vwLtbkDQ0/rsAXqVKFdStW1ea1tTUhJmZmdLVy7z23cxev36F27dj8PXXwzB9+n/7c2pqKoyMyivV1dPTU7rdkYiIik+RJBYZScU///yD48eP5/hlAby7H1tHR6cowig1HBwcoFAoEBERke2zKRERETA1NUWlSpXytT4rKytERkbi6NGjOHLkCD7//HMsWrQIf/31FxITE6GpqYmwsDCVg+LMByh6enoqB1JNmjSBvb09tm7dis8++wy7d+/G+vXrpfmJiYkYNWqU0vMcGaytrREVFZWv+LNTrlw5tG7dGq1bt8bkyZMxZ84czJo1C5MnT5Zux/Dw8MCmTZtQqVIl3L59Gx4eHioPq2a+5UihUKjcgqRQKGQ9qJ6YmAgLC4tsn0PISKBmzpyJgQMH4sCBA/jzzz/h7++PrVu3ZvveA+9Gs3n27JlKuYaGhnRQX7t2bcTExOCzzz7Dxo0bAQBnzpyBt7c3AgIC4OHhgfLly2Pr1q1YvHixtI6st95l3Za89pWsiVt2vv76a3z99dfStIGBgVIy8tNPP6F8+fJYu3Yt5syZo7L81q1bMXHiRCxevBgtWrSAkZERFi1ahNDQ0Dy3IbMOHTpgy5YtOHTokHT7Xn7VqFEDCQkJePDgAczNzQG86wMHBwdoaSn/m8zrc5BB7r6XmJgIT09PLFiwQGWehYWF9LeBgYHK/M6dO+d6O5CNjQ1u3LihVGZnZ6dyEiAxMRGNGjXK9lmpzP+vstvW3LY/P/tuZq9fJwIA5sxZi48+aqY0T0NDed99+vRpvv+XEhGReqk9schIKqKjoxEUFJTjfdUfEjMzM3To0AGrVq3C+PHjlQ6UHjx4gE2bNsHHx0fpQP/s2bNK6zh79qx0JhR4d7Dl6ekJT09P+Pr6olatWrh27RoaNGiAtLQ0xMfHo3Xr1gWO1dvbG5s2bUK1atWgoaGBLl26SPMaNmyI8PDwHM9g16pVC6mpqQgLC0OTJk0AvBtpJusD4flRp04dpKamIikpCdHR0Xjy5Am++eYb6WpWbg9DF1TGAWyGs2fPokaNGioH28C7Pnjw4AG0tLRga2ub4zodHR3h6OiI8ePHY8CAAQgMDMwxsWjQoAHCw8PzjHPKlCmwt7fH+PHj0bBhQ5w+fRo2NjZKD/7/888/SsvUq1cPx44dw9ChQ7NtN699xdLSMs/fVahQoUKu8zOGzH3z5k2280NCQuDi4oLPP/9cKst8NtzIyAi2trY4duwY2rZtm2M73bp1g6enJwYOHAhNTU30798/17gy6927N6ZMmYIFCxbkep8/kPfnID9q166NjRs3IikpSbpqkfUz37BhQ+zcuRO2trYqyU1efvrppxz7G1BNBHLSsGFDbNu2DZUrV871BFFB5WffzaxixSqoXNkSd+7cQrduuSeN169fh5ubm7pCJSKiAijww9uJiYm4fPmydLARGxuLy5cv4/bt20hJSUHv3r1x4cIFbNq0CWlpaXjw4AEePHggaxjE98H333+P5ORkeHh44MSJE7hz5w4OHjyIDh06oGrVqiq3uISEhGDhwoWIiorCypUrsX37dowdOxbAu1Gdfv75Z1y/fh23bt3Cr7/+Cj09PdjY2MDR0RHe3t7w8fHBrl27EBsbi3PnzmH+/Pk4cOBAnnF6e3vj4sWLmDt3Lnr37q10NWny5Mk4ffo0Ro8ejcuXLyM6Ohp79+6VHlqtWbMmOnXqhFGjRiE0NBRhYWEYPnx4nmec3dzcsGbNGoSFhSEuLg5//PEHvv76a7Rt2xbGxsawtraGtrY2VqxYgVu3bmHfvn2YPXt2Qd+CHN2+fRsTJkxAZGQktmzZghUrVkh9nZW7uztatGiB7t274/Dhw4iLi8Pp06cxbdo0XLhwAW/evMHo0aMRHByMf/75ByEhITh//rxSUpiVh4cHTp06lWecVlZW6NGjB2bMmAHg3Vn227dvY+vWrYiJicF3332nMhqOv78/tmzZAn9/f0RERODatWvSGfD87CsZt0Ll9sqaWCQnJ0uf+4iICHzxxRfS2ffs1KhRAxcuXMChQ4cQFRUFPz8/nD9/XqnOzJkzsXjxYnz33XeIjo7GxYsXVR7CB4AePXpg48aNGDp0KHbs2JFnn2awtrbG4sWLsXz5cgwePBhBQUGIi4vDxYsX8d133wGAlGjm9TnIj4EDB0KhUGDEiBEIDw/HH3/8gW+//Vapjq+vL54+fYoBAwbg/PnziImJwaFDhzB06NA8f0ixatWqub5nOd2SlpW3tzcqVqwILy8vnDx5ErGxsQgODsaYMWNk/RBdfvbdrMaMCcCaNfPxyy/fITY2CpGR17BzZyDWrVsi1Xn9+jXCwsLQsWPHQsdGRESFV+DE4sKFC2jQoAEaNGgA4N395A0aNMCMGTNw9+5d7Nu3D//++y/q168PCwsL6XX69Gm1B1+WZBw8Va9eHX379oW9vT1GjhyJtm3b4syZMyoHZ19++aXU13PmzMGSJUukoSRNTEywdu1atGzZEvXq1cPRo0exf/9+6epQYGAgfHx88OWXX6JmzZro3r07zp8/r3SbRk4cHBzQtGlTXL16VeV2knr16uGvv/5CVFQUWrduLb3vlpaWUp3AwEBYWlrC1dUVPXv2xMiRI1G5cuVc2/Tw8MCGDRvQsWNH1K5dG1988QU8PDykUYEqVaqE9evXY/v27ahTpw6++eYblYMwOXx8fPDmzRs0bdoUvr6+GDt2LEaOHJltXYVCgT/++ANt2rTB0KFD4ejoiP79++Off/5BlSpVoKmpiSdPnsDHxweOjo7o27cvOnfujICAgBzb9/b2xo0bN6QRg3Izfvx4HDhwAOfOnUO3bt0wfvx4jB49GvXr18fp06elZ1IyuLm5Yfv27di3bx/q16+Pdu3aKT1jImdfycnBgwelz32zZs1w/vx5bN++PcezyKNGjULPnj3Rr18/NGvWDE+ePFG6egEAgwcPxrJly7Bq1So4OTmha9euSqOcZda7d29s2LABgwYNwq5duwC8S0xyu8IEAF988QUOHz6MR48eoXfv3qhRowb+97//ITY2FgcPHpRGXsvP5yAvhoaG2L9/v3SVcdq0aSq3PFlaWiIkJARpaWno2LEjnJ2dMW7cOJiYmCg9z1CU9PX1ceLECVhbW6Nnz56oXbs2hg0bhqSkJFlXMPKz72bVt+9wzJ37E3buDETXrs74+GNX7Nq1HtWq2Ul19u7dC2tr60JdrSUiIvkUoiBPFBeDFy9eoHz58khISFD54kpKSkJsbCzs7OyyfdiVqKDc3NxQv3596cH4kvLVV1/hxYsXWLNmTYnG8b4aPHgwFAqF0jNDVHrlkDPmadCg5hgzZgwGDhyo3oDywO8mUrvgTFd43faX7XVQmZfbsXlWxXPai4hyNW3aNNjY2JToL6C/r4QQCA4OVuvtc1T6PH36GD179sSAAQNKOhQiog9WkQ03S0T5Z2JiojSyEqmPQqHI9cFgej9UqFAxxx84JCKi4sHEgj5o2Q0bS0REREQFx1uhiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgtQgJCYGzszPKlSuH7t27l3Q4uYqLi4NCocDly5dLOpRCKa74/fz8MHLkyCJto7QKDg6GQqHA8+fPc6wzc+ZM1K9fv9hiKmm2trYl/gv16lDQ9+3x48eoXLky/v3336ILiojoPfH+JBbBnsX7KoQHDx7giy++QPXq1aGjowMrKyt4enri2LFjau6MvCkUCuzZs0dt65swYQLq16+P2NhYrF+/Xi3rVHeMBREbG4uBAwfC0tISurq6qFatGry8vPD333+XSDzF7cGDB1i+fDmmTZsmlQ0ZMgQKhQIKhQLlypWDnZ0dJk2ahKSkpBKMNHf9+/dHp06dlMoOHjwIhUKBmTNnKpXPnDkT1tbW+V73xIkTlT67Q4YMKdKk+ubNm/jkk09gbW0NHR0dVK1aFe3bt8emTZuQmppaZO2WNEdHBRwdFbh8+axS+du3yWja1AyOjgqEhgYXWfsVK1aEj48P/P39i6wNIqL3xfuTWJRycXFxaNSoEY4fP45Fixbh2rVrOHjwINq2bQtfX9+SDq/QUlJSAAAxMTFo164dqlWrBhMTk5INSqaUlBR06NABCQkJ2LVrFyIjI7Ft2zY4Ozvnegb7ffLTTz/BxcUFNjY2SuWdOnXC/fv3cevWLSxduhRr1qwp1Qdcbdu2RUhIiNKBd1BQEKysrFR+HDEoKAht27bN97oNDQ1hZmamrlBzde7cOTRs2BARERFYuXIlrl+/juDgYAwfPhyrV6/GjRs3iiUOOd6+fVvoZS0srLBzZ6BS2eHDu6Gvbyg3rHwZOnQoNm3ahKdPnxZLe0REZRUTi2Ly+eefQ6FQ4Ny5c+jVqxccHR3h5OSECRMm4OzZ/87E3b59G15eXjA0NISxsTH69u2Lhw8fSvOzOys6btw4uLm5SdNubm4YM2YMJk2ahAoVKsDc3Fzp7KytrS0AoEePHlAoFNI0AOzduxcNGzaErq4uqlevjoCAAKWDMoVCgdWrV6Nbt24wMDDAiBEjoFAo8OTJE3zyySdQKBRYv3490tLSMGzYMNjZ2UFPTw81a9bE8uXLVfpl3bp1cHJygo6ODiwsLDB69OhcY8zP9h88eBCtWrWCiYkJzMzM0LVrV8TExOTwzqi6ceMGYmJisGrVKjRv3hw2NjZo2bIl5syZg+bNm0v1Jk+eDEdHR+jr66N69erw8/OTEi3gv1su1q1bB2traxgaGuLzzz9HWloaFi5cCHNzc1SuXBlz585Vaj+jjzt37gw9PT1Ur14dO3bsyDXm69evo3PnzjA0NESVKlUwaNAgPH78WJq/Y8cOODs7Q09PD2ZmZnB3d8erV69yXN/WrVvh6al6ZU5HRwfm5uawsrJC9+7d4e7ujiNHjkjznzx5ggEDBqBq1arQ19eHs7MztmzZorSO9PR0LFy4EA4ODtDR0YG1tbVSH9y5cwd9+/aFiYkJKlSoAC8vL8TFxeW6/Tlp27YtEhMTceHCBaksODgYU6ZMQWhoqHS1JSkpCaGhoSqJRVhYGBo3bgx9fX24uLggMjJSmpf5lpqZM2diw4YN2Lt3r3RVJyNxkbs9QggMGTIEjo6OCAkJgaenJ2rUqIEaNWpgwIABOHXqFOrVqyfVz6u9jM/Qt99+CwsLC5iZmcHX11dp342Pj4enpyf09PRgZ2eHTZs2qcT1/PlzDB8+HJUqVYKxsTHatWuHK1euqPTPTz/9BDs7O+jq6uZ7m7Pq0WMwDhzYiqSkN1LZzp3r0KPHYJW6eX0us/PTTz+hdu3a0NXVRa1atbBq1Sql+U5OTrC0tMTu3bsLvQ1ERB8CJhbF4OnTpzh48CB8fX1hYGCgMj/jDH96ejq8vLzw9OlT/PXXXzhy5Ahu3bqFfv36FbjNDRs2wMDAAKGhoVi4cCFmzZolHQCeP38eABAYGIj79+9L0ydPnoSPjw/Gjh2L8PBwrFmzBuvXr1c58J05cyZ69OiBa9euISAgAPfv34exsTGWLVuG+/fvo1+/fkhPT0e1atWwfft2hIeHY8aMGfj666/x22+/SetZvXo1fH19MXLkSFy7dg379u2Dg4NDrjHmx6tXrzBhwgRcuHABx44dg4aGBnr06IH09PR8LV+pUiVoaGhgx44dSEtLy7GekZER1q9fj/DwcCxfvhxr167F0qVLlerExMTgzz//xMGDB7Flyxb8/PPP6NKlC/7991/89ddfWLBgAaZPn47Q0FCl5fz8/NCrVy9cuXIF3t7e6N+/PyIiIrKN4/nz52jXrh0aNGiACxcu4ODBg3j48CH69u0LALh//z4GDBiATz75BBEREQgODkbPnj0hhMh2fU+fPkV4eDgaN26caz9dv34dp0+fhra2tlSWlJSERo0a4cCBA7h+/TpGjhyJQYMG4dy5c1KdqVOn4ptvvoGfnx/Cw8OxefNmVKlSBcC7q0UeHh4wMjLCyZMnERISAkNDQ3Tq1Ek6471p0yYYGhrm+jp58iQAwNHREZaWlggKCgIAvHz5EhcvXkSfPn1ga2uLM2fOAABOnz6N5ORklcRi2rRpWLx4MS5cuAAtLS188skn2fbFxIkT0bdvX+mKzv379+Hi4pKv7cnL5cuXERERgYkTJ0JDI/t/2QqFIt/9B7y7OhMTE4OgoCBs2LAB69evV7qFcciQIbhz5w6CgoKwY8cOrFq1CvHx8Upt9unTB/Hx8fjzzz8RFhaGhg0bon379kpn9W/evImdO3di165d0jNB8+bNy/P9u3fvtlJbTk6NULWqLQ4d2gkAuHfvNs6fPwEvr0EqfZGfz2VmmzZtwowZMzB37lxERERg3rx58PPzw4YNG5TqNW3aVNqviIgoB6KUSUhIEABEQkKCyrw3b96I8PBw8ebNG9UFg7oW76sAQkNDBQCxa9euXOsdPnxYaGpqitu3b0tlN27cEADEuXPnhBBCDB48WHh5eSktN3bsWOHq6ipNu7q6ilatWinVadKkiZg8ebI0DUDs3r1bqU779u3FvHnzlMo2btwoLCwslJYbN26cSuzly5cXgYGBuW6fr6+v6NWrlzRtaWkppk2blmP97GLMz/Zn9ejRIwFAXLt2TQghRGxsrAAgLl26lOMy33//vdDX1xdGRkaibdu2YtasWSImJibH+kIIsWjRItGoUSNp2t/fX+jr64sXL15IZR4eHsLW1lakpaVJZTVr1hTz58+XpgGITz/9VGndzZo1E5999lm28c+ePVt07NhRqf6dO3cEABEZGSnCwsIEABEXF5dr/BkuXbokACjth0K863tNTU1hYGAgdHR0BAChoaEhduzYkev6unTpIr788kshhBAvXrwQOjo6Yu3atdnW3bhxo6hZs6ZIT0+XypKTk4Wenp44dOiQtI7o6OhcX69fv5aW9/b2lvrnwIEDok6dOkIIIUaOHClmzJghhBDCz89P2NnZScsEBQUJAOLo0aNS2YEDBwQA6f+Pv7+/+Oijj5T6J+u+mZ/tycvWrVsFAHHx4kWp7OHDh8LAwEB6rVy5Mt/tDR48WNjY2IjU1FSpTp8+fUS/fv2EEEJERkYq/c8RQoiIiAgBQCxdulQIIcTJkyeFsbGxSEpKUorV3t5erFmzRuqfcuXKifj4eKU6T548yfP9Cw9PEVFRQkRFvfs8rFy5W0ybtkw0b95WREUJMWZMgOjQoYe4cOGZACA2bgwSUVHZ9192n8vM75u9vb3YvHmz0jKzZ88WLVq0UCobP368cHNzy7aNXL+biAqjkMcbpXIdVObldmyelVZxJjEfKpHDmeGsIiIiYGVlBSsrK6msTp06MDExQUREBJo0aZLvNjPfGgEAFhYWKmccs7py5QpCQkKUrlCkpaUhKSkJr1+/hr6+PgDkeSY7w8qVK7Fu3Trcvn0bb968wdu3b6VbR+Lj43Hv3j20b98+39uUX9HR0ZgxYwZCQ0Px+PFj6UrF7du3Ubdu3Xytw9fXFz4+PggODsbZs2exfft2zJs3D/v27UOHDh0AANu2bcN3332HmJgYJCYmIjU1FcbGxkrrsbW1hZGRkTRdpUoVaGpqKp15rlKlisp706JFC5XpnEaBunLlCoKCgmBoqHq/eUxMDDp27Ij27dvD2dkZHh4e6NixI3r37g1TU9Ns1/fmzbvbTbK7daVt27ZYvXo1Xr16haVLl0JLSwu9evWS5qelpWHevHn47bffcPfuXbx9+xbJycnSvhMREYHk5OQc3/crV67g5s2bSn0GvLsSknE7m5GRkcr83Li5uWHcuHFISUlBcHCwdNucq6sr1qxZA+Dd7VHZPV+R+XNkYWEB4N2+m9+HvPOzPYVhZmYm7Q9ubm7S1Yj8tufk5ARNTU1p2sLCAteuXQPw7j3S0tJCo0aNpPm1atVSenbqypUrSExMVHnG5M2bN0rt2NjYoFKlSkp1KlSogAoVKuS6fdHRqmXdun2Mb7+dgtu3b2HXrvXw8/su22Xz87nM8OrVK8TExGDYsGEYMWKEVJ6amory5csr1dXT08Pr169zjZuI6EPHxKIY1KhRAwqFQi0jCmloaKgkKtndP1yuXDmlaYVCkeetQImJiQgICEDPnj1V5mU+yMzudq6stm7diokTJ2Lx4sVo0aIFjIyMsGjRIumWHz09vTzXkZ38bL+npydsbGywdu1aWFpaIj09HXXr1i3ww6NGRkbw9PSEp6cn5syZAw8PD8yZMwcdOnTAmTNn4O3tjYCAAHh4eKB8+fLYunUrFi9erLSO7N6Hwrw3uUlMTISnpycWLFigMs/CwgKampo4cuQITp8+jcOHD2PFihWYNm0aQkNDYWdnp7JMxYoVAQDPnj1TOSg0MDCQbldbt24dPvroI/z8888YNmwYAGDRokVYvnw5li1bBmdnZxgYGGDcuHFS3+f1vicmJqJRo0bZ3tOfEcumTZswatSoXNfz559/onXr1gDeJUOvXr3C+fPnERQUhK+++grAu8Tik08+wdOnTxEaGprtOjO/Vxm3GxXkvcrP9uSlRo0aAIDIyEg0aNAAAKCpqSm9D1pa//0bz297cvfBxMREWFhYqDwAD0ApAcnuf8W8efMwb968XNd/4EA4LC2VkzdTUzO0bdsV06YNw9u3SWjTpjNevXqpVCe/n8vM2wEAa9euRbNmzZTmZU68gHe3COb3PSMi+lAxsSgGFSpUgIeHB1auXIkxY8aofNk+f/4cJiYmqF27Nu7cuYM7d+5IVy3Cw8Px/Plz1KlTB8C7g4Pr168rLX/58mWVA4W8lCtXTuX5gYYNGyIyMlI6YJEjJCQELi4u+Pzzz6WyzGcyjYyMYGtri2PHjuU4Ek92Mea1/U+ePEFkZCTWrl0rHVieOnVK9vYoFArUqlULp0+fBvDunnwbGxul4Vj/+ecf2e1kOHv2LHx8fJSmMw4qs2rYsCF27twJW1tbpYPMrPG3bNkSLVu2xIwZM2BjY4Pdu3djwoQJKnXt7e1hbGyM8PBwODo65hijhoYGvv76a0yYMAEDBw6Enp4eQkJC4OXlhY8//hjAu4PwqKgoaf+tUaMG9PT0cOzYMQwfPjzbbdm2bRsqV66c41nmbt26qRwEZlW1alWl7bGyssK+fftw+fJluLq6SnWqVq2KxYsX4+3btwUaESo72tra2X6m8tqevDRo0AC1atXCt99+i759++b4nIW62qtVqxZSU1MRFhYmXSWNjIxUGhGtYcOGePDgAbS0tJQGf8iPTz/9VHr+JycpKZbZlvfq9QlGjPgfRoyYrHLgDxT8c1mlShVYWlri1q1b8Pb2zjWm69evKw0SQUREqvjwdjFZuXIl0tLS0LRpU+zcuRPR0dGIiIjAd999J9324u7uDmdnZ3h7e+PixYs4d+4cfHx84OrqKt1+1K5dO1y4cAG//PILoqOj4e/vr3KgnR8ZB/UPHjzAs2fPAAAzZszAL7/8goCAANy4cQMRERHYunUrpk+fXuD116hRAxcuXMChQ4cQFRUFPz8/lQewZ86cicWLF+O7775DdHQ0Ll68iBUrVuQaY17bb2pqCjMzM/z444+4efMmjh8/nu3Bc24uX74MLy8v7NixA+Hh4bh58yZ+/vlnrFu3Dl5eXtL23b59G1u3bkVMTAy+++47tY4Ys337dqxbtw5RUVHw9/fHuXPnpBGzsvL19cXTp08xYMAAnD9/HjExMTh06BCGDh2KtLQ0hIaGYt68ebhw4QJu376NXbt24dGjR6hdu3a269PQ0IC7u3u+ErI+ffpAU1MTK1euBPCuXzKujkRERGDUqFFKo5rp6upi8uTJmDRpEn755RfExMTg7Nmz+PnnnwEA3t7eqFixIry8vHDy5EnExsYiODgYY8aMkX6gzMjICA4ODrm+sl4Zadu2LVatWgUHBwfpQXHg3VWLFStWSA95y2Fra4urV68iMjISjx8/RkpKSr62Jy8KhQKBgYGIjIxEy5YtsW/fPkRHRyM8PBw//PADHj16JB1kq6O9mjVrolOnThg1ahRCQ0MRFhaG4cOHK/Wpu7s7WrRoge7du+Pw4cOIi4vD6dOnMW3aNKURuLJToUKFPN+/nBLkNm064ezZRxg7dla28wvzuQwICMD8+fPx3XffISoqCteuXUNgYCCWLFki1Xn9+jXCwsLQsWPHXNdFRPShY2JRTKpXr46LFy+ibdu2+PLLL1G3bl106NABx44dw+rVqwG8O4DYu3cvTE1N0aZNG7i7u6N69erYtm2btB4PDw/4+flh0qRJaNKkCV6+fKl0Zju/Fi9ejCNHjsDKyko6E+7h4YHff/8dhw8fRpMmTdC8eXMsXbpU5bcM8mPUqFHo2bMn+vXrh2bNmuHJkydKVy8AYPDgwVi2bBlWrVoFJycndO3aFdGZbq7OKcbctl9DQwNbt25FWFgY6tati/Hjx2PRokUFir1atWqwtbVFQEAAmjVrhoYNG2L58uUICAiQzoR269YN48ePx+jRo1G/fn2cPn0afn5+Be6nnAQEBGDr1q2oV68efvnlF2zZskU665+VpaUlQkJCkJaWho4dO8LZ2Rnjxo2DiYkJNDQ0YGxsjBMnTuB///sfHB0dMX36dCxevBidO3fOsf3hw4dj69ated4eo6WlhdGjR2PhwoV49eoVpk+fjoYNG8LDwwNubm4wNzdXGR7Yz88PX375JWbMmIHatWujX79+0jMm+vr6OHHiBKytrdGzZ0/Url0bw4YNQ1JSUqHPwAPvEouXL1+qnHF2dXXFy5cvZV+tAIARI0agZs2aaNy4MSpVqoSQkJB8bU/Gr3znNgRt8+bNERYWhpo1a8LX1xd16tSBi4sLtmzZgqVLl+Kzzz4DoL7+CwwMhKWlJVxdXdGzZ0+MHDkSlStXluYrFAr88ccfaNOmDYYOHQpHR0f0798f//zzj1Lipm4KhQIVKlRUGokss8J8LocPH46ffvoJgYGBcHZ2hqurK9avX690m+DevXthbW0tXQUlIqLsKUR+nywuJi9evED58uWRkJCg8kWYlJSE2NhY2WOiE5VmCoUCu3fvLtJfcc6LEALNmjXD+PHjMWDAgBKL40MQGBiIefPmITw8vMC3NL6vsnt4Oz/+/3EUtWvevDnGjBmDgQMHZjuf302kdsGZfkfIbX/ZXgeVebkdm2fFKxZEpEKhUODHH39U+nFEKhp//PEH5s2bx6SilHr8+DF69uzJBJuIKB/48DYRZat+/frS8MBUdLZv317SIbw3CnOlI6+rHBUrVsSkSZMKFxAR0QeGiQVRKVPK7k4kIiIiyhfeCkVERERERLIxsSAiIiIiItnKZGIh51eKiYiI1Im3LxIRvVOmnrHQ1taGhoYG7t27h0qVKkFbWxsKhaKkwyIiIjXK8gPmRSopSd7yQgg8evQICoWCI3sR0QevTCUWGhoasLOzw/3793Hv3r2SDoeIiIrA//9eYrFQx8UGhUKBatWqSb+ATkT0oSpTiQXw7qqFtbU1UlNTkVacp7WIiKhYLFxYfG2tXi1/HeXKlWNSQUSEMphYAJAuOfOyMxHR++fx4+Jriz+UTUSkPmXy4W0iIiIiIipdmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREshU4sThx4gQ8PT1haWkJhUKBPXv2KM0XQmDGjBmwsLCAnp4e3N3dER0dra54iYiIiIioFCpwYvHq1St89NFHWLlyZbbzFy5ciO+++w4//PADQkNDYWBgAA8PDyQlJckOloiIiIiISietgi7QuXNndO7cOdt5QggsW7YM06dPh5eXFwDgl19+QZUqVbBnzx70799fXrRERERERFQqqfUZi9jYWDx48ADu7u5SWfny5dGsWTOcOXNGnU0REREREVEpUuArFrl58OABAKBKlSpK5VWqVJHmZZWcnIzk5GRp+sWLF+oMiYiIiIiIioFaE4vCmD9/PgICAko6DCIionzz9Cy+tvbvL762iHIUnGWnd8vnjpl5ufwuQ2WWWm+FMjc3BwA8fPhQqfzhw4fSvKymTp2KhIQE6XXnzh11hkRERERERMVArYmFnZ0dzM3NcezYMansxYsXCA0NRYsWLbJdRkdHB8bGxkovIiIiIiIqWwp8K1RiYiJu3rwpTcfGxuLy5cuoUKECrK2tMW7cOMyZMwc1atSAnZ0d/Pz8YGlpie7du6szbiIiIiIiKkUKnFhcuHABbdu2laYnTJgAABg8eDDWr1+PSZMm4dWrVxg5ciSeP3+OVq1a4eDBg9DV1VVf1EREREREVKoUOLFwc3ODECLH+QqFArNmzcKsWbNkBUZERERERGWHWp+xICIiIiKiDxMTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxaJR0AEREVjqdn4Zbbv1+9cRDRByg40z8gNzX8U1H3+qhE8IoFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSTe2JRVpaGvz8/GBnZwc9PT3Y29tj9uzZEEKouykiIiIiIioltNS9wgULFmD16tXYsGEDnJyccOHCBQwdOhTly5fHmDFj1N0cERERERGVAmpPLE6fPg0vLy906dIFAGBra4stW7bg3Llz6m6KiIiIiIhKCbXfCuXi4oJjx44hKioKAHDlyhWcOnUKnTt3VndTRERERERUSqj9isWUKVPw4sUL1KpVC5qamkhLS8PcuXPh7e2dbf3k5GQkJydL0y9evFB3SEREREREVMTUnlj89ttv2LRpEzZv3gwnJydcvnwZ48aNg6WlJQYPHqxSf/78+QgICFB3GERElANPz4Ivs39/8bVVnEp7fIVVFrarsPsUqVlwpp3FTQ1vSnAZ2PmoyKj9VqivvvoKU6ZMQf/+/eHs7IxBgwZh/PjxmD9/frb1p06dioSEBOl1584ddYdERERERERFTO1XLF6/fg0NDeV8RVNTE+np6dnW19HRgY6OjrrDICIiIiKiYqT2xMLT0xNz586FtbU1nJyccOnSJSxZsgSffPKJupsiIiIiIqJSQu2JxYoVK+Dn54fPP/8c8fHxsLS0xKhRozBjxgx1N0VERERERKWE2hMLIyMjLFu2DMuWLVP3qomIiIiIqJRS+8PbRERERET04WFiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIikk2rpAMgorLP07OkIyga+/eXdARERGoQnOWftBv/uVHR4BULIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkW5EkFnfv3sXHH38MMzMz6OnpwdnZGRcuXCiKpoiIiIiIqBTQUvcKnz17hpYtW6Jt27b4888/UalSJURHR8PU1FTdTRERERERUSmh9sRiwYIFsLKyQmBgoFRmZ2en7maIiIiIiKgUUfutUPv27UPjxo3Rp08fVK5cGQ0aNMDatWvV3QwREREREZUiak8sbt26hdWrV6NGjRo4dOgQPvvsM4wZMwYbNmzItn5ycjJevHih9CIiIiIiorJF7bdCpaeno3Hjxpg3bx4AoEGDBrh+/Tp++OEHDB48WKX+/PnzERAQoO4wiIhk8/Qs6QhKD/YFkRoEZ/ogue1X7/rUsVxh16duWeNQR19RsVD7FQsLCwvUqVNHqax27dq4fft2tvWnTp2KhIQE6XXnzh11h0REREREREVM7VcsWrZsicjISKWyqKgo2NjYZFtfR0cHOjo66g6DiIiIiIiKkdqvWIwfPx5nz57FvHnzcPPmTWzevBk//vgjfH191d0UERERERGVEmpPLJo0aYLdu3djy5YtqFu3LmbPno1ly5bB29tb3U0REREREVEpofZboQCga9eu6Nq1a1GsmoiIiIiISiG1X7EgIiIiIqIPDxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbFolHQARERHRBy3Ys6QjKBLnzitPz16cc12/Nv/93bRJPhvI2m9u+/O5IBUVXrEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyFXli8c0330ChUGDcuHFF3RQREREREZWQIk0szp8/jzVr1qBevXpF2QwREREREZWwIkssEhMT4e3tjbVr18LU1LSomiEiIiIiolKgyBILX19fdOnSBe7u7kXVBBERERERlRJaRbHSrVu34uLFizh//nyedZOTk5GcnCxNv3jxoihCIiIiIiKiIqT2xOLOnTsYO3Ysjhw5Al1d3Tzrz58/HwEBAeoOg+iD5+lZ8GX271d/HERE2eH/qHwKztJRbqWwE7LGmA9+bQqwTOb1q3v7y0L/liFqvxUqLCwM8fHxaNiwIbS0tKClpYW//voL3333HbS0tJCWlqZUf+rUqUhISJBed+7cUXdIRERERERUxNR+xaJ9+/a4du2aUtnQoUNRq1YtTJ48GZqamkrzdHR0oKOjo+4wiIiIiIioGKk9sTAyMkLdunWVygwMDGBmZqZSTkRERERE7wf+8jYREREREclWJKNCZRUcHFwczRARERERUQnhFQsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2rZIOgEhdPD0Lt9z+/eqNIzeFibE44yMiInnOLfrvH/3sE8r/wP3a/Pf37MX//b3/ywI0EFzILzuZzp0vkWZVFef2Z23LjV/IeeEVCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJJvaE4v58+ejSZMmMDIyQuXKldG9e3dERkaquxkiIiIiIipF1J5Y/PXXX/D19cXZs2dx5MgRpKSkoGPHjnj16pW6myIiIiIiolJCS90rPHjwoNL0+vXrUblyZYSFhaFNmzbqbo6IiIiIiEqBIn/GIiEhAQBQoUKFom6KiIiIiIhKiNqvWGSWnp6OcePGoWXLlqhbt262dZKTk5GcnCxNv3jxoihDIiIiIiKiIlCkiYWvry+uX7+OU6dO5Vhn/vz5CAgIKMowiCifPD1LOgIiyoqfy5JR2H73a5P575xXknneufOFa6swmjYpvraKXXCm/nbbn/M8da8/67qztv3/CrNP7c9+VaVWkd0KNXr0aPz+++8ICgpCtWrVcqw3depUJCQkSK87d+4UVUhERERERFRE1H7FQgiBL774Art370ZwcDDs7Oxyra+jowMdHR11h0FERERERMVI7YmFr68vNm/ejL1798LIyAgPHjwAAJQvXx56enrqbo6IiIiIiEoBtd8KtXr1aiQkJMDNzQ0WFhbSa9u2bepuioiIiIiISokiuRWKiIiIiIg+LEX+OxZERERERPT+Y2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZGNiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSTaukAyjNPD0Lt9z+/aW7rcIqTIzFGR8REb3fzi3674to9omcv2D82ih/YeVUN2s9Zfn7Ast9HaXPufMl13Z++0otMZ7PX1tNmxRy/cFl630vLrxiQUREREREsjGxICIiIiIi2ZhYEBERERGRbEwsiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiIiIiEg2JhZERERERCQbEwsiIiIiIpKNiQUREREREcnGxIKIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERycbEgoiIiIiIZCuyxGLlypWwtbWFrq4umjVrhnPnzhVVU0REREREVMKKJLHYtm0bJkyYAH9/f1y8eBEfffQRPDw8EB8fXxTNERERERFRCSuSxGLJkiUYMWIEhg4dijp16uCHH36Avr4+1q1bVxTNERERERFRCVN7YvH27VuEhYXB3d39v0Y0NODu7o4zZ86ouzkiIiIiIioFtNS9wsePHyMtLQ1VqlRRKq9SpQr+/vtvlfrJyclITk6WphMSEgAAL168UHdoBZaSUrjlChN6cbZVWIWJsbTHB5T+GAsbX2H7g4iotEpM+u8fW0pKzv8cM9fLrW7WevlZpiDroNLvxausBZne91fqPbAo7cdROcfwLgghRJ511Z5YFNT8+fMREBCgUm5lZVUC0ahH+fLvZ1uFUdrjA0p/jKU9PiKi4nLoUOapnP85KtfLua5qvfytP//roLJHHV+66vviLk3HAC9fvkT5PAJSe2JRsWJFaGpq4uHDh0rlDx8+hLm5uUr9qVOnYsKECdJ0eno6nj59CjMzMygUCnWHV2a9ePECVlZWuHPnDoyNjUs6nPcO+7dosX+LFvu3aLF/ixb7t2ixf4vWh9C/Qgi8fPkSlpaWedZVe2Khra2NRo0a4dixY+jevTuAd8nCsWPHMHr0aJX6Ojo60NHRUSozMTFRd1jvDWNj4/d2xy0N2L9Fi/1btNi/RYv9W7TYv0WL/Vu03vf+zetKRYYiuRVqwoQJGDx4MBo3boymTZti2bJlePXqFYYOHVoUzRERERERUQkrksSiX79+ePToEWbMmIEHDx6gfv36OHjwoMoD3URERERE9H4osoe3R48ene2tT1Q4Ojo68Pf3V7ltjNSD/Vu02L9Fi/1btNi/RYv9W7TYv0WL/atMIfIzdhQREREREVEuiuSXt4mIiIiI6MPCxIKIiIiIiGRjYkFERERERLIxsSjF5s6dCxcXF+jr6+f7tz2EEJgxYwYsLCygp6cHd3d3REdHF22gZdTTp0/h7e0NY2NjmJiYYNiwYUhMTMx1mQcPHmDQoEEwNzeHgYEBGjZsiJ07dxZTxGVLYfoXAM6cOYN27drBwMAAxsbGaNOmDd68eVMMEZcthe1f4N3/ic6dO0OhUGDPnj1FG2gZVdD+ffr0Kb744gvUrFkTenp6sLa2xpgxY5CQkFCMUZdeK1euhK2tLXR1ddGsWTOcO3cu1/rbt29HrVq1oKurC2dnZ/zxxx/FFGnZVJD+Xbt2LVq3bg1TU1OYmprC3d09z/fjQ1fQ/TfD1q1boVAopN91+xAwsSjF3r59iz59+uCzzz7L9zILFy7Ed999hx9++AGhoaEwMDCAh4cHkpKSijDSssnb2xs3btzAkSNH8Pvvv+PEiRMYOXJkrsv4+PggMjIS+/btw7Vr19CzZ0/07dsXly5dKqaoy47C9O+ZM2fQqVMndOzYEefOncP58+cxevRoaGjwX1VWhenfDMuWLYNCoSjiCMu2gvbvvXv3cO/ePXz77be4fv061q9fj4MHD2LYsGHFGHXptG3bNkyYMAH+/v64ePEiPvroI3h4eCA+Pj7b+qdPn8aAAQMwbNgwXLp0Cd27d0f37t1x/fr1Yo68bCho/wYHB2PAgAEICgrCmTNnYGVlhY4dO+Lu3bvFHHnZUND+zRAXF4eJEyeidevWxRRpKSGo1AsMDBTly5fPs156erowNzcXixYtksqeP38udHR0xJYtW4owwrInPDxcABDnz5+Xyv7880+hUCjE3bt3c1zOwMBA/PLLL0plFSpUEGvXri2yWMuiwvZvs2bNxPTp04sjxDKtsP0rhBCXLl0SVatWFffv3xcAxO7du4s42rJHTv9m9ttvvwltbW2RkpJSFGGWGU2bNhW+vr7SdFpamrC0tBTz58/Ptn7fvn1Fly5dlMqaNWsmRo0aVaRxllUF7d+sUlNThZGRkdiwYUNRhVimFaZ/U1NThYuLi/jpp5/E4MGDhZeXVzFEWjrwNOB7JDY2Fg8ePIC7u7tUVr58eTRr1gxnzpwpwchKnzNnzsDExASNGzeWytzd3aGhoYHQ0NAcl3NxccG2bdvw9OlTpKenY+vWrUhKSoKbm1sxRF12FKZ/4+PjERoaisqVK8PFxQVVqlSBq6srTp06VVxhlxmF3X9fv36NgQMHYuXKlTA3Ny+OUMukwvZvVgkJCTA2NoaWVpH9ZFSp9/btW4SFhSl9L2loaMDd3T3H76UzZ84o1QcADw8Pfo9lozD9m9Xr16+RkpKCChUqFFWYZVZh+3fWrFmoXLnyB3nFkonFe+TBgwcAoPIL51WqVJHm0TsPHjxA5cqVlcq0tLRQoUKFXPvqt99+Q0pKCszMzKCjo4NRo0Zh9+7dcHBwKOqQy5TC9O+tW7cAADNnzsSIESNw8OBBNGzYEO3bt+dzQlkUdv8dP348XFxc4OXlVdQhlmmF7d/MHj9+jNmzZ+f79rT31ePHj5GWllag76UHDx7weyyfCtO/WU2ePBmWlpYqyRwVrn9PnTqFn3/+GWvXri2OEEsdJhbFbMqUKVAoFLm+/v7775IOs8wq6v718/PD8+fPcfToUVy4cAETJkxA3759ce3aNTVuRelVlP2bnp4OABg1ahSGDh2KBg0aYOnSpahZsybWrVunzs0otYqyf/ft24fjx49j2bJl6g26DCmu/78vXrxAly5dUKdOHcycOVN+4ERF5JtvvsHWrVuxe/du6OrqlnQ4Zd7Lly8xaNAgrF27FhUrVizpcErEh3t9toR8+eWXGDJkSK51qlevXqh1Z9za8PDhQ1hYWEjlDx8+RP369Qu1zrImv/1rbm6u8uBVamoqnj59muMtIjExMfj+++9x/fp1ODk5AQA++ugjnDx5EitXrsQPP/yglm0ozYqyfzP22Tp16iiV165dG7dv3y580GVIUfbv8ePHERMTozLCXK9evdC6dWsEBwfLiLxsKMr+zfDy5Ut06tQJRkZG2L17N8qVKyc37DKtYsWK0NTUxMOHD5XKHz58mGNfmpubF6j+h6ww/Zvh22+/xTfffIOjR4+iXr16RRlmmVXQ/o2JiUFcXBw8PT2lsoyTZlpaWoiMjIS9vX3RBl3CmFgUs0qVKqFSpUpFsm47OzuYm5vj2LFjUiLx4sULhIaGFmhkqbIsv/3bokULPH/+HGFhYWjUqBGAdwde6enpaNasWbbLvH79GgBURijS1NSU/nG874qyf21tbWFpaYnIyEil8qioKHTu3Fl+8GVAUfbvlClTMHz4cKUyZ2dnLF26VOlL8H1WlP0LvPt/6+HhAR0dHezbt49ngAFoa2ujUaNGOHbsmDTkZnp6Oo4dO4bRo0dnu0yLFi1w7NgxjBs3Tio7cuQIWrRoUQwRly2F6V/g3QiSc+fOxaFDh5SeJSJlBe3fWrVqqdzBMH36dLx8+RLLly+HlZVVcYRdskr66XHK2T///CMuXbokAgIChKGhobh06ZK4dOmSePnypVSnZs2aYteuXdL0N998I0xMTMTevXvF1atXhZeXl7CzsxNv3rwpiU0o1Tp16iQaNGggQkNDxalTp0SNGjXEgAEDpPn//vuvqFmzpggNDRVCCPH27Vvh4OAgWrduLUJDQ8XNmzfFt99+KxQKhThw4EBJbUapVdD+FUKIpUuXCmNjY7F9+3YRHR0tpk+fLnR1dcXNmzdLYhNKtcL0b1bgqFA5Kmj/JiQkiGbNmglnZ2dx8+ZNcf/+femVmppaUptRKmzdulXo6OiI9evXi/DwcDFy5EhhYmIiHjx4IIQQYtCgQWLKlClS/ZCQEKGlpSW+/fZbERERIfz9/UW5cuXEtWvXSmoTSrWC9u8333wjtLW1xY4dO5T208zHFvSfgvZvVh/aqFBMLEqxwYMHCwAqr6CgIKkOABEYGChNp6enCz8/P1GlShWho6Mj2rdvLyIjI4s/+DLgyZMnYsCAAcLQ0FAYGxuLoUOHKv1jjY2NVenvqKgo0bNnT1G5cmWhr68v6tWrpzL8LL1TmP4VQoj58+eLatWqCX19fdGiRQtx8uTJYo68bChs/2bGxCJnBe3foKCgbP9fAxCxsbElsxGlyIoVK4S1tbXQ1tYWTZs2FWfPnpXmubq6isGDByvV/+2334Sjo6PQ1tYWTk5OPHmTh4L0r42NTbb7qb+/f/EHXkYUdP/N7ENLLBRCCFGMF0iIiIiIiOg9xFGhiIiIiIhINiYWREREREQkGxMLIiIiIiKSjYkFERERERHJxsSCiIiIiIhkY2JBRERERESyMbEgIiIiIiLZmFgQEREREZFsTCyIiKhMc3Nzw7hx40o6DCKiDx4TCyIiIiIiko2JBRERlbiUlJSSDoGIiGRiYkFE9J45ePAgWrVqBRMTE5iZmaFr166IiYkBALi4uGDy5MlK9R89eoRy5crhxIkTAID79++jS5cu0NPTg52dHTZv3gxbW1ssW7YsX+3//fffaNWqFXR1dVGnTh0cPXoUCoUCe/bsAQDExcVBoVBg27ZtcHV1ha6uLjZt2oQnT55gwIABqFq1KvT19eHs7IwtW7YorfvVq1fw8fGBoaEhLCwssHjxYpX2k5OTMXHiRFStWhUGBgZo1qwZgoODC9aJRERUYEwsiIjeM69evcKECRNw4cIFHDt2DBoaGujRowfS09Ph7e2NrVu3Qggh1d+2bRssLS3RunVrAICPjw/u3buH4OBg7Ny5Ez/++CPi4+Pz1XZaWhq6d+8OfX19hIaG4scff8S0adOyrTtlyhSMHTsWERER8PDwQFJSEho1aoQDBw7g+vXrGDlyJAYNGoRz585Jy3z11Vf466+/sHfvXhw+fBjBwcG4ePGi0npHjx6NM2fOYOvWrbh69Sr69OmDTp06ITo6uqBdSUREBSGIiOi99ujRIwFAXLt2TcTHxwstLS1x4sQJaX6LFi3E5MmThRBCRERECADi/Pnz0vzo6GgBQCxdujTPtv7880+hpaUl7t+/L5UdOXJEABC7d+8WQggRGxsrAIhly5blub4uXbqIL7/8UgghxMuXL4W2trb47bffpPlPnjwRenp6YuzYsUIIIf755x+hqakp7t69q7Se9u3bi6lTp+bZHhERFZ5WiWY1RESkdtHR0ZgxYwZCQ0Px+PFjpKenAwBu376NunXromPHjti0aRNat26N2NhYnDlzBmvWrAEAREZGQktLCw0bNpTW5+DgAFNT03y1HRkZCSsrK5ibm0tlTZs2zbZu48aNlabT0tIwb948/Pbbb7h79y7evn2L5ORk6OvrAwBiYmLw9u1bNGvWTFqmQoUKqFmzpjR97do1pKWlwdHRUWndycnJMDMzy9c2EBFR4TCxICJ6z3h6esLGxgZr166FpaUl0tPTUbduXbx9+xYA4O3tjTFjxmDFihXYvHkznJ2d4ezsXOxxGhgYKE0vWrQIy5cvx7Jly+Ds7AwDAwOMGzdOijs/EhMToampibCwMGhqairNMzQ0VEvcRESUPT5jQUT0Hnny5AkiIyMxffp0tG/fHrVr18azZ8+U6nh5eSEpKQkHDx7E5s2b4e3tLc2rWbMmUlNTcenSJans5s2bKuvISc2aNXHnzh08fPhQKjt//ny+lg0JCYGXlxc+/vhjfPTRR6hevTqioqKk+fb29ihXrhxCQ0OlsmfPninVadCgAdLS0hAfHw8HBwelV+arKEREpH5MLIiI3iOmpqYwMzPDjz/+iJs3b+L48eOYMGGCUh0DAwN0794dfn5+iIiIwIABA6R5tWrVgru7O0aOHIlz587h0qVLGDlyJPT09KBQKPJsv0OHDrC3t8fgwYNx9epVhISEYPr06QCQ5/I1atTAkSNHcPr0aURERGDUqFFKCYqhoSGGDRuGr776CsePH8f169cxZMgQaGj891Xm6OgIb29v+Pj4YNeuXYiNjcW5c+cwf/58HDhwIF99SEREhcPEgojoPaKhoYGtW7ciLCwMdevWxfjx47Fo0SKVet7e3rhy5Qpat24Na2trpXm//PILqlSpgjZt2qBHjx4YMWIEjIyMoKurm2f7mpqa2LNnDxITE9GkSRMMHz5cGhUqr+WnT5+Ohg0bwsPDA25ubjA3N0f37t2V6ixatAitW7eGp6cn3N3d0apVKzRq1EipTmBgIHx8fPDll1+iZs2a6N69O86fP6+ynUREpF4KITKNOUhERJTFv//+CysrKxw9ehTt27cv8PIhISFo1aoVbt68CXt7+yKIkIiISgMmFkREpOT48eNITEyEs7Mz7t+/j0mTJuHu3buIiopCuXLl8lx+9+7dMDQ0RI0aNXDz5k2MHTsWpqamOHXqVDFET0REJYW3QhERkZKUlBR8/fXXcHJyQo8ePVCpUiUEBwejXLly2LRpEwwNDbN9OTk5AQBevnwJX19f1KpVC0OGDEGTJk2wd+/eEt4qIiIqarxiQURE+fby5UulB6ozK1euHGxsbIo5IiIiKi2YWBARERERkWy8FYqIiIiIiGRjYkFERERERLIxsSAiIiIiItmYWBARERERkWxMLIiIiIiISDYmFkREREREJBsTCyIiIiIiko2JBRERERERyfZ/WU7D65OORWoAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -1114,10 +1114,10 @@ "id": "c6baef0b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:31:37.022712Z", - "iopub.status.busy": "2024-10-19T23:31:37.022340Z", - "iopub.status.idle": "2024-10-19T23:32:29.248204Z", - "shell.execute_reply": "2024-10-19T23:32:29.247560Z" + "iopub.execute_input": "2024-10-22T00:57:28.948748Z", + "iopub.status.busy": "2024-10-22T00:57:28.948376Z", + "iopub.status.idle": "2024-10-22T00:58:24.673079Z", + "shell.execute_reply": "2024-10-22T00:58:24.672404Z" } }, "outputs": [ @@ -1174,7 +1174,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 373.34it/s]" + "Fitting causal mechanism of node Gender: 100%|██████████| 5/5 [00:00<00:00, 377.93it/s]" ] }, { @@ -1186,12 +1186,12 @@ }, { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 0.0$" + "$\\displaystyle -1.11022302462516 \\cdot 10^{-15}$" ], "text/plain": [ - "0.0" + "-1.1102230246251565e-15" ] }, "execution_count": 12, @@ -1223,16 +1223,16 @@ "id": "84b1c218", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:29.250470Z", - "iopub.status.busy": "2024-10-19T23:32:29.250221Z", - "iopub.status.idle": "2024-10-19T23:32:29.584226Z", - "shell.execute_reply": "2024-10-19T23:32:29.583554Z" + "iopub.execute_input": "2024-10-22T00:58:24.675513Z", + "iopub.status.busy": "2024-10-22T00:58:24.675229Z", + "iopub.status.idle": "2024-10-22T00:58:25.031102Z", + "shell.execute_reply": "2024-10-22T00:58:25.030463Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/main/example_notebooks/do_sampler_demo.html b/main/example_notebooks/do_sampler_demo.html index a8a37974e..0a794ffc4 100644 --- a/main/example_notebooks/do_sampler_demo.html +++ b/main/example_notebooks/do_sampler_demo.html @@ -653,7 +653,7 @@

Demo#
-$\displaystyle 1.62585044692902$
+$\displaystyle 1.63689405412668$

So the naive effect is around 60% high. Now, let’s build a causal model for this data.

@@ -725,7 +725,7 @@

Demo#
-$\displaystyle 0.959364599217468$
+$\displaystyle 1.1185969100772$

Now we’re much closer to the true effect, which is around 1.0!

diff --git a/main/example_notebooks/do_sampler_demo.ipynb b/main/example_notebooks/do_sampler_demo.ipynb index 3d6c26c01..a00e6cac4 100644 --- a/main/example_notebooks/do_sampler_demo.ipynb +++ b/main/example_notebooks/do_sampler_demo.ipynb @@ -64,10 +64,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:34.199327Z", - "iopub.status.busy": "2024-10-19T23:32:34.198907Z", - "iopub.status.idle": "2024-10-19T23:32:34.204748Z", - "shell.execute_reply": "2024-10-19T23:32:34.204296Z" + "iopub.execute_input": "2024-10-22T00:58:29.613604Z", + "iopub.status.busy": "2024-10-22T00:58:29.613422Z", + "iopub.status.idle": "2024-10-22T00:58:29.619427Z", + "shell.execute_reply": "2024-10-22T00:58:29.618941Z" } }, "outputs": [], @@ -81,10 +81,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:34.206481Z", - "iopub.status.busy": "2024-10-19T23:32:34.206148Z", - "iopub.status.idle": "2024-10-19T23:32:35.628044Z", - "shell.execute_reply": "2024-10-19T23:32:35.627490Z" + "iopub.execute_input": "2024-10-22T00:58:29.621239Z", + "iopub.status.busy": "2024-10-22T00:58:29.620905Z", + "iopub.status.idle": "2024-10-22T00:58:31.050382Z", + "shell.execute_reply": "2024-10-22T00:58:31.049739Z" }, "scrolled": true }, @@ -100,10 +100,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.630348Z", - "iopub.status.busy": "2024-10-19T23:32:35.630066Z", - "iopub.status.idle": "2024-10-19T23:32:35.634944Z", - "shell.execute_reply": "2024-10-19T23:32:35.634434Z" + "iopub.execute_input": "2024-10-22T00:58:31.052509Z", + "iopub.status.busy": "2024-10-22T00:58:31.052203Z", + "iopub.status.idle": "2024-10-22T00:58:31.057207Z", + "shell.execute_reply": "2024-10-22T00:58:31.056753Z" } }, "outputs": [], @@ -122,21 +122,21 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.636643Z", - "iopub.status.busy": "2024-10-19T23:32:35.636318Z", - "iopub.status.idle": "2024-10-19T23:32:35.691980Z", - "shell.execute_reply": "2024-10-19T23:32:35.691355Z" + "iopub.execute_input": "2024-10-22T00:58:31.059009Z", + "iopub.status.busy": "2024-10-22T00:58:31.058656Z", + "iopub.status.idle": "2024-10-22T00:58:31.114422Z", + "shell.execute_reply": "2024-10-22T00:58:31.113762Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle 1.62585044692902$" + "$\\displaystyle 1.63689405412668$" ], "text/plain": [ - "1.6258504469290211" + "1.636894054126685" ] }, "execution_count": 4, @@ -160,10 +160,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.694086Z", - "iopub.status.busy": "2024-10-19T23:32:35.693624Z", - "iopub.status.idle": "2024-10-19T23:32:35.697174Z", - "shell.execute_reply": "2024-10-19T23:32:35.696572Z" + "iopub.execute_input": "2024-10-22T00:58:31.116581Z", + "iopub.status.busy": "2024-10-22T00:58:31.116197Z", + "iopub.status.idle": "2024-10-22T00:58:31.119536Z", + "shell.execute_reply": "2024-10-22T00:58:31.119048Z" } }, "outputs": [], @@ -193,10 +193,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.699141Z", - "iopub.status.busy": "2024-10-19T23:32:35.698696Z", - "iopub.status.idle": "2024-10-19T23:32:35.703753Z", - "shell.execute_reply": "2024-10-19T23:32:35.703153Z" + "iopub.execute_input": "2024-10-22T00:58:31.121158Z", + "iopub.status.busy": "2024-10-22T00:58:31.120988Z", + "iopub.status.idle": "2024-10-22T00:58:31.125670Z", + "shell.execute_reply": "2024-10-22T00:58:31.125165Z" } }, "outputs": [], @@ -216,10 +216,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.705770Z", - "iopub.status.busy": "2024-10-19T23:32:35.705328Z", - "iopub.status.idle": "2024-10-19T23:32:35.712307Z", - "shell.execute_reply": "2024-10-19T23:32:35.711846Z" + "iopub.execute_input": "2024-10-22T00:58:31.127561Z", + "iopub.status.busy": "2024-10-22T00:58:31.127198Z", + "iopub.status.idle": "2024-10-22T00:58:31.134467Z", + "shell.execute_reply": "2024-10-22T00:58:31.133841Z" } }, "outputs": [], @@ -251,10 +251,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.714081Z", - "iopub.status.busy": "2024-10-19T23:32:35.713766Z", - "iopub.status.idle": "2024-10-19T23:32:35.725595Z", - "shell.execute_reply": "2024-10-19T23:32:35.725034Z" + "iopub.execute_input": "2024-10-22T00:58:31.136415Z", + "iopub.status.busy": "2024-10-22T00:58:31.136061Z", + "iopub.status.idle": "2024-10-22T00:58:31.147676Z", + "shell.execute_reply": "2024-10-22T00:58:31.147182Z" } }, "outputs": [], @@ -267,21 +267,21 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:35.727684Z", - "iopub.status.busy": "2024-10-19T23:32:35.727245Z", - "iopub.status.idle": "2024-10-19T23:32:35.745208Z", - "shell.execute_reply": "2024-10-19T23:32:35.744593Z" + "iopub.execute_input": "2024-10-22T00:58:31.149432Z", + "iopub.status.busy": "2024-10-22T00:58:31.149132Z", + "iopub.status.idle": "2024-10-22T00:58:31.166657Z", + "shell.execute_reply": "2024-10-22T00:58:31.166138Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle 0.959364599217468$" + "$\\displaystyle 1.1185969100772$" ], "text/plain": [ - "0.9593645992174682" + "1.1185969100771995" ] }, "execution_count": 9, diff --git a/main/example_notebooks/dowhy-conditional-treatment-effects.html b/main/example_notebooks/dowhy-conditional-treatment-effects.html index 697543681..09fc4da64 100644 --- a/main/example_notebooks/dowhy-conditional-treatment-effects.html +++ b/main/example_notebooks/dowhy-conditional-treatment-effects.html @@ -620,19 +620,19 @@

Conditional Average Treatment Effects (CATE) with DoWhy and EconML 
          X0        X1   Z0        Z1        W0        W1 W2 W3         v0  \
-0  1.491701 -1.737984  1.0  0.300386  0.563925 -1.215789  3  0  14.891185
-1  0.692461 -1.791338  0.0  0.458973  0.056990 -0.571325  3  1   8.496209
-2  0.872723 -0.166527  1.0  0.844340  0.023435 -0.135442  3  2  22.756933
-3  0.655365  0.379222  1.0  0.940465  1.431118 -0.576851  2  2  27.321689
-4  0.485370 -1.121584  0.0  0.534082  0.448856 -1.454139  3  0   6.868651
+0  2.023455  1.562361  1.0  0.158316 -2.917928 -0.905594  0  2   1.906373
+1  1.480928  0.525396  1.0  0.883311  1.098866  0.322358  1  3  38.704043
+2 -0.786564  0.115644  1.0  0.207903  0.034024 -1.382215  0  3  18.700457
+3  1.635684  0.822219  1.0  0.322280  0.366915  0.257753  3  0  19.561754
+4  2.261918 -0.308412  1.0  0.602596 -1.818019 -0.114524  0  0   8.478407
 
             y
-0  137.397772
-1   58.552502
-2  301.496443
-3  394.580374
-4   57.178667
-True causal estimate is 9.90297950800993
+0   28.438381
+1  572.167489
+2  171.981562
+3  313.928792
+4  100.054705
+True causal estimate is 12.326444409564749
@@ -689,9 +689,9 @@

Conditional Average Treatment Effects (CATE) with DoWhy and EconML 
-True causal estimate is 9.90297950800993
+True causal estimate is 12.326444409564749

@@ -889,23 +889,23 @@

EconML methods 
-[[15.34215019]
- [14.90055945]
- [14.15823019]
- [14.77471051]
- [14.90021874]
- [14.7392562 ]
- [14.83142581]
- [16.29700014]
- [14.56724122]
- [15.94164365]]
+[[13.65743569]
+ [11.25520921]
+ [13.51849661]
+ [14.75180693]
+ [13.57443238]
+ [14.32896054]
+ [12.8873053 ]
+ [12.01943554]
+ [10.65435693]
+ [10.15937714]]

@@ -1032,7 +1032,7 @@

Can also retrieve the raw EconML estimator object for any further operations 
-<econml.dml.dml.DML object at 0x7fe519451a00>
+<econml.dml.dml.DML object at 0x7f50568ba2b0>
@@ -1067,17 +1067,17 @@

Binary treatment, Binary outcome 
             X0        X1   Z0        W0        W1        W2        W3  v0  y
-0     0.737371 -0.988791  0.0  1.315618 -0.459543  1.092568  1.518797   1  1
-1     0.721527  0.564647  0.0  2.142769  0.395111  1.847626  0.161921   1  1
-2     3.244662 -1.704488  1.0  1.258298 -0.041071  2.464063  0.140888   1  1
-3     1.373530 -1.057219  0.0 -0.091845 -1.011054  1.744316  0.501808   0  1
-4     0.039205  0.064237  1.0 -2.113835 -1.106553  2.352720 -1.011937   0  0
+0     1.187002  2.129281  1.0  0.225979  1.520578 -0.462146  1.104043   1  1
+1     0.319704 -1.603687  0.0 -1.050686  1.183747 -0.434124  1.927326   1  1
+2     0.736122 -1.360670  1.0 -0.383549 -0.925797 -1.607262 -0.784307   0  0
+3     1.053287 -1.567965  0.0 -0.911918 -0.927349 -0.626209  0.647853   0  1
+4     0.391219  1.150939  0.0  0.014391  3.230884  0.387733  0.988914   1  1
 ...        ...       ...  ...       ...       ...       ...       ...  .. ..
-9995  1.441554 -0.454320  1.0 -0.455541  0.105561 -0.139760 -0.901243   1  1
-9996  2.459208 -0.186497  0.0  0.406960 -1.386559  0.612979 -0.141619   0  1
-9997  2.364170 -1.539729  0.0  0.085340 -0.113041  0.280497 -1.187246   1  1
-9998  0.358942  0.023838  1.0  1.088847 -1.587456  1.154165  1.042078   1  1
-9999  0.201153  0.665577  1.0  1.740281  0.240991  1.508345 -1.355040   1  1
+9995  0.400726 -1.221735  0.0  0.046251  0.605087 -1.082564 -0.150353   0  0
+9996 -1.029194 -0.604113  1.0  0.901130  0.324673 -1.156698  1.271759   1  1
+9997 -1.184801 -1.120879  1.0 -0.607204  0.970222 -0.631891  0.605802   1  1
+9998  1.181210 -1.491519  0.0  0.708097  0.720793 -0.993977 -0.766604   0  0
+9999 -0.354285 -0.105086  0.0  0.172579  0.497326  0.166035  0.258179   1  1
 
 [10000 rows x 9 columns]
@@ -1117,25 +1117,25 @@

Using DRLearner estimator 
             X0        X1        X2        X3        X4   Z0        Z1  \
-0     1.194694  0.806424  3.378051  1.066913 -1.762461  0.0  0.042603
-1     0.210889 -0.866121 -0.036220  2.189422 -0.133892  1.0  0.255470
-2     0.994021  0.356651  1.642013  0.319378  1.683821  0.0  0.574598
-3     0.974397 -1.042993  1.734511 -0.220498 -0.545196  1.0  0.228140
-4     0.723457 -0.935144  1.068031  0.576495  0.740326  0.0  0.078338
+0    -0.086925 -0.100078  0.571303 -0.828653  1.344718  1.0  0.389962
+1     0.280182  0.470249 -0.375436 -2.764916 -1.072220  1.0  0.079642
+2     0.123467  1.309425  0.476237 -0.106417 -0.895539  0.0  0.733598
+3     2.834800  0.230924 -0.666959 -0.828474 -2.011372  0.0  0.447600
+4     1.963431  1.069789 -0.394695 -0.459135 -0.751069  0.0  0.744901
 ...        ...       ...       ...       ...       ...  ...       ...
-9995  0.238972  1.086033  0.053144  0.610416 -1.248808  0.0  0.344754
-9996  0.894745 -1.155823  0.090509  0.859903  0.165150  0.0  0.163243
-9997  0.143863 -2.810947 -0.974355  0.820165 -1.555686  0.0  0.614141
-9998 -1.351503 -1.443587  2.677787  0.725252 -0.779037  1.0  0.206247
-9999  0.533301  0.557858  1.470046  1.655571 -0.140108  0.0  0.601939
+9995  0.097454  0.801626 -1.719491 -1.689063 -2.283606  1.0  0.009369
+9996  0.963483  1.062666 -0.515928 -0.444270 -1.900877  0.0  0.802029
+9997  2.137832 -0.146762  0.527316 -0.725324  0.121707  0.0  0.712036
+9998 -0.080499 -0.660111  0.237723 -0.246514 -2.791567  0.0  0.401258
+9999 -0.788721  1.639797 -1.126375 -1.565304 -0.038917  0.0  0.995005
 
             W0        W1        W2        W3        W4  v0          y
-0     1.442070  0.181076 -1.064983  1.689094  0.055662   1  26.705185
-1    -2.113033 -0.435568 -0.524926  0.086432 -0.242206   1   3.194405
-2    -0.027573 -0.570038 -0.923142  4.924895  1.241415   1  24.708508
-3     0.124236  1.725932  0.494322  0.869966  0.520273   1  22.239015
-4     0.021272 -0.616530  0.169321  0.429555  1.769569   1  20.260806
+0    -0.244303  0.266005 -0.060768 -1.208970  0.657691   1  14.009190
+1    -0.259919  1.107581  1.013343 -0.576170  0.785727   1   9.425114
+2     1.172103 -2.312375  1.224012  0.729851  0.266905   1  20.818196
+3    -1.500939  0.032816  1.983530  0.401027  2.369935   1  23.159152
+4    -1.728847  1.182353 -0.503303  0.137425  1.013635   1  15.171032
 ...        ...       ...       ...       ...       ...  ..        ...
-9995 -0.039791  0.527084  0.293256  2.452390 -0.205648   1  22.632414
-9996 -0.061491  0.964071 -0.595591 -0.367892  1.588571   0   9.045665
-9997  0.459640  1.143104 -0.054694  0.328811  0.083701   1  10.594588
-9998 -0.902153  0.840342  1.524773  0.998653 -1.077402   1   5.035180
-9999 -0.745454  0.777239  1.253353  1.271281 -1.389755   1  21.136632
+9995  0.611338  0.775651  0.447902  0.201046 -0.223241   1  -0.536255
+9996 -1.367978 -1.128647  1.219540  0.502938 -0.306668   1   3.482780
+9997  0.845712  0.613335  1.259556  0.220525  0.967669   1  34.807461
+9998 -1.174897 -0.269773  0.150152 -0.064002  0.298968   0  -3.497418
+9999 -1.950860 -0.652349  0.677681  1.184015  1.058340   1  11.260721
 
 [10000 rows x 14 columns]
@@ -1281,25 +1281,25 @@

Metalearners 
 Refute: Add a random common cause
-Estimated effect:15.045243609692054
-New effect:15.040517118845221
-p value:0.86
+Estimated effect:12.680681626503253
+New effect:12.695571695534525
+p value:0.8400000000000001
@@ -1402,8 +1402,8 @@

Adding an unobserved common cause variable 
 Refute: Add an Unobserved Common Cause
-Estimated effect:15.045243609692054
-New effect:15.079754922662948
+Estimated effect:12.680681626503253
+New effect:12.78054614324095
@@ -1428,9 +1428,9 @@

Replacing treatment with a random (placebo) variable 
 Refute: Use a Placebo Treatment
-Estimated effect:15.045243609692054
-New effect:-0.021354476275475112
-p value:0.4443630005685122
+Estimated effect:12.680681626503253
+New effect:-0.027534656014193824
+p value:0.3319205721239611
@@ -1454,9 +1454,9 @@

Removing a random subset of the data 
 Refute: Use a subset of data
-Estimated effect:15.045243609692054
-New effect:15.031111389585709
-p value:0.40967889817265557
+Estimated effect:12.680681626503253
+New effect:12.71535645089526
+p value:0.24567680945783077
diff --git a/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb b/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb index f180f1662..75a2a5b34 100644 --- a/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb +++ b/main/example_notebooks/dowhy-conditional-treatment-effects.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:37.575619Z", - "iopub.status.busy": "2024-10-19T23:32:37.575203Z", - "iopub.status.idle": "2024-10-19T23:32:37.595235Z", - "shell.execute_reply": "2024-10-19T23:32:37.594765Z" + "iopub.execute_input": "2024-10-22T00:58:33.209986Z", + "iopub.status.busy": "2024-10-22T00:58:33.209569Z", + "iopub.status.idle": "2024-10-22T00:58:33.227514Z", + "shell.execute_reply": "2024-10-22T00:58:33.227046Z" } }, "outputs": [], @@ -36,10 +36,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:37.597203Z", - "iopub.status.busy": "2024-10-19T23:32:37.597032Z", - "iopub.status.idle": "2024-10-19T23:32:39.059365Z", - "shell.execute_reply": "2024-10-19T23:32:39.058764Z" + "iopub.execute_input": "2024-10-22T00:58:33.229447Z", + "iopub.status.busy": "2024-10-22T00:58:33.229084Z", + "iopub.status.idle": "2024-10-22T00:58:34.706365Z", + "shell.execute_reply": "2024-10-22T00:58:34.705658Z" } }, "outputs": [], @@ -64,10 +64,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.061517Z", - "iopub.status.busy": "2024-10-19T23:32:39.061237Z", - "iopub.status.idle": "2024-10-19T23:32:39.116213Z", - "shell.execute_reply": "2024-10-19T23:32:39.115616Z" + "iopub.execute_input": "2024-10-22T00:58:34.708824Z", + "iopub.status.busy": "2024-10-22T00:58:34.708521Z", + "iopub.status.idle": "2024-10-22T00:58:34.765280Z", + "shell.execute_reply": "2024-10-22T00:58:34.764164Z" } }, "outputs": [ @@ -76,19 +76,19 @@ "output_type": "stream", "text": [ " X0 X1 Z0 Z1 W0 W1 W2 W3 v0 \\\n", - "0 1.491701 -1.737984 1.0 0.300386 0.563925 -1.215789 3 0 14.891185 \n", - "1 0.692461 -1.791338 0.0 0.458973 0.056990 -0.571325 3 1 8.496209 \n", - "2 0.872723 -0.166527 1.0 0.844340 0.023435 -0.135442 3 2 22.756933 \n", - "3 0.655365 0.379222 1.0 0.940465 1.431118 -0.576851 2 2 27.321689 \n", - "4 0.485370 -1.121584 0.0 0.534082 0.448856 -1.454139 3 0 6.868651 \n", + "0 2.023455 1.562361 1.0 0.158316 -2.917928 -0.905594 0 2 1.906373 \n", + "1 1.480928 0.525396 1.0 0.883311 1.098866 0.322358 1 3 38.704043 \n", + "2 -0.786564 0.115644 1.0 0.207903 0.034024 -1.382215 0 3 18.700457 \n", + "3 1.635684 0.822219 1.0 0.322280 0.366915 0.257753 3 0 19.561754 \n", + "4 2.261918 -0.308412 1.0 0.602596 -1.818019 -0.114524 0 0 8.478407 \n", "\n", " y \n", - "0 137.397772 \n", - "1 58.552502 \n", - "2 301.496443 \n", - "3 394.580374 \n", - "4 57.178667 \n", - "True causal estimate is 9.90297950800993\n" + "0 28.438381 \n", + "1 572.167489 \n", + "2 171.981562 \n", + "3 313.928792 \n", + "4 100.054705 \n", + "True causal estimate is 12.326444409564749\n" ] } ], @@ -110,10 +110,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.118928Z", - "iopub.status.busy": "2024-10-19T23:32:39.118403Z", - "iopub.status.idle": "2024-10-19T23:32:39.160885Z", - "shell.execute_reply": "2024-10-19T23:32:39.160344Z" + "iopub.execute_input": "2024-10-22T00:58:34.768104Z", + "iopub.status.busy": "2024-10-22T00:58:34.767554Z", + "iopub.status.idle": "2024-10-22T00:58:34.810529Z", + "shell.execute_reply": "2024-10-22T00:58:34.809987Z" } }, "outputs": [], @@ -128,10 +128,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.163523Z", - "iopub.status.busy": "2024-10-19T23:32:39.163035Z", - "iopub.status.idle": "2024-10-19T23:32:39.336605Z", - "shell.execute_reply": "2024-10-19T23:32:39.336010Z" + "iopub.execute_input": "2024-10-22T00:58:34.812615Z", + "iopub.status.busy": "2024-10-22T00:58:34.812408Z", + "iopub.status.idle": "2024-10-22T00:58:34.984595Z", + "shell.execute_reply": "2024-10-22T00:58:34.983957Z" } }, "outputs": [ @@ -167,10 +167,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.338577Z", - "iopub.status.busy": "2024-10-19T23:32:39.338385Z", - "iopub.status.idle": "2024-10-19T23:32:39.673800Z", - "shell.execute_reply": "2024-10-19T23:32:39.673231Z" + "iopub.execute_input": "2024-10-22T00:58:34.986614Z", + "iopub.status.busy": "2024-10-22T00:58:34.986416Z", + "iopub.status.idle": "2024-10-22T00:58:35.329877Z", + "shell.execute_reply": "2024-10-22T00:58:35.329247Z" }, "scrolled": true }, @@ -185,9 +185,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -226,10 +226,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:39.675835Z", - "iopub.status.busy": "2024-10-19T23:32:39.675443Z", - "iopub.status.idle": "2024-10-19T23:32:40.017093Z", - "shell.execute_reply": "2024-10-19T23:32:40.016520Z" + "iopub.execute_input": "2024-10-22T00:58:35.331987Z", + "iopub.status.busy": "2024-10-22T00:58:35.331643Z", + "iopub.status.idle": "2024-10-22T00:58:35.681869Z", + "shell.execute_reply": "2024-10-22T00:58:35.681210Z" } }, "outputs": [ @@ -246,43 +246,43 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2+v0*X1+v0*X0\n", + "b: y~v0+W3+W1+W2+W0+v0*X0+v0*X1\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: 9.903186220972193\n", + "Mean value: 12.326487748766223\n", "### Conditional Estimates\n", - "__categorical__X1 __categorical__X0 \n", - "(-4.517, -1.479] (-2.7729999999999997, -0.206] -0.854567\n", - " (-0.206, 0.392] 2.729455\n", - " (0.392, 0.893] 4.593359\n", - " (0.893, 1.491] 6.473340\n", - " (1.491, 4.942] 9.569572\n", - "(-1.479, -0.9] (-2.7729999999999997, -0.206] 2.647748\n", - " (-0.206, 0.392] 5.898428\n", - " (0.392, 0.893] 7.793650\n", - " (0.893, 1.491] 9.867785\n", - " (1.491, 4.942] 12.992648\n", - "(-0.9, -0.382] (-2.7729999999999997, -0.206] 4.765016\n", - " (-0.206, 0.392] 7.855087\n", - " (0.392, 0.893] 9.846105\n", - " (0.893, 1.491] 11.863300\n", - " (1.491, 4.942] 15.080355\n", - "(-0.382, 0.202] (-2.7729999999999997, -0.206] 6.856693\n", - " (-0.206, 0.392] 9.979435\n", - " (0.392, 0.893] 11.944220\n", - " (0.893, 1.491] 13.891133\n", - " (1.491, 4.942] 17.220567\n", - "(0.202, 3.39] (-2.7729999999999997, -0.206] 10.099547\n", - " (-0.206, 0.392] 13.267852\n", - " (0.392, 0.893] 15.331551\n", - " (0.893, 1.491] 17.310951\n", - " (1.491, 4.942] 20.560836\n", + "__categorical__X0 __categorical__X1 \n", + "(-3.0589999999999997, -0.0333] (-3.3049999999999997, -0.58] 4.640146\n", + " (-0.58, 0.00381] 7.961317\n", + " (0.00381, 0.517] 10.003873\n", + " (0.517, 1.107] 12.066093\n", + " (1.107, 4.088] 15.330083\n", + "(-0.0333, 0.555] (-3.3049999999999997, -0.58] 6.137589\n", + " (-0.58, 0.00381] 9.427405\n", + " (0.00381, 0.517] 11.380003\n", + " (0.517, 1.107] 13.484874\n", + " (1.107, 4.088] 16.902725\n", + "(0.555, 1.057] (-3.3049999999999997, -0.58] 6.981416\n", + " (-0.58, 0.00381] 10.295854\n", + " (0.00381, 0.517] 12.303682\n", + " (0.517, 1.107] 14.439904\n", + " (1.107, 4.088] 17.655446\n", + "(1.057, 1.657] (-3.3049999999999997, -0.58] 7.649759\n", + " (-0.58, 0.00381] 11.126852\n", + " (0.00381, 0.517] 13.170195\n", + " (0.517, 1.107] 15.298160\n", + " (1.107, 4.088] 18.680237\n", + "(1.657, 4.502] (-3.3049999999999997, -0.58] 9.274378\n", + " (-0.58, 0.00381] 12.532155\n", + " (0.00381, 0.517] 14.667815\n", + " (0.517, 1.107] 16.736425\n", + " (1.107, 4.088] 20.003727\n", "dtype: float64\n" ] } @@ -314,10 +314,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:40.019218Z", - "iopub.status.busy": "2024-10-19T23:32:40.018833Z", - "iopub.status.idle": "2024-10-19T23:32:44.756035Z", - "shell.execute_reply": "2024-10-19T23:32:44.754779Z" + "iopub.execute_input": "2024-10-22T00:58:35.684113Z", + "iopub.status.busy": "2024-10-22T00:58:35.683718Z", + "iopub.status.idle": "2024-10-22T00:58:40.552555Z", + "shell.execute_reply": "2024-10-22T00:58:40.551317Z" } }, "outputs": [ @@ -334,23 +334,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: Data subset defined by a function\n", "\n", "## Estimate\n", - "Mean value: 13.753748963935088\n", - "Effect estimates: [[ 8.73122277]\n", - " [15.23876743]\n", - " [17.29460755]\n", + "Mean value: 13.93194215968301\n", + "Effect estimates: [[19.58502909]\n", + " [14.50763963]\n", + " [15.96006866]\n", " ...\n", - " [15.87908319]\n", - " [11.24352305]\n", - " [25.84231277]]\n", + " [12.86547484]\n", + " [17.1101046 ]\n", + " [16.39475864]]\n", "\n" ] } @@ -377,10 +377,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:44.760753Z", - "iopub.status.busy": "2024-10-19T23:32:44.760509Z", - "iopub.status.idle": "2024-10-19T23:32:44.839846Z", - "shell.execute_reply": "2024-10-19T23:32:44.839313Z" + "iopub.execute_input": "2024-10-22T00:58:40.556361Z", + "iopub.status.busy": "2024-10-22T00:58:40.556074Z", + "iopub.status.idle": "2024-10-22T00:58:40.620240Z", + "shell.execute_reply": "2024-10-22T00:58:40.619129Z" } }, "outputs": [ @@ -388,7 +388,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "True causal estimate is 9.90297950800993\n" + "True causal estimate is 12.326444409564749\n" ] } ], @@ -401,10 +401,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:44.841527Z", - "iopub.status.busy": "2024-10-19T23:32:44.841356Z", - "iopub.status.idle": "2024-10-19T23:32:48.505837Z", - "shell.execute_reply": "2024-10-19T23:32:48.504469Z" + "iopub.execute_input": "2024-10-22T00:58:40.622945Z", + "iopub.status.busy": "2024-10-22T00:58:40.622705Z", + "iopub.status.idle": "2024-10-22T00:58:44.269453Z", + "shell.execute_reply": "2024-10-22T00:58:44.267707Z" } }, "outputs": [ @@ -421,23 +421,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: 9.919879528997198\n", - "Effect estimates: [[ 8.81651721]\n", - " [ 5.69962658]\n", - " [12.56157938]\n", + "Mean value: 12.232711770496296\n", + "Effect estimates: [[19.50191592]\n", + " [14.48628432]\n", + " [ 8.77201276]\n", " ...\n", - " [ 5.72285068]\n", - " [ 9.26534355]\n", - " [ 1.82141633]]\n", + " [ 9.31526092]\n", + " [ 1.20345234]\n", + " [12.42482179]]\n", "\n" ] } @@ -469,10 +469,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:32:48.509952Z", - "iopub.status.busy": "2024-10-19T23:32:48.509178Z", - "iopub.status.idle": "2024-10-19T23:34:57.171270Z", - "shell.execute_reply": "2024-10-19T23:34:57.170680Z" + "iopub.execute_input": "2024-10-22T00:58:44.274163Z", + "iopub.status.busy": "2024-10-22T00:58:44.273918Z", + "iopub.status.idle": "2024-10-22T01:01:00.858452Z", + "shell.execute_reply": "2024-10-22T01:01:00.857821Z" } }, "outputs": [ @@ -489,28 +489,28 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.866981805100442\n", - "Effect estimates: [[ 8.76788198]\n", - " [ 5.63768763]\n", - " [12.51641217]\n", + "Mean value: 12.314628691878262\n", + "Effect estimates: [[19.5655154 ]\n", + " [14.51963354]\n", + " [ 9.01097059]\n", " ...\n", - " [ 5.65328576]\n", - " [ 9.21045244]\n", - " [ 1.74479777]]\n", - "95.0% confidence interval: [[[ 8.73665267 5.56645407 12.56084318 ... 5.58889059 9.2246464\n", - " 1.54243643]]\n", + " [ 9.31578975]\n", + " [ 1.17294938]\n", + " [12.49848647]]\n", + "95.0% confidence interval: [[[19.81494888 14.70998842 8.93032579 ... 9.27559761 0.54856956\n", + " 12.65799309]]\n", "\n", - " [[ 8.96365154 5.72125451 12.73561168 ... 5.70241494 9.32302885\n", - " 1.76524244]]]\n", + " [[20.45154685 15.03025232 9.35074604 ... 9.58882303 1.34584941\n", + " 12.86393751]]]\n", "\n" ] } @@ -547,10 +547,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:34:57.174413Z", - "iopub.status.busy": "2024-10-19T23:34:57.173448Z", - "iopub.status.idle": "2024-10-19T23:35:00.817084Z", - "shell.execute_reply": "2024-10-19T23:35:00.815772Z" + "iopub.execute_input": "2024-10-22T01:01:00.861113Z", + "iopub.status.busy": "2024-10-22T01:01:00.860869Z", + "iopub.status.idle": "2024-10-22T01:01:04.600661Z", + "shell.execute_reply": "2024-10-22T01:01:04.599554Z" } }, "outputs": [ @@ -558,16 +558,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[15.34215019]\n", - " [14.90055945]\n", - " [14.15823019]\n", - " [14.77471051]\n", - " [14.90021874]\n", - " [14.7392562 ]\n", - " [14.83142581]\n", - " [16.29700014]\n", - " [14.56724122]\n", - " [15.94164365]]\n" + "[[13.65743569]\n", + " [11.25520921]\n", + " [13.51849661]\n", + " [14.75180693]\n", + " [13.57443238]\n", + " [14.32896054]\n", + " [12.8873053 ]\n", + " [12.01943554]\n", + " [10.65435693]\n", + " [10.15937714]]\n" ] } ], @@ -600,10 +600,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:00.820120Z", - "iopub.status.busy": "2024-10-19T23:35:00.819899Z", - "iopub.status.idle": "2024-10-19T23:35:00.917684Z", - "shell.execute_reply": "2024-10-19T23:35:00.917031Z" + "iopub.execute_input": "2024-10-22T01:01:04.604482Z", + "iopub.status.busy": "2024-10-22T01:01:04.603467Z", + "iopub.status.idle": "2024-10-22T01:01:04.704127Z", + "shell.execute_reply": "2024-10-22T01:01:04.703611Z" } }, "outputs": [ @@ -611,7 +611,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -639,10 +639,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:00.919667Z", - "iopub.status.busy": "2024-10-19T23:35:00.919469Z", - "iopub.status.idle": "2024-10-19T23:35:01.895868Z", - "shell.execute_reply": "2024-10-19T23:35:01.895296Z" + "iopub.execute_input": "2024-10-22T01:01:04.706215Z", + "iopub.status.busy": "2024-10-22T01:01:04.705736Z", + "iopub.status.idle": "2024-10-22T01:01:05.684616Z", + "shell.execute_reply": "2024-10-22T01:01:05.684046Z" } }, "outputs": [ @@ -651,17 +651,17 @@ "output_type": "stream", "text": [ " X0 X1 Z0 W0 W1 W2 W3 v0 y\n", - "0 0.737371 -0.988791 0.0 1.315618 -0.459543 1.092568 1.518797 1 1\n", - "1 0.721527 0.564647 0.0 2.142769 0.395111 1.847626 0.161921 1 1\n", - "2 3.244662 -1.704488 1.0 1.258298 -0.041071 2.464063 0.140888 1 1\n", - "3 1.373530 -1.057219 0.0 -0.091845 -1.011054 1.744316 0.501808 0 1\n", - "4 0.039205 0.064237 1.0 -2.113835 -1.106553 2.352720 -1.011937 0 0\n", + "0 1.187002 2.129281 1.0 0.225979 1.520578 -0.462146 1.104043 1 1\n", + "1 0.319704 -1.603687 0.0 -1.050686 1.183747 -0.434124 1.927326 1 1\n", + "2 0.736122 -1.360670 1.0 -0.383549 -0.925797 -1.607262 -0.784307 0 0\n", + "3 1.053287 -1.567965 0.0 -0.911918 -0.927349 -0.626209 0.647853 0 1\n", + "4 0.391219 1.150939 0.0 0.014391 3.230884 0.387733 0.988914 1 1\n", "... ... ... ... ... ... ... ... .. ..\n", - "9995 1.441554 -0.454320 1.0 -0.455541 0.105561 -0.139760 -0.901243 1 1\n", - "9996 2.459208 -0.186497 0.0 0.406960 -1.386559 0.612979 -0.141619 0 1\n", - "9997 2.364170 -1.539729 0.0 0.085340 -0.113041 0.280497 -1.187246 1 1\n", - "9998 0.358942 0.023838 1.0 1.088847 -1.587456 1.154165 1.042078 1 1\n", - "9999 0.201153 0.665577 1.0 1.740281 0.240991 1.508345 -1.355040 1 1\n", + "9995 0.400726 -1.221735 0.0 0.046251 0.605087 -1.082564 -0.150353 0 0\n", + "9996 -1.029194 -0.604113 1.0 0.901130 0.324673 -1.156698 1.271759 1 1\n", + "9997 -1.184801 -1.120879 1.0 -0.607204 0.970222 -0.631891 0.605802 1 1\n", + "9998 1.181210 -1.491519 0.0 0.708097 0.720793 -0.993977 -0.766604 0 0\n", + "9999 -0.354285 -0.105086 0.0 0.172579 0.497326 0.166035 0.258179 1 1\n", "\n", "[10000 rows x 9 columns]\n" ] @@ -694,10 +694,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:01.897833Z", - "iopub.status.busy": "2024-10-19T23:35:01.897596Z", - "iopub.status.idle": "2024-10-19T23:35:10.833455Z", - "shell.execute_reply": "2024-10-19T23:35:10.832879Z" + "iopub.execute_input": "2024-10-22T01:01:05.686826Z", + "iopub.status.busy": "2024-10-22T01:01:05.686386Z", + "iopub.status.idle": "2024-10-22T01:01:15.006908Z", + "shell.execute_reply": "2024-10-22T01:01:15.006279Z" } }, "outputs": [ @@ -714,25 +714,25 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W1,W2,U) = P(y|v0,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W0,U) = P(y|v0,W3,W1,W2,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 0.29243078341186574\n", - "Effect estimates: [[0.28844194]\n", - " [0.28652766]\n", - " [0.34885284]\n", + "Mean value: 0.3758135379923914\n", + "Effect estimates: [[0.43224547]\n", + " [0.36184538]\n", + " [0.37576416]\n", " ...\n", - " [0.32772384]\n", - " [0.27842901]\n", - " [0.27403682]]\n", + " [0.32902103]\n", + " [0.38562862]\n", + " [0.36359694]]\n", "\n", - "True causal estimate is 0.2696\n" + "True causal estimate is 0.3519\n" ] } ], @@ -763,10 +763,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:10.836176Z", - "iopub.status.busy": "2024-10-19T23:35:10.835949Z", - "iopub.status.idle": "2024-10-19T23:35:39.881809Z", - "shell.execute_reply": "2024-10-19T23:35:39.880859Z" + "iopub.execute_input": "2024-10-22T01:01:15.009705Z", + "iopub.status.busy": "2024-10-22T01:01:15.009452Z", + "iopub.status.idle": "2024-10-22T01:01:42.721527Z", + "shell.execute_reply": "2024-10-22T01:01:42.720148Z" }, "scrolled": true }, @@ -791,18 +791,18 @@ "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W1+W2 | X1,X0\n", + "b: y~v0+W3+W1+W2+W0 | X0,X1\n", "Target units: Data subset defined by a function\n", "\n", "## Estimate\n", - "Mean value: 10.32704802755174\n", - "Effect estimates: [[ 8.79935732]\n", - " [ 5.64897801]\n", - " [12.56897615]\n", + "Mean value: 12.498979396226137\n", + "Effect estimates: [[19.35420994]\n", + " [14.47770887]\n", + " [ 9.20311738]\n", " ...\n", - " [ 5.66277359]\n", - " [ 9.24276854]\n", - " [ 1.73144656]]\n", + " [ 9.448214 ]\n", + " [ 1.58237698]\n", + " [12.53520843]]\n", "\n" ] } @@ -832,10 +832,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:39.885756Z", - "iopub.status.busy": "2024-10-19T23:35:39.885529Z", - "iopub.status.idle": "2024-10-19T23:35:40.463398Z", - "shell.execute_reply": "2024-10-19T23:35:40.462772Z" + "iopub.execute_input": "2024-10-22T01:01:42.726197Z", + "iopub.status.busy": "2024-10-22T01:01:42.725055Z", + "iopub.status.idle": "2024-10-22T01:01:43.335284Z", + "shell.execute_reply": "2024-10-22T01:01:43.334719Z" } }, "outputs": [ @@ -844,30 +844,30 @@ "output_type": "stream", "text": [ " X0 X1 X2 X3 X4 Z0 Z1 \\\n", - "0 1.194694 0.806424 3.378051 1.066913 -1.762461 0.0 0.042603 \n", - "1 0.210889 -0.866121 -0.036220 2.189422 -0.133892 1.0 0.255470 \n", - "2 0.994021 0.356651 1.642013 0.319378 1.683821 0.0 0.574598 \n", - "3 0.974397 -1.042993 1.734511 -0.220498 -0.545196 1.0 0.228140 \n", - "4 0.723457 -0.935144 1.068031 0.576495 0.740326 0.0 0.078338 \n", + "0 -0.086925 -0.100078 0.571303 -0.828653 1.344718 1.0 0.389962 \n", + "1 0.280182 0.470249 -0.375436 -2.764916 -1.072220 1.0 0.079642 \n", + "2 0.123467 1.309425 0.476237 -0.106417 -0.895539 0.0 0.733598 \n", + "3 2.834800 0.230924 -0.666959 -0.828474 -2.011372 0.0 0.447600 \n", + "4 1.963431 1.069789 -0.394695 -0.459135 -0.751069 0.0 0.744901 \n", "... ... ... ... ... ... ... ... \n", - "9995 0.238972 1.086033 0.053144 0.610416 -1.248808 0.0 0.344754 \n", - "9996 0.894745 -1.155823 0.090509 0.859903 0.165150 0.0 0.163243 \n", - "9997 0.143863 -2.810947 -0.974355 0.820165 -1.555686 0.0 0.614141 \n", - "9998 -1.351503 -1.443587 2.677787 0.725252 -0.779037 1.0 0.206247 \n", - "9999 0.533301 0.557858 1.470046 1.655571 -0.140108 0.0 0.601939 \n", + "9995 0.097454 0.801626 -1.719491 -1.689063 -2.283606 1.0 0.009369 \n", + "9996 0.963483 1.062666 -0.515928 -0.444270 -1.900877 0.0 0.802029 \n", + "9997 2.137832 -0.146762 0.527316 -0.725324 0.121707 0.0 0.712036 \n", + "9998 -0.080499 -0.660111 0.237723 -0.246514 -2.791567 0.0 0.401258 \n", + "9999 -0.788721 1.639797 -1.126375 -1.565304 -0.038917 0.0 0.995005 \n", "\n", " W0 W1 W2 W3 W4 v0 y \n", - "0 1.442070 0.181076 -1.064983 1.689094 0.055662 1 26.705185 \n", - "1 -2.113033 -0.435568 -0.524926 0.086432 -0.242206 1 3.194405 \n", - "2 -0.027573 -0.570038 -0.923142 4.924895 1.241415 1 24.708508 \n", - "3 0.124236 1.725932 0.494322 0.869966 0.520273 1 22.239015 \n", - "4 0.021272 -0.616530 0.169321 0.429555 1.769569 1 20.260806 \n", + "0 -0.244303 0.266005 -0.060768 -1.208970 0.657691 1 14.009190 \n", + "1 -0.259919 1.107581 1.013343 -0.576170 0.785727 1 9.425114 \n", + "2 1.172103 -2.312375 1.224012 0.729851 0.266905 1 20.818196 \n", + "3 -1.500939 0.032816 1.983530 0.401027 2.369935 1 23.159152 \n", + "4 -1.728847 1.182353 -0.503303 0.137425 1.013635 1 15.171032 \n", "... ... ... ... ... ... .. ... \n", - "9995 -0.039791 0.527084 0.293256 2.452390 -0.205648 1 22.632414 \n", - "9996 -0.061491 0.964071 -0.595591 -0.367892 1.588571 0 9.045665 \n", - "9997 0.459640 1.143104 -0.054694 0.328811 0.083701 1 10.594588 \n", - "9998 -0.902153 0.840342 1.524773 0.998653 -1.077402 1 5.035180 \n", - "9999 -0.745454 0.777239 1.253353 1.271281 -1.389755 1 21.136632 \n", + "9995 0.611338 0.775651 0.447902 0.201046 -0.223241 1 -0.536255 \n", + "9996 -1.367978 -1.128647 1.219540 0.502938 -0.306668 1 3.482780 \n", + "9997 0.845712 0.613335 1.259556 0.220525 0.967669 1 34.807461 \n", + "9998 -1.174897 -0.269773 0.150152 -0.064002 0.298968 0 -3.497418 \n", + "9999 -1.950860 -0.652349 0.677681 1.184015 1.058340 1 11.260721 \n", "\n", "[10000 rows x 14 columns]\n" ] @@ -891,10 +891,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:40.465428Z", - "iopub.status.busy": "2024-10-19T23:35:40.465238Z", - "iopub.status.idle": "2024-10-19T23:35:48.817290Z", - "shell.execute_reply": "2024-10-19T23:35:48.816650Z" + "iopub.execute_input": "2024-10-22T01:01:43.337648Z", + "iopub.status.busy": "2024-10-22T01:01:43.337248Z", + "iopub.status.idle": "2024-10-22T01:01:51.504236Z", + "shell.execute_reply": "2024-10-22T01:01:51.503495Z" } }, "outputs": [ @@ -911,25 +911,25 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W4,W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W4,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W0,W1,W2,U) = P(y|v0,W4,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W4,W0,U) = P(y|v0,W3,W1,W2,W4,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+X1+X4+X2+X0+X3+W4+W3+W0+W1+W2\n", + "b: y~v0+X0+X2+X4+X3+X1+W3+W1+W2+W4+W0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 17.090990931755716\n", - "Effect estimates: [[23.15233028]\n", - " [18.7117749 ]\n", - " [20.73704997]\n", + "Mean value: 14.22779046611777\n", + "Effect estimates: [[18.51798921]\n", + " [ 7.55906167]\n", + " [21.53200484]\n", " ...\n", - " [ 8.88761776]\n", - " [14.08657328]\n", - " [30.4898313 ]]\n", + " [30.12004075]\n", + " [-2.74523381]\n", + " [13.90682249]]\n", "\n", - "True causal estimate is 11.867334844408562\n" + "True causal estimate is 8.001323825995941\n" ] } ], @@ -966,10 +966,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:48.819269Z", - "iopub.status.busy": "2024-10-19T23:35:48.819080Z", - "iopub.status.idle": "2024-10-19T23:35:48.868227Z", - "shell.execute_reply": "2024-10-19T23:35:48.867585Z" + "iopub.execute_input": "2024-10-22T01:01:51.506595Z", + "iopub.status.busy": "2024-10-22T01:01:51.506192Z", + "iopub.status.idle": "2024-10-22T01:01:51.557535Z", + "shell.execute_reply": "2024-10-22T01:01:51.556938Z" } }, "outputs": [ @@ -986,23 +986,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W4,W3,W0,W1,W2])\n", + "─────(E[y|W3,W1,W2,W4,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W3,W0,W1,W2,U) = P(y|v0,W4,W3,W0,W1,W2)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W1,W2,W4,W0,U) = P(y|v0,W3,W1,W2,W4,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+X1+X4+X2+X0+X3+W4+W3+W0+W1+W2\n", + "b: y~v0+X0+X2+X4+X3+X1+W3+W1+W2+W4+W0\n", "Target units: Data subset provided as a data frame\n", "\n", "## Estimate\n", - "Mean value: 17.44536912162681\n", - "Effect estimates: [[21.85051043]\n", - " [11.91231284]\n", - " [ 8.88761776]\n", - " [14.08657328]\n", - " [30.4898313 ]]\n", + "Mean value: 10.425956082465873\n", + "Effect estimates: [[ 1.30116632]\n", + " [ 9.54698467]\n", + " [30.12004075]\n", + " [-2.74523381]\n", + " [13.90682249]]\n", "\n", - "True causal estimate is 11.867334844408562\n" + "True causal estimate is 8.001323825995941\n" ] } ], @@ -1037,10 +1037,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:35:48.870098Z", - "iopub.status.busy": "2024-10-19T23:35:48.869911Z", - "iopub.status.idle": "2024-10-19T23:41:54.369614Z", - "shell.execute_reply": "2024-10-19T23:41:54.368475Z" + "iopub.execute_input": "2024-10-22T01:01:51.559574Z", + "iopub.status.busy": "2024-10-22T01:01:51.559374Z", + "iopub.status.idle": "2024-10-22T01:08:03.541530Z", + "shell.execute_reply": "2024-10-22T01:08:03.540517Z" } }, "outputs": [ @@ -1049,9 +1049,9 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:15.045243609692054\n", - "New effect:15.040517118845221\n", - "p value:0.86\n", + "Estimated effect:12.680681626503253\n", + "New effect:12.695571695534525\n", + "p value:0.8400000000000001\n", "\n" ] } @@ -1073,10 +1073,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:41:54.372988Z", - "iopub.status.busy": "2024-10-19T23:41:54.372769Z", - "iopub.status.idle": "2024-10-19T23:41:58.089380Z", - "shell.execute_reply": "2024-10-19T23:41:58.088008Z" + "iopub.execute_input": "2024-10-22T01:08:03.546925Z", + "iopub.status.busy": "2024-10-22T01:08:03.546643Z", + "iopub.status.idle": "2024-10-22T01:08:07.276258Z", + "shell.execute_reply": "2024-10-22T01:08:07.275304Z" } }, "outputs": [ @@ -1085,8 +1085,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:15.045243609692054\n", - "New effect:15.079754922662948\n", + "Estimated effect:12.680681626503253\n", + "New effect:12.78054614324095\n", "\n" ] } @@ -1110,10 +1110,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:41:58.093678Z", - "iopub.status.busy": "2024-10-19T23:41:58.093458Z", - "iopub.status.idle": "2024-10-19T23:42:34.804857Z", - "shell.execute_reply": "2024-10-19T23:42:34.803535Z" + "iopub.execute_input": "2024-10-22T01:08:07.280459Z", + "iopub.status.busy": "2024-10-22T01:08:07.279307Z", + "iopub.status.idle": "2024-10-22T01:08:44.152544Z", + "shell.execute_reply": "2024-10-22T01:08:44.151589Z" } }, "outputs": [ @@ -1122,9 +1122,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:15.045243609692054\n", - "New effect:-0.021354476275475112\n", - "p value:0.4443630005685122\n", + "Estimated effect:12.680681626503253\n", + "New effect:-0.027534656014193824\n", + "p value:0.3319205721239611\n", "\n" ] } @@ -1149,10 +1149,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:42:34.809143Z", - "iopub.status.busy": "2024-10-19T23:42:34.808247Z", - "iopub.status.idle": "2024-10-19T23:43:04.361698Z", - "shell.execute_reply": "2024-10-19T23:43:04.360767Z" + "iopub.execute_input": "2024-10-22T01:08:44.157646Z", + "iopub.status.busy": "2024-10-22T01:08:44.156647Z", + "iopub.status.idle": "2024-10-22T01:09:13.951379Z", + "shell.execute_reply": "2024-10-22T01:09:13.950699Z" } }, "outputs": [ @@ -1161,9 +1161,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:15.045243609692054\n", - "New effect:15.031111389585709\n", - "p value:0.40967889817265557\n", + "Estimated effect:12.680681626503253\n", + "New effect:12.71535645089526\n", + "p value:0.24567680945783077\n", "\n" ] } diff --git a/main/example_notebooks/dowhy-simple-iv-example.html b/main/example_notebooks/dowhy-simple-iv-example.html index 29b64c4c0..39ace709b 100644 --- a/main/example_notebooks/dowhy-simple-iv-example.html +++ b/main/example_notebooks/dowhy-simple-iv-example.html @@ -761,7 +761,7 @@

Using DoWhy to estimate the causal effect of education on future income @@ -784,9 +784,9 @@

Using DoWhy to estimate the causal effect of education on future income 
 Refute: Use a Placebo Treatment
-Estimated effect:4.130869232919477
-New effect:0.019853343491346664
-p value:0.94
+Estimated effect:4.15323575009908
+New effect:0.05746995723853913
+p value:0.8200000000000001
@@ -813,22 +813,22 @@

Using DoWhy to estimate the causal effect of education on future income IV2SLS Regression Results - Dep. Variable: income R-squared: 0.904 + Dep. Variable: income R-squared: 0.903 - Model: IV2SLS Adj. R-squared: 0.904 + Model: IV2SLS Adj. R-squared: 0.902 - Method: Two Stage F-statistic: 1815. + Method: Two Stage F-statistic: 1853. - Least Squares Prob (F-statistic): 8.27e-227 + Least Squares Prob (F-statistic): 9.90e-230 - Date: Sat, 19 Oct 2024 + Date: Tue, 22 Oct 2024 - Time: 23:43:13 + Time: 01:09:22 No. Observations: 1000 @@ -845,24 +845,24 @@

Using DoWhy to estimate the causal effect of education on future income coef std err t P>|t| [0.025 0.975] - Intercept 8.7593 0.893 9.811 0.000 7.007 10.511 + Intercept 8.8450 0.899 9.836 0.000 7.080 10.610 - education 4.1309 0.097 42.604 0.000 3.941 4.321 + education 4.1532 0.096 43.050 0.000 3.964 4.343 - + - + - + - +
Omnibus: 3.915 Durbin-Watson: 2.128Omnibus: 0.661 Durbin-Watson: 1.971
Prob(Omnibus): 0.141 Jarque-Bera (JB): 4.009Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.575
Skew: 0.096 Prob(JB): 0.135Skew: -0.054 Prob(JB): 0.750
Kurtosis: 3.243 Cond. No. 25.2Kurtosis: 3.048 Cond. No. 25.9
diff --git a/main/example_notebooks/dowhy-simple-iv-example.ipynb b/main/example_notebooks/dowhy-simple-iv-example.ipynb index e2e600b1b..6f22b189e 100644 --- a/main/example_notebooks/dowhy-simple-iv-example.ipynb +++ b/main/example_notebooks/dowhy-simple-iv-example.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:07.777808Z", - "iopub.status.busy": "2024-10-19T23:43:07.777363Z", - "iopub.status.idle": "2024-10-19T23:43:07.793487Z", - "shell.execute_reply": "2024-10-19T23:43:07.793040Z" + "iopub.execute_input": "2024-10-22T01:09:17.374210Z", + "iopub.status.busy": "2024-10-22T01:09:17.373760Z", + "iopub.status.idle": "2024-10-22T01:09:17.389787Z", + "shell.execute_reply": "2024-10-22T01:09:17.389120Z" } }, "outputs": [], @@ -29,10 +29,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:07.795271Z", - "iopub.status.busy": "2024-10-19T23:43:07.794940Z", - "iopub.status.idle": "2024-10-19T23:43:09.232379Z", - "shell.execute_reply": "2024-10-19T23:43:09.231755Z" + "iopub.execute_input": "2024-10-22T01:09:17.391971Z", + "iopub.status.busy": "2024-10-22T01:09:17.391777Z", + "iopub.status.idle": "2024-10-22T01:09:18.972429Z", + "shell.execute_reply": "2024-10-22T01:09:18.971756Z" } }, "outputs": [], @@ -60,10 +60,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.234802Z", - "iopub.status.busy": "2024-10-19T23:43:09.234321Z", - "iopub.status.idle": "2024-10-19T23:43:09.266001Z", - "shell.execute_reply": "2024-10-19T23:43:09.265450Z" + "iopub.execute_input": "2024-10-22T01:09:18.974977Z", + "iopub.status.busy": "2024-10-22T01:09:18.974495Z", + "iopub.status.idle": "2024-10-22T01:09:19.008836Z", + "shell.execute_reply": "2024-10-22T01:09:19.008258Z" } }, "outputs": [], @@ -115,10 +115,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.267708Z", - "iopub.status.busy": "2024-10-19T23:43:09.267533Z", - "iopub.status.idle": "2024-10-19T23:43:09.429333Z", - "shell.execute_reply": "2024-10-19T23:43:09.428708Z" + "iopub.execute_input": "2024-10-22T01:09:19.011249Z", + "iopub.status.busy": "2024-10-22T01:09:19.010867Z", + "iopub.status.idle": "2024-10-22T01:09:19.183916Z", + "shell.execute_reply": "2024-10-22T01:09:19.183300Z" } }, "outputs": [ @@ -170,10 +170,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.431588Z", - "iopub.status.busy": "2024-10-19T23:43:09.431365Z", - "iopub.status.idle": "2024-10-19T23:43:09.676994Z", - "shell.execute_reply": "2024-10-19T23:43:09.676345Z" + "iopub.execute_input": "2024-10-22T01:09:19.186291Z", + "iopub.status.busy": "2024-10-22T01:09:19.185856Z", + "iopub.status.idle": "2024-10-22T01:09:19.465176Z", + "shell.execute_reply": "2024-10-22T01:09:19.464594Z" } }, "outputs": [ @@ -215,10 +215,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:09.679113Z", - "iopub.status.busy": "2024-10-19T23:43:09.678645Z", - "iopub.status.idle": "2024-10-19T23:43:12.573679Z", - "shell.execute_reply": "2024-10-19T23:43:12.573058Z" + "iopub.execute_input": "2024-10-22T01:09:19.467292Z", + "iopub.status.busy": "2024-10-22T01:09:19.466901Z", + "iopub.status.idle": "2024-10-22T01:09:22.392442Z", + "shell.execute_reply": "2024-10-22T01:09:22.391683Z" } }, "outputs": [ @@ -260,7 +260,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 4.130869232919477\n", + "Mean value: 4.15323575009908\n", "p-value: [0, 0.001]\n", "\n" ] @@ -289,10 +289,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:12.575552Z", - "iopub.status.busy": "2024-10-19T23:43:12.575363Z", - "iopub.status.idle": "2024-10-19T23:43:13.017205Z", - "shell.execute_reply": "2024-10-19T23:43:13.016653Z" + "iopub.execute_input": "2024-10-22T01:09:22.394557Z", + "iopub.status.busy": "2024-10-22T01:09:22.394104Z", + "iopub.status.idle": "2024-10-22T01:09:22.845322Z", + "shell.execute_reply": "2024-10-22T01:09:22.844724Z" } }, "outputs": [ @@ -301,9 +301,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:4.130869232919477\n", - "New effect:0.019853343491346664\n", - "p value:0.94\n", + "Estimated effect:4.15323575009908\n", + "New effect:0.05746995723853913\n", + "p value:0.8200000000000001\n", "\n" ] } @@ -335,10 +335,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:13.019276Z", - "iopub.status.busy": "2024-10-19T23:43:13.018914Z", - "iopub.status.idle": "2024-10-19T23:43:13.070342Z", - "shell.execute_reply": "2024-10-19T23:43:13.069778Z" + "iopub.execute_input": "2024-10-22T01:09:22.847500Z", + "iopub.status.busy": "2024-10-22T01:09:22.847012Z", + "iopub.status.idle": "2024-10-22T01:09:22.896720Z", + "shell.execute_reply": "2024-10-22T01:09:22.896053Z" } }, "outputs": [ @@ -348,22 +348,22 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -380,24 +380,24 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
IV2SLS Regression Results
Dep. Variable: income R-squared: 0.904Dep. Variable: income R-squared: 0.903
Model: IV2SLS Adj. R-squared: 0.904Model: IV2SLS Adj. R-squared: 0.902
Method: Two Stage F-statistic: 1815.Method: Two Stage F-statistic: 1853.
Least Squares Prob (F-statistic): 8.27e-227 Least Squares Prob (F-statistic): 9.90e-230
Date: Sat, 19 Oct 2024 Date: Tue, 22 Oct 2024
Time: 23:43:13 Time: 01:09:22
No. Observations: 1000 coef std err t P>|t| [0.025 0.975]
Intercept 8.7593 0.893 9.811 0.000 7.007 10.511Intercept 8.8450 0.899 9.836 0.000 7.080 10.610
education 4.1309 0.097 42.604 0.000 3.941 4.321education 4.1532 0.096 43.050 0.000 3.964 4.343
\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Omnibus: 3.915 Durbin-Watson: 2.128Omnibus: 0.661 Durbin-Watson: 1.971
Prob(Omnibus): 0.141 Jarque-Bera (JB): 4.009Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.575
Skew: 0.096 Prob(JB): 0.135Skew: -0.054 Prob(JB): 0.750
Kurtosis: 3.243 Cond. No. 25.2Kurtosis: 3.048 Cond. No. 25.9
" ], @@ -405,12 +405,12 @@ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", - "\\textbf{Dep. Variable:} & income & \\textbf{ R-squared: } & 0.904 \\\\\n", - "\\textbf{Model:} & IV2SLS & \\textbf{ Adj. R-squared: } & 0.904 \\\\\n", - "\\textbf{Method:} & Two Stage & \\textbf{ F-statistic: } & 1815. \\\\\n", - "\\textbf{} & Least Squares & \\textbf{ Prob (F-statistic):} & 8.27e-227 \\\\\n", - "\\textbf{Date:} & Sat, 19 Oct 2024 & \\textbf{ } & \\\\\n", - "\\textbf{Time:} & 23:43:13 & \\textbf{ } & \\\\\n", + "\\textbf{Dep. Variable:} & income & \\textbf{ R-squared: } & 0.903 \\\\\n", + "\\textbf{Model:} & IV2SLS & \\textbf{ Adj. R-squared: } & 0.902 \\\\\n", + "\\textbf{Method:} & Two Stage & \\textbf{ F-statistic: } & 1853. \\\\\n", + "\\textbf{} & Least Squares & \\textbf{ Prob (F-statistic):} & 9.90e-230 \\\\\n", + "\\textbf{Date:} & Tue, 22 Oct 2024 & \\textbf{ } & \\\\\n", + "\\textbf{Time:} & 01:09:22 & \\textbf{ } & \\\\\n", "\\textbf{No. Observations:} & 1000 & \\textbf{ } & \\\\\n", "\\textbf{Df Residuals:} & 998 & \\textbf{ } & \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -419,15 +419,15 @@ "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", - "\\textbf{Intercept} & 8.7593 & 0.893 & 9.811 & 0.000 & 7.007 & 10.511 \\\\\n", - "\\textbf{education} & 4.1309 & 0.097 & 42.604 & 0.000 & 3.941 & 4.321 \\\\\n", + "\\textbf{Intercept} & 8.8450 & 0.899 & 9.836 & 0.000 & 7.080 & 10.610 \\\\\n", + "\\textbf{education} & 4.1532 & 0.096 & 43.050 & 0.000 & 3.964 & 4.343 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 3.915 & \\textbf{ Durbin-Watson: } & 2.128 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.141 & \\textbf{ Jarque-Bera (JB): } & 4.009 \\\\\n", - "\\textbf{Skew:} & 0.096 & \\textbf{ Prob(JB): } & 0.135 \\\\\n", - "\\textbf{Kurtosis:} & 3.243 & \\textbf{ Cond. No. } & 25.2 \\\\\n", + "\\textbf{Omnibus:} & 0.661 & \\textbf{ Durbin-Watson: } & 1.971 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.719 & \\textbf{ Jarque-Bera (JB): } & 0.575 \\\\\n", + "\\textbf{Skew:} & -0.054 & \\textbf{ Prob(JB): } & 0.750 \\\\\n", + "\\textbf{Kurtosis:} & 3.048 & \\textbf{ Cond. No. } & 25.9 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{IV2SLS Regression Results}\n", @@ -438,25 +438,25 @@ "\"\"\"\n", " IV2SLS Regression Results \n", "==============================================================================\n", - "Dep. Variable: income R-squared: 0.904\n", - "Model: IV2SLS Adj. R-squared: 0.904\n", - "Method: Two Stage F-statistic: 1815.\n", - " Least Squares Prob (F-statistic): 8.27e-227\n", - "Date: Sat, 19 Oct 2024 \n", - "Time: 23:43:13 \n", + "Dep. Variable: income R-squared: 0.903\n", + "Model: IV2SLS Adj. R-squared: 0.902\n", + "Method: Two Stage F-statistic: 1853.\n", + " Least Squares Prob (F-statistic): 9.90e-230\n", + "Date: Tue, 22 Oct 2024 \n", + "Time: 01:09:22 \n", "No. Observations: 1000 \n", "Df Residuals: 998 \n", "Df Model: 1 \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "Intercept 8.7593 0.893 9.811 0.000 7.007 10.511\n", - "education 4.1309 0.097 42.604 0.000 3.941 4.321\n", + "Intercept 8.8450 0.899 9.836 0.000 7.080 10.610\n", + "education 4.1532 0.096 43.050 0.000 3.964 4.343\n", "==============================================================================\n", - "Omnibus: 3.915 Durbin-Watson: 2.128\n", - "Prob(Omnibus): 0.141 Jarque-Bera (JB): 4.009\n", - "Skew: 0.096 Prob(JB): 0.135\n", - "Kurtosis: 3.243 Cond. No. 25.2\n", + "Omnibus: 0.661 Durbin-Watson: 1.971\n", + "Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.575\n", + "Skew: -0.054 Prob(JB): 0.750\n", + "Kurtosis: 3.048 Cond. No. 25.9\n", "==============================================================================\n", "\"\"\"" ] diff --git a/main/example_notebooks/dowhy_causal_api.html b/main/example_notebooks/dowhy_causal_api.html index eb48aa999..c19c2ed5f 100644 --- a/main/example_notebooks/dowhy_causal_api.html +++ b/main/example_notebooks/dowhy_causal_api.html @@ -632,33 +632,33 @@

Demo for the DoWhy causal API
-$\displaystyle 4.97996296363016$
+$\displaystyle 4.8534431379023$ 
[9]:
@@ -1074,7 +1074,7 @@ 
Comparing the estimate to Linear Regression
 

-$\displaystyle 0.232608819702524$
+$\displaystyle 0.21798855853523$

Comparing to the estimate from OLS.

@@ -1095,19 +1095,19 @@

Comparing the estimate to Linear Regression OLS Regression Results - Dep. Variable: y R-squared (uncentered): 0.970 + Dep. Variable: y R-squared (uncentered): 0.963 - Model: OLS Adj. R-squared (uncentered): 0.970 + Model: OLS Adj. R-squared (uncentered): 0.963 - Method: Least Squares F-statistic: 1.608e+04 + Method: Least Squares F-statistic: 1.301e+04 - Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00 + Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00 - Time: 23:43:16 Log-Likelihood: -1420.5 + Time: 01:09:26 Log-Likelihood: -1420.6 No. Observations: 1000 AIC: 2845. @@ -1127,24 +1127,24 @@

Comparing the estimate to Linear Regression - Omnibus: 0.410 Durbin-Watson: 2.037 + Omnibus: 0.524 Durbin-Watson: 2.068 - Prob(Omnibus): 0.815 Jarque-Bera (JB): 0.454 + Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.565 - Skew: -0.047 Prob(JB): 0.797 + Skew: 0.054 Prob(JB): 0.754 - Kurtosis: 2.955 Cond. No. 2.22 + Kurtosis: 2.957 Cond. No. 1.74

Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

diff --git a/main/example_notebooks/dowhy_causal_api.ipynb b/main/example_notebooks/dowhy_causal_api.ipynb index 34bbbc96c..b4739cdaf 100644 --- a/main/example_notebooks/dowhy_causal_api.ipynb +++ b/main/example_notebooks/dowhy_causal_api.ipynb @@ -13,10 +13,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:14.815124Z", - "iopub.status.busy": "2024-10-19T23:43:14.814950Z", - "iopub.status.idle": "2024-10-19T23:43:16.221720Z", - "shell.execute_reply": "2024-10-19T23:43:16.221067Z" + "iopub.execute_input": "2024-10-22T01:09:24.919407Z", + "iopub.status.busy": "2024-10-22T01:09:24.918967Z", + "iopub.status.idle": "2024-10-22T01:09:26.468497Z", + "shell.execute_reply": "2024-10-22T01:09:26.467766Z" } }, "outputs": [], @@ -36,10 +36,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.224066Z", - "iopub.status.busy": "2024-10-19T23:43:16.223760Z", - "iopub.status.idle": "2024-10-19T23:43:16.287489Z", - "shell.execute_reply": "2024-10-19T23:43:16.286909Z" + "iopub.execute_input": "2024-10-22T01:09:26.470980Z", + "iopub.status.busy": "2024-10-22T01:09:26.470670Z", + "iopub.status.idle": "2024-10-22T01:09:26.521690Z", + "shell.execute_reply": "2024-10-22T01:09:26.521072Z" } }, "outputs": [ @@ -72,33 +72,33 @@ " \n", " \n", " 0\n", - " 2.189678\n", + " 1.043443\n", " False\n", - " 5.600430\n", + " 3.288370\n", " \n", " \n", " 1\n", - " 1.388721\n", - " False\n", - " 2.093958\n", + " -0.719086\n", + " True\n", + " 3.827861\n", " \n", " \n", " 2\n", - " 0.310346\n", - " True\n", - " 8.289784\n", + " 0.967549\n", + " False\n", + " 2.757559\n", " \n", " \n", " 3\n", - " 1.913677\n", + " -0.676069\n", " False\n", - " 4.320300\n", + " -2.196697\n", " \n", " \n", " 4\n", - " 1.694236\n", - " False\n", - " 3.616701\n", + " 0.240388\n", + " True\n", + " 6.114017\n", " \n", " \n", " ...\n", @@ -108,33 +108,33 @@ " \n", " \n", " 995\n", - " -0.417768\n", - " False\n", - " -0.910359\n", + " -1.173342\n", + " True\n", + " 3.233798\n", " \n", " \n", " 996\n", - " 0.822609\n", + " 1.061021\n", " False\n", - " 0.387237\n", + " 0.766653\n", " \n", " \n", " 997\n", - " -1.059541\n", + " 1.812507\n", " True\n", - " 2.176587\n", + " 8.387034\n", " \n", " \n", " 998\n", - " 2.330745\n", + " 1.389300\n", " False\n", - " 4.708835\n", + " 1.748483\n", " \n", " \n", " 999\n", - " 0.382361\n", + " 1.041059\n", " True\n", - " 6.688796\n", + " 8.247229\n", " \n", " \n", "\n", @@ -143,17 +143,17 @@ ], "text/plain": [ " W0 v0 y\n", - "0 2.189678 False 5.600430\n", - "1 1.388721 False 2.093958\n", - "2 0.310346 True 8.289784\n", - "3 1.913677 False 4.320300\n", - "4 1.694236 False 3.616701\n", + "0 1.043443 False 3.288370\n", + "1 -0.719086 True 3.827861\n", + "2 0.967549 False 2.757559\n", + "3 -0.676069 False -2.196697\n", + "4 0.240388 True 6.114017\n", ".. ... ... ...\n", - "995 -0.417768 False -0.910359\n", - "996 0.822609 False 0.387237\n", - "997 -1.059541 True 2.176587\n", - "998 2.330745 False 4.708835\n", - "999 0.382361 True 6.688796\n", + "995 -1.173342 True 3.233798\n", + "996 1.061021 False 0.766653\n", + "997 1.812507 True 8.387034\n", + "998 1.389300 False 1.748483\n", + "999 1.041059 True 8.247229\n", "\n", "[1000 rows x 3 columns]" ] @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.289451Z", - "iopub.status.busy": "2024-10-19T23:43:16.289091Z", - "iopub.status.idle": "2024-10-19T23:43:16.449650Z", - "shell.execute_reply": "2024-10-19T23:43:16.448998Z" + "iopub.execute_input": "2024-10-22T01:09:26.523796Z", + "iopub.status.busy": "2024-10-22T01:09:26.523412Z", + "iopub.status.idle": "2024-10-22T01:09:26.718046Z", + "shell.execute_reply": "2024-10-22T01:09:26.717483Z" }, "scrolled": true }, @@ -204,7 +204,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -227,10 +227,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.451574Z", - "iopub.status.busy": "2024-10-19T23:43:16.451383Z", - "iopub.status.idle": "2024-10-19T23:43:16.566580Z", - "shell.execute_reply": "2024-10-19T23:43:16.565965Z" + "iopub.execute_input": "2024-10-22T01:09:26.720112Z", + "iopub.status.busy": "2024-10-22T01:09:26.719729Z", + "iopub.status.idle": "2024-10-22T01:09:26.862788Z", + "shell.execute_reply": "2024-10-22T01:09:26.862198Z" } }, "outputs": [ @@ -246,7 +246,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -269,10 +269,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.568494Z", - "iopub.status.busy": "2024-10-19T23:43:16.568308Z", - "iopub.status.idle": "2024-10-19T23:43:16.589229Z", - "shell.execute_reply": "2024-10-19T23:43:16.588612Z" + "iopub.execute_input": "2024-10-22T01:09:26.864886Z", + "iopub.status.busy": "2024-10-22T01:09:26.864502Z", + "iopub.status.idle": "2024-10-22T01:09:26.886262Z", + "shell.execute_reply": "2024-10-22T01:09:26.885688Z" } }, "outputs": [], @@ -295,10 +295,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.591018Z", - "iopub.status.busy": "2024-10-19T23:43:16.590835Z", - "iopub.status.idle": "2024-10-19T23:43:16.599571Z", - "shell.execute_reply": "2024-10-19T23:43:16.599094Z" + "iopub.execute_input": "2024-10-22T01:09:26.888572Z", + "iopub.status.busy": "2024-10-22T01:09:26.888180Z", + "iopub.status.idle": "2024-10-22T01:09:26.897261Z", + "shell.execute_reply": "2024-10-22T01:09:26.896761Z" }, "scrolled": true }, @@ -334,43 +334,43 @@ " \n", " \n", " 0\n", - " -1.019356\n", + " 1.251503\n", " False\n", - " -2.347354\n", - " 0.500862\n", - " 1.996559\n", + " 4.648686\n", + " 0.445524\n", + " 2.244549\n", " \n", " \n", " 1\n", - " 0.721223\n", + " 2.575259\n", " False\n", - " 1.808889\n", - " 0.483995\n", - " 2.066135\n", + " 6.555148\n", + " 0.396166\n", + " 2.524197\n", " \n", " \n", " 2\n", - " 2.625687\n", + " 0.840849\n", " False\n", - " 5.881100\n", - " 0.465584\n", - " 2.147840\n", + " 1.729413\n", + " 0.461105\n", + " 2.168702\n", " \n", " \n", " 3\n", - " 1.135806\n", + " 2.991209\n", " False\n", - " 1.664204\n", - " 0.479982\n", - " 2.083411\n", + " 5.811982\n", + " 0.381035\n", + " 2.624432\n", " \n", " \n", " 4\n", - " 0.240817\n", + " 1.165415\n", " False\n", - " 0.878289\n", - " 0.488649\n", - " 2.046460\n", + " 1.450720\n", + " 0.448782\n", + " 2.228251\n", " \n", " \n", " ...\n", @@ -382,43 +382,43 @@ " \n", " \n", " 995\n", - " 1.062827\n", + " 1.095525\n", " False\n", - " 2.114951\n", - " 0.480688\n", - " 2.080350\n", + " 2.874825\n", + " 0.451431\n", + " 2.215177\n", " \n", " \n", " 996\n", - " 1.936814\n", + " 1.061021\n", " False\n", - " 4.098610\n", - " 0.472236\n", - " 2.117587\n", + " 0.766653\n", + " 0.452740\n", + " 2.208774\n", " \n", " \n", " 997\n", - " 0.820606\n", + " 1.095525\n", " False\n", - " 1.303303\n", - " 0.483033\n", - " 2.070251\n", + " 2.874825\n", + " 0.451431\n", + " 2.215177\n", " \n", " \n", " 998\n", - " -0.382583\n", + " -0.010993\n", " False\n", - " -2.777227\n", - " 0.494690\n", - " 2.021470\n", + " 0.013939\n", + " 0.493636\n", + " 2.025785\n", " \n", " \n", " 999\n", - " 0.093856\n", + " 0.379926\n", " False\n", - " -0.035757\n", - " 0.490072\n", - " 2.040515\n", + " 0.652875\n", + " 0.478684\n", + " 2.089062\n", " \n", " \n", "\n", @@ -427,17 +427,17 @@ ], "text/plain": [ " W0 v0 y propensity_score weight\n", - "0 -1.019356 False -2.347354 0.500862 1.996559\n", - "1 0.721223 False 1.808889 0.483995 2.066135\n", - "2 2.625687 False 5.881100 0.465584 2.147840\n", - "3 1.135806 False 1.664204 0.479982 2.083411\n", - "4 0.240817 False 0.878289 0.488649 2.046460\n", + "0 1.251503 False 4.648686 0.445524 2.244549\n", + "1 2.575259 False 6.555148 0.396166 2.524197\n", + "2 0.840849 False 1.729413 0.461105 2.168702\n", + "3 2.991209 False 5.811982 0.381035 2.624432\n", + "4 1.165415 False 1.450720 0.448782 2.228251\n", ".. ... ... ... ... ...\n", - "995 1.062827 False 2.114951 0.480688 2.080350\n", - "996 1.936814 False 4.098610 0.472236 2.117587\n", - "997 0.820606 False 1.303303 0.483033 2.070251\n", - "998 -0.382583 False -2.777227 0.494690 2.021470\n", - "999 0.093856 False -0.035757 0.490072 2.040515\n", + "995 1.095525 False 2.874825 0.451431 2.215177\n", + "996 1.061021 False 0.766653 0.452740 2.208774\n", + "997 1.095525 False 2.874825 0.451431 2.215177\n", + "998 -0.010993 False 0.013939 0.493636 2.025785\n", + "999 0.379926 False 0.652875 0.478684 2.089062\n", "\n", "[1000 rows x 5 columns]" ] @@ -456,10 +456,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.601442Z", - "iopub.status.busy": "2024-10-19T23:43:16.601057Z", - "iopub.status.idle": "2024-10-19T23:43:16.609814Z", - "shell.execute_reply": "2024-10-19T23:43:16.609209Z" + "iopub.execute_input": "2024-10-22T01:09:26.899268Z", + "iopub.status.busy": "2024-10-22T01:09:26.898893Z", + "iopub.status.idle": "2024-10-22T01:09:26.907993Z", + "shell.execute_reply": "2024-10-22T01:09:26.907484Z" } }, "outputs": [ @@ -494,43 +494,43 @@ " \n", " \n", " 0\n", - " 0.179666\n", + " 0.212877\n", " True\n", - " 7.359646\n", - " 0.510759\n", - " 1.957871\n", + " 3.122988\n", + " 0.514930\n", + " 1.942011\n", " \n", " \n", " 1\n", - " -1.304697\n", + " 0.791773\n", " True\n", - " 2.206922\n", - " 0.496372\n", - " 2.014616\n", + " 9.022931\n", + " 0.537027\n", + " 1.862104\n", " \n", " \n", " 2\n", - " 2.146927\n", + " 0.626781\n", " True\n", - " 11.696896\n", - " 0.529794\n", - " 1.887525\n", + " 6.875759\n", + " 0.530740\n", + " 1.884162\n", " \n", " \n", " 3\n", - " 0.931729\n", + " 0.119495\n", " True\n", - " 7.280872\n", - " 0.518043\n", - " 1.930343\n", + " 6.846478\n", + " 0.511358\n", + " 1.955578\n", " \n", " \n", " 4\n", - " 0.256639\n", + " -0.397422\n", " True\n", - " 5.648205\n", - " 0.511505\n", - " 1.955016\n", + " 4.782325\n", + " 0.491573\n", + " 2.034286\n", " \n", " \n", " ...\n", @@ -542,43 +542,43 @@ " \n", " \n", " 995\n", - " -1.304697\n", + " 0.592020\n", " True\n", - " 2.206922\n", - " 0.496372\n", - " 2.014616\n", + " 7.671610\n", + " 0.529414\n", + " 1.888880\n", " \n", " \n", " 996\n", - " -0.799042\n", + " -0.193414\n", " True\n", - " 4.638932\n", - " 0.501274\n", - " 1.994918\n", + " 4.251908\n", + " 0.499382\n", + " 2.002477\n", " \n", " \n", " 997\n", - " 0.387640\n", + " -0.764507\n", " True\n", - " 7.164544\n", - " 0.512774\n", - " 1.950178\n", + " 5.038481\n", + " 0.477535\n", + " 2.094087\n", " \n", " \n", " 998\n", - " 0.036980\n", + " 0.669331\n", " True\n", - " 5.724190\n", - " 0.509376\n", - " 1.963185\n", + " 5.780822\n", + " 0.532362\n", + " 1.878420\n", " \n", " \n", " 999\n", - " 2.513577\n", + " 0.322424\n", " True\n", - " 10.959737\n", - " 0.533334\n", - " 1.874997\n", + " 5.712695\n", + " 0.519119\n", + " 1.926342\n", " \n", " \n", "\n", @@ -586,18 +586,18 @@ "" ], "text/plain": [ - " W0 v0 y propensity_score weight\n", - "0 0.179666 True 7.359646 0.510759 1.957871\n", - "1 -1.304697 True 2.206922 0.496372 2.014616\n", - "2 2.146927 True 11.696896 0.529794 1.887525\n", - "3 0.931729 True 7.280872 0.518043 1.930343\n", - "4 0.256639 True 5.648205 0.511505 1.955016\n", - ".. ... ... ... ... ...\n", - "995 -1.304697 True 2.206922 0.496372 2.014616\n", - "996 -0.799042 True 4.638932 0.501274 1.994918\n", - "997 0.387640 True 7.164544 0.512774 1.950178\n", - "998 0.036980 True 5.724190 0.509376 1.963185\n", - "999 2.513577 True 10.959737 0.533334 1.874997\n", + " W0 v0 y propensity_score weight\n", + "0 0.212877 True 3.122988 0.514930 1.942011\n", + "1 0.791773 True 9.022931 0.537027 1.862104\n", + "2 0.626781 True 6.875759 0.530740 1.884162\n", + "3 0.119495 True 6.846478 0.511358 1.955578\n", + "4 -0.397422 True 4.782325 0.491573 2.034286\n", + ".. ... ... ... ... ...\n", + "995 0.592020 True 7.671610 0.529414 1.888880\n", + "996 -0.193414 True 4.251908 0.499382 2.002477\n", + "997 -0.764507 True 5.038481 0.477535 2.094087\n", + "998 0.669331 True 5.780822 0.532362 1.878420\n", + "999 0.322424 True 5.712695 0.519119 1.926342\n", "\n", "[1000 rows x 5 columns]" ] @@ -624,21 +624,21 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.611767Z", - "iopub.status.busy": "2024-10-19T23:43:16.611334Z", - "iopub.status.idle": "2024-10-19T23:43:16.657554Z", - "shell.execute_reply": "2024-10-19T23:43:16.656955Z" + "iopub.execute_input": "2024-10-22T01:09:26.909808Z", + "iopub.status.busy": "2024-10-22T01:09:26.909611Z", + "iopub.status.idle": "2024-10-22T01:09:26.958108Z", + "shell.execute_reply": "2024-10-22T01:09:26.957524Z" } }, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 4.97996296363016$" + "$\\displaystyle 4.8534431379023$" ], "text/plain": [ - "4.979962963630156" + "4.8534431379023" ] }, "execution_count": 8, @@ -655,21 +655,21 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.659442Z", - "iopub.status.busy": "2024-10-19T23:43:16.659091Z", - "iopub.status.idle": "2024-10-19T23:43:16.674816Z", - "shell.execute_reply": "2024-10-19T23:43:16.674338Z" + "iopub.execute_input": "2024-10-22T01:09:26.960001Z", + "iopub.status.busy": "2024-10-22T01:09:26.959769Z", + "iopub.status.idle": "2024-10-22T01:09:26.977067Z", + "shell.execute_reply": "2024-10-22T01:09:26.976461Z" } }, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 0.232608819702524$" + "$\\displaystyle 0.21798855853523$" ], "text/plain": [ - "0.23260881970252437" + "0.21798855853522991" ] }, "execution_count": 9, @@ -693,10 +693,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:16.676669Z", - "iopub.status.busy": "2024-10-19T23:43:16.676326Z", - "iopub.status.idle": "2024-10-19T23:43:16.769138Z", - "shell.execute_reply": "2024-10-19T23:43:16.768581Z" + "iopub.execute_input": "2024-10-22T01:09:26.979075Z", + "iopub.status.busy": "2024-10-22T01:09:26.978875Z", + "iopub.status.idle": "2024-10-22T01:09:26.997555Z", + "shell.execute_reply": "2024-10-22T01:09:26.996869Z" } }, "outputs": [ @@ -706,19 +706,19 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -738,24 +738,24 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.970Dep. Variable: y R-squared (uncentered): 0.963
Model: OLS Adj. R-squared (uncentered): 0.970Model: OLS Adj. R-squared (uncentered): 0.963
Method: Least Squares F-statistic: 1.608e+04Method: Least Squares F-statistic: 1.301e+04
Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00
Time: 23:43:16 Log-Likelihood: -1420.5Time: 01:09:26 Log-Likelihood: -1420.6
No. Observations: 1000 AIC: 2845. coef std err t P>|t| [0.025 0.975]
x1 2.3400 0.028 84.977 0.000 2.286 2.394x1 2.3089 0.031 74.423 0.000 2.248 2.370
x2 4.9894 0.050 100.109 0.000 4.892 5.087x2 5.0562 0.047 108.194 0.000 4.964 5.148
\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
Omnibus: 0.410 Durbin-Watson: 2.037Omnibus: 0.524 Durbin-Watson: 2.068
Prob(Omnibus): 0.815 Jarque-Bera (JB): 0.454Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.565
Skew: -0.047 Prob(JB): 0.797Skew: 0.054 Prob(JB): 0.754
Kurtosis: 2.955 Cond. No. 2.22Kurtosis: 2.957 Cond. No. 1.74


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], @@ -763,11 +763,11 @@ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", - "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.970 \\\\\n", - "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.970 \\\\\n", - "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 1.608e+04 \\\\\n", - "\\textbf{Date:} & Sat, 19 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", - "\\textbf{Time:} & 23:43:16 & \\textbf{ Log-Likelihood: } & -1420.5 \\\\\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.963 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.963 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 1.301e+04 \\\\\n", + "\\textbf{Date:} & Tue, 22 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 01:09:26 & \\textbf{ Log-Likelihood: } & -1420.6 \\\\\n", "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 2845. \\\\\n", "\\textbf{Df Residuals:} & 998 & \\textbf{ BIC: } & 2855. \\\\\n", "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n", @@ -777,15 +777,15 @@ "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", - "\\textbf{x1} & 2.3400 & 0.028 & 84.977 & 0.000 & 2.286 & 2.394 \\\\\n", - "\\textbf{x2} & 4.9894 & 0.050 & 100.109 & 0.000 & 4.892 & 5.087 \\\\\n", + "\\textbf{x1} & 2.3089 & 0.031 & 74.423 & 0.000 & 2.248 & 2.370 \\\\\n", + "\\textbf{x2} & 5.0562 & 0.047 & 108.194 & 0.000 & 4.964 & 5.148 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 0.410 & \\textbf{ Durbin-Watson: } & 2.037 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.815 & \\textbf{ Jarque-Bera (JB): } & 0.454 \\\\\n", - "\\textbf{Skew:} & -0.047 & \\textbf{ Prob(JB): } & 0.797 \\\\\n", - "\\textbf{Kurtosis:} & 2.955 & \\textbf{ Cond. No. } & 2.22 \\\\\n", + "\\textbf{Omnibus:} & 0.524 & \\textbf{ Durbin-Watson: } & 2.068 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.770 & \\textbf{ Jarque-Bera (JB): } & 0.565 \\\\\n", + "\\textbf{Skew:} & 0.054 & \\textbf{ Prob(JB): } & 0.754 \\\\\n", + "\\textbf{Kurtosis:} & 2.957 & \\textbf{ Cond. No. } & 1.74 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{OLS Regression Results}\n", @@ -800,11 +800,11 @@ "\"\"\"\n", " OLS Regression Results \n", "=======================================================================================\n", - "Dep. Variable: y R-squared (uncentered): 0.970\n", - "Model: OLS Adj. R-squared (uncentered): 0.970\n", - "Method: Least Squares F-statistic: 1.608e+04\n", - "Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00\n", - "Time: 23:43:16 Log-Likelihood: -1420.5\n", + "Dep. Variable: y R-squared (uncentered): 0.963\n", + "Model: OLS Adj. R-squared (uncentered): 0.963\n", + "Method: Least Squares F-statistic: 1.301e+04\n", + "Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 01:09:26 Log-Likelihood: -1420.6\n", "No. Observations: 1000 AIC: 2845.\n", "Df Residuals: 998 BIC: 2855.\n", "Df Model: 2 \n", @@ -812,13 +812,13 @@ "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "x1 2.3400 0.028 84.977 0.000 2.286 2.394\n", - "x2 4.9894 0.050 100.109 0.000 4.892 5.087\n", + "x1 2.3089 0.031 74.423 0.000 2.248 2.370\n", + "x2 5.0562 0.047 108.194 0.000 4.964 5.148\n", "==============================================================================\n", - "Omnibus: 0.410 Durbin-Watson: 2.037\n", - "Prob(Omnibus): 0.815 Jarque-Bera (JB): 0.454\n", - "Skew: -0.047 Prob(JB): 0.797\n", - "Kurtosis: 2.955 Cond. No. 2.22\n", + "Omnibus: 0.524 Durbin-Watson: 2.068\n", + "Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.565\n", + "Skew: 0.054 Prob(JB): 0.754\n", + "Kurtosis: 2.957 Cond. No. 1.74\n", "==============================================================================\n", "\n", "Notes:\n", diff --git a/main/example_notebooks/dowhy_causal_discovery_example.html b/main/example_notebooks/dowhy_causal_discovery_example.html index 903143633..51280afe3 100644 --- a/main/example_notebooks/dowhy_causal_discovery_example.html +++ b/main/example_notebooks/dowhy_causal_discovery_example.html @@ -759,7 +759,7 @@

Causal Discovery with causal-learn
-
+
@@ -951,7 +951,7 @@

Causal Discovery with causal-learn

-
+
@@ -1075,7 +1075,7 @@

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b/main/example_notebooks/dowhy_causal_discovery_example.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:18.347869Z", - "iopub.status.busy": "2024-10-19T23:43:18.347667Z", - "iopub.status.idle": "2024-10-19T23:43:19.776909Z", - "shell.execute_reply": "2024-10-19T23:43:19.776292Z" + "iopub.execute_input": "2024-10-22T01:09:28.857104Z", + "iopub.status.busy": "2024-10-22T01:09:28.856908Z", + "iopub.status.idle": "2024-10-22T01:09:30.487298Z", + "shell.execute_reply": "2024-10-22T01:09:30.486670Z" } }, "outputs": [], @@ -47,10 +47,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:19.779448Z", - "iopub.status.busy": "2024-10-19T23:43:19.778907Z", - "iopub.status.idle": "2024-10-19T23:43:19.784107Z", - "shell.execute_reply": "2024-10-19T23:43:19.783474Z" + "iopub.execute_input": "2024-10-22T01:09:30.489881Z", + "iopub.status.busy": "2024-10-22T01:09:30.489379Z", + "iopub.status.idle": "2024-10-22T01:09:30.494723Z", + "shell.execute_reply": "2024-10-22T01:09:30.494223Z" } }, "outputs": [], @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:19.786095Z", - "iopub.status.busy": "2024-10-19T23:43:19.785639Z", - "iopub.status.idle": "2024-10-19T23:43:19.798955Z", - "shell.execute_reply": "2024-10-19T23:43:19.798476Z" + "iopub.execute_input": "2024-10-22T01:09:30.496770Z", + "iopub.status.busy": "2024-10-22T01:09:30.496362Z", + "iopub.status.idle": "2024-10-22T01:09:30.510807Z", + "shell.execute_reply": "2024-10-22T01:09:30.510249Z" } }, "outputs": [ @@ -239,17 +239,17 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:43:19.800818Z", - "iopub.status.busy": "2024-10-19T23:43:19.800442Z", - "iopub.status.idle": "2024-10-19T23:43:20.126232Z", - "shell.execute_reply": "2024-10-19T23:43:20.125536Z" + "iopub.execute_input": 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"HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "fb32c0efc4b14f199f33a5e8a59f7c5d": { + "f75cd1577ba549008b2d10b588466730": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1991,7 +1984,7 @@ "width": null } }, - "ffea50907537462eaed0e70212a4367c": { + "f940bcbe1b9340078cbca1f9598e2f82": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/main/example_notebooks/dowhy_confounder_example.html b/main/example_notebooks/dowhy_confounder_example.html index 16dc08de1..4a5d55006 100644 --- a/main/example_notebooks/dowhy_confounder_example.html +++ b/main/example_notebooks/dowhy_confounder_example.html @@ -616,11 +616,11 @@

Let’s create a mystery dataset for which we need to determine whether ther 
    Treatment    Outcome        w0
-0   6.176718  12.620508  0.327005
-1   8.459975  16.672067  2.160446
-2   1.045561   2.423261 -4.774266
-3   6.610747  13.749108  1.021957
-4   5.539514  10.965522 -0.768080
+0   2.429087   4.844044 -3.608025
+1   5.686840  11.918230 -0.140240
+2   9.558291  19.287474  3.546276
+3   9.146309  18.691248  3.308028
+4   2.140224   4.728417 -3.769274

@@ -744,7 +744,7 @@

STEP 3: Estimate the causal effect 
-Causal Estimate is 0.9829405717720379
+Causal Estimate is 0.005268900430303702

@@ -771,8 +771,8 @@

Checking if the estimate is correct 
-DoWhy estimate is 0.9829405717720379
-Actual true causal effect was 1
+DoWhy estimate is 0.005268900430303702
+Actual true causal effect was 0

@@ -797,9 +797,9 @@

Adding a random common cause variable 
 Refute: Add a random common cause
-Estimated effect:0.9829405717720379
-New effect:0.9829458586586253
-p value:0.9199999999999999
+Estimated effect:0.005268900430303702
+New effect:0.005277215320695472
+p value:0.92
@@ -822,9 +822,9 @@

Replacing treatment with a random (placebo) variable 
 Refute: Use a Placebo Treatment
-Estimated effect:0.9829405717720379
-New effect:6.157230339285392e-05
-p value:1.0
+Estimated effect:0.005268900430303702
+New effect:-7.902808168136487e-06
+p value:0.96
@@ -847,9 +847,9 @@

Removing a random subset of the data 
 Refute: Use a subset of data
-Estimated effect:0.9829405717720379
-New effect:0.9825105635614834
-p value:0.96
+Estimated effect:0.005268900430303702
+New effect:0.004281457098964161
+p value:0.72
diff --git a/main/example_notebooks/dowhy_confounder_example.ipynb b/main/example_notebooks/dowhy_confounder_example.ipynb index 8c9f32c40..cdc3b8b94 100644 --- a/main/example_notebooks/dowhy_confounder_example.ipynb +++ b/main/example_notebooks/dowhy_confounder_example.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:14.683729Z", - "iopub.status.busy": "2024-10-19T23:48:14.683547Z", - "iopub.status.idle": "2024-10-19T23:48:16.122205Z", - "shell.execute_reply": "2024-10-19T23:48:16.121531Z" + "iopub.execute_input": "2024-10-22T01:14:25.010514Z", + "iopub.status.busy": "2024-10-22T01:14:25.010307Z", + "iopub.status.idle": "2024-10-22T01:14:26.605358Z", + "shell.execute_reply": "2024-10-22T01:14:26.604652Z" } }, "outputs": [], @@ -61,10 +61,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.124553Z", - "iopub.status.busy": "2024-10-19T23:48:16.124256Z", - "iopub.status.idle": "2024-10-19T23:48:16.132491Z", - "shell.execute_reply": "2024-10-19T23:48:16.132002Z" + "iopub.execute_input": "2024-10-22T01:14:26.607987Z", + "iopub.status.busy": "2024-10-22T01:14:26.607451Z", + "iopub.status.idle": "2024-10-22T01:14:26.615962Z", + "shell.execute_reply": "2024-10-22T01:14:26.615398Z" } }, "outputs": [ @@ -73,11 +73,11 @@ "output_type": "stream", "text": [ " Treatment Outcome w0\n", - "0 6.176718 12.620508 0.327005\n", - "1 8.459975 16.672067 2.160446\n", - "2 1.045561 2.423261 -4.774266\n", - "3 6.610747 13.749108 1.021957\n", - "4 5.539514 10.965522 -0.768080\n" + "0 2.429087 4.844044 -3.608025\n", + "1 5.686840 11.918230 -0.140240\n", + "2 9.558291 19.287474 3.546276\n", + "3 9.146309 18.691248 3.308028\n", + "4 2.140224 4.728417 -3.769274\n" ] } ], @@ -96,16 +96,16 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.134416Z", - "iopub.status.busy": "2024-10-19T23:48:16.133997Z", - "iopub.status.idle": "2024-10-19T23:48:16.496685Z", - "shell.execute_reply": "2024-10-19T23:48:16.496045Z" + "iopub.execute_input": "2024-10-22T01:14:26.617737Z", + "iopub.status.busy": "2024-10-22T01:14:26.617556Z", + "iopub.status.idle": "2024-10-22T01:14:26.990732Z", + "shell.execute_reply": "2024-10-22T01:14:26.989804Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -133,10 +133,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.498832Z", - "iopub.status.busy": "2024-10-19T23:48:16.498482Z", - "iopub.status.idle": "2024-10-19T23:48:16.624218Z", - "shell.execute_reply": "2024-10-19T23:48:16.623648Z" + "iopub.execute_input": "2024-10-22T01:14:26.993209Z", + "iopub.status.busy": "2024-10-22T01:14:26.992686Z", + "iopub.status.idle": "2024-10-22T01:14:27.125229Z", + "shell.execute_reply": "2024-10-22T01:14:27.124625Z" } }, "outputs": [ @@ -173,10 +173,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.626347Z", - "iopub.status.busy": "2024-10-19T23:48:16.626127Z", - "iopub.status.idle": "2024-10-19T23:48:16.631961Z", - "shell.execute_reply": "2024-10-19T23:48:16.631398Z" + "iopub.execute_input": "2024-10-22T01:14:27.127797Z", + "iopub.status.busy": "2024-10-22T01:14:27.127445Z", + "iopub.status.idle": "2024-10-22T01:14:27.132263Z", + "shell.execute_reply": "2024-10-22T01:14:27.131784Z" } }, "outputs": [ @@ -209,10 +209,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.634023Z", - "iopub.status.busy": "2024-10-19T23:48:16.633808Z", - "iopub.status.idle": "2024-10-19T23:48:16.641632Z", - "shell.execute_reply": "2024-10-19T23:48:16.641029Z" + "iopub.execute_input": "2024-10-22T01:14:27.134465Z", + "iopub.status.busy": "2024-10-22T01:14:27.134068Z", + "iopub.status.idle": "2024-10-22T01:14:27.141795Z", + "shell.execute_reply": "2024-10-22T01:14:27.141255Z" } }, "outputs": [ @@ -262,10 +262,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:16.643917Z", - "iopub.status.busy": "2024-10-19T23:48:16.643686Z", - "iopub.status.idle": "2024-10-19T23:48:17.055305Z", - "shell.execute_reply": "2024-10-19T23:48:17.054636Z" + "iopub.execute_input": "2024-10-22T01:14:27.143767Z", + "iopub.status.busy": "2024-10-22T01:14:27.143380Z", + "iopub.status.idle": "2024-10-22T01:14:27.548052Z", + "shell.execute_reply": "2024-10-22T01:14:27.547496Z" } }, "outputs": [ @@ -273,12 +273,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 0.9829405717720379\n" + "Causal Estimate is 0.005268900430303702\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -308,10 +308,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:17.057465Z", - "iopub.status.busy": "2024-10-19T23:48:17.057096Z", - "iopub.status.idle": "2024-10-19T23:48:17.060111Z", - "shell.execute_reply": "2024-10-19T23:48:17.059577Z" + "iopub.execute_input": "2024-10-22T01:14:27.550098Z", + "iopub.status.busy": "2024-10-22T01:14:27.549724Z", + "iopub.status.idle": "2024-10-22T01:14:27.552794Z", + "shell.execute_reply": "2024-10-22T01:14:27.552286Z" } }, "outputs": [ @@ -319,8 +319,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "DoWhy estimate is 0.9829405717720379\n", - "Actual true causal effect was 1\n" + "DoWhy estimate is 0.005268900430303702\n", + "Actual true causal effect was 0\n" ] } ], @@ -350,10 +350,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:17.062009Z", - "iopub.status.busy": "2024-10-19T23:48:17.061645Z", - "iopub.status.idle": "2024-10-19T23:48:18.791934Z", - "shell.execute_reply": "2024-10-19T23:48:18.790937Z" + "iopub.execute_input": "2024-10-22T01:14:27.554439Z", + "iopub.status.busy": "2024-10-22T01:14:27.554256Z", + "iopub.status.idle": "2024-10-22T01:14:29.321078Z", + "shell.execute_reply": "2024-10-22T01:14:29.320384Z" } }, "outputs": [ @@ -362,9 +362,9 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:0.9829405717720379\n", - "New effect:0.9829458586586253\n", - "p value:0.9199999999999999\n", + "Estimated effect:0.005268900430303702\n", + "New effect:0.005277215320695472\n", + "p value:0.92\n", "\n" ] } @@ -386,10 +386,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:18.795093Z", - "iopub.status.busy": "2024-10-19T23:48:18.794862Z", - "iopub.status.idle": "2024-10-19T23:48:20.892051Z", - "shell.execute_reply": "2024-10-19T23:48:20.891473Z" + "iopub.execute_input": "2024-10-22T01:14:29.323363Z", + "iopub.status.busy": "2024-10-22T01:14:29.323137Z", + "iopub.status.idle": "2024-10-22T01:14:31.473157Z", + "shell.execute_reply": "2024-10-22T01:14:31.472585Z" } }, "outputs": [ @@ -398,9 +398,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:0.9829405717720379\n", - "New effect:6.157230339285392e-05\n", - "p value:1.0\n", + "Estimated effect:0.005268900430303702\n", + "New effect:-7.902808168136487e-06\n", + "p value:0.96\n", "\n" ] } @@ -423,10 +423,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:20.894955Z", - "iopub.status.busy": "2024-10-19T23:48:20.894572Z", - "iopub.status.idle": "2024-10-19T23:48:22.816763Z", - "shell.execute_reply": "2024-10-19T23:48:22.815939Z" + "iopub.execute_input": "2024-10-22T01:14:31.476564Z", + "iopub.status.busy": "2024-10-22T01:14:31.475763Z", + "iopub.status.idle": "2024-10-22T01:14:33.409987Z", + "shell.execute_reply": "2024-10-22T01:14:33.409428Z" } }, "outputs": [ @@ -435,9 +435,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:0.9829405717720379\n", - "New effect:0.9825105635614834\n", - "p value:0.96\n", + "Estimated effect:0.005268900430303702\n", + "New effect:0.004281457098964161\n", + "p value:0.72\n", "\n" ] } diff --git a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html index a3dbc4c25..bd4a2ddc0 100644 --- a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html +++ b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.html @@ -679,43 +679,43 @@

Create the Dataset @@ -880,8 +880,8 @@

Using a Randomly Generated Outcome
 Refute: Use a Dummy Outcome
 Estimated effect:0
-New effect:-8.325535477770815e-05
-p value:0.94
+New effect:-8.194888015547049e-05
+p value:0.92
@@ -925,7 +925,7 @@

Using a Function that Generates the Outcome from the Confounders
 Refute: Use a Dummy Outcome
 Estimated effect:0
-New effect:-0.00011766505684934831
+New effect:-4.858144270064502e-05
 p value:0.98
diff --git a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb index 8cb517d03..2b9355b48 100644 --- a/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb +++ b/main/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb @@ -20,10 +20,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:25.233283Z", - "iopub.status.busy": "2024-10-19T23:48:25.232817Z", - "iopub.status.idle": "2024-10-19T23:48:26.663448Z", - "shell.execute_reply": "2024-10-19T23:48:26.662824Z" + "iopub.execute_input": "2024-10-22T01:14:35.598225Z", + "iopub.status.busy": "2024-10-22T01:14:35.598043Z", + "iopub.status.idle": "2024-10-22T01:14:37.056273Z", + "shell.execute_reply": "2024-10-22T01:14:37.055667Z" } }, "outputs": [], @@ -70,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.665807Z", - "iopub.status.busy": "2024-10-19T23:48:26.665486Z", - "iopub.status.idle": "2024-10-19T23:48:26.715605Z", - "shell.execute_reply": "2024-10-19T23:48:26.714517Z" + "iopub.execute_input": "2024-10-22T01:14:37.058757Z", + "iopub.status.busy": "2024-10-22T01:14:37.058446Z", + "iopub.status.idle": "2024-10-22T01:14:37.108668Z", + "shell.execute_reply": "2024-10-22T01:14:37.107822Z" } }, "outputs": [ @@ -108,55 +108,55 @@ " \n", " \n", " 0\n", - " 1.0\n", - " -0.406629\n", - " 0.657206\n", - " 13.601381\n", - " 135.071109\n", + " 0.0\n", + " -1.076630\n", + " 0.649139\n", + " -1.653619\n", + " -18.700173\n", " \n", " \n", " 1\n", - " 1.0\n", - " -0.339279\n", - " 0.137269\n", - " 9.438659\n", - " 93.251089\n", + " 0.0\n", + " -0.904553\n", + " 1.344766\n", + " 1.596628\n", + " 17.062082\n", " \n", " \n", " 2\n", - " 1.0\n", - " 1.975973\n", - " 1.463278\n", - " 22.909722\n", - " 237.632926\n", + " 0.0\n", + " -0.203244\n", + " -0.922881\n", + " -2.108656\n", + " -25.281342\n", " \n", " \n", " 3\n", - " 1.0\n", - " 2.179468\n", - " 2.888625\n", - " 27.138411\n", - " 281.855087\n", + " 0.0\n", + " 0.402820\n", + " 1.054545\n", + " 0.785004\n", + " 13.328140\n", " \n", " \n", " 4\n", - " 1.0\n", - " -0.503646\n", - " 0.315278\n", - " 11.930401\n", - " 117.719475\n", + " 0.0\n", + " -0.584076\n", + " 0.160731\n", + " 0.221487\n", + " 0.354862\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Z0 W0 W1 v0 y\n", - "0 1.0 -0.406629 0.657206 13.601381 135.071109\n", - "1 1.0 -0.339279 0.137269 9.438659 93.251089\n", - "2 1.0 1.975973 1.463278 22.909722 237.632926\n", - "3 1.0 2.179468 2.888625 27.138411 281.855087\n", - "4 1.0 -0.503646 0.315278 11.930401 117.719475" + " Z0 W0 W1 v0 y\n", + "0 0.0 -1.076630 0.649139 -1.653619 -18.700173\n", + "1 0.0 -0.904553 1.344766 1.596628 17.062082\n", + "2 0.0 -0.203244 -0.922881 -2.108656 -25.281342\n", + "3 0.0 0.402820 1.054545 0.785004 13.328140\n", + "4 0.0 -0.584076 0.160731 0.221487 0.354862" ] }, "execution_count": 2, @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.717936Z", - "iopub.status.busy": "2024-10-19T23:48:26.717696Z", - "iopub.status.idle": "2024-10-19T23:48:26.724036Z", - "shell.execute_reply": "2024-10-19T23:48:26.723465Z" + "iopub.execute_input": "2024-10-22T01:14:37.111261Z", + "iopub.status.busy": "2024-10-22T01:14:37.110779Z", + "iopub.status.idle": "2024-10-22T01:14:37.117207Z", + "shell.execute_reply": "2024-10-22T01:14:37.116683Z" } }, "outputs": [], @@ -217,10 +217,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.727080Z", - "iopub.status.busy": "2024-10-19T23:48:26.726291Z", - "iopub.status.idle": "2024-10-19T23:48:26.891726Z", - "shell.execute_reply": "2024-10-19T23:48:26.891091Z" + "iopub.execute_input": "2024-10-22T01:14:37.119586Z", + "iopub.status.busy": "2024-10-22T01:14:37.119110Z", + "iopub.status.idle": "2024-10-22T01:14:37.269856Z", + "shell.execute_reply": "2024-10-22T01:14:37.269172Z" } }, "outputs": [ @@ -260,10 +260,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:26.893975Z", - "iopub.status.busy": "2024-10-19T23:48:26.893752Z", - "iopub.status.idle": "2024-10-19T23:48:27.037212Z", - "shell.execute_reply": "2024-10-19T23:48:27.036607Z" + "iopub.execute_input": "2024-10-22T01:14:37.272091Z", + "iopub.status.busy": "2024-10-22T01:14:37.271860Z", + "iopub.status.idle": "2024-10-22T01:14:37.427998Z", + "shell.execute_reply": "2024-10-22T01:14:37.427308Z" } }, "outputs": [ @@ -315,10 +315,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:27.039321Z", - "iopub.status.busy": "2024-10-19T23:48:27.038949Z", - "iopub.status.idle": "2024-10-19T23:48:27.052683Z", - "shell.execute_reply": "2024-10-19T23:48:27.052196Z" + "iopub.execute_input": "2024-10-22T01:14:37.429896Z", + "iopub.status.busy": "2024-10-22T01:14:37.429701Z", + "iopub.status.idle": "2024-10-22T01:14:37.444297Z", + "shell.execute_reply": "2024-10-22T01:14:37.443665Z" }, "scrolled": true }, @@ -361,7 +361,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.997204381724089\n", + "Mean value: 10.005290320123438\n", "\n" ] } @@ -393,10 +393,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:27.054639Z", - "iopub.status.busy": "2024-10-19T23:48:27.054277Z", - "iopub.status.idle": "2024-10-19T23:48:28.321053Z", - "shell.execute_reply": "2024-10-19T23:48:28.320405Z" + "iopub.execute_input": "2024-10-22T01:14:37.446334Z", + "iopub.status.busy": "2024-10-22T01:14:37.446141Z", + "iopub.status.idle": "2024-10-22T01:14:38.839019Z", + "shell.execute_reply": "2024-10-22T01:14:38.838376Z" } }, "outputs": [ @@ -406,8 +406,8 @@ "text": [ "Refute: Use a Dummy Outcome\n", "Estimated effect:0\n", - "New effect:-8.325535477770815e-05\n", - "p value:0.94\n", + "New effect:-8.194888015547049e-05\n", + "p value:0.92\n", "\n" ] } @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:28.323207Z", - "iopub.status.busy": "2024-10-19T23:48:28.322845Z", - "iopub.status.idle": "2024-10-19T23:48:28.326154Z", - "shell.execute_reply": "2024-10-19T23:48:28.325545Z" + "iopub.execute_input": "2024-10-22T01:14:38.841006Z", + "iopub.status.busy": "2024-10-22T01:14:38.840807Z", + "iopub.status.idle": "2024-10-22T01:14:38.844219Z", + "shell.execute_reply": "2024-10-22T01:14:38.843753Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:28.328066Z", - "iopub.status.busy": "2024-10-19T23:48:28.327695Z", - "iopub.status.idle": "2024-10-19T23:48:29.536261Z", - "shell.execute_reply": "2024-10-19T23:48:29.535626Z" + "iopub.execute_input": "2024-10-22T01:14:38.846078Z", + "iopub.status.busy": "2024-10-22T01:14:38.845748Z", + "iopub.status.idle": "2024-10-22T01:14:40.082710Z", + "shell.execute_reply": "2024-10-22T01:14:40.082113Z" } }, "outputs": [ @@ -490,7 +490,7 @@ "text": [ "Refute: Use a Dummy Outcome\n", "Estimated effect:0\n", - "New effect:-0.00011766505684934831\n", + "New effect:-4.858144270064502e-05\n", "p value:0.98\n", "\n" ] diff --git a/main/example_notebooks/dowhy_efficient_backdoor_example.html b/main/example_notebooks/dowhy_efficient_backdoor_example.html index fa5647f79..0352cb1f6 100644 --- a/main/example_notebooks/dowhy_efficient_backdoor_example.html +++ b/main/example_notebooks/dowhy_efficient_backdoor_example.html @@ -706,13 +706,13 @@

The design of an observational studyDoWhy: Different estimation methods for causal inference 9995 0.0 - 0.480650 - -2.274500 - -1.333670 - -0.846298 - -0.679329 - 0.319555 + 0.197731 + 0.557255 + 0.325835 + -1.212832 + -1.164383 + -1.807983 False - -17.951348 + -9.234784 9996 - 0.0 - 0.153102 - 0.333702 - -0.345303 - 0.553626 - -0.964486 - 1.010486 + 1.0 + 0.510984 + 0.796696 + 2.164684 + -2.138929 + -0.917557 + 0.373379 True - 13.289393 + 16.813862 9997 0.0 - 0.315476 - 0.192321 - -0.560291 - -0.133407 - 0.101016 - 1.491129 - True - 12.239693 + 0.440900 + 0.927370 + 0.256726 + -2.347163 + -0.547954 + -0.784016 + False + -5.154270 9998 - 0.0 - 0.729459 - 0.781247 - -1.315457 - -0.489871 - -0.292444 - 1.243067 + 1.0 + 0.024808 + -0.324998 + -0.876410 + 0.281398 + -0.965584 + 0.601558 True - 6.919374 + 6.902531 9999 0.0 - 0.304672 - 1.213176 - -1.136650 - -1.339962 - -0.014332 - -0.644697 - True - -0.400405 + 0.619958 + -0.400779 + 0.895544 + -1.862085 + -0.293696 + 0.244533 + False + 0.190840 @@ -853,9 +853,9 @@

Identifying the causal estimand @@ -949,18 +949,18 @@

Method 2: Distance Matching\n", " 0\n", " 0.0\n", - " 0.700924\n", - " 2.003474\n", - " -1.384330\n", - " -1.077363\n", - " 1.578925\n", - " -0.015912\n", + " 0.396511\n", + " 2.284501\n", + " 1.046547\n", + " -0.186356\n", + " 0.535768\n", + " -0.387440\n", " True\n", - " 6.892037\n", + " 18.171403\n", " \n", " \n", " 1\n", " 0.0\n", - " 0.069260\n", - " 0.352670\n", - " -0.725968\n", - " -0.308867\n", - " -1.251763\n", - " 0.279309\n", - " False\n", - " -5.471840\n", + " 0.553483\n", + " 1.504512\n", + " 0.146214\n", + " -0.940229\n", + " -1.702890\n", + " 0.413664\n", + " True\n", + " 10.745640\n", " \n", " \n", " 2\n", " 0.0\n", - " 0.681990\n", - " -0.396417\n", - " -0.213663\n", - " -1.934848\n", - " 1.269992\n", - " 0.853344\n", + " 0.828142\n", + " 1.488155\n", + " -1.509005\n", + " -0.563991\n", + " -0.620357\n", + " -0.310603\n", " False\n", - " -5.707764\n", + " -6.365163\n", " \n", " \n", " 3\n", - " 1.0\n", - " 0.464012\n", - " -0.798440\n", - " -0.502134\n", - " -1.255451\n", - " -1.032031\n", - " 0.936978\n", - " True\n", - " 0.598095\n", + " 0.0\n", + " 0.849105\n", + " -0.372715\n", + " 0.372227\n", + " -0.418977\n", + " 0.210340\n", + " 0.749899\n", + " False\n", + " 3.566787\n", " \n", " \n", " 4\n", - " 1.0\n", - " 0.242159\n", - " -1.118668\n", - " 0.142072\n", - " -0.788882\n", - " 1.235869\n", - " -0.847403\n", + " 0.0\n", + " 0.905093\n", + " 1.525682\n", + " -0.156306\n", + " -1.610560\n", + " -0.407204\n", + " 0.159589\n", " True\n", - " 3.251620\n", + " 9.840732\n", " \n", " \n", " ...\n", @@ -181,62 +181,62 @@ " \n", " 9995\n", " 0.0\n", - " 0.480650\n", - " -2.274500\n", - " -1.333670\n", - " -0.846298\n", - " -0.679329\n", - " 0.319555\n", + " 0.197731\n", + " 0.557255\n", + " 0.325835\n", + " -1.212832\n", + " -1.164383\n", + " -1.807983\n", " False\n", - " -17.951348\n", + " -9.234784\n", " \n", " \n", " 9996\n", - " 0.0\n", - " 0.153102\n", - " 0.333702\n", - " -0.345303\n", - " 0.553626\n", - " -0.964486\n", - " 1.010486\n", + " 1.0\n", + " 0.510984\n", + " 0.796696\n", + " 2.164684\n", + " -2.138929\n", + " -0.917557\n", + " 0.373379\n", " True\n", - " 13.289393\n", + " 16.813862\n", " \n", " \n", " 9997\n", " 0.0\n", - " 0.315476\n", - " 0.192321\n", - " -0.560291\n", - " -0.133407\n", - " 0.101016\n", - " 1.491129\n", - " True\n", - " 12.239693\n", + " 0.440900\n", + " 0.927370\n", + " 0.256726\n", + " -2.347163\n", + " -0.547954\n", + " -0.784016\n", + " False\n", + " -5.154270\n", " \n", " \n", " 9998\n", - " 0.0\n", - " 0.729459\n", - " 0.781247\n", - " -1.315457\n", - " -0.489871\n", - " -0.292444\n", - " 1.243067\n", + " 1.0\n", + " 0.024808\n", + " -0.324998\n", + " -0.876410\n", + " 0.281398\n", + " -0.965584\n", + " 0.601558\n", " True\n", - " 6.919374\n", + " 6.902531\n", " \n", " \n", " 9999\n", " 0.0\n", - " 0.304672\n", - " 1.213176\n", - " -1.136650\n", - " -1.339962\n", - " -0.014332\n", - " -0.644697\n", - " True\n", - " -0.400405\n", + " 0.619958\n", + " -0.400779\n", + " 0.895544\n", + " -1.862085\n", + " -0.293696\n", + " 0.244533\n", + " False\n", + " 0.190840\n", " \n", " \n", "\n", @@ -245,30 +245,30 @@ ], "text/plain": [ " Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 0.0 0.700924 2.003474 -1.384330 -1.077363 1.578925 -0.015912 True \n", - "1 0.0 0.069260 0.352670 -0.725968 -0.308867 -1.251763 0.279309 False \n", - "2 0.0 0.681990 -0.396417 -0.213663 -1.934848 1.269992 0.853344 False \n", - "3 1.0 0.464012 -0.798440 -0.502134 -1.255451 -1.032031 0.936978 True \n", - "4 1.0 0.242159 -1.118668 0.142072 -0.788882 1.235869 -0.847403 True \n", + "0 0.0 0.396511 2.284501 1.046547 -0.186356 0.535768 -0.387440 True \n", + "1 0.0 0.553483 1.504512 0.146214 -0.940229 -1.702890 0.413664 True \n", + "2 0.0 0.828142 1.488155 -1.509005 -0.563991 -0.620357 -0.310603 False \n", + "3 0.0 0.849105 -0.372715 0.372227 -0.418977 0.210340 0.749899 False \n", + "4 0.0 0.905093 1.525682 -0.156306 -1.610560 -0.407204 0.159589 True \n", "... ... ... ... ... ... ... ... ... \n", - "9995 0.0 0.480650 -2.274500 -1.333670 -0.846298 -0.679329 0.319555 False \n", - "9996 0.0 0.153102 0.333702 -0.345303 0.553626 -0.964486 1.010486 True \n", - "9997 0.0 0.315476 0.192321 -0.560291 -0.133407 0.101016 1.491129 True \n", - "9998 0.0 0.729459 0.781247 -1.315457 -0.489871 -0.292444 1.243067 True \n", - "9999 0.0 0.304672 1.213176 -1.136650 -1.339962 -0.014332 -0.644697 True \n", + "9995 0.0 0.197731 0.557255 0.325835 -1.212832 -1.164383 -1.807983 False \n", + "9996 1.0 0.510984 0.796696 2.164684 -2.138929 -0.917557 0.373379 True \n", + "9997 0.0 0.440900 0.927370 0.256726 -2.347163 -0.547954 -0.784016 False \n", + "9998 1.0 0.024808 -0.324998 -0.876410 0.281398 -0.965584 0.601558 True \n", + "9999 0.0 0.619958 -0.400779 0.895544 -1.862085 -0.293696 0.244533 False \n", "\n", " y \n", - "0 6.892037 \n", - "1 -5.471840 \n", - "2 -5.707764 \n", - "3 0.598095 \n", - "4 3.251620 \n", + "0 18.171403 \n", + "1 10.745640 \n", + "2 -6.365163 \n", + "3 3.566787 \n", + "4 9.840732 \n", "... ... \n", - "9995 -17.951348 \n", - "9996 13.289393 \n", - "9997 12.239693 \n", - "9998 6.919374 \n", - "9999 -0.400405 \n", + "9995 -9.234784 \n", + "9996 16.813862 \n", + "9997 -5.154270 \n", + "9998 6.902531 \n", + "9999 0.190840 \n", "\n", "[10000 rows x 9 columns]" ] @@ -317,10 +317,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.085287Z", - "iopub.status.busy": "2024-10-19T23:48:39.084705Z", - "iopub.status.idle": "2024-10-19T23:48:39.127764Z", - "shell.execute_reply": "2024-10-19T23:48:39.127175Z" + "iopub.execute_input": "2024-10-22T01:14:49.912782Z", + "iopub.status.busy": "2024-10-22T01:14:49.912241Z", + "iopub.status.idle": "2024-10-22T01:14:49.955139Z", + "shell.execute_reply": "2024-10-22T01:14:49.954555Z" } }, "outputs": [], @@ -340,10 +340,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.130316Z", - "iopub.status.busy": "2024-10-19T23:48:39.130103Z", - "iopub.status.idle": "2024-10-19T23:48:39.294682Z", - "shell.execute_reply": "2024-10-19T23:48:39.294174Z" + "iopub.execute_input": "2024-10-22T01:14:49.957711Z", + "iopub.status.busy": "2024-10-22T01:14:49.957220Z", + "iopub.status.idle": "2024-10-22T01:14:50.135748Z", + "shell.execute_reply": "2024-10-22T01:14:50.135191Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.297096Z", - "iopub.status.busy": "2024-10-19T23:48:39.296723Z", - "iopub.status.idle": "2024-10-19T23:48:39.338994Z", - "shell.execute_reply": "2024-10-19T23:48:39.338370Z" + "iopub.execute_input": "2024-10-22T01:14:50.137813Z", + "iopub.status.busy": "2024-10-22T01:14:50.137589Z", + "iopub.status.idle": "2024-10-22T01:14:50.180652Z", + "shell.execute_reply": "2024-10-22T01:14:50.180043Z" } }, "outputs": [ @@ -402,10 +402,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.341184Z", - "iopub.status.busy": "2024-10-19T23:48:39.340968Z", - "iopub.status.idle": "2024-10-19T23:48:39.601145Z", - "shell.execute_reply": "2024-10-19T23:48:39.600463Z" + "iopub.execute_input": "2024-10-22T01:14:50.182850Z", + "iopub.status.busy": "2024-10-22T01:14:50.182639Z", + "iopub.status.idle": "2024-10-22T01:14:50.440132Z", + "shell.execute_reply": "2024-10-22T01:14:50.439457Z" } }, "outputs": [ @@ -419,9 +419,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -429,9 +429,9 @@ " ⎡ -1⎤\n", " ⎢ d ⎛ d ⎞ ⎥\n", "E⎢─────────(y)⋅⎜─────────([v₀])⎟ ⎥\n", - " ⎣d[Z₀ Z₁] ⎝d[Z₀ Z₁] ⎠ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + " ⎣d[Z₁ Z₀] ⎝d[Z₁ Z₀] ⎠ ⎦\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", @@ -459,10 +459,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.603286Z", - "iopub.status.busy": "2024-10-19T23:48:39.602916Z", - "iopub.status.idle": "2024-10-19T23:48:39.654629Z", - "shell.execute_reply": "2024-10-19T23:48:39.653784Z" + "iopub.execute_input": "2024-10-22T01:14:50.442172Z", + "iopub.status.busy": "2024-10-22T01:14:50.441833Z", + "iopub.status.idle": "2024-10-22T01:14:50.493265Z", + "shell.execute_reply": "2024-10-22T01:14:50.492684Z" } }, "outputs": [ @@ -479,19 +479,19 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.99993066743126\n", + "Mean value: 9.999992741562023\n", "p-value: [0.]\n", "\n", - "Causal Estimate is 9.99993066743126\n" + "Causal Estimate is 9.999992741562023\n" ] } ], @@ -517,10 +517,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:39.657279Z", - "iopub.status.busy": "2024-10-19T23:48:39.657001Z", - "iopub.status.idle": "2024-10-19T23:48:41.672000Z", - "shell.execute_reply": "2024-10-19T23:48:41.671414Z" + "iopub.execute_input": "2024-10-22T01:14:50.495753Z", + "iopub.status.busy": "2024-10-22T01:14:50.495542Z", + "iopub.status.idle": "2024-10-22T01:14:52.408904Z", + "shell.execute_reply": "2024-10-22T01:14:52.408214Z" } }, "outputs": [ @@ -537,18 +537,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: att\n", "\n", "## Estimate\n", - "Mean value: 10.37814721851544\n", + "Mean value: 10.921410307145099\n", "\n", - "Causal Estimate is 10.37814721851544\n" + "Causal Estimate is 10.921410307145099\n" ] } ], @@ -575,10 +575,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:41.674119Z", - "iopub.status.busy": "2024-10-19T23:48:41.673729Z", - "iopub.status.idle": "2024-10-19T23:48:42.102517Z", - "shell.execute_reply": "2024-10-19T23:48:42.101977Z" + "iopub.execute_input": "2024-10-22T01:14:52.411087Z", + "iopub.status.busy": "2024-10-22T01:14:52.410712Z", + "iopub.status.idle": "2024-10-22T01:14:52.907120Z", + "shell.execute_reply": "2024-10-22T01:14:52.906543Z" } }, "outputs": [ @@ -595,18 +595,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: att\n", "\n", "## Estimate\n", - "Mean value: 10.043191997278369\n", + "Mean value: 9.92168802722978\n", "\n", - "Causal Estimate is 10.043191997278369\n" + "Causal Estimate is 9.92168802722978\n" ] } ], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.104497Z", - "iopub.status.busy": "2024-10-19T23:48:42.104138Z", - "iopub.status.idle": "2024-10-19T23:48:42.161189Z", - "shell.execute_reply": "2024-10-19T23:48:42.160645Z" + "iopub.execute_input": "2024-10-22T01:14:52.909073Z", + "iopub.status.busy": "2024-10-22T01:14:52.908733Z", + "iopub.status.idle": "2024-10-22T01:14:52.956643Z", + "shell.execute_reply": "2024-10-22T01:14:52.955974Z" } }, "outputs": [ @@ -652,18 +652,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 9.863997269458439\n", + "Mean value: 10.085510450492208\n", "\n", - "Causal Estimate is 9.863997269458439\n" + "Causal Estimate is 10.085510450492208\n" ] } ], @@ -692,10 +692,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.163178Z", - "iopub.status.busy": "2024-10-19T23:48:42.162799Z", - "iopub.status.idle": "2024-10-19T23:48:42.209723Z", - "shell.execute_reply": "2024-10-19T23:48:42.209217Z" + "iopub.execute_input": "2024-10-22T01:14:52.958731Z", + "iopub.status.busy": "2024-10-22T01:14:52.958426Z", + "iopub.status.idle": "2024-10-22T01:14:53.006986Z", + "shell.execute_reply": "2024-10-22T01:14:53.006406Z" } }, "outputs": [ @@ -712,18 +712,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W3,W0,W2,W1,W4])\n", + "─────(E[y|W2,W4,W1,W3,W0])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W0,W2,W1,W4,U) = P(y|v0,W3,W0,W2,W1,W4)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W4,W1,W3,W0,U) = P(y|v0,W2,W4,W1,W3,W0)\n", "\n", "## Realized estimand\n", - "b: y~v0+W3+W0+W2+W1+W4\n", + "b: y~v0+W2+W4+W1+W3+W0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 10.118877157276382\n", + "Mean value: 10.69830260335515\n", "\n", - "Causal Estimate is 10.118877157276382\n" + "Causal Estimate is 10.69830260335515\n" ] } ], @@ -750,10 +750,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.211631Z", - "iopub.status.busy": "2024-10-19T23:48:42.211272Z", - "iopub.status.idle": "2024-10-19T23:48:42.249673Z", - "shell.execute_reply": "2024-10-19T23:48:42.249072Z" + "iopub.execute_input": "2024-10-22T01:14:53.008844Z", + "iopub.status.busy": "2024-10-22T01:14:53.008650Z", + "iopub.status.idle": "2024-10-22T01:14:53.049021Z", + "shell.execute_reply": "2024-10-22T01:14:53.048454Z" }, "scrolled": true }, @@ -773,9 +773,9 @@ " ⎡ -1⎤\n", " ⎢ d ⎛ d ⎞ ⎥\n", "E⎢─────────(y)⋅⎜─────────([v₀])⎟ ⎥\n", - " ⎣d[Z₀ Z₁] ⎝d[Z₀ Z₁] ⎠ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + " ⎣d[Z₁ Z₀] ⎝d[Z₁ Z₀] ⎠ ⎦\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "## Realized estimand\n", "Realized estimand: Wald Estimator\n", @@ -788,17 +788,17 @@ " ⎡ d ⎤\n", "E⎢───(v₀)⎥\n", " ⎣dZ₀ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n", "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n", "\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.419142541349753\n", + "Mean value: 10.208659706642896\n", "\n", - "Causal Estimate is 9.419142541349753\n" + "Causal Estimate is 10.208659706642896\n" ] } ], @@ -823,10 +823,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:42.251664Z", - "iopub.status.busy": "2024-10-19T23:48:42.251325Z", - "iopub.status.idle": "2024-10-19T23:48:42.289549Z", - "shell.execute_reply": "2024-10-19T23:48:42.288900Z" + "iopub.execute_input": "2024-10-22T01:14:53.050907Z", + "iopub.status.busy": "2024-10-22T01:14:53.050713Z", + "iopub.status.idle": "2024-10-22T01:14:53.091060Z", + "shell.execute_reply": "2024-10-22T01:14:53.090479Z" } }, "outputs": [ @@ -845,9 +845,9 @@ " ⎡ -1⎤\n", " ⎢ d ⎛ d ⎞ ⎥\n", "E⎢─────────(y)⋅⎜─────────([v₀])⎟ ⎥\n", - " ⎣d[Z₀ Z₁] ⎝d[Z₀ Z₁] ⎠ ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + " ⎣d[Z₁ Z₀] ⎝d[Z₁ Z₀] ⎠ ⎦\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "\n", "## Realized estimand\n", "Realized estimand: Wald Estimator\n", @@ -860,17 +860,17 @@ " ⎡ d ⎤\n", "E⎢──────────────────(v₀)⎥\n", " ⎣dlocal_rd_variable ⎦\n", - "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n", - "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n", + "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n", + "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n", "Estimand assumption 3, treatment_effect_homogeneity: Each unit's treatment ['v0'] is affected in the same way by common causes of ['v0'] and ['y']\n", "Estimand assumption 4, outcome_effect_homogeneity: Each unit's outcome ['y'] is affected in the same way by common causes of ['v0'] and ['y']\n", "\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 10.018266612768816\n", + "Mean value: 4.036255557227603\n", "\n", - "Causal Estimate is 10.018266612768816\n" + "Causal Estimate is 4.036255557227603\n" ] } ], diff --git a/main/example_notebooks/dowhy_functional_api.html b/main/example_notebooks/dowhy_functional_api.html index 1af6fb9ee..6f5bbf107 100644 --- a/main/example_notebooks/dowhy_functional_api.html +++ b/main/example_notebooks/dowhy_functional_api.html @@ -724,9 +724,9 @@

Identify Effect - Functional API (Preview)2.Identify 
-Causal Estimate is 4.0287482183892624
+Causal Estimate is 4.028748218389719
 ATE 4.021121012430829
@@ -996,8 +996,8 @@

4. Refute 
 Refute: Add a random common cause
-Estimated effect:4.0287482183892624
-New effect:4.028748218389261
+Estimated effect:4.028748218389719
+New effect:4.028748218389719
 p value:1.0
@@ -1018,8 +1018,8 @@

4. Refute 
 Refute: Use a Placebo Treatment
-Estimated effect:4.0287482183892624
-New effect:-0.474358716591812
+Estimated effect:4.028748218389719
+New effect:-0.43873349315010624
 p value:0.04
@@ -1042,9 +1042,9 @@

4.3 Data Subset Refuter 
 Refute: Use a subset of data
-Estimated effect:4.0287482183892624
-New effect:4.021811738587015
-p value:0.82
+Estimated effect:4.028748218389719
+New effect:4.026465486389576
+p value:0.88
diff --git a/main/example_notebooks/dowhy_ihdp_data_example.ipynb b/main/example_notebooks/dowhy_ihdp_data_example.ipynb index c9038a7f6..d1403ba13 100644 --- a/main/example_notebooks/dowhy_ihdp_data_example.ipynb +++ b/main/example_notebooks/dowhy_ihdp_data_example.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:54.340432Z", - "iopub.status.busy": "2024-10-19T23:48:54.340225Z", - "iopub.status.idle": "2024-10-19T23:48:55.766638Z", - "shell.execute_reply": "2024-10-19T23:48:55.766040Z" + "iopub.execute_input": "2024-10-22T01:15:05.708445Z", + "iopub.status.busy": "2024-10-22T01:15:05.708002Z", + "iopub.status.idle": "2024-10-22T01:15:07.163619Z", + "shell.execute_reply": "2024-10-22T01:15:07.162914Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:55.769104Z", - "iopub.status.busy": "2024-10-19T23:48:55.768601Z", - "iopub.status.idle": "2024-10-19T23:48:55.871588Z", - "shell.execute_reply": "2024-10-19T23:48:55.871056Z" + "iopub.execute_input": "2024-10-22T01:15:07.166105Z", + "iopub.status.busy": "2024-10-22T01:15:07.165670Z", + "iopub.status.idle": "2024-10-22T01:15:07.341949Z", + "shell.execute_reply": "2024-10-22T01:15:07.341394Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:55.873513Z", - "iopub.status.busy": "2024-10-19T23:48:55.873219Z", - "iopub.status.idle": "2024-10-19T23:48:56.115454Z", - "shell.execute_reply": "2024-10-19T23:48:56.114836Z" + "iopub.execute_input": "2024-10-22T01:15:07.343914Z", + "iopub.status.busy": "2024-10-22T01:15:07.343553Z", + "iopub.status.idle": "2024-10-22T01:15:07.587098Z", + "shell.execute_reply": "2024-10-22T01:15:07.586538Z" } }, "outputs": [ @@ -321,10 +321,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.117710Z", - "iopub.status.busy": "2024-10-19T23:48:56.117323Z", - "iopub.status.idle": "2024-10-19T23:48:56.136300Z", - "shell.execute_reply": "2024-10-19T23:48:56.135654Z" + "iopub.execute_input": "2024-10-22T01:15:07.589556Z", + "iopub.status.busy": "2024-10-22T01:15:07.589192Z", + "iopub.status.idle": "2024-10-22T01:15:07.614840Z", + "shell.execute_reply": "2024-10-22T01:15:07.614274Z" } }, "outputs": [ @@ -338,13 +338,13 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "────────────(E[y_factual|x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13 ↪\n", + "────────────(E[y_factual|x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x ↪\n", "d[treatment] ↪\n", "\n", "↪ \n", - "↪ ,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16])\n", + "↪ 20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16,U) = P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7,U) = P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -382,10 +382,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.138347Z", - "iopub.status.busy": "2024-10-19T23:48:56.138150Z", - "iopub.status.idle": "2024-10-19T23:48:56.164780Z", - "shell.execute_reply": "2024-10-19T23:48:56.163683Z" + "iopub.execute_input": "2024-10-22T01:15:07.616963Z", + "iopub.status.busy": "2024-10-22T01:15:07.616754Z", + "iopub.status.idle": "2024-10-22T01:15:07.643872Z", + "shell.execute_reply": "2024-10-22T01:15:07.643363Z" } }, "outputs": [ @@ -402,23 +402,23 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "────────────(E[y_factual|x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13 ↪\n", + "────────────(E[y_factual|x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x ↪\n", "d[treatment] ↪\n", "\n", "↪ \n", - "↪ ,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16])\n", + "↪ 20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16,U) = P(y_factual|treatment,x5,x15,x22,x9,x7,x14,x4,x17,x2,x19,x18,x25,x1,x24,x13,x11,x20,x8,x23,x3,x21,x12,x10,x6,x16)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7,U) = P(y_factual|treatment,x3,x1,x15,x22,x9,x17,x4,x12,x10,x13,x14,x18,x19,x21,x20,x24,x5,x8,x2,x6,x11,x23,x25,x16,x7)\n", "\n", "## Realized estimand\n", - "b: y_factual~treatment+x5+x15+x22+x9+x7+x14+x4+x17+x2+x19+x18+x25+x1+x24+x13+x11+x20+x8+x23+x3+x21+x12+x10+x6+x16\n", + "b: y_factual~treatment+x3+x1+x15+x22+x9+x17+x4+x12+x10+x13+x14+x18+x19+x21+x20+x24+x5+x8+x2+x6+x11+x23+x25+x16+x7\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 3.9286717508727147\n", + "Mean value: 3.928671750872714\n", "p-value: [1.58915682e-156]\n", "\n", - "Causal Estimate is 3.9286717508727147\n", + "Causal Estimate is 3.928671750872714\n", "ATE 4.021121012430829\n" ] } @@ -450,10 +450,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.167172Z", - "iopub.status.busy": "2024-10-19T23:48:56.166962Z", - "iopub.status.idle": "2024-10-19T23:48:56.191612Z", - "shell.execute_reply": "2024-10-19T23:48:56.191066Z" + "iopub.execute_input": "2024-10-22T01:15:07.646453Z", + "iopub.status.busy": "2024-10-22T01:15:07.645987Z", + "iopub.status.idle": "2024-10-22T01:15:07.670692Z", + "shell.execute_reply": "2024-10-22T01:15:07.670143Z" } }, "outputs": [ @@ -488,10 +488,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.194643Z", - "iopub.status.busy": "2024-10-19T23:48:56.193923Z", - "iopub.status.idle": "2024-10-19T23:48:56.242752Z", - "shell.execute_reply": "2024-10-19T23:48:56.242206Z" + "iopub.execute_input": "2024-10-22T01:15:07.673232Z", + "iopub.status.busy": "2024-10-22T01:15:07.672680Z", + "iopub.status.idle": "2024-10-22T01:15:07.721444Z", + "shell.execute_reply": "2024-10-22T01:15:07.720871Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.245315Z", - "iopub.status.busy": "2024-10-19T23:48:56.245106Z", - "iopub.status.idle": "2024-10-19T23:48:56.262634Z", - "shell.execute_reply": "2024-10-19T23:48:56.262102Z" + "iopub.execute_input": "2024-10-22T01:15:07.723970Z", + "iopub.status.busy": "2024-10-22T01:15:07.723417Z", + "iopub.status.idle": "2024-10-22T01:15:07.741585Z", + "shell.execute_reply": "2024-10-22T01:15:07.741025Z" } }, "outputs": [ @@ -537,7 +537,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 4.0287482183892624\n", + "Causal Estimate is 4.028748218389719\n", "ATE 4.021121012430829\n" ] } @@ -566,10 +566,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:56.265649Z", - "iopub.status.busy": "2024-10-19T23:48:56.264895Z", - "iopub.status.idle": "2024-10-19T23:48:57.183003Z", - "shell.execute_reply": "2024-10-19T23:48:57.182378Z" + "iopub.execute_input": "2024-10-22T01:15:07.744007Z", + "iopub.status.busy": "2024-10-22T01:15:07.743467Z", + "iopub.status.idle": "2024-10-22T01:15:08.701617Z", + "shell.execute_reply": "2024-10-22T01:15:08.700931Z" } }, "outputs": [ @@ -578,8 +578,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:4.0287482183892624\n", - "New effect:4.028748218389261\n", + "Estimated effect:4.028748218389719\n", + "New effect:4.028748218389719\n", "p value:1.0\n", "\n" ] @@ -603,10 +603,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:57.184896Z", - "iopub.status.busy": "2024-10-19T23:48:57.184677Z", - "iopub.status.idle": "2024-10-19T23:48:58.132004Z", - "shell.execute_reply": "2024-10-19T23:48:58.131428Z" + "iopub.execute_input": "2024-10-22T01:15:08.703811Z", + "iopub.status.busy": "2024-10-22T01:15:08.703379Z", + "iopub.status.idle": "2024-10-22T01:15:09.661150Z", + "shell.execute_reply": "2024-10-22T01:15:09.660551Z" } }, "outputs": [ @@ -615,8 +615,8 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:4.0287482183892624\n", - "New effect:-0.474358716591812\n", + "Estimated effect:4.028748218389719\n", + "New effect:-0.43873349315010624\n", "p value:0.04\n", "\n" ] @@ -640,10 +640,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:48:58.133925Z", - "iopub.status.busy": "2024-10-19T23:48:58.133709Z", - "iopub.status.idle": "2024-10-19T23:48:59.009657Z", - "shell.execute_reply": "2024-10-19T23:48:59.009072Z" + "iopub.execute_input": "2024-10-22T01:15:09.663065Z", + "iopub.status.busy": "2024-10-22T01:15:09.662707Z", + "iopub.status.idle": "2024-10-22T01:15:10.527657Z", + "shell.execute_reply": "2024-10-22T01:15:10.527034Z" } }, "outputs": [ @@ -652,9 +652,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:4.0287482183892624\n", - "New effect:4.021811738587015\n", - "p value:0.82\n", + "Estimated effect:4.028748218389719\n", + "New effect:4.026465486389576\n", + "p value:0.88\n", "\n" ] } diff --git a/main/example_notebooks/dowhy_interpreter.html b/main/example_notebooks/dowhy_interpreter.html index 457e900ea..2d66c1f98 100644 --- a/main/example_notebooks/dowhy_interpreter.html +++ b/main/example_notebooks/dowhy_interpreter.html @@ -615,7 +615,7 @@

DoWhy: Interpreters for Causal Estimators 
-7633
+7514
@@ -656,62 +656,62 @@

DoWhy: Interpreters for Causal Estimators @@ -942,7 +942,7 @@

Textual Interpreter 
-Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9899959475311039 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.
+Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9940453251583979 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.

@@ -999,18 +999,18 @@

Method 2: Propensity Score Matching @@ -1027,7 +1027,7 @@

Method 2: Propensity Score Matching 
-Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9969548315933395 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.
+Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0031657778165648 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.

Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator.

@@ -1062,18 +1062,18 @@

Method 3: Weighting @@ -1090,7 +1090,7 @@

Method 3: Weighting 
-Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0480577208389077 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.
+Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0234846748653175 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.
diff --git a/main/example_notebooks/dowhy_interpreter.ipynb b/main/example_notebooks/dowhy_interpreter.ipynb index b421846ad..3b66f9852 100644 --- a/main/example_notebooks/dowhy_interpreter.ipynb +++ b/main/example_notebooks/dowhy_interpreter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "83ffbcdf", + "id": "e0d169dc", "metadata": {}, "source": [ "# DoWhy: Interpreters for Causal Estimators\n", @@ -16,13 +16,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "a49c29c2", + "id": "e9cfbba3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:01.364992Z", - "iopub.status.busy": "2024-10-19T23:49:01.364815Z", - "iopub.status.idle": "2024-10-19T23:49:01.379907Z", - "shell.execute_reply": "2024-10-19T23:49:01.379407Z" + "iopub.execute_input": "2024-10-22T01:15:12.721690Z", + "iopub.status.busy": "2024-10-22T01:15:12.721508Z", + "iopub.status.idle": "2024-10-22T01:15:12.737959Z", + "shell.execute_reply": "2024-10-22T01:15:12.737385Z" } }, "outputs": [], @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "b2c531c3", + "id": "81d1f768", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:01.381556Z", - "iopub.status.busy": "2024-10-19T23:49:01.381380Z", - "iopub.status.idle": "2024-10-19T23:49:02.838234Z", - "shell.execute_reply": "2024-10-19T23:49:02.837614Z" + "iopub.execute_input": "2024-10-22T01:15:12.739760Z", + "iopub.status.busy": "2024-10-22T01:15:12.739596Z", + "iopub.status.idle": "2024-10-22T01:15:14.317164Z", + "shell.execute_reply": "2024-10-22T01:15:14.316509Z" } }, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "e5f1c131", + "id": "e3b85430", "metadata": {}, "source": [ "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome.\n", @@ -67,13 +67,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "16416f60", + "id": "98f3cf81", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:02.840599Z", - "iopub.status.busy": "2024-10-19T23:49:02.840121Z", - "iopub.status.idle": "2024-10-19T23:49:03.127574Z", - "shell.execute_reply": "2024-10-19T23:49:03.126992Z" + "iopub.execute_input": "2024-10-22T01:15:14.319668Z", + "iopub.status.busy": "2024-10-22T01:15:14.319293Z", + "iopub.status.idle": "2024-10-22T01:15:14.607647Z", + "shell.execute_reply": "2024-10-22T01:15:14.606864Z" } }, "outputs": [ @@ -81,7 +81,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "7633\n" + "7514\n" ] }, { @@ -120,62 +120,62 @@ " \n", " 0\n", " 0.0\n", - " 0.025228\n", - " 1.100087\n", - " 0.251319\n", - " 0.967396\n", - " -0.070943\n", - " 3\n", + " 0.686451\n", + " -1.880858\n", + " 0.312247\n", + " 0.506022\n", + " -0.432920\n", + " 1\n", " True\n", - " 2.441736\n", + " 0.667578\n", " \n", " \n", " 1\n", " 0.0\n", - " 0.050578\n", - " -1.059087\n", - " -1.024157\n", - " -0.931269\n", - " -0.008544\n", - " 2\n", + " 0.812954\n", + " 0.194572\n", + " 0.112574\n", + " -0.866071\n", + " -1.220483\n", + " 0\n", " False\n", - " -1.351059\n", + " -0.450874\n", " \n", " \n", " 2\n", " 0.0\n", - " 0.983814\n", - " -0.265495\n", - " 0.138566\n", - " -0.201379\n", - " -0.793082\n", + " 0.060108\n", + " -1.404344\n", + " 1.963471\n", + " 0.351305\n", + " 0.376100\n", " 1\n", " True\n", - " 0.703998\n", + " 2.343394\n", " \n", " \n", " 3\n", " 0.0\n", - " 0.058638\n", - " -0.389303\n", - " -0.224825\n", - " 0.529911\n", - " -1.109725\n", - " 1\n", - " False\n", - " 0.053730\n", + " 0.416385\n", + " -0.356534\n", + " -0.529168\n", + " -1.218528\n", + " -1.951671\n", + " 3\n", + " True\n", + " 0.617386\n", " \n", " \n", " 4\n", - " 0.0\n", - " 0.835845\n", - " 0.042306\n", - " 0.345074\n", - " 0.125246\n", - " -1.127950\n", - " 1\n", - " False\n", - " 0.046581\n", + " 1.0\n", + " 0.939757\n", + " -0.878371\n", + " 0.922175\n", + " -0.788306\n", + " -0.424262\n", + " 2\n", + " True\n", + " 1.445012\n", " \n", " \n", " ...\n", @@ -191,63 +191,63 @@ " \n", " \n", " 9995\n", - " 0.0\n", - " 0.601954\n", - " 0.295718\n", - " -1.746804\n", - " -0.417831\n", - " 0.891260\n", + " 1.0\n", + " 0.777358\n", + " -1.824224\n", + " -1.168284\n", + " -1.137629\n", + " -1.102965\n", " 1\n", " True\n", - " 0.074727\n", + " -1.712360\n", " \n", " \n", " 9996\n", " 0.0\n", - " 0.844898\n", - " 1.390792\n", - " -1.198053\n", - " -0.444348\n", - " 0.175100\n", - " 3\n", + " 0.626805\n", + " 0.442451\n", + " 0.021703\n", + " 0.527621\n", + " 0.276867\n", + " 1\n", " True\n", - " 0.458489\n", + " 2.129588\n", " \n", " \n", " 9997\n", - " 0.0\n", - " 0.194669\n", - " -0.483547\n", - " -2.524919\n", - " -1.561724\n", - " -0.043256\n", + " 1.0\n", + " 0.634063\n", + " -2.355253\n", + " -0.180250\n", + " -0.651788\n", + " -0.054815\n", " 3\n", " True\n", - " -1.613163\n", + " 0.016879\n", " \n", " \n", " 9998\n", " 0.0\n", - " 0.984473\n", - " 0.324328\n", - " -0.450699\n", - " 0.346091\n", - " -0.452536\n", - " 3\n", - " True\n", - " 1.252557\n", + " 0.066714\n", + " -0.069945\n", + " -0.068083\n", + " 1.255475\n", + " -0.428094\n", + " 2\n", + " False\n", + " 1.572778\n", " \n", " \n", " 9999\n", - " 1.0\n", - " 0.329549\n", - " 0.553322\n", - " -1.821996\n", - " 0.470235\n", - " -1.458865\n", - " 3\n", - " True\n", - " 0.333653\n", + " 0.0\n", + " 0.690870\n", + " -1.695679\n", + " 0.045482\n", + " -1.015905\n", + " 0.701215\n", + " 1\n", + " False\n", + " -1.382399\n", " \n", " \n", "\n", @@ -256,30 +256,30 @@ ], "text/plain": [ " Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 0.0 0.025228 1.100087 0.251319 0.967396 -0.070943 3 True \n", - "1 0.0 0.050578 -1.059087 -1.024157 -0.931269 -0.008544 2 False \n", - "2 0.0 0.983814 -0.265495 0.138566 -0.201379 -0.793082 1 True \n", - "3 0.0 0.058638 -0.389303 -0.224825 0.529911 -1.109725 1 False \n", - "4 0.0 0.835845 0.042306 0.345074 0.125246 -1.127950 1 False \n", + "0 0.0 0.686451 -1.880858 0.312247 0.506022 -0.432920 1 True \n", + "1 0.0 0.812954 0.194572 0.112574 -0.866071 -1.220483 0 False \n", + "2 0.0 0.060108 -1.404344 1.963471 0.351305 0.376100 1 True \n", + "3 0.0 0.416385 -0.356534 -0.529168 -1.218528 -1.951671 3 True \n", + "4 1.0 0.939757 -0.878371 0.922175 -0.788306 -0.424262 2 True \n", "... ... ... ... ... ... ... .. ... \n", - "9995 0.0 0.601954 0.295718 -1.746804 -0.417831 0.891260 1 True \n", - "9996 0.0 0.844898 1.390792 -1.198053 -0.444348 0.175100 3 True \n", - "9997 0.0 0.194669 -0.483547 -2.524919 -1.561724 -0.043256 3 True \n", - "9998 0.0 0.984473 0.324328 -0.450699 0.346091 -0.452536 3 True \n", - "9999 1.0 0.329549 0.553322 -1.821996 0.470235 -1.458865 3 True \n", + "9995 1.0 0.777358 -1.824224 -1.168284 -1.137629 -1.102965 1 True \n", + "9996 0.0 0.626805 0.442451 0.021703 0.527621 0.276867 1 True \n", + "9997 1.0 0.634063 -2.355253 -0.180250 -0.651788 -0.054815 3 True \n", + "9998 0.0 0.066714 -0.069945 -0.068083 1.255475 -0.428094 2 False \n", + "9999 0.0 0.690870 -1.695679 0.045482 -1.015905 0.701215 1 False \n", "\n", " y \n", - "0 2.441736 \n", - "1 -1.351059 \n", - "2 0.703998 \n", - "3 0.053730 \n", - "4 0.046581 \n", + "0 0.667578 \n", + "1 -0.450874 \n", + "2 2.343394 \n", + "3 0.617386 \n", + "4 1.445012 \n", "... ... \n", - "9995 0.074727 \n", - "9996 0.458489 \n", - "9997 -1.613163 \n", - "9998 1.252557 \n", - "9999 0.333653 \n", + "9995 -1.712360 \n", + "9996 2.129588 \n", + "9997 0.016879 \n", + "9998 1.572778 \n", + "9999 -1.382399 \n", "\n", "[10000 rows x 9 columns]" ] @@ -305,7 +305,7 @@ }, { "cell_type": "markdown", - "id": "2ea3292a", + "id": "091bc2cd", "metadata": {}, "source": [ "Note that we are using a pandas dataframe to load the data." @@ -313,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "2022753b", + "id": "893396dd", "metadata": {}, "source": [ "## Identifying the causal estimand" @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "93732956", + "id": "a0be0d1a", "metadata": {}, "source": [ "We now input a causal graph in the GML graph format." @@ -330,13 +330,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "fe9f13d2", + "id": "175662b2", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.129959Z", - "iopub.status.busy": "2024-10-19T23:49:03.129542Z", - "iopub.status.idle": "2024-10-19T23:49:03.171982Z", - "shell.execute_reply": "2024-10-19T23:49:03.171417Z" + "iopub.execute_input": "2024-10-22T01:15:14.610616Z", + "iopub.status.busy": "2024-10-22T01:15:14.609867Z", + "iopub.status.idle": "2024-10-22T01:15:14.653716Z", + "shell.execute_reply": "2024-10-22T01:15:14.653115Z" } }, "outputs": [], @@ -354,13 +354,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "728b131a", + "id": "40a97c28", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.174205Z", - "iopub.status.busy": "2024-10-19T23:49:03.173750Z", - "iopub.status.idle": "2024-10-19T23:49:03.353135Z", - "shell.execute_reply": "2024-10-19T23:49:03.352567Z" + "iopub.execute_input": "2024-10-22T01:15:14.656148Z", + "iopub.status.busy": "2024-10-22T01:15:14.655943Z", + "iopub.status.idle": "2024-10-22T01:15:14.838423Z", + "shell.execute_reply": "2024-10-22T01:15:14.837772Z" } }, "outputs": [ @@ -382,13 +382,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "90d90f25", + "id": "ccae709c", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.355562Z", - "iopub.status.busy": "2024-10-19T23:49:03.355349Z", - "iopub.status.idle": "2024-10-19T23:49:03.396296Z", - "shell.execute_reply": "2024-10-19T23:49:03.395767Z" + "iopub.execute_input": "2024-10-22T01:15:14.840730Z", + "iopub.status.busy": "2024-10-22T01:15:14.840292Z", + "iopub.status.idle": "2024-10-22T01:15:14.882910Z", + "shell.execute_reply": "2024-10-22T01:15:14.882297Z" } }, "outputs": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "5bf54323", + "id": "9ad7e25f", "metadata": {}, "source": [ "We get a causal graph. Now identification and estimation is done." @@ -419,13 +419,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "ce514903", + "id": "2a08c3a6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.398428Z", - "iopub.status.busy": "2024-10-19T23:49:03.398061Z", - "iopub.status.idle": "2024-10-19T23:49:03.655264Z", - "shell.execute_reply": "2024-10-19T23:49:03.654589Z" + "iopub.execute_input": "2024-10-22T01:15:14.885309Z", + "iopub.status.busy": "2024-10-22T01:15:14.884966Z", + "iopub.status.idle": "2024-10-22T01:15:15.150286Z", + "shell.execute_reply": "2024-10-22T01:15:15.149642Z" } }, "outputs": [ @@ -439,9 +439,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -467,7 +467,7 @@ }, { "cell_type": "markdown", - "id": "2ca12db4", + "id": "1d306793", "metadata": {}, "source": [ "## Method 1: Propensity Score Stratification\n", @@ -478,13 +478,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "c6112302", + "id": "8de65e8f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:03.657227Z", - "iopub.status.busy": "2024-10-19T23:49:03.657030Z", - "iopub.status.idle": "2024-10-19T23:49:04.203596Z", - "shell.execute_reply": "2024-10-19T23:49:04.202988Z" + "iopub.execute_input": "2024-10-22T01:15:15.152644Z", + "iopub.status.busy": "2024-10-22T01:15:15.152169Z", + "iopub.status.idle": "2024-10-22T01:15:15.729264Z", + "shell.execute_reply": "2024-10-22T01:15:15.728643Z" } }, "outputs": [ @@ -501,18 +501,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W4+W2+W3+W1\n", + "b: y~v0+W2+W3+W1+W0+W4\n", "Target units: att\n", "\n", "## Estimate\n", - "Mean value: 0.9899959475311039\n", + "Mean value: 0.9940453251583979\n", "\n", - "Causal Estimate is 0.9899959475311039\n" + "Causal Estimate is 0.9940453251583979\n" ] } ], @@ -526,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "5e64153a", + "id": "f7ac113b", "metadata": {}, "source": [ "### Textual Interpreter\n", @@ -537,13 +537,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "dd999510", + "id": "c9bea51f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.205749Z", - "iopub.status.busy": "2024-10-19T23:49:04.205360Z", - "iopub.status.idle": "2024-10-19T23:49:04.237761Z", - "shell.execute_reply": "2024-10-19T23:49:04.237260Z" + "iopub.execute_input": "2024-10-22T01:15:15.731378Z", + "iopub.status.busy": "2024-10-22T01:15:15.730965Z", + "iopub.status.idle": "2024-10-22T01:15:15.765371Z", + "shell.execute_reply": "2024-10-22T01:15:15.764760Z" } }, "outputs": [ @@ -551,7 +551,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9899959475311039 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" + "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9940453251583979 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" ] } ], @@ -562,7 +562,7 @@ }, { "cell_type": "markdown", - "id": "d78ba452", + "id": "7d968dcb", "metadata": {}, "source": [ "### Visual Interpreter\n", @@ -578,19 +578,19 @@ { "cell_type": "code", "execution_count": 10, - "id": "d0b99494", + "id": "b4a2cbb7", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.239468Z", - "iopub.status.busy": "2024-10-19T23:49:04.239278Z", - "iopub.status.idle": "2024-10-19T23:49:04.934119Z", - "shell.execute_reply": "2024-10-19T23:49:04.933360Z" + "iopub.execute_input": "2024-10-22T01:15:15.767548Z", + "iopub.status.busy": "2024-10-22T01:15:15.767110Z", + "iopub.status.idle": "2024-10-22T01:15:16.490140Z", + "shell.execute_reply": "2024-10-22T01:15:16.489559Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -606,7 +606,7 @@ }, { "cell_type": "markdown", - "id": "92214fb0", + "id": "6ee814c4", "metadata": {}, "source": [ "This plot shows how the SMD decreases from the unadjusted to the stratified units. " @@ -614,7 +614,7 @@ }, { "cell_type": "markdown", - "id": "433052d5", + "id": "510813b1", "metadata": {}, "source": [ "## Method 2: Propensity Score Matching\n", @@ -625,13 +625,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "92c3c44d", + "id": "e561313d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.936630Z", - "iopub.status.busy": "2024-10-19T23:49:04.936219Z", - "iopub.status.idle": "2024-10-19T23:49:04.995859Z", - "shell.execute_reply": "2024-10-19T23:49:04.995285Z" + "iopub.execute_input": "2024-10-22T01:15:16.492282Z", + "iopub.status.busy": "2024-10-22T01:15:16.491907Z", + "iopub.status.idle": "2024-10-22T01:15:16.552614Z", + "shell.execute_reply": "2024-10-22T01:15:16.552045Z" } }, "outputs": [ @@ -648,18 +648,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W4+W2+W3+W1\n", + "b: y~v0+W2+W3+W1+W0+W4\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 0.9969548315933395\n", + "Mean value: 1.0031657778165648\n", "\n", - "Causal Estimate is 0.9969548315933395\n" + "Causal Estimate is 1.0031657778165648\n" ] } ], @@ -674,13 +674,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "62608e0d", + "id": "6b6bed00", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:04.997953Z", - "iopub.status.busy": "2024-10-19T23:49:04.997495Z", - "iopub.status.idle": "2024-10-19T23:49:05.029628Z", - "shell.execute_reply": "2024-10-19T23:49:05.029123Z" + "iopub.execute_input": "2024-10-22T01:15:16.554654Z", + "iopub.status.busy": "2024-10-22T01:15:16.554263Z", + "iopub.status.idle": "2024-10-22T01:15:16.586510Z", + "shell.execute_reply": "2024-10-22T01:15:16.585981Z" } }, "outputs": [ @@ -688,7 +688,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 0.9969548315933395 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" + "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0031657778165648 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" ] } ], @@ -699,7 +699,7 @@ }, { "cell_type": "markdown", - "id": "e1c2635c", + "id": "e63272ee", "metadata": {}, "source": [ "Cannot use propensity balance interpretor here since the interpreter method only supports propensity score stratification estimator." @@ -707,7 +707,7 @@ }, { "cell_type": "markdown", - "id": "bcd9f15e", + "id": "aa01387e", "metadata": {}, "source": [ "## Method 3: Weighting\n", @@ -721,13 +721,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "cad2287b", + "id": "ae674424", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:05.031479Z", - "iopub.status.busy": "2024-10-19T23:49:05.031113Z", - "iopub.status.idle": "2024-10-19T23:49:05.080436Z", - "shell.execute_reply": "2024-10-19T23:49:05.079883Z" + "iopub.execute_input": "2024-10-22T01:15:16.588442Z", + "iopub.status.busy": "2024-10-22T01:15:16.588082Z", + "iopub.status.idle": "2024-10-22T01:15:16.637085Z", + "shell.execute_reply": "2024-10-22T01:15:16.636459Z" } }, "outputs": [ @@ -744,18 +744,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W0,W4,W2,W3,W1])\n", + "─────(E[y|W2,W3,W1,W0,W4])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W4,W2,W3,W1,U) = P(y|v0,W0,W4,W2,W3,W1)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W3,W1,W0,W4,U) = P(y|v0,W2,W3,W1,W0,W4)\n", "\n", "## Realized estimand\n", - "b: y~v0+W0+W4+W2+W3+W1\n", + "b: y~v0+W2+W3+W1+W0+W4\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 1.0480577208389077\n", + "Mean value: 1.0234846748653175\n", "\n", - "Causal Estimate is 1.0480577208389077\n" + "Causal Estimate is 1.0234846748653175\n" ] } ], @@ -771,13 +771,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "54e13a2c", + "id": "6c050a3f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:05.082399Z", - "iopub.status.busy": "2024-10-19T23:49:05.081974Z", - "iopub.status.idle": "2024-10-19T23:49:05.115636Z", - "shell.execute_reply": "2024-10-19T23:49:05.115057Z" + "iopub.execute_input": "2024-10-22T01:15:16.639067Z", + "iopub.status.busy": "2024-10-22T01:15:16.638721Z", + "iopub.status.idle": "2024-10-22T01:15:16.672307Z", + "shell.execute_reply": "2024-10-22T01:15:16.671773Z" } }, "outputs": [ @@ -785,7 +785,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0480577208389077 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" + "Increasing the treatment variable(s) [v0] from 0 to 1 causes an increase of 1.0234846748653175 in the expected value of the outcome [['y']], over the data distribution/population represented by the dataset.\n" ] } ], @@ -797,19 +797,19 @@ { "cell_type": "code", "execution_count": 15, - "id": "0115afed", + "id": "18a66e9b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:05.117413Z", - "iopub.status.busy": "2024-10-19T23:49:05.117225Z", - "iopub.status.idle": "2024-10-19T23:49:05.466453Z", - "shell.execute_reply": "2024-10-19T23:49:05.465819Z" + "iopub.execute_input": "2024-10-22T01:15:16.674113Z", + "iopub.status.busy": "2024-10-22T01:15:16.673919Z", + "iopub.status.idle": "2024-10-22T01:15:17.022930Z", + "shell.execute_reply": "2024-10-22T01:15:17.022368Z" } }, "outputs": [ { "data": { - "image/png": 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RUFCQatasqXHjxlnOcmSnbt262rhxo4YOHaqqVavqoYce0rPPPqvLly9n+tSlVq1aGjBggKpXr67WrVvrySef1I4dO6w+pbh8+bKmTZum2rVry9/fX6NHj852G+3YsaNKlSpldVAUExOjo0ePqkePHtn2+cyZM3rzzTfl4+MjLy8vSyD7+++/JUmff/65ypUrp9dee021atVSmzZtFBYWZvWp3q0cHR3l7Oxs+dvcfLlHcnKypk2bJk9PTwUEBKh79+6Ki4uz7AMiIiJUuXJlzZo1S7Vq1ZKfn59mz56tpKQkffbZZ1nOr1mzZjp//ryOHz8u6cZNoQ8++KCaNWtmdb3vnj17LAc1ixYtUsOGDTV+/HhVr15dQUFBevPNN3X8+HF98803Wc7n888/18MPP6ynn35aNWrUUL9+/Syfzt3q0Ucf1X//+195eHho0KBBcnJyspx1yuo9GR0drfr16yswMFAPPvigWrRooeXLl+uZZ57Jdj0XZ9SO4l077mZfW758+Ux/51v3S6mpqXrvvff02GOP6fnnn1e1atXUokULjRs3Tnv27LHab589e1ZjxoyRl5dXtpewVaxYUevXr7fsV6pVq6ahQ4cqLS3Naj1krNfRo0froYceUuPGjTV48GAdO3ZMR44csYxz4cIFvf766/Lx8VGdOnU0efJkpaSkZDoTIt3Ydj08PKzOov7777/avn37bWvAnbafjBvhZ86cqTp16qhJkyaaNWvWbcPr7bbx8+fPa9q0aapbt67q1Kmjvn376vLly5YzDMuWLZOtra3lQwAvLy+98cYbKlWqlJYtW5bl/Jo1a6a0tDTL3ysmJkb//vuvunbtahUudu/erZCQEEk5qzWff/656tatq7CwMD300EPq0qWLunXrluW4jRs3Vo8ePeTh4aEePXqoZs2alhrg7u4uW1tbOTo6qnz58rK3t1d0dLS8vLzUtGlTPfjggwoKCtKSJUsIFzlx/fp1Scpyp9WsWTOlp6fr8ccf1yeffKK//vpLTk5Oqlevnuzt7W87XV9f3zvO29fX1+pGTS8vL0myHKAY4dChQzKbzQoKCrIaHhgYKElWO38nJyd5enpafs94M2Z3EJeUlKQTJ05kmna5cuVUtWrV2xaWrDRv3lwJCQmW1/30009q0qSJJKlp06b6888/deHCBaWlpennn3+2+uQpJiZG7777riZPnpyja9QbNGhg9buXl5fl73DgwAGVLFlSjRo1shqnadOmOnHiRJaf4ks3rvmvVq2aKlSoYDU8MDBQJ0+etLpc5tbt5eDBg6pWrZqqVatmNbxJkyaWSyuy4uTkpP379+upp55ScHCwAgMDLadqTSaT1bi3/t3q1q0rs9ls9Umsn5+f1TZat25dScryUysnJyd17txZ69atsxT5TZs2ycPDQ40bN862zx4eHlY7/jJlykj6v+0uNjZW3t7eKlHi/24VCwkJyXS99N3y8fGxev9mzDvj73jw4EE1adLEap9Qrlw51a5dO9tt2t/fXy4uLvrll18k3SggQUFBatiwoeWSjBMnTujMmTMKCQlRUlKSjh8/nunSFW9vb7m5uWU5n2vXruncuXOZtpWWLVtm2aebP4G1tbWVm5tbttuqdOOT+M8++0yvvPKKtmzZon///VfVqlXLsxsvCxtqR/GuHfeyr72dmJgYJSUlZdoXZPT/5nXh4OBgtZ6z4uDgoGPHjmnQoEEKCQlRYGCgZVq39qtBgwaysbGx/J7V/r1atWpW1+VXrVpVpUuXzrIG2NjYqGfPntq8ebNlX/Ptt9+qZMmSeuSRR7Lt8522n5MnT6patWpWZ7+9vLz0n//857brIju31pysakC9evWs7ndycHBQ/fr1s62/FSpUkKenp1UNqFevnoKDgy3DLl++rKioKMv2l5NaExsbm+m+zuxqQMZ79eblvFMN2L59u+VM5YULF1SpUiXL/uVuFKsbum/n77//lo2NTZYbqY+PjyIjI/X+++/rnXfe0eTJk1WrVi299NJLatOmzW2nezc34WWk6gwZN5zd7antu5FxAHtrfzI+Pbl5Q7v5hreb3Xr95K3TzpjWrdO/3UaclWbNmsnGxkY//fSTXFxcdOrUKct1x/Xr11fJkiW1a9cuValSRUlJSZY3aHp6usaPH6+OHTtm+ynundz6t3jggQcsl+UkJSUpNTU1UwDJOLiIj4/PsiglJSVluR3cvO4zfr51/klJSYqNjc20c0hNTVVqaqpSUlKyPEhZtmyZpk+frt69e2vcuHFydXXV2bNns7wu9da+Zfz9bz4guJtxbtarVy99/PHH2r17t5o2bapNmzbpf//7n1UBu9Wt213GuBnbnclkynQ5hL29fY5vdH7ggQduO7+kpCStW7dOX375pdV4165dy/bA0M7OTk2bNtXevXvVs2dP7dmzR3369FGDBg00fvx4JScna/fu3SpXrpy8vb0tl00sWLAg05moK1eu6Ny5c5nmkXFgcOtyZ3cj8a03AdrY2GT7Xpakl156STVr1tSaNWs0YsQISTeetDJhwgTDb/orCqgdxbt23Mu+9nYy1sWECRM0adKkTO03nxm7m23j22+/1bBhw/Tf//5Xb731lsqVKycbGxu1a9cu07g5qQEZ42VXA7p166a33npLX331lbp3764vv/xSHTt2zHYbuXm+t7q5BmS1v8/4IOpe3U0N+P333zPV35SUlNs+uCEkJMTyYdLu3bvVqFEj+fv7KzExUTExMfrnn39kNpst22ZOak1W68KoGtCrVy9VrFhRH3/8sV555RWlpKSoSZMmGj9+fLaXtN2KcPH/ffPNN6pbt262fxwvLy/NmDFDZrNZUVFRWrJkiV588UVt2rTpvj/Ru3UHmlEYMjacrDaEey0eGUUo41rCDBm/31qk7kVGYbj5E/gMSUlJ2T7dIzvu7u6qW7eu9u3bp7Jly6pUqVLy9/eXdONTg8DAQP3888+Ki4uTg4OD5UzC6dOn9dtvv+ngwYOWa8olKS0tTTY2NtqwYYOWLVuW6czDzZKTk612OJcvX7b8HVxcXOTo6Gi5v+JWWV0HnPG6rE7bZqz7rArrza+tWrWqlixZkmX7zZ/i32zDhg0KCAjQ5MmTLcOyu3bz1u0v4/ebPx26m3Fu5uXlpcDAQG3cuFFlypTRyZMnsz1le7fs7e0z3VScmpp6zwcgd8vFxUUhISF68cUXs+xLdpo1a6ZFixbp0qVLOnLkiBo1aqTKlSurXLly2rdvn/bs2WM5CMoo2P369cvycoGsim3GmZpb18W9fEp6OzY2NurSpYu6dOmi5ORk/fDDD5o1a5ZeeuklrVy50pB5FCXUjuJdO+5lX3s7GfvSl19+OcuAc69PC9uwYYMqVqyouXPnWm5Mz+rDCilnNSBjWHZ/f3d3d7Vv314bN25U69attXv3bn366af3tAy3yqoGSNmHjvvl4uKiSpUqZfn9Kbe72b9Zs2b66KOPdOXKFf3888/q06ePHBwc5OfnZ9n+AgICrD5UvNdak9W6MKoGSDc+UMp4ZPLOnTs1e/ZsPf/88/ruu+9u+yFhBi6L0o3vJYiOjtYLL7yQZfu+fft04MABSTd21v7+/po6darS0tL0xx9/WMa7XRK8nYMHD1ptJBmn22rXri3pxk4lMTHR8gm5JEt/bpVdH3x9fWVra2v1tAxJlutl7+exqc7OzqpVq1amaZ87dy7LU3d3o3nz5vrtt9+0f/9+NWrUyOogumnTptq3b59+++03NWzY0JLKK1SooC+++ELr1q2z+pfxFIiMn2/n5msizWazDh8+bPk7BAQE6OrVq7py5Yo8PDws/xwdHeXi4pLtTsDf31+xsbGZbqTet2+fatasedudYkBAgE6fPi1nZ2eredrZ2als2bLZ7uBSU1MzfZqTcQ/ErdvIrTfdHTp0SCVLllTVqlUtw7LbRmvUqJFt33v16qVvv/1Wa9asUWhoaKZn098rDw8PRUdHKy0tzTJs69atd/04x3t9fwYEBCgmJsZqvXt4eOj69eu3XZZmzZrpn3/+0eeff64HH3zQcoAUFBSkn3/+WXv37rV8YlqqVCl5enrqr7/+yjSflJQUlS1bNtP03d3d5erqmulpXdndn3E3MtbNlStX9OWXX1o+jSxVqpQeffRRPf3001bXX+MGage14172tVnJGKdGjRpycXFRbGys1X6gSpUqun79+j0/4jg1NVWurq5WNSK7ft26/jO2o4ceesgy7O+//7aqYX///beSkpKsxrlVr1699PPPP2vFihWWewnuh4eHh06cOKFLly5Zhh06dCjLS7OykpMa8Ndff+nBBx+0+puYzeZMlznfrGHDhrKxsVFkZKTlEcrS/9WAX375xeqSvJzUGg8Pj1ypAenp6dq8ebPlA1F7e3u1bNlSw4YNU1xcnNW6v51iFS7S09MVHx+v+Ph4nT17Vr/99psmTpyoadOmaeDAgWrbtm2Wr9u2bZsGDx6szZs3Ky4uTsePH9eiRYvk6OhoebO4uLjoxIkTioqKuu3NRVlxdHTU+PHj9ccff+jgwYOaNm2aKlasqODgYEk3Dk5TU1O1aNEixcbGasuWLZkeN5nxCcNPP/2kw4cPZ3oTlS9fXl27drU8rSg2Nlbfffedpk+frsaNG1s+3cmp5557Ttu3b9f8+fN14sQJ7d+/X8OHD5ebm1umx7LdjZCQEF26dEnffPON5TrRDE2bNtXx48czvUFLliwpT0/PTP+cnJzk4uJi+fl2li9frh07duivv/7SjBkzFBcXp65du0q6keQ9PT01atQo7dy5U3Fxcfrhhx/01FNPaeLEidlOs1u3bnJzc1NYWJgOHjyov/76S++8845+/PFHPf/887ftT7du3eTq6qphw4Zp3759OnXqlDZt2qQePXrc9ovPAgICtGfPHu3cuVN///23Zs2apfT0dNnZ2engwYNWn6z98ccfioiI0IkTJ7R161Z9/PHHevjhh60+KbO3t7faRmfOnKkKFSrc9lu5H3nkEdnY2Ojjjz++7U18d+uRRx5RfHy8Zs6cqb/++kvbtm3TkiVL7viJXsZ744cffrA83vduPPvss/r99981efJkHT16VCdOnFBERIQ6deqU7SMYJalKlSqqXr26li1bZnWPSVBQkDZt2qTz589brbeBAwfqu+++07x58xQTE6Njx45pxowZ6tq1a7bX2/73v//Vd999p9WrV+vvv//WihUrrG6svVv29vZydHTU/v37dfToUV25ckUzZ87U6NGjdfDgQZ0+fVq//vqrNmzYcNszfkUdtYPakV3tuJd97c1u3S+VKFFCzz77rD755BMtX75cJ06c0JEjR/TKK6+oR48emT6cupOAgAAdO3ZMmzZtUmxsrN577z0dOHBADz74oA4fPmx1FiMxMVHTp09XTEyM9uzZo3fffVf+/v6W7+TI6O+4ceMUHR2to0ePasqUKXJ0dLztPRQNGzZUjRo1FBERYVgNSE1N1ZQpU3Ts2DH9/PPPmjRp0h3PcN1pG89O3759lZycrJEjRyoqKkqxsbH67LPP1KVLF0VGRmb7OgcHBwUFBWnZsmVW91cFBQVp7969io6OttzMLeWs1jzyyCOKiorS0qVL9ffff2v9+vVZ3lx/N1xcXHT48GEdOXJEFy9e1NKlSzVixAj98ssvOn36tKKjo/Xpp5/K09Pzrr8Dp1hdFnXx4kXLH9TGxkaurq6qV6+eli5davWHvtXw4cNlZ2enGTNm6Ny5c3JycpK3t7eWLFliuRTmmWee0ejRo/XEE0/opZdeko+Pz133KyQkRJ6ennruued04cIFeXt7a9GiRZYb9R599FHt379fH3/8sZYuXarAwEC9/vrr6tChg2Uafn5+atOmjT744AOtWbNG27dvzzSfyZMny93dXeHh4YqPj1eZMmXUtm3bbB9heS+6dOmi9PR0ffDBB5bi2ahRI02bNu2eP3GRbuwYS5curUuXLlmuS8zg5+cnJycn/fvvv5meUX4/7Ozs9Oqrr2ry5Mk6cuSI3Nzc9Morr6hVq1aSbhyILVu2TOHh4Ro5cqQuXbqkcuXKqUOHDpm+2+Bm7u7uWrFihWbOnKlnnnlG165d00MPPaQZM2aoS5cut+2Tm5ubPv74Y4WHh+uFF17Q5cuX9eCDD+rpp5/Wc889l+3rRowYofj4eA0dOlQODg7q3LmzJk2aJCcnJ33yySeysbHRkCFDJN14vOPx48fVs2dPpaSkqFmzZnr11VetptewYUP5+flp4MCBio+PtzwbPbvLsqQbO9iMZ2jn9B6Ym3Xs2FEnT57UypUr9emnn8rf31/Tp09Xnz59bnuZUmhoqOrXr68333xTnp6emQ6ushMUFKSlS5dq3rx5evzxx5Weni4vLy/NnTv3jtfLh4SE6KOPPrI6IA8KCtLkyZPl6+tr9Z7o2LGjbG1ttWTJEi1evFglSpSQn5+fli5dmu2npRmP6H3jjTdkZ2enli1b6tVXX1Xv3r3v6Vu8bWxsNHjwYC1atEhPPvmkli5dqmXLlmnmzJl67rnnlJycrPLly6t58+Y5evpaUUHtoHZk5272tVk9xjmr/dLAgQNVqlQprVy5UjNnzpS9vb0aNmyolStX3vP9Tn379tXx48c1adIk2djYqFWrVpo5c6ZWrVqlt956S6NGjbI8Cr1z584qUaKE+vbtq8TERAUGBmratGlW06tataq6du2ql156SXFxcfLw8NCCBQvueL/DI488oqVLl6pz58731P+sBAYGaurUqVq4cKG6deum2rVr65VXXtH06dNvWwPuZhvPioeHh1asWKG5c+eqb9++Sk1NVfXq1TVmzBj17t37tq8NCQnRTz/9ZPlyTenGfT8XL16Uq6ur5aZ5KWe1ZsCAAYqPj1dERITeffddNWrUSG+88YYeeeSRe6oB0o0PuKZNm6bevXtr+vTpWrBggWbMmKHhw4fr0qVLKlOmjBo1aqTXXnvt7idqBlAsxcbGmj09Pc0ff/zxbcdr1aqVecSIEfc8/eTkZHOzZs3MS5cuzWkXraSnp5vPnj1rTk9PtwwzmUxmT09P85IlSwyZR2GRkpJiPn/+vNWwb7/91uzp6Wk+cOBAPvUKQGHj6elpnjVr1m3Heeqpp8w9evS452mnpaWZO3fubH7ttddy2r1MLly4YE5JSbH8npqaam7UqJF58uTJhs2jMLh+/br53LlzVsMOHz5s9vT0NG/atCmfevV/itVlUQByX1JSko4dO6bhw4fLycnJ8k2092vXrl1q3ry55syZo5MnT+ro0aN65ZVX5OTkpI4dOxoyj8Ji/vz5atWqldavX6+4uDj9/PPPmjNnjurWrXtXjzAFgNxy+fJlnTx5UuPHj9eZM2c0aNAgQ6YbExOj5s2b69VXX1VMTIxiYmI0ZcoUJSYmWp0hKA7Wrl2rkJAQffjhhzp16pQOHjyoKVOmqFKlSoZcKXC/itVlUQBy34oVK7RgwQIFBgYqIiIi0+P+cio4OFizZs3SBx98oI8++kj29vby9vbWsmXLbvtFY0XRiy++aPlyp7Nnz8rd3V2NGjXSqFGjbvsUEwDIbVu2bNHYsWNVp04dLVmy5L4f5pGhZs2aWrRokRYsWKAePXrI1tZWtWrV0uLFi60uMyoOevTooeTkZEVGRmrOnDkqXbq0/P39NW3atFx5cta9sjGbc/iYCgAAAAC4CR9xAQAAADAE4QIAAACAIQgXAAAAAAzBDd268YzhlJQUw246AoDCJj4+Xvb29vrll1/yuysFCvUBQHF3r/WBcCHp2rVrSktLy+9uAEC+uX79+l1/c21xQn0AUNzda30gXEiqUKGCJOm7777L554AQP6407eOF1fUBwDF3b3WB+65AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwgUKrZ8+emjdvXn53AwAAoEDLy2OmEnkylyKo+tgv83R+J97scE/jt27dWs8995x69+5tNfzHH3/Uc889p99///2O01i9erVat24td3f3e5r33dq8ebO8vLzk4eGRK9MHAAD5j2Om+1eYjpk4c4EspaWl6c0331RCQkKuzeOdd97R33//nWvTBwAAyG0cM1kjXBRjrVu31qpVq/T8888rMDBQDz/8sHbs2CFJatSokf7991899thjmj9/vvbs2aPAwEAtW7ZM9evX12+//SZJ+uijj/TII4+oXr166tChg7Zs2WKZ/sWLFzVs2DA1bdpUQUFBeu6553T69GlJUufOnfXnn39q8ODBeuWVVyRJR48e1dNPP62goCA1adJEU6dOVWpqqmV6CxYsUEhIiBo3bqwFCxbk1WoCAADFHMdMd49wUcy99957Gjp0qPbs2aNGjRrpjTfekCStX7/e8v/QoUMlSampqfr777+1c+dOBQQEaPPmzZo/f75mzZqlffv2afjw4RoxYoT++ecfSdKsWbOUnJys7777Tj/88IMkWaa/YcMGSdK7776r6dOn68qVK3r22WcVHBysnTt3atWqVdqzZ4/ee+89SdKOHTsUERGht99+Wz/++KPMZrP++OOPvFtRAACgWOOY6e4QLoq5Vq1ayd/fX/b29mrfvr1OnDih9PT0LMdNTU3VE088IUdHR9nY2Gj16tXq3r27fH19VaJECbVr104NGjTQxo0bJUmvvfaa5s2bJycnJ5UqVUoPP/ywDh06lOW0v//+e5nNZg0cOFD29vaqWrWqBgwYYHnDfvvtt2rRooUaNGggBwcHy3gAAAB5gWOmu8MN3cVclSpVLD87OjoqLS3N6rTarf7zn/9Yfj558qR++uknffjhh5ZhZrNZtWrVkiT9/fffevPNN3Xw4EFdvXpV6enpcnNzy3K6sbGxunDhgvz8/KymlfFmOHv2rGrUqGFpK1mypFXfAQAAchPHTHeHcFFElSxZUlevXs00PCkpSQ4ODpbfbW3v7eRViRL/t8k4Ojpq5MiR6t+/f6bx0tPTNXDgQDVo0EDffPON3N3dtWrVKr311ltZTtfBwUG1a9fWF198kWV7SkqKrl+/nmkeAAAA94NjJmNxWVQRVaNGDUVHR2ca/ttvv8nT09OQeVSrVi3T49n++ecfmc1mnT9/XnFxcerTp4/lsWyHDx++7bRiY2OVnJxsGZaQkKCkpCRJUoUKFXTmzBlLW0pKimJjYw1ZDgAAUHxxzGQswkUR9eyzz+rrr7/Wp59+qsuXL+vy5ctas2aNIiMjNXbs2Du+3tHRUZJ04sQJy8Z6q8cff1ybNm3S999/r+vXr2v37t3q2LGjDhw4IHd3dzk5OWn//v26du2avvjiCx05ckRJSUmWN4ODg4P+/vtvJSUlKSQkRO7u7poxY4aSkpIUHx+v4cOHKzw8XJLUokUL7dixw3K6cP78+Zy5AAAA941jJmMRLoqooKAgrVixQps3b1arVq3UqlUrrV27VvPmzVNQUNAdX1+uXDm1b99ew4cPz/a0XLNmzTRmzBhNmTJF9evX15QpUzR58mQFBASoRIkSmjx5siIiIhQcHKy9e/dq3rx5qlSpktq1aydJ6tWrl2bOnKmXX35ZJUuW1Lvvvqvjx4+rWbNm6tKli6pXr64xY8ZIkh555BH17dtXL7zwgkJDQ2Vvb6+AgACjVhcAACimOGYylo3ZbDbn2dwKqDZt2kiSvvvuu3zuCQDkD/aDWWO9ACju7nU/yJkLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiiR3x0AwsPDdeDAAa1YsSK/uwIYb7JrHs/vUt7OD8gl1cd+mWfzOvFmhzybF3A/CsMxE+EipwrwAUP//v21d+9eSVJaWprS09NVsmRJS/vXX3+typUrG9a16OhoXbp0ScHBwYZNEwAAFBEcM1kUh2MmwkUR9P7771t+njdvnrZv367PPvss1+a3Zs0aOTk5Fek3CgAAKHo4ZjIe91wUQ15eXlq2bJlCQkIUEREhSdq1a5cef/xxBQYGqnnz5lqwYIFlfLPZrPDwcIWGhiowMFBdu3a1pPzXX39dH3/8sd5//321bdtWkmQymTRq1CiFhIQoMDBQgwYN0tmzZy3T27p1q9q3b6/AwECNGDFCV69ezcOlBwAAuDscM907zlwUU1u2bNG6detUtmxZnTlzRoMHD9akSZPUqVMnHTt2TM8++6yqVaumTp06af369Vq3bp1Wr16t8uXLa+HChRo2bJh27NihiRMn6o8//lC9evU0atQoSdLYsWNVokQJffnll7Kzs9OkSZP0yiuv6P3331diYqLCwsI0atQoPf7449q1a5dGjhwpb2/vfF4jAGA87hsACj+Ome4NZy6KqUceeUTlypWTjY2NNm7cqNq1a6tLly6ys7OTl5eXevXqpfXr10uSOnXqpK+++kqVKlWSnZ2dOnTooIsXL+qff/7JNN0LFy5o27ZtCgsLk6urq5ydnTVq1Cj99NNPio+P144dO+Tk5KQnn3xS9vb2Cg0NVVBQUF4vPgAAwF3hmOnecOaimPrPf/5j+fnkyZOKioqSn5+fZZjZbFaNGjUkSVeuXNEbb7yhH3/8UZcu/d9NUikpKZmmGxsbK0nq0qWL1XA7OzudPn1aZ86c0YMPPihb2//LtdWrV1d0dLQhywUAAGAkjpnuDeGimLKzs7P87OjoqNDQUC1atCjLcV977TX9/vvvWrlypTw8PBQbG2u5VvBWjo6OkqQff/xRZcqUydS+c+dOpaWlWQ1LT0/P6WIAKMLi4uL0xhtv6JdffpGdnZ1atGihcePGKTExUW3atJG9vb3V+CNGjNCAAQMkSZs2bdLChQt16tQp1ahRQy+99JJCQkIk3djnvP3229q4caMSExPl7++vyZMnq2rVqnm+jAAKPo6Z7g3hAqpWrZq2bNkis9ksGxsbSVJ8fLxcXV1lb2+vgwcPqkePHqpevbok3TYxV65cWba2tvr999/VpEkTSVJqaqouXryoihUrqkKFCjp79qzVvGJiYnJ3AQEUSi+88IJ8fX21detW/fvvvxoyZIhmzJihQYMGSZKioqKyfN2RI0c0ZswYzZ8/X02aNNE333yjoUOH6uuvv1alSpW0cuVKffHFF1qyZIkqVqyouXPnasiQIVq/fr1lv4RipgA/KhUFC8dMd8Y9F1CHDh1kMpn07rvv6urVq4qNjVX//v314YcfSpKqVKmiqKgopaSkaP/+/fryyxs3KJ47d06S5ODgoFOnTunSpUsqXbq0Hn30UYWHh+vMmTO6evWq5syZo/79+8tsNis4OFhJSUn69NNPlZKSoi1btujAgQP5tuwACqbExET5+vpq5MiRKlWqlCpVqqSuXbvql19+ueNrV61apdDQUIWGhsrBwUGdO3eWp6enNmzYIEmKjIxUv379VLNmTTk7OyssLEwxMTHsiwDcEcdMd8aZC6hMmTJ69913NXPmTC1atEju7u567LHH1L9/f0nSyJEjNXr0aDVq1Ej16tXTzJkzJUmDBw/WRx99pG7dumnChAlq166ddu7cqYkTJ+r1119Xhw4dZGtrq4CAAL377ruysbFRpUqVNHv2bIWHh2vGjBlq0aKFnnjiCf3222/5uQoAFDAuLi6aPn261bDTp0+rQoUKlt9Hjx6tnTt36vr16+rRo4eGDRumkiVLKjo6WqGhoVav9fHxUVRUlK5evapjx47Jx8fH0ubs7CwPDw9FRUUpICAgV5crV/HpO5DrDD9msn9Prye6qMPDzWUrKaBcqt5tkCib19xUSdLsxg4KD5+gGa+/qhb/SdETVa/rtxMXcvZ+z6P3LOEipwrJTvXFF1/Uiy++aDXs999/zzRekyZNtHbt2iyn4ePjo40bN1oNmz9/vuXnunXr6tFHH7X87ubmptmzZ2fbp/bt26t9+/Z31X8AkG5cAvXRRx9p4cKFsre3V2BgoNq2batp06bpyJEjevHFF1WiRAkNHz5cJpNJrq7WhdfV1VXHjh3TpUuXZDabs2xPSEjIy0UCig+OmSw/ZzpmcjBrdnD266d91WtqX/XaXfW/oCBcAAAKtH379mnQoEEaOXKk5VttP/30U0u7v7+/Bg4cqMWLF2v48OGSbjy95Xbu1A7gHuTlWbNCElSKs3y95yIuLk5DhgxR48aNFRwcrLFjxyoxMVHSjRvynnrqKTVo0EDt2rWz+np26caTQDp16qTAwEB169ZNO3bssLSlp6dr7ty5atOmjRo2bKgBAwZYHvcFACg8tm7dqueff17jxo1T3759sx2vcuXKOn/+vMxms8qUKSOTyWTVbjKZ5O7uLjc3N9na2mbZXrZs2VxYAgAoXvI1XLzwwgtycXHR1q1btXbtWv3555+aMWOGrl69qoEDB6pJkybavn275s6dq8WLF2vz5s2S/u9JIKNGjdLu3bvVr18/DR06VGfOnJEky5NAIiIitG3bNlWvXl1DhgzhkyoAKER+/fVXjRkzRm+//bbVc+B37dqlhQsXWo17/PhxVa5cWTY2NvL19dWhQ4es2qOiolSvXj05ODiodu3aVk9wSUxM1MmTJ+Xv75+rywMAxUG+hYvbPQnk+++/V2pqqgYNGiQnJyfVrVtXPXr0UGRkpCSeBAIARd3169c1YcIEjRo1yvL9FBlKly6tBQsWaP369UpNTVVUVJTee+899e7dW5LUs2dP7dy5U99//72uXbum1atX68SJE+rcubMkqXfv3lq+fLliYmKUlJSk8PBweXt7W30pFgAgZ/LtnovbPQkkOjpaXl5eVl9a4uPjo1WrVklS8X0SCAAUE/v371dMTIymTp2qqVOnWrV9/fXXmjt3rubPn69XX31VpUuXVp8+ffT0009Lkjw9PRUeHq7p06crLi5OtWrV0uLFi1W+fHlJUq9evRQfH68+ffooOTlZjRs3trrhEgCQcwXmhu6bnwTy1VdfycXFxardzc1NJpNJ6enpPAkEyGs84hJ5LCgoKMuntGSoXLlytt96K0nt2rVTu3btsmyzsbHRsGHDNGzYsPvuJwDAWoH4Er19+/ZpwIABVk8CycrN35zKk0AAAACAgiXfw0VWTwJxd3fPdJbBZDJZnvLBk0AAAACAgidfw0V2TwLx9fXV77//ruvXr1uGZTzpI6OdJ4EAAAAABUu+hYvbPQkkNDRUzs7OWrhwoa5cuaIDBw5o9erVPAkEAAAAKMDy7YbuOz0JZNGiRZo0aZIiIiJUrlw5hYWFqWXLlpJ4EggAAABQEOVbuLjTk0Ak6ZNPPsm2jSeBAAAAAAVLvt/QDQAAAKBoIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwRIn87gAAAABQEFQf+2Wezu+EY57OLk9w5gIAAACAIQgXAAAAAAxBuAAAAABgCO65AAAAKEK4bwD5iTMXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBA8LQrFx2TXPJ7fpbydHwAAQD7jzAUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiiRH53YPv27RozZowaN26suXPnWoZPmDBB69evtxo3LS1Njz32mKZPn66xY8dqw4YNsrOzs7Q7ODjol19+kSSZTCZNnjxZP//8s2xtbRUaGqqJEyfK0dExbxYMAAAAKGbyNVwsWbJEq1evloeHR6a2qVOnaurUqZbfr1+/ri5duui///2vZdigQYP04osvZjntiRMnKiUlRRs3blRqaqqGDx+u8PBwTZgwwfgFAQAAAJC/l0U5ODhkGy5u9eGHH+o///mPQkND7zju+fPntWXLFoWFhcnd3V0VK1bU4MGDtWbNGqWmphrRdQAAAAC3yNdw0bdvX5UuXfqO4yUmJmrRokV6+eWXrYbv3r1bXbp0UWBgoLp3765Dhw5Jko4cOSI7Ozt5eXlZxq1bt64uX76s48ePG7sQAAAAACQVkhu6P/roIzVs2FC1a9e2DKtatao8PDy0ePFibd++XUFBQerfv78SEhJkMpnk7OwsGxsby/iurq6SpISEhDzvPwAAAFAc5PsN3XeSlpamlStXavbs2VbDhwwZYvX7yy+/rI0bN2rLli1ydHSU2WzOy24CAAAAxV6BP3Oxd+9epaSkKCgo6Lbj2dnZ6cEHH9S5c+fk7u6upKQkpaWlWdpNJpMkqWzZsrnZXQAAAKDYKvDh4rvvvlOTJk1UosT/nWQxm82aPn26jh49ahmWkpKikydPqmrVqvL29pbZbLZqj4qKkouLi2rUqJGn/QcAAACKiwIfLo4cOaIqVapYDbOxsdGpU6f02muv6ezZs0pOTlZ4eLhKliyphx9+WO7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3q5ACAAAAwDj5eqTt5+cn6cZ3WEjSli1bJN04y5AhPj5e5cqVy/TaadOmacaMGerWrZuSkpLk7++vDz/8UE5OTpKkKVOmaNKkSWrTpo1Kliypjh07KiwsLLcXCQAAACi28jVc3BwisvPNN99kOdzNzU3Tp0/P9nWlS5fWnDlzctw3AAAAAPeGa4QKm8mueTy/S3k7PwAAABRaBf6eCwAAAACFA+ECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAUCDFxcVpyJAhaty4sYKDgzV27FglJiZKko4cOaKnnnpKDRo0ULt27fT+++9bvXbTpk3q1KmTAgMD1a1bN+3YscPSlp6errlz56pNmzZq2LChBgwYoNjY2DxdNgAoqggXAIAC6YUXXpCLi4u2bt2qtWvX6s8//9SMGTN09epVDRw4UE2aNNH27ds1d+5cLV68WJs3b5Z0I3iMGTNGo0aN0u7du9WvXz8NHTpUZ86ckSStXLlSX3zxhSIiIrRt2zZVr15dQ4YMkdlszs/FBYAigXABAChwEhMT5evrq5EjR6pUqVKqVKmSunbtql9++UXff/+9UlNTNWjQIDk5Oalu3brq0aOHIiMjJUmrVq1SaGioQkND5eDgoM6dO8vT01MbNmyQJEVGRqpfv36qWbOmnJ2dFRYWppiYGB04cCA/FxkAigTCBQCgwHFxcdH06dNVrlw5y7DTp0+rQoUKio6OlpeXl+zs7CxtPj4+OnTokCQpOjpaPj4+VtPz8fFRVFSUrl69qmPHjlm1Ozs7y8PDQ1FRUbm8VABQ9BEuAAAFXlRUlD766CMNGjRIJpNJLi4uVu1ubm4ymUxKT0+XyWSSq6urVburq6sSEhJ06dIlmc3mbNsBAPeHcAEAKND27dunAQMGaOTIkQoODs52PBsbG8vPd7p/gvsrACB3EC4AAAXW1q1b9fzzz2vcuHHq27evJMnd3T3TWQaTySQ3NzfZ2tqqTJkyMplMmdrd3d0t42TVXrZs2dxcFAAoFggXAIAC6ddff9WYMWP09ttvq0uXLpbhvr6++v3333X9+nXLsKioKNWrV8/SnnH/xa3tDg4Oql27tqKjoy1tiYmJOnnypPz9/XN3gQCgGCBcAAAKnOvXr2vChAkaNWqUQkJCrNpCQ0Pl7OyshQsX6sqVKzpw4IBWr16t3r17S5J69uypnTt36vvvv9e1a9e0evVqnThxQp07d5Yk9e7dW8uXL1dMTIySkpIUHh4ub29v+fn55flyAkBRUyK/OwAAwK3279+vmJgYTZ06VVOnTrVq+/rrr7Vo0SJNmjRJERERKleunMLCwtSyZUtJkqenp8LDwzV9+nTFxcWpVq1aWrx4scqXLy9J6tWrl+Lj49WnTx8lJyercePGmj9/fl4vIgAUSYQLAECBExQUpN9///2243zyySfZtrVr107t2rXLss3GxkbDhg3TsGHD7quPAIDMuCwKAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYIh8Dxfbt29XcHCwwsLCrIavXbtWderUkZ+fn9W/gwcPSpLS09M1d+5ctWnTRg0bNtSAAQMUGxtreb3JZNKIESMUHByskJAQjR8/XlevXs3TZQMAAACKk3wNF0uWLNHUqVPl4eGRZXvDhg0VFRVl9c/f31+StHLlSn3xxReKiIjQtm3bVL16dQ0ZMkRms1mSNHHiRF25ckUbN27UmjVrFBMTo/Dw8DxbNgAAAKC4yddw4eDgoNWrV2cbLm4nMjJS/fr1U82aNeXs7KywsDDFxMTowIEDOn/+vLZs2aKwsDC5u7urYsWKGjx4sNasWaPU1NRcWBIAAAAA+Rou+vbtq9KlS2fbfvr0aT3zzDNq2LCh2rRpo/Xr10uSrl69qmPHjsnHx8cyrrOzszw8PBQVFaUjR47Izs5OXl5elva6devq8uXLOn78eO4tEAAAAFCMlcjvDmTH3d1d1atX10svvaRatWrp22+/1ejRo1WhQgU99NBDMpvNcnV1tXqNq6urEhIS5ObmJmdnZ9nY2Fi1SVJCQkKeLgcAAABQXOT7Dd3ZadmypZYuXSofHx/Z29urQ4cOatu2rdauXWsZJ+P+iqzcrg0AAACA8QpsuMhK5cqVde7cObm5ucnW1lYmk8mq3WQyqWzZsnJ3d1dSUpLS0tKs2iSpbNmyedhjAAAAoPgosOHik08+0aZNm6yGxcTEqGrVqnJwcFDt2rUVHR1taUtMTNTJkyfl7+8vb29vmc1mHT161NIeFRUlFxcX1ahRI8+WAQAAAChOCmy4SElJ0euvv66oqCilpqZq48aN+vHHH9WrVy9JUu/evbV8+XLFxMQoKSlJ4eHh8vb2lp+fn9zd3dW+fXu99dZbunjxos6cOaMFCxaoe/fuKlGiwN5mAgAAABRq+Xqk7efnJ0m6fv26JGnLli2Sbpxl6Nu3r5KTkzV8+HDFx8erSpUqWrBggXx9fSVJvXr1Unx8vPr06aPk5GQ1btxY8+fPt0x7ypQpmjRpktq0aaOSJUuqY8eOmb6oDwAAAIBx8jVcREVFZdtmY2OjwYMHa/Dgwdm2Dxs2TMOGDcuyvXTp0pozZ44h/QQAAABwZwX2sigAAAAAhQvhAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBD5Hi62b9+u4OBghYWFZWrbvHmzOnfurMDAQLVv316fffaZpW3evHny9vaWn5+f1b/z589Lkq5du6ZXX31VLVq0UOPGjTVs2DAlJCTk2XIBAAAAxU2+hoslS5Zo6tSp8vDwyNR28OBBjRo1SsOGDdPevXs1btw4TZkyRb/88otlnMcee0xRUVFW/8qVKydJmjt3rqKjoxUZGalvvvlGZrNZr7zySp4tGwAAAFDc5Gu4cHBw0OrVq7MMFyaTSQMHDtTDDz+sEiVKKDQ0VJ6enlbhIjvXr1/X6tWrNXjwYD344INyc3PTiBEj9P333+vs2bO5sSgAAABAsZev4aJv374qXbp0lm0tWrTQkCFDLL9fv35d8fHxqlixomXY77//rl69eql+/frq0KGDduzYIUk6efKk/v33X9WtW9cybs2aNeXo6Kjo6OhcWhoAAACgeMv3ey7uVnh4uJycnPToo49KkipVqqSqVatqxowZ+umnn9SjRw+98MILOn78uEwmkyTJxcXFahouLi7cdwEAAADkkgIfLsxms2bNmqWNGzdq4cKFcnBwkCT16NFD77zzjjw8PPTAAw+oX79+8vb21oYNG6xeCwAAACBvlMjvDtxOenq6XnnlFR08eFCffPKJqlatetvxK1eurHPnzsnd3V3Sjfs2SpUqZWm/dOmSypYtm6t9BgAAAIqrAn3m4o033tCff/6ZZbB49913tWvXLqthMTExqlq1qqpWrSpXV1er+yv++OMPpaSkyNfXN0/6DgAAABQ3BTZc7Nu3Txs2bFBERITc3NwytZtMJr322ms6fvy4rl27pvfff18nT55U165dZWdnp549e2rRokU6ffq0EhISNGfOHLVt29byqFoAAAAAxsrXy6L8/Pwk3XgSlCRt2bJFkhQVFaU1a9bo33//VatWraxe07BhQ73//vsaOXKkJKlfv34ymUyqVauWli1bpkqVKkmShg0bpuTkZD322GO6fv26WrVqpcmTJ+fRkgEAAADFT76Gi6ioqGzb3njjDb3xxhvZtjs4OGjcuHEaN25clu329vaaNGmSJk2adN/9BAAAAHBnBfayKAAAAACFC+ECAAAAgCEIFwCAAmn79u0KDg5WWFiY1fC1a9eqTp068vPzs/p38OBBSTceYz537ly1adNGDRs21IABAxQbG2t5vclk0ogRIxQcHKyQkBCNHz9eV69ezdNlA4CiinABAChwlixZoqlTp8rDwyPL9oYNGyoqKsrqn7+/vyRp5cqV+uKLLxQREaFt27apevXqGjJkiOWLVSdOnKgrV65o48aNWrNmjWJiYhQeHp5nywYARRnhAgBQ4Dg4OGj16tXZhovbiYyMVL9+/VSzZk05OzsrLCxMMTExOnDggM6fP68tW7YoLCxM7u7uqlixogYPHqw1a9YoNTU1F5YEAIoXwgUAoMDp27evSpcunW376dOn9cwzz6hhw4Zq06aN1q9fL0m6evWqjh07Jh8fH8u4zs7O8vDwUFRUlI4cOSI7Ozt5eXlZ2uvWravLly/r+PHjubdAAFBM5OujaAEAuFfu7u6qXr26XnrpJdWqVUvffvutRo8erQoVKuihhx6S2WyWq6ur1WtcXV2VkJAgNzc3OTs7y8bGxqpNkhISEvJ0OQCgKOLMBQCgUGnZsqWWLl0qHx8f2dvbq0OHDmrbtq3Wrl1rGSfj/oqs3K4NAHB/CBcAgEKvcuXKOnfunNzc3GRrayuTyWTVbjKZVLZsWbm7uyspKUlpaWlWbZJUtmzZPOwxABRNhAsAQKHyySefaNOmTVbDYmJiVLVqVTk4OKh27dqKjo62tCUmJurkyZPy9/eXt7e3zGazjh49ammPioqSi4uLatSokWfLAABFFeECAFCopKSk6PXXX1dUVJRSU1O1ceNG/fjjj+rVq5ckqXfv3lq+fLliYmKUlJSk8PBweXt7y8/PT+7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3lSjBbYgAcL/YkwIAChw/Pz9J0vXr1yVJW7ZskXTjLEPfvn2VnJys4cOHKz4+XlWqVNGCBQvk6+srSerVq5fi4+PVp08fJScnq3Hjxpo/f75l2lOmTNGkSZPUpk0blSxZUh07dsz0RX0AgJwhXAAACpyoqKhs22xsbDR48GANHjw42/Zhw4Zp2LBhWbaXLl1ac+bMMaSfAABrXBYFAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxheLgwm81GTxIAUARQHwCg6MtRuGjfvn2WwxMTExUcHHxfHQIAFF7UBwAo3krcy8i7du3Szp07FRcXpzlz5mRqP3XqlFJSUgzrHACgcKA+AACkewwXrq6uunz5stLS0vTbb79land0dNTUqVMN6xwAoHCgPgAApHsMFz4+PvLx8ZGNjY0mTJiQW30CABQy1AcAgHSP4SLDhAkTFBUVpZiYGF27di1T++OPP37fHQMAFD7UBwAo3nIULt544w0tX75c7u7ucnR0tGqzsbGheABAMUV9AIDiLUfhYt26dfrggw/UtGlTo/sDACjEqA8AULzl6FG09vb2CgoKMrovAIBCjvoAAMVbjsJFv3799P777xvdFwBAIUd9AIDiLUeXRf3666/69ddftWLFCv3nP/+Rra11Rvn0008N6RwAoHChPgBA8ZajcJHxyEEAAG5GfQCA4i1H4WLo0KFG9wMAUARQHwCgeMtRuJg/f/5t2ykuAFA8UR8AoHjLUbjYvn271e9paWmKi4uT2WxW/fr1DekYAKDwoT4AQPGWo3ARGRmZaVh6eroWLVoke3v7++4UAKBwoj4AQPGWo0fRZjkhW1s999xzPIIQAGCF+gAAxYdh4UKS9u7dq+vXrxs5SQBAEUB9AIDiIUeXRYWEhGQadvXqVSUnJ6tfv3732ycAQCFFfQCA4i1H4WLkyJGZhjk4OMjDw0N169a9704BAAon6gMAFG85Chddu3aVJKWmpurcuXOysbFRxYoVZWdnZ2jnAACFC/UBAIq3HIWLxMRETZo0SVu2bLFcQ+vg4KCOHTtq4sSJcnBwMLSTAIDCgfoAAMVbjsLF5MmTFR8fr/nz58vDw0OSFBMTo0WLFik8PFzjx483tJMAgMKB+gAAxVuOv0Tvm2++kbu7u2VY9erV5evrq169elE8AKCYoj4AQPGWo0fR2tnZ6YEHHsg03MXFRZcvX77vTgEACifqAwAUbzkKF/Xr19eUKVN08eJFy7CLFy/q9ddfl5+fn2GdAwAULtQHACjecnRZ1KRJkzRo0CA1a9ZMLi4ukqRLly6pZs2aWrhwoaEdBAAUHtQHACje7jlcpKSkKCUlRWvXrtXRo0d16tQppaSkqEKFCgoMDORxgwBQTFEfAAD3FC4uXbqkJ554QvXq1dMbb7yhOnXqqE6dOpKkbt26qVSpUnrvvfdkb2+fK50FABRM1AcAgHSP91zMnz9f7u7umjBhQqa2lStXymw2a8mSJYZ1DgBQOFAfAADSPYaLbdu2afz48XJycsrU9sADD2j8+PHauHGjYZ0DABQO1AcAgHSP4eLChQvy8vLKtr1OnTo6c+bMfXcKAFC4UB8AANI9hgsnJyclJCRk237u3Lksn28OACjaqA8AAOkew0XTpk21bNmybNtnzpypJk2a3G+fAACFDPUBACDd49OihgwZou7duys2NlZPPvmkatSoobS0NB07dkzvv/++Dhw4oM8++yy3+goAKKCoDwAA6R7DRY0aNfTRRx/p9ddf11NPPSUbGxtJktlsVqNGjfTRRx+pRo0audJRAEDBRX0AAEg5+BI9b29vffzxx7p48aJiY2NlY2OjatWqyc3NLRe6BwAoLKgPAIB7DhcZ3N3d5e7ubmRfAABFAPUBAIqve7qhOzds375dwcHBCgsLy9S2adMmderUSYGBgerWrZt27NhhaUtPT9fcuXPVpk0bNWzYUAMGDFBsbKyl3WQyacSIEQoODlZISIjGjx+vq1ev5skyAQAAAMVRvoaLJUuWaOrUqfLw8MjUduTIEY0ZM0ajRo3S7t271a9fPw0dOtTynPSVK1fqiy++UEREhLZt26bq1atryJAhMpvNkqSJEyfqypUr2rhxo9asWaOYmBiFh4fn6fIBAAAAxUm+hgsHBwetXr06y3CxatUqhYaGKjQ0VA4ODurcubM8PT21YcMGSVJkZKT69eunmjVrytnZWWFhYYqJidGBAwd0/vx5bdmyRWFhYXJ3d1fFihU1ePBgrVmzRqmpqXm9mAAAAECxkK/hom/fvipdunSWbdHR0fLx8bEa5uPjo6ioKF29elXHjh2zand2dpaHh4eioqJ05MgR2dnZWX1bbN26dXX58mUdP348dxYGAAAAKOby/Z6L7JhMJrm6uloNc3V1VUJCgi5duiSz2Zxtu8lkkrOzs+VRiBltkm77DbIAAAAAcq7AhgtJlvsnctJ+p9cCAAAAMFaBDRdlypSRyWSyGmYymeTu7i43NzfZ2tpm2V62bFm5u7srKSlJaWlpVm2SVLZs2VzuOQAAAFA8Fdhw4evrq0OHDlkNi4qKUr169eTg4KDatWsrOjra0paYmKiTJ0/K399f3t7eMpvNOnr0qNVrXVxc+IZYAAAAIJcU2HDRs2dP7dy5U99//72uXbum1atX68SJE+rcubMkqXfv3lq+fLliYmKUlJSk8PBweXt7y8/PT+7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3lSiR4+8NBAAAAHAb+Xqk7efnJ0m6fv26JGnLli2Sbpxl8PT0VHh4uKZPn664uDjVqlVLixcvVvny5SVJvXr1Unx8vPr06aPk5GQ1btxY8+fPt0x7ypQpmjRpktq0aaOSJUuqY8eOWX5RHwAAAABj5Gu4iIqKum17u3bt1K5duyzbbGxsNGzYMA0bNizL9tKlS2vOnDn33UcAAAAAd6fAXhYFAAAAoHAhXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAABRI27dvV3BwsMLCwjK1bdq0SZ06dVJgYKC6deumHTt2WNrS09M1d+5ctWnTRg0bNtSAAQMUGxtraTeZTBoxYoSCg4MVEhKi8ePH6+rVq3myTABQ1BEuAAAFzpIlSzR16lR5eHhkajty5IjGjBmjUaNGaffu3erXr5+GDh2qM2fOSJJWrlypL774QhEREdq2bZuqV6+uIUOGyGw2S5ImTpyoK1euaOPGjVqzZo1iYmIUHh6ep8sHAEUV4QIAUOA4ODho9erVWYaLVatWKTQ0VKGhoXJwcFDnzp3l6empDRs2SJIiIyPVr18/1axZU87OzgoLC1NMTIwOHDig8+fPa8uWLQoLC5O7u7sqVqyowYMHa82aNUpNTc3rxQSAIodwAQAocPr27avSpUtn2RYdHS0fHx+rYT4+PoqKitLVq1d17Ngxq3ZnZ2d5eHgoKipKR44ckZ2dnby8vCztdevW1eXLl3X8+PHcWRgAKEYIFwCAQsVkMsnV1dVqmKurqxISEnTp0iWZzeZs200mk5ydnWVjY2PVJkkJCQm533kAKOIIFwCAQifj/omctN/ptQCAnCNcAAAKlTJlyshkMlkNM5lMcnd3l5ubm2xtbbNsL1u2rNzd3ZWUlKS0tDSrNkkqW7ZsLvccAIo+wgUAoFDx9fXVoUOHrIZFRUWpXr16cnBwUO3atRUdHW1pS0xM1MmTJ+Xv7y9vb2+ZzWYdPXrU6rUuLi6qUaNGni0DABRVhAsAQKHSs2dP7dy5U99//72uXbum1atX68SJE+rcubMkqXfv3lq+fLliYmKUlJSk8PBweXt7y8/PT+7u7mrfvr3eeustXbx4UWfOnNGCBQvUvXt3lShRIp+XDAAKP/akAIACx8/PT5J0/fp1SdKWLVsk3TjL4OnpqfDwcE2fPl1xcXGqVauWFi9erPLly0uSevXqpfj4ePXp00fJyclq3Lix5s+fb5n2lClTNGnSJLVp00YlS5ZUx44ds/yiPgDAvSNcAAAKnKioqNu2t2vXTu3atcuyzcbGRsOGDdOwYcOybC9durTmzJlz330EAGTGZVEAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBN9zcZ+qj/0yT+d3wjFPZwcAAADcNc5cAAAAADBEgT1zsXfvXvXv399qmNlsVmpqqpYvX66+ffvK3t7eqn3mzJl65JFHJEnLly/XypUrFR8fLy8vL40fP16+vr551n8AAACguCmw4aJhw4aKioqyGrZo0SIdPXpUklS5cmVt3bo1y9du3bpV8+bN09KlS+Xl5aXly5frhRde0ObNm+Xk5JTrfQcAAACKo0JzWdQ///yjDz74QKNHj77juJGRkerWrZvq1asnR0dHPfvss5Kkbdu25XY3AQAAgGKr0ISLt99+W//73//0n//8R5KUnJysIUOGqHHjxmrevLk++OADmc1mSVJ0dLR8fHwsr7W1tZW3t3emMyEAAAAAjFNgL4u62alTp7R582Zt3rxZkuTs7CxPT089/fTTmjt3rn7++WcNHz5cpUuXVvfu3WUymeTq6mo1DVdXVyUkJORH9wEAAIBioVCcuVi5cqXatWun8uXLS5Lq1q2rFStWqFGjRrK3t1dISIh69eqltWvXWl6TcRYDAAAAQN4oFOHim2++UevWrW87TuXKlXXu3DlJUpkyZWQymazaTSaT3N3dc6uLAAAAQLFX4MPFkSNHFBcXp2bNmlmGffXVV/r444+txjt+/LiqVq0qSfL19VV0dLSlLS0tTYcPH1a9evXyptMAAABAMVTgw8Xhw4fl5uYmZ2dny7CSJUtqxowZ2rFjh1JTU/XTTz9pzZo16t27tySpd+/eWrdunfbv368rV65o4cKFsre3V8uWLfNpKQAAAICir8Df0H3+/HnLvRYZHn74YY0bN06vv/66Tp8+rXLlymncuHFq166dJKlFixZ66aWXNGLECF24cEF+fn6KiIiQo6NjfiwCAAAAUCwU+HAxcOBADRw4MNPwxx9/XI8//ni2r3viiSf0xBNP5GbXAAAAANykwF8WBQAAAKBwIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGKJHfHUDxVX3sl3k6vxOOeTo7AACAYoczFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEAU6XHh5ecnX11d+fn6Wf6+//rokadeuXerevbvq16+vDh06aMOGDVavXb58udq3b6/69eurd+/eOnToUH4sAgAAAFBslMjvDtzJ119/rSpVqlgNO3funAYPHqzx48erU6dO2rdvnwYNGqQaNWrIz89PW7du1bx587R06VJ5eXlp+fLleuGFF7R582Y5OTnl05IAAAAARVuBPnORnS+++ELVq1dX9+7d5eDgoODgYLVu3VqrVq2SJEVGRqpbt26qV6+eHB0d9eyzz0qStm3blp/dBgAAAIq0Ah8uZs+erZYtWyooKEgTJ05UcnKyoqOj5ePjYzWej4+P5dKnW9ttbW3l7e2tqKioPO07AAAAUJwU6HAREBCg4OBgbd68WZGRkdq/f79ee+01mUwmubi4WI3r5uamhIQESZLJZJKrq6tVu6urq6UdAAAAgPEK9D0XkZGRlp9r1qypUaNGadCgQWrQoMEdX2s2m3Oza0C+qj72yzyd3wnHPJ0dAAAopAr0mYtbValSRWlpabK1tZXJZLJqS0hIkLu7uySpTJkymdpNJpOlHQAAAIDxCmy4OHz4sN58802rYTExMbK3t1doaGimR8seOnRI9erVkyT5+voqOjra0paWlqbDhw9b2gEAAAAYr8CGi7JlyyoyMlIRERFKSUnRX3/9pbfffluPP/64HnvsMcXFxWnVqlW6du2afvjhB/3www/q2bOnJKl3795at26d9u/frytXrmjhwoWyt7dXy5Yt83ehAAAAgCKswN5zUbFiRUVERGj27NmWcNC1a1eFhYXJwcFBixcv1tSpU/Xaa6+pcuXKmjVrlurUqSNJatGihV566SWNGDFCFy5ckJ+fnyIiIuToyIXjAAAAQG4psOFCkho2bKhPP/0027b169dn+9onnnhCTzzxRG51DQAAAMAtCuxlUQAAAAAKF8IFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiiRH53AADyUvWxX+bp/E445unsAADIV5y5AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQCg0PHy8pKvr6/8/Pws/15//XVJ0q5du9S9e3fVr19fHTp00IYNG6xeu3z5crVv317169dX7969dejQofxYBAAokkrkdwcAAMiJr7/+WlWqVLEadu7cOQ0ePFjjx49Xp06dtG/fPg0aNEg1atSQn5+ftm7dqnnz5mnp0qXy8vLS8uXL9cILL2jz5s1ycnLKpyUBgKKDMxcAgCLjiy++UPXq1dW9e3c5ODgoODhYrVu31qpVqyRJkZGR6tatm+rVqydHR0c9++yzkqRt27blZ7cBoMggXAAACqXZs2erZcuWCgoK0sSJE5WcnKzo6Gj5+PhYjefj42O59OnWdltbW3l7eysqKipP+w4ARRXhAgBQ6AQEBCg4OFibN29WZGSk9u/fr9dee00mk0kuLi5W47q5uSkhIUGSZDKZ5OrqatXu6upqaQcA3B/uuQAAFDqRkZGWn2vWrKlRo0Zp0KBBatCgwR1fazabc7NrAFCsceYCAFDoValSRWlpabK1tZXJZLJqS0hIkLu7uySpTJkymdpNJpOlHQBwfwgXAIBC5fDhw3rzzTethsXExMje3l6hoaGZHi176NAh1atXT5Lk6+ur6OhoS1taWpoOHz5saQcA3B/CBQCgUClbtqwiIyMVERGhlJQU/fXXX3r77bf1+OOP67HHHlNcXJxWrVqla9eu6YcfftAPP/ygnj17SpJ69+6tdevWaf/+/bpy5YoWLlwoe3t7tWzZMn8XCgCKCO65AAAUKhUrVlRERIRmz55tCQddu3ZVWFiYHBwctHjxYk2dOlWvvfaaKleurFmzZqlOnTqSpBYtWuill17SiBEjdOHCBfn5+SkiIkKOjo75vFQAUDQQLgAAhU7Dhg316aefZtu2fv36bF/7xBNP6IknnsitrgFAscZlUQAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCnS4iIuL05AhQ9S4cWMFBwdr7NixSkxM1KlTp+Tl5SU/Pz+rf++9957ltZs2bVKnTp0UGBiobt26aceOHfm4JAAAAEDRVyK/O3A7L7zwgnx9fbV161b9+++/GjJkiGbMmKFBgwZJkqKiorJ83ZEjRzRmzBjNnz9fTZo00TfffKOhQ4fq66+/VqVKlfJyEQAAAIBio8CeuUhMTJSvr69GjhypUqVKqVKlSuratat++eWXO7521apVCg0NVWhoqBwcHNS5c2d5enpqw4YNedBzAAAAoHgqsOHCxcVF06dPV7ly5SzDTp8+rQoVKlh+Hz16tEJCQtSkSRPNnj1bqampkqTo6Gj5+PhYTc/HxyfbMx0AAAAA7l+BDRe3ioqK0kcffaRBgwbJ3t5egYGBatu2rbZt26aIiAht2LBB7777riTJZDLJ1dXV6vWurq5KSEjIj64DAAAAxUKhCBf79u3TgAEDNHLkSAUHB6tChQr69NNP1bZtW5UsWVL+/v4aOHCg1q5da3mN2WzOxx4DAAAAxU+BDxdbt27V888/r3Hjxqlv377Zjle5cmWdP39eZrNZZcqUkclksmo3mUxyd3fP5d4CAAAAxVeBDhe//vqrxowZo7fffltdunSxDN+1a5cWLlxoNe7x48dVuXJl2djYyNfXV4cOHbJqj4qKUr169fKi2wAAAECxVGDDxfXr1zVhwgSNGjVKISEhVm2lS5fWggULtH79eqWmpioqKkrvvfeeevfuLUnq2bOndu7cqe+//17Xrl3T6tWrdeLECXXu3Dk/FgUAAAAoFgrs91zs379fMTExmjp1qqZOnWrV9vXXX2vu3LmaP3++Xn31VZUuXVp9+vTR008/LUny9PRUeHi4pk+frri4ONWqVUuLFy9W+fLl82NRAAAAgGKhwIaLoKAg/f7779m2V65cWW3bts22vV27dmrXrl1udA0AAABAFgrsZVEAAAAAChfCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFAAAAAEMQLgAAAAAYgnABAAAAwBCECwAAAACGIFwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCGKbLiIi4vT888/r8aNG6tVq1aaNWuW0tPT87tbAIACgBoBALmjRH53ILe8+OKLqlu3rrZs2aILFy5o4MCBKleunJ555pn87hoAIJ9RIwAgdxTJMxdRUVE6evSoRo0apdKlS6t69erq16+fIiMj87trAIB8Ro0AgNxTJMNFdHS0KleuLFdXV8uwunXr6q+//lJSUlI+9gwAkN+oEQCQe4rkZVEmk0kuLi5WwzKKSEJCgpydna3azp07p7S0NLVp0+ae52V/8XLOO5oDbWzK5en8tP3e18ndYt3lHOsu51h3WTt9+rTs7OwM7kzBdC814n7qg5S321th2dbuFusu54r0fo51l3N5VB+KZLiQJLPZfNfjOjg4KCUlJUfzqerulKPX5Vz1PJ5f7mHd5RzrLudYd1krUaKE7O3t87sbeeZua8T91Acpr7e36nk4r9zHuss59nM5x7rL7F7rQ5EMF+7u7jKZTFbDTCaTbGxs5O7unmn8X375JY96BgDIb/dSI6gPAHBviuQ9F76+vjp9+rQuXrxoGRYVFaVatWqpVKlS+dgzAEB+o0YAQO4pkuHCx8dHfn5+mj17tpKSkhQTE6MPPvhAvXv3zu+uAQDyGTUCAHKPjflebk4oRM6cOaOJEyfq559/lrOzs3r16qWhQ4fKxsYmv7sGAMhn1AgAyB1F8syFJFWqVElLlizRgQMH9NNPP+nFF18sFEXjXr41dvny5Wrfvr3q16+v3r1769ChQ3nc24Jl+/btCg4OVlhY2G3HS09P19y5c9WmTRs1bNhQAwYMUGxsbB71smCKi4vTkCFD1LhxYwUHB2vs2LFKTEzMctxNmzapU6dOCgwMVLdu3bRjx4487m3BcvToUT399NNq0KCBgoODNWLECMXHx2c5Lu/ZgqMw1gjqw/2hRuQcNSLnimWNMKNA6dq1q3nChAnmxMRE819//WVu166d+f3338803nfffWcOCgoy79+/33zlyhXz4sWLzc2aNTMnJyfnQ6/zX0REhLldu3bmXr16mUeMGHHbcZcvX25u1aqV+dixY+Z///3XPGXKFHOnTp3M6enpedTbgqdjx47msWPHmpOSksynT582d+vWzTxu3LhM4x0+fNjs6+tr/v77781Xr141r1+/3lyvXj3z6dOn86HX+e/atWvmpk2bmufPn2++du2a+cKFC+annnrKPHjw4Ezj8p7F/aI+5Bw14v5QI3KmuNaIInvmojC6l2+NjYyMVLdu3VSvXj05Ojrq2WeflSRt27Ytr7tdIDg4OGj16tXy8PC447iRkZHq16+fatasKWdnZ4WFhSkmJkYHDhzIg54WPImJifL19dXIkSNVqlQpVapUSV27ds3yKTmrVq1SaGioQkND5eDgoM6dO8vT01MbNmzIh57nvytXrigsLEwDBw6Uvb293N3d1bZtW/3555+ZxuU9i/tBfbg/1Iico0bkXHGtEYSLAuRevjU2OjpaPj4+lt9tbW3l7e2tqKioPOtvQdK3b1+VLl36juNdvXpVx44ds1p3zs7O8vDwKLbrzsXFRdOnT1e5cv/3RT6nT59WhQoVMo1763Yn3bg5triuO1dXV/Xo0UMlStx4qvfx48f1+eef65FHHsk0Lu9Z3A/qw/2hRuQcNSLnimuNIFwUIHf61thbx725yGSMe+t4sHbp0iWZzWbW3W1ERUXpo48+0qBBgzK1sd1lLS4uTr6+vnr00Ufl5+enYcOGZRqHdYf7QX3IG9SIO6NG3LviViMIFwWM+R4e3nUv48Ia6y5r+/bt04ABAzRy5EgFBwdnOQ7rLrPKlSsrKipKX3/9tU6cOKHRo0dnOR7rDveD+pB3WH9Zo0bkTHGrEYSLAuRevjW2TJkyWY6b1TeQ4/+4ubnJ1tY2y3VXtmzZ/OlUAbF161Y9//zzGjdunPr27ZvlOGx32bOxsVH16tUVFhamjRs3Wn1Bm8S6w/2hPuQNakT2qBH3pzjVCMJFAXIv3xrr6+ur6Ohoy+9paWk6fPiw6tWrl2f9LYwcHBxUu3Ztq3WXmJiokydPyt/fPx97lr9+/fVXjRkzRm+//ba6dOmS7Xi+vr6ZHo0XFRVVbLe7Xbt2qX379laPA7W1vbFbLVmypNW4vGdxP6gPeYMakTVqRM4U1xpBuChA7vStsf/9738tT2fo3bu31q1bp/379+vKlStauHCh7O3t1bJly3xcgoLp7Nmz+u9//2t5Tnnv3r21fPlyxcTEKCkpSeHh4fL29pafn18+9zR/XL9+XRMmTNCoUaMUEhKSqf3pp5/Wpk2bJEk9e/bUzp079f333+vatWtavXq1Tpw4oc6dO+d1twsEX19fJSUladasWbpy5YouXryoefPmKSgoSKVLl+Y9C8NQH3IPNeL2qBE5V1xrRIn87gCsvfPOO5o4caKaNWtm+dbYJ554QpL0119/6fLly5KkFi1a6KWXXtKIESN04cIF+fn5KSIiQo6OjvnZ/XyTsdO/fv26JGnLli2Sbnxikpqaqr/++kspKSmSpF69eik+Pl59+vRRcnKyGjdurPnz5+dPxwuA/fv3KyYmRlOnTtXUqVOt2r7++mvFxsbq0qVLkiRPT0+Fh4dr+vTpiouLU61atbR48WKVL18+P7qe70qXLq33339fU6dOVZMmTeTk5KQmTZpo2rRpknjPwljUh5yjRuQcNSLnimuNsDEXlbtHAAAAAOQrLosCAAAAYAjCBQAAAABDEC4AAAAAGIJwAQAAAMAQhAsAAAAAhiBcAAAAADAE4QIAAACAIQgXAAAAAAxBuAAAAABgCMIFkIu6deummTNnWg2Ljo6Wl5eXNm/ebDV8+fLlCgkJkdlstgz78MMP5eXlpVOnTuVJfwEAeYcagaKIcAHkoubNm2vnzp1Ww3766Sc5OTllGr5z506FhITIxsZGknT27Fm9//77edZXAEDeokagKCJcALmoefPmOnr0qC5evGgZtmvXLnXt2lW7du2yDLt+/br27t2r5s2bW4ZNmzZNvXr1ytP+AgDyDjUCRRHhAshFAQEBcnZ2tnwClZKSol9//VV9+/bVmTNn9M8//0iSDh48qMuXL6tZs2aSpB9++EG///67BgwYkG99BwDkLmoEiiLCBZCLSpQooeDgYP3000+SpH379qlixYqqXr26AgICLAVl586d8vPzk5ubm65evarXX39dr776quzt7fOz+wCAXESNQFFEuABy2c3X1O7cuVNNmjSRJDVt2tRy2nvXrl2W090LFy6Ur6+v5RMqAEDRRY1AUUO4AHJZ8+bNdebMGcXExGj37t1q2rSpJKlJkybavXu3Ll++rAMHDqh58+aKiYnRZ599pldeeSWfew0AyAvUCBQ1hAsgl1WqVEm1a9fWjz/+qCNHjqhx48aSJF9fX125ckVr165VqVKl5O/vr6+++kr//vuvOnfurMaNG1vG7datm5YsWZKfiwEAyAXUCBQ1NuabH5gMIFfMmDFD3377rZydnbVu3TrL8IEDB+r48ePy9fXV3LlzlZSUpKSkJKvXhoaGKjIyUrVq1ZKzs3Me9xwAkNuoEShKOHMB5IHmzZsrNjbWci1thqZNm+rkyZOWa2mdnZ1VqVIlq3+SVK5cOYoGABRR1AgUJZy5AAAAAGAIzlwAAAAAMAThAgAAAIAhCBcAAAAADEG4AAAAAGAIwgUAAAAAQxAuAAAAABiCcAEAAADAEIQLAAAAAIYgXAAAAAAwBOECAAAAgCEIFwAAAAAMQbgAAAAAYIj/B9oTecIMgDdmAAAAAElFTkSuQmCC", + "image/png": 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"text/plain": [ "
" ] @@ -825,7 +825,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f75ffffc", + "id": "242ea0ef", "metadata": {}, "outputs": [], "source": [] diff --git a/main/example_notebooks/dowhy_lalonde_example.html b/main/example_notebooks/dowhy_lalonde_example.html index f01609cb1..e13bcfa19 100644 --- a/main/example_notebooks/dowhy_lalonde_example.html +++ b/main/example_notebooks/dowhy_lalonde_example.html @@ -613,7 +613,7 @@

