diff --git a/README.md b/README.md index e8dc8007..a4b1a583 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ If you find this package useful and have any suggestions please feel free to ope 3. A bathymetry file that at least covers your domain 4. 3D ocean forcing files *of any resolution* on your choice of A, B or C Arakawa grid 5. Surface forcing files (eg ERA or JRA reanalysis) +6. [GFDL's FRE tools](https://github.com/NOAA-GFDL/FRE-NCtools) must be downloaded and compiled on the machine you are using. Check out the the [documentation](https://regional-mom6.readthedocs.io/en/latest/) and browse through the [demos](https://regional-mom6.readthedocs.io/en/latest/demos.html). @@ -60,4 +61,9 @@ pip install git+https://github.com/COSIMA/regional-mom6.git@061b0ef80c7cbc04de05 ## Getting started -The two example notebooks walk you through how to use the package on two different sets of input datasets. Make sure that you can get at least one of these working on your setup with your MOM6 executable, and then try to modify them to apply to your domain with your bathymethry / forcing / boundaries. +The two example notebooks walk you through how to use the package using two different sets of input datasets. +Make sure that you can get at least one of these working on your setup with your MOM6 executable, and then try to modify them to apply to your domain with your bathymethry, forcing, and boundary conditions. + +The `xesmf` the package will attempt to regrid in parallel, and if it's not able it will throw a warning and run it in serial. +You will also get a print out of the relevant `mpirun` command which you could use as a backup. +Depending on your setup of your machine, you may need to write scripts that implement the package to access more computational resources than might be available, e.g., on the HPC machine of you are working on. diff --git a/demos/model-forced.ipynb b/demos/model-forced.ipynb index ca92cd94..bb468940 100644 --- a/demos/model-forced.ipynb +++ b/demos/model-forced.ipynb @@ -4,136 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Forcing with model output" + "# Forcing with model output\n", + "\n", + "### This example is most useful for people with access to Australia's National Computational Infrastructure facility, because the model output being used is hosted here. For others, the 'reanalysis-forced' example will be more helpful as it relies only on open source data." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-06-15 12:00:18,586 - distributed.nanny - ERROR - Worker process died unexpectedly\n", - "2023-06-15 12:00:18,586 - distributed.nanny - ERROR - Worker process died unexpectedly\n", - "Process Dask Worker process (from Nanny):\n", - "Process Dask Worker process (from Nanny):\n", - "Process Dask Worker process (from Nanny):\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 44, in run\n", - " return loop.run_until_complete(main)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\n", - " self.run_forever()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 1869, in _run_once\n", - " event_list = self._selector.select(timeout)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/selectors.py\", line 469, in select\n", - " fd_event_list = self._selector.poll(timeout, max_ev)\n", - "KeyboardInterrupt\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 44, in run\n", - " return loop.run_until_complete(main)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\n", - " self.run_forever()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 1869, in _run_once\n", - " event_list = self._selector.select(timeout)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/selectors.py\", line 469, in select\n", - " fd_event_list = self._selector.poll(timeout, max_ev)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/process.py\", line 202, in _run\n", - " target(*args, **kwargs)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/nanny.py\", line 990, in _run\n", - " asyncio.run(run())\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 47, in run\n", - " _cancel_all_tasks(loop)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 63, in _cancel_all_tasks\n", - " loop.run_until_complete(\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\n", - " self.run_forever()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 1869, in _run_once\n", - " event_list = self._selector.select(timeout)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/selectors.py\", line 469, in select\n", - " fd_event_list = self._selector.poll(timeout, max_ev)\n", - "KeyboardInterrupt\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 44, in run\n", - " return loop.run_until_complete(main)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\n", - " self.run_forever()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 1869, in _run_once\n", - " event_list = self._selector.select(timeout)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/selectors.py\", line 469, in select\n", - " fd_event_list = self._selector.poll(timeout, max_ev)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/process.py\", line 202, in _run\n", - " target(*args, **kwargs)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/nanny.py\", line 990, in _run\n", - " asyncio.run(run())\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 47, in run\n", - " _cancel_all_tasks(loop)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 63, in _cancel_all_tasks\n", - " loop.run_until_complete(\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\n", - " self.run_forever()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 1869, in _run_once\n", - " event_list = self._selector.select(timeout)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/selectors.py\", line 469, in select\n", - " fd_event_list = self._selector.poll(timeout, max_ev)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/process.py\", line 202, in _run\n", - " target(*args, **kwargs)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/nanny.py\", line 990, in _run\n", - " asyncio.run(run())\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 47, in run\n", - " _cancel_all_tasks(loop)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/runners.py\", line 63, in _cancel_all_tasks\n", - " loop.run_until_complete(\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\n", - " self.run_forever()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/asyncio/base_events.py\", line 1869, in _run_once\n", - " event_list = self._selector.select(timeout)\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/selectors.py\", line 469, in select\n", - " fd_event_list = self._selector.poll(timeout, max_ev)\n", - "KeyboardInterrupt\n" - ] - }, { "data": { "text/html": [ @@ -141,7 +21,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-5fbe715e-0b20-11ee-863b-00000741fe80

\n", + "

Client-7784bb69-574d-11ee-a4f4-0000076bfe80

\n", " \n", "\n", " \n", @@ -154,7 +34,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -163,6 +43,10 @@ "
\n", - " Dashboard: /proxy/8787/status\n", + " Dashboard: /proxy/41687/status\n", "
\n", "\n", " \n", + " \n", + " \n", "\n", " \n", "
\n", @@ -172,11 +56,11 @@ "
\n", "
\n", "

LocalCluster

\n", - "

9f0c0fc9

\n", + "

44080638

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -209,11 +93,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-a0cb1cd8-eecf-4699-8ff5-5e88cff0cb65

\n", + "

Scheduler-92b24ab3-f341-4add-a698-9b1a75691e43

\n", "
\n", - " Dashboard: /proxy/8787/status\n", + " Dashboard: /proxy/41687/status\n", " \n", " Workers: 4\n", @@ -184,10 +68,10 @@ "
\n", - " Total threads: 8\n", + " Total threads: 16\n", " \n", - " Total memory: 32.00 GiB\n", + " Total memory: 64.00 GiB\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -232,7 +116,7 @@ " Started: Just now\n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:33217\n", + " Comm: tcp://127.0.0.1:43307\n", " \n", " Workers: 4\n", @@ -221,10 +105,10 @@ "
\n", - " Dashboard: /proxy/8787/status\n", + " Dashboard: /proxy/41687/status\n", " \n", - " Total threads: 8\n", + " Total threads: 16\n", "
\n", - " Total memory: 32.00 GiB\n", + " Total memory: 64.00 GiB\n", "
\n", @@ -255,29 +139,29 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -300,29 +184,29 @@ "
\n", - " Comm: tcp://127.0.0.1:46853\n", + " Comm: tcp://127.0.0.1:45487\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: /proxy/37903/status\n", + " Dashboard: /proxy/42799/status\n", " \n", - " Memory: 8.00 GiB\n", + " Memory: 16.00 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:36907\n", + " Nanny: tcp://127.0.0.1:33009\n", "
\n", - " Local directory: /jobfs/87853448.gadi-pbs/dask-worker-space/worker-qf1xk05a\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-4_h5msco\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -345,29 +229,29 @@ "
\n", - " Comm: tcp://127.0.0.1:37619\n", + " Comm: tcp://127.0.0.1:46083\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: /proxy/41601/status\n", + " Dashboard: /proxy/33509/status\n", " \n", - " Memory: 8.00 GiB\n", + " Memory: 16.00 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:34089\n", + " Nanny: tcp://127.0.0.1:34913\n", "
\n", - " Local directory: /jobfs/87853448.gadi-pbs/dask-worker-space/worker-hd3yop43\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-6gdlx6fq\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -390,29 +274,29 @@ "
\n", - " Comm: tcp://127.0.0.1:40755\n", + " Comm: tcp://127.0.0.1:41395\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: /proxy/41063/status\n", + " Dashboard: /proxy/40835/status\n", " \n", - " Memory: 8.00 GiB\n", + " Memory: 16.00 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:43061\n", + " Nanny: tcp://127.0.0.1:41617\n", "
\n", - " Local directory: /jobfs/87853448.gadi-pbs/dask-worker-space/worker-8mu8fo37\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-dul55glp\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -439,10 +323,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -460,8 +344,13 @@ "import subprocess\n", "from scipy.ndimage import binary_fill_holes\n", "from importlib import reload\n", - "# os.chdir(\"/path/to/where/you/cloned/the/regional-mom6/repository\")\n", - "os.chdir(\"/home/149/ab8992/cosima_regional/regional-mom6/regional_mom6/\")\n", + "\n", + "\n", + "## For NCI users, uncomment the following line if you just want to import from my copy of the code and sidestep the installation process\n", + "## In this case just use the latest version of the analysis env. HOWEVER! Note that without the latest version of xesmf which is not yet\n", + "## available on analysis3, the regridding will only work in serial and won't be suitable for large domains\n", + "\n", + "# os.chdir(\"/home/149/ab8992/cosima_regional/regional-mom6/\")\n", "\n", "import regional_mom6 as rm\n", "from dask.distributed import Client\n", @@ -509,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -530,11 +419,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "expt_name = \"tasmania-om2test\"\n", + "expt_name = \"tasmania-example\"\n", "\n", "## Choose your coordinates and the name of your experiment\n", "yextent = [-48,-38.95] ## latitude\n", @@ -584,268 +473,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n", - "Task exception was never retrieved\n", - "future: .wait() done, defined at /g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py:2176> exception=AllExit()>\n", - "Traceback (most recent call last):\n", - " File \"/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.01/lib/python3.9/site-packages/distributed/client.py\", line 2185, in wait\n", - " raise AllExit()\n", - "distributed.client.AllExit\n" - ] - } - ], + "outputs": [], "source": [ "\n", "########## TWO OPTIONS: ############################\n", @@ -917,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -934,7 +564,7 @@ } ], "source": [ - "reload(ml)\n", + "reload(rm)\n", "expt = rm.experiment(\n", " xextent,\n", " yextent,\n", @@ -960,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1035,20 +665,20 @@ "\n", "==>NOTE from check_mask: Checking for possible masking:\n", "==>NOTE from check_mask: Assume 4 halo rows\n", - "==>NOTE from check_mask: Total domain size is 140, 249\n", + "==>NOTE from check_mask: Total domain size is 300, 383\n", "\n", "_______________________________________________________________________\n", "\n", "NOTE from check_mask: The following is for using model source code with version older than siena_201207,\n", "Possible setting to mask out all-land points region, for use in coupler_nmlTotal number of domains = 100\n", - "Number of tasks (excluded all-land region) to be used is 98\n", - "Number of regions to be masked out = 2\n", + "Number of tasks (excluded all-land region) to be used is 94\n", + "Number of regions to be masked out = 6\n", "The layout is 10, 10\n", "Masked and used tasks, 1: used, 0: masked\n", + "0000001111\n", "1111111111\n", "1111111111\n", "1111111111\n", - "1111001111\n", "1111111111\n", "1111111111\n", "1111111111\n", @@ -1056,13 +686,13 @@ "1111111111\n", "1111111111\n", " domain decomposition\n", - " 14 14 14 14 14 14 14 14 14 14\n", - " 25 25 25 25 25 25 25 25 25 24\n", - " used=98, masked=2, layout=10,10\n", + " 30 30 30 30 30 30 30 30 30 30\n", + " 39 39 39 38 38 38 38 38 38 38\n", + " used=94, masked=6, layout=10,10\n", " To chose this mask layout please put the following lines in ocean_model_nml and/or ice_model_nml\n", - " nmask = 2\n", + " nmask = 6\n", "layout = 10, 10\n", - "mask_list = 5,7,6,7\n", + "mask_list = 1,10,2,10,3,10,4,10,5,10,6,10\n", "\n", "\n", "_______________________________________________________________________\n", @@ -1096,22 +726,22 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1139,27 +769,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INITIAL CONDITIONS\n", - "Regridding Velocities...Done.\n", - "Regridding Tracers...\n", - "Done.\n", - "Regridding Free surface...\n", - "Saving outputs... done.\n", - "BRUSHCUT BOUNDARIES\n", - "Processing south...Done.\n", - "Processing north...Done.\n", - "Processing west...Done.\n", - "Processing east...Done.\n" - ] - } - ], + "outputs": [], "source": [ "## FOR ACCESS OM2: \n", "expt.ocean_forcing(\n", @@ -1183,9 +795,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 6 (optional): Select number of processors \n", + "## Step 6 Run the FRE tools\n", "\n", - "This is just a wrapper for check_mask FRE tool. Choose the number of processors in the X and Y directions respectively. Having run `.bathymetry()`, you already have one set up for 10x10 by default." + "This is just a wrapper for the FRE tools needed to make the mosaics and masks for the experiment. The only thing you need to tell it is the processor layout. In this case we're saying that we want a 10 by 10 grid of 100 processors. " ] }, { @@ -1194,7 +806,7 @@ "metadata": {}, "outputs": [], "source": [ - "expt.processor_mask((10,10))" + "expt.FRE_tools((10,10))\n" ] }, { @@ -1209,14 +821,14 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "ln: failed to create symbolic link '/home/149/ab8992/mom6_rundirs/tasmania-om2test//inputdir/tasmania-om2test': File exists\n" + "ln: failed to create symbolic link '/home/149/ab8992/mom6_rundirs/tasmania-september20-23//inputdir/tasmania-september20-23': File exists\n" ] } ], @@ -1350,9 +962,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:analysis3]", + "display_name": "Python [conda env:analysis3-23.04] *", "language": "python", - "name": "conda-env-analysis3-py" + "name": "conda-env-analysis3-23.04-py" }, "language_info": { "codemirror_mode": { @@ -1364,7 +976,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/demos/reanalysis-forced.ipynb b/demos/reanalysis-forced.ipynb index e0854082..ec44edb2 100644 --- a/demos/reanalysis-forced.ipynb +++ b/demos/reanalysis-forced.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -19,7 +19,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-d0f6f0dd-0b4b-11ee-88d7-00000771fe80

\n", + "

Client-42ab73fe-5753-11ee-8b2f-0000076bfe80

\n", "
\n", - " Comm: tcp://127.0.0.1:34663\n", + " Comm: tcp://127.0.0.1:41149\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: /proxy/43605/status\n", + " Dashboard: /proxy/42273/status\n", " \n", - " Memory: 8.00 GiB\n", + " Memory: 16.00 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:42699\n", + " Nanny: tcp://127.0.0.1:38813\n", "
\n", - " Local directory: /jobfs/87853448.gadi-pbs/dask-worker-space/worker-e2axshri\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-i1ngkq1c\n", "
\n", "\n", " \n", @@ -32,7 +32,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -41,6 +41,10 @@ "
\n", - " Dashboard: /proxy/42579/status\n", + " Dashboard: /proxy/44739/status\n", "
\n", "\n", " \n", + " \n", + " \n", "\n", " \n", "
\n", @@ -50,11 +54,11 @@ "
\n", "
\n", "

LocalCluster

\n", - "

8b3e24a6

\n", + "

a6df0577

\n", " \n", " \n", " \n", "
\n", - " Dashboard: /proxy/42579/status\n", + " Dashboard: /proxy/44739/status\n", " \n", " Workers: 4\n", @@ -87,11 +91,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-edf0f5e7-aa7e-476a-97d9-e4031b2ad3b0

\n", + "

