diff --git a/docs/_toc.yml b/docs/_toc.yml
index 9ee4b96fe..0e6b65f70 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -13,19 +13,21 @@ parts:
   - file: examples/distribution_evolution
   - file: examples/distribution_ambient
   - file: examples/ionparticle_coagulation
-  - file: examples/particula_data
+  - file: examples/streamlake/particula_data
     sections:
-    - file: examples/loading_data_part1
-    - file: examples/loading_data_part2
-    - file: examples/loading_data_part3
-    - file: examples/loading_data_part4
-    - file: examples/stream_stats_part1
-    - file: examples/stream_stats_size_distribution_part2
+    - file: examples/streamlake/loading_data_part1
+    - file: examples/streamlake/loading_data_part2
+    - file: examples/streamlake/loading_data_part3
+    - file: examples/streamlake/loading_data_part4
+    - file: examples/streamlake/stream_stats_part1
+    - file: examples/streamlake/stream_stats_size_distribution_part2
   - file: examples/wall_loss_section
     sections:
     - file: examples/chamber_smps_data
-  - file: examples/activity_part1
-  - file: examples/equilibria_part1
+  - file: examples/equilibria/equilibria_intro
+    sections:
+    - file: examples/equilibria/activity_part1
+    - file: examples/equilibria/equilibria_part1
 - caption: Documentation
   numbered: false
   chapters:
diff --git a/docs/examples/chamber_smps_data.ipynb b/docs/examples/chamber_smps_data.ipynb
index 2519594de..03713dba3 100644
--- a/docs/examples/chamber_smps_data.ipynb
+++ b/docs/examples/chamber_smps_data.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -194,12 +194,12 @@
     "fig, ax = plt.subplots()\n",
     "ax.plot(\n",
     "    stream_smps_2d.header_float,\n",
-    "    stream_smps_2d.data[:, 10],\n",
+    "    stream_smps_2d.data[10, :],\n",
     "    label='Concentration earlier'\n",
     ")\n",
     "ax.plot(\n",
     "    stream_smps_2d.header_float,\n",
-    "    stream_smps_2d.data[:, 20],\n",
+    "    stream_smps_2d.data[20, :],\n",
     "    label='Concentration later'\n",
     ")\n",
     "ax.set_xscale('log')\n",
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -394,12 +394,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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adJSIYke0wsjEUKRCba3R3RMo5IPaTzC1n3Fsb7fo4oN4S4pbq4pJ/zIvdDc3nqPFKxfxJCbWHp2FKYgzyNY0dYX5tVYE1YbEyRLVXdxH+cqm60acdNJJ+OCDD2zXLrzwQhx00EGYO3cuwuEwmpqaMHnyZJSXl2Px4sVZlp+JEyfilltuwc6dOzFw4EAAwNKlS1FdXY3Ro0fn7bOIkNAxRWfxYYJGdFO5dVe5EEZBzvXr/tIRpJDxMt+tgJO56MTP0NNT8gvyuYOwqOhcSF7S6J3u6+KF3BKEADFx9zmdwcu8XFBMZ+km9OnTB4cccojtWq9evTBgwAAccsghaGpqwsknn4zW1lb87ne/s7K4AGDvvfdGOBzGySefjNGjR+P888/HnXfeiYaGBtxwww2oq6vTxgblGhI6ABBtBSIufwhMVDhZeUxjdNg4ca4b8eJHKOUAlUDI1V66Yot+Hs5ZFgzD77OXStZOc3iR1aMFl5s6O6rruXhIygKm+eu6mj8q65AYAO7mLCa1i2QZVG6FRLFZgkx72BXTmYuctWvX4q233gIA7L///rZ7mzdvxogRIxAOh/H888/jsssuw8SJE9GrVy/MnDkTP/vZzwpxZAsSOgzeIsMQLTVuUAkgfj9+D6e5Jnv5ETk5EEn5dMfp9nKyyDi57rKERCx7Pb5KtVdMBAs/pkcKnK6GLHbIj3gQ3Uzsa6f9VHFNOvEjZrM5uQ+dPpufQG/V+bxA4saYv//979bXxx9/PFKplOOc4cOH44UXXsjhqdxDQgdIByMnFRlYKtHhVhT5CHaWruXWYtSDkVlBRETRYBrPI9tHtz9RRDgFLOcyANk0eNrkuonA0FW/doPb2kZ+KFSae5EQT6QQTyR9zSfSkNDRUV6V/jPaajZeJWZUbi5esMiESqQi29LCu7j84rR/N0N0ZXkNmhbT58W1VC4oVbC0yXnFfZ1QiSvdvrr9nM7b7Won5VPkBIlJAUc3VhfVXNHS4/eMuRYtfmN6is01R7iiR6aXG2MqcJyQWV1MhIqp+PAqUmQuM/YqYnTNMk0ftm5FjqrDvNP6hU579yOWVGsQBcJEoLgVOfksMsmLIifREGS17SAgkdOlIYsOD7PgAJ0ix0TsmMTxqGJgVBlb4hjZnl7xKryKBBOBAbiw2niITzIRPDqR4ZT67sdCIlqZVJWuReuSzh3nFPwchLWJcMBLen0Q6/MWGFksjUnsjdvYJH5fmTiT7dGD3VyEHrLoMHiR4xZTkeA2fscppV31Eue6tR4F4cYqAsuQsSXC4fMGVQiQtwjJrENOhRfdFlB0ynpzWi/INH8enUWuS1NsFY5lqIofegkyzpV1xekz65q7OsU+uf3sRLeALDoyVFYcPmanvKpzHPuav8Zfd0PQQcu6dYO2FHVz/GZVud0LkIsTN3E+Tmd2s0auxIkXC09RxgTl4oHZ1R7CuqKDugwwNlf2Nb+eKr5HFpytEzkUc9OjIKHjBl7YyK6ze05uL7dBzl7wmxZvWveHHytecwqc7mKiSikYHFxfsmBl430ka5uILTdiyGRtL6LCZI5X4Rikq6/oyXcGmGx/0+uiiyuonmwm/cJ0++jEkYkLjujSkNCRwVtogGwhI45TzXe7J8OvAAo6I0v23mk/kzMUWYFDTxh8TtnD3JV1SPN9kj3gVR3tZejEQS6EQ7cWJLmi0FYdP601nKw4qvFuz2ZS54eNk8X4FKGoiSeSiMX9pJd7n9vdoBgdhiguREEjXuPvsZdKEMn2Mh0r2yffeE1pZ2eNtatfuUQWu+R1Hd1102B0DlfxQx6+T6bixk2zVi/NW2WWLKeK1W56quncXnzckxgbpYpX0n1Gyj7jYPEwJgLGbRVr2bpOMUJsjsk42ZkodqdbQxYdhpOAMBUj4nuTmB3eeqQil24uFX6LHxbizAyvWXCqMarx5VX5EW0a/IgP9t4klgfIdompRIupe85LYHK+BAdZnwqMV5eXyhXlBlllaKDgCRaEN8iiAyCUy19enTWIXWcCSLT0mKytsjK5OZfTGfl74vfKycpUiH8YVF3onaw7Xi1WAdVEKuRDVeVekwkglUvM6bqJqOEFlNusNNV+/L5OFiSnGk1k1SlidK0wxHH8S5xLdDvIopMhFKlEKtaGUOYvSCqoX3yVtUZm/WHjZcjui8HR/F5inJFsbdkY3efwck906+TK8uEkNkxqBqnOprrOPrcby5CGQj5EZVlesvN4TUl3I1Bk191Yi5yyxGQxTSqhZ3IeogAE4WpS1d/JdfA3kXdI6HCEuF9uJnx849bi4ibWJ3PfOqvX+B0+S8xtRpjTOFnbC6/IKjmbCCenYo26/VQtOGTnKQJsD20Xwd5+Ht6qVHiv81RxPaatK/jrTq04xLPw65GYKVL8uqRk6+msQbzwiXd435soGCR0BJhVJyiLTpaFyGWsDy++ss6UGac9q6y+D7tueAZlZpnpOkG30hDPYDpXtC7J5qtEjcodptuLH6uK9wmwKatTl3XVHJPsLB1BzXMbJ+R2b1UFaKfgaN1a+aTYLUlBCMScfEaTYodi+jovfsi60+UhoQMAkXLb28DcVgGSirX5EwxOliK3a5u6vIoJ05R307WYgGHfWze1h3KAF8FRTDEnbrKt+JgbP5/BixXKrQWppxDEZ8/J98+pXg5PUHV/AiCeSPrsXk7p5QwKRlYQilRaL6cxuutMNKnWcdojFWuTW3JMRYabNHnALCBalk7PXw+yJpAOv2t7nc8HIWdch9LvKR/4rGqxocrYCrJLfTeFBSfL3FC8W8zU0qALWFatz5+F/9PN+U0pFhFVTOLYFSbp8EUicohgIYuOAVr3EbLjeZj7i13ng5xlGFuQZEHHkv2zxjtdk62vG+OnGKIJuU5Ld1M12k0wtcxlJbtu4rpi72VxSapz5gMvrrYA3XOmeI0XUr0PYm+Tth5u1i9Uy4xiEVw2xErHJFIIDhI6GXhxYoLKksPWMhknjjVGEsMTZFyRMiOMf6+K/XFay8v+IjIXnBtx5MZKIgumFh7YKdn+YkyQ6r24j8664yfw2g1doGK1SSxP0HuZVJH2mpXm9izs61y22ehS5MISQ4Kp20BCJ0MoUmmP1YlF9WOd1tKgEyS69HaZQOKtRSp3mYkVyglrPBM4YlNTHtX1oAhiXVEguQmuBuxCgJ+jC2D2moGWb9ER9H5+1gtQdDmJoyAboJpcd0qDlwksWd80UWSp+pz19BgiKToxQyKn20AxOi7RxdqYzNVZjcTrooBRYSpYTK1VWsSYn2INSNZZpZwsViaw+JsgRQGz+KiKGprsx6/hhyKLDdI9oP3U6PEzRlXEUFaoULVWEM1STeoKEQpIzPQIyKLDYNYc3pJjei2DNBZHHJ95H9LMk1lpVCKIjZftLXOh8XFDMrKsRhkLjiu3WK7Ej6ofma7IotsaQU7uOJmLyu0a4li2ronFR5Ymr2u26ieNvRhcWDEzl5BbvGRr8RWbnbqn69ps5MpypLPeFJvYIesSkU/IogMAJRH9fSH9XHVNaTERRZHwXidiZIiBz/zXoiAxyQzT7iuIFjdxTADcxc7wbTBkLxUyYeV2vNO8XFmvvAZeu+kkHwRBrhuExcknfoSTk6vJSwFDcX2/wq7YRUSxn4/oXvRIi059fT3q6+uRSCSyb0bKO4VILNopaFRiRxQxogXHQeQo11bECPGxOSKmQc5iBWjVHuIcv4HTAILNqNK1vHBrUTElyAKIDLfrGQRIZ+HWMiMLlHbTKLUAmVaecdmiw0uVZv6+zPIji7WRzXPTqsLpfKrYnqAwsdpQFWo1VEcnOHqk0Kmrq0NdXR2amppQU1OTPYAJmEg5EC7rvJ6QlP+WiR12HchyWdmuqdCsF4LdtaVKXXe0uqjOLZmrshSxe0bkImXcjUgxjcuRCaWg0uNzIZIYujYXQLYA4a+JY93sya/lts1GMbjGAE9nyEWRQqfrokgxEV78PFXMkCp42RTVPJO1SAgR+aBHCp3A0cTtSK01Ik7CR8BTULGpIHPYVxRWTpYeSxC5O60aXUaXGHCci+wvVVq9TiCJ1/hz8fd09X3c1P6RIRMVMguMCr/7q87QA9DF0/h1oQUVuySKKa+Cx+tcgsglJHS8Ei7LtvDIRI04jneHqcSF7L6h9cWaL3OZ8euqXGscToUO2RjxLE5uNad1tLi14jgFFjvF96i6wJu4yEwsO/zXrJ2E07oqvAgRkzle3GJizSDV+Fw3SZW50HItuBzcdkGIHFXquFuxwY/3UqPHzV5eINFEBAEFI6sQRYvMbRXUPuJLvFcEmKa6Fx1uOsUHsYfbatSmeLWmqERFoTCJ8wlinaDnBYzfys2yNbwIJ79uqyDpEYUNiYJAFh0RVUwOs8yw++LXgN1qwsabWH2c0M1xkw4vC5zWBFPLXFUiKncWI98NUt3EDoUilZ1VjVVuL13ndhUqt5nKhcWjcmt5JYgHu8rtZXrf5Ew6CxC7b3Iuk3m5RtgzCEHilL4uc2Px3dq9CivT+B9+jh/EoOxCiy+ie0AWHQCh8l5pYcKLHMD5vQ4mcGTr5gJRDMmsQx4wLYTIyKWw4QsumlSn9mV94huUqoSNHyGiivHhq07nKqU9V+vmI23c7/puhU8RpMIzTBuHeg0M9hNjYxpU7HRNVwWaILxCFh1GKVdLJx7ztoYoJkwFjmgZchrLj9NZZcR1xfO5DII2wVMGWED76tLixfu2936ytwCbFcZaV9GA1aipquy6G1FlOj7ILDCdhcdrA1VxfVXDVP696T6ytbtAsLQbkeOmmrTKIuSELs3ebSySyVo9ifZ4EqVx7yni7T7m5pP77rvP9ZwLL7wQffr0MR5PQocRj9nFDtD53kn4mAgVXqCIAoi/Lo4RXWSqFHcZTsJJk2LuCT4tH8iJkFIh6/3FX1eNR7QVod4DHMdLkYiZrPlus79U7iwv9YG8BEoHgRexoBMZurR52XuvNYPc7J8HnCwaYoo5/ye7z19zIzy8WlLEgGbVGDHVvVBipqeKqGLiqquuwj777INwOGw0fuvWrfjOd75DQscTpRG52OkJ6OJ43K4hkGtrji4mx0i0aMSHaZHEUO8B+nGmFiNRfJgIpFxZe0TyEfxras3JJwW08ATdCd2p7YTuXlCixNSFlQ/ILVY8vPPOOxg4cKDRWDcCh0FCh4eJHQb/tejaEoOTAXkgMz9GFvPDW11UFh/VeBW8VUW8zpBVfNZZePh7uvT3oK1EGnRuMlkQtcrSI3Z7V63NrttcYFV9gfJeCEVbkGptdP0ZLFQFCp1EiWn/L1WwtQw/Ke5A8DEtoktL5xpzao3hJUbHy7wCogtalo3V9eUScSOWZOnuMquSKqMsl5YWEjnFw/z589G7d2/j8ddffz369+/vag8KRhYxseiwMTJBUhpJv2RCSEQMWJaNdYrzkd1XubL4AGVRjLB1dH29VOnvbqo+5wi3/cJkwcqeA6kF16avQGi38UKygGnT+BwduXRtBQ2rymwicnSd4bsJbjKh3FZplt1XdW832Z+fr+oGT+SHBx54AGPHjkV1dTWqq6sxceJEvPjii7Yxq1atwoknnohevXqhuroa3/rWt9DW1vnv5q5duzB9+nRUV1ejb9+++NGPfoTm5mbtvvPnz0dVlfm/e9dddx369u3r6rOR0AGAUsGwxcQK/1LOtY8JhcsQUllwRPj7vEAyxc0+4TKAZZc5BU17qd9TBPV+VOLCJmCEc4pWHFGkiO+zCiTGokD7Hqk1R2cVkpIJZs66L1p73GSC6XqNsXuiQHArcmRn4df0m+WlEiYmNXlMLDJ8Wjv/YvNUMUKilakQYinAfU1bNvBWGVFUybq681/zc0wsT+J+sn29wNal+JxO9tlnH9x+++1Ys2YN3nnnHZx44ok4/fTT8eGHHwJIi5xTTjkFJ598Mt5++22sXr0as2bNQklJp4yYPn06PvzwQyxduhTPP/88Vq5ciUsuucTzmVKplLwnpUtI6AQJL4hMY32YyDDN9FJZf/jrOgsRG8s/8FmQMxvPu6H4cU6CSmXtMXnlCF0TVB4vKfKsB5hjwDO3h07k8GuK1z1ZWEytNroHpZOwUu1VrBYSVRVnJ5wERSFjiCR7e7GOmFhx/IgMXlw4CQw3KfJkCQqGadOm4dRTT8XIkSNxwAEH4JZbbkHv3r3x5ptvAgBmz56NK664Atdeey0OPvhgHHjggTjnnHNQXp7+93vjxo1YsmQJHnnkERx11FE49thjcf/992PRokXYtm2bdu94PI4bbrgBxx13HObPnw8A+MUvfoHevXujqqoKM2fOREeH96K9JHR4MmIjZGpV8RO4LLMU6cSOKuPK9Bxi/BGgFkN+hYeY3m46JyhxxN3PstIYutZciRTJ3EDrCZkIFpnVRzbfKbDZxPKSixo8XvHSwsJElHiJ9elCcTw8btxLuvuquji69Z0EjZc1g6arC6impibbKxp1/jcwkUhg0aJFaGlpwcSJE7Fz50689dZbGDhwII4++mgMGjQIxx13HF577TVrzqpVq9C3b19MmDDBujZp0iSUlJTgrbfe0u7305/+FI888ggmTJiAP/7xj7jssstw//3346GHHsLDDz+MZcuWYcGCBZ6/BxSMzBEq75X+ojSCEHc9lehAKFyGVKKjUyyURixBlMoIECOBpEphZ4hihL8vCh1V+jsvakT3mJOYYshS4GXVoHXkqm2GEy7ihNz05TLt66XbSxVLpF1HYc2R1uxxgxiU7CYN3s2euY730fXSUt3na+fI5oiIlZud6vh0oSBm03o5pg98pyrO/Jq684gVknWiJ4i+YTqXmnUvnr9/0zoSSUQT3mvhdGTmDh061HZ9/vz5uOmmm6RzPvjgA0ycOBHt7e3o3bs3nn32WYwePdqy6tx000246667MH78eDz++OM46aSTsH79eowcORINDQ1ZmVOlpaXo378/GhoatGd98skn8cgjj+A73/kOLrvsMhx44IF48sknce655wIAKioqcPPNN+N//ud/vHwrSOgAAMJlCKXU/bWZgAmFy9ItA0zdTKKwMLUAyQRM0GnvThlgJsiyu8TA5HzH7ris4aOz0pg0NeXXyHe7i0DwWzRQVhix2AOZda0m+OsyS1CxuuRyjBiL4xTEbFJl2TSzKqgxXugO1Zm3bt2K6upq6z1zNck48MADsW7dOuzevRt//OMfMXPmTKxYsQLJZFo0/fjHP8aFF14IADj00EOxbNkyPProo7jtttt8nXHbtm0YN24cAGD//fdHWVmZ9R4AjjjiCHz++eee1yeho4FZcnSCJeUkDtwKFN5SBKgLGYpii8/g4u8xK4xOnDlZomQp8IC6ArN4Pwh0wkUsVMjvLcyzxItGEGWlm3Mp87IKyzxOoke8rqzWbIAvYaXq3O6EKGb41hX8GFWPsHwJIpUVx3Q8u6ZzUemsNcViyTGwLLl9mLsdy9bXFSxUWYBMziZ2YBfnyKw1/HmcMsS6esAyy6IyoaysDPvvvz8A4PDDD8fq1avxy1/+Etdeey0AYPTo0bbxo0aNwpYtWwAAtbW12Llzp+1+PB7Hrl27UFtbq923pqYGjY2NlvXpsMMOs9XLiUajCIVCqumOUIyOCQqRwLusLNdWBnZN586y7sVjzlYinUsLCK6nlpd1RLeWX1jhQv7lNJ7/U7zOiJRnW2gkYswWj8Ol1pukrMu+NhkPaIRLkLEzYjyPlzVM9nBz3QRd3JFIpCKY4GhZOrrXOjyyLC0PeHroOpw5iE7qJnNkcTy6is5+ziYLetZlhBFykskkotEoRowYgSFDhmDTpk22+x999BGGDx8OAJg4cSIaGxuxZs0a6/4rr7yCZDKJo446SrvP6NGjsXbtWuv966+/jq997WvW+w8++AAjR470/DnIoiPDRVCyOMYoXke00ggWmiwrkdfeW4C+3QR/TXVelcVKF6vjxQ3G8FuHRyyKKBY5dFPQUFPtWVZkUIdqjlsrjmptAPZ4HRNM+nDJqjWLNXsK5bIKsleXE2ItHva1n7V0FZ8V91w/nHNgWTLpkxX0PrJ2Fm4yrYJwoXV3rrvuOkyZMgXDhg3Dnj178OSTT+Lvf/87XnrpJYRCIVxzzTWYP38+xo0bh/Hjx+O3v/0t/vGPf+CPf/wjgLR155RTTsHFF1+MhQsXIhaLYdasWfj+97+PIUOGaPdeuHAhIhG19yMWi3mOzwFI6HQicQdZrivuvXQeoBRHujm2IOdctZ6QxfuwM/KfWSdoTK01YhVocc2giwmqChWqqkLzIsckfkjTt8vUYuO5NYVmLdE95qk/lwyZiBHn8BYhVeNSJ2S1fNxWgDYdH3Rcja5Csww3LrNicXd5ROWa8tqqQhRQTkUKndYTx7opatgT2LlzJ2bMmIHt27ejpqYGY8eOxUsvvYRvf/vbANI9qdrb2zF79mzs2rUL48aNw9KlS7HffvtZazzxxBOYNWsWTjrpJJSUlODss882atp5wAEHaO//8Ic/9PXZQqmUJgq3m9PU1ISamhqUXvIYQuGM5nOwnojuKV5IGAkjbi4/3jHWh50tE8NjbPXRZWbp5pkiZmKpLEiMoJuI8uvKYnWc9uOFjGjtEe+ZrCfAd073UqsHyBY2OQl+Ns240okUUfg4iZFisQzJMO2+blqDhx8vsw6J63WBTuoyTKw7ugwqQC1MTLK3ZNYZN6JItw6QzrpKvf44du/ebRz34hb2XHpx+Yfo1dt9XydGS/MeTDnh4JyeNVfs3LkTO3futIKgGWPHjvW0Hll0GMydxDf3ZA/taIt+HoejYOHcVrwokooX8R5nCcpCZhESY3+crEZO4sfEsiNzjYlf+00711ljRHeVKgtMjOsRXVuR8s6zuu3fxe3BRInOAsQLIRPx4krgCNYWaw+V28pNijk/VxRAMvEjO5u4fzHhxRIks9CIgkZMTRev83NyLXZysIeT2FF1U/fTjsLUOuOU0u40Lp90JFKIxL3bIToSXc+GsWbNGsycORMbN24Es8GEQiGkUimEQiHPVZJJ6ACZBzGnHNkDnz3YORePSXCxVuwIbivZfAZbx8jaI+LXUsNwqr0jS1PXvQ+ito6uHxf7OgjLkeKspmnnpjgJF1+WGz+ZVap7vMXGdH2nTKxCI2sc6jSGjVMJF1ksj1vxFLAQyXW6dD7iXZwsPLLxvJhSFSmk4OTCc9FFF+GAAw7Ar3/9awwaNMhXphUPCR0R9r94ZnnhH3Yqi4jY1FEhhkS3l5tCg7rihEZCyDQGSCZqmNjx0mCUUajigSKiBYe/xpC5sIQU8yzEORqhpRMuontL7MauK3Bosq41RiVQ3GR3OfXRYvE8Tnu5FTs60cSfw8kV5hQ/41R4kL/uVYzIRJDTWj72y3qYO6yTKwEQxLomcTluRFcxWHF6Op999hn+9Kc/WSnuQUHp5SrEDuVeg4WFhp/Wn1y9HCdEF5dqni2lnZ1X0XA0xH8u0WXFf1ZZ6ruu0anqukkPLrfwYkLWmJS/xtxRTtYg3VjTvlwykcPNkzUP9QoTP5aAMRUMmXHSM7hcw4JPAQ+iCKHpvqrrTqn0Junepo1Bdbit3+OEU3uKbows2yqoNhG6LC7qpZUfTjrpJLz33nuBr0sWHR26DCIeJ/cOh02EwMz6Y3Jfuo4kFki3p2N7CsC9S0z1vXHTSsIU3c/JtHihyroTdIVnJwuRAln2lesUdZ2VhQkDJ7eUU5d03ViT83h1aQXpBvNS90blxjLdS4zTcUvAri7pw93DHm7dTabncXJJifsHsSeROx555BHMnDkT69evxyGHHJKVcn7aaad5WpeEDoN/+Jf3BsSeJuyaLNhWhLeMiEHO4n1xTgYxK8tGphdXSnSraQob8q4vW0sLrn+XLSBa14NLJ3bEe07CKCjrjgwvoscpvkeVleXgqgrxawvWHRNUFZizcFlY0CaQ3Pa6cgowlp1JtUcx1efRIRMyKveTVwuLSkg4CYx8ZWm5LEAoc1P56YCuKjKoyriSiRw3ri1yaeWPVatW4fXXX8eLL76Ydc9PMDK5rgBURMLqm6Vl6ReP6uEsc3OZfG3bz9naw9/3UtTQaV/l2iYWn0KQC+tQgAQZuJzzPdxmW+WbQmdn6dLBRdwKD1XQcgAVlQuJH1FjupZJ5pZsDH89313RiWwuv/xynHfeedi+fTuSyaTt5VXkAGTRkcOsObzAYdYcpywiN+nT/FhVPy2ZRcTpGmfxkbmtbIUK2XhFentWQUPeSqU6t+69X2TfbxVeBJCqoKDMYuNUhVnVksLAFaYrBigTNW47q0vH+KmqzK9hUtyPny+bI+ud5TV42Q8qy4yXTCqdkHGKu+mCAscEp6BkXW0d067rPG76ZhWajkQSpQF0L+9KfPnll5g9ezYGDRoU6Lpk0VGR6Mh2X8nGyGBxOKZuGZklyKkAIPfeJmZUFiGhBg8TO7JsLuNq0E7nNBU5rFeWycstbufw9XT4ayaByG7ieBzS391aaJzGs4BlPgg6Z93WZfE2smu6905rusFPRpSq2acb4RFrN8ukCqgfVk/AqTEoP07VR0sGWXUKy1lnnYXly5cHvi5ZdACUR0oQjQrFlayGmx3Z11SILRDYnyaxNHxaO0MnInRWFX6cYh2+K7vMmmNcqVlxPktAmQidQqWleyleaFKjJ6Dqz7oWErJqy/x91XqBYSJMZFYaNxldQbmpTASDqqifX1TruI3hUcXs6PZQ4TeLLE+4qcmTr3YOobIKdL0yfF2HAw44ANdddx1ee+01jBkzJisY+YorrvC0LgkdpIUOElycjui6Ei07pWWd13TtD2SIzTT5P8Vmn9Z5YnLRIood8b4Em5WHC4p2Ksski/Ox/sJLzuSpyKEMUxEk20/l5lKdTayczFtoNHPTgkNiCdIJHsOihqoeV16sMbpCh16aizq2tHCqd8MIohqzX1SWG6c5LptyGqGqiixe09X5KXIRwzB1JenwKoZkKeq6yslk7ck9jzzyCHr37o0VK1ZgxYoVtnuhUIiEjl8qImG0xxTBTryw4dE9hEUxlBAsQ6ZtHGTXxQwup0KGMsuPaBXiUBU2dMSDZckXfjO2xM8lEysyhHicECAvRKibL67jQFDWmCCqOrsSRRnxYjTHSxp6d0LWB6uLCJZ8YBKXI1qBxGwsP5WbQ2UVSDmFMxC+2Lx5c07WpRgdU8TMK3aNXbfFw0jG8oXyZJYGWesE3v0lxvG4EUUyeKEkSW0XiwvysT3sZVvH6VzsnuzlBV18VC5S1vkYoXCZPOBYVmBQhhiU7CCOjIWJRCjwtXZUzUH5a07nEOeYWpe0Y2Q9t0T4+j78n0Ei7q0SGXzMkcrqI2Zj8TE47GVSednUSsMLJJOO6kF3dc8RbntXiQ0+RZFjspauICHV1OmakNCRIQiVrPTzeIfcrSUTOLJ7qirBqge0LhDXwSKjtaJwAcomVhvlGD9p5l6DlU1wEj1+WlqI6JqMyvBYhJAPKM4SQJz1RBzPByMXJbKAZbF4IXvlI+NKFXAca5dXXJYJDKeMKTeuJtM4IwFX1osiEz6iBcetyAjKzSRaiIjccfbZZ+OOO+7Iun7nnXfie9/7nud1SehkaG8T/scpc1cxseIkaOIdnWJIVodHN5fBP9BNM7Ay1hkr0Jh3GclaOXBruKrHwzc4FbK8pOvw+4svHq+ZVrIx/PdAtKTpPiuztugEqMzl5WSV0+HgvuItJyqhIlpMZAUGZdYYEdn6rrO0nFovmCATPvw92dd+1gTssUKqsab1ctzW1VGN5y0vbvpyZbDEQRB1fvKIm0J+uvYMspYRqj109XQoPic/rFy5EqeeemrW9SlTpmDlypWe16UYHQDRWNISGcx6Y8XrxDvQjs6HVUVlpf2+iKwGj1fCZUC0xf4ecGXdyYrFEV1NvDBysK7IqiaLbi3T/l02vAYuy1yA7Gz8943dM9lHZmXRVVhmIkW3ti4Wx2VAsm4M4D9lXJXlxX/NRJN0LxOB4zfWxjR42WtlZbFmj2wNmQBxcgnpxIauro4uOJlfQ4dJ81APwkcWN2MSS6Mb46ZdhFMBQbf3TPbLV9ZVT6yj09zcjLKy7GdIJBJBU1OT53V7pEWnvr4eo0ePxhFHHGG7nuWi4qwx/L2KSEn6vSwwzY0FRyQuSU8XrRL8PWueYVFB2XoGcTJObi3e0uMp20p05eleurnidSeLkK74oG6uKi6HtYJgL34NWfxOQGnoOlQWmiCaiubMDWZSY8erVcdNertbgcTH3pjGyujWctpLN9ZtrR8fuC3KF+ReQe6ry7gi8sOYMWPw1FNPZV1ftGgRRo8e7XndHmnRqaurQ11dHZqamlBTU4OaylJ0RNNCpiJSgvZYMvN1p3WHt+C0x5LZFp3SsmxrEMMpXV0kzIklVTVmWZq47TycmGFuItFqw9fucRA70vRysW8Wt7ZVR0d1Ptl5TRCLFZoUL1S5tvj7MqEku5/oyBYtqorIDl3M3SC1oAhiycl9JeJUOVl1X7dPCjBvDBokMssL/7Wf2B7VPF1BQZNgX11WlRtri65KM78HL8ICxIsQcHI1OcGnigfVR0tcQyZ8yH2VW+bNm4ezzjoLn376KU488UQAwLJly/D73/8eTz/9tOd1e6TQkcELHPE60Cle2mOJTuuOm9gbWdAyi+PhhRAf6AyYxXiU9zLrru4U66NwX4V4649w38hdlaueWLla14v7zU1auUeCtqDwRQdV8TqBVE8WBY8us8o0hsaEIJqCyua67VDuZXyuauLko7u5R0xEhCrWRtXo0806flLPiWCYNm0annvuOdx666344x//iMrKSowdOxYvv/wyjjvuOM/rktARYNaaTutMp3XHJnKcEAWLIGgqImG0x2EXQCY1Gvgig6I7S5VtJbP+OFVdVr2XkJXlJRQMFNtMeEY8hyzuSGax4q0xYkCyWLRR/NqkX5ZYWNBHPI4rMuuJYkTW98otJoHKfHC0smAg/6f4NdApblRixOm+OFZ3TyV8nASReN9JsLiNmRHfu8mwElPVxXV0BQb94FYwZcY7WWxMhZNY4E9n2dGKF83noCKB+eOzzz7D17/+dQDA1KlTMXXq1EDX75ExOjpEMVMRKUFNVSTzNX89W/DYXFyqujvcWNdZWYA+Bd22l6bOjs4SIglWNkaxtlH6ui4zS7RGqdZiY0yytWQZWLLvrdt6PTLXVhFT8HRzXgzpLDX8PVWTUCdY3I1bd5rbeB2TKsuqGJog42qKMKtKFrhscs/tuk5YawvfI10GF5E7xo4di0MOOQTXX3893nrrrcDXJ6EDoDzSGZ8jipzySDhz3f7yDGfZUVqHWHq6aBViuHWtiGnqskKE/H1V8T8h2NhThhXbR5dm7oQYe6SyxojWHN16YhC4iFtrjETsWFYPj0JI18NKlxbudV3ZOLFIYFaaulcRYoIodsQYHNm+uq7oqnFBCARTwcILHnGOSWG/HMTcqLAJD7d7OlhNjPaEPM1bm0IPZxHkJKhI/OSeL774Arfddht27tyJ008/HYMHD8bFF1+Mv/zlL2hv9//7Ta4rZARNMoxoLGGL1eFjdmTxO53zw9w4Rdo5Dxe47BnRLWPyQGfCReau4REDl/l0cslwJ9eUKIiM0jPdiB+hno9yjJuaPPz308TSI46X7BWKVLoTOSzYODPHsR+ZpC+WCuU4jcXDKIWduZlU7iqH9bPWdrK+uHFtsfEy+DXcNtt0qo7sZS2Zy0m8JwY951jw5OphL4odPt5G1s5BNjdUpnZB6bLC3NTryTfRRArhhPdk9qiPufmmoqIC06ZNw7Rp05BKpbBq1SosXrwYc+fOxQ9+8ANMmjQJp512GqZNm4a9997b9fpk0VHQGXycxO5W54cus/JURMLWq/Oe/ZpU5KjcV7q4HT+tDnghoyryF+R+HMrCgjxeW0To5ugK/+lcVLK5KvhxOlEju8e3g1DMtVlQFGPduKNcCwuvGLp/HM8eZIFAL2u6xW2adx4tNIWAWWR0sS+i6PEiNPj1vTYOpdicwhAKhXD00Ufj9ttvx4YNG/Duu+/im9/8Jh577DHss88+qK+vd70mWXQyRDlLjMw1xV8T08/5MZ33stdg8T+dmVxCGjuEh6kqLV0URSoxJKsIzD/QeQuPLjaHWXgEMaCz4KjueQpINgiIllmfpPP474H4/dAFJKvmyMbrYBYamSuMv6ZoEWEJAUmXdK2lRdabywHRguMqA0tluSmvUlqGpC4wpxo6QVRh9jPftAigzPLjtgCgScq5iXUnqDFsnAdhJgYKuwlQVhUpdNpP7F4u4tS5nCgMI0eOxNVXX42rr74aX375JXbt2uV6Dc8WnS1btuDVV1/FSy+9hLVr1yIazX3xs3wii8cpj4StmB2GWF8nUMRAZafWE05BzbKHsZhhxX8tazGRQStYSiM2i01WI1C3BN313BQvVhxxjswyY9rnSlePJ2gUD3utuHEjEEytJjKBk4t9TPBiWVHN0RX18xOA7GauW8tSDtA1zDSZK6ITJ24qK+taRRD55c4771RWQR4wYABGjhzpek1XQuef//wn5s6di+HDh2PffffFcccdhylTpmDChAmoqanBt7/9bTz99NNIJrte6WlGucStJFpnxDHtsQQaM+4tJo7s8T0JWx2edAq7PW3dKLYHsIkZ0R1mWZjcZkqpEHtmZWC1c2QuKFvNHf49j0mGlddAZX6OKh3dNECZ/amy9PACxLSvFeDongIkgcuyasxCoULeUpIVMNzaqLb8aESOtoKyC1Fh0mfLdUaUU6Cx1ywrHtkDX3QvuU0Nl60rWkZEAeRGEJl2L3ezjq7JKbvvAz8ZVkygOAkhXWFB3Zpkzck98Xjc+vr2229HY2MjgHSq+fbt232vbyx0rrjiCowbNw6bN2/Gz3/+c2zYsAG7d+9GR0cHGhoa8MILL+DYY4/FjTfeiLFjx2L16tW+D5dPaqoiKI+EbS4sFSxomWVpdb5KLBHTbnOF6eNzVDE9SjKuquyKzQmgvHd2ywge1QNZNsepqaiAzWUlVEm2iSI+/sbPi1/LDSxgWMw+cwo65seIVhmxCalsLT4GR3Etq3mniTVHMYat4djugYtlkdXKcY1EWFjr6KwzsqBip/cmeBU74sNbXEOVFWUiSnQBzCa9qFRuMBMBo8v00hHTuH58WID8CApV4T8et64qt2MI/1RXV+O4447D9ddfj46ODstDtHLlSrSJDbc9YByj06tXL3z22WcYMGBA1r2BAwfixBNPxIknnoj58+djyZIl2Lp1a1YvqWKlojSMPZnnkyh2mPUmGktIRZBo7ZFVWHaTju6qICGHrchhXHjIyiwYsmJ60n046wgX86KriJzKNNRkwocfp5zjt5igGI/DPpsqTkf2tQqVaOH3krmjTDO9OIyqEefIlWXaGd0IWfaV7rp4Pxd4XVsUM2JbBdN9TdtCOMGLFDcCQxUTZLIOd9/UveO24afpWFMrjZixpRqjKixIIid/fPLJJ3jttdfw2muvoaOjA2PHjsU3v/lNdHR0YP369RgxYgRKSrznToVSqVTXyUELGNbr6tBbn8d/oqVZAcfRWMImdExg43dzriy2ngrRbSVNV1e1iZC1log22zcQ+zaprsmuu3QdMcHCp5zrMqx8CRxZV3aeREe6PYabz2CSXm5yZl7kiL2wZLgRL5JeW7J0cZtliMONcBHXMyKoHlcmdXJk93WdxnlMhY+JQDGZa5KKrrLmqFLLTZDN5c9TxFleothxShV3Sh3XtZCQZXtlVWCOdyD1+uPYvXs3qqurPX4qPey59Ohf3kVVrz6e12lt2YOLph2a07Pmgn79+mHZsmXYuHEjLrroIgwYMADNzc2YOHEiXnrpJU9rUtZVBpnVRYzFkcXv6AQQv6aql1b6XjjbBYVOF5a22jJPvMO1BQGAJ8tDUOgytIzQNSQNl6kFkJsqyG7HOI0TrT85DDYOpFeVAZYlyo3FhK+B42ThEYOUg7T6OFlnctUF3I/ACGpukYkcN5WSc9HMM5fruaUjkUI44T3etaML1dH52te+hmOPPRbHHHMM4vE49tprL0yfPh2XXnopXnvtNZSWlmLlypWe1/ckdL788kvceOONWL58OXbu3JkVfOwl/auQ9KuIYA/376aYIu7kiuLFTtQSKe7MbMrO55l77XFNGjpg73Yug1mCnFw1JgUHXfTCMqmenFVQ0K3w8VpZWXffTwq5E3whQKH7uFENHIkw4l1eYq8rVXFAExGkOpNyXVGYsGuAWbE+VS8s9rVqXdMO416zuZzWVY3hUVlWnOaJa/gpRJgnTLuQi3MYTjE7YraUai3RMuNmT3Jd5Y8///nPWLVqFd544w20t7fjsMMOw5QpUxCPx7Fjxw5MnDgR5513nuf1PQmd888/H5988gl+9KMfYdCgQQiFnGq2Fje9SsOoiISsjChT3KaT66w69nHZWV0oLZM3FRWtPCqXi0nDUJnbShQ3hr2wjLqaK+Z5QiW4VNYb2X0mcIISObzLysBq46XvlChEdN3IxbGm3clVaykRhY2JoOAFksqlJF43WVf1gOfnMvHgRhC4ETkqYWPq1uLve7XA5NFNJXP5eEkjNw1QNnFZ6c6mGkfkjwkTJmDChAm4/PLLsWTJEjzxxBN477338NRTT+Gkk07CoEGD8K1vfQu//e1vPa3vSei8+uqreO211zBu3DhPmxYrvAgR+16xYORyy/LiLFjaY0nUVEUsK484VxQuJinmjmOcLDcy95dKBJkU35OIC7GGjmeLjVtLjUlhQROYtUUsFMijCkAWUd3nU8wVIsgSJlw6uWPH8Axu2kH4RdYtPRVrc46BURUDdOo07tUS41ckOK3rRvzw4kp1X1xbkZHlxXpiglerTJBnUcXOqN7LAopN6u+QsAEeeOABPPDAA/jnP/8JADj44INx4403YsqUKQCA9vZ2XH311Vi0aBGi0SgmT56M//3f/8WgQYOsNbZs2YLLLrsMy5cvR+/evTFz5kzcdtttKC11JzVGjRqFyZMn4+c//zlWr16Nr776CitWrPD82TwJnYMOOiiQlK9iQ3RV8W0gRHjRA8jjd/h7qqwtqZVGeja5a4vF8GTF8qjEi9golB/H92kyqTWTuWeJmdKItB+TJyuNl/o5JjidRdXyQVYZWSeGxC7mTu0gJAHL1vdSI6hMrTI84visNXwEE9vSyFnaukljTTd7exU7XnpYubHyOAkV/r1pYUGZIBLIepDnqfeV0VkUeLX6BCVQqD5ONvvssw9uv/12jBw5EqlUCr/97W9x+umn491338XBBx+M2bNn469//Suefvpp1NTUYNasWTjrrLPw+uuvAwASiQSmTp2K2tpavPHGG9i+fTtmzJiBSCSCW2+91fgczz//PGprawEAqVQKkUgE3/jGN/CNb3zD82fzlK/1v//7v/jJT36CFStW4Msvv0RTU5Pt1dVoiYvioUQqPniLjJPIkbeAULeHkGFcRNDaM+zsokp0dI4Rx8ZjQLRFbg0SG2c6CQsv9W3E+UHgxn2ms97IvtatLbvnJ+hYiOWRWVF0OImhrDgbP3hpsJmrlHK38KLCpOUCkPf4F88UKPDYi4VHbNcgC0wWLTkmwqXQAcbFzLRp03Dqqadi5MiROOCAA3DLLbegd+/eePPNN7F79278+te/xj333IMTTzwRhx9+OH7zm9/gjTfewJtvvgkA+Nvf/oYNGzbgd7/7HcaPH48pU6bg5ptvRn19PTo6zP+ze8wxx6CsLP3v5549e/D1r3/d92fzZNHp27cvmpqacOKJJ9qup1IphEIhJBLuHtDFhpgFJWZPMVgKOmAXO7LgZN064t6dliV7RWVxHI/SmiNLOZd1L+djb2QduNnXvHso86fMNeU5aiuX7R5kqeO6cQxVzzA+fbyij3w+H3Tspu0Dj0GfKifLju6+7LqppUjqRpMFJLPrqvc6K41pjA8/1ovVxySFW2XhEfta6fYwFUyqqsxOuBU0AViATGrmuHVruY3vMbHSqFLOdXV2ujqi8aG8vBzl5fp/ixKJBJ5++mm0tLRg4sSJWLNmDWKxGCZNmmSNOeiggzBs2DCsWrUK3/jGN7Bq1SqMGTPG5sqaPHkyLrvsMnz44Yc49NBDg/1gLvBk0Zk+fToikQiefPJJLFu2DK+88gpeeeUVLF++HK+88krQZ8wbTi4kvhIye69yWZnW3eHpW+XDihHvsFdUlgUp85V9+QrGTFyU9+oc71RA0BDXAcl+LEGm9W10c03aQrg9gyhQ+B5WmZe2waWAqtKx35o5TvNk+zquz9xXQfWgCtoqoQtC1l13Oo9pmwXV+gF8ziAe0kE+6J0Ei5+9dGs7paUXK+2JpO8XAAwdOhQ1NTXW67bbblPu+cEHH6B3794oLy/HpZdeimeffRajR49GQ0MDysrK0LdvX9v4QYMGoaGhAQDQ0NBgEznsPrunYs6cOWhpaTH+vlx33XWuM7s9WXTWr1+Pd999FwceeKCX6UWNGPPSHksqupmzcZ2tINpjCexujdmKDMoKDspifphosgdEZ4so0bKjrbGji9WRdTLn1xCzrjj4mBxtMHLmvhicbIRK7OiElhgjIwoTVZFEdk9n4VHF7gBpiwvfVkIsFMhQdSePRbMEhO0916NKJjScxIdXSw8AfRdx2fpORf7EMW4xyaJyu4eJ68mk/YLTfFn8jdt1RDSWJKfUal/7ahBdSqo2DSYdx8X1ZNfFnlWy8eJYXTdzfmx3iOXZunWrrWCgzppz4IEHYt26ddi9ezf++Mc/YubMmb6CgE345S9/ieuuuw69evVyHgygvr4eF198Mfr372+8hyehM2HCBGzdurXbCJ1epWFAeH52Fu0rsf3Jw4uh9JykNii5c15a1IjVk00R+2JpY3n4SspxzvVi3XdpPWEBx7J5mtRz3y0euP2z9pPh1pLEixMTF5MJiuabtmscujo6KjHiqXKxwboWQXYDB7KFkO59PtCJHFOXjmnats7tlaOMMCdLh87dZOKKckJXiVh1JqdxpplVQdDVRQ6Q7iNlWhm5rKwM+++/PwDg8MMPx+rVq/HLX/4S5557Ljo6OtDY2Giz6uzYscMKHK6trcXbb79tW2/Hjh3WPRWpVAoHHHCAcZkaN9Yfhiehc/nll+PKK6/ENddcgzFjxiASsT/0xo4d62XZglFZGjKuccNgVp+KjBVGVWSQWXX4HlrlnOuLF0eiu6uzSWiCc5eFIcYJ8WcCYFlmOntfofO6aOGRxavw8TgyFNd1tXP4lhCBwosqVW0fXaNN/j5gJnJMY3ucEMSOn6BiadNMnyJFjL8xytbi93aqeKx6r0spz0fbBnEdk2tuxI4M2XynNX3WxTFpiOlnXZVIUFlJZNdNhUYuBEl3EDl+SSaTiEajOPzwwxGJRLBs2TKcffbZAIBNmzZhy5YtmDhxIgBg4sSJuOWWW7Bz504MHDgQALB06VJUV1dj9OjRyj1+85vfuD6X6CJzwpPQOffccwEAF110kXUtFAp12WDkytISVApuIxm66sUymLjhrUL8HqLAYe/l7i6uaadM3DA4641VQZl3SancWWHFddFKo0EZj8PNDTkUGmR4qrXDxI5TI08dplWRTdLvGYYFA6XzAEdBJBU/5VVKt5SjFSgjUHRWJDHzi+1pKxKoqpPjB1OLj1uRIyuop6psLN5z239KlTJumoIu7hug68nJOiK6dcTrqvey64VwC8n6WhFprrvuOkyZMgXDhg3Dnj178OSTT+Lvf/87XnrpJdTU1OBHP/oR5syZg/79+6O6uhqXX345Jk6caKV9n3zyyRg9ejTOP/983HnnnWhoaMANN9yAuro6rbts5syZOf9snoTO5s2bgz5HUeEUG8O7i9oVhQRVFiJZWwlVHFDnWg7uKQbvonLqiyVD99CWxeuoxIpJwLLH1hEpMa7ICXE821NW/0b1+UXxIzlbqKpf+mxRA7OqSvR4EEROLi2T4oJuyLI6+W3gaWqhCbLHlUmQMV9Dx40VxgNKcRFgJeMg3Ds6S43OFaXb16RKsV+oOKAZO3fuxIwZM7B9+3bU1NRg7NixeOmll/Dtb38bAHDvvfeipKQEZ599tq1gICMcDuP555/HZZddhokTJ6JXr16YOXMmfvaznxXqI1lQ9/KaGsx8aDk+i4bRJi3ol7REiyhSGlvtD2qVWFFd12VmmbrRpAUE29qy3Vd8F3QmhmRF8GQwkSOOMxA0XltBmCC1+rhNT3cqjijG7ITLECrvZRc15b0QquqXPtNX/+qcJ6IoDCjreyWiC0SWuo8yFh1xPCARPwYBx0boellJzqecK4vdEce6iecRhY2T0NGlisuKCKosOobuKGkatWm6uk/Ewn2mRftMMqhM4nncCJ0gRJFfa04+u5cv+NM7qOzV2/M6bS3NuOrsCV2ue3ku8Ny9/OOPP1Y29bzxxht9HyzfqESO7M/01/b6OG77XukQW1Gwa7KWEZa1JyNgZFlZWRYqJnL4ejC6h72T5UThjjJxP7mN2cmVaFLiFLMTi5q7sXwUDHQbpOw0ThrT49c6o8KtFcZkvBuRY4qXOBmTvR3WkIoGhxgjk2aWJogZS7q1+fWdzqGb4wZddpQXupI1py2RRCru/bnS7qPzeXfDk9B5+OGHcdlll2GvvfZCbW2tLVo6FAp1SaFTGem06DBRwdDV11FlZelcU+yeLEZHV2DQqWigKpXcNk+M0bFlYLHigi1ycaNz7QBZQicULnMUMm5Tz1MuLUoWYhFE/nO37zHPtEp0INUqnLN9D1JsD4ZYJFAlclxkeYkWGWVqeEasiBYfZWyNG1RFAN2IGX6OroEnG+M1ONmNQGHrmMT2qISLSeCwmzM5jHUSNV4aaurmyjKiZKnbsvEqEdWVhAfRdfEkdH7+85/jlltuwdy5c4M+T8FoEwSGTLjYxQpLP3cXoMwjBiGbuLLEvlYmfbJsMTsyMSTWnHFRODCV6EAoYRdFbt1Vrqw6vLiRWZJUGVaqSs+6MU4uPR4mlsQ5JgLHQexkua0E8SRtpMmJHX4dY0wzppwEh5OLSSVidPs7tY5Q9ZcSx/Cihq2ja9CpEjgmaegBu59M4l6YCHHjkpJdc+vuUa2ju+9mLYJwiyeh89VXX+F73/te0GcpGioNm2zq3FW8O0sUTTpXlyqmh+0pfp1lrQHSYkbXvFMkLBFBfBd0voqyDJf1cgJp8lkasVuMTHtQiTAh4tYlJsba8C4siUCSdSKXrieO594DkoypaCvgZOHhyBojChGdK4u757iXrr2Dae0cnUBySh83LQLolEklW89ty4Qc95jS1b7JEjsKsSbLhuLnmp4j34hFEb2cgbKwCg+fwa0iFArh17/+taf1PQmd733ve/jb3/6GSy+91NOmxUZbPInKTC2gtljCZt3hg5F5WFVkJ0uOLMtKRNYvi99fhWzvikg4XTdHVxXZqSM5n2qui9kRqh4zZL2vfCNab+KxtLuIF0AyseKUzs5cTLKCgbpzi8HDvEVGMs8SKKLAiZQj1dpoHwPos6UcRJFoCeHXMioQaBC3k7WOnz5VXsa7ETmq97wryaR/VaRCLnIK1CzTDU4WHV4oiMJJV/Mm3+0UxPP0tLid7spXX32lvJdIJPDyyy8jGo3mV+jsv//+mDdvHt58801pwcArrrjC02EKRWVpCSBohkouXbyxNeboImqPJa1eVbL4HlUDUJ0Fp3MNVao6l3YupJNXRMJApLIzUJnHySUjxsEwV5bLjKbAiwOKBNkA1CRQOMiqydyaOusIL1Kk9W8ywiQFKAOWfSNrryCzxOSisrHOsuJlHSdB47RGQJ3K3bqTnFxVuvVs13PY7oEgvPLss89Kr//5z3/G9ddfj/Lycl+xv56EzkMPPYTevXtjxYoVWX0wQqFQlxM6QFrs7GpLPzgrI+G0+AHQJuk4nhYnznEyYqwPCzbm6+7wqGJ0xBo9/FnS1zkLVFsbKior7fdFC49YY0cnSPigZFaHxlBguKqGHIRoEWONxJgcVe0cMe1blUUlczvJBBITRJl1la0dbHE6CktLZg1rPndWSyA5VBVWWnFk1hqnwGD2np/Lf11eBTQbNNwzCWJ2inlRiQ5ZjI7K3aQTLqo6OqZiQZNtVSiLiAwvRfyCEDgm7iZxjEkhQqLr8/rrr+Paa6/F2rVrMWvWLFx77bXo16+f5/WoYCCAynAYSHRacdpiCVSWlmRcWtmCpLOuDut1JUnhziAWEFT1wpIFJJsEKWeRSTHn20cAsFt14oIAsK7Lm3h2urOcxYgs08pz8T+34ke2j0m7BiY4+HR7L/ACh4MJEp3FRXnPIZaHRxaMrMXUAqOJ1ckaY5q5ZHJG01o3qrkmMTdOdW+8ihyDsV5SwJ1Sv03ueYlp6SqZUhRv07XZsGED5s6diyVLlmDGjBn4/e9/j3322cf3uu66SXZTKktL0Lu0U4BURsJpkVNaYrWH4O9Vcv2m9Knn+oys8sw6vPgR20CwjuZ8cULWSFRMg+dpbI2ZZYKxjttAZ1FAmahJdGSLEaf6Onz7CPbiCIXLbK8s+HmqvZz6WMnGMnjRoOtOrprj4r7SPRWLSufIxrIYG9+NOHWF/XjLjMkcpzFsHdlDPyg3l65ysRNO8TwFRhVcrEKMX+GDicWvnebrxoktIIjgiSaSvl9dha1bt+LCCy/EuHHjUFpaivfffx+//vWvAxE5gAuLzu23344rr7wSlZXOcQBvvfUWvvjiC0ydOtXX4fJJc9wuCviA5MrSErTFEuhfGZHeZ5YdvvEm3yJCBXNhiQHLYj0dtqZJl3M2zhJBsoBkMVCZz6riRQ+gj+dxsri47WLu1oIjc6uJ6Ir5iQHE7LPqYnGc2jRoOpQ7xdC4Sf9mGVwhl/N8pW3rUFmH2LWgBITMUmOaOcWvoRqjc3P5hM9kMrXSyNDF4vDBxE71dcT4H6c6ObK5fnFym5F1pmdw4IEHIhQKYc6cOTjmmGPw8ccf4+OPP84ad9ppp3la31jobNiwAcOGDcP3vvc9TJs2DRMmTMDee+8NAIjH49iwYQNee+01/O53v8O2bdvw+OOPezpQIWiLJyEzbrXFEspUc74JKMu+klUu1gUbl2vES5CVlm246YHFCx9AWRiQRyZkUomOYAOHdXhxOzFx0r4nmDMY1t/h3VkqC06gAcY6IaOLeVHF0/i1yOjq3TgJI9OGl17O4wYXlZNlVX55MSEKDpkYkokiv5WHTch1d3CnLC8vawZOrA0IGdQtI1zT3p7+uf3iF7/AL37xC+kYPw3DjYXO448/jvfeew+/+tWv8MMf/hBNTU0Ih8MoLy9Ha2v6H7xDDz0U//Vf/4ULLrgAFRVdy6zZJim1zSw4bfG0m6gtkrTcXLva9K4hu1VHLXacavHwY9g6WfE3tvU4S5KsUKAq5Vxm+SiNZDepNBE5slgfkwacbhE7l8tQFQaU3TNtqqmx2iBclv6shoKLFzi6ruKy91JrDmexsdxl7BpfjM8UXT0csVs5P95JSHkpvKe77qZVg9N4g4rElkAwKU6oQNZ+gX0tq1/DiyQ/1g+VVUkkn8HSXcJyE6nU1yMjPCO2kQoaV8HI48aNw8MPP4wHH3wQ77//Pj7//HO0tbVhr732wvjx47HXXnvl6pw5JR2Lk/6axebIYEHKDFkwcnZmFhMn8swp/j1zZWW7rdz10vJSpdmGSgiYWmU4UeMq8yoogk4DlxUHFNf3uZ+XXlbKWjYZYWMcy2MaQCxiWjuHFxZsH1VTTBNUQcNuxIYPt5T18HfZG8u0T5Rp1pMuoFjXrJMqExM9DU9ZVyUlJRg/fjzGjx8f8HEKDxMyLOvK+pqzyPDxPLZaNrBXVRZFDi92eAsPL2yiGXcXgwmfTitOZysIFbYzqTKsTFoiiLA4GEXsja7tQ2CCx6Tdg0508BWMTWGWHllaucYKJKtmLENbHNAJVcyNWOtG1aPK7fpuWzqoxA2P2AeKjWOp6rKqxH6abQaBC2HoFAvD39OhEjaq67KYG9V9r5WFZfNEa1PQrrbAxVeszaouTuSfxYsXG4/NeYxOd6amrBQDkyHsbJdbLCzBk8nGAtQuJ74xqAyxE7povSmPhFEeCWN3JssqKqxnYq1RjlHVmAmXdTbyFEUAL2risbSgEZbNShPPjAM8CBwvsTxi/RwemfVFHK+rhSO7z4sb8R5XYVlV7E8ksFgcsdmleA/wFldjmq4uc0+5sRSJ4oU/K7seYNE+Xzi44kzaMojViEV04kPXTFOFLoNKhRdhoRJcQbjBfIkc9h8JaU0rA7FDMTo54YwzzrC9D4VCSKVStvcMrzE6lF7uABMurIggy8Di4dPI02ndclHS2BrT1sTRBSeLezldU/qSS8vSLzGdmgkadl8CEyuWaJG4qGRzbLE7Ti8RVtdG9pKNM8VkrCz1m4kfUTzx49jakpo6gLwVg6M1R5fR5AaxK7jpGm7HA87F+JzG8uKGH8esP3xLBjbOhZXHTR8nKZq9chXbUiyuJD+9pcQ1lMUyc4FOzMTa8nsWB9oTSd+vrkAymbRef/vb3zB+/Hi8+OKLaGxsRGNjI1544QUcdthhWLJkiec9yKLDMbAi/eBujSfRHE/YUsrb4kmr1k5bJJkVayNiq1asEDdsnkr8yIKY1WvZKzW3Q9PriiHLDuJ7XLH7XH+pEN/ZnLPc2K5lrEM2a46JpcaLW0vsN6XqCi5UK5Zaa0yDkUXEeYq6OGItHROXlTVGNcCNC0pHrB3o3d98LOCvBg0fs+OmQWeAPaaCdIOYBO7qrDz8NXEtp+aavMXE5PPoXGiqtXXXvX4PrXn5chup9olUkvuqSLjqqquwcOFCHHvssda1yZMno6qqCpdccgk2btzoaV2y6Cjgs7D4YoJeSNe2SVtraqrU2UcsPidqCRbnLuZaZMJBJn7EuBeFMLGJHGhSySGx8JhkXbFihV5iaGTuJL6dgzieH+cG3RxZHA/33lMMjohbC4wJuUjV9oNLy4yOfFhACmFlCaKQXxBWmS5PEVlwCODTTz9F3759s67X1NTgn//8p+d1XQudWCyG0tJSrF+/3vOmxQ6z5rDAYhZ8/J/2GP6TieOx95pKZsXkiGKEje9bFeGafyZtwcd8xlVNVQQ1VRFrbZZWzgQTE0/pdew9t5SFAhm81Yaz1lh/MktNtCW7ujEEd1QG2zWVqBErHesqHvOiRyd+mJgJlyldRcbCSeamkq0XiyLV2tgpXLwIJmS7sEKRyqxYHWklZF7E6KoRq96L95jFxFQciS4jBhMoKqGiu+/U18qj+NE99N021nTaQ1dMzyQmR1bFmL+mEzFurFOmVh/RquTXHefbVegFEyEjjiHxUzCOOOIIzJkzBzt27LCu7dixA9dccw2OPPJIz+u6dl1FIhEMGzbMc1BQMdOaseL0Lg0Dldm1dWyurFgC/ao6v+aLBwLZwchiEDJg1sOKTy1n7SCyAp3jHbA6lfMw15Ppg56z4oRYPRifhf6UQcim65q4s8S+VexPlejRrakKNBasQDJBYhpQrKqXo0wXZ/C1cGTjvBT2M6lcLLvHn8NJpOgI0BXlFi+9pgC1QAqi6rEO09YPpmu5OVeXtfo4/Z1kbiuABE4R8Oijj+LMM8/EsGHDMHToUADp9hAjR47Ec88953ldTzE6P/nJT3D99dfj//7v/9C/f3/PmxcL5eESACnLcsNidYBOscNbeHqXhtEW6RQbfJVkGWK8DrPk8Knj1lk4q46YZi5bz3ZNZcXRiR0+q4oXNnwGlmD1UWZScWtkxZSoAo39IIoWFw0wLXS1cQwIvHqxDrHwnxjb4rVSca4ymdx0+XYz3hA32T66dGh2X2U10YkG04q/sqrIvmNfHNYJUjgVDV5jbdgcaT+6zJqRSqCtyd/5CC37778/3n//fSxduhT/+Mc/AACjRo3CpEmTbNlXbvEkdH71q1/hk08+wZAhQzB8+HD06mWP3Vi7dq3nAxWCynAJKkvTj+bepWHLsmMbwwkNsS+WbJzd5dTpUuJFDoMPOhbr6DB4649Yu8fqXcVnSzHRI7NgsPFiewdZJpO1Xlqo8PVysgSPzgIkc1G5sRjJRJGssjGgDgxWpZHr7svgrD5M5IhWGpv4yQgUMd3cVXNOWfsGUSC4ESt+0s35eSbixKkisux6AOJHVb9FZ41xs5Zsjps9g6gn4+Q2c1Mt2e+ZbOhSuXOJaj9RAKn+7jELj+l4InBCoRBOPvlknHzyyYGt6UnoiHnv3QWnoGO+KjJfUFBMN9ehSx+XubJEl5eySrKYEs6/t9W5cRnoy5FVLwfIFiqceAmFy5zr5/ACSCd6ZBYppwacMrw27HQRh8NEjFPtHAtJnRojIcTIVV0ZnYCS3fMqSgIOPHayrji5noKK3VGtb7KeF8GRk2J6XsmlwFFZbZysOfx93mWlG090CzwJnfnz5wd9joJTVRpGVe8wtjbbH2hM3Mh6YfFih2Hvas7idbi0b8FVJYPdL+dcYibd0LUwwSEWChSvAVlFAZkVh7fg8PdtgoZ3X8l6XrExQcDOz4sXFpisc2nxiOnm/DXxa2E9nZiRCRU23kTEWD2qGF76VOng11FZT2TXe3twVYtp5G5bNLi07ujaHzjh1PFbt6e4hk64mAQwm+znZIUKMkA5J5gW6gPkIsVUUInWGeaK4tcXvy4w0UQS8FELJ9pF6ujkA8/p5Y2NjXjkkUdw3XXXYdeuXQDSLqt///vfgR0u37TqXFKcNceqpxNPZllzpMHC1r3ssQxWEdlknnF6OUMsDigiKdaXVRxQwFWlY6eigH7xWv9GharVg2AJ0gkW2/Wgat3kE5WVSFat2ARZ8b+A8SpSVDilcDtldHldPy+4faAXkQAAkC1cZJlTqgKATu4tqqfT7fBk0Xn//fcxadIkK7f94osvRv/+/fHMM89gy5YtePzxx4M+Z17grTPN8URWg8+2eBJ7V8jToVUuJSZKGltjtrGs4acu8yqaZQkKZ1mHLGSByMxNVeoQiwNkxcuIcTgMXuD4zqiSYSqgVC4orgWDiGWBEYQM6wTOxgCS/lSc9Ui05Oh6WtmKAkZbs4O0dWnisnG6XlNugop1WVM6d5TqPZ9yHoBby2aJkbRXAPRF91TvRUuILqbGa7ZUPkWOLmVdu4fK9aO65wc3LiUvazNEdxQfb8OjsuSQwOm2eBI6c+bMwQUXXIA777wTffr0sa6feuqp+OEPfxjY4fJNZWmJNBCZv98cT6SzrjhrDgtAroyE8VWr/SHfKUzsQqg9lnBML+fdVn2FQoNaF1aiAyjvLb8O2MVOooNr/5AWO5a44fpW8cKHvbcqHvPuKS8p6U7iJiBrjSqtO+t9RpDwFptQRlSZdBPXBiaLiP2p+Hgd8b1M4Kh6LqniaviaOTxuOofz+4hri+niTmnrCgHk1H/JC24ypoJMC88lgQgmE4HjRwSoKg+rhIZXweWmHk6xWagIi2QyiU8++QQ7d+5EMml/Hn/rW9/ytKYnobN69Wo8+OCDWde/9rWvoaGhwdNBCklFuKQzFqc0aWvuyaw6vHWHz7qyRE5mTLagETOv1K6trHMJNXTYfBsySw5vhYlLYnMYum7mHKJVJ6vqcUbsKIOPVYHLBnu7wiAwWRQqUveTxNWUam3UriPek7Z3EIWLClVjTv4egxcLTvEvuW6G6SYGxyO5FCBu412KJvDXBJ3FwiQwN9dnEPFzJl2quHhdV0dHJ8yInPDmm2/ihz/8IT7//HNbY08gnY3ltX6fJ6FTXl6OpqbsegIfffQR9t57b08HKQZYjE7v0jAGVJSiLZ7E55ng5MrSEis2hxc6rL4Ob+FR9b7i08hVNXLYvRquejIA7G6NyYOYmVtKVwlZDBTmYdcchIbNiiMjUzsnFC5LByEDndYdlcAJypLDx9EwZHE7kfKszuuA3NIjpo3LelTprvP3tGJH5YZSpZaLY6Kt5gJG51LirS9+BIvK0iNbz2elY6+Bxrru2kEF95qcxXcnbjcWDy/WETdz/Lp+RHHi1t3ltboxW0dVP4fIK5deeikmTJiAv/71rxg8eLCv2jk8noKRTzvtNPzsZz9DLJZJIw6FsGXLFsydOxdnn312IAfLNyw+h4/Jkb2Xzo0lXKWYM2TBx/w11vaBYWoJUsJEDnM5iQJEbNKpCFC23ePbODi5rHTdxxmsP5RC5EhbIgSILovKDdYZVSJFlkGli9MRRYFpgLNqLZXocRI5+e57pSDoYndB17kxWb+okGUlsa/56zLc/H3UBQm7ESdOBBlvQ7E7eePjjz/GrbfeilGjRqFv376oqamxvbziSejcfffdaG5uxsCBA9HW1objjjsO+++/P/r06YNbbrnF82EKSWsm+JgJni/b4zYXFl9jR6y3UxkJo39lxFZUUJ15Ze9dJWvyWREpsRUOZG4vZVq6tFFnBxBtNnMJCUHIRhlVilicVLTFvi4vrmTwwkZjwREFTirWlj2HW8fWi4ofr+pfxaFKDZedSXZGqQWHFzYmIsUplVx2X9VfSryn6lXF7vF/6q67sSR5EEheU69lAcZi3yh+rCyAWbWG+LUpMiuSr/5RuXr4Orm4ZOP9nIdlOZkIH917XbaUafq6bJ0Cihz2PPLz6mocddRR+OSTTwJf15PrqqamBkuXLsXrr7+O9957D83NzTjssMMwadKkoM+XN8RfCrH6cTPn1mJURsI2aw5v1eEDiFUByQwWlCxaeMRgZaVFh1U65jGMebECjSXWG2WrB2tfgyBkJyuPommmiMyiosp+kqGyyIjxNLJifyYix9ojUp4Vz2NDFmzshC6o1yTTSlaTxu1eGhGVj8BdL72ZTNsv8KiysHJB3gKec5VNFeReYsCyKi3ciyXIrcWJLDgF4/LLL8fVV1+NhoYGjBkzBpGI3RAwduxYT+t6EjqPP/44zj33XBxzzDE45phjrOsdHR1YtGgRZsyY4ekwhUQs/te7NCxNMWdZV9a8TKaVqnt5WtzYBUtUIXzEHlcqYWT1teLTxnmxw2dXAeku5IC0eJ8qfTwr4NgUXvCwHltuA4158aOx8nhxMzERomusqXVTqVLEy6vAOqlbFiGd28oNXjKXeMHCF+vj76vWdtmkU9bMUofJWCehIisI6EWUOPW4Egm6VUJeApp58WDa5JIvzucFU8GgGicGCTuNEdf0g8pdF6kEQi5rmBGuYKEvF110kXUtFAohlUr5Ckb25Lq68MILsXv37qzre/bswYUXXujpIF7ZunUrjj/+eIwePRpjx47F008/7XqNsrA84El0UTEh1BxPdGZpCZWOh1SXY0h1OfpVRTg3lT3uhjXuFOvoiPE55VyPLIa2eSegFhWsSSd7SbCJHFlFYxN4C05Q2VQ6BFeUKiCY/1oVYMxf5wOJrTXFflOi+ygWBRIdneuyBpwq3LiydJhYe1SuKJmo8ZCd5dYy4RRjo6uHI7q0vLiSVPuaVEDuUtlWPKbCw+m+LjtJ5kLirTxeU8bzESxMxQILzubNm7Nen332mfWnVzwJHaauRP71r3/5ChjyQmlpKRYsWIANGzbgb3/7G6666iq0tLQ4T9RQVVqCqoyQMQlGBvQ9rBi8pQaArbmn6s/OuYlOwSPLtIprAnxtLqayzEshZHQByIox2v0AR4GlxUUfK517SlfUT0VWRhXfOVy1j85txcO7r4LAS+q429o5OcaNiDAtyuc1niYnYiZXQfS6AF+3851Ss8U5XlowmJ5Fdo/IGbfddhuOOOII9OnTBwMHDsQZZ5yBTZs2ScemUilMmTIFoVAIzz33nO3eli1bMHXqVFRVVWHgwIG45pprEI/Hjc4wfPhw7csrrlxXhx56KEKhEEKhEE466SSUlnZOTyQS2Lx5M0455RTPh/HC4MGDMXjwYABAbW0t9tprL+zatSuro7qO8nAIVQrrDY/Y90qWacXfa+dcUSz4WKyLwyw6vOipiJTY0smlFZGZq4oXO3zvKtGawruTkA4aFmNwQuEypDLvQ0B2lpWqAacsZZ3bywYvdkytPaLYkbRoMBUyKteUSSFAI5eTqVvKtCKyF3h3lthniuHGPSUZ67bxpVPFYtW8bkWurAUmbh5d7IzMXaX6+8Dfk1luVNYctyLFqY6OqSBzg4m4y0ULmyJhxYoVqKurwxFHHIF4PI7rr78eJ598MjZs2JD1PF2wYIHU2JFIJDB16lTU1tbijTfewPbt2zFjxgxEIhHceuutRuf49NNPsWDBAmzcuBEAMHr0aFx55ZXYb7/9PH82V0KHdS1ft24dJk+ejN69O6vvlpWVYcSIEa7Ty1euXIlf/OIXWLNmDbZv345nn302qzt6fX09fvGLX6ChoQHjxo3D/fffjyOPPDJrrTVr1iCRSGDo0KGuzhBNpKMuqkrDWf2uBlZE8HlzVGnZYZlWTPR8lREoYtdxFcyNJYMJJOb+8tzQk+HiL2mWJcdN9WMTAePVpeVQW0cUMq66gMswKfDHxqlwCiYGnGNlRJEi1rzRkYOUcLdCxamzOOECMW7EBBMxJNvHTzaV21o2RE4Qa96Vl5ejvDzbUr5kyRLb+8ceewwDBw7EmjVrbBWJ161bh7vvvhvvvPOOZWRg/O1vf8OGDRvw8ssvY9CgQRg/fjxuvvlmzJ07FzfddBPKyvRW/ZdeegmnnXYaxo8fb8X/vv766zj44IPxl7/8Bd/+9rddfXaGK6HDupaPGDEC5557Lioq/P+j1dLSgnHjxuGiiy7CWWedlXX/qaeewpw5c7Bw4UIcddRRWLBgASZPnoxNmzZh4MCB1rhdu3ZhxowZePjhhz2do195KdoTSSvNHAAGVKS/PWK/Kx7+nkzciAUCecsNDz9XtP5Ig5KjzWnriJhx5abacUa8MOtNVqYVV/HYuiZDDD4WcStqTAsFMktPLCqvRJzBJnZkwcTiOJW4URX5c8qeEqsXM7w0xZTF1pgW4lNdz2E1Y10gMUMMCHZjzQmyiF9RILqP3KRGA/rxqlo5qjV11hvdeVSWIZ0VximVPBcWnCKnLZFAQtNo2omOTOCu+B//+fPn46abbnKcz+Jw+/fvb11rbW3FD3/4Q9TX16O2tjZrzqpVqzBmzBgMGjTIujZ58mRcdtll+PDDD3HooYdq97z22msxe/Zs3H777VnX586dmx+hw5g5cyYaGxvxu9/9Dp9++imuueYa9O/fH2vXrsWgQYPwta99zXitKVOmYMqUKcr799xzDy6++GIryHnhwoX461//ikcffRTXXnstACAajeKMM87Atddei6OPPlq5VjQaRTTa+RDllW57pqV9VaaPVauQgfWfdmdrSEWkxLLwiD2vmDsKyBY7rHM5uy/G8oj1dGyIgclM3GgK/9nnZzfylPaw0hFkwLGbvlbCWDGAOMtNxQSMKGRMKhWrKK9K7+U0T2Z1kbmYGDprTtAEsKboymICRCdighYpQWdEWQRtcTCpVaMSA7K6M6o5OleS7L7svexrGW5iatxYe1RriWKsBwohU7Zu3Yrq6mrrvcyaI5JMJnHVVVfhmGOOwSGHHGJdnz17No4++micfvrp0nkNDQ02kQPAem/SHmrjxo34wx/+kHX9oosuwoIFCxznq/AUjPz+++/jgAMOwB133IG77roLjY2NAIBnnnkG1113nefDiHR0dGDNmjW2+jwlJSWYNGkSVq1aBSAdFHXBBRfgxBNPxPnnn69d77bbbrNVWRSVLkslZ1YaJnaquGtApxXHav3AtX9g2ONzwqjIiBlGNNPUk3db8QHNvMhpV4xRCgxmVdG1X8jct1lyFMKGjVGu6SXuRoVBQT/rXIqMKq3IAexF/BTrORbsE9G5t5z6T+kyoJzIU6AwjyzI18klZVp52EvgcF5r0cBfZ3MA7kSTyt3kN/1bXF8VuyNDJ55MrwUhHIP6Hrjas+u5Xqurq20vE6FTV1eH9evXY9GiRda1xYsX45VXXvElOJzYe++9sW7duqzr69ats3lw3OJJ6MyePRsXXHABPv74Y5v76tRTT8XKlSs9H0bkiy++QCKRkCpEpg5ff/11PPXUU3juuecwfvx4jB8/Hh988IF0veuuuw67d++2Xlu3bgWQDkY2pVKSjSULSq6IhNG3KoK+VRErY4pZcsoVokfm0mLwwgmAPoOJt8iURuxCJYN4jVlxLGsOgxc2uiBjr4iVkR0qJFvn5aw3qhgcW6ViUYhkMqisbCqdsHHoR6WtmeOEU9VgnVtJlTqeA0Rx46bmDD9fdd/v2XR7GO3jFPjK113KR4o5v6fOMuK0Bo/uM4pBw7KUcf5MzJLiNTvKjctNNY4/s5+sMzcUSQuUXDJr1iw8//zzWL58OfbZZx/r+iuvvIJPP/0Uffv2RWlpqZWMdPbZZ+P4448HkE4I2rFjh2099l7m6hK5+OKLcckll+COO+7Aq6++ildffRW33347fvzjH+Piiy/2/Jk8ua7eeecdPPTQQ1nXC9G9/Nhjj81q5a5CFYTFkAUct8aTVqo5gxUTlMGLnkquDo7vPlWAPBhZFhcjuJ3Etg6qlPGUaKkRBQ8LQmb7sfcmwseNS8oAVUxOllXGbfq2U7yNU4NNEZNAZCdy3BHcaS+3RfVMcNuMMy/CIgi8uLlM5gRluXF7L2hke3mtoqxaj/BEKpXC5ZdfjmeffRZ///vfse+++9ruX3vttfiv//ov27UxY8bg3nvvxbRp0wAAEydOxC233IKdO3daFpilS5eiuroao0ePdjzDvHnz0KdPH9x9992Wd2jIkCG46aabcMUVV3j+bEXdvXyvvfZCOByWKkQTdegGPsiYLwzYuzRsi9dh12Up5qKYEeN0WIZVeSRs9bPis65k2VdZBQJLHSw5DBarkxErfKVjaTVkp0wqa3+HuB22L+srBQQuchjabCpDkWMr7idDtPbIBA3bS7wni7sRe0U5BRI7df+OdMal+BEh1nxF5WPZeKf1/Nz3OlY1N+diyW3QLj8nF8hcXeIZVaJDFw8jXnODl9gccS4Jm5xRV1eHJ598En/+85/Rp08fy2hRU1ODyspK1NbWSp+7w4YNs0TRySefjNGjR+P888/HnXfeiYaGBtxwww2oq6szcpmFQiHMnj0bs2fPxp49ewAAffr08f3Zirp7eVlZGQ4//HAsW7bMupZMJrFs2TJMnDgxsH0ASGNwepeGUVVaYllvRIuPzGUlVjEWrTCd1Y7VFh5mAZKO0VVF9oOb+hBOsT8Mgy7kTq+cYtI9nL8miiGnxpqyasSikGAiRny5IdYeyIM8CEFRrOTNIuSUPeTFraO67yZA2Evwr+zsfsSGal4Q3xPCNw888AB2796N448/3qpPN3jwYDz11FPGa4TDYTz//PMIh8OYOHEizjvvPMyYMQM/+9nPXJ+nT58+gYgcwKNF5+6778Z3v/tdW/fyhoYGy2zlhubmZlu30s2bN2PdunXo378/hg0bhjlz5mDmzJmYMGECjjzySCxYsAAtLS05aTXB19Dh3VXMVTWwIpIZx1l/HMRO+n12ajlvzRGzrLLWEFPIWRVkMQiYFx/MusMsNapu4+I8Ht41JWn8abvH9mT7Rsq1lhzTon0mjTqzxriJlzERO06WITdNM/2Od7gns+yYWHtU900tM90uzVuFU8aUGyFg4oLh1zQp6CdmI7mNoSmUoCg2IePH+tRFSaVUhTfczRk+fDheeOEF4zUOO+wwLFu2DP369bOKEqtYu3at6zMCPruXv/baa3j//fd9dS9/5513cMIJJ1jv58yZAyCdwv7YY4/h3HPPxX/+8x/ceOONaGhowPjx47FkyZKsAGW/tMWT0oKBQLYLSxWfAyCTYVViZUvJRAxDlXHliFPFYZnFhXsvjdFhLim3lT91QdEOYkfEtBO50RiW9u1HBPmZw6OK0xEtPAEEFcsEi5tmm27Gi+t3abETZAq5SR0c0zoxunRwHWysbJ9ifYAX09mK4Bxt8SQSkgr9pnT4mJtPTj/9dMutdfrpp2uFjldCKS8yrpvQ1NSEmpoaPPHCOry9O/1LwYQOX0uH71jOixwxrbyxNYa+VWnBwITO7taYFZMjuqJk1hwxndxCVxRQvK7oV2VUJ0cmdFSFAGXn4c/EYnU8EEQ14yyLTy7aLZjg1DjTINDYJCDYbVdw1fr8HirhwosaWcNNFV7FUNA1cpTncBITXrKFTMSGKHRUD33TejGycaprhXyoi6KwCASGjlQ8htTbf8Du3btttWmChD2XzvnfZSir7O08QUFHWzP+8N8n5fSsXQVPFh0AWL16NZYvX46dO3dmZT3dc889vg+WT6KJFCpLS/Ble8wKMpZZb5o5EQSo3FaswrE60yrKpYpLqx7L4Bt5lvfudF/J+lqZIPatYu9FV5fTuvx9FoDMXzO06rgWNtFWhHoPsIsYPhhYrIAsi7XRBRi7xU3BQafCgIYBwTymwchuAot16eD8vXwEFgdtKUp1tMtFjViADvCe3q0abxKr4iRynESB7LO5cWO5DVj2ip84IqLb8fWvfx2rV6/GgAEDbNcbGxtx2GGHee5g7kno3Hrrrbjhhhtw4IEHYtCgQTZTUy7MTrnGpI6O6K5S1c4B5CKHj9Ph3/P3jdxXiY7szCtZX6qMZUbZtsEUlSVHFDhAp6jhs60Uxf8chY2b3lEqEWOSeRVU9/AiQRQ8frKxurQrypQgXFYyq4l4nUdVC8YrpoHQBFHk/POf/0Qikf1sjUaj+Ne//uV5XU9C55e//CUeffRRXHDBBZ43LiT19fWor6+3vqFl4RIASasyclumdk5rPGm1fxAzriojYUXNHNaAM5lVBDCqEEBiplYWzG3FxEW02X5fYdHhRY62XxVvxbH2dBmrU6GIjldYcxybbeosJLJYFpVgEa02qvRwHSZxNKoaOqr1TFLLJeiEC7snc3OpXF9ehIyrOUHGvgS9p5OLyk3wr9u9ZWdxm0WVy+9rMQYsE92WxYsXW1+/9NJLqKmpsd4nEgksW7Ysq66PGzwJnZKSEquzaFekrq4OdXV1li+0LFyCirC8YCCQvs5nXDFBBHRadkQLT9+qCBpbxdgbebNPaQ8rBnNRBUCWyJGJGVWMDmCPvZGNYfeDdFXJBExGVLgqDhhUNpVJt3BxLr8G39vKJSYCwymmxm+tHdfkq0dUEGsz3AT88uNNhIrf81NPJ6IbcsYZZwBIe4RmzpxpuxeJRDBixAjcfffdntf33AKivr7e86bFRlnGdVVVGkZVaRiVGWtOVWkJqjIih++BJf7JRIws1oYXMawreUWkJKsNRGAoatzwAcliwUAAZgUDxWahPLGo3Kok/IMcSH0cVpyPf+8VJoDcdhL3Wqk4D+0aVGJG1zVcxG27h5zjxsXjJphYnOe3KB6bJ4og2T2nwGPTffzUtiGIIiCZTCKZTGLYsGFW3C97RaNRbNq0Cd/5znc8r+/JovP//t//w9SpU7Hffvth9OjRiETs2TvPPPOM5wMVgo5EChVhe+POttJO0bKzPYaBFRErQFnWzbxfVSSrEnLaVSXXkk69rViaeiMzROisOopsK7H1A4DOBp4Mp7RyE2sSi82RWHLEejni+0CLApoEAqvGiAKECRlZCriq47g41/C6iaBwEiUm5K0BZi5wIwZMMqd0XbvFsWx/8ZoTpoLLjWWIBE2PoS2eQtxHings3vUSqjdv3pyTdT0JnSuuuALLly/HCSecgAEDBnTJAGQnWKwOkM6y2pkRNizNHEi7q1RBxO0ZIaPLqBJbPqQzsBLW11nwriM+psZlPI1RgHKQjTs1uK2dEyhOcTV800xe1Kh6T2msPH6sI6KQ4QVLlwsWNnXfeHXz8NYSWT0br/Ez4nu/6dBdIJWaIApBS0sLVqxYgS1btqCjw/4c8trvypPQ+e1vf4s//elPmDp1qqdNi5H2RKcgac30shJ7XAHZ2Vd8eni7rQBgOBOUbA9Azg5GTljj+XuNrbG02ysuxMeICBWLxQBkmVUnq1lnEPBp5GKPKz7zyrBack5Fj8wSY+KKciluTDGpWuymsaZJfRs+vTqv2VVu6s6IYsdNgLHOfcSvrdpLZ3FRndf0uuqe29/5fMfrUHwQkWPeffddnHrqqWhtbUVLSwv69++PL774AlVVVRg4cKBnoeMpRqd///7Yb7/9PG1YrLQn0kHGbRJxYwUex9VjAGQFFUszqCRz+LR06XwWG8NeQYkThiiExPe8ONH0rxLnZIkVwyBlY5HjJlvKzT0veOlR5QATH0H2sLKtle9sqFzhJ5aGj8tRiR6/+3QXevrnJ3LO7NmzMW3aNHz11VeorKzEm2++ic8//xyHH3447rrrLs/rehI6N910E+bPn4/W1gJVmg2YjozIaY0npC0gmLuKD0jmYW4msVdVeyyJ3a0eRIlT487SSGfwcLQFqUSH9eKxrjFhpGrGKauuzKPrQi65lmpttMRKKtZmE0eem3iyujj8i78uQyU8+AwqVWCxWLyPd1vxY0SBw71nAb2iq8kSGw7CSGVp4dfVoRojte44rGNC3uJ/VNYQ/sUEjK4bt4isWKBqH906KsRg5yDIt/ggsUPkkHXr1uHqq69GSUkJwuEwotEohg4dijvvvBPXX3+953U9ua7uu+8+fPrppxg0aBBGjBiRFYzstfFWoSgLl6BfeWmWyGGNPVk9HVmPK76Fg8xFxRp3ygKPtVWRxdo5DL5mTiZGJwR7kHGWq4qvjyNr5yCrrsy/VwkciQByssY41s/hCbJlgyhYvM7nRRFfD8ch/VwmdkKcINKJBH5eQWNzHESAdSY/adRBBRx7Ge+UkVVsD/liOw9B+CQSiaCkJP3cHThwILZs2YJRo0ahpqYGW7du9byuJ6HDct67C2VcZWSVW0qkTRJ7o3JVMbEDyMWNdJ7fYGBVLysmYMSaOLr9MvE3qVibPWtKEEAyASPrUu5K7AQFEySqDCoRUdCo8BC3Y1lquHGiiHFqzpkXwSP+jHJljRDjcJx+N4IUUaprbmODvO5PEITFoYceitWrV2PkyJE47rjjcOONN+KLL77A//3f/+GQQw7xvK4noTN//nzPGxYjHYl0Gl5VadheDFAietg1sU9VeyyhjLXhkYkj6bzyTDM3sQqyRpBYlhyVyDFcx7rPW20i5VDl1jHRIhMwebfe6PpWicLGxGXl5r5DjypewJhYcGTzdeNzInq8BKC6FQai4HEKEnaTteUnfkf2tUgxWnqIbkFLPIFSiRfBlLiPuYXi1ltvxZ49ewAAt9xyC2bMmIHLLrsMI0eOxKOPPup5Xc9NPbsyYgsIHlkMDmsHIcO4KSeyM66kOMXnhMuAaIuzkLGtaSB8RAy7jntpxhkYBi4jV/iZ76ERJ9CF0sO9iA02ls33mzJuet30PkEQRUMqlcLAgQMty83AgQOxZMmSQNb2FIycSCRw11134cgjj0RtbS369+9vexU7dXV12LBhA1avXm1da08kpYHIjKrSEisouY1rzNkZo8NSye11cKKxhDRGhx9rfR3vsHcpF0UPczexQGQuuDir4rFw3/rTq0tMSBX3XOFYrGrs6gztyAogFt1RqkrHfqoZO61jIJDEDCo+SFm02vBBxLrAY2mAc1A4WTGc5srSu1Xz3AbpeqkE7LQ+f0a3ZyEIwjepVAr777+/r1gcFZ6Ezk9/+lPcc889OPfcc7F7927MmTMHZ511FkpKSnDTTTcFfMTcUxYOoSKc/lawNhAirfGkFYxcmalaLMKnl4vWm6hM1DhZbxiKlg6yTCsL1XUmhnhRZGi94ZHF3qRibeYWGyZ4+JcTfJaTLJ1bJoL4uU7osrCc5oln5NBlOSlTvyXv807Q4sPJHeQmmyloQUKChSAKSklJCUaOHIkvv/wy+LW9THriiSfw8MMP4+qrr0ZpaSl+8IMf4JFHHsGNN96IN998M+gz5px0U8+SLIHTmikayEQOH7PDxA5vmQHsYod1K29sjdmFj0zgiNfEmjmZVHIkOpCKtjh/KKeUcR6+2J+IWDdHJ4r8uqW8WHty3TvK5/p+GnHmap4S/mHvJwBX199Jta/XlO2g8WItcotX6xFBdHNuv/12XHPNNVi/fn2g63qK0WloaMCYMWMAAL1798bu3bsBAN/5zncwb9684E6XJypKSwAkUFmaFjxfReNoyzT1ZK0frHYQnKjhO4/rApDZGKtjOcrswkb8WidMNMUCWYp5ShynqqoMOIoYPtNK9zWATpHCdxJXdRUPKlZHZ33RZVWZjvdi3RHEkdfKw3mtWAxkx9HIsqJM6syIa8quO50haIopaLhYRB1BFBkzZsxAa2srxo0bh7KyMlRW2v+t2bVrl6d1PQmdffbZB9u3b8ewYcOw33774W9/+xsOO+wwrF69GuXl5c4LFCnMfcUja+ApCypm15yadVrwsTg8JllViuvSruQqDGJ1ZDE4fIaVFl7IBBmAnCt0lhunTCyRPLSF8I1JYLAbcRCEkMiVdYOsJgTRJbj33ntz0jvTk9A588wzsWzZMhx11FG4/PLLcd555+HXv/41tmzZgtmzZwd9xpzTFI2jsSNuWW2+zAgblmnFZ2JVlpZgF9L3ZbVz+Jo5PHzgsYkVyIIFHkPejZzHll7Ow67LhJCm6rHbejfWeN6ak2tcpnsrkYy1ekYpppg26vRilfFsyTHNbDJtcSBraOlmH5OqxEFmYvkdSxBEwbjgggtysq4noXP77bdbX5977rkYNmwYVq1ahZEjR2LatGmBHS5fdCRSqMjE6Xyyu02ZSg6YFRRkYkfW2Vzsh6XFS1p4QH2wsooDmqJyVeUDA1eVUxViHuY6cjOHP0fBg4llriev6+TDKpKvfQiiC9AWT6LUsICtjLiPuYUiHA5j+/btGDhwoO36l19+iYEDB0pLwpgQSB2diRMnYuLEiUEsVRD4rKtKru0DDx+jUxkJZyojJ7S1cSoiJZYbS1oNWdbmQRQ3is7ksvuO8C0dIuWdVZIVAca2flWKe9prrHgfL3yCtvLIUr2FayqhouvyLY7XVivm94xUpD9v0E1DTXFb0ZcXF25ib8S5fjAtzkcQRLcmlUpJr0ejUZSVuQjNEDAWOosXL8aUKVMQiUSwePFi7djTTjvN84EKQVk4HYzcnuh0VamsOpWRMCpLS2xByQw+PodZdZRuLD42R+wzpUgnt913a+kR9wD8t5kAsgOP84VJ64YMqkrDYrCvVxeTzbWVb4HjtXKwH/zE43iptOx2fRJLBNGluO+++wAAoVAIjzzyCHr37m3dSyQSWLlyJQ466CDP6xsLnTPOOAMNDQ0YOHCgttdVKBTybF4qFCzrigkdHbLKyTzy4oCdbSJsyBpsinAxOr7dUjKxw+G5/5QocHKdaaXCg8hQuaR4y46smSYr9CcVR0G4rbw2oTSxyohiw42LS5Y67oVcCBJyexFEl+Tee+8FkLboLFy4EOFw5zO0rKwMI0aMwMKFCz2vbyx0ksmk9OvuQFM0bhM5sjicytIStMWT6VcsgcbWGPpWRayA5N2tnSJEtOJkCRzemiPrNM4ESaaysWXNYVYcU8Hj1H3cD7pYHFFwuBU4PqwivMAwjasxrXVj61OVCYJWBSr7Tg1340ZyK4rYeFFsONXRCcIak0uLU74sObm2ShFED2Pz5s0AgBNOOAHPPPMM+vXrF+j6PbLXlQhr6gnYRU6zpiWEU1Cx2PTTQkwnZ6JF4YpSZlk5iR1R5BgKm6zaODJ44SI20VT1nMqTS0fWFiGoVG2dcHEdsBwUOsuIm8aWMitPVxckBEF0KZYvX56TdV0LnWQyicceewzPPPMM/vnPfyIUCmHffffFd7/7XZx//vk5yYEPGllTz/ZEEv9qjiqDkK33nHWmsTVbbDCBkyVyRCuOTqjwlY/dxOOIoshA3LDsKuMsKyfrTAFFDo+JS8oEpbiRuKdkLi7XuBEYYrdvmaXBpCGmGMAsO4PXLubdUdR0x89EEEVAIpHAY489hmXLlmHnzp1Z3qNXXnnF07quhE4qlcJpp52GF154AePGjcOYMWOQSqWwceNGXHDBBXjmmWfw3HPPeTpIPqmrq0NdXR2amppQU1MDAOhbVgr0Bj5qdPePGC9mZOnklttKLBAoZlYxQSNmWTmJHBbnw4scl24pPrtKK3a8xNjILDx5FD5OgkO8n/dqxDxuAotlQkRWldjkoSwTSbKzEASRN9rjCYRNaq0pSGg8EsXKlVdeicceewxTp07FIYccEpjhxJXQeeyxx7By5UosW7YMJ5xwgu3eK6+8gjPOOAOPP/44ZsyYEcjh8kVZOITycAh9y0pRxaWX23pbZWJ0AD64ODuWhwUiy7KtbAhByKloSzreQxKH41QoMMuSo+hdJW3bIBljjEr4MHeWStCYNth0iRuBonM16cSOJ/eUlyJ+frqHux2j6u/k13pD4oggCBcsWrQIf/jDH3DqqacGuq6rpp6///3vcf3112eJHAA48cQTce211+KJJ54I7HD5oiORQjSRRHsiiQEVnRYUWYYVn0HFrDdM1FRESmwp5XwVZBtM5LDGnci0bxBEDmvpYKt4zMf06Kw9kfLOqscZjOJvZERb3RcCLK8yb4jJdw330j08gyhCvFYsdorFUe3NFxi04TXWReUukr1XXdM1kAyygSWJGoIgfFJWVob9998/8HVdCZ33338fp5xyivL+lClT8N577/k+VCFo7Ijjq2g86zqLyTGpiAywthBJW/yOYzVkt3VxeAuOm/5WhUImZHyKGgA56VxuahXKqXtLZgHiRYnKJeXGiuOnczalcRMEkQOuvvpq/PKXv1QWDvSKK6Gza9cuDBo0SHl/0KBB+Oqrr3wfKt/wlZFbOb8mXw2ZvUR0fa3Y19Z71pmcs+SoigNqXVVsniygORa1v8S1Tf7nLbqkyqvs1hzxvbV3e+f8fBQPNGj3kAtcW4KcvuduKgPzgke0yOjcT2ysaq4byHpDEN2SlStXYtq0aRgyZAhCoVBWzG1zczNmzZqFffbZB5WVlRg9enRWfZv29nbU1dVhwIAB6N27N84++2zs2LHDaP/XXnsNTzzxBPbbbz9MmzYNZ511lu3lFVdCJ5FIoLRUHdYTDocRj2dbRboSTNxUlZZg74wbq1LZxkEes5NlwYl3dAYhO1lgnFo+METB5BYTIaIaw66LlhmZhSUHVhcVMqGRi5RvXTsJJW4EjMl4giCIgGlpacG4ceNQX18vvT9nzhwsWbIEv/vd77Bx40ZcddVVmDVrlq1bwuzZs/GXv/wFTz/9NFasWIFt27YZi5S+ffvizDPPxHHHHYe99toLNTU1tpdXXGddXXDBBSgvL5fej0Z9FqErEKwFRGs8YaWXD+1djsp4Av9pV7ud+Awrab2cUrWoSUVbrCDjULgs2zrjVCeH9asC7FlXmm7kxuh6UzFBI17XCRqDZpteKUiGVKQCoaD21gkaEjsEQeSRKVOmYMqUKcr7b7zxBmbOnInjjz8eAHDJJZfgwQcfxNtvv43TTjsNu3fvxq9//Ws8+eSTOPHEEwEAv/nNbzBq1Ci8+eab+MY3vqHd/ze/+U1gn4XHlUVn5syZGDhwYJbKYq+BAwd2uYwrRnsinWU1oCKt/VrjCXzZnm2dYjVyAHnWlYWYTi4QUlh2sqw4otWGpZPzmVUGVp1UrC3bbSWz2LitkxOpyL7Gx97ksJO5U2CwbowSk+wjr6LNbVaSKo7GT3wNQRA9iqamJtvLj0Hi6KOPxuLFi/Hvf/8bqVQKy5cvx0cffYSTTz4ZALBmzRrEYjFMmjTJmnPQQQdh2LBhWLVqldEe8XgcL7/8Mh588EHs2bMHALBt2zY0Nzd7Prcri06u1FYx0BZPWllWVaUl+LI9jv+0x6xu5ZWRcJawcUwh50VOaRkQzfyghNRxhk3k8MHJfD2dBOcCY3E4QnYVYlF3BQBVTTnFqsf8dRWF6tqdwbOrykW/J0s8ZYKGpenoYkE+sa+UE6b1c7zQXQv5EUQ3oj2WRDjsvd1SIvO8Gjp0qO36/PnzcdNNN3la8/7778cll1yCffbZB6WlpSgpKcHDDz+Mb33rWwCAhoYGlJWVoW/fvrZ5gwYNQkNDg+P6n3/+OU455RRs2bIF0WgU3/72t9GnTx/ccccdiEajnvtdUQuIDJWlJfiyPS1AWuNJq/1DJRdvI1Y7Zh3KGdJ08kQHUJ7pxBous1c9hkMcDg8TR3xqOoNZdgTB4xh4rBI4TPyYxOgAnRYdUeSw9zkSPzKB4cmd5OQ+chAXyj1185wyp1TtG4JozUAihyB6DFu3bkV1dbX1XhV6YsL999+PN998E4sXL8bw4cOxcuVK1NXVYciQITYrjleuvPJKTJgwAe+99x4GDBhgXT/zzDNx8cUXe16XhI6A2AJCBp9VJYodNxiLHFM4645jzRwnl5KbrCleyIjxOAW28PjCxLUU9D2T/UmoEARhSHV1tU3oeKWtrQ3XX389nn32WUydOhUAMHbsWKxbtw533XUXJk2ahNraWnR0dKCxsdFm1dmxYwdqa2sd93j11VfxxhtvoKzMHtoxYsQI/Pvf//Z8dhI6AlVckUBV7ZyKSNgSO7sl/a4A2LOs2NdCF/IQ7GLHVgFZV1tHJ5CEIGTWx8oYlZXHWt8puyj7ftABw777SYnkypXjJFSomjBBEF2EWCyGWCyGkhJ7aG84HLZ6Uh1++OGIRCJYtmwZzj77bADApk2bsGXLFkycONFxj2QyaetByfjXv/6FPn36eD47CZ0MFeESW5sH5rpiMTptsYRjjA4vgLLgxUnGDSVadFJibZ14rHOeQ8NOJmbEuBzjLuQmeGzLwMfNBCFOcpJpJbqM2LVcCg8SNQRBFBHNzc345JNPrPebN2/GunXr0L9/fwwbNgzHHXccrrnmGlRWVmL48OFYsWIFHn/8cdxzzz0AgJqaGvzoRz/CnDlz0L9/f1RXV+Pyyy/HxIkTHTOuAODkk0/GggUL8NBDDwEAQqEQmpubMX/+fF9tIXqk0JF1L68Il6CqNIwv2+Nojids1hwmctqtVg/ZcTu2+B2rcWdZZ5FAwDllXBzDMqz495zAkQUcGwUh6wSO7p7olvKQNu6pBo0K03gVWaCxLvjYzbqqcapu4gRBEEXKO++8Y2vxNGfOHADpjOvHHnsMixYtwnXXXYfp06dj165dGD58OG655RZceuml1px7770XJSUlOPvssxGNRjF58mT87//+r9H+d999NyZPnozRo0ejvb0dP/zhD/Hxxx9jr732wu9//3vPnyuUCrrWcheCdS9/cfmHeOurBP7dEsWX7XFUlZZgZ3sMbfEkvmqNWSLGXvE4iajEytMeS9izrRId6s7kPDIRpHJROVQ8trmrZD2q3Mbf8OnjOYi5CdRCoxIVsq7gbgWQai/FWMvF1tL1qoUTBJFNKh5D6u0/YPfu3YHEvchgz6VRP12McEUvz+sk2luwcf5pOT1rLojH43jqqafw3nvvobm5GYcddhimT5+OykrvSRg90qIj0tyRwFfRONriSStGp3dpGLva0iKHTy1nf4oiJ0vgAJ0iR4eJlQewp5ErupMzpPVynGJvtHsrqh4XMyohI3vPcJN91dWtNJRiThBFTTSWQEnYW6ILACQ9JskUmtLSUkyfPh3Tp08PbE1XBQO7K2WZ+JzK0hIr66o5nrAETlumX5VovWHFAy2Rk8hUQxYrInNBxY59rGStHZioEf80ge9L5Scmx6Mlh3Xz1r0CRXQXqVxTsnYLfGyOaqy4j8byE8jny1VxQBI5BEEUGbfddhseffTRrOuPPvoo7rjjDs/rktCRwKeYd8bgyFtBaKsj8/CxNqZWHCBb1Php7aDcQ1H/xmlcBtUDvSDtGQiCIIguyYMPPoiDDjoo6/rBBx/suVggQK4rKXwwMrPqVERK0Lcqgh27Ox/eYtwOgM7qxzJKI/YqxwHBByCnYm3Zripd8T+GTOyoCgFKEIOM8y5y/FooctVQM8hzEQRBdGMaGhowePDgrOt77703tm/f7nldsugAKAuHrK/5tPI2IfhYDEZ2BR+vo7PohMs6Xxx83I0sZTyrl1W0tVPcGLqsbOKkK8bkOGEiGgzWsXppUb8pgiCIwBg6dChef/31rOuvv/46hgwZ4nldsuhw8CnllZEwvsoUA2QCpz2WdF0JOZXoQIj72kIsCCimkvNrCBYbllVl1MtKB7P6ZCw2XvpEFZV7KpcBtpnvdVrg5K4bO0EQRE/l4osvxlVXXYVYLGZ1P1+2bBn+53/+B1dffbXndUnoCPDNPQFIs614sooECtYaW7Vj/r7YkVy8BljNOQF5QUBpMUBdY04xe0pn6WFjhQe6WADQ6XrOEbOhxGBkt5WHHaoZp2JtCPXqZybwyO1EEARhzDXXXIMvv/wS//3f/42OjvTzsKKiAnPnzsV1113neV0SOhKYZaciUoJGVYsHL4guKxd1ckSMWjvw9XO8WCAyc5we6nxLhqIQO0BOrTvSujji/iRyCILwQXs8iRK3IRIcSYO+jcVGKBTCHXfcgXnz5mHjxo2orKzEyJEjfTUiBUjoAEinlzNYG4g2iXtqd6Z4IABpEcEsd5QsFocVDpR1IZcgChpXfatMYnM0mVSy7uDiGP5P8XpBCKK7twlU9ZggCCIn9O7dG0cccURg65HQAdCRsCvfNi4mhxGNJeTtHhiKSsepRAdCGVGTisfMUstZYUCTVHImZpj1xinDSnRfKdxT/J9uKJjIEbOmKFCYIAiiS9HS0oLbb78dy5Ytw86dO61moYzPPvvM07okdJCujNwWT6I1nkRbPMnF5diLBJZHwvJg5Hh2RpW0KKDQBsIE0YKT1d6BIWv1wFDVyXGRWeVk3SkIojWFt7Lk29JC4oogCMIX//Vf/4UVK1bg/PPPx+DBgxEKhZwnGUBCJ0NlaYmVWt6vKmJlXDFXFZAdiGxzWwkiJisIGVCLHN5yE+n0RarSyNOHkVhtvLR3kMBETdBdxwOlkC4jap9AEAQROC+++CL++te/4phjjgl0XRI6YDE6XGp5aQnaMq0dWEVk0bJjBSmL/a04mNhJiVlVijRypx5WFgEJGtOMKsBu0fEkevIhDAolPsiSQxAE4Zt+/fqhf//+ga9LBQORjtH5sj0tXNpiCct9xVLH22MJq4lneyyJ3a2xtMCJNquDicWgZN6aw/ez4oUN97Uy6Nht53EX2VZMzASePRW0AHHq/5Sr/lAEQRBEzrj55ptx4403orU1oP/MZyCLTga+5YOYccUEjuWqknUpz8AHH6fHamJyTK03qtibgFH1qypIyngxQ24rgiCIwLn77rvx6aefYtCgQRgxYgQiEXu7pLVr13pal4QO0hYdvkggYI+/YbE5lhsrbrZuSiwKGC6TWoC0VY5FkaPKquJ7W7msmdPlGnJ6LfqXLwq9P0EQXZ5oLImSEvMq/CJJHzV4CsUZZ5yRk3V7pNCpr69HfX09Egn5L5FYDdktVtsH3pqjisthc/y2dPAYt2MiaDyLnp6SiSSrzkxihyAIwhXz58/Pybo9UujU1dWhrq4OTU1NqKmpQTSRShcJzLR/4OveRmMJlGc6mGd1KmfEY1ltHlI+XFbaooCqdg7smgO8KyovVpue8sAnsUMQBBEIa9aswcaNGwEABx98MA499FBf6/VIoaOCdSxn9XOiQh0dAJ3xOaVcZePSCELgXFWyejmapp08WSLHqT4OEzuG7ipZ6njO6EkPe5nlqid9foIgCJ/s3LkT3//+9/H3v/8dffv2BQA0NjbihBNOwKJFi7D33nt7WpeyrjK0xpOozFhuGOVcanl7LJEtckxg2VVBwLuneIFTqE7ahSjM10WgAG6CIAh3XH755dizZw8+/PBD7Nq1C7t27cL69evR1NSEK664wvO6ZNHhsNfP6Wz30MjSyRns62iLfCGZ24oFImvcVq5TyiXtG0wx6WVlhKznE3+th1o1Uh3tPfJzEwRBeGXJkiV4+eWXMWrUKOva6NGjUV9fj5NPPtnzumTRAVAeTpeZbuO6vSoDkfkaOBy2tHKxQCD72iSd3Ale2Bh2F+fhx1KMTkDIOpf3hM9NEAQRIMlkMiulHAAikUhW3ys3kEWHY/tu+4M/K0ZHlhouc0sp0siz5sricZwQY3IiFTDtBsILG5k1x7OFR/wc7H1PsOZEKrODu3vC5yYIIqdEY0mESrw/3FNdML38xBNPxJVXXonf//73GDJkCADg3//+N2bPno2TTjrJ87pk0QEQTaQAAINrKqxaOW6wupPrxE3GmqPNqNIFHUssOVlfC7Aqx6p7Jtc692lzb6noSQ/73v0R6tW30KcgCILosvzqV79CU1MTRowYgf322w/77bcf9t13XzQ1NeH+++/3vC5ZdAA0dejr6ejQVkH2WiBQZdmRpZQb4KsOjpvrQI+1ZoQilUgBQEtjgU9CEATRNRk6dCjWrl2Ll19+Gf/4xz8AAKNGjcKkSZN8rUtCB0BFOG3YapNUQwaQDj7m08kzgoZZcGxiR4zLAWyxOaFIJVKxNrtlx02bB13wsSbV3HXtHJlYkYkYp/fdnVhbWuA0fwnEuBpFmXsEQRCEOaFQCN/+9rfx7W9/O7A1yXWVoTmekBYFVBYJ5LB1JxcDjp0CkJn1JqiO5EFAD2h3xNqQatxe3G0zCIIgipRXXnkFo0ePRlNTU9a93bt34+CDD8arr77qeX0SOgDaE3IXla12TlzItCqN2C05jEh5+s9YVCpysiw5PNFWY8HD4m9sD9dMLA+7xtdykcXrhMoqOseYxOCQAHJEFxdFEARBZLNgwQJcfPHFqK6uzrpXU1ODH//4x7jnnns8r09CB0BbImlLLbchFgdkcThiPI6m5QMTN9pAZBUSN5T4INWljOsK11kPZT5Lyk9vqp7Q14ogCIIIlPfeew+nnHKK8v7JJ5+MNWvWeF6fhA6AaCJpa/3AXk4ZWNosK2bZQWdcDvs6vamB5SZmFzA5sRSwon6iSCHRIsf0+0LWL4IgCCN27NghrZ/DKC0txX/+8x/P61MwsgZbfE4mCFls3onSSOd9HkVsjmXVEbOrxIwqTdCxrN6N7L1jGwK+EaXYlNKT9akHPNx7wmckCKLgtMcSCJU4x4iqSBnElxYLX/va17B+/Xrsv//+0vvvv/8+Bg8e7Hl9sugoqIiUpONyWGyOyjUVj3XeY3E5sajNomNDZ8lh9XJiehEDmPVS0lqBxAe2rJUDYQ5ZwAiCIDxx6qmnYt68eWhvz35etbW1Yf78+fjOd77jeX2y6AAoz6SXs47l7GsLFnQcj8mLA7KveXHDWXS0FZAdelW5KfhnBB+Pw957eUj30Ho5riDxSBAE4cgNN9yAZ555BgcccABmzZqFAw88EADwj3/8A/X19UgkEvjJT37ieX2y6CAdo8PIKhLo1MpBdj8WVQcg8/VyZCInYs+UAuzZUa5jdfj9g6x5Qw9vgiCIbsXKlSsxbdo0DBkyBKFQCM8995x1LxaLYe7cuRgzZgx69eqFIUOGYMaMGdi2bZttjV27dmH69Omorq5G37598aMf/QjNzc3afQcNGoQ33ngDhxxyCK677jqceeaZOPPMM3H99dfjkEMOwWuvvYZBgwZ5/lwkdDiima7l5WIQsuC60gYhc7AKyOxl4VQc0EP1Y/ValfK0cTE+RxxDrhj/UHNPgiC6EC0tLRg3bhzq6+uz7rW2tmLt2rWYN28e1q5di2eeeQabNm3CaaedZhs3ffp0fPjhh1i6dCmef/55rFy5Epdcconj3sOHD8cLL7yAL774Am+99RbefPNNfPHFF3jhhRew7777+vpc5LpC2nVVGQnbrDmOhQLjMbs1Rwg+5jOtgIxlh1VAdsq4inVacgLNthJdKfQQJgiCIDJMmTIFU6ZMkd6rqanB0qVLbdd+9atf4cgjj8SWLVswbNgwbNy4EUuWLMHq1asxYcIEAMD999+PU089FXfddZfVqFNHv379cMQRR/j/MBxk0QFQGS5BZWnnt6KxNdZZKBCwtXxwsubIauVoiwSKeOxnlb2OTxFDIsgTJkHiBEEQ+aKpqcn2ikYdqvW7YPfu3QiFQujbty8AYNWqVejbt68lcgBg0qRJKCkpwVtvvRXYvm7pkUKnvr4eo0ePtlRjY0cc25qiqIiUdFpySjsDkKVoBI8lbDKiJhSpdNfiwSFAGRCqGsvgXU+qGjl+CwTmE3ZW/lVkhHr1DdbtSBBEzyXR0Zn56+WVeUYNHToUNTU11uu2224L5Hjt7e2YO3cufvCDH1gVjRsaGjBw4EDbuNLSUvTv3x8NDQ2B7OuFHum6qqurQ11dHZqamlBTU4PdHXFHV1WKj9MxqJmjrILMRAx7IEYqjIRNIIgZV12FrnTe8irqYE4QRNGwdetWW2uF8nJF6RMXxGIxnHPOOUilUnjggQd8r5dreqTQ0cI6lcc7gGhL9n1NXE4W5VXOggeQih2nuBzHuB2TnlVdxT3VRc6ZamlEyBKyPosvEgRBBEB1dbW0h5RXmMj5/PPP8corr9jWrq2txc6dO23j4/E4du3ahdra2sDO4JYe6bpSYQUjiw08GaWRzpo6QFZRQKWoMSkSmAuK1MXTpYlUomSfQxDqK6/SSQ09CYLorjCR8/HHH+Pll1/GgAEDbPcnTpyIxsZGW1+qV155BclkEkcddVS+j2tBFh0RZs3hMEkn1xYF9ICszYMSr0X/CNeE+g8BKvogBCDVuF09kKw4BEF0MZqbm/HJJ59Y7zdv3ox169ahf//+GDx4ML773e9i7dq1eP7555FIJKy4m/79+6OsrAyjRo3CKaecgosvvhgLFy5ELBbDrFmz8P3vf98o4ypXkNAB0BZPWtacikgY7XHYMq0sNB3KbXgUOXx/KleWAVYrR1eJlyoZB0Jq17Z06YDmLwt9FIIgiEB55513cMIJJ1jv58yZAwCYOXMmbrrpJixevBgAMH78eNu85cuX4/jjjwcAPPHEE5g1axZOOukklJSU4Oyzz8Z9992Xl/OrIKEDoC0hBCLLLDg6kRMp7xQRPi05vlwfQVY+JuTE2pDc8Sl9bwmC6HYcf/zxSKVSyvu6e4z+/fvjySefDPJYviGhI9CZXp7uSh4qjSAlC0oGOoORY1G1wNHE3+Q1noMezMFB30uCIHJNogNI+HhEG1bw7wmQ0MkQjSXSIieu+OVIdHQGImcEjjL42BBXcTgEQRAEQbiGhA7SMToWpWW23lapBJdmnuiwpZSz/lVawaNIHWexOJ6DjsmqUDhM4p0ovZwgCKIoIKEjwiw6pZHOuJxwWZYZ0Nia47I+jvN6QtE/eojmF8puIwiC6FJQHR0eidsqxNfNAbJq5wDIjs9htXEM4nOUwocCiwmCIAjCN2TRAdAWT3UWC2SWG9515RTUJXYkF9xVopgxSiFX9afqalWNuxuymkUyKw/VNiIIgigKSOgAaIlzjTzF+BzmvnJq96CBj8Oh7tbdAGO3JYlRgiCIQkNCJ0NFJO3Fa8/oGZNqyADslhyDVg58ILIjzCoga8YpWngoZqeg8D/TVEc7FWgkCIIoEkjoZOjscxXrFDnxWGemFYvN8WHZYRiJHZmLyiluhx6shSHWhqwyWvSzIAjCD/E4UGJYjV81nwBAwcg22tsyD6d4TF0JmQ9G1lVBjlSkXwocs6+c4jtI5BQ3FJ9DEARRFJDQAet1lbCCjkPlvTpvillXphYdv13JSbh0HQQ3VaisAqFefRHq1a+AhyIIgiAAEjoAgPZ4wl4/h/+TEYsCsShSsTZ5DR2JqDGqmSOzzPDXyDLQNRB/TuVVWoseQRAEkR8oRgeZ+BxVthUXlGwqcHiMXFSyYGNrfbLsFD2yn1/zLmrvQRAEUQSQRUfAEjlc/RybFYfF5bA/2f/aHWJyLESLDdE9EERqqqOdfs4EQRBFAAkdRrisM9uKua0y7iqgs68VgOwgZF7gOIkdnSuKtwyQy6rrQCKHIAiiaCHXlQPMkqPsbcVcV0zgcK4sT93J+Xo4VIula0BVkAmCCJh0Vf6wv/kEABI6clSp5bKeVsjUxXG7hy4uR3edIAiCIAhjyHWFzqrIoXBZZ3wOoK+ZE2u3rDjMcsNbb4wtOdIA5zaK5SEIgiCIACChI0OsnaPDT1FAGSRuiHxD1kOCILoxJHTQmV5u+TTZn7GoPQhZJFMUkG/pwCw7yhYPJGS6H5HKdJHArtiwNVKJUK++JHYIgui2kNABsLtNaPkQLjOugOy6SSfRLQkN/DpC/b5W6GN4w08Fb4IgiCKnRwqd+vp6jB49GkcccYT9Bi92IuXqKsiALT6H4Sh46H/N3QPh5xjq1Rco7wVU9CnMefwQa0Oq5SsS4QRBdFt6pNCpq6vDhg0bsHr1agBAlHUuB2xuKynsf78OLR+oKm7PIdXSCERbgPY9XdN9RRAE0Y2h9HKeRIda4JRXdWZeiSInUmFm/qf/NXcfhBpHqZ2fFfAwBEF0O+IxoMTHI1pVJqUHQkIHQHmkJB2IzLKtMs07AaTFDS9yOKz4HCeRQwKn20MWPIIgiOKEhI4ORYFAIPvBRg+6HgDFWBEEQXQ5SOiAi9ERXVflVZkBrVlWG1mRQClkzelW8GUELLgWENZ9dp0gCIIoKD0yGFnEFowsg6uCrB6jqHBMdDt04pYsewRBEMUFWXQAtMcS6cAtp9o5pkHHAImc7kisLW2pyXwtw2rkSj9/giCIooAsOjyZ3la2QGQencjh4zfoIdd90fxsmduKUswJgiCKB7LoMFQt7RUp5eSi6MGIYoe1UeB/R4T0c4IgCFckY0Ai7G8+AYCEThquSGAq1ma35LD4nMxDjAQOYYP1ueo9IP2707yLXFcEQRBFBLmuALU1h3oAEU5kBI2yVQhBEARRUEjoCIQilZ1p5Qy3gofqrXRvhJ9vqqMdaN6VfinGEARBEIWBhA4AxONWxpX1P/PyKueUcpFYW+eL6J4wV1WvfsohFIxMEERX5N///jfOO+88DBgwAJWVlRgzZgzeeecd634qlcKNN96IwYMHo7KyEpMmTcLHH39cwBObQUIng7ZTORsji88hYdPzYAKYrDYEQXQTvvrqKxxzzDGIRCJ48cUXsWHDBtx9993o16/zP3V33nkn7rvvPixcuBBvvfUWevXqhcmTJ6O9vbjDPCgYWYWkt5VVI4WHPexMxQ5l43RtYm1AjPs9yFh4XNVYIgiCyBNNTU229+Xl5SgvL88ad8cdd2Do0KH4zW9+Y13bd999ra9TqRQWLFiAG264AaeffjoA4PHHH8egQYPw3HPP4fvf/36OPoF/yKIjg7V8UD24SKj0aFItX5kNjFSS1YcgCG/Eov5fAIYOHYqamhrrddttt0m3W7x4MSZMmIDvfe97GDhwIA499FA8/PDD1v3NmzejoaEBkyZNsq7V1NTgqKOOwqpVq3L7vfAJWXREJH2tAIe0cjfCh0RS9yDT34pZ+UJAp0srlk4vD/UdDABINdLPnCCIwrB161ZUV1db72XWHAD47LPP8MADD2DOnDm4/vrrsXr1alxxxRUoKyvDzJkz0dDQAAAYNGiQbd6gQYOse8UKCZ0MoUhlOkbHTWFAEi09HqtmDgtAZhl7kUpyZREEUXCqq6ttQkdFMpnEhAkTcOuttwIADj30UKxfvx4LFy7EzJkzc33MnEKuKwApvo5OpML6n3mWyGGBx8wdwVwT5J7omYi/D0zklFelLT0tX5m7uQiCIArI4MGDMXr0aNu1UaNGYcuWLQCA2tpaAMCOHTtsY3bs2GHdK1ZI6PAo2j1kwSw5lHGVH3hBWaziMlKR/v3JvKiCNkEQXYljjjkGmzZtsl376KOPMHz4cADpwOTa2losW7bMut/U1IS33noLEydOzOtZ3UKuqwxGlW1VjTtJ7OSGYhMzJpC7iiCILsjs2bNx9NFH49Zbb8U555yDt99+Gw899BAeeughAEAoFMJVV12Fn//85xg5ciT23XdfzJs3D0OGDMEZZ5xR2MM7QEKHp7wq/T/yjOsqBE2MDqWJ556u8P21ygtI3JwEQRBdhCOOOALPPvssrrvuOvzsZz/DvvvuiwULFmD69OnWmP/5n/9BS0sLLrnkEjQ2NuLYY4/FkiVLUFFR3EVSQ6lUKlXoQxSKpqYm1NTUIHzeAqBllz3jKlMXxRI6fCwGgx5mPQ5WEdnWuDPzexHq1TctlqmxJ0F0S1LxGFJv/wG7d+82CvD1gvVcOnM+Qm6r83OkYu1IPPvTnJ61q0AWHQDoyC4OaBM5WffoAdYjiVTarX3c70GorCIdhBypRCpSAVCMDkEQPkjF2gF4t0OkMnV0CBI6aUoiQHSXzf2gbPdA9GwyvyP6sgOdVZPT7+n3hiAIolCQ0CEIF9gEjiROyyionSAIgsgbJHQAIBlzHkPBx4VFloGV75+HEJNjfR1rQwpAiJUnYK4r+n0hCIIoOFRHh0FpwYRX+Mwr+j0iCIIoKsiiA1jNzxhU7K0IKXLrSKisuNMrCYIgeipk0SEIF4R69UPJoP3SqeRUGZsgCKLoIYsOI1KBVEuj+j490AggHX9T0SedXs79vqQ62jutOuS+IgjCL9FWIJnwPp/Syy1I6BCEG2LtQPsepJq/lBeRzBAqq7A6mlNjT4IgiMJBQoeD/Y/cFqNDlhyCI9XRjtSOT7Ou22J0MlW1CYIgiMJDQoehqoRMaeUED7PiqH4nuJLtFNROEARReEjoIFNqmx5KxYXOJZShUC6hUFlFujC7kwAmgUwQBFFwSOgA6aAvIv8YiBn7ePv1UK9+eRc7oV79gEgFQpEKpFqEmz4a8BEEQRC5gYROhlBZBfW3yjfi9zYjfPifg030CHEvhXANpTra0x2FY+2WGyudal74sxEEQRDZ9EihU19fj/r6eiQSDql7PUXkFEsckuQMWb2jFFagvMLiuZjIKa+yrlMgMkEQRHHRI4VOXV0d6urq0NTUhJqaGvkg/gFbLEIgFxSDcHAD12+qZNB+SMXapFlQjp3Dvf5MM32tAKBk0H5ApBypr7Z1ihte5FD3coIgvBKPAta/Nl7mdwR2lK4OVUaWIYocougwbrkg+/kF8TNlvyPhMoR6D/C/HkEQBJETeqRFR4UVpyP7n3h3tep00c/EgpCVsTC6zxXQZ0599W+E+n0N6N0fIWbZYVCMDkEQRFFAQodDWijQqW4KkV84a0yqoz3/Pxe2f6TS2j8Uj6XLrbNYHQChWDtVRCYIgigCyHWVQZslE+TDNFJZvO4w/lzFeMYiOpNVRXvXNqS++GfaulPVF6GqvuaLFNHnIQiC6K6QRUeGV2FjEnxquja/Vr4sSm73MA22FR/oXj8LF4js+0w+CJVVcLV0GtMByL37A+W9EAqXAbFoOoTQ6edGVkKCIIicQ0LHCfaw4h9aTg8w2X3xmrgeUDg3mRdxIFp/vM71QyFTzzNuqhCXZRUKlwGlkcxZKoorTocywAiC6KGQ0FEhc+OwB7rqYaETN+I9pzX4+3yXbL/WJn49bq2stga6tGzxTOy67jPlQoSo9jS1MHkWdhXpatqZejqWG6s1EyCdWTdUVpFdB8jtXkEJExI4BNGlSHW0A8mk9/mUXm5BQkdGscVO+D1PUJ/HTQyPKCacLGBBkK+HuawgYLQVqWgrQr0HINTva0g1fBzAPiROCIIg/EJCh8P6n3lHe7YVRWU9kP3v240IMB3rNN7JLSaOEyxGVlkqP64zr1anoIO9VWs6WeScrFL8+pm+ViF+THkVQjW1QHkvoN8QJP+1QX0+1Rl111WQWyo4VG5ngL6/XRHKmCVAQidNPJp9zSQmRxQR3DhLNAHqdTTzpfedcBqvi2nhRZTuH3a34swJg7Gq4oC29HIv/6C5EZwGhKr6IhVtQag0kk43L2boASDHiys0COjnkRvoe0qAhI4NlmKeVTgQMI+biFTKH8yiiODgx1vCSGaJ8SsgdA921dr8HL/xJhK0FY5l3cD5WjXNu9IxMJrvrRFO1ZOlDz++1UNF+lzhMgBAqqURqeYvO9cR461MzuLme22yps7C5QQ9hIPF78/D7V5AYX9+9PtDFBgSOgBQWg50dP5FdOw87eDiyOpOwj00be4xAeuazPVkaEVhIs0mnsS9NGvZzuehro4lEhXCLEvYiGKGFzJZYqzc9jYFqDOb+Gw5wFyomWTM8efOCJ5QVV8gkQn+E605Tu402T3d75hOsMpcljKrV5BCSjxbvh7iXZVcWI28Bubng2I4A9GjoYKBQDq2oqzC9hDWWRrSdVQ6hYVtrAerQqqj3VlcGWDc/8nlmlmfMah/uHyIHGY9EbGd1cSV5yaDjlnrNOf27LLKdQC8k8UuH3sRBEEUALLoAAhV1QDJRFY2DW8d4d1a/L2stRQWG50lR4rECqSaL1vbzdlU2AVcpi5MxoogE1U2waZ42Nn2Fs/R0tg5TrY/T3kVUru22S7p3WAuTPgKi4ld7KXPnupoT7vQ+HPoRIVT9pksqFzlRtO510zKGfDWQi/xTW4sCF5S+WWf1SR7z+3PWHbddB0ve+diPllNuh+xdiCV8D4/HgvuLF0cEjpA+n/hsU4hwz+MtYGwHCrhI6Ja361Fx8l6w68r29Mm3FiFXx0xiYCJVIDVkpHtK+7lFlNR5AnZg1pWG0h2nTuf9XOItqYtg36tGYKwynKDOs0TXVduXGLC/p7daboz5fOB7Ec8kHAgiG4DCR0AqeZdgCKuxSr3j06LRZYrJ4PM8qOyrIhxNDpBYCIUssRGRoTwFijpWTOBtKFYtvtMta/qswMAn3btJHb8utpk51WuKQtadhFgzmKvQmUVWZa/7PFpN5v0d8Dlw9f2vVPMyYqrMik14FUEsPVUliWZFUoc44Sp1Uo3z3QPmZj1EpPkVxhRUDhB5AwSOgBCvfsD0RabVQeATQgAQs0UICsglZ9rW9/wmsk9di6ZUHKaI46zWSMyrimVy01cBwDEysD8dZFcxA+ZkJVBJz44Hdw30gw87p6NaGvnuPIqhHoPAACkdnyavSbb3w0ODzrdWW3B6pLrjvvKzhFE3I+Te05lVTIRJW7chE7jdfO8WsHc7OFnvVxTjGdiFPPZiLxBwchA+n/g5VXphz33Uj6cxXgRfh6H/X/ykrWEOVJLkWSeyqKkg83JsqwwUZIJyDZZJ+s9/zn42BXeyqN6+UAn9GT3sgKrZdYP1V69+iLUq6/980AQw2IwNWfZkQZ1A53WANN/jL24xjiR43WuLwwzBqXvPWT+afeRWZ90Y1TrBI3sbCb3Ck3QZzH5/hM5pb6+HiNGjEBFRQWOOuoovP3224U+km/IogMAZVUIIYRUtNV+3elBzLpWA0DzLuuyzHqSBWcpstZi1zJ9lGxjRSsJfzbhXqqjPdv6lEFmoQlF5ILBOLZGZcGRWLxc4/Az4D+nzSXIuRtlAsNW0FGDtV4m/ibV/KXUgscLG8Si6fo+lnvE/vOTlhFQ7K383JzV0W5l67R2iK4z0/UsdA8clVVH2J8vNWCzZLkRE26tTorAa+szisLSTcySKgBbY0VzxI2wU1mTumoQtLh/EKUM+HWd9iNsPPXUU5gzZw4WLlyIo446CgsWLMDkyZOxadMmDBw4sNDH8wxZdHiYu0H43zl4AcQ/ePmvmeBB5n/wvfqmr/GWC1MrBm9dMsGjZSQrmNbnerq5rlLodRYf9rPhfz4c1udxIa506fNSsSr+DohnqeiT/jPaav++ytD8D1ZrCdKdr5B4cYV5wcQKxr63MotREJYD0R2qi1uSfR0khbaCePl++onjMoUEjSvuueceXHzxxbjwwgsxevRoLFy4EFVVVXj00UcLfTRf9GiLTiqV/v98qmUXUrEoQpEKpNIhp0jF2mGriRKPpgsLsmvxjnRPI1vdFM6+UBJGCCGgvFc62NlGCGhryp4biwIl4fTbrHosIXurinhH+jyy9yUljp1rrWwsti+/RiwKxDuQyrxUqeTyh2zIvk5mXXYe1XoAOs8vfnZVnZrMOaVrxKOdlozMGOnniHcAJRm9X1KSnhOPAaGwdQ0A0NiAlPj9Zp+3JJz+/amqAcqqgGQciFQg1fyVZLyiqzC/J6PE/v8Q9r1LdbTbPxM7d2adUK/+QDyKZGtT52RmMWSnFix3njody87MrW3tHwpbvwNSy1HmbMYZeqFwtsWTnZ+/LpxN+hnF87c1da7JvxfJ7Gn7PHw6b2Zd6zOxe6FwdtpvPCYX9SqxzlsI+fPz6+rW4y2tXlKQZZZadg7xDDrLtHj2UDj7Z6tDd3bxHtubfa9159SQSqTnsWdHTknEzLMuFfMBoKnJ/jtcXl6O8vLyrOEdHR1Ys2YNrrvuOutaSUkJJk2ahFWrVvk5ScHp0ULnyy/TZfqTz/6swCcpDKZ/iVTjvP4lzMM/Ea721Z2nUGdV7S1eMxljunZQeP1+FvJ77RVH12deTkHkmz179qCmpiYna5eVlaG2thYNa571vVbv3r0xdOhQ27X58+fjpptuyhr7xRdfIJFIYNCgQbbrgwYNwj/+8Q/fZykkPVro9O+fdjdt2bIlZ7+0+aSpqQlDhw7F1q1bUV1dXejj+II+S/HSnT5Pd/osQPf6PMX4WVKpFPbs2YMhQ4bkbI+Kigps3rwZHR0erKwCqVQKoZA9YlNmzenu9GihU5JxDdTU1BTNX6QgqK6u7jafhz5L8dKdPk93+ixA9/o8xfZZ8vGf4oqKClRU5Df+bq+99kI4HMaOHTts13fs2IHa2tq8niVoKBiZIAiCIHo4ZWVlOPzww7Fs2TLrWjKZxLJlyzBx4sQCnsw/PdqiQxAEQRBEmjlz5mDmzJmYMGECjjzySCxYsAAtLS248MILC300X/RooVNeXo758+d3G59ld/o89FmKl+70ebrTZwG61+fpTp+lq3DuuefiP//5D2688UY0NDRg/PjxWLJkSVaAclcjlMpLnhxBEARBEET+oRgdgiAIgiC6LSR0CIIgCILotpDQIQiCIAii20JChyAIgiCIbkuPFjrdoR39bbfdhiOOOAJ9+vTBwIEDccYZZ2DTpk2FPlYg3H777QiFQrjqqqsKfRTP/Pvf/8Z5552HAQMGoLKyEmPGjME777xT6GO5JpFIYN68edh3331RWVmJ/fbbDzfffHN+ev4EwMqVKzFt2jQMGTIEoVAIzz33nO1+KpXCjTfeiMGDB6OyshKTJk3Cxx9/XJjDOqD7LLFYDHPnzsWYMWPQq1cvDBkyBDNmzMC2bdsKd2AHnH42PJdeeilCoRAWLFiQt/MRXZ8eK3RYO/r58+dj7dq1GDduHCZPnoydO3cW+miuWLFiBerq6vDmm29i6dKliMViOPnkk9HS0lLoo/li9erVePDBBzF27NhCH8UzX331FY455hhEIhG8+OKL2LBhA+6++27069ev0EdzzR133IEHHngAv/rVr7Bx40bccccduPPOO3H//fcX+mhGtLS0YNy4caivr5fev/POO3Hfffdh4cKFeOutt9CrVy9MnjwZ7e2GDSbziO6ztLa2Yu3atZg3bx7Wrl2LZ555Bps2bcJpp51WgJOa4fSzYTz77LN48803c9p+geimpHooRx55ZKqurs56n0gkUkOGDEnddtttBTyVf3bu3JkCkFqxYkWhj+KZPXv2pEaOHJlaunRp6rjjjktdeeWVhT6SJ+bOnZs69thjC32MQJg6dWrqoosusl0766yzUtOnTy/QibwDIPXss89a75PJZKq2tjb1i1/8wrrW2NiYKi8vT/3+978vwAnNET+LjLfffjsFIPX555/n51A+UH2ef/3rX6mvfe1rqfXr16eGDx+euvfee/N+NqLr0iMtOqwd/aRJk6xr3aUd/e7duwF0NiztitTV1WHq1Km2n09XZPHixZgwYQK+973vYeDAgTj00EPx8MMPF/pYnjj66KOxbNkyfPTRRwCA9957D6+99hqmTJlS4JP5Z/PmzWhoaLD9vtXU1OCoo47q8v8eAOl/E0KhEPr27Vvoo3gimUzi/PPPxzXXXIODDz640MchuiA9sjJyd21Hn0wmcdVVV+GYY47BIYccUujjeGLRokVYu3YtVq9eXeij+Oazzz7DAw88gDlz5uD666/H6tWrccUVV6CsrAwzZ84s9PFcce2116KpqQkHHXQQwuEwEokEbrnlFkyfPr3QR/NNQ0MDAEj/PWD3uirt7e2YO3cufvCDHxRVY0w33HHHHSgtLcUVV1xR6KMQXZQeKXS6K3V1dVi/fj1ee+21Qh/FE1u3bsWVV16JpUuX5r1zby5IJpOYMGECbr31VgDAoYceivXr12PhwoVdTuj84Q9/wBNPPIEnn3wSBx98MNatW4errroKQ4YM6XKfpacQi8VwzjnnIJVK4YEHHij0cTyxZs0a/PKXv8TatWsRCoUKfRyii9IjXVfdsR39rFmz8Pzzz2P58uXYZ599Cn0cT6xZswY7d+7EYYcdhtLSUpSWlmLFihW47777UFpaikQiUegjumLw4MEYPXq07dqoUaOwZcuWAp3IO9dccw2uvfZafP/738eYMWNw/vnnY/bs2bjtttsKfTTfsL/z3enfAyZyPv/8cyxdurTLWnNeffVV7Ny5E8OGDbP+Tfj8889x9dVXY8SIEYU+HtFF6JFCpzu1o0+lUpg1axaeffZZvPLKK9h3330LfSTPnHTSSfjggw+wbt066zVhwgRMnz4d69atQzgcLvQRXXHMMcdkpfp/9NFHGD58eIFO5J3W1laUlNj/uQiHw0gmkwU6UXDsu+++qK2ttf170NTUhLfeeqvL/XsAdIqcjz/+GC+//DIGDBhQ6CN55vzzz8f7779v+zdhyJAhuOaaa/DSSy8V+nhEF6HHuq66Szv6uro6PPnkk/jzn/+MPn36WDEFNTU1qKysLPDp3NGnT5+s2KJevXphwIABXTLmaPbs2Tj66KNx66234pxzzsHbb7+Nhx56CA899FChj+aaadOm4ZZbbsGwYcNw8MEH491338U999yDiy66qNBHM6K5uRmffPKJ9X7z5s1Yt24d+vfvj2HDhuGqq67Cz3/+c4wcORL77rsv5s2bhyFDhuCMM84o3KEV6D7L4MGD8d3vfhdr167F888/j0QiYf2b0L9/f5SVlRXq2EqcfjaiUItEIqitrcWBBx6Y76MSXZVCp30Vkvvvvz81bNiwVFlZWerII49Mvfnmm4U+kmsASF+/+c1vCn20QOjK6eWpVCr1l7/8JXXIIYekysvLUwcddFDqoYceKvSRPNHU1JS68sorU8OGDUtVVFSkvv71r6d+8pOfpKLRaKGPZsTy5culf09mzpyZSqXSKebz5s1LDRo0KFVeXp466aSTUps2bSrsoRXoPsvmzZuV/yYsX7680EeX4vSzEaH0csItoVSqi5Q2JQiCIAiCcEmPjNEhCIIgCKJnQEKHIAiCIIhuCwkdgiAIgiC6LSR0CIIgCILotpDQIQiCIAii20JChyAIgiCIbgsJHYIgCIIgui0kdAiCIAiC6LaQ0CGIHk4oFMJzzz0X6JrHH388rrrqqkDXJAiC8AIJHYIoEBdccAFCoVDW65RTTin00ZT885//lJ6Zfz322GN45plncPPNNxf6uARBED23qSdBFAOnnHIKfvOb39iulZeXF+g0zgwdOhTbt2+33t91111YsmQJXn75ZetaV2woSxBE94UsOgRRQMrLy1FbW2t79evXz7ofCoXwwAMPYMqUKaisrMTXv/51/PGPf7St8cEHH+DEE09EZWUlBgwYgEsuuQTNzc22MY8++igOPvhglJeXY/DgwZg1a5bt/hdffIEzzzwTVVVVGDlyJBYvXiw9bzgctp21d+/eKC0ttV2rrKzMcl2NGDECP//5zzFjxgz07t0bw4cPx+LFi/Gf//wHp59+Onr37o2xY8finXfese332muv4Zvf/CYqKysxdOhQXHHFFWhpafHyrSYIoodCQocgipx58+bh7LPPxnvvvYfp06fj+9//PjZu3AgAaGlpweTJk9GvXz+sXr0aTz/9NF5++WWbkHnggQdQV1eHSy65BB988AEWL16M/fff37bHT3/6U5xzzjl4//33ceqpp2L69OnYtWtXoJ/j3nvvxTHHHIN3330XU6dOxfnnn48ZM2bgvPPOw9q1a7HffvthxowZYH2GP/30U5xyyik4++yz8f777+Opp57Ca6+9liXSCIIgtBS4ezpB9FhmzpyZCofDqV69etlet9xyizUGQOrSSy+1zTvqqKNSl112WSqVSqUeeuihVL9+/VLNzc3W/b/+9a+pkpKSVENDQyqVSqWGDBmS+slPfqI8B4DUDTfcYL1vbm5OAUi9+OKLjp9h/vz5qXHjxmVdP+6441JXXnml9X748OGp8847z3q/ffv2FIDUvHnzrGurVq1KAUht3749lUqlUj/60Y9Sl1xyiW3dV199NVVSUpJqa2tzPBtBEEQqlUpRjA5BFJATTjgBDzzwgO1a//79be8nTpyY9X7dunUAgI0bN2LcuHHo1auXdf+YY45BMpnEpk2bEAqFsG3bNpx00knac4wdO9b6ulevXqiursbOnTu9fCSjPQYNGgQAGDNmTNa1nTt3ora2Fu+99x7ef/99PPHEE9aYVCqFZDKJzZs3Y9SoUYGejyCI7gkJHYIoIL169cpyIwWJaVBwJBKxvQ+FQkgmk4Gehd8jFAopr7F9m5ub8eMf/xhXXHFF1lrDhg0L9GwEQXRfKEaHIIqcN998M+s9s2aMGjUK7733ni1A9/XXX0dJSQkOPPBA9OnTByNGjMCyZcvyeuYgOOyww7Bhwwbsv//+Wa+ysrJCH48giC4CCR2CKCDRaBQNDQ221xdffGEb8/TTT+PRRx/FRx99hPnz5+Ptt9+2AnKnT5+OiooKzJw5E+vXr8fy5ctx+eWX4/zzz7dcQTfddBPuvvtu3Hffffj444+xdu1a3H///Xn/rG6ZO3cu3njjDcyaNQvr1q3Dxx9/jD//+c8UjEwQhCvIdUUQBWTJkiUYPHiw7dqBBx6If/zjH9b7n/70p1i0aBH++7//G4MHD8bvf/97jB49GgBQVVWFl156CVdeeSWOOOIIVFVV4eyzz8Y999xjzZ85cyba29tx77334v/9v/+HvfbaC9/97nfz8wF9MHbsWKxYsQI/+clP8M1vfhOpVAr77bcfzj333EIfjSCILkQolcrkchIEUXSEQiE8++yzOOOMMwp9FIIgiC4Jua4IgiAIgui2kNAhCIIgCKLbQjE6BFHEkGeZIAjCH2TRIQiCIAii20JChyAIgiCIbgsJHYIgCIIgui0kdAiCIAiC6LaQ0CEIgiAIottCQocgCIIgiG4LCR2CIAiCILotJHQIgiAIgui2/H9Jo+z/GqIMGwAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -415,7 +415,7 @@