2. Run DoWhy analysis: model, identify, estimate 
-Causal Estimate is 1639.8323003718579
+Causal Estimate is 1639.8298498687827

@@ -633,10 +633,10 @@

2. Run DoWhy analysis: model, identify, estimate @@ -667,7 +667,7 @@

2. Run DoWhy analysis: model, identify, estimate 
-Causal Estimate is 1639.8323003718551
+Causal Estimate is 1639.8298498687882

diff --git a/main/example_notebooks/dowhy_lalonde_example.ipynb b/main/example_notebooks/dowhy_lalonde_example.ipynb index a478c5fcd..12f12c02d 100644 --- a/main/example_notebooks/dowhy_lalonde_example.ipynb +++ b/main/example_notebooks/dowhy_lalonde_example.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:07.930708Z", - "iopub.status.busy": "2024-10-19T23:49:07.930277Z", - "iopub.status.idle": "2024-10-19T23:49:10.277784Z", - "shell.execute_reply": "2024-10-19T23:49:10.277155Z" + "iopub.execute_input": "2024-10-22T01:15:19.383913Z", + "iopub.status.busy": "2024-10-22T01:15:19.383732Z", + "iopub.status.idle": "2024-10-22T01:15:22.029032Z", + "shell.execute_reply": "2024-10-22T01:15:22.028367Z" } }, "outputs": [], @@ -46,10 +46,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:10.280097Z", - "iopub.status.busy": "2024-10-19T23:49:10.279697Z", - "iopub.status.idle": "2024-10-19T23:49:10.332866Z", - "shell.execute_reply": "2024-10-19T23:49:10.332238Z" + "iopub.execute_input": "2024-10-22T01:15:22.031289Z", + "iopub.status.busy": "2024-10-22T01:15:22.030923Z", + "iopub.status.idle": "2024-10-22T01:15:22.084804Z", + "shell.execute_reply": "2024-10-22T01:15:22.084242Z" } }, "outputs": [ @@ -57,7 +57,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 1639.8323003718579\n" + "Causal Estimate is 1639.8298498687827\n" ] }, { @@ -75,10 +75,10 @@ " Method: Least Squares F-statistic: 6.743\n", "\n", "\n", - " Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00972\n", + " Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00972\n", "\n", "\n", - " Time: 23:49:10 Log-Likelihood: -4544.7\n", + " Time: 01:15:22 Log-Likelihood: -4544.7\n", "\n", "\n", " No. Observations: 445 AIC: 9093.\n", @@ -98,10 +98,10 @@ " coef std err t P>|t| [0.025 0.975] \n", "\n", "\n", - " Intercept 4555.0707 406.706 11.200 0.000 3755.759 5354.383\n", + " Intercept 4555.0711 406.705 11.200 0.000 3755.759 5354.383\n", "\n", "\n", - " treat[T.True] 1639.8323 631.497 2.597 0.010 398.731 2880.934\n", + " treat[T.True] 1639.8298 631.497 2.597 0.010 398.729 2880.931\n", "\n", "\n", "\n", @@ -109,7 +109,7 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -126,8 +126,8 @@ "\\textbf{Dep. Variable:} & re78 & \\textbf{ R-squared: } & 0.015 \\\\\n", "\\textbf{Model:} & WLS & \\textbf{ Adj. R-squared: } & 0.013 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 6.743 \\\\\n", - "\\textbf{Date:} & Sat, 19 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00972 \\\\\n", - "\\textbf{Time:} & 23:49:10 & \\textbf{ Log-Likelihood: } & -4544.7 \\\\\n", + "\\textbf{Date:} & Tue, 22 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00972 \\\\\n", + "\\textbf{Time:} & 01:15:22 & \\textbf{ Log-Likelihood: } & -4544.7 \\\\\n", "\\textbf{No. Observations:} & 445 & \\textbf{ AIC: } & 9093. \\\\\n", "\\textbf{Df Residuals:} & 443 & \\textbf{ BIC: } & 9102. \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -137,13 +137,13 @@ "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", - "\\textbf{Intercept} & 4555.0707 & 406.706 & 11.200 & 0.000 & 3755.759 & 5354.383 \\\\\n", - "\\textbf{treat[T.True]} & 1639.8323 & 631.497 & 2.597 & 0.010 & 398.731 & 2880.934 \\\\\n", + "\\textbf{Intercept} & 4555.0711 & 406.705 & 11.200 & 0.000 & 3755.759 & 5354.383 \\\\\n", + "\\textbf{treat[T.True]} & 1639.8298 & 631.497 & 2.597 & 0.010 & 398.729 & 2880.931 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", "\\textbf{Omnibus:} & 303.265 & \\textbf{ Durbin-Watson: } & 2.085 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 4770.760 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 4770.748 \\\\\n", "\\textbf{Skew:} & 2.709 & \\textbf{ Prob(JB): } & 0.00 \\\\\n", "\\textbf{Kurtosis:} & 18.098 & \\textbf{ Cond. No. } & 2.47 \\\\\n", "\\bottomrule\n", @@ -162,8 +162,8 @@ "Dep. Variable: re78 R-squared: 0.015\n", "Model: WLS Adj. R-squared: 0.013\n", "Method: Least Squares F-statistic: 6.743\n", - "Date: Sat, 19 Oct 2024 Prob (F-statistic): 0.00972\n", - "Time: 23:49:10 Log-Likelihood: -4544.7\n", + "Date: Tue, 22 Oct 2024 Prob (F-statistic): 0.00972\n", + "Time: 01:15:22 Log-Likelihood: -4544.7\n", "No. Observations: 445 AIC: 9093.\n", "Df Residuals: 443 BIC: 9102.\n", "Df Model: 1 \n", @@ -171,11 +171,11 @@ "=================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "---------------------------------------------------------------------------------\n", - "Intercept 4555.0707 406.706 11.200 0.000 3755.759 5354.383\n", - "treat[T.True] 1639.8323 631.497 2.597 0.010 398.731 2880.934\n", + "Intercept 4555.0711 406.705 11.200 0.000 3755.759 5354.383\n", + "treat[T.True] 1639.8298 631.497 2.597 0.010 398.729 2880.931\n", "==============================================================================\n", "Omnibus: 303.265 Durbin-Watson: 2.085\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.760\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.748\n", "Skew: 2.709 Prob(JB): 0.00\n", "Kurtosis: 18.098 Cond. No. 2.47\n", "==============================================================================\n", @@ -226,10 +226,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:10.334867Z", - "iopub.status.busy": "2024-10-19T23:49:10.334490Z", - "iopub.status.idle": "2024-10-19T23:49:10.633170Z", - "shell.execute_reply": "2024-10-19T23:49:10.632554Z" + "iopub.execute_input": "2024-10-22T01:15:22.086793Z", + "iopub.status.busy": "2024-10-22T01:15:22.086489Z", + "iopub.status.idle": "2024-10-22T01:15:22.384040Z", + "shell.execute_reply": "2024-10-22T01:15:22.383384Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:10.635279Z", - "iopub.status.busy": "2024-10-19T23:49:10.634922Z", - "iopub.status.idle": "2024-10-19T23:49:10.640311Z", - "shell.execute_reply": "2024-10-19T23:49:10.639761Z" + "iopub.execute_input": "2024-10-22T01:15:22.385971Z", + "iopub.status.busy": "2024-10-22T01:15:22.385669Z", + "iopub.status.idle": "2024-10-22T01:15:22.390952Z", + "shell.execute_reply": "2024-10-22T01:15:22.390497Z" } }, "outputs": [ @@ -272,7 +272,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Causal Estimate is 1639.8323003718551\n" + "Causal Estimate is 1639.8298498687882\n" ] } ], diff --git a/main/example_notebooks/dowhy_mediation_analysis.html b/main/example_notebooks/dowhy_mediation_analysis.html index c7829aa89..b4335238c 100644 --- a/main/example_notebooks/dowhy_mediation_analysis.html +++ b/main/example_notebooks/dowhy_mediation_analysis.html @@ -604,12 +604,12 @@

Creating a dataset 
-         FD0        W0        v0          y
-0   6.489292  0.733013  1.220239  34.601677
-1  -5.289214  0.068313 -1.238072 -24.993585
-2   2.263775  0.210433  0.165696  11.842802
-3   0.094201 -0.544390  0.277355  -2.182567
-4 -18.464862 -1.645708 -4.082562 -96.327466
+        FD0        W0        v0          y
+0  3.787914 -0.227960  1.412614   4.343411
+1  4.690292  0.815757  2.508414  10.676013
+2 -0.028364 -0.311694 -0.166239  -1.553875
+3 -0.963158 -0.309174 -0.739863  -2.860223
+4 -5.910934 -0.533363 -3.582746 -11.060230
@@ -761,7 +761,7 @@

Natural Indirect Effect @@ -779,7 +779,7 @@

Natural Indirect Effect 
-19.47167530678116 19.518019667969362
+2.594882809806675 2.6008692268961195

The parameter is called ate because in the simulated dataset, the direct effect is set to be zero.

@@ -829,7 +829,7 @@

Natural Direct Effect diff --git a/main/example_notebooks/dowhy_mediation_analysis.ipynb b/main/example_notebooks/dowhy_mediation_analysis.ipynb index 521745ebe..f44feb937 100644 --- a/main/example_notebooks/dowhy_mediation_analysis.ipynb +++ b/main/example_notebooks/dowhy_mediation_analysis.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:12.337167Z", - "iopub.status.busy": "2024-10-19T23:49:12.336989Z", - "iopub.status.idle": "2024-10-19T23:49:13.771559Z", - "shell.execute_reply": "2024-10-19T23:49:13.770959Z" + "iopub.execute_input": "2024-10-22T01:15:24.068018Z", + "iopub.status.busy": "2024-10-22T01:15:24.067591Z", + "iopub.status.idle": "2024-10-22T01:15:25.523197Z", + "shell.execute_reply": "2024-10-22T01:15:25.522595Z" } }, "outputs": [], @@ -43,10 +43,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.773833Z", - "iopub.status.busy": "2024-10-19T23:49:13.773501Z", - "iopub.status.idle": "2024-10-19T23:49:13.783396Z", - "shell.execute_reply": "2024-10-19T23:49:13.782805Z" + "iopub.execute_input": "2024-10-22T01:15:25.525406Z", + "iopub.status.busy": "2024-10-22T01:15:25.525091Z", + "iopub.status.idle": "2024-10-22T01:15:25.534817Z", + "shell.execute_reply": "2024-10-22T01:15:25.534322Z" } }, "outputs": [ @@ -54,12 +54,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " FD0 W0 v0 y\n", - "0 6.489292 0.733013 1.220239 34.601677\n", - "1 -5.289214 0.068313 -1.238072 -24.993585\n", - "2 2.263775 0.210433 0.165696 11.842802\n", - "3 0.094201 -0.544390 0.277355 -2.182567\n", - "4 -18.464862 -1.645708 -4.082562 -96.327466\n" + " FD0 W0 v0 y\n", + "0 3.787914 -0.227960 1.412614 4.343411\n", + "1 4.690292 0.815757 2.508414 10.676013\n", + "2 -0.028364 -0.311694 -0.166239 -1.553875\n", + "3 -0.963158 -0.309174 -0.739863 -2.860223\n", + "4 -5.910934 -0.533363 -3.582746 -11.060230\n" ] } ], @@ -88,10 +88,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.785235Z", - "iopub.status.busy": "2024-10-19T23:49:13.785051Z", - "iopub.status.idle": "2024-10-19T23:49:13.896181Z", - "shell.execute_reply": "2024-10-19T23:49:13.895570Z" + "iopub.execute_input": "2024-10-22T01:15:25.536630Z", + "iopub.status.busy": "2024-10-22T01:15:25.536265Z", + "iopub.status.idle": "2024-10-22T01:15:25.649977Z", + "shell.execute_reply": "2024-10-22T01:15:25.649448Z" } }, "outputs": [ @@ -144,10 +144,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.898273Z", - "iopub.status.busy": "2024-10-19T23:49:13.898060Z", - "iopub.status.idle": "2024-10-19T23:49:13.912625Z", - "shell.execute_reply": "2024-10-19T23:49:13.912056Z" + "iopub.execute_input": "2024-10-22T01:15:25.651961Z", + "iopub.status.busy": "2024-10-22T01:15:25.651750Z", + "iopub.status.idle": "2024-10-22T01:15:25.665950Z", + "shell.execute_reply": "2024-10-22T01:15:25.665381Z" } }, "outputs": [ @@ -182,10 +182,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:13.914674Z", - "iopub.status.busy": "2024-10-19T23:49:13.914463Z", - "iopub.status.idle": "2024-10-19T23:49:14.140529Z", - "shell.execute_reply": "2024-10-19T23:49:14.139972Z" + "iopub.execute_input": "2024-10-22T01:15:25.667980Z", + "iopub.status.busy": "2024-10-22T01:15:25.667775Z", + "iopub.status.idle": "2024-10-22T01:15:25.893819Z", + "shell.execute_reply": "2024-10-22T01:15:25.893257Z" } }, "outputs": [ @@ -233,10 +233,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:14.142362Z", - "iopub.status.busy": "2024-10-19T23:49:14.142188Z", - "iopub.status.idle": "2024-10-19T23:49:14.190538Z", - "shell.execute_reply": "2024-10-19T23:49:14.189381Z" + "iopub.execute_input": "2024-10-22T01:15:25.895823Z", + "iopub.status.busy": "2024-10-22T01:15:25.895458Z", + "iopub.status.idle": "2024-10-22T01:15:25.943166Z", + "shell.execute_reply": "2024-10-22T01:15:25.942596Z" } }, "outputs": [ @@ -264,7 +264,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 19.47167530678116\n", + "Mean value: 2.594882809806675\n", "\n" ] } @@ -295,10 +295,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:14.193098Z", - "iopub.status.busy": "2024-10-19T23:49:14.192567Z", - "iopub.status.idle": "2024-10-19T23:49:14.196799Z", - "shell.execute_reply": "2024-10-19T23:49:14.196263Z" + "iopub.execute_input": "2024-10-22T01:15:25.945974Z", + "iopub.status.busy": "2024-10-22T01:15:25.945465Z", + "iopub.status.idle": "2024-10-22T01:15:25.949757Z", + "shell.execute_reply": "2024-10-22T01:15:25.949284Z" } }, "outputs": [ @@ -306,7 +306,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "19.47167530678116 19.518019667969362\n" + "2.594882809806675 2.6008692268961195\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:14.199161Z", - "iopub.status.busy": "2024-10-19T23:49:14.198690Z", - "iopub.status.idle": "2024-10-19T23:49:14.264863Z", - "shell.execute_reply": "2024-10-19T23:49:14.264285Z" + "iopub.execute_input": "2024-10-22T01:15:25.952297Z", + "iopub.status.busy": "2024-10-22T01:15:25.951850Z", + "iopub.status.idle": "2024-10-22T01:15:26.019230Z", + "shell.execute_reply": "2024-10-22T01:15:26.018668Z" } }, "outputs": [ @@ -360,7 +360,7 @@ "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 3.5485295992288e-05\n", + "Mean value: 2.27800892300678e-05\n", "\n" ] } diff --git a/main/example_notebooks/dowhy_multiple_treatments.html b/main/example_notebooks/dowhy_multiple_treatments.html index 85fd8981e..e301b7d67 100644 --- a/main/example_notebooks/dowhy_multiple_treatments.html +++ b/main/example_notebooks/dowhy_multiple_treatments.html @@ -630,63 +630,63 @@

Estimating effect of multiple treatments\n", "

\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Omnibus: 303.265 Durbin-Watson: 2.085
Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.760Prob(Omnibus): 0.000 Jarque-Bera (JB): 4770.748
Skew: 2.709 Prob(JB): 0.00
01.355903-0.387930-2.327660-1.73596900-5.521305-15.649951-155.383932-0.349212-0.009528-0.294154-1.3261553212.1254568.37434068.072214
1-0.320515-0.417196-1.171597-2.57441820.772577-2.1043550.9489460.77859232.823160-3.8304119.204169021.5136507.646989170.307903
20.060713-0.593707-0.2065850.3848682-0.3968740.159136-0.8320590.168977310.66917413.004237211.838941113.2469011.618905125.310203
31.5411700.797725-0.2185120.3563133-0.740952-2.030404-0.026773-0.054814110.09404313.653444473.957283315.69082210.933237-855.902869
41.044167-0.849258-0.6694860.624146000.888019-0.6957460.529529-0.7298950.7011852.4242680.4233963233.44794423.229281-668.944083
\n", @@ -138,18 +138,18 @@ ], "text/plain": [ " X0 X1 W0 W1 W2 W3 v0 v1 \\\n", - "0 1.355903 -0.387930 -2.327660 -1.735969 0 0 -5.521305 -15.649951 \n", - "1 -0.320515 -0.417196 -1.171597 -2.574418 2 3 2.823160 -3.830411 \n", - "2 0.060713 -0.593707 -0.206585 0.384868 2 3 10.669174 13.004237 \n", - "3 1.541170 0.797725 -0.218512 0.356313 3 1 10.094043 13.653444 \n", - "4 1.044167 -0.849258 -0.669486 0.624146 0 0 0.888019 -0.695746 \n", + "0 -0.349212 -0.009528 -0.294154 -1.326155 3 2 12.125456 8.374340 \n", + "1 0.772577 -2.104355 0.948946 0.778592 3 0 21.513650 7.646989 \n", + "2 -0.396874 0.159136 -0.832059 0.168977 3 1 13.246901 1.618905 \n", + "3 -0.740952 -2.030404 -0.026773 -0.054814 1 3 15.690822 10.933237 \n", + "4 -0.729895 0.701185 2.424268 0.423396 3 2 33.447944 23.229281 \n", "\n", " y \n", - "0 -155.383932 \n", - "1 9.204169 \n", - "2 211.838941 \n", - "3 473.957283 \n", - "4 0.529529 " + "0 68.072214 \n", + "1 170.307903 \n", + "2 125.310203 \n", + "3 -855.902869 \n", + "4 -668.944083 " ] }, "execution_count": 2, @@ -174,10 +174,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.675471Z", - "iopub.status.busy": "2024-10-19T23:49:17.675250Z", - "iopub.status.idle": "2024-10-19T23:49:17.681318Z", - "shell.execute_reply": "2024-10-19T23:49:17.680778Z" + "iopub.execute_input": "2024-10-22T01:15:29.520680Z", + "iopub.status.busy": "2024-10-22T01:15:29.520127Z", + "iopub.status.idle": "2024-10-22T01:15:29.527036Z", + "shell.execute_reply": "2024-10-22T01:15:29.526475Z" } }, "outputs": [], @@ -192,10 +192,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.683561Z", - "iopub.status.busy": "2024-10-19T23:49:17.683169Z", - "iopub.status.idle": "2024-10-19T23:49:17.852337Z", - "shell.execute_reply": "2024-10-19T23:49:17.851673Z" + "iopub.execute_input": "2024-10-22T01:15:29.529777Z", + "iopub.status.busy": "2024-10-22T01:15:29.529515Z", + "iopub.status.idle": "2024-10-22T01:15:29.692315Z", + "shell.execute_reply": "2024-10-22T01:15:29.691677Z" } }, "outputs": [ @@ -231,10 +231,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.854343Z", - "iopub.status.busy": "2024-10-19T23:49:17.854124Z", - "iopub.status.idle": "2024-10-19T23:49:17.875059Z", - "shell.execute_reply": "2024-10-19T23:49:17.874541Z" + "iopub.execute_input": "2024-10-22T01:15:29.694490Z", + "iopub.status.busy": "2024-10-22T01:15:29.694144Z", + "iopub.status.idle": "2024-10-22T01:15:29.711124Z", + "shell.execute_reply": "2024-10-22T01:15:29.710464Z" } }, "outputs": [ @@ -248,9 +248,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────────(E[y|W3,W1,W2,W0])\n", + "─────────(E[y|W1,W3,W0,W2])\n", "d[v₀ v₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W1,W2,W0,U) = P(y|v0,v1,W3,W1,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W1,W3,W0,W2,U) = P(y|v0,v1,W1,W3,W0,W2)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -284,10 +284,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.877012Z", - "iopub.status.busy": "2024-10-19T23:49:17.876806Z", - "iopub.status.idle": "2024-10-19T23:49:17.939568Z", - "shell.execute_reply": "2024-10-19T23:49:17.939000Z" + "iopub.execute_input": "2024-10-22T01:15:29.713367Z", + "iopub.status.busy": "2024-10-22T01:15:29.712886Z", + "iopub.status.idle": "2024-10-22T01:15:29.768955Z", + "shell.execute_reply": "2024-10-22T01:15:29.768316Z" } }, "outputs": [ @@ -304,16 +304,16 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────────(E[y|W3,W1,W2,W0])\n", + "─────────(E[y|W1,W3,W0,W2])\n", "d[v₀ v₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W1,W2,W0,U) = P(y|v0,v1,W3,W1,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W1,W3,W0,W2,U) = P(y|v0,v1,W1,W3,W0,W2)\n", "\n", "## Realized estimand\n", - "b: y~v0+v1+W3+W1+W2+W0+v0*X0+v0*X1+v1*X0+v1*X1\n", + "b: y~v0+v1+W1+W3+W0+W2+v0*X1+v0*X0+v1*X1+v1*X0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 21.052209166265527\n", + "Mean value: 64.93760915623568\n", "\n" ] } @@ -339,10 +339,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:17.942029Z", - "iopub.status.busy": "2024-10-19T23:49:17.941800Z", - "iopub.status.idle": "2024-10-19T23:49:18.294726Z", - "shell.execute_reply": "2024-10-19T23:49:18.294055Z" + "iopub.execute_input": "2024-10-22T01:15:29.771479Z", + "iopub.status.busy": "2024-10-22T01:15:29.771238Z", + "iopub.status.idle": "2024-10-22T01:15:30.130811Z", + "shell.execute_reply": "2024-10-22T01:15:30.130094Z" } }, "outputs": [ @@ -359,43 +359,43 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────────(E[y|W3,W1,W2,W0])\n", + "─────────(E[y|W1,W3,W0,W2])\n", "d[v₀ v₁] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W3,W1,W2,W0,U) = P(y|v0,v1,W3,W1,W2,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0,v1} and U→y then P(y|v0,v1,W1,W3,W0,W2,U) = P(y|v0,v1,W1,W3,W0,W2)\n", "\n", "## Realized estimand\n", - "b: y~v0+v1+W3+W1+W2+W0+v0*X0+v0*X1+v1*X0+v1*X1\n", + "b: y~v0+v1+W1+W3+W0+W2+v0*X1+v0*X0+v1*X1+v1*X0\n", "Target units: \n", "\n", "## Estimate\n", - "Mean value: 21.052209166265527\n", + "Mean value: 64.93760915623568\n", "### Conditional Estimates\n", - "__categorical__X0 __categorical__X1 \n", - "(-2.799, 0.0662] (-4.933000000000001, -1.667] 5.125910\n", - " (-1.667, -1.093] 9.297402\n", - " (-1.093, -0.58] 12.095977\n", - " (-0.58, 0.0346] 14.987859\n", - " (0.0346, 3.349] 19.223786\n", - "(0.0662, 0.654] (-4.933000000000001, -1.667] 10.174763\n", - " (-1.667, -1.093] 14.694029\n", - " (-1.093, -0.58] 17.647011\n", - " (-0.58, 0.0346] 20.455978\n", - " (0.0346, 3.349] 25.069082\n", - "(0.654, 1.171] (-4.933000000000001, -1.667] 13.549221\n", - " (-1.667, -1.093] 18.195581\n", - " (-1.093, -0.58] 20.914473\n", - " (-0.58, 0.0346] 23.862252\n", - " (0.0346, 3.349] 28.547426\n", - "(1.171, 1.753] (-4.933000000000001, -1.667] 16.987343\n", - " (-1.667, -1.093] 21.747473\n", - " (-1.093, -0.58] 24.462509\n", - " (-0.58, 0.0346] 27.240189\n", - " (0.0346, 3.349] 31.896187\n", - "(1.753, 4.583] (-4.933000000000001, -1.667] 22.703744\n", - " (-1.667, -1.093] 27.237801\n", - " (-1.093, -0.58] 29.801650\n", - " (-0.58, 0.0346] 32.810708\n", - " (0.0346, 3.349] 37.503156\n", + "__categorical__X1 __categorical__X0\n", + "(-4.646, -1.595] (-2.444, 0.1] -83.223076\n", + " (0.1, 0.722] -21.272362\n", + " (0.722, 1.212] 19.736681\n", + " (1.212, 1.786] 57.274426\n", + " (1.786, 5.117] 121.408808\n", + "(-1.595, -1.003] (-2.444, 0.1] -55.416964\n", + " (0.1, 0.722] 7.615118\n", + " (0.722, 1.212] 47.655290\n", + " (1.212, 1.786] 84.543374\n", + " (1.786, 5.117] 146.107955\n", + "(-1.003, -0.484] (-2.444, 0.1] -36.333046\n", + " (0.1, 0.722] 26.080394\n", + " (0.722, 1.212] 66.385902\n", + " (1.212, 1.786] 102.107474\n", + " (1.786, 5.117] 165.431383\n", + "(-0.484, 0.0986] (-2.444, 0.1] -18.863482\n", + " (0.1, 0.722] 44.673243\n", + " (0.722, 1.212] 83.519976\n", + " (1.212, 1.786] 122.690563\n", + " (1.786, 5.117] 182.880352\n", + "(0.0986, 2.92] (-2.444, 0.1] 10.171146\n", + " (0.1, 0.722] 73.169405\n", + " (0.722, 1.212] 113.105707\n", + " (1.212, 1.786] 150.266148\n", + " (1.786, 5.117] 213.857166\n", "dtype: float64\n" ] } diff --git a/main/example_notebooks/dowhy_optimize_backdoor_example.html b/main/example_notebooks/dowhy_optimize_backdoor_example.html index a7f095bb4..53cd783a1 100644 --- a/main/example_notebooks/dowhy_optimize_backdoor_example.html +++ b/main/example_notebooks/dowhy_optimize_backdoor_example.html @@ -557,9 +557,9 @@

Testing optimized backdoor search 
-Time taken for initializing model = 0.004712104797363281
-Time taken for vanilla identification = 0.0002238750457763672
-Time taken for optimized backdoor identification = 0.00014019012451171875
+Time taken for initializing model = 0.004521846771240234
+Time taken for vanilla identification = 0.01575326919555664
+Time taken for optimized backdoor identification = 0.012814521789550781

It can be observed that the optimized backdoor search makes causal identification faster as compared to the vanilla implementation.

diff --git a/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb b/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb index 12906ab65..8f5993c06 100644 --- a/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb +++ b/main/example_notebooks/dowhy_optimize_backdoor_example.ipynb @@ -14,10 +14,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:19.848769Z", - "iopub.status.busy": "2024-10-19T23:49:19.848573Z", - "iopub.status.idle": "2024-10-19T23:49:21.265860Z", - "shell.execute_reply": "2024-10-19T23:49:21.265230Z" + "iopub.execute_input": "2024-10-22T01:15:32.047687Z", + "iopub.status.busy": "2024-10-22T01:15:32.047506Z", + "iopub.status.idle": "2024-10-22T01:15:33.561774Z", + "shell.execute_reply": "2024-10-22T01:15:33.561114Z" } }, "outputs": [], @@ -48,10 +48,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:21.268143Z", - "iopub.status.busy": "2024-10-19T23:49:21.267844Z", - "iopub.status.idle": "2024-10-19T23:49:21.285472Z", - "shell.execute_reply": "2024-10-19T23:49:21.284965Z" + "iopub.execute_input": "2024-10-22T01:15:33.564216Z", + "iopub.status.busy": "2024-10-22T01:15:33.563733Z", + "iopub.status.idle": "2024-10-22T01:15:33.581274Z", + "shell.execute_reply": "2024-10-22T01:15:33.580741Z" } }, "outputs": [ @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:21.287366Z", - "iopub.status.busy": "2024-10-19T23:49:21.287014Z", - "iopub.status.idle": "2024-10-19T23:49:21.296025Z", - "shell.execute_reply": "2024-10-19T23:49:21.295499Z" + "iopub.execute_input": "2024-10-22T01:15:33.583236Z", + "iopub.status.busy": "2024-10-22T01:15:33.582882Z", + "iopub.status.idle": "2024-10-22T01:15:33.620182Z", + "shell.execute_reply": "2024-10-22T01:15:33.619619Z" } }, "outputs": [ @@ -107,9 +107,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time taken for initializing model = 0.004712104797363281\n", - "Time taken for vanilla identification = 0.0002238750457763672\n", - "Time taken for optimized backdoor identification = 0.00014019012451171875\n" + "Time taken for initializing model = 0.004521846771240234\n", + "Time taken for vanilla identification = 0.01575326919555664\n", + "Time taken for optimized backdoor identification = 0.012814521789550781\n" ] } ], diff --git a/main/example_notebooks/dowhy_refutation_testing.html b/main/example_notebooks/dowhy_refutation_testing.html index 1249f1057..bff79d12d 100644 --- a/main/example_notebooks/dowhy_refutation_testing.html +++ b/main/example_notebooks/dowhy_refutation_testing.html @@ -1049,13 +1049,13 @@

IHDP#Lalonde# Estimand name: backdoor Estimand expression: d -────────(E[re78|hisp,black,nodegr,educ,married,age]) +────────(E[re78|hisp,black,age,married,educ,nodegr]) d[treat] -Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,black,nodegr,educ,married,age,U) = P(re78|treat,hisp,black,nodegr,educ,married,age) +Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,black,age,married,educ,nodegr,U) = P(re78|treat,hisp,black,age,married,educ,nodegr) ### Estimand : 2 Estimand name: iv @@ -1129,7 +1129,7 @@

IHDP# 
-The Causal Estimate is 4.028748218389782
+The Causal Estimate is 4.0287482183899
@@ -1153,7 +1153,7 @@

Lalonde# 
-The Causal Estimate is 1639.796346804399
+The Causal Estimate is 1639.7917591321402
@@ -1184,8 +1184,8 @@

Add Random Common Cause 
 Refute: Add a random common cause
-Estimated effect:4.028748218389782
-New effect:4.028748218389782
+Estimated effect:4.0287482183899
+New effect:4.028748218389901
 p value:1.0
@@ -1214,9 +1214,9 @@

Replace Treatment with Placebo 
 Refute: Use a Placebo Treatment
-Estimated effect:4.028748218389782
-New effect:-0.47708408750457004
-p value:0.02
+Estimated effect:4.0287482183899
+New effect:-0.47856889604057196
+p value:0.0
@@ -1243,9 +1243,9 @@

Remove Random Subset of Data 
 Refute: Use a subset of data
-Estimated effect:4.028748218389782
-New effect:4.030673559907819
-p value:0.98
+Estimated effect:4.0287482183899
+New effect:4.021788149954388
+p value:0.92
@@ -1275,8 +1275,8 @@

Add Random Common Cause 
 Refute: Add a random common cause
-Estimated effect:1639.796346804399
-New effect:1639.7963468043993
+Estimated effect:1639.7917591321402
+New effect:1639.7917591321404
 p value:1.0
@@ -1305,9 +1305,9 @@

Replace Treatment with Placebo 
 Refute: Use a Placebo Treatment
-Estimated effect:1639.796346804399
-New effect:-220.33816894361186
-p value:0.66
+Estimated effect:1639.7917591321402
+New effect:-130.77125442290446
+p value:0.94
@@ -1335,9 +1335,9 @@

Remove Random Subset of Data 
 Refute: Use a subset of data
-Estimated effect:1639.796346804399
-New effect:1621.9710142661302
-p value:0.94
+Estimated effect:1639.7917591321402
+New effect:1636.6887995233171
+p value:1.0
diff --git a/main/example_notebooks/dowhy_refutation_testing.ipynb b/main/example_notebooks/dowhy_refutation_testing.ipynb index 4ea136963..eba0ab132 100644 --- a/main/example_notebooks/dowhy_refutation_testing.ipynb +++ b/main/example_notebooks/dowhy_refutation_testing.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:22.925856Z", - "iopub.status.busy": "2024-10-19T23:49:22.925510Z", - "iopub.status.idle": "2024-10-19T23:49:24.345759Z", - "shell.execute_reply": "2024-10-19T23:49:24.345154Z" + "iopub.execute_input": "2024-10-22T01:15:35.365742Z", + "iopub.status.busy": "2024-10-22T01:15:35.365563Z", + "iopub.status.idle": "2024-10-22T01:15:36.819888Z", + "shell.execute_reply": "2024-10-22T01:15:36.819209Z" } }, "outputs": [], @@ -80,10 +80,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:24.348066Z", - "iopub.status.busy": "2024-10-19T23:49:24.347785Z", - "iopub.status.idle": "2024-10-19T23:49:24.405661Z", - "shell.execute_reply": "2024-10-19T23:49:24.405143Z" + "iopub.execute_input": "2024-10-22T01:15:36.822397Z", + "iopub.status.busy": "2024-10-22T01:15:36.821901Z", + "iopub.status.idle": "2024-10-22T01:15:36.856382Z", + "shell.execute_reply": "2024-10-22T01:15:36.855818Z" } }, "outputs": [ @@ -346,10 +346,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:24.407622Z", - "iopub.status.busy": "2024-10-19T23:49:24.407257Z", - "iopub.status.idle": "2024-10-19T23:49:25.073611Z", - "shell.execute_reply": "2024-10-19T23:49:25.073049Z" + "iopub.execute_input": "2024-10-22T01:15:36.858236Z", + "iopub.status.busy": "2024-10-22T01:15:36.858056Z", + "iopub.status.idle": "2024-10-22T01:15:38.004874Z", + "shell.execute_reply": "2024-10-22T01:15:38.004328Z" } }, "outputs": [ @@ -516,10 +516,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:25.075647Z", - "iopub.status.busy": "2024-10-19T23:49:25.075288Z", - "iopub.status.idle": "2024-10-19T23:49:25.319265Z", - "shell.execute_reply": "2024-10-19T23:49:25.318778Z" + "iopub.execute_input": "2024-10-22T01:15:38.006944Z", + "iopub.status.busy": "2024-10-22T01:15:38.006620Z", + "iopub.status.idle": "2024-10-22T01:15:38.250648Z", + "shell.execute_reply": "2024-10-22T01:15:38.250121Z" } }, "outputs": [ @@ -575,10 +575,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:25.321904Z", - "iopub.status.busy": "2024-10-19T23:49:25.321474Z", - "iopub.status.idle": "2024-10-19T23:49:25.467992Z", - "shell.execute_reply": "2024-10-19T23:49:25.467398Z" + "iopub.execute_input": "2024-10-22T01:15:38.252760Z", + "iopub.status.busy": "2024-10-22T01:15:38.252544Z", + "iopub.status.idle": "2024-10-22T01:15:38.405751Z", + "shell.execute_reply": "2024-10-22T01:15:38.404523Z" } }, "outputs": [ @@ -634,10 +634,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:49:25.470354Z", - "iopub.status.busy": "2024-10-19T23:49:25.469944Z", - "iopub.status.idle": "2024-10-19T23:50:20.487755Z", - "shell.execute_reply": "2024-10-19T23:50:20.487059Z" + "iopub.execute_input": "2024-10-22T01:15:38.408117Z", + "iopub.status.busy": "2024-10-22T01:15:38.407876Z", + "iopub.status.idle": "2024-10-22T01:16:33.420762Z", + "shell.execute_reply": "2024-10-22T01:16:33.420118Z" } }, "outputs": [ @@ -651,13 +651,13 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d ↪\n", - "────────────(E[y_factual|x11,x19,x1,x18,x3,x2,x17,x24,x5,x12,x20,x25,x9,x14,x2 ↪\n", + "────────────(E[y_factual|x12,x7,x21,x25,x8,x24,x13,x20,x23,x6,x22,x1,x11,x2,x1 ↪\n", "d[treatment] ↪\n", "\n", "↪ \n", - "↪ 1,x15,x22,x7,x10,x23,x8,x13,x4,x16,x6])\n", + "↪ 4,x18,x4,x5,x15,x3,x16,x10,x17,x9,x19])\n", "↪ \n", - "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x11,x19,x1,x18,x3,x2,x17,x24,x5,x12,x20,x25,x9,x14,x21,x15,x22,x7,x10,x23,x8,x13,x4,x16,x6,U) = P(y_factual|treatment,x11,x19,x1,x18,x3,x2,x17,x24,x5,x12,x20,x25,x9,x14,x21,x15,x22,x7,x10,x23,x8,x13,x4,x16,x6)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x12,x7,x21,x25,x8,x24,x13,x20,x23,x6,x22,x1,x11,x2,x14,x18,x4,x5,x15,x3,x16,x10,x17,x9,x19,U) = P(y_factual|treatment,x12,x7,x21,x25,x8,x24,x13,x20,x23,x6,x22,x1,x11,x2,x14,x18,x4,x5,x15,x3,x16,x10,x17,x9,x19)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -688,10 +688,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.489809Z", - "iopub.status.busy": "2024-10-19T23:50:20.489581Z", - "iopub.status.idle": "2024-10-19T23:50:20.503010Z", - "shell.execute_reply": "2024-10-19T23:50:20.502505Z" + "iopub.execute_input": "2024-10-22T01:16:33.422835Z", + "iopub.status.busy": "2024-10-22T01:16:33.422464Z", + "iopub.status.idle": "2024-10-22T01:16:33.435975Z", + "shell.execute_reply": "2024-10-22T01:16:33.435462Z" } }, "outputs": [ @@ -705,9 +705,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "────────(E[re78|hisp,black,nodegr,educ,married,age])\n", + "────────(E[re78|hisp,black,age,married,educ,nodegr])\n", "d[treat] \n", - "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,black,nodegr,educ,married,age,U) = P(re78|treat,hisp,black,nodegr,educ,married,age)\n", + "Estimand assumption 1, Unconfoundedness: If U→{treat} and U→re78 then P(re78|treat,hisp,black,age,married,educ,nodegr,U) = P(re78|treat,hisp,black,age,married,educ,nodegr)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -745,10 +745,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.504764Z", - "iopub.status.busy": "2024-10-19T23:50:20.504559Z", - "iopub.status.idle": "2024-10-19T23:50:20.535009Z", - "shell.execute_reply": "2024-10-19T23:50:20.534270Z" + "iopub.execute_input": "2024-10-22T01:16:33.438074Z", + "iopub.status.busy": "2024-10-22T01:16:33.437699Z", + "iopub.status.idle": "2024-10-22T01:16:33.469315Z", + "shell.execute_reply": "2024-10-22T01:16:33.468468Z" } }, "outputs": [ @@ -756,7 +756,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The Causal Estimate is 4.028748218389782\n" + "The Causal Estimate is 4.0287482183899\n" ] } ], @@ -781,10 +781,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.537983Z", - "iopub.status.busy": "2024-10-19T23:50:20.536999Z", - "iopub.status.idle": "2024-10-19T23:50:20.571075Z", - "shell.execute_reply": "2024-10-19T23:50:20.570504Z" + "iopub.execute_input": "2024-10-22T01:16:33.471901Z", + "iopub.status.busy": "2024-10-22T01:16:33.471420Z", + "iopub.status.idle": "2024-10-22T01:16:33.504664Z", + "shell.execute_reply": "2024-10-22T01:16:33.504088Z" } }, "outputs": [ @@ -792,7 +792,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The Causal Estimate is 1639.796346804399\n" + "The Causal Estimate is 1639.7917591321402\n" ] } ], @@ -831,10 +831,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:20.574234Z", - "iopub.status.busy": "2024-10-19T23:50:20.573449Z", - "iopub.status.idle": "2024-10-19T23:50:21.625440Z", - "shell.execute_reply": "2024-10-19T23:50:21.624889Z" + "iopub.execute_input": "2024-10-22T01:16:33.507892Z", + "iopub.status.busy": "2024-10-22T01:16:33.506958Z", + "iopub.status.idle": "2024-10-22T01:16:34.436698Z", + "shell.execute_reply": "2024-10-22T01:16:34.436051Z" } }, "outputs": [ @@ -843,8 +843,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:4.028748218389782\n", - "New effect:4.028748218389782\n", + "Estimated effect:4.0287482183899\n", + "New effect:4.028748218389901\n", "p value:1.0\n", "\n" ] @@ -872,10 +872,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:21.627389Z", - "iopub.status.busy": "2024-10-19T23:50:21.627025Z", - "iopub.status.idle": "2024-10-19T23:50:22.578447Z", - "shell.execute_reply": "2024-10-19T23:50:22.577874Z" + "iopub.execute_input": "2024-10-22T01:16:34.438680Z", + "iopub.status.busy": "2024-10-22T01:16:34.438494Z", + "iopub.status.idle": "2024-10-22T01:16:35.368453Z", + "shell.execute_reply": "2024-10-22T01:16:35.367847Z" } }, "outputs": [ @@ -884,9 +884,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:4.028748218389782\n", - "New effect:-0.47708408750457004\n", - "p value:0.02\n", + "Estimated effect:4.0287482183899\n", + "New effect:-0.47856889604057196\n", + "p value:0.0\n", "\n" ] } @@ -914,10 +914,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:22.580424Z", - "iopub.status.busy": "2024-10-19T23:50:22.580054Z", - "iopub.status.idle": "2024-10-19T23:50:23.445116Z", - "shell.execute_reply": "2024-10-19T23:50:23.444461Z" + "iopub.execute_input": "2024-10-22T01:16:35.370727Z", + "iopub.status.busy": "2024-10-22T01:16:35.370240Z", + "iopub.status.idle": "2024-10-22T01:16:36.238020Z", + "shell.execute_reply": "2024-10-22T01:16:36.237315Z" } }, "outputs": [ @@ -926,9 +926,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:4.028748218389782\n", - "New effect:4.030673559907819\n", - "p value:0.98\n", + "Estimated effect:4.0287482183899\n", + "New effect:4.021788149954388\n", + "p value:0.92\n", "\n" ] } @@ -962,10 +962,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:23.447330Z", - "iopub.status.busy": "2024-10-19T23:50:23.446958Z", - "iopub.status.idle": "2024-10-19T23:50:24.333411Z", - "shell.execute_reply": "2024-10-19T23:50:24.332776Z" + "iopub.execute_input": "2024-10-22T01:16:36.240012Z", + "iopub.status.busy": "2024-10-22T01:16:36.239794Z", + "iopub.status.idle": "2024-10-22T01:16:37.154916Z", + "shell.execute_reply": "2024-10-22T01:16:37.154283Z" } }, "outputs": [ @@ -974,8 +974,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:1639.796346804399\n", - "New effect:1639.7963468043993\n", + "Estimated effect:1639.7917591321402\n", + "New effect:1639.7917591321404\n", "p value:1.0\n", "\n" ] @@ -1003,10 +1003,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:24.335563Z", - "iopub.status.busy": "2024-10-19T23:50:24.335137Z", - "iopub.status.idle": "2024-10-19T23:50:25.285665Z", - "shell.execute_reply": "2024-10-19T23:50:25.285040Z" + "iopub.execute_input": "2024-10-22T01:16:37.156994Z", + "iopub.status.busy": "2024-10-22T01:16:37.156756Z", + "iopub.status.idle": "2024-10-22T01:16:38.112721Z", + "shell.execute_reply": "2024-10-22T01:16:38.112071Z" } }, "outputs": [ @@ -1015,9 +1015,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:1639.796346804399\n", - "New effect:-220.33816894361186\n", - "p value:0.66\n", + "Estimated effect:1639.7917591321402\n", + "New effect:-130.77125442290446\n", + "p value:0.94\n", "\n" ] } @@ -1045,10 +1045,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:25.287665Z", - "iopub.status.busy": "2024-10-19T23:50:25.287373Z", - "iopub.status.idle": "2024-10-19T23:50:26.141226Z", - "shell.execute_reply": "2024-10-19T23:50:26.140516Z" + "iopub.execute_input": "2024-10-22T01:16:38.114809Z", + "iopub.status.busy": "2024-10-22T01:16:38.114595Z", + "iopub.status.idle": "2024-10-22T01:16:39.015522Z", + "shell.execute_reply": "2024-10-22T01:16:39.014944Z" } }, "outputs": [ @@ -1057,9 +1057,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:1639.796346804399\n", - "New effect:1621.9710142661302\n", - "p value:0.94\n", + "Estimated effect:1639.7917591321402\n", + "New effect:1636.6887995233171\n", + "p value:1.0\n", "\n" ] } diff --git a/main/example_notebooks/dowhy_simple_example.html b/main/example_notebooks/dowhy_simple_example.html index 849657ab0..305deba9a 100644 --- a/main/example_notebooks/dowhy_simple_example.html +++ b/main/example_notebooks/dowhy_simple_example.html @@ -642,68 +642,68 @@

Basic Example for Calculating the Causal Effect @@ -1016,7 +1016,7 @@

Adding a random common cause variable
-
+
@@ -1024,8 +1024,8 @@

Adding a random common cause variable 
 Refute: Add a random common cause
-Estimated effect:12.1455097789681
-New effect:12.145509778968098
+Estimated effect:10.32668021161025
+New effect:10.32668021161025
 p value:1.0

@@ -1047,7 +1047,7 @@

Replacing treatment with a random (placebo) variable

-
+
@@ -1055,9 +1055,9 @@

Replacing treatment with a random (placebo) variable 
 Refute: Use a Placebo Treatment
-Estimated effect:12.1455097789681
-New effect:0.055367443227466485
-p value:0.8799999999999999
+Estimated effect:10.32668021161025
+New effect:0.012367961362952769
+p value:0.98

@@ -1078,7 +1078,7 @@

Removing a random subset of the data
-
+
@@ -1086,9 +1086,9 @@

Removing a random subset of the data 
 Refute: Use a subset of data
-Estimated effect:12.1455097789681
-New effect:11.96555527788205
-p value:0.36
+Estimated effect:10.32668021161025
+New effect:10.297025170147867
+p value:0.56

@@ -1109,7 +1109,7 @@

Removing a random subset of the data
-
+
@@ -1119,14 +1119,14 @@

Removing a random subset of the data @@ -1135,9 +1135,9 @@

Removing a random subset of the data 
 Refute: Use a subset of data
-Estimated effect:12.1455097789681
-New effect:11.953778728959206
-p value:0.34
+Estimated effect:10.32668021161025
+New effect:10.293059888977698
+p value:0.5

@@ -1146,7 +1146,7 @@

Removing a random subset of the data 
-[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   13.0s finished
+[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.9s finished
@@ -1175,8 +1175,8 @@

Adding an unobserved common cause variable 
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.1455097789681
-New effect:10.382153142787415
+Estimated effect:10.32668021161025
+New effect:9.989729726705876
@@ -1205,8 +1205,8 @@

Adding an unobserved common cause variable 
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.1455097789681
-New effect:(8.382168111045187, 12.004268127514308)
+Estimated effect:10.32668021161025
+New effect:(9.026401102591986, 10.283703220838348)
@@ -1237,8 +1237,8 @@

Adding an unobserved common cause variable 
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.1455097789681
-New effect:(4.164924356241952, 12.037138430567376)
+Estimated effect:10.32668021161025
+New effect:(6.9550266796339475, 10.294311909679474)
@@ -1276,14 +1276,14 @@

Adding an unobserved common cause variable 
 Refute: Add an Unobserved Common Cause
-Estimated effect:12.1455097789681
-New effect:(2.8781532276244874, 11.864340607802907)
+Estimated effect:10.32668021161025
+New effect:(-0.2588918365645012, 10.23917200252175)

Conclusion: Assuming that the unobserved confounder does not affect the treatment or outcome more strongly than any observed confounder, the causal effect can be concluded to be positive.

diff --git a/main/example_notebooks/dowhy_simple_example.ipynb b/main/example_notebooks/dowhy_simple_example.ipynb index c84286a3b..0440f5227 100644 --- a/main/example_notebooks/dowhy_simple_example.ipynb +++ b/main/example_notebooks/dowhy_simple_example.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:32.702691Z", - "iopub.status.busy": "2024-10-19T23:50:32.702277Z", - "iopub.status.idle": "2024-10-19T23:50:34.143525Z", - "shell.execute_reply": "2024-10-19T23:50:34.142922Z" + "iopub.execute_input": "2024-10-22T01:16:45.939252Z", + "iopub.status.busy": "2024-10-22T01:16:45.939081Z", + "iopub.status.idle": "2024-10-22T01:16:47.512380Z", + "shell.execute_reply": "2024-10-22T01:16:47.511810Z" } }, "outputs": [], @@ -44,10 +44,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.146010Z", - "iopub.status.busy": "2024-10-19T23:50:34.145491Z", - "iopub.status.idle": "2024-10-19T23:50:34.292757Z", - "shell.execute_reply": "2024-10-19T23:50:34.292124Z" + "iopub.execute_input": "2024-10-22T01:16:47.514954Z", + "iopub.status.busy": "2024-10-22T01:16:47.514622Z", + "iopub.status.idle": "2024-10-22T01:16:47.663628Z", + "shell.execute_reply": "2024-10-22T01:16:47.662980Z" } }, "outputs": [], @@ -68,10 +68,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.295636Z", - "iopub.status.busy": "2024-10-19T23:50:34.295178Z", - "iopub.status.idle": "2024-10-19T23:50:34.308728Z", - "shell.execute_reply": "2024-10-19T23:50:34.308261Z" + "iopub.execute_input": "2024-10-22T01:16:47.666172Z", + "iopub.status.busy": "2024-10-22T01:16:47.665959Z", + "iopub.status.idle": "2024-10-22T01:16:47.682055Z", + "shell.execute_reply": "2024-10-22T01:16:47.681461Z" } }, "outputs": [ @@ -111,68 +111,68 @@ " \n", " \n", " 0\n", - " 2.433025\n", - " 1.0\n", - " 0.446315\n", - " 0.565050\n", - " 1.584307\n", - " 0.044203\n", - " 0.998964\n", - " 0\n", + " -1.480699\n", + " 0.0\n", + " 0.682122\n", + " 1.092432\n", + " -0.403973\n", + " -0.545784\n", + " 1.560930\n", + " 3\n", " True\n", - " 29.326757\n", + " 14.906206\n", " \n", " \n", " 1\n", - " -0.615400\n", + " -0.085327\n", " 0.0\n", - " 0.686974\n", - " 1.094997\n", - " 0.789523\n", - " 0.220281\n", - " -0.269505\n", - " 2\n", - " False\n", - " 4.139483\n", + " 0.023824\n", + " 1.109181\n", + " 0.085558\n", + " 1.882593\n", + " -0.660220\n", + " 0\n", + " True\n", + " 13.612012\n", " \n", " \n", " 2\n", - " 0.464745\n", - " 0.0\n", - " 0.694356\n", - " -0.211672\n", - " -0.423395\n", - " 1.916099\n", - " 0.775553\n", - " 2\n", + " -0.111724\n", + " 1.0\n", + " 0.355073\n", + " 1.655963\n", + " -0.580056\n", + " 1.430628\n", + " -0.566377\n", + " 3\n", " True\n", - " 20.520108\n", + " 20.258636\n", " \n", " \n", " 3\n", - " 1.419616\n", - " 1.0\n", - " 0.964398\n", - " 0.086734\n", - " 0.598756\n", - " 0.791396\n", - " 0.424756\n", + " -0.116541\n", + " 0.0\n", + " 0.777078\n", + " 0.500918\n", + " 1.786132\n", + " -1.350500\n", + " -1.842522\n", " 2\n", - " True\n", - " 22.203880\n", + " False\n", + " -3.794496\n", " \n", " \n", " 4\n", - " 0.117763\n", + " -1.012609\n", " 0.0\n", - " 0.301013\n", - " 0.463781\n", - " 2.771895\n", - " -0.385546\n", - " -0.401188\n", - " 1\n", + " 0.458027\n", + " 0.694989\n", + " 1.747985\n", + " 1.535833\n", + " 0.911330\n", + " 0\n", " True\n", - " 18.342432\n", + " 13.092393\n", " \n", " \n", "\n", @@ -180,18 +180,18 @@ ], "text/plain": [ " X0 Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 2.433025 1.0 0.446315 0.565050 1.584307 0.044203 0.998964 0 True \n", - "1 -0.615400 0.0 0.686974 1.094997 0.789523 0.220281 -0.269505 2 False \n", - "2 0.464745 0.0 0.694356 -0.211672 -0.423395 1.916099 0.775553 2 True \n", - "3 1.419616 1.0 0.964398 0.086734 0.598756 0.791396 0.424756 2 True \n", - "4 0.117763 0.0 0.301013 0.463781 2.771895 -0.385546 -0.401188 1 True \n", + "0 -1.480699 0.0 0.682122 1.092432 -0.403973 -0.545784 1.560930 3 True \n", + "1 -0.085327 0.0 0.023824 1.109181 0.085558 1.882593 -0.660220 0 True \n", + "2 -0.111724 1.0 0.355073 1.655963 -0.580056 1.430628 -0.566377 3 True \n", + "3 -0.116541 0.0 0.777078 0.500918 1.786132 -1.350500 -1.842522 2 False \n", + "4 -1.012609 0.0 0.458027 0.694989 1.747985 1.535833 0.911330 0 True \n", "\n", " y \n", - "0 29.326757 \n", - "1 4.139483 \n", - "2 20.520108 \n", - "3 22.203880 \n", - "4 18.342432 " + "0 14.906206 \n", + "1 13.612012 \n", + "2 20.258636 \n", + "3 -3.794496 \n", + "4 13.092393 " ] }, "execution_count": 3, @@ -231,10 +231,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.311422Z", - "iopub.status.busy": "2024-10-19T23:50:34.310975Z", - "iopub.status.idle": "2024-10-19T23:50:34.317127Z", - "shell.execute_reply": "2024-10-19T23:50:34.316604Z" + "iopub.execute_input": "2024-10-22T01:16:47.684661Z", + "iopub.status.busy": "2024-10-22T01:16:47.684429Z", + "iopub.status.idle": "2024-10-22T01:16:47.691943Z", + "shell.execute_reply": "2024-10-22T01:16:47.691359Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.318919Z", - "iopub.status.busy": "2024-10-19T23:50:34.318697Z", - "iopub.status.idle": "2024-10-19T23:50:34.483757Z", - "shell.execute_reply": "2024-10-19T23:50:34.483177Z" + "iopub.execute_input": "2024-10-22T01:16:47.694711Z", + "iopub.status.busy": "2024-10-22T01:16:47.694330Z", + "iopub.status.idle": "2024-10-22T01:16:47.863465Z", + "shell.execute_reply": "2024-10-22T01:16:47.862869Z" } }, "outputs": [ @@ -280,10 +280,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.485996Z", - "iopub.status.busy": "2024-10-19T23:50:34.485621Z", - "iopub.status.idle": "2024-10-19T23:50:34.492025Z", - "shell.execute_reply": "2024-10-19T23:50:34.491476Z" + "iopub.execute_input": "2024-10-22T01:16:47.865840Z", + "iopub.status.busy": "2024-10-22T01:16:47.865301Z", + "iopub.status.idle": "2024-10-22T01:16:47.872759Z", + "shell.execute_reply": "2024-10-22T01:16:47.872151Z" }, "scrolled": true }, @@ -329,10 +329,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.494153Z", - "iopub.status.busy": "2024-10-19T23:50:34.493759Z", - "iopub.status.idle": "2024-10-19T23:50:34.664237Z", - "shell.execute_reply": "2024-10-19T23:50:34.663557Z" + "iopub.execute_input": "2024-10-22T01:16:47.874894Z", + "iopub.status.busy": "2024-10-22T01:16:47.874682Z", + "iopub.status.idle": "2024-10-22T01:16:48.050495Z", + "shell.execute_reply": "2024-10-22T01:16:48.049879Z" } }, "outputs": [ @@ -346,9 +346,9 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", @@ -391,10 +391,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:34.666451Z", - "iopub.status.busy": "2024-10-19T23:50:34.666012Z", - "iopub.status.idle": "2024-10-19T23:50:35.003677Z", - "shell.execute_reply": "2024-10-19T23:50:35.003082Z" + "iopub.execute_input": "2024-10-22T01:16:48.052633Z", + "iopub.status.busy": "2024-10-22T01:16:48.052223Z", + "iopub.status.idle": "2024-10-22T01:16:48.366769Z", + "shell.execute_reply": "2024-10-22T01:16:48.366128Z" }, "scrolled": true }, @@ -412,16 +412,16 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W4+W3+W0\n", + "b: y~v0+W4+W2+W3+W0+W1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 12.1455097789681\n", + "Mean value: 10.32668021161025\n", "\n" ] } @@ -444,10 +444,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.005772Z", - "iopub.status.busy": "2024-10-19T23:50:35.005379Z", - "iopub.status.idle": "2024-10-19T23:50:35.288112Z", - "shell.execute_reply": "2024-10-19T23:50:35.287460Z" + "iopub.execute_input": "2024-10-22T01:16:48.368942Z", + "iopub.status.busy": "2024-10-22T01:16:48.368552Z", + "iopub.status.idle": "2024-10-22T01:16:48.630220Z", + "shell.execute_reply": "2024-10-22T01:16:48.629529Z" } }, "outputs": [ @@ -464,18 +464,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W4+W3+W0\n", + "b: y~v0+W4+W2+W3+W0+W1\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 11.868218360802024\n", + "Mean value: 10.42646085358387\n", "\n", - "Causal Estimate is 11.868218360802024\n" + "Causal Estimate is 10.42646085358387\n" ] } ], @@ -500,10 +500,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.290148Z", - "iopub.status.busy": "2024-10-19T23:50:35.289851Z", - "iopub.status.idle": "2024-10-19T23:50:35.293087Z", - "shell.execute_reply": "2024-10-19T23:50:35.292589Z" + "iopub.execute_input": "2024-10-22T01:16:48.632549Z", + "iopub.status.busy": "2024-10-22T01:16:48.632145Z", + "iopub.status.idle": "2024-10-22T01:16:48.635704Z", + "shell.execute_reply": "2024-10-22T01:16:48.635150Z" }, "scrolled": true }, @@ -523,10 +523,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.294955Z", - "iopub.status.busy": "2024-10-19T23:50:35.294575Z", - "iopub.status.idle": "2024-10-19T23:50:35.418793Z", - "shell.execute_reply": "2024-10-19T23:50:35.418113Z" + "iopub.execute_input": "2024-10-22T01:16:48.637709Z", + "iopub.status.busy": "2024-10-22T01:16:48.637334Z", + "iopub.status.idle": "2024-10-22T01:16:48.783111Z", + "shell.execute_reply": "2024-10-22T01:16:48.782448Z" } }, "outputs": [ @@ -550,10 +550,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.421057Z", - "iopub.status.busy": "2024-10-19T23:50:35.420638Z", - "iopub.status.idle": "2024-10-19T23:50:35.425528Z", - "shell.execute_reply": "2024-10-19T23:50:35.424913Z" + "iopub.execute_input": "2024-10-22T01:16:48.785428Z", + "iopub.status.busy": "2024-10-22T01:16:48.785171Z", + "iopub.status.idle": "2024-10-22T01:16:48.791415Z", + "shell.execute_reply": "2024-10-22T01:16:48.790796Z" } }, "outputs": [ @@ -587,10 +587,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.427340Z", - "iopub.status.busy": "2024-10-19T23:50:35.427153Z", - "iopub.status.idle": "2024-10-19T23:50:35.527840Z", - "shell.execute_reply": "2024-10-19T23:50:35.527280Z" + "iopub.execute_input": "2024-10-22T01:16:48.793633Z", + "iopub.status.busy": "2024-10-22T01:16:48.793420Z", + "iopub.status.idle": "2024-10-22T01:16:48.906498Z", + "shell.execute_reply": "2024-10-22T01:16:48.905906Z" } }, "outputs": [], @@ -610,10 +610,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.529626Z", - "iopub.status.busy": "2024-10-19T23:50:35.529432Z", - "iopub.status.idle": "2024-10-19T23:50:35.812018Z", - "shell.execute_reply": "2024-10-19T23:50:35.811389Z" + "iopub.execute_input": "2024-10-22T01:16:48.908462Z", + "iopub.status.busy": "2024-10-22T01:16:48.908083Z", + "iopub.status.idle": "2024-10-22T01:16:49.166593Z", + "shell.execute_reply": "2024-10-22T01:16:49.165985Z" } }, "outputs": [ @@ -630,18 +630,18 @@ "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", - "─────(E[y|W2,W1,W4,W3,W0])\n", + "─────(E[y|W4,W2,W3,W0,W1])\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W4,W3,W0,U) = P(y|v0,W2,W1,W4,W3,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W3,W0,W1,U) = P(y|v0,W4,W2,W3,W0,W1)\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W4+W3+W0\n", + "b: y~v0+W4+W2+W3+W0+W1\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 12.1455097789681\n", + "Mean value: 10.32668021161025\n", "\n", - "Causal Estimate is 12.1455097789681\n" + "Causal Estimate is 10.32668021161025\n" ] } ], @@ -685,17 +685,17 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:50:35.814043Z", - "iopub.status.busy": "2024-10-19T23:50:35.813856Z", - "iopub.status.idle": "2024-10-19T23:51:04.311699Z", - "shell.execute_reply": "2024-10-19T23:51:04.311018Z" + "iopub.execute_input": "2024-10-22T01:16:49.168700Z", + "iopub.status.busy": "2024-10-22T01:16:49.168277Z", + "iopub.status.idle": "2024-10-22T01:17:14.411254Z", + "shell.execute_reply": "2024-10-22T01:17:14.410647Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c359e565e6ae4d3a8320924f3092a413", + "model_id": "f7665810416e4f69a627a0e32c80fc07", "version_major": 2, "version_minor": 0 }, @@ -711,8 +711,8 @@ "output_type": "stream", "text": [ "Refute: Add a random common cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:12.145509778968098\n", + "Estimated effect:10.32668021161025\n", + "New effect:10.32668021161025\n", "p value:1.0\n", "\n" ] @@ -735,17 +735,17 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:51:04.313589Z", - "iopub.status.busy": "2024-10-19T23:51:04.313386Z", - "iopub.status.idle": "2024-10-19T23:51:30.751914Z", - "shell.execute_reply": "2024-10-19T23:51:30.751224Z" + "iopub.execute_input": "2024-10-22T01:17:14.413283Z", + "iopub.status.busy": "2024-10-22T01:17:14.412888Z", + "iopub.status.idle": "2024-10-22T01:17:39.582841Z", + "shell.execute_reply": "2024-10-22T01:17:39.582302Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "521fef3fa630438399a9c99ce9fa21d9", + "model_id": "a289d04f173d48df994c1e73591f26aa", "version_major": 2, "version_minor": 0 }, @@ -761,9 +761,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:12.1455097789681\n", - "New effect:0.055367443227466485\n", - "p value:0.8799999999999999\n", + "Estimated effect:10.32668021161025\n", + "New effect:0.012367961362952769\n", + "p value:0.98\n", "\n" ] } @@ -786,17 +786,17 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:51:30.753860Z", - "iopub.status.busy": "2024-10-19T23:51:30.753658Z", - "iopub.status.idle": "2024-10-19T23:51:56.640854Z", - "shell.execute_reply": "2024-10-19T23:51:56.640223Z" + "iopub.execute_input": "2024-10-22T01:17:39.585024Z", + "iopub.status.busy": "2024-10-22T01:17:39.584695Z", + "iopub.status.idle": "2024-10-22T01:18:02.497611Z", + "shell.execute_reply": "2024-10-22T01:18:02.497006Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "49166b74edc243cd997ed2a91258d69d", + "model_id": "8991d6af3bab4fd6889e6ab0041f81f2", "version_major": 2, "version_minor": 0 }, @@ -812,9 +812,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:12.1455097789681\n", - "New effect:11.96555527788205\n", - "p value:0.36\n", + "Estimated effect:10.32668021161025\n", + "New effect:10.297025170147867\n", + "p value:0.56\n", "\n" ] } @@ -841,17 +841,17 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:51:56.642984Z", - "iopub.status.busy": "2024-10-19T23:51:56.642577Z", - "iopub.status.idle": "2024-10-19T23:52:09.655444Z", - "shell.execute_reply": "2024-10-19T23:52:09.654693Z" + "iopub.execute_input": "2024-10-22T01:18:02.499736Z", + "iopub.status.busy": "2024-10-22T01:18:02.499362Z", + "iopub.status.idle": "2024-10-22T01:18:14.426891Z", + "shell.execute_reply": "2024-10-22T01:18:14.425395Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b137779c5e844e68418c223c9b563ba", + "model_id": "4e074034b0314ac88de0e3234827a52f", "version_major": 2, "version_minor": 0 }, @@ -887,56 +887,56 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 4.4s\n" + "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 4.2s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 24 tasks | elapsed: 4.9s\n" + "[Parallel(n_jobs=-1)]: Done 24 tasks | elapsed: 4.8s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 6.1s\n" + "[Parallel(n_jobs=-1)]: Done 33 tasks | elapsed: 5.8s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 7.0s\n" + "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 6.6s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 53 tasks | elapsed: 8.2s\n" + "[Parallel(n_jobs=-1)]: Done 53 tasks | elapsed: 7.6s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 64 tasks | elapsed: 9.2s\n" + "[Parallel(n_jobs=-1)]: Done 64 tasks | elapsed: 8.5s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 77 tasks | elapsed: 10.7s\n" + "[Parallel(n_jobs=-1)]: Done 77 tasks | elapsed: 9.9s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 90 tasks | elapsed: 12.0s\n" + "[Parallel(n_jobs=-1)]: Done 90 tasks | elapsed: 11.0s\n" ] }, { @@ -944,9 +944,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:12.1455097789681\n", - "New effect:11.953778728959206\n", - "p value:0.34\n", + "Estimated effect:10.32668021161025\n", + "New effect:10.293059888977698\n", + "p value:0.5\n", "\n" ] }, @@ -954,7 +954,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.0s finished\n" + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 11.9s finished\n" ] } ], @@ -984,10 +984,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:09.657394Z", - "iopub.status.busy": "2024-10-19T23:52:09.657177Z", - "iopub.status.idle": "2024-10-19T23:52:09.958038Z", - "shell.execute_reply": "2024-10-19T23:52:09.957342Z" + "iopub.execute_input": "2024-10-22T01:18:14.431075Z", + "iopub.status.busy": "2024-10-22T01:18:14.430748Z", + "iopub.status.idle": "2024-10-22T01:18:14.705541Z", + "shell.execute_reply": "2024-10-22T01:18:14.704799Z" } }, "outputs": [ @@ -996,8 +996,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:10.382153142787415\n", + "Estimated effect:10.32668021161025\n", + "New effect:9.989729726705876\n", "\n" ] } @@ -1021,16 +1021,16 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:09.960114Z", - "iopub.status.busy": "2024-10-19T23:52:09.959726Z", - "iopub.status.idle": "2024-10-19T23:52:11.278904Z", - "shell.execute_reply": "2024-10-19T23:52:11.278243Z" + "iopub.execute_input": "2024-10-22T01:18:14.707710Z", + "iopub.status.busy": "2024-10-22T01:18:14.707499Z", + "iopub.status.idle": "2024-10-22T01:18:15.900519Z", + "shell.execute_reply": "2024-10-22T01:18:15.899882Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1043,8 +1043,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:(8.382168111045187, 12.004268127514308)\n", + "Estimated effect:10.32668021161025\n", + "New effect:(9.026401102591986, 10.283703220838348)\n", "\n" ] } @@ -1070,16 +1070,16 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:11.280919Z", - "iopub.status.busy": "2024-10-19T23:52:11.280562Z", - "iopub.status.idle": "2024-10-19T23:52:15.861961Z", - "shell.execute_reply": "2024-10-19T23:52:15.861346Z" + "iopub.execute_input": "2024-10-22T01:18:15.902699Z", + "iopub.status.busy": "2024-10-22T01:18:15.902302Z", + "iopub.status.idle": "2024-10-22T01:18:19.982846Z", + "shell.execute_reply": "2024-10-22T01:18:19.982011Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1092,8 +1092,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:12.1455097789681\n", - "New effect:(4.164924356241952, 12.037138430567376)\n", + "Estimated effect:10.32668021161025\n", + "New effect:(6.9550266796339475, 10.294311909679474)\n", "\n" ] } @@ -1118,10 +1118,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:15.864399Z", - "iopub.status.busy": "2024-10-19T23:52:15.863850Z", - "iopub.status.idle": "2024-10-19T23:52:42.457268Z", - "shell.execute_reply": "2024-10-19T23:52:42.456524Z" + "iopub.execute_input": "2024-10-22T01:18:19.985242Z", + "iopub.status.busy": "2024-10-22T01:18:19.984891Z", + "iopub.status.idle": "2024-10-22T01:18:42.595021Z", + "shell.execute_reply": "2024-10-22T01:18:42.594291Z" } }, "outputs": [ @@ -1135,7 +1135,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income< 
-Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00,  2.31it/s]
+Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00,  2.27it/s]
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Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income< 

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Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income< 

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+/tmp/ipykernel_4021/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string "ro-" (-> color='r'). The keyword argument will take precedence.
   plt.plot((x, x), (ci[0], ci[1]), 'ro-', color='orange')
diff --git a/main/example_notebooks/gcm_401k_analysis.ipynb b/main/example_notebooks/gcm_401k_analysis.ipynb index c1959184d..49b0ed868 100644 --- a/main/example_notebooks/gcm_401k_analysis.ipynb +++ b/main/example_notebooks/gcm_401k_analysis.ipynb @@ -64,10 +64,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:47.696657Z", - "iopub.status.busy": "2024-10-19T23:52:47.696481Z", - "iopub.status.idle": "2024-10-19T23:52:47.946510Z", - "shell.execute_reply": "2024-10-19T23:52:47.945939Z" + "iopub.execute_input": "2024-10-22T01:18:47.838819Z", + "iopub.status.busy": "2024-10-22T01:18:47.838627Z", + "iopub.status.idle": "2024-10-22T01:18:48.115189Z", + "shell.execute_reply": "2024-10-22T01:18:48.114481Z" } }, "outputs": [], @@ -81,10 +81,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:47.948821Z", - "iopub.status.busy": "2024-10-19T23:52:47.948422Z", - "iopub.status.idle": "2024-10-19T23:52:47.965599Z", - "shell.execute_reply": "2024-10-19T23:52:47.965069Z" + "iopub.execute_input": "2024-10-22T01:18:48.117639Z", + "iopub.status.busy": "2024-10-22T01:18:48.117217Z", + "iopub.status.idle": "2024-10-22T01:18:48.135124Z", + "shell.execute_reply": "2024-10-22T01:18:48.134498Z" } }, "outputs": [ @@ -299,10 +299,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:47.967502Z", - "iopub.status.busy": "2024-10-19T23:52:47.967303Z", - "iopub.status.idle": "2024-10-19T23:52:49.173676Z", - "shell.execute_reply": "2024-10-19T23:52:49.173074Z" + "iopub.execute_input": "2024-10-22T01:18:48.137274Z", + "iopub.status.busy": "2024-10-22T01:18:48.136883Z", + "iopub.status.idle": "2024-10-22T01:18:49.444529Z", + "shell.execute_reply": "2024-10-22T01:18:49.443807Z" } }, "outputs": [], @@ -331,10 +331,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:49.175950Z", - "iopub.status.busy": "2024-10-19T23:52:49.175587Z", - "iopub.status.idle": "2024-10-19T23:52:49.387522Z", - "shell.execute_reply": "2024-10-19T23:52:49.386980Z" + "iopub.execute_input": "2024-10-22T01:18:49.446958Z", + "iopub.status.busy": "2024-10-22T01:18:49.446643Z", + "iopub.status.idle": "2024-10-22T01:18:49.665622Z", + "shell.execute_reply": "2024-10-22T01:18:49.665001Z" } }, "outputs": [ @@ -369,10 +369,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:49.390130Z", - "iopub.status.busy": "2024-10-19T23:52:49.389703Z", - "iopub.status.idle": "2024-10-19T23:52:50.835809Z", - "shell.execute_reply": "2024-10-19T23:52:50.835153Z" + "iopub.execute_input": "2024-10-22T01:18:49.668360Z", + "iopub.status.busy": "2024-10-22T01:18:49.667779Z", + "iopub.status.idle": "2024-10-22T01:18:51.211985Z", + "shell.execute_reply": "2024-10-22T01:18:51.211298Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:50.837705Z", - "iopub.status.busy": "2024-10-19T23:52:50.837515Z", - "iopub.status.idle": "2024-10-19T23:52:50.841060Z", - "shell.execute_reply": "2024-10-19T23:52:50.840569Z" + "iopub.execute_input": "2024-10-22T01:18:51.214310Z", + "iopub.status.busy": "2024-10-22T01:18:51.213866Z", + "iopub.status.idle": "2024-10-22T01:18:51.218094Z", + "shell.execute_reply": "2024-10-22T01:18:51.217548Z" } }, "outputs": [], @@ -453,10 +453,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:50.842649Z", - "iopub.status.busy": "2024-10-19T23:52:50.842470Z", - "iopub.status.idle": "2024-10-19T23:52:57.662535Z", - "shell.execute_reply": "2024-10-19T23:52:57.662032Z" + "iopub.execute_input": "2024-10-22T01:18:51.219963Z", + "iopub.status.busy": "2024-10-22T01:18:51.219678Z", + "iopub.status.idle": "2024-10-22T01:18:58.040257Z", + "shell.execute_reply": "2024-10-22T01:18:58.039730Z" } }, "outputs": [ @@ -481,7 +481,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node e401: 6%|▌ | 1/18 [00:01<00:21, 1.28s/it]" + "Fitting causal mechanism of node e401: 6%|▌ | 1/18 [00:01<00:21, 1.25s/it]" ] }, { @@ -489,7 +489,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node net_tfa: 6%|▌ | 1/18 [00:01<00:21, 1.28s/it]" + "Fitting causal mechanism of node net_tfa: 6%|▌ | 1/18 [00:01<00:21, 1.25s/it]" ] }, { @@ -660,10 +660,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:57.664515Z", - "iopub.status.busy": "2024-10-19T23:52:57.664193Z", - "iopub.status.idle": "2024-10-19T23:52:57.668513Z", - "shell.execute_reply": "2024-10-19T23:52:57.668059Z" + "iopub.execute_input": "2024-10-22T01:18:58.042085Z", + "iopub.status.busy": "2024-10-22T01:18:58.041895Z", + "iopub.status.idle": "2024-10-22T01:18:58.046369Z", + "shell.execute_reply": "2024-10-22T01:18:58.045936Z" } }, "outputs": [], @@ -691,10 +691,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:52:57.670247Z", - "iopub.status.busy": "2024-10-19T23:52:57.669906Z", - "iopub.status.idle": "2024-10-19T23:53:41.044103Z", - "shell.execute_reply": "2024-10-19T23:53:41.043530Z" + "iopub.execute_input": "2024-10-22T01:18:58.047990Z", + "iopub.status.busy": "2024-10-22T01:18:58.047811Z", + "iopub.status.idle": "2024-10-22T01:19:42.054793Z", + "shell.execute_reply": "2024-10-22T01:19:42.054225Z" } }, "outputs": [ @@ -711,7 +711,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 1%| | 1/100 [00:00<00:43, 2.28it/s]" + "Estimating bootstrap interval...: 1%| | 1/100 [00:00<00:43, 2.26it/s]" ] }, { @@ -719,7 +719,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 2%|▏ | 2/100 [00:00<00:42, 2.30it/s]" + "Estimating bootstrap interval...: 2%|▏ | 2/100 [00:00<00:42, 2.29it/s]" ] }, { @@ -727,7 +727,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 3%|▎ | 3/100 [00:01<00:42, 2.30it/s]" + "Estimating bootstrap interval...: 3%|▎ | 3/100 [00:01<00:42, 2.29it/s]" ] }, { @@ -735,7 +735,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 4%|▍ | 4/100 [00:01<00:41, 2.31it/s]" + "Estimating bootstrap interval...: 4%|▍ | 4/100 [00:01<00:41, 2.30it/s]" ] }, { @@ -743,7 +743,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 5%|▌ | 5/100 [00:02<00:41, 2.31it/s]" + "Estimating bootstrap interval...: 5%|▌ | 5/100 [00:02<00:41, 2.30it/s]" ] }, { @@ -751,7 +751,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 6%|▌ | 6/100 [00:02<00:40, 2.31it/s]" + "Estimating bootstrap interval...: 6%|▌ | 6/100 [00:02<00:40, 2.30it/s]" ] }, { @@ -759,7 +759,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 7%|▋ | 7/100 [00:03<00:40, 2.31it/s]" + "Estimating bootstrap interval...: 7%|▋ | 7/100 [00:03<00:40, 2.30it/s]" ] }, { @@ -767,7 +767,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 8%|▊ | 8/100 [00:03<00:39, 2.31it/s]" + "Estimating bootstrap interval...: 8%|▊ | 8/100 [00:03<00:40, 2.30it/s]" ] }, { @@ -775,7 +775,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 9%|▉ | 9/100 [00:03<00:39, 2.31it/s]" + "Estimating bootstrap interval...: 9%|▉ | 9/100 [00:03<00:40, 2.27it/s]" ] }, { @@ -783,7 +783,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 10%|█ | 10/100 [00:04<00:38, 2.31it/s]" + "Estimating bootstrap interval...: 10%|█ | 10/100 [00:04<00:39, 2.27it/s]" ] }, { @@ -791,7 +791,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 11%|█ | 11/100 [00:04<00:38, 2.31it/s]" + "Estimating bootstrap interval...: 11%|█ | 11/100 [00:04<00:38, 2.28it/s]" ] }, { @@ -799,7 +799,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 12%|█▏ | 12/100 [00:05<00:38, 2.31it/s]" + "Estimating bootstrap interval...: 12%|█▏ | 12/100 [00:05<00:38, 2.27it/s]" ] }, { @@ -807,7 +807,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 13%|█▎ | 13/100 [00:05<00:37, 2.31it/s]" + "Estimating bootstrap interval...: 13%|█▎ | 13/100 [00:05<00:38, 2.26it/s]" ] }, { @@ -815,7 +815,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 14%|█▍ | 14/100 [00:06<00:37, 2.31it/s]" + "Estimating bootstrap interval...: 14%|█▍ | 14/100 [00:06<00:38, 2.26it/s]" ] }, { @@ -823,7 +823,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 15%|█▌ | 15/100 [00:06<00:36, 2.31it/s]" + "Estimating bootstrap interval...: 15%|█▌ | 15/100 [00:06<00:37, 2.24it/s]" ] }, { @@ -831,7 +831,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 16%|█▌ | 16/100 [00:06<00:36, 2.31it/s]" + "Estimating bootstrap interval...: 16%|█▌ | 16/100 [00:07<00:37, 2.26it/s]" ] }, { @@ -839,7 +839,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 17%|█▋ | 17/100 [00:07<00:35, 2.31it/s]" + "Estimating bootstrap interval...: 17%|█▋ | 17/100 [00:07<00:36, 2.27it/s]" ] }, { @@ -847,7 +847,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 18%|█▊ | 18/100 [00:07<00:35, 2.31it/s]" + "Estimating bootstrap interval...: 18%|█▊ | 18/100 [00:07<00:35, 2.28it/s]" ] }, { @@ -855,7 +855,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 19%|█▉ | 19/100 [00:08<00:35, 2.31it/s]" + "Estimating bootstrap interval...: 19%|█▉ | 19/100 [00:08<00:35, 2.29it/s]" ] }, { @@ -863,7 +863,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 20%|██ | 20/100 [00:08<00:34, 2.30it/s]" + "Estimating bootstrap interval...: 20%|██ | 20/100 [00:08<00:34, 2.29it/s]" ] }, { @@ -871,7 +871,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 21%|██ | 21/100 [00:09<00:34, 2.30it/s]" + "Estimating bootstrap interval...: 21%|██ | 21/100 [00:09<00:34, 2.29it/s]" ] }, { @@ -879,7 +879,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 22%|██▏ | 22/100 [00:09<00:33, 2.31it/s]" + "Estimating bootstrap interval...: 22%|██▏ | 22/100 [00:09<00:34, 2.28it/s]" ] }, { @@ -887,7 +887,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 23%|██▎ | 23/100 [00:09<00:33, 2.31it/s]" + "Estimating bootstrap interval...: 23%|██▎ | 23/100 [00:10<00:33, 2.29it/s]" ] }, { @@ -895,7 +895,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 24%|██▍ | 24/100 [00:10<00:32, 2.31it/s]" + "Estimating bootstrap interval...: 24%|██▍ | 24/100 [00:10<00:33, 2.29it/s]" ] }, { @@ -903,7 +903,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 25%|██▌ | 25/100 [00:10<00:32, 2.31it/s]" + "Estimating bootstrap interval...: 25%|██▌ | 25/100 [00:10<00:32, 2.29it/s]" ] }, { @@ -911,7 +911,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 26%|██▌ | 26/100 [00:11<00:32, 2.31it/s]" + "Estimating bootstrap interval...: 26%|██▌ | 26/100 [00:11<00:33, 2.22it/s]" ] }, { @@ -919,7 +919,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 27%|██▋ | 27/100 [00:11<00:31, 2.31it/s]" + "Estimating bootstrap interval...: 27%|██▋ | 27/100 [00:11<00:32, 2.22it/s]" ] }, { @@ -927,7 +927,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 28%|██▊ | 28/100 [00:12<00:31, 2.30it/s]" + "Estimating bootstrap interval...: 28%|██▊ | 28/100 [00:12<00:32, 2.22it/s]" ] }, { @@ -935,7 +935,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 29%|██▉ | 29/100 [00:12<00:31, 2.29it/s]" + "Estimating bootstrap interval...: 29%|██▉ | 29/100 [00:12<00:32, 2.20it/s]" ] }, { @@ -943,7 +943,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 30%|███ | 30/100 [00:13<00:30, 2.30it/s]" + "Estimating bootstrap interval...: 30%|███ | 30/100 [00:13<00:31, 2.22it/s]" ] }, { @@ -951,7 +951,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 31%|███ | 31/100 [00:13<00:30, 2.27it/s]" + "Estimating bootstrap interval...: 31%|███ | 31/100 [00:13<00:30, 2.24it/s]" ] }, { @@ -959,7 +959,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 32%|███▏ | 32/100 [00:13<00:29, 2.28it/s]" + "Estimating bootstrap interval...: 32%|███▏ | 32/100 [00:14<00:30, 2.26it/s]" ] }, { @@ -975,7 +975,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 34%|███▍ | 34/100 [00:14<00:28, 2.28it/s]" + "Estimating bootstrap interval...: 34%|███▍ | 34/100 [00:14<00:28, 2.29it/s]" ] }, { @@ -1007,7 +1007,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 38%|███▊ | 38/100 [00:16<00:26, 2.31it/s]" + "Estimating bootstrap interval...: 38%|███▊ | 38/100 [00:16<00:27, 2.29it/s]" ] }, { @@ -1015,7 +1015,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 39%|███▉ | 39/100 [00:16<00:26, 2.31it/s]" + "Estimating bootstrap interval...: 39%|███▉ | 39/100 [00:17<00:26, 2.29it/s]" ] }, { @@ -1023,7 +1023,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 40%|████ | 40/100 [00:17<00:26, 2.31it/s]" + "Estimating bootstrap interval...: 40%|████ | 40/100 [00:17<00:26, 2.29it/s]" ] }, { @@ -1031,7 +1031,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 41%|████ | 41/100 [00:17<00:25, 2.31it/s]" + "Estimating bootstrap interval...: 41%|████ | 41/100 [00:18<00:25, 2.29it/s]" ] }, { @@ -1039,7 +1039,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 42%|████▏ | 42/100 [00:18<00:25, 2.31it/s]" + "Estimating bootstrap interval...: 42%|████▏ | 42/100 [00:18<00:25, 2.26it/s]" ] }, { @@ -1047,7 +1047,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 43%|████▎ | 43/100 [00:18<00:24, 2.31it/s]" + "Estimating bootstrap interval...: 43%|████▎ | 43/100 [00:18<00:25, 2.25it/s]" ] }, { @@ -1055,7 +1055,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 44%|████▍ | 44/100 [00:19<00:24, 2.31it/s]" + "Estimating bootstrap interval...: 44%|████▍ | 44/100 [00:19<00:24, 2.27it/s]" ] }, { @@ -1063,7 +1063,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 45%|████▌ | 45/100 [00:19<00:23, 2.31it/s]" + "Estimating bootstrap interval...: 45%|████▌ | 45/100 [00:19<00:24, 2.28it/s]" ] }, { @@ -1071,7 +1071,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 46%|████▌ | 46/100 [00:19<00:23, 2.31it/s]" + "Estimating bootstrap interval...: 46%|████▌ | 46/100 [00:20<00:23, 2.29it/s]" ] }, { @@ -1079,7 +1079,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 47%|████▋ | 47/100 [00:20<00:22, 2.31it/s]" + "Estimating bootstrap interval...: 47%|████▋ | 47/100 [00:20<00:23, 2.26it/s]" ] }, { @@ -1087,7 +1087,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 48%|████▊ | 48/100 [00:20<00:22, 2.31it/s]" + "Estimating bootstrap interval...: 48%|████▊ | 48/100 [00:21<00:22, 2.27it/s]" ] }, { @@ -1095,7 +1095,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 49%|████▉ | 49/100 [00:21<00:22, 2.30it/s]" + "Estimating bootstrap interval...: 49%|████▉ | 49/100 [00:21<00:22, 2.28it/s]" ] }, { @@ -1103,7 +1103,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 50%|█████ | 50/100 [00:21<00:21, 2.31it/s]" + "Estimating bootstrap interval...: 50%|█████ | 50/100 [00:22<00:21, 2.28it/s]" ] }, { @@ -1111,7 +1111,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 51%|█████ | 51/100 [00:22<00:21, 2.31it/s]" + "Estimating bootstrap interval...: 51%|█████ | 51/100 [00:22<00:21, 2.29it/s]" ] }, { @@ -1119,7 +1119,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 52%|█████▏ | 52/100 [00:22<00:20, 2.31it/s]" + "Estimating bootstrap interval...: 52%|█████▏ | 52/100 [00:22<00:20, 2.29it/s]" ] }, { @@ -1127,7 +1127,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 53%|█████▎ | 53/100 [00:22<00:20, 2.31it/s]" + "Estimating bootstrap interval...: 53%|█████▎ | 53/100 [00:23<00:20, 2.29it/s]" ] }, { @@ -1135,7 +1135,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 54%|█████▍ | 54/100 [00:23<00:19, 2.31it/s]" + "Estimating bootstrap interval...: 54%|█████▍ | 54/100 [00:23<00:20, 2.29it/s]" ] }, { @@ -1143,7 +1143,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 55%|█████▌ | 55/100 [00:23<00:19, 2.31it/s]" + "Estimating bootstrap interval...: 55%|█████▌ | 55/100 [00:24<00:19, 2.29it/s]" ] }, { @@ -1151,7 +1151,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 56%|█████▌ | 56/100 [00:24<00:19, 2.31it/s]" + "Estimating bootstrap interval...: 56%|█████▌ | 56/100 [00:24<00:19, 2.29it/s]" ] }, { @@ -1159,7 +1159,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 57%|█████▋ | 57/100 [00:24<00:18, 2.31it/s]" + "Estimating bootstrap interval...: 57%|█████▋ | 57/100 [00:25<00:18, 2.29it/s]" ] }, { @@ -1167,7 +1167,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 58%|█████▊ | 58/100 [00:25<00:18, 2.31it/s]" + "Estimating bootstrap interval...: 58%|█████▊ | 58/100 [00:25<00:18, 2.29it/s]" ] }, { @@ -1175,7 +1175,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 59%|█████▉ | 59/100 [00:25<00:17, 2.31it/s]" + "Estimating bootstrap interval...: 59%|█████▉ | 59/100 [00:25<00:17, 2.29it/s]" ] }, { @@ -1183,7 +1183,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 60%|██████ | 60/100 [00:26<00:17, 2.31it/s]" + "Estimating bootstrap interval...: 60%|██████ | 60/100 [00:26<00:17, 2.30it/s]" ] }, { @@ -1191,7 +1191,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 61%|██████ | 61/100 [00:26<00:16, 2.31it/s]" + "Estimating bootstrap interval...: 61%|██████ | 61/100 [00:26<00:16, 2.30it/s]" ] }, { @@ -1199,7 +1199,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 62%|██████▏ | 62/100 [00:26<00:16, 2.31it/s]" + "Estimating bootstrap interval...: 62%|██████▏ | 62/100 [00:27<00:16, 2.30it/s]" ] }, { @@ -1207,7 +1207,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 63%|██████▎ | 63/100 [00:27<00:16, 2.31it/s]" + "Estimating bootstrap interval...: 63%|██████▎ | 63/100 [00:27<00:16, 2.30it/s]" ] }, { @@ -1215,7 +1215,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 64%|██████▍ | 64/100 [00:27<00:15, 2.31it/s]" + "Estimating bootstrap interval...: 64%|██████▍ | 64/100 [00:28<00:15, 2.30it/s]" ] }, { @@ -1223,7 +1223,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 65%|██████▌ | 65/100 [00:28<00:15, 2.31it/s]" + "Estimating bootstrap interval...: 65%|██████▌ | 65/100 [00:28<00:15, 2.30it/s]" ] }, { @@ -1231,7 +1231,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 66%|██████▌ | 66/100 [00:28<00:14, 2.31it/s]" + "Estimating bootstrap interval...: 66%|██████▌ | 66/100 [00:28<00:14, 2.29it/s]" ] }, { @@ -1239,7 +1239,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 67%|██████▋ | 67/100 [00:29<00:14, 2.31it/s]" + "Estimating bootstrap interval...: 67%|██████▋ | 67/100 [00:29<00:14, 2.27it/s]" ] }, { @@ -1247,7 +1247,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 68%|██████▊ | 68/100 [00:29<00:13, 2.31it/s]" + "Estimating bootstrap interval...: 68%|██████▊ | 68/100 [00:29<00:14, 2.18it/s]" ] }, { @@ -1255,7 +1255,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 69%|██████▉ | 69/100 [00:29<00:13, 2.31it/s]" + "Estimating bootstrap interval...: 69%|██████▉ | 69/100 [00:30<00:13, 2.22it/s]" ] }, { @@ -1263,7 +1263,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 70%|███████ | 70/100 [00:30<00:12, 2.31it/s]" + "Estimating bootstrap interval...: 70%|███████ | 70/100 [00:30<00:13, 2.24it/s]" ] }, { @@ -1271,7 +1271,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 71%|███████ | 71/100 [00:30<00:12, 2.31it/s]" + "Estimating bootstrap interval...: 71%|███████ | 71/100 [00:31<00:12, 2.26it/s]" ] }, { @@ -1279,7 +1279,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 72%|███████▏ | 72/100 [00:31<00:12, 2.31it/s]" + "Estimating bootstrap interval...: 72%|███████▏ | 72/100 [00:31<00:12, 2.27it/s]" ] }, { @@ -1287,7 +1287,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 73%|███████▎ | 73/100 [00:31<00:11, 2.31it/s]" + "Estimating bootstrap interval...: 73%|███████▎ | 73/100 [00:32<00:11, 2.27it/s]" ] }, { @@ -1295,7 +1295,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 74%|███████▍ | 74/100 [00:32<00:11, 2.31it/s]" + "Estimating bootstrap interval...: 74%|███████▍ | 74/100 [00:32<00:11, 2.28it/s]" ] }, { @@ -1303,7 +1303,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 75%|███████▌ | 75/100 [00:32<00:10, 2.29it/s]" + "Estimating bootstrap interval...: 75%|███████▌ | 75/100 [00:32<00:10, 2.28it/s]" ] }, { @@ -1311,7 +1311,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 76%|███████▌ | 76/100 [00:32<00:10, 2.30it/s]" + "Estimating bootstrap interval...: 76%|███████▌ | 76/100 [00:33<00:10, 2.26it/s]" ] }, { @@ -1319,7 +1319,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 77%|███████▋ | 77/100 [00:33<00:10, 2.30it/s]" + "Estimating bootstrap interval...: 77%|███████▋ | 77/100 [00:33<00:10, 2.27it/s]" ] }, { @@ -1327,7 +1327,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 78%|███████▊ | 78/100 [00:33<00:09, 2.30it/s]" + "Estimating bootstrap interval...: 78%|███████▊ | 78/100 [00:34<00:09, 2.27it/s]" ] }, { @@ -1335,7 +1335,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 79%|███████▉ | 79/100 [00:34<00:09, 2.30it/s]" + "Estimating bootstrap interval...: 79%|███████▉ | 79/100 [00:34<00:09, 2.28it/s]" ] }, { @@ -1343,7 +1343,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 80%|████████ | 80/100 [00:34<00:08, 2.31it/s]" + "Estimating bootstrap interval...: 80%|████████ | 80/100 [00:35<00:08, 2.27it/s]" ] }, { @@ -1351,7 +1351,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 81%|████████ | 81/100 [00:35<00:08, 2.31it/s]" + "Estimating bootstrap interval...: 81%|████████ | 81/100 [00:35<00:08, 2.28it/s]" ] }, { @@ -1359,7 +1359,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 82%|████████▏ | 82/100 [00:35<00:07, 2.31it/s]" + "Estimating bootstrap interval...: 82%|████████▏ | 82/100 [00:36<00:07, 2.28it/s]" ] }, { @@ -1367,7 +1367,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 83%|████████▎ | 83/100 [00:35<00:07, 2.31it/s]" + "Estimating bootstrap interval...: 83%|████████▎ | 83/100 [00:36<00:07, 2.29it/s]" ] }, { @@ -1383,7 +1383,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 85%|████████▌ | 85/100 [00:36<00:06, 2.31it/s]" + "Estimating bootstrap interval...: 85%|████████▌ | 85/100 [00:37<00:06, 2.29it/s]" ] }, { @@ -1391,7 +1391,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 86%|████████▌ | 86/100 [00:37<00:06, 2.31it/s]" + "Estimating bootstrap interval...: 86%|████████▌ | 86/100 [00:37<00:06, 2.30it/s]" ] }, { @@ -1399,7 +1399,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 87%|████████▋ | 87/100 [00:37<00:05, 2.31it/s]" + "Estimating bootstrap interval...: 87%|████████▋ | 87/100 [00:38<00:05, 2.30it/s]" ] }, { @@ -1407,7 +1407,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 88%|████████▊ | 88/100 [00:38<00:05, 2.31it/s]" + "Estimating bootstrap interval...: 88%|████████▊ | 88/100 [00:38<00:05, 2.30it/s]" ] }, { @@ -1415,7 +1415,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 89%|████████▉ | 89/100 [00:38<00:04, 2.31it/s]" + "Estimating bootstrap interval...: 89%|████████▉ | 89/100 [00:39<00:04, 2.30it/s]" ] }, { @@ -1423,7 +1423,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 90%|█████████ | 90/100 [00:39<00:04, 2.31it/s]" + "Estimating bootstrap interval...: 90%|█████████ | 90/100 [00:39<00:04, 2.27it/s]" ] }, { @@ -1431,7 +1431,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 91%|█████████ | 91/100 [00:39<00:03, 2.31it/s]" + "Estimating bootstrap interval...: 91%|█████████ | 91/100 [00:40<00:04, 2.18it/s]" ] }, { @@ -1439,7 +1439,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 92%|█████████▏| 92/100 [00:39<00:03, 2.31it/s]" + "Estimating bootstrap interval...: 92%|█████████▏| 92/100 [00:40<00:03, 2.22it/s]" ] }, { @@ -1447,7 +1447,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 93%|█████████▎| 93/100 [00:40<00:03, 2.31it/s]" + "Estimating bootstrap interval...: 93%|█████████▎| 93/100 [00:40<00:03, 2.25it/s]" ] }, { @@ -1455,7 +1455,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 94%|█████████▍| 94/100 [00:40<00:02, 2.31it/s]" + "Estimating bootstrap interval...: 94%|█████████▍| 94/100 [00:41<00:02, 2.26it/s]" ] }, { @@ -1463,7 +1463,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 95%|█████████▌| 95/100 [00:41<00:02, 2.31it/s]" + "Estimating bootstrap interval...: 95%|█████████▌| 95/100 [00:41<00:02, 2.24it/s]" ] }, { @@ -1471,7 +1471,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 96%|█████████▌| 96/100 [00:41<00:01, 2.31it/s]" + "Estimating bootstrap interval...: 96%|█████████▌| 96/100 [00:42<00:01, 2.26it/s]" ] }, { @@ -1479,7 +1479,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 97%|█████████▋| 97/100 [00:42<00:01, 2.31it/s]" + "Estimating bootstrap interval...: 97%|█████████▋| 97/100 [00:42<00:01, 2.28it/s]" ] }, { @@ -1487,7 +1487,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 98%|█████████▊| 98/100 [00:42<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 98%|█████████▊| 98/100 [00:43<00:00, 2.29it/s]" ] }, { @@ -1495,7 +1495,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 99%|█████████▉| 99/100 [00:42<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 99%|█████████▉| 99/100 [00:43<00:00, 2.29it/s]" ] }, { @@ -1503,7 +1503,7 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.27it/s]" ] }, { @@ -1511,15 +1511,15 @@ "output_type": "stream", "text": [ "\r", - "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.31it/s]" + "Estimating bootstrap interval...: 100%|██████████| 100/100 [00:43<00:00, 2.27it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'ate': 6625.920676709266, '0%-20%': 4189.075458725501, '20%-40%': 3377.758863663636, '40%-60%': 5708.485661944268, '60%-80%': 7650.805116771037, '80%-100%': 12053.7668997243}\n", - "{'ate': array([5068.14408455, 8530.33139243]), '0%-20%': array([2852.5444978 , 5813.86194937]), '20%-40%': array([1542.59819043, 5847.81952526]), '40%-60%': array([3555.12606816, 8280.83280268]), '60%-80%': array([ 4452.12672782, 10727.92604392]), '80%-100%': array([ 5246.02924007, 19210.44069844])}\n" + "{'ate': 6754.262821634007, '0%-20%': 4311.355389405208, '20%-40%': 3326.9069591859184, '40%-60%': 5818.682773291764, '60%-80%': 7765.007274775296, '80%-100%': 12318.072193752065}\n", + "{'ate': array([4960.09827088, 8710.01062402]), '0%-20%': array([3045.2342499, 5818.661168 ]), '20%-40%': array([1572.75217838, 5499.96751887]), '40%-60%': array([3575.11437279, 8306.54464987]), '60%-80%': array([ 4659.97458355, 11206.3565352 ]), '80%-100%': array([ 5589.58571445, 19310.90794145])}\n" ] }, { @@ -1569,10 +1569,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:41.046119Z", - "iopub.status.busy": "2024-10-19T23:53:41.045779Z", - "iopub.status.idle": "2024-10-19T23:53:41.166599Z", - "shell.execute_reply": "2024-10-19T23:53:41.166065Z" + "iopub.execute_input": "2024-10-22T01:19:42.056910Z", + "iopub.status.busy": "2024-10-22T01:19:42.056454Z", + "iopub.status.idle": "2024-10-22T01:19:42.156465Z", + "shell.execute_reply": "2024-10-22T01:19:42.155796Z" } }, "outputs": [ @@ -1580,13 +1580,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_4015/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n", + "/tmp/ipykernel_4021/1344202636.py:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro-\" (-> color='r'). The keyword argument will take precedence.\n", " plt.plot((x, x), (ci[0], ci[1]), 'ro-', color='orange')\n" ] }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/main/example_notebooks/gcm_basic_example.html b/main/example_notebooks/gcm_basic_example.html index 1bf68ef3e..7737bf683 100644 --- a/main/example_notebooks/gcm_basic_example.html +++ b/main/example_notebooks/gcm_basic_example.html @@ -633,33 +633,33 @@

Step 1: Modeling cause-effect relationships as a structural causal model (SC 0 - 0.743675 - 3.381843 - 9.769149 + 2.028617 + 3.339915 + 11.424105 1 - -0.371102 - -0.376468 - -2.062196 + 0.929315 + 1.362639 + 2.781130 2 - -0.750238 - -3.046208 - -8.310544 + -0.203330 + -1.045402 + -5.305729 3 - -0.018300 - 1.331412 - 3.343701 + 0.814279 + 1.467193 + 4.137201 4 - 0.292231 - 1.661945 - 6.149433 + 0.269044 + -0.659678 + -1.931055 @@ -715,19 +715,19 @@

Step 1: Modeling cause-effect relationships as a structural causal model (SC Node Y is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node. This represents the causal relationship as Y := f(X) + N. For the model selection, the following models were evaluated on the mean squared error (MSE) metric: -LinearRegression: 1.0776648471428856 +LinearRegression: 1.1002813947388796 Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)), - ('linearregression', LinearRegression)]): 1.0779355716960863 -HistGradientBoostingRegressor: 1.2198942819273426 + ('linearregression', LinearRegression)]): 1.1008911568888382 +HistGradientBoostingRegressor: 1.2658372486275384 --- Node: Z -Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node. +Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node. This represents the causal relationship as Z := f(Y) + N. For the model selection, the following models were evaluated on the mean squared error (MSE) metric: -LinearRegression: 0.9814349659546397 Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)), - ('linearregression', LinearRegression)]): 0.9834430345330534 -HistGradientBoostingRegressor: 1.4602664103257639 + ('linearregression', LinearRegression)]): 0.9322685457630433 +LinearRegression: 0.934716145618161 +HistGradientBoostingRegressor: 1.4779386097949847 ===Note=== Note, based on the selected auto assignment quality, the set of evaluated models changes. @@ -764,7 +764,7 @@

Step 2: Fitting the SCM to the data 
-Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 414.63it/s]
+Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 505.32it/s]

Fitting means, we learn the generative models of the variables in the SCM according to the data.

@@ -782,8 +782,8 @@

Step 2: Fitting the SCM to the data 
-Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3744.91it/s]
-Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.71it/s]
+Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 4291.58it/s]
+Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 19.30it/s]
@@ -812,21 +812,21 @@

Step 2: Fitting the SCM to the data 0 - 1.641232 + 1.593606 2.34 - 7.099762 + 6.023067 1 - -1.637951 + -0.940958 2.34 - 7.350658 + 6.497881 2 - 0.327314 + 0.225591 2.34 - 8.630947 + 8.664585 3 - -0.056939 + 0.003608 2.34 - 7.843168 + 7.991808 4 - 1.069523 + -0.329552 2.34 - 6.189711 + 8.322043 diff --git a/main/example_notebooks/gcm_basic_example.ipynb b/main/example_notebooks/gcm_basic_example.ipynb index d4b526a7d..3be09e247 100644 --- a/main/example_notebooks/gcm_basic_example.ipynb +++ b/main/example_notebooks/gcm_basic_example.ipynb @@ -25,10 +25,10 @@ "id": "f22337ab", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:43.592579Z", - "iopub.status.busy": "2024-10-19T23:53:43.592401Z", - "iopub.status.idle": "2024-10-19T23:53:43.874546Z", - "shell.execute_reply": "2024-10-19T23:53:43.874015Z" + "iopub.execute_input": "2024-10-22T01:19:45.171789Z", + "iopub.status.busy": "2024-10-22T01:19:45.171319Z", + "iopub.status.idle": "2024-10-22T01:19:45.488531Z", + "shell.execute_reply": "2024-10-22T01:19:45.487836Z" } }, "outputs": [], @@ -51,10 +51,10 @@ "id": "0367caeb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:43.876832Z", - "iopub.status.busy": "2024-10-19T23:53:43.876548Z", - "iopub.status.idle": "2024-10-19T23:53:45.018934Z", - "shell.execute_reply": "2024-10-19T23:53:45.018303Z" + "iopub.execute_input": "2024-10-22T01:19:45.491180Z", + "iopub.status.busy": "2024-10-22T01:19:45.490687Z", + "iopub.status.idle": "2024-10-22T01:19:46.712407Z", + "shell.execute_reply": "2024-10-22T01:19:46.711690Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:53:45.021187Z", - "iopub.status.busy": "2024-10-19T23:53:45.020894Z", - "iopub.status.idle": "2024-10-19T23:53:45.031251Z", - "shell.execute_reply": "2024-10-19T23:53:45.030673Z" + "iopub.execute_input": "2024-10-22T01:19:46.715110Z", + "iopub.status.busy": "2024-10-22T01:19:46.714525Z", + "iopub.status.idle": "2024-10-22T01:19:46.724991Z", + "shell.execute_reply": "2024-10-22T01:19:46.724502Z" }, "jupyter": { "outputs_hidden": false @@ -129,45 +129,45 @@ " \n", " \n", " 0\n", - " 0.743675\n", - " 3.381843\n", - " 9.769149\n", + " 2.028617\n", + " 3.339915\n", + " 11.424105\n", " \n", " \n", " 1\n", - " -0.371102\n", - " -0.376468\n", - " -2.062196\n", + " 0.929315\n", + " 1.362639\n", + " 2.781130\n", " \n", " \n", " 2\n", - " -0.750238\n", - " -3.046208\n", - " -8.310544\n", + " -0.203330\n", + " -1.045402\n", + " -5.305729\n", " \n", " \n", " 3\n", - " -0.018300\n", - " 1.331412\n", - " 3.343701\n", + " 0.814279\n", + " 1.467193\n", + " 4.137201\n", " \n", " \n", " 4\n", - " 0.292231\n", - " 1.661945\n", - " 6.149433\n", + " 0.269044\n", + " -0.659678\n", + " -1.931055\n", " \n", " \n", "\n", "

" ], "text/plain": [ - " X Y Z\n", - "0 0.743675 3.381843 9.769149\n", - "1 -0.371102 -0.376468 -2.062196\n", - "2 -0.750238 -3.046208 -8.310544\n", - "3 -0.018300 1.331412 3.343701\n", - "4 0.292231 1.661945 6.149433" + " X Y Z\n", + "0 2.028617 3.339915 11.424105\n", + "1 0.929315 1.362639 2.781130\n", + "2 -0.203330 -1.045402 -5.305729\n", + "3 0.814279 1.467193 4.137201\n", + "4 0.269044 -0.659678 -1.931055" ] }, "execution_count": 3, @@ -214,10 +214,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:53:45.033288Z", - "iopub.status.busy": "2024-10-19T23:53:45.032857Z", - "iopub.status.idle": "2024-10-19T23:53:48.738883Z", - "shell.execute_reply": "2024-10-19T23:53:48.737831Z" + "iopub.execute_input": "2024-10-22T01:19:46.726934Z", + "iopub.status.busy": "2024-10-22T01:19:46.726504Z", + "iopub.status.idle": "2024-10-22T01:19:49.869944Z", + "shell.execute_reply": "2024-10-22T01:19:49.868845Z" }, "jupyter": { "outputs_hidden": false @@ -245,10 +245,10 @@ "id": "4f1493d8-9a0b-4ed5-97be-7b5f39cbd7d4", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.742312Z", - "iopub.status.busy": "2024-10-19T23:53:48.741781Z", - "iopub.status.idle": "2024-10-19T23:53:48.751262Z", - "shell.execute_reply": "2024-10-19T23:53:48.750637Z" + "iopub.execute_input": "2024-10-22T01:19:49.872927Z", + "iopub.status.busy": "2024-10-22T01:19:49.872701Z", + "iopub.status.idle": "2024-10-22T01:19:49.880831Z", + "shell.execute_reply": "2024-10-22T01:19:49.880337Z" } }, "outputs": [ @@ -282,19 +282,19 @@ "Node Y is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Y := f(X) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 1.0776648471428856\n", + "LinearRegression: 1.1002813947388796\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 1.0779355716960863\n", - "HistGradientBoostingRegressor: 1.2198942819273426\n", + " ('linearregression', LinearRegression)]): 1.1008911568888382\n", + "HistGradientBoostingRegressor: 1.2658372486275384\n", "\n", "--- Node: Z\n", - "Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", + "Node Z is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n", "This represents the causal relationship as Z := f(Y) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 0.9814349659546397\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 0.9834430345330534\n", - "HistGradientBoostingRegressor: 1.4602664103257639\n", + " ('linearregression', LinearRegression)]): 0.9322685457630433\n", + "LinearRegression: 0.934716145618161\n", + "HistGradientBoostingRegressor: 1.4779386097949847\n", "\n", "===Note===\n", "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n", @@ -322,10 +322,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.753443Z", - "iopub.status.busy": "2024-10-19T23:53:48.753025Z", - "iopub.status.idle": "2024-10-19T23:53:48.756863Z", - "shell.execute_reply": "2024-10-19T23:53:48.756286Z" + "iopub.execute_input": "2024-10-22T01:19:49.882770Z", + "iopub.status.busy": "2024-10-22T01:19:49.882432Z", + "iopub.status.idle": "2024-10-22T01:19:49.885758Z", + "shell.execute_reply": "2024-10-22T01:19:49.885190Z" }, "jupyter": { "outputs_hidden": false @@ -369,10 +369,10 @@ "id": "fe5f99f0", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.758973Z", - "iopub.status.busy": "2024-10-19T23:53:48.758557Z", - "iopub.status.idle": "2024-10-19T23:53:48.772149Z", - "shell.execute_reply": "2024-10-19T23:53:48.771541Z" + "iopub.execute_input": "2024-10-22T01:19:49.887672Z", + "iopub.status.busy": "2024-10-22T01:19:49.887298Z", + "iopub.status.idle": "2024-10-22T01:19:49.899165Z", + "shell.execute_reply": "2024-10-22T01:19:49.898570Z" } }, "outputs": [ @@ -413,7 +413,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 414.63it/s]" + "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 505.32it/s]" ] }, { @@ -450,10 +450,10 @@ "id": "671cd8d8-75b0-4019-b503-8aa83ad91615", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:48.774425Z", - "iopub.status.busy": "2024-10-19T23:53:48.773985Z", - "iopub.status.idle": "2024-10-19T23:53:49.841482Z", - "shell.execute_reply": "2024-10-19T23:53:49.840861Z" + "iopub.execute_input": "2024-10-22T01:19:49.900885Z", + "iopub.status.busy": "2024-10-22T01:19:49.900707Z", + "iopub.status.idle": "2024-10-22T01:19:50.957297Z", + "shell.execute_reply": "2024-10-22T01:19:50.956722Z" } }, "outputs": [ @@ -470,7 +470,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3744.91it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 4291.58it/s]" ] }, { @@ -493,7 +493,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 12.23it/s]" + "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 13.03it/s]" ] }, { @@ -501,7 +501,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.65it/s]" + "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.99it/s]" ] }, { @@ -509,7 +509,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.71it/s]" + "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 19.30it/s]" ] }, { @@ -521,7 +521,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -547,21 +547,21 @@ "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n", "\n", "--- Node X\n", - "- The KL divergence between generated and observed distribution is 0.1083365946213318.\n", + "- The KL divergence between generated and observed distribution is 0.07624922073720022.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Y\n", - "- The MSE is 1.0790193944800426.\n", - "- The NMSE is 0.45097097135917863.\n", - "- The R2 coefficient is 0.7964795543107239.\n", - "- The normalized CRPS is 0.25672986729669534.\n", + "- The MSE is 1.097888062196601.\n", + "- The NMSE is 0.49439332888932563.\n", + "- The R2 coefficient is 0.754451960243354.\n", + "- The normalized CRPS is 0.2843681015505487.\n", "The estimated CRPS indicates a good model performance.\n", "\n", "--- Node Z\n", - "- The MSE is 0.9654404823689962.\n", - "- The NMSE is 0.1414731008457315.\n", - "- The R2 coefficient is 0.9799289646454417.\n", - "- The normalized CRPS is 0.0809725428180018.\n", + "- The MSE is 0.9317194069647587.\n", + "- The NMSE is 0.15106453639402395.\n", + "- The R2 coefficient is 0.97699833188681.\n", + "- The normalized CRPS is 0.08688513089875502.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "==== Evaluation of Invertible Functional Causal Model Assumption ====\n", @@ -569,13 +569,13 @@ "--- The model assumption for node Y is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", - "--- The model assumption for node Z is not rejected with a p-value of 0.49802363047370024 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Z is not rejected with a p-value of 0.6816413250088438 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 0.014318906648073039\n", + "The overall average KL divergence between the generated and observed distribution is 0.0036988615988169265\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", @@ -637,10 +637,10 @@ "id": "52452496", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:49.843639Z", - "iopub.status.busy": "2024-10-19T23:53:49.843250Z", - "iopub.status.idle": "2024-10-19T23:53:49.855543Z", - "shell.execute_reply": "2024-10-19T23:53:49.854931Z" + "iopub.execute_input": "2024-10-22T01:19:50.959474Z", + "iopub.status.busy": "2024-10-22T01:19:50.959092Z", + "iopub.status.idle": "2024-10-22T01:19:50.971486Z", + "shell.execute_reply": "2024-10-22T01:19:50.970833Z" } }, "outputs": [ @@ -673,33 +673,33 @@ " \n", " \n", " 0\n", - " 1.641232\n", + " 1.593606\n", " 2.34\n", - " 7.099762\n", + " 6.023067\n", " \n", " \n", " 1\n", - " -1.637951\n", + " -0.940958\n", " 2.34\n", - " 7.350658\n", + " 6.497881\n", " \n", " \n", " 2\n", - " 0.327314\n", + " 0.225591\n", " 2.34\n", - " 8.630947\n", + " 8.664585\n", " \n", " \n", " 3\n", - " -0.056939\n", + " 0.003608\n", " 2.34\n", - " 7.843168\n", + " 7.991808\n", " \n", " \n", " 4\n", - " 1.069523\n", + " -0.329552\n", " 2.34\n", - " 6.189711\n", + " 8.322043\n", " \n", " \n", "\n", @@ -707,11 +707,11 @@ ], "text/plain": [ " X Y Z\n", - "0 1.641232 2.34 7.099762\n", - "1 -1.637951 2.34 7.350658\n", - "2 0.327314 2.34 8.630947\n", - "3 -0.056939 2.34 7.843168\n", - "4 1.069523 2.34 6.189711" + "0 1.593606 2.34 6.023067\n", + "1 -0.940958 2.34 6.497881\n", + "2 0.225591 2.34 8.664585\n", + "3 0.003608 2.34 7.991808\n", + "4 -0.329552 2.34 8.322043" ] }, "execution_count": 9, diff --git a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html index b4d74c337..cd24a805f 100644 --- a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html +++ b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.html @@ -706,7 +706,7 @@

Modeling of the graph 
-Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 29.27it/s]
+Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 28.70it/s]

Optionally, we can now also evaluate the fitted causal model:

@@ -723,8 +723,8 @@

Modeling of the graph 
-Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3573.68it/s]
-Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.62it/s]
+Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3630.38it/s]
+Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.40it/s]
@@ -761,15 +761,15 @@

Modeling of the graphModeling of the graphAnswering Alice’s counterfactual queries 
-<matplotlib.legend.Legend at 0x7fc9afb1e460>
+<matplotlib.legend.Legend at 0x7fb375b17c70>

diff --git a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb index 493f7e3d1..aea6bf708 100644 --- a/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb +++ b/main/example_notebooks/gcm_counterfactual_medical_dry_eyes.ipynb @@ -34,10 +34,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:54.598000Z", - "iopub.status.busy": "2024-10-19T23:53:54.597820Z", - "iopub.status.idle": "2024-10-19T23:53:54.834392Z", - "shell.execute_reply": "2024-10-19T23:53:54.833818Z" + "iopub.execute_input": "2024-10-22T01:19:55.671251Z", + "iopub.status.busy": "2024-10-22T01:19:55.671061Z", + "iopub.status.idle": "2024-10-22T01:19:55.911778Z", + "shell.execute_reply": "2024-10-22T01:19:55.911152Z" } }, "outputs": [ @@ -128,10 +128,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:54.836529Z", - "iopub.status.busy": "2024-10-19T23:53:54.836157Z", - "iopub.status.idle": "2024-10-19T23:53:55.377698Z", - "shell.execute_reply": "2024-10-19T23:53:55.376996Z" + "iopub.execute_input": "2024-10-22T01:19:55.913882Z", + "iopub.status.busy": "2024-10-22T01:19:55.913523Z", + "iopub.status.idle": "2024-10-22T01:19:56.457003Z", + "shell.execute_reply": "2024-10-22T01:19:56.456368Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:55.379905Z", - "iopub.status.busy": "2024-10-19T23:53:55.379543Z", - "iopub.status.idle": "2024-10-19T23:53:58.718575Z", - "shell.execute_reply": "2024-10-19T23:53:58.717823Z" + "iopub.execute_input": "2024-10-22T01:19:56.459077Z", + "iopub.status.busy": "2024-10-22T01:19:56.458715Z", + "iopub.status.idle": "2024-10-22T01:19:59.774175Z", + "shell.execute_reply": "2024-10-22T01:19:59.773438Z" } }, "outputs": [ @@ -227,7 +227,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Vision: 67%|██████▋ | 2/3 [00:00<00:00, 19.92it/s]" + "Fitting causal mechanism of node Vision: 67%|██████▋ | 2/3 [00:00<00:00, 19.63it/s]" ] }, { @@ -235,7 +235,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Condition: 67%|██████▋ | 2/3 [00:00<00:00, 19.92it/s]" + "Fitting causal mechanism of node Condition: 67%|██████▋ | 2/3 [00:00<00:00, 19.63it/s]" ] }, { @@ -243,7 +243,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 29.27it/s]" + "Fitting causal mechanism of node Condition: 100%|██████████| 3/3 [00:00<00:00, 28.70it/s]" ] }, { @@ -279,10 +279,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:53:58.721217Z", - "iopub.status.busy": "2024-10-19T23:53:58.720562Z", - "iopub.status.idle": "2024-10-19T23:54:12.687356Z", - "shell.execute_reply": "2024-10-19T23:54:12.686665Z" + "iopub.execute_input": "2024-10-22T01:19:59.776815Z", + "iopub.status.busy": "2024-10-22T01:19:59.776166Z", + "iopub.status.idle": "2024-10-22T01:20:13.752958Z", + "shell.execute_reply": "2024-10-22T01:20:13.752307Z" } }, "outputs": [ @@ -299,7 +299,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3573.68it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 3/3 [00:00<00:00, 3630.38it/s]" ] }, { @@ -322,7 +322,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.62it/s]" + "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 89.40it/s]" ] }, { @@ -334,7 +334,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -368,15 +368,15 @@ "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Vision\n", - "- The MSE is 0.003261792953176713.\n", - "- The NMSE is 0.18251379349564886.\n", - "- The R2 coefficient is 0.9666840172193771.\n", - "- The normalized CRPS is 0.10629621558204352.\n", + "- The MSE is 0.0032629985147496613.\n", + "- The NMSE is 0.18256886571617365.\n", + "- The R2 coefficient is 0.9666647696233858.\n", + "- The normalized CRPS is 0.10663383830931623.\n", "The estimated CRPS indicates a very good model performance.\n", "\n", "==== Evaluation of Invertible Functional Causal Model Assumption ====\n", "\n", - "--- The model assumption for node Vision is not rejected with a p-value of 0.7411494488201407 (after potential adjustment) and a significance level of 0.05.\n", + "--- The model assumption for node Vision is not rejected with a p-value of 0.8870605896160012 (after potential adjustment) and a significance level of 0.05.\n", "This implies that the model assumption might be valid.\n", "\n", "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n", @@ -391,7 +391,7 @@ "+-------------------------------------------------------------------------------------------------------+\n", "| The given DAG is not informative because 2 / 6 of the permutations lie in the Markov |\n", "| equivalence class of the given DAG (p-value: 0.33). |\n", - "| The given DAG violates 0/2 LMCs and is better than 33.3% of the permuted DAGs (p-value: 0.67). |\n", + "| The given DAG violates 0/2 LMCs and is better than 66.7% of the permuted DAGs (p-value: 0.33). |\n", "| Based on the provided significance level (0.2) and because the DAG is not informative, |\n", "| we do not reject the DAG. |\n", "+-------------------------------------------------------------------------------------------------------+\n", @@ -420,10 +420,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.689456Z", - "iopub.status.busy": "2024-10-19T23:54:12.689224Z", - "iopub.status.idle": "2024-10-19T23:54:12.698614Z", - "shell.execute_reply": "2024-10-19T23:54:12.698073Z" + "iopub.execute_input": "2024-10-22T01:20:13.755167Z", + "iopub.status.busy": "2024-10-22T01:20:13.754772Z", + "iopub.status.idle": "2024-10-22T01:20:13.763000Z", + "shell.execute_reply": "2024-10-22T01:20:13.762374Z" } }, "outputs": [ @@ -506,17 +506,17 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.700819Z", - "iopub.status.busy": "2024-10-19T23:54:12.700406Z", - "iopub.status.idle": "2024-10-19T23:54:12.886077Z", - "shell.execute_reply": "2024-10-19T23:54:12.885520Z" + "iopub.execute_input": "2024-10-22T01:20:13.765051Z", + "iopub.status.busy": "2024-10-22T01:20:13.764700Z", + "iopub.status.idle": "2024-10-22T01:20:13.949169Z", + "shell.execute_reply": "2024-10-22T01:20:13.948619Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -620,10 +620,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.888295Z", - "iopub.status.busy": "2024-10-19T23:54:12.887911Z", - "iopub.status.idle": "2024-10-19T23:54:12.900345Z", - "shell.execute_reply": "2024-10-19T23:54:12.899851Z" + "iopub.execute_input": "2024-10-22T01:20:13.951264Z", + "iopub.status.busy": "2024-10-22T01:20:13.951050Z", + "iopub.status.idle": "2024-10-22T01:20:13.964087Z", + "shell.execute_reply": "2024-10-22T01:20:13.963625Z" } }, "outputs": [], @@ -663,10 +663,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:12.902051Z", - "iopub.status.busy": "2024-10-19T23:54:12.901866Z", - "iopub.status.idle": "2024-10-19T23:54:12.906041Z", - "shell.execute_reply": "2024-10-19T23:54:12.905545Z" + "iopub.execute_input": "2024-10-22T01:20:13.965929Z", + "iopub.status.busy": "2024-10-22T01:20:13.965576Z", + "iopub.status.idle": "2024-10-22T01:20:13.969572Z", + "shell.execute_reply": "2024-10-22T01:20:13.969082Z" } }, "outputs": [], diff --git a/main/example_notebooks/gcm_cps2015_dist_change_robust.html b/main/example_notebooks/gcm_cps2015_dist_change_robust.html index a130e49e7..985211f58 100644 --- a/main/example_notebooks/gcm_cps2015_dist_change_robust.html +++ b/main/example_notebooks/gcm_cps2015_dist_change_robust.html @@ -503,7 +503,7 @@

Read and prepare data

Causal Model#

To use our causal change attribution method, we need a causal model linking the outcome (wage) and explanatory variables. (In practice, the method only requires knowing a causal ordering.) We will work with the following DAG:

-

fc0b2807cc2847eaac46978e8fb71ed3

+

d1f2baf9f521423a922a927f1cbe40ac

The DAG above implies the following causal Markov factorization of the distribution of the data:

\[P(\mathtt{wage} \mid \mathtt{occup}, \mathtt{educ}) \times P(\mathtt{occup} \mid \mathtt{educ}) \times P(\mathtt{educ})\]
@@ -561,7 +561,7 @@

Implementation with 
-Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.72it/s]
+Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00,  3.71it/s]

diff --git a/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb b/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb index 6eb5a17df..fc173525d 100644 --- a/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb +++ b/main/example_notebooks/gcm_cps2015_dist_change_robust.ipynb @@ -27,10 +27,10 @@ "id": "6e926b16-37e2-4921-9afb-1c99db9c9feb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:17.509624Z", - "iopub.status.busy": "2024-10-19T23:54:17.509200Z", - "iopub.status.idle": "2024-10-19T23:54:17.838633Z", - "shell.execute_reply": "2024-10-19T23:54:17.838090Z" + "iopub.execute_input": "2024-10-22T01:20:18.478777Z", + "iopub.status.busy": "2024-10-22T01:20:18.478601Z", + "iopub.status.idle": "2024-10-22T01:20:18.807637Z", + "shell.execute_reply": "2024-10-22T01:20:18.807054Z" }, "tags": [] }, @@ -78,10 +78,10 @@ "id": "2ca5daf9-be47-4c12-bd02-bb5b01014e0c", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:17.841079Z", - "iopub.status.busy": "2024-10-19T23:54:17.840677Z", - "iopub.status.idle": "2024-10-19T23:54:19.036765Z", - "shell.execute_reply": "2024-10-19T23:54:19.036201Z" + "iopub.execute_input": "2024-10-22T01:20:18.810107Z", + "iopub.status.busy": "2024-10-22T01:20:18.809661Z", + "iopub.status.idle": "2024-10-22T01:20:20.010162Z", + "shell.execute_reply": "2024-10-22T01:20:20.009446Z" } }, "outputs": [], @@ -117,10 +117,10 @@ "id": "39dcb294-25df-4358-81cb-3ee5a41e0419", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:19.039023Z", - "iopub.status.busy": "2024-10-19T23:54:19.038697Z", - "iopub.status.idle": "2024-10-19T23:54:22.598855Z", - "shell.execute_reply": "2024-10-19T23:54:22.598195Z" + "iopub.execute_input": "2024-10-22T01:20:20.012630Z", + "iopub.status.busy": "2024-10-22T01:20:20.012290Z", + "iopub.status.idle": "2024-10-22T01:20:23.695495Z", + "shell.execute_reply": "2024-10-22T01:20:23.694878Z" }, "tags": [] }, @@ -146,7 +146,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00, 3.72it/s]" + "Evaluating set functions...: 100%|██████████| 8/8 [00:02<00:00, 3.71it/s]" ] }, { @@ -216,10 +216,10 @@ "id": "f7febdc3-8c1e-46cf-816b-4a62251d641b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:22.600858Z", - "iopub.status.busy": "2024-10-19T23:54:22.600644Z", - "iopub.status.idle": "2024-10-19T23:54:22.615644Z", - "shell.execute_reply": "2024-10-19T23:54:22.615149Z" + "iopub.execute_input": "2024-10-22T01:20:23.697638Z", + "iopub.status.busy": "2024-10-22T01:20:23.697233Z", + "iopub.status.idle": "2024-10-22T01:20:23.711765Z", + "shell.execute_reply": "2024-10-22T01:20:23.711272Z" }, "tags": [] }, @@ -250,10 +250,10 @@ "id": "5f9e1bad-ae6e-432d-bbb8-1f65ab7bbab6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:22.617710Z", - "iopub.status.busy": "2024-10-19T23:54:22.617273Z", - "iopub.status.idle": "2024-10-19T23:54:24.915512Z", - "shell.execute_reply": "2024-10-19T23:54:24.914778Z" + "iopub.execute_input": "2024-10-22T01:20:23.713705Z", + "iopub.status.busy": "2024-10-22T01:20:23.713358Z", + "iopub.status.idle": "2024-10-22T01:20:26.008798Z", + "shell.execute_reply": "2024-10-22T01:20:26.008175Z" }, "tags": [] }, @@ -344,10 +344,10 @@ "id": "e6d0b582-20c6-476b-a07c-9d336ccda3fa", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:24.917824Z", - "iopub.status.busy": "2024-10-19T23:54:24.917339Z", - "iopub.status.idle": "2024-10-19T23:54:29.487039Z", - "shell.execute_reply": "2024-10-19T23:54:29.486334Z" + "iopub.execute_input": "2024-10-22T01:20:26.011016Z", + "iopub.status.busy": "2024-10-22T01:20:26.010622Z", + "iopub.status.idle": "2024-10-22T01:20:30.556328Z", + "shell.execute_reply": "2024-10-22T01:20:30.555781Z" }, "tags": [] }, @@ -396,10 +396,10 @@ "id": "95ec0312-84c2-4405-a823-740c98482090", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:29.489217Z", - "iopub.status.busy": "2024-10-19T23:54:29.488828Z", - "iopub.status.idle": "2024-10-19T23:54:29.641186Z", - "shell.execute_reply": "2024-10-19T23:54:29.640534Z" + "iopub.execute_input": "2024-10-22T01:20:30.558437Z", + "iopub.status.busy": "2024-10-22T01:20:30.558035Z", + "iopub.status.idle": "2024-10-22T01:20:30.707566Z", + "shell.execute_reply": "2024-10-22T01:20:30.706970Z" }, "tags": [] }, @@ -491,10 +491,10 @@ "id": "a5b5dbf8-046b-4d9d-848c-694eb742aafb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:29.643707Z", - "iopub.status.busy": "2024-10-19T23:54:29.643412Z", - "iopub.status.idle": "2024-10-19T23:54:29.807622Z", - "shell.execute_reply": "2024-10-19T23:54:29.807085Z" + "iopub.execute_input": "2024-10-22T01:20:30.709828Z", + "iopub.status.busy": "2024-10-22T01:20:30.709481Z", + "iopub.status.idle": "2024-10-22T01:20:30.872318Z", + "shell.execute_reply": "2024-10-22T01:20:30.871689Z" }, "tags": [] }, @@ -571,10 +571,10 @@ "id": "8b977dca-89e4-406c-80a9-6d24dd8a3aca", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:29.809760Z", - "iopub.status.busy": "2024-10-19T23:54:29.809345Z", - "iopub.status.idle": "2024-10-19T23:54:30.093594Z", - "shell.execute_reply": "2024-10-19T23:54:30.093064Z" + "iopub.execute_input": "2024-10-22T01:20:30.874373Z", + "iopub.status.busy": "2024-10-22T01:20:30.874156Z", + "iopub.status.idle": "2024-10-22T01:20:31.046767Z", + "shell.execute_reply": "2024-10-22T01:20:31.046115Z" }, "tags": [] }, diff --git a/main/example_notebooks/gcm_draw_samples.html b/main/example_notebooks/gcm_draw_samples.html index 2c12c2958..6ba4888cc 100644 --- a/main/example_notebooks/gcm_draw_samples.html +++ b/main/example_notebooks/gcm_draw_samples.html @@ -613,33 +613,33 @@

Basic Example for generating samples from a GCM 
-Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 490.70it/s]
+Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 456.83it/s]

We now learned the generative models of the variables, based on the defined causal graph and the additive noise model assumption. To generate new samples from this model, we can now simply call:

@@ -708,33 +708,33 @@

Basic Example for generating samples from a GCM 
-Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.12it/s]
+Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.69it/s]
@@ -772,7 +772,7 @@

Basic Example for generating samples from a GCM\n", " \n", " 0\n", - " 0.052003\n", - " 1.483941\n", - " 4.916561\n", + " 0.625450\n", + " 0.820544\n", + " 1.621421\n", " \n", " \n", " 1\n", - " -1.182643\n", - " -1.100917\n", - " -3.034945\n", + " -0.921440\n", + " -2.291946\n", + " -5.542924\n", " \n", " \n", " 2\n", - " 1.160655\n", - " 1.535785\n", - " 5.887016\n", + " -0.578712\n", + " -1.317664\n", + " -5.610970\n", " \n", " \n", " 3\n", - " -0.717647\n", - " -0.051851\n", - " 0.529103\n", + " 2.235428\n", + " 3.111105\n", + " 8.982018\n", " \n", " \n", " 4\n", - " 0.285487\n", - " 1.114898\n", - " 3.832859\n", + " -0.556047\n", + " 1.068834\n", + " 2.799001\n", " \n", " \n", "\n", @@ -99,11 +99,11 @@ ], "text/plain": [ " X Y Z\n", - "0 0.052003 1.483941 4.916561\n", - "1 -1.182643 -1.100917 -3.034945\n", - "2 1.160655 1.535785 5.887016\n", - "3 -0.717647 -0.051851 0.529103\n", - "4 0.285487 1.114898 3.832859" + "0 0.625450 0.820544 1.621421\n", + "1 -0.921440 -2.291946 -5.542924\n", + "2 -0.578712 -1.317664 -5.610970\n", + "3 2.235428 3.111105 8.982018\n", + "4 -0.556047 1.068834 2.799001" ] }, "execution_count": 1, @@ -136,10 +136,10 @@ "id": "0367caeb", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:34.783077Z", - "iopub.status.busy": "2024-10-19T23:54:34.782516Z", - "iopub.status.idle": "2024-10-19T23:54:39.714403Z", - "shell.execute_reply": "2024-10-19T23:54:39.713726Z" + "iopub.execute_input": "2024-10-22T01:20:35.588094Z", + "iopub.status.busy": "2024-10-22T01:20:35.587672Z", + "iopub.status.idle": "2024-10-22T01:20:40.669265Z", + "shell.execute_reply": "2024-10-22T01:20:40.668581Z" } }, "outputs": [ @@ -180,7 +180,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 490.70it/s]" + "Fitting causal mechanism of node Z: 100%|██████████| 3/3 [00:00<00:00, 456.83it/s]" ] }, { @@ -220,10 +220,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:54:39.716664Z", - "iopub.status.busy": "2024-10-19T23:54:39.716233Z", - "iopub.status.idle": "2024-10-19T23:54:39.726624Z", - "shell.execute_reply": "2024-10-19T23:54:39.726016Z" + "iopub.execute_input": "2024-10-22T01:20:40.671681Z", + "iopub.status.busy": "2024-10-22T01:20:40.671255Z", + "iopub.status.idle": "2024-10-22T01:20:40.682752Z", + "shell.execute_reply": "2024-10-22T01:20:40.682135Z" }, "jupyter": { "outputs_hidden": false @@ -262,33 +262,33 @@ " \n", " \n", " 0\n", - " -0.713625\n", - " -1.676018\n", - " -3.091644\n", + " -0.058468\n", + " 0.518959\n", + " 2.993328\n", " \n", " \n", " 1\n", - " -0.861692\n", - " -1.707183\n", - " -3.505719\n", + " 0.744540\n", + " 2.547959\n", + " 7.710327\n", " \n", " \n", " 2\n", - " 0.704378\n", - " 2.330441\n", - " 7.048587\n", + " 0.752173\n", + " -0.243187\n", + " -0.805433\n", " \n", " \n", " 3\n", - " -1.681803\n", - " -2.035157\n", - " -5.782651\n", + " 0.426648\n", + " 0.242184\n", + " -0.267344\n", " \n", " \n", " 4\n", - " -0.233174\n", - " 1.122552\n", - " 2.060792\n", + " 0.025863\n", + " -0.826618\n", + " -3.154461\n", " \n", " \n", "\n", @@ -296,11 +296,11 @@ ], "text/plain": [ " X Y Z\n", - "0 -0.713625 -1.676018 -3.091644\n", - "1 -0.861692 -1.707183 -3.505719\n", - "2 0.704378 2.330441 7.048587\n", - "3 -1.681803 -2.035157 -5.782651\n", - "4 -0.233174 1.122552 2.060792" + "0 -0.058468 0.518959 2.993328\n", + "1 0.744540 2.547959 7.710327\n", + "2 0.752173 -0.243187 -0.805433\n", + "3 0.426648 0.242184 -0.267344\n", + "4 0.025863 -0.826618 -3.154461" ] }, "execution_count": 3, @@ -332,10 +332,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-19T23:54:39.728613Z", - "iopub.status.busy": "2024-10-19T23:54:39.728420Z", - "iopub.status.idle": "2024-10-19T23:54:40.456847Z", - "shell.execute_reply": "2024-10-19T23:54:40.456257Z" + "iopub.execute_input": "2024-10-22T01:20:40.684960Z", + "iopub.status.busy": "2024-10-22T01:20:40.684554Z", + "iopub.status.idle": "2024-10-22T01:20:41.400001Z", + "shell.execute_reply": "2024-10-22T01:20:41.399334Z" }, "jupyter": { "outputs_hidden": false @@ -358,7 +358,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 12.23it/s]" + "Test permutations of given graph: 33%|███▎ | 2/6 [00:00<00:00, 13.03it/s]" ] }, { @@ -366,7 +366,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.18it/s]" + "Test permutations of given graph: 67%|██████▋ | 4/6 [00:00<00:00, 12.50it/s]" ] }, { @@ -374,7 +374,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.12it/s]" + "Test permutations of given graph: 100%|██████████| 6/6 [00:00<00:00, 18.69it/s]" ] }, { @@ -386,7 +386,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -401,7 +401,7 @@ "Evaluated and the overall average KL divergence between generated and observed distribution and the graph structure. The results are as follows:\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 0.016457558998081354\n", + "The overall average KL divergence between the generated and observed distribution is 0.04850074524068953\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", diff --git a/main/example_notebooks/gcm_falsify_dag.html b/main/example_notebooks/gcm_falsify_dag.html index 0039772a9..eb6fe91f9 100644 --- a/main/example_notebooks/gcm_falsify_dag.html +++ b/main/example_notebooks/gcm_falsify_dag.html @@ -637,7 +637,7 @@

Synthetic Data 
-Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00,  1.19it/s]
+Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00,  1.21it/s]

@@ -723,7 +723,7 @@

Synthetic Data 
-Test permutations of given graph: 100%|██████████| 20/20 [00:17<00:00,  1.11it/s]
+Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00,  1.11it/s]

@@ -843,7 +843,7 @@

Real Data (Protein Network dataset by Sachs et al., 2005) 
-Test permutations of given graph: 100%|██████████| 100/100 [12:04<00:00,  7.24s/it]
+Test permutations of given graph: 100%|██████████| 100/100 [12:21<00:00,  7.41s/it]

@@ -888,7 +888,7 @@

Edge Suggestions 
-Test permutations of given graph: 100%|██████████| 20/20 [00:19<00:00,  1.05it/s]
+Test permutations of given graph: 100%|██████████| 20/20 [00:19<00:00,  1.04it/s]

diff --git a/main/example_notebooks/gcm_falsify_dag.ipynb b/main/example_notebooks/gcm_falsify_dag.ipynb index a7b6c1798..3ef0af6d2 100644 --- a/main/example_notebooks/gcm_falsify_dag.ipynb +++ b/main/example_notebooks/gcm_falsify_dag.ipynb @@ -18,10 +18,10 @@ "id": "13a5f7f6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:44.631754Z", - "iopub.status.busy": "2024-10-19T23:54:44.631559Z", - "iopub.status.idle": "2024-10-19T23:54:46.053017Z", - "shell.execute_reply": "2024-10-19T23:54:46.052280Z" + "iopub.execute_input": "2024-10-22T01:20:45.366172Z", + "iopub.status.busy": "2024-10-22T01:20:45.365987Z", + "iopub.status.idle": "2024-10-22T01:20:46.817464Z", + "shell.execute_reply": "2024-10-22T01:20:46.816831Z" } }, "outputs": [], @@ -56,10 +56,10 @@ "id": "08d67fd1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:46.055493Z", - "iopub.status.busy": "2024-10-19T23:54:46.055187Z", - "iopub.status.idle": "2024-10-19T23:54:46.165216Z", - "shell.execute_reply": "2024-10-19T23:54:46.164656Z" + "iopub.execute_input": "2024-10-22T01:20:46.819840Z", + "iopub.status.busy": "2024-10-22T01:20:46.819371Z", + "iopub.status.idle": "2024-10-22T01:20:46.929964Z", + "shell.execute_reply": "2024-10-22T01:20:46.929325Z" } }, "outputs": [ @@ -105,10 +105,10 @@ "id": "953753d0", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:54:46.167789Z", - "iopub.status.busy": "2024-10-19T23:54:46.167423Z", - "iopub.status.idle": "2024-10-19T23:55:06.901968Z", - "shell.execute_reply": "2024-10-19T23:55:06.901318Z" + "iopub.execute_input": "2024-10-22T01:20:46.932300Z", + "iopub.status.busy": "2024-10-22T01:20:46.932090Z", + "iopub.status.idle": "2024-10-22T01:21:07.460714Z", + "shell.execute_reply": "2024-10-22T01:21:07.460026Z" } }, "outputs": [ @@ -125,7 +125,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 1/20 [00:01<00:26, 1.40s/it]" + "Test permutations of given graph: 5%|▌ | 1/20 [00:01<00:24, 1.31s/it]" ] }, { @@ -133,7 +133,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 2/20 [00:02<00:24, 1.35s/it]" + "Test permutations of given graph: 10%|█ | 2/20 [00:02<00:23, 1.29s/it]" ] }, { @@ -141,7 +141,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 3/20 [00:04<00:22, 1.34s/it]" + "Test permutations of given graph: 15%|█▌ | 3/20 [00:03<00:21, 1.28s/it]" ] }, { @@ -149,7 +149,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 4/20 [00:04<00:18, 1.16s/it]" + "Test permutations of given graph: 20%|██ | 4/20 [00:04<00:17, 1.11s/it]" ] }, { @@ -157,7 +157,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 5/20 [00:05<00:15, 1.07s/it]" + "Test permutations of given graph: 25%|██▌ | 5/20 [00:05<00:15, 1.03s/it]" ] }, { @@ -165,7 +165,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 6/20 [00:06<00:14, 1.00s/it]" + "Test permutations of given graph: 30%|███ | 6/20 [00:06<00:13, 1.02it/s]" ] }, { @@ -173,7 +173,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 7/20 [00:08<00:14, 1.10s/it]" + "Test permutations of given graph: 35%|███▌ | 7/20 [00:07<00:14, 1.10s/it]" ] }, { @@ -189,7 +189,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 45%|████▌ | 9/20 [00:10<00:12, 1.11s/it]" + "Test permutations of given graph: 45%|████▌ | 9/20 [00:10<00:12, 1.12s/it]" ] }, { @@ -197,7 +197,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 10/20 [00:11<00:11, 1.16s/it]" + "Test permutations of given graph: 50%|█████ | 10/20 [00:11<00:11, 1.17s/it]" ] }, { @@ -205,7 +205,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 12/20 [00:12<00:06, 1.21it/s]" + "Test permutations of given graph: 60%|██████ | 12/20 [00:12<00:06, 1.22it/s]" ] }, { @@ -213,7 +213,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 13/20 [00:13<00:05, 1.19it/s]" + "Test permutations of given graph: 65%|██████▌ | 13/20 [00:12<00:05, 1.21it/s]" ] }, { @@ -221,7 +221,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 14/20 [00:14<00:05, 1.16it/s]" + "Test permutations of given graph: 70%|███████ | 14/20 [00:13<00:04, 1.20it/s]" ] }, { @@ -229,7 +229,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 85%|████████▌ | 17/20 [00:15<00:01, 1.77it/s]" + "Test permutations of given graph: 85%|████████▌ | 17/20 [00:14<00:01, 1.82it/s]" ] }, { @@ -237,7 +237,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 18/20 [00:15<00:01, 1.58it/s]" + "Test permutations of given graph: 90%|█████████ | 18/20 [00:15<00:01, 1.63it/s]" ] }, { @@ -245,7 +245,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 95%|█████████▌| 19/20 [00:16<00:00, 1.44it/s]" + "Test permutations of given graph: 95%|█████████▌| 19/20 [00:16<00:00, 1.47it/s]" ] }, { @@ -253,7 +253,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00, 1.19it/s]" + "Test permutations of given graph: 100%|██████████| 20/20 [00:16<00:00, 1.21it/s]" ] }, { @@ -317,10 +317,10 @@ "id": "529de3f5", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:06.904060Z", - "iopub.status.busy": "2024-10-19T23:55:06.903666Z", - "iopub.status.idle": "2024-10-19T23:55:06.907175Z", - "shell.execute_reply": "2024-10-19T23:55:06.906575Z" + "iopub.execute_input": "2024-10-22T01:21:07.462772Z", + "iopub.status.busy": "2024-10-22T01:21:07.462557Z", + "iopub.status.idle": "2024-10-22T01:21:07.465711Z", + "shell.execute_reply": "2024-10-22T01:21:07.465209Z" } }, "outputs": [ @@ -350,10 +350,10 @@ "id": "4623dbc3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:06.909101Z", - "iopub.status.busy": "2024-10-19T23:55:06.908753Z", - "iopub.status.idle": "2024-10-19T23:55:07.002287Z", - "shell.execute_reply": "2024-10-19T23:55:07.001615Z" + "iopub.execute_input": "2024-10-22T01:21:07.467482Z", + "iopub.status.busy": "2024-10-22T01:21:07.467118Z", + "iopub.status.idle": "2024-10-22T01:21:07.561099Z", + "shell.execute_reply": "2024-10-22T01:21:07.560487Z" } }, "outputs": [ @@ -382,10 +382,10 @@ "id": "fcd847fa", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:07.004435Z", - "iopub.status.busy": "2024-10-19T23:55:07.004217Z", - "iopub.status.idle": "2024-10-19T23:55:27.816916Z", - "shell.execute_reply": "2024-10-19T23:55:27.816291Z" + "iopub.execute_input": "2024-10-22T01:21:07.563339Z", + "iopub.status.busy": "2024-10-22T01:21:07.563125Z", + "iopub.status.idle": "2024-10-22T01:21:28.372558Z", + "shell.execute_reply": "2024-10-22T01:21:28.371899Z" } }, "outputs": [ @@ -402,7 +402,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:46, 2.47s/it]" + "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:47, 2.51s/it]" ] }, { @@ -410,7 +410,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 2/20 [00:04<00:44, 2.46s/it]" + "Test permutations of given graph: 10%|█ | 2/20 [00:04<00:44, 2.49s/it]" ] }, { @@ -418,7 +418,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:35, 2.07s/it]" + "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:35, 2.09s/it]" ] }, { @@ -426,7 +426,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 4/20 [00:08<00:30, 1.90s/it]" + "Test permutations of given graph: 20%|██ | 4/20 [00:08<00:30, 1.93s/it]" ] }, { @@ -434,7 +434,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 5/20 [00:09<00:26, 1.80s/it]" + "Test permutations of given graph: 25%|██▌ | 5/20 [00:09<00:27, 1.82s/it]" ] }, { @@ -442,7 +442,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:22, 1.62s/it]" + "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:22, 1.63s/it]" ] }, { @@ -450,7 +450,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 7/20 [00:12<00:19, 1.52s/it]" + "Test permutations of given graph: 35%|███▌ | 7/20 [00:12<00:19, 1.51s/it]" ] }, { @@ -458,7 +458,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:17, 1.45s/it]" + "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:17, 1.43s/it]" ] }, { @@ -466,7 +466,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 45%|████▌ | 9/20 [00:14<00:15, 1.40s/it]" + "Test permutations of given graph: 45%|████▌ | 9/20 [00:14<00:15, 1.38s/it]" ] }, { @@ -482,7 +482,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 13/20 [00:17<00:05, 1.18it/s]" + "Test permutations of given graph: 65%|██████▌ | 13/20 [00:17<00:05, 1.19it/s]" ] }, { @@ -490,7 +490,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.45it/s]" + "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.46it/s]" ] }, { @@ -498,7 +498,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 20/20 [00:17<00:00, 1.11it/s]" + "Test permutations of given graph: 100%|██████████| 20/20 [00:18<00:00, 1.11it/s]" ] }, { @@ -557,10 +557,10 @@ "id": "3fe68e25", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:27.818943Z", - "iopub.status.busy": "2024-10-19T23:55:27.818573Z", - "iopub.status.idle": "2024-10-19T23:55:27.913167Z", - "shell.execute_reply": "2024-10-19T23:55:27.912532Z" + "iopub.execute_input": "2024-10-22T01:21:28.374617Z", + "iopub.status.busy": "2024-10-22T01:21:28.374263Z", + "iopub.status.idle": "2024-10-22T01:21:28.457490Z", + "shell.execute_reply": "2024-10-22T01:21:28.456879Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "id": "03e8ffa3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:27.915514Z", - "iopub.status.busy": "2024-10-19T23:55:27.915291Z", - "iopub.status.idle": "2024-10-19T23:55:28.017291Z", - "shell.execute_reply": "2024-10-19T23:55:28.016725Z" + "iopub.execute_input": "2024-10-22T01:21:28.460161Z", + "iopub.status.busy": "2024-10-22T01:21:28.459892Z", + "iopub.status.idle": "2024-10-22T01:21:29.040059Z", + "shell.execute_reply": "2024-10-22T01:21:29.039468Z" } }, "outputs": [], @@ -624,10 +624,10 @@ "id": "3d30a01b", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:28.019698Z", - "iopub.status.busy": "2024-10-19T23:55:28.019203Z", - "iopub.status.idle": "2024-10-19T23:55:28.133439Z", - "shell.execute_reply": "2024-10-19T23:55:28.132822Z" + "iopub.execute_input": "2024-10-22T01:21:29.042241Z", + "iopub.status.busy": "2024-10-22T01:21:29.041898Z", + "iopub.status.idle": "2024-10-22T01:21:29.168495Z", + "shell.execute_reply": "2024-10-22T01:21:29.167921Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "072544c1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:28.135593Z", - "iopub.status.busy": "2024-10-19T23:55:28.135274Z", - "iopub.status.idle": "2024-10-19T23:55:28.138828Z", - "shell.execute_reply": "2024-10-19T23:55:28.138216Z" + "iopub.execute_input": "2024-10-22T01:21:29.171119Z", + "iopub.status.busy": "2024-10-22T01:21:29.170689Z", + "iopub.status.idle": "2024-10-22T01:21:29.174566Z", + "shell.execute_reply": "2024-10-22T01:21:29.174044Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "id": "bbfe658d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-19T23:55:28.140675Z", - "iopub.status.busy": "2024-10-19T23:55:28.140488Z", - "iopub.status.idle": "2024-10-20T00:07:43.760554Z", - "shell.execute_reply": "2024-10-20T00:07:43.759945Z" + "iopub.execute_input": "2024-10-22T01:21:29.176450Z", + "iopub.status.busy": "2024-10-22T01:21:29.176227Z", + "iopub.status.idle": "2024-10-22T01:34:01.630770Z", + "shell.execute_reply": "2024-10-22T01:34:01.630104Z" } }, "outputs": [ @@ -712,7 +712,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 1%| | 1/100 [00:10<17:54, 10.86s/it]" + "Test permutations of given graph: 1%| | 1/100 [00:10<17:48, 10.79s/it]" ] }, { @@ -720,7 +720,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 2%|▏ | 2/100 [00:21<17:44, 10.86s/it]" + "Test permutations of given graph: 2%|▏ | 2/100 [00:21<17:33, 10.75s/it]" ] }, { @@ -728,7 +728,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 3%|▎ | 3/100 [00:32<17:16, 10.68s/it]" + "Test permutations of given graph: 3%|▎ | 3/100 [00:32<17:15, 10.68s/it]" ] }, { @@ -736,7 +736,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 4%|▍ | 4/100 [00:43<17:11, 10.74s/it]" + "Test permutations of given graph: 4%|▍ | 4/100 [00:43<17:13, 10.77s/it]" ] }, { @@ -744,7 +744,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 5/100 [00:54<17:13, 10.88s/it]" + "Test permutations of given graph: 5%|▌ | 5/100 [00:54<17:16, 10.91s/it]" ] }, { @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 6%|▌ | 6/100 [01:03<16:21, 10.44s/it]" + "Test permutations of given graph: 6%|▌ | 6/100 [01:03<16:25, 10.48s/it]" ] }, { @@ -760,7 +760,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 7%|▋ | 7/100 [01:11<14:49, 9.56s/it]" + "Test permutations of given graph: 7%|▋ | 7/100 [01:11<14:58, 9.66s/it]" ] }, { @@ -768,7 +768,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 8%|▊ | 8/100 [01:22<15:21, 10.02s/it]" + "Test permutations of given graph: 8%|▊ | 8/100 [01:22<15:29, 10.10s/it]" ] }, { @@ -776,7 +776,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 9%|▉ | 9/100 [01:33<15:42, 10.35s/it]" + "Test permutations of given graph: 9%|▉ | 9/100 [01:34<15:53, 10.47s/it]" ] }, { @@ -784,7 +784,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 10/100 [01:44<15:35, 10.40s/it]" + "Test permutations of given graph: 10%|█ | 10/100 [01:44<15:48, 10.54s/it]" ] }, { @@ -792,7 +792,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 11%|█ | 11/100 [01:54<15:27, 10.43s/it]" + "Test permutations of given graph: 11%|█ | 11/100 [01:55<15:42, 10.60s/it]" ] }, { @@ -800,7 +800,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 12%|█▏ | 12/100 [02:04<15:17, 10.42s/it]" + "Test permutations of given graph: 12%|█▏ | 12/100 [02:06<15:32, 10.60s/it]" ] }, { @@ -808,7 +808,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 13%|█▎ | 13/100 [02:14<14:35, 10.06s/it]" + "Test permutations of given graph: 13%|█▎ | 13/100 [02:15<14:47, 10.20s/it]" ] }, { @@ -816,7 +816,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 14%|█▍ | 14/100 [02:22<13:28, 9.40s/it]" + "Test permutations of given graph: 14%|█▍ | 14/100 [02:23<13:38, 9.52s/it]" ] }, { @@ -824,7 +824,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 15/100 [02:32<13:48, 9.75s/it]" + "Test permutations of given graph: 15%|█▌ | 15/100 [02:34<13:59, 9.88s/it]" ] }, { @@ -832,7 +832,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 16%|█▌ | 16/100 [02:43<13:58, 9.98s/it]" + "Test permutations of given graph: 16%|█▌ | 16/100 [02:44<14:10, 10.13s/it]" ] }, { @@ -840,7 +840,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 17%|█▋ | 17/100 [02:50<12:39, 9.15s/it]" + "Test permutations of given graph: 17%|█▋ | 17/100 [02:52<12:49, 9.27s/it]" ] }, { @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 18%|█▊ | 18/100 [03:00<12:58, 9.50s/it]" + "Test permutations of given graph: 18%|█▊ | 18/100 [03:02<13:09, 9.63s/it]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 19%|█▉ | 19/100 [03:11<13:09, 9.75s/it]" + "Test permutations of given graph: 19%|█▉ | 19/100 [03:12<13:20, 9.88s/it]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 20/100 [03:18<12:15, 9.19s/it]" + "Test permutations of given graph: 20%|██ | 20/100 [03:20<12:23, 9.30s/it]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 21%|██ | 21/100 [03:29<12:32, 9.52s/it]" + "Test permutations of given graph: 21%|██ | 21/100 [03:31<12:43, 9.66s/it]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 22%|██▏ | 22/100 [03:39<12:41, 9.76s/it]" + "Test permutations of given graph: 22%|██▏ | 22/100 [03:41<12:53, 9.92s/it]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 23%|██▎ | 23/100 [03:46<11:18, 8.81s/it]" + "Test permutations of given graph: 23%|██▎ | 23/100 [03:48<11:32, 8.99s/it]" ] }, { @@ -896,7 +896,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 24%|██▍ | 24/100 [03:54<10:59, 8.68s/it]" + "Test permutations of given graph: 24%|██▍ | 24/100 [03:57<11:15, 8.89s/it]" ] }, { @@ -904,7 +904,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 25/100 [04:04<11:24, 9.13s/it]" + "Test permutations of given graph: 25%|██▌ | 25/100 [04:07<11:43, 9.38s/it]" ] }, { @@ -912,7 +912,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 26%|██▌ | 26/100 [04:13<11:05, 8.99s/it]" + "Test permutations of given graph: 26%|██▌ | 26/100 [04:17<11:26, 9.28s/it]" ] }, { @@ -920,7 +920,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 27%|██▋ | 27/100 [04:21<10:34, 8.69s/it]" + "Test permutations of given graph: 27%|██▋ | 27/100 [04:25<10:50, 8.91s/it]" ] }, { @@ -928,7 +928,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 28%|██▊ | 28/100 [04:30<10:43, 8.94s/it]" + "Test permutations of given graph: 28%|██▊ | 28/100 [04:34<10:54, 9.09s/it]" ] }, { @@ -936,7 +936,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 29%|██▉ | 29/100 [04:41<11:01, 9.31s/it]" + "Test permutations of given graph: 29%|██▉ | 29/100 [04:45<11:13, 9.49s/it]" ] }, { @@ -944,7 +944,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 30/100 [04:48<10:04, 8.63s/it]" + "Test permutations of given graph: 30%|███ | 30/100 [04:52<10:16, 8.80s/it]" ] }, { @@ -952,7 +952,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 31%|███ | 31/100 [04:55<09:30, 8.27s/it]" + "Test permutations of given graph: 31%|███ | 31/100 [04:59<09:41, 8.43s/it]" ] }, { @@ -960,7 +960,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 32%|███▏ | 32/100 [05:03<09:22, 8.28s/it]" + "Test permutations of given graph: 32%|███▏ | 32/100 [05:08<09:36, 8.48s/it]" ] }, { @@ -968,7 +968,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 33%|███▎ | 33/100 [05:11<09:05, 8.15s/it]" + "Test permutations of given graph: 33%|███▎ | 33/100 [05:16<09:20, 8.37s/it]" ] }, { @@ -976,7 +976,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 34%|███▍ | 34/100 [05:19<08:44, 7.95s/it]" + "Test permutations of given graph: 34%|███▍ | 34/100 [05:24<08:57, 8.14s/it]" ] }, { @@ -984,7 +984,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 35/100 [05:28<08:59, 8.30s/it]" + "Test permutations of given graph: 35%|███▌ | 35/100 [05:33<09:16, 8.57s/it]" ] }, { @@ -992,7 +992,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 36%|███▌ | 36/100 [05:37<09:00, 8.44s/it]" + "Test permutations of given graph: 36%|███▌ | 36/100 [05:42<09:18, 8.72s/it]" ] }, { @@ -1000,7 +1000,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 37%|███▋ | 37/100 [05:44<08:36, 8.20s/it]" + "Test permutations of given graph: 37%|███▋ | 37/100 [05:50<08:54, 8.48s/it]" ] }, { @@ -1008,7 +1008,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 38%|███▊ | 38/100 [05:50<07:37, 7.38s/it]" + "Test permutations of given graph: 38%|███▊ | 38/100 [05:56<07:51, 7.61s/it]" ] }, { @@ -1016,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 39%|███▉ | 39/100 [06:00<08:23, 8.26s/it]" + "Test permutations of given graph: 39%|███▉ | 39/100 [06:06<08:40, 8.53s/it]" ] }, { @@ -1024,7 +1024,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 40/100 [06:08<08:12, 8.21s/it]" + "Test permutations of given graph: 40%|████ | 40/100 [06:15<08:27, 8.46s/it]" ] }, { @@ -1032,7 +1032,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 41%|████ | 41/100 [06:14<07:26, 7.57s/it]" + "Test permutations of given graph: 41%|████ | 41/100 [06:21<07:39, 7.79s/it]" ] }, { @@ -1040,7 +1040,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 42%|████▏ | 42/100 [06:23<07:50, 8.11s/it]" + "Test permutations of given graph: 42%|████▏ | 42/100 [06:31<08:05, 8.37s/it]" ] }, { @@ -1048,7 +1048,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 43%|████▎ | 43/100 [06:29<06:56, 7.31s/it]" + "Test permutations of given graph: 43%|████▎ | 43/100 [06:36<07:10, 7.56s/it]" ] }, { @@ -1056,7 +1056,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 44%|████▍ | 44/100 [06:36<06:50, 7.33s/it]" + "Test permutations of given graph: 44%|████▍ | 44/100 [06:44<07:05, 7.59s/it]" ] }, { @@ -1064,7 +1064,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 45%|████▌ | 45/100 [06:44<06:55, 7.55s/it]" + "Test permutations of given graph: 45%|████▌ | 45/100 [06:52<07:09, 7.81s/it]" ] }, { @@ -1072,7 +1072,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 46%|████▌ | 46/100 [06:47<05:33, 6.18s/it]" + "Test permutations of given graph: 46%|████▌ | 46/100 [06:55<05:44, 6.39s/it]" ] }, { @@ -1080,7 +1080,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 47%|████▋ | 47/100 [06:55<05:57, 6.75s/it]" + "Test permutations of given graph: 47%|████▋ | 47/100 [07:04<06:07, 6.94s/it]" ] }, { @@ -1088,7 +1088,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 48%|████▊ | 48/100 [07:02<05:45, 6.64s/it]" + "Test permutations of given graph: 48%|████▊ | 48/100 [07:10<05:54, 6.82s/it]" ] }, { @@ -1096,7 +1096,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 49%|████▉ | 49/100 [07:11<06:17, 7.41s/it]" + "Test permutations of given graph: 49%|████▉ | 49/100 [07:20<06:28, 7.61s/it]" ] }, { @@ -1104,7 +1104,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 50/100 [07:16<05:32, 6.66s/it]" + "Test permutations of given graph: 50%|█████ | 50/100 [07:24<05:39, 6.80s/it]" ] }, { @@ -1112,7 +1112,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 51%|█████ | 51/100 [07:24<05:51, 7.18s/it]" + "Test permutations of given graph: 51%|█████ | 51/100 [07:33<06:01, 7.38s/it]" ] }, { @@ -1120,7 +1120,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 52%|█████▏ | 52/100 [07:33<06:13, 7.78s/it]" + "Test permutations of given graph: 52%|█████▏ | 52/100 [07:43<06:23, 7.98s/it]" ] }, { @@ -1128,7 +1128,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 53%|█████▎ | 53/100 [07:39<05:33, 7.09s/it]" + "Test permutations of given graph: 53%|█████▎ | 53/100 [07:48<05:40, 7.25s/it]" ] }, { @@ -1136,7 +1136,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 54%|█████▍ | 54/100 [07:46<05:27, 7.11s/it]" + "Test permutations of given graph: 54%|█████▍ | 54/100 [07:55<05:34, 7.27s/it]" ] }, { @@ -1144,7 +1144,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 55%|█████▌ | 55/100 [07:54<05:36, 7.48s/it]" + "Test permutations of given graph: 55%|█████▌ | 55/100 [08:04<05:44, 7.66s/it]" ] }, { @@ -1152,7 +1152,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 56%|█████▌ | 56/100 [07:59<04:51, 6.61s/it]" + "Test permutations of given graph: 56%|█████▌ | 56/100 [08:09<04:59, 6.81s/it]" ] }, { @@ -1160,7 +1160,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 57%|█████▋ | 57/100 [08:06<04:44, 6.63s/it]" + "Test permutations of given graph: 57%|█████▋ | 57/100 [08:16<04:53, 6.83s/it]" ] }, { @@ -1168,7 +1168,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 58%|█████▊ | 58/100 [08:13<04:42, 6.73s/it]" + "Test permutations of given graph: 58%|█████▊ | 58/100 [08:23<04:52, 6.97s/it]" ] }, { @@ -1176,7 +1176,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 59%|█████▉ | 59/100 [08:17<04:07, 6.03s/it]" + "Test permutations of given graph: 59%|█████▉ | 59/100 [08:28<04:15, 6.23s/it]" ] }, { @@ -1184,7 +1184,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 60/100 [08:25<04:18, 6.45s/it]" + "Test permutations of given graph: 60%|██████ | 60/100 [08:35<04:25, 6.63s/it]" ] }, { @@ -1192,7 +1192,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 61%|██████ | 61/100 [08:29<03:49, 5.89s/it]" + "Test permutations of given graph: 61%|██████ | 61/100 [08:40<03:56, 6.06s/it]" ] }, { @@ -1200,7 +1200,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 62%|██████▏ | 62/100 [08:35<03:42, 5.86s/it]" + "Test permutations of given graph: 62%|██████▏ | 62/100 [08:46<03:49, 6.03s/it]" ] }, { @@ -1208,7 +1208,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 63%|██████▎ | 63/100 [08:41<03:37, 5.88s/it]" + "Test permutations of given graph: 63%|██████▎ | 63/100 [08:52<03:44, 6.07s/it]" ] }, { @@ -1216,7 +1216,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 64%|██████▍ | 64/100 [08:48<03:45, 6.28s/it]" + "Test permutations of given graph: 64%|██████▍ | 64/100 [08:59<03:52, 6.46s/it]" ] }, { @@ -1224,7 +1224,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 65/100 [08:54<03:33, 6.09s/it]" + "Test permutations of given graph: 65%|██████▌ | 65/100 [09:05<03:39, 6.27s/it]" ] }, { @@ -1232,7 +1232,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 66%|██████▌ | 66/100 [08:59<03:15, 5.74s/it]" + "Test permutations of given graph: 66%|██████▌ | 66/100 [09:10<03:19, 5.88s/it]" ] }, { @@ -1240,7 +1240,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 67%|██████▋ | 67/100 [09:05<03:13, 5.86s/it]" + "Test permutations of given graph: 67%|██████▋ | 67/100 [09:16<03:18, 6.01s/it]" ] }, { @@ -1248,7 +1248,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 68%|██████▊ | 68/100 [09:09<02:52, 5.39s/it]" + "Test permutations of given graph: 68%|██████▊ | 68/100 [09:21<02:56, 5.52s/it]" ] }, { @@ -1256,7 +1256,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 69%|██████▉ | 69/100 [09:16<03:05, 6.00s/it]" + "Test permutations of given graph: 69%|██████▉ | 69/100 [09:28<03:10, 6.14s/it]" ] }, { @@ -1264,7 +1264,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 70/100 [09:26<03:28, 6.94s/it]" + "Test permutations of given graph: 70%|███████ | 70/100 [09:38<03:33, 7.11s/it]" ] }, { @@ -1272,7 +1272,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 71%|███████ | 71/100 [09:31<03:08, 6.49s/it]" + "Test permutations of given graph: 71%|███████ | 71/100 [09:43<03:13, 6.66s/it]" ] }, { @@ -1280,7 +1280,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 72%|███████▏ | 72/100 [09:38<03:05, 6.61s/it]" + "Test permutations of given graph: 72%|███████▏ | 72/100 [09:50<03:10, 6.79s/it]" ] }, { @@ -1288,7 +1288,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 73%|███████▎ | 73/100 [09:42<02:34, 5.73s/it]" + "Test permutations of given graph: 73%|███████▎ | 73/100 [09:54<02:38, 5.88s/it]" ] }, { @@ -1296,7 +1296,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 74%|███████▍ | 74/100 [09:48<02:34, 5.95s/it]" + "Test permutations of given graph: 74%|███████▍ | 74/100 [10:01<02:38, 6.08s/it]" ] }, { @@ -1304,7 +1304,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 75%|███████▌ | 75/100 [09:53<02:18, 5.55s/it]" + "Test permutations of given graph: 75%|███████▌ | 75/100 [10:06<02:22, 5.70s/it]" ] }, { @@ -1312,7 +1312,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 76%|███████▌ | 76/100 [09:57<02:06, 5.27s/it]" + "Test permutations of given graph: 76%|███████▌ | 76/100 [10:10<02:09, 5.41s/it]" ] }, { @@ -1320,7 +1320,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 77%|███████▋ | 77/100 [10:03<02:01, 5.28s/it]" + "Test permutations of given graph: 77%|███████▋ | 77/100 [10:16<02:05, 5.45s/it]" ] }, { @@ -1328,7 +1328,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 78%|███████▊ | 78/100 [10:08<01:58, 5.37s/it]" + "Test permutations of given graph: 78%|███████▊ | 78/100 [10:22<02:02, 5.56s/it]" ] }, { @@ -1336,7 +1336,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 79%|███████▉ | 79/100 [10:13<01:48, 5.15s/it]" + "Test permutations of given graph: 79%|███████▉ | 79/100 [10:27<01:52, 5.34s/it]" ] }, { @@ -1344,7 +1344,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 80%|████████ | 80/100 [10:19<01:52, 5.60s/it]" + "Test permutations of given graph: 80%|████████ | 80/100 [10:33<01:56, 5.82s/it]" ] }, { @@ -1352,7 +1352,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 81%|████████ | 81/100 [10:24<01:41, 5.33s/it]" + "Test permutations of given graph: 81%|████████ | 81/100 [10:38<01:44, 5.51s/it]" ] }, { @@ -1360,7 +1360,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 82%|████████▏ | 82/100 [10:29<01:32, 5.16s/it]" + "Test permutations of given graph: 82%|████████▏ | 82/100 [10:43<01:35, 5.33s/it]" ] }, { @@ -1368,7 +1368,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 83%|████████▎ | 83/100 [10:33<01:24, 4.97s/it]" + "Test permutations of given graph: 83%|████████▎ | 83/100 [10:48<01:27, 5.13s/it]" ] }, { @@ -1376,7 +1376,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 84%|████████▍ | 84/100 [10:41<01:32, 5.76s/it]" + "Test permutations of given graph: 84%|████████▍ | 84/100 [10:56<01:35, 5.96s/it]" ] }, { @@ -1384,7 +1384,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 85%|████████▌ | 85/100 [10:46<01:23, 5.55s/it]" + "Test permutations of given graph: 85%|████████▌ | 85/100 [11:01<01:26, 5.76s/it]" ] }, { @@ -1392,7 +1392,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 86%|████████▌ | 86/100 [10:54<01:26, 6.14s/it]" + "Test permutations of given graph: 86%|████████▌ | 86/100 [11:09<01:28, 6.35s/it]" ] }, { @@ -1400,7 +1400,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 87%|████████▋ | 87/100 [11:00<01:21, 6.24s/it]" + "Test permutations of given graph: 87%|████████▋ | 87/100 [11:15<01:23, 6.45s/it]" ] }, { @@ -1408,7 +1408,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 88%|████████▊ | 88/100 [11:07<01:18, 6.53s/it]" + "Test permutations of given graph: 88%|████████▊ | 88/100 [11:23<01:20, 6.72s/it]" ] }, { @@ -1416,7 +1416,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 89%|████████▉ | 89/100 [11:11<01:02, 5.72s/it]" + "Test permutations of given graph: 89%|████████▉ | 89/100 [11:27<01:04, 5.87s/it]" ] }, { @@ -1424,7 +1424,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 90/100 [11:17<00:58, 5.80s/it]" + "Test permutations of given graph: 90%|█████████ | 90/100 [11:33<00:59, 5.95s/it]" ] }, { @@ -1432,7 +1432,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 91%|█████████ | 91/100 [11:20<00:45, 5.07s/it]" + "Test permutations of given graph: 91%|█████████ | 91/100 [11:36<00:46, 5.18s/it]" ] }, { @@ -1440,7 +1440,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 92%|█████████▏| 92/100 [11:26<00:41, 5.22s/it]" + "Test permutations of given graph: 92%|█████████▏| 92/100 [11:42<00:42, 5.35s/it]" ] }, { @@ -1448,7 +1448,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 93%|█████████▎| 93/100 [11:32<00:38, 5.55s/it]" + "Test permutations of given graph: 93%|█████████▎| 93/100 [11:48<00:39, 5.69s/it]" ] }, { @@ -1456,7 +1456,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 94%|█████████▍| 94/100 [11:36<00:29, 4.87s/it]" + "Test permutations of given graph: 94%|█████████▍| 94/100 [11:52<00:29, 5.00s/it]" ] }, { @@ -1464,7 +1464,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 95%|█████████▌| 95/100 [11:43<00:28, 5.69s/it]" + "Test permutations of given graph: 95%|█████████▌| 95/100 [11:59<00:29, 5.81s/it]" ] }, { @@ -1472,7 +1472,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 96%|█████████▌| 96/100 [11:48<00:21, 5.38s/it]" + "Test permutations of given graph: 96%|█████████▌| 96/100 [12:04<00:22, 5.51s/it]" ] }, { @@ -1480,7 +1480,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 97%|█████████▋| 97/100 [11:53<00:16, 5.42s/it]" + "Test permutations of given graph: 97%|█████████▋| 97/100 [12:10<00:16, 5.56s/it]" ] }, { @@ -1488,7 +1488,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 98%|█████████▊| 98/100 [11:55<00:08, 4.40s/it]" + "Test permutations of given graph: 98%|█████████▊| 98/100 [12:12<00:09, 4.52s/it]" ] }, { @@ -1496,7 +1496,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 99%|█████████▉| 99/100 [12:02<00:05, 5.00s/it]" + "Test permutations of given graph: 99%|█████████▉| 99/100 [12:19<00:05, 5.15s/it]" ] }, { @@ -1504,7 +1504,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 100/100 [12:04<00:00, 4.11s/it]" + "Test permutations of given graph: 100%|██████████| 100/100 [12:21<00:00, 4.23s/it]" ] }, { @@ -1512,7 +1512,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 100/100 [12:04<00:00, 7.24s/it]" + "Test permutations of given graph: 100%|██████████| 100/100 [12:21<00:00, 7.41s/it]" ] }, { @@ -1580,10 +1580,10 @@ "id": "58650ea8", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:07:43.762570Z", - "iopub.status.busy": "2024-10-20T00:07:43.762365Z", - "iopub.status.idle": "2024-10-20T00:08:06.376500Z", - "shell.execute_reply": "2024-10-20T00:08:06.375801Z" + "iopub.execute_input": "2024-10-22T01:34:01.633030Z", + "iopub.status.busy": "2024-10-22T01:34:01.632608Z", + "iopub.status.idle": "2024-10-22T01:34:24.594983Z", + "shell.execute_reply": "2024-10-22T01:34:24.594243Z" } }, "outputs": [ @@ -1600,7 +1600,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:46, 2.44s/it]" + "Test permutations of given graph: 5%|▌ | 1/20 [00:02<00:48, 2.53s/it]" ] }, { @@ -1608,7 +1608,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 2/20 [00:04<00:44, 2.47s/it]" + "Test permutations of given graph: 10%|█ | 2/20 [00:05<00:45, 2.53s/it]" ] }, { @@ -1616,7 +1616,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:35, 2.10s/it]" + "Test permutations of given graph: 15%|█▌ | 3/20 [00:06<00:36, 2.15s/it]" ] }, { @@ -1624,7 +1624,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 4/20 [00:07<00:28, 1.78s/it]" + "Test permutations of given graph: 20%|██ | 4/20 [00:08<00:28, 1.81s/it]" ] }, { @@ -1632,7 +1632,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 25%|██▌ | 5/20 [00:10<00:30, 2.04s/it]" + "Test permutations of given graph: 25%|██▌ | 5/20 [00:10<00:30, 2.05s/it]" ] }, { @@ -1640,7 +1640,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:23, 1.65s/it]" + "Test permutations of given graph: 30%|███ | 6/20 [00:11<00:23, 1.66s/it]" ] }, { @@ -1648,7 +1648,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 35%|███▌ | 7/20 [00:12<00:21, 1.64s/it]" + "Test permutations of given graph: 35%|███▌ | 7/20 [00:13<00:21, 1.66s/it]" ] }, { @@ -1656,7 +1656,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:16, 1.39s/it]" + "Test permutations of given graph: 40%|████ | 8/20 [00:13<00:16, 1.41s/it]" ] }, { @@ -1664,7 +1664,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 55%|█████▌ | 11/20 [00:14<00:06, 1.32it/s]" + "Test permutations of given graph: 55%|█████▌ | 11/20 [00:14<00:06, 1.31it/s]" ] }, { @@ -1672,7 +1672,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 65%|██████▌ | 13/20 [00:15<00:04, 1.51it/s]" + "Test permutations of given graph: 65%|██████▌ | 13/20 [00:15<00:04, 1.49it/s]" ] }, { @@ -1680,7 +1680,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 14/20 [00:16<00:04, 1.41it/s]" + "Test permutations of given graph: 70%|███████ | 14/20 [00:16<00:04, 1.40it/s]" ] }, { @@ -1688,7 +1688,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.33it/s]" + "Test permutations of given graph: 75%|███████▌ | 15/20 [00:17<00:03, 1.32it/s]" ] }, { @@ -1704,7 +1704,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 20/20 [00:19<00:00, 1.05it/s]" + "Test permutations of given graph: 100%|██████████| 20/20 [00:19<00:00, 1.04it/s]" ] }, { @@ -1763,10 +1763,10 @@ "id": "ab8ef360", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:06.378479Z", - "iopub.status.busy": "2024-10-20T00:08:06.378280Z", - "iopub.status.idle": "2024-10-20T00:08:06.466401Z", - "shell.execute_reply": "2024-10-20T00:08:06.465871Z" + "iopub.execute_input": "2024-10-22T01:34:24.597090Z", + "iopub.status.busy": "2024-10-22T01:34:24.596877Z", + "iopub.status.idle": "2024-10-22T01:34:24.678447Z", + "shell.execute_reply": "2024-10-22T01:34:24.677784Z" } }, "outputs": [ @@ -1800,10 +1800,10 @@ "id": "073a54c4", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:06.468822Z", - "iopub.status.busy": "2024-10-20T00:08:06.468571Z", - "iopub.status.idle": "2024-10-20T00:08:06.569473Z", - "shell.execute_reply": "2024-10-20T00:08:06.568903Z" + "iopub.execute_input": "2024-10-22T01:34:24.680461Z", + "iopub.status.busy": "2024-10-22T01:34:24.680247Z", + "iopub.status.idle": "2024-10-22T01:34:24.757000Z", + "shell.execute_reply": "2024-10-22T01:34:24.756324Z" } }, "outputs": [ diff --git a/main/example_notebooks/gcm_icc.html b/main/example_notebooks/gcm_icc.html index e7ce89e0a..2408af717 100644 --- a/main/example_notebooks/gcm_icc.html +++ b/main/example_notebooks/gcm_icc.html @@ -627,7 +627,7 @@

Intrinsic influence on car MPG consumption 
-Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 29.97it/s]
+Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 29.35it/s]

Optionally, we can get some insights into the performance of the causal mechanisms by using the evaluation method:

@@ -644,7 +644,7 @@

Intrinsic influence on car MPG consumption 
-Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 4523.62it/s]
+Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 3311.99it/s]
@@ -670,36 +670,36 @@

Intrinsic influence on car MPG consumption 
-Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.14it/s]
+Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.13it/s]

For a better interpretation of the results, we convert the variance attribution to percentages by normalizing it over the total sum.