Scheduler-03233fab-1e0e-4db4-8d02-5f4b7bd9c35a

\n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:33981\n", + " Comm: tcp://127.0.0.1:45817\n", " \n", " Workers: 4\n", @@ -99,7 +103,7 @@ "
\n", - " Dashboard: /proxy/42579/status\n", + " Dashboard: /proxy/44739/status\n", " \n", " Total threads: 16\n", @@ -133,7 +137,7 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -178,7 +182,7 @@ "
\n", - " Comm: tcp://127.0.0.1:40923\n", + " Comm: tcp://127.0.0.1:38687\n", " \n", " Total threads: 4\n", @@ -141,7 +145,7 @@ "
\n", - " Dashboard: /proxy/46115/status\n", + " Dashboard: /proxy/41735/status\n", " \n", " Memory: 16.00 GiB\n", @@ -149,13 +153,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:33827\n", + " Nanny: tcp://127.0.0.1:41549\n", "
\n", - " Local directory: /jobfs/87881662.gadi-pbs/dask-worker-space/worker-6hyhe91a\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-hn9y5hob\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -223,7 +227,7 @@ "
\n", - " Comm: tcp://127.0.0.1:42353\n", + " Comm: tcp://127.0.0.1:35617\n", " \n", " Total threads: 4\n", @@ -186,7 +190,7 @@ "
\n", - " Dashboard: /proxy/46207/status\n", + " Dashboard: /proxy/36911/status\n", " \n", " Memory: 16.00 GiB\n", @@ -194,13 +198,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:36239\n", + " Nanny: tcp://127.0.0.1:36389\n", "
\n", - " Local directory: /jobfs/87881662.gadi-pbs/dask-worker-space/worker-v9v5yvr1\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-_lj8t8bo\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -268,7 +272,7 @@ "
\n", - " Comm: tcp://127.0.0.1:45685\n", + " Comm: tcp://127.0.0.1:43353\n", " \n", " Total threads: 4\n", @@ -231,7 +235,7 @@ "
\n", - " Dashboard: /proxy/46481/status\n", + " Dashboard: /proxy/38847/status\n", " \n", " Memory: 16.00 GiB\n", @@ -239,13 +243,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:45421\n", + " Nanny: tcp://127.0.0.1:33113\n", "
\n", - " Local directory: /jobfs/87881662.gadi-pbs/dask-worker-space/worker-ayyo3zav\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-hanya5z3\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -317,10 +321,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -338,8 +342,13 @@ "import subprocess\n", "from scipy.ndimage import binary_fill_holes\n", "from importlib import reload\n", - "# os.chdir(\"/path/to/where/you/cloned/the/regional-mom6/repository\")\n", - "os.chdir(\"/home/149/ab8992/cosima_regional/regional-mom6/regional_mom6/\")\n", + "\n", + "## For NCI users, uncomment the following line if you just want to import from my copy of the code and sidestep the installation process\n", + "## In this case just use the latest version of the analysis env. HOWEVER! Note that without the latest version of xesmf which is not yet\n", + "## available on analysis3, the regridding will only work in serial and won't be suitable for large domains\n", + "\n", + "# os.chdir(\"/home/149/ab8992/cosima_regional/regional-mom6/regional_mom6/\")\n", + "\n", "\n", "import regional_mom6 as rm\n", "from dask.distributed import Client\n", @@ -385,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -407,11 +416,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "expt_name = \"reanalysistest\"\n", + "expt_name = \"tasmania-example-reanalysis\"\n", "\n", "## Choose your coordinates and the name of your experiment\n", "yextent = [-48,-38.95] ## latitude\n", @@ -457,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -523,11 +532,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NOTE from make_solo_mosaic: there are 0 contacts (align-contact)\n", + "congradulation: You have successfully run make_solo_mosaic\n", + "FRE TOOLS: Make solo mosaic\n", + "\n", + "\n", + "CompletedProcess(args=['/home/157/ahg157/repos/mom5/src/tools/make_solo_mosaic/make_solo_mosaic', '--num_tiles', '1', '--dir', '.', '--mosaic_name', 'ocean_mosaic', '--tile_file', 'hgrid.nc'], returncode=0)\n" + ] + } + ], "source": [ - "reload(ml)\n", "expt = rm.experiment(\n", " xextent,\n", " yextent,\n", @@ -553,9 +574,123 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Starting weight generation with these inputs: \n", + " Source File: bathy_original.nc\n", + " Destination File: topog_raw.nc\n", + " Source variable names: elevation\n", + " Destination variable names: elevation\n", + " Souce Grid has a mask, using missingvalue 1.0000000000000000E+020\n", + " Source File is in GRIDSPEC format with coordinate names lon lat\n", + " Source Grid is a regional grid\n", + " Destination File is in GRIDSPEC format with coordinate names lon lat\n", + " Destination Grid is a regional grid\n", + " Regrid Method: bilinear\n", + " Pole option: NONE\n", + "\n", + " Completed file regrid successfully.\n", + "\n", + "NOTE from make_solo_mosaic: there are 0 contacts (align-contact)\n", + "congradulation: You have successfully run make_solo_mosaic\n", + "MAKE SOLO MOSAIC\n", + "\n", + "CompletedProcess(args='/home/157/ahg157/repos/mom5/src/tools/make_solo_mosaic/make_solo_mosaic --num_tiles 1 --dir . --mosaic_name ocean_mosaic --tile_file hgrid.nc', returncode=0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "cp: './ocean_mosaic.nc' and 'ocean_mosaic.nc' are the same file\n", + "cp: './hgrid.nc' and 'hgrid.nc' are the same file\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cp ./hgrid.nc hgrid.nc \n", + "\n", + "NOTE from make_coupler_mosaic: the ocean land/sea mask will be determined by field depth from file topog.nc\n", + "mosaic_file is grid_spec.nc\n", + "\n", + "***** Congratulation! You have successfully run make_quick_mosaic\n", + "QUICK MOSAIC\n", + "\n", + "CompletedProcess(args='/home/157/ahg157/repos/mom5/src/tools/make_quick_mosaic/make_quick_mosaic --input_mosaic ocean_mosaic.nc --mosaic_name grid_spec --ocean_topog topog.nc', returncode=0)\n", + "\n", + " ===>NOTE from check_mask: when layout is specified, min_pe and max_pe is set to layout(1)*layout(2)=100\n", + "\n", + " ===>NOTE from check_mask: Below is the list of command line arguments.\n", + "\n", + "grid_file = ocean_mosaic.nc\n", + "topog_file = topog.nc\n", + "min_pe = 100\n", + "max_pe = 100\n", + "layout = 10, 10\n", + "halo = 4\n", + "sea_level = 0\n", + "show_valid_only is not set\n", + "nobc = 0\n", + "\n", + " ===>NOTE from check_mask: End of command line arguments.\n", + "\n", + " ===>NOTE from check_mask: the grid file is version 2 (mosaic grid) grid which contains field gridfiles\n", + "\n", + "==>NOTE from get_boundary_type: x_boundary_type is solid_walls\n", + "\n", + "==>NOTE from get_boundary_type: y_boundary_type is solid_walls\n", + "\n", + "==>NOTE from check_mask: Checking for possible masking:\n", + "==>NOTE from check_mask: Assume 4 halo rows\n", + "==>NOTE from check_mask: Total domain size is 140, 249\n", + "\n", + "_______________________________________________________________________\n", + "\n", + "NOTE from check_mask: The following is for using model source code with version older than siena_201207,\n", + "Possible setting to mask out all-land points region, for use in coupler_nmlTotal number of domains = 100\n", + "Number of tasks (excluded all-land region) to be used is 98\n", + "Number of regions to be masked out = 2\n", + "The layout is 10, 10\n", + "Masked and used tasks, 1: used, 0: masked\n", + "1111111111\n", + "1111111111\n", + "1111111111\n", + "1111001111\n", + "1111111111\n", + "1111111111\n", + "1111111111\n", + "1111111111\n", + "1111111111\n", + "1111111111\n", + " domain decomposition\n", + " 14 14 14 14 14 14 14 14 14 14\n", + " 25 25 25 25 25 25 25 25 25 24\n", + " used=98, masked=2, layout=10,10\n", + " To chose this mask layout please put the following lines in ocean_model_nml and/or ice_model_nml\n", + " nmask = 2\n", + "layout = 10, 10\n", + "mask_list = 5,7,6,7\n", + "\n", + "\n", + "_______________________________________________________________________\n", + "\n", + "NOTE from check_mask: The following is for using model source code with version siena_201207 or newer,\n", + " specify ocean_model_nml/ice_model_nml/atmos_model_nml/land_model/nml \n", + " variable mask_table with the mask_table created here.\n", + " Also specify the layout variable in each namelist using corresponding layout\n", + "\n", + "***** Congratulation! You have successfully run check_mask\n", + "CHECK MASK CompletedProcess(args='/home/157/ahg157/repos/mom5/src/tools/check_mask/check_mask --grid_file ocean_mosaic.nc --ocean_topog topog.nc --layout 10,10 --halo 4', returncode=0)\n" + ] + } + ], "source": [ "expt.bathymetry(\n", " '/g/data/ik11/inputs/GEBCO_2022/GEBCO_2022.nc',\n", @@ -575,9 +710,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PsRGkoBmPXkve4U4hn3gJStIoQA+SU5KGr5OTv7kLBlQQJd8BHVIw7aC3yKnqt4v9LR+tGdmMXHQLyXZmeSltV53YQHEmbO1xXkaaT/sXh9J21qrJth0bNzZ8gQL8vRbNbwmaIAopmIVaYMDvg6bBBhGDwWAwGAsUIUUU0swtotkcu9jABlGzwNWxweLG1WDHvUIMxhQ8EHxzRsed7rxD/g/fs22HybNkpOAa7Dulc2iClxqcHOGumA6ye7rl32t+2rarobbtlnTn1I/DrunbBNOicLyEhlghquK+EDcE3iKrJ+48rEmajwrSK7SkZzJtH9D1suwDTpRTBlgPz6oPavY12xh7Pni2mwksO/1EYZR+KArlR7c/g9FBnOH9n/RzuvMO7YexcKDMJe3YF+cj/IQRURhRVKmmH4Rd9dOPUw7lpxbVfWz4UETpx5uQHyuQHzuUH0t8IvlRhm/pP54bpJ8DV+2kA1ftpMiL0k+mq5p+DluyI/10O5X0U4BPKwjITj+dQtiG/1rBhg0byLIs5TM4OJj+PYoi2rBhA61atYry+Tydcsop9Jvf/Ebpo1Kp0JVXXknLli2jrq4uOu+882jr1q3KPkNDQ7R+/XoqFotULBZp/fr1NDw8POPr1A7wW5rBYDAYjAWKIIpm/WkVRx55JG3bti39PP744+nfbr75ZvrsZz9Lt912G/3iF7+gwcFBOv3002lsbCzd5+qrr6bvfOc7dM8999BPf/pTGh8fp3POOYeCQHrWLrjgAnrsscfovvvuo/vuu48ee+wxWr9+/ewu1izBlBliBp4cywF/b4QRhvNoazZB7zEWFxQ6BiYl9BKJfVpJ7V+o0H4HwzNpzharf3YW4jVolYITdL6VgazBsvSUWDm53faBPvPx2iT3ClwiE/OEnh8ffwLNsIOM3BhmYQdHtquQ+daXTQLDC5Ly82vy70iNVSCi24GBe5qBowfIadHDsjfAdV3FKyQQRRHdcsst9LGPfYze+ta3EhHRHXfcQStWrKC7776bLr/8choZGaGvf/3rdOedd9Ib3/hGIiK66667aPXq1fTggw/SunXr6KmnnqL77ruPHnnkETrhhBOIiOhrX/sarV27lp5++mk67LDDOvdlAWwQtRMLkRZbgMaRmJznPdtmkUIx4mfaR4N4l/k2qvTn0cd9RG0OB2l4fZt4Dmd6nRoZQTHi8YWj4+kWuw/ijSrSuCDwLlhoEFlT/iUip6ofc+iCHhLaVOIy4WEwXSn7QtxQBAvPJZk4Rqh3iYwVqoHBdEBBxg3VQv3vEgqpAogLCuEcIehhCEPJn4HXZaZoV1D16Oiosj2bzVI2m9UdQr///e9p1apVlM1m6YQTTqAbb7yRDjzwQNqyZQtt376dzjjjDKWfk08+mTZt2kSXX345bd68mWq1mrLPqlWr6KijjqJNmzbRunXr6OGHH6ZisZgaQ0REJ554IhWLRdq0adO8GUQL8A3OYDAYDAaDKDZogll8hEG0evXqNF6nWCzSTTfdpD3fCSecQP/0T/9E//mf/0lf+9rXaPv27XTSSSfRrl27aPv27UREtGLFCuWYFStWpH/bvn07ZTIZWrJkybT7DAwM0FQMDAyk+8wH2EO0t2OBeIUaQV3Nmla57FFqN+6v3ZO2z/D+T93fm7n+rfwurdBTC9Lj2gSQkuz4uWfoWYpqQBvlQZixCt+lS1Jp3nhCu4FOUZAFr4rhzaLoECWOKEXvEzSJFB0igAWuowk/HutJq7ak25Aas8GzgvQZag4FiZsL9YY8W37v+dYhaheef/556u3tTf/f5B0666yz0vbRRx9Na9eupYMOOojuuOMOOvHEE4moXpAyiqKGIpVT99Ht30w/cwk2iBgNMeMUYEMfYn9TvwsxTmNvhc4IQrTbCDX+tnuo8bOnQLnuqXK+NAosKOkQ1aQxYGG6O8QTRYmkNMYVobI0psljGQ+MOUrT7iFjPgCVacUgyskx5TLSeBuuxsKMxxWfS7dNgky2SaU5hEFlkxii3AISY0S0izLr7e1VDKJm0dXVRUcffTT9/ve/pze/+c1EFHt4Vq5cme6zY8eO1Gs0ODhI1WqVhoaGFC/Rjh076KSTTkr3eemll+rOtXPnzjrvUyfBsxBj1sAUYFM6sGVb6YexeIH3genD6BBAPoSCIP1Yo+PyU/XlJ4jSD9kWkW0pKfOYXo9p8mT6iGHAcUoKvhOlHycTpp9ivpx++rOT1J+dpPEgl348208/NkXpx7HC9INwKCKHIiWlvhY56SeM7PRTCb300ynMR5YZolKp0FNPPUUrV66kAw44gAYHB+mBBx5I/16tVumhhx5KjZ3jjjuOPM9T9tm2bRs98cQT6T5r166lkZER+vnPf57u87Of/YxGRkbSfeYD7CFiMBgMBoNBRETXXXcdnXvuubTffvvRjh076IYbbqDR0VG68MILybIsuvrqq+nGG2+kQw45hA455BC68cYbqVAo0AUXXEBERMVikS699FK69tpraenSpdTf30/XXXcdHX300WnW2eGHH05nnnkmXXbZZfSVr3yFiIje+9730jnnnDNvAdVEbBApsGyrnhttA33TiCKaU+ioiBbjilpR0FVP075rx1g80P3mTKO2Ecnzb3mSWlLEGSdlrTCrkJdtoMyCTH3afWTIEENvEJbXE04Wv6BPtUfKzHbkidDDsywTZ8pNBpJ3Q2Vpz5YZcw7pY4jk3zEVXx8H5kVJUVfod64REs0q8b/VY7du3UrvfOc76eWXX6bly5fTiSeeSI888gitWbOGiIg+9KEPUalUove97300NDREJ5xwAt1///3U09OT9vG5z32OXNel888/n0qlEp122mm0ceNGciAD8xvf+AZdddVVaTbaeeedR7fddtssvunsYUVRB/MHFyhGR0epWCzSqZm3z0m1ex06NsG3Oai6E+NmI4gxFc0E3TOaQ6o3lcHgnQB3SJv2ShnP4a/sS9vlgTjmCI0gYSQRqQHWfl62awW5fy0JZ6n2gkFUgLimbhnTs2KZTBlf0SUFAPcrxAVb81BBtoBtW7bRCNIZR2gQNSrnURr36arjHqGRkZEZxeU0A/Fe+s1TA9TTM/PolrGxkI48fMecjnVvAXuIGAwGg8FYoAgimmW1+/aNZW8HG0SMljCv9B9j0aLVlP+96v5s5OWdYYZeBAVdleuI7RAyzgLw3iRv2Wq3pEDQW6S08SfCdij6wkFBMwSPE7Rduz5lHoGp9pgtlgUPkeotCpO+QEIAvsDUIGzG3gs2iBYYFjod0IkxNVOKgMEgmkbLSBgJum3zAdM42jG+Zmhx6DtVim8msxyNIEjHF2n15SXymXSAZUJRaDSOtPpE+Fjb+rihck2GMuwuy7imbjcuOVJwGqfMY6o9EmLC4FH/Xq9OHe+TfO+wc3NRp2OIFjPYIGIwGAwGY4EiJEvrCWvleEZzYINoAaDtno89RJ2asXeg02riJg/iA+G36radbr8dD5TtTnuLmjlfp59bOF8E6V+oBE1VGZSMHiJBmblluWtpuf40oJOoZp+Jc+NbCGuIBUBhgUemGsgDykHsObINfhBHo0gdn0f2Xaa4D0eh2lBlUjZFH5WIjYy9EWwQISwrnrgaTZwznLiaeXG0m5Jqd5FIBmMqOmIEGbKgEKc776jfuBAVsBfggkX5DdFQqkpyyZqspG1vLIk/GoQK8mAc1bplG9Pq0c5wJ5JsN8xgVyq6ymalIimzWq4+cwwzy2yDqnNokMwWW5XSHZa+dIcs89G53zCM4s9sjmc0BzaIGAwGg8FYoAhmSZnN5tjFBjaIELYVe4nC+VtVzthzpLi/Dfu2UECTvUKMhQTL0QfK7jH36QL0CjWDCCkzX7pyIpFtapoqsTQH0F0hBEr7XaIzOC4L2V8Z0AXKSU9VzpPtgWysSYReIaTJ0NMzCUXTUG1O1PpCw8GGNm4XXqbQ+MUZezLYIAJYrkuW5Spu4vnETI2gB4JvTnvYgomraBG668GZZ4sbC4babSU1fqEbR6Z5ANSsvdGYPrNCGSBkEHcm0EakEAQbw/5kni1DintOGl1oBC3pmkzba7qH5DiSFHwMHPYM6fUIpMF0+6DBM9/GD3uIOgc2iBgMBoPBWKAIIytN95/p8YzmwAYRwFrWT5adJdrxcroNxcvkju1d7XV6ZYvZOHuqt4jBIFrgXqGZ7ruAoFCV2E4qPllw/UMHaKac3DXMYGkOcCMlm70lEKztyfm2JwfbDe6nSpJxlgVRJSzXgZljnqXvw9FQZiYIb5LJ88TYs8EGEcLziByPyIXLIrJbTAJwLcDo3m/FEIF976/dM6NxNAU2jhh7ADpOmTXx7OvS/xHKImQ+gc+16RnHYteuo98uNsHlx1JgELpDVIXzJMVbayU536JBhOjLyhS2rOPXtW04uW2gzLIN6pPVQhlZ5IDxNN/GD1NmnQMbRAwGg8FgLFAEZCtq2a0fz2gWbBABSvsVyXVzlAe5+uiFbaLVUl+tBES3td8WYaTP1JPWb1sgXqNmrgcHXjNmA0xSaMa7g/s08hbNB9Jq9008FxFkllk+vFr9eE5A5wm23XHYFSrcoxvJGo89TpEj55KSDSqOeJjVk7YxJiafeH0C2IZZZsvcMehENrGyvRBhNHmCHM3c30zVk3YhmmUMUcQxRE2DDSJA5FgUuRZV9utLt2XHJ4iIKBwekTvO0ORW3fsLL+ukKeNIgCk1xiKB8izAvd6qoTRTtPRcItrxXJrqCiaGklMBA8eHtHV4s2RGILNsEkQOc0nsDsQbRb4cs+/LfSu+7HCkIg94NuonIqK+TEkeB4XUMB2/6MpMNR10hg9jcYENIgaDwWAwFig4hqhzYIMIENnxJ8hgsKHmZmq3psgC9LDM2Ftkgu6adeh7m2g1ptIYrcKk8dUKNbYQaTTjM6w8t+ANqsQeIrcE2/rkrjbERtsyWYzQSRNU4udvsgu9zbIZgqBjDbxFZQeCn22R9YVB1bI9mJWefdQeCiEmp5xQZs14iIRx0cm4nCCyKZiFFlLAjq+mwQaRBiEaRL1JUR6kzJp4cHQv21nF/ywQWm3G0FFs80y7id+DDaM9Fx3JLFuACxYjWhirsbYi9oHbPfm6iLKJEVEBZWkQWIxgIYkvmQAzzsS6KABKDWg3LO5aqUEmmivNkVwSepCBtPw+T1pdmGpfgSwyjBcShhBuQzFG9LCIfTsZQ8ToHNggYjAYDAZjgSIkS/FotX48u4iaBRtEgGqvQ0HGUdzAkRvfiHZfMd0W7hqqO7YZNKOZsmBKEbQbupXrnrTyZixe7Ene2Sa8rqln1OTmwD4g+SNKEkyIpCCjFfSm2xSaDHWIwMmECU9BPv7XnZDnqPVAvTQ40AIaLONIr09PJubjDuiSYrorM9Kbj96drMED1IrO0HwIM3IMUefABhHAnQzJrYVkAekaZeJL1OotZXRHa7CojKAFiFZ+K8bCwoyLIc8QpzvvSNuNagbOBUT80XyIO1rwPEfFuDKrOykNGG9S/zoJIeAm9EDNOrEpgjzE/2TlzpYNBVttaYC4YBBN+limVXPuGRo+CM4+Wzxgg4jBYDAYjAWK2QdVs0HXLNggAgQZiyzPIleqxFNlWezXzb80e8+BadXKXiEGo70QnqO2P1t7En3WCMp3aSJMGLxx1nis++NkIVC5JiOmnRp4k+A4KDOW0mpOCbLJdoMwY6/k3coQeD0CnqPefDxZPze5NN1mosyoBW8RHmeqgdYpxDFEsyjuypRZ02CDCBC5FoWeRSFy4WJiFdlmRGSNjMpjsPgrTDA6V35TyrB7inHEhg9jD0C76eiFkjLfssJ8g+fVSBtDH+GolJ+2vdgQsmpAcZky97HGGdgWTkmzL2aW5WUbfzpMwa8FcbvHk6vYP1X60/YSyPN3GtQyQ3rNRJPJLLM9ZJ5mtAQ2iBgMBoPBWKAIZ1nLjLPMmgcbRIDQiTMvQrwqyWIpysmN9sCytB29sH3W5zWtzubTW2Rcde7lniEOsN47wBpTcwyNYK1bku6f0IV5DHfNAu2WPGoWOG4s1CmC6Q8DrBFChLEH4xwAThOB1KEmPsekSeTMA33GMUSdAxtEACusd/sGufhG9Iuyfk7mhZ2dHJYRHSsiuZcbQYy9D4vJEJpprTPF+G9VabAaBwNZJRkUZEEVVzSIVMpM/k+QSVSf89DtEr0BYzmQcebIfXqysSH0p0lJkx3SvaPh8HVGEMYNmWY8qVTdufsrJJt1iDoEftMxGAwGg8FY9GAPEcDPWRRlLLJ9sKijKf8SEXV3pU2rJF21UQWK9rQAI02myWhR9m2z93Y+tE0WMpg+YywawFwTgRij8b5341dH2CU95xY+LzCN+aA9pOgQJZ6hABLLoiwkpjjT02REsmTHipxMdClAKlsr2kOmQGkdfabzMM0VgsiiIJr5/DObYxcb2CAC2EFEdhApaaKiHmDoQcZDUbqGbVBvnalBNGPMkMpiw4fBYLQMNJqSuc7yZZZtkJXzoqJInZH/U5XJuuQnBlGEhg8eCHFDrgfxSbBPtxcbPwOZsXRbM3FDCEF/YXo9FoJFQ2leirvOMqg6YMqsaTBlxmAwGAwGY9GDPUSA0LHIciyyXCjdkSxGwixUP+6WPl47L13GNCJXKTMWcDMdp6kQj+7sZgKsxT6WI1c/RoqOA6kZewHakqlpeBZMntaFolU0l0jnkLJMEcM50s+Dl930lonq/47eIttAmWU96ZUquLGnahe4ngazI3XHEJkDoYUHyESDzXctsDCyZ0XRhZxl1jTYIAJEbvxBl2zkNriZctI4ssA4iiYndXs3hskQ0RhKymQPxzVKmTe+JNgIMoLjifZMtFvGAmuZKc/LQlew1o1vFs97JGInB2R2V7Vb9lfuhywzrGUGafWiGKy7Gyg1GJMSpgCxR3lPGmF+GBtm4yQ7biZuqFEqPdJkSJ91sqirAFNmnQO/ARkMBoPBYCx6sIdIA7umyTIDRA5kSvRIEQ1nt7ycUQdWj2ZPhYES6zAWisgkgzEnMDzXHdMH06ApTaJGnqEmapxFYbwPzkBIkwXgCUKnisL8JLs7qKkY6IOqHVd2MlCQJUSGq/H8e2RxW7qtVXpJUGKhodYZeohE5bZOZpmFNLtMsQXuu1xQYINIA7j/U+PHz8EDEEIGQglqmRV7ZRuyzxqlJCwGY2FvMo6YPttzMKf32p5EmSHEWJugzEz3up1PFoK+/N42TIVoBFVk3VXF+PELcd+ZIYOIIxhBy3rkfHpQ98tpO5vIXGfx5AaY1J51NNgkaAGESuZbMvYO/t6zF2ZkIqhZ8JViMBgMBoOx6MEeIoCoZUagmyFEGp2yfqUZ5OUldBwQNMMAa/QWpTugLdqEqoUmy8yEGa+KO5RlJlaae7qniLHIYXgW98Qss1a9nmEyp1kD0v2DgrZWKPtAr1CtW+4TenG7eoKcH8NtUssol5PB0wVPii3mQXixkeYQeoVCpTQHBE0nxStNfWFAc5ZiT5TVwUDl2dcyY79Hs2CDCGERka26bcW9FIKRRBX9hBFl5OVUUtvbOca5RBuMoGYm044YQh0y7riI6CIG3FcPBN+cx4F0BorRlGR9hb1y4YcCjGhbgP1C7iQ8J0mzMirpqaUHDck/w0SccyQllrOloaR72TdjADiaGKEKBD6hEeRAFI6XUHNBExRduxCSpRhzMzme0RzYIGIwGAwGY4GCPUSdAxtEAEGZKYt9XZaZSSrI1VNmaYB1oKfG2hFw3IrLey49NAsm4NgU8MpaS4zZYAFmls0YbXguIggTcKo4rxiefdgcZhLvKmSWTZSll+aIFS+l7WOLz6dtLLHR6GVv8o5oPUsGrxAGbOespK3RLmLs+WCDSAd4hlLKDFLtlTaIh0UepKd5Hk2LNmcpLBiqqoVzmwxBtYCtnHh0LxoltXgejZ0FYwgy5h0LxThqKgVfA+Ve1mfdk5VJDBfYF2tAKnaIyTbyk1hCT86FuYykw3KObOsoLhPQCKpBRjAaNo1oJCwgixTdWBAvdMtBre6YucLshRl5Edgs2CBCWPEHniGy04ccggFdeGErhWDhxrOAT08qQ0cGD1FTaIMBNZ+eo4bng+93f635l0jLk36bFXsZiwCzePbEPbnHeI2mAksFQVwkOZpnBkpERBbOkbALSpokVQC8LjB8QHvID/UK0SII2gQMmEbDBoEeIuFxQs8TtvHcPUmEuOd0MIYostTU/xkcz2gO/CZgMBgMBoOx6MEeIkBkJcVckTJLFimmVQ5mn6GCdeRBxllvT7ztZXCzGvl7gxephbT7maIpCquBF6klugi+SztW0DOlCNodY8T02fyjLd7ONj5r80GjtfQMNAG8pnbiLbKr0lNiVKQGoPdd7IPZZBhDhLTWuC9jMtHr4zWI5WmUlo/74L7zUbPMhHCWlBkLMzYPNogAYYbIyqiKq2ESChSCLeOgXYPGEwRVkyaeyMrIuKKoUoED9RXsjZXoOwB+kTMYex5aMoJaNP4Vyiwbp8rXitJQqRVwcSh39aW0EIVZlKKO27UKlDyqQYHYZfrXE1JAtaS0CBozgVK4VW5HAwu376jGC9b9c7vk0ODvGGAtDJNOGkyzr3bPBlGz4CvFYDAYDAZj0YM9RIgo/oSQICa8RRg8jTF9mGWmtPOyE6cau4EFdUZEFO0ED1Ez6ABl1m7skUrUpuvLgdeMNsHkxZkpldZuaswEKycFFKMl8VwWZuRzUenTB1IrGoZl9DzHO4WQdn/0QVvT9v5du9M2ZpYhZWYnIQaYTYaAKAZtIDUR0WB2JOkXgqftMuwrv8BYGNdw8zuYdh+QlRagnenxjObABhEiiR+yNJQZ3v8h/B2rOuPkEGbgAU7oMwtT8dH93Io+0VwWlDS99DWU3qyMnaS/uYylmHE8kQmsZcTYWzDD+xcNojCZ0/w8VIiHebEm135UGZDzmzMu9w964gOWLpPV630wWrKQyYUGkYMZvwl9hkYSUlwmQ2kykhO3l/RtikdyrPrzdTJziymzzoGvFIPBYDAYjEUP9hABIif+hOD1SSkzzCzzQGMIHCVYy8dBb1Eh7tApy2hsuyAjDUMs/trI69Mpr5Bpn+T87VDX3mPRgreIM84YzeJ05x3yfxo8553KVFMDqeXE6PfE3iIL7m8s4opOCW9I9lFbIefAlaviumX79Iyk25ZkJtN2JZCvJ9eePUWFXibMvPKFhyjQ6xBNwguhHMVe/koHPUQBzY72Yk3t5jGvBtFNN91E3/72t+m3v/0t5fN5Oumkk+jTn/40HXbYYek+URTR9ddfT1/96ldpaGiITjjhBPrCF75ARx55ZLpPpVKh6667jv75n/+ZSqUSnXbaafTFL36R9t1335bGE2SJKKdSZuLBDoDtUgoXYsYZvPACMIioEB9sT0AqfjekXkzKScCcdS+oqhYpMx3FNhvKp0Es00I0jkwvj07FXjAYTaGFxU7L926DZ964wHHhFQGUf5CLjYjyEjAyYCGJc6Qo0UFEZI/J/V96uZeIiKqB3Fbuludb0y1jiPBFFYAxIoaNFJZjsB2QVquF9TuNB0AJggGShUleiEKWg86RK0yZdQ7zeqUeeughuuKKK+iRRx6hBx54gHzfpzPOOIMmJqTH5Oabb6bPfvazdNttt9EvfvELGhwcpNNPP53GxsbSfa6++mr6zne+Q/fccw/99Kc/pfHxcTrnnHMomI0yNIPBYDAY8wxR3HU2H0ZzmFcP0X333af8/+23304DAwO0efNm+rM/+zOKoohuueUW+tjHPkZvfetbiYjojjvuoBUrVtDdd99Nl19+OY2MjNDXv/51uvPOO+mNb3wjERHdddddtHr1anrwwQdp3bp1zQ9Ik2UmFgpOMGU/0cTY6CxQaSEGG8YHBD1Ss8OtSDeUDdln4fAodF6/YlRXcm240dscLGyihZRV5wIJShaeo1l5itrhdWPMGHPqkVwomZ1zmUihPR3MY0iZufUByqVlct/yAPwW4IERJTqIVN02LxfPgRnHEMysq6xtAHp/UMRQLQRL2u06YDC23cI4GHs2FlQM0chIzCX39/cTEdGWLVto+/btdMYZZ6T7ZLNZOvnkk2nTpk10+eWX0+bNm6lWqyn7rFq1io466ijatGmT1iCqVCpUAWHE0dHYCAlzEVEuIgvdqeK5wU3Ss6r8wVImZ/lQWsmTaPnycjtZoM8ge0OpEeTXT36zegHoXtozrXI9m9iY+X7BTEFbMtI4C23vQJvvzbak0s/R82J8bvH+BcV9FJ71C3G76yU5D1Qh7b66b1X2MYE10OpPhzFEy7Nj9TtMAxEXlIU4B51wY3xqEFtsMGUpWWsaEcZmFLDbhYishsVoGx3PaA4LZuaOooiuueYaet3rXkdHHXUUERFt376diIhWrFih7LtixYr0b9u3b6dMJkNLliwx7jMVN910ExWLxfSzevXqdn8dBoPBYDBmDabMOocF4yF6//vfT7/+9a/ppz/9ad3fLEu1cKMoqts2FdPt85GPfISuueaa9P9HR0dp9erVFNlxELVSkTnpAr1CmE2B9FroQ7CfD9oVbqJdAdlpRvqs2CuP2z0MA1lY2V2zyphawB6UtusXATjjjLEQ0Mq9Z0FQdQT0mTceu87LfeAJR6fJuDyusI/0+pTGQdwx8cS/NNmdbkMPEQolotfHs6HmGNV7ajCbDD1HJipNQKllZqDJxJgWUq0zRvuwIAyiK6+8kv71X/+VfvKTnyiZYYODg0QUe4FWrlyZbt+xY0fqNRocHKRqtUpDQ0OKl2jHjh100kknac+XzWYpm83W/8GJiJyIIjCkgsRuQSMoyMMxJdlUFKyhHeSS+jcG2tquyg7tCF6aJXnSCDPRxN/n0zhqMa5BN9b5KHi5UKD7vdhIag5tv9dbSHHfozITW1l4mPbFRSXOTclvgAWtbWDJbFgcTo7Jxd/ggKTH8l58wP49MpsMqaosKD1WYELVZZnh39HYUQwpw3YYtdwX04fnGWFkzUoIspMikns65nWpHkURvf/976dvf/vb9MMf/pAOOOAA5e8HHHAADQ4O0gMPPJBuq1ar9NBDD6XGznHHHUee5yn7bNu2jZ544gmjQcRgMBgMxp6AIKl2P5sPoznMq4foiiuuoLvvvpu+973vUU9PTxrzUywWKZ/Pk2VZdPXVV9ONN95IhxxyCB1yyCF04403UqFQoAsuuCDd99JLL6Vrr72Wli5dSv39/XTdddfR0UcfnWadNYuIYoos6JIrRjeRmg/kIoecypSDEiB95it0XRJUDcHamIXm90gBDzcEt61fhA7j1VJUhWUYY06xx3oG9kI04xV6IPhm2k5/rybK0Rj703gtO+XJnLF2VrszRUGMEYOqBVOFiSSYZYaZZQS1ynbslhm1rhd7bFZ1ycxa9Aoh9TVTNMomI5JeKVOgNNNjiwfzahB96UtfIiKiU045Rdl+++2300UXXURERB/60IeoVCrR+973vlSY8f7776eeHvlgfe5znyPXden8889PhRk3btxIjtPaAxUWAqJ8QA5kRfj5+GFxJ/SFC6sQy+0Cq+VBsoRQsMZn08rhxCV/htCTgo0ejN+pxS7caPcQdCw73JPoM4E9iSZrNFZjVlALLyjT77YYqLR23LNaY6HFDK096Z6cNeDeVO4xnDcNcZh+d7wPFnRVlKrhOG87CC/uKw2enn1jvbkeFwupYiaY/p7A7cJoUuqNKR4ROUfaxv7C5Nxy32wDyizooJHElFnnMO+Ume4jjCGiOKB6w4YNtG3bNiqXy/TQQw+lWWgCuVyObr31Vtq1axdNTk7Svffey5ljDAaDwdjjEZI9689McdNNN6VMjUAURbRhwwZatWoV5fN5OuWUU+g3v/mNclylUqErr7ySli1bRl1dXXTeeefR1q1blX2GhoZo/fr1abb3+vXraXh4eMZjbQcWRFD1QoMiNV8WGWLy75HhqikijZp6aFjrDL1MEWgPhRCkaPmyE3uiO+kL9DYMIo5NiSPOJxaYDtGcgvWJjGjL/biY7iUyiIka7qsZexYNpTswy6yW6BApXiGs97hCxhXYB8u2W5X9iUyuXRWZZbZvYQj+3vjZ0XmRXPD0IO1WgKhvVbAxSXoBr0/OgrqTmmr3EdZ32kvxi1/8gr761a/SMccco2wX1SM2btxIhx56KN1www10+umn09NPP50yN1dffTXde++9dM8999DSpUvp2muvpXPOOYc2b96cMjcXXHABbd26NRVofu9730vr16+ne++9t7NfFMAGEcByI7LciCJ4EKNa/AAEXShPLZtOCYwZuJqKvmLCgqEXNgReHRWuMzYWS4S+u+KsOKciKTUb4o3C0XEY38xeEs1MoNqXWBPnw+P2Rlqime9kjP9oY4HYdqT2z6aPloycNhgzLVGZM+xjIUKJb4OisE39Xo0Mc+zD078ixOIOYyuDrPztM1lpMBQL0mpatlSWZXKT9PnBHCzsAK14NoZrMlN3Z1kaWCvzMqsNjaBuT44ppd0MqfY6QUe/idikdiGILCWzbibHt4rx8XF617veRV/72tfohhtuSLe3q3rEU089Rffddx898sgjdMIJJxAR0de+9jVau3YtPf3000o9006Cl6wMBoPBYCxQiBii2XxaxRVXXEFnn312XWJSo+oRRNSwegQR0cMPP0zFYjE1hoiITjzxRCoWi+k+8wH2ECEs+CSIBH2WgdUseIVMsXdVSBDLDMf/ogcpAgrOQc8RuJ39Lvk/7mR8sF2SNJoVdsl2Ta7IohL6seuptFapCqY22gNj1lqjawMr+gVDe84Uhu/aDk/PTPrdK6BcU0MiiSmAWvN3u0d6WDCzLMxhkkd8H5aXQl/g3bah3Z2RlFm3J9tLMnEWit1EILUJ40lYwQuTcsJFA6CEsQveBOxT7wsIYOKfDKVOHQZYCy9TJ1PZo1lWu4+SY0WJKgGTHt8999xDv/zlL+kXv/hF3d+mqx7x3HPPpfs0qh6xfft2GhgYqOt/YGDAWGGiE2CDCGDZIVlOSEodsmz8MIQVmAyqMElgqj3Qaplh+XCFyTOJHDty7+ipxTgjTGTwe+IT2eAadqHWmVXsgZ3BOII0/RmLAbYSB2MUeNvDX+Rthu5FbXzpz2HWWjsMrJnSqK0YK4vCsGk3Zhqzlpc8WJTBGCIwzEXRa5jHcG0YgsRIyZeTpB9ibbH4vkHlaaR3TMbRElcaNpUgHl+3K+c5H4wHNLY8UMYNNPW9KjCZY3HXglMfh1QNZ05hzRemJhp98pOfpA0bNijbnn/+efrABz5A999/P+VyOTKhHdUjdPs3089cgg0iBoPBYDAWKAKytAZcK8cTxcZOb68sDaXzDm3evJl27NhBxx13nDw+COgnP/kJ3XbbbfT0008T0eyrRwwODtJLL71Ud/6dO3fWeZ86CTaIAJYVkWVFZGdQ3ydeYYQl0AoC+iwsyFWMO4T7yH4FVYaCjugBRS8TlvdQyn8knqMwK1crQY/0FjkBeovkTR8NDcvt/gwzI3Q6Q7iNs6fagqbE+NqQtdaKV6hlD1IyPvbozC2ayTJDtBQcDx4RpcJ9t5yQyv3xdpznIg/uFcPpdFXbTUG/3eB+wgKlGBzd68a1k2pZGCe44rsUFV0JDKAWBgOeA7PTsJ3u28EK8mE0Oy0h8Qj39vYqBpEOp512Gj3++OPKtosvvphe8YpX0Ic//GE68MAD0+oRxx57LBHJ6hGf/vSniUitHnH++ecTkawecfPNNxMR0dq1a2lkZIR+/vOf0/HHH09ERD/72c9oZGRkXitMsEEEcDIBOZlAeZirE7G1YjnwsKMBA/FEOCEEcGWtpK6PAyLTSgFZgxFEOTkQvxq37R65gxUAZ1+B2CKoOWT3SW49Go3VImeldt0G40dM5vzSbA4zjj2aKZr5jeHc/DvOL2acXo9xRTkoutolqZIghwUaoc7YSNz28/Lc1VXy7z2QWRaAKj++2Cvh9K+fMqwUMWUet4uU+D4PikoCHE3x1/g4pOmEuCPQa2B0mQq97o3o6emp0/nr6uqipUuXptvbUT3i8MMPpzPPPJMuu+wy+spXvkJEcdr9OeecM28ZZkRsEDEYDAaDsWARzjKoejbH6tCu6hHf+MY36Kqrrkqz0c477zy67bbb2jrWVmFFUbR4zF8DRkdHqVgs0jHfuo6cQpbGJ+VqqVaJbcaoBjfVpPxRLdATUryoqFWUiDu64/p9HVjcIGWG1aMTzzBlR2EVMw7UHniLctukJpE1AVGPE3FWRziKdUX0ehpzWVE8Ss7JnoXOgbO09i4YtYcaZZMhMLOsvy9tB/ssT9tYZ7HWI+e90rK4PfQK2V20Ss416GTs7pYT3LIuGRC9X3cswpiHNFukslyYDDHAWkexYQ20ZuqXFYBKEwZDYDAcPOg7lwgylsd9+ujxP6KRkZGGNNRMId5L63/0Tsp0ZxofYEB1vEp3vuGf53SsewvYQwQII4usyFIyJKIgeUjQIEK6y4MXPYoqjqOrOe7Pl5qKCn0WyFAgimA7PteCq1fUrotyIHYV1FT3lzd94Y8QW5QYIrYvOw4noQCbwThqC5SYo0Vvg3ccbNgwpkUvpNp7QHFlMKNWtqu9SX1GZKS2SarNL8q5pJyBgq15qGsWxNQXGkQIpNcwO61R/E5oyFJCoUddIVekxrwWDSzG3gE2iBgMBoPBWKCYD6XqxQo2iABhaJE1RV9CZJmhkCIuUCwXVhrjsBO6eJNMNEGdEamS97g9UjLL6rurFVCnSLZdCPpGT1VlJaRZviD6QgE10EuakN4izEjb48UAGYy9DDMOpEaYah5CPUV8lyr1HJPtIAlEZOE8Jj06PlBttUC2e9yYtlICmI3eYznPlgIZ0pBPRBOxdAd6nAoG75Mua03nNSKaEoOT7NPZLLOFFUO0N4MNIoDnBuS4AWXAxVtJn3yI0clLXmtiBCwbu94IIpKZaH6XfODccXABgxwEPpNoNAk6PerS74vziBXpY5WiJGPEqun5aEXtGtP4QTitkXHUbtE/BoNRj3bUrFOKuAKNjgKMbhkKpYIxI8SbPQhHDHEeQ/V9HzJxFRXp2MLC+B8cUpeLcT7yOF2x1V5Xxi+h0CNCFX1Ewcb4/LpUfCI17d5OttdYeX+vBBtEDAaDwWAsUIQ0s3pkeDyjObBBBMg6PrmuQ9WavCzCW9STl6uVoTGIjsZSPt1yWRRMSP9ymI9XE1ZFrpSCPNTvAS2jGpT/cEsQVJicEr1CKJuvbgfhyAzqJMUrnSgnx2aBa1t5bJAyq6GgY32A4UxXqJj5xEG/DEbzeCD4ZtpWMs4MpcwaoibnLgu8w0Fe/4pwNFJm6NHG7NuwLAc1WZVzz4uTMZ2f79HTWl0k59wCnBDpLqFlpIgnGsp/oJYR0nTCM4TaRKSINMq2qGsWGOi1uUBE1qyMmogNoqbBBhEg59bIdW2qZYDnzsZWx44xqbFgwUOWKciHuToJgmF5NCiSBwoZJDCO/G7g0KFOWpCDuKCEn8e0fAcTxLLg4u2CbIoa1O8pxONDft8Zg1TZDIg7gqAjDY/IdguuYqOhFIkis8xtMxjzBnw+oYwDSngoiDRtfMThcXaxvvSIfM3U+uXcM1aJLaidGZnhloOYn15PdmKTNGZsMGYqySsMs9BaLRYr9kfKDFeYOSjuKgyvTlJmM61Yj8czmgO/kRgMBoPBYCx6sIcIkHUC8hyfAk+uRiaTAGS0svM5+fdSWXpVMnmgzHxw4YoFDWoW4Ynhf0LIprBQ8j4b72QZMtJ80DISpUKIiPwcUGZ27CHKjICLukuuDB2sRAyBlcr6IvEWIY1meVARu2aol6ZbUcE2ps8YjA4DI5gN+rwW7OPUIFHET9pVmI/AY41OiZp0ACllPMaTuXNrJL3RfXkp4rgkIzs01TUrJam4WAYEPUQ2RHfbtv47pllYBhpM6U+MoYOUGWeZdQ5sEAFyrk+ea9OEJgurNy8fyNGSJMs9D7IzlPkF3dHxH4IKuHW75IMa1oD4h0yOIAeu33JSTBF+sZpk8ciV4tRkQ7ZHaZl8GPIvx/35XbITx8EgKMjkgKKOtiajBTPSwpFRmhF0RWMZDEZD4ALCcmYaOARwUW1WNtV6iZAlmyzMajhnYK1G1KXNYDYbLPKSOCMHDJWSJ+eVsZqcZ7tBWRqzzIQhhAaRh3EFpI9PQoiMMjQbMCZJqFMjnA7WN2PKrHNg05HBYDAYDMaiB3uIAGXfpcB3Ke9BbZ1E02K4LDmpLKxiMo5cjYxMyhUNahlVq/Fl9rrlEsrHlRLQa9QFq5sy0F3JLhho7Yzq9YawRIgLddKEqGNkgygZmMQheIscF0QfcZUo3GBImUF/qF+k1C9jTSIGY2EBNcOQLldc3bA7zANCvFFhY2AOQlHZCDxEIZRAshIx2SqGFwClNgoeomVZ6QLPgsfGblCCwxRUratbVtFkk9X1nZxPd965QjjLLDNOu28ebBABXDskd4qol3i40EjKOvKBHCpJ6wNT80uQXprLxsdidloAM4YHsUc+0GohGCVhIgzpjUEWGjJ7OHHBsxyAt9fO1dcfsgKgxlChFtohZN1lq37yXbA6LRh0VWn0tSbiyM5KBmMmUEQaZ8qeOYaXJsYCdYHhksw9qF5dA9FYpPMpq68FZjv1RoUf6OcBpMSQAhKijhgn49r68+FxNShIGaaUmbyOSJlhUcn5mKWYMusc+C3EYDAYDAZj0YM9RICiV6JMJqDhqqTH/GTlUczIoOrhivx7ISM9IlVfXs4cepQSim1kUh6HlBquiry8XKVUgVYTXp1ar1ytuGNQLwhpMqwvBGJpYrWH7nGltAdsz4zDKmy3/C5p+Y8KRG6XQXSkGVg6O1x+L844YzDmHpYL0z/S3i0IrWLwdE2WTaTqEhA+zEHbhXbiIcpn5fzSX5CZZX0ZyfdjeQ+EoMRsS3rnUZMIRRoDQxKHcI4htVQDjxQGfaO3qFNgD1HnwAYRQEikI23WnaRO+EDp5FxIr0eqx5UPLdJqZT/2K6ORpHLY8mcoTerrjEW9SX/j4DrOQmwPpL+iGxt9gGJOwRR9tIiU43B0oDTrVOLx2eNyAiJTqn0jwKTE9BmD0SEkhoFVlBYMxg8i/Y6LpDTVHoDTGGa6hljcNQevGZh7UoMI5kVdnbKpbVOtMgFTCr5D+uPEXGwq7jrfYIOoc+A3D4PBYDAYjEUP9hABXCsg1w6ooMjExysWpNG6QbixHMhL2AUrDB+WTiJTLZ+Rf8eg6zCEVRgEGlpAnwWl+DwRuJ8jcA2HmNWB8YATVt0+KKtRBqott1u2nSpklGAmWlIbDct/YMmPqCI9R6rXp17LSPEEGXSIsE4T1m9iMBizhIEaiyCZQ0mu8Or3xwQO29FvR4RBfR+lmpwLHfD+uIb5tOhJKi2flPpQhHOh7hkGR0/CJLnMG9fuo0OAVFpauoN1iPZGsEEE8KyQPCukXogX2l2NUye6Pfmin/Tlg4XGEWLSlw95V7IPPvhImWH2me3o1WNDN54cIhBuDKFALBaOdXw9DaYTLPUm9H/3c5B2X8L+EoMoI28dKwvW2CRMLooRVG/wmGmyzvP0DMaeBIytw0WDKnUBc4zO+AELJoJM0QhEWcMsFDktQH/JIwp6iSrljtMYGkEQF1mtxAfg/Kdk4qKqNYjl+jk51uW5MSJSi7VixplniPnB+VdHmS0k+iyi2aXOs+BJ82CDCFBwq5R1I4WD7nLjJ15J6zQ8LOO+DDTudqH8R2Ic1cCjgw9+LoMxOLI9AWVBhGaHjR6iKi7JwHiCQGqMMxIVqN0Jg8GEYrWwMvRhIhTy/RHGBaC3CCfWEL5XC6rUpqKwHGzNYLQRWfnwoxGEzz46F9QyHkKHCOYXWIih/I9lMI7CZH+h00akKvz7IJJWyMjt6JUX3g+1LId+fkFNovFAztXCaPLg76hDpFOlZqXqvRMcQ8RgMBgMBmPRgz1EAMeKyLEiKjj1BHgN3LfIUU/ASqMA2ydBNbGaeIYykHnmGDIlRko57fZsLh6TD6quNXRFFzDmB1Yv45qfONJ7iJC1wpppygIjWSWGsHpzIIaIMtBhAO5qFGlskHZvhPY4BmPxAb2lLT8XSeHmyEOaDOYEQ9wQZr5HicfaBcUNPw8eIhR/RcX9fD2lF4ICdgBzF44JM3sxC1iXjm8b1KlN++j66HNk+j8KNoo4JF9T32yuwB6izoENIoBrBeROmVxEqmYW6CQMzsvbmEqP2vWymROGEPSBbt9J4MezoNPhQoC10CpSVFqhFEgIWka1Evys+Cxonguk2JWCjGgowfZqb/wl3BJQdwVpFFpQviQqQ4ABXlZhHDVR0JVLfjAYs4eS1CC2eZh9YenbSiewi3j8Me2+hM+qvg8l2kDz+FtgEOH8lwNJk4xGidpUogNhwwmVlP5krBhvtNvvTttFNI5S3SOmzPZG8JKbwWAwGAzGogd7iADdTplyTqCkYQp3KUb5Y1p+xZKulHGgyTK2X9fG7DRcYSCV5sHqB1NNK8lP5YLLuQyp+zVYBdgurIRwFSYcM5hliqwWeIXQI+wXQBRtIu4kyCBlBqqunuGWUladDVZXFmbHcIFYBmMqjFlmBiiJCpqU8RCDqk3LZOzCEftCADa0Fc9zBl1L0F8k5DcM3iSDFwbnVjFX2/CdmvIWwT4i2QUDszGoejLMwnHxfORz2v1eCTaIAHHafTBlW/z/ZbAW0GgZr2G2gnx5ey4Ueg3iY5H7LljAicNxaHhVIcVePMBYHsR35DhqEFsU+ujHrs8CURSuawalaiwWC0ZVrTtxL5cgNTcPBlFGr7Rt90gXdDg8mgyoNcMHJ/X0JQC0G2eeMRgAhZLWaO1AuQ6riRc8GkrC+AkNEj4g80NlmGOQlhexjoqNBC/vUkVOSGOenGcHctB5AizX4cACE8MYGlUkwVR7zCpGns9LaDfbkMk2F4giS7kuMzme0RyYMmMwGAwGg7HowR4iwFJ3jPKuS2NhfaYXqpVOooYFeDawwKDwChERuYlHCak2pMMwSLAa1tN1yjjsJmxYRTwEju2Kz+OMyp89ADVsS6MiS0QUQmy0GLafBeFGzEqBAGt7SZ88bhwUIMVSbRYUmFbtmsFYJJhNlpldiOXpA0VvCJ59pMFM8dWiUDR6jTBGG5OwGj3mqFME9Jnj6Q/EqgGCDspDZjDWkUSPf6NAaCVUArxCqmRsktzSQV9CSNashBlnc+xiAxtEAM/yybOIckBnNZJ1z+K+4JYObXTVxg+ib6DGULAR+V5fM9Gheiu6QnEisT1QbYXtVuK6RoVrjDFygAZDkTUsCxImqbCqpD+MCQQboyU98sCR0brvoviwTUrVDQQdtTQacZkPBgOBNHQ4Fqs7WzV4zgzvd8WG0GSshpiuD5R7rZv00PWHtpijzwR7aUgWovV75XyZ6Ym/Ay4wXVhgLnFlhlhg+JJiO8YhYbV7z5lf5XyOIeoceHnNYDAYDAZj0YM9RICCVaXCFI2LkSB2L6PrtceRamRK8UDIMsOaZF7i2QggOwKDAAODBY+WfaChhjALwwX9oqAG+2JttGT1ZcG2aAxTy7TDUFZ+4isGQJkFOXk+G2T47ZKhwqNwzxsCOU1FYbXUgKkoLJf5YDCmhVWW8xFSZjgdNXQuGJJHcc6wKzBXZOqfefRuo56awrrBYTuHpOc5k+gTrcjLQGtlbjWs+cOo3oOPWFC1zDioumNggwjQbZepy3aoBqkQwhDCBwtf8zam44NSdaDJXcX6OSFMQM2kiQplaxfctxmYSCqQgu94kBWRg0kveTCqkzBboXGkSaslUoUZxUSHxV+dCtJakHFWhjEVpcs7GhqOG6ZUfMw+Q8Yymj4TzVQslo0jxt4GvI/P8P7PjPqwxiCuj+D51GST1bdFA/rDzFSYJJ0yGESgZk2Cdod5LPAxJAD6g5T4Kihfb92xhIiIivvKRSqGI6gCjLBwgywxIb6Li15sY90ykX0WGrUJ2g+mzDoHNogYDAaDwVigYA9R58AGEcAnh2rkkAcpEgU7TrFywDuBmkRK1WO475BKE/uja9a0wkCRRl12AFr7Ffj5shnIsgD6bKIiaTyhVYRUW4i1gyCrQyEOwZOTBkviQq+K3iIYawa0QXq60radRHJHY9LNHZl0UBpoEilZZqZSIFwDjbGXAb2eljN94ocJSuYneofREwRdB0o5n+T5U7xJ8HdIxAhykMmKj7PIasVsN0wCwbcTJs6iVzvp4/nRvnTbyrxM4FB15SCsAL1FyaA8CGmwFxBlxugc2CACBJFNQWQrD1FO+H7h+UD3bRkzE3D2gLbYjsYMZkK4mCEBHBEqsoqCi6bUUVwFTFT1itgCTkaeG2sH4dRhQXq/jUKPGnUzTMF3CqBgXYY01l4pZeBU42tqVaURF5VK+GX058MMu2D6zA9THBLTZ4xFg0YLAXzOsKkUedZLeGjrIhpsCGVatDUcm20YhzINyP/p7pH02NKu2Khb2SWNoH1zQ2nblCUcaKZRVYwRjSeY3xpqCLQf0SwpM/YQNQ82iBgMBoPBWKCIyJh/0vTxjObABhHAs3zKWJEiiCgC8UJYbWUw4A5omrGgXtCRCLxMCFhN+Rg5DIHZNviMXc3yqxzIv1cC/U+J9FguodUUb9I4VKqHMh9U1rvNxSIKEurIljpp5INMv90NHqIaeK0m43NaQJlZnvTHR1V5DUyQlJkhC43pMwYjhaWrW2Goam+izEJHv13Ah+lPKQOEma46L5PhjR0pmmuQtQshAV1ePFcsyUi9IQw1wLkXE12UYOukjX9Hz5Iq7hgq/zL2LrBBBPCsgDxLfTBEPFGAcTSQSYXZCvjwoYu3ksYQQWaD4YFy4UH0sZ38i6n9Jjdq1oPCshhPlFBp5YocP/LxIKRtdH/7haT+kJIZAl10YzwR7JOFySgXj8MpgCVVwyTbJpAYNlz8lbHYgfe91vAxwOoqwP+Qto21ypS4oKSNU1ClH/bFzFQ0nmDeiKz6WmbqAPVUmm7eM82FSrwnqk8r8ZzxsWgEoaCto9B8nRdpDMkii5WqOwI2iBgMBoPBWKDgLLPOgQ0igENR/MFVgFgcANOiiCRi/RtYxjjgORJeJlyBmNyzWANNV8ssBzV7Ci5kskGU4HhV0mAVH2i1RDTRhyDpoCr7iLCWWVaOP8gADZZogICkkrJoAsZPEW8MoY8wF5/TyYOPfZcMhDRCCbBu4A1CLSODF0kEWHNwNWNPA96zWLKmJSBNbaDJcDt6fdKahuBkgioZ5MukUkKtWywbpARTi02goYbuJ0z+yIAWm6j9iAHROg24qdvRmy/mX2VOBrdWmeR1EvRZJ3WIGJ0DG0QAmyKFAouRvLxhu+lhcAy1ynRqqSYXr+rKrd9e9uXDifV7MIbIB7XXIJx+dYAijjaIONbKUJOsAoZcUiAWZ013EiZNJU0XJ1OccOPxRS4YLTrhRqLZRRMyGIsBGC9neeb9ph7WIylrRZRVJ8BI6sJH7OJB5j7GFSprSoP+qlxsGp5xmAsxDEBXWywLGbmmzDI1pAEWrOJfGDRSbTVNxnCtg/NSGFlksTBjR8AGEYPBYDAYCxRRNMssM15TNg02iAAZK6CMFU3RnYhXErVIrhiUlYZBsNHWeIVwBYJlPhBKtXty6rabZOkRGATowNOQSVZZGVhtVWvgWfIhswK8RT56rRKKLQQRx9AzuNUVWSbYx0s8RFl5vSxXv6pTMmF0T3YT1JgC+L2YKmPsFWg1czLZv7ZEeogUSruFt4LqWZJtKPdIFlDxGM8inDQmmgwr3+tqORIR9WdL2u06qJQYlBjSZJDoKDXG3g82iDQwcdCNgDXJalhTtY6GU6FkN2BqKBg/gjLD9PsMkPOYEouxR6L4IZGcVHByQeOpjOMHQ0lRj/UTgUh0iePlMnhndan7oQfxVzkZlGQv6UvbIdJnCOGzbzGzjI0gxl4HRWKi+Ze3Bc+OQpPhM9xgKsREMCV+EGU5INs0LNQ/r2EN6PecNEQcMIgw1T7nyn3EojDr6LNUcdFY0ywwiYi8ZDLL6uRRiAhJSLFY7mSuGQdVdw5sEDEYDAaDsUDBBlHnwAYRwLIisq1IG0CN23AdYdqO0AVVN+OGdVE+PqGOMMsMAwNdoIt8Ry9AJjIyShCYXQJPEHqOwsCwNEy6E54ioimaRaj0b2KtEm9W5EHwd59MV3HKlbpj4g4b0GdNgEt3MPY6GCgzkyaR3RcnMPgmzSLT+xO3h/WbcB7A5ArFc1QFsUWRyYqJZ6CLZsOc5th6+kwkluBc2UwQsZLxq6Hj0MuONc5E6Y5OkmgcVN05sEEEyFBAGYqmTAjxw1BVlEshBieSVI9S0BVijkaCmKsfBylX3LcCfHYtwocdzylmIDkxYGYF0mfjvhzTOChEi0y0Sah1Vq5C1hq4rtELH0GaviUmNJM9ggJqcHcpGWdJG+OKlOJCSlwQTKBh8+qwai0znPA6L6zGYMwlWhFjJCKKKrGFggsSJQvUVLBV87ZQijk3keDmlCBGUswDmAmr1DLTfy9fozjtN7HAtOHZDwnjierPo60uQPNDmTE6BzaIGAwGg8FYoOAss86BDSKALSgzcI+IwOaMQaNCOR58vxnwIhXsxGcMV7sS1ot9ERF56BrGwD8hCAYrm9Ga9DhVwUfth3rKTOcadh3MzIIARB/TxeoOU6As5AxZJ8p2p95DhJpElIOITOwCvUUNqt1zGQ/GngCkcE3QUbt4nOW0RuBYK5YREVHo6qk2kw6R7jk3eZOUJArUU82iuFpSuiPQe4IsE+euAeqw5YGjw/kSvUhIseUT7zpmm5UN8/N81DCLDaLZxBC1cTB7OdggAoSRRWFkKTE/gYZQxwcng25YU/prshkfrJotH9pJKBJUiLC4qzzPSCIJq4o16jPOfKUNE0KSiVEGYwe5edwXlWGVemfJdoXuUowdgv+haYETr9IHCjZmJf0XlppPsTWCi7syFioMxYkbKlE3c0/DPkLuAuUyalCDENd7iowGtMVU1oxBRAajSrSdAtBTSpggxD9CrGNXRs6RaVwkDLrLBR4PrqNtMLDkolEOtAd0A3RZx6xUvXeCDSIGg8FgMBYoOMusc2CDqAFEMHMAKw0PQ+pMmh0az6qiD2Rh5gJkqsEyrAxtEeRXhn498BCZfCcYgFhN3MpInSnCjBBUHfpIT2FGmZX8CyfBQEjDs6dUv04COFE7SfE4gaeKMrBcnYRiSTNEqwGoDEa7YaTJlBIc03sgTDSZ8f7GmmSZ+GEMDaV1UEPIJLQqSnbgvljf0BSMHYGgq5UJkzHDvjjXWPrvUqrW13vEGpAIk1cI6TMhgNttS69QTUlogWzf5IvNVKtuJoioobO94fGM5sAGESAgiwKytEKKGEMUKj5gPF5ux0w0uR3r7chLjw8txiHlYH9BxwUwe+QNatdoBNl+fTyOiVNWsz3qjaC4nfyLNiEmiGF2Gk6EFho/Ce2mZLbo44mQMmMw9jo0QXdpjZwWqV97aX/aruXjB7NWgNAAmCaMsUCwPU2YhaFhQdcwg6KPOJD6YmZoBNmGVaVSnxHmB1GEtQwGEc7fOCcr8ZTQFtvLlr4aQc1QwJux92FeidCf/OQndO6559KqVavIsiz67ne/q/z9oosuIsuylM+JJ56o7FOpVOjKK6+kZcuWUVdXF5133nm0devWDn4LBoPBYDDmBoIym82H0Rzm1UM0MTFBr3zlK+niiy+mt73tbdp9zjzzTLr99tvT/89kVI/H1VdfTffeey/dc889tHTpUrr22mvpnHPOoc2bN5PTYvaFQ1EqvCWgc7kGSiV72c6BNCN6kUTWWmCQ80L6DIFKGCKAWld3Z+o4fEPGmaDKIs3qiEjVPTRCZJcYsskivfNsyj5W/TbQRImgpAdBjTPLhXIi1eTqRJ3P+mAw5hpG6qsVzxDuC2Ktta74Oar2wDyGdBc8fqHBc+QmHH2lqP97kAMPEdBkkQuUmZhEDC5r9Fhj3zWouZj34nlgEsRmR31Zow0DrMMG63+k3Uw6RPMC5sw6hnk1iM466yw666yzpt0nm83S4OCg9m8jIyP09a9/ne6880564xvfSEREd911F61evZoefPBBWrduXUvjKdgBFeyIalG9wYOGkqldNhg84uHCQrBYdBVTPFXlawe2x/ujy9bEj2P2mW4fGzPI4O/KvGSIERJtpVtD2r2pLey1EIXhIP0ft9t5mJHRwBWilCgl0KJxJGI5WLGasVcC5UOW9qZtvyt+jtTiy7KtGEEGyqwm6LEmZDYUmgxpeZG9ips8vZwGqlbjnJUKM8I8gBln+UgfVuBo5kVcSI6AUVWw6/uodlLWY7ZeHvYQNY0Fnzv44x//mAYGBujQQw+lyy67jHbs2JH+bfPmzVSr1eiMM85It61atYqOOuoo2rRpk7HPSqVCo6OjyofBYDAYDMbixYIOqj7rrLPo7W9/O61Zs4a2bNlCn/jEJ+jUU0+lzZs3Uzabpe3bt1Mmk6ElS5Yox61YsYK2b99u7Pemm26i66+/vqkxiBUIUlJKEHQT2WcigFoJ1saga+CqsMI9CkSKTIcAs0XAz63WLNP/rEJzyORZUj1B068qlJUjMH6Khgm0lerXvmbfLFBmFWhDxpkNGWdRxVDvrBGQRmhB+I3BmA3akVlmgpFeA4q5VpQirpXeZE4DmiyQf1a9RYZyHLVeTSgB0mS24dnC7aIuIj6HhtIdqPFmeyiUmARmw74o0oil6k31zsTl8w0efl0tSt22uQIrVXcOC9ogesc7pCDZUUcdRa9+9atpzZo19G//9m/01re+1XhcFEXGlE0ioo985CN0zTXXpP8/OjpKq1evJjuJIRqO5Ns7lxgugcFAwJgexeDRUEqYQYFpm45hQkAIgwwFwTD7AbcjreZaelezFgY3tyKgKDLEUF3WcKmDvH67U0u+C6biQ9p9mAGDKAc75WAGHxvXd94COAWfMd9AuhaNJqy/p7tPm7l3rYP2S9sBLDjCTP3zZ0qTD7IGY8UTx9UbOESkVD/Fvi2MIXLq0+7RCEJxWMfRhwHk3DgcoeDKmJ8+T4qQ4ByZhUUo9iFS8LOOPpZTVyfN72DsIusQdQ4LnjJDrFy5ktasWUO///3viYhocHCQqtUqDQ0NKfvt2LGDVqxYYewnm81Sb2+v8mEwGAwGg7F4saA9RFOxa9cuev7552nlypVERHTccceR53n0wAMP0Pnnn09ERNu2baMnnniCbr755hmfpw+C6ESANeYchAaLGykzrddHcbRg8LSEKWhaBGGrXiE91aaIMUKgoMgyC1BXCM5tmVZ7ynaRGSI3mVaXmKiBgZqCPsNYRTsLVGENV7OgAVLslu0gvn7hy7uJwVjoaCZwH/c5w/s/8g8zzCyzJiWt7BdkOph4/kxB1ea2YX4Qf4dsMsqAZxqTOCA42nYTCt9Ar2FZoawnvTcubBdzIM6VgS6Dg9Q5uQYeOF0IgS7oet4QWbMLjGYPUdOYVw/R+Pg4PfbYY/TYY48REdGWLVvoscceoz/96U80Pj5O1113HT388MP07LPP0o9//GM699xzadmyZfSWt7yFiIiKxSJdeumldO2119IPfvAD+tWvfkV/8Rd/QUcffXSaddYK/MiiWmRRGFH6EWKNAdnpx7MC+ITpp8uuph/P8tPPTJGx/PQj+irY1fQjitHaVkSlwEs/fmhrPwKOHcmPE2o/li0/Cqz4E7lR+gk9/SfIUvqJbPkJ3eTjWeknyMiPn5WfWrebfsJCJv2Q78cf25KfVmHZXNuMsVfB7u5KP+UDlqafWredfkInthMi08eCd3AEH0BkR/HHkx+y5ScKKf2QFcGH0o9lRWRZEYWBnX6Uc4COTqXmph+c08q+F38CN/1UQ/mpwCeIrPSD8ENHyTCb9vpSmH46BRFDNJtPK/jSl75ExxxzTMqerF27lv7jP/4DxhPRhg0baNWqVZTP5+mUU06h3/zmN0ofzegDDg0N0fr166lYLFKxWKT169fT8PDwTC9TWzCvb4NHH32Ujj32WDr22GOJiOiaa66hY489lv76r/+aHMehxx9/nN70pjfRoYceShdeeCEdeuih9PDDD1NPT0/ax+c+9zl685vfTOeffz699rWvpUKhQPfee2/LGkQMBoPBYCx27LvvvvR3f/d39Oijj9Kjjz5Kp556Kr3pTW9KjZ6bb76ZPvvZz9Jtt91Gv/jFL2hwcJBOP/10GhsbS/u4+uqr6Tvf+Q7dc8899NOf/pTGx8fpnHPOoSCQLMoFF1xAjz32GN13331033330WOPPUbr16/v+PdFWFHEMeijo6NULBbp0d+soO4eWyu8qMi+G6KITTLxE0mQdjmSKQ9YumMizMJ2acihPtHuIBb+2FGV8U5jvkwNGanKCObdFdmuwcqnFsTtaiC3YS0zFDwLYLXmV+T2SNQ7K+lLWNs1oL7K0IakMCeJeUSNyQwoH2TGwFU+JB+gzIjk2NxnXiQionBEPoSYsRO1oBMSwUOKfbA+EWO+cEbmgmn/rgRVYwbW8qVpe/KoVWl7YqV8zkvLEu0eyUCrpTsg8FmtWl9fjkPZFwOmXcyegy4gQ8wSFBzOHxA8rQRSg6fagXH05uP6Y54t+13TI2NKcR7uz0ykbZxnhcabC31g/TJbo3tUGa/R/+9136ORkZE5i0EV76U1X/sE2YVc4wMMCCfL9NxlfzOrsfb399NnPvMZuuSSS2jVqlV09dVX04c//GEiir1BK1asoE9/+tN0+eWX08jICC1fvpzuvPPONDHqxRdfpNWrV9O///u/07p16+ipp56iI444gh555BE64YQTiIjokUceobVr19Jvf/tbOuyww2b8fWeDPSqGqFOYqlZNRCpnrtQvMxlHYAwkB6iii3g+rL0jjyvY0op4YmJfIiLaXgaBNQjeQaVWLN6KE4Iu2wAzz3ACQmARyNTd3AP7jilFy6ANTUXFNjmfrKVIBqUAJTPenpRXLfKnpyLxhdHIOMJCmZFeF47BmHM0Ezekyy6z+/vk/0CqvRWiYYO1BMXfsZN6YyfeN9K2ddyCZYgxUowjgMhUVZQw4IHHtgtzUw7iiXCuEzBJkOyuymJreQeqCogC3igaiSLfGNfZQapMYD6zzIIgoG9961s0MTFBa9eupS1bttD27dsV7b9sNksnn3wybdq0iS6//PKG+oDr1q2jhx9+mIrFYmoMERGdeOKJVCwWadOmTWwQLVSIh8GkQ2RKx0eIY02FAW0ldV+fVn9UV8y/lsID0m0vTMpASYwRUiYSeIAj3WQKqzr0HKGHSFG2zsaTkV8GgQ+leKNshopkv9yeeo5Mlw4lEyLDJKwzcpT8XQzqbME44lR8xhzApENkqlrfClCTK9xnedqu9ciHzi/AAeIxMT0KzQRSCPkNMIJUgwiMCNge+jCPJnOPEqdoklQCgwinBBGwjUbQMHjLc5BKjwVgcTEppEnQQ4QxRVhEO0wmsmqw580TUwWIs9ksZQ3Fsx9//HFau3Ytlctl6u7upu985zt0xBFHpILHU7O4V6xYQc899xwRUVP6gNu3b6eBgYG68w4MDEyrITjX4IhSBoPBYDAWMqJZfBKsXr06DWAuFot00003GU932GGH0WOPPUaPPPII/eVf/iVdeOGF9OSTT6Z/n6rz10j7T7ePbv9m+plLsIeoSaDbtBbp7Ujcrggvpl4mCXS9hkpsEXhpFPHGeP9XdG1Lt+0syyCAsg+FT02yAMkKCPn2MlBtWF/Nc4FDhxVcuSRydvHLwArQA88MuOmdEnhpkq+FcQsupvcqLnt09UMKfjY+WFGsDpWlI3TSgreIs84Yc4E5vK+sgnT/+EX5UAUgZREqnlvxd7lNLdbcmCYT+5u8Qphqj14h0jl/UT4Exf6Vlyf0B52UavGXwRT93WV5PQa7ZIwhzpGuLeeSTDIf2kCvZWzpWcJ53Uvm4Wq45wkzPv/880oMkck7RBQXUT/44IOJiOjVr341/eIXv6DPf/7zadzQ9u3bU/kbIlX7D/UB0Uu0Y8cOOumkk9J9Xnrppbrz7ty5c1oNwbkGG0QAkb1d09x8SJlVQYbVpElkOjY9zuCcQyMIH3yxfbk7VncMEdFIWbqJRQVoIqKMU6/fgWPOgcKrScm6Ws7Ub8R9s+DOBgOGynCdsARAcijMOcYikpELBgxoElFPYgyO6q+HEajTklwPE412uiOV0h8IvtnaeRgMBBrlJppshnpD0RKZdYu0eC0Phga++5KhqAXdYa7pgpc9Bk0rekKaIWGh6FA/L+LiijQGkaJUjXQ/JrVA3yIRxMXFF4xjZ0nGDS3JSgXrMpT30GkZIaWWgdpE1WSfWidDiTSyBy0fTzQrEeIoiqhSqdABBxxAg4OD9MADD6TZ4dVqlR566CH69Kc/TUTN6QOuXbuWRkZG6Oc//zkdf/zxRET0s5/9jEZGRlKjaT7ABhGDwWAwGAwiIvroRz9KZ511Fq1evZrGxsbonnvuoR//+Md03333kWVZdPXVV9ONN95IhxxyCB1yyCF04403UqFQoAsuiDMjUR9w6dKl1N/fT9ddd52iD3j44YfTmWeeSZdddhl95StfISKi9773vXTOOefMW0A1ERtEDaGjx5pJwUcPkKDMasYo4sYQKf2Yin9M8YW0PQSp9qNlmaKZK8hloFgBKUqv2Ea1awiqRnd0lIndy5jjFdYMK150pyNfKMSuYTUbYKFXrHEGHiKscRZ2xd/RBrognJyEAw30GYOxlyHMQ4ZpDp4RQ2FW4QhxoBxgAEHXARRXDkO9JzhNUWumULRCk2kyXWFnDJgOwROEc64iD5JwgZHBy4RJL9gH1i0T2zO2PsU058zGPdMOCDXL2RzfPF566SVav349bdu2jYrFIh1zzDF033330emnn05ERB/60IeoVCrR+973PhoaGqITTjiB7r///jp9QNd16fzzz6dSqUSnnXYabdy4UdEH/MY3vkFXXXVVmo123nnn0W233TaL7zl7sA4RSb2Hn/1mkLp7bDUWqMHNhDE/VSyrQfV9KBpDUEDWpEOk9h1bCUrqPvz9TxWpP/LgC9LC7s3K3PZi0kbXMGaWYer+eAXGBBkXpUq8j1+VVktQRZ0icHnDxGpBVoZdjdsYV+QB85VFTaJRSLfdJScxoUlkj8rvZ+3YlbaNxpEOhkKNqE90f+2e6ftgMJqEkl6PaCHV3srLBVB4mCziOrGvtGxKS+HZHpTH+t2JDAjohPl5iNHBqvVKpmh9NpiuWGu8s/araN/NmIWGGWehL69Hb1HSXWIOIpLUnKkobCEnM8QwdhJLgQhgIeyejIxN7PYgiy95N9QmqvSd02/viA7R6i9tIDs/Cx2iUpme/8sNczrWvQW8dGYwGAwGg7HowZRZkwhmYTsGGtoNXbkmYUZPs5rS9UVE1A1S0Evy0juC3qCCG6+WqgYVxIyjX0EFVU0hRHSVY3YJsmdY7BGDLJMmFnzFoE8sK6SIQublH/yEY/N8DFbFVBrMjrH029MDDRlp4N7FVT17ixhtwwzpXGul1HCpFeXDgzS0ojKNj0Dy+PtLDSqkilK1QWdI7iH/jtlpWEC6QaFU5Chc8EKh51nnFSJS9dIEUMm6Ap7sKkxOFcg46y/E82UFAq2tWr06NZEMK/CVgPQ5RpuCqhmNwQYRwKGIHIoUFWmdIfT7qkwLHAukK/MV2W11+yJMRhVudxSZeLmPEHWsksY4IaIekH0+qEdSRxUwfoQAJIqOKfFEMNGg+xj3Een4SnYdThiQKhuieJlX/72UhBPIEsF0fB9Th5fK717tjneSOSREni9F6WxQ7A1fltej4QvIYBzhds4+Y8wbEiPdXyKpMTSCMG7IVLU+jeGDmCALFy+41lEWPrJpa1TtXSjL4WNcoSGcr65wNKnUV66rqt2O8Y2CVlOy2nBuguuBsZD4vcaqsUGJC8JSTU5CGDIg5kW/k2lmXO2+Y2DKjMFgMBgMxqIHe4gAo1GGwsjWCnEh9vd2pu2aq7+EumBsE92lUmbgAVJqn9Vncpmy3VblhuVxsLrZVYv9KaErV152YEhFgc2+Ijg5/WrDh1sqQvEyWMGlOpQQgI00WU1WJFEKUGIQdibJkCkvkQO1K1A4siozHmh4RJ67CrpLjcomaDSL4k7kOERJBi4Ey5gO6FVsplyHsXhrb3xfB+BRDTPoIYI5wSC8mHYLQctWTj4Xjqen0tBLI+oehhoqPB6yKftMEyCOx2HJD6UPKJeEHiKduCMKQcK8aWewTprcw0ue7b6cDNzeMSEnnhqoWop9fb9zHqIoUmnFmRzPaA5sEAEyFFCGIiX9XCe8GCgGk6/djs63mabbI33mJXnrqnBjfcVmIqKCLQ2eQJOhMmTg9LGmWgYsFF0NNtyGLuwIJlNknJTrmMQkYa2zsE/vsncmDargyWnsXqD5QEDS8mUnblFmVkTjsuJ1mkVmmjEM2WlKMdhkH6xVxcYRY87QF9/LQR7qlEGqvZ/TU89hFp41QZVl4VkFehvFUF2DcSSA8TpKjUedrDWpCwth8JhUmF2gsNCAQYMocETaPabu65WvfTAAa/AM59x4Dh8GcVuEroCsaXE7J+AYoo6BDSIGg8FgMBh7JIaHh+nnP/857dixg8IpJVXe/e53t9QXG0QaeJZcVugCoTOgMthIe8jUhwlIn1U1dc1wbLVI73pXxg9l5oUOR8EB9zgsHzAAewKWl1Ws/OxOn14xWYVlKaxMFCFHkWWGGSw1cN9XkQ6AlS1sD5LFXA1c9pNwPSxfjsMZBw4Oap+lnh5fjg61h9TstOlro0Xwd/YWLXyYqs8j2vrbKVpX+udWpzdERMp9GObi+xpFSitF8BChwCJI1yhOmEgMCTxBOXnfO5DphYfZSlByMh5jiQ702IAXyan3KEWYqQZ9+KCRht4npNXcRCg2UGqn4bMK/VWQzq9PTslBySMPvit6wEVbl902Z+CgaiPuvfdeete73kUTExPU09NTVzyWDaI2AI0EMU00o05tQkp3GQwjpKpCpaAr+jrjl3ZoyDLDBx8NpQqknQijygWDqQK3QGj4XjmNqitejyqksGK6Po5PUZ1NJxP9+QwZ/UqNM5Gy76OlBWMqLYfsukDGE+UqcEBCnyGNptJh8BLDnw6No2SAKDcQTc8yMOYJzRhBuv3bYRhhH4owoynrEePXIE7R740DgyaXgQQFGD5Ik9V6UFQR2skiw0LDR1djjFTjCJnl9NnGZxJT7Q1UGmZ6uZpMtWpNftcaZKph3zqjinCcQP+FkxgMCZlqGbl/SZPtptRl06hdB0HjOLB2wYrMguDNHr+34tprr6VLLrkkLR8yW7BBxGAwGAzGQgXHEBnxwgsv0FVXXdUWY4iIDSIF24JeKgQO9VhS0ydjCe9Oa16hRkF36P3BLAZFkwjoM7FixDhfXcB33AespuBpqGm8O+idQs+RCy4PH1z8QqtDCaCE8QtNDyLV1axkkojVIwq9yThwVcQRN8PdaieOHhR0DOByVSHYGjPp7KqkzzLPx9/FAk9QOCYLPCneIqTSFDE6QZmhgJ08jjWL9gA00KZqOwWqZC82psksSAqoFuN7udYt/17pl4cZ8joo6pGeUXGkk9XTZOjFUagqTeaYo3hS9PUPkT5DD7KYS0x1ynzwFqlB3zAOTdC3okmEorLgIUKfkKDSqoZrILTXiIhq1fj6B0Fr7wPG3GDdunX06KOP0oEHHtiW/tggAuzjjlC3q06OYwk3U4v0l8qmenerCUh3DUM1xZyFMT36FHxdujvGCuHEVQN3bqiRDUBKDSk6bGPdH2yXEuOiDHn5noP76icS5NxFWq+PkwrOtTCBuqNIT8Huog1/Dgzlfsp9YByB4Fpkx/Xfsn+E6wjGTAjp+go0sUU6kTl1oBxbtKDQikJ0p4oDm9LxsQhyNh5LpQ/+DIsJH5RKwwIYCxCjZ3fF8w1mkKkxOgRtfeyOpbEHHODUTOUDdYs4pOJMsTlKwVaQ60jVrE1Z/hAbpcQWacINQsyWBaOqhoaZ2Nfv0D1BxDFEU/Cv//qvafvss8+mD37wg/Tkk0/S0UcfTZ6nysicd955LfXNBhGDwWAwGAsVTJkpePOb31y37VOf+lTdNsuyKAhaC+hkgwhQmyLKSAQB1ka9IT0c8MyI/XOWXMoVbL2XBiks1fskqt1jFhSOXfaXtaXH6VfDq9P2YC4uI6+W7gi0bRMdh7SaQBW9KlAlOoBVVgVWWWFaowjOgfRZrfGKJtJ00Yw0SLkf6IqkdpGzUnIO7taX07bdJb144YSsDxfV6itlG8X2TBHijI4DvXJIZZqgo7NMFKgivKg5zlj/DukzU0B/VvLCVpAERMOtVB7UC6AiXWT1gRhrQg3hKDHAWZdNRqTP9FICjhWvC2xHbwtQYsIbFJjEHQ2lQvCcUUKDRX6LXpCg/pwKJYihCSgqm7TDTmaZMRRMTa1vJ9ggaoByVK/k3ApNRiSNI3xlIk02GckJLzSIPjqJMWWi0bAQbJ8jX96nLHk6bT8yGvOseUi7z4OBY6TgwNgSlFjFUCBWycYzWChyQtPXScLDqktgoq5AXEUyoYENR2irIcOJcUY+7FNaGn8vpyZ3cCZlvIY1PCbbJRlXplBmIgMI093QOMLxwXEcW7QwYYzpEX/Hgr+ZC7TbdSrT63Lvkps8V3tcZFjNRhm4n5KuHSmqTHYZ5oQ8GDNFqAUGL3A7G9+rDhgZ2DaluOsEWtGWQaMKjwsNlKOYK1DcMQSuLcIsNHykgK7SLTEsU7wRGjy5+rHimAMDfZaesJNrG/YQGfFP//RP9I53vIOysGggIqpWq3TPPfe0nHbPZi6DwWAwGAsVURs+eykuvvhiGhmpj/UcGxujiy++uOX+2EPUAOXE1dBjl7V/b4Y+E/ugzg9SagW7UnfM1H0mEjeHSafIFMSYA/rsyO4XiYjo5ZrU5fE0FNjUc6PXp5JcD6zxhlWigyqOD/aBAOsoqWBfK0OwogcucRwIuMIx+NJK6ppF9Z5vTScSqNMivnoIpQqqyyVN5kG9KBs8RAplJlLbDKtghUpDV39YT7sx2g9Fe8j0G5m8Qpr9W9m3mb+bvEIKX4SMmLgnFX0u8Kp0g5cXtnt56S0SyQ6Y9KBoBWkSMaYOKd0XBqIILMKzg+MLFa92vF2hzMAVZNICUsZhTfl3StuC+Uih5ZEGs0SmqOG3xWud7GMqN8LoLKIoUsQYBbZu3UrFYlFzxPRggwgQkE0B2TQGHMtyZzz9m4ASX2MQVWx4LqUmmaGgK/A+afYZ/vYooAbj0NF8REQr3JGkX30NNEzXR0HHCvQnsszUWCfZR4+HMURI7xmuXwLFOMK/G7h8SsTl7KrpBaXfrFyzxMbB+T+ELEO/V6ateQMyzsjOy+3R7uH4X+S1VQU76FwfLyLoM6bO5gDzaQSZYFq9YNc5abmHPUCpJ48iitS7UPQ4mETlfAkMsUkNIuCbdcYO0TT0WdLGZ1kxWpRwHDS2dCdBeQ68NrCAVGKIlIPjPRUheRgHGouG7DlxGr14rNpfahCZjKe5AGeZ1eHYY48ly7LIsiw67bTTyAXx0iAIaMuWLXTmmWe23C8bRAwGg8FgLFCwUnU9RKbZY489RuvWraPu7u70b5lMhvbff39629ve1nK/MzKINm7cSOeff37b1CEXCpykfnwPUFjCM4TZZzkIds6hhwW2oxemmriBJyPgawwIG2gPoRfKFGCNXiZH8zSsyexK22Mg3jMC2kjo9el2JF2UTYLBTXXUcg6sZrGeG3hHMNMkheI1hzHj0hbLD4ivCBlpygpQn/BCKBfklRKXfQZWlAb3PS2TD5yTz9bvAeU/ojJQoAZvkRXVn4cDrdsPvI5KyYyFCLw/uqWgUGW5rMIeJS4W9GqqYoz6tx8KLyJVJk89vSfItB3vY6UqvIHiUsr5JPtYpD8HQvEb4XOezBWKxwa9QoTPNvSneJGS8df0c5oaVG2p/zLmBZ/85CeJiGj//fend7zjHZTLGUToWsSMDKKPfOQjdNVVV9Hb3/52uvTSS+mkk05qy2DmGw5FyQeLuyaZEPA0Ic1kqicWwPauJEOsDIrJXWB0IcVlEoBsVCBWNaSg8KMm6wuNmUCpjQZxTY4cX01TZNZED/ooIWCY6Ewp/RIwocHkpmSPJKcJPXDHV3Dikk0Io6KMTBxL44nQCMJJPVJiKTAWCDpPUvbtSWkw2dtl6n6EsUcYL+JoYiWgACiLOLYHeB3N0ggLJLcE3/RAy6LBLqaQ0gq5q9+LUhxwbyq1yuA5SVYFGY1hFA9DP78p8T8J3aYYQYbLaJtcFMmxkeHPGAOlxBPp+jNNKQo1pp8fRAp+ZAp50I2Ps8wWBC688EIiInr00UfpqaeeIsuy6PDDD6fjjjtuRv3NaCbYunUr3XXXXTQ0NERveMMb6BWveAV9+tOfpu3bt89oEAwGg8FgMBit4IUXXqDXv/71dPzxx9MHPvABuuqqq+g1r3kNve51r6Pnn3++5f5m5CFyHIfOO+88Ou+882jHjh1011130caNG+kTn/gEnXnmmXTppZfSueeeS7a9QFZeTcK2ovrVTPK/zdQyw8wrD2iricQD1AuZaqbAZ6S7MJZTeGYaeYqagVKHrMXVg8hKAxkU8sGDhKKPjaAEYaKbG2ug4dfFnyDZHx1qQVbuAPqQij5RgHIVonJ1BlaiWD4OgykxUyYL3h2xGbx/TpekO7BSeTQyCufWUGn4ZeE3Ym/RLDAL708jTaKWYOhL0S8aWJa2K6uXpO0QIpHDRHAMHLhKCRxnCdK1cjvWEOvKYOHA6YGecU9XwR44aKxdaKLa0KPUyFOMApHGrC6dLpDS1lPqiu5Z+gybMtk0/HsHKTNryhBmcvzeiosvvphqtRo99dRTdNhhhxER0dNPP02XXHIJXXrppXT//fe31N+sg6oHBgbota99LT399NP0u9/9jh5//HG66KKLqK+vj26//XY65ZRTZnuKjiFDIWWIqNoGowNptUxCwU1A/a+qKQYHBBsbQY0nArc6jF8RW0zGhPXSHhvbL233etJgm4QX/D7Z4bpzVwJ560z4MjZquCpd/SUfstNqsi0mILQJtOJnUxA5OMkmDYMIm5+T21GbTTF40iHBRIhCipAZb2MaMViRaVZaDuONpLijMyZNRxtm2XBomKbCROlEranP7zUwqUnPZXxVQyOoVQNL05+NsZdLZGpwbVDeN343CDbCbSH0UPHeRKo4gGzN3n4Z12ZrUt/RaDHR27ahrYO60Gr+NYzGB4o7+k3UNWuUdq9YEk2k1Wv7wNAoX4xB3xWjs/iv//ov2rRpU2oMEREddthhdOutt9JrX/valvub8Zv/pZdeor//+7+nI488kk455RQaHR2l73//+7RlyxZ68cUX6a1vfWvK7zEYDAaDwZgBRNr9bD57Kfbbbz+q1eqdCL7v0z777NNyfzPyEJ177rn0n//5n3TooYfSZZddRu9+97upv1/qtOTzebr22mvpc5/73Ey6nzfYVvxRqja34HB0DK6NnEb8cIIkd+NRY3HELit2hWMle6TdFJFGQ2kRXQmR1xdlaY9/33VM2h6vqVLoAgNJVHKvKz0fu2tyxesaqr6j5kmUVrsAzRQMmrT1qzoLPETpM46ZZ5CRFmbheqArHIeX7KIsIoHJRO3E0NcvGa0kEBoDMjGZMOiT18bqlt4zXImk3iJDFlrkL65g6/Q7quIy9X+fZp/0z00EUnfCK2Rl4KZwgDYqyvujvEw+c35e9lErgCdYdINfG54BOyOfM/SwLMmhpzLR8GrRK+RjwoHIEDN4mcIWOB6TN0mpk9bopY5OI6Tfcf7Q57805pTwthL9dVSHiDio2oCbb76ZrrzySvrCF75Axx13HFmWRY8++ih94AMfoL//+79vub8ZGUQDAwP00EMP0dq1a437rFy5krZs2TKT7ucNYWTWTDMZOybo5licMExZa8b+NEaOLqXetK8JGTDW1hR2p+3Hh1el7Ud3SFptn54RIiI6qFtmUvW4QLUBfVaSnBRNojBjKMTUmrimWM8IqS3xnoMfDDPOlMwxJW4ImiJ0B88Ahg+qWtsGYWkr+V6KlqRalVLuW4PffJWMF3GS7LNwbBw6xtpT0pAKq83Hf+zpUA2VxrFpUdjAcGnVsGlD9pmV1FjC+mWYQVYeqE+pJ1Lj4fD+FSoZeG+GYBB5hvT6nDs9FY+GDy5q0BDRUWImwdVmlJxFf0g/IWWmxBsF+v7EHBLhxI3ZYgYqXhsjZDLMNEbV3qjtsyfioosuosnJSTrhhBNScUbf98l1XbrkkkvokksuSffdvXu3qZsUMzKIvv71r9MPfvAD+uhHP0o7duyoqz77j//4j2RZFq1Zs2Ym3TMYDAaDwSBiD9E0uOWWW9ra34wMok996lN0/fXX06tf/WpauXKltpbInoiArPQj0CiQsBnvjpMGM8t9TcHTptpowqM0FsoVpadUqpcuDAc1iRqEidmGukVIFaKrfPtEXAcNV4P7FIbTds6R38ux5dIW650JV74DK1ErJ8fv+7gyRG4AxufXe2bwLsRAVAzGxpW38PpA/DjZ2Fay06YP+rbBm4AJhOgVspFygBIhlE9+U/QQKSU/9PeYLujYRP9Ehj4wQNkUxAydyOPaTNcp506uJeo2Wa4+KxPH1EpWmK4ifcto4nxR4tGzeqVOVdgnawmGGSgVA4kAWF9PR+mEkBlJWL8MPCz4zOloMNPc5sKc4Cs6ZfAcaY/Uw5RZJrxMzVBjtoFSF99XKa9hGog3Q8tgng0KVqo2o91xyjMyiL70pS/Rxo0baf369W0dzEJBq/RYs/DgzuyxJfVRhtxxTN1HdWwxpv29l7XHlSFwRSFVWvgqxxaeS9u/Hx9I21lXGivj5djIMcUZFFx59m5PtmuQji+KOboQS4EFHm2gmTDOyEclWR2HD4aPYubBJOugkZNcMmTlQoz/Uc6h4doA+AKzfbw2SrRQ2rJQniCnUTBXaDdoevpHNjV4wEBQaQS94YvqzZYu/V85rr7+2tR9WjGUlHNrYn0sGyQLmqj/NeXgurG1JY2+xT7S2CGQX6gOyLghFF0M8aeFr4sGe5DYzhgjl+mSzxmKLWYMxVuFwWMyiBoLp8rYIYX1hsMwLkhRn9aq78M1MMTm4Dyg1DWz6mOZlCF5+vs+QgouTaXX7jqFD9echDGveOaZZ+j222+nZ555hj7/+c/TwMAA3XfffbR69Wo68sgjW+prRkujarW616hTMxgMBoOxYBG14bOX4qGHHqKjjz6afvazn9G3v/1tGh+Pvey//vWv0/IerWBGHqL3vOc9dPfdd9MnPvGJmRy+V0Et46HfrtuWAW9BCA7oKqzCdX0gNaZ6luR2DKouk4Fq0AD7OKhrZ9reVZIr2pIT9zc8Kak7XOH15yblOGDVlnUgKjlZNJdrODagDkwLVLwcYmmKq09T3C14hRRNl0TkThFuxH2hC6X2GEaBihVqM3HsmNmEHoAk+8w+cLXc+OwL8nSGkh9IpaVB5hjYCmly6LlRano1oouM1eKn9xw1UyEevUI6DxBuM1KBqBulzShrQ7mOVj1L4N0TwdRRATLICpBd6elpMigxqIiPCi9S1APPO9xM+YykrHuzMtkhYzdPcoWtZHcZoGafSeAo0qQQuJcC0DdTgq3B26UGbyf/omcJayHibWXSOmvwHeedcuIYIiP+6q/+im644Qa65pprqKdHUtFveMMb6POf/3zL/c3IICqXy/TVr36VHnzwQTrmmGPI89SX7mc/+9mZdLsgoVOobjnjTLOtqsT5ND/pIKWmCDDiVAPdiXR9IqKJUJ9Kr+vvNd1/TNsV8OX/aOshRERULhuUtmH2wBghzKpraZLFUAoUZhQvS4gLiCAsS/XSwwSP7FSSnWMZ3PTYhy/tP3KVQYkZWW5y4AWKFIAFgo4BxI6Iy5EZAdpNGYjBGAPKkYLpLbIzMhfI7kwCkBqjxEwz6Y0ZYZi1Sk+1tD8aVa6B3pvDuCD9mOB3RoX+Qnzj1PqBJlOMILmraqzLti+FzynIJfGIEHPXlQd1akDBkFkmYgIxNlDJLGuDtnEzcUG6GCKF+jJksGlhYrdxdYXnmZ7FMws6cnHXBYXHH3+c7r777rrty5cvp127dmmOmB4zMoh+/etf06te9SoiInriiSeUv+0tAdYMBoPBYMw3OKjajL6+Ptq2bRsdcMAByvZf/epXnRNm/NGPfjSTwxY8pmaYEcmVCW4PYQmCVe0zEXpEoA/of2q/8fbmV0JYdqPPlvQUen9wH9Q+ymgEIk2iaDim1/X+Lm3/tjcus/27UVlue2JU+vfRgdGdm14zB1eoSlC1oQ6S4oZPvDtBTUMbESluc4XtQo9NNW4rGT1IZYHDITKsQJ1kAWDDYtwHusOpwjjAM5Adhd8o8RzZk/J6RaHB42PyFgnPBlxTO6MJ1o47l02k4yyhD9VaBpZy3Q3B2y1hpt6dTnuFlFOD16oA7sTkJvJ7pMsngGwypZI93DcYc49epKA39gwVMtJDhB6W5V1jaVsRStR4fcZAfLXqy5MUQcSxFfoMz9cMQSf2r2oEH6ciUqhgbGt+c7x9sW5PxdLvI5y8uE3nFSKan6Dq2apN78XerAsuuIA+/OEP07e+9S2yLIvCMKT//u//puuuu47e/e53t9zfrGuZ7U0II6vuoRdGTKBQXBKOYgQZuOspfU0HNAZ0omg4jt9XB9P2oDus7Q8nQq1gI7xEsL4a1knD1Py1S7cQEdHTf5TCjVSR+05GIDQH40eROEuzZAkNxpEyVM28pKjSGuIFME3XqkHG2WSS3o2CjjBpqqn70DXQGeIrKlnSGJPk6SdhRUIgoX3CgjRgnG7Jk0ST8gWlGDA6II1mKriELxE0OE2qzq2gHUZJqxllzfbbbuANidcOfrvqfrGCP6pNh4Z7SckmQ+MIiw8n3WTBICpAsVakyUwq02JeycO+24dkHbUMZJV6rcQeGeY3xZiBn1ZknuJlxGffZBzp5o8pA5EwKHpbIGCZzhWYeWaMN9Jsm2twDJERf/u3f0sXXXQR7bPPPhRFER1xxBHk+z69613voo9//OMt98cGEYPBYDAYjD0OnufRN77xDfqbv/kb+uUvf0lhGNKxxx5LhxxyyIz6Y4NoBkDPDVJjqBsUaFYxNYPooqnvRqKQiB2+XOENuiOyP8Kx1nuIUB8IvUKKRwy2H5nfSkREAyuH5bmfWSo7HJf9lQLwFhVlxotti/pfjVfvxj2Sa6NK5pjc3NBGTZ9k8SuoMyI1owe1M0NDsp7QNcJVfwSx6yA3Rblh8GbBjSMyjWo94CHq75PjAA2baFj+tjMG1kyz52gKaMLLowgv6rLBWg2SnktvkAYmmizol9kutZ74+qKwp+J5xLh48ApVl8C9Ap4NpxDflDnw4qCQomtjNpaeivej+qDqPNDbL49