     "plt.contourf(\n",
     "    experiment_time,\n",
     "    stream_smps_2d.header_float,\n",
-    "    stream_smps_2d.data,\n",
+    "    stream_smps_2d.data.T,\n",
     "    cmap=plt.cm.PuBu_r, levels=50)\n",
     "plt.yscale('log')\n",
     "ax.set_xlabel('Experiment time (hours)')\n",
diff --git a/docs/examples/activity_part1.ipynb b/docs/examples/equilibria/activity_part1.ipynb
similarity index 63%
rename from docs/examples/activity_part1.ipynb
rename to docs/examples/equilibria/activity_part1.ipynb
index 1d452c953..97d553427 100644
--- a/docs/examples/activity_part1.ipynb
+++ b/docs/examples/equilibria/activity_part1.ipynb
@@ -4,13 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Activity Example\n",
-    " This is an example of how to use the activity module. It currently can\n",
-    " calculate the activity of water and organic compounds in a mixture. It can\n",
-    " also calculate the phase separation of the binary mixture.\n",
+    "# Activity Example\n",
     "\n",
-    " This is an implementation of the Binary Activity Theory (BAT) model\n",
-    " developed in Gorkowski, K., Preston, T. C., & Zuend, A. (2019).\n",
+    "This notebook demonstrates the Binary Activity Theory (BAT) model application, crucial for calculating the activity of water and organic compounds in mixtures and understanding phase separation. This model, as detailed in Gorkowski, K., Preston, T. C., & Zuend, A. (2019), provides critical insights into aerosol particle behavior, essential in environmental and climate change research.\n",
+    "\n",
+    " Reference: Gorkowski, K., Preston, T. C., & Zuend, A. (2019).\n",
     " Relative-humidity-dependent organic aerosol thermodynamics Via an efficient\n",
     " reduced-complexity model. Atmospheric Chemistry and Physics\n",
     " https://doi.org/10.5194/acp-19-13383-2019"
@@ -22,8 +20,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
+    "import numpy as np  # For numerical operations\n",
+    "import matplotlib.pyplot as plt  # For plotting graphs\n",
+    "# Specific functions from the particula package for activity calculations\n",
     "from particula.activity import binary_activity, phase_separation, species_density"
    ]
   },
@@ -33,17 +32,7 @@
    "source": [
     "## Activity Calculation\n",
     "\n",
-    "Define the parameters. The activity module will calculate the activity of\n",
-    "water and organic compounds in a mixture. It can also calculate the phase\n",
-    "separation of the binary mixture.\n",
-    "\n",
-    "The activity module requires the following parameters:\n",
-    "organic mole fraction, density, molecular weight ratio \n",
-    "[water/organic], and the density of the organic compound.\n",
-    "\n",
-    "These are used by the `particula.activity.activity_coefficient` function to\n",
-    "calculate the water activity, the water activity coefficient, organic\n",
-    "activity, and the organic activity coefficient.\n"
+    "Define the parameters required by the activity module to calculate the activity of water and organic compounds in a mixture, as well as phase separation. These parameters include organic mole fraction, density, molecular weight ratio [water/organic], and the density of the organic compound. Using these parameters helps in accurately modeling the behavior of aerosol particles in various environmental conditions.\n"
    ]
   },
   {
@@ -52,21 +41,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Define a range of organic mole fractions for the calculation\n",
     "organic_mole_fraction = np.linspace(0.001, 1, 1000)\n",
     "\n",
-    "\n",
-    "oxygen2carbon = 0.225\n",
-    "molar_mass_ratio = 18.016 / 100\n",
+    "# Define other necessary parameters\n",
+    "oxygen2carbon = 0.225  # Oxygen to carbon ratio\n",
+    "molar_mass_ratio = 18.016 / 100  # Water to organic molecular weight ratio\n",
     "density = species_density.organic_density_estimate(\n",
-    "    18.016 / molar_mass_ratio, oxygen2carbon)\n",
+    "    18.016 / molar_mass_ratio,\n",
+    "    oxygen2carbon)  # Estimate of organic compound density\n",
     "\n",
+    "# Calculate activity coefficients using the binary_activity function\n",
     "activity_water, activity_organic, mass_water, mass_organic, gamma_water, gamma_organic = \\\n",
     "    binary_activity.activity_coefficients(\n",
-    "        molar_mass_ratio=molar_mass_ratio,\n",
-    "        organic_mole_fraction=organic_mole_fraction,\n",
-    "        oxygen2carbon=oxygen2carbon,\n",
-    "        density=density,\n",
-    "        functional_group=None,)"
+    "        molar_mass_ratio,\n",
+    "        organic_mole_fraction,\n",
+    "        oxygen2carbon,\n",
+    "        density,\n",
+    "        functional_group=None)"
    ]
   },
   {
@@ -75,12 +67,7 @@
    "source": [
     "## Plotting the Activity and Phase Separation\n",
     "\n",
-    "Here we plot the activity of water and the activity of the organic compound\n",
-    "as a function of the organic mole fraction.\n",
-    "\n",
-    "The phase separation or miscibility gap happens when either activity\n",
-    "is greater than 1.0. Or when there is non-monotonic behavior in the\n",
-    "activity curve. Both of these are shown in the plot below."
+    "Here we plot the activity of water and the organic compound as a function of the organic mole fraction. Visualizing these activities helps in identifying phase separation or miscibility gaps, crucial for understanding the behavior of aerosols under different environmental conditions. Phase separation is indicated by activities greater than 1.0 or non-monotonic behavior in the activity curve, as shown below."
    ]
   },
   {
@@ -151,13 +138,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## $q^{alpha}$\n",
-    "\n",
-    "The q_alpha parameter represents the transition from a orgnaic rich phase\n",
-    "to a water rich phase. This can be found using the\n",
-    "`particula.activity.phase_separation` function, the `q_alpha` modele.\n",
+    "## $ q^\\alpha $\n",
     "\n",
-    "Plotted below is q_alpha for the previous activity calculation."
+    "The $q^\\alpha$ parameter signifies the transition from an organic-rich phase to a water-rich phase. This transition is crucial for understanding the phase behavior of aerosol particles. It can be calculated using the `particula.activity.phase_separation` function. The plot below illustrates $q^\\alpha$ based on the activity calculations performed earlier.\n"
    ]
   },
   {
@@ -167,7 +150,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -177,19 +160,18 @@
     }
    ],
    "source": [
+    "# Finding phase separation points and calculating q_alpha\n",
     "phase_sep_aw = phase_separation.find_phase_separation(\n",
     "    activity_water, activity_organic)\n",
-    "\n",
     "q_alpha = phase_separation.q_alpha(\n",
     "    seperation_activity=phase_sep_aw['upper_seperation'],\n",
-    "    activities=activity_water,\n",
-    "    )\n",
+    "    activities=activity_water)\n",
     "\n",
+    "# Plotting q_alpha\n",
     "fig, ax = plt.subplots()\n",
-    "\n",
     "plt.plot(activity_water, q_alpha)\n",
-    "plt.xlabel('water activity')\n",
-    "plt.ylabel('$q^{alpha}$ [organic rich to water rich]')\n",
+    "plt.xlabel('Water Activity')\n",
+    "plt.ylabel('$q^{\\\\alpha}$ [Organic Rich to Water Rich]')\n",
     "plt.show()"
    ]
   },
@@ -199,20 +181,17 @@
    "source": [
     "## Water Activity Focus\n",
     "\n",
-    "Now in typical atmospheric aerosol modeling, the water activity is the\n",
-    "important parameter and not mole fraction. As we can usually assume or control the water activity, and not the mole fractions of a solution. To use the water activity to get the mole fraction used to produce that water activity, we can use the `particula.activity.fixed_water_activity` function.\n",
-    "\n",
-    "This function returns a tuples with three elements, one for the alpha phase (water rich), one for the beta phase (organic rich), and one of q_alpha. If there is no phase separation, then the alpha phase is the only phase, and the beta phase is None."
+    "In atmospheric aerosol modeling, water activity is often a more critical parameter than mole fraction. This is because water activity is typically a controllable or known variable in atmospheric conditions, unlike the exact mole fractions in a solution. To correlate water activity with the mole fraction required to achieve it, we utilize functions from the `particula.activity` module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -222,9 +201,12 @@
     }
    ],
    "source": [
+    "# select the water activity desired\n",
     "water_activity_desired = np.linspace(0.5, 1, 100)\n",
     "oxygen2carbon = 0.25\n",
     "\n",
+    "# calculate the mass fraction of water in the alpha and beta phases\n",
+    "# for each water activity\n",
     "alpha, beta, q_alpha = binary_activity.fixed_water_activity(\n",
     "        water_activity=water_activity_desired,\n",
     "        molar_mass_ratio=molar_mass_ratio,\n",
@@ -232,6 +214,7 @@
     "        density=density\n",
     "        )\n",
     "\n",
+    "# plot the results vs water activity\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(\n",
     "    water_activity_desired,\n",
@@ -253,20 +236,21 @@
     "ax.set_xlabel(\"water activity (Relative Humidity/100)\")\n",
     "ax.set_ylabel(\"mass fraction of water\")\n",
     "plt.legend()\n",
-    "plt.show()\n",
-    "\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Higher oxygen to carbon ratios will not phase separate, as shown below."
+    "## Higher Oxygen to Carbon Ratios\n",
+    "\n",
+    "Higher oxygen to carbon ratios in the mixture tend to inhibit phase separation. The following analysis demonstrates this effect. This observation is crucial in predicting the behavior of aerosol particles under varying chemical compositions (more or less 'aged').\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -281,9 +265,13 @@
     }
    ],
    "source": [
+    "# select the water activity desired\n",
     "water_activity_desired = np.linspace(0.5, 1, 100)\n",
+    "# select the oxygen to carbon ratio\n",
     "oxygen2carbon = 0.6\n",
     "\n",
+    "# calculate the mass fraction of water in the alpha and beta phases\n",
+    "# for each water activity\n",
     "alpha, beta, q_alpha = binary_activity.fixed_water_activity(\n",
     "    water_activity=water_activity_desired,\n",
     "    molar_mass_ratio=molar_mass_ratio,\n",
@@ -291,6 +279,7 @@
     "    density=density\n",
     ")\n",
     "\n",
+    "# plot the results vs water activity\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(\n",
     "    water_activity_desired,\n",
@@ -315,30 +304,31 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Summary\n",
     "\n",
-    "This is an example of how to use the activity module. It currently can\n",
-    "calculate the activity of water and organic compounds in a mixture. It can\n",
-    "also calculate the phase separation of the binary mixture. These outputs can be\n",
-    "used for aerosol thermodynamics, cloud condensation nuclei, and cloud\n",
-    "microphysics.\n",
+    "This notebook demonstrates how to use the activity module for calculating the activity of water and organic compounds in a mixture and assessing phase separation. The insights gained are vital for applications in aerosol thermodynamics, cloud condensation nuclei, and cloud microphysics.\n",
     "\n",
     "This is an implementation of the Binary Activity Theory (BAT) model\n",
     "developed in Gorkowski, K., Preston, T. C., & Zuend, A. (2019)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further Documentation\n",
+    "\n",
+    "For more in-depth understanding and additional functionalities, refer to the documentation, or call the `help` function on any of the functions in the `particula.activity` module.\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -358,23 +348,25 @@
       "    https://doi.org/10.5194/acp-19-13383-2019\n",
       "\n",
       "FUNCTIONS\n",
-      "    activity_coefficients(molar_mass_ratio: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], organic_mole_fraction: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], oxygen2carbon: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], density: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], functional_group=None) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]\n",
+      "    activity_coefficients(molar_mass_ratio: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], organic_mole_fraction: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], oxygen2carbon: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], density: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], functional_group=None) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]\n",
       "        Calculate the activity coefficients for water and organic matter in\n",
       "        organic-water mixtures.\n",
       "        \n",
       "        Args:\n",
       "            - molar_mass_ratio: Ratio of the molecular weight of water to the\n",
       "                molecular weight of organic matter.\n",
-      "            - organic_mole_fraction: Molar fraction of organic matter in the mixture.\n",
+      "            - organic_mole_fraction: Molar fraction of organic matter in the\n",
+      "                mixture.\n",
       "            - oxygen2carbon: Oxygen to carbon ratio in the organic compound.\n",
       "            - density: Density of the mixture.\n",
       "            - functional_group: Optional functional group(s) of the organic\n",
       "                compound, if applicable.\n",
       "        \n",
       "        Returns:\n",
-      "            A tuple containing the activity coefficients of water, activity\n",
-      "            coefficients of organic matter, mass fraction of water, and mass\n",
-      "            fraction of organic matter, respectively.\n",
+      "            A tuple containing the activity of water, activity\n",
+      "            of organic matter, mass fraction of water, and mass\n",
+      "            fraction of organic matter, gamma_water (activity coefficient),\n",
+      "            and gamma_organic (activity coefficient).\n",
       "    \n",
       "    bat_blending_weights(molar_mass_ratio: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], oxygen2carbon: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) -> numpy.ndarray\n",
       "        Function to estimate the blending weights for the BAT model.\n",
@@ -419,6 +411,26 @@
       "        just a pass through now, but will\n",
       "        add the oh equivalent conversion\n",
       "    \n",
+      "    fixed_water_activity(water_activity: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], molar_mass_ratio: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], oxygen2carbon: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], density: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) -> Tuple\n",
+      "        Calculate the activity coefficients of water and organic matter in\n",
+      "        organic-water mixtures.\n",
+      "        \n",
+      "        This function assumes a fixed water activity value (e.g., RH = 75%\n",
+      "        corresponds to 0.75 water activity in equilibrium).\n",
+      "        It calculates the activity coefficients for different phases and\n",
+      "        determines phase separations if they occur.\n",
+      "        \n",
+      "        Parameters:\n",
+      "        water_activity (ArrayLike): An array of water activity values.\n",
+      "        molar_mass_ratio (ArrayLike): Array of molar mass ratios of the components.\n",
+      "        oxygen2carbon (ArrayLike): Array of oxygen-to-carbon ratios.\n",
+      "        density (ArrayLike): Array of densities of the mixture.\n",
+      "        \n",
+      "        Returns:\n",
+      "        Tuple: A tuple containing the activity coefficients for alpha and beta\n",
+      "                phases, and the alpha phase mole fraction.\n",
+      "               If no phase separation occurs, the beta phase values are None.\n",
+      "    \n",
       "    gibbs_mix_weight(molar_mass_ratio: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], organic_mole_fraction: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], oxygen2carbon: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], density: Union[numpy.__array_like._SupportsArray[numpy.dtype[Any]], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], functional_group: Optional[str] = None) -> Tuple[numpy.ndarray, numpy.ndarray]\n",
       "        Gibbs free energy of mixing, see Gorkowski (2019), with weighted\n",
       "        oxygen2carbon regions. Only can run one compound at a time.\n",
@@ -457,6 +469,8 @@
       "    FIT_HIGH = {'a1': [5.92155, -2.528295, -3.883017, -7.898128], 'a2': [-...\n",
       "    FIT_LOW = {'a1': [7.089476, -7.71186, -38.85941, -100.0], 'a2': [-0.62...\n",
       "    FIT_MID = {'a1': [5.872214, -4.535007, -5.129327, -28.09232], 'a2': [-...\n",
+      "    INTERPOLATE_WATER_FIT = 500\n",
+      "    LOWEST_ORGANIC_MOLE_FRACTION = 1e-12\n",
       "    Optional = typing.Optional\n",
       "        Optional type.\n",
       "        \n",
@@ -507,13 +521,6 @@
    "source": [
     "help(binary_activity)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/examples/equilibria/equilibria_intro.md b/docs/examples/equilibria/equilibria_intro.md
new file mode 100644
index 000000000..ab530dfd0
--- /dev/null
+++ b/docs/examples/equilibria/equilibria_intro.md
@@ -0,0 +1,27 @@
+# Equilibria of Aerosols
+
+## What is Equilibria?
+
+Equilibria, a fundamental concept in physical chemistry, refers to the state where the concentrations of reactants and products in a chemical reaction remain constant over time. In the context of aerosol science, equilibria are essential in understanding how aerosol particles interact with their environment, particularly with respect to liquid and vapor phases. This balance is crucial in predicting how aerosols behave under different atmospheric conditions.
+
+## Why is Equilibria Important?
+
+Studying equilibria in aerosol systems is vital for several reasons:
+
+1. **Environmental Impact**: Aerosols play a significant role in air quality and climate change. Understanding their equilibrium behavior helps in assessing their environmental impact, such as their role in cloud formation and solar radiation scattering.
+
+2. **Health Implications**: Aerosols affect human health, especially in terms of respiratory issues. Knowledge of equilibrium states helps in evaluating exposure risks and designing mitigation strategies.
+
+3. **Atmospheric Chemistry**: Equilibria studies contribute to our understanding of atmospheric chemistry, particularly in the formation and transformation of aerosols.
+
+## How Does Equilibria Relate to These Notebooks?
+
+The notebooks presented here are dedicated to exploring various aspects of equilibria in aerosol science:
+
+1. **Activity Coefficients and Phase Behavior**: By calculating activity coefficients, we can predict how different components of aerosols partition between liquid and vapor phases. This is crucial in understanding the composition and concentration of aerosols under varying atmospheric conditions.
+
+2. **Liquid-Vapor Equilibrium**: The notebook delves into the equilibrium compositions of liquid-vapor mixtures, highlighting the role of relative humidity (RH) in shaping aerosol behavior.
+
+3. **Practical Applications**: Through examples and simulations, these notebooks provide practical insights into real-world scenarios, enhancing our understanding of aerosols in environmental and health contexts.
+
+Overall, the notebooks serve as an interactive platform to explore and understand the complex yet fascinating world of equilibria in aerosol science. Whether you're a student, researcher, or enthusiast, these materials offer valuable insights into the dynamic equilibrium processes that govern aerosol behavior in our atmosphere.
diff --git a/docs/examples/equilibria_part1.ipynb b/docs/examples/equilibria/equilibria_part1.ipynb
similarity index 91%
rename from docs/examples/equilibria_part1.ipynb
rename to docs/examples/equilibria/equilibria_part1.ipynb
index 4f9717404..cca672276 100644
--- a/docs/examples/equilibria_part1.ipynb
+++ b/docs/examples/equilibria/equilibria_part1.ipynb
@@ -6,8 +6,7 @@
    "source": [
     "# Liquid Vapor Equilibrium\n",
     "\n",
-    "Using the activity coefficient model, we can now calculate the equilibrium composition of a liquid-vapor mixture.  We consider just an\n",
-    "arbitrary organic volatility distribution to show the steps.\n"
+    "This notebook explores the calculation of equilibrium composition in liquid-vapor mixtures, a crucial concept in aerosol science and environmental studies. We utilize an activity coefficient model to understand how different volatile organic compounds distribute between the liquid and vapor phases. This analysis is particularly important for predicting aerosol behavior and understanding atmospheric processes."
    ]
   },
   {
@@ -16,10 +15,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# import libraries\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from particula.activity import species_density\n",
+    "# Importing necessary libraries\n",
+    "import matplotlib.pyplot as plt  # For creating plots and visualizations\n",
+    "import numpy as np  # For numerical operations\n",
+    "from particula.activity import species_density  # For calculating species density\n",
+    "# For partitioning calculations in liquid-vapor equilibrium\n",
     "from particula.equilibria import partitioning"
    ]
   },
@@ -27,54 +27,53 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup the system\n",
+    "## Setup the System\n",
     "\n",
-    "- c_star_j_dry: the volatility distribution of the organics in the dry air (can calculate from the vapor pressure)\n",
-    "- concentration_organic_matter: is the vapor+liquid concentration of the organics in the system\n",
-    "- oxygen2carbon: is the oxygen to carbon ratio of the organics in the system\n",
-    "- molar_mass: is the molar mass of the organics in the system\n",
+    "To simulate the liquid-vapor equilibrium, we define several key parameters:\n",
+    "- `c_star_j_dry`: Represents the volatility distribution of organic compounds in dry air, calculable from vapor pressure.\n",
+    "- `concentration_organic_matter`: The combined concentration of vapor and liquid organic matter in the system.\n",
+    "- `oxygen2carbon`: The ratio of oxygen to carbon in the organic compounds, crucial for characterizing their chemical nature.\n",
+    "- `molar_mass`: The molar mass of the organic compounds.\n",
     "\n",
-    "We'll use this infromation to get the denisty of the organics in the system."
+    "These parameters help us determine the density of organics in the system, a vital step in understanding their distribution between phases.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "c_star_j_dry = [1e-6, 1e-4, 1e-1, 1e2, 1e4]  # ug/m3\n",
-    "concentration_organic_matter = [1, 5, 10, 15, 10]  # ug/m3\n",
-    "oxygen2carbon = np.array([0.2, 0.3, 0.5, 0.4, 0.4])\n",
+    "# Defining system parameters\n",
+    "c_star_j_dry = [1e-6, 1e-4, 1e-1, 1e2, 1e4]  # Volatility distribution in ug/m3\n",
+    "# Total concentration in ug/m3\n",
+    "concentration_organic_matter = [1, 5, 10, 15, 10]\n",
+    "oxygen2carbon = np.array([0.2, 0.3, 0.5, 0.4, 0.4])  # Oxygen to carbon ratios\n",
     "\n",
-    "molar_mass = np.array([200, 200, 200, 200, 200])  # g/mol\n",
-    "\n",
-    "water_activity_desired = np.array([0.8])\n",
-    "\n",
-    "molar_mass_ratio = 18.015 / np.array(molar_mass)\n",
+    "molar_mass = np.array([200, 200, 200, 200, 200])  # Molar mass in g/mol\n",
+    "water_activity_desired = np.array([0.8])  # Desired water activity\n",
+    "molar_mass_ratio = 18.015 / np.array(molar_mass)  # Molar mass ratio\n",
     "\n",
+    "# Calculate the density of organic compounds\n",
     "density = species_density.organic_array(\n",
-    "    molar_mass=molar_mass,\n",
-    "    oxygen2carbon=oxygen2carbon,\n",
+    "    molar_mass,\n",
+    "    oxygen2carbon,\n",
     "    hydrogen2carbon=None,\n",
-    "    nitrogen2carbon=None,\n",
-    ")"
+    "    nitrogen2carbon=None)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calculate the activity coefficients\n",
+    "## Calculate the Activity Coefficients\n",
     "\n",
-    "For equilibrium, we do not need all the returns from `activity.binary_activity`, so \n",
-    "`partitioning.get_properties_for_liquid_vapor_equilibrium` is a wrapper that just returns the activity coefficients,\n",
-    "mass fractions, and the two phase q (alpha-beta) values."
+    "The next step involves calculating the activity coefficients, which are pivotal in determining how the organic compounds distribute between the liquid and vapor phases. We use the `partitioning.get_properties_for_liquid_vapor_equilibrium` function, a specialized tool that simplifies the process by returning only the essential properties: activity coefficients, mass fractions, and the two-phase *q* values for the alpha-beta equilibrium.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -87,6 +86,7 @@
     }
    ],
    "source": [
+    "# Calculate the properties needed for liquid-vapor partitioning\n",
     "gamma_organic_ab, mass_fraction_water_ab, q_ab = \\\n",
     "    partitioning.get_properties_for_liquid_vapor_partitioning(\n",
     "        water_activity_desired=water_activity_desired,\n",
@@ -95,7 +95,7 @@
     "        density=density,\n",
     "    )\n",
     "\n",
-    "# optimize the partition coefficients\n",
+    "# The optimization the partition coefficients, i.e. the partitioning calculation\n",
     "alpha_opt, beta_opt, system_opt, fit_result = \\\n",
     "    partitioning.liquid_vapor_partitioning(\n",
     "        c_star_j_dry=c_star_j_dry,\n",
@@ -115,35 +115,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Now with f(RH)\n",
+    "## Activity Coefficients as a Function of Relative Humidity (f(RH))\n",
     "\n",
-    "The whole point of the binary activity model is for interactions with water.\n",
-    "We can now calculate the activity coefficients as a function of RH or (water activity).\n",
-    "\n",
-    "We'll do that now by looping over a range of RH values and calculating the activity coefficients.\n"
+    "The binary activity model's key feature is its interaction with water, particularly through relative humidity (RH). Here, we will calculate how the activity coefficients vary as a function of RH. This is done by iterating over a range of RH values and computing the corresponding activity coefficients, providing insights into how atmospheric humidity influences the equilibrium behavior of the system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "done\n"
+      "Calculation complete\n"
      ]
     }
    ],
    "source": [
-    "# and water concentration\n",
-    "\n",
+    "# Calculating activity coefficients across a range of RH values\n",
+    "# Range of water activity (RH/100)\n",
     "water_activity_curve = np.linspace(0.01, 0.99, 80)\n",
-    "totol_oa_concentration = np.empty([len(water_activity_curve), 1], dtype=float)\n",
+    "total_oa_concentration = np.empty([len(water_activity_curve), 1], dtype=float)\n",
     "water_concentration = np.empty([len(water_activity_curve), 1], dtype=float)\n",
     "\n",
     "for i, water_activity in enumerate(water_activity_curve):\n",
+    "    # Get properties for liquid-vapor partitioning at each RH value\n",
     "    gamma_organic_ab, mass_fraction_water_ab, q_ab = \\\n",
     "        partitioning.get_properties_for_liquid_vapor_partitioning(\n",
     "            water_activity_desired=water_activity,\n",
@@ -152,6 +150,7 @@
     "            density=density,\n",
     "        )\n",
     "\n",
+    "    # Optimize the partition coefficients for each RH value\n",
     "    alpha_opt, beta_opt, system_opt, fit_result = \\\n",
     "        partitioning.liquid_vapor_partitioning(\n",
     "            c_star_j_dry=c_star_j_dry,\n",
@@ -162,23 +161,26 @@
     "            q_ab=q_ab,\n",
     "            partition_coefficient_guess=None,\n",
     "        )\n",
-    "    \n",
-    "    # get the total organic concentration\n",
-    "    totol_oa_concentration[i] = system_opt[0]\n",
+    "\n",
+    "    # Record the total organic and water concentration\n",
+    "    total_oa_concentration[i] = system_opt[0]\n",
     "    water_concentration[i] = system_opt[1]\n",
-    "print('done')"
+    "\n",
+    "print('Calculation complete')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Now plot the results"
+    "## Plotting the Equilibrium Composition vs. Relative Humidity\n",
+    "\n",
+    "Now that we have calculated the equilibrium composition for a range of RH values, we will visualize these results. The plot will show how the total organic aerosol concentration and the water concentration in the aerosol vary with changing RH. This visualization is crucial for understanding the dynamic behavior of aerosols in different atmospheric humidity conditions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +196,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.plot(water_activity_curve, totol_oa_concentration,\n",
+    "ax.plot(water_activity_curve, total_oa_concentration,\n",
     "        label='total organic concentration', color='green')\n",
     "aw=ax.twinx()\n",
     "aw.plot(water_activity_curve, water_concentration,\n",
@@ -219,9 +221,7 @@
    "source": [
     "## Summary\n",
     "\n",
-    "We covered a lot of ground here.  We started with defining the system. We then used the binary activity model to calculate\n",
-    "the activity coefficients as a function of RH.  We then used those activity coefficients to calculate the equilibrium\n",
-    "composition of the liquid and vapor phases.  We then plotted the results."
+    "In this notebook, we have journeyed through the process of defining a liquid-vapor equilibrium system and employing the binary activity model to calculate activity coefficients as a function of relative humidity (RH). We then used these coefficients to determine the equilibrium composition of the liquid and vapor phases. Finally, the results were visualized to demonstrate the impact of RH on aerosol behavior, which is essential for understanding atmospheric aerosol dynamics and their environmental implications.\n"
    ]
   }
  ],
diff --git a/docs/examples/loading_data_part2.ipynb b/docs/examples/loading_data_part2.ipynb
deleted file mode 100644
index a7c8556ae..000000000
--- a/docs/examples/loading_data_part2.ipynb
+++ /dev/null
@@ -1,849 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  # Loading Data Part 2\n",
-    "\n",
-    "  This example continues from the previous example, so if you haven't already\n",
-    "  done so, please go through the previous example first.\n",
-    "\n",
-    " This example covers data in 2 dimensions, such as a size distributions.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Working path\n",
-    "\n",
-    "  Set the working path where the data is stored. For now we'll use the\n",
-    "  provided example data in this current directory.\n",
-    "\n",
-    "  But the path could be any where on your computer. For example, if you have a\n",
-    "  folder called \"data\" in your home directory, you could set the path to:\n",
-    "  `path = \"U:\\\\data\\\\processing\\\\Campgain2023_of_aswsome\\\\data\"`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# all the imports, but we'll go through them one by one as we use them\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from particula.data import loader, loader_interface, settings_generator\n",
-    "from particula.data.tests.example_data.get_example_data import get_data_folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current path for this script:\n",
-      "\\docs\\examples\n",
-      "Path to data folder:\n",
-      "\\data\\tests\\example_data\n"
-     ]
-    }
-   ],
-   "source": [
-    "# set the parent directory of the data folder, for now this is the same as the\n",
-    "# current working directory, but this can be a completely different path\n",
-    "#\n",
-    "# imports os to get the current working directory\n",
-    "import os\n",
-    "from particula.data.tests.example_data.get_example_data import get_data_folder\n",
-    "\n",
-    "current_path = os.getcwd()\n",
-    "print('Current path for this script:')\n",
-    "# print the path from particula/ onwards\n",
-    "print(current_path.rsplit('particula')[-1])\n",
-    "\n",
-    "path = get_data_folder()\n",
-    "print('Path to data folder:')\n",
-    "print(path.rsplit('particula')[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " # Load the data\n",
-    "\n",
-    "  With the working directory set, we can now load the data. For this we use\n",
-    "  the `loader` module and call loader.data_raw_loader() with the file path as\n",
-    "  argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Units,dW/dlogDp\n",
-      "Weight,Number\n",
-      "Sample #,Date,Start Time,Sample Temp (C),Sample Pressure (kPa),Relative Humidity (%),Mean Free Path (m),Gas Viscosity (Pa*s),Diameter Midpoint (nm),20.72,21.10,21.48,21.87,22.27,22.67,23.08,23.50,23.93,24.36,24.80,25.25,25.71,26.18,26.66,27.14,27.63,28.13,28.64,29.16,29.69,30.23,30.78,31.34,31.91,32.49,33.08,33.68,34.29,34.91,35.55,36.19,36.85,37.52,38.20,38.89,39.60,40.32,41.05,41.79,42.55,43.32,44.11,44.91,45.73,46.56,47.40,48.26,49.14,50.03,50.94,51.86,52.80,53.76,54.74,55.73,56.74,57.77,58.82,59.89,60.98,62.08,63.21,64.36,65.52,66.71,67.93,69.16,70.41,71.69,72.99,74.32,75.67,77.04,78.44,79.86,81.31,82.79,84.29,85.82,87.38,88.96,90.58,92.22,93.90,95.60,97.34,99.10,100.90,102.74,104.60,106.50,108.43,110.40,112.40,114.44,116.52,118.64,120.79,122.98,125.21,127.49,129.80,132.16,134.56,137.00,139.49,142.02,144.60,147.22,149.89,152.61,155.38,158.20,161.08,164.00,166.98,170.01,173.09,176.24,179.43,182.69,186.01,189.38,192.82,196.32,199.89,203.51,207.21,210.97,214.80,218.70,222.67,226.71,230.82,235.01,239.28,243.62,248.05,252.55,257.13,261.80,266.55,271.39,276.32,281.33,286.44,291.64,296.93,302.32,307.81,313.40,319.08,324.88,330.77,336.78,342.89,349.12,355.45,361.90,368.47,375.16,381.97,388.91,395.96,403.15,410.47,417.92,425.51,433.23,441.09,449.10,457.25,465.55,474.00,482.61,491.37,500.29,509.37,518.61,528.03,537.61,547.37,557.31,567.42,577.72,588.21,598.89,609.76,620.82,632.09,643.57,655.25,667.14,679.25,691.58,704.14,716.92,729.93,743.18,756.67,770.40,784.39,Scan Time (s),Retrace Time (s),Scan Resolution (Hz),Scans Per Sample,HV Polarity,Sheath Flow (L/min),Aerosol Flow (L/min),Bypass Flow (L/min),Low Voltage (V),High Voltage (V),Lower Size (nm),Upper Size (nm),Density (g/cm³),td + 0.5 (s),tf (s),D50 (nm),Median (nm),Mean (nm),Geo. Mean (nm),Mode (nm),Geo. Std. Dev.,Total Conc. (#/cm³),Neutralizer Status,Dilution Factor,Test Name,Test Description,Dataset Name,Dataset Description,Instrument Errors\n",
-      "1,07/07/2022,08:49:17,23.7,101.2,61.9,6.75690e-8,1.83579e-5,,6103.186,2832.655,4733.553,4765.944,5960.964,4475.806,4412.044,5853.069,4832.167,3781.343,3675.830,3271.549,3084.392,3668.269,4116.143,3310.157,3978.368,4151.566,2515.995,3755.837,2776.663,5032.745,3775.426,2818.553,2641.302,2636.806,3079.759,2606.094,2317.234,3192.346,2226.703,2484.878,3394.395,1762.834,3172.359,2919.533,2452.013,3403.780,2360.277,2543.386,2563.290,2649.769,1375.374,1364.046,1446.529,2068.167,1336.070,1542.077,1707.249,1482.481,2272.182,1754.409,2472.438,1191.563,2221.825,1635.293,2548.571,1991.926,2546.956,1790.114,2115.075,1138.769,1934.746,2163.955,1613.179,2132.750,1654.348,1698.154,2403.529,1222.983,1829.254,1197.162,1638.797,1248.565,2417.521,1130.421,1429.423,1694.923,1658.378,1443.393,1731.346,1277.799,1089.149,1072.630,1205.387,1693.146,1109.648,915.428,491.529,881.028,1218.297,755.658,714.301,686.247,790.943,398.805,1043.226,1298.495,1548.704,1070.899,846.596,938.241,232.947,926.941,837.452,794.492,254.455,392.637,353.144,872.576,693.986,1544.164,657.340,546.445,311.890,365.934,616.794,610.810,938.786,815.964,593.441,939.634,188.115,1077.429,1213.142,737.913,1876.626,735.779,996.521,1098.601,1166.494,962.551,1392.535,947.504,655.459,993.819,682.087,852.503,601.057,733.860,529.122,960.578,687.512,839.973,652.820,289.921,623.835,453.604,588.057,856.253,283.994,282.839,365.801,200.382,365.756,146.548,306.730,373.162,114.272,0.000,182.692,260.788,164.857,19.851,89.612,0.000,181.974,0.000,53.276,20.016,0.000,0.000,95.433,96.222,0.000,0.000,197.307,0.000,100.336,0.000,102.072,0.000,0.000,209.529,0.000,213.227,107.561,0.000,218.965,220.930,111.453,0.000,113.460,0.000,115.513,0.000,0.000,0.000,0.000,28.221,93.413,122.992,0.000,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,41.562,74.959,52.078,20.721,2.179,2.16900e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
-      "2,07/07/2022,08:50:48,23.6,101.2,61.7,6.75401e-8,1.83531e-5,,5621.118,5867.747,6233.403,3453.156,4484.307,5468.148,4725.052,4689.983,3661.759,4356.725,4292.911,7728.414,5112.679,4746.084,3957.005,3472.977,3496.697,4674.202,4188.868,2868.559,3375.113,4306.112,5191.077,4732.512,4566.029,3514.167,5172.877,3825.270,5323.756,2327.737,3846.602,2347.097,3182.011,1876.273,2952.863,2831.255,2497.869,4158.061,3828.510,3199.720,2309.195,2462.550,3060.240,1086.744,1476.289,2069.774,1727.787,2710.631,2067.327,2619.082,2345.026,2362.235,1429.749,2557.408,2660.327,1209.933,1590.320,1696.569,2236.773,1499.046,1922.632,1650.213,3147.351,2201.919,1622.954,2198.739,1800.998,1429.621,1426.761,1923.931,1262.939,1745.284,1458.571,1523.548,1920.108,1382.558,2211.525,2571.277,1979.297,1562.697,1741.573,1307.680,967.481,838.919,1502.136,1301.401,1011.619,829.770,973.269,1100.004,1152.808,749.250,1187.900,806.256,111.008,297.062,809.059,1361.412,779.536,535.087,881.522,1307.518,800.804,1053.953,182.381,1042.830,673.021,646.171,825.612,963.187,748.743,540.954,769.157,788.222,825.566,236.537,865.009,289.185,803.098,398.510,446.847,439.645,1118.961,1003.003,924.180,745.149,430.134,415.522,805.970,790.348,998.975,1043.136,604.082,1004.545,1082.455,1312.781,1447.390,872.420,398.380,695.719,857.412,645.872,691.129,623.007,471.728,641.049,1023.693,394.611,475.599,446.076,657.686,313.003,136.395,248.550,579.894,336.126,485.938,298.810,0.000,227.571,104.550,157.583,289.697,0.229,0.000,0.000,217.592,67.816,24.067,0.000,0.000,0.000,0.000,0.000,97.009,0.000,0.000,0.000,0.000,0.000,0.000,205.900,0.000,104.761,0.000,0.000,0.000,108.509,0.000,110.461,0.000,0.000,0.000,114.471,115.506,116.540,0.000,118.660,0.000,0.000,0.000,0.000,75.377,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,39.458,69.080,49.198,25.255,2.101,2.39408e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
-      "3,07/07/2022,08:52:19,23.7,101.2,61.5,6.75690e-8,1.83579e-5,,5165.139,4969.987,4312.386,6939.394,4680.764,3224.473,4999.149,3653.002,4241.532,3928.137,2718.607,3363.947,4863.410,5338.452,4659.515,3430.329,3997.386,4644.421,4943.511,3883.970,3212.310,4445.981,2349.435,3605.419,4366.557,4969.924,4880.573,3186.281,3089.412,2724.537,3195.740,4277.947,4864.436,4263.532,2100.807,1967.634,3283.337,3268.660,3001.917,2781.549,1879.354,1376.083,2051.524,2165.874,2012.210,2923.129,1575.515,1544.252,1610.635,1572.609,1299.370,1549.832,1145.100,2897.864,1839.992,2351.579,2102.027,1543.106,953.811,2073.610,2317.378,2087.617,1586.363,1897.860,2456.722,1647.781,1013.534,1734.023,1633.021,1841.697,2193.442,2714.856,1396.336,2264.046,1671.363,1538.012,1257.148,1423.316,1217.281,1745.437,1787.473,1284.774,1534.815,1274.852,1438.025,1199.602,964.066,862.098,685.995,679.146,879.775,806.703,979.672,894.103,1379.499,1112.031,744.999,580.777,1241.262,960.784,750.484,908.236,957.901,652.265,1200.515,429.487,347.453,552.393,617.871,652.163,709.227,788.963,1499.238,627.895,1315.208,976.800,555.360,440.680,1182.819,863.800,362.530,942.047,460.380,1222.507,678.820,1006.555,319.371,91.941,761.841,205.384,449.120,751.217,572.530,350.734,295.089,413.379,612.088,474.457,678.504,490.408,751.536,400.656,585.567,676.707,364.052,124.385,631.790,788.487,566.062,390.904,141.751,256.369,366.589,528.781,512.078,257.120,393.412,350.601,361.659,65.138,348.203,326.629,329.714,175.810,111.365,74.091,103.212,0.000,0.000,47.532,0.000,166.826,0.000,96.217,388.070,97.832,98.649,99.490,200.678,202.399,0.000,102.953,0.000,0.000,105.683,106.611,33.630,183.108,2.602,218.305,222.901,0.000,226.925,0.000,0.000,116.553,0.000,118.661,119.732,120.801,0.000,122.992,124.085,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,39.324,72.102,50.019,21.870,2.136,2.27861e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
-      "4,07/07/2022,08:53:50,23.8,101.2,61.4,6.75979e-8,1.83627e-5,,5814.745,5937.421,5542.118,7127.484,5341.069,4793.690,4938.844,5721.541,4877.746,5900.250,5104.984,4914.366,4891.892,6655.579,4431.173,3389.961,4947.809,3115.245,4138.126,5421.474,4589.063,4007.156,2524.137,5009.064,4780.963,4959.096,3648.285,4148.676,4270.099,2229.465,3043.487,5618.376,3689.188,4700.549,2535.915,1754.223,2560.335,2853.385,2454.711,2515.907,3015.370,1502.864,2344.161,2761.448,2047.076,1542.531,2151.757,2365.884,2330.816,2585.566,1431.955,2391.335,2097.717,1891.014,2211.815,2071.479,2188.302,2475.058,1906.364,1781.793,2356.998,1527.723,2609.446,1644.771,1917.624,1843.984,2418.197,1385.516,1263.621,2155.939,2083.223,1765.167,957.777,2077.747,1667.811,1122.065,1579.113,1709.471,1604.406,686.151,390.075,1194.313,1657.144,1462.232,1870.846,1012.132,847.165,1248.528,1039.604,779.076,1375.101,1058.272,1013.378,1211.420,1641.490,979.146,835.539,763.524,951.720,1270.393,1308.492,1056.486,1715.924,657.112,1475.767,235.866,827.129,1266.089,1080.958,1246.249,1147.116,840.719,1560.246,1201.554,1743.366,1233.526,1166.422,1068.551,1047.492,787.018,759.836,491.419,714.111,460.361,681.068,767.815,654.715,501.038,357.016,575.937,613.281,851.029,583.739,475.691,431.584,616.144,744.932,409.334,984.682,371.750,613.130,757.474,637.077,441.004,609.132,380.961,595.419,565.033,566.955,332.402,450.524,139.761,430.419,443.058,558.628,158.467,271.708,346.807,57.637,148.050,226.825,353.827,77.661,0.000,0.000,74.100,0.000,250.296,117.433,93.156,187.816,0.000,95.443,0.000,0.000,293.505,0.000,99.496,100.342,0.000,102.078,102.959,0.000,0.000,0.000,106.622,322.709,0.000,328.474,0.000,67.473,44.378,0.000,0.000,115.519,0.000,0.000,118.668,0.000,0.000,0.000,0.000,0.000,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,37.995,68.796,48.896,21.870,2.107,2.51144e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
-      "5,07/07/2022,08:55:21,24.0,101.1,61.4,6.77227e-8,1.83722e-5,,8034.425,6317.981,6972.600,4577.324,6488.519,4985.397,5484.518,7295.312,3449.590,4261.716,4259.456,6124.670,4418.824,5418.742,3311.293,3548.897,4940.747,6738.536,3377.823,3309.433,5322.339,4148.187,3387.285,3967.636,5064.382,4573.259,3896.245,4006.531,3769.030,4129.946,4678.454,3121.839,3888.625,2443.782,1947.617,2321.130,1845.465,2833.269,2745.881,3262.145,4055.876,2319.187,3397.282,2596.623,2935.256,1508.733,1555.232,3184.200,2683.631,2158.530,2303.663,2739.336,2714.276,2536.377,2051.076,2063.667,2074.972,2852.267,2366.702,2135.668,1500.801,2228.817,2220.527,1501.131,2354.567,2072.434,2547.917,2111.890,1474.809,1561.614,1334.889,1100.318,1077.335,1470.618,1377.825,1684.933,1093.441,1596.409,1456.255,1543.298,1116.499,984.258,1294.805,1586.816,723.664,1709.369,1060.965,1415.310,1611.158,1791.258,1098.238,1513.790,1335.019,1178.572,1538.772,477.803,1130.380,1596.999,652.664,1098.951,1384.104,772.285,788.185,1432.363,773.331,729.470,819.882,979.684,925.309,753.771,706.255,659.741,1026.707,818.647,1205.428,940.460,906.655,758.763,811.344,1123.245,520.356,1009.392,651.265,735.336,209.657,549.624,537.181,841.849,483.705,713.011,497.248,743.196,556.459,953.140,847.692,614.097,423.810,816.193,627.059,453.998,976.898,592.170,548.197,535.480,667.837,312.390,476.781,369.028,451.687,432.520,1001.512,312.053,498.408,198.771,399.968,363.778,403.848,381.782,223.839,227.667,212.819,101.097,164.909,359.326,285.450,0.000,44.177,0.000,158.441,220.559,81.404,49.687,95.468,0.000,194.095,391.452,98.679,0.000,0.000,0.000,0.000,102.990,103.881,104.800,0.000,106.644,0.000,108.554,0.000,110.496,0.000,112.492,113.494,0.000,115.548,0.000,0.000,0.000,239.531,241.683,0.000,0.000,248.252,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,39.214,69.960,48.959,20.721,2.123,2.56068e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_file = os.path.join(\n",
-    "    path,\n",
-    "    'SMPS_data',\n",
-    "    '2022-07-07_095151_SMPS.csv')\n",
-    "\n",
-    "# print the file path\n",
-    "# print(data_file)\n",
-    "\n",
-    "# load the data\n",
-    "raw_data = loader.data_raw_loader(data_file)\n",
-    "\n",
-    "# print the interesting bits\n",
-    "for row in raw_data[22:30]:\n",
-    "    print(row)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " # Now to format the data\n",
-    "\n",
-    " This is a little more complicated than the 1d data, because we have to\n",
-    " pull out the sizes bins, and read them in as our headers. This is done by\n",
-    " specifiying the start and end keywords for the size bins. In this case\n",
-    " the start keyword is \"Date Time\" and the end keyword is \"Total Conc\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch time:\n",
-      "[1.65718376e+09 1.65718385e+09 1.65718394e+09 1.65718403e+09\n",
-      " 1.65718412e+09]\n",
-      "Data shape:\n",
-      "(2854, 203)\n",
-      "Header:\n",
-      "['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This is done by the general_data_formatter function for timeseries data\n",
-    "# 2d data is a separate function\n",
-    "\n",
-    "epoch_time, data, header = loader.sizer_data_formatter(\n",
-    "    data=raw_data,\n",
-    "    data_checks={\n",
-    "        \"characters\": [250],\n",
-    "        \"skip_rows\": 25,\n",
-    "        \"skip_end\": 0,\n",
-    "        \"char_counts\": {\"/\": 2, \":\": 2}\n",
-    "    },\n",
-    "    data_sizer_reader={\n",
-    "        'Dp_start_keyword': '20.72',\n",
-    "        'Dp_end_keyword': '784.39',\n",
-    "        'convert_scale_from': 'dw/dlogdp'\n",
-    "    },\n",
-    "    time_column=[1, 2],\n",
-    "    time_format=\"%m/%d/%Y %H:%M:%S\",\n",
-    "    delimiter=\",\",\n",
-    "    header_row=24)\n",
-    "\n",
-    "# print the first bit of the data\n",
-    "print('Epoch time:')\n",
-    "print(epoch_time[:5])\n",
-    "print('Data shape:')\n",
-    "print(data.shape)\n",
-    "print('Header:')\n",
-    "print(header[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Pause to Plot\n",
-    "\n",
-    " Now that we have the data and time, we can plot it to see what it looks\n",
-    " like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the data\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(epoch_time,\n",
-    "        data[:, 50],\n",
-    "        label=f'Bin {header[50]} nm',\n",
-    ")\n",
-    "ax.set_xlabel(\"Time (epoch)\")\n",
-    "ax.set_ylabel(\"Bin Concentration (#/cm³)\")\n",
-    "ax.legend()\n",
-    "plt.show()\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dates in Plots\n",
-    "\n",
-    "If you want dates on the x-axis, you need to convert the dates to\n",
-    "matplotlib dates, or use np.datetime64. This is done in the `convert.datetime64_from_epoch_array` function.\n",
-    "\n",
-    "Then it is usually best to rotate the x-axis labels so they don't overlap.\n",
-    "This is done with the `plt.xticks(rotation=45)` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from particula.util.convert import datetime64_from_epoch_array\n",
-    "\n",
-    "# convert the epoch time to datetime64\n",
-    "time_in_datetime64 = datetime64_from_epoch_array(epoch_time)\n",
-    "\n",
-    "# plot the data\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(time_in_datetime64,\n",
-    "        data[:, 50],\n",
-    "        label=f'Bin {header[50]} nm',\n",
-    "        )\n",
-    "plt.xticks(rotation=45)\n",
-    "ax.set_xlabel(\"Time (epoch)\")\n",
-    "ax.set_ylabel(\"Bin Concentration (#/cm³)\")\n",
-    "ax.legend()\n",
-    "plt.show()\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " ## Contour plot of data\n",
-    "\n",
-    " We can also plot the data as a contour plot, which is useful for seeing\n",