@@ -778,7 +778,7 @@

Intrinsic influence on car MPG consumption#

In the next example, we look at different recordings taken of the river flows (\(m^3/s\)) at a 15 minute frequency across 5 different measuring stations in England at Henthorn, New Jumbles Rock, Hodder Place, Whalley Weir and Samlesbury. Here, obtaining a better understanding of how the river flows behave can help to plan potential mitigation steps to avoid overflows. The data is taken from the UK Department for Environment Food & Rural Affairs website. Here is a map of the rivers:

-

2cdd5ab2dbaf4648a04c39a409915f2b

+

c8733c9f1d6c44a6b966501bcc600b6b

New Jumbles Rock lies at a confluence point of the 3 rivers passing Henthorn, Hodder Place, and Whalley Weir and New Jumbles Rock flows into Samlesbury. The water passing a certain measuring station is certainly a mixture of some fraction of the amount observed at the next stations further upstream plus some amount contributed by streams and little rivers entering the river in between. This defines our causal graph as:

[8]:
@@ -801,7 +801,7 @@ 
Intrinsic influence on river flow

+
4a30b35579dd4bfb995006c68ecd443b

 
Nevertheless, we still expect the ICC algorithm to provide some insights into the contribution to the river flow of Samlesbury, even with the hidden confounder in place:

 

 
[9]:
@@ -823,8 +823,8 @@ 
Intrinsic influence on river flow
 
-Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 325.76it/s]
-Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 270.95it/s]
+Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 298.61it/s]
+Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 259.49it/s]
 

 

 

diff --git a/main/example_notebooks/gcm_icc.ipynb b/main/example_notebooks/gcm_icc.ipynb
index 79bdf5517..50bd4ff48 100644
--- a/main/example_notebooks/gcm_icc.ipynb
+++ b/main/example_notebooks/gcm_icc.ipynb
@@ -46,10 +46,10 @@
    "id": "6bfa09f9-67e2-4c65-a27c-903c2742fe0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:11.135897Z",
-     "iopub.status.busy": "2024-10-20T00:08:11.135689Z",
-     "iopub.status.idle": "2024-10-20T00:08:12.666159Z",
-     "shell.execute_reply": "2024-10-20T00:08:12.665475Z"
+     "iopub.execute_input": "2024-10-22T01:34:29.409966Z",
+     "iopub.status.busy": "2024-10-22T01:34:29.409772Z",
+     "iopub.status.idle": "2024-10-22T01:34:31.162535Z",
+     "shell.execute_reply": "2024-10-22T01:34:31.161847Z"
     }
    },
    "outputs": [
@@ -101,10 +101,10 @@
    "id": "04f25d22-df49-45c8-9e4d-6fe05bc30e88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:12.668164Z",
-     "iopub.status.busy": "2024-10-20T00:08:12.667973Z",
-     "iopub.status.idle": "2024-10-20T00:08:16.737382Z",
-     "shell.execute_reply": "2024-10-20T00:08:16.736717Z"
+     "iopub.execute_input": "2024-10-22T01:34:31.165004Z",
+     "iopub.status.busy": "2024-10-22T01:34:31.164743Z",
+     "iopub.status.idle": "2024-10-22T01:34:35.679961Z",
+     "shell.execute_reply": "2024-10-22T01:34:35.679211Z"
     }
    },
    "outputs": [
@@ -153,7 +153,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node horsepower:  80%|████████  | 4/5 [00:00<00:00, 24.55it/s]"
+      "Fitting causal mechanism of node horsepower:  80%|████████  | 4/5 [00:00<00:00, 24.12it/s]"
      ]
     },
     {
@@ -161,7 +161,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node mpg:  80%|████████  | 4/5 [00:00<00:00, 24.55it/s]       "
+      "Fitting causal mechanism of node mpg:  80%|████████  | 4/5 [00:00<00:00, 24.12it/s]       "
      ]
     },
     {
@@ -169,7 +169,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 29.97it/s]"
+      "Fitting causal mechanism of node mpg: 100%|██████████| 5/5 [00:00<00:00, 29.35it/s]"
      ]
     },
     {
@@ -200,10 +200,10 @@
    "id": "ec3eadc5-e305-4291-864a-fab105c27443",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:16.739452Z",
-     "iopub.status.busy": "2024-10-20T00:08:16.739249Z",
-     "iopub.status.idle": "2024-10-20T00:08:17.822782Z",
-     "shell.execute_reply": "2024-10-20T00:08:17.822203Z"
+     "iopub.execute_input": "2024-10-22T01:34:35.682023Z",
+     "iopub.status.busy": "2024-10-22T01:34:35.681816Z",
+     "iopub.status.idle": "2024-10-22T01:34:36.890186Z",
+     "shell.execute_reply": "2024-10-22T01:34:36.889611Z"
     }
    },
    "outputs": [
@@ -220,7 +220,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 4523.62it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 5/5 [00:00<00:00, 3311.99it/s]"
      ]
     },
     {
@@ -252,36 +252,36 @@
       "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n",
       "\n",
       "--- Node displacement\n",
-      "- The MSE is 1046.1386127285114.\n",
-      "- The NMSE is 0.3122627933469142.\n",
-      "- The R2 coefficient is 0.9023228951821402.\n",
-      "- The normalized CRPS is 0.17491878800736213.\n",
+      "- The MSE is 1041.6789617242168.\n",
+      "- The NMSE is 0.31110594400261765.\n",
+      "- The R2 coefficient is 0.9030196864757061.\n",
+      "- The normalized CRPS is 0.17389510458463725.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node weight\n",
-      "- The MSE is 78405.98159688411.\n",
-      "- The NMSE is 0.3305052973475223.\n",
-      "- The R2 coefficient is 0.8907286116580121.\n",
-      "- The normalized CRPS is 0.1812969474437766.\n",
+      "- The MSE is 79878.78338201882.\n",
+      "- The NMSE is 0.3339593114124556.\n",
+      "- The R2 coefficient is 0.8875230464804005.\n",
+      "- The normalized CRPS is 0.18324396514993632.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node horsepower\n",
-      "- The MSE is 213.07861084063614.\n",
-      "- The NMSE is 0.38752088273071295.\n",
-      "- The R2 coefficient is 0.8454044157528504.\n",
-      "- The normalized CRPS is 0.20674889754173442.\n",
+      "- The MSE is 212.60668614086336.\n",
+      "- The NMSE is 0.3824548766720538.\n",
+      "- The R2 coefficient is 0.8510773450667953.\n",
+      "- The normalized CRPS is 0.20279112012331843.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node mpg\n",
-      "- The MSE is 16.060486823938973.\n",
-      "- The NMSE is 0.5203406299058366.\n",
-      "- The R2 coefficient is 0.727082619378933.\n",
-      "- The normalized CRPS is 0.28605114114490204.\n",
+      "- The MSE is 15.748779043326902.\n",
+      "- The NMSE is 0.5079018424532107.\n",
+      "- The R2 coefficient is 0.7403548726484821.\n",
+      "- The normalized CRPS is 0.2775001467867037.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 1.0618178820046136\n",
-      "The estimated KL divergence indicates some mismatches between the distributions.\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.9807451423111999\n",
+      "The estimated KL divergence indicates a good representation of the data distribution, but might indicate some smaller mismatches between the distributions.\n",
       "\n",
       "==== NOTE ====\n",
       "Always double check the made model assumptions with respect to the graph structure and choice of causal mechanisms.\n",
@@ -307,16 +307,16 @@
    "id": "6257a9e2-a1f4-4c16-b629-b5a13bbf32dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:17.825015Z",
-     "iopub.status.busy": "2024-10-20T00:08:17.824492Z",
-     "iopub.status.idle": "2024-10-20T00:08:18.445225Z",
-     "shell.execute_reply": "2024-10-20T00:08:18.444664Z"
+     "iopub.execute_input": "2024-10-22T01:34:36.892464Z",
+     "iopub.status.busy": "2024-10-22T01:34:36.892055Z",
+     "iopub.status.idle": "2024-10-22T01:34:37.528911Z",
+     "shell.execute_reply": "2024-10-22T01:34:37.528224Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -352,10 +352,10 @@
    "id": "9cbc1002-57e4-4b79-8b8b-cab7dff25d4a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:18.447508Z",
-     "iopub.status.busy": "2024-10-20T00:08:18.447149Z",
-     "iopub.status.idle": "2024-10-20T00:08:41.584791Z",
-     "shell.execute_reply": "2024-10-20T00:08:41.584195Z"
+     "iopub.execute_input": "2024-10-22T01:34:37.531075Z",
+     "iopub.status.busy": "2024-10-22T01:34:37.530858Z",
+     "iopub.status.idle": "2024-10-22T01:35:00.982462Z",
+     "shell.execute_reply": "2024-10-22T01:35:00.981858Z"
     }
    },
    "outputs": [
@@ -372,7 +372,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  50%|█████     | 16/32 [00:02<00:02,  6.31it/s]"
+      "Evaluating set functions...:  50%|█████     | 16/32 [00:02<00:02,  6.27it/s]"
      ]
     },
     {
@@ -380,7 +380,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:07<00:02,  2.84it/s]"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:07<00:02,  2.82it/s]"
      ]
     },
     {
@@ -388,7 +388,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:12<00:02,  1.82it/s]"
+      "Evaluating set functions...:  88%|████████▊ | 28/32 [00:12<00:02,  1.80it/s]"
      ]
     },
     {
@@ -404,7 +404,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.14it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:14<00:00,  2.13it/s]"
      ]
     },
     {
@@ -433,10 +433,10 @@
    "id": "885195c7-f9d7-449e-a96f-894ce09562d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:41.587117Z",
-     "iopub.status.busy": "2024-10-20T00:08:41.586716Z",
-     "iopub.status.idle": "2024-10-20T00:08:41.590211Z",
-     "shell.execute_reply": "2024-10-20T00:08:41.589707Z"
+     "iopub.execute_input": "2024-10-22T01:35:00.984886Z",
+     "iopub.status.busy": "2024-10-22T01:35:00.984536Z",
+     "iopub.status.idle": "2024-10-22T01:35:00.988224Z",
+     "shell.execute_reply": "2024-10-22T01:35:00.987611Z"
     }
    },
    "outputs": [],
@@ -452,16 +452,16 @@
    "id": "a80ca005-23aa-46b0-889d-4de7144adb33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:41.592016Z",
-     "iopub.status.busy": "2024-10-20T00:08:41.591642Z",
-     "iopub.status.idle": "2024-10-20T00:08:41.685289Z",
-     "shell.execute_reply": "2024-10-20T00:08:41.684669Z"
+     "iopub.execute_input": "2024-10-22T01:35:00.990174Z",
+     "iopub.status.busy": "2024-10-22T01:35:00.989812Z",
+     "iopub.status.idle": "2024-10-22T01:35:01.097576Z",
+     "shell.execute_reply": "2024-10-22T01:35:01.096884Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -530,10 +530,10 @@
    "id": "c3cb5016-edca-4314-ae34-dc64e3686ce5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:41.687698Z",
-     "iopub.status.busy": "2024-10-20T00:08:41.687185Z",
-     "iopub.status.idle": "2024-10-20T00:08:41.766286Z",
-     "shell.execute_reply": "2024-10-20T00:08:41.765655Z"
+     "iopub.execute_input": "2024-10-22T01:35:01.100103Z",
+     "iopub.status.busy": "2024-10-22T01:35:01.099599Z",
+     "iopub.status.idle": "2024-10-22T01:35:01.181018Z",
+     "shell.execute_reply": "2024-10-22T01:35:01.180388Z"
     }
    },
    "outputs": [
@@ -587,10 +587,10 @@
    "id": "5c5a439d-4b43-4a38-bb78-975b0694a2c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:08:41.768392Z",
-     "iopub.status.busy": "2024-10-20T00:08:41.768198Z",
-     "iopub.status.idle": "2024-10-20T00:08:46.817186Z",
-     "shell.execute_reply": "2024-10-20T00:08:46.816613Z"
+     "iopub.execute_input": "2024-10-22T01:35:01.183445Z",
+     "iopub.status.busy": "2024-10-22T01:35:01.182938Z",
+     "iopub.status.idle": "2024-10-22T01:35:06.243556Z",
+     "shell.execute_reply": "2024-10-22T01:35:06.242900Z"
     }
    },
    "outputs": [
@@ -647,7 +647,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 325.76it/s]"
+      "Fitting causal mechanism of node Samlesbury: 100%|██████████| 5/5 [00:00<00:00, 298.61it/s]"
      ]
     },
     {
@@ -670,7 +670,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 216.42it/s]"
+      "Evaluating set functions...:  75%|███████▌  | 24/32 [00:00<00:00, 199.45it/s]"
      ]
     },
     {
@@ -678,7 +678,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 270.95it/s]"
+      "Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 259.49it/s]"
      ]
     },
     {
@@ -690,7 +690,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
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diff --git a/main/example_notebooks/gcm_online_shop.html b/main/example_notebooks/gcm_online_shop.html
index 950d6bf04..6ae840c43 100644
--- a/main/example_notebooks/gcm_online_shop.html
+++ b/main/example_notebooks/gcm_online_shop.html
@@ -856,64 +856,64 @@ 
Step 1: Define causal model
 
-Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 411.28it/s]
+Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 400.09it/s]
 

 

 
The fit method learns the parameters of the generative models in each node. Before we continue, let’s have a quick look into the performance of the causal mechanisms and how well they capture the distribution:

@@ -955,8 +955,8 @@ 
Step 2: Fit causal models to data
 
-Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 855.76it/s]
-Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  3.26it/s]
+Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 809.14it/s]
+Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00,  3.22it/s]
@@ -985,67 +985,67 @@

Step 2: Fit causal models to data 0 False - $999.00 - $1,293.03 - 11583 - 2350 - $2,347,650.00 - $1,676,296.14 - $671,353.86 + $1,046.68 + $1,171.92 + 11595 + 1992 + $2,084,991.02 + $1,497,179.83 + $587,811.19 1 False $999.00 - $1,383.61 - 11897 - 2410 - $2,407,590.00 - $1,706,391.01 - $701,198.99 + $1,221.67 + 11657 + 2309 + $2,306,691.00 + $1,655,733.37 + $650,957.63 2 False $999.00 - $1,309.75 - 11690 - 2317 - $2,314,683.00 - $1,659,828.40 - $654,854.60 + $1,215.97 + 11841 + 2328 + $2,325,672.00 + $1,665,220.65 + $660,451.35 3 False $999.00 - $1,123.75 - 11701 - 2412 - $2,409,588.00 - $1,707,126.26 - $702,461.74 + $1,450.41 + 11645 + 2339 + $2,336,661.00 + $1,670,958.88 + $665,702.12 4 - True - $913.46 - $2,764.01 - 20618 - 6035 - $5,512,713.18 - $3,520,266.23 - $1,992,446.95 + False + $999.00 + $1,228.42 + 11771 + 2376 + $2,373,624.00 + $1,689,231.49 + $684,392.51 5 False $999.00 - $1,128.02 - 11753 - 2466 - $2,463,534.00 - $1,734,142.88 - $729,391.12 + $1,132.55 + 11742 + 2332 + $2,329,668.00 + $1,667,136.70 + $662,531.30 6 False $999.00 - $1,308.80 - 11731 - 2319 - $2,316,681.00 - $1,660,817.37 - $655,863.63 + $1,199.07 + 11552 + 2343 + $2,340,657.00 + $1,672,710.83 + $667,946.17 7 False $999.00 - $1,428.01 - 11750 - 2309 - $2,306,691.00 - $1,655,943.41 - $650,747.59 + $1,306.93 + 11892 + 2389 + $2,386,611.00 + $1,695,817.89 + $690,793.11 8 False $999.00 - $1,178.90 - 11501 - 2301 - $2,298,699.00 - $1,651,689.56 - $647,009.44 + $1,143.05 + 11664 + 2295 + $2,292,705.00 + $1,648,652.94 + $644,052.06 9 False $999.00 - $1,279.35 - 11677 - 2274 - $2,271,726.00 - $1,638,281.46 - $633,444.54 + $1,412.59 + 11782 + 2370 + $2,367,630.00 + $1,686,422.20 + $681,207.80 @@ -1324,7 +1324,7 @@

What are the key factors influencing the variance in profit? 
-Evaluating set functions...: 100%|██████████| 116/116 [02:40<00:00,  1.39s/it]
+Evaluating set functions...: 100%|██████████| 118/118 [02:41<00:00,  1.37s/it]

@@ -1363,7 +1363,7 @@

What are the key factors influencing the variance in profit? 
-<matplotlib.collections.LineCollection at 0x7f5c8eb25d90>
+<matplotlib.collections.LineCollection at 0x7f7905ada190>

@@ -1430,7 +1430,7 @@

What are the key factors explaining the Profit drop on a particular day? 
-Evaluating set functions...: 100%|██████████| 122/122 [00:03<00:00, 38.00it/s]
+Evaluating set functions...: 100%|██████████| 113/113 [00:03<00:00, 36.90it/s]

diff --git a/main/example_notebooks/gcm_online_shop.ipynb b/main/example_notebooks/gcm_online_shop.ipynb index 0e10fa91f..09e39fc59 100644 --- a/main/example_notebooks/gcm_online_shop.ipynb +++ b/main/example_notebooks/gcm_online_shop.ipynb @@ -53,10 +53,10 @@ "id": "3e2cc4f9-539c-415d-98a5-3a35ebe226a8", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:51.696193Z", - "iopub.status.busy": "2024-10-20T00:08:51.695764Z", - "iopub.status.idle": "2024-10-20T00:08:51.706794Z", - "shell.execute_reply": "2024-10-20T00:08:51.706193Z" + "iopub.execute_input": "2024-10-22T01:35:11.443902Z", + "iopub.status.busy": "2024-10-22T01:35:11.443715Z", + "iopub.status.idle": "2024-10-22T01:35:11.454875Z", + "shell.execute_reply": "2024-10-22T01:35:11.454191Z" } }, "outputs": [ @@ -136,10 +136,10 @@ "id": "49665408-6239-4d17-ab3f-fca7a8bfbbd3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:51.708710Z", - "iopub.status.busy": "2024-10-20T00:08:51.708371Z", - "iopub.status.idle": "2024-10-20T00:08:51.989604Z", - "shell.execute_reply": "2024-10-20T00:08:51.989072Z" + "iopub.execute_input": "2024-10-22T01:35:11.457150Z", + "iopub.status.busy": "2024-10-22T01:35:11.456774Z", + "iopub.status.idle": "2024-10-22T01:35:11.763961Z", + "shell.execute_reply": "2024-10-22T01:35:11.763368Z" } }, "outputs": [], @@ -175,10 +175,10 @@ "id": "47243f27-c8ef-40e2-9d88-ac9418a96393", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:51.991836Z", - "iopub.status.busy": "2024-10-20T00:08:51.991545Z", - "iopub.status.idle": "2024-10-20T00:08:53.273065Z", - "shell.execute_reply": "2024-10-20T00:08:53.272429Z" + "iopub.execute_input": "2024-10-22T01:35:11.766387Z", + "iopub.status.busy": "2024-10-22T01:35:11.766048Z", + "iopub.status.idle": "2024-10-22T01:35:13.151255Z", + "shell.execute_reply": "2024-10-22T01:35:13.150681Z" } }, "outputs": [ @@ -213,10 +213,10 @@ "id": "9d4f5efd-5873-46ed-9c3a-d5b644380f84", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:53.275509Z", - "iopub.status.busy": "2024-10-20T00:08:53.274991Z", - "iopub.status.idle": "2024-10-20T00:08:53.291098Z", - "shell.execute_reply": "2024-10-20T00:08:53.290560Z" + "iopub.execute_input": "2024-10-22T01:35:13.153899Z", + "iopub.status.busy": "2024-10-22T01:35:13.153403Z", + "iopub.status.idle": "2024-10-22T01:35:13.170119Z", + "shell.execute_reply": "2024-10-22T01:35:13.169488Z" } }, "outputs": [ @@ -371,10 +371,10 @@ "id": "ffe4eb1d-3bf5-486b-947d-5d25115b5ab3", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:53.293326Z", - "iopub.status.busy": "2024-10-20T00:08:53.293111Z", - "iopub.status.idle": "2024-10-20T00:08:58.109402Z", - "shell.execute_reply": "2024-10-20T00:08:58.108683Z" + "iopub.execute_input": "2024-10-22T01:35:13.172431Z", + "iopub.status.busy": "2024-10-22T01:35:13.172193Z", + "iopub.status.idle": "2024-10-22T01:35:18.345754Z", + "shell.execute_reply": "2024-10-22T01:35:18.344981Z" } }, "outputs": [], @@ -404,10 +404,10 @@ "id": "941a24d8-cb6c-442e-81d9-0d64bb5ec933", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:58.111655Z", - "iopub.status.busy": "2024-10-20T00:08:58.111454Z", - "iopub.status.idle": "2024-10-20T00:08:58.122268Z", - "shell.execute_reply": "2024-10-20T00:08:58.121751Z" + "iopub.execute_input": "2024-10-22T01:35:18.348478Z", + "iopub.status.busy": "2024-10-22T01:35:18.347965Z", + "iopub.status.idle": "2024-10-22T01:35:18.359627Z", + "shell.execute_reply": "2024-10-22T01:35:18.358977Z" } }, "outputs": [ @@ -441,64 +441,64 @@ "Node Unit Price is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Unit Price := f(Shopping Event?) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 170.2896958076838\n", + "LinearRegression: 140.17291970017308\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 170.31426299250154\n", - "HistGradientBoostingRegressor: 425.84284365749255\n", + " ('linearregression', LinearRegression)]): 140.18220715240867\n", + "HistGradientBoostingRegressor: 423.52475724553994\n", "\n", "--- Node: Ad Spend\n", "Node Ad Spend is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Ad Spend := f(Shopping Event?) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 16319.371837554645\n", + "LinearRegression: 16319.015589914266\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 16609.292691721013\n", - "HistGradientBoostingRegressor: 80383.83457077148\n", + " ('linearregression', LinearRegression)]): 16347.986303481035\n", + "HistGradientBoostingRegressor: 80624.32478842209\n", "\n", "--- Node: Page Views\n", "Node Page Views is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the discrete causal relationship as Page Views := f(Ad Spend,Shopping Event?) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 89536.99249298338\n", + "LinearRegression: 80794.31766180889\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 111056.20714304021\n", - "HistGradientBoostingRegressor: 1382067.8968955562\n", + " ('linearregression', LinearRegression)]): 100308.0900693651\n", + "HistGradientBoostingRegressor: 1494674.9787405445\n", "\n", "--- Node: Sold Units\n", "Node Sold Units is a non-root node with discrete data. Assigning 'Discrete AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the discrete causal relationship as Sold Units := f(Page Views,Shopping Event?,Unit Price) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 9816.125538225946\n", + "LinearRegression: 11037.833647061916\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 23017.09648916649\n", - "HistGradientBoostingRegressor: 279953.3697878738\n", + " ('linearregression', LinearRegression)]): 23452.767029787286\n", + "HistGradientBoostingRegressor: 252818.86004050882\n", "\n", "--- Node: Revenue\n", "Node Revenue is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n", "This represents the causal relationship as Revenue := f(Sold Units,Unit Price) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 3.683316969539796e-19\n", - "LinearRegression: 72775370.9536945\n", - "HistGradientBoostingRegressor: 126628611605.11624\n", + " ('linearregression', LinearRegression)]): 6.814136393648623e-19\n", + "LinearRegression: 52994220.20337007\n", + "HistGradientBoostingRegressor: 155123248537.05835\n", "\n", "--- Node: Operational Cost\n", - "Node Operational Cost is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using Pipeline' to the node.\n", + "Node Operational Cost is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Operational Cost := f(Ad Spend,Sold Units) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", + "LinearRegression: 38.81943962208829\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 38.473104806220924\n", - "LinearRegression: 38.76570032466681\n", - "HistGradientBoostingRegressor: 16048870912.075459\n", + " ('linearregression', LinearRegression)]): 39.103417791382334\n", + "HistGradientBoostingRegressor: 16297592562.688583\n", "\n", "--- Node: Profit\n", "Node Profit is a non-root node with continuous data. Assigning 'AdditiveNoiseModel using LinearRegression' to the node.\n", "This represents the causal relationship as Profit := f(Operational Cost,Revenue) + N.\n", "For the model selection, the following models were evaluated on the mean squared error (MSE) metric:\n", - "LinearRegression: 1.8493444385269016e-18\n", + "LinearRegression: 1.1263449624091704e-18\n", "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(include_bias=False)),\n", - " ('linearregression', LinearRegression)]): 4.304225709266537e-06\n", - "HistGradientBoostingRegressor: 25975916143.442066\n", + " ('linearregression', LinearRegression)]): 1.1126361777340421e-06\n", + "HistGradientBoostingRegressor: 27847798723.59491\n", "\n", "===Note===\n", "Note, based on the selected auto assignment quality, the set of evaluated models changes.\n", @@ -540,10 +540,10 @@ "id": "2b032fa2-9348-418c-b62b-9ae77f49c342", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:58.124143Z", - "iopub.status.busy": "2024-10-20T00:08:58.123799Z", - "iopub.status.idle": "2024-10-20T00:08:58.148676Z", - "shell.execute_reply": "2024-10-20T00:08:58.148165Z" + "iopub.execute_input": "2024-10-22T01:35:18.361730Z", + "iopub.status.busy": "2024-10-22T01:35:18.361510Z", + "iopub.status.idle": "2024-10-22T01:35:18.388403Z", + "shell.execute_reply": "2024-10-22T01:35:18.387674Z" } }, "outputs": [ @@ -624,7 +624,7 @@ "output_type": "stream", "text": [ "\r", - "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 411.28it/s]" + "Fitting causal mechanism of node Operational Cost: 100%|██████████| 8/8 [00:00<00:00, 400.09it/s]" ] }, { @@ -653,10 +653,10 @@ "id": "ab315928-4d60-4008-abb9-a9030e4cad44", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:08:58.150588Z", - "iopub.status.busy": "2024-10-20T00:08:58.150228Z", - "iopub.status.idle": "2024-10-20T00:09:31.448673Z", - "shell.execute_reply": "2024-10-20T00:09:31.448124Z" + "iopub.execute_input": "2024-10-22T01:35:18.390448Z", + "iopub.status.busy": "2024-10-22T01:35:18.390206Z", + "iopub.status.idle": "2024-10-22T01:35:52.145807Z", + "shell.execute_reply": "2024-10-22T01:35:52.145166Z" } }, "outputs": [ @@ -673,7 +673,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 855.76it/s]" + "Evaluating causal mechanisms...: 100%|██████████| 8/8 [00:00<00:00, 809.14it/s]" ] }, { @@ -696,7 +696,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 2%|▏ | 1/50 [00:00<00:28, 1.75it/s]" + "Test permutations of given graph: 2%|▏ | 1/50 [00:00<00:24, 1.98it/s]" ] }, { @@ -704,7 +704,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 4%|▍ | 2/50 [00:01<00:25, 1.85it/s]" + "Test permutations of given graph: 4%|▍ | 2/50 [00:00<00:21, 2.22it/s]" ] }, { @@ -712,7 +712,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 6%|▌ | 3/50 [00:01<00:23, 2.04it/s]" + "Test permutations of given graph: 6%|▌ | 3/50 [00:01<00:21, 2.23it/s]" ] }, { @@ -720,7 +720,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 8%|▊ | 4/50 [00:01<00:21, 2.11it/s]" + "Test permutations of given graph: 8%|▊ | 4/50 [00:01<00:14, 3.11it/s]" ] }, { @@ -728,7 +728,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 10%|█ | 5/50 [00:02<00:22, 2.04it/s]" + "Test permutations of given graph: 10%|█ | 5/50 [00:02<00:17, 2.51it/s]" ] }, { @@ -736,7 +736,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 12%|█▏ | 6/50 [00:02<00:21, 2.02it/s]" + "Test permutations of given graph: 12%|█▏ | 6/50 [00:02<00:19, 2.30it/s]" ] }, { @@ -744,7 +744,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 14%|█▍ | 7/50 [00:03<00:20, 2.11it/s]" + "Test permutations of given graph: 14%|█▍ | 7/50 [00:02<00:19, 2.26it/s]" ] }, { @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 16%|█▌ | 8/50 [00:03<00:19, 2.14it/s]" + "Test permutations of given graph: 16%|█▌ | 8/50 [00:03<00:19, 2.15it/s]" ] }, { @@ -760,7 +760,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 18%|█▊ | 9/50 [00:04<00:18, 2.26it/s]" + "Test permutations of given graph: 18%|█▊ | 9/50 [00:04<00:19, 2.09it/s]" ] }, { @@ -768,7 +768,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 20%|██ | 10/50 [00:04<00:16, 2.36it/s]" + "Test permutations of given graph: 20%|██ | 10/50 [00:04<00:19, 2.09it/s]" ] }, { @@ -776,7 +776,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 22%|██▏ | 11/50 [00:05<00:16, 2.32it/s]" + "Test permutations of given graph: 22%|██▏ | 11/50 [00:04<00:18, 2.12it/s]" ] }, { @@ -784,7 +784,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 24%|██▍ | 12/50 [00:05<00:15, 2.38it/s]" + "Test permutations of given graph: 24%|██▍ | 12/50 [00:05<00:18, 2.11it/s]" ] }, { @@ -792,7 +792,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 26%|██▌ | 13/50 [00:05<00:16, 2.28it/s]" + "Test permutations of given graph: 26%|██▌ | 13/50 [00:05<00:15, 2.43it/s]" ] }, { @@ -800,7 +800,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 28%|██▊ | 14/50 [00:06<00:14, 2.41it/s]" + "Test permutations of given graph: 28%|██▊ | 14/50 [00:05<00:13, 2.75it/s]" ] }, { @@ -808,7 +808,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 30%|███ | 15/50 [00:06<00:12, 2.81it/s]" + "Test permutations of given graph: 30%|███ | 15/50 [00:06<00:13, 2.60it/s]" ] }, { @@ -816,7 +816,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 32%|███▏ | 16/50 [00:06<00:12, 2.70it/s]" + "Test permutations of given graph: 32%|███▏ | 16/50 [00:06<00:13, 2.50it/s]" ] }, { @@ -824,7 +824,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 34%|███▍ | 17/50 [00:07<00:12, 2.67it/s]" + "Test permutations of given graph: 34%|███▍ | 17/50 [00:07<00:13, 2.40it/s]" ] }, { @@ -832,7 +832,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 36%|███▌ | 18/50 [00:07<00:13, 2.46it/s]" + "Test permutations of given graph: 36%|███▌ | 18/50 [00:07<00:11, 2.68it/s]" ] }, { @@ -840,7 +840,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 38%|███▊ | 19/50 [00:08<00:11, 2.61it/s]" + "Test permutations of given graph: 38%|███▊ | 19/50 [00:07<00:11, 2.66it/s]" ] }, { @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 40%|████ | 20/50 [00:08<00:11, 2.70it/s]" + "Test permutations of given graph: 40%|████ | 20/50 [00:08<00:10, 2.77it/s]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 42%|████▏ | 21/50 [00:08<00:11, 2.46it/s]" + "Test permutations of given graph: 42%|████▏ | 21/50 [00:08<00:09, 3.06it/s]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 44%|████▍ | 22/50 [00:09<00:10, 2.76it/s]" + "Test permutations of given graph: 44%|████▍ | 22/50 [00:08<00:08, 3.22it/s]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 46%|████▌ | 23/50 [00:09<00:09, 2.99it/s]" + "Test permutations of given graph: 46%|████▌ | 23/50 [00:09<00:08, 3.05it/s]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 48%|████▊ | 24/50 [00:09<00:08, 3.18it/s]" + "Test permutations of given graph: 48%|████▊ | 24/50 [00:09<00:09, 2.73it/s]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 50%|█████ | 25/50 [00:10<00:08, 3.10it/s]" + "Test permutations of given graph: 50%|█████ | 25/50 [00:09<00:07, 3.33it/s]" ] }, { @@ -896,7 +896,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 52%|█████▏ | 26/50 [00:10<00:07, 3.11it/s]" + "Test permutations of given graph: 52%|█████▏ | 26/50 [00:10<00:07, 3.13it/s]" ] }, { @@ -904,7 +904,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 54%|█████▍ | 27/50 [00:10<00:07, 2.92it/s]" + "Test permutations of given graph: 54%|█████▍ | 27/50 [00:10<00:06, 3.49it/s]" ] }, { @@ -912,7 +912,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 56%|█████▌ | 28/50 [00:11<00:07, 3.11it/s]" + "Test permutations of given graph: 56%|█████▌ | 28/50 [00:10<00:06, 3.34it/s]" ] }, { @@ -920,7 +920,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 58%|█████▊ | 29/50 [00:11<00:06, 3.44it/s]" + "Test permutations of given graph: 58%|█████▊ | 29/50 [00:10<00:05, 3.67it/s]" ] }, { @@ -928,7 +928,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 60%|██████ | 30/50 [00:11<00:06, 3.30it/s]" + "Test permutations of given graph: 60%|██████ | 30/50 [00:11<00:06, 3.25it/s]" ] }, { @@ -936,7 +936,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 64%|██████▍ | 32/50 [00:12<00:04, 4.09it/s]" + "Test permutations of given graph: 62%|██████▏ | 31/50 [00:11<00:05, 3.38it/s]" ] }, { @@ -944,7 +944,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 66%|██████▌ | 33/50 [00:12<00:04, 4.11it/s]" + "Test permutations of given graph: 64%|██████▍ | 32/50 [00:11<00:05, 3.54it/s]" ] }, { @@ -952,7 +952,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 68%|██████▊ | 34/50 [00:12<00:03, 4.64it/s]" + "Test permutations of given graph: 66%|██████▌ | 33/50 [00:12<00:04, 3.41it/s]" ] }, { @@ -960,7 +960,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 70%|███████ | 35/50 [00:12<00:03, 4.40it/s]" + "Test permutations of given graph: 68%|██████▊ | 34/50 [00:12<00:04, 3.47it/s]" ] }, { @@ -968,7 +968,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 72%|███████▏ | 36/50 [00:12<00:02, 4.88it/s]" + "Test permutations of given graph: 70%|███████ | 35/50 [00:12<00:04, 3.74it/s]" ] }, { @@ -976,7 +976,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 74%|███████▍ | 37/50 [00:12<00:02, 5.40it/s]" + "Test permutations of given graph: 72%|███████▏ | 36/50 [00:12<00:03, 4.43it/s]" ] }, { @@ -984,7 +984,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 76%|███████▌ | 38/50 [00:13<00:02, 5.85it/s]" + "Test permutations of given graph: 74%|███████▍ | 37/50 [00:12<00:03, 4.19it/s]" ] }, { @@ -992,7 +992,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 78%|███████▊ | 39/50 [00:13<00:02, 5.16it/s]" + "Test permutations of given graph: 76%|███████▌ | 38/50 [00:13<00:03, 3.78it/s]" ] }, { @@ -1000,7 +1000,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 80%|████████ | 40/50 [00:13<00:02, 4.75it/s]" + "Test permutations of given graph: 78%|███████▊ | 39/50 [00:13<00:02, 3.85it/s]" ] }, { @@ -1008,7 +1008,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 82%|████████▏ | 41/50 [00:13<00:01, 4.50it/s]" + "Test permutations of given graph: 80%|████████ | 40/50 [00:13<00:02, 4.55it/s]" ] }, { @@ -1016,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 84%|████████▍ | 42/50 [00:14<00:01, 4.34it/s]" + "Test permutations of given graph: 82%|████████▏ | 41/50 [00:13<00:02, 4.50it/s]" ] }, { @@ -1024,7 +1024,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 88%|████████▊ | 44/50 [00:14<00:01, 5.78it/s]" + "Test permutations of given graph: 84%|████████▍ | 42/50 [00:14<00:01, 4.65it/s]" ] }, { @@ -1032,7 +1032,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 90%|█████████ | 45/50 [00:14<00:00, 5.43it/s]" + "Test permutations of given graph: 86%|████████▌ | 43/50 [00:14<00:01, 4.76it/s]" ] }, { @@ -1040,7 +1040,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 92%|█████████▏| 46/50 [00:14<00:00, 5.17it/s]" + "Test permutations of given graph: 88%|████████▊ | 44/50 [00:14<00:01, 4.63it/s]" ] }, { @@ -1048,7 +1048,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 94%|█████████▍| 47/50 [00:14<00:00, 5.70it/s]" + "Test permutations of given graph: 90%|█████████ | 45/50 [00:14<00:01, 4.30it/s]" ] }, { @@ -1056,7 +1056,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 96%|█████████▌| 48/50 [00:15<00:00, 5.42it/s]" + "Test permutations of given graph: 94%|█████████▍| 47/50 [00:15<00:00, 5.59it/s]" ] }, { @@ -1064,7 +1064,7 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00, 5.88it/s]" + "Test permutations of given graph: 96%|█████████▌| 48/50 [00:15<00:00, 5.44it/s]" ] }, { @@ -1072,7 +1072,15 @@ "output_type": "stream", "text": [ "\r", - "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00, 3.26it/s]" + "Test permutations of given graph: 98%|█████████▊| 49/50 [00:15<00:00, 5.23it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Test permutations of given graph: 100%|██████████| 50/50 [00:15<00:00, 3.22it/s]" ] }, { @@ -1084,7 +1092,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1110,67 +1118,67 @@ "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n", "\n", "--- Node Shopping Event?\n", - "- The KL divergence between generated and observed distribution is 0.027990336402803684.\n", + "- The KL divergence between generated and observed distribution is 0.008904394006508259.\n", "The estimated KL divergence indicates an overall very good representation of the data distribution.\n", "\n", "--- Node Unit Price\n", - "- The MSE is 152.00707808895962.\n", - "- The NMSE is 0.720411285360626.\n", - "- The R2 coefficient is 0.16175071478019634.\n", - "- The normalized CRPS is 0.1407033490352388.\n", - "The estimated CRPS indicates a very good model performance.\n", - "The mechanism is better or equally good than all 7 baseline mechanisms.\n", + "- The MSE is 148.11430498673633.\n", + "- The NMSE is 1342.263881817742.\n", + "- The R2 coefficient is -9002278.035382235.\n", + "- The normalized CRPS is 163.24112659743213.\n", + "The estimated CRPS indicates a very bad model performance. Consider trying alternative causal mechanism types.\n", + "6 of 7 baseline mechanisms performed significantly better. The best baseline mechanism is: AdditiveNoiseModel using HistGradientBoostingRegressor with a CRPS of 24.342782551962898. Seeing this, consider changing the mechanism to a AdditiveNoiseModel using HistGradientBoostingRegressor or, if you are using an auto assigment, consider a better assignment quality level such as AssignmentQuality.BETTER.\n", "\n", "--- Node Ad Spend\n", - "- The MSE is 16195.430869243308.\n", - "- The NMSE is 0.48896423364213043.\n", - "- The R2 coefficient is 0.7443950435230015.\n", - "- The normalized CRPS is 0.280859718724849.\n", + "- The MSE is 15800.804127125453.\n", + "- The NMSE is 0.49216705827116514.\n", + "- The R2 coefficient is 0.7462501575346435.\n", + "- The normalized CRPS is 0.2852065536532278.\n", "The estimated CRPS indicates a good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Page Views\n", - "- The MSE is 84221.1506849315.\n", - "- The NMSE is 0.15291392779783552.\n", - "- The R2 coefficient is 0.9754630013432213.\n", - "- The normalized CRPS is 0.06611624028756846.\n", + "- The MSE is 76340.72328767124.\n", + "- The NMSE is 0.1686117400922747.\n", + "- The R2 coefficient is 0.9693701076006261.\n", + "- The normalized CRPS is 0.06765478644055958.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Sold Units\n", - "- The MSE is 9025.345205479452.\n", - "- The NMSE is 0.28032677135492834.\n", - "- The R2 coefficient is 0.8096081043090505.\n", - "- The normalized CRPS is 0.11314055052761643.\n", + "- The MSE is 8566.380821917808.\n", + "- The NMSE is 0.12831895732544357.\n", + "- The R2 coefficient is 0.9823127200152071.\n", + "- The normalized CRPS is 0.05706410449649901.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Revenue\n", - "- The MSE is 3.261517768189271e-19.\n", - "- The NMSE is 8.375167717432583e-16.\n", + "- The MSE is 5.655673798390138e-19.\n", + "- The NMSE is 1.1956498945603868e-15.\n", "- The R2 coefficient is 1.0.\n", - "- The normalized CRPS is 2.0778033783637062e-16.\n", + "- The normalized CRPS is 1.9089910778478803e-16.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Operational Cost\n", - "- The MSE is 39.44346210470364.\n", - "- The NMSE is 1.7996147680273706e-05.\n", - "- The R2 coefficient is 0.9999999996466071.\n", - "- The normalized CRPS is 1.010554646963904e-05.\n", + "- The MSE is 38.51417507707491.\n", + "- The NMSE is 1.754174369432478e-05.\n", + "- The R2 coefficient is 0.9999999996840231.\n", + "- The normalized CRPS is 9.745765566210271e-06.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "--- Node Profit\n", - "- The MSE is 1.6909098265407491e-18.\n", - "- The NMSE is 4.409180107629312e-15.\n", + "- The MSE is 1.1088195026347474e-18.\n", + "- The NMSE is 4.631086458497623e-15.\n", "- The R2 coefficient is 1.0.\n", - "- The normalized CRPS is 3.152869746103322e-16.\n", + "- The normalized CRPS is 3.900711108759761e-16.\n", "The estimated CRPS indicates a very good model performance.\n", "The mechanism is better or equally good than all 7 baseline mechanisms.\n", "\n", "==== Evaluation of Generated Distribution ====\n", - "The overall average KL divergence between the generated and observed distribution is 1.1642537304338478\n", + "The overall average KL divergence between the generated and observed distribution is 1.1503944967087358\n", "The estimated KL divergence indicates some mismatches between the distributions.\n", "\n", "==== Evaluation of the Causal Graph Structure ====\n", @@ -1179,9 +1187,9 @@ "+-------------------------------------------------------------------------------------------------------+\n", "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov |\n", "| equivalence class of the given DAG (p-value: 0.00). |\n", - "| The given DAG violates 6/18 LMCs and is better than 88.0% of the permuted DAGs (p-value: 0.12). |\n", + "| The given DAG violates 6/18 LMCs and is better than 74.0% of the permuted DAGs (p-value: 0.26). |\n", "| Based on the provided significance level (0.2) and because the DAG is informative, |\n", - "| we do not reject the DAG. |\n", + "| we reject the DAG. |\n", "+-------------------------------------------------------------------------------------------------------+\n", "\n", "==== NOTE ====\n", @@ -1227,10 +1235,10 @@ "id": "74ed17eb-45ec-444d-9364-2aa850911a05", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.450693Z", - "iopub.status.busy": "2024-10-20T00:09:31.450396Z", - "iopub.status.idle": "2024-10-20T00:09:31.467505Z", - "shell.execute_reply": "2024-10-20T00:09:31.466926Z" + "iopub.execute_input": "2024-10-22T01:35:52.147942Z", + "iopub.status.busy": "2024-10-22T01:35:52.147547Z", + "iopub.status.idle": "2024-10-22T01:35:52.164771Z", + "shell.execute_reply": "2024-10-22T01:35:52.164105Z" } }, "outputs": [ @@ -1269,112 +1277,112 @@ " \n", " 0\n", " False\n", - " $999.00\n", - " $1,293.03\n", - " 11583\n", - " 2350\n", - " $2,347,650.00\n", - " $1,676,296.14\n", - " $671,353.86\n", + " $1,046.68\n", + " $1,171.92\n", + " 11595\n", + " 1992\n", + " $2,084,991.02\n", + " $1,497,179.83\n", + " $587,811.19\n", " \n", " \n", " 1\n", " False\n", " $999.00\n", - " $1,383.61\n", - " 11897\n", - " 2410\n", - " $2,407,590.00\n", - " $1,706,391.01\n", - " $701,198.99\n", + " $1,221.67\n", + " 11657\n", + " 2309\n", + " $2,306,691.00\n", + " $1,655,733.37\n", + " $650,957.63\n", " \n", " \n", " 2\n", " False\n", " $999.00\n", - " $1,309.75\n", - " 11690\n", - " 2317\n", - " $2,314,683.00\n", - " $1,659,828.40\n", - " $654,854.60\n", + " $1,215.97\n", + " 11841\n", + " 2328\n", + " $2,325,672.00\n", + " $1,665,220.65\n", + " $660,451.35\n", " \n", " \n", " 3\n", " False\n", " $999.00\n", - " $1,123.75\n", - " 11701\n", - " 2412\n", - " $2,409,588.00\n", - " $1,707,126.26\n", - " $702,461.74\n", + " $1,450.41\n", + " 11645\n", + " 2339\n", + " $2,336,661.00\n", + " $1,670,958.88\n", + " $665,702.12\n", " \n", " \n", " 4\n", - " True\n", - " $913.46\n", - " $2,764.01\n", - " 20618\n", - " 6035\n", - " $5,512,713.18\n", - " $3,520,266.23\n", - " $1,992,446.95\n", + " False\n", + " $999.00\n", + " $1,228.42\n", + " 11771\n", + " 2376\n", + " $2,373,624.00\n", + " $1,689,231.49\n", + " $684,392.51\n", " \n", " \n", " 5\n", " False\n", " $999.00\n", - " $1,128.02\n", - " 11753\n", - " 2466\n", - " $2,463,534.00\n", - " $1,734,142.88\n", - " $729,391.12\n", + " $1,132.55\n", + " 11742\n", + " 2332\n", + " $2,329,668.00\n", + " $1,667,136.70\n", + " $662,531.30\n", " \n", " \n", " 6\n", " False\n", " $999.00\n", - " $1,308.80\n", - " 11731\n", - " 2319\n", - " $2,316,681.00\n", - " $1,660,817.37\n", - " $655,863.63\n", + " $1,199.07\n", + " 11552\n", + " 2343\n", + " $2,340,657.00\n", + " $1,672,710.83\n", + " $667,946.17\n", " \n", " \n", " 7\n", " False\n", " $999.00\n", - " $1,428.01\n", - " 11750\n", - " 2309\n", - " $2,306,691.00\n", - " $1,655,943.41\n", - " $650,747.59\n", + " $1,306.93\n", + " 11892\n", + " 2389\n", + " $2,386,611.00\n", + " $1,695,817.89\n", + " $690,793.11\n", " \n", " \n", " 8\n", " False\n", " $999.00\n", - " $1,178.90\n", - " 11501\n", - " 2301\n", - " $2,298,699.00\n", - " $1,651,689.56\n", - " $647,009.44\n", + " $1,143.05\n", + " 11664\n", + " 2295\n", + " $2,292,705.00\n", + " $1,648,652.94\n", + " $644,052.06\n", " \n", " \n", " 9\n", " False\n", " $999.00\n", - " $1,279.35\n", - " 11677\n", - " 2274\n", - " $2,271,726.00\n", - " $1,638,281.46\n", - " $633,444.54\n", + " $1,412.59\n", + " 11782\n", + " 2370\n", + " $2,367,630.00\n", + " $1,686,422.20\n", + " $681,207.80\n", " \n", " \n", "\n", @@ -1382,28 +1390,28 @@ ], "text/plain": [ " Shopping Event? Unit Price Ad Spend Page Views Sold Units \\\n", - "0 False $999.00 $1,293.03 11583 2350 \n", - "1 False $999.00 $1,383.61 11897 2410 \n", - "2 False $999.00 $1,309.75 11690 2317 \n", - "3 False $999.00 $1,123.75 11701 2412 \n", - "4 True $913.46 $2,764.01 20618 6035 \n", - "5 False $999.00 $1,128.02 11753 2466 \n", - "6 False $999.00 $1,308.80 11731 2319 \n", - "7 False $999.00 $1,428.01 11750 2309 \n", - "8 False $999.00 $1,178.90 11501 2301 \n", - "9 False $999.00 $1,279.35 11677 2274 \n", + "0 False $1,046.68 $1,171.92 11595 1992 \n", + "1 False $999.00 $1,221.67 11657 2309 \n", + "2 False $999.00 $1,215.97 11841 2328 \n", + "3 False $999.00 $1,450.41 11645 2339 \n", + "4 False $999.00 $1,228.42 11771 2376 \n", + "5 False $999.00 $1,132.55 11742 2332 \n", + "6 False $999.00 $1,199.07 11552 2343 \n", + "7 False $999.00 $1,306.93 11892 2389 \n", + "8 False $999.00 $1,143.05 11664 2295 \n", + "9 False $999.00 $1,412.59 11782 2370 \n", "\n", - " Revenue Operational Cost Profit \n", - "0 $2,347,650.00 $1,676,296.14 $671,353.86 \n", - "1 $2,407,590.00 $1,706,391.01 $701,198.99 \n", - "2 $2,314,683.00 $1,659,828.40 $654,854.60 \n", - "3 $2,409,588.00 $1,707,126.26 $702,461.74 \n", - "4 $5,512,713.18 $3,520,266.23 $1,992,446.95 \n", - "5 $2,463,534.00 $1,734,142.88 $729,391.12 \n", - "6 $2,316,681.00 $1,660,817.37 $655,863.63 \n", - "7 $2,306,691.00 $1,655,943.41 $650,747.59 \n", - "8 $2,298,699.00 $1,651,689.56 $647,009.44 \n", - "9 $2,271,726.00 $1,638,281.46 $633,444.54 " + " Revenue Operational Cost Profit \n", + "0 $2,084,991.02 $1,497,179.83 $587,811.19 \n", + "1 $2,306,691.00 $1,655,733.37 $650,957.63 \n", + "2 $2,325,672.00 $1,665,220.65 $660,451.35 \n", + "3 $2,336,661.00 $1,670,958.88 $665,702.12 \n", + "4 $2,373,624.00 $1,689,231.49 $684,392.51 \n", + "5 $2,329,668.00 $1,667,136.70 $662,531.30 \n", + "6 $2,340,657.00 $1,672,710.83 $667,946.17 \n", + "7 $2,386,611.00 $1,695,817.89 $690,793.11 \n", + "8 $2,292,705.00 $1,648,652.94 $644,052.06 \n", + "9 $2,367,630.00 $1,686,422.20 $681,207.80 " ] }, "execution_count": 9, @@ -1445,10 +1453,10 @@ "id": "efe77b22-e99c-43e9-876f-4f7198d5b112", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.469498Z", - "iopub.status.busy": "2024-10-20T00:09:31.469172Z", - "iopub.status.idle": "2024-10-20T00:09:31.746480Z", - "shell.execute_reply": "2024-10-20T00:09:31.745875Z" + "iopub.execute_input": "2024-10-22T01:35:52.166750Z", + "iopub.status.busy": "2024-10-22T01:35:52.166547Z", + "iopub.status.idle": "2024-10-22T01:35:52.440722Z", + "shell.execute_reply": "2024-10-22T01:35:52.440023Z" } }, "outputs": [ @@ -1491,10 +1499,10 @@ "id": "b3ff0966-ddaf-4b19-b9ba-1fe854092f43", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.748588Z", - "iopub.status.busy": "2024-10-20T00:09:31.748106Z", - "iopub.status.idle": "2024-10-20T00:09:31.794711Z", - "shell.execute_reply": "2024-10-20T00:09:31.794197Z" + "iopub.execute_input": "2024-10-22T01:35:52.442692Z", + "iopub.status.busy": "2024-10-22T01:35:52.442489Z", + "iopub.status.idle": "2024-10-22T01:35:52.491495Z", + "shell.execute_reply": "2024-10-22T01:35:52.490873Z" } }, "outputs": [ @@ -1531,16 +1539,16 @@ "id": "ecef5abd-f551-444b-8ec4-53e224f6d529", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:31.796831Z", - "iopub.status.busy": "2024-10-20T00:09:31.796439Z", - "iopub.status.idle": "2024-10-20T00:09:32.108325Z", - "shell.execute_reply": "2024-10-20T00:09:32.107676Z" + "iopub.execute_input": "2024-10-22T01:35:52.493614Z", + "iopub.status.busy": "2024-10-22T01:35:52.493201Z", + "iopub.status.idle": "2024-10-22T01:35:52.810006Z", + "shell.execute_reply": "2024-10-22T01:35:52.809244Z" } }, "outputs": [ { "data": { - "image/png": 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", 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o1WrlQHbkyBHa2tqAj1bIuFzOsSRJIhQKcerUKerq6rBYLIwbNw6LxTLYTbsg8vBbX18f27dvx2w2ExsbS2Fh4XkfEwwGWbduHQsWLKCoqOjSNXYI8Pl87N69m40bN9Le3k4oFEKn0wEffWdaWlqQJInu7m7Gjx9Pdnb2ILf4dDqdTlmfNjs7m46ODqWHrqamhqqqKlavXo3b7SYnJ4fMzEwSEhLEyZpwyYkeO+GS6H8w37p1K8uWLWP37t1MnDiRmTNncs8995CdnT2kz3IlSUKSJF566SVeffVV1q9fzze/+U1mz57NxIkTiYqKorOzk4qKCtavX8/vf/971Go1V1xxBWvWrDnr+b773e+yYcMGqqqqqKmpwWq1XhYlFSRJwu128/vf/56XXnqJzMxM/vCHP5CVlTXYTbtgPT09VFRUcOWVV5KZmcn8+fP53e9+d977O51OcnNz+d73vsd1111Hfn7+JWztxSMX/VWr1Uqu2Zmampp44IEHOHbsGJMmTeL+++9n2rRp9PX1UVZWxgsvvMAbb7zBjBkzWLRoEY899hgwtEsUSZKEy+Viz549rFy5kueeew6r1cqECRO4/fbbuf766zEYDKjV6iH9OoSRZegeRYURxeVy8dJLL/H3v/+drq4uUlJSeP311xk3bhxxcXHo9fphcWZbVlbGxo0b2bNnD1/+8pf52te+RlpamrJ6hTxLd9KkSfT29rJ9+/bBbvKQJOcthcNhJWAWhq+vfOUrlJaWkpuby6uvvvqxva8PPPAAU6dOZebMmWi1WiwWC9HR0RQVFVFSUkJJSQnd3d3cddddREVFDemTPZVKhcViYdasWUydOpUHHniAZcuWsW3bNh588EFuuukmvvrVrzJlyhTMZvNgN1e4TAzdb4ww7MkH70OHDrFt2zZeeeUVUlJSuPLKK5k1axaTJk0iKioKo9E42E391BoaGuju7kalUpGWlobFYlGGk+CjHb1arcZsNjNt2jROnjxJOBwexBYPTQaDgeuvv54xY8Zgs9mIi4sb7CZdEkajkaeffpqxY8eSkJAw2M0ZMC6XS0lBOF+QHhERwbe+9S0SExOJjY1Vvjfy0n82m42kpCTa29txuVw0NjZit9uHdGAH/26/RqMhNTWVm2++mTFjxrB79262bdvG0qVLOXLkCLfffjtRUVHD4gRWGN6G9jdGGLbknXtNTQ27du1i9erV9PT0cO211zJr1ixmzZo1LIcnXC4Xfr//E3uZVCoVY8eOpbi4GJfLdQlbOPTJB8Lx48czfvz4wW7OJVFVVUU4HCYiIoLbbrtt2G33A8FsNnPllVee8zaVSoVGo8FoNKLRaAiFQrhcrmF1UiSf0I0fP57k5GSSk5OVepzd3d1kZ2czY8YMLBbLZZFyIQweEdgJF40kSfzlL39h69atNDc388c//pEZM2bgcDgGu2mfWVRUFCaTCa/Xy549e7jnnnvOCvDkXrsLDVzkYFF+vjMP/mdePvP/9r98rsf2v/189z3f9fLf/UtAfFK7Pq4N53rPzvRZ23iu+11omwc68Prf//1fvF4vV111Fbfddhtarfa878X5/v/53rMzP9sz73u+7eizfpYf93j5cv+A7NPMaJe3+6amJlwuF3a7nYiIiGHbuxUTE8OMGTP4xz/+wZNPPsm6dev42te+xjvvvENOTo4y018QLgYR2AkXRVtbG6+99hrvvPMOU6dO5b/+679YsGCBMnN0uCoqKiI7Oxuj0cjWrVt57LHHmDVrFnPnzqW4uPgzHYjC4TBtbW0sXbqUPXv2cPjwYdRqNampqRQWFvK1r32NpKSk04Z84aPyC93d3fzzn//kwIED1NTU0NHRgclkIjk5mfz8fG677TbGjx+PxWLB6/Xy0ksv8corr3Dq1Cml+PNXv/pVsrKy+Ne//sWxY8fw+XxotVoKCgq44447KCwsJCMjA4B169axceNGXnnlFQDsdjt5eXk88cQT/OMf/6C8vJzGxkZCoRC5ublMnDiRxYsXM378eDQaDd3d3ezevZsf/OAHdHV1Ka9l+fLlFBQUIEkSW7Zs4emnn+bIkSPK7d///veZNGkS//rXv9izZw8+nw+DwcDYsWO57777GDVqFDExMWe9t9XV1axdu5atW7dy8OBBgsEgUVFRZGRkcO+997J69WoOHz7M8ePHAZg2bRpz5szhP/7jPy74c7xQoVAIp9PJlVdeSXt7+2m3TZw4kUWLFnHvvfciSRIrV67k0UcfVW6Pioriiiuu4Oc//zkOh4NwOEx3dzevvvoqBw4coKysjLa2NkwmEzExMYwaNYr777+fzMxMHA4HkiSxbds23nrrLd555x3lea+55hp+8IMf8Mwzz3Do0CHa2trQaDQUFhayaNEi5syZQ2JiIiqViiNHjrB+/XqeeeYZWlpa8Pv9dHR0UFBQoARzGo2GrVu3kpyc/LHvRUtLC1u2bKGyshKDwUBWVhY5OTno9fqBfdMvIZVKRWxsLI899hhXXHEFP/rRj/jhD3/Ifffdx5IlS4bliIUwPIjAThhwLpeLuro6li9fTn5+PtOmTaO4uBiTyTTsd2RWq5Vp06bR1tbG22+/zcGDB+ns7KSsrIzc3FxSUlKIi4sjOTmZ3NzcTzUpJBgMsnLlStRqNRMnTmTMmDE0NzdTUVHBtm3bsNvtPPjgg0otPAC3282xY8f44IMP2LZtGwkJCUyfPh2bzYbf76elpYWamhr++te/8uUvf5kxY8aQmJhIYWEhgUCAffv2sXLlSiorK9m8eTNtbW0UFhZSUFBAX18fnZ2dHDx4kFdeeYXq6mpuuOEGUlNTSU9PZ+bMmZhMJp5//nnlgP7OO++QmJhIYmIiwWBQWed3586dtLS08O1vf5ukpCQMBgMZGRnceeed7Ny5k9LSUiorK/H5fMBHw1lpaWncdNNNpKamsnnzZk6cOMH27duRJInc3FySk5Pp6emhtbWV3bt3ExUVxYIFC/jCF76gvD/9g97Dhw/T2dnJjBkziIuLU2Ypbt68mZKSEiorK2lra+O73/0uubm5n7nMRnNzMzt27ODHP/7xWbft3buXpKSk066TS+TccccdbNmyhdLSUurq6rjzzjuZOnUqhYWFyvclKyuLe+65hxdffJG0tDRGjRrFggULMBqN+Hw+Wltbef7559m3bx9arZbJkycTFRWF3++nu7ub6upq/v73v7No0SKmTp1KfHw8ycnJzJ07F5PJxPLly6murqa0tJRVq1aRkZFBYmIiLpeL1tZWdu3aBXw0q/eBBx4APuqVmjBhAvfddx8vvfQSjY2NxMTEcMcddygBmVqtxmq1nvP9CofDdHR0cOLECQ4dOsTKlSsJhUJMnz6da665ZlgHdfDR56vVaomPj2f8+PHcfvvtvPbaaxw4cIC8vDzGjRs32E0URigR2AkDrquri1OnTrFz505+/OMfc8UVV5CamjrYzfrcVCoVer2eyZMno9frqaiooLa2liNHjnDgwAFMJhMFBQXk5OQoQ7BxcXHYbLaPnREXDAbZtWsXN910E+PGjSMlJYWysjL++te/sm7dOl599VVuu+220xKv5YPts88+i9Vq5corr2TevHmMGjWKzs5ONm7cyNq1a3nllVdwOByo1WqSk5OZOnUq06ZNY+PGjezbt4/KykoqKyuJjo7mgQceIDs7G6fTyalTp6iqqmLNmjW0t7eTnp5OSkoK+fn55Ofns3DhQjZt2kRFRQVdXV3s37+fRx99lLy8PBwOBzU1NfzpT39i586d7Nixg9mzZzNz5kwlKBk1ahQvvPACPT09VFZWKu+FVqtV/se4ceNoaGigsrKSU6dOkZiYyJe//GWlhtjBgwdZsWIFK1euxGKxsHDhQqUXRF6e7sUXX0StVjNq1CjuuusuRo8eTTAYpKGhgd/97nc0NjbS29uL0Wjk8ccfx2azfebto6urC6/Xq9Qm7K+tre2s59ZoNJjNZr797W8TGxtLIBCgrq6O66+/nmnTpinfGZVKxahRo3jsscfYvHkzCxYsYO7cucycORP4qLervLycv/3tb1gsFubMmcMtt9xCcXExnZ2dHDp0iOeff55XXnkFvV5PVFQUCQkJZGVlkZWVxcyZMykvL6elpYX29nZKSkr42te+Rnp6OoBSvmf79u309vZy3333oVarlUB+zpw57N69G6fTSUZGBt///vfPG8z1Jxcp//DDD9m/fz87duwgNTWV+fPnc8011wz7k0CZXq8nNTWV22+/nddff53jx4+ze/duEdgJF40I7IQBt2/fPjZu3Eh0dDS33HILmZmZg92kAZWSkkJycjJXXnkla9asoaSkhH379rF9+3a2bdvGli1bAHA4HNx111184Qtf4Kqrrjrv8+n1er74xS8yffp04uPjAZg8eTJHjx6lubmZVatW0draSnx8vFJG4pVXXmH16tW0trby//7f/2PevHnKGrpWq5W77rqLqVOnsnXrVv75z3/i8/koKioiNjb2tP/t8Xi47rrruP3228nLywM+Gl6Ni4vjt7/9LXfccQclJSX8+te/5pprrjkr6TsQCBAZGcn//M//MHr0aGWofezYsXzve9/jrbfe4gc/+AG/+tWv+NWvfvWZ1/mdNGkSixcvpri4WGmjzWZj3rx5bNy4kVOnTtHd3a3kb9bU1PDb3/6WlpYWvvzlL3P77bczY8YM5fliYmL43e9+x+233059ff2ApAiMHj2a+fPn89vf/vas2x5++GG8Xu95Hzt16lT8fj+rVq3i7bffRqVSnXYy1NXVxeHDh2lqaiI3N5dp06Ypt23bto2XX36ZhoYGfvrTn3L11VczefJk4KNVTeLi4hg3bhxTpkxRimrPnj37nO1IT0/nlltuYcKECcpnHRsby7x589iyZQvHjh2jq6uLyMjIzz1bta+vjxtvvBGj0Uh6ejp33303jzzyCImJiSOuNIjRaCQ3N5fFixdz4MAB3nvvPaXnUxAGmgjshAFXX19PY2MjM2fOxGw2j5gzb5n8evR6PXPmzGHSpEncdddd9PT0cPz4cY4fP87OnTv58MMPefXVVzl58iRpaWlkZWWdlScHHw1XyWU/+r9XVqtVCVQ6OztxuVxKYHfgwAGOHj2KRqNh+vTpxMbGnvZYjUaDzWZj8uTJbNmyhbq6Ovbt28fChQtPu5+cj5eenn7a9VqtlpycHBISEqivr+fEiRO0tbURGxt7WhCk0+mw2+3k5+ej1+tPe46UlBTS09OJjo7m1KlTNDY20t3dTWRk5AW/50lJSWRlZZ31GuPj49HpdHi9Xtrb24mKikKlUilLVwUCATIyMhg/fvxpj1Wr1URFRZGSkkJsbCy9vb0X3Kbz+Szbe1paGhMnTiQ/P5+dO3eSmZnJ1VdfrWwTNTU1vPbaa9x4441kZ2efFmBXVlaye/duVCoVxcXF5OTknNYGvV5PTEwMmZmZVFZWcuLECVpaWoiOjj4rOIuIiGDUqFGn5X9JkkRsbCwWi4Xe3l7a2tqwWq0DUoZEkiQefvhhFixYQE5OjlIGZaTuM6644goqKio4fvw4TqdTzJAVLgoR2AkDzuVy4Xa7lZUkRtpOGv49009eYkieCRgXF0dGRgZJSUnKMOOJEyc4cuQIKSkp5wzs5Oc58zatVqtcFwgElGXHwuEwXV1dyhBidHT0WT1OckmR+Ph41Gq1kit15n2MRiMmk+msWoJy6QabzYZOp8Pj8eB0OomKijorsDOZTOfsYTEYDFgsFmw2Gx0dHTidTtxu92cK7OS2nNl+eX3dUCiE3+8HPsrd8vl8tLe3o1KpsFqtZ/3P/rXTzGbzgAZ25zJr1iwCgcBZQZfMaDQSGxvLtGnTWLlyJVVVVZSVlSkrMzQ2NnL48GG+973vkZCQoMyEldctlidfbNq0iaqqqrO2pVAoRGdnJ263G6fTSXt7+zl73fR6PZGRkWe10WAwKDN55XI/AyUpKYmcnJwRka7xSeQcT4/Hg8vlwmQyicBOGHAisBMGXCAQIBAIjIjJEv3Jy6L5fD5CoRB2u125TZ4BmJaWRlpaGlOmTKGtrY0333xTyVuaP3/+OSvyywHKmTv4/mUi+h9Ig8Gg0gatVqtMBjiTnLiuVquVBczPPCDLdcMkSTrnZ2UymZQDUV9fH8Fg8LTb5f9/vhIn8soCKpUKn8+Hx+M553v7SfR6/TmHS+UVP+Df71EoFCIQCODz+dDr9ej1+nMG1PLznu+2gXTHHXd84n2sViuLFi1i06ZNyqSWKVOm0NraSk1NDQ0NDUybNu20STRyQCtPPvn73//+sT1pOp0Oi8VCZ2fnOWvEabVaTCbTWdf3f5+HU225ocZoNCoBstvtFu+lcFEMzyJBwpAWGRlJREQEJ0+eJBAIDHZzBlRpaSnf/e53ueGGGz6210KtVjNt2jTi4+MJBoN0d3cP2E5cr9djsVjQ6/X4fL5zBlzwUQDY1tZGKBRSyl6cWQ+uq6tLCQrOpbu7m76+PlQqFTExMWfNVHS73R/b2+Xz+ejo6ECSJGw2GxEREZ/hFV8YrVaL0WjEarUSCATweDznDSh7enpwOp0XvU2fhs1mY/HixWRnZ1NfX8/LL7+Mz+dj+fLlHD9+nNtvv/2sXjadTofZbMZut6PRaFi1ahUNDQ00Nzef92fHjh3MmDFjQFd8+TS16oSPClX39PRgMBjOWcJIEAaCCOyEAZeVlUV2djZ79uyhtbUVt9s92E0aMOFwGKfTSXNzM8ePHz9vQrwkSVRVVSnDpVlZWQOSkyQfQMeOHUtubi6hUIj9+/fT2dl52v3kyv2lpaUEg0ESExMZN27cWQffQCBAU1MTtbW1p10fDAaprq6mra0NlUpFRkYGDofjrMAuFArR19dHRUXFWUF8c3MzDQ0NdHV1kZaWRnx8/Gm9nBeLSqXCbrczceJEtFotdXV1HD169LT7yMPZzc3NZ713F5PX6+V73/ser7zyCocOHTqr3Tqdjvnz55OTk0NLSwvvvvsu69ato6uri+uvv/601AZ5W0hLS6O4uJhwOExdXR0dHR1KT6X8o9VqWb16NT//+c/5+c9/PuBr88pLavU/waisrOShhx7i7bff5uTJk2c9xmq18tZbb7Fo0aJz1iAcaSRJYvPmzfj9fsaOHXtWTqogDBQR2AkDLi0tjdzcXHp6ejh48CANDQ0jZqF3SZKUHrgNGzZQVVVFR0cHXq+XUCiE1+uls7OTU6dOsW/fPtxuN7GxsRQUFAxoXa7i4mLGjRuHwWBg69atVFRU0NnZSTAYxOVyUVVVRWlpKQ0NDWRkZJCXl3dWHTX4qGexsrKSgwcP0tXVhd/vx+1209raytatW+ns7MThcDBlypTzDhd7vV52795NS0sLLpdLqSFXUlJCWVkZoVCIqVOnkpiYeMlqk0VFRTFr1izMZjOVlZXs3LmTtrY2fD4fXq+Xrq4u9uzZQ1dXF6FQ6JK0CT4KhHfs2EFFRcVZRYnlFUsmT55MdnY2Ho+HVatWKZMV8vPzzznknpWVxRVXXIHJZGL//v0cPHiQtrY2AoEAfr8fp9OpFGo+cuTIOcuxfF42mw2j0YjT6cTj8dDb20tdXR0rV66kvb1dyX+U9fX1UV9fj0qloqOj46K0aSjxer3U1dVx6NAhTCaTUsxcBHbCxSBy7IQBl5eXh8/nIzY2lpdffhlJksjMzPzYXK7hQq1Wo9Vq6enp4cknn8Tj8TBhwgRycnKIjIykq6uL6upqtm3bxhtvvEFsbCyTJk1i/vz56PV6JcANBoOEQiHlst/vV3o75Jw4+T7wUQ+afJ1Go2HhwoWEw2F2797NCy+8gFarJRgMMm7cOFpbW1m9ejWrVq3C6XRy8803s2DBgrOGQVUqFWazmZKSEoLBIA6Hg9zcXHp7ezl+/Di//e1v6erqYvbs2dx7773n7HGUh4OXLVumzI6NiYnh5MmTPP/88+zfvx+r1cp9991Hbm7uaa8/GAwqw9NyXqYc3Mh5cvLJgPz6NRoNGo2GcDiM3+9X3sNwOKy8P1qtlsTERO666y7efPNN9u/fT1tbG9nZ2YwfP16pF/fHP/6Rnp4erFbrxw5Hn4/cZrmdcjvODGL68/v9nzgkP3PmTI4dO8bbb7/NsmXLuPrqqxk3btxpuXX9FRcXY7Vaeffdd3nnnXdobm5Wil1LkkRzczMffvghb731FkuWLOGmm25S3kP5fQ6Hw6e1X6vVKkFk//e5/2cXCoWUQD8lJQWHw0FlZSVNTU0EAgGOHz9Oe3s7OTk5xMXFndbm+vp61qxZw49//GOuvvpqrr32Wu67774LefuHjXA4TGdnJ6tXr+bIkSPcfvvtLFq0aLCbJYxkkiAMsHA4LHV1dUkffPCBlJ2dLc2bN0/69a9/LYVCISkcDg928z6zcDgs+Xw+6ciRI9Ibb7whfeMb35CuvvpqadKkSVJeXp4UExMjJSYmSrm5udLMmTOl//zP/5Q2bdoktbe3S+FwWAqHw1J9fb303nvvSVFRUZLBYJA0Go2kUqmkyMhI6atf/ar08ssvS36/X8rOzpYsFouk1+slQLJYLNL48eOlr33ta1IoFJJCoZDU29srlZeXS9/4xjekuXPnSrm5uVJsbKyUnJwsTZw4Ubrtttukd999V2ptbZUCgcBpr2XDhg3SuHHjpOTkZOmXv/yltHz5cmnx4sVSdna2lJiYKCUnJ0uzZs2S/vnPf0oHDx6U/H7/aZ9dIBCQ5syZI2VnZ0tXXHGFtHfvXunee++Vpk2bJqWkpEhxcXHSpEmTpIceekhas2aN5PF4pFAoJHV0dEgrVqyQsrKyJLPZLGm1WgmQbDabNGnSJOnrX/+6tGvXLunqq6+WbDabpNPpJEAym83SqFGjpJ/97GdSZ2en9OSTT0qRkZGS0WiUVCqVpNPpJIfDIX3xi1+Udu3aJYXDYSkQCEgHDhyQfvrTn0oLFiyQEhMTpcTERKmwsFBavHixtGrVKunOO++Uxo4dK0VHR0u9vb0XtD3cddddUnx8vGSz2SRA0mg0ksFgkKKios77ExkZKWk0Gulb3/qWtGHDhvNuZxs3bpTuv/9+SaVSSX/84x+lEydOfOJ2WVVVJf30pz+VlixZIo0ZM0aKjY2V0tPTpfHjx0vXX3+99Morr0gVFRXKZ3no0CHp29/+thQZGSnpdDpJpVJJer1eiouLk/77v/9bKisrk7Zu3SpFRUVJRqNR0mg0klqtlux2u7Ro0SJp6dKlShtOnDghPffcc9K0adOk5ORkKSsrS5o1a5b05JNPSp2dnVIoFDqtzUePHpV+97vfSVarVbr55pulf/zjHxf03g8nR48elf70pz9JDodDeuCBB6S1a9dKgUBgWO8LhaFN9NgJA07uCSoqKuKLX/wiR44c4b333iMmJoaFCxcSFxc3bKf463Q6UlNTsdvtpKSk0NbWhsfjUYb41Go1er0eq9WqzJCNiIhQeikjIiIYO3Ysv/zlL8967qysLNLS0lCr1fz4xz8+K3/PbrcrpS7k91iuaD9//nx6e3vxer1KDTuHw8GoUaM+tpisvF5scXExDzzwAO3t7QSDQdRqNdHR0YwZM0apLXY+BoOB7Oxs7rjjDqWsiSRJREZGkpCQoKz5KZdQKSgo4Mc//vFZvWQREREkJiaSnp7Oww8/zJIlS0673Ww2M2rUKEwmE/PmzTur/Im8uoZck0+j0ZCVlcX1119PUVERbW1thMNhTCYTERERjBs3jldeeYVAIKDM3L0Qd955J7Nnz/5MQ7mFhYXKyg5nkleauPvuu5k8eTKzZs1SClef7/46nY6EhASuv/56pkyZQnt7Oy6XS5lIEhERQUFBAdHR0eh0OiRJIjExkRtvvFEpTC1Tq9UUFhaSkJBARETEObfVxMREcnJylMsJCQnMmjULh8NBR0cHWq0Wm81GXl4eFovlrCHkhIQEpRc5JSXlMxeuHspCoRAlJSW8++67lJaWMnr0aG6++WYKCgoGJN9WEM5HJUkjIPFJGJKk/1to/N1332Xt2rVkZWVx//33U1BQQGJiIkajcVgPyw5nGzdu5LHHHqOzs5Mnn3ySL33pSxf0+GAwyJVXXkl9fT0ZGRmsX7/+IrX0swmHwwSDQY4fP058fPxZBZyl/xv+/spXvkJJSQmRkZFs2bLlnOVoBOFCyBOXWlpaeP3111m/fj19fX0sWrSIxx577LQTPUG4GMRpg3DRqFQqZs2aRWJiIkVFRTz66KMcP36cK6+8kocffpjRo0efdl9BGCiBQIC2tjZuuukmHnvsMR588MHTeklCoRBNTU00NDQAMHHixHNOTBCET6N//4jX62Xfvn3885//5PXXX2fhwoXcdtttPPTQQ4DY1wkXnwjshIsuPT1dWdLoL3/5Czt27GDNmjUsWbKExYsXM2bMmM+0GoEgfBKn08k777xDX18fd911F3a7nd7eXiorK3nyySepr6+nqKiIr33ta5dsxq4w8kiShMvl4oMPPuD111/n5MmTeDwennrqKWbOnElWVtZgN1G4jIjATrjo5PVECwoKuPnmmzly5AiHDx9m//79OJ1OsrOzKS4uPm9JDWFgBAIBSkpK2L9/P/v27aOlpQWPx8Pbb79Nc3Mz48ePZ9q0aedceUB2/PhxTpw4QUlJCTU1NfT29iJJEj/96U+VtU6zs7Mv4as6NznPcMmSJfT29lJeXs5zzz2nrNTQ19eH3+9n0aJFTJo0iYyMDNFjJ1wwSZKUtZQPHjzIhx9+SE9PD3l5eRQWFjJnzhzS0tIuSf1GQZCJwE64JORF12+88UYmT57Mvn37eOqpp1i3bh3bt2+nqqqKyMhI4uPjsVqtyuLYYthi4ASDQQ4fPsybb77JqVOnMBgMGAwGdu/eTXV1NcFgkOLi4o8N7Gpqati2bRuvvPIK8FGR2WAwyLPPPovX68VisQyJwE6r1WK327nnnnvYvn07paWlrF69mvb2djQaDXa7nSlTpnDLLbeQn59PVFTUYDdZGAakfuWJfD4fLpeLffv2sWXLFjZv3kx3dzcLFixg9uzZXHfddTgcDrEPEy45MXlCuKT6b25ut5v333+fDRs28Nprr2EymZg6dSqzZs3i1ltvJSEhQQyPDaD+7/25vvb9VzT4pOf4rI+/lM5s45mX+7dzqLRZGNokScLj8bBv3z527tzJa6+9Rnl5ORkZGcycOZPHH3+c5OTk05ZrE9uWcKmJwE4YFNL/FUPt7u6mo6OD6upqVq5cSXl5OU1NTeh0OqZOncr48eOZOHEixcXFYj1KQRAGRVtbG9XV1Xz44YesXbtWWYM5JyeHefPmkZ+fT1ZWFklJSUppH0EYLGIoVhgUcp2x6OhoIiIiSEhIIBAIkJyczMmTJzlx4gSnTp2ip6eHyspKTp06RVJSEtHR0cTFxREVFSV2noIgXBTysmhtbW3U1dVRVVVFXV2dsj50WloaCQkJTJgwgRkzZpCYmCiG84UhQ/TYCUNKKBSip6eHLVu28NZbb3HkyBGqqqpISEhg2rRpFBYWMmXKFMaNG6ecGWu1WmXChejREwThQsh5c6FQiFAoRDAYpKWlhZMnT7Jv3z7Wr1/PiRMn8Pv9JCUlcfvttzNz5kwKCgrEbH5hSBKBnTCkyJujvI5le3s71dXVvP3222zfvp3a2lo6OjpIT09n8uTJFBUVMWfOHCXQE4GdIAgXQvq/9XSPHDnC/v37lTVdvV4vZrOZefPmMWvWLMaNG8ekSZOUE0mRGiIMVSKwE4Y0v9+Py+WisbGR9vZ2WltbqaqqoqysjNbWVlwuF2q1mvT0dFJTU0lNTWX06NHk5uYSERGB2Wwe7JcgCMIQEQ6HCQQCVFRUUFNTo5QqOXHiBL29vQSDQeLi4sjMzCQlJUXJm4uJiSEiIkIZbhUBnTCUicBOGFacTicNDQ3s2rWLY8eOUV1dTW1trbI2aUxMDGlpaeTl5REbG0t0dDRRUVHYbDYsFgtWqxW1Wi12zIIwwsllSTweD11dXfT29tLX10d3dzdHjx6loaGBtrY2mpqacDqd2Gw24uLimDBhAsXFxWRkZJCRkYFWqxX7C2FYEYGdMKy5XC4qKirYsGEDZWVllJeXU1paSigUIjIykvT0dGbNmsX48ePJzc2loKAAs9msDKOcazhF7MQFYfg4swSPnDMXDodpa2ujqqqKbdu2UVpaysmTJykvL8fn8xEfH09aWhqTJk1i7ty5jBkzhqysLHQ6ndgHCMOaCOyEYU1Oevb7/QSDQYLBIE6nk6NHj1JZWcnx48fZt28fdXV1uFwuDAYDGRkZZGdnk5mZydixYxk1ahSJiYnExsYCIrAThOEkGAzidrs5ceIElZWVVFVVcfDgQcrKyujo6MDlcmGz2cjPzycjI4MxY8YwY8YM4uPjsdls6HQ6dDodGo1GTMISRgQR2AkjiiRJBINBOjo66O7upr29ncbGRpqammhra6OxsZGenh4CgQDBYFDp2YuKiiImJobU1FQSExNxOBzExsaSmJiIyWRCp9MN9ksThMuWJEkEAgHa29uV73VdXR0tLS10dnbS2tpKZ2cnoVAISZLQ6/UYDAZsNhsOh4OMjAxiYmKU73lKSgomk0kUQBdGJBHYCZeFnp4e2traqKio4OjRo9TW1tLQ0EBVVRV9fX2o1WqMRiPp6emkpKQQFxdHUlISWVlZREVFYTKZlCW49Ho9Op1O+Vvk7AnC5ycHb/KPz+fD5/Ph9/vx+/04nU7q6+tpbW2lsbGR6upqmpqa6OzspLu7G5PJpNS5zM/PJzc3l9TUVNLT08nKyhKzWIXLhgjshMtWOBzG5/NRVlZGVVUVJ0+eZP/+/ZSVlSk9AQARERHExMSQnZ3NmDFjSEtLIyUlhby8PLKysjCbzZ9YLFkcUITL2ac9zNTX1ysnXRUVFRw/fpza2lrq6+upqakhFAqh1WqxWCwUFRUxatQoMjMzKSgoYMKECURGRoqZ8MJlTwR2wmWr/4LeZ/YSuN1uuru7OXnypDKM29TURH19Pe3t7TidTlQqFSaTCYfDoSRix8fHEx0dTUJCAunp6cTFxREREYHFYhnslysIgyYYDOLz+WhsbKSxsZGOjg5aW1upra2lubmZ9vZ2Ghoa6O7uJhAIoFKpcDgcJCcnExMTQ2xsLGlpacqQakxMDEajUek91+v1GI1G1Gq1WJFGuOyJwE4QziEQCOD1emlpaVFKJXR0dNDW1nba5Z6eHkKhkLJEmjzco9FoiIyMxG63Y7FYiIiIIDIyUim5EhERgdVqxWQyYTKZsNlsmEwmtFqxyp8wfMgnRi6XC6/Xi8vlwul00tfXh8vlore3l56eHlwuF319fXR2dtLT04PP5yMYDBIOh5UfSZKwWCzY7XbsdjvR0dHExsYq353Y2FhiYmKwWq1YLBYxtCoI5yECO0G4QKFQCK/XS21tLVVVVTQ1NdHU1ERtbe1phZQ9Hg+hUAi1Wo3FYiE5OZno6Giio6OVWbjyxI2kpCQiIyMxGo3K7Dy59+HMy/J14sAmDDT5cBAKhZRgS15qSw7A+l8OhULKSU5XVxft7e20tLTQ0dFBZ2cnzc3NNDU10d3djcvlQqVSodPpsFqtOBwO0tPTSUhIIC4ujpSUFHJzc5U1oUUvtyB8NiKwE4QLdK6vTP/r5INhfX09TU1NtLa2UlNTQ1VVFc3NzbS0tFBdXU1rayt+v59wOAyAXq/HYrEQFxenDEE5HA4SExNPCwpjY2OJjY3FbDZjNBov2esWRr5QKKTMPpVnlre1tVFfX09HRwcdHR00NDQokxY6Ojro7e09bfuXUxPi4uJIS0sjMzOThIQE4uPjyczMJC0tDYvFctZMc1FPUhAGhgjsBGGAyV8peUafnLvn9XqVy16vF6/Xi9vtVmbsdnd309fXh9PpVIavPB4Pvb29OJ1OwuEwarVaqbul0+kwGo1ER0djtVqVId7IyEjlcmRkpJLjJ19nsVjQ6/Wi1MMIJfekOZ1O3G43Ho8Hl8tFZ2enMkTa3d2tbG99fX1KeoHb7aavr++0ckDBYBCLxYLZbMZisWCz2bDb7cplh8OBw+HAbrcTFRWlbF/yzPH+uXBGoxGDwSBy4QThIhKBnSAMklAohM/nU3KP5CWPenp66OzsVHKV5IOwx+NRAkKfz0coFAJQDpparVY5mGq1WrRaLXq9HrPZrOTyWSwWpX6XXK6l/33lgLH/ZXnRc71erwwJy9f1/63VasUB+wLIk3fkwtryEGf/vwOBgPK3vL3I9w8EAkphbvnv/uVC5BMDn8+nnCDIt/W/bzAYVC7LQ6z9S/sYjUYiIyOx2WxYrVaioqKUkwe5TpzdbsdqtWK329Hr9aK3TRAGkQjsBGGIk7+iLpeLnp4eZZjM6XTS3d1NS0sLPT09OJ3O04bQenp66OjowOfzKflS8vPJPX9yb57ZbFaS1uWeGPlgbjQaT5vgodfrMZlM5w0Y5Qkg8sG9/0H+Qv8+l897u+zT7vrOd78zh98/zW3nWvaqr68Pj8ej/LjdbmV2tjzRQA7O5Fw1t9uN0+mkt7cXl8ulbBtOp1MJ/vvnYKpUKrRaLTabTQnGYmNjlc88Pj5emezjcDiUYr7yJAaxzJYgDB8isBOEYeB862Ge7+/+l+WDv9PppKurSxmOk4d8+/r6lEBBHgKWh+t6e3uVQKGnpwf4+IBMntghD/0ajUalDIU8JCf/yGUqNBoNZrNZmRBit9uVQORcNQJtNtvHFoWWey7Pl38o94p5PJ6PDe76+vqUlQxkgUAAj8dDOBzG6/UqPV7ysLvcm+Z2u08bEgWU2aPyDFK/34/P5zvtcz3X3/LwpclkUmZWm81mbDabEpjLM68jIiKUwDwqKkoZerfZbMTExCgL2p8Z9J0ZhJ9rHWUR2AnC8CACO0EY4eTgo/8wnTzMd67LcnDSf3hOvl0OXs5cFUAeHpYfK/+4XC7a2to4ePAgOTk5xMfHYzAYTpt1KQdA/Ycm4aNcMflvWf/7ni/QCAaDpwW3Z5KvNxgMZ90mv1ZJkjAYDMrQs6z/zGQ5uJQD0v73lQMoQOntkoNeechao9Gg0+lO6+nUaDSsW7eOiooKmpubmT17Ntdeey2pqalKYCwPe/cfNj/X5f731Wq1GAwGMZNaEC4DomiWIIxw8gHfZDJ97ufy+XynTQaRgzs5/08O/jweD+3t7Zw6dYrGxkaMRiM5OTnk5+ej1+uVAFHuPZOHi8PhsNKTFgqF8Hg8yv+Wg0Cv16vMJD6XQCBAR0cHhw4dIjExkejoaOx2+2n3UalU5yynIQ9l19bWMm/ePOx2+2mzN/vnGvYPouTASg7aTCaTEkTJNde0Wq3Sgyn3WhoMBsxmMwaDQVmIXqvVEhcXx9GjR7HZbERHR5OdnU12drbSmykIgnA+osdOEIQBJe9Sjhw5wnvvvceTTz7Jddddx3/8x38wbdq0SzK54sMPP+Tuu+/m7rvv5rrrrmPixImf6nEbN25k7dq1PPPMM+zZs4fs7OwBCYgv1IkTJ9i2bRv/+Z//icFgYNq0aTz66KNMnTpVCTRFgCcIwrmI6WuCIAyoYDDI0aNH+a//+i/eeOMN5s2bx69//WsmTpx4yYKRQCBAZ2cndrv9nEOu5zNx4kRmz56NSqVi7dq1VFRUXMRWnl9mZia33norGzduZPHixdTU1HDbbbfxs5/9jAMHDpw1RC0IgiATQ7GCIAwYt9tNc3Mzf/rTn+jq6mLMmDF86UtfIiYm5pLNrJTzAb1er1JT7dOyWCzEx8czfvx4SkpKyM3NZfz48Rextecm59tlZGRwyy23kJuby7Zt29i4cSNdXV1MmjSJa6+9lujoaDQazSVvnyAIQ5fosRMEYUAEAgGam5spKSnhgw8+ICYmhpkzZ7Jw4cJLWi6jf522c61w8HG0Wi1RUVFMnjyZ8vJy6uvr8fl8n7o0ykBSqVSYzWZmzpzJjTfeyOLFi/H5fOzYsYPly5ezb98+WlpacLvdHztZRBCEy4sI7ARB+NwkSaK9vZ033niD73//+yQmJvLd736Xe++995IXrJVLuQBERERc0FAsQHR0NDfeeCN1dXUcPXqUU6dOXYxmfmoqlYrU1FS+9KUvsWzZMmbPns3x48f54he/yD/+8Q+OHDkyqO0TBGFoEUOxgiB8LsFgkK6uLh5//HFOnTpFamoqzzzzDFlZWYOybJm8TJZKpSIiIuKC19O1Wq1MmjSJjIwM6uvrWbduHaNHj75Irf30VCoV6enpfO9732PJkiW8++67vPzyy2zevJnJkyfzne98B4fDcUE9lIIgjDwisBME4TMLBoO0tbXx9ttvU1FRQVJSEnPmzCE3N1dZE/RS83q9SpkUq9V6wYGOWq3GYrFQWFhIU1MT+/fvJxQKKfXqBotKpcJgMJCQkIDJZCIQCOB2u6mpqWHfvn08++yzzJs3j8zMTBITEwetnYIgDC4xFCsIwgWTc7p6e3upqKjgb3/7GwDTp0/nK1/5CiaTadCS+uWluYALzrGDf6+6MG3aNDQaDYcOHcLlcn1s7bxLSa1WExUVxbx583jkkUeYNm0afX19/PGPf2TlypUcOnSI7u7u05aREwTh8iF67ARB+EwCgQDLli3jzTffpKOjg5deeolx48YRFRU1qO3q6+vD6XSi0Wiw2WwXnGMnu+666zh8+DD79+9nzZo1XHXVVYP+2s5UUFBAbm4uX/3qV/nNb37DP//5T1577TVmzZrF//7v/xIRETEow+GCIAwe0WMnCMIFkVeFeOqpp3j//fdxuVz88pe/pKCgQFkZYTCHLN1uNz6fD7vd/rl6DR0OB3l5eeTk5LBy5Up6e3sHsJWfn/w+63Q64uLi+PrXv87//M//MHv2bPbs2cNDDz3E8uXLqaysHOymCoJwCYkeO0EQLkhvby/Hjx9nw4YNhEIhiouLmT17NlFRUUMicV9e2sxqtSrruV4oOZ8tPT2d3Nxc9u7dS2dnJwkJCZ+5B/BiUavVGI1GcnNzkSQJs9lMIBDg8OHDrF+/ntbWVqZPn05BQcElLTsjCMLgED12giB8asFgkNraWpYuXcqBAwcoKirigQceICMjY0gEdfBRjp3P58Nms33uICY7O5tJkyZRVlZGfX39kOu1O1NeXh4333wzP/3pTykoKGDNmjU8/fTTLF26lPb2dmVNXkEQRi7RYycIwqe2fPlyVq1axSuvvMJvfvMb5s6dS15e3mA36zR9fX14vV6io6M/d2CXl5eHWq0mJiaGjRs3otPpWLhw4QC19OIwGo2kpqby7LPPsm3bNjZs2KD8fffdd3PttdcOuc9MEISBIwI7QRA+UTAYZPv27bzzzjvU1NRwxx13MHfuXFJSUobcklYejwe/34/NZvvc5VY0Gg12u50ZM2ZQVlZGWloaV1999aDnEX4cuW1Go5Fx48YRGRlJSkoKr7/+OitWrGD//v3ccMMNXHXVVcpwtSAII4cI7ARB+Fg+n4/Ozk7Wrl1LVVUVNpuN66+/nqysrCGXbwan59h93uBLpVJhMpmYNm0aL730EjU1NfT09BAZGTkwjb3I4uLiiIiIIDExkdraWkpKSjhy5AgajYaoqCiys7OJj4/HYDAM2UBVEIQLI07VBEE4L0mSaG1tZceOHTz99NOkpaVxxx13cP311w/JoA4+WlLM5/MRERExIMGKxWLh+uuvJxAIcOrUKQ4ePDis6sMZDAaSkpL45S9/yQ9/+EOuvPJKXnnlFb73ve/x8ssv09TUJNaaFYQRRAR2giCcV21tLe+88w7f//73WbBgAQ8++CBLliwZ7GZ9rL6+PjweD1FRUQMyzKjT6cjOzmb8+PH4/X7eeeedYTkBQaVSMWPGDL7zne+wcuVKHA4HL774IjfffDPLli2jra1tsJsoCMIAEIGdIAhnCYfD9Pb28uabb7Jt2zZsNht33nkn+fn5mM3mwW7ex/J6vUqO3UD02Mm14oqKirBarZSUlOB2uwmFQgPQ2ktDzrszGAw4HA7Gjh3LPffcw7XXXktsbCzLli3jpZdeYsuWLbhcLtF7JwjDmMixEwThNOFwGJ/PR3l5OStXrsTpdDJhwgS+8IUvYDKZhnwulsfjIRAIYLFYBrStRUVFVFZWsn37dlpbW0lKShryQe65aLVaYmJi+OIXv0h6ejp2u52lS5fi8/loa2vDarWSm5uL2WxGqxWHCEEYbkSPnSAIpwkEAtTV1fHQQw/R3NzMnDlz+M1vfoPZbB7yQR18lGPn9XqJiooa0PbOmjWLcePGEQ6HWblyJa2trQP23Jea3As5ffp0vv/97/Pcc8+hVqt54YUXWLJkiVLYWBCE4UecjgmCAHw0USIcDrNu3TreeustWltb+e///m9mzJiB3W4HGNKBnSRJeDwevF4vkiQRGRk5oKU8jEYjWVlZTJ8+nffee4+pU6eSkZExYM9/qcmfpV6vZ8qUKfz+979n//79vP/++3zve99j9uzZzJ49my996UsYjcYh/dkLgvBvIrATBEGxe/duPvzwQ44dO8bChQuZNGkSaWlpQ65W3blIkoTb7SYYDKLRaAak3El/arWahIQEJkyYwHPPPUdjYyM9PT1EREQM2P8YDGq1Whl+1ev1qFQqvF4v9fX1rF27llAoxIIFC4iNjcVqtQ52cwVB+AQisBMEgXA4jNfr5a233mL37t14PB4eeeQR8vPzsVgsg928T83pdCqB3UBNnugvMTGRqVOn8otf/IKqqioaGxuHfWAnMxgM5OXlkZWVRXR0NEuXLqWkpIQDBw5gsVgoKioiPT0dk8kEDO3eW0G4nInAThAE2traeOONN3jppZeYP38+d999N0VFRcPq4C1JEl1dXfj9fnQ6HQ6HY8DbHx8fz5QpU8jJyWHfvn1YLBZGjx49oP9jsGk0GhYuXMjUqVMpLS3lt7/9LQ899BDTpk1j4cKFfP3rXxeTKgRhCBOTJwThMldfX8+OHTv461//ylVXXcUNN9zA5MmTh/SyWeciSRLd3d0EAgG0Wi0REREDvlyWSqVCr9dz1VVXUVdXx/79+/H5fCOqPIhKpUKtVmO32ykuLuanP/0pjz76KBqNhmXLlvGtb32L0tJSuru7B7upgiCcgzjtEoTLlDzZoLS0lO3bt+N0Opk9ezbjx48nOjp6sJt3wSRJwuVyEQqFMBgMF20Wr1arZerUqezatYv6+noaGhpIT08fFnmIF0Kn0xEVFUVkZCR9fX1otVr27t3L3r17SU5OZvz48YwZM0aZQDKcTgIEYSQTPXaCcBmSl5BqbGzkrbfe4t1332X69OksXrx42A4tSpJEb28vkiSh1+svWs09nU7HvHnziIyMpK2tjV27dhEMBgf8/wwFcu/dnDlzePTRR3n88cfRarU89dRTPPXUU6xcuRKv10s4HB5RvZaCMJyJwE4QLkOhUIju7m4efPBBDh48yKRJk/jDH/5ATEzMYDftM5Mkic7OTgwGAzab7aL9H5VKRWxsLJMnTyY6OpqXX34Zv99/0f7fUBETE8PMmTNZuXIlP/nJT7BYLDzxxBPcfvvtbNmyRQzNCsIQIYZiBeEyI0kSlZWVvP3229TX13Pttddy1VVXKXXfhuuQmiRJ9PX1odPpLuqKEHLuYXFxMR0dHbz//vs0NTWh0WiG5UoUn5a8bdjtdq6++mqSkpIYM2YMa9eu5U9/+hPjxo3j2muvZcKECWJyhSAMIvHtE4TLTGtrK0eOHGHVqlUkJCQwbdo0pk+fPiIOxi6XC61Wq5TkuJjy8vI4deoUnZ2dnDx5EpvNNqIDO/h3UJubm4vD4SA5OZlTp05x8uRJuru70Wq1WCwWkpKSsNvtIy7vUBCGg+G/JxcE4VORc6BWrVrFqlWrOHDgAG+++Sbjx4/H4XAMcus+P3lWrE6nuyS190aPHk1TUxOxsbGsXr2aqKgoEhIShm2P54WKjo7G4XDwr3/9i3/+85+sXr2an/zkJ+zbt4/77ruPWbNmDYsVSwRhpBE5doJwmQgEAqxbt46lS5dSXV3NT37yE6ZNmzas8+r6kwM7vV5/UXPsZCqViqSkJJYsWcKqVauoqKggEAhc9P871BgMBu6++25+/etf8/e//51jx47xX//1Xzz44IPs2rXrssg/FIShRAR2gnAZ8Hq9NDU1sWzZMtRqNePHj2fBggVYLJYRM1wmSRJOpxOdTndJhmJVKhUxMTHMnj0bp9NJVVUVp06duuj/dyiRZ83abDbS09OZMWMG9913H/n5+bS2tvLnP/+Z5cuXc/z4cYLBoJg5KwiXgBiKFYQRTpIkOjo6OHLkCKtXr+aaa65hzpw5jB8/frCbNuD6+vqIiIi4JIEdQEREBBMnTsRgMFBTU0NZWdmwLRfzeVksFnJzc7nvvvuIjIxkw4YNrF+/nkAggMvlQq/Xk5SUhE6nG/DC0YIg/Jv4dgnCCOf3+9m4cSM//vGPSUhI4Ktf/Spf/OIXB7tZA+5SD8UC6PV64uLimDVrFg0NDaxZs+aS/N+hLCoqivvvv58//OEP/OpXv2LXrl088cQT3HfffRw9ehSv1zvYTRSEEU0EdoIwwv3zn//k3Xffxel08utf/5rCwkJ0Ot1gN2vAXerJE/DvocgbbrgBjUbDwYMHqaysvCxz7eDfs2ZVKhVRUVFcd911PPfcc9xxxx14PB6++tWv8swzz7Bt2zZR1FgQLhIxFCsII5TP5+PUqVNs2bKFnp4errjiCoqKii7KGqqDTV5Jw+VyXbIcO5lKpaKwsJDY2Fjq6+s5cOAAcXFxIzJ4vhBarRaHw0FxcTHhcBiz2czGjRvZuXMnXV1ddHZ2Mnv2bCwWy2X/XgnCQBKBnSCMQOFwmN7eXtasWcO+ffsoLi7mrrvuIjY2dsQFdbJQKKQEdpeynpxKpSI/P5/MzEzKy8vZuHEjs2bNwmKxXPZlPuSeuwULFjBp0iTi4+P561//SllZGfv27SMhIYHMzEwiIiLQ6/WD3VxBGBFG5h5eEC5zLS0tSm7TggUL+MpXvsKCBQtGbKDh9/vp7e0lGAxisVguWY6dTKVSMWXKFKZOncqrr75Ke3v7ZTscez4RERHcf//9vPLKKzz66KO0tbWxaNEifvGLX7Bx48bBbp4gjBiix04QRhBJknC73axatYrly5eTk5PDzTffTFFR0YjtqYOPavQ5nU4kScJsNl+yHDv4d/HdgoICuru7efbZZ9m3bx96vZ6cnJxL1o6hTH6PNBoNqamp3HDDDYwaNYqXXnqJPXv2cPToUUpLS7n33nuJjIwUvXeC8DmM3D29IFxm5ET00tJS9u7dS2VlJQsWLKCgoIDY2NhBbt3FJZfUgI8K5hqNxkvehri4ODIyMoiJieHw4cPU1dVd8jYMBxaLhfT0dGbOnMk111xDeno6brebjRs3snbtWsrKyujo6BjsZgrCsCUCO0EYISRJIhAI8I9//IN9+/YRGRnJd7/7XeLj40d0bx18FNj19vYOSo+dzGw2k5iYyBVXXMHu3bs5fvy4mPl5HhqNBqvVyl133cUPfvADbrnlFk6ePMnjjz/OCy+8QGlpKYFAQJkUIwjCpzey9/aCcBlpaGjg97//PatXr2bixIn84he/IC4uDq125Gdc+Hw+urq6ALBarZc8x04WHR3NXXfdRVVVFQcPHrzsVqL4LMaMGcO9997Lxo0bWbRoEevXr+e+++7j8ccfp7a2VuQqCsIFGvl7fEG4DDQ2NlJSUsIbb7zBnDlzmDNnDqNHj1Zqio10co6dXq/HYDAMWvkMk8lEcXExcXFxtLa2sn37dpFn9wm0Wq3S23nPPfcwevRoDh06xNatWwmFQsyYMYPp06eTkpJyWWzLgvB5icBOEIYxSZIIBoMcO3aMvXv3Ul1dzX333UdRURExMTGD3bxLJhAI4Ha7laBusNa/1el0JCUlkZWVhdPpZO/evdx5551oNBoRlHwMtVqNwWBgxowZREdHk5CQQHV1Ndu2bcPj8QAwffp0YmNjMRgM4r0UhI8hhmIFYZiS8486Ojp46aWXeOutt1iwYAG33HLLZbdeqd/vp6enZ9Drock9pAsXLsRgMLB+/Xr6+voIhUKD1qbhZtSoUdx66628+uqrFBcXs337dr72ta/xhz/8gbq6OkKhkMi9E4SPIXrsBGGYklda+Pa3v82JEycoKCjg5z//OXa7fbCbdsnJdewiIyOHxCoGixYtorKykpKSEpYvX861115LXFzcYDdr2NBqtcTGxvLLX/6S0tJStmzZwtKlSykpKWHOnDl85StfIS0tTfTcCcI5iMBOEIappqYmdu/eTWlpKZMmTWL27NkkJycP2jDkYJLLnVit1iExWcThcJCRkUF6ejqbNm1ShhFFIPLpqFQqNBoNsbGxFBYWYjKZcLvdHDp0iF27duF0Olm8eDGjRo0iOjpavK+C0M/g7wEFQbhgHo+HkydPsnz5crxeLzNmzOCaa67BYDAMdtMGxVAK7FQqFQaDgfT0dAoLC9m4cSPt7e2kp6eLwrsXQA7WEhMTiY2NJTExkWeeeYa9e/fy6quvKj2zo0ePJiIiQuQxCsL/EYGdIAxDu3btYvny5bz++uv86U9/Yv78+aSkpAx2swaN3++nq6trSK1aMG7cOMLhMH//+9/Zv38/UVFRjBo1arCbNSxpNBqys7P53e9+x+HDh3njjTd46qmnWLVqFbNnz+b73/8+8fHxl2VvtSCcSQR2gjCMhEIhmpqaePbZZ6mpqWHJkiUsXLjwsh+OknPsUlJShkSOHXxU0y4/P5+xY8eye/dubDabCOw+o/5LkuXl5fHQQw+Rn5/PihUr+PDDDzly5AiPPfYY48ePJzU19VM9ZzgcBhjxxbuFy48I7ARhmJDXgV2zZg1VVVVERkayaNEiYmNjh0wwM1jkcicWi2XQh2JlOp2OiIgIJk+ezKlTp6ioqMDtdmMymS7rIPzzUKlUWCwWTCYTs2fPxufzUVJSwqFDh3jvvfeor69n3LhxTJw4EZ1Od86gTZIkPB4PR48exe12M336dDGMK4woQ2MPKAjCJ/L7/bS1tfH3v/8dgOLiYpYsWTJkApnBFAgE6Ovrw2q1Dqkg12g0ctVVV7Fz506OHz9Oa2urmM05ANRqNZmZmdx9993MmjWLpUuX8vzzz7Nv3z6mTJlCamoqMTEx6PX604Zn5TIpzc3NvPnmmzQ3N1NQUEBkZKQYxhVGDNEHLQjDxM6dO/nd737HsWPHePTRR3nooYcGZbH7oUheUiwqKmrIBXY33HAD6enptLS08Prrr4uadgNIr9eTn5/Pz3/+c1566SXGjRvHBx98wPz583nnnXeoqqo66zGBQIDHH3+cZcuWsWbNGn7yk5/Q3d196RsvCBeJCOwEYYiTJImysjI2bdrEli1buP/++ykuLlbKZ1zuvT/hcBi/34/b7cZmsw2pwE6eITtlyhQiIyNZu3Ytfr9fye8SPh+VSoVarUar1VJcXMxDDz3Ej370I1JSUvjrX//K//7v//Liiy/i8/kIh8O0tLSwevVq9u3bR3t7O52dnXzwwQfs2LGD+vr6wX45gjAgxBiOIAxh4XCYQCDAjh07OHbsGOFwmIULF5KcnIzJZBrs5g0JgUAAv99PMBgcUjl28O96bOPHj6epqYnVq1fT1NREUlISZrN5sJs3YqhUKmJjY7FarSQkJNDS0sK6des4fvw4breb5ORkMjIyqK+vZ/Xq1TQ3N+Pz+QCorq5m48aNmEwmYmJixJJlwrA3dPaAgiCcRpIkQqEQ7e3t/POf/0SSJObOncuVV14pZvL143K58Hg8SJKE3W4fUj12shkzZlBXV8err77Kzp07mTt3rgjsLgKTyURmZiZPPPEEY8eOZcWKFbz33nscPXqUu+66i46ODp577jn8fr/yGJVKxT/+8Q90Oh15eXmkpqYiSZII7oRhSxwdBGGIkiSJuro6Hn30UXp7e7n66qt54oknxAHnDL29vbhcLlQqFQ6HY8jUsesvNjaWMWPGMGPGDJYtW0Z1dfVgN2nE+8IXvsAvfvELli9fjt1uZ9myZbzyyitnDYOHw2F8Ph/Lly/nRz/6kciBFIY9EdgJwhB17NgxNmzYwIEDB/jCF77A7NmziYqKGuxmDTlutxuv14tKpcJmsw2poViZWq0mKSmJ2bNnc+zYMaqqqmhvbx/sZo1oBoOB6OhosrOzycvLw+Vy0dbWds78Rrk+ZGlpKWvXrqWnp2cQWiwIA0MEdoIwxMj16g4ePMiHH34IwIIFCygsLESv14seuzP0D+yGWo5df7GxsUyePJmuri6qq6upq6sb7CaNeHKOo0ajwe1243K5zhnYSZJEX18ftbW1fPDBBzQ1NSk5eIIw3AzNPaAgXKbkOlvl5eW899577Ny5k29961tMmTIFh8Mx2M0bkvr6+nC73ahUKiIiIoZkjh18FNhNnTqV9PR0Dh06REREBEVFRQAiWL8IJEmis7OTQ4cOsXTp0tPy6s53/56eHv7yl79QXFyMTqcjOztbfDbCsCN67ARhCAmFQnR3d/Of//mfNDQ0MG/ePL761a8SEREx2E0bsnp7ewkEAkqpk6F8INbr9XzpS1+iubmZLVu24HQ6kSRpsJs1Ivl8PjZs2MAPf/hDwuHwp94uVCoVTz75JEuXLhWfjzAsicBOEIaQtrY2Vq5cSXV1Nbm5uSxcuJDIyEgxC/ZjeDwepdTJUK7rJw8Lzpw5E6vVSmtrKwcOHCAYDA5200akXbt2sWvXLk6ePEk4HP7UAZokSTQ2NnLgwAHWrl0rag4Kw444WgjCEOHxeKisrOS1115Dr9czbdo0rrnmmiEdrAwFbrebUCiE1Wod7KZ8Io1Gw8SJE0lKSsLr9bJlyxb8fr/oFboISkpKOHHiBOFw+Lzrxp6LvJbssWPHePPNN3E6nSL4FoYVkWMnCEPE5s2bWblyJWvXruXll19WVisQPp7T6SQUChEVFTUsAmCLxcKMGTPweDz861//4sEHH8RsNg+Ltg8nV199NWlpaUyaNIl169ZRUVFBT08PkiSh1WoJhULn7Y0LhUJUV1fT1tbGG2+8wTXXXENaWtolfgWC8NmIwE4QBlkoFKKmpobXX3+d8vJyvvKVrzBt2jRiYmLEwf5T6OvrIxgMYrfbB7spn0j+PKdPn47L5WLFihXs2rWLiRMnkpqaOsitG1mys7NJSkpi6tSp3HLLLfT09NDR0UF1dTV79uyhrKyMuro6urq6UKvVSq9p/95Tj8fDU089hc1mw2g0EhcX96n+tyRJhMNhpcaix+Ohr6+Pnp4eXC4XLpcLp9OJ1+vF7/fj8/lwuVz4/X5lJRWPx4PH4yEQCAAf9UyfOaQcCoUIBAIEAgHlenmZNb1ef9ZEIpVKpZxEaLVaDAYDRqMRvV6PXq/HZDJhMpmUx5pMJmw2GyaTCYvFgt1ux2azYTabsVgsREREoNVq0Wg0n+uzEgaWCOwEYRCFw2G8Xi9r166lpqYGq9XKtddeqyxtJHwyj8dDKBRScuyGg8TERLKysoiOjmbfvn0kJiaKwG6Amc1mzGYz0dHRpKWl4ff7cTqdZGdnEx8fz5gxY2hoaKCpqYm2tja6urro6uqitbX1tJ68EydO8OGHH2K1WlmwYAEejwefz4fP58Pj8eB2u5XLcukdv9+P3+9XZmzLt/X19REIBAgGgwSDQaXXMBwOEwwGlb/l64PBoBKwyQFef/Is+nOV+JGXIzzX9XJ6h1qtRqPRoFarlTV35ctyTqhOp0Oj0aDVapUAz2g0YjabsVqtpwWFZrMZg8GAXq/HbDYrQaLRaMRisWAwGNBoNMPmezpcicBOEAaR3++nvb2dP//5z9jtdmbOnMkNN9wgzoAvgMvlIhQKYbPZBrspn1pkZCTp6elMnDiRrVu3UlBQwKRJk8TnfhHp9Xqio6OJjo6msLAQAK/XS21tLTt27ODw4cOUlZWxe/duZYk6tVqN1+vl/fffp6enh1GjRtHS0kJHRwddXV20tLTQ3NysBIXNzc20t7fjdDpxOp1KANM/kLJYLFitViIjI5Vgx2g0Kr2Cer0eg8GA1WpVAiO1Wo3RaFSCLJkcmFksltNeayAQwOfznZUbKPciyveRewV9Ph9erxeXy3VaoOpyueju7lZ6GPvng8pBpcFgwGw2ExsbS0JCAlFRUURGRpKQkEBsbCxRUVHExMSQnJxMdHQ0JpNJCe7kAFIOLPvnE4vg77MTgZ0gDKJ9+/bx3HPP0dTUxDe/+U0WLlwoDu4XSC534nA4htXBICkpifvuu4+vfOUr7N+/nwkTJpCXlzesXsNwZzAYyMnJITMzk87OTlpaWjh+/Lgym/bkyZNUV1fT2NjIq6++yltvvXVaL1l0dDTx8fFERUXhcDi44ooriI2NJSIigoiICGJiYrDb7VitVux2Ow6HA61We94A5nx/n+vyZ3XmRJ3+l8/1d//fvb29dHd309fXR29vL52dnfT09NDb20t7eztNTU10dnbS0NDA+vXraW9vx+fzKb2EBoMBu91OSkoKmZmZJCUlER8fT0ZGBunp6SQkJBAXFzcklwUcTkRgJwiD5ODBg2zZsoXt27fzwAMPMHHiRKKjo8WB/QK5XC6CweCwmBXbn81mY8KECaSnp1NXV8eWLVvIy8sb7GaNWPJs146ODmpqamhpaaG9vZ2Ghgbq6uro6emhr6+Pvr4+/H4/arWaqKgo0tLSMJvNqNVq/H4/U6ZMIS4ujoiICGVYsv9wpMFgQKfTKdfpdDq0Wq1y3XCd5S5JEjqdDpvNRjAYVHIB5Rw/eWhaHob2eDx4vV48Hg9Op5P29nal90/uATxy5AglJSV4vV6MRqOS05eSkkJiYiIxMTEkJCSQnp5OXFycUqtS+HgisBOES0ySJHw+H7t27aKsrAz4aAZfamoqRqNxkFs3/AzHHDv4aGgwLi6O0aNH09XVRUlJCXffffcFleYQziZPXOjr68Pj8eByuZSgrbe3l5aWFqqrq5Xepo6ODjo6OgiHw2g0GkwmEwkJCURGRhIdHU1ycjI2mw2NRoPf72f69OkkJCQM6VVOLgaVSoVOp7ug1xwOh/H5fDidTlpbW2lvb6erq4u2tjYaGhqUIe3W1lbcbjdOp5OmpiZaW1txOBzY7XYluE5ISFCus9vtWCwWzGYzdrsdrVYrvjP9iMBOEC4hSZIIBoM0Nzfz0ksv4fV6ueGGG5gxY8ZldZAYCPIQUV9fH8CwmBV7JpVKxbXXXsuyZcvYsWMHnZ2dxMTEiIPUp3Tm0KEc1Hm9XsrKyjh16hTl5eXs2bOH8vJyOjs78fl86HQ6UlJSSE5OJj8/n0WLFpGWlkZqaio5OTlYLBaREjEA1Gq1MtP2fDOKJUkiEAhQW1tLY2MjNTU1HDlyhIqKCg4dOkRVVRW9vb3KWtCZmZlMnjyZvLw8cnNzmTRpEg6HQ9l/ijw9EdgJwiXX3t7OY489RldXF1deeSXf/e53h+zC9UNdIBDA7XZjMpmGTR27/lQqFddddx2lpaVUVlaybNkyvvrVrxITEzPYTRsW5Lyv48ePs337dioqKqisrOTo0aN4PB6MRiORkZGMGzeOe+65h5SUFLKyssjLy8NoNKLVapUfeTaomLV56el0OjIyMkhLS2Py5MnceOONhEIhpZxLbW0tNTU11NfXc+LECQ4fPsyGDRuUUjXyY3Nzc5k+fTrjx48nLi4Ok8k02C9tUIijiSBcQpWVlezdu5cDBw5www03MG/ePBwOx2A3a1iSh9uCwSBarXZYzYrtz2q1kpeXR05ODuvWrWPJkiU4HA7Ra3cOPp+Prq4u6uvrOX78OLW1tbS0tNDQ0IDb7Uar1RIREcGCBQuUWZjR0dEkJibicDiIiIggMjISh8MhArghQv4MznVyK/fIysPjo0ePZsKECcyZM4euri46Ozupqamht7cXn8/HkSNHKCsrIy4ujri4OFJTU8nPzyc1NVWZoXs5EIGdIFwCkiTh9/spLy9n8+bNBAIB5syZw8SJE8UQ7GckSRJ9fX2EQiF0Ot2wy7EDlEKxOTk5jB07lqVLl9LS0kJcXNywmwwy0OShVTkpv7e3l46ODhoaGigvL6e0tJSqqiq6uroIh8NkZGSQkpJCRkYGY8eOZcyYMcTExBARETHYL0X4jOTvszzLuD+5LmFZWRnHjh2jpqaGyspKysrKKC8vx2g0kpKSQk1NDfn5+aSnp5OWlqYUWDaZTMoM5ZFGBHaCcInU1NSwevVqli9fzte//nWuuOIKkpKSBrtZw1Y4HKarq4tgMIherx/WZ+MTJ05EpVLx1FNP8eGHH6LX65k4ceJgN+uSO7MUh8fj4fjx45SUlPDuu++yZ88euru7UavVTJ06lSuuuIKxY8cyffp0cnJyRErDZUSuSzh79mxmz54NfLRP6OzspKSkhCNHjrB7926WLl1KT08ParWa3Nxcrr/+eqZOnaqs2dx/mxspQZ5KEqtPC8JFJVeAf+CBB6iursZut/Pcc88RFRUleus+B6/Xy65du3jkkUcYN24czzzzzLBdhk1em/TRRx8lFApx00038fDDDw92sy4puYfu5MmTHD9+nC1btrBjxw46OjoIBoPKMFxubi6jR48mNjYWo9GIwWBQCvkOx89eGDiSJBEKhU5bGcTpdFJdXc2pU6fYu3cv+/fvx+PxoNfrKSoqYubMmYwdO5aioiKsVuuI2IbE6Y0gXGS9vb3s3r2bsrIyMjMzmT9//rANQIYSOccuHA6j0+kwm82D3aTPTKPRYLfbmTNnDi+//DKVlZU0NzcTHx8/4reTQCCA0+mktraW0tJSTpw4QV1dHW1tbdjtdjIyMkhMTKSgoIC8vDwSExNJSkrCaDSO+PdGuDByaoNWq8VisSizpB0Oh1IbLzc3l8bGRlpbW+np6WHTpk0cPnyYXbt2UVhYSHZ2tlKncLgSgZ0gXESBQIDm5mZef/11+vr6GDduHDfeeOOwLVI6lEiShNPpBFAWLB/O5LVIly5dSk1NDRUVFcTHxw92sy4KeU1UucZcbW0t27dv56233qKtrQ1Jkhg1ahTXXHMNEyZMoLCwcFjOehYGl7zerTyZoqCggMWLF1NfX095eTkrV65kx44d7Nmzh1AoxNy5c5k/fz6FhYVkZmZisVjOWsZtOBCBnSBcRBUVFWzcuJHnn3+e3/zmN8yfP5/ExERxgBoAcj6NvDD5cH9PjUYjkyZNoqioiPb2dl588UVmzJgx7A4qn4bb7aapqYm//vWvrF27ltraWlQqFTfffDNXX301kydPJiMjAxg5eU/C4JPXpU1LSyMtLY2rrrqKvr4+jh8/zrp163j++ed57733MJlMTJs2jYceeojCwkJSUlIGu+kXRAR2gnARyDM2ly9fzoYNG5g1axZXX3016enp4kA1QMLhMD09PcpSRMP9fZXbf+2117JmzRq2b99OfX098fHxI2JFknA4jNPpZNWqVezdu5c9e/bgdrsZN24ct956K1OmTCEjIwOHw4HNZhPlXoSL4sz1eC0WC6NGjSI+Pp758+dz+PBhysvLOXDgAD/84Q/Jzc2lqKiIW2+9ldTUVAwGwyC2/tMRgZ0gXAThcJg9e/Zw6NAhenp6uPXWW0lJSbnsS1gMJDnHzmAwjIjAR1ZUVERZWRnbt2/n4MGDTJ8+fdi+PnlChLyE1KFDh/jwww9pbGxEkiSmTp3KpEmTGD16NOPGjcNsNg/7AF0YXjQaDTabDavVSkpKCjExMaSmpmK329m5cyft7e3s2rULjUbDhAkTlLI6QznHUwR2gjDA5PURly5dyqlTp0hOTub+++8XQd0Ak3Ps5B67kaKwsJCCggIiIyN5//33yc3NJTo6esgeRM5HTlx3uVzs37+fdevW8eKLL2IymSguLubee+/l9ttvx2AwiN45YdDJec85OTnk5ORwzTXXsHPnTlasWMGGDRv4n//5H+bNm8fVV1/NddddR1pamrJayVAjAjtBGGB1dXWsX7+eFStW8MADD7BkyZJhXWNtqAqFQnR2dmI2m7FYLIPdnAGjVqspLCzk5ptv5qmnnuK6664jJSVl2K2s4fF4OHHiBP/5n/9JRUUFgUCAhx56iNtuu420tDTMZrOoOycMaXK9u29+85usX7+ev/3tbzz99NP86U9/4kc/+hFXXXUViYmJg93Ms4hvlSAMoK6uLg4dOsQLL7zA7NmzmT59OqNGjRp2vS3DgbxOqN1uH1E9diqVivT0dObOncszzzxDSUkJDoeDmTNnDnbTPpE89Lp//35KSkp477336Onp4corr2TKlClMmTKF9PR0zGbziJwUIowc8oxatVqNw+Fgzpw5REdHs3//fnbv3s1f/vIXDh06xNSpU7n66qux2+1DZj8vAjtBGAByne+KigoOHDhARUUFjz/+OKNHjxZrwV4kco5dTEzMsEhovhCRkZHk5uaSmprK8ePHSUpK4oorrkCtVg+Zg8eZ5OKw5eXlbN26lT179lBZWcnMmTNZsGABs2fPJi4ubsi2XxDORaVSodfrSU1NJT4+nri4OAwGA6+++ip79uyhr68Pu93OFVdcMWR6oQe/BYIwQgQCAV566SV2795NSkoKd911lxiCvYjkWbEmk2lE9djBRwui2+12brjhBlasWIHRaOSuu+4asq9TDuqcTie/+MUv2Lt3LxqNhocffpi7776biIiIIRHQySdg4XAY+Hf5i4F4LtlnCb77LwAl93qei/y8Z/4eyc73Pn+ez+6zkpf6KywsZNq0aTz55JOsW7eOTZs28eKLL5Kbm0tkZOSgfy5DL+tPEIYhj8fDH//4R7Zu3UpUVBRPPfUUERERQzKxdqQ4s47dSGMymbjnnnuIiori1KlTvPvuuwSDwcFu1jm5XC42b97MV77yFbZu3cqSJUv4y1/+wv3334/dbh/s5incbjfHjx9n1KhRzJgxg+985zuf+bl6e3tZvnw5kyZNIjU1ldTUVLKzs9m9e/dner5wOMz69ev59a9/zeLFixk7dqySyD9x4kRuv/12nnjiCV599VXq6urw+/2fue3DSTgc5oMPPuCLX/yi8j5nZWXx5z//edDapNPpmDJlCn/84x/56U9/SnR0NHfeeSfPPvssZWVlg9YumeixE4TPSV4O6b333iM9PZ0pU6YwatQoNBrNoJ+5jWRyrcCRVu5EplariY+PZ8yYMRw/fpy1a9dy4403otVqh8x2JUkSwWCQFStWsG3bNurq6rj77rtZsGABBQUFQy7g7uzsZNeuXTQ0NNDW1obNZqOlpYWYmJgLzvkzGAyMGjWKhx56iO3bt7N9+3YaGxs/U8Dl8/l46623WLduHV1dXSQlJTF58mT0ej1qtZpAIEBDQwPl5eWUlJSwe/duHnvsMaWI80imUqnIy8vjtttuIycnh3fffZfGxkb6+voGtU16vZ74+HimTJnCAw88wLPPPsuHH35Id3c3jzzyCHFxcYM2LCsCO0H4HMLhMK2trRw4cIDDhw/z8MMPM2vWLGJiYga7aSOaPPTncrkwGAwjLscOPjp4mM1mxo0bpwQk3d3daLVa9Hr9YDcP+Gj7b2hoYNWqVRw9epTY2FjuuOMOUlNTh9wsXkmS6OjoYNeuXWi1Wvr6+mhoaODUqVNERERccGBnNBoZNWoUo0aNwuFwUF1dTWNj4wW3KxgM0tvby+uvv87hw4dJTk5m8eLFXHXVVUqvf2dnJ6tXr2b16tXs3r2bPXv2cNddd10WgZ1arSYvL4+8vDwKCwvZv38/LS0tg90s4KOh2czMTOLj4zl69ChbtmyhtbWV6dOnM2fOHMxm86CM2ohxIkH4HHw+Hxs3buRnP/sZkyZN4qabbmL27NmD3awRLxAI4PF48Pl8WCyWIdczNJCuvfZapk2bRk1NDVu2bKG5uXmwm6RwuVz8/Oc/Z/fu3aSlpfG3v/2NUaNGDbmgDj4K7BobG3n//ffJysoiIiKCrq4u3nnnHdxu96C1q6Ojg4MHD7Jy5Ury8/P50pe+xD333KOUuLFYLKSmpvLAAw9w//33c8MNNwyZHlvhowLHVquVX/3qV9xyyy0YDAYeeeQRqqqq8Hq9g9ImEdgJwufw4osvsmbNGgKBAE888QQ5OTmD3aTLgtfrpaenBwCbzYbZbB7kFl08CQkJjB49mqlTp/Laa69x6tSpwW4SgJL39+6777J48WIeeughUlJShmzQcfToUSoqKjAYDPzgBz+guLiY7u5uVqxYQV9f31nJ+ZdKT08PVVVVSJKkzLqUi+We+ZOdnS1OHIcotVrNLbfcwte+9jUkSeJ3v/sdW7ZsGZy2DMp/FYRhLhAIUFdXx5YtW+jt7WXmzJmMGjVKLIl0ifj9ftxuN5IkYTabR2SOnUyn05GUlMTMmTMpLy+nqqqK9vb2QW1TMBjk5MmTbNiwgbS0NCZPnkxBQQF6vX7Ibv/l5eU0NTVRUFBAcXExGRkZmM1mGhsbqaqqoqura1Da1X92Z1dXF93d3ee9b2xsLBMmTOChhx4iPj7+ErVQ+CRy4J2QkEBhYSFXX301hw8f5vDhw8ryeZeSyLEThAskSRIej4eSkhL27t3LmDFjuOWWW4iKihKzYC8Rv9+vJE+P9MAOID4+ngULFvDHP/6RiooKqqurBzWPs6+vj2PHjrF582a+/OUvU1xcTEpKyqC15+PI5UMOHz5Ma2srs2bNIj09nYyMDBISEjh69CiHDx8mOjqa6Ojocz5HKBQiGAzi8XgIBAJIkqQUsDWbzYRCoc/cPqPRqOw7ampqOHnyJN3d3ZhMJrRa7WnlUxwOBw6Hg6KiotNen8fjwePxKLOm1Wo1JpMJnU6H2+0mGAwqwYVOp8NoNKLT6c5K7pffK7/fr/z0Lw3T/7FyTmIgEDjt+yj/f4fDgdvtJhAIEAwGlfdLp9NhMpnOObksHA4rubPBYFD531qtFqPROGRnhcuMRiPp6encfffdrFmzhrKyMo4dO3bJV6cQgZ0gXCCfz0dlZSXf/va3ycnJ4Qtf+AKLFi0asj0VI5HH41F6Nux2+4jOsQOIjo5m2rRpFBcXc+jQIXQ6HZMmTRq09mzatImSkhJCoRBf//rXh/RkIUmS6O7uZvfu3Wi1Wm6++WalXEV7eztHjx5lxYoVxMTEMHbs2HM+R1NTEwcOHOC5555jx44d+Hw+ZVbsY489Rm1t7WduX3x8PJMnTyYuLo7y8nKam5s5duwYjzzyCIWFhSQkJHzsvsXv9/OPf/yDpUuXcuTIEeCj4fsHHniAiRMn8vvf/57Dhw/jdrvRarUUFRVx7733MnnyZPLz8896vq6uLjZs2MDatWvZtGkTbW1tqFQqbDYbU6dO5fbbb2fChAlkZWUBUFZWxsaNG3n88ceV50hKSmLLli387W9/Y+fOnRw5cgSNRkNeXh7Tpk3j4YcfJj09/azAsru7m8rKSn71q1+xf/9+Ojs7gY/WT7711luH9HYms1qtTJ8+nRkzZtDa2srrr7/O/PnzL2kbRGAnCBdox44drF69GrfbzZ133sn06dNFT90l5vP56OvrQ6vVYjKZRuSs2P5UKhVarZYbb7yR9957jz179tDY2DhoJRX279+P2+1m4sSJREVFDYlq++cTCATYtGkTVquVlJQUEhMTUavVjBkzho6ODjQaDQcOHGDWrFn09PSctjRUOBymubmZP/zhD+zYsYPOzk6+8Y1vkJ6eTmRkJB6Ph61bt3L8+PHPPFNTq9XicDj40Y9+xAsvvMCJEyfYsWMHdXV1xMXFER8fz+jRoykqKiInJ4e0tLTThrx1Oh033XQTU6dOZe/evTz99NN0dnayfv162trauPvuu4mMjMTlctHY2MiyZcv4wx/+wOTJk3nooYcYM2YMGo2GQCBAT08PP/zhDzlx4gSBQIBHHnmE9PR04KNSMW+88QZ/+MMfGDt2LI8++ih5eXlkZ2cTFRXFuHHjePrppzl06BDd3d38+c9/ZtSoUUydOhWDwUBFRQXLly9n7dq1dHd389vf/paIiAjlfWhpaeGDDz5g2bJlNDU1cdNNNzFq1CiSk5NxuVyUlZWxY8cOmpqaPlcP6cUmfy5z5sxh9erVlJaW4vP5lNI1l8LQ/TYKwhAjSRLt7e0cOHCAkpISioqKKCoqIjk5ebCbdtmRc+zkYaHLIbBWq9VMmTKFLVu2UFtbS2lpKfPmzRuUoKqmpoZAIEBubu4lPWB9FoFAgN27dxMdHU12drayekd0dDRpaWkkJyfT3NxMXV0d1dXVjBs3TnlsKBRi79697Nq1i+rqaoqLi5k/fz4ZGRlYrVY6Ozuprq7m5MmTymSeC6VWqzEajcydO5fOzk4OHjxIZWUlTU1NNDQ0oNfrqaqqora2VilYXFxcTHx8PAaDAbVaTVpaGmlpaUiShNVqpbm5GY1Gg8PhYPLkySQlJeHz+WhoaODYsWOsWrWKXbt2MXr0aPLz89FoNPT19VFSUsKWLVswGo0UFBQwd+5csrKylH1fVVUV77zzDrt27aKwsJDMzExsNhs2m420tDTefPNNTp06RXd3t7J9yIFfamoq+/bto7a2lg8//JC+vj6sVqsypHv48GF27drF/v37mT59OjNnzmT8+PEkJibS3t6O2+2moaGBzs7OQZvociHy8/P58MMPaW9vp6uri5iYmEv2PRm630ZBGELk3JPS0lJ27tzJqVOneOihh0hPTx+yyzyNZD6fD7fbjd1uH1IFey8mtVrN5MmTlZnXb7/9Ni6X65InZgPU1dURCoXIyckZ0u+9JEn4fD42bdpEWloa48ePV24zmUzExcUxZcoUtFotlZWV7Ny587TH+/1+XnnlFU6cOEFMTAz33nsvEyZMIDExUQlm7r77biZOnEhvb+9nbqdWq2XUqFF873vf41e/+hXf+MY3mDNnDomJiXg8Hvbs2cM//vEPnnjiCb7zne+wadMmenp6zhvg+P1+pk6dyh133EF+fj42m42YmBjGjBnDN7/5TZKSkjh58iT/+te/lJzBtrY2XnzxRerr6xkzZgx33HEHxcXFREZGEhUVRXZ2Nt/85jfJzMyktraWpUuX4vP5zrn9GY1GrrzySgoLC0lOTsZsNlNYWKjkYsrBn8/nUx6zYsUKdu3ahSRJ3HvvvcycOZOsrCxMJhOpqaksXryYxYsX09PTMyjb/IXKzs7G4XDg9Xqpqak57bVebCKwE4RPIRQK0d3dzRNPPEFHRwc333wz11133ZBaLuly4vV6cTqdOByOIT0MONA0Gg1XXXUVc+fO5bXXXqO6unpQKvDLB9fzTTYYKjo7Ozl27BiVlZWMGzfurLzE6OhovvSlL2GxWDh8+DDvvffeabcHg0E2b95Me3s7UVFRzJ07F51Op9yuUqmIiYkhNTVVGbL8PLRaLenp6XzpS19SVjIoLS3lrbfe4s477yQjI4OqqioeeeQR1q9fT0NDwzmfRw6Gziy/JAeQKSkpqNVqjh49SnNzM16vl66uLlatWoXP5yM3N5cZM2ac9liVSkVsbCxZWVnY7XYOHTpEQ0PDOWsA6nQ6iouLz8p9dTgcxMTEKIFk/8fu3r2bkydPotfrmTt3LpGRkac9NiIigqSkJLKysobFdz46Ohqz2Uw4HKajo+OSDh8P/XdHEIaApqYm3nzzTTo7O/nCF77ATTfddNn0FA1FPp8Pl8uFzWa74BUDhit5WxszZgydnZ3861//YvPmzWg0GoqLiy9pW3Q6HSqVasivV1pfX8+2bdtwuVw8/fTTvPrqq6dtL36/n9bWVpxOJ5IkUV1dTWVlJcnJyej1egKBAH19fUiShMFgwGKxnPWdl4dSB+IkTy6boVarlRmoBoOBiRMnEh0dzahRo5RF5w8dOkRSUhKpqalnPY/ZbEav15/13ZBnplqtVkwmEx6Ph56eHhwOBz6fD6fTSTgcZvny5Zw8efKcAdTevXtpbW0lFArR3NxMbGzsWQGcWq3GarWeNfSo0WiUNoVCIWUkJBgMKjNo9Xo9FovlnG3X6XRERkZ+rskql4rf7ycUCqFSqTAYDJf0WCECO0H4BE6nk6qqKj744AMSEhIYP378aeUGhEtPzrGz2WxDOr/rYoiPjyc3N5fMzEx2795NZmYmY8eOvaQnGnKA83mGHy82SZKU2az5+fm4XC5qamrOed+4uDi6urro6OigrKwMh8OBTqdTAo/+Ade5qNXqz9yL5Pf7cblcnDhxgry8vNN6quSgIDExkcTEREKhEJ2dnWzcuJGGhgZaW1vP+Zxye+W2n6u98msJhUKEw2EkSVKGdnt6eqipqTnnY+WyMBqN5rxL28lB2JnvV///CyhDqv3/d/+CzOd63uFyQt3b24vX61VmFF/KE1AR2AnCecg7nWPHjrFlyxY2b97Mv/71L6ZPn47Vah3k1l3evF4vLpeLyMjIYTEsM5C0Wi0JCQncdttt/Pa3v2X06NHMnTsXh8NxydqQlJREb2+vsmLC+QKIwRQMBqmurmbfvn08//zzFBYWnjW8Bx99z3/2s5+xcuVKjhw5wsqVK5X76vV6zGazkg/mdrvP6rWT8/icTudnamdHRwelpaXcd999vPTSS8ybN++872VCQoJSksXtdp93ySqn06nkv53ZVvl2l8uFSqUiKioKs9mMwWDAZrPhcrl48MEHefzxx89Za65/MDYQn7lKpVLeZ41Gg9/vx+PxoNFoTvtuy+tD9/b2DovJE9XV1XR2dqLT6UhPT7+k6ztfXqe6gnCBurq6+Ne//sV7773Hl7/8ZRYsWHDOoQ/h0vJ4PPT29g75UhsXS0xMDHfccQfR0dEcPnyYN99885L+/4kTJ6LT6di8efOQLRq7a9cuampqiImJYeLEiR87VHr99dczduxYPB4Py5cvp6WlhWAwiFarZc6cOcTExNDV1cXWrVsJBALK4yRJoqenh6ampvP2Bn4Subeqo6NDyQc8n9bWVo4dOwZAVlbWeWfk+3w+amtrqaioOO36UCjE8ePHaWhoQJIkRo8eTXx8vFIkeeHCheh0Ourq6jh8+PA5n/uDDz7ghz/8IbNmzaKrq2vAJjJMmTKFnJwc/H4/W7ZsOWsFDpfLRWtrK5WVlUN2m+tPnuCSl5dHTEzMJd1PicBOEM4jGAwquSZWq5Wbb775suwhGorks/rLKceuP61WS1RUFFOmTMHr9fLhhx/i9XovWU9GUVERCQkJtLa2cuDAgSE5JHvgwAFcLhcTJkxQyoKcjzz5QR6SraiooL6+Hq1Wy3XXXUd6ejrd3d289dZblJeXK+U3WltbWbFiBQcPHvxcKQFyb9TatWt59913OXjwIN3d3Xi9Xnw+H11dXezatYsNGzawfft2kpKSKCoqUooEn8lgMFBeXs769eupra3F7XYrxX9ffvllWlpaSE5O5oYbblBq4jkcDhYvXkxCQgLHjx/n7bffpqKigt7eXjweD06nkz179rBmzRqOHTtGUVGRkms5EObNm8f48eNRqVS8/fbbHDhwgIaGBnw+H62trWzevJn169efthLHUBQMBunp6aGkpAS1Ws3EiRPP2fN5MYkjlCCcg9/vp6uri3feeQe3201BQQFXXnnlJe1OF85PzrE7V4L25UCtVqPX65k1axbvvPMOpaWltLS0kJiYeEmKNY8ZM4b09HTC4TBbtmwhNjb2tJpkgyUUCuHz+aivr2fv3r0EAgHy8/Opq6sjPj7+rNJEcuK+XIMuIiKCpqYmSkpKMJvNSJLE/Pnz2bVrF9u2bWPbtm1MnjyZvLw8oqKi6O7uZtu2bdTX12OxWPB4PNTX11NVVYXFYvlUtcu0Wi1ms5nU1FTKy8sJBoOYzWb6+vqUYd/u7m527NjBnj17qK2tZfLkyRQVFZ139MBoNNLe3k5ZWRkZGRkkJSXhcrmorq7m/fffR6vVMn78eL7whS8oJ6qRkZEsWLCAN998k7q6OrZu3arUzTOZTAQCAbZv387Ro0ex2+1cf/31yjJfbreblpYWenp68Pv9BAIBTp48qWwXFouFyspKmpublSHr+vp6oqKigI9yHKdPn05DQwNHjhxh27ZtZGVl4Xa7ycjIoLOzk927d3P48GGsViter5f29nZOnjyJzWZTciKHAnllovLyciZPnsyUKVMufSMkQRDOcvLkSekvf/mLZDQapSeeeEI6cOCAFA6HpXA4PNhNEyRJ+t3vfifl5eVJTz/9tNTY2DjYzRkU4XBY6unpkf7rv/5LysjIkH7+859fsvciHA5LGzdulO69914pISFBevPNN6X29vZL8r8/TldXl7Rp0yYJUH60Wq00ZcoUadeuXWfd3+v1SpWVlVJKSsppjwEkk8kk5ebmSl6vV6qvr5dWrFgh3XDDDVJ0dLRksVikqKgoafLkydLzzz8v/eQnP5FmzpypPDYrK0t6+OGHJY/H84ltDofDktfrlbZt2yb9+te/lu655x5pypQpUkpKimS1WiW9Xi/ZbDZp/Pjx0p133in94Q9/kPr6+qRgMHjW/mjHjh3SuHHjpJSUFOlnP/uZtGHDBumaa66R4uLiJIvFItntdmn27NnSCy+8IB07duy0fZr8d2dnp/Tmm29KDz74oJSVlSWZzWbJYrFICQkJ0lVXXSX99a9/PW1/eOrUKem555476/0DpLvvvlt68803pZ6eHslkMp11+5gxY6SHH35Yea6Ojg5p37590k033SSlp6dLVqtVMpvN0oQJE6Sf//zn0t///ndp/vz5ksFgkADJZrNJ9913n1RdXT0wG9AAKC8vl2666SYpLy9PeuqppySv13vJjxuix04QziAPe/z5z39myZIlzJs3j/z8/CHd/X+5kP4vn+dynjzRn8ViYcKECdTU1PDiiy9y9dVXEx0dfdF7llUqFUVFRVitVnbu3Mlzzz1HVVUV3/jGNwZ0eO5C2Ww2Jk2axIEDB05rq8lkOmc+mk6nIykpidWrV5+WOyc/Tq/Xo9PpiIuLY9asWYwZM0YpCi3PWI2JicHv9/PlL38Zl8sFgF6vx2azferPQafTUVRURG5uLl6vF6/XSyAQUEqCyG0xmUyYzWZMJtN5Z47KoqOjmTx5Mn/605/weDzKML3JZFJqrPV/vDyL1mazsWDBAqZMmcI3v/lNJZ9NrVZjMpmIiIg4bQKJPKTb/z2XRUVFKfffuXPnWfl4RqPxtOey2+2MHj2a3/72t3g8HuV/GwwG5bs+b9485TNQq9VERkaSkJDwqd7ni23Hjh1s3LiRzZs388QTTzBv3rxB6Um8fPeIgnAGeaezd+9eSkpKcDqdLFy4UKl+LgwNfr9fqRF1uQ7Fwr9rkuXm5jJ16lRWrFjB8ePHiYmJISMj46L/f5vNRlZWFtdddx07duxg9+7dbNq0iblz5562lumlJNdo+7TliNRqNQaDgYKCgk+8X0RExGlrm57pswYXcoBmsVjOqgf3eeh0OmW5rwtpi1arJTIy8pwziM/FYDBgMBg+sVh1/1U/zker1aLVasnMzDzvfYZiUexwOMzJkyfZvHkze/fuZfTo0UydOlUpBH2picBOEPrxeDy8//77lJaWkpKSIlaXGII8Hg8+n49wOHzZTp7oLzs7m1AohFarZe/evURHRyurIFzM4EoOAB566CFOnDjB8ePHWbp0KWPGjCEmJgaj0Sh6uYURLxgM4vF42LRpE6tXr6a9vZ2HH36YgoKCQTt2XJ6nuoJwDi6XixdffJF169Zhs9l48sknsdvtl22P0FDV09ODx+NBrVZfdkuKnYs8zHj77bezadMmNm3adNaw4sWiVqvJysriRz/6Ebfeeitr167l3nvvZd26dedcakoQRppTp07x+9//nu985ztERkby6KOP8h//8R8X1FM60C7vPaIg/B+5Kv2zzz5Lfn4+c+bMYfTo0UN+av3lyOl04vV6leGxyz2wU6lUWK1Wbr31VrZt20ZFRQVbtmxhwYIFF33blZ8/NzcXnU5HVFQUf/nLX3j22WcpLS3l3nvvJSEh4bL/jC6mQCDA+++/z5o1aygtLaWqqopAIMBTTz3F2rVrWbRoEYsXL/7UQ6vCJ5P+ryj1+++/z9atW1m/fv3/Z++8w+Mqrzz8m96rpNFo1Ea9V1uSu2zLNjY2xkDAoTqBbMiSZAPZZDcJ2UASQno2MSSEULJgCC0UG1fcu2RZ1ZLVu0Zteu9z9w+4N5ZtjPvMSPd9Hj0qM6M5c8v5zncqHnjgAaxZswbl5eVhjyLQdxvNrIbMq9PpdDh16hSGhoZw6623Yu7cuZfMp6EJHy6XCz6f73PnUc5GuFwu8vLykJiYCIfDgWPHjmHp0qVfmGB/vZBKpcjIyACfz6fuoxMnTkCtVmPhwoVISEigWlvQG6XrDzneKz4+HvHx8dTfeTzeTe+hNtMh5+s2NjZi37596OvrQ0xMDG655RbMmTMHSUlJ4RYRDOL8MhUamlkE8Vlj0HfffRcvv/wy9Ho9XnrpJaqzPk1kQRAEDhw4gL///e84evQozp49Cz6fH/YdcqTwy1/+EsePH8fo6CiOHTsGoVB4Uw3fUCgEnU6Hv/zlL9i1axdVKbtq1SpUVlZSFbO0oUETTZBmUjAYxOjoKFpaWvDf//3fsNvtKC0txTe/+U2sXLkyYtYM2mNHM+vZunUrtm3bhu7ubrz++uvIzc2lQ0cRjMVigd/vB5/Pp9o+0HzKPffcA5/Ph+PHj+Pjjz9GdXU1NBrNTXt/BoOBxMREPPnkk3jggQfw5ptv4tVXX8VHH32E0tJS/OAHP6CrzGmijlAoBJvNhldeeQX79+9HfX09Kioq8Nhjj6GsrAzx8fERtWZEjiQ0NDeZQCAAu92OrVu3wmKxYOnSpSgsLLxgyDdNZEH2sBKLxQDo0N65xMfHIycnBwUFBdi6dSuys7OhVqtvmteO9MYJhUKkpKTg7rvvhkKhQGtrKwYGBvA///M/WLhwIYqLi1FeXg6lUkmfP5qIhCAI+P1+dHd3o62tDfv370dPTw9EIhE2btyI9evXo6ioCDExMRHjqSOhDTuaWYvH40FbWxtaWlqQlpaG5cuXIy4ujl5oIhyy2vJ69vyaKYjFYqSlpaGiogLbtm3D0NAQMjIybnriPJPJhEQiQWlpKWQyGRQKBfx+PxobG+F2uzE1NQWfz4f8/HwolUoIBAJ6XB9NRBAKheD1emG1WjE8PIzTp0/j9OnTOHHiBOLj41FQUICVK1eipqYmYvN7acOOZlZCEAQmJibwi1/8AqFQCPPnz8d9990XbrFoLgNy4LxEIqGN8IuQm5uL+++/Hy+++CKOHz8OmUyG5cuXA7j53k0Gg4H09HSkpaVh48aN2LlzJ/76179iy5YteOGFF/DAAw9gw4YNKCwsREJCwrTX0dDcLM4tNfB4PBgZGcHu3bvx5z//GUajERKJBJs2bcJXvvIVJCQkgM/nh1HaL4Y27GhmJY2NjThw4ACOHj2K3//+91i8eDHtMYgSyIHtdPuGiyORSJCZmYnbb78dp06dgt/vx5IlS8KeAyQSibBu3TosXLgQnZ2dOHLkCN5//33s2rULCoUCFRUV2LhxI/Lz8yNmRBTN7MDlcmFiYgLvvvsujhw5goGBATgcDixduhTLly9HRUUFkpKSIBaLw34fXQ6RLyENzXWEIAg4HA6cOnUKJ06cQGlpKcrKypCYmBixbnWa6ZDzOOlQ7MUhZ3quXbsWL730Enp7e9HS0oLS0tKwLUrnjs0iw65isRhKpRJdXV2YmprCwMAAXn31VaSnp0Or1SI3NxfZ2dmQSCTg8XhhkZtmZkKGW/v7+9HT04Ph4WH09PSgt7cXTCYT5eXlyMzMxNy5c5GTk4Pk5GQIhcJwi33Z0IYdzaxjeHgY9fX16OzsxP3334/MzEza+xNFuFwuEARBG3aXgMPhYNmyZfjggw8wOjqKI0eOID8/PyJ6mjGZTKhUKsTFxWHu3Llobm5GU1MTPvnkExw7dgyNjY1Qq9VYtGgR3G43NBoNFAoFRCIR1dqG3oTRXAkEQVBNhZ1OJ1wuF8xmM44dO4ba2lr09fVhaGgIWq0W1dXVqKysxNKlS6N28hDdx45mVhEIBPDwww+jo6MDcXFxePfdd296ry+aq4cgCHz5y19GKBRCQUEBnnrqqbAbKpEIqdb//Oc/Y//+/WhtbcXx48cRFxcXMT3/zl96CIJAa2srDhw4gOPHj2P//v1wuVxITk5GcXExNmzYgCVLlkCtVtPtUmiuiFAoBLvdjsbGRmzduhX19fVoa2uD0+nEnDlzMGfOHKxYsQK33noruFzuNJ0SjfqF9tjRzBqmpqZw8uRJHDp0CKtXr8Ztt91G90GLQmw2G0QiUVhnMUY65DW9atUqhEIhHDx4ELt27cKSJUuQkZERZuk+5WL3XXZ2NhISErB+/XoYjUY0NTWhv78f/f39+NWvfoXnnnsOKpUKGRkZKCsrQ2FhIVJSUuicPJppeL1emEwmdHd3o66uDp2dnRgaGsLo6CgkEgmSk5Px7//+71i6dCk0Gg1kMhmkUill1EX7mkAbdjSzAq/Xi9HRUezatQuxsbEoLCxEcXFxxHgvaC4fp9MJkUhEh2Ivg4SEBGRmZiItLQ1HjhxBamoqtFptRF73ZP87oVAIlUqF1NRUSKVSpKWlIT09HQqFAuPj4/D7/ejv74der8fZs2ehVquh1WqpkK1cLqd6i0X7Ak1zacgQq8Vigc1mg8VigU6nw9TUFPR6PUZGRqDX6+F0OsHlclFSUoKMjAykpaUhMzMT5eXlUVMQcSXMrE9DQ/M5mM1mnD17Fh988AHuu+8+VFRUIDU1Ndxi0VwBZOju/AbFNJ+PRCKhejT+85//RFVVFSoqKiLe28lgMMDhcJCXl4e8vDyEQiFYrVbU1tbi7NmzaG5uxrFjx7B3716wWCwkJiZiwYIFyM7ORkZGBoqLiyGRSMBms8FisajvTCaTNvaiFNKICwaD0768Xi96enowODiIrq4unDx5Et3d3bBarWCz2SgrK0NRUREKCgqwaNEiaLXaGd8Bgc6xo5kV/OY3v8H+/fvR39+Pffv2Qa1W05V2UQap1PPy8lBSUoJNmzZh3bp19EL9BbjdbkxMTGDRokVYtmwZbrvtNmzcuDHcYl0R5DJFEARCoRCVCN/Z2UmF244cOQKdTgeLxQI2m42cnBykp6cjJycHVVVVKCgogEqlglQqDfOnobka/H4/rFYrurq6cOrUKXR3d6OnpwfNzc1wOBzgcDiQy+VYsGABKioqkJ+fj7KyMiiVSqpo6NyvmQztsaOZ0fj9ftTW1uLIkSNwOBx47LHHInIEDM0XEwwGYbfbEQgEwOPx6AX6MuFyuVCpVFi5ciXGxsawe/du3H777eDxeFGzwJFyMhgMMJlMEAQBFouF7OxsaDQalJSU4K677oLZbIbRaMTg4CCmpqZgNptRX1+Pffv2QSwWQywWQ6FQIC0tDWq1GnFxcUhOToZWq4VEIqGLMsJMKBRCIBDAxMQEdDod9Ho9xsbGMDg4iLGxMRiNRpjNZhAEAYlEAoVCgXvuuYfKs9RoNIiNjYVcLodEIoFMJpuVIXnasKOZsQSDQbhcLuzfvx8WiwXx8fFYtmwZ+Hw+XQUbhYRCITidToRCIXA4HDrH7jJhsVjg8/lYsmQJ/vnPf6KrqwsDAwPIyMiI2pAU6XWRSqWQSqXQaDQAPvVO2u12DAwMoL+/H6OjoxgaGkJvby9sNhscDgf0ej1MJhP1WpVKBa1WC6VSCYlEQvXa4/F44PP5VJsVLpcbtccrUggGg/D7/fB4PHA6nfB4PPB6vXC73dTvLpcL4+PjmJiYgNVqhdVqhcFggNVqhd/vB5/Ph1qtRkJCAhITE5GZmYn09HSqhc5s8Mh9EbRhRzNj8Xq9mJycxMsvv4yqqiqsWLECpaWl4RaL5ioJBoOw2WwIhULg8Xh0jt0VwGQycfvtt+P06dPYv38/tm3bhn/7t3+DQqGYUYugQCCAQCCASqVCVVUVgE83BBaLBZ2dnRgeHkZfXx/OnDmDvr4+jI2NYWJigsrZVCqVSE9PR3JyMtRqNRITE5Geno6kpCTExsYiJibmglYY53oTz/1+7nNmMueGyb/ou8vlgtVqxcjICPr6+jA+Po7JyUkMDQ2hv78fU1NTMBqN1PmIjY1FRkYGCgsLkZ6ejpSUFOTl5SElJWXGFTxcT+gjQzNjOXXqFF566SWw2Wzce++9qKmpCbdINNdAIBCA0WhEKBSCQCCgm0pfIQqFAkuXLoXL5cLzzz+PdevWQSQSzfhcUwaDAblcjoqKCsyZM4cK94VCIfh8PlitVnR0dECn02FsbAwjIyMYGRlBU1MTpqamYLfbwWQyKS9xUlISlEollEol1Go14uPjoVAoqN/JPD6pVBqR1cfXG6fTCYfDAYPBgImJCRiNRphMJkxMTECv18NsNsNkMmFkZARmsxkejwehUAgsFguxsbGIjY1FUlISli9fjvj4eGg0GuTn5yMhIYHqMcpisajiF7pB9RdDG3Y0M5LOzk6cOnUKjY2NuPfee5GbmxvxlYA0l4bMsQuFQtRIKprLg/QsFRcXw2azYevWrThx4gSYTCby8vLCLd4NhfzsFzMGQqEQJBIJhEIhcnJy4HA4YLfbYbfb4XK54HQ6YTKZYDKZqL/7/X4EAgE4nU50dnaitbWVKugQCAQQCoVU2FYsFlNhXbKVC+lVFAqF4PF44HA44HA44PP54HA4YLPZYLPZVPj3XIOGrO4lYTKZYDKZ05rqkkVGgUAAwWCQei5ZeOL1egGAqij1+XwIBAIIBALw+XzweDzUZyTDpB6PBx6PBw6HAy6Xa9rv5P9wu91wuVzwer0IBoMQi8Xgcrlgs9mIiYmhGkuLRCIolUrExMRALBZDKBRCIpFAKpVCKBRCLBYjJiYGQqGQ9spdJfRRo5lRkMqrsbERZ8+ehcfjwa233gqNRkMriSgnFArB4XCAwWCAy+XOeE/TjSAlJQVFRUWIjY1FXV0d4uPjkZ2dPWvbgDCZTPD5fCpH73zINiuTk5MwGAyYnJy8IPdLr9dT+Xterxd+vx/BYBChUAhCoRB8Ph98Ph9CoZDK3yNz98437LhcLjgcDmUI8vn8aS1byOeSMBgMygg8F9JIO9ewC4VCCAaDcLvd1HNIo83n81FG3ecZdm63+6KGHSkHh8Oheso5HA5kZ2ejpKQEarUaSqUS8fHxUKlUlJEXHx8fESPuZiL0SkczoyBHx7z22muwWq1Yt24d5s+fTxt1M4BgMAiLxQI+nw+BQEBXNl8FQqEQKSkpuO+++/D6669DpVJhyZIlkMlk4RYtImEymVAoFFAoFJd8XigUgt/vx8TEBKampijDb2pqijL6LBYLTCYTlWNmNpupggGy6IP0/IUTiUQCPp8PHo8HkUgEuVwOsVgMiURCNYEmPWxKpRIymQwymQwqlQp+vx9vvvkm3nrrLUilUnzzm99EYWEhda/SRtzNgV7taGYUFosFv/nNb6DT6bBgwQI88cQTsyLPZTYQCARgNpuphYfm6oiJicEjjzyCHTt2oKWlBa+//jq+/e1vh1usqIb0Ims0GsTHxyMUClEeMvLn87+CwSDVdJeMNPh8PrS2tuKVV16BwWDA+vXrUV5eToVQzw+vklWmZHiVhMlkgsfjXVDFy2KxqGpyDodDeQ/J5woEAsp7S4avyS8Gg0HlupGPkWFi8meCIPDwww+juLgY3//+9/HTn/4Ua9aswcMPP0xvxG4itGFHM2OwWCzo6+vD/v37UVBQgMrKSiQkJIRbLJrrBBmKJXOWaK4ONpuN+Ph4VFVVYWBgAIcOHcL9998PmUxGe7avEtITdX6o9EpxOp3o7+/H5OQk5s2bh6qqKpSWllIFH6QxSEIaiIFA4AJ5OBzOBZtaBoNB3TssFouSl8zrux7tXBITEwEAd955JxoaGnD48GHExsZi/fr1s7KnXDig72KaqIdUdDqdjsqt27RpE6qqquiGozOIYDBIGXZ0P7Grh0y2J8eMnTx5EgMDA8jNzaULUsKM3W7HxMQE+vv78cQTTyAnJyfqNqdisRhpaWl45JFH0N/fj66uLrz11luoqqpCXFxcVDXGjlbommGaGYHX68XOnTuxefNmLFu2DKtXr0ZhYWG4xaK5jpwbiqUN9mvn9ttvR3V1NWQyGX7/+99Dp9OFW6RZz+HDh3H8+HEIhUKsWrUKycnJ4RbpquBwOCgtLcUvf/lL3Hbbbdi/fz9++MMforW1New5hLMB2mNHMyN46623cOLECTAYDPzXf/0X1Go1vSucYZDFE2KxmM6xuw6wWCzMmTMHd999NzZv3ozbbrsNQqEwao2JaIbMszt58iSGhoawZs0aSCSSqA2Nk7o3MzMTGzZsgEQiwXPPPQepVIqRkRHcddddYZZwZkN77GiimkAgAJPJhIMHD8LlcqGkpARFRUW0R2cGQubYkW0haK4eMjk+OTkZlZWV4HA4aGpqQnt7O2Vk0Nw8QqEQdDodBgYG4PP5sGDBgmm96aIVkUgErVaLpUuXoqCgAL29vTh48CDa2trg9/vDLd6MhTbsaKIWgiDgdrvR3d2Nw4cPQyqV4o477oBCoYjanS7N50M2KCYbu9JcO2q1GuXl5SgtLcWJEydw/PhxesENA4FAAI2NjdDpdBAIBKiurp4xOkyhUKCkpASPPvoofD4fDh48iK1bt8LhcEyr8KW5ftCGHU3UEgqF0NfXhyeeeALJyclYs2YN7rjjjnCLRXODIEeK0Tl21xeJRIInn3wSTqcTx48fx969exEKhcIt1qzC6/Viy5YtUCgUqKioQFZW1owam8Vms7Fu3Tp8//vfx/Lly/Hzn/8cb731Fnp6esIt2oxkZmwJaGYlTU1NOHjwIPr7+/HUU0+hsrKS7lk3gwkGg7DZbLTH7jrD4XCQm5uLyspK9Pf3Y8uWLVi6dCnV04zmxuJ2uzE1NYX6+nqsXbsWc+bMmXHHneyBN3fuXLBYLAwPD+ONN96gpmDk5OREfdg5kphZVw/NrIAgCLhcLjQ1NaGhoQFxcXGoqqpCcnIylTtEM7Mg51+6XC66j911hslkQqlUorKyEiqVCg0NDRgaGoLL5Qq3aLMCi8WC3t5emEwmZGRkIDs7O9wi3TDi4+NRVFSEW2+9FWazGY2NjTh27Bjsdjsdlr2O0IYdTVRBJnaPjo5i3759aGxsxEMPPYTs7Gx6LNIMhuyw73K5IBaLacPuBrB27VrMnz8fer0eO3fuxNjYGF1EcYMhCAJDQ0PYu3cvZDIZSkpKkJubG26xbigajQaPPPIIVq1ahc7OTmzevBnd3d3w+Xz09XadoA07mqjD7/fjqaeewvDwMMrKyvCNb3yDbqw6w3E6nbBarSAIAjKZjM6xuwHEx8ejoqICGzduxF/+8he0tLTQXrsbTCgUQmdnJ3bu3ImNGzciKSlpVqSTsFgsPP300/jyl78MiUSCBx98EI2NjbBareEWbUZAG3Y0UYXRaMSJEyfQ1NSE3NxcrF27FmKxmA6/znA8Hg9lZNAeuxsDk8lESkoK1q5dC4/Hg9OnT+PUqVPhFmtG093djd7eXlitVlRXVyMmJmbG6zLy80mlUixfvhz33HMPgsEg3n77bRw9ehRer5f23F0jdPEETdQQCAQwPj6OAwcOwOVyobi4GIsWLZoVO9zZjtfrpQw7oVBI97G7QcTGxlIzljs6OiCXyzF//nx6DNQNoq2tDSMjI+DxeCgpKYFEIgm3SDcFspiisLAQIpEIBw4cwLFjxyAUCpGdnY3s7GwQBEFfc1cJ7bGjiRqsVisaGxvx17/+FXfccQcWL16M9PT0cItFcxNwuVyw2+0APt3p06HYGwOXy4VSqcSjjz6KiYkJbN++HYODg3Ri+3WGzBXeu3cvJiYmsGDBAiQmJs66iSo8Hg/p6enYvHkzeDwe9uzZg1/+8pcIBALhFi2qoQ07mqhhy5Yt+PDDDyGXy/GNb3xjxicZ0/wL0rBjMpmQSqWzbgG8mXA4HHzpS1/CvHnzEAqF8Mwzz8DhcIRbrBlFIBDA8PAwGhoawOFwcOedd864FieXC4fDgUajwVNPPYWqqiocOHAAzz//PEZHR8MtWtRCh2JpIp5AIID+/n6cOnUKFosFa9asQWJiIu21mUV4vV54PB7w+Xyw2exZuwjeLORyOaqqquB0OnHs2DF0dXUhOzsbCoUi3KLNCDweD+rq6uD1eqkWILM17MhgMMDhcFBcXIyJiQmMjY1hx44dSEtLA4PBQFJSUrhFjDpow44moiEIAj6fDydOnEBnZyfi4uLw5S9/GUKhkM6tm0V4PB54PB6IRCKwWKxZuwjeDMhekPPmzYPL5cK7776L2tpaiEQiyOVy+thfIwRBwOl04sCBAxAIBEhNTUVGRka4xQo7iYmJWLhwIYLBIP7zP/8TR44cAZfLhUqlAofDoa+7K4A27GgiGr/fj6mpKfz0pz9FUVERVq5cifnz54dbLJqbjNPphNPphFKppA36m0R6ejo8Hg9uu+02PP/882Cz2cjIyACfz6cX2WsgEAjAYDDgvffew4MPPoiqqiraA/0ZWVlZUKvVaG9vx7Zt29DZ2QmVSoWysjL6vr8C6KuJJqJpa2vDG2+8AZfLhbVr12L16tX0dIlZiNvthsfjgVwupxfBmwQZBnvssccQCoVQV1eHHTt2hFusqKe3txcnT56Ey+XCokWLUFhYSOu0z2AwGBAKhXjsscdQXV0Nu92Op556CuPj4/B6veEWL2qgNSRNxGKz2dDZ2Ynjx4+jqKgI+fn5SExMDLdYNGHA6/XC6/XSPQtvMiKRCPn5+cjNzYXBYMDRo0fhdDrpKtlrYHh4GGfOnIFGo0FKSgqUSmW4RYooWCwWMjIysHTpUmRlZaGrqwuHDh3C+Pg43d/uMqENO5qIg2wFMDg4iMbGRpw+fRoPPPAAMjMzIRQKwy0eTRggPXZSqZT22N1E2Gw25HI5br/9dgQCAezfvx+jo6N0O4qrgJx33NXVhYaGBsyfPx/x8fF0Edh5MBgMMJlM3HHHHVi/fj2EQiFefPFFnDlzhvbaXSa0hqSJSAiCwO9+9zvU1taisrISX/7ylxEfHx9usWjCBJ1jFz4YDAYeeOABLFmyhBoFNTk5GW6xopLu7m60tLRgdHQUmzZtor11l0AkEmHJkiV44YUX0NnZiTfeeANvvfVWuMWKCmjDjibicDgc2L17N9ra2qBWq7Fp0yZwuVw6BDeLIatiaY9deODz+ViwYAHWrFmDY8eOoampie4zdhUcOnQIBoMBiYmJKC4upvsxXgIGgwGxWIzs7Gzcd9990Ov12LZtG1paWuDz+cItXkRDa0iaiCIQCMBisWDv3r0gCAIZGRmYP38+nVw8y6Fz7MIHGRrLzs5GVVUVvF4vTp8+jb6+PoRCITrv6TIgCAKBQAD19fXw+XzIysqCSqUCm003prgU5CSU1atXQy6XY2RkBAcPHoTdbqfTAS4BbdjRRBROpxMDAwP4+9//jgULFqC6uhrJycn0Yj7LcbvdcLvdtMcujCQlJaGiogKLFi3Ctm3bcPjwYfj9/nCLFRUEg0E4HA4cO3YMIpEINTU1AEDrtcuAxWJhzZo1WL16NRITE/HLX/4S/f39cDqd9Kbic6A1JE1EsXPnTjz//POQyWR44IEHsHjx4nCLRBMBnJtjRxt24SM2NhZPP/00AODYsWN4/fXX6cX1MjAYDPjggw/gcDhQUFCAW265JdwiRR133303vvnNb0KhUOBHP/oRtm/fjmAwSF9/F4HWkDQRAUEQ6OnpQUNDA3p7e/GlL30JycnJdBXsLIcgCPj9fni9XgSDQUgkEtqwCyMcDgdarRaLFi0Ci8XCjh07YDAY6JynL8BiseDQoUPIyspCWloa5HJ5uEWKOiQSCbKzs7Fp0yaMjY3hxIkTOHz4cLjFikhoDUkTdgiCQCgUwunTp9HT0wOfz4f169dDoVDQFZA08Hq98Pv9IAgCYrGYNuzCCIvFgkwmw9KlSxEXF4empiZ0dHTA6XSGW7SIxev1wmAw4PTp0ygqKkJqaip4PB4dhr1C2Gw24uPjceedd0IsFqO7uxv79u2Dy+Wi+yqeB60hacJOKBSCy+XCH/7wB9hsNqxZswZLliyBSCQKt2g0EYDZbIbX6wWbzYZCoaANuwjgjjvuwMqVKxETE4Nnn30WAwMD4RYpYhkcHERLSwu6urqwZs0a5ObmhlukqEUgECA7OxtPPPEE+Hw+3njjDZw6dQoulyvcokUUtIakCTujo6P485//jImJCcyfPx8PP/xwuEWiiRAIgoDVaoXX6wWLxaJHikUILBYL8+bNw7e+9S3U1tZi3759aGpqCrdYEUltbS1qa2uRm5uLwsJCxMbGhlukqGfNmjVYu3YtcnNz8eMf/xh9fX108+JzoDUkTVix2WwYGBjA3r17UVxcjKKiIiQlJYVbLJoIwuFwwO/3g8Vi0aHYCIHBYFDD2dPS0tDU1ITa2lp4vV46mf0zCIKA2+1Gd3c3RkdHUVVVBalUCg6HE27RohoGgwGZTIaysjIsW7YMQ0NDOHnyJLq7u8MtWsRAa0iasECODRsbG0NbWxvq6uqwevVqlJSUQCKR0PknNBTnGnZ08UTkIJPJkJmZiZqaGnR0dODIkSPQ6/W0YfcZBEHAaDSit7cXJpMJK1asAI/HC7dYM4aioiKsXbsWAoEA+/btQ11dHb2x+AxaQ9KEDYIgqDExhYWF2LhxI51/QjMNgiBgsVgQCATA5XJpoz/CEIlE+OEPfwitVouuri784Q9/oENinxEMBvH+++9Dp9NBrVbj9ttvpydNXEcEAgEyMjLw61//Gi0tLfjwww9x9OhR2rADbdjRhIlgMIidO3eivr4eoVAI3/3ud+lFm+aiWK1WsFgsiEQi+vqIMMiw2IYNG1BUVIStW7eira0NZrM53KKFFXLSxO7du6FUKlFVVQWBQEBfv9cRBoMBgUCAxYsXo7q6Gm63G5s3b4bVap31Uylow47mphMMBuF0OrF37154vV6kpaWhsrKSngdLcwEEQcDlcoHJZEIoFNKj5SIMBoMBDoeD8vJyFBYWwufz4fDhwxgbG0MoFAq3eGHD4/FgfHwc3d3dUKvVKCoqAovFoq/d6wyLxUJcXByWLFmCxMREdHR0oL6+HhaLJdyihRXasKO56ZBK77333kNCQgJWrFgBrVZL96yjuSh2u53y2NFEJsXFxViwYAEKCwuxZcsWtLe3z+qmxWazGc3NzZiYmEBWVhYqKirCLdKMZsOGDVi8eDFCoRD+7//+D0NDQ7O6tx09gZjmptPc3IwXXngBXC4XGzZswJo1a8ItEk2EQhAEzGYz2Gw2pFJpuMWhuQSFhYV45plnsGbNGuzYsQMAcM8994RZqvAwMDCAt956C+Xl5SguLkZKSkq4RZrRSKVS1NTUgMPh4LHHHkNubi4IgsDcuXPDLVpYoD12NDeVvr4+nD59GrW1tbjvvvuQl5dHjw2juSS0xy46EAqF0Gq1WL16NXQ6HXbt2gWDwTDrPCd2ux3Dw8Nobm7G0qVLodFo6EruGwyDwUB8fDzmz5+PiooK1NfXY8+ePfD5fLOymIK+2mhuCgRBIBgMorm5Ge3t7fB6vVixYgU0Gg3YbNpxTPP5OJ1OKseOJnLhcDiQyWRYsWIFWCwWOjo60NTUNOsW1/HxcQwPD8NgMKCiooJuSHyTEIvFSE1NxdKlS2EwGNDU1IShoaFZmetJG3Y0NwWCIOD1evGPf/wDzc3NWLZsGRYuXEgrPZpLQhAEbDYb1ZyYJrJhsVjYuHEjysvL4fV68atf/WrWJLKTvTmPHz+OlpYWSKVSLFq0CCqVKtyizRp4PB4ee+wxxMfHo7e3F2+99das7G1HG3Y0NwWHw4HNmzejs7MT6enp+OEPf0h3YKe5LOgcu+iCy+Xi3nvvxVe+8hXU1tZi69at6OjoCLdYN4VgMIj9+/dDp9PhS1/6EoRCIR2GvYkwGAzExcXhrrvuQklJCV588UX09fXB4XCEW7SbCn3F0dxwnE4ndDoddu7ciczMTFRVVSE5OZku/ae5LBwOBx2KjRLIdjTJyckoLS1FeXk5PvnkEzQ1NcHpdM5oz0kgEEBHRwdGRkbA4/GwZMkSsNlsWs/dRBgMBlgsFubOnYuKigowmUzs2rULg4OD4RbtpkIbdjQ3DDI0YTQacfbsWbS0tKC8vByVlZUQi8W0wqP5QgiCoAw7gUAQbnFoLhOFQoGMjAysWLECzc3NaG5uxsjISLjFuqH4/X40NDTAZrNBLpejrKyM9taFiaysLJSWliIjIwN79+5Fd3c33G53uMW6adBXHc0N58iRI3juueeQnZ2N9evXY/78+eEWiSaKsFqt4HA4dCg2ykhISMB3v/tdaDQa1NbW4s9//nO4RbqhuN1uvP3224iPj0d5eTmSk5Npwy5MsFgs5Obm4gc/+AHq6upw8OBBNDY2hlusmwZ91dHcUD755BPs378fQ0NDePLJJ5GSkkJ76mgui0AgAIfDAZ/PBz6fD4lEEm6RaK4AMnz++OOPIzExEXv37sWBAwdgs9nCLdpVEQqF4Pf7LxpOttvtGBoaQl1dHcrLyzFv3jxaz4URBoMBuVyOqqoq1NTUoL29Ha+++uqsqdCmDTuaqyYQCODs2bMwmUzw+/3THguFQvB4PDhw4ACmpqaQlZWF8vJyOgRLc9kEAgEqL4vH49E5dlEGg8EAk8lEeXk5cnNzIRKJsG3bNkxNTV2gL0gjvqWlBXq9PkwSXxqTyYT9+/dj9+7daG5uxuTkJJVuMjk5idbWVoRCIWRnZyMtLS3c4s56yIKrW265BQKBAJ2dnWhubobX6w23aDcc2rCjuSoIgoDf78e+ffvQ1dUFvV4Pj8dDKTq/34+pqSns3bsXfr8fK1asQHJyMng8XrhFp4kSAoEA7HY7CIIAn8+nDbsohMFgID09HXPnzkVhYSHee+899Pf3w263U8/x+Xyw2Wzo7+/Hzp07MTg4GJFelfHxcbz55pv461//ip07d6KlpQUmkwkulwu9vb04ceIEVCoVsrOzkZSUFG5xafCp13j9+vVITU2FyWTC7t27Z3wRD0CPFKO5SgiCgNPpxE9/+lMwGAwUFRXh61//Ou644w7weDyMj4/j6aefhtVqxfr16/H1r3893CLTRBl+vx8mkwkEQUAkEkEmk4VbJJqrZNmyZUhMTMSePXvw8ssvY2JiAg899BAAoLa2Fnv27MFf/vIX+P1+yOVylJaWRlw7JL/fD4PBgEOHDmH79u3g8XhITk7Gl7/8ZXR1deHUqVN48MEH6UkTEUZCQgJWr16NUCiE5557DrfffjtEIhH4fH64Rbth0IYdzVXhcDgwMjICp9OJUCiElpYW/PrXv8bx48dRVlaGUCiEvXv34v7778eSJUvo5rI0V4zf74fNZqM8dvRIseiFz+cjMTERjz32GD766CPs3bsXiYmJOHr0KE6ePImenh44HA6EQiHodDoMDAwgOzs73GJPg/QgB4NBKtVkdHQU//jHP+ByueByudDT04OWlhYQBEGHYyMAMu1nzpw58Pv9+Oc//4ldu3bB5/OhoqIizNLdOGjDjuaqcDgcGBsboxKKzWYzLBYLXC4XxsbGIBAI4HA4MHfuXGRkZNBjw2iumGAwCIfDAQaDAR6PN6N32DMdFotFDWrfsWMHent7sWPHDpw4cQJdXV3UdAoGgwG9Xo+RkZGIM+xCoRDcbjcVxiMIAi6XC/39/QA+HanW09ODffv2YXR0FHl5edBqtYiNjYVIJAKLxQqn+LOauLg4ZGVlIScnB/X19UhKSkJxcTG4XO6MzPmmV1uaq8JqtaK3t3faTUEQBPr7+9HX1wcejwetVousrCwolUoEg0EwmcwZeRPR3BhIDwmHwwGPxwOXyw23SDRXCUEQ4HK5qKioQHZ2No4fP44///nPCIVCCAaD1PPYbDbGxsbQ1dWFmpqaMEp8IcFgEC6X64L8LHIWqdfrRXNzMxoaGiCVSpGWloavfvWrqK6uRmZmJu1xDiM8Hg8JCQn40pe+hD/+8Y/QarVYsWIF4uPjwy3aDYFOBKC5KgwGA86cOUN1mic5N0zR19eH9evX4z/+4z+wffv2MEpLE434fD6YzWYoFAq66CbK8Xg8GBgYwDe/+U3s3bsXQ0ND8Pv9CAQC0wwlv9+Pjo4O1NfXh1HaixMMBr8w8Z78PDabDWfOnMF///d/48UXX8Thw4dvoqQ0FyMmJgYPP/wwEhIS0NbWhtdeey3cIt0waI8dzVVhsVjQ09NzgWImIStj9Xo9Dh8+DJ1Oh46ODtx6661ISUmBXC6/+ULTRBWkx04sFtPeuiiFIAi0tbXhxIkTOHbsGOrq6mCxWCgv18WYmprC0NAQvF4vOBxOxBQiBINBqvL/iyAIAgwGA0uWLMGiRYtQWFh4EySkuRRMJhNisRhLly5FW1sbDh06hG984xsQiUQzLlVoZn0amptCKBSCzWbDyMjItDDK+RAEAZ/Ph4GBAYyOjsJoNEKr1UIsFtOGHc0XQnpIhEJhxFVI0nwxgUAAVqsVhw8fxq5du3DkyBG43e5L6gzg0/xdg8EAk8mEuLi4iDHsQqHQZfdA4/F4iImJwerVqzF37lykpKTcYOlovghyjuyCBQug0+lQW1uLwcFBZGZmzjjDLjLuGJqogiyWGBoa+sLnkrvbQCCAlpYWNDY2YmBg4EaLSDMDIKtipVIp7bGLQmw2G44cOYKf/exn2LlzJxwOxxcadcC/Wik1NTXB4/HcBEm/GIIgLiie+DxYLBY0Gg1qamrw6KOPIisr6yZJSXM5rFy5EiUlJQiFQnjvvfdgMBhmXF872rCjuWL6+vqg0+kuGU4hYbFY4PF4SEpKwmuvvYZvfetbWLhw4U2QkibaIXPsZDIZnWMXhchkMtxyyy343e9+h/Xr14PP51+WZ4TFYsHr9eLEiRMRZdgFg8ELJmacD5vNRnZ2Nu644w78+te/piu5IxA+n485c+bg1ltvxeuvv06F/WcSM8v/SHNTGBsbg9FoBIPBuOROh8lkQiKRoKysDNXV1Zg/fz5UKhWt7GguC7LdiUajoUOxUQiLxYJAIMD8+fPB4XCQnJyMrVu3Qq/XX3JmZygUgsvlwpkzZyJmwfX7/fD5fJ/7OBnmUygUWL9+PWpqahAXF3dBcRlNeCHPR1paGhYuXIgtW7bg7NmzUKlUyM3NDbd41w3asKO5Ysh8OSaT+bmhFSaTCZFIhKysLKxevRobN25ESkoKreRoLhtyfqhIJKINuyiFwWAgKysL8fHxyM/Px8jICJqbm2E0GuF0Oi/6GjIU29HRAbfbjVAoFPY8O5/Pd0nDjslkQiAQID8/H+vXr0dZWdmMy9uaSWg0GpSVlUEkEuHMmTNISEhATk4OAMyINYoOxdJcMT09PRgdHf3cxxkMBiQSCSoqKvDaa6/h29/+Nm3U0Vwxfr8fFouFzrGbAUgkEhQVFeGdd97Bt771LcybN++S3iyfz4fe3l4YDAa43e6bLO2FeL3ezw0Lk57J5ORk/P3vf0d5eTkdlYhwuFwuVCoVbr/9dpw6dQpHjx6dUXl2tGFHc8V0d3dfsiK2pKQEDzzwAJ5//nlotVrweDzaqKO5Yvx+P6xWK6RSKZ1jF+WQRhyXy8VDDz2En/zkJ/j5z38OiURyyZYm7e3t0Ol0N1naC/F4PBcNCzMYDLDZbNxyyy349a9/DY1GM2OnGcwkGAwGhEIhNm7ciFAohM7OTtTV1V1W3ng0QPuKaS4bsn2JwWCAzWab9hiDwQCTyURVVRWWLl2KBQsWIDs7m542QXNVEASBQCBAtTuhw1rRD2ncxcfHg81mg8fjYWhoCHV1dRgZGYHFYpnmNWEwGOjv70dubm7Yx4t9Xo4d2atu6dKlKC8vp426KILD4SAnJwdqtRoejwenTp3C3LlzZ8ToN9pjR3PZhEIh2O12mM3mafkxTCYTXC4XCoUCmzZtwle+8hWsXbsWLBaLVnI0VwU5g9jlctENimcgMTExqKiowE9+8hOsWrUK6enpEAqF057DZDLR3d2NiYmJMEn5Ly6WY0cWh23atAlr1qyBRqOh9V0UwWKxkJCQgPz8fHC5XBw6dOhzG+5HG7RhR3PZeL1eNDU1TTPq2Gw2BAIBFixYgLfffhubNm1CZmZmGKWkmQk4nU44HA6EQiF6pNgMhcFgIDExEc888wxeffVVPProoxAIBJRnLxAI4OTJk+jr6wu3qPB4PBfk+iUmJuJ///d/ceuttyI9PT1MktFcKytXrkR6ejoOHz6M8fHxiGmxcy3Q8Q2ay8bn86G7uxter5cq79doNHjwwQexePFilJaWgsPh0LtWmmvG5XLB5XIBAF08MUMh9QSXy0VaWhoeeeQRKBQK7Ny5k2pObDAYMDk5CaPRCKVSGTbd4vP5puXYzZs3D8uWLcPKlSshFotpnRfFlJSUoLe3FxwOB4cOHUJNTQ1SU1PDLdY1QRt2EQKZU+T3+xEMBhEMBhEIBBAMBqmu536/H6FQCKFQiHrOua89H/K5F3Mts9nsC5QRuVMmQ6hMJhNMJhNsNhtMJhNWqxVdXV3wer3g8XhQKBSYP38+lixZgrKyMiiVyhtzcGhmHW63m9o5z8RZjjT/gqyiz83NxapVq+B2u8FkMtHa2gqHw4GJiQl0dXUhPT2dahQcDAYp/RYKhSj9R+rKiyXBX0ofkrruYgYak8lET08PFRKWyWRITU1Feno6gsEgpqamqNefqy9ZLBbYbDb1e7hbttBcnJiYGCQmJiIxMRH19fUoLS2lDTuaf3GuwiB//rzv5//t3Pw1l8sFt9sNm80Gr9dL7Rbtdjs8Hg98Ph/cbjc1d5GcqXk+Xq8Xfr9/mtFHvqdUKr0gSZTJZILD4VAd4smfJRIJeDwenE4nTpw4AYfDAblcjsLCQjz22GNQq9UIBoNwu93T8urOVZLn/+3zvtPQAP/y2JGLPu2xiw6uVgeSPxcUFEAul6OkpARPPfUU+vv70dPTg48//hgVFRXw+XzweDxwuVzTdKPT6QRBEPD7/Z9b6HAxfUjC4XDA5XIv2i+Rx+NhYmICQ0NDVF++mJgYOJ1O7Ny5k7o+SX0pFovB4/HA5/Mhl8up38/fTF+NnqS5/rDZbGg0GsyfPx8HDhzAunXrQBBEVB9z2rC7zgQCAej1elgsFpjNZoyNjcFgMMBqtcJkMsFoNMJms8HhcFCGnMPhgNvtht/vB0EQ075IGAwGlUTO5XLB4/EgFAopz5pEIrlAFtI4O39RDIVCGBsbu2BXGwgE4PV6EQgEqC/SoCSTh8l5j263GwaDAbW1tdROl8FgQC6XQyKRQCQSQSaTQS6XQyQSQSKRID4+HnFxcZDL5VAqlVCr1YiLi4NYLKb7PtFMw263w263AwCdYxdl2Gw2WCwWWK1WGAwGGAwGWCwWWCwWGI1GWCwWOJ1OOJ1OWCwW2O12uFwuOBwOKq8yEAhQzYkbGxtx5swZsNlsEAQBDocDgUBA6UIulwuBQAAWi0V5yS6mT0jv2fnXEkEQsNlsCAQCF+hEgiDgcrkovS0Wi2E0GrFr1y5s374dfr+fkvlcI+z8n9lsNoRCIWQyGUQiEaUfpVIpRCIRxGIx4uLiEBsbC6lUCoVCgfj4eCgUCojFYojF4ht0tmgAIDU1FevXr8ff//53dHR0IC8vL6rzJmnD7jLx+XxwuVyUsrJYLLDZbDCbzTAYDLDb7XA6nZSS8nq9VFPLc13ybDabGj0TFxdHKSOyyaVIJKKUFZ/PB4fDoV7P5XLBYrEoN/+5+WwX82iQ3rPzPXPkzvb8kAQZ5jg3xBEMBikPoc1mQ1dXFzIyMsDlcuH3++H1euF2u6nvZCiZVM7BYJBS6D09PdSsRRaLBZFIBD6fDz6fD5FIRBmFUqkUSqUSMTExkEqlkMlkUKlUEAgEdEhulkDeQ3w+H1wulw5jhRGCIOB2uylj22w2UxtSm80Gk8k0zThzOp3T9F8gEJims8jQJI/HQ2JiIqXTzt2I8ng8yngj9UtqauoFevTc15IbTPK9zocMh17sMbIa8mIh3EAggP7+fng8HsTGxkIsFk/TkT6fj4qckDqR9Bye61kk9eG5oWTyGAaDQbS2tlLpNWRRGo/HozbxMpkMQqEQIpEISqXyAn1JPk5vkq8cqVSK7OxsKBQKDA8Po7Ozkzbsoh3SwCFvRo/HQ4U8SVc/eQMaDAaMjY3BbDZPM+wcDgdcLhecTielcEjXvFwuh0AggFwup1zz5C5NIpFAKBRCKBRSRgyfzwePx6M8dKTCCycejwcWiwXNzc0oLi4Gn8+H1+ulPjP5ndyNn7sQkLtzo9EIg8EAl8sFj8czLSzC5XKpHaxEIqEMO7lcjpiYGCQlJVHH5vwdO2kc0nksMwe32w2fz0d5YujzeuMg+1OSBgkZ7vT5fJROJCMQZrMZU1NTMJvN1P1tMpmm6b9gMDhNBwoEAkrnKZVKSCQSCAQCSucJhULqOeRj5O98Pp+KaOTl5V2ymfGNpLe3F8FgEKmpqRdtuE4adaQDgFw7SP3ndrupSm8yzYD0UJLHzWg0wmq1Uvml526SWSwWZbgJhcILDLuEhARqI0yGfjkcDrUGkb+TIeNoDjPeCHg8HuLi4pCcnIzJyUn09PSEW6RrYtYZdp/Xo4ZMgh0bG0N3dzc6OjowPDyMkZER9Pf3Y3JyksrbYLPZlNs8OTkZ2dnZiIuLQ0xMDNRqNeLj4ynDJC4ubkY06eXz+VCr1Vi9evU1/y/SUB4fH8fU1BRMJhN17KempjA1NYXa2loMDg5SShIAxGIx5HI5EhMTUVBQAK1Wi+TkZGRlZSEvL4/ydn4e0X4OZhNOpxM+nw8KhYI+b9fIF/XlCgQCGBsbw8DAAMbGxiiPxcjICCYnJzE+Pg6r1UrlHbFYLKjVasTGxiI+Ph4pKSlQqVRUFILUhXK5HHFxcRfN5402vqiFE2nI8vl8SKXSa3ov0iCcmJiAyWSiokTj4+MwmUwwmUwYGhpCfX09ld5zrqdPIpEgNTUVGo0GGo0GmZmZyMrKgkajQWJiIpKSkr7wmpht9xwZLq+urkZdXR0aGxupYxSNx2LWGXZ2ux2Tk5NobW1Fb28vxsbGMDQ0hN7eXrhcLipEqdFooFKpkJ6ejsWLF0OlUiEmJgbx8fFITEykkmFJb9q5IdJzQwS0p+FCuFwutdvMyMiYFp44tyI4EAhQHoHR0VFMTk7CZDJBr9djcHAQp0+fhs1mg8/nA4/Hg1KphEqlohRZSkoK0tLSkJube9EqYJrIxeFwwOfzQS6X0+ftGiGr5vv6+jA8PIzR0VH09vZicHAQExMT0Ov1VI4Zl8uFRCJBcnIytFotysrKEBsbC7VajZiYGCiVSsTHx1ORBDJN5Hz9R/5M68Arh8PhUPrx3NQYMgfw3I4JZB70+Pg49Ho9zGYz9Ho9JicnYTAYMDg4iBMnTlCGOYfDgUwmQ1JSElQqFRITE5GbmwutVguNRoOUlJSoN8KvFjabjZUrV6K+vh79/f3o7e1FWlpaVKb/RJ/ElwFZeEAaAiaTCePj49QOyGg0YmJigip9ZzKZyMnJoUIBpKdNJpNBJpNNc3FLJBLI5XJ6qsI1QO76L0eBkOHe5ORkqujEZrNhamqKStAmQ7xk6EKn00Gn04HH40EikSApKQmxsbFQKpWIi4uDRqOhkpTpasvIhMzXlEgk9H12BdhsNqpQa2JigipWMBgMmJiYgM1mg9vtprwRCoUCMTExkEgkUCgUkEgkkMlkiI2NpdJGyL+JxWIIhUJIJJIZEYWIVK5EP5J50SqVispztNvtVA44uTE2Go2w2+3U3wDAarXCarWio6MDUqkUUqkUcXFxUKvVkMvlVAGHSqWizv1MhslkIjs7GzExMRgaGkJrayuSk5Npwy5ckEmvZF4Imcjf3t6OoaEhjIyMoKurC729vZSHRyKRID09HRqNBlqtFtnZ2UhOToZarUZKSgrt4YkQyORhhUJx0ce9Xi8mJyfR29tLGXS9vb3o6uqCyWSCzWajOtyr1Wqkp6cjNzeXOvdk3h6Zr0fmMtLnPryQhh3d/PXikAs6mctFJu2Pjo5Cp9NhaGgI3d3dGB4epsKpACAUCiGXy5GZmYm0tDSo1WokJCQgPT0dycnJkEqlEAgEYf50NJcLGUJUKpWf20eUIAg4nU4qnNvf30+F2UdHR9HZ2UmlPjAYDKSlpSEhIQEJCQnIyMhATk4OFbHi8XiUnuTxeDPKwcFgMJCUlISEhATodDqcPn0aq1atispiFAYxAwajkYn9J06cQH19Pc6cOYOTJ0/CYrFQiaZFRUXIz89Heno6cnJyUFVVRVWZfh4z5YKdyVzq8iUTkk+dOoX29nb09/ejvb0dZ86cQTAYhEgkQnZ2NpYtW4aSkhIUFxejsLBwWrsCmvDw9NNPo7W1FQqFAps3b4ZIJAq3SBGFz+eD0WhEfX09zp49i7Nnz+LYsWMYGxuD3+8Hh8NBeno6MjMzkZKSgqysLFRWViI9PR2xsbGXDI/S1/7M44uWeTLHubW1FWfOnMHg4CAGBwfR3t5OFTHFxMSgsrISZWVlyMvLQ3l5OZKSkmZM6JY8Rq+++ioOHjyI9vZ27N27F7GxsWGW7MqJSsPObDZjfHwcR48eRWtrK0ZGRqDT6eDxeKBSqRAXF4fU1FSUlJRQ1UJkGTj5RXoCaCU2cyFzUMiqNHLe4/j4OAYGBiivRn9/P3w+H5hMJhITE1FYWIjs7GwUFRVRY9LoPKGby/e+9z10dnYiOzsbzzzzzIwPA10Kv98Pk8mE3t5e1NbWoqenBzqdDqOjo/D5fJBKpYiNjYVWq0VmZiaVJE9WlZKV45/XKJeG5tyKXrK9jMfjgc1mQ29vL0ZHRzE8PIze3l7o9Xr4/X7w+Xykp6dTX2VlZSgsLKQq2aOVuro6fPLJJ/jNb36Dw4cPIzMz85oLYm42URGKJVtnkMpsfHwco6Oj6O7uht1uRyAQoHKnkpOTodFoKM+cUqmkmzvOUsg8lXMbkhIEAa1Wi9TUVIyNjSEtLQ09PT2YmpqC0WiE0+lEZ2cnxsfH0dHRgY6ODqqRckJCAlQq1YwKP0QqZLsHkUg06461z+ej+sPpdDpMTExQkw/GxsaoIi+yAj8+Ph5qtRoZGRnQarVU2Iw24GguF7I1yrmecbLoJjExEZOTk9DpdEhPT8fo6CiMRiPV5qa7uxtjY2Po6elBS0sLdf0lJiZGZf/RxMREJCcnw+/3Y3BwkKrsjiYi8miTOXPk7NSJiQkMDg7i4MGDOHjwIHQ6HaxWKzUGpKCgAKWlpaisrKTGYdHQXAxygkd2djays7NRXV0N4NNQxODgII4fP46TJ0+ira0NAwMDEAqFKC0tRWFhIRYtWoTKykqIRCKqJxTt9b0xkG1uZnrxBBkwObfBrdlsxsDAAFpbW3H48GG0tLTAZDIhGAyiqKgIhYWFyMnJQUlJCcrKyiAUCsPe55Jm5sFgMMDhcKDVaqHVaqm/e71eGAwGdHZ2ora2Fm1tbeju7sbHH38MPp8PrVaLvLw8LF68GBUVFYiPj4dEIqGaU0e6zkxISEBqairEYjE6OzuptlrRRESGYr1eL8bGxnDo0CG8+eabVCK8QqFATU0NysvLMWfOHJSXl1/QcRygc0RorozzZ/aSHeHHx8fx8ccf4+TJk+jq6kJ/fz/UajWqqqqwaNEi3HXXXYiPj6cX1RvA3XffDZ1Oh4cffhgPPvjgjB0pRk6BOXv2LJqamvDxxx/j+PHjcDgc4HA4qKiowLx581BUVIQ5c+ZQkxpIfUfPEqW52ZD6kuwqEQqFqJYrBw8eREtLC86cOYOGhgYqvaW8vBx33303KioqkJCQENHOF4Ig0NLSgh/84AeQy+V48MEHsXbt2nCLdUVEjGFHzuM7ePAgGhoaMDg4iPHxcahUKqSlpSE1NRX5+flQq9VQKBTUFAeAVmo0159AIACPx0O1jDAYDBgaGkJDQwN0Oh1sNhtkMhnmzp2LgoICFBUVIS8vL+J3o5EOWe15xx13YGpqCv/5n/+JDRs2zLi2NGRFYkNDA06ePImxsTHY7XbIZDJkZGQgJSUFWq0WSUlJUCqV1PxQPp9PX180EQfZlUKv18NqtcJisVAVt6OjoxgdHYXNZoNarUZycjLKysqwaNEiqNXqiAxz9vb24q9//SsOHjyIb3/72/jKV74SbpGuiLCazaSlT+bO9fT04MiRIxgYGIDX64VCoUBVVRVKSkqQnZ2NrKwsun8SzU2BzWZDLBYjMzMTmZmZ8Hq9mJqagkKhQEtLC7q6ujA8PIyGhgZMTU1Bp9PBZDIhMTGR2ngA9KbjaiBnawKYMTl2BEHA6/XCbrdjZGSEKtppa2tDb28v2Gw25HI5ysrKMG/ePGRkZCA1NRVCoXBGfH6amQ2TyYRQKERqaiqAT9d2p9OJjIwMdHd3o7W1FU1NTVT/UXIkXXp6OjUNQ6lURkz+skAgQFZWFt577z2YTCa43e6oagN00w27c8NePp8Po6Oj2LJlC44cOYLa2lrExcVh2bJlWLBgAW677Tao1eqorrChmRnweDwkJyfj3nvvxd133w2r1YqDBw/igw8+wLFjx/D6668jMzMT9913HxYuXIj58+dT120kKKpogSAI2O12+P1+sNlsyGSycIt01Zyr68iRhc3NzXj11VepqSkpKSlYv349Fi1ahJKSEmg0Gvp6oYl6mEwmJBIJqqqqUFVVhVAohImJCRw5cgR1dXXYtWsX/vnPf1JdCL7yla+guroaUqk0IlIMJBIJiouL4XQ6MTk5iYmJCWi12qi5N296KJYgCIyNjeHw4cPYt28ftm/fDqVSidLSUqxevRpr1qyBQCCgktNpDx1NpEFONiGbwk5NTaG7uxsvvvgimpubwWQykZ+fj29/+9tUyx2ayyMQCKC7uxuPPvoo2Gw2nn/+eeTm5kbl5i4YDMLhcGDnzp14//33qWrrqqoqrFq1CmVlZSguLqYavl7utAEammjj/IJIr9eLkydP4tChQ6irq0NraytycnIwb948rFixAqtXr6by58NBMBiEx+NBcXExFixYgDvvvBMbNmyIGlvkpnnsQqEQrFYrTp06hY8//hjDw8OwWq1YtWoV5s+fj6ysLGRkZFDNM6PlANLMPsg8OnIqBtkm4Bvf+Aba29sxODiIvr4+/OUvf0FhYSFKSkqwatWqGTEM/UZDeuyCwSD4fH5UVsUSBIH+/n60tLTg9OnTOH36NJhMJkpLS3HXXXdh7ty5SE9PR3x8/OdOVKGhmUmcOyaNy+VCJBKhvLwccXFxqKysREtLCzo6OqgUhdOnT2Pp0qXIysoKS0Uqk8kEl8uFVquF1+tFX1/fTZfhWrjhhh1BEHC73TAYDOjr68Pu3btx8OBBCIVCpKWlYcOGDaiurkZMTAzdBJYmKhEIBBAIBEhISEBxcTHa29uxZ88e7Nq1C1NTUxgZGYFSqURBQQHkcjmVqxFtBsvNgCAIOBwOBAIBsFisqJo44ff7qQTy48eP4/jx46ivr0cwGMTChQtRUVGB6upqaLVaWtfRzFpIvafRaKDRaFBaWorS0lJs3boVjY2NaGtrw8jICNWYOxgMQq1WU17tmyUjk8lEamoqLBYLhoeHb8r7Xi9uaCiWDFk1NTXhlVdewf79+zE6OoonnngCa9asQVVVVcQkS9LQXE+CwSBOnDiBV155BYcPH4bJZMIPfvADrFixAnPmzAl7Dkmk4vV6sX37dvziF79AYmIiPvzww6jQEQRBQK/Xo729Hb/73e9w6NAhpKamYuHChXjyySehVqujcuYkDc3NxGKxYHBwEM888wyOHDkCDoeDBQsW4Kc//SmSk5Nv6uzoUCiEP/zhDzh69Cj8fj927NgBIDp09g312A0NDeGTTz7BH/7wB4jFYlRUVOCvf/0r8vPz6bAUzYyGyWRi7ty50Gq16Ovrw9tvv41XXnkFu3fvxrx58/DjH/8YQqGQvgfOgyAImM1mKlwTDccnGAzigw8+wAcffIDm5mbw+Xz86le/QmlpKbKzs6lqPxoamksjkUiQl5eHzZs34/Tp06irq8OOHTtw55134s4770RNTQ1qampumjxpaWk4evQoxsbG4PV6qab0kc51N+zIJMmGhgacOnUKu3fvRnJyMiorKzFnzhyUlJRAJpPdNEXn8XgwNTWFN95446KPazQaLFy4EOnp6ZRMBEHg1KlT6OnpucAFy+Vy8eCDD0Iul0dd09QDBw6goaEBfr//c5/DYDDA5/MRExOD+Ph4FBUVIT4+nl6YrhAGgwGBQACNRgOBQIANGzaAzWajv78ftbW1eO2113DrrbciISEhqsrobzRkKJbD4UAgEES0EvX7/ZicnER9fT3ee+89WK1W5OTkoLq6GkuXLkViYiKUSuVNl6ulpQWffPLJF97nPB4PMpkMcXFxmDt3LmJjY2dcv0DgU8+LXq/H7t27odPppj3GZrMhkUhw//33X9BPjSx+efHFFxEIBKY9tmrVKsydO/eGyz7bIPPwNBoN5syZQ/Wr3bNnD1pbW2EymeByubB06dIbvjFmMBiUp93tdkOv10OtVkdFGsV1NexIo25qagq7d+/GsWPH0N7ejv/6r//CLbfcgpycnJuuqH0+H8bGxvDCCy/A4/FQQ45FIhFEIhEqKiqg0Wig1WqnXSTNzc3Yu3cvTpw4AaPRCD6fDx6PB6VSiVtvvRUikeiaDTsyVO1wOOByuaheVjeqK3dtbS1effVV2O12GI1GAJ+28Tg/gVsgECA+Ph7Z2dkIBAJYuHAhxGLxVctFtnswGAxgsVhUUvxsgMViITY2FrfccguSkpLw7rvvYuvWrXjxxRehUqkwZ84cpKSk0HM9P+N8wy4SIfWcwWBAc3MztmzZgrq6OtTU1GDVqlX48pe/HNbwcUdHB/72t7/BbrfDbDYjEAhQ9/m5MvH5fCiVSqSmplIe5piYmBk3SYU8Vx988AFqa2spfcvn8yGTyZCZmYkNGzZc1LCz2+148cUXYTAYEAgEqEIptVqN4uJiGI1GsNlsCIXCqMoHjQYSExORkJCAiooKcLlcfPTRR9i/fz/MZjOSk5ORmpp6QXuU601CQgLEYjE12jRq7g/iOhIIBAi9Xk+sW7eOSE5OJpYtW0Zs376d8Hq9RCgUIkKh0PV8u8uCfN9gMEjs2LGD+MpXvkIIBALie9/7HtHR0UEEg8GLyka+RqfTEdXV1cSzzz5LHDx48HOff7WyORwO4he/+AVRUFBArFu3jujr67vm/3up93M6nURDQwORlpZGpKenE5s2bSL8fj8RDAYJj8dD6PV64qOPPiJuu+02QqlUEjwej/jHP/5B9Pf3X/X7er1eoqenh8jOziZqamqI3//+99fxU0UPoVCIMJvNRF1dHVFSUkIkJSURX/3qV4mzZ88SwWAw3OJFBHa7nfjud79LrFmzhvj+978fbnEuSjAYJEZHR4mvfvWrRFZWFpGQkEC8//77xPDwMKUbwqHrSEKhEOHz+YjGxkZi8eLFhEqlIu68807CYrEQwWCQ8Pl8hMlkInbu3En827/9GxEbG0twuVziT3/6E3HmzJmwyX2jOHcNOH36NLFp0yaCw+EQa9euJf7yl79QOv1Sr3vmmWeIBx98kPjNb35D2O12wu12Ew0NDURWVhaxatUq4pVXXgnDJ5v5nHs/NTc3E3/84x+JmJgYorCwkPjDH/5ATE1N3bB7LRQKEV6vl3jiiSeIlJQU4v333ycsFssNea/rzXX1Kba0tOAvf/kLmpub8dBDD+G///u/MX/+/LAO/iXfl8lkXuBCPXfe4vmyXWwW46Wef7UQn+3+ySHgxEVqWU6ePImVK1fi0UcfxbFjx676vUi5z0/cJz8Xh8OBTCbDokWL8MADD+DOO++Ez+fDK6+8gvr6+qt+X+Izz+SlPuNsgMFgQCwWIzc3F3/5y19QWVmJ7u5u/OhHP8Lk5CS8Xm+4RQw7BEHAarWCx+NFpAfE4/FAp9Ph8ccfR1tbG4qKivDSSy9h8eLFUKlUF9xj4eBcXUX+Tn5nMplU+HHevHm4++678ZWvfAWBQADvvPMO9uzZEza5bxTnHo+EhASsWbMGHA4HbW1tOHny5CVfR16PHR0dMBqNuOWWW6giGFqn3XjOvZ9Iz+rLL78MkUiEjz76CE899RSsVusFofLrBZvNhkgkglAoxMTExA17n+vNdYv5DQ4O4vTp0zhy5AgKCwuxYMEClJWVhSXHJJpgs9koKirC+vXrERcXd9EQpdlsRm1tLex2+w0dRkwavzExMSgoKMD4+DgIgkB7ezt0Oh18Ph84HM4VL1osFgsSiQQbNmxATEwM8vLybtAniHzIRbW8vBwrV67EkSNH0NTUhAMHDqC6uhqJiYmzOiRLEAScTic4HE7EVZGGQiGMjIzg+PHjOHPmDMrLy7Fo0SLMnz8fcrk8KnJvgE8XSzabDYVCgaysLBiNRhAEge7ubgwMDFz1fR4NSKVSFBQUQCaTwWq1YmhoCOPj44iPj79oqkkoFEJ3dze8Xi9kMhlSUlLAZDLBYrGgVCpxxx13IDY2FpmZmWH4NLMLkUgELpcLoVCImpoaNDQ0oLGxEfv378eyZcsuSDW4VkiDUigUQigUwmKxIBgMXrf/fyO5ZsOO9MYcO3YM+/fvx5kzZ/DSSy+hoqICcXFx10PGGQtZqHD77bfj9ttvD7c409BoNNTcv4mJCRiNRng8nqvKL2Cz2VCr1fjd7353vcWMSsjzfv/990MikaCjowMvvPACNBoN4uPjoyOH4wZBEARsNhvkcjmEQmG4xZmG1+tFQ0MDXnzxRXC5XNx7771YvHgxNRc4GomJiaGMEoPBAL1eD5fLFdWj3C6FWCymmt52d3djYmICzc3NWLZs2UUNu0AggMOHD0MikSA1NZU610wmE2lpabROu8lwOBzExsbiO9/5Dl566SW89dZb2Lx5M7KysiAWi29I8Y9QKIREIqF66kUD17zFDAQCGB8fx+bNmzE+Po7vfOc7WLNmDWJjY6+HfDRh4vzQQrjDSzMRsViMlStX4ve//z1aWlqwbds2HDp0KNxihRWCIKhipUgrsHn33Xfx4YcfYnBwEC+//DKWLFkyIw2gmX6fs1gs3HnnnUhLS4PRaMTbb78Np9N50ef6/X68++67SEtLw7Jly26ypDSfR1xcHB566CE888wzqK+vxxtvvIGjR4/ekPcSCAQQiUSwWq0IhUI35D2uN9fssXM4HNi6dSt8Ph/Ky8uxfv36WVHh19PTg6amJrz33nvU3xITE/GjH/0IH3/8MXp7ezE2NgYmk4nk5GTk5+dj2bJliIuLA5PJhN1ux9mzZ/Hiiy/CbrdT/+PZZ59FWloaQqEQPvnkE+zbtw8NDQ3weDzo6enBr3/9a2zZsgXAp7vGO++8Exs3brzun0+n02FgYADAp5VBMTExYDAYqK+vx+uvv47JyUnquf/2b/8GhUKBI0eOoKOjAy6Xi2q1cO+996Kvrw+nTp2inr948WJ84xvfmBbuIUNwLS0tOHPmDNra2mCxWMBisSAUCqkqtIyMDOTm5lKvDQQCcDqd2LVrF3p6ejA6Ogqr1Uq1bElJScG6deuQkJAQcR4gBoMBmUyG3NxcLFu2DJ2dnRCJRKipqZm1hjTpseNyuRFTFRsKheByubBnzx6YTCbcdtttyMzMhFAojPpzpNfr0dXVBQBQqVRQqVQQCoUIhULo6elBb28vGhsbodPp4PF4QBAEYmNjMW/ePBQUFCAvL++i16rZbMbw8DD27NlDhTLJHLeamho4HA40Nzejo6MDBEEgJiYGZWVleOihhyAQCKhWI5988gm6u7sxMjJC5V4qFAqkpKTgtttug1qthlgsvqLPzGQyUV1djWPHjqGjowMnT56E2WyGTCab5vGxWq3o7++H0WiEVqtFTk4O1XB/z549aGxspJ67bt063HfffdPWPuKz8XinTp1Ce3s7urq6YLFYqLyt9PR0rFy5EsnJyYiJiQEAvPfee2hqakJPTw/1v/l8PrRaLb773e9CoVDA5/Ohq6sLr7zyCpRKJW677TaUlJSAyWRibGwMu3fvxv79++Hz+aj/8fvf/x4ajQZutxs7d+5Ee3s7pqamYLfbweFwoNFokJKSgrlz56KwsBA8Hi9ir21SrtjYWBQUFGD9+vWoq6uDTCbDggULwOfzr6vsHA4HPB4PLpdrdhh25GJ8+PBhKl8jJyfneskW0QQCAbjdbphMJpw9exZWqxXx8fFYs2YNhoeHMTk5CYPBAIfDge7ubuh0OojFYqxevRpMJpMaIm+xWNDV1YXx8XGYzWZ873vfg1arBUEQ8Hg8sFqtsNvtIAgCgUAADocDZrMZwKcKyuPxXLfPRBZxWCwWnDlzBmfOnAGDwUBJSQmSkpLAYrHg9/thtVrR29uLkZERGI1G5OTkIC0tDaOjozAajTCZTDAYDOjo6EBNTQ2sViumpqbQ0dFBNZ/9+te/Pu19fT4f6urqcOLECbS1tcFmsyEYDILNZoPP50Ov18NgMGBiYgJCoRDp6ekIBAIwm804deoUPvnkEypcHAwGwWKxMDk5iYGBAfD5fCxZsgQpKSkRl5BPttBZsmQJPvjgA3R0dMBkMkGpVEasYr2RnJtjFymGXSAQwMjICLq7u6FSqbB8+fKb2ovzekMm/VutVnR1deH06dNgMBjIzc1FRkYGWCwW9Ho96urq0NLSgtHRUWpwO9m7z263Uxuo9PT0af/b4/HgzJkzOH36NHbv3g2/3z/tfDY3N2NsbAxHjx5Fc3MzCgsLwWaz4XK5AHx6vC0WC+rq6vDJJ59gamrqove1QCDAwoULkZaWdkXGHZmIr9FowOfzMTIygtHRUSiVymmRJrPZjM7OTsjlcqjVasTGxlJ62+FwYGxsDN3d3TCbzUhKSsLGjRupcG4wGITX68WJEydw6NAh9PT0wOFwIBQKUYUco6OjYDAYmDNnDubMmQOJRAK3243JyUm0trait7cXarUaqampiImJoYwKn8+H1tZWbN++HfHx8UhJSUFRURGYTCaCwSBcLhd6e3upZP+8vDwEg0EYjUb09/djz549sFqt8Hq9CAQCYDKZsNlsGB8fh8FgQFxcHOLi4iJuI3w+ZFuulStX4g9/+AN6enowNjY27Xq8HrDZbLDZbHi93ugpkrmWktpgMEicPXuWUCgUxH/+538Se/fuvZZ/d8PZtWvXtHYnnZ2dX/iasbExorq6mvjlL39JHDp06ILHA4EA8eCDDxLp6emEVColNmzYQHz88cdET08PYTKZiH379hFVVVWEWq0mFi1aRDidzgvKs1977TWipqaGAEDU1tYSPp9v2uM7duwgxGIxUVVVRWzduvWajoHL5SIaGxupdicPPfQQ4fV6Cb/fT7hcLkKv1xMffvghcdtttxExMTEEn88n3nrrrQvanezatYtYs2YNwWAwiKKiIuLrX/86cfjwYcJqtRLd3d3E66+/TnC5XOLUqVOEw+Eg9Ho9sWHDBkKhUBD33Xcf4Xa7qePg8XiI4eFhory8nEhKSiLmzJlDbN++nRgfHyeMRiMxMDBAPPvss0RGRgZRUlJCbN68mfB6vYTJZCJ2795NpKSkEAkJCcS///u/EwcOHCDsdjvR3d1N/O1vfyMWL15MCAQC4re//S3R1tZ2TcfuRhEIBIienh6ipqaGmDt3LrFv3z7C6/WGW6ybTigUIvR6PSGTyYjvfe97EaNPzGYzsXnzZiI3N5f45je/Sdjt9rC2M7kc/H4/0dzcTLU7ueOOOwiTyUT4/X7C4/EQRqOR2LFjB/G1r32NiIuLI3g8HrF582aira2N8Pv9xI4dO4hFixYRGRkZxK9+9StidHSUMBgMRF9fH/Hcc89R9+lPfvITIhAIUMcjGAwSPT09xAMPPEAolUoiOTmZeOutt4iOjg7CbDYTbW1txCOPPEKUl5cTMTExBI/HI7Zt2zZNdrPZTBw8eJC6rx9++GFi3759hM1mI3p6eoj/+7//I5YsWUIIhULi5z//OdHa2npVx+j3v/89MW/ePAIA8bOf/Yyoq6ujHguFQsSxY8eIr3/968Tjjz9+0ffo7+8n1q9fT4jFYuLxxx+fptOcTifR09ND5OTkEBqNhqiuriZ27txJGAwGYnh4mDhw4ACxbNkyQqlUEhs3biRqa2uplkf9/f3E5s2bCYFAQDzyyCPEzp07p8ml1+uJTZs2EbGxsYRWqyU2bdpEtRQjz8H27duJJ554gvja175GvXb37t3E/fffT8jlcuJ///d/icbGRsLpdBITExPE3//+d+K+++4jGAwG8e677xKDg4NXdUxvNoFAgDCbzcS8efOImpoa4tVXXyUCgcB1fY9//OMfxH333UesW7eOGBgYuK7/+0ZxTR47vV6Pvr4+WCwWzJ07F9nZ2dfH2oxS5HI57rvvPixYsIBqnLh48WIsW7YM+/btQ2trKwwGA+Lj4yNiasXQ0BDluj8XoVAIjUaDDRs24J577sG8efMu6elau3Ytbr31VlRVVVHNOpOSkrBq1SoolUqw2Wy43e7Pff3g4CA2b96M7u5urFu3DnfddRdWrFhBhTVkMhn+4z/+A1NTU5iamqJet337dmzduhU6nQ4///nPUVNTg9LSUrDZbKSlpSEmJgalpaVYvnw5tmzZAr1ej1//+tfXfuCuM0wmE1qtFunp6ejs7ERdXR3mz58/I6cAXArSy+FyuaiE5UjA7Xbj6NGjyMrKQl5eXsR7Ms7HYDBg586dFzSIFwgEiI2NxdKlS/Hwww9jzpw505qV5+TkoKioCN/61reo8JZMJsPDDz+Mvr4+nDhxAlu2bMHjjz8OiUQCNpuNQCCAP/zhD6irq4NCocCPf/xj3HLLLZBIJGAymRCLxXjqqafw9ttv4+mnn76ovHv27MGHH36I0dFRPPXUU1ixYgXmzp0LNpsNrVaLmJgYFBcXY/ny5XjnnXdgMBjwxz/+8YqPS2VlJdVx4OOPP0ZSUhIqKysBfOoV6+3txd69e7FlyxYkJSVd0f8+e/YsXnrpJQwODuKBBx7AunXrsHz5cnA4HMjlcsTExOCFF17APffcg5MnT8LtduP999+nwtW33XYbfvazn6GpqQkKhQJr1qwB8C9v5p49eyAUCuHz+bB//37Y7XbIZDKw2WwQBIGPP/4YKpUK8+fPp2TS6XRoaGhAdnY25s2bR3lK+Xw+Nm7ciIyMDNTV1UWVJ5rJZEIqlWLOnDkYGRnByZMn8eCDD96w94uWKMo1GXYOhwMmkwkEQUClUkV8IvG5uSDEFbhUyed+0Unl8XjIzs6GUCikXPJcLhcxMTGQSCTw+XywWCxQKBQRYdjJ5XKkp6fjy1/+8rRWDXw+H3K5HCqVCvn5+V84s5PsAk4aImSvrMsNpdlsNpw8eRIejwdJSUkoLi6+YCafSCTCXXfdBafTSY046+3tRVtbGwiCQFlZGVJSUqbJIJFIkJiYCLVaDb1ej/7+flgsFsrojhTObT8hEomg1+ujJpfjekKGuAiCgEAgiJhQbDAYxPj4ODIzM6FUKiPq2rkcyNSFe++9d1oLGR6PB4lEgtjYWJSWllKGQSgUQl5eHu69914qH4yEvLdTUlLQ1taG/v5+jI+Pg8vlUq9tamqCXq9HSkoKKioqIBKJKH3IZDIRFxdHhTbPzdUlGRgYQEtLCwiCQElJCbRa7QX3dVJSEtRqNYxGI+VcuNL7Oi0tDYWFhRAIBBgcHMTQ0BCmpqYQFxeHnp4eTExMQCqVIi0t7YqNeYPBgFOnTiEQCCAjI4PKWyPh8/lISkpCQkICjEYjurq6YDabIZfLweFwoFQqkZubi6mpKXR2dsJms0EsFmNqago9PT1Qq9XIysrCxMQEWlpa0NLSgpKSEsjlcjidTpw9exZpaWkoKiqi3lMulyMpKQnt7e3Yv38/HA4HsrKykJiYCD6fj+zsbPzP//wPiouLL5hGFKmQazqp4ycnJ697uJT4rPNHtBh1wDUadl6vl6omkkgkEaOIPw8Wi0XltwUCgcu+APx+PzXD7lKw2WxoNJoLyuaFQiF1bJxOZ8Q0OZTL5cjPz8d3vvOda9qlKZXKq1YExGc5VT09PQiFQoiNjUVycvJFn7to0SLqNQRBYGJiAiMjIwA+vRbHxsag1+unvcbpdEIgEGBiYgJ6vR4mkyliPEHnIxKJwOfzqZzK2Ybf76f0CZ/Pj5g+dqFQiFpYIy1H83IQiUTIzMzEY489dsHYrIvBYDCQkpIChUJB3ZuBQAChUIjacDidTgSDQQQCARgMBmg0GqrZ+uDgIBwOB/h8PtLS0i7Qh3w+H1KpFDExMdM88OR9PTk5Sc3o9nq9GB8fp0Ygkni9XvD5fIyNjWFqagpGoxFisfiKDDuVSoX09HSoVCqMjo5ieHgYQ0NDiIuLw9mzZ2EwGKDVaq94hi7xWQFQT08PlZNnNptx5syZC57LZDIRCAQwNTUFk8lENcMVCAQoLi7G/v37MTw8TOWOjY+Po6urC7m5uaiqqsKZM2dQX1+PxsZGpKSkQCAQYHJyElarFXK5nGpZBXzawqqiogJdXV04deoU7HY7DAYDcnNzIRKJIBAIcMcdd1zxcYwEpFIpOBzOBfr/ekBe99F0TK7JsLtaD1i44PP54HK5lDFxOT1pQqEQ7HY7uFzuF3rZyN3k+RcAmXwJ/Et5zSQu59h8HoFAAB6PBw6HAzweDwKB4LKqmvx+P1wuF5VwvWHDhi98L7fbjeHhYSQnJ0dsuCHadobXE6/XC6vVCuDTVjBXWu14IyGnEMy0e/fzcDgc2L59O44fP46jR49ifHwcTqfzgukoMpmMKmwgi6BI45y8ny92PXM4nIsameff15dT8e/1ejE8PIykpKQrmmdN9kRbvXo1/vGPf6CjowN79uzB3LlzcfjwYbjdbqxbt+6KF3S/3w+Px0Mdh6eeegpPPfXUJV8jlUoxPDyM2NhYCIVCMJlMrFmzBl1dXeju7sbOnTvxyCOPoLu7GydOnMCGDRswd+5cMJlMvPHGG9i1axfmzZsHHo+HPXv2YM6cOdBqtdOcLVVVVSguLkZ+fj52796N3bt347nnngMAZGdno7i4GBs2bMDq1asj3klzM/F6vXC73ZDJZFFj3F2TYSeRSKgmxOPj40hJSYFarb4ugt0INBoNlEolQqEQent7P7d3EUkwGKSMAYVCQZWkh4OZutiTOR5isRgulwtutxtut/tzFwQSchi3SCSC1+vFzp07odFoLnnj8fl8qFSqK1L+NxO9Xg+bzUZVuM02vF4vLBYLCIKIKO8Ym81GYmIipqambohHINLwer34zne+g9OnT4PNZuOee+5BdXU11Q6EwWBg27Zt2LdvH+rr6yljl8lkgsvlQiQSwe12U/mSAoHgguvZ6/XCZDJd8N7n3tcejwcfffQR0tLSLnk/8Hi8q27sTU6P+Oijj9Dd3Q02m40HH3wQZ86cQWZmJlatWnXFm0ByaopEIoHL5cKzzz6LW2+99ZL/h8lkIikpifJSM5lMLFy4ENu2bcPZs2exbds2rFmzBoODg+jv70dNTQ1kMhnS09NRUVGBhoYGDA4OwufzYdu2bXjooYeQkZFxwfvw+XzcdtttWLZsGWV8NjU14fjx4+jo6MB//Md/4Hvf+x6WLVuG4uLiKzuYYWR0dBRutxsajea6/2+XywW73Y709PSIXTvO55qklEqlUKlU4PP5GBoaQlpaWkQbdkqlEnFxcZBKpRgaGoJer4fD4fhcz4DBYEB/fz/YbDbi4uLC1mGezMEKhUKUEiUIAv/85z8hFouh1WqRm5sblcYfg8GASCRCdnY2WltbodfrMTIyctFCnL1798JqtSIuLg4LFy5EQkICUlJS0N3dDbFYjKSkpAvyPAOBAJqamtDX1wepVDotNBEphEIhWK1W6PV6eL1eaLXaiPUo3kjIUCyTyYyoUCyHw0FmZiaampowMTExo0dukWkq9fX1mJiYQE5ODpYvX46CggKIRCLKeKqrq7sg74xs45GWlkZ5Ofr7+5GTkzMtlOl2u2G1WqlRZue+nsFgID4+HlqtFh0dHRCJRNBoNBekegSDQTQ1NaG/v5/q83Y154PP5yM3NxdxcXEYGRnB4OAgPvroI2paztVMTyKLTLKystDa2go+n4/Y2NiLro1tbW0YHh6GXq/H/fffP82AlUgk0Gq1SEhIQE9PD44dOwaj0UgdDzabDZVKhbKyMpw6dQqtra3Q6XQYHx9HTk7OBY6I5uZmNDQ0YMOGDdQGNxAIQCAQQCKRQCKR4Pjx42hqakJubm5UGHakl3hoaAgejwcFBQXX/b70eDxwu90XjcZFKtckpUQiQXx8PGJjY9Hd3Y3h4eGIDlfI5XJqVNbIyAjVi40MJZCyn9sLqKGhAQkJCUhISAhbcQjZpNfn88Hv9yMQCMDn8+HPf/4z3nnnHbS1tYVFruuFTCbD/PnzqZ5SLS0t8Hq9VHgnEAjAarXiH//4B1566SUcPHgQgUAAmZmZKC4uBoPBwNmzZ6HT6eD1eqmcCJ/PB6vViu3bt+Pll1/G1q1bI7IogSAISrkTBIHc3NxZa9g5HA6wWCwqbSIS4PF4KCkpgV6vx/Dw8EU9TTMJsliE+KwZcWlpKaRSKVVx6fP5YLPZLtpDk8lkory8HHFxcXC5XKivr4fD4aBy9Px+PyYmJqi+nRcjPT0dpaWlYDAY6OzsxMjIyLT72u/3w263Y8eOHXjllVfw/vvvX/V9zePxkJKSAq1WC6FQiKmpKfzf//0fVCoVsrKyvjBy8HmQTZzZbDaGh4fR3d09LWRNNlY/ePAg3njjDfztb3+blvdNbuYzMzORm5uLsbEx7NixAxaLBSUlJeBwOGAymYiPj8e8efPAYDBQV1eHY8eOIRAIQKvVXuCIOHbsGH7xi1+gp6cHdrud6hOanp6OFStWYPny5QA+7ZZgsViu6njebEKhEPR6PQYGBhAMBm+YYedyuSKu6O5SXLOUMpkMd911F44ePYra2toLcjAijcrKSnz/+98Hm83G7373O/zwhz/E7t27qea2Xq8Xk5OTeO+99/C///u/ePnll/E///M/SE5ODtsOnRxcPTo6ipGREQwNDaGxsREDAwMQiUQoLS0Ni1zXi9TUVPzgBz9AdnY2Tp48id/85jfYv38/VezQ19eHH//4x2hoaIBSqcRXv/pVcLlcrFu3Dl/72teQkpKCX/7yl/jb3/6GEydOwG63w2KxoLW1FVu2bMEf//hHVFRU4L777otIg8nr9eLvf/87HA4HMjMzqQVhtkE25FYoFBE1L1ckEuGOO+6AXC5HZ2cn3nzzzYjcIFwvOBwOqqqqwOFwqOk4Y2NjcDgcsFgsOHHiBN555x3U19df8Fo2m40nnngCVVVVsFqtePrpp/HJJ5+gt7eXaoj8q1/9Ctu2bfvcsZO33HILHn30UaSmpuL3v/89XnjhBRw7dgw2mw0WiwXt7e3YsmUL/vSnP6GgoACbNm265vt67dq1KCgogM/nQ3NzMxYsWICKioqr/n/5+fn4/ve/j4yMDHz00Ud49tlncejQIZhMJlitVuh0Orz55pt47rnn4HA48L3vfe+CTgAAUF5eThlcW7duRSAQoFqfAJ9OO1qxYgWkUinV3H3Dhg2fmyNnt9vxox/9CAcPHqQMZqPRiB07duDDDz8EQRCYO3fuFbd3CRc2mw0vv/wyLBYLMjIysGbNmutufJGh2NjY2KjRywziGt1rbrcbra2t+Pd//3ckJibi3nvvvaB9RiTh8XhgNpuxc+dOnDx5EuPj4/B4PFRHcDLnKzY2FklJScjMzMTq1asvWGxGRkZQV1eH5557Dl1dXbBarWAymZgzZw5uv/12LF68GBkZGbj77ruh0+lgNBphNptRXFyMkpIS5OfnY968efjd735HldZbLBaUlZUhLy8P5eXlePzxx6lO67W1tfjNb34D4NNdJpfLpXJAVq5ceVkFBy+++CL++c9/wmw2o729HcCnhnlOTg7i4+OxePFifPvb3/7c1/t8PvT09OBXv/oVWlpaoNPpYDKZUFBQAKVSCS6Xiw8++IBKiiYIAg0NDdi9ezd27dpFHSelUkmNWLv77ruRk5MDv9+PkydP4vjx42htbYXZbIbf76fOBzkupqioCBUVFeDxeNSUjKamJnz44YdUR3yfz0cVYUgkEpSWlmLZsmVIS0uLuIHt5NiiL33pS1iyZAmWL1+OBx54AMDMzav8PE6fPo2tW7fivffew4svvojq6upwiwTgX5Mafvvb36K2thY6nQ7/+Mc/kJSUFJE97Xbu3Ik//elPMBqN6O3thcvlokbXkWOYnnnmmc99fTAYxMmTJ7F161ZqtB+Hw6F6C2ZkZMBkMqG1tRWnT59Gfn4+8vLysHTpUnzjG9+Ax+NBU1MTdT7dbjc1eUKtVqOyshKjo6PYu3cvzp49i3feeQfr1q2j3j8QCMBms6GhoQHbtm3D+Pg4bDYbfD4f+Hw+BAIBxGIxSktLsWTJEmRmZl5Tew6CINDR0YHnn38eL7/8MuRyOd555x1UVFRckKYTCoVw6tQpfPTRRzh06BB6e3thsViQkJCA3NxcVFdX48EHH0RycjK8Xi9OnjyJI0eOoKurCyaTiQrj83g8asNeWlqKyspKamzjubjdbjQ2NmLjxo0IBAJ47LHH8J3vfAdSqZQq6HE4HHjooYdQW1uLxMRE/Pa3v8XChQsv8Hi3t7fj+PHjOHbsGOXEcLvdYDKZVEpMeno6HnroISQnJ0d8+zKj0Yj29nY88sgjWLRoEW655RZ86UtfAovFui66k/hsisqPfvQjfPDBB/jTn/6E5cuXX1Zlebi5ZvOTy+UiKysLubm5MBgM2LdvH5YtWwalUhkRvdrOh0ygX758OcRiMQYGBqDT6agBvywWi8pby8vLQ35+PuLi4i64UFgsFkQiERWmPRcyyZjJZEKtVkOlUk17XK1WQyaTgc/nIz4+HkKhECUlJdTj5/cElMlkKC8vx7p166DT6aiE5JqaGuTn5192BZNEIqHmvmZmZk57LCYm5rIuWC6Xi7i4OOTn5yM/P3/aY2w2+wKDnsvlQi6XIzk5eVobExaLBZlMRoUUuFwu5s6dCy6Xi9jYWLS3t1OzYkUiEbKysrBw4UKkpKRQuVdk77d58+ZRY85GR0dhNpshEAggk8mgVqtRU1ODtLS0iLsh/X4/hoaGcODAAfh8PhQUFFCh5dlIIBCAy+WCWCyOqJ0xueGbP38+DAYD2trasGfPHqxevXpa/8ZIgTSgFArFBfe5TCb7QiOIyWSipKQETqcTGo0Gvb29sNls4HA4kEgkSE1NRWFhIVJTU6HVagF8qtMkEgkYDAYEAgHy8/Op3p3d3d3w+XxgsVhITExEcXEx5WHj8XgX7SJApmc4HA709PRgeHgYZrOZapVC3tcXCzleDUlJSVi4cCHsdjukUim0Wu1Fi3cYDAY1r1ar1VKfH/hU15G9AMk80crKSjCZTKhUKnR2dsJsNlNN3BMTE7FkyRKkp6d/rvdSIBAgOTkZX/rSlxAIBFBeXj5Nj5HyrFmzBomJiVAqlcjKyrqoBzM5ORlLly4Fk8nE0NAQzGYzNSuWHJtWXl6OzMzMiFy7SUiD6+zZszh06BDcbjcqKytRUFBw3fWG3W6Hx+MBg8GAXC6PyIjPRbleIyzefvtt4t577yVUKhXx8ccfE5OTk9SIFBoamn8RCoWo0UB/+9vfiOzsbOKWW24hjh8/PqvvmcOHDxNf//rXiaVLlxK1tbXhFucC/H4/8dFHHxHLli0j0tPTibfeeosYHx8ngsFgxI8Yu1mQ17bH4yFcLhf1+7kEg0HinXfeIbKysgitVkt88sknYZKWJtoIhUJEMBgkBgYGiB/+8IdEZmYmsWbNGqKzs/O6685QKES0trYSDz74IJGRkUG0t7cTbrf7ur7HjeK6mbd33nknBAIBHA4Hvva1r+GZZ57B8uXLr/tAXhqaaIf4LHzy5JNPoqGhASwWC5s3b0ZiYuKs9dYB/8qxUyqVEZVjR8JisVBTU4PMzEzcc889ePbZZ3HgwAH85Cc/gUajmdXn7lz0ej2efvppjIyM4IMPPrjAizI5OUmNM7z99tuhVCrDJClNtEE2rH7kkUcwNTUFrVaLl1566aJRtWuFIAj09PTAYrFAKBQiIyMjIvXSxbhuiXBsNhvl5eX4+te/jtTUVCpPprGxMWImLdDQhBun04n+/n48/fTTaGxsRHJyMr773e9Co9FcVp7kTMbn88HhcFBzRyMNMsyYnJyMJ598EmlpaWhvb8eTTz6J+vr6C6YjzFaIzyYvdHV14eWXX8bIyAjsdjucTid6e3vx2muv4eDBgxCLxbjzzjuRmJgYbpFpooChoSHs27cPP/rRj2CxWLB06VJ8+9vfpooaboTuHB0dpVrwRFOLo+umPRkMBhISEiASiVBdXY0jR46gvr4eSqWSmjsqEomi5sDQ0FxPiM+mnfT29qKpqQn79u2DSqXC3LlzsWrVKvrewL9y7L5oNnE4IXNwb7nlFgwPD+PEiRM4deoUsrOz4XK5kJOTQ+XcztbzyeFwqHyvkydPQqlUUsn+Y2NjqK+vp/Ki5s6dG3EFTTSRA/HZmLrR0VHU1tairq4ODQ0NqKioQHV1Naqrqy9aTXy9mJycpOYbnztpK9K5rttiFosFuVyOZ599Fn/+85+xe/duPPXUU/B4PLj99tunddSPlgNEQ3MtEOc0lO7s7MTrr7+ODz74ALGxsfjhD3+I0tLSsE40iSS8Xi/sdjtVVBOpMJlMxMTE4PHHH8eyZcvw/PPP4xe/+AVqampw66234pFHHqE8jrNNzzEYDCiVSmzatAklJSV4//338fTTT8NoNMLpdEImk6GiogLr16/Hhg0bkJKSMuuOEc3lQRp1brcbr732Gt5//31MTU1h7dq1+NnPfoa4uLgbqicIgsDQ0BBYLNbnzi+PVG5IvIPFYuGRRx7BwoULsXXrVrzwwgv45JNPMG/ePHzrW99CUlJSRCtuGprrhd/vx+joKN588028/vrrEIlEWLJkCZ555hmo1eqIma4QCZCzYuVyeVToBx6Ph7KyMvzxj39EeXk5tm/fjmeffRbvvvsuHn/8cZSUlEyrmpxNZGVlQavVYvXq1fD7/dTUHCaTSbX7oK99mkvR2tqK48eP46233kJ3dzfWrFmD5cuX47bbbrtpzYJ7e3svqH6OBq67YUfuvsRiMTIyMrBu3Tp4PB50dnaiubkZv/3tb7F27Vrk5eUhNTWV6qFDQzOTCIVCGBgYQG1tLZqbm1FXV4eioiKUlJSgsrISiYmJVKsXmk8hB8hLJJKIDcWeC4PBAJfLhUKhoJrEtra2oqmpCa+//jpyc3NRWlpK9b6KBmP1esHhcKjedzQ0l4vb7YbRaMS+fftQW1uLsbExsNlsPPLII5g/fz7y8vKuqWfh5UJOnzIajcjOzr6gpVmkc0MzlBUKBSoqKiCRSPDPf/4TR44cwfbt2wF82h8mFAohNjYWYrH4ujUVpKEJJz6fj6ruPHHiBLZv34729nZwOBw88MADWLBgAQoKCgDMvjDdpSBHRXk8HohEoogsnvg8mEwmioqKoNFoUFhYCJ/Ph4MHD2J4eBgDAwNQKBRITU2FXC6HRCIBj8ejzz0NzWecO/5xcnISfX19ePfdd9Hf3081pv/a176G+Pj4i/YWvBEEAgFYLBbYbDYIhcKLzvmNZG649mQymcjLy8OTTz6Jr371q3jzzTfxpz/9Ce+++y4SExPx2GOPYf369VCpVFGxS6ehuRQjIyM4ffo03n33XWzbtg0FBQVYtmwZ/uu//gsJCQnUNU4v7NNxu91wu90IBAIRN1LsclEqlaisrERlZSU1ueGjjz7CO++8g/nz56O6uhp33XUXNQeVhobm081wV1cX/v73v+PEiRNobW1FamoqHnvsMSxZsiQsIzPtdjuam5sRCASQkJCA7Ozsmy7DtXBTtsUMBoMaWEy6VGtra3Hy5EkqJ6W8vBwrV67E0qVLLzrBgIYmUjGZTBgYGMBrr72GU6dOwel0Ii4uDps3b0ZpaSlSU1MRGxtLe6Uvgd1uh9vtpjq8R5PHjoQ8twRBUPl1GzduxCeffIIDBw7g448/xvvvv4/q6mrU1NSgqKgIubm5015LQzPTIQgCBEGgsbERx44dQ319PU6dOgW5XI7c3Fzcf//9WLlyZVg7aTgcDrS0tCA+Ph5qtZqq6o4Wbpr2JPNRYmNjUVJSAoFAgLi4OMTGxmJsbIyapdff34/U1FQkJiYiLS0NIpGINvJoIopQKASv14vR0VH09PRgcHAQ3d3dOHPmDDWTs7y8HEuWLEFSUlLEz1yMBFwuFzW6J9JGil0p5NxNgUAAhUKBUCgEuVyO/v5+DAwMYHR0FLt27UJTUxOysrKQkZGBpKQkxMfHXzCblIZmJkC2e9LpdBgfH8fAwACam5sxOjoKi8VCzUfPzc1FXl4ecnJywpp/73K50NvbC41GA4VCEXX66KZLS+7IKysrKS/dO++8g2PHjmHHjh04cOAAKisrMWfOHKxatYqaDcrhcGZtCwGa8EN8Ngje4/HA4/HAZDLhyJEj2LNnD7q7uzE2NoaCggKsW7cO8+bNQ0VFRbhFjiqcTie8Xi8YDEbENii+UphMJgQCAebOnYu5c+dCr9ejs7MTr7zyCk6dOgWDwQCZTIbly5djwYIFKCkpQUpKCng8HthsNtV0ldZ3NNEG6ZXz+/3w+/3URri2thZNTU04cuQI9Ho9UlJSUFhYiPvuuw8LFiyAWCwO+/VOEARcLhd6enpuWrHG9YZBkI22wsC5Pb5sNht0Oh22bNmCvXv3YmBgAB6PB2vWrEF1dTUqKiqogcrhPvE0sw+n04mhoSF88MEHOHz4MFpaWmCz2VBdXY0FCxZg6dKlmD9/PlgsFn2NXgXHjh3DW2+9hbfffhs9PT2QSqUzwrg7F1LfhUIhDA0NobOzE7t378a2bdtgMpnAYrFQVVWFdevWoaysDAUFBZDJZPS1RBN1BINBWK1WHD9+HKdPn8aRI0dw8uRJiEQiJCcno6amBnfffTfS0tIQGxs7bQMT7us9EAjg8OHD2LBhA37+859TaRPRRFgNu3MJBALUHLjJyUmMjo6isbERjY2NsNvtAIDExERUVFQgNzcX2dnZyM3NpXe0NDcEstT9xIkTOHPmDAYGBtDf349AIACNRgOtVouFCxciLS0NMTExUCqVVMiVvh6vnF27duH999/Hjh070N/fP+PHq7ndbrhcLhgMBkxMTKC3txe9vb1oa2vDxMQE1ey9qKgIBQUFyMjIQE5ODmJjY+nUFJqIw+/3w2g0orGxET09Pejr60NbWxtsNht4PB5UKhXKysqQn58PjUaDuLg4xMfHg8/ng8vlhlv8aYyMjGDfvn149NFHsXXrVpSWltLtTq4WMvSQmZkJrVaLrKwsqFQq8Pl89PX1YXJyEuPj4zh16hRGRkbQ2dmJvr4+qFQqyGQyyOVyxMTEUB4TGprLgdzXeDweOJ1O6PV6GI1G6PV6jI6Oorm5GVNTU3A4HOByucjNzUV+fj7y8/Mxf/78GelZCgdutxvBYBB8Pv+GzX2MJAQCAQQCAZRKJTIyMpCamor09HTI5XKcOXMGBoMBDocD7e3tVAj37Nmz1HOkUinkcjmV/0PrPJqbRTAYhNfrhc1mg9FohMVigcViwfDwMM6ePQuDwQCz2QyPxwONRgONRoPs7GwsXLgQmZmZUCgUEX296vV6TE5Ogs1mIyEhARKJJNwiXTERuSKx2WyoVCqoVCosW7YMer2eGgC8Z88eNDY2YmpqCnFxcZg3bx7y8/NRWlqK+fPnUz2wmEzmtCrEmb5Q0Fwe54bDyJE1gUAAExMTGBgYwNGjR1FXV4e+vj4MDw8jNjYWlZWVWL58Oaqrq1FZWUn3IbsBOBwOhEIhSKXScItyU2EwGGCz2VR3+2XLlsFut2NgYACtra3Yt28f6uvrMT4+Drfbjby8PBQWFiInJwclJSUoLy+HRCIBl8sFk8mk0gBovUdzrZybKnWuvnS73ZiamsLZs2dRV1eHtrY29PX1oa+vDzKZDNnZ2SgqKkJNTQ2qqqoQFxcXVVNGRkdHodPpoFAokJCQcNN6511PIiYUeynIiyoQCCAQCFCG3u7du3H69GkMDQ1Bp9NBIBAgJycHWVlZmDt3LhYtWoSkpCRqFiet5GgIgoDBYEBXVxc6OztRV1eH2tpa6PV6uFwuKJVKlJeXo6CgABUVFVi4cCHlRWKxWHQfuhvE888/j9raWoyOjmLv3r1R2cfuekEuoMFgkPKOTE5Ooq2tDQcPHsTZs2cxPDyMsbEx8Pl8JCcnIy0tDZWVlSgrK0NaWhq0Wi29AaG5JkKhEDweD86ePYu+vj50dHSgtrYW3d3dMJlM8Pv90Gq1yM7ORk5ODubPn4+KigqIxWKq2JF0rkTTdfjb3/4Wx48fB5PJxCuvvAK5XB5V8gMR6rE7H9Jty2KxwOPxwGKxIBaLqYoyk8mEqakp9PX1wWazwW6346OPPsKePXsglUqhUCiQnJyM1NRUqFQqJCQkICMjgw5hzHDcbjcsFgv6+/uh0+kwgTFJrgABAABJREFUNTWF0dFRDA0NwWKxwO12g8ViIS8vD9XV1dBoNMjKykJcXByUSiXi4uIgl8vpYoibgNPpRCgUioiquHDDYDCmbSJ4PB64XC7EYjFSUlJgNpthsVgwOTmJwcFBmM1mOBwOHDt2DIcPH4ZAIIBMJkNCQgLi4+MRExMDtVqN5ORkxMfH0+kDNBRk9Sp5TY2Pj2NkZAQGg4FyoBgMBng8HgQCAUgkEixcuBAxMTHQarVITU2FQqGATCZDfHw8YmNjweFwovIeJo/F6OgojEYjli1bFrVpIVF5d/P5fPD5fMTGxgL4tHO13W7HmTNn0NvbS4UxdDodBgcHwWKxoFarodFoEB8fj6SkJExOTkIqlVL/SygUUomcZOJ2NJ7Q2QR5I3q9Xrjdbni9Xupnl8sFq9WKqakpdHV1YXx8HEajERMTEzCbzeDz+ZBKpUhNTUV5eTnVTyw9PZ0+92HA7XYjFApFZdjjRsNkMiEUCiEUCpGYmAjg02R1m82Gs2fPYnBwEIODg+js7MTg4CAmJiYQCAQgk8kQGxsLuVyOuLg4pKWlITk5GSqVCmKxGAKBAFwuFzwej9KDpJeFZmYRCASotiNkyyafzwev1wu73Y6pqSkYDAaMjIxgfHwcZrMZZrMZExMT1KYiJiYG+fn5yM7ORlpaGnJychAXFzejnCN2ux16vR5utxvZ2dlRuwGKTqnPg8vlIiYmBkuXLsXSpUupnmPt7e3o6elBT08PmpqacOzYMUxNTcFms4HL5SIrKwvJycnIzMxEQUEBtFotEhMTkZGRAS6X+4ULPL3431gulSVAGnUejwc6nY4KT42MjKCjowNtbW0wmUxwOp0QCoXIyspCeno6ampqMHfuXOTk5CAxMZEeUh4h2O12BIPBqOwZFQ44HA5iYmKwePFiLF68mPo76XHp7u5Gc3Mzurq60NLSgr6+PrjdbnC5XEilUiplJTExEUlJScjOzkZ6ejoUCsU045rWf5HNF+lIEpvNRnWb6O7uRn9/P8bHxzE0NIS2tjZqYyUQCFBYWIj09HTMmzcPRUVFKCkpgUqlglKpvBkfKWyEQiF0d3djamoKbDYb5eXlUZsSMiMMu4tBhtiysrIQCATg8/ng9/upEEZLSwsGBgYwOTmJpqYmfPTRR/D7/QA+DX2kpaUhISEBiYmJ0Gq100q0k5OTKcOP5sZBEAQcDgfMZjOGh4cxPj4OvV4PnU6Hnp4ejIyMwGg0Tku8J0ME9957L5KSkpCWloa8vDyqyTWHwwGXy6U9ExGGzWZDMBiMynyWSIJcgMlm2X6/H4FAAB6PB729vRgeHqYiGcPDw2hoaIDJZILH4wGbzYZQKIRCoUBmZial89RqNZKSkhAbGwulUgm1Wh21C95Mgyz+IlNNTCYTJiYmMDo6iomJCSpcbzAY4PP5EAqFwOfzkZCQgNjYWOTl5WH9+vXUGpecnEzpShaLBS6XS/080wkGg6irq4PP54NarUZmZmbUfu4ZadiRCwOXy72gR45SqYRKpUJMTAyMRiOsVivMZjP0ej0sFgtsNhtMJhPlpu7r60Nvby+YTCZ4PB7EYjEUCgWkUinEYjGkUimUSiVEIhGEQiHEYjEkEgkVOiFn3dGL1b8gexbabDY4nU643W44nU7YbDY4HA44nU4qFOBwOOBwOGCxWCjFRB7PlJQUZGRkQCaTQaVSQSqVUuEnMgRFnm86Ty6yIXPs6FDstUHm5vF4POpvZDGGSCRCSkoKrFYrjEYjTCYT7HY77HY71aLC7XbD4/HA7/dTvRsJggCXy6V0GnmfiUQiiMViyOVyKrRL6kAytUUoFEIgEMyocN2NhjTEnU4nXC4X3G433G43bDYblWZCrlVOpxMOhwNWqxUOhwNerxd+v39adXR6ejqKioogEoko/SiTyag89fj4eGpNIzdWs1FXhkIhdHZ2Uk2Uo3nzMiMNu0vB4/HA4/GoSlngX4rPaDTCYDBgYGAAPT09GB8fx/j4OIaHh6HX6ymFFwqFIJFIIJVKqR2tTCajCjVIo4LsrUfueMgee+TX+b+f26KFvDFJhRgJivH8ViHkcbvUVzAYnPYzOZbL4XBQO0yr1QqLxTKtJ9Lk5CSmpqbg8/kAfBp6IhUPWfySlJSEhIQEaLVapKWlQSAQRPXNOJtxuVwIBoO0YXcDIIsxyBZS5xMMBqnCoqmpKeh0OgwMDGBsbAwGgwFTU1Mwm80IBALU/XyuIaBSqSjjjtzonttrTy6XU4Vq5HdSv507qeV8/Xfu30ljI1KNDlIfntsa5HL04/m6kqyCttvtMBqNMJvNsNvtlMOBNMTJnDi73Q6HwzHNoJdKpUhMTKTOd2pqKrRaLeLj46FSqRAXFxeRxzCckOlb3d3dUCgUSElJiepjNOsMu4tBKo+4uDjExcUhLy/vguf4fD5YLBaMjY1hYGAAOp1uWs6CXq+HyWSC0WhEMBikXsdmsylFR3oKSW+fUqmEQqGgvHwKhYLKcSF3w+cWdEQCfr+f2kGe62mz2+2UcUYqG4vFQikjq9WKyclJTE5Owul0UmFv4F9GG1nFp1KpsHTpUkoZaTQapKenQyqV0obbDMVms4HP50Mul4dblFkHk8mEWq2GWq3+3OeQSeV6vR4jIyMYGRmBXq/H1NQUhoaG0NTUBIvFAoPBAJfLNe21LBYLMpkMSqUS8fHxkMvlEIlEkEgkkMvlkEgklDeJ1IcikQhyuZxq5Mzn88Hj8SAQCCIyPOb3+6koj8PhoAoUSMPM6XRSG1gyKmGxWChPm91ux/j4OEwmE9xu9zT9SM4cTkj4f/buOzyu6lr48G96l2bUe7GKLbnIHeNecIxtaoDQIZBCEkLqDUluSCWkh04giUMH4wCmuuHeC+5FclW1ep2Rppfz/cE953PFBdmjst/n0WNLGmnWjGbOWWftvddOxeFwEBcXR1ZWFuPHjychIYHk5GRycnJITU0lNjb2nPOGe3PCcqnIi/B27drFLbfcQnFxcbRD+kJEYvd/zvVi1+l0xMXFYbPZyM3NVTY3llcayT32AoEAbW1tuFwunE4nzc3NyhtbHlaUV2vu378ft9utrFCSJOmMVTw58ZT7A8nzxCwWi/J9eePwEx+HvJvHqcPRcqk/EokoX5P7BPp8PgDlsQUCATweD36/n0AgQCgUOu1KVL6KlyRJGYKRh2Hk4eiEhASGDx+uDOHo9Xr+9a9/0draSkZGBt/61rcYN26cUnWTh9G1Wi06nU5pcyMOSn2LXAWWd/YQe6Nefud6viVJwmKxYDAYSElJYeDAgcrcPfkYIVfzQqGQsmPGzp07efPNNzGbzQwbNozi4mI8Ho9yPGlsbKS8vByfz6esZvf5fKeNaJw6gqHVapUWMCf+Kx//5FXEpz4ujUaj/Oyp5J1PTjwmwmfVTDlRlY91crzyvG154cGJTXxP7L0qTx8xGAzK8VFOUs1mM8nJyWRnZzN+/HglqZUTYXnI1Gw2K8fzE+e+ycfHE+fCiffPhevo6KCsrAy326305uvNRGJ3nuSDjU6nO+sVkfymlq/I5Ku0rq4uPB6P8nW5NYdc8ZLbdMgHjBMPmnJ5PhwOn9QcNxKJ4PF4lDdxV1fXafPIJElShjtOjVGuqvn9frKzs0/6nvz75Z+Xr5gjkYhyYD3xQz6oGAwG5QRgNBqVA5ec6MnD12azGZ1Oh9vtZsuWLXg8Hg4cOMDgwYOVip04OPUP8pVyMBhEo9GIodge6MS+evJ7/Ezk48euXbuU0Yvc3FymTp1KUVERWVlZSmInV7c8Ho9y3PP5fHi9XiVRlP+VP+SFAnJMchInN9JVq9XKMaurq+u0YdszHQ/lr8ujLKfe/sRj4onDrXKCZTQalQts+Tk6ceqNwWBQEi/5YldORuVqpHy8jImJwWKxYDKZsFgsyrFSTgSFS8flcnHkyBFMJhOJiYlKK7XeSiR23Ug+aMTGxiobwp+vSCSiJIIej0eZOCtfGfp8Pjo7O5XP5ZK/fOCR5yiduMT9xAPkidRqNYFAgKamJhobG5k0aZKSsJnNZiWBlZM1m82mzE2UrzRP/JAPSPKeeueTlEmSRGFhIa+99hrLli3jlVdeQa/XM2fOHGUo+nx/l9B7SZKE2+0mFAqh1WqxWq3RDkm4CHJ1qrOzkzVr1rBo0SLq6uqYMWMGDz/8MElJSefc7F0+lsnHPpfLpQxpnpj8nfpvIBDA6/UqownhcJiurq7Tfv+JoxCnOnEl6IlUKhU2m+2kOYAnJlvyopITq4cnXtzKFTe9Xt8jh5CFz7S3t3PgwAGl80Vvb7skErseQqVSYbValRPb2foTnW/fonNZtWoV8+fPZ8mSJfzqV79S9tj9vPgu5Ovnw2AwcP/99zNr1iw++ugjfvOb37Bu3TomT57Mb3/7W6VRtNB3hcNh2traCIVCGAwGHA6H+Jv3Qj6fj08//ZRnn32WRYsWMXfuXL72ta9x1113XVCTV7Vajc1mw2azkZycDFz6Y+G5XEwvP/Ea7l0aGhrYsGEDkyZNOuMCo95GJHY9xKkHgkt5YJAkibS0NOx2O+FwmMbGRrKzsy9ruV9+fPJqvWuuuYZIJMLatWtZtWoVXV1dfOc73yEnJ0epBAp9TyQSwel0EolE0Ov14m/dy4TDYRoaGnjttdfYunUrhw4d4uGHH2bixIkMGjRISerO53gmkiQhGtxuN42NjVRUVHDPPff0iUbMIrHrp+SFIJIk0dTURGpqatR2YTAajWRlZTFz5kxlvuHmzZvJz89n9OjRDBkyhLi4OHGA74PkOanhcBidTifm2PUibreb1tZWli9fzoYNG2hra2Po0KFcffXVFBQU9IkTpND3NTU10dTUhMfjYeDAgcTExEQ7pC8s+s3RhKhITExUViAeP34cv98f1XhUKhWDBg3ivvvu43/+53/QaDT8/e9/Z968eezZs0eZPyP0LZFIBJfLJSp2vYS8eCAcDlNXV8eaNWv42c9+RnV1NaNHj+Yvf/kLY8aMEUmd0GvIeyzr9XqGDBlywfPjeyJRseuHVCoVWq0Wu91OcnIy5eXleL3eaIcFgMPh4Morr+Tjjz/m0UcfZdu2bdx99938+Mc/5uabbyYrKyvaIQrdSJ5jJ682F6v/eoc333yTjz/+mJUrV3LVVVfx4IMPMnToUGJiYkRlXehVdu7cSVNTE2PGjMFms13QnNCeqvc/AuGiyIs1EhMTqauri3rFTiavyE1ISODuu++moKCA9evXs2DBAjo7O7nyyiuZOnUqOp1OnED6ALliJ68yFH/Tnsvv99Pc3Mzbb7/NJ598gkql4qabbuK2226jqKgIm83WI3bIEYTzIVefjx49SmdnJyUlJX2mD6BI7Poxs9lMfHw8jY2NZ2wBEC0qlQqdTse4ceOIjY3FaDTyzDPPsHbtWtxut7J5tWgh0PvJ7U5Er66eS5IkvF4vNTU17N+/n/feew+3283o0aO5/vrrmTJlikjohF5HPvbU1tYSDocpKirqM69jkdj1Y/I2Xps3b8bn8ykNPHsKlUpFcXExeXl55Obm8thjj/Hmm2+ybt06Xn75ZTIyMrBYLD0qZuHChMNhnE6nskuJ0LPI81rLy8v5z3/+wwcffEAgEODxxx9n7Nix5OTkRDdAQbhIwWCQsrIyGhsbycrK4oorrhCJndD7xcbGkp6eTmVl5Wn7O/Yker2eyZMnk5yczNq1a5k3bx533HEH999/PzNnzmTgwIHRDlG4SJFIhLa2NiwWi0jseqDW1lbWr1/P7373OyRJYvjw4TzyyCPk5eWJFcxCr+b3+1m7di1arZbMzEzy8vL6TJFAJHb9mMViITExUdnH1uv19siTq0qlwmw2k5eXhyRJBINB3nvvPdasWUNTUxM33HADQ4YMOWdne6Hnkbe3E0OxPYc892j//v3s2rWLDz/8ELPZzOjRo5kwYQIFBQWYzWYxDULo1YLBIPv37ychIYH09PQ+df4QiV0/ZrFYSEpKIhAI4HQ6cbvdPTKxk8XExDBs2DByc3M5fvw4mzdv5tixY5jNZuLi4khMTFTi7ytXXn2dnNjZ7XaMRmO0w+n35FYmDQ0NrF27lnXr1rFhwwbuuOMObrjhBiZMmNBnJpgL/VckEsHv93PgwAGGDBlCZmZmn3pNi8SuH4uNjVXah9TX19PY2EhiYmKUo/p8Go0Gu93OE088weLFi1myZAmPPPII+/bt49Zbb+Xqq69Gp9NFO0zhPEUiEdrb20lPT+/RFxX9hd/vp6mpiW9+85uUlZWRmJjI008/zXXXXack3n3pBCj0Tz6fj+bmZvbs2cONN97IoEGDoh1StxKJXT9mNBqJj4/HZrPR0tJCc3NztEM6J5VKpSzymDhxItnZ2QwYMIBXXnlF2e/v4Ycfxm6394l+RH1dOBymo6MDs9mMyWSKdjj92tGjR1m/fj1vvPEGDQ0NfPWrX2XixImMGjVKtKIR+pSqqiq2bNmCVqtl6NChFBQURDukbtU3loAIF0Wj0aDX64mLi6Ozs5OOjo5oh3ReVCoVKpUKu93OgAEDmDFjhjJEtG3bNt555x3Ky8vp7OyMdqjCOchbiok5dtETDAY5ePAgH3/8MWvWrKG1tZWZM2cyZcoUhg8fTlxcXJ9ZLSgIAI2NjRw8eJDMzEySkpKwWq3RDqlbiZJGP6fRaEhNTaWrq4v29vZoh3PBLBYLJSUl/PCHP2T+/Pm88847/OEPf0Cv13PFFVdQUFAgmhn3UJIknbR4Qsyxu3zkNibBYJCOjg6WLl3KvHnzCIVCjB07lp///OfY7fY+NaFcEOTXfW1tLQcOHGDEiBHY7fY+N31HXIb1cxqNhry8PNra2mhoaIh2OBctPz+fH/3oR7zxxhsMHDiQX/ziF/zP//wPCxcuJBKJRDs84QwikQjBYBCn04nVahXtMy6zQCDAsmXL+NGPfsTDDz9MSUkJjzzyCP/+979JTEzscyc7QQDo7Ozk2LFj7N+/nzlz5vTJfY1Fxa6f02g0ZGRkUFNTQ2tra7TDuSjy0KzZbCY3N5ff/OY3zJ8/nyNHjvDEE0/Q1NTE3LlzycnJES0aehC/309XVxeSJGG1WkXF7jIJhUK0t7fz/PPPs337dmpqavif//kfZs2aRX5+vlKlE1VuoS/as2cP1dXV6HQ6rrjiCmw2W7RD6nYisevn1Go1ycnJeDwenE4n4XAYtVrdKw/qGo0Gq9XKuHHjaG1txWAwsHr1apYtW0ZMTAxer5fCwkIxNNtDBAIBvF4vACaTScyxuwzcbjfNzc1s2bKFdevWEQgEKCwsZO7cuQwdOpSYmJhohygIl1RZWRlOp5PExMQ+179OJhK7fk6u2Hm9Xtrb2wkEAr26cqJSqdBoNFx33XUMHjyYIUOG8D//8z/U1tYyZcoUfvazn5GUlNRrk9e+xOfzKQtcrFarWBV7icjziiKRCFVVVWzatIlHHnmE5ORkbrnlFu6++26ysrLE+0Ho0+T3waefforP52PYsGFYLJY+uTBIJHb9nDzHzmg00tXVRVVVFQUFBX1iyDInJ4eUlBTy8vJ46qmnWLp0KZs3b+bZZ58lPz+/T86t6E28Xi9OpxP4rKei6GN36YTDYf7zn//w7rvvcvDgQSZNmsQjjzxCTk5On1sRKAhnEolE8Hg8bN26Vemm0FcvZvpeqipcELVaTWpqKkajEb/fT319vXJl09tpNBpMJhPFxcXce++93HLLLahUKp588kk++OADysrKoh1ivybPsQMwm81iKPYSCAQCHDt2jHnz5rFgwQLUajVXX301DzzwALm5uVitVrGThNAveL1e9u7dS1dXF4mJiQwbNizaIV0yomLXz6nVaux2OyaTCY/HQ2NjY59J7OCzx5eQkMD06dNJSkqisbGRtWvXotPpCIfDxMTEkJSUhFarFSe3y8zv9+P1etFqtRgMBrEKsxtJkoTf76eqqoodO3bw/vvv09DQwMyZM5kxYwZXXXVVtEMUhMvK6/WyZ88eNBoNycnJ5OTkRDukS0Ykdv2cSqXCYDAQHx9PMBikurq6T7YHsVqtjB07lhEjRvCnP/2JDz/8kGXLlnHkyBF+/OMfk5CQAIiVgJeTz+ejq6sLm82GXq/vE8P/PYHcH7C6upo//OEPbNq0ia6uLp5//nlGjx5NRkZGtEMUhMtKkiQ6OztZuXIleXl55OXl9cnVsDKR2AkApKam0tnZSWVlZZ+q2J1IpVKh1+t56KGHGDVqFOvWrePVV1+lqqqK6667jmuvvRar1SqSu8vE6/XS1dWFw+EQSV03qqurY9euXTzyyCOoVCrGjRvHt771LYYOHSrmMQr9UjAYpK2tjfXr1/PNb36T/Pz8aId0SYnETgAgLi4Ok8lEY2Njn6zYwf+vxsXFxTF8+HAMBgNtbW2UlpayZMkSXC4XN910U5/sRN4TyUOxVqu1T65Mu5wkSUKSJHbv3s327dtZu3YtZrOZyZMnM3bsWAYPHiyeZ6Hfamxs5NixY7hcLoqKikhPT492SJeUSOwEAOLj4zEajRw7dqzPVuxOlJGRQVxcHHFxcfziF79g9+7dHDlyhIKCAoYMGYLdbheT+S+xQCCAz+fDZrOJit0XEIlECIVCtLa2snz5ctavX8+uXbu48847ueuuuxg0aBBarTjUC/1XdXU1Bw8eRKfTMXDgQJKTk6Md0iUlLt8EANLS0rBarVRUVPTZit2pTCYTI0eO5OWXX+aBBx4gFArx5S9/mVdeeYX9+/dHO7w+z+v10tnZKYZivyCfz8fRo0e57777+Nvf/obX6+UPf/gDf/jDHxg8eLB4boV+b9euXWzdupXJkyeTlpbW56ckiMs4AYCkpCTsdjudnZ20t7ej1+v7fMVKpVIhSRLx8fHceOONDBkyhKeeeoq33nqLTZs2MXfuXO69916xU8Ul4vV6cbvd2O12kXxcBEmSqKys5KOPPmLJkiWUl5fzgx/8gCuuuIKSkhLRxkTo9yRJwul0cujQIaqrq/nWt76F2Wzu8+8LUbETAHA4HMTGxipDOn6/P9ohXRYqlQqdTkdKSgojRozg2muvJScnB6fTyZIlS1i5ciV1dXWEQqFoh9rnyEOxYu7XhfP5fJSXl/PBBx+wadMmWlpamD59OlOnTqWkpITExMQ+f/IShHORJIkjR47Q2NiISqVi5MiRfb5gAaJiJ/yfhIQEHA4HkiRRX19Pampqv9o3UqPREBsby/33309KSgqLFy9m/vz5eDwevv3tbzNhwgTi4uJQqVTihNlN/H4/Ho+HmJgYUbE7D/Lc11AoRHNzM6tWreLJJ58kJiaGkSNH8rvf/Q6HwyEW/gjC/5EkiU2bNtHa2kpcXBwjRowQiZ3QfzgcDhISErBYLFRUVFBQUEBKSkq0w4qKWbNmUVJSwuzZs3nwwQf53//9X0aPHs1vfvMbMjMzxUT0buLxeMQcuwsUDof54IMPWLRoEfPnz+fWW2/l5ptv5qqrrurVezwLwqUQiUT44IMPsNlsjBgxot+8R8T4hwB8NiRpMplITk6mrq4Oj8cT7ZCiRqPRkJCQwOjRo3nssccYNmwYR48e5eGHH2bLli00NTVFO8Q+wefziYrdeQoEAjQ3N/Poo4/y4osvUl5ezkMPPcQDDzzAmDFjlCqEqCYLwme8Xi/V1dUcO3aMlJQUxowZ029GXETpQQA+OyEYjUYSExNpaWnB6/VGO6SoMhgMJCUlMWvWLJxOp9JCYsWKFbjdboYMGUJaWlq/OEh0N7nnWiAQwO/3izl259DV1UVdXR27d+9mxYoVqNVqBgwYwLXXXsvIkSP7TRVCEC5EV1cXhw8fxuv1kpqaSmFhYbRDumxEYicozGYz6enp/b5iJ1OpVCQkJPD1r3+diRMn8sQTT/DEE0+wfft2rr32Wr7+9a8rCYlI8C5MMBjE7/cTCASIjY0VFbszkBPgY8eOsXTpUp5++mlsNhvf/e53mTNnDgMGDIh2iILQI0mSREtLC8uXL8fhcJCfn9/nd5s4kUjsBIXFYiEnJ4ePPvqIzs7OaIfTY+h0OoqLi3nyyScZO3YsH3zwAY899hg7d+7k4YcfJjMzE71eH+0we5WOjg68Xi8ajYa4uDiR2J2Bz+fjnXfe4YUXXqClpYVRo0bxpz/9ifT0dKxWa7TDE4QeS14E+PHHHzNnzhzy8vKiHdJlJRI7QWEymUhJSaGlpQW32004HBYnXD6rxmm1WmJiYpg2bRparZb09HS2bt3KCy+8wKRJk5g0aRJ2u11U7s5TV1cXgUAAtVotdp44RSgU4tChQ2zevJm33noLi8XC8OHDmTNnDtnZ2RiNRvF8CcLnqK2tpby8nKamJsaOHUtqamq/OjaLxE5QGI1GkpKS6OzsxOPx4Pf7+3yH7guhUqkYNGgQRqORhIQE9u3bx4oVK/B6vcTExDBq1ChMJpNYNXseTk3sxBy7z6oMwWCQqqoqNm/ezEcffURFRQU33HAD06ZNY/bs2f1m8rcgfBGVlZVUVFQgSRLFxcXExcVFO6TLSpyBBIXZbCY7OxtJkmhtbaW5uZns7Oxoh9Xj5OTkkJaWxrBhw/j+97/P4sWLWbJkCS+88AKjRo3C4XCIk+85uFwufD4farVa7DzB/0/q6uvr+elPf8r+/fsJhUI8++yzXHHFFcTHx0c7REHoFSRJYuvWrZSWljJs2DByc3P7VU9WEO1OhBNYLBays7NRq9U0NzdTV1cX7ZB6LJ1OR2ZmJk888QQ//elPycvL44EHHuBvf/sbK1asiHZ4PZ7T6SQQCChD3P09sauoqOD999/ny1/+MhUVFVx99dW88sorTJ48GbvdHu3wBKFXiEQiOJ1Otm3bRn19PV/+8pf75fxnUbETFFqtFovFgs1mw+1209raGu2QeiyVSoVeryc7O5sJEyagVqt566232LlzJy6Xi2AwyLRp0zAYDGKY8QzcbjeSJGEymfptUievet21axcbNmzg008/RaVSMXfuXMaPH8+QIUOwWCyi+isI5ykcDrN3716am5sxmUyMHTu2X06N6X+PWDgrtVqNXq8nPj4er9crErvzoNfrGTx4MAMGDCAYDPLKK6+wcuVKWltbld07LBaLSO5O0dXVBaBsyN3fkhd56NXpdLJ06VKWL19OZWUlc+fO5Wtf+xrp6en9stIgCF9EKBRi48aNeDwecnJyKCkpEYmdIKhUKnJzc/F6vdTW1kY7nF7DZDLx7W9/m5EjRyo9xyorK3nooYf40pe+REJCQrRD7DEkSaKjowOVStVvhxl9Ph/79u3jL3/5C8uWLWPatGn84he/4Gtf+1q/S3IFobsEAgEWLFhAYWEhkydPxmKxRDukqBCJnXAStVpNRkYGtbW1Yuus83Tiibi4uJjY2FgKCgp44okn+Ne//sWGDRt48MEHKSgoEFWY/9PZ2amsiO1PIpEItbW1vPHGG2zcuJFDhw7xv//7v1xxxRUUFRWJyq4gXKSWlhZKS0upqKjgpptuYuzYsf32IkkcRYTTJCUlEQgExFDsRYiJiSE3N5fJkyczY8YMDAYDpaWlvPfeexw8eJC2trZoh9gjeDweVCpVv2qn43a7qaqq4oMPPmDr1q10dHQwYcIErrrqKkpKSkhNTY12iILQazU3N7Nnzx6MRiM5OTlkZWVFO6SoEYmdcBKVSkVaWhp+v5+WlhZlgrdw/oxGIxkZGfzyl79k7ty5GAwG/vKXv/DRRx9x+PBh/H5/v3hew+Ew4XCYUCik/D8SiSBJkrKzSV/fQUH+O8utTNatW8djjz1GTU0NI0aM4A9/+AMjR44U7UwE4QuQJInjx4+zdu1aCgsLGTBgAMnJydEOK2rEUKxwEnmDcbVaTXt7O21tbaLP2EWyWCx861vf4pprruGNN97g6aefZvHixVx11VX84he/QKfTRTvES6qsrIyWlhY8Hg9xcXHYbDYsFgsxMTG0tbWhUqmIjY2NdpiXXCQS4aWXXlIWSdx0003cf//9SkPr/jpcJAjdxeVycfDgQdauXcvvfvc7MjIyoh1SVInETjiJSqUiNTUVs9lMa2srdXV1YsuniyCfrHU6HSkpKdx0000EAgF2797N4sWLkSSJO++8k8zMzD47HFldXc3mzZtZu3Yter0enU6HVqtFp9OxY8cONBoNFRUVdHV1YbFYMJlMmM1mbDYbw4cPJzMzk5SUlGg/jIvm9/tpbW3lpZdeYuXKlajVar761a9yxx13UFhYiMlkEnPqBKEbbN++ncOHD6PX65kwYUK/22niVCKxE06iUqlISEjAZDIRDodpbGykoKAg2mH1WvI8sqKiIubOnYtKpWL16tUsXbqUzMxMRo4cSWFhIVartc9VbiRJorGxkfXr15/x+1qtlsbGRuXiwWw2Y7VacTgcxMfH99qhFEmS8Hg8VFdXs3//fpYsWUIkEmHo0KFce+21jB07VlwoCUI3kKez7Nmzh/r6etLS0sjJyemzF8vnSyR2wmkSExOx2WxIkkRNTQ2hUCjaIfV6KpWKcePGkZOTw+TJk/n2t7/N73//e6ZNm8Z3v/tdRowYgVqt/tzk7tQ5eT09EZTnupxNKBQiFArh8XhO+rrBYOCuu+7qMa1QTnzez/Wcy3PqysvLmT9/PvPnz0eSJH7/+98zfvz4z30+BEG4cJIksX79elwuFzNmzMBsNvf7CycxDiCcRqVSkZiYiMPh4NixYyKx60aJiYlMmDCBRYsWMX36dPbu3cttt93GggULzrmFm9/vp62tjYqKCnw+32WK+OLl5OSQm5t73i1N1Go1VquVW265hYKCgh6zsCIYDHLkyBH8fv85b9vR0cG7777L3XffzYcffsiwYcNYtGgR1113Xb9epScIl4LP52PTpk0cOnQIu93OLbfcIqY3ICp2winkioTdbsdms1FfX08kEolyVH2HRqPBYDCQmZnJ7bffTm5uLhs2bOC1116jvr6eMWPGKFuUyX8LuQq0b98+1q9fj9vt5p577iErK6tHV+20Wi0Oh4PCwkL27NlzzgsESZLQaDRcddVVJCYm9ogDdDAYpLGxkWeeeYarr76aoqKi06pukiQpWxnt3LmThQsXkpSURElJCRMnTiQnJwej0djvqwiC0N28Xi+rVq3CbDaTnZ1NTk5Ojz4mXi4isRPOyG63ExMTQ0NDA+FwONrh9ClqtRqTycRVV11FSkoKOp2OF154gVAoREdHBxkZGWRkZKDVapXkpqWlhU8//ZQFCxYQCASYPHkyCQkJPbqzurzqtaioiAMHDhAOhz+3xYtWq8VmszFu3DgcDsdljPTs2tvbOXjwIG+88QbhcJhgMEhqaipGoxGVSkUkEiEUClFVVcW6detYt24d+/fv56tf/SozZ85k/Pjx5xxiFwThwoXDYTo7O1m/fj0pKSnk5eWJtkH/RyR2whklJCTgcDjYu3evGIq9RFQqFUOHDmXQoEEMGDCAp59+mhdffJEdO3bw/PPPk5ycjNFoBOCll15iyZIlbN++HUmSWLJkCZIkMXXq1Og+iHOIj49n7NixvPvuu597O41GQ2pqKmPGjCEvL6/H7O+4fv165s+fT0dHB//6179oamoiPT2dUaNGoVKp8Pv9NDY28u1vf5sjR44QExPD448/ztVXX60k3SKpE4Tu53K5qKysZPXq1fzhD39g3Lhx0Q6px+gZR0+hx0lMTCQuLo6amhoCgQCRSKRHDI31JSe2RLn66qtJTU1l48aNvPzyy9x///3cfvvtjBs3jsrKSl577TUqKiqUYfE333yTrq4uSkpKsNvtPTZ5SEhIYOzYscDpiz9OFA6HGThwIHfccUePeJ1JksTevXtZuXIly5cvV4bD161bR3NzM/Pnz6e9vZ3t27fzr3/9i46ODu6++25mzJjByJEjMZvNPfZvIgh9gdw6Kjk5mfHjxzNo0KBoh9RjiMROOKPY2FjsdjterxeXy4Xf78dkMkU7rD5JHrIsLi5Gq9XS3t7OqlWrWL16NYcPH6axsZGampqTVo82NTVRVlbGunXrmDt3bo+pcJ3KZDKRlpaG1WolGAyetfprs9nIzMxk6NChUU+I5Dlzy5YtY+/evcouGZIk4XQ6OXjwIG+99RbNzc0cP34cv9/PDTfcwPTp0xk6dGi/aLosCNEi7+RSXl7O3r17GT16NMnJyeL8dIKeeTYQos5utxMfH084HKalpYXU1FTxxrnEEhISlL1mq6ur2b59O++//z6BQOC0eY6hUIiKigreffddpk+f3mOX+BsMBhISEkhKSsLr9dLV1XXabeRt7PLy8sjPz49ClCcLhUI4nU4WLlzI4cOHUalUJ20N1tHRwTPPPENHRwfx8fFMnz6d73//+zgcjj6/m4gg9AQul4sjR45w+PBhfvzjHxMbG9sjKv09hXgmhDNKSkoiPT0dlUpFVVUV7e3t0Q6pX5B3qnj88ceZPn26crA6dRgzHA4rid369etpaWmJRrjnRa1WM3XqVNLS0s6YfKpUKr7yla8wefLkKER3urq6Op588kkOHz6M0+k8aVW4JEkEAgFqampIT0/ny1/+Mv/4xz9ITEzssVVTQehrPvjgA/bt24fJZOK2227r9ztNnEokdsIZ6fV6ZReAhoYGXC5XtEPqF+TE4fHHH2fLli1nrNadKBgM8tRTT7F///4eu8hFrVYzfPhw7Hb7aa1z5J055DkyKpUqqkOxzc3N7N27l/nz59PZ2XnWVj/hcJjKykq2b9/OqlWrCIVCUR9CFoS+TpIkIpEIS5cuVRaPxcbG9sjRimgSiZ1wRnK/tfj4eFpbW3G73dEOqV/o7Oxk586drFu3Ttn141yLDnbv3s3+/fupqqq6jJGeP7VaTWFhITExMac9FoPBwMCBA8nMzIzqThPyUGtZWRlbt26lurr6nImy2+2moqKCRYsW0dzcfF4NjAVBuHiBQIC6ujoOHz6M1WplzJgxaLVacVF1CpHYCWel1+tJT0+nublZmUAuXDrhcJiqqir+85//UFpaSltb2zmbQ0ciEVpaWli/fj3r1q0jEol8biIYDWq1miFDhuBwOE46AKvVamJiYrjmmmuIj4+P+lBmMBjkk08+YeHChef9M1VVVfz73/9m3759tLe397jnXhD6ks7OTjZv3kxDQwOZmZlMmTIl2iH1SCKxE87KZDKRl5dHbW2tmGN3Gaxbt47XX3+dV199Fbfbfd5JQiQSYdGiRbz66qtUVVX1yIbSDoeD7Ozsk7bVkiQJm83GvffeG/WGxOFwmNdff51Vq1Zx+PDh8x7WliQJv9/Pgw8+yOLFi8UFkCBcQo2NjcybN48RI0YwatQocnJyoh1SjyQSO+GsRMXu8tLr9cTHxzNkyBD0er2yY8H5DDMEg0GqqqqYN2/eSW1RegL5MeTk5FBYWIhGo0Gj0ZCdnc3IkSNJTk6O6mpSv9+v9KY7evToeSXG8mNSqVSo1Wri4uIIBAK0trZehogFof9pbW2lsrKSffv2MXnyZAoLC8WuLmchlnEJZ6XT6UhOTsbpdNLV1UUoFEKj0Yg30iUSFxdHYWEhM2fOJDY2lo6ODjo7O2lvb8fpdBIOh5XWG6eKRCK0trbyySefcPfdd6PVajGbzVF4FGeXnp6u7OUoSRLZ2dmMGjUKk8kU1deU0+lU9nl1Op3nvL1KpcJmsxEbG6ssMBozZgzJycmXIVpB6J9qa2s5cuQIXV1dDB8+nPT09GiH1GOJxE44K4PBQFZWFl1dXTidTjweDzabLdph9VlFRUUUFRVxww030NnZyZ49e9i1axdLlixh06ZNeDweVCrVGRdUSJKEy+Vi586drFq1iqlTp1JUVNQtCVN3zRvLzc2lsLBQGeYcMmQIs2fPvqj76K5EUJIkDh06xJNPPonH4znrnEa5MgefLSwqLi5mypQpjB49milTphAfHy/6aAnCJSAfGzZv3sz69evJzc1lxIgRpKSkRDmynkskdsJZyXPstFotbW1tVFdXM3jw4GiH1S9YrVbGjh3L8OHDuf3226mtreXQoUPs3r2bJUuWcPToUTweD1qtlmAwqPycJEn8+c9/JhQKkZmZidVq/cJJkLwTg8fjwev1EggECAQCeL1eIpEIoVDojMO/gUDgpN0mvF4vnZ2dqNVqsrKyMJlMtLS0sGHDBuVnVCoVFosFtVqNVqvFaDRiMBiUf00mU7e2Nti6dStLly5lzZo1+Hy+k76n1WqJRCJoNBry8/P50pe+xPDhwykpKSEtLQ2j0YhOp8NgMIgqtiBcQo2NjWzatIkjR47wwAMPYLVaox1SjyYSO+GsdDodiYmJ6PV6PB4Pzc3N0Q6pX5DnbhkMBgwGA1arFbPZjMPhICsri0GDBnHkyBGOHz9OZWUlu3fvxu12K73UGhsb2b59OwMGDGDq1Km43W68Xq/y4ff7lQ+v14vP5yMQCODz+fB6vUoy5vf7CYVChEIhwuEwgUBA+b/8dXkVbiAQOO1xhMNhIpGIMmdNHi6WJImOjg7Wr19PTU3NaY9dr9crFTKtVqt8aDQadDodOp0OjUaDVqvFYDCg0+nQ6/VK4mc0GtHr9RiNRkwmEwaDQfncarUqt1Gr1Sxfvpx169bh8/lQq9VEIhF0Oh0JCQkMHTqUjIwM0tLSlPmBqamppKamir1gBeEyCYfDrFu3jqamJux2OxMmTBAXU+cgEjvhrDQaDbGxsZjNZrxer0jsokSuYpnNZrKyshg9ejQVFRUcPHiQTZs20dzcTFNTE263m0gkQiAQYO/evVitVqUPYWdnJy6Xi87OTtxut5LEdXV1KYmfx+PB7XafVJGTGyRHIpGTFnOcODQJnHEY8sTbysLhMCkpKZjNZlpbW8+4Y4Y8HCr3lpObksr/l5M6nU6nJG4GgwGz2awkwUajEYvFgtVqVRI8+fmIiYnBZrOh1WpZtWoVu3btQqVSYTQa0Wg02Gw28vPzmTFjBiNHjqS4uJjU1FTlMQmCcHnI2/itWrUKn89HdnY2xcXFYuu+cxCJnfC51Go16enphMPh06orwuUXDAapr6+nubmZSCRCVlYW119/PeXl5dTU1FBeXk5LSwt79uxhz549PP/88yf9vMFgICYmBqvVis1mIy4uTkl00tPTcTgcGI1GjEYjNpsNk8mEXq9Hr9ef9LnBYMBisaBSqdBoNMTExJwWq1xdMxgMyteam5tZuXIl06dPR6/Xnza3LhKJ4HK5kCSJUCikVBTlD7fbfVLFUU5UPR4PnZ2dtLW10dTURFdXFy6Xi7a2Nrq6uvB4PCcNWcNnFy7hcBiNRqNMOygsLCQnJ4eCggJycnJISEgAPjvBiKROEC6vYDBIW1sb77//PrNnz2b27NkiqTsPIrETzkpevZiRkUFnZye1tbXRDqlPCwQCdHZ2UldXx7Fjx2hubqatrY36+nrq6+tpbW2lvb1dqaSpVCp0Oh1xcXGYzWYSEhLIzs7GZrOdNLR6/fXXk56eTkxMDGazGY1Gg1qtVoY61Wq18jX5XzlhO7E6J3+cWrE7tXonO1PFLi4ujquvvhqr1YparT7jookTF+hEIpGTqnWnfi5XE+WPcDh80hCw/H85SWxtbaWxsZGamhrWrVuHw+FAp9MhSRJOp5OmpiaqqqpYtGgRwWBQqQ6azWZSUlKIi4sjISGB9PR0kpOTSUxMJCMjg5ycHAwGg9jaSBC6UXV1NR999BEAo0ePZvLkyeIC6zyIxE74XCqVisTERFwuV4/eaL63CAaD+Hw+WltblTYyLpeL9vZ2Ojo6cLlcyhBlMBgkHA4TDAYJBoMYDAaSkpKUIUZ56NFut5/0udVqJRQKKUOq48aNIz4+HrPZrMxfixatVnvOZsSXYgeKSCRCMBjE5XLR0dFBc3MzaWlpWK1WtFot4XAYl8ulDE27XC5lJbg8TA2ftUZxu93U19crizscDgcpKSlKJdRut+NwOJSqaEJCAnq9XiR9gnABQqEQ9fX1rF+/nuLiYnJycoiLi4t2WL2CSOyEc0pOTubYsWM0NTUBYljqXOQq1KkVo1AopAwXlpaWUllZSUNDA7W1tcrz6/V6lT5pSUlJJCUlkZ6eTkZGBomJiSQlJZGVlUVaWprSR004N7VajcFgIDExkcTERAoKChg/fvxZby9JEu3t7bS2ttLc3Ex1dTW1tbW0tLTQ1NSkfO5yufB6vUiSpFTzsrOzycvLUxZaDB48GIfDoVT05IUg8geIuXuCcCq3201VVRWbNm3iwQcfJDs7G71eH+2wegWR2AnnlJ2dzcaNG6mpqVHmJAmfLxQKcfToUSoqKqiqqmLv3r3s2LGD48eP09zcjCRJxMfHk5KSQk5ODjNnziQzM5P09HTy8/MpKChAp9MpJ/yz/StcOg6HA4fDQV5eHuPGjVMS9hP/lauv+/bto6KiguPHj1NRUcHSpUupr69Xqtzx8fGkp6dTVFTE6NGjyc/PV1Y4izlDgnC6FStWsGbNGkKhEHfeeaeygEk4N5HYCeeUmpqKxWLB5/PR1NREYmKiOBmdQJ6btXfvXg4fPszx48eprq6mvb2dUCiEVqslPj6ekpISpk2bRkJCAnl5ecqcN5PJhMViURYtyKs6z3c7MaH7nfi8f97fQK1Wo9PpsFgslJSUKPMa5cUc7e3tHD16VNlvuaWlhVdffRWNRoPBYCA+Pp5BgwaRk5OjrPhLS0sTlQmh35Lnzq5bt46qqipmzZqlTGcQzo9I7IRzSkxMxGw2EwwGaWlpUSac9zfySk25dUhbW5syH66uro7Dhw/T0tKifN9sNpOYmIjD4SA7O5sBAwaQmpqqVOm6u9mucPnJw6lGo/G07wUCAdxuNwUFBVRXV5807N7R0YHH46GpqYlQKERtbS0HDx7k6NGjZGdnY7fbiY2NJSEhQRnGvRRzDwWhpwmHw1RXV3PkyBGCwSCTJ09WWhEJ50ccKYRzSk1NJSYmhnA4zPHjx8nOzsZkMkU7rEtOHnKTV2FGIhE6OjooKyujtLSULVu2sG3bNqWHXFxcHCNGjGDw4MEMHTqU0aNHk5GRgd1uF9tN9UNymxiHw8HIkSMBlNfR8ePHqampobS0lM2bN1NaWsry5cvp6OggISGB3NxcBg8ezIQJExgzZgwpKSnKjhwnrkwWhL5Ebna+atUqjh8/Tl5eHnPmzBEXNRdIPFvCOdlsNhwOBxaLhWPHjjF69Ohoh3TZOJ1Odu/ezb59+1i/fj2bNm3C7Xaj0WgYMGAAkydPprCwkKKiIq644gr0ev1Jk+PFCVg4lVqtVuZTjhkzhrvuukvZlu3gwYPs2LGDI0eOcOjQIf773/+i0WhITEykqKiIadOmceWVV5Kfn4/dbo/2QxGEbud2u/nHP/6h7JyTnp4ujqEXSCR2wueS31AxMTHEx8dTW1t7WqPXvkIeaj148KCys0NpaSktLS34/X5MJhOzZ88mMzOT1NRUpY+Z3N7C4XCIRE74XCcufpH7CMJnrz2z2cyQIUNITk6mo6OD9vZ2qqurOXr0KE1NTbS0tLBgwQJWrlxJSkoKAwcOZNSoUeTm5pKamqpcSAhCb9XQ0MDu3buprq7mxhtvZOzYsWK04yKIxE44L1arFYfDQWNjY59K7CRJwu/343a7cTqdNDQ0sH37do4cOcLhw4epqanBYrGQmJjIoEGDGD9+PIMGDVI2sRcnUqE7qFQqtFotCQkJym4XkUgEt9vNvn37OHz4MHv37uXTTz+loqKCyspKampqaGtro7i4mIKCAhITE4mLi8NoNPbLObBC7yZJErW1tXz66adotVqKiorIz8+Pdli9kkjshPNit9tJSkqisrLyjBu+9zYn7nggH0zWrFnDu+++S1dXFykpKYwaNYqf/exnjB07lqysLMxmcxQjFvobtVqNzWZj/PjxjB8/HkmS6OzsZNeuXezevZtVq1bx1FNPAZ8tcPryl7/MDTfcQF5eHsnJycrvERcfQm8QiUTYv38/7733HjNmzKCoqIjExMRoh9UricROOC9xcXGkpKSwevVq/H5/tMP5wsrLy9m3bx8LFixg+/bthMNh7HY79913H9OmTSM7O5vExERlk3mxIkvoCaxWK1dccQUjRozg9ttvp6amhj179rBjxw4+/vhj/vvf/5KcnMzQoUO5//77KS4uPudOH4LQE6xdu5ZNmzbR2NjIc889R0ZGRrRD6rVEYiecF5vNRnx8PG1tbXi9XoLBYK8b7nG73dTW1rJp0yZKS0uprq6mpaWFMWPGkJGRQW5uLkOGDKGgoAC73X7GFhaCEC3y/E2536HNZsNsNmOz2cjKyqKgoICysjJaWlqoqanhhRdeYOjQoRQUFDBs2DBycnLEBYrQ48j7Pq9fv57GxkYGDhxIXl5ev+i8cKmIxE44L1arlfj4eLq6uujq6sLv9/eKxE5ePu9yuairq2PHjh3Mnz+f48ePI0kSY8eO5eabb2bw4MHk5uaKxQ9Cr6FSqbBarRQWFlJQUMC0adPYsWMH27dvZ+3ataxevZqysjIGDRqkrOR2OByYTCbR7FXoMeS5pJs3byYcDjNu3DgSEhLERcgXIBI74bzExcWRmZkJQF1dHa2trb1in9JwOExpaSkvvPAC69ev5+DBg0yfPp3vfOc7TJkyhaFDh550e5HUCb2VWq1mzJgxjBkzhm984xts27aN+fPns2nTJt58803Gjh3LXXfdxZe+9CUKCwujHa4gANDV1cWSJUsoKytj+vTp3HfffWIl7BckEjvhvMTExJCcnIxWq6WhoYG2tjays7OjHdZZ+Xw+9u/fz/Lly3nrrbcwmUwMHz6cn//854waNYqEhASsVqtI5IQ+4dTXsVarpaSkhMzMTOrr69m3bx9vvvkmL730Eu+++y433HADt956K/Hx8b2i8i70TZIk0dbWxosvvkhRURFjxowhKysr2mH1eiKxE86LVqvFbDYTExNDR0cHLpcr2iGdUSgUoqmpiU2bNimTyuPj4xk1ahQlJSVMmjSJjIwMUeYX+jR5mNZqtZKQkEB8fLyyora2tpYVK1agVqsZNmyYsvpQXOQIl1tLSwtHjx6ltLSUu+++m6KiIjG3uRuIxE44L/Jm53LzVKfTGe2QTiPv47pjxw6eeeYZqqurMZlM/PSnP2X69OlkZGRc9pOXPDE4EAgQDAaJRCLKFmXy1lDyc6vVapUto76oYDBIMBjE5/MB/78hbkxMzHk9B5IkEQwG8Xq9hMNh5XdYLJbznp8ViUQIBAJ4PJ4zfl+v1yt7oMoxSZKEz+cjEAgo9yvTaDRYrVbleRPOj9lsJi8vj+9///ts3bqVtWvX8sILL1BaWsqsWbO4+eabGT9+PHq9XgyBCZeF3G6qoqKC7du309LSwsSJEykuLo5yZH2DeBcL502n05Gfn09TUxPNzc3RDuc0O3fu5LnnnuPWW29Fp9Px3e9+l5UrV3LXXXdFbem8JEk0Njby6quv8pWvfIVx48aRn59PRkYGgwcPZurUqdx7773MmzePHTt2dNvzumXLFh599FFSU1NJSUkhLy+P6dOnEwqFzuvnPR4P77//PuPGjSMlJYXU1FSysrJYtmzZecfQ2trKyy+/TEpKyhk/HnroIdatW3fSz3i9Xv7yl78o93vix7Rp0zh69GifapB9OalUKq644gp+8IMfsH79eqZNm8bGjRu5++67ef3113vke1rouyRJYs2aNbz//vtcf/31DBs2jKSkpGiH1SeIip1w3rRaLRkZGRw5coT29vZohwP8/03VX3vtNdasWcOBAwe45ZZbuPXWWxk0aBDx8fFRrfAsXbqUNWvWsGzZMiZMmMD06dNJT0/HZDLh8XhoaGjgyJEj/Pvf/+aDDz5g4sSJ/PrXv/7C91tcXExCQgITJkzgT3/6EzU1NReUEBkMBiZMmMBzzz3H+vXreeeddzh69OhJjZ3PJSYmhquvvpq3336bLVu28MEHH9DU1MS0adO45ZZbKCgoOC3h1uv13HbbbUyaNIkdO3bw+uuvM378eEpKSigqKiI9Pb1bNgSXK4PPPvssx48fJzk5mR//+McYDIYv/Lt7ohO3MtPr9SQlJfHQQw+xceNG1q5dyzPPPMPx48eZNGkS06ZNE6vDhUtu69at7N+/n66uLu644w7sdrt4zXUTkdgJ502r1ZKcnMyOHTtwuVzKST5ab0ZJknC73Rw6dIhPPvmE+vp6HA4Hc+fOZdy4ccrerdGKzeVysX37dtatW4fT6WTkyJGMGDGC3NxcrFYrTqeTqqoqbDYbK1asYN++fd3WTDY+Pp74+HiKiop49dVXL7gao9VqSU9PJz09Hb/fz+rVqzl69OgF/Q6DwUBOTg4ZGRkEAgHWr19PZ2cn+fn5zJo1i9jY2DPe78CBAxk4cCAGg4ElS5YwdOhQpk6dyqBBgy7o/j+PvC/wjh07OHToELm5uacN/QIcOnSIyspKrFYrJSUlvWIl+Lmo1Wr0ej3Dhg1Do9Gg0WgoLy9n69atRCIRkpKSGDx4sDjJCpeEfDG+adMmmpubiY+PZ+TIkWJuXTcSiZ1w3rRaLZmZmcoG5ZFIJKpzciKRCLW1tTz55JOsWrWKuXPncueddyoVh2iSJImjR48qe3vecMMN3HjjjcTHxyu3SUxMJDExkeLiYsrKyli1alUUI+6fNBoNWq32rItp5s+fz9NPP01RUREvvfRSn2kTIr8/hgwZQl5eHklJSTz66KMsXLiQtrY2/vrXv2I0GqP+PhL6Hnl/7nfffRer1crEiROjMv+5LxOJnXDe5Dl2AE6nk4aGBtLS0qIWz4oVK1i2bBnvvfcejz/+OOPHj2fgwIFRi+dEkiRx5MgRnE4nBoOBvLy8sw4h6nQ6rrnmGkpLSy9zlP2XvGr0n//8J+FwGI1G02873RuNRubOnYvFYmHRokW89tprFBYWMmfOHLEJu9DtGhsbWbZsGUePHuXrX/869913X7RD6nNEYiecN41GQ1paGjqdDrfbTWNjI6mpqVFZaVpXV8eaNWvYuXMn11xzDePGjSMrK6tH9eQym81otVp8Ph+HDx8+68IFjUZDcXEx3//+93tU/H2Z/JrtC0OrX5Q8727YsGF0dnZSU1PDO++8Q0ZGBg6H46QqsyB8EaFQiPr6ej744AOGDh1KcXFxVM4hfZ1I7ITzptFoSEhIwGAw4Pf7aWpqikockUiE/fv3s3//fjo6OvjGN75BXl4eFoslKvGciUqlIiEhAYvFQjAYpLS0lMOHD5OTk4PNZlNilduQpKamkpqaesbfJUkS4XCYrq4unE4nLpdLWQihVqsxm83Y7XasVitms/mCY5XbmjQ1NdHV1UUkEkGlUmEymUhKSsLv91/8E9HN5LmLra2ttLW1KV93OBykpaVRX1+Px+MhGAyiUqkwm83ExcURExOjtGnx+Xx0dXVRWVmp/LxarWbo0KFotVqCwSBtbW3U19dTV1dHOBzG7Xazf/9+pX+jSqUiPz+f2NhYpaWN0+mks7PzrH8fs9ms/H164oksJSWF4cOH09zczCOPPMK+ffvIysoSiZ3QbTo6OqioqGDbtm3cc889FBQUiIurS0AkdsJ502g02O12YmNjCYVC1NTUXNAqye4gT3r/xz/+QUdHB6NHj+b222/vcSdKlUrFsGHDyM7OZvPmzWzevJmf//znXHXVVcyYMYOxY8eiVqtPe/5OfRzy9zs7O9mwYQOLFy/mk08+oampiXA4jMlkYtSoUVx//fVceeWVjBgx4oy/50zk393S0sK+fft47rnn2LJlCx6PB61Wy5AhQ/jOd77T49pgbN26lZdffpn58+crX7v55pt59NFHefTRR9m+fTtNTU1oNBpGjhzJbbfdxlVXXaV0tK+pqWHDhg18/etfJxKJAGAymaiqqiIuLo7m5mbefPNNHnvsMaVf4549e7jpppuU+9NoNHz44YfMmTMH+Cw5XrNmDStXrmTFihXU1dUBYLFYGDVqFNdddx2jRo1i+PDhPbpXXG5uLnfeeSevvfYa69evx+/3M2rUKKBnJqNC7yAfazZv3syqVatwOp3ceuut5OXlRTmyvkkkdsIFS0tLU1Z0Xu7Ezu12U11dzebNm/nKV77Cdddd12NPOGazmXvvvZfc3Fz+/ve/s337dvbu3cu//vUvkpOTGT58OIWFhQwZMoTJkydjNBpPm8QfDodpb2/nu9/9LocPHwbgkUceYeDAgeh0Otrb25k3bx7//Oc/WbBgAd/97ne59tprz3u+WE1NDU888QRLly4lEAjwyCOPUFRURFJSEq2trSxcuJCysrKoJPFnM3HiREpKSvjhD3/IL37xC7Zt28aePXt48sknueOOO/jhD39IOBzm8OHDPPLIIzz33HPs2LGD5557DoCcnBxSUlKYMmUKjz/+OOvWrTtpxW9ycjLf+MY3uPnmm3nyySd55ZVXyM/P589//jM5OTnAZ0lOSkoK8Nnf6E9/+hNr167F6/Xy61//mqKiIlQqFR0dHbz33ns8/fTTDB06lB/96EeMGjWqx75mVSoVRqORb3/72/zrX/9iy5Yt1NTUkJ6eLnZrEb4Qj8fDkiVL2LlzJ/fccw9ZWVk9apSlLxGJnXDBEhIS8Hg8NDY2Xvb77uzs5ODBg0QiEXJzcxk0aFCPPEnKfcByc3PRaDQEg0F27txJbW0tjY2NHDt2jK6uLg4dOsTOnTvZtWsXV1xxBfn5+SftwdvZ2cny5cvZs2cPNpuNsWPHcuWVV5KamopGo8HtdnPjjTfy73//m2PHjvHOO+8wfvx4kpKSzrlDRCQSYdWqVezevZvOzk5mzZrFlVdeSXZ2NlarleTkZGVoc/fu3Zf4GTs/8vCq2WxW/q9Wq7FYLGRlZVFcXKwMHTocDrKysqivr6e0tBSPx4PRaESn06HT6bDZbNjt9tN612m1Wux2O3a7HYfDgVqtxmAwkJmZecYKgyRJ7N69m/b2dlJSUhgzZgzp6enAZyczn8/H1q1b8fv9+Hy+HpMgn4lKpUKj0TB69Gj++9//0tDQwMGDB0lOThaJnXDRwuEwW7dupbKyEp1Ox6xZs5T3rtD9xLMqXLCEhAS0Wi2NjY2X/STlcrkoKyvDarWSkZERtR0lzldCQgLDhw/nhz/8IV/96le59tprlcTM5XJx4MABPvroI/7617+ycOFCdu3aRSAQUJ5Xl8vFBx98QG1tLZmZmVx33XUMHDiQ2NhYrFYrSUlJ3HTTTQwZMoRwOMzHH39MXV0dXq/3nLGFw2GWLl3KsWPHsFgsfPnLX6a4uJjExERMJhPJyclcffXVjBgxQtma7GKdmHxfitdMUlKSsoAmJiaGmJgY8vLyKC4uRq1W09DQgMvlUoZeu1tDQwOSJBEbG6v0ItTpdMTFxfGlL32JUaNGkZOT0ysaIKtUKgoKCkhKSkKSJEpLS897xxJBOJW8peInn3yiXPxMnjz5vLcmFC6cqNgJFyw9PZ1Dhw5x6NChy57YdXV1UVFRwaBBg6LagPhCyKsOZ8+ezezZs5V5grt27WL79u2sWrWK999/nxdeeIG9e/eSk5PDkCFD0Gq1dHZ2smzZMjweD1lZWUyYMOG036/T6Rg0aBDFxcWsWLGCzZs3Yzabz9gA+ESRSISNGzfS2NjIsGHDlOHgEzkcDtLT08nOzqa8vPyinwN59w+5Oen5kBeNnM/OIQ6HgyFDhpxWAUhOTsZisdDV1UVTUxN2u71bdq44kUqlYvz48SxdupSVK1fy85//nJtvvpmBAweSkZGBWq3mmWee6db7vNTUajWZmZnU19dz5MgRkdgJF01eaPfSSy8xa9YsZs2ahd1uj3ZYfZpI7IQLlpqaitFopKmpCa/Xq3SvvxzC4TBerxej0XjS5vE92Zli1Ol0DB48mJycHCZPnkxMTAwrV66kubmZ1atXK/34/H4/XV1dSp+1U+fOndi2IyYmBoD29vZzVuwikQjBYBCPx4MkSRgMBmV489Tfr9frv/CB2GQyodFolBWm53NBIK8EltvGfB6dTnfG+TparVZZpHKpqnUajYaHHnqIkSNHsnv3bkpLS/nlL3+JWq3GarVSXFzMlClTKCoqoqio6JLE0J3k14DBYECn0+H1env08LHQsx07dowPP/wQlUrFxIkTmTJlSq84bvdmIrETLlhcXBwWiwWfz6c04L1ciZ1arUan0xEIBM64BVRPIUkSzc3NlJaWEggE+NKXvqR878RkzGq1YrfbGTlyJNu3b6erq4uGhgal5YjcDgU+S8bOtttHOBxWng85mTmXE3+3nPic7ee+yIldpVLhcDjQ6/WEQiFaW1vPmWRFIhH8fj8dHR3ExMSccwhTnht2pq9/0ZPI+fx8dnY24XCYxMREkpKSqKiooKOjA5fLxcGDB3G73dTW1gIwcODAXjG3KBgMEgqF0Ol04kQsXBSv10tVVRUbNmxg6NChyi4nwqUlEjvhgiUmJmK324lEItTX1xMbG3vZ9vnT6XTExsZy+PBh/H4/kiT1yJOOJEmUl5fzwgsv0N7ezvTp09FoNGeMVa1Wk5+fj8ViweVyKRPsNRoNBoOBmJgY2tvb8fl8uN3uk4ZY5YSrq6uLzs5OJYk616pYOUG2Wq24XC6lMhgTE3NSgiTPj+ns7Lzo50JeQSr39KuuriYQCHxuIun3++ns7KSlpYXk5OSL6s/XXc7UgqarqwudTqdsSdbZ2Ul6ejq5ubnMnDlTaQe0f/9+XnrpJd577z127dpFKBQiPz+/Ryd2cl++zs5OPB6PsoBEEC5US0sLZWVlbN68md/+9rfk5uaKuXWXgXi3ChcsPT2dxMREVCoV5eXluN3uy3bfdrudkpISSktLqauru6z3faHkk2NdXR07duwgEAic8XbhcJi9e/ficrmwWq3K/DoAm83GnDlzsFqtVFZWsn79+tN+PhAIUFpayv79+zEajUyYMEHp2fZ5VCoVkydPJi0tDZfLxerVq09bJOF0Oqmvr6e8vPyiq3YqlYqsrCzy8vKIjY1lx44dHDt2TOkRdyZbtmxh165dJCQkMHDgwKjNydHpdBgMBoLB4EkLWqZMmcLvf/97Vq9eTTAYZMyYMTzzzDNUV1cDn1VNc3JymDNnDi+//DIFBQXU19ezZs2aSzYk3J2cTifl5eW0trYycuTIbp+XKPQPr732GitXriQ9PZ0777zzvI5Lwhcn3q3CBdNqtVitVuLj4zl+/PgXXjF5IeLi4hg1ahQ6nY6ysjK2b9/O1KlTL9v9XyhJkqitreUvf/kL9913H/n5+SQnJyuVsrq6Og4cOMCCBQsIh8MMHz6cadOmKVuLORwO7r33Xvbu3cuxY8d4+eWXSU5OZsCAAeh0Ojo6Ovjvf//L9u3bMZvNfOUrXyEtLe28KqgajYbbbruN1tZWdu7cyb///W8cDgcFBQU4HA7a2tpYuHAh69atw2Aw4PF4Luo5kIdDp06ditfr5d///jdPPPEE06ZN48orr6SgoACTyUQwGMTlcrFr1y7eeustnE4n9957LyaTKWpV2bi4ONLS0qitraW5uRmLxUJdXR3V1dXEx8crbU38fj+rVq0iGAxyxx13kJycrOySsWrVKtra2oiNje2x7XlOFAqFWLlyJa2trcTFxTFy5Eix1Z1wQeSK9caNG/H7/dx+++1YLBbRMucyEYmdcMHUajUmkwmHw0FTU9Nl3XLKZDKRkZFBVlYWNTU17Nixg/Hjx/fIeUDyibylpYWamhq2bt1KfX09CQkJ6PV6urq6lJ52gUCAYcOGMWHCBLKzs5WhL5PJxIgRI5g6dSqlpaXU19ezdu1aysvL0Wq1uFwutmzZouxAMXv2bGJiYnC5XLS0tFBRUUFDQwNut5tAIMDy5cvJyMggMTGRlJQURowYwfjx45W5MGvWrKGyspL4+Hh8Ph81NTX4/X5iY2Pp6upi165dxMbGKpW0C6nkFBcX43a72bNnD5WVlWzbtg2v10tlZSUmk0lZGHP48GG6urpITk5m+vTpJ/1t5SpoTU0NBw4coKmpiWAwSF1dHatWrSI/P5+0tDT8fj+ffvopR48exeVy4fV62bx5M263m8TERCwWCwcOHKC8vByn00k4HGb16tVkZmaSlJSk9KvLzMykpKSE+vp6tm7dyrFjx2hvbyc9PZ2srCySkpJQqVSMGDECr9dLaWkp69evx+FwKEO2mzdvJiEhgaysLEaPHt2jhzXlv8HatWuBz+YOpqWliROycEHk919jYyPZ2dlMmTKl1yx26wtUkljuJFyExYsX8/zzz2O1WvnJT37CyJEjL9t9B4NBfve737FixQosFgtvvvkm8fHx59UW43KR5ykdO3aMw4cPs27dOj799FOam5tpb2+ntbUVg8GAw+EgIyODOXPmcO2115Kfn39atU3eh3TDhg0sWbKE5cuX09zcTCgUwmQyMXLkSK677rqTthRbt24dixcv5u9///tpsd16663MnTuX22+/HYDGxkYOHDigbCnmdrvRaDQUFhZy//3309HRwdKlS9mwYQORSIT09HRmz57NX/7yF2w22wU9L3JT5hdffJEDBw5QVVVFc3MzkiQpFwsjR47khhtuYMSIEQwaNOi052LPnj3885//ZN68eSd9z2Aw8L3vfY8777yT+vp6Zs+efdr9T548mXHjxlFYWMg3v/nN04ZFr7zySr70pS/xyCOPANDc3MyhQ4f45S9/qTTGjo+P58EHH+Saa64hOzubSCRCaWkpa9asYdu2bezatUvZUsxoNJKbm8vcuXMZP348kydPBnrm9lySJOH1eqmrq2Py5MlMmTKFmTNncv/990c7NKEXkSSJxsZG7rrrLnw+H7NmzVLeTz3xdd8XicROuCgbN27kzTffZMeOHfztb39j4sSJl+2+JUmiqamJX//616xbt46SkhKeeuopZd5fTyC/reTVqvIKw0gkoqxAPXHVq8FgQK/Xn3GBhZwkBoNBAoEAwWDwpIREq9ViMBjQarXKkFkgECAQCJyx7Yl8X3ICGYlECIVCeL1eQqGQErtGo8FoNCoLKE7c2F6v12OxWC64+iT38PP5fMpzIz8W+bnQarXo9Xq0Wu1pFUG5t53X6z3jFACTyYTRaCQSiZxxDp+864RGoznjghD5+3LrlEgkorRoCYfDSJKkVKzlv5ccUzAYPOnvfOJjOvXv01NepycKh8OsW7eOp556ir179/L73/+e2bNnKw2XBeF8HDt2jM2bN/O1r32NP//5z8yYMYMhQ4YAPfN13xeJoVjhopjNZpKSkmhoaFBWcV7ON63D4WDmzJloNBo++eQTFi5cyMSJExk8eDAQ/QOIfP9ycvJFdhyQE0CDwXDev0ev16PX67Faree8rZyofd5qte5alapSqZTk6WJ/XqvVYrPZzlktTExM/Nzvn888RLVajVqt/tzFG3JMWq32vPfo7UnkRH7FihWsWLGCsrIy7r33XoYOHXrBFVmh/5IvQEtLS/nkk08oLi5m6NChZGZmRv143N+IxE64KGazmcTERKUZbjgcvmwr5+SmuWPGjCESibB8+XKWLVuGXq/H4XCQmpqq3E4QhLOTK8F1dXV88skn7N69G7PZzHXXXUdWVpZYDStckPb2dsrKyti1axdTp04lNzdX7DIRBeJdK1wUm81GZmYmHo+HtrY2nE6nsvn65ZKVlYXVakWr1fKd73yHyspK9u7dyx//+EelsiWSO0E4M3lYvKmpiZ/+9KesW7eOoUOH8qc//Ylhw4aJBRPCeZOrvkuXLmXTpk24XC6++93vKhfZwuUlEjvhothsNrKzs1GpVDQ2NtLY2HjZEzv4bOXpVVddxT/+8Q8+/PBDFi9eTHl5OT//+c8pKioSV4uCcAaSJNHa2srHH3/M22+/zb59+3jwwQeZOHEiY8aM6dErd4WeJxKJ4PP5ePXVV4lEItx8883k5OSINjlRIhI74aIYDAbsdjsGgwGn00l7e3tU4tBoNFitVkaNGoXP58NoNLJx40befvttRowYwZgxYygsLBQnKkHgs4TO5/Nx7Ngxli9fzo4dO2htbeXmm29m6tSpFBQUnHHPXUH4PG63m61bt1JbW8vIkSOZPn06er1ejJhEiUjshIui1+uVCeydnZ20tbVFLRZ5ZwObzUZeXh5lZWUsWrSIo0eP4vV6cTgcxMTEKKsYBaG/kVfuut1uGhoaWLlyJS+//DIqlYqCggK+//3vk5KS8oUW+Qj9UyQSoa2tjY8//phwOExhYeFl7ZIgnE6UMYSLplarycnJwel00tDQEO1wsNvtjBo1ivfff5+77roLl8vF9773Pe666y6WLl1KY2NjtEMUhKipqqriySef5N577+XHP/4xw4YN45FHHuHFF18kKytL7OEpXJSOjg5KS0v5z3/+w/XXX8+UKVOIiYmJdlj9mqjYCRdNrVaTmZlJV1cXzc3N0Q5H6RlmtVr56le/ypQpU9i9ezevv/46f/7zn0lPT2f69OnceOONxMXFnVe7C0HozZxOJ5WVlaxcuZIPPvgAn89HXFwcL7zwAuPGjSMlJUVpZSOGzYSLsWbNGpYsWUJcXBzXXHMNgwcPFq+lKBOJnXDR1Go1ycnJHDlyhI6OjmiHA/z/nmKZmZnExMQQFxdHY2Mje/fupa2tjdWrVxMOh8nPzycrK4sBAwZgNBrFHDyhzwgGg8rWbIcOHeLYsWPs3LmTcDhMUVERQ4cOZfr06WRmZorJ7cJFkxfg7N27l8OHD3PllVeSk5MjqnU9gEjshIumVqtJS0tjz549tLa2Kkvee8rVWmxsLDExMfz2t7/lk08+Ye3atSxcuJBVq1YxevRoJkyYwO23305qaioGgwGNRtOjtiUThPMlz6ELhUJ0dnZSV1fHSy+9xOrVq2ltbSU+Pp5vfetbTJ8+nUGDBon+dMIXIjcjlnvWyS1zHA6HmMfcA4gtxYSL5vf7+eijj/jzn/9McnIy77zzDgaDoUclRvLLOxKJEAwGcbvdvPXWWyxdupTdu3fT1dXFrFmzmDBhAhMmTGDEiBE9Kn5BOB9er5edO3fy4Ycfsm3bNnbv3o3dbuf6669n0qRJzJgxA4vFomxZJ17jwhchbzN43XXX4fV6KSoq4h//+Ie4MO4hxGWbcNHUajXp6ekYDAa8Xi8NDQ1kZmb2qCs2+SAjV+O0Wi2zZ89m6NChVFZWsmXLFo4dO8bChQv5+OOPGTx4sNIiZdCgQZjNZnGgEnoceb/crVu3cuDAAQ4ePEhZWRkqlYq4uDjuvfdepkyZQm5uLklJSdhsNnHSFbpNS0sL27dv59ChQ8yZM4drrrmmRx33+zuR2AkXTa1Wk5SUhF6vJxAI0NzcTEZGRrTDOit5/t2AAQPIyspi8ODBxMfHs2rVKo4dO0ZdXR1bt27F5XJRXV1NQ0MD6enp2O12rFYrdrtdqXgIwuUUiUQIh8O0trbidDqVlegbN27k6NGj1NXVKXPoiouLGTVqFJMmTUKn04n5o0K3CofDNDY2smbNGvR6PYMGDaKkpCTaYQknEImdcNHkip3ZbKapqYmamppe8wbXarXEx8czd+5c5s6dS0NDA4cPH2b+/PmsWLGCBQsWADBp0iSuvPJKhg8fzoQJE4iJiVFOlHKCJxI9obvJUwjkuUzBYBCXy8WKFSvYvHkze/fuZceOHej1eoYNG8bkyZO59dZblSqzIFwqPp+PI0eO8Prrr3P99dczevRo0tPTox2WcAKR2AlfiMFgIDExEZfLRXl5OeFwONohXZTExETi4uIYPnw4HR0dVFZWsn37dtasWcMbb7zBs88+i16vZ/z48ZSUlDBkyBCuuOIK4uLixMpCodt5PB5aW1vZuHEjW7ZsoaysjNLSUgKBAHl5eRQUFHDbbbcxbdo04uLisFgsGI1GsShCuOTef/99li1bRjgc5tvf/jY5OTnRDkk4hTgKCBdNrlQ5HA5MJhP19fVEIpEoR3VxNBoNGo0GvV6PyWTCbDYTFxdHfn4+tbW1NDQ0UFFRgdPpZNOmTWzdupWPPvqIrKwsUlJSSE1NJS8vj7S0NCwWizjBCudFrsa1tLTQ1NREZWUllZWVNDY20tTURH19PWq1GpvNxsyZMxk4cCDp6ekkJyeTmZlJdna2sqJbEC4leYeJzZs3c/z4ca6++mrS0tIwmUzRDk04hTj7CF+Yw+HAbDbT2NhIX1hkrdPpSEhIICEhgSFDhuD1emlubmbXrl2sXbuWQ4cOUVFRwe7du0lMTCQ1NZXs7GyGDh3KwIEDSUhIwGq1YjKZ0Ov16PV65eQrhm37L3menM/nw+/3EwgE8Pv9tLe3U1VVRWVlJWVlZRw+fJi2tjY6OzuJjY1l6NChDBo0iBEjRjB+/HgsFouoEguXXTgc5tChQ5SVlREIBJg9ezZWq1VcxPZAot2J8IX961//YsOGDZSXl7No0SJiY2OjHdIlFQgEcLlcbN++nU2bNnHgwAEOHDjAoUOHlKRw0KBBjB07lsLCQgYMGMDQoUOx2+1nncguEr6+42yHVDmJ27FjBwcPHqS8vJz9+/eza9cuvF4vWq2WwsJCxo8fT3FxMUOGDGHcuHGiIif0CC6Xi7vvvpv6+nrGjh3Lk08+KS5WeyiRagtfWHJyMjabjcrKyl47FHshdDoddrudiRMnMnLkSPx+P16vl7q6OkpLS6mpqaGqqoo1a9bw/vvv4/f7MRgMZGdnk5GRQVZWFoMGDSInJ4ekpCSSk5MxmUziANkHyBW5yspKGhoaqKuro6ysjCNHjtDQ0EBjYyN+vx+73Y7D4SA7O5svfelL5Ofnk5mZqWzxpdfrMRqNGI1G8boQoq62tpbt27ezbt06vv3tbzNr1ixxsdGDicRO+MLsdjs2m43Ozk48Hg82m61Pl+fltilWqxWr1Qp8NsyWnJxMYmIizc3N1NfXc/z4cVpaWmhvb6elpYVQKKS0qzh06BB2u52YmBjsdjsJCQkntVWJi4vDZrNhNpuxWq2iB1kPIe/w4HK5cLlcuN1unE4nra2tuFwuOjs7aWtro7m5mc7OTtxuN4FAgFAoRFxcHMnJycrrJC4ujpSUFLKzs0lJSSE+Ph6LxSL+zkKPEgqFKC8v55NPPiEtLY1hw4ZRWFgoXqc9WN89+wqXTVxcHLGxsXi9Xjo6OrDb7X06sTsTtVpNbGzsacPQbrebtrY2Dh48yL59+6ioqKCqqoo9e/bQ1dVFMBhEpVKRkpKiTIpPT08nLy+P1NRUEhMTSUlJwWAwoFarlUUe8v9P/FfsKHDxTtyhRP4Ih8OEw+GT/h8Oh/H7/Rw/fpzjx4/T1NTE8ePHKS8vp76+nubmZpqbm9FoNJhMJmJiYigqKqKkpISsrCzy8vIoKSnBarWi1+uj/KgF4dw6Ozs5cOAAn3zyCdOmTWPw4MGkpqZGOyzhc/Svs69wSaSmppKamookSVRWVirtFwQwm82YzWYyMjKYMWMG8P97k9XV1VFfX091dTVlZWVUVFRQW1vLxo0bqampUVrHaDQa0tPTlWHb3NxcMjMzlYUbGRkZJCQkYLPZMBqN0Xy4vZrX66WlpYW2tjYaGxuV4dTGxkaqqqqorq5Wvh8KhVCpVFgsFlJTUykqKmLUqFGkp6dTUFDAwIEDSUpKwuFwAKLnodB7LViwgGXLluH3+/nZz34mkrpeQCR2whcWExODw+HAaDRSW1tLYWFhtEPqMU48kcv/l6tDycnJ2O12cnNzGTNmDF6vV5mv53Q6aWtro729naamJtrb23G73XR1dVFWVsbWrVsJBAKEw2EMBgM6nQ69Xo/VaiUuLo6YmBhsNhsOhwOHw6EMG8fExCg9zywWizLcq9fre/0OBZIkEYlE8Hg8+Hw+3G63Mj3A6/XS2dlJR0cHXV1duFwu2tvbaW9vp7OzE6fTqVRQg8EggUBAaX1jsVhwOBxMnDgRi8VCTEwMKSkpJCYmYrPZlOfQaDRiMBiUn9HpdGIektBrhUIhGhsb+eijj/D5fNxxxx3K6IHQs4nETvjCdDodZrOZ2NhYmpub8Xg80Q6pR5MTPIPBgMFgICYm5qTvS5KE3+9Xto5qaWmhpaUFp9NJR0cHzc3NtLa20tXVpSQufr+fUCiEy+XC7/fT2tqKTqdTkg2dTqd8bjKZlH+tVqtyG/l28nCvvB2VPNx74udqtRqdTqc8Fvk2p5J/16mCweBpq0flxExegBOJRAiFQspwaCQSIRgMnvR5KBQ66cPn8ykJssfjwe12K5/LH3LiJq9SbWpqorq6mpSUFFJSUkhKSsJoNOJwOIiJiSE2NpakpCTi4+OJjY3FbreTlJREXFyc0tJGEPoar9fLxo0bqaurIz8/n2nTpmE0Gnv9BWB/IBI74QtTqVQYjUbS0tJobGwUid0XJD+fRqOR5OTks1ZAA4EAXV1d1NXV0dLSoiR9TU1NJ1X7mpub6ejooKOjA5fLRSAQQJIkVCqV0g9No9EoSYx831ar9aRqYGxsLHq9XvncZrMp8/rkpPHUg75OpzttWF6SJDo7O09bQR0KhZTES/68q6tL6fkmP95TP/d6vXg8Hrq6unA6nSc9j3LiqdFosFqtJCYmKitS5dXcADU1NUyZMoWZM2cydOhQ0tPTT9o+ThD6k0gkQnt7Oy+++CI6nY6SkhKmT58uphL0EiKxE7qFxWJhwIABVFVVnXRyFS4dnU6Hw+FQFmzIc/fO9hEMBtm3bx+/+tWvOHbsGAMHDuQ73/kOoVAIj8dDR0cHTqdTaaArJ1Fyhau6ulr5XP6+XGWTv35iFS4SiRAIBPD5fKfFbrFYlEUfMo1Gg1arVeYJypVgg8GgNHq22WwnJZhWqxWz2axUH+XVxPIwszwMbTKZTqowykmfJEkcOnSIhQsX8uSTT1JeXs706dP54Q9/eCn/dILQox08eJB169axcuVKnnrqKaZOndrvFsT1ZuIvJXQLo9FIamoqmzdvxu12RzucfuHEatS5yHuPvvDCCwQCASZNmsQ999zDkCFDAJS5ZX6/XxnqPHXYMxAInLRiNBQKKUmj/LUTne3rAFqt9rRqmEqlOmlIV61Wo9VqlQRQHv49cTWwVqtVbqPT6ZQKo1arVSqLJ/7MqSRJIjc3l5tvvhm1Ws22bdtYvnw57e3tfPOb3yQ9PV2p6glCXydfAG7fvp0lS5Ywbtw4Ro4cSUZGhqjW9SIisRO6hcFgICkpidbWVrxeL5FIRAxj9RCBQICamhq2bdvG5s2bGTt2LJMnT2batGknVbH6I5VKRWxsLEOGDCESieDz+ZTkrrCwkJEjR5Kfn4/D4ejXz5PQf1RXV7Nv3z6OHj3KzTffTHZ29mnzgIWeTSR2QrcwGo1kZmYqjVlDoZCYVB5l8rBoW1sbS5Ys4emnnyYpKYkHHniAcePGib/PCdRqNcOHDyczM5Ndu3bxq1/9il/84hdce+213HLLLcyePVu5rUjwhL5Irr6//fbb7NixA51Ox7e//W3i4+OjHZpwgURJRegW8hw7SZJoaWmhoaEh2iH1e/IE6Icffpg333yT5ORkXn75ZcaMGSP63Z2Fw+FgwoQJ/Pe//+WrX/0qhw8f5qGHHuLXv/41x48f7xdb5gn9k8/n4/Dhw7zyyitYrVYeeOABEhMTxdy6Xkj8xYRuYTAYSElJQa1W43K5aG1tJSsrK9ph9VuhUIjm5mZeffVVDh8+TGZmJjNnziQzM1PsP/o51Go1RqORlJQUbrjhBhITE9mxYwcrVqxAr9czbtw4xo0bh9VqFc+h0GfIF+TvvvsuarWaIUOGMHHiRLGVYS8lEjuhW+j1euLj49Hr9XR2dtLS0hLtkPqlE4dfy8rKmD9/PlarlTFjxnDTTTdhNpvFgfoc5L2AJ02aREJCAsnJyfztb39j0aJFdHR0EBMTw+DBgzEajaKaIfQJHo+H6upqPvroI3JycigpKaG4uDjaYQkXSRyVhG6hVqsxmUwkJSXh8Xioq6uLdkj9VjAY5L333uPNN9+kvr6el156idGjR5OQkCCSugs0aNAg8vLyGDduHD/5yU947733WLhwIc8//7zynApCb/fpp5+ydOlSduzYwYcffsiIESNEta4XE4md0C3kvmCZmZn4/X6R2EWBJEmEw2Eee+wx1q9fj9Pp5Pnnn2fMmDFiVedFkqt3ubm5/PWvf2XdunUsXLiQhx56iBtuuIGpU6cye/bsk5ohC0JvIUkSTqeTjz76iI0bN3LHHXdQUlJCfHy8eD33YiKxE7qNSqUiOTlZmWMnXD6SJNHe3s727dtZv349RqORadOmMXbsWGJjY8WQ4Rcgz7sbMGCA0rvv3XffZf/+/bjdbiRJYurUqZhMJrE3rNCrhMNh1q1bx9GjR1Gr1cyZMwe73a7sSCP0TuJoL3SrlJQUWltbaW5ujnYo/YrP56OiooKXX36Z8vJybr31Vu644w7S09PFlXc3MRgMDB06lMLCQqxWK//+979Zvnw5NTU1ZGdnk5GRgc1mE0m00CtEIhG8Xi9vv/02TU1N5OTkMHfuXLFivg8QRyCh26hUKnJzc9m5cyfHjx+Pdjj9ysKFC1m0aBHvv/8+L7zwAuPHjycvL08kdZeAXq/njjvuYNiwYaxcuZK///3v3HzzzTzwwANce+21FBQURDtEQTin1tZWdu3axXvvvcfXv/51brzxxtP2dRZ6J5HYCd1GpVKRnp6ORqOhtbUVl8uF1WoVO1BcQsFgkLfffpsFCxbQ1NTET37yE6ZMmUJiYqJI6i4ReT7dgAEDsFgs5Obm8uyzz/L++++zZcsW7rvvPiZPnixOkkKPFQqFKC0t5fnnn2f48OFMmDCBYcOGiWNGHyESO6FbJSYmotfr8fv9tLW1YTKZRGJ3iXR1dVFTU8OSJUtwuVzk5OQwa9YsUlNTxa4Sl4HVasVgMBAXF0dZWRlbtmzh+PHjfPzxxxgMBgoKCkhLSxOrC4Uep6qqigMHDrBv3z5uvfVWCgsLcTgc0Q5L6CbijCt0G5VKRVpaGhaLhUAgwPHjxwmFQtEOq08Kh8NUV1fz4Ycf8t5771FUVMQ999zD+PHjRVJ3Gel0Oux2Ow8//DDf+MY3GDFiBC+++CJPP/00a9asURZXyP0FBSGaJEkiEomwevVq1q5di8fj4Z577iE/Pz/aoQndSCR2QrdKSUnBbrejUqk4duwYwWAw2iH1SWvXrmXevHn88Y9/5Ic//CEPPPAAM2fOjHZY/ZZarWbWrFn86le/4qWXXuLw4cM89thj3H333VRVVeH3+6MdoiAQCoUoKyvj9ddfp7Kykp/85CdkZ2djNpujHZrQjURiJ3QrjUZDbGwsNpuN2tpaUbHrZoFAgN27d/PGG2+wd+9eZsyYwY033khOTo5oURAl8pw7nU6n7DX7ox/9iPHjx1NXV8cvf/lLli9fTkVFRbRDFfqxSCSC2+1m3rx5+Hw+iouLmTVrFjqdTkwV6GPEHDuh28gHh5iYGGw2G42NjYTD4ShH1XfI8xZXrVrF3r17sdvtzJ49m6FDh4qkrofQ6/VkZGQwd+5cZXu9zZs3k5iYiMfjQavVKi1oxMlUuJy6urqorKxk+fLl5OXlMWLECAYOHCjmQPdB4i8qdLu4uDji4+Oprq4WFbtuIkkSDQ0NbNiwgUceeYTk5GRuuukmvva1r4m+aT2MSqUiNTWVO+64g6effpoZM2bw1ltv8fvf/55///vf+P1+Me9OuKwkSaKsrIy3336b8vJybrrpJr785S+j0WjEBUYfJM4IQrdLSEggISGBjRs3ijl23UCSJI4dO8Zbb73FG2+8wZQpU/jpT39KSUlJtEMTPodGoyEpKYk//vGPTJ48mdWrV/Of//yH0tJSvvnNbzJ27Fjsdnu0wxT6OEmSOH78OMuXL2f+/Pl873vfY9y4caSkpEQ7NOESEYmd0O1iY2Ox2+00NTURDAaJRCKi3H+RQqEQHR0dzJ8/n507d2K327nnnnsoLCzEZrOJq+0eTKVSodFoiI+P58orr8RsNmM2m9m0aZNSOZkzZ47S+1EQuptcGV66dCn79+/HYrEwZ84ckpKSRKW/DxN/WaHbyYldW1sbPp+PcDgsEruLEA6Hcbvd7N+/nw8//BCdTseYMWO48cYb0ev1IqnrRfLy8oiPjyc7O5uDBw+yZcsWqquriY+PZ9KkSdjtdrGVk9DtwuEwLpeLJUuW0NzczODBg7niiivEnNw+TpxthW6XkJBAWloawWCQ+vp6XC5XtEPqlVwuF7t27eKrX/0qALfccgt//OMfMRgMIqnrhWJjYxk5ciTvvvsut99+O16vl7vuuovnnnuOffv2RTs8oQ/q6OjgtddeY+vWreTl5fGLX/xCXBT2A6JiJ3S7+Ph40tLSUKlU1NbWkpOTQ3x8fLTD6jUkScLpdPLqq6+yaNEiLBYLv/rVrygpKVGqOuLA3PuoVCokScJisXD//fczYcIE3n33Xd566y12797NxIkT+c53voPZbBZDs8IX5nQ6OXjwIM899xzTp0/n6quvVhoRi+NH3yYSO6HbGY1GrFYrZrOZ1tZWurq6oh1SrxGJRAgEAixevJgtW7bQ0dHBddddR0lJCSkpKeKE38vJ8+6Sk5PR6XR4vV5aWlpoampi3bp1JCYmMn36dBITE8Ves8JFkVdbHzx4kI0bN+JyuZg4cSJDhgzBZDJFOTrhchCJndDtdDodZrMZh8NBS0uLGIo9T5Ik4ff7aWpq4p///Ccul4sBAwbw/e9/n4SEBDHZuQ9RqVTEx8cza9Ys4uLieO2111i+fDl//etfsVqtjBw5kqysLNE8Vrgofr+fzZs3s3jxYjIzM5k2bRoDBgyIdljCZSLm2AmXhMFgIC8vj/r6etra2qIdTq8QCATYsWMH9913HxUVFdx+++08/vjjJCcni0pdHzZ69Gh+85vfMG/ePBwOB9/73vf42c9+xpIlS0SvO+GCSZLEO++8w+LFi2loaODpp58mIyNDHEP6EVECEC4JnU5HRkYGzc3NOJ3OaIfTo0mSRDgc5u2332bVqlVUVlbys5/9jIkTJ5KcnCwqNn2YvANFTEwMgwcP5i9/+Qvz58/n6NGj/OlPf6KyspLrrruOrKwscWIWzikQCCgLJjQaDbNnz6aoqEgsuOpnRMVOuCR0Oh3Jycl0dHSIOXafQ07q9uzZw9q1azly5AjFxcVMnTqV3Nxc0QKjn9DpdNjtdq688kpmzpzJ4MGDkSSJ5cuXs3btWvbt24fH4xEVPOFzuVwutm/fTnl5OWlpaUyePBm73S4uCvoZkdgJl4RcsZPn2IktlM5MkiTcbjfPPfcc69evR6/X88gjj1BQUIDNZot2eMJlJC+suOGGG3jwwQd58MEH+fTTT/nb3/7GvHnzqKurIxwOi/eRcEaRSITq6mqef/55TCYTEyZMYM6cOdEOS4gCMRQrXBIGg4H8/Hw6Oztpa2ujs7NTJCpnsGPHDt566y3effddHnroIWbNmsXo0aPFFXY/l5OTQ2pqKkVFRfz1r39l5cqVLF++nCeeeIIRI0aQmpoa7RCFHmbTpk0sW7aMlStX8tJLL3HFFVdgMBiiHZYQBaJiJ1wSer2etLQ0NBoNnZ2dNDc3RzukHmfNmjV8+OGHrF69mjvuuIPp06dTVFSEVqsV82H6OY1Gg9FoJD8/n/vvv5877riD5ORknn32WV5//XXWr19PJBIR1TsBSZLo7Oxk1apV7Nixg4kTJzJixAgSExPFcaSfEhU74ZLQarXKfoQej4fm5max3P7/hMNhWlpalANxMBjk+uuvZ+jQoSQkJEQ7PKGHUKvV2O12pkyZQkJCAm63m/feew+NRoPb7SYpKYns7Gz0er3Ysq8fi0QiHD58mF27dtHS0sItt9xCRkYGZrM52qEJUSISO+GS0Ol0pKamYjKZcLvd1NbWRjukHkGSJLq6uliwYAGvvvoqWVlZPPzww8ycOVMMvwpnZDQaGTVqFCUlJQwcOJCXXnqJ559/nu3bt/P444+TkZGByWQS1Zl+SO59+be//Y2KigqKior41re+JRZd9XMisRMuGY1GQ1paGpIkUV1dHe1weoSDBw+yYcMGHn30UW644Qauuuoq5syZIyouwjlpNBpuvvlmBg8ezJYtW3j22We5++67ufHGG7n++usZNGiQSO76mePHj7NlyxaWLl3K17/+dWbPno3FYhGvg35OJHbCJSHvi5mYmIjP56OpqSnaIUWVJEmUl5ezatUqVqxYweDBg5k5cyYjR44Ui0qE86JSqbDZbBQUFKDX6+nq6mLx4sVs2rSJtrY2vvzlLzNkyBCsVmu0QxUuA7fbTVlZGR988AEFBQWMHDmSQYMGiYtEQSR2wqWVmJhIbW0tTU1NSJLUL68kI5EIfr+fbdu2sWbNGnbv3s1DDz3E5MmTSUlJiXZ4Qi/jcDiw2WxkZmbS2NjItm3bOHjwICaTCZvNRlZWllK16Y/vt75OXjBTX1/Pnj17WL16NXfddRfDhg0jLS0tytEJPYFKEsuqhEtEkiT+/ve/s2XLFpxOJ8uWLeuXV5NOp5Pt27dz//33k56ezowZM/jd734HIE68wkWRD9uSJLF+/XoWLVrE3//+d2bPns3111/PnXfeKebd9VHy3/7HP/4x27Ztw+VysWrVKmJjY9HpdFGOTugJRMVOuKTS09NRq9XU1tYSiUT6XRWhtraWHTt28Nvf/paBAwdy/fXXc+211wIiqRMunjzVQaVSKX3t0tLSeOutt3jxxRfZsmULv/zlL0lOTsZkMkU7XKEbeTwelixZwtq1a0lMTOQ73/kOMTExaLXidC58pv+VT4TLKiEhAYPBgMfjobOzk3A4HO2QLgtJkujo6GDTpk2sWLECp9PJVVddxRVXXEFGRka0wxP6gBP3mc3Ozmb69OlMnz6d5ORkSktLWbBgAbt27aKhoSHaoQrdxO/309zczKJFizCZTBQXFzN27FjR+1I4iUjxhUsqOTkZq9WK3++npaUFo9HY568s5f1fjx49ynvvvcfWrVsZNmwYd955JykpKf1yOFq4tAwGA8OGDSMlJYVPPvmEefPm8fe//522tjZmzJhBbGys0gJDJAC9jzz82tHRwZEjR3jvvfe44447mDp1Kvn5+VGOTuhp+vYZVoi6rKws4uLiCIVCHD16lISEhD4/NOT1eqmvr+f+++9Ho9EwadIknnzySWw2m0jqhEsqMTGRr3zlK3zpS1/iscce49133+W9997j+uuv55FHHsFms4nErpcKh8N8/PHHvPjii+Tm5nLfffdRUlIS7bCEHkgkdsIlZbFYiImJwWw2U1tbSyAQiHZIl1RXVxe7du1i3rx5SJLEDTfcwMyZM7FarajVanFSFS4plUqFTqfDbrfzjW98g+zsbHbu3MnixYuJRCLMnTuX0aNHExMTE+1QhQu0ZMkS1q9fT0tLCz//+c/Jysrq86MfwsURrwrhkpFPMhaLBZvNRmNjI8FgMNphXRKSJBEKhdi/fz8bNmxg27ZtXHHFFUycOJHhw4eLA7Bw2ahUKvR6PUOGDCEcDivvvU2bNmEymQgEAowZMwa73S52O+kFwuEwXV1drF69mvr6ejIzM5kyZQp2u12MAAhnJF4VwiUXExNDUlISNTU1+P3+aIfT7SRJQpIkXC4X8+bNY/78+eh0On71q18xfvx4sWejEDUlJSXcfvvtPPHEE6hUKt544w1+/etfs2PHDrxeL5FIRHn9Cj2PJEn4fD4OHz7Me++9h06n48477yQnJweDwRDt8IQeSiR2wiXncDjIyMigvLwcn88X7XC6XSgUoqmpifvuu49NmzZRXFzMG2+8QWZmptizUYg6i8XCoEGDePvtt3nwwQex2Wzcdttt/PWvf2Xz5s3RDk/4HPIirO9973ukpKQwZ84cbrvttmiHJfRwYnxIuOSsVivx8fHs3LnzjHPsIpEI4XC4Vy7ZD4fD7Nu3j08++YRDhw4xa9YsJk2aRE5OTq98PELfo1Kp0Gg0JCYmMmvWLFJTU4mLi2Pt2rU0NDRw7NgxbrrpJoxGoxia7WF27tzJ2rVrqamp4Qc/+AFXXHGFuFgUzkkkdsIlIw/vmM1mYmNjaW5uprm5mfr6ekKhED6fj2AwSDgcJhKJUFxc3Gs6p8uPrba2lq1bt7J48WIcDgczZsxg3LhxYv9XoUc5cd5deno6KpWK5557jl27dtHS0kJhYSH5+fnExMSg1+ujHW6fF4lECAaD+Hw+dDrdadM1JEnC7Xazfft2Nm3ahN1uZ/r06RQUFIiLReGcRGIndIuzzdGRJAm9Xo/FYqG5uZmPP/6YLVu20NjYyKFDhzh+/Dgej4eYmBjWrFlDQkLCZY78zDGf6NQDqfz9SCTCM888w/r166mvr+e///0vRUVFYsWh0GOpVCri4uK4/fbbyc/P56233mLBggXcfffdPPbYY1x55ZVKA22RQFw6oVCI6upqysrKSE9PZ+TIkSd9X5Ikdu7cyaJFizh06BC//vWvKSgoEBeMwnkRe8UK3SISiRAKhXjppZc4fvw4zc3NVFdXU1tbi8vlorOzk9bWVuLi4tBqtUQiEdxuN+FwmLi4OMaPH89//vMf7HZ7tB8KFRUVqFQqEhISlM3UTyRJEs3NzTz99NO8/fbbDB06lDvuuIPZs2djMBjESjWhx5MkCb/fT1NTE0eOHOHRRx+lubmZwYMHc/vtt3PttdeKldyXUEdHB2+99RYvvPACdrudadOm8eMf/xiTyYQkSTidTm6++WZlJOPPf/6z0jJJEM5FvHOFbiEnP+Xl5ezevZuamhra2tpob28nGAwqVa729nZlK6RwOIxarcZgMJCVldVjDlqbN2+moaEBi8XC7bffjtlsPukkd+zYMXbs2MHy5csZPHgwkydPZtSoURiNRlHlEHoFlUqF0WgkJSUFo9HIXXfdxZIlS2hvb2fBggUAjB49mtTU1LMmeHKLnyNHjpCVlYXVar2cD6FX8/l87Ny5k+PHjytTU2w2G9OmTUOv17Nnzx5qamq46qqrmDlzphgFEC6ISOyEbqFSqVCr1QSDQWpqaigrKzvj7U5traBSqTCZTGRlZUV94rYkSQSDQVavXs2OHTuAz05uubm5xMbGolar6erq4tNPP2Xx4sWUl5fzta99jYkTJ5KdnR3V2AXhYuj1ehITE/nqV7+KXq9nxYoVLF++nEAggFqtZuzYsSQmJqLRaE67aPH7/XR0dLBmzRquvvpqDAZDr5kjG02RSASv18uuXbvwer34fD7a29uprq4mEolgs9n45JNPMBgMjBs3junTp0c7ZKG3kQShm0QiEamiokK68847JY1GIwHn9VFSUiItW7ZM8vl8UY3f7/dLu3fvlkaMGCGpVCpJq9VKAwYMkF555RWpsbFRCofD0jPPPCNNnjxZSktLk+bNmye1trZKkUgkqnELwhcRiUSUj6qqKumtt96SkpKSpIEDB0r33HOPVF5eLgWDwdN+bvPmzdJPfvITSavVSk899ZR08ODBKETf+7jdbmnbtm2nHSO1Wq2k1Wolk8kkpaSkSPPnz5cqKyvF8UW4YD1j7EvoM1JTU7nyyiuZMmXKeVfgzGYz+fn5Ua/Yeb1e/vvf/9Le3o5arSYcDtPY2MhTTz3FT37yE15//XX++c9/YrVaeeCBB7jmmmuIiYkRw69CryZPjVCpVCQnJzN9+nReeOEFRo4cyYEDB/j617/O6tWrqaurU35m7969LF68mDfffJNwOMzrr7/OggULCAQCotnxOVRXV7N7924ikchJXw+FQoRCIaUSunDhQvbt24fT6YxSpEJvJYZihW6jUqkwGAwMGTKEuro6NmzYQDgc/tyf0Wq1mM1m4uPjozrHLhQK0dnZycaNG3E6ncqQsdz13el04vf7iUQiDB48mGnTppGUlCSSOqFPMRgMxMfHM378eNrb27FarWzfvp2PPvqI5uZmhg0bRnZ2NitXrmTLli3U1dUhSRJHjx5l9+7d7Nu3j+HDh0f9Iq0na2ho4PDhw6hUqjMmwZFIhEAgoPTHdLvdTJw4keTkZLGgRTgv4lUidLvhw4cTCoV49tlnCYVCp12ZnkjucRcbG3sZIzyd1+uloaGBzZs3n7TYIxwO4/F4qKqqorq6mhtvvJEpU6Zw5ZVXiqRO6JPUajXJycncddddjB07lueff5433niDAwcOMHnyZG666SZee+01Dh48qPyM0+mktLSUhQsXMmjQIEwmU49ZDNVTyMeU48ePs2/fvs89fkQiEY4dO8bx48fZsGEDWq2W6dOnY7PZRHInnJN45wndLiYmhoEDB/LNb34Th8PxuQew7OxsBgwYcBmjO7N9+/bx3//+96xX0HKCumjRIpYtW8b27dvFkJPQp+l0OoqLi3n88cf585//jE6n4/HHH+e6667j0KFDJw27RiIRjhw5wrPPPsuuXbtob2+PcvQ9Uzgc5tChQ2zatOmcoxnBYBC3283evXu58847+d3vfsf69esvU6RCbyYSO6HbqVQqHA4HN998s9K37ky0Wi1paWmkp6df5gj/P+n/WjYcOnSI5cuXEw6Hz5qwRSIRfD4fS5cu5YknnmDbtm14PJ7LHLEgXB7ySne9Xs/MmTOZOHEi+fn5NDY24vf7T0tM5KkL//jHP9i/f/85E5f+6OjRozQ0NFzQcUOr1RIXF0dJSQlpaWmXMDqhrxA1XeGSMBqNFBcXU1BQQGdnJw0NDafdRqVSER8fT2JiYhQi/P+am5uprKzk6NGjnztsLEmSMkTicrkoLi7GarWSmZkp+kwJfZrJZEKv16NSqfD5fGd8n0iSRDgcZuPGjUyYMIG8vDxlFwvhM8eOHaOlpeVzjzMnstvtpKSkMGrUKAYPHtwjduYRej6R2AmXhEajwWq1csMNNxCJRPjkk09OO5gFg0GSk5OjWrGDzxoSl5WV4Xa7z3lbeVFFU1MTjz76KAaDgauuuorRo0dfhkgF4fKSX+/r1q1jzZo1Sn/HswmHwxw/fpzVq1djsVi49957AbE9GXz2XG7fvp36+nq0Wi3BYPCMt5MrpQAlJSVcddVV/PCHP8RoNIp5i8J5EYmdcEndcsstNDc3s23bNtrb208b5szIyIh6c9/58+ezbdu287qtfILS6/Xk5OSQkJCAyWS6lOEJQtR4PB4qKip45JFHqK6uPq+fiUQiLFmyhIaGBq688koGDBggGhf/n1WrVnHkyJGzJnUajQadTkdMTAx/+MMfGDduHDk5OeIYI1wQkdgJl5TNZmPw4MHMmDGDd99996TETp47Eh8fH5XY/H4/NTU1HD16lJaWlnMuhlCpVNhsNoqKihgyZIiylVhqauplilgQLp9AIEBlZSX/+te/qKurw+fznffPyu+tN954gx/+8IfY7fZ+XbULBoO0t7dTX19PV1fXWW+Xk5OjHC8nT55MSkoKFovlMkYq9AUisRMuGZVKhUajoaCggOnTp/PRRx8RCASUIVm51Um09pj0er3s2bOHxsbGz53MrNFoMBgMOBwOBgwYwOTJk5k4cSIzZ85ErVb36xOW0HcFg0FaWlrYtGkTkiQpTbvP1n/tRJIk0d7ezkcffcStt96KwWDAbDZfpsh7HjnRdTqdBAKBk74nHyeTk5MZO3YsU6ZM4bbbbhPNz4WLJhI74ZIbNGgQMTEx/P3vf6e2thafz4dKpSIvLw+bzRaVeSOSJNHa2sorr7yCx+NBrVafcUKzWq3GZrNRUFDAXXfdxd13301MTIxowCr0ecFgkNjYWG666Sbef/99ysvLaWlpQafTEQqFTtv3+USRSITOzk727t3LwoULufrqqxk9enS/TVQ6OjpYvXo1oVDopMRYq9Wi0Wiw2+089thjzJw5U6x8Fb4wkdgJl5xKpSI2NpYf/OAHPP7441RUVKDT6Rg8eHDUGhM7nU7Ky8tZs2YNbrf7pKROrVYrO2Lce++9zJgxg0GDBhEfH09MTIyYwCz0CzabjUGDBpGZmcntt99Oa2srdXV1bNmyhbVr11JRUUF9fT0ajeasbYIikQgvvPACkiSRkZFBSkpKv0zu2tvbWbFiBV6vV/maSqWiqKiImTNncvPNN1NUVCSGXYVuIRI74ZKTtxqbMGEC77zzDi0tLfj9fjIzM6M2PFNRUcHevXvxeDxKUqdWq1Gr1RQXFzNgwACKioqYPn06xcXFJCUliY7vQr+i0WjQaDQYjUbi4+NJSkoiPT0du91OTk4OtbW11NbWUlVVRUVFBa2trco+yydW85qbm9mxYwcDBgzgzjvvRJKkfpXcBYNBnE4nhw8fJhgMolKpsFqtjBs3junTpzNmzBgGDx6MzWbrV8+LcOmIM5VwVid2lZcP1JFIRPmQP5e/d6aPEythSUlJJCYmYrPZ8Pl8WCwWXC4XtbW1J92vvCH5qU6slJ04t03+/4mfn/g1jUajtBCQv1ZWVsbmzZuVz/V6PSaTidjYWGbMmMGUKVOYMWMGFotFHGwFgc/mxJrNZlJSUpg0aRKdnZ00NzezevVqNmzYwOHDhzl27BjBYBCfz0cgEFB2bNm9ezcajYZrrrkGnU6nDEfKx4cTjzWyE5PDU/9/6tdO/JkT/z2b87nd2Y5Dpzq1gi//jPyvx+OhpqaGqqoq4LMV9QkJCcydO5drrrmG+Ph4JEmis7NTuc8TP+T7ONPnZ7p/QVBJYl8k4SzkA2drayudnZ3KgbytrQ2Xy4XL5aK1tRW3243P58Pr9dLZ2YnX68Xn89HV1UVXVxfhcJhwOIzL5SIQCOD3+/H7/dhsNvx+P6FQ6KT71Wg0pzX8ValUWCwWdDodarWamJgYzGYzRqMRs9mM1WrFZDJhNBqx2WzY7XZsNhuxsbEkJSUpn9vtdgwGA//7v//LSy+9hNfrRavVMnLkSGbOnMmDDz5IXFwcer3+pPsWBOFkpyZHbreburo6Fi5cyKZNmygtLaW8vFy5fWxsLPfffz/FxcXo9Xq8Xi8ulwuv10sgECAcDivHi1AopCSHwWBQSRbD4TCBQACfz4ckSXg8npNah/j9fiWh/Ly45fv5vNOffLF3rsTJZrMpF48ABoMBnU6HTqdDr9fj8/no6Ojg2LFjxMbGkp2dTVZWFmlpaSddVKrVaqxWK0ajEZPJpPzfYDBgsViw2WzK5/LxzmQy9etFKcKZicSuH5JXrDmdTtrb22lqaqK1tRWn00lbWxuNjY20t7fT2dmprOIKhUKEw2GCwSCSJKHVapUDjnwQO/FD/r5er1euLuXO9ZWVlezYsYNrrrlG+Xn5oChfxZ+6ciwSiRAMBpVq4YkH/GAwqMQYCoXw+/14vV4lgZTnzGk0GuXfiooKmpubsVqtDBo0iAEDBlBYWMiIESNITk7G4XDgcDiIj48XiZ3Qp0mSRCAQoKurC4/Hg8fjweVy4fF48Hq9eDwe5YLN6/UqF2zyxZycnHk8HpxOJy0tLTidTjwez0ktUjQaDSkpKVitVvR6PTqdDoPBoLwn5R5ucsX91M/l44T8dfgs+TpxIZNcmf+8vnny79BoNGdN2iKRiJJgfh75mHiiYDCoJI3BYJAjR47Q1NREJBIhIyMDk8mETqdTRjtCoZDyO+TjmHx8k29zYrzyc3Pi/+WLWvlC1/b/2DvzsLjKe/F/hnUYYIZh2PedsISQkJ0EkphEzWaMS6tttWr1Wlu11tva3+3tdu12W6u1tbbWtlaNdYlLFrPvCUIWEhKWsEPYGQaYGdbZz+8P7zmFBGJiYiDJ+TwPDwxz5pz3nHmX7/td/f0lwc/f3x9fX9/zXo88dqSgKHPtIwt21yGCIGC1WhkcHGR4eJjBwcFRE/XAwAA9PT309fVhNpsxmUzS7licrK1WqyQ0iZOwOBGLE4g4SYgCnJeX16i/xePECcjb2xt3d3eampooKSnh1ltvxc/Pb0zB7tycWS6XS6pPKb4vtlEU4MTJULxncSESNQKi0NfQ0ICHh4fUPnGxEU1NarVa0vYFBwdLk6K4a1apVKPuXTaFyEwWRCFBHBMjx/Hw8LD0t9VqxWKxSJuioaGhUZp3UXCz2+3SOUVBRzyHqIkfufFzOp2SEDJSWBM1WuLGUKVSERAQIJUqEzdd4t/ia/Gz4vyhUCik4wBJMBQZec0LIV5nvLErClznbjDPxel0YrVaR2n+RO2juAGtqanBaDRK84lCoZDms5HXEf838vsTN6uiACk+Y/G7EP8eKRyLz038n5eX16iNrTiPi3O5r6+vNKepVCppDh8534vzv1KpHPM92f94ciELdtco5/qZiBOJ+LfBYKC5uZmOjg6am5upr6+nvb2dzs5OmpqaGBwclHJTKZVKQkJCJAfp6OhoQkJCJI1VSEgI/v7++Pv7ExgYSEBAwGUN5MHBQXp7e4mMjPzChSKXyyVpJ/v7++nt7eW1115Do9GgVqsZGhqira2Nnp4euru7aW1tlSZmAF9fXyIiIggLCyM6OprExEQiIiIIDw8nKSkJnU43Sqsw0r8PZDOuzOVzIR/Wc3/ETVxvby/d3d0YjUZpA9fV1UVfXx/9/f309PRILhbi/2C0f5goFIguDH5+fvj6+qLRaNBoNPj6+uLr60tgYKCk8RGFBNFkKG6AlEolHh4emEwmbDYbWq32hqkjq9frcTgcn6t0oijAiVpTcUMrbtItFgtDQ0OStlXUoJpMJkmzajabMZvNDA4OSq/FTTL8u3+5ublJ/n9qtRq1Wo1Op0On0+Hn54darZbcWtRqNYGBgQQHB6PRaFCpVGP6B47nMyjPi18ssmB3jSJq5URBrbS0lDNnztDU1ER9fT2tra24XC48PDykPGwRERGEhoaSkJBAXFwcISEh0s9I59wLDcYrMTDFieRinZMvh7EcrcXINNGB+9xgD3HR6+zslJ5lZ2cnLS0t1NbW0t/fj9VqRaFQEBERQVRUFImJiUybNo3U1FSio6OJj49HqVTKE5jMZTMwMEBfXx9dXV20tbXR3d0t9c/29napv3Z0dGA2myXzobhYe3t7S9oicUMTGBhIUFAQ/v7+oxZsPz8/aQOnUqkk9wnggvPAeL/P/XskN4qme6Tg9Hk+e6G/L+b3WP8T3VX6+/tHbXyNRiMGg0Hyoe7u7qa3t1dyy+nq6hqVZH6kj6C44RWFQXG9EQXAqKgotFqtPC9eBWTB7hrA5XLhcDior6+npaWF9vZ26uvrOXv2rLQTczgckpnQz8+PsLAwgoODCQgIkHZYI30sRjrmKpVKQN5FiUNBNOuKE5+4Exa1IeIk197ejsFgkHbLJpNJSg+h0WhISEiQnKQTExOJi4uTzNEyMqIZs6urC5PJhMlkwmAwYDAYJP9Xg8EgmUhF7YxoUhM1YiNdH0Qtmiiw+fn5Sc78om/XSFOc6OAvmtg8PDykc3t4eNwwwteNhmjCFU3xI32VR5rozzXni6Z6cW4U1x9Reyi6vYjzp81mk4S/kebegIAAyZdZ3HSIGw8x0O1GX48uB1mwm4SIPhc9PT2j1OlnzpyhtbUVvV4v7dxHmlLj4uIICwsjKiqKlJQUwsPDpUEic+UQzSLNzc00NDTQ0dFBW1sbtbW19Pb2MjQ0hM1mIzg4WNKIxsfHM2XKFEkrIk5goo+KzPWHuCETNwbiQjkysGdgYID29nZ6e3ul4KWRZjSTyYTT6ZQCAry9vUe5RYzUwgUFBUlJtEVzqRioICNzpRD7sLj5EE29ojuL+Fp0BRjZ310uF+7u7vj5+aHVaiUlQ0BAAMHBwQQGBkr1w0f6a4u+f+JmRvS3lBkbWbCbBJyrNu/v76e9vZ3t27dz4sQJamtrqaysxGKxEBQURGRkJLNnz2b27NmSJig6Olru6JOA9vZ22traqKmpoaioiNLSUs6ePUtbWxtKpZLY2FhSU1PJzc1l0aJFREdHExQUJPufXIOMl0NN/NtqtdLf309lZSV1dXV0dHTQ0tJCQ0PDKL9O0Z9NqVQSGRlJXFwcoaGhhIeHS5u1wMBAyf9V1vrKXCs4nU6Ghobo6emhtbWVrq4uuru7aW9vlzbFer2epqYmKVm8QqHA39+fqKgoKSl2UlISsbGx0t8RERGjUszA+fPmjTyPyoLdJEB02j9w4AAFBQXSxO/t7U1aWpqk7Zk9ezahoaGo1WopMkk0y8jF6CcHIyPWRkYjtrW1UVxcTH19PQ0NDZSUlEhO5AkJCSxdupRZs2YRHx//uZysZa4+giAwPDxMV1cX1dXVtLS00NHRQWNjI7W1tRgMBkwmEw6HAx8fHwICAqQgHFE7ERYWRlxcHEFBQWg0mlFRoWIqi3NTW8CNvWjJXDucm9heDPITtdkj/y+m3erq6qK5uRm9Xi+l32pubqa3txeLxYKHhwdarZawsDBJ0EtISCA8PFx6LQa03ajIgt0EYLVaMZvNVFdXc+rUKWlBMBgMeHl54evri1arJTU1VXJGDQ0NJTIyEl9fX9m0co3hcDgYHBwcpaVpaWmhvr5eSvZss9kICAggKCiI+Ph4pk+fTnx8PKGhoXJuqQlGTK/T0dFBb28vBoOBtrY29Hq95BvX09MjOZSLKSVE3zUx4lz0exPN8WIOsYCAAMnUdCMvRjI3NiPTc5lMJsm/eWSAhxjQIW6YxE20y+WSTLaiv55Wq5XWzZH+5jcCsmB3lRAzqYvVGlpbW/nkk084fPgwBoMBm81GWFgYc+bMIT09nalTp5KdnS07MF+HiLmrKioqqKmpobS0lEOHDklJTHU6HXl5ecyYMYPk5GRJSyv6nMh8cYh528RgBdEvzmg0UlNTI5na6+vraWtrkyoYiPVUg4ODiYuLkzQI4eHhJCQkoNPp5O9ORuYyENdPMX1XR0eHlLmgpaVFSqLtdDoJCQkhKCiI6OhoEhISiI2NJTw8nIiICClQSNx4iRuq60kLLgt2Vwm9Xk9JSQlvvfUWRUVFtLW14enpyapVq8jNzWXWrFnk5OSMW3dQ5vri3GHncrmorKykrKyMXbt2sXnzZoaHh/H39+emm27izjvvJCsri6SkpAlq8Y1BW1sbLS0tnD59mpMnT1JXV8fZs2dpbGxEEAQ0Gg3h4eHk5OSQlpZGXFwcycnJpKamolKpxs3vKI9jGZnL40KiisvlwmQy0d7eTkVFBZWVlTQ2NlJZWUl5eTk2mw03Nzd0Oh3Z2dkkJiaSlJRETk4OGRkZaDSa6yrJsizYfUGIztNHjx5l8+bNlJWVUVdXR2hoKFlZWWRkZDBv3jxCQ0NHlXsBeRG40RiZZkWssdvd3U11dTXV1dUUFhZy9uxZtFotycnJ3HHHHcyfPx+dTidrcz8nLpcLs9lMVVUVjY2NVFdXc+zYMTo7OxkYGEAQBCIjIwkKCiI4OJjk5GTS09MlXzgxMm9kxZXrbdcvI3OtICbmF7XtYooWi8XC4OAgnZ2ddHR0UFtbS1tbG52dnej1esxms5THMSEhgZycHFJTU4mJiSE2Nha4NtdjWbC7goiPsr6+XkoUXFxcTHt7O4IgoNVqpSS2cXFxpKSkyGHbMuchmmrFKMrS0lJOnDhBV1cXAwMDhIeHM3PmTJKSkkhOTiY5OVkWKi6AWLPTYDDQ2to6qspIW1vbqFyFSqUSPz8/QkJCJP/WwMBAoqOjiYqKkvI/ysjITH7EwA2TyYTRaJSEOjFfZHNzMz09Pdjtdil1mOijJ0bhBgcHo9PpiIqKwsPD45qYZ2XB7gohJnscGBhg69atHD58mMLCQrq7u5k/fz7z5s1j5cqVpKWlyakKZC4JQRAkzd2ePXvYs2cPgYGBpKWlcfPNN7N27Vqpbu31ZE74vIiReGJtYLvdTl9fH6WlpRw9epS6ujopOlksoZScnMy8efPIzMwkISFBFpZlZK5zHA4HLS0tlJSUUFtbS0VFBUePHsVsNktuF6IGLyUlhfnz50sZKcR6xVejetLnQRbsrhANDQ2cPHmS//3f/6WmpoaEhAQWLFjAf/zHfxAbG4uvr6+cp0zmcyMKK3a7nbq6Ot555x0KCgo4cuQImZmZfP3rX2fRokVkZGRMdFMnHHGHXl5eztatWzl27BhVVVV0dXURGxtLcnIymZmZLFiwgOzsbIKDg/Hx8bmkclgyMjLXNmOVWhMrPNXV1XHixAkKCgqoq6ujs7MTLy8vFixYwIwZM5g1axY33XQTPj4+k3IzLQt2l4GoCXj99dc5duwYjY2N+Pj4sHbtWqZMmUJ8fDxRUVEolUpZSydzRXC5XAwPD9PZ2UlbWxuVlZVs2LCBwcFBtFot+fn5fPWrX0Wn091QJkOz2UxraysHDx6kvLxcSiGkVCqlfFdZWVkkJiZKFRnEqixiPkgZGZkbF1EUEt0y+vr6pFRGYvCjmKJqYGCA4OBg0tPTSU5OZtq0acycOXPS5M+bfKLmNYIYgVNcXMzBgwcxm81otVpyc3NZunQpUVFRN0zOHJmrh5ubG76+viQmJkrl43p6ejhx4gQGg4EDBw6g0+kkISYwMPC61DqJCU47OjpobW2ltbWVuro6ysrKJJ+ZoKAgUlJSpASm06ZNIywsTC7hJiMjcx7iPCnWWw4KCgI+TbNiNpsJDQ3l9OnTNDU10dzcjMFgkCwBTU1N6PV6yScvIiJiQv3nZY3dJSA+KofDwalTp9i/fz8vvvgi/v7+3HbbbaxYsYL8/PwJbqXMjUhxcTGHDh3ilVdeYXh4mNtvv51169Yxf/58yVRwrQt4I8ef3W7HZDKxY8cONm/eTGVlJS0tLaSmpjJ//nymT5/OwoULSUxMnJSmEhkZmWuT4eFhent72bVrF4cPH6aiooKKigoCAwNZuHAh8+fPZ+XKlYSEhODl5SX56l7N+VcW7C4B0Qz2wgsv8MEHH2A0GpkzZw4///nPCQsLm7T2dpnrH6fTKSXS/dGPfsTx48cxm808/PDDfOMb3yA0NHRSmAguB7vdTm9vL//6178oKCiguLhYGoOzZs1i8eLFzJ07VzKHiKW4rnWBVkZGZvIwMjjL6XQyMDBAe3s7b7/9NocPH6a+vh6bzcby5ctZvHgxubm5ZGRkyILdZMTlclFVVcXOnTv517/+RUxMDOnp6SxfvpycnBy5MLfMhONyubDb7Zw8eZKioiJOnDhBVVUV69atY+HCheTm5l5zgo4gCNhsNsrLyyktLWXfvn20trbi4+NDSEgIOTk5JCUlSVnlg4ODr6n7k5GRubZxOBwMDQ3R0NBAa2urlOC8sbERALVaTX5+PkuXLiUiIgK1Wv2Ft0lWL30GonTe2NjI0aNH2b59Oy6Xi9mzZ5OXl8e8efOuanvq6+vR6/VSXcrxcHNzw8vLCx8fHwIDAwkJCbnmFnWZS8PNzQ1vb2/mzZsn1SStrq7mwIEDCIJAcHAwKSkp10Q/EBN8G41G6uvrKSoqoqSkhJKSEmJiYkhLSyMnJ4dly5YREBAw6f3mhoaGGBgYwGQy0dfXJ9W3FHNniXVjg4ODcXd3n/Tfj8wXx/DwMD09PTQ3N0t9xNPTk6ysLLy9vT/3eW02G8PDw1I+TLF8npubGx4eHnh4eODt7Y1arZaS5ospPWTGx8PDA7VaTXZ2NpmZmRiNRmJjY9mxYwdVVVXU1NQwPDyMh4cHqampxMfHExER8YWWC5U1dp+BqDH40Y9+xMGDB2lsbOSf//wnM2fOJCQk5Kq35/HHH+cf//gHw8PDo0qsnDv4vL29CQ8PJzU1lVWrVnH//fePSrkic/0zNDREcXExjz/+ODabjRkzZvDKK6/g4+MzabXLYp92Op00Nzezc+dOnnvuOcxmM8HBwdx///18/etfR6fTTXphDv59P5WVlZw4cYKdO3dSVFREb28vNpsNLy8v4uPjmTp1KjNnzuQrX/kKarVaFu4uknNTVkzWvGKXQl1dHe+//z7PPvssQ0NDeHp6EhISQmFhITExMZd0rpFrRGdnJ1VVVbzxxhucOnUKvV6PyWRCqVSi1WoJDAwkJiaG3NxcMjMzSUlJuaarL0wGqqqqOHr0KL/5zW9obW0lISGBm2++me985ztotdovTHCWBbvPoLe3l1/+8pds376dlJQUHnroIZYsWTJhplej0UhPTw91dXU88MADqFQqZs+ezW9+8xsUCgUulwubzUZjYyPvvvsux48fp6WlhUcffZS1a9cye/bsq95mmYlBNM0WFBSwadMmPvroI+655x4eeughUlNTJ7p5Y+J0Ouno6OAvf/kLBQUFVFVVsXjxYlasWMGsWbOIiIjA19f3mtA6AvT19fHyyy/z3nvv4e3tzfz581mxYgUhISEolUpsNhsVFRUcOHCAEydOMDQ0xLPPPsuMGTMueRG/EbFYLJSUlPD973+f2NhY7r33XlasWDHRzbosRNNef38/zz//PB9//DGDg4OfW7ATBIFXXnmFvXv3UlJSwm233cbChQuJjo4mMDCQgYEBOjo6qKmpYcuWLZw+fRqXy0VycjKHDh2SNxmXgd1ux2azYTab2bNnD4cPH2bfvn2oVCoeeugh8vPzmT59+hW/rmyKvQBdXV1UVFSwd+9esrKyyM3NZfr06aOSmV5ttFot3t7e9Pf34+7ujoeHB76+vkRFRUmCncvlwt/fn4GBAXx8fPjTn/7E7t27SU9PJysr64bKb3YjI5pmMzIyMJlM9Pf3s3fvXqZNm4a3tzdxcXET3cRRmM1mWlpaeP311yktLUWpVPKVr3yF/Px80tPTiYyMlOopXwucPXuWkpISNm/eTEREBJmZmSxfvpyMjAwpf57D4cDHxwc/Pz8iIiJ4/fXXee+99zAajaxevRqdTndDLqodHR288sorBAcHM3PmTObMmTPmcYIgYLFY6OjoQKVSMTw8fJVbeuURTXviz+UE5A0MDFBYWMjWrVuxWCxSxGZSUhJarRaVSoXVaiU4OJiwsDC0Wi0vvvgiDQ0NGAwGJlrvU1FRwYYNGwgNDeWmm24iJSVlQttzqXh6euLh4YFKpWL+/PkEBQURFxfHjh072LdvHy0tLfT09JCXl4enp+cVG+uyYDcGYkHhxsZGPvnkE5qamnjkkUfIz88nMjJyopt3Qdzc3HBzcyM0NJRFixbhcDh45ZVXKCkpobGxkf7+flmwu8EICwtjzpw5eHh4sGXLFo4cOYJarSYqKmrS7MaHhoZoamrik08+4Z133iE+Pp558+bxjW98g4SEhElrOh4LMWKuqqqKbdu2cfLkSX74wx+ybNky5s6dO+pYT09PEhISiI6OJikpiYKCAvbs2YObmxtpaWnodLoJuouJxWAw8NxzzzFlyhTc3NzGFewUCgU+Pj7ExsZK2lyZT3E4HPT29rJx40aOHz/O/PnzufPOO1myZMmoMS8qB8LCwsjIyODo0aMMDw9jtVonsPWfUlNTw+9+9zsyMzOJj4+/5gQ7+LcZOykpidjYWGbNmsXAwAC7d++mubkZu91OYmIioaGhV0xpJAt249Db28vHH3/Ma6+9xj333MMtt9wy6TQcn0VAQABhYWEEBwfT2dlJZ2cnjY2NBAcHT3TTZK4ykZGRaDQavvzlL7N37146OzuZNWsWISEhk0KwO3r0KBs2bODNN99k9erVPPXUU0ybNu2a8KMbi7a2Nvbs2cNbb71Feno6a9asISsra9zjPTw8iIyM5L/+67+4//77OXz4MHa7nX/961/XlFB7tfH29mbOnDns2bNnopsy6ejp6aG0tJS//vWvpKWlkZeXx5o1ay443t3d3fn6179OX18fhYWFV7G1NwYeHh4EBQXxy1/+ksWLF7N9+3b+/Oc/YzabeeCBB65YHlxZsBuH999/n/LyckJCQnjqqacIDw+fFAvgpSC2V1Sne3h4XLMLpczloVAoUKlUfOc736GlpYX29nbWr1/Pt7/97QnV4FqtVmpra/nNb35DX18fd955J//93/9NeHj4FTVNXG127NhBWVkZnp6eUuTuhe5F1DzNmDGDiIgIKisrOXbsGK2trYSGhspa9nG4VvvH1eD06dPs3r0bl8vFvHnzSElJ+cw+KAgCaWlp/M///A9DQ0NyXtYrzMjnP2/ePMLDw4mNjeX3v/89np6e9Pb2sm7dusu+jvytnYNohj127BhWq5WsrCxiYmKuyQ4+PDzMwMCANED9/f3PK3Mm+qhUV1fT2dlJV1eXdLxSqSQoKIisrCwCAgLw8fHBZrOxceNGenp6Rp0nICCA1NRUpk2bhru7O11dXXzwwQfS+x4eHsTExDBnzhy0Wq103fr6ejo6Oujs7GRoaEjyCwsODiYzMxOtVoufnx/wqd+N6LckEhQUxC233MLJkyfp6uqir68PhUKBTqcjOjqa5ORk/P39USgUmM1mampqKCkpweFw4OnpiU6nY/ny5fj5+eFwOGhvb6ewsJDe3l7gU43A4sWLSUhIGHW/LpcLq9VKRUUFer2enp4e6bmpVCp0Oh3Z2dloNJpJsyi7ublJ+RdLSkooKCjgkUcembDSNy6Xi/7+frZu3UpPTw+JiYmsWrWKuLi4ayY4Yjyqqqro7OzE3d2dxMTEi+oDbm5u+Pn5ERISQmNjI2azmaamJgwGAyaTiZqaGuDTPhkUFMTcuXMpKSmht7eXwcFBBEEgMDCQiIgIEhMTx9TG2mw2+vv7OXPmDK2trQwPD2O326WxEB4ePqoU3fDwMB0dHezfv3+UaW7FihVYLBYaGxvp6OjAZrPhcrlwd3fnnnvuwcfHB7vdTltbG3V1dfT19TE0NITNZsPPzw+dTkdUVBRpaWnSdy0IAuXl5dTW1nLixAnsdjsGg4FDhw6NinpNTk5myZIlNDc3U1JSQkdHh9SuOXPmkJ2dPar/iKlzamtr6ejooKuri8HBQSkllE6nIz09naCgICnHWFtbG/X19ZSXl0vnDg8PZ8mSJVL5vv7+fhQKBUFBQcTGxpKQkCDNNeJ1BUGgq6sLvV5PU1MTRqMRu92OIAj4+/uTkJBARESE5OJzJft8e3s7tbW1AMTExEglsi6EuAEUo2FHIj7Huro6Ojo60Ov1n/kc4d/l/06fPk1HRwdms5nh4WHc3d1Rq9VotVoSExOJiIjAy8sLh8MhVXM4ePAgdrsdvV7Pxx9/TENDg9TO2bNnk5OTc4We1tXH39+fuLg48vPz2bVrF21tbezfv5/8/HwCAgIuS1N/7UkrXzBidYmTJ0+SkJDAokWLrinNwch0EV1dXbS3t9PX10dISAhhYWGjUrQIgoDdbqe1tZWtW7dy8uRJKisrMRqNeHl5odFoSExM5MEHHyQzM5PIyEgsFgt//vOfOXnyJBaLBbvdjp+fH2lpadx7772kp6ejUChoa2vjxz/+Mf39/bi5uaFWq7n11ltJTEwkICAAp9NJW1sbO3fu5NixY5SXl2M0GiUBNDk5ma997WtMnTqV+Ph4PDw8aG5uZteuXfzxj3+kv78fh8PB1KlTSU9P54MPPqC0tJTm5mYEQSA2Npa8vDzuvvtuKet3b28vBw4c4A9/+APd3d14eHgwbdo0Zs2aJQl2dXV1vPTSS5SVlTE0NIRareZvf/vbKMFOjDxubW3lo48+orS0VCoOrVQqCQwMJCEhgUceeYSMjAxCQ0MnxcZAoVDg5eXFrFmz6O3tZdOmTQwODqJSqSakfQ6HA4PBwD//+U/i4+PJz89n7dq117xQB9DY2IjBYMDd3Z3o6Gi8vLwu+rNhYWEEBATQ1tZGTU0NdrudsrIy3nvvPcxmM/7+/mRlZfGTn/yEN954g9raWvR6PXa7nZiYGObOncvKlSvJz8+XqnCIQobRaKS2tpb169dz4sQJent7GR4eRqVSkZiYyIwZM1i5ciWzZ8/G09OToaEhzpw5w69+9Sv0ej1WqxW73c4777zDwMAAe/bsobi4GJPJxNDQEC6Xi2XLlhEUFITJZOLAgQPs3LmTtrY2enp6pOLpycnJzJs3j6ioKPz8/PDw8MDlcnHs2DHef/99jhw5gs1mo729nS1btrB7927gU+H3zjvvZNGiRVRXV/P3v/+dTz75RBIaf/nLX5KZmSk9b9Hfsa2tje3bt3PixAnOnDkjjX8/Pz/i4+O55557mD59OsnJyXh5eXH27Fm2bdvGq6++KuV8mzdvHomJiWzYsIGysjLa2toQBIG4uDiWLVvGbbfdRkZGxqjv0mKxUF5eLtUUb2lpYXBwEKfTSUhICEuWLGHhwoUEBARccf9Ag8HA2bNnpT6l0Wg+97lGPscdO3ZQXFxMRUWF9Bx9fX1HPceUlBQplYdYMebDDz+kpKSEtrY2aY2JiIggISGB5cuXs3z5crRaLXa7ncLCQl577TWqq6uxWq20tLTw5ptvShYnd3d3/t//+3/XtGAHSGP5S1/6EuvXr+fAgQPU1dVJCpLPjSAzCrPZLOzfv18IDw8XnnjiCaGlpUVwuVwT3axRDA4OCidPnhQiIyOFlJQU4aGHHpLa6HQ6heHhYeH06dPC448/LkybNk1QKpXCiy++KJSXl4+6l4GBAaGsrExITk4WIiMjhZUrVwoff/yxYDAYhNraWuHDDz8U8vPzBa1WK3z7298WSkpKBJfLJdhsNmHjxo3C4sWLBXd3d+Evf/mL0NzcLDgcDsHlcgkul0twOp2C1WoV7rjjDuFb3/qW8P777wt2u11wuVzC0NCQUFdXJ6SnpwtRUVHCokWLhM2bNwudnZ1CfX29sGPHDiE/P18ICgoS7rvvPqG4uFg6p8PhEAYGBoS7775biIqKEgIDA4V169YJ27ZtE2pqaoTOzk5h+/btwtSpU4WoqChh2bJlgtVqFQRBEFwul+BwOASbzSY8/vjjQlBQkJCbmys0NzdL7zudTsFmswmbNm0SsrOzhaCgIOHDDz8c9fzNZrNQVFQkJCYmChEREcJXvvIVYfv27UJPT49QVVUlvP7668LChQsFtVot/PSnPxXKysquUs+4ONrb24U///nPgpeXl7Bt2zaho6NjQtpRVlYmvPTSS4K7u7uwadMmQa/XS/3nWsblcgnz5s0T/P39hZCQEKGoqEjo6+u76M8/88wzQk5OjuDn5yf8/Oc/F+rr6wW73S709fUJc+fOFcLCwoSQkBBhwYIFwpYtW4SamhpBr9cLRUVFwurVq4XY2FghJCRE2LNnj9Dd3S21qbOzU/jud78rxMfHC2FhYcLf/vY34eTJk0JXV5dw6tQp4YEHHhAyMzOFoKAgYf/+/UJ3d/eoMfHqq68KixcvFgBh9uzZwjPPPCMcPHhQMBqNwqlTp4Rf/vKXgkqlEurr64WjR48Kv/vd7wR3d3fhP//zP4Xt27cLer1eaG9vF15//XXhnnvuEfz8/ISnn35aKC8vl9rocDgEu90unDhxQlCpVMKMGTOEl156SbBarYLVahVsNps0jzidTsFutwslJSXCqlWrBG9vb+GXv/ylYLFYpD40MDAgVFZWClOmTBGioqKEW265Rdi0aZM01+zcuVO4/fbbBbVaLaxdu1Z49913R801RqNRWLdunfTM77jjDmHXrl1CXV2d0NbWJmzfvl1ITk4WYmNjhVWrVglOp1P6Hu12u7B9+3ZhyZIlQkJCgvCLX/xCaGxsFDo7O4Xq6mrh97//vRATEyPMnDlT+NnPfiY4HI7z+sJPf/pTIT09XYiMjBSampouqR/+93//txAcHCwAwjvvvHPJnx/J4OCgUF1dLaSlpQlRUVHC8uXLpefY0NAg7Nq1S7j99tsFjUYjrFmzRnjnnXek76CwsFB48MEHhaCgIOFnP/uZUFhYKJjNZqG1tVVYv3698PWvf11wd3cXXn/9daGurm7UPP3ee+8Jfn5+wpw5c4QtW7aM6gdjPa9rEZfLJdjtduHnP/+5MG/ePOH++++/7Dl54tUIkwyr1Up9fT0+Pj4EBQVdlPp6ImltbWXHjh3ceuut0v9cLheDg4P4+voyY8YMnnrqKfLy8s4rt1ReXs6mTZtoa2vjzjvv5KabbmL27NloNBp8fX1RqVQ888wzPPzwwxQVFeHt7S05tKekpHDbbbdx+PBhjh49SlBQEHfccYd0bovFIoXMT506lfnz50sRmGICzpaWFm699VZuuukm5s6dS0BAAGq1Gj8/P37wgx/wxBNPcPLkSf7xj3+QnZ0t7WBELYRCoUCj0bBs2TKysrLQarV4eHgwc+ZMZs6cybFjx6iursZsNkvVCdzd3aWfcxGTm4qZ2MfTGh07doxt27bR1tbG17/+dZYuXcrMmTNRq9UolUop4ef999/Pnj17EASBzMzMK/WVXzZiJRJ/f3/Onj1LUlISYWFhV70dzc3NnDlzhqysLGJjYz/TD+1aQvg/DRlcvnlN7I9iv7fb7QQFBfHNb36TnJwcNBoNnp6epKWl8cgjj/DRRx/x1ltv8corr/CDH/wAnU6Hy+Xirbfe4ujRo7hcLh5++GHy8vIICwuT+qxYU/iPf/wjf/3rX3nyySeZM2eONCZGRlAvXbqURYsWMXXqVPz8/EhISOBLX/oSM2fOJCwsDJPJhI+PDxkZGdxxxx0kJSWh0WgQBIGlS5eiUCjo6Ohg06ZNLF68WDJXi+NSHH/itcfSeI4cq+Nl8K+srGTjxo20tLRwxx13sGTJEubNmyfNNWq1mmeeeYaqqipKS0uxWq0sXbpUSjMiPnMAnU7HsmXLmDp1KhqNBjc3N2muKSkpkeYaf39/SQMuCAKRkZGEhITw5S9/mbCwMNzc3PD392ft2rWcOnWKiooKtmzZwpNPPomfn98VDZa5Un2wqqqKjz76iObmZm6//XZuuukm5s2bh0ajQaPR4O/vzw9+8ANqa2spLy/nn//8J8uWLUOtVtPT08Pp06ellD/p6en4+vri7e3NkiVLCAoKYvfu3bi7uyMIAgqFQpqjxeeoUCjw8PC4JM33tYS7uzsZGRm0trayZ8+ey07bIwt25yCGiPv4+KBSqSaNf9R4iINALDUjFoNvbm6WFvDo6GgiIyPPGxStra0UFhYyPDzM1KlTmT59uhQx6+npiaenJ3PmzCEwMBC9Xi+ZX729vQkJCWHGjBloNBqqq6uJiYlh9erVktl6aGiI48ePExAQQERExCgTsF6vp6CggKGhIaZMmcLMmTNHXdfLy4s5c+YQFBREU1MTx48fZ3h4eMyKCSqViszMTHQ6nfRdBQUFERUVRWVlJa2trfT39+Pn53fFAkcaGho4duwYFouFnJwcpk6dKm0APDw8CA8Px9vbG41GQ1NTE6WlpQwPD+Pt7f2FlZC5FLy9vVGpVPj6+mI0GrFYLBPSDqPRiF6vJyYmBn9//+tq0vb29pb6m+hTdbHY7XacTqcUUDFWn1Gr1cydO5fg4GBp8dNoNOTk5HDmzBnc3Nw4duwYXV1d2Gw23NzcKCoqorm5GW9vbyl1k0qlAj4dd1OnTqWmpgYfHx+OHj1KR0cHVqt1zDJW6enpJCcno9VqgU9NSqLPGCD5Ta1evZrU1FTpOEDyAUxMTKSwsFAqcfVFzLVtbW0UFhYyNDRERkYGM2bMGDXXiCW0YmNjKS0tlXwPk5KSznNP8PPzk+Ya8bv18vIiOjqa6upq2tra6Ovrk1wbFAoFISEhzJ8/Hw8Pj1HuHF5eXsTGxpKSkkJjYyMVFRUYjcYrmvhevD/4d5/6vLS3t0sm7/T09FHPEZCeY0xMDKdOneLMmTO0tLSQlJQkrVGDg4MYDAY6OjoICQlBo9EQFhaGIAisXr2a6OjoGzJdjSh0h4aGEhkZSVtbGxaLBafT+bn7gizYnYOo7VKpVJdVl+9qERkZycKFC3n11Vclgaqzs5Nnn32Wffv2UVBQQHh4ODNnzhzlKygIAh0dHZw8eRKFQkFCQgJRUVHnLfI+Pj5ERETQ1dUl+Q2FhYWh0+nIysoiJSWFuro6AgMDMZlMkoBjNBr58MMPmT9/PhkZGVIHFQQBg8HA8ePHJV+4uLi4Ma8bHh5Oc3OzpPkLDw8/r6MrlUqSk5PPE9oCAgLw8/NDEAR6e3sJCgq67OS24uLc1NTE6dOnJUfukJCQ89qvUqmIjIyksrKSs2fP0t3dTXh4+KQQ7ODfSVAHBwex2+0T0obBwUH6+vpISkq67qK1RyZ/NZvNl7SoDgwMYLFYpCCgc5+Np6fnKCFqJOHh4UREREibiq6uLvr7+/H396e4uJiuri5SU1OZOnUqwKh+6+npKW3EysrKaG9vx2w2j1k6MTY29oJpk+Li4oiNjWXJkiVSoNFI4dbLy4vIyEjJF9hgMFxx64ggCHR2dlJcXIwgCKSkpJCUlDTqGLEOa1paGu3t7bS3t3Py5EnCw8MloVdEpVKRlJR03hyk1Wrx9fXF5XLR29uLTqeTNnEzZswgOztbegZiu0REAcdut9Pc3HxFg618fX0lX83+/v7PvYETBAG9Xi89x+TkZJKTk0cdo1AopOfY1tZGS0sLJSUlhIeHExQUxPTp03n99dd5//33aW1tZfny5VLt2+DgYF566aXrwrf2cggICCAoKEgKerRaref1wYtFFuzOwc3NDR8fH8lJ+FpDTNb5u9/9ju9///tSnpxbbrmFqVOnEhISIgVNDA8P09/fD8B99903rgO96OwbERFBY2OjVONOqVTyyCOP8Pvf/576+nreeOMNvvWtb9Hf3091dTVHjhzhpz/9KVOmTJHOde51H3/8cb773e+Oed2hoSEcDgcajYbGxkYCAgLOm/TEyKpzJwTR7AqfCuuXojG5EHa7XSroDrBq1apxd1UDAwO4XC5CQ0NpaGgYpV2ZaJxOJ0NDQyiVyglrk4+PD76+vvT29l6WNmEyMmPGDPR6PWfOnKGiooLs7OxRWqsLUV9fT2dnJ15eXsyfP/+8SHaxUsV4KJVKdDoder2evr4+TCaTVK3GarVSXl5OcnLymIuow+GQ6lB3dXVJ2pWxrnEhDavFYqGlpYUNGzawb98+9Ho9ZrNZGvdOpxOHw4HT6cRms30hyXBHjlVvb298fX3HFZp0Oh2+vr44nU4MBgMOh+O8Y8TN0LnPzcPDY9TGdST9/f3s2rWLoqIiCgsLpUhS8X5tNht2u12qmuFyua7ErQOfCtfZ2dlUVFRQX1/PlClTSEtLu+TziHP2xTzHwMBAScjt6urCbreTk5NDamoqM2fO5JNPPmHPnj288soruLu7Ex8fT1paGrfffjv5+fn4+/tf7m1fswwODmI2m3F3d8fPz++yFEuTY5WZRIgFl/v7+6WJ8Isq1PtFIPqdaDQa8vLy6O/v5/3332fjxo2SCVVUjXt5eUlC7KOPPsqUKVMueJ++vr4kJydLg9rDw4Pc3Fw+/PBDSktL2bFjBw8++CBlZWWcOnVKKqEysoOee9377ruPnJycC17X29ubKVOmjLt7uZzC3w6HY0yhb+QCNxLR7C3W+Xz66aelcm7jERAQQEpKyqQR6qxWK4ODgxiNRoKDgyesTFdQUBAREREcOHBAinKeLM/ocpk/fz61tbWUlpZy9OhR1qxZ85mfEV0ourq6UKlUpKamEhIScp7Gzmazjeqb5/Y9u90upT9RKpWSaVCpVOLp6UlERAQ//OEPP1N7PH36dKKiosZ877PG2+7du9m/fz8HDhwgJyeHvLw8QkNDpXtpb2/n1KlTbNy4cZQ/4pVEnGu8vb2x2+3SZn0sgVQUtkT/t/E2a2Pd93jPwm638/LLL1NYWIjZbGbGjBmkpqaO8iU8cuQIJ0+epK6u7oo/g5SUFObOncvbb79NWVmZpKW9EIIgsHXrVk6fPo3ZbOapp54iMDDwkp6jzWZDoVBIz9Hd3R1/f38WL15Mamoqvb29Ur3z5uZmWlpaeOGFF+jr65OEwJFcK2vv5dLS0kJDQwNJSUkolcrLsu5cH7PoFcTT05PIyEisVismk4ne3t4JcSy/HETBLTs7G4PBwNatW/nkk0+YMWMG06ZNkwacn58fwcHBtLW1kZeXx5IlS87TBIg7d7PZjMPhIDw8XHrPzc2N+Ph4EhISqK+vp6ysTFLB19TUkJeXd94k6e7ujkqlIiQkhLa2NubOncu6devG3KkZDAbMZjMWi4WwsLArOsDFXbbFYjlvQnU4HFitVkkrJyI+V7VaTVBQEB0dHSxbtozp06efJ3SK5guTyYSbm9uk6kNms5ne3l6GhoYIDw+fML+WkJAQ4uPjeeutt2hpaZGqpFwPpKWlkZKSQlBQEBUVFbS3txMRETEqv9dIRBeQY8eO0dfXR2hoKHPmzMHf3/+8CV4U7AYGBvD19R01LiwWCwMDA/T396NSqfD398fX1xd3d3dCQkIYGBhAq9Vy5513olarzxNghoaGMBqNGAwGKaDlUhDH0okTJ9i+fTsNDQ184xvfYM6cOSQnJ0v3X15ejkKhYNOmTeOeayzhqq6uDpfLhVarJSgo6DOrKPj6+hIcHExLSwsmk4m+vr5RJl9BEHC5XBgMBikPZVhY2GW7Bgj/lx5k8+bN1NbWEhYWRl5eHvn5+aPMrV5eXpKQI37uShEVFUVmZiZRUVGcPXuWxsZGenp6pByF5+JyubBYLOzZs0dKN/PYY48Bn5qhxedoNpsxm82jxqoonHd3dzM4OIinpyfh4eF4eXnR29tLc3MzqampxMXFoVAocDqdlJWVUVRUxCeffMLbb79NSkoKWq12lGAnKipGCv+CIFBVVSX5MY93P9cKgiAwNDREY2MjTU1NZGZmXnZu0cnh8DOJUKlUUpH01tZWTpw4MdFN+txMnTqV3NxckpOTKSsr49ixY5SVlUmDJDo6mtzcXFwuF3V1dTQ1NUnvjfz54IMPeOaZZ3j00UdHmSjESKX8/Hzmzp1LT08PGzZsYPPmzdTU1PClL31pTIFNnOQEQaCxsZH6+voxr7tp0yZ+/OMf87Wvfe2SHdA/Cz8/P/z8/DAYDDidzlHXNZvNGAwG9Hr9mNdMSkpi9uzZOJ1OyWn63LY7HA7efvttnnjiCf7zP/9zUpkaz5w5Q1VVFR4eHmRmZhIYGDgh7UhNTZX6waZNmygqKvrCtDdXE4VCQUREBIsWLeKuu+6ipqZGyv01Vj8XXSPa29t5/vnnsVqt5Obm8uijj45rLjWbzZw5c0YaF+JPS0uLJMQkJSURHh4umQ8XLlxIeHg4AwMDnD59WvJ7G/nT0NDAP/7xD9atW8ehQ4c+l2nQ5XLR3NxMY2MjOp2ONWvWkJOTM0qoFYO8xsPNzQ2lUonT6Rw1Pn/84x/zve99jy1btoxpLj2XqKgocnNzUSgUVFRUUFlZed4922w2Tp8+jV6vl4JSLmTqvhjEOaC8vByHw0FiYiJ33333eVVEbDbbRd3H50Gr1ZKens7DDz9Mb28vhYWF7Ny5c9w+aLFYOHv2LFu3bsXpdJKfn09sbCze3t5ERkayYMEC3Nzcxn2OVquV0tJSOjs78ff3Z86cOfj5+bFz505WrlxJTU0Ng4OD0roxffp0Hn74YZ5++mkUCgUlJSVS3j0RsR84HA7JpUYQBB599FF+/vOfS/kNr1XE+6msrKSwsJCamhruuuuuyzZJyxq7c/Dw8ECn0zF37ly6u7v58MMPWbly5UQ363MTGxvLU089xeOPP87evXux2WzMmzcPgJkzZ+Lv709RURFvvPEGZ86c4b777iMrKwuFQkFPTw9FRUW89NJLTJ8+nbvvvntMU1leXh5DQ0Ns2LCBl19+mSlTprBw4UJCQ0PH3HVnZGTw1FNPceTIET766CNqamp45JFHyMrKwt3dnf7+fg4ePMjLL79MbGws3/nOd654kujExESmTp3Kli1beP/998nLyyMjI4Pe3l7Wr19PUVERgYGBY066ixYtQqvVUlxczEsvvUR5eTnr1q0jKysLl8tFZ2cnR48e5Y9//KOUeHMy1fvcs2cP5eXl5OfnExwcPGFBQiqVivj4eJ555hneeOMNzGYzGo2GhQsXXtM7cJGZM2cSHR3N0NAQW7Zs4ciRIxw7dozbbrtNSjMiChW7d++msLCQ7u5unn32WebOnStVIzgXpVKJyWTiueee47HHHiMpKQm1Wk1TUxPPP/88x44dw9/fnx/+8IeSk7u7uzvf/va3cTgc7Ny5k+9///t8//vfJzMzk4iICFwuF0VFRezZs4cPPviAp59+moyMjM9lDnJzcyMpKYkpU6ZQUVHB+vXrWbhwIRkZGfj4+FBdXc2uXbt4//33xxUclUolGRkZVFdX09LSQltbG11dXTQ0NBAaGkpOTs5FtW3GjBn4+flRVFTEBx98QGNjIw6HgxkzZjA0NERDQ4OU5HnWrFncfvvtBAUFXfZ4FYMJZs2aRXl5OVVVVbz66qusXLkStVqN0+mksrKS999/n1OnTl3WtS6ETqfj29/+tpT090c/+hFNTU0sWbKE2NhYAgMDMRqNVFZWcuLECdavX49Wq+Xee+/lrrvuks6TnZ3Nk08+SVFRERs3buTs2bPScxweHqaxsZHXX3+d6upqcnJyuP322wkODpaeo9Vq5ac//Slf/epXmTlzprTBOH78OPv370cQBLKzs8+rx67RaEhLS5OilZubm+nu7ubs2bNkZ2dfsP7ytYDNZqO7u5vvfe97WK1W8vPzWbVq1WW7x8iC3TmI5ra8vDz27t1LeXk5ZWVlJCUlfe4IlSvJ1q1bOXr0qOQYbbFYOHnyJD/5yU9ITEwkLS2N2bNnA0h53mbPnk1cXJwkqP3kJz8hPDycqVOnkpqaymOPPcapU6fo6+vjvffeY/v27Xh6ekoln3Jzc5k/fz4zZ86UzjsSMcx95syZFBQUkJCQwJw5c8b1l/L19SUuLo5HH32U0tJSyQ9wx44dkgmkr6+PmTNnkp2dzdy5c1EoFHR3d1NZWcmuXbsoKyvDbDbjcrl49tlnmT9/Punp6Wg0Gl588UWOHTtGXV0dFouFv/71r2RlZZGZmcmSJUuAT7WZ/f39NDc3U1RUJC0Yfn5++Pr6EhsbS01NDW1tbbz99ts0NjYydepU8vLyCAgIIC0tjW9961sUFxfT0dHBu+++y9atW/H09MTpdDIwMMBNN93EkiVLyM7OnhSCisPhoLKykoqKCqxWK2vWrJlQ/1HRn2n58uWcPHkSo9HI+vXrpYon17ojtVKpJDQ0lDvuuAOtVktPTw9NTU28/vrrUu41QRAYHh6mr6+PuLg4li5dyvz584mMjBx3/Hh6ehIYGMi8efM4cuQIBQUF0gLR1NQkVUIQc9yJREREsGLFCnQ6HSdOnJCESTEQQoycXLFihZT30mq10tXVxb/+9S9pnAD85S9/ITQ0FH9/f773ve9JQpbYl+bPn4/T6cTLy4ujR4/S0NBAUFAQ/v7+eHt7YzAYiImJoaamhn379tHd3c3x48f52te+ho+PDxqNhttuu41//etfVFRU8OKLL+JwOIiJiSE7O5vw8HBqa2v55JNPKC4upqqqCofDwZ49e7Db7aSnp7Ns2TL8/PxITEzkscce48SJE1gsFjZs2MDu3btxOBwMDg7S3t7O6tWrmTdvHgsXLpRKIpaVlbF3714qKioYGBigoaGBn//85+Tm5pKZmYmnpycvv/wyR44cobGxkcHBQf7yl78wbdo0pk2bxpw5c7j33nvZvXs3ra2t7Nq1i+rqavz8/KTcgQEBAeh0OoxGI2+++SanTp0iJiaG6OhoDhw4wP79+zEYDFgsFp5//nmmTp3KlClTmD9//kWNW3d3dzQaDbfccgthYWGUlpbS0tLCBx98gJeXF25ubpJpf2hoiJSUFPLy8pg7d+6ooJmxnuP777/P7t27pUCs9vZ2Vq1axdy5c6XnqFAoSEpK4mtf+xptbW0cPnyY06dP4+7ujtPppKenB5PJxJo1a7j55pvP86+LiIhg9erVGAwGqSqDw+EgJyeH7OzsSeXicqkMDAxQV1fHli1b6OrqIj8/n2XLll0R1xhZsBuHBQsWUFVVxfHjxzl48KAUwj7Rmpfjx4/z/vvvY7VaJR8Hs9nMW2+9JSUBFgU7+FQrkpyczOzZsykvL6erq4u33nqL9PR0VCoV8+fP5+GHH2bTpk2cOHFCMkkolUo0Go1UJiY9PZ3o6Ogx2yT6UyxdupTOzk5ycnIuWOrFw8ODgIAAHnroIXbs2MGxY8c4ceIEer0eLy8v/P39SUxM5M477yQjI0NK62A2m6moqODtt98GkHxl3nnnHalWobu7O+vXr5euFRUVxf79++nu7sblckmCXWpqKiqViubmZo4ePUpTUxNOp5PU1FTuv/9+dDodzc3NuFwuTpw4QUdHB06nk3nz5uHn50d4eDiPPPIIarWakpIS6bmpVCq0Wi1xcXF87WtfIz09ndDQ0Mv4xq8MLpeLoaEhDhw4IPmzLV68eMKDFZRKJTNnzmTp0qXs2bOHXbt2kZaWhru7O3FxcZJJbDIIxp8HpVIpaUfKyso4dOgQRUVFmEwmbDYbnp6exMbGkpaWxrRp01i9erXkEzceHh4ehISEsHr1al566SUqKyul/ik6zN9yyy3Ex8ePErh8fX1ZvHgxKSkpeHt788knn0g55MS6ldOmTeOmm24iMzNT0p4bDAY2bNgg+YsmJCSwb98+4NMoyKeffvo87dns2bMJCQnBZrNRVFREU1MTFosFf39/cnJyCA4OZuHChTQ0NNDS0oLRaKS1tZUvf/nL+Pj4oFarWb16NbW1tVRWVrJz507UajW33XYb8+bNIzAwkLKyMnbt2kVxcTHwqXVCTC1kNBqZP38+Go0GnU7Hww8/TGhoKCdOnODUqVN0dXVJUa4pKSnce++9pKWlSXOcyWTi9OnTvPfeewCSkPPuu+/i4+MjaVzfeust4FMBKiwsjL1799LT04Onpye5ubmsW7cOb29vjhw5IiUj9vLyQqvVMnv2bLKysvDy8mJ4eJgjR47Q0dFBUlISM2bM4O2338ZqtUo5Ards2UJHRwcul4v58+dfdB9UKBTMmzdPOu+GDRsoLy/HYDDQ19eHt7c3YWFhxMbGsmLFCm677bbzfKPFzcQ3vvENwsLCKC4ulp6jGByRmprKl7/8ZdLT04mJiZE+m5KSwoMPPsgHH3xAdXU1p06doru7G29vb6mm92233caSJUvO80ENDw9n5cqVnDp1ioaGBiorK/Hz85M0fxPlRnI5iD51DQ0NFBUV8fbbbxMaGioJdlcChXCtO7R8QQiCwI4dO3jvvff46KOPeOONN5g3b96EO3dfzNd17iJ4pb7iCy2uY13jUo//rOteifu4EucSC5Zf6jUnErPZTF1dHTfffDOLFi3i1ltv5cEHHwQmR/tcLhcFBQWsX7+e1157jXXr1nHrrbfyla985YJVQK4VLrW/jXW/FouFm266ic7OTjIzM9m4ceMln+dS++3FHn8l55xLHV9X8lxXcq75Ivk84+Fy++DnnfMu57qf9dlrcV6w2Wzs2LGDF198kdraWiIjI3njjTeIjIzEx8fnityTrLEbB4VCwZw5c1Cr1Rw9epS//vWvNDY28uCDD+Ln5zdhHerzXPdqtPVSrzHR93G557qWJpTBwUH27dvHm2++SWhoKF/60pcmnR+bQqFg+vTphIWFER4ezt69e/nDH/7Anj17+OY3v0lKSsqEb6ouhy/iWV+NMfR52z2ZxtdEzzWThYn6Ti7nutfT9yCWVtu5cycfffQRcXFx3HPPPdxzzz1ER0dfUbcYWbC7ABqNhuTkZFasWEFhYSFHjhwhPDycVatWTQqzrIzMhXC5XNhsNvbv38+hQ4dobGzk1ltvJSMjY9IJSWLeK6VSybJly7Db7VRXV3P27Fk2bNggVTnJyMgYM02HjIyMzGTD6XQyPDzMmTNnKCkpobKykjNnzhAfH09+fj6zZs0iPT39igcHyoLdBXB3d0er1fLYY49RX19PeXk5LS0tTJ8+XSo5M1lKRMnIjMTlcmG32+ns7OTvf/87DQ0N+Pr68s1vfpOIiIhJKxh5enqyYMECEhISOHHiBK+//jpvvPEG8fHxzJgxg4ceekjyjxQDEK6nXf1Y2Gw2bDablMRZ/G57e3ulpMPXW0k2GZlrlZFpdAYHB6Xgui1btmC1WomOjuaZZ55h9uzZX5j/texj9xmIj6e5uZnNmzfzwgsvoNVq+f73v8+iRYsmhWO8jMy59Pb2cubMGZ544gn6+/tZtWoVjz/+uJQgdLILQ2LiWIfDwaFDh9iwYQOHDx+mqamJ22+/ncWLF7Nw4UJSUlIm/b1cLu+//z7btm3jrbfekvLWKRQKvLy8+OY3v8ntt99OXl7eRDdTRkaGTyuO9PX1sWHDBnbt2sWRI0dwOBzcd999LF68mEWLFkmVJb6ouUsW7C4SMXnj0aNHefHFFwkMDCQjI4O7776b2bNnyztmmUmBIAgcO3aM3bt3U1BQQEdHh5T5f+rUqSiVymtKEBIEgd7eXlpbW2lsbOTgwYOcOnVKqq85Y8YMcnNzSUlJITExUUqxcD3R3t6OXq+nra3tvPfi4uIIDw9Hp9NNQMtkZGTgUwtJQ0ODFPl8/PhxDAYDWq2W6OhobrnlFlJTUwkNDUWn033hm2vZFHuRKJVKYmNj8fX1pbi4mNLSUk6fPo2fnx9eXl7ExMQQGBgoC3gyE4LD4WBgYICmpiZ27tzJiRMn6O7uJjc3l0WLFhEfHz9hNWEvB4VCgU6nIzAwUBp/CoWC+vp69Ho9xcXFDA4O0tDQIKXkEQu6T4a8k1eCiIgIIiIimD59+kQ3RUZGBiRTq9FoxGQyYTAYOHXqFGfOnKG5uZnOzk6ioqLIyMggOzub5cuXo1KprpoLjKyxu0TE0imvv/4627dvZ9u2bXzpS1/ijjvuYOHChQQEBJyXrFNG5otAHLpiIunKykpeeeUVPvroI2kyefrpp6VEpNcD4j03NjZy+vRp3nrrLT755BOGh4cJDAzk7rvvZsGCBaPqUoomD3k8ysjIfB5GzrWCIOB0Ounu7qawsJDi4mL2799PaWkpkZGRTJkyhdtvv501a9YQEBCAl5fXVW+vLNhdIuLjslqttLe3c/DgQX79619js9kIDw/nP/7jP1i9evUoAU9G5otA3GS89957vP/++5SXl+N0OnnmmWeYP38+qampUl3K60WoGTnBivVG29raqKio4ODBg2zfvh2z2YynpydxcXEsX76cnJwc0tPTpUTXMjIyMpeCw+HAaDRy6NAhTp06RUlJCUePHkUQBEJDQ8nOzuauu+4iIyOD0NBQvLy8pPQlEzH3yoLdZTA0NERHRweHDh3i5MmT1NfXMzw8zNy5c5k2bRrZ2dmSc/f1srDKTCzicG1oaKCmpkYqKeXl5UVoaCi5ubnk5eURFhY2qpzU9czQ0BA9PT20trZSX1/P2bNn6ezspKOjg76+PqlElVhyLyIigsjISGJiYq5LnzwZGZnLw2q10tPTQ21tLS0tLbS0tFBdXY1er0cQBKlcYGJiIuHh4URFRZGSkkJgYOCkcHmRBbvLRBAEBgcHKSws5PDhw2zevBlvb28yMjKYO3cuCxYsQKfTSTVI4frRnshcHcQharFYGBgYwGAwUFxcTElJCQUFBdjtdvLy8sjNzWXlypU3bBoeMc1AU1MTjY2NnDhxQgogEctmZWRkEBMTQ1xcHBkZGfj7++Pj44OPjw/+/v54eXlN2lQwMjIyVx6HwyHVyx0YGGBoaAiz2UxLSwunT5/m7NmztLW10dLSglarlUoALly4kKlTp6LT6SbE3HohZMHuCiJGzv72t7/lyJEj1NbWkp6ezr333ktubi5z5swZtWjIAp7MhRg5NF0uF5WVlRQWFvLyyy9TX19PQEAAc+fO5cc//jGxsbHn1Vm80REFvcbGRqqrq9m1axcHDhygtbWV3t5efH19mTZtGqmpqWRnZ5Ofn09MTMwFNZ3ymJWRuTYZT9Tp7e2VhDjR1Nrc3Ex3dzdqtZopU6YwdepUbr75ZvLy8tBqtZNOkDsXWbC7ggiCgN1ux2Qy0d7eTk1NDe+++y4VFRUAxMTEcMcddzB37lzi4+PlhVjmggwODqLX69m3bx9btmzh7NmzmM1mcnJyWLFiBWlpaSQkJKDVavH09LwhtXQXQpzaRu7Ih4eHMRqNdHZ2UlxcTHV1NS0tLTQ1NWG32wkMDCQsLIz09HQyMzOJjY0lOjqahIQE2WwrI3MNY7PZ0Ov1UuRqU1MTp0+fpqGhgYGBAVwuF8nJySQkJBAbG0t6ejrp6en4+fnh7e2NSqVCqVReE/OALNh9QQwMDNDT08PRo0cpLS2ltbWVjo4OfHx8iIqKIjIykuTkZDIzMwkNDSUgIACQNQI3MoIgMDQ0RHt7O+Xl5Zw9e1byG7NYLAQEBBAVFcXs2bOZNm3aqH4jc/FYLBb6+/slE4vBYKCzs5OmpibMZjM2mw03Nzfc3Nzw8/NDo9EQFhYmPW+dTkd4eDhBQUH4+vpKASoyMjITiyAIUqCDyWSiu7ubjo4Oenp6MBqN6PV69Ho9NpsNp9OJ0+nE29sbtVpNaGiolBcyODiYiIgIwsLC8PDwuObWZVmwuwp0dnZSUVHB7t27pag9b29vMjMzufnmm8nIyCA2NhalUomPj8+oEkHXWoeSuXjE6gp2u13SJhkMBkpKSti2bRvV1dV0d3ej0WhYvnw5ubm5zJs3j8jISLlfXEHExeDMmTNUVlbS0NDAmTNnqKiooK+vD4vFgpubG3FxcURERBAdHc2UKVNISkoiODiYgIAAPDw8cHd3x8PDAw8PD8lXT059JCNzZRFdLETBzGaz4XA4cDgcOJ1OBgYGaGxspK2tjfr6eqqqqmhpacFgMDAwMIBKpSIkJITo6GimTp1KVlYWiYmJJCUloVKprouxKgt2VwHxEYvpKU6ePMmJEyfYvHkzhYWFuLu7o9PpyMvLY9myZUydOvULKQwsM7lwOp3o9Xqqq6vZvHkzBQUFNDY20tvbS1ZWlhQQsWLFivMCIuR+ceUYbwoUBAGj0UhHRwcnTpyQhL6amhrKy8txuVx4e3uj0+lISUmRNPFpaWlkZmYSERFBcHDwDVHPVkbmaiEIAmazmc7OTtra2igvL6eyspK2tjZaW1uprq7GZrPh4eFBYGAg06ZNIz4+nri4OKZPn8706dOlQKmxuB7GqizYXUXEncbg4CADAwMYjUaqqqqorq6mtraW6upqzGYzSqWSkJAQ5s6dS0ZGBnFxcSQlJaFWq6+LTncjIggCAwMDUmmsEydOcOrUKfR6PWazWYq2iouLY+rUqSQnJxMQEIBarZZyIsrf/dVH9M8bHBxkaGgIq9XK8PAwXV1dNDU10dXVhV6vp7Ozk97eXvr6+jCZTJJPjp+fn+SnFxwcTGhoKFFRUZKmT6vVyumQZGRG4HK56OvrQ6/XYzQa6erqoqWlhY6ODgwGAy0tLfT29mKxWHA6nfj4+BAcHIxGoyEwMJDw8HBpvAUHB+Pr64u3tzdKpRKVSoWvr+8obfr1iCzYTSCCIGAwGKT0DBUVFdTX12MymRgcHESr1aLT6dDpdERHRxMZGUlgYCAajYagoCCCgoJkp/lJiJg812AwYDQaMZvNdHd3097eTkdHB3q9nq6uLvr6+vDw8ECj0ZCamkpSUhKxsbGkpqYSEhIif6+TFEEQGB4epqOjg97eXrq6umhra5P8eLq6ujCZTFgsFux2u5RORVxcNBrNKKFdrVZLx/j5+eHn54ePj48kGF5PlUNkbmycTid2u52hoSH6+/slF5SBgQEGBgYYHh5maGgIo9FIb28v/f39mM1mhoeHsVgs0sbKw8NDSlEUFBREWFiYtF5GRkYSHh6ORqO5YXJ5noss2E0y2traqKmpobi4mD179lBdXY3JZMLlcpGamioJANnZ2WRnZ6NWq6WJX9yFiD9wfaiVJzMul0v6cTqduFwuHA4Hg4ODFBcXU1lZSV1dHadOnaKurg6n04lKpSIrK4sFCxaQlZXF9OnTiYmJkb+r6wCXy4XNZqO+vn6UaaihoYGOjg7a29vp6enB5XIB4OnpSUxMDCEhIQQHB0vm3JCQEMLCwqQ0NqJbxsjyaOe+HqnVlfuSzNVipKuRWHLr3L/F11arlYGBAdrb2zl79ix6vZ7u7m5aW1tpbm7GYDDQ3d1Nf38/CoUCT09PlEoliYmJxMTEEBERQUJCAikpKURHRxMWFiZbssZAFuwmGSMHhNPpZHh4mO7ubqqqqigsLJS0erW1tXh4eEgRehkZGaSmphIfH09qaioZGRnXRFj2tU5raytNTU3U1tZSXl5ObW0tzc3NNDQ0YLFYCAwMJCIighkzZjB37lzS09NJTExEo9HIQvh1yMhFbqwf0czU2dlJT08PHR0dtLa20t7eTmdnJ42NjTQ1NTE4OIjdbkehUODl5YWvry+BgYGSEBgYGIhOpyMkJETS3osLnbe3t6zhk7lq2O12LBYL3d3dtLS00N3dLZlMu7u76evrw2g00tzcjF6vZ3BwUFrj/P390Wq1xMfHExkZiU6nIzg4mKSkJOLi4iRzqriWjfUD8tx5LrJgN8kR62H29/dLg8RkMklpMERTn+jfo1AoUKlUhIaGEhoaSmBgICEhIURERKDT6SRBQ/QzkBkfQRDo7+/HYDDQ29uLwWCQTG7iomw0GhkeHsZmsxEQEIBGo5FSZCQmJo7y/dDpdPj7++Pn53dNhtDLXD5iBK5oVrJYLAwNDWGxWBgeHpb8b/v7+zGZTJLJXjRZDQ8PY7VasVqtDA4OYrFYcHd3x8vLSzJN+fr64u/vL/VFX19fqU/6+vqOel+lUkkmX7k/3tiI2mbRFUiswNDf38/Q0BADAwPnvRbnP7FPDg8PA+Du7i65EyiVSsm/Tcz8oNVqCQ0NxcfHB6VSKbkfeHl5Sa9VKhXe3t54e3sDsvB2KciC3TWImAS5trZW8ttqaGigubkZs9nMwMAADocDtVqNn58farWawMBAtFotWq2WyMhItFotSqVSGkje3t54enpKfkBeXl54enpOaCHjLwJRcyKmGGlra8Nms0n3Ki644i60t7cXvV5Pb28vPT09kt+HOKmJvh5qtZqEhASio6MJDQ0lOjqa1NRUNBqNnOdM5pJwuVzSoio6kIuCnsFgwGw209fXR29vr+RELvZXl8uFh4eHtJCO9O0TF1GlUom/vz8BAQGSM7lGo8HT01NK2eLp6XleCpeRf4/3eqRpWObqIG4WRrqC2O12nE6nlAZEfH+813a7XZoTjUYjg4ODDA4OSv5tIzch4kZWLHEouhWMDFDw8/OTFAliXwsKCpJ8SsUgB7HPyVxZZMHuOsLhcNDT00NTUxOnTp3i7Nmzki9DdXU1/f39WCwWAMnRVMznExYWJr2OiYkhODiYwMBAgoKCJOFO5NxJe7xJ/FImd/HYS+mOF0pTMd5rQRCkXWljYyP//Oc/MRgMkmaztbWVzs5ODAYDer2enp4e4FNfKLVaLaW1iIiIICUlhYyMDKKjo4mIiJC1cDJXFXFBF01dbW1tUnRuT08P3d3do0xher1e0sZYrdZR5/L09ESr1eLn5yeZx0TtnxjkIWr/RKd0UROo1WpHBYBcaBxcaHwoFAoEQbioMXSlxtl457lSy+KVmM8u9J64Ue3r65OELvH77u/vl4Qzs9ksaYNNJpP0nrhhMJlMUvDCSPOmu7u79P0GBAQQGhqKVquVrBChoaFSgF94eDiRkZGS5k1m4pAFu+uIkYkbHQ7HKId+p9MpmWxFPx5RAyWGkZtMJnp6eqTM+6JDtkajwd/fX5rwRZOiuPMSVeZKpRJfX99R6nWVSiXt5r29vc+L8FMqlaN2bWJbBwcHR92XzWbDarVK9yaaCsRdpPi36JwrTlR9fX2SlkP8v9lslp6LqOXw8/MjMDCQKVOmEBISglarJSgoiISEBCIjIwkKCkKn0+Hh4SH5xYkaC9lPTmYiONefT+zTI/10z3VmFzU6/f399PT0SNo/g8EgCQeDg4OSAChGLI4UHPr7+7Hb7QBj+jyJ5rORKV9ETaEo/ImaHdE85+XlhZeXFy6XS3rf09MTlUolaQL9/PwA8PLyQqVSAf+eP0aOPzc3N/z9/S/47EQBdDxtkWiWFDfC4x0jaktHLqNDQ0PS/Ds0NCTNWeIcJmrJxHQdI+c2MfpzZHodUVsmbsxFM/xIzdlY/pzwqUlUnLtFDa4olPv4+EiCuugiImrWNBoNarUarVYrzXEjA3RG/h7rb3kenFhkwe4GQpwgxB3cyIla/HtoaEgSikZOLiMDOsQFZOSPuHAAkmlGrKAhDvaRE4TIWP8TqzGM5Nzr2u32UWYE+PciIwpd4o5zZMQwQElJCW1tbZjNZun8np6eBAYG8tBDDzF79mxCQkJQqVQEBATg7+8vLUgyMtc6otAiCgZWq1Ua53a7XRIWxIz+4jHia3EeGflZUcARSzWNFx05cr4Q55OR75nNZtzd3SVTMPx7XIvVeMT3xb/PDRITA04uxFjzzrmIAtl4iBpTcd4Tsdvt0j2K74sbVqfTKR3n7u4uaSjd3NwQBGFUkMC5Ec/jCVcKhULSkolRpOJm28vLS/otzscjX4vvi/8The+Rr2VB7drDY6IbIHP1EAfwhXL7iNn2e3t7JQGwp6dHUuObzWZJ8BNfj3QEF4XAc4U+YNTfImP9DzhvJ32uVmBkRKlCoZB2/aKWQPQdEjVxarUaNzc3BgYGaGpqoru7e9T5Rf8Sb29vUlJSiIuLkwU5mesSNzc3SYP2ebFYLJK2vK+vTwrkEINAREFQDAgZ+XrkfCFqnkwmE01NTbS0tODm5oavry+xsbHnCYXAqL/PnT/O1ViNx0gBazzOjby82GPO1R6ONG2O1Pb7+PiMSunh5uYmCV4jcx+ODCgQX4tazZHvid+paD4XPy9z4yFr7GSuGOIOXJzcxQlf3PmKJoqRXW5gYOA87Zxo/h2JGKQgmm1Ffx/RnHAxqV1OnTrFr371KzZu3IjNZhvzGA8PD371q1+xdOlSpk2bJu9UZWS+QMRkz5988gk/+MEPKC0tRavVMnPmTP75z38SGBiIQqGQ5o+R2kaAwcHBUWNZ1OKL7493zf7+/nE3lSKenp74+PiMq9kThTLxGJGR85T4t1g/WBTgZGS+SGTBTuaKcW6iypG7bRhbOzfe7vrcye/cnfG5pgnxmAuh1+s5fPgwjz76KEaj8TwTikhERATLli3jiSeeYNq0aXLUlozMF4BYO/uPf/wjO3bsoLCwELVazcMPP8y6devIysoaNfbG0tidO3+MnGsuxGe9L/JZQtjI+Wfk/0bOUyM1drJZU+ZqIJtiZa4Y55ocJhv+/v5kZWWxevVqCgoKqK+vH1Oo1Ov1FBcX8/bbb0sJYOWUJTIyVw5RqNu8eTMHDhygqqoKd3d37rzzTvLz80lKSpJ87ETkDZaMzMUhC3YyNwwqlYqkpCTuu+8+KVO6yWQ67zin00l1dTVdXV0sWrSI6dOnExoaKi8sMjJXAEEQsFgsdHZ28o9//IOTJ08yPDxMXFwcDz74IImJiajV6olupozMNcvkU6vIyHyBKBQKFi1axBNPPMFTTz017nEOh4Pu7m4eeeQRtm3bRldX11VspYzM9YvL5aKwsJDvfe977Nmzh8HBQebOncvrr79OVlbWDVu4XUbmSiFr7GRuKERzcUZGBj4+PrS1tfHOO+/Q19d33rGCIGAwGHj11Vepq6vje9/7HlqtdlKamWVkJjui28Nbb73Frl27OHjwIB4eHjzwwAMsX76c1NRUOcm3jMwVQF6hZG5I1Go1cXFxrFixgqysLIKDg8c8zm63U1VVxeHDh9m1axcDAwMXlSpBRkZmNDabjZKSEvbu3cuJEyfo6+sjLy9PcneQ69XKyFwZ5KhYmRsaQRB44YUX2Lp1K4cOHRo3Iamvry8JCQm8/fbbxMbG4uvrKy9CMjIXicPhwGAw8L//+7+888479Pb2EhQUxKuvvsqsWbMICQmZ6CbKyFw3yIKdzA2NIAjY7Xa2bdvG+vXr+eCDD8Y91s3Njby8PP7jP/6D22+//bwaujIyMmNz6NAhNm/ezB/+8AdcLhd5eXk8+uijrF27Fk9PT3kcychcQWQfO5kbHk9PT2bPno2Pjw+tra1UVFQwMDBw3nEul4uysjLef/99+vr6eOihhz6zLJGMzI2MIAgcOnSIjRs3snXrVux2O+vWrWPZsmXk5ubKQp2MzBeALNjJ3NCIi0pYWBg+Pj4sW7aMvr4+zp49O2b2+p6eHoqLixEEgfnz55OYmCjVU5SRkfk3NpuNnp4e9uzZQ2FhIY2NjSQlJbFs2TLmz59PZGTkRDdRRua6RBbsZGT41MwaEBDAf/3Xf9Hb28u+ffuoqqoa89impiaMRiMqlYpnn32WmJgYWbCTkRmBIAiYTCZ27NjBK6+8Qnd3Nzqdjscee4y1a9cSFhY20U2UkblukX3sZGT+D3EodHZ2smPHDp577jkqKyvHrE6hUCjw9vbmkUceYeXKlSxfvvxqN1dGZtJSWlrKwYMH+elPf4rJZCI3N5eVK1fyrW99Cx8fHznZt4zMF4ic7kRG5v8Q6zjqdDrmzJnDI488Qnh4ON7e3ucdK2bP37t3L9u3b+eTTz656PqTMjLXKy6Xi9raWrZs2cKHH36IyWRi5syZ3Hzzzdx88834+vrKQp2MzBeMbIqVkTkHLy8vEhMT0Wg07Nq1i5KSEvR6/ZiCW0VFBd7e3qjVaqZMmYJarcbT03MCWi0jM7E4nU6GhoY4cuQIu3fv5ujRo6jVapYsWUJ+fj7Z2dkT3UQZmRsC2RQrIzMOLpeLs2fP8uMf/5ht27ZhNBrHPVatVvPcc8+xYsUK2Slc5oakp6eHsrIy7r77boxGI1qtllWrVvG///u/BAUFyX6oMjJXCVmwk5EZBzHHXWlpKfv27eMXv/gF/f39Y/rcubm5ER4ezn/+53+yePFipk2bNgEtlpGZGFpaWti/f7/kl5qamsqCBQv44Q9/SHh4OO7u7rJgJyNzlZBNsTIy46BQKCSzrNVqZd26dXzwwQcMDg6eZ5Z1uVy0t7eza9cunE4nQUFBhIeHy3VlZa5rXC4XZrOZXbt2sWfPHurr64mKiuKmm25i6dKlREZGSr6rMjIyVwd51ZGR+Qy0Wi1Tp07lm9/8JjExMfj4+Ix5nCAI7Nmzh02bNnHy5EmsVqscUCFz3eJyubDZbNTU1PDOO++wY8cOBEFg7ty5rF27lltvvVVO4C0jMwHIplgZmYtAEAQEQWDXrl389a9/ZfPmzTidzjGP9fT0RKvV8sEHH5CWloZOp7vKrZWR+eLp7++nsbGRdevW0dbWhp+fHzNmzGD9+vVotVrZ/CojM0HIplgZmYtAXKBmz57N0NAQISEhvPLKK2Me63A4MBqN/OhHP+KRRx5hyZIlhIaGXs3mysh8oZjNZnbv3s2bb75JW1sboaGh5Obm8u1vfxuNRiMLdTIyE4gs2MnIXCQKhYLAwECys7NxuVwcO3aMyspKLBbLqOPEoIsTJ05w4MABfHx8uPnmm/H29pZ97mSuaVwuF3a7ncLCQvbv38+xY8fw9vZm3rx53HTTTWRnZ8v1X2VkJhhZsJORuUQSEhLQarUYDAZ+/etf09raOqYvXX9/Px9++CEtLS1MmzaNqKgo2ZFc5ppFEAScTicGg4E//vGPlJSU0NPTQ1ZWFg899BBz584d1/9URkbm6iH72MnIfA5ErdwLL7zA9u3bOXjw4JjHKRQKfH19SU9P54033iA2NhalUnmVWysjc/lYLBbq6+t59NFHOXXqFO7u7iQmJvLOO+8QGRmJj4+PvGmRkZkEyBo7GZnPgZgKZc2aNSiVSux2O0VFRefluBMEgeHhYWpqanjppZdYvXo1+fn5eHl5yYugzDWBuInZt28f27dv58yZM3h5ebFgwQLuvvtuIiIi8Pb2lvuzjMwkQRbsZGQugylTpjAwMEBPTw+1tbWYTCbsdvuoY5xOJyaTiV27dhEQEEB4eDgZGRkA8mIoM6kRo8Grqqo4ePAge/fupa+vj7lz55KXl8fSpUtRqVRyP5aRmUTIplgZmcvE5XJhMpl47LHHOHDgAHq9ftxjU1JSyM3N5S9/+QseHh6yz53MpEUU6qxWK9/4xjcoKiqiubkZnU7Ha6+9xowZMwgLC5voZsrIyJyDHKInI3OZKBQKNBoNL7zwAmvXriUlJWXcYxsaGvj44495+umnaWlpuYqtlJG5NARBoKamhu9+97ts374dvV5PcnIyb775JvPnzyc4OHiimygjIzMGsmAnI3OZKBQK3NzcCA4OZu3ataxZs4aIiIgxNXEOhwOTycS+ffv4+OOPOXXq1NVvsIzMBRA1dceOHWPLli0cOHCA4eFhZs2axb333ktWVha+vr64u7tPdFNlZGTGQPaxk5G5AigUCjw8PMjPz8fb25vKykqMRuOYZcXsdjtnzpxh69atKBQK4uLiCAgIkE2yMpMCQRDo6enh4MGD7Nixg+rqamJjY1m0aBG33347oaGhcl+VkZnEyD52MjJXEEEQsNlsGAwG1qxZQ319PX19feMeP2vWLL7yla/wrW99Cw8PeZ8lM/FYrVb+53/+hw8++ICamhq8vLz4xz/+wbx584iLi5OFOhmZSY4s2MnIXGFcLhcOh4PCwkJeeukl9uzZg9lsHvNYlUpFWFgYv/rVr1iwYAERERFXubUyMv+mpqaG7du385vf/Ibe3l6io6N59NFH+cpXvkJAQADe3t4T3UQZGZnPQFYRyMhcYdzc3PD09GTq1KncfPPNAHz88cdYrdbzjh0aGqK1tZUPP/wQlUoFIAt3MhNCXV0dRUVFbN26Fb1eT3p6OvPnz2fx4sUEBgbi6ek50U2UkZG5CGTBTkbmC0ChUKDT6Vi5ciVBQUEUFxfT1taGw+E471ibzcaGDRsIDQ3F09OToKCgK15v0+FwYLPZGBwcvOj2e3p64uvri4eHh5R42el04nQ6sdls2O12yX9QDCDx8PDAy8sLLy8v6f+fB7F8lc1mk/wUBUFAoVDg7u6Op6en9HMx13E6nQwMDGC326U2ypUSPkVMaVJYWMi2bdvYvXs3vr6+3HTTTaxcuZLp06dLx7lcLmw2GzabDafTed737+7ujo+PD+7u7hesiyz2J4vFMupcI8/j7e0tjYOL/Z7E84r93eFwSH1WPLebmxtKpRJPT0/c3NzGPHd/fz92ux2n0znutdzc3PD29kalUsk1oGUmFbIpVkbmC0QQBPr7+yktLeW+++6jtbX1vATGIl5eXsycOZP/+Z//YeHChZJwdCUoLy9n586d/Nd//deYdW3PxcfHh4ULF/L888+TmpoqLZhVVVWcOXOG/fv3c/jwYfR6PYODg/j5+REXF8fMmTNZvHgxa9euvawcfQ6HQ0oNI9bb7e/vx9vbm8zMTObNm0d+fj6LFy++qOs0NTXx5JNPcvDgQbKysli3bh2PP/64vCADg4ODfPTRR/zyl7+kuroaDw8PfvKTn3D77beTmpoqPaOhoSEMBgM7duxg165d1NfX09bWhsViISgoiLCwMKZMmcJjjz1GYmIigYGB417T5XIxNDTEa6+9xv79+6murqa9vR2lUklMTAxTpkzhrrvuIj8/Hz8/v0sS7IaHhykpKWHHjh2UlZVRWVlJa2srKpWKgIAAoqOj+drXvsbChQtJTEwc89yPPvooBw4coL6+ftxrBQUFcc899/CDH/yAkJCQi2qfjMzVQNbYych8gSgUClQqFZmZmXznO99h8+bN7N+/f0zhym63U1VVxc9//nOef/554uPjCQgIuCLtELUtDofjogQ7hUJBamqqVNRdEATa2tr405/+xOnTp9HpdHz7298mMDAQLy8v+vv72bFjB+Xl5Rw/fhyDwcDq1asv2awsao9ee+01Tp48SXt7O/fccw8hISEolUqGhob4+OOPKSgo4OjRoyQkJBAeHn7B+rvV1dUUFhZSWFhIf3//KE3TjU5rayunTp3id7/7Hc3NzcTHx3PTTTdx1113ERERMUrwPXr0KHv27GHz5s0sWrSIhQsXEh0djbe3NwaDgZqaGvbv389TTz3FXXfdxeLFi8nKyjrvmlarlWPHjrFhwwbq6uqYM2cOa9euJSgoiIGBAYqLi6moqODHP/4xf/zjH0lLS7ugkDgSs9lMVVUV3/3udwkKCiI1NZU777wTrVbL0NAQTU1NFBQU8Pvf/57Tp0+Tn5/P7bffft55RC3fWBp2EXEsyboRmcmGLNjJyHzBeHh4oNFoWLRoEUajkZ6enjHz1wmCgMlkori4mJ07d7JkyRIyMjLw9fW9Iu1wc3MjPDyclJSUcQXGoaEhKZI3Oztb8vsTBIGhoSFJsyIu7KGhoSiVSsxmM4ODg3z88cecOHGC7du3M2/ePMLCwi5JK+ZwODh9+jSHDx+mvb2dqVOnMm/ePKKjo/H19WVwcJDu7m42b95MRUUFAwMD4wppopBYVlZGYWEhSqVSNr3+H4Ig0Nvby6lTpyTNVlhYGNOnT2fFihXEx8efF6VtMBhoaGhgeHiYrKwscnJySEhIwMfHh/b2drRaLa2trZJbgb+//5iCXW1tLUePHqWgoIDZs2eTk5NDZmYm4eHhklbWarWyY8cOjEYjNpvtou/LZrPR29vL8ePHeeihh5g5cyYLFiwgLCyM/v5+GhsbcTqd7N+/n4KCAjw9PVmzZs2YJtnw8HBiYmLQaDRjXkutVpOVlSUHlMhMOmTBTkbmKqBQKMjKykKhUBAYGMh//ud/Yrfbz9vtu1wuBgYG+O1vf4vdbker1ZKUlCSd43Ku7+XlxZw5c/jv//5vyW9qJIIgUF9fz5/+9Ce2bt3KsmXLRi1qCoUCpVLJlClT+O53v4tGo5GENqVSyde//nWGhoYoKChg+/btfPvb38bpdF60YCcIAhaLhX/+85+cOnWKtLQ0fvSjHxEUFCSdw8fHhwceeACTycTZs2c/85kYDAYOHjzInj17WLJkCRs3brzIJ3b9IiYgLi0t5cMPP+Stt94CIC8vj1WrVnHbbbeN+Tl3d3cCAgK4+eabWbly5ShtbHx8PIGBgURGRrJx40YKCgpQKBQ88MADwKd9R+zrota6p6eHH/7wh4SEhEjCkbe3N7fccgvh4eH885//xN3d/ZL7vbu7OyqVigcffJBp06ZJmxOdTodWqyU1NZVXXnmFqqoqvLy8sNvtYwpns2bN4qtf/SrTpk2TTfYy1xSyYCcjcxVJT08nPDwcq9XKiy++SGtr65jHGY1G/vSnP1FUVMRf//pXKbDi86JWq0lMTGR4eHhcDQTAe++9R19fH0uWLBmViNbNzY3ExETeeecdAGmxFBETNGs0GkJDQ+nt7cVqtWK32y+63RaLhba2Nt566y0WLFjAokWLCAoKOm9h9/Hx4emnn+aJJ564oOO6y+XiJz/5CVarlZUrVzJz5kx27959UW25nhkaGqKiooLvfOc71NfX4+HhwZ133skPfvADUlNTx/3cmjVrWLFiBcCYgpBarSY9PR2VSoXRaKSjo2PU+y6Xi56eHjZv3szg4CC33XbbmP3ay8uLGTNm0NLSIgViXCxBQUEsWbKEtra2MatjiBuc8PBwzGazFJzj5eUla3NlrhvkbYiMzFXE3d0dtVrNypUrWblyJVOnTh3zOEEQMBqNlJeX87vf/Y729vYx06VcLDqdjqysLFasWDFmjU+Xy4XFYqGwsBB3d3fy8vJGBSWI0ai+vr74+vqeF7AgatvMZjMmk4mYmBjUavUlBYAYDAZKS0sZHh4mKiqKmJiY80xk4nWVSiX+/v7janSMRiOHDx+mtraW5ORkVq9eLZfAAnp7eyktLeW5556jqakJjUbDvHnzeOSRR4iJibmgEO7p6YmPjw8+Pj5jCtMulwu73Y7dbketVhMUFDTqfavVysmTJzEajfj5+TFlypQxTaDiJkGtVkuRqxeLmGpIo9Hg4eFx3rmdTqcUBKJSqQgPD5ejo2WuO2SNnYzMVcbLy4u0tDSWLl2K0+mks7MTg8Fw3nE2mw29Xs/mzZtZsGABM2bMGFPYuRhEgSwqKmrM9202G21tbbS0tJCSksK0adMu6rwulwun08nw8DD19fW0tLTgcrmYPXs2gYGBl1RNw2g0UlNTg8vlQqfT4ePjw9mzZ+nt7cXhcCAIgrRoBwQEoNFoxhTs7HY7er2evXv34uvry5QpU8jKymLPnj0X3ZbrDdHfsKamhsOHD7Njxw7c3d2ZMmUKN910E7m5uZ/L7DkSq9VKV1cXdrud+Ph4EhISRp1PDA4aHh7Gx8eH0NBQWlpaGBgYwGq14nA4pBQ7fn5+hIWFXXabxHt3Op1YrVZMJhMNDQ0MDQ0RGxvL1KlTx918WCwW9Ho9ZWVlUuoThUKBj48PGo0Gf39/tFrtZbVNRuaLQBbsZGQmiDvuuIPQ0FDsdjvr168fM2eWzWajoaGBZ599lvvvv59HHnkElUol5XS7EohBG1u3bsXX15ekpCQyMjI+8zPw6WJuNpupq6vj+eef5+zZs8TExPCjH/2IuLi4S2qHyWSirq4O+NTc2tHRwbZt29iyZQs9PT04HA50Oh1Lly7llltuYenSpQQEBJz3LMxmM6Wlpfz5z3/m1VdfZebMmVJ0742I+F21trbyj3/8gw8++ID+/n6WLFnC1772Nb7yla9ctgAlCAIGg4EDBw7gdDpZsGABK1euHHWMzWajuroai8WCp6cnHh4e/OlPf6KgoIDGxkZMJhMhISFkZ2czd+5cHn30UQICAj7XRmZkuwRBYHBwkMbGRo4ePcr69etRqVTcddddrF69etzPtra2smXLFoqKimhra2NoaAh3d3fS0tLIz89n4cKFkmla1vjJTCZkwU5GZgKZOXMmMTExmEwmjhw5gl6vH/O4M2fO8Prrr9PU1MRvfvObMc1Ml4PBYOCNN95g1apVF6WtEwSBv//971IUrMPhICEhgVtuuYUVK1YwZcqUS659Ozw8THd3t3TuqKgoUlJS+Pvf/45Go2F4eJji4mLJ9/Ddd9/l73//O35+fqPMrOvXr+f48eOsWbOG3NxcAgMDGRgYuORncr1gtVrR6/U88sgjVFRUYLfbmTdvHr/+9a9JSUm5IjWK+/r6KC4u5oUXXmDVqlWsXbuWBQsWjDrG6XRKGr1jx47R0dHBtGnTePLJJ0lKSkKpVFJQUMCWLVt45ZVX2LlzJ6+88grx8fEXTGdzIUpLS9mxYwcvv/wyNpsNX19f4uLiePHFF8nMzCQsLGzcz4qJmH/5y19K0d09PT28+eab7Nq1i48++ohjx47x+OOPn2d2lpGZSGTBTkZmghB9xcLCwvjqV7+K0+nkxIkT5zmdw6dmrKamJg4fPsyHH37IkiVLCA4OviLCnV6vp6GhgY6ODqZPn05MTMxFtX3q1Km4ubmRlZVFX18fXV1dtLa28sEHH2C325k+ffol5eETK03Apw7+arWaNWvWkJGRgY+PDw6HA19fX44dO0ZZWRllZWUUFBQwf/58tFotTqeTU6dOcebMGex2O1/60pckX6sbleHhYWpra/nggw84c+YMAKmpqTz44IMkJiZeUvLfsRATAm/bto2jR4+i0+m4++67mTJlynnCmGgOFgQBh8OBxWJh9erVTJ8+nZCQELy8vHBzc8NgMDA4OEh5eTmFhYUoFAqmTJnyudoXHBzMrFmzePDBB+nr62NgYACj0cjGjRslwTInJ+e8z91222309fVJKU3UarWULuiOO+6QKnVs376dW2+9FQ8PjyuWc1JG5nK5cWc8GZlJgBilt2bNGpqbm7FarfT09IyZu8tkMnHmzBn+9a9/ERUVhbe39wUjXC+WpqYmqqqqUCgUpKWlERoaelHtnjt3LrNmzcJisdDZ2clHH33E/v372bVrF4IgEBISgo+Pz0Xn+RJ9oeDTqNv4+Hgp6EEUPjQaDUuWLKG3t5eCggL27dtHRkYGGo0Gi8XCvn376O7uJjw8nKVLl17R6h3XEmJC6ra2No4dO8Ybb7yB2WwmLS2N+fPn8+Uvf/myS2EJgoDdbqehoYFdu3bR0dHBnDlzuPXWW8+LmhYRv19PT08CAwNZtWoVSqVSakdGRgZ6vZ6+vj4OHTrEJ598IlW0+DxEREQQHh7OggULpCTKu3fv5tVXX6W1tRWj0UhaWtqoNgCsWrVqzPOpVCpWrlxJQ0MDtbW1HD9+nKqqKoKCgmTBTmbSIAt2MjKTAE9PTx599FGmTZtGd3c3paWlY/rcWSwWtmzZgoeHB6tXr5byhF0On3zyCYWFhdx+++3odLpLSqvi5uaGSqUiISGBp59+mqSkJFwuF3/4wx+Ijo5m+fLl40b+nouHh4fkC5eenk56evqYkazz58/nxIkT7N27l8OHD0v585qbm3nuuef4zne+w4oVK254vzqj0cj//u//smvXLlpaWpgyZQqPP/4469atuyJJrx0OB11dXTz00EOEh4ezfPlynnjiiXGjj8UqLAqFgtDQUObMmTNmTeSUlBS6u7sBKC4uHlOjdql4enoSERFBREQEubm5NDc3U1BQQH19PdOmTSM3N/eS+ktaWhq5ubkcP36co0ePEhYWJuWblJGZaGTBTkZmghEXNqVSyfTp0/nDH/7AY489xtmzZ+nv7x/zM/v27aO/vx8PDw++/OUvf64cd4Ig0NraSmlpKe3t7Xz3u9+VUplcattFoqKimDt3Ljt37qS0tJSYmJiLFuxUKhVhYWGSADBeWzQaDSqVCpfLhdFoxOFwoNfr+eSTT+jr6+Oll17izTffHLVQO51OzGYzBoMBs9lMU1MT69evByA3N5fvf//755XQulax2+0YjUaefvppCgoKMJlMxMbG8txzz5GVlXXZ5lf49HkePnyY9957D51Oxze+8Q1ycnIkoW6s83t4eBAREYGXlxdeXl5oNJox6/z6+vpKmui+vj6Gh4cvq63nnt/NzY358+dTW1tLa2srhw8fJicn55IEOz8/Pyki1mQyMTQ0dFltlJG5ksiCnYzMJMHNzQ21Ws3UqVO57bbb2L17t5TX7VzEmpgff/wxWVlZxMXFXbJZ1uVyUVJSQn9/P1qtluTk5MtKggyfCqeiScpkMmE2my/6s/7+/lI6FofDgd1uH/M4h8MhpZ4QE8uqVCri4uJ44IEHxo0ubmpqoqurCz8/P6KjoyWBMzk5+bopC+V0Ojl79qykhTWbzURERLBixQqmT59OYGDgFcnnV1xcTHFxMW1tbdxyyy1kZmaOSmh95MgRenp6mDlzJkFBQbi7u+Ph4UFMTAxeXl6j/CnHugexRquHh8cVzz+oUCjQarV4e3tLWsdLrR3sdDql/vlFtFFG5nKQBTsZmUmEmJj1sccew+Fw0N3dTUNDw5iFxltbW9m0aRM5OTmsXLkSlUp10YKZ6M8m5nabMmXKqBJRYx0/PDyMy+XC3d39orQbNpvtgkXUz0Wr1ZKSkoK7uzt9fX309fVJ9z1S62IymRgeHsbNzU0yHYeHhxMeHs6yZcvGPLfJZGLLli1UV1cTHx/PHXfcwZNPPnldaOjg3ylN+vr6OHr0KH/+8585e/YskZGRzJ49myeffJKQkJDLFkDERNabNm2ioaGBgIAAvvGNb5zno/bRRx9x6tQpfvaznxEQEIC7uzuenp6kpaWhUqmw2Wz09PTgcrnOS1czODgobQi0Wu24/npjYbPZpFJ9F6paIT4vl8slBXSI/xcrpox0DTj3c/39/SbceEwAADVeSURBVPT29gIQEBBwSW2UkfmikQU7GZlJSFhYGN/61reYOXMmDz/8MGazeUytgtVq5Uc/+hF6vZ41a9aQn59/Ued3OByYzWY2btzIbbfddsF8XvCpb98vfvELuru7SUpK4nvf+96Yx4m+S4IgkJSURGxs7EW1Bz414y5atIjw8HCqq6uJiorCarWep00rKCigrq4OHx8fbrnlFtlp/f9wOBw8++yz7Nu3j7KyMgIDA/nv//5vli1bRnR09BW5Rm9vLy+//DLHjx/npptu4pvf/OZFmzB9fHy46aabiI+Pp7S0lMOHD2OxWHB3dx8lgFVVVVFUVATAwoULSUlJuej27d69Wwqg+fnPfz7mfbtcLg4dOkRHRwcqlYrs7GwpyGZgYIANGzbw0UcfSXWVx6KsrIyDBw8Cn5ryL6WNMjJfNLJgJyMzyRC1F0FBQcycOZOf/exn/Pa3v6Wjo2NMDZjNZmPTpk3o9Xr8/f3JzMz8zGjQrq4uDh8+LKWSuJjcdWI6kdraWqKioliwYAEBAQF4eHjQ19fHwYMH2blzJ0VFRSQlJZGfnz/qvGazmbKyMn79618jCAKrV6/m1ltvJSYmRipZplarefzxx3nzzTc5ffo0v/3tb/nqV7+KRqPBbrdTXl7Ohg0b0Ov1ZGZmsnbt2hs++78YKPHXv/6V3bt309bWRmBgID/5yU/Iy8uT/BYvl5qaGo4dO8b69evp7e2lpaWFAwcOjKkVq6qqOu97EU3n9957LwEBAezatYvf/va33HrrrSQlJeHj40NJSQmbNm3i8OHDpKamsmLFilHJsnt7ezl8+DCvvvoqgiBw//33k5ubS2RkJPCp0Nbf38/evXuZNm0as2bNIjU1lYCAAAYGBmhpaeHYsWPs3r0bl8tFZmYmN9988yjh1OVycfbsWYaGhggNDeXWW2+VNg+9vb1s3LiRnTt3YjQaWb16NVlZWYSEhFz285WRuVLIgp2MzCRFqVQSGhrK8uXL+eSTTyguLh7XLNvY2IhCoWDnzp2EhYURGBg4blJXQRDo6emhsLCQqKgooqKi0Ol0F2yLm5sbSUlJVFdXYzKZOHbsGHa7XUqZ0d/fz/Hjx2lra0On05GXl0d6evqourQ2m02qJiEIAomJieTl5Unvi3n9lixZQm1tLXV1dRQXFxMSEoKfnx8Oh4P6+nqGh4dJTEwkJyeHxMTEcc3PgiBQWVmJwWCgo6ODU6dOYbFY6O3t5cyZM2zdupWAgADCwsJITk6+mK9kUiH2g87OTioqKti+fTvNzc1oNBqmT5/OsmXLiIqKumLRwS0tLZw4cYLa2loAenp6qK6uHvf4c6uXiML7rFmz6O/vl74TpVJJXV0d3t7eVFdX09bWhlqtZtasWaSlpREYGCidw2q1cvbsWakPLVy4kOnTp0vvBwcHk5iYSFBQEJWVlVitVlpbW/Hx8WFwcJDOzk7Ky8vx8fEhNTWVuXPnEhcXJ+U6dHd3JzIykoSEBCkhtlKplIJ1TCYTRUVFOBwO0tPTWblyJRERETd0BLbM5EMW7GRkJjFKpZKUlBSefPJJ3nzzTV577TUsFst5x7lcLhobG/n1r39NVlYW2dnZREREjKup6erqYv/+/dx2223ExMR8pu+Vl5cXd999N9HR0ZSUlFBQUMCOHTswGo0MDQ3h6elJXFwcGRkZUhoWb2/v887r5uaGt7c3giDg5eV1XvJgDw8PZsyYwVNPPcWxY8d49913+elPf8rw8LAUVblixQqWLl3KggULpOCJsXC5XLzxxhvs2rWLiooK6f8DAwPU19fzxhtvMHPmTNasWcMzzzxzwfufrDidTgoLC3n33Xc5dOgQvr6+zJ8/n+985zskJydfUR/C5uZmysrKLjo34FipTODTVCE6nY6MjAx+//vf8/rrr0u5G2NiYpg1axYrVqzgvvvuw9vbe9Q9iMLheH0oOzub6OhoQkJC2LNnD1u3bqWzsxO9Xi/5r0ZFRXHvvfdy8803k5mZOcrU7+Pjw5IlS/D29ubkyZMUFRXxq1/9iu7ubmw2G15eXmRkZJCXl0dubi4333zzZTxRGZkvBoUw1vZfRkZm0iAGOpSWlrJ3717+3//7f2NGfsKnC19UVBSPPPIIX/3qV4mNjT1vcRUz/4sC2VgC1lhtgE/9uMQfl8s1yu/Pzc1Nin4UF8uR13a5XDgcDqm8l1KplBbuc9soRkaeG4Dh5uaGp6enVGv03Guc2+bh4WGpgPtYeHh44OnpeU1qXARB4IUXXmDLli0UFhYiCAI///nPWbx4MdOnT7/s+q/nYrVasVqtFx0Q4+7ujq+v75jtcLlcOJ1OLBYLTqdT6l8X04dsNpuUXkQMGBI3EGJtWLvdjt1ul/royCCckX3o3LZdTD8X2yeeQ0ZmsiELdjIy1whGo5HGxkaef/55du/eTVdX15jHeXt7k5OTw+LFi3nqqadu+LJa1yNi5Y3nnnuOmpoarFYrX/7yl3nggQdISkoaZb6UkZG5sZBnexmZawStVotSqWTt2rU0NTVhs9kwmUznHWe1Wjl9+jRDQ0MsXryYnJwc/P395Vxb1wl9fX00NDTw8ccfU1JSgqenJ6mpqaxbt44pU6agVqsnuokyMjITiKyxk5G5Bvnb3/7G1q1b2bhx47jHeHl5kZSUxGuvvUZGRoaUa+tKmudkrh7iVL1nzx62b9/OCy+8gEKhYO3atXz5y1/mrrvukr9bGRkZWbCTkbkW6evr49ixY7z22mu899574/o9eXp6smDBAu68806+/vWv4+PjIy/+1yhOp5OtW7fy97//nUOHDmEymfjWt77F6tWryc3NveRycDIyMtcnsilWRuYaRK1Wk56ezp133smZM2doaGigr6/vvOPsdjsVFRVotVo0Gg133XXXuNGKMpOXvr4+mpqaePfddzl9+jSCILBkyRIpz5ufn99EN1FGRmaSIAt2MjLXKOHh4dxyyy0cOnQIm81GXV3dmPU3u7q6KCoqYnh4mAULFhAaGoq3t7cs3F0jWK1W2tvbOXjwIJs2bUIQBBISEli3bh0LFy7E399/opsoIyMziZBNsTIy1yji0LXZbDz33HNs2rSJ48ePj3u8t7c3a9eu5Qc/+AFZWVnXTZ3U650TJ06wZcsWfv3rX2O1Wlm2bBkrVqzgySefBGSfSRkZmdHIGjsZmWsUhUKBIAh4enry1a9+lcTERH7xi19QVVU1bumx3bt3ExAQwM0338ztt98+Aa2WuVjsdjtVVVU8//zzFBYW4nQ6ueWWW/j6179OXl6eLNDJyMiMibxll5G5hhETrkZERJCTk8PatWsJCQkZlU1fRBAEent7KSoqYv/+/Zw+fXrcxL0yE4vFYkGv1/PRRx9RXFyM0WgkLi6O1atXM336dMLCwia6iTIyMpMUWWMnI3MdIJb0euKJJzh+/DilpaV0dnaOWVe2tLQUi8WCVqslLi4OPz8/OcfdJMLlctHT08Pp06d58cUXGRwcJDIykvz8fO677z5UKpWsrZORkRkX2cdORuY6QRzKzc3NvPzyy7z55pt0dHSMeaxCocDb25tnn32WFStWkJ6efjWbKnMBmpqa+Ne//sUf//hHOjo6mDNnDqtWreK73/2uVPpMFuxkZGTGQ9bYychcJ4g+d6Ghoaxbtw6dTscvf/lL+vv7R9W6hE+FQKvVyj//+U+ptua0adMmqOUy8Gl9UqPRyO9//3sKCgowm81kZmby0EMPsWDBApRKJSALdTIyMhdGFuxkZK4jFAoFSqWSKVOm4OHhwf79+ykqKqKvr+88s6wgCJw5c4ZDhw6hVCqJjIxEq9XKZtkJwOFwYDKZOHToEPv376elpQW1Ws2yZcvIzc0lOTlZjmKWkZG5KGRTrIzMdcrw8DDl5eX8x3/8B5WVlVgsljGPc3d3JzU1ld/85jcsWrRI9uG6iojTr9lspqKiggceeIDm5mYCAwOZPn06b731Fn5+fnh4yHtwGRmZi0MW7GRkrlMEQcDhcHDo0CHee+89XnvtNex2+5jHenp6otPp+Otf/8qMGTOIjIy8yq29MREEAZPJxKuvvsrbb79NaWkpcXFxfOlLX+LRRx8lKioKhUIhC9oyMjIXjbwNlJG5TlEoFHh6epKZmYnZbGZoaIh//etf5/nbwac507q7u3nttdcwmUzccsstBAUFjStQCIKAy+XCarXidDrl6gefA6fTidVq5W9/+xs7d+6kubkZnU7HAw88QH5+PqGhobJQJyMjc8nIgp2MzHVOaGgoOTk5uFwuCgoK6OzsHNMs63A42Lt3LwEBAYSHh7Nw4UI8PT3P8+0SBAFBEGhubmZwcBB3d3fS0tKu1u1cF7hcLgYHB6mrq+Ojjz6irq4Ol8vFrFmzWLVqFUlJSWPmIpSRkZH5LGRTrIzMDcLw8DCvv/46f/zjHzlz5sy4x/n7+5OamsqGDRsIDw/Hy8tL0hqJQp3FYuHb3/42NpuNhIQEfvaznwFyxOZnMbIM3MmTJ/nBD35AUVERKpWKrKws3njjDSIiIvDy8prglsrIyFyryIKdjMwNgsvloq+vjzfeeIOdO3eybdu2MY9zc3PDx8eHWbNm8atf/YrMzEz8/PyATysilJeX8+KLL7Jt2zYEQSAjI4PNmzejVqvliNrPQPR7/Nvf/sa2bdvYu3cvSqWS++67j7vvvptZs2bh4eEhC8gyMjKfG9kUKyNzg+Dm5kZAQAALFy4EQK/Xc+rUqfPKirlcLoaHhykrK2PTpk309/eTl5eHw+HgwIEDHD58mKKiIkwmk3SekpIS5syZg6+v79W+rQnn3L3xhfwS7Xa7JNCVlZUhCALr1q1jyZIlUooaWaiTkZG5HGTBTkbmBmP69OkolUoMBgP19fX09/ePKdz19PTwwQcfYLPZSEpKYmBggPfff5/9+/fT1NQkHWs2mzl06BBTp069YVOlWCwWbDYbgiCg0WiA0QKeIAjYbDa6u7t56623KCoqwmg0Eh0dzf33309aWhqBgYET1XwZGZnrCNkUKyNzAyJWnnjsscc4cOAAjY2N4x4bEhJCYmIi3d3dtLe3Mzg4OOp9T09PIiIi2LVrF0lJSTdcIl2Xy8Xu3bspKirC4XDw05/+FHd39/MEuyNHjvDqq6/y5ptv4ubmRk5ODr/+9a+ZO3cunp6eN6RALCMjc+WRNXYyMjcoXl5efO973yMwMJAdO3ZQUVEx5nFGo5Hy8nLsdjs2m+289x0OB52dnZw8eRIvLy/i4uK+4JZPLlwuFxs3buTYsWMMDQ3hcrl44oknCA4OlnwOP/zwQ3bt2sXWrVtRKBTce++93HzzzWRnZ8vmVxkZmSuKLNjJyNyAiIJESkoKixYtwm63o9fr6e3tPS/Pnd1uHzexMfxb+1dSUkJ4ePgNJdjZbDZMJhMlJSU0NjZitVrZvXs3GRkZ5OTkEBsbS01NDXv27OHo0aP09vaSm5vL4sWLmTVrFmq1eqJvQUZG5jrjxrKZyMjISCgUCtzd3Vm6dCn3338/GRkZKJXKz609KigooLq6GpfLdV5AwfXK0NAQdXV1nDlzBqPRyNDQEMXFxfzpT39i9+7dtLe3895777FlyxYqKir+f3t3Hhx1ned//NlH+ky6cx/k7pAQLgMBBUGYoIAXIjoysrVaszsyWGPNzjrjzOzsP6uzh7VVWjuuO+PsOrruaOkO6yDoDjJQRECCcgQSyEESSMh90Z2jr/T9/f3Br3tBDtEBcr0fVSnt5Nvdn+5U2pfvz+f9+WC1Wvne977HPffcQ1FR0XgPXwgxBckaOyGmuWi35vnz53niiSeor6/Hbrd/5cfRaDRs3ryZn/3sZ+Tn50+L6cWTJ0/yyiuv8N57710yTa1Wq0lJSSExMZFz584RiURYunQpW7ZsYdOmTVfc+FkIIW4E+WQRYppTqVRotVpSU1NZtWoVM2fO/FqPEw6HaW1tZf/+/dOiYufxeOjq6uLgwYOEQqFLfhaJRBgZGaGnpwdFUaioqGD9+vWsWrVKQp0Q4qaSTxchBMFgkP7+fvx+/xXPkr1e3d3dHDlyhHA4POXDXW9vL62trXR2dl71/F2v1wuAxWIhNTWV9PT0aVHJFEKMH2meEGKaigYvRVGw2+1s3bqVf//3f2doaOhrP+a5c+dQFIWxsTHMZvOUPIki+r599tlnHDp06JqNJXChknn8+HFmzJjBmjVryMnJAeT4NSHEzSEVOyGmud27d/PSSy/xD//wDwwPD/9Jj+X3+xkcHGT79u2cP3/+Bo1w4vH5fOzdu5d9+/Zd1/U9PT1s3bqVxx9/nJaWlsv2AhRCiBtFgp0Q01D0zNJt27bx+9//nr179+J2u2/I9KnP56OysjJ25NhUEwqFqK6uprOzE6fTeV33iUQijI6O0tLSwquvvsrRo0dxOBw3eaRCiOlIgp0Q01A4HMbj8bBz50727dt31c2Jv45AIMCRI0cYHBzE7/ffsMedCKKB+NChQ/T3919xw+arCYVCDA8Ps23bNk6ePMng4OBNHKkQYrqSYCfENOR2u2lpaeHEiRNfa2uTawmHw7S0tHDy5MlrHlU2Wfl8Pt599116enq+1v0VRaGzs/OS83aFEOJGkeYJIaahhIQE5s+fz/vvv8/hw4c5fPgw27Ztw263/0ldsRerrKzEZDJRWlp6Qx5vIujt7eX48eO0tbUxNjZ23fczGAwUFRWxcuVK/vIv/5IZM2ZgtVpv4kiFENOVBDshpiGNRhMLG2q1mvT0dNLS0jhx4gRnzpyhs7PzKwWXKzl9+jTFxcW43W7MZvOU6ALt6+vj0KFD+Hy+L12PqFarMRgMzJ07l7KyMkpKSpg/fz5z5szBYDBMyY5hIcT4k2AnxDQVPVJs5syZ2Gw27r77bnbu3Mnu3bs5cOAA3d3djI2NEQ6Hv9bjt7a20tzcTH9/PzabbdIHu1AoREdHBwcOHPjSUGcwGIiPjycjI4P169fz2GOPkZeXh8lkukWjFUJMV3KkmBDikqDi8/kYHBzkzTff5L//+7/p6ur62k0Q8+bN48///M/50Y9+hE6nu1HDHRdnz57l7bff5h//8R+vGuxUKhVxcXE88sgjPPjgg6xZs+aSTYkne7gVQkx8EuyEEJeIRCIEAgH6+/tpa2ujvr6egwcPsnPnzq98MkVSUhKzZ8/mj3/8I2azGbVajaIoeL1enE4nTqeT8+fP43A4cDqduFwuXC4XXq+XsbExvF4vLpeLYDBIKBSKTQ+Hw2FCoVCsK1Wj0RAXF4dWq41VIg0GAzqdDoPBgNFoxGw2Ex8fj9FoxGQykZKSQkpKChaLhaSkJNLT0zEYDGi1V57I+NWvfsXOnTvZtWvXZT/TaDTMmzePhQsXsm7dOkpKSkhPTycpKYm4uDgJdEKIW0amYoUQl4iuDSsoKCA9PZ2MjAysVitqtZqzZ8/S19dHf3//de1553a7aW1t5eDBgxgMBkKhEG63+5IQNzQ0hNfrJRAIEAgEYiEu+hUMBolEIkQikdiZrIqixL4HxK6LnsGqVqvxer2o1Wo0Gk0s+MXFxaHRaNBqtRgMBkwmE2azGavVSkpKCmazGaPRSHx8PBaLBZPJhNFoxGAwUF1dTWtr6yWvz2g0kpmZyZw5c1i4cCFlZWVUVFSQmJgoa+iEEONCKnZCiC8ViUTo7+9n27Zt7N+/nz179uDz+a7rTFi1Wk1FRQVwIeh1dHTgdDpRFAWVSoVKpSIlJYXExESSkpJITk4mISEBs9lMQkICSUlJ6PV6dDpdbI1aNJwZjUbgQrDz+/2x473C4XCs6ufxePB4PIyOjjIyMoLb7cblctHf38/w8DCBQCAWFM1mM4mJieTn51NcXEx2djaZmZmkp6fzs5/9jLNnzxIOh2NTrrm5udxzzz389V//NTk5OVgslpv3SxBCiOsgwU4I8aUURYltzutyuWhvb+eVV17h888/v6yKdSUWi4WlS5cya9YsCgoKyM3NjU2Fzpgx45Iu0WjY++K/R29fLHr7Sh9jF5+Fe/FruPjf/X4/LpeLwcFBuru7GRgY4Pz583R0dNDS0kJvby92ux2/34/f70dRFNRqNTabjXXr1rFs2TK+8Y1vkJKSglqtlilXIcS4k6lYIcSXigYsj8dDf38/TU1NOJ1OQqEQKpXqS6t2Xq+XOXPmcO+992Kz2TCbzej1evR6PUajEY1GMy6hKDoda7Vayc7Oxufz4fP5cLvdjI6O4nQ6aW9v5/nnnycjI4OEhAQsFgtut5v9+/dz4sQJtm3bxuzZsykoKCAvL4/Zs2eTmpp61bV6QghxM8knjxDiqgKBAF6vl9HRUc6fP09PTw89PT00NTURDAbJyMjAYrEQHx+Pz+djZGQk1ghxsVAohE6nIykpiZKSknF6NZeLrr8zGAwkJiZe9vPoGsEdO3aQnZ1NcnIyVquV7u5u+vr6GB0dpaGhAYfDQVtbG9nZ2fT19VFYWEhiYiIWi4XU1FTi4uJi6/+EEOJmkqlYIUTMxR8HiqLQ399PY2MjBw8e5IMPPqCzsxO/34/VamXVqlWUl5czb948lixZQnt7O5WVlXz00Ud8/vnnl3XPrlq1ivvvv58f//jHwOTY+qOnp4e+vj58Ph8LFiwgPj4+9rPR0VF6e3upra1l9+7d1NbW0t7ejtPpZObMmZSXl7Ns2TIefvhh0tPT0ev115xWFkKIG0GCnRACuBDkwuEwzc3N1NTU8OGHH3Ly5Em8Xi8Gg4GKigoWLVpESUkJpaWlGI3GWKepTqeLNTA4nU6qqqp45513OHXqFF1dXQDEx8ezfPlyfve732GxWCZFBSscDscaRL5YdYtEIoTDYYLBYGwN3ujoKHV1dXz66ac0NDRw5swZQqEQZWVlLFiwgBUrVnDPPfdIBU8IcdNIsBNimgsGg7EpxaNHj9LW1kZPTw/hcJjk5GQyMjIoKChg1qxZ5OTkkJqaSkpKymWNDVGhUIienh5qa2tpbm6mtraWTz75BJfLRXFxMS+//DJ33XUXBoNhHF7tzRPd/29wcJBz587R09NDd3c3jY2NDA8PEw6H0el0LFy4kLlz51JUVERpaSk6nU6qd0KIG0bW2AkxDUW39xgdHcVut9PZ2UllZSV79+7F7Xaj0+lYuXIlFRUVlJaWMmvWrOtucNBqteTn55OXl0dfXx+zZ8/G5XLR1taGVqvl6NGj3HHHHZdNTU520f3/8vLyyMvLi225cvjwYQ4cOEBDQwP19fW0tLRw++23s2DBAhRFYcaMGSQkJKDX66WKJ4T4k0nFTohpKBQK4fV6ef311/nwww85deoUiqLwrW99i9WrV7N8+XJycnIuuc9XDWFfXK939OhRjh49ys6dO3nnnXdIS0ubUsHui7740ep2u+nu7ub1119n7969dHR0oNVq+f73v899991HeXn5lAu7QohbT4KdENNEtEp36NAhDh8+zMcff0x3dzfz5s1jwYIF3HfffWRnZ8c2B44ez3UjnheIbRY8NDREbm4uer1+Wp3OED0GbWhoiPPnz9PW1saePXvYt28fZrOZwsJC/uzP/oy7774bq9UqAU8I8bXIVKwQ04DP52N0dJT9+/fz+eef09HRgaIorF27loULFzJnzhxuu+02DAbDDZ8OjAYUk8l0ybYi0y24RLdWycrKIjk5mZSUFOLi4jAYDLS3tzM0NMQHH3zA8PAw8+bNo7S0FIvFMu3eJyHEn0YqdkJMUdE/bZ/Px+DgIM3Nzfz93/89PT09pKWlce+99/L000+Tmpo65RoZJgtFUXA4HHzyySdUVlayY8cO8vPzWb16NY8++iizZ8+OhW0JeEKI6yHBTogpKvqn/dvf/padO3eyZ88eiouL+eEPf8iyZcvIy8uLVeckNIyPi484Gxsbo6mpieeff57GxkZ8Ph8/+clP+OY3v0lubq78joQQ10WCnRBTUCAQYGBggDfeeIM9e/ZgMpmYM2cOmzZtwmazkZSUJFW6CSbaRdvS0sLx48epqqri+PHjVFRUsHLlSh5++GFprhBCfClZYyfEFBKt/Jw5c4aamhr27duHyWTitttu45577uGOO+64YU0R4sbSaDQkJCTETrgwGAz09vbS2NhIKBTCYrGwfPlyTCaTnEMrhLgqqdgJMUUoioKiKLS1tfHWW2+xfft2PB4P//zP/8ySJUuw2WzjPUTxFQQCAY4cOcI//dM/cebMGVJSUnjjjTfIz8+XpgohxFVJsBNiivD5fPT39/PEE08wOjpKXl4e//Iv/0Jubu5N6XYVN1f0iLeuri4++eQT/vVf/xVFUXjuued44IEHSE9PH+8hCiEmIKnnCzEFuN1umpub+c1vfoPb7WbVqlWsXbuWnJwcCXWTlEqlQqvVkpmZyYoVK9BqtfziF7/go48+YmBggC1btmC1WuV3K4S4hHwiCDGJRTcdPnfuHEeOHKGyspLi4uLYcWAmk0n+wz/JGY1GCgoKeOCBBygrK6O3t5f9+/dTW1uL3++/7IQLIcT0JlOxQkxiiqIQCAR44YUX2LNnD4FAgPfff5+8vDxMJtNNe86v+rERXQ82EdaFXbzFyLWoVKoJMd4oRVE4f/48L774IpWVlVgsFrZu3UpWVta0OsFDCHFtEuyEmMQCgQAvv/wyH330ETqdjhdffJFFixbd1APlq6qqeOqppxgbG7tiOIqGoYSEBNLT05k1axabN2+mqKiIpKSkmzKmr2J4eJiOjg5+85vf0NDQgN1ux+12E4lEUBQFrVaL2Wxm7969ZGZmjvdwY6LV2dbWVg4ePMhPf/pTnn76adatW8eyZcvGe3hCiAlC1tgJMUl5PB66u7v54x//SEZGBgsXLmT27Nk3NdQB5OTksHnzZnp6eti3bx/Nzc3YbDbWr1+PxWIBwO/343A46O/vp7q6Gr/fz4YNG1i0aBE5OTk3bWzXoigKTqeTzz77jJ07d9LU1ERJSQlLly7FZDIRiUTo6+ujs7OTI0eOEA6Hx2WcV6NSqdBoNMyYMYOysjLWrl1LVVUVWVlZ2Gw2MjIyJlSFUQgxPiTYCTFJjYyMcOrUKerq6tiyZQtr1qwhJSXlpj9vQUEBP/7xj6mvr2dgYID29nZKSkr4/ve/T05ODpFIBI/HQ0NDAwcOHKChoYH33nuP+Ph4zGbzuAU7gP7+fqqqqvjd737HggULWL16NeXl5WRnZxMOh2lububYsWPY7fYr7hU3OjqK3W5Hp9ORmpqK0Wi85a8hPj6ewsJCHn30UZ577jlOnTpFWVkZGRkZt3wsQoiJR1ZVCzFJNTU18dprrzF//nzWrFkzYabjVCoV8fHxLFmyhMcff5yf/OQnqFQqtm/fzttvvz2uY9u1axdHjx5FpVLxd3/3d6xdu5aioiIMBgNms5ny8nKefvppjh49esXtRN5//33Ky8vZtGkTNTU14/AKLkhOTuab3/wmS5Ysie1bKIQQIMFOiEmpubmZ48ePx6p1JSUlE2YaLtp0oFKpSE5Opry8HLVazeDgIJ2dnbhcLiKRyLiMra+vj5GRETQaDcXFxRiNxkvG+8WvqxnvpcnR8T3xxBOkpaVx+PBhTp48idfrHddxCSHGnwQ7ISahM2fO0NXVhdlsZsGCBROiKeFKtFot8fHxqFQqgsEgfr9/XNeu+Xw+AoEAarUas9k86btJ58+fT3Z2NpFIRIKdEAKQNXZCTCrRSlFDQwN9fX0UFRVRVFQ0Lmu9roeiKASDQQDUajUajQa1Wk0wGCQQCBAIBGLX6nQ6jEYjY2NjhEKhS6p6JpMJvV6PSqVCURRCoRCBQIBgMBi7Lrqhr06nQ6vVxtbIhUIhgsEgXq8Xj8cTe2yn00kwGLxqo4lOp0Ov16PVavH7/fh8Ptxud+z5nU4nDocjdr3ZbMZgMNzw9/BqVCoVNpuNoqIi0tLS+Pzzz6moqCAlJWXCVG+FELeeBDshJqHq6mocDgf33nvvhK462e12Dh8+TDgcJisri8LCQhISEtizZw9vvvkmO3bsiF37+OOP86Mf/YiXX36Zmpoa+vv7cbvdALz00kt8+9vfxmq1Mjo6ysGDB9m9ezd79+5lcHCQSCSC0WikvLycDRs2sGTJEsrKygA4ffo0+/fv57nnniMcDseCYFFR0TUD0KOPPsqTTz7JsmXLeOutt3j11Vfp7u4mHA5TXV3NunXrYqFQrVbzy1/+ks2bN9+kd/LqSkpKWLx4MTt27OCZZ5655c8vhJhYJNgJMYlEIhHsdjuDg4PExcVRVlY24U6WUBSFsbExzp49y2effca7776LoiisXr2adevWAVBeXk5aWhrr16/nV7/6FadOnaK2tpb/+q//Ys2aNTz22GMEAgE6Ozv5+c9/TiQSIRQKMTw8zM9//nOamprweDw89dRT5OTkoNFocDqd7Nq1i3fffZf9+/fz7W9/m4qKCgoKCnjooYeYOXMmv/71rzl58iROp5P//M//RKfTXfL+eb1e6uvreeGFF2IhMD4+ng0bNrBgwQI+/PBD3njjDfLz89m8eTOzZ8+O3Xfu3Lm3/L0GyMrKorS0lIGBARwOBx6Ph/j4+HEZixBi/EmwE2ISiUQiDA8P4/V6SU1NJTs7e9yn3YLBIH19fezZs4eUlBQURcHn89HR0UFTUxN2u51ly5axcuVKFixYgEqlIi0tjbS0NDIzM/n973+PVquNBamFCxeSm5uLSqWir6+PmpoaioqKCAaDNDY2sm/fPhRFYdasWVRUVGCz2dBqtYyMjDA0NMTWrVs5duwYGRkZLFq0iMTERAoKCsjPz2fnzp20tbXh9/tZs2YNRqPxkoqny+W6bDo1Li4Om82GzWajra0NrVZLYmIiS5cunRCdyFarlYyMDHw+H06nk7GxMQl2QkxjEuyEmEQikQgOh4NgMIheryc1NXXcg93Y2BjV1dXU1tYC/3fyhMViITMzk7KyMn7wgx8wa9asa+6zV1RUxCOPPML8+fNjVbSkpCTee+89ANra2vjtb3/LuXPnWLt2LX/xF3/B7bffHns+q9XKs88+y5kzZ/jf//1f3njjDb7zne9gNBqvuCfdVGE2m2PNMyMjI7jdbtLS0sZ5VEKI8TJ1P+2EmIKiwS4UCqHX6yfEQvmEhARWrFjBSy+9xIwZM2Lfj27JodForus0jNTUVObPn3/V1zM6OsrHH3+M1+ulsLCQu+6667JrdDodc+fOpa2tjU8//ZSqqip0Oh2zZs36017kBGY2m2OBeXh4OLYuUQgxPUmwE2ISUalUGAwGVCoV4XAYv9+PTqcb9zHFxcVhsVhITEz82o+j1Wqv2t0bDocJBAI4nU40Gg1GoxGz2XzZOOBC0ExISEBRFIaHhxkbG/vaY5oMQqEQfr8fAL1eT1xc3DiPSAgxnibWqmshxDWpVKrY/mvhcBiPxzPum+XeKGq1+qpTpoqiEIlEYh2tarX6qhXA6LYqcCH0TJX352oCgUBs/zqj0Yherx/nEQkhxpMEOyEmEbVaTWpqKnFxcfj9/kv2UZvKtFoter2e+Ph4wuEwY2Njl1XiFEVBURTcbjdOpxO4cPTWjd5b7otTxdHnjX7dah6Ph6GhIeDCmkRpnBBiepNgJ8QkotFoyM7OJiEhAY/HQ11d3bgdz3WrWSwW1q5di9FopKOjg88+++yya4LBIKdPn6apqQmj0cidd95Jdnb2DRuDVqvFYDBcUgkMhUJs2LCB559/nt27d9+w57pe/f39tLS0YDKZSEtLw2q13vIxCCEmDgl2Qkwi0TV2ubm56PV66uvrp02wS0pKYv369WRlZdHe3s4f/vAHWlpacDqduN1uBgcH+eCDD2hsbESr1XLfffeRlZV1Q0/lMJvNZGRk4HA4GBkZweFw0NXVRX19PX6/H4vFcsOe63r19/fT2tpKSUkJCQkJU7oDWAjx5eQTQIhJJHpsls1mw+VycerUKQKBAFqt9pZtVDw2NkZ/fz89PT243W4ikQgej4euri7C4TBxcXFkZWVdtbtVUZRYI0RnZycej4dIJILL5aK9vR2dTkdmZiYmk+mS+0Urdtu3b6e1tZWqqirKy8spLS1Fq9XicDjYtm0bDoeDgoICNm7cSHJyMoqiMDo6isPhwOl0EggECIfDdHR0kJSUhNlsxmKxMDg4yNDQEAMDA8CFKc6BgQE6OztJT09Hp9ORlJTErFmzOHDgAG1tbZjNZhwOB4qikJ6eTm5u7k1//y9+H8PhMD09PZw9e5bbbruN+Pj4CbdhtRDiFlOEEJPOxx9/rDzzzDNKcnKycvbsWcXr9d6y5967d69iNpsVlUqlAJd8JScnK3fddZcSCoWuev9IJKIcP35cefLJJy+7f/Tr448/vuL9IpGI4nA4lO3btytbtmxRCgsLFbPZrBiNRiU5OVlZs2aN8tprryknTpyIXX/y5EnllVdeueLzrF69Wnn11VcVn8+nbNiwQZkxY8Zl1+Tk5Ciffvqp4nK5lKGhIaW6ulqpqKhQZsyYoSQmJioFBQXKyy+/rNTX1yuRSORmvvWXvR89PT3Ks88+q2RnZyvvvPOOYrfbb9nzCyEmJpWiTPGWMSGmILvdzkcffcQLL7zAd77zHTZu3HjLjrRyOp2cOXPmilPA0a1ISktLr1mx83q9sSOwrqS4uPiKW6co/79K5XK5GB0dxel0EgwGgf/rGI42EEQrfl6vF5fLRWdn52WPF92iJS0tjdbWVjweT+zxouLi4igqKsJkMqEoCn6/n66uLnw+H5FIBI1GQ0ZGBlar9YZO+34ZRVH4xS9+QWVlJXa7nR07dsQaa4QQ05cEOyEmoVAoxKeffsorr7yC1+vl2Wef5Rvf+AYJCQnjPTRxCwSDQZxOJ08//TQej4eCggJeffVVtFrtuG9YLYQYX7IYQ4hJSKvVUlBQwLp166iurqa+vp6enp5x23JD3DqKojA2NkZzczPHjh0jKSmJBx54QEKdEAKQip0Qk1Z0SnLDhg14PB7Kysr4t3/7t9jJFGJq8vv91NTUsGXLFuLj49m8eTNPPvmkBDshBCAVOyEmLbVajdls5oc//CFZWVnU1dXxH//xH7jdbqnaTUHRamxlZSVbt27F4XDw3e9+l+XLl8sWJ0KIGAl2QkxSKpUKjUbDnXfeyfz58zGbzezevZuGhoZpcyLFdBENdU1NTVRVVVFXV8fs2bO58847yc3NRaVSSbVOCAFIsBNiUlOr1aSnp7Nx40buv/9+Dhw4wJtvvklNTQ3hcFjW3E0B0d+hz+fj9ddf5w9/+ANDQ0P89Kc/JT8//7L9/oQQ05vU74WYAubNm0dKSgpJSUk8//zzdHd309jYyF/91V9JJWeSUxSFuro63nnnHd5++202bdrEgw8+SEVFhWxtIoS4jDRPCDFFjI2N0d3dzWuvvUZzczM+n4+NGzfy8MMPk5aWJiFgkolW6Xbt2sWhQ4fYv38/5eXlPPTQQyxevJgZM2aM9xCFEBOQTMUKMUUYjUZmzpzJ+vXrsdlsOBwOPvjgA44fPx7bUFf+P25yCIVCuN1u6uvr2bVrF4cPHwbg4YcfllAnhLgmqdgJMQW1t7dz9OhRfvCDH5CYmMiDDz7I5s2bKS0tjV0jU7QTy8UfxXa7ndOnT/O9730Pl8vFkiVL+Ju/+RsWLlyIRqMZx1EKISY6CXZCTEHBYBCPx8Pp06d56aWXaG1tRVEUvvvd77Ju3Try8vIkIEwwiqIQDAbZunUrn3zyCVVVVaSlpfHMM89w++23k5+fj16vl0AuhLgmCXZCTFHhcBi3282uXbs4fvw4dXV1qFQqli1bxvz58ykvLycnJwe1WlZkjLehoSF6eno4dOgQlZWVuFwuDAYD999/P3fffTc5OTm39BxaIcTkJV2xQkxRGo0Gq9XKxo0bmTt3LpWVlfzyl7+kt7eXxsZGQqEQWq0Wi8WCXq+X5opbLBKJEAqFGB0dpbm5mZqaGt566y3cbjeLFy/moYceYuPGjWg0GqnSCSGum1TshJjion/i4XCY1tZWfv3rX3PgwAHq6upYvXo1mzZtYsWKFRQVFY3zSKcXt9vN2bNnefHFF6mursZut7NixQr+9m//ltLSUlJSUmLXSrATQlwvCXZCTBPR7TMGBgbo6OjgxIkTbN++Ha/XS3x8PHfeeScPPfQQRUVFZGRkjPdwp6RwOEx9fT1VVVXU1tZSXV2NwWBg0aJFLF68mCVLlpCdnY3RaJQKqhDia5GpWCGmCZVKhdFopKCggOTkZJKSkvB4PDQ0NDA4OEhtbS2hUIiioiJsNhtFRUVkZWWh1+ul0eJrUhSFUCjEwMAAg4ODdHV1cezYMVpbWxkaGiI5OZmlS5dyxx13MH/+fAoLC6U6J4T4k0jFTohpKvqnX1tby7Fjx/if//kfampqsFqtFBUV8a1vfYuVK1eSnp6OwWAgLi4OtVot55Jew8XT3uFwmFAohMvl4uDBgxw9epR9+/bR3NxMSUkJCxYs4JFHHmH16tXS7SqEuGEk2AkxTUX/9BVFIRKJEA6HOX78OAcPHuTAgQMcOHAAo9GIzWZjyZIlPPbYY8yZM4eUlBTppL2K6HtZXV3NsWPHOHz4MLt378bv95OVlcXixYt56qmnmD17NikpKWg0mth7KcFOCHEjSLATQgAXQsnIyAh2u52BgQHa29tpaGigt7eX/v5+fD4f6enpZGZmUlxcTFlZGQUFBWRkZGAwGMZ7+OMiHA4TDAZpbGykra2Njo4OGhsbaW9vx+/3o9VqKSwsZOHCheTl5ZGdnY3NZiMhIQGdTjfewxdCTEES7IQQl4lEIng8HmpqamhububUqVOcOHGCQCCAXq8nMzOTkpISbDYb2dnZpKWlYbFYMJlMmEwmLBbLlNumIxwO4/P58Hq9jI6O4na78Xg8DA8PU1dXR3d3NwMDA3R3d6NWq0lNTSUvL4+77rqLJUuWkJ6ejtlsHu+XIYSY4iTYCSG+VDgcpqWlhSNHjlBXV8eRI0eoqakhEolgNpuZN28eixYtYubMmRQXF7No0SJMJlMs3EUD3sVBbyKGvounp7/4T4/HQ29vL83NzRw7doyGhgba2to4c+YMkUiEwsJCiouLWbFiBXfffTf5+fmkpaWN22sRQkxPEuyEEF9KUZTYtGMoFCIYDDI4OEhzczMtLS3U1dXR2NjI+fPncTqdGAwG8vLyyMzMJC8vj+LiYvLy8sjKyooFnonYaTsyMoLD4aCjo4OWlhZ6e3vp7e3l7NmznDt3Do/HQyQSITs7m5kzZ5Kfn09paSnLly8nJSUFk8lEXFwcOp3ukvVzQghxq0iwE0J8ZdE98YaGhnA4HAwODjI4OBi73dnZic/nIxgMEgwG8fv9aDQa9Ho9FosFq9WK2WwmPj6exMRErFYrJpMJs9mMyWTCaDSi0+kwGAyYTCa0Wi1arRa9Xg9cqPZpNBri4uJQqVSxDtRwOAxcqDAGAoHYcwcCAcbGxhgbG8Pn8+Hz+XA6nTidTjweDy6Xi6GhITweDx6PB6fTSSgUQqPRoNVq0el0mEwmrFYr6enpZGdnk5KSQmJiIqmpqeTk5KDX69FqZQcpIcT4kmAnhLhhvF4vIyMj1NXV0d7ezsDAAD09PbS2tjI8PIzH4yEQCKBWqzGbzSQkJMQCUnx8fOx7JpMJg8GA2WzGarWi0+mIi4vDZDIBF45L02g0saaNi4Nc9LbX68Xv9zM2NobX68XlcuHxeGIBb2RkhJGREdxuN06nE7vdTiQSQaVSodfrSU9PJyMjg4yMDAoLC7HZbOTm5pKXl0dycvKEnEoWQggJdkKIWyJaFWttbaWrqwu73Y7dbqenp4eenh6GhoYYGhpieHgYr9cbq77dSCaTifj4eBISEsjMzCQrK4vU1FQyMjLIzc0lIyOD9PR0ioqKSExMnJDTxUIIcS0S7IQQt0Q4HCYSiRAIBGLTphev24v+PPo9v9+P2+3G5XIRDAYJBAK43W7gQtduKBTC5/MBFyp40aoegFarxWw2x6ZQjUZjbIsRtVqNWq2OTe9Gp1svvh29TqpyQojJRoKdEGLCia6Zi06lXnwbLgS7SCQSm3pVq9WxgBa9HV3zptPpYuv1ptoWLEII8UUS7IQQQgghpgjpxRdCCCGEmCIk2AkhhBBCTBES7IQQQgghpggJdkIIIYQQU4QEOyGEEEKIKUKCnRBCCCHEFCHBTgghhBBiipBgJ4QQQggxRUiwE0IIIYSYIiTYCSGEEEJMERLshBBCCCGmCAl2QgghhBBThAQ7IYQQQogpQoKdEEIIIcQU8f8As3dV0hwGrbQAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -1585,10 +1593,10 @@ "id": "43e49972-b137-4cae-9ee7-5c5879ff01ed", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:09:32.110711Z", - "iopub.status.busy": "2024-10-20T00:09:32.110395Z", - "iopub.status.idle": "2024-10-20T00:12:23.629257Z", - "shell.execute_reply": "2024-10-20T00:12:23.628502Z" + "iopub.execute_input": "2024-10-22T01:35:52.812224Z", + "iopub.status.busy": "2024-10-22T01:35:52.812000Z", + "iopub.status.idle": "2024-10-22T01:38:49.679394Z", + "shell.execute_reply": "2024-10-22T01:38:49.678665Z" } }, "outputs": [ @@ -1597,7 +1605,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 0%| | 0/116 [00:00" ] @@ -1888,17 +1896,17 @@ "id": "a3853007-fb0c-4a1d-bcca-f7bdd4dcef13", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.766450Z", - "iopub.status.busy": "2024-10-20T00:12:23.766234Z", - "iopub.status.idle": "2024-10-20T00:12:23.956558Z", - "shell.execute_reply": "2024-10-20T00:12:23.956016Z" + "iopub.execute_input": "2024-10-22T01:38:49.817141Z", + "iopub.status.busy": "2024-10-22T01:38:49.816499Z", + "iopub.status.idle": "2024-10-22T01:38:50.014603Z", + "shell.execute_reply": "2024-10-22T01:38:50.013876Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -1953,10 +1961,10 @@ "id": "c9be4254-9ac2-4abc-a3f3-906925dac53d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.958556Z", - "iopub.status.busy": "2024-10-20T00:12:23.958258Z", - "iopub.status.idle": "2024-10-20T00:12:23.976597Z", - "shell.execute_reply": "2024-10-20T00:12:23.976125Z" + "iopub.execute_input": "2024-10-22T01:38:50.016981Z", + "iopub.status.busy": "2024-10-22T01:38:50.016586Z", + "iopub.status.idle": "2024-10-22T01:38:50.037280Z", + "shell.execute_reply": "2024-10-22T01:38:50.036599Z" } }, "outputs": [ @@ -1994,10 +2002,10 @@ "id": "16215ca0-00c9-411c-b293-675a4cb595c2", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.978678Z", - "iopub.status.busy": "2024-10-20T00:12:23.978230Z", - "iopub.status.idle": "2024-10-20T00:12:23.993340Z", - "shell.execute_reply": "2024-10-20T00:12:23.992874Z" + "iopub.execute_input": "2024-10-22T01:38:50.039431Z", + "iopub.status.busy": "2024-10-22T01:38:50.039192Z", + "iopub.status.idle": "2024-10-22T01:38:50.056559Z", + "shell.execute_reply": "2024-10-22T01:38:50.055846Z" } }, "outputs": [ @@ -2036,10 +2044,10 @@ "id": "00c6b5bf-e6b7-456f-84f6-91e2cbda62d6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:23.995162Z", - "iopub.status.busy": "2024-10-20T00:12:23.994847Z", - "iopub.status.idle": "2024-10-20T00:12:27.924356Z", - "shell.execute_reply": "2024-10-20T00:12:27.923788Z" + "iopub.execute_input": "2024-10-22T01:38:50.058663Z", + "iopub.status.busy": "2024-10-22T01:38:50.058440Z", + "iopub.status.idle": "2024-10-22T01:38:53.858973Z", + "shell.execute_reply": "2024-10-22T01:38:53.858213Z" } }, "outputs": [ @@ -2048,15 +2056,7 @@ "output_type": "stream", "text": [ "\r", - "Evaluating set functions...: 0%| | 0/122 [00:00" ] @@ -2219,10 +2219,10 @@ "id": "e48b4b77-7ee6-4669-be82-05d0d15f4065", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:12:27.926881Z", - "iopub.status.busy": "2024-10-20T00:12:27.926499Z", - "iopub.status.idle": "2024-10-20T00:13:04.831361Z", - "shell.execute_reply": "2024-10-20T00:13:04.830809Z" + "iopub.execute_input": "2024-10-22T01:38:53.861073Z", + "iopub.status.busy": "2024-10-22T01:38:53.860863Z", + "iopub.status.idle": "2024-10-22T01:39:31.896840Z", + "shell.execute_reply": "2024-10-22T01:39:31.896087Z" } }, "outputs": [], @@ -2244,16 +2244,16 @@ "id": "4c7b083f-ef7f-47e0-b72f-2d0d1b9501e1", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:04.833607Z", - "iopub.status.busy": "2024-10-20T00:13:04.833242Z", - "iopub.status.idle": "2024-10-20T00:13:04.958825Z", - "shell.execute_reply": "2024-10-20T00:13:04.958289Z" + "iopub.execute_input": "2024-10-22T01:39:31.899275Z", + "iopub.status.busy": "2024-10-22T01:39:31.898900Z", + "iopub.status.idle": "2024-10-22T01:39:32.034933Z", + "shell.execute_reply": "2024-10-22T01:39:32.034313Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2304,10 +2304,10 @@ "id": "d70a7edb-9374-4fce-b8d3-e9ef9f53f328", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:04.961173Z", - "iopub.status.busy": "2024-10-20T00:13:04.960820Z", - "iopub.status.idle": "2024-10-20T00:13:04.984942Z", - "shell.execute_reply": "2024-10-20T00:13:04.984451Z" + "iopub.execute_input": "2024-10-22T01:39:32.037190Z", + "iopub.status.busy": "2024-10-22T01:39:32.036957Z", + "iopub.status.idle": "2024-10-22T01:39:32.060721Z", + "shell.execute_reply": "2024-10-22T01:39:32.060078Z" } }, "outputs": [ @@ -2347,10 +2347,10 @@ "id": "7f4a5065-e436-46cd-be5e-e6c1274fc3b7", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:04.986665Z", - "iopub.status.busy": "2024-10-20T00:13:04.986490Z", - "iopub.status.idle": "2024-10-20T00:13:20.422247Z", - "shell.execute_reply": "2024-10-20T00:13:20.421537Z" + "iopub.execute_input": "2024-10-22T01:39:32.063000Z", + "iopub.status.busy": "2024-10-22T01:39:32.062610Z", + "iopub.status.idle": "2024-10-22T01:39:48.130700Z", + "shell.execute_reply": "2024-10-22T01:39:48.130072Z" } }, "outputs": [], @@ -2371,16 +2371,16 @@ "id": "fc2467b0-eb84-4f72-b895-954f5a8634e6", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:20.424503Z", - "iopub.status.busy": "2024-10-20T00:13:20.424117Z", - "iopub.status.idle": "2024-10-20T00:13:20.553787Z", - "shell.execute_reply": "2024-10-20T00:13:20.552798Z" + "iopub.execute_input": "2024-10-22T01:39:48.133037Z", + "iopub.status.busy": "2024-10-22T01:39:48.132718Z", + "iopub.status.idle": "2024-10-22T01:39:48.247067Z", + "shell.execute_reply": "2024-10-22T01:39:48.246495Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -2407,10 +2407,10 @@ "id": "34b11c19-1256-4ae0-9067-2eb374ce5147", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:20.556956Z", - "iopub.status.busy": "2024-10-20T00:13:20.556131Z", - "iopub.status.idle": "2024-10-20T00:13:20.573046Z", - "shell.execute_reply": "2024-10-20T00:13:20.572570Z" + "iopub.execute_input": "2024-10-22T01:39:48.249269Z", + "iopub.status.busy": "2024-10-22T01:39:48.249042Z", + "iopub.status.idle": "2024-10-22T01:39:48.269824Z", + "shell.execute_reply": "2024-10-22T01:39:48.269253Z" } }, "outputs": [ @@ -2463,10 +2463,10 @@ "id": "1c4785f9-5ec1-4340-8837-6d0cdaf4c406", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:13:20.574899Z", - "iopub.status.busy": "2024-10-20T00:13:20.574708Z", - "iopub.status.idle": "2024-10-20T00:13:20.664123Z", - "shell.execute_reply": "2024-10-20T00:13:20.663570Z" + "iopub.execute_input": "2024-10-22T01:39:48.272380Z", + "iopub.status.busy": "2024-10-22T01:39:48.271973Z", + "iopub.status.idle": "2024-10-22T01:39:48.367635Z", + "shell.execute_reply": "2024-10-22T01:39:48.367041Z" } }, "outputs": [], diff --git a/main/example_notebooks/gcm_rca_microservice_architecture.html b/main/example_notebooks/gcm_rca_microservice_architecture.html index 5c537d4c3..c43980bee 100644 --- a/main/example_notebooks/gcm_rca_microservice_architecture.html +++ b/main/example_notebooks/gcm_rca_microservice_architecture.html @@ -566,7 +566,7 @@