JD9fqJcPaPtIxzyIjLfV6GzzC2J8abK3ZX5eBOgUKfaYcm/SH3eKYlO2a880xOIZIxTXXXDPt3x955JG03WqCFxtEGjSitjxNLA6RmjKfgXxgXX8mBWkTZSbOORbKWbPHllRKnyPjiRQoRli9QfRfY4el7dd0b0nbNZi1VaotvmWuPfiBdMtXs69P2398AgLZwDgqkxx3YUk8VsWVjkNuZWJVMuDR4NDTZAqSr+VOyE1+Af4MLy7HUMtMXF7MXsN4IrcU1e1LpMaFWImr3gLezV8CBXN3DRlOXp/+v2DQhHGiGGM6A6oNtcTmElaPVJ8mUKKuLYHYnO6EDkWaDIxhjDdTCgsDTRbm5PO3pFsuLNJtmsKtRGabVMwruO9A9/i0+05tp9tgbgsMteSUPjRxlKHhOOW7YG1CUK/Xs8LIwUEbaTLYLvqwggZGEMnYIlNm6pyAKTMFv/rVr5T/37x5MwVBQIcdFr/Lfve735HjOHTccce13DcbRAwGg8FgMPYIYFLXZz/7Werp6aE77riDlixZQkREQ0NDdPHFF9PrX/96UxdGsEEE0GWZyTpkJvHEUNvGfhrVE0PovEI4jj7wCpk8WaZ6aCKrA1dpJ3Y/k7Z3BXKVi7XWlCw48R3h1Jeu/u+0/fHHZEkHBwKUUQ5k0onphWy31E8xZZGYkO6DuxoOU4IpYUy1YvxdnCroscjLq3iLkOaw0VskkrvgSUIPEWawWajXg8GjSd+YheZ3SXeBM7hMHvfCS/K4MujPmAKoW4HwMpk8T204R1STF8/KYHS6QXxyptB9lzbAwsw9GGcI5VeqPaDRlHiDMJtM8UJ6+jYmArg99dmaXRn522O5HIQP84Bv8MLoYPJSI4Q3yETxm7xFCPHTNEW1ofNGQ5815VVulDmmBFXD+XQB1h0Oqp4V7bWXeYgQ//AP/0D3339/agwRES1ZsoRuuOEGOuOMM+jaa69tqT82iAAOReRQ1NSEoIMpfb5RLJDJCNLVNcNtniG5FcehixtC7qZgy4n1uap88eYgAwXpM904EYcf92za/sNDB6Tt7C55zkoSqFPzZB9uRt9faJrE0m2GNsAyGE2CVisNwjUfld8VvyJeRqXmVNJ2kMlQzgFtjHfCudfTbISdq8tkTEcmXJ627d2jcv9q/HuFJbDoTMZAK/Ra2w0KUzCWRoHbqHZtsnx1tNvMDbo06w7GbOVAYqIof5fJNVC8VSe8iL89fFU0jmpL5A2HRnwhLw2evBf/zn2gQo3p7iYjSJcB1kqM4lTo+jPF/JjSvv0gvhAo4OoHBkPKdCsnu+NiQzGOULUaZTRMBo9u23wbFEyZGTE6OkovvfRSXc2yHTt20NjYmOEoM9ggAthWZJwk0BujeoIa899if5PCdWBh3/p9xHacdHYGMngTU/4xtgj706puw5sejSNHMcacujbKDaBx9H9W/jxtf/IgKQuQeVoGnWZ3x9esYkGsRVEaYG7WELCDKeIkVob4d/2+SrxuDl46fqK7AytDoQRMRASVTNQJUuMNUtLywZAKQZIGz2OFmoGjbBAqH8Oufr98CXsT9QrFtgveiQkZV2bUNWoHdEYHbvPhQrqGKUdJYdcZvk3M6q0YegZDCaUHrGxyf3ZBmZolMnmhvEr+FugJVAyi5Lug0rniIcLfHOPeslCYFQwGUY6j4Bi8QkqSxPTXA4OxdbE9U6Hz+jSlGwQ/HfbhOs2n9NsYKI0hQmH9NvWB0ccbWboU+6CF+4exIPCWt7yFLr74YvqHf/gHOvHEE4koDqr+4Ac/SG9961tb7o8NIgaDwWAwFirYQ2TEl7/8ZbruuuvoL/7iL6hWi1enruvSpZdeSp/5zGda7o8NIoCgzNCTkqaGtphSqsYWTX9HeobKoMpxyekxdX+1J7OP/qcsC4P2ZaDAqk6lzNLHPS13pYsRla91VCB6iPAc6Km66BiZ/njXs29I295I/G/3KPTrQvHLbtmuHSy9XZYhnbYlYCyCyDoBj0QAmSh2CVbbEASF9JhYkOPQMBYEF/JuBbx1EFMi4kWQHsTriDFJlg8q073S1WCNJNvR2wFeIUwRb4vnyOSN0W03eYXacb42QPEKQeZY1Bd7g4I+6d6pFqVLJ8gCPZUHmQqNwCLSpbUeoLi64PeEmwg9IsW8vOG6vfiGQg90FbxCfhP1v9ymKhHX96GjwUzUWCuhBphlpmpdwrwCXrLJkvwNxDSkxB2Geu+U4hUCMVZFwboBxNftpC+J0+7NKBQK9MUvfpE+85nP0DPPPENRFNHBBx9MXV1djQ/WgA0iDXQ0E26rGipoo3FhMnIanc9EcaWUGfy9hlXmHZk2i1XrMcBaGi62dt8XazIwzTaMP9U2MajB9lhy8j4sty1tD57wYtre+eNY5Rpf9NjOjMh2ZTeI+vSCCq87/fVVAy+VP0A7+QMobeuUrKeOCekP3Tnw0gUw/EofvFyQRUoumUK7YeVzePHWeuXLIPSAfvDig+0hSJ82lO6wusCQQmMluTbRCMQmYUX66vSlWJqBEpQMVNqMDTNDAHhq5GBsEpzPKkrqS1H3hkLVtRWxceTnZR+KEYTq00rKPPx2yfYaZOiHWT2dizo5BUg4wBihnJPEiqGukIEmMylVi0KuM61kj2gmmDnULDCJ5CITn1WnxcDsMKXE9AaRsV3WxBlhjBGeGuePxKjai22MPRJdXV10zDHHzLqfhS30wWAwGAwGg9EBsIdIA13wMa5sUHTRtI9updaqCrXOW4Rjw2DmfQy1zBqFLZroPFMtM53nCL1Q5UgulZVg630fTdv/cOTp8XHPSFdLZlj2h6fIDEN2WhaVEuvHYVKzNdY7E23PsDSEfXGFrzCZSTvSJEkRqZ4vpFIwgSpdKZuKyaKnfxLoAAc9FPEAIlBJRsrMxgDsCrrmNBk2S/vl/0DWmslDZHnoHknOCRSd4oFBKJllM/MQoYdL8T7lEtccFlotSe9l1Cd/0KBbZo753ViENUkthyBppDpDw+ypFGNNuva74B7Ey2FrbiYiynpQTwyCjzOJ5kMVTmKqIYbImZRF00HJpsmjM1OPklJkFh0zyXXAIsoi84xIpZBD8IJ1FeS9PFJ2xaBTmLy1ilcbizxrgqmV+oYmYddOgWOIOgY2iAAihgjRTEFWgYbVoJswgnQ0GRFROXnjvuD3pduWKjQZZIzAOHIgmjOZpLTgOaqaDLKp++RIM5nCV7UVGk9PJ2Lm21Wv+iEREX2ufHq6ze+Rt2LXcxDvgpQClNgIMwlF5GJuPDSx2j1md6HRJA7AlxJkofndIF9QgpcExBMJuSbFjgL7QFEnMGQip7QaMilKApncGYuE2hX4zXPJ9cBrZMuLV+2XhoFTlr+nXQatq5EktigAwwfoNTsPxsUEyHujYrMwhFAjCWk5lAVQquBC9l9iHClxT2jcQfFUpLioB4qqDsaGIdKKqpEJhiVmiCmFV+NjgwxuMxhHBj0hEYrnF+DHRQM8A3FecM8uLcjr25+Fayb6Ncw1OMfMNK1eMWBaMIJM9JkLixekwUSVezyu5kM8Fz6rof73oiRNPzLZ0zq9IZpiKKX9Ynaa/rj0UeygjcQxRJ0DU2YMBoPBYDAWPdhDpIGHQX6J/1XxFGGcHqxATN6d6bZNtw+yGT1JFC5SY8MgclMz6LR44N0R3qAMrMxDLOKqaJGArYzeIM3SSr0GGLAtz1ODqqmi+OzRB21Ntz3xp1Vpe9yCLBI02TEzpMG1VGRmcDWnxODG/6N4kwwryghrSykBl/H+tqnWmUF0Ga+iuKR4aVGrBrPayksgABzoBS9xFqJHBMfvdwEVAYHBDujdRMmxdllSalYA18aD1XseosUhSy/Kxu4Ry5O/d21AepAy22XAduTCWF/YAecMknNIKgvbwfI++b169IHjwpMTwfXwocAq1ECd4q1Dr09CU6OHCIPdYTsGz9cka0l+d3z9ojwUslU8mVCYFcRJexUl6nq9KYTi0THQXS3BcFijAGr06Hi23iuE05SgxCo1ea+Y6peZxiG8vwrthd6dFq6BXdX7BzD7T5y6DfHorYG9PB0BG0QAUbrDbmDMmGi0nEG9udUxpG2MYUksg2dqUqkYy2v02qhcKycjTJ8XYotlME4KlqRHio50zY9j6W2AyKRDwweNHUQNX9hwbUSc0Z8Pbk63uTCB/qq6Jm1bUCBWsfk0pTtURWowLBXRxHqDB2uIOhCvUTO50GG7CCeLlJgEOAVSfhjnAJcs1TIM9fvii1eJVUL6Jol3MSU3BrBvrSDbGbx+fvLydvUvhiAHBlGvvK8iNIiStlOBUhZFySGFXl/a9sbkvWcdIA1iezLZDsFY/nJpZQRZvZWJGWCCVsGx4Q2C8Vz4ztTRYErZDSzGCkYQPi61Xnipi8KseO/aegPcg7ghXGRg5lUtNFjYCVpJqcdnp5nSHs2oTwtgmjzOAzj+QEOZqXFDcA0MWaXpkJDGVnZtbLkI0dUQ6UvsT4kxijTnmGNwDFHHwJQZg8FgMBiMRQ/2EAEmIo+syFaCiGVxV8gyw9IdBsG4RsHYzdBnCBE0PRJILmVpZgf8Xa89pDtnTftXolflnkvbmyYPSdsvQ4rVsoSbMekUobcIx6Tu4yR/l66U1/b/IW3/pleW/Ii2y3Njb2FCc2HANNI/iAjFCuHapMdiSRAM5Mygm0aubG3IOmnkOsefwpSVJFe5cBx6lgzJWOP7yM5zu+OBdG+Vvy7SZD54hXAcWNhWUAam1W+InqAcXEcbPS8JhdEl/z6+So6jW8pRUQB1wbwx+PLFeu9kkDGUyAE3KlKBIuC8BuNAb1EtD98FWUYsy5I4qmoQw42eoGof3CtwHHoaUo0bA29rK+KD+oBoncdazQLF40LtPuqx9TetQq/NkDJDrxCOGT27GDRdq8XtwFC/zDIEiKMwY+rl1SfrNUVtpZRYqL8n8IIIb5LVQc6Mg6o7BzaIAJXQJTe0lRRVYUQ0k21WjpAiqn+rtBpDpItJen3+mbpjpmICZuellqTBhhMfP1JmmCaPwpKHZaWo4v2lo9O2MIhGIMilR6lsKoHGkRJblIgB4KTZ78ismqU9sj2xUxpEFahLVBEGEVayh0AeU4aKDcEjtcQyQKMKX+7ZXhm7UcU3YUVjHCETh+FXODmbDCJNPTSk3TBrSbmFwLKtdscDQLFApHpMGVFlEIt0JwV1oL/X8btUeiEmCfrzc4KqgiHD9672gEAgKHdbuJMwcnQ1zUh9yfmYIQZtP6H30AiyaxAbCOOrdRkosUS7ESlLH4wjxQjKIt8J+yQvW6Rl8X7EuKGcJ3/QjCEoTRgzpgWJahxBxiRQVToZkGbQiDIzxSxhXJPJ+Em7RZoMKawG51bifDQxfnEnaDXhPtMOSZkTxM5RJ60Mpsw6hnmlzH7yk5/QueeeS6tWrSLLsui73/2u8vcoimjDhg20atUqyufzdMopp9BvfvMbZZ9KpUJXXnklLVu2jLq6uui8886jrVu3EoPBYDAYDEazmFcP0cTEBL3yla+kiy++mN72trfV/f3mm2+mz372s7Rx40Y69NBD6YYbbqDTTz+dnn76aerpiYMsr776arr33nvpnnvuoaVLl9K1115L55xzDm3evJkcZ/oAxKnod0rUPUVErhXqqwtpqwYZZ0rwtCK2qHeV1xqs5hTdIENwd0/CAaBXSNENwjpIYCtXgGMZ0wRb42rVpEMURPUB1kpGDHzX81f/Mm1/pfestI0MnFNKNGIKqF8D3p2s3Bnd9PhLeLl4n1oZPFk1u+7vRES5pdLTVibQ4xlN+sZMPNACUn55gw6KuLwo4mhaquDlDTAwO2mXlskd3BJ4YJBaKujvpckVcSeZMaQIILAZ63WB16RarO9PCVpWSplgALbcHsKz6pYFzYTjgM4VD4yl3y6qzGPgcy9k6GG8PPycWH1eUGW4DctupAHTRGpGIlatT9oWaA956BXKyh99sFvWEix68n6rmLjWBM1kk+moNB0VN912hM675MA5yr68AaqQRRbqAqiVemk4ZvCWNwr6hgxUxSmEjjYcMmqPCQ+xKYkVvc2O+LdzbhemzDqHeTWIzjrrLDrrrLO0f4uiiG655Rb62Mc+Rm9961uJiOiOO+6gFStW0N13302XX345jYyM0Ne//nW688476Y1vfCMREd111120evVqevDBB2ndunWzHmMjmksj9EtERFVNOj4WZjXVOkOVYzRyxLG4DakvjBvqtSvafXT7egbjaXu1mLYn4O23vRrzCAMZOXmbjCBEqCk6iTQauveLjiw+esCpz6btp7bITKQU46BU3C2zlrAwJLrbfXDZi0nWQ+MJjCO/Jr9XFl5cmX5JEVaTNHNnJ9QYAxoPRRwReNkF44hZSyg0Z5rQkBoQP9HEKsy2gXMbas+hQriotYYZabkhMHYxDgkMHhwehLhp/16D7+1CUU2dve9NgPHh6g0fQRUSEXlgAJaWJtQdfFc0fJDlRYkDTJ8P8gk94uE4DLE2aAS59TFEmTxIGcBhPTn5rI5V5clLWXk/eUDzVpOjXUU9HsaHBocxBV/UMqun0aYDxhDpil4r8wpka5oKxEaGGKf0OEPavePJvgNhpOMNhMrSuN7DWwjFMUUtw1B/j2nRSW6FKbOOYcFmmW3ZsoW2b99OZ5xxRrotm83SySefTJs2bSIios2bN1OtVlP2WbVqFR111FHpPgwGg8FgMBiNsGCDqrdv305ERCtWrFC2r1ixgp577rl0n0wmQ0uWLKnbRxyvQ6VSoUpFrsxGR2OxOFG6A70+uWRFU4blRQ8uQFDbBFz8DlQRE1ZnTXFbyz6aCdgWK7LhQC5zUYeoBrbtn3x5PbrAWyRWcKFC5xG0IcjZlWVBxmty5drlxF6Yblhim7JZTIGfwjPkwTUy0WdvB62i2orH0vaPhw4jIqIdJRl0/dxOWYMLV5euwVsksrp8XCljkLbBZd+dl9fUz8S/wdiodJmoInGyiY40H4UekyBmF6phKAG7psrbuFkscrFeGlBwxiBnKPou4nixbhvWSzN5W3TxrkgzIW3hGaS6FPrMFePQnxsZJBHEHe9f/xzhOANsA/Or/C5QYkME7Cu3Nz4wSiAvBvfD7oknwq/KQWdz0pNZBTq3G2p05R25j+75aoomM90syWVywfPkNwh2JlKfhzC5KS2DdwrhgreoUVC1YyjFY/oqua74OtXAmxvahnNgFlkVNLVEwzY9XJrv1bkkM/YQdRAL1iASsKaktUdRVLdtKhrtc9NNN9H1119ft10IM5bhDTSW3Pk9kPXhwYznKMqA8sWLzEG6FQymiiHGyGQciVT/5ZCNhRTcrrCr7hgiNV5Ih5xGyZpIrZPWn5XnxIm6FehiGGpUH1dEZDaOcvCGP63/KSIiKoMF8IPM4Wn7f57bR465S465kJFtQQGgsYMTMhaFdTXFZBE20D9OGWgLxfCR++MLNI05QobLVA8N42Q0t4piWOBhhsKxaCREyeVFAwZVlz3JkioGhRq7k5wbDTPYNTTEFikGT0LZYaIVxjUhdYfGm2J/izAkjLNCihFigZTxu5rt+KI0vVwM042gPh2gaCJDzIwJ+OyI7DPzIgSzzAwp+CLL3ECpNVMPTZepht8LnyklLghS8x1NFqFCy9n6axZAtmlK2RkyyDCDlNAYQ2NW8+MpRj4au6FmhzkGxxB1DguWMhscjLVopnp6duzYkXqNBgcHqVqt0tDQkHEfHT7ykY/QyMhI+nn++efbPHoGg8FgMNqAqA0fRlNYsAbRAQccQIODg/TAAw+k26rVKj300EN00kknERHRcccdR57nKfts27aNnnjiiXQfHbLZLPX29iofEwSNhqhEYfopR0H6qUVR+gmJ0o9nWeRNWQp6VpR+dOebek7hvcLPaJhNPz1WOf102ZX041GQfsTf1XH46afPKaUfHEefV0o/thVN667H8VVCL/3o97HTTy1y0g8iJCv9BJGdfmyKyKaIPCtIP6csfTr9WE6UfkqTmfTj2FH6KWRqVMjUKAcfzwvSj+uE6cex5Sfv1dKPbUdk25Ey+Xij8pPbYaUfq0bpJ3Ki9GMFsRfICuETyE+QlR+7Kj/upPykgHFEtvwEGfmJXPnBvoNc/PEL8hN68lPtkx/sI3LkJz0HfD+lj6L81Ary4+fhk4s/tbz8TA5Y6Qf7EGOe+tH9PcxE6YcsSj+RF6UfRGRHineAiNTj4GNCFFnJh9KP8ixEVvop+Z78BJn041lh+tHBtsL049lB+mkE8SzbVpQ+TzZF6nb46JB1/PRjWVH6aQaOE8bJD3BNxTbHCXGzgii00k/oxx+EOg7oRbhapo7Pjuo+eP8q5zZs35tw00030Wte8xrq6emhgYEBevOb30xPP/20sk+7JHGGhoZo/fr1VCwWqVgs0vr162l4eHiuv6IR80qZjY+P0x/+IBWKt2zZQo899hj19/fTfvvtR1dffTXdeOONdMghh9AhhxxCN954IxUKBbrggguIiKhYLNKll15K1157LS1dupT6+/vpuuuuo6OPPjrNOpsJcOLRKVVXDc+CiVQJkmMrSpYGadsYW6Sj0lAlu8uW9I8ithjqaTJBUWHskfpdZXs4lKRfAc6jyyizDdwHXg80igQ9ZlLMxtgiHBNyItXk+zqGq768T1J+27csTdu7Pfm9BpfEsWMY41CqTU8xEqlZP+JFgdlkyFLaMnuaci/LfSZWy3Z1SVIfDrOu4DLD5VcUp3XafWosDrj68Z5FmgMm9pRCwX1NL3tkKDQ/vxKzhErgEMdRw3UI9icoHbyOBqZWoSFN1FfasWFfbOMS0ZryLw5uatfKdaqn2KImqKVyM/decrFDgzyHEh+oySwTR0wdZis0WdxOnmFDbTWVSpPbkXoWcUtoQKlZaEA9GzLO0r/7SNFpbiYiZfmvKE03MOCUx0EwdHtxltlDDz1EV1xxBb3mNa8h3/fpYx/7GJ1xxhn05JNPUldXHJrRLkmcCy64gLZu3Ur33XcfERG9973vpfXr19O99947iy88c8yrQfToo4/SG97whvT/r7nmGiIiuvDCC2njxo30oQ99iEqlEr3vfe+joaEhOuGEE+j+++9PLzgR0ec+9zlyXZfOP/98KpVKdNppp9HGjRtb1iBiMBgMBmOhodMxRMI4Ebj99ttpYGCANm/eTH/2Z3/WNkmcp556iu677z565JFH6IQTTiAioq997Wu0du1aevrpp+mwww6b+ZeeIebVIDrllFOUOlNTYVkWbdiwgTZs2GDcJ5fL0a233kq33nrrrMdTi2yqRbbiNRGUFi4IarAKayZbTFSfR3oMhRYV2szS960rIYLjHIPUGzX7rN4wRA+MCehlOjQv47h+VxpMxq8PiFYEJ2G5jeJywsOCx1Wo3oM03ZiECFwWAq3RozYB1dYt0CWpTsrtQ5nYW9SblzRiXw4EGEFczrSKLSb77+4BXRgQd1TKeIBHp/s5CNw/NP6DImZY1bkqpkHqmYEVtkGDRefBwP9RqKOgiXNbhrZmE47DxOqIWyWEcWDwt2XInkM08hCZhBS1MF2CZt40wqNguH/Q64KlO4ar8gv3uvL+FJlhYaSfBzDRArMndd4iVYcIA7Dls4reJxyrn3i2HOU4EEDFuQtoxwAdvlZ9vyYPkRGa66sErWPwNHqZUCtK531CB5Livkz+NWWkLWCIbGqBbDZL2WzWsLfEyMgIERH198dZvI0kcS6//PKGkjjr1q2jhx9+mIrFYmoMERGdeOKJVCwWadOmTYvPIFpomEyKu9bg5d1jx5ORWUgRDACDsSJg8rKWGxRjNQHPbRsJOwldUVVEYBghGjaFJMsMKbAa3EboskcDRknJFUPFpA9l4q2nLHH8RESjyRsyC2/HJZ7Mhnv1yj+l7V85+6btkWFJmQlDqDsj051dY5yGHIcL2S9lP/7uBx3xQrpt16TM+BveDdl/YyAiCYZGekqohxVB2x4G0UcwmlAtWgwvMyL7rUDx0TBnMoJgs6X+S6S+UCyl5lsLRoSS9SPbIRj/uncfKn5b8idSDCX8Lrg9NepajfXQ7G6qTWd5+r4tFGlMrpMN16sAafd5MIK6PEjHBypqFAqs9XlJwBhcL5MwqintXsxTyj0N80fGYKmGykKwPkPTmD1nMI6iqH4iQGpMR1URTbknxT1rmv6Utab+eshhGwatU99twlZrG9pEma1evVrZ/MlPfnJaZwNRHCt0zTXX0Ote9zo66qijiKh9kjjbt2+ngYGBunMODAxMK5szl2CDiMFgMBiMBYp2UWbPP/+8kkDUjHfo/e9/P/3617+mn/70p/X9tkESR7d/M/3MFdggApQjl5zIoWGoPzARxcvwPlum8pi8RRjgix6R54N6jaClTqlu21ToKDjFa4SeFGgrEvqEdJYQZoRATvCGYckP9NK87MuYraGk3DcKM5pWqHg9cijOloxvElwcSKnhyhVd/S54tkQbzz0CNRjW5Hen7eX7ygDrTdkD0vZkNT4/BkkXgDJTKAKDF68vG1+H4Ypcxffm5LXJDUiebKIov+/YVhD4EecD1UIM2qztDyKYKCiHtFpyq9SWwv2Bgd7gcVKEI9HZYot+9RHHCh2HlJTucUDv3ziKDBkCulEXKBkfvgTcCQgWhu/ldxneFMKLoKPOSKV0THOv2Ef5u0mTSBFmhHs2qVuWz0rvT86V98TyvPRq5hzpLVqWAZVOgEl/SAeTMKpwMmJgNgZHo2AjeoV8TT0xB7yl+Iyoz7BBoDW5sNgH3jioIYTXFOcE6VFCnSUDTYa/Of6OYhdD+IZ28x4o7tMoo3oqrrzySvrXf/1X+slPfkL77iu97CiJs3LlynS7SRIHvUQ7duxIM8AHBwfppZdeqjvvzp07p5XNmUuwQQQQaeY6SgkFDgPw+w9A3S00YIahMJWI4+myZiZqiEDvLQpI1rCumVIDTU50Oas+LSk00GQ48e6AdKBxP36pI0WXhXQnNAQLIOK4oyoNgImkYNSWMaksvWNM/r1UltfaBa7/sIEdaXufwjAREXU70ogrQcpRYIg/eM0ySaX9aTI+f0YZv0nlVr+5nFRYRRoN98UMtmJBGlu1Qfm7lMfi6xFOyO+NlFSmF1SL4QUfuHpDI+3DwZUY/EERH0TDJnlBTcqxBd0Y9AF9KJSZVd/EuJE8vMyA+lIELLOaFwxsqnXrjTH8ubA/UZNMEeZDo8XRvBCnnDNNMnMNP77hBeuCYZv14nsrA/fBQEEa6H0ZOX90z1D0FGFarKmLq/oYIoSSTQZfEu/xTLLQQiNJMYJsXBzqY4vENakG+ppqEfRhKXQcLFQSpe/I9L19NJTgD3jLJv3hM6JQcDrazTZxdHOANlFmTe8eRXTllVfSd77zHfrxj39MBxxwgPJ3lMQ59thjiUhK4nz6058mIlUS5/zzzyciKYlz8803ExHR2rVraWRkhH7+85/T8ccfT0REP/vZz2hkZGRa2Zy5BBtEDAaDwWAsVHTYILriiivo7rvvpu9973vU09OTxvMUi0XK5/NkWVZbJHEOP/xwOvPMM+myyy6jr3zlK0QUp92fc8458xJQTcQGkYJYmCzU1v/qgW0Z0mdVIdSVWi3pA3WD0HPTOOtLADPP8Bz94KmahMjbKmSZCW8QlujoA+pO7U+uYl8oFdP2YC7OVHi+JN2gh3Wj2xO9VvI8TwxL1+pwOQ6I7s/LMa8qjsgu5OloO3iOtgxJj5LAfoWhtJ11NMI8pNJx6MFalYvPOQ7UnQ/UgRK0Dh4FXNGmfzdkDuVdSYNs2SH1kLq75P2UTeqhhbDaDg0aLAFm5GBpg+RYXNkiXRCZhHdgthQrZL9ffx2VFXRg8D4l41MCszNA50LVelGig4jIhkxAcduomkCGAGb4vkEPPEca2o0ySkS3tj+kLW2NZ8hEr9mgryO8QkSymj3+nkivIhzwkuVtk0pXcj6D9peJJlMjw5N9I7xn5XH4UgibEHhM940a74tB2EHyG1gabw0RkYMOUGQqMUjbEd4u/b1uzDIDOMnvbGmpOPlsITqqQ9RhfOlLXyKiOAsccfvtt9NFF11ERNQ2SZxvfOMbdNVVV6XZaOeddx7ddtttc/sFp4EVTZf3vkgwOjpKxWKRfvnkCurusbWp9IowI8bgAFXVBRlP2wMospSgD1T6WjGCiGRqPmak4ZjQwEJDRMRA4Xakzjxo94HRh5Tfl7afmrZ1om2YFtzryu+4tSyNpnFf9icMiqrB+MAsF4xLeHFEWkpjQ3G8ULZHjnllUaaVHrFEZikgrTZcg/iwxBDyDeJyCByrrzFccJyKMWOg7raPQnBj8gKt1OS9FCkvADCIlBpRQEsl1IAiZgd0gellYGmyxSKs+YSUk2V4uUBbZ7dEGoOJiMgCeQJnov4Ng3XgdAVkiaYYTZjdJeKMMFkIhC+DosE4yoMoaGJAORjbZTB81Rc5vFij+jgZ/I0w1f4V/ZIS7jfEEDWCB+epKfcpGCLJnIb3pm+gzzC2qKq043sVnwXl77BoqBmec3Ev4z2Nx4UGgx77ENcVi9Nifz4WfUXxRk2cl2W4v3WikOFkmbZc/Lc0MjLSUlxOKxDvpSPedyM5Wb0R3QyCSpme/OJH53SsewvYQ8RgMBgMxkJFhymzxQw2iABhFH+QBhtLAnV7DGUyEHjcIFBOAjWDdwc9TrpyHUQySBhLd5h0j3B8ShmMZAWkHKf9Jqq3aJ/8cNp+LglEPrDwcroNPVLoFXpsx6q0rZPsX9alXwVP1DLa7bgKFNRGZZcUsNsyLFdRz78sx9HXK6m5I/olvdeTeLYm0HsF9JrJg6WDsVo4evECfSadgMlXi9cOV8oObk9WsdiHUu3epMGC3qBoyr9EqlfIINKIQceRr+ES0JOCwczAClmQOSY0k7DMR+ghRaT3WmEfIigdM+owcNuegEBeZKdGwBOYUHBBQe/NdfPyQFeh1+BZLMX3skIxwjVweiH4uEPiNmk5IsM9gd4kU000cf9WDa8Qk6BjDelmza2iaoKCp0e5T4FqTX5nTIRxDaJEeiIYgqphmyIgqslI62QtM6523zmwQQQQRUcRPZoiSkiNIVWFsTu6Fx4aRBOQtYYUVtjAnC9DTJCp5pApNb+VlF3sY9+MTGEXsUM4Ub4IMUaYOYYv7yrQQeJFPgaxFCZKIQMpyj15aaR5bvKy6oI6a+Ae9yE9fWxSnuf39vK0LVSmez3Zr058brq2AAo6+opasGwPlSRdV67I339sJDbq8t1yHN052cYYC/G9iYhqPigRJ9fUh23GmmS4HTLKnMl4rH4vUESmzBw0AIzZZ/XjV940OfguWHetktS4UjJ9wKDAU8OLUpECEMrd+kdBGWfo6i+OncgaRFg8FJo+GFV42ZXHryeeK7ycvI+RrqlU5XPxzPCytL1sQC6o8DlvZJibsiuNsUUJcGGEcWpIJyvGkegO4vZwnFWrcQablRhH+OzYcFwNQ8LAONYtPhxD1hcaMwE8O0GD2miEcUOwq3jOdPFljD0fbBAxGAwGg7FQwZRZx8AGEaAcOeRGNk2Ano0QUDSV0VAoLqMLun41guKJZfgZkPrCbDaxHT1YSJ+VI73nSFu3DCg1RV+JpLcFv9dh2W1p+z+rR8b7FuT4R2uStqr6sm/FzQ0QKqQBiioZAisnq/p6YuVkuyIMBx4iXNWhFsnLI1Ikc/u2PiIiyvTI771mqfSG5Ry9vhJ6kQQ1YBJuxN9ieZdc9S8t1NOFSlYbHFeCmmpOqKcRxLEuepBKQNHBijYEr4RdwVSeZFvJ9LvJdtStXyGndJDJO6XsC32g6KNoGCg6JeDcJLyYUGwWaDERfFel/Adm2mloN2OtNqRNFO8UeDCS657NyHsJA6kxIw2fHZP3V/d3k9fIJHCq9yLps9Yc0OhCD3dKd+k1FdV7Ga4NBmELzxBS02pwuv4eiyCbTQSwm4LdHciQwfkGn+FAk0UWGTLm0vprbmtJMbMGGzUdARtEAD9yqBbZitiiQAEmuRrcnFW4Ux0Dd53OyWC04OOExgcaQbpJMWNQCFSy1jRZcngeByg//DtSfg5QhVggdqQa008YN4STX00zuRDpax7hJDZZ06fGq/WR4DdIXvZVMIIUesTWv6AQme74O+LEO16V8UQ+CB+6itAcGKVOfbFNE0wvsckkZioDBhgWlt01Jo24XBbo2mp9BBhSAXnIwKsCRYcK1jaIGaaxNAZmFW8xH1JnqUvzcjBcc6XIKaZEg9HqdscDCWr4htVntZHOgCGShguKOHqGKBIcBxrxqQifIcUNlMKdvOy7UJDXXdTLy4H8Qi/UzmtVmFE3Jxip8xa264q/EqnPsy7eECkpG9P/YRJS1K6BEgsTA8UOwGCyweCHc6PaNT6LYp/AkOWJz7axHETyDJvmGl2GW9CCHAFjzwEbRAwGg8FgLFBwUHXnwAYRIG/XqGDbijfIS1Y3mIhSMwTYmqrFi5peuJLTldGI+wCNEsVbJPrSZ6HhmDyDaFu5QRYLZmpg3/u7Q3IcKUWk1+UZm5ABzMrKKqxffVWBugkgCBpF7kL0ANXqV+8WBuMqX8/gocAgyyQSFreVauglaxw4WfbrHyGTJ8iUiSauB3qFClD5fGXfSNrG7LlQm9Glb+M1DZDCWCLvQzfJsMISGLUeCHaHEhyUg0BYoA/Eb45ClorjFOOTcXyg9SP2d0BIURHNg+8dghcPs9JS8UGkNgy3P14bnRMBs/mEiCYRkQ/UrvAEEaneoELSLijboNo91C9z59HrgKV4sJyPbfAcZZM5Bj3FNvzQJu9UoCmTgzXcUK/MhyyzEOgupKfFdZ2EZyeATFEdHUakPtsiWNwxeIFRRyn1bjsd/K04hqhjYIMIkLNCyllEDmYaJf+OwYNlEkHEyQOpL5EGjwrR6AJGA8Zk8PSkKfMSZVQw1lBjU8fkJJwSxg0h0O086EpXPgpVHr/sOSIiKoG6M05G+PJAmiaAeBYxb+ILDOmOEGMKyjA5wzhC8UIGFWR8sSFMrnDd0gkNN3zhRQY3vJg4oyYoM1Pqe6qwDSzUBFB3+IJds1zGOGH9t7FhmcEmUAUDMtOlVz62uuQLSmuiY1FYiENCIxLbIpspMsSQGH8LjZJ2IV/V7osZegEMGg0vIbbnaeqKTe0PX5qYSZXGi6DyOBiqaORgwVZ8wYs6eVjvD9utwmRoNIJtKsbXaF8Laav6OCSMUTTVRcTrV4BrI/p4dkIquOO1U+g1Q5FZ8fzh9TcJQSIszXbXsADC7UI2wGLKbK8EG0QMBoPBYCxQMGXWObBBBMhaEeWsiNC3Xo6EZwal7yEjCrw+6BUajeqr3ecgmBm9OA7pvUWIiWR3JWBQ6QM0QAylRUQZDxwHAmk8pOByMCaxusX6YAN5mT0l6pQREZXGgD5DYToxVNjmQGaT4iwA6gVLOdiJrgvqu2CGFerxNKpLpFTYhpMjHYbVymuaWmZKQLfBWWTaR3goTNXCEbhaXQrClvv2DRMR0ShoOw1Pyt8CNZrIRa8aXN+ues8FBjtn8/r7Rtlf/IuB8Rq6lGiKHs+w9DiKTLUwL49bUpAlYfB76wJsifSBxpg1aPIG6OhOxcMBdBcGDne5MlBayfJMaOhmssJm6v1pFWIsTSUCGKg0/VhhHoOtOoFZPP/SrPw9KyBeioKkij4YZoslXm2FmkYxVKd5+gz1kPB5R0FVcd/4XuNnoW1gyqxjYIMI4JFFHlla53IfuEiHYYcxAsMHjKOltqScsMCqQDnEVHt97I46YU3v8jYVmUVjS4zJZHSpx+mpu0NzcQr+tpqMZXm5Kuu2lTCbqQTUF2YDJYJ8mT75ElFUl2EcmDKPGS2CTlEKn2JWiqN3+0cNvjuKHarZbtAdGmzJdkfJRILxK8aA3I4GT5AYWErhS1TlNry8J0ugsD0WGxTF5dI47crKl/doIA2l3j55bwYaI8IU9xQYakvpqMDAIGxnpi9l0y0kWWYG2QakdvGxQApLvLiwLp4SwwfUjK0x+BEmYwafI1NhYV2Ke6OU+lbRCh3WzDiMafya8wRGoVgwumErCj2WkqxWs5ik7A9/F9WoqjeIMFuzCsYMxh7pCjQj7YZGVTcYwcIwq7kzpz0ZCxdsEDEYDAaDsUDBlFnnwAYRwLNs8ixbKZ9RTVwAk7A6RhoKPUEIXN10JTlqKGw2SZIiUGqPWVVo61Zkjd3ciq4R0GCCSsOg8C74u+LaJo0bhIhyiVhNGTSL9skNp+3feINpexK0WVA7JpsIIbpNZGpYcIcqmUYa6X30ujiwLMXAT/Rc6PpAKBW0oa3ziLhKppI+YBcx6cvfP58V3i79vhXQG8IzI0VYTb7L6C6pWRQskTQTZkeFhmsgFtlKgDgMSa3iDrtovEghltTAYHi8pkjHFaW3UMBHbSJLf30bBTZ3GbR9TBld6K1Nx9xEba9aA7rOhFZpsnZ6g9B7HBgzy/QQWaZ4vUxB1eo4wGOXeMnRs248Du58/O105YiQXsMsPqTjqnb9q6/g1t+DpnNUm9CMahuYMusY2CACVKKQMpHq4s0kPAcaSb2g6KyILcON5ykFPmOM4MQKhkgzKfgCzShm45hwu8hKw8lvOJS0SwGqXCqxSjCJ9SUG4JaSzAx5Vc/zabs3J9OPJwvype9oDAaTEYGIDGKXOpTK8ny1XZIisovy9+ruLsMR8XXA+JrQQAvh+NGgEDE4OuqJSJ2LMlhLCV72tUTkEA0OUd+MiMjZAfE1QD36oLZsD8STOcbrKAYfvHP8Fow73FepqQptnWI2Avd1Pb1BobtmLmSnYeYQZt0pmZ1Aq4i2+vLU09GNKORGdcCawWzigxoZQe2g4NA4MvWHcYNiTGgEBco1bWwoibgrO5DnxnMgTWb6jewGGXtozORhfhuHLFlhNJnmVt087LkcQ7Q3gg0iQEARBRRRFW6gsWT1girOWAm+D6yP3aC4ikHJwiODBV0x9d30IOomAU+JZ9B/D9yOlQvEg42eqhp4p3oI1akNAYjJmLaMSoPomO4X0vZBvS+n7e3DvWlb0RyqxG1rVG7DF71avFO2fdDEcfoSlWn0koyC4YB9bJPG0UQEgd6JQZHbbyzdVgPFbFRKFmOOOwRDJAlQVvSN5J5KMHkN3F1oGAiDoQrxS94L0lB1YOGKtrNfAI9eYpQUitIr5BpkCLCMBMZMid8ogmtanpTXFL8LlgLBttCTipQyKmC0QJmU0FCaQ8SKFXJwP0LMB3ppTMHRwgDBmBU0jtQFRPMGRzOByLpxtBvmws7tjSdCoJGj8+rgtSmFaHAYvClJbJ8So1PDRaM+RgsNL/H74m/oKfeEKTkB7oUG3x3PVwrieds09zL2bLBBxGAwGAzGAgXHEHUObBBpgGuK4SQ750VfZlX1OzKTpxzJdgFWHTshq0d4gwJDAVBTWJAu20fxJllIAej7wHHo4iMKULOsrNRaM7i5kxgn5OO3VYtp+8juF9P2w9YBabu2U47DqSRihrDI9MbA2yEXlwrcUVTHjncKi+C6huuV3QUimCXYRVnYJnTokBx/7QAQAzQpVSMtJTwh6DVCeg0zpeA3qgJlJorSBpPymnaPyH1tDG2A7gLpREpVvD233vNENJUKlNs9TZFKpBDR++OjICJ6z+BYK/H6RAYF8QD6dhR1ahhfcqHQe+V4MA5UX4f7EDPHBJpRf0ZapRVvUTNwreZr3TWDdtBjjfpQ6ETl+mlqlUVIteFxkLFlUm7XeG8UAUvH1+6LXm0BFIr17HLd34nkbxG3tbtosasqBVBFvbmok1YGU2YdAxtEAJF2P4ay7UnwxQTE2qD20P6efHOhUYIPrTCEMNhPZ5zE2w1FRElI5eufZFOwdY9d767OKa5vSEWFQBOUBeiCF43ob1leaodsK0uDYr/srrTd1yUtke2uVFLWGW81mbmvlI5AA0Z5V4kA4BH5krZ65ARaWSF3zW6HuIRhuV0MA7z7VPgdBLsPgBRAUfataCqJceALPYsp+LDd1/9GwlhB5W40dtwJ2BmNiwIYHdlAGQ/2S2ROd1dkCzRp7hg7FShFUDGgCA5IaEgLK8Fj0LWm/MrU8+jS/02LCVW1uP6F3UyskMkImmlwtELpJDdwM8HQzZxPUGK4YGkm/qdhv6iWbzAWHM11Cg3zTjOq1TojEek1RZcLvyPcWyLmyKR1ZFIFN82/OuBvKwxsLu66d4INIgaDwWAwFiisKCIrmrmbZzbHLjawQQQYDYnCUFVmFthSWZ62j+96Jm3rgqeJ1BVhr0hVN9QQQ6AAJNZPa5RubxJSRIjxoYcIV2GYOoyrQexPeKr2KQyn23ZWpEsZV16vWf6ntP1vT8nrFwovgomRAg+LktmEqd7VeKxKDbQKCEFiTbIunBDgu4wmW7D+J7QLz0OG1TbpOZrYX7M6RMXnMjxWWPMLaCRU6aZyUh+pJrehVwi/NzjuVJpxWf2QTJlvCEVROvm3BgHkShYafEdFnBK8PuL3UDQXUbQSb81I72USngOcyE1ZiK14RNqRLWZCoGRlzn3Wl02NPRQ6j06rwHR3kzco3VfJrMUAdn16v+44BM4l+F2y5Nftg56gZjxm2J/4jqbrdVBhR9qeTFy3noaenTMwZdYxsEEEyNsRFeyIdgb12WDLPJmJtBRiiHYFMj06Y3DDCoMCDYs+u6Tdd8ygRdMKlFR7TVzQRIQ0g56WwO+CGXaUGHeD2dF007aSpMxwwhvMAp0IlBOlZTogngD+bAV6wwAudWpUQYiUchxp6DUi1QgTxoVtmNsUrzgwjz1PQyxTIvtTGsS0NjDogI6LqgbrTsTHgJVR69XfB7Ve+B+0M5zpZz1VMVsfT5TSfmiAQRwPUn4WxEARxPeIcVioPI5xI7b+OiHEOJB2UTShQGQKKbMqZJQJKjjfQLNmKloxmtph+JhooVbQDsPHBCMlFtVLeChjgu+CxgpmpzUysBAY64jzaCUpr2OiwHSGz9TtcpxyIjD1V0wCEksZVqreG8EGEYPBYDAYCxScZdY5sEGkwXJwh/6yHGeXTULkLXpgehyZ0aAqWEsPkNAOerYmI2WHQ+nuQG9RLZqe5sjAyssz3OnlBoHSNdjWgylMyOjAKkwnHNkN3xvrRVXAm4QrLgKPQWYo3gf/rMQ+IksDjjTU46n0J+OzlEGnQG8RLvYcTQIK0lCYwBKBV8jkIRf9ZXfJa15ZCqvSEnhjkDLDJy+p7YbRrJVBUBjvA49eHrwx4HEqFmPBTPT4KHQo6PjgXaPUVEu8QUoBXPQshQYPFzYFfaaIQgL1hcrX6EXSqIZj8D16mdBbJEQtiVRvkfDehAZazoRGPqR2KEs34xVqiQqcB05E5xlCr0oOHu6sIogILt2kD4Wej/A3lNvHlOOgB50Ct8FjptBuGm+QyYOoFOVO+ggMYrpzAqbMOgY2iABBFH/wcTohv4WIiHYGsiQCPoRoBJkmNxFntJ8rKSRUk54M60XOiIh6FA49/leJK2oilbcLHuacpgDlMEw0pokEJ79cYnW8Mifjg35GB2qPwwmyf5mkGcefjw1DNIKUCveogQhD0s2JNhgFEVBBap1J7Ly+D+wXM84IKDqV0oNj8+LcchtmyYUZMAYC3A6Gje53RBpqQt4f2T5p0YkCt0REw3+KacvcCllKxpRNpihYw7UWafAoDh4qOgVwrTFZTOkvOafyA4ABoEnznzpW3TZso6J3jyetZKxQLpStM4YinKY0eFwsZDUvvWaeOQWJBYiGUatGkFZ+o4mXvlHmow3IaXhmE+XU58h7crW3O23/IUkFDZRFoEnJfPqYKdP3xnEiHdrvSC5eHIuZxM2UE2HsfWCDiMFgMBiMBQqmzDoHNogAIdULvXclK5MeVwYRj8HqAbPCoHIHVTQrySp4WjKwDC9iDSbwcgwjdZH03Uw2mQm6EiG9QJlhaRGTZos456Arg8xREC9rqId25NLtafvnYVz2QxFgRGYDy3VI+SJypZMpvR6YaYXeHexbYdXAG5RJvoIPGkh4iZSgahif3wWbxU9n0kvCrh29t4gSb5ExMHqFXmiuBEKJYtzlCfnFe/vkylynMURE5ENJFVtX6gMvHlJ+sKuWlQIvjgPeMKVmMHiWbI0Okan0CAaID1ekGy8Ez5yXeItWdw+n2/o9eT18gwdAp5HVSpmPeJ8GAe4GDaGZijdicLLJr9FK4LXJs6RoFSX/oicI2zgPYEA0emz2y8RlfrbW+pseW92YNHMajgOLUK8Azbg+8BAJzbiaBQHfmGxi+L06BqbMOgY2iACVyCYvspWH7LHKPkSkToQDjjQGwkgGuSB9VtVMuKshZQofq0mYgCYxfkZj/ATK5Kc3jky10UQh11ZFIQumNKwEK3LyemSAZsCJZL+8dJX/THQN83+tCPEkObg6kNlU7YMXaKJ2rWSkgd2AczpqU3oT9duVd4XhOFe+SxUbYXxfISGA1B3saxAwVN45ScZW5DaebKuTcoLPdMnfxeqN29bL4PbvlsauB6rQqETtw60g4o8s5X0I38tQe8xCei/5DpFvoGsM6fgYy5RJxqrEQEG2GwE1tnNEWrPRH2S7tk/83XvWyGtgK4YN0Iko5Ae0cloHTVn0QE21FsT9TGhGYFGXfo4vejQyTDE4raCZ48R5MJbQMdCCSDPiuEUWYTPX0TSn6YB0F2YH98OKSrlmmsxek1Ervvdc0pFTwR6izqFzvyqDwWAwGAzGAgV7iAATkUdWZNNvK4PpNuFOxVXTjrojY/RD8GC/BavzZHWDnqBmPDrN7NPo78MQKChWPbiCQrFIDPxEDRgvQrd4/erxlYXn0nYF+sMV2e/L8ppWlyb9IQWDTRQzRCoKhAuF5wWdV4pcEno+MGkK7ng/8dQrAd2Ovq0EXkMfuZcTzx2U2kAKTgktxvOgV0Us4UwqlAB7t6TEAqCiBP3kTUAfv5SiRfZrhmUbrq+OJlNquFmGNZNBbFF8FzsL4p9Y2w08aei1UgQik67HJuVFD8DjNGnJi93dLT20hf/1UtoWnh6sdTbuy+OQ5nXhnh6pSgpuaTa+f3VBzfH2mdVAa0epDZNXKDS4IT1NCQvT+JvRaxLzIWbfFiElFD1fkzAHVeA8VTG3NuH9aXR9MRMMrw3Odb2QzVsFb76uNpqpPy/xgE86s/cONg2mzDoGNogAfXaVum2b9oNMCF2cDhoRo6GctJEmwxgb4VTGB980CbTiGlbq90AbqbsJUAbsSYy0qmIQyX17oCiiLtWeCAXZ5PkGIXtuZ9AL+8pJ8YTuP6Tt/9d9bNxAAwDnO8xaAsrMLsMEngxPUUQG40iJT4L5HWumia+uMIKGrDaMZVIuTTI8NMDy2/XZWA5kxI0cBuKNCUWIFJJJmTkEOjGagAzHvF9/PqAQq//Tl7bto2Q8HBolgjpA40NRmW6C0pMH6sefAUE7k5J2qRL/eLVx+BExxgjGUSrLfXyQFihkYr6zKyN5z0kfjH9F9lxfiPSlcnwvL89i8Jo+HoqayJTSnhvQjIhgwxgmk3GkHUdrtd10hhJuMylSK20MD0iMqVZjtHTncZQMMvl7dWGMpCGLTJdRhscVoC1CApwO1zJj2qszYMqMwWAwGAzGogd7iBpArB5wdeEoAY9ydYmrCtT3EZlcJlG3mirqIs+j8RaZMstwX3Txd1lyhSyEGSfBa2QKaMQxKVpLyfgUfSML3fdwbqfe1UxEqQtXKQUB9BkKDqLJjt4RK/Eu4IJSCZ6WTiu1vAdc0iqWwUh3gCZcGtSv9LvBu5MIRJl0ikzOgq7n5Kp0ct+4j6jXIPaGt0EWvAWgTxQm9dA86KImS8xRBq7H5B/lH9wD5GpaBDZHKJIFmW8Bfi+g1ZyMnvpKx4Z15TA42iAPJTxUXrf8QX2oU4flRALMzIP+Ai/uowKUGXrdhsGz68J36fbkOYXgaAlK+eQdgzsRb9kGdc1MFBx6hZDiQi+MaNsGr1E76rWZkjV0fePYdoNWG+r8IMbCejEx05ibyegS+2Cw9h8g5OGw3Itp+8XakrSN2kgiVMCD3xDnK0wUEfNlOwLqm0YUqYqrMzme0RTYIALUIotqkUU7AvnCGAlirgQfAJ36LBGRB6JzA+C2FZMKlnZtxrCpKhkoUd3fEdhfD1gGPTDZb08CXUKkKpRsEL3DEA2oPivm4TGWCFP3ET2KArd8ib3+sJg++6//OUzu7BpeLiWgb2AXkfWF2yx9F2r2GWzPDsX/Vova4RuzzNxJoCeToqrodS8NyLNkRvSyAHjJMkPJy7sbM9WQtgJDEI2PKvyOk3EbC9miQCQahbkdcvtEl+QCC4PxPavQdYa2jQasJoZIrZEGL3RMtQc6TomvSuLDsMisqp0A3wv2CX15nYar8Q/iAiXY0yVvhLyH8hByn6oNhldiGOILGxcK+DzjCxSNhFpyKNZwc9DexNgdw5xQU4pG1z//tmER1Qr1ZYKxRlhyngB+/DDS02QjWIRQ00fFFIvVggoBnhtT/tFQwphGnbFlK4rU+iw/LxGm9TqYfs9ZZp0DU2YMBoPBYDAWPdhDBMhaIeUsoiMzUkRQiBXuDGQ0rloVXq4McbWHNcQyyarCVKcM9x2G1RRSUf1JIS8UjmtGmHF3gNkU8fkxqLpLk31CRDQcSs8BrrJEdkaPvSvdVoAAQ0fJpgEdGej71cVniYjokSX7p9uQElGoNHRQgGeglnjnPb1nnnxYlHoYE6tkrSWbwINkKuNRxQw2TQ02Q4ULqvZh8C54VXz0HCXbwRsWATUGSVVKkHOUg9V7ostkT4BnyUDdKQHgL8iBV/riL+xiJW/8LfDcKBUFwczaEhxIe0LANgpVKqJ/SQZeCHpJhPcHeqQwSw7jv4UnDbZhDTQfPEEeXBDMSiskN4gPpXV8haOFpqkGYbIdszZtQ8ICen/G/eZrdyEc1CzC508TRIyCic1A53HCFXVooO1N5xHjwLEdlXshbaMX539Ka9I2ZraJuRi9ODgvYqiD8htA3yKLDOdbZV6f7zQtzjLrGNggAuwKclQObIVy6k1icPbJyMyz7TBpomt4HwdqSwE1IIyqcqi/3Fg/CScHzPoS9c5sg8FhpNJg/xwlqciWp90XsRwov5otv+MLfszDY223NZBRhzSZqUDsai82poIavBzx5WfK9AZDJJ2bFYPJ0MaXtyaeSHl/Kan2elkASyNQGHlyBxdS34Ms/EZIccE7IszE+4fQB6oxKwrX8PK2gT4Ls8nAQS7dGUUhSxg/jFupJ7cjfnlE+0C/vt7oDg3K1yIWTPk7/hYWGlV6+kwUfXV75f1vUnHGjLigCoZLYkC5kP6PVHGopPnrY3p8jWCfi9akgTXBRYErbkSYM7IW0nVAh1l6AwYhKJsQeUoDBWZaMOn6bib2qFHcjGNhzODMYmwwzgfRravKTHLcJsVsNIJU6lFu76L44cgYDFVVZDdul6OZfb+ZwArN6vfNHs9oDkyZMRgMBoPBWPRgDxHgRb9IBd+h1d5Quk2sJIuwxB4EeX+TRVmGVYXoAz1BpgBmXN3giibXwL3dTJC2bqmAukE5yEhThcuQYouvgyriKDGINd/AXY1Bp0uTDJRwTLq+RSkOIlI4EaSWFK+P+FomUUW4vFWgvhQnnegDqQ8UeoTjsL9Il9mEdBJkaWWHIeNIOs/URf1k/XFBDkqWwKBDyIxD8UOrOx64PSqvuZK8CKdTvFNwPcT+flXvnWgmyBUzwHRArxDSXbUSeF3d+tpuDuyreHRwnwJWNtdQd+idgj9jMDN6joQXBrfhb4HuNQwWdzW0Go4HKR+l/hcGZhvoHR1MYowm6AKzEYqHpUF9NaVchyFAHOc6kaRCRFSETC+Bl2oyw2FVRs7DSGd1eXIuFnOTyUOEWa+rveG0rdN5Qn05BPqCxHeptBCYPmswZdYxsEEEODSzk7oztmLM/LJ8ABERPQIPy9G559P2IFBLYzDRIe32TG05ERFl4EFF48M0QaGRIxSn+wwZXQg0gkYhQ2x3MhmtBiFFHH/Z4KZHWrAv+V45zMLA1FwbJ6v6LDkiGU+kUEFAJ2FdMMWGw9iR6curqdBkpxHJdwcK1