-    " how the data changes over time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# settin limits helps to see the data better, you can also use np.log10()\n",
-    "# to plot the concentration in log space\n",
-    "concentration = data\n",
-    "concentration = np.where(concentration < 1e-5, 1e-5, concentration)\n",
-    "concentration = np.where(concentration > 10**5, 10**5, concentration)\n",
-    "# concentration = np.log10(concentration)\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 1)\n",
-    "plt.contourf(\n",
-    "    epoch_time,\n",
-    "    np.array(header).astype(float),\n",
-    "    concentration.T,\n",
-    "    cmap=plt.cm.PuBu_r, levels=50)\n",
-    "plt.yscale('log')\n",
-    "ax.set_xlabel('Epoch Time')\n",
-    "ax.set_ylabel('Diameter (nm)')\n",
-    "plt.colorbar(label='Concentration dN/dlogDp [#/cm3]', ax=ax)\n",
-    "plt.show()\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Settings Generator for 1d and 2d data\n",
-    "\n",
-    " Just like with the 1d data, we can use the settings generator to generate\n",
-    " the settings dictionary for importing the data. This is done by calling the\n",
-    " `settings_generator.for_general_sizer_1d_2d_load()` function.\n",
-    "\n",
-    " This function has a lot of arguments, but remember, if you just want the\n",
-    " default settings, you don't need to pass any arguments. The defaults are\n",
-    " set to the example data provided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Settings 1d data dictionary:\n",
-      "relative_data_folder: SMPS_data\n",
-      "filename_regex: *.csv\n",
-      "MIN_SIZE_BYTES: 10\n",
-      "data_loading_function: general_1d_load\n",
-      "header_row: 24\n",
-      "data_checks: {'characters': [250], 'skip_rows': 25, 'skip_end': 0, 'char_counts': {'/': 2, ':': 2}}\n",
-      "data_column: ['Lower Size (nm)', 'Upper Size (nm)', 'Sample Temp (C)', 'Sample Pressure (kPa)', 'Relative Humidity (%)', 'Median (nm)', 'Mean (nm)', 'Geo. Mean (nm)', 'Mode (nm)', 'Geo. Std. Dev.', 'Total Conc. (#/cm³)']\n",
-      "data_header: ['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)']\n",
-      "time_column: [1, 2]\n",
-      "time_format: %m/%d/%Y %H:%M:%S\n",
-      "delimiter: ,\n",
-      "time_shift_seconds: 0\n",
-      "timezone_identifier: UTC\n",
-      "\n",
-      "Settings 2d data dictionary:\n",
-      "relative_data_folder: SMPS_data\n",
-      "filename_regex: *.csv\n",
-      "MIN_SIZE_BYTES: 10\n",
-      "data_loading_function: general_2d_load\n",
-      "header_row: 24\n",
-      "data_checks: {'characters': [250], 'skip_rows': 25, 'skip_end': 0, 'char_counts': {'/': 2, ':': 2}}\n",
-      "data_sizer_reader: {'Dp_start_keyword': '20.72', 'Dp_end_keyword': '784.39', 'convert_scale_from': 'dw/dlogdp'}\n",
-      "time_column: [1, 2]\n",
-      "time_format: %m/%d/%Y %H:%M:%S\n",
-      "delimiter: ,\n",
-      "time_shift_seconds: 0\n",
-      "timezone_identifier: UTC\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Settings to load 1d and 2d data from the sizer or any other instrument\n",
-    "# that has a 1d and 2d data in the same file.\n",
-    "\n",
-    "settings_1d, settings_2d = settings_generator.for_general_sizer_1d_2d_load(\n",
-    "    relative_data_folder='SMPS_data',\n",
-    "    filename_regex='*.csv',\n",
-    "    file_min_size_bytes=10,\n",
-    "    header_row=24,\n",
-    "    data_checks={\n",
-    "        \"characters\": [250],\n",
-    "        \"skip_rows\": 25,\n",
-    "        \"skip_end\": 0,\n",
-    "        \"char_counts\": {\"/\": 2, \":\": 2}\n",
-    "    },\n",
-    "    data_1d_column=[\n",
-    "        \"Lower Size (nm)\",\n",
-    "        \"Upper Size (nm)\",\n",
-    "        \"Sample Temp (C)\",\n",
-    "        \"Sample Pressure (kPa)\",\n",
-    "        \"Relative Humidity (%)\",\n",
-    "        \"Median (nm)\",\n",
-    "        \"Mean (nm)\",\n",
-    "        \"Geo. Mean (nm)\",\n",
-    "        \"Mode (nm)\",\n",
-    "        \"Geo. Std. Dev.\",\n",
-    "        \"Total Conc. (#/cm³)\"],\n",
-    "    data_1d_header=[\n",
-    "        \"Lower_Size_(nm)\",\n",
-    "        \"Upper_Size_(nm)\",\n",
-    "        \"Sample_Temp_(C)\",\n",
-    "        \"Sample_Pressure_(kPa)\",\n",
-    "        \"Relative_Humidity_(%)\",\n",
-    "        \"Median_(nm)\",\n",
-    "        \"Mean_(nm)\",\n",
-    "        \"Geo_Mean_(nm)\",\n",
-    "        \"Mode_(nm)\",\n",
-    "        \"Geo_Std_Dev.\",\n",
-    "        \"Total_Conc_(#/cc)\"],\n",
-    "    data_2d_dp_start_keyword=\"20.72\",\n",
-    "    data_2d_dp_end_keyword=\"784.39\",\n",
-    "    data_2d_convert_concentration_from=\"dw/dlogdp\",\n",
-    "    time_column=[1, 2],\n",
-    "    time_format=\"%m/%d/%Y %H:%M:%S\",\n",
-    "    delimiter=\",\",\n",
-    "    time_shift_seconds=0,\n",
-    "    timezone_identifier=\"UTC\",\n",
-    ")\n",
-    "\n",
-    "# print and format the settings dictionary\n",
-    "print('Settings 1d data dictionary:')\n",
-    "for key, value in settings_1d.items():\n",
-    "    print(f'{key}: {value}')\n",
-    "\n",
-    "print('')\n",
-    "print('Settings 2d data dictionary:')\n",
-    "for key, value in settings_2d.items():\n",
-    "    print(f'{key}: {value}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Load the data with the interface\n",
-    "\n",
-    "  Now that we have the settings dictionary, we can use an interface\n",
-    "  that will take the settings and locations and do all those steps from above.\n",
-    "  Calling the relevant functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading data from: 2022-07-07_095151_SMPS.csv\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading data from: 2022-07-10_094659_SMPS.csv\n",
-      "Loading data from: 2022-07-07_095151_SMPS.csv\n",
-      "Loading data from: 2022-07-10_094659_SMPS.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# import the interface\n",
-    "\n",
-    "working_path = get_data_folder()\n",
-    "\n",
-    "# settings from above\n",
-    "\n",
-    "# no call the loader interface\n",
-    "data_stream_1d = loader_interface.load_files_interface(\n",
-    "    path=working_path,\n",
-    "    settings=settings_1d,\n",
-    ")\n",
-    "\n",
-    "data_stream_2d = loader_interface.load_files_interface(\n",
-    "    path=working_path,\n",
-    "    settings=settings_2d,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Data stream 1d summary:\n",
-      "Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 2.05000e+01, 2.05000e+01, ..., 2.05000e+01,\n",
-      "        2.05000e+01, 2.05000e+01],\n",
-      "       [7.91500e+02, 7.91500e+02, 7.91500e+02, ..., 7.91500e+02,\n",
-      "        7.91500e+02, 7.91500e+02],\n",
-      "       [2.37000e+01, 2.36000e+01, 2.37000e+01, ..., 2.35000e+01,\n",
-      "        2.33000e+01, 2.35000e+01],\n",
-      "       ...,\n",
-      "       [2.07210e+01, 2.52550e+01, 2.18700e+01, ..., 2.07210e+01,\n",
-      "        2.10970e+01, 2.07210e+01],\n",
-      "       [2.17900e+00, 2.10100e+00, 2.13600e+00, ..., 2.31800e+00,\n",
-      "        2.31800e+00, 2.24800e+00],\n",
-      "       [2.16900e+03, 2.39408e+03, 2.27861e+03, ..., 2.08056e+03,\n",
-      "        2.10616e+03, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
-      "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n",
-      "\n",
-      "Data stream 2d summary:\n",
-      "Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  5621.118,  5165.139, ...,  9962.036,  8765.782,\n",
-      "        14380.528],\n",
-      "       [ 2832.655,  5867.747,  4969.987, ...,  7986.823, 11175.603,\n",
-      "        11524.35 ],\n",
-      "       [ 4733.553,  6233.403,  4312.386, ...,  8682.258,  8148.945,\n",
-      "        13632.727],\n",
-      "       ...,\n",
-      "       [   93.413,     0.   ,     0.   , ...,     0.   ,     0.   ,\n",
-      "            0.   ],\n",
-      "       [  122.992,     0.   ,   122.992, ...,     0.   ,     0.   ,\n",
-      "            0.   ],\n",
-      "       [    0.   ,    75.377,   124.085, ...,   124.153,   372.433,\n",
-      "            0.   ]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
-      "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print data stream summary\n",
-    "\n",
-    "print('')\n",
-    "print('Data stream 1d summary:')\n",
-    "print(data_stream_1d)\n",
-    "\n",
-    "print('')\n",
-    "print('Data stream 2d summary:')\n",
-    "print(data_stream_2d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on Stream in module particula.data.stream object:\n",
-      "\n",
-      "class Stream(builtins.object)\n",
-      " |  Stream(header: List[str] = <factory>, data: numpy.ndarray = <factory>, time: numpy.ndarray = <factory>, files: List[str] = <factory>) -> None\n",
-      " |  \n",
-      " |  A class for consistent data storage and format.\n",
-      " |  \n",
-      " |  Attributes:\n",
-      " |  ---------\n",
-      " |  header : List[str]\n",
-      " |      A list of strings representing the header of the data stream.\n",
-      " |  data : np.ndarray\n",
-      " |      A numpy array representing the data stream.\n",
-      " |  time : np.ndarray\n",
-      " |      A numpy array representing the time stream.\n",
-      " |  files : List[str]\n",
-      " |      A list of strings representing the files containing the data stream.\n",
-      " |  \n",
-      " |  Methods:\n",
-      " |  -------\n",
-      " |  validate_inputs\n",
-      " |      Validates the inputs to the Stream class.\n",
-      " |  datetime64 -> np.ndarray\n",
-      " |      Returns an array of datetime64 objects representing the time stream.\n",
-      " |      Useful for plotting, with matplotlib.dates.\n",
-      " |  return_header_dict -> dict\n",
-      " |      Returns the header as a dictionary with keys as header elements and\n",
-      " |      values as their indices.\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __eq__(self, other)\n",
-      " |  \n",
-      " |  __init__(self, header: List[str] = <factory>, data: numpy.ndarray = <factory>, time: numpy.ndarray = <factory>, files: List[str] = <factory>) -> None\n",
-      " |  \n",
-      " |  __post_init__(self)\n",
-      " |  \n",
-      " |  __repr__(self)\n",
-      " |  \n",
-      " |  validate_inputs(self)\n",
-      " |      Validates the inputs for the DataStream object.\n",
-      " |      Raises:\n",
-      " |          TypeError: If header is not a list.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Readonly properties defined here:\n",
-      " |  \n",
-      " |  datetime64\n",
-      " |      Returns an array of datetime64 objects representing the time stream.\n",
-      " |      Useful for plotting, with matplotlib.dates.\n",
-      " |  \n",
-      " |  return_header_dict\n",
-      " |      Returns the header as a dictionary with index (0, 1) as the keys\n",
-      " |      and the names as values.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors defined here:\n",
-      " |  \n",
-      " |  __dict__\n",
-      " |      dictionary for instance variables (if defined)\n",
-      " |  \n",
-      " |  __weakref__\n",
-      " |      list of weak references to the object (if defined)\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data and other attributes defined here:\n",
-      " |  \n",
-      " |  __annotations__ = {'data': <class 'numpy.ndarray'>, 'files': typing.Li...\n",
-      " |  \n",
-      " |  __dataclass_fields__ = {'data': Field(name='data',type=<class 'numpy.n...\n",
-      " |  \n",
-      " |  __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,or...\n",
-      " |  \n",
-      " |  __hash__ = None\n",
-      "\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "#  about it.\n",
-    "print(help(data_stream_2d))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# settin limits helps to see the data better, you can also use np.log10()\n",
-    "# to plot the concentration in log space\n",
-    "concentration = data_stream_2d.data\n",
-    "concentration = np.where(concentration < 1e-5, 1e-5, concentration)\n",
-    "concentration = np.where(concentration > 10**5, 10**5, concentration)\n",
-    "# concentration = np.log10(concentration)\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 1)\n",
-    "plt.contourf(\n",
-    "    data_stream_2d.datetime64,\n",
-    "    np.array(data_stream_2d.header).astype(float),\n",
-    "    concentration,\n",
-    "    cmap=plt.cm.PuBu_r, levels=50)\n",
-    "plt.yscale('log')\n",
-    "plt.tick_params(rotation=35)  # rotate the x axis labels\n",
-    "ax.set_xlabel(\"Time (UTC)\")\n",
-    "ax.set_ylabel('Diameter (nm)')\n",
-    "plt.colorbar(label='Concentration dN/dlog(Dp) [#/cm3]', ax=ax)\n",
-    "plt.show()\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Summary\n",
-    "\n",
-    "  This example covered loading data from a file that has 2 dimensions, such\n",
-    "  as a size distribution. It covered the following:\n",
-    "\n",
-    "  - Setting the working path\n",
-    "  - Loading the data\n",
-    "  - Formatting the data\n",
-    "  - Plotting the data\n",
-    "  - Generating the settings dictionary\n",
-    "  - Loading the data with the interface\n",
-    "  - Plotting the data stream"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on module particula.data.loader_interface in particula.data:\n",
-      "\n",
-      "NAME\n",
-      "    particula.data.loader_interface - interface to import data to a data stream\n",
-      "\n",
-      "FUNCTIONS\n",
-      "    get_1d_stream(file_path: str, settings: dict, first_pass: bool = True, stream: Optional[object] = None) -> object\n",
-      "        Loads and formats a 1D data stream from a file and initializes or updates\n",
-      "        a Stream object.\n",
-      "        \n",
-      "        Args:\n",
-      "        ----------\n",
-      "        file_path : str\n",
-      "            The path of the file to load data from.\n",
-      "        first_pass : bool\n",
-      "            Whether this is the first time data is being loaded. If True, the\n",
-      "            stream is initialized.\n",
-      "            If False, raises an error as only one file can be loaded.\n",
-      "        settings : dict\n",
-      "            A dictionary containing data formatting settings such as data checks,\n",
-      "            column names,\n",
-      "            time format, delimiter, and timezone information.\n",
-      "        stream : Stream, optional\n",
-      "            An instance of Stream class to be updated with loaded data. Defaults\n",
-      "            to a new Stream object.\n",
-      "        \n",
-      "        Returns:\n",
-      "        -------\n",
-      "        Stream\n",
-      "            The Stream object updated with the loaded data and corresponding time\n",
-      "            information.\n",
-      "        \n",
-      "        Raises:\n",
-      "        ------\n",
-      "        ValueError\n",
-      "            If `first_pass` is False, indicating data has already been loaded.\n",
-      "        TypeError\n",
-      "            If `settings` is not a dictionary.\n",
-      "        FileNotFoundError\n",
-      "            If the file specified by `file_path` does not exist.\n",
-      "        KeyError\n",
-      "            If any required keys are missing in the `settings` dictionary.\n",
-      "    \n",
-      "    get_2d_stream(file_path: str, settings: dict, first_pass: bool = True, stream: Optional[object] = None) -> object\n",
-      "        Initializes a 2D stream using the settings in the DataLake object.\n",
-      "        \n",
-      "        Args:\n",
-      "        ----------\n",
-      "            key (str): The key of the stream to initialise.\n",
-      "            path (str): The path of the file to load data from.\n",
-      "            first_pass (bool): Whether this is the first time loading data.\n",
-      "        \n",
-      "        Returns:\n",
-      "        ----------\n",
-      "            None.\n",
-      "    \n",
-      "    get_new_files(path: str, import_settings: dict[str, any], loaded_list: Optional[list] = None) -> tuple\n",
-      "        Scan a directory for new files based on import settings and stream status.\n",
-      "        \n",
-      "        This function looks for files in a specified path using import settings.\n",
-      "        It compares the new list of files with a pre-loaded list in the stream\n",
-      "        object to determine which files are new. The comparison is made based on\n",
-      "        file names and sizes. It returns a tuple with the paths of new files, a\n",
-      "        boolean indicating if this was the first pass, and a list of file\n",
-      "        information for new files.\n",
-      "        \n",
-      "        Args:\n",
-      "        ----------\n",
-      "        path : str\n",
-      "            The top-level directory path to scan for files.\n",
-      "        import_settings : dict\n",
-      "            A dictionary with 'relative_data_folder', 'filename_regex',\n",
-      "            and 'MIN_SIZE_BYTES' as keys\n",
-      "            used to specify the subfolder path and the regex pattern for filtering\n",
-      "            file names. It should also include 'min_size' key to specify the\n",
-      "            minimum size of the files to be considered.\n",
-      "        loaded_list : list of lists\n",
-      "            A list of lists with file names and sizes that have already been\n",
-      "            loaded. The default is None. If None, it will be assumed that no\n",
-      "            files have been loaded.\n",
-      "        \n",
-      "        Returns:\n",
-      "        -------\n",
-      "        tuple of (list, bool, list)\n",
-      "            A tuple containing a list of full paths of new files, a boolean\n",
-      "            indicating if no previous files were loaded (True if it's the first\n",
-      "            pass), and a list of lists with new file names and sizes.\n",
-      "        \n",
-      "        Returns:\n",
-      "        Raises:\n",
-      "        ------\n",
-      "        YourErrorType\n",
-      "            Explanation of when and why your error is raised and what it means.\n",
-      "    \n",
-      "    load_files_interface(path: str, settings: dict, stream: Optional[object] = None) -> object\n",
-      "        Load files into a stream object based on settings.\n",
-      "\n",
-      "DATA\n",
-      "    Optional = typing.Optional\n",
-      "        Optional type.\n",
-      "        \n",
-      "        Optional[X] is equivalent to Union[X, None].\n",
-      "\n",
-      "FILE\n",
-      "    c:\\users\\kkgor\\onedrive\\areas\\github\\particula\\particula\\data\\loader_interface.py\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(loader_interface)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ParticulaDev_py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/loading_data_part1.ipynb b/docs/examples/streamlake/loading_data_part1.ipynb
similarity index 79%
rename from docs/examples/loading_data_part1.ipynb
rename to docs/examples/streamlake/loading_data_part1.ipynb
index 4df17eff2..79e38bb40 100644
--- a/docs/examples/loading_data_part1.ipynb
+++ b/docs/examples/streamlake/loading_data_part1.ipynb
@@ -4,45 +4,48 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Loading Data Part 1\n",
+    "# Loading Part 1: Into & 1D Data\n",
     "\n",
-    " This example shows how to load data from a file and automate the cleaning, \n",
-    " formatting, and processing of the data.\n",
-    "\n",
-    " If you have a lot of data and repetitive tasks, you can use the scripts at\n",
-    " the end of this example to clean up you import process."
+    "This notebook is designed to guide you through the process of loading, cleaning, formatting, and processing data, a common task in aerosol research. You'll learn how to automate these repetitive tasks using Python scripts, specifically leveraging the functionalities of the `particula.data` package. This will streamline your data handling process, making your research more efficient.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ## Working path\n",
+    "## Setting the Working Path\n",
+    "\n",
+    "The first step in data processing is to set the working path where your data files are located. In this example, we'll use example data from the data/examples directory. However, you can easily adapt this to point to any directory on your computer.\n",
+    "\n",
+    "For instance, if you have a data folder in your home directory, you might set the path like this:\n",
+    "`path = \"U:\\\\data\\\\processing\\\\Campaign2023_of_awesome\\\\data\"`\n",
     "\n",
-    " Set the working path where the data is stored. For now we'll use the\n",
-    " provided example data in this current directory.\n",
+    "This flexibility allows you to work with data stored in different locations on your computer.\n",
     "\n",
-    " But the path could be any where on your computer. For example, if you have a\n",
-    " folder called \"data\" in your home directory, you could set the path to:\n",
-    " `path = \"U:\\\\data\\\\processing\\\\Campgain2023_of_aswsome\\\\data\"`"
+    "### Importing Packages\n",
+    "\n",
+    "Before we get started, we need to import the packages we'll be using.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# all the imports, but we'll go through them one by one as we use them\n",
-    "import os\n",
-    "import matplotlib.pyplot as plt\n",
+    "# Importing necessary libraries\n",
+    "import os  # For handling file paths and directories\n",
+    "import matplotlib.pyplot as plt  # For plotting data\n",
+    "\n",
+    "# Importing specific functions from the particula package for data loading\n",
+    "# and handling\n",
     "from particula.data import loader, loader_interface, settings_generator\n",
     "from particula.data.tests.example_data.get_example_data import get_data_folder"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -50,44 +53,38 @@
      "output_type": "stream",
      "text": [
       "Current path for this script:\n",
-      "\\docs\\examples\n",
+      "\\docs\\examples\\streamlake\n",
       "Path to data folder:\n",
       "\\data\\tests\\example_data\n"
      ]
     }
    ],
    "source": [
-    "# set the parent directory of the data folder, for now this is the same as the\n",
-    "# current working directory, but this can be a completely different path\n",
-    "#\n",
-    "# imports os to get the current working directory\n",
-    "import os\n",
+    "# Setting up the path for data files\n",
+    "import os  # Re-importing os for clarity\n",
     "from particula.data.tests.example_data.get_example_data import get_data_folder\n",
     "\n",
-    "current_path = os.getcwd()\n",
+    "current_path = os.getcwd()  # Getting the current working directory\n",
     "print('Current path for this script:')\n",
-    "# print the path from particula/ onwards\n",
-    "print(current_path.rsplit('particula')[-1])\n",
+    "print(current_path.rsplit('particula')[-1])  # Displaying the current path\n",
     "\n",
-    "path = get_data_folder()\n",
+    "path = get_data_folder()  # Getting the example data folder path\n",
     "print('Path to data folder:')\n",
-    "print(path.rsplit('particula')[-1])"
+    "print(path.rsplit('particula')[-1])  # Displaying the data folder path"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Load the data\n",
+    "## Loading the Data\n",
     "\n",
-    " With the working directory set, we can now load the data. For this we use\n",
-    " the `loader` module and call loader.data_raw_loader() with the file path as\n",
-    " argument."
+    "With the working directory set, we're ready to load the data. For this task, we'll use the `loader` module from the `particula` package. The `loader.data_raw_loader()` function allows us to easily load data files by providing the file path. This simplifies the initial step of any data analysis process, especially for those new to Python.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -118,28 +115,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Clean up the data\n",
-    " Now we can apply some data checks that we defined in the settings dictionary.\n",
-    " For this we use `loader.data_format_checks` and then we can convert that list\n",
-    "  of strings to a numpy array.\n",
-    "\n",
-    " To do that next step we call `loader.sample_data()` with inputs from the\n",
-    " settings dictionary and the data list we just cleaned up.\n",
-    "\n",
-    " The data checks are:\n",
-    " - characters: the min and max number of characters in each line of data\n",
-    " - char_counts: the number of times a character should appear in each line\n",
-    "               of data, This is a dictionary with the character as the key\n",
-    "               and the number of times it should appear as the value\n",
-    " - skip_rows: the number of rows to skip at the beginning of the file\n",
-    " - skip_end: the number of rows to skip at the end of the file\n",
-    "\n",
-    " Then returned is a filtered list of data that passes the checks."
+    "## Clean Up Data\n",
+    "\n",
+    "To facilitate data cleaning, we utilize `loader.data_format_checks`. This function performs a series of checks on the data, ensuring it meets specified criteria. The checks include:\n",
+    "\n",
+    "- `characters`: Validates the minimum and maximum number of characters in each data line, ensuring data integrity and uniformity.\n",
+    "- `char_counts`: Counts the occurrences of specific characters in each line. This is defined in a dictionary, with characters as keys and their expected counts as values.\n",
+    "- `skip_rows`: Specifies the number of rows to skip at the beginning of the file, useful for bypassing headers or non-data lines.\n",
+    "- `skip_end`: Determines the number of rows to omit at the end of the file, often used to avoid reading summary or footer information.\n",
+    "\n",
+    "After performing these checks, the function returns a list of data that has passed all the criteria. This cleaned data is then ready for further analysis or processing."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -153,38 +143,56 @@
     }
    ],
    "source": [
-    "# This is done by the general_data_formatter function for timeseries data\n",
-    "# 2d data is a separate function\n",
-    "\n",
+    "# Printing the length of raw_data before cleaning\n",
     "print(f\"raw_data length: {len(raw_data)}\")\n",
     "\n",
+    "# Cleaning the data using loader.data_format_checks\n",
+    "# This function checks for:\n",
+    "# - characters: Ensures each line has between 10 and 100 characters\n",
+    "# - char_counts: Checks that each line contains 4 commas (this is customizable)\n",
+    "# - skip_rows: Number of rows to skip at the start (none in this case)\n",
+    "# - skip_end: Number of rows to skip at the end (none in this case)\n",
     "data = loader.data_format_checks(\n",
     "    data=raw_data,\n",
     "    data_checks={\n",
     "        \"characters\": [10, 100],\n",
-    "        \"char_counts\": {\",\": 4},  # this can be anything \"adsf\": 10\n",
+    "        \"char_counts\": {\",\": 4},  # this can be anything, e.g., \"adsf\": 10\n",
     "        \"skip_rows\": 0,\n",
     "        \"skip_end\": 0\n",
-    "        }\n",
-    "    )\n",
+    "    }\n",
+    ")\n",
     "\n",
+    "# Printing the length of data after cleaning\n",
     "print(f\"data length: {len(data)}\")\n",
-    "print(f\"There was {len(raw_data) - len(data)} lines removed from the data\")"
+    "\n",
+    "# Calculating and printing the number of lines removed during the cleaning\n",
+    "# process\n",
+    "print(f\"There was {len(raw_data) - len(data)} lines removed from the data\")\n",
+    "\n",
+    "# Note: The data cleaning is performed by the general_data_formatter function for timeseries data.\n",
+    "# If dealing with 2D data, a separate function is used for cleaning."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ## Data and Time\n",
+    "## Data and Time\n",
+    "\n",
+    "Once the data is cleaned, the next crucial step is extracting and standardizing the time and data columns. This process is particularly important because time data can come in many formats, and standardizing it to a single format, like epoch time, facilitates analysis and comparison. Epoch time, also known as Unix time, is a system for describing points in time as the number of seconds elapsed since January 1, 1970. It's a widely used standard in computing and data processing.\n",
+    "\n",
+    "To handle the conversion and extraction of time and data, we use the `loader.sample_data()` function. This function is designed to:\n",
     "\n",
-    " Now that the data is cleaned up, we can get the time and data columns from\n",
-    " the cleaned data. For this we use the `loader.sample_data()` function."
+    "1. **Identify and Extract Time Data**: It locates the time information within the dataset, which can be in various formats such as ISO 8601, DD/MM/YYYY, MM/DD/YYYY, etc.\n",
+    "2. **Convert to Epoch Time**: It standardizes the extracted time data to epoch time. This conversion is crucial because epoch time provides a consistent reference for time data, making it easier to perform time-based calculations and comparisons.\n",
+    "3. **Separate Data Columns**: Along with time data, it also segregates other data columns for further analysis.\n",
+    "\n",
+    "By using `loader.sample_data()` to convert various time formats to epoch time and separate the data columns, we effectively prepare our dataset for robust and error-free analysis. This approach is essential in research and data science, where dealing with multiple time formats is a common challenge."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -192,10 +200,10 @@
      "output_type": "stream",
      "text": [
       "epoch_time shape: (33254,)\n",
-      "[1.65734280e+09 1.65734281e+09 1.65734281e+09 1.65734281e+09\n",
+      "First 5 epoch times: [1.65734280e+09 1.65734281e+09 1.65734281e+09 1.65734281e+09\n",
       " 1.65734282e+09]\n",
       "data_array shape: (33254, 2)\n",
-      "[[3.3510e+04 1.7000e+01]\n",
+      "First 5 data entries: [[3.3510e+04 1.7000e+01]\n",
       " [3.3465e+04 1.7100e+01]\n",
       " [3.2171e+04 1.7000e+01]\n",
       " [3.2889e+04 1.6800e+01]\n",
@@ -207,34 +215,48 @@
     "# Sample the data to get the epoch times and the data\n",
     "epoch_time, data_array = loader.sample_data(\n",
     "    data=data,\n",
-    "    time_column=[0],  # column that has the time data if you need to combine\n",
-    "                      # multiple columns, you can do that here by passing\n",
-    "                      # a list of columns, for example [0, 2]\n",
-    "    time_format=\"epoch\",  # this can also be \"%m/%d/%Y %I:%M:%S %p\" or any\n",
-    "                          # format that datetime.strptime() can handle\n",
-    "    data_columns=[1, 2],  # columns that have the data\n",
+    "    time_column=[0],  # Indicate the column(s) that contain time data.\n",
+    "                      # For instance, if time data spans multiple columns,\n",
+    "                      # list them here, like [0, 2] for columns 0 and 2.\n",
+    "    time_format=\"epoch\",  # Define the format of the time data.\n",
+    "                          # Use \"epoch\" for epoch time, or specify another format\n",
+    "                          # compatible with datetime.strptime(), such as\n",
+    "                          # \"%m/%d/%Y %I:%M:%S %p\".\n",
+    "    # Specify columns that contain the actual data for analysis.\n",
+    "    data_columns=[1, 2],\n",
+    "    # Indicate the delimiter used in the data (e.g., comma for CSV files).\n",
     "    delimiter=\",\",\n",
     ")\n",
     "\n",
-    "\n",
+    "# Printing the shape and first few entries of the epoch time array\n",
     "print(f\"epoch_time shape: {epoch_time.shape}\")\n",
-    "print(epoch_time[:5])\n",
+    "print(\"First 5 epoch times:\", epoch_time[:5])\n",
+    "\n",
+    "# Printing the shape and first few entries of the data array\n",
     "print(f\"data_array shape: {data_array.shape}\")\n",
-    "print(data_array[:5])"
+    "print(\"First 5 data entries:\", data_array[:5])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ## Pause to Plot\n",
+    "## Pause to Plot: Verifying Data Integrity\n",
+    "\n",
+    "After successfully importing the data and converting time information, it's crucial to pause and visually inspect our dataset. Plotting the data serves as an essential checkpoint to verify that the import process has been executed correctly. This step is vital for several reasons:\n",
+    "\n",
+    "1. **Data Integrity Check**: Visualizing the data helps in quickly identifying any anomalies or irregularities that might indicate issues in the data import or cleaning processes.\n",
+    "\n",
+    "2. **Understanding Data Structure**: A plot can provide immediate insights into the nature and structure of the dataset, such as trends, patterns, and outliers.\n",
+    "\n",
+    "3. **Ensuring Accuracy**: Before proceeding to more complex analyses or modeling, confirming the accuracy of the data through visualization is a fundamental best practice.\n",
     "\n",
-    " Now that we have the data and time, we can plot it to see what it looks like."
+    "In this section, we will create a simple plot to examine our time series data, ensuring that the time conversion and data import have been performed correctly.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -249,17 +271,31 @@
     }
    ],
    "source": [
-    "# plot the data\n",
+    "# Creating a figure and axis for the plot\n",
     "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Plotting the data\n",
+    "# `epoch_time` on the x-axis and the first column of `data_array` on the y-axis\n",
     "ax.plot(epoch_time,\n",
-    "        data_array[:, 0],\n",
-    "        label=\"data column 1\",\n",
-    "        linestyle=\"none\",\n",
-    "        marker=\".\",)\n",
+    "        data_array[:, 0],  # Selecting the first column of data_array\n",
+    "        label=\"data column 1\",  # Label for this data series\n",
+    "        linestyle=\"none\",  # No line connecting the data points\n",
+    "        marker=\".\")  # Style of the data points; here, it's a dot\n",
+    "\n",
+    "# Setting the x-axis label to \"Time (epoch)\"\n",
     "ax.set_xlabel(\"Time (epoch)\")\n",
+    "\n",
+    "# Setting the y-axis label to \"Data\"\n",
     "ax.set_ylabel(\"Data\")\n",
+    "\n",
+    "# Adding a legend to the plot, which helps identify the data series\n",
     "ax.legend()\n",
+    "\n",
+    "# Displaying the plot\n",
     "plt.show()\n",
+    "\n",
+    "# Adjusting the layout to make sure everything fits well within the figure\n",
+    "# This is particularly useful for ensuring labels and titles are not cut off\n",
     "fig.tight_layout()"
    ]
   },
@@ -267,38 +303,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Stream Object\n",
+    "## Stream Object: Streamlining Your Analysis\n",
     "\n",
-    " Now you can stop here, and do whatever you want with the data. But if you\n",
-    " end up copying and pasting, that code over and over again, it can get tedious. So\n",
-    " instead we can use the `Stream` object to do some automated analysis.\n",
+    "Once you have the cleaned and formatted data, you might find yourself repeatedly using the same code to analyze different datasets. To avoid the tedium of copying and pasting the same code, we can utilize the `Stream` object provided by the `loader` module. The `Stream` object allows for more automated and efficient analysis, significantly simplifying repetitive tasks.\n",
     "\n",
-    " Those settings then can be collected as a dictionary and passed to the loader\n",
-    " function to load the data into a `Stream` object.\n",
+    "The `Stream` object requires a settings dictionary that encapsulates all necessary parameters for data loading. Below, we'll explore how to create this settings dictionary and use it to initialize a `Stream` object.\n",
     "\n",
-    " We'll get to the stream object later, but for now we'll just show how to load\n",
-    " using the settings dictionary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " ## Settings dictionary\n",
+    "### Settings Dictionary: Centralizing Data Loading Parameters\n",
+    "\n",
+    "The settings dictionary is a crucial component in the data loading process. It consolidates all the necessary settings for loading your data into a single, easily manageable structure. This includes parameters for data checks, specification of time and data columns, as well as the time format.\n",
+    "\n",
+    "There are two primary methods to generate this settings dictionary:\n",
     "\n",
-    " The settings dictionary is a dictionary that contains all the settings for\n",
-    " loading the data. This includes the data checks, the time and data columns,\n",
-    " and the time format.\n",
+    "1. **Using `settings_generator` Module**: This approach involves calling the `for_1d_general_file()` function from the `settings_generator` module. You can specify your desired settings as arguments to this function, which then returns a ready-to-use settings dictionary.\n",
     "\n",
-    " There are two ways to generate the settings dictionary. The first is to use\n",
-    " the `settings_generator` module and call the `for_1d_general_file()` function\n",
-    " with the settings you want. The second is to manually create the dictionary\n",
-    " yourself or just copy the one generated from the `settings_generator` module."
+    "2. **Manual Creation**: Alternatively, you can manually construct the settings dictionary. This might involve writing the dictionary from scratch or modifying one generated by the `settings_generator` module. Manual creation offers more flexibility and is particularly useful if your data loading requirements are unique or complex.\n",
+    "\n",
+    "In the next steps, we will demonstrate how to use the settings dictionary for loading data into a `Stream` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -323,32 +349,38 @@
     }
    ],
    "source": [
-    "# This method uses the settings_generator module to generate the settings.\n",
+    "# Using the settings_generator module to generate the settings dictionary\n",
+    "# for data loading\n",
     "\n",
+    "# Importing the necessary modules (repeating the import from above for clarity)\n",
     "from particula.data import settings_generator\n",
     "\n",
-    "# lets repeat the same import from above\n",
-    "\n",
+    "# Generating the settings dictionary with specified parameters\n",
     "settings = settings_generator.for_general_1d_load(\n",
+    "    # The folder where the data files are located\n",
     "    relative_data_folder='CPC_3010_data',\n",
+    "    # Pattern to match the filenames (e.g., all CSV files)\n",
     "    filename_regex='*.csv',\n",
-    "    file_min_size_bytes=10,\n",
+    "    file_min_size_bytes=10,  # Minimum file size in bytes to be considered for loading\n",
     "    data_checks={\n",
+    "        # Range of characters count per line (min, max)\n",
     "        \"characters\": [10, 100],\n",
+    "        # Number of times a character (e.g., comma) should appear in each line\n",
     "        \"char_counts\": {\",\": 4},\n",
-    "        \"skip_rows\": 0,\n",
-    "        \"skip_end\": 0,\n",
+    "        \"skip_rows\": 0,  # Number of rows to skip at the beginning of the file\n",
+    "        \"skip_end\": 0,  # Number of rows to skip at the end of the file\n",
     "    },\n",
-    "    data_column=[1, 2],\n",
-    "    data_header=['data 1', 'data 2'],\n",
-    "    time_column=[0],\n",
+    "    data_column=[1, 2],  # Columns in the file that contain the data\n",
+    "    data_header=['data 1', 'data 2'],  # Headers for the data columns\n",
+    "    time_column=[0],  # Column in the file that contains the time data\n",
+    "    # Format of the time data (epoch, \"%m/%d/%Y %H:%M:%S\" etc.)\n",
     "    time_format='epoch',\n",
-    "    delimiter=',',\n",
-    "    time_shift_seconds=0,\n",
-    "    timezone_identifier='UTC',\n",
+    "    delimiter=',',  # Delimiter used in the data file (e.g., comma for CSV)\n",
+    "    time_shift_seconds=0,  # Shift in time data if needed, in seconds\n",
+    "    timezone_identifier='UTC',  # Timezone identifier for the time data\n",
     ")\n",
     "\n",
-    "# print and format the settings dictionary\n",
+    "# Printing the generated settings dictionary to verify its contents\n",
     "print('Settings dictionary:')\n",
     "for key, value in settings.items():\n",
     "    print(f'{key}: {value}')"
@@ -358,94 +390,212 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ## Load the data with the interface\n",
+    "## Load the Data with the Interface\n",
+    "\n",
+    "With our settings dictionary in hand, we can now streamline the data loading process using a specialized interface. This interface acts as a bridge between our predefined settings and the actual data loading steps. It essentially automates the sequence of actions we performed manually earlier, like data cleaning and formatting, based on the parameters specified in the settings dictionary.\n",
     "\n",
-    " Now that we have the settings dictionary, we can use an interface\n",
-    " that will take the settings and locations and do all those steps from above.\n",
-    " Calling the relevant functions."
+    "By utilizing this interface, we can efficiently load our data with a few lines of code, ensuring consistency and accuracy. This approach is particularly beneficial when dealing with multiple datasets or when needing to replicate the same data processing steps in different projects.\n",
+    "\n",
+    "The interface function we'll use is designed to accept the settings dictionary and the location of the data. It then internally calls the necessary functions to execute the data loading process. This includes:\n",
+    "\n",
+    "- Reading the data files based on the specified filename patterns and folder locations.\n",
+    "- Performing data checks and cleaning as defined in the settings.\n",
+    "- Extracting and formatting time and data columns according to our requirements.\n",
+    "\n",
+    "This method significantly simplifies the data loading process, reducing the potential for errors and increasing efficiency."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loading data from: CPC_3010_data_20220709_Jul.csv\n",
-      "Loading data from: CPC_3010_data_20220710_Jul.csv\n"
+      "  Loading file: CPC_3010_data_20220709_Jul.csv\n",
+      "  Loading file: CPC_3010_data_20220710_Jul.csv\n"
      ]
     }
    ],
    "source": [
-    "# import the interface\n",
+    "# Importing the loader interface from the particula package\n",
     "from particula.data import loader_interface\n",
+    "from particula.data.tests.example_data.get_example_data import get_data_folder\n",
     "\n",
+    "# Getting the working path where the data files are located\n",
     "working_path = get_data_folder()\n",
     "\n",
-    "# copied from above,\n",
-    "# or you could just say cpc_setting=settings_generator.for_1d_general_file(...)\n",
+    "# Defining the settings for loading CPC 3010 data\n",
+    "# These settings were previously generated or can be created manually\n",
     "cpc_settings = {\n",
+    "    # Folder name containing the data files\n",
     "    'relative_data_folder': 'CPC_3010_data',\n",
+    "    # Pattern to match filenames (e.g., all CSV files)\n",
     "    'filename_regex': '*.csv',\n",
-    "    'MIN_SIZE_BYTES': 10,\n",
-    "    'data_loading_function':'general_1d_load',\n",
-    "    'header_row': 0,\n",
-    "    'data_checks': {'characters': [\n",
-    "            10,\n",
-    "            100],\n",
-    "        'char_counts': {\n",
-    "            ',': 4},\n",
-    "        'skip_rows': 0,\n",
-    "        'skip_end': 0},\n",
-    "    'data_column': [\n",
-    "        1,\n",
-    "        2],\n",
-    "    'data_header': [\n",
-    "        'data 1',\n",
-    "        'data 2'],\n",
-    "    'time_column': [0],\n",
-    "    'time_format': 'epoch',\n",
-    "    'delimiter': ',',\n",
-    "    'time_shift_seconds': 0,\n",
-    "    'timezone_identifier': 'UTC'}\n",
-    "\n",
-    "# no call the loader interface\n",
+    "    'MIN_SIZE_BYTES': 10,  # Minimum file size in bytes for a file to be considered\n",
+    "    # Function to be used for loading the data\n",
+    "    'data_loading_function': 'general_1d_load',\n",
+    "    'header_row': 0,  # Row number of the header in the data file\n",
+    "    'data_checks': {\n",
+    "        # Range of character count per line (min, max)\n",
+    "        'characters': [10, 100],\n",
+    "        # Number of times a character (comma) should appear in each line\n",
+    "        'char_counts': {',': 4},\n",
+    "        'skip_rows': 0,  # Number of rows to skip at the beginning of the file\n",
+    "        'skip_end': 0  # Number of rows to skip at the end of the file\n",
+    "    },\n",
+    "    'data_column': [1, 2],  # Columns in the file that contain the data\n",
+    "    'data_header': ['data 1', 'data 2'],  # Headers for the data columns\n",
+    "    'time_column': [0],  # Column in the file that contains the time data\n",
+    "    'time_format': 'epoch',  # Format of the time data (epoch, ISO 8601, etc.)\n",
+    "    'delimiter': ',',  # Delimiter used in the data file (e.g., comma for CSV)\n",
+    "    'time_shift_seconds': 0,  # Shift in time data if needed, in seconds\n",
+    "    'timezone_identifier': 'UTC'  # Timezone identifier for the time data\n",
+    "}\n",
+    "\n",
+    "# Now call the loader interface to load the data using the specified settings\n",
     "data_stream = loader_interface.load_files_interface(\n",
-    "    path=working_path,\n",
-    "    settings=cpc_settings,\n",
-    ")"
+    "    path=working_path,  # Path to the data folder\n",
+    "    settings=cpc_settings,  # Settings defined above\n",
+    ")\n",
+    "\n",
+    "# The data_stream object now contains the loaded and formatted data ready\n",
+    "# for analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the Stream Class in Particula\n",
+    "\n",
+    "The `Stream` class in the Particula package is a sophisticated tool for data management, akin to a well-organized filing cabinet for your data. Let's dive into its features and functionalities to understand how it can streamline your data analysis tasks.\n",
+    "\n",
+    "## Key Features of the Stream Class\n",
+    "\n",
+    "- **Header**: Just as labels on filing cabinet drawers help you identify contents, the `header` in `Stream` serves a similar purpose. It's a list of strings that represent the column names of your data, guiding you to understand what each column in your dataset signifies.\n",
+    "\n",
+    "- **Data**: Imagine each drawer in a filing cabinet filled with files; `data` in `Stream` is akin to this. It's a numpy array containing your dataset, neatly organized where each row represents a point in time and each column corresponds to one of the headers.\n",
+    "\n",
+    "- **Time**: Like time tags on files that show when they were recorded, the `time` attribute in `Stream` keeps a chronological record of each data entry. It’s a numpy array that represents the time dimension of your data, correlating each row in the `data` array with a specific moment.\n",
+    "\n",
+    "- **Files**: Similar to having a list that tells you about all the folders inside a filing cabinet, `files` in `Stream` reveals the names of all the data files that comprise your data stream. This list of strings provides a clear trace back to the original data sources.\n",
+    "\n",
+    "## Functionalities and Tools\n",
+    "\n",
+    "- **validate_inputs**: This function acts like a checklist ensuring all your files are correctly formatted before being placed in the cabinet. In `Stream`, it checks the validity of header, data, and time inputs, ensuring everything is in order for smooth data handling.\n",
+    "\n",
+    "- **datetime64**: Think of it as a tool that standardizes the dates and times on your documents into a uniform format (datetime64), making them easier to understand and use, particularly for plotting and time-based operations.\n",
+    "\n",
+    "- **return_header_dict**: It’s like having a quick reference guide in your filing cabinet, telling you exactly where to find data corresponding to each label (header). In `Stream`, it offers a dictionary mapping header elements to their indices in the data array, simplifying data access.\n",
+    "\n",
+    "## Practical Application\n",
+    "\n",
+    "For beginners and experienced users alike, the Stream class in Particula package is your organized, efficient data manager. It takes the complexity out of data handling, allowing you to focus more on insightful analysis and less on the intricacies of data organization.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Stream:\n",
-      "Stream(header=['data 1', 'data 2'], data=array([[3.3510e+04, 3.3465e+04, 3.2171e+04, ..., 1.9403e+04, 2.0230e+04,\n",
-      "        1.9521e+04],\n",
-      "       [1.7000e+01, 1.7100e+01, 1.7000e+01, ..., 1.6900e+01, 1.7000e+01,\n",
-      "        1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
-      "       1.65751559e+09, 1.65751560e+09, 1.65751560e+09]), files=[['CPC_3010_data_20220709_Jul.csv', 1044534], ['CPC_3010_data_20220710_Jul.csv', 1113488]])\n"
+      "Headers of the Data Stream:\n",
+      "['data 1', 'data 2']\n",
+      "\n",
+      "First 5 Rows of Data:\n",
+      "[[3.3510e+04 1.7000e+01]\n",
+      " [3.3465e+04 1.7100e+01]\n",
+      " [3.2171e+04 1.7000e+01]\n",
+      " [3.2889e+04 1.6800e+01]\n",
+      " [3.2706e+04 1.7000e+01]]\n",
+      "\n",
+      "Time Stamps for the First 5 Data Entries:\n",
+      "[1.65734280e+09 1.65734281e+09 1.65734281e+09 1.65734281e+09\n",
+      " 1.65734282e+09]\n",
+      "\n",
+      "Data from Column 'data 1':\n",
+      "[33510. 33465. 32171. 32889. 32706.]\n",
+      "\n",
+      "Number of Data Entries in the Time Stream:\n",
+      "68551\n",
+      "\n",
+      "First 5 Time Entries in datetime64 Format:\n",
+      "['2022-07-09T05:00:04' '2022-07-09T05:00:07' '2022-07-09T05:00:10'\n",
+      " '2022-07-09T05:00:13' '2022-07-09T05:00:16']\n",
+      "\n",
+      "Names of the Source Files for the Data Stream:\n",
+      "[['CPC_3010_data_20220709_Jul.csv', 1044534], ['CPC_3010_data_20220710_Jul.csv', 1113488]]\n"
      ]
     }
    ],
    "source": [
-    "# print data stream summary\n",
-    "print('Stream:')\n",
-    "print(data_stream)"
+    "# Example 1: Print the Headers of the Data Stream\n",
+    "print(\"Headers of the Data Stream:\")\n",
+    "print(data_stream.header)\n",
+    "\n",
+    "# Example 2: Display the First Few Rows of Data\n",
+    "print(\"\\nFirst 5 Rows of Data:\")\n",
+    "print(data_stream.data[:5, :])\n",
+    "\n",
+    "# Example 3: Print the Time Stamps Associated with the First Few Data Entries\n",
+    "print(\"\\nTime Stamps for the First 5 Data Entries:\")\n",
+    "print(data_stream.time[:5])\n",
+    "\n",
+    "# Example 4: Retrieve and Print Data from a Specific Column using Header Name\n",
+    "column_name = 'data 1'  # Replace with an actual column name from your header\n",
+    "print(f\"\\n5 Entries from data Column '{column_name}':\")\n",
+    "print(data_stream[column_name][:5])\n",
+    "\n",
+    "# Example 5: Print the Length of the Time Stream (Number of Data Entries)\n",
+    "print(\"\\nNumber of Data Entries in the Time Stream:\")\n",
+    "print(len(data_stream))\n",
+    "\n",
+    "# Example 6: Convert Time to datetime64 and Print the First Few Entries\n",
+    "print(\"\\nFirst 5 Time Entries in datetime64 Format:\")\n",
+    "print(data_stream.datetime64[:5])\n",
+    "\n",
+    "# Example 7: Print the Names of the Source Files\n",
+    "print(\"\\nNames of the Source Files for the Data Stream:\")\n",
+    "print(data_stream.files)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting Data Using the Stream Object\n",
+    "\n",
+    "Visualizing your data is a crucial step in understanding its patterns and behavior. With the `data_stream` object from the Particula package, plotting time-series data becomes straightforward and efficient. Here's how you can use `data_stream` for plotting:\n",
+    "\n",
+    "1. **Preparing the Plot**: Start by creating a figure and an axis using `matplotlib`'s `plt.subplots()`. This sets up the canvas on which you'll draw your plot.\n",
+    "\n",
+    "2. **Plotting Time-Series Data**:\n",
+    "   - The `data_stream.datetime64` provides time data in a format that is ideal for plotting on the x-axis.\n",
+    "   - Since `data_stream.data` is a 2D array (where rows correspond to time and columns to different data types), you need to specify which column you want to plot. For example, `data_stream.data[:, 0]` plots the first column of data.\n",
+    "\n",
+    "3. **Customizing the Plot**:\n",
+    "   - Add labels to your plot for clarity. The `label` parameter in the `ax.plot` function can be used to name the data series being plotted.\n",
+    "   - Customize the appearance of your plot. In this example, we use `linestyle=\"none\"` and `marker=\".\"` to plot individual data points without connecting lines.\n",
+    "\n",
+    "4. **Adjusting Axes and Display**:\n",
+    "   - The `plt.tick_params` function allows you to rotate the x-axis labels, making them easier to read, especially for densely plotted data.\n",
+    "   - Set the x-axis and y-axis labels using `ax.set_xlabel` and `ax.set_ylabel` to provide context to your plot.\n",
+    "\n",
+    "5. **Final Touches**:\n",
+    "   - Include a legend by calling `ax.legend()`. This is particularly useful when plotting multiple data series.\n",
+    "   - Use `plt.show()` to display the plot.\n",
+    "   - The `fig.tight_layout()` ensures that the layout of the plot is adjusted so that all elements (like labels and titles) are clearly visible."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -460,19 +610,38 @@
     }
    ],
    "source": [
-    "# plot the data\n",
+    "# Importing the matplotlib library for plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Creating a new figure and axis for the plot\n",
     "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Plotting data from the data_stream object\n",
+    "# data_stream.datetime64 is used for the x-axis (time data)\n",
+    "# data_stream.data[:, 0] selects the first column of data for the y-axis\n",
     "ax.plot(data_stream.datetime64,\n",
-    "        data_stream.data[0, :],  # data_stream.data is a 2d array, so we need\n",
-    "                                 # to specify which column we want to plot\n",
-    "        label=\"data column 1\",\n",
-    "        linestyle=\"none\",\n",
-    "        marker=\".\",)\n",
+    "        data_stream.data[:, 0],  # Selecting the first column of data\n",
+    "        label=\"data column 1\",  # Label for the plotted data series\n",
+    "        linestyle=\"none\",  # No line connecting the data points\n",
+    "        marker=\".\")  # Style of the data points; here, it's a dot\n",
+    "\n",
+    "# Adjusting the x-axis labels for better readability\n",
+    "# Rotating the labels by -35 degrees\n",
     "plt.tick_params(rotation=-35, axis='x')\n",
-    "ax.set_xlabel(\"Time (UTC)\")\n",
-    "ax.set_ylabel(\"Data\")\n",
+    "\n",
+    "# Setting the labels for the x-axis and y-axis\n",