Finding the Root Cause of Elevated Latencies in a Microservice Architecture#

In this case study, we identify the root causes of “unexpected” observed latencies in cloud services that empower an online shop. We focus on the process of placing an order, which involves different services to make sure that the placed order is valid, the customer is authenticated, the shipping costs are calculated correctly, and the shipping process is initiated accordingly. The dependencies of the services is shown in the graph below.

-

d9c1de193bb04bd5a5f3d25c99453ad9

+

0ecc9127dfbd4a2098726f1699e5b01f

This kind of dependency graph could be obtained from services like Amazon X-Ray or defined manually based on the trace structure of requests.

We assume that the dependency graph above is correct and that we are able to measure the latency (in seconds) of each node for an order request. In case of Website, the latency would represent the time until a confirmation of the order is shown. For simplicity, let us assume that the services are synchronized, i.e., a service has to wait for downstream services in order to proceed. Further, we assume that two nodes are not impacted by unobserved factors (hidden confounders) at the same time (i.e., causal sufficiency). Seeing that, for instance, network traffic affects multiple services, this assumption might be typically violated in a real-world scenario. However, weak confounders can be neglected, while stronger ones (like network traffic) could falsely render multiple nodes as root causes. Generally, we can only identify causes that are part of the data.

@@ -799,9 +799,9 @@

Setting up the causal model 
-Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 75.92it/s]
-Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.65it/s]
-Test permutations of given graph: 100%|██████████| 50/50 [01:13<00:00,  1.47s/it]
+Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 74.18it/s]
+Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.32it/s]
+Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.52s/it]

@@ -830,68 +830,68 @@

Setting up the causal model#

Next, let us imagine a scenario where permanent degradation has happened as in scenario 2 and we’ve successfully identified Caching Service as the root cause. Furthermore, we figured out that a recent deployment of the Caching Service contained a bug that is causing the overloaded hosts. A proper fix must be deployed, or the previous deployment must be rolled back. But, in the meantime, could we mitigate the situation by shifting over some resources from Shipping Service to Caching Service? And would that help? Before doing it in reality, let us simulate it first and see whether it improves the situation.

-

53c5a8d8cea84ec094700625a3ada33d

+

980f176209c4437d883587417fbc7c0c

Let’s perform an intervention where we say we can reduce the average time of Caching Service by 1s. But at the same time we buy this speed-up by an average slow-down of 2s in Shipping Cost Service.