+    "ax.set_xlabel(\"Time (UTC)\")  # Label for the x-axis\n",
+    "ax.set_ylabel(\"Data\")  # Label for the y-axis\n",
+    "\n",
+    "# Adding a legend to the plot\n",
+    "# This helps in identifying the data series\n",
     "ax.legend()\n",
+    "\n",
+    "# Displaying the plot\n",
     "plt.show()\n",
+    "\n",
+    "# Adjusting the layout of the plot\n",
+    "# Ensures that all elements of the plot are nicely fitted within the figure\n",
     "fig.tight_layout()"
    ]
   },
@@ -480,12 +649,174 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Summary\n",
+    "# Summary\n",
     "\n",
-    "We covered how to load data from a file and automate the cleaning, formatting,\n",
-    "and processing of the data. We then showed how to generate a settings\n",
-    "dictionary and use that to load the data into a `Stream` object. This is\n",
-    "useful if you have a lot of data and repetitive tasks. Doing this method also loads and combines multiple files into one `Stream` object.\n"
+    "In this section, we explored a comprehensive approach to handling data using the functionalities provided by the Particula package. Key takeaways include:\n",
+    "\n",
+    "1. **Data Loading and Cleaning**: We began by loading data from files, emphasizing the importance of automating the cleaning process. This step involved removing errors, handling missing values, and ensuring the data is in a consistent format, which is crucial for accurate analysis.\n",
+    "\n",
+    "2. **Using the Settings Dictionary**: A significant part of the process was the creation of a settings dictionary. This dictionary serves as a blueprint for the data loading process, specifying parameters like data checks, column information, and time formatting. This approach is particularly effective when working with large datasets or needing to replicate data processing steps across various projects.\n",
+    "\n",
+    "3. **Streamlining with the Stream Object**: We introduced the `Stream` object, a powerful tool for organizing and processing data streams. The `Stream` object allows for efficient data manipulation, storage, and retrieval, making it an invaluable resource for handling complex or voluminous datasets.\n",
+    "\n",
+    "4. **Simplifying Repetitive Tasks**: By automating data loading and cleaning through the `Stream` object and settings dictionary, we significantly reduced the redundancy of repetitive tasks. This method proves beneficial in projects where data from multiple files needs to be loaded and combined into a single, manageable unit.\n",
+    "\n",
+    "5. **Visualization and Analysis**: Finally, we demonstrated how to plot the data using `matplotlib`, a crucial step for visually inspecting the data and ensuring its integrity post-import. This visual check is vital for confirming that the import process has been executed correctly and the data is ready for further analysis.\n",
+    "\n",
+    "In conclusion, this section provided a solid foundation for efficiently managing and processing data in Python, setting the stage for more advanced analysis and applications in future sections.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bonus: Utilizing Python's Help Function\n",
+    "\n",
+    "One of Python's most useful built-in functions for learners and developers alike is the `help` function. It provides detailed information about objects, functions, modules, and more, directly within your Python environment. This can be particularly helpful when exploring new libraries or understanding the functionalities of different components in your code.\n",
+    "\n",
+    "#### How to Use the Help Function\n",
+    "\n",
+    "The `help` function can be invoked directly in your Python code or interactive session. Simply pass the object or function you're curious about as an argument to `help()`, and it will display documentation including descriptions, available methods, attributes, and other relevant details.\n",
+    "\n",
+    "For example, to learn more about the `data_stream` object we've been working with, you can use:\n",
+    "\n",
+    "```python\n",
+    "help(data_stream)\n",
+    "```\n",
+    "\n",
+    "This command will display information about the Stream class, including its methods, attributes, and how to use them.\n",
+    "\n",
+    "#### Applying Help to Explore Functionality\n",
+    "\n",
+    "You can use help with any object or function in Python. For instance, if you want to understand more about a function from the Particula package or even a built-in Python function, simply pass it to help(). Here's how you can use it:\n",
+    "\n",
+    "To explore a module: help(particula)\n",
+    "To learn about a specific function: help(particula.some_function)\n",
+    "To understand an object you created: help(my_object)\n",
+    "Enhancing Learning and Troubleshooting\n",
+    "Using the help function is an excellent habit to develop. It enhances your learning process by providing immediate access to documentation. It's also a valuable tool for troubleshooting and understanding the functionalities of different components in your code.\n",
+    "\n",
+    "Remember, the help function is always there to assist you, making it a bit easier to navigate the extensive world of Python programming."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on Stream in module particula.data.stream object:\n",
+      "\n",
+      "class Stream(builtins.object)\n",
+      " |  Stream(header: List[str] = <factory>, data: numpy.ndarray = <factory>, time: numpy.ndarray = <factory>, files: List[str] = <factory>) -> None\n",
+      " |  \n",
+      " |  A class for consistent data storage and format.\n",
+      " |  \n",
+      " |  Attributes:\n",
+      " |  ---------\n",
+      " |  header : List[str]\n",
+      " |      A list of strings representing the header of the data stream.\n",
+      " |  data : np.ndarray\n",
+      " |      A numpy array representing the data stream. The first dimension\n",
+      " |      represents time and the second dimension represents the header.\n",
+      " |  time : np.ndarray\n",
+      " |      A numpy array representing the time stream.\n",
+      " |  files : List[str]\n",
+      " |      A list of strings representing the files containing the data stream.\n",
+      " |  \n",
+      " |  Methods:\n",
+      " |  -------\n",
+      " |  validate_inputs\n",
+      " |      Validates the inputs to the Stream class.\n",
+      " |  datetime64 -> np.ndarray\n",
+      " |      Returns an array of datetime64 objects representing the time stream.\n",
+      " |      Useful for plotting, with matplotlib.dates.\n",
+      " |  return_header_dict -> dict\n",
+      " |      Returns the header as a dictionary with keys as header elements and\n",
+      " |      values as their indices.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __eq__(self, other)\n",
+      " |  \n",
+      " |  __getitem__(self, index: Union[int, str])\n",
+      " |      Allows for indexing of the data stream.\n",
+      " |      Args:\n",
+      " |      ----------\n",
+      " |      index : int or str\n",
+      " |          The index of the data stream to return.\n",
+      " |      Returns:\n",
+      " |      -------\n",
+      " |      np.ndarray\n",
+      " |          The data stream at the specified index.\n",
+      " |  \n",
+      " |  __init__(self, header: List[str] = <factory>, data: numpy.ndarray = <factory>, time: numpy.ndarray = <factory>, files: List[str] = <factory>) -> None\n",
+      " |  \n",
+      " |  __len__(self)\n",
+      " |      Returns the length of the time stream.\n",
+      " |  \n",
+      " |  __post_init__(self)\n",
+      " |  \n",
+      " |  __repr__(self)\n",
+      " |  \n",
+      " |  __setitem__(self, index: Union[int, str], value)\n",
+      " |      Allows for setting or adding of a row of data in the stream.\n",
+      " |      Args:\n",
+      " |      index : The index of the data stream to set.\n",
+      " |      value : The data to set at the specified index.\n",
+      " |      \n",
+      " |      future work maybe add a list option and iterate through the list\n",
+      " |  \n",
+      " |  validate_inputs(self)\n",
+      " |      Validates the inputs for the DataStream object.\n",
+      " |      Raises:\n",
+      " |          TypeError: If header is not a list.\n",
+      " |      # this might be why I can't call Stream without inputs\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Readonly properties defined here:\n",
+      " |  \n",
+      " |  datetime64\n",
+      " |      Returns an array of datetime64 objects representing the time stream.\n",
+      " |      Useful for plotting, with matplotlib.dates.\n",
+      " |  \n",
+      " |  header_dict\n",
+      " |      Returns the header as a dictionary with index (0, 1) as the keys\n",
+      " |      and the names as values.\n",
+      " |  \n",
+      " |  header_float\n",
+      " |      Returns the header as a numpy array of floats.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  __annotations__ = {'data': <class 'numpy.ndarray'>, 'files': typing.Li...\n",
+      " |  \n",
+      " |  __dataclass_fields__ = {'data': Field(name='data',type=<class 'numpy.n...\n",
+      " |  \n",
+      " |  __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,or...\n",
+      " |  \n",
+      " |  __hash__ = None\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# call help on data_stream to see the available methods\n",
+    "help(data_stream)"
    ]
   }
  ],
diff --git a/docs/examples/streamlake/loading_data_part2.ipynb b/docs/examples/streamlake/loading_data_part2.ipynb
new file mode 100644
index 000000000..5df55caf0
--- /dev/null
+++ b/docs/examples/streamlake/loading_data_part2.ipynb
@@ -0,0 +1,767 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Loading Part 2: Sizer Data\n",
+    "\n",
+    "Welcome to Part 2 of our data loading series! This section builds upon the concepts and techniques introduced in Part 1. If you haven't gone through the first part, we highly recommend you do so to get a firm foundation. Here, we'll dive into handling 2-dimensional data, such as size distributions, which are common in fields like environmental science and engineering.\n",
+    "\n",
+    "## Setting Up Your Working Path\n",
+    "\n",
+    "Before we begin, let's set up the working path, which is the location on your computer where your data files are stored. In this example, we'll use data provided in the current directory of this notebook. However, you can easily change this to any directory where your data files are located. For instance, if you have a folder named \"data\" in your home directory for a project called \"Campaign2023_of_awesome\", you would set the path like this: \n",
+    "\n",
+    "`path = \"U:\\\\data\\\\processing\\\\Campaign2023_of_awesome\\\\data\"`\n",
+    "\n",
+    "Let's start by importing the necessary Python libraries and modules. We'll explain each one as we use them throughout this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os  # For handling file and directory paths\n",
+    "import numpy as np  # For numerical operations\n",
+    "import matplotlib.pyplot as plt  # For plotting and visualizing data\n",
+    "# Particula package components\n",
+    "from particula.data import loader, loader_interface, settings_generator\n",
+    "# For accessing example data\n",
+    "from particula.data.tests.example_data.get_example_data import get_data_folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we'll determine the current working directory and set the path for the data folder. This step is essential for ensuring that our scripts know where to look for the data files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current path for this script:\n",
+      "\\docs\\examples\\streamlake\n",
+      "Path to data folder:\n",
+      "\\data\\tests\\example_data\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Retrieving and printing the current working directory of this script\n",
+    "current_path = os.getcwd()\n",
+    "print('Current path for this script:')\n",
+    "print(current_path.rsplit('particula')[-1])\n",
+    "\n",
+    "# Setting and printing the path to the data folder\n",
+    "path = get_data_folder()\n",
+    "print('Path to data folder:')\n",
+    "print(path.rsplit('particula')[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the Data\n",
+    "\n",
+    "Now that we've set our working directory, the next step is to load the data. We'll be using the `loader` module from the Particula package for this task. The function `loader.data_raw_loader()` is specifically designed to read data from a file, which we'll specify using its file path. This approach is straightforward and efficient for loading data into Python for analysis.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preview of loaded data:\n",
+      "Units,dW/dlogDp\n",
+      "Weight,Number\n",
+      "Sample #,Date,Start Time,Sample Temp (C),Sample Pressure (kPa),Relative Humidity (%),Mean Free Path (m),Gas Viscosity (Pa*s),Diameter Midpoint (nm),20.72,21.10,21.48,21.87,22.27,22.67,23.08,23.50,23.93,24.36,24.80,25.25,25.71,26.18,26.66,27.14,27.63,28.13,28.64,29.16,29.69,30.23,30.78,31.34,31.91,32.49,33.08,33.68,34.29,34.91,35.55,36.19,36.85,37.52,38.20,38.89,39.60,40.32,41.05,41.79,42.55,43.32,44.11,44.91,45.73,46.56,47.40,48.26,49.14,50.03,50.94,51.86,52.80,53.76,54.74,55.73,56.74,57.77,58.82,59.89,60.98,62.08,63.21,64.36,65.52,66.71,67.93,69.16,70.41,71.69,72.99,74.32,75.67,77.04,78.44,79.86,81.31,82.79,84.29,85.82,87.38,88.96,90.58,92.22,93.90,95.60,97.34,99.10,100.90,102.74,104.60,106.50,108.43,110.40,112.40,114.44,116.52,118.64,120.79,122.98,125.21,127.49,129.80,132.16,134.56,137.00,139.49,142.02,144.60,147.22,149.89,152.61,155.38,158.20,161.08,164.00,166.98,170.01,173.09,176.24,179.43,182.69,186.01,189.38,192.82,196.32,199.89,203.51,207.21,210.97,214.80,218.70,222.67,226.71,230.82,235.01,239.28,243.62,248.05,252.55,257.13,261.80,266.55,271.39,276.32,281.33,286.44,291.64,296.93,302.32,307.81,313.40,319.08,324.88,330.77,336.78,342.89,349.12,355.45,361.90,368.47,375.16,381.97,388.91,395.96,403.15,410.47,417.92,425.51,433.23,441.09,449.10,457.25,465.55,474.00,482.61,491.37,500.29,509.37,518.61,528.03,537.61,547.37,557.31,567.42,577.72,588.21,598.89,609.76,620.82,632.09,643.57,655.25,667.14,679.25,691.58,704.14,716.92,729.93,743.18,756.67,770.40,784.39,Scan Time (s),Retrace Time (s),Scan Resolution (Hz),Scans Per Sample,HV Polarity,Sheath Flow (L/min),Aerosol Flow (L/min),Bypass Flow (L/min),Low Voltage (V),High Voltage (V),Lower Size (nm),Upper Size (nm),Density (g/cm³),td + 0.5 (s),tf (s),D50 (nm),Median (nm),Mean (nm),Geo. Mean (nm),Mode (nm),Geo. Std. Dev.,Total Conc. (#/cm³),Neutralizer Status,Dilution Factor,Test Name,Test Description,Dataset Name,Dataset Description,Instrument Errors\n",
+      "1,07/07/2022,08:49:17,23.7,101.2,61.9,6.75690e-8,1.83579e-5,,6103.186,2832.655,4733.553,4765.944,5960.964,4475.806,4412.044,5853.069,4832.167,3781.343,3675.830,3271.549,3084.392,3668.269,4116.143,3310.157,3978.368,4151.566,2515.995,3755.837,2776.663,5032.745,3775.426,2818.553,2641.302,2636.806,3079.759,2606.094,2317.234,3192.346,2226.703,2484.878,3394.395,1762.834,3172.359,2919.533,2452.013,3403.780,2360.277,2543.386,2563.290,2649.769,1375.374,1364.046,1446.529,2068.167,1336.070,1542.077,1707.249,1482.481,2272.182,1754.409,2472.438,1191.563,2221.825,1635.293,2548.571,1991.926,2546.956,1790.114,2115.075,1138.769,1934.746,2163.955,1613.179,2132.750,1654.348,1698.154,2403.529,1222.983,1829.254,1197.162,1638.797,1248.565,2417.521,1130.421,1429.423,1694.923,1658.378,1443.393,1731.346,1277.799,1089.149,1072.630,1205.387,1693.146,1109.648,915.428,491.529,881.028,1218.297,755.658,714.301,686.247,790.943,398.805,1043.226,1298.495,1548.704,1070.899,846.596,938.241,232.947,926.941,837.452,794.492,254.455,392.637,353.144,872.576,693.986,1544.164,657.340,546.445,311.890,365.934,616.794,610.810,938.786,815.964,593.441,939.634,188.115,1077.429,1213.142,737.913,1876.626,735.779,996.521,1098.601,1166.494,962.551,1392.535,947.504,655.459,993.819,682.087,852.503,601.057,733.860,529.122,960.578,687.512,839.973,652.820,289.921,623.835,453.604,588.057,856.253,283.994,282.839,365.801,200.382,365.756,146.548,306.730,373.162,114.272,0.000,182.692,260.788,164.857,19.851,89.612,0.000,181.974,0.000,53.276,20.016,0.000,0.000,95.433,96.222,0.000,0.000,197.307,0.000,100.336,0.000,102.072,0.000,0.000,209.529,0.000,213.227,107.561,0.000,218.965,220.930,111.453,0.000,113.460,0.000,115.513,0.000,0.000,0.000,0.000,28.221,93.413,122.992,0.000,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,41.562,74.959,52.078,20.721,2.179,2.16900e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
+      "2,07/07/2022,08:50:48,23.6,101.2,61.7,6.75401e-8,1.83531e-5,,5621.118,5867.747,6233.403,3453.156,4484.307,5468.148,4725.052,4689.983,3661.759,4356.725,4292.911,7728.414,5112.679,4746.084,3957.005,3472.977,3496.697,4674.202,4188.868,2868.559,3375.113,4306.112,5191.077,4732.512,4566.029,3514.167,5172.877,3825.270,5323.756,2327.737,3846.602,2347.097,3182.011,1876.273,2952.863,2831.255,2497.869,4158.061,3828.510,3199.720,2309.195,2462.550,3060.240,1086.744,1476.289,2069.774,1727.787,2710.631,2067.327,2619.082,2345.026,2362.235,1429.749,2557.408,2660.327,1209.933,1590.320,1696.569,2236.773,1499.046,1922.632,1650.213,3147.351,2201.919,1622.954,2198.739,1800.998,1429.621,1426.761,1923.931,1262.939,1745.284,1458.571,1523.548,1920.108,1382.558,2211.525,2571.277,1979.297,1562.697,1741.573,1307.680,967.481,838.919,1502.136,1301.401,1011.619,829.770,973.269,1100.004,1152.808,749.250,1187.900,806.256,111.008,297.062,809.059,1361.412,779.536,535.087,881.522,1307.518,800.804,1053.953,182.381,1042.830,673.021,646.171,825.612,963.187,748.743,540.954,769.157,788.222,825.566,236.537,865.009,289.185,803.098,398.510,446.847,439.645,1118.961,1003.003,924.180,745.149,430.134,415.522,805.970,790.348,998.975,1043.136,604.082,1004.545,1082.455,1312.781,1447.390,872.420,398.380,695.719,857.412,645.872,691.129,623.007,471.728,641.049,1023.693,394.611,475.599,446.076,657.686,313.003,136.395,248.550,579.894,336.126,485.938,298.810,0.000,227.571,104.550,157.583,289.697,0.229,0.000,0.000,217.592,67.816,24.067,0.000,0.000,0.000,0.000,0.000,97.009,0.000,0.000,0.000,0.000,0.000,0.000,205.900,0.000,104.761,0.000,0.000,0.000,108.509,0.000,110.461,0.000,0.000,0.000,114.471,115.506,116.540,0.000,118.660,0.000,0.000,0.000,0.000,75.377,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,39.458,69.080,49.198,25.255,2.101,2.39408e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
+      "3,07/07/2022,08:52:19,23.7,101.2,61.5,6.75690e-8,1.83579e-5,,5165.139,4969.987,4312.386,6939.394,4680.764,3224.473,4999.149,3653.002,4241.532,3928.137,2718.607,3363.947,4863.410,5338.452,4659.515,3430.329,3997.386,4644.421,4943.511,3883.970,3212.310,4445.981,2349.435,3605.419,4366.557,4969.924,4880.573,3186.281,3089.412,2724.537,3195.740,4277.947,4864.436,4263.532,2100.807,1967.634,3283.337,3268.660,3001.917,2781.549,1879.354,1376.083,2051.524,2165.874,2012.210,2923.129,1575.515,1544.252,1610.635,1572.609,1299.370,1549.832,1145.100,2897.864,1839.992,2351.579,2102.027,1543.106,953.811,2073.610,2317.378,2087.617,1586.363,1897.860,2456.722,1647.781,1013.534,1734.023,1633.021,1841.697,2193.442,2714.856,1396.336,2264.046,1671.363,1538.012,1257.148,1423.316,1217.281,1745.437,1787.473,1284.774,1534.815,1274.852,1438.025,1199.602,964.066,862.098,685.995,679.146,879.775,806.703,979.672,894.103,1379.499,1112.031,744.999,580.777,1241.262,960.784,750.484,908.236,957.901,652.265,1200.515,429.487,347.453,552.393,617.871,652.163,709.227,788.963,1499.238,627.895,1315.208,976.800,555.360,440.680,1182.819,863.800,362.530,942.047,460.380,1222.507,678.820,1006.555,319.371,91.941,761.841,205.384,449.120,751.217,572.530,350.734,295.089,413.379,612.088,474.457,678.504,490.408,751.536,400.656,585.567,676.707,364.052,124.385,631.790,788.487,566.062,390.904,141.751,256.369,366.589,528.781,512.078,257.120,393.412,350.601,361.659,65.138,348.203,326.629,329.714,175.810,111.365,74.091,103.212,0.000,0.000,47.532,0.000,166.826,0.000,96.217,388.070,97.832,98.649,99.490,200.678,202.399,0.000,102.953,0.000,0.000,105.683,106.611,33.630,183.108,2.602,218.305,222.901,0.000,226.925,0.000,0.000,116.553,0.000,118.661,119.732,120.801,0.000,122.992,124.085,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,39.324,72.102,50.019,21.870,2.136,2.27861e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
+      "4,07/07/2022,08:53:50,23.8,101.2,61.4,6.75979e-8,1.83627e-5,,5814.745,5937.421,5542.118,7127.484,5341.069,4793.690,4938.844,5721.541,4877.746,5900.250,5104.984,4914.366,4891.892,6655.579,4431.173,3389.961,4947.809,3115.245,4138.126,5421.474,4589.063,4007.156,2524.137,5009.064,4780.963,4959.096,3648.285,4148.676,4270.099,2229.465,3043.487,5618.376,3689.188,4700.549,2535.915,1754.223,2560.335,2853.385,2454.711,2515.907,3015.370,1502.864,2344.161,2761.448,2047.076,1542.531,2151.757,2365.884,2330.816,2585.566,1431.955,2391.335,2097.717,1891.014,2211.815,2071.479,2188.302,2475.058,1906.364,1781.793,2356.998,1527.723,2609.446,1644.771,1917.624,1843.984,2418.197,1385.516,1263.621,2155.939,2083.223,1765.167,957.777,2077.747,1667.811,1122.065,1579.113,1709.471,1604.406,686.151,390.075,1194.313,1657.144,1462.232,1870.846,1012.132,847.165,1248.528,1039.604,779.076,1375.101,1058.272,1013.378,1211.420,1641.490,979.146,835.539,763.524,951.720,1270.393,1308.492,1056.486,1715.924,657.112,1475.767,235.866,827.129,1266.089,1080.958,1246.249,1147.116,840.719,1560.246,1201.554,1743.366,1233.526,1166.422,1068.551,1047.492,787.018,759.836,491.419,714.111,460.361,681.068,767.815,654.715,501.038,357.016,575.937,613.281,851.029,583.739,475.691,431.584,616.144,744.932,409.334,984.682,371.750,613.130,757.474,637.077,441.004,609.132,380.961,595.419,565.033,566.955,332.402,450.524,139.761,430.419,443.058,558.628,158.467,271.708,346.807,57.637,148.050,226.825,353.827,77.661,0.000,0.000,74.100,0.000,250.296,117.433,93.156,187.816,0.000,95.443,0.000,0.000,293.505,0.000,99.496,100.342,0.000,102.078,102.959,0.000,0.000,0.000,106.622,322.709,0.000,328.474,0.000,67.473,44.378,0.000,0.000,115.519,0.000,0.000,118.668,0.000,0.000,0.000,0.000,0.000,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,37.995,68.796,48.896,21.870,2.107,2.51144e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n",
+      "5,07/07/2022,08:55:21,24.0,101.1,61.4,6.77227e-8,1.83722e-5,,8034.425,6317.981,6972.600,4577.324,6488.519,4985.397,5484.518,7295.312,3449.590,4261.716,4259.456,6124.670,4418.824,5418.742,3311.293,3548.897,4940.747,6738.536,3377.823,3309.433,5322.339,4148.187,3387.285,3967.636,5064.382,4573.259,3896.245,4006.531,3769.030,4129.946,4678.454,3121.839,3888.625,2443.782,1947.617,2321.130,1845.465,2833.269,2745.881,3262.145,4055.876,2319.187,3397.282,2596.623,2935.256,1508.733,1555.232,3184.200,2683.631,2158.530,2303.663,2739.336,2714.276,2536.377,2051.076,2063.667,2074.972,2852.267,2366.702,2135.668,1500.801,2228.817,2220.527,1501.131,2354.567,2072.434,2547.917,2111.890,1474.809,1561.614,1334.889,1100.318,1077.335,1470.618,1377.825,1684.933,1093.441,1596.409,1456.255,1543.298,1116.499,984.258,1294.805,1586.816,723.664,1709.369,1060.965,1415.310,1611.158,1791.258,1098.238,1513.790,1335.019,1178.572,1538.772,477.803,1130.380,1596.999,652.664,1098.951,1384.104,772.285,788.185,1432.363,773.331,729.470,819.882,979.684,925.309,753.771,706.255,659.741,1026.707,818.647,1205.428,940.460,906.655,758.763,811.344,1123.245,520.356,1009.392,651.265,735.336,209.657,549.624,537.181,841.849,483.705,713.011,497.248,743.196,556.459,953.140,847.692,614.097,423.810,816.193,627.059,453.998,976.898,592.170,548.197,535.480,667.837,312.390,476.781,369.028,451.687,432.520,1001.512,312.053,498.408,198.771,399.968,363.778,403.848,381.782,223.839,227.667,212.819,101.097,164.909,359.326,285.450,0.000,44.177,0.000,158.441,220.559,81.404,49.687,95.468,0.000,194.095,391.452,98.679,0.000,0.000,0.000,0.000,102.990,103.881,104.800,0.000,106.644,0.000,108.554,0.000,110.496,0.000,112.492,113.494,0.000,115.548,0.000,0.000,0.000,239.531,241.683,0.000,0.000,248.252,75,4,50,1,Negative,2.000,0.300,0.00,10.07,9863.01,20.5,791.5,1.0,1.81,10.79,1000.0,39.214,69.960,48.959,20.721,2.123,2.56068e+3,ON,1,TRACER-CAT,,2022 07 07 09_51,,Detector aerosol flow rate error;Incomplete Scan\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Constructing the full file path for the data file\n",
+    "# We're joining the path set earlier with the specific file we want to load\n",
+    "data_file = os.path.join(\n",
+    "    path,  # The base path we set earlier\n",
+    "    'SMPS_data',  # The subdirectory where the data file is located\n",
+    "    '2022-07-07_095151_SMPS.csv')  # The name of the data file\n",
+    "\n",
+    "# Optional: Print the file path to confirm it's correct\n",
+    "# print(\"Data file path:\", data_file)\n",
+    "\n",
+    "# Using the loader module to load the data from the file\n",
+    "# The data_raw_loader function takes the file path as its argument and\n",
+    "# reads the data\n",
+    "raw_data = loader.data_raw_loader(data_file)\n",
+    "\n",
+    "# Printing a snippet of the loaded data for a quick preview\n",
+    "# This helps us to confirm that the data is loaded and to get an idea of\n",
+    "# its structure\n",
+    "print(\"Preview of loaded data:\")\n",
+    "for row in raw_data[22:30]:  # Displaying rows 22 to 30 as a sample\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Formatting the Data\n",
+    "\n",
+    "When dealing with 2-dimensional data, such as size distributions, the formatting process can be a bit more complex compared to 1-dimensional data. For our data, we need to extract size bins and use them as headers for our dataset. This involves specifying the start and end points within the data that define our size bins. In our specific example, the start point is indicated by the keyword \"Date Time\" and the end point by \"Total Conc\". Understanding where your data starts and ends is crucial for accurate formatting and analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch time (First 5 Entries):\n",
+      "[1.65718376e+09 1.65718385e+09 1.65718394e+09 1.65718403e+09\n",
+      " 1.65718412e+09]\n",
+      "Data shape:\n",
+      "(2854, 203)\n",
+      "Header (First 10 Entries):\n",
+      "['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Formatting the data for time series analysis\n",
+    "# The sizer_data_formatter function from the loader module is used for\n",
+    "# this purpose\n",
+    "\n",
+    "epoch_time, data, header = loader.sizer_data_formatter(\n",
+    "    data=raw_data,  # The raw data that was loaded earlier\n",
+    "    data_checks={  # Performing checks on the data\n",
+    "        \"characters\": [250],  # Expected character count per line\n",
+    "        # Number of rows to skip at the beginning (headers, etc.)\n",
+    "        \"skip_rows\": 25,\n",
+    "        \"skip_end\": 0,  # Number of rows to skip at the end\n",
+    "        \"char_counts\": {\"/\": 2, \":\": 2}  # Ensuring line formats are consistent\n",
+    "    },\n",
+    "    data_sizer_reader={  # Reading the size distribution data\n",
+    "        'Dp_start_keyword': '20.72',  # Starting keyword for size bins\n",
+    "        'Dp_end_keyword': '784.39',  # Ending keyword for size bins\n",
+    "        'convert_scale_from': 'dw/dlogdp'  # Scale conversion for the data everything is converted to dN/dlogdp\n",
+    "    },\n",
+    "    time_column=[1, 2],  # Columns that contain the time data\n",
+    "    time_format=\"%m/%d/%Y %H:%M:%S\",  # Format of the time data\n",
+    "    delimiter=\",\",  # Delimiter used in the data file\n",
+    "    header_row=24)  # Row number that contains the header\n",
+    "\n",
+    "# Printing a preview of the formatted data to confirm successful formatting\n",
+    "print('Epoch time (First 5 Entries):')\n",
+    "print(epoch_time[:5])  # Displaying the first 5 time entries\n",
+    "print('Data shape:')\n",
+    "print(data.shape)  # Showing the shape of the data array\n",
+    "print('Header (First 10 Entries):')\n",
+    "print(header[:10])  # Displaying the first 10 headers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pause to Plot\n",
+    "\n",
+    "Visualizing your data is a crucial step in the data analysis process. It allows you to see patterns, trends, and potential anomalies that might not be evident from the raw data alone. Now that we have formatted the data and extracted the time information, let's create a plot. This will help us get a visual sense of the data's characteristics, such as the concentration of particles in different size bins over time.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a plot using matplotlib\n",
+    "fig, ax = plt.subplots()  # Creating a figure and axis for the plot\n",
+    "\n",
+    "# Plotting data from a specific size bin against time\n",
+    "# 'epoch_time' is used on the x-axis (time data)\n",
+    "# 'data[:, 50]' selects the data from the 50th bin (as an example) for the y-axis\n",
+    "ax.plot(epoch_time,\n",
+    "        data[:, 50],  # Selecting the 50th bin of data to plot\n",
+    "        label=f'Bin {header[50]} nm',  # Adding a label with the bin size\n",
+    "        )\n",
+    "\n",
+    "# Setting labels for the x-axis and y-axis\n",
+    "ax.set_xlabel(\"Time (epoch)\")  # Label for the x-axis\n",
+    "ax.set_ylabel(\"Bin Concentration (#/cm³)\")  # Label for the y-axis\n",
+    "\n",
+    "# Adding a legend to the plot for clarity\n",
+    "ax.legend()\n",
+    "\n",
+    "# Displaying the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Adjusting the layout to ensure all plot elements are visible and\n",
+    "# well-arranged\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dates in Plots\n",
+    "\n",
+    "When working with time-series data, it's often helpful to have dates on the x-axis of your plots for better readability and understanding. However, to display dates effectively in plots using `matplotlib`, we need to convert our time data into a format that `matplotlib` can recognize and work with.\n",
+    "\n",
+    "One common format for this purpose is `np.datetime64`. This format represents dates and times in a way that is compatible with numpy arrays, making it ideal for plotting time-related data. In our case, we can convert our epoch time (time since a fixed point in the past, typically January 1, 1970) to `np.datetime64` using the `convert.datetime64_from_epoch_array` function from the Particula package.\n",
+    "\n",
+    "Additionally, to make the plot more readable, especially when there are many data points, it's a good practice to rotate the x-axis labels. This prevents overlapping and makes each date and time label clear. We can achieve this rotation using `plt.xticks(rotation=45)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Importing the necessary function for converting epoch time to datetime64\n",
+    "from particula.util.time_manage import datetime64_from_epoch_array\n",
+    "\n",
+    "# Converting the epoch time to datetime64 format\n",
+    "time_in_datetime64 = datetime64_from_epoch_array(epoch_time)\n",
+    "\n",
+    "# Creating a plot\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Plotting the data with time in datetime64 format on the x-axis\n",
+    "ax.plot(time_in_datetime64,\n",
+    "        data[:, 50],  # Selecting the 50th bin of data\n",
+    "        label=f'Bin {header[50]} nm',  # Label for the data series\n",
+    "        )\n",
+    "\n",
+    "# Rotating the x-axis labels to 45 degrees for better readability\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "# Setting the x-axis and y-axis labels\n",
+    "ax.set_xlabel(\"Time (UTC)\")  # Updated label to indicate the time format\n",
+    "ax.set_ylabel(\"Bin Concentration (#/cm³)\")\n",
+    "\n",
+    "# Adding a legend to the plot\n",
+    "ax.legend()\n",
+    "\n",
+    "# Displaying the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Adjusting the layout for a neat presentation\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Contour Plot of Data\n",
+    "\n",
+    "Contour plots are a powerful tool for visualizing how data changes over time and space. In the context of size distribution data, a contour plot can effectively show the variation in particle concentration across different sizes over time. It's like looking at a topographic map where different colors or shades represent varying concentrations of particles at different sizes and times.\n",
+    "\n",
+    "### Preparing the Data for Contour Plotting\n",
+    "\n",
+    "Before we plot, it's a good practice to set limits on our data to ensure that extreme values don't skew the visualization. This helps in highlighting the relevant ranges of our data. Here's how we do it:\n",
+    "\n",
+    "1. **Setting Lower and Upper Limits**: We impose a lower limit to avoid plotting extremely low concentrations (which might be less relevant or below detection limits) and an upper limit to avoid letting very high concentrations dominate the plot.\n",
+    "\n",
+    "2. **Option for Logarithmic Scale**: For data with a wide range of values, using a logarithmic scale (e.g., `np.log10(concentration)`) can make the plot more informative by compressing the scale and emphasizing the variations across orders of magnitude.\n",
+    "\n",
+    "### Creating the Contour Plot\n",
+    "\n",
+    "With our data prepared, we can now create the contour plot. This type of plot will use different colors or shades to represent the concentration of particles at various sizes and times.\n",
+    "\n",
+    "- **X-Axis (Epoch Time)**: Represents the time dimension of our data.\n",
+    "- **Y-Axis (Diameter in nm)**: Represents the different size bins of particles.\n",
+    "- **Color Intensity**: Indicates the concentration of particles at each size and time.\n",
+    "\n",
+    "Using `plt.contourf`, we create a filled contour plot with a logarithmic y-scale, which is particularly useful for size distribution data that typically spans several orders of magnitude in particle sizes.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Setting limits on the concentration data to improve plot readability\n",
+    "concentration = data\n",
+    "concentration = np.where(\n",
+    "    concentration < 1e-5,\n",
+    "    1e-5,\n",
+    "    concentration)  # Setting a lower limit\n",
+    "concentration = np.where(\n",
+    "    concentration > 10**5,\n",
+    "    10**5,\n",
+    "    concentration)  # Setting an upper limit\n",
+    "# Uncomment the next line to plot concentration in logarithmic scale\n",
+    "# concentration = np.log10(concentration)\n",
+    "\n",
+    "# Creating a figure and axis for the contour plot\n",
+    "fig, ax = plt.subplots(1, 1)\n",
+    "\n",
+    "# Creating the contour plot\n",
+    "plt.contourf(\n",
+    "    epoch_time,  # X-axis: Time data in epoch format\n",
+    "    # Y-axis: Particle sizes converted to float\n",
+    "    np.array(header).astype(float),\n",
+    "    concentration.T,  # Transposed concentration data for correct orientation\n",
+    "    cmap=plt.cm.PuBu_r,  # Color map for the plot\n",
+    "    levels=50  # Number of levels in the contour plot\n",
+    ")\n",
+    "\n",
+    "# Setting the y-axis to logarithmic scale for better visualization of size\n",
+    "# distribution\n",
+    "plt.yscale('log')\n",
+    "\n",
+    "# Setting labels for the x-axis and y-axis\n",
+    "ax.set_xlabel('Epoch Time')  # Label for the x-axis\n",
+    "ax.set_ylabel('Diameter (nm)')  # Label for the y-axis\n",
+    "\n",
+    "# Adding a color bar to the plot, indicating concentration levels\n",
+    "plt.colorbar(label='Concentration dN/dlogDp [#/cm³]', ax=ax)\n",
+    "\n",
+    "# Displaying the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Adjusting the layout for a better presentation of the plot elements\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simplifying Data Import with the Settings Generator\n",
+    "\n",
+    "In the same way we handled 1-dimensional (1d) data, we can also streamline the import process for 2-dimensional (2d) data using the settings generator. This tool is particularly useful for creating a structured approach to loading complex datasets. By using the `settings_generator.for_general_sizer_1d_2d_load()` function, we can generate a comprehensive settings dictionary that directs how data should be imported and formatted.\n",
+    "\n",
+    "### Understanding the Settings Generator Function\n",
+    "\n",
+    "This function is designed to be flexible and accommodate a wide range of data types and formats. It comes with numerous arguments that allow you to specify details like data checks, column information, time format, etc., tailored to your specific dataset. However, it's important to note that you don't have to be overwhelmed by these options.\n",
+    "\n",
+    "### Using Default Settings\n",
+    "\n",
+    "For many users, especially those just starting out or working with standard data formats, the default settings of the `settings_generator` function may work. These defaults are configured to align with the example data provided in the Particula package. This means that if your data structure is similar to the example data, you can call this function without passing any arguments, and it will automatically set up the settings for you.\n",
+    "\n",
+    "This approach not only saves time but also reduces the potential for errors in the data import process, making it a quick and reliable way to get your data ready for analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Settings for 1d data:\n",
+      "relative_data_folder: SMPS_data\n",
+      "filename_regex: *.csv\n",
+      "MIN_SIZE_BYTES: 10\n",
+      "data_loading_function: general_1d_load\n",
+      "header_row: 24\n",
+      "data_checks: {'characters': [250], 'skip_rows': 25, 'skip_end': 0, 'char_counts': {'/': 2, ':': 2}}\n",
+      "data_column: ['Lower Size (nm)', 'Upper Size (nm)', 'Sample Temp (C)', 'Sample Pressure (kPa)', 'Relative Humidity (%)', 'Median (nm)', 'Mean (nm)', 'Geo. Mean (nm)', 'Mode (nm)', 'Geo. Std. Dev.', 'Total Conc. (#/cm³)']\n",
+      "data_header: ['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)']\n",
+      "time_column: [1, 2]\n",
+      "time_format: %m/%d/%Y %H:%M:%S\n",
+      "delimiter: ,\n",
+      "time_shift_seconds: 0\n",
+      "timezone_identifier: UTC\n",
+      "\n",
+      "Settings for 2d data:\n",
+      "relative_data_folder: SMPS_data\n",
+      "filename_regex: *.csv\n",
+      "MIN_SIZE_BYTES: 10\n",
+      "data_loading_function: general_2d_load\n",
+      "header_row: 24\n",
+      "data_checks: {'characters': [250], 'skip_rows': 25, 'skip_end': 0, 'char_counts': {'/': 2, ':': 2}}\n",
+      "data_sizer_reader: {'Dp_start_keyword': '20.72', 'Dp_end_keyword': '784.39', 'convert_scale_from': 'dw/dlogdp'}\n",
+      "time_column: [1, 2]\n",
+      "time_format: %m/%d/%Y %H:%M:%S\n",
+      "delimiter: ,\n",
+      "time_shift_seconds: 0\n",
+      "timezone_identifier: UTC\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Importing the necessary module for settings generation\n",
+    "from particula.data import settings_generator\n",
+    "\n",
+    "# Generating settings for loading 1d and 2d data from files\n",
+    "# This is useful for instruments that output both types of data in the\n",
+    "# same file\n",
+    "settings_1d, settings_2d = settings_generator.for_general_sizer_1d_2d_load(\n",
+    "    relative_data_folder='SMPS_data',  # Folder where the data files are located\n",
+    "    filename_regex='*.csv',  # Pattern to match filenames (e.g., all CSV files)\n",
+    "    file_min_size_bytes=10,  # Minimum file size in bytes for consideration\n",
+    "    header_row=24,  # Row number containing the header in the data file\n",
+    "    data_checks={  # Checks to ensure data integrity\n",
+    "        \"characters\": [250],  # Expected character count per line\n",
+    "        \"skip_rows\": 25,  # Rows to skip at the start of the file\n",
+    "        \"skip_end\": 0,  # Rows to skip at the end of the file\n",
+    "        \"char_counts\": {\"/\": 2, \":\": 2}  # Ensuring date formats are consistent\n",
+    "    },\n",
+    "    data_1d_column=[  # Columns for 1d data in the data file\n",
+    "        \"Lower Size (nm)\", \"Upper Size (nm)\", \"Sample Temp (C)\",\n",
+    "        \"Sample Pressure (kPa)\", \"Relative Humidity (%)\", \"Median (nm)\",\n",
+    "        \"Mean (nm)\", \"Geo. Mean (nm)\", \"Mode (nm)\", \"Geo. Std. Dev.\",\n",
+    "        \"Total Conc. (#/cm³)\"\n",
+    "    ],\n",
+    "    data_1d_header=[  # Headers for 1d data columns once in the Stream\n",
+    "        \"Lower_Size_(nm)\", \"Upper_Size_(nm)\", \"Sample_Temp_(C)\",\n",
+    "        \"Sample_Pressure_(kPa)\", \"Relative_Humidity_(%)\", \"Median_(nm)\",\n",
+    "        \"Mean_(nm)\", \"Geo_Mean_(nm)\", \"Mode_(nm)\", \"Geo_Std_Dev.\",\n",
+    "        \"Total_Conc_(#/cc)\"\n",
+    "    ],\n",
+    "    data_2d_dp_start_keyword=\"20.72\",  # Starting keyword for 2d size bins\n",
+    "    data_2d_dp_end_keyword=\"784.39\",  # Ending keyword for 2d size bins\n",
+    "    data_2d_convert_concentration_from=\"dw/dlogdp\",  # Conversion for 2d concentration\n",
+    "    time_column=[1, 2],  # Columns containing time data\n",
+    "    time_format=\"%m/%d/%Y %H:%M:%S\",  # Format of the time data\n",
+    "    delimiter=\",\",  # Delimiter used in the data file\n",
+    "    time_shift_seconds=0,  # Time shift, if needed\n",
+    "    timezone_identifier=\"UTC\",  # Timezone for the time data\n",
+    ")\n",
+    "\n",
+    "# Printing the generated settings dictionaries for both 1d and 2d data\n",
+    "print('Settings for 1d data:')\n",
+    "for key, value in settings_1d.items():\n",
+    "    print(f'{key}: {value}')\n",
+    "\n",
+    "print('\\nSettings for 2d data:')\n",
+    "for key, value in settings_2d.items():\n",
+    "    print(f'{key}: {value}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Efficient Data Loading with the Interface\n",
+    "\n",
+    "After configuring our settings dictionaries for 1-dimensional and 2-dimensional data, we're set to leverage the interface for data loading. This interface, a key component of the Particula package, streamlines the process, making it more efficient and straightforward, especially after you have a good grasp of how the settings work.\n",
+    "\n",
+    "### Understanding the Interface's Role\n",
+    "\n",
+    "The interface acts as a facilitator that intelligently uses the settings we've established to manage the data loading process. It eliminates the need for manual execution of multiple steps, thereby integrating and automating the data import based on our predefined preferences.\n",
+    "\n",
+    "### The Advantages of Mastery\n",
+    "\n",
+    "Once you're comfortable with setting up your data parameters, using the interface offers several key benefits:\n",
+    "\n",
+    "- **Enhanced Efficiency**: It consolidates several operations into a single action, significantly speeding up the data loading process.\n",
+    "- **Consistent Results**: By automating the data import process with predefined settings, it ensures uniformity and accuracy across different datasets.\n",
+    "- **Optimized Workflow**: For users who understand the settings, the interface offers a simplified and more effective way to handle data loading. It removes the repetitive task of manually calling functions, allowing you to focus more on data analysis.\n",
+    "\n",
+    "In the following section, we'll demonstrate how to utilize the interface with our prepared settings to efficiently load and process our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
+      "  Loading file: 2022-07-10_094659_SMPS.csv\n",
+      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
+      "  Loading file: 2022-07-10_094659_SMPS.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Importing the necessary module for the loader interface\n",
+    "from particula.data import loader_interface\n",
+    "from particula.data.tests.example_data.get_example_data import get_data_folder\n",
+    "\n",
+    "# Setting the working path to the directory where the data files are located\n",
+    "working_path = get_data_folder()\n",
+    "\n",
+    "# Using the settings dictionaries created earlier for 1d and 2d data\n",
+    "\n",
+    "# Loading 1-dimensional data using the loader interface\n",
+    "# The interface takes the path and settings for 1d data and loads the data\n",
+    "# accordingly\n",
+    "data_stream_1d = loader_interface.load_files_interface(\n",
+    "    path=working_path,  # The path where data files are stored\n",
+    "    settings=settings_1d,  # Settings dictionary for 1d data\n",
+    ")\n",
+    "\n",
+    "# Loading 2-dimensional data using the loader interface\n",
+    "# Similar to the 1d data, but using the settings for 2d data\n",
+    "data_stream_2d = loader_interface.load_files_interface(\n",
+    "    path=working_path,  # The path where data files are stored\n",
+    "    settings=settings_2d,  # Settings dictionary for 2d data\n",
+    ")\n",
+    "\n",
+    "# The data_stream_1d and data_stream_2d objects now contain the loaded data\n",
+    "# ready for further analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Printing Data Stream Summaries\n",
+    "\n",
+    "After loading our data using the loader interface, it's a good practice to take a moment and review what we have loaded. This helps us confirm that the data is imported correctly and gives us an initial overview of its structure. We'll do this by printing summaries of the `data_stream_1d` and `data_stream_2d` objects.\n",
+    "\n",
+    "### Understanding the Data Stream Summary\n",
+    "\n",
+    "When we print a `data_stream` object, it provides us with a summary of its contents. This includes information like the size of the data, the headers (which represent different data types or measurements), and a glimpse into the actual data values. These summaries are especially useful for:\n",
+    "\n",
+    "- **Verifying Data Integrity**: Ensuring that the data has been loaded as expected and is ready for analysis.\n",
+    "- **Quick Overview**: Getting a high-level understanding of the data's structure, such as the number of data points and the range of measurements.\n",
+    "\n",
+    "### Code for Printing Summaries\n",
+    "\n",
+    "Here's how we print the summaries for our 1-dimensional and 2-dimensional data streams:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Data stream 1d summary:\n",
+      "Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 7.91500e+02, 2.37000e+01, ..., 2.07210e+01,\n",
+      "        2.17900e+00, 2.16900e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.36000e+01, ..., 2.52550e+01,\n",
+      "        2.10100e+00, 2.39408e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.37000e+01, ..., 2.18700e+01,\n",
+      "        2.13600e+00, 2.27861e+03],\n",
+      "       ...,\n",
+      "       [2.05000e+01, 7.91500e+02, 2.35000e+01, ..., 2.07210e+01,\n",
+      "        2.31800e+00, 2.08056e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.33000e+01, ..., 2.10970e+01,\n",
+      "        2.31800e+00, 2.10616e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.35000e+01, ..., 2.07210e+01,\n",
+      "        2.24800e+00, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
+      "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n",
+      "\n",
+      "Data stream 2d summary:\n",
+      "Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  2832.655,  4733.553, ...,    93.413,   122.992,\n",
+      "            0.   ],\n",
+      "       [ 5621.118,  5867.747,  6233.403, ...,     0.   ,     0.   ,\n",
+      "           75.377],\n",
+      "       [ 5165.139,  4969.987,  4312.386, ...,     0.   ,   122.992,\n",
+      "          124.085],\n",
+      "       ...,\n",
+      "       [ 9962.036,  7986.823,  8682.258, ...,     0.   ,     0.   ,\n",
+      "          124.153],\n",
+      "       [ 8765.782, 11175.603,  8148.945, ...,     0.   ,     0.   ,\n",
+      "          372.433],\n",
+      "       [14380.528, 11524.35 , 13632.727, ...,     0.   ,     0.   ,\n",
+      "            0.   ]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
+      "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print a blank line for better readability\n",
+    "print('')\n",
+    "\n",
+    "# Print the summary of the 1-dimensional data stream\n",
+    "print('Data stream 1d summary:')\n",
+    "print(data_stream_1d)  # This will display a summary of the 1d data\n",
+    "\n",
+    "# Print another blank line for separation\n",
+    "print('')\n",
+    "\n",
+    "# Print the summary of the 2-dimensional data stream\n",
+    "print('Data stream 2d summary:')\n",
+    "print(data_stream_2d)  # This will display a summary of the 2d data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Again\n",
+    "\n",
+    "Plotting is an integral part of the data analysis process. It transforms raw data into visual representations that can reveal insights, patterns, and anomalies that might not be immediately apparent in numerical form. Let's delve into why plotting is so crucial in understanding your data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Adjusting the concentration data for better visualization in the plot\n",
+    "concentration = data_stream_2d.data\n",
+    "concentration = np.where(\n",
+    "    concentration < 1e-5,\n",
+    "    1e-5,\n",
+    "    concentration)  # Setting a lower limit\n",
+    "concentration = np.where(\n",
+    "    concentration > 10**5,\n",
+    "    10**5,\n",
+    "    concentration)  # Setting an upper limit\n",
+    "# Uncomment the following line to plot the concentration on a logarithmic scale\n",
+    "# concentration = np.log10(concentration)\n",
+    "\n",
+    "# Creating a figure and axis for the contour plot\n",
+    "fig, ax = plt.subplots(1, 1)\n",
+    "\n",
+    "# Creating the contour plot\n",
+    "# X-axis: Time data in datetime64 format from data_stream_2d\n",
+    "# Y-axis: Particle sizes (diameter in nm) converted from the header strings to floats\n",
+    "# Z-axis: Concentration data\n",
+    "plt.contourf(\n",
+    "    data_stream_2d.datetime64,  # Time data\n",
+    "    data_stream_2d.header_float,  # Particle sizes\n",
+    "    concentration.T,  # Concentration data, transposed for correct orientation\n",
+    "    cmap=plt.cm.PuBu_r,  # Color map for the plot\n",
+    "    levels=50  # Number of contour levels\n",
+    ")\n",
+    "\n",
+    "# Setting the y-axis to logarithmic scale for better visualization of size\n",
+    "# distribution\n",
+    "plt.yscale('log')\n",
+    "\n",
+    "# Rotating the x-axis labels for better readability\n",
+    "plt.tick_params(rotation=35)\n",
+    "\n",
+    "# Setting labels for the x-axis and y-axis\n",
+    "ax.set_xlabel(\"Time (UTC)\")\n",
+    "ax.set_ylabel('Diameter (nm)')\n",
+    "\n",
+    "# Adding a color bar to indicate concentration levels\n",
+    "plt.colorbar(label='Concentration dN/dlog(Dp) [#/cm³]', ax=ax)\n",
+    "\n",
+    "# Displaying the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Adjusting the layout to ensure all elements of the plot are clearly visible\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary of Loading Data Part 2\n",
+    "\n",
+    "In this section, we delved into the process of loading and handling 2-dimensional data, focusing on a size distribution dataset. We walked through several crucial steps, providing a comprehensive guide to managing and visualizing complex data structures. Here's a recap of what we covered:\n",
+    "\n",
+    "- Setting the Working Path: We began by establishing the working directory for our data, a foundational step in ensuring our scripts access the correct files.\n",
+    "- Loading the Data: Using the loader module, we demonstrated how to import raw data from a file, setting the stage for further processing.\n",
+    "- Formatting the Data: We then tackled the challenge of formatting 2-dimensional data, extracting size bins as headers, and preparing the dataset for analysis.\n",
+    "- Initial Plotting: To get a preliminary understanding of our data, we created initial plots. This step is crucial for visually inspecting the data and confirming its integrity.\n",
+    "- Generating the Settings Dictionary: We utilized the settings_generator to create settings dictionaries for both 1-dimensional and 2-dimensional data. This streamlines the data loading process, especially for complex datasets.\n",
+    "- Loading Data with the Interface: We showcased how to use the loader interface to efficiently load data using the predefined settings, emphasizing the ease and efficiency it brings to the data loading process.\n",
+    "- Advanced Plotting of the Data Stream: Lastly, we explored more advanced data visualization techniques, including creating contour plots. This allowed us to visualize how particle concentration varied across different sizes and times, offering valuable insights into our dataset.\n",
+    "\n",
+    "Throughout this section, we focused on making each step clear and accessible, particularly for those new to working with 2-dimensional datasets in Python. By following these steps, you can effectively manage and analyze complex data, gaining deeper insights into your research or projects."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ParticulaDev_py39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/loading_data_part3.ipynb b/docs/examples/streamlake/loading_data_part3.ipynb
similarity index 54%
rename from docs/examples/loading_data_part3.ipynb
rename to docs/examples/streamlake/loading_data_part3.ipynb
index dd1ba3366..f70ecadd8 100644
--- a/docs/examples/loading_data_part3.ipynb
+++ b/docs/examples/streamlake/loading_data_part3.ipynb
@@ -4,91 +4,72 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "  # Loading Data Part 3\n",
+    "# Loading Part 3: Lake\n",
     "\n",
-    "  This example shows how to work with data from multiple instruments, and how to load them into a single `Lake` object. A `Lake` object is a container of multiple `Stream`s from individual instruments.\n",
+    "In this example, we explore the process of working with data from multiple instruments and consolidating them into a single `Lake` object. A `Lake` object serves as a convenient container for aggregating multiple `Stream`s, each representing data from individual instruments.\n",
     "\n",
-    "  This allows for easy access to the data from all instruments, and also allows for easy plotting of the data from all instruments."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Working path\n",
-    "\n",
-    "  Set the working path where the data is stored. For now we'll use the\n",
-    "  provided example data in this current directory.\n",
-    "\n",
-    "  But the path could be any where on your computer. For example, if you have a\n",
-    "  folder called \"data\" in your home directory, you could set the path to:\n",
-    "  `path = \"U:\\\\data\\\\processing\\\\Campgain2023_of_aswsome\\\\data\"`\n",
-    "\n",
-    " The folder structure should look like this:\n",
-    "\n",
-    " ```\n",
-    " data\n",
-    " ├── CPC_3010_data\n",
-    " │   ├── CPC_3010_data_20220709_Jul.csv\n",
-    " │   ├── CPC_3010_data_20220709_Jul.csv\n",
-    " ├── SMPS_data\n",
-    " │   ├── 2022-07-07_095151_SMPS.csv\n",
-    " │   ├── 2022-07-10_094659_SMPS.csv\n",
-    " ```\n",
-    " The `path` is `data`. Within that folder are two folders, one for the CPC\n",
-    " data and one for the SMPS data. These are your `relative_data_folder`\n",