[14]:
diff --git a/main/example_notebooks/gcm_rca_microservice_architecture.ipynb b/main/example_notebooks/gcm_rca_microservice_architecture.ipynb
index 707e13e59..83c963e1b 100644
--- a/main/example_notebooks/gcm_rca_microservice_architecture.ipynb
+++ b/main/example_notebooks/gcm_rca_microservice_architecture.ipynb
@@ -38,10 +38,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:13:27.136435Z",
-     "iopub.status.busy": "2024-10-20T00:13:27.136252Z",
-     "iopub.status.idle": "2024-10-20T00:13:27.393960Z",
-     "shell.execute_reply": "2024-10-20T00:13:27.393298Z"
+     "iopub.execute_input": "2024-10-22T01:39:55.169686Z",
+     "iopub.status.busy": "2024-10-22T01:39:55.169477Z",
+     "iopub.status.idle": "2024-10-22T01:39:55.439061Z",
+     "shell.execute_reply": "2024-10-22T01:39:55.438470Z"
     }
    },
    "outputs": [
@@ -194,10 +194,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:13:27.396190Z",
-     "iopub.status.busy": "2024-10-20T00:13:27.395712Z",
-     "iopub.status.idle": "2024-10-20T00:13:37.966003Z",
-     "shell.execute_reply": "2024-10-20T00:13:37.965356Z"
+     "iopub.execute_input": "2024-10-22T01:39:55.440944Z",
+     "iopub.status.busy": "2024-10-22T01:39:55.440753Z",
+     "iopub.status.idle": "2024-10-22T01:40:06.333976Z",
+     "shell.execute_reply": "2024-10-22T01:40:06.333322Z"
     }
    },
    "outputs": [
@@ -244,10 +244,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:13:37.969373Z",
-     "iopub.status.busy": "2024-10-20T00:13:37.968953Z",
-     "iopub.status.idle": "2024-10-20T00:13:38.931903Z",
-     "shell.execute_reply": "2024-10-20T00:13:38.931280Z"
+     "iopub.execute_input": "2024-10-22T01:40:06.337371Z",
+     "iopub.status.busy": "2024-10-22T01:40:06.336806Z",
+     "iopub.status.idle": "2024-10-22T01:40:07.421915Z",
+     "shell.execute_reply": "2024-10-22T01:40:07.421150Z"
     }
    },
    "outputs": [],
@@ -276,10 +276,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:13:38.934339Z",
-     "iopub.status.busy": "2024-10-20T00:13:38.933785Z",
-     "iopub.status.idle": "2024-10-20T00:13:39.180165Z",
-     "shell.execute_reply": "2024-10-20T00:13:39.179623Z"
+     "iopub.execute_input": "2024-10-22T01:40:07.424830Z",
+     "iopub.status.busy": "2024-10-22T01:40:07.424059Z",
+     "iopub.status.idle": "2024-10-22T01:40:07.677524Z",
+     "shell.execute_reply": "2024-10-22T01:40:07.676850Z"
     }
    },
    "outputs": [
@@ -317,10 +317,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:13:39.182305Z",
-     "iopub.status.busy": "2024-10-20T00:13:39.181942Z",
-     "iopub.status.idle": "2024-10-20T00:13:39.185452Z",
-     "shell.execute_reply": "2024-10-20T00:13:39.184987Z"
+     "iopub.execute_input": "2024-10-22T01:40:07.679924Z",
+     "iopub.status.busy": "2024-10-22T01:40:07.679446Z",
+     "iopub.status.idle": "2024-10-22T01:40:07.683488Z",
+     "shell.execute_reply": "2024-10-22T01:40:07.682867Z"
     }
    },
    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:13:39.187273Z",
-     "iopub.status.busy": "2024-10-20T00:13:39.186925Z",
-     "iopub.status.idle": "2024-10-20T00:15:08.888366Z",
-     "shell.execute_reply": "2024-10-20T00:15:08.887670Z"
+     "iopub.execute_input": "2024-10-22T01:40:07.685727Z",
+     "iopub.status.busy": "2024-10-22T01:40:07.685263Z",
+     "iopub.status.idle": "2024-10-22T01:41:40.194966Z",
+     "shell.execute_reply": "2024-10-22T01:41:40.194240Z"
     }
    },
    "outputs": [
@@ -458,7 +458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Product DB:  91%|█████████ | 10/11 [00:00<00:00, 85.03it/s]"
+      "Fitting causal mechanism of node Product DB:  91%|█████████ | 10/11 [00:00<00:00, 83.24it/s]"
      ]
     },
     {
@@ -466,7 +466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Order DB:  91%|█████████ | 10/11 [00:00<00:00, 85.03it/s]  "
+      "Fitting causal mechanism of node Order DB:  91%|█████████ | 10/11 [00:00<00:00, 83.24it/s]  "
      ]
     },
     {
@@ -474,7 +474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 75.92it/s]"
+      "Fitting causal mechanism of node Order DB: 100%|██████████| 11/11 [00:00<00:00, 74.18it/s]"
      ]
     },
     {
@@ -497,7 +497,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...:  73%|███████▎  | 8/11 [00:02<00:00,  3.39it/s]"
+      "Evaluating causal mechanisms...:  73%|███████▎  | 8/11 [00:02<00:00,  3.15it/s]"
      ]
     },
     {
@@ -505,7 +505,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.65it/s]"
+      "Evaluating causal mechanisms...: 100%|██████████| 11/11 [00:02<00:00,  4.32it/s]"
      ]
     },
     {
@@ -528,7 +528,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   2%|▏         | 1/50 [00:02<02:06,  2.59s/it]"
+      "Test permutations of given graph:   2%|▏         | 1/50 [00:02<02:08,  2.63s/it]"
      ]
     },
     {
@@ -536,7 +536,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   4%|▍         | 2/50 [00:05<02:02,  2.56s/it]"
+      "Test permutations of given graph:   4%|▍         | 2/50 [00:04<01:48,  2.26s/it]"
      ]
     },
     {
@@ -544,7 +544,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   6%|▌         | 3/50 [00:07<01:57,  2.50s/it]"
+      "Test permutations of given graph:   6%|▌         | 3/50 [00:06<01:42,  2.17s/it]"
      ]
     },
     {
@@ -552,7 +552,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:   8%|▊         | 4/50 [00:09<01:51,  2.42s/it]"
+      "Test permutations of given graph:   8%|▊         | 4/50 [00:09<01:45,  2.30s/it]"
      ]
     },
     {
@@ -560,7 +560,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  10%|█         | 5/50 [00:12<01:46,  2.36s/it]"
+      "Test permutations of given graph:  10%|█         | 5/50 [00:11<01:40,  2.23s/it]"
      ]
     },
     {
@@ -568,7 +568,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  12%|█▏        | 6/50 [00:14<01:43,  2.36s/it]"
+      "Test permutations of given graph:  12%|█▏        | 6/50 [00:13<01:37,  2.21s/it]"
      ]
     },
     {
@@ -576,7 +576,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  14%|█▍        | 7/50 [00:16<01:35,  2.21s/it]"
+      "Test permutations of given graph:  14%|█▍        | 7/50 [00:15<01:37,  2.28s/it]"
      ]
     },
     {
@@ -584,7 +584,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  16%|█▌        | 8/50 [00:18<01:25,  2.04s/it]"
+      "Test permutations of given graph:  16%|█▌        | 8/50 [00:17<01:31,  2.18s/it]"
      ]
     },
     {
@@ -592,7 +592,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  18%|█▊        | 9/50 [00:19<01:18,  1.92s/it]"
+      "Test permutations of given graph:  18%|█▊        | 9/50 [00:19<01:28,  2.17s/it]"
      ]
     },
     {
@@ -600,7 +600,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  20%|██        | 10/50 [00:21<01:18,  1.97s/it]"
+      "Test permutations of given graph:  20%|██        | 10/50 [00:22<01:26,  2.15s/it]"
      ]
     },
     {
@@ -608,7 +608,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  22%|██▏       | 11/50 [00:23<01:18,  2.03s/it]"
+      "Test permutations of given graph:  22%|██▏       | 11/50 [00:23<01:20,  2.06s/it]"
      ]
     },
     {
@@ -616,7 +616,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  24%|██▍       | 12/50 [00:26<01:19,  2.08s/it]"
+      "Test permutations of given graph:  24%|██▍       | 12/50 [00:25<01:14,  1.95s/it]"
      ]
     },
     {
@@ -624,7 +624,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  26%|██▌       | 13/50 [00:27<01:05,  1.76s/it]"
+      "Test permutations of given graph:  26%|██▌       | 13/50 [00:27<01:08,  1.86s/it]"
      ]
     },
     {
@@ -632,7 +632,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  28%|██▊       | 14/50 [00:28<01:03,  1.76s/it]"
+      "Test permutations of given graph:  28%|██▊       | 14/50 [00:29<01:04,  1.80s/it]"
      ]
     },
     {
@@ -640,7 +640,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  30%|███       | 15/50 [00:30<00:58,  1.66s/it]"
+      "Test permutations of given graph:  30%|███       | 15/50 [00:30<01:02,  1.78s/it]"
      ]
     },
     {
@@ -648,7 +648,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  32%|███▏      | 16/50 [00:31<00:52,  1.54s/it]"
+      "Test permutations of given graph:  32%|███▏      | 16/50 [00:32<00:57,  1.69s/it]"
      ]
     },
     {
@@ -656,7 +656,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  34%|███▍      | 17/50 [00:33<00:53,  1.62s/it]"
+      "Test permutations of given graph:  34%|███▍      | 17/50 [00:33<00:55,  1.68s/it]"
      ]
     },
     {
@@ -664,7 +664,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  36%|███▌      | 18/50 [00:35<00:52,  1.63s/it]"
+      "Test permutations of given graph:  36%|███▌      | 18/50 [00:35<00:55,  1.75s/it]"
      ]
     },
     {
@@ -672,7 +672,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  38%|███▊      | 19/50 [00:36<00:48,  1.56s/it]"
+      "Test permutations of given graph:  38%|███▊      | 19/50 [00:37<00:51,  1.65s/it]"
      ]
     },
     {
@@ -680,7 +680,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  40%|████      | 20/50 [00:37<00:46,  1.54s/it]"
+      "Test permutations of given graph:  40%|████      | 20/50 [00:38<00:48,  1.62s/it]"
      ]
     },
     {
@@ -688,7 +688,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  42%|████▏     | 21/50 [00:39<00:45,  1.58s/it]"
+      "Test permutations of given graph:  42%|████▏     | 21/50 [00:40<00:47,  1.64s/it]"
      ]
     },
     {
@@ -696,7 +696,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  44%|████▍     | 22/50 [00:40<00:39,  1.42s/it]"
+      "Test permutations of given graph:  44%|████▍     | 22/50 [00:42<00:46,  1.64s/it]"
      ]
     },
     {
@@ -704,7 +704,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  46%|████▌     | 23/50 [00:41<00:36,  1.35s/it]"
+      "Test permutations of given graph:  46%|████▌     | 23/50 [00:43<00:46,  1.72s/it]"
      ]
     },
     {
@@ -712,7 +712,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  48%|████▊     | 24/50 [00:43<00:37,  1.44s/it]"
+      "Test permutations of given graph:  48%|████▊     | 24/50 [00:45<00:42,  1.65s/it]"
      ]
     },
     {
@@ -720,7 +720,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  50%|█████     | 25/50 [00:45<00:38,  1.53s/it]"
+      "Test permutations of given graph:  50%|█████     | 25/50 [00:46<00:36,  1.46s/it]"
      ]
     },
     {
@@ -728,7 +728,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:46<00:34,  1.43s/it]"
+      "Test permutations of given graph:  52%|█████▏    | 26/50 [00:47<00:33,  1.38s/it]"
      ]
     },
     {
@@ -736,7 +736,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:47<00:32,  1.42s/it]"
+      "Test permutations of given graph:  54%|█████▍    | 27/50 [00:48<00:31,  1.35s/it]"
      ]
     },
     {
@@ -744,7 +744,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:49<00:30,  1.37s/it]"
+      "Test permutations of given graph:  56%|█████▌    | 28/50 [00:50<00:28,  1.29s/it]"
      ]
     },
     {
@@ -752,7 +752,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:50<00:26,  1.27s/it]"
+      "Test permutations of given graph:  58%|█████▊    | 29/50 [00:51<00:26,  1.26s/it]"
      ]
     },
     {
@@ -760,7 +760,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  60%|██████    | 30/50 [00:51<00:28,  1.42s/it]"
+      "Test permutations of given graph:  60%|██████    | 30/50 [00:52<00:24,  1.23s/it]"
      ]
     },
     {
@@ -768,7 +768,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:52<00:24,  1.31s/it]"
+      "Test permutations of given graph:  62%|██████▏   | 31/50 [00:53<00:23,  1.22s/it]"
      ]
     },
     {
@@ -776,7 +776,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:53<00:20,  1.15s/it]"
+      "Test permutations of given graph:  64%|██████▍   | 32/50 [00:54<00:21,  1.17s/it]"
      ]
     },
     {
@@ -784,7 +784,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:55<00:20,  1.23s/it]"
+      "Test permutations of given graph:  66%|██████▌   | 33/50 [00:55<00:19,  1.13s/it]"
      ]
     },
     {
@@ -792,7 +792,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:56<00:18,  1.18s/it]"
+      "Test permutations of given graph:  68%|██████▊   | 34/50 [00:56<00:17,  1.11s/it]"
      ]
     },
     {
@@ -800,7 +800,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  70%|███████   | 35/50 [00:57<00:17,  1.18s/it]"
+      "Test permutations of given graph:  70%|███████   | 35/50 [00:58<00:17,  1.17s/it]"
      ]
     },
     {
@@ -808,7 +808,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  72%|███████▏  | 36/50 [00:58<00:16,  1.17s/it]"
+      "Test permutations of given graph:  72%|███████▏  | 36/50 [00:58<00:14,  1.06s/it]"
      ]
     },
     {
@@ -816,7 +816,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  74%|███████▍  | 37/50 [00:59<00:15,  1.16s/it]"
+      "Test permutations of given graph:  74%|███████▍  | 37/50 [01:00<00:15,  1.17s/it]"
      ]
     },
     {
@@ -824,7 +824,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  76%|███████▌  | 38/50 [01:00<00:12,  1.05s/it]"
+      "Test permutations of given graph:  76%|███████▌  | 38/50 [01:01<00:14,  1.17s/it]"
      ]
     },
     {
@@ -832,7 +832,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  78%|███████▊  | 39/50 [01:01<00:13,  1.19s/it]"
+      "Test permutations of given graph:  78%|███████▊  | 39/50 [01:02<00:13,  1.25s/it]"
      ]
     },
     {
@@ -840,7 +840,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  80%|████████  | 40/50 [01:03<00:11,  1.16s/it]"
+      "Test permutations of given graph:  80%|████████  | 40/50 [01:03<00:11,  1.18s/it]"
      ]
     },
     {
@@ -848,7 +848,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  82%|████████▏ | 41/50 [01:04<00:10,  1.18s/it]"
+      "Test permutations of given graph:  82%|████████▏ | 41/50 [01:05<00:11,  1.24s/it]"
      ]
     },
     {
@@ -856,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  84%|████████▍ | 42/50 [01:05<00:09,  1.18s/it]"
+      "Test permutations of given graph:  84%|████████▍ | 42/50 [01:06<00:09,  1.20s/it]"
      ]
     },
     {
@@ -864,7 +864,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  86%|████████▌ | 43/50 [01:06<00:07,  1.13s/it]"
+      "Test permutations of given graph:  86%|████████▌ | 43/50 [01:07<00:08,  1.23s/it]"
      ]
     },
     {
@@ -872,7 +872,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  88%|████████▊ | 44/50 [01:07<00:06,  1.14s/it]"
+      "Test permutations of given graph:  88%|████████▊ | 44/50 [01:09<00:07,  1.31s/it]"
      ]
     },
     {
@@ -880,7 +880,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  90%|█████████ | 45/50 [01:08<00:04,  1.09it/s]"
+      "Test permutations of given graph:  90%|█████████ | 45/50 [01:10<00:06,  1.25s/it]"
      ]
     },
     {
@@ -888,7 +888,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  92%|█████████▏| 46/50 [01:09<00:04,  1.03s/it]"
+      "Test permutations of given graph:  92%|█████████▏| 46/50 [01:11<00:04,  1.17s/it]"
      ]
     },
     {
@@ -896,7 +896,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  94%|█████████▍| 47/50 [01:10<00:03,  1.04s/it]"
+      "Test permutations of given graph:  94%|█████████▍| 47/50 [01:12<00:03,  1.24s/it]"
      ]
     },
     {
@@ -904,7 +904,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  96%|█████████▌| 48/50 [01:11<00:02,  1.03s/it]"
+      "Test permutations of given graph:  96%|█████████▌| 48/50 [01:13<00:02,  1.22s/it]"
      ]
     },
     {
@@ -912,7 +912,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph:  98%|█████████▊| 49/50 [01:12<00:01,  1.02s/it]"
+      "Test permutations of given graph:  98%|█████████▊| 49/50 [01:14<00:01,  1.17s/it]"
      ]
     },
     {
@@ -920,7 +920,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [01:13<00:00,  1.03s/it]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.07s/it]"
      ]
     },
     {
@@ -928,7 +928,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "Test permutations of given graph: 100%|██████████| 50/50 [01:13<00:00,  1.47s/it]"
+      "Test permutations of given graph: 100%|██████████| 50/50 [01:15<00:00,  1.52s/it]"
      ]
     },
     {
@@ -940,7 +940,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
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@@ -966,68 +966,68 @@
       "We will mostly utilize the CRPS for comparing and interpreting the performance of the mechanisms, since this captures the most important properties for the causal model.\n",
       "\n",
       "--- Node Customer DB\n",
-      "- The KL divergence between generated and observed distribution is 0.04053244561112437.\n",
+      "- The KL divergence between generated and observed distribution is 0.04027380777121141.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Shipping Cost Service\n",
-      "- The KL divergence between generated and observed distribution is 0.00516151353196099.\n",
+      "- The KL divergence between generated and observed distribution is 0.05228673903078925.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Product DB\n",
-      "- The KL divergence between generated and observed distribution is 0.07907442198078539.\n",
+      "- The KL divergence between generated and observed distribution is 0.04453439493387481.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Order DB\n",
-      "- The KL divergence between generated and observed distribution is 0.07950995311601591.\n",
+      "- The KL divergence between generated and observed distribution is 0.02945564832908269.\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "--- Node Auth Service\n",
-      "- The MSE is 0.014409310390136789.\n",
-      "- The NMSE is 0.5462705245729054.\n",
-      "- The R2 coefficient is 0.7014376755538484.\n",
-      "- The normalized CRPS is 0.3027521836699161.\n",
+      "- The MSE is 0.014414661073635477.\n",
+      "- The NMSE is 0.5468392311614381.\n",
+      "- The R2 coefficient is 0.7008581544246362.\n",
+      "- The normalized CRPS is 0.3027241612157213.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Caching Service\n",
-      "- The MSE is 0.023158343257168923.\n",
-      "- The NMSE is 0.8427188063479955.\n",
-      "- The R2 coefficient is 0.28958072725807565.\n",
-      "- The normalized CRPS is 0.46295292151719725.\n",
+      "- The MSE is 0.02313891078313382.\n",
+      "- The NMSE is 0.8424878275526769.\n",
+      "- The R2 coefficient is 0.2901027347604022.\n",
+      "- The normalized CRPS is 0.46256528437410704.\n",
       "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n",
       "\n",
       "--- Node Order Service\n",
-      "- The MSE is 0.014397735388957358.\n",
-      "- The NMSE is 0.5489274923469949.\n",
-      "- The R2 coefficient is 0.6986121921831244.\n",
-      "- The normalized CRPS is 0.304260526459383.\n",
+      "- The MSE is 0.014401944309636233.\n",
+      "- The NMSE is 0.5486734295896927.\n",
+      "- The R2 coefficient is 0.6988465430408508.\n",
+      "- The normalized CRPS is 0.3034588760610286.\n",
       "The estimated CRPS indicates a good model performance.\n",
       "\n",
       "--- Node Product Service\n",
-      "- The MSE is 0.020441689979414278.\n",
-      "- The NMSE is 0.6493818414893976.\n",
-      "- The R2 coefficient is 0.5781215893815632.\n",
-      "- The normalized CRPS is 0.3639297409683348.\n",
+      "- The MSE is 0.02044996767396589.\n",
+      "- The NMSE is 0.6494615124205818.\n",
+      "- The R2 coefficient is 0.5780071956796627.\n",
+      "- The normalized CRPS is 0.3639706645045675.\n",
       "The estimated CRPS indicates only a fair model performance. Note, however, that a high CRPS could also result from a small signal to noise ratio.\n",
       "\n",
       "--- Node API\n",
-      "- The MSE is 0.0144834135564434.\n",
-      "- The NMSE is 0.20978740048124056.\n",
-      "- The R2 coefficient is 0.9559825162420468.\n",
-      "- The normalized CRPS is 0.11587627273143704.\n",
+      "- The MSE is 0.014487845771135594.\n",
+      "- The NMSE is 0.20979051156046288.\n",
+      "- The R2 coefficient is 0.9559736211348916.\n",
+      "- The normalized CRPS is 0.11621315648767647.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node www\n",
-      "- The MSE is 0.011043981547566816.\n",
-      "- The NMSE is 0.13770141930364738.\n",
-      "- The R2 coefficient is 0.9810206381989447.\n",
-      "- The normalized CRPS is 0.07781201996784066.\n",
+      "- The MSE is 0.011042719234612093.\n",
+      "- The NMSE is 0.13763928951285248.\n",
+      "- The R2 coefficient is 0.9810497649925918.\n",
+      "- The normalized CRPS is 0.07784582906798679.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "--- Node Website\n",
-      "- The MSE is 0.020771984383852027.\n",
-      "- The NMSE is 0.18531485117535818.\n",
-      "- The R2 coefficient is 0.9656314099183605.\n",
-      "- The normalized CRPS is 0.1022386286114898.\n",
+      "- The MSE is 0.02078048115561805.\n",
+      "- The NMSE is 0.18515791235912968.\n",
+      "- The R2 coefficient is 0.9656876357594084.\n",
+      "- The normalized CRPS is 0.10198831290029271.\n",
       "The estimated CRPS indicates a very good model performance.\n",
       "\n",
       "==== Evaluation of Invertible Functional Causal Model Assumption ====\n",
@@ -1035,10 +1035,10 @@
       "--- The model assumption for node www is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Website is not rejected with a p-value of 0.2889508066141974 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Website is not rejected with a p-value of 0.6537764264060258 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
-      "--- The model assumption for node Auth Service is not rejected with a p-value of 0.5622900739464742 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Auth Service is not rejected with a p-value of 0.46869441012563545 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "--- The model assumption for node API is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
@@ -1047,7 +1047,7 @@
       "--- The model assumption for node Product Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might not be valid. This is, the relationship cannot be represent with this type of mechanism or there is a hidden confounder between the node and its parents.\n",
       "\n",
-      "--- The model assumption for node Order Service is not rejected with a p-value of 1.0 (after potential adjustment) and a significance level of 0.05.\n",
+      "--- The model assumption for node Order Service is not rejected with a p-value of 0.7488000687727592 (after potential adjustment) and a significance level of 0.05.\n",
       "This implies that the model assumption might be valid.\n",
       "\n",
       "--- The model assumption for node Caching Service is rejected with a p-value of 0.0 (after potential adjustment) and a significance level of 0.05.\n",
@@ -1056,7 +1056,7 @@
       "Note that these results are based on statistical independence tests, and the fact that the assumption was not rejected does not necessarily imply that it is correct. There is just no evidence against it.\n",
       "\n",
       "==== Evaluation of Generated Distribution ====\n",
-      "The overall average KL divergence between the generated and observed distribution is 0.10478608550501169\n",
+      "The overall average KL divergence between the generated and observed distribution is 0.11130986058049971\n",
       "The estimated KL divergence indicates an overall very good representation of the data distribution.\n",
       "\n",
       "==== Evaluation of the Causal Graph Structure ====\n",
@@ -1065,7 +1065,7 @@
       "+-------------------------------------------------------------------------------------------------------+\n",
       "| The given DAG is informative because 0 / 50 of the permutations lie in the Markov                     |\n",
       "| equivalence class of the given DAG (p-value: 0.00).                                                   |\n",
-      "| The given DAG violates 4/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |\n",
+      "| The given DAG violates 2/63 LMCs and is better than 100.0% of the permuted DAGs (p-value: 0.00).      |\n",
       "| Based on the provided significance level (0.2) and because the DAG is informative,                    |\n",
       "| we do not reject the DAG.                                                                             |\n",
       "+-------------------------------------------------------------------------------------------------------+\n",
@@ -1113,10 +1113,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:15:08.890634Z",
-     "iopub.status.busy": "2024-10-20T00:15:08.890244Z",
-     "iopub.status.idle": "2024-10-20T00:15:08.902032Z",
-     "shell.execute_reply": "2024-10-20T00:15:08.901538Z"
+     "iopub.execute_input": "2024-10-22T01:41:40.197119Z",
+     "iopub.status.busy": "2024-10-22T01:41:40.196914Z",
+     "iopub.status.idle": "2024-10-22T01:41:40.208825Z",
+     "shell.execute_reply": "2024-10-22T01:41:40.208179Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1207,10 +1207,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:15:08.903876Z",
-     "iopub.status.busy": "2024-10-20T00:15:08.903496Z",
-     "iopub.status.idle": "2024-10-20T00:15:08.951444Z",
-     "shell.execute_reply": "2024-10-20T00:15:08.950819Z"
+     "iopub.execute_input": "2024-10-22T01:41:40.211060Z",
+     "iopub.status.busy": "2024-10-22T01:41:40.210600Z",
+     "iopub.status.idle": "2024-10-22T01:41:40.261554Z",
+     "shell.execute_reply": "2024-10-22T01:41:40.260884Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1258,10 +1258,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:15:08.953608Z",
-     "iopub.status.busy": "2024-10-20T00:15:08.953250Z",
-     "iopub.status.idle": "2024-10-20T00:16:02.083392Z",
-     "shell.execute_reply": "2024-10-20T00:16:02.082622Z"
+     "iopub.execute_input": "2024-10-22T01:41:40.263721Z",
+     "iopub.status.busy": "2024-10-22T01:41:40.263361Z",
+     "iopub.status.idle": "2024-10-22T01:42:34.713972Z",
+     "shell.execute_reply": "2024-10-22T01:42:34.713386Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1301,10 +1301,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:16:02.085918Z",
-     "iopub.status.busy": "2024-10-20T00:16:02.085464Z",
-     "iopub.status.idle": "2024-10-20T00:16:02.473128Z",
-     "shell.execute_reply": "2024-10-20T00:16:02.472485Z"
+     "iopub.execute_input": "2024-10-22T01:42:34.716489Z",
+     "iopub.status.busy": "2024-10-22T01:42:34.716071Z",
+     "iopub.status.idle": "2024-10-22T01:42:34.875835Z",
+     "shell.execute_reply": "2024-10-22T01:42:34.875153Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1313,7 +1313,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -1349,10 +1349,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:16:02.475170Z",
-     "iopub.status.busy": "2024-10-20T00:16:02.474789Z",
-     "iopub.status.idle": "2024-10-20T00:16:02.488659Z",
-     "shell.execute_reply": "2024-10-20T00:16:02.488054Z"
+     "iopub.execute_input": "2024-10-22T01:42:34.878024Z",
+     "iopub.status.busy": "2024-10-22T01:42:34.877824Z",
+     "iopub.status.idle": "2024-10-22T01:42:34.892174Z",
+     "shell.execute_reply": "2024-10-22T01:42:34.891544Z"
     }
    },
    "outputs": [
@@ -1503,10 +1503,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:16:02.490679Z",
-     "iopub.status.busy": "2024-10-20T00:16:02.490319Z",
-     "iopub.status.idle": "2024-10-20T00:16:02.505938Z",
-     "shell.execute_reply": "2024-10-20T00:16:02.505443Z"
+     "iopub.execute_input": "2024-10-22T01:42:34.894438Z",
+     "iopub.status.busy": "2024-10-22T01:42:34.894191Z",
+     "iopub.status.idle": "2024-10-22T01:42:34.912181Z",
+     "shell.execute_reply": "2024-10-22T01:42:34.911621Z"
     }
    },
    "outputs": [
@@ -1550,16 +1550,16 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:16:02.507842Z",
-     "iopub.status.busy": "2024-10-20T00:16:02.507468Z",
-     "iopub.status.idle": "2024-10-20T00:17:55.847934Z",
-     "shell.execute_reply": "2024-10-20T00:17:55.847183Z"
+     "iopub.execute_input": "2024-10-22T01:42:34.914410Z",
+     "iopub.status.busy": "2024-10-22T01:42:34.914010Z",
+     "iopub.status.idle": "2024-10-22T01:44:26.789938Z",
+     "shell.execute_reply": "2024-10-22T01:44:26.789285Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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TnTt31mkPDg7Gzp079RKKiIiIqDSVuAAq6nH3ChUqQKPR6CUUERERUWkqcQHk7e2N1atX67SvWrUKDRo00EsoIiIiotJU4kHQkyZNQs+ePXHu3Dm0b98eABATE4OVK1fil19+0XtAIiIiIn0rcQHUtWtXrFu3DrNmzcKvv/4KS0tL+Pj4YNu2bWjTpk1pZCQiIiLSK5UQQsgdQmk0Gg1sbGyQmZkJa2truePQ01j3kTsBoFkldwIiIiqh554HCAAePHiA1atXIzs7Gx07doSXl5e+chERERGVmmIXQKGhoXj48CG++eYbAEBubi6aN2+OEydOwMrKCuPGjcPff/+NFi1alFpYIiIiIn0o9lNgW7duRceOHaXtn3/+GZcuXcLZs2dx584dvPHGG5gxY0aphCQiIiLSp2IXQJcuXdJ6zH3r1q343//+h1q1akGlUmH06NE4fPhwqYQkIiIi0qdiF0BqtRpPjpfet28fmjdvLm3b2trizp07+k1HREREVAqKXQDVr18fGzZsAAAcP34cly5dQrt27aT9Fy9ehIODg/4TEhEREelZsQdBjxs3Dn369MHGjRtx/PhxdO7cGe7u7tL+TZs2oVmzZqUSkoiIiEifit0D9Prrr2PTpk3w8fHBRx99pLMchpWVFYYPH673gERERET6xokQC8GJEA0IJ0IkIqLnUOLFUImIiIgMHQsgIiIiMjqyFkA7d+5E165d4ezsDJVKhXXr1j3zc2JjY/HSSy/B3Nwcnp6eWLZsmc4xERERcHNzg4WFBfz9/XHgwAH9hyciIiKDJWsBlJ2djcaNGyMiIqJYx6ekpKBLly5o164dEhMT8eGHH2Lw4MHYsmWLdMzq1asRGhqKKVOmICEhAY0bN0ZgYCCuX79eWm+DiIiIDIxiBkGrVCr8/vvv6NGjR5HHjB8/Hhs3bsSxY8ektj59+iAjIwObN28GAPj7++Pll1/GwoULAQD5+flwdXXFyJEjMWHChGJl4SBoA8JB0ERE9BxK3AOUnp6Od955B87OzjA1NYWJiYnWqzTt3bsXAQEBWm2BgYHYu3cvgIIFWg8dOqR1jFqtRkBAgHRMYXJycqDRaLReREREVH4VeyLEx959911cunQJkyZNgpOTE1QqVWnkKlRaWprObNMODg7QaDS4f/8+7ty5g7y8vEKPOXXqVJHnnT17NqZOnVoqmYmIiEh5SlwAxcfHY9euXfD19S2FOPIICwtDaGiotK3RaODq6ipjIiIiIipNJS6AXF1dIdewIUdHR6Snp2u1paenw9raGpaWltJtuMKOcXR0LPK85ubmMDc3L5XMREREpDwlHgMUHh6OCRMm4MKFC6UQ5+latGiBmJgYrba///4bLVq0AACYmZmhadOmWsfk5+cjJiZGOoaIiIioxD1AvXv3xr1791C7dm1YWVmhQoUKWvtv375d7HNlZWUhOTlZ2k5JSUFiYiLs7e1Rs2ZNhIWF4erVq1i+fDkAYNiwYVi4cCHGjRuHgQMHYvv27VizZg02btwonSM0NBQhISHw8/NDs2bNEB4ejuzsbAwYMKCkb5WIiIjKqRIXQOHh4Xr7xw8ePIh27dpJ24/H4YSEhGDZsmVITU3FpUuXpP3u7u7YuHEjPvroI3z99deoUaMGlixZgsDAQOmY3r1748aNG5g8eTLS0tLg6+uLzZs36wyMJiIiIuOlmHmAlITzABkQzgNERETPocQ9QACQl5eHdevW4eTJkwCAhg0bolu3bqU+DxARERGRPpS4AEpOTkbnzp1x9epV1K1bF0DBPDqurq7YuHEjateurfeQRERERPpU4qfARo0ahdq1a+Py5ctISEhAQkICLl26BHd3d4waNao0MhIRERHpVYl7gOLi4rBv3z7Y29tLbVWqVMGcOXPwyiuv6DUcERERUWkocQ+Qubk57t69q9OelZUFMzMzvYQiIiIiKk0lLoBee+01DB06FPv374cQAkII7Nu3D8OGDUO3bt1KIyMRERGRXpW4AFqwYAFq166NFi1awMLCAhYWFnjllVfg6emJr7/+ujQyEhEREelViccA2draYv369Th79qy0wnr9+vXh6emp93BEREREpeG55gECAC8vL3h5eekzCxEREVGZKFYBFBoaiunTp6NixYrSchVFmT9/vl6CEREREZWWYhVAhw8fxsOHD6WPiYiIiAwZ1wIrBNcCMyBcC4yIiJ5DiZ8CGzhwYKHzAGVnZ2PgwIF6CUVERERUmkpcAEVHR+P+/fs67ffv38fy5cv1EoqIiIioNBX7KTCNRiNNfHj37l1YWFhI+/Ly8rBp0yZUr169VEISERER6VOxCyBbW1uoVCqoVCrUqVNHZ79KpcLUqVP1Go6IiIioNBS7ANqxYweEEGjfvj1+++03rcVQzczMUKtWLTg7O5dKSCIiIiJ9KnYB1KZNGwBASkoKatasCZVKVWqhiIiIiEpTiWeCvnjxIi5evFjk/ldfffWFAhERERGVthIXQG3bttVpe7I3KC8v74UCEREREZW2Ej8Gf+fOHa3X9evXsXnzZrz88svYunVraWQkIiIi0qsS9wDZ2NjotHXs2BFmZmYIDQ3FoUOH9BKMiIiIqLSUuAeoKA4ODjh9+rS+TkdERERUakrcA3TkyBGtbSEEUlNTMWfOHPj6+uorFxEREVGpKXEB5OvrC5VKhf+uodq8eXMsXbpUb8GIiIiISkuJC6CUlBStbbVajWrVqmktjUFERESkZCUugGrVqlUaOYiIiIjKzHMNgo6JicFrr72G2rVro3bt2njttdewbds2fWcjIiIiKhUlLoC+/fZbBAUFoXLlyhg9ejRGjx4Na2trdO7cGREREaWRkYiIiEivVOK/o5mfoUaNGpgwYQI++OADrfaIiAjMmjULV69e1WtAOWg0GtjY2CAzMxPW1tZyx6Gnse4jdwJAs0ruBEREVEIl7gHKyMhAUFCQTnunTp2QmZmpl1BEREREpanEBVC3bt3w+++/67SvX78er732ml5CEREREZWmYj0FtmDBAunjBg0aYObMmYiNjUWLFi0AAPv27cPu3bsxZsyY0klJREREpEfFGgPk7u5evJOpVDh//vwLh5IbxwAZEI4BIiKi51CsW2ApKSnFej1v8RMREQE3NzdYWFjA398fBw4cKPLYtm3bQqVS6by6dOkiHfPuu+/q7C9s3BIREREZpxJPhKhvq1evRmhoKCIjI+Hv74/w8HAEBgbi9OnTqF69us7xa9euRW5urrR969YtNG7cGG+88YbWcUFBQfjhhx+kbXNz89J7E0RERGRQilUAhYaGYvr06ahYsSJCQ0Ofeuz8+fNLFGD+/PkYMmQIBgwYAACIjIzExo0bsXTpUkyYMEHneHt7e63tVatWwcrKSqcAMjc3h6OjY4myEBERkXEoVgF0+PBhPHz4EACQkJAAlUpV6HFFtRclNzcXhw4dQlhYmNSmVqsREBCAvXv3FuscUVFR6NOnDypWrKjVHhsbi+rVq8POzg7t27fHjBkzUKVKlULPkZOTg5ycHGlbo9GU6H0QERGRYSlWAbRjxw7p49jYWL394zdv3kReXh4cHBy02h0cHHDq1Klnfv6BAwdw7NgxREVFabUHBQWhZ8+ecHd3x7lz5/DJJ58gODgYe/fuhYmJic55Zs+ejalTp77YmyEiIiKDUaIxQA8fPoSlpSUSExPRqFGj0spUbFFRUfD29kazZs202vv0+ffJIG9vb/j4+KB27dqIjY1Fhw4ddM4TFhamdWtPo9HA1dW19IITERGRrEo0EWKFChVQs2ZN5OXl6eUfr1q1KkxMTJCenq7Vnp6e/szxO9nZ2Vi1ahUGDRr0zH/Hw8MDVatWRXJycqH7zc3NYW1trfUiIiKi8qvEM0F/+umn+OSTT3D79u0X/sfNzMzQtGlTxMTESG35+fmIiYmRJlksyi+//IKcnBy8/fbbz/x3rly5glu3bsHJyemFMxMREZHhK/Fj8AsXLkRycjKcnZ1Rq1YtncHHCQkJJTpfaGgoQkJC4Ofnh2bNmiE8PBzZ2dnSU2H9+/eHi4sLZs+erfV5UVFR6NGjh87A5qysLEydOhW9evWCo6Mjzp07h3HjxsHT0xOBgYElfbtERERUDpW4AOrevXuJn/Z6mt69e+PGjRuYPHky0tLS4Ovri82bN0sDoy9dugS1Wruj6vTp04iPj8fWrVt1zmdiYoIjR44gOjoaGRkZcHZ2RqdOnTB9+nTOBUREREQAirkUhrHhUhgGhEthEBHRcyjxGCAPDw/cunVLpz0jIwMeHh56CUVERERUmkpcAF24cKHQp8BycnJw5coVvYQiIiIiKk3FHgP0xx9/SB9v2bIFNjY20nZeXh5iYmKKvWo8ERERkZyKXQD16NFD+jgkJERrX4UKFeDm5oYvv/xSb8GIiIiISkuxC6D8/HwAgLu7O/755x9UrVq11EIRERERlaYSjwGaOnUqKleurNOem5uL5cuX6yUUERERUWkq8WPwJiYmSE1NRfXq1bXab926herVq+ttmQw58TF4A8LH4ImI6DmUuAdICFHoRIhXrlzRGhhNREREpFTFHgPUpEkTqFQqqFQqdOjQAaam/35qXl4eUlJSEBQUVCohiYiIiPSpxE+BJSYmIjAwEJUqVZL2mZmZwc3NDb169dJ7QCIiIiJ9K3YBNGXKFACAm5sbevfuDQsLC51jjh07hkaNGukvHREREVEpKPEYoJCQEK3i5+7du1i8eDGaNWuGxo0b6zUcERERUWkocQH02M6dOxESEgInJyd88cUXaN++Pfbt26fPbERERESloti3wAAgLS0Ny5YtQ1RUFDQaDd58803k5ORg3bp1aNCgQWllJCIiItKrYvcAde3aFXXr1sWRI0cQHh6Oa9eu4ZtvvinNbERERESlotg9QH/99RdGjRqF999/H15eXqWZiYiIiKhUFbsHKD4+Hnfv3kXTpk3h7++PhQsX4ubNm6WZjYiIiKhUFLsAat68Ob7//nukpqbivffew6pVq+Ds7Iz8/Hz8/fffuHv3bmnmJCIiItKbEq8F9qTTp08jKioKP/74IzIyMtCxY0f88ccf+swnC64FZkC4FhgRET2H534MHgDq1q2LefPm4cqVK1i5cqW+MhERERGVqhfqASqv2ANkQNgDREREz+GFeoCIiIiIDBELICIiIjI6LICIiIjI6LAAIiIiIqPDAoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOCyAiIiIyOiyAiIiIyOiwACIiIiKjo4gCKCIiAm5ubrCwsIC/vz8OHDhQ5LHLli2DSqXSellYWGgdI4TA5MmT4eTkBEtLSwQEBODs2bOl/TaIiIjIQMheAK1evRqhoaGYMmUKEhIS0LhxYwQGBuL69etFfo61tTVSU1Ol18WLF7X2z5s3DwsWLEBkZCT279+PihUrIjAwEA8ePCjtt0NEREQGQPYCaP78+RgyZAgGDBiABg0aIDIyElZWVli6dGmRn6NSqeDo6Ci9HBwcpH1CCISHh2PixIno3r07fHx8sHz5cly7dg3r1q0rg3dERERESidrAZSbm4tDhw4hICBAalOr1QgICMDevXuL/LysrCzUqlULrq6u6N69O44fPy7tS0lJQVpamtY5bWxs4O/vX+Q5c3JyoNFotF5ERERUfslaAN28eRN5eXlaPTgA4ODggLS0tEI/p27duli6dCnWr1+Pn376Cfn5+WjZsiWuXLkCANLnleScs2fPho2NjfRydXV90bdGRERECib7LbCSatGiBfr37w9fX1+0adMGa9euRbVq1bBo0aLnPmdYWBgyMzOl1+XLl/WYmIiIiJRG1gKoatWqMDExQXp6ulZ7eno6HB0di3WOChUqoEmTJkhOTgYA6fNKck5zc3NYW1trvYiIiKj8krUAMjMzQ9OmTRETEyO15efnIyYmBi1atCjWOfLy8nD06FE4OTkBANzd3eHo6Kh1To1Gg/379xf7nERERFS+mcodIDQ0FCEhIfDz80OzZs0QHh6O7OxsDBgwAADQv39/uLi4YPbs2QCAadOmoXnz5vD09ERGRgY+//xzXLx4EYMHDwZQ8ITYhx9+iBkzZsDLywvu7u6YNGkSnJ2d0aNHD7neJhERESmI7AVQ7969cePGDUyePBlpaWnw9fXF5s2bpUHMly5dglr9b0fVnTt3MGTIEKSlpcHOzg5NmzbFnj170KBBA+mYcePGITs7G0OHDkVGRgZatWqFzZs360yYSERERMZJJYQQcodQGo1GAxsbG2RmZnI8kNJZ95E7AaBZJXcCIiIqIYN7CoyIiIjoRbEAIiIiIqPDAoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOCyAiIiIyOiyAiIiIyOiwACIiIiKjwwKIiIiIjA4LICIiIjI6LICIiIjI6LAAIiIiIqPDAoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOCyAiIiIyOiyAiIiIyOiwACIiIiKjwwKIiIiIjA4LICIiIjI6LICIiIjI6LAAIiIiIqPDAoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOCyAiIiIyOiyAiIiIyOiwACIiIiKjo4gCKCIiAm5ubrCwsIC/vz8OHDhQ5LHff/89WrduDTs7O9jZ2SEgIEDn+HfffRcqlUrrFRQUVNpvg4iIiAyE7AXQ6tWrERoaiilTpiAhIQGNGzdGYGAgrl+/XujxsbGx6Nu3L3bs2IG9e/fC1dUVnTp1wtWrV7WOCwoKQmpqqvRauXJlWbwdIiIiMgAqIYSQM4C/vz9efvllLFy4EACQn58PV1dXjBw5EhMmTHjm5+fl5cHOzg4LFy5E//79ART0AGVkZGDdunXFypCTk4OcnBxpW6PRwNXVFZmZmbC2ti75m6KyY91H7gSAZpXcCYiIqIRk7QHKzc3FoUOHEBAQILWp1WoEBARg7969xTrHvXv38PDhQ9jb22u1x8bGonr16qhbty7ef/993Lp1q8hzzJ49GzY2NtLL1dX1+d4QERERGQRZC6CbN28iLy8PDg4OWu0ODg5IS0sr1jnGjx8PZ2dnrSIqKCgIy5cvR0xMDObOnYu4uDgEBwcjLy+v0HOEhYUhMzNTel2+fPn53xQREREpnqncAV7EnDlzsGrVKsTGxsLCwkJq79Pn39si3t7e8PHxQe3atREbG4sOHTronMfc3Bzm5uZlkpmIiIjkJ2sPUNWqVWFiYoL09HSt9vT0dDg6Oj71c7/44gvMmTMHW7duhY+Pz1OP9fDwQNWqVZGcnPzCmYmIiMjwyVoAmZmZoWnTpoiJiZHa8vPzERMTgxYtWhT5efPmzcP06dOxefNm+Pn5PfPfuXLlCm7dugUnJye95CYiIiLDJvtj8KGhofj+++8RHR2NkydP4v3330d2djYGDBgAAOjfvz/CwsKk4+fOnYtJkyZh6dKlcHNzQ1paGtLS0pCVlQUAyMrKwscff4x9+/bhwoULiImJQffu3eHp6YnAwEBZ3iMREREpi+xjgHr37o0bN25g8uTJSEtLg6+vLzZv3iwNjL506RLU6n/rtO+++w65ubn43//+p3WeKVOm4LPPPoOJiQmOHDmC6OhoZGRkwNnZGZ06dcL06dM5zoeIiIgAKGAeICXSaDSwsbHhPECGgPMAERHRc5D9FhgRERFRWWMBREREREaHBRAREREZHRZAREREZHRYABEREZHRYQFERERERocFEBERERkdFkBERERkdFgAERERkdFhAURERERGhwUQERERGR0WQERERGR0WAARERGR0WEBREREREaHBRAREREZHRZAREREZHRYABEREZHRYQFERERERocFEBERERkdFkBERERkdFgAERERkdFhAURERERGhwUQERERGR0WQERERGR0WAARERGR0WEBREREREaHBRAREREZHRZAREREZHRYABEREZHRYQFERERERocFEBERERkdFkBERERkdFgAERERkdFRRAEUEREBNzc3WFhYwN/fHwcOHHjq8b/88gvq1asHCwsLeHt7Y9OmTVr7hRCYPHkynJycYGlpiYCAAJw9e7Y03wIREREZENkLoNWrVyM0NBRTpkxBQkICGjdujMDAQFy/fr3Q4/fs2YO+ffti0KBBOHz4MHr06IEePXrg2LFj0jHz5s3DggULEBkZif3796NixYoIDAzEgwcPyuptERERkYKphBBCzgD+/v54+eWXsXDhQgBAfn4+XF1dMXLkSEyYMEHn+N69eyM7Oxt//vmn1Na8eXP4+voiMjISQgg4OztjzJgxGDt2LAAgMzMTDg4OWLZsGfr06fPMTBqNBjY2NsjMzIS1tbWe3qnxuJebj/pfpQEATn7kCCuzUqyzrZ/9/7PUaVbJnUB2Zfr/3Ajw60lU+mT9rsrNzcWhQ4cQEBAgtanVagQEBGDv3r2Ffs7evXu1jgeAwMBA6fiUlBSkpaVpHWNjYwN/f/8iz5mTkwONRqP1UqJ7ufmoNfcaas29hnu5+XLHoTLC/+/6w6+lfhnC19MQMgLMKQdTOf/xmzdvIi8vDw4ODlrtDg4OOHXqVKGfk5aWVujxaWlp0v7HbUUd81+zZ8/G1KlTn+s9kMzY+0JESvEiPdIVzIBxcwo+dnoXeJj7/Ofiz8VikbUAUoqwsDCEhoZK2xqNBq6urjImKpyVmRoXxzvLHeOZDCWnoeDXU38M5WvJnPpTphlfpPDIzQf+/7YnUpcBvO1Z6mQtgKpWrQoTExOkp6drtaenp8PR0bHQz3F0dHzq8Y//m56eDicnJ61jfH19Cz2nubk5zM3Nn/dtEBk9Q/hFSKRkhvI9ZCg5i0PWEtPMzAxNmzZFTEyM1Jafn4+YmBi0aNGi0M9p0aKF1vEA8Pfff0vHu7u7w9HRUesYjUaD/fv3F3lOIiIiMi6y3wILDQ1FSEgI/Pz80KxZM4SHhyM7OxsDBgwAAPTv3x8uLi6YPXs2AGD06NFo06YNvvzyS3Tp0gWrVq3CwYMHsXjxYgCASqXChx9+iBkzZsDLywvu7u6YNGkSnJ2d0aNHD7neJhERESmI7AVQ7969cePGDUyePBlpaWnw9fXF5s2bpUHMly5dglr9b0dVy5YtsWLFCkycOBGffPIJvLy8sG7dOjRq1Eg6Zty4ccjOzsbQoUORkZGBVq1aYfPmzbCwsCjz90dERETKI/s8QErEeYCIiIjKNw4zJyIiIqPDAoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOCyAiIiIyOiyAiIiIyOiwACIiIiKjwwKIiIiIjA4LICIiIjI6si+GqkSPl0fTaDQyJyEiIqKSqly5MlQq1VOPYQFUiLt37wIAXF1dZU5CREREJVWcxcy5Gnwh8vPzce3atWJVkGVNo9HA1dUVly9fVvRK9cypX4aQ0xAyAsypb4aQ0xAyAsypT+wBek5qtRo1atSQO8ZTWVtbK/bCexJz6pch5DSEjABz6psh5DSEjABzlhUOgiYiIiKjwwKIiIiIjA4LIANjbm6OKVOmwNzcXO4oT8Wc+mUIOQ0hI8Cc+mYIOQ0hI8CcZY2DoImIiMjosAeIiIiIjA4LICIiIjI6LICIiIjI6LAAIiIiIqPDAoj05tGjR9i2bRsWLVokLSdy7do1ZGVlyZxMV25uLk6fPo1Hjx7JHaVYcnNzFfl1NBSGdG0SUdlgAUR6cfHiRXh7e6N79+4YMWIEbty4AQCYO3cuxo4dK3O6f927dw+DBg2ClZUVGjZsiEuXLgEARo4ciTlz5sicrsAPP/yAkSNH4ueffwYAhIWFoXLlyrCxsUHHjh1x69YtmRMW7vz58zh+/Djy8/PljqLFUK7NJ/+/Xr58GZMnT8bHH3+MXbt2yZiqcLt27cLbb7+NFi1a4OrVqwCAH3/8EfHx8TIn+5chZDQUS5cuRUpKitwx9E+QIn300UfFeilF9+7dxdtvvy1ycnJEpUqVxLlz54QQQuzYsUN4enrKnO5fo0aNEk2bNhW7du0SFStWlHKuW7dO+Pr6ypxOiBkzZghLS0sREBAg7O3txbBhw4Sjo6OYM2eOmDdvnqhRo4YYNmyYrBlzc3PF5MmTxWuvvSZmzJghHj16JPr06SPUarVQq9Wifv36IiUlRdaMT1L6tXnkyBFRq1YtoVarRd26dcXhw4eFg4ODqFSpkrC2thYmJibi999/lzum5NdffxWWlpZi8ODBwtzcXPp6fvPNNyI4OFjmdAUMIeNjycnJ4tNPPxV9+vQR6enpQgghNm3aJI4dOyZzsn95enoKtVotXF1dxdtvvy2+//57cfbsWbljvTAWQArVtm3bZ77atWsnd0yJvb29OHXqlBBCaP2SSUlJEZaWlnJG01KzZk2xd+9eIYR2zrNnz4rKlSvLGU0IUfCDZsWKFUIIIf755x+hVqvFr7/+Ku3ftGmTqFmzplzxhBBChIaGimrVqonBgwcLDw8P0a1bN1G3bl2xatUqsWbNGuHt7S3eeustWTM+SenXZlBQkHjttddEfHy8eO+994SLi4sYOHCgyMvLE3l5eWL48OHC399f7pgSX19fER0dLYTQ/nomJCQIBwcHOaNJDCGjEELExsZKf/CYmZlJOWfPni169eolczptV65cET/99JMYOnSoqFu3rlCr1cLFxUX069dP7mjPjQUQ6YWtra04fvy4EEL7B86uXbtE9erV5YymxdLSUsr2ZM7ExERhbW0tZzQhhBBmZmbi0qVLWtuPf3kLUfBDqEKFCnJEk9SsWVNs3LhRCCHE6dOnhUqlEps2bZL2x8bGChcXF7ni6VD6tVmlShWRlJQkhBDi7t27QqVSiYMHD0r7T548KWxsbGRKp8vS0lLq4Xvy63nu3Dlhbm4uY7J/GUJGIYRo3ry5+PLLL4UQ2jn379+vqO+hJ2VnZ4vNmzeLkJAQYWpqKkxMTOSO9Nw4BkjBNBoN/v77b2zcuFEat6BUnTp1Qnh4uLStUqmQlZWFKVOmoHPnzvIF+w8/Pz9s3LhR2lapVACAJUuWoEWLFnLFkjx8+FBrenkzMzNUqFBB2jY1NUVeXp4c0STXrl1D48aNAQB16tSBubk5PD09pf116tRBWlqaXPF0KP3avH37NhwdHQEAlSpVQsWKFWFnZyftt7OzkwZuK4GjoyOSk5N12uPj4+Hh4SFDIl2GkBEAjh49itdff12nvXr16rh586YMiQq3detWfPLJJ2jZsiWqVKmCsLAw2NnZ4ddff1X876anMZU7ABUuMTERnTt3ln6RVK5cGWvWrEFgYKDMyQr35ZdfIjAwEA0aNMCDBw/w1ltv4ezZs6hatSpWrlwpdzzJrFmzEBwcjBMnTuDRo0f4+uuvceLECezZswdxcXFyxwMAnDhxQvr/LoTAqVOnpKeVlPBDMS8vT6coMzExkbbVajWEglbYMYRr83EhXtS2kgwZMgSjR4/G0qVLoVKpcO3aNezduxdjx47FpEmT5I4HwDAyAoCtrS1SU1Ph7u6u1X748GG4uLjIlEpXUFAQqlWrhjFjxmDTpk2wtbWVO5JecC0whQoMDERWVha++OILWFhYYPr06Th69CjOnj0rd7QiPXr0CKtXr0ZSUhKysrLw0ksvoV+/frC0tJQ7mpZz585hzpw5WjnHjx8Pb29vuaNBrVZDpVIVWkA8blepVLL2AqnVakRHR8PGxgYA0LdvX4SHh8PBwQEAkJGRgQEDBsjeU/UkJV+barUawcHBUs/fhg0b0L59e1SsWBEAkJOTg82bNyvm6ymEwKxZszB79mzcu3cPQMHimGPHjsX06dNlTlfAEDICwNixY7F//3788ssvqFOnDhISEpCeno7+/fujf//+mDJlitwRAQDh4eHYuXMndu7cCXNzc7Rp0wZt27ZF27ZtUadOHbnjPTcWQApVtWpVbN26FS+99BKAgl8q9vb2yMjIgLW1tczpqLRcvHixWMfVqlWrlJMUTa1+9p1zuYs0QzJgwIBiHffDDz+UcpKSyc3NRXJyMrKystCgQQNUqlRJ7kg6lJ4xNzcXI0aMwLJly5CXlyfd4n7rrbewbNkyrZ5VpTh69Cji4uKwfft2/Pnnn6hevTquXLkid6znwgJIodRqNdLS0lC9enWprXLlyjhy5IhOd6kSzJ49Gw4ODhg4cKBW+9KlS3Hjxg2MHz9epmTaNm3aBBMTE51biVu2bEF+fj6Cg4NlSkalxVCuTUORmZmJvLw82Nvba7Xfvn0bpqamivgDzRAyPuny5cs4evQosrKy0KRJE3h5eckdSYcQAocPH0ZsbCx27NiB+Ph43L17F97e3jh8+LDc8Z4LB0Er2IkTJ3DkyBHpJYTAyZMntdqUYtGiRahXr55Oe8OGDREZGSlDosJNmDCh0J4JIQQmTJggQ6LCnT17Fl988QU++OADjBw5EvPnz8f58+fljmWQDOXaBAquw5s3byp2sksA6NOnD1atWqXTvmbNGvTp00eGRLoMISMATJs2Dffu3YOrqys6d+6MN998E15eXrh//z6mTZsmdzxJ165dUaVKFTRr1gw///wz6tSpg+joaNy8edNgix8AnAhRqVQqlVCr1UKlUhX5UqvVcseUmJubi/Pnz+u0K+2xUwsLi0In6UtJSRFWVlZlH6gQs2bNEqampkKtVgtHR0fh4OAg1Gq1qFChgvj888/ljieJiYkRI0aMEF26dBGvvfaaGDlypIiLi5M7lg5DuDZTU1PFO++8I2xsbKQJJW1tbcWAAQNEWlqa3PG02NnZiRMnTui0nzx5Utjb28uQSJchZBRCCLVaLU1++KSbN28q6uf72LFjxYYNG0RGRobcUfSKT4EpVHGmHVfSo7Gurq7YvXu3zu253bt3w9nZWaZUumxsbHD+/Hm4ublptScnJ0uDTuW0Y8cOTJw4EZMmTcLo0aOlx6Fv376N8PBwTJgwAc2aNcOrr74qa85hw4Zh8eLFsLOzQ506dSCEwJ49exAREYHhw4fjm2++kTXfk5R+bWo0GrRs2RJZWVkYMGAA6tWrByEETpw4gZUrVyI+Ph4JCQmKGb+Sk5NT6Bp6Dx8+xP3792VIpMsQMgKQHmr4r6SkJJ3bd3KaPn06LCws5I6hfzIXYFRCGo1GLFq0SDRr1kxRfyHMnTtXVKlSRSxdulRcuHBBXLhwQURFRYkqVaqIWbNmyR1PMnToUOHt7S2Sk5OltrNnzwofHx8xaNAgGZMVePPNN8XQoUOL3D9kyBDRp0+fMkyka+3atcLMzEz88MMPIj8/X2rPy8sTUVFRwszMTKxfv17GhNqUfm1OmzZNeHp6iuvXr+vsS09PF56enmLmzJkyJCtc27ZtxQcffKDTPnz4cNGqVSsZEulSekZbW1thZ2cn9fTZ2dlJL2tra6FWq8Xw4cPljikxNzcXrVu3FhMnThTbtm0T9+7dkzuSXrAAMhBxcXGif//+omLFisLLy0uMHz9eHDhwQO5Ykvz8fDFu3DhhYWEhdeFbWVmJqVOnyh1NS0ZGhmjevLkwNTUVbm5uws3NTZiamop27dqJO3fuyB1PuLm5iV27dhW5f+fOncLNza0ME+nq2rWrmDBhQpH7x40bJ7p161aGiZ5O6demv7+/WLp0aZH7o6KiRPPmzcsw0dPFx8cLCwsL0bp1a/HZZ5+Jzz77TLRu3VpYWFiInTt3yh1PCKH8jMuWLRM//PCDUKlU4uuvvxbLli2TXitWrBB79uyRO6KWXbt2iZkzZ4qOHTuKihUrCnNzc/HKK6+ITz75RGzdulXueM+NT4EpWFpaGpYtW4aoqChoNBq8+eabiIyMRFJSEho0aCB3vEJlZWXh5MmTsLS0hJeXl9asxkohhMDff/+NpKQkWFpawsfHR/ZbSo9ZWVnhzJkzqFGjRqH7r1y5Ig2SlEuNGjWwdu1aNGvWrND9+/fvR69evRT3aKxSr017e3vs3bsXdevWLXT/qVOn0LJlS9y+fbuMkxUtMTERn3/+ORITE6XvobCwMEU9vWQIGePi4tCyZUutiUWV7tGjR/jnn3+waNEi/Pzzz8jPzzfYKS9YAClU165dsXPnTnTp0gX9+vVDUFAQTExMUKFCBUUXQPRiCpv+4Enp6elwdnaW9QeOhYUFzp8/X+T4matXr8LT01NRYy2UzNTUFFevXpUmkvyvtLQ01KhRo9AxLWR4NBqN9Bi+RqN56rFKelz/zJkziI2NlV45OTl49dVX0bZtW4wePVrueM+Fg6AV6q+//sKoUaPw/vvvK+ovlif17NkTy5Ytg7W1NXr27PnUY9euXVtGqXQtWLAAQ4cOhYWFBRYsWPDUY0eNGlVGqYq2ZMmSIge8KmHge25u7lP/YjU1NUVubm4ZJtJlKNcmUNAj+bTJJYuaGbwsGcIvbUPICBSs7Zaamorq1avD1ta20EHQQgEzvj/JxcUF9+/fl2Z/Hj9+PHx8fBS9ZEtxsABSqPj4eERFRaFp06aoX78+3nnnHUXNXwEUPFH1+Bvg8bIISvTVV1+hX79+sLCwwFdffVXkcSqVSvYCqGbNmvj++++feYzcJk2aBCsrq0L3PV56QE6Gcm0CBb/s6tSpU+QvE7mLH8AwfmkbQkYA2L59u/SE144dO2TLURLVqlXDqVOnkJaWhrS0NKSnp+P+/ftF/gwwFLwFpnDZ2dlYvXo1li5digMHDiAvLw/z58/HwIEDUblyZbnjkRFq27Ztsf7yM5Qf7nKLjo4u1nEhISGlnKRocXFxeOWVV2BqaorY2Nin/v9v06ZNGSb7lyFkNGQZGRnYuXMn4uLiEBcXhxMnTsDX1xft2rXDzJkz5Y73XFgAGZDTp08jKioKP/74IzIyMtCxY0f88ccfcscCAMyYMQP9+vVT5DIdT4qPj0erVq3kjkFlyFCuTTI+mzdvRqVKlaSfSREREfj+++/RoEEDRERESPOAKcmtW7cQGxuL9evXY+XKlQY9CJpLYRiQunXrYt68ebhy5QpWrlwpdxwtv/zyCzw9PdGyZUt8++23uHnzptyRCtW+fXu4u7vjk08+wfHjx+WOQ2XAUK5NQ+Hl5YXPPvsMZ8+elTtKkQwhIwB8/PHH0nilo0ePIjQ0FJ07d0ZKSgpCQ0NlTvevtWvXYtSoUfDx8YGDgwPef/99ZGVl4csvv0RCQoLc8Z5f2T95T+XVsWPHRFhYmHB3dxcVKlQQnTt3Fj///LPIzs6WO5rkxo0b4ptvvhEtW7YUKpVKNG7cWMybN09cvnxZ7mhUigzh2jQU8+fPF35+fkKtVgs/Pz8RHh4uUlNT5Y6lxRAyCiFExYoVpaV5pkyZInr16iWEEOLQoUPCwcFBxmTaqlWrJv73v/+Jb775Rhw5ckTuOHrDW2BUKnbv3o0VK1bgl19+wYMHD575VIYcUlJSsGLFCqxcuRKnTp3Cq6++iu3bt8sdi0qZIVybhuDMmTP4+eefsXLlSqSkpKBdu3Z4++230b9/f7mjSZSe0d7eHvHx8WjQoAFatWqF/v37Y+jQobhw4QIaNGigiAcKAODtt99G+/bt0aZNG9SuXVvuOHrDW2BUKipWrAhLS0uYmZnh4cOHcscplLu7OyZMmIA5c+bA29sbcXFxckcCUDDR2PLly5Geni53lHLJEK5NQ1CnTh1MnToVZ86cwa5du3Djxg0MGDBA7lhalJ6xVatWCA0NxfTp03HgwAF06dIFAJ46GaocLC0tMWfOHNSpUweurq54++23sWTJEsXfYnwWFkCkNykpKZg5cyYaNmwIPz8/HD58GFOnTkVaWprc0XTs3r0bw4cPh5OTE9566y00atQIGzdulDsWgIJ5dIYNG4YHDx7IHaVIjx49wrRp0xQ323NRDOHanDZtWqF/8d+/fx/Tpk2TIdGzHThwAB9++CFef/11nDlzBm+88YbckXQoOePChQthamqKX3/9Fd999x1cXFwAFMwDFxQUJHO6f33//fc4c+YMLl26hHnz5qFSpUr48ssvUa9ePUUVaiUm9z04Kh/8/f2FWq0Wvr6+4vPPPxdXrlyRO1Khxo8fL9zc3ISZmZno0qWLWLFihSLHgbRp00asW7dO7hhPValSJWn8gpIZyrWpVqtFenq6TvvNmzcVtfDx6dOnxeTJk4WXl5cwNTUVnTp1EtHR0eLu3btyR5MYQkZDlJ2dLbZs2SImTJggmjdvLszMzISvr6/csZ4bJ0IkvejQoQOWLl2q+CU6du3ahY8//hhvvvkmqlatKnecIg0fPhyhoaG4fPkymjZtiooVK2rt9/HxkSnZv9q3b4+4uDi4ubnJHeWpDOXaFP8/Sd9/JSUlSRPnKUG9evXw8ssvY8SIEejTp0+RS3jIyRAyPpaXl4fff/8dJ0+eBADUr18fPXr0gKmpcn49f/LJJ4iNjcXhw4dRv359tGnTBhMmTMCrr76qyEf1i4uDoOmFPXz4EPXq1cOff/6J+vXryx2nSA8fPsR7772HSZMmKX5OmMKWRni8JILcM9k+FhkZialTp6Jfv36FFmndunWTKdm/DOHatLOzg0qlQmZmJqytrbWKoLy8PGRlZWHYsGGIiIiQMeW/eZYuXYr//e9/iv3FZwgZHzt+/Di6du2K9PR0aTHcM2fOoFq1atiwYQMaNWokc8ICarUa1apVw0cffYSePXuiTp06ckfSCxZApBcuLi7Ytm2bYn/JPGZjY4PExETFF0AXL1586v5atWqVUZKiPWv9KiUUaYDyr83o6GgIITBw4ECEh4drLd1hZmYGNzc3tGjRQsaE2iwsLHDy5ElFfw8ZQkYAaNGiBapVq4bo6GipWLtz5w7effdd3LhxA3v27JE5YYGkpCTExcUhNjYWu3btgpmZGdq0aSOtDWaoBRELINKLWbNm4cyZM1iyZImium7/KyQkBL6+vvjoo4/kjkJlxFCuzSeXclAyPz8/zJ07Fx06dJA7SpEMISNQ8HTVwYMH0bBhQ632Y8eO4eWXX8b9+/dlSvZ0SUlJ+Oqrr/Dzzz8b9EzQyv5OI4Pxzz//ICYmBlu3boW3t7fO7RC5V9x+zMvLC9OmTcPu3bsLvW0j92KoT/rxxx8RGRmJlJQU7N27F7Vq1UJ4eDjc3d3RvXt3ueNpefDgASwsLOSOUShDuTazs7MRExODwMBArfYtW7YgPz8fwcHBMiXTNmPGDIwdOxbTp08v9HtIzpXWHzOEjEDBY/rp6ek6BdD169fh6ekpUypdQggcPnwYsbGxiI2NRXx8PDQaDXx8fAx6XTX2AJFePGtujR9++KGMkjzd07rEVSoVzp8/X4Zpivbdd99h8uTJ+PDDDzFz5kwcO3YMHh4eWLZsGaKjoxWx0GheXh5mzZqFyMhIpKen48yZM/Dw8MCkSZPg5uaGQYMGyR0RgOFcmz4+PpgzZw46d+6s1b5582aMHz8eSUlJMiXT9uStzyfHKylpfJqSMz458WZ8fDzGjRuHzz77DM2bNwcA7Nu3D9OmTSv0WpCLnZ0dsrKy0LhxY+nWV+vWrWFrayt3tBfCAohIgRo0aIBZs2ahR48eqFy5MpKSkuDh4YFjx46hbdu2iljPatq0aYiOjsa0adMwZMgQqUhbvXo1wsPDsXfvXrkjGhRLS0ucPHlS56m6CxcuoGHDhsjOzpYn2H88a8JQJfQIKDmjWq3WKcqAfwu1J7eVUEwCwMaNG9G6dWvF9JzpC2+Bkd48evQIsbGxOHfuHN566y1UrlwZ165dg7W1NSpVqiR3PC25ublISUlB7dq1FTnmIiUlBU2aNNFpNzc3V8wvwuXLl2Px4sXo0KEDhg0bJrU3btwYp06dkjGZLkO4Nm1sbHD+/HmdAig5OVnnFo6clFDgPIuSMyqh97akHs9QXd4o7yc/GaSLFy8iKCgIly5dQk5ODjp27IjKlStj7ty5yMnJQWRkpNwRAQD37t3DyJEjER0dDQDSbZuRI0fCxcUFEyZMkDlhAXd3dyQmJuo87bV582bFPM109erVQscp5OfnK2qJCUO5Nrt3744PP/wQv//+u7TeUnJyMsaMGaOIKQWetGvXLixatAjnz5/HL7/8AhcXF/z4449wd3dHq1at5I4HQLkZlVycGRsuhUF6MXr0aPj5+eHOnTuwtLSU2l9//XXExMTImExbWFgYkpKSEBsbqzVoNyAgAKtXr5YxmbbQ0FCMGDECq1evhhACBw4cwMyZMxEWFoZx48bJHQ9AwW26Xbt26bT/+uuvhfZeycVQrs158+ahYsWKqFevHtzd3eHu7o769eujSpUq+OKLL+SOJ/ntt98QGBgIS0tLJCQkICcnBwCQmZmJWbNmyZyugCFkfGzXrl14++230bJlS1y9ehVAwQMQ8fHxMiczAmU88zSVU/b29uLUqVNCiIIlEs6dOyeEECIlJUVYWlrKGU1LzZo1xd69e4UQ2jnPnj0rKleuLGc0HT/99JPw9PQUKpVKqFQq4eLiIpYsWSJ3LMm6deuEjY2NmDNnjrCyshKff/65GDx4sDAzMxNbt26VO57EUK5NIYTIz88XW7ZsEfPmzRPffPONiIuLkzuSDl9fXxEdHS2E0P56JiQkCAcHBzmjSQwhoxBC/Prrr8LS0lIMHjxYmJubSzm/+eYbERwcLHO68o+3wEgvipoL4sqVK6hcubIMiQp348YNVK9eXac9Ozu70GUI5NSvXz/069cP9+7dQ1ZWVqG55dS9e3ds2LAB06ZNQ8WKFTF58mS89NJL2LBhAzp27Ch3PImhXJtAwcDXTp06oVOnTnJHKdLp06fx6quv6rTb2NggIyOj7AMVwhAyAgWP60dGRqJ///5YtWqV1P7KK69gxowZMiYzDiyASC86deqE8PBwLF68GEDBD/KsrCxMmTJFMY9yAgUTpG3cuBEjR44E8O+TF0uWLFHUbLtPsrKygpWVldwxCtW6dWv8/fffcsd4KkO5Np+14vvkyZPLKMnTOTo6Ijk5WWewdnx8PDw8POQJ9R+GkBEwnEKtvGIBRHrx5ZdfIjAwEA0aNMCDBw/w1ltv4ezZs6hatSpWrlwpdzzJrFmzEBwcjBMnTuDRo0f4+uuvceLECezZs+eZj86WpVu3bmHy5MnYsWMHrl+/jvz8fK39t2/flimZ4TGUa/P333/X2n748CFSUlJgamqK2rVrK6YAGjJkCEaPHo2lS5dCpVLh2rVr2Lt3L8aOHYtJkybJHQ+AYWQEDKdQK69YAJFe1KhRA0lJSVi9ejWSkpKQlZWFQYMGoV+/floDT+XWqlUrJCYmYs6cOfD29sbWrVvx0ksvYe/evfD29pY7nuSdd95BcnIyBg0aBAcHB8Xcnnu8cGdxKKVIM5Rr8/DhwzptGo0G7777Ll5//XUZEhVuwoQJyM/PR4cOHXDv3j28+uqrMDc3x9ixY6WeVbkZQkbAcAq18ooTIRIpUOXKlREfH4/GjRvLHUXL4+kDgIJeqhkzZiAwMFC6fbh3715s2bIFkyZN4nprenL06FF07doVFy5ckDuKltzcXCQnJyMrKwsNGjRQzHxKT1JqxpSUFLi7u0MIgVmzZmH27Nm4d+8eAEiF2vTp02VOWf6xAKIXcubMGWRkZKBZs2ZSW0xMDGbMmIHs7Gz06NEDn3zyiYwJCzx69Ah5eXkwNzeX2tLT0xEZGYns7Gx069ZNMfOXAMDLL7+Mb775RpoeX4l69eqFdu3a4YMPPtBqX7hwIbZt24Z169bJE+z/Gcq1+Szx8fHo2rUr7ty5I3eUQl28eBHZ2dmoV6+e1hIUSqK0jGq1GrVq1UK7du3Qrl07tG3bFnfv3lVcoVbesQCiF/L666/D29tbGsCZkpKChg0bonXr1qhXrx6WLl2K6dOn48MPP5Q154ABA2BmZoZFixYBAO7evYuGDRviwYMHcHJywokTJ7B+/XrFDIr9559/MGHCBEyePBmNGjVChQoVtPYrYUr6SpUqITExUWcyxOTkZPj6+iIrK0umZAUM5dp8bMGCBVrbQgikpqbixx9/RJs2bbBixQqZkhVYunQpMjIyEBoaKrUNHToUUVFRAIC6detiy5YtcHV1lSuiQWQEIC0qGhsbi/379yM3NxceHh5o37492rdvj7Zt28LBwUHWjEZBvifwqTyoUaOG2LNnj7Q9ffp00bhxY2l7yZIlWtty8fLyElu2bJG2Fy5cKJydnUVGRoYQQohx48aJtm3byhVPx5kzZ4Sfn59Qq9VaL5VKJdRqtdzxhBAFcyp98cUXOu1ffPGFqFmzpgyJtBnKtfmYm5ub1svDw0P4+/uLsLAwodFo5I4n/P39xdKlS6Xtv/76S5iamoqffvpJHDp0SLRo0UIMGjRIxoSGkfG/7t+/L2JiYsSkSZNE69athbm5uVCr1aJBgwZyRyv3OAiaXsjNmzdRo0YNaXvHjh3o2rWrtN22bVuMGTNGjmharl69Ci8vL2k7JiYGvXr1go2NDQAgJCREMauCAwVzAFWoUAErVqxQ1CDoJ02dOhWDBw9GbGws/P39AQD79+/H5s2b8f3338ucznCuzcdSUlLkjvBUZ8+ehZ+fn7S9fv16dO/eHf369QNQ8ITlgAED5IoHwDAy/peFhQXat2+PVq1aoV27dvjrr7+waNEixa2nVx6xAKIXYm9vj9TUVLi6uiI/Px8HDx7U6n7Ozc2VVjeWk4WFBe7fvy9t79u3D59//rnWfrlv2Tzp2LFjOHz4MOrWrSt3lCK9++67qF+/PhYsWIC1a9cCAOrXr4/4+HipIJKToVybhuL+/ftat1737NmDQYMGSdseHh5IS0uTI5rEEDI+lpubi3379mHHjh3SrTBXV1e8+uqrWLhwIdcMKwMsgOiFtG3bFtOnT8e3336LX375Bfn5+Wjbtq20/8SJEzpzXMjB19cXP/74I2bPno1du3YhPT0d7du3l/afO3cOzs7OMibU5ufnh8uXLyu2AHr48CHee+89TJo0CT///LPccQplCNdmz549i33s4yJTLrVq1cKhQ4dQq1Yt3Lx5E8ePH8crr7wi7U9LS5N6VOViCBkBoH379ti/fz/c3d3Rpk0bvPfee1ixYgWcnJzkjmZUWADRC5k5cyY6duyIWrVqwcTEBAsWLEDFihWl/T/++KNWoSGXyZMnIzg4GGvWrEFqaireffddrR82v//+u9YPSrmNHDkSo0ePxscffwxvb2+dQdA+Pj4yJStQoUIF/Pbbb4qeq8QQrs0nfxkLIfD777/DxsZGuo1z6NAhZGRklKhQKi0hISEYMWIEjh8/ju3bt6NevXpo2rSptH/Pnj1o1KiRjAkNIyNQsACqk5OTNOC5TZs2qFKlityxjA6fAqMX9ujRIxw/fhzVqlXT6UVJSkpCjRo1FPHNffLkSWzduhWOjo544403tB6HXbx4MZo1awZfX1/5Aj6hsEd1VSoVhBBQqVSFrm1V1kJCQuDr66vo+X4M5doEgPHjx+P27duIjIyEiYkJACAvLw/Dhw+HtbW11i1bOeTn5+Ozzz7Dhg0b4OjoiPnz56N+/frS/jfeeANBQUFat5yYsXDZ2dnYtWsXYmNjsWPHDiQmJqJOnTpo06aNVBBVq1ZN1ozGgAUQkQJdvHjxqftr1apVRkmKNmPGDHz55Zfo0KEDmjZtqtW7AgCjRo2SKZlhqlatGuLj43Vue54+fRotW7bErVu3ZEpGpe3u3buIj4+XxgMlJSXBy8sLx44dkztaucZbYEQKpIQC51mioqJga2uLQ4cO4dChQ1r7VCoVC6ASevToEU6dOqVTAJ06dUpnLTgqXypWrAh7e3vY29vDzs4OpqamOHnypNyxyj0WQEQKde7cOYSHh0s/CBs0aIDRo0ejdu3aMicroPTHtg3NgAEDMGjQIJw7d06avXr//v2YM2eO4h7dphfz+KnEx7fAdu/ejezsbLi4uKBdu3aIiIhAu3bt5I5Z7vEWGJECbdmyBd26dYOvr680OHv37t1ISkrChg0b0LFjR5kT/uvmzZsAgKpVq8qcxLDl5+fjiy++wNdff43U1FQAgJOTE0aPHo0xY8ZI44LI8FlbWyM7OxuOjo5ay2Eo5Y8bY8ECiEiBmjRpgsDAQMyZM0erfcKECdi6dSsSEhJkSlYgIyMDn376KVavXi2tUWVnZ4c+ffpgxowZsLW1lTXfY48ePcKsWbMwcOBArUkRlU6j0QBQxpInpH+LFi1Cu3btUKdOHbmjGDUWQKQ3GRkZOHDgAK5fv64zZqF///4ypTJMFhYWOHr0qNbs1UDBAp8+Pj548OCBTMmA27dvo0WLFrh69Sr69esnPWVz4sQJrFixAq6urtizZw/s7Oxky/ikypUr4+jRo7LP+VNcN27cwOnTpwEA9erVU1zP2rRp0zB27FhYWVlptd+/fx+ff/45Jk+eLFOyfxlCRlIAOdbfoPLnjz/+EJUrVxYqlUrY2NgIW1tb6WVnZyd3PMnjPP992dvbC2dnZ/Hqq69qrSUklxo1aog1a9botK9evVq4urrKkOhfo0ePFo0aNRJpaWk6+1JTU4W3t7f48MMPZUhWuG7duolly5bJHeOZsrKyxIABA4SJiYlQqVRCpVIJU1NTMXDgQJGdnS13PIlarRbp6ek67Tdv3lTMOnWGkJHkx0HQpBdjxozBwIEDMWvWLJ2/upRk8uTJmDlzJoKDg6WBpgcOHMDmzZsxYsQIpKSk4P3338ejR48wZMgQ2XIOGTIEQ4cOxfnz59GyZUsABWOA5s6dq7WcgxzWrVuHRYsWFbpataOjI+bNm4dhw4bhq6++kiGdruDgYEyYMAFHjx4t9HH9bt26yZRMW2hoKOLi4rBhwwZp3Fd8fDxGjRqFMWPG4LvvvpM5YQHx/3NR/VdSUhLs7e1lSKTLEDKSAshdgVH5YGVlJc6dOyd3jGfq2bOn+O6773TaIyMjRc+ePYUQQixYsEA0atSorKNpyc/PF/PnzxcuLi5Sb4CLi4sIDw8X+fn5smYzMzMTly9fLnL/5cuXhbm5eRkmerrHX7/CXkrqDahSpYrYsWOHTvv27dtF1apVyz7QfzzuPVWr1To9qdbW1kKtVovhw4czIxkMjgEivejZsyf69OmDN998U+4oT1WpUiUkJibC09NTqz05ORm+vr7IysrCuXPn4OPjg+zsbJlSart79y6AgrEsSuDi4oLVq1ejVatWhe7ftWsXevfujWvXrpVxMsNmZWWFQ4cOac1cDADHjx9Hs2bNZL8eo6OjIYTAwIEDER4errWMh5mZGdzc3NCiRQsZExpGRlIO3gKj5/bHH39IH3fp0gUff/wxTpw4UejaVUq5zWBvb48NGzboLN+wYcMGqWs8Oztb9mKjffv2WLt2LWxtbbWyaDQa9OjRA9u3b5ctW2BgID799FP8/fffMDMz09qXk5ODSZMmISgoSKZ0T/fgwQNYWFjIHaNQLVq0wJQpU7B8+XIp4/379zF16lRF/NIOCQkBALi7u+OVV16Bqanyfn0YQkZSDvYA0XMrbL2qwihl7SoA+P777/H++++jc+fO0higf/75B5s2bUJkZCQGDRqEL7/8EgcOHMDq1atly6lWq5GWlobq1atrtV+/fh0uLi54+PChTMmAK1euwM/PD+bm5hgxYgTq1asHIQROnjyJb7/9Fjk5OTh48CBcXV1ly/ikvLw8zJo1C5GRkUhPT8eZM2fg4eGBSZMmwc3NTfZ1oR47evQogoKCkJOTg8aNGwMoGLNiYWGBLVu2oGHDhjInLLBp0yaYmJggMDBQq33Lli3Iz89HcHCwTMn+lZCQgAoVKsDb2xsAsH79evzwww9o0KABPvvsM53CnYyUjLffiGQRHx8v+vTpI5o0aSKaNGki+vTpI3bv3i13LCGEEElJSSIpKUmoVCqxY8cOaTspKUkkJCSIWbNmiVq1askdU5w/f14EBQUJtVqtNZ4mMDBQnD17Vu54WqZOnSo8PDzETz/9JCwtLaWxaqtWrRLNmzeXOZ227OxssXjxYhEaGipCQ0PF999/L+7duyd3LC3e3t5i48aNOu1//fWX8PHxkSGRLj8/P/Hrr78KIYQ4d+6cMDc3F3379hWenp5i9OjR8oYjxWABRHoRHR0tHjx4oNOek5MjoqOjZUhkmB4XEk8WFk++rKysRFRUlNwxJbdv3xb79+8X+/fvF7du3ZI7TqFq164ttm3bJoQQolKlSlIBdPLkSWFraytnNElubq7w8PAQJ06ckDvKM1lYWIiUlBSd9pSUFGFlZVX2gQphbW0tkpOThRBCzJkzR3Tq1EkIUfDHT40aNeSMRgrCG6SkFwMGDEBQUJDOLZu7d+9iwIABipoIMT8/H8nJyYVO2Pjqq6/KlKpASkoKhBDw8PDAgQMHUK1aNWmfmZkZqlevrqglEezs7KRbiUp19epVnUHvQMF1IOetxCdVqFBB1sktS8LGxgbnz5/XmVgyOTlZZ4oBuQghpO/tbdu24bXXXgMAuLq6Sku3ELEAIr0QRcy7ceXKFa0nMeS2b98+vPXWW7h48SLEf4a/KWGs0uNV4Ln6t/40aNAAu3btkr62j/36669o0qSJTKl0jRgxAnPnzsWSJUsUPXi3e/fu+PDDD/H7779La1clJydjzJgxinnYwc/PDzNmzEBAQADi4uKkOZRSUlIKnb+KjJNyv8vIIDRp0gQqlQoqlQodOnTQ+sGdl5eHlJQURT0RNGzYMPj5+WHjxo1wcnIqtGhTgujoaFStWhVdunQBAIwbNw6LFy9GgwYNsHLlSp1f5lS0yZMnIyQkBFevXkV+fj7Wrl2L06dPY/ny5fjzzz/ljif5559/EBMTg61bt8Lb21unN2Xt2rUyJdM2b948BAUFoV69etL6aleuXEHr1q3xxRdfyJyuQHh4OPr164d169bh008/lXoAf/31V2liUSI+BUYvZOrUqdJ/x4wZg0qVKkn7Hs+70atXL8U8dVGxYkUkJSUVektESerWrYvvvvsO7du3x969e9GhQweEh4fjzz//hKmpqWJ+GRqKXbt2Ydq0aUhKSkJWVhZeeuklTJ48GZ06dZI7mmTAgAFP3f/DDz+UUZJnE0Lg77//RlJSEiwtLeHj4yP77ePiePDgAUxMTHSm6SDjxAKI9CI6Ohq9e/dW7Bwrj7Vv3x7jxo1TVK9UYaysrHDq1CnUrFkT48ePR2pqKpYvX47jx4+jbdu2uHHjhtwRiRTv0KFDOHnyJICCW6EvvfSSzIlISXgLjPTi8QRkSjdy5EiMGTMGaWlphU7Y6OPjI1MybZUqVcKtW7dQs2ZNbN26VVr/y8LCAvfv35c5HelTfn4+Pv/8c/zxxx/Izc1Fhw4dMGXKFFhaWsodrVDTpk176n4lrLR+/fp19O7dG3FxcbC1tQUAZGRkoF27dli1apXWwwVkvNgDRHqhVqufOp5G7sHFjxU2eaNKpZIGcSslZ79+/XDq1Ck0adIEK1euxKVLl1ClShX88ccf+OSTT3Ds2DG5IyqanZ1dscd33b59u5TTPN306dPx2WefISAgAJaWltiyZQv69u2LpUuXypqrKP8dOP7w4UOkpKTA1NQUtWvXRkJCgkzJ/tW7d2+cP38ey5cvl5YWOXHiBEJCQuDp6YmVK1fKnJCUgD1ApBdr167V+oXz8OFDHD58GNHR0dI4ISVISUmRO0KxREREYOLEibh8+TJ+++03VKlSBUBBl37fvn1lTqd84eHh0se3bt3CjBkzEBgYKC0psXfvXmzZsgWTJk2SKeG/li9fjm+//RbvvfcegILHtrt06YIlS5YUe7b1snT48GGdNo1Gg3fffRevv/66DIl0bd68Gdu2bdNaV61BgwaIiIhQ1Lgvkhd7gKhUrVixAqtXr8b69evljkJGqlevXmjXrh0++OADrfaFCxdi27ZtWLdunTzB/p+5uTmSk5O1lg6xsLBAcnKy9JSVITh69Ci6du2KCxcuyB0FlStXxq5du+Dr66vVfvjwYbRp0wYajUaeYKQoLICoVJ0/fx4+Pj7IysqSLcMff/yB4OBgVKhQQWsB18IoZR6TnTt3PnW/ITxxoxSVKlVCYmKizpN/ycnJ8PX1lfXaBAATExOkpaVpjUupXLkyjhw5And3dxmTlUx8fDy6du2KO3fuyB0F3bt3R0ZGBlauXAlnZ2cABRNi9uvXD3Z2dvj9999lTkhKwFtgVGru37+PBQsWwMXFRdYcPXr0kBYW7dGjR5HHKWkMUNu2bXXanrzFqJSchqBKlSpYv349xowZo9W+fv166dainIQQePfdd2Fubi61PXjwAMOGDdOaC0gpUx8sWLBAa1sIgdTUVPz444+KWAgVKOjd69atG9zc3KSetcuXL6NRo0b46aefZE5HSsECiPTiv4NOhRC4e/curKysZP+B8+SsyoYyw/J//4p+PKZq0qRJmDlzpkypDNPUqVMxePBgxMbGwt/fHwCwf/9+bN68Gd9//73M6Qp/gvLtt9+WIUnxfPXVV1rbarUa1apVQ0hICMLCwmRKpc3V1RUJCQnYtm0bTp06BQCoX78+AgICZE5GSsJbYKQX0dHRWtuPfyj6+/vDzs5OplTlT1xcHEJDQ3Ho0CG5oxiU/fv3Y8GCBdKcMPXr18eoUaOkgoiIjA8LICp1x44dQ6NGjeSOIYmJiUFMTEyhi6Eq9dHjx06dOgU/Pz/Zx60YiocPH+K9997DpEmTDGo8DT2f7du344MPPsC+fftgbW2ttS8zMxMtW7ZEZGQkWrduLVNCUhIWQFQq7t69i5UrV2LJkiU4dOiQYsasTJ06FdOmTYOfn1+ha4EpZXDkkSNHtLYfj7OYM2cOHj16hPj4eJmSGR4bGxskJiayAHoBPXv2LPaxco5V6tatG9q1a4ePPvqo0P0LFizAjh07FPN9TvLiGCDSq507dyIqKgq//fYbnJ2d0bNnT0RERMgdSxIZGYlly5bhnXfekTvKU/n6+koTND6pefPmiu+lUpoePXpg3bp1Rf5SpGezsbGRPhZC4Pfff4eNjQ38/PwAFMxPlZGRUaJCqTQkJSVh7ty5Re7v1KmTYhZsJfmxAKIXlpaWhmXLliEqKgoajQZvvvkmcnJysG7dOjRo0EDueFpyc3MNYjXo/07Y+HhMldLXWlMiLy8vTJs2Dbt370bTpk11VlkfNWqUTMkMx5MLsY4fPx5vvvkmIiMjYWJiAqDgqcThw4fr3HYqa+np6U9d6NTU1JTr6JGEt8DohXTt2hU7d+5Ely5d0K9fPwQFBUmrLSclJSmuABo/fjwqVaqkiBmAqWw87daXSqXC+fPnyzCN4atWrRri4+NRt25drfbTp0+jZcuWuHXrlkzJgNq1a+PLL78scrqLtWvXYuzYsfx/TgDYA0Qv6K+//sKoUaPw/vvvw8vLS+44hXq8kChQ8Bj84sWLsW3bNvj4+Oj8tTh//vyyjqeFgzj1z1CWPzEUjx49wqlTp3QKoFOnTsk+zUTnzp0xadIkBAUF6fSW3r9/H1OmTMFrr70mUzpSGhZA9ELi4+MRFRWFpk2bon79+njnnXfQp08fuWNp+e/aRY+nx//vgqLFXTyzNIWHh2PIkCGF3kqwsbHBe++9h/nz57MAeg43b94EAFStWlXmJIZtwIABGDRoEM6dO4dmzZoBKJhmYM6cORgwYICs2SZOnIi1a9eiTp06+OCDD6Qi7dSpU4iIiEBeXh4+/fRTWTOSgggiPcjKyhJRUVHilVdeERUqVBBqtVqEh4cLjUYjdzSDUrNmTXHixIki9588eVK4urqWYSLDdufOHTF8+HBRpUoVoVarhVqtFlWqVBEjRowQd+7ckTueQcrLyxNz584Vzs7OQqVSCZVKJZydncXcuXPFo0eP5I4nLly4IIKDg4VarZbyqdVqERwcLM6fPy93PFIQjgEivTt9+jSioqLw448/IiMjAx07dnzmGlxlJTMzE3l5ebC3t9dqv337NkxNTWUfxGlhYYFjx47prFv1WHJyMry9vXH//v0yTmZ4bt++jRYtWkhrQD1eGfzEiRNYsWIFXF1dsWfPHk7U+QIeLyoq9/dNYe7cuYPk5GQIIeDl5cX/z6RDLXcAKn/q1q2LefPm4cqVK1i5cqXccbT06dMHq1at0mlfs2aNIm7dubi46Nyae9KRI0fg5ORUhokM17Rp02BmZoZz585h0aJF+PDDD/Hhhx9i8eLFSE5ORoUKFTBt2jS5YxqsGzdu4MiRIzhy5Ih0e1FJ7Ozs8PLLL6NZs2YsfqhQ7AEio2Jvb4/du3dLvQGPnTp1Cq+88oqsT7AAwMiRIxEbG4t//vmn0EGczZo1Q7t27XQWpCRdbm5uWLRoEQIDAwvdv3nzZgwbNgwXLlwo22AGLjs7GyNHjsTy5culQc8mJibo378/vvnmG1hZWcmckKh42ANERiUnJwePHj3SaX/48KEibitNnDgRt2/fRp06dTBv3jysX78e69evx9y5c1G3bl3cvn2bgziLKTU1FQ0bNixyf6NGjZCWllaGicqH0NBQxMXFYcOGDcjIyEBGRgbWr1+PuLg4jBkzRu54RMXGHiAyKu3atUOjRo3wzTffaLWPGDECR44cwa5du2RK9q+LFy/i/fffx5YtW6SZoFUqFQIDAxEREcElHYrJxcUFq1evRqtWrQrdv2vXLvTu3RvXrl0r42SGrWrVqvj111/Rtm1brfYdO3bgzTff5ESDZDBYAJFR2b17NwICAvDyyy+jQ4cOAAoWR/3nn3+wdetWRT1ezkGcL2bgwIE4d+4c/v77b5iZmWnty8nJQWBgIDw8PLi0SAlZWVnh0KFDOreRjx8/jmbNmiE7O1umZEQlwwKIjE5iYiI+//xzJCYmwtLSEj4+PggLC1PsRI70fK5cuQI/Pz+Ym5tjxIgRqFevHoQQOHnyJL799lvk5OTg4MGDcHV1lTuqQenQoQOqVKmC5cuXS+PU7t+/j5CQENy+fRvbtm2TOSGKfOpUpVLBwsICnp6e7EklFkBEVH6lpKRg+PDh2Lp1q9btxI4dO2LhwoVFTjdARTt69CiCgoKQk5ODxo0bAyhYhNTCwgJbtmx56rirsqJWqwtdTPhxm0qlQqtWrbBu3Tr2rBoxFkBktB48eIDc3FytNiXOZ0Iv7s6dOzh79iwAwNPTU2ceKCqZe/fu4eeff8apU6cAAPXr10e/fv1gaWkpc7ICMTEx+PTTTzFz5kxptuoDBw5g0qRJmDhxojSrur+/P6KiomROS3JhAURG5d69exg3bhzWrFlT6CPveXl5MqQiMgwPHz5EvXr18Oeff+qMAVKSRo0aYfHixWjZsqVW++7duzF06FAcP34c27Ztw8CBA3Hp0iWZUpLc+Bg8GZWPP/4Y27dvx3fffQdzc3MsWbIEU6dOhbOzM5YvXy53PCJFq1ChAh48eCB3jGc6d+5cob251tbW0krwXl5eipzAkcoOCyAyKhs2bMC3336LXr16wdTUFK1bt8bEiRMxa9Ys/Pzzz3LHI1K8ESNGYO7cuYXOp6UUTZs2xccff6z1SP6NGzcwbtw4vPzyywCAs2fPcgC8keNq8GRUbt++DQ8PDwAFfw3evn0bANCqVSu8//77ckYjMgj//PMPYmJisHXrVnh7e6NixYpa+9euXStTsn9FRUWhe/fuqFGjhlTkXL58GR4eHli/fj0AICsrCxMnTpQzJsmMBRAZFQ8PD6SkpKBmzZqoV68e1qxZg2bNmmHDhg2wtbWVOx6R4tna2qJXr15yx3iqunXr4sSJE9i6dSvOnDkjtXXs2BFqdcGNjx49esiYkJSAg6DJqHz11VcwMTHBqFGjsG3bNnTt2hVCCDx8+BDz58/H6NGj5Y5IRERlgAUQGbWLFy/i0KFD8PT0hI+Pj9xxiBQrPz8fn3/+Of744w/k5uaiQ4cOmDJlimIeff+vmJgYxMTE4Pr169KirY9x9m8CeAuMjFytWrVQq1YtuWMQKd7MmTPx2WefISAgAJaWlvj6669x/fp1RRYTU6dOxbRp0+Dn5wcnJyeoVCq5I5ECsQeIjML27dvxwQcfYN++fTqPx2ZmZqJly5aIjIxU1FpgREri5eWFsWPH4r333gMAbNu2DV26dMH9+/elcTVK4eTkhHnz5uGdd96ROwopmLKuWqJSEh4ejiFDhhQ6N8jjWWHnz58vQzIiw3Dp0iV07txZ2g4ICIBKpcK1a9dkTFW43NxcnUkQif6LBRAZhaSkJAQFBRW5v1OnTjh06FAZJiIyLI8ePZIWP32sQoUKePjwoUyJijZ48GCsWLFC7hikcBwDREYhPT0dFSpUKHK/qamp1qRpRKRNCIF3330X5ubmUtuDBw8wbNgwrbmAlDAP0IMHD7B48WJs27YNPj4+Ot/77O0lgAUQGQkXFxccO3asyNW/jxw5AicnpzJORWQ4QkJCdNrefvttGZI825EjR+Dr6wsAOHbsmNY+DoimxzgImozCyJEjERsbi3/++UenG//+/fto1qwZ2rVrhwULFsiUkIiIyhILIDIK6enpeOmll2BiYoIPPvgAdevWBQCcOnUKERERyMvLQ0JCAhwcHGROSkREZYEFEBmNixcv4v3338eWLVvw+LJXqVQIDAxEREQE3N3dZU5IRM+rZ8+eWLZsGaytrdGzZ8+nHquEcUokP44BIqNRq1YtbNq0CXfu3EFycjKEEPDy8oKdnZ3c0YjoBdnY2Ejje2xsbGROQ4aAPUBERERkdNgDRERE5dL169dx+vRpAAWrwVevXl3mRKQknAiRiIjKFY1Gg3feeQcuLi5o06YN2rRpAxcXF7z99tvIzMyUOx4pBAsgIiIqV4YMGYL9+/fjzz//REZGBjIyMvDnn3/i4MGD0lpmRBwDRERE5UrFihWxZcsWtGrVSqt9165dCAoKQnZ2tkzJSEnYA0REROVKlSpVCn0SzMbGhk99koQFEBERlSsTJ05EaGgo0tLSpLa0tDR8/PHHmDRpkozJSEl4C4yIiMqVJk2aIDk5GTk5OahZsyYA4NKlSzA3N4eXl5fWsQkJCXJEJAXgY/BERFSu9OjRQ+4IZADYA0RERERGhz1ARERULh08eBAnT54EADRo0ABNmzaVOREpCQsgIiIqV65cuYK+ffti9+7dsLW1BQBkZGSgZcuWWLVqFWrUqCFvQFIEPgVGRETlyuDBg/Hw4UOcPHkSt2/fxu3bt3Hy5Enk5+dj8ODBcscjheAYICIiKlcsLS2xZ88eNGnSRKv90KFDaN26Ne7duydTMlIS9gAREVG54urqiocPH+q05+XlwdnZWYZEpEQsgIiIqFz5/PPPMXLkSBw8eFBqO3jwIEaPHo0vvvhCxmSkJLwFRkRE5YqdnR3u3buHR48ewdS04Fmfxx9XrFhR69jbt2/LEZEUgE+BERFRuRIeHi53BDIA7AEiIiIio8MeICIiMngajQbW1tbSx0/z+DgybuwBIiIig2diYoLU1FRUr14darUaKpVK5xghBFQqFfLy8mRISErDHiAiIjJ427dvh729PQBgx44dMqchQ8AeICIiIjI67AEiIqJyJyMjAwcOHMD169eRn5+vta9///4ypSIlYQ8QERGVKxs2bEC/fv2QlZUFa2trrfFAKpWKc/8QABZARERUztSpUwedO3fGrFmzYGVlJXccUigWQEREVK5UrFgRR48ehYeHh9xRSMG4FhgREZUrgYGBWuuAERWGg6CJiMjg/fHHH9LHXbp0wccff4wTJ07A29sbFSpU0Dq2W7duZR2PFIi3wIiIyOCp1cW7ocGJEOkxFkBERERkdDgGiIiIiIwOCyAiIioX9u7diz///FOrbfny5XB3d0f16tUxdOhQ5OTkyJSOlIYFEBERlQvTpk3D8ePHpe2jR49i0KBBCAgIwIQJE7BhwwbMnj1bxoSkJBwDRERE5YKTkxM2bNgAPz8/AMCnn36KuLg4xMfHAwB++eUXTJkyBSdOnJAzJikEe4CIiKhcuHPnDhwcHKTtuLg4BAcHS9svv/wyLl++LEc0UiAWQEREVC44ODggJSUFAJCbm4uEhAQ0b95c2n/37l2dOYHIeLEAIiKicqFz586YMGECdu3ahbCwMFhZWaF169bS/iNHjqB27doyJiQl4UzQRERULkyfPh09e/ZEmzZtUKlSJURHR8PMzEzav3TpUnTq1EnGhKQkHARNRETlSmZmJipVqgQTExOt9tu3b6NSpUpaRREZLxZAREREZHQ4BoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOCyAiIiIyOiyAiIiIyOj8HxPxinhuSgkfAAAAAElFTkSuQmCC",
       "text/plain": [
        "
"
       ]
@@ -1607,10 +1607,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:17:55.850082Z",
-     "iopub.status.busy": "2024-10-20T00:17:55.849888Z",
-     "iopub.status.idle": "2024-10-20T00:17:56.397818Z",
-     "shell.execute_reply": "2024-10-20T00:17:56.397196Z"
+     "iopub.execute_input": "2024-10-22T01:44:26.791929Z",
+     "iopub.status.busy": "2024-10-22T01:44:26.791720Z",
+     "iopub.status.idle": "2024-10-22T01:44:27.363751Z",
+     "shell.execute_reply": "2024-10-22T01:44:27.363038Z"
     }
    },
    "outputs": [],
@@ -1639,16 +1639,16 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:17:56.400136Z",
-     "iopub.status.busy": "2024-10-20T00:17:56.399692Z",
-     "iopub.status.idle": "2024-10-20T00:17:56.461262Z",
-     "shell.execute_reply": "2024-10-20T00:17:56.460612Z"
+     "iopub.execute_input": "2024-10-22T01:44:27.366439Z",
+     "iopub.status.busy": "2024-10-22T01:44:27.365954Z",
+     "iopub.status.idle": "2024-10-22T01:44:27.430206Z",
+     "shell.execute_reply": "2024-10-22T01:44:27.429474Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1689,10 +1689,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:17:56.463413Z",
-     "iopub.status.busy": "2024-10-20T00:17:56.463217Z",
-     "iopub.status.idle": "2024-10-20T00:17:56.475616Z",
-     "shell.execute_reply": "2024-10-20T00:17:56.475005Z"
+     "iopub.execute_input": "2024-10-22T01:44:27.432709Z",
+     "iopub.status.busy": "2024-10-22T01:44:27.432231Z",
+     "iopub.status.idle": "2024-10-22T01:44:27.445009Z",
+     "shell.execute_reply": "2024-10-22T01:44:27.444315Z"
     }
    },
    "outputs": [],
@@ -1780,10 +1780,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:17:56.477556Z",
-     "iopub.status.busy": "2024-10-20T00:17:56.477369Z",
-     "iopub.status.idle": "2024-10-20T00:17:56.483347Z",
-     "shell.execute_reply": "2024-10-20T00:17:56.482880Z"
+     "iopub.execute_input": "2024-10-22T01:44:27.447199Z",
+     "iopub.status.busy": "2024-10-22T01:44:27.446991Z",
+     "iopub.status.idle": "2024-10-22T01:44:27.454492Z",
+     "shell.execute_reply": "2024-10-22T01:44:27.453835Z"
     }
    },
    "outputs": [],
diff --git a/main/example_notebooks/gcm_supply_chain_dist_change.html b/main/example_notebooks/gcm_supply_chain_dist_change.html
index a33737392..66eabaeaf 100644
--- a/main/example_notebooks/gcm_supply_chain_dist_change.html
+++ b/main/example_notebooks/gcm_supply_chain_dist_change.html
@@ -567,7 +567,7 @@
 
Finding Root Causes of Changes in a Supply Chain#

 
In a supply chain, the number of units of each product in the inventory that is available for shipment is crucial to fulfill customers’ demand faster. For this reason, retailers continuously buy products anticipating customers’ demand in the future.

 
Suppose that each week a retailer submits purchase orders (POs) to vendors taking into account future demands for products and capacity constraints to consider for demands. The vendors will then confirm whether they can fulfill some or all of the retailer’s purchase orders. Once confirmed by the vendors and agreed by the retailer, products are then sent to the retailer. All of the confirmed POs, however, may not arrive at once.

-
ce90858bc76f4e4d94a1dd558815c13d

+
18c330b3cc5a45aeaeb5fd75a2632e96

 

 
Week-over-week changes#

 
For this case study, we consider synthetic data inspired from a real-world use case in supply chain. Let us look at data over two weeks, w1 and w2 in particular.

@@ -1007,9 +1007,8 @@ 
Attributing change
 
-Evaluating set functions...:  88%|████████▊ | 28/32 [00:00<00:00, 232.91it/s]
-Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 225.70it/s]
-Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 283.04it/s]
+Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 187.18it/s]
+Evaluating set functions...: 100%|██████████| 32/32 [00:00<00:00, 261.35it/s]
diff --git a/main/example_notebooks/gcm_supply_chain_dist_change.ipynb b/main/example_notebooks/gcm_supply_chain_dist_change.ipynb index 7a5166fe9..5d459a2ec 100644 --- a/main/example_notebooks/gcm_supply_chain_dist_change.ipynb +++ b/main/example_notebooks/gcm_supply_chain_dist_change.ipynb @@ -27,10 +27,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.398032Z", - "iopub.status.busy": "2024-10-20T00:18:02.397596Z", - "iopub.status.idle": "2024-10-20T00:18:02.624842Z", - "shell.execute_reply": "2024-10-20T00:18:02.624300Z" + "iopub.execute_input": "2024-10-22T01:44:33.505294Z", + "iopub.status.busy": "2024-10-22T01:44:33.505105Z", + "iopub.status.idle": "2024-10-22T01:44:33.756627Z", + "shell.execute_reply": "2024-10-22T01:44:33.756015Z" } }, "outputs": [], @@ -46,10 +46,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.626902Z", - "iopub.status.busy": "2024-10-20T00:18:02.626708Z", - "iopub.status.idle": "2024-10-20T00:18:02.636384Z", - "shell.execute_reply": "2024-10-20T00:18:02.635866Z" + "iopub.execute_input": "2024-10-22T01:44:33.758857Z", + "iopub.status.busy": "2024-10-22T01:44:33.758647Z", + "iopub.status.idle": "2024-10-22T01:44:33.769200Z", + "shell.execute_reply": "2024-10-22T01:44:33.768605Z" }, "jupyter": { "outputs_hidden": false @@ -143,10 +143,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.638271Z", - "iopub.status.busy": "2024-10-20T00:18:02.637923Z", - "iopub.status.idle": "2024-10-20T00:18:02.644515Z", - "shell.execute_reply": "2024-10-20T00:18:02.644034Z" + "iopub.execute_input": "2024-10-22T01:44:33.771084Z", + "iopub.status.busy": "2024-10-22T01:44:33.770881Z", + "iopub.status.idle": "2024-10-22T01:44:33.778467Z", + "shell.execute_reply": "2024-10-22T01:44:33.777909Z" }, "jupyter": { "outputs_hidden": false @@ -250,10 +250,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:02.646323Z", - "iopub.status.busy": "2024-10-20T00:18:02.645989Z", - "iopub.status.idle": "2024-10-20T00:18:03.088970Z", - "shell.execute_reply": "2024-10-20T00:18:03.088290Z" + "iopub.execute_input": "2024-10-22T01:44:33.780453Z", + "iopub.status.busy": "2024-10-22T01:44:33.780212Z", + "iopub.status.idle": "2024-10-22T01:44:34.230769Z", + "shell.execute_reply": "2024-10-22T01:44:34.230190Z" }, "jupyter": { "outputs_hidden": false @@ -280,10 +280,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:03.091032Z", - "iopub.status.busy": "2024-10-20T00:18:03.090585Z", - "iopub.status.idle": "2024-10-20T00:18:03.095182Z", - "shell.execute_reply": "2024-10-20T00:18:03.094573Z" + "iopub.execute_input": "2024-10-22T01:44:34.232994Z", + "iopub.status.busy": "2024-10-22T01:44:34.232439Z", + "iopub.status.idle": "2024-10-22T01:44:34.237587Z", + "shell.execute_reply": "2024-10-22T01:44:34.237052Z" } }, "outputs": [ @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:03.097186Z", - "iopub.status.busy": "2024-10-20T00:18:03.096769Z", - "iopub.status.idle": "2024-10-20T00:18:03.276184Z", - "shell.execute_reply": "2024-10-20T00:18:03.275633Z" + "iopub.execute_input": "2024-10-22T01:44:34.239693Z", + "iopub.status.busy": "2024-10-22T01:44:34.239377Z", + "iopub.status.idle": "2024-10-22T01:44:34.451937Z", + "shell.execute_reply": "2024-10-22T01:44:34.451372Z" } }, "outputs": [ @@ -388,10 +388,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:18:03.278205Z", - "iopub.status.busy": "2024-10-20T00:18:03.278020Z", - "iopub.status.idle": "2024-10-20T00:18:04.339675Z", - "shell.execute_reply": "2024-10-20T00:18:04.339073Z" + "iopub.execute_input": "2024-10-22T01:44:34.454138Z", + "iopub.status.busy": "2024-10-22T01:44:34.453744Z", + "iopub.status.idle": "2024-10-22T01:44:35.560419Z", + "shell.execute_reply": "2024-10-22T01:44:35.559810Z" }, "jupyter": { "outputs_hidden": false @@ -433,10 +433,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:04.342317Z", - "iopub.status.busy": "2024-10-20T00:18:04.341800Z", - "iopub.status.idle": "2024-10-20T00:18:07.994614Z", - "shell.execute_reply": "2024-10-20T00:18:07.993934Z" + "iopub.execute_input": "2024-10-22T01:44:35.563319Z", + "iopub.status.busy": "2024-10-22T01:44:35.562744Z", + "iopub.status.idle": "2024-10-22T01:44:39.786524Z", + "shell.execute_reply": "2024-10-22T01:44:39.785819Z" } }, "outputs": [], @@ -468,10 +468,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:07.996946Z", - "iopub.status.busy": "2024-10-20T00:18:07.996713Z", - "iopub.status.idle": "2024-10-20T00:18:08.004294Z", - "shell.execute_reply": "2024-10-20T00:18:08.003804Z" + "iopub.execute_input": "2024-10-22T01:44:39.788968Z", + "iopub.status.busy": "2024-10-22T01:44:39.788636Z", + "iopub.status.idle": "2024-10-22T01:44:39.796449Z", + "shell.execute_reply": "2024-10-22T01:44:39.795907Z" } }, "outputs": [ @@ -553,16 +553,16 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:08.006003Z", - "iopub.status.busy": "2024-10-20T00:18:08.005815Z", - "iopub.status.idle": "2024-10-20T00:18:23.984179Z", - "shell.execute_reply": "2024-10-20T00:18:23.983551Z" + "iopub.execute_input": "2024-10-22T01:44:39.798457Z", + "iopub.status.busy": "2024-10-22T01:44:39.798056Z", + "iopub.status.idle": "2024-10-22T01:44:55.805640Z", + "shell.execute_reply": "2024-10-22T01:44:55.805109Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -619,10 +619,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:23.986310Z", - "iopub.status.busy": "2024-10-20T00:18:23.985939Z", - "iopub.status.idle": "2024-10-20T00:18:42.418061Z", - "shell.execute_reply": "2024-10-20T00:18:42.417375Z" + "iopub.execute_input": "2024-10-22T01:44:55.807827Z", + "iopub.status.busy": "2024-10-22T01:44:55.807453Z", + "iopub.status.idle": "2024-10-22T01:45:14.523970Z", + "shell.execute_reply": "2024-10-22T01:45:14.523385Z" } }, "outputs": [], @@ -641,16 +641,16 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:42.420309Z", - "iopub.status.busy": "2024-10-20T00:18:42.419937Z", - "iopub.status.idle": "2024-10-20T00:18:42.532671Z", - "shell.execute_reply": "2024-10-20T00:18:42.531840Z" + "iopub.execute_input": "2024-10-22T01:45:14.526328Z", + "iopub.status.busy": "2024-10-22T01:45:14.525970Z", + "iopub.status.idle": "2024-10-22T01:45:14.644761Z", + "shell.execute_reply": "2024-10-22T01:45:14.644138Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -676,10 +676,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:18:42.534789Z", - "iopub.status.busy": "2024-10-20T00:18:42.534548Z", - "iopub.status.idle": "2024-10-20T00:19:38.908429Z", - "shell.execute_reply": "2024-10-20T00:19:38.907688Z" + "iopub.execute_input": "2024-10-22T01:45:14.647419Z", + "iopub.status.busy": "2024-10-22T01:45:14.646983Z", + "iopub.status.idle": "2024-10-22T01:46:13.832454Z", + "shell.execute_reply": "2024-10-22T01:46:13.831717Z" } }, "outputs": [ @@ -689,8 +689,8 @@ "text": [ "\r", "Evaluating set functions...: 0%| | 0/32 [00:00" ] @@ -802,10 +808,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2024-10-20T00:19:39.133912Z", - "iopub.status.busy": "2024-10-20T00:19:39.133640Z", - "iopub.status.idle": "2024-10-20T00:19:39.145929Z", - "shell.execute_reply": "2024-10-20T00:19:39.145290Z" + "iopub.execute_input": "2024-10-22T01:46:14.073385Z", + "iopub.status.busy": "2024-10-22T01:46:14.073116Z", + "iopub.status.idle": "2024-10-22T01:46:14.086714Z", + "shell.execute_reply": "2024-10-22T01:46:14.086041Z" }, "jupyter": { "outputs_hidden": false diff --git a/main/example_notebooks/graph_conditional_independence_refuter.ipynb b/main/example_notebooks/graph_conditional_independence_refuter.ipynb index 9ef39c871..1fb77b1ab 100644 --- a/main/example_notebooks/graph_conditional_independence_refuter.ipynb +++ b/main/example_notebooks/graph_conditional_independence_refuter.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:44.100309Z", - "iopub.status.busy": "2024-10-20T00:19:44.099888Z", - "iopub.status.idle": "2024-10-20T00:19:44.116104Z", - "shell.execute_reply": "2024-10-20T00:19:44.115612Z" + "iopub.execute_input": "2024-10-22T01:46:19.068158Z", + "iopub.status.busy": "2024-10-22T01:46:19.067966Z", + "iopub.status.idle": "2024-10-22T01:46:19.083798Z", + "shell.execute_reply": "2024-10-22T01:46:19.083263Z" } }, "outputs": [], @@ -33,10 +33,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:44.118124Z", - "iopub.status.busy": "2024-10-20T00:19:44.117727Z", - "iopub.status.idle": "2024-10-20T00:19:45.572491Z", - "shell.execute_reply": "2024-10-20T00:19:45.571891Z" + "iopub.execute_input": "2024-10-22T01:46:19.085901Z", + "iopub.status.busy": "2024-10-22T01:46:19.085704Z", + "iopub.status.idle": "2024-10-22T01:46:20.667141Z", + "shell.execute_reply": "2024-10-22T01:46:20.666521Z" } }, "outputs": [], @@ -69,10 +69,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.574865Z", - "iopub.status.busy": "2024-10-20T00:19:45.574365Z", - "iopub.status.idle": "2024-10-20T00:19:45.854316Z", - "shell.execute_reply": "2024-10-20T00:19:45.853553Z" + "iopub.execute_input": "2024-10-22T01:46:20.669667Z", + "iopub.status.busy": "2024-10-22T01:46:20.669147Z", + "iopub.status.idle": "2024-10-22T01:46:20.952402Z", + "shell.execute_reply": "2024-10-22T01:46:20.951203Z" } }, "outputs": [ @@ -128,10 +128,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.857114Z", - "iopub.status.busy": "2024-10-20T00:19:45.856550Z", - "iopub.status.idle": "2024-10-20T00:19:45.896364Z", - "shell.execute_reply": "2024-10-20T00:19:45.895847Z" + "iopub.execute_input": "2024-10-22T01:46:20.955237Z", + "iopub.status.busy": "2024-10-22T01:46:20.954947Z", + "iopub.status.idle": "2024-10-22T01:46:20.997660Z", + "shell.execute_reply": "2024-10-22T01:46:20.996997Z" } }, "outputs": [], @@ -243,10 +243,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.898487Z", - "iopub.status.busy": "2024-10-20T00:19:45.898281Z", - "iopub.status.idle": "2024-10-20T00:19:45.939282Z", - "shell.execute_reply": "2024-10-20T00:19:45.938727Z" + "iopub.execute_input": "2024-10-22T01:46:21.001472Z", + "iopub.status.busy": "2024-10-22T01:46:21.000668Z", + "iopub.status.idle": "2024-10-22T01:46:21.045485Z", + "shell.execute_reply": "2024-10-22T01:46:21.044810Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:45.941683Z", - "iopub.status.busy": "2024-10-20T00:19:45.941287Z", - "iopub.status.idle": "2024-10-20T00:19:46.106486Z", - "shell.execute_reply": "2024-10-20T00:19:46.105883Z" + "iopub.execute_input": "2024-10-22T01:46:21.048402Z", + "iopub.status.busy": "2024-10-22T01:46:21.048140Z", + "iopub.status.idle": "2024-10-22T01:46:21.221513Z", + "shell.execute_reply": "2024-10-22T01:46:21.220845Z" } }, "outputs": [ @@ -291,10 +291,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.108807Z", - "iopub.status.busy": "2024-10-20T00:19:46.108433Z", - "iopub.status.idle": "2024-10-20T00:19:46.150685Z", - "shell.execute_reply": "2024-10-20T00:19:46.150098Z" + "iopub.execute_input": "2024-10-22T01:46:21.223974Z", + "iopub.status.busy": "2024-10-22T01:46:21.223731Z", + "iopub.status.idle": "2024-10-22T01:46:21.266997Z", + "shell.execute_reply": "2024-10-22T01:46:21.266394Z" }, "scrolled": true }, @@ -336,10 +336,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.153086Z", - "iopub.status.busy": "2024-10-20T00:19:46.152663Z", - "iopub.status.idle": "2024-10-20T00:19:46.389788Z", - "shell.execute_reply": "2024-10-20T00:19:46.389179Z" + "iopub.execute_input": "2024-10-22T01:46:21.269430Z", + "iopub.status.busy": "2024-10-22T01:46:21.268987Z", + "iopub.status.idle": "2024-10-22T01:46:21.525152Z", + "shell.execute_reply": "2024-10-22T01:46:21.524613Z" }, "scrolled": false }, @@ -353,10 +353,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.392032Z", - "iopub.status.busy": "2024-10-20T00:19:46.391701Z", - "iopub.status.idle": "2024-10-20T00:19:46.422253Z", - "shell.execute_reply": "2024-10-20T00:19:46.421658Z" + "iopub.execute_input": "2024-10-22T01:46:21.527422Z", + "iopub.status.busy": "2024-10-22T01:46:21.527018Z", + "iopub.status.idle": "2024-10-22T01:46:21.558040Z", + "shell.execute_reply": "2024-10-22T01:46:21.557521Z" } }, "outputs": [ @@ -396,10 +396,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.424167Z", - "iopub.status.busy": "2024-10-20T00:19:46.423802Z", - "iopub.status.idle": "2024-10-20T00:19:46.472213Z", - "shell.execute_reply": "2024-10-20T00:19:46.471536Z" + "iopub.execute_input": "2024-10-22T01:46:21.560021Z", + "iopub.status.busy": "2024-10-22T01:46:21.559673Z", + "iopub.status.idle": "2024-10-22T01:46:21.610876Z", + "shell.execute_reply": "2024-10-22T01:46:21.610164Z" } }, "outputs": [], @@ -420,10 +420,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.474268Z", - "iopub.status.busy": "2024-10-20T00:19:46.473902Z", - "iopub.status.idle": "2024-10-20T00:19:46.503588Z", - "shell.execute_reply": "2024-10-20T00:19:46.502978Z" + "iopub.execute_input": "2024-10-22T01:46:21.613296Z", + "iopub.status.busy": "2024-10-22T01:46:21.612906Z", + "iopub.status.idle": "2024-10-22T01:46:21.644434Z", + "shell.execute_reply": "2024-10-22T01:46:21.643880Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.505633Z", - "iopub.status.busy": "2024-10-20T00:19:46.505269Z", - "iopub.status.idle": "2024-10-20T00:19:46.534106Z", - "shell.execute_reply": "2024-10-20T00:19:46.533500Z" + "iopub.execute_input": "2024-10-22T01:46:21.646572Z", + "iopub.status.busy": "2024-10-22T01:46:21.646218Z", + "iopub.status.idle": "2024-10-22T01:46:21.677641Z", + "shell.execute_reply": "2024-10-22T01:46:21.677032Z" } }, "outputs": [], @@ -563,10 +563,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.535853Z", - "iopub.status.busy": "2024-10-20T00:19:46.535654Z", - "iopub.status.idle": "2024-10-20T00:19:46.564750Z", - "shell.execute_reply": "2024-10-20T00:19:46.564137Z" + "iopub.execute_input": "2024-10-22T01:46:21.679692Z", + "iopub.status.busy": "2024-10-22T01:46:21.679494Z", + "iopub.status.idle": "2024-10-22T01:46:21.711432Z", + "shell.execute_reply": "2024-10-22T01:46:21.710762Z" } }, "outputs": [], @@ -584,10 +584,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.566825Z", - "iopub.status.busy": "2024-10-20T00:19:46.566340Z", - "iopub.status.idle": "2024-10-20T00:19:46.716031Z", - "shell.execute_reply": "2024-10-20T00:19:46.715383Z" + "iopub.execute_input": "2024-10-22T01:46:21.713690Z", + "iopub.status.busy": "2024-10-22T01:46:21.713318Z", + "iopub.status.idle": "2024-10-22T01:46:21.871444Z", + "shell.execute_reply": "2024-10-22T01:46:21.870764Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.718061Z", - "iopub.status.busy": "2024-10-20T00:19:46.717861Z", - "iopub.status.idle": "2024-10-20T00:19:46.750474Z", - "shell.execute_reply": "2024-10-20T00:19:46.749867Z" + "iopub.execute_input": "2024-10-22T01:46:21.873900Z", + "iopub.status.busy": "2024-10-22T01:46:21.873475Z", + "iopub.status.idle": "2024-10-22T01:46:21.917795Z", + "shell.execute_reply": "2024-10-22T01:46:21.917144Z" } }, "outputs": [ @@ -639,10 +639,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:46.752370Z", - "iopub.status.busy": "2024-10-20T00:19:46.752177Z", - "iopub.status.idle": "2024-10-20T00:19:47.830059Z", - "shell.execute_reply": "2024-10-20T00:19:47.829366Z" + "iopub.execute_input": "2024-10-22T01:46:21.920254Z", + "iopub.status.busy": "2024-10-22T01:46:21.919888Z", + "iopub.status.idle": "2024-10-22T01:46:23.069075Z", + "shell.execute_reply": "2024-10-22T01:46:23.068410Z" } }, "outputs": [], @@ -662,10 +662,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:47.832529Z", - "iopub.status.busy": "2024-10-20T00:19:47.832082Z", - "iopub.status.idle": "2024-10-20T00:19:47.862810Z", - "shell.execute_reply": "2024-10-20T00:19:47.862167Z" + "iopub.execute_input": "2024-10-22T01:46:23.071403Z", + "iopub.status.busy": "2024-10-22T01:46:23.071173Z", + "iopub.status.idle": "2024-10-22T01:46:23.103531Z", + "shell.execute_reply": "2024-10-22T01:46:23.102889Z" } }, "outputs": [ diff --git a/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb b/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb index 1f546537b..46dbd6543 100644 --- a/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb +++ b/main/example_notebooks/identifying_effects_using_id_algorithm.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:50.080103Z", - "iopub.status.busy": "2024-10-20T00:19:50.079663Z", - "iopub.status.idle": "2024-10-20T00:19:51.521426Z", - "shell.execute_reply": "2024-10-20T00:19:51.520769Z" + "iopub.execute_input": "2024-10-22T01:46:25.714117Z", + "iopub.status.busy": "2024-10-22T01:46:25.713918Z", + "iopub.status.idle": "2024-10-22T01:46:27.343798Z", + "shell.execute_reply": "2024-10-22T01:46:27.343125Z" } }, "outputs": [], @@ -55,10 +55,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.523928Z", - "iopub.status.busy": "2024-10-20T00:19:51.523454Z", - "iopub.status.idle": "2024-10-20T00:19:51.628681Z", - "shell.execute_reply": "2024-10-20T00:19:51.628146Z" + "iopub.execute_input": "2024-10-22T01:46:27.346479Z", + "iopub.status.busy": "2024-10-22T01:46:27.345951Z", + "iopub.status.idle": "2024-10-22T01:46:27.467642Z", + "shell.execute_reply": "2024-10-22T01:46:27.467065Z" } }, "outputs": [ @@ -136,10 +136,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.630866Z", - "iopub.status.busy": "2024-10-20T00:19:51.630631Z", - "iopub.status.idle": "2024-10-20T00:19:51.741067Z", - "shell.execute_reply": "2024-10-20T00:19:51.740485Z" + "iopub.execute_input": "2024-10-22T01:46:27.470470Z", + "iopub.status.busy": "2024-10-22T01:46:27.469849Z", + "iopub.status.idle": "2024-10-22T01:46:27.585791Z", + "shell.execute_reply": "2024-10-22T01:46:27.585192Z" } }, "outputs": [ @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.743513Z", - "iopub.status.busy": "2024-10-20T00:19:51.743294Z", - "iopub.status.idle": "2024-10-20T00:19:51.845437Z", - "shell.execute_reply": "2024-10-20T00:19:51.844834Z" + "iopub.execute_input": "2024-10-22T01:46:27.588381Z", + "iopub.status.busy": "2024-10-22T01:46:27.587815Z", + "iopub.status.idle": "2024-10-22T01:46:27.696657Z", + "shell.execute_reply": "2024-10-22T01:46:27.695970Z" } }, "outputs": [ @@ -302,10 +302,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.847605Z", - "iopub.status.busy": "2024-10-20T00:19:51.847389Z", - "iopub.status.idle": "2024-10-20T00:19:51.962810Z", - "shell.execute_reply": "2024-10-20T00:19:51.962198Z" + "iopub.execute_input": "2024-10-22T01:46:27.698990Z", + "iopub.status.busy": "2024-10-22T01:46:27.698752Z", + "iopub.status.idle": "2024-10-22T01:46:27.821761Z", + "shell.execute_reply": "2024-10-22T01:46:27.821079Z" } }, "outputs": [ @@ -386,10 +386,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:51.965270Z", - "iopub.status.busy": "2024-10-20T00:19:51.964687Z", - "iopub.status.idle": "2024-10-20T00:19:52.099902Z", - "shell.execute_reply": "2024-10-20T00:19:52.099262Z" + "iopub.execute_input": "2024-10-22T01:46:27.824587Z", + "iopub.status.busy": "2024-10-22T01:46:27.824128Z", + "iopub.status.idle": "2024-10-22T01:46:27.967761Z", + "shell.execute_reply": "2024-10-22T01:46:27.967093Z" } }, "outputs": [ @@ -470,10 +470,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:52.102606Z", - "iopub.status.busy": "2024-10-20T00:19:52.102201Z", - "iopub.status.idle": "2024-10-20T00:19:52.245123Z", - "shell.execute_reply": "2024-10-20T00:19:52.244528Z" + "iopub.execute_input": "2024-10-22T01:46:27.972768Z", + "iopub.status.busy": "2024-10-22T01:46:27.970832Z", + "iopub.status.idle": "2024-10-22T01:46:28.121773Z", + "shell.execute_reply": "2024-10-22T01:46:28.121151Z" } }, "outputs": [ diff --git a/main/example_notebooks/lalonde_pandas_api.html b/main/example_notebooks/lalonde_pandas_api.html index ca34025b2..560968195 100644 --- a/main/example_notebooks/lalonde_pandas_api.html +++ b/main/example_notebooks/lalonde_pandas_api.html @@ -799,87 +799,87 @@

The do 0 False - 31.0 - 10.0 + 17.0 + 11.0 1.0 0.0 0.0 1.0 + 4080.730 + 3796.029 + 0.0000 0.0 - 0.00 - 17014.590 - 1.0 - 1.0 - 0.616379 - 1.622378 + 0.0 + 0.649916 + 1.538661 1 - True + False 23.0 - 8.0 + 11.0 + 1.0 0.0 0.0 1.0 + 0.000 + 0.000 + 0.0000 1.0 - 0.0 - 1713.15 - 4232.309 1.0 - 0.0 - 0.486703 - 2.054643 + 0.640709 + 1.560772 2 False - 22.0 - 9.0 + 19.0 + 11.0 1.0 0.0 0.0 1.0 - 0.0 - 0.00 - 12898.380 + 0.000 + 0.000 + 0.0000 1.0 1.0 - 0.618674 - 1.616361 + 0.646859 + 1.545933 3 - False - 32.0 - 12.0 - 0.0 - 1.0 + True + 27.0 + 11.0 1.0 0.0 0.0 - 0.00 - 6735.320 1.0 + 0.000 + 0.000 + 549.2984 1.0 - 0.518670 - 1.928009 + 1.0 + 0.365488 + 2.736067 4 False - 19.0 - 10.0 - 1.0 - 0.0 + 28.0 + 11.0 0.0 1.0 - 0.0 - 0.00 - 4309.878 1.0 1.0 - 0.635214 - 1.574272 + 3472.948 + 0.000 + 6771.6220 + 0.0 + 1.0 + 0.694635 + 1.439605 @@ -920,7 +920,7 @@

Treatment Effect Estimation
-$\displaystyle 1380.0630450237$

+$\displaystyle 1087.95177287071$

We could get some rough error bars on the outcome using the normal approximation for a 95% confidence interval, like

@@ -939,7 +939,7 @@

Treatment Effect Estimation
-$\displaystyle 1176.21721690883$

+$\displaystyle 1331.19037172705$

but note that these DO NOT contain propensity score estimation error. For that, a bootstrapping procedure might be more appropriate.

This is just one statistic we can compute from the interventional distribution of 're78'. We can get all of the interventional moments as well, including functions of 're78'. We can leverage the full power of pandas, like

@@ -958,13 +958,13 @@

Treatment Effect Estimation 
 count      445.000000
-mean      5160.692072
-std       6301.457031
+mean      5621.276648
+std       7162.567400
 min          0.000000
 25%          0.000000
-50%       3194.010000
-75%       7693.400000
-max      39483.530000
+50%       3708.719000
+75%       8472.158000
+max      60307.930000
 Name: re78, dtype: float64
@@ -1120,87 +1120,87 @@

Specifying Interventions\n", " 0\n", " False\n", - " 31.0\n", - " 10.0\n", + " 17.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", + " 4080.730\n", + " 3796.029\n", + " 0.0000\n", " 0.0\n", - " 0.00\n", - " 17014.590\n", - " 1.0\n", - " 1.0\n", - " 0.616379\n", - " 1.622378\n", + " 0.0\n", + " 0.649916\n", + " 1.538661\n", " \n", " \n", " 1\n", - " True\n", + " False\n", " 23.0\n", - " 8.0\n", + " 11.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", + " 0.000\n", + " 0.000\n", + " 0.0000\n", " 1.0\n", - " 0.0\n", - " 1713.15\n", - " 4232.309\n", " 1.0\n", - " 0.0\n", - " 0.486703\n", - " 2.054643\n", + " 0.640709\n", + " 1.560772\n", " \n", " \n", " 2\n", " False\n", - " 22.0\n", - " 9.0\n", + " 19.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", - " 0.00\n", - " 12898.380\n", + " 0.000\n", + " 0.000\n", + " 0.0000\n", " 1.0\n", " 1.0\n", - " 0.618674\n", - " 1.616361\n", + " 0.646859\n", + " 1.545933\n", " \n", " \n", " 3\n", - " False\n", - " 32.0\n", - " 12.0\n", - " 0.0\n", - " 1.0\n", + " True\n", + " 27.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 0.00\n", - " 6735.320\n", + " 1.0\n", + " 0.000\n", + " 0.000\n", + " 549.2984\n", " 1.0\n", " 1.0\n", - " 0.518670\n", - " 1.928009\n", + " 0.365488\n", + " 2.736067\n", " \n", " \n", " 4\n", " False\n", - " 19.0\n", - " 10.0\n", - " 1.0\n", - " 0.0\n", + " 28.0\n", + " 11.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", - " 0.00\n", - " 4309.878\n", " 1.0\n", " 1.0\n", - " 0.635214\n", - " 1.574272\n", + " 3472.948\n", + " 0.000\n", + " 6771.6220\n", + " 0.0\n", + " 1.0\n", + " 0.694635\n", + " 1.439605\n", " \n", " \n", "\n", "" ], "text/plain": [ - " treat age educ black hisp married nodegr re74 re75 re78 \\\n", - "0 False 31.0 10.0 1.0 0.0 0.0 1.0 0.0 0.00 17014.590 \n", - "1 True 23.0 8.0 0.0 0.0 1.0 1.0 0.0 1713.15 4232.309 \n", - "2 False 22.0 9.0 1.0 0.0 0.0 1.0 0.0 0.00 12898.380 \n", - "3 False 32.0 12.0 0.0 1.0 1.0 0.0 0.0 0.00 6735.320 \n", - "4 False 19.0 10.0 1.0 0.0 0.0 1.0 0.0 0.00 4309.878 \n", + " treat age educ black hisp married nodegr re74 re75 \\\n", + "0 False 17.0 11.0 1.0 0.0 0.0 1.0 4080.730 3796.029 \n", + "1 False 23.0 11.0 1.0 0.0 0.0 1.0 0.000 0.000 \n", + "2 False 19.0 11.0 1.0 0.0 0.0 1.0 0.000 0.000 \n", + "3 True 27.0 11.0 1.0 0.0 0.0 1.0 0.000 0.000 \n", + "4 False 28.0 11.0 0.0 1.0 1.0 1.0 3472.948 0.000 \n", "\n", - " u74 u75 propensity_score weight \n", - "0 1.0 1.0 0.616379 1.622378 \n", - "1 1.0 0.0 0.486703 2.054643 \n", - "2 1.0 1.0 0.618674 1.616361 \n", - "3 1.0 1.0 0.518670 1.928009 \n", - "4 1.0 1.0 0.635214 1.574272 " + " re78 u74 u75 propensity_score weight \n", + "0 0.0000 0.0 0.0 0.649916 1.538661 \n", + "1 0.0000 1.0 1.0 0.640709 1.560772 \n", + "2 0.0000 1.0 1.0 0.646859 1.545933 \n", + "3 549.2984 1.0 1.0 0.365488 2.736067 \n", + "4 6771.6220 0.0 1.0 0.694635 1.439605 " ] }, "execution_count": 6, @@ -464,10 +464,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.798360Z", - "iopub.status.busy": "2024-10-20T00:19:56.798016Z", - "iopub.status.idle": "2024-10-20T00:19:56.850927Z", - "shell.execute_reply": "2024-10-20T00:19:56.850417Z" + "iopub.execute_input": "2024-10-22T01:46:33.338888Z", + "iopub.status.busy": "2024-10-22T01:46:33.338417Z", + "iopub.status.idle": "2024-10-22T01:46:33.395006Z", + "shell.execute_reply": "2024-10-22T01:46:33.394462Z" } }, "outputs": [ @@ -502,21 +502,21 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.852854Z", - "iopub.status.busy": "2024-10-20T00:19:56.852476Z", - "iopub.status.idle": "2024-10-20T00:19:56.869245Z", - "shell.execute_reply": "2024-10-20T00:19:56.868642Z" + "iopub.execute_input": "2024-10-22T01:46:33.397217Z", + "iopub.status.busy": "2024-10-22T01:46:33.396760Z", + "iopub.status.idle": "2024-10-22T01:46:33.415562Z", + "shell.execute_reply": "2024-10-22T01:46:33.414840Z" } }, "outputs": [ { "data": { - "image/png": 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"text/latex": [ - "$\\displaystyle 1380.0630450237$" + "$\\displaystyle 1087.95177287071$" ], "text/plain": [ - "1380.0630450236968" + "1087.951772870707" ] }, "execution_count": 8, @@ -540,21 +540,21 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.871240Z", - "iopub.status.busy": "2024-10-20T00:19:56.870819Z", - "iopub.status.idle": "2024-10-20T00:19:56.889221Z", - "shell.execute_reply": "2024-10-20T00:19:56.888615Z" + "iopub.execute_input": "2024-10-22T01:46:33.417637Z", + "iopub.status.busy": "2024-10-22T01:46:33.417410Z", + "iopub.status.idle": "2024-10-22T01:46:33.437263Z", + "shell.execute_reply": "2024-10-22T01:46:33.436714Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/latex": [ - "$\\displaystyle 1176.21721690883$" + "$\\displaystyle 1331.19037172705$" ], "text/plain": [ - "1176.2172169088303" + "1331.1903717270516" ] }, "execution_count": 9, @@ -587,10 +587,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.891289Z", - "iopub.status.busy": "2024-10-20T00:19:56.890865Z", - "iopub.status.idle": "2024-10-20T00:19:56.897111Z", - "shell.execute_reply": "2024-10-20T00:19:56.896516Z" + "iopub.execute_input": "2024-10-22T01:46:33.439400Z", + "iopub.status.busy": "2024-10-22T01:46:33.439032Z", + "iopub.status.idle": "2024-10-22T01:46:33.445777Z", + "shell.execute_reply": "2024-10-22T01:46:33.445157Z" } }, "outputs": [ @@ -598,13 +598,13 @@ "data": { "text/plain": [ "count 445.000000\n", - "mean 5160.692072\n", - "std 6301.457031\n", + "mean 5621.276648\n", + "std 7162.567400\n", "min 0.000000\n", "25% 0.000000\n", - "50% 3194.010000\n", - "75% 7693.400000\n", - "max 39483.530000\n", + "50% 3708.719000\n", + "75% 8472.158000\n", + "max 60307.930000\n", "Name: re78, dtype: float64" ] }, @@ -622,10 +622,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.898835Z", - "iopub.status.busy": "2024-10-20T00:19:56.898633Z", - "iopub.status.idle": "2024-10-20T00:19:56.904605Z", - "shell.execute_reply": "2024-10-20T00:19:56.904121Z" + "iopub.execute_input": "2024-10-22T01:46:33.447804Z", + "iopub.status.busy": "2024-10-22T01:46:33.447491Z", + "iopub.status.idle": "2024-10-22T01:46:33.456137Z", + "shell.execute_reply": "2024-10-22T01:46:33.454465Z" } }, "outputs": [ @@ -664,10 +664,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.906470Z", - "iopub.status.busy": "2024-10-20T00:19:56.906117Z", - "iopub.status.idle": "2024-10-20T00:19:56.910902Z", - "shell.execute_reply": "2024-10-20T00:19:56.910391Z" + "iopub.execute_input": "2024-10-22T01:46:33.458819Z", + "iopub.status.busy": "2024-10-22T01:46:33.458417Z", + "iopub.status.idle": "2024-10-22T01:46:33.463848Z", + "shell.execute_reply": "2024-10-22T01:46:33.463294Z" } }, "outputs": [], @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:56.912556Z", - "iopub.status.busy": "2024-10-20T00:19:56.912370Z", - "iopub.status.idle": "2024-10-20T00:19:57.175422Z", - "shell.execute_reply": "2024-10-20T00:19:57.174783Z" + "iopub.execute_input": "2024-10-22T01:46:33.465801Z", + "iopub.status.busy": "2024-10-22T01:46:33.465372Z", + "iopub.status.idle": "2024-10-22T01:46:33.750737Z", + "shell.execute_reply": "2024-10-22T01:46:33.750157Z" } }, "outputs": [ @@ -699,7 +699,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -719,10 +719,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.177676Z", - "iopub.status.busy": "2024-10-20T00:19:57.177312Z", - "iopub.status.idle": "2024-10-20T00:19:57.342585Z", - "shell.execute_reply": "2024-10-20T00:19:57.341974Z" + "iopub.execute_input": "2024-10-22T01:46:33.752948Z", + "iopub.status.busy": "2024-10-22T01:46:33.752483Z", + "iopub.status.idle": "2024-10-22T01:46:33.925779Z", + "shell.execute_reply": "2024-10-22T01:46:33.925093Z" } }, "outputs": [ @@ -738,7 +738,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -765,10 +765,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.344774Z", - "iopub.status.busy": "2024-10-20T00:19:57.344535Z", - "iopub.status.idle": "2024-10-20T00:19:57.384005Z", - "shell.execute_reply": "2024-10-20T00:19:57.383462Z" + "iopub.execute_input": "2024-10-22T01:46:33.928075Z", + "iopub.status.busy": "2024-10-22T01:46:33.927689Z", + "iopub.status.idle": "2024-10-22T01:46:33.969381Z", + "shell.execute_reply": "2024-10-22T01:46:33.968686Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.386028Z", - "iopub.status.busy": "2024-10-20T00:19:57.385542Z", - "iopub.status.idle": "2024-10-20T00:19:57.397802Z", - "shell.execute_reply": "2024-10-20T00:19:57.397311Z" + "iopub.execute_input": "2024-10-22T01:46:33.971812Z", + "iopub.status.busy": "2024-10-22T01:46:33.971409Z", + "iopub.status.idle": "2024-10-22T01:46:33.984130Z", + "shell.execute_reply": "2024-10-22T01:46:33.983576Z" } }, "outputs": [ @@ -834,106 +834,106 @@ " \n", " 0\n", " True\n", - " 19.0\n", - " 9.0\n", + " 20.0\n", + " 10.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.00\n", - " 0.000\n", - " 8173.908\n", - " 1.0\n", - " 1.0\n", - " 0.376593\n", - " 2.655385\n", + " 5005.7310\n", + " 2777.3550\n", + " 5615.1890\n", + " 0.0\n", + " 0.0\n", + " 0.366342\n", + " 2.729693\n", " \n", " \n", " 1\n", " True\n", - " 27.0\n", - " 11.0\n", + " 21.0\n", + " 9.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.00\n", - " 0.000\n", - " 7506.146\n", - " 1.0\n", - " 1.0\n", - " 0.365484\n", - " 2.736099\n", + " 6416.4700\n", + " 5749.3310\n", + " 743.6666\n", + " 0.0\n", + " 0.0\n", + " 0.379746\n", + " 2.633342\n", " \n", " \n", " 2\n", " True\n", - " 38.0\n", - " 12.0\n", - " 0.0\n", - " 0.0\n", + " 40.0\n", + " 11.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", - " 0.00\n", - " 0.000\n", - " 4941.849\n", " 1.0\n", + " 0.0000\n", + " 0.0000\n", + " 23005.6000\n", " 1.0\n", - " 0.600062\n", - " 1.666494\n", + " 1.0\n", + " 0.385928\n", + " 2.591158\n", " \n", " \n", " 3\n", " True\n", - " 20.0\n", - " 9.0\n", - " 0.0\n", + " 18.0\n", + " 11.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", - " 12260.78\n", - " 5875.049\n", - " 1358.643\n", + " 858.2543\n", + " 214.5636\n", + " 929.8839\n", " 0.0\n", " 0.0\n", - " 0.272011\n", - " 3.676321\n", + " 0.351611\n", + " 2.844049\n", " \n", " \n", " 4\n", " True\n", - " 31.0\n", - " 11.0\n", + " 21.0\n", + " 13.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0000\n", + " 0.0000\n", + " 17094.6400\n", " 1.0\n", " 1.0\n", - " 0.00\n", - " 0.000\n", - " 14509.930\n", - " 1.0\n", - " 1.0\n", - " 0.421973\n", - " 2.369819\n", + " 0.519464\n", + " 1.925062\n", " \n", " \n", "\n", "" ], "text/plain": [ - " treat age educ black hisp married nodegr re74 re75 \\\n", - "0 True 19.0 9.0 1.0 0.0 0.0 1.0 0.00 0.000 \n", - "1 True 27.0 11.0 1.0 0.0 0.0 1.0 0.00 0.000 \n", - "2 True 38.0 12.0 0.0 0.0 0.0 0.0 0.00 0.000 \n", - "3 True 20.0 9.0 0.0 1.0 0.0 1.0 12260.78 5875.049 \n", - "4 True 31.0 11.0 1.0 0.0 1.0 1.0 0.00 0.000 \n", + " treat age educ black hisp married nodegr re74 re75 \\\n", + "0 True 20.0 10.0 1.0 0.0 0.0 1.0 5005.7310 2777.3550 \n", + "1 True 21.0 9.0 1.0 0.0 0.0 1.0 6416.4700 5749.3310 \n", + "2 True 40.0 11.0 1.0 0.0 0.0 1.0 0.0000 0.0000 \n", + "3 True 18.0 11.0 1.0 0.0 0.0 1.0 858.2543 214.5636 \n", + "4 True 21.0 13.0 1.0 0.0 0.0 0.0 0.0000 0.0000 \n", "\n", - " re78 u74 u75 propensity_score weight \n", - "0 8173.908 1.0 1.0 0.376593 2.655385 \n", - "1 7506.146 1.0 1.0 0.365484 2.736099 \n", - "2 4941.849 1.0 1.0 0.600062 1.666494 \n", - "3 1358.643 0.0 0.0 0.272011 3.676321 \n", - "4 14509.930 1.0 1.0 0.421973 2.369819 " + " re78 u74 u75 propensity_score weight \n", + "0 5615.1890 0.0 0.0 0.366342 2.729693 \n", + "1 743.6666 0.0 0.0 0.379746 2.633342 \n", + "2 23005.6000 1.0 1.0 0.385928 2.591158 \n", + "3 929.8839 0.0 0.0 0.351611 2.844049 \n", + "4 17094.6400 1.0 1.0 0.519464 1.925062 " ] }, "execution_count": 16, @@ -964,10 +964,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:19:57.399495Z", - "iopub.status.busy": "2024-10-20T00:19:57.399309Z", - "iopub.status.idle": "2024-10-20T00:19:57.402472Z", - "shell.execute_reply": "2024-10-20T00:19:57.402002Z" + "iopub.execute_input": "2024-10-22T01:46:33.986272Z", + "iopub.status.busy": "2024-10-22T01:46:33.986070Z", + "iopub.status.idle": "2024-10-22T01:46:33.989786Z", + "shell.execute_reply": "2024-10-22T01:46:33.989267Z" } }, "outputs": [ diff --git a/main/example_notebooks/load_graph_example.html b/main/example_notebooks/load_graph_example.html index 832024289..81f7dc79c 100644 --- a/main/example_notebooks/load_graph_example.html +++ b/main/example_notebooks/load_graph_example.html @@ -636,55 +636,55 @@

I. Generating dummy data\n", " \n", " 0\n", - " 0\n", + " 6\n", " 0\n", " 0\n", " \n", " \n", " 1\n", - " 2\n", + " 1\n", " 1\n", " 10\n", " \n", " \n", " 2\n", - " 9\n", + " 0\n", " 2\n", " 20\n", " \n", " \n", " 3\n", - " 6\n", + " 2\n", " 3\n", " 30\n", " \n", " \n", " 4\n", - " 8\n", + " 5\n", " 4\n", " 40\n", " \n", " \n", " 5\n", - " 4\n", + " 8\n", " 5\n", " 50\n", " \n", " \n", " 6\n", - " 1\n", + " 9\n", " 6\n", " 60\n", " \n", " \n", " 7\n", - " 3\n", + " 4\n", " 7\n", " 70\n", " \n", " \n", " 8\n", - " 5\n", + " 3\n", " 8\n", " 80\n", " \n", @@ -165,15 +165,15 @@ ], "text/plain": [ " Z X Y\n", - "0 0 0 0\n", - "1 2 1 10\n", - "2 9 2 20\n", - "3 6 3 30\n", - "4 8 4 40\n", - "5 4 5 50\n", - "6 1 6 60\n", - "7 3 7 70\n", - "8 5 8 80\n", + "0 6 0 0\n", + "1 1 1 10\n", + "2 0 2 20\n", + "3 2 3 30\n", + "4 5 4 40\n", + "5 8 5 50\n", + "6 9 6 60\n", + "7 4 7 70\n", + "8 3 8 80\n", "9 7 9 90" ] }, @@ -202,10 +202,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.466980Z", - "iopub.status.busy": "2024-10-20T00:20:01.466483Z", - "iopub.status.idle": "2024-10-20T00:20:01.572713Z", - "shell.execute_reply": "2024-10-20T00:20:01.572160Z" + "iopub.execute_input": "2024-10-22T01:46:38.348043Z", + "iopub.status.busy": "2024-10-22T01:46:38.347855Z", + "iopub.status.idle": "2024-10-22T01:46:38.445036Z", + "shell.execute_reply": "2024-10-22T01:46:38.444360Z" } }, "outputs": [ @@ -255,10 +255,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.575104Z", - "iopub.status.busy": "2024-10-20T00:20:01.574886Z", - "iopub.status.idle": "2024-10-20T00:20:01.687203Z", - "shell.execute_reply": "2024-10-20T00:20:01.686564Z" + "iopub.execute_input": "2024-10-22T01:46:38.447341Z", + "iopub.status.busy": "2024-10-22T01:46:38.446858Z", + "iopub.status.idle": "2024-10-22T01:46:38.530871Z", + "shell.execute_reply": "2024-10-22T01:46:38.530232Z" } }, "outputs": [ @@ -309,10 +309,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.689460Z", - "iopub.status.busy": "2024-10-20T00:20:01.689113Z", - "iopub.status.idle": "2024-10-20T00:20:01.800333Z", - "shell.execute_reply": "2024-10-20T00:20:01.799758Z" + "iopub.execute_input": "2024-10-22T01:46:38.532956Z", + "iopub.status.busy": "2024-10-22T01:46:38.532758Z", + "iopub.status.idle": "2024-10-22T01:46:38.624676Z", + "shell.execute_reply": "2024-10-22T01:46:38.624187Z" } }, "outputs": [ @@ -356,10 +356,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:01.802653Z", - "iopub.status.busy": "2024-10-20T00:20:01.802117Z", - "iopub.status.idle": "2024-10-20T00:20:01.916719Z", - "shell.execute_reply": "2024-10-20T00:20:01.916127Z" + "iopub.execute_input": "2024-10-22T01:46:38.626805Z", + "iopub.status.busy": "2024-10-22T01:46:38.626433Z", + "iopub.status.idle": "2024-10-22T01:46:38.711917Z", + "shell.execute_reply": "2024-10-22T01:46:38.711236Z" } }, "outputs": [ diff --git a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html index 71d0f43d4..7d115d9d7 100644 --- a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html +++ b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.html @@ -625,7 +625,7 @@

Initialize dataset
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Initialize dataset

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Initialize dataset

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Initialize dataset

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Fit predictor and start training

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Evaluate on test domain

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Evaluate on test domain

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Prediction with CACM
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Prediction with CACM

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"tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb index 64632dae9..a5ff8c846 100644 --- a/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb +++ b/main/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb @@ -90,10 +90,10 @@ "id": "8c441b37", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:03.681902Z", - "iopub.status.busy": "2024-10-20T00:20:03.681701Z", - "iopub.status.idle": "2024-10-20T00:20:05.318343Z", - "shell.execute_reply": "2024-10-20T00:20:05.317715Z" + "iopub.execute_input": "2024-10-22T01:46:43.592980Z", + "iopub.status.busy": "2024-10-22T01:46:43.592800Z", + "iopub.status.idle": "2024-10-22T01:46:45.276498Z", + "shell.execute_reply": "2024-10-22T01:46:45.275777Z" }, "scrolled": true }, @@ -117,10 +117,10 @@ "id": "23c5c320", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:05.320697Z", - "iopub.status.busy": "2024-10-20T00:20:05.320431Z", - "iopub.status.idle": "2024-10-20T00:20:08.047413Z", - "shell.execute_reply": "2024-10-20T00:20:08.046768Z" + "iopub.execute_input": "2024-10-22T01:46:45.279145Z", + "iopub.status.busy": "2024-10-22T01:46:45.278833Z", + "iopub.status.idle": "2024-10-22T01:46:51.930557Z", + "shell.execute_reply": "2024-10-22T01:46:51.929857Z" } }, "outputs": [ @@ -128,7 +128,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Failed to download (trying next):\n", "\n", "\n", @@ -145,7 +151,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1ffbb62b3d94ac7a34f5002f869a663", + "model_id": "283eefa5e0cb46e5b3e1161b9f7ae3c3", "version_major": 2, "version_minor": 0 }, @@ -168,7 +174,13 @@ "output_type": "stream", "text": [ "\n", - "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Failed to download (trying next):\n", "\n", "\n", @@ -185,7 +197,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1fe87d8c4e443f8b34a38b2b1bc2d43", + "model_id": "b32fb2e6e5d742b19d0c3e5f83bb5ef2", "version_major": 2, "version_minor": 0 }, @@ -225,7 +237,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f44f1dc26974e3a9cee0837bacbb6bf", + "model_id": "a956dfa80b12412795cfa1bc7e13afdc", "version_major": 2, "version_minor": 0 }, @@ -240,26 +252,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n" + "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Failed to download (trying next):\n", "\n", "\n", - "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ef95b193d7a417db298378090b2ba68", + "model_id": "ea72732ce8cf48668ab2170cb4aa409e", "version_major": 2, "version_minor": 0 }, @@ -309,10 +327,10 @@ "id": 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"2024-10-22T01:46:52.491588Z" } }, "outputs": [ @@ -372,10 +390,10 @@ "id": "5201cd08", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:08.534978Z", - "iopub.status.busy": "2024-10-20T00:20:08.534274Z", - "iopub.status.idle": "2024-10-20T00:20:14.299204Z", - "shell.execute_reply": "2024-10-20T00:20:14.298063Z" + "iopub.execute_input": "2024-10-22T01:46:52.496388Z", + "iopub.status.busy": "2024-10-22T01:46:52.495985Z", + "iopub.status.idle": "2024-10-22T01:46:53.354055Z", + "shell.execute_reply": "2024-10-22T01:46:53.352930Z" } }, "outputs": [], @@ -398,10 +416,10 @@ "id": "afbd74ef", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.302248Z", - "iopub.status.busy": "2024-10-20T00:20:14.301926Z", - "iopub.status.idle": "2024-10-20T00:20:14.307427Z", - "shell.execute_reply": "2024-10-20T00:20:14.306799Z" + "iopub.execute_input": "2024-10-22T01:46:53.356897Z", + "iopub.status.busy": "2024-10-22T01:46:53.356568Z", + "iopub.status.idle": "2024-10-22T01:46:53.362553Z", + "shell.execute_reply": "2024-10-22T01:46:53.362048Z" } }, "outputs": [], @@ -432,10 +450,10 @@ "id": "5b977c7f", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.309236Z", - "iopub.status.busy": "2024-10-20T00:20:14.308902Z", - "iopub.status.idle": "2024-10-20T00:20:14.313526Z", - "shell.execute_reply": "2024-10-20T00:20:14.312932Z" + "iopub.execute_input": "2024-10-22T01:46:53.364276Z", + "iopub.status.busy": "2024-10-22T01:46:53.364101Z", + "iopub.status.idle": "2024-10-22T01:46:53.368775Z", + "shell.execute_reply": "2024-10-22T01:46:53.368272Z" } }, "outputs": [], @@ -457,10 +475,10 @@ "id": "9a663fe7", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.315526Z", - "iopub.status.busy": "2024-10-20T00:20:14.315190Z", - "iopub.status.idle": "2024-10-20T00:20:14.323187Z", - "shell.execute_reply": "2024-10-20T00:20:14.322679Z" + "iopub.execute_input": "2024-10-22T01:46:53.370647Z", + "iopub.status.busy": 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"2024-10-22T01:46:53.387057Z", + "iopub.status.busy": "2024-10-22T01:46:53.386625Z", + "iopub.status.idle": "2024-10-22T01:46:53.389930Z", + "shell.execute_reply": "2024-10-22T01:46:53.389453Z" } }, "outputs": [], @@ -533,10 +551,10 @@ "id": "27f43e54", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:14.334623Z", - "iopub.status.busy": "2024-10-20T00:20:14.334444Z", - "iopub.status.idle": "2024-10-20T00:20:37.621333Z", - "shell.execute_reply": "2024-10-20T00:20:37.620753Z" + "iopub.execute_input": "2024-10-22T01:46:53.391854Z", + "iopub.status.busy": "2024-10-22T01:46:53.391502Z", + "iopub.status.idle": "2024-10-22T01:47:17.799886Z", + "shell.execute_reply": "2024-10-22T01:47:17.799250Z" } }, "outputs": [ @@ -595,7 +613,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29351c6347174fd6a69148c6d330b3e4", + "model_id": "f1746bc39d84478ba2ec663d2c538169", "version_major": 2, "version_minor": 0 }, @@ -609,7 +627,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d58a9ec4c87d4ec08f3f984d9b3044c4", + "model_id": "82e5de3a9ef9494b884b7d4c3dac5f06", "version_major": 2, "version_minor": 0 }, @@ -623,7 +641,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cddc6b0a652e4f71a54ff60c8cee06b5", + "model_id": "afcea8aad865470aa4d32ba989701ef8", "version_major": 2, "version_minor": 0 }, @@ -637,7 +655,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "208d8b6981a3496589181fd2ae1a81ca", + "model_id": "e4b99fbacca4474abc1a55319f0b3aee", "version_major": 2, "version_minor": 0 }, @@ -651,7 +669,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8cfd3d075b1d4c57910e4617cc93aec9", + "model_id": "484d2165bd0b48e8b138c35f88adb34b", "version_major": 2, "version_minor": 0 }, @@ -665,7 +683,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c915594898449948b467b4ed6aeb7b6", + "model_id": "d5f178b0083d41ffbdc2d78ea652ad3b", "version_major": 2, "version_minor": 0 }, @@ -679,7 +697,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8cc5b734a784414b8eeb5bfa8a00dfc", + "model_id": "ee8e735949434438a4960b449b6c474b", "version_major": 2, "version_minor": 0 }, @@ -723,10 +741,10 @@ "id": "4f382191", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:37.623387Z", - "iopub.status.busy": "2024-10-20T00:20:37.622921Z", - "iopub.status.idle": "2024-10-20T00:20:38.350456Z", - "shell.execute_reply": "2024-10-20T00:20:38.349903Z" + "iopub.execute_input": "2024-10-22T01:47:17.801828Z", + "iopub.status.busy": "2024-10-22T01:47:17.801635Z", + "iopub.status.idle": "2024-10-22T01:47:18.562363Z", + "shell.execute_reply": "2024-10-22T01:47:18.561722Z" }, "scrolled": true }, @@ -748,7 +766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64a715023f2f460d9f91ea3a33e843d5", + "model_id": "be50f052ceeb42bea431f336c3f259f7", "version_major": 2, "version_minor": 0 }, @@ -766,8 +784,8 @@ "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", " Test metric DataLoader 0\n", "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_acc 0.16920000314712524\n", - " test_loss 1.8549073934555054\n", + " test_acc 0.17059999704360962\n", + " test_loss 1.7162753343582153\n", "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" ] } @@ -793,10 +811,10 @@ "id": "08881e8d", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:20:38.352509Z", - "iopub.status.busy": "2024-10-20T00:20:38.352149Z", - "iopub.status.idle": "2024-10-20T00:20:38.356041Z", - "shell.execute_reply": "2024-10-20T00:20:38.355436Z" + "iopub.execute_input": 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"2024-10-20T00:21:10.369023Z" + "iopub.execute_input": "2024-10-22T01:47:18.574555Z", + "iopub.status.busy": "2024-10-22T01:47:18.574196Z", + "iopub.status.idle": "2024-10-22T01:47:51.879819Z", + "shell.execute_reply": "2024-10-22T01:47:51.879247Z" } }, "outputs": [ @@ -881,7 +899,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20cc2c46920340ec868faa7f861a5fff", + "model_id": "e6aefba72bd6419aa1ece8461ff9c185", "version_major": 2, "version_minor": 0 }, @@ -895,7 +913,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ac5cc4168c4462781f5fcfc3a23fa29", + "model_id": "37cd226c16b040388a4f19c93770987a", "version_major": 2, "version_minor": 0 }, @@ -909,7 +927,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e96258f822d4e6d9f5bb032bbd8f365", + "model_id": "d95e362a14034a909b6f6845679b397d", "version_major": 2, "version_minor": 0 }, @@ -923,7 +941,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2b5ae131173482a9573426211f2d37e", + "model_id": "a96231384054499bac249cca49982b1e", "version_major": 2, "version_minor": 0 }, @@ -937,7 +955,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c2b32805ae04f7e8a598f3e5f7d5fd3", + "model_id": "fe04b2f3b04f4df292688f2d95029298", "version_major": 2, "version_minor": 0 }, @@ -951,7 +969,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aced5188e32a49d7b0a3406bc829d517", + "model_id": "9287361d87814bc7a609350b4aa993c6", "version_major": 2, "version_minor": 0 }, @@ -965,7 +983,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "69e0d570592d4cc88ea429c0aef65ffd", + "model_id": "a3bceb7cd6b749ecbf66082aae3f69a3", "version_major": 2, "version_minor": 0 }, @@ -996,10 +1014,10 @@ "id": "cf1640b9", "metadata": { "execution": { - "iopub.execute_input": "2024-10-20T00:21:10.371364Z", - "iopub.status.busy": 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b/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.html @@ -927,9 +927,9 @@

Obtaining a causal estimate using Model, Identify, Estimate steps

@@ -1037,13 +1037,13 @@

Sensitivity Analysis using the Refute step
 Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization
 
-Original Effect Estimate : 11.679948861035195
-Robustness Value : 0.44
+Original Effect Estimate : 11.738281416879683
+Robustness Value : 0.45
 
 Robustness Value (alpha=0.05) : 0.41000000000000003
 
 Interpretation of results :
-Any confounder explaining less than 44.0% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0
+Any confounder explaining less than 45.0% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0
 
 For a significance level of 5.0%, any confounder explaining more than 41.0% percent of the residual variance of both the treatment and the outcome would be strong enough to make the estimated effect not 'statistically significant'
 
@@ -1066,7 +1066,7 @@ 
Sensitivity Analysis using the Refute step
 

-$\displaystyle 0.44$

+$\displaystyle 0.45$
 
 
The robustness value measures the minimal equal strength of \(\eta^2_{T\sim U | W}\) and \(\eta^2_{Y \sim U | T, W}\) such the bound for the average treatment effect would include zero. It can be between 0 and 1. A robustness value of 0.45 implies that confounders with \(\eta^2_{T\sim U | W}\) and \(\eta^2_{Y \sim U | T, W}\) values less than 0.4 would not be sufficient enough to bring down the estimates to zero. In general, a low robustness value implies that the results can be
 changed even by the presence of weak confounders whereas a robustness value close to 1 means the treatment effect can handle even strong confounders that may explain all residual variation of the treatment and the outcome.

@@ -1164,14 +1164,14 @@ 
Sensitivity Analysis using the Refute step
 
@@ -1267,7 +1267,7 @@ 
II. Sensitivity Analysis for general non-parametric models
 Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization
 
-Original Effect Estimate : 11.815195660841939
+Original Effect Estimate : 11.80537621178161
 Robustness Value : 0.66
 
 Robustness Value (alpha=0.05) : 0.64
diff --git a/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb b/main/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
index 86529ae49..31008a3d5 100644
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-     "shell.execute_reply": "2024-10-20T00:21:39.131445Z"
+     "iopub.execute_input": "2024-10-22T01:48:21.391199Z",
+     "iopub.status.busy": "2024-10-22T01:48:21.391008Z",
+     "iopub.status.idle": "2024-10-22T01:48:21.564589Z",
+     "shell.execute_reply": "2024-10-22T01:48:21.563956Z"
     }
    },
    "outputs": [
@@ -498,10 +498,10 @@
    "id": "4eaec5dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:21:39.134092Z",
-     "iopub.status.busy": "2024-10-20T00:21:39.133897Z",
-     "iopub.status.idle": "2024-10-20T00:21:39.176314Z",
-     "shell.execute_reply": "2024-10-20T00:21:39.175641Z"
+     "iopub.execute_input": "2024-10-22T01:48:21.566962Z",
+     "iopub.status.busy": "2024-10-22T01:48:21.566533Z",
+     "iopub.status.idle": "2024-10-22T01:48:21.624397Z",
+     "shell.execute_reply": "2024-10-22T01:48:21.623757Z"
     },
     "scrolled": true
    },
@@ -516,9 +516,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W1,W6,W2,W4,W5,W3])\n",
+      "─────(E[y|W5,W3,W6,W2,W1,W4])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W6,W2,W4,W5,W3,U) = P(y|v0,W1,W6,W2,W4,W5,W3)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W2,W1,W4)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -543,10 +543,10 @@
    "id": "56889b39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:21:39.178266Z",
-     "iopub.status.busy": "2024-10-20T00:21:39.178075Z",
-     "iopub.status.idle": "2024-10-20T00:21:40.787312Z",
-     "shell.execute_reply": "2024-10-20T00:21:40.786754Z"
+     "iopub.execute_input": "2024-10-22T01:48:21.626915Z",
+     "iopub.status.busy": "2024-10-22T01:48:21.626507Z",
+     "iopub.status.idle": "2024-10-22T01:48:23.250374Z",
+     "shell.execute_reply": "2024-10-22T01:48:23.249689Z"
     }
    },
    "outputs": [
@@ -577,17 +577,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W1,W6,W2,W4,W5,W3])\n",
+      "─────(E[y|W5,W3,W6,W2,W1,W4])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W6,W2,W4,W5,W3,U) = P(y|v0,W1,W6,W2,W4,W5,W3)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W2,W1,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W6+W2+W4+W5+W3 | \n",
+      "b: y~v0+W5+W3+W6+W2+W1+W4 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.679948861035195\n",
-      "Effect estimates: [[11.67994886]]\n",
+      "Mean value: 11.738281416879683\n",
+      "Effect estimates: [[11.73828142]]\n",
       "\n"
      ]
     }
@@ -631,16 +631,16 @@
    "id": "2488ccbb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:21:40.789338Z",
-     "iopub.status.busy": "2024-10-20T00:21:40.788961Z",
-     "iopub.status.idle": "2024-10-20T00:21:41.845551Z",
-     "shell.execute_reply": "2024-10-20T00:21:41.845013Z"
+     "iopub.execute_input": "2024-10-22T01:48:23.252460Z",
+     "iopub.status.busy": "2024-10-22T01:48:23.252223Z",
+     "iopub.status.idle": "2024-10-22T01:48:24.366491Z",
+     "shell.execute_reply": "2024-10-22T01:48:24.365905Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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AYGBgksvBgweTNTPInTt3khNwgGzZsnHz5k3z1/3798fb25uyZctSoEABunfvnmxufHq1aNGCBw8emAu3u3fvsnLlSvMai7Tep7u7O4sWLQLg9u3b/PTTT7zxxhvPvM/EP/aJJ6MpuXr1Kvfv3+fFF19M9r3IyEhMJlOyufdPFk6JJxr/fa6f5vTp0099rMTvp0WuXLksWihuNBqTFCwABQsWBHhqu+i0PFepkfgzP+1+E0+A0+tZj1OoUKEkz33lypW5ePEix44dY9OmTRgMBsqXL5+kCNq4cSMVK1Y0n1Rb+jtr6b8Z6P8GsbGxz3xNJ/6slrzO0vOaTumx8+fPn+x388k8R48eBaBdu3bJnq8vv/ySuLg4bt++bVHO48ePYzQan3kSn/i41atXT/a4a9euTVOjmAIFCiT52mAwkD9/fvPvkiWvPUs97zlJvO8nMz4tjxBC2JJ0Y3Mybm5ulClThjJlylCwYEE6dOjAkiVLGDJkCCaTCYPBwKpVq1Lsjubt7Z3k66d1UNP+s+A8MjKSw4cP89NPP7F69WqWLl3KtGnTGDx4MMOGDbPKz/TSSy8RFhbGt99+S6tWrfjxxx958OBBskXclsiWLRuvvPIKixYtYvDgwXz33XfExcUlGcnKSKl5rtPraUVcYoOHJ3l4eFjtsQXmEZE//viDEydOULJkSby8vKhcuTKfffYZd+/eZefOnYwaNcp8G0t/ZzPTv1lGvKaflDhq8/HHHz+13Xxa3udS+7gLFiwgODg42fddXdX+KTYYDCn+PE/73VfxbyeEEGklxY4TS5yScvHiRQAiIiLQNI3w8HDzJ+7W4OXlRYsWLWjRogXx8fE0adKEUaNGMXDgwKe2kbZ0RKZ58+ZMmjSJ2NhYFi9eTFhYGC+99NIzb/O8x2jbti0NGzZk69atLFq0iBIlSlCkSJFn3iZx5GLfvn1PvU5gYCCenp4cPnw42fcOHTqE0WgkT548z3yclDzt58mXL99THyvx+/Dvp7O3bt1Kcr30fAL8XyaTiRMnTiR5bR05cgTgqfviWPJcWfKaSfyZn3a/AQEBeHl5pfr+UvM41atXT/K9w4cPJ9mcNG/evOTNm5eNGzdy4sQJ89SgKlWq0LdvX5YsWUJCQgJVqlQx38ZWv7P/FRgYiK+v7zNf05D615kt5MuXj3379qFpWpLXwZN5IiIiAPD19aVmzZpWeeyIiAhMJhMHDhx4agGV+LhBQUFWe9zE0aJEmqZx7NgxoqKiAMtee9myZUtx+mBaf/cT7/vJjImPLYQQGUmmsTmB9evXp/iJW+Lc7sRpBU2aNMHFxYVhw4Ylu76maVy/ft3ix37yNm5ubhQuXBhN03j06NFTb+fl5ZXspPtZWrRoQVxcHPPmzWP16tU0b978ubdJPJl92uPUrVuXgIAAxo4dy++//56qUZ3AwECqVKnC7NmzOXPmTJLvJT6nLi4u1K5dmxUrViSZvnX58mW++uorKlWqhK+v73MfK6WfJ6WfpV69emzZsoXNmzebj927d48ZM2YQFhZmnn6TeEL2xx9/mK+XkJBg1V3rp0yZYv5/TdOYMmUKWbJkoUaNGile35Ln6nn/nv8VEhJCdHQ08+bNS3L9ffv2sXbtWurVq2f5D5eC0qVLExQUxPTp05O0QV61ahUHDx6kfv36Sa5fuXJlfvvtN7Zs2WIudqKjo/Hx8eGjjz7Cw8ODUqVKma9vi9/ZJxmNRho1asSPP/7Itm3bkn0/8XFT+zqzhXr16nHhwgW+++4787H79+8ne+2WKlWKiIgIPvnkkyRr/BKl1G7+eRo1aoTRaGT48OHJ1vskPjd16tTB19eX0aNHp/i+l5bHnT9/Pnfu3DF//d1333Hx4kXq1q0LWPbai4iI4NChQ0ly7N69O81Tjv/7+/XfaYG//PJLmtcICiFEWsnIjhPo0aMH9+/fp3HjxhQqVIj4+Hg2bdpkHgFJXLAcERHByJEjGThwIKdOnaJRo0b4+Phw8uRJli1bRteuXXn33XcteuzatWsTHBxMxYoVyZkzJwcPHmTKlCnUr18/yeLaJ5UqVYrPP/+ckSNHkj9/foKCgpJ9OvlfJUuWJH/+/AwaNIi4uLhUTWGLiIjA39+f6dOn4+Pjg5eXF+XKlSM8PByALFmy8PrrrzNlyhRcXFxo2bJlqn7mzz77jEqVKlGyZEm6du1KeHg4p06d4ueff2bXrl0AjBw5kl9++YVKlSrx1ltv4erqyhdffEFcXFyKexWlxtOeswEDBvD1119Tt25devbsSfbs2Zk3bx4nT55k6dKl5vUfRYoU4aWXXmLgwIHcuHGD7Nmz88033/D48eM05XlS1qxZWb16Ne3ataNcuXKsWrWKn3/+mffff5/AwMCn3i61z1V0dDQuLi6MHTuW27dv4+7uTvXq1VPcSwr0qUx169alfPnydOrUiQcPHjB58mT8/PzM+7mkV5YsWRg7diwdOnQgJiaGli1bcvnyZSZNmkRYWBh9+vRJcv3KlSuzaNEiDAaDeVqbi4sLFSpUYM2aNVStWjXJmhtb/M6mZPTo0axdu5aYmBi6du1KZGQkFy9eZMmSJfz555/4+/un+nVmC126dGHKlCm0bduW7du3ExISwoIFC5JtmGo0Gvnyyy+pW7cuRYoUoUOHDuTKlYvz58+zfv16fH19+fHHHy167MT3nREjRlC5cmWaNGmCu7s7W7duJTQ0lDFjxuDr68vnn39OmzZtKFmyJK+//jqBgYGcOXOGn3/+mYoVKyb5ICA1smfPTqVKlejQoQOXL19m4sSJ5M+fny5dugCWvfY6duzIhAkTqFOnDp06deLKlStMnz6dIkWKpKkBCMCYMWOoX78+lSpVomPHjty4ccO851pKhaYQQthMRrZ+E2qsWrVK69ixo1aoUCHN29tbc3Nz0/Lnz6/16NFDu3z5crLrL126VKtUqZLm5eWleXl5aYUKFdK6d++uHT582HydmJiYFFtKP9mq9IsvvtCqVKmi5ciRQ3N3d9ciIiK09957T7t9+7b5Oim1PL506ZJWv359zcfHRwPMbVFTaoOcaNCgQRqg5c+fP8XnIaX2qitWrNAKFy6subq6ptiGesuWLRqg1a5dO8X7fJp9+/ZpjRs31vz9/bWsWbNqL774YrL9ZHbs2KHVqVNH8/b21jw9PbVq1appmzZtSnKdxOfmyZa/KT0PT3vONE3fh+a1114z5ylbtqz2008/Jct9/PhxrWbNmpq7u7uWM2dO7f3339d++eWXFFtPP6+l+H+1a9dO8/Ly0o4fP67Vrl1b8/T01HLmzKkNGTIkWbtenmg9ndrnStM0bebMmdoLL7ygubi4pKoN9a+//qpVrFhR8/Dw0Hx9fbUGDRpoBw4cSHKd9LSeTrR48WKtRIkSmru7u5Y9e3btjTfe0M6dO5fs9vv37ze3Zf6vkSNHprgnUaL0/M6m1unTp7W2bdtqgYGBmru7u/bCCy9o3bt3T9I+OjWvs6c9n4ltjf/7O5ja1tOJ+V599VXN09NTCwgI0Hr16qWtXr06xX+PnTt3ak2aNDG/L+XLl09r3ry5tm7duuc+9tNatM+ePdv8b5wtWzYtJiZG++WXX5L97HXq1NH8/Py0rFmzahEREVr79u21bdu2JXu+nybx+fv666+1gQMHakFBQZqHh4dWv379ZO3uNS31r72FCxdqL7zwgubm5qZFR0dra9aseWrr6Y8//jjZ7VP6vV26dKkWGRmpubu7a4ULF9a+//77ZPcphBC2ZtA0WVEoxNPs3r2b6Oho5s+fT5s2bVTHsVvt27fnu+++k090hRBCCJGhZM2OEM8wc+ZMvL29adKkieooQgghhBDCQrJmR4gU/Pjjjxw4cIAZM2bw9ttvW6UzlxCZzd27d5872hYYGPjUVsPCeuLj47lx48Yzr+Pn55ep2ocLIYQ9kGJHiBT06NGDy5cvU69ePavtByREZvPJJ5889/V98uTJp7YGF9azadMmqlWr9szrzJkzh/bt22dMICGEcBCZYs3O1KlT+fjjj7l06RLFixdn8uTJlC1bNsXrVq1ald9//z3Z8Xr16vHzzz/bOqoQQjiMEydOpLi/yn9VqlTpqfthCeu5efMm27dvf+Z1ihQpQkhISAYlEkIIx6C82Fm8eDFt27Zl+vTplCtXjokTJ7JkyRIOHz6cYsvYGzduEB8fb/76+vXrFC9enC+//FI+8RJCCCGEEEKYKS92ypUrR5kyZcx7DJhMJvLkyUOPHj0YMGDAc28/ceJEBg8ezMWLF2VdhRBCCCGEEMJM6Zqd+Ph4tm/fzsCBA83HjEYjNWvWTLIL97PMmjWL119//amFTlxcXJLdo00mEzdu3CBHjhwYDIb0/QBCCCGEEM+haRp37twhNDTUphvsCiGSU1rsXLt2jYSEBHLmzJnkeM6cOTl06NBzb79lyxb27dvHrFmznnqdMWPGyAJzIYQQQih39uxZcufOrTqGEE7FrruxzZo1i2LFij21mQHAwIED6du3r/nr27dvkzdvXs6ePYuvr29GxFRqyRL47jtYvFh1EnVmzYI//4Q5c1QnEam1L1dt7rftRtkxjVVHEU/x/fcwZQr89pvqJI7n5k0oXBi2bYNcuVSnUe/BAyhQADZuhPBw1WnSJjY2ljx58uDj46M6ihBOR2mxExAQgIuLC5cvX05y/PLlywQHBz/ztvfu3eObb75h+PDhz7yeu7s77u7uyY77+vo6RbGTIwc8egRO8KM+Vfny8MUXzv0c2Jv4qs3w+HkNvlPbqY4inuL116FvXzh/HiIjVadxLL6+0KSJ/kHVhx+qTqNe4vPx008waJDqNOkj0+eFyHhKJ466ublRqlQp1q1bZz5mMplYt24d5cuXf+ZtlyxZQlxcHK1bt7Z1TLvm6al/KubMihWD48fh3j3VSURqvfBOY4qe/pn4u/HPv7JQImtWaNkS5s5VncQxdemij0qbTKqTZA6tWsGiRaB+swwhhL1Rvkqub9++zJw5k3nz5nHw4EG6devGvXv36NChAwBt27ZN0sAg0axZs2jUqBE5cuTI6Mh2xcMD7t9XnUItLy+IiIA9e1QnEamVt+oLXMoaxp6JMkcqM+vQAebPh8ePVSdxPJUrg7s7/Pqr6iSZQ/XqcOMG7N6tOokQwt4oL3ZatGjBJ598wuDBg4mOjmbXrl2sXr3a3LTgzJkzXLx4McltDh8+zJ9//kmnTp1URLYrMrKjK1ECdu1SnUJY4mK5xtxftEx1DPEMpUpBQACsXas6ieMxGKBzZ/jyS9VJMgdXV2jRAr76SnUSIYS9Ub7PTkaLjY3Fz8+P27dvO8WanQMH4OWX4cwZ1UnUGjsWTpzQ1+4I+3D4291ka1mHHA/O4+LmojqOeIrx4+Hvv/VmKMK6Ll/WR6VPnoTAQNVp1NuyBZo2hdOnwd66N2e2cw9N03j8+DEJCQmqowiRJlmyZMHFJXXnBnbdjU08n4zs6KKj9e5Rwn4UfC2Ks294sn/W30R1q6g6jniK1q1h8GC4fl1viCKsJ2dO/cOq+fPhnXdUp1GvTBl9at/GjRATozqN/YqPj+fixYvcd/Y57sKuGQwGcufOjbe393OvK8WOg5M1O7roaH3NzuPH+nQIkfkZjAZOFG8Ms5aBFDuZVs6cULMmfP01vP226jSOp3Nn6N1b73zn7I28DAZ44w29UYEUO2ljMpk4efIkLi4uhIaG4ubmJh3ihN3RNI2rV69y7tw5ChQo8NwRHjntc3CJIzua5tx/KHPmhGzZ4MgRff8KYR+yd2yMX692aKaPMRid+AWcybVvDyNHSrFjC7Vq6e/hf/0FlSqpTqNeq1b6dgKTJ+ujPMIy8fHxmEwm8uTJg6enp+o4QqRZYGAgp06d4tGjR88tduxs1quwlIeHXujExalOol50tDQpsDdFOpfHM+EuR7/fqzqKeIb69fV1gdLx0PpcXKBjR5g5U3WSzOHFF+GFF2D1atVJ7JvR3hY9CfEES0Yk5dXu4FxdIUsWWbcDUuzYIxc3Fw6+2JALU6UrW2bm5qav3ZE9d2yjY0d9zeGtW6qTZA6tWklXNiFE6kmx4wRk3Y4uOhp27lSdQljKo2VjQv6WYieza98eFi6ER49UJ3E8efLo++58/bXqJJnD66/Dzz9DbKzqJEIIeyDFjhOQjmy6xL12nKvZuv2L6l2d4IcnOfvHSdVRxDMULw65c+snocL6unSRqWyJQkOhXDlYvlx1EvHIST7d2LBhAwaDgVv/P7z65NeZydChQ4mOjk5yLD4+nvz587Np0yY1oSw0ffp0GjRoYLX7k2LHCXh6ysgO6PtVPHgAFy6oTiIs4e7rzv689Tg+XkZ3MrsOHWQqm6288gqcPw87dqhOkjkkdmUT6syePRtvb29mz56dIY9XtWpVevfunez43Llz8ff3z5AMiSpUqMDFixfx8/Ozyv2lVKBY0/Tp0wkPD6dChQrmY6NGjaJChQp4eno+9fnr2bMnpUqVwt3dPVX5Tp06hcFgSPGyJIXN2K5fv07u3LmTFY4dO3Zkx44dbNy40dIfNUVS7DgBDw8Z2QF9E7rixWXdjl1q0phsG6TYyexatoRff4UrV1QncTxZsuhTBWV0R9ekCfz5p77xqsh4s2fPpnPnzsTHx9O5c+cMK3gyCzc3N4KDg+2ibbemaUyZMoVOnTolOR4fH0+zZs3o1q3bM2/fsWNHWrRokarHypMnDxcvXkxyGTZsGN7e3tStWzfZ9Tt16kRUVFSy425ubrRq1YrPPvssVY/7PFLsOAEZ2fmXrNuxT8Xeq0uB2O1c3SdnNplZQIC+CebChaqTOKbOnfV1O/fuqU6inr+//lpbvFh1EueTWOho/z8nXNO0TFPwtG/fnkaNGvHJJ58QEhJCjhw56N69e5LpdgsWLKB06dL4+PgQHBxMq1atuPLEJzQrV66kYMGCeHh4UK1aNU6dOpXk+09OY0tpZGbixImEhYUluU3ZsmXx8vLC39+fihUrcvr0aebOncuwYcPYvXu3eRRk7v8Pkd+6dYvOnTsTGBiIr68v1atXZ/fu3Uke56OPPiJnzpz4+PjQqVMnHj58mOT727dv5/jx49SvXz/J8WHDhtGnTx+KFSv21Ofzs88+o3v37rzwwgtPvc5/ubi4EBwcnOSybNkymjdvnmzzz88//5xbt27x7rvvpnhfDRo04IcffuCBFT6tl2LHCUiDgn8lrtsR9sUn1Id9QdU5OO4H1VHEc3ToAHPmyNo4WyhQQP/AJoXZIE5JurJlvCcLnUSZqeBZv349x48fZ/369cybN4+5c+eaiwfQ1xmNGDGC3bt3s3z5ck6dOkX79u3N3z979ixNmjShQYMG7Nq1i86dOzNgwIB0ZXr8+DGNGjUiJiaGPXv2sHnzZrp27YrBYKBFixa88847FClSxDwakjiS0qxZM65cucKqVavYvn07JUuWpEaNGty4cQOAb7/9lqFDhzJ69Gi2bdtGSEgI06ZNS/LYGzdupGDBgvj4+KTrZ0iL7du3s2vXrmSjSgcOHGD48OHMnz//qW3QS5cuzePHj/nnn3/SnUOKHScgDQr+Je2n7Vdc3cZ4rF6uOoZ4jjp14No1WVtiK126wJdfqk6ROdSvD4cOwbFjqpM4h6cVOokyS8GTLVs2pkyZQqFChXjllVeoX78+69atM3+/Y8eO1K1blxdeeIGXXnqJzz77jFWrVnH37l1AH3GIiIhg/PjxvPjii7zxxhtJiqG0iI2N5fbt27zyyitEREQQGRlJu3btyJs3Lx4eHnh7e+Pq6moeDfHw8ODPP/9ky5YtLFmyhNKlS1OgQAE++eQT/P39+e677wB99KhTp0506tSJF198kZEjR1L4iZ3TT58+TWhoaLryp9WsWbOIjIxMslYoLi6Oli1b8vHHH5M3b96n3tbT0xM/Pz9Onz6d7hxS7DgBGdn5V5EicPq0tCy1R5H9X6XI1fXEnpN/vMzM1RXatNFHd4T1NWkCBw7oF2eXNav+fEhLbtt7XqGTKDMUPEWKFMHFxcX8dUhISJJpatu3b6dBgwbkzZsXHx8fYmJiADhz5gwABw8epFy5cknus3z58unKlD17dtq3b0+dOnVo0KABkyZN4uLFi8+8ze7du7l79y45cuTA29vbfDl58iTHjx9PddYHDx6QNWvWdOVPiwcPHvDVV18lG9UZOHAgkZGRtG7d+rn34eHhwX0rnMBKseMEZGTnXx4e+g7cT0x5FXYgIDKQo76l2ffxKtVRxHO0b6+fgMbFqU7ieDw89A1cZ81SnSRzSOzKJtMmbefRo0d069btuYVOIk3T6Natm9XbUvv6+nL79u1kx2/dupWkK1qWLFmSfN9gMGAymQC4d+8ederUwdfXl0WLFrF161aWLdOb38THx6c5m9FoTPb8PPnzz5kzh82bN1OhQgUWL15MwYIF+fvvv596n3fv3iUkJIRdu3YluRw+fJj33nsv1dkCAgK4efOmZT+QFXz33Xfcv3+ftm3bJjn+22+/sWTJElxdXXF1daVGjRrmnEOGDEly3Rs3bhAYGJjuLFLsOAEZ2UlK1u3Yr5vVGsMy6cqW2RUuDPnzww+yxMomOneGefOkmASoWlUfqZfGM7aTJUsWPv/881R3HjMYDHz++efJio70evHFF9mRwvzYHTt2ULBgwVTdx6FDh7h+/TofffQRlStXplChQsmaE0RGRrJly5Ykx55VlAAEBgZy6dKlJAXPrhRONEqUKMHAgQPZtGkTRYsW5av/X3Tm5uZGQkJCkuuWLFmSS5cu4erqSv78+ZNcAgICzFmfXNPyZNYSJUpw6NChVBer1jJr1ixeffXVZMXK0qVL2b17t7l4+/L/5+Vu3LiR7t27m693/PhxHj58SIkSJdKdRYodJyAjO0nJuh37lf/dxhQ9u5K4WDnLy+wSGxUI64uK0vcNW7FCdRL1XFzg9ddlzx1b69ixI19++eVzCx6DwcCXX35Jx44drZ6hW7duHDlyhJ49e7Jnzx4OHz7MhAkT+Prrr3nnnXdSdR958+bFzc2NyZMnc+LECX744QdGjBiR5DpvvvkmR48e5b333uPw4cN89dVXSRocpKRq1apcvXqVcePGcfz4caZOncqqVf/OQjh58iQDBw5k8+bNnD59mrVr13L06FEiIyMBCAsL4+TJk+zatYtr164RFxdHzZo1KV++PI0aNWLt2rWcOnWKTZs2MWjQILZt2wZAr169mD17NnPmzOHIkSMMGTKE/fv3J8lWrVo17t69m+z4mTNn2LVrF2fOnCEhIcFcfCSuXQI4duwYu3bt4tKlSzx48MB8ncRRsPPnz1OoUKFkxeGxY8f4448/6Ny5c7LnKiIigqJFi5ov4eHhgF64BQUFma+3ceNGXnjhBSIiIp753KeK5mRu376tAdrt27dVR8kw/ftr2qBBqlNkHr/+qmklS6pOIdLqoEe0tmXoz6pjiOe4eVPTvLw07fx51Ukc04wZmlarluoUmcPWrZoWGqppjx+rTvJ0meXc48GDB9qBAwe0Bw8epOn2s2bN0gwGgwYkuxgMBm3WrFlWTpzUli1btFq1ammBgYGan5+fVq5cOW3ZsmXm77dr105r2LBhktv06tVLi4mJMX/91VdfaWFhYZq7u7tWvnx57YcfftAAbefOnebr/Pjjj1r+/Pk1d3d3rXLlytrs2bM1QLt586amaZq2fv36JF9rmqZ9/vnnWp48eTQvLy+tbdu22qhRo7R8+fJpmqZply5d0ho1aqSFhIRobm5uWr58+bTBgwdrCQkJmqZp2sOHD7WmTZtq/v7+GqDNmTNH0zRNi42N1Xr06KGFhoZqWbJk0fLkyaO98cYb2pkzZ8yPO2rUKC0gIEDz9vbW2rVrp/Xr108rXrx4kuegefPm2oABA5Ica9euXYr/juvXrzdfJyYmJsXrnDx5UtM0TTt58mSy22iapg0cOFDLkyeP+ed7lpSeS03TtNq1a2tjxox56u0seS0bNM25ZrrGxsbi5+fH7du38fX1VR0nQwwfDrdvw/jxqpNkDtevQ2go3L2rb9Qn7MuG6sMxXjhLlUOyu2Jm17KlPpLav7/qJI7nzh3IlUtff/j/H4w6LU2DQoXg88+henXVaVKWWc49Hj58yMmTJwkPD0/zovWUmhXYckQnM1qzZg1169bl4cOHuLm5qY7zXHv27KFWrVocP3482X43mdH+/fupXr06R44cSbIe678seS3LNDYnIGt2ksqRA3LmhIMHVScRaRHavTGRR1aQEJ/w/CsLpWTPHdvx8YHmzSETbGuinMEge+5kpCentDlboXP58mVWrFhBgQIF7KLQAYiKimLs2LGcPHlSdZRUuXjxIvPnz39qoWMpKXacgKzZSU7W7divAo2Lct/Fl30zNqmOIp6jRg24dw+ssCecSEGXLnqx8/ix6iTqtWoFS5fCE5vHCxtJLHjc3NycqtABqFevHr/++itTp05VHcUi7du3p1ixYqpjpErNmjWpU6eO1e5Pih0n4OkpIztPkmLHfhmMBk5GN+bmHOnKltm5uEDbttKowFbKloXs2WH1atVJ1CtQQL+sks70GaZjx47cvXvXqQod0PfpOXLkCDVr1lQdRaSSFDtOwMNDRnaeFB0trUrtWY7OjYnYswzNJPOjMrv27WHxYvnAxRYMBr0N9UxZvgb8u+eOyDjWbi8thC1IseMEZGQnucS9dmQtgX0q0ukl3EwPObJEdofN7AoUgKJFYfly1UkcU5s2sG4dXLigOol6LVroIzsp7DspnuBkvamEA7LkNSzFjhOQkZ3kwsLAZIIzZ1QnEWlhdDVy+MWGXJwmU9nsgey5YzvZs8Orr8JztgFxCsHBULEifP+96iSZV+JIzH35BFTYucS9flxcXJ57XVdbhxHqychOcgbDv+t28uVTnUakhecbjfEd+R4wTHUU8RzNm0Pv3vqHC3nzqk7jeLp00aezDRgARif/CLNVK30qW4cOqpNkTi4uLvj7+3PlyhUAPD09n7tRqBCZjclk4urVq3h6euLq+vxSRoodJyAjOylLLHYaNlSdRKRFVK9qPPjgDKd/O06+6lbYYVnYjI8PNG4M8+fDBx+oTuN4YmL0D3DWr9c74DmzJk3g7bfh4kUICVGdJnMKDg4GMBc8Qtgjo9FI3rx5U1WsS7HjBGRkJ2UlSsg6Anvm5u3G1nz1eTRhGfmqv6s6jniODh30EYhBg/QTc2E9RqM+svPll1Ls+PpC3bp6U4zevVWnyZwMBgMhISEEBQXx6NEj1XGESBM3NzeMqRzKlmLHCcimoimLjoahQ1WnEOlhbNqY7DM/BaTYyexiYiAhATZuhCpVVKdxPO3a6c0grl/XN052Zm+8AaNGSbHzPC4uLqla7yCEvXPy2b3OQTYVTVnhwnoHo5s3VScRaVXsvZeJuLOLK3suqY4insNo1E/IZSG9bYSEQK1asGCB6iTq1a0Lx47BkSOqkwghMgMpdpxA4pod6TSZlJubXvDslu7Fdss72Jt9OWtwaOwK1VFEKrRrB999B3fvqk7imBKnsjn7e727O7z2Gnz1leokQojMQIodJ+Dhof/34UO1OTKjEiVkc1F7F1+vMR5rl6uOIVIhPBxKl9YLHmF9L78Mt27B33+rTqJeq1Z6sePshZ8QQoodp+Dioo9iyLqd5BI7sgn7Vbh/A4pc+53bZ2QnQXvQvr1MZbMVFxfo2FEf3XF2Varof/O2bVOdRAihmhQ7TkLW7aRMih37l+PFAI74lWX/xytVRxGp0LSpPpp64oTqJI6pY0dYsgRiY1UnUcvFBV5/XaayCSGk2HEa0n46ZcWLw8GDEBenOolIj9vVG2NYvkx1DJEKXl7QrJmM7thKWBiULw/ffKM6iXpvvKE/DwkJqpMIIVSSYsdJyMaiKfP3h9y5Yf9+1UlEekS804ii51bx8JYsTLMH7dvDvHlgMqlO4pg6d4aZM1WnUC86Wn+PX79edRIhhEpS7DgJGdl5OpnKZv9yV8zHOY+C7Jnwq+ooIhUqVtQ7ZslJqG00bAinTsn7msGgj+4sWqQ6iRBCJSl2nISM7DydFDuO4XKFxsR9I1PZ7IHBoI/uzJmjOoljcnPT23xLowJo2RK+/17+/gnhzKTYcRIysvN0Uuw4htw9GhN57AceP3ysOopIhTZtYPlyuC1N9Gyic2d9RMPZT/IjIvT91H7+WXUSIYQqUuw4CRnZeboSJfRiR9YP2LeIBoW545qdvdM2qo4iUiFPHn0627ffqk7imAoVgqJFYelS1UnUS9xzRwjhnKTYcRIysvN0uXNDlixw8qTqJCI9DEYDp8u8RuxcObuzFx06yFQ2W+rSRRoVADRvDmvWwM2bqpMIIVSQYsdJyMjO0xkMMpXNUeR8qykv7v8e02MZprMHDRvqrd8PH1adxDG99hrs3g1HjqhOolbOnFC5sr52RwjhfKTYcRIysvNsUuw4hkItSxBvzMq+mZtVRxGp4OGhb/woe+7Yhqen3o1MGhVIVzYhnJkUO05CRnaerUQJfVd3Yd8MRgMnopty40uZymYvOnSA+fNl40db6dxZ39MoPl51ErUaNYItW+D8edVJhBAZTYodJyEjO88mIzuOI+DN18i/eymaSVMdRaRCmTLg5we//KI6iWMqUUJvBvHjj6qTqOXjA6+8At98ozqJECKjSbHjJDw8pNh5lhdfhGvX4OpV1UlEehVuVwYjJg4u2KY6ikgFg0EaFdha584ylQ2kK5sQzkqKHSfh6SnT2J4lSxa9Tevu3aqTiPQyuho5WqwpV6Z9pzqKSKXWreGnn+DGDdVJHFOrVvDnn3D6tOokar38st5189Ah1UmEEBlJih0nIdPYnk/W7TgO/05NCd8hU9nsRUgIVKsmU4xsxddX78zm7KNnbm7QrJmM7gjhbKTYcRLSoOD5ZN2O4yjatQIeprscWSJDdfZCprLZVufOMHu2NIJI7MqmyecgQjgNKXachIzsPJ8UO47Dxc2FQ5FNuDhFurLZi1degRMnYN8+1UkcU4UK4O0Na9eqTqJWpUpgMslUNiGciRQ7TkJGdp4vKkrffE+eJ8fg3a4pebbIuh174e6uf+oue+7YhsGgj+7MnKk6iVpGIxw4AJGRqpMIITKKFDtOQkZ2ns/HB8LDYe9e1UmENUT1iMH/0VWO/XBAdRSRSh06wIIF8OiR6iSOqW1bWLMGLl1SnUQtDw/VCYQQGUmKHSchIzupI1PZHIdrVlcOFGjEuYkyumMvoqMhOBhWrVKdxDEFBOjTBefPV51ECCEyjhQ7TkJGdlJHih3H4tHmNUI2y7ode5G4545MZbOdLl30PXdkgb4QwllIseMkZGQndaTYcSxRvauTM+4Mp345qjqKSKU33tCnWskGv7ZRvTo8fgx//KE6iRBCZAwpdpyEjOykTokSsGePtGd1FG7ebuwLf5VT42V0x14EBkLt2np7YGF9RiN06iSNCp6U8P9v+iaTSXESIYS1SbHjJGRkJ3WCg8HLC44dU51EWEuW15sStFGKHXsiU9lsq317WL4cbt5UnSRzOHHiBHXr1gXAaJTTIiEcjfxWOwlPT3j4UN9fQDydwSBT2RxN8fdqk/v+Yc79eUp1FJFKdevCxYuwc6fqJI4pVy59OtvChaqTZA4vvPAC27dv56effgL+HeURQjgGKXacRNas+n8fPlSbwx5IseNYsvpnZV/e+hz7+HvVUUQqZckCrVvDnDmqkziuxD13nL1Rgfb/T0CHDh345JNPFKcRQtiCFDtOwmjUCx5Zt/N8JUpIseNoDM1fI8dv0oLannToAF99BXFxqpM4pnr14No12LpVdRK1DAYDAD169OCPP/7gxo0buLi4mIsgIYT9k2LHiXh6yrqd1IiOlukzjibqvZcJv7uHi9vOq44iUqloUX2T3/+fWSSszNVVLyi//FJ1EvU0TSNfvnxUqFCBjz/+GJCpbEI4Eil2nIiHh4zspEaBAhAbK7uMOxKvIC/25nqZIx/JVDZ70r69TGWzpY4d4Ztv4O5d1Ukyhz59+jD3/ztjuLq6qg0jhLAaKXaciLSfTh0XF4iKkqlsjkZr3BS/X6Urmz1p2RLWr9ebFQjri4iAsmVh8WLVSdRKnMrWtGlTLl++bG5UIIRwDMqLnalTpxIWFkbWrFkpV64cW7Zseeb1b926Rffu3QkJCcHd3Z2CBQuycuXKDEpr36T9dOrJuh3HU7R/fQrc3srVfZdVRxGplD07vPKKdA2zpc6dZSob/Lu/zsyZM/H39zcfj42NZceOHYpSCSGsQWmxs3jxYvr27cuQIUPYsWMHxYsXp06dOly5ciXF68fHx1OrVi1OnTrFd999x+HDh5k5cya5cuXK4OT2SUZ2Uk/W7Tge39y+7MtZg4NjlquOIiyQOJVN1ovbRuPGcPQo7NunOok6mqaZ99fp1KkTlSpVMn9v48aN9OnThxUrVqiKJ4RIJ6XFzoQJE+jSpQsdOnSgcOHCTJ8+HU9PT2bPnp3i9WfPns2NGzdYvnw5FStWJCwsjJiYGIoXL57Bye2TjOyknrSfdkyPGjTFa7V0ZbMntWvD7dvSNcxW3N2hTRvnHt1JnMYG+gjPo0ePzN3Y6tevT8mSJRk2bJiqeEKIdFJW7MTHx7N9+3Zq1qz5bxijkZo1a7J58+YUb/PDDz9Qvnx5unfvTs6cOSlatCijR49+ZteUuLg4YmNjk1yclYzspF6xYnDihCzcdTRF33+VyBt/cePoddVRRCq5uEDbttKowJY6d4YFC5xzHzZN04iPj2f16tWAfh6SJUsWDAYDK1eupFq1anz22Wf4+vpy7949xWmFEGmhrNi5du0aCQkJ5MyZM8nxnDlzcukpbbBOnDjBd999R0JCAitXruTDDz9k/PjxjBw58qmPM2bMGPz8/MyXPHnyWPXnsCdeXiDv1anj6QmRkfJpsqPxD8/G3pw12TvwK9VRhAU6d4ZFi/R9YYT1FSmif8Dz/43InIrBYOD48ePUq1cP0M9N+vfvT0BAAC1btiQ8PJy//vqLlStX4uXlpTitECItlDcosITJZCIoKIgZM2ZQqlQpWrRowaBBg5g+ffpTbzNw4EBu375tvpw9ezYDE2cu3t4yUmGJKlXgjz9UpxDWZujaldCfvkAzySIQexERoTcq+Owz1Ukc1/vvw9ix8Pix6iQZLzIykhIlShASEkJoaCi//PILI0eO5PDhw8yePZuXXnoJT09P1TGFEGmkrNgJCAjAxcWFy5eTdka6fPkywcHBKd4mJCSEggUL4uLiYj4WGRnJpUuXiI+PT/E27u7u+Pr6Jrk4Kyl2LBMTI8WOIyr5/st4Pb7NvpkpT5cVmdPAgTBlir4HlrC+WrUgRw593x1nNHDgQK5du8a+ffvYtGkTb7755lPPRYQQ9kVZsePm5kapUqVYt26d+ZjJZGLdunWUL18+xdtUrFiRY8eOmVtEAhw5coSQkBDc3NxsntneSbFjmcqVYfNmeEodLeyUa1ZXjlTuzO1xX6iOIixQrJj+O/n556qTOCaDAQYNgjFj4D9/Yp3Ga6+9RkJCAvHx8WTNmjXZ900mE1u3bmX79u0K0gkh0kPpNLa+ffsyc+ZM5s2bx8GDB+nWrRv37t2jQ4cOALRt25aBAwear9+tWzdu3LhBr169OHLkCD///DOjR4+me/fuqn4Eu+LjI8WOJYKDIU8ekL9tjufFcZ0ofuJ7bp28qTqKsMDAgTBhgnSVtJWGDfUW387aZXns2LFcv643L9Ge6HWuaRo7d+5k0KBBnDt3TkU8IUQaKS12WrRowSeffMLgwYOJjo5m165drF692ty04MyZM1z8z9bZefLkYc2aNWzdupWoqCh69uxJr169GDBggKofwa54e8OdO6pT2BdZt+OYQsrk5kDOaux+d4HqKMICL70ERYvCrFmqkzgmo1EvKEeNcs59jd577z1iYmKApO2oNU3DxcWFzp07kyNHDkaPHq0qohAiDQzakx9fOLjY2Fj8/Py4ffu2063fmTsXfv4ZlixRncR+LFgAX38NK1eqTiKsbevQn/H/qD/57+/FYDQ8/wYiU/jtN32j0WPHQGYvW9/jx1CgAHzxhb7HkbOZNm0aly5dokGDBpQpU8Z8/PHjx7i6unLs2DFq167NiRMnLLpfZz73EEI1u+rGJtJH1uxYrkoV+PNPeMZWTsJOlXz/ZTwfx7L3i02qowgLVKsGoaF6K2phfa6u0L8/OOvgRUBAACNHjuS9996jYsWK/Pbbb9y/fx9XV1dMJhM7d+4kPDycCxcuqI4qhEglKXaciKzZsVy+fJAtG+zerTqJsDYXNxeOVulM7CfSqMCeJC6k/+gj+RDCVtq3hyNH4K+/VCfJeM2bN8fPz4/x48fTvn173nrrLerVq0fLli2ZPn06n376KTExMYSGhqqOKoRIJSl2nIis2UkbWbfjuF4c25HoE99z8/gN1VGEBerXB3d3WLpUdRLHlDUrvPOO847u1KtXj4ULF9KlSxf+/vtv+vXrR+7cuVm2bBmlSpWic+fOqiMKISwga3acyO7d0LSpPtddpN7MmbBqFXz/veokwhb+CWnIw/LVifm+l+oowgJff61vgrlzpz7aI6zr7l0IC4NffoESJVSnyVi7d++mSpUq3L5922r36cznHkKoJiM7TkTW7KRN4siOc30s4DyM/+tKrpUz0EzyD2xPmjeHe/f0DyKE9Xl7Q8+e+r47zqZ48eIUKFCAXbt2mY8lfi5sMpmStaUWQmRuUuw4EVmzkzYFC+qLdg8eVJ1E2ELJ91/G4/Ed9k53wgUKdszFRV9I76xtkjPC22/DmjVw+LDqJBlv27ZtREdHm79ObEVtNBqTtKUWQmR+Uuw4EW9v/ZNQZ9wdOz0MBlm348jMjQrGz1AdRViobVs4c0Z+N20le3b43//0ZhDOyCR/LIVwCFLsOBEPD/3E/d491UnsjxQ7ju3FcZ2kUYEdcnODd9913oX0GaFvX/juOzh9WnWSjGc0yimSEI4gzb/Jx44dY82aNTx48ABA5rDaAYNB1u2klazbcWwhpXOxP7gGe95boDqKsFDnznqTgq1bVSdxTMHB+gjaxx+rTiKEEGljcbFz/fp1atasScGCBalXrx4XL14EoFOnTrzzzjtWDyisS9btpE3RovqImIWbZgs7Ynzzf+T++QtpVGBnvLygd2/nXEifUfr1g/nz4dIl1UnUiY2FefNUpxBCpIXFxU6fPn1wdXXlzJkzeHp6mo+3aNGC1atXWzWcsD7ZaydtjEaoXFmmsjmykgPr4J5wTxoV2KG33oL162H/ftVJHFO+fPDzz5Ajh+ok6hiN0KePvMaEsEcWFztr165l7Nix5M6dO8nxAgUKcNoZJ/XaGZnGlnaybsexubi5cCymM7GffKE6irCQvz906+a8C+kzQuXKkCWL6hTqeHvrUyYnTlSdRAhhKYuLnXv37iUZ0Ul048YN3N3drRJK2I4UO2knxY7je3FsR6JPLpNGBXaod29YsUKmmgrb6dEDvvkGrl5VnUQIYQmLi53KlSszf/5889cGgwGTycS4ceOoVq2aVcMJ6/PxkWlsaVWiBFy+DOfOqU4ibEVvVFCTPe/Of/6VRaYSFAQdOsC4caqTCEeVJw+88gpMn646iRDCEhYXO+PGjWPGjBnUrVuX+Ph4+vXrR9GiRfnjjz8YO3asLTIKK5KRnbTLkgUqVICNG1UnEbZkfLMruVfOkEYFdujdd2HRIrhwQXUS4aj69IGpUyEuTnUSIURqWVzsFC1alCNHjlCpUiUaNmzIvXv3aNKkCTt37iQiIsIWGYUVSbGTPjKVzfElNirYM+1P1VGEhfLkgRYtYMIE1Umcx93//4OSkJCgOEnGKFsWIiLg669VJxFCpJZBc7INcmJjY/Hz8+P27dv4+vqqjpPheveGwEAYNEh1Evu0cSO8+aZ05HF0G2qMwPXkESqdkH137M3Ro1C6tL52x5m7h2WEvXv30rZtW3bu3Kk6SoZauhSGD4ddu/T961LD2c89hFApTZuKPnz4kC1btvDTTz/xww8/JLmIzE3W7KRPmTL6SZQsUHVsiY0Kbhy9rjqKsFCBAlCvHnz2meokjq9YsWJcvnyZuXPnAvD48WO1gTJIo0b6vju//aY6iRAiNVwtvcHq1atp27Yt165dS/Y9g8HgNEPZ9srbG86eVZ3CfmXNqk9j+PNPaNxYdRphKyGlc/FPcE0e9ltAzLLequMICw0cCFWrwjvvgHyIbhuapmEwGHjnnXf46KOPaN++PS4uLqpjZQgXF+jZEz79FGrUUJ1GCPE8Fo/s9OjRg2bNmnHx4kVMJlOSixQ6mZ+s2Um/KlXg999VpxC2Zuz2P3Kv/EIaFdihqCioVEm6ZtmS4f/nb3Xr1o3Tp0/zzz//ONUHnp066R96HT6sOokQ4nksLnYuX75M3759yZkzpy3yCBvz8ZFiJ72kSYFzKDmgNu4J96VRgZ16/329UcGDB6qTOC5N0/D09KRNmzYMGTJEdZwM5esLHTvKJqNC2AOLi53XXnuNDRs22CCKyAje3rJmJ73Kl9cbFNy+rTqJsCUXNxeOVe3C3fFfqI4i0uCll6BwYZg9W3USx5U4utO/f3/Wr1/P9evXcXFxwWQyKU6WMXr21FudX5elfUJkahZ3Y7t//z7NmjUjMDCQYsWKkSVLliTf79mzp1UDWpuzd0RZuxaGDYO//lKdxL6VKwdDhugLoYXjurTjAt6lChJ/5DTZC0hrL3uzbp3+6fuxY/o+WcL6EtfuFC9enGrVqjHRyYY6mjWDkiX1dWLP4uznHkKoZHGDgq+//pq1a9eSNWtWNmzYYP5kB/RPeTJ7sePsZM2OdSROZZNix7EFlwzl75BaPHxvPlWX91EdR1ioenUIDtY/fW/fXnUax2QymXBxceGHH34w77lz8OBBDh06hJ+fH2XLlsXb21txStvp00cveN55B9zcVKcRQqTE4mlsgwYNYtiwYdy+fZtTp05x8uRJ8+XEiRO2yCisSNbsWIes23EeLm92Jc+qGdKowA4ZDPranY8+AidZN5/hXFxcSEhI4NGjR0RERHDhwgV69uxJ+/btGTBgAH379lUd0abKl4fcueHbb1UnEUI8jcXFTnx8PC1atMBoTNMWPUIxWbNjHZUqwY4dcP++6iTC1vRGBQ/YM3Wj6igiDRo00KewLVumOoljevjwIWPGjKF79+40a9aMTz75hKioKK5evcovv/zC0qVLOXjwoOqYNmMwQN++ehtq59qiXQj7YXHF0q5dOxYvXmyLLCIDyDQ268iWDQoVgr//Vp1E2JreqKAzdyfMUB1FpIHRqK+nGD1aTkZt4ffff2fmzJm8/PLLvPLKK8ydO5dKlSrh5uaGn58fDRo0YOHChapj2lTTpvpG0xvl8xAhMiWL1+wkJCQwbtw41qxZQ1RUVLIGBRMmTLBaOGF93t56K9aEBH1jNJF2iVPZqldXnUTYWqFxHfEuVZAbR69LowI71Lw5DB4Mq1dD3bqq0ziWDRs20LJlS/r00de0nT17ljFjxtC4cWOOHDnCvn37qOfgixtdXaFHD73VeZUqqtMIIZ5k8cjO3r17KVGiBEajkX379rFz507zZdeuXTaIKKwpa1a9yLl3T3US+yfrdpxHcMlQ9oXUYs9781VHEWng6gr9++ujO8K6IiIi+O2338xft2zZkvj4eIoVK0bt2rXJmTMnNWrUUJgwY3TpAuvX653/hBCZi8Wtp+2dtH8Ef399n5hcuVQnsW+XL0N4ONy6JV14nMG2kavJPqI34Q8OYjAann8DkanExUFEBHz1lXz6bm0hISFMnDiRHDlyMGLECBo3bkz16tVJSEggZ86chIaGqo6YIRKb0X72WfLvybmHEOqkq8vAuXPnOHfunLWyiAwi63asI2dOyJsXtm5VnURkhBL9auGW8FAaFdgpd3d4910Z3bGF8ePHM23aNPr27UtoaCj16tUjKiqKEiVKOE2hA9CrF8ybp38AJoTIPCwudkwmE8OHD8fPz498+fKRL18+/P39GTFihNPsmmzvpNixHpnK5jxc3Fw4Vq0Ld8d/oTqKSKMuXWD7dti2TXUSx9KqVStWrlzJ7Nmz+fDDDylYsKDqSEpEREC1ajBzpuokQoj/StM+O1OmTOGjjz4yr9UZPXo0kydP5sMPP7RFRmFlsteO9Uix41wKje1A8dMruHH0uuooIg28vKB3bxgzRnUSx6JpGl5eXpQuXZrChQurjqNU374weTI8fqw6iRAikcXFzrx58/jyyy/p1q0bUVFRREVF8dZbbzFz5kzmzp1rg4jC2mSvHeupUgX++kv+sDkLvVFBbfa8O091FJFG3bvDunVw4IDqJI7DYPh3DZuTLQNOpnJlCAyEpUtVJxFCJLK42Llx4waFChVKdrxQoULcuHHDKqGEbck0NuvJmxeyZ4fdu1UnERnF9a2u5F09A83k3Cd19srfH7p1g48+Up3EMf238HFGBgP06aO3oXbyuk+ITMPiYqd48eJMmTIl2fEpU6ZQvHhxq4QStiXFjnXJVDbnUnJAbdwSHrJ7svyj26s+fWD5cjh5UnUS4YiaN4dz52DzZtVJhBCQhmJn3LhxzJ49m8KFC9OpUyc6depE4cKFmTt3Lh9//LEtMgor8/GRaWzWFBMjxY4zMboaOVatC/c+naE6ikijoCBo3x7GjVOdxDEdOfJvG2Zn5OYGb78Nn36qOokQAtJQ7MTExHD48GEaN27MrVu3uHXrFk2aNOHw4cNUrlzZFhmFlcnIjnVVqQIbN4I0I3QekR93pPjpFVw/fE11FJFG770HCxfCxYuqkzie0FC9BfPx46qTqPO//8GaNTJ6KERmIJuKOqHBg/UN9saOVZ3EMWia/sf911+hSBHVaURG+TtXUx6WrkjVFX1VRxFp1KmTvuZOJiVYX5cu+l5kI0eqTqJOt27g4aGv35FzDyHUsXhkZ86cOSxZsiTZ8SVLljBvnnQosgcysmNdBoOs23FG0qjA/vXvDzNmwHXpJG51nTvDnDnO3amyd2+YPRtiY1UnEcK5WVzsjBkzhoCAgGTHg4KCGC1bU9sFWbNjfVWqwO+/q04hMlLJ/rXIYoqTRgV2rGBBqFtX3xdFWFfZspAtmz6Vy1m9+CJUqgSzZqlOIoRzs7jYOXPmDOHh4cmO58uXjzNnzlgllLAtGdmxvsSRHeeaFOrcjK5Gjlfrwr1Pv1AdRaTDwIF6sSMfAFmXwaCP7nz5peokavXpA599BgkJqpMI4bwsLnaCgoLYs2dPsuO7d+8mR44cVgklbEuKHesrUgQePnTuBbnOqPAnHSl++gdpVGDHiheH8uVh+nTVSRxP69bwyy9w+bLqJOpUrw6+vvDTT6qTCOG8LC52WrZsSc+ePVm/fj0JCQkkJCTw22+/0atXL15//XVbZBRWJsWO9RmN+s7Zsm7HuQRFBbMvtA5735X1ivZs0CAYP17/wEJYT0AA1K8P8+erTqJO4iajKWxPKITIIBYXOyNGjKBcuXLUqFEDDw8PPDw8qF27NtWrV2fUqFG2yCisTNbs2IY0KXBOWd7+H/nWSKMCe1a+PERG6ovJhXV16qRPZXPmKb4tW8L9+6pTCOG80tx6+ujRo+zatQsPDw+KFStGvnz5rJ3NJqT9I+zbBw0aSP9/a9u6FVq0gBMnVCcRGcn02MQ5j/zc+Hg20b2rqo4j0ujXX/U1JkePQpYsqtM4DpMJwsNh0SJ9sb6zun07Fn9/5z73EEIVi0d2hg8fzv379ylQoADNmjXjlVdeIV++fDx48IDhw4fbIqOwMpnGZhslSsDVq3D2rOokIiMZXY2cqN6F+9KowK7VqAFBQfD116qTOBajETp2lEYFBoPqBEI4L4tHdlxcXLh48SJBQUFJjl+/fp2goCASMnnLERnZgWvXIE8eePBAdRLH8/LL0LYttGqlOonISFf2XMKzeH4eHjhJQGSg6jgijZYv17uz7d+vn6QL6zhzRm/icv68vljfGcm5hxDqWPx2rmkahhQ+oti9ezfZs2e3SihhWz4++kJcZ97szVZk3Y5zCooKZm+ul9n3njQqsGevvgouLrBsmeokjiVvXqhYEb75RnUSIYQzSnWxky1bNrJnz47BYKBgwYJkz57dfPHz86NWrVo0b97cllmFlbi5gaurTGWzBSl2nJdb967kWyuNCuyZ0aiP7Iwa5dwL6m0hsVGBEEJktFRPY5s3bx6aptGxY0cmTpyIn5+f+Xtubm6EhYVRvnx5mwW1FhlK1mXLBnv3Qu7cqpM4lrg48PeH06f1+f/CeSQ2Krg+dhYl+lZTHUek0ePH+s73U6fq01KFdcTF6X9v1q2DqCjVaTKenHsIoY5raq/Yrl07AMLDw6lQoQJZpF2NXfPxkZEdW3B3h3LlYONGaNpUdRqRkRIbFbhNmgFS7NgtV1fo3x9Gj5Zix5rc3aFNG5g1CyZNUp1GCOFMLF6zEx4ezsWLFzlz5kyKF2EfvL1lrx1bkalszqvwxx2IOvMj1w5eVR1FpEO7dnD8uP6hhbCeTp1g4ULZvFUIkbFSPbKTKCwsLMUGBYkyezc2oZP207ZTpQq8957qFEKFoKhgNud6mbj35lH1p3dVxxFp5O4Oc+ZArlyqkziWIkWgYEG9693rr6tOI4RwFhYXOzt37kzy9aNHj9i5cycTJkxg1KhRVgsmbEuKHdspXx4OHIBbt/T1O8K5uL39P4IHd0czvYPBKJtr2KvatVUncEydOulT2aTYEUJkFIuLneLFiyc7Vrp0aUJDQ/n4449p0qSJVYIJ25I1O7bj5aVvMPrXX1C/vuo0IqOVeLcGZz9MYNfEDdKoQIgntGgBffvCyZMQHq46jRDCGVht27QXX3yRrVu3WuvuhI3Jmh3biomRdTvOyuhq5GSNLjyY+IXqKEJkOj4+0KyZPk1QCCEygsXFTmxsbJLL7du3OXToEB988AEFChSwRUZhAzKNzbakSYFzKzyuPVFnf5JGBUKkoHNnvdiRJb5CiIxgcbHj7+9PtmzZzJfs2bNTuHBhNm/ezOeff26LjMIGpNixrYoVYedOuHdPdRKhQlBUMHtz1WXfu3NVRxFWlJCQwJEjR8z/L9LmpZf0EZ61a1UnEUI4A4vX7Kxfvz7J10ajkcDAQPLnz4+rq8V3JxTx8ZFpbLbk7w+RkfD331Cjhuo0QgW3t7sSMvgtNNO70qjAQezYsYOGDRty4cIFXFxc0DTtmd1JRcoMhn8bFdStqzqNEMLRWTyyExMTk+RSuXJlChUqJIWOnZGRHduTqWzOrcS7NTBoJnZ9uv75VxZ2oUyZMmTLlo3hw4cDMrqTHm3awKpVcOWK6iRCCEeXpgYFx48fp0ePHtSsWZOaNWvSs2dPjh8/bu1swoak2LG9KlXg999VpxCqmBsVTJqhOoqwAk3TABg5ciTjx48HkA/50iEoSB/VWbBAdRIhhKOzuNhZs2YNhQsXZsuWLURFRREVFcU///xDkSJF+OWXX9IUYurUqYSFhZE1a1bKlSvHli1bnnrduXPnYjAYklyyZs2apsd1ZlLs2F7lyvDPPxAXpzqJUKXIJx2IOvsTV/fLx9f2LnG6WuPGjcmZMycTJkwA4PHjxypj2bXOnfWpbP9fRwohhE1YXOwMGDCAPn368M8//zBhwgQmTJjAP//8Q+/evenfv7/FARYvXkzfvn0ZMmQIO3bsoHjx4tSpU4crzxjb9vX15eLFi+bL6dOnLX5cZydrdmwvKAjCwkA6sjuvwKI52ZurLvv7zVMdRVhB4ujOkCFDGD16NCCjO+lRq5b+odvmzaqTCCEcmcXFzsGDB+nUqVOy4x07duTAgQMWB5gwYQJdunShQ4cOFC5cmOnTp+Pp6cns2bOfehuDwUBwcLD5kjNnTosf19nJyE7GkHU7wr3n/wj7ZQamxybVUUQ6JY7uvPHGG3h7ezNlyhRA1u6klYsLdOigj+4IIYStWFzsBAYGsmvXrmTHd+3aRVBQkEX3FR8fz/bt26lZs+a/gYxGatasyeZnfNRz9+5d8uXLR548eWjYsCH79+9/6nXj4uKS7Q0kpNjJKFLsiOi+1TFoJnZP2qA6irCCxNGdPn368P333wPg4uKiMpJd69ABliyRmQZCCNuxuNjp0qULXbt2ZezYsWzcuJGNGzfy0Ucf8b///Y8uXbpYdF/Xrl0jISEh2chMzpw5uXTpUoq3efHFF5k9ezYrVqxg4cKFmEwmKlSowLlz51K8/pgxY/Dz8zNf8uTJY1FGR+XjA1L32V7VqvDnn/DggeokQhWjq5GTtd/k8djxqqMIK0gc3enVqxc///xzku/JCI/lwsKgQgV4xmQOIYRIF4OmWbY0UNM0Jk6cyPjx47lw4QIAoaGhvPfee/Ts2dOiPQcuXLhArly52LRpE+XLlzcf79evH7///jv//PPPc+/j0aNHREZG0rJlS0aMGJHs+3FxccT9Z4V4bGwsefLk4fbt2/j6+qY6q6O5cAEKFZKCJyOULg1DhkCDBqqTCFXuXbnH3ZD8XJq4mOI9qqiOI9Lpxo0bDBs2jIIFCxIfH0+VKlV4+PAhpUqV4vHjx3h4eMhojwU2boTmzeH4cfD0VJ3GNmJjY/Hz83P6cw8hVLB4ZaXBYKBPnz706dOHO/8/7uzj45OmBw8ICMDFxYXLly8nOX758mWCg4NTdR9ZsmShRIkSHDt2LMXvu7u74+7unqZ8jixbNn3awOPHIOtrbatJE1i2TIodZ+YV5MX25oPJ9n5/tO6bZJNRO3fp0iWmTp1Kv379uHTpEps3b+bBgwfkzJmTLVu2UKNGDT799FPVMe1G5cpQrBhMnw59+6pOI4RwNGnaZyeRj49PmgsdADc3N0qVKsW6devMx0wmE+vWrUsy0vMsCQkJ7N27l5CQkDTncEZZs4KbG9y+rTqJ42vSBFas0AtL4bzKz+qMT9w1tgxaoTqKSKfChQvTpEkTbt26xezZs/n2229ZtGgRLi4uuLq6snz58qdOxRYpGzYMxo6Fe/dUJxFCOJp0FTvW0LdvX2bOnMm8efM4ePAg3bp14969e3To0AGAtm3bMnDgQPP1hw8fztq1azlx4gQ7duygdevWnD59ms6dO6v6EeySwaCP7ty8qTqJ4ytUSG9DLY0KnFsWzyxc6D6KgAkDefxQKl9798EHH7Bs2TIOHDjAiBEjqFevHufPn+f1119n9uzZZMuWTXVEu1K+PJQqBdOmqU4ihHA0yicwtWjRgqtXrzJ48GAuXbpEdHQ0q1evNjctOHPmDEbjvzXZzZs36dKlC5cuXSJbtmyUKlWKTZs2UbhwYVU/gt3Klg1u3VKdwjkkTmWrXl11EqHSSx+/xqEZ49j85jwqz03ewl/YD29vb3x8fKhduzYVK1akfv36VK1alaioKLy8vFTHs0vDhkG9evDmm3oTHSGEsAaLGxTYO1kk+K/y5WH4cH1jN2Fb27dDw4Zw5gwYlY+nCpV2jPuVkIHt8b96FI/sHqrjiDTYtWsXLVu2JCgoiF27drF69WrKli2bpCmBpmkWNewRuldf1f82/WdCh0OQcw8h1JHTLicmIzsZp2RJfQO9rVtVJxGqlexXkwv+kfzTZorqKCKNoqOjGThwIJMmTaJFixYYjcZk3dek0EmbYcPgk0+kU6gQwnrSNLKzbt061q1bx5UrVzCZku4KPjuTN8uXT1f+1aqVvg9M166qkziH3r31xhAffaQ6iVDt4MLt5GxbG+PxY/iHy9oOe5Q4cvP48WNcpaWlVTVpAiVKwIcfqk5iPXLuIYQ6Fo/sDBs2jNq1a7Nu3TquXbvGzZs3k1yE/ZAGBRmrSRNYuhSca+KoSElk61Icyl2LXS3Hqo4i0ihx5EYKHesbOhQ+/VRmHgghrMPid+np06czd+5c2rRpY4s8IgPJNLaMVbGi3up7/34oWlR1GqFarjkj8a9ZiovbehBSOpfqOEJkGlFRULOmXvAMG6Y6jRDC3lk8shMfH0+FChVskUVkMH9/GdnJSC4uepOCZctUJxGZQb4a+dlVtDVH28jZnBBPGjIEPvsMbtxQnUQIYe8sLnY6d+7MV199ZYssIoPJyE7Ga9IEvv9edQqRWUQu+pDihxZzYuUh1VGEFRw6JNNUraVIEahbF8aPV51ECGHvUtWgoG/fvub/N5lMzJs3j6ioKKKiosiSJUuS606YMMH6Ka1IFgn+a+lSmDED1qxRncR5xMXpG4zu2gXh4arTiMxgQ+UPyXriAC+dX6o6ikiHhAQoUEDfFPPll1WncQyHDsFLL8GxYxAQoDpN+si5hxDqpGrNzs6dO5N8HR0dDcC+ffusHkhkHGlQkPHc3aF+fX0q238+QxBOrOTX7xGfN4J9X/5N0c4vqY4j0sjFRV9fMmAA1K4t+2lZQ6FC+r47H38MY6WXhxAijWRTUSe2cye0aAFHjqhO4ly++w4mToQ//1SdRGQWvzeeiN/65RS/sR6DUfZnsVcJCfqeWv36wRtvqE7jGI4ehdKl9b9TOXOqTpN2cu4hhDoWf/bUsWNH7ty5k+z4vXv36Nixo1VCiYwhDQrUePllvdC8dEl1EpFZvDSvGwF3T7Ft5GrVUUQ6uLjAmDHwwQf6lFWRfgUKQNOmMG6c6iRCCHtlcbEzb948Hjx4kOz4gwcPmD9/vlVCiYyROI3Nucb21PP21tuqrlihOonILNx93TnVcTi+YwZiemx6/g1EplW3LuTLB198oTqJ4/jgA5g5Ey5eVJ1ECGGPUl3sxMbGcvv2bTRN486dO8TGxpovN2/eZOXKlQQFBdkyq7AyX18wmeDePdVJnI90ZRNPKj/lDQyY2Nzza9VRRDoYDPr6kpEjITZWdRrH8MIL+pTrjz5SnUQIYY9SvWbHaDSad4xO8Y4MBoYNG8agQYOsFs4WZN5sUtmywZ49kCeP6iTO5cYNyJ0bzp/X/w2EANg69GeCR71N0PVDuPu6q44j0qFpU33zYNkU0zpOndI3Gz1wQH/vtDdy7iGEOqkudn7//Xc0TaN69eosXbqU7Nmzm7/n5uZGvnz5CA0NtVlQa5E3nKReeEGfTlWsmOokzqdmTWjfHlq3Vp1EZBaaSWNPthhu1XyNmKU9VccR6XD4MJQrp//XnhfWZybduuld7qZOVZ3EcnLuIYQ6FndjO336NHnz5n3mKE9mJm84SZUsqXcGq1JFdRLnM20a/PqrTGcTSe2dsZngNxuS9dxxfEJ9VMcR6dC1K7i5wZQpqpM4hrNn9c1G9+2DvHlVp7GMnHsIoU6qip09e/ZQtGhRjEYje/bseeZ1o6KirBbOFuQNJ6kaNaBnT2jYUHUS53P+PBQsCFevgqen6jQiM/k7tDEPCxan6oahqqOIdDh/HgoXhu3bIX9+1WkcQ48eEB9vfw0g5NxDCHVSVewYjUYuXbpEUFCQee1OSjczGAwkJCTYJKi1yBtOUk2b6pu2tWunOolzKl9e35OjcWPVSURmcvyngwQ0KE/83sMEFpU5UPZs4EA4eRK++UZ1Esdw4YK+2eju3RAerjpN6sm5hxDquKbmSidPniQwMND8/8JxJLafFmokdmWTYkf8V8QrkWws0BRTq5HE7JmsOo5Ih/799VGd7duhVCnVaexfaCh06gSjRsGXX6pOI4SwBxav2bF38ulKUu+9B15eMHSo6iTO6dgxfXfwK1f0uf1CJLrwz1m8XirK7fU7yVv1BdVxRDp88gmsWQO//KI6iWO4dAlefBF27ICICNVpUkfOPYRQx+JNRfPmzUvbtm2ZNWsWx48ft0UmkYH8/WVkR6X8+fWFths2qE4iMpvQcnnYWeZ/nG3/oeooIp3eflvvyibFjnUEB+vNH0aMUJ1ECGEPLC52Ro8eTdasWRk7diwFChQgT548tG7dmpkzZ3L06FFbZBQ2JNPY1JMNRsXTFP96AIXPrOLQ1ztVRxHpkDWrvt/OgAH6Rs4i/fr1g2XL4MgR1UmEEJmdxcVO69atmTFjBkeOHOH8+fN8/PHHALz11lsUKlTI6gGFbWXLBrduqU7h3Bo3huXLIZP39hAKZIvIzu46/bnbY6DqKCKd2raFuDhYskR1EscQGAhvvQXDh6tOIoTI7CwudgDu37/P2rVrmTx5MpMmTeK7776jaNGi9Owpm+DZG5nGpl5UlL5u6u+/VScRmVHZBT3IdXMvO8f/pjqKSAcXFxgzBgYN0lsni/R791346Sc4eFB1EiFEZmZxsVOhQgVy5MjBgAEDePjwIQMGDODixYvs3LmTTz/91BYZhQ3JNDb1DAaZyiaezjPAk6NvDMVtyAA0k1P1k3E4r7yirzeZOVN1EseQI4e+786wYaqTCCEyM4uLnUOHDuHl5UWhQoUoVKgQkZGRZMuWzRbZRAbw95dpbJlB48Z6seNcvRFFalWY0QGPR7H8/d5S1VFEOhgMMHasvrD+7l3VaRxD3756p7u9e1UnEUJkVhYXO9evX+e3337jpZdeYs2aNVSsWJFcuXLRqlUrZsrHVXZHRnYyh5degocP9Y3yhHiSa1ZXrvQaTfDkQTx++Fh1HJEOFStCuXIwYYLqJI4hWzbo3VtGd4QQT5eufXY0TWP79u1MmTKFRYsWYTKZSMjkq6yl131ScXF6p6C4ONnnRbW33oKAAFlwK1KmmTT2+5bnRqOOVFnYVXUckQ7790OlSnonsf/fr1ukw+3b8MILsG4dREerTpMyOfcQQh2LR3Z27NjBhAkTePXVV8mRIwfly5dnz5499OjRg+9l0YHdcXcHDw+ZypYZJE5lEyIlBqOBxyM/ouDXQ7l/7b7qOCIdihTRf99HjlSdxDH4+enT2WRzbCFESiwe2XF1daVEiRLExMQQExNDlSpV8PPzs1U+q5NPV5LLlQt++03fkVqo8+gRBAXBP/9AwYKq04jMamtQPe6VqEzVNdKO2p6dPQtFi8KuXRAerjqN/btzR38e16yBUqVUp0lOzj2EUMfV0hvcuHFDflEdjDQpyByyZIFXX9U3yuvfX3UakVn5ThlD/tercfP4/8gWkV11HJFGefJA167w4YewcKHqNPbPxwfeew+GDNHbUQshRCKLp7FJoeN4pElB5iFT2cTzvNi8OAfy1WP362NURxHpNHAgrFypj+6I9OveHbZs0UfHhRAiUZo2FRWOJVs2GdnJLGrX1hcvnzunOonIzPLOG0HJbV9wfvMZ1VFEOmTPro/iDpQZiVbh7a0/n0OGqE4ihMhMpNgR+PvLyE5m4ekJL78My5erTiIyszxVwtlZvAMn2g1VHUWkU48esGcPrF+vOolj6NZNHyn76y/VSYQQmYUUO0KmsWUyMpVNpEbhRYOIOvo9x1bsVx1FpIOnp75HTP/+sqmwNXh66iNlMrojhEhkUbHz6NEjIiIiOHjwoK3yCAWkQUHmUr8+/P03XLumOonIzAKLBLGzah+uv/m+6igindq317uJLV2qOolj6NoVDhyA339XnUQIkRlYVOxkyZKFhw8f2iqLUERGdjIXf3+IiYEff1SdRGR2pRb15YXLf7Pnc5mzY89cXWH0aBg0SG9BL9LHwwPefx8GD5bRMiFEGqaxde/enbFjx/L48WNb5BEKSIOCzEemsonU8An14UDTD9EGDEAzyVmdPWvUSG9YMHu26iSOoXNnOHFC1kIJIdKwqWjjxo1Zt24d3t7eFCtWDC8vryTf/z6Tn6HJxl7JLV8OU6bAr7+qTiISXb4ML7wAly7p+0cI8TTxd+O5mC2SywMnUXb4K6rjiHT4/Xdo2RKOHoUn/rSKNJg+Xd/DaONGMBjUZpFzDyHUsXhkx9/fn6ZNm1KnTh1CQ0Px8/NLchH2R6axZT45c0LJkrBqleokIrNz83bjXNcRZBs3gIT4BNVxRDrExECJEjBpkuokjqFjRzh7Vj7IE8LZWTyyY+/k05Xkdu+GJk3g+HHVScR/ffqpvjneN9+oTiIyO9NjE0d8S3Htjd5UmtlOdRyRDnv26EXPsWOQI4fqNPbvyy9h1izYtEnt6I6cewihTppaTz9+/Jhff/2VL774gjt37gBw4cIF7t69a9VwImPIyE7m1Lixvru69AQRz2N0NXJ30EeEzRnMw1vygrFnUVHQoIHesECkX7t2+rTg1atVJxFCqGJxsXP69GmKFStGw4YN6d69O1evXgVg7NixvPvuu1YPKGwvsUGByaQ6ifivsDDInx/WrVOdRNiDUgNrc9Ungr/bf646ikin4cP1EYnTp1UnsX9Zsuhd2aQzmxDOy+Jip1evXpQuXZqbN2/i4eFhPp7YuEDYH29vMBr1fR5E5tKkiXRlE6ljMBpwnziWoj+M5vaZ26rjiHQIC4NOnWRjTGtp3Vr/QO+nn1QnEUKoYHGxs3HjRj744APc3NySHA8LC+P8+fNWCyYyjsGg7+0iU9kynyZN4IcfQDq9i9Qo3K4MR0OrsrPVx6qjiHR6/31YsQL27lWdxP65uuqFo4zuCOGcLC52TCYTCQnJO/6cO3cOH+mRa7f8/WWvncwoMlLfe+PPP1UnEfYieNYoSvw1mcu7LqqOItIhIADefVcvekT6tWwJDx7oWy0IIZyLxcVO7dq1mThxovlrg8HA3bt3GTJkCPXq1bNmNpGBpElB5mQwyFQ2YZnwOgXZHdmSw28MVx1FpFPv3rBtm75PjEgfFxcYOlQf4ZH1qUI4F4uLnfHjx/PXX39RuHBhHj58SKtWrcxT2MaOHWuLjCIDSLGTeTVpAsuWyfQLkXoFFw6m+IGvOPXLUdVRRDp4eekn5/37y++/NTRrphc6S5eqTiKEyEgWFzu5c+dm9+7dvP/++/Tp04cSJUrw0UcfsXPnToKCgmyRUWQAmcaWeZUurf93yxa1OYT9CC4Zys7y3bnQ6QPVUUQ6deoE167p63dE+iSO7gwdCinMxhdCOCjZVFQA8L//QcGC8M47qpOIlAwYALGxMG2a6iTCXtw+fYvH4fm5PHc1hduWVh1HpMOSJfri+r179cX2Iu1MJihRQn9Pbdky4x5Xzj2EUCdVxc4PP/yQ6jt89dVX0xXI1uQNJ2X9+4ObG4wYoTqJSMmRI1CuHFy4AP/p+C7EM21oMB7fv1ZR8savqqOIdNA0KFsW3nxTH+kR6bN8uf43b//+jCse5dxDCHVSVewYjUlnuxkMBp68mcFgAEixU1tmIm84KfvoIzh3DqZMUZ1EPE3lyvrJzhtvqE4i7MXDWw+5HlCQSyNnUWpALdVxRDr89hu0bQtHj8oHHumlaVCqFPTpA23aZMxjyrmHEOqkas2OyWQyX9auXUt0dDSrVq3i1q1b3Lp1i1WrVlGyZElWr15t67zCRqRBQebXsSPMnq06hbAnWf2zcqLtMDyHD8D0WFpQ2bPq1aFoUZg8WXUS+2cwwPDhMGyY7GEmhDOwuEFB7969mTRpEnXq1MHX1xdfX1/q1KnDhAkT6Nmzpy0yigwgDQoyv2bNYOtWOHFCdRJhTypMb4urKY6/31miOopIpzFjYOxY+WDKGurX1/cwW7BAdRIhhK1ZXOwcP34cf3//ZMf9/Pw4deqUFSIJFWRkJ/Pz9obmzWHuXNVJhD1xcXPhxjujyTVtEPF341XHEelQogS8/LI+7VikT+LozvDh8OiR6jRCCFuyuNgpU6YMffv25fLly+Zjly9f5r333qNs2bJWDScyTrZsMrJjDzp21IudTL40TmQyZUc04LZHMJs7f6k6ikinESNg+nQ4e1Z1EvtXpw6EhMgHSEI4OouLndmzZ3Px4kXy5s1L/vz5yZ8/P3nz5uX8+fPMmjXLFhlFBvD3l5Ede1C+PHh6wq/SXEtYwGA0wEcfEblkOHcv3VUdR6TDCy9Au3b6XjEifRJHd0aOhLg41WmEELaSpn12NE3jl19+4dChQwBERkZSs2ZNc0e2zEw6oqTs2jXInRsePlSdRDzPuHGwfTssXqw6ibA3/wS/yoMiZai67kPVUUQ6XLmi74u2aRMULqw6jX3TNIiJ0ffc6dbNdo8j5x5CqCObigpAn7Ps5gYPHkDWrKrTiGe5dAkiIuDMGciRQ3UaYU+OLttHYNPKPN5/hIDIQNVxRDoMGwY7d+p7xoj0Wb9eb0F97Jjt/v7JuYcQ6qSp2Fm3bh3r1q3jypUrmExJ25nOzuS9ceUN5+l8fPTNK0NCVCcRz9OwIdSsCT16qE4i7M2f+duT4JuNmB2fqo4i0uHOHcifH5YtgwoVVKexf9WqQdOm8Pbbtrl/OfcQQh2L1+wMGzaM2rVrs27dOq5du8bNmzeTXIT9ko5s9qNjR5g1S5+CIYQlwhcMJ3rnbM79dVp1FJEOPj7w4YfQv7+8D1jDsGEwerQ+u0EI4VgsHtkJCQlh3LhxtMmobYetTD5debqoKL3Lj3xKmPk9egR58sDKlVCypOo0wt78XrIPxju3qHx0juooIh3i4yEyEiZNgldeUZ3G/tWqpe+/07u39e9bzj2EUMfikZ34+HgqyNmwQ5KRHfuRJQu0bQuZfNaoyKSKLHqfqGPLOPbDAdVRRDq4uemdxAYMkHb01jBsmL6H0b17qpMIIazJ4mKnc+fOfPXVV7bIIhTz95e9duxJhw6waJFMuxCWC4gMZGeVXlzrJl3Z7F2LFnrRs2CB6iT2r0IFfePWzz9XnUQIYU0WFzsPHz5kwoQJxMTE0KNHD/r27ZvkkhZTp04lLCyMrFmzUq5cObZs2ZKq233zzTcYDAYaNWqUpscVScnIjn2JjNQv0o1JpEXJRe+Q/+If7J+TuvdbkTkZjfpoxODBsnWANQwbprf3vyvbUQnhMCwudvbs2UN0dDRGo5F9+/axc+dO82XXrl0WB1i8eDF9+/ZlyJAh7Nixg+LFi1OnTh2uXLnyzNudOnWKd999l8qVK1v8mCJlUuzYn06dZCqbSBvf3L7sazCQuHfeVx1FpFOtWvq+O1Onqk5i/8qWhXLlYMoU1UmEENaifJ+dcuXKUaZMGab8/zuLyWQiT5489OjRgwEDBqR4m4SEBKpUqULHjh3ZuHEjt27dYnkqP96WRYJPN2wYxMbC+PGqk4jUunMHQkNh714IC1OdRtibh7ceciOgAJdGz6Fkv5qq44h02LYNXn5Z3yvG3191Gvu2YwfUrg0nToC1ThPk3EMIdSwe2bGm+Ph4tm/fTs2a//6RNRqN1KxZk82bNz/1dsOHDycoKIhOnTo99zHi4uKIjY1NchEpy5YNbtxQnUJYwscHXnsNZs5UnUTYo6z+WTn+xhA8hvbj0f1HquOIdChdGmrUgOHDVSexfyVLQuXK8PHHqpMIIazB1dIbVKtWDYPB8NTv//bbb6m+r2vXrpGQkEDOnDmTHM+ZMyeHDh1K8TZ//vkns2bNSvWUuTFjxjBs2LBUZ3JmQUHwnNmDIhN65x2oVAn69oUcOVSnEfamwswO7F82l7/qjaLqhqGq44h0mDBBX2Bfp45+EWk3fjyUKgWNG0t7fyHsncUjO9HR0RQvXtx8KVy4MPHx8ezYsYNixYrZIqPZnTt3aNOmDTNnziQgICBVtxk4cCC3b982X86ePWvTjPYsJAQuXVKdQliqaFF9b4hx41QnEfbIxc2FbD8toPgfn7Hvy79VxxHpkCsXzJmjt6W/eFF1Gvv2wgswZoz+XErjByHsm8UjO59++mmKx4cOHcpdC9uXBAQE4OLiwuXLl5Mcv3z5MsHBwcmuf/z4cU6dOkWDBg3Mx0wmEwCurq4cPnyYiIiIJLdxd3fH3d3dolzOKjhYih17NWyY/ilk79560SqEJfJUCefPjhPI0601d1/ZhXewt+pIIo3q14c33oA2bWDtWr1bm0ib//0Pvv8ehgyBsWNVpxFCpJXV3gZbt27NbAvbQrm5uVGqVCnWrVtnPmYymVi3bh3ly5dPdv1ChQqxd+9edu3aZb68+uqrVKtWjV27dpEnT550/xzOLDgYLl+WzensUf780Lw5jB6tOomwVxVntONCzmh2VO2jOopIpzFj9D3T5AQ9fQwGvdvll1/Cpk2q0wgh0spqxc7mzZvJmjWrxbfr27cvM2fOZN68eRw8eJBu3bpx7949OnToAEDbtm0ZOHAgAFmzZqVo0aJJLv7+/vj4+FC0aFHc3Nys9eM4JV9ffXO669dVJxFp8eGHMG8enD6tOomwRwajgRfXf0HBoz/zz/srVMcR6eDuDt98oy+wl5P09MmdGyZOhHbt4N491WmEEGlh8TS2Jk2aJPla0zQuXrzItm3b+PBDy3fjbtGiBVevXmXw4MFcunSJ6OhoVq9ebW5acObMGYwyDp8hDIZ/p7IFBalOIyyVNy906KB3Y5o1S3UaYY+yF8jBtuFzeOHDNlx5vRxBUcmnEwv7kD+/vldMy5awa5febVOkTevW+nS2AQNg8mTVaYQQlrJ4n53EEZdERqORwMBAqlevTu3ata0azhak1/2zVaigz0+WTj726dIlePFF2LpV32RQiLT4vXhPPC8ep/SlnzAYn959U2R+nTrpm0UvXap/oCXS5vJlKFYMvv5ab/FtKTn3EEId5ZuKZjR5w3m2pk3h1Vf1IXthnwYMgDNn4KuvVCcR9urBjQdcCCnF+SY9qPJ1N9VxRDrcu6fvwdOjB7z1luo09u277/QW/3v3gp+fZbeVcw8h1Enz/LDt27ezcOFCFi5cyM6dO62ZSSgkHdnsX79+sGqV/gdZiLTwyO7Bo7mLKP7NQE6sOqw6jkgHLy9YvBjefx9271adxr699pq+2Wgf6eEhhF2xuNi5cuUK1atXp0yZMvTs2ZOePXtSqlQpatSowdWrV22RUWSg4GDZn8HeZc+u/zFOwxI6IcwKtSzBzpcH8vC11jy6/0h1HJEOUVF6h7YWLWSRfXpNmQJr1sCPP6pOIoRILYuLnR49enDnzh3279/PjRs3uHHjBvv27SM2NpaePXvaIqPIQLKxqGPo3Rv+/FNfuyNEWlVe8S6PXD34q85w1VFEOr35JhQpok9nE2mXLZveirprV+lcKoS9sLjYWb16NdOmTSMyMtJ8rHDhwkydOpVVq1ZZNZzIeDKNzTH4+kL//vDBB6qTCHvm4uZCjp/nU/yvqez9QnoY2zODQT9J/+03WLRIdRr7VrcuNGgga6CEsBcWFzsmk4ksWbIkO54lSxZMJpNVQgl1ZBqb4+jeXV+388cfqpMIe5a7Uhj7u07C9+023LlwR3UckQ7ZsundxHr0gGPHVKexb+PHw5Yt+nooIUTmZnGxU716dXr16sWFCxfMx86fP0+fPn2okZZ+jCJTkWlsjsPTEwYN0i/O1XNRWFvFaa05H1KaXVV7q44i0ql8eb2JSYsWEBenOo398vGBuXPh7bflA0IhMjuLi50pU6YQGxtLWFgYERERREREEB4eTmxsLJNlty27FxQEsbFw/77qJMIaOneGs2f1BbVCpJXBaCBy/efkP76Gv/t9rzqOSKd+/fRGJgMGqE5i32Ji9A1Hu3aVD5SEyMzStM+Opmn8+uuvHDp0CIDIyEhq1qxp9XC2IL3uny8wEP75B154QXUSYQ2zZ8O0aXqzAtlUUKTHjnG/kmdAK0w7dpMzOkR1HJEOly5BdDTMnKmvPxFp8+ABlCihr5F8Ys/1JOTcQwh1UlXsZM+enSNHjhAQEEDHjh2ZNGkSPj4+GZHP6uQN5/miomD6dKhQQXUSYQ2PH+tdmD76CBo3Vp1G2LvfS/bB8+whSl9eicEo1bM9W7sW3ngDdu6E3LlVp7Ff//yjNy3YuRPy5Uv5OnLuIYQ6qZrGFh8fT2xsLADz5s3j4cOHNg0l1JImBY7F1RWGDdP33UlIUJ1G2Ltyv43B/85Z/nh9muooIp1q14ZOnfSCR94b0q5cOejWDTp2BOnTJETmk6qRnVq1anH58mVKlSrFvHnzaNGiBR4eHiled/bs2VYPaU3y6crztW2rv3l37646ibAWk0mfatGvn35iI0R6HP52N8EtYrj242YiXol8/g1EpvXoEVSpAi+/DEOGqE5jv+LjoUwZ6NJFb1rwJDn3EEKdVI3sLFy4kHr16nH37l0MBgO3b9/m5s2bKV6E/ZOObI7HaIQRI2DoUP3kRoj0eLF5cXbW/4D45q2JvxuvOo5IhyxZ9HbUn30Gv/+uOo39cnOD+fP1EfSjR1WnEUL8l8UNCsLDw9m2bRs5cuSwVSabkk9Xnu/TT2H/fn0DOuE4NA1eekn/5LFzZ9VphL0zPTaxO7AGtyPLU3XTaNVxRDp99x307g27dkFAgOo09mv0aPjpJ9i4EVxc/j0u5x5CqGNx6+mTJ0/abaEjUic4WEZ2HJHBACNHwvDhsr+GSD+jq5GglfOI+vsLdk/ZqDqOSKfXXtO7srVvL22U06NfP3390yefqE4ihEjkmpYbrVu3jnXr1nHlyhVMT6zGy+xrdsTzyTQ2x1Wzpt5S/IsvoGdP1WmEvctVPi9/dZtMvt5tiG20B9/c8om1PZswAcqWhUmT9FEeYTlXV5g3Tx9Fr1cPihVTnUgIYfHIzrBhw6hduzbr1q3j2rVrsmbHAUk3NsdlMMCoUfpUi3v3VKcRjqDi1Faczl2BPVWlerZ3Hh6weLHevXH7dtVp7FehQvpz2K6d3rhACKGWxWt2QkJCGDduHG3atLFVJpuSebPPd/s25Mihv0kbLS6HhT2oV0/f/bt/f9VJhCO4dfIm9wtEcbrXp5Qf/5rqOCKdZs2CMWNgxw6QP5NpYzJB9er6++ywYXLuIYRKFp/KxsfHU0F2m3Rovr56h57r11UnEbYyYgSMG6cXtkKkl394Ni6Pm0+BT7txcdt51XFEOnXsqE9n69ZN1u+kldEIc+boXe62blWdRgjnZnGx07lzZ7766itbZBGZhMEgU9kcXalSULWqPkdfCGso0bca+0q240KdDpgey86K9sxggOnT4e+/9fUnIm3Cw/UPldq1gwcPVKcRwnlZPI2tV69ezJ8/n6ioKKKiosiSJUuS70/I5GdPMpScOhUq6Huy1K6tOomwlf37oWJFOHZMWs0K64iLjeN0zjJcrNeZmKWyhsfebdsGtWrBpk0QKXvHpommQf36EBERy5Qpcu4hhAoWd2Pbs2cP0dHRAOzbty/J9wwGg1VCCfWk/bTjK1JEbzU7bpx+ESK93H3dYeEiol+rzLEVNcjfsIjqSCIdSpfWN8l8/XV9lMfDQ3Ui+2Mw6HvWvfyy6iRCOC+LR3bsnYzspM5bb0G+fLKA3dEdO6ZPaTt0SG85LoQ1bGj0KcFr5xN+6W+9ABJ2y2TSPxQJC4OpU1WnsV+3b8fi7y/nHkKokK5eW+fOnePcuXPWyiIyEdlrxznkzw8tWujtqIWwlirf9eJ+1hxsrjVYdRSRTkYjzJ0Ly5fD99+rTmO/ZOKLEOpYXOyYTCaGDx+On58f+fLlI1++fPj7+zNixIhkG4wK+yXT2JzHhx/C/Plw6pTqJMJRGF2NhKyZS7Gts9g16XfVcUQ6BQbCokXQtSucPq06jRBCWMbiYmfQoEFMmTKFjz76iJ07d7Jz505Gjx7N5MmT+fDDD22RUSgg3dicR548eqvZESNUJxGOJKRMbg73mErAO225fUZ6nNu7qlWhe3do2RIePVKdRgghUs/iNTuhoaFMnz6dV199NcnxFStW8NZbb3H+fObeY0HW7KTOtm3QurW+lkM4vsuXoWBBfT+IggVVpxGO5M8X9A2oK51YoDiJSK/Hj/WNMitXlqmvlpJzDyHUsXhk58aNGxQqVCjZ8UKFCnHjxg2rhBLqyciOc8mZU29KMWSI6iTC0RTbMIWwM3+wqddi1VFEOrm66tPZZsyAX39VnUYIIVLH4mKnePHiTJkyJdnxKVOmULx4cauEEuoFBUFsLNy/rzqJyCjvvQerV8OePaqTCEfil9ePa+Pn8+Lk7lzcKg1t7F2ePDBrFrRpo48ICyFEZmfxNLbff/+d+vXrkzdvXsqXLw/A5s2bOXv2LCtXrqRy5co2CWotMpSceoGBsGWLvgu0cA4jRuhTGFesUJ1EOJoN5frje3Q70VfWYnRNVyNQkQn06qVPc161Su/YJp5Nzj2EUMfit6iYmBgOHz5M48aNuXXrFrdu3aJJkyYcPnw40xc6wjIylc359O6t75a+ZYvqJMLRlP9lOJ4Pr/PHa5NURxFWMG4cXL0Kn3yiOokQQjybbCoqnqpWLejWDZo0UZ1EZKTx42HNGli7VnUS4WiOrdhPYKOKXP5uIwWbFlMdR6TT0aNQrpw+ulOunOo0mZucewihjsUjO3PmzGHJkiXJji9ZsoR58+ZZJZTIHGRjUef01luwbx/8LtujCCvL37AIu5oMh9ZvEBcbpzqOSKcCBfSRnY4dIT5edRohhEiZxcXOmDFjCAgISHY8KCiI0aNHWyWUyBxkGptz8vCADz6AQYPAucZ9RUaovPht7ngGs7nGB6qjCCvo0EHv5jhunOokQgiRMouLnTNnzhCewor1fPnycebMGauEEplDcLCM7Dirzp3h/Hl9OpsQ1mR0NZLrl7kU3T6XnRPWq44j0slggOnT9RGeI0dUpxFCiOQsLnaCgoLYk0Jv2t27d5MjRw6rhBKZg0xjc15ubvqeOx98IKM7wvqCS4ZytM/n5OzXllsnb6qOI9KpYEF45x148015vxBCZD4WFzstW7akZ8+erF+/noSEBBISEvjtt9/o1asXr7/+ui0yCkVkGptza90a7t6FZctUJxGOqPz41zgRVoP91bqrjiKsoH9/fd8dWborhMhsLC52RowYQbly5ahRowYeHh54eHhQu3ZtqlevLmt2HIxMY3Nurq4wbBh8+CEkJKhOIxxR1IbPyHt+M391/0p1FJFObm7wxRf65sRXr6pOI4QQ/0pz6+mjR4+ya9cuPDw8KFasGPny5bN2NpuQ9o+pd+uWvrFoXJxsGuesTCYoWRLefVcf6RHC2nZP2Uieno148NdOcpXPqzqOSKf//Q8ePID581UnyVzk3EMIdWSfHfFUmqZ35jp7Vi96hHP68Ufo0wcOHoQsWVSnEY5oQ4X38Tu4meJX12F0lU9W7NnNm1C4MCxYADVrqk6Teci5hxDqyF8V8VQGgzQpEPDKKxAQAHPnqk4iHFWFtUPJGhfLH40mqI4i0ilbNpg4UW9W8OCB6jRCCCHFjngOWbcjDAYYORKGD4eHD1WnEY7IzdsNt28XUuLnkRz+drfqOCKdmjfXO7SNGKE6iRBCSLEjnkM6sgmAGjUgf359AbIQthDxSiS7XhuJse0bPLwlVbU9Mxhg2jT9snev6jRCCGcnxY54JpnGJkA/eRk1CkaPhnv3VKcRjqrK4u7c8snD3zXeVx1FpFNYmN7J8X//0xudCCGEKq6puVJKm4g+TVRUVJrDiMxHprGJRBUqQOnSMHkyDBigOo1wRAajgby/zMalZBQ7Pq5PyfdqqI4k0qFXL1i0SB8R7tZNdRohhLNKVTc2o9GIwWBA0zQMBsMzr5uQyTfkkI4olpkxA9avh6+/Vp1EZAY7dkCtWnD8OPj7q04jHNXf/b4n3/ieZD2yh2wR2VXHEemwbRvUrg379kFoqOo06si5hxDqpGoa28mTJzlx4gQnT55k6dKlhIeHM23aNHbu3MnOnTuZNm0aERERLF261NZ5RQaTaWziv0qWhGrVYII0zRI29NK4JhyLqM3Bat3QTE61O4LDKV0a2raFnj1VJxFCOCuL99kpW7YsQ4cOpV69ekmOr1y5kg8//JDt27dbNaC1yacrltm6Fdq0gUOHVCcRmcWBA/qUtqNHZf8lYTt3LtzhRr5oznUeRsXPZUdbe3bnDhQpAlOnQoMGqtOoIeceQqhjcYOCvXv3Eh4enux4eHg4Bw4csEookXnImh3xpMKF4dVXYdw41UmEI/MJ9SF2ygIiv+jFub9Oq44j0sHHRy90uneHu3dVpxFCOBuLi53IyEjGjBlDfHy8+Vh8fDxjxowhMjLSquGEejlzwu3bsjmcSGroUH0914ULqpMIR1bsfxXYU/EtrtVvS0J85l4PKp6tQQMoW1bv0CaEEBnJ4mlsW7ZsoUGDBmiaZu68tmfPHgwGAz/++CNly5a1SVBrkaFkywUE6NPZUhjQE07szTfBxUX/xFYIW3l0/xFHAytwJaYZVVf2Ux1HpMOFC1C0KKxdq6/lcSZy7iGEOhYXOwD37t1j0aJFHPr/hRyRkZG0atUKLy8vqwe0NnnDsVzRojBzJpQvrzqJyEzOndOntO3Zo++pIYStnFh1mOz1X+LSot8o1LKE6jgiHaZN0/+ebN0Krqna/MIxyLmHEOqkqdixZ/KGY7lateCtt6BxY9VJRGbTp48+zXH2bNVJhKP7o+XnhC6bQq4L2/DI7qE6jkgjkwkqVYKmTeGdd1SnyThy7iGEOhav2QFYsGABlSpVIjQ0lNOn9YWjn376KStWrLBqOJE5BAfDxYuqU4jMaOBA+P57OHxYdRLh6CovepObvmFsqS472tozo1Ff7zdiBJw6pTqNEMIZWFzsfP755/Tt25e6dety8+ZN8yai2bJlY+LEidbOJzIB6cgmniYoSO+wNGSI6iTC0RmMBvL9OovCe75m+5i1quOIdChaVJ8t8NZb4FxzS4QQKlhc7EyePJmZM2cyaNAgXP8z4bZ06dLs3bvXquFE5iAbi4pnefddfcHx7t2qkwhHFxQVzIkBMwn9oAM3jl5XHUekw4cfwpEj8O23qpMIIRydxcXOyZMnKVEi+QJRd3d37t27Z5VQInORaWziWbJlg759paWsyBjlRjfkaIF6HK72PzSTDAvYKw8PmD4deveGmzdVpxFCODKLi53w8HB27dqV7Pjq1atlnx0HJdPYxPP06gV//w3//KM6iXAGJTd8SujlXfz1v/mqo4h0qFlTvwyQZVhCCBuyuNjp27cv3bt3Z/HixWiaxpYtWxg1ahQDBw6kXz/ZA8ERyTQ28Tw+PvoJywcfqE4inIF3sDd3Pl+Icc0q1VFEOk2YoDc5+fNP1UmEEI4qTa2nFy1axNChQzl+/DgAoaGhDBs2jE6dOlk9oLVJ+0fL3bypL0SPi9M76QiRkgcPIH9+WLQIqlZVnUYIYS/mzoVx42DnTnB3V53GNuTcQwh10rXPzv3797l79y5BQUHWzGRT8oZjOU3T51efOwcBAarTiMzs889h4UL9U1qDQXUaIYQ90DSoUQOqVXPctX9y7iGEOhZ/Tj9y5EhOnjwJgKenp10VOiJtDAZZtyNSp1MnuHABVq9WnUQIYS8MBr1Zwfjxeoc2IYSwJouLnSVLlpA/f34qVKjAtGnTuHbtWrpDTJ06lbCwMLJmzUq5cuXYsmXLU6/7/fffU7p0afz9/fHy8iI6OpoFCxakO4N4NunIJlLDzQ2GDtXX7sj+GUKlYz/8wJ9vvsmmXr049PXXquOI5yhYEN55B958U947hBDWZXGxs3v3bvbs2UPVqlX55JNPCA0NpX79+nz11Vfcv3/f4gCLFy+mb9++DBkyhB07dlC8eHHq1KnDlStXUrx+9uzZGTRoEJs3b2bPnj106NCBDh06sGbNGosfW6SeNCkQqdW6Ndy/ry86FkKFDfXqEdiwId7ffIPH/PnkeOMN9s2apTqWeI7+/eHyZZg3T3USIYQjSdeaHYC//vqLr776iiVLlvDw4UNiY2Mtun25cuUoU6YMU6ZMAcBkMpEnTx569OjBgFT2oyxZsiT169dnxIgRz72uzJtNm27d4IUX4L33VCcR9mDJEhgyBPbuBRcX1WmEM/kzIoLoEyfY/dZbFO7bl2wREfzRujXZVqyg4PnzuMv7fqb255/QuDEcOACBgarTWI+cewihTrp7a3l5eeHh4YGbmxuPHj2y6Lbx8fFs376dmjVr/hvIaKRmzZps3rz5ubfXNI1169Zx+PBhqlSpkuJ14uLiiI2NTXIRlpNpbMISTZvqU9oWLlSdRDiTv8LDiTh1ikvLl1Nx6lSyRUQA4BoYSIKLC6bHjxUnFM9TqRI0aaJvNirT2YQQ1pCmYufkyZOMGjWKIkWKULp0aXbu3MmwYcO4ZOE8p2vXrpGQkEDOnDmTHM+ZM+cz7+v27dt4e3vj5uZG/fr1mTx5MrVq1UrxumPGjMHPz898yZMnj0UZhS5fPvj/vhRCPJfRCJMm6XPwjx1TnUY4g/1z5pDj8mWuz5hB/oYNSYiPN38vfuNG7ubIgZu3N5rJpDClSI2xY2HbNkjFZA0hhHgui4udl156ifz58/Pdd9/RoUMHTp8+zbp16+jUqRN+fn62yJiMj48Pu3btYuvWrYwaNYq+ffuyYcOGFK87cOBAbt++bb6cPXs2QzI6mkKF4OBB1SmEPYmJ0ac9Nm2qr+ERwpbuHjvGY6ORFxo2BMDFzQ2ADdWrU277drJ27IiLmxsG2Sws0/P3h19+gS+/hM8+U51GCGHvXC29QY0aNZg9ezaFCxdO94MHBATg4uLC5cuXkxy/fPkywcHBT72d0Wgkf/78AERHR3Pw4EHGjBlD1RR2MnR3d8fdUXcpy0CRkXD8OMTH69OThEiNfv3gn3/gf/+D+fNl7x1hOwYXF3zi4vAMCCAuNpbYM2c43LAhZU+cYO+HH1J20CDVEYUF8ubVC54qVfTip21b1YmEEPbKoo+4Hj16xDfffIPBSmcsbm5ulCpVinXr1pmPmUwm1q1bR/ny5VN9PyaTibi4OKtkEinz89MXix4/rjqJsCcGA8yZA1u2wLRpqtMIR1Z2+HDOh4Rw0NOTQ3nycLVcOXKdPcu5JUsoO3y46ngiDV58EVatgj59YMUK1WmEEPbKopGdLFmy8PDhQ6sG6Nu3L+3ataN06dKULVuWiRMncu/ePTp06ABA27ZtyZUrF2PGjAH0NTilS5cmIiKCuLg4Vq5cyYIFC/j888+tmkskFxmpT2WLjFSdRNgTPz+9DXWlSlCiBFSooDqRcFQVzpxhc9++mP7/71RhqbDtXsmSsGwZNGyov49Uq6Y6kRDC3lg8ja179+6MHTuWL7/8EldXi2+eTIsWLbh69SqDBw/m0qVLREdHs3r1anPTgjNnzmD8zxzre/fu8dZbb3Hu3Dk8PDwoVKgQCxcupEWLFunOIp4tsdgRwlJFisDnn0OzZrBjBzzRk0QIqyk/YYLqCMLKqlTROzs2bQpr1kCZMqoTCSHsicX77DRu3Jh169bh7e1NsWLF8PLySvL97zP5ToLS6z7tpk6FzZulnbBIuz59YOdO+PVXsMJnJUKk2q2TNzlW5nVyrZ5NSOlcquOINPj6a+jVCzZsACssG85Qcu4hhDoWt6Xx9/enadOm1KlTh9DQ0CRtnTOqG5tQQzqyifQaNw5MJhg4UHUS4Wz88vlzt1AZ4ipU5eLWc6rjiDRo2RKGDYPateHUKdVphBD2wuKRHXsnn66k3YULUKAA3Lmj76MiRFpcuqTPw580SZ/WJkRG0Uwav1cdQvjmr8jy53pCy8m+a/Zo9GiYPRv+/FPf8NoeyLmHEOqk6ZT18ePH/Prrr3zxxRfcuXMHgAsXLnD37l2rhhOZS0iIPvXonHwoKtIhOBi+/VZvRy0jhSIjGYwGYjYM42SFN3hUqSrnN59RHUmkwcCB0LixPsJz86bqNEKIzM7iYuf06dMUK1aMhg0b0r17d65evQrA2LFjeffdd60eUGQeBoM0KRDWUakSDB2qn7DExqpOI5yJwWig6u/DOFmpLQlVqnLur9OqIwkLGQz6lNiyZaF+fbh3T3UiIURmZnGx06tXL0qXLs3Nmzfx8PAwH09sXCAcmxQ7wlp69NCns3XsCM41mVZkBlXXD+FE5fZoVaty7s9TquMICxkM8MUXkCsXNGkCstWeEOJpLC52Nm7cyAcffICbm1uS42FhYZw/f95qwUTmJMWOsBaDAWbOhMOHYfx41WmEM6r622COx3QCKXjskovLv91BW7eGhAS1eYQQmZPFxY7JZCIhhXeUc+fO4ePjY5VQIvOSYkdYk5cXLF0KY8bA+vWq0whnVPXXDzhWrQtUjeHsHydVxxEWcnfXNxs9f15fByijxEKIJ1lc7NSuXZuJEyeavzYYDNy9e5chQ4ZQr149a2YTmZC0nxbWVrCg3lnp9del+YVQo+ovgzhW400M1atyZsMJ1XGEhby84OefYcsW6NdPCh4hRFIWt54+d+4cderUQdM0jh49SunSpTl69CgBAQH88ccfBAUF2SqrVUj7x/R5/Fj/w3L+PAQEqE4jHMmgQbBuHfz+u/5prRAZbUPdsRT4ZSqP164nX/UI1XGEhS5d0pufdOqU+fbyknMPIdRJ0z47jx8/ZvHixezevZu7d+9SsmRJ3njjjSQNCzIrecNJv2LF4PPP9T8qQlhLQgK8/LI+0jN1quo0wlltqP8xBdd8xqM168lXI7/qOMJCJ0/qf5s++AC6dVOd5l9y7iGEOrKpqLBY8+ZQqxZ06aI6iXA0165BqVIwYgS0bas6jXBWG175hBdXTSRu9XrCahVQHUdYaP9+qFoVPvsMWrZUnUYn5x5CqGPxmp158+bx888/m7/u168f/v7+VKhQgdP/1959hzV1/X8Af4ctyFKZgqKiggPcCg4cuGetVatV6q7V1pZaR2sddW+/jmrddVTcddZZ96gLHAwV9wLFhdbByPn9cX9EI6AJJrkhvF/Pcx/Jzc3NO4cQ7yfn3nNucL6CvICDFJC+FCoErFsHDBgAREXJnYbyqrpbB+Jii3BYN6mLazsvyR2HtFS2rHQNT79+0r9ElLdpXeyMGzdOdbrasWPHMHv2bEyaNAmFChXC999/r/OAZHxY7JA+Va0qTRjYti1nRyf51N0UjkutfoRNs3q4+vdFueOQlqpVk744+eIL4NAhudMQkZy0LnZu3boFX1/pPOa//voL7dq1Q+/evTF+/Hgc4idKnsAR2UjfevYE6tWTDlSUSrnTUF4VsvE7XGo9CLYtWPDkRvXrA0uXAm3aAGfOyJ2GiOSidbGTP39+PHz4EACwa9cuNGzYEABgY2ODly9f6jYdGaXSpaUhgl+8kDsJmSqFApg9WxpdacwYudNQXhayYQAufjJUKni2x8kdh7TUujUwfTrQtKk0gTER5T0W2j6gYcOG6NmzJypWrIhLly6p5taJjo6Gj4+PrvOREcqXDyhSRPqPo2JFudOQqcqXT5pwtGpVaWnaVO5ElFeFrPsGB9qbwa9lPVzZ9A9KtPCXOxJpoWtX6ZTYhg2Bw4el/7+IKO/Qumdnzpw5CAoKwoMHD7B+/XoULFgQAHD69Gl8bizDnpDe8bodMgQfH2DFCqBLF+Aq53okGYWs6Ye4z36Bfet6iN8cI3cc0tKAAUD37lLBc/++3GmIyJA49DTlyMCB0jfvo0fLnYTygjFjpF6eo0el9x2RXA5+Phd+a0YhecNe+LYuK3cc0oIQwHffSQMW7NsHODoa7rl57EEkH617dgDg8ePHmDJlCnr06IEePXpgypQpePToka6zkRFjzw4Z0k8/AV5e0iSBeevrGTI2dVb1RVzHUXBo2wCXN16QOw5pQaGQrt8pXx5o2ZLXnRLlFVoXOwcPHoSPjw9mzpyJx48f4/Hjx5g1axaKFSuGgwcP6iMjGSEWO2RIZmbA8uXS+fbz58udhvK6Oiv7IO7zX+H0aQNcWn9e7jikBTMzYNEioEAB4LPPgNRUuRMRkb5pfRpb+fLlERQUhLlz58Lc3BwAkJ6ejq+//hpHjx7F+fPG/cHPrmTdePQIcHeXvhmz0HqYC6KcOXcOCAkBduwAqleXOw3ldYfCFsJv+c94vGY3SrULkDsOaeHVK6BZM+n/seXLgf8/nNEbHnsQyUfrnp34+Hj88MMPqkIHAMzNzREeHo74+HidhiPjVaAA4OzMi8bJsAICgFmzgHbteJExya/2Hz0R13UcnNuH4uKas3LHIS3Y2ACbNgHx8cA33/D0WCJTpnWxU6lSJcRmcf5SbGwsAgMDdRKKcgeeykZy+OILaZLAjh2BtDS501BeV3tpD8SFTUDBjqG4uDpK7jikBXt74O+/gQMHgGHD5E5DRPqi0QlI586dU/387bffYsCAAYiPj0eNGjUAAMePH8ecOXMwYcIE/aQko5RR7LRuLXcSymumTgXq1pUOUPixQ3KrvaQ7Dpubwe/zhohT7oLf55yALLcoWBDYtQuoVUs6W2HgQLkTEZGuaXTNjpmZGRQKBT60qUKhQHp6us7C6QPPm9WdmTOB06eBP/6QOwnlRXfuAJUrA7/9BrRtK3caIuBwrz/gt2ggHizfCf/OleSOQ1qIjwdq15aGue/RQ/f757EHkXw06tm5du2avnNQLuTvL034SCSHwoWBiAip0ClbFihdWu5ElNfVWhCGw+Zm8O/SCLFiJ/y/qCx3JNKQry+wcydQv740/067dnInIiJd0ajYKVq0qL5zUC7k5wfExUkXdioUcqehvKhuXeDnn6WC599/gfz55U5EeV2teV1wRKGAX9fGiFHuQJmuVeSORBoKCAC2bAGaNwccHIBGjeRORES6kKNJRa9cuYJvvvkGoaGhCA0NxbfffosrV67oOhsZOS8vqdC5e1fuJJSXhYdLPTs9enBEJTIONed+gbi+M+H2ZRPE/HFS7jikhaAgqce4Y0fg6FG50xCRLmhd7OzcuRNlypTBiRMnEBAQgICAAPz7778oW7Ysdu/erY+MZKQUCql3hyOykZwUCmmSwPPngRkz5E5DJKk5pxMu9psF925NEL3khNxxSAuNGkmTF7dqJc3tRUS5m9bTQQ4ZMgTff/99ppHXhgwZgsGDB6Nhw4Y6C0fGL2NEttBQuZNQXmZvD2zYAAQHA6VKSaehEMkteNbnOGpmBr8eTXFBuR3lenAm3NyiXTvg6VOgcWPg0CHpmh4iyp207tmJjY1FjyyGKunevTtiYmJ0EopyD861Q8bCzw9Yt06ah+fIEbnTEEmC/9cBcQPmwrNXM5yff0zuOKSFHj2AH34AGjaURn8kotxJ62LHxcUFUVFRmdZHRUXB1dVVF5koF2GxQ8akfn1g4UJp7qcLF+ROQyQJnt4eF7+bB6+vmuP877wQJDcZOBDo1Ek6te3hQ7nTEFFOaH0aW69evdC7d29cvXoVwcHBAIAjR45g4sSJCA8P13lAMm68ZoeMzaefAklJ0uknR44APj5yJyICgqZ9hmPmZijdtyXOKTcjoG9NuSORhsaMAR4/Bpo2BfbulU6bJaLcQ6NJRd8mhMCMGTMwdepU3P3/Ybg8PT3x448/4ttvv4XCyMcg5sReupWaCtjZAYmJ0uzTRMZizBhg2TLg8GGAnc5kLI4P2oDSU3ri1uzNCPi6ltxxSENKpXSKbEICsH07YGOj3eN57EEkH62Lnbc9e/YMAGCfi77m4AeO7pUpI42GFRQkdxKiN4QABgyQho/dt4/fxpLxOD54I0pP7oGbMzchsH9tueOQhlJTgU8+ASwspOsDLbQ4N4bHHkTyydE8Oxns7e1zVaFD+sHrdsgYKRTSUNSlSgFt2gCvX8udiEhSY+InuDRkCYp82xpnZx2UOw5pyNISWLMGePRIGrxAqZQ7ERFpQutiJzExEV26dIGnpycsLCxgbm6utlDew2KHjJWZGbB0qXSQ8sUXQHq63ImIJNXHtcaloUtRZEAbRP3vgNxxSEO2tsCWLdK8Xt9/z4mMiXIDrQco+PLLL3Hz5k388ssv8PDwMPprdEj//P2BVavkTkGUNSsrYP16oEEDoF8/YO5cqdeHSG7Vx7bCCbM/UOr7NogSG1Hhu7pyRyINODoCO3YAtWsDv/4KjBghdyIieh+ti53Dhw/j0KFDqFChgh7iUG7Enh0ydnZ2wLZt0sHJiBHSAQqRMag2uiVOKJajZHhbRKavQ8Uf6ssdiTTg6grs3g3UqgU4OUnXBxKRcdK62PH29sZHjGlAJqh0aeDGDeDVK+1HqCEylIIFgZ07gZo1ARcX4Jtv5E5EJKn2awucNF+Bkj9+ijPKdaj0YwO5I5EGihSRCp46daTRSLt2lTsREWVF62t2ZsyYgSFDhuD69et6iEO5kZ0dULgwcOmS3EmI3s/bG9i1Cxg9mqdeknGpOqIZLo/8E8UHt8OZSXvkjkMaKl0a+Ptv6fqdTZvkTkNEWdG6Z6dDhw548eIFSpQoAVtbW1haWqrd/+jRI52Fo9wj41S2gAC5kxC9n58fsHUr0KQJUKCANPkokTGoOrwpTpmtgu+Qz3BauQaVhzSUOxJpoFIlYONGoHVr6frA+jwTkcioaF3szJgxQw8xKLfjdTuUm1SrBkREAB07St/KVq8udyIiSZVhTXBKEQHfn9rjtFiNykMbyR2JNFCnDrByJdCunTR4QbVqciciogxaFzthYWH6yEG5nL8/sHev3CmINNeoEfDbb0DLlsCBA9J7mMgYVPm5MU6brUaJnzvglDICVX5m92Nu0KwZMGcO0Lw5sH8/ULas3ImICMhBsXPz5s333l+kSJEch6Hcy98fmD1b7hRE2unYEUhKkgqfo0ela3qIjEHloY1wxnwtfId8hpPpf6Lq8KZyRyINfP458PSp9Jly+DBQrJjciYhI62LHx8fnvXPrpHPWvjzJz08aoCA9HeDcspSb9O8PPHjw5uCkYEG5ExFJKg0KxRnFOpQc3A4nxUpUHdFM7kikga++Ah4/Bho2BA4dAjw85E5ElLdpXexERkaq3U5NTUVkZCSmTZuGsWPH6iwY5S4uLkD+/MD160CJEnKnIdLOyJHA/fvS6Sd79kjvZSJjUOnHBog0Ww/fH9vhpFiOqiObyx2JNDBkCPDokTQAyoED/BKQSE4KoaNJc7Zt24bJkydj//79utid3iQnJ8PR0RFPnz6Fg4OD3HFMSu3awODBQIsWcich0l56unRa27NnwObNgJWV3ImI3oictg/FBrbFpZ+XodrolnLHIQ0IAfTqBcTEAOvXJ8PTk8ceRHLQep6d7JQuXRonT57U1e4oF+KIbJSbmZsDK1YAaWnAl18CSqXciYjeqBheD9enbUTJsWH49+fNcschDSgUwO+/S/PQde4sdxqivEvrYic5OVltefr0KeLi4jBs2DCULFlSHxkpl2CxQ7mdtbU0X8alS8CAAdI3s0TGosJ3dXHzf3+h1Pgv8e9PnMEyN8j4EqV1a7mTEOVdWhc7Tk5OcHZ2Vi0FChRAmTJlcOzYMcydO1cfGSmXYLFDpsDeXpp7Z9cugJchkrEJ/KYObs7chFITuuH44I1yxyENWFsD3brJnYIo79J6gIJ9+/ap3TYzM4OLiwt8fX1hYaH17siEZBQ7Qkjd90S5lYsLsHMnULOm9HOfPnInInojsH9tnDPbjNL9W+G4EKgxqa3ckYiIjJbW1UlISIg+cpAJ8PYGUlOBxETA3V3uNEQfx8dHKnjq1ZOGo27XTu5ERG8EfF0L5xRbULpfSxwTAkGTP5U7EhGRUWJXDOmMmRlQurTUu8Nih0xBuXLApk3SkNTOzkCDBnInInojoG9NnDfbCr++LXAsXYmgaZ/JHYmIyOjobDQ2IoDX7ZDpCQ4GVq4EPvsMOH1a7jRE6sr3CcbtedtQesZXOPr9GrnjEBEZHRY7pFMsdsgUNWsG/O9/0r+XLsmdhkhd+d5BuDt/G/z+1xdHB6yWOw4RkVHRqNiZOXMmXr16BQC4efMmdDQPKZkgFjtkqrp0kWZFb9QIuHtX7jRE6sr1rIG7C7bDb9bXOPrNKrnjEBEZDY2KnfDwcCQnJwMAihUrhgcPHug1FOVeLHbIlH3/PdCpE9C4MfD4sdxpiNSV61Ed9xb9jdJzvsGRfn/KHYeIyChoNECBp6cn1q9fj2bNmkEIgdu3b6t6et5VpEgRnQak3MXXF7h/H0hOBhwc5E5DpHtjx0rv8RYtgN27AVtbuRMRvVG2WzXEmO+A35dNcEQI1Pyts9yRiIhkpRAanJM2f/58fPPNN0hLS8t2GyEEFAoF0tPTdRpQ15KTk+Ho6IinT5/CgUfjelG6NLB8OVCtmtxJiPQjLU0asCA1Fdi4EbC0lDsRkbrYFafh2rUxYntPR615XeSOk+fx2INIPhoVOwDw7Nkz3LhxAwEBAdizZw8KFiyY5XaBgYE6Dahr/MDRvzZtgE8+AcLC5E5CpD+vXgFNmgBFigBLl0pDrxMZk9iVZ+DSpTHiek5Frfld5Y6Tp/HYg0g+Gs+zY29vj3LlymHJkiWoWbMmrK2t9ZmLcjFet0N5gY2NNAdP3brAjz8CU6YACoXcqYje8O9cCXFmu+DXuREOC4FaC/gNFBHlPVp/FxkWFgZra2ucPn0aK1aswIoVK3DmzJmPCjFnzhz4+PjAxsYG1atXx4kTJ7LddsGCBahduzacnZ3h7OyM0NDQ925Phsdih/IKR0dgxw6p6Jk0Se40RJn5fV4RD1ftht+igTjUfYnccYiIDE7jnp0M9+/fR8eOHbF//344OTkBAJ48eYJ69eohIiICLi4uWu1v9erVCA8Px7x581C9enXMmDEDjRs3xsWLF+Hq6ppp+/379+Pzzz9HcHAwbGxsMHHiRDRq1AjR0dEoXLiwti+H9IDFDuUlbm7Arl1AzZpAoUJAjx5yJyJSV7pDBVxU7IFfx4Y4JARqL+kudyQiIoPR+JqdDB06dMDVq1exbNky+Pv7AwBiYmIQFhYGX19frFql3fj+1atXR9WqVTF79mwAgFKphLe3N7755hsMGTLkg49PT0+Hs7MzZs+eja5dP3xOMs+b1b9nzwBnZ+C//wCe7Uh5xdmzQP36wKJF0nVrRMbm0rpzcG7fEHFdxqL2Hz3ljpOn8NiDSD5an8a2Y8cO/Pbbb6pCBwDKlCmDOXPm4O+//9ZqXykpKTh9+jRCQ0PfBDIzQ2hoKI4dO6bRPl68eIHU1FQUKFAgy/tfv36N5ORktYX0y94ecHcH4uPlTkJkOIGB0shs3boBBw7InYYos1LtAvB47R74Lf8ZB7+YL3ccIiKD0LrYUSqVsMxinFVLS0solUqt9pWUlIT09HS4ubmprXdzc0NCQoJG+xg8eDA8PT3VCqa3jR8/Ho6OjqrF29tbq4yUM+XLAx95KRdRrlOnDvDHH0Dr1sD06YCRj8RPeVCpT8vjyfq9KLVqJA6XCENSLCcJJyLTpnWxU79+fQwYMAB3795Vrbtz5w6+//57NGjQQKfhPmTChAmIiIjAxo0bYWNjk+U2Q4cOxdOnT1XLrVu3DJoxr2rcGNi+Xe4URIbXqhVw5AiwcqU0Uht7OMnYlPykHGzio6G0soGirD8OfbkIyjTtvqwkIsottC52Zs+ejeTkZPj4+KBEiRIoUaIEihUrhuTkZMyaNUurfRUqVAjm5uZITExUW5+YmAh3d/f3PnbKlCmYMGECdu3ahYCAgGy3s7a2hoODg9pC+teihTRK1XvmoSUyWWXLAseOSUV/lSrArFmAlh3fRHrlVMwZdWJ/x505m+AeMQPnC9ZF/OYYuWMREemc1sWOt7c3zpw5g23btuG7777Dd999h+3bt+PMmTPw8vLSal9WVlaoXLky9u7dq1qnVCqxd+9eBAUFZfu4SZMmYfTo0dixYweqVKmi7UsgA/D1BVxdgaNH5U5CJA9LS2DYMOn6ncWLpcELrl6VOxWRuoC+NeHz6AweBzeHS+tg7K81DC8fvZQ7FhGRzmg9GpuurV69GmFhYfj9999RrVo1zJgxA2vWrEFcXBzc3NzQtWtXFC5cGOPHjwcATJw4EcOHD8eff/6JmjVrqvaTP39+5M+f/4PPxxFRDOeHHwBzc84/QpSSAowbB/zvf9K/ffoAZlp/1USkX7cOXkPCp1/D9cllJP36GyoPbSR3JJPBYw8i+cj+322HDh0wZcoUDB8+HBUqVEBUVBR27NihGrTg5s2buHfvnmr7uXPnIiUlBe3atYOHh4dqmTJlilwvgbLRvDmwdavcKYjkZ2UFjBwJ7N0L/PYb0KgRcOOG3KmI1HnXKYYqidtxp/84FB72JY74dML9c5oNFkREZKxk79kxNH67YjgpKYCLCxAZCRQvLncaIuPw+jXw66/A3LlSr2ePHoBCIXcqInVPbz5FVIufUf7CKlzoOBa1lvWGmYXs34/mWjz2IJIPP7lIb6yspAu0t22TOwmR8bC2BsaOBXbuBKZNA5o2BW7fljsVkTrHIo4IOTcb9xb9DbeN8xDtXAuX1p+XOxYRkdZY7JBeNW/OYocoK1WrSnNRBQZKy9KlQN7qZ6fcoGy3aijx+BQe1m8Ht3a1sb/6YPx3/z+5YxERaYzFDulV06bAwYPA8+dyJyEyPjY2wMSJ0rVt48ZJc/S8NYUZkVGwsLFA3U3heH70HPLdiMVjz7I4OYoTqRFR7qBxsZOamopBgwbB19cX1apVw+LFi9XuT0xMhLm5uc4DUu7m6goEBEgXZhNR1oKCgKgooGRJ6e9l5Ur28pDxKRxUBNXubsLtH6bD69feOOb1GRLOsDonIuOmcbEzduxYLFu2DF999RUaNWqE8PBw9OnTR22bPDbWAWmoRQuOykb0Iba20jU8GzcCI0YAbdsC78y3TCQ7hZkCNSZ+gvy3YpHi6gXrKuVwoP0cKNM4ay4RGSeNi52VK1di4cKFGDhwIMaMGYNTp07hn3/+Qbdu3VRFjoJDClEWMq7bYS1M9GG1awNnzwJeXkC5csDq1XInIsrM3tMeIWemI2HZbrhuXYQLBWrjytZYuWMREWWicbFz584dlCtXTnXb19cX+/fvx9GjR9GlSxekp6frJSDlfhUqSEPrRkbKnYQod7CzA2bNAtasAYYMAdq3Bx48kDsVUWb+X1SGb9K/eFSrNQq1DML++r8i5XmK3LGIiFQ0Lnbc3d1x5coVtXWFCxfGvn37cPLkSXz55Ze6zkYmQqHgqGxEOVGvHnDuHFCwoNTLs2GD3ImIMrO0tUTd7YPweNdJOEbuw41ClXBh4XG5YxERAdCi2Klfvz7+/PPPTOs9PT3xzz//4Nq1azoNRqaF1+0Q5Yy9vTQB6YoVwHffAZ06AQ8fyp2KKDOfhiVR4eE/SOj4PTx7N8eBCgPwPIFDcRKRvDQudn755Re0b98+y/sKFy6MAwcOZBqhjShDgwbA+fO84Joopxo2lP6G7OykXp7Nm+VORJSZwkyB2kt7IPXMBVgl3cETr7I4+evfcsciojxMIfLYEGrJyclwdHTE06dP4eDgIHecPKVpU6BDB4BnPBJ9nL//Bnr2lL5E+N//AGdnuRMRZe344I0oOqU/rhaph9Lbp6OQv4vckWTBYw8i+Wg9qWgeq41Ih1q04HU7RLrQtClw4QJgZib18mzn/I5kpGpM/AS2V6ORni8/RNmyONJ3BYSSxxFEZDhaFTspKSn47LPP9JWFTFzz5sDOnUAKB+oh+mjOzsDSpcC8eVIvT48ewNOncqciysyxqBPqxMzDnelr4bnoV5xya4bbR27IHYuI8giNi53nz5+jadOmSEtL02ceMmE+PkCRIsChQ3InITIdLVtKvTyvXgHlywO7d8udiChrFQaEwOP+OfxXqhLy166AA23/h/QUTltBRPqlUbGTlJSEkJAQmJubY+3atfrORCaMQ1AT6V6BAsDKlcCMGcAXXwBffQU8eyZ3KqLMbJxsUPfIWCRG7IfLzhWILVATlzdekDsWEZkwjYqdWrVqwc7ODn/99RcsLS31nYlMGIegJtKftm2lXp7Hj6Venn/+kTsRUdZKtw9EqYfHkNSgPVzb1sL+OsPxOvm13LGIyARpVOxcuXIFTZo0ga2trb7zkIkLCgKSkoBLl+ROQmSaXFyA1auBSZOAjh2B/v2B55zqhIyQhY0F6m4Kx9N9Z2AffRy3XSrg3G+H5Y5FRCZGo2JnzZo1GDNmDBYsWKDvPGTiLCyAJk14KhuRvrVvL83Lc/cuEBgIHDwodyKirBWpWxyVHuzEva5DULh/Gxwo3w/Jt5PljkVEJkKjYueTTz7Btm3bMGjQIPz555/6zkQmjtftEBmGmxuwfj0werR0itt33wEvXsidiigzhZkCtRaEQXkuGpbJj/Bf0TI48csWuWMRkQnQalLR06dPo1WrVrhz544+M+kVJ/aS38OHgJcXkJgI8FdAZBj37gG9ewMXLwJLlgA1a8qdiCh7J4Zvhde4vrjuGYyS22fCpZyb3JE+Co89iOSj1Tw7lStXxr59+/SVhfKIggWBKlU4RC6RIXl4AJs3A8OGAa1bA506Adevy52KKGvVfm2B/NejkerkArOAsjjccyknIyWiHNGq2AGAUqVK6SMH5THNm3NUNiJDUyiArl2By5cBT0/pWp7Bg4EnT+RORpSZg5cDQs7Nxp05m+CxfBIiCzXEzf1X5Y5FRLmM1sXO+6xbt06XuyMT1qIFsH07oFTKnYQo73F2BqZMASIjgRs3gJIlgdmzgdRUuZMRZRbQtya8HkQiuXxNONarhP0tpyLtFSc4JyLNaFXspKWl4cKFC7j0zrjBmzZtQmBgIDp37qzTcGS6ypYFbGyAU6fkTkKUdxUvDkREAFu2AKtWSX+Xf/0FaH4lJ5FhWDtYo+6BUbi/4TAK7VuLSwVr4OLqKLljEVEuoHGxc+HCBfj6+iIwMBD+/v5o27YtEhMTERISgu7du6Np06a4cuWKPrOSCVEopN4djspGJL8aNYDDh4Hx44GBA4GQEODkSblTEWVW8pNy8H90BA+adIXb5/WwP2goXj56KXcsIjJiGhc7gwcPhq+vLzZt2oSOHTvir7/+Qt26ddGyZUvcvn0bEyZMgJeXlz6zkonhdTtExkOhAD79FIiJkf5t2pSDGJBxMrcyR8j6b/H8UBTs4qOQ4B6IqP8dkDsWERkpjYeednV1xa5du1ChQgU8ffoUzs7O+OOPP9ClSxd9Z9QpDv9oPF6+BAoVenOxNBEZj8ePgbFjgQULgK++AoYOBZyc5E5FpE4oBY72/xOl5n2P2NJtELhjEhyLOskdKxMeexDJR+OenaSkJHj+/xGpo6Mj7OzsUKNGDb0FI9OXLx/QoAGwYoXcSYjoXVkNYjBrFgcxIOOiMFOg5m+doYiOhtnrl3hZvAyOD94odywiMiIaFzsKhQLPnj1DcnIynj59CoVCgZcvXyI5OVltIdLG6NHSdQL798udhIiy8vYgBhERHMSAjFMhfxfUuroct0cthtfU73C88KdIjLondywiMgIan8ZmZmYGhUKhui2EyPJ2enq67lPqELuSjc9ffwG9egHHjwMlSsidhoiyIwSwYYM0N4+nJzB1KlC1qtypiNQ9T3iO002HofzZ5YjuMhG1lvSAwkzx4QfqEY89iOSjcbFz4IBmF/+FhIR8VCB94weOcZowAVi2DDh2DHB0lDsNEb1PSgowd67UM9uoETBuHODjI3cqInUXFv0L63498TxfITivmQ+fhiVly8JjDyL5aFzsmAp+4BgnIYCwMOD+fWmENgsLuRMR0YdwEAMydinPU3C09URU/GcqIpsMRc314bC0tTR4Dh57EMlHq0lFifRFoQDmzweSk6V5PojI+HEQAzJ2VvmtUHfvL0jacgwFjmzBlULVELvitNyxiMiAWOyQ0bCxATZulJb58+VOQ0SaensQg9WrpUEMNm7kIAZkPEq08Ee5Rwdxv3VvuHdtiP1Vf8SLpBdyxyIiA2CxQ0bFzU06YBo8GNi3T+40RKSNGjWAQ4eka/AGDQJCQoATJ+RORSQxszBDnVV98fLYWdjejMMDj/I4M3mv3LGISM9Y7JDRCQgA/vgDaN8eiI+XOw0RaUOhANq2BaKjgU8/BZo1Azp1Aq5flzsZkcSzujeq3tuMO/3Hw2tIZxwq1R2PrzySOxYR6QmLHTJKrVpJ1+60bAk8fSp3GiLSlpUVMGAAcPkyULgwEBgo9fY8eSJ3MiJpMtLg6e1heSkGEAKppcrgWPhaCCXPvSQyNRqNxta2bVuNd7hhw4aPCqRvHBEl9xAC+PJLIDGRI7QR5XZXrwI//QTs3QsMHy6N3mZp+EGxiLJ0ZtIeFPq5N+4VKo8im+fAo6qXTvfPYw8i+WjUs+Po6KjxQqQrGSO0PXvGEdqIcjsOYkDGrNKgUBS8cx4vvUrBpnoADn4+F8o0pdyxiEgHOM8OGb3796VZ2n/+GejdW+40RPSxhJAKncGDAQ8PafjqatXkTkUkiVl2CuZ9euKVlT3sVy1A8WZ+H71PHnsQyYfX7JDRc3WVvg0eMoQjtBGZAg5iQMasTNcqKP7wJB4HNUfB5jWwP3QMUp6nyB2LiHIoR8XOunXr0L59e9SoUQOVKlVSW4j0ISAAWLqUI7QRmRIOYkDGytLWEnV3DMGjHSfgdGoPrrtUQfQSjqNOlBtpXezMnDkT3bp1g5ubGyIjI1GtWjUULFgQV69eRdOmTfWRkQiANELbjz9KI7TxYIjIdDg7A5MnA1FRwK1bQMmSwKxZQAq/TCeZFWtcCgFJ/yDhs2/g0aMpDlT6Hs8Tnssdi4i0oHWx89tvv2H+/PmYNWsWrKysMGjQIOzevRvffvstnnKMYNKzH3+Uzu3v2BFIS5M7DRHpUrFiwKpV0uiLq1cD5cpxEAOSn5mFGeos64WUU+dhnXADT7zK4dTYnXLHIiINaV3s3Lx5E8HBwQCAfPny4dmzZwCALl26YNWqVbpNR/SOjBHanj8HfvhB7jREpA/VqwOHDgETJkintYWEACd4BhHJzL2SJ2rc3YDb4dPgPfxLHC7RFQ8vJskdi4g+QOtix93dHY8eSTMNFylSBMePHwcAXLt2DXlsYDeSibU1sGEDsGkT8PvvcqchIn14exCDdu04iAEZjxqT2sI6PgZKS2so/cvgSL8/ORkpkRHTutipX78+Nm/eDADo1q0bvv/+ezRs2BAdOnTAJ598ovOARFlxdQU2bwaGDuUIbUSmzMoK+PZbDmJAxsWpmDPqxC3A7SmrUXjBCJxyb4E7x27KHYuIsqD1PDtKpRJKpRIW/z+dfUREBI4ePYqSJUuiT58+sLKy0ktQXeFY96Zlyxage3fg6FHpomYiMm3XrgE//QTs2QMMHw706SMVRERyefnoJf5tNgoVTvyOs21/Ra0/v4a5lbnaNjz2IJIPJxWlXG/yZGDRIuD4ccDJSe40RGQI//4rXbd3/z4wcSLQpo106huRXOJWRUL06Ik0cyvkW7EQvq3Lqu7jsQeRfDQqds6dO4dy5crBzMwM586de++2AQEBOgunD/zAMT1CAN26AffuAdu2Af/f6UhEJk4IabS2wYMBd3dg6lRptEYiuaS9SsPhdjNQcdsYRIZ8h6DNQ2HtYM1jDyIZaVTsmJmZISEhAa6urjAzM4NCochyMAKFQoH09HS9BNUVfuCYptevgQYNgMqVgf/9T+40RGRIKSnAvHnAr78CDRsC48cDPj5yp6K87MY/V/CwXW84/ncPL2YuRNHPy/HYg0gmGn0Hfu3aNbi4uKh+JjI2GSO0VasGlCkjncdPRHlDxiAGXbsC48ZJgxj06SNd28NTW0kOReuXQJGkPTjccynK9G2FwzM/lTsSUZ6l0WhsRYsWheL/T4a+ceMGChcujKJFi6othQsXxo0bN/Qaluh9XF2lAQuGDgX++UfuNERkaE5OwKRJQFQUcOuWNGjJrFlSzw+RoSnMFKi9uBvSoy7ALKCc3HGI8iytBygwNzfHvXv34Orqqrb+4cOHcHV15WlsJDuO0EZEgDSIwcCBQGIiBzEgefHYg0g+Ws+zI4RQ9fK87eHDh7Czs9NJKKKP0bKlNA9Hy5aci4MoL6teHTh4EJgwQfpMqFMHOHFC7lRERGRIGo9b1bZtWwDSIARffvklrK2tVfelp6fj3LlzCA4O1n1CohwYOBCIiQE6dOAIbUR5mUIBtG0LtGghDWLQrBkHMSAiyks07tlxdHSEo6MjhBCwt7dX3XZ0dIS7uzt69+6NFStW6DMrkcYUCunA5sULIDxc7jREJLeMQQzi4wFvb2kQg/XrNXussZ+eTURE2dPqmh0hBLp3745Zs2Yhf/78+sylNzxvNm958EAaoW3wYOCrr+ROQ0TG4to1wNYWcHPLfpu3T9tOSkrCxIkT8dlnn6EaJ/MhLfHYg0g+WhU7SqUSNjY2iI6ORslceuU3P3DyngsXpHP1160D6teXOw0R5SZJSUmYOnUqrl+/jtWrV6N8+fI4e/as3LEol+GxB5F8tBqgwMzMDCVLlsTDhw/1lYdI58qVA5Ytk67fuXxZ7jRElBvcvHkT8+fPR+vWrfH48WN07doVbm5umDRpEgDpyz8iIjJ+Wo/GNmHCBPz444+4cOGCPvIQ6UWLFtKpbByhjYg+5N9//8Xnn3+Ou3fvYtSoUZg1axa2bdsGHx8fNG7cGID05R8RERk/refZcXZ2xosXL5CWlgYrKyvky5dP7f5Hjx7pNKCusSs57xIC6NEDuHOHI7QRUdaePn2KPn36YMOGDUhKSoKDgwOOHz+OJk2aYPv27QgODs52Cgai7PDYg0g+Wh/uzZgxQw8xiPRPoQDmzgVCQ6UR2mbOlDsRERkbR0dHTJkyBfnz50epUqXQpEkTPHjwAC1btkRwcDCUSiV7dYiIchGti52wsDCdBpgzZw4mT56MhIQEBAYGYtasWdmOdBMdHY3hw4fj9OnTuHHjBqZPn47vvvtOp3nItFlbAxs2SCO0lSnDEdqIKDMvLy8sXLgQUVFR+Oqrr3Dq1Cn06dMHANijQ0SUy3zU11OvXr1CcnKy2qKN1atXIzw8HCNGjMCZM2cQGBiIxo0b4/79+1lu/+LFCxQvXhwTJkyAu7v7x0SnPMzFBdiyBfjpJ+Cff+ROQ0TGqkiRIkhJSVFdp5OYmMhih4gol9G62Pnvv//Qv39/uLq6ws7ODs7OzmqLNqZNm4ZevXqhW7duKFOmDObNmwdbW1ssXrw4y+2rVq2KyZMno2PHjrC2ttY2OpFKxght7dtzhDYiytqzZ89gaWmJDRs2YM6cOXB1dVXd9/y5NODJ/v3y5SMiog/TutgZNGgQ/vnnH8ydOxfW1tZYuHAhRo0aBU9PTyxbtkzj/aSkpOD06dMIDQ19E8bMDKGhoTh27Ji2sbL1+vXrj+p9ItPVogUwdCjQoAEwfrw0szoRUYaiRYvi33//hbW1NZRKpVqvjo0N0KoV0KkTEBQEfPstsGgRcPIk8PKljKGJiEiN1sXOli1b8Ntvv+HTTz+FhYUFateujWHDhmHcuHFYuXKlxvtJSkpCeno63N6ZvtrNzQ0JCQnaxsrW+PHj4ejoqFq8vb11tm/K/cLDgXnzgLg4oEoVoGJFYNw49vYQkbp3ByWwsAB69ZK+JBk6VDo99u+/peLHwQHw9wc6dpS+SNm2Dbh9WxoRkoiIDEvrAQoePXqE4sWLAwAcHBxUQ03XqlULffv21W06HRg6dCjCw8NVt5OTk1nwkIpCATRrJi2vXwN79gBr10oDGBQtCnz2mbSUKiV3UiIyRra2Ug9Pq1Zv1j1/Dly4AJw9Ky1btwLnzgFWVkBgIBAQIP0bGCgNlGJjI19+IiJTp3WxU7x4cVy7dg1FihSBn58f1qxZg2rVqmHLli1wcnLSeD+FChWCubk5EhMT1dYnJibqdPABa2trXt9DGrG2Bpo3l5aUFKnwWbcOqFED8PJ6U/j4+cmdlIiMWf780udGjRpv1imVwPXrbwqgzZuBMWOAW7ekL1PeLoACAwF3d+nLGCIi+jhaFzvdunXD2bNnERISgiFDhqBly5aYPXs2UlNTMW3aNI33Y2VlhcqVK2Pv3r1o06YNAECpVGLv3r3o37+/trGIdMrK6k2Pz7x50qhta9cCNWsCnp5vCh9/f7mTElFuYGYGFC8uLZ988mZ9cjJw/vybIuivv6Tb+fKpFz+BgdLnjZWVbC+BiChXUgjxcWcR37hxA6dPn4avry8CAgK0euzq1asRFhaG33//HdWqVcOMGTOwZs0axMXFwc3NDV27dkXhwoUxfvx4ANKgBjExMQCAZs2aoXPnzujcuTPy588PX19fjZ6TsxjTx0hNlQqfdeuAjRulb18zCp8yZeROR0SmQKkErlx5UwCdOyf9e/eu1LOcUfxk9Aa9c+krGSEeexDJR+NiR6lUYvLkydi8eTNSUlLQoEEDjBgxAvny5fuoALNnz1ZNKlqhQgXMnDkT1atXBwDUrVsXPj4+WLp0KQDg+vXrKFasWKZ9hISEYL+G43/yA4d0JTVVGnZ27VppolJX1zeFT9myPAWFiHTryZM3hU/GvxcuSAMivFsA+fkBlpZyJ6YMPPYgko/Gxc7o0aMxcuRIhIaGIl++fNi5cyc+//zzbOfEMVb8wCF9SEtTL3wKFXpT+JQrx8KHiPQjPV0aPfLtAujsWeD+fam3+d1rgQoVkjtx3sRjDyL5aFzslCxZEgMHDkSfPn0AAHv27EHz5s3x8uXLTENyGjN+4JC+paUBBw++KXycnYF27aTCJyCAhQ8R6d/Dh+rXAp09C0RHAwULZi6ASpWShtIm/eGxB5F8NC52rK2tER8frzZss42NDeLj4+Hl5aW3gLrGDxwypPR09cLHweFNj09gIAsfIjKctDTg0iX1AujsWeDRI+nU27cLoIAAoEABuRObDh57EMlH42LH3NwcCQkJcHFxUa2zt7fHuXPnsryOxljxA4fkkp4OHD4sFT7r10vD02YUPhUqsPAhInk8eKB+Cty5c0BMjHQd4rvXApUsCZiby5049+GxB5F8NC52zMzM0LRpU7U5a7Zs2YL69evDzs5OtW7Dhg26T6lD/MAhY5CeDhw58qbwsbV9c6pbpUosfIhIXqmpQFyceg/QuXPSUNnlyqmfCle+vHS6LmWPxx5E8tG42OnWrZtGO1yyZMlHBdI3fuCQsVEq1QsfG5s3hU/lyix8iMh4JCZmHhI7Lk4a+KBMGel0uLf/5alwEh57EMnno+fZyW34gUPGTKkEjh2TCp9166ShY98ufHj6CBEZm9RUID5eOvUtOvrNvxcvSgMiZFUEFSwod2rD4rEHkXxY7BAZKaUSOH5cKnrWrweSkqRTRypWfLOUKyf1BBERGZu0tOyLIGfnrIsgUx0am8ceRPJhsUOUSyQkAJGR6svNm9LkgW8XQIGBgJOT3GmJiLKWlgZcuZK5CIqLkz67siqC3hobKVfisQeRfFjsEOViT59K58y/XQDFxgJeXuoFUMWKgIcHr/8hIuOVlgZcvapeAMXESEWQg0PmAqhs2dxTBPHYg0g+LHaITMyrV9JBQkbxExUlFUR2dpkLoBIlgFw0JzAR5UFpacC1a1kXQfnzZ18EGdOXOzz2IJIPix2iPCA9XTp3/t3T4F6/lk57yyh+KlSQDhSsrOROTET0funpWRdBsbHSlztZFUGurvIUQTz2IJIPix2iPEoI4M6dzAXQvXvSgcG71wHZ28udmIjow9LTgevXsy6C8uXLughyc9NvEcRjDyL5sNghIjWPHkmnvmWcAhcZKY2eVKxY5tPgXF3lTktEpJn0dODGjcxFUEyMVARlNTCCu7tuiiAeexDJh8UOEX3Qy5fA+fPqPUDnzkkTBmac/pZRABUrZlznyhMRvY9SmX0RZG2ddRGk7YAvPPYgkg+LHSLKkbQ0qcfn3dPghFAvfipWlIbHtrSUOzERkeaUSml4/6yKIEvLrIsgT8+siyAeexDJh8UOEemMENI3pG+fAhcZCTx4AJQvr14ABQQAtra6ff7o6GicOHEChQsXRuXKlVEwr03TTkR6p1QCt25Jxc+7hZCFRdZFkL19MpyceOxBJAcWO0Skdw8eqBc/kZHSpIK+vpmvA8ppfbJhwwbMmzcPqampePLkCVJSUjBr1izUr19fp6+FiCgrGUXQu5OlxsQAAQHJOHqUxx5EcmCxQ0SyeP5cuu7n7QLowgVpVKR3CyBv7w+fHx8SEgJ/f3/Mnj0bFhYWGDZsGE6ePIk//vgD7u7uhnlRRETvEAK4eTMZPj489iCSg4XcAYgob8qfHwgOlpYMqanS8LAZxc+0aVKPkIUFEBYm3c5O7dq1YW5uDgsL6WOtatWqWLduHaytrfX7QoiI3kOhAJyd5U5BlHex2CEio2FpKV3LExAgFTeAdGrItWvAs2fvf2zHjh0RFhYGZ2dnFC1aFHPmzEGrVq3gzKMMIiKiPIunsRFRrhcZGYlhw4bBysoKSUlJuHLlCpo3b44FCxbIHY2IiMceRDJizw4R5XrLli3Dw4cPcfDgQVhZWeHBgwfo2LEjpk2bhvDwcLVtJ08G1q0DfHyAokXf/Jux2NvL8hKIiIhID1jsEFGu9+TJE7i4uMDKygoA4OLiAjs7O8THx2fatnNnaR6gGzeA69eBY8eAiAjp5zt3ACenzEXQ2z87O3PSVCIiotyCxQ4R5Xrff/89hgwZgt69e6N58+a4cOECTp48iZ49e2ba1tNTWrKSmgrcvi0VQhnF0LlzwJYt0s+3bgE2NpmLoLeLIVdXFkNERETGgtfsEJFJOHz4MNasWYMzZ87A0dERAwcORJ06dWBubq6z50hPBxISpMLn7YLo7Z8VCqBIkex7hzw8AB1GIqJcgMceRPJhsUNEpCNCSBOoZlUEZfz76pU0b1B2xZCXlzQqHRGZDh57EMmHp7EREemIQiGdxubqClStmvl+IYAnTzIXQ5s3v/n58WOgcOGsrxfy8ZF6jWxsDPu6iIiIcisWO0REBpIxuaCzszRIQlaeP8/cI7R375ufExMBN7fsi6GiRaUJW4mIiIinsckdh4hIK69eATdvZn+qXMaIctmNJufjI93PQRSIDIfHHkTyYc8OEVEuYmMDlColLVnJakS5s2elU+UyRpSztgbc3aUeoozl7dtv/2xra8hXR0REpFssdoiITIilJVCsmLRkJT1dOhUuY0lIePPzxYvqtx8+BOzs3l8YvX2bhRERERkbFjtERHmIufn75xp6W2qqNLqcNoXR+3qJWBgREZGhsdghIqIsWVqyMCIiotyNxQ4REX00QxdG2RVKLIyIiOhtLHaIiMigdFUYXbqUs8Io4zYLIyIi08dih4iIjJYhCqPseolcXYGCBaXF2Vm63omIiHIXFjtERGQSPqYwers4yiiMHjyQiqKHD4GUFGl+ooziR9OFvUdERPJisUNERHmONoWREMCLF28Kn3eXpCTpWqN31z95Is2LpG2B5OTEXiQiIl1hsUNERPQeCoV0ypudHVCkiOaPS0sDHj/OvkiKjc16fVqadNqctkVSvnz6awMiotyKxQ4REZEeWFgALi7SoikhgOfPsy+QEhOBmJjM65OTpWInJ71IZmZ6awIiItmx2CEiIjISCgVgby8tPj6aPy41FXj0KPsiKTo66/VKZc56kWxs9NYEREQ6xWKHiIgol7O0fDOanKaEAJ49y75AuncPuHAh8/pnz6SBFzIKnwIFpIIpY3Fyyv62k5OUlYjIUFjsEBER5UEKBeDgIC3Fimn+uJQU9V6kx4/Vl4QE6XqkjNtPnrz5+dUr6donTYujd2/zuiQi0haLHSIiItKYlZU0F5G7u/aPffVKvfh5txh6/Bi4fj3r+589A6yttSuO3r5tby8VeESUt7DYISIiIoOwscl5oZSWJhU+7yuW4uLUb2f8/OSJVOg4OeWsWOJw4ES5F4sdIiIiMnoWFkChQtKiLaVS6hl6X6F08yZw7lzW96ekSKf7ZXUNkiaFExHJh8UOERERmTQzM8DRUVqKFtXusUIAL19mfcpdxu2kJODy5azv/+8/IDhY96+JiDTDYoeIiIgoGwqFNPqcrS1QuLD2j09JkeZH0mZCWiLSHU4lRkRERKQnVlZSjxIRyYPFDhERERERmSQWO0REREREZJJY7BARERERkUlisUNERERERCaJxQ4REREREZkkFjtERERERGSSWOwQEREREZFJYrFDREREREQmicUOERERERGZJBY7RERERERkkljs6FFqaqrcEYiIiIiI8iwWO3qyePFi5M+fH4sXL5Y7ChERERFRnmQhdwBTtHjxYvTs2RNCCPTs2RMA0L17d5lTERERERHlLUbRszNnzhz4+PjAxsYG1atXx4kTJ967/dq1a+Hn5wcbGxuUL18e27dvN1DSD3u70AGgKnjYw0NEREREZFiyFzurV69GeHg4RowYgTNnziAwMBCNGzfG/fv3s9z+6NGj+Pzzz9GjRw9ERkaiTZs2aNOmDS5cuGDg5Jm9W+hkYMFDRERERGR4CvHukbmBVa9eHVWrVsXs2bMBAEqlEt7e3vjmm28wZMiQTNt36NAB//33H7Zu3apaV6NGDVSoUAHz5s374PMlJyfD0dERT58+hYODg85eR3aFztsUCgUWLlzIU9qIiIjyEH0dexDRh8nas5OSkoLTp08jNDRUtc7MzAyhoaE4duxYlo85duyY2vYA0Lhx42y3f/36NZKTk9UWXdOk0AHYw0NEREREZEiyFjtJSUlIT0+Hm5ub2no3NzckJCRk+ZiEhAStth8/fjwcHR1Vi7e3t27C/7/U1FT07dv3g4VOBiEE+vbty2GpiYiIiIj0TPZrdvRt6NChePr0qWq5deuWTvdvaWmJuXPnQqFQaLS9QqHA3LlzYWlpqdMcRERERESkTtahpwsVKgRzc3MkJiaqrU9MTIS7u3uWj3F3d9dqe2tra1hbW+smcDYyrsHhNTtERERERMZD1p4dKysrVK5cGXv37lWtUyqV2Lt3L4KCgrJ8TFBQkNr2ALB79+5stzeU7t27Y+HChdn28LDQISIiIiIyLNknFQ0PD0dYWBiqVKmCatWqYcaMGfjvv//QrVs3AEDXrl1RuHBhjB8/HgAwYMAAhISEYOrUqWjevDkiIiJw6tQpzJ8/X86XASD7Hh4WOkREREREhid7sdOhQwc8ePAAw4cPR0JCAipUqIAdO3aoBiG4efMmzMzedEAFBwfjzz//xLBhw/DTTz+hZMmS+Ouvv1CuXDm5XoKadwseFjpERERERPKQfZ4dQzPUWPeLFy9G3759MXfuXBY6REREeRjn2SGSD4sdPUpNTeWoa0RERHkcix0i+Zj80NNyYqFDRERERCQfFjtERERERGSSWOwQEREREZFJYrFDREREREQmicUOERERERGZJBY7RERERERkkljsEBERERGRSWKxQ0REREREJonFDhERERERmSQWO0REREREZJJY7BARERERkUlisUNERERERCaJxQ4REREREZkkFjtERERERGSSWOwQEREREZFJYrFDREREREQmicUOERERERGZJAu5AxiaEAIAkJycLHMSIiIiygsyjjkyjkGIyHDyXLHz7NkzAIC3t7fMSYiIiCgvefjwIRwdHeWOQZSnKEQe+5pBqVTi7t27sLe3h0Kh0NvzJCcnw9vbG7du3YKDg4Penie3Yztphu2kGbbTh7GNNMN20gzbSTNPnz5FkSJF8PjxYzg5OckdhyhPyXM9O2ZmZvDy8jLY8zk4OPA/AA2wnTTDdtIM2+nD2EaaYTtphu2kGTMzXipNZGj8qyMiIiIiIpPEYoeIiIiIiEwSix09sba2xogRI2BtbS13FKPGdtIM20kzbKcPYxtphu2kGbaTZthORPLJcwMUEBERERFR3sCeHSIiIiIiMkksdoiIiIiIyCSx2CEiIiIiIpPEYoeIiIiIiEwSi52PMGfOHPj4+MDGxgbVq1fHiRMn3rv92rVr4efnBxsbG5QvXx7bt283UFJ5adNO0dHR+PTTT+Hj4wOFQoEZM2YYLqjMtGmnBQsWoHbt2nB2doazszNCQ0M/+P4zBdq00YYNG1ClShU4OTnBzs4OFSpUwPLlyw2YVj7afjZliIiIgEKhQJs2bfQb0Eho005Lly6FQqFQW2xsbAyYVj7avp+ePHmCfv36wcPDA9bW1ihVqlSe+P9Om3aqW7dupveTQqFA8+bNDZiYKI8QlCMRERHCyspKLF68WERHR4tevXoJJycnkZiYmOX2R44cEebm5mLSpEkiJiZGDBs2TFhaWorz588bOLlhadtOJ06cEAMHDhSrVq0S7u7uYvr06YYNLBNt26lTp05izpw5IjIyUsTGxoovv/xSODo6itu3bxs4ueFo20b79u0TGzZsEDExMSI+Pl7MmDFDmJubix07dhg4uWFp204Zrl27JgoXLixq164tWrdubZiwMtK2nZYsWSIcHBzEvXv3VEtCQoKBUxuetu30+vVrUaVKFdGsWTNx+PBhce3aNbF//34RFRVl4OSGpW07PXz4UO29dOHCBWFubi6WLFli2OBEeQCLnRyqVq2a6Nevn+p2enq68PT0FOPHj89y+/bt24vmzZurratevbro06ePXnPKTdt2elvRokXzTLHzMe0khBBpaWnC3t5e/PHHH/qKKLuPbSMhhKhYsaIYNmyYPuIZjZy0U1pamggODhYLFy4UYWFheaLY0badlixZIhwdHQ2Uznho205z584VxYsXFykpKYaKaBQ+9vNp+vTpwt7eXjx//lxfEYnyLJ7GlgMpKSk4ffo0QkNDVevMzMwQGhqKY8eOZfmYY8eOqW0PAI0bN852e1OQk3bKi3TRTi9evEBqaioKFCigr5iy+tg2EkJg7969uHjxIurUqaPPqLLKaTv9+uuvcHV1RY8ePQwRU3Y5bafnz5+jaNGi8Pb2RuvWrREdHW2IuLLJSTtt3rwZQUFB6NevH9zc3FCuXDmMGzcO6enphoptcLr4DF+0aBE6duwIOzs7fcUkyrNY7ORAUlIS0tPT4ebmprbezc0NCQkJWT4mISFBq+1NQU7aKS/SRTsNHjwYnp6emQpqU5HTNnr69Cny588PKysrNG/eHLNmzULDhg31HVc2OWmnw4cPY9GiRViwYIEhIhqFnLRT6dKlsXjxYmzatAkrVqyAUqlEcHAwbt++bYjIsshJO129ehXr1q1Deno6tm/fjl9++QVTp07FmDFjDBFZFh/7GX7ixAlcuHABPXv21FdEojzNQu4ARPRxJkyYgIiICOzfvz/PXDCtKXt7e0RFReH58+fYu3cvwsPDUbx4cdStW1fuaEbh2bNn6NKlCxYsWIBChQrJHceoBQUFISgoSHU7ODgY/v7++P333zF69GgZkxkXpVIJV1dXzJ8/H+bm5qhcuTLu3LmDyZMnY8SIEXLHM0qLFi1C+fLlUa1aNbmjEJkkFjs5UKhQIZibmyMxMVFtfWJiItzd3bN8jLu7u1bbm4KctFNe9DHtNGXKFEyYMAF79uxBQECAPmPKKqdtZGZmBl9fXwBAhQoVEBsbi/Hjx5tssaNtO125cgXXr19Hy5YtVeuUSiUAwMLCAhcvXkSJEiX0G1oGuvhssrS0RMWKFREfH6+PiEYhJ+3k4eEBS0tLmJubq9b5+/sjISEBKSkpsLKy0mtmOXzM++m///5DREQEfv31V31GJMrTeBpbDlhZWaFy5crYu3evap1SqcTevXvVvvl7W1BQkNr2ALB79+5stzcFOWmnvCin7TRp0iSMHj0aO3bsQJUqVQwRVTa6ei8plUq8fv1aHxGNgrbt5Ofnh/PnzyMqKkq1tGrVCvXq1UNUVBS8vb0NGd9gdPF+Sk9Px/nz5+Hh4aGvmLLLSTvVrFkT8fHxqqIZAC5dugQPDw+TLHSAj3s/rV27Fq9fv8YXX3yh75hEeZfcIyTkVhEREcLa2losXbpUxMTEiN69ewsnJyfVUKRdunQRQ4YMUW1/5MgRYWFhIaZMmSJiY2PFiBEj8szQ09q00+vXr0VkZKSIjIwUHh4eYuDAgSIyMlJcvnxZrpdgENq204QJE4SVlZVYt26d2vClz549k+sl6J22bTRu3Dixa9cuceXKFRETEyOmTJkiLCwsxIIFC+R6CQahbTu9K6+MxqZtO40aNUrs3LlTXLlyRZw+fVp07NhR2NjYiOjoaLlegkFo2043b94U9vb2on///uLixYti69atwtXVVYwZM0aul2AQOf27q1WrlujQoYOh4xLlKSx2PsKsWbNEkSJFhJWVlahWrZo4fvy46r6QkBARFhamtv2aNWtEqVKlhJWVlShbtqzYtm2bgRPLQ5t2unbtmgCQaQkJCTF8cAPTpp2KFi2aZTuNGDHC8MENSJs2+vnnn4Wvr6+wsbERzs7OIigoSERERMiQ2vC0/Wx6W14pdoTQrp2+++471bZubm6iWbNm4syZMzKkNjxt309Hjx4V1atXF9bW1qJ48eJi7NixIi0tzcCpDU/bdoqLixMAxK5duwyclChvUQghhEydSkRERERERHrDa3aIiIiIiMgksdghIiIiIiKTxGKHiIiIiIhMEosdIiIiIiIySSx2iIiIiIjIJLHYISIiIiIik8Rih4iIiIiITBKLHSIiIiIiMkksdihXun79OhQKBaKiojR+zJdffok2bdroNMfGjRthYWGBUqVK4f79+zrd94fMnz8f3t7eMDMzw4wZMwz63G/Lye/CmBnL60lISEDDhg1hZ2cHJycnWbMAgEKhwF9//SVrBk1/N3Xr1sV3331nkExERGTcWOyQznz55ZdQKBRQKBSwsrKCr68vfv31V6SlpX30ft8tUry9vXHv3j2UK1fuo/b9tv3796vyKxQKuLi4oFmzZjh//nyW2+/btw+dOnXCyJEj4erqiiZNmiA5OVltm+vXr6NHjx4oVqwY8uXLhxIlSmDEiBFISUn5qKzJycno378/Bg8ejDt37qB3794ftT8yPtOnT8e9e/cQFRWFS5cuyR3HKLz7d5/xN/vkyZOP3rc+vgx5n5EjR6JChQoGe74P0WVbEhEZExY7pFNNmjTBvXv3cPnyZfzwww8YOXIkJk+enKN9paenQ6lUZnmfubk53N3dYWFh8TFxs3Tx4kXcu3cPO3fuxOvXr9G8efNMxcnp06fxySefYPr06Rg2bBh27tyJAgUKoHXr1nj9+rVqu7i4OCiVSvz++++Ijo7G9OnTMW/ePPz0008flfHmzZtITU1F8+bN4eHhAVtb24/aX27zscVibnDlyhVUrlwZJUuWhKurq9xxPpoufmf6/LvXVGpqqmzPTUREOSCIdCQsLEy0bt1abV3Dhg1FjRo1hBBCTJ06VZQrV07Y2toKLy8v0bdvX/Hs2TPVtkuWLBGOjo5i06ZNwt/fX5ibm4uwsDABQG3Zt2+fuHbtmgAgIiMjhRBCpKWlie7duwsfHx9hY2MjSpUqJWbMmPHBfG/bt2+fACAeP36sWrd582YBQJw9e1a1Li4uTri7u4tly5apPf7Vq1eiZcuW4pNPPhFpaWnZPs+kSZNEsWLFsr1fCCFu3LghWrVqJezs7IS9vb347LPPREJCgqqd3m2Ta9euafR6IiMj1bbPaPMdO3YIPz8/YWdnJxo3bizu3r2rekx6eroYNWqUKFy4sLCyshKBgYHi77//Vt2f8btYtWqVCAoKEtbW1qJs2bJi//79qm0ePXokOnXqJAoVKiRsbGyEr6+vWLx4ser+mzdvis8++0w4OjoKZ2dn0apVK7XXlPG7GzNmjPDw8BA+Pj5i6NCholq1apled0BAgBg1apTq9oIFC4Sfn5+wtrYWpUuXFnPmzFHb/t9//xUVKlQQ1tbWonLlymLDhg1q762svHr1SgwaNEh4eXkJKysrUaJECbFw4ULV/fv37xdVq1YVVlZWwt3dXQwePFikpqaq7g8JCRHffPON+PHHH4Wzs7Nwc3MTI0aMUN1ftGhRtd9vWFiYEOL974u32+ltAwYMECEhIRo/txBCXLp0SdSuXVtYW1sLf39/sWvXLgFAbNy4UbVNTn5n73ry5IkwMzMTJ0+eFEJI7zVnZ2dRvXp11TbLly8XXl5eQgih9nef8XNW7aTJa3zbiBEj3vs5ExERIerUqSOsra3FkiVLhBAffl8NGjRIlCxZUuTLl08UK1ZMDBs2TKSkpAghsv4bztgvADFv3jzRvHlzkS9fPuHn5yeOHj0qLl++LEJCQoStra0ICgoS8fHxas/3119/iYoVKwpra2tRrFgxMXLkSLX3HACxYMEC0aZNG5EvXz7h6+srNm3apNauWbUlEVFux2KHdCarA61WrVqJSpUqCSGEmD59uvjnn3/EtWvXxN69e0Xp0qVF3759VdsuWbJEWFpaiuDgYHHkyBERFxcnnj59Ktq3by+aNGki7t27J+7duydev36dqdhJSUkRw4cPFydPnhRXr14VK1asELa2tmL16tXvzfe2d4uDJ0+eiE6dOgkAIjY2VidtJIQQP//8s6hcuXK296enp4sKFSqIWrVqiVOnTonjx4+LypUrqw5YX7x4Ifbs2SMAiBMnToh79+5lWVxpWuxYWlqK0NBQcfLkSXH69Gnh7+8vOnXqpHrMtGnThIODg1i1apWIi4sTgwYNEpaWluLSpUtCiDcHSl5eXmLdunUiJiZG9OzZU9jb24ukpCQhhBD9+vUTFSpUECdPnhTXrl0Tu3fvFps3bxZCSL87f39/0b17d3Hu3DkRExMjOnXqJEqXLi1ev34thJB+d/nz5xddunQRFy5cUC0A1A76MtZdvnxZCCHEihUrhIeHh1i/fr24evWqWL9+vShQoIBYunSpEEKIZ8+eCRcXF9GpUydx4cIFsWXLFlG8ePEPFjvt27cX3t7eYsOGDeLKlStiz549IiIiQgghxO3bt4Wtra34+uuvRWxsrNi4caMoVKiQ2sF2SEiIcHBwECNHjhSXLl0Sf/zxh1AoFGLXrl1CCCHu378vmjRpItq3by/u3bsnnjx58sH3RUY7aVLsvO+509PTRbly5USDBg1EVFSUOHDggKhYsaJasZPT31lWKlWqJCZPniyEECIqKkoUKFBAWFlZqb4I6dmzp+jcubPaey0yMlKkpaWJ9evXCwDi4sWLqnbS5DW+69mzZ+/9nPHx8VG9h+7evfvB95UQQowePVocOXJEXLt2TWzevFm4ubmJiRMnCiGkv+EffvhBlC1bVvV8L168EEJIRUnhwoXF6tWrxcWLF0WbNm2Ej4+PqF+/vtixY4eIiYkRNWrUEE2aNFE918GDB4WDg4NYunSpuHLliti1a5fw8fERI0eOVG2T8Tf6559/isuXL4tvv/1W5M+fXzx8+PC9bUlElNux2CGdeftAS6lUit27dwtra2sxcODALLdfu3atKFiwoOp2xredUVFR2e43w7vFTlb69esnPv300/fu520ZxYGdnZ2ws7NTfcPZqlWrbB+jrcuXLwsHBwcxf/78bLfZtWuXMDc3Fzdv3lSti46OVhU3QmQuWt73ej5U7LxbMMyZM0e4ubmpbnt6eoqxY8eq7btq1ari66+/FkK8+V1MmDBBdX9qaqrw8vJSHdy1bNlSdOvWLcucy5cvF6VLlxZKpVK17vXr1yJfvnxi586dQgjpd+fm5qY6kM4QGBgofv31V9XtoUOHqvUKlChRQvz5559qjxk9erQICgoSQgjx+++/i4IFC4qXL1+q7p87d+5731sXL14UAMTu3buzvP+nn37K9HrmzJkj8ufPL9LT04UQ0sF4rVq11B5XtWpVMXjwYNXt1q1bq327rsn7QtNi533PvXPnTmFhYSHu3Lmjuv/vv/9WK3Y+5nf2rvDwcNG8eXMhhBAzZswQHTp0UOs99PX1Vf29vPt3n9V7XJPXmJX3fc6820v8ofdVViZPnqz2JceIESNEYGBgpu0AiGHDhqluHzt2TAAQixYtUq1btWqVsLGxUd1u0KCBGDdunNp+li9fLjw8PLLd7/PnzwUAVTtn15ZERLmdfCc+k0naunUr8ufPj9TUVCiVStUF/ACwZ88ejB8/HnFxcUhOTkZaWhpevXqFFy9eqK45sbKyQkBAQI6ee86cOVi8eDFu3ryJly9fIiUlJUcXAB86dAi2trY4fvw4xo0bh3nz5uUoz7vu3LmDJk2a4LPPPkOvXr2y3S42Nhbe3t7w9vZWrStTpgycnJwQGxuLqlWr6iRPBltbW5QoUUJ128PDQzWyXHJyMu7evYuaNWuqPaZmzZo4e/as2rqgoCDVzxYWFqhSpQpiY2MBAH379sWnn36KM2fOoFGjRmjTpg2Cg4MBAGfPnkV8fDzs7e3V9vfq1StcuXJFdbt8+fKwsrJS26Zz585YvHgxfvnlFwghsGrVKoSHhwMA/vvvP1y5cgU9evRQa++0tDQ4OjoCkNo6ICAANjY2Wb6OrERFRcHc3BwhISFZ3h8bG4ugoCAoFAq19nr+/Dlu376NIkWKAECm9/nb7Z7dfnX1vnjfc2c8j6enp+r+d9vkY35n7woJCcGiRYuQnp6OAwcOoFGjRnB3d8f+/fsREBCA+Ph41K1bV+PXpslr1FaVKlVUP2vyvgKA1atXY+bMmbhy5QqeP3+OtLQ0ODg4aJ3dzc0NgNSWb6979eoVkpOT4eDggLNnz+LIkSMYO3asapv09PRMn69v79fOzg4ODg4GH0WSiMjQWOyQTtWrVw9z586FlZUVPD09VRcSX79+HS1atEDfvn0xduxYFChQAIcPH0aPHj2QkpKi+s84X758ageJmoqIiMDAgQMxdepUBAUFwd7eHpMnT8a///6r9b6KFSsGJycnlC5dGvfv30eHDh1w8OBBrffztrt376JevXoIDg7G/PnzP2pfmjIzk8YfEUKo1mV1cbWlpaXabYVCofYYXWjatClu3LiB7du3Y/fu3WjQoAH69euHKVOm4Pnz56hcuTJWrlyZ6XEuLi6qn+3s7DLd//nnn2Pw4ME4c+YMXr58iVu3bqFDhw4AgOfPnwMAFixYgOrVq6s9ztzcPMevJV++fDl+7NuyavfsBuTQlJmZWabfnaa/c22e+2N+Z++qU6cOnj17hjNnzuDgwYMYN24c3N3dMWHCBAQGBsLT0xMlS5bUOFsGXbbv269Dk/fVsWPH0LlzZ4waNQqNGzeGo6MjIiIiMHXqVK2zZ3weZrUu4/U8f/4co0aNQtu2bTPt6+1CXh/vOSIiY8dih3TKzs4Ovr6+mdafPn0aSqUSU6dOVR2Er1mzRqN9WllZIT09/b3bHDlyBMHBwfj6669V697+hjmn+vXrh/Hjx2Pjxo345JNPcrSPO3fuoF69eqhcuTKWLFmiev3Z8ff3x61bt3Dr1i3Vt/gxMTF48uQJypQpo/HzZhx03rt3D87OzgCg9dwxDg4O8PT0xJEjR9R6Mo4cOYJq1aqpbXv8+HHUqVMHgPQt9+nTp9G/f3+1PGFhYQgLC0Pt2rXx448/YsqUKahUqRJWr14NV1dXjb/5zuDl5YWQkBCsXLkSL1++RMOGDVUjl7m5ucHT0xNXr15F586ds3y8v78/li9fjlevXqkOCo8fP/7e5yxfvjyUSiUOHDiA0NDQLPe5fv16CCFUB6VHjhyBvb09vLy8tHp97+73Q+8LFxcXXLhwQe1xUVFRmQ5yNXmee/fuwcPDA0DmNvmY39m7nJycEBAQgNmzZ8PS0hJ+fn5wdXVFhw4dsHXr1mx70ACoeo0+9PmgCU0+ZwDN3ldHjx5F0aJF8fPPP6vW3bhxI0fPp4lKlSrh4sWLWX72akqXbUlEZEw49DQZhK+vL1JTUzFr1ixcvXoVy5cv1/j0MB8fH5w7dw4XL15EUlJSlt9UlyxZEqdOncLOnTtx6dIl/PLLLzh58uRH57a1tUWvXr0wYsSIHPV23LlzB3Xr1kWRIkUwZcoUPHjwAAkJCUhISMj2MaGhoShfvjw6d+6MM2fO4MSJE+jatStCQkLUTqf5EF9fX3h7e2PkyJG4fPkytm3bpvE3y2/78ccfMXHiRKxevRoXL17EkCFDEBUVhQEDBqhtN2fOHGzcuBFxcXHo168fHj9+jO7duwMAhg8fjk2bNiE+Ph7R0dHYunUr/P39AUinohUqVAitW7fGoUOHcO3aNezfvx/ffvstbt++/cF8nTt3RkREBNauXZvp4HPUqFEYP348Zs6ciUuXLuH8+fNYsmQJpk2bBgDo1KkTFAoFevXqhZiYGGzfvh1Tpkx57/P5+PggLCwM3bt3x19//aXKm1G8f/3117h16xa++eYbxMXFYdOmTRgxYgTCw8M/WOi+jybvi/r16+PUqVNYtmwZLl++jBEjRmQqfjR5nlKlSiEsLAxnz57FoUOH1A7agY//nb2rbt26WLlypaqwKVCgAPz9/bF69er3FjtFixaFQqHA1q1b8eDBA1WvS05o8jmT4UPvq5IlS+LmzZuIiIjAlStXMHPmTGzcuDHT8127dg1RUVFISkpSG7JeW8OHD8eyZcswatQoREdHIzY2FhERERg2bJjG+9BlWxIRGRMWO2QQgYGBmDZtGiZOnIhy5cph5cqVGD9+vEaP7dWrF0qXLo0qVarAxcUFR44cybRNnz590LZtW3To0AHVq1fHw4cP1Xp5Pkb//v0RGxuLtWvXav3Y3bt3Iz4+Hnv37oWXlxc8PDxUS3YUCgU2bdoEZ2dn1KlTB6GhoShevDhWr16t1XNbWlpi1apViIuLQ0BAACZOnIgxY8Zo/Rq+/fZbhIeH44cffkD58uWxY8cObN68OdOpRRMmTFCdenT48GFs3rwZhQoVAiB9azx06FAEBASgTp06MDc3R0REBACpoDx48CCKFCmCtm3bwt/fHz169MCrV6806jVo164dHj58iBcvXmSaFLJnz55YuHAhlixZgvLlyyMkJARLly5FsWLFAAD58+fHli1bcP78eVSsWBE///wzJk6c+MHnnDt3Ltq1a4evv/4afn5+6NWrF/777z8AQOHChbF9+3acOHECgYGB+Oqrr9CjRw+tDjyzosn7onHjxvjll18waNAgVK1aFc+ePUPXrl21eh4zMzNs3LgRL1++RLVq1dCzZ0+1a0GAj/+dvSskJATp6elq1+bUrVs307p3FS5cGKNGjcKQIUPg5uam1pOoLU0+ZzJ86H3VqlUrfP/99+jfvz8qVKiAo0eP4pdfflHbx6effoomTZqgXr16cHFxwapVq3KcvXHjxti6dSt27dqFqlWrokaNGpg+fTqKFi2q8T502ZZERMZEIXR9cj4REREREZERYM8OERERERGZJBY7RERERERkkljsEBERERGRSWKxQ0REREREJonFDhERERERmSQWO0REREREZJJY7BARERERkUlisUNERERERCaJxQ4REREREZkkFjtERERERGSSWOwQEREREZFJ+j/vDiTtb799UgAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -654,13 +654,13 @@
      "text": [
       "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n",
       "\n",
-      "Original Effect Estimate : 11.679948861035195\n",
-      "Robustness Value : 0.44\n",
+      "Original Effect Estimate : 11.738281416879683\n",
+      "Robustness Value : 0.45\n",
       "\n",
       "Robustness Value (alpha=0.05) : 0.41000000000000003\n",
       "\n",
       "Interpretation of results :\n",
-      "Any confounder explaining less than 44.0% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n",
+      "Any confounder explaining less than 45.0% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n",
       "\n",
       "For a significance level of 5.0%, any confounder explaining more than 41.0% percent of the residual variance of both the treatment and the outcome would be strong enough to make the estimated effect not 'statistically significant'\n",
       "\n",
@@ -694,21 +694,21 @@
    "id": "52b2e904",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:21:41.847458Z",
-     "iopub.status.busy": "2024-10-20T00:21:41.847133Z",
-     "iopub.status.idle": "2024-10-20T00:21:41.892523Z",
-     "shell.execute_reply": "2024-10-20T00:21:41.892026Z"
+     "iopub.execute_input": "2024-10-22T01:48:24.368589Z",
+     "iopub.status.busy": "2024-10-22T01:48:24.368252Z",
+     "iopub.status.idle": "2024-10-22T01:48:24.416424Z",
+     "shell.execute_reply": "2024-10-22T01:48:24.415732Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle 0.44$"
+       "$\\displaystyle 0.45$"
       ],
       "text/plain": [
-       "0.44"
+       "0.45"
       ]
      },
      "execution_count": 10,
@@ -754,16 +754,16 @@
    "id": "85eefe08",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:21:41.894473Z",
-     "iopub.status.busy": "2024-10-20T00:21:41.894287Z",
-     "iopub.status.idle": "2024-10-20T00:21:57.328659Z",
-     "shell.execute_reply": "2024-10-20T00:21:57.328048Z"
+     "iopub.execute_input": "2024-10-22T01:48:24.418789Z",
+     "iopub.status.busy": "2024-10-22T01:48:24.418380Z",
+     "iopub.status.idle": "2024-10-22T01:48:39.843839Z",
+     "shell.execute_reply": "2024-10-22T01:48:39.843116Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -808,16 +808,16 @@
    "id": "df23a49b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:21:57.331092Z",
-     "iopub.status.busy": "2024-10-20T00:21:57.330533Z",
-     "iopub.status.idle": "2024-10-20T00:22:12.324500Z",
-     "shell.execute_reply": "2024-10-20T00:22:12.323862Z"
+     "iopub.execute_input": "2024-10-22T01:48:39.846313Z",
+     "iopub.status.busy": "2024-10-22T01:48:39.845918Z",
+     "iopub.status.idle": "2024-10-22T01:48:56.230065Z",
+     "shell.execute_reply": "2024-10-22T01:48:56.229431Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -827,7 +827,7 @@
     },
     {
      "data": {
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z51i5ciXdunXjww8/tOqxa9WqRWBgIOXLlycgIIATJ04wdepU6tevj6+vb5y3K168ONOmTeOzzz4jV65c+Pv7U61atTjPL1asGLly5WLw4ME8ffo0XtPocubMScqUKZk+fTq+vr4kT56c0qVLkz17dgA8PT1p1aoVU6dOxd3dndatW8frc/7qq6+oUKECxYoVo1u3bmTPnp3z58/z66+/Wtouf/bZZ2zcuJEKFSrw3nvv4eHhwYwZM3j69Gmse0HFR1zP2YABA1i0aBF169alV69epE6dmnnz5nHu3DmWL1+Om5u6NpU/f37KlCnDwIEDuXPnDqlTp2bx4sVERES8Up7nJUmShHXr1tGhQwdKly7N2rVr+fXXXxk0aBDp0qWL83bxfa6KFCmCu7s7Y8eOJTQ0FG9vb6pVqxbrXl0AX3zxBXXr1qVs2bJ06dKFx48fM2XKFPz8/Bg+fPhrfa5hYWFUr16dFi1acOrUKb755hsqVKhAw4YN47xNgwYNWLFiBY0bN6Z+/fqcO3eO6dOnky9fvmiNRd5++23u3LlDtWrVyJQpExcuXGDKlCkUKVLkhe3JhRBCOAhN3fCEcAlr1641OnfubAQHBxs+Pj6Gl5eXkStXLqNnz57G9evXY5y/fPlyo0KFCkby5MmN5MmTG8HBwcb7779vnDp1ynJO5cqVY23D3aFDByNr1qyW92fMmGFUqlTJSJMmjeHt7W3kzJnT+Oijj4zQ0FDLObG1ib527ZpRv359w9fX1wAsbY1jax0dZfDgwQZg5MqVK9bn4fn2yIZhGD///LORL18+w8PDI9bW3Xv37jUAo1atWrHeZ1yOHj1qNG7c2EiZMqWRJEkSI0+ePMbQoUOjnfPXX38ZtWvXNnx8fIxkyZIZVatWNXbu3BntnKjnZt++fdGOx/Y8xPWcGYZhnDlzxmjWrJklT6lSpYzVq1fHyH3mzBmjRo0ahre3txEQEGAMGjTI2LhxY6ztul/Whv1ZHTp0MJInT26cOXPGqFWrlpEsWTIjICDAGDZsmKVdeBSea9cd3+fKMAxj1qxZRo4cOQx3d/d4te7etGmTUb58eSNp0qRGihQpjDfeeMM4fvx4tHNepV339u3bjW7duhmpUqUyfHx8jLZt20ZrC24YMb8fzWaz8fnnnxtZs2Y1vL29jaJFixqrV6+O8TO1bNkyo1atWoa/v7/h5eVlZMmSxXjnnXeMq1evvjSfEEII+2cyjARcQSyEEAnk0KFDFClShO+//5527drpjuOwOnbsyLJly6KNfAghhBAiJlljJISwS7NmzcLHx4cmTZrojiKEEEIIFyBrjIQQdmXVqlUcP36cmTNn0qNHj9fqUCaEEEIIEV9SGAkh7ErPnj25fv069erVY8SIEbrjCCGEEMJFyBojIYQQQgghhMuTNUZCCCGEEEIIlyeFkRBCCCGEEMLlyRqjWJjNZv799198fX0xmUy64wghhBDCyRmGwf3798mQIYNl82chROKSwigW//77L5kzZ9YdQwghhBAu5tKlS2TKlEl3DCFckhRGsfD19QXUL6cUKVJoTiOEEEIIZ3fv3j0yZ85seQ0ihEh8UhjFImr6XIoUKaQwEkIIIUSikSn8Qugjk1iFEEIIIYQQLk8KIyGEEEIIIYTLk8JICCGEEEII4fJkjZEQQgghhIgmMjKS8PBw3TGEeG2enp64u7vH61wpjIQQQgghhMWDBw+4fPkyhmHojiLEazOZTGTKlAkfH5+XniuFkRBCCCGEANRI0eXLl0mWLBnp0qWTLnnCoRmGwc2bN7l8+TJBQUEvHTmSwkgIIYQQQgAQHh6OYRikS5eOpEmT6o4jxGtLly4d58+fJzw8/KWFkTRfEEIIIYQQ0chIkXAW1nwvS2EkhBBCCCGEcHlSGAkhhBBCCCFcnhRGQgghhBBC2FDHjh0xmUyYTCZ++ukn3XGsynPq1CkCAwO5f/9+4oR7gQEDBtCzZ0+b3b8URkIIIYQQwuF9/fXXZMuWjSRJklC6dGn27t37wvNnzZpFxYoVSZUqFalSpaJGjRovvU3//v3Jli1bjCLhjTfeoFKlSpjN5jhvW6dOHa5evUrdunUtx0wmE+fPnwcgffr0jBkzJtptBgwYgMlkYtu2bdGOV6lShXbt2gFw9epV2rRpQ+7cuXFzc6NPnz4xHnv48OF07NjR8v7kyZO5evXqCz/XKAMHDqRnz574+voC8OTJEzp27EjBggXx8PCgUaNG8bqfO3fu0LZtW1KkSEHKlCnp0qULDx48sHz81KlTVK1alYCAAJIkSUKOHDkYMmRItP20PvzwQ+bNm8fZs2fj9ZjWksJICCGEEEIkPMOAffvUvza2ZMkS+vXrx7Bhw/jrr78oXLgwtWvX5saNG3HeZtu2bbRu3ZqtW7eya9cuMmfOTK1atbhy5Uqct/n000/x8fGhX79+lmPfffcdW7duZc6cObi5xf3S2tvbm8DAQLy9vWP9eJUqVWIUQFu3biVz5szRjj958oTdu3dTrVo1AJ4+fUq6dOkYMmQIhQsXjvPxn+Xn50dgYOBLz7t48SKrV6+OVlRFRkaSNGlSevXqRY0aNeL1eABt27bl2LFjbNy4kdWrV/Pbb7/RrVs3y8c9PT1p3749GzZs4NSpU0yaNIlZs2YxbNgwyzlp06aldu3aTJs2Ld6PaxVDxBAaGmoARmhoqO4oMfz7r2HcvJk4j3XlimHcupU4j2Urp08bxoMHulPYB7PZMA4eVP+KhGM2G8b+/bpTiNiEhRnG7t26UwhbuHDBME6e1J0iYdnLa4/Hjx8bx48fNx4/fvz6d/b994YBhvHDD69/Xy9RqlQp4/3337e8HxkZaWTIkMEYPXp0vO8jIiLC8PX1NebNm/fC8/7880/D09PTWLt2rXHhwgUjRYoUxtdff/3C23To0MF48803YxwHjHPnzhmGYRgzZswwfHx8jPDwcMMwDOPevXuGp6enMXXqVKNy5cqW22zZsiXa7Z5VuXJlo3fv3jGODxs2zOjQoUOsj79y5co4c3/xxRdGiRIlrP68nnf8+HEDMPbt22c5tnbtWsNkMhlXrlyJ83Z9+/Y1KlSoEO3YvHnzjEyZMr30MaNY8z0tI0YaRUbC8OHq3/j67DOYPdtmkaIZNAgWLUqcx7KVtm3h9991p7APkZFQowYcO6Y7iXO5fRuqVoV//tGdRDzv9GmoUwdOntSdRCS09euhdm3491/dSUScIiIg6kr/sGHqfRsJCwtj//790UYv3NzcqFGjBrt27Yr3/Tx69Ijw8HBSp079wvOKFy/OwIEDefvtt2nXrh2lSpWie/fur5w/StWqVXnw4AH79u0DYMeOHeTOnZumTZuyZ88enjx5AqhRpGzZspEtW7bXfsyX2bFjByVKlHjt+9m1axcpU6aMdl81atTAzc2NPXv2xHqb06dPs27dOipXrhzteKlSpbh8+bJlCmJCksJII7MZRoxQ/8aXh4dNf7dEky4dvGAE2iEUKQIHD+pOYR88PKBRI1i+XHcS55I2LXTrBqNG6U4inpc3LwweDG+9Bc9MURdOoGtXaNVKFb537+pOI2K1aBGcO6f+f/YsLF5ss4e6desWkZGRBAQERDseEBDAtWvX4n0//fv3J0OGDPGaHjZkyBDLi/rZs2e/8r5PhmFYCpygoCAyZsxomTa3bds2KleuTGBgIFmyZLEUedu2baNq1apWPc7w4cOZO3eu1fkuXLhAhgwZrL7d865du4a/v3+0Yx4eHqROnTrG16hcuXIkSZKEoKAgKlasyKeffhrt41F5Lly48Nq5nieFkYNxd7duhOl1+PvDzZuJ81i2IoVRdM2aSWFkCx9+CCtWwJkzupOI5/XtCz4+MHKk7iQioY0eDaVKQYMG8OiR7jQimqjRoqhiwc3N5qNGr2vMmDEsXryYlStXkiRJkpeev3HjRq5du4bZbLaM8CSEZ9cZbdu2jSpVqgBQuXJltm3bxuPHj9mzZ4/VhdGrevz4cbyej4S0ZMkS/vrrLxYuXMivv/7K+PHjo308adKkgBrhS2hSGDkYD4/ELYxkxMi5VK0Kly7B33/rTuJcAgLUFezPP9edRDzP3R3mzYOpU2H3bt1pREIymWD6dAgMVBd9ZFTQjkSNFkU1XTCbbTpqlDZtWtzd3bl+/Xq049evX49Xg4Hx48czZswYNmzYQKFChV56/t27d+natStDhgxh8ODBvPfee9y6deuV8z+ratWq/PHHH9y+fZsDBw5YppFVrlyZrVu3snPnTsLCwiyNF2wtbdq03E2AYdnAwMAYjTAiIiK4c+dOjK9R5syZyZcvH61bt2bMmDEMHz6cyGde/N65cweAdOnSvXau50lh5GDc3RPvgoszFEYFC6p1Bg8f6k5iH7y8oGFDGTWyhY8+gqVL/z9zRNiPrFlh0iRo1w6e6QwrnICHByxYAE+fQseO1k1NFzby/GhRFBuOGnl5eVG8eHE2b95sOWY2m9m8eTNly5Z94W3HjRvHyJEjWbduXbzX0vTs2ZPAwEAGDRrE4MGDyZgxI++///5rfQ5RqlatysOHD/nyyy8JCgqyTD+rVKkSe/fuZe3atZYpd4mhaNGiHD9+/LXvp2zZsoSEhLB//37LsS1btmA2myldunSctzObzYSHh0drg3706FE8PT3Jnz//a+d6nhRGDiYxR4zSpXP8qXTJk0POnHD0qO4k9qNpUymMbCEwEDp3llEje9WuHRQurKY9CueSJAn89JMaCbeDfTPF86NFUWw8atSvXz9mzZrFvHnzOHHiBN27d+fhw4d06tTJck779u0ZOHCg5f2xY8cydOhQvvvuO7Jly8a1a9e4du1atL11nrdy5UqWLl3KvHnz8PDwwMPDg3nz5vHTTz+xPAH+uObIkYMsWbIwZcqUaE0HMmfOTIYMGZg5c2as0+gOHjzIwYMHefDgATdv3uTgwYMJUtDUrl2bXbt2RRuxATh+/DgHDx7kzp07hIaGWh4/yt69ewkODra0Ps+bNy916tSha9eu7N27lz/++IMePXrQqlUry5qhBQsW8OOPP3LixAnOnj3Ljz/+yMCBA2nZsiWenp6W+96xYwcVK1a0TKlLUPHudedCEqtlZliY6mIZFhb/2wwbZhgffGCzSNFcuGAYqVIlzmPZUsuWhjF9uu4U9uPxY8Pw9TWMWLp8itd05Yo8t/bs5k3DSJ/eMFav1p1E2MKDB469HYFTtOsODzeM7NkNw2RSL3Cef3NzM4wcOdR5NjBlyhQjS5YshpeXl1GqVClj93P9+itXrhytZXXWrFkNIMbbsGHDYr3/mzdvGv7+/saoUaNifGzUqFGGv7+/cTOOPVXi29Y66lzAWLx4cbTjHTt2NABj0aJFMW4T2+eRNWvWlz4WL2nXHR4ebmTIkMFYt25dtONxPXdRtm7dGqOl+O3bt43WrVsbPj4+RooUKYxOnToZ9+/ft3x88eLFRrFixQwfHx8jefLkRr58+YzPP/88xvdinjx5Yn0O4mLN97TJMBJh1y0Hc+/ePfz8/AgNDSVFihQ2e5zwcDW1KSwMnimEX+izz1R74IkTbRbL4vFjSJZMTVHw8rL949nKmDFw4QLYai8wR9S6NZQsCc/sTycSSK9e6mdmxgzdSURs1q6FTp3gyBE1Ki5cS9TGmFEL2u1JYr32eJknT55w7tw5smfPbv2i+23b1GLWl9m6Fezwa2BLHTt2JCQkhJ/sbFjTZDKxcuVKGjVqFOc5X3/9Nb/88gvr169PvGBxWLt2LR988AGHDx/Gw8MjXrex5ns6fvco7EZirjFKmlR1c7p1CxKgU6M2RYrAzz/rTmFfmjaFL7+UwsgW+vf/f5voLFl0pxHPq1sXmjRRLdZXrIi5DEI4L7PZzLp16/juu+9YuXIlkydP1h3J+ZQtCz/+qK4OxcXbW53nglavXo2Pjw+LFy+mQYMGWrO8++67zJ8/P17nvvPOO4SEhHD//n18fX1tnOzFHj58yJw5c+JdFFlLRoxiYc8jRl98oabufvONzWJFkzOnevFQuHDiPJ4tXLumPo9791RhKVQzioAAOHUKEmn9pkvp0UOtBZRRSvv08CEULQoDB6rRI+E6IiMjmTlzJj179mTy5MkJtmA+ITjFiJGI040bN7h37x4A6dOnJ3ny5JInkVjzPS3NFxxMYo4YgXN0pgsMBF9f1Z1OKMmTq13jV67UncQ5DRigOmVduqQ7iYhN8uTwww/wwQfSRdDVHD58mFOnTlG1alXLpppCJAZ/f39y5cpFrly57KIIsbc89kIKIweTmBu8gpqD7+iFEch+RrGR7nS2kykTvPUWjB2rO4mIS+nSaj1Y+/aJ+ztV6LN7926mT5/OqVOn+Pjjj6lfv77uSEIIOyOFkYNJzHbdoEaMHL1lN0hhFJsGDWDPHucofO3RgAHw/ffwX6dSYYcGD1ZLIZ7bVF04iUePHln+v2PHDr799lsuX75M//79qVmzJgCymiBu8twIZ2HN97IURg5GptK9GimMYkqRAqpXl30/bCVLFtX9T0aN7Jenp5pSN3q0/H5wNhEREfTt25dvv/2W48ePM3v2bK5fv86gQYMsHekMw8Ak3TdicP9vMW5YWJjmJEIkjKjvZfd4LDSXrnQOJrFHjNKlg2PHEu/xbKVwYXnhE5umTdVefN266U7inAYOhEKF1OiRI3d2dGZ58qhNed96C/78U20WKhyfh4cH7du3p2LFihQsWJCCBQsyZMgQSpcubTnn2aLo2SLJ1QsmDw8PkiVLxs2bN/H09MTNTa6hC8dlNpu5efMmyZIli1cnOymMHIyOEaNt2xLv8Wwld24IDVUd6gIDdaexHw0bwnvvwd27kCqV7jTOJ1s2aNkSxo2DSZN0pxFx6d4dVq1SU+smTNCdRiSU8uXLs2PHDqpUqUKzZs0oXbo0ZrM5xgv954+FhYXh7e2d2HHthslkIn369Jw7d44LFy7ojiPEa3NzcyNLlizxuuAhhZGD8fBI/MLo+vXEezxbcXeHggXVqFGdOrrT2I/UqaFCBbXPU8eOutM4p0GD1Ihl//6QPr3uNCI2JhN8950a3atfH6pV051IJJTy5cuzfPlyWrRoQe3atSlVqlSMc9zc3Hj06BG9e/fm7t27hIWF0bFjR5o0aaIhsX3w8vIiKChIptMJp+Dl5RXvkU8pjBxMYnelc5Y1RqD2LTlwQAqj57Voofbjk8LINrJnV6NGn38OU6boTiPikj49zJihutQdPAhp0+pOJBJKw4YN2b59O4ULF+bWrVukTZs22nS5Xbt20bp1a/z9/WnTpg3e3t60adOGVatWWZo0uCI3NzfZx0i4HJk46mASe41RQIBzjBgBFC8O+/frTmF/mjSB7dvh9m3dSZzX0KEwbx7IrBT71qSJ6tbYpQtIQy7nUrp0aa5cuUKTJk24fPmypSjav38/rVq1omzZsuzdu5c+ffrQvXt3Bg4cyLp16zCbzdKdTQgXIoWRg0nsqXRp08Ljx2qneEdXvDj89ZfuFPYndWqoUgVWrNCdxHllyQKdO8Onn+pOIl7myy/VZtDffKM7iUhoOXLkYMKECZZC5/HjxzRt2pRy5cqxaNEiy3kPHz5k3759eHt74+bm5tKNGIRwNVIYOZjEbr7g4QFp0jjHqFH+/GpPmTt3dCexP61aweLFulM4t4EDYdkyOHVKdxLxIsmSqZ+FIUPgyBHdaURCK1myJJkzZwbghx9+IE2aNCxYsABQTRgADh48SGhoKAULFrTc7tk9kYQQzksKIweT2FPpwHmm03l7Q4ECMmoUmzffhN274epV3UmcV0AA9OgBw4bpTiJepmBB+OwzdcFAXg87txw5ckRblH3kyBG6dOlCmjRpaN26NQBLly6lTp06HHOGvSuEEC8khZGDSeypdOA8hRHIdLq4pEihmlIsW6Y7iXP78EPYsEH21HIE770HuXJBv366kwhb8fPzY/fu3YSGhnLq1CmWL19O06ZNyZEjBz/9t/P1/PnzmT59OmFhYVSqVIlTMuQrhFOTwsjBJHZXOnCuwqhYMWnAEBeZTmd7qVLBBx+oZgzCvkW18F69GpYs0Z1G2ELLli1p0qQJFStWpHbt2kyfPp26deuyZs0aADZv3swnn3xC6dKl2b17NwMHDqRKlSqcOXNGc3IhhK1IYeRgZMTo9UhnurjVrw+HD8PFi7qTOLfevWHvXti1S3cS8TJp0qiLBd27wz//6E4j4sUwYN++eLcVnDx5Mt9//z2//PILs2fPZvLkyYDa5DVr1qxUq1aNX375BYAPP/yQli1bMmLECJvFF0LoJYWRg9FVGDnLXkYFC6oX/iEhupPYn2TJoGFDtaeRsB0fH9WIYfBg3UlEfFSoAAMGQPPmqkOnsHPz50OpUvBfQ4X4KFKkCIUKFSJbtmyEh4fz4MEDvLy8yJUrF99++y3BwcG0a9cOgEmTJjFr1ixbpRdCaCaFkYPRMZXO3995RoySJIF8+dRGryImmU6XON59V41AbN6sO4mIjw8/hEyZoE8f3UnEC0VE/L+7ybBhVl9FjIyMZMKECfz130LUBw8eANCnTx9u3bpFaGgoAN7e3gmXWQhhV6QwcjAyle71yXS6uNWqBWfOyLQhW0uSBD75RI0ayd6R9s/NTW3Qu3YtLFyoO42I06JFcO6c+v/Zs1Zf5XF3d+f06dMMGDAAAB8fHwC2bNlCSEiIFERCuAApjByMFEavr1gx6UwXF29vaNxYFpsnho4d4fZtWLVKdxIRH2nSqGmmPXrAyZO604gYokaLojZjdXN7pVGjb7/9Fj8/P2rUqMG6desYPnw4hw8fpnbt2nh6etoguBDCnkhh5GBkH6PXJyNGLybT6RKHpyeMGKE2Ev1vX0lh58qUUV+v5s1lfyO7EzVaFDUEaza/0qgRwNq1a8mXLx9Tpkxh69at5M+fnz59+uDu7p7AoYUQ9sZkGDKR43n37t3Dz8+P0NBQUqRIYbPHCQ8HLy8IC1MvkuLj+HGoWxcuXLBZrBiePlVTfx4/Vv86useP1b49t2+rf0V0ERGQPj1s3ao2xBW2YzZD4cIwaBD8t5eksHOGoUZV06SB2bN1pxGA+qWVOzecPx99bqqbG2TLBqdOqauKVnr8+DFubm54enri5uaGYRiYokakbCCxXnsIIeImI0YORseIkbc3pEzpPJ3pkiaF4GBpwBAXDw91RVym09memxt89pna1ygsTHcaER8mE8yZA1u2qHVHwg48P1oU5TVGjQCSJk2Kt7d3ohRFQgj7IIWRg9GxxghUZzpnKYxATaeTdUZxi5pOJ+PJttewoRqhmzFDdxIRX6lSqfVGffrAsWO607i459cWPe8V1xo97/miSKZSCuGcpDByMO7uegojWWfkWipUUH/45TmyPZMJvvgCPv0U/usGLBxAyZJqjVjz5vBfV2ehw++/xz5aFCVq1Oj33xP0YZs2hbFjE/QuhRB2wPpJt0IrXSNGgYFw7VriP66tFC8OX3+tO4X9cnNTo0YLF0KJErrTOL8yZaBqVRg1CsaN051GxFfPnur19ttvq9lcMtNKg7Jl1fDd06dxn+Ptrc5LQF9/rX5mw8NVQw4hhHOQ5guxsOfmC9evQ65ccP++zWLFqlcvNWo0eHDiPq6tPH4Mfn5w86b6V8R04ADUqweXLr3SumVhpXPnoEgRNcUzZ07daUR83b8PpUur4qhfP91pRGK6cEEVR2+9pUYPX7cwluYLQugnU+kcjIwYJYykSVXHtT//1J3EfhUpAqlTq0XmwvayZ4f334ePP9adRFjD1xdWrlRNNLZt051GJKasWeG339R6zFmzdKcRQiQEKYwcjK7CKH165yqMQK0R2LdPdwr7ZTKpK6E//KA7iesYOBB27pQX2I4mTx747jto2RIuX9adRiSmTJlUcSTt9oVwDlIYOZiowiixJ0A624gRQKlSsHev7hT2rW1b+OknWVyeWHx91Tqjvn0Tvy2/eD2NGqnpdE2bvni5i3A+gYHqZ1cI4fikMHIwURtvm82J+7jOWBjJiNHLZcmiGlX8/LPuJK6jQwc1Wjd3ru4kwlqffqr2fOvdW3cSIYQQr0IKIwcTtQg+safTOWNhlC8f3LkDV6/qTmLf3noL5s/XncJ1uLvDxImq0cm9e7rTCGu4u6tOjuvXw+zZutMIIYSwlhRGDiZqxCixC6N06eDhQ+eaUuXhAcWKyajRyzRrBjt2OF9hbM8qV1Z7SY0erTuJsFaaNLB8OXz4oTR3EUIIRyOFkYMxmfRs8urhAf7+zvfiWNYZvVzKlFC3ruq8JBLPuHHwzTeqjbdwLMWKwaRJ0KSJ2hJACCGEY5DCyAFJy+6EU6qUjBjFh0ynS3w5csC770r7bkfVoQO88YbqVqbj97UQQgjr2UVh9PXXX5MtWzaSJElC6dKl2fuSS/hLly4lODiYJEmSULBgQdasWRPt4x07dsRkMkV7q1Onji0/hUTl4aGnY5UzFkZRDRhkm+MXq1tXjVycOKE7iWsZPFi1At6xQ3cS8SomToRHj5xnY2whhHB22gujJUuW0K9fP4YNG8Zff/1F4cKFqV27Njdu3Ij1/J07d9K6dWu6dOnCgQMHaNSoEY0aNeLo0aPRzqtTpw5Xr161vC1atCgxPp1EISNGCSd7dnBzgzNndCexb15e0KKF7GmU2FKkUBuH9umT+J0oxevz8oJly+D77+HHH3WnEUII8TLaC6Mvv/ySrl270qlTJ/Lly8f06dNJliwZ3333XaznT548mTp16vDRRx+RN29eRo4cSbFixZg6dWq087y9vQkMDLS8pUqVKjE+nUShY40ROGdhZDKpUSNZZ/RyHTuqF3iyv07i6txZ/bx//73uJOJVZMigiqN33oEDB3SnEUII8SJaC6OwsDD2799PjRo1LMfc3NyoUaMGu3btivU2u3btinY+QO3atWOcv23bNvz9/cmTJw/du3fn9u3bceZ4+vQp9+7di/Zmz2TEKGHJOqP4KVUK/PxUK2KReNzd1UL+QYOcqyukKylfHiZMUJvAxjEZQgghhB3QWhjdunWLyMhIAgICoh0PCAjgWhyvwK9du/bS8+vUqcP333/P5s2bGTt2LNu3b6du3bpExnGpe/To0fj5+VneMmfO/JqfmW3pKozSp3fOwkhGjOLHZIIuXSCOwVxhQ1WrQunSMGaM7iTiVXXuDI0bQ9OmEBamO40QQojYaJ9KZwutWrWiYcOGFCxYkEaNGrF69Wr27dvHtm3bYj1/4MCBhIaGWt4uXbqUuIGtpHPEyBk3Qy1ZUk1xCQ/XncT+tWsH69ZJC2IdvvgCpkyBCxd0JxGvavx4SJIEevSQhi9CCGGPtBZGadOmxd3dnevXr0c7fv36dQIDA2O9TWBgoFXnA+TIkYO0adNy+vTpWD/u7e1NihQpor3ZM5lKl7ACAtQeTceO6U5i/9Klg9q1pQmDDrlyQbdu0r7bkXl4wJIlsGULfP217jRCCCGep7Uw8vLyonjx4mzevNlyzGw2s3nzZsqWLRvrbcqWLRvtfICNGzfGeT7A5cuXuX37NunTp0+Y4Jp5eOgZ3QgMhOvXnXPxfalSsGeP7hSO4e23YdYsueKtw5AhsG0bbN+uO4l4ValTwy+/wLBhsl5PCCHsjfapdP369WPWrFnMmzePEydO0L17dx4+fEinTp0AaN++PQMHDrSc37t3b9atW8eECRM4efIkw4cP588//6RHjx4APHjwgI8++ojdu3dz/vx5Nm/ezJtvvkmuXLmoXbu2ls8xoenaxyhFCvD2hlu3Ev+xba1MGYij34d4Tq1a8Pix7K2jg58fjB0L778vUz8dWb58sGgRtGkDz+00IYQQQiPthVHLli0ZP348n3zyCUWKFOHgwYOsW7fO0mDh4sWLXH1mYUu5cuVYuHAhM2fOpHDhwixbtoyffvqJAgUKAODu7s7hw4dp2LAhuXPnpkuXLhQvXpwdO3bg7e2t5XNMaLqm0plMqvXsv/8m/mPbWtmysHu37hSOwd0dunaFGTN0J3FN7durixTP7VAgHEytWvD551C/vnNOURZCCEdkMgyZEPO8e/fu4efnR2hoqE3XG4WHqw0Aw8LA0zP+tyteXO2oXqmSzaLFqVIlGDAA6tVL/Me2padP1dX4f/9VU13Ei129CkFBcP48pE2rO43rOXAAqldX6+KcZIawy/rwQ/jtNzVFMlky3WmETon12kMIETftI0bCerpGjMB5R4y8vaFIERk1iq/06VUThrlzdSdxTUWLqmlY0ojB8Y0dCxkzqpFAs1l3GiGEcG1SGDkgnYVR+vTOWRiBTKez1jvvwMyZ0oRBl5EjYcMGNdogHJe7O8yfr0ZfBw3SnUYIIVybFEYOSPeIkTPuZQSqMJIGDPFXo4ZqArJ1q+4krilVKhg9Wu2Jo+v3gUgYyZOrTnULFsDs2brTCCGE65LCyAHpatcNzjuVDlRnuj17nLMduS24ual9daQJgz4dO6oX1bInjuPLkAFWr4aPPoLndqQQQgiRSKQwckCennqn0jnriFHmzODrC8eP607iODp1gl9/hRs3dCdxTW5uqigaMUI6mzmDwoXV5sktW8KJE7rTCCGE65HCyAHpnkrnrCNGJpOsM7KWv79qNzxnju4krqtYMWjVShoxOIv69dXmr/Xrw82butMIIYRrkcLIAeluvnDtmvNON5ONXq0X1YRBOmrp89lnsH69avksHF/PntCgATRqBE+e6E4jhBCuQwojB6SzMEqRQrW2vnVLz+PbmjRgsF7Vqqqz1qZNupO4rtSp4csvVZEqL6Sdw8SJqsFGp07S+VEIIRKLFEYOSGdhZDI593S6YsXg7Fm4e1d3EsdhMqkX5NKEQa82bSBbNtWpTjg+d3dYtEitNRo2THcaIYRwDVIYOSCdhRE4dwOGpEnVAug9e3QncSwdOqipXM76feEITCb45huYPFkW7jsLX19YtQq+/Ra+/153GiGEcH5SGDkg3YWRM48YgUynexVp08Kbb8J33+lO4tpy5oQBA9QInqz5cg6ZM6viqHdv2L5ddxohhHBuUhg5IN2FUfr0zl8YSWc6673zDsya5byNORzFBx9ASIgUqc6keHGYNw+aNoVjx3SnEUII5yWFkQPSXRhlyODcU6aiNnqVK+7WqVgRkiWDDRt0J3Ftnp6qS+CAAXD9uu40IqE0bAgjR0LdunDliu40QgjhnKQwckAeHhAeru/xM2Rw7j/MWbOqF/iy0at1TCZ49121zkXoVaaM2tuoZ0/dSURC6t4d2rWD2rXhzh3daYQQwvlIYeSAPD31jhhlzOjchZHJBOXLwx9/6E7ieDp0gN9+gzNndCcRo0fD3r2wfLnuJCIhffYZlCunNoB9+FB3GiGEcC5SGDkg3VPpnL0wAlUY/f677hSOx89PXdH++mvdSYSvr1rz9f77zrvvmCsymWDaNNWUoUkTePpUdyIhhHAer1wYnT59mvXr1/P48WMADNmBLtHonkqXMSPcuKE3g61VqqQ6QMm3tfV69IA5c+DBA91JRM2a8MYbqqOZcB7u7vDDD2rfqtu3dacRQgjnYXVhdPv2bWrUqEHu3LmpV68eV/9bhd+lSxc++OCDBA8oYtI9lS5ZMjUy4MwNGAoXVi/sz57VncTxBAdDqVLqhZvQb/x4VeT/8ovuJCIheXurTZUzZNCdRAghnIfVhVHfvn3x8PDg4sWLJEuWzHK8ZcuWrFu3LkHDidjpnkoHkCmTc0+nc3eHypVhyxbdSRxTz54wZYqMuNkDPz/Vpe7dd+HuXd1pRGKLjIzk8ePH3JahJSGEeCmrC6MNGzYwduxYMmXKFO14UFAQFy5cSLBgIm66p9KBmk53+bLeDLZWrZoURq+qXj0IC4PNm3UnEaC+HjVrQr9+upOIxHTjxg1atWpF7dq1adiwIX369NEdSQgh7JrVhdHDhw+jjRRFuXPnDt7e3gkSSryY7ql04BoNGKpWha1bZdTjVbi5qUX/X32lO4mIMnEirF8Pa9fqTiISw59//kn+/PkJDw+nS5cuTJkyhTVr1jBixAjd0YQQwm5ZXRhVrFiR77//3vK+yWTCbDYzbtw4qlatmqDhROzsYSqdKxRG+fOrTV5PnNCdxDF16gTbtsk6LXuROrXqZtatG4SG6k4jbOn48eNUr16dpk2bsmLFCjp06ECxYsUYOnQoBw8eJDw8XBomCSFELKwujMaNG8fMmTOpW7cuYWFhfPzxxxQoUIDffvuNsWPH2iKjeI69TKVz9sLIZPr/qJGwXsqU8NZb0rrbnrz5JlSsCB99pDuJsJU7d+7QokULmjZtyvTp03FzU3/mzWYzR48eJVWqVHh6emIymTQnFUII+2N1YVSgQAH+/vtvKlSowJtvvsnDhw9p0qQJBw4cIGfOnLbIKJ4jU+kSj6wzej09esB330nrbnvy1Vfw88+waZPuJMIWjh07hre3NwMHDgRU8wVQo0iLFy+mUKFCOuMJIYRd83iVG/n5+TF48OCEziLiyR6m0jl7V7oo1arBoEFqSp2bbIdstXz5oEQJmD9fdUUT+qVNq0bx3n4bjhxRG8EK5xEaGkqyZMkICgoCwN3dnZMnT1KrVi0qV64sDRiEEOIFXqkwevLkCYcPH+bGjRuYzeZoH2vYsGGCBBNxs5epdJcvq8YEzjwjI1cuSJoUDh+GIkV0p3FMvXrBgAHwzjvO/b3iSJo1g8WL1ddFpjo6l1y5cnH8+HF+/vlnAgMDOXv2LO+//z61a9dm0aJFgBpFcnd315xUCCHsj9WF0bp162jfvj23bt2K8TGTyWQZthe2Yw8jRmnTqqLozh1Ik0ZvFlsymdSo0datUhi9qnr1oE8fNSWxenXdaUSUr7+GAgWgRQu1Z5dwDsHBwfzwww8MGDAAd3d3fHx86NWrF8OHD7ec82xRtH37dpIkSULSpEllmp0QwuVZPTmoZ8+eNG/enKtXr2I2m6O9SVGUOOxhjZHJpHZcd4XpdFWryjqj1+Hurlp3T5miO4l4VkAATJ4MXbrAo0e604iEVK9ePdatW8f69etZsWKFpSgym82WbnRhYWF0796dOnXqMHLkSN58881oHWeFEMIVWV0YXb9+nX79+hEQEGCLPCIe7GEqHbhWA4bfftNfjDqyzp1VcXnunO4k4lmtW6u29LJk1PlkyJABf39/Hjx4wNn/eua7ublZutGNGTOGxYsXc+TIEVavXs3ixYv56KOPOHTokM7YQgihldWFUbNmzdi2bZsNooj4soepdOA6hVHWrGq64F9/6U7iuFKmhLZt4ZtvdCcRzzKZYPp0+OEH+OMP3WmELfz777988MEH0aa/m81m9uzZw6BBg8iVKxeRkZGULl2avHnzcsUVfqkLIUQcrF5jNHXqVJo3b86OHTsoWLAgnp6e0T7eq1evBAsnYufpaR8jRpkyqQYMrqBaNdi8GUqV0p3EcfXsCRUqwLBh4OOjO42Ikj49TJigNuQ9cACSJ9edSCSk8uXLkyZNGtKmTQvA06dP8fLyIlWqVJbp7+7u7uzYsYN79+5ZZoMYhiF7HQkhXI7VhdGiRYvYsGEDSZIkYdu2bdF+cZpMJimMEoG9jBhlzqza/bqCmjVhxgz4b2sQ8Qry5YPSpWHOHFUkCfvRvj2sXKm61MlaMOcTHBwMwB9//MHjx4+pUaMGbdu2pV27dvj7+3P+/Hl2796Nj48P6dOnB5CiSAjhkqyeSjd48GBGjBhBaGgo58+f59y5c5a3qHnMwrbsacTo0iXdKRJH9eqwaxc8fKg7iWPr1w8mTrSPwl78n8kEM2fC0qWwbp3uNMJW7t69S9euXTl37hx169Zl5cqV/PPPP3z22WccOnSIKVOmkCFDBi5dusTSpUtZvnw5Z86c0R1bCCESjdWFUVhYGC1btsRNdrvUxh660oEaMXKVwihtWrVI/bffdCdxbDVqQKpUsGSJ7iTief7+8N130LEjXL+uO42whQYNGtCmTRsqVarEtGnTWLp0KefOnSN79uzs2LGDwoULs3DhQvLnz88XX3zBN998Q9myZaUhgxDCZVhd3XTo0IEl8qpGK3vpShdVGP3X/dXp1aoFGzboTuHYTCYYNAhGj4bn9oYWdqBePWjVShVH8vVxTqNGjWL48OHcvHmTkJAQKleuzObNm8mdOzcTJ06kS5cuDB48mO3bt7N582YGDx7Mhx9+yOPHj3VHF0IIm7N6jVFkZCTjxo1j/fr1FCpUKEbzhS+//DLBwonY2cuIUUAAPH0KoaGq65izq1lT1sYkhMaNYcgQWLUK3nxTdxrxvLFj1VqwSZPU1EfhfLp06RLjWFS77lmzZtGpUyfL8bRp0/Lo0SOZJSKEcAlWF0ZHjhyhaNGiABw9ejTax2SxZuKwlxEjd3fVsvvSJdcojMqVg/PnVYvyjBl1p3Fcbm5qkf/nn0PDhmoUSdgPb29YtEh9v1epAsWK6U4kbO38+fN8+OGHfPHFF9GKogcPHnD06FEKFCiAh4eHdKoTQjg9qwujrVu32iKHsIK9NF+A/0+nK1hQdxLb8/aGSpVg0ybo0EF3GsfWpg188ona9LV6dd1pxPPy5oVx49QGsPv3S3t1Z/fw4UMCAwNp1KgRZrPZMjr0888/88MPPzBu3Djc3d01pxRCCNt7rbHxy5cvc9lVNrKxI/YylQ5cqwEDyDqjhOLpCR9/rEaNhH16+211waN3b91JhK2Fhoby+PFjsmfPjpubGzdv3mTWrFl06dKFXr160aZNG90RhRAiUVhdGJnNZj799FP8/PzImjUrWbNmJWXKlIwcORKzrNZNFPYylQ5crzCqWVONGMm3+uvr3BmOHoXdu3UnEbExmWDWLNi4EX78UXcaYUvlypUjZ86clClThn79+tGjRw8+//xzvvjiCz7++GNAbfgqhBDO7pX2MZo6dSpjxozhwIEDHDhwgM8//5wpU6YwdOhQW2QUz7HHqXSuIl8+VZgePqw7ieNLmlQt7h89WncSEZdUqWDBAujeXa2vE87rl19+oWHDhoSHh1OqVCnmzJlDz/+6zcjaIiGEqzAZVl4GypAhA9OnT6dhw4bRjv/888+89957XLlyJUED6nDv3j38/PwIDQ0lRYoUNnuc8HDw8oKwMFXsxNe1a5ArFzx4YLNo8fbzzzB5slor4io6dlR7Gn30ke4kju/ePciWDbZvd411ao5q2DA1Urp9u7owIIRIeIn12kMIETerR4zu3LlDcHBwjOPBwcHcuXMnQUKJF5MRI71knVHCSZEC3n8fxozRnUS8yNChamrdyJG6k4jEINPmhBCuyurCqHDhwkydOjXG8alTp1K4cOEECSVezN6aL1y+7DqbvALUqAF//AGy32HC6N1b7Wl05ozuJCIuHh5qSt3XX8Nvv+lOI2wttmlzixbBkycawgghRCKyeird9u3bqV+/PlmyZKFs2bIA7Nq1i0uXLrFmzRoqVqxok6CJyd6n0j16BMmTQ2Sk2hNGJ8NQa0UuXYJ06fRmSUxFi6qNMGvV0p3EOfTtq76vZ8zQnUS8yNKlal3YoUOQOrXuNCKxREaqPcfMZli5EpIk0Z3IOclUOiH0s/pldeXKlTl16hSNGzcmJCSEkJAQmjRpwqlTp5yiKHIEUUWUPUynM5kgUybXm05Xs6bq1iUSxgcfwMKFavNcYb+aN4fataFrV9caJXZ17u6wYoX6t1EjGTkSQjgvq0eMXIG9jxgZhhopevBAjRzpVrUq9OkDb76pO0ni2bRJvZg/dEh3EufRtSv4+sKXX+pOIl7k4UMoXlyNHHXrpjuNSExPn0LTpupv18qVkCyZ7kTORUaMhNDP6hGjOXPmsHTp0hjHly5dyrx58xIklHgxk0ldubOHESOALFlcb8SoQgU4fRquXtWdxHn07w+zZ8ONG7qTiBdJnlytN+nfH44c0Z1GJCZvb1i+XE2frlsXQkN1JxJCiIRldWE0evRo0qZNG+O4v78/n8s29onGnjrTZckCFy/qTpG4kiSBKlVg3TrdSZxHrlxqms4XX+hOIl6maFEYNUpNrbt/X3cakZi8vdVas2zZ1O/A69d1JxJCiIRjdWF08eJFsmfPHuN41qxZuehqr441sqfCKGtWuHBBd4rEV68erFmjO4VzGTpUNWC4dk13EvEy3burAknWG7keT0+YMweqVYPy5eHcOd2JhBAiYVhdGPn7+3P48OEYxw8dOkSaNGkSJJR4OSmM9KtXT+1nZC9fB2eQK5cahRg7VncS8TImE8ycCQcPwjff6E4jEpubG4wfD2+/rYojmVYphHAGVhdGrVu3plevXmzdupXIyEgiIyPZsmULvXv3plWrVrbIKGJhT4WRK06lA8ieHTJmVHsaiYQzZIhaa/Tvv7qTiJfx9YVly9TXbN8+3WlEYjOZYMAAGDFCNeH5/XfdiYQQ4vVYXRiNHDmS0qVLU716dZImTUrSpEmpVasW1apVY9SoUbbIKGJhb4XRtWuqY5Grkel0CS97dmjdGkaP1p1ExEeBAjBpkhrpu3NHdxqhQ9euavTwjTfg1191pxFCiFf3yu26//nnHw4ePEjSpEkpWLAgWbNmTehs2th7u26AHDng55+hYEHbZLNWQIAaOcmVS3eSxLVlC/TqBUeP6k7iXC5eVC+4jx2DzJl1pxHx8fbbaiH+zz/r33ha6LFlCzRrBl99BW+9pTuN45F23ULoZ/Wfr08//ZRHjx4RFBRE8+bNadCgAVmzZuXx48d8+umntsgoYmFPI0bguuuMKlRQL+Jd8XO3pSxZoF07kEaXjmPKFPWzIF0FXVe1amrd5QcfwOTJutMIIYT1rC6MRowYwYMHD2Icf/ToESNGjEiQUOLl7K0wctV1Rl5eULOmTKezhYEDYcECKTodRdKkar3R2LGwfbvuNEKXEiXgt9/URs1DhkjHQiGEY7G6MDIMA5PJFOP4oUOHSJ06dYKEEi9nb4WRq44YgawzspVMmaBTJ7VfjnAMQUHw7bfQsqXrbfos/i9PHjW1esUK1dY9MlJ3IiGEiJ94F0apUqUiderUmEwmcufOTerUqS1vfn5+1KxZkxYtWtgyq3iGFEb2o25dNbf+yRPdSZzPgAGweDGcPas7iYivJk2gSxdo2lR+JlxZpkywY4dq596qlWs25xFCOB6P+J44adIkDMOgc+fOjBgxAj8/P8vHvLy8yJYtG2XLlrVJSBGTvRVGWbKoRdeuKEMGdYV0+3aoXVt3GueSPr3qePXZZ/Ddd7rTiPj69FM4cADee0+1Xo9lkoFwAWnSwKZNqkiuXx9WrlQt3oUQwl7FuzDq0KEDANmzZ6dcuXJ4WttGTSQoeyuMXHnECP4/nU4Ko4T38ceQO7dacxQUpDuNiA93d7U+rGRJmD5dTacSrsnHB1atgvbtVXOGtWshbVrdqYQQInZWrzHKnj07V69e5eLFi7G+icRhj4XRpUtgNutOoke9emr/DllonPACAuDdd2HkSN1JhDVSpYKffoJBg2TjT1fn5aUK5VKl/t/JUwgh7FG8R4yiZMuWLdbmC1EiZZVlorC3wihVKkiSBK5ccc19Z0qXhrt34Z9/1OiGSFgffaT2yDp5EoKDdacR8VWggNr4s3lz+PNPyJhRdyKhi7s7TJ0KI0ZA+fKqrXfevLpTCSFEdFYXRgcOHIj2fnh4OAcOHODLL79klLSPSjQeHvZVGJlMaprTP/+4ZmHk7q6m0a1ZI4WRLaRNC++/r9auLFyoO42wRvPm8Ndfap3J9u3g7a07kdDFZILhw9XPc+XK8MsvUKaM7lRCCPF/Vk+lK1y4cLS3EiVK0LVrV8aPH89XX31li4wiFvY2YgT/L4xcVf360rbblj74QK1POH5cdxJhrc8+Az8/6NlTdxJhD3r0UBsC162riiMhhLAXVhdGccmTJw/79u1LqLsTL2GvhdHff+tOoU/t2motxf37upM4p9SpoVcvGDZMdxJhLXd3WLQINm+Gb77RnUbYg5Yt1Rq0zp1VkSSEEPbA6sLo3r170d5CQ0M5efIkQ4YMIUhaRiUaLy/7K4zy5IFTp3Sn0CdtWihWDNav153EefXrB1u3qvUqwrGkTq1GB4YOhY0bdacR9qByZbXX0aRJahQpIkJ3IiGEq7O6MEqZMiWpUqWyvKVOnZp8+fKxa9cupk2bZouMIhb2OGLk6oURwJtvuu5+TonBz0+17R4wQHcS8Sry54f586F1a9VIQ4i8eWH3brUR7BtvQGio7kRCCFdmdfOFrVu3Rnvfzc2NdOnSkStXLjw8rL478YrssTDKnRvOnYOwMDWi5YrefBNGj1ZfG9nqyzbefx8mT1brjerW1Z1GWKtuXTVq1KAB7NmjNgEVri1dOjXN8u23Vce61ashWzbdqYQQrsjqSqZy5cq2yCGs5OWlChB7kiIF+PvDmTOu24Y1d261787vv0PVqrrTOKckSWDMGNWMoUYNKUAdUa9ecOKE6lS3YYPrXkgR/+ftDd9/D6NGqU51K1dC2bK6UwkhXM0rNV84c+YMPXv2pEaNGtSoUYNevXpx5syZhM4mXsAeR4xAptOBTKdLDK1bq0J85kzdScSrMJnUgnt3d3jvPdkYWSgmEwwZAl99pTbNXrxYdyIhhKuxujBav349+fLlY+/evRQqVIhChQqxZ88e8ufPz0ZZUZto7LUwCg6WwiiqMJIXe7ZjMsHEiWpPlLt3dacRr8LTE5Yuhd9+U19LIaK0aAHr1kHfvmrvMvldKoRILFZPpRswYAB9+/ZlzJgxMY7379+fmjVrJlg4ETdPT/ubSgdqxOjwYd0p9CpdGh4/hiNHoFAh3WmcV9myULOm2iNnwgTdacSrSJ1arScpV05NQ23QQHciYS9Kl1ZNGRo0UBfbZs9W02iFEMKWrB4xOnHiBF26dIlxvHPnzhyXnRcTjT226waZSgfg5qa6K8l0OtsbMwa+/da1NxZ2dLlzw48/Qvv2clFFRJc1K/zxhxoVrl4dbtzQnUgI4eysLozSpUvHwYMHYxw/ePAg/v7+CZFJxIO9TqXLk0fa8IKsM0osWbJAz57w8ce6k4jXUa2aKnLfeAOuX9edRtiTFCnU/lfFi6tRJLn+KoSwJaun0nXt2pVu3bpx9uxZypUrB8Aff/zB2LFj6devX4IHFLGz18Ioa1Z4+BBu3VIbnrqq6tWhVSu4fBkyZdKdxrkNGABBQWrjV+kE6Li6dVOd6ho3hi1bZNqU+D8PD9WQIU8eqFgRFi2CWrV0pxJCOCOrR4yGDh3KJ598wpQpU6hcuTKVK1dm6tSpDB8+nCFDhtgio4iFva4xcneHXLlkOl3SpOoP9y+/6E7i/Hx8VIvffv0gMlJ3GvE6xo+HVKmgSxdZcC9iev99WLBAXXSS/eSFELZgdWFkMpno27cvly9fJjQ0lNDQUC5fvkzv3r0xmUy2yChiYa9rjEDWGUWR6XSJp0MH9e/cuVpjiNfk7q5GAw4fVk01hHhenTqqk+HYsdCnj1wMEUIkrFfaxyiKr68vvr6+CZVFWMFep9KBtOyO0qAB7Ngh7aQTg7u7avk8ZAjcv687jXgdKVLAqlXwzTdqw08hnlegAOzdq94aNICQEN2JhBDO4rUKI6GPvU6lAxkxipImjZoPv3Kl7iSuoUoVKFNGLeIXji1bNvj1VzUisGmT7jTCHvn7q7VoAQGqKYM0/RFCJAS7KIy+/vprsmXLRpIkSShdujR79+594flLly4lODiYJEmSULBgQdasWRPnue+++y4mk4lJkyYlcGq97HnESAqj/2vZUrUiFonjiy9g6lS4cEF3EvG6ihVT60latoRDh3SnEfYoSRKYMwe6d4fy5eEFLwWEECJetBdGS5YsoV+/fgwbNoy//vqLwoULU7t2bW7EsWHBzp07ad26NV26dOHAgQM0atSIRo0acfTo0Rjnrly5kt27d5MhQwZbfxqJzt7XGJ05AxERupPo16iRmg9/65buJK4hVy7o2lV1qhOOr25dtZakfn24dEl3GmGPTCY1srhkiVprOG6cNO4QQrw67YXRl19+SdeuXenUqRP58uVj+vTpJEuWjO+++y7W8ydPnkydOnX46KOPyJs3LyNHjqRYsWJMnTo12nlXrlyhZ8+eLFiwAE9PzxdmePr0Kffu3Yv2Zu/seSpdypSqs9TZs7qT6Jc6tWohvWKF7iSuY8gQ2LxZbQwpHN/bb0PnzjBjhu4kwp7VqAG7dsG8edCmjdo2QgghrGX1PkYAmzdvZvPmzdy4cQOz2RztY3EVNLEJCwtj//79DBw40HLMzc2NGjVqsGvXrlhvs2vXrhj7JdWuXZuffvrJ8r7ZbKZdu3Z89NFH5M+f/6U5Ro8ezYgRI+Kd2x7Y84gRQL58ak+S3Ll1J9GvTRuYOVPt0yJsL2VKtc7ozz/V9Brh+EaMkFEA8XK5csHu3dCpk1pvuHy5/A0SQljH6hGjESNGUKtWLTZv3sytW7e4e/dutDdr3Lp1i8jISAICAqIdDwgI4Nq1a7He5tq1ay89f+zYsXh4eNCrV6945Rg4cKCl9XhoaCiXHGDOhj2PGIEqjGSHcuXNN+Gvv+DiRd1JXEfnztC7t+4UIqGYTOCmfX6DcAS+vrB0qZpWV7YsPHPNVAghXsrqEaPp06czd+5c2rVrZ4s8r23//v1MnjyZv/76K977Knl7e+Pt7W3jZAnLEUaMdu/WncI++PhAw4Zqf5b+/XWnEcI5mc1mnj59yuPHj0mdOrXuOEIjkwk+/BCKF4fWrWHPHhg5EjxeaY6MEMKVWH0NLiwsjHLlyiXIg6dNmxZ3d3euX78e7fj169cJDAyM9TaBgYEvPH/Hjh3cuHGDLFmy4OHhgYeHBxcuXOCDDz4gW7ZsCZLbHnh5yYiRI3nrLZg/X3cKIZzTrVu3aN26NXXq1KFhw4b07dtXdyRhB6pWhf37Yft2tTHszZu6Ewkh7J3VhdHbb7/NwoULE+TBvby8KF68OJs3b7YcM5vNbN68mbJly8Z6m7Jly0Y7H2Djxo2W89u1a8fhw4c5ePCg5S1Dhgx89NFHrF+/PkFy2wNHmEp34gQ8twTNZdWsCdevw+HDupMI4Vz2799Pvnz5ePz4MR07dmTixImsXr3a4daNCtvImBG2bVMbjxcrpjaFFUKIuMRrYPnZZgdms5mZM2eyadMmChUqFKPj25dffmlVgH79+tGhQwdKlChBqVKlmDRpEg8fPqRTp04AtG/fnowZMzJ69GgAevfuTeXKlZkwYQL169dn8eLF/Pnnn8ycOROANGnSkCZNmmiP4enpSWBgIHny5LEqmz2z96l0/v6QNKnaTyZ7dt1p9PPwgFat1KjRuHG604iQkBCWLVtGQEAAGTNmpFixYrojiVdw8uRJqlevTosWLZg2bRru7u4ADB8+nGXLlhEeHo6Hh0e8p1UL5+TlpfY3K10aatdWzVm6dVNT7oQQ4lnxKowOHDgQ7f0iRYoAxLp3kLVatmzJzZs3+eSTT7h27RpFihRh3bp1lgYLFy9exO2ZVbflypVj4cKFDBkyhEGDBhEUFMRPP/1EgQIFXjuLI7H3qXQm0/+n00lhpLRtC82aqT/KspBcD7PZzKeffsq4ceMoVqwYjx8/5tGjR4wYMYIWLVrojiescPfuXZo1a0bjxo0tF8ZAfY0PHz5MypQpX7pVg3At7dpB4cLQpIlaA/vNN+oCnhBCRDEZhjRBfd69e/fw8/MjNDSUFClS2OxxwsP/X+BY+/f73DkoVcq+50y/845qn/rRR7qT2AfDgKAg+PZbqFJFdxrXc+HCBd58801u3LjBl19+Sb169UiWLBmbN2+mc+fOXLp0KdpFGGHffvvtN/r27cvixYsJCgoiMjISd3d3jh49SoMGDejdu7esNRKxCgmB9u3VpsHLl0OOHLoTKYn12kMIETerXwV07tyZ+/fvxzj+8OFDOnfunCChxMvZ+1Q6kAYMzzOZpAmDLmfPnqVZs2YEBwdz8uRJWrVqRYoUKfDw8CAoKIjAwEBOnjypO6awQmhoKMmSJSMoKAgAd3d3/v77b+rUqUPp0qWjFUVR1//kOqAAtdfZTz+pEfxSpeDXX3UnEkLYC6sLo3nz5vH48eMYxx8/fsz333+fIKHEy9n7VDqQwig2bdvCsmXw5InuJK7l+PHjRERE8Pnnn5MiRQoiIiIsH1u0aBEZMmQgX758GhMKawUFBXH8+HFWrVrFvn37WLx4MWXLlqV8+fIsWbIEgMjISADLGqMLFy5oyyvsi5sbDB4MCxeqfc8GDLD/i41CCNuLd2F07949QkNDMQyD+/fvc+/ePcvb3bt3WbNmDf7+/rbMKp5h713p4P+FkVyk/b+gIMiTR65QJrZ9+/ZRtGhRcvw3Z8bjvw1Nhg4dyrhx42jUqBEgIwqOJDg4mHnz5jF48GC6du3K1KlTef/99y1FEWBpxmAYBiEhIeTNm5cZM2boiizsUK1aagPunTtVe28H2N9dCGFD8d7uLGXKlJhMJkwmE7lz547xcZPJJO1RE5GXF0RGqnbY9rosIkMGle3yZcicWXca+9G2LSxYAE2b6k7iOmrXrk3NmjXp27cvYWFh3Lx5k48//phr166xaNEi6tWrByDdyxxMgwYNKFasmKXQjbo4Zzabo60XM5lMpEyZkg0bNtC4cWNSpEhB69attWQW9idjRtiyBYYNU5vCzp0L//1KEEK4mHg3X9i+fTuGYVCtWjWWL18ebWdxLy8vsmbNSoYMGWwWNDE5QvOFiAh1mydPwNvbNvkSQtmyMHy4apEqlOvX1WLfy5chVSrdaVzH0KFD2bhxI2FhYVy7do2GDRsyevRoUqVKZRkpksLIcZ07dw6TyRTnRt4RERF4eHjwxx9/UK9evWgFsRBR1q1TjRk6d4aRI63/2/w6pPmCEPpZ3ZXuwoULZMmSxalfQDhCYWQYajTm3j3w9bVNvoTQpQsUKADSHCq6unVVy9iuXXUncS03btzg5s2b+Pn5kSlTJkBNszKZTJauZsIx/f7770yYMIFvv/02xl52USNIUcVRs2bNWLlyJbt27aJUqVKaEgt7dfkytG6t/s4uXgz//aqwOSmMhNAvXlPpDh8+TIECBXBzcyM0NJQjR47EeW6hQoUSLJyIm8kkDRgcWdu2qm23FEaJy9/fn+TJk5M8eXIASzFkGIalKLp58yY+Pj4klQ1OHEqFChVImzatpSh6djqdm5sbhw8fZuXKlSxevJhkyZLRqlUrac8uYpUpE2zdCp98AsWKwbx56mKWEML5xWvEyM3NjWvXruHv74+bmxsmkynWRcpRV10dnSOMGIEaKfrnHwgMTPhsCWXtWvjsM/jjD91J7MuDB5A+PRw7Blmy6E7jWmbMmMGtW7cYPHiwZQQBYMeOHXz44YekTJmSGzdu0KRJE7p06eI0U4Rd1Q8//MCePXuYPXs2tWrVIm/evAwdOhR3d3eSJEnCvXv3ePz4sWVTcSGeFTW1rksXNbXOI94rs60nI0ZC6BevH/Fz586RLl06y/+FffD0hKdPdad4sXz51It/w1CjXELx8YGGDVWr2AEDdKdxLR07dmT+/PnRiqKePXvy9ddfU6FCBfr27Yu7uzsrV66kWbNm7Ny5U3NiYa1Hjx4xefJkli1bxr179yhSpAhLliyhZs2a0UYC79y5wyeffMK2bdtYuHChzHgQMdSpo7rWtW6tutYtWCAXs4RwZlavMXIFjjJiFBgIv/8OuXIlfLaEYjaDnx+cOJF487QdxYYN0KuXem6kaNTj1q1btG3blmPHjpEjRw5y587Nv//+y5o1awDInj07n3zyCZ06ddKcVFhrwYIFbNq0iVGjRuHr64uvr69lPRmor/2oUaO4efMmt2/fZu/evfzxxx8EBwdrTi7sUUSEaiQ0bZp6a9Ei4R9DRoyE0M/qCdZZsmShffv2zJ49mzNnztgik4gnb2/7X2Pk5gYFC8ILlqW5rOrV4fFjtX+G0OPIkSPcvHmTZcuWsWbNGr799lvu37/PmDFjAJg2bRpPnz6V/Y0cUNu2bZkzZw4ZMmTA978ONVFFUVhYGCtWrGDZsmWUL1+etWvXMmTIEIoVK8b58+c1phb2ysNDTQtfsQI++EBNrXvwQHcqIURCs7ow+vzzz0mSJAljx44lKCiIzJkz89ZbbzFr1iz++ecfW2QUcXCE5gsghVFc3N2hY0eYPVt3Ete1c+dOChUqRJkyZfD+r+99pUqVuHbtGgAlSpSgXbt2Tt2F09nFVtR6enrSrFkzatWqxez/fgD79u3L0KFDZeqkeKHKleHQIQgJUY0Z9u/XnUgIkZCsLozeeustZs6cyd9//82VK1f44osvAHjvvfdkCkIic6TC6PBh3SnsU6dOsGwZ3L+vO4lrCggIYP9/r2w8PT0JCwtj/fr1BAUFAZA2bVpLBzvhmJ4tam/cuMH9+/cxmUykTp2a2bNnkz17dgYOHAjAwIEDaWGLOVLCqaROrX5vf/wx1KgBX3yhpo0LIRzfK/UqffToERs2bGDKlCmWBa4FChSgV69eCZ1PvIAjFUYyYhS7bNmgdGn48UfdSVzT22+/Tbp06ahXrx6dO3cmY8aMeHh40KpVKwBCQ0P54osvGD16ND///DMRERGaE4tXdeXKFdq2bcvly5cBNZ0OIDg4mH///ddynoct244Jp2Eywdtvw65dsGgR/PCD7kRCiIRgdWFUrlw50qRJw4ABA3jy5AkDBgzg6tWrHDhwgIkTJ9oio4iDIxVGJ0+qZhMips6dZTqdTosXL6ZDhw54eXkxdOhQdu/eTapUqRgzZgyZM2dmzpw5/Pnnn4waNYoOHTrojiteUcaMGfH396frf5uHeXl5ce/ePa5fv07KlCmB2KfdCfEiwcGqOGrbVncSIURCsPrS2MmTJ0mePDnBwcEEBweTN29eUqVKZYts4iW8vOy/XTeoaQfp0sHff0P+/LrT2J/GjVUL2EePIFky3Wlcj7+/Py1btqRly5YAPH78mDp16nDs2DEmTpxIly5dLN3M0qVLx+7duylTpozm1OJVLFiwgPr161O9enUKFizI/fv32b17N+PGjQOQtWTilfy3PFEI4QSsHjG6ffs2W7ZsoUyZMqxfv57y5cuTMWNG2rRpw6xZs2yRUcTBUUaMQNYZvUiSJLB6tRRFupnNZm7dukXdunWJiIjg9OnTdOnSBVAvmB8/fkxQUBAhISF6g4rX8uuvv1K/fn3Cw8NJlSoVEyZMoH79+rpjCSGEsANWjxiZTCYKFSpEoUKF6NmzJ/v372fq1KksWLCAJUuWWKYpCNtzhHbdUQoVUuuMWrfWnUSI2Lm5uRESEsLDhw/59ttvSZkyJZGRkbi7u/PkyRPmz5+PYRgUK1ZMd1Txmvr16wcQbV8jIYQQwurC6K+//mLbtm1s27aN33//nfv371OwYEF69uxJ5cqVbZFRxMHRRoyWLNGdQogXO3LkCJGRkRQuXBgAd3d37t+/z7fffsvUqVN5++23SZMmjbygdmKRkXDnjpr+K4QQwrVYXRiVKlWKokWLUrlyZbp27UqlSpXw8/OzRTbxEo5WGA0ZojuFEC/WuHFjunXrxtSpU8mUKRNhYWFMnz6dPXv2MHnyZN5++23dEUUCiq24/ekn6N8ftmyBLFkSP5MQQgh9rC6M7ty5Q4oUKWyRRVjJkQqj4GC4cgVCQ0HqaOvcvHmTR48ekTVrVt1RXMKKFSuYOHEif//9N/7+/mTOnJkVK1ZE61wmo0XOq0kTNe23cmXYulW11BdCCOEarC6MpCiyH45UGHl7Q548cPQolC+vO439M5vNzJ49m+nTp3Pt2jUyZ85M0aJF6du3L7lz59Ydz6lVrFiREiVKEBYWxoMHD8iYMSOAZb2RFEXOzWSC4cPBw+P/xVGOHLpTCSGESAyvtMGrsA+O0q47imz0+mJRe6jMmTOH9OnTM3jwYGrUqMHPP/9Mnz59CAsLo3v37ppTuoakSZPi5+dnKYoMw8Dd3V1zKpGYhgyB995TxdGpU7rTCCGESAyyxbcDc6QRI5DC6GVMJhPbtm3jq6++on///vTo0QMvLy8ASpQoQatWrQgICODWrVukTZtWc1rXIqNErql/f0iaVBVHa9dC0aK6EwkhhLAlGTFyYI5YGMleRnF7+vQp8+fPp1ixYrzzzjt4eXkREREBqKl106ZNIygoiCdPnmhO6pq2b4e5c3WnEImtVy8YOxZq1IDff9edRgghhC1ZVRiFh4eTM2dOTpw4Yas8wgre3o41lS5qL6P/ZoyJ53h7e3Pw4EFatGhB8uTJAfDwUIO6a9euZf78+TRt2pRMmTLpjOmyUqWCPn3g7791JxGJrUMHmDUL3nhDjRwJIYRwTlYVRp6ennK12o442ohRlixqYfOFC7qT2K/GjRszfPhwlixZwqFDh5g5cyZFixalUaNGlCxZks6dO+uO6LIKFYKBA6FNG8f6uRMJo0kTWLYM3npLRg6FEMJZWb3G6P3332fs2LF8++23lqvZQg9HK4xMJihSBA4ckBa4cRkwYAD3799n6tSp3Lx5k/v379O2bVt+/fVXMmTIoDuey/voI9iwQS3MHzdOdxqR2KpXh82boV49uHwZBg9Wv9eEEEI4B6srm3379rF582Y2bNhAwYIFLVN+oqxYsSLBwokXc7SpdKAWLx84AI0b605in9zd3RkzZgwPHjzg8uXLBAcHWz5mNpsxmUzSCEAjNzf4/ntV4NeqpdadCNdSpAjs3Al16qjiaOpU1dpbCCGE47P613nKlClp2rSpLbIIK3l7O9aIEajC6Mcfdaewf7dv37YURREREXh4eODmJr1S7EHGjPDtt9C+PRw6BOnS6U4kElu2bPDHH9CwITRtCosWQbJkulMJIYR4XVYXRnPmzLFFDvEKHG0fI1CF0cCBulPYv2+++YbSpUvTpEkTPDw8MAwDk8lk2WRU6PXmm7B+PXTuDL/8ItOpXFGaNLBpk1pzVr06rFoF0kVfCCEc2ytdgo6IiGDTpk3MmDGD+/fvA/Dvv//y4MGDBA0nXswRp9IFB8Pt23Dzpu4k9u2tt94iRYoUADx48ACTyUR4eHisRZEhbf60mDABzp6FKVN0JxG6JE2qGjIUKwbly8O5c7oTCSGEeB1WjxhduHCBOnXqcPHiRZ4+fUrNmjXx9fVl7NixPH36lOnTp9sip4iFozVfADUXv2BBtc6oVi3daexXwYIFKViwIADLli1jypQpZMmShbx58+Lp6cmbb74JQLFixWTNkSZJk6ppoRUqQOnS6k24Hnd3tc5ozBgoVw5+/VUVSkIIIRyP1SNGvXv3pkSJEty9e5ekSZNajjdu3JjNmzcnaDjxYo44YgT/b8AgXixqJCh58uQcPHiQCRMmkCJFCnx9fenTpw9jxowhf/78LFy4UHNS15U/P3z1FTRvrkZChWsymdQU4aiNYDds0J1ICCHEq7C6MNqxYwdDhgzBy8sr2vFs2bJx5cqVBAsmXs4Rmy+AFEbxFTUS9MYbb5A9e3auXLnCgAED6NmzJxMmTMDX15cTJ06wZs0azUldW7t2qn1zu3ZgNutOI3Rq3x4WL4ZWrVT3QiGEEI7F6sLIbDYTGRkZ4/jly5fx9fVNkFAifhyx+QJIYWStJEmS0KpVKwYPHsy6deuoX78+tWvX5p9//mHDhg3Mnz9fd0SXN2kSXL8Oo0frTiJ0q1VLNWXo3199P8gSQCGEcBxWF0a1atVi0qRJlvdNJhMPHjxg2LBh1KtXLyGziZdw1Kl0BQuqRevSqyN+Tpw4QUREBH/++Sdt2rQhb9687Ny5k99++40aspGOXUiSRC3CnzhRbQAqXFuxYmqvo7lzoUcPiOVaohBCCDtkdWE0YcIE/vjjD/Lly8eTJ09o06aNZRrd2LFjbZFRxMERmy+A2u8jd261B4x4uREjRjBnzhyyZMlC06ZNmTJlSrSNXwH279/P48ePNSUUANmzw3ffQdu2ILOKRfbsaq+jv/5Sa9Dkx1MIIeyf1YVRpkyZOHToEIMGDaJv374ULVqUMWPGcODAAfz9/W2RUcTBUUeMQKbTWWPq1Klcv36d3bt306xZMyIiIgAIDw9n0aJFtG7dmjp16jBu3DjNSUXDhtCxo1pjEh6uO43QLW1aNYJoNkPVqmq6pRBCCPtlMmQTlBju3buHn58foaGhlr1kbCE8/P+jPp6e1t/+xAmoXRsuXkz4bLY2YQIcPw6zZ+tO4jjMZjOGYeDu7s79+/eZPn0648ePp0ePHgBMmjSJ29IaTbuICLXhZ6lS8MUXutMIexAZqdYcLV0Kq1er6cRCPC+xXnsIIeIWr32Mfvnll3jfYcOGDV85jLCOozZfADVitGCB7hSOIzw8nE2bNpErVy6CgoLw9fWlbdu2jB8/nnr16lG8eHFWr17N7Nmz6dKli+64Ls3DQ3UmK1ZM7WvTuLHuREI3d3cYP15NIa5SRf3uq1NHdyohhBDPi1dh1KhRo2jvm0wmnh9oimotHFvHOmEbjj6VrlQp3Skch2EYvPfee3z//fcEBQXx+PFjMmTIQI0aNfjxxx8pXrw43bp1Y8iQIVIY2YH06WHhQmjWDAoVgpw5dScS9qBbN7X2qFUrGDFCNWYQQghhP+K1xshsNlveNmzYQJEiRVi7di0hISGEhISwdu1aihUrxrp162ydVzzDUZsvAKRKBdOn607hOLy8vChbtizLly8HsGyunD9/fsvFiC5duvDNN99gls107ELVqvDhh6o4koX3IkrNmvD77/Dll9Czp5p6KYQQwj5YvcaoQIECTJ8+nQoVKkQ7vmPHDrp168aJEycSNKAOjrLGKDQUUqeWVrCu4tixY9SsWZONGzfi6+vLsWPHaN++PVOnTqVly5a644lYmM1qKp2PD8yfD/8NrAvBzZvQpIn6G7BkiWrUIFybrDESQj+ru9KdOXOGlClTxjju5+fH+fPnEyCSiC9vb/XCS644uob8+fPz7rvv0rp1a5o0acLUqVNp27Yt9evX1x1NxMHNDX74AQ4eVGtMhIiSLp3qWJcnD5QsKdsXCCGEPbB6xKhSpUokSZKEH374gYCAAACuX79O+/btefLkCdu3b7dJ0MTkKCNGhqFeeN2/r65IC+cXERHB33//zcmTJwkPD6d+/fr4yBff7p0+DWXKqCKpbl3daYS9mTULPv4YZsyAFi10pxG6yIiREPpZXRidPn2axo0b8/fff5M5c2YALl26RFBQED/99BO5cuWySdDE5CiFEcC//0JgoCqQhBD2a8MGaN0adu5UowRCPOuPP9R6tE6dYORI1clOuBYpjITQ75X2MTIMg40bN3Ly5EkA8ubNS40aNSyd6RydIxVGQgjHMXGiajqyZw/EMiNZuLgrV9SatHTpVEtv+R5xLVIYCaGfbPAaCymMEl9ISAibNm1i165dnD9/nuLFi9O5c2cCAwN1RxMiwRiGGhG4cQNWrZJRARHTkyfw7ruwaxf89BPkzas7kUgsUhgJod8rFUabN29m8+bN3LhxI0Zr4O+++y7BwukihVHiOX78OIMHD2b16tWkTJmSQoUKUahQIf766y8iIyP5/PPPqVSpku6YQiSYJ0/UJp+VK8PYsbrTCHtkGDBlitrraO5ceOMN3YlEYpDCSAj94rXB67NGjBjBp59+SokSJUifPr3TTJ8Tie/Ro0eMHz8es9nMzp07KVmypOVjYWFhDBs2jIkTJ0ph9BJTp6o9c/Ln151ExEeSJLBihepEVrgwtGmjO5GwNyYT9OoFBQtCy5Zqv6PBg2UtqRBC2JrVhdH06dOZO3cu7dq1s0Ue4UJ+//13Dh8+zJIlS8iZMyeGYWA2m3F3d7dsaLpt2zbdMe3etWvw+edqTYJwDBkywMqVULs25M4NJUroTiTsUdWqsHcvNGqkWr7PnQu+vppDCSGEE7P6+lNYWBjlypWzRRbhYgICArh06RI5c+YEwGQy4f7foos9e/YwbNgwmjVrpjOiQ+jTB1avVi2hheMoVQomT1aL7a9d051G2Kts2VTHOk9PKF0anGAPdSGEsFtWrzHq378/Pj4+DB061FaZtHOkNUZPnjzh8uXLnDx5kitXrhAeHo6Pjw9FixalcOHCCRvYBkqWLElQUBBt2rQhZcqUHD58mF9//ZWjR49Ss2ZNvv76a7y9vXXHtHsffAD37qn9UIRj+eAD1cJ7yxZImlR3GmGvDAO++kqtO/rmG2jVSncikdBkjZEQ+lldGPXu3Zvvv//eskje87lX9F9++WWCBtTBUQqj8PBwBg8ezNKlS3n06BEpU6bE09OTpEmT4unpyYABA3jjjTfseh3YgQMHmDdvHlu2bOHMmTNkypSJChUq0KpVK6pXr46bmxuGYdj152AP/v0XgoPh2DH4b3sx4SAiI9Wokbc3LFki60jEi+3cqTaBbdwYJkxQf0OEc5DCSAj9rC6MqlatGvedmUxs2bLltUPp5iiFUf/+/dm+fTsjRoygWrVqliL1xo0bzJ07l++//55NmzbZbcvrZwue69evExAQoDmRY+veXX0/TZ6sO4mw1sOHqktd9erSqU683I0bqmnHgwewdKlcDHEWUhgJoZ/sYxQLRymMcuTIwY8//kiJ/1ZuR0ZG4ubmZik2MmfOzE8//UTx4sUTMnaCCgkJ4ffff6dBgwaYzWZMJhMmkyla0SQjRvFz7pzqcnb6NPj7604jrHX1KpQpA4MGwTvv6E4j7F1kJAwfDjNmwPz5UKuW7kTidUlhJIR+MmnDgaVOnZojR44QHh4OgLu7OyaTibCwMH7++WdSpkxp9+tzvL29mTFjBkC0ou7ZQkiKovjJnl1Nr5k4UXcS8SrSp4dff1WF0bp1utMIe+fuDiNHqk51bdvCp5/Cc9sKCiGEsNIrTaV70QtVmUoXf687YrRo0SLGjRtH2bJlKVmyJG5ubty4cYNz586xe/duOnbsyLvvvouXnU9C3759O2XLlo2W89lRoosXL+Lm5kamTJl0RXQYJ0+qUYdz5yBVKt1pxKvYtEmtIdm6VY0ACvEy589Ds2aQNq0aPUqbVnci8SpkxEgI/awujPr27Rvt/fDwcA4ePMjRo0fp0KEDk51ggYMjFEZRhcPOnTsZP348hw4dAiBFihTkzJmTJk2a0LJlS0v7a3v1bAEUGRkZI29kZCRfffUVly5dcorGHomheXO1MeQnn+hOIl7Vd9+pr9/u3SDXA0R8PHkCffvCmjXw44+qtbdwLFIYCaFfgq0xGj58OA8ePGD8+PEJcXdaOUJhpG4fjru7O24O3sbqzJkzTJs2jZYtW1KyZElArT3aunUrjRs3Zv/+/TRt2pTz58/rDeogDh2CatXgzBlImVJ3GvGqhg6FFStgxw5InVp3GuEo5s+Hnj3VlMwPPpAuh45ECiMh9Euwwuj06dOUKlWKO3fuJMTdaeUohdH06dNJlSoVLVu2JDw8HA8PDwBLEwNHKZjOnTtHxYoVOXfuHB4eHphMJiIiIsiVK5elGEqdOjVnz54lpbzSj5cWLVT77k8/1Z1EvCrDUJ0GDx1S0+uSJ9edSDiKU6fUPkcBATBvnvpX2D8pjITQL8FeOe/atYskSZIk1N2JeEibNq2luUJUQWEymSyjSI7ScDB79uw8evSIu3fvWqbVeXh4cO/ePVavXs3du3fJkycPZ86c0ZzUcYwYoTaDvHlTdxLxqkwm+PprNZWuaVN1AUWI+MiTR03DzJNHrVPbsEF3IiGEcAxWjxg1adIk2vuGYXD16lX+/PNPhg4dyrBhwxI0oA6OMmLkTCpXrkyrVq3o2rUrAPv376ddu3a4u7vj4+NDhQoV+PTTT/H19dWc1HF06ADp0oETzG51aU+fQoMGakH9ggUyNUpYZ9Uq6NxZvX32mfytsWcyYiSEflYXRp06dYr2vpubG+nSpaNatWrUcpKNFBylMDIMg/Dw8Fi7uT148IDTp0+TJk0aMjvA7n9Lly7liy++IFOmTBQsWBCz2Uz9+vU5cuQIR48e5e2336ZgwYK6YzqUs2ehSBHVqS5DBt1pxOt48ECtGytVCqZMUaNJQsTXlSuqpffjx7BoEeTIoTuRiI0URkLoJxu8xsJRCqM///yT7du388EHH8T42KNHjxg/fjxJkiTh448/ToC0thUZGcnevXv54osvuH37Nt26daNt27YxzrH3Lnv25t131X4nX3+tO4l4XbduQcWK0LKl2thTCGtERsLnn6t9zr78Uo0oS4FtX6QwEkK/Vy6M9u/fz4kTJwDInz8/RYsWTdBgOjlKYbRy5Uo++OADzp49y40bNzCbzTx9+pSIiAgePHjA1KlTOXPmjMPuLRUaGkpISAhPnz4lJCSEEiVKOExDCXtx+TLkyweHD0O2bLrTiNd16RKULw8ffaQ6jwlhrT174K23VEv/GTPUdFthH6QwEkI/D2tvcOPGDVq1asW2bdssHcJCQkKoWrUqixcvJp38lk00gYGBnD9/nv79+3P79m3Cw8MtbwBnz54llQPt8mk2m3Fzc2P//v1MmzaN3bt3c/bsWZ4+fUrOnDlp0KAB3bp1Izg4WHdUh5EpE3TporrTffed7jTidWXOrBbSV6oEadJAmza6EwlHU7o0HDwIH34IhQrB7NlQr57uVEIIYR+svvzes2dP7t+/z7Fjx7hz5w537tzh6NGj3Lt3j169etkio4hDVGEaFhZGsmTJyJAhA3nz5qVMmTJUr16d3r17O0wzDMMwcHNzY8WKFTRq1IizZ8/So0cPDh48SHh4OLNnz+by5csMlzlEVhswAJYvVy18heMLDoZff4UePWDtWt1phCNKnhymTYNvv1UXTrp3h4cPdacSQgj9rJ5K5+fnx6ZNmywbcUbZu3cvtWrVIiQkJCHzaeEoU+nOnz9PwYIFuX//fsKH0+DChQt06tSJWrVqMWDAgBgfv3z5MkWKFOHWrVsa0jm2IUPg+HG1YahwDps3Q/PmsHo1lCunO41wVDdvQrducOwY/PCDGlESeshUOiH0s3rEyGw24xnLq3hPT0/MZnOChBLxkyZNGur9NwciMjISs9lMZGQk4eHhhIWFERYWZplW5wg8PDw4cuRIrEVRaGgo3377LeXKlePJkyca0jm2/v1h507Yvl13EpFQqleHWbOgYUM4ckR3GuGo0qVTF0wGDYK6dVVjDwf6syGEEAnK6sKoWrVq9O7dm3///ddy7MqVK/Tt25fq1asnaDjxYr6+vixZsgTAsqmru7s7np6eeHl54eXlFWsRa68yZsyIu7s7K1eu5Pz589y9e5c7d+6wc+dOJkyYwC+//ELPnj1lI+FX4Our9jDp1w/k+oXzaNoURo+GOnXg3DndaYSjMpmgY0fYv1+NRJYvL1NvhRCuyeqpdJcuXaJhw4YcO3bMsj/OpUuXKFCgAL/88guZMmWySdDE5ChT6QBOnz5tWV8EcPfuXY4ePcrNmzd58OABZrOZDh06YHKQvqwLFixg/PjxpEqVCi8vLw4fPszdu3cpWrQoH3zwAU2bNtUd0WFFRkLRomrRdfv2utOIhDRmjFov8ttvsmeVeD2RkWpT6DFjVHvvd9+Vtt6JRabSCaHfK7XrNgyDTZs2cfLkSQDy5s1LjRo1EjycLo5UGBUrVozBgwdbCoYTJ07QqFEjAgICSJkyJZs2beLatWsO9Uv22rVrrFu3jqtXr1KkSBEqVKiAr6+v7lhOYdMmdWX41Cm1AFs4B8OAwYPVlKjt2yEgQHci4egOHlRtvbNkUZ3r0qfXncj5SWEkhH7xKoxSp07N33//Tdq0aencuTOTJ0926heqjlQYBQQEsG/fPrJkyUJERAQeHh5kzZqVyZMnU6ZMGYKCgjh+/LhldM9RRX2bOsrIlz1r0ABKlYJPPtGdRCQkw1D7G61bB1u3yv404vU9eaLWHv3wg9rzqEkT3YmcmxRGQugXrzVGYWFh3Lt3D4B58+bJ4vcE4uYGw4apf19V0qRJLZ0APTzUtlSenp7kz5+fwMBAAgICCA0NTYC0ic8wjGgFkRRFCWP8eLXz/TPLBIUTMJngiy+gWjWoUQNu39adSDi6JEnU74rFi6F3bzXa7KB/ToQQIl7itcFr2bJladSoEcWLF8cwDHr16kXSpEljPfc72UUy3tzdVQeg11GqVCl+/PFH/Pz8SJUqFT/88APZs2e3dAi8e/cuN27ceP2wGkghZBvBwdCunWrhLT+uzsVkgsmT1b40NWuqhfQOtMezsFPVq6vOhz16QOHCMG8eVK6sO5UQQiS8eBVG8+fPZ+LEiZw5cwaTyURoaKiMGtmJoUOH0q5dO/bu3UvOnDnZuHEjgwcPJmfOnJaPO/o0OpHwhg2DPHlgzx7Zt8TZmEzwzTdqb5rq1WHDBkibVncq4ehSpoT582HJEtUNsXlz1aDBz093MiGESDhWN1/Inj07f/75J2nSpEmwEF9//TVffPEF165do3DhwkyZMoVSpUrFef7SpUsZOnQo58+fJygoiLFjx1r28wEYPnw4ixcv5tKlS3h5eVG8eHFGjRpF6Xi+AnS0eb4XL15k6dKlXL58mQYNGljaphuG4dCjLr//Do8fqyvfIuFNnw4zZ8K+fWr0UjgXs1ld4f/tN9V0IzBQdyLhLG7cgD59VKOPqVOhcWPdiZyDo732EMIZWb265dy5cwlaFC1ZsoR+/foxbNgw/vrrLwoXLkzt2rXjnP61c+dOWrduTZcuXThw4ACNGjWiUaNGHD161HJO7ty5mTp1KkeOHOH3338nW7Zs1KpVi5s3byZYbnuSJUsWPvjgAyZOnBhtLylHLooA/vkHJk3SncJ5de0KHh4wbZruJMIW3Nzg66+hdm2oVAkuXdKdSDgLf39YuFC1iO/TRzVluHJFdyohhHh9r9Sue/PmzWzevJkbN25Y1rJEsXaNUenSpSlZsiRTp04FwGw2kzlzZnr27MmAAQNinN+yZUsePnzI6tWrLcfKlClDkSJFmD59eqyPEXUVZtOmTfHahFau2tiHq1chVy61iFz2dLWNP/+EWrXg+HEZUXBWhqE6EM6fD1u2QPbsuhMJZ/LgAQwdqtYdjRoF77zzeg2FXJm89hBCP6t/fY0YMYJatWqxefNmbt26xd27d6O9WSMsLIz9+/dH2wPJzc2NGjVqsGvXrlhvs2vXrhh7JtWuXTvO88PCwpg5cyZ+fn4ULlw41nOePn3KvXv3or0J/dKnh6AgNRVI2EaJEtC6tWrzLJyTyQQjR6oRwkqV4O+/dScSzsTHByZOhPXrVUvvSpXUhRYhhHBE8Wq+8Kzp06czd+5c2rVr99oPfuvWLSIjIwl4bjfCgIAAy+axz7t27Vqs51+7di3asdWrV9OqVSsePXpE+vTp2bhxI2njWIE8evRoRowY8RqfyesJCQnhwYMH+Pj4kDJlSm057FHdumpfllq1dCdxXp99pjrVbdsGVaroTiNsZdAgSJZMdRPbuBEKFNCdSDiTkiXVesUvv4Ry5dQUu4EDwdtbdzIhhIg/q0eMwsLCKFeunC2yJKiqVaty8OBBdu7cSZ06dWjRokWc65YGDhxIaGio5e1SIkzGj4iIYNiwYWTJkoXUqVOTNWtWUqdOTZYsWRg2bBgRERFW3d/162qxtbOpUwfWrtWdwrmlSqX2v3nvPbXZsHBeffrAiBFqr6O//tKdRjgbT0/o319N0f39dyhaVP0rhBCOwurC6O2332bhwoUJ8uBp06bF3d2d69evRzt+/fp1AuNY8BAYGBiv85MnT06uXLkoU6YMs2fPxsPDg9mzZ8d6n97e3qRIkSLam6317duXX3/9lcmTJ3PmzBlu3rzJ6dOnmTx5MqtWraJPnz5W3V9QkFqT42zKlVOLes+f153EubVrp1o6T5yoO4mwtW7d1Ca/tWrB7t260whnlCuXGpX8+GNo1EjtqyUbwwohHIHVU+mePHnCzJkz2bRpE4UKFcLT0zPax7/88st431dUK+3NmzfTqFEjQDVf2Lx5Mz169Ij1NmXLlmXz5s3RCoeNGzdStmzZFz6W2Wzm6dOn8c5mawsWLODEiRPRpgWmTp2aHDlyUL58eYKDgy0NKeIjXTq4eRMyZrRFWn08PaFGDTV//Z13dKdxXlF731SooNYcZcmiO5GwpfbtIWlSqFcPVq6UzTpFwjOZoGNH9T3Wty/kzataezdpojuZEELEzerC6PDhwxQpUgQgWotseLX20P369aNDhw6UKFGCUqVKMWnSJB4+fEinTp0AaN++PRkzZmT06NEA9O7dm8qVKzNhwgTq16/P4sWL+fPPP5k5cyYADx8+ZNSoUTRs2JD06dNz69Ytvv76a65cuULz5s2tzmcrJpOJuBoCGoaBm5VtfaIKI2dUpw6sWSOFka0VKKBGE/r0gRUrdKcRtta8uVr/0aiR2rRT1vEJW/D3hwUL1JTo7t3hhx9UgeRsF/GEEM7B6sJo69atCRqgZcuW3Lx5k08++YRr165RpEgR1q1bZxlJuXjxYrQioVy5cixcuJAhQ4YwaNAggoKC+Omnnyjw30pid3d3Tp48ybx587h16xZp0qShZMmS7Nixg/z58ydo9tfRrl076tevz8CBAylatCh+fn6EhIRw8OBBRo0aRZs2bay6v3Tp4NYtG4XVrE4d+OADtf7Fy0t3Guf2ySfqyu6vv0L9+rrTCFtr2FAVRS1bwvffwxtv6E4knFXdunD0qPodU7CgtPYWQtinV9rHKMrly5cByJQpU4IFsgeJsZeA2Wxm9OjRzJo1K1qzhyxZstClSxcGDRpk1ahR585QpAj06mWDsHYgf361WaV0TbO95ctV++5jx9R0K+H8tm+Hxo1Vu2U7GlgXTmrfPjU67eWlpvAWL647kX2QfYyE0M/qazVms5lPP/0UPz8/smbNStasWUmZMiUjR46MsdmriJubmxuDBw/m/PnzhISEcOnSJUJCQjh37hxDhgyxeipd2rTOO5UOpDtdYmrSBPLkgf9mrwoXULmymq7avTt8+63uNMLZRbX2btsWataEHj0gJER3KiGEeIXCaPDgwUydOpUxY8Zw4MABDhw4wOeff86UKVMYOnSoLTI6pe7du1v+7+vrS4YMGfD19X3l+3PmNUbw//2MhO2ZTDBlCkyeDP/8ozuNSCxlysCWLWqq0+efw6vPJRDi5Tw81AyH48dVURQcrKZzyvedEEInqwujefPm8e2339K9e3cKFSpEoUKFeO+995g1axZz5861QUTn9CqNKl7E2QujihXhzBn491/dSVxDrlyqk1SPHvJCxZUUKgR//AFz50Lv3s65N5qwL4GBMH8+LF4MY8eq0cvn+joJIUSisbowunPnDsHBwTGOBwcHc+fOnQQJ5Qq++eabBL0/Zy+MvL2halUZNUpM/furYnTZMt1JRGLKnl0VR7t2QZs2YEe7HAgnVqUKHDigGoBUqKAa7ty/rzuVEMLVWF0YFS5cONb9daZOnUrhwoUTJJSrCQkJ4fLly4S8xiRrZ+5KF6VOHSmMElPSpGpKXZ8+8gLF1aRLp6bV3bkDDRrI118kDi8v1fjlyBG4cEFNr1uyREathRCJx+qudNu3b6d+/fpkyZLFsqnqrl27uHTpEmvWrKFixYo2CZqYEqMzTEREBCNHjmTOnDlcvnzZsq9RpkyZ6NSpE0OHDsXDI/7d1M+dUwtanbk4OnsWihVTI2PP7SssbKhZM8iQAb76SncSkdjCwtQmncePw+rV4GQNSIWdW79eTefNmBEmTVKdV52ZdKUTQj+rR4wqV67MqVOnaNy4MSEhIYSEhNCkSRNOnTrlFEVRYunbty+//vorkydP5syZM9y8eZPTp08zefJkVq1aRZ8+fay6P39/uH0bwsNtk9ce5MgBmTOr1sIi8UyZojZo/OMP3UlEYvPyUus/3ngDSpeG/ft1JxKupHZttd6oQQM1lbpbN7hxQ3cqIYQze619jJxVYly1SZ06NSdOnLBsZPusGzduvNKaLR8f+PtvdXXfWQ0dCnfvqp3TReKZO1e17z54UPY2clXz5qlplXPmQKNGutMIV3P9uvr9v2wZDBqkOto524bfMmIkhH5WjxjNmTOHpUuXxji+dOlS5s2blyChXEHU1LnYGIZh9T5GAAEB6o+HM2vUCH76SeacJ7YOHdSi/GHDdCcRunTooH72unaFCRPkZ1AkroAAmDkTNm+GVavUpt+rVsn3oRAiYVn96nv06NGkTZs2xnF/f38+//zzBAnlCtq1a0f9+vVZtmwZZ86c4datW5w+fZply5ZRp04d2rRpY/V9BgQ4/zSDYsXUPjsypSdxmUxq48/vvoNt23SnEbpUrgw7d8KMGfDuu849dVfYp6JF1e+g0aPVqFGNGvDXX7pTCSGchdWF0cWLF8mePXuM41mzZuXixYsJEsoVfPnllzRp0oQPP/yQ3LlzExAQQJ48efjwww9p2rQpkyZNsvo+XWHEyGT6/6iRSFyZMsG0adCunZrOKFxTUJBq5X3yJNSrpzbnFCIxmUyqKcyJE2rz75o11e8leQkihHhdVhdG/v7+HD58OMbxQ4cOkSZNmgQJ5Qrc3NwYPHgw58+fJyQkhEuXLhESEsL58+cZMmSITKV7ASmM9GneXF2hfecdmcLiytKkgY0bVbewcuVUV0whEluSJPDhh2ptbbp0ULCg2n9NinUhxKuy+tV369at6dWrF1u3biUyMpLIyEi2bNlC7969adWqlS0yOr3IyEjMZjORkZGvdT+uUhhVqgRXrsA//+hO4pq++kpNZfz+e91JhE5eXqoRw1tvQZkyaoqdEDqkSQNffqk2iL1wQY1qTp6s2s0LIYQ1rC6MRo4cSenSpalevTpJkyYladKk1KpVi2rVqskaIytEREQwbNgwsmTJQurUqcmaNSupU6cmS5YsDBs2jIiICKvv01UKI09P1b715591J3FNvr6qfXffvnDmjO40QieTSXUImzIF6teHxYt1JxKuLEcO9T3466+wfDnkywdLl8rothAi/l65Xfc///zDwYMHSZo0KQULFiRr1qwJnU2bxGiZ2bNnT3bt2sXgwYMpUqQIfn5+hISEcOjQIUaOHEm5cuWYamVP6uXL1aLoDRtsEtmuLF8OEyfC77/rTuK6Pv0U1q6FHTvAir2IhZPavVtNc33/fRgyRBVNQuhiGPDLL2pqXapUMH48lC+vO9WLSbtuIfSTfYxi4aj7GP3+u3pRcuhQQqW0Xw8eqBGys2fVvyLxRUSoLmU1a8Lw4brTCHtw/rwazS1YEGbPhmTJdCcSri48XHXUHD5cFUZjxkDu3LpTxU4KIyH0s36Fv0gQso/R6/HxgWrV1D4WQg8PD5g/X605kvUlAiBbNvW98OSJehF6/rzuRMLVeXpC9+5w+jQUKAAlS6oLiM6+tYUQ4tVIYaSJrfYxunkTXrOHg8OQ7nT6Zc+uCqO33oJ793SnEfYgRQo11bVJEyhVSm3IKYRuvr5q+u+JE/D0KeTJA6NGwaNHupMJIeyJTKWLRWIMZ5vNZkaPHs2sWbO4dOmS5XiWLFno0qULgwYNsnrUyDDU1JULF8DfP6ET258bN9QV6uvX1R89oYdhQJs2qkvZvHm60wh7smoVdOyo1hz16SPrjoT9OHJErT86fFgVTO3b618rKVPphNBPCqNYJPYvp/v373P//n18fX3xfc1X+NmyqRcjBQsmTDZ7V7Gi2v28eXPdSVxbSAgULgzjxkHLlrrTCHty6hS8+SaUKAGzZkHSpLoTCfF/mzerAunhQxg5Epo21VfAS2EkhH7xKoxi29A1LoUKFXqtQPbAkX85lS6tpgfUqKE7SeIYP17tXbFgge4k4rff1PSpv/6CLFl0pxH2JDQU2rVT+4+tXCnfH8K+GAasWKFGNpMnh88/V01lErtAcuTXHkI4i3gVRm5ubpZmAaaX/KZ43U1K7YEj/3Jq2FBdsW/bVneSxPHPP2ox7Y0baiqX0GvwYPjjD3UV1t1ddxphT8xmNWVp2jRYsgSqVNGdSIjoIiLUxtXDh0OuXKpAKlMm8R7fkV97COEs4rWI5dy5c5w9e5Zz586xfPlysmfPzjfffMOBAwc4cOAA33zzDTlz5mT58uW2ziteIiAArl3TnSLxBAVBpkywdavuJALUC4qHD9ULCiGe5eamvj9mzFAji+PGqWJJCHvh4QGdO8Pff6uLjA0bqvbz+/bpTiaESCxWrzEqVaoUw4cPp169etGOr1mzhqFDh7J///4EDaiDI1+1GTZMvTAdP153ksQzciScOwfffac7iQD1tShZEhYtUtNRhHje33+rdYGZM6uGHWnS6E4kREwPHsDXX6u/p2XKqL+vJUrY7vEc+bWHEM7C6nbdR44cIXv27DGOZ8+enePHjydIKFcxY4balDUhpU8PV68m7H3au5Yt1fzwp091JxGgWnjPm6emc168qDuNsEe5c8Pu3ZAxIxQtqv4vhL3x8VGNGc6dgwoVoG5deOMN+PNP3cmEELZidWGUN29eRo8eTVhYmOVYWFgYo0ePJm/evAkaztlt2aKunCakDBng338T9j7tXe7ckDMnrF+vO4mIUr8+vPOOGhWQglXEJmlSdXFo9Gj1gnPCBLUIXgh7IwWSEK7D6sJo+vTprF+/nkyZMlGjRg1q1KhBpkyZWL9+PdOnT7dFRmEFVxwxAmjdGhYv1p1CPGv4cLXZ5wcf6E4i7FnbtrBrF8ydqzZtvntXdyIhYhdbgdSwITjBCgIhxH+sLoxKlSrF2bNn+eyzzyhUqBCFChVi1KhRnD17llKlStkio7CCK44YAbRoofZvevhQdxIRxd0dFi6En39W/woRl+Bg2LMH0qVTU+v27tWdSIi4PVsglS8PdeqA9J4SwjnIBq+xSKwFkC1bQu3aqgtOQomIUG2r791Tv7xdSYUKarPXFi10JxHP2r1bXVn9/XfIn193GmHvvv8eeveGoUOhTx/VzU4Ie/bggboQ9LqbF0vzBSH0e6U/OT/88AMVKlQgQ4YMXLhwAYCJEyfy888/J2g4YT0PD/D3d83pdK1ayXQ6e1SmjNq/pmlTuH9fdxph79q3h507VYFUp45rjoALx+Lj8/pFkRDCPlhdGE2bNo1+/fpRt25d7t69a9nQNVWqVEyaNCmh84lXkCGDaxZGzZqpBgyhobqTiOf16KGmSHXpIgvsxcvlzaum1hUurN5WrtSdSAghhCuwujCaMmUKs2bNYvDgwXh4eFiOlyhRgiNHjiRoOPFq0qd3zausgYFQtiz89JPuJOJ5JhPMmgVHj4JcPxHx4e0NX3yh9sPq0QO6dlVTloQQQghbsbowOnfuHEWLFo1x3Nvbm4ey8t0uuOqIEUh3Onvm46P2m/rsM9i4UXca4Shq1IDDh1W3uqJFYd8+3YmEEEI4K6sLo+zZs3Pw4MEYx9etWyf7GNkJVx0xAmjcGLZvh1u3dCcRsQkOhvnzVQF78qTuNMJRpEkDS5fCwIFQqxaMGgX/zeIWQgghEozVhVG/fv14//33WbJkCYZhsHfvXkaNGsXAgQP5+OOPbZFRWMlVW3YDpE4N1avDsmW6k4i41K2rOo698Qbcvq07jXAUJpPq4LlvH/zyC1StCv/1/hFCCCEShNWF0dtvv83YsWMZMmQIjx49ok2bNkybNo3JkyfTqlUrW2QUVnLlqXSgulp9953uFOJFevWCatVUw4ywMN1phCPJlUu1fq9SBYoVgwULdCcSQgjhLF6pXXfbtm35559/ePDgAdeuXePy5ct06dIlobOJV+TKU+kA3nxTbbx36JDuJCIuJhNMnar+36OHdKoT1vH0VC3gf/kFhgyBtm0hJER3KiGEEI7O6sLos88+49y5cwAkS5YMf3//BA8lXo+rjxh5eUGHDqoLmrBfnp5qyuPWrfDVV7rTCEdUvjwcPKg21yxcGHbs0J1ICCGEI7O6MFq6dCm5cuWiXLlyfPPNN9ySVe52JyBAbaTpyk0C335bTbF59Eh3EvEiadLAqlXq6v/atbrTCEfk56c2gx07Fho1gsGDITxcdyohhBCOyOrC6NChQxw+fJgqVaowfvx4MmTIQP369Vm4cCGP5FWoXfDwAH9/155OFxwMBQvCjz/qTiJeJjhYtVh/6y119V+IV9GqFRw4oNYflSkDsq2eEEIIa73SGqP8+fPz+eefc/bsWbZu3Uq2bNno06cPgYGBCZ1PvKJMmeDyZd0p9OrRQ61jkfUr9q9mTbWZZ926cPas7jTCUWXJAlu2qCK7YkUYOVKaewghhIi/VyqMnpU8eXKSJk2Kl5cX4TJ/wW5kzgyXLulOoVfjxnDtGuzcqTuJiI/O/2vvzuNjut4/gH8me4Qk1mxCQoIQSexLVSwhVGsvxZdYq2qr2FuE1lqUUrW1qNautqIqlFLUloRaYo3EFrvEnu38/ji/TDKEzshMbmbm83697mtm7tyZee7MZHKfe855Tk9g8GAgNBS4fVvpaMhYWVoCQ4bIst67dsnKdfwNICIibbxVYhQXF4dJkyahUqVKqF69OqKjozFhwgQkJibqOz56S56ebDGytgY+/ZQD+43JyJHA++/LlqPkZKWjIWPm6ysLewwZArRsKX8LkpKUjoqIiPIznROj2rVrw8fHB+vXr0ePHj0QHx+P3bt3o1evXnBycjJEjPQWSpZkixEA9OkDbN/OJNFYqFTAzJly3FGbNsCLF0pHRMbMwgLo1Qs4fVqW8/bzA379ld1riYgoZzonRo0bN8a///6L6OhoDBs2DB4eHoaIi3KJXemk4sWBDh2A+fOVjoS0ZWEBLF0qW/y6dgXS05WOiIydiwuwcqWc+HnYMFm9jr+PRET0Mp0So9TUVKxevRoqlcpQ8ZCeMDHKMnAgsGgR8OyZ0pGQtmxs5BxHV67IcUc8w0/60KwZcOoUUK4cEBAAfPstE28iIsqiU2JkbW2N58+fGyoW0iOOMcoSFARUrAisWqV0JKSLggWBbduAyEhg4kSloyFT4eAgKyD++Sfw889AnTosE09ERJLOXen69++PadOmIS0tzRDxkJ64uwMPHnCC00yDB8siDGx5MC7FiwM7dwILF8qFSF+qVAEOHwa6dAEaNgRGjODvJRGRudM5MTp69Cg2bNiAUqVKITQ0FG3bttVYKH+wtgZcXdmdLlPLljJR3L9f6UhIV6VLAzt2AF98AWzYoHQ0ZEosLeVJkxMngLNnAX9/4I8/lI6KiIiUYqXrA5ydndGuXTtDxEJ6ltmdrnx5pSNRnpUV0L+/HFNQv77S0ZCu/P2BTZuADz4AihYFgoOVjohMSalSwJYtsmJdjx6yBWnWLKBECaUjIyKivKRzYrR06VJDxEEGwJLdmnr3Br76CoiPl60QZFzq1QN++glo2xbYvVuOHSPSF5UKaN8eCAkBRo+W4xKnTwe6d5f3ERGR6XurCV7T0tKwa9cuLFy4EI8ePQIA3LhxA48fP9ZrcJQ7rEynqUgRoHNn4PvvlY6E3lbLlvJgtXlz4Px5paMhU+TsLMv7b94MzJgBNGoEnDmjdFRERJQXdE6M4uPjUblyZbRq1Qr9+/fHnTt3AADTpk3DsGHD9B4gvT1WpnvVwIHADz9wkLUx69kTGDlSHrBevKh0NGSq3nkHiI4GmjQB6tYFhg4FkpOVjoqIiAxJ58Ro8ODBqF69Oh48eAB7e3v1+jZt2mD37t16DY5yhy1Gr/L3l12wVqxQOhLKjc8+k0ujRkBcnNLRkKmysQE+/xw4eRJISAAqVJC/HaxuSURkmnROjPbv348xY8bAxsZGY72XlxeuX7+ut8Ao9zjGKGeDB8siDDy4MW7DhgGffioHysfHKx0NmbJSpYB164Dly+WcWsHBMlkiIiLTonNilJGRgfQcpgq/du0aChUqpJegSD/YYpSzFi1kV7o9e5SOhHJr1ChZVKNhQ37XyfBCQmRp75YtZXI0aBDw8KHSURERkb7onBg1bdoUs2fPVt9WqVR4/PgxIiIi8N577+kzNsolNzfg8WPg/+tj0P+ztAQGDJDleMn4jRkDdOvGliPKGzY2srXy9Gng3j05HcLChQDnPCciMn46J0YzZ87EgQMHULFiRTx//hydO3dWd6ObNm2aIWKkt2RlBXh48GAxJ717AwcPsjuMqYiIkGWV69UDYmOVjobMgbu7HG+0fj3w449AYCDw++/soktEZMx0nseoZMmSOHHiBNasWYMTJ07g8ePH6NWrF7p06aJRjIHyB29vOTjd31/pSPIXR0fZDWbiRGDtWqWjodxSqWTLkbOz7OL0++9A1apKR0Xm4N13gX/+AVavBj75BChXTpb5DgxUOjIiItKVzokRAFhZWaFLly7o0qWLvuMhPfP2Bq5cUTqK/GnQIPn+nD0L+PkpHQ3pw4ABgJOTLLG8aZM8aCUyNAsLOUdamzaysEuDBnIi4q++ki1LRERkHHTuSvfTTz9h27Zt6tsjRoyAs7Mz6tati3j22cp3vLxYzvh1CheWVc0mT1Y6EtKnrl2BJUvkAPnt25WOhsyJvb0sCHLuHGBnB1SsCEyYADx5onRkRESkDZ0To8mTJ6u7zB06dAjfffcdvv76axQrVgxDhgzRe4CUO2wxerMhQ4AtWzhRqKlp1UqO/fjf/4A1a5SOhsxNiRLAvHnAoUPAsWOAry+waBGQmqp0ZERE9CY6J0ZXr16Fj48PAGDTpk1o3749Pv74Y0yZMgX79+/Xe4CUO2wxerPixYE+fYApU5SOhPStcWM51mjAAGDxYqWjIXPk5wf89hvwyy+yQEPFinIsUkaG0pEREVFOdE6MChYsiHv37gEAdu7ciSZNmgAA7Ozs8OzZM/1GR7mWWXyBXm/oUDl5I3uCmp5ateR8VRERwPTpSkdD5qpRI1mgYfp0Oe6oWjXZzZMV7IiI8hedE6MmTZqgd+/e6N27N86fP6+eu+j06dPw8vLSd3yUS+7ucjJTTkL4em5uQFgYwGrzpsnfH9i/H5g/H/j8cx6MkjJUKqB1azlFwJAhQP/+QP36wN9/Kx0ZERFl0jkxmjdvHurUqYM7d+7g119/RdGiRQEAx48fR6dOnfQeIOWOpSVQqhRbjf7LiBGyu8v160pHQoZQtqxMjjZvlgek7MpESrG0lBMSx8YCHTsC7dsDLVoAMTFKR0ZERCoheP70ZcnJyXByckJSUhIcHR0N9jodOwKhoUDPngZ7CQBASIg8GGzTxrCvY+w+/hhwcABmzVI6EjKUe/eAZs2A8uWBpUsBa2ulIyJz9+SJLPE9Y4b8f/Dll7JYA5mfvDr2IKLX07nFCAAePHiAGTNmoFevXujVqxdmzJiB+/fv6zs20hOOM9LOqFGyzPPt20pHQoZStCiwe7dsGWzXDuCwSFKag4Ps4nnxIlC6NFC9OtC3L1uviYiUoHNitG/fPnh5eWHOnDl48OABHjx4gLlz58Lb2xv79u0zRIyUS15eLNmtjTJlZKvazJlKR0KG5OgoB76rVLJy3Z07SkdEBBQpAkydKrvYWVrKinbDh8tWTiIiyhs6J0b9+/dHx44dERcXhw0bNmDDhg24fPkyPvroI/Tv398QMVIuscVIe6NHAwsW8GDE1NnbAxs2yLPztWsD588rHRGR5OYGfP89EB0N3LwJ+PjISnaPHysdGRGR6dM5Mbp48SKGDh0KS0tL9TpLS0uEh4fjImfJzJeYGGmvfHngvfdkn38ybZaWwJw5wMCBwDvvyOIMRPlF2bKyIMy+fXKS2LJl5fhHdv8kIjIcnROjqlWr4uzZs6+sP3v2LAIDA/USFOlXZlc6ltnQzhdfAN99ByQlKR0J5YXPPgMWLQJatgRWrlQ6GiJNlSvLaoqbNskJi8uUAWbPZoJERGQIVtpsdPLkSfX1QYMGYfDgwbh48SJq164NAPjnn38wb948TJ061TBRUq64ugLp6cDdu0Dx4kpHk//5+wMNGgBz5wJjxigdDeWFNm0ADw+ZHMXFycHwKpXSURFlqVMH2LlTzns0YYKcd23UKFlN095e6eiIiEyDVuW6LSwsoFKp8F+bqlQqpKen6y04pZhauW4AqFABWL4cqFnT8K9lCk6cABo2BM6dYzJpTuLi5JwytWrJCWHt7JSOiChn+/fLBOnMGVmkoU8foGBBpaOi3GC5biLladWVLi4uDpcvX0ZcXNwbl8uXLxs6XnpLZcsCly4pHYXxCAyU5ZxHjlQ6EspL3t7AwYPArVtA/frA1atKR0SUs3ffBXbtAtasASIjZanviAjZM4CIiN6OVl3pSpcubeg4yMDKlWPlLV1NnSpb2g4ckIPzyTw4OwO//SYPMmvUkAeewcFKR0WUs3fflcuJE7J7XZkyshdCeDhQqpTS0RERGZe3muD10qVLGDhwIEJCQhASEoJBgwbhEpsj8jVfX+DCBaWjMC5FiwJTpgCffgqkpSkdDeUlS0tg4kTZna5NG1m9jsVLKD8LDJTFQ2JigJQUOVaye3fZ1Y6IiLSjc2L0xx9/oGLFijhy5AgCAgIQEBCAw4cPo1KlSoiMjDREjKQHTIzeTs+eQIECskodmZ82bWSL4fffA926AU+fKh0R0ZuVKSO/rxcuAO7usrW7dWvgn3+UjoyIKP/TqvhCdlWqVEFoaOgrFehGjRqFnTt3IioqSq8BKsEUiy9cuQJUqQLcv89qW7qKjgYaNQJOn5YHGmR+kpJkYnT1qpwY1stL6YiItJOUBCxcKOdAKl9ejpts1oz/B/IjFl8gUp7OLUZnz55Fr169Xlnfs2dPnHnLNvt58+bBy8sLdnZ2qFWrFo4cOfLG7detW4cKFSrAzs4OlStXxvbt29X3paamYuTIkahcuTIcHBzg7u6Obt264caNG28Vm6nw9JTzXty7p3QkxqdKFaBrV2DoUKUjIaU4OQEbN8oz7zVqyEHvRMbAyQkYMUJWXOzUCRg8GKhUCVi8mHMhERG9TOfEqHjx4oiJiXllfUxMDEqUKKFzAGvWrEF4eDgiIiIQFRWFwMBAhIaG4vbt2zluf/DgQXTq1Am9evVCdHQ0WrdujdatW+PUqVMAgKdPnyIqKgpjx45FVFQUNmzYgHPnzqFly5Y6x2ZKLC1lZTp2p3s7X30F7N0L7N6tdCSkFAsLYNw4YOlS2do7fTrHHZHxsLMD+vYFYmNlkYaVK2VxhogIWYWRiIjeoivdl19+iVmzZmHUqFGoW7cuAODAgQOYNm0awsPDMXbsWJ0CqFWrFmrUqIHv/n8QR0ZGBjw9PTFw4ECMGjXqle07duyIJ0+eYOvWrep1tWvXRlBQEBYsWJDjaxw9ehQ1a9ZEfHw8SmlRpscUu9IB8mx3mzZAWFjevJ6pWbFCJkgnTwI2NkpHQ0o6f17+Lfn7A0uWAA4OSkdEpLvoaNnFbuNG4MMPgSFDgMqVlY7KfLErHZHydG4xGjt2LMaNG4e5c+ciODgYwcHB+O677zB+/HiMGTNGp+dKSUnB8ePHERISkhWQhQVCQkJw6NChHB9z6NAhje0BIDQ09LXbA0BSUhJUKhWcnZ1zvP/FixdITk7WWEwRCzDkTufOgJsbMHOm0pGQ0sqVk4PZMzJk17p//1U6IiLdVakiJ/6OjQVcXYEGDYAmTYDff5ffbSIic6NzYqRSqTBkyBBcu3YNSUlJSEpKwrVr1zB48GCodBzNeffuXaSnp8PFxUVjvYuLCxITE3N8TGJiok7bP3/+HCNHjkSnTp1eewZmypQpcHJyUi+enp467YexKFeOiVFuqFTAvHmyG0p8vNLRkNIKFQLWrgX695fzyMyfz651ZJw8PIDJk4GEBKBtWzkOyd8fWLSI45CIyLy81TxGmQoVKoRChQrpKxa9S01NRYcOHSCEwPz581+73ejRo9VJXlJSEq6a6HT3bDHKvYoVZT/9wYOVjoTyA5VKJkZ//QV8+y3Qvj3w4IHSURG9HQcHoF+/rHFIq1fLcUjjxgGvOfdIRGRSdE6Mbt26ha5du8Ld3R1WVlawtLTUWHRRrFgxWFpa4tZLIz9v3boFV1fXHB/j6uqq1faZSVF8fDwiIyPf2F/X1tYWjo6OGosp8vWVYyN4Vjt3xo4Fjh8Htm1TOhLKLwID5XfCyQkICgIOHlQ6IqK3Z2EBfPAB8OefwM6dcroHX1+gRw85xpKIyFTpnBh1795dXfVt/fr12LBhg8aiCxsbG1SrVg27s5X6ysjIwO7du1GnTp0cH1OnTh2N7QEgMjJSY/vMpOjChQvYtWsXihYtqlNcpsrdXSZFPPOXOwULytaBgQPZzYSyODjIQgyTJwPvvy8v09OVjooodzLHIZ07J/+HNGwIhIQAmzfz+01EpkfnqnSFChXC/v37ERQUpJcA1qxZg7CwMCxcuBA1a9bE7NmzsXbtWsTGxsLFxQXdunWDh4cHpkyZAkCW6w4ODsbUqVPRokULrF69GpMnT0ZUVBT8/f2RmpqK9u3bIyoqClu3btUYj1SkSBHYaFFOzFSr0gHybPacOUD9+nn3mqZICOC99+TA+y+/VDoaym8uXgQ++ghwdgZ+/lkW7SAyBU+eyO/0vHnAo0fAp58CvXoBPP+Ye6xKR6Q8nVuMPD09oWMu9UYdO3bEjBkzMG7cOAQFBSEmJgY7duxQJzQJCQm4efOmevu6deti5cqVWLRoEQIDA7F+/Xps2rQJ/v7+AIDr169jy5YtuHbtGoKCguDm5qZeDrJ/i7o7HeWOSgXMnSuTTI7bopf5+MjudAEB8mTEjh1KR0SkHw4OwCefyC51P/0EHDkCeHnJE3xRUUpHR0SUOzq3GO3cuRMzZ87EwoUL4eXlZaCwlGXKLUaffy67P0yblnevacrGjwcOHZIHvjoWZSQzsW2bHJvRvTswcSLnwCLTc/UqsHChrGLn6wsMGAC0a8fvuq7YYkSkPJ1bjDp27Ii9e/eibNmyKFSoEIoUKaKxUP7GFiP9GjlSdptat07pSCi/atFCTqR59Kgs680WRjI1np4y6b96VVa1+/ZbWc1u7FhObUBExsVK1wfMnj3bAGFQXvHzA77+WukoTIe9vTxT2qULEBwMvDTFFhEAOU/Mrl2ypbZGDWDCBFm8wyJXEyYQ5S+2tsD//ieXo0fl3F6VKwN16wK9ewMtW7IViYjyN5270pkDU+5Kl5wsB8k+fiz/iZF+DBwoz4xu3swudfRmJ08CYWGAoyOwdClQpozSEREZTnIysGoVsHixbFEKC5NJUrlySkeW/7ArHZHydD5fmZCQ8MaF8jdHR1ly9dw5pSMxLdOmyfd02TKlI6H8LiBADlhv2BCoVk2eVc/IUDoqIsNwdJSTYh87JsdiPnkC1KwJNGggizc8eaJ0hEREWXRuMbKwsIDqDafE001gYgNTbjECZJnprl2BTp3y9nVN3eHDQPPmsjKTidYlIT2LipJn0EuUABYskGMAiUzd06fA+vWyxfT4caB9e1mgpF49825xZ4sRkfJ0bjGKjo5GVFSUejl8+DAWLFiAcuXKYR1HoBuFSpWAU6eUjsL01KoF9O8vq4+xBYC0UbWqPJPeoIEce/TVV8CLF0pHRWRYBQoA3boBe/YAJ04ApUvL276+8m+AnU+ISCk6J0aBgYEaS/Xq1dGnTx/MmDEDc+bMMUSMpGf+/sDp00pHYZrGjpWTHrJGCWnL1lZ+b44cAfbulfMe7dundFREecPbG4iIAC5dAn74QV5WqgSEhAArVsjWJSKivKK3mkjly5fH0aNH9fV0ZECVKjExMhQbG2D5cuDLL/kek27KlZOV6z7/XHYt6tULuHdP6aiI8oaFhWw5XbYMuHFDVvpcuBBwcwP69JETJrNUFBEZms6JUXJyssaSlJSE2NhYjBkzBr7sIG8U/PyAuDieiTOUSpWAcePkOK6UFKWjIWOiUsnvzdmz8rafH/DzzzwgJPNSqJAcc7RvnxyD5OYmx8SWLw9Mngxcu6Z0hERkqnROjJydnVG4cGH1UqRIEVSsWBGHDh3C/PnzDREj6ZmDg+zTHRurdCSm67PPZDWmr75SOhIyRkWLAj/+KCcOnjxZdivixMxkjnx8ZAt8XJys4Hj2rDxhEBoqy4A/e6Z0hERkSnSe4HXPnj0aty0sLFC8eHH4+PjAykrnpyOFZHanq1pV6UhMk4WF7BJSrZrsHtK4sdIRkTEKDgZiYuSkzDVrAkOHAiNGcA4yMj8WFvJ3tHFjOTfS2rXAd98Bn3wCtGkjJ5Vt2BCwtFQ6UiIyZpzgNQemXq4bkOMY0tPl/DtkOFu2yP7xx48DJUsqHQ0Zs/PngX79gOvX5diL4GClIyJS3uXLwMqVslBDUhLw0UdyfFLVqsZX+pvluomUp7fiC2RcWIAhb7RsKQfRf/ghxxtR7mQWZ/jiC/l96tmTxRmIypQBxowBzpwBtm6V6z74QHa3mzhRJk5ERNpiYmSmmBjlna++kuO6wsOVjoSMXfbiDCqVPPhbvpzFGYhUKtlK9M03wNWrspvdpUtAlSpA3brAvHnAnTtKR0lE+R0TIzNVoYKs7PP4sdKRmD5LSzlIePNm2d2DKLeyF2eYMkWOu2BxBiLJ0lIWLFm6FEhMlMVwdu4EvLyA99+Xv8dPnigdJRHlR1olRnPmzMHz588BAAkJCeCwJONnZye7IJw5o3Qk5qF4cWD9emDAAODkSaWjIVORWZyhYUOgRg1g9Gie7CDKzt4e6NBBnphKSJCJ0fffyxLgXbsCO3YAaWlKR0lE+YVWiVF4eDiSk5MBAN7e3rjD9miT4O8PnDqldBTmo1YtYNIkoF074OFDpaMhU2FrC4wdC0RHyxL85coBv/zC7nVELytaVFax279fnqCqWFFWenR3B/r3l+szMpSOkoiUpFVi5O7ujl9//RXx8fEQQuDatWtISEjIcSHjwXFGea9fP6BOHaB7d/4DJv0qUwbYuFGOOZoyBXjnHeDwYaWjIsqfvLxkC+upU7KoibOz/F0uVUqOBz14kL/RROZIq8RozJgx+Oyzz1CmTBmoVCrUqFED3t7eGouXlxe8vb0NHS/pUUAAcOKE0lGYF5UKWLBAju8aMULpaMgUhYTI7nUffSS7DbVty8mciV5HpZL/CydNAi5elCcXLCzkvEgeHvJk1s6dQGqq0pESUV7Qeh6jR48eIT4+HgEBAdi1axeKFi2a43aBgYF6DVAJ5jCPESDLmFavLkv+Gtt8D8YuMVG2HIWHAwMHKh0NmarkZGDmTODbb4H27YHx4zmfFpE2hJAnDjduBDZskCezMk80hIYCBQro/zU5jxGR8nSe4PWnn37CRx99BFsTnnrdXBIjIYAiReTYBC8vZWIwZ7GxwLvvAosWyZnbiQzl1i05p8tPPwF9+8ouREWKKB0VkfHIbE3asAH491+gaVP5u/3++0Dhwvp5DSZGRMrTuVx3WFgYbG1tcfz4cfzyyy/45ZdfEBUVZYjYyMAy533gx6eMChXkP9mePYFDh5SOhkyZiwswd67sYnfjBuDjI8chPX2qdGRExsHHBxg+XP5Wnzsnu6wuXy672zVtChw7pnSERKQPOidGt2/fRqNGjVCjRg0MGjQIgwYNQvXq1dG4cWNWqzNCVasCx48rHYX5evddYOFCoFUrzkNDhlemjJxL688/ZQUuHx855o3jJ4i05+EBfPopEBkpu9j973/6azUiImXpnBgNHDgQjx49wunTp3H//n3cv38fp06dQnJyMgYNGmSIGMmA2GKkvA4dgFGjgObNgdu3lY6GzEFQELB9u5zo8qefZIXKtWtZhYtIV0WKAN26AWXLKh0JEemDzonRjh078P3338PPz0+9rmLFipg3bx5+//13vQZHhpfZYsQ5T5Q1ZIjsq/7BB5yRnfJOcLAsS/z117IwQ82awLZt/D0gIiLzpHNilJGRAWtr61fWW1tbI4OnG42Ory/w7Jkcd0DKUamAb76RXTQ6dwbS05WOiMyFSgW0bi0nvBwwABg8GKhRA9iyhQkSERGZF50To0aNGmHw4MG4ke1I+vr16xgyZAgaN26s1+DI8CwsZLcajjNSnqWlHP9x544s4c2DUspLVlZygsvYWJkcDR8uW5Q3bmQXOyIiMg86J0bfffcdkpOT4eXlhbJly6Js2bLw9vZGcnIy5s6da4gYycCqVeM4o/zC3l6eqd+1S3ZvIsprVlZA167AmTNyEuIvvgCqVAHWr2eCREREps1K1wd4enoiKioKu3btQuz/T6fu5+eHkJAQvQdHeaNqVXnQQ/lDsWLA778D77wDeHrKrnVEec3SEujUSRYH+fVXYMIEICICGDsW+PBDeT8REZEp0XmCV3NgLhO8ZsqcaPTWLdm1jvKHI0fk92PjRqBBA6WjIXOXkSG/i19+CaSkAGPGyN8wK51PrxFRTjjBK5HyeBhMKF8esLUFTpxQOhLKrmZNOYFg+/ZyYk4iJVlYAO3aAdHRcnLYmTOBihVlue+0NKWjIyIiyj0mRgSVCmjSBNi5U+lI6GUffCAPQJs2ZeJK+YOFhaxid/y4rKT43Xfy5MqSJZwoloiIjBsTIwIgD7wjI5WOgnISFgZMnSqT15MnlY6GSFKp5NxbR47I5GjRIqBcOWDxYtnVjoiIyNgwMSIAQOPGcqLHZ8+UjoRy0rMnMHmyTI5OnVI6GqIsKhXQvDlw6JBMjn76CfD2BmbMAJKTlY6OiIhIe0yMCABQooTsDrN/v9KR0Ov07g189RUQEsLkiPKfzC65f/8NrFkD/PUX4OUFfP45J5AmIiLjoHVilJqaihEjRsDHxwc1a9bEkiVLNO6/desWLFm/1ag1awZs26Z0FPQmH38sq4I1bCgPQInyo3r1gN9+A/btk0lRuXLARx/J7yzroBIRUX6ldWI0adIkLF++HJ988gmaNm2K8PBw9O3bV2MbVv42bq1bA5s28cAlv/v4Y2DhQlmYYeNGpaMhej1/f2DZMuDyZSAgQM6LVLUq8OOP7LZLRET5j9aJ0YoVK/DDDz9g2LBhmDhxIo4dO4Y///wTPXr0UCdEKpXKYIGS4dWoIatKsTR0/te2rTwj36cPMH++0tEQvVmJErJLXVycnP/o55+BkiWBESOAK1eUjo6IiEjSOjG6fv06/P391bd9fHywd+9eHDx4EF27dkV6erpBAqS8k1mGl60QxqFePdlVacoU4Isv2NJH+Z+VlZwLae9euSQlAZUrA61aAbt28TtMRETK0joxcnV1xaVLlzTWeXh4YM+ePTh69Ci6d++u79hIAZnd6cg4VKwoq4H99hvQowfnkSHjUbmy7BKakAAEBwN9+8rv87x5wKNHSkdHRETmSOvEqFGjRli5cuUr693d3fHnn38iLi5Or4GRMho0kAcqL+XAlI95eMiWo/h4oGVL4PFjpSMi0l7hwkB4OHDhgizxvXUr4OkJDBjASY2JiChvaZ0YjR07Fh06dMjxPg8PD/z111+vVKoj42NjA7RowVYjY+PsDOzYATg6yop1t28rHRGRbiws5G/P77/LSWPt7GRp+lq1gB9+YMJPRESGp3ViVLp0aYSGhr72fnd3d4SFheklKFJWmzYcZ2SMbG2BVavk2KO6dYGLF5WOiOjtlCsnW4+uXQOGDpXzInl4yIqMR49yLBIRERmGzhO8siS36WvWDIiKAm7dUjoS0pWFBfDNN8AnnwDvvCMPIomMla0t0KEDEBkJREcDxYvL7qJVqsixSA8fKh0hERGZEp0So5SUFHz44YeGioXyiYIFgcaNgS1blI6E3oZKBQwbBsyaBYSGyq5JRMauTBlg0iQ5BnL8eGD7djkWKSwMOHCArUhERJR7WidGjx8/RvPmzZGWlmbIeCifYHU649e5M7BuHdClC7B0qdLREOmHtbX8fdq2DThzRiZMnToBlSrJkwF37yodIRERGSutEqO7d+8iODgYlpaWWLdunaFjonygZUtgzx4gOVnpSCg3GjcG/vxTznM0ZgzA6cbIlHh6AhERcuLYmTOB/fsBLy+ZKP35J5CRoXSERERkTLRKjOrVqwcHBwds2rQJ1tbWho6J8oHixYHq1WWlMzJuQUHA4cPAzp3A++8DDx4oHRGRfllaAs2bAxs2yKIjgYGyUIOvLzBhgkyciIiI/otWidGlS5fQrFkzFChQwNDxUD7SujWr05kKT08515GHh0x4T55UOiIiw3B1BUaNAs6fB378Uc7vFRgI1K8vbyclKR0hERHlV1olRmvXrsXEiROxePFiQ8dD+Ujr1nKA84sXSkdC+mBnByxeDIwYAQQHy9LeRKbKwkJOWL1kCXDzJtC3b1bZ786dZWs4h8wSEVF2WiVGbdq0wbZt2zBixAisXLnS0DFRPlGmjOyvv2eP0pGQvqhU8gBx+3ZZuS48nAeHZPocHGQRkp07gXPnZPfSoUOBUqWA4cOBf/9VOkIiIsoPtK5K17BhQ+zatQvDhw83ZDyUz7Rpw+p0pqhOHeD4ceDYMaBJE+D2baUjIsobHh6y1fTUKeC332SLeMOGQNWqwOzZQGKi0hESEZFSdJrHqFq1atjD5gOz0q4d8OuvQEqK0pGQvrm6Art3AwEBctm2TemIiPKOSgVUqwbMmQPcuCGr2+3fD5QtKxOl+fM5yTURkbnRKTECgHLlyhkiDsqnKleW3ek42atpsrYGvv1WjsPo3Rvo1w948kTpqIjylo0N0KqVPAl086b8W9ixQ3YnbtQI+O474No1paMkIiJD0zkxepP169fr8+kon+jdG/jhB6WjIEN67z1ZqS4xEahSBThyROmIiJTh6CjHI23eLFuSevaU4ywrVABq1QKmTZMV74iIyPTolBilpaXh1KlTOP/Sf4XNmzcjMDAQXbp00WtwlD906gQcOAAkJCgdCRlS8eJyHpjRo4FmzeT8LyzMQObMyQn43/9kS9Lt2/Jv48wZmSBVqgSMHQtERQFCKB0pERHpg9aJ0alTp+Dj44PAwED4+fmhbdu2uHXrFoKDg9GzZ080b94cly5dMmSspBBHR6B9e2DpUqUjIUNTqYAePWRRhshIoF494MIFpaMiUl6BAnIKg59+kknSt98C9+/LSZO9vYEhQ+RcYenpSkdKRERvS+vEaOTIkfDx8cHmzZvx0UcfYdOmTWjQoAE++OADXLt2DVOnTkXJkiUNGSspqFcvOQ6F//TNQ5kywF9/AS1bAjVrAosW8aw4USZrayAkBJg3T449Wr1ajlPq1QtwcwP69OEccERExkglhHaHOyVKlMDOnTsRFBSEpKQkFC5cGD/99BO6du1q6BjzXHJyMpycnJCUlARHR0eDvU7HjkBoqOzDnt8JAfj5ybOkoaFKR0N56fhx2Z3Ix0eONXNxUToiovxJCOD0adkldeNG4PJlOX6vTRvZPdWA/07IBOTVsQcRvZ7WLUZ3796Fu7s7AMDJyQkODg6oXbu2wQKj/EWlkkUYfvxR6Ugor1WrJpMjLy85rmLxYiAjQ+moiPIflQrw9wfGjQOio4GYGKBGDWDuXHlCoUkTeXKJvc6JiPInrRMjlUqFR48eITk5GUlJSVCpVHj27BmSk5M1FjJd3brJ7iF37igdCeW1AgXkwd2WLfKyXj1ZxY6IXs/bGwgPl/MjXbsGdO8OHDokTzb4+QHDh8txSSxyQkSUP2idGAkhUK5cORQuXBhFihTB48ePUaVKFRQuXBiFCxeGs7MzChcubMhYSWElSsjuID//rHQkpJS6dWXrUbt2QP368sDu8WOloyLK/4oWlWXAV6+WJ5fmz5dd7z7+WFaE7NwZWLlSFnQgIiJlaD3G6K+//tLqCYODg3MVUH7AMUav9/vvwNChsh+9SqV0NKSkhARg8GCZKM2dKyfIJCLdXbgAbNsGbN0qp0aoUUNWu3v/fdmyxN9a88AxRkTK0zoxMidMjF4vPV2ONVmzRrYeEG3ZAgwcCAQFAXPmAKVLKx0RkfFKSpKl8rdulcmSo2NWklS/PmBrq3SEZChMjIiUp9MEr0SWljKRYxEGytSypZz00s8PCAwEpk8HUlOVjorIODk5yXnjli0DEhOBX34BChYEhg2T3fHef1+20F64wBL6RET6xsSIdNajB7BuHcBaG5TJwQGYOlUOMt+8GahaVXYJIqK3Z2kJ1KkDTJoEnDgBnDsnx/f9/TdQqxZQtizQr5/8m3v0SOloiYiMHxMj0pmXl/xnvWaN0pFQflO5sqyy9dlncsxRnz7AvXtKR0VkGjw85ImpNWtkAYeVKwFXV2DaNFkcJzgYmDIFiIpiSX0iorfBxIjeSq9ecrJPopdZWMjvx9mzsgyxn5/sFsRuP0T6Y2kJ1K4NREQABw8CN24AAwbIOZJatgTc3ICuXWVXvNu3lY6WiMg4MDGit9KqlfwH/O+/SkdC+VXx4sDSpbLb5ddfAw0ayLFIRKR/hQsDH34oT1hdvQr8+SdQpQqwfLls5a9WDfj8c+Cvv4AXL5SOlogof7LSZqO2bdtq/YQbNmx462DIeNjayskK580DFixQOhrKz4KDgZgY4JtvZBfMTp3kAVqpUkpHRmSaVCqgUiW5hIcDT5/KhOiPP+SYpLg4OUbp3Xdlpbs6dWSBByIic6dVi5GTk5PWC5mPwYNlH/ebN5WOhPI7Gxtg1CjZvc7KCvD3B/r2Ba5cUToyItNXoADQvDkwe7ZstY2PlyX2Hz0CRo6U1e5q1pSV77Zs4SSzRGS+OI9RDjiPkfZ695ZdOKZPVzoSMibXr8vudUuXAh06yBakMmWUjorIPCUlyXFK+/bJypLHjgG+vlktSu++Kws/kGFxHiMi5XGMEeXKiBHAokU8w0i68fAAvv1Wlh92dJSTw/boAVy8qHRkRObHyUm2KE2ZIkuBP3gg50pyc5Nz1pUrJ0uD9+ghT2ZcvMhiKkRkmt6qxWj9+vVYu3YtEhISkJKSonFfVFSU3oJTCluMdNOxo+zLPm6c0pGQsbp1C5gxQybZrVoBY8bIgzEiUl5qKhAdLVuU9u2TyZO1NVC3btZSrRpgZ6d0pMaNLUZEytO5xWjOnDno0aMHXFxcEB0djZo1a6Jo0aK4fPkymjdvbogYKZ8bPRqYMwd4/FjpSMhYubjI7pgXLwLu7kCNGkCXLnJMEhEpy9pacwzS3bvA3r3A++8DsbGyS7WzsyziMHQosGEDx54SkXHSOTH6/vvvsWjRIsydOxc2NjYYMWIEIiMjMWjQICQlJRkiRsrngoJkhaNFi5SOhIxd8eLA1KmyFHzmRMIffQScOqV0ZESUycJCzk/Wq5fsanf2rBw3OGaMrFg6Z47selemDPC//wHffy8rU6anKx05EdGb6ZwYJSQkoG7dugAAe3t7PHr0CADQtWtXrFq1Sr/RkdH4/HPZFerpU6UjIVNQrBgwaRJw+bLsUvfuu3KOlpMnlY6MiHJStCjQogUwebJsTUpKAtaulS1N+/YBH3wgW5WaNJGT0v7xhxzLRESUn+icGLm6uuL+/4+0L1WqFP755x8AQFxcHFjgzny98w5Qtao8mCXSlyJFgC+/lPOu+PvLSWLbtpXjHYgo/7K2BqpXBwYNAlavlpPOnjkju909fChbl1xdgfLlgW7d5Jx4x44BLw1bJiLKUzonRo0aNcKWLVsAAD169MCQIUPQpEkTdOzYEW3atNF7gGQ85s6V/9zOnFE6EjI1zs7yLHNcnEzAQ0Lk8ttvQEaG0tERkTY8PWWxnm+/BY4elQnSsmXyb/rvv2XpficnWcxhyBCZUMXFsQIeEeUdnROjRYsW4YsvvgAA9O/fH0uWLIGfnx++/PJLzJ8/X+cA5s2bBy8vL9jZ2aFWrVo4cuTIG7dft24dKlSoADs7O1SuXBnbt2/XuH/Dhg1o2rQpihYtCpVKhZiYGJ1jorfj7S0n8ezXj//IyDCcnOSZ5oQE2bVu5Eh5xnnuXDlZJREZD3t7OY7ws8+AVatk19n4eNk1u2BBYMkSoEoVWZzlgw+AiROByEiZUBERGYLOiZGFhQWsrKzUtz/66CPMmTMHAwcOhI2NjU7PtWbNGoSHhyMiIgJRUVEIDAxEaGgobt++neP2Bw8eRKdOndCrVy9ER0ejdevWaN26NU5lG5n95MkT1KtXD9OmTdN110gPwsNlxaLly5WOhEyZgwPQty9w+rRspfz9d3k2OjxcnmEmIuNUooSsdvfVV8DOnXKOvH37gPbtgRs3ZBVUFxegQgUgLEz+/R8+DDx/rnTkRGQKtJrH6OTJk/D394eFhQVO/sfo54CAAK1fvFatWqhRowa+++47AEBGRgY8PT0xcOBAjBo16pXtO3bsiCdPnmDr1q3qdbVr10ZQUBAWLFigse2VK1fg7e2N6OhoBAUFvTGOFy9e4MWLF+rbycnJ8PT05DxGbynzn9jZs3JALlFeiI2V1bB++UV2sxs8GKhfH1CplI6MiPTp2TM5zvDwYTku6dgxeUKkUiU5rilzqVwZ0PF8raI4jxGR8qz+exMgKCgIiYmJKFGiBIKCgqBSqXIstKBSqZCuZT3OlJQUHD9+HKNHj1avs7CwQEhICA4dOpTjYw4dOoTw8HCNdaGhodi0aZNWr/k6U6ZMwYQJE3L1HJSlfn15xm/UKGDxYqWjIXNRoYIsCzxpEvDDD0DXrjIx/+wzWfLb1lbpCIlIH+ztsyaWzfTwIRAVJZOk3buBadNkC1NAgGayVLEiYKXVkQ8RmSOtfh7i4uJQvHhx9XV9uHv3LtLT0+Hi4qKx3sXFBbGxsTk+JjExMcftExMTcxXL6NGjNRKuzBYjentffy3nuejeXVasI8orhQsDw4fLwdubNgGzZwMjRsixb598IithEZFpcXYGGjWSS6Z794Djx2WytHUrMH687OodFKSZLJUvD1haKhQ4EeUrWiVGpUuXVl+Pj49H3bp1NcYZAUBaWhoOHjyosa2xsLW1hS1PJ+tVsWLyjN0nn8izeNbWSkdE5sbKSnbpbN9eVsD69lvA1xdo1052s6tSRekIiciQihYFmjaVS6Zbt7KSpfXrZQGX5GSZLFWtKn8XqlaVLUvG1A2PiPRD5+ILDRs2VM9jlF1SUhIaNmyo9fMUK1YMlpaWuHXrlsb6W7duwfU1p3RdXV112p6U1b27rCI2e7bSkZC5q1FDjj06d04WaWjaFAgOBtas4bwpRObExQV47z1g3Dhgyxbg5k3g4kVZCa9ECTkFQOvWQKFCsjWpTx9g/nw5nunZM6WjJyJD0zkxEkJAlcNo5nv37sHBwUHr57GxsUG1atWwe/du9bqMjAzs3r0bderUyfExderU0dgeACIjI1+7PSnLwgJYsECO+fj3X6WjIQLc3WW1q4QEWdFq5ky5rl8/WTSEcyIRmR83N6BFC2DsWGDjRuDKFTk+aepUoFw5+dvQrRvg6Ci73bVrJ+dVW7dOFhlKS1N6D4hIX7Qegti2bVsAssBC9+7dNbqepaen4+TJk6ibfSSkFsLDwxEWFobq1aujZs2amD17Np48eYIePXoAALp16wYPDw9MmTIFADB48GAEBwdj5syZaNGiBVavXo1jx45h0aJF6ue8f/8+EhIScOPGDQDAuXPnAMjWJrYs5T1/f2DyZKBtW9mdydlZ6YiI5ODtnj3lcvq0nEOle3cgNRXo1Ano3BkIDGRFOyJzVbRo1kTSmR4/lonQqVPyZN/ixfL6vXtyPGPz5oqFS0R6onVi5OTkBEC2GBUqVAj29vbq+2xsbFC7dm306dNHpxfv2LEj7ty5g3HjxiExMRFBQUHYsWOHusBCQkICLCyyGrXq1q2LlStXYsyYMfj888/h6+uLTZs2wd/fX73Nli1b1IkVIOdZAoCIiAiMHz9ep/hIP/r1A44ckVXCNm+WLUlE+UWlSnLiyK++kt/TlSuBZs1kEYfOnWWi5OOjdJREpLSCBWW33Bo1NNffuwfY2SkTExHpl1bzGGUSQqBnz56YO3cuChYsaMi4FJVXcwmY6jxGOXn2TFana91a9u0mys/S0oC9e2WStGGD7D7TuTPQoYPsdkNEpG+cx4hIeTqduxdCYMWKFbh586ah4iETZW8vDzDnzgW2b1c6GqI3s7KSXWiWLAESE2Xlqr//li1HmesfPlQ6SiIiItInnRIjCwsL+Pr64t69e4aKh0yYlxewYoUcxHrpktLREGnHzk6OkVu3Tg7I7tpVVrPz8ADatAFWr5blfomIiMi46TzaY+rUqRg+fDhOnTpliHjIxDVtCgwdKg8onzxROhoi3Tg5yWp2f/wBXL4MNG4sKy+6usoSwIsXy3lSiIiIyPjoNMYIAAoXLoynT58iLS0NNjY2GkUYAOQ4x5Gx4RgjwxJClju1t5dzy7DyFxm7O3fk/CcbNwK7d8sJItu0kUuZMkpHR0TGgGOMiJSndVW6TLM5WyflkkoFLFsG1KwJzJkDDB6sdEREuVO8eFb570ePgB07ZJL05ZeyC2lmkhQQwBMBRERE+ZXOiVFYWJgh4iAz4+goDxzfeQcICgKCg5WOiEg/ChUCPvxQLi9eAHv2yO96aCjg4CArM7ZpA9SpA1haKh0tERERZcrVjDLPnz9HcnKyxkKkLT8/OSajY0fg+nWloyHSP1tbOSfSwoXyO758uWwxCgsD3N2Bjz8Gfv9dJlBERESkLJ0ToydPnmDAgAEoUaIEHBwcULhwYY2FSBft2gHduwPt2/PgkEybpaVsIZ0xA7h4EYiMlJXtRo8GihWTLUkLFwIJCUpHSkREZJ50ToxGjBiBP//8E/Pnz4etrS1++OEHTJgwAe7u7li+fLkhYiQTN3GinFF8yBClIyHKGyqVHG8UEQHExACnTwPNm8vWo0qVAH9/YPhw2Q0vJUXpaImIiMyDzonRb7/9hu+//x7t2rWDlZUV3n33XYwZMwaTJ0/GihUrDBEjmTgrK2DVKmDbNmDpUqWjIcp7pUoBffsCmzYBd+8C334rqzcOGCBbk9q0kd1Or11TOlIiIiLTpXNidP/+fZT5//qzjo6O6vLc9erVw759+/QbHZmNYsWADRuA8HDgwAGloyFSjq2tnB9pxgzZkvTvv7Jww9atQIUKQOXKwMiRwN69QGqq0tESERGZDp0TozJlyiAuLg4AUKFCBaxduxaAbElydnbWa3BkXqpVA378EWjZEjh0SOloiPKH0qWBTz4BNm8G7t0DZs0C0tKATz8FihaV4/R++IGtSURERLmlc2LUo0cPnDhxAgAwatQozJs3D3Z2dhgyZAiGDx+u9wDJvLRtCyxaBLz/PpMjopfZ2gIhIcDMmcCZM8DJk/L2li2yNaliRWDQIDnZLIuEEhER6UYlhBC5eYL4+HgcP34cPj4+CAgI0Fdcisqr2ac7dpRdZHr2NNhLGK3162Up42XLZAsSEb1ZSgrwzz+y2t2uXUB0tCzw8M47QN268tLdXekoieh18urYg4heT+sJXjMyMjB9+nRs2bIFKSkpaNy4MSIiIlC6dGmULl3akDGSGWrfHihSBOjUCTh+XFbvssjVrFtEps3GBqhfXy5ffQU8fCgTpQMHgAULgB495Fi+zCSpbl05XslK52m+iYiITJPW/xInTZqE8ePHIyQkBPb29vj2229x+/ZtLFmyxJDxkRlr1Ag4elSOoTh2DPjlF4BTZRFpx9lZTi7brJm8nZYmu94dPAj8/Tfw9dfA/ftArVoySapbF6hdWz6OiIjIHGndlc7X1xfDhg1D3759AQC7du1CixYt8OzZM1iY2Kl8dqXLX54/B/r3l1W4Nm6U3YOIKPeuXZOJ0sGDsmXp5EmgbFmgZs2sJSBAtkYRkWGxKx2R8rROjGxtbXHx4kV4enqq19nZ2eHixYsoWbKkwQJUAhOj/EcIOY/LyJHAvHlA585KR0Rkep4+lWOTjhzJWq5fB4KCNJMlHx92bSXSNyZGRMrTuitdWloa7OzsNNZZW1sjlRNpUB5QqWQxhoAAOf7oyBFg+nTA2lrpyIhMR4ECcvzRO+9krbt7V3ZpPXJETsQ8ZIjsllejRlaiVKMG4OamXNxERET6oHViJIRA9+7dYWtrq173/PlzfPLJJ3BwcFCv27Bhg34jJMqmdm1ZjKFjRzkJ5tq1gKur0lERma5ixYDmzeUCyNbb+PisFqUZM+TfZNGiWclS9epAlSqygAoREZGx0DoxCgsLe2Xd//73P70GQ6QNFxdZknjkSDkp7Pr1QJ06SkdFZB5UKsDLSy4dOsh1aWlyXqXMZGntWuDUKdmKVLWqTJKqVpULW5aIiCi/yvU8RqaIY4yMx6pVwKefAlOmAH37yoM2IlJeSopMlqKi5LilqCggJgYoVOjVZMnLi3+7RBxjRKQ8zmBBRq1TJ6BSJaBtW3mmet48wN5e6aiIyMZGFm0ICspal54OXLggk6SoKOD77+UlIBOl7MlSuXKApaUSkRMRkbliYkRGLyBADg7/3/+Ad98Ffv0V4JzDRPmPpSVQoYJcMitLCgFcuZLVqrRiBTB0KJCcLE96BARoLkWLKroLRERkwpgYkUkoXBj47Tfgyy/lAPAlS4D331c6KiL6LyoV4O0tl7Zts9bfuiXnVTp5UiZMy5bJrnnFir2aLJUvz7mWiIgo95gYkcmwsADGj5dVsfr2lQdS334LeHgoHRkR6crFBWjSRC6ZUlOB8+dlsvTvv3KM4ejRQGIi4Of3asLk6sqxS0REpD0mRmRy3ntPnlmOiAD8/WWy1L8/YMVvO5FRs7aW3esqVZLjCzM9eCATpcwWpl9/lbft7YGKFbMek3m9RAkmTERE9CpWpcsBq9KZjuho4JNPZDnhBQtkNzsiMn0ZGUBcHHD6tDxRcvq0XM6eBRwcmDBR/sOqdETK4zl0MmlVqgAHDwKLFgHNmskB3xMnAk5OSkdGRIZkYQGULSuXli2z1qeny2IPmQnT4cNyTOLZs0CBAq8mS0yYiIjMBxMjMnmWlkC/fkCbNsCQIXIswuzZwIcf8mCHyNxYWmqXMB05AixdKhMmOzv5u5FZUS/zurc3u+gSEZkS/qST2XB1lYO1d+6Uk8IuXAiMGwfUr88EicjcvS5hysgAEhKA2FiZJMXGAtu2ycsHDwBfX/mYzMp62ZdChZTbHyIi0h0TIzI7TZvKgdmLF8u5j0qVAj7/XBZtYIJERNlZWABeXnJp1kzzvvv3ZYJ0+bIcz3TyJLB5s7x+9aqcRsDbGyhTRj6+dGnNhYkTEVH+wuILOWDxBfORkgL88gswdaocXzB6NNC+vTx7TET0tlJTZUtTXJxc4uNlV734eLlcvw44O8sEKXvSlP164cI8WWNOWHyBSHlMjHLAxMj8pKfLEr+TJwNPngCjRgFdu3LSSCIyjNRU4Nq1rEQpe9J05YpscbKze7WVydNTtnJ7egLu7hzjZEqYGBEpjz+pRJAtRB06yIIMv/8OTJoEfPEF0L070KuXHEdARKQv1tZZY5Fykp4uJ67NnjTFxQF//SWTpqtXgeRkmRxlT5ayXy9VCihWjK1ORETaYmJElI1KJccaNW8OREUBP/4o5z4KDAR69wbatZNd7oiIDMnSEvDwkEvdujlv8+hRVpKUkCAvo6LkOKfMdSoVULKkZuJUsqR83pIl5VK0KJMnIiKAiRFRjlQqoFo1ucyYAaxfL4s1DBok50Lq1QuoWlXpKInInBUqJOdbqlgx5/uFAO7e1Uycrl4F9u2T3fiuXZNjnYCsRCl7wpT9uosLu+0RkenjzxzRfyhQAOjWTS7nzsnJIJs3lwcNvXvLRMnZWekoiYg0qVRA8eJyed2JHCGAe/eykqTMy5gYYOvWrHWPHwNublnJkrt7zouzM1ufiMh4sfhCDlh8gf5Laqo8aPjxR9nnv3VrmSRxTiQiMkWPHmUlSdeuATdvAjduaC43b8ougK9LmrIvLFX+KhZfIFIeW4yI3oK1NdCmjVyuXQOWLZOFGqytZTe7sDA5oSwRkSkoVAioUEEur5ORIVufXk6YLl8G/v4763ZiomyJd3OTi6vr668XLSrnkiIiygtsMcoBW4zobWRkAHv2AD/8APz2GxASIj/bpk1l2V0iIpIV927dkgnSzZtyybyefd3Nm3JbF5dXE6fsly4ucjH2wjhsMSJSHluMiPTEwgJo3Fgu9+7JiWPHjwe6dJFjktq0kRXvnJyUjpSISDnZu9u9iRBAUlLOCdSBA/Ly1i253LsHFCyYlSRlT5iyXzeVJIqIDIOJEZEBFC0KDB4sl/h4YNMmYNEi2c2ufn05JqlVK3nGk4iIXqVSyWIOzs5v7sIHyHGfd+5kJUqJiVnXz53TvH33ruwamD1pKlHi9Uvhwhw7SmQumBgRGVjp0llJ0t27smjDxo3A0KFAQIBsSWrdGihXTulIiYiMk7W1dq1QQM5J1J07wO3bwOHD8vL2bXnf7duy5ap4cc1k6eVkqmZNuQ0RGTcmRkR5qFgxWaShe3fgyRNg506ZJE2dKluPWreWS/XqPENJRGQIuiRRmd35MpOl7MvVq8CxY/L6V18xMSIyBUyMiBTi4JBV2S41VU66uGkT0LatvL9VK3lf/fryHzkREeWt7N352KpPZPpYBJMoH7C2lkUb5s6VM9Rv3CiLNAwaJLtsdOggq93FxysdKREREZFpYmJElM+oVLIr3aRJwOnTss97cLAsAV65MlC+PNCnD7BgAXDkCPD8udIRExERERk/dqUjyud8feXSv7/scnf4MHDwILB3LzBzpmxF8vMDqlYFqlWTl4GBsqseEREREWmHiRGREbG2BurVk0umpCQgOhqIigIOHQLmzQMuXpT94TMTpcxkiXMoEREREeWMiRGRkXNyAho0kEumR4+AEydksnT8uByfdO6cHK/k7w9UqiQXf3+gYkU5MSIRERGROWNiRGSCChV6tWUpJQU4f16OWzp1Ss6nNG0acOkSULLkqwmTnx9gb6/cPhARERHlJSZGRGbCxkYmPP7+QMeOWeufPwdiY7MSpvXrgQkT5NglLy/NhKliRcDHhy1MREREZHqYGBGZOTs7IChILtk9eQKcPZuVMP3yi7ydkCC75JUrJ4tCZF76+gJly8rnIyIiIjI2TIyIKEcODrJsePXqmutfvAAuXwYuXJBd8y5cAH7/XV6/eRPw9Hw1YSpXTrY+caJaIiIiyq+YGBGRTmxt5fgjP79X73vyRI5ZykyYTp6UXfMuXADu3we8vWVXPG9vmSh5e2ddL1JEzuFEREREpAQmRkSkNw4OQECAXF6WlCTLiF+4AFy5Iq/v2gXExcnxTLa2ryZL2S8dHfN2X4iIiMi8MDEiojzh5CTnVapW7dX7MjJkN7wrV2SiFBcH/PsvsGWLXJeQIB+fPVny9NRcSpQALCzydp+IiIjIdDAxIiLFWVgAHh5yeeedV+9PSwOuX5cJU2bydOIEsG0bcPWqXFJS5OMzE6WSJTUTp5IlgeLF2V2PiIiIcsbEiIjyPSsroHRpueRECODhQ5kgXbuWlSwdPQps2JC1ToishKlkSZlIubvLxc0t65KV9YiIiMwPEyMiMnoqFVC4sFxyGt8EyKTo3r2spOnqVdl9LyYG2L5dXr9xA7h7Vz7PywlTTtdtbfN0N4mIiMiAmBgRkVlQqYBixeRSpcrrt0tJARITZZJ040ZWwhQVlXX9xg2ZZBUpAri6ynmdXre4usrxTzY2ebevREREpDsmRkRE2djYAKVKyeVNXryQCVRiInDrVtbl9esyibp1K2tJSpKtUK9LnkqUkOOfMhdHR46FIiIiymtMjIiI3oKt7ZvHPWX3/Dlw+7ZmApW5nDwp77tzRy5378oxVcWKaSZL2ZeX7ytShBX5iIiIcouJkcKePlU6AiIyNDs77VqhAFm6/MEDmSBlJkvZl7NnX70vNRUoWlQmTNkvCxWSr21rKy+zLy+vy7yd/TJzsbMDrK3ZikVERKaNiZGCOncGevaU87rUqaN0NESUH1hYyKSmaFGgfPn/3l4I4PFjmSDduyeXu3fl8vix7PL35Ilc//x51vLihebt7Osz78u8LoR8reyJ0suJU/bb2RcbG83L111/3brM5eXb2Re2lhERkT4wMVJQq1bAggVAixbA1q1A3bpKR0RExkalki1DhQoBZcro//mFkPNIvZws5ZRAZd5OScm6/fL1Z89kafU3bZN5PTU1a93LS/aEzcrq9UlT9sXa+u1uZ7/MXN50+7+2zVyY0BER5S9MjBT24YfywOb994Hffst5cksiIqWoVFkH8gULKh2NprS01ydNmZepqXLJvE/b60+fat7OvP9N17XZNi0tK34Li5wTpjctVlavXs9p3X9df/mxr1v3X9u/vGS/j4kfERkbJkb5QPv28uDjgw+ALVuAevWUjoiIKP/LPAAvUEDpSLSX2QKXmSjpsmR/XE7XX3f/kyevPk/m8vLjtVmXmgqkp+f8PBkZWfuqUr05cXrTYmmp3XYvb/u21193qc02lpZyXjMHB+W+V0SkH0yM8ol27eTZtZYtgc2bgXffVToiIiLSt+wtcKYoI+PVpOnlJCvzdk7b5bTuddtkf46XH5eeLlvqXndfTs/58nNpc5l5fdkyIDRU6XefiHKLiVE+0qaN/KfZqhWwaRNQv77SEREREWnPwiKriyARkbFhYpTPtG6tmRwFBysdERERERGR6ePQyHyoVSvgp59kC9LevUpHQ0RERERk+pgY5VMtWwLLl8vkaM8epaMhIiIiIjJtTIzysfffB375BWjbFvjzT6WjISIiIiIyXUyM8rkWLYAVK2TVul27lI6GiIiIiMg0MTEyAu+9B6xaJSeDjYxUOhoiIiIiItPDxMhINGsGrF4NdOgA7NypdDRERERERKYlXyRG8+bNg5eXF+zs7FCrVi0cOXLkjduvW7cOFSpUgJ2dHSpXrozt27dr3C+EwLhx4+Dm5gZ7e3uEhITgwoULhtyFPBEaCqxZA3TsCPzxh9LREBERERGZDsUTozVr1iA8PBwRERGIiopCYGAgQkNDcfv27Ry3P3jwIDp16oRevXohOjoarVu3RuvWrXHq1Cn1Nl9//TXmzJmDBQsW4PDhw3BwcEBoaCieP3+eV7tlME2bAmvXAh99BOzYoXQ0RERERESmQSWEEEoGUKtWLdSoUQPfffcdACAjIwOenp4YOHAgRo0a9cr2HTt2xJMnT7B161b1utq1ayMoKAgLFiyAEALu7u4YOnQohg0bBgBISkqCi4sLli1bho8++ug/Y0pOToaTkxOSkpLg6Oiopz3Vr1275JijFSvkGCQiIiIyXsZw7EFk6qyUfPGUlBQcP34co0ePVq+zsLBASEgIDh06lONjDh06hPDwcI11oaGh2LRpEwAgLi4OiYmJCAkJUd/v5OSEWrVq4dChQzkmRi9evMCLFy/Ut5OTk3OzW3kiJARYvx5o3x4YMAAoWFDpiIiIiMxT27aAr6/SURBRbimaGN29exfp6elwcXHRWO/i4oLY2NgcH5OYmJjj9omJier7M9e9bpuXTZkyBRMmTHirfVBS48bA1q2yYt2DB0pHQ0REZJ6ePVM6AiLSB0UTo/xi9OjRGq1QycnJ8PT0VDAi7b3zjlyIiIiIiOjtKVp8oVixYrC0tMStW7c01t+6dQuurq45PsbV1fWN22de6vKctra2cHR01FiIiIiIiMh8KJoY2djYoFq1ati9e7d6XUZGBnbv3o06derk+Jg6depobA8AkZGR6u29vb3h6uqqsU1ycjIOHz782uckIiIiIiLzpnhXuvDwcISFhaF69eqoWbMmZs+ejSdPnqBHjx4AgG7dusHDwwNTpkwBAAwePBjBwcGYOXMmWrRogdWrV+PYsWNYtGgRAEClUuGzzz7DxIkT4evrC29vb4wdOxbu7u5o3bq1UrtJRERERET5mOKJUceOHXHnzh2MGzcOiYmJCAoKwo4dO9TFExISEmBhkdWwVbduXaxcuRJjxozB559/Dl9fX2zatAn+/v7qbUaMGIEnT57g448/xsOHD1GvXj3s2LEDdnZ2eb5/RERERESU/yk+j1F+xLkEiIiIKC/x2INIeYqOMSIiIiIiIsoPmBgREREREZHZY2JERERERERmj4kRERERERGZPSZGRERERERk9pgYERERERGR2WNiREREREREZo+JERERERERmT0mRkREREREZPaYGBERERERkdljYkRERERERGaPiREREREREZk9JkZERERERGT2mBgREREREZHZY2JERERERERmj4kRERERERGZPSZGRERERERk9pgYERERERGR2bNSOoD8SAgBAEhOTlY4EiIiIjIHmcccmccgRJT3mBjl4NGjRwAAT09PhSMhIiIic/Lo0SM4OTkpHQaRWVIJnpp4RUZGBm7cuIFChQpBpVIZ7HWSk5Ph6emJq1evwtHR0WCvQznj+688fgbK42egLL7/yssvn4EQAo8ePYK7uzssLDjSgUgJbDHKgYWFBUqWLJlnr+fo6Mh/iAri+688fgbK42egLL7/yssPnwFbioiUxVMSRERERERk9pgYERERERGR2WNipCBbW1tERETA1tZW6VDMEt9/5fEzUB4/A2Xx/VcePwMiysTiC0REREREZPbYYkRERERERGaPiREREREREZk9JkZERERERGT2mBgREREREZHZY2KkR/PmzYOXlxfs7OxQq1YtHDly5I3br1u3DhUqVICdnR0qV66M7du3a9wvhMC4cePg5uYGe3t7hISE4MKFC4bcBaOn789gw4YNaNq0KYoWLQqVSoWYmBgDRm8a9PkZpKamYuTIkahcuTIcHBzg7u6Obt264caNG4beDaOl77+B8ePHo0KFCnBwcEDhwoUREhKCw4cPG3IXjJ6+P4PsPvnkE6hUKsyePVvPUZsOfb//3bt3h0ql0liaNWtmyF0gIqUI0ovVq1cLGxsbsWTJEnH69GnRp08f4ezsLG7dupXj9gcOHBCWlpbi66+/FmfOnBFjxowR1tbW4t9//1VvM3XqVOHk5CQ2bdokTpw4IVq2bCm8vb3Fs2fP8mq3jIohPoPly5eLCRMmiMWLFwsAIjo6Oo/2xjjp+zN4+PChCAkJEWvWrBGxsbHi0KFDombNmqJatWp5uVtGwxB/AytWrBCRkZHi0qVL4tSpU6JXr17C0dFR3L59O692y6gY4jPItGHDBhEYGCjc3d3FrFmzDLwnxskQ739YWJho1qyZuHnzpnq5f/9+Xu0SEeUhJkZ6UrNmTdG/f3/17fT0dOHu7i6mTJmS4/YdOnQQLVq00FhXq1Yt0bdvXyGEEBkZGcLV1VVMnz5dff/Dhw+Fra2tWLVqlQH2wPjp+zPILi4ujomRFgz5GWQ6cuSIACDi4+P1E7QJyYv3PykpSQAQu3bt0k/QJsZQn8G1a9eEh4eHOHXqlChdujQTo9cwxPsfFhYmWrVqZZB4iSh/YVc6PUhJScHx48cREhKiXmdhYYGQkBAcOnQox8ccOnRIY3sACA0NVW8fFxeHxMREjW2cnJxQq1at1z6nOTPEZ0C6yavPICkpCSqVCs7OznqJ21TkxfufkpKCRYsWwcnJCYGBgfoL3kQY6jPIyMhA165dMXz4cFSqVMkwwZsAQ/4N7N27FyVKlED58uXRr18/3Lt3T/87QESKY2KkB3fv3kV6ejpcXFw01ru4uCAxMTHHxyQmJr5x+8xLXZ7TnBniMyDd5MVn8Pz5c4wcORKdOnWCo6OjfgI3EYZ8/7du3YqCBQvCzs4Os2bNQmRkJIoVK6bfHTABhvoMpk2bBisrKwwaNEj/QZsQQ73/zZo1w/Lly7F7925MmzYNf/31F5o3b4709HT97wQRKcpK6QCIiLSRmpqKDh06QAiB+fPnKx2OWWnYsCFiYmJw9+5dLF68GB06dMDhw4dRokQJpUMzecePH8e3336LqKgoqFQqpcMxSx999JH6euXKlREQEICyZcti7969aNy4sYKREZG+scVID4oVKwZLS0vcunVLY/2tW7fg6uqa42NcXV3fuH3mpS7Pac4M8RmQbgz5GWQmRfHx8YiMjGRrUQ4M+f47ODjAx8cHtWvXxo8//ggrKyv8+OOP+t0BE2CIz2D//v24ffs2SpUqBSsrK1hZWSE+Ph5Dhw6Fl5eXQfbDWOXV/4EyZcqgWLFiuHjxYu6DJqJ8hYmRHtjY2KBatWrYvXu3el1GRgZ2796NOnXq5PiYOnXqaGwPAJGRkertvb294erqqrFNcnIyDh8+/NrnNGeG+AxIN4b6DDKTogsXLmDXrl0oWrSoYXbAyOXl30BGRgZevHiR+6BNjCE+g65du+LkyZOIiYlRL+7u7hg+fDj++OMPw+2MEcqrv4Fr167h3r17cHNz00/gRJR/KF39wVSsXr1a2NraimXLlokzZ86Ijz/+WDg7O4vExEQhhBBdu3YVo0aNUm9/4MABYWVlJWbMmCHOnj0rIiIicizX7ezsLDZv3ixOnjwpWrVqxXLdb2CIz+DevXsiOjpabNu2TQAQq1evFtHR0eLmzZt5vn/GQN+fQUpKimjZsqUoWbKkiImJ0SiX++LFC0X2MT/T9/v/+PFjMXr0aHHo0CFx5coVcezYMdGjRw9ha2srTp06pcg+5neG+B16GavSvZ6+3/9Hjx6JYcOGiUOHDom4uDixa9cuUbVqVeHr6yueP3+uyD4SkeEwMdKjuXPnilKlSgkbGxtRs2ZN8c8//6jvCw4OFmFhYRrbr127VpQrV07Y2NiISpUqiW3btmncn5GRIcaOHStcXFyEra2taNy4sTh37lxe7IrR0vdnsHTpUgHglSUiIiIP9sY46fMzyCyTntOyZ8+ePNoj46LP9//Zs2eiTZs2wt3dXdjY2Ag3NzfRsmVLceTIkbzaHaOk79+hlzExejN9vv9Pnz4VTZs2FcWLFxfW1taidOnSok+fPupEi4hMi0oIIZRpqyIiIiIiIsofOMaIiIiIiIjMHhMjIiIiIiIye0yMiIiIiIjI7DExIiIiIiIis8fEiIiIiIiIzB4TIyIiIiIiMntMjIiIiIiIyOwxMSIiIiIiIrPHxIhM0pUrV6BSqRATE6P1Y7p3747WrVvrNY6NGzfCysoK5cqVw+3bt/X63P9l0aJF8PT0hIWFBWbPnp2nr53d23wW+Vl+2Z/ExEQ0adIEDg4OcHZ2VjQWAFCpVNi0aZOiMWj72TRo0ACfffZZnsRERETGg4kR5Znu3btDpVJBpVLBxsYGPj4++PLLL5GWlpbr5305ofH09MTNmzfh7++fq+fObu/ever4VSoVihcvjvfeew///vtvjtvv2bMHnTt3xvjx41GiRAk0a9YMycnJGttcuXIFvXr1gre3N+zt7VG2bFlEREQgJSUlV7EmJydjwIABGDlyJK5fv46PP/44V89H+c+sWbNw8+ZNxMTE4Pz580qHky+8/Hef+Tf78OHDXD+3IU6cvMn48eMRFBSUZ6/3X/T5XhIR5VdMjChPNWvWDDdv3sSFCxcwdOhQjB8/HtOnT3+r50pPT0dGRkaO91laWsLV1RVWVla5CTdH586dw82bN/HHH3/gxYsXaNGixSuJzPHjx9GmTRvMmjULY8aMwR9//IEiRYqgVatWePHihXq72NhYZGRkYOHChTh9+jRmzZqFBQsW4PPPP89VjAkJCUhNTUWLFi3g5uaGAgUK5Or5jE1uE0tjcOnSJVSrVg2+vr4oUaKE0uHkmj4+M0P+3WsrNTVVsdcmIqJcEkR5JCwsTLRq1UpjXZMmTUTt2rWFEELMnDlT+Pv7iwIFCoiSJUuKfv36iUePHqm3Xbp0qXBychKbN28Wfn5+wtLSUoSFhQkAGsuePXtEXFycACCio6OFEEKkpaWJnj17Ci8vL2FnZyfKlSsnZs+e/Z/xZbdnzx4BQDx48EC9bsuWLQKAOHHihHpdbGyscHV1FcuXL9d4/PPnz8UHH3wg2rRpI9LS0l77Ol9//bXw9vZ+7f1CCBEfHy9atmwpHBwcRKFChcSHH34oEhMT1e/Ty+9JXFycVvsTHR2tsX3me75jxw5RoUIF4eDgIEJDQ8WNGzfUj0lPTxcTJkwQHh4ewsbGRgQGBorff/9dfX/mZ7Fq1SpRp04dYWtrKypVqiT27t2r3ub+/fuic+fOolixYsLOzk74+PiIJUuWqO9PSEgQH374oXBychKFCxcWLVu21NinzM9u4sSJws3NTXh5eYnRo0eLmjVrvrLfAQEBYsKECerbixcvFhUqVBC2traifPnyYt68eRrbHz58WAQFBQlbW1tRrVo1sWHDBo3vVk6eP38uRowYIUqWLClsbGxE2bJlxQ8//KC+f+/evaJGjRrCxsZGuLq6ipEjR4rU1FT1/cHBwWLgwIFi+PDhonDhwsLFxUVERESo7y9durTG5xsWFiaEePP3Ivv7lN3gwYNFcHCw1q8thBDnz58X7777rrC1tRV+fn5i586dAoDYuHGjepu3+cxe9vDhQ2FhYSGOHj0qhJDftcKFC4tatWqpt/n5559FyZIlhRBC4+8+83pO75M2+5hdRETEG39nVq9eLerXry9sbW3F0qVLhRD//b0aMWKE8PX1Ffb29sLb21uMGTNGpKSkCCFy/hvOfF4AYsGCBaJFixbC3t5eVKhQQRw8eFBcuHBBBAcHiwIFCog6deqIixcvarzepk2bRJUqVYStra3w9vYW48eP1/jOARCLFy8WrVu3Fvb29sLHx0ds3rxZ433N6b0kIjIlTIwoz+R0UNayZUtRtWpVIYQQs2bNEn/++aeIi4sTu3fvFuXLlxf9+vVTb7t06VJhbW0t6tatKw4cOCBiY2NFUlKS6NChg2jWrJm4efOmuHnzpnjx4sUriVFKSooYN26cOHr0qLh8+bL45ZdfRIECBcSaNWveGF92LycSDx8+FJ07dxYAxNmzZ/XyHgkhxBdffCGqVav22vvT09NFUFCQqFevnjh27Jj4559/RLVq1dQHt0+fPhW7du0SAMSRI0fEzZs3c0zEtE2MrK2tRUhIiDh69Kg4fvy48PPzE507d1Y/5ptvvhGOjo5i1apVIjY2VowYMUJYW1uL8+fPCyGyDqpKliwp1q9fL86cOSN69+4tChUqJO7evSuEEKJ///4iKChIHD16VMTFxYnIyEixZcsWIYT87Pz8/ETPnj3FyZMnxZkzZ0Tnzp1F+fLlxYsXL4QQ8rMrWLCg6Nq1qzh16pR6AaBxgJi57sKFC0IIIX755Rfh5uYmfv31V3H58mXx66+/iiJFiohly5YJIYR49OiRKF68uOjcubM4deqU+O2330SZMmX+MzHq0KGD8PT0FBs2bBCXLl0Su3btEqtXrxZCCHHt2jVRoEAB8emnn4qzZ8+KjRs3imLFimkcmAcHBwtHR0cxfvx4cf78efHTTz8JlUoldu7cKYQQ4vbt26JZs2aiQ4cO4ubNm+Lhw4f/+b3IfJ+0SYze9Nrp6enC399fNG7cWMTExIi//vpLVKlSRSMxetvPLCdVq1YV06dPF0IIERMTI4oUKSJsbGzUJ0169+4tunTpovFdi46OFmlpaeLXX38VAMS5c+fU75M2+/iyR48evfF3xsvLS/0dunHjxn9+r4QQ4quvvhIHDhwQcXFxYsuWLcLFxUVMmzZNCCH/hocOHSoqVaqkfr2nT58KIWQC4+HhIdasWSPOnTsnWrduLby8vESjRo3Ejh07xJkzZ0Tt2rVFs2bN1K+1b98+4ejoKJYtWyYuXbokdu7cKby8vMT48ePV22T+ja5cuVJcuHBBDBo0SBQsWFDcu3fvje8lEZEpYWJEeSb7QVlGRoaIjIwUtra2YtiwYTluv27dOlG0aFH17cyzqDExMa993kwvJ0Y56d+/v2jXrt0bnye7zETCwcFBODg4qM+ctmzZ8rWP0dWFCxeEo6OjWLRo0Wu32blzp7C0tBQJCQnqdadPn1YnQkK8muC8aX/+KzF6ObmYN2+ecHFxUd92d3cXkyZN0njuGjVqiE8//VQIkfVZTJ06VX1/amqqKFmypPpA8IMPPhA9evTIMc6ff/5ZlC9fXmRkZKjXvXjxQtjb24s//vhDCCE/OxcXF/VBd6bAwEDx5Zdfqm+PHj1ao7WhbNmyYuXKlRqP+eqrr0SdOnWEEEIsXLhQFC1aVDx79kx9//z589/43Tp37pwAICIjI3O8//PPP39lf+bNmycKFiwo0tPThRDywL1evXoaj6tRo4YYOXKk+narVq00ztpr873QNjF602v/8ccfwsrKSly/fl19/++//66RGOXmM3tZeHi4aNGihRBCiNmzZ4uOHTtqtEr6+Pio/15e/rvP6TuuzT7m5E2/My+3Pv/X9yon06dP1zghEhERIQIDA1/ZDoAYM2aM+vahQ4cEAPHjjz+q161atUrY2dmpbzdu3FhMnjxZ43l+/vln4ebm9trnffz4sQCgfp9f914SEZkS5Tpik1naunUrChYsiNTUVGRkZKiLEwDArl27MGXKFMTGxiI5ORlpaWl4/vw5nj59qh4jY2Njg4CAgLd67Xnz5mHJkiVISEjAs2fPkJKS8laDm/fv348CBQrgn3/+weTJk7FgwYK3iudl169fR7NmzfDhhx+iT58+r93u7Nmz8PT0hKenp3pdxYoV4ezsjLNnz6JGjRp6iSdTgQIFULZsWfVtNzc3dYW95ORk3LhxA++8847GY9555x2cOHFCY12dOnXU162srFC9enWcPXsWANCvXz+0a9cOUVFRaNq0KVq3bo26desCAE6cOIGLFy+iUKFCGs/3/PlzXLp0SX27cuXKsLGx0dimS5cuWLJkCcaOHQshBFatWoXw8HAAwJMnT3Dp0iX06tVL4/1OS0uDk5MTAPleBwQEwM7OLsf9yElMTAwsLS0RHByc4/1nz55FnTp1oFKpNN6vx48f49q1ayhVqhQAvPI9z/6+v+559fW9eNNrZ76Ou7u7+v6X35PcfGYvCw4Oxo8//oj09HT89ddfaNq0KVxdXbF3714EBATg4sWLaNCggdb7ps0+6qp69erq69p8rwBgzZo1mDNnDi5duoTHjx8jLS0Njo6OOsfu4uICQL6X2dc9f/4cycnJcHR0xIkTJ3DgwAFMmjRJvU16evorv6/Zn9fBwQGOjo55Xk2TiEhJTIwoTzVs2BDz58+HjY0N3N3d1YOkr1y5gvfffx/9+vXDpEmTUKRIEfz999/o1asXUlJS1P+47e3tNQ4otbV69WoMGzYMM2fORJ06dVCoUCFMnz4dhw8f1vm5vL294ezsjPLly+P27dvo2LEj9u3bp/PzZHfjxg00bNgQdevWxaJFi3L1XNqysJC1V4QQ6nU5DRy3trbWuK1SqTQeow/NmzdHfHw8tm/fjsjISDRu3Bj9+/fHjBkz8PjxY1SrVg0rVqx45XHFixdXX3dwcHjl/k6dOmHkyJGIiorCs2fPcPXqVXTs2BEA8PjxYwDA4sWLUatWLY3HWVpavvW+2Nvbv/Vjs8vpfX9dsRFtWVhYvPLZafuZ6/LaufnMXla/fn08evQIUVFR2LdvHyZPngxXV1dMnToVgYGBcHd3h6+vr9axZdLn+5t9P7T5Xh06dAhdunTBhAkTEBoaCicnJ6xevRozZ87UOfbM38Oc1mXuz+PHjzFhwgS0bdv2lefKnvQb4jtHRGRMmBhRnnJwcICPj88r648fP46MjAzMnDlTfcC+du1arZ7TxsYG6enpb9zmwIEDqFu3Lj799FP1uuxnrt9W//79MWXKFGzcuBFt2rR5q+e4fv06GjZsiGrVqmHp0qXq/X8dPz8/XL16FVevXlW3Dpw5cwYPHz5ExYoVtX7dzAPUmzdvonDhwgCg89w8jo6OcHd3x4EDBzRaSA4cOICaNWtqbPvPP/+gfv36AOTZ8+PHj2PAgAEa8YSFhSEsLAzvvvsuhg8fjhkzZqBq1apYs2YNSpQoofUZ9UwlS5ZEcHAwVqxYgWfPnqFJkybqCm4uLi5wd3fH5cuX0aVLlxwf7+fnh59//hnPnz9XH0D+888/b3zNypUrIyMjA3/99RdCQkJyfM5ff/0VQgj1AeyBAwdQqFAhlCxZUqf9e/l5/+t7Ubx4cZw6dUrjcTExMa8cEGvzOjdv3oSbmxuAV9+T3HxmL3N2dkZAQAC+++47WFtbo0KFCihRogQ6duyIrVu3vrZlDoC6Neq/fh+0oc3vDKDd9+rgwYMoXbo0vvjiC/W6+Pj4t3o9bVStWhXnzp3L8bdXW/p8L4mI8iuW66Z8wcfHB6mpqZg7dy4uX76Mn3/+Wesual5eXjh58iTOnTuHu3fv5ngG3NfXF8eOHcMff/yB8+fPY+zYsTh69Giu4y5QoAD69OmDiIiIt2pFuX79Oho0aIBSpUphxowZuHPnDhITE5GYmPjax4SEhKBy5cro0qULoqKicOTIEXTr1g3BwcEaXXr+i4+PDzw9PTF+/HhcuHAB27Zt0/qMdXbDhw/HtGnTsGbNGpw7dw6jRo1CTEwMBg8erLHdvHnzsHHjRsTGxqJ///548OABevbsCQAYN24cNm/ejIsXL+L06dPYunUr/Pz8AMjucMWKFUOrVq2wf/9+xMXFYe/evRg0aBCuXbv2n/F16dIFq1evxrp16145UJ0wYQKmTJmCOXPm4Pz58/j333+xdOlSfPPNNwCAzp07Q6VSoU+fPjhz5gy2b9+OGTNmvPH1vLy8EBYWhp49e2LTpk3qeDMT/U8//RRXr17FwIEDERsbi82bNyMiIgLh4eH/mRS/iTbfi0aNGuHYsWNYvnw5Lly4gIiIiFcSJW1ep1y5cggLC8OJEyewf/9+jQN8IPef2csaNGiAFStWqJOgIkWKwM/PD2vWrHljYlS6dGmoVCps3boVd+7cUbfmvA1tfmcy/df3ytfXFwkJCVi9ejUuXbqEOXPmYOPGja+8XlxcHGJiYnD37l2NMv+6GjduHJYvX44JEybg9OnTOHv2LFavXo0xY8Zo/Rz6fC+JiPIrJkaULwQGBuKbb77BtGnT4O/vjxUrVmDKlClaPbZPnz4oX748qlevjuLFi+PAgQOvbNO3b1+0bdsWHTt2RK1atXDv3j2N1qPcGDBgAM6ePYt169bp/NjIyEhcvHgRu3fvRsmSJeHm5qZeXkelUmHz5s0oXLgw6tevj5CQEJQpUwZr1qzR6bWtra2xatUqxMbGIiAgANOmTcPEiRN13odBgwYhPDwcQ4cOReXKlbFjxw5s2bLlle5NU6dOVXd/+vvvv7FlyxYUK1YMgDwbPXr0aAQEBKB+/fqwtLTE6tWrAcjkc9++fShVqhTatm0LPz8/9OrVC8+fP9eqNaJ9+/a4d+8enj59+soEnb1798YPP/yApUuXonLlyggODsayZcvg7e0NAChYsCB+++03/Pvvv6hSpQq++OILTJs27T9fc/78+Wjfvj0+/fRTVKhQAX369MGTJ08AAB4eHti+fTuOHDmCwMBAfPLJJ+jVq5dOB6k50eZ7ERoairFjx2LEiBGoUaMGHj16hG7duun0OhYWFti4cSOePXuGmjVronfv3hpjV4Dcf2YvCw4ORnp6usZYogYNGryy7mUeHh6YMGECRo0aBRcXF40WSl1p8zuT6b++Vy1btsSQIUMwYMAABAUF4eDBgxg7dqzGc7Rr1w7NmjVDw4YNUbx4caxateqtYw8NDcXWrVuxc+dO1KhRA7Vr18asWbNQunRprZ9Dn+8lEVF+pRL6HixARERERERkZNhiREREREREZo+JERERERERmT0mRkREREREZPaYGBERERERkdljYkRERERERGaPiREREREREZk9JkZERERERGT2mBgREREREZHZY2JERERERERmj4kRERERERGZPSZGRERERERk9v4PusAn9nWVopEAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
"
       ]
@@ -855,10 +855,10 @@
    "id": "934eef00",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:12.326659Z",
-     "iopub.status.busy": "2024-10-20T00:22:12.326230Z",
-     "iopub.status.idle": "2024-10-20T00:22:12.370559Z",
-     "shell.execute_reply": "2024-10-20T00:22:12.370043Z"
+     "iopub.execute_input": "2024-10-22T01:48:56.232155Z",
+     "iopub.status.busy": "2024-10-22T01:48:56.231738Z",
+     "iopub.status.idle": "2024-10-22T01:48:56.275974Z",
+     "shell.execute_reply": "2024-10-22T01:48:56.275430Z"
     }
    },
    "outputs": [
@@ -896,14 +896,14 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.048629\n",
-       "      0.052047\n",
-       "      11.679949\n",
-       "      1.025023\n",
-       "      10.654926\n",
-       "      12.704971\n",
-       "      9.508112\n",
-       "      13.825284\n",
+       "      0.048682\n",
+       "      0.055065\n",
+       "      11.738281\n",
+       "      1.058551\n",
+       "      10.67973\n",
+       "      12.796833\n",
+       "      9.528129\n",
+       "      13.9211\n",
        "    \n",
        "  \n",
        "\n",
@@ -911,10 +911,10 @@
       ],
       "text/plain": [
        "     r2tu_w   r2yu_tw  short estimate      bias  lower_ate_bound  \\\n",
-       "0  0.048629  0.052047       11.679949  1.025023        10.654926   \n",
+       "0  0.048682  0.055065       11.738281  1.058551         10.67973   \n",
        "\n",
        "   upper_ate_bound  lower_confidence_bound  upper_confidence_bound  \n",
-       "0        12.704971                9.508112               13.825284  "
+       "0        12.796833                9.528129                 13.9211  "
       ]
      },
      "execution_count": 13,
@@ -941,10 +941,10 @@
    "id": "7cff3837",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:12.372540Z",
-     "iopub.status.busy": "2024-10-20T00:22:12.372350Z",
-     "iopub.status.idle": "2024-10-20T00:22:12.962666Z",
-     "shell.execute_reply": "2024-10-20T00:22:12.962041Z"
+     "iopub.execute_input": "2024-10-22T01:48:56.278095Z",
+     "iopub.status.busy": "2024-10-22T01:48:56.277725Z",
+     "iopub.status.idle": "2024-10-22T01:48:56.871617Z",
+     "shell.execute_reply": "2024-10-22T01:48:56.871061Z"
     }
    },
    "outputs": [
@@ -975,17 +975,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W1,W6,W2,W4,W5,W3])\n",
+      "─────(E[y|W5,W3,W6,W2,W1,W4])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W6,W2,W4,W5,W3,U) = P(y|v0,W1,W6,W2,W4,W5,W3)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W2,W1,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W1+W6+W2+W4+W5+W3 | \n",
+      "b: y~v0+W5+W3+W6+W2+W1+W4 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 11.815195660841939\n",
-      "Effect estimates: [[11.81519566]]\n",
+      "Mean value: 11.80537621178161\n",
+      "Effect estimates: [[11.80537621]]\n",
       "\n"
      ]
     }
@@ -1017,17 +1017,17 @@
    "id": "946e1237",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:12.964673Z",
-     "iopub.status.busy": "2024-10-20T00:22:12.964378Z",
-     "iopub.status.idle": "2024-10-20T00:22:27.998838Z",
-     "shell.execute_reply": "2024-10-20T00:22:27.998195Z"
+     "iopub.execute_input": "2024-10-22T01:48:56.873608Z",
+     "iopub.status.busy": "2024-10-22T01:48:56.873218Z",
+     "iopub.status.idle": "2024-10-22T01:49:12.034724Z",
+     "shell.execute_reply": "2024-10-22T01:49:12.034044Z"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1041,7 +1041,7 @@
      "text": [
       "Sensitivity Analysis to Unobserved Confounding using partial R^2 parameterization\n",
       "\n",
-      "Original Effect Estimate : 11.815195660841939\n",
+      "Original Effect Estimate : 11.80537621178161\n",
       "Robustness Value : 0.66\n",
       "\n",
       "Robustness Value (alpha=0.05) : 0.64\n",
diff --git a/main/example_notebooks/sensitivity_analysis_testing.html b/main/example_notebooks/sensitivity_analysis_testing.html
index 529d68833..d39f799b0 100644
--- a/main/example_notebooks/sensitivity_analysis_testing.html
+++ b/main/example_notebooks/sensitivity_analysis_testing.html
@@ -908,9 +908,9 @@ 
Step 4: IdentificationStep 5: Estimation
 
 {'estimate': 10.697677486880917,
- 'standard_error': 0.5938735661282945,
+ 'standard_error': 0.5938735661282948,
  'degree of freedom': 492,
- 't_statistic': 18.013392238727626,
- 'r2yt_w': 0.3974149837266682,
- 'partial_f2': 0.6595168698094567,
- 'robustness_value': 0.5467445572181009,
- 'robustness_value_alpha': 0.5076289101030926}
+ 't_statistic': 18.013392238727615,
+ 'r2yt_w': 0.39741498372666795,
+ 'partial_f2': 0.6595168698094559,
+ 'robustness_value': 0.5467445572181008,
+ 'robustness_value_alpha': 0.5076289101030925}
 

 
 

@@ -1129,13 +1129,13 @@ 
Parameter List for plot function
 
 {'converted_estimate': 2.457447065735422,
- 'converted_lower_ci': 2.228856727004314,
- 'converted_upper_ci': 2.709481505797994,
+ 'converted_lower_ci': 2.2288567270043136,
+ 'converted_upper_ci': 2.7094815057979944,
  'evalue_estimate': 4.349958364289862,
- 'evalue_lower_ci': 3.883832733630413,
+ 'evalue_lower_ci': 3.883832733630412,
  'evalue_upper_ci': None}
 

 
@@ -1311,14 +1311,14 @@ 
Step 6b: Refutation and Sensitivity Analysis - Method 2Step 6b: Refutation and Sensitivity Analysis - Method 2Step 6b: Refutation and Sensitivity Analysis - Method 2
 
@@ -1603,14 +1603,14 @@ 
Step 6b: Refutation and Sensitivity Analysis - Method 2Step 6b: Refutation and Sensitivity Analysis - Method 2
 

 
-{'estimate': 10.081924375588304,
- 'standard_error': 0.10446229543424757,
+{'estimate': 10.081924375588297,
+ 'standard_error': 0.10446229543424743,
  'degree of freedom': 491,
- 't_statistic': 96.51256784735541,
- 'r2yt_w': 0.9499269594066172,
- 'partial_f2': 18.970826379817478,
+ 't_statistic': 96.51256784735548,
+ 'r2yt_w': 0.9499269594066173,
+ 'partial_f2': 18.970826379817506,
  'robustness_value': 0.9522057801012398,
  'robustness_value_alpha': 0.950386691319526}
 

diff --git a/main/example_notebooks/sensitivity_analysis_testing.ipynb b/main/example_notebooks/sensitivity_analysis_testing.ipynb
index 82f2fffa0..54b8dd839 100644
--- a/main/example_notebooks/sensitivity_analysis_testing.ipynb
+++ b/main/example_notebooks/sensitivity_analysis_testing.ipynb
@@ -34,10 +34,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:32.906159Z",
-     "iopub.status.busy": "2024-10-20T00:22:32.905665Z",
-     "iopub.status.idle": "2024-10-20T00:22:34.342142Z",
-     "shell.execute_reply": "2024-10-20T00:22:34.341530Z"
+     "iopub.execute_input": "2024-10-22T01:49:17.285794Z",
+     "iopub.status.busy": "2024-10-22T01:49:17.285613Z",
+     "iopub.status.idle": "2024-10-22T01:49:18.798503Z",
+     "shell.execute_reply": "2024-10-22T01:49:18.797849Z"
     }
    },
    "outputs": [],
@@ -82,10 +82,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:34.344525Z",
-     "iopub.status.busy": "2024-10-20T00:22:34.344002Z",
-     "iopub.status.idle": "2024-10-20T00:22:34.360838Z",
-     "shell.execute_reply": "2024-10-20T00:22:34.360234Z"
+     "iopub.execute_input": "2024-10-22T01:49:18.801016Z",
+     "iopub.status.busy": "2024-10-22T01:49:18.800502Z",
+     "iopub.status.idle": "2024-10-22T01:49:18.817596Z",
+     "shell.execute_reply": "2024-10-22T01:49:18.817051Z"
     }
    },
    "outputs": [],
@@ -105,10 +105,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:34.362813Z",
-     "iopub.status.busy": "2024-10-20T00:22:34.362438Z",
-     "iopub.status.idle": "2024-10-20T00:22:34.519800Z",
-     "shell.execute_reply": "2024-10-20T00:22:34.519241Z"
+     "iopub.execute_input": "2024-10-22T01:49:18.819794Z",
+     "iopub.status.busy": "2024-10-22T01:49:18.819392Z",
+     "iopub.status.idle": "2024-10-22T01:49:18.982923Z",
+     "shell.execute_reply": "2024-10-22T01:49:18.982310Z"
     }
    },
    "outputs": [
@@ -277,10 +277,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:34.521903Z",
-     "iopub.status.busy": "2024-10-20T00:22:34.521668Z",
-     "iopub.status.idle": "2024-10-20T00:22:34.666560Z",
-     "shell.execute_reply": "2024-10-20T00:22:34.665921Z"
+     "iopub.execute_input": "2024-10-22T01:49:18.985277Z",
+     "iopub.status.busy": "2024-10-22T01:49:18.984908Z",
+     "iopub.status.idle": "2024-10-22T01:49:19.150369Z",
+     "shell.execute_reply": "2024-10-22T01:49:19.149737Z"
     }
    },
    "outputs": [
@@ -444,10 +444,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:34.668515Z",
-     "iopub.status.busy": "2024-10-20T00:22:34.668324Z",
-     "iopub.status.idle": "2024-10-20T00:22:34.683703Z",
-     "shell.execute_reply": "2024-10-20T00:22:34.683111Z"
+     "iopub.execute_input": "2024-10-22T01:49:19.153006Z",
+     "iopub.status.busy": "2024-10-22T01:49:19.152471Z",
+     "iopub.status.idle": "2024-10-22T01:49:19.172636Z",
+     "shell.execute_reply": "2024-10-22T01:49:19.172031Z"
     }
    },
    "outputs": [
@@ -461,9 +461,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W5,W6,W1,W3,W2,W0])\n",
+      "─────(E[y|W1,W3,W0,W2,W6,W5])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W3,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W0,W2,W6,W5)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -494,10 +494,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:34.685523Z",
-     "iopub.status.busy": "2024-10-20T00:22:34.685339Z",
-     "iopub.status.idle": "2024-10-20T00:22:34.702098Z",
-     "shell.execute_reply": "2024-10-20T00:22:34.701480Z"
+     "iopub.execute_input": "2024-10-22T01:49:19.174964Z",
+     "iopub.status.busy": "2024-10-22T01:49:19.174552Z",
+     "iopub.status.idle": "2024-10-22T01:49:19.195621Z",
+     "shell.execute_reply": "2024-10-22T01:49:19.194957Z"
     }
    },
    "outputs": [
@@ -514,12 +514,12 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                          \n",
-      "─────(E[y|W5,W6,W1,W3,W2,W0])\n",
+      "─────(E[y|W1,W3,W0,W2,W6,W5])\n",
       "d[v₀]                        \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W3,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W0,W2,W6,W5)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W5+W6+W1+W3+W2+W0\n",
+      "b: y~v0+W1+W3+W0+W2+W6+W5\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
@@ -557,10 +557,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:34.703979Z",
-     "iopub.status.busy": "2024-10-20T00:22:34.703793Z",
-     "iopub.status.idle": "2024-10-20T00:22:39.042465Z",
-     "shell.execute_reply": "2024-10-20T00:22:39.041917Z"
+     "iopub.execute_input": "2024-10-22T01:49:19.197908Z",
+     "iopub.status.busy": "2024-10-22T01:49:19.197694Z",
+     "iopub.status.idle": "2024-10-22T01:49:23.561626Z",
+     "shell.execute_reply": "2024-10-22T01:49:23.561038Z"
     },
     "scrolled": false
    },
@@ -599,10 +599,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:39.044477Z",
-     "iopub.status.busy": "2024-10-20T00:22:39.044087Z",
-     "iopub.status.idle": "2024-10-20T00:22:39.048304Z",
-     "shell.execute_reply": "2024-10-20T00:22:39.047675Z"
+     "iopub.execute_input": "2024-10-22T01:49:23.563472Z",
+     "iopub.status.busy": "2024-10-22T01:49:23.563279Z",
+     "iopub.status.idle": "2024-10-22T01:49:23.567575Z",
+     "shell.execute_reply": "2024-10-22T01:49:23.566967Z"
     }
    },
    "outputs": [
@@ -610,13 +610,13 @@
      "data": {
       "text/plain": [
        "{'estimate': 10.697677486880917,\n",
-       " 'standard_error': 0.5938735661282945,\n",
+       " 'standard_error': 0.5938735661282948,\n",
        " 'degree of freedom': 492,\n",
-       " 't_statistic': 18.013392238727626,\n",
-       " 'r2yt_w': 0.3974149837266682,\n",
-       " 'partial_f2': 0.6595168698094567,\n",
-       " 'robustness_value': 0.5467445572181009,\n",
-       " 'robustness_value_alpha': 0.5076289101030926}"
+       " 't_statistic': 18.013392238727615,\n",
+       " 'r2yt_w': 0.39741498372666795,\n",
+       " 'partial_f2': 0.6595168698094559,\n",
+       " 'robustness_value': 0.5467445572181008,\n",
+       " 'robustness_value_alpha': 0.5076289101030925}"
       ]
      },
      "execution_count": 8,
@@ -633,10 +633,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:39.050094Z",
-     "iopub.status.busy": "2024-10-20T00:22:39.049780Z",
-     "iopub.status.idle": "2024-10-20T00:22:39.057855Z",
-     "shell.execute_reply": "2024-10-20T00:22:39.057234Z"
+     "iopub.execute_input": "2024-10-22T01:49:23.569420Z",
+     "iopub.status.busy": "2024-10-22T01:49:23.569048Z",
+     "iopub.status.idle": "2024-10-22T01:49:23.576576Z",
+     "shell.execute_reply": "2024-10-22T01:49:23.576099Z"
     }
    },
    "outputs": [
@@ -757,10 +757,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:39.059975Z",
-     "iopub.status.busy": "2024-10-20T00:22:39.059559Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.562103Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.561470Z"
+     "iopub.execute_input": "2024-10-22T01:49:23.578305Z",
+     "iopub.status.busy": "2024-10-22T01:49:23.577978Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.129916Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.129269Z"
     }
    },
    "outputs": [
@@ -792,10 +792,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.564285Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.563933Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.567326Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.566839Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.132073Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.131715Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.135223Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.134704Z"
     },
     "scrolled": true
    },
@@ -809,13 +809,13 @@
       "Unadjusted Estimates of Treatment ['v0'] :\n",
       "Coefficient Estimate : 10.697677486880917\n",
       "Degree of Freedom : 492\n",
-      "Standard Error : 0.5938735661282945\n",
-      "t-value : 18.013392238727626\n",
-      "F^2 value : 0.6595168698094567\n",
+      "Standard Error : 0.5938735661282948\n",
+      "t-value : 18.013392238727615\n",
+      "F^2 value : 0.6595168698094559\n",
       "\n",
       "Sensitivity Statistics : \n",
-      "Partial R2 of treatment with outcome : 0.3974149837266682\n",
-      "Robustness Value : 0.5467445572181009\n",
+      "Partial R2 of treatment with outcome : 0.39741498372666795\n",
+      "Robustness Value : 0.5467445572181008\n",
       "\n",
       "Interpretation of results :\n",
       "Any confounder explaining less than 54.67% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0 \n",
@@ -850,10 +850,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.568951Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.568767Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.876714Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.876062Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.137325Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.137023Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.499281Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.498686Z"
     }
    },
    "outputs": [
@@ -890,10 +890,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.878664Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.878460Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.882580Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.881993Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.501439Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.501017Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.505168Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.504679Z"
     }
    },
    "outputs": [
@@ -901,10 +901,10 @@
      "data": {
       "text/plain": [
        "{'converted_estimate': 2.457447065735422,\n",
-       " 'converted_lower_ci': 2.228856727004314,\n",
-       " 'converted_upper_ci': 2.709481505797994,\n",
+       " 'converted_lower_ci': 2.2288567270043136,\n",
+       " 'converted_upper_ci': 2.7094815057979944,\n",
        " 'evalue_estimate': 4.349958364289862,\n",
-       " 'evalue_lower_ci': 3.883832733630413,\n",
+       " 'evalue_lower_ci': 3.883832733630412,\n",
        " 'evalue_upper_ci': None}"
       ]
      },
@@ -922,10 +922,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.884211Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.884025Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.890955Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.890374Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.506957Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.506765Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.514223Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.513599Z"
     }
    },
    "outputs": [
@@ -1044,10 +1044,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.892838Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.892474Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.895374Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.894911Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.516204Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.515778Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.518934Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.518428Z"
     }
    },
    "outputs": [
@@ -1059,14 +1059,14 @@
       "\n",
       "Unadjusted Estimates of Treatment: v0\n",
       "Estimate (converted to risk ratio scale): 2.457447065735422\n",
-      "Lower 95% CI (converted to risk ratio scale): 2.228856727004314\n",
-      "Upper 95% CI (converted to risk ratio scale): 2.709481505797994\n",
+      "Lower 95% CI (converted to risk ratio scale): 2.2288567270043136\n",
+      "Upper 95% CI (converted to risk ratio scale): 2.7094815057979944\n",
       "\n",
       "Sensitivity Statistics: \n",
       "E-value for point estimate: 4.349958364289862\n",
-      "E-value for lower 95% CI: 3.883832733630413\n",
+      "E-value for lower 95% CI: 3.883832733630412\n",
       "E-value for upper 95% CI: None\n",
-      "Largest Observed Covariate E-value: 1.6811401167656788 (W1)\n",
+      "Largest Observed Covariate E-value: 1.6811401167656792 (W1)\n",
       "\n",
       "Interpretation of results:\n",
       "Unmeasured confounder(s) would have to be associated with a 4.35-fold increase in the risk of y, and must be 4.35 times more prevalent in v0, to explain away the observed point estimate.\n",
@@ -1092,10 +1092,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.897047Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.896770Z",
-     "iopub.status.idle": "2024-10-20T00:22:43.912309Z",
-     "shell.execute_reply": "2024-10-20T00:22:43.911799Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.520671Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.520483Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.536532Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.535925Z"
     }
    },
    "outputs": [],
@@ -1115,10 +1115,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:43.914233Z",
-     "iopub.status.busy": "2024-10-20T00:22:43.913869Z",
-     "iopub.status.idle": "2024-10-20T00:22:44.045954Z",
-     "shell.execute_reply": "2024-10-20T00:22:44.045273Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.538696Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.538483Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.677507Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.676924Z"
     }
    },
    "outputs": [
@@ -1279,10 +1279,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:44.047903Z",
-     "iopub.status.busy": "2024-10-20T00:22:44.047687Z",
-     "iopub.status.idle": "2024-10-20T00:22:44.071970Z",
-     "shell.execute_reply": "2024-10-20T00:22:44.071353Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.679866Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.679642Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.711994Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.711400Z"
     }
    },
    "outputs": [
@@ -1296,9 +1296,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                             \n",
-      "─────(E[y|W5,W6,W1,W4,W3,W2,W0])\n",
+      "─────(E[y|W1,W3,W4,W0,W2,W6,W5])\n",
       "d[v₀]                           \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W4,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W4,W3,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W4,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W4,W0,W2,W6,W5)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -1321,10 +1321,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:44.073813Z",
-     "iopub.status.busy": "2024-10-20T00:22:44.073601Z",
-     "iopub.status.idle": "2024-10-20T00:22:44.089601Z",
-     "shell.execute_reply": "2024-10-20T00:22:44.088985Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.714137Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.713913Z",
+     "iopub.status.idle": "2024-10-22T01:49:28.734904Z",
+     "shell.execute_reply": "2024-10-22T01:49:28.734232Z"
     }
    },
    "outputs": [
@@ -1341,16 +1341,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                             \n",
-      "─────(E[y|W5,W6,W1,W4,W3,W2,W0])\n",
+      "─────(E[y|W1,W3,W4,W0,W2,W6,W5])\n",
       "d[v₀]                           \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W6,W1,W4,W3,W2,W0,U) = P(y|v0,W5,W6,W1,W4,W3,W2,W0)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W3,W4,W0,W2,W6,W5,U) = P(y|v0,W1,W3,W4,W0,W2,W6,W5)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W5+W6+W1+W4+W3+W2+W0\n",
+      "b: y~v0+W1+W3+W4+W0+W2+W6+W5\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.081924375588304\n",
+      "Mean value: 10.081924375588297\n",
       "\n"
      ]
     }
@@ -1365,10 +1365,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:44.091415Z",
-     "iopub.status.busy": "2024-10-20T00:22:44.091230Z",
-     "iopub.status.idle": "2024-10-20T00:22:48.579565Z",
-     "shell.execute_reply": "2024-10-20T00:22:48.579023Z"
+     "iopub.execute_input": "2024-10-22T01:49:28.737302Z",
+     "iopub.status.busy": "2024-10-22T01:49:28.736934Z",
+     "iopub.status.idle": "2024-10-22T01:49:33.236416Z",
+     "shell.execute_reply": "2024-10-22T01:49:33.235809Z"
     }
    },
    "outputs": [
@@ -1406,10 +1406,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:48.581405Z",
-     "iopub.status.busy": "2024-10-20T00:22:48.581201Z",
-     "iopub.status.idle": "2024-10-20T00:22:52.975105Z",
-     "shell.execute_reply": "2024-10-20T00:22:52.974431Z"
+     "iopub.execute_input": "2024-10-22T01:49:33.238267Z",
+     "iopub.status.busy": "2024-10-22T01:49:33.238077Z",
+     "iopub.status.idle": "2024-10-22T01:49:37.679150Z",
+     "shell.execute_reply": "2024-10-22T01:49:37.678543Z"
     }
    },
    "outputs": [
@@ -1433,10 +1433,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:52.977117Z",
-     "iopub.status.busy": "2024-10-20T00:22:52.976923Z",
-     "iopub.status.idle": "2024-10-20T00:22:52.980286Z",
-     "shell.execute_reply": "2024-10-20T00:22:52.979701Z"
+     "iopub.execute_input": "2024-10-22T01:49:37.680973Z",
+     "iopub.status.busy": "2024-10-22T01:49:37.680784Z",
+     "iopub.status.idle": "2024-10-22T01:49:37.684303Z",
+     "shell.execute_reply": "2024-10-22T01:49:37.683828Z"
     },
     "scrolled": true
    },
@@ -1448,14 +1448,14 @@
       "Sensitivity Analysis to Unobserved Confounding using R^2 paramterization\n",
       "\n",
       "Unadjusted Estimates of Treatment ['v0'] :\n",
-      "Coefficient Estimate : 10.081924375588304\n",
+      "Coefficient Estimate : 10.081924375588297\n",
       "Degree of Freedom : 491\n",
-      "Standard Error : 0.10446229543424757\n",
-      "t-value : 96.51256784735541\n",
-      "F^2 value : 18.970826379817478\n",
+      "Standard Error : 0.10446229543424743\n",
+      "t-value : 96.51256784735548\n",
+      "F^2 value : 18.970826379817506\n",
       "\n",
       "Sensitivity Statistics : \n",
-      "Partial R2 of treatment with outcome : 0.9499269594066172\n",
+      "Partial R2 of treatment with outcome : 0.9499269594066173\n",
       "Robustness Value : 0.9522057801012398\n",
       "\n",
       "Interpretation of results :\n",
@@ -1477,10 +1477,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:52.982188Z",
-     "iopub.status.busy": "2024-10-20T00:22:52.981755Z",
-     "iopub.status.idle": "2024-10-20T00:22:52.985815Z",
-     "shell.execute_reply": "2024-10-20T00:22:52.985234Z"
+     "iopub.execute_input": "2024-10-22T01:49:37.686033Z",
+     "iopub.status.busy": "2024-10-22T01:49:37.685683Z",
+     "iopub.status.idle": "2024-10-22T01:49:37.689727Z",
+     "shell.execute_reply": "2024-10-22T01:49:37.689146Z"
     },
     "scrolled": true
    },
@@ -1488,12 +1488,12 @@
     {
      "data": {
       "text/plain": [
-       "{'estimate': 10.081924375588304,\n",
-       " 'standard_error': 0.10446229543424757,\n",
+       "{'estimate': 10.081924375588297,\n",
+       " 'standard_error': 0.10446229543424743,\n",
        " 'degree of freedom': 491,\n",
-       " 't_statistic': 96.51256784735541,\n",
-       " 'r2yt_w': 0.9499269594066172,\n",
-       " 'partial_f2': 18.970826379817478,\n",
+       " 't_statistic': 96.51256784735548,\n",
+       " 'r2yt_w': 0.9499269594066173,\n",
+       " 'partial_f2': 18.970826379817506,\n",
        " 'robustness_value': 0.9522057801012398,\n",
        " 'robustness_value_alpha': 0.950386691319526}"
       ]
@@ -1512,10 +1512,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:52.987660Z",
-     "iopub.status.busy": "2024-10-20T00:22:52.987255Z",
-     "iopub.status.idle": "2024-10-20T00:22:52.994932Z",
-     "shell.execute_reply": "2024-10-20T00:22:52.994429Z"
+     "iopub.execute_input": "2024-10-22T01:49:37.691504Z",
+     "iopub.status.busy": "2024-10-22T01:49:37.691203Z",
+     "iopub.status.idle": "2024-10-22T01:49:37.699179Z",
+     "shell.execute_reply": "2024-10-22T01:49:37.698589Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/timeseries/effect_inference_timeseries_data.html b/main/example_notebooks/timeseries/effect_inference_timeseries_data.html
index bd9165bed..1b9144f56 100644
--- a/main/example_notebooks/timeseries/effect_inference_timeseries_data.html
+++ b/main/example_notebooks/timeseries/effect_inference_timeseries_data.html
@@ -769,28 +769,28 @@ 
Causal Effect Estimation
 Collecting tigramite
   Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)
-     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 296.8/296.8 kB 15.6 MB/s eta 0:00:00
+     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 296.8/296.8 kB 47.7 MB/s eta 0:00:00
 Requirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)
 Requirement already satisfied: scipy>=1.10.0 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.10.1)
 Requirement already satisfied: six in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.16.0)
diff --git a/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb b/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
index 979c383a3..ff8975a3e 100644
--- a/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
+++ b/main/example_notebooks/timeseries/effect_inference_timeseries_data.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:56.069874Z",
-     "iopub.status.busy": "2024-10-20T00:22:56.069472Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.493071Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.492516Z"
+     "iopub.execute_input": "2024-10-22T01:49:40.333747Z",
+     "iopub.status.busy": "2024-10-22T01:49:40.333569Z",
+     "iopub.status.idle": "2024-10-22T01:49:41.848211Z",
+     "shell.execute_reply": "2024-10-22T01:49:41.847596Z"
     }
    },
    "outputs": [],
@@ -40,10 +40,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.495452Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.495030Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.499979Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.499499Z"
+     "iopub.execute_input": "2024-10-22T01:49:41.850754Z",
+     "iopub.status.busy": "2024-10-22T01:49:41.850234Z",
+     "iopub.status.idle": "2024-10-22T01:49:41.855699Z",
+     "shell.execute_reply": "2024-10-22T01:49:41.855166Z"
     }
    },
    "outputs": [],
@@ -67,10 +67,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.501772Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.501476Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.613416Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.612838Z"
+     "iopub.execute_input": "2024-10-22T01:49:41.857503Z",
+     "iopub.status.busy": "2024-10-22T01:49:41.857280Z",
+     "iopub.status.idle": "2024-10-22T01:49:41.958747Z",
+     "shell.execute_reply": "2024-10-22T01:49:41.958018Z"
     }
    },
    "outputs": [
@@ -116,10 +116,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.615650Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.615258Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.733312Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.732768Z"
+     "iopub.execute_input": "2024-10-22T01:49:41.960962Z",
+     "iopub.status.busy": "2024-10-22T01:49:41.960599Z",
+     "iopub.status.idle": "2024-10-22T01:49:42.063822Z",
+     "shell.execute_reply": "2024-10-22T01:49:42.063326Z"
     }
    },
    "outputs": [
@@ -159,10 +159,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.735606Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.735384Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.740195Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.739605Z"
+     "iopub.execute_input": "2024-10-22T01:49:42.065833Z",
+     "iopub.status.busy": "2024-10-22T01:49:42.065655Z",
+     "iopub.status.idle": "2024-10-22T01:49:42.070000Z",
+     "shell.execute_reply": "2024-10-22T01:49:42.069570Z"
     }
    },
    "outputs": [],
@@ -175,10 +175,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.742599Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.742173Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.842694Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.842093Z"
+     "iopub.execute_input": "2024-10-22T01:49:42.071785Z",
+     "iopub.status.busy": "2024-10-22T01:49:42.071595Z",
+     "iopub.status.idle": "2024-10-22T01:49:42.150376Z",
+     "shell.execute_reply": "2024-10-22T01:49:42.149737Z"
     }
    },
    "outputs": [
@@ -205,10 +205,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.844924Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.844682Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.858149Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.857578Z"
+     "iopub.execute_input": "2024-10-22T01:49:42.152531Z",
+     "iopub.status.busy": "2024-10-22T01:49:42.152144Z",
+     "iopub.status.idle": "2024-10-22T01:49:42.163708Z",
+     "shell.execute_reply": "2024-10-22T01:49:42.163070Z"
     }
    },
    "outputs": [
@@ -330,10 +330,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.860158Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.859940Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.864373Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.863888Z"
+     "iopub.execute_input": "2024-10-22T01:49:42.165653Z",
+     "iopub.status.busy": "2024-10-22T01:49:42.165447Z",
+     "iopub.status.idle": "2024-10-22T01:49:42.169883Z",
+     "shell.execute_reply": "2024-10-22T01:49:42.169258Z"
     }
    },
    "outputs": [
@@ -361,10 +361,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.866436Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.866231Z",
-     "iopub.status.idle": "2024-10-20T00:22:57.963937Z",
-     "shell.execute_reply": "2024-10-20T00:22:57.963318Z"
+     "iopub.execute_input": "2024-10-22T01:49:42.171708Z",
+     "iopub.status.busy": "2024-10-22T01:49:42.171517Z",
+     "iopub.status.idle": "2024-10-22T01:49:42.258872Z",
+     "shell.execute_reply": "2024-10-22T01:49:42.258186Z"
     }
    },
    "outputs": [
@@ -381,28 +381,28 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "   d                    \n",
-      "────────(E[V_6_0|V4_-7])\n",
+      "────────(E[V_6_0|V4_-3])\n",
       "d[V₅ ₋₁]                \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-7,U) = P(V6_0|V5_-1,V4_-7)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{V5_-1} and U→V6_0 then P(V6_0|V5_-1,V4_-3,U) = P(V6_0|V5_-1,V4_-3)\n",
       "\n",
       "## Realized estimand\n",
-      "b: V6_0~V5_-1+V4_-7+V5_-1*V7_-3+V5_-1*V7_-5\n",
+      "b: V6_0~V5_-1+V4_-3+V5_-1*V7_-3+V5_-1*V7_-5\n",
       "Target units: \n",
       "\n",
       "## Estimate\n",
-      "Mean value: -0.12612138021763197\n",
-      "p-value: [0.75401158]\n",
+      "Mean value: -0.008318651389864762\n",
+      "p-value: [0.75812695]\n",
       "### Conditional Estimates\n",
       "__categorical__V7_-3  __categorical__V7_-5\n",
-      "(-0.001, 0.6]         (-0.001, 5.4]           0.111915\n",
-      "(0.6, 5.4]            (7.8, 9.0]             -0.257861\n",
-      "                      (9.0, 10.0]            -0.314176\n",
-      "(5.4, 7.8]            (-0.001, 5.4]           0.073537\n",
-      "                      (7.8, 9.0]             -0.285273\n",
-      "(7.8, 9.0]            (-0.001, 5.4]          -0.034356\n",
-      "                      (5.4, 7.8]             -0.216503\n",
-      "                      (7.8, 9.0]             -0.296238\n",
-      "(9.0, 10.0]           (7.8, 9.0]             -0.261853\n",
+      "(-0.001, 0.6]         (-0.001, 5.4]           0.101279\n",
+      "(0.6, 5.4]            (7.8, 9.0]             -0.167641\n",
+      "                      (9.0, 10.0]            -0.167956\n",
+      "(5.4, 7.8]            (-0.001, 5.4]           0.175782\n",
+      "                      (7.8, 9.0]             -0.114425\n",
+      "(7.8, 9.0]            (-0.001, 5.4]           0.111134\n",
+      "                      (5.4, 7.8]             -0.028648\n",
+      "                      (7.8, 9.0]             -0.093138\n",
+      "(9.0, 10.0]           (7.8, 9.0]             -0.050250\n",
       "dtype: float64\n"
      ]
     },
@@ -451,10 +451,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:22:57.965879Z",
-     "iopub.status.busy": "2024-10-20T00:22:57.965499Z",
-     "iopub.status.idle": "2024-10-20T00:23:00.583315Z",
-     "shell.execute_reply": "2024-10-20T00:23:00.582644Z"
+     "iopub.execute_input": "2024-10-22T01:49:42.261077Z",
+     "iopub.status.busy": "2024-10-22T01:49:42.260691Z",
+     "iopub.status.idle": "2024-10-22T01:49:44.948980Z",
+     "shell.execute_reply": "2024-10-22T01:49:44.948275Z"
     }
    },
    "outputs": [
@@ -471,15 +471,8 @@
      "text": [
       "  Downloading tigramite-5.2.6.2-py3-none-any.whl (296 kB)\r\n",
       "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/296.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25h"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)\r\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.8/296.8 kB\u001b[0m \u001b[31m47.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
+      "\u001b[?25hRequirement already satisfied: numpy>=1.18 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.24.4)\r\n",
       "Requirement already satisfied: scipy>=1.10.0 in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.10.1)\r\n",
       "Requirement already satisfied: six in /github/home/.cache/pypoetry/virtualenvs/dowhy-oN2hW5jr-py3.8/lib/python3.8/site-packages (from tigramite) (1.16.0)\r\n"
      ]
@@ -517,10 +510,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:00.585790Z",
-     "iopub.status.busy": "2024-10-20T00:23:00.585360Z",
-     "iopub.status.idle": "2024-10-20T00:23:00.593575Z",
-     "shell.execute_reply": "2024-10-20T00:23:00.593107Z"
+     "iopub.execute_input": "2024-10-22T01:49:44.951399Z",
+     "iopub.status.busy": "2024-10-22T01:49:44.951041Z",
+     "iopub.status.idle": "2024-10-22T01:49:44.959727Z",
+     "shell.execute_reply": "2024-10-22T01:49:44.959203Z"
     }
    },
    "outputs": [],
@@ -541,10 +534,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:00.595461Z",
-     "iopub.status.busy": "2024-10-20T00:23:00.595099Z",
-     "iopub.status.idle": "2024-10-20T00:23:01.489338Z",
-     "shell.execute_reply": "2024-10-20T00:23:01.488770Z"
+     "iopub.execute_input": "2024-10-22T01:49:44.961822Z",
+     "iopub.status.busy": "2024-10-22T01:49:44.961321Z",
+     "iopub.status.idle": "2024-10-22T01:49:45.880191Z",
+     "shell.execute_reply": "2024-10-22T01:49:45.879575Z"
     }
    },
    "outputs": [
@@ -569,10 +562,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:01.491700Z",
-     "iopub.status.busy": "2024-10-20T00:23:01.491307Z",
-     "iopub.status.idle": "2024-10-20T00:23:01.496692Z",
-     "shell.execute_reply": "2024-10-20T00:23:01.496229Z"
+     "iopub.execute_input": "2024-10-22T01:49:45.882290Z",
+     "iopub.status.busy": "2024-10-22T01:49:45.881862Z",
+     "iopub.status.idle": "2024-10-22T01:49:45.887551Z",
+     "shell.execute_reply": "2024-10-22T01:49:45.886934Z"
     }
    },
    "outputs": [],
@@ -592,10 +585,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:01.498450Z",
-     "iopub.status.busy": "2024-10-20T00:23:01.498102Z",
-     "iopub.status.idle": "2024-10-20T00:23:01.678192Z",
-     "shell.execute_reply": "2024-10-20T00:23:01.677549Z"
+     "iopub.execute_input": "2024-10-22T01:49:45.889385Z",
+     "iopub.status.busy": "2024-10-22T01:49:45.889034Z",
+     "iopub.status.idle": "2024-10-22T01:49:46.072993Z",
+     "shell.execute_reply": "2024-10-22T01:49:46.072391Z"
     }
    },
    "outputs": [
@@ -626,10 +619,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:01.680169Z",
-     "iopub.status.busy": "2024-10-20T00:23:01.679826Z",
-     "iopub.status.idle": "2024-10-20T00:23:02.069006Z",
-     "shell.execute_reply": "2024-10-20T00:23:02.068356Z"
+     "iopub.execute_input": "2024-10-22T01:49:46.075133Z",
+     "iopub.status.busy": "2024-10-22T01:49:46.074755Z",
+     "iopub.status.idle": "2024-10-22T01:49:46.458815Z",
+     "shell.execute_reply": "2024-10-22T01:49:46.458181Z"
     }
    },
    "outputs": [
@@ -767,13 +760,7 @@
       "\n",
       "Testing condition sets of dimension 0:\n",
       "\n",
-      "    Link (V1 -1) -?> V2 (1/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V1 -1) -?> V2 (1/21):\n",
       "    Subset 0: () gives pval = 0.12317 / val = -0.591\n",
       "    Non-significance detected.\n",
       "\n",
@@ -781,7 +768,13 @@
       "    Subset 0: () gives pval = 0.87304 / val =  0.068\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V1 -3) -?> V2 (3/21):\n",
+      "    Link (V1 -3) -?> V2 (3/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.40658 / val =  0.342\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2019,7 +2012,13 @@
       "    Subset 0: () gives pval = 0.73304 / val =  0.144\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V6 -3) -?> V3 (18/21):\n",
+      "    Link (V6 -3) -?> V3 (18/21):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.71062 / val =  0.157\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2181,13 +2180,7 @@
       "    Subset 0: () gives pval = 0.42170 / val =  0.332\n",
       "    Non-significance detected.\n",
       "\n",
-      "    Link (V3 -2) -?> V5 (8/21):\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    Link (V3 -2) -?> V5 (8/21):\n",
       "    Subset 0: () gives pval = 0.70342 / val = -0.161\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2594,7 +2587,13 @@
       "    Link (V3  0) o?o V4 (16/44):\n",
       "    Iterate through 1 subset(s) of conditions: \n",
       "    with conds_y = [ ]\n",
-      "    with conds_x = [ ]\n",
+      "    with conds_x = [ ]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Subset 0: () gives pval = 0.30341 / val =  0.417\n",
       "    Non-significance detected.\n",
       "\n",
@@ -2671,13 +2670,7 @@
       "    Link (V5  0) o?o V7 (31/44):\n",
       "    Iterate through 1 subset(s) of conditions: \n",
       "    with conds_y = [ ]\n",
-      "    with conds_x = [ ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    with conds_x = [ ]\n",
       "    Subset 0: () gives pval = 0.32066 / val = -0.404\n",
       "    Non-significance detected.\n",
       "\n",
@@ -6109,10 +6102,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:02.071502Z",
-     "iopub.status.busy": "2024-10-20T00:23:02.071312Z",
-     "iopub.status.idle": "2024-10-20T00:23:02.140404Z",
-     "shell.execute_reply": "2024-10-20T00:23:02.139787Z"
+     "iopub.execute_input": "2024-10-22T01:49:46.460772Z",
+     "iopub.status.busy": "2024-10-22T01:49:46.460575Z",
+     "iopub.status.idle": "2024-10-22T01:49:46.530397Z",
+     "shell.execute_reply": "2024-10-22T01:49:46.529750Z"
     }
    },
    "outputs": [
diff --git a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html
index f854bff3a..d9da93b33 100644
--- a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html
+++ b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html
@@ -768,9 +768,9 @@ 
The four steps of causal inference
 
 Refute: Use a Placebo Treatment
-Estimated effect:10.065477684628421
-New effect:0.018320036935283782
-p value:0.8400000000000001
+Estimated effect:10.021268689755129
+New effect:0.021116924365320297
+p value:0.6799999999999999
 
 

 
@@ -952,11 +952,11 @@ 
A mystery dataset: Can you find out if if there is a causal effect?
 