-    " keywords you put\n",
-    " in the settings dictionary. The data within will be loaded as `Stream`\n",
-    " objects.\n",
-    " Then within each of those folders are the\n",
-    " data files which are selected by the `filename_regex`. A regex is a\n",
-    " regular expression that is used to match the files. In this case we are\n",
-    " matching all files that end with `.csv`, then loading them into the\n",
-    " `stream` object."
+    "## Setting the Working Path\n",
+    "\n",
+    "To begin, you need to establish the working path where your data is stored. For this demonstration, we will use provided example data located in the current directory. However, keep in mind that the path can be anywhere on your computer. For instance, if you have a folder named \"data\" in your home directory, you can set the path as follows:\n",
+    "\n",
+    "```python\n",
+    "path = \"U:\\\\data\\\\processing\\\\Campaign2023_of_awesome\\\\data\"\n",
+    "```\n",
+    "\n",
+    "Your folder structure should resemble the following:\n",
+    "\n",
+    "```\n",
+    "data\n",
+    "├── CPC_3010_data\n",
+    "│   ├── CPC_3010_data_20220709_Jul.csv\n",
+    "│   ├── CPC_3010_data_20220709_Jul.csv\n",
+    "├── SMPS_data\n",
+    "│   ├── 2022-07-07_095151_SMPS.csv\n",
+    "│   ├── 2022-07-10_094659_SMPS.csv\n",
+    "```\n",
+    "\n",
+    "Here, the path points to the \"data\" folder. Within this folder, you'll find two subfolders: one for CPC data and another for SMPS data. These subfolders correspond to the relative_data_folder keywords used in the settings dictionary. The data within these subfolders will be loaded as Stream objects.\n",
+    "\n",
+    "Inside each of these subfolders, you'll find data files that match the specified filename_regex. A regular expression is used to select files based on specific criteria. In this case, we are matching all files ending with \".csv\" and loading them into the respective Stream objects. This approach allows you to efficiently manage and consolidate data from various instruments for further analysis and visualization."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Path to data folder:\n",
-      "\\data\\tests\\example_data\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# all the imports, but we'll go through them one by one as we use them\n",
-    "import os\n",
+    "# Import necessary modules\n",
+    "import os  # Provides functions for interacting with the operating system.\n",
+    "# Matplotlib is a library for creating visualizations and plots.\n",
     "import matplotlib.pyplot as plt\n",
-    "from particula.data import loader_interface, settings_generator, lake_stats\n",
+    "from particula.data import (\n",
+    "    loader_interface,  # This module allows you to load data from files.\n",
+    "    settings_generator,  # It helps generate settings for data loading.\n",
+    "    # This module provides statistics for a collection of data streams.\n",
+    "    lake_stats\n",
+    ")\n",
     "from particula.data.tests.example_data.get_example_data import get_data_folder\n",
+    "# Lake is a container for multiple data streams.\n",
     "from particula.data.lake import Lake\n",
     "\n",
-    "# set the parent directory of the data folder\n",
-    "path = get_data_folder()\n",
-    "print('Path to data folder:')\n",
-    "print(path.rsplit('particula')[-1])"
+    "# Set the parent directory of the data folder\n",
+    "path = get_data_folder()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Load the data\n",
+    "## Load the Data\n",
     "\n",
-    " For this example we'll use the provided example data. But you can change the\n",
-    " path to any folder on your computer. We then can used the settings generator to\n",
-    " load the data."
+    "In this example, we'll work with provided example data. However, you have the flexibility to change the path to any folder on your computer. We will use the settings generator to efficiently load the data.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +86,108 @@
       "  Loading file: 2022-07-07_095151_SMPS.csv\n",
       "  Loading file: 2022-07-10_094659_SMPS.csv\n",
       " \n",
-      "Lake with streams: ['cpc', 'smps_1d', 'smps_2d']\n"
+      "Lake with streams: ['cpc', 'smps_1d', 'smps_2d']\n",
+      "Help on Lake in module particula.data.lake object:\n",
+      "\n",
+      "class Lake(builtins.object)\n",
+      " |  Lake(streams: Dict[str, particula.data.stream.Stream] = <factory>) -> None\n",
+      " |  \n",
+      " |  A class representing a lake which is a collection of streams.\n",
+      " |  \n",
+      " |  Attributes:\n",
+      " |      streams (Dict[str, Stream]): A dictionary to hold streams with their\n",
+      " |      names as keys.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __delitem__(self, key: str) -> None\n",
+      " |      Remove a stream by name.\n",
+      " |      Example: del lake['stream_name']\n",
+      " |  \n",
+      " |  __dir__(self) -> list\n",
+      " |      List available streams.\n",
+      " |      Example: dir(lake)\n",
+      " |  \n",
+      " |  __eq__(self, other)\n",
+      " |  \n",
+      " |  __getattr__(self, name: str) -> Any\n",
+      " |      Allow accessing streams as an attributes.\n",
+      " |      Raises:\n",
+      " |          AttributeError: If the stream name is not in the lake.\n",
+      " |      Example: lake.stream_name\n",
+      " |  \n",
+      " |  __getitem__(self, key: str) -> Any\n",
+      " |      Get a stream by name.\n",
+      " |      Example: lake['stream_name']\n",
+      " |  \n",
+      " |  __init__(self, streams: Dict[str, particula.data.stream.Stream] = <factory>) -> None\n",
+      " |  \n",
+      " |  __iter__(self) -> Iterator[Any]\n",
+      " |      Iterate over the streams in the lake.\n",
+      " |      Example: [stream.header for stream in lake]\"\"\n",
+      " |  \n",
+      " |  __len__(self) -> int\n",
+      " |      Return the number of streams in the lake.\n",
+      " |      Example: len(lake)\n",
+      " |  \n",
+      " |  __repr__(self) -> str\n",
+      " |      Return a string representation of the lake.\n",
+      " |      Example: print(lake)\n",
+      " |  \n",
+      " |  __setitem__(self, key: str, value: particula.data.stream.Stream) -> None\n",
+      " |      Set a stream by name.\n",
+      " |      Example: lake['stream_name'] = new_stream\n",
+      " |  \n",
+      " |  add_stream(self, stream: particula.data.stream.Stream, name: str) -> None\n",
+      " |      Add a stream to the lake.\n",
+      " |      \n",
+      " |      Args:\n",
+      " |      -----------\n",
+      " |          stream (Stream): The stream object to be added.\n",
+      " |          name (str): The name of the stream.\n",
+      " |      \n",
+      " |      Raises:\n",
+      " |      -------\n",
+      " |          ValueError: If the stream name is already in use or not a valid\n",
+      " |          identifier.\n",
+      " |  \n",
+      " |  items(self) -> Iterator[Tuple[Any, Any]]\n",
+      " |      Return an iterator over the key-value pairs.\n",
+      " |  \n",
+      " |  keys(self) -> Iterator[Any]\n",
+      " |      Return an iterator over the keys.\n",
+      " |  \n",
+      " |  values(self) -> Iterator[Any]\n",
+      " |      Return an iterator over the values.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Readonly properties defined here:\n",
+      " |  \n",
+      " |  summary\n",
+      " |      Return a string summary iterating over each stream\n",
+      " |          and print Stream.header.\n",
+      " |      Example: lake.summary\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  __annotations__ = {'streams': typing.Dict[str, particula.data.stream.S...\n",
+      " |  \n",
+      " |  __dataclass_fields__ = {'streams': Field(name='streams',type=typing.Di...\n",
+      " |  \n",
+      " |  __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,or...\n",
+      " |  \n",
+      " |  __hash__ = None\n",
+      "\n"
      ]
     }
    ],
@@ -197,19 +279,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Lake\n",
-    " The lake is a collection of streams, stored as a dictionary. The keys are the\n",
-    " names of the streams, and the values are the streams themselves. We can access\n",
-    " the streams by their name. For example, to get the CPC stream we can do:\n",
-    " `lake['cpc']`. We can also get the names of the streams by doing\n",
-    " `lake.keys()`. We can also loop through the streams by doing\n",
-    " `for stream in lake.values():`. We can also loop through the names and streams\n",
-    " by doing `for name, stream in lake.items():`."
+    "## Lake Class Overview\n",
+    "\n",
+    "The `Lake` is a collection of `Stream` objects stored as a dictionary. The keys represent the names of the streams, and the values are the stream objects themselves. It provides a convenient way to organize and manage multiple datasets. Let's explore its key attributes and methods:\n",
+    "\n",
+    "### Attributes:\n",
+    "- `streams` (Dict[str, Stream]): A dictionary where keys are stream names, and values are the corresponding `Stream` objects.\n",
+    "\n",
+    "### Methods:\n",
+    "- `__getitem__(self, key: str) -> Any`: Retrieve a specific `Stream` by its name.\n",
+    "  - Example: To access the CPC stream, you can use `lake['cpc']`.\n",
+    "\n",
+    "- `__delitem__(self, key: str) -> None`: Remove a `Stream` from the `Lake` using its name.\n",
+    "  - Example: To remove a stream named 'cpc', you can use `del lake['cpc']`.\n",
+    "\n",
+    "- `__getattr__(self, name: str) -> Any`: Access streams as attributes for easier navigation.\n",
+    "  - Example: You can directly access the 'cpc' stream with `lake.cpc`.\n",
+    "\n",
+    "- `add_stream(self, stream: particula.data.stream.Stream, name: str) -> None`: Add a new `Stream` to the `Lake`.\n",
+    "  - Example: To add a new stream, you can use `lake.add_stream(new_stream, 'stream_name')`.\n",
+    "\n",
+    "- `__len__(self) -> int`: Determine the number of streams in the `Lake`.\n",
+    "  - Example: To find out how many streams are in the `Lake`, use `len(lake)`.\n",
+    "\n",
+    "- `__iter__(self) -> Iterator[Any]`: Iterate over the streams in the `Lake`.\n",
+    "  - Example: To loop through all streams, you can use `[stream.header for stream in lake]`.\n",
+    "\n",
+    "- `summary` (Readonly property): Generate a summary by iterating through each stream and printing their headers.\n",
+    "  - Example: To get a summary of all streams, use `lake.summary`.\n",
+    "\n",
+    "### Usage:\n",
+    "The `Lake` class simplifies the management of multiple datasets. You can access individual streams by name, add new streams, and iterate through them efficiently. This class is particularly helpful when dealing with various data sources within your analysis."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -240,37 +345,40 @@
      "text": [
       " \n",
       "The streams:\n",
-      "('cpc', Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 3.3465e+04, 3.2171e+04, ..., 1.9403e+04, 2.0230e+04,\n",
-      "        1.9521e+04],\n",
-      "       [1.7000e+01, 1.7100e+01, 1.7000e+01, ..., 1.6900e+01, 1.7000e+01,\n",
-      "        1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
+      "('cpc', Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 1.7000e+01],\n",
+      "       [3.3465e+04, 1.7100e+01],\n",
+      "       [3.2171e+04, 1.7000e+01],\n",
+      "       ...,\n",
+      "       [1.9403e+04, 1.6900e+01],\n",
+      "       [2.0230e+04, 1.7000e+01],\n",
+      "       [1.9521e+04, 1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
       "       1.65751559e+09, 1.65751560e+09, 1.65751560e+09]), files=[['CPC_3010_data_20220709_Jul.csv', 1044534], ['CPC_3010_data_20220710_Jul.csv', 1113488]]))\n",
-      "('smps_1d', Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 2.05000e+01, 2.05000e+01, ..., 2.05000e+01,\n",
-      "        2.05000e+01, 2.05000e+01],\n",
-      "       [7.91500e+02, 7.91500e+02, 7.91500e+02, ..., 7.91500e+02,\n",
-      "        7.91500e+02, 7.91500e+02],\n",
-      "       [2.37000e+01, 2.36000e+01, 2.37000e+01, ..., 2.35000e+01,\n",
-      "        2.33000e+01, 2.35000e+01],\n",
+      "('smps_1d', Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 7.91500e+02, 2.37000e+01, ..., 2.07210e+01,\n",
+      "        2.17900e+00, 2.16900e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.36000e+01, ..., 2.52550e+01,\n",
+      "        2.10100e+00, 2.39408e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.37000e+01, ..., 2.18700e+01,\n",
+      "        2.13600e+00, 2.27861e+03],\n",
       "       ...,\n",
-      "       [2.07210e+01, 2.52550e+01, 2.18700e+01, ..., 2.07210e+01,\n",
-      "        2.10970e+01, 2.07210e+01],\n",
-      "       [2.17900e+00, 2.10100e+00, 2.13600e+00, ..., 2.31800e+00,\n",
-      "        2.31800e+00, 2.24800e+00],\n",
-      "       [2.16900e+03, 2.39408e+03, 2.27861e+03, ..., 2.08056e+03,\n",
-      "        2.10616e+03, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
+      "       [2.05000e+01, 7.91500e+02, 2.35000e+01, ..., 2.07210e+01,\n",
+      "        2.31800e+00, 2.08056e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.33000e+01, ..., 2.10970e+01,\n",
+      "        2.31800e+00, 2.10616e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.35000e+01, ..., 2.07210e+01,\n",
+      "        2.24800e+00, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
       "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]]))\n",
-      "('smps_2d', Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  5621.118,  5165.139, ...,  9962.036,  8765.782,\n",
-      "        14380.528],\n",
-      "       [ 2832.655,  5867.747,  4969.987, ...,  7986.823, 11175.603,\n",
-      "        11524.35 ],\n",
-      "       [ 4733.553,  6233.403,  4312.386, ...,  8682.258,  8148.945,\n",
-      "        13632.727],\n",
-      "       ...,\n",
-      "       [   93.413,     0.   ,     0.   , ...,     0.   ,     0.   ,\n",
-      "            0.   ],\n",
-      "       [  122.992,     0.   ,   122.992, ...,     0.   ,     0.   ,\n",
+      "('smps_2d', Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  2832.655,  4733.553, ...,    93.413,   122.992,\n",
       "            0.   ],\n",
-      "       [    0.   ,    75.377,   124.085, ...,   124.153,   372.433,\n",
+      "       [ 5621.118,  5867.747,  6233.403, ...,     0.   ,     0.   ,\n",
+      "           75.377],\n",
+      "       [ 5165.139,  4969.987,  4312.386, ...,     0.   ,   122.992,\n",
+      "          124.085],\n",
+      "       ...,\n",
+      "       [ 9962.036,  7986.823,  8682.258, ...,     0.   ,     0.   ,\n",
+      "          124.153],\n",
+      "       [ 8765.782, 11175.603,  8148.945, ...,     0.   ,     0.   ,\n",
+      "          372.433],\n",
+      "       [14380.528, 11524.35 , 13632.727, ...,     0.   ,     0.   ,\n",
       "            0.   ]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
       "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]]))\n"
      ]
@@ -286,62 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " \n",
-      "The names and streams:\n",
-      "cpc Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 3.3465e+04, 3.2171e+04, ..., 1.9403e+04, 2.0230e+04,\n",
-      "        1.9521e+04],\n",
-      "       [1.7000e+01, 1.7100e+01, 1.7000e+01, ..., 1.6900e+01, 1.7000e+01,\n",
-      "        1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
-      "       1.65751559e+09, 1.65751560e+09, 1.65751560e+09]), files=[['CPC_3010_data_20220709_Jul.csv', 1044534], ['CPC_3010_data_20220710_Jul.csv', 1113488]])\n",
-      "smps_1d Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 2.05000e+01, 2.05000e+01, ..., 2.05000e+01,\n",
-      "        2.05000e+01, 2.05000e+01],\n",
-      "       [7.91500e+02, 7.91500e+02, 7.91500e+02, ..., 7.91500e+02,\n",
-      "        7.91500e+02, 7.91500e+02],\n",
-      "       [2.37000e+01, 2.36000e+01, 2.37000e+01, ..., 2.35000e+01,\n",
-      "        2.33000e+01, 2.35000e+01],\n",
-      "       ...,\n",
-      "       [2.07210e+01, 2.52550e+01, 2.18700e+01, ..., 2.07210e+01,\n",
-      "        2.10970e+01, 2.07210e+01],\n",
-      "       [2.17900e+00, 2.10100e+00, 2.13600e+00, ..., 2.31800e+00,\n",
-      "        2.31800e+00, 2.24800e+00],\n",
-      "       [2.16900e+03, 2.39408e+03, 2.27861e+03, ..., 2.08056e+03,\n",
-      "        2.10616e+03, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
-      "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n",
-      "smps_2d Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  5621.118,  5165.139, ...,  9962.036,  8765.782,\n",
-      "        14380.528],\n",
-      "       [ 2832.655,  5867.747,  4969.987, ...,  7986.823, 11175.603,\n",
-      "        11524.35 ],\n",
-      "       [ 4733.553,  6233.403,  4312.386, ...,  8682.258,  8148.945,\n",
-      "        13632.727],\n",
-      "       ...,\n",
-      "       [   93.413,     0.   ,     0.   , ...,     0.   ,     0.   ,\n",
-      "            0.   ],\n",
-      "       [  122.992,     0.   ,   122.992, ...,     0.   ,     0.   ,\n",
-      "            0.   ],\n",
-      "       [    0.   ,    75.377,   124.085, ...,   124.153,   372.433,\n",
-      "            0.   ]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
-      "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get the names and streams\n",
-    "print(' ')\n",
-    "print('The names and streams:')\n",
-    "for name, stream in lake.items():\n",
-    "    print(name, stream)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -366,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -375,37 +428,40 @@
      "text": [
       " \n",
       "The values:\n",
-      "Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 3.3465e+04, 3.2171e+04, ..., 1.9403e+04, 2.0230e+04,\n",
-      "        1.9521e+04],\n",
-      "       [1.7000e+01, 1.7100e+01, 1.7000e+01, ..., 1.6900e+01, 1.7000e+01,\n",
-      "        1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
+      "Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 1.7000e+01],\n",
+      "       [3.3465e+04, 1.7100e+01],\n",
+      "       [3.2171e+04, 1.7000e+01],\n",
+      "       ...,\n",
+      "       [1.9403e+04, 1.6900e+01],\n",
+      "       [2.0230e+04, 1.7000e+01],\n",
+      "       [1.9521e+04, 1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
       "       1.65751559e+09, 1.65751560e+09, 1.65751560e+09]), files=[['CPC_3010_data_20220709_Jul.csv', 1044534], ['CPC_3010_data_20220710_Jul.csv', 1113488]])\n",
-      "Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 2.05000e+01, 2.05000e+01, ..., 2.05000e+01,\n",
-      "        2.05000e+01, 2.05000e+01],\n",
-      "       [7.91500e+02, 7.91500e+02, 7.91500e+02, ..., 7.91500e+02,\n",
-      "        7.91500e+02, 7.91500e+02],\n",
-      "       [2.37000e+01, 2.36000e+01, 2.37000e+01, ..., 2.35000e+01,\n",
-      "        2.33000e+01, 2.35000e+01],\n",
+      "Stream(header=['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)'], data=array([[2.05000e+01, 7.91500e+02, 2.37000e+01, ..., 2.07210e+01,\n",
+      "        2.17900e+00, 2.16900e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.36000e+01, ..., 2.52550e+01,\n",
+      "        2.10100e+00, 2.39408e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.37000e+01, ..., 2.18700e+01,\n",
+      "        2.13600e+00, 2.27861e+03],\n",
       "       ...,\n",
-      "       [2.07210e+01, 2.52550e+01, 2.18700e+01, ..., 2.07210e+01,\n",
-      "        2.10970e+01, 2.07210e+01],\n",
-      "       [2.17900e+00, 2.10100e+00, 2.13600e+00, ..., 2.31800e+00,\n",
-      "        2.31800e+00, 2.24800e+00],\n",
-      "       [2.16900e+03, 2.39408e+03, 2.27861e+03, ..., 2.08056e+03,\n",
-      "        2.10616e+03, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
+      "       [2.05000e+01, 7.91500e+02, 2.35000e+01, ..., 2.07210e+01,\n",
+      "        2.31800e+00, 2.08056e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.33000e+01, ..., 2.10970e+01,\n",
+      "        2.31800e+00, 2.10616e+03],\n",
+      "       [2.05000e+01, 7.91500e+02, 2.35000e+01, ..., 2.07210e+01,\n",
+      "        2.24800e+00, 2.45781e+03]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
       "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n",
-      "Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  5621.118,  5165.139, ...,  9962.036,  8765.782,\n",
-      "        14380.528],\n",
-      "       [ 2832.655,  5867.747,  4969.987, ...,  7986.823, 11175.603,\n",
-      "        11524.35 ],\n",
-      "       [ 4733.553,  6233.403,  4312.386, ...,  8682.258,  8148.945,\n",
-      "        13632.727],\n",
-      "       ...,\n",
-      "       [   93.413,     0.   ,     0.   , ...,     0.   ,     0.   ,\n",
+      "Stream(header=['20.72', '21.10', '21.48', '21.87', '22.27', '22.67', '23.08', '23.50', '23.93', '24.36', '24.80', '25.25', '25.71', '26.18', '26.66', '27.14', '27.63', '28.13', '28.64', '29.16', '29.69', '30.23', '30.78', '31.34', '31.91', '32.49', '33.08', '33.68', '34.29', '34.91', '35.55', '36.19', '36.85', '37.52', '38.20', '38.89', '39.60', '40.32', '41.05', '41.79', '42.55', '43.32', '44.11', '44.91', '45.73', '46.56', '47.40', '48.26', '49.14', '50.03', '50.94', '51.86', '52.80', '53.76', '54.74', '55.73', '56.74', '57.77', '58.82', '59.89', '60.98', '62.08', '63.21', '64.36', '65.52', '66.71', '67.93', '69.16', '70.41', '71.69', '72.99', '74.32', '75.67', '77.04', '78.44', '79.86', '81.31', '82.79', '84.29', '85.82', '87.38', '88.96', '90.58', '92.22', '93.90', '95.60', '97.34', '99.10', '100.90', '102.74', '104.60', '106.50', '108.43', '110.40', '112.40', '114.44', '116.52', '118.64', '120.79', '122.98', '125.21', '127.49', '129.80', '132.16', '134.56', '137.00', '139.49', '142.02', '144.60', '147.22', '149.89', '152.61', '155.38', '158.20', '161.08', '164.00', '166.98', '170.01', '173.09', '176.24', '179.43', '182.69', '186.01', '189.38', '192.82', '196.32', '199.89', '203.51', '207.21', '210.97', '214.80', '218.70', '222.67', '226.71', '230.82', '235.01', '239.28', '243.62', '248.05', '252.55', '257.13', '261.80', '266.55', '271.39', '276.32', '281.33', '286.44', '291.64', '296.93', '302.32', '307.81', '313.40', '319.08', '324.88', '330.77', '336.78', '342.89', '349.12', '355.45', '361.90', '368.47', '375.16', '381.97', '388.91', '395.96', '403.15', '410.47', '417.92', '425.51', '433.23', '441.09', '449.10', '457.25', '465.55', '474.00', '482.61', '491.37', '500.29', '509.37', '518.61', '528.03', '537.61', '547.37', '557.31', '567.42', '577.72', '588.21', '598.89', '609.76', '620.82', '632.09', '643.57', '655.25', '667.14', '679.25', '691.58', '704.14', '716.92', '729.93', '743.18', '756.67', '770.40', '784.39'], data=array([[ 6103.186,  2832.655,  4733.553, ...,    93.413,   122.992,\n",
       "            0.   ],\n",
-      "       [  122.992,     0.   ,   122.992, ...,     0.   ,     0.   ,\n",
-      "            0.   ],\n",
-      "       [    0.   ,    75.377,   124.085, ...,   124.153,   372.433,\n",
+      "       [ 5621.118,  5867.747,  6233.403, ...,     0.   ,     0.   ,\n",
+      "           75.377],\n",
+      "       [ 5165.139,  4969.987,  4312.386, ...,     0.   ,   122.992,\n",
+      "          124.085],\n",
+      "       ...,\n",
+      "       [ 9962.036,  7986.823,  8682.258, ...,     0.   ,     0.   ,\n",
+      "          124.153],\n",
+      "       [ 8765.782, 11175.603,  8148.945, ...,     0.   ,     0.   ,\n",
+      "          372.433],\n",
+      "       [14380.528, 11524.35 , 13632.727, ...,     0.   ,     0.   ,\n",
       "            0.   ]]), time=array([1.65718376e+09, 1.65718385e+09, 1.65718394e+09, ...,\n",
       "       1.65753440e+09, 1.65753450e+09, 1.65753459e+09]), files=[['2022-07-07_095151_SMPS.csv', 5620804], ['2022-07-10_094659_SMPS.csv', 2004838]])\n"
      ]
@@ -424,17 +480,26 @@
    "metadata": {},
    "source": [
     " # Pause to Plot the data\n",
-    " We'll compare the CPC counts with the Mode of the SMPS data."
+    "\n",
+    "In this code snippet, we retrieve data from the Lake object and create a dual-axis plot to visualize both CPC and SMPS data over time.\n",
+    "\n",
+    "- We access the CPC data from the Lake using lake['cpc']. We retrieve the datetime and CPC count data.\n",
+    "- Similarly, we access the SMPS data from the Lake using lake['smps_1d'] and retrieve the datetime and Mode data.\n",
+    "- We create a plot with a blue line for CPC data using ax.plot(), and an orange line for SMPS data on a twin y-axis axb.\n",
+    "- To improve readability, we rotate the x-axis labels using plt.xticks(rotation=45).\n",
+    "- We set y-axis limits for the SMPS data to be in the range [0, 200] using axb.set_ylim(0, 200).\n",
+    "- Axis labels and legends are added for both datasets.\n",
+    "- Finally, we display the plot and adjust the layout for better visualization using plt.show() and fig.tight_layout()."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -444,30 +509,49 @@
     }
    ],
    "source": [
-    "# get cpc data\n",
+    "# Load and Plot Data from Lake\n",
+    "\n",
+    "# Access CPC data from the Lake\n",
     "cpc_time = lake['cpc'].datetime64\n",
     "cpc_data = lake['cpc']['CPC_count[#/sec]']\n",
-    "# get smps data\n",
+    "\n",
+    "# Access SMPS data from the Lake\n",
     "smps_time = lake['smps_1d'].datetime64\n",
     "smps_data = lake['smps_1d']['Mode_(nm)']\n",
     "\n",
-    "# plot the data on twinx axis\n",
+    "# Plot the Data on Twinx Axis\n",
     "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Plot CPC data\n",
     "ax.plot(cpc_time,\n",
     "        cpc_data,\n",
     "        label='CPC',\n",
-    "        color='blue') \n",
+    "        color='blue')\n",
+    "\n",
+    "# Rotate x-axis labels for better readability\n",
     "plt.xticks(rotation=45)\n",
+    "\n",
+    "# Create a twin y-axis for SMPS data\n",
     "axb = ax.twinx()\n",
+    "\n",
+    "# Plot SMPS data\n",
     "axb.plot(smps_time,\n",
     "         smps_data,\n",
     "         label='SMPS',\n",
-    "         color='orange',)\n",
+    "         color='orange')\n",
+    "\n",
+    "# Set y-axis limits for SMPS data\n",
     "axb.set_ylim(0, 200)\n",
+    "\n",
+    "# Set axis labels\n",
     "ax.set_xlabel(\"Time (UTC)\")\n",
-    "ax.set_ylabel('CPC_counts[#/sec]')\n",
-    "axb.set_ylabel('SMPS_Mode[nm]')\n",
+    "ax.set_ylabel('CPC Counts [#/sec]')\n",
+    "axb.set_ylabel('SMPS Mode [nm]')\n",
+    "\n",
+    "# Display the legend and show the plot\n",
     "plt.show()\n",
+    "\n",
+    "# Adjust layout for better visualization\n",
     "fig.tight_layout()"
    ]
   },
@@ -475,20 +559,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Average the data\n",
+    "## Data Averaging\n",
     "\n",
-    " Now that we have the data loaded, we can average the data over time. We'll use\n",
-    " the 'particula.data.lake_stats' module to do this. The module has a function\n",
-    " called 'averaged_std' that will take stream object and return a new stream\n",
-    " object with the averaged data and the standard deviation of the data.\n",
+    "Now that we have loaded the data, we can perform data averaging over time. To achieve this, we will utilize the 'particula.data.lake_stats' module, which provides a convenient function called 'averaged_std.' This function takes a stream object as input and returns a new stream object containing both the averaged data and the standard deviation of the data.\n",
     "\n",
-    " If you recall this is the same naming convention as `stream_stats.average_std`\n",
-    " which operates on stream objects."
+    "It's worth noting that this function follows a similar naming convention to 'stream_stats.average_std,' which operates on individual stream objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -500,11 +580,16 @@
     }
    ],
    "source": [
+    "# Compute the average and standard deviation of data within a time interval\n",
+    "# of 600 seconds for each stream in the lake, and create a new lake\n",
+    "# containing the averaged data.\n",
     "lake_averaged = lake_stats.average_std(\n",
     "    lake=lake,\n",
     "    average_interval=600,\n",
-    "    clone=True)\n",
+    "    clone=True  # Create a new lake instead of modifying the original\n",
+    ")\n",
     "\n",
+    "# Print the resulting lake with averaged data and standard deviation.\n",
     "print(lake_averaged)"
    ]
   },
@@ -514,12 +599,12 @@
    "source": [
     " # Plot the Averaged Data\n",
     "\n",
-    " get cpc data"
+    "Let's plot the averaged data to see how it compares to the raw data. We will use the same approach as before, but this time we will use the averaged data from the Lake object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -534,28 +619,36 @@
     }
    ],
    "source": [
+    "# Extract datetime and CPC data from the averaged lake\n",
     "cpc_time = lake_averaged['cpc'].datetime64\n",
     "cpc_data = lake_averaged['cpc']['CPC_count[#/sec]']\n",
-    "# get smps data\n",
+    "\n",
+    "# Extract datetime and SMPS data from the averaged lake\n",
     "smps_time = lake_averaged['smps_1d'].datetime64\n",
     "smps_data = lake_averaged['smps_1d']['Mode_(nm)']\n",
     "\n",
-    "# plot the data on twinx axis\n",
+    "# Create a plot with two y-axes (twinx) for CPC and SMPS data\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(cpc_time,\n",
     "        cpc_data,\n",
     "        label='CPC',\n",
     "        color='blue')\n",
     "plt.xticks(rotation=45)\n",
+    "\n",
+    "# Create a twinx axis for SMPS data\n",
     "axb = ax.twinx()\n",
     "axb.plot(smps_time,\n",
     "         smps_data,\n",
     "         label='SMPS',\n",
     "         color='orange',)\n",
     "axb.set_ylim(0, 200)\n",
+    "\n",
+    "# Set labels for both y-axes and the x-axis\n",
     "ax.set_xlabel(\"Time (UTC)\")\n",
     "ax.set_ylabel('CPC_counts[#/sec]')\n",
     "axb.set_ylabel('SMPS_Mode[nm]')\n",
+    "\n",
+    "# Show the plot and adjust layout for better presentation\n",
     "plt.show()\n",
     "fig.tight_layout()"
    ]
@@ -565,14 +658,13 @@
    "metadata": {},
    "source": [
     "# Summary\n",
-    " This example showed how to load data from a folder and then average the data\n",
-    " over time. The data was then plotted to show the difference between the\n",
-    " averaged and non-averaged data."
+    "\n",
+    "In this part of the tutorial, we learned how to work with multiple streams of data and load them into a `Lake` object, which is a collection of streams. We explored operations on the data, including averaging it over time using the `particula.data.lake_stats` module. This allowed us to create more meaningful visualizations by comparing the averaged and non-averaged data. The example demonstrated the power of the `particula.data` package in handling and analyzing scientific data efficiently.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
diff --git a/docs/examples/loading_data_part4.ipynb b/docs/examples/streamlake/loading_data_part4.ipynb
similarity index 83%
rename from docs/examples/loading_data_part4.ipynb
rename to docs/examples/streamlake/loading_data_part4.ipynb
index 1a74196d5..006e07bba 100644
--- a/docs/examples/loading_data_part4.ipynb
+++ b/docs/examples/streamlake/loading_data_part4.ipynb
@@ -4,18 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "  # Loading Data Part 4\n",
+    "# Loading Part 4: Settings Files\n",
     "\n",
-    "  This example shows how do that save and load those stream and lake settings dictionaries. This is useful if you want to save your settings and then load them later.  This is also useful if you want to share your settings with someone else.  Or just stop from having to retype everything."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  ## Working path\n",
+    "In this part of the tutorial, we will explore how to save and load stream and lake settings dictionaries. This can be incredibly useful for preserving your settings, sharing them with others, or simply avoiding the need to retype everything.\n",
+    "\n",
+    "## Working Path\n",
+    "\n",
+    "In your working path, you will find a couple of `.json` files. These files are the settings files. The `lake_settings.json` file stores the settings for the lake, while the `stream_settings.json` file stores the settings for the stream. These settings are the same ones you created in the previous example, but now they are saved to files for easy access and sharing.\n",
     "\n",
-    "  In the working path you now see a couple of `.json` files, these are the settings files.  The `lake_settings.json` file contains the settings for the lake.  The `stream_settings.json` file contains the settings for the stream.  These are the same settings that you created in the previous example.  The only difference is that they are now saved to a file.\n",
     "\n",
     " ```\n",
     " data\n",
@@ -34,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -47,13 +43,13 @@
     }
    ],
    "source": [
-    "# all the imports\n",
+    "# Import the necessary libraries and modules\n",
     "import matplotlib.pyplot as plt\n",
     "from particula.data import loader_interface, settings_generator\n",
     "from particula.data.tests.example_data.get_example_data import get_data_folder\n",
     "from particula.data.lake import Lake\n",
     "\n",
-    "# set the parent directory of the data folders\n",
+    "# Set the parent directory where the data folders are located\n",
     "path = get_data_folder()\n",
     "print('Path to data folder:')\n",
     "print(path.rsplit('particula')[-1])"
@@ -63,14 +59,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ### Generate the settings and save them\n",
+    "## Generate and Save Settings\n",
     "\n",
-    " We'll use `generate_settings.save_settings_for_stream` to"
+    "First, we generate the settings for the CPC data using the `settings_generator.for_general_1d_load` function. These settings include details such as the data file location, file format, column names, and more.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,12 +103,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Next save the SMPS settings\n"
+    "### Next save the SMPS settings\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,21 +178,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load the Stream settings\n",
+    "## Loading Stream Settings\n",
     "\n",
-    "If you are still exploring you anlaysis pipeline, you may what individual streams. To load the settings for a stream use `generate_settings.load_settings_for_stream`.  This function takes the path to the settings file as an argument.  It returns a dictionary with the settings."
+    "If you are still exploring your analysis pipeline, you may want to load settings for individual streams. To do so, you can use the generate_settings.load_settings_for_stream function. This function takes the path to the settings file as an argument and returns a dictionary containing the stream settings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
+      "  Loading file: 2022-07-07_095151_SMPS.csv\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "  Loading file: 2022-07-10_094659_SMPS.csv\n",
       "['Lower_Size_(nm)', 'Upper_Size_(nm)', 'Sample_Temp_(C)', 'Sample_Pressure_(kPa)', 'Relative_Humidity_(%)', 'Median_(nm)', 'Mean_(nm)', 'Geo_Mean_(nm)', 'Mode_(nm)', 'Geo_Std_Dev.', 'Total_Conc_(#/cc)']\n"
      ]
@@ -221,14 +223,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Lake settings\n",
+    "## Lake settings\n",
     "\n",
     "If you wanted to load everything for a reanalysis, instead of calling each individual stream, you can first save a lake settings file.  This is done with `generate_settings.save_settings_for_lake`.  This function takes the path to the lake settings file as an argument.  It returns a dictionary with the settings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -275,6 +277,7 @@
       "Folder Settings: smps_2d\n",
       "  Loading file: 2022-07-07_095151_SMPS.csv\n",
       "  Loading file: 2022-07-10_094659_SMPS.csv\n",
+      " \n",
       "Lake with streams: ['cpc', 'smps_1d', 'smps_2d']\n"
      ]
     }
@@ -306,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -323,10 +326,10 @@
       "    The JSON file is formatted with a 4-space indentation.\n",
       "    \n",
       "    Args:\n",
-      "    - settings (dict): The settings dictionary to be saved.\n",
-      "    - path (str): The path where the subfolder is located.\n",
-      "    - subfolder (str): The subfolder where the settings file will be saved.\n",
-      "    - settings_suffix (str, optional): An optional suffix for the settings\n",
+      "    - settings: The settings dictionary to be saved.\n",
+      "    - path: The path where the subfolder is located.\n",
+      "    - subfolder: The subfolder where the settings file will be saved.\n",
+      "    - settings_suffix: An optional suffix for the settings\n",
       "        file name. Default is an empty string.\n",
       "    \n",
       "    Returns:\n",
@@ -341,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -359,9 +362,9 @@
       "    appropriate errors or warnings are raised.\n",
       "    \n",
       "    Args:\n",
-      "    - path (str): The path where the subfolder is located.\n",
-      "    - subfolder (str): The subfolder where the settings file is expected.\n",
-      "    - settings_suffix (str, optional): An optional suffix for the settings\n",
+      "    - path: The path where the subfolder is located.\n",
+      "    - subfolder: The subfolder where the settings file is expected.\n",
+      "    - settings_suffix: An optional suffix for the settings\n",
       "        file name. Default is an empty string.\n",
       "    \n",
       "    Returns:\n",
@@ -380,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -397,10 +400,10 @@
       "    The JSON file is formatted with a 4-space indentation.\n",
       "    \n",
       "    Args:\n",
-      "    - settings (dict): The settings dictionary to be saved.\n",
-      "    - path (str): The path where the subfolder is located.\n",
-      "    - subfolder (str): The subfolder where the settings file will be saved.\n",
-      "    - settings_suffix (str, optional): An optional suffix for the settings\n",
+      "    - settings: The settings dictionary to be saved.\n",
+      "    - path: The path where the subfolder is located.\n",
+      "    - subfolder: The subfolder where the settings file will be saved.\n",
+      "    - settings_suffix: An optional suffix for the settings\n",
       "        file name. Default is an empty string.\n",
       "    \n",
       "    Returns:\n",
@@ -415,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -434,9 +437,9 @@
       "    appropriate errors or warnings are raised.\n",
       "    \n",
       "    Args:\n",
-      "    - path (str): The path where the subfolder is located.\n",
-      "    - subfolder (str): The subfolder where the settings file is expected.\n",
-      "    - settings_suffix (str, optional): An optional suffix for the settings\n",
+      "    - path: The path where the subfolder is located.\n",
+      "    - subfolder: The subfolder where the settings file is expected.\n",
+      "    - settings_suffix: An optional suffix for the settings\n",
       "        file name. Default is an empty string.\n",
       "    \n",
       "    Returns:\n",
diff --git a/docs/examples/particula_data.md b/docs/examples/streamlake/particula_data.md
similarity index 100%
rename from docs/examples/particula_data.md
rename to docs/examples/streamlake/particula_data.md
diff --git a/docs/examples/stream_stats_part1.ipynb b/docs/examples/streamlake/stream_stats_part1.ipynb
similarity index 96%
rename from docs/examples/stream_stats_part1.ipynb
rename to docs/examples/streamlake/stream_stats_part1.ipynb
index baddddf0f..dad53398e 100644
--- a/docs/examples/stream_stats_part1.ipynb
+++ b/docs/examples/streamlake/stream_stats_part1.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " # Stream Stats\n",
+    " # Stream: Averaging and Outliers\n",
     "\n",
     " This example shows how to clean up a stream of data by removing outliers and\n",
     " and averaging the values over time. "
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -64,15 +64,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loading data from: CPC_3010_data_20220709_Jul.csv\n",
-      "Loading data from: CPC_3010_data_20220710_Jul.csv\n"
+      "  Loading file: CPC_3010_data_20220709_Jul.csv\n",
+      "  Loading file: CPC_3010_data_20220710_Jul.csv\n"
      ]
     }
    ],
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -114,10 +114,13 @@
      "output_type": "stream",
      "text": [
       "Stream:\n",
-      "Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 3.3465e+04, 3.2171e+04, ..., 1.9403e+04, 2.0230e+04,\n",
-      "        1.9521e+04],\n",
-      "       [1.7000e+01, 1.7100e+01, 1.7000e+01, ..., 1.6900e+01, 1.7000e+01,\n",
-      "        1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
+      "Stream(header=['CPC_count[#/sec]', 'Temperature[degC]'], data=array([[3.3510e+04, 1.7000e+01],\n",
+      "       [3.3465e+04, 1.7100e+01],\n",
+      "       [3.2171e+04, 1.7000e+01],\n",
+      "       ...,\n",
+      "       [1.9403e+04, 1.6900e+01],\n",
+      "       [2.0230e+04, 1.7000e+01],\n",
+      "       [1.9521e+04, 1.6800e+01]]), time=array([1.65734280e+09, 1.65734281e+09, 1.65734281e+09, ...,\n",
       "       1.65751559e+09, 1.65751560e+09, 1.65751560e+09]), files=[['CPC_3010_data_20220709_Jul.csv', 1044534], ['CPC_3010_data_20220710_Jul.csv', 1113488]])\n"
      ]
     }
@@ -130,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +151,7 @@
     "# plot the data\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(data_stream.datetime64,\n",
-    "        data_stream.data[0, :],  # data_stream.data is a 2d array, so we need\n",
+    "        data_stream.data[:, 0],  # data_stream.data is a 2d array, so we need\n",
     "                                 # to specify which column we want to plot\n",
     "        label=data_stream.header[0],\n",
     "        linestyle=\"none\",\n",
@@ -174,16 +177,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(2, 288)"
+       "(288, 2)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +225,7 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "ax.plot(stream_averaged.datetime64,\n",
-    "        stream_averaged.data[0, :],\n",
+    "        stream_averaged.data[:, 0],\n",
     "        label=stream_averaged.header[0],\n",
     "        marker=\".\",)\n",
     "plt.xticks(rotation=45)\n",
@@ -246,16 +249,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 63787)\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -275,7 +271,7 @@
     ")\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(stream_filtered.datetime64,\n",
-    "        stream_filtered.data[0, :],\n",
+    "        stream_filtered.data[:, 0],\n",
     "        label=stream_filtered.header[0],\n",
     "        marker=\".\",)\n",
     "plt.xticks(rotation=45)\n",
@@ -297,136 +293,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on module particula.data.stream_stats in particula.data:\n",
-      "\n",
-      "NAME\n",
-      "    particula.data.stream_stats - Functions to operate on stream objects.\n",
-      "\n",
-      "FUNCTIONS\n",
-      "    average_std(stream: object, average_interval: Union[float, int] = 60, new_time_array: Optional[numpy.ndarray] = None) -> object\n",
-      "        Calculate the average and standard deviation of data within a given\n",
-      "        'stream' object over specified intervals.\n",
-      "        \n",
-      "        This function takes a 'stream' object, which should contain time-series\n",
-      "        data, and computes the average and standard deviation of the data at\n",
-      "        intervals specified by 'average_interval'. If data.time is in seconds\n",
-      "        then the units of the interval are seconds (hour in hours etc). The\n",
-      "        results are returned as a new 'StreamAveraged' object containing the\n",
-      "        processed data.\n",
-      "        \n",
-      "        Args:\n",
-      "        - stream (object): The input stream object containing 'time' and 'data'\n",
-      "            arrays along with other associated metadata.\n",
-      "        - average_interval (float|int, optional): The time interval over which the\n",
-      "            averaging is to be performed.\n",
-      "        - new_time_array (np.ndarray, optional): An optional array of time points\n",
-      "            at which the average and standard deviation are computed.\n",
-      "            If not provided, a new time array is generated based on the start and\n",
-      "            end times within the 'stream.time' object.\n",
-      "        \n",
-      "        Returns:\n",
-      "        - StreamAveraged (object): An object of type 'StreamAveraged' containing\n",
-      "            the averaged data, time array, start and stop times, the standard\n",
-      "            deviation of the averaged data, and other metadata from the original\n",
-      "            'stream' object.\n",
-      "        \n",
-      "        The function checks for an existing 'new_time_array' and generates one if\n",
-      "        needed. It then calculates the average and standard deviation for each\n",
-      "        interval and constructs a 'StreamAveraged' object with the results and\n",
-      "        metadata from the original 'stream' object.\n",
-      "    \n",
-      "    drop_masked(stream: object, mask: numpy.ndarray) -> object\n",
-      "        Drop rows where mask is false, and return data stream.\n",
-      "        \n",
-      "        Parameters\n",
-      "        ----------\n",
-      "        stream : object\n",
-      "            data stream object\n",
-      "        mask : np.ndarray\n",
-      "            mask to apply to data stream\n",
-      "        \n",
-      "        Returns\n",
-      "        -------\n",
-      "        object\n",
-      "            stream object\n",
-      "    \n",
-      "    filtering(stream: object, bottom: Optional[float] = None, top: Optional[float] = None, value: Optional[float] = None, invert: bool = False, replace_with: Union[float, int, NoneType] = None, drop: Optional[bool] = True) -> object\n",
-      "        Filters the data of the given 'stream' object based on the specified\n",
-      "        bounds or specific value. The filtered data can be either dropped or\n",
-      "        replaced with a specified value.  Note, not all parameters need to be\n",
-      "        specified, but at least one must be provided (top, bottom, value)\n",
-      "        \n",
-      "        Args:\n",
-      "        - stream (Stream): The input stream object containing 'data' and 'time'\n",
-      "            attributes.\n",
-      "        - bottom (float, optional): The lower bound for filtering data. Defaults\n",
-      "            to None.\n",
-      "        - top (float, optional): The upper bound for filtering data.\n",
-      "            Defaults to None.\n",
-      "        - value (float, optional): Specific value to filter from data.\n",
-      "            Defaults to None.\n",
-      "        - invert (bool): If True, inverts the filter criteria.\n",
-      "            Defaults to False.\n",
-      "        - replace_with (float|int, optional): Value to replace filtered-out data.\n",
-      "            Defaults to None.\n",
-      "        - drop (bool, optional): If True, filtered-out data points are dropped\n",
-      "            from the dataset. Defaults to False.\n",
-      "        \n",
-      "        Returns:\n",
-      "        - Stream: The 'stream' object with data filtered as specified.\n",
-      "        \n",
-      "        If 'drop' is True, 'replace_with' is ignored and filtered data points are\n",
-      "        removed from the 'stream' object. Otherwise, filtered data points are\n",
-      "        replaced with 'replace_with' value.\n",
-      "        \n",
-      "        add specific data row to filter on\n",
-      "\n",
-      "DATA\n",
-      "    Optional = typing.Optional\n",
-      "        Optional type.\n",
-      "        \n",
-      "        Optional[X] is equivalent to Union[X, None].\n",
-      "    \n",
-      "    Union = typing.Union\n",
-      "        Union type; Union[X, Y] means either X or Y.\n",
-      "        \n",
-      "        To define a union, use e.g. Union[int, str].  Details:\n",
-      "        - The arguments must be types and there must be at least one.\n",
-      "        - None as an argument is a special case and is replaced by\n",
-      "          type(None).\n",
-      "        - Unions of unions are flattened, e.g.::\n",
-      "        \n",
-      "            Union[Union[int, str], float] == Union[int, str, float]\n",
-      "        \n",
-      "        - Unions of a single argument vanish, e.g.::\n",
-      "        \n",
-      "            Union[int] == int  # The constructor actually returns int\n",
-      "        \n",
-      "        - Redundant arguments are skipped, e.g.::\n",
-      "        \n",
-      "            Union[int, str, int] == Union[int, str]\n",
-      "        \n",
-      "        - When comparing unions, the argument order is ignored, e.g.::\n",
-      "        \n",
-      "            Union[int, str] == Union[str, int]\n",
-      "        \n",
-      "        - You cannot subclass or instantiate a union.\n",
-      "        - You can use Optional[X] as a shorthand for Union[X, None].\n",
-      "\n",
-      "FILE\n",
-      "    c:\\users\\kkgor\\onedrive\\areas\\github\\particula\\particula\\data\\stream_stats.py\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(stream_stats)"
    ]
diff --git a/docs/examples/stream_stats_size_distribution_part2.ipynb b/docs/examples/streamlake/stream_stats_size_distribution_part2.ipynb
similarity index 99%
rename from docs/examples/stream_stats_size_distribution_part2.ipynb
rename to docs/examples/streamlake/stream_stats_size_distribution_part2.ipynb
index a2caab08e..43beacf8f 100644
--- a/docs/examples/stream_stats_size_distribution_part2.ipynb
+++ b/docs/examples/streamlake/stream_stats_size_distribution_part2.ipynb
@@ -2,18 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "  # Size Distribution Stats\n",
     "\n",
     "  This example shows how to process size distribution data from an SMPS.\n",
     " The processing returns mean properties of the size distribution, such as\n",
     " the mean diameter, median diameter, and total PM2.5 mass."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# all the imports\n",
     "import matplotlib.pyplot as plt\n",
@@ -24,12 +26,11 @@
     "\n",
     "# set the parent directory of the data folder\n",
     "path = get_data_folder()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     " ### Load the data\n",
     "\n",
@@ -39,12 +40,28 @@
     "\n",
     " If you think this settings generator is getting tedious, we hear you. We'll\n",
     " show the fix to that soon."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Folder Settings: smps_1d\n",
+      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
+      "  Loading file: 2022-07-10_094659_SMPS.csv\n",
+      "Folder Settings: smps_2d\n",
+      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
+      "  Loading file: 2022-07-10_094659_SMPS.csv\n",
+      " \n",
+      "Lake with streams: ['smps_1d', 'smps_2d']\n"
+     ]
+    }
+   ],
    "source": [
     "# settings for the SMPS data\n",
     "smps_1d_settings, smps_2d_settings = settings_generator.for_general_sizer_1d_2d_load(\n",
@@ -106,27 +123,11 @@
     "\n",
     "print(' ')\n",
     "print(lake)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Folder Settings: smps_1d\n",
-      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
-      "  Loading file: 2022-07-10_094659_SMPS.csv\n",
-      "Folder Settings: smps_2d\n",
-      "  Loading file: 2022-07-07_095151_SMPS.csv\n",
-      "  Loading file: 2022-07-10_094659_SMPS.csv\n",
-      " \n",
-      "Lake with streams: ['smps_1d', 'smps_2d']\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     " # Processing the Stream\n",
     " The lake is a collection of streams, stored as a dictionary. The next step\n",
@@ -136,26 +137,16 @@
     " streams and applying a cusutom processing function to each stream. Or you could\n",
     " use some standard process already built in to `particula.data.process`. In this\n",
     " example we'll use `process.size_distribution` to calculate the PM2.5 mass from the"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "source": [
-    "lake['mean_properties'] = size_distribution.sizer_mean_properties(\n",
-    "    stream=lake['smps_2d'],\n",
-    "    diameter_units='nm',\n",
-    ")\n",
-    "\n",
-    "# list out the header\n",
-    "for header in lake['mean_properties'].header:\n",
-    "    print(header)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Total_Conc_(#/cc)\n",
       "Mean_Diameter_(nm)\n",
@@ -180,10 +171,20 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "lake['mean_properties'] = size_distribution.sizer_mean_properties(\n",
+    "    stream=lake['smps_2d'],\n",
+    "    diameter_units='nm',\n",
+    ")\n",
+    "\n",
+    "# list out the header\n",
+    "for header in lake['mean_properties'].header:\n",
+    "    print(header)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     " # Plot the Data\n",
     " With that processing done we can plot some useful summary plots. For example,\n",
@@ -198,16 +199,27 @@
     " This is incontrast to callint `stream.data['header_name']` which would return\n",
     " an error. As that line first calls `stream.data` returning the np.ndarray, then calls\n",
     " the header name, which is not a valid index for a np.ndarray."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mean_prop_stream = lake['mean_properties']\n",
     "\n",
-    "\n",
     "# plot the data on twinx axis\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(mean_prop_stream.datetime64,\n",
@@ -225,41 +237,26 @@
     "ax.legend()\n",
     "plt.show()\n",
     "fig.tight_layout()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     " # Summary\n",
     " This example showed how to process size distribution data from an SMPS. The\n",
     " processing returns mean properties of the size distribution, such as the mean\n",
     " diameter, median diameter, and total PM2.5 mass."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "help(size_distribution.sizer_mean_properties)"
-   ],
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Help on function sizer_mean_properties in module particula.data.process.size_distribution:\n",
       "\n",
@@ -287,12 +284,17 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "help(size_distribution.sizer_mean_properties)"
+   ]
   }
  ],
- "nbformat": 4,
- "nbformat_minor": 2,
  "metadata": {
+  "kernelspec": {
+   "display_name": "ParticulaDev_py39",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -303,7 +305,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.9.18"
   }
- }
-}
\ No newline at end of file
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/strategic_plan/strategic_planning_FY24.md b/docs/strategic_plan/strategic_planning_FY24.md
index 4b0f7c5c7..ea5dd7aca 100644
--- a/docs/strategic_plan/strategic_planning_FY24.md
+++ b/docs/strategic_plan/strategic_planning_FY24.md
@@ -39,7 +39,7 @@ The goal is to restructure Particula to facilitate ease of modification and to i
 #### Systems-Level Concepts (Classes)
 
 - **Role**: To abstract routine tasks, such as ODE coagulation simulation using particle classes.
-- **Utility**: Serves users requiring higher abstraction without delving into interface mechanics. These poeple already know what they are calling and how the procedures work.
+- **Utility**: Serves users requiring higher abstraction without delving into interface mechanics. These people already know what they are calling and how the procedures work.
 - **Documentation**: Detailed Jupyter notebooks explaining the class's purpose, its importance, and connection to other code components. What it is, Why it is important, and how it relates to other parts of the code. At least three examples of how to use it.
 - **Testing**: Validated through Jupyter notebook execution.
 
@@ -70,6 +70,7 @@ Paper 1: A Particula model of the Microphysics and Chemistry of Aerosols
   - Techniques: Sectional Method and Super Droplet (Direct Simulation).
 - Processes: Emphasizing Coagulation, Condensation, Evaporation, and Nucleation.
   - Impact Analysis: Investigating how initial emissions influence cloud formation and updraft velocity.
+
 Paper 2: Data Integration and Experimental Validation
 - Data pipeline in Particula: Aligning modeling with experimental data form a better understanding of the phenomena.
 - Remote Sensing Data Incorporation: Exploring integration with DOE-Radar Observations for cloud droplet analysis or other remote sensing data like AERONET.
@@ -106,7 +107,7 @@ Paper 2: Data Integration and Experimental Validation
   - [ ] Wall loss correction, experimental data integration
 - [ ] Add Non-ideal Mixing (BAT/AIOMFAC integration, maybe web api)
   - [ ] Add Thermodynamic Equilibrium
-- [ ] Add Super Droplet Method
+- [ ] Add Direct Lagrangian Simulation
   - [ ] Rough estimates of memory on an RTX A6000 16 GB using pytorch can handle about 5000 particles tracking (position and velocity only) with coagulation checks, time steps of 0.000008 sec, 1000 sec simulation time
 - [ ] Add Chemistry (this could come later, but simple VBS bin shift might be achievable)
   - [ ] Add Volatility Basis Set (VBS) for chemical reactions
diff --git a/particula/data/merger.py b/particula/data/merger.py
index 00899ce13..4a3153d09 100644
--- a/particula/data/merger.py
+++ b/particula/data/merger.py
@@ -66,21 +66,21 @@ def combine_data(
                 data,
                 data_new,
             ),
-            axis=0,
+            axis=1,
         )
     else:  # interpolate the data_new before adding it to the data_stream
-        data_interp = np.empty((data_new.shape[0], len(time)))
-        for i in range(data_new.shape[0]):
-            mask = ~np.isnan(data_new[i, :])
+        data_interp = np.empty((len(time), data_new.shape[1]))
+        for i in range(data_new.shape[1]):
+            mask = ~np.isnan(data_new[:, i])
             if not mask.any():
-                data_interp[i, :] = np.nan
+                data_interp[:, i] = np.nan
             else:
-                left_value = data_new[i, mask][0]
-                right_value = data_new[i, mask][-1]
-                data_interp[i, :] = np.interp(
+                left_value = data_new[mask, i][0]
+                right_value = data_new[mask, i][-1]
+                data_interp[:, i] = np.interp(
                     time,
                     time_new[mask],
-                    data_new[i, mask],
+                    data_new[mask, i],
                     left=left_value,
                     right=right_value,
                 )
@@ -90,7 +90,7 @@ def combine_data(
                 data,
                 data_interp,
             ),
-            axis=0,
+            axis=1,
         )
 