    Treatment    Outcome        w0         s        w1
-0   9.283407  18.635052 -1.656601  0.621639  1.292029
-1  20.387799  40.985144 -3.792526  7.455493 -0.185157
-2   9.351217  17.920173 -1.572169  8.558150  0.983524
-3  16.044158  31.827899 -3.058927  7.970089  1.207837
-4  21.913201  43.921734  3.983981  2.873435  0.176288
+0  16.062299  31.926540  3.158977  9.675626 -0.715888
+1   6.438101  12.599912 -0.728012  8.808864 -1.557705
+2   8.451761  16.537359 -1.569776  0.639963 -0.800780
+3  24.371373  47.793866 -4.250825  0.127575 -0.277237
+4  12.892423  26.003995  2.648309  1.837378  0.357678
 

 
 

@@ -1012,9 +1012,9 @@ 
Identify the correct estimand for the target quantity based on the causal mo
 Estimand name: backdoor
 Estimand expression:
      d
-────────────(E[Outcome|w0,w1])
+────────────(E[Outcome|w1,w0])
 d[Treatment]
-Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)
+Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)
 
 ### Estimand : 2
 Estimand name: iv
@@ -1058,18 +1058,18 @@ 
Estimate the target estimand
@@ -1116,17 +1116,17 @@ 
Estimate the target estimand
 
 Refute: Add a random common cause
-Estimated effect:1.1610439951723444
-New effect:1.131708565840576
-p value:0.24
+Estimated effect:0.9894564817819103
+New effect:0.9788569428730344
+p value:0.72
 
 

 
@@ -1172,9 +1172,9 @@ 
Check robustness of the estimate using refutation tests
 
 Refute: Use a Placebo Treatment
-Estimated effect:1.1610439951723444
-New effect:-2.4894090262337903e-05
-p value:0.4671139184878982
+Estimated effect:0.9894564817819103
+New effect:0.00013356798350309588
+p value:0.4157368947724852
 
 

 
diff --git a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
index 9b47a678a..e2d091bc9 100644
--- a/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
+++ b/main/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
@@ -104,10 +104,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:04.354094Z",
-     "iopub.status.busy": "2024-10-20T00:23:04.353654Z",
-     "iopub.status.idle": "2024-10-20T00:23:06.026530Z",
-     "shell.execute_reply": "2024-10-20T00:23:06.025819Z"
+     "iopub.execute_input": "2024-10-22T01:49:48.766649Z",
+     "iopub.status.busy": "2024-10-22T01:49:48.766463Z",
+     "iopub.status.idle": "2024-10-22T01:49:50.559322Z",
+     "shell.execute_reply": "2024-10-22T01:49:50.558613Z"
     }
    },
    "outputs": [],
@@ -152,10 +152,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:06.029540Z",
-     "iopub.status.busy": "2024-10-20T00:23:06.029155Z",
-     "iopub.status.idle": "2024-10-20T00:23:06.033205Z",
-     "shell.execute_reply": "2024-10-20T00:23:06.032460Z"
+     "iopub.execute_input": "2024-10-22T01:49:50.562535Z",
+     "iopub.status.busy": "2024-10-22T01:49:50.562000Z",
+     "iopub.status.idle": "2024-10-22T01:49:50.566782Z",
+     "shell.execute_reply": "2024-10-22T01:49:50.566249Z"
     }
    },
    "outputs": [],
@@ -181,10 +181,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:06.035548Z",
-     "iopub.status.busy": "2024-10-20T00:23:06.035177Z",
-     "iopub.status.idle": "2024-10-20T00:23:06.204608Z",
-     "shell.execute_reply": "2024-10-20T00:23:06.204051Z"
+     "iopub.execute_input": "2024-10-22T01:49:50.569446Z",
+     "iopub.status.busy": "2024-10-22T01:49:50.569212Z",
+     "iopub.status.idle": "2024-10-22T01:49:50.755120Z",
+     "shell.execute_reply": "2024-10-22T01:49:50.754404Z"
     }
    },
    "outputs": [
@@ -227,10 +227,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:06.206678Z",
-     "iopub.status.busy": "2024-10-20T00:23:06.206471Z",
-     "iopub.status.idle": "2024-10-20T00:23:06.356151Z",
-     "shell.execute_reply": "2024-10-20T00:23:06.355571Z"
+     "iopub.execute_input": "2024-10-22T01:49:50.758557Z",
+     "iopub.status.busy": "2024-10-22T01:49:50.757966Z",
+     "iopub.status.idle": "2024-10-22T01:49:50.916025Z",
+     "shell.execute_reply": "2024-10-22T01:49:50.915381Z"
     }
    },
    "outputs": [
@@ -278,10 +278,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:06.358647Z",
-     "iopub.status.busy": "2024-10-20T00:23:06.358248Z",
-     "iopub.status.idle": "2024-10-20T00:23:06.608367Z",
-     "shell.execute_reply": "2024-10-20T00:23:06.607822Z"
+     "iopub.execute_input": "2024-10-22T01:49:50.918290Z",
+     "iopub.status.busy": "2024-10-22T01:49:50.918059Z",
+     "iopub.status.idle": "2024-10-22T01:49:51.084865Z",
+     "shell.execute_reply": "2024-10-22T01:49:51.084238Z"
     }
    },
    "outputs": [
@@ -295,9 +295,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W3,W2,W1,W0,W4])\n",
+      "─────(E[y|W1,W0,W3,W2,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W2,W1,W0,W4,U) = P(y|v0,W3,W2,W1,W0,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W3,W2,W4,U) = P(y|v0,W1,W0,W3,W2,W4)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -338,10 +338,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:06.610351Z",
-     "iopub.status.busy": "2024-10-20T00:23:06.609995Z",
-     "iopub.status.idle": "2024-10-20T00:23:07.092906Z",
-     "shell.execute_reply": "2024-10-20T00:23:07.092236Z"
+     "iopub.execute_input": "2024-10-22T01:49:51.086929Z",
+     "iopub.status.busy": "2024-10-22T01:49:51.086610Z",
+     "iopub.status.idle": "2024-10-22T01:49:51.663101Z",
+     "shell.execute_reply": "2024-10-22T01:49:51.662501Z"
     }
    },
    "outputs": [
@@ -358,16 +358,16 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W3,W2,W1,W0,W4])\n",
+      "─────(E[y|W1,W0,W3,W2,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W2,W1,W0,W4,U) = P(y|v0,W3,W2,W1,W0,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W3,W2,W4,U) = P(y|v0,W1,W0,W3,W2,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W3+W2+W1+W0+W4\n",
+      "b: y~v0+W1+W0+W3+W2+W4\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 10.065477684628421\n",
+      "Mean value: 10.021268689755129\n",
       "\n"
      ]
     }
@@ -385,10 +385,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:07.094904Z",
-     "iopub.status.busy": "2024-10-20T00:23:07.094582Z",
-     "iopub.status.idle": "2024-10-20T00:23:12.167769Z",
-     "shell.execute_reply": "2024-10-20T00:23:12.166775Z"
+     "iopub.execute_input": "2024-10-22T01:49:51.665273Z",
+     "iopub.status.busy": "2024-10-22T01:49:51.664809Z",
+     "iopub.status.idle": "2024-10-22T01:49:56.772367Z",
+     "shell.execute_reply": "2024-10-22T01:49:56.771524Z"
     }
    },
    "outputs": [
@@ -405,17 +405,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "  d                       \n",
-      "─────(E[y|W3,W2,W1,W0,W4])\n",
+      "─────(E[y|W1,W0,W3,W2,W4])\n",
       "d[v₀]                     \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W2,W1,W0,W4,U) = P(y|v0,W3,W2,W1,W0,W4)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W3,W2,W4,U) = P(y|v0,W1,W0,W3,W2,W4)\n",
       "\n",
       "## Realized estimand\n",
-      "b: y~v0+W3+W2+W1+W0+W4 | \n",
+      "b: y~v0+W1+W0+W3+W2+W4 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 9.948342659047578\n",
-      "Effect estimates: [[9.94834266]]\n",
+      "Mean value: 9.984673963903383\n",
+      "Effect estimates: [[9.98467396]]\n",
       "\n"
      ]
     }
@@ -450,10 +450,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:12.170790Z",
-     "iopub.status.busy": "2024-10-20T00:23:12.169975Z",
-     "iopub.status.idle": "2024-10-20T00:23:47.768027Z",
-     "shell.execute_reply": "2024-10-20T00:23:47.767361Z"
+     "iopub.execute_input": "2024-10-22T01:49:56.775184Z",
+     "iopub.status.busy": "2024-10-22T01:49:56.774686Z",
+     "iopub.status.idle": "2024-10-22T01:50:32.061786Z",
+     "shell.execute_reply": "2024-10-22T01:50:32.061095Z"
     }
    },
    "outputs": [
@@ -462,9 +462,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:10.065477684628421\n",
-      "New effect:0.018320036935283782\n",
-      "p value:0.8400000000000001\n",
+      "Estimated effect:10.021268689755129\n",
+      "New effect:0.021116924365320297\n",
+      "p value:0.6799999999999999\n",
       "\n"
      ]
     }
@@ -503,10 +503,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:47.769937Z",
-     "iopub.status.busy": "2024-10-20T00:23:47.769707Z",
-     "iopub.status.idle": "2024-10-20T00:23:47.773195Z",
-     "shell.execute_reply": "2024-10-20T00:23:47.772725Z"
+     "iopub.execute_input": "2024-10-22T01:50:32.064093Z",
+     "iopub.status.busy": "2024-10-22T01:50:32.063647Z",
+     "iopub.status.idle": "2024-10-22T01:50:32.067283Z",
+     "shell.execute_reply": "2024-10-22T01:50:32.066696Z"
     }
    },
    "outputs": [],
@@ -530,10 +530,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:47.774838Z",
-     "iopub.status.busy": "2024-10-20T00:23:47.774604Z",
-     "iopub.status.idle": "2024-10-20T00:23:48.105209Z",
-     "shell.execute_reply": "2024-10-20T00:23:48.104544Z"
+     "iopub.execute_input": "2024-10-22T01:50:32.069391Z",
+     "iopub.status.busy": "2024-10-22T01:50:32.068950Z",
+     "iopub.status.idle": "2024-10-22T01:50:32.433757Z",
+     "shell.execute_reply": "2024-10-22T01:50:32.433053Z"
     }
    },
    "outputs": [
@@ -542,16 +542,16 @@
      "output_type": "stream",
      "text": [
       "   Treatment    Outcome        w0         s        w1\n",
-      "0   9.283407  18.635052 -1.656601  0.621639  1.292029\n",
-      "1  20.387799  40.985144 -3.792526  7.455493 -0.185157\n",
-      "2   9.351217  17.920173 -1.572169  8.558150  0.983524\n",
-      "3  16.044158  31.827899 -3.058927  7.970089  1.207837\n",
-      "4  21.913201  43.921734  3.983981  2.873435  0.176288\n"
+      "0  16.062299  31.926540  3.158977  9.675626 -0.715888\n",
+      "1   6.438101  12.599912 -0.728012  8.808864 -1.557705\n",
+      "2   8.451761  16.537359 -1.569776  0.639963 -0.800780\n",
+      "3  24.371373  47.793866 -4.250825  0.127575 -0.277237\n",
+      "4  12.892423  26.003995  2.648309  1.837378  0.357678\n"
      ]
     },
     {
      "data": {
-      "image/png": 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bmM1AdXVoxkNERESkhMUC7Nvnu12PHkDbtsEfDwWN1SYwbt4OCDf32X82bt4OWG3uWpA/EhIS8PHHH8NkMnltt3nzZuTn5wMA4uPj8dVXX6Gtj9fbQw89hMsuuwwA0NDQgJkzZ7pt16pVK8XjvfXWW3HhhRcCAGpqarBo0SLFx/rr2Wefxbnnnuvx/kceecTxfUNDA4QQmDlzJtLT0922NxgMePTRRx23165d67EY40svveRYSnLbbbdh+PDhXsealpbm8jzPmjULp06d8noMAAwaNAjPPPOMx/sTExNdCoiuWbPGawFJPfE74LB27VqsWrXKcXv69OmaDOjrr792fN+5c2fceOONXtvfdNNN6NSpk+P2v//9b03GobnUVGDVKvlh7MnUqbIdERERkV4kJgK/r1P26uhRGZygiLW+pAJllfUe7xcAyirrsb6kInSDinJXXXWVow6AN847OVx11VXo06ePov7vvvtux/dLlixRPT53/vjHPzq+X79+vSZ9evPQQw95vb9Lly7Iyspy3O7Vqxcuvvhir8cMHDjQ8X1dXR1KSkqatamoqMC8efMct59++mlF47344ovR4/drvtraWqxZs8bnMc4BEE8GDx7s+L6urg579uxRNJ5w87to5Lvvvuv4ftCgQTjzzDMDHkxdXR0WL17suD1s2DCPxU3s4uPjMWzYMHz88ccAgIKCAtTX1yMpKSng8WiushJw88fs8M47wIgRLLZERERE+mGxAL8XQ/PKZuMW3xHu6CnPwQZ/2pFv//d//6eo3fLlyx3fX3nllYr7/8Mf/uD4ftOmTRBCeM1IP3bsGBYvXoxffvkFhw4dQlVVVbPtI52z0Q8ePKh4LP7o0aMHOnbs6LNdx44dceDAAQCuARFPnCesAbgUzbRbsWIFbDYbAJlZ369fPyVDBiCfd3sQY+PGjRgyZIjX9oMGDfLZZ2ZmpsvtkydPKh5POPkdcLCn9ADw+QQqVVRU5PIHreSJt7ezBxzq6+tRVFTkNe0mLIQAfO2isXMnsGgRMGxYaMZERERE5IvJBEycCDz8sPd29fVyaSizNSNW+xRlE3ZK25Fv2dnZPtsIIbBt2zbH7Y8++gjff/+9ov7r6uoc31ssFlRVVSEtLa1Zu3379uEf//gHvv32WzQ2NirqGwj+Ra+SYAMAtGzZUtUxzu0BmYnQ1JYtWxzfWywWDFNxjbZ161bH98eOHfPZXsmYk5OTXW67G7Me+RVw2Lt3L44cOeK4bY+cHT58GB999BG+/fZblJSUoLq6Gm3atEHv3r0xZMgQ3H///WjTpo3Hfrdv3+5yOzc3V9F4mrbbsWOH/gIOFgvgZmubZsaMAYYOZZYDERER6YMQwMsv+27Xpg2QkhL88VDQDOjRGp3SknC4st5tHQcDgI5pcotM0kaqggBdZWWlSxBg8+bNfj9eZWVls4DDhg0bMGTIEL+CB02zH7SWmJgYkmPsdRqcOW89Wl1d7Xe9isrKSp9tfNXwcMfdmPXIrxoOTf/IO3fujNmzZ6NXr14YPXo0Nm7ciPLycpjNZhw6dAg//PAD/vnPf6JHjx545513PPa7t8kez0rWMwFyD1Rn7tbghF1iItAkDcatgwe5/pGIiIj0w2wGdu/23e74cZnlQBHLGGfA2OvyAMjggjP77bHX5cEYx4kxrcTF+b4cq6mp0ezx7EsEnPu+6aabHMGGhIQE3HHHHZgzZw62bt2KiooK1NfXQ8jdDSGEwNixYzUbj55p9bw3fc5jjV8ZDsePH3e5/e2332Ly5MmO2507d0ZOTg4aGhqwY8cOR1Tn1KlTeOyxx7B//3689NJLzfqtqqpyue2psmhTTaN0viqBms1ml2hc08cNCovFe/0GAGjRQhaW9CPCRURERBQUambRGhrk+QxFrGF9O2HGHf0wbt4OlwKSHdOSMPa6PAzr28nL0RQMTa+Jvv76a/y///f/NOn7448/dtRhSEhIwOLFi12KE7qjZNeFaOD8vOfl5TXLxidl/Ao4NE23sQcbzjjjDMyYMcOx/QoANDY24vPPP8fIkSMdgYcpU6agf//+uPnmm136qW6yJWQLhR9YTdv5ehFMnjwZ48aNU9S3ZhISfG+LCXjfxYKIiIgo1JwKentlNAIqttcj/RrWtxOuzOuI9SUVOHqqHu1T5DIKZjaER3JyMlq1auW4VnJe2h4o57p8t912m89gAwBHccZo51xX4ejRo2EcSWTza0lFvZt0ua5du2LVqlUuwQZA7iJx9913Y/HixS7raZ599tlme4c2NLkg97VDhad2TftpatSoUaisrHR8heRFc+qU76US9mJLRERERHogBPD3vytra7XK8x2KCsY4A/6Y3QbXn9MFf8xuw2BDmF144YWO75Vss6jUvn37HN8PGDDAZ3shBFavXq2ob+flIpFSb8CZ83N+/PhxFBcXh3E0yjVdphPu596vgEPTCpkAMHXqVLRt29bjMf3798fjjz/uuL1r1y78+OOPXvt1F9hwp2k7d+NzZjKZkJqa6vIVdEreGNq2ZbElIiIi0g+zGWhSY8uj1q0BPW5LThQFrrrqKsf33333nUtBw0D4mqhtKj8/H6WlpYratnLKeHLeLSNS9O/f32XDgw8//DCMo1GuVZNMs3A/934FHFKaXBSnpaXhhhtu8Hncvffe63J72bJlLrebPjlKt/po2q7p+MJOCGD0aN/tjh1jsSUiIiLSD4MBcLOFnluFhaxDRRQk999/P1q3lruD1NTU4LHHHtOk386dOzu+/+mnn7y2ra2txVNPPaW4706dTtf72Llzp/rBhVl8fLzL//fNN99EYWFhGEekjPPzDoT/ufcr4NCuXTuX2+eccw6MRqPP4/r06YMkp8j37iYVj5v2W1ZWpmg8Tdt5y7QIC4tF7j6hhMooIxEREVHQmEzAnXcqaztrVlCHQhTLUlJSMGnSJMftr776CrfeeitOnDjh89iNGzfirrvuwhdffNHsPufl8N988w3mz5/vto/y8nJce+21+O233xSP+bzzznN8v2XLFr+3lQynESNGIDc3F4DMFBgyZIjH58hZZWUl3n33XQwZMiTYQ2ymU6dOLkGHqVOnumyrGmp+FY3My8tzue2cauKNwWBA69atcejQIQBARUWFy/29evVyub1v3z6cddZZPvt1XnsEAL1791Y0npAxmYD164EzzvBexyEpicWWiIiISD9sNuC995S1nTwZeP55QME2f0Sk3sMPP4yff/4ZM2fOBCCDDt9//z1uueUWXHzxxejSpQtMJhMqKyuxf/9+bN68GYsXL8be35dFNa21BwAPPfQQpkyZgurqathsNlx//fW48847cd1116FDhw44ceIEVqxYgY8++gjl5eVITU3FNddcgy+//NLneC+77DJ06dIFpaWlEEJg2LBh6N27N7p37+5S22/EiBFux6YHKSkp+O677zBo0CCcOHEC5eXluO6669C/f39cf/31OPvss5GRkYH6+nqUl5dj+/btWLduHZYtWwaLxYJu3bqFZdx33XUXpkyZAgD47LPPsHDhQpx99tkuKwH69u2LiRMnBn0sfgUcsrOz0aJFC8d6EOctJn1xrrfQdHeJPn36uNwuLCzEtdde67PPpqktTQMiupCRAfiKLFkssmhkKGpKEBEREflSXQ0oXOIKs1kWjVS6BIOIVJsxYwaysrLwr3/9CzabDdXV1fjwww/9ri/Qvn17fPLJJ7jlllvQ2NgIm82GTz75BJ988kmztsnJyZgzZw7WrVunqO/4+Hh88sknuP7661FTUwMAKCoqQlFRkUs7JUvzw6l3795Yv349brjhBsfWmBs2bMCGDRvCPDLPxowZgx9++AEbN24EIIteLl261KVN050ng8WvEHRcXBwuueQSx+09e/YoOu7EiRMuaT/OW40AQFZWFrKzsx23ly9frqhf53Y5OTnIzMxUdFxIpaYCU6d6b/PQQww2EBERkX6kpgI//QQo2ao8PZ1FI4mCzGAwYPTo0di6dStuv/12tGzZ0mv7jIwM/PnPf8Z//vMf/PWvf3Xb5qabbsKSJUvQt29ft/cbjUYMGTIEhYWFLsUrlbj88suxbds2jBo1Cn/84x/Rtm1bJCQkqOpDD3JyclBYWIh33323WVZ+UwaDAeeccw7+9a9/YcmSJSEaoatWrVph1apV+PDDD3HttdeiW7duaNmyJQyG0O82YxB+7pPx8ccf47777pOdGAzYs2cPunfv7vWYOXPm4LbbbnPc/uSTT3DXXXe5tHnmmWfwyiuvAJCBjZKSEnTt2tVjn/v370ePHj1gs9kcx9vTR5SqqqpCWloaKisrg7djhRDABRcAGzfK791JSJBFI5mKSERERHohBNCxI+BrH/rPPgPuuCNowwjJ+ZpG6uvrUVJSgh49erjULyPSmsViwbp167Br1y4cP34cDQ0NaNWqFbp06YJevXqhd+/ezbZJ9EQIgcLCQmzcuBHl5eVISUlBp06dcNFFFzWbKI51Bw4cwNq1a3H06FGcPHkSJpMJGRkZyMnJwVlnneUo8Bmt1LzH+R1wqKqqQnZ2No4fPw4AuO+++7ym8jQ0NKB///745ZdfAMjlFPv27WtWKLKoqAhnnXUWrFYrAOCBBx7A+++/77HfBx54wPG4RqMR27Zt8xl1cvd/CfoHmNkMdOsGHDnivd3cucB11wVnDERERERqLVwIXH2173b9+wPr1smdLYKAAQciIn1Q8x7n91R6amoqxo4d67j90Ucf4fXXX3fb1mKx4N5773UEGwDg0UcfbRZsAOQambvvvttx+4MPPsAHH3zgtt+ZM2e6BDnuuece1cGGkLEXjszK8t7ub3/znAFBREREFEpCAPffr6zt/v3ei2MTEVHM8TvDAQCsViv+9Kc/YcGCBY6fXXTRRbjzzjuRm5uLxsZGbN68Ge+//z6Ki4sdbc477zysWLGiWdFIu+PHj2PgwIEu22b+6U9/wq233orOnTujtLQUX375pcuWJDk5OVizZo1fW2KGLGJeXw8kJ8uKz54YDLI4E6PhREREFG51dYCPNeIOmzYB/foFbSjMcCAi0oeQLKmwq62txQ033IDFixcran/RRRfhm2++QYcOHby2Ky4uxtChQ1FSUuKzzx49eqCgoAA5OTmKxtBUSAMOrVoBvy8X8ai2VllxJiIiIqJgmjcP+NOflLU9cgRo3z5oQ2HAgYhIH0KypMKuZcuWWLRoEd577z2vF/xZWVl48803sXTpUp/BBgDIzc3Fli1bMGLECI8fKmlpaRgxYgS2bNnid7AhpAwGuT2mLwqDN0RERERBIwQwfryyti1bcjtMIiJqJuAMh6Z+/vlnbN++HWVlZQCAdu3a4bzzzkPfvn393oajvr4ey5cvx969e1FeXo42bdqge/fuuOSSS2AymQIec0gj5v/4B/Dqq97bnHEG8OuvQSu6REREROST2SxrTx075rttt25ASUlQz12Y4UBEpA8hXVIRDUL2AWazyRoO9fXe28XHA6dOsY4DERERhdeuXcD55wOVld7bheDchQEHIiJ9COmSClKhutp3sAGQKYnMbiAiIqJwy8kBPOxC5iIjg+cuRETUDAMOoZSS4ntbTIMBKCyU22gSERERhZMQwDvveG+TlARs3MhzFyIiaoYBh1Aym4FDh7y3EUIGHIiIiIjCzWKRdaW8qa8HfvklNOMhIqKIwoCDHj36qAw8EBEREYVTQgLQ2Oi73bhxPHchIqJmGHAIJYMBUFLkqKxMWa0HIiIiomBSWn/qwAGZDUFEROSEAYdQMpl8b4lpt2RJcMdCRERE5EtqKvDaa97b/O1vwKZNrOFARETNMOAQSkIAU6Yoa/v000xNJCIiovASApg0yXubTz4BunQJzXiIiCiiMOAQSmYzsGePsrYlJbI9ERERUbjU1QHl5d7blJfLdkRERE0w4BBKBgOQlqasbWoq97MmIiKi8MrP17YdERHFFAYcQslkAiZMUNZ2/HiuhSQiIqLwEQKYPFlZ20mTuBSUiIiaYcAhlIQApk5V1vb55/nBTUREROFjsQD79ilry10qiIjIDQYcQklNDYeKCq6HJCIiovBRsrvWjTcCn37KXSqIiMit+HAPIKYYDEB6ugwmKFFTA7RsGdQhEREREbklBPDcc97brF8P/Oc/rDtFRERuMcMhlEwm4OefgQ4dfLf93/+Adu2CPiQiIiIit+rrgdJS721KS2U7IiIiNxhwCLX27YFjx3y327w56EMhIiIi8qihwXfmgsEg2xEREbnBgEM42Gy+27z8srJ2RERERMGQkgJkZnpvk5kp2xEREbnBgEM4xCl42uvqgOrq4I+FiIiIyB2zGSgr897m0CHZjoiIyA0GHPTIYAB++glITQ33SIiIiChWGQy+i1ebTCwYSUREHnGXinAQwvf9558fmrEQERERuZOYCHTtCmzb5rmNzQYkJIRuTEQ6tW3bNnz//fdYvHgx9u/fj6NHj6K+vh7t2rVDhw4dMHDgQFx11VW4/PLLkZSUFO7hEoUMMxxCTQjfAQeA9RuIiIgovCwWYM8e723q64EFC0IzHiId2rJlC66++mqcddZZePbZZ/HDDz+guLgYlZWVMJvNOHjwIDZt2oS3334b1157LXJzc/Hxxx/DpoNz/VmzZsFgMMBgMKB79+7hHg5FKQYcQk1JxWcAyM8P/liIiIiIPElIkEEHX154QdlkClGUeeedd9CvXz8sXLjQ5eedOnVC//79MXjwYJx55plIcMoCOnjwIO677z4MGTIElZWVoR4yUcgx4BBqKSnAmWf6bjdpEj+8iYiIKHyqqoDGRt/tDhxQFpggiiJjxozBY489BqvVCgAwGo14/PHHsWXLFhw6dAjr16/HsmXL8Ouvv+Lo0aP46KOPkJWV5Tj+hx9+wEUXXYRjx46F679AFBIMOISaxQJUVPhut3s3P7yJiIgofFau9N0mNRVYvVoWjySKEV9//TVefPFFx+127dph7dq1mD59Os4666xm7dPT03HvvfeiqKgIN998s+Pn27Ztw1133QXBSUaKYgw4hJrJBGzcCMyfLwsxedK1qyzWRERERBRqQgDjx/tu9/bbQHZ28MdDpBMHDx7Egw8+6LidkpKCH3/8EecrKPienJyMOXPm4Oqrr3b8LD8/H9OmTQvKWIn0gAGHcMjKAuLjgf37PbfZsgUoKAjdmIiIiIjsLBagpMR3uzfe4BJQiimvvvoqqqqqHLenTJmCPn36KD4+Pj4eH374IdLT0x0/e+mll1BfX++2/QsvvOAo7HjJJZcoeoxly5Y5jjG4qR13ySWXwGAw4N5773X8bN++fS7HOH/dc889Xh/PYrHg888/x913343evXujTZs2SEhIQFpaGs4++2zceeedmDVrluKaFWvWrMFTTz2Fc845B+3atUNiYiI6duyIAQMGYPTo0dixY0dAz8ORI0fw0ksv4YILLkCHDh2QmJiIzMxM/PWvf8WGDRvc9rV161YMHz4cvXr1QsuWLZGcnIzevXvj73//Ow4fPqxoPM4aGxvx1Vdf4a677kKvXr3QunVrmEwmdOnSBZdffjleeeUVVCjJio8EgkRlZaUAICorK0PzgDabEP362fer8Px1/vmyLREREVGoPfWU73OVVq2EqK8PyXBCfr4WgLq6OrFjxw5RV1cX7qGQhk6cOCFatWolAAgAIjs7W9j8PFcfP368ox8A4r333nPbbuzYsY42gwcPVtT3jz/+6NJ3U4MHD3a539fX3Xff7fGxvvjiC5GVlaWonxYtWohDhw557OvYsWPi+uuv99mP0WgUDz30kM/Xl7vnYe7cuaJ169Ye+zYYDGLmzJmOPmw2mxg1apSIi4vzeExaWppYsWKFj9/Kafn5+SI3N9fn/zM9PV18+OGHivsNJTXvcfEaxCxILYsFKCry3a64WLblukgiIiIKJZsNeOcd3+3q6+VuFkQxoKCgANXV1Y7b999/v9sMAiXuu+8+jBs3zlF08ttvv3VZqhFMAwYMQFJSEkpLS7Ft2zYAQFJSEgYPHuy2vbu6FADwj3/8A6+++qrLz5KSkhwz9jU1Ndi9ezeOHz8OAKirq4PZbHbb18GDB3H55Zdj586djp/FxcWhT58+aNu2LcrKyvDrr78CAKxWK9577z3s2LEDCxYsQEpKiqL/95IlS3DjjTfCarUiPj4eZ511FtLT03HgwAHs2rULACCEwCOPPIJOnTrhuuuuwxNPPIG3334bgFw+k5eXh6SkJPz66684cuQIAKCyshLXXHMNioqK0LlzZ69jmDFjBp544gnH7x0AWrdujTPOOAMtWrTAwYMHUVxcDAA4efIk7r//fpSVlWH06NGK/o+6FPz4h/6FPGJutQoRH+971qB7d2Y4EBERUejNnev7PMX+dfJkSIbEDIcwWLxYiN695b8kHnvsMZcZ6J9//jmg/s4++2xHX6mpqcJqtTZrE4wMB7uPP/7Y0aZbt26qxj516lSXx8jMzBSffPKJqKmpadb2t99+ExMmTBCdO3cWJSUlze63Wq3i4osvdunvnnvuaZYNUVxcLK666iqXdnfddZfHMTZ9Htq0aSMMBoN49tlnxYkTJ1zarly5UnTo0MHRtk+fPuKLL75wZBp89NFHwmKxONrbbDbx7rvvCqPR6Djmvvvu8/qcLViwQBgMBkf7Cy+8UCxfvrxZlszOnTvFtdde65J1sWjRIq99h5qa9zgGHEQYPsBOnFD2AW40ChHpH1REREQUWWw2Ic47T9m5SlxcyM5VGHAIMZtNiP795e+5f39Oggkh+vfv77gITEpKEg0NDQH1d//997tcEO/YsaNZGz0GHHbu3Cni4+Mdx5511lni8OHDPo8zm83CbDY3+/n777/vMt6nn37aYx9Wq1XcfPPNLu2XLl3qtm3T5wGAePfddz32vWTJEpe2iYmJokWLFqKwsNDjMaNGjXK0T05OdhtwEUKIU6dOiXbt2jna/vWvfxWNjY0e+7XZbOL22293CYDoiZr3OBaNDIc4hU97y5aAn2laRERERH6xWICDB5W1VXpOQ5GnoACwF9DbsIHFzAEcPXrU8X1mZibi4wNbnd6tWzeP/evZlClT0NjYCABo0aIFvvnmG3To0MHncYmJiUh0swuf8y4deXl5mDx5ssc+4uLi8P7776NNmzaOn73xxhuKxn3ZZZfh4Ycf9nj/5ZdfjtzcXMdti8WCUaNG4dxzz/V4zKOPPur4vqamBps3b3bb7oMPPsCxY8cAAD169MCHH34Io9HosV+DwYB33nnHUVx0+/btWLp0qcf2esZPiXBo1UrZB3SbNtwak4iIiELLZAJWrFA26ZGezsmRaCQE8PzzgP2CyGiUt2N8RxLnXQOcd5nwV9M+ImFXAvvuCnZ33HEHzjjjDL/7Ky4udtSRAIARI0b4DORkZGTgvvvuc9xeuHChx10+nD300EM+2wwcOFDVMZmZmcjMzHTc9rSDxqxZsxzfP/7440hKSvI5ltTUVNx4442O20uWLPF5jB4x4BAOVVWyGJMvBw4AHgqrEBEREQXNnj2+Ly4vvxz4+WcWt45G9uwGe2E7q5VZDoDLRa1Jg7/7pheddXV1AfcZbIWFhS6FM2+55ZaA+luzZo3L7euuu07Rcddff73j+4aGBo/bWTr74x//6LNNx44dHd/36NFDUeZGp06dHN+fOHGi2f0nT57E1q1bHbevvPJKn33a/eEPf3B8v3HjRsXH6Ql3qQgHpTMBLVpw1oCIiIhCSwjgued8t1u6FHA60aYo4Zzd4FRJ35HlMGRIzJ6fZmRkOJY9VFZWBtxf0z5at24dcJ/B1nQGv3///gH1Z9+RAQDatm3rc5cHu7PPPtvl9q5du3DxxRd7PcY5mOBJy5YtVbVvekxtbW2z+7du3Qqb02Tzk08+iQSFu/uUlpY6vrcvyYg0DDiEQ1IS0Lo14Cttqnt3LqkgIiKi0LJYgN+3iPNKCKC8HGjfPvhjotBxrt3gzDnLYejQ0I9LB1q3bu0IOJSXlwfcX9M+IiHg4Lzsw2QyITU1NaD+nDMC2rVrp/i4lJQUmEwmxzab7jILmnJXP0LL9oDcVrOppr9nf2sxaBHkCgcuqQgHkwnYvFm+aXspFoJt22I+dY2IiIhCLDFRTowo8fPPwR0LhZY9u8FTrbG4uJiu5ZCdne34vqysLOAij84FBg0GA3r27BlQf6Gg9bISs9PycbUX+M6Pr6SGQ7jU1NRo0o9NyZJ8HWLAIVyysoD8fNdUNXfGjInZN3UiIiIKA7NZ1pHyxWCI6YvPqGSxAPv3e641ZrPJvw2LJbTj0ommKftN6w+oYbPZXNbk9+7dG23btvW7P2dWX9cXAcjIyHB8f+rUqYAvgp0LZ546dUrxcUIIl1oSWhTxDJamYzt69CiEEKq/9u7dG5bxB4pLKsLFZgMmTPDdbtcu+abOgkxEREQUCsuW+Z4QAWSgwX7xyfOU6GAyyQxcb2vF27eP2d/3ZZdd5nJ79uzZLsUL1Vi4cCGOHz/uuH3ppZe6bec869/Q0KCobyXLC/zlXCBRCIHi4mKceeaZfvfX3mlJ1oEDB2CxWBRlOpSUlLgEO9rreGlX01oQR44cUbV8JNIxwyFc5s9XFh3OzGQdByIiIgoNIWR2pRJTp8qL0xi9+IxaWVlAv36ev5y2AIw1/fv3x7nnnuu4/d1336GkpMSvvqZNm+Zy++GHH3bbzrlGgtJtM513RPAmzmnpjLvaA+5ceOGFLrd//PFHRcd5ct555zm+b2hoULwTw+rVq11un3/++QGNI5jOPvtsJCcnO24HkhkTiRhwCAchgHHjlLU9ciRm09aIiIgoxCwW4OBBZW1nzAC6dAnueIh05plnnnF839DQgIceekh1H19++SUWL17suD1s2DCcddZZbtt269bN8f2uXbvc7oLQ1LfffqtoHK1atXJ8r3RLzvbt27sEXd59913FwQp3LrjgArRo0cJx+7PPPlN03CeffOL4vlu3bujevbvfYwi2hIQEXHHFFY7bH330URhHE3oMOISDxaJsbSQATJnCmQMiIiIKDZMJWL9eWSBh925Z74EohvzlL3/BlVde6bi9ZMkSPPHEE4qPX7lypUuQolWrVs2yHZw5ZwA0Njbiv//9r9f+Z8+erTjDwXl5xLFjx3Dy5ElFxz311FOO73/55Re8+uqrio5zJzk5GX/9618dtz/88ENs377d6zELFy7EkiVLHLc9ZYfoyT/+8Q/H92vXrsWMGTPCOJrQYsAhHEwmYPVqQMk2Mm+/zWJMREREFDrt2wNlZb7bRWjFdKJAxMXFYfbs2S4X62+99RZuuukmlJaWejzOZrPhrbfewrBhw1yKHc6YMQNnnHGGx+M6d+7ssozhueee8/g4CxYswCOPPKL4/3L22WcjISHBcfuVV15RlK1w2223oX///o7b//znPzFlyhSvxSrr6urw9ttvu93Z45lnnnFkOTQ0NOCaa65BcXGx237WrFmD2267zXG7Y8eOqv7P4TJo0CDceuutjttPPPEEpkyZgsbGRq/HNTQ0YN68ebj00kuxb9++YA8zKFg0MlxycoCNG4Fevbx/YJeWshgTERERhZbSyQ4GHSgGtW/fHsuWLcPVV1+N3bt3A5DLGPLz83H11Vdj6NChyMrKQosWLXD48GFs2rQJ33zzjUu9B6PRiBkzZuCOO+7w+XijRo3CddddB0AWVjz33HMxYsQIXHDBBUhISEBJSQn++9//Yv78+QCAe+65B7NmzfLZb3JyMm666SZ89dVXAIBJkyZh1qxZ6NOnD1q2bOlod9lll2HEiBGO2/Hx8fj6668xYMAAHDt2DEIIPPvss/joo49w22234bzzzkNGRgZqamqwa9curFq1CvPnz8epU6dwzTXXNBvHGWecgalTp2L48OEAgH379uEPf/gD7r33XlxxxRVo06YNDh8+jPnz5+OLL75wBDaMRiNmzZrlsnOGnn344YfYuXMnCgsLYbVa8eyzz+Kdd97BX/7yF1xwwQVo164dhBA4efIkdu7ciU2bNqGgoMCReRLI0pWwEiQqKysFAFFZWRnaB164UAj5ke7567PPQjsmIiIiim319UK0bOn7HAUQ4ujRkA0rbOdrfqirqxM7duwQdXV14R4KBdHRo0fFDTfcIACo+urRo4fIz89X9ViPP/64or5feOEF8eOPP7r8zJvS0lLRs2dPr33efffdbo/dvXu36N27t6r/e0lJicexvPHGG8JgMCjqJykpSXz77bde/29qnge7sWPHOtoPHjxY0TGDBw92HDN27FivbU+dOiVuvvlm1X8zvp67UFPzHsclFeEiBOAUKfTojTe4pIKIiIhCJzERMBi8t2nVCpg7F4ihrd2ImmrXrh2+/fZbrFq1CjfeeKNLEcamDAYD+vXrh2nTpuHXX3/F0KFDVT3W9OnT8cYbbyA9Pd3t/WeccQbmzp2LsWPHquq3c+fO2Lx5M9544w1ceeWV6NKli0sRR2969uyJwsJCTJ06FV27dvXaNicnBy+88EKzLSKdjRw5Ehs2bMCQIUNcdtBwlpiYiFtuuQXbtm3DDTfcoGicetKqVSt88803WLhwIS655BIYjUav7bt3747hw4dj5cqVui6M6Y1BCF7NVlVVIS0tDZWVlS5bzwRVfT2QnKwsFTE/H1D5pkRERETkl5oaGVDwpbYWUHhhooWwnK/5qb6+HiUlJejRoweSkpLCPRwKEYvFgnXr1mH//v04evQozGYz2rZtiw4dOmDAgAHo0KFDwI9hNpuxfPly/Pbbb6iurkaHDh3Qt29fDBgwQIP/QWB27NiBwsJCHDt2DDU1NUhJSUHXrl1xzjnnoEePHqr6Ki8vx/Lly3Ho0CFUVVUhIyMDXbt2xeDBg70GdiJNZWUlVq1ahYMHD6K8vBwGgwFpaWno3r078vLyXHYp0RM173EMOCACAg7nny8rRvuabSAiIiIK1JgxwIsv+m538iSQlhb04dgx4EBEpA9q3uO4pCKclAYQDhyQhSOJiIiIgslmA15+2Xc7o5EFrYmIyCcGHMLFYACcqr961KkTsGEDP9SJiIgo+ObPBxoafLfLyGDmJRER+cSAQ7gkJgJKCn8cPcqCTERERBR8QgDjxvluFxcHrFnDyRAiIvKJAYdwsViAI0d8t7NageXLgz8eIiIiim0WC3DwoO92rVsDWVnBHw8REUU8BhzCxWQCXn1VWdsxY7g1JhEREQWXyQRs3Ahcfrn3dr16MbuBiIgUYcAhXIQAJkxQ1nbXLhaNJCIiouDr3BlYutR7m5UrgcbG0IyHiIgiGgMO4WI2A3v2KGublSVrPhAREREFU0WFsqzK//43+GMhIqKIx4BDuBgMQHq6srbHjjHDgYiIiIKvXTvgr3/13e7VV7nck4iIfGLAIVxMJmX7XBsMwIoVXCtJREREwWezAd9+67vdvn2cDCEiIp8YcAgXIYB33lHWNjMzuGMhIiIiAoDqaqC+3ne7iRM5GUJERD4x4BAuSreeEgJYtizowyEiIiJCaipw//2+273/PpdUEBGRTww4hIvJBKxbp2x24Ikn+KFOREREwWezAR9+6Lvd/v1cUqGA4PkbEUUhNe9tDDiEU0aG3K3Clz17lLUjIiIiCkRlpbJJjqVLuaTCi7g4eYpts9nCPBIiIu3Z39vs73XeMOAQTqmpwCuv+G5nMAR/LERERETTpvluExcH9OwZ/LFEMKPRCABoaGgI80iIiLRnf2+zv9d5w4BDOAkBfPlluEdBREREJJdTvPSS73acCPHJaDQiKSkJNTU14R4KEZHmqqurkZSUxICD7iktHNmiBT/ciYiIKLhOnVK2hDMtjeclCrRq1QrV1dWs40BEUUUIgZqaGrRq1UpRewYcwslkAjZuBF54wXu75GQgMTEkQyIiIqIYZTIpCySsXcv6DQqkpKTAZrPh2LFj4R4KEZFmjh07BpvNhpSUFEXtGXAIt8xM4L33vLc5cgTIzw/NeIiIiCh2KSgAhh07gj+OKJCUlIT27dujvLwcFRUVzHQgoogmhEBFRQXKy8vRvn17JCUlKTrOIPjuh6qqKqSlpaGyshKpqamhffC6OqBlS9/tcnKAnTuZwkhERETBYTYDnToBJ054b9e5s1wSGuJzkrCer/lJCIEjR47gxIkTSEhIQFpaGlq0aAGj0QgDz+mISOeEELBarairq0NlZSUaGhqQkZGBDh06KH4Piw/yGMmXxYuVtdu7V54IKIwkEREREaliMgF33w288Yb3docOAfX1ssYUeWUwGNChQwekpaXh5MmTqKio4FaZRBRx4uLikJqaivT0dCQlJakKmDLgEE5CAOPHK2vLAk1EREQUTDYb8M47ytr+8ANw7bXBHU+UMBgMaNGiBVq0aIEOHTrAarXCarWGe1hERIoYjUYYjUbEKVly5wYDDuGkdJcKALjnHhZoIiIiouA5dUqemygxbhxwzTWcDFEpLi4OcXFxSEhICPdQiIhCgkUjw8lkAiZNUtb2zTflzAMRERFRMKxapbztgQPKgxNERBSzGHAIJyGAd99V1rahAaiqCu54iIiIKDYJATz4oLK2nToBGzYw85KIiHxiwCGcLBZg/37l7VeuDN5YiIgo9JYsAfLy5L9K7/d1DJE/8vNlMUgljh0D2rUL7niIiCgqMOAQTiaTnCGYPl1Z+/Hj5QwEkRb8vWjhxQ6RNoQAnnsOKCqS/wrh+vpyd7+7nxEFSghg9Gjl7TMyWLuBiIgUYcAh3DIzgVmzlLXdv5/rJUkb/l60OB/32GMMPBAFoqBABp0B+e+iRa6vy0WLXO8vKABeeqn5z4gCZbEoz24AuJyCiIgUY8Ah3CwWYN8+ZW3Hj+cHPGmj6YXOSy95z1yw3+d8sbNzJ2dZifwlBDBmjOvP7r/f9XU5ciRgNJ6+f8QIYMKE07eNRuD55/n6o8DZMy4fekhZ+48/Du54iIgoahiE4JlKVVUV0tLSUFlZidTU1NAPYNcuoG9fwGz23q5fP2DjRqYxUmCEAHr1kgEDu5Ytgc6d5d/iGWcAv/56+u9MCOCCC+TJaMuWQF1d8wuc/Hxg6NDQ/R+IIt3kyTJYpwW+/kgLNpt8j/d1LgLIdqdOAX7uye6vsJ+vERGRasxw0IOePZV9aO/ezSUV5J+XX5YzWC+/LNO0nYMNAFBbK4MNgLxv0aLT9zlnQ9TWNg82xMXJWdbFi7nEgkgJm801UyFQI0Ywy4ECd+qUsmADANTXA9XVwR0PERFFBQYc9KC6Wn54+5KZCSQmBn88FF1sNmDcOBmsGjcOeOIJ38eMHHk6gDBihPeAmM0mAxKPP84lFkRK3H+/zBTSij1IyIKuFAg1SzaXLQOYYUBERApwSQV0kKInhNxeqrzce7sOHWS9B9ZxIDVefLH5WnElcnOB4mJlbQ0G1yDDpEnAqFHqH5Mo2lmtQHy89v3m5gJpaXLZXf/+wLp1XH5H6uTnA1ddpaxtbS3QokVwx+NG2M/XiIhINWY46EF9ve9gAwAsX85gA6ljs8mLf38oDTYAzTMaJk6USzE420qxrmnWwYsvBudxdu+WwQbgdCFYIqWEkNlsSnF5JxERKcSAgx4ovSDTcs0vxYbJk+VMVKjV1gL33Xd6iQXrO1Asarr97KJFwNixwXksm8319sSJzX9G5InZDJSUKGubkgIkJQV3PEREFDUYcAg3IYAXXlDW9osvZDoukTfOW1j6s5RCK6Wl8t8NG4B775UXXY89xvoOFDuabj979dWhe+zaWhlwZF0HUsJgUL5EokcP1pMiIiLFWMMBYV4TaDYDXbsCR48qa3/kCNC+fXDHRJHLeQvLhASgoSHcI2pe32HhQmDYsPCNhygUFi8G/vQnmXoerkyDli2B3r2BTZtY14G8EwI4+2xg2zbfbcNYT4o1HIiIIg8zHMLNZAJWrlS+lzU/YMkb5xlVPQQbgOYZDSNHMsuBopsQcteW+vrwLmuorZXBBkC+LxQUMOOB3LNY5ISGL2PHylohrCdFREQKMeCgB127yuriSnBNLnkiBPD884DRGO6ReGffwo8oWi1aJP/O9cRolEusRo3i9rXUnMkE3H2373YLFgBdugR/PEREFDUYcNADk0l5RfH8/OCOhSKXPbshEup8MMuBos3LL8v38ilT5N+33litcmbaeSeLgoLwjon0w2YD3n7bd7t9+7hDBRERqcKAgx4IAbz/vrK2L73ECzVqLlKyG+x27pQF7Yiigc0G/Otf8kJszBj9ZTe4YzTK9wx+nhAAVFfLJUC+TJjA5RRERKQKAw56YLEAu3Ypa7t/P2cXqLlIym6wGz+eS4QoOkyaJAsAA0BjY3jHopTVyiwHOi0lBcjN9d1u3DgGqYiISBUGHPQgMVH5mkjOLlBT9uyGSKs+bzYD8+eHexREgbHZgIkTwz0K/zDLgewsltNbGXtz6JCyTAgiIqLfMeCgB0qrQwPAjBk8OSTXSvMWi8x8icS/i/HjI3PcRHbO2Q2Rxp7loLSGEEWvxETlS/Ii9e+diIjCggEHPTCZlKe17tnDJRWxTghZYd5eaT4xEXjllXCPyj+bNjGlmyJXJGc3OJs4kcubYt3ChUBVlbK2kZZNR0REYcWAg1707q2sXXq6vMCk2GWv1wDIfxctkpkCkSgujindFLnuuy86Zntra4Hvvw/3KChc7EFsJZKS5BcREZFCDDjoxbJlytrt2ycvMCk2Nd2NwmgEHnhAedFRvbHZWLiOIpPVCnzySbhHoR0ub4pdFguwd6/y9gkJQRsKERFFHwYc9EAIuZWaUqNH88QwVjXdjcJqVVboS88MBuCGG4DFi8M9EiLl/ve/cI9AWxs3MvAXqxITgcxMZW3NZrmFJhERkUIMOOiBxQIcOKC8/YEDrOMQS+wFIhcvltkNcVH2shVCVj0fNYqBNIoMQgCTJ4d7FNobM4avwVhksQBlZcraLlsGpKYGdThERBRdouzKJUKZTMCqVUCrVsrar17NrTFjhXOByMcfl9kN0VrcjQUkKVIUFMi/12jDLIfYZDIprwPUq1dwx0JERFGHAQe9yM4GunZV1rZz5+COhfTDuUDkzp3hHUuwGY0sIEn65LwNrb2OSrRlGtkxyyH2CAHMmqWsbTQG2oiIKKii9IwpAhUUADt2KGtbUxPcsZA+NC0QGe2sVhaQJP1pug3tokXRnWnEJXuxR03RSNaQIiIilRhw0AOlM2Y9ewLz5gHt2oVmXBReTQtExgJmOZDeNN2GduTI8I4nmCZMkMsquGQvtphMwODBytoeOsSAFBERqWIQgmf2VVVVSEtLQ2VlJVLDUQzJbAa6dQOOHPHdtqEBiI8P/pgovIQALrgAKCyMrYCD3aRJsogkUTi5ex3GxUVvdsMZZwC//ip3jqHYYbXKrS6VnA4WFwM5OcEfkwdhP18jIiLVmOGgByaTnDnbsMF3lsNVV4VmTBResZjd4GzcOKB3b7lmnihc3L0OozXYAMg6MQsXhnsUFGoVFcqCDUaj8u0ziYiIfseAg15kZckTPV8ns0uWAI2NoRkThYcQwIgR4R5FeJnNcqb1uee4vILCI9qLQ3ry1FN8zcWadu2A9u19t2vdmtkvRESkWoydSemYzaZ8X/f//Ce4Y6HwMpuBPXvCPQp9YBFJChd7dkM0ZzS4s3OnLIxJscNiAY4e9d3ulVdY34OIiFRjwEEvqquB+nplbadM4QxUNFu+nFksdnFxLCJJoRfrWUbXXQcsXhzuUVCojBmjrN2rr/K9mIiIVGPAQS9SU4E1a4AHH/TdtriYVaKjVaymcXtiszHLgUIv1rOMGhuBxx7jxWUssNmAt99W1ra0lOceRESkGq9q9GTAAODnn323s1eUpugTq2nc3jDLgUKNWUYysP3SS+EeBQXb998DtbXK2mZmAomJwR0PERFFHQYc9MRiAfbv992urk6eJFB0YXaDe8xyoFDi6/C0ceMY/IxmQgATJihvf+QIMxyIiEg1nlHpiX17zLQ0320nTOCMb7RhdoNnzHKgUOHr8DSzWXkxY4o8Fguwb5/y9iwaSUREfmDAQW+2bwcqK32327ePMw3RxD6ryi3H3LPZgAMH+DdPwcXshuYmTWLwJVqZTMCKFcrbv/46g75ERKQaz6r0RAjg2WeVtX3hBc40RBP7TBNP5lzl5gIbNwKbNslZZ/7NUzAxu6G52lpmOUSzjh2Vtz10iEFfIiJSjQEHPbFY5CyuEm+/zYvTaGIyAatWASkp4R6JvhQXy4vAfv1kwTKiYGF2g2fMcoheLVsqaxcfD6xezaAvERGpxjMrPTGZgNdeU9b2+HHONESb7GygRw9e8DQ1caK82FmyBMjLk/8Sac1etDdcF9ZGI3Dmmd7bGAzyNTB1amjGZFdby0LF0WrcOGXtrFb5+URERKQSr2z0RAjgnXd8tzMYgPXrOdMQTZYsAbp1A7Zs0e9MYnIy0L49cMUVoX3c2lrg/vuB554Diorkv8zuIa3Zi/Zu3Aj07h2ax3Te3thqBX77zXt7IYDycuDzz0Nf7+Xpp/m6izY2m/JJDiGU1ZciIiJqggEHPbFYgL17fbcTgqn30UQIYNQo5ctptJSbC8yfD8ye7X7JgsEg22zYAOTkAEePqisyppVZs+QYAG6RScGTlSWzx4qKQvN4DQ3eAwdxcTKjwV7HZNMm4NVX5b+hvvjfvVvuWkHRo7pabrOtVEND8MZCRERRK6gBh9tvvx0Gg8Hla6+SC2onpaWlmDJlCgYNGoQuXbrAZDKhS5cuGDRoEKZMmYLS0tLgDD4cTCZg2TJlbX/4IahDoRAqKJAXFOFQVSUzFtq2BQ4ebH6/ELKOwqJFwC+/yJ+F+6LDaOQWmRQc4dgtxtvfsc0G7NghgyD9+gHnngu8+aZ8DYSa1QqccQaXNEWT1FS51aUScXGyPRERkUoGIYJz1j537lxcf/31zX5eUlKC7t27K+rj3XffxdNPP42amhqPbVq1aoVXX30VDz/8sL9DRVVVFdLS0lBZWYnUcH+gVlYC6em+251zDlBYyG0UI50QwAUXyBnLUCyl6NEDmDBBft+6NXDWWUCXLkCvXsDOne6PMRiAFi1koMFqDf4YlcrPB4YODfcoKJosWgQMGxbuUbiKiwPOOw9Yt04GJ8M9vvPPl0v6+NkT+YQA+veXnz++pKbKDLcwL+XU1fkaEREpEpQMhxMnTuCRRx4JqI/x48dj+PDhLsGG3NxcDB48GNnZ2Y6fVVdX45FHHsHEiRMDejzdWLtWWbuSEhaNjAah3oavrg7485+B228HrrpKLqMwm4E9ezwfI4Sso6CnYAOzHEhr9uyGUOjaVXlbm00utzKb9bGLxsaNwEsvhXcMpA2LxX1mmzs9egCJicEdDxERRaWgnLk8+eSTKCsrAwAMGTJE9fHfffcdxo4d67idl5eHTZs2YefOnVi2bBl27dqFDRs2oLdTYa/nn38ec+fODXzw4SQEMGaMsrZJSfzwj3Sh3oava1f3xUaXLwcaG0/fnj799HrxjRvlGvJwpHB7Y7WylgNpy2KRy4dCYf9+3xkCzvUbNmyQ7cO5i4azF17QxzgoMPZCqUqyZo4c4SQHERH5RfMrnQULFuDTTz8FAFxzzTW47bbbVB3f0NCAp59+2nE7MzMTK1euRL9+/VzanX/++Vi5ciW6dOni+NnTTz+NRucLp0hjsQD79ilre+SITCmnyBXq7IayMqBdO9ef2YMe9oCC0Qh8+qlcK96vn1w7vmOH5+yG11+XSzPCIS6OWQ6knZ9+Arws39Ocr79b5/oNmZmnLw43bQLWrAnf6w6Qn1WTJ4fv8Uk7nTsrO5d45ZWwL6cgIqLIpGnAobKyEg899BAAICUlBTNmzFDdx5w5c7Br1y7H7alTpyIjI8Nt29atW2Oq037kxcXFmDNnjurH1A2TCVi1SvmM9+jRvNiKVOEoTpeW1vzx7EEPe0DBOXPAVwZGXBzwxRfAzz/Li6Dp04M7/qbsqeacdaNACQHce29oq/C3aSMDB86ZRE1fa02XDmVlyUDgwIHA5s3y2O+/l7vM2L86dAjN+JnlEB3uvVdZu2nTeL5BRER+0TTg8Le//c2xa8RLL72ErKws1X18/fXXju87d+6MG2+80Wv7m266CZ06dXLc/ve//636MXWla1fAQ4ClmUOHeLEVqSwWmR4dyhO4l192naFqmt1gZ7/IMZu9p3DbL/g7dJAZEZ98EryxN5WZeTrVnLNuFKj8fCAUOx45L1favFkGDpwziZq+1rwtHbIHH66+WtZkuf12GcQ4ciT4/w9ALsOaNCk0j0XBYbUCn32mrO3+/TzfICIiv2gWcFi0aBE++ugjAMBFF12E4cOHq+6jrq4OixcvdtweNmwY4uPjvR4THx+PYU7rDwsKClBfX6/6sXXDZAKcsja8evVVXmxFKnt69OrVoctymDHDNcDRNLvBzn6Rs3z56RRuT1/2C36LBXDKTAq6Q4eAPn1k4IEoEEIADz4YmsdyXq5k/9tVkkmkZOmQmhpAWhk/nlkOkez4ceVtV67k+QYREflFk4BDVVUVHvz9hM1kMuGDDz6AwY+LqKKiIpjNZsftQYMGKTrOuV19fT2KiopUP7ZuCHF620Jf3niDKY6RLCtLboMaqt+h8/IDpRc5mZny4sjTl/2iKTFRZueEKnhis8nAXF4esGRJaB6TolOoshsA90uA7NlOvjKJfM0uq9lxQCsNDcD8+aF9TNLOunXK2hkMoVuqQ0REUcd7+oBC//jHP3DgwAEAwL/+9S+ceeaZfvWzfft2l9u5ubmKjmvabseOHTj33HP9GkPYmc1yy0slDh6UJ5mcdYhMixcDf/pT8Pp//32gRQtZXK5DB6B9+9N/K2oucpT8fVksMpU7lAGwF16QFzzPPQdcfnlo62FQdBACeOCB4D9Omzbywtxe/NGZPdvp2DHPxzu/dj3x1s+qVcCIEerHrcSjjwLXXcfXX6QRAhg3zne7pCT5WZWaGvwxERFRVAo44PDDDz/gvffeAwD84Q9/wDPPPON3X3v37nW53VXhXuXdunVzuV2i9IJdjwwGWdyvosJ329GjGWyIVEIAzz4bnCJ1/+//Af/4B9C/v+c2Wl3kOPe3fj1w1VXAr7+GJs3a/tzZ17gPHRr8x6Tokp8vl+cES9euwH//KwN+3pb/ZGXJr0C560cI4JZbAu/bk9JSoL5eBjcpcihdBpebCyjMNiUiInInoCUV1dXVeOD32SGj0YgPPvjAZ80Fb6qqqlxup6enKzouLS3N5fapU6e8tjebzaiqqnL50g2TCVixQlnbyZO5pCJSvfQSUFgYnL6//x447zzf7exF53wtl1CqqMh94btga1rJn8ibJUvkUpzFi4M362+3f78M6oWz1oiarDl//fBDcPsn7SUmKgsScScgIiIKUEABh3/+85+OrISnnnoK559/fkCDqa6udrndQuGMSdN2vgIOkydPRlpamuPLn900gqp3b2UnqGVlcmaJIovNprxOhz/q64Emr6Wg81UTIpi8VfInciaEXIJTVCQzjEKRDTdyZHiDYQYDoDB477dx4xjwizT19cp2NGnVSgYniIiI/OT31cGyZcswY8YMAEB2djbGjx8f8GAamqSXK82WaNquaT9NjRo1CpWVlY4ve/0J3TCbgcOHlbV12tWDIsTkyUBdXfD6f/310K+39VUTItiY5UBK2HdmAWSGUSj+Xvfske/p4WIyAT//LDOfZs8GunfX/jF27eIseKRRupzv4MHw/v0SEVHE8yvgUFtbi/vvvx/i95P7999/X3E2gjfJyckut5Vub9m0XdN+mjKZTEhNTXX50hWDQfl62Kef5kVWJLHZgBdfDF7/cXHyoiLUfxP2mhBNt83cuBHo3Dn4j88sB/LFnoVjNMrbBkNoXicZGeEvqJiVBVx9NfDnPyurD6SWycRZ8EiTmqp8KQy3PiUiogD4VXDh2WefxZ49ewAADzzwAC699FJNBtOqVSuX27W1tWjZsqXP42pra11up6SkaDKesLFvMdhk1w63du+Wsw9JScEfFwUu2NkNaneX0JK7gnX19cDRo6F5fPtWnkOGhP8Cj/THObsB0D7Y8MgjwEUXnd4Vxk5N8dVgS0yUy/V27NC23yNHZAHOq67Stl8KLqUB2upqQMG5GBERkTuqAw47duzAW2+9BQDo1KkTXnnlFc0G065dO5fbZWVlaNu2rc/jysrKXG4rOUbXLBbvuwc4a92aF1eRwmYDJk0K7mNMnw7ccIN+LnDsu66Ulwf/scIZbCF9c85usFq17z8uTmb0vPOOvt+PCwq0DzbYjRwJDBum7/8/nWazAW++6btdixbyPZyIiMhPqpdUHD161LGUoqysDBkZGTAYDB6/7r33Xpfje/To4biv6S4UvXr1crm9b98+RWNq2q53794q/1c6Y09Pz8nx3s5olCe5vLiKDAsWAE2ycTRlMACffgp06RK8x1DLZALeeCP4j5OXJ5dvbNjA1wM1Z89uCEawAXANdulV0yUlWtu7l2v9I0l1tbJsu+xsLpchIqKAhKGkvGd9+vRxuV2ocNvApu3y8vI0G1PYdOjgO8vBagV++SU046HACCEruQf7MfR20SOEnEUL9u4VO3YAx4+Hd/tB0qdg7qBy5pky0LVpk/6DXcEOuqSlMbshkqSkAE0medw6dkxfnylERBRxVC+pSEhIQJs2bRS3N5vNLttdZmRkIO73E7+0Jml6WVlZyM7Oxu7duwEAy5cvV/QYzu1ycnKQGQ0XHUpnFP72N+Daa3mip3f2XRyCoU0bYP58+Tejp/XiQGh3rxgzhvUbqLlg/g2WlQF9++rrNeeOc9AlWK/FuDhgxQrgiiuC0z9py2IBTpzw3W7FCv3/fRMRka6pnvIZNGgQjh8/rvhr+vTpLscXFhY67rMHFpzddNNNju+XLVuG/T4u0vbv3+8ScHA+PqLV1QGVlb7bhXvLNVLGvkxGyx0bjEZg9Wpg82Zg4ECgXz/9zfA7717R5L1Ac9yaj9wJ5t9g166RkW4eisDf0aPAY4/J5U1LlgTvcUgbJhPw8su+2339dfDHQkREUU1XSyoA4N5774Xx9zWmNpsNEyZM8Np+/PjxsP1+EmU0GpvVjIhYCxcqa8fZ3MixYwdw6JB2/bVurc8gQ1NZWcC558r6EsFcWpGZGRkXfxR69r9BrQMO27ZFxlasnrat3bQJWLxYu8fZuRMoKgKee47bNeudEMCoUb7bTZjAbTGJiCggugs49O7dG3fffbfj9gcffIAPPvjAbduZM2fiww8/dNy+5557mhWejEhCABMnKmubnMygQyQQAhgxQpu+4uJkZkNhYeSkuoZihrW0lBkO5JnZLDPCtGQwyKUKkXBxnZUlA5RNvy6/XBYG1NKGDZERiIll+fnKAuBmM3DqVPDHQ0REUUt1DYdQmDJlCpYvX+5YcvHggw9i3rx5uPXWW9G5c2eUlpbiyy+/xPz58x3H5OTk4KWXXgrXkLVlsQAlJcraJiVxVjcSmM2AmyVEfjv3XPm7jxT2GVZ7IdT33gNmztT2MbKy+Fogz15/HWhs1LZP50KtkRL8a8pi0f6C0h6IYU0VfRICGD1aWduMjMj6rCEiIt3RZcChbdu2WLhwIYYOHYqS3y+8586di7lz57pt36NHDyxcuBBt27YN5TCDJzER6NYN2LLFd9ujR+VMxVVXBX9cFBitZvcjtRp8Vpb8stmAzz7Tvv9ff43sCz8KHptNedaYL7fcAgwaJLct7tBBf4Va1TKZ5E4bhYXAXXcBVVWB9ynE6SyHoUMD74+0ZbHIjDBf4uL0v/sKERHpnu6WVNjl5uZiy5YtGDFiBFJTU922SUtLw4gRI7Blyxbk5OSEeIRBZLEA+/Ypb//AA5GR0hvLfvxRu74eeCCyTwCrq4H6eu37tVhkIU2ipiZPBmprtenr55+Bxx+XQd5IqKGiRFYWcP31wNatsuijFiJpuUmsMZkAJRmhNhvQvXvQh0NERNHNIIT+zwbq6+uxfPly7N27F+Xl5WjTpg26d++OSy65BCYNLryqqqqQlpaGyspKj8GNkBICOOssYPt25cfU1gItWgRvTOS/xYuBa64BGhq06a9lS5kCHcwCjMG2bp3cVaK8XM6oWiyyOFmgEhKABQu4NR+dZrMBKSnaBRzi4+XrLxrTzBctAoYN07bP/HxmOeiNEEC7dvL915fDh2Umj07o7nyNiIh80uWSiqaSkpIwNJZOWCwW4Phx5e0NBnkxy4CD/gght4rTKtgAyOyA6mogkk+2LrhAftnNm6dNvw0NwLPPyjTgSFx2QtqbNEm7YAMgA37R+LclhMxIiIvTbvkXaznoU329smADIIPDf/pTcMdDRERRLYKnSKOYyQSsXy9TW32dpLVvD6xdG9kXn9Fs0SKguFi7/p55Jvp+30LI7AatLkg2bWKFfJJsNrmcQks9ekRncdJg7CQjhKwNoXSbZwqNujrlbceP57IYIiIKCAMOetWhg5yB8PVBf/QoUFERmjGROkIAY8Zo2+ePPwLnn69tn+FWUCAzErQ8qX3iCZ4kk7a1G+yOHInO7VftO8ls2iQD3lowGACrFXjhBb4e9WTxYuVtud0wEREFiAEHvTKZgFdfVdaWF1f6VFAgT961ZN+CL1rY07i1TrcuLpbZJRS7bDa5nEJLbdvKi/FILtrqTVaWLIR54IA2/dk/l+w7VlD4CQFMmaKsbUYGsHJl9P69ExFRSDDgoFdCANOmKWu7axcvrvRG6+yG6dNl8CLatiizp3EHI2A2YgQDcbFswQLtsxuOH5dBh2gmhLIdDNQwGrljhV5YLHILYSVWrACys4M7HiIiinoMOOiV/UJMqdGjeTKnJwUFcm97rXzyCXDuudGxBZ8zNZk8ajHLIXYJAYwbF5y+f/ghOP3qhdrPHiWsVmY56EVCgvx9KNGzZ3DHQkREMYEBB70ymWQqo1IHD0ZXqn0kC0btho0bo/NkXQjgzTeDt8Unsxxik9YBPzuDIfqL6DnXclixQrt+meWgD6dOKT9XiPbgGhERhQQDDnrWtatcQ6nE5MnRlWofyRYulJXZtRQXF50n68GojO9szx7AbA5O36RPwQj4OfcdbXVU3LHXctBydwlmOejDmjXK20Z7cI2IiEKCAQc9M5mAl19W1vbFF3lioAdCAGPHan8BbbNF54WO82zqxo1yK1gtWa3AsmXa9kn6ZrHIujZaMZmAWbNkTYhorKPiic0GTJ2qbZ/RGjiNFGqDcfv3R99nDhERhVx8uAdAXqgp3rV3r5zJTUoK6pDIiyVLgPvu067Cu9306cCFFwLt20fnhU5Wlvwym+VWsFobMwYYOlT7nTBInxITZXbYyZPa9Ne5M3DXXbH397NgAVBfr22fzoHTaHwv0zuLRS6/VGrFCv6eiIgoYMxw0DOzGdi3T1nblJTYOyHWEyGAUaO0DzbExQGffhqdBSObMpnkloO9e2vb7549nKWLJVoXPTxwIPaW5Qgh0+m1lJUls5hiJUNEj9QW6S0qCt5YiIgoZjDgoGfLlwONjcradusmZ/YoPIJVpC5al1J4UlSk/UlueroM2CxZom2/pE+JiUBqqnb9ZWTEXjA3GDtVHDgAHDsW/YFTPRMCeOMN5e1feIHLX4iIKGAMOOiV2rWW+/bFzkWp3gSjSF1urgxgxNKacSHk+m6jUdt+S0pkEOO553jyHAteekmbi+WEBPnaKyyMjdefM+faKlpmOnDXmPBSW9/k0CGeVxARUcAYcNArtTNM6enMcAiXYGQ3HDoE9O0rK8XHyoxgQYG8yFG6R7xarJAf/Ww2OSurhYYGGfiLlddfU1lZMjNo3jzttq0tLgYWLdKmL1LPXt9EifHj5edarAXbiIhIcww46JXJJD/s588H4hXU9ty/P/bWGetBsLbgs9nkDGussGc3aHVh4w4r5Ee/q67SbkY2Lo4XW/YgoJa77jDLIXwKCoCtW5W1/e47oEuX4I6HiIhiAgMOepaVBVx0kbI6DrG2xlgvglW7oa5OVomPFfaMHq23E3VmszHLIZo1Nmr7u7XZZB2dWBWsJU67dzM4Hg7236fSc4WDB7mcgoiINMGAg96tXausHWeMQi+Ys/IGg0xpjZXfq/OacfvXmjVA69baPo7RyCyHaHXvvdr2F+sZMcFa4mSzAcuWadsn+WYP6ir5ezYa5Y5BsZ7hQ0REmmDAQc/UXNAKEbsnxuESzFl5IWJrdwpAZvT063f6a+BAYPNmGXx4+GFtHsNqZZZDNLJagc8/17bPWM6ICfYSpzFj+HkVavag7tChvttarUBaWvDHREREMYEBBz1Te0EbiyfG4WQ/gduwQVmdDTXatAFWruQMU1YWcM45wGefaddnrM9cR6P77w/O7zNW/1aCvcRpzx6gd29uVRtqnTsrO08wGvnZQ0REmmHAQc/sF7Rr1gDJyb7b//3vsXdiHG6//QbccIOyOhtqvPEGkJ2tbZ+RqroaqK/Xrj+bLfayR6KZ1Qp8+mlw+o7VvxV3S5y02iIzPl4GEn/7jVvVhtp33yl7vpOTWReKiIg0w4CD3mVlARdcAPTs6bvt7t3cciyUhABGjQJKS7Xt12gE3nyTJ+J2qanA6tUyy6Fjx8D6euMNeeG0YQNn8KLFhAnav1YMBiAvTxaEjdW/laZLnLTaIrOxEdiyRX4fq0tWwkEIYPJkZW27d+c220REpBkGHCKBxQLs26es7ejRvFANlWDtUME6A81dcAHQti1w+HBg/Tz9NFBeDmRmajMuCi+bDXjlFe37FQKoqAD69uXfil0wllmwiGvoqPm8Onw49rJ6iIgoaBhwiASJiUDXrsraciur0Ah2UTWeiLsSQhaaC1RjI/DYY3xeo4XWy20AWT9lzZrYzWzwxHmZxfTp2vTJ4GpoqP28evVV/u0TEZFmGHCIBAUFwLZtytouXcoThVCwbxkXrKJqPBF3ZbHIYJoWiou59ChapKbK4qpGozb9GY1yK+KBA5nZ4E5Wllxa8emn2q3xZ3A1+NRmp0ydyt8HERFphgEHvbPPTCil1UUZeaZldkNCguf7YrVCvjsmk9wXvndvbfobMYLPa7Q4flwG6LRgtcpaOOSZfYmfVq8fBleDz/7+2auXsvYlJcyUJCIizTDgoHf2mQmlWPU7+LTMbmho8HxfrFbI96RDB1l/QQvMcogOQgDDh2vXH4N8vplMwKpVQEaGdn3yeQ++oiLg11+Vtc3IYNFIIiLSTHy4B0A+mEzAihVyZlfJLF5RkbxA5bKK4BBCzo5rqXdvuQODuxTl9u35u7QzmWTRs61bgR9/lOuMAzFypDwB5/ZvkSs/X9tdYpyDfHzdeZaTI3fSufNObfrj8x5c/mRKms1AUlLwxkRERDGDAYdI0LUrkJYmq6b70tDgPU2fAmM2A3v2aNtnUZFMCx86VNt+o1FWllxbP3Jk4H3t3CmzHIYNC7wvCj0htPk7sFu1Sl5gMcjnmxDAtGna9DV8OPDAA3zeg8likcsklMrIYCCWiIg0wyUVkcBkAiZNUta2sRGoqgrueGLZ8uXyOdYS04nVWbQI2LVLm76eeALIywOWLNGmPwqdRYvk0hitlJcD/fqxWKQSFot2r8H33wfOOYfPezCZTMCECcrbr17N4A8REWmGAYdIIIQ8KVNqxYrgjSWWabU1Y1Os1aCc1r+DPXtkhglrn0QWrbMbAHlBxr8BZRITtVvjzyB58AkBzJypvL1WwSQiIiIw4BAZ1G4JOH48T5yDoaBA1hDQgtEoZ5E2bZJfGzZwRkkJLbfHBE4X/mSV/MhiNmu/mwSDfsrV1wNHj2rXH4PkwWWxAIcOKW/PXXyIiEhDDDhEAvuWVikpytpv3MiLJ61pPbNutcpZvX79mMathskkgwObNgFr1sjaJlowGrmsJZIsXx74VpitWgGvvQYsWMCgn1oNDdqu8R83jq+9YDKZgHXrlLffs0cG9YiIiDRgEIKf8lVVVUhLS0NlZSVSU1PDPRz3amuB5GTl7c8/XwYpWPhJG1oXFzQaZaBh3Tr+jgJx4IDcteLQIeDBBwPvLz+fxTv1TghgwIDAso2mTwduuIGBvkCsWydT77dsAV5+OfD++NoLrm+/BW66SVnbdu3ke6sOA3ARcb5GREQumOEQKaqr1bVnerB2glG7wWplGr8WsrKAq68Gli4NvC9mOUQGiwXYsSOwPj75BOjSRZvxxKoLLgD++le5Ra0WQdMxY/jaCxYhlBeN7NVLZvwoCDZYbQJrdpfju82lWLO7HFYbf39ERNQcMxwQIRHzujqgZUtlbXNy5AVYVlZwxxQrzGagY0fg5Elt+42LA847j1kOgbJa5VawWr2VcaZV32w2ID4+sN93RgZQVqbLGdyIYjYD3boBR45o0x9fe8FhNgPp6bL2hhIKfg8LthzCmO+2oaKmwfGzTmlJGHtdHob17RTAYL2LiPM1IiJywQyHELM02vDhij3413fb8OGKPbA02pQd2NDgu41dVZXc05y0kZgIdO2qfb/cnUIbEydqF2wwGJjloHdVVYH/fjIztdtlIZaZTMDKlUDr1oH3xdde8CQknC6Qq4SP38PkBTvw6Bc/uwQbAKCssh7DZxcif1uZvyMlIqIoFB/uAcSSyQt24P0VJXDOOnxxQREevLgHRl2d5/3glBSZuaBku6pJkzhzpyWLRfuK+G++CQwaJAND/F35z2bTZv24nRCng0D8vejTW28F3sfWrXI5E2fTA5eTA2zeDBw7JjPxLrrIv36EAPbv52svGKqr1QW29+3z+HtYsKUMM38q8XioADBu3g5cmdcRxjhm7hERETMcQmbygh2Y+ZNrsAEAbAKY+VMJJi/wsSbZYlGetjp5MmeJtKR2dkiJt94Czj2XResCtWCBLKiqhbg4IC9PFlvlBY8+2WxyR4NAcTZdW1lZsgju4cOB9fPDD3ztBUNqKjB1qrK2BoOsy+Hm92C1CYz5bpvPLsoq67G+pELtKImIKEox4BAClkYb3l/heUYAAN5fUeJ9eUViItC2rbIH3L1b7qpA2rj/fjlzp6WdO/k7CpQQwPjx2tW/sNlkMcJACxJS8FRVAY2NgffjnMlC2hACePbZwPpgIc/gEAJ49VXf7VJSgJ9+koFXN9aXVKCiRtlr5ugphfUiiIgo6jHgEAKfrdnbLLOhKZuQ7Twym+UJslKs+K0Nq1VWtA8G/o4CY7HIFGytn0P+XvRrxQrt+nrlFc6ma8lsBvbsCayPZcvkxe6SJZoMiX5XXy+3DvbljTe8Lok5XKU8iNA+JUlxWyIiim4MOIRASXlN4O0MBiAtTfmDHjzI2TstvPhi8C4+S0v5OwqEySS3Fl29WttdPjZu5HaleiQE8PTT2vQVFyfrqDCwpB0hAl969v/+H1BUBDz3HH83WlJadHr6dK/P+/FTZkXdpCTFY0APDQqJEhFRVGDAIRSUnjh5a2cyAWvWKL+wWreOs3eBstmAKVO06Ss7W17Ibtp0+mvDBv6OApWVJWthxGn8VsYsB/1ZtEguRdICd4jRnpqdlDyxL5fZsIFBPy2lpCirF7R3r9fXxIlaZQGHQdmtWTCSiIgcGHAIgdQkZduv+Wy3a5fyi6CxY5W1I88mT9auIGFNDdC3ryysZv9iwUhtLF8ul75oafduXozqiRDAyJGB9/Paawz4BUtqKvD669r0FRfHop5aMptl1qMvWVlet4s1QFkQIbtditKRERFRDGDAIQRsUJZm6rWdEMDo0cof9NNPtb8IiyU2m9xeVAsvv8yLm2ARQl6YaJ3h0LWr1xNvCjGzWZutaT//XGbEMOCnPSGA2bO16ctmY5aDlpQWWj161GugVWkNBzW1HoiIKPox4BACp+qUfdgv//WY5zstFmVFn+yEAL77Tnl7cqXldotz5rD6erDYC0dqvW3pL7/wYkdPtMpiKSlh5kqwWCzKZtGV4tal2pk4UVm7RYs8BsatNoElO5Rtzf1D0VFYfVXKJiKimMGAQwgcVVhoqehIteetMU0mYOVKoFUr5Q88aRJP1vwhBPD3v2vXH4tDBo+9cKQ9TX7NGqC1BsXKeLGjH/YslkAZDMxcCSbn1+KaNeo+q9wRglkOWrDZgKlTfbe7807gD3/wePf6kgpU1iubPDlZ14D1JRVKR0hERFGOAYcQqLUo3zd+1H9+8XxndjbQo4fyB960iSdr/tBieze7zp25nCLYsrJO18UYOBDYvFn+7X/1lf99CsGignphsQD79gXejxDAkSP8nQaT/bU4cCDw6qva9MnAX2Dmz1dW0PObb7xmih09pW6ZhNr2REQUvRhwCIHWLZXPqM3fcthzKqLFItdYqsFq++otX658zasvR44A7dpp0xcpY7/ouf56oEUL//owGIC1axko0gOTCbj99sD6eP11FooMJSGA997Tpq99+xgk8pcQwPDhytrW1QHV1R7vbpus7nXTPiVJVXsiIopeDDiEgNGo/Gk2W22eUxFNJmDpUnUPzlladezp20ajNv1ZrcCyZdr0ReokJgLduvl3rBBAYaG24yH/2GzA228H1oe9WCQLRYaGlvUc7riDQSJ/1dcrr/2UmSm3z/TApmLiItFowIAeGixtIyKiqMCAQwh0yVA3y+o1FbFXL+UV+efNAzZu5MmaGgUFchZUyx0+Ro9mlkk42AtK+mvyZP7e9GDy5MCDphs3cnlZKNnrOWRnB97Xu+9qXxQ2VtSrWNZw+LBcTujBupJyxV11yWgBY5yyLTSJiCj6MeAQAhdmt1XV3msq4qlTyk+++vfnjJ4aWmc32B08yCyTcEhIUHfC3RRTucOvoEAuCwtUXBxrAYRaUZE2W5nW1sqgE6mnZmlgerpcSubBip1edtFqomMKC7MSEdFpDDiEwMCebZCcoOypTk8yek9FTEqSX0p88IGydiQFI7shIwNYvZpZJuEwb15gM6N3383fWzgJAfzzn9r0ZbNxeVko2YO3Xi5gVZk0iVkO/mjdWtnvID5e1jjx8H5nabThl9IqxQ8reGpJRERO+KkQAsY4A9IVFo7smN7SeypiQoLyBx4/nidpStlPkJUuV/Fl+XJ5ArdlizZpxaSOEMDYsYH18fbbfP2EU0GB3HHEX3FxwEMPAZ9+yoKRoWZfzqRVRkltLbBggTZ9xZKJE5X9DhobgV8875A1a1WJqofdX1Gjqj0REUU3BhxCwGoTOKxwi6h4X+seq6uVp4lbLHJLLPLNfoKs1QXmRRfJnRK4pCU8Cgq8nkArUlsLfP+9NuMhdYTQZinFK68Ad97J12Ko2Ws4bNzof+HWpsaN45IYNWw24OWXlbf38vwW7Dis6qHrLBpmCRIRUcRjwCEE1pdUwKrwOvZkreeiTQCA1FR1Mz1PP82TNCXsJ8jTp2vTH9cch4+W2Sp8/YRHQYG8WPWXwSB3h0lN1WxIpFJWFtC3b2B1VJxxSYw61dVyq0ulNm3SrLBq22TWcCAiotMYcAgBr7tONHHwpBkLtpR5bzRsGNChg7IO9+zxWnmanGRmArNmadMX1xyHj5bZKrt38/UTakLInV0C7aOGad1hZzIB69fL3ZUC0bYt8PrrXBKjRmoqcM896o7xUFj1yryOqro5rzu3xCQiotMYcAgBr7tOuPHMf7bAavMyq2o2A8ePK+ssI0O7wl3RTsu94+vr5QwThZ49W2XTJvm1bJn/fVmtgR1P6hUUyN9boMaMYXaKHhQVAb/+Glgfx48DU6fy96mGzQbMmaPuGA9ZJB2S1QV6lNasIiKi2MCAQwgM6NEaCSr2pK42N2LtHh97Xis58Xr+eeDnnzkrpJT9QvXNN/3vo2VL4LXXgLVrmc4dTllZct1+v36Bz3SPHMkLnVDRqnYDoGmKOPnJvrxJCxs38vephpolFQaD3E3JTWFVq01g9Nytqh76cJVGy2iIiCgqMOAQAsY4Ay7oqS7FcM1uHwEHJeniH3wAdOmi6nFj3q+/Ak895d+xBoMsVPi3vwH9+2s7LvKPEMCoUYH1sXMnsGiRNuMh7ywWYNcubfqKi/OYIk4hYrEA+/Zp1x+zVpRLSVFeKDUlxWNh1fUlFaixqFuexl8RERE5Y8AhRC49s72q9it3HfV8548/KuukrEy7gl2xQAjg3ntlGr2/x2t1sUTasFiAw+oqrLvFLIfQSEwEunbVpi+bjYUGw81kAlatApKTtemPWQ7Kmc3K3/u6dZOvPTfU1KCy65KhbhkpERFFNwYcQuTOP3ZX1f6XA1WwNLqZVRACeOIJ5R3x5Ey5/HygtDSwPjgDpy/2ZTI5OYH1wyyH0CgoALZsCbyfnj3lxambFHEKsexsoHt37frje6wyBgPQqpWytvv3ewzMqa1BBQCDstupPoaIiKIXAw4hkhgfh/atlBdSEgA+W7O3+R1mM1BSovyB77uPJ2dKCAE88EDg/Rw8yBlVvenQAaisDLwfZjkEl5br/ffskYUGlaaUU/BYLMCxY9r1xywHZUwm5btUdO3qMcNhQI/WSFRxpphoNGBgdhvlBxARUdRjwCGE2qeqmynYV1Hb/IcGA9CihfJOKirU7cUdq/LzgUOHAu/nxx85o6o3JpMsiNayZWD97NrFLTKDyb6dqRaMRtZv0Av7soq0NO36ZJaDbzYbMH26srbHjnkNlBvilJ8qZrdrBaOKItlERBT9GHAIobQWCaraZ6a7CSwkJsr1lmpMmaKufawRAhgxIrA+UlKAlSuB3r21GRNpKzsbaNs2sD6Sk7nFbDCZTMC6dUCSBuu/rVa5nIIz4fqQkyMzE1JStOmPtTl8mzdPeT2iFSs8BsrXl1TA7G55pweZGSomRIiIKCYw4BBCD1zYQ1X7Bnc7UVgsQLmPHSyamjJF2a4WsWrRosCKPS5aBOzYAQwapN2YSFuLFgU+e96jh8e0Y9JIRoZ2hW65S4W+ZGfL15AWXnmFmWTeCAHcf7/y9p07e7xL7RaXQ/I6qGpPRETRjwGHEIqLVzc7urToSPMfmkzA+vVAbq7yjsxmYPJkVY8dM9QW4XTnggu4VlzPtMhgAYCtW4GFCwPvhzxLTQWGDdOmL+5SoS8WC3DEzWeaPyZMYCDJm4UL1U1M1NR4vOvISXVLMjNba7QjCRERRQ0GHELo25/V7YBwytzo/o4OHdSfuE2axCwHd8zmwLIb4uI406Z3aguteiIE8NRTvNAJJqs18N1A2rQB1qwBNm3iLhV6YjLJ1H0V9QA8Ki5mPRVPhADuukt5+y++ANp53lVi+U4vW3S7cV63DFXtiYgo+jHgEEIHT6ibKejVIdX9Hf7UcaivB6qr1R0TC5SucfXEZgOWL9dmLBQcBgOQnq5NXzt3Ai+9pE1f1Jy/u+oYDEBenqwTsHkzMHAg0K8fM4/0pmvXwIu32i1bpk0/0aauTl12w9VXe7378Cl1gZ1N+06oak9ERNGPAYcQ6pKurhha3y4eqnqrTU1NSJBV+lM9BDBimRYFNblOXN9MJqCwUM56Z2gw+zZuHLOFgsFqBT791L9jhZB1VLgVpr75Eyz3ZNQovu+688orytsmJfks0pqoMiPl6CmNarAQEVHUYMAhhP7cL0tV+7JKDxkRJpPcEUGphgZt90GPFjYb8NprgffDdeL6l5UlZ73Xrw98FwTWRAmOCRMCO55FIvWvoADYvl2bvjZv5i4kTdlswMsvK29vMMgJCQ+sNoF9FZ7rO7jTPkWDXWaIiCiqMOAQQhfmqtuW77tfDsFq83DynJWlbi3s+PE8EW9q8mSgtjbwfl5+mevEI0XPntpsbTlhArMctGSzARMnBt4Hg3/6JYQMCGm5tezo0fxcc7ZggbrPtLo6r0st15dUoL5R+fPbKS0JA3q0Vv74REQUExhwCCFjnAF9Oiqv4FxR04D1JRXu7zQY1KWH79/PE3FnNpsspKmFadN40hspqqu12XaRWQ7amjs3sHoqRqNcNsYikfplscjPIS3fK/m5dpoQcmJBTUCnSxcgJcXj3Wq3xHz+mt4wxmkYUCIioqjAgEOIXXxGe1XtD3nakspkUlesMDeXJ+LOvv9em+wGACgt5UlvpEhJAc48U5u+mOWgDSFkschAtGrFIpF6Z9/SOS9PuyyHqVP5uWbnT0Dn8GGvu30cV1kwMiOZvwsiImqOAYcQizcaVbX/brOXrTSLi5V3tHIl0Ohhm81YI0Tg68UBOaPKrfciS0EB8Ouv2vRlNgPz52vTVyyrqwNOBFDZ3mAAuneXBQlJ3zp0kDsoaJXlwKWCp5lM8rNo9Wrlx1itXnf7+H7rIVVDYMFIIiJyhwGHEPtjdhtV7dfuKXdfx0EI4Mkn1T34f/6jrn20sliAffsC76eqirOqkcS+hlxl0M8rXvAE7uGHAzteCLlrD7OM9M9+Ubxpk9zCNC8vsP727PE6Qx9zsrKAykp1x4wZ4/Y9zNJow+YD6vpiwUgiInKHAYcQG9hTXcDBYhVYu9vNntpms/qL5tde48URIE96tdidghXxI0tBgbzYCaRWQFMHD/JCNxBWKzB7tv/HGwxyaRmzjCJHVpYM1PbtK7MdAuFjhj7mCCELaarhYUngqP9uUdVNnAEsGElERG4x4BBixjgDUkzqnvY1e467v0Pl/tis4P47IYA33wx8HTGfz8hhz25Q+5rxhTuUBGb8eP+PTUkBfvoJ+L//Y5ZRJHLOdhg50v9+Roxg4NfOYgF271be/uWX3QbrrDaB+VvULacYlN2GBSOJiMgtBhzCINnked9r99x8iBsMslCaUllZsmAXL45Oz3QHcpKani7rYvD5jAz2gmpaF3l86ile7Pgr0J1ipk0DLrpIu/FQ6GVlAeeeKwNH/uKyitMSE+Vnk1Lvvy93qmhifUkFzCq2wwSAGXecr6o9ERHFDgYcwqBnO+VbYwIe6j4kJspCaUpn6Q8cALZvV/W4Uck+0x2oH38EsrMD74dCw3k2dc0aoLVGqb8VFcDChdr0FUuWLJEXm4EUsp00icGeaGCxyM8nf1mtwA8/aDeeSGY2yyUSSnkI1vhT/HFrqcraEUREFDMYcAiDhy9Wd6FaWesmbd9ikYXS1Jxwjx7NE3SLBdi1y//jU1OBuXOBc87RbEgUIva14wMHAps3y+DDpk3y9xmIBx/k60oNIYBRo4BD6lK2mykp4cx2NDCZ5PaWgRg7lq9BQE5ApKYG3I0/xR+5QwUREXnCgEMYXHRGO3eLJDwaP39H850q7DO2b76pvKOdO1lzIDER6NzZ/+PNZmDIEO3GQ+FhDz706yer5Qfi0CGgnifbihUUBP6cAzJLJdA6LBR+QsjlMYHYtAl46SVtxhPJTCbglVeUt/fwGjpRoz6Qxx0qiIjIEwYcwiReRXGlw1VmrC+paH5HZibw0UfKH7SxEUhQWz8iyhQUBLa0xGIB4uO1Gw+FV6B1BOwWLw68j1hgX9IUaKBg1SqgsJA1VKKBxSJ3ewnUxIna12iJNEIAkycra5uUJAN/bgpGjpun7jMynjtUEBGRFww4hMH6kgo0NM1Y8MFtuqLaE7X6eqC6WtXjRhV/tgxz18d332kzHgq/778PrI6A3T/+wZRuJbQo2AoAffpwZ4poYTLJgsZduwbWT22t8ovtaLVokfIlgxaL2wKT60sqcOSUukzIjORE7lBBREQeMeAQBv6sdXSbrmgyqZtZfe01TdZ3RqyCApl6GwiDQabu8uIy8gkBjBunTV+7drGegC9aZTcAwNq1gfdB+tG+vbpih55MmhS7WQ5CAA88oLz9smVuzwf8OT/pkMpMIyIi8owBhzDwZ63jed0y3N/xhz8o3y3h889j90JZCGDMGG36OXCAtTCigVap3IC8yInV15ZSWmU3ACyAS+7FcpZDfr66oM3Jk25/7M/5yUU57VQfQ0REsYMBhzAY0KO16id+g7saDoC8aDp+XFknhYXypD8WaVWoLj0dWLmSa8ejgckkf5cZHoJ5agVaaT+aabUdrd3u3Qz6RRODQbvX4YQJsZflIITcLUeNCRPcBu0G9GgNo8qHvziXAQciIvKMAYcwMMYZ0K1NS1XHrNnjIaiQmAh066a8o1icGdQqu+Gqq4CtW5VnlJD+5eTILTJzcwPvKxYvdJSyWIDiYu3669pVvvdRdDCZZEBci+KrZrOszRJL6uvVL0nZv99t0M4YZ0CHVOVZDqZ4AwZmt1H32EREFFMYcAiTP2Slq2rv8TrGYgEOH1be0bZtsTczaLEA+/YF3s/y5YFtqUn61KEDUFUVeD9mc+ymc/uSmKjdDDYAHDkSe+9j0S4rC7j8cqBjx8D7evrp2Aqs//CDuvYGA7B0qcdMPZOKzaz+OqArC0YSEZFXDDiEyY3ndFHVfuvBk+7vMJnkUgGl6cpmc+xt62gyaZPuXlsb27t8RCuTSdYW0OJvhFkO7i1aBJSUBN5P27Zy9trNdn4UBcxm5UsEvSkpiZ0irv4Uvz3zTKB3b7d3WW0CByqUF468orcGASIiIopqDDiESXy8uqd+TUk5rJ620szMBObPV97ZsWOqHjviCQFMmxZ4P2eeCaSkBN4P6U9mJvDll4H3wyyH5rRa0gQAlZXAZZdxS8xoZTAALVoE3k9Dg8xIiwX+1CfykiG0dnc5GtUkhzC5gYiIfGDAIUyOV6ubfWm0yRMBtwoKgJ9/Vt7ZM8+oeuyIp1XByMpKpnFHK/sOClpgloMrLbajtYulC8lYlJgo63MEymiUWX/RvqzC32BeSorHGiird6vLMFF7LkNERLGHAYcw8WfrqdXuCkf6s7f97NmA1ar68SOSVrOrbdpwdwrIdNs1u8vx3eZSrNntJesmkvjzGvLGbAZOndKmr0hnf26NauveexAXFxsXkrHKYgGOHg28H6tVBhCjfVcmi0UWf1SrtNTjkpP1JR4mNjzw51yGiIhiS4wt5tePAT1aI61FAirrGhQfc6CirvkP7Sccak7AhQD+9z/g5puVHxOp/D0ha+qNN6JqdwqrTWB9SQWOnqpH+5QkuRWaj8Jf+dvKMG7eDpRVnl7f2yktCWOvy8Owvp2CPeTg8ec15I3RGPOBKQctM0cAmTly4ID8nfE5jj4mE7BqlawvoEVQfMQI4NdftQsm6o3JBLz6KnDXXeqO8/B8WG0ChQdOKu4mKSEOA3q0VvfYREQUc5jhECbGOAPu/qOK7SwBbHQ382AyyZn39HR1A5g0KTZmCVesAFJT5brvQEybFjXP14Ith9D/xcW47f21GDlnM257fy0umrIU+dvKPB6Tv60Mw2cXugQbAOBwZT2Gzy70eqzu2YtGbtokvzZuDCyt22pl2j9wOrshTsOPmV69gPXrozbY4CuDyGoTWFV8HK8u+hWvLvoNq3Ydj44sI2ddu2q3o8mePdFdPFIIYOJE9celp7sNOry1dBesKlaDDc5twx0qiIjIJ2Y4hFFKkoq9pwAcqjLD0mhDYtOCkzk58iS8Vy/la8cLC+Xs49ChqsYQUYQAnnsO2LVLfgVi9+6omFWdvGAHZv7UfLeAst8DBzPu6NcsW8FqExg3bwfcXdYIyJph4+btwJV5HSP35DMrS34Bck/7Q4cC62/0aGDIkOidWVXCnjmiZT2LX38Fduw4/buKcJZGGz5ZvRcb9pbjZG0Ddh45hZN1jY77O6aacNuArujeNhl7j9fgo1UlqHS6/60fd6GVKR4v33w2rj47grOMnNl3XsrNlTU7AtHYKIN/0fo5t2gRsHOnumOMRmDNmmafZVabwEer1O0kk5TAU0giIvKNGQ5hdOBErepjPlm91/0d/pyAjxkTNbP2bmmVzm0wyFk3D0W29M4+azpu7na3wQY7ARk4aDpjunZ3ebPMhqbHlVXWY31JhUYjDjODQX3GUFO//cYCo/bMkY0bZTBUC1FSDNBqE3j8i0KcMWYhXlxQhIIdR7F+7wmXYAMAHK4y4/UlxRg5ZzNeX1LsEmywqzY34tEvCvHi9ztCNfzg27Il8GADEN01P/ytT5SR4fZ8YX1JhaolngCwYNvh6MuwISIizTHgEEbdWrdUfcyGvV4u6tTOJG7cGL1FtYSQ63e16svLNmJ6lr+tDINeWorb3l+Ljz0Fq5yUVdbjhbnbYGm0OY5/9HNlOwws3nHY7c8jrtCkySSXVnjYp16RxkYgQV0GU1TKypLvMb/+qk1/UVAMcMGWMvT5Vz7mb9F2GdL7K0rw4vfbNe0zLIQAxo3Tpi+bLeL/XjzyZ/eX5GSZ3egmU6/spJsaUT40WEX0BJqJiChomA8XRnf+sTteXFAENddfLRM1qvZu9/zz0Zn67U+qqTsGA7BsGdCzZ0Qsp7AXgzxcVY9VxcfwTWGp6j4+W7sfn63djx5tW6LkuPIsnI9W7YUAkJneAq2TE9ExrQVO1Fgwbt52HDl1eh11hxQTxl3fR9+FJouK5Je/6uuByZPl0opYZrP5t8bcG/ustY7ft6w2gbV7yrF693GUnqhD5/QkDMpuh2U7j+L9FerS1tV4f8VeNFoFhvTp5Cjmp7Y4bNhZLMDBg9r1FwF/L6o510dRM9FQUyN3XHLj5wMn/BrK0VOes9+IiIgAwCBENOYaqlNVVYW0tDRUVlYiNTU1pI/94vc7VJ2A/mPoGXjs0tzmd9TWytkLtTp2BPbujYiLacWEkDOrpeovtl107w589RUwYIAmwwo2d7tI6Nm7bupF6IIQwAUXyAygQN4eW7QAqqu1LZoYaebNA/70J+371dn7lvOuL3uP1+Lj1SU4WavBkoAAtEyMQ4IxzmUZRsTsKnPggHz/vvRSGbzTQn5+9NRyMJuBbt1k5p1a//438Oc/N/vxmP9txey16nd0+vLBgfhjtvsgRjCE83yNiIj8E8NnwvpwWa8OqtovLfJwgrFwofoH7907Oiu+5+cHHmwA5AXNCf9mfULN0y4Sevb3r3/R5/IKrbbJrKuTWQ6xSghgwgRt+srLkwEg+04iGzbo5n0rf1sZLpqy1LHry+tLdoY92AAAtRZbs5oPZZX1eGR2ISbM267v5U1ZWTLQq1VGgsEQXbUcnOujnHmmumMnTnT7PHRrrX7CIq1FPLfFJCIinxhwCDO16YibD1Q2P0kUAnjxRfUPXlEBtG+v/jg9EwJ48EFt+oqQgmPedpHQsxqLFSt3Hgv3MJrzd6tZdyZN0naXhkiiVdFWACgvB/r2Bfr1k1+Zmdr0G6BIDPQBwIer9iraDjesqqu1y24QQmZNRGAdHo+ysoDjx2WBWjV++cVtTYucNuoDDlf2juCdiYiIKGQYcAiztsnqZumsAs2LNFks/m3jd9FFupkl1Ex9vTbZDYC8UIyAk9T1JRURd8Fj9/Dnm/R5wZOTA7z2WuD91NbKTKIlSwLvK5LY15hrMUNtMAA//qi79ypLow3Pfbst4gJ9zuwZDws0LmCpidRUYOpU7fp75RXd/Q0FRAj/asR4yPaY8dNu1V0Nym2r/vGJiCjmMOAQbn6cjxdsb3JyaDLJpRFqUyv/+19Z9T2aaHmC2qtXRCw5OVwVmcEGAKhvsGH47EL9BR2EAN56K/B+EhNl8dLnntN9poym7NkNWvyf33gjsB1DgiB/WxkGTl6Cihp9ByOVevzLQizY4kfQOpiEAGbP1q6/N9+MrtegP7tUAG6zPaw2gZ8PnlTdVcfUJPWPT0REMYcBhzA7Xm323aiJbwoPNl9W0aGD+noDQgDffaf68XXLZpMp7Fo5eTLsS07cbSnZ9Gcrdh4N6xi1MG7eDn2tJ7dY5Em5Fv0A0bs1nzv27AatfPaZri4U7csoKmrCX6dBKzYBPPrFz/oK/Pl7Qe1JBGSrKebvayw9HVizplkNlPUlFWiwqnuNtTIZWb+BiIgU4baYYdY+Rf0Mwal6K9aXVLhWhrYXkRo4EChTcdL47LPAjTdGx3ZhCxbIFHYtTJ8O3HBDWLMb3O06kdYiHo02gRpz9GSmCMjU7mZ/0+FkMgGvvw7ceac2/UXj1nyeWCzAvn3a9We/UNRBplGk1ktR6tn/bsWVeTpYl2+/oDYatcnCS0gA1q3Txd+QJvypj9Kliww2ZGU1u8ufLLkHLuoZ/r8TIiKKCMxwCLMBPVojOdGo+ji3xSbbtweOqSzCt2eP3GIr0gkBjB+vXX+zZskTtDDxVIyusq4xqoINzlbtOqafLAchgGnTtOvPZoudLAd70c1AffSRDCJu3BjWC0WrTWDVruN4ddFv+PvXmyO2XooSJ2sb8NbSXeEexukLaq2W/DU0yGKJ0cDf7IayMqBdO7d3HVdZvBoAnrjczfbcREREbjDDIcyMcQY8cHFPTPuhWNVxbjMjDAYgLU1WdFcqWmZbtZ5VLS0N26xqtM+ievLWj7vxxfoDuOGczrgyryMG9Ggdvhm0ggJ5oaulWMpy2K2+AF0zN90k38/CKH9bGZ7971ZdbHMZKu+t2I3hl2QjMT5M8xH2C+q4OG13eHn6aeDaayP/tefvZ52XZUlq/74z05OY3UBERIoxw0EHRlyeq6p2ZBzgfu2kyQSsXQu0bKm8s9TUyD8BA+T/fdUqoFWrwPtKS5MztGGaVV27uzyqZ1G9qaix4KPft+w7b8JiTFuyM/RZD1rusOAsVrIchAAefzywPtLTgaTwFqTL31aGR2YXxlSwAQBqzFYMnPxD+Oo5WCzA/v3abye7cyewaJG2fYaDyeTfNthxnk/3dh+rUdWVWWW9ByIiim0MOOiAMc6AszNTFbfv7G12ITsb6NlT+YPffXf0rGu1/98DvVBcvVr2FQb528rw2BeFYXlsvTlZ14DXlxTjvImLQ3vxY59BDEahQg9b0kWVRYsCy3BITAz7MgpLow1P/3tL2B4/3CpqLOHbPcZej2jTptNfa9YA8RokZI4cGfmvPSGAmTPVH9e6tdvPRqtNYNlv6goPt0+JknMGIiIKCQYcdMBqE6pmGFomePm1WSzq6jjMmKH9TFKoLVkC5OUBL70EbNkS2Allr15h24LPXrfhZF1szaj6crK2IbQXP/ZsmdZBqMDuZku6qLJ4MXD99YH306NH4H34KX9bGS6YtATV5sawjUEPBIAX5m4PT12VrCygX7/TXwMGeJ2hVywaahb5s9yrd28ZuHETxHtraTHqG9WdA1zTt6O6xyciopjGgIMOrC+pQLWKQoA7j9V6Pgk0mWQ1bqPCQpT19UB1teLH1h0hgOeeA4qKgIkTA89uOHEiLBeDsVq3QSmBEG+dmZMDbN7sOsOqZqmSJ1OnNtuSLmoIAYwaFfjrx2IBTp3SZkwq2YN+J2JsGYUnh6vMmK6yvlBQnDqlzftyYyOwfHng/YSLc30LNSoq3G7xbLUJfLxqr+phdEpvofoYIiKKXQw46IA/W1JNW7LT851pacqreyckaFP3IFyctwerrQ0suyE3N2wXg+tLKmK2boNS9q0zQ8Z5lnXgQOCttwLvc8aMsO5+ElQFBTI4E6iMjLDUb2DQz703fijG5AU7wjuItWu162vMmMhdVmH/vFOTlWg0AuvXu/1cW19S4VdGXcc0BhyIiEg5Bhx0oKJafYrnez/t9jzb+/bbyjtqaAC+/1714+uCv7M9npw65XYWKBTcbnNKzYTteRJCBgsCVVwcHYXrmhJCXshp4c03GfTTmZk/lWDBlkPheXCt3+f374/MJU3+Pg9WqywO7cbhyjrVw0hOiHNftJqIiMgDBhx0oHVyoupj6huF+9lemw2YNEldZ3//e2TO+Pgz2+PJmWd6nAUKhb3Ha8PyuJGm+MgprNldHvp15fbK+Vp44IHIfL15o9U2onFxMuAQhueHQT/vxny3LTz1HLTetWLQIGDFCln3Z8kSbfoMhUA+71ascPvjihr1gZehfTtyS0wiIlIloICD2WzG0qVL8a9//QvXXnstevbsiZSUFCQmJqJdu3Y499xz8cgjj6CgoADCzxPI0tJSTJkyBYMGDUKXLl1gMpnQpUsXDBo0CFOmTEFpaWkg/wVd8Dc90e0JcnW1XFqgxu7dkVdIyz7bo7RWhS8nT4Ytu2HBljK87m2JDDm89eNu3Pb+Wlw0ZWloK+ibTDIglZsbeF+lpUB+fuD96IWW2Q02W9iKarZPCe82nHpXUdOAqQW/hT7g57xrxerVgff37bfAP/8p6/4891xkBP8CzfKYMMHt/zOjpfrJDi6nICIitQzCj0jAkSNH8OSTT+L777/HKYXFvfr06YMPP/wQF1xwgeLHeffdd/H000+jpsbzDg6tWrXCq6++iocfflhxv01VVVUhLS0NlZWVSPWQehhMVpvA+RMLcKJWXVX0z++/AINy2za/47XXgKefVjeI2lqgRQSdSCxaBAwbpl1/H34I3Hefdv0ptGDLITz+5c8Ix8RhJLPPr824ox+G9e0UmgfV8m+uc2fg4MHAi5zqgRbPS3y8LMwZFycDf5mZ2oxNIatNYPWu47jzo/UhfdxI1SktCWOvywvda8/ObJavnQoNa7nk5wNDh2rXXzCYzUC3bsCRI/734eb/+f5Pu/Higl9VdfPYpdn4x9Be/o8jQOE+XyMiIvX8CpcfOHAAc+bMaRZs6NSpE/r374/LLrsMeXl5iHOKxm/fvh0XXXQR/vvf/yp6jPHjx2P48OEuwYbc3FwMHjwY2dnZjp9VV1fjkUcewcSJE/35r+iCMc6AF284S/Vxq3a72f5SCGDOHPWDWLxY/THhovWaXgB4552Qz3TlbyvDo18w2OAP+1MWsp0rtP6bO3RI7hAT6bTKbmhsBA4flgU6QxxsyN9Whgsn/8BggwpllfWh3arWzmSSu8f87W/a9Gc0yte13rMc7Fke06f7d3xcnNv/50k/dmNJb5Hg3xiIiChmBXz2PHDgQLz77rsoKSnBoUOHsH79evzwww/Yvn07SktL8fjjj8Pw+yxeY2MjbrvtNvz2229e+/zuu+8wduxYx+28vDxs2rQJO3fuxLJly7Br1y5s2LABvXv3drR5/vnnMXfu3ED/O2Fz9dmdcc1Z6va2/mBFSfOLLX/Xmo8bp/+TLjut1/QCMs09hGnc9or45D8BeeEza5Wb14HWtKwXYhdJQT5PLBZg1y5t+grDe1D+tjI8MrsQR05F2JIyHRAAXpi7PfR1HTIzAa0+661W+bouKNCmv2DKzAQ+/dS/rCgPS5V2H/OcPepJ21ZRuKUvEREFlV8Bh7i4OFx//fXYtGkT1qxZg4cffhjdu3dv1q5jx46YPn06pk2b5viZxWLB6NGjPfbd0NCAp52WA2RmZmLlypXo16+fS7vzzz8fK1euRBenLeaefvppNDaqW5agJz3aqtue0mJ1UzjSZJLrNdUqLIyMky7g9GzP+vXapKRnZYW8YCQr4mtnwvdFwa3pYM9u0Hr5w/jxkRPk8yQxEejaVZu+Nm4M+nuQ1SawZnc5vttcilXFx/G3r38J6uNFu8NVZry1VKOAk1JmM7B3r3b9RUqWgz3Q7s84H3202ZbPVpvAj7+pX6LBGg5ERKSWXwGHfv364X//+1+zIIAnTzzxBAYMGOC4/f3336PWQ2HDOXPmYJfTjNnUqVORkZHhtm3r1q0xdepUx+3i4mLM8Wc5gW6oP5FoVjhSCOD99/17+Eg46bLLypLbeWox3oMHgXbtAu9HBVbE19bhYKZ4B3Ki782mTZET5PPEYglsXbkzgyGo70H528pw0ZSluO39tRg5ZzNu/3Adai3WoDxWLHl9yc7QLq0wGID0dO36i5QsB3ugfeNG9f//WbNk7Qsna/eUw9yo7rUWHwduiUlERKqFbFvM66+/3vF9fX099nqYofj6668d33fu3Bk33nij135vuukmdOp0unDVv//978AGGkZ/7OmmAKQPzSqrB7J93759kbM/eUGBTMHWghDAsmXa9KWA1SZwtIop3FoKak0HkwlYuRLwEPj0W5AvsEPCZALWrtWmLyHke1cQ3oPyt5Vh+OxCZhUFyd++3hy6pRUmk8zIW7NGu6w0DzUOdCcrCzh6VO6qpEZdndzBysmqXcdVP/xlZ7bnlphERKRayAIOrVu7RsWrqqqatamrq8Nip3XNw4YNQ3x8vNd+4+PjMcypQnpBQQHqI7QY28DsNqp/IQU7Drn+wD4L0smP6uGTJoV0WYHfhJApoloaPTokJ5v528pw3sTFeHFBUdAfK9bYazo0W2akhZwc4JdfgPnzgR49tOlTiLBtAampWbO06adNG2DVKs3fg+z1UnR+KRnRai02TP+hOHQPmJUFXHCBdpkOYdyOVRUhgPvvV3/cmWcCKSkuPyo9Uae6mzM6pvhuRERE1ETIAg5NMxrat2/frE1RURHM5tMzv4MGDVLUt3O7+vp6FBVF5sWcMc6A6/+gLlDw8ar9sDQ2KWTXvr2cBVErRBfdAZs8Gdi9W9s+Q1A00l6gzp/K4FpLCNkrP/SCtlwlKwu44gq5hawWzjhDbmF77rnAkiXa9BlqNhvw8suB9dG6tZyt3rwZcNqBSCuslxIaH6wMQfFWZ/X12izneeghubypSY0DXcrPB8r8WL5y8mSzz7djp9QHHPzJwiQiIgrJZYcQAt98843jdqdOndDDzSzh9u3bXW7n5uYq6r9pux07Irf6f+eMlqqP+WT1XtcfGAz+pX8fOaL/rfpsNv+KYrozZgzw5pvAggVyXWwQTzatNoEX5ob/73JQdht8/sAFmHXfBeEeStA0W2akpUC3p3N27BgwdSpQVAQ891xkBPsAGRzJy5P/vviiTNcOxNq1wMCBQdsOM1rqpSSbjOEeglfV5sbQ7Bhjp1WQ7uOPgXPOCfl2rKoJATzxhPrj8vLcFkWuV1m/Ia1FPAZmt1H/+EREFPNCEnD44osvsNtpRvr22293bJXprGkWRFeF1c+7devmcrukpET9IHXC3fPiy4a95a4/sK9xdSqoqZjWSxW0NnmyNkGRtDS5S8ATTwBXXRX0k831JRU4XBX+C59H/i8bg3La4nh1dNaQyGiZEPyiZvbt6QIVHy9nVoHIKFoHyIuee+6RQZJ77pGvx0AYDECHDlqMzKOgBqACpObd/t3bz0Pr5ISgjUULQd8xxk4IGXjWYueYhgbAzRJP3Vm0yL/MvvJymfXYRKbKyY0pN5/N+g1EROSXoAccDh48iJEjRzpup6enY9SoUW7bNq3rkK5wfWZaWprL7VOnTnltbzabUVVV5fKlF3/0YwahRYKbma/MTODzz9UP4JNPZNVuPbLZ5IyqFiorA5+ZVUEvs6wVdTKtVulFWEbLBFUXReE2sGeb4J8UB1KY1dmxY6e/j5Sidfn5cvkRIP8N5DV0553AunVAaqo2Y/NgQI/W6Jiqn1T5jqlJePeOfnj3jn7omKbsddgpLQkX5rTFpBvPggHqAhWhVlZZj0dmF2LBlkO+G/tL651j3n5bm36CRQhgxAj/jp0wwW32Xt/Oyl936UlGDOvrR10oIiIiBDngUFtbi5tuugnl5adn4GfOnNmsgKRddZMqyi1aKNvvuWk7XwGHyZMnIy0tzfGVlZWl6HFCYWDPNkiKV3c6efikm5N+i0UWwVLLPnOkR5MnaxskWLRIu758aJusjwsee6BhQI/W6JSW5PHCxQB5kfPiDX0dt5veDwBPXZGLabeeg9FX9wrGcFU7UhmCIJLJJFOUe/fWrk+bTf9ZDkIADz6oXX9FRcD552vXnweLdxxGfdM6N2Hy1BW5WPXsZRjWtxOG9e2Elf+8DF8+OBD3DeoOwP3rzABg7HV5MMYZMKxvJ8xwE6jIaBmPYXntYFL52RFMj37xMxZsCVKmg31p05o1QEv1yxCbmTRJvgb1ymwG/M3cfO89t4EZNbtUtNNxlhAREelf0AIOjY2NuPXWW7FhwwbHzx577DH85S9/8XhMQ4NrMT1fO1R4ate0n6ZGjRqFyspKx9cBfy7Mg8QYZ8At/ZUtJbFbt+9k88KRJpOs+J7iR1XpV17R38mXltkNdpMmhWxG+bM1e0LyON50SktyLDcwxhkw9ro8AJ6DCWOvy8PVZ3d2e4HTMU3O0o684gxcf04X3HdRT6S3DH+6d+GByuDOrNoVFckvLek9y8E5u0ELQdoC09mCLYd0UajVFG9wvF6cM3CMcQb8MbsN/nVdH7cZDx3TkjDjjn4us8vOgYppt56DLx8ciI1jhuDduwZg2q3nhuz/pMSjXxQGb3mFfacKLXaNqa0NfHlQMBkMchmgP3bvbvY6szTasLxYecDhvO7p/j02ERERAGVX9CrZbDbceeedmDdvnuNnf/nLXzBt2jSvxyUnJ7vcrq+vR0sFsxdNt8Fs2k9TJpMJJh1Xo+7a2r/CkQ/+X0/XH+bkAG+8oX4brdpaWUjx2mtVjyNovv9e+yUQ9m3Qglws8i8zV2PTvpNBewwlnGdJ7eyzpePm7XCp4t8xLQljr8tzXOQM69sJV+Z1xPqSChw9VY/2KTJw0fTC6aWbzsIjswtD9n/yZOSczbgiryMS44MUTxVCFhzVmnOWw9Ch2vcfCK2zG4Cgb8O7YEsZHv/y56D1r8YHd/XHxWe089pGyevMzh6ocNfHu3f0w7P/3Rr2IIvd377+BSlJCcFZ7lRQADQpNu23SZOAUaNk4E9vTCbg9deBu+5Sd5zBAHTtCiQmuvx41H+3qOrm3K5BrotDRERRTfOAg81mwz333IM5c+Y4fnbzzTfj888/h9Hovcp2q1atXG7X1tYqCjjUNtmmLsWfWX0dSW+Z6LtRE/O3lDYPOAgBTJmifgAGgyyoeM012hTlCpRzgTAtZn/T0oAvvgDOPjuoFzz528rw7H+24mRdaE/8DQCcn6VOTQIIzpRe5Hi6wGna17t39MMLc7fjcFX4ilI22AT6jM3H9NvODc66Y61qOLhjMMgshyFD9PHas9M6uwEA3noLuO++oPw/87eV4dEvwh/8AmQdlAtzlG0nqOR15ov9Nb12dznW7DkOwAAhBN5epvFWwgrVWqy4/YN1Xt+H/CKEfK3ExWmTkafHQLudEMA//+nfcUeOuATW87eV4T+F6l7L20orcUt/9Q9PREQEaBxwsNlsuP/++/HZZ585fnbjjTdizpw5ipZHtGvnOgNUVlaGtm19n6iVNdmXWskxelZRoz7N+LfDp2C1CdcLxUWLgJ071Q9ACH3NtGpdIKyyErj0UkBhjRB/5G8rw/DZhQhHcvz0W89FmxSTz1lSOy0ucuyaBjAOV9Zh8sLfNOlbjQarwCOzC/Fuk3R0TZhMchvVLVvkLida7oojREgyb1QJRnYDAJSVBeX/abUJjJsX/i1o7SbfdFbIq/sb4wwYlNsWg3LlZ+Ga3eVhCzjYHa6sx/DZhc2WiPjN/rmg5fK/ceP0E2h3Vl8vXy9qxMUBK1fKpSe/v8bk9sz+ZITo7PkgIqKIolnuoM1mwwMPPIBZs2Y5fnbDDTfgq6++UlyLoVcv18Jz+/btU3Rc03a9tSzmFgYna9UHHOobBdaXVJz+QaBp3/aZVj2sJ7cX6VO4TapPBoPcCi0IrDaBVcXH8fevfwlLsOHh/+uBa8/pjD9mt8H153TBH7NDsGtDE/YAxvXndMEDF2ejZaL3zKZgGvXfrbDagvCbyMoCrrhCzopqKS1N/q3rJdgAyIsdLbMbnnhCziRv3BiU/+f6kgqXJUKh0vRV1un3Oid6qO7vq0hsKNhfhePm7dDmNWkvHLlpk/xbys0NvM9t24JeV8QvS5aoPyYjA+jXz2XLZ7k9s/rss+5tNCjMSUREMUuTDAd7sOHjjz92/OyGG27A119/jYQE5YXk+vTp43K7sLAQ1ypIbywsdE2dzcvLU/yYeuTv5EqZ824VFgtw8KD/gxDidFE3PVz87NihTRr7TTcBzz6r+VZ8VpvAW0uL8fGqvSFfQgEArUxGvHzz2bj67M4hf2xvjHEGTP3LH8JW2+FEbQOm/1CMJ688Q/vO7Rc8e/YAl1yiTZ+VlUAbbbJNNNPQoN1yJkAGG6ZNC9oscsH2IBUp9MAAYOTluXj00hxs2ndCcWZRKNmLxA6fXdhsyVUoCchtM9eXVGiTVZWVJb/MZuDkycD7q68HFE6QhIwQwFNPqTsmLg5Yu7bZZ7e/2zP/9YJufh1HREQEaJDhoFWwAQCysrKQnZ3tuL18+XJFxzm3y8nJQaZTRD8S/bGnf0tCfj5w4vQN+8XQ6tX+F8F69VV9BBsC2YO8qYULgfPO06av3+VvK8N5Exfj9SXFIQ82pLdMwFNXnIFfxg7VXbDBbljfTnjqiiBc8Cs07Yfi4O1ckZUFDBzo324wnlx/PZCX59+sZjCkpgLLlmkXINi3T14gBkH+tjJ8vFpZZpxW3v7ruXjyyjOQGB8X1swiXzxtqdk6OQGX9/Je0FJr/l74emQy+VevyJ377tOmH60sWiR3mlDDZnN7TNtW/n2ebz5w0q/jiIiIgAADDu6CDTfeeKNfwQa7m266yfH9smXLsN/HrPb+/ftdAg7Ox0eqgdltYPKjwr6t6QxkVpZMqczI8G8g06bpY0nFokVAcbE2fdXXA9XV2vSF07UaQl0R/rFLsvHlgwOxacyVGHlFru4ubpp6/LIcdEwNT/BKAHj0i58xbcnO4CyvMJmAwkKgSdFbvy1ZIrfcfO45fbz+ALl0RKuxpKcHJbvB0mjDc99u07xfT+xLJvQa6HPH3ZaaG0ZfiQ/vGRDSoOCeY9q9BwOQf5szZmjT16efAlarNn0FSgjg8cf9O3bMmGavWZdllypoHiAiIqKY4nfAQQiBBx980CXYcNNNN+Grr77yO9gAAPfee69jNwubzYYJEyZ4bT9+/HjYfi8aZTQace+99/r92HphjDPgkcE9fTds4kCFm20jTSbg559lMSy1Nm6UhSPDSavshttvlwGUdes0W05hL04X6kvCNsmJ+NuQM3U5i+qJMc6AF/7UBwaEr/zY60uKMeilpcjfFoSU+5wcuT1f+/ba9Wkv3BpuWmYY9ewpgzMaZ07lbyvDwMlL/Cq464/P7h2Alf+8TBf1GdRyrrHi/B4SyqDgByv2aBv803rnGL2cR/iT3WBnLz77O6tN4JPVe/3qqn1Kku9GREREHvgVcBBC4OGHH8ZHH33k+Nmf//zngIMNgCz4ePfddztuf/DBB/jggw/ctp05cyY+/PBDx+177rmnWeHJSPXYpeoLYP1UfNz9xVRmJvC///k3kBEjwjvLqlV2wy+/yGJ1/bXb2ytcxekmXN83YgINzjyldIfS4SpZKT8oQYf27YEK/2YQ3YqL00fhVi0zjI4e1TYog9NZRhU1ockySjYZcWFu24h8DXpjDwqGQo3FhreWavQ3BWhfWPizz8Kf5SCEzHLyR8eOMmDpFNhbX1Lh15K/1i0TMKBHa//GQUREBMAghPqz2a+//hq33HLL6U4MBlx22WWKd6MAgL///e+48sor3d53/PhxDBw4ELudIvt/+tOfcOutt6Jz584oLS3Fl19+ifnz5zvuz8nJwZo1a/zaErOqqgppaWmorKxEqsbFBP21Znc5bnt/rerjOqUlYeU/L3M9GTabgW7d5H7cahmNcglCUhguEoUAzjxTm4ud5GSgvFzTmdXvNpdi5JzNmvWnxOW92uHDewaE9DG1ZrUJx7aZy349im83B6m+ggcGAB3dvU4CZTYD7doBp05p1ycA5OeHb3taLV+DANChg9zeT6MlFVabwEVTloY88PflgwM120pWbxZsOYTHv/wZwVh95CytRTwKnx+i3Wuwvl7WUmls1Ka/b74Bbr5Zm778YTYDXbrIzy214uKAmhqXz21/P6+evDw3OEV3/aTH8zUiIvLOr3LMtU22ghNC4IcfflDVx6233urxvrZt22LhwoUYOnQoSn7f437u3LmYO3eu2/Y9evTAwoUL/Qo26JW/aybdVgC3F9S65x71HVqtsmDcsGF+jScgZrPcAUALNhsQYPZNU61bJGranxIPXJztu5HO2VO6AVnELNQBB80r5dslJgJt22ofcBgzBhgyJGi7OnilZXYDIIOe+fnAVVdp0l24soyieU371Wd3xogj1XjjBw1/725U1jXiraW7MPIKDbazBOTrIz0dOH5cm/5GjJC7GoXjdbdkiXz8O+6QSwHVstmaZUbtPe7fFr492iX7dRwREZFdwLtUBEtubi62bNmCESNGeIxip6WlYcSIEdiyZQtycnJCPMLgCmTN5OGqJifDQgCvv+7/YNwUnwqqJUtklf7XXtMurbWuDpg8WZu+INO4H/lik2b9KdEpLSnqUlsH9myD9JbaBoKUWrLjsLYdms1y3bTWwlVLRQj52teahsu0mr3XhUi0r2kP1UXm60t2areDjMkErFkjgw5aOHRIZk2Emn0pRVER8P77/gc8nCaBrDaBL9f7V+Mi2v/W6f+3d+/xMd35/8BfZyY3CUkkLolrIrREKClBqaJUStGqb4ulqtZXi9a3u7/d0uqieqG73S560dUuVS3Vrq2qNO5LKZUmVCKucSmSuCRIiFzMnN8fpzNmJjPJzJlzzlzyej4eedSMc3lX5pw5533en/eHiEh9soZUaK28vBw7d+7EmTNnUFRUhOjoaMTFxaFfv34IVqBE3htL9AxGEd3fkNcE7dWhHTDpfoumk+4MqTDRqqxbFIEePaTxp4KgbKIjNFR6+ix3mlBIv5f3tp/Au1vVffpnSwDw4bhkn2xSV5v0nAI8uypL8/3WD9bjlzmDlSvpdqcEujbduklj1LV82lpRAURHS6XZSgoMBEpKFBmmtWzXKbyRdkSBoJyj2nAcLyN3SJ8cOgF4b0wyhnRW6Nx24gTQvr30lN9dLVoAy5cDAwe6vy1nbdqkTEVh9+5Sk2RBkP37jAwNRObsQV71WffG6zUiIqqZ11Y4WAoJCcHgwYMxZcoUvPzyy5gyZQoGDx6sSLLBW+l1Ah7tIm+6tagwm1L/4GDpBv7tt+UFo2Xzus2bpVgB5fdXVgakpclePT2nAL0XbNc82RAbEeK3yQZAaib5TO84zfd7o8KgfOO6AweA115TbpsmeXlWHec1ERioTuM8BafF/Pmsgk06a2GKeM6wRK+6AVNDSnyUZpVHRhGY+oWCjVxPnVIm2QAA588DU6dKFXdbtyqzzZqIovRd60ZS3Mxilgq5lUAT74v3+886ERGpzycSDnXVoMQYWevl2ZvjvEULqTxTDqNRmyn6TBdbaj3FFQTpZlBGIsPUCV/rEu5Xh3bw2en3XCH3s+6u5XvOKDs9X4sWwIYNytwwmAiC1H0/SMOeIVu3AlFRypeUt28PZGYq0rz1rbRcbDrsRtWWi2L8PPHnafM25Lp/LCp5w25y4oQ0vOHll9VPupsS7u4kTKKipKElFrNUXCmtcHkzgToB0wf411BVIiLyDCYcvFhKfBRiwl0vO35vR171p0UVFcBvDThlEQT1qxxMF1tq7UMUq81N7gyDUcS8DbnQeuxRZGggnu5dN54wpcRHITYiBFr/n167VYV3txzH3rwiZRIPlZXAr78q94QVkD63v/yiXR8HUQReegm4fl35bV+7psi0mGmH8vHRLjfOZy6a3j+hTiT+TPafLsa1Mm2mGTUxNXJ1ixI37I5kZAALFkh/NvUZUrLqQYlkSViYNPSqZ08p+fmbq2WuV0cNaN+kTnz3EBGR+phw8GJ6nYAxKfLmFa/2tEgQgIgI+cHIvFl3aftqVTeYnvhkZlabm9wZ+04VeaQT/rWyKmxRurGhl9LrBMwZlggAmicd3ttxEmOW7UOfhdvdL+s2DV/KzAT27FEmQECbhJ/J5s1AlsI9NZYskX382TIYRby49qAycTmpd9vGdermy1OzcLh1vlO7Qg4A5s+XhhmZmjoqWfWgRLIkIQFo06ba23I+uve2big/DiIiIgtMOHi5uEahstar9rQoONi9UmadDvjhB0VKoe1Sq7rhq6+kp8M9ewLJyVZPfZyRnlOAySt/VjYmJwlQqMzYR6QmxeLDccmIibCu6okMDUSwXv2bvcLr5XhulQJjyVu2lD5rxQr2F6gt4afUE1dTh3wl6XTAypVA164uH3/2LN52AhW3tTkmBPjn7DC18dTMBOsP5ss/35mqi9RMyt26BUyadKfPkFJDDZUaClJYaPcc0b21659fOVURRERE9jDh4OXcmh7z+i3rNxo2lIZWyCEIitws2GW62FJa+/bA44/Ljts0e0JZpQqN85wgQqEyYx+SmhSL3S8NwOrJPbFodBesntwTmbMH4V8TU1Tft/jbj2JjyefNUyIs6SYkMVEqlbaX8LOcRs/dJ65qVDcYjYpVRxmMIt5XstFnDepSk0hbnhriVHSzUv75zrK6aMkSZQOz9OmngF4v/VmvV6bySImhWE2aSFPo2jlHHL9kp69TLTxR1UdERP6JCQcvlxIfhbAgvax195y0mZ6vQQNp2j45RBHYvl3eurWprATOnlV+u9euyb7JMRhFzFyXrWw8MnmqvNlT9DoBvRKiMaJLc/RKiIZeJ6Bnm2jERmjz1FWRJE9lpdRsTglGI5CbC6xaZf/vLWd2ceeJqygCr7wib11HEhOlmyAFhlIAwI8nr0Cj4oY63STSk0Ocvs8pkN9TpWVLqZJm5UplG0faMs3eYjAoU+VgSpZkZEizw8hRVAQ0bmz3r84Wy5jatm4U1hERkQaYcPByep2AyfdXH5PpjK+zzluXh1dUAPn58gIxGoE5c6o/yVGilDs4GNi1S/769rRv7/iJcC0MRhEvff2L5k3THPFUebM3sbwB0oLbvTOCgqSZJZQ0b550Y2N5vJmqg5x54lrbsbp5s/R0WElFRUBSkmLVUYu3HVdkOzWJDA3E55N61KkmkfbUNMQpQMWKj5V7z7rXU0WNxq01UarKoWVL4P33gSqZ3ztRUQ77Vxhuu/5v0awhv3eIiEgZTDj4gOcfbCd7XavycEGQhlXI9fPP1k9ylCjlNt0EvfGG/LjsKSiQ1Q0/PacAvRdsw9dZF5SNR4a6On7ckdSkWLw48C5N9vXF/l/dG1axeTOQrXCFTEUFMHSo9fFmqm6o7Ymro2PVdPxt2aJ8dUN8vGKVDYA0DWbG2WuKbMsRAcCCkZ3Qu12jOjeMwh5HQ5ye7K7S8DoLBXJ7qlgOrVC6eas9rlY5OEr8GQzScA05apluNvNX1yu2esU3khcLERGRDSYcfIBeJ6Bb60hZ61qVhwcHAwcPAjNmyAvEtlO+nFJuy4utLVuAYcOkm6DPPpMXkyNRUdJTZhek5xTguVVZKCyR2edCQXV5/HhNpg9oi5hwlRqXWiivMuL/1sjsZaBUAzh7bt+W/muaou/RR6vvR6eT3t+y5c579o5VyyTErFnA0aPKxnr6tDQURAFphwpUnwazYWhgnR1CURN7Q5xmD+2oyb5FAHO/Pex68s/UuDU5WaqwUXPmCkA65pypchBFYNo06ZibOBHo0OFO4uGZZ+RXSdSQYE/PKcDxS2Uub1KnQaNeIiKqG5hw8BHT+raVva5VD4AWLeQ/8RFF65sVZ0u5Lde3vMGZNg0oV6k/wblzLjXINBhFzNuQ67Fhq5Gh1uN26/L48ZrodQLmDu+oybjyDYcKUSmjFFmTkm5BkIZYlJdX34/RKL0/fbp0zDk6VjdtupOEyMxU/lh09iasFgajiD+oOA1mZL1AvDiwHX6ePYjHm5PqBekxKNH1CjI5Cksq8N72k/I3EB4O/PgjEBenWEzV2DZGdVTFsGkTcPy3YUHnz0tJvmnTgPR0qe+EXC1a2E2wG4wi/rj2F1mbvHLD84l3IiLyDwGeDoCcc7JIRtOn31j1ADDdDLlj0iTg44/v3KwA1mWlgwfbX8/yKevPKk812bChS0+19p8u9lhX7hcH3oXpA9pi/+liXCotR5MG0jAKVjbYZxpb/vJ/slF8U90+G5/tPYNJrvZQMZV0X74sHW+DBgE3XO8SXyNRrD2hdvy4dIMjCPaP1RkzpOSDwSAlB0zDMpRieRPmxrCKGasPoFxO4qcWcdGheGtkZx5rMi17qjsmr8zAltxLqu/r3a3HcXdMffkJoa5dpeNRLa1aAbt3S59z2+FLDz4oHYOiaL+68PhxYPhw9/Z/5Yrd4+zHk1dwU+YsS+wdRERESmGFg484d9X1kkhAenpn1QMgOFi62f/uO1k9DgAAFy4Azz9/54mpSU1VDrZPWdUQHAx88on0tDYry6WbHE/NBBFRLwDTB7S1W7ZMjqUmxWLfrIFoGCqzo7uTzhbLO+7MJd09egBt5DV9VcQrrwCzZ1c/7nQ66UbHlGRQuhojMhLYu9etHg4Go4h3Nh/Dd9kyGgc6obzKwGSDm5Y91R1HXktF33bqj/d3a7rawEB1K45+/RU4fFj6s6OhhpbVDbbkNoo0eestu8fZVxnnZG0uJjyYvYOIiEgxTDj4iNZRobLW693WzgV1y5bSU5diN6b+O3my+hPRmppn2Ta3U0N2tjQONjnZ5a74nnqa80zvNrzhkSkoQIen74tTdR83bsmbVtWsshIodHPGC2dNnVr9vawsKcFoe9ypefP12mvSsdizp+zZKdJzCnDv61uwxJ1S+loUllS4P/0poV6QHisn9cDvUlqquh+3pqtNSwNu3VI2IFszZkjHlb3hS0aj/N5JtREE4MMP7Sb695y6ImuTc4d35PcSEREphgkHHzG+V5ys9XYcu2z/qZAgSGNblWZv3LYW1Q2CADRtKnv1lPgoTZoRWooMDcT0AfJ7cxAQ1yhM1e2vO1iA9QfdmLHEVFG0caNUaRCg4ii2jz9W9xhz1vr1QPPmsldPzynAs6uyNJmW1lOVTf6oTeP6qu+jsETG70sUpSSY2o4flxLe9maNeestx9UN7hJF6/4RvzEYRRTJGHL24sB27GVCRESKYsLBRwQF6DC0U4zL65VVGrEvr6j6XwQHA2++qUBkNoxGqfrB8uJHi+qGZs2ABg1kr67XCRiT0krBgGq3YGQnPkVykxaVKTPWHMTklRm1L+hIy5bAkCHSTU+HDsoFZquyUt1jzFkXLlS7+XGWqXmrVjhOXTnje8VB7dPZq99kuz5NphJ9i5z16af2hy/Nn6/ePvfssTt0afcJ13tWBOoFTB8gfxpuIiIie5hw8CGLxyTLWu/zn85Uf1MUgX/+072AAPtVEq1a3emYvWWLNEWf2i5edGlWCntaRav7tNwkNiIESzkDhSJS4qMQGxGi+qwVW3Iv4Y2Nh93byObN0lADT2jVSropadxY3f20bw/s3y+7b4NWzVsFSMchx6krJyhAh8n3x6u6jxsVBjy7Ksu1pIOpiWtmpv1hR0qzN3zJze+mGhUX2x26tOyHUy5vqmNsOJPgRESkOCYcfIheJ+Dupq7fFKflXKx+gVZZKU3L5a6Skurv/fLLnakzZ81Sb+pLSy7OSmErPacAM/99SMGArE3rn4BFo7tg9eSe2P3SACYbFKLXCZgzLBEAVE86LPvhjLxpMgHpWHjlFWUDckVBgVR5oGanfkC6+ZHbjBbAllz1+12YPidzhiXy5kphs4YkYkrfeNWPxbnfHnatgWTLlkCXLsCKFWqF5BmCIFVO2enfcLbI9Ya3Z4vL5DfmJCIicoAJBx/zYHvXh1UAwMx12dYXEsHBwN/+plBUNgRB6tmwaZP0VEltjRpJ+5H5VNU0ZrxChan3TPq0bcwZKFRimiYzJkL98vhPfzwtb0Uty7qffbb6e1VVwPjx6u/7v/91a1aKdVkKJEFrERMRgg9ZYaSaWUMS8bf/uUfVfRSWVOC97SdcW+nGDfUbR2rNQf+G9JwCnLvq+v/r1bIqNlIlIiLFMeHgY3rLnH7sWlmVdS8HUQQWLVIoKhuiKJWwzpihTRO7K1ekpIMMBqOIP3/9i8IBWWvaIIil2ypLTYrF7pcGYPXknniqV2vV9vNV5jl5VQ7BwdJYa50Gp1xHQ6VKS9Xdb4sW0pAKmfafLsa1W7cVDKi6V4d2YIWRBppF1lN9H+9uPeHa0IrwcGmq1qFD1QvKHjcq72r0n/9IiXab/g0Go4iZ6+QP3WIjVSIiUhoTDj6me5z8G9e9llNkKTWkwhFBkLpya9HEThBkz2O+aOtxlJSrG+O8EUmsatCAXiegV0I0HlbxZvL4xZto/+r3eCtNRmPDU6fUnY7SRIt92HP+vFTVJJOawylMPRue7h3PY1EDpt4qapu3Ide1IQDduwM7dqgXkD12hjsoIiDA7hTQS7Ydd2uGFzZSJSIipTHh4GN+sjfjhNMsLrQtG2mpMT+4WhdZ9mzZImuKz7fScrF4+0kVApIEB+jYHNID1G4kaRSBj3addi3pYJoaVosKB0+aPVvWsV9524gv9qs75IQ9G7Rj2VtFTQXXy10bAnDjhjY9hbQwb161Yy09pwD/2Cb/O42NVImISA1+fvXrf/59QH5VQg/bC4mWLYGuXYHdu92MysNu3nR5lbRD+fhol8zx+E4IC9Ije+5gJhs8QKubnWU/nHZ+eIWph4Onqg+0kpkpNYx1QXpOATrOSUd5lTr/NrHs2eARqUmx+GCsvJmVXFF43YVeBeHhwI8/AqtWAS++qF5QWrDp3WAwipj7rXtTyjIpR0REamDCwceUVcov/99/xs6ToM2btWnsqCYHXbodMRhFzF6fo2JAwDtP3IOgAB5enmJuJBkur4GhM4wi8NneM84tbKoo+vFHbfqaeIpOJ1VyOHk8mhq2VhnUqYiKCgvEzj/1Z7LBQ4Z0jsXTKvZUAYDim5W1L2SpRw9g7Fhg40Z1AtLKX/9q1bth/+liFJbIr954PLk5jxMiIlIF74h8jDs9HP61+7T1eFdTmbevO3++WpduRwxGESv2nEbxTfljXGuiE4APxnblhZsXSE2KxZ6ZDyIlrqFq+1i+x4UqmZYtpTHXYa5PbeszjEYpseJElYPBKOIPa9Vt2Fp8swqZZ6+qug+q2WCVz4W7T16pfSFbFRVAXp7ywWhp/nyrxJ67zR77tJXXeJmIiKg2TDj4mAn3xcle92alwXq8q5ZT9aklOlrq/u/EVHzpOQXos3A75m88olo4741JxpDOzVTbPrlGrxMw48G7VNv++Wvl+O5gvvMrBAUBcXHqda73Bk5WOSzZdsKtii1nseu+Z6XERyEmXL1GhDuOXXZ95hhB8P3E36lTUuLkN+42e4yJUH9mESIiqpuYcPAxQQE6DO0UI3t9q/GuwcFS/4bISPcD01pAgFSefvAgkJBQ6+LpOQV4blUWCq6rc/MRqBewdFwyhnRmZYO36ZkQjch6gapt/8W1B5zvlL95M3DokLZNVbVmNFYbX27LYBTx4U5tnjCz675n6XUC5g5Xt6fKn786iL15Rc4fh/6Q+GvY0Cr+lPgoRIUGydpUTHgwm0USEZFqmHDwQQ91lJ9wqDbetW1bYMkSNyPygIYN7U4JZo/BKGLehlyoeYs3+f54DqPwUnqdgAWPd1Jt+1VGYNzHe2u/2fHHmSqWLJF6wNj+ZGQ4rDqqvG3EnPXZqHD1qbSLTFNh8kbK81KTYvHiQPUqjb75pQBjlu1Dn4XbkZ5TUPsKlZXAxYu+mfiLipL6Txw4YHWM6XUCWkXLq1L4yyNsFklEROoJ8HQA5Dp3ntidKy6zfkMUgcWLpSclvnLx1aEDsGmTU8MoAKmZllqVDSa9Exqrun1yz6DEGETWC8S1W+r07th76ip6L9iOucMTHSee/HGmik8/BaZNc/pJ8VtpuVj2w2k4+yBaLlM07LrvPeIahaq+j4Lr5XhuVVbts5KYmrheviwdl4MGSVNm+oLr14EBA4AQ6+uA9JwCHDx3XdYmG4ap11yXiIjIjx611R0p8VEI0su7iF6x96z1EyDTTZCvJBsAoKgIaNLE6cXVHsMdGRqIngnRqu6D3LP/dLFqyQaTwhLpZsfhE1bTTU5mJvDNN9IUfb6ulqETlt5Ky8VHu9RPNgBADKfC9DpaDW0RAczbkFt7xZGpiWuPHtLwCh9mMIqYuS5b9vrsc0JERGpiwsEH6XUCeraRf4NrdTFmOV2fL4xnbdhQitXJ6gZA/QvdBSM78Smql9PqgloEMGtdtuObHdNNzogRwPvvaxKTaiIjnW7YeqvSgH/ucmFGDxme75eARaO7YPXkntj90gAmG7xMSnwUYiO0SToUXC+3bpBck8pKqdLBVxgMwM6dVm8t2XYC18rkJ1TZ54SIiNTEhIOPerRLc9nrFlwvx75TRXfeaNkS6NpVgahUJAhSouHQIaeaRFq66uo87U6KCAnAUj5F9QlaXlBfLavCe9tP1LyQaSiTr/ZzSEx0+lhMzylAyptbVe6hEoc/prbHiC7N0SshmglAL6TXCZgzLBFa/Wa25hY6t6Bl5VFmpjREyJvZzAKTnlOAf2yr5XxTg8jQQPY5ISIiVfno1S7FRro3hdW0z+2Ufnt7hUPXrk41ibRkMIqYvzFX8VA6xNRH1l8eYrLBR5iermr1CV++50zNJd2+3s8hN1f6qYVpdpjS8tuqhKETgCl94/HK0I6qbJ+UlZoUiw/HJWtS6fDJnjPONZAE7lQedekCfPKJqnG5zWIWGINRxMx/yx9KAQAT74tngo6IiFTFhIOPSomPcmuqv2u3qqqPN/fmhIMoAjt2uLSKwShixZ7TqjSMfHkIm9H5EtPTVa1cu1VVc0m37VPVFSuA+fOB//s/rUJ03+zZNfZ+UXt2mLub1sfR+Q9j1hDtfq/kvtSkWOx+aQBWT+6JRaO74IX+bVXb18v/yUalK7OhvPUWUO7l/QyWLDHPArNk2wm3etOEBAiYPkC9f38iIiKACQefpdcJmNg73q1tWDXXEgSpP4I3e+EFp5tbpucUoM/C7Zi/8YgqoRSXqTNMg9Sj5dNVwIm+EaanqsnJwIQJ0g18aqomsSmiloaRas8OMyixKYIC+BXmi/Q6Ab0SojGiS3P8YfDdeG90F1X2U3yzCj3f2uZcpYPRCLzxhipxKEYQgJUrgebN3R5KAQBjUlozcU5ERKrj1ZoPmz6gLeoH693ahrm5VnAwkJUlPW39+98VilBhp08DFRW1LpZ2KB/PrspS9WaHTbZ8k+XT1afva63qEItTl2+6toIoSmOzvbXS6NlngVWrpJ+0NODnn2tsGKl2o85ebRqpun3SziNdmmNKX/cS6I4U36zEszXNHmOycSNw65YqMShGFIGMDBjSN2HeBveHCj7UMUaBoIiIiGrGhIMP0+sELHisk9vbMd8YmJpHrlrl9jZVYTAA//1vjYukHSrAtNUHVA0jNiKETbZ8mOnp6tzhSfiHSk9WAWDRthNYtPUE1h+8gL15RbVP07d5s1Qq7a1T1G7fDowdC/zud8DDD9faT+XMFRcTLi4ICdBxKlo/M2tIIrrHqVdlV+PsMaIoDWnyhSaugoBbL72MgmvuJUf0Avg9RkREmvCBb1eqSbQCT9obhVk8paysBE6dcnubqpkxw+ENWXpOAaZ+kaX6/dqcYezf4C9GdGmO+OhQ1bb/7tbjmLHmIMYs24c+C7c7fspqqm7wZsePA5s2ObVoek4B3t3qXrl3Tfq3b8xj0A+90L+datu+WlZlPTuTJVOyzxeauIoicO4cggzuNWKNiw7lMURERJpgwsHHKVK2bHnNERQEtGrlvWXdDoZVGIwi5n6r/GwUtl4ceBdnpvAzQztr8/ssvF5evVGrSWUlcPasJnG4pYaEn4mpWaSaxvWIU3X75Bn3tWuk6kXJ3jw7CQdTss+bqxtCQ6XGsmlpMGT8jFGT/oHKAPlNowHgie6tlImNiIioFl78DUvOUKKXwKVSixv4ykrg4kXvLeuOjLSbDNl/uhiFJeqOGW8QrGdHbz+kVS8A0xFlbtRqKTgYeOcdTeJwixN9VNRuFgkA12+xaas/0usETO3fRrXt510urf6mL0xRm5AAPPUU8PDD2N8wDkcDIt3epLtNp4mIiJzFhIOPS4mPQky4e0mH3Scu33lhmq5v714gyovGdwoCkJgoNbW006hO7QZ1APDmyM4sQfVDPROiERnq3tNCZ4mwaNRq9RcisHgxoHevCazqqqqAnTtrXKTwuvqN9+ZvPFJ7TwzySb0SGqu27e9zLlavMLKdojYz0/u+/7KzpWEfUOa7blBiE87wQkREmuE3jo/T6wSMSXGvNHLT4ULri/eWLYGePYGDB4EdO9wLUCmiCBQXA02aWL1tMIrYm1eEExftPLlS0KDEJhh2TzNV90GeodcJWDDS/earrqh202AaQ24waBqHy3Q6qfy8hgqoPSevqB6G3aQN+YUrN2qficgddiuMLKeoTU6+8/2XmQn8+KPnhxgKgvm4s+q5JFPOhRIm7IiISDMBng6A3BfXyL2mdzcqDNh/uhi9bLu+t2wJRES4tW1F6HTA7t1SPBbVDek5BZi3IVf18u1+d0Vj2VPdVd0HeVZqUiyWjkvG3G8Po7BE3RsewGYolOUYcm8u6wak+M6dk8rQbSqNDEYR720/ga+zLmgSihZVTaQ9taccNiWrqn3f2WrZUvqpqJCqHYocNJzUwm+NIjcdOIs56Xlub87pfwMiIiIFMOHgB5Tp4+Dg4j08HNi3Txpf/tVXbu9HFqMRuH4d6NXL/FZ6TgGeW5UFLZ7RTHlAvc7p5D1Sk2IxKDEG/9p9Cm+kHVVtP6FBOuvp6Lx5DHmHDsBnn1k/4W3SpFqyIT2nAHO/zVW9j4oltW9MyTNS4qMQGRqIa2VVqu3Dpc9pcLD0HZicDJSqW0lnlpgIzJwJdOxofmtHsYhn1x5W7DuPCTsiItIKEw5+QOrjEOzWk9m8moYkdO8ObNwoe9uKmD0bGDwYEARzF3wtkg2xESGcq7wO0esENHGzJ0ptyiqNWPD9EbwyNFF6wzSG/PJvvVR+/BF4/nlVY3BacTGQlGS3b4pJek4Bnl2VpVlIAoAYHpfkhpe+Ooh6gTrnZxxKSADi4qReClo4cgQYO9bc08VgFPHywu2KfucxYUdERFphDwc/oNcJmDu8Y+0L1mDxjjz70/UBQFoaUFbm1vbdduGC9CQYwL68ItWHUZjMGZbIRpF1jBYX4st+OI3vDloMPTCNIe/aFVi50jum6NPrpaFMNSQbDEYRM9dpdBOGOzP48rj0X/tPF6ta3QAAlUbg2VVZWLT1uHO9DCorpaFEWhFFYP1680ulZ35hIp2IiLTkBVe1pITUpFh8MDbZrW3M/Hc29py8Yn0BJorAa69p3zRLpwO++eZO1/CMDCA4GOk5BZj6eabquxcAfDC2q/NPwMhvpMRHISpM/Vkrpq85WD3J503DK6KipERIDfadKlL95tBSTEQIPhyXzOPSj2lZ6v/u1hPovWC742S7SVAQ0Mq95swuW7DA3JxV6X8TJuyIiEhLHFLhR4Z0jkW33RH4+dfrsta/dqsKv/v4J8RGhGDOsETpot50A1RDV3pVREcDqanVmkRq1bfh/bHJGNKZNzV1kV4n4PURSZj6xQHV9zVvQy4GJcbcufi3HV5hSRSB0aOBvDx1jsclS4D77rvz2k6vBltazEgRHRaE2UM7ICaiHlLio3ij5Oe0LvUvLCnHc6uyak5kbd4MHDqkaVz49Vdzc1al/k0i6wVgweOdmbAjIiJNscLBzzzU0f0LicLr0gVYek6BdMOxe7f2c5L//e9WNzta9m2Y8WA7JhvquCGdm2FK33jV91NwvRzvbjmOvXlFdyqLbKfoM/1cuQKcPKlOskGvl4ZydO16Z38tWtS4isEoYvuRi8rHYkEA8MZjSXgsuQV6JUQz2VAHpMRHITYiBFr/pu1OlwncmUVGyyq/6Ghgzx7zd2BKfBTqB7v3fKhN41BkvvoQkw1ERKQ5Jhz8TKP67s/RbbrkMl+AtW17Z07yzEzgu++AeJVvxhYtsrqxUnoMqyMRIQF44UHOSkHArCGJ+GBsMqLCglTdz3s7TmLMsn3os7CG0m7LqTPVYDBIlRWbN9e42K1KA2b/Jxt9396Ou2en4ejFG+rEA2mcOYdP1D16nYA5w6SGqlrd4ou4M1VkNVpW+bVvD/z8s/R9m5Bg9VdG0b1hVmUVBrfWJyIikosJBz9ztaxSke1UuwCzfOo6cKD6TSTPnTM3iQS0G9e7cFRnPkUlsyGdY7Fv1oOIClU36QDYVBbZ0qK3g04nJTUc3FhNXpmBDn9Jx6qffsWvxbdwW6VQkltGYPXkntj90gAmG+qo1KRYfDguGTER2g6vsPs9ExwM/O1v2gRw9KhUyWRTXbTvVBHKKt074ApLKuwnVIiIiFTGHg5+RumnsQ4vwPbvBx5+GMjNdX8n4eHA4sVAwG8fx6gooFMnqyEVWozrfW90F97gUDWZZ6+iWKFEXk1ESE90q/V1AOz3dhBFYMwY4MQJZQIwGu8k+mx6N0xemYEtuZeU2U8tJvSOR6+EaE32Rd4rNSkWgxJjsP90MbbmFuKTPWdU3+eu45fxSOdm1seeKErfTzqd+s1cTUm/hx6yGsLx2d4zimxey4acREREJkw4+JmYiHqKbs/hjf6RI8okG6KjgZ9+qlY+auvy9Vvu76u2UDgvOdmh5UW6ZWVRtZvuli2tZ42oqABKSpTZsalhpJ1GkbcqDZolGwDtmwaS99LrBPRKiEavhGh0j4/CzHXZqs6K8u+sC9hz8grmDu94J/ms5cwxdpJ+6TkFSD+sTK8UHltEROQJTDj4mZT4KDQICUBp+W23t+Vwrm7L8eSuXIQ1bQpMnw5EREh9IZo2lW5wamlO98bGXCz74bSL0buOT3/IHk9cpDv1WbSsehBF4KmnpJJsOTdGK1YA06bZbYw35bOfXd+eTFFhgfbPOVTnmSoe9uUVYdG249h/5qoq+yksqbCetcLRzDHffQfMmeP+DqOigI0bpak3Aaukn6lZshLqB+t4bBERkUcw4eBn9DoBbz7WCc+vdn9Kv6Tm4fb7Gch94nPxItC9OzB4sNOrvJWmTbIB4NMfsi8lPgox4SEoLNEuIZWeU4gmDUJqnwbSVPWwaZN7FUfHjtkdSmEwith3qkj+dl30+ogk9lAhh/Q6Ab3bNcKVmxWqJRwAqdLIamiTbXWRKAL/+7/K7GzRIqBnT7t/pWSz5AWPsT8RERF5BptG+qFh9zTDwA6N3d7OltxLmP55ZvWpwkxPfEyzVmRmSp21ExNr7qJfS1M6W5W3jfinRskGh9UcVOfpdQLmDk/UdJ/f5xTWPnOFianiSK+Xv0ODAQgMrPb2/tPFqDRoMRktMKVvPIZ0bqbJvsi3aZEcdjhrBSAl586dq30jwbXMGqXTSf0hHHwnnr+qTHPmjrEN8EiX5opsi4iIyFVMOPipj8Z3h16BhxnfZRcief7m6jc9lrNWJCdLnbVzc2uuejAanZp6z2AUsTevCH/+6hdNZiITAMwZlsinP+RQalIslo5LRmRo9ZtyNRXUNHOFyebN0nFlcDDt3ciRte/o1i0gLa3a299n5zsZqXz1gwPwwdhkzBqibVKHfFdKfBSiwtQ/FvecvIL1By9gb16RdeI9OFhKsm/cCKxaZf9n40bgrrtq3oFlzwY7Fm89psj/x+xHOiqyHSIiIjkEUdTils67lZSUICIiAtevX0d4eLinw1HE3rwijFm2T9FtLjWNabUlikCPHlKlQ23DLAQB6NZNahRpZ7x42qF8vPJNNq6Wud+DwhmxESGYMyyRs1OQUwxGEbtPXMaE5Rma7rdhaCB+nj2oelKstmNPpwNCQmqfxtbOcZl2KB/TvjgANb8gXhnSAc/0iWeyj1w2f8NhTWauMAkL0uP397fBCw+2c+7zumkTkJrq+O8tG7Va9DEyGEXsP12Mf/6Qhx1HLzte30mxESHY/dIAvznG/PF6jYjI37HCwU+p0QBx3obc6sMrANd6Ooiiwyc6b6XlYuoXBzRJNgQF6PD5pB7Y/dIAJhvIaXqdgAfuboLJ98dput+rZVV4b7ud6S9rO/aMRqDciXOBzXGZdqgAU1VONgBAk/Bgv7kRIm0NTIzRdH83Kw1YtO0EkuakY9HW4/YrH0wsGyvbo9MBK1cCXbtaJRvScwrQZ+F2jFm2T5FkA8DqPSIi8jw2jfRTaoxxdThdn6mnw4ULwNChQLGDca+ANA3m7t3VxramHSrAR7u06dcAAItHd0Hvdo002x/5l1eGdsTpK2XYekS76SI/2nUK0wfYPF0NDobhp/04fPAkissqERUahI7NI6yXuX0bCAgACguBq3Ya7UVF3ZkxJjgY3x3Mx/Q17jeddQYbtZJcKfFRiI0IUayporNuVRnx7tY7yT+7VXLOJALtTH/53KosRZN8Lwxoy4Q6ERF5HBMOfkqti7HC67fs/4Wpi/fBg9WnD7NkZxpMg1HEK99kKxdkDQTgznRnRG74eEJ3vL7hMD7WqKy7rNKAfXlFVomy9JwCzNtwwuI4v4XYCLHaDZCpTPtSaXmNs1+8sfEwlv1wRuX/EwkbtZI79DoBc4YlKn6T7qrC3/qsWH2vOJhKs+q2ERsP5aOgpByRrZvjcX0ggnBn+kul/z96tImufSEiIiKVsYcD/HdMYHpOAZ5dlaXoNqPCgvDmY0mybtgtb3oa1Q8GRODKzQpcKqnAG2lHFI3TkaiwIGS8MpAlpqSYtEP5+H9f/4KySheniZUhqVkDPNa1Bcb3isP2oxdrvNl6cWA7TB/QDltyCzFvQ65V8jE2IgSvDk1Ew7AgcxJiW+5FfLxHuyojhz1hiFyQnlOAl/+TjeKbVR6LQQAQU0uvBNMUz5YjMAQAQzvHYkz3VvjdJz8pHtei0V0wws9mp/DX6zUiIn/GhAP8+wssPacAf1z7C25WOuhg7yLTpZSrVQLSk9hczctf7Vk9uWf1YSFEbjAYRfxu2T7sczSNnsIEAIEBAipv13z6jqwXiGu3PHcj5siMB9vixUF3ezoM8hOVt43o+dY2FN+0P9uDVlZP7omU+Khq1URvpx+pcchgUIAOlbeVT1j643edP1+vERH5Kw6p8HOpSbEY0L4pkuako9Lgfm7JtIV5G3IxoH1TZJ69WmuZthpjU92hRkNNqtv0OgFjerTSLOEgArUmGwB4ZbIhNEiPFx6sZbpAIhcEBejw+oiOmPqFNr1HHHkzLRdXblRaJdabNgjGpRsVNa6nRrKBQ5aIiMhbMOFQBwQF6PCPJ7ti6hfKDa8ouF6Oe+Ztwq2qOxdKts2zDEYR+04VYea/s70m2QCwUR2pg58r50zpm8AhTaS4hmHBtS+ksuwLJdXeu1hac7JBLZydgoiIvAUTDnXEkM6xSNzRALkFpYpt0zLZAEhJiGdXZeHFge3Qrkl9zN94xCuGUFjiUx9Si6e65vuSiHoBmD6grafDID/EyjVJoE7AkrFd2R+FiIi8hoNJoskf3dtamxvtd7eewNQvDnjdjZcAPvUh9Zi65pNjCx/vzOOPVMEKI8m/nu7OZAMREXkVJhzqkLjoUE+H4DGxESGcDpNUl5oUixcHsj+BLQHAB2N5/JF6TBVGdVnD0EDc17ZR7QsSERFpiAmHOmR8rzjUtYeLk3rHYfXkntj90gDe7JAmpg9oi5hwz48n9ybvj+2KIZ15/JF6TBVGdewrzspbIzuxgoiIiLwOEw51SFCADpPvj/d0GJpZMqYrXh3WEb0SonkRRprR6wTMHd6xTt/4mIQG6bF0XDKGdG7m6VCoDkhNisWH45LrZKXD/z3Yjkl1IiLySmwaWcfMGiKNMf/nrtNeNXOE0ibfH4dh9/AmhzzDdOMzb0Ou1/Uy0cojnWOxaHRXJvtIU6lJsRiUGIP9p4tReP0Wdh6/jG8O5ns6LNXFNw7zdAhERER2MeFQB80akog+CY0xfvl+T4eiiil9482JFSJPsbzxWfB9Ln45X33KPH/1Qv8E/GFwe0+HQXWUXiegV0I0ACAmol6dSDiwaSYREXkrDqmoo4pvVXo6BMXdn9AIx19/mMkG8hqmG5/72zX2dCiaWpt5HgajP9dQka+oC80kI0MDOd0zERF5LSYc6ih/exoSGRqIFZNSEBTAjzR5o7o1rKCwpAL7Txd7OgyiOtFMcuJ98Ry6REREXot3Z3VUSnwUosICPR2GYnjBRd7MVN5dl1wqrZu9K8j7mHqq1A/2v1GkkaGBmD6grafDICIicogJhzpKrxPw+ogkT4ehmLhGoZ4Ogcihnm2iERnqPwk+Z/hbFRX5ttSkWGS9OghhwXpPh6KoBZwKk4iIvBwTDnXYkM7N0MtPxn3y5oa8mV4nYMHITp4OQxMCgNiIEI4pJ68TFKDDO/9zj6fDUMx7o7tyKkwiIvJ6TDjUcZ9O6uHTY1t5c0O+YlBijN9XOZjOJXOGJfKpK3ml1KRYvDiwnafDcNuQpKZ4pAunfiYiIu/HhEMdFxSgw//2jfd0GLLw5oZ8yf7TxbhWVuXpMFQVExGCD8cl86krebXpA9ohJty3q+IGdeQxRkREvsH/OiiRy2YNScS54jKk5Vz0dCguiYkIwZxhiby5IZ/gj00UW0eH4s1HO+HKzQo0aSBVGjH5R95OrxMwd3ginluVBV+dvLX4RoWnQyAiInIKEw4EAFgy9l7smJOOW1VGT4dSq2n9EtCnXWPe3JBP8cc+IwtGdq6TM3CQ7zPNXDH321wUlvheMjAqLMjTIRARETmFQyoIgPTE590nu3g6DKcIgoBeCdFMNpBPSYmPQmxEiE/3TLHE3ink61KTYrHrz/19cuaKmIh6ng6BiIjIKUw4kFlqUixmPOgLzbR8tQiW6jK9TsCcYYkA4BdJB/ZOIX+QefYqblYYPB2GS5jsIyIiX8KEA1l54cF2iKzn3SNterVp5OkQiGQxlXHHRPj28IoXB7Zj7xTyC77WW0UAk31ERORbvPvOkjSn1wlY8HhnPLsqy9Oh2BUapEdPjhknH5aaFItBiTHYf7oYl0rLceLiDby346Snw3JaVFggpg/whUoootr5Um+VqLBAvPlYJyb7iIjIp7DCgapJTYrF0nHJiPDCSocpfdvwyQ75PL1O6kMyoktz9G7rWxU7r49I4jFIfsNXeqtEhwVh36yBTDYQEZHPYcKB7EpNikXWqw/hxYF3ITTIOxpqNQzlk1XyP75ywwMAU/rGY0jnZp4Og0gx3t5bRfjt543HkhAUwEs2IiLyPfz2Iof0OgEzBrZD9tzBeKSzZ5+qCADeGtmJT1bJ71je8Hir6LAgfDA2GbOGeHecRHI46q0SGxGCUcnNPRSVJCYiBB+OS2ZlAxER+SxBFMU63/K/pKQEERERuH79OsLDwz0djtdKO5SP51cfgEHjT0xsRAjmDEvkBRf5tfScAszbkIuC697RxO6RzrEYlNgUTRpIHfGZ7CN/ZzCK5t4qps/9vA2HsXLvWc1jiQgJwAfj7kXPNpwC2hKv14iIfI/3DdInrzWkczMMTorFmH/uxf4zV1Xf38NJMXiqVxxvdqhOsGwmuSW3EN8czEfxzUrN44gKC8TrI5I4dILqHFNvFUuto0I9EsvCUZ19rr8LERGRPaxwADPmrjIYRXSam46ySqNq+4gMDUTm7EFMNFCdZfm0tVH9YPxx7UFcLKmAmifs6f0T8OKgu3ncEf2m8rYR7V/9HkaNrpQEAO+PTcYQDw9j9Fa8XiMi8j3s4UAu0+sE/P2JLqruYwH7NVAdZzuTxdzhHQGo29iud9vGPO6ILAQF6DD5/njN9vf+2K5MNhARkV9hwoFkMU2dGRMufw7zAB0QHGB9cxMbEYKlbJBFVI2jxnZKCQ8JQEp8lCrbJvJls4YkYkrfeKiZizN993EoExER+RsOqQBL9NxhKvvemluIT/accWqdIL0Oy8bfiz53NQaAak26+ISVyDGroRZhwfjjV7+gsMT9RpNLxnTFsHt4s0PkSOVtIz7bewZni8tQVmHA11nn3d7mM73jMCgxht99TuL1GhGR72HCAfwCU0raoQJMX51V61jXDzg+lUgx6TkFeG5VFgDI7u8wKLEJlj3VXbmgiOqA9JwCzFyXjWtlVVbvR4YGIqFxGDLPXnO4bmS9ACx4vDOr+VzE6zUiIt/DhAP4BaaktEP5mPrFAYd/P6VvPGYNSdQwIiL/Z29KzdiIEIzu3gpxjUJx6vJNLN2Zh4rb1Ru9Tr4/Dq8M7ahluER+w2AUse9UEfbmFQEQ0atNI/RMkKayTDuUj9nrc1B8805CIrJeICb2jsP0Ae1Y0SADr9eIiHwPEw7gF5jS7N38cKo9InVZDrWwNzzJYBTx48kr+HfWeZRV3kb3uGhMuC8OQQFs5UOkltqOS3INr9eIiHwPEw7gF5gaeJFFRERESuL1GhGR7wnwdADkn0xT+hEREREREVHdxFpaIiIiIiIiIlIcEw5EREREREREpDgmHIiIiIiIiIhIcUw4EBEREREREZHimHAgIiIiIiIiIsUx4UBEREREREREimPCgYiIiIiIiIgUx4QDERERERERESmOCQciIiIiIiIiUhwTDkRERERERESkOCYciIiIiIiIiEhxTDgQERERERERkeKYcCAiIiIiIiIixQV4OgBvIIoiAKCkpMTDkRARERGRPabrNNN1GxEReT8mHACUlpYCAFq2bOnhSIiIiIioJqWlpYiIiPB0GERE5ARBZJoYRqMR+fn5aNCgAQRBUH1/JSUlaNmyJc6dO4fw8HDV90e+j58ZchU/M+QqfmbIVVp/ZkRRRGlpKZo1awadjqOCiYh8ASscAOh0OrRo0ULz/YaHh/OijlzCzwy5ip8ZchU/M+QqLT8zrGwgIvItTA8TERERERERkeKYcCAiIiIiIiIixTHh4AHBwcGYM2cOgoODPR0K+Qh+ZshV/MyQq/iZIVfxM0NERLVh00giIiIiIiIiUhwrHIiIiIiIiIhIcUw4EBEREREREZHimHAgIiIiIiIiIsUx4UBEREREREREimPCwQkXLlzAwoUL0bt3bzRv3hzBwcFo3rw5evfujYULF+LChQuqxyCKItLS0jB+/Hi0b98e4eHhCA8PR/v27TF+/HikpaWB/T89q6KiAtu3b8df/vIXPPLII2jTpg0aNGiAoKAgNG7cGF27dsWzzz6LzZs3q/a7evrppyEIgss/S5cuVSUecuzMmTOyflcxMTGqxcTzjPeS81mx/ImLi1MsFp5nPKu0tBS7du3CP/7xDzz11FPo2LEjAgICFP1de8N1D8BzEhGRXxCpRh9++KEYFhYmAnD4U79+fXHp0qWqxXD27Fmxf//+NcYAQBwwYIB49uxZ1eIg+woLC8XRo0eLDRo0qPV3ZPrp2LGjuG/fPsVjmTBhgtMxWP58+OGHisdCNTt9+rSs31XTpk1ViYfnGe8m57Ni+XPvvfcqFgvPM55z1113iYIg1Pjv3Lp1a7f24Q3XPaLIcxIRkb8IsJeEIMlrr72GOXPmWL3Xrl07NGvWDOfPn0deXh4A4MaNG3j22Wdx+fJlzJ49W9EY8vPz0adPH5w7d878XoMGDZCYmAhRFHHkyBGUlpYCALZv3477778f+/btQ2xsrKJxkGPnzp3DmjVrqr0fGxuLFi1aoEGDBigsLMTRo0dhNBoBAIcPH0afPn3w5ZdfYuTIkarE1axZM3Tq1MmpZVu1aqVKDOS8vn37ol69erUuFxUVpfi+eZ7xfoMHD3Zp+TNnzuDYsWPm1+PGjVM6JAA8z2jt+PHjqm7fG657AJ6TiIj8iocTHl7rm2++scqgJyYmipmZmVbLZGRkiB06dLBabv369YrFYDAYxG7dupm3LQiCOG/ePPHGjRvmZUpLS8U5c+ZYPfHo1q2baDAYFIuDapaRkWH+t+/Zs6e4dOlS8fTp09WWKygoEKdPn271uwoKChKPHj2qWCyWTx4nTJig2HZJebYVDvY+M1rgecY/DR8+3Oo8c+XKFcW2zfOM55j+3cPCwsT77rtPfP7558Xly5eLqampblc4eMN1jyjynERE5G+YcLCjsrJSbNu2rflLrEWLFmJxcbHdZYuKisTmzZubl23Xrp1YVVWlSBz/+te/rL7U3377bYfLLliwwGrZFStWKBID1S4zM1McMWJEtQszRxYvXmz1u3r88ccVi4U3Ar7DWxIOPM/4n4KCAjEgIMD8e3riiScU3T7PM56zatUqMTc3t9qNteXvRE7CwVuue0SR5yQiIn/DhIMdK1eutPoCW7t2bY3Lf/nll1bLf/bZZ4rE0aZNG/M2k5KSaszcGwwGMSkpybx8QkKCIjGQOlJSUsy/q5CQEPHmzZuKbJc3Ar7DWxIOPM/4n4ULF1p9tjZt2qTo9nme8T7uJhy85bpHFHlOIiLyN5ylwo61a9ea/9ysWTM89thjNS4/cuRIq3GDX331ldsxZGZm4tSpU+bXU6dOhU7n+Nel0+nw3HPPmV/n5eXhwIEDbsdB6hgxYoT5z+Xl5Thz5ozngqE6i+cZ//Svf/3L/OdWrVph4MCBHoyGfIE3XPcAPCcREfkjJhxs3Lp1C1u2bDG/Tk1NRUBAzb01AwICkJqaan69efNmlJeXuxXHt99+a/X6kUceqXUd22XWr1/vVgykHtvGfyUlJR6KhOoynmf8z+7du62aRU6cOLHGGzYib7nuAXhOIiLyR7wKsXHkyBFUVFSYX/fu3dup9SyXKy8vx5EjR9yKwzJD37JlS7Rs2bLWdVq1aoUWLVrY3QZ5F9uKhiZNmngmEKrTeJ7xP5bVDYIgYOLEiR6MhnyBt1z3ADwnERH5IyYcbBw+fNjqdbt27Zxaz3a53NxcxeJwNgbbZd2NgdQhiiK+/vpr8+vY2FjEx8crvp+MjAw8/PDDaN68OYKDgxEeHo74+HgMHz4cf/vb33Dx4kXF90nyvfTSS+jcuTMiIyMRFBSEpk2bIjk5GdOnT8emTZsgiqLi++R5xr+UlpZalcYPHDgQrVu3VnWfPM/4Pm+57rGNheckIiL/wISDDdsnz87OG257UXf69Gm34jh79qzLMdjG4W4MpI4vvvjCPJc5APzud7+DIAiK7yc3Nxfp6enIz89HZWUlSktLcebMGWzYsAF/+tOf0Lp1a/zhD3+werJFnrN27VpkZ2fj+vXrqKqqwqVLl3DgwAG8//77SE1NRadOnbB3715F98nzjH/58ssvcfPmTfPrSZMmqb5Pnmd8n7dc9wA8JxER+SMmHGzYjqWPjIx0ar2IiAir16WlpbJjuHnzJgwGg8sx2MZhMBhw69Yt2XGQ8s6fP48ZM2aYX0dGRmLWrFmq7CsgIADt27dH37590a9fP3Ts2BF6vd789xUVFXj33XfRq1cvXL16VZUYyHnR0dFISUnBgw8+iB49eqBRo0ZWf3/48GH07dsXn3zyiSL743nG/1gOp4iKisKjjz6q+j55nvF93nDdA/CcRETkr5hwsHHjxg2r1/Xq1XNqPdvl3PnilRuD0nGQssrKyjBy5EgUFRWZ3/voo4+qNZB0R3h4OKZMmYLNmzfjxo0bOHLkCHbu3IkdO3YgJycHV69exQcffICmTZua1zlw4AAeffRRGI1GxeKg2gmCgG7duuH999/HqVOncOXKFfz000/YunUr9u3bh8uXL+Pnn3/GqFGjzOvcvn0bU6ZMwaZNm9zeP88z/uXIkSNWFTDjxo1DcHCwKvvieca/eMN1jztxqBELEREphwkHG1VVVVava+vU7Gg52+1oEYPScZBybt++jdGjRyMjI8P83rRp0/DEE08oup/Fixdj6dKlGDRokN2bjQYNGuC5557DL7/8gsTERPP7u3btwsqVKxWNhWrWunVrZGRkYOrUqQ57eNx777346quvsHjxYvN7BoMB06dPd/vY5nnGv1hWNwDqDqfgeca/eMN1jztxqBELEREphwkHG2FhYVavnZ3myXY52+1oEYPScZAyjEYjxo8fjw0bNpjfe+KJJ7Bo0SKPxdS0aVOsX78eQUFB5vfeeecdj8VDNXv++efxzDPPmF+fPHmy2vRxruJ5xn9UVVVZ3ch369YNnTt39mBEEp5nfIM3XPe4E4casRARkXKYcLBRv359q9dlZWVOrWe7XIMGDTSPQek4yH1GoxFPP/001qxZY37v8ccfx+eff241ztkT2rZtiyeffNL8OicnB+fPn/dgRFSTV155xer1999/79b2eJ7xH9999x0uXbpkfv373//eg9FY43nG+3nDdY87cagRCxERKYcJBxuNGze2el1QUODUerbL2TZ8c0VgYKBVAyRnY7BdNjIy0uM3tXWZ0WjEpEmT8Nlnn5nfe+yxx7BmzRqXSkXVNGDAAKvXR48e9VAkVJs2bdpYdWJ393fF84z/sBxOERoaijFjxngwmup4nvFu3nDdA/CcRETkr5hwsNG+fXur15ZTNNXEdrkOHTooFoezMdgu624MJJ/RaMTvf/97rFixwvzeo48+ii+//NJrkg0AEBsba/X6ypUrHoqEnGH5+1Lid8XzjO/Lz8+3qnYZNWoUwsPDPRhRdTzPeDdvue6xjYXnJCIi/8CEg42OHTtavc7KynJqPdvlLBtluRvH4cOHUVlZWes6FRUVOHz4sGIxkDymZMPy5cvN7z366KNYu3YtAgMDPRhZdbZlqK50BSftWf6+lPhd8Tzj+z799FOrqQTVbBYpF88z3s1brntsY+E5iYjIPzDhYKNly5ZISEgwv965c6dT61ku17ZtW7Ro0cKtOPr162f+c3l5OX766ada1/npp59QUVFhft2/f3+3YiDX+VKyAZDGU1uKiYnxUCRUm4qKCpw8edL8WonfFc8zvs/yXNOuXTv07dvXg9HYx/OMd/OW6x6A5yQiIn/EhIMdI0eONP/5v//9L3799dcal//111+tvngt15dr2LBhVjeozkwlZrlMUFAQHnnkEbfjIOfZSzY89thjXptsMBgMWL16tfl1WFgYunbt6sGIqCbr1q2zelLcp08ft7fJ84xv27VrF06cOGF+bTmTibfgecY3eMN1D8BzEhGRP2LCwY6JEyeaGw4ZjUbMnz+/xuVfe+01GI1GAIBer8fEiRPdjiEyMhKjRo0yv/7888+tnm7aOnHiBL744gvz61GjRlk1XyJ1iaKIyZMnWyUbRo4ciS+//NIrkw0A8Ne//hVHjhwxvx4+fLjV9HXkPS5evIiZM2eaX+t0OkUu8Hme8W2WzSL1ej0mTJjgwWjs43nGN3jDdQ/AcxIRkV8Sya5nnnlGBGD+WbZsmd3lli5darXcpEmTHG7z9OnTVstOmDChxhhOnjwpBgYGmpdPTk4WL126VG25wsJCsUuXLublgoKCxLy8PJf+f0k+o9EoTp482ep3O2rUKLGqqsrtbbvymZk2bZr42WefieXl5TVus7KyUpw7d64oCIJ5u4GBgeKJEyfcjpec8+OPP4pTpkwRjx49Wuuyhw4dEhMTE60+B08//bTD5XmeqRuuX78uhoaGmn8fw4YNk70tnmd834QJE8z/zq1bt5a1DTWue0SR5yQiorrOe9rle5mFCxdi586dyMvLAwBMnjwZGzZswOjRo9GsWTNcuHABq1evxnfffWdep23btliwYIFiMSQkJODtt9/Giy++CEBq0HTPPfdg6tSp6N69O0RRREZGBj744AMUFhaa13v77bfRpk0bxeKgmn311VdYtmyZ+bUgCLh69apLZZ1//OMfMWjQILfiyMnJwfvvv4+pU6ciNTUV9957L+6++25ERkZCp9Ph0qVL2L9/P1avXo3z589bxfvxxx+jbdu2bu2fnFdRUYGPPvoIH330Ee655x4MGDAAnTt3RkxMDBo0aIAbN27g5MmT2LRpEzZu3Gh+kggAXbt2xaJFixSLhecZ37RmzRqrITZaNYvkecazXn/9dbz++uvV3q+qqjL/+ezZswgJCam2zPjx462+q2x5w3UPwHMSEZHf8XDCw6sdP35cjI+Pt8rMO/qJj4+v9cmNq1l+k5dfftnqKZGjH0EQxNmzZyvwf06uWL58uVOfkZp+li9fbnfbrnxmHnjgAZf3GxkZKa5Zs0adfxhyaMeOHbI+J8OGDRMvX75c47Z5nqkbevToYf6dNG3a1K2KKp5nfMecOXNkf884cy5Q+rpHFHlOIiKq69jDoQbt2rXDoUOH8MILLzic1zwiIgIvvPACDh06pNqTmzfeeANbtmxBt27dHC7TrVs3bN26tdZxl+S/Ro0ahR49ejjVMyImJgazZs1Cbm4unnzySQ2iI0txcXF48sknERsbW+uyOp0OgwYNwvr16/Htt9+iUaNGqsTE84zvOHz4sFX3/gkTJiAgQJuCRZ5n/Ju3XPcAPCcREfkLQRRF0dNB+ILy8nLs3LkTZ86cQVFREaKjoxEXF4d+/fohODhYszhOnDiBjIwMFBQUAABiY2PRvXt3tGvXTrMYyLtVVlbi8OHDyMvLQ0FBAW7cuAGj0YiIiAg0btwYycnJ/Lx4kfz8fOTm5uLXX39FcXExbt26hXr16iEyMhJt27ZFt27dUL9+fU1j4nmGasPzjP/zlusegOckIiJfxoQDERERERERESmOQyqIiIiIiIiISHFMOBARERERERGR4phwICIiIiIiIiLFMeFARERERERERIpjwoGIiIiIiIiIFMeEAxEREREREREpjgkHIiIiIiIiIlIcEw5EREREREREpDgmHIiIiIiIiIhIcUw4EBEREREREZHimHAgIiIiIiIiIsUx4UBE5CX69esHQRAgCALmzp3r6XCIiIiIiNzChAMRkZPOnDljTggo/XPmzBlP/+8RERERESmKCQciIiIiIiIiUlyApwMgIvIV9erVw+DBg2tdbv/+/bh69SoAICQkBA888IBT2yYiIiIi8ieCKIqip4MgIvIn/fr1w86dOwEArVu35nAJIiIiIqqTOKSCiIiIiIiIiBTHhAMRERERERERKY4JByIiL+HstJhxcXHm5VasWAEAMBgM+PrrrzFixAi0adMGISEhiIyMxP33349ly5bBYDBU205paSneeecd9OnTBw0bNkRQUBCaNWuGxx9/HNu2bZP1/3DgwAHMmjULKSkpaNasGYKDgxEdHY3OnTtjxowZyMjIkLVdIiIiIvI9bBpJROTj8vPzMXbsWHPfCJOKigrs3r0bu3fvxpdffokNGzaYm1Pu2rULo0ePRkFBgdU6BQUFWLduHdatW4c//OEPeOedd5yK4dKlS5g2bRq+/vrran9XXFyM4uJiZGdnY/HixRgzZgyWLVuGsLAwmf/HREREROQLmHAgIvJhN27cwEMPPYTDhw8DAOLj49G6dWuUlZXh4MGDqKysBABs27YN48ePx9dff40ffvgBgwcPRnl5OQRBQMeOHdGkSRNcunQJOTk55m3//e9/R5s2bTBt2rQaYzh27BhSU1OtmmMGBgYiMTER0dHRKCkpQXZ2NioqKgAAq1evxrFjx/Df//4XDRo0UPhfhIiIiIi8BYdUEBH5sLlz5+Lw4cO47777kJWVhVOnTmHHjh346aefkJ+fjxEjRpiX/fe//41NmzbhySefRHl5OZ555hlcuHAB2dnZ2LZtG7Kzs5GdnY277rrLvM7LL7+MGzduONx/aWkphg0bZk42REZG4v3338fVq1dx8OBBbNu2DRkZGSguLsbbb7+NoKAgAEBWVhaee+45df5RiIiIiMgrMOFAROTDioqK8MADD2D79u3o2rWr1d9FR0dj7dq1aNOmjfm94cOHo6CgADNnzsQnn3yC2NhYq3WSkpLwzTffQK/XAwBKSkqwbt06h/v/85//jBMnTgAAYmNjkZmZialTp1YbLhEaGoo//elPWL9+PXQ66avn888/Z08HIiIiIj/GhAMRkQ/T6/VYvnw5goOD7f59UFAQJk2aZH5dWVmJu+66C/Pnz3e4zQ4dOqB///7m17t377a7XGFhIZYvX25+vWLFCqvkhj2pqal4+umnza+XLFlS4/JERERE5LuYcCAi8mGDBg1CfHx8jcv07NnT6vUzzzyDgICaW/hYrpObm2t3mTVr1pj7MnTq1AkPPfSQMyFjwoQJ5j/LnQ2DiIiIiLwfEw5ERD6sV69etS4TExPj8jqWQy2uXr1qdxnLWTEGDRpU6zZN7rnnHvOf8/PzkZ+f7/S6REREROQ7OEsFEZEPs00m2BMaGurWOmVlZXaXOXTokPnPGzduNM+U4arLly+jWbNmstYlIiIiIu/FhAMRkQ8zzfqg5jqiKNp9v6ioyPznY8eO4dixYy7HAgDXr1+XtR4REREReTcOqSAiIllu3rypyHaMRqMi2yEiIiIi78KEAxERyRIZGWn+89tvvw1RFGX99OvXz2P/D0RERESkHiYciIhIFsteEBcvXvRgJERERETkjZhwICIiWe677z7zn/fu3evBSIiIiIjIGzHhQEREsjz88MPmP+/duxdHjhzxYDRERERE5G2YcCAiIlmGDx+Ou+++G4A0k8WUKVNQVVXl4aiIiIiIyFsw4UBERLLodDq8++67EAQBAPDDDz8gNTUVFy5cqHXdI0eOYPr06fjrX/+qdphERERE5CEBng6AiIh818MPP4w333wTs2bNAgBs374dbdq0weOPP47+/fujdevWCA0NRUlJCfLz83Hw4EFs377dPPxizpw5ngyfiIiIiFTEhAMREbll5syZaNq0KaZOnYry8nJUVlZi9erVWL16tadDIyIiIiIP4pAKIiJy28SJE3Hs2DFMmzYNERERNS5bv359DB06FJ9++in+9Kc/aRQhEREREWlNEEVR9HQQRETkPwwGA7KyspCbm4uioiLcunULYWFhiImJQfv27dGxY0cEBgZ6OkwiIiIiUhkTDkRERERERESkOA6pICIiIiIiIiLFMeFARERERERERIpjwoGIiIiIiIiIFMeEAxEREREREREpjgkHIiIiIiIiIlIcEw5EREREREREpDgmHIiIiIiIiIhIcUw4EBEREREREZHimHAgIiIiIiIiIsUx4UBEREREREREimPCgYiIiIiIiIgUx4QDERERERERESmOCQciIiIiIiIiUhwTDkRERERERESkuP8PtxafW5My8+QAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
"
       ]
@@ -586,10 +586,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:48.107318Z",
-     "iopub.status.busy": "2024-10-20T00:23:48.106929Z",
-     "iopub.status.idle": "2024-10-20T00:23:48.228228Z",
-     "shell.execute_reply": "2024-10-20T00:23:48.227659Z"
+     "iopub.execute_input": "2024-10-22T01:50:32.435896Z",
+     "iopub.status.busy": "2024-10-22T01:50:32.435646Z",
+     "iopub.status.idle": "2024-10-22T01:50:32.542527Z",
+     "shell.execute_reply": "2024-10-22T01:50:32.541791Z"
     }
    },
    "outputs": [
@@ -626,10 +626,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:48.230383Z",
-     "iopub.status.busy": "2024-10-20T00:23:48.230161Z",
-     "iopub.status.idle": "2024-10-20T00:23:48.238805Z",
-     "shell.execute_reply": "2024-10-20T00:23:48.238193Z"
+     "iopub.execute_input": "2024-10-22T01:50:32.544636Z",
+     "iopub.status.busy": "2024-10-22T01:50:32.544443Z",
+     "iopub.status.idle": "2024-10-22T01:50:32.550742Z",
+     "shell.execute_reply": "2024-10-22T01:50:32.550050Z"
     }
    },
    "outputs": [
@@ -643,9 +643,9 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w0,w1])\n",
+      "────────────(E[Outcome|w1,w0])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -683,10 +683,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:48.241177Z",
-     "iopub.status.busy": "2024-10-20T00:23:48.240787Z",
-     "iopub.status.idle": "2024-10-20T00:23:48.642105Z",
-     "shell.execute_reply": "2024-10-20T00:23:48.641515Z"
+     "iopub.execute_input": "2024-10-22T01:50:32.552657Z",
+     "iopub.status.busy": "2024-10-22T01:50:32.552472Z",
+     "iopub.status.idle": "2024-10-22T01:50:32.944449Z",
+     "shell.execute_reply": "2024-10-22T01:50:32.943852Z"
     },
     "scrolled": true
    },
@@ -704,23 +704,23 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w0,w1])\n",
+      "────────────(E[Outcome|w1,w0])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
       "\n",
       "## Realized estimand\n",
-      "b: Outcome~Treatment+w0+w1\n",
+      "b: Outcome~Treatment+w1+w0\n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.998434097264643\n",
+      "Mean value: 1.9993789424751534\n",
       "\n",
-      "Causal Estimate is 1.998434097264643\n"
+      "Causal Estimate is 1.9993789424751534\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -755,10 +755,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:48.644302Z",
-     "iopub.status.busy": "2024-10-20T00:23:48.643904Z",
-     "iopub.status.idle": "2024-10-20T00:23:50.477642Z",
-     "shell.execute_reply": "2024-10-20T00:23:50.477050Z"
+     "iopub.execute_input": "2024-10-22T01:50:32.946620Z",
+     "iopub.status.busy": "2024-10-22T01:50:32.946134Z",
+     "iopub.status.idle": "2024-10-22T01:50:34.791585Z",
+     "shell.execute_reply": "2024-10-22T01:50:34.790680Z"
     }
    },
    "outputs": [
@@ -775,17 +775,17 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "     d                        \n",
-      "────────────(E[Outcome|w0,w1])\n",
+      "────────────(E[Outcome|w1,w0])\n",
       "d[Treatment]                  \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,w1,U) = P(Outcome|Treatment,w0,w1)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w1,w0,U) = P(Outcome|Treatment,w1,w0)\n",
       "\n",
       "## Realized estimand\n",
-      "b: Outcome~Treatment+w0+w1 | \n",
+      "b: Outcome~Treatment+w1+w0 | \n",
       "Target units: ate\n",
       "\n",
       "## Estimate\n",
-      "Mean value: 1.1610439951723444\n",
-      "Effect estimates: [[1.161044]]\n",
+      "Mean value: 0.9894564817819103\n",
+      "Effect estimates: [[0.98945648]]\n",
       "\n"
      ]
     }
@@ -825,10 +825,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:23:50.480543Z",
-     "iopub.status.busy": "2024-10-20T00:23:50.480122Z",
-     "iopub.status.idle": "2024-10-20T00:28:12.017965Z",
-     "shell.execute_reply": "2024-10-20T00:28:12.017082Z"
+     "iopub.execute_input": "2024-10-22T01:50:34.794376Z",
+     "iopub.status.busy": "2024-10-22T01:50:34.793997Z",
+     "iopub.status.idle": "2024-10-22T01:54:58.039304Z",
+     "shell.execute_reply": "2024-10-22T01:54:58.038532Z"
     }
    },
    "outputs": [
@@ -837,9 +837,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:1.1610439951723444\n",
-      "New effect:1.131708565840576\n",
-      "p value:0.24\n",
+      "Estimated effect:0.9894564817819103\n",
+      "New effect:0.9788569428730344\n",
+      "p value:0.72\n",
       "\n"
      ]
     }
@@ -854,10 +854,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-20T00:28:12.020921Z",
-     "iopub.status.busy": "2024-10-20T00:28:12.020099Z",
-     "iopub.status.idle": "2024-10-20T00:28:49.213038Z",
-     "shell.execute_reply": "2024-10-20T00:28:49.212259Z"
+     "iopub.execute_input": "2024-10-22T01:54:58.042038Z",
+     "iopub.status.busy": "2024-10-22T01:54:58.041487Z",
+     "iopub.status.idle": "2024-10-22T01:55:35.273295Z",
+     "shell.execute_reply": "2024-10-22T01:55:35.272497Z"
     }
    },
    "outputs": [
@@ -866,9 +866,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:1.1610439951723444\n",
-      "New effect:-2.4894090262337903e-05\n",
-      "p value:0.4671139184878982\n",
+      "Estimated effect:0.9894564817819103\n",
+      "New effect:0.00013356798350309588\n",
+      "p value:0.4157368947724852\n",
       "\n"
      ]
     }
diff --git a/main/index.html b/main/index.html
index 054553fd3..e56536d47 100644
--- a/main/index.html
+++ b/main/index.html
@@ -446,7 +446,7 @@
 
DoWhy documentation#

 

 