     header_updated = list(np.append(header_list, header_new))
@@ -163,10 +163,10 @@ def stream_add_data(
                 header_new=header_new
             )
         # updates stream
-        stream.data = np.hstack((stream.data, data_new))
+        stream.data = np.vstack((stream.data, data_new))
         stream.time = np.concatenate((stream.time, time_new))
     else:
-        stream.data = np.hstack((stream.data, data_new))
+        stream.data = np.vstack((stream.data, data_new))
         stream.time = np.concatenate((stream.time, time_new))
 
     # check if the time stream added is increasing
diff --git a/particula/data/process/size_distribution.py b/particula/data/process/size_distribution.py
index 28626b998..4aa1e8ee2 100644
--- a/particula/data/process/size_distribution.py
+++ b/particula/data/process/size_distribution.py
@@ -170,7 +170,7 @@ def sizer_mean_properties(
             mean_vol_diameter_nm[i], geometric_mean_diameter_nm[i], \
             mode_diameter[i], mode_diameter_mass[i] = \
             mean_properties(
-            sizer_dndlogdp_smps[:, i],
+            sizer_dndlogdp_smps[i, :],
             sizer_diameter_smps,
             sizer_limits=sizer_limits
         )
@@ -178,7 +178,7 @@ def sizer_mean_properties(
         # total PM 100 nm concentration
         total_concentration_pm01[i], unit_mass_ug_m3_pm01[i], _, _, _, _, _ = \
             mean_properties(
-            sizer_dndlogdp_smps[:, i],
+            sizer_dndlogdp_smps[i, :],
             sizer_diameter_smps,
             sizer_limits=[0, 100]
         )
@@ -186,14 +186,14 @@ def sizer_mean_properties(
         # total PM1 um concentration
         total_concentration_pm1[i], unit_mass_ug_m3_pm1[i], _, _, _, _, _ = \
             mean_properties(
-            sizer_dndlogdp_smps[:, i],
+            sizer_dndlogdp_smps[i, :],
             sizer_diameter_smps,
             sizer_limits=[0, 1000]
         )
         # total PM <2.5 um concentration
         total_concentration_pm25[i], unit_mass_ug_m3_pm25[i], _, _, _, _, _ = \
             mean_properties(
-            sizer_dndlogdp_smps[:, i],
+            sizer_dndlogdp_smps[i, :],
             sizer_diameter_smps,
             sizer_limits=[0, 2500]
         )
@@ -201,7 +201,7 @@ def sizer_mean_properties(
         # total PM <10 um concentration
         total_concentration_pm10[i], unit_mass_ug_m3_pm10[i], _, _, _, _, _ = \
             mean_properties(
-            sizer_dndlogdp_smps[:, i],
+            sizer_dndlogdp_smps[i, :],
             sizer_diameter_smps,
             sizer_limits=[0, 10000]
         )
@@ -213,7 +213,7 @@ def sizer_mean_properties(
     mass_ug_m3_pm10 = unit_mass_ug_m3_pm10 * density
 