-
Date: Oct 20, 2024 Version: main

+
Date: Oct 22, 2024 Version: main

 
Related resources:
 Source Repository |
 Issues & Ideas |
diff --git a/main/searchindex.js b/main/searchindex.js
index bcf169d4a..f6cf7b944 100644
--- a/main/searchindex.js
+++ b/main/searchindex.js
@@ -1 +1 @@
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36, 43, 48, 50, 53, 57, 68, 89, 100], "librari": [0, 1, 2, 4, 6, 18, 22, 24, 31, 35, 38, 39, 42, 47, 49, 53, 55, 56, 65, 71, 79, 92, 100, 102, 108, 109], "causal": [0, 2, 4, 5, 6, 7, 9, 11, 13, 17, 18, 19, 22, 23, 24, 26, 27, 28, 34, 37, 38, 40, 42, 44, 46, 48, 50, 52, 53, 59, 62, 72, 75, 77, 78, 82, 84, 86, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 105, 110, 114, 118, 119, 123, 124], "infer": [0, 1, 4, 5, 6, 7, 13, 17, 18, 19, 22, 26, 27, 28, 34, 39, 44, 47, 55, 57, 59, 60, 63, 65, 66, 75, 79, 85, 87, 101, 102, 109, 113, 121], "2020": [0, 1, 7, 13, 14, 15, 18, 45, 66], "http": [0, 1, 3, 4, 7, 9, 13, 14, 15, 18, 21, 22, 24, 25, 26, 31, 38, 42, 44, 45, 48, 53, 54, 59, 64, 65, 66, 68, 70, 71], "arxiv": [0, 9, 13, 14, 15, 18, 19, 21, 26, 45, 53, 64, 65], "org": [0, 4, 9, 13, 14, 15, 18, 21, 22, 24, 25, 26, 31, 44, 45, 53, 54, 64, 65, 66], "ab": [0, 4, 9, 13, 14, 15, 18, 31, 45, 51, 53, 54, 55, 64, 65, 67, 89, 109], "2011": [0, 7, 13, 18, 19, 44], "04216": 0, "patrick": [0, 18, 89, 93, 94, 95], "bl\u00f6baum": [0, 18, 53, 89, 93, 94, 95], "peter": [0, 18, 19], "g\u00f6tz": 0, "kailash": [0, 18, 89, 93, 94], "budhathoki": [0, 18, 56, 57, 89, 93, 94], "atalanti": [0, 89], "A": [0, 1, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 63, 64, 65, 67, 69, 76, 77, 80, 85, 87, 89, 93, 94, 95, 97, 102, 103, 110, 112, 113, 114, 117], "mastakouri": [0, 18, 53, 89], "dominik": [0, 18, 89, 92, 93, 94, 95], "janz": [0, 18, 19, 53, 56, 89, 92, 93, 94, 95], "gcm": [0, 4, 26, 48, 49, 50, 53, 54, 55, 56, 57, 63, 69, 71, 87, 88, 89, 92, 93, 94, 95, 97, 99, 101, 102, 103, 105, 107, 111, 112], "extens": [0, 18, 30, 61, 65, 71, 79, 117], "graphic": [0, 1, 4, 7, 18, 34, 44, 48, 52, 54, 55, 63, 71, 79, 80, 89, 96, 97, 98, 99, 100, 101, 102, 103, 109, 110, 114], "model": [0, 1, 2, 4, 6, 7, 8, 9, 13, 15, 18, 19, 20, 24, 25, 26, 27, 29, 30, 31, 34, 35, 37, 39, 43, 45, 47, 48, 52, 53, 54, 59, 61, 62, 67, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 108, 110, 111, 114, 116, 117, 119, 120, 122, 124], "2024": [0, 18, 29, 30, 40, 51, 71, 89, 94], "mloss": 0, "25": [0, 4, 18, 26, 28, 29, 42, 45, 46, 48, 51, 55, 58, 60, 64, 67, 99], "147": 0, "1": [0, 4, 5, 6, 9, 11, 13, 14, 15, 17, 18, 19, 21, 24, 27, 28, 29, 30, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 73, 75, 78, 79, 80, 89, 92, 93, 94, 95, 99, 105, 109, 110, 111, 112, 113, 114, 117, 124], "7": [0, 4, 5, 13, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68, 92, 93, 99, 110, 114], "jmlr": [0, 34], "paper": [0, 18, 19, 26, 34, 48, 51, 53, 55, 59, 89, 92, 93, 94, 95, 112], "v25": 0, "22": [0, 28, 30, 33, 42, 44, 47, 51, 55, 60, 66, 67], "1258": 0, "html": [0, 1, 4, 9, 13, 18, 21, 25, 42, 48, 57, 64, 66, 68], "bibtex": 0, "articl": [0, 1, 7, 9, 13, 24], "titl": [0, 9, 11, 12, 18, 23, 26, 50, 57], "author": [0, 3, 9, 11, 12], "journal": [0, 7, 9, 19, 34, 44], "preprint": [0, 18], "year": [0, 9, 11, 12, 26, 36, 44, 48, 55], "bl": 0, "o": [0, 4, 5, 14, 15, 17, 27, 29, 58, 60, 61, 66, 67], "baum": 0, "g": [0, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 24, 25, 26, 31, 34, 36, 43, 45, 48, 49, 50, 52, 54, 55, 56, 58, 60, 64, 65, 68, 79, 85, 89, 92, 93, 94, 96, 97, 103, 108, 110, 111, 112, 113, 114], "tz": 0, "machin": [0, 1, 3, 4, 9, 13, 18, 26, 28, 31, 51, 54, 55, 63, 65, 69, 71, 72, 79, 93, 94, 97, 102, 109, 112], "learn": [0, 1, 4, 9, 11, 13, 14, 15, 18, 19, 25, 26, 28, 45, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 71, 72, 79, 89, 93, 94, 97, 101, 102, 103, 109, 110, 112, 113, 114], "research": [0, 13, 18, 26, 27, 34, 51, 55, 66, 68, 69, 75], "volum": [0, 9, 14, 45], "number": [0, 4, 6, 7, 9, 13, 14, 15, 17, 18, 19, 25, 31, 33, 43, 45, 53, 54, 55, 57, 58, 64, 66, 82, 92, 106, 107, 109, 111, 115], "page": [0, 1, 4, 15, 18, 19, 37, 55], "url": [0, 9], "i": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 64, 66, 67, 69, 70, 71, 72, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 118, 121, 122, 123, 124], "host": [1, 56], "github": [1, 2, 3, 4, 25, 42, 47, 63, 64, 67, 70], "can": [1, 2, 3, 4, 5, 6, 7, 11, 13, 17, 18, 19, 21, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 73, 75, 76, 78, 79, 80, 82, 84, 85, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 124], "brows": [1, 71], "code": [1, 2, 4, 7, 9, 13, 14, 15, 24, 25, 35, 39, 45, 51, 55, 59, 64, 68, 69, 70, 71, 82, 89, 94, 102, 109], 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92, 93, 94, 96, 97, 101, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 118, 120, 124], "experiment": [1, 4, 6, 28, 44], "stage": [1, 4, 6, 13, 25, 27, 29, 34, 41, 65, 75, 79, 87, 102], "allow": [1, 3, 4, 5, 6, 13, 17, 18, 19, 28, 48, 49, 51, 60, 67, 68, 71, 79, 80, 92, 94, 97, 112, 113], "modular": [1, 18, 71], "differ": [1, 3, 4, 5, 6, 7, 13, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27, 28, 31, 34, 36, 37, 39, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 63, 65, 66, 75, 80, 88, 89, 90, 92, 93, 94, 95, 96, 97, 100, 102, 111, 112, 114, 124], "includ": [1, 3, 4, 13, 14, 15, 18, 24, 26, 36, 45, 48, 51, 55, 56, 60, 65, 66, 67, 68, 70, 71, 81, 87, 89, 93, 95, 109, 114], "separ": [1, 4, 6, 7, 9, 18, 45, 53, 71, 79, 90, 102, 104, 106], "fit": [1, 4, 6, 9, 13, 14, 15, 18, 20, 21, 26, 27, 28, 29, 30, 31, 34, 37, 40, 48, 50, 51, 52, 54, 56, 57, 66, 68, 69, 71, 75, 77, 78, 80, 89, 92, 93, 94, 95, 97, 99, 101, 109, 110, 111, 112], "method": [1, 2, 4, 5, 6, 7, 9, 13, 14, 17, 18, 19, 20, 21, 22, 24, 26, 27, 30, 31, 32, 34, 37, 40, 41, 45, 47, 51, 53, 54, 55, 56, 57, 58, 60, 63, 65, 68, 69, 71, 75, 76, 81, 82, 83, 84, 86, 87, 90, 95, 99, 100, 102, 103, 105, 109, 110, 111, 112, 114, 115, 120, 124], "leav": [1, 13, 51, 54, 60], "feedback": [1, 37], "old": [1, 18, 37, 48, 94, 111], "causalmodel": [1, 4, 7, 23, 25, 27, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 47, 58, 59, 61, 65, 66, 67, 68, 69, 73, 75, 79, 82, 83, 84, 86, 87, 108], "should": [1, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27, 29, 33, 36, 41, 45, 47, 49, 50, 51, 52, 54, 55, 56, 58, 60, 64, 66, 67, 70, 75, 79, 89, 92, 97, 100, 103, 104, 105, 106, 109, 110, 114, 115, 116, 117, 118, 119, 120, 121, 124], "befor": [1, 2, 13, 14, 15, 18, 23, 25, 27, 36, 39, 47, 48, 49, 50, 53, 54, 55, 56, 57, 60, 65, 75, 87, 94, 105, 109, 113], "andresmor": 1, "m": [1, 3, 4, 13, 14, 15, 18, 24, 26, 29, 34, 45, 51, 53, 54, 64, 89, 97], "analys": [1, 28, 48], "mani": [1, 3, 13, 18, 19, 25, 27, 36, 45, 49, 50, 52, 53, 54, 55, 56, 58, 68, 75, 80, 82, 97, 100, 103, 104, 107, 109, 110, 111, 114], "now": [1, 3, 25, 26, 27, 28, 31, 33, 35, 36, 39, 41, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 68, 70, 80, 89, 93, 95, 99, 106, 109, 110, 111, 113, 114], "joblib": [1, 13], "parallel": [1, 4, 5, 13, 17, 18, 19, 47, 56, 60, 94, 111], "process": [1, 4, 9, 10, 13, 15, 17, 18, 24, 25, 26, 27, 29, 31, 36, 47, 49, 52, 54, 57, 60, 64, 65, 71, 75, 78, 79, 80, 85, 87, 93, 94, 97, 100, 102, 109, 110, 111, 112, 113, 114, 117], "show": [1, 4, 6, 13, 18, 23, 26, 28, 29, 30, 31, 32, 33, 34, 37, 39, 40, 45, 47, 48, 51, 54, 55, 56, 57, 59, 61, 64, 65, 66, 67, 68, 69, 79, 81, 106, 107, 109, 124], "progress": [1, 4, 13, 18, 37, 55, 57], "bar": [1, 4, 18, 24, 26, 27, 30, 39, 50, 51, 55, 56, 57, 60, 75], "astoeffelbau": 1, "yemaedahrav": 1, "non": [1, 4, 7, 13, 14, 15, 18, 19, 25, 26, 27, 34, 37, 41, 44, 45, 48, 49, 50, 52, 54, 55, 56, 57, 63, 64, 67, 68, 69, 71, 75, 79, 89, 93, 95, 97, 102, 104, 109, 110, 113, 114, 118, 121], "linear": [1, 4, 6, 7, 12, 13, 15, 18, 19, 24, 26, 27, 32, 33, 34, 35, 37, 39, 41, 46, 47, 49, 50, 51, 52, 55, 56, 57, 66, 68, 75, 78, 79, 87, 89, 93, 95, 109, 113, 114, 118, 121, 124], "chernozhukov": [1, 13, 51, 65], "cinelli": [1, 13, 66], "newei": [1, 13], "syrgkani": [1, 13], "2021": [1, 4, 7, 9, 11, 18, 34, 55, 56, 57, 94, 95], "exampl": [1, 2, 3, 6, 7, 13, 15, 18, 21, 24, 25, 26, 28, 30, 36, 37, 42, 50, 51, 53, 55, 61, 64, 65, 67, 68, 69, 71, 79, 84, 86, 87, 95, 101, 102, 103, 104, 106, 109, 110, 112, 114, 117, 118], "anusha0409": 1, "e": [1, 3, 4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 79, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 103, 108, 109, 110, 111, 112, 113, 114], "valu": [1, 4, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 75, 77, 78, 81, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 105, 107, 110, 111, 112, 113, 114, 117, 118, 124], "ding": [1, 13, 66], "vanderweel": [1, 13, 66], "2016": 1, "www": [1, 3, 7, 13, 24, 53, 56, 68], "ncbi": [1, 7, 13, 24], "nlm": [1, 7, 13, 24], "nih": [1, 7, 13, 24], "gov": [1, 7, 13, 24], "pmc": [1, 7, 13, 24], "pmc4820664": [1, 13], "jlgleason": 1, "unit": [1, 4, 6, 13, 18, 23, 25, 26, 27, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 55, 57, 65, 66, 67, 68, 92, 94], "chang": [1, 3, 4, 13, 18, 23, 25, 26, 28, 33, 36, 37, 39, 40, 42, 45, 47, 49, 51, 54, 55, 56, 58, 63, 65, 66, 68, 79, 87, 88, 90, 92, 93, 95, 96, 97, 100, 101, 102, 111, 112, 113, 114, 116, 118, 120, 124], "attribut": [1, 4, 5, 7, 9, 11, 17, 21, 24, 26, 31, 34, 51, 53, 54, 60, 63, 67, 88, 89, 92, 95, 96, 101, 102, 113], "kailashbuki": 1, "qualiti": [1, 18, 49, 50, 55, 57, 102, 114], "option": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 19, 24, 25, 26, 46, 47, 48, 49, 50, 51, 54, 55, 57, 60, 64, 70, 95, 109, 114, 124], "best": [1, 4, 13, 18, 19, 24, 49, 51, 55, 57, 64, 68, 69, 102, 107, 109, 114], "auto": [1, 4, 6, 26, 28, 48, 49, 50, 52, 54, 55, 56, 57, 69, 80, 89, 92, 93, 94, 95, 97, 99, 110, 111, 112, 113], "assign": [1, 4, 6, 9, 17, 18, 25, 26, 35, 39, 48, 49, 50, 52, 55, 56, 57, 71, 79, 87, 89, 93, 97, 101, 103, 110, 111, 112, 113], "mechan": [1, 18, 25, 26, 48, 49, 50, 51, 52, 54, 55, 56, 57, 68, 71, 89, 92, 93, 94, 95, 96, 97, 101, 102, 103, 110, 111, 113, 114], "which": [1, 4, 5, 6, 7, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 28, 29, 31, 33, 45, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 61, 64, 65, 66, 67, 68, 69, 75, 80, 82, 88, 89, 91, 93, 94, 95, 97, 102, 104, 106, 109, 111, 112, 114], "ml": [1, 4, 18, 28, 48, 49, 51, 53, 56, 64, 68, 89, 109, 113], "autogluon": [1, 4, 18, 111], "bloebp": 1, "condit": [1, 4, 6, 7, 9, 13, 17, 18, 19, 24, 36, 42, 45, 49, 50, 52, 53, 54, 55, 56, 57, 60, 63, 65, 66, 67, 68, 69, 71, 76, 79, 87, 88, 89, 90, 92, 94, 101, 102, 103, 105, 106, 107, 109, 110, 112, 113, 114, 118], "through": [1, 4, 6, 13, 17, 18, 24, 27, 33, 37, 41, 45, 50, 52, 56, 59, 60, 65, 67, 68, 69, 71, 75, 79, 90, 91, 92, 93, 100, 102, 104, 110], "packag": [1, 25, 28, 35, 36, 39, 47, 48, 52, 58, 60, 64, 67, 68, 71, 73, 95, 96, 101, 104, 109, 110, 111, 113], "algorithm": [1, 4, 7, 8, 13, 18, 26, 31, 34, 37, 49, 50, 51, 52, 54, 55, 56, 57, 63, 67, 68, 69, 71, 72, 85, 87, 94, 102, 104, 109, 110, 111, 112, 114], "comput": [1, 4, 6, 7, 9, 12, 13, 15, 18, 24, 26, 27, 28, 30, 34, 37, 44, 47, 48, 51, 54, 56, 57, 60, 64, 65, 68, 75, 80, 88, 89, 94, 96, 98, 100, 101, 113], "effici": [1, 4, 7, 13, 27, 34, 65, 75], "backdoor": [1, 4, 13, 17, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 83, 85, 86, 87, 88, 101, 102, 124], "set": [1, 4, 5, 7, 10, 13, 14, 15, 17, 18, 19, 21, 24, 26, 27, 28, 32, 33, 36, 37, 41, 44, 45, 46, 47, 48, 49, 51, 53, 54, 55, 57, 60, 63, 65, 66, 67, 68, 70, 71, 75, 76, 80, 82, 85, 87, 89, 93, 94, 95, 96, 97, 102, 104, 106, 111, 112, 113, 114, 124], "esmucl": 1, "control": [1, 4, 5, 6, 7, 18, 19, 25, 27, 35, 39, 45, 47, 48, 49, 60, 75, 78, 101, 113, 115], "direct": [1, 4, 7, 11, 13, 18, 24, 25, 26, 31, 34, 43, 49, 51, 54, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 67, 79, 88, 90, 101, 102, 103, 104, 106, 113, 114], "multi": [1, 4, 9, 26, 42, 94], "egorkraevtransferwis": 1, "pydata": 1, "theme": 1, "homepag": 1, "get": [1, 4, 7, 13, 17, 18, 19, 24, 25, 27, 31, 35, 39, 47, 48, 49, 51, 54, 55, 56, 64, 66, 68, 70, 71, 75, 80, 93, 94, 111, 114], "start": [1, 2, 4, 13, 15, 17, 25, 43, 44, 50, 55, 71, 79, 82, 102, 109], "guid": [1, 2, 4, 33, 49, 69, 71, 112], "revis": 1, "user": [1, 4, 5, 7, 13, 18, 24, 27, 36, 47, 49, 50, 60, 63, 64, 69, 70, 71, 72, 75, 79, 92, 103, 104, 106, 109, 112, 117], "petergtz": 1, "contribut": [1, 18, 48, 51, 54, 55, 57, 68, 71, 89, 90, 93, 95, 96, 100, 111], 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50, 51, 52, 54, 55, 56, 57, 69, 71, 75, 89, 90, 93, 95, 101, 102, 103, 106, 109, 110, 112, 113, 114], "updat": [1, 2, 3, 4, 7, 18, 37, 55, 56, 67, 70], "check": [1, 2, 3, 4, 7, 13, 18, 21, 24, 25, 27, 29, 36, 37, 41, 46, 47, 49, 50, 52, 54, 55, 56, 58, 60, 65, 69, 71, 72, 75, 79, 83, 84, 85, 86, 87, 100, 102, 103, 106, 107, 110, 114, 117, 120, 124], "out": [1, 2, 4, 9, 12, 13, 19, 25, 26, 27, 37, 45, 47, 51, 54, 55, 56, 64, 69, 75, 79, 84, 85, 87, 88, 94, 101, 102, 103, 107, 109, 112, 117], "elikl": 1, "itsoum": 1, "ryanrussel": 1, "whether": [1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 17, 18, 19, 24, 25, 26, 36, 41, 44, 47, 50, 53, 55, 56, 57, 58, 60, 68, 71, 85, 87, 92, 97, 102, 105, 106, 107, 114, 124], "data": [1, 4, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 26, 27, 29, 30, 33, 34, 35, 36, 37, 39, 41, 42, 43, 52, 54, 57, 58, 59, 62, 63, 65, 66, 69, 71, 75, 78, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, 112, 114, 115, 117, 118, 124], "conform": [1, 7], "assum": [1, 4, 7, 13, 17, 18, 25, 29, 30, 34, 36, 40, 47, 48, 49, 50, 55, 56, 57, 58, 65, 66, 69, 71, 89, 94, 97, 102, 105, 107, 109, 111, 112, 113, 114, 124], "specif": [1, 4, 5, 6, 7, 9, 13, 17, 18, 19, 24, 26, 27, 28, 31, 47, 50, 55, 60, 64, 66, 71, 75, 82, 90, 91, 92, 93, 95, 97, 103, 104, 108, 111, 112, 124], "paramet": [1, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 31, 33, 37, 41, 45, 47, 49, 51, 53, 55, 57, 58, 60, 64, 65, 67, 68, 71, 75, 79, 89, 92, 97, 109, 111, 114, 117, 121], "directli": [1, 4, 18, 28, 37, 48, 51, 54, 56, 63, 68, 73, 90, 93, 95, 97, 104, 107, 108, 111], "its": [1, 3, 4, 5, 6, 7, 18, 19, 25, 28, 31, 32, 34, 49, 50, 51, 55, 56, 57, 60, 63, 65, 66, 69, 79, 88, 89, 90, 92, 93, 94, 95, 97, 102, 106, 109, 111, 113, 114, 120], "own": [1, 4, 6, 17, 27, 28, 34, 54, 71, 75, 92, 112, 113, 114], "init": [1, 6], "custom": [1, 4, 5, 6, 13, 18, 24, 25, 36, 49, 55, 56, 57, 63, 71, 89, 95, 96, 101, 102, 112, 113, 114, 117], "consist": [1, 4, 13, 18, 21, 26, 31, 45, 48, 49, 50, 54, 55, 56, 57, 64, 69, 93, 104, 105, 106, 113, 114], "init_param": [1, 28, 46, 65, 68, 73, 79], "add": [1, 3, 4, 7, 13, 15, 18, 24, 25, 28, 32, 35, 37, 38, 39, 41, 45, 47, 51, 53, 58, 67, 68, 70, 79, 80, 89, 113, 114, 118, 120, 121], "glm": [1, 6], "hotel": [1, 62, 63, 103], "case": [1, 3, 4, 7, 13, 14, 17, 18, 19, 25, 26, 27, 28, 34, 36, 43, 47, 48, 49, 52, 54, 55, 56, 57, 63, 65, 71, 75, 87, 89, 92, 93, 95, 96, 97, 102, 103, 104, 109, 110, 112, 113, 114, 124], "studi": [1, 13, 14, 15, 26, 44, 45, 48, 56, 57, 65], "ae": 1, "foster": 1, "major": [1, 67, 100], "identif": [1, 4, 5, 6, 7, 13, 17, 25, 27, 31, 35, 39, 41, 59, 60, 68, 69, 71, 75, 81, 82, 83, 85, 86, 87, 102, 103, 108], "minim": [1, 4, 7, 9, 17, 18, 34, 45, 49, 53, 55, 57, 64, 65, 68, 82, 114], "adjust": [1, 4, 7, 13, 18, 19, 26, 38, 39, 45, 48, 49, 50, 55, 56, 63, 65, 66, 67, 68, 82, 91, 114], "maxim": [1, 7, 38, 82], "exhaust": [1, 7, 82], "search": [1, 7, 9, 11, 13, 15, 31, 34, 50, 55, 82], "coverag": 1, "discoveri": [1, 4, 18, 19, 22, 26, 63, 67, 69, 101, 103], "from": [1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 68, 69, 70, 75, 79, 80, 81, 82, 84, 87, 89, 90, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 108, 111, 112, 113, 114, 117, 118], "like": [1, 2, 4, 13, 14, 17, 18, 24, 25, 26, 27, 33, 36, 45, 47, 48, 50, 51, 54, 55, 56, 58, 60, 64, 68, 69, 71, 75, 90, 94, 96, 100, 102, 109, 111, 112], "cdt": [1, 4, 53, 101, 103], "id": [1, 3, 4, 7, 25, 34, 37, 58, 61, 63, 65, 66, 85, 87], "text": [1, 3, 4, 23, 53, 80, 93], "interpret": [1, 2, 13, 18, 33, 41, 42, 47, 49, 50, 54, 55, 56, 63, 65, 66, 89, 91, 92, 93, 95, 114, 124], "": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 73, 75, 79, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 109, 111, 112, 113, 114, 124], "distanc": [1, 6, 9, 13, 18, 19, 76, 87], "match": [1, 4, 6, 13, 14, 33, 36, 45, 58, 76, 79, 87, 104, 117], "reli": [1, 18, 55, 68, 73, 112], "metric": [1, 6, 9, 18, 26, 35, 49, 50, 54, 55, 56, 57, 64, 92, 114], "between": [1, 4, 6, 7, 9, 10, 11, 13, 18, 19, 24, 25, 26, 27, 32, 33, 35, 39, 43, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 64, 65, 66, 67, 71, 75, 79, 80, 90, 92, 93, 94, 102, 103, 109, 110, 111, 112, 114, 118, 124], "input": [1, 4, 13, 18, 19, 20, 21, 24, 26, 31, 35, 39, 45, 50, 53, 56, 57, 63, 64, 67, 68, 70, 88, 92, 95, 96, 101, 102, 104, 109, 111, 112, 114, 124], "heurist": [1, 48, 55], "default": [1, 3, 4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 24, 26, 27, 31, 37, 47, 51, 53, 54, 56, 58, 60, 64, 65, 66, 71, 75, 82, 89, 92, 94, 95, 111, 114, 124], "strata": [1, 6], "automat": [1, 3, 4, 5, 6, 13, 15, 18, 19, 27, 45, 47, 48, 49, 52, 55, 57, 60, 65, 71, 75, 79, 89, 93, 106, 108, 110, 113, 114, 121], "propens": [1, 4, 6, 13, 23, 27, 42, 45, 47, 60, 68, 75, 76, 79, 87], "score": [1, 4, 6, 13, 15, 18, 23, 26, 27, 45, 47, 49, 50, 51, 54, 55, 56, 57, 60, 67, 68, 75, 79, 87, 93, 94, 95, 111, 114], "stratif": [1, 4, 23, 47, 68, 79, 87], "confid": [1, 4, 6, 13, 18, 24, 25, 27, 29, 30, 37, 48, 55, 56, 57, 60, 65, 66, 75, 79, 101, 113], "interv": [1, 4, 6, 13, 18, 24, 30, 48, 55, 56, 57, 60, 66, 79, 101, 113], "regress": [1, 4, 6, 13, 18, 25, 26, 28, 29, 32, 33, 40, 41, 48, 49, 51, 55, 57, 63, 65, 68, 76, 79, 87, 94, 112, 114, 122], "version": [1, 3, 4, 13, 18, 21, 26, 31, 32, 33, 37, 44, 46, 54, 55, 66, 69, 70, 71], "bootstrap": [1, 4, 6, 13, 18, 19, 27, 37, 46, 48, 51, 56, 57, 60, 75, 79], "without": [1, 3, 4, 18, 19, 30, 47, 55, 56, 65, 66, 85, 105, 109, 111, 114], "need": [1, 4, 6, 7, 12, 13, 18, 19, 21, 25, 27, 28, 34, 36, 37, 47, 51, 54, 55, 59, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 89, 96, 97, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 115, 117], "refit": 1, "andrewc19": 1, "ha2trinh": 1, "siddhanthaldar": 1, "vojavocni": 1, "also": [1, 2, 3, 6, 7, 13, 18, 24, 25, 26, 29, 31, 32, 34, 35, 39, 42, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 63, 65, 66, 67, 68, 69, 71, 79, 80, 82, 87, 89, 90, 92, 93, 95, 96, 97, 99, 100, 102, 103, 105, 106, 109, 110, 111, 112, 113, 114, 115, 117, 118, 124], "iv": [1, 4, 6, 25, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 42, 44, 46, 47, 62, 65, 66, 68, 79, 81], "move": [1, 28, 48, 88, 100, 104], "matplotlib": [1, 13, 25, 26, 31, 32, 45, 48, 50, 51, 55, 57, 60, 66, 67, 68], "instal": [1, 3, 18, 24, 26, 51, 63, 64, 67, 68, 71, 104], "pip": [1, 3, 24, 26, 64, 67, 68, 69], "plot": [1, 3, 4, 13, 18, 23, 26, 30, 32, 34, 37, 39, 40, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 65, 67, 68, 109, 124], "align": [1, 56], "all": [1, 2, 3, 4, 6, 7, 9, 10, 12, 13, 15, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 32, 34, 35, 36, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 67, 68, 70, 72, 73, 75, 79, 80, 82, 83, 86, 89, 93, 97, 100, 102, 103, 105, 109, 110, 111, 112, 113, 114, 124], "continu": [1, 4, 5, 13, 14, 15, 17, 18, 19, 21, 23, 37, 44, 45, 49, 50, 54, 55, 56, 57, 58, 60, 67, 92, 97, 106, 109, 112, 114], "log": [1, 13, 18, 24, 25, 28, 29, 30, 32, 33, 35, 37, 39, 40, 41, 44, 46, 56, 66, 68, 93, 109], "configur": [1, 9, 13, 18, 19, 25, 29, 37, 55], "dummyoutcomerefut": [1, 4, 13], "arshiaarya": 1, "n8sty": 1, "moprescu": 1, "conda": [1, 69], "formula": [1, 4, 13, 17, 24, 27, 39, 40, 75], "front": [1, 4, 6, 79, 87], "door": [1, 4, 6, 7, 27, 48, 75, 79, 87], "criterion": [1, 6, 13, 18, 26, 41, 68, 79, 85, 86, 87], "path": [1, 4, 7, 24, 26, 27, 34, 35, 39, 41, 48, 51, 58, 59, 60, 61, 64, 66, 70, 75, 91, 92], "d": [1, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 51, 53, 57, 58, 60, 64, 65, 66, 67, 68, 70, 75, 92, 93, 106], "logist": [1, 4, 6, 18, 24, 25, 51, 66, 78, 87], "erikhambardzumyan": 1, "visual": [1, 4, 23, 31, 45, 51, 55, 56, 68], "how": [1, 4, 13, 17, 18, 19, 23, 25, 26, 31, 33, 34, 36, 39, 40, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 79, 82, 88, 90, 96, 100, 102, 106, 107, 109, 112, 113, 114, 121, 124], "distribut": [1, 3, 4, 9, 12, 13, 14, 17, 18, 23, 24, 26, 27, 39, 40, 48, 49, 50, 51, 52, 54, 55, 56, 57, 60, 68, 70, 75, 76, 80, 85, 88, 89, 90, 92, 93, 95, 96, 97, 101, 102, 103, 104, 109, 110, 111, 112, 113, 114], "friendlier": 1, "error": [1, 4, 6, 7, 13, 18, 19, 24, 27, 30, 34, 37, 40, 49, 50, 51, 53, 54, 55, 56, 57, 60, 64, 66, 68, 70, 75, 94, 103, 111, 114], "messag": [1, 3, 13, 18], "when": [1, 4, 5, 6, 7, 9, 10, 13, 14, 18, 19, 21, 24, 25, 27, 31, 34, 36, 37, 42, 45, 47, 49, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 78, 79, 80, 90, 92, 93, 94, 97, 106, 109, 110, 111, 112, 114, 116, 117, 119, 121], "enough": [1, 18, 25, 36, 65, 66, 68, 114], "bin": [1, 4, 6, 13, 14, 18, 26, 48], "dummi": [1, 13, 33, 79, 118], "outcom": [1, 4, 5, 6, 7, 13, 17, 18, 19, 20, 23, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 58, 59, 60, 61, 63, 65, 66, 67, 68, 69, 75, 76, 78, 79, 87, 88, 90, 95, 97, 100, 101, 102, 108, 112, 118, 121, 124], "compon": [1, 5, 19, 24, 51, 60, 94, 103], "simul": [1, 4, 6, 13, 29, 33, 35, 36, 37, 39, 41, 47, 51, 53, 65, 66, 79, 88, 92, 94, 97, 98, 101, 117, 118, 121], "zero": [1, 4, 13, 14, 18, 25, 29, 30, 33, 36, 41, 44, 47, 51, 53, 54, 55, 65, 66, 79, 89, 107, 118, 119, 124], "too": [1, 4, 13, 18, 25, 27, 47, 53, 72, 73, 75, 79, 80, 95, 114, 121, 124], "readi": [1, 3, 26, 31, 71, 80, 88, 109, 113], "book": [1, 57, 62, 63, 69, 85, 103], "cancel": [1, 51, 62, 63, 103], "membership": [1, 36, 62], "reward": [1, 15, 62, 63, 102], "program": [1, 13, 15, 24, 48, 57, 62, 63, 79, 102], "multipl": [1, 2, 4, 6, 18, 19, 26, 45, 47, 51, 55, 56, 59, 63, 64, 65, 68, 69, 71, 79, 80, 93, 95, 102, 104, 109, 111, 114, 115], "tutori": [1, 3, 18, 59, 63, 69], "organ": [1, 71, 102], "websit": [1, 36, 54, 56], "commun": [1, 2, 51, 71], "creat": [1, 3, 4, 6, 10, 13, 18, 19, 24, 27, 29, 34, 36, 38, 44, 47, 48, 49, 55, 60, 63, 64, 67, 68, 69, 70, 75, 92, 93, 108, 113, 117, 124], "guidelin": 1, "allcontributor": 1, "bot": 1, "so": [1, 2, 3, 4, 5, 12, 14, 18, 25, 26, 27, 29, 31, 37, 45, 47, 49, 50, 55, 58, 60, 65, 68, 75, 79, 97, 103, 109, 124], "just": [1, 4, 12, 24, 27, 30, 34, 37, 45, 47, 49, 50, 51, 53, 54, 55, 56, 60, 65, 68, 79, 107, 114], "after": [1, 4, 12, 13, 18, 23, 24, 34, 36, 39, 47, 49, 50, 51, 52, 54, 55, 56, 57, 63, 65, 66, 79, 87, 89, 93, 94, 97, 102, 110, 114, 118, 120], "pull": [1, 2, 66, 109], "request": [1, 2, 4, 25, 56, 103], "ar": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 35, 36, 39, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 68, 69, 71, 75, 77, 79, 80, 82, 87, 88, 89, 90, 92, 93, 94, 95, 97, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 118, 122, 123, 124], "merg": [1, 3, 18, 45], "tanmai": 1, "kulkarni101": 1, "sid": [1, 25], "darthvad": [1, 25], "increas": [1, 13, 18, 25, 26, 29, 39, 47, 48, 50, 55, 56, 57, 66, 92, 111, 124], "In": [1, 3, 4, 9, 11, 13, 14, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 31, 33, 34, 36, 40, 43, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 64, 65, 67, 68, 69, 70, 71, 73, 75, 78, 79, 80, 87, 89, 90, 92, 93, 95, 96, 97, 102, 103, 104, 106, 108, 109, 110, 111, 112, 113, 114, 117], "addit": [1, 3, 4, 6, 7, 13, 14, 17, 18, 19, 24, 25, 28, 29, 31, 36, 47, 48, 49, 50, 52, 53, 55, 56, 57, 59, 61, 65, 67, 68, 71, 73, 78, 79, 89, 90, 97, 109, 110, 111, 112, 113, 114, 117, 121, 124], "random": [1, 4, 10, 13, 14, 18, 19, 21, 24, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 65, 66, 68, 69, 75, 79, 80, 89, 92, 93, 94, 95, 97, 99, 105, 107, 109, 110, 113, 114, 117, 118, 119], "dummyoutcom": 1, "arbitrari": [1, 4, 13, 18, 21, 89, 93, 95], "alwai": [1, 7, 26, 34, 49, 50, 52, 54, 55, 56, 68, 110, 111, 114], "thi": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 75, 77, 78, 79, 80, 81, 82, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110, 111, 112, 113, 114, 116, 122, 123, 124], "inspir": [1, 57], "idea": [1, 4, 17, 19, 21, 26, 27, 48, 54, 71, 75, 92, 94, 96], "t": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 25, 26, 27, 28, 29, 30, 31, 34, 37, 40, 45, 48, 49, 50, 51, 53, 55, 56, 59, 60, 65, 66, 67, 75, 80, 89, 93, 94, 97, 105, 108, 111, 113], "learner": [1, 28], "we": [1, 2, 3, 4, 5, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 64, 65, 66, 67, 68, 69, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 124], "provid": [1, 3, 4, 5, 7, 13, 14, 18, 19, 20, 24, 25, 26, 29, 31, 34, 35, 37, 40, 42, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 65, 66, 67, 68, 71, 72, 73, 79, 80, 89, 90, 95, 97, 100, 102, 104, 105, 106, 108, 109, 110, 111, 112, 114, 124], "Of": [1, 36], "cours": [1, 13, 36, 45], "specifi": [1, 4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 30, 35, 39, 40, 41, 45, 50, 53, 55, 64, 65, 68, 73, 80, 84, 89, 101, 103, 109, 114], "ani": [1, 2, 3, 4, 6, 7, 13, 14, 15, 18, 19, 20, 21, 24, 25, 26, 27, 29, 30, 32, 34, 36, 37, 45, 47, 49, 50, 54, 55, 57, 64, 65, 66, 67, 68, 70, 71, 75, 76, 79, 82, 89, 92, 93, 97, 102, 103, 105, 106, 112, 113, 115, 117, 118, 124], "bootstraprefut": [1, 4, 13], "issu": [1, 2, 4, 18, 27, 55, 56, 70, 71, 75], "measur": [1, 4, 13, 14, 18, 19, 31, 34, 37, 44, 51, 53, 54, 55, 56, 65, 66, 89, 90, 93, 94, 97, 105, 107, 114], "rather": [1, 5, 18, 51, 55, 56, 57, 60, 68, 89, 94, 95, 97, 109], "than": [1, 4, 5, 13, 14, 18, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 68, 80, 88, 89, 92, 95, 97, 102, 107, 110, 114, 124], "simpl": [1, 4, 6, 24, 30, 32, 36, 52, 54, 63, 68, 69, 71, 79, 86, 93, 95, 104, 110, 112, 113, 117], "sampl": [1, 4, 5, 6, 10, 13, 14, 15, 17, 18, 19, 24, 25, 26, 27, 28, 33, 35, 37, 39, 46, 47, 48, 49, 50, 51, 53, 56, 57, 58, 60, 63, 64, 68, 69, 75, 80, 93, 94, 97, 99, 101, 102, 111, 112, 113, 114], "nois": [1, 4, 13, 18, 26, 29, 30, 41, 48, 49, 50, 52, 54, 55, 56, 57, 64, 89, 90, 93, 94, 95, 96, 97, 109, 110, 111, 112, 113, 114], "select": [1, 4, 6, 9, 13, 18, 19, 26, 27, 47, 49, 55, 57, 64, 67, 71, 75, 79, 82, 114, 116, 124], "signific": [1, 4, 6, 13, 18, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 65, 66, 67, 79, 107, 110, 114], "causalml": [1, 4, 42, 68, 71], "call": [1, 2, 4, 5, 7, 9, 12, 14, 18, 26, 27, 28, 34, 37, 41, 45, 49, 50, 51, 52, 55, 56, 57, 60, 65, 68, 71, 73, 75, 79, 82, 87, 94, 102, 103, 106, 109, 110, 111, 113], "name": [1, 3, 4, 5, 6, 7, 13, 14, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 48, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 84, 102, 108, 109], "scheme": [1, 21, 35, 39], "To": [1, 3, 4, 5, 9, 13, 18, 21, 24, 25, 26, 27, 34, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 70, 75, 78, 80, 82, 83, 84, 86, 89, 92, 94, 96, 97, 99, 100, 102, 103, 105, 106, 107, 109, 110, 111, 112, 113, 114, 124], "achiev": [1, 18, 24, 26, 47, 68, 92, 94, 97], "suggest": [1, 4, 18, 21, 26, 57, 64, 65, 68, 97], "prepend": 1, "intern": [1, 9, 11, 12, 13, 14, 15, 18, 21, 27, 35, 45, 51, 64, 75, 89, 93, 94, 95, 109], "string": [1, 4, 5, 6, 13, 14, 15, 18, 19, 21, 23, 24, 28, 31, 34, 46, 47, 48, 49, 55, 57, 58, 60, 61, 66, 108, 109, 112, 114], "For": [1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 34, 35, 36, 37, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 75, 79, 80, 85, 86, 87, 89, 91, 92, 93, 94, 95, 97, 100, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 124], "propensity_score_match": [1, 4, 35, 36, 37, 38, 39, 46, 69, 77, 79], "Not": [1, 6, 13, 25], "break": [1, 3], "keep": [1, 3, 4, 13, 17, 18, 21, 25, 26, 27, 55, 68, 94, 97, 113], "sinc": [1, 4, 12, 13, 18, 25, 26, 27, 29, 30, 31, 34, 39, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 68, 80, 89, 94, 97, 102, 103, 105, 107, 109, 110, 111, 112, 114, 124], "modifi": [1, 4, 6, 17, 18, 21, 24, 26, 28, 31, 42, 47, 53, 54, 73, 93, 95, 104, 105, 118, 124], "subset": [1, 4, 9, 13, 18, 19, 24, 25, 26, 27, 37, 46, 56, 65, 67, 68, 75, 79, 89, 95, 116], "warn": [1, 3, 13, 25, 26, 28, 29, 33, 34, 36, 37, 41, 42, 44, 46, 47, 64, 66, 67, 68], "outsid": [1, 25, 26, 56], "tri": [1, 18, 79, 87, 112], "ci": [1, 4, 18, 25, 48, 53, 66], "standard": [1, 4, 6, 13, 14, 18, 19, 23, 24, 25, 30, 39, 40, 49, 50, 51, 54, 55, 56, 66, 68, 72, 89, 112, 114], "correspond": [1, 4, 6, 7, 10, 13, 14, 18, 24, 28, 34, 46, 48, 49, 51, 52, 53, 54, 55, 56, 66, 68, 89, 93, 94, 109, 110, 113, 114], "parametr": [1, 4, 5, 7, 13, 18, 19, 24, 27, 34, 37, 41, 48, 49, 55, 57, 60, 63, 69, 75, 79, 104, 109, 114, 123], "form": [1, 4, 7, 13, 14, 15, 18, 33, 34, 45, 49, 51, 54, 55, 57, 58, 80, 85, 97, 103, 109, 112, 113, 114], "conveni": [1, 4, 17, 18, 24, 82], "seed": [1, 4, 10, 13, 14, 21, 31, 37, 45, 48, 51, 53, 57, 58, 65, 66, 92], "import": [1, 4, 13, 18, 22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 68, 69, 70, 73, 79, 80, 82, 84, 87, 89, 91, 92, 93, 94, 95, 97, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 113, 114, 124], "simpler": [1, 28], "multivari": [1, 18, 19], "averag": [1, 13, 18, 21, 24, 25, 26, 31, 36, 38, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 79, 87, 88, 90, 101, 102, 110, 114], "estimate_effect": [1, 2, 4, 6, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 81, 87, 91], "other": [1, 4, 5, 6, 7, 13, 17, 18, 25, 26, 27, 34, 36, 37, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 60, 64, 65, 68, 69, 71, 75, 82, 87, 88, 89, 90, 92, 93, 94, 95, 101, 102, 103, 104, 105, 106, 107, 109, 110, 112, 114], "target": [1, 4, 6, 7, 9, 18, 19, 21, 25, 26, 29, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 46, 47, 50, 51, 54, 55, 57, 58, 61, 65, 66, 67, 69, 71, 80, 85, 89, 90, 92, 93, 94, 95, 96, 108, 112], "estimand": [1, 4, 5, 6, 7, 13, 17, 28, 29, 31, 32, 34, 36, 37, 38, 41, 42, 43, 44, 47, 60, 65, 66, 67, 69, 71, 78, 87], "att": [1, 4, 25, 28, 35, 36, 39, 47, 74, 77], "atc": [1, 4, 25, 28, 35, 39, 47, 77], "reproduc": [1, 4, 47, 55, 57, 92], "j": [1, 18, 19, 24, 26, 44, 46, 51, 58, 64, 67, 93, 94], "chou": 1, "ktmud": 1, "jrfiedler": 1, "shounak112358": 1, "lnk2past": 1, "pywhi": [2, 70, 71, 104], "welcom": [2, 31, 37], "There": [2, 11, 13, 18, 25, 26, 27, 28, 44, 45, 47, 48, 49, 50, 51, 55, 56, 64, 66, 69, 75, 82, 93, 95, 114, 124], "wai": [2, 3, 4, 9, 18, 27, 29, 33, 35, 47, 49, 51, 52, 54, 55, 56, 57, 63, 68, 70, 71, 75, 79, 82, 89, 96, 97, 102, 104, 110, 114, 124], "some": [2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18, 25, 27, 28, 32, 34, 36, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 61, 65, 68, 69, 70, 79, 80, 88, 89, 92, 93, 94, 95, 97, 99, 100, 102, 105, 106, 108, 109, 110, 111, 113, 114, 118, 124], "ad": [2, 3, 4, 5, 12, 13, 15, 24, 26, 30, 45, 48, 51, 53, 54, 55, 60, 64, 68, 80, 90], "jupyt": [2, 63, 68], "describ": [2, 4, 7, 13, 15, 18, 34, 39, 41, 50, 52, 54, 55, 57, 60, 64, 65, 66, 68, 69, 88, 100, 102, 110, 112, 113], "solv": [2, 13, 15, 68, 70, 71], "problem": [2, 3, 6, 15, 18, 21, 25, 27, 29, 35, 36, 37, 46, 47, 50, 55, 56, 68, 69, 70, 71, 75, 79, 87, 95, 102, 103, 104, 106, 112, 118], "help": [2, 4, 13, 18, 19, 25, 33, 47, 49, 54, 55, 56, 60, 64, 65, 66, 68, 71, 89, 97, 102, 103, 113, 114], "document": [2, 3, 4, 6, 13, 18, 25, 28, 31, 55, 70, 87], "new": [2, 3, 4, 5, 6, 9, 13, 15, 18, 19, 24, 25, 27, 29, 32, 33, 36, 37, 38, 44, 45, 46, 47, 52, 54, 57, 60, 64, 67, 68, 69, 70, 71, 75, 80, 85, 88, 92, 94, 102, 110, 111, 114, 118, 124], "four": [2, 4, 28, 29, 69, 71, 79, 82, 87, 93, 94], "step": [2, 3, 4, 6, 7, 13, 17, 18, 26, 27, 28, 29, 46, 47, 50, 54, 57, 59, 64, 67, 69, 70, 75, 79, 80, 87, 88, 94, 103, 114], "analysi": [2, 4, 6, 13, 18, 25, 32, 36, 37, 48, 54, 63, 68, 69, 71, 73, 79, 87, 88, 89, 90, 92, 93, 94, 97, 100, 101, 103, 106, 113, 115], "identifi": [2, 4, 5, 6, 7, 13, 18, 19, 26, 27, 28, 29, 42, 43, 44, 47, 55, 56, 60, 62, 63, 66, 67, 69, 70, 71, 76, 78, 81, 82, 83, 84, 86, 87, 88, 89, 93, 94, 96, 101, 112, 124], "estim": [2, 4, 6, 7, 9, 13, 14, 15, 18, 19, 21, 23, 27, 34, 45, 48, 49, 50, 51, 52, 55, 56, 57, 62, 63, 64, 69, 71, 75, 77, 78, 85, 86, 88, 89, 90, 92, 93, 94, 95, 96, 97, 100, 101, 102, 103, 108, 110, 112, 113, 114, 116, 117, 119, 120, 121, 124], "refut": [2, 4, 13, 18, 29, 45, 62, 69, 87, 101, 102, 103, 104, 122, 123, 124], "integr": [2, 3, 18, 26, 79, 102, 104], "api": [2, 4, 6, 13, 18, 27, 40, 49, 52, 56, 57, 63, 66, 68, 69, 71, 73, 75, 78, 79, 80, 82, 84, 87, 100, 102, 110, 113], "extern": [2, 26, 79], "seamlessli": 2, "identify_effect": [2, 4, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 59, 65, 66, 67, 68, 69, 79, 82, 83, 84, 86, 87, 91, 124], "refute_estim": [2, 4, 25, 28, 29, 32, 33, 36, 37, 38, 44, 45, 46, 47, 65, 66, 68, 69, 79, 87, 116, 117, 119, 120, 124], "extend": [2, 26, 48, 55, 109, 114], "support": [2, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 35, 37, 39, 41, 42, 47, 51, 55, 58, 60, 61, 63, 64, 66, 68, 70, 71, 72, 76, 78, 87, 88, 91, 97, 100, 101, 104, 109, 112, 124], "function": [2, 4, 5, 6, 7, 9, 10, 12, 13, 14, 18, 19, 20, 21, 24, 25, 26, 28, 34, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 67, 71, 77, 78, 82, 84, 87, 89, 90, 94, 95, 96, 97, 102, 105, 108, 109, 110, 111, 112, 113, 114, 117], "counterfactu": [2, 6, 18, 36, 63, 68, 69, 71, 88, 96, 98, 100, 101, 102, 112, 113], "predict": [2, 4, 6, 13, 14, 15, 18, 19, 20, 21, 25, 26, 33, 49, 50, 54, 55, 56, 63, 65, 71, 88, 89, 95, 100, 101, 102, 104, 109, 111, 112, 114], "would": [2, 4, 13, 18, 25, 26, 34, 36, 49, 50, 51, 52, 53, 54, 55, 56, 57, 65, 66, 68, 69, 80, 88, 89, 93, 94, 96, 97, 103, 104, 105, 109, 110, 111, 112, 113, 114], "rais": [2, 5, 7, 13, 18, 34, 60], "info": [2, 32], "have": [2, 4, 5, 6, 7, 13, 14, 17, 18, 19, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 79, 82, 88, 89, 93, 94, 96, 97, 102, 103, 104, 106, 107, 109, 111, 112, 113, 114, 124], "question": [2, 18, 24, 36, 49, 50, 54, 57, 68, 69, 80, 88, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 106, 112, 113, 114], "open": [2, 4, 55, 70], "list": [2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 31, 34, 42, 45, 51, 56, 59, 60, 64, 67, 102, 106, 114], "md": [2, 13], "our": [2, 18, 25, 27, 29, 31, 32, 33, 36, 45, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 68, 69, 71, 75, 80, 89, 92, 93, 94, 95, 96, 97, 105, 106, 109, 110, 111, 112, 113, 114], "conduct": [2, 18, 26, 45, 68, 102], "avail": [2, 3, 4, 6, 18, 24, 25, 26, 47, 48, 55, 57, 63, 64, 65, 67, 68, 69, 79, 82, 85, 87, 106, 114], "join": [2, 26, 45, 51, 56, 71, 108], "develop": [2, 3, 34, 63, 70, 79, 102], "channel": 2, "discord": [2, 37, 71], "basic": [3, 13, 33, 37, 63, 69, 80, 82, 93, 99, 113], "knowledg": [3, 25, 31, 47, 48, 49, 53, 55, 56, 57, 68, 69, 72, 79, 87, 97, 101, 103, 104, 109, 113, 114, 124], "git": [3, 70], "requir": [3, 4, 5, 6, 7, 13, 17, 18, 24, 25, 27, 31, 35, 38, 39, 45, 47, 51, 55, 57, 58, 60, 61, 64, 65, 67, 68, 70, 75, 80, 87, 89, 97, 100, 102, 103, 109, 111, 112, 114, 124], "unfamiliar": 3, "workflow": [3, 45, 79, 87], "checkout": [3, 18], "fork": [3, 106], "main": [3, 4, 7, 19, 28, 53, 55, 56, 57, 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120], "strength": [4, 6, 13, 18, 47, 54, 55, 65, 66, 88, 90, 101, 113, 121], "param": [4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 24, 33, 46, 60, 64], "dictionari": [4, 5, 6, 7, 10, 13, 14, 15, 17, 18, 21, 24, 26, 45, 51, 60, 64, 80], "size": [4, 6, 10, 11, 12, 13, 18, 19, 23, 24, 27, 29, 30, 31, 36, 45, 48, 49, 50, 52, 55, 56, 57, 64, 66, 69, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "boolean": [4, 6, 13, 14, 15, 21, 26, 28, 45], "split": [4, 6, 10, 13, 18, 36, 40, 44, 45, 51], "enabl": [4, 6, 13, 64, 94, 100, 102, 109], "instanc": [4, 6, 7, 9, 11, 12, 13, 15, 18, 19, 21, 23, 26, 31, 34, 45, 47, 49, 55, 56, 57, 60, 66, 67, 69, 71, 80, 82, 83, 84, 86, 89, 92, 93, 100, 105, 108, 109, 111, 112, 113, 114], "bootstrapestim": 4, "tupl": [4, 13, 18, 21, 24, 26, 42, 58, 66], "alia": [4, 6, 13], "field": [4, 5, 13, 60], "default_confidence_level": 4, "default_interpret_method": 4, "default_notimplementederror_msg": 4, "yet": [4, 27], "next": [4, 17, 29, 34, 51, 54, 55, 56, 64, 87, 88, 89, 92, 93, 94, 97, 99, 103, 106, 110, 111], "microsoft": [4, 13, 69], "default_number_of_simulations_ci": 4, "default_number_of_simulations_stat_test": 4, "default_sample_size_fract": 4, "temp_cat_column_prefix": 4, "__categorical__": 4, "x": [4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 26, 28, 30, 31, 33, 34, 36, 38, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 65, 66, 69, 80, 87, 88, 89, 92, 93, 94, 95, 96, 97, 99, 103, 105, 106, 108, 109, 110, 111, 112, 113, 114], "data_df": [4, 6, 31], "oper": [4, 5, 7, 14, 17, 18, 25, 55, 56, 85, 109], "expect": [4, 9, 13, 17, 18, 19, 21, 33, 36, 39, 46, 47, 49, 50, 53, 54, 55, 56, 64, 65, 76, 80, 87, 89, 92, 94, 95, 97, 104, 105, 109, 112, 114], "interven": [4, 5, 26, 27, 49, 52, 54, 60, 75, 80, 97, 99, 100, 110], "appli": [4, 6, 7, 9, 11, 13, 14, 18, 19, 20, 21, 23, 24, 31, 36, 40, 51, 53, 54, 55, 63, 67, 68, 92, 102, 103, 111], "estimate_confidence_interv": 4, "estimate_valu": 4, "estimate_effect_na": 4, "panda": [4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 63, 66, 67, 68, 69, 75, 80, 89, 92, 93, 94, 95, 97, 99, 104, 105, 108, 109, 110, 111, 113, 114], "estimate_std_error": 4, "get_new_estimator_object": 4, "same": [4, 6, 13, 14, 18, 19, 20, 25, 26, 27, 28, 29, 33, 35, 36, 37, 45, 47, 49, 51, 52, 53, 55, 56, 57, 60, 61, 65, 66, 67, 68, 69, 71, 75, 79, 80, 83, 86, 94, 104, 105, 106, 107, 110, 111, 113, 114], "type": [4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 76, 80, 84, 89, 92, 97, 101, 109, 113, 114], "respect": [4, 5, 7, 9, 10, 13, 18, 34, 36, 49, 50, 52, 54, 55, 56, 57, 58, 60, 66, 67, 71, 79, 80, 92, 94, 95, 96, 97, 110, 112, 114], "pathwai": [4, 66, 91], "emploi": [4, 48, 50, 51, 66, 90, 92, 94, 112], "had": [4, 25, 26, 36, 50, 55, 65, 88, 96, 97, 105, 112], "static": [4, 7, 18, 23, 34], "is_bootstrap_parameter_chang": 4, "bootstrap_estimates_param": 4, "given_param": 4, "fresh": 4, "again": [4, 5, 49, 53, 55, 60, 69, 70, 97, 105, 109, 111, 113, 114], "current": [4, 5, 6, 7, 9, 13, 18, 19, 24, 26, 36, 37, 41, 42, 45, 47, 51, 58, 60, 64, 66, 79, 97, 109], "denot": [4, 6, 13, 18, 24, 59, 65, 66, 68, 92, 112, 114], "reset_encod": 4, "remov": [4, 7, 13, 18, 24, 25, 27, 29, 31, 33, 34, 35, 36, 37, 39, 46, 53, 54, 64, 65, 66, 67, 68, 90, 92, 124], "encod": [4, 12, 13, 19, 21, 47, 48, 49, 51, 52, 68, 79, 87, 102, 103, 110], "re": [4, 13, 14, 18, 25, 27, 40, 44, 51, 55, 56, 60, 65, 66, 69, 75, 94, 111], "It": [4, 5, 6, 7, 13, 18, 19, 25, 26, 27, 32, 37, 43, 47, 50, 53, 54, 57, 60, 64, 65, 66, 68, 69, 71, 75, 78, 79, 82, 83, 86, 90, 97, 100, 106, 107, 109, 114, 116, 117, 120, 121, 124], "produc": [4, 6, 13, 18, 26, 33, 49, 55, 57, 60, 114], "inconsist": [4, 52, 110], "output": [4, 6, 9, 13, 15, 18, 19, 23, 24, 26, 37, 44, 46, 47, 50, 53, 55, 56, 57, 58, 60, 65, 66, 76, 104, 107, 124], "particular": [4, 7, 18, 26, 48, 50, 54, 56, 57, 71, 92, 97], "hot": [4, 21, 48], "map": [4, 5, 14, 15, 18, 19, 21, 24, 26, 45, 51, 54, 93], "must": [4, 5, 7, 13, 18, 23, 24, 26, 27, 45, 56, 59, 60, 66, 68, 109, 111], "ident": [4, 6, 8, 12, 18, 53], "train": [4, 8, 9, 10, 12, 13, 15, 18, 19, 21, 26, 44, 49, 50, 51, 69, 89, 97, 109, 113], "time": [4, 5, 18, 21, 24, 25, 29, 30, 31, 40, 43, 44, 49, 51, 55, 56, 60, 64, 66, 67, 68, 89, 92, 105, 111, 114], "common": [4, 6, 9, 13, 17, 18, 24, 25, 29, 33, 35, 38, 39, 54, 65, 66, 68, 73, 78, 79, 80, 81, 93, 95, 97, 100, 102, 108, 112, 114, 118, 121, 124], "signif_results_tostr": 4, "signif_result": 4, "target_units_tostr": 4, "procedur": [4, 13, 27, 47, 60, 68, 75, 115, 118, 124], "self": [4, 6, 7, 13, 15, 24, 27, 35, 39, 48, 66, 75, 109], "update_input": 4, "target_unit": [4, 6, 25, 28, 35, 36, 37, 39, 40, 45, 47, 73, 74, 77, 78, 79], "realizedestimand": 4, "estimator_nam": [4, 6], "update_assumpt": 4, "estimator_assumpt": 4, "update_estimand_express": 4, "estimand_express": 4, "identifier_nam": [4, 7], "ate": [4, 5, 7, 25, 28, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 60, 65, 66, 68, 77, 78], "fit_estim": [4, 28], "method_param": [4, 6, 28, 33, 35, 38, 39, 40, 41, 42, 45, 46, 65, 68, 73, 74, 77, 79, 81, 91], "dict": [4, 5, 6, 7, 13, 14, 15, 17, 18, 21, 24, 26, 32, 33, 37, 44, 46, 48, 49, 52, 56, 57, 60, 66, 69, 80, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "convent": 4, "doubl": [4, 28, 47, 49, 50, 52, 54, 55, 56, 68, 79, 110, 114], "dml": [4, 13, 28, 37, 46, 65, 68, 79], "locat": [4, 34], "insid": 4, "demo": [4, 53, 63], "group": [4, 9, 13, 26, 36, 39, 47, 48], "variat": [4, 13, 25, 51, 65, 66, 68], "treat": [4, 6, 15, 23, 25, 36, 39, 40, 44, 45, 60, 71, 79, 80, 113], "three": [4, 13, 17, 18, 19, 25, 26, 27, 28, 31, 50, 55, 56, 61, 64, 75, 76, 79, 89, 100, 105, 106, 109, 111, 112], "2": [4, 6, 7, 9, 10, 11, 13, 15, 18, 20, 21, 24, 27, 28, 29, 30, 31, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 74, 79, 80, 89, 92, 93, 94, 95, 97, 98, 99, 105, 109, 110, 111, 112, 113, 114, 117], "lambda": [4, 6, 9, 13, 18, 24, 26, 28, 36, 47, 48, 49, 50, 51, 55, 56, 57, 73, 79, 80, 89, 97, 99, 111], "onli": [4, 5, 6, 7, 9, 13, 14, 15, 18, 19, 21, 24, 25, 26, 28, 29, 34, 36, 37, 39, 41, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 66, 67, 68, 80, 87, 92, 93, 94, 97, 104, 105, 109, 110, 111, 112, 114], "thei": [4, 5, 13, 18, 19, 24, 25, 26, 27, 29, 34, 36, 48, 49, 50, 52, 54, 55, 56, 57, 58, 60, 65, 66, 68, 75, 82, 89, 102, 103, 106, 110, 113, 114, 118], "previous": [4, 18, 21], "causalgraph": [4, 34, 37], "common_cause_nam": 4, "instrument_nam": [4, 7, 32, 33, 35, 39, 68], "mediator_nam": 4, "observed_node_nam": [4, 34], "missing_nodes_as_confound": [4, 41], "accept": [4, 26, 105], "networkx": [4, 18, 24, 26, 31, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 67, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "digraph": [4, 5, 7, 17, 18, 24, 25, 26, 31, 36, 43, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 59, 60, 61, 65, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "gml": [4, 39, 47, 53, 58, 108], "dot": [4, 22, 24, 31, 32, 33, 35, 44, 47, 51, 58, 67, 68, 70, 117], "edg": [4, 7, 17, 18, 24, 26, 27, 31, 34, 36, 43, 48, 49, 55, 61, 65, 66, 67, 75, 80, 90, 92, 103, 109, 113, 114], "node": [4, 7, 15, 18, 24, 25, 26, 31, 34, 36, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 65, 66, 67, 69, 71, 80, 89, 90, 92, 93, 94, 95, 96, 97, 103, 106, 109, 110, 111, 112, 113, 114], "style": [4, 15], "dash": [4, 13, 15, 53], "ensur": [4, 13, 15, 18, 21, 48, 49, 55, 57, 67, 68, 70, 96, 97, 100, 114], "drawn": [4, 13, 18, 25, 55, 56, 111], "line": [4, 13, 24, 32, 36, 47, 53, 54, 55, 56, 65, 66, 68, 71, 94], "confound": [4, 6, 9, 13, 18, 23, 25, 27, 29, 36, 37, 40, 41, 47, 50, 54, 56, 59, 63, 64, 66, 68, 79, 87, 108, 118, 121, 124], "instrument": [4, 6, 7, 13, 31, 32, 33, 39, 48, 59, 63, 68, 79, 83, 85, 87, 102, 108], "add_missing_nodes_as_common_caus": 4, "add_node_attribut": 4, "color": [4, 11, 13, 21, 24, 26, 48, 51, 55, 56, 64, 66, 102], "grai": 4, "all_observ": 4, "node_nam": [4, 7, 18, 24, 109], "build_graph": [4, 37], "semant": 4, "consid": [4, 6, 13, 14, 15, 18, 19, 21, 25, 26, 34, 45, 48, 49, 50, 52, 54, 55, 56, 57, 64, 65, 66, 67, 68, 87, 89, 105, 106, 108, 110, 114], "represent": [4, 9, 11, 13, 18, 49, 50, 52, 53, 54, 55, 56, 65, 72, 100, 108, 110, 114], "thu": [4, 13, 18, 25, 26, 28, 31, 34, 36, 52, 53, 55, 65, 68, 70, 73, 79, 89, 95, 102, 104, 110, 114], "unless": [4, 58, 64, 102], "taxonomi": 4, "vanderwheel": [4, 7], "robin": [4, 17], "modif": [4, 18, 45], "acycl": [4, 7, 18, 24, 26, 31, 49, 55, 59, 63, 102, 103, 104, 106, 113], "epidemiologi": 4, "2007": [4, 18, 19], "check_dsepar": 4, "nodes1": [4, 7], "nodes2": [4, 7], "nodes3": 4, "new_graph": 4, "dseparation_algo": [4, 7], "check_valid_backdoor_set": 4, "backdoor_path": [4, 7], "second": [4, 6, 9, 13, 18, 19, 27, 29, 34, 41, 45, 51, 53, 56, 75, 94, 102, 107, 109, 111, 118], "third": [4, 13, 14, 15, 18, 27, 45, 51, 71, 75, 105, 106], "candid": [4, 102, 104, 106], "check_valid_frontdoor_set": 4, "candidate_nod": 4, "frontdoor_path": 4, "frontdoor": [4, 7, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 65, 66, 68, 79, 85, 86, 87, 102], "check_valid_mediation_set": 4, "mediation_path": 4, "mediat": [4, 6, 7, 13, 34, 59, 63, 87, 88, 90, 101, 103, 108], "do_surgeri": 4, "remove_outgoing_edg": 4, "remove_incoming_edg": 4, "target_node_nam": 4, "remove_only_direct_edges_to_target": 4, "concept": [4, 26, 54, 71, 89, 95, 113], "surgeri": 4, "focal": 4, "outgo": 4, "incom": [4, 18, 92], "filter_unobserved_vari": 4, "get_adjacency_matrix": 4, "adjac": [4, 18, 24, 67], "matrix": [4, 13, 14, 18, 19, 21, 24, 30, 40, 56], "get_all_directed_path": 4, "singleton": [4, 15], "get_all_nod": 4, "include_unobserv": [4, 7], "get_ancestor": 4, "get_backdoor_path": 4, "get_caus": 4, "remove_edg": [4, 53], "get_common_caus": 4, "get_descend": 4, "get_effect_modifi": [4, 78], "get_instru": 4, "treatment_nod": 4, "outcome_nod": [4, 7, 17, 27, 34, 37], "get_par": 4, "get_unconfounded_observed_subgraph": 4, "has_directed_path": 4, "action_nod": [4, 7, 17, 27, 34, 37], "least": [4, 7, 13, 15, 18, 28, 29, 30, 34, 40, 45, 47, 48, 66, 79, 112, 115, 124], "And": [4, 31, 36, 56, 106, 111], "conditioned_nod": 4, "criteria": [4, 25, 34, 45, 69], "decid": [4, 13, 18, 25, 27, 31, 34, 50, 68, 75], "block": [4, 24, 27, 34, 41, 48, 75], "view_graph": [4, 34], "layout": [4, 24, 32, 44, 68], "file_nam": 4, "common_caus": [4, 5, 7, 27, 29, 30, 32, 38, 40, 44, 45, 47, 60, 68, 108], "estimand_typ": [4, 5, 6, 7, 17, 34, 37, 41, 60, 91], "nonparametr": [4, 5, 7, 13, 36, 41, 44, 46, 49, 60, 91], "proceed_when_unidentifi": [4, 5, 25, 27, 28, 29, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 60, 65, 66, 68, 91], "identify_var": 4, "store": [4, 6, 7, 13, 18, 32, 51, 55], "state": [4, 5, 12, 17, 24, 26, 34, 48, 60, 64, 68, 71, 79, 89, 100], "At": [4, 13, 18, 47, 49, 51, 55, 58, 65, 68, 89, 113], "later": [4, 5, 25, 34, 36, 45, 48, 51, 57, 60, 70], "dag": [4, 18, 26, 31, 47, 49, 50, 51, 52, 53, 55, 56, 57, 59, 67, 103, 104, 106, 107, 110, 113, 114], "_outcom": 4, "futur": [4, 7, 13, 36, 57, 60, 68, 97, 104], "proce": [4, 5, 56, 60, 70], "potenti": [4, 18, 27, 49, 50, 54, 55, 56, 64, 69, 75, 79, 80, 100, 102, 103, 108, 114], "unobserv": [4, 7, 13, 18, 25, 26, 29, 36, 37, 49, 55, 56, 57, 66, 68, 79, 89, 94, 97, 112, 113, 114, 118, 124], "while": [4, 12, 13, 14, 17, 18, 25, 26, 27, 29, 45, 52, 54, 55, 56, 57, 66, 68, 71, 73, 75, 82, 89, 92, 93, 94, 95, 97, 100, 110, 112, 114], "sens": [4, 26, 34, 51, 53, 54, 55, 89, 111, 114], "intervent": [4, 5, 6, 7, 17, 18, 26, 31, 34, 44, 48, 49, 52, 54, 63, 71, 80, 88, 89, 90, 97, 98, 100, 101, 102, 110, 112], "These": [4, 5, 13, 14, 18, 19, 27, 31, 34, 45, 46, 51, 55, 57, 60, 63, 66, 68, 75, 89, 92, 102, 103, 106, 112, 115], "explicit": [4, 17, 27, 71, 75, 79, 100, 103], "estimation_method_nam": 4, "propensity_score_stratif": [4, 35, 38, 39, 47, 68, 77], "invers": [4, 6, 18, 20, 23, 27, 35, 39, 75, 79, 87], "weight": [4, 5, 6, 9, 10, 13, 15, 17, 18, 21, 23, 27, 30, 31, 40, 51, 54, 55, 60, 64, 67, 75, 79, 87, 94], "propensity_score_weight": [4, 25, 35, 38, 39, 40, 44, 45, 46, 77], "linear_regress": [4, 28, 31, 32, 35, 38, 42, 45, 66, 67, 68, 73, 78], "generalized_linear_model": 4, "instrumental_vari": [4, 7, 29, 33, 35, 81], "discontinu": [4, 6, 79, 87], "regression_discontinu": [4, 35, 81], "two_stage_regress": [4, 41, 91], "addition": [4, 18, 25, 55, 68, 94, 102, 114], "signfic": 4, "rel": [4, 15, 18, 50, 51, 55, 65, 90, 93, 114], "compar": [4, 13, 18, 19, 21, 23, 26, 34, 36, 38, 43, 46, 49, 50, 53, 54, 55, 56, 60, 65, 66, 68, 79, 80, 87, 94, 100, 107, 112, 114], "sequenti": [4, 9, 64, 68, 94], "get_estim": 4, "retriev": 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"dowhy-interpreters-package"]], "dowhy.interpreters.confounder_distribution_interpreter module": [[23, "module-dowhy.interpreters.confounder_distribution_interpreter"]], "dowhy.interpreters.propensity_balance_interpreter module": [[23, "module-dowhy.interpreters.propensity_balance_interpreter"]], "dowhy.interpreters.textual_effect_interpreter module": [[23, "module-dowhy.interpreters.textual_effect_interpreter"]], "dowhy.interpreters.textual_interpreter module": [[23, "module-dowhy.interpreters.textual_interpreter"]], "dowhy.interpreters.visual_interpreter module": [[23, "module-dowhy.interpreters.visual_interpreter"]], "dowhy.utils package": [[24, "dowhy-utils-package"]], "dowhy.utils.api module": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit module": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers module": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp module": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations module": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting module": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting module": [[24, "module-dowhy.utils.networkx_plotting"]], "dowhy.utils.ordered_set module": [[24, "module-dowhy.utils.ordered_set"]], "dowhy.utils.plotting module": [[24, "module-dowhy.utils.plotting"]], "dowhy.utils.propensity_score module": [[24, "module-dowhy.utils.propensity_score"]], "dowhy.utils.regression module": [[24, "module-dowhy.utils.regression"]], "Exploring Causes of Hotel Booking Cancellations": [[25, "Exploring-Causes-of-Hotel-Booking-Cancellations"]], "Data Description": [[25, "Data-Description"]], "Feature Engineering": [[25, "Feature-Engineering"]], "Calculating Expected Counts": [[25, "Calculating-Expected-Counts"]], "Using DoWhy to estimate the causal effect": [[25, "Using-DoWhy-to-estimate-the-causal-effect"]], "Step-1. Create a Causal Graph": [[25, "Step-1.-Create-a-Causal-Graph"]], "Step-2. Identify the Causal Effect": [[25, "Step-2.-Identify-the-Causal-Effect"]], "Step-3. Estimate the identified estimand": [[25, "Step-3.-Estimate-the-identified-estimand"]], "Step-4. Refute results": [[25, "Step-4.-Refute-results"]], "Method-1": [[25, "Method-1"]], "Method-2": [[25, "Method-2"]], "Method-3": [[25, "Method-3"]], "Counterfactual Fairness": [[26, "Counterfactual-Fairness"]], "When to apply counterfactual fairness?": [[26, "When-to-apply-counterfactual-fairness?"]], "What is required to estimate counterfactual fairness?": [[26, "What-is-required-to-estimate-counterfactual-fairness?"]], "Notation": [[26, "Notation"]], "1. Load and Clean the Dataset": [[26, "1.-Load-and-Clean-the-Dataset"]], "2. A Formal Definition of Counterfactual Fairness": [[26, "2.-A-Formal-Definition-of-Counterfactual-Fairness"]], "2.1 Measuring Counterfactual Fairness": [[26, "2.1-Measuring-Counterfactual-Fairness"]], "3. Measuring Counterfactual Fairness using DoWhy": [[26, "3.-Measuring-Counterfactual-Fairness-using-DoWhy"]], "3.1 Univariate Analysis": [[26, "3.1-Univariate-Analysis"]], "3.2 Intersectional Analysis": [[26, "3.2-Intersectional-Analysis"]], "3.3 Some Additional Uses of the Counterfactuals df_obs , df_cf for Fairness": [[26, "3.3-Some-Additional-Uses-of-the-Counterfactuals-df_obs-,-df_cf-for-Fairness"]], "4. Limitation of Counterfactual Fairness": [[26, "4.-Limitation-of-Counterfactual-Fairness"]], "References": [[26, "References"], [44, "References"], [64, "References"]], "Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unfair Estimator": [[26, "Appendix-:-Fairness-Through-Unawareness-(FTU)-creates-A-Counterfactually-Unfair-Estimator"]], "Do-sampler Introduction": [[27, "Do-sampler-Introduction"]], "Pearlian Interventions": [[27, "Pearlian-Interventions"], [75, "pearlian-interventions"]], "Statefulness": [[27, "Statefulness"], [75, "statefulness"]], "Integration": [[27, "Integration"]], "Specifying Interventions": [[27, "Specifying-Interventions"], [60, "Specifying-Interventions"]], "Demo": [[27, "Demo"]], "Conditional Average Treatment Effects (CATE) with DoWhy and EconML": [[28, "Conditional-Average-Treatment-Effects-(CATE)-with-DoWhy-and-EconML"]], "Linear Model": [[28, "Linear-Model"]], "EconML methods": [[28, "EconML-methods"]], "CATE Object and Confidence Intervals": [[28, "CATE-Object-and-Confidence-Intervals"]], "Can provide a new inputs as target units and estimate CATE on them.": [[28, "Can-provide-a-new-inputs-as-target-units-and-estimate-CATE-on-them."]], "Can also retrieve the raw EconML estimator object for any further operations": [[28, "Can-also-retrieve-the-raw-EconML-estimator-object-for-any-further-operations"]], "Works with any EconML method": [[28, "Works-with-any-EconML-method"]], "Binary treatment, Binary outcome": [[28, "Binary-treatment,-Binary-outcome"]], "Using DRLearner estimator": [[28, "Using-DRLearner-estimator"]], "Instrumental Variable Method": [[28, "Instrumental-Variable-Method"]], "Metalearners": [[28, "Metalearners"]], "Avoiding retraining the estimator": [[28, "Avoiding-retraining-the-estimator"]], "Refuting the estimate": [[28, "Refuting-the-estimate"], [47, "Refuting-the-estimate"]], "Adding a random common cause variable": [[28, "Adding-a-random-common-cause-variable"], [32, "Adding-a-random-common-cause-variable"], [47, "Adding-a-random-common-cause-variable"]], "Adding an unobserved common cause variable": [[28, "Adding-an-unobserved-common-cause-variable"], [47, "Adding-an-unobserved-common-cause-variable"]], "Replacing treatment with a random (placebo) variable": [[28, "Replacing-treatment-with-a-random-(placebo)-variable"], [32, "Replacing-treatment-with-a-random-(placebo)-variable"], [47, "Replacing-treatment-with-a-random-(placebo)-variable"]], "Removing a random subset of the data": [[28, "Removing-a-random-subset-of-the-data"], [32, "Removing-a-random-subset-of-the-data"], [47, "Removing-a-random-subset-of-the-data"]], "Simple example on using Instrumental Variables method for estimation": [[29, "Simple-example-on-using-Instrumental-Variables-method-for-estimation"]], "Loading the dataset": [[29, "Loading-the-dataset"]], "Using DoWhy to estimate the causal effect of education on future income": [[29, "Using-DoWhy-to-estimate-the-causal-effect-of-education-on-future-income"]], "Demo for the DoWhy causal API": [[30, "Demo-for-the-DoWhy-causal-API"]], "Comparing the estimate to Linear Regression": [[30, "Comparing-the-estimate-to-Linear-Regression"]], "Causal Discovery example": [[31, "Causal-Discovery-example"]], "Utility function": [[31, "Utility-function"]], "Experiments on the Auto-MPG dataset": [[31, "Experiments-on-the-Auto-MPG-dataset"]], "1. Load the data": [[31, "1.-Load-the-data"], [31, "id1"], [40, "1.-Load-the-data"]], "Causal Discovery with causal-learn": [[31, "Causal-Discovery-with-causal-learn"], [31, "id2"]], "Estimate causal effects using Linear Regression": [[31, "Estimate-causal-effects-using-Linear-Regression"]], "Experiments on the Sachs dataset": [[31, "Experiments-on-the-Sachs-dataset"]], "Estimate effects using Linear Regression": [[31, "Estimate-effects-using-Linear-Regression"]], "Confounding Example: Finding causal effects from observed data": [[32, "Confounding-Example:-Finding-causal-effects-from-observed-data"]], "Let\u2019s create a mystery dataset for which we need to determine whether there is a causal effect.": [[32, "Let's-create-a-mystery-dataset-for-which-we-need-to-determine-whether-there-is-a-causal-effect."]], "Using DoWhy to resolve the mystery: Does Treatment cause Outcome?": [[32, "Using-DoWhy-to-resolve-the-mystery:-Does-Treatment-cause-Outcome?"]], "STEP 1: Model the problem as a causal graph": [[32, "STEP-1:-Model-the-problem-as-a-causal-graph"]], "STEP 2: Identify causal effect using properties of the formal causal graph": [[32, "STEP-2:-Identify-causal-effect-using-properties-of-the-formal-causal-graph"]], "STEP 3: Estimate the causal effect": [[32, "STEP-3:-Estimate-the-causal-effect"]], "Checking if the estimate is correct": [[32, "Checking-if-the-estimate-is-correct"]], "Step 4: Refuting the estimate": [[32, "Step-4:-Refuting-the-estimate"]], "A Simple Example on Creating a Custom Refutation Using User-Defined Outcome Functions": [[33, "A-Simple-Example-on-Creating-a-Custom-Refutation-Using-User-Defined-Outcome-Functions"]], "Insert Dependencies": [[33, "Insert-Dependencies"]], "Create the Dataset": [[33, "Create-the-Dataset"]], "Creating the Causal Model": [[33, "Creating-the-Causal-Model"]], "Identify the Estimand": [[33, "Identify-the-Estimand"]], "Estimating the Effect": [[33, "Estimating-the-Effect"]], "Refuting the Estimate": [[33, "Refuting-the-Estimate"]], "Using a Randomly Generated Outcome": [[33, "Using-a-Randomly-Generated-Outcome"]], "Using a Function that Generates the Outcome from the Confounders": [[33, "Using-a-Function-that-Generates-the-Outcome-from-the-Confounders"]], "Finding optimal adjustment sets": [[34, "Finding-optimal-adjustment-sets"]], "Preliminaries": [[34, "Preliminaries"]], "The design of an observational study": [[34, "The-design-of-an-observational-study"]], "An example in which sufficient conditions to guarantee the existence of an optimal backdoor set do not hold": [[34, "An-example-in-which-sufficient-conditions-to-guarantee-the-existence-of-an-optimal-backdoor-set-do-not-hold"]], "An example in which there are no observable adjustment sets": [[34, "An-example-in-which-there-are-no-observable-adjustment-sets"]], "An example with costs": [[34, "An-example-with-costs"]], "DoWhy: Different estimation methods for causal inference": [[35, "DoWhy:-Different-estimation-methods-for-causal-inference"]], "Identifying the causal estimand": [[35, "Identifying-the-causal-estimand"], [39, "Identifying-the-causal-estimand"]], "Method 1: Regression": [[35, "Method-1:-Regression"]], "Method 2: Distance Matching": [[35, "Method-2:-Distance-Matching"]], "Method 3: Propensity Score Stratification": [[35, "Method-3:-Propensity-Score-Stratification"]], "Method 4: Propensity Score Matching": [[35, "Method-4:-Propensity-Score-Matching"]], "Method 5: Weighting": [[35, "Method-5:-Weighting"]], "Method 6: Instrumental Variable": [[35, "Method-6:-Instrumental-Variable"]], "Method 7: Regression Discontinuity": [[35, "Method-7:-Regression-Discontinuity"]], "Estimating the Effect of a Member Rewards Program": [[36, "Estimating-the-Effect-of-a-Member-Rewards-Program"]], "I. Formulating the causal model": [[36, "I.-Formulating-the-causal-model"]], "The importance of time": [[36, "The-importance-of-time"]], "II. Identifying the causal effect": [[36, "II.-Identifying-the-causal-effect"]], "III. Estimating the effect": [[36, "III.-Estimating-the-effect"]], "IV. Refuting the estimate": [[36, "IV.-Refuting-the-estimate"]], "Functional API Preview": [[37, "Functional-API-Preview"]], "Import Dependencies": [[37, "Import-Dependencies"], [46, "Import-Dependencies"]], "Create the Datasets": [[37, "Create-the-Datasets"], [46, "Create-the-Datasets"]], "Identify Effect - Functional API (Preview)": [[37, "Identify-Effect---Functional-API-(Preview)"]], "Estimate Effect - Functional API (Preview)": [[37, "Estimate-Effect---Functional-API-(Preview)"]], "Refute Estimate - Functional API (Preview)": [[37, "Refute-Estimate---Functional-API-(Preview)"]], "Backwards Compatibility": [[37, "Backwards-Compatibility"]], "Identify Effect": [[37, "Identify-Effect"], [46, "Identify-Effect"]], "Estimate Effect": [[37, "Estimate-Effect"], [46, "Estimate-Effect"]], "Refute Estimate": [[37, "Refute-Estimate"], [46, "Refute-Estimate"]], "DoWhy example on ihdp (Infant Health and Development Program) dataset": [[38, "DoWhy-example-on-ihdp-(Infant-Health-and-Development-Program)-dataset"]], "Loading Data": [[38, "Loading-Data"]], "1.Model": [[38, "1.Model"]], "2.Identify": [[38, "2.Identify"]], "3. Estimate (using different methods)": [[38, "3.-Estimate-(using-different-methods)"]], "3.1 Using Linear Regression": [[38, "3.1-Using-Linear-Regression"]], "3.2 Using Propensity Score Matching": [[38, "3.2-Using-Propensity-Score-Matching"]], "3.3 Using Propensity Score Stratification": [[38, "3.3-Using-Propensity-Score-Stratification"]], "3.4 Using Propensity Score Weighting": [[38, "3.4-Using-Propensity-Score-Weighting"]], "4. Refute": [[38, "4.-Refute"]], "4.3 Data Subset Refuter": [[38, "4.3-Data-Subset-Refuter"]], "DoWhy: Interpreters for Causal Estimators": [[39, "DoWhy:-Interpreters-for-Causal-Estimators"]], "Method 1: Propensity Score Stratification": [[39, "Method-1:-Propensity-Score-Stratification"]], "Textual Interpreter": [[39, "Textual-Interpreter"]], "Visual Interpreter": [[39, "Visual-Interpreter"]], "Method 2: Propensity Score Matching": [[39, "Method-2:-Propensity-Score-Matching"]], "Method 3: Weighting": [[39, "Method-3:-Weighting"]], "DoWhy example on the Lalonde dataset": [[40, "DoWhy-example-on-the-Lalonde-dataset"]], "2. Run DoWhy analysis: model, identify, estimate": [[40, "2.-Run-DoWhy-analysis:-model,-identify,-estimate"]], "3. Interpret the estimate": [[40, "3.-Interpret-the-estimate"]], "4. Sanity check: compare to manual IPW estimate": [[40, "4.-Sanity-check:-compare-to-manual-IPW-estimate"]], "Mediation analysis with DoWhy: Direct and Indirect Effects": [[41, "Mediation-analysis-with-DoWhy:-Direct-and-Indirect-Effects"]], "Creating a dataset": [[41, "Creating-a-dataset"]], "Step 1: Modeling the causal mechanism": [[41, "Step-1:-Modeling-the-causal-mechanism"]], "Step 2: Identifying the natural direct and indirect effects": [[41, "Step-2:-Identifying-the-natural-direct-and-indirect-effects"]], "Step 3: Estimation of the effect": [[41, "Step-3:-Estimation-of-the-effect"]], "Natural Indirect Effect": [[41, "Natural-Indirect-Effect"]], "Natural Direct Effect": [[41, "Natural-Direct-Effect"]], "Step 4: Refutations": [[41, "Step-4:-Refutations"]], "Estimating effect of multiple treatments": [[42, "Estimating-effect-of-multiple-treatments"]], "Linear model": [[42, "Linear-model"]], "More methods": [[42, "More-methods"]], "Example to demonstrate optimized backdoor variable search for Causal Identification": [[43, "Example-to-demonstrate-optimized-backdoor-variable-search-for-Causal-Identification"]], "Create Random Graph": [[43, "Create-Random-Graph"]], "Testing optimized backdoor search": [[43, "Testing-optimized-backdoor-search"]], "Applying refutation tests to the Lalonde and IHDP datasets": [[44, "Applying-refutation-tests-to-the-Lalonde-and-IHDP-datasets"]], "Import the Dependencies": [[44, "Import-the-Dependencies"]], "Loading the Dataset": [[44, "Loading-the-Dataset"]], "Infant Health and Development Program Dataset (IHDP)": [[44, "Infant-Health-and-Development-Program-Dataset-(IHDP)"]], "Reference": [[44, "Reference"]], "Lalonde Dataset": [[44, "Lalonde-Dataset"]], "Step 1: Building the model": [[44, "Step-1:-Building-the-model"]], "IHDP": [[44, "IHDP"], [44, "id1"], [44, "id3"], [44, "id5"]], "Lalonde": [[44, "Lalonde"], [44, "id2"], [44, "id4"], [44, "id6"]], "Step 2: Identification": [[44, "Step-2:-Identification"]], "Step 3: Estimation (using propensity score weighting)": [[44, "Step-3:-Estimation-(using-propensity-score-weighting)"]], "Step 4: Refutation": [[44, "Step-4:-Refutation"]], "Add Random Common Cause": [[44, "Add-Random-Common-Cause"], [44, "id7"]], "Replace Treatment with Placebo": [[44, "Replace-Treatment-with-Placebo"], [44, "id8"]], "Remove Random Subset of Data": [[44, "Remove-Random-Subset-of-Data"], [44, "id9"]], "Assessing Support and Overlap with OverRule": [[45, "Assessing-Support-and-Overlap-with-OverRule"]], "Motivation": [[45, "Motivation"]], "Acknowledgements": [[45, "Acknowledgements"]], "Table of contents": [[45, "Table-of-contents"]], "Illustration on a simple 2D example": [[45, "Illustration-on-a-simple-2D-example"]], "Problem Data": [[45, "Problem-Data"]], "Applying OverRule with default arguments": [[45, "Applying-OverRule-with-default-arguments"]], "Example usage using CausalModel": [[45, "Example-usage-using-CausalModel"]], "Example usage using the Functional API": [[45, "Example-usage-using-the-Functional-API"]], "Only fitting support or overlap rules": [[45, "Only-fitting-support-or-overlap-rules"]], "Interpreting the output of OverRule": [[45, "Interpreting-the-output-of-OverRule"]], "Configuring OverRule": [[45, "Configuring-OverRule"]], "Illustration on Lalonde and PSID datasets": [[45, "Illustration-on-Lalonde-and-PSID-datasets"]], "Checking for Support/Overlap in the Experimental Sample": [[45, "Checking-for-Support/Overlap-in-the-Experimental-Sample"]], "Assessing Support/Overlap on the combined sample": [[45, "Assessing-Support/Overlap-on-the-combined-sample"]], "Filtering with OverRule before performing a causal analysis": [[45, "Filtering-with-OverRule-before-performing-a-causal-analysis"]], "Performing causal inference after filtering": [[45, "Performing-causal-inference-after-filtering"]], "Iterating over multiple refutation tests": [[46, "Iterating-over-multiple-refutation-tests"]], "Inspection Parameters": [[46, "Inspection-Parameters"]], "Estimator List": [[46, "Estimator-List"]], "Refuter List": [[46, "Refuter-List"]], "Inspect Data": [[46, "Inspect-Data"]], "Create the CausalModels": [[46, "Create-the-CausalModels"]], "View Models": [[46, "View-Models"]], "Identified Estimands": [[46, "Identified-Estimands"]], "Estimate Values": [[46, "Estimate-Values"]], "Refutation Values": [[46, "Refutation-Values"]], "Basic Example for Calculating the Causal Effect": [[47, "Basic-Example-for-Calculating-the-Causal-Effect"]], "Interface 1 (recommended): Input causal graph": [[47, "Interface-1-(recommended):-Input-causal-graph"]], "DoWhy philosophy: Keep identification and estimation separate": [[47, "DoWhy-philosophy:-Keep-identification-and-estimation-separate"]], "Identification": [[47, "Identification"], [91, "identification"]], "Estimation": [[47, "Estimation"], [91, "estimation"]], "Interface 2: Specify common causes and instruments": [[47, "Interface-2:-Specify-common-causes-and-instruments"]], "Impact of 401(k) eligibility on net financial assets": [[48, "Impact-of-401(k)-eligibility-on-net-financial-assets"]], "Background": [[48, "Background"]], "Data": [[48, "Data"]], "Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income": [[48, "Effect-of-401(k)-Eligibility-on-Net-Financial-Assets,-Conditioned-on-Income"]], "Basic Example for Graphical Causal Models": [[49, "Basic-Example-for-Graphical-Causal-Models"]], "Step 1: Modeling cause-effect relationships as a structural causal model (SCM)": [[49, "Step-1:-Modeling-cause-effect-relationships-as-a-structural-causal-model-(SCM)"]], "Step 2: Fitting the SCM to the data": [[49, "Step-2:-Fitting-the-SCM-to-the-data"]], "Step 3: Answering a causal query based on the SCM": [[49, "Step-3:-Answering-a-causal-query-based-on-the-SCM"]], "Counterfactual Analysis in a Medical Case": [[50, "Counterfactual-Analysis-in-a-Medical-Case"]], "The data": [[50, "The-data"]], "Modeling of the graph": [[50, "Modeling-of-the-graph"]], "Answering Alice\u2019s counterfactual queries": [[50, "Answering-Alice's-counterfactual-queries"]], "Appendix: What the tele-app uses internally. Data generation of the patients\u2019 log": [[50, "Appendix:-What-the-tele-app-uses-internally.-Data-generation-of-the-patients'-log"]], "Decomposing the Gender Wage Gap": [[51, "Decomposing-the-Gender-Wage-Gap"]], "Read and prepare data": [[51, "Read-and-prepare-data"]], "Causal Model": [[51, "Causal-Model"]], "Implementation with dowhy.gcm.distribution_change_robust": [[51, "Implementation-with-dowhy.gcm.distribution_change_robust"]], "Manual Implementation (Advanced)": [[51, "Manual-Implementation-(Advanced)"]], "Interpretation": [[51, "Interpretation"]], "Basic Example for generating samples from a GCM": [[52, "Basic-Example-for-generating-samples-from-a-GCM"]], "Falsification of User-Given Directed Acyclic Graphs": [[53, "Falsification-of-User-Given-Directed-Acyclic-Graphs"]], "Synthetic Data": [[53, "Synthetic-Data"]], "Real Data (Protein Network dataset by Sachs et al., 2005)": [[53, "Real-Data-(Protein-Network-dataset-by-Sachs-et-al.,-2005)"]], "Edge Suggestions": [[53, "Edge-Suggestions"]], "Estimating intrinsic causal influences in real-world examples": [[54, "Estimating-intrinsic-causal-influences-in-real-world-examples"]], "Intrinsic influence on car MPG consumption": [[54, "Intrinsic-influence-on-car-MPG-consumption"]], "Intrinsic influence on river flow": [[54, "Intrinsic-influence-on-river-flow"]], "Causal Attributions and Root-Cause Analysis in an Online Shop": [[55, "Causal-Attributions-and-Root-Cause-Analysis-in-an-Online-Shop"]], "The scenario": [[55, "The-scenario"]], "Step 1: Define causal model": [[55, "Step-1:-Define-causal-model"]], "Step 2: Fit causal models to data": [[55, "Step-2:-Fit-causal-models-to-data"]], "Step 3: Answer causal questions": [[55, "Step-3:-Answer-causal-questions"]], "Generate new samples": [[55, "Generate-new-samples"]], "What are the key factors influencing the variance in profit?": [[55, "What-are-the-key-factors-influencing-the-variance-in-profit?"]], "What are the key factors explaining the Profit drop on a particular day?": [[55, "What-are-the-key-factors-explaining-the-Profit-drop-on-a-particular-day?"]], "What caused the profit drop in Q1 2022?": [[55, "What-caused-the-profit-drop-in-Q1-2022?"]], "Data generation process": [[55, "Data-generation-process"]], "Finding the Root Cause of Elevated Latencies in a Microservice Architecture": [[56, "Finding-the-Root-Cause-of-Elevated-Latencies-in-a-Microservice-Architecture"]], "Setting up the causal model": [[56, "Setting-up-the-causal-model"]], "Scenario 1: Observing a single outlier": [[56, "Scenario-1:-Observing-a-single-outlier"]], "Attributing an outlier latency at a target service to other services": [[56, "Attributing-an-outlier-latency-at-a-target-service-to-other-services"]], "Scenario 2: Observing permanent degradation of latencies": [[56, "Scenario-2:-Observing-permanent-degradation-of-latencies"]], "Attributing permanent degradation of latencies at a target service to other services": [[56, "Attributing-permanent-degradation-of-latencies-at-a-target-service-to-other-services"]], "Scenario 3: Simulating the intervention of shifting resources": [[56, "Scenario-3:-Simulating-the-intervention-of-shifting-resources"]], "Appendix: Data generation process": [[56, "Appendix:-Data-generation-process"]], "Finding Root Causes of Changes in a Supply Chain": [[57, "Finding-Root-Causes-of-Changes-in-a-Supply-Chain"]], "Week-over-week changes": [[57, "Week-over-week-changes"]], "Why did the average value of received quantity change week-over-week?": [[57, "Why-did-the-average-value-of-received-quantity-change-week-over-week?"]], "Ad-hoc attribution analysis": [[57, "Ad-hoc-attribution-analysis"]], "Causal Attribution Analysis": [[57, "Causal-Attribution-Analysis"]], "Graphical causal model": [[57, "Graphical-causal-model"]], "Attributing change": [[57, "Attributing-change"]], "Appendix: Ground Truth": [[57, "Appendix:-Ground-Truth"]], "Testing Assumptions in model with DoWhy: A simple example": [[58, "Testing-Assumptions-in-model-with-DoWhy:-A-simple-example"]], "Step 1: Load dataset": [[58, "Step-1:-Load-dataset"]], "Step 2: Input causal graph": [[58, "Step-2:-Input-causal-graph"]], "Step 3: Create Causal Model": [[58, "Step-3:-Create-Causal-Model"], [66, "Step-3:-Create-Causal-Model"]], "Step 4: Testing for Conditional Independence": [[58, "Step-4:-Testing-for-Conditional-Independence"]], "Testing for a set of edges": [[58, "Testing-for-a-set-of-edges"]], "Testing with a wrong graph input": [[58, "Testing-with-a-wrong-graph-input"]], "Identifying Effect using ID Algorithm": [[59, "Identifying-Effect-using-ID-Algorithm"]], "Case 1": [[59, "Case-1"]], "Case 2": [[59, "Case-2"]], "Case 3": [[59, "Case-3"]], "Case 4": [[59, "Case-4"]], "Case 5": [[59, "Case-5"]], "Case 6": [[59, "Case-6"]], "Lalonde Pandas API Example": [[60, "Lalonde-Pandas-API-Example"]], "Getting the Data": [[60, "Getting-the-Data"]], "The causal Namespace": [[60, "The-causal-Namespace"]], "The do Operation": [[60, "The-do-Operation"]], "Treatment Effect Estimation": [[60, "Treatment-Effect-Estimation"]], "Different ways to load an input graph": [[61, "Different-ways-to-load-an-input-graph"]], "I. Generating dummy data": [[61, "I.-Generating-dummy-data"]], "II. Loading GML or DOT graphs": [[61, "II.-Loading-GML-or-DOT-graphs"]], "GML format": [[61, "GML-format"]], "DOT format": [[61, "DOT-format"]], "Case Studies": [[62, null]], "Example notebooks": [[63, "example-notebooks"]], "Introductory examples": [[63, "introductory-examples"]], "Real world-inspired examples": [[63, "real-world-inspired-examples"]], "Examples on benchmark datasets": [[63, "examples-on-benchmark-datasets"]], "Modeling and refuting causal assumptions": [[63, "modeling-and-refuting-causal-assumptions"]], "Miscellaneous": [[63, "miscellaneous"]], "Demo for DoWhy Causal Prediction on MNIST": [[64, "Demo-for-DoWhy-Causal-Prediction-on-MNIST"]], "Multi-attribute distribution shift datasets": [[64, "Multi-attribute-distribution-shift-datasets"]], "Multi-attribute MNIST": [[64, "Multi-attribute-MNIST"]], "Domains in multi-attribute MNIST": [[64, "Domains-in-multi-attribute-MNIST"]], "Initialize dataset": [[64, "Initialize-dataset"]], "Initialize data loaders": [[64, "Initialize-data-loaders"]], "Method 1: Provide validation domain explicitly": [[64, "Method-1:-Provide-validation-domain-explicitly"]], "Method 2: Validation set using subset of training data": [[64, "Method-2:-Validation-set-using-subset-of-training-data"]], "Initialize model and algorithm": [[64, "Initialize-model-and-algorithm"]], "Initialize algorithm class: ERM": [[64, "Initialize-algorithm-class:-ERM"]], "Fit predictor and start training": [[64, "Fit-predictor-and-start-training"]], "Evaluate on test domain": [[64, "Evaluate-on-test-domain"]], "Prediction with CACM": [[64, "Prediction-with-CACM"]], "Extending to different datasets and algorithms": [[64, "Extending-to-different-datasets-and-algorithms"]], "MNIST Independent and Causal+Independent datasets": [[64, "MNIST-Independent-and-Causal+Independent-datasets"]], "MNISTIndAttribute: Single-attribute Independent shift": [[64, "MNISTIndAttribute:-Single-attribute-Independent-shift"]], "MNISTCausalIndAttribute: Multi-attribute Causal+Independent shift": [[64, "MNISTCausalIndAttribute:-Multi-attribute-Causal+Independent-shift"]], "Additional datasets and algorithms": [[64, "Additional-datasets-and-algorithms"]], "Sensitivity analysis for non-parametric causal estimators": [[65, "Sensitivity-analysis-for-non-parametric-causal-estimators"]], "I. Sensitivity analysis for partially linear models": [[65, "I.-Sensitivity-analysis-for-partially-linear-models"]], "Creating a dataset with unobserved confounding": [[65, "Creating-a-dataset-with-unobserved-confounding"]], "Obtaining a causal estimate using Model, Identify, Estimate steps": [[65, "Obtaining-a-causal-estimate-using-Model,-Identify,-Estimate-steps"]], "Sensitivity Analysis using the Refute step": [[65, "Sensitivity-Analysis-using-the-Refute-step"]], "II. Sensitivity Analysis for general non-parametric models": [[65, "II.-Sensitivity-Analysis-for-general-non-parametric-models"]], "Sensitivity Analysis for Regression Models": [[66, "Sensitivity-Analysis-for-Regression-Models"]], "Step 1: Load required packages": [[66, "Step-1:-Load-required-packages"]], "Step 2: Load the dataset": [[66, "Step-2:-Load-the-dataset"]], "Step 4: Identification": [[66, "Step-4:-Identification"]], "Step 5: Estimation": [[66, "Step-5:-Estimation"]], "Step 6a: Refutation and Sensitivity Analysis - Method 1": [[66, "Step-6a:-Refutation-and-Sensitivity-Analysis---Method-1"]], "Parameter List for plot function": [[66, "Parameter-List-for-plot-function"]], "Step 6b: Refutation and Sensitivity Analysis - Method 2": [[66, "Step-6b:-Refutation-and-Sensitivity-Analysis---Method-2"]], "Effect inference with timeseries data": [[67, "Effect-inference-with-timeseries-data"]], "Loading timeseries data and causal graph": [[67, "Loading-timeseries-data-and-causal-graph"]], "Dataset Shifting and Filtering": [[67, "Dataset-Shifting-and-Filtering"]], "Causal Effect Estimation": [[67, "Causal-Effect-Estimation"]], "Importing temporal causal graph from Tigramite library": [[67, "Importing-temporal-causal-graph-from-Tigramite-library"]], "Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)": [[68, "Tutorial-on-Causal-Inference-and-its-Connections-to-Machine-Learning-(Using-DoWhy+EconML)"]], "Why causal inference?": [[68, "Why-causal-inference?"]], "Defining a causal effect": [[68, "Defining-a-causal-effect"]], "The difference between prediction and causal inference": [[68, "The-difference-between-prediction-and-causal-inference"]], "Two fundamental challenges for causal inference": [[68, "Two-fundamental-challenges-for-causal-inference"]], "The four steps of causal inference": [[68, "The-four-steps-of-causal-inference"]], "The DoWhy+EconML solution": [[68, "The-DoWhy+EconML-solution"]], "A mystery dataset: Can you find out if if there is a causal effect?": [[68, "A-mystery-dataset:-Can-you-find-out-if-if-there-is-a-causal-effect?"]], "Model assumptions about the data-generating process using a causal graph": [[68, "Model-assumptions-about-the-data-generating-process-using-a-causal-graph"]], "Identify the correct estimand for the target quantity based on the causal model": [[68, "Identify-the-correct-estimand-for-the-target-quantity-based-on-the-causal-model"]], "Estimate the target estimand": [[68, "Estimate-the-target-estimand"]], "Check robustness of the estimate using refutation tests": [[68, "Check-robustness-of-the-estimate-using-refutation-tests"]], "Case-studies using DoWhy+EconML": [[68, "Case-studies-using-DoWhy+EconML"]], "Estimating the impact of a customer loyalty program": [[68, "Estimating-the-impact-of-a-customer-loyalty-program"]], "Recommendation A/B testing at an online company": [[68, "Recommendation-A/B-testing-at-an-online-company"]], "User segmentation for targeting interventions": [[68, "User-segmentation-for-targeting-interventions"]], "Multi-investment attribution at a software company": [[68, "Multi-investment-attribution-at-a-software-company"]], "Connections to fundamental machine learning challenges": [[68, "Connections-to-fundamental-machine-learning-challenges"]], "Further resources": [[68, "Further-resources"], [69, "further-resources"]], "DoWhy+EconML libraries": [[68, "DoWhy+EconML-libraries"]], "Video Lecture on causal inference and its connections to machine learning": [[68, "Video-Lecture-on-causal-inference-and-its-connections-to-machine-learning"]], "Detailed KDD Tutorial on Causal Inference": [[68, "Detailed-KDD-Tutorial-on-Causal-Inference"]], "Book chapters on causality and machine learning": [[68, "Book-chapters-on-causality-and-machine-learning"]], "Causality and Machine Learning group at Microsoft": [[68, "Causality-and-Machine-Learning-group-at-Microsoft"]], "Getting Started": [[69, "getting-started"]], "Installation": [[69, "installation"], [70, "installation"]], "\u201cHello causal inference world\u201d": [[69, "hello-causal-inference-world"]], "Effect inference": [[69, "effect-inference"]], "Graphical causal model-based inference": [[69, "graphical-causal-model-based-inference"]], "Installing with pip": [[70, "installing-with-pip"]], "Installing with Conda": [[70, "installing-with-conda"]], "Installing on Azure Machine Learning": [[70, "installing-on-azure-machine-learning"]], "DoWhy documentation": [[71, "dowhy-documentation"]], "Key differences compared to available causal inference software": [[71, "key-differences-compared-to-available-causal-inference-software"]], "Predicting outcome for out-of-distribution inputs": [[72, "predicting-outcome-for-out-of-distribution-inputs"]], "Estimating conditional average causal effect": [[73, "estimating-conditional-average-causal-effect"]], "Linear regression": [[73, "linear-regression"]], "DML method from EconML": [[73, "dml-method-from-econml"]], "Distance-based matching": [[74, "distance-based-matching"]], "Do-sampler": [[75, "do-sampler"]], "Using do-sampler": [[75, "using-do-sampler"]], "Estimating average causal effect using backdoor": [[76, "estimating-average-causal-effect-using-backdoor"]], "Propensity-based methods": [[77, "propensity-based-methods"]], "Propensity-Based Matching": [[77, "propensity-based-matching"]], "Propensity-based Stratification": [[77, "propensity-based-stratification"]], "Inverse Propensity Weighting": [[77, "inverse-propensity-weighting"]], "Regression-based methods": [[78, "regression-based-methods"]], "Effect Estimation Using specific Effect Estimators (for ACE, mediation effect, \u2026)": [[79, "effect-estimation-using-specific-effect-estimators-for-ace-mediation-effect"]], "Generating sample data and the causal model": [[79, "generating-sample-data-and-the-causal-model"]], "Identify a target estimand under the model": [[79, "identify-a-target-estimand-under-the-model"]], "Supported identification criteria": [[79, "supported-identification-criteria"]], "Estimate causal effect based on the identified estimand": [[79, "estimate-causal-effect-based-on-the-identified-estimand"]], "Supported estimation methods": [[79, "supported-estimation-methods"]], "Using EconML and CausalML estimation methods in DoWhy": [[79, "using-econml-and-causalml-estimation-methods-in-dowhy"]], "Refute the obtained estimate": [[79, "refute-the-obtained-estimate"]], "Supported refutation methods": [[79, "supported-refutation-methods"]], "Comparison to other packages": [[79, "comparison-to-other-packages"]], "Key difference: Causal assumptions as first-class citizens": [[79, "key-difference-causal-assumptions-as-first-class-citizens"]], "Estimating average causal effect using GCM": [[80, "estimating-average-causal-effect-using-gcm"]], "How to use it": [[80, "how-to-use-it"], [89, "how-to-use-it"], [92, "how-to-use-it"], [93, "how-to-use-it"], [94, "how-to-use-it"], [95, "how-to-use-it"], [97, "how-to-use-it"], [99, "how-to-use-it"], [111, "how-to-use-it"]], "Related example notebooks": [[80, "related-example-notebooks"], [89, "related-example-notebooks"], [91, "related-example-notebooks"], [92, "related-example-notebooks"], [93, "related-example-notebooks"], [94, "related-example-notebooks"], [97, "related-example-notebooks"], [99, "related-example-notebooks"], [113, "related-example-notebooks"]], "Understanding the method": [[80, "understanding-the-method"], [89, "understanding-the-method"], [92, "understanding-the-method"], [93, "understanding-the-method"], [94, "understanding-the-method"], [97, "understanding-the-method"]], "Estimating average causal effect with natural experiments": [[81, "estimating-average-causal-effect-with-natural-experiments"]], "Instrumental variable estimator for binary action": [[81, "instrumental-variable-estimator-for-binary-action"]], "Regression discontinuity estimator": [[81, "regression-discontinuity-estimator"]], "Backdoor criterion": [[82, "backdoor-criterion"]], "Frontdoor criterion": [[83, "frontdoor-criterion"]], "ID algorithm for discovering new identification strategies": [[84, "id-algorithm-for-discovering-new-identification-strategies"]], "Identifying causal effect": [[85, "identifying-causal-effect"]], "Natural experiments and instrumental variables": [[86, "natural-experiments-and-instrumental-variables"]], "Estimating Causal Effects": [[87, "estimating-causal-effects"]], "Performing Causal Tasks": [[88, "performing-causal-tasks"]], "Quantifying Intrinsic Causal Influence": [[89, "quantifying-intrinsic-causal-influence"]], "Quantify Causal Influence": [[90, "quantify-causal-influence"]], "Mediation Analysis: Estimating natural direct and indirect effects": [[91, "mediation-analysis-estimating-natural-direct-and-indirect-effects"]], "Direct Effect: Quantifying Arrow Strength": [[92, "direct-effect-quantifying-arrow-strength"]], "Customize the distance measure": [[92, "customize-the-distance-measure"]], "Anomaly Attribution": [[93, "anomaly-attribution"]], "Attributing Distributional Changes": [[94, "attributing-distributional-changes"]], "Feature Relevance": [[95, "feature-relevance"]], "Root-Cause Analysis and Explanation": [[96, "root-cause-analysis-and-explanation"]], "Computing Counterfactuals": [[97, "computing-counterfactuals"]], "Asking and Answering What-If Questions": [[98, "asking-and-answering-what-if-questions"]], "Simulating the Impact of Interventions": [[99, "simulating-the-impact-of-interventions"]], "Foreword": [[100, "foreword"]], "The need for causal inference": [[100, "the-need-for-causal-inference"]], "User Guide": [[101, "user-guide"]], "Introduction to DoWhy": [[102, "introduction-to-dowhy"]], "Supported causal tasks": [[102, "supported-causal-tasks"]], "Testing validity of a causal analysis": [[102, "testing-validity-of-a-causal-analysis"]], "Who this user guide is for": [[102, "who-this-user-guide-is-for"]], "Modeling Causal Relations": [[103, "modeling-causal-relations"]], "Learning causal structure from data": [[104, "learning-causal-structure-from-data"]], "Graph discovery using CDT": [[104, "graph-discovery-using-cdt"]], "Graph discovery using dodiscover": [[104, "graph-discovery-using-dodiscover"]], "Graph discovery using causal-learn": [[104, "graph-discovery-using-causal-learn"]], "Performing independence tests": [[105, "performing-independence-tests"]], "Refuting a Causal Graph": [[106, "refuting-a-causal-graph"]], "Graph refutations": [[107, "graph-refutations"]], "Specifying a causal graph using domain knowledge": [[108, "specifying-a-causal-graph-using-domain-knowledge"]], "Customizing Causal Mechanism Assignment": [[109, "customizing-causal-mechanism-assignment"]], "Using ground truth models": [[109, "using-ground-truth-models"]], "Creating causal model (GCM) from equations": [[109, "creating-causal-model-gcm-from-equations"]], "Generate samples from a GCM": [[110, "generate-samples-from-a-gcm"]], "Estimating Confidence Intervals": [[111, "estimating-confidence-intervals"]], "Computing confidence intervals for causal queries": [[111, "computing-confidence-intervals-for-causal-queries"]], "Conveniently bootstrapping graph training on random subsets of training data": [[111, "conveniently-bootstrapping-graph-training-on-random-subsets-of-training-data"]], "Runtime cost versus confidence": [[111, "runtime-cost-versus-confidence"]], "Understanding the need for confidence intervals": [[111, "understanding-the-need-for-confidence-intervals"]], "Types of graphical causal models": [[112, "types-of-graphical-causal-models"]], "Available Causal Mechanisms": [[112, "available-causal-mechanisms"]], "Modeling Graphical Causal Models (GCMs)": [[113, "modeling-graphical-causal-models-gcms"]], "Fitting an SCM to the data": [[113, "fitting-an-scm-to-the-data"]], "Evaluating a fitted SCM": [[113, "evaluating-a-fitted-scm"]], "Other topics": [[113, "other-topics"]], "Evaluate a GCM": [[114, "evaluate-a-gcm"]], "Summary of auto assignment": [[114, "summary-of-auto-assignment"]], "Evaluating a fitted GCM": [[114, "evaluating-a-fitted-gcm"]], "Refuting causal estimates": [[115, "refuting-causal-estimates"]], "Data Subsample Refuter": [[116, "data-subsample-refuter"]], "Dummy Outcome Refuter": [[117, "dummy-outcome-refuter"]], "Testing for zero causal effect": [[117, "testing-for-zero-causal-effect"]], "Testing for non-zero causal effect": [[117, "testing-for-non-zero-causal-effect"]], "Refuting Effect Estimates": [[118, "refuting-effect-estimates"]], "Refutations based on negative control": [[118, "refutations-based-on-negative-control"]], "Refutations based on sensitivity analysis": [[118, "refutations-based-on-sensitivity-analysis"]], "Placebo Treatment Refuter": [[119, "placebo-treatment-refuter"]], "Random Common Cause Refuter": [[120, "random-common-cause-refuter"]], "Sensitivity Analysis": [[121, "sensitivity-analysis"]], "Partial-R2 based sensitivity analysis for linear estimators": [[122, "partial-r2-based-sensitivity-analysis-for-linear-estimators"]], "Reisz estimator-based sensitivity analysis for non-linear estimators": [[123, "reisz-estimator-based-sensitivity-analysis-for-non-linear-estimators"]], "Simulation-based sensitivity analysis": [[124, "simulation-based-sensitivity-analysis"]], "Automatically inferring effect strength parameters": [[124, "automatically-inferring-effect-strength-parameters"]]}, "indexentries": {"auto (dowhy.causal_refuter.significancetesttype attribute)": [[4, "dowhy.causal_refuter.SignificanceTestType.AUTO"]], "bootstrap (dowhy.causal_refuter.significancetesttype attribute)": [[4, "dowhy.causal_refuter.SignificanceTestType.BOOTSTRAP"]], "causalestimate (class in dowhy.causal_estimator)": [[4, "dowhy.causal_estimator.CausalEstimate"]], "causalestimator (class in dowhy.causal_estimator)": [[4, "dowhy.causal_estimator.CausalEstimator"]], "causalestimator.bootstrapestimates (class in dowhy.causal_estimator)": [[4, "dowhy.causal_estimator.CausalEstimator.BootstrapEstimates"]], "causalgraph (class in dowhy.causal_graph)": [[4, "dowhy.causal_graph.CausalGraph"]], "causalmodel (class in dowhy)": [[4, "dowhy.CausalModel"]], "causalmodel (class in dowhy.causal_model)": [[4, "dowhy.causal_model.CausalModel"]], "causalrefutation (class in dowhy.causal_refuter)": [[4, "dowhy.causal_refuter.CausalRefutation"]], "causalrefuter (class in dowhy.causal_refuter)": [[4, "dowhy.causal_refuter.CausalRefuter"]], "default_confidence_level (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.DEFAULT_CONFIDENCE_LEVEL"]], "default_interpret_method (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.DEFAULT_INTERPRET_METHOD"]], "default_notimplementederror_msg (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.DEFAULT_NOTIMPLEMENTEDERROR_MSG"]], "default_number_of_simulations_ci (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.DEFAULT_NUMBER_OF_SIMULATIONS_CI"]], "default_number_of_simulations_stat_test (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST"]], "default_num_simulations (dowhy.causal_refuter.causalrefuter attribute)": [[4, "dowhy.causal_refuter.CausalRefuter.DEFAULT_NUM_SIMULATIONS"]], "default_sample_size_fraction (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.DEFAULT_SAMPLE_SIZE_FRACTION"]], "dimensionalityreducer (class in dowhy.data_transformer)": [[4, "dowhy.data_transformer.DimensionalityReducer"]], "directedgraph (class in dowhy.graph)": [[4, "dowhy.graph.DirectedGraph"]], "dosampler (class in dowhy.do_sampler)": [[4, "dowhy.do_sampler.DoSampler"]], "estimandtype (class in dowhy)": [[4, "dowhy.EstimandType"]], "graphlearner (class in dowhy.graph_learner)": [[4, "dowhy.graph_learner.GraphLearner"]], "hasedges (class in dowhy.graph)": [[4, "dowhy.graph.HasEdges"]], "hasnodes (class in dowhy.graph)": [[4, "dowhy.graph.HasNodes"]], "interpreter (class in dowhy.interpreter)": [[4, "dowhy.interpreter.Interpreter"]], "nonparametric_ate (dowhy.estimandtype attribute)": [[4, "dowhy.EstimandType.NONPARAMETRIC_ATE"]], "nonparametric_cde (dowhy.estimandtype attribute)": [[4, "dowhy.EstimandType.NONPARAMETRIC_CDE"]], "nonparametric_nde (dowhy.estimandtype attribute)": [[4, "dowhy.EstimandType.NONPARAMETRIC_NDE"]], "nonparametric_nie (dowhy.estimandtype attribute)": [[4, "dowhy.EstimandType.NONPARAMETRIC_NIE"]], "normal (dowhy.causal_refuter.significancetesttype attribute)": [[4, "dowhy.causal_refuter.SignificanceTestType.NORMAL"]], "num_quantiles_to_discretize_cont_cols (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS"]], "progress_bar_color (dowhy.causal_refuter.causalrefuter attribute)": [[4, "dowhy.causal_refuter.CausalRefuter.PROGRESS_BAR_COLOR"]], "realizedestimand (class in dowhy.causal_estimator)": [[4, "dowhy.causal_estimator.RealizedEstimand"]], "supported_estimators (dowhy.interpreter.interpreter attribute)": [[4, "dowhy.interpreter.Interpreter.SUPPORTED_ESTIMATORS"]], "supported_models (dowhy.interpreter.interpreter attribute)": [[4, "dowhy.interpreter.Interpreter.SUPPORTED_MODELS"]], "supported_refuters (dowhy.interpreter.interpreter attribute)": [[4, "dowhy.interpreter.Interpreter.SUPPORTED_REFUTERS"]], "significancetesttype (class in dowhy.causal_refuter)": [[4, "dowhy.causal_refuter.SignificanceTestType"]], "temp_cat_column_prefix (dowhy.causal_estimator.causalestimator attribute)": [[4, "dowhy.causal_estimator.CausalEstimator.TEMP_CAT_COLUMN_PREFIX"]], "add_effect_strength() (dowhy.causal_estimator.causalestimate method)": [[4, "dowhy.causal_estimator.CausalEstimate.add_effect_strength"]], "add_estimator() (dowhy.causal_estimator.causalestimate method)": [[4, "dowhy.causal_estimator.CausalEstimate.add_estimator"]], "add_missing_nodes_as_common_causes() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.add_missing_nodes_as_common_causes"]], "add_node_attributes() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.add_node_attributes"]], "add_params() (dowhy.causal_estimator.causalestimate method)": [[4, "dowhy.causal_estimator.CausalEstimate.add_params"]], "add_refuter() (dowhy.causal_refuter.causalrefutation method)": [[4, "dowhy.causal_refuter.CausalRefutation.add_refuter"]], "add_significance_test_results() (dowhy.causal_refuter.causalrefutation method)": [[4, "dowhy.causal_refuter.CausalRefutation.add_significance_test_results"]], "add_unobserved_common_cause() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.add_unobserved_common_cause"]], "all_observed() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.all_observed"]], "build_graph() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.build_graph"]], "build_graph() (in module dowhy.graph)": [[4, "dowhy.graph.build_graph"]], "build_graph_from_str() (in module dowhy.graph)": [[4, "dowhy.graph.build_graph_from_str"]], "check_dseparation() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.check_dseparation"]], "check_dseparation() (in module dowhy.graph)": [[4, "dowhy.graph.check_dseparation"]], "check_valid_backdoor_set() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.check_valid_backdoor_set"]], "check_valid_backdoor_set() (in module dowhy.graph)": [[4, "dowhy.graph.check_valid_backdoor_set"]], "check_valid_frontdoor_set() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.check_valid_frontdoor_set"]], "check_valid_frontdoor_set() (in module dowhy.graph)": [[4, "dowhy.graph.check_valid_frontdoor_set"]], "check_valid_mediation_set() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.check_valid_mediation_set"]], "check_valid_mediation_set() (in module dowhy.graph)": [[4, "dowhy.graph.check_valid_mediation_set"]], "choice() (in module dowhy.datasets)": [[4, "dowhy.datasets.choice"]], "choose_variables() (dowhy.causal_refuter.causalrefuter method)": [[4, "dowhy.causal_refuter.CausalRefuter.choose_variables"]], "choose_variables() (in module dowhy.causal_refuter)": [[4, "dowhy.causal_refuter.choose_variables"]], "construct_col_names() (in module dowhy.datasets)": [[4, "dowhy.datasets.construct_col_names"]], "construct_symbolic_estimator() (dowhy.causal_estimator.causalestimator method)": [[4, "dowhy.causal_estimator.CausalEstimator.construct_symbolic_estimator"]], "convert_continuous_to_discrete() (in module dowhy.datasets)": [[4, "dowhy.datasets.convert_continuous_to_discrete"]], "convert_to_binary() (in module dowhy.datasets)": [[4, "dowhy.datasets.convert_to_binary"]], "convert_to_categorical() (in module dowhy.datasets)": [[4, "dowhy.datasets.convert_to_categorical"]], "create_discrete_column() (in module dowhy.datasets)": [[4, "dowhy.datasets.create_discrete_column"]], "create_dot_graph() (in module dowhy.datasets)": [[4, "dowhy.datasets.create_dot_graph"]], "create_gml_graph() (in module dowhy.datasets)": [[4, "dowhy.datasets.create_gml_graph"]], "dataset_from_random_graph() (in module dowhy.datasets)": [[4, "dowhy.datasets.dataset_from_random_graph"]], "disrupt_causes() (dowhy.do_sampler.dosampler method)": [[4, "dowhy.do_sampler.DoSampler.disrupt_causes"]], "do() (dowhy.causalmodel method)": [[4, "dowhy.CausalModel.do"]], "do() (dowhy.causal_estimator.causalestimator method)": [[4, "dowhy.causal_estimator.CausalEstimator.do"]], "do() (dowhy.causal_model.causalmodel method)": [[4, "dowhy.causal_model.CausalModel.do"]], "do_sample() (dowhy.do_sampler.dosampler method)": [[4, "dowhy.do_sampler.DoSampler.do_sample"]], "do_surgery() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.do_surgery"]], "do_surgery() (in module dowhy.graph)": [[4, "dowhy.graph.do_surgery"]], "dowhy": [[4, "module-dowhy"]], "dowhy.causal_estimator": [[4, "module-dowhy.causal_estimator"]], "dowhy.causal_graph": [[4, "module-dowhy.causal_graph"]], "dowhy.causal_model": [[4, "module-dowhy.causal_model"]], "dowhy.causal_refuter": [[4, "module-dowhy.causal_refuter"]], "dowhy.data_transformer": [[4, "module-dowhy.data_transformer"]], "dowhy.datasets": [[4, "module-dowhy.datasets"]], "dowhy.do_sampler": [[4, "module-dowhy.do_sampler"]], "dowhy.graph": [[4, "module-dowhy.graph"]], "dowhy.graph_learner": [[4, "module-dowhy.graph_learner"]], "dowhy.interpreter": [[4, "module-dowhy.interpreter"]], "dowhy.plotter": [[4, "module-dowhy.plotter"]], "edges (dowhy.graph.hasedges property)": [[4, "dowhy.graph.HasEdges.edges"]], "estimate_conditional_effects() 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"dowhy.causal_estimator.CausalEstimator.BootstrapEstimates.estimates"]], "evaluate_effect_strength() (dowhy.causal_estimator.causalestimator method)": [[4, "dowhy.causal_estimator.CausalEstimator.evaluate_effect_strength"]], "filter_unobserved_variables() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.filter_unobserved_variables"]], "generate_random_graph() (in module dowhy.datasets)": [[4, "dowhy.datasets.generate_random_graph"]], "get_adjacency_matrix() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.get_adjacency_matrix"]], "get_adjacency_matrix() (in module dowhy.graph)": [[4, "dowhy.graph.get_adjacency_matrix"]], "get_all_directed_paths() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.get_all_directed_paths"]], "get_all_directed_paths() (in module dowhy.graph)": [[4, "dowhy.graph.get_all_directed_paths"]], "get_all_nodes() (dowhy.causal_graph.causalgraph method)": [[4, 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"dowhy.causal_estimator.CausalEstimate.get_confidence_intervals"]], "get_descendants() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.get_descendants"]], "get_descendants() (in module dowhy.graph)": [[4, "dowhy.graph.get_descendants"]], "get_effect_modifiers() (dowhy.causalmodel method)": [[4, "dowhy.CausalModel.get_effect_modifiers"]], "get_effect_modifiers() (dowhy.causal_graph.causalgraph method)": [[4, "dowhy.causal_graph.CausalGraph.get_effect_modifiers"]], "get_effect_modifiers() (dowhy.causal_model.causalmodel method)": [[4, "dowhy.causal_model.CausalModel.get_effect_modifiers"]], "get_estimator() (dowhy.causalmodel method)": [[4, "dowhy.CausalModel.get_estimator"]], "get_estimator() (dowhy.causal_model.causalmodel method)": [[4, "dowhy.causal_model.CausalModel.get_estimator"]], "get_instruments() (dowhy.causalmodel method)": [[4, "dowhy.CausalModel.get_instruments"]], "get_instruments() (dowhy.causal_graph.causalgraph method)": [[4, 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(dowhy.data_transformer.dimensionalityreducer method)": [[4, "dowhy.data_transformer.DimensionalityReducer.reduce"]], "refute_estimate() (dowhy.causalmodel method)": [[4, "dowhy.CausalModel.refute_estimate"]], "refute_estimate() (dowhy.causal_model.causalmodel method)": [[4, "dowhy.causal_model.CausalModel.refute_estimate"]], "refute_estimate() (dowhy.causal_refuter.causalrefuter method)": [[4, "dowhy.causal_refuter.CausalRefuter.refute_estimate"]], "refute_graph() (dowhy.causalmodel method)": [[4, "dowhy.CausalModel.refute_graph"]], "refute_graph() (dowhy.causal_model.causalmodel method)": [[4, "dowhy.causal_model.CausalModel.refute_graph"]], "reset() (dowhy.do_sampler.dosampler method)": [[4, "dowhy.do_sampler.DoSampler.reset"]], "reset_encoders() (dowhy.causal_estimator.causalestimator method)": [[4, "dowhy.causal_estimator.CausalEstimator.reset_encoders"]], "sales_dataset() (in module dowhy.datasets)": [[4, "dowhy.datasets.sales_dataset"]], "sample() (dowhy.do_sampler.dosampler 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"dowhy.causal_refuters.dummy_outcome_refuter.noise"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.num_thresh"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig.num_thresh"]], "other (dowhy.causal_refuters.dummy_outcome_refuter.testfraction attribute)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.TestFraction.other"]], "partial_correlation() (dowhy.causal_refuters.graph_refuter.graphrefuter method)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefuter.partial_correlation"]], "partial_r2_func() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.partial_r2_func"]], "perform_benchmarking() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.perform_benchmarking"]], "permute() (in module dowhy.causal_refuters.dummy_outcome_refuter)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.permute"]], "plot() (dowhy.causal_refuters.evalue_sensitivity_analyzer.evaluesensitivityanalyzer method)": [[13, "dowhy.causal_refuters.evalue_sensitivity_analyzer.EValueSensitivityAnalyzer.plot"]], "plot() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.plot"]], "plot() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.plot"]], "plot_estimate() 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dowhy.gcm.density_estimators)": [[18, "dowhy.gcm.density_estimators.KernelDensityEstimator1D"]], "local_violation_insight (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.LOCAL_VIOLATION_INSIGHT"]], "linearpredictionmodel (class in dowhy.gcm.unit_change)": [[18, "dowhy.gcm.unit_change.LinearPredictionModel"]], "mec (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.MEC"]], "method (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.METHOD"]], "meandeviationscorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.MeanDeviationScorer"]], "mechanismperformanceresult (class in dowhy.gcm.model_evaluation)": [[18, "dowhy.gcm.model_evaluation.MechanismPerformanceResult"]], "mediancdfquantilescorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.MedianCDFQuantileScorer"]], "mediandeviationscorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.MedianDeviationScorer"]], "not_rejected (dowhy.gcm.validation.rejectionresult attribute)": [[18, "dowhy.gcm.validation.RejectionResult.NOT_REJECTED"]], "n_tests (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.N_TESTS"]], "n_violations (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.N_VIOLATIONS"]], "permutation (dowhy.gcm.shapley.shapleyapproximationmethods attribute)": [[18, "dowhy.gcm.shapley.ShapleyApproximationMethods.PERMUTATION"]], "perm_graphs (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.PERM_GRAPHS"]], "perm_violations (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.PERM_VIOLATIONS"]], "p_value (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.P_VALUE"]], "p_values (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.P_VALUES"]], "postnonlinearmodel (class in dowhy.gcm.causal_mechanisms)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel"]], "probabilisticcausalmodel (class in dowhy.gcm.causal_models)": [[18, "dowhy.gcm.causal_models.ProbabilisticCausalModel"]], "probabilityestimatormodel (class in dowhy.gcm.causal_mechanisms)": [[18, "dowhy.gcm.causal_mechanisms.ProbabilityEstimatorModel"]], "rejected (dowhy.gcm.validation.rejectionresult attribute)": [[18, "dowhy.gcm.validation.RejectionResult.REJECTED"]], "rankbasedanomalyscorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.RankBasedAnomalyScorer"]], "rejectionresult (class in dowhy.gcm.validation)": [[18, "dowhy.gcm.validation.RejectionResult"]], "rescaledmediancdfquantilescorer (class in dowhy.gcm.anomaly_scorers)": [[18, "dowhy.gcm.anomaly_scorers.RescaledMedianCDFQuantileScorer"]], "subset_sampling (dowhy.gcm.shapley.shapleyapproximationmethods attribute)": [[18, "dowhy.gcm.shapley.ShapleyApproximationMethods.SUBSET_SAMPLING"]], "scipydistribution (class in dowhy.gcm.stochastic_models)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution"]], "shapleyapproximationmethods (class in dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.ShapleyApproximationMethods"]], "shapleyconfig (class in dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.ShapleyConfig"]], "sklearnlinearregressionmodel (class in dowhy.gcm.unit_change)": [[18, "dowhy.gcm.unit_change.SklearnLinearRegressionModel"]], "stochasticmodel (class in dowhy.gcm.causal_mechanisms)": [[18, "dowhy.gcm.causal_mechanisms.StochasticModel"]], "structuralcausalmodel (class in dowhy.gcm.causal_models)": [[18, "dowhy.gcm.causal_models.StructuralCausalModel"]], "validate_cm (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.VALIDATE_CM"]], "validate_lmc (dowhy.gcm.falsify.falsifyconst attribute)": [[18, "dowhy.gcm.falsify.FalsifyConst.VALIDATE_LMC"]], "validate_pd (dowhy.gcm.falsify.falsifyconst attribute)": [[18, 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"dowhy.gcm.causal_models.StructuralCausalModel.causal_mechanism"]], "classifier_model (dowhy.gcm.causal_mechanisms.classifierfcm property)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.classifier_model"]], "clone() (dowhy.gcm.causal_mechanisms.additivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.AdditiveNoiseModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.clone"]], "clone() (dowhy.gcm.causal_mechanisms.conditionalstochasticmodel method)": [[18, "dowhy.gcm.causal_mechanisms.ConditionalStochasticModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.clone"]], "clone() (dowhy.gcm.causal_mechanisms.stochasticmodel method)": [[18, 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(in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.estimate_distribution_change_scores"]], "estimate_entropy_discrete() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_discrete"]], "estimate_entropy_kmeans() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_kmeans"]], "estimate_entropy_of_probabilities() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_of_probabilities"]], "estimate_entropy_using_discretization() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_using_discretization"]], "estimate_ftest_pvalue() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.estimate_ftest_pvalue"]], "estimate_gaussian_entropy() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_gaussian_entropy"]], "estimate_geometric_median() (in module dowhy.gcm.confidence_intervals)": [[18, "dowhy.gcm.confidence_intervals.estimate_geometric_median"]], "estimate_kl_divergence_categorical() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_categorical"]], "estimate_kl_divergence_continuous_clf() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_continuous_clf"]], "estimate_kl_divergence_continuous_knn() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_continuous_knn"]], "estimate_kl_divergence_of_probabilities() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_of_probabilities"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.estimate_noise"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.invertiblefunctionalcausalmodel method)": [[18, "dowhy.gcm.causal_mechanisms.InvertibleFunctionalCausalModel.estimate_noise"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.estimate_noise"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.estimate_probabilities"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.probabilityestimatormodel method)": [[18, "dowhy.gcm.causal_mechanisms.ProbabilityEstimatorModel.estimate_probabilities"]], "estimate_shapley_values() (in module dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.estimate_shapley_values"]], "estimate_variance() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_variance"]], "evaluate() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.functionalcausalmodel method)": [[18, "dowhy.gcm.causal_mechanisms.FunctionalCausalModel.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.evaluate"]], "evaluate_causal_model() (in module dowhy.gcm.model_evaluation)": [[18, "dowhy.gcm.model_evaluation.evaluate_causal_model"]], "falsify_graph() (in module dowhy.gcm.falsify)": [[18, "dowhy.gcm.falsify.falsify_graph"]], "feature_relevance_distribution() (in module dowhy.gcm.feature_relevance)": [[18, "dowhy.gcm.feature_relevance.feature_relevance_distribution"]], "feature_relevance_sample() (in module dowhy.gcm.feature_relevance)": [[18, "dowhy.gcm.feature_relevance.feature_relevance_sample"]], "find_best_model() (in module dowhy.gcm.auto)": [[18, "dowhy.gcm.auto.find_best_model"]], "find_suitable_continuous_distribution() (dowhy.gcm.stochastic_models.scipydistribution static method)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution.find_suitable_continuous_distribution"]], "fit() (dowhy.gcm.anomaly_scorer.anomalyscorer method)": [[18, "dowhy.gcm.anomaly_scorer.AnomalyScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.itanomalyscorer method)": [[18, "dowhy.gcm.anomaly_scorers.ITAnomalyScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.inversedensityscorer method)": [[18, "dowhy.gcm.anomaly_scorers.InverseDensityScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.meandeviationscorer method)": [[18, "dowhy.gcm.anomaly_scorers.MeanDeviationScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.mediancdfquantilescorer method)": [[18, "dowhy.gcm.anomaly_scorers.MedianCDFQuantileScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.mediandeviationscorer method)": [[18, "dowhy.gcm.anomaly_scorers.MedianDeviationScorer.fit"]], "fit() (dowhy.gcm.anomaly_scorers.rankbasedanomalyscorer method)": [[18, "dowhy.gcm.anomaly_scorers.RankBasedAnomalyScorer.fit"]], "fit() 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"dowhy.gcm.density_estimators.GaussianMixtureDensityEstimator.fit"]], "fit() (dowhy.gcm.density_estimators.kerneldensityestimator1d method)": [[18, "dowhy.gcm.density_estimators.KernelDensityEstimator1D.fit"]], "fit() (dowhy.gcm.stochastic_models.bayesiangaussianmixturedistribution method)": [[18, "dowhy.gcm.stochastic_models.BayesianGaussianMixtureDistribution.fit"]], "fit() (dowhy.gcm.stochastic_models.empiricaldistribution method)": [[18, "dowhy.gcm.stochastic_models.EmpiricalDistribution.fit"]], "fit() (dowhy.gcm.stochastic_models.scipydistribution method)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution.fit"]], "fit() (in module dowhy.gcm.fitting_sampling)": [[18, "dowhy.gcm.fitting_sampling.fit"]], "fit_and_compute() (in module dowhy.gcm.confidence_intervals_cms)": [[18, "dowhy.gcm.confidence_intervals_cms.fit_and_compute"]], "fit_causal_model_of_target() (in module dowhy.gcm.fitting_sampling)": [[18, "dowhy.gcm.fitting_sampling.fit_causal_model_of_target"]], "get_class_names() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.get_class_names"]], "graph_falsification (dowhy.gcm.model_evaluation.causalmodelevaluationresult attribute)": [[18, "dowhy.gcm.model_evaluation.CausalModelEvaluationResult.graph_falsification"]], "has_linear_relationship() (in module dowhy.gcm.auto)": [[18, "dowhy.gcm.auto.has_linear_relationship"]], "interventional_samples() (in module dowhy.gcm.whatif)": [[18, "dowhy.gcm.whatif.interventional_samples"]], "intrinsic_causal_influence() (in module dowhy.gcm.influence)": [[18, "dowhy.gcm.influence.intrinsic_causal_influence"]], "intrinsic_causal_influence_sample() (in module dowhy.gcm.influence)": [[18, "dowhy.gcm.influence.intrinsic_causal_influence_sample"]], "invertible_function (dowhy.gcm.causal_mechanisms.postnonlinearmodel property)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.invertible_function"]], "is_probability_matrix() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.is_probability_matrix"]], "map_scipy_distribution_parameters_to_names() (dowhy.gcm.stochastic_models.scipydistribution static method)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution.map_scipy_distribution_parameters_to_names"]], "marginal_expectation() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.marginal_expectation"]], "mechanism_change_test() (in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.mechanism_change_test"]], "mechanism_performances (dowhy.gcm.model_evaluation.causalmodelevaluationresult attribute)": [[18, "dowhy.gcm.model_evaluation.CausalModelEvaluationResult.mechanism_performances"]], "merge_p_values_average() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.merge_p_values_average"]], "merge_p_values_fdr() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.merge_p_values_fdr"]], "merge_p_values_quantile() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.merge_p_values_quantile"]], "nmse() (in module dowhy.gcm.model_evaluation)": [[18, "dowhy.gcm.model_evaluation.nmse"]], "noise_model (dowhy.gcm.causal_mechanisms.postnonlinearmodel property)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.noise_model"]], "overall_kl_divergence (dowhy.gcm.model_evaluation.causalmodelevaluationresult attribute)": [[18, "dowhy.gcm.model_evaluation.CausalModelEvaluationResult.overall_kl_divergence"]], "parameters (dowhy.gcm.stochastic_models.scipydistribution property)": [[18, "dowhy.gcm.stochastic_models.ScipyDistribution.parameters"]], "parent_relevance() (in module dowhy.gcm.feature_relevance)": [[18, "dowhy.gcm.feature_relevance.parent_relevance"]], "permute_features() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.permute_features"]], "plot_evaluation_results() (in module dowhy.gcm.falsify)": [[18, "dowhy.gcm.falsify.plot_evaluation_results"]], "plot_falsification_histogram 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module dowhy.gcm.falsify)": [[18, "dowhy.gcm.falsify.validate_tpa"]], "apply_delta_kernel() (in module dowhy.gcm.independence_test.kernel_operation)": [[19, "dowhy.gcm.independence_test.kernel_operation.apply_delta_kernel"]], "apply_rbf_kernel() (in module dowhy.gcm.independence_test.kernel_operation)": [[19, "dowhy.gcm.independence_test.kernel_operation.apply_rbf_kernel"]], "apply_rbf_kernel_with_adaptive_precision() (in module dowhy.gcm.independence_test.kernel_operation)": [[19, "dowhy.gcm.independence_test.kernel_operation.apply_rbf_kernel_with_adaptive_precision"]], "approx_kernel_based() (in module dowhy.gcm.independence_test.kernel)": [[19, "dowhy.gcm.independence_test.kernel.approx_kernel_based"]], "approximate_delta_kernel_features() (in module dowhy.gcm.independence_test.kernel_operation)": [[19, "dowhy.gcm.independence_test.kernel_operation.approximate_delta_kernel_features"]], "approximate_rbf_kernel_features() (in module dowhy.gcm.independence_test.kernel_operation)": 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"transform() (dowhy.gcm.util.catboost_encoder.catboostencoder method)": [[21, "dowhy.gcm.util.catboost_encoder.CatBoostEncoder.transform"]], "variance_of_deviations() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.variance_of_deviations"]], "variance_of_matching_values() (in module dowhy.gcm.util.general)": [[21, "dowhy.gcm.util.general.variance_of_matching_values"]], "cdt (class in dowhy.graph_learners.cdt)": [[22, "dowhy.graph_learners.cdt.CDT"]], "ges (class in dowhy.graph_learners.ges)": [[22, "dowhy.graph_learners.ges.GES"]], "lingam (class in dowhy.graph_learners.lingam)": [[22, "dowhy.graph_learners.lingam.LINGAM"]], "dowhy.graph_learners": [[22, "module-dowhy.graph_learners"]], "dowhy.graph_learners.cdt": [[22, "module-dowhy.graph_learners.cdt"]], "dowhy.graph_learners.ges": [[22, "module-dowhy.graph_learners.ges"]], "dowhy.graph_learners.lingam": [[22, "module-dowhy.graph_learners.lingam"]], "get_discovery_class_object() (in module dowhy.graph_learners)": 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"dowhy.utils.propensity_score.binarize_discrete"]], "binary_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.binary_treatment_model"]], "categorical_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.categorical_treatment_model"]], "compute_ci() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.compute_ci"]], "conditional_mi() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.conditional_MI"]], "continuous_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.continuous_treatment_model"]], "convert_to_binary() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.convert_to_binary"]], "convert_to_undirected_graph() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.convert_to_undirected_graph"]], "create_polynomial_function() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.create_polynomial_function"]], "daggity_to_dot() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.daggity_to_dot"]], "del_edge() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.del_edge"]], "difference() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.difference"]], "discrete_to_integer() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.discrete_to_integer"]], "dowhy.utils": [[24, "module-dowhy.utils"]], "dowhy.utils.api": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting": [[24, 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"dowhy.utils.regression.generate_moment_function"]], "generation_process() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.generation_process"]], "get_all() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.get_all"]], "get_numeric_features() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.get_numeric_features"]], "get_random_node_pair() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.get_random_node_pair"]], "get_simple_ordered_tree() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.get_simple_ordered_tree"]], "get_type_string() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.get_type_string"]], "induced_graph() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.induced_graph"]], "intersection() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.intersection"]], "is_connected() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.is_connected"]], "is_empty() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.is_empty"]], "parse_state() (in module dowhy.utils.api)": [[24, "dowhy.utils.api.parse_state"]], "partial_corr() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.partial_corr"]], "plot() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.plot"]], "plot_adjacency_matrix() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.plot_adjacency_matrix"]], "plot_causal_graph_graphviz() (in module dowhy.utils.graphviz_plotting)": [[24, "dowhy.utils.graphviz_plotting.plot_causal_graph_graphviz"]], "plot_causal_graph_networkx() (in module dowhy.utils.networkx_plotting)": [[24, "dowhy.utils.networkx_plotting.plot_causal_graph_networkx"]], "pretty_print_graph() (in module dowhy.utils.plotting)": [[24, 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\ No newline at end of file
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18, 36, 43, 48, 50, 53, 57, 68, 89, 100], "librari": [0, 1, 2, 4, 6, 18, 22, 24, 31, 35, 38, 39, 42, 47, 49, 53, 55, 56, 65, 71, 79, 92, 100, 102, 108, 109], "causal": [0, 2, 4, 5, 6, 7, 9, 11, 13, 17, 18, 19, 22, 23, 24, 26, 27, 28, 34, 37, 38, 40, 42, 44, 46, 48, 50, 52, 53, 59, 62, 72, 75, 77, 78, 82, 84, 86, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 105, 110, 114, 118, 119, 123, 124], "infer": [0, 1, 4, 5, 6, 7, 13, 17, 18, 19, 22, 26, 27, 28, 34, 39, 44, 47, 55, 57, 59, 60, 63, 65, 66, 75, 79, 85, 87, 101, 102, 109, 113, 121], "2020": [0, 1, 7, 13, 14, 15, 18, 45, 66], "http": [0, 1, 3, 4, 7, 9, 13, 14, 15, 18, 21, 22, 24, 25, 26, 31, 38, 42, 44, 45, 48, 53, 54, 59, 64, 65, 66, 68, 70, 71], "arxiv": [0, 9, 13, 14, 15, 18, 19, 21, 26, 45, 53, 64, 65], "org": [0, 4, 9, 13, 14, 15, 18, 21, 22, 24, 25, 26, 31, 44, 45, 53, 54, 64, 65, 66], "ab": [0, 4, 9, 13, 14, 15, 18, 31, 45, 51, 53, 54, 55, 64, 65, 67, 89, 109], "2011": [0, 7, 13, 18, 19, 44], "04216": 0, "patrick": [0, 18, 89, 93, 94, 95], "bl\u00f6baum": [0, 18, 53, 89, 93, 94, 95], "peter": [0, 18, 19], "g\u00f6tz": 0, "kailash": [0, 18, 89, 93, 94], "budhathoki": [0, 18, 56, 57, 89, 93, 94], "atalanti": [0, 89], "A": [0, 1, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 63, 64, 65, 67, 69, 76, 77, 80, 85, 87, 89, 93, 94, 95, 97, 102, 103, 110, 112, 113, 114, 117], "mastakouri": [0, 18, 53, 89], "dominik": [0, 18, 89, 92, 93, 94, 95], "janz": [0, 18, 19, 53, 56, 89, 92, 93, 94, 95], "gcm": [0, 4, 26, 48, 49, 50, 53, 54, 55, 56, 57, 63, 69, 71, 87, 88, 89, 92, 93, 94, 95, 97, 99, 101, 102, 103, 105, 107, 111, 112], "extens": [0, 18, 30, 61, 65, 71, 79, 117], "graphic": [0, 1, 4, 7, 18, 34, 44, 48, 52, 54, 55, 63, 71, 79, 80, 89, 96, 97, 98, 99, 100, 101, 102, 103, 109, 110, 114], "model": [0, 1, 2, 4, 6, 7, 8, 9, 13, 15, 18, 19, 20, 24, 25, 26, 27, 29, 30, 31, 34, 35, 37, 39, 43, 45, 47, 48, 52, 53, 54, 59, 61, 62, 67, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 108, 110, 111, 114, 116, 117, 119, 120, 122, 124], "2024": [0, 18, 29, 30, 40, 51, 71, 89, 94], "mloss": 0, "25": [0, 4, 18, 26, 29, 33, 45, 46, 48, 51, 55, 58, 60, 64, 67, 99], "147": 0, "1": [0, 4, 5, 6, 9, 11, 13, 14, 15, 17, 18, 19, 21, 24, 27, 28, 29, 30, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 73, 75, 78, 79, 80, 89, 92, 93, 94, 95, 99, 105, 109, 110, 111, 112, 113, 114, 117, 124], "7": [0, 4, 5, 13, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 68, 92, 93, 99, 110, 114], "jmlr": [0, 34], "paper": [0, 18, 19, 26, 34, 48, 51, 53, 55, 59, 89, 92, 93, 94, 95, 112], "v25": 0, "22": [0, 28, 29, 30, 40, 44, 47, 51, 55, 60, 66, 67, 71], "1258": 0, "html": [0, 1, 4, 9, 13, 18, 21, 25, 42, 48, 57, 64, 66, 68], "bibtex": 0, "articl": [0, 1, 7, 9, 13, 24], "titl": [0, 9, 11, 12, 18, 23, 26, 50, 57], "author": [0, 3, 9, 11, 12], "journal": [0, 7, 9, 19, 34, 44], "preprint": [0, 18], "year": [0, 9, 11, 12, 26, 36, 44, 48, 55], "bl": 0, "o": [0, 4, 5, 14, 15, 17, 27, 29, 58, 60, 61, 66, 67], "baum": 0, "g": [0, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 24, 25, 26, 31, 34, 36, 43, 45, 48, 49, 50, 52, 54, 55, 56, 58, 60, 64, 65, 68, 79, 85, 89, 92, 93, 94, 96, 97, 103, 108, 110, 111, 112, 113, 114], "tz": 0, "machin": [0, 1, 3, 4, 9, 13, 18, 26, 28, 31, 51, 54, 55, 63, 65, 69, 71, 72, 79, 93, 94, 97, 102, 109, 112], "learn": [0, 1, 4, 9, 11, 13, 14, 15, 18, 19, 25, 26, 28, 45, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 71, 72, 79, 89, 93, 94, 97, 101, 102, 103, 109, 110, 112, 113, 114], "research": [0, 13, 18, 26, 27, 34, 51, 55, 66, 68, 69, 75], "volum": [0, 9, 14, 45], "number": [0, 4, 6, 7, 9, 13, 14, 15, 17, 18, 19, 25, 31, 33, 43, 45, 53, 54, 55, 57, 58, 64, 66, 82, 92, 106, 107, 109, 111, 115], "page": [0, 1, 4, 15, 18, 19, 37, 55], "url": [0, 9], "i": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 64, 66, 67, 69, 70, 71, 72, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 118, 121, 122, 123, 124], "host": [1, 56], "github": [1, 2, 3, 4, 25, 42, 47, 63, 64, 67, 70], "can": [1, 2, 3, 4, 5, 6, 7, 11, 13, 17, 18, 19, 21, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 73, 75, 76, 78, 79, 80, 82, 84, 85, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 124], "brows": [1, 71], "code": [1, 2, 4, 7, 9, 13, 14, 15, 24, 25, 35, 39, 45, 51, 55, 59, 64, 68, 69, 70, 71, 82, 89, 94, 102, 109], 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92, 93, 94, 96, 97, 101, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 118, 120, 124], "experiment": [1, 4, 6, 28, 44], "stage": [1, 4, 6, 13, 25, 27, 29, 34, 41, 65, 75, 79, 87, 102], "allow": [1, 3, 4, 5, 6, 13, 17, 18, 19, 28, 48, 49, 51, 60, 67, 68, 71, 79, 80, 92, 94, 97, 112, 113], "modular": [1, 18, 71], "differ": [1, 3, 4, 5, 6, 7, 13, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27, 28, 31, 34, 36, 37, 39, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 63, 65, 66, 75, 80, 88, 89, 90, 92, 93, 94, 95, 96, 97, 100, 102, 111, 112, 114, 124], "includ": [1, 3, 4, 13, 14, 15, 18, 24, 26, 36, 45, 48, 51, 55, 56, 60, 65, 66, 67, 68, 70, 71, 81, 87, 89, 93, 95, 109, 114], "separ": [1, 4, 6, 7, 9, 18, 45, 53, 71, 79, 90, 102, 104, 106], "fit": [1, 4, 6, 9, 13, 14, 15, 18, 20, 21, 26, 27, 28, 29, 30, 31, 34, 37, 40, 48, 50, 51, 52, 54, 56, 57, 66, 68, 69, 71, 75, 77, 78, 80, 89, 92, 93, 94, 95, 97, 99, 101, 109, 110, 111, 112], "method": [1, 2, 4, 5, 6, 7, 9, 13, 14, 17, 18, 19, 20, 21, 22, 24, 26, 27, 30, 31, 32, 34, 37, 40, 41, 45, 47, 51, 53, 54, 55, 56, 57, 58, 60, 63, 65, 68, 69, 71, 75, 76, 81, 82, 83, 84, 86, 87, 90, 95, 99, 100, 102, 103, 105, 109, 110, 111, 112, 114, 115, 120, 124], "leav": [1, 13, 51, 54, 60], "feedback": [1, 37], "old": [1, 18, 37, 48, 94, 111], "causalmodel": [1, 4, 7, 23, 25, 27, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 47, 58, 59, 61, 65, 66, 67, 68, 69, 73, 75, 79, 82, 83, 84, 86, 87, 108], "should": [1, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27, 29, 33, 36, 41, 45, 47, 49, 50, 51, 52, 54, 55, 56, 58, 60, 64, 66, 67, 70, 75, 79, 89, 92, 97, 100, 103, 104, 105, 106, 109, 110, 114, 115, 116, 117, 118, 119, 120, 121, 124], "befor": [1, 2, 13, 14, 15, 18, 23, 25, 27, 36, 39, 47, 48, 49, 50, 53, 54, 55, 56, 57, 60, 65, 75, 87, 94, 105, 109, 113], "andresmor": 1, "m": [1, 3, 4, 13, 14, 15, 18, 24, 26, 29, 34, 45, 51, 53, 54, 64, 89, 97], "analys": [1, 28, 48], "mani": [1, 3, 13, 18, 19, 25, 27, 36, 45, 49, 50, 52, 53, 54, 55, 56, 58, 68, 75, 80, 82, 97, 100, 103, 104, 107, 109, 110, 111, 114], "now": [1, 3, 25, 26, 27, 28, 31, 33, 35, 36, 39, 41, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 68, 70, 80, 89, 93, 95, 99, 106, 109, 110, 111, 113, 114], "joblib": [1, 13], "parallel": [1, 4, 5, 13, 17, 18, 19, 47, 56, 60, 94, 111], "process": [1, 4, 9, 10, 13, 15, 17, 18, 24, 25, 26, 27, 29, 31, 36, 47, 49, 52, 54, 57, 60, 64, 65, 71, 75, 78, 79, 80, 85, 87, 93, 94, 97, 100, 102, 109, 110, 111, 112, 113, 114, 117], "show": [1, 4, 6, 13, 18, 23, 26, 28, 29, 30, 31, 32, 33, 34, 37, 39, 40, 45, 47, 48, 51, 54, 55, 56, 57, 59, 61, 64, 65, 66, 67, 68, 69, 79, 81, 106, 107, 109, 124], "progress": [1, 4, 13, 18, 37, 55, 57], "bar": [1, 4, 18, 24, 26, 27, 30, 39, 50, 51, 55, 56, 57, 60, 75], "astoeffelbau": 1, "yemaedahrav": 1, "non": [1, 4, 7, 13, 14, 15, 18, 19, 25, 26, 27, 34, 37, 41, 44, 45, 48, 49, 50, 52, 54, 55, 56, 57, 63, 64, 67, 68, 69, 71, 75, 79, 89, 93, 95, 97, 102, 104, 109, 110, 113, 114, 118, 121], "linear": [1, 4, 6, 7, 12, 13, 15, 18, 19, 24, 26, 27, 32, 33, 34, 35, 37, 39, 41, 46, 47, 49, 50, 51, 52, 55, 56, 57, 66, 68, 75, 78, 79, 87, 89, 93, 95, 109, 113, 114, 118, 121, 124], "chernozhukov": [1, 13, 51, 65], "cinelli": [1, 13, 66], "newei": [1, 13], "syrgkani": [1, 13], "2021": [1, 4, 7, 9, 11, 18, 34, 55, 56, 57, 94, 95], "exampl": [1, 2, 3, 6, 7, 13, 15, 18, 21, 24, 25, 26, 28, 30, 36, 37, 42, 50, 51, 53, 55, 61, 64, 65, 67, 68, 69, 71, 79, 84, 86, 87, 95, 101, 102, 103, 104, 106, 109, 110, 112, 114, 117, 118], "anusha0409": 1, "e": [1, 3, 4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 79, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 103, 108, 109, 110, 111, 112, 113, 114], "valu": [1, 4, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 75, 77, 78, 81, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 105, 107, 110, 111, 112, 113, 114, 117, 118, 124], "ding": [1, 13, 66], "vanderweel": [1, 13, 66], "2016": 1, "www": [1, 3, 7, 13, 24, 53, 56, 68], "ncbi": [1, 7, 13, 24], "nlm": [1, 7, 13, 24], "nih": [1, 7, 13, 24], "gov": [1, 7, 13, 24], "pmc": [1, 7, 13, 24], "pmc4820664": [1, 13], "jlgleason": 1, "unit": [1, 4, 6, 13, 18, 23, 25, 26, 27, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 55, 57, 65, 66, 67, 68, 92, 94], "chang": [1, 3, 4, 13, 18, 23, 25, 26, 28, 33, 36, 37, 39, 40, 42, 45, 47, 49, 51, 54, 55, 56, 58, 63, 65, 66, 68, 79, 87, 88, 90, 92, 93, 95, 96, 97, 100, 101, 102, 111, 112, 113, 114, 116, 118, 120, 124], "attribut": [1, 4, 5, 7, 9, 11, 17, 21, 24, 26, 31, 34, 51, 53, 54, 60, 63, 67, 88, 89, 92, 95, 96, 101, 102, 113], "kailashbuki": 1, "qualiti": [1, 18, 49, 50, 55, 57, 102, 114], "option": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 19, 24, 25, 26, 46, 47, 48, 49, 50, 51, 54, 55, 57, 60, 64, 70, 95, 109, 114, 124], "best": [1, 4, 13, 18, 19, 24, 49, 51, 55, 57, 64, 68, 69, 102, 107, 109, 114], "auto": [1, 4, 6, 26, 28, 48, 49, 50, 52, 54, 55, 56, 57, 69, 80, 89, 92, 93, 94, 95, 97, 99, 110, 111, 112, 113], "assign": [1, 4, 6, 9, 17, 18, 25, 26, 35, 39, 48, 49, 50, 52, 55, 56, 57, 71, 79, 87, 89, 93, 97, 101, 103, 110, 111, 112, 113], "mechan": [1, 18, 25, 26, 48, 49, 50, 51, 52, 54, 55, 56, 57, 68, 71, 89, 92, 93, 94, 95, 96, 97, 101, 102, 103, 110, 111, 113, 114], "which": [1, 4, 5, 6, 7, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 28, 29, 31, 33, 45, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 61, 64, 65, 66, 67, 68, 69, 75, 80, 82, 88, 89, 91, 93, 94, 95, 97, 102, 104, 106, 109, 111, 112, 114], "ml": [1, 4, 18, 28, 48, 49, 51, 53, 56, 64, 68, 89, 109, 113], "autogluon": [1, 4, 18, 111], "bloebp": 1, "condit": [1, 4, 6, 7, 9, 13, 17, 18, 19, 24, 36, 42, 45, 49, 50, 52, 53, 54, 55, 56, 57, 60, 63, 65, 66, 67, 68, 69, 71, 76, 79, 87, 88, 89, 90, 92, 94, 101, 102, 103, 105, 106, 107, 109, 110, 112, 113, 114, 118], "through": [1, 4, 6, 13, 17, 18, 24, 27, 33, 37, 41, 45, 50, 52, 56, 59, 60, 65, 67, 68, 69, 71, 75, 79, 90, 91, 92, 93, 100, 102, 104, 110], "packag": [1, 25, 28, 35, 36, 39, 47, 48, 52, 58, 60, 64, 67, 68, 71, 73, 95, 96, 101, 104, 109, 110, 111, 113], "algorithm": [1, 4, 7, 8, 13, 18, 26, 31, 34, 37, 49, 50, 51, 52, 54, 55, 56, 57, 63, 67, 68, 69, 71, 72, 85, 87, 94, 102, 104, 109, 110, 111, 112, 114], "comput": [1, 4, 6, 7, 9, 12, 13, 15, 18, 24, 26, 27, 28, 30, 34, 37, 44, 47, 48, 51, 54, 56, 57, 60, 64, 65, 68, 75, 80, 88, 89, 94, 96, 98, 100, 101, 113], "effici": [1, 4, 7, 13, 27, 34, 65, 75], "backdoor": [1, 4, 13, 17, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 83, 85, 86, 87, 88, 101, 102, 124], "set": [1, 4, 5, 7, 10, 13, 14, 15, 17, 18, 19, 21, 24, 26, 27, 28, 32, 33, 36, 37, 41, 44, 45, 46, 47, 48, 49, 51, 53, 54, 55, 57, 60, 63, 65, 66, 67, 68, 70, 71, 75, 76, 80, 82, 85, 87, 89, 93, 94, 95, 96, 97, 102, 104, 106, 111, 112, 113, 114, 124], "esmucl": 1, "control": [1, 4, 5, 6, 7, 18, 19, 25, 27, 35, 39, 45, 47, 48, 49, 60, 75, 78, 101, 113, 115], "direct": [1, 4, 7, 11, 13, 18, 24, 25, 26, 31, 34, 43, 49, 51, 54, 55, 56, 57, 58, 59, 61, 63, 64, 65, 66, 67, 79, 88, 90, 101, 102, 103, 104, 106, 113, 114], "multi": [1, 4, 9, 26, 42, 94], "egorkraevtransferwis": 1, "pydata": 1, "theme": 1, "homepag": 1, "get": [1, 4, 7, 13, 17, 18, 19, 24, 25, 27, 31, 35, 39, 47, 48, 49, 51, 54, 55, 56, 64, 66, 68, 70, 71, 75, 80, 93, 94, 111, 114], "start": [1, 2, 4, 13, 15, 17, 25, 43, 44, 50, 55, 71, 79, 82, 102, 109], "guid": [1, 2, 4, 33, 49, 69, 71, 112], "revis": 1, "user": [1, 4, 5, 7, 13, 18, 24, 27, 36, 47, 49, 50, 60, 63, 64, 69, 70, 71, 72, 75, 79, 92, 103, 104, 106, 109, 112, 117], "petergtz": 1, "contribut": [1, 18, 48, 51, 54, 55, 57, 68, 71, 89, 90, 93, 95, 96, 100, 111], 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28, 50, 51, 52, 54, 55, 56, 57, 69, 71, 75, 89, 90, 93, 95, 101, 102, 103, 106, 109, 110, 112, 113, 114], "updat": [1, 2, 3, 4, 7, 18, 37, 55, 56, 67, 70], "check": [1, 2, 3, 4, 7, 13, 18, 21, 24, 25, 27, 29, 36, 37, 41, 46, 47, 49, 50, 52, 54, 55, 56, 58, 60, 65, 69, 71, 72, 75, 79, 83, 84, 85, 86, 87, 100, 102, 103, 106, 107, 110, 114, 117, 120, 124], "out": [1, 2, 4, 9, 12, 13, 19, 25, 26, 27, 37, 45, 47, 51, 54, 55, 56, 64, 69, 75, 79, 84, 85, 87, 88, 94, 101, 102, 103, 107, 109, 112, 117], "elikl": 1, "itsoum": 1, "ryanrussel": 1, "whether": [1, 4, 5, 6, 9, 10, 11, 13, 14, 15, 17, 18, 19, 24, 25, 26, 36, 41, 44, 47, 50, 53, 55, 56, 57, 58, 60, 68, 71, 85, 87, 92, 97, 102, 105, 106, 107, 114, 124], "data": [1, 4, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 26, 27, 29, 30, 33, 34, 35, 36, 37, 39, 41, 42, 43, 52, 54, 57, 58, 59, 62, 63, 65, 66, 69, 71, 75, 78, 80, 85, 87, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, 112, 114, 115, 117, 118, 124], "conform": [1, 7], "assum": [1, 4, 7, 13, 17, 18, 25, 29, 30, 34, 36, 40, 47, 48, 49, 50, 55, 56, 57, 58, 65, 66, 69, 71, 89, 94, 97, 102, 105, 107, 109, 111, 112, 113, 114, 124], "specif": [1, 4, 5, 6, 7, 9, 13, 17, 18, 19, 24, 26, 27, 28, 31, 47, 50, 55, 60, 64, 66, 71, 75, 82, 90, 91, 92, 93, 95, 97, 103, 104, 108, 111, 112, 124], "paramet": [1, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 31, 33, 37, 41, 45, 47, 49, 51, 53, 55, 57, 58, 60, 64, 65, 67, 68, 71, 75, 79, 89, 92, 97, 109, 111, 114, 117, 121], "directli": [1, 4, 18, 28, 37, 48, 51, 54, 56, 63, 68, 73, 90, 93, 95, 97, 104, 107, 108, 111], "its": [1, 3, 4, 5, 6, 7, 18, 19, 25, 28, 31, 32, 34, 49, 50, 51, 55, 56, 57, 60, 63, 65, 66, 69, 79, 88, 89, 90, 92, 93, 94, 95, 97, 102, 106, 109, 111, 113, 114, 120], "own": [1, 4, 6, 17, 27, 28, 34, 54, 71, 75, 92, 112, 113, 114], "init": [1, 6], "custom": [1, 4, 5, 6, 13, 18, 24, 25, 36, 49, 55, 56, 57, 63, 71, 89, 95, 96, 101, 102, 112, 113, 114, 117], "consist": [1, 4, 13, 18, 21, 26, 31, 45, 48, 49, 50, 54, 55, 56, 57, 64, 69, 93, 104, 105, 106, 113, 114], "init_param": [1, 28, 46, 65, 68, 73, 79], "add": [1, 3, 4, 7, 13, 15, 18, 24, 25, 28, 32, 35, 37, 38, 39, 41, 45, 47, 51, 53, 58, 67, 68, 70, 79, 80, 89, 113, 114, 118, 120, 121], "glm": [1, 6], "hotel": [1, 62, 63, 103], "case": [1, 3, 4, 7, 13, 14, 17, 18, 19, 25, 26, 27, 28, 34, 36, 43, 47, 48, 49, 52, 54, 55, 56, 57, 63, 65, 71, 75, 87, 89, 92, 93, 95, 96, 97, 102, 103, 104, 109, 110, 112, 113, 114, 124], "studi": [1, 13, 14, 15, 26, 44, 45, 48, 56, 57, 65], "ae": 1, "foster": 1, "major": [1, 67, 100], "identif": [1, 4, 5, 6, 7, 13, 17, 25, 27, 31, 35, 39, 41, 59, 60, 68, 69, 71, 75, 81, 82, 83, 85, 86, 87, 102, 103, 108], "minim": [1, 4, 7, 9, 17, 18, 34, 45, 49, 53, 55, 57, 64, 65, 68, 82, 114], "adjust": [1, 4, 7, 13, 18, 19, 26, 38, 39, 45, 48, 49, 50, 55, 56, 63, 65, 66, 67, 68, 82, 91, 114], "maxim": [1, 7, 38, 82], "exhaust": [1, 7, 82], "search": 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49, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 73, 75, 79, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 109, 111, 112, 113, 114, 124], "distanc": [1, 6, 9, 13, 18, 19, 76, 87], "match": [1, 4, 6, 13, 14, 33, 36, 45, 58, 76, 79, 87, 104, 117], "reli": [1, 18, 55, 68, 73, 112], "metric": [1, 6, 9, 18, 26, 35, 49, 50, 54, 55, 56, 57, 64, 92, 114], "between": [1, 4, 6, 7, 9, 10, 11, 13, 18, 19, 24, 25, 26, 27, 32, 33, 35, 39, 43, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 64, 65, 66, 67, 71, 75, 79, 80, 90, 92, 93, 94, 102, 103, 109, 110, 111, 112, 114, 118, 124], "input": [1, 4, 13, 18, 19, 20, 21, 24, 26, 31, 35, 39, 45, 50, 53, 56, 57, 63, 64, 67, 68, 70, 88, 92, 95, 96, 101, 102, 104, 109, 111, 112, 114, 124], "heurist": [1, 48, 55], "default": [1, 3, 4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 24, 26, 27, 31, 37, 47, 51, 53, 54, 56, 58, 60, 64, 65, 66, 71, 75, 82, 89, 92, 94, 95, 111, 114, 124], "strata": [1, 6], "automat": [1, 3, 4, 5, 6, 13, 15, 18, 19, 27, 45, 47, 48, 49, 52, 55, 57, 60, 65, 71, 75, 79, 89, 93, 106, 108, 110, 113, 114, 121], "propens": [1, 4, 6, 13, 23, 27, 42, 45, 47, 60, 68, 75, 76, 79, 87], "score": [1, 4, 6, 13, 15, 18, 23, 26, 27, 45, 47, 49, 50, 51, 54, 55, 56, 57, 60, 67, 68, 75, 79, 87, 93, 94, 95, 111, 114], "stratif": [1, 4, 23, 47, 68, 79, 87], "confid": [1, 4, 6, 13, 18, 24, 25, 27, 29, 30, 37, 48, 55, 56, 57, 60, 65, 66, 75, 79, 101, 113], "interv": [1, 4, 6, 13, 18, 24, 30, 48, 55, 56, 57, 60, 66, 79, 101, 113], "regress": [1, 4, 6, 13, 18, 25, 26, 28, 29, 32, 33, 40, 41, 48, 49, 51, 55, 57, 63, 65, 68, 76, 79, 87, 94, 112, 114, 122], "version": [1, 3, 4, 13, 18, 21, 26, 31, 32, 33, 37, 44, 46, 54, 55, 66, 69, 70, 71], "bootstrap": [1, 4, 6, 13, 18, 19, 27, 37, 46, 48, 51, 56, 57, 60, 75, 79], "without": [1, 3, 4, 18, 19, 30, 47, 55, 56, 65, 66, 85, 105, 109, 111, 114], "need": [1, 4, 6, 7, 12, 13, 18, 19, 21, 25, 27, 28, 34, 36, 37, 47, 51, 54, 55, 59, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 89, 96, 97, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 115, 117], "refit": 1, "andrewc19": 1, "ha2trinh": 1, "siddhanthaldar": 1, "vojavocni": 1, "also": [1, 2, 3, 6, 7, 13, 18, 24, 25, 26, 29, 31, 32, 34, 35, 39, 42, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 63, 65, 66, 67, 68, 69, 71, 79, 80, 82, 87, 89, 90, 92, 93, 95, 96, 97, 99, 100, 102, 103, 105, 106, 109, 110, 111, 112, 113, 114, 115, 117, 118, 124], "iv": [1, 4, 6, 25, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 42, 44, 46, 47, 62, 65, 66, 68, 79, 81], "move": [1, 28, 48, 88, 100, 104], "matplotlib": [1, 13, 25, 26, 31, 32, 45, 48, 50, 51, 55, 57, 60, 66, 67, 68], "instal": [1, 3, 18, 24, 26, 51, 63, 64, 67, 68, 71, 104], "pip": [1, 3, 24, 26, 64, 67, 68, 69], "plot": [1, 3, 4, 13, 18, 23, 26, 30, 32, 34, 37, 39, 40, 47, 48, 50, 51, 53, 54, 55, 56, 57, 60, 65, 67, 68, 109, 124], "align": [1, 56], "all": [1, 2, 3, 4, 6, 7, 9, 10, 12, 13, 15, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 32, 34, 35, 36, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 67, 68, 70, 72, 73, 75, 79, 80, 82, 83, 86, 89, 93, 97, 100, 102, 103, 105, 109, 110, 111, 112, 113, 114, 124], "continu": [1, 4, 5, 13, 14, 15, 17, 18, 19, 21, 23, 37, 44, 45, 49, 50, 54, 55, 56, 57, 58, 60, 67, 92, 97, 106, 109, 112, 114], "log": [1, 13, 18, 24, 25, 28, 29, 30, 32, 33, 35, 37, 39, 40, 41, 44, 46, 56, 66, 68, 93, 109], "configur": [1, 9, 13, 18, 19, 25, 29, 37, 55], "dummyoutcomerefut": [1, 4, 13], "arshiaarya": 1, "n8sty": 1, "moprescu": 1, "conda": [1, 69], "formula": [1, 4, 13, 17, 24, 27, 39, 40, 75], "front": [1, 4, 6, 79, 87], "door": [1, 4, 6, 7, 27, 48, 75, 79, 87], "criterion": [1, 6, 13, 18, 26, 41, 68, 79, 85, 86, 87], "path": [1, 4, 7, 24, 26, 27, 34, 35, 39, 41, 48, 51, 58, 59, 60, 61, 64, 66, 70, 75, 91, 92], "d": [1, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 51, 53, 57, 58, 60, 64, 65, 66, 67, 68, 70, 75, 92, 93, 106], "logist": [1, 4, 6, 18, 24, 25, 51, 66, 78, 87], "erikhambardzumyan": 1, "visual": [1, 4, 23, 31, 45, 51, 55, 56, 68], "how": [1, 4, 13, 17, 18, 19, 23, 25, 26, 31, 33, 34, 36, 39, 40, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 60, 64, 65, 66, 67, 68, 79, 82, 88, 90, 96, 100, 102, 106, 107, 109, 112, 113, 114, 121, 124], "distribut": [1, 3, 4, 9, 12, 13, 14, 17, 18, 23, 24, 26, 27, 39, 40, 48, 49, 50, 51, 52, 54, 55, 56, 57, 60, 68, 70, 75, 76, 80, 85, 88, 89, 90, 92, 93, 95, 96, 97, 101, 102, 103, 104, 109, 110, 111, 112, 113, 114], "friendlier": 1, "error": [1, 4, 6, 7, 13, 18, 19, 24, 27, 30, 34, 37, 40, 49, 50, 51, 53, 54, 55, 56, 57, 60, 64, 66, 68, 70, 75, 94, 103, 111, 114], "messag": [1, 3, 13, 18], "when": [1, 4, 5, 6, 7, 9, 10, 13, 14, 18, 19, 21, 24, 25, 27, 31, 34, 36, 37, 42, 45, 47, 49, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 78, 79, 80, 90, 92, 93, 94, 97, 106, 109, 110, 111, 112, 114, 116, 117, 119, 121], "enough": [1, 18, 25, 36, 65, 66, 68, 114], "bin": [1, 4, 6, 13, 14, 18, 26, 48], "dummi": [1, 13, 33, 79, 118], "outcom": [1, 4, 5, 6, 7, 13, 17, 18, 19, 20, 23, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 58, 59, 60, 61, 63, 65, 66, 67, 68, 69, 75, 76, 78, 79, 87, 88, 90, 95, 97, 100, 101, 102, 108, 112, 118, 121, 124], "compon": [1, 5, 19, 24, 51, 60, 94, 103], "simul": [1, 4, 6, 13, 29, 33, 35, 36, 37, 39, 41, 47, 51, 53, 65, 66, 79, 88, 92, 94, 97, 98, 101, 117, 118, 121], "zero": [1, 4, 13, 14, 18, 25, 29, 30, 33, 36, 41, 44, 47, 51, 53, 54, 55, 65, 66, 79, 89, 107, 118, 119, 124], "too": [1, 4, 13, 18, 25, 27, 47, 53, 72, 73, 75, 79, 80, 95, 114, 121, 124], "readi": [1, 3, 26, 31, 71, 80, 88, 109, 113], "book": [1, 57, 62, 63, 69, 85, 103], "cancel": [1, 51, 62, 63, 103], "membership": [1, 36, 62], "reward": [1, 15, 62, 63, 102], "program": [1, 13, 15, 24, 48, 57, 62, 63, 79, 102], "multipl": [1, 2, 4, 6, 18, 19, 26, 45, 47, 51, 55, 56, 59, 63, 64, 65, 68, 69, 71, 79, 80, 93, 95, 102, 104, 109, 111, 114, 115], "tutori": [1, 3, 18, 59, 63, 69], "organ": [1, 71, 102], "websit": [1, 36, 54, 56], "commun": [1, 2, 51, 71], "creat": [1, 3, 4, 6, 10, 13, 18, 19, 24, 27, 29, 34, 36, 38, 44, 47, 48, 49, 55, 60, 63, 64, 67, 68, 69, 70, 75, 92, 93, 108, 113, 117, 124], "guidelin": 1, "allcontributor": 1, "bot": 1, "so": [1, 2, 3, 4, 5, 12, 14, 18, 25, 26, 27, 29, 31, 37, 45, 47, 49, 50, 55, 58, 60, 65, 68, 75, 79, 97, 103, 109, 124], "just": [1, 4, 12, 24, 27, 30, 34, 37, 45, 47, 49, 50, 51, 53, 54, 55, 56, 60, 65, 68, 79, 107, 114], "after": [1, 4, 12, 13, 18, 23, 24, 34, 36, 39, 47, 49, 50, 51, 52, 54, 55, 56, 57, 63, 65, 66, 79, 87, 89, 93, 94, 97, 102, 110, 114, 118, 120], "pull": [1, 2, 66, 109], "request": [1, 2, 4, 25, 56, 103], "ar": [1, 2, 3, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 24, 25, 26, 27, 28, 29, 31, 32, 35, 36, 39, 42, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 68, 69, 71, 75, 77, 79, 80, 82, 87, 88, 89, 90, 92, 93, 94, 95, 97, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 118, 122, 123, 124], "merg": [1, 3, 18, 45], "tanmai": 1, "kulkarni101": 1, "sid": [1, 25], "darthvad": [1, 25], "increas": [1, 13, 18, 25, 26, 29, 39, 47, 48, 50, 55, 56, 57, 66, 92, 111, 124], "In": [1, 3, 4, 9, 11, 13, 14, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 31, 33, 34, 36, 40, 43, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 64, 65, 67, 68, 69, 70, 71, 73, 75, 78, 79, 80, 87, 89, 90, 92, 93, 95, 96, 97, 102, 103, 104, 106, 108, 109, 110, 111, 112, 113, 114, 117], "addit": [1, 3, 4, 6, 7, 13, 14, 17, 18, 19, 24, 25, 28, 29, 31, 36, 47, 48, 49, 50, 52, 53, 55, 56, 57, 59, 61, 65, 67, 68, 71, 73, 78, 79, 89, 90, 97, 109, 110, 111, 112, 113, 114, 117, 121, 124], "random": [1, 4, 10, 13, 14, 18, 19, 21, 24, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 65, 66, 68, 69, 75, 79, 80, 89, 92, 93, 94, 95, 97, 99, 105, 107, 109, 110, 113, 114, 117, 118, 119], "dummyoutcom": 1, "arbitrari": [1, 4, 13, 18, 21, 89, 93, 95], "alwai": [1, 7, 26, 34, 49, 50, 52, 54, 55, 56, 68, 110, 111, 114], "thi": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 75, 77, 78, 79, 80, 81, 82, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110, 111, 112, 113, 114, 116, 122, 123, 124], "inspir": [1, 57], "idea": [1, 4, 17, 19, 21, 26, 27, 48, 54, 71, 75, 92, 94, 96], "t": [1, 3, 4, 5, 6, 13, 14, 15, 17, 18, 25, 26, 27, 28, 29, 30, 31, 34, 37, 40, 45, 48, 49, 50, 51, 53, 55, 56, 59, 60, 65, 66, 67, 75, 80, 89, 93, 94, 97, 105, 108, 111, 113], "learner": [1, 28], "we": [1, 2, 3, 4, 5, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 64, 65, 66, 67, 68, 69, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 120, 121, 124], "provid": [1, 3, 4, 5, 7, 13, 14, 18, 19, 20, 24, 25, 26, 29, 31, 34, 35, 37, 40, 42, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 65, 66, 67, 68, 71, 72, 73, 79, 80, 89, 90, 95, 97, 100, 102, 104, 105, 106, 108, 109, 110, 111, 112, 114, 124], "Of": [1, 36], "cours": [1, 13, 36, 45], "specifi": [1, 4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 30, 35, 39, 40, 41, 45, 50, 53, 55, 64, 65, 68, 73, 80, 84, 89, 101, 103, 109, 114], "ani": [1, 2, 3, 4, 6, 7, 13, 14, 15, 18, 19, 20, 21, 24, 25, 26, 27, 29, 30, 32, 34, 36, 37, 45, 47, 49, 50, 54, 55, 57, 64, 65, 66, 67, 68, 70, 71, 75, 76, 79, 82, 89, 92, 93, 97, 102, 103, 105, 106, 112, 113, 115, 117, 118, 124], "bootstraprefut": [1, 4, 13], "issu": [1, 2, 4, 18, 27, 55, 56, 70, 71, 75], "measur": [1, 4, 13, 14, 18, 19, 31, 34, 37, 44, 51, 53, 54, 55, 56, 65, 66, 89, 90, 93, 94, 97, 105, 107, 114], "rather": [1, 5, 18, 51, 55, 56, 57, 60, 68, 89, 94, 95, 97, 109], "than": [1, 4, 5, 13, 14, 18, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 68, 80, 88, 89, 92, 95, 97, 102, 107, 110, 114, 124], "simpl": [1, 4, 6, 24, 30, 32, 36, 52, 54, 63, 68, 69, 71, 79, 86, 93, 95, 104, 110, 112, 113, 117], "sampl": [1, 4, 5, 6, 10, 13, 14, 15, 17, 18, 19, 24, 25, 26, 27, 28, 33, 35, 37, 39, 46, 47, 48, 49, 50, 51, 53, 56, 57, 58, 60, 63, 64, 68, 69, 75, 80, 93, 94, 97, 99, 101, 102, 111, 112, 113, 114], "nois": [1, 4, 13, 18, 26, 29, 30, 41, 48, 49, 50, 52, 54, 55, 56, 57, 64, 89, 90, 93, 94, 95, 96, 97, 109, 110, 111, 112, 113, 114], "select": [1, 4, 6, 9, 13, 18, 19, 26, 27, 47, 49, 55, 57, 64, 67, 71, 75, 79, 82, 114, 116, 124], "signific": [1, 4, 6, 13, 18, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 65, 66, 67, 79, 107, 110, 114], "causalml": [1, 4, 42, 68, 71], "call": [1, 2, 4, 5, 7, 9, 12, 14, 18, 26, 27, 28, 34, 37, 41, 45, 49, 50, 51, 52, 55, 56, 57, 60, 65, 68, 71, 73, 75, 79, 82, 87, 94, 102, 103, 106, 109, 110, 111, 113], "name": [1, 3, 4, 5, 6, 7, 13, 14, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 48, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 84, 102, 108, 109], "scheme": [1, 21, 35, 39], "To": [1, 3, 4, 5, 9, 13, 18, 21, 24, 25, 26, 27, 34, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 69, 70, 75, 78, 80, 82, 83, 84, 86, 89, 92, 94, 96, 97, 99, 100, 102, 103, 105, 106, 107, 109, 110, 111, 112, 113, 114, 124], "achiev": [1, 18, 24, 26, 47, 68, 92, 94, 97], "suggest": [1, 4, 18, 21, 26, 57, 64, 65, 68, 97], "prepend": 1, "intern": [1, 9, 11, 12, 13, 14, 15, 18, 21, 27, 35, 45, 51, 64, 75, 89, 93, 94, 95, 109], "string": [1, 4, 5, 6, 13, 14, 15, 18, 19, 21, 23, 24, 28, 31, 34, 46, 47, 48, 49, 55, 57, 58, 60, 61, 66, 108, 109, 112, 114], "For": [1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 17, 18, 19, 21, 25, 26, 27, 28, 34, 35, 36, 37, 39, 41, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 75, 79, 80, 85, 86, 87, 89, 91, 92, 93, 94, 95, 97, 100, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 117, 124], "propensity_score_match": [1, 4, 35, 36, 37, 38, 39, 46, 69, 77, 79], "Not": [1, 6, 13, 25], "break": [1, 3], "keep": [1, 3, 4, 13, 17, 18, 21, 25, 26, 27, 55, 68, 94, 97, 113], "sinc": [1, 4, 12, 13, 18, 25, 26, 27, 29, 30, 31, 34, 39, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 68, 80, 89, 94, 97, 102, 103, 105, 107, 109, 110, 111, 112, 114, 124], "modifi": [1, 4, 6, 17, 18, 21, 24, 26, 28, 31, 42, 47, 53, 54, 73, 93, 95, 104, 105, 118, 124], "subset": [1, 4, 9, 13, 18, 19, 24, 25, 26, 27, 37, 46, 56, 65, 67, 68, 75, 79, 89, 95, 116], "warn": [1, 3, 13, 25, 26, 28, 29, 33, 34, 36, 37, 41, 42, 44, 46, 47, 64, 66, 67, 68], "outsid": [1, 25, 26, 56], "tri": [1, 18, 79, 87, 112], "ci": [1, 4, 18, 25, 48, 53, 66], "standard": [1, 4, 6, 13, 14, 18, 19, 23, 24, 25, 30, 39, 40, 49, 50, 51, 54, 55, 56, 66, 68, 72, 89, 112, 114], "correspond": [1, 4, 6, 7, 10, 13, 14, 18, 24, 28, 34, 46, 48, 49, 51, 52, 53, 54, 55, 56, 66, 68, 89, 93, 94, 109, 110, 113, 114], "parametr": [1, 4, 5, 7, 13, 18, 19, 24, 27, 34, 37, 41, 48, 49, 55, 57, 60, 63, 69, 75, 79, 104, 109, 114, 123], "form": [1, 4, 7, 13, 14, 15, 18, 33, 34, 45, 49, 51, 54, 55, 57, 58, 80, 85, 97, 103, 109, 112, 113, 114], "conveni": [1, 4, 17, 18, 24, 82], "seed": [1, 4, 10, 13, 14, 21, 31, 37, 45, 48, 51, 53, 57, 58, 65, 66, 92], "import": [1, 4, 13, 18, 22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38, 39, 40, 41, 42, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 68, 69, 70, 73, 79, 80, 82, 84, 87, 89, 91, 92, 93, 94, 95, 97, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 113, 114, 124], "simpler": [1, 28], "multivari": [1, 18, 19], "averag": [1, 13, 18, 21, 24, 25, 26, 31, 36, 38, 48, 49, 50, 51, 52, 54, 55, 56, 63, 65, 69, 79, 87, 88, 90, 101, 102, 110, 114], "estimate_effect": [1, 2, 4, 6, 25, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 65, 66, 67, 68, 69, 73, 74, 77, 78, 79, 81, 87, 91], "other": [1, 4, 5, 6, 7, 13, 17, 18, 25, 26, 27, 34, 36, 37, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 60, 64, 65, 68, 69, 71, 75, 82, 87, 88, 89, 90, 92, 93, 94, 95, 101, 102, 103, 104, 105, 106, 107, 109, 110, 112, 114], "target": [1, 4, 6, 7, 9, 18, 19, 21, 25, 26, 29, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 46, 47, 50, 51, 54, 55, 57, 58, 61, 65, 66, 67, 69, 71, 80, 85, 89, 90, 92, 93, 94, 95, 96, 108, 112], "estimand": [1, 4, 5, 6, 7, 13, 17, 28, 29, 31, 32, 34, 36, 37, 38, 41, 42, 43, 44, 47, 60, 65, 66, 67, 69, 71, 78, 87], "att": [1, 4, 25, 28, 35, 36, 39, 47, 74, 77], "atc": [1, 4, 25, 28, 35, 39, 47, 77], "reproduc": [1, 4, 47, 55, 57, 92], "j": [1, 18, 19, 24, 26, 44, 46, 51, 58, 64, 67, 93, 94], "chou": 1, "ktmud": 1, "jrfiedler": 1, "shounak112358": 1, "lnk2past": 1, "pywhi": [2, 70, 71, 104], "welcom": [2, 31, 37], "There": [2, 11, 13, 18, 25, 26, 27, 28, 44, 45, 47, 48, 49, 50, 51, 55, 56, 64, 66, 69, 75, 82, 93, 95, 114, 124], "wai": [2, 3, 4, 9, 18, 27, 29, 33, 35, 47, 49, 51, 52, 54, 55, 56, 57, 63, 68, 70, 71, 75, 79, 82, 89, 96, 97, 102, 104, 110, 114, 124], "some": [2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18, 25, 27, 28, 32, 34, 36, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 61, 65, 68, 69, 70, 79, 80, 88, 89, 92, 93, 94, 95, 97, 99, 100, 102, 105, 106, 108, 109, 110, 111, 113, 114, 118, 124], "ad": [2, 3, 4, 5, 12, 13, 15, 24, 26, 30, 45, 48, 51, 53, 54, 55, 60, 64, 68, 80, 90], "jupyt": [2, 63, 68], "describ": [2, 4, 7, 13, 15, 18, 34, 39, 41, 50, 52, 54, 55, 57, 60, 64, 65, 66, 68, 69, 88, 100, 102, 110, 112, 113], "solv": [2, 13, 15, 68, 70, 71], "problem": [2, 3, 6, 15, 18, 21, 25, 27, 29, 35, 36, 37, 46, 47, 50, 55, 56, 68, 69, 70, 71, 75, 79, 87, 95, 102, 103, 104, 106, 112, 118], "help": [2, 4, 13, 18, 19, 25, 33, 47, 49, 54, 55, 56, 60, 64, 65, 66, 68, 71, 89, 97, 102, 103, 113, 114], "document": [2, 3, 4, 6, 13, 18, 25, 28, 31, 55, 70, 87], "new": [2, 3, 4, 5, 6, 9, 13, 15, 18, 19, 24, 25, 27, 29, 32, 33, 36, 37, 38, 44, 45, 46, 47, 52, 54, 57, 60, 64, 67, 68, 69, 70, 71, 75, 80, 85, 88, 92, 94, 102, 110, 111, 114, 118, 124], "four": [2, 4, 28, 29, 69, 71, 79, 82, 87, 93, 94], "step": [2, 3, 4, 6, 7, 13, 17, 18, 26, 27, 28, 29, 46, 47, 50, 54, 57, 59, 64, 67, 69, 70, 75, 79, 80, 87, 88, 94, 103, 114], "analysi": [2, 4, 6, 13, 18, 25, 32, 36, 37, 48, 54, 63, 68, 69, 71, 73, 79, 87, 88, 89, 90, 92, 93, 94, 97, 100, 101, 103, 106, 113, 115], "identifi": [2, 4, 5, 6, 7, 13, 18, 19, 26, 27, 28, 29, 42, 43, 44, 47, 55, 56, 60, 62, 63, 66, 67, 69, 70, 71, 76, 78, 81, 82, 83, 84, 86, 87, 88, 89, 93, 94, 96, 101, 112, 124], "estim": [2, 4, 6, 7, 9, 13, 14, 15, 18, 19, 21, 23, 27, 34, 45, 48, 49, 50, 51, 52, 55, 56, 57, 62, 63, 64, 69, 71, 75, 77, 78, 85, 86, 88, 89, 90, 92, 93, 94, 95, 96, 97, 100, 101, 102, 103, 108, 110, 112, 113, 114, 116, 117, 119, 120, 121, 124], "refut": [2, 4, 13, 18, 29, 45, 62, 69, 87, 101, 102, 103, 104, 122, 123, 124], "integr": [2, 3, 18, 26, 79, 102, 104], "api": [2, 4, 6, 13, 18, 27, 40, 49, 52, 56, 57, 63, 66, 68, 69, 71, 73, 75, 78, 79, 80, 82, 84, 87, 100, 102, 110, 113], "extern": [2, 26, 79], "seamlessli": 2, "identify_effect": [2, 4, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 59, 65, 66, 67, 68, 69, 79, 82, 83, 84, 86, 87, 91, 124], "refute_estim": [2, 4, 25, 28, 29, 32, 33, 36, 37, 38, 44, 45, 46, 47, 65, 66, 68, 69, 79, 87, 116, 117, 119, 120, 124], "extend": [2, 26, 48, 55, 109, 114], "support": [2, 4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 19, 21, 35, 37, 39, 41, 42, 47, 51, 55, 58, 60, 61, 63, 64, 66, 68, 70, 71, 72, 76, 78, 87, 88, 91, 97, 100, 101, 104, 109, 112, 124], "function": [2, 4, 5, 6, 7, 9, 10, 12, 13, 14, 18, 19, 20, 21, 24, 25, 26, 28, 34, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 67, 71, 77, 78, 82, 84, 87, 89, 90, 94, 95, 96, 97, 102, 105, 108, 109, 110, 111, 112, 113, 114, 117], "counterfactu": [2, 6, 18, 36, 63, 68, 69, 71, 88, 96, 98, 100, 101, 102, 112, 113], "predict": [2, 4, 6, 13, 14, 15, 18, 19, 20, 21, 25, 26, 33, 49, 50, 54, 55, 56, 63, 65, 71, 88, 89, 95, 100, 101, 102, 104, 109, 111, 112, 114], "would": [2, 4, 13, 18, 25, 26, 34, 36, 49, 50, 51, 52, 53, 54, 55, 56, 57, 65, 66, 68, 69, 80, 88, 89, 93, 94, 96, 97, 103, 104, 105, 109, 110, 111, 112, 113, 114], "rais": [2, 5, 7, 13, 18, 34, 60], "info": [2, 32], "have": [2, 4, 5, 6, 7, 13, 14, 17, 18, 19, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 71, 75, 79, 82, 88, 89, 93, 94, 96, 97, 102, 103, 104, 106, 107, 109, 111, 112, 113, 114, 124], "question": [2, 18, 24, 36, 49, 50, 54, 57, 68, 69, 80, 88, 89, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 106, 112, 113, 114], "open": [2, 4, 55, 70], "list": [2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 31, 34, 42, 45, 51, 56, 59, 60, 64, 67, 102, 106, 114], "md": [2, 13], "our": [2, 18, 25, 27, 29, 31, 32, 33, 36, 45, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 64, 65, 68, 69, 71, 75, 80, 89, 92, 93, 94, 95, 96, 97, 105, 106, 109, 110, 111, 112, 113, 114], "conduct": [2, 18, 26, 45, 68, 102], "avail": [2, 3, 4, 6, 18, 24, 25, 26, 47, 48, 55, 57, 63, 64, 65, 67, 68, 69, 79, 82, 85, 87, 106, 114], "join": [2, 26, 45, 51, 56, 71, 108], "develop": [2, 3, 34, 63, 70, 79, 102], "channel": 2, "discord": [2, 37, 71], "basic": [3, 13, 33, 37, 63, 69, 80, 82, 93, 99, 113], "knowledg": [3, 25, 31, 47, 48, 49, 53, 55, 56, 57, 68, 69, 72, 79, 87, 97, 101, 103, 104, 109, 113, 114, 124], "git": [3, 70], "requir": [3, 4, 5, 6, 7, 13, 17, 18, 24, 25, 27, 31, 35, 38, 39, 45, 47, 51, 55, 57, 58, 60, 61, 64, 65, 67, 68, 70, 75, 80, 87, 89, 97, 100, 102, 103, 109, 111, 112, 114, 124], "unfamiliar": 3, "workflow": [3, 45, 79, 87], "checkout": [3, 18], "fork": [3, 106], "main": [3, 4, 7, 19, 28, 53, 55, 56, 57, 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114, 116, 117, 118, 119, 120], "strength": [4, 6, 13, 18, 47, 54, 55, 65, 66, 88, 90, 101, 113, 121], "param": [4, 5, 6, 7, 9, 13, 14, 15, 17, 18, 24, 33, 46, 60, 64], "dictionari": [4, 5, 6, 7, 10, 13, 14, 15, 17, 18, 21, 24, 26, 45, 51, 60, 64, 80], "size": [4, 6, 10, 11, 12, 13, 18, 19, 23, 24, 27, 29, 30, 31, 36, 45, 48, 49, 50, 52, 55, 56, 57, 64, 66, 69, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "boolean": [4, 6, 13, 14, 15, 21, 26, 28, 45], "split": [4, 6, 10, 13, 18, 36, 40, 44, 45, 51], "enabl": [4, 6, 13, 64, 94, 100, 102, 109], "instanc": [4, 6, 7, 9, 11, 12, 13, 15, 18, 19, 21, 23, 26, 31, 34, 45, 47, 49, 55, 56, 57, 60, 66, 67, 69, 71, 80, 82, 83, 84, 86, 89, 92, 93, 100, 105, 108, 109, 111, 112, 113, 114], "bootstrapestim": 4, "tupl": [4, 13, 18, 21, 24, 26, 42, 58, 66], "alia": [4, 6, 13], "field": [4, 5, 13, 60], "default_confidence_level": 4, "default_interpret_method": 4, "default_notimplementederror_msg": 4, "yet": [4, 27], "next": [4, 17, 29, 34, 51, 54, 55, 56, 64, 87, 88, 89, 92, 93, 94, 97, 99, 103, 106, 110, 111], "microsoft": [4, 13, 69], "default_number_of_simulations_ci": 4, "default_number_of_simulations_stat_test": 4, "default_sample_size_fract": 4, "temp_cat_column_prefix": 4, "__categorical__": 4, "x": [4, 5, 6, 7, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 24, 26, 28, 30, 31, 33, 34, 36, 38, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 65, 66, 69, 80, 87, 88, 89, 92, 93, 94, 95, 96, 97, 99, 103, 105, 106, 108, 109, 110, 111, 112, 113, 114], "data_df": [4, 6, 31], "oper": [4, 5, 7, 14, 17, 18, 25, 55, 56, 85, 109], "expect": [4, 9, 13, 17, 18, 19, 21, 33, 36, 39, 46, 47, 49, 50, 53, 54, 55, 56, 64, 65, 76, 80, 87, 89, 92, 94, 95, 97, 104, 105, 109, 112, 114], "interven": [4, 5, 26, 27, 49, 52, 54, 60, 75, 80, 97, 99, 100, 110], "appli": [4, 6, 7, 9, 11, 13, 14, 18, 19, 20, 21, 23, 24, 31, 36, 40, 51, 53, 54, 55, 63, 67, 68, 92, 102, 103, 111], "estimate_confidence_interv": 4, "estimate_valu": 4, "estimate_effect_na": 4, "panda": [4, 5, 6, 13, 14, 15, 17, 18, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 63, 66, 67, 68, 69, 75, 80, 89, 92, 93, 94, 95, 97, 99, 104, 105, 108, 109, 110, 111, 113, 114], "estimate_std_error": 4, "get_new_estimator_object": 4, "same": [4, 6, 13, 14, 18, 19, 20, 25, 26, 27, 28, 29, 33, 35, 36, 37, 45, 47, 49, 51, 52, 53, 55, 56, 57, 60, 61, 65, 66, 67, 68, 69, 71, 75, 79, 80, 83, 86, 94, 104, 105, 106, 107, 110, 111, 113, 114], "type": [4, 5, 6, 9, 13, 14, 17, 18, 19, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 68, 70, 76, 80, 84, 89, 92, 97, 101, 109, 113, 114], "respect": [4, 5, 7, 9, 10, 13, 18, 34, 36, 49, 50, 52, 54, 55, 56, 57, 58, 60, 66, 67, 71, 79, 80, 92, 94, 95, 96, 97, 110, 112, 114], "pathwai": [4, 66, 91], "emploi": [4, 48, 50, 51, 66, 90, 92, 94, 112], "had": [4, 25, 26, 36, 50, 55, 65, 88, 96, 97, 105, 112], "static": [4, 7, 18, 23, 34], "is_bootstrap_parameter_chang": 4, "bootstrap_estimates_param": 4, "given_param": 4, "fresh": 4, "again": [4, 5, 49, 53, 55, 60, 69, 70, 97, 105, 109, 111, 113, 114], "current": [4, 5, 6, 7, 9, 13, 18, 19, 24, 26, 36, 37, 41, 42, 45, 47, 51, 58, 60, 64, 66, 79, 97, 109], "denot": [4, 6, 13, 18, 24, 59, 65, 66, 68, 92, 112, 114], "reset_encod": 4, "remov": [4, 7, 13, 18, 24, 25, 27, 29, 31, 33, 34, 35, 36, 37, 39, 46, 53, 54, 64, 65, 66, 67, 68, 90, 92, 124], "encod": [4, 12, 13, 19, 21, 47, 48, 49, 51, 52, 68, 79, 87, 102, 103, 110], "re": [4, 13, 14, 18, 25, 27, 40, 44, 51, 55, 56, 60, 65, 66, 69, 75, 94, 111], "It": [4, 5, 6, 7, 13, 18, 19, 25, 26, 27, 32, 37, 43, 47, 50, 53, 54, 57, 60, 64, 65, 66, 68, 69, 71, 75, 78, 79, 82, 83, 86, 90, 97, 100, 106, 107, 109, 114, 116, 117, 120, 121, 124], "produc": [4, 6, 13, 18, 26, 33, 49, 55, 57, 60, 114], "inconsist": [4, 52, 110], "output": [4, 6, 9, 13, 15, 18, 19, 23, 24, 26, 37, 44, 46, 47, 50, 53, 55, 56, 57, 58, 60, 65, 66, 76, 104, 107, 124], "particular": [4, 7, 18, 26, 48, 50, 54, 56, 57, 71, 92, 97], "hot": [4, 21, 48], "map": [4, 5, 14, 15, 18, 19, 21, 24, 26, 45, 51, 54, 93], "must": [4, 5, 7, 13, 18, 23, 24, 26, 27, 45, 56, 59, 60, 66, 68, 109, 111], "ident": [4, 6, 8, 12, 18, 53], "train": [4, 8, 9, 10, 12, 13, 15, 18, 19, 21, 26, 44, 49, 50, 51, 69, 89, 97, 109, 113], "time": [4, 5, 18, 21, 24, 25, 29, 30, 31, 40, 43, 44, 49, 51, 55, 56, 60, 64, 66, 67, 68, 89, 92, 105, 111, 114], "common": [4, 6, 9, 13, 17, 18, 24, 25, 29, 33, 35, 38, 39, 54, 65, 66, 68, 73, 78, 79, 80, 81, 93, 95, 97, 100, 102, 108, 112, 114, 118, 121, 124], "signif_results_tostr": 4, "signif_result": 4, "target_units_tostr": 4, "procedur": [4, 13, 27, 47, 60, 68, 75, 115, 118, 124], "self": [4, 6, 7, 13, 15, 24, 27, 35, 39, 48, 66, 75, 109], "update_input": 4, "target_unit": [4, 6, 25, 28, 35, 36, 37, 39, 40, 45, 47, 73, 74, 77, 78, 79], "realizedestimand": 4, "estimator_nam": [4, 6], "update_assumpt": 4, "estimator_assumpt": 4, "update_estimand_express": 4, "estimand_express": 4, "identifier_nam": [4, 7], "ate": [4, 5, 7, 25, 28, 29, 33, 35, 36, 37, 38, 39, 40, 41, 42, 45, 46, 47, 48, 60, 65, 66, 68, 77, 78], "fit_estim": [4, 28], "method_param": [4, 6, 28, 33, 35, 38, 39, 40, 41, 42, 45, 46, 65, 68, 73, 74, 77, 79, 81, 91], "dict": [4, 5, 6, 7, 13, 14, 15, 17, 18, 21, 24, 26, 32, 33, 37, 44, 46, 48, 49, 52, 56, 57, 60, 66, 69, 80, 89, 92, 93, 94, 95, 97, 99, 105, 109, 110, 111, 113, 114], "convent": 4, "doubl": [4, 28, 47, 49, 50, 52, 54, 55, 56, 68, 79, 110, 114], "dml": [4, 13, 28, 37, 46, 65, 68, 79], "locat": [4, 34], "insid": 4, "demo": [4, 53, 63], "group": [4, 9, 13, 26, 36, 39, 47, 48], "variat": [4, 13, 25, 51, 65, 66, 68], "treat": [4, 6, 15, 23, 25, 36, 39, 40, 44, 45, 60, 71, 79, 80, 113], "three": [4, 13, 17, 18, 19, 25, 26, 27, 28, 31, 50, 55, 56, 61, 64, 75, 76, 79, 89, 100, 105, 106, 109, 111, 112], "2": [4, 6, 7, 9, 10, 11, 13, 15, 18, 20, 21, 24, 27, 28, 29, 30, 31, 33, 34, 36, 37, 42, 43, 45, 46, 48, 50, 51, 52, 53, 54, 57, 60, 61, 65, 67, 68, 69, 74, 79, 80, 89, 92, 93, 94, 95, 97, 98, 99, 105, 109, 110, 111, 112, 113, 114, 117], "lambda": [4, 6, 9, 13, 18, 24, 26, 28, 36, 47, 48, 49, 50, 51, 55, 56, 57, 73, 79, 80, 89, 97, 99, 111], "onli": [4, 5, 6, 7, 9, 13, 14, 15, 18, 19, 21, 24, 25, 26, 28, 29, 34, 36, 37, 39, 41, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 66, 67, 68, 80, 87, 92, 93, 94, 97, 104, 105, 109, 110, 111, 112, 114], "thei": [4, 5, 13, 18, 19, 24, 25, 26, 27, 29, 34, 36, 48, 49, 50, 52, 54, 55, 56, 57, 58, 60, 65, 66, 68, 75, 82, 89, 102, 103, 106, 110, 113, 114, 118], "previous": [4, 18, 21], "causalgraph": [4, 34, 37], "common_cause_nam": 4, "instrument_nam": [4, 7, 32, 33, 35, 39, 68], "mediator_nam": 4, "observed_node_nam": [4, 34], "missing_nodes_as_confound": [4, 41], "accept": [4, 26, 105], "networkx": [4, 18, 24, 26, 31, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 67, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "digraph": [4, 5, 7, 17, 18, 24, 25, 26, 31, 36, 43, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 59, 60, 61, 65, 69, 80, 89, 92, 93, 94, 95, 97, 99, 107, 108, 109, 110, 111, 113, 114], "gml": [4, 39, 47, 53, 58, 108], "dot": [4, 22, 24, 31, 32, 33, 35, 44, 47, 51, 58, 67, 68, 70, 117], "edg": [4, 7, 17, 18, 24, 26, 27, 31, 34, 36, 43, 48, 49, 55, 61, 65, 66, 67, 75, 80, 90, 92, 103, 109, 113, 114], "node": [4, 7, 15, 18, 24, 25, 26, 31, 34, 36, 43, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 65, 66, 67, 69, 71, 80, 89, 90, 92, 93, 94, 95, 96, 97, 103, 106, 109, 110, 111, 112, 113, 114], "style": [4, 15], "dash": [4, 13, 15, 53], "ensur": [4, 13, 15, 18, 21, 48, 49, 55, 57, 67, 68, 70, 96, 97, 100, 114], "drawn": [4, 13, 18, 25, 55, 56, 111], "line": [4, 13, 24, 32, 36, 47, 53, 54, 55, 56, 65, 66, 68, 71, 94], "confound": [4, 6, 9, 13, 18, 23, 25, 27, 29, 36, 37, 40, 41, 47, 50, 54, 56, 59, 63, 64, 66, 68, 79, 87, 108, 118, 121, 124], "instrument": [4, 6, 7, 13, 31, 32, 33, 39, 48, 59, 63, 68, 79, 83, 85, 87, 102, 108], "add_missing_nodes_as_common_caus": 4, "add_node_attribut": 4, "color": [4, 11, 13, 21, 24, 26, 48, 51, 55, 56, 64, 66, 102], "grai": 4, "all_observ": 4, "node_nam": [4, 7, 18, 24, 109], "build_graph": [4, 37], "semant": 4, "consid": [4, 6, 13, 14, 15, 18, 19, 21, 25, 26, 34, 45, 48, 49, 50, 52, 54, 55, 56, 57, 64, 65, 66, 67, 68, 87, 89, 105, 106, 108, 110, 114], "represent": [4, 9, 11, 13, 18, 49, 50, 52, 53, 54, 55, 56, 65, 72, 100, 108, 110, 114], "thu": [4, 13, 18, 25, 26, 28, 31, 34, 36, 52, 53, 55, 65, 68, 70, 73, 79, 89, 95, 102, 104, 110, 114], "unless": [4, 58, 64, 102], "taxonomi": 4, "vanderwheel": [4, 7], "robin": [4, 17], "modif": [4, 18, 45], "acycl": [4, 7, 18, 24, 26, 31, 49, 55, 59, 63, 102, 103, 104, 106, 113], "epidemiologi": 4, "2007": [4, 18, 19], "check_dsepar": 4, "nodes1": [4, 7], "nodes2": [4, 7], "nodes3": 4, "new_graph": 4, "dseparation_algo": [4, 7], "check_valid_backdoor_set": 4, "backdoor_path": [4, 7], "second": [4, 6, 9, 13, 18, 19, 27, 29, 34, 41, 45, 51, 53, 56, 75, 94, 102, 107, 109, 111, 118], "third": [4, 13, 14, 15, 18, 27, 45, 51, 71, 75, 105, 106], "candid": [4, 102, 104, 106], "check_valid_frontdoor_set": 4, "candidate_nod": 4, "frontdoor_path": 4, "frontdoor": [4, 7, 25, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 47, 65, 66, 68, 79, 85, 86, 87, 102], "check_valid_mediation_set": 4, "mediation_path": 4, "mediat": [4, 6, 7, 13, 34, 59, 63, 87, 88, 90, 101, 103, 108], "do_surgeri": 4, "remove_outgoing_edg": 4, "remove_incoming_edg": 4, "target_node_nam": 4, "remove_only_direct_edges_to_target": 4, "concept": [4, 26, 54, 71, 89, 95, 113], "surgeri": 4, "focal": 4, "outgo": 4, "incom": [4, 18, 92], "filter_unobserved_vari": 4, "get_adjacency_matrix": 4, "adjac": [4, 18, 24, 67], "matrix": [4, 13, 14, 18, 19, 21, 24, 30, 40, 56], "get_all_directed_path": 4, "singleton": [4, 15], "get_all_nod": 4, "include_unobserv": [4, 7], "get_ancestor": 4, "get_backdoor_path": 4, "get_caus": 4, "remove_edg": [4, 53], "get_common_caus": 4, "get_descend": 4, "get_effect_modifi": [4, 78], "get_instru": 4, "treatment_nod": 4, "outcome_nod": [4, 7, 17, 27, 34, 37], "get_par": 4, "get_unconfounded_observed_subgraph": 4, "has_directed_path": 4, "action_nod": [4, 7, 17, 27, 34, 37], "least": [4, 7, 13, 15, 18, 28, 29, 30, 34, 40, 45, 47, 48, 66, 79, 112, 115, 124], "And": [4, 31, 36, 56, 106, 111], "conditioned_nod": 4, "criteria": [4, 25, 34, 45, 69], "decid": [4, 13, 18, 25, 27, 31, 34, 50, 68, 75], "block": [4, 24, 27, 34, 41, 48, 75], "view_graph": [4, 34], "layout": [4, 24, 32, 44, 68], "file_nam": 4, "common_caus": [4, 5, 7, 27, 29, 30, 32, 38, 40, 44, 45, 47, 60, 68, 108], "estimand_typ": [4, 5, 6, 7, 17, 34, 37, 41, 60, 91], "nonparametr": [4, 5, 7, 13, 36, 41, 44, 46, 49, 60, 91], "proceed_when_unidentifi": [4, 5, 25, 27, 28, 29, 31, 32, 33, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47, 60, 65, 66, 68, 91], "identify_var": 4, "store": [4, 6, 7, 13, 18, 32, 51, 55], "state": [4, 5, 12, 17, 24, 26, 34, 48, 60, 64, 68, 71, 79, 89, 100], "At": [4, 13, 18, 47, 49, 51, 55, 58, 65, 68, 89, 113], "later": [4, 5, 25, 34, 36, 45, 48, 51, 57, 60, 70], "dag": [4, 18, 26, 31, 47, 49, 50, 51, 52, 53, 55, 56, 57, 59, 67, 103, 104, 106, 107, 110, 113, 114], "_outcom": 4, "futur": [4, 7, 13, 36, 57, 60, 68, 97, 104], "proce": [4, 5, 56, 60, 70], "potenti": [4, 18, 27, 49, 50, 54, 55, 56, 64, 69, 75, 79, 80, 100, 102, 103, 108, 114], "unobserv": [4, 7, 13, 18, 25, 26, 29, 36, 37, 49, 55, 56, 57, 66, 68, 79, 89, 94, 97, 112, 113, 114, 118, 124], "while": [4, 12, 13, 14, 17, 18, 25, 26, 27, 29, 45, 52, 54, 55, 56, 57, 66, 68, 71, 73, 75, 82, 89, 92, 93, 94, 95, 97, 100, 110, 112, 114], "sens": [4, 26, 34, 51, 53, 54, 55, 89, 111, 114], "intervent": [4, 5, 6, 7, 17, 18, 26, 31, 34, 44, 48, 49, 52, 54, 63, 71, 80, 88, 89, 90, 97, 98, 100, 101, 102, 110, 112], "These": [4, 5, 13, 14, 18, 19, 27, 31, 34, 45, 46, 51, 55, 57, 60, 63, 66, 68, 75, 89, 92, 102, 103, 106, 112, 115], "explicit": [4, 17, 27, 71, 75, 79, 100, 103], "estimation_method_nam": 4, "propensity_score_stratif": [4, 35, 38, 39, 47, 68, 77], "invers": [4, 6, 18, 20, 23, 27, 35, 39, 75, 79, 87], "weight": [4, 5, 6, 9, 10, 13, 15, 17, 18, 21, 23, 27, 30, 31, 40, 51, 54, 55, 60, 64, 67, 75, 79, 87, 94], "propensity_score_weight": [4, 25, 35, 38, 39, 40, 44, 45, 46, 77], "linear_regress": [4, 28, 31, 32, 35, 38, 42, 45, 66, 67, 68, 73, 78], "generalized_linear_model": 4, "instrumental_vari": [4, 7, 29, 33, 35, 81], "discontinu": [4, 6, 79, 87], "regression_discontinu": [4, 35, 81], "two_stage_regress": [4, 41, 91], "addition": [4, 18, 25, 55, 68, 94, 102, 114], "signfic": 4, "rel": [4, 15, 18, 50, 51, 55, 65, 90, 93, 114], "compar": [4, 13, 18, 19, 21, 23, 26, 34, 36, 38, 43, 46, 49, 50, 53, 54, 55, 56, 60, 65, 66, 68, 79, 80, 87, 94, 100, 107, 112, 114], "sequenti": [4, 9, 64, 68, 94], 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"dowhy.gcm.ml.autogluon module": [[20, "dowhy-gcm-ml-autogluon-module"]], "dowhy.gcm.ml.classification module": [[20, "module-dowhy.gcm.ml.classification"]], "dowhy.gcm.ml.prediction_model module": [[20, "module-dowhy.gcm.ml.prediction_model"]], "dowhy.gcm.ml.regression module": [[20, "module-dowhy.gcm.ml.regression"]], "dowhy.gcm.util package": [[21, "dowhy-gcm-util-package"]], "dowhy.gcm.util.catboost_encoder module": [[21, "module-dowhy.gcm.util.catboost_encoder"]], "dowhy.gcm.util.general module": [[21, "module-dowhy.gcm.util.general"]], "dowhy.gcm.util.plotting module": [[21, "module-dowhy.gcm.util.plotting"]], "dowhy.graph_learners package": [[22, "dowhy-graph-learners-package"]], "dowhy.graph_learners.cdt module": [[22, "module-dowhy.graph_learners.cdt"]], "dowhy.graph_learners.ges module": [[22, "module-dowhy.graph_learners.ges"]], "dowhy.graph_learners.lingam module": [[22, "module-dowhy.graph_learners.lingam"]], "dowhy.interpreters package": [[23, "dowhy-interpreters-package"]], "dowhy.interpreters.confounder_distribution_interpreter module": [[23, "module-dowhy.interpreters.confounder_distribution_interpreter"]], "dowhy.interpreters.propensity_balance_interpreter module": [[23, "module-dowhy.interpreters.propensity_balance_interpreter"]], "dowhy.interpreters.textual_effect_interpreter module": [[23, "module-dowhy.interpreters.textual_effect_interpreter"]], "dowhy.interpreters.textual_interpreter module": [[23, "module-dowhy.interpreters.textual_interpreter"]], "dowhy.interpreters.visual_interpreter module": [[23, "module-dowhy.interpreters.visual_interpreter"]], "dowhy.utils package": [[24, "dowhy-utils-package"]], "dowhy.utils.api module": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit module": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers module": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp module": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations module": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting module": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting module": [[24, "module-dowhy.utils.networkx_plotting"]], "dowhy.utils.ordered_set module": [[24, "module-dowhy.utils.ordered_set"]], "dowhy.utils.plotting module": [[24, "module-dowhy.utils.plotting"]], "dowhy.utils.propensity_score module": [[24, "module-dowhy.utils.propensity_score"]], "dowhy.utils.regression module": [[24, "module-dowhy.utils.regression"]], "Exploring Causes of Hotel Booking Cancellations": [[25, "Exploring-Causes-of-Hotel-Booking-Cancellations"]], "Data Description": [[25, "Data-Description"]], "Feature Engineering": [[25, "Feature-Engineering"]], "Calculating Expected Counts": [[25, "Calculating-Expected-Counts"]], "Using DoWhy to estimate the causal effect": [[25, "Using-DoWhy-to-estimate-the-causal-effect"]], "Step-1. Create a Causal Graph": [[25, "Step-1.-Create-a-Causal-Graph"]], "Step-2. Identify the Causal Effect": [[25, "Step-2.-Identify-the-Causal-Effect"]], "Step-3. Estimate the identified estimand": [[25, "Step-3.-Estimate-the-identified-estimand"]], "Step-4. Refute results": [[25, "Step-4.-Refute-results"]], "Method-1": [[25, "Method-1"]], "Method-2": [[25, "Method-2"]], "Method-3": [[25, "Method-3"]], "Counterfactual Fairness": [[26, "Counterfactual-Fairness"]], "When to apply counterfactual fairness?": [[26, "When-to-apply-counterfactual-fairness?"]], "What is required to estimate counterfactual fairness?": [[26, "What-is-required-to-estimate-counterfactual-fairness?"]], "Notation": [[26, "Notation"]], "1. Load and Clean the Dataset": [[26, "1.-Load-and-Clean-the-Dataset"]], "2. A Formal Definition of Counterfactual Fairness": [[26, "2.-A-Formal-Definition-of-Counterfactual-Fairness"]], "2.1 Measuring Counterfactual Fairness": [[26, "2.1-Measuring-Counterfactual-Fairness"]], "3. Measuring Counterfactual Fairness using DoWhy": [[26, "3.-Measuring-Counterfactual-Fairness-using-DoWhy"]], "3.1 Univariate Analysis": [[26, "3.1-Univariate-Analysis"]], "3.2 Intersectional Analysis": [[26, "3.2-Intersectional-Analysis"]], "3.3 Some Additional Uses of the Counterfactuals df_obs , df_cf for Fairness": [[26, "3.3-Some-Additional-Uses-of-the-Counterfactuals-df_obs-,-df_cf-for-Fairness"]], "4. Limitation of Counterfactual Fairness": [[26, "4.-Limitation-of-Counterfactual-Fairness"]], "References": [[26, "References"], [44, "References"], [64, "References"]], "Appendix : Fairness Through Unawareness (FTU) creates A Counterfactually Unfair Estimator": [[26, "Appendix-:-Fairness-Through-Unawareness-(FTU)-creates-A-Counterfactually-Unfair-Estimator"]], "Do-sampler Introduction": [[27, "Do-sampler-Introduction"]], "Pearlian Interventions": [[27, "Pearlian-Interventions"], [75, "pearlian-interventions"]], "Statefulness": [[27, "Statefulness"], [75, "statefulness"]], "Integration": [[27, "Integration"]], "Specifying Interventions": [[27, "Specifying-Interventions"], [60, "Specifying-Interventions"]], "Demo": [[27, "Demo"]], "Conditional Average Treatment Effects (CATE) with DoWhy and EconML": [[28, "Conditional-Average-Treatment-Effects-(CATE)-with-DoWhy-and-EconML"]], "Linear Model": [[28, "Linear-Model"]], "EconML methods": [[28, "EconML-methods"]], "CATE Object and Confidence Intervals": [[28, "CATE-Object-and-Confidence-Intervals"]], "Can provide a new inputs as target units and estimate CATE on them.": [[28, "Can-provide-a-new-inputs-as-target-units-and-estimate-CATE-on-them."]], "Can also retrieve the raw EconML estimator object for any further operations": [[28, "Can-also-retrieve-the-raw-EconML-estimator-object-for-any-further-operations"]], "Works with any EconML method": [[28, "Works-with-any-EconML-method"]], "Binary treatment, Binary outcome": [[28, "Binary-treatment,-Binary-outcome"]], "Using DRLearner estimator": [[28, "Using-DRLearner-estimator"]], "Instrumental Variable Method": [[28, "Instrumental-Variable-Method"]], "Metalearners": [[28, "Metalearners"]], "Avoiding retraining the estimator": [[28, "Avoiding-retraining-the-estimator"]], "Refuting the estimate": [[28, "Refuting-the-estimate"], [47, "Refuting-the-estimate"]], "Adding a random common cause variable": [[28, "Adding-a-random-common-cause-variable"], [32, "Adding-a-random-common-cause-variable"], [47, "Adding-a-random-common-cause-variable"]], "Adding an unobserved common cause variable": [[28, "Adding-an-unobserved-common-cause-variable"], [47, "Adding-an-unobserved-common-cause-variable"]], "Replacing treatment with a random (placebo) variable": [[28, "Replacing-treatment-with-a-random-(placebo)-variable"], [32, "Replacing-treatment-with-a-random-(placebo)-variable"], [47, "Replacing-treatment-with-a-random-(placebo)-variable"]], "Removing a random subset of the data": [[28, "Removing-a-random-subset-of-the-data"], [32, "Removing-a-random-subset-of-the-data"], [47, "Removing-a-random-subset-of-the-data"]], "Simple example on using Instrumental Variables method for estimation": [[29, "Simple-example-on-using-Instrumental-Variables-method-for-estimation"]], "Loading the dataset": [[29, "Loading-the-dataset"]], "Using DoWhy to estimate the causal effect of education on future income": [[29, "Using-DoWhy-to-estimate-the-causal-effect-of-education-on-future-income"]], "Demo for the DoWhy causal API": [[30, "Demo-for-the-DoWhy-causal-API"]], "Comparing the estimate to Linear Regression": [[30, "Comparing-the-estimate-to-Linear-Regression"]], "Causal Discovery example": [[31, "Causal-Discovery-example"]], "Utility function": [[31, "Utility-function"]], "Experiments on the Auto-MPG dataset": [[31, "Experiments-on-the-Auto-MPG-dataset"]], "1. Load the data": [[31, "1.-Load-the-data"], [31, "id1"], [40, "1.-Load-the-data"]], "Causal Discovery with causal-learn": [[31, "Causal-Discovery-with-causal-learn"], [31, "id2"]], "Estimate causal effects using Linear Regression": [[31, "Estimate-causal-effects-using-Linear-Regression"]], "Experiments on the Sachs dataset": [[31, "Experiments-on-the-Sachs-dataset"]], "Estimate effects using Linear Regression": [[31, "Estimate-effects-using-Linear-Regression"]], "Confounding Example: Finding causal effects from observed data": [[32, "Confounding-Example:-Finding-causal-effects-from-observed-data"]], "Let\u2019s create a mystery dataset for which we need to determine whether there is a causal effect.": [[32, "Let's-create-a-mystery-dataset-for-which-we-need-to-determine-whether-there-is-a-causal-effect."]], "Using DoWhy to resolve the mystery: Does Treatment cause Outcome?": [[32, "Using-DoWhy-to-resolve-the-mystery:-Does-Treatment-cause-Outcome?"]], "STEP 1: Model the problem as a causal graph": [[32, "STEP-1:-Model-the-problem-as-a-causal-graph"]], "STEP 2: Identify causal effect using properties of the formal causal graph": [[32, "STEP-2:-Identify-causal-effect-using-properties-of-the-formal-causal-graph"]], "STEP 3: Estimate the causal effect": [[32, "STEP-3:-Estimate-the-causal-effect"]], "Checking if the estimate is correct": [[32, "Checking-if-the-estimate-is-correct"]], "Step 4: Refuting the estimate": [[32, "Step-4:-Refuting-the-estimate"]], "A Simple Example on Creating a Custom Refutation Using User-Defined Outcome Functions": [[33, "A-Simple-Example-on-Creating-a-Custom-Refutation-Using-User-Defined-Outcome-Functions"]], "Insert Dependencies": [[33, "Insert-Dependencies"]], "Create the Dataset": [[33, "Create-the-Dataset"]], "Creating the Causal Model": [[33, "Creating-the-Causal-Model"]], "Identify the Estimand": [[33, "Identify-the-Estimand"]], "Estimating the Effect": [[33, "Estimating-the-Effect"]], "Refuting the Estimate": [[33, "Refuting-the-Estimate"]], "Using a Randomly Generated Outcome": [[33, "Using-a-Randomly-Generated-Outcome"]], "Using a Function that Generates the Outcome from the Confounders": [[33, "Using-a-Function-that-Generates-the-Outcome-from-the-Confounders"]], "Finding optimal adjustment sets": [[34, "Finding-optimal-adjustment-sets"]], "Preliminaries": [[34, "Preliminaries"]], "The design of an observational study": [[34, "The-design-of-an-observational-study"]], "An example in which sufficient conditions to guarantee the existence of an optimal backdoor set do not hold": [[34, "An-example-in-which-sufficient-conditions-to-guarantee-the-existence-of-an-optimal-backdoor-set-do-not-hold"]], "An example in which there are no observable adjustment sets": [[34, "An-example-in-which-there-are-no-observable-adjustment-sets"]], "An example with costs": [[34, "An-example-with-costs"]], "DoWhy: Different estimation methods for causal inference": [[35, "DoWhy:-Different-estimation-methods-for-causal-inference"]], "Identifying the causal estimand": [[35, "Identifying-the-causal-estimand"], [39, "Identifying-the-causal-estimand"]], "Method 1: Regression": [[35, "Method-1:-Regression"]], "Method 2: Distance Matching": [[35, "Method-2:-Distance-Matching"]], "Method 3: Propensity Score Stratification": [[35, "Method-3:-Propensity-Score-Stratification"]], "Method 4: Propensity Score Matching": [[35, "Method-4:-Propensity-Score-Matching"]], "Method 5: Weighting": [[35, "Method-5:-Weighting"]], "Method 6: Instrumental Variable": [[35, "Method-6:-Instrumental-Variable"]], "Method 7: Regression Discontinuity": [[35, "Method-7:-Regression-Discontinuity"]], "Estimating the Effect of a Member Rewards Program": [[36, "Estimating-the-Effect-of-a-Member-Rewards-Program"]], "I. Formulating the causal model": [[36, "I.-Formulating-the-causal-model"]], "The importance of time": [[36, "The-importance-of-time"]], "II. Identifying the causal effect": [[36, "II.-Identifying-the-causal-effect"]], "III. Estimating the effect": [[36, "III.-Estimating-the-effect"]], "IV. Refuting the estimate": [[36, "IV.-Refuting-the-estimate"]], "Functional API Preview": [[37, "Functional-API-Preview"]], "Import Dependencies": [[37, "Import-Dependencies"], [46, "Import-Dependencies"]], "Create the Datasets": [[37, "Create-the-Datasets"], [46, "Create-the-Datasets"]], "Identify Effect - Functional API (Preview)": [[37, "Identify-Effect---Functional-API-(Preview)"]], "Estimate Effect - Functional API (Preview)": [[37, "Estimate-Effect---Functional-API-(Preview)"]], "Refute Estimate - Functional API (Preview)": [[37, "Refute-Estimate---Functional-API-(Preview)"]], "Backwards Compatibility": [[37, "Backwards-Compatibility"]], "Identify Effect": [[37, "Identify-Effect"], [46, "Identify-Effect"]], "Estimate Effect": [[37, "Estimate-Effect"], [46, "Estimate-Effect"]], "Refute Estimate": [[37, "Refute-Estimate"], [46, "Refute-Estimate"]], "DoWhy example on ihdp (Infant Health and Development Program) dataset": [[38, "DoWhy-example-on-ihdp-(Infant-Health-and-Development-Program)-dataset"]], "Loading Data": [[38, "Loading-Data"]], "1.Model": [[38, "1.Model"]], "2.Identify": [[38, "2.Identify"]], "3. Estimate (using different methods)": [[38, "3.-Estimate-(using-different-methods)"]], "3.1 Using Linear Regression": [[38, "3.1-Using-Linear-Regression"]], "3.2 Using Propensity Score Matching": [[38, "3.2-Using-Propensity-Score-Matching"]], "3.3 Using Propensity Score Stratification": [[38, "3.3-Using-Propensity-Score-Stratification"]], "3.4 Using Propensity Score Weighting": [[38, "3.4-Using-Propensity-Score-Weighting"]], "4. Refute": [[38, "4.-Refute"]], "4.3 Data Subset Refuter": [[38, "4.3-Data-Subset-Refuter"]], "DoWhy: Interpreters for Causal Estimators": [[39, "DoWhy:-Interpreters-for-Causal-Estimators"]], "Method 1: Propensity Score Stratification": [[39, "Method-1:-Propensity-Score-Stratification"]], "Textual Interpreter": [[39, "Textual-Interpreter"]], "Visual Interpreter": [[39, "Visual-Interpreter"]], "Method 2: Propensity Score Matching": [[39, "Method-2:-Propensity-Score-Matching"]], "Method 3: Weighting": [[39, "Method-3:-Weighting"]], "DoWhy example on the Lalonde dataset": [[40, "DoWhy-example-on-the-Lalonde-dataset"]], "2. Run DoWhy analysis: model, identify, estimate": [[40, "2.-Run-DoWhy-analysis:-model,-identify,-estimate"]], "3. Interpret the estimate": [[40, "3.-Interpret-the-estimate"]], "4. Sanity check: compare to manual IPW estimate": [[40, "4.-Sanity-check:-compare-to-manual-IPW-estimate"]], "Mediation analysis with DoWhy: Direct and Indirect Effects": [[41, "Mediation-analysis-with-DoWhy:-Direct-and-Indirect-Effects"]], "Creating a dataset": [[41, "Creating-a-dataset"]], "Step 1: Modeling the causal mechanism": [[41, "Step-1:-Modeling-the-causal-mechanism"]], "Step 2: Identifying the natural direct and indirect effects": [[41, "Step-2:-Identifying-the-natural-direct-and-indirect-effects"]], "Step 3: Estimation of the effect": [[41, "Step-3:-Estimation-of-the-effect"]], "Natural Indirect Effect": [[41, "Natural-Indirect-Effect"]], "Natural Direct Effect": [[41, "Natural-Direct-Effect"]], "Step 4: Refutations": [[41, "Step-4:-Refutations"]], "Estimating effect of multiple treatments": [[42, "Estimating-effect-of-multiple-treatments"]], "Linear model": [[42, "Linear-model"]], "More methods": [[42, "More-methods"]], "Example to demonstrate optimized backdoor variable search for Causal Identification": [[43, "Example-to-demonstrate-optimized-backdoor-variable-search-for-Causal-Identification"]], "Create Random Graph": [[43, "Create-Random-Graph"]], "Testing optimized backdoor search": [[43, "Testing-optimized-backdoor-search"]], "Applying refutation tests to the Lalonde and IHDP datasets": [[44, "Applying-refutation-tests-to-the-Lalonde-and-IHDP-datasets"]], "Import the Dependencies": [[44, "Import-the-Dependencies"]], "Loading the Dataset": [[44, "Loading-the-Dataset"]], "Infant Health and Development Program Dataset (IHDP)": [[44, "Infant-Health-and-Development-Program-Dataset-(IHDP)"]], "Reference": [[44, "Reference"]], "Lalonde Dataset": [[44, "Lalonde-Dataset"]], "Step 1: Building the model": [[44, "Step-1:-Building-the-model"]], "IHDP": [[44, "IHDP"], [44, "id1"], [44, "id3"], [44, "id5"]], "Lalonde": [[44, "Lalonde"], [44, "id2"], [44, "id4"], [44, "id6"]], "Step 2: Identification": [[44, "Step-2:-Identification"]], "Step 3: Estimation (using propensity score weighting)": [[44, "Step-3:-Estimation-(using-propensity-score-weighting)"]], "Step 4: Refutation": [[44, "Step-4:-Refutation"]], "Add Random Common Cause": [[44, "Add-Random-Common-Cause"], [44, "id7"]], "Replace Treatment with Placebo": [[44, "Replace-Treatment-with-Placebo"], [44, "id8"]], "Remove Random Subset of Data": [[44, "Remove-Random-Subset-of-Data"], [44, "id9"]], "Assessing Support and Overlap with OverRule": [[45, "Assessing-Support-and-Overlap-with-OverRule"]], "Motivation": [[45, "Motivation"]], "Acknowledgements": [[45, "Acknowledgements"]], "Table of contents": [[45, "Table-of-contents"]], "Illustration on a simple 2D example": [[45, "Illustration-on-a-simple-2D-example"]], "Problem Data": [[45, "Problem-Data"]], "Applying OverRule with default arguments": [[45, "Applying-OverRule-with-default-arguments"]], "Example usage using CausalModel": [[45, "Example-usage-using-CausalModel"]], "Example usage using the Functional API": [[45, "Example-usage-using-the-Functional-API"]], "Only fitting support or overlap rules": [[45, "Only-fitting-support-or-overlap-rules"]], "Interpreting the output of OverRule": [[45, "Interpreting-the-output-of-OverRule"]], "Configuring OverRule": [[45, "Configuring-OverRule"]], "Illustration on Lalonde and PSID datasets": [[45, "Illustration-on-Lalonde-and-PSID-datasets"]], "Checking for Support/Overlap in the Experimental Sample": [[45, "Checking-for-Support/Overlap-in-the-Experimental-Sample"]], "Assessing Support/Overlap on the combined sample": [[45, "Assessing-Support/Overlap-on-the-combined-sample"]], "Filtering with OverRule before performing a causal analysis": [[45, "Filtering-with-OverRule-before-performing-a-causal-analysis"]], "Performing causal inference after filtering": [[45, "Performing-causal-inference-after-filtering"]], "Iterating over multiple refutation tests": [[46, "Iterating-over-multiple-refutation-tests"]], "Inspection Parameters": [[46, "Inspection-Parameters"]], "Estimator List": [[46, "Estimator-List"]], "Refuter List": [[46, "Refuter-List"]], "Inspect Data": [[46, "Inspect-Data"]], "Create the CausalModels": [[46, "Create-the-CausalModels"]], "View Models": [[46, "View-Models"]], "Identified Estimands": [[46, "Identified-Estimands"]], "Estimate Values": [[46, "Estimate-Values"]], "Refutation Values": [[46, "Refutation-Values"]], "Basic Example for Calculating the Causal Effect": [[47, "Basic-Example-for-Calculating-the-Causal-Effect"]], "Interface 1 (recommended): Input causal graph": [[47, "Interface-1-(recommended):-Input-causal-graph"]], "DoWhy philosophy: Keep identification and estimation separate": [[47, "DoWhy-philosophy:-Keep-identification-and-estimation-separate"]], "Identification": [[47, "Identification"], [91, "identification"]], "Estimation": [[47, "Estimation"], [91, "estimation"]], "Interface 2: Specify common causes and instruments": [[47, "Interface-2:-Specify-common-causes-and-instruments"]], "Impact of 401(k) eligibility on net financial assets": [[48, "Impact-of-401(k)-eligibility-on-net-financial-assets"]], "Background": [[48, "Background"]], "Data": [[48, "Data"]], "Effect of 401(k) Eligibility on Net Financial Assets, Conditioned on Income": [[48, "Effect-of-401(k)-Eligibility-on-Net-Financial-Assets,-Conditioned-on-Income"]], "Basic Example for Graphical Causal Models": [[49, "Basic-Example-for-Graphical-Causal-Models"]], "Step 1: Modeling cause-effect relationships as a structural causal model (SCM)": [[49, "Step-1:-Modeling-cause-effect-relationships-as-a-structural-causal-model-(SCM)"]], "Step 2: Fitting the SCM to the data": [[49, "Step-2:-Fitting-the-SCM-to-the-data"]], "Step 3: Answering a causal query based on the SCM": [[49, "Step-3:-Answering-a-causal-query-based-on-the-SCM"]], "Counterfactual Analysis in a Medical Case": [[50, "Counterfactual-Analysis-in-a-Medical-Case"]], "The data": [[50, "The-data"]], "Modeling of the graph": [[50, "Modeling-of-the-graph"]], "Answering Alice\u2019s counterfactual queries": [[50, "Answering-Alice's-counterfactual-queries"]], "Appendix: What the tele-app uses internally. Data generation of the patients\u2019 log": [[50, "Appendix:-What-the-tele-app-uses-internally.-Data-generation-of-the-patients'-log"]], "Decomposing the Gender Wage Gap": [[51, "Decomposing-the-Gender-Wage-Gap"]], "Read and prepare data": [[51, "Read-and-prepare-data"]], "Causal Model": [[51, "Causal-Model"]], "Implementation with dowhy.gcm.distribution_change_robust": [[51, "Implementation-with-dowhy.gcm.distribution_change_robust"]], "Manual Implementation (Advanced)": [[51, "Manual-Implementation-(Advanced)"]], "Interpretation": [[51, "Interpretation"]], "Basic Example for generating samples from a GCM": [[52, "Basic-Example-for-generating-samples-from-a-GCM"]], "Falsification of User-Given Directed Acyclic Graphs": [[53, "Falsification-of-User-Given-Directed-Acyclic-Graphs"]], "Synthetic Data": [[53, "Synthetic-Data"]], "Real Data (Protein Network dataset by Sachs et al., 2005)": [[53, "Real-Data-(Protein-Network-dataset-by-Sachs-et-al.,-2005)"]], "Edge Suggestions": [[53, "Edge-Suggestions"]], "Estimating intrinsic causal influences in real-world examples": [[54, "Estimating-intrinsic-causal-influences-in-real-world-examples"]], "Intrinsic influence on car MPG consumption": [[54, "Intrinsic-influence-on-car-MPG-consumption"]], "Intrinsic influence on river flow": [[54, "Intrinsic-influence-on-river-flow"]], "Causal Attributions and Root-Cause Analysis in an Online Shop": [[55, "Causal-Attributions-and-Root-Cause-Analysis-in-an-Online-Shop"]], "The scenario": [[55, "The-scenario"]], "Step 1: Define causal model": [[55, "Step-1:-Define-causal-model"]], "Step 2: Fit causal models to data": [[55, "Step-2:-Fit-causal-models-to-data"]], "Step 3: Answer causal questions": [[55, "Step-3:-Answer-causal-questions"]], "Generate new samples": [[55, "Generate-new-samples"]], "What are the key factors influencing the variance in profit?": [[55, "What-are-the-key-factors-influencing-the-variance-in-profit?"]], "What are the key factors explaining the Profit drop on a particular day?": [[55, "What-are-the-key-factors-explaining-the-Profit-drop-on-a-particular-day?"]], "What caused the profit drop in Q1 2022?": [[55, "What-caused-the-profit-drop-in-Q1-2022?"]], "Data generation process": [[55, "Data-generation-process"]], "Finding the Root Cause of Elevated Latencies in a Microservice Architecture": [[56, "Finding-the-Root-Cause-of-Elevated-Latencies-in-a-Microservice-Architecture"]], "Setting up the causal model": [[56, "Setting-up-the-causal-model"]], "Scenario 1: Observing a single outlier": [[56, "Scenario-1:-Observing-a-single-outlier"]], "Attributing an outlier latency at a target service to other services": [[56, "Attributing-an-outlier-latency-at-a-target-service-to-other-services"]], "Scenario 2: Observing permanent degradation of latencies": [[56, "Scenario-2:-Observing-permanent-degradation-of-latencies"]], "Attributing permanent degradation of latencies at a target service to other services": [[56, "Attributing-permanent-degradation-of-latencies-at-a-target-service-to-other-services"]], "Scenario 3: Simulating the intervention of shifting resources": [[56, "Scenario-3:-Simulating-the-intervention-of-shifting-resources"]], "Appendix: Data generation process": [[56, "Appendix:-Data-generation-process"]], "Finding Root Causes of Changes in a Supply Chain": [[57, "Finding-Root-Causes-of-Changes-in-a-Supply-Chain"]], "Week-over-week changes": [[57, "Week-over-week-changes"]], "Why did the average value of received quantity change week-over-week?": [[57, "Why-did-the-average-value-of-received-quantity-change-week-over-week?"]], "Ad-hoc attribution analysis": [[57, "Ad-hoc-attribution-analysis"]], "Causal Attribution Analysis": [[57, "Causal-Attribution-Analysis"]], "Graphical causal model": [[57, "Graphical-causal-model"]], "Attributing change": [[57, "Attributing-change"]], "Appendix: Ground Truth": [[57, "Appendix:-Ground-Truth"]], "Testing Assumptions in model with DoWhy: A simple example": [[58, "Testing-Assumptions-in-model-with-DoWhy:-A-simple-example"]], "Step 1: Load dataset": [[58, "Step-1:-Load-dataset"]], "Step 2: Input causal graph": [[58, "Step-2:-Input-causal-graph"]], "Step 3: Create Causal Model": [[58, "Step-3:-Create-Causal-Model"], [66, "Step-3:-Create-Causal-Model"]], "Step 4: Testing for Conditional Independence": [[58, "Step-4:-Testing-for-Conditional-Independence"]], "Testing for a set of edges": [[58, "Testing-for-a-set-of-edges"]], "Testing with a wrong graph input": [[58, "Testing-with-a-wrong-graph-input"]], "Identifying Effect using ID Algorithm": [[59, "Identifying-Effect-using-ID-Algorithm"]], "Case 1": [[59, "Case-1"]], "Case 2": [[59, "Case-2"]], "Case 3": [[59, "Case-3"]], "Case 4": [[59, "Case-4"]], "Case 5": [[59, "Case-5"]], "Case 6": [[59, "Case-6"]], "Lalonde Pandas API Example": [[60, "Lalonde-Pandas-API-Example"]], "Getting the Data": [[60, "Getting-the-Data"]], "The causal Namespace": [[60, "The-causal-Namespace"]], "The do Operation": [[60, "The-do-Operation"]], "Treatment Effect Estimation": [[60, "Treatment-Effect-Estimation"]], "Different ways to load an input graph": [[61, "Different-ways-to-load-an-input-graph"]], "I. Generating dummy data": [[61, "I.-Generating-dummy-data"]], "II. Loading GML or DOT graphs": [[61, "II.-Loading-GML-or-DOT-graphs"]], "GML format": [[61, "GML-format"]], "DOT format": [[61, "DOT-format"]], "Case Studies": [[62, null]], "Example notebooks": [[63, "example-notebooks"]], "Introductory examples": [[63, "introductory-examples"]], "Real world-inspired examples": [[63, "real-world-inspired-examples"]], "Examples on benchmark datasets": [[63, "examples-on-benchmark-datasets"]], "Modeling and refuting causal assumptions": [[63, "modeling-and-refuting-causal-assumptions"]], "Miscellaneous": [[63, "miscellaneous"]], "Demo for DoWhy Causal Prediction on MNIST": [[64, "Demo-for-DoWhy-Causal-Prediction-on-MNIST"]], "Multi-attribute distribution shift datasets": [[64, "Multi-attribute-distribution-shift-datasets"]], "Multi-attribute MNIST": [[64, "Multi-attribute-MNIST"]], "Domains in multi-attribute MNIST": [[64, "Domains-in-multi-attribute-MNIST"]], "Initialize dataset": [[64, "Initialize-dataset"]], "Initialize data loaders": [[64, "Initialize-data-loaders"]], "Method 1: Provide validation domain explicitly": [[64, "Method-1:-Provide-validation-domain-explicitly"]], "Method 2: Validation set using subset of training data": [[64, "Method-2:-Validation-set-using-subset-of-training-data"]], "Initialize model and algorithm": [[64, "Initialize-model-and-algorithm"]], "Initialize algorithm class: ERM": [[64, "Initialize-algorithm-class:-ERM"]], "Fit predictor and start training": [[64, "Fit-predictor-and-start-training"]], "Evaluate on test domain": [[64, "Evaluate-on-test-domain"]], "Prediction with CACM": [[64, "Prediction-with-CACM"]], "Extending to different datasets and algorithms": [[64, "Extending-to-different-datasets-and-algorithms"]], "MNIST Independent and Causal+Independent datasets": [[64, "MNIST-Independent-and-Causal+Independent-datasets"]], "MNISTIndAttribute: Single-attribute Independent shift": [[64, "MNISTIndAttribute:-Single-attribute-Independent-shift"]], "MNISTCausalIndAttribute: Multi-attribute Causal+Independent shift": [[64, "MNISTCausalIndAttribute:-Multi-attribute-Causal+Independent-shift"]], "Additional datasets and algorithms": [[64, "Additional-datasets-and-algorithms"]], "Sensitivity analysis for non-parametric causal estimators": [[65, "Sensitivity-analysis-for-non-parametric-causal-estimators"]], "I. Sensitivity analysis for partially linear models": [[65, "I.-Sensitivity-analysis-for-partially-linear-models"]], "Creating a dataset with unobserved confounding": [[65, "Creating-a-dataset-with-unobserved-confounding"]], "Obtaining a causal estimate using Model, Identify, Estimate steps": [[65, "Obtaining-a-causal-estimate-using-Model,-Identify,-Estimate-steps"]], "Sensitivity Analysis using the Refute step": [[65, "Sensitivity-Analysis-using-the-Refute-step"]], "II. Sensitivity Analysis for general non-parametric models": [[65, "II.-Sensitivity-Analysis-for-general-non-parametric-models"]], "Sensitivity Analysis for Regression Models": [[66, "Sensitivity-Analysis-for-Regression-Models"]], "Step 1: Load required packages": [[66, "Step-1:-Load-required-packages"]], "Step 2: Load the dataset": [[66, "Step-2:-Load-the-dataset"]], "Step 4: Identification": [[66, "Step-4:-Identification"]], "Step 5: Estimation": [[66, "Step-5:-Estimation"]], "Step 6a: Refutation and Sensitivity Analysis - Method 1": [[66, "Step-6a:-Refutation-and-Sensitivity-Analysis---Method-1"]], "Parameter List for plot function": [[66, "Parameter-List-for-plot-function"]], "Step 6b: Refutation and Sensitivity Analysis - Method 2": [[66, "Step-6b:-Refutation-and-Sensitivity-Analysis---Method-2"]], "Effect inference with timeseries data": [[67, "Effect-inference-with-timeseries-data"]], "Loading timeseries data and causal graph": [[67, "Loading-timeseries-data-and-causal-graph"]], "Dataset Shifting and Filtering": [[67, "Dataset-Shifting-and-Filtering"]], "Causal Effect Estimation": [[67, "Causal-Effect-Estimation"]], "Importing temporal causal graph from Tigramite library": [[67, "Importing-temporal-causal-graph-from-Tigramite-library"]], "Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)": [[68, "Tutorial-on-Causal-Inference-and-its-Connections-to-Machine-Learning-(Using-DoWhy+EconML)"]], "Why causal inference?": [[68, "Why-causal-inference?"]], "Defining a causal effect": [[68, "Defining-a-causal-effect"]], "The difference between prediction and causal inference": [[68, "The-difference-between-prediction-and-causal-inference"]], "Two fundamental challenges for causal inference": [[68, "Two-fundamental-challenges-for-causal-inference"]], "The four steps of causal inference": [[68, "The-four-steps-of-causal-inference"]], "The DoWhy+EconML solution": [[68, "The-DoWhy+EconML-solution"]], "A mystery dataset: Can you find out if if there is a causal effect?": [[68, "A-mystery-dataset:-Can-you-find-out-if-if-there-is-a-causal-effect?"]], "Model assumptions about the data-generating process using a causal graph": [[68, "Model-assumptions-about-the-data-generating-process-using-a-causal-graph"]], "Identify the correct estimand for the target quantity based on the causal model": [[68, "Identify-the-correct-estimand-for-the-target-quantity-based-on-the-causal-model"]], "Estimate the target estimand": [[68, "Estimate-the-target-estimand"]], "Check robustness of the estimate using refutation tests": [[68, "Check-robustness-of-the-estimate-using-refutation-tests"]], "Case-studies using DoWhy+EconML": [[68, "Case-studies-using-DoWhy+EconML"]], "Estimating the impact of a customer loyalty program": [[68, "Estimating-the-impact-of-a-customer-loyalty-program"]], "Recommendation A/B testing at an online company": [[68, "Recommendation-A/B-testing-at-an-online-company"]], "User segmentation for targeting interventions": [[68, "User-segmentation-for-targeting-interventions"]], "Multi-investment attribution at a software company": [[68, "Multi-investment-attribution-at-a-software-company"]], "Connections to fundamental machine learning challenges": [[68, "Connections-to-fundamental-machine-learning-challenges"]], "Further resources": [[68, "Further-resources"], [69, "further-resources"]], "DoWhy+EconML libraries": [[68, "DoWhy+EconML-libraries"]], "Video Lecture on causal inference and its connections to machine learning": [[68, "Video-Lecture-on-causal-inference-and-its-connections-to-machine-learning"]], "Detailed KDD Tutorial on Causal Inference": [[68, "Detailed-KDD-Tutorial-on-Causal-Inference"]], "Book chapters on causality and machine learning": [[68, "Book-chapters-on-causality-and-machine-learning"]], "Causality and Machine Learning group at Microsoft": [[68, "Causality-and-Machine-Learning-group-at-Microsoft"]], "Getting Started": [[69, "getting-started"]], "Installation": [[69, "installation"], [70, "installation"]], "\u201cHello causal inference world\u201d": [[69, "hello-causal-inference-world"]], "Effect inference": [[69, "effect-inference"]], "Graphical causal model-based inference": [[69, "graphical-causal-model-based-inference"]], "Installing with pip": [[70, "installing-with-pip"]], "Installing with Conda": [[70, "installing-with-conda"]], "Installing on Azure Machine Learning": [[70, "installing-on-azure-machine-learning"]], "DoWhy documentation": [[71, "dowhy-documentation"]], "Key differences compared to available causal inference software": [[71, "key-differences-compared-to-available-causal-inference-software"]], "Predicting outcome for out-of-distribution inputs": [[72, "predicting-outcome-for-out-of-distribution-inputs"]], "Estimating conditional average causal effect": [[73, "estimating-conditional-average-causal-effect"]], "Linear regression": [[73, "linear-regression"]], "DML method from EconML": [[73, "dml-method-from-econml"]], "Distance-based matching": [[74, "distance-based-matching"]], "Do-sampler": [[75, "do-sampler"]], "Using do-sampler": [[75, "using-do-sampler"]], "Estimating average causal effect using backdoor": [[76, "estimating-average-causal-effect-using-backdoor"]], "Propensity-based methods": [[77, "propensity-based-methods"]], "Propensity-Based Matching": [[77, "propensity-based-matching"]], "Propensity-based Stratification": [[77, "propensity-based-stratification"]], "Inverse Propensity Weighting": [[77, "inverse-propensity-weighting"]], "Regression-based methods": [[78, "regression-based-methods"]], "Effect Estimation Using specific Effect Estimators (for ACE, mediation effect, \u2026)": [[79, "effect-estimation-using-specific-effect-estimators-for-ace-mediation-effect"]], "Generating sample data and the causal model": [[79, "generating-sample-data-and-the-causal-model"]], "Identify a target estimand under the model": [[79, "identify-a-target-estimand-under-the-model"]], "Supported identification criteria": [[79, "supported-identification-criteria"]], "Estimate causal effect based on the identified estimand": [[79, "estimate-causal-effect-based-on-the-identified-estimand"]], "Supported estimation methods": [[79, "supported-estimation-methods"]], "Using EconML and CausalML estimation methods in DoWhy": [[79, "using-econml-and-causalml-estimation-methods-in-dowhy"]], "Refute the obtained estimate": [[79, "refute-the-obtained-estimate"]], "Supported refutation methods": [[79, "supported-refutation-methods"]], "Comparison to other packages": [[79, "comparison-to-other-packages"]], "Key difference: Causal assumptions as first-class citizens": [[79, "key-difference-causal-assumptions-as-first-class-citizens"]], "Estimating average causal effect using GCM": [[80, "estimating-average-causal-effect-using-gcm"]], "How to use it": [[80, "how-to-use-it"], [89, "how-to-use-it"], [92, "how-to-use-it"], [93, "how-to-use-it"], [94, "how-to-use-it"], [95, "how-to-use-it"], [97, "how-to-use-it"], [99, "how-to-use-it"], [111, "how-to-use-it"]], "Related example notebooks": [[80, "related-example-notebooks"], [89, "related-example-notebooks"], [91, "related-example-notebooks"], [92, "related-example-notebooks"], [93, "related-example-notebooks"], [94, "related-example-notebooks"], [97, "related-example-notebooks"], [99, "related-example-notebooks"], [113, "related-example-notebooks"]], "Understanding the method": [[80, "understanding-the-method"], [89, "understanding-the-method"], [92, "understanding-the-method"], [93, "understanding-the-method"], [94, "understanding-the-method"], [97, "understanding-the-method"]], "Estimating average causal effect with natural experiments": [[81, "estimating-average-causal-effect-with-natural-experiments"]], "Instrumental variable estimator for binary action": [[81, "instrumental-variable-estimator-for-binary-action"]], "Regression discontinuity estimator": [[81, "regression-discontinuity-estimator"]], "Backdoor criterion": [[82, "backdoor-criterion"]], "Frontdoor criterion": [[83, "frontdoor-criterion"]], "ID algorithm for discovering new identification strategies": [[84, "id-algorithm-for-discovering-new-identification-strategies"]], "Identifying causal effect": [[85, "identifying-causal-effect"]], "Natural experiments and instrumental variables": [[86, "natural-experiments-and-instrumental-variables"]], "Estimating Causal Effects": [[87, "estimating-causal-effects"]], "Performing Causal Tasks": [[88, "performing-causal-tasks"]], "Quantifying Intrinsic Causal Influence": [[89, "quantifying-intrinsic-causal-influence"]], "Quantify Causal Influence": [[90, "quantify-causal-influence"]], "Mediation Analysis: Estimating natural direct and indirect effects": [[91, "mediation-analysis-estimating-natural-direct-and-indirect-effects"]], "Direct Effect: Quantifying Arrow Strength": [[92, "direct-effect-quantifying-arrow-strength"]], "Customize the distance measure": [[92, "customize-the-distance-measure"]], "Anomaly Attribution": [[93, "anomaly-attribution"]], "Attributing Distributional Changes": [[94, "attributing-distributional-changes"]], "Feature Relevance": [[95, "feature-relevance"]], "Root-Cause Analysis and Explanation": [[96, "root-cause-analysis-and-explanation"]], "Computing Counterfactuals": [[97, "computing-counterfactuals"]], "Asking and Answering What-If Questions": [[98, "asking-and-answering-what-if-questions"]], "Simulating the Impact of Interventions": [[99, "simulating-the-impact-of-interventions"]], "Foreword": [[100, "foreword"]], "The need for causal inference": [[100, "the-need-for-causal-inference"]], "User Guide": [[101, "user-guide"]], "Introduction to DoWhy": [[102, "introduction-to-dowhy"]], "Supported causal tasks": [[102, "supported-causal-tasks"]], "Testing validity of a causal analysis": [[102, "testing-validity-of-a-causal-analysis"]], "Who this user guide is for": [[102, "who-this-user-guide-is-for"]], "Modeling Causal Relations": [[103, "modeling-causal-relations"]], "Learning causal structure from data": [[104, "learning-causal-structure-from-data"]], "Graph discovery using CDT": [[104, "graph-discovery-using-cdt"]], "Graph discovery using dodiscover": [[104, "graph-discovery-using-dodiscover"]], "Graph discovery using causal-learn": [[104, "graph-discovery-using-causal-learn"]], "Performing independence tests": [[105, "performing-independence-tests"]], "Refuting a Causal Graph": [[106, "refuting-a-causal-graph"]], "Graph refutations": [[107, "graph-refutations"]], "Specifying a causal graph using domain knowledge": [[108, "specifying-a-causal-graph-using-domain-knowledge"]], "Customizing Causal Mechanism Assignment": [[109, "customizing-causal-mechanism-assignment"]], "Using ground truth models": [[109, "using-ground-truth-models"]], "Creating causal model (GCM) from equations": [[109, "creating-causal-model-gcm-from-equations"]], "Generate samples from a GCM": [[110, "generate-samples-from-a-gcm"]], "Estimating Confidence Intervals": [[111, "estimating-confidence-intervals"]], "Computing confidence intervals for causal queries": [[111, "computing-confidence-intervals-for-causal-queries"]], "Conveniently bootstrapping graph training on random subsets of training data": [[111, "conveniently-bootstrapping-graph-training-on-random-subsets-of-training-data"]], "Runtime cost versus confidence": [[111, "runtime-cost-versus-confidence"]], "Understanding the need for confidence intervals": [[111, "understanding-the-need-for-confidence-intervals"]], "Types of graphical causal models": [[112, "types-of-graphical-causal-models"]], "Available Causal Mechanisms": [[112, "available-causal-mechanisms"]], "Modeling Graphical Causal Models (GCMs)": [[113, "modeling-graphical-causal-models-gcms"]], "Fitting an SCM to the data": [[113, "fitting-an-scm-to-the-data"]], "Evaluating a fitted SCM": [[113, "evaluating-a-fitted-scm"]], "Other topics": [[113, "other-topics"]], "Evaluate a GCM": [[114, "evaluate-a-gcm"]], "Summary of auto assignment": [[114, "summary-of-auto-assignment"]], "Evaluating a fitted GCM": [[114, "evaluating-a-fitted-gcm"]], "Refuting causal estimates": [[115, "refuting-causal-estimates"]], "Data Subsample Refuter": [[116, "data-subsample-refuter"]], "Dummy Outcome Refuter": [[117, "dummy-outcome-refuter"]], "Testing for zero causal effect": [[117, "testing-for-zero-causal-effect"]], "Testing for non-zero causal effect": [[117, "testing-for-non-zero-causal-effect"]], "Refuting Effect Estimates": [[118, "refuting-effect-estimates"]], "Refutations based on negative control": [[118, "refutations-based-on-negative-control"]], "Refutations based on sensitivity analysis": [[118, "refutations-based-on-sensitivity-analysis"]], "Placebo Treatment Refuter": [[119, "placebo-treatment-refuter"]], "Random Common Cause Refuter": [[120, "random-common-cause-refuter"]], "Sensitivity Analysis": [[121, "sensitivity-analysis"]], "Partial-R2 based sensitivity analysis for 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"dowhy.causal_refuters.partial_linear_sensitivity_analyzer": [[13, "module-dowhy.causal_refuters.partial_linear_sensitivity_analyzer"]], "dowhy.causal_refuters.placebo_treatment_refuter": [[13, "module-dowhy.causal_refuters.placebo_treatment_refuter"]], "dowhy.causal_refuters.random_common_cause": [[13, "module-dowhy.causal_refuters.random_common_cause"]], "dowhy.causal_refuters.refute_estimate": [[13, "module-dowhy.causal_refuters.refute_estimate"]], "dowhy.causal_refuters.reisz": [[13, "module-dowhy.causal_refuters.reisz"]], "filter_dataframe() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.filter_dataframe"]], "fit() (dowhy.causal_refuters.assess_overlap_overrule.overruleanalyzer method)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverruleAnalyzer.fit"]], "fit() (dowhy.causal_refuters.reisz.pluginreisz method)": [[13, "dowhy.causal_refuters.reisz.PluginReisz.fit"]], "fit() 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dowhy.causal_refuters.reisz)": [[13, "dowhy.causal_refuters.reisz.get_generic_regressor"]], "get_phi_lower_upper() (dowhy.causal_refuters.non_parametric_sensitivity_analyzer.nonparametricsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.non_parametric_sensitivity_analyzer.NonParametricSensitivityAnalyzer.get_phi_lower_upper"]], "get_phi_lower_upper() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_phi_lower_upper"]], "get_regression_r2() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.get_regression_r2"]], "include_simulated_confounder() (dowhy.causal_refuters.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.AddUnobservedCommonCause.include_simulated_confounder"]], "include_simulated_confounder() (dowhy.causal_refuters.add_unobserved_common_cause.addunobservedcommoncause method)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.AddUnobservedCommonCause.include_simulated_confounder"]], "include_simulated_confounder() (in module dowhy.causal_refuters.add_unobserved_common_cause)": [[13, "dowhy.causal_refuters.add_unobserved_common_cause.include_simulated_confounder"]], "is_benchmarking_needed() (dowhy.causal_refuters.partial_linear_sensitivity_analyzer.partiallinearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.partial_linear_sensitivity_analyzer.PartialLinearSensitivityAnalyzer.is_benchmarking_needed"]], "itermax (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.iterMax"]], "itermax (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": 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"dowhy.causal_refuters.dummy_outcome_refuter.noise"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.overlapconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.OverlapConfig.num_thresh"]], "num_thresh (dowhy.causal_refuters.assess_overlap_overrule.supportconfig attribute)": [[13, "dowhy.causal_refuters.assess_overlap_overrule.SupportConfig.num_thresh"]], "other (dowhy.causal_refuters.dummy_outcome_refuter.testfraction attribute)": [[13, "dowhy.causal_refuters.dummy_outcome_refuter.TestFraction.other"]], "partial_correlation() (dowhy.causal_refuters.graph_refuter.graphrefuter method)": [[13, "dowhy.causal_refuters.graph_refuter.GraphRefuter.partial_correlation"]], "partial_r2_func() (dowhy.causal_refuters.linear_sensitivity_analyzer.linearsensitivityanalyzer method)": [[13, "dowhy.causal_refuters.linear_sensitivity_analyzer.LinearSensitivityAnalyzer.partial_r2_func"]], "perform_benchmarking() 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(in module dowhy.gcm.distribution_change)": [[18, "dowhy.gcm.distribution_change.estimate_distribution_change_scores"]], "estimate_entropy_discrete() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_discrete"]], "estimate_entropy_kmeans() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_kmeans"]], "estimate_entropy_of_probabilities() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_of_probabilities"]], "estimate_entropy_using_discretization() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_entropy_using_discretization"]], "estimate_ftest_pvalue() (in module dowhy.gcm.stats)": [[18, "dowhy.gcm.stats.estimate_ftest_pvalue"]], "estimate_gaussian_entropy() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_gaussian_entropy"]], "estimate_geometric_median() (in module dowhy.gcm.confidence_intervals)": [[18, "dowhy.gcm.confidence_intervals.estimate_geometric_median"]], "estimate_kl_divergence_categorical() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_categorical"]], "estimate_kl_divergence_continuous_clf() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_continuous_clf"]], "estimate_kl_divergence_continuous_knn() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_continuous_knn"]], "estimate_kl_divergence_of_probabilities() (in module dowhy.gcm.divergence)": [[18, "dowhy.gcm.divergence.estimate_kl_divergence_of_probabilities"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.estimate_noise"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.invertiblefunctionalcausalmodel method)": [[18, "dowhy.gcm.causal_mechanisms.InvertibleFunctionalCausalModel.estimate_noise"]], "estimate_noise() (dowhy.gcm.causal_mechanisms.postnonlinearmodel method)": [[18, "dowhy.gcm.causal_mechanisms.PostNonlinearModel.estimate_noise"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.estimate_probabilities"]], "estimate_probabilities() (dowhy.gcm.causal_mechanisms.probabilityestimatormodel method)": [[18, "dowhy.gcm.causal_mechanisms.ProbabilityEstimatorModel.estimate_probabilities"]], "estimate_shapley_values() (in module dowhy.gcm.shapley)": [[18, "dowhy.gcm.shapley.estimate_shapley_values"]], "estimate_variance() (in module dowhy.gcm.uncertainty)": [[18, "dowhy.gcm.uncertainty.estimate_variance"]], "evaluate() (dowhy.gcm.causal_mechanisms.classifierfcm method)": [[18, "dowhy.gcm.causal_mechanisms.ClassifierFCM.evaluate"]], "evaluate() (dowhy.gcm.causal_mechanisms.discreteadditivenoisemodel method)": [[18, "dowhy.gcm.causal_mechanisms.DiscreteAdditiveNoiseModel.evaluate"]], "evaluate() 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"dowhy.utils.propensity_score.binarize_discrete"]], "binary_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.binary_treatment_model"]], "categorical_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.categorical_treatment_model"]], "compute_ci() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.compute_ci"]], "conditional_mi() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.conditional_MI"]], "continuous_treatment_model() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.continuous_treatment_model"]], "convert_to_binary() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.convert_to_binary"]], "convert_to_undirected_graph() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.convert_to_undirected_graph"]], "create_polynomial_function() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.create_polynomial_function"]], "daggity_to_dot() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.daggity_to_dot"]], "del_edge() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.del_edge"]], "difference() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.difference"]], "discrete_to_integer() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.discrete_to_integer"]], "dowhy.utils": [[24, "module-dowhy.utils"]], "dowhy.utils.api": [[24, "module-dowhy.utils.api"]], "dowhy.utils.cit": [[24, "module-dowhy.utils.cit"]], "dowhy.utils.cli_helpers": [[24, "module-dowhy.utils.cli_helpers"]], "dowhy.utils.dgp": [[24, "module-dowhy.utils.dgp"]], "dowhy.utils.graph_operations": [[24, "module-dowhy.utils.graph_operations"]], "dowhy.utils.graphviz_plotting": [[24, "module-dowhy.utils.graphviz_plotting"]], "dowhy.utils.networkx_plotting": [[24, "module-dowhy.utils.networkx_plotting"]], "dowhy.utils.ordered_set": [[24, "module-dowhy.utils.ordered_set"]], "dowhy.utils.plotting": [[24, "module-dowhy.utils.plotting"]], "dowhy.utils.propensity_score": [[24, "module-dowhy.utils.propensity_score"]], "dowhy.utils.regression": [[24, "module-dowhy.utils.regression"]], "entropy() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.entropy"]], "find_ancestor() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.find_ancestor"]], "find_c_components() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.find_c_components"]], "find_predecessor() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.find_predecessor"]], "generate_data() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.generate_data"]], "generate_moment_function() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.generate_moment_function"]], "generation_process() (dowhy.utils.dgp.datageneratingprocess method)": [[24, "dowhy.utils.dgp.DataGeneratingProcess.generation_process"]], "get_all() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.get_all"]], "get_numeric_features() (in module dowhy.utils.regression)": [[24, "dowhy.utils.regression.get_numeric_features"]], "get_random_node_pair() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.get_random_node_pair"]], "get_simple_ordered_tree() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.get_simple_ordered_tree"]], "get_type_string() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.get_type_string"]], "induced_graph() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.induced_graph"]], "intersection() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.intersection"]], "is_connected() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.is_connected"]], "is_empty() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.is_empty"]], "parse_state() (in module dowhy.utils.api)": [[24, "dowhy.utils.api.parse_state"]], "partial_corr() (in module dowhy.utils.cit)": [[24, "dowhy.utils.cit.partial_corr"]], "plot() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.plot"]], "plot_adjacency_matrix() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.plot_adjacency_matrix"]], "plot_causal_graph_graphviz() (in module dowhy.utils.graphviz_plotting)": [[24, "dowhy.utils.graphviz_plotting.plot_causal_graph_graphviz"]], "plot_causal_graph_networkx() (in module dowhy.utils.networkx_plotting)": [[24, "dowhy.utils.networkx_plotting.plot_causal_graph_networkx"]], "pretty_print_graph() (in module dowhy.utils.plotting)": [[24, "dowhy.utils.plotting.pretty_print_graph"]], "propensity_of_treatment_score() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.propensity_of_treatment_score"]], "query_yes_no() (in module dowhy.utils.cli_helpers)": [[24, "dowhy.utils.cli_helpers.query_yes_no"]], "state_propensity_score() (in module dowhy.utils.propensity_score)": [[24, "dowhy.utils.propensity_score.state_propensity_score"]], "str_to_dot() (in module dowhy.utils.graph_operations)": [[24, "dowhy.utils.graph_operations.str_to_dot"]], "union() (dowhy.utils.ordered_set.orderedset method)": [[24, "dowhy.utils.ordered_set.OrderedSet.union"]]}})
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