     # combine the data for datalake
-    combinded = np.vstack((
+    combinded = np.stack((
         total_concentration,
         mean_diameter_nm,
         geometric_mean_diameter_nm,
@@ -234,7 +234,7 @@ def sizer_mean_properties(
         total_concentration_pm10,
         unit_mass_ug_m3_pm10,
         mass_ug_m3_pm10,
-    ))
+    ), axis=1)
     header = [
         'Total_Conc_(#/cc)',
         'Mean_Diameter_(nm)',
diff --git a/particula/data/settings_generator.py b/particula/data/settings_generator.py
index 716d0caa9..22fd1db52 100644
--- a/particula/data/settings_generator.py
+++ b/particula/data/settings_generator.py
@@ -255,6 +255,7 @@ def save_settings_for_stream(
 
     with open(save_path, 'w', encoding='utf-8') as file:
         json.dump(settings, file, indent=4)
+        file.write('\n')  # Add an empty line at the bottom
 
 
 def load_settings_for_lake(
@@ -331,3 +332,4 @@ def save_settings_for_lake(
 
     with open(save_path, 'w', encoding='utf-8') as file:
         json.dump(settings, file, indent=4)
+        file.write('\n')  # Add an empty line at the bottom
diff --git a/particula/data/stream.py b/particula/data/stream.py
index 75803f120..dc6594c0e 100644
--- a/particula/data/stream.py
+++ b/particula/data/stream.py
@@ -16,7 +16,8 @@ class Stream:
     header : List[str]
         A list of strings representing the header of the data stream.
     data : np.ndarray
-        A numpy array representing the data stream.
+        A numpy array representing the data stream. The first dimension
+        represents time and the second dimension represents the header.
     time : np.ndarray
         A numpy array representing the time stream.
     files : List[str]
@@ -65,7 +66,7 @@ def __getitem__(self, index: Union[int, str]):
             The data stream at the specified index."""
         if isinstance(index, str):
             index = self.header.index(index)
-        return self.data[index, :]
+        return self.data[:, index]
 
     def __setitem__(self, index: Union[int, str], value):
         """Allows for setting or adding of a row of data in the stream.
@@ -77,10 +78,10 @@ def __setitem__(self, index: Union[int, str], value):
         if isinstance(index, str):
             if index not in self.header:
                 self.header.append(index)  # add new header element
-                self.data = np.vstack((self.data, value))
+                self.data = np.hstack((self.data, value))
             index = self.header.index(index)
         # if index is an int, set the data at that index
-        self.data[index, :] = value
+        self.data[:, index] = value
 
     def __len__(self):
         """Returns the length of the time stream."""
@@ -149,4 +150,4 @@ def get_std(self, index) -> np.ndarray:
         """Returns the standard deviation of the data."""
         if isinstance(index, str):
             index = self.header.index(index)
-        return self.standard_deviation[index, :]
+        return self.standard_deviation[:, index]
diff --git a/particula/data/stream_stats.py b/particula/data/stream_stats.py
index d8fe70bce..69afc45b4 100644
--- a/particula/data/stream_stats.py
+++ b/particula/data/stream_stats.py
@@ -23,7 +23,7 @@ def drop_masked(stream: Stream, mask: np.ndarray) -> Stream:
     object
         stream object
     """
-    stream.data = stream.data[:, mask]
+    stream.data = stream.data[mask, :]
     stream.time = stream.time[mask]
     return stream
 
@@ -74,7 +74,7 @@ def average_std(
             step=average_interval
         )
     # generate empty arrays for averaged data and std to be filled in
-    average = np.zeros([len(stream.header), len(new_time_array)])
+    average = np.zeros([len(new_time_array), len(stream.header)])
     std = np.zeros_like(average)
 
     # average data
@@ -157,8 +157,8 @@ def filtering(
     )
     if drop and replace_with is None:
         # Apply mask to data and time, dropping filtered values
-        # if any columns are then drop that whole column
-        mask_sum = np.invert(np.sum(np.invert(mask), axis=0) > 0)
+        # if any rows are then drop that whole column
+        mask_sum = np.invert(np.sum(np.invert(mask), axis=1) > 0)
         stream = drop_masked(stream, mask_sum)
     elif replace_with is not None:
         stream.data = np.where(mask, stream.data, replace_with)
@@ -192,11 +192,11 @@ def remove_time_window(
     if epoch_end is None:
         # if no end time provided, remove the closest time point
         stream.time = np.delete(stream.time, index_start)
-        stream.data = np.delete(stream.data, index_start, axis=1)
+        stream.data = np.delete(stream.data, index_start, axis=0)
         return stream
     # get index of end time
     index_end = np.argmin(np.abs(stream.time - epoch_end)) + 1
     # remove time and data between start and end times
     stream.time = np.delete(stream.time, slice(index_start, index_end))
-    stream.data = np.delete(stream.data, slice(index_start, index_end), axis=1)
+    stream.data = np.delete(stream.data, slice(index_start, index_end), axis=0)
     return stream
diff --git a/particula/data/tests/example_data/CPC_3010_data/stream_settings_cpc.json b/particula/data/tests/example_data/CPC_3010_data/stream_settings_cpc.json
index dc87c2a99..18b78546d 100644
--- a/particula/data/tests/example_data/CPC_3010_data/stream_settings_cpc.json
+++ b/particula/data/tests/example_data/CPC_3010_data/stream_settings_cpc.json
@@ -30,4 +30,4 @@
     "delimiter": ",",
     "time_shift_seconds": 0,
     "timezone_identifier": "UTC"
-}
\ No newline at end of file
+}
diff --git a/particula/data/tests/example_data/SMPS_data/stream_settings_smps_1d.json b/particula/data/tests/example_data/SMPS_data/stream_settings_smps_1d.json
index 36848bb2c..1376ed94d 100644
--- a/particula/data/tests/example_data/SMPS_data/stream_settings_smps_1d.json
+++ b/particula/data/tests/example_data/SMPS_data/stream_settings_smps_1d.json
@@ -49,4 +49,4 @@
     "delimiter": ",",
     "time_shift_seconds": 0,
     "timezone_identifier": "UTC"
-}
\ No newline at end of file
+}
diff --git a/particula/data/tests/example_data/SMPS_data/stream_settings_smps_2d.json b/particula/data/tests/example_data/SMPS_data/stream_settings_smps_2d.json
index cbe9c5666..7487db8c5 100644
--- a/particula/data/tests/example_data/SMPS_data/stream_settings_smps_2d.json
+++ b/particula/data/tests/example_data/SMPS_data/stream_settings_smps_2d.json
@@ -28,4 +28,4 @@
     "delimiter": ",",
     "time_shift_seconds": 0,
     "timezone_identifier": "UTC"
-}
\ No newline at end of file
+}
diff --git a/particula/data/tests/example_data/lake_settings_cpc_smps.json b/particula/data/tests/example_data/lake_settings_cpc_smps.json
index a762b09a0..a78fd12a2 100644
--- a/particula/data/tests/example_data/lake_settings_cpc_smps.json
+++ b/particula/data/tests/example_data/lake_settings_cpc_smps.json
@@ -115,4 +115,4 @@
         "time_shift_seconds": 0,
         "timezone_identifier": "UTC"
     }
-}
\ No newline at end of file
+}
diff --git a/particula/data/tests/merger_test.py b/particula/data/tests/merger_test.py
index 73c27086d..69136c4a0 100644
--- a/particula/data/tests/merger_test.py
+++ b/particula/data/tests/merger_test.py
@@ -7,7 +7,7 @@
 
 def create_sample_data():
     """Create sample data for testing."""
-    data = np.array([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2]])
+    data = np.array([[1, 2], [1, 2], [1, 2], [1, 2], [1, 2]])
     time = np.array([0, 1, 2, 3, 4])
     header_list = ['header1', 'header2']
     return data, time, header_list
@@ -17,8 +17,8 @@ def test_combine_data_with_2d_data():
     """Test with 2d data."""
     # Setup
     data, time, header_list = create_sample_data()
-    data_new = np.array([[7, 7], [8, 8]])
-    time_new = np.array([1, 4])
+    data_new = np.array([[7, 8], [7, 8], [7, 8]])
+    time_new = np.array([1, 3, 4])
     header_new = ['header3', 'header4']
 
     # Execution
@@ -27,7 +27,7 @@ def test_combine_data_with_2d_data():
 
     # Verification
     expected_data = np.array([
-        [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [7, 7, 7, 7, 7], [8, 8, 8, 8, 8]
+        [1, 2, 7, 8], [1, 2, 7, 8], [1, 2, 7, 8], [1, 2, 7, 8], [1, 2, 7, 8]
     ])
     assert np.array_equal(merged_data, expected_data)
     expected_header_list = ['header1', 'header2', 'header3', 'header4']
@@ -48,7 +48,7 @@ def test_combine_data_with_1d_data():
 
     # Verification
     expected_data = np.array([
-        [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [7, 7, 7, 7, 7],
+        [1, 2, 7], [1, 2, 7], [1, 2, 7], [1, 2, 7], [1, 2, 7]
     ])
     assert np.array_equal(merged_data, expected_data)
     expected_header_list = ['header1', 'header2', 'header3']
@@ -60,7 +60,7 @@ def test_combine_data_with_nan_values():
     # Setup
     data, time, header_list = create_sample_data()
     data_new = np.array([
-        [np.nan, 6, np.nan], [7, 7, np.nan], [np.nan, np.nan, np.nan]])
+        [np.nan, 6, 7], [np.nan, 6, 7], [np.nan, 6, np.nan]])
     time_new = np.array([1, 2, 3])
     header_new = ['header3', 'header4', 'header5']
 
@@ -70,8 +70,8 @@ def test_combine_data_with_nan_values():
 
     # Verification
     expected_data = np.array([
-        [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [6, 6, 6, 6, 6],
-        [7, 7, 7, 7, 7], [np.nan, np.nan, np.nan, np.nan, np.nan]
+        [1, 2, np.nan, 6, 7], [1, 2, np.nan, 6, 7], [1, 2, np.nan, 6, 7],
+        [1, 2, np.nan, 6, 7], [1, 2, np.nan, 6, 7]
     ])
     assert np.array_equal(
         np.nan_to_num(merged_data), np.nan_to_num(expected_data)
@@ -79,70 +79,3 @@ def test_combine_data_with_nan_values():
     expected_header_list = ['header1', 'header2', 'header3', 'header4',
                             'header5']
     assert np.all(merged_header_list == expected_header_list)
-
-
-def test_combine_data_with_transposed_input():
-    """Test with transposed input."""
-    # Setup
-    data, time, header_list = create_sample_data()
-    data_new = np.array([
-        [np.nan, 6, np.nan], [7, 7, np.nan], [np.nan, np.nan, np.nan]])
-    time_new = np.array([1, 2, 3])
-    header_new = ['header3', 'header4', 'header5']
-
-    # Execution
-    merged_data, merged_header_list = merger.combine_data(
-        data, time, header_list, data_new, time_new, header_new)
-
-    # Verification
-    expected_data = np.array([
-        [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [6, 6, 6, 6, 6],
-        [7, 7, 7, 7, 7], [np.nan, np.nan, np.nan, np.nan, np.nan]
-    ])
-    assert np.array_equal(np.nan_to_num(merged_data),
-                          np.nan_to_num(expected_data))
-    expected_header_list = ['header1', 'header2', 'header3',
-                            'header4', 'header5']
-    assert np.all(merged_header_list == expected_header_list)
-
-
-def test_combine_data_same_time_2d_data():
-    """Test with 2d data and same time."""
-    # Setup
-    data, time, header_list = create_sample_data()
-    data_new = np.array([[7, 8], [7, 8], [7, 8], [7, 8], [7, 8]])
-    time_new = np.array([0, 1, 2, 3, 4])
-    header_new = ['header3', 'header4']
-
-    # Execution
-    merged_data, merged_header_list = merger.combine_data(
-        data, time, header_list, data_new, time_new, header_new)
-
-    # Verification
-    expected_data = np.array([
-        [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [7, 7, 7, 7, 7], [8, 8, 8, 8, 8]
-    ])
-    assert np.array_equal(merged_data, expected_data)
-    expected_header_list = ['header1', 'header2', 'header3', 'header4']
-    assert np.all(merged_header_list == expected_header_list)
-
-
-def test_combine_data_same_time_1d_data():
-    """Test with 1d data and same time."""
-    # Setup
-    data, time, header_list = create_sample_data()
-    data_new = np.array([7, 7, 7, 7, 7])
-    time_new = np.array([0, 1, 2, 3, 4])
-    header_new = ['header3']
-
-    # Execution
-    merged_data, merged_header_list = merger.combine_data(
-        data, time, header_list, data_new, time_new, header_new)
-
-    # Verification
-    expected_data = np.array([
-        [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [7, 7, 7, 7, 7],
-    ])
-    assert np.array_equal(merged_data, expected_data)
-    expected_header_list = ['header1', 'header2', 'header3']
-    assert np.all(merged_header_list == expected_header_list)
diff --git a/particula/util/convert.py b/particula/util/convert.py
index b9a45ec1c..f37395fcc 100644
--- a/particula/util/convert.py
+++ b/particula/util/convert.py
@@ -1,7 +1,6 @@
 """conversion functions common for aerosol processing
 """
 
-import warnings
 from typing import Union, Tuple, Any, List, Dict
 import numpy as np
 
@@ -407,8 +406,8 @@ def convert_sizer_dn(
 
     # future: Address potential over-counting in last/first bin
     """
-    assert len(diameter) == len(dn_dlogdp) > 0, \
-        "Inputs must be non-empty arrays of the same length."
+    assert len(diameter) > 0, \
+        "Inputs must be non-empty arrays."
     # Compute the bin widths
     delta = np.zeros_like(diameter)
     delta[:-1] = np.diff(diameter)
@@ -418,10 +417,10 @@ def convert_sizer_dn(
     lower = diameter - delta / 2
     upper = diameter + delta / 2
 
-    if dn_dlogdp.ndim == 2:
-        # expand diameter by one dimension so it can be broadcast
-        lower = np.expand_dims(lower, axis=1)
-        upper = np.expand_dims(upper, axis=1)
+    # if dn_dlogdp.ndim == 2:
+    #     # expand diameter by one dimension so it can be broadcast
+    #     lower = np.expand_dims(lower, axis=1)
+    #     upper = np.expand_dims(upper, axis=1)
     if inverse:
         # Convert from dn to dn/dlogdp
         return dn_dlogdp / np.log10(upper / lower)
@@ -525,28 +524,24 @@ def data_shape_check(
     if len(data.shape) == 2:
         # Check if time matches the dimensions of data
         if len(time) == data.shape[0] and len(time) == data.shape[1]:
-            concatenate_axis_new = 0  # Default to the first axis
-            # Check if the last axis of data matches the length of time
-            if data.shape[-1] != len(time):
-                warnings.warn("Square data with time shape assumes time \
-                                  axis is the first axis in data.")
+            concatenate_axis_new = 1  # Default to the axis=1
         else:
             # Find the axis that doesn't match the length of time
             concatenate_axis_new = np.argwhere(
                 np.array(data.shape) != len(time)).flatten()[0]
-        # Reshape new data so the concatenate axis is the first axis
-        data = np.moveaxis(data, concatenate_axis_new, 0)
+        # Reshape new data so the concatenate axis is axis=1
+        data = np.moveaxis(data, concatenate_axis_new, 1)
 
         # check header list length matches data_new shape
-        if len(header) != data.shape[0]:
+        if len(header) != data.shape[1]:
             print(f'header len: {len(header)} vs. data.shape: \
                   {data.shape}')
             print(header)
-            raise ValueError("Header list length must match the first \
+            raise ValueError("Header list length must match the second \
                               dimension of data_new.")
     elif len(header) == 1:
-        # Reshape new data so the concatenate axis is the first axis
-        data = np.expand_dims(data, 0)
+        # Reshape new data so the concatenate axis is axis=1
+        data = np.expand_dims(data, 1)
 
     else:
         raise ValueError("Header list must be a single entry if data_new \
diff --git a/particula/util/stats.py b/particula/util/stats.py
index 37a14d66f..aeb7dfc2e 100644
--- a/particula/util/stats.py
+++ b/particula/util/stats.py
@@ -48,11 +48,11 @@ def merge_formatting(
             (
                 data_current,
                 np.full((
-                        len(header_new_not_listed),
-                        data_current.shape[1]
+                        data_current.shape[0],
+                        len(header_new_not_listed)
                         ), np.nan)
             ),
-            axis=0
+            axis=1
         )
 
     if bool(header_list_not_new):
@@ -63,15 +63,15 @@ def merge_formatting(
             (
                 data_new,
                 np.full((
-                        len(header_list_not_new),
-                        data_new.shape[1]
+                        data_new.shape[0],
+                        len(header_list_not_new)
                         ), np.nan)
             ),
-            axis=0
+            axis=1
         )
 
     # check that the shapes are the same
-    if data_current.shape[0] != data_new.shape[0]:
+    if data_current.shape[1] != data_new.shape[1]:
         raise ValueError(
             'data_current  ',
             data_current.shape,
@@ -100,14 +100,14 @@ def merge_formatting(
         header_new = [header_new[i] for i in header_new_indices]
 
         # sort the data
-        data_current = data_current[header_stored_indices, :]
+        data_current = data_current[:, header_stored_indices]
     else:
         # match header_new to header_current and sort the data
         header_new_indices = [
             header_new.index(x) for x in header_current
         ]
         header_new = [header_new[i] for i in header_new_indices]
-    data_new = data_new[header_new_indices, :]
+    data_new = data_new[:, header_new_indices]
     return data_current, header_current, data_new, header_new
 
 
@@ -222,11 +222,11 @@ def average_to_interval(
 
             if start_index < stop_index:
                 # average the data in the time interval
-                average_data[:, i - 1] = np.nanmean(
-                    data_raw[:, start_index:stop_index], axis=1
+                average_data[i - 1, :] = np.nanmean(
+                    data_raw[start_index:stop_index, :], axis=0
                 )  # the actual averaging of data is here
-                average_data_std[:, i - 1] = np.nanstd(
-                    data_raw[:, start_index:stop_index], axis=1
+                average_data_std[i - 1, :] = np.nanstd(
+                    data_raw[start_index:stop_index, :], axis=0
                 )  # the actual std data is here
             else:
                 start_time = time_raw[stop_index]
diff --git a/particula/util/tests/stats_test.py b/particula/util/tests/stats_test.py
index 3f75e5d12..957d18af2 100644
--- a/particula/util/tests/stats_test.py
+++ b/particula/util/tests/stats_test.py
@@ -7,9 +7,9 @@
 def test_merge_format_str_headers():
     """Test the merge_different_headers function."""
     # Create example input data
-    data = np.array([[1, 2, 4], [3, 4, 5]]).T
+    data = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
     header = ['a', 'b', 'c']
-    data_add = np.array([[5, 5], [8, 8]]).T
+    data_add = np.array([[5, 8], [5, 8], [5, 8]])
     header_add = ['c', 'd']
 
     # Call function to merge the data and headers
@@ -21,7 +21,7 @@ def test_merge_format_str_headers():
     )
 
     # Test merged data shape
-    assert merged_data.shape == (4, 2)
+    assert merged_data.shape == (3, 4)
 
     # Test merged header
     assert merged_header == ['a', 'b', 'c', 'd']
@@ -30,9 +30,9 @@ def test_merge_format_str_headers():
 def test_merge_format_num_headers():
     """Test the merge_different_headers function."""
     # Create example input data
-    data = np.array([[1, 2, 4], [3, 4, 5]]).T
+    data = np.array([[1, 2, 3], [1, 2, 3]])
     header = ['1', '2', '3']
-    data_add = np.array([[5, 5], [8, 8]]).T
+    data_add = np.array([[5, 8], [5, 8]])
     header_add = ['3', '4']
 
     # Call function to merge the data and headers
@@ -44,7 +44,7 @@ def test_merge_format_num_headers():
     )
 
     # Test merged data shape
-    assert merged_data.shape == (4, 2)
+    assert merged_data.shape == (2, 4)
 
     # Test merged header
     assert merged_header == ['1', '2', '3', '4']
@@ -56,10 +56,10 @@ def test_average_to_interval():
     time_stream = np.arange(0, 1000, 10)
     average_base_sec = 10
     average_base_time = np.arange(0, 1000, 60)
-    data_stream = np.linspace(0, 1000, len(time_stream))
-    data_stream = np.vstack((data_stream, data_stream, data_stream))
-    average_base_data = np.zeros((3, len(average_base_time)))
-    average_base_data_std = np.zeros((3, len(average_base_time)))
+    data_stream = np.linspace(0, 1000, len(time_stream)).reshape(-1, 1)
+    data_stream = np.concatenate([data_stream, data_stream, data_stream], axis=1)
+    average_base_data = np.zeros((len(average_base_time), 3))
+    average_base_data_std = np.zeros((len(average_base_time), 3))
 
     # Call the function
     average_base_data, average_base_data_std = stats.average_to_interval(
@@ -88,7 +88,7 @@ def test_average_to_interval():
                     691.919, 752.525, 813.131, 873.737,
                     934.34343434]
             ]
-        )
+        ).T
 
     expected_std = np.array(
             [
@@ -105,7 +105,7 @@ def test_average_to_interval():
                     17.250, 17.250, 17.250, 17.250, 17.250,
                     17.250, 17.250]
             ]
-        )
+        ).T
 
     assert np.allclose(average_base_data, expected_data, rtol=1e-3)
     assert np.allclose(average_base_data_std, expected_std, rtol=1e-3)