Sqp+PqMbiWGJd3HMGb09ENtUQqRmsvUnwMz42yghTCeyxqEF1dC41kmigu+o4P0GbAEThJ/5LtgCXbBDibxTI3CtmL42Pr7W6FJsWvRHwptGkQrMeXfAhrJTjI+FeMJ+sCXdC1AKgWztBL6D34ZlUaTF1VnBBER2UmwFcYhIRx4BkaBz/VsvRGUPotN0CBZWx/fmGtQm9AkC6DLzMK5YVu1L21vHtovbb8wJo2cAghlititiao04l8ekrGa3d3yITh6YFvaXlt8Jm0Lo2oslEGDDw8flLZ9+J17XHnjL89Imv+03t8QkSpRoqtFSURUS37zsINWBmeZdQ5sEDEYDAaDsVDBOkQdAxtEGuyCgGEhNoaZErsg48wjfQBfAZa3A45cjQhgFpeS8QDH4eqrq4WMEPQs7QYX9a5EXwldygcCxeXBUqIHFv1leKBEPTYloBv2fQnStFC8saypF4SUiPLnAB9g1OaRW9P99UlGyqoInWqYRSYoLFwMogcJaavINNb0HECZQB9I89XkbaV4iETtM78AAfA7wSsBGXNhEZUXwRUxGQ8qMwr7AgOA31uXJUcks/HCLAQfZ5F3g2sQoAdI7iI8Q0rMr2FfBejZEl4YS++ii0ADCTPHMMhceKVQGwzfC46tp+Bq8MNY4j5EZ4HJM2Pr90k9ThqvEZEqHImBzUj1BDCA9Llrhr5UaiHC+VuIslUoM/jxxPZ/23F0uu3JP+yTtnMv6Omn8XpnopodCAMd75I37X8Py3ns5f3kg9Sb0GdV+N3275IZsEshDfXgnKx1h8kfuoSUjCFrrZAEYEedFGZkdAxsEAE8KyLPihRRxf+Vfzb5m3wA+uBhQqAKawZmRbH/hIGjRndvDaqZhhBTIjJkagauCPlvzE7Ddjmhz5CP/y9IZ0UsBRGzMZACEEZaHyjR5hTDR45jtTuctpGKmEioRWtcr1qMWVpKHA/crek701DEFed8Q9gHVZOEFswEU9LhMcsMi3pm8M2aCNQBHeP3QwzRdnnySj+8hDGtPjkU3z2TYIhEPSiDDecuAdVTrqdS0IZW4qsUVWVoJ6dB4y5EKQBFKRwNVQzIseq2KfsaqAaFJk2K+CphGmAERXo7SXmx6mKIMMushvaXwcoJkuxKH7UuDIaPAk06vg2D9o0ZZNC1YVHvOPpMKF3fAWRC4fOXxgQqYrMSuBAzqUVvrfYTEdEzO5al27yd8ht4YJgrv6OGbjbH7cF9CIdN1OROx/c/Vze2Cjzwa7Ivp22cs3Ahq4u/NGXwzocZxJRZ58AGEYPBYDAYCxUcVN0xsEEEGA4z5Ic2rQY9oWqSAtQHy21cMbzgS/ctVpbHshuCLkIKyXFkYPOwhWKM+hWg8DLhyteUAYLjWwoeIhGQ/ePJQ9NtmPGyrvs32v5qzqh2u0CXSb9D0QCpDwxG7R4MoK1BYLOi5A/eG0Fh2ZghhBlT6FEw0C1T+yIixRNhBGrYaAKGcVtltbxvMNCYFA9L0oDjlLptFQg4x+BzuB6iTAfcjuSCaKXi4cdhoPcs+Q1ccICGOaBJPIN3B+mupBkZqE6dhlPcObQb/ARKH3A9FF9GLr4nA6DUQkwEACotAM8RaggFGh0iBc14ixpNsehNMizldZo+jbK/iFRPCWbp1ZLtOUdfbkSh7nCAQPXtm+iyHbevTDDZ0rM0bW9/UeoJuS8DLYh1CjU6YAj05mLw/HFL5TlXZoaJiOhPlaWwTc6tpgw9R1PupBmhW3Gcw+I+eyXYIAJEkUVhZNHTNRkjJLKw+sB9OwH+7IMgdWg41IsSCkNkQhd8QqoRZMpuEEaVEFckUhWu///tnXuQZVV59t9zP31vem49DTPDYECuIg4KGAmoBENUYrTiLQ7EmAsxKohGMVifaCEQUxoqRk0glpqghZXSWJr4GYcEUT8U4sDITWGAAQaYnltPT/d097nv74+z116/t89ac85c6JnuXk/V1OzeZ521176ctd/1PO+Fkyn9dbj9RHW5iIic37052cfIOIIGzNbaULI9mJ6Ox2wtmAkPxU5DiVLastin6vgTbUbwp7YuS7YjZCWmfMNLY7pm8kRFzafd+6N8o2W/8mXyzXPqpY5tY9h4ortYHNVrDCTh/0rHsaAU6DCCRKw8poKdKEVAj/HN5Wa/ipKb8fiQdFGTbNXgvDR91PpCFPFEx3nskVQ30l4w7D7XGi6uQ+3hK4T0AKwzRt8dqbf+Fik9qRB93DtGNuUPMoFfluk3+DuPpbd0+3d326zWdeVjxPPqwDiKce7gk8n2K+IM9CIi21faVQ2j53jNpmrN6LJH9yxP9s1U3G4Fpy/dmWy/uHu05XMddu9eoHVSLLYdTHRifQ4dlYNkNncIBlFAQEBAQMDRihBlNmcIBhGws94rU/WMihYzDMtEZJkgJWt18KyZlVjRE5kw3mh1WhaxbEzLMWPk4eLniuISEdndsBrK8bnmKmsadEFRVel2LztzDgaLq8sesbJQg8knVd4lpvVvrtSWdNnze4YlFrhIz8HZFtKRZB1jpdzli1Di11xLJ5/HMaEGGHflYX9S8N5lZJmSAo0jcto9ZvW4Zd2MU+IQXWVbDIQsGU+x0brfl0AyAkOk9vO6m2vjYzDasULsj9cIEXWNDCLBuuzFYRkPIxFFSq+zm3VVNoT7cY5tkjuq/GFtyIe0+l6b/EsiUmdyT0e9QV/ixqyqZUbmCyVOkuO55UYVheZJQOisa5ZqldREROp5Rqe19nfOgGWZmLiR/TE4xAUej2xRPxKB5Tx13pLPMZ8yyozsttlfDVFmCxLBIAIyqYZkUin1o21Xk8eXfLCiQmUbLZ8z+7Oiez1xDBNxYkaG3/vqHXHyXZL2VD919LEbUXKjNZtM7Vhk7jaGTRVvWxpdNHz40qH/lDGm1iI89oHsSLLNMG3KNBGkysRIcCVJFBFBH1HeY9g43sepHCQ1lSW71QjifhUlxfeXCvXG92AoJf5QyjfJ3YeKCmP4fJxaIO05tjYE7aZykzH+P7Q78fioSLUsX96tEWUuX639jUkZZibavYSX+DH2xFJZZESGEZSKWjtPw3cm7Ul2yOK0DMc3Nge79RpMHnnPRKhVUu7fqoLHJ4l+TTPxs9ebdc8DnGNqSmqzHRoDy1XwdTZ8NcTMQ0KjpQf5LcZq1u3AN9eZpIp9OMYQHjjKl76aZCajvu8Y3Sk7pnZFWilNugw3ETt3deJvdLgQJLO5QzCIAgICAgICjlaEKLM5QzCIgGqUlWqUUSsTQ5fmGshDhNWZornFzYj0xavOabAIzEmkygh46HSzuqG8xlXR8cj581ycgLGTvsn0jIOuJtPD/Ya6rjoYMBFNL6tU+GhvpD5VD03VuOIvGIwNsv2ZYbsinESkI/ks6U6Vuxc3KJNR+oqZHl8UF8E2LJpuCAouStNVN1tEBkg7d7ce25f/xZeXyZx7hjlAyeJgf4ZevXS2jiPRXAzYbKQcch1R78a9J7kDp/sUoo+iGtmbuHSHh7lRUhrzzDRaaZo6s0nimmfBPpFqSzki1WoHuEynszjnI1OlvdpwMyYsEdKfs8wLGRYzZ3FuKIBunK4X0Nbn2R6fOz7nPDHtCdZQspVDduO5ljGXsLaiS9r3OZCrmmoeJ/L97RPReefM/NuObQqYnwgGEZCWhqQlJYOgcM1LmxPKGCYM1ixjCDjrglXj0N8hhLlORXgTYc6ZQkFUl+6vx2t/lI9VbYK0TMrtO2D28xjU7DlZsd6Rpq5TLX2QKlf7YRB1Y9tMTKsLVjIb6LOTzthuS7dHCKtWxozxEfGW13VD1dVqE7qsfHQY+k5DxLz42S+Mp0bBbYyp218zshsOHrm3OWS+c4xhU3Psmz1mGmOU3VwJIpWSQpuwxDb7v45typ61fpB8kdu4z5TuKjSO3NmnXfB9TsM86VdJY27jKIP7T9nNFIitOiLWZoMv9SzGRyPH1D7zGUFZ3FwaTS4/JOJA3AR8oCFV9/SnC68229B44vzB/VWmEEhR/ms+wJxf6PNIn0xG6B4IuAg174N2BXIPJ4JkNncIBlFAQEBAQMDRikbkT13e6fcDOkIwiIDpRkGkkZE8VhhGImJEFyOzfBXuXcleppWTsXu1Uk3ZW9KO4mXkhYpUY4XndCttPiV2FbYCle953r4EkSaajVEYT1VsDqHBjI0cY04ly0PZ1e2pxefsOHptvbexXVbyo7NyutfSGYnjdcZDn9Cp2jcfmKWTNyLKLfuQLTILchIArK5AhiWtEktiGCb/j29hzvEzuov10HiBY/AJzLBvn5povuCJSHNKdDKr/lt83etdnvxLZNK4n/ma4nveyLbuaw4f+5UWaDcNi6RqqnnoKR9bxKSOySHwclGRYLg4KTKq8YOhnLU997kGaa7GEj6UJB0Pqzc3EmUybJfj+UEFcDiY5E5Ap2qfYzbZZhd7U0LU6z4U3ZvENpNMKsfseDfnOTqh0yWgzkhXTw3KdpiOk+9OH2R+qYNC8CGaMwSDCBjITElPJjOL4m3+4Ev4QdJYYNsp5T/TGlU1BGmhRKeIBqQ0zOojeNOMxm88GiKcgChP9cEJhAabiSKrehNEItkiIs7oG2AmGxZHZBTaBCaxJys24dqpBWv8mHPg8bKYWPPd9D2y42fEUFLniEYLLykmuZRKHGg3k4ki5TGqkAqAIecNGlvxpWQWZyWTAUp5YTEtV3OP0eKd3IxtR6Olg+8pwyz+rid3og7pp18T1gHmZ8KkkXVG+TFdAt/t9db97KPhixQEmAncyE/KGFZGFXzWPJoCA+WTfSpSDQYMDCVXNnlldNF4xkNbUoVe998fkYcclvFZ944isqyb6PPBocHjWpRRauN8RCPIV0zWnIvXTwmgLMjouW7l8NaEKozr8AcVOXhZ0EhzUchUvSARDKKAgICAgICjFCk5RB+iwzaShY+j2iC67rrr5JOf/KTat2LFChkdbaZuj6JIPvnJT8ott9wie/bskXPOOUe+8IUvyGmnnXZQxytHeclEGSkxd0y8KkqrSCo3fbsEzsVVlgmIV0BTiFSr4jFl/h/S1c8jl4dhmRjRlfOsUrYihxCpZMMiFbOoqaYqOWNFzvIDOMedtaacNZlyj7kA1ocrxntmXpRsn1Fs1iIi09abs9euso+ewVgpM/KmZlbetimDgTKF9itAwygwl42SUqpulqnei5w4cRtGVRH8nq7kbY+T22fYLvu5j41R5J5jklQSmC/yzeeknW1tq87Kc0ld9dC0TMnnCtfX42Bta7vhc8qNiuXzOLMbucsVVSia5Usjr5GPLbKfw9m5zm2wRVk4FMc10zhMRugxqo2XFyqk5OEobUqE+FijHkTAsvK9ZnJMDiHbh8/pmk7mnOtMAElZ5WFrONsSZKcNc0QZbSDjZt9Vkkk8GKYN5yCyw3TSnoRbwWg0mGy/KL/DOVZ7DMh8HbBZhx0hU/Wc4ag2iERETjvtNLnjjjuSvzPIUvuZz3xGPve5z8lXv/pVOemkk+T666+X3/7t35ZHH31U+vr6XN3tF/Uo1fyHH9TuejPpICOpqvQnQlXSSdDmLmOFwThVh9E1G1XHvvGGla84uSxDAVZOJJMwtgwmMDH0Q/rqBKZvXqOhuN6biJ54mXBtrGyNu/H4mg7Df0lJZr2QzMbgRwDDpTjY7JsyCSPI0o7Q59ntk8R6fKcy0R/9XQp4aTLU32GT+qQqXwLgal8cqo53SG7SHsOXHFEZWKn9H1tte4wjJzqYS13HVMFazHWI2nQ+XyWTMqHhkPNEZl1HX/BWfB+ZPFEZYKx150n5kFLWYBN8lpRRwj4od5njQGrzRZzRsClD4urN27nH+BBpSQ0XEnZNMUPZqnVuogzFY9fSkNJw83QfzRvSnbFjo1HFOciXEbsQj4PyGhdU3WlGjrldBcx+yvojuXFn26fLNhL3zO5nkm0zpzKJo3ZNaH1NNoJjzoLEUW8QZbNZGR4ebtkfRZHcfPPNcu2118qb3/xmERH52te+JitWrJBvfOMb8ud//udzPdSAgICAgIDDihB2P3c46g2izZs3y8jIiBQKBTnnnHPkhhtukBNOOEG2bNkio6OjcvHFFydtC4WCXHDBBXL33XcflEFkEjO6cvB0ks+CUlodLJJZkdHhMYcVRjeOx5WaqmAf6xKMoGDUBJmjnDp2a/2eOtguRqiQdk6LbcNVYHf8XVXvDVEidFxklAhhZLeRrHXG7sq4+DCRFPMQsXp7XyxVYaXPRHleR1RH1FHKw4KrEiLoI+VwAFbJB/G5T7/XtcziMUN2q1M1dJToEBHJ4jE07ck8+XIPeZGa9b/4/c19SRUTtcsj+fGRoEoDZcP2xZ9ZB44QuryKcdjF5x5pjPdfM4RtDphFTp0aavhBPjPPWCHPxFMsDwOWBifJ3+VMzf6mumK5exq/s25I4JWGe0pv4AEwUlVaabGU1FqZoOb37H4TBecvQcQaiZ56aLFD+faqlfhV/qK0uw9XOZGlYKk5H+2qWaVg85QN8vif7Scl21255vUrQq477xhbX21NfleyTVZ7zhCizOYMR7VBdM4558i//Mu/yEknnSTbt2+X66+/Xl75ylfKww8/nPgRrVixQn1nxYoV8vTTT++333K5LOWyfbNMTDTlpmK6KsV0Q1Gn09I6U9P4KCK6ixlNlaQUGy45zLCTeKEMYrIaB41Nw8skeKvg4Z6K3AbHdGTHzIkp49B32LZUtxOJDqe1j4mJLmM0GQ3IofS+lrYibulO6/4eHxwWR+1DaHNsCOXwUmJoM194eTzlFbyATHh0veY2fJRhQB8hV3JHXzi5inJCf75QdMexGVKfzqEJsybHl5rvQ+XG4QndZ3uPTeoaklbgotb9NJjq/Anx0jDBoqewbfI5LxefCc8AjYGqfI8o3SijD/KZq0ivxxhreIrT0pCqVpsnqZ5H3MMqJVwa3Qwdx5xg2tAPkNFp9DeqQVOt4LdRdNxoLrTSuB6M6GLSR7O4KgsXgXb828v9LccQcf/OGQk2mLNzBsdEI4juC0ZiyygaxM55W0tDyfbmcZseZPd91jgyX+Vv4bEX27bvPulndnyZpn5dapP4M2B+4gh4iHWOSy65RN7ylrfIGWecIRdddJH853/+p4g0pTGD1CyhP4qiln2zceONN8rAwEDyb9WqVYd/8AEBAQEBAYeIVBQd8r8DwY9//GN54xvfKCMjI5JKpeQ73/mO+jyKIrnuuutkZGREurq65MILL5SHH35YtSmXy/L+979fli5dKj09PXLppZfKs88+q9rs2bNH1q9fn7yH169fL+Pj4wdziQ4bjmqGaDZ6enrkjDPOkM2bN8ub3vQmEREZHR2VlStXJm127NjRwhrNxsc+9jG5+uqrk78nJiZk1apVMtkoSK2elcGcjfHIp5rbFQ81zBUPc/csB4U7FlO4TOZVxIqGrFBVpbxvlYBUJIcnOVhPurVMRvOY1fh4LNdhV1OsBt6P6DSeu2GUWGdoumYpAJ2ErXVFyXMh83T+wGPJ9o/FRqRFyO+T7kLiyHzMulG2gMMrnVx9MmRytsoBGKv0mpvpccpS/JyOuZTPfLmFTBvPvOVRQTQTEW9n4SPvjTij1Ab2xrXo7cT/wFXqQ/VLR/Cc59pgOx/n6KwgLkIlWASbpBigrGM/GR+U9lA5iUpgYNqwdd79HudtE+0W4dms1TB/MHeShyXjM1uNGc4iaM8+OF0L2CdfwkYTwMB9eY+mqphiV6JK/BhqPmkM46A0flJPk+Uni9MLeZ44Lj+WbOt6aI7cSFjnL8nbeXhixj6Uip2M/+8+xUr4r11l5yPmSzM11apzyRA1xBm8cUDfPwBMTU3JmWeeKe9+97vlLW95S8vnnQQzXXXVVfK9731Pbr/9dlmyZIl86EMfkje84Q2ycePGJDDqne98pzz77LPygx/8QERE/uzP/kzWr18v3/ve9w7hZA8N88ogKpfL8qtf/UrOP/98Wbt2rQwPD8uGDRvkrLPOEhGRSqUid911l/zN3/zNfvspFApSKLRKYTlpSD5VV4aN+XFRRhvO2qzKVc8kMED9O/5/Em+2bkw0y2CIjMHpgUZEw/gF4XeoIt88yRbZh2mzJG3DlpjhmsbMVGTpdk4IQ6nmBLOsYKPadtdtBBnBMNdTkJXa7KfP1Qk5G/paLNjjlbvhl5BDosR4QsojBXON1yvNl60K6UtgDCgVzs+CnVWmmbab7ppj7WU3X5i5yS3nyxUHlwhttDjqjCkJDO8WX5005ctkgu48c70KJvNkzHYZdTSCstO2c46PvlGm76J13VCyYSMPY5fXxmG0Kt8u3gsYUvRTc9lDvj68QHvzXVUP0CXLiQgfU/rGKcM3vkk0pLgQKNHfKGcXRlnWV0tkN7ePTl6F4LvntySpoueCpB0LoNnt759YLSIiT08ek+zbMW6tYP7eL1j1eLJ9du9Tto3jR8PIuNV5Wy/xVcdtSbZ/KmuTbVOHbkmPnRcna/YdwPE/Xm4G+JQqNRF5pOXYCwGXXHKJXHLJJc7POglm2rt3r3z5y1+Wf/3Xf5WLLrpIRERuu+02WbVqldxxxx3yute9Tn71q1/JD37wA/n5z38u55xzjoiI3HrrrXLeeefJo48+Ki9+8Yvn5mRn4ag2iD784Q/LG9/4Rlm9erXs2LFDrr/+epmYmJDLL79cUqmUXHXVVXLDDTfIiSeeKCeeeKLccMMN0t3dLe985zsP6ni5VE1yqUj5thgHvYm6dfzjj3q0Nphsn1HQlKDBQBzGOoS5ZRoTJH2LyAqRRSo5XpYZ5RDtNmaWpC3bNRF7rvrYLt23O0OtMRB3wlnRFxLLPrZWlyTbxsCaSoFZgkPjij5rcO7bB+MUDq8m74sqiQCo8GgYTWQaumImcKpsr9c+TISqdISPKjHD9vgE0RhIsyI6DQATMo9ueVtYdkPnMmrd9hVjjXwGnTjadMKwe3ySXMhUYAzQYFMFYrE/vjY0zHJ2oS8lGz2tsmRHKoN4bDjghiu/J5Zf8aRRSAzEdGu/LR0CKjeWGVMNzwSzdWtnJrvJa8qyMbGRUC5Zw4dO3JUs2GY8bzR+xBhKDNHH53S2znrohXYlRFSIPtinyar9ff3i6aZBVNtnz6Wwwz4gZTwr/zVzarL9yDIbdfyKpU1/0ZO7tjnHRodt4rTl25Nt4xt1TN7OlUty1jhiORHDmNU7sowPDw5G9pr9fRHrK2vgIwb2h06CmTZu3CjValW1GRkZkdNPP13uvvtued3rXic/+9nPZGBgIDGGRETOPfdcGRgYkLvvvjsYRC48++yz8o53vEN27doly5Ytk3PPPVd+/vOfy5o1a0RE5CMf+YjMzMzIe9/73iQx4w9/+MODykEUEBAQEBBw1OEwRZnN9pX9xCc+Idddd90BddVJMNPo6Kjk83k55phjWtqY74+Ojsry5ctlNpYvX560ORI4qg2i22+/fb+fp1Ipue666w74pvowFNcyI+VqVr+rs1bDpqRGqeeJqo1MeAL9Hp9r0rYnwt9likFJCJPnAp++RYkPEWof9Xh0/25xh/HnXWwQxlHF0SseCa4Us0+MopuCwwhlMhaL3Vu3+5+qNpf45/ZaGpzX/Lie8WR7S8YySzUkaWwUm32TCcpl3NeDbQjD+tAPSUkVOfhwIWS7oaS0uP20L0MgD7j/bco/beICRMQtbZGBUWwM2AAvQxT/r4LoPOyPzz/JnAPlMJ4Xt2s9djs/zs7jcbhrj0rRqiAy0zqn6vZkXUqkydgWkt4M65bFTSn5eLJnKx8yMlUxu0OWKQW2SBWt9UXaUdKNI+xSYKqqOB7lREatVcEcmTmBCR/Tdfcbt6H8GPfvjEJZbh/9CuE3dM+D1j8wH7NBRbB/BKYMKZXt+Jd0WSZneb7JekyDOtUZuO2xV0Dm789ZetLMPfRf6nOE9otYBryT+muHDYcpU/XWrVulv99G/x0oO0QcTDDT7Dau9p3080LiqDaI5hrTUV4kyqhcGIOx5ERZiD5ExLFIKVzETS3FD+SDlR7sw5sBYN9FSnfxmChfjSN5S9FRRVpkVlHa2DeAbSt1Sly2bzpV01gZzrSe+wTGMdaw/kSDkOtWIH/Hz2u/ISI6rf6SjL12/Vl77GWDdrYc3WFnyMpM7KiOGOahbhiCafeLoY4XUM3hJFrsbi0W2fwe8rRgvyk4qyKwKZ/NuF/CqtxFfNlVbh98frD+m6qeKKUlj/+PeSy8+YZ8xhFdTuJxqxxIHv8logpSNxe/tzIO+Wr28bqtUqLGXR1o/lEruqVMLVW5x2fOPeIkznRCkALVPcLLy0hmDA7wOZbxWjNLt3L0jxtFnlIxdd5n+i05HiK1UChab3yVbwhzCQ2etONG8nv9CJ9/eLcNehGUwzHPJF2WmH+rBoOIpXgmKnbeMH5Ip/dZH0XOY5y3V8Lq3lG1hoE5F+Y98pUxMvvnsNb9YUN/f78yiA4GJkny/oKZhoeHpVKpyJ49exRLtGPHDnnlK1+ZtNm+fbvMxs6dO9sGRb2QOKrD7gMCAgICAhYzTKbqQ/l3uMBgJgMTzGSMnXXr1kkul1Nttm3bJg899FDS5rzzzpO9e/fKvffem7S55557ZO/evUmbI4HAEAGT9S6pz6ozZJickzPWmu3BqoMriXHoAVyldMfc/5l5u3zb3rDsyShCaPrQ97RjSc7jkWUi23Js1lLDg9lWxoNZsntYKAtgWOlzdbt8n4qPyUSLpjaZiA7jZ3j/c1W7UjBOloySI87utdEgD+wZSba5Iq9PNR/d6bw974EuyyzVPPWiXGysqqOmItlsG67I8wVG5xiPaDRmsdhO6J02TXzZolUbk+2ayRrB+qc88pNTBfExJtzN/YyYMw7RVBWr7m06iCsnczMrkYpzZPZuHhDfA+mamXEcD87MGaQnYJZsF3um0gbgKqj6anzpOKLIGFmmHO2rHrZIRaqho1iubaC2XqQeanfdMFdufTJIvJ9lhPR3IQFktoGAj9gJ2xfaT5A56hqxjG+l1JxX6gX8rouQB/vtBe7vcUtYp/Zua9lHVoiRrKwh52K4qqBtG/BqdxV0rTYOo5XRDnNc3HXfvn3y+OPWnWHLli2yadMmGRoaktWrV7cNZhoYGJD3vOc98qEPfUiWLFkiQ0ND8uEPfzjJJygicsopp8jv/M7vyJ/+6Z/KP/3TP4lIM+z+DW94wxFzqBYJBpETLBQ4Hc+We7Hvgm7rIUTDwZVrR8TKXdvqdlpamcGPL2ONo2kHPdscR3O2392wxgfD4Sk5TTJDLWZT4yPEyLOKUDJjhJudVPpguBgJjtKZS0YTscaTiA47tuO0nPggrkE/9HtF+zN8ezzOB1J05yFiJl8WyuRLpxa3zzjyPYmIzgUDuaXueOGloe80UO1cVfHlu48vciOJqLh2fM2zTST78Xla+Q15opwcSo5yNWu0ft5ybP5hjumQntQ4RUQq7v0uI42GnpKTqHxRpjP94RjKRKZRCOPIFaWnDCJPcd20L8u38UPi2GAE+SP+3KkF7IVSsfi2bQEXsgfPJIz0etyHWxz2owdh/Oa3QxmNZUO4yDhxcGeyvavLzl/Pxr+ZqSlrkdK24+Kkp2CPPdxl55vpWGMbyFLyQ642iFvlhttNwYARsmWcC+cEM1dEcxhlNtf4xS9+Ia9+9auTv03Ovssvv1y++tWvdhTM9Hd/93eSzWblrW99q8zMzMhrX/ta+epXv6qKs3/961+XD3zgA0k02qWXXir/8A//MEdn6UYwiAICAgICAo5SpBp+w7nT7x8ILrzwQon2wyp1EsxULBbl85//vHz+85/3thkaGpLbbrvtwAb3AiMYRMBAHGVGOagv5tbpFDyOpSFZFTozT8M7cChmP7rBMuyNuKS0qw2XIzVBVojgmLtVcha7aRgsMjfMe8QV0hTPkbmRYtrhOeRlolzHSDbmKnIVXFyZG0/2kZZmDTTmGYogExSfabbft8LtOEppwxd9ZhzKGzhv3zzARHiqifnDt2Ak4+CuCezM/+NLpHggqCKKK6Uinw69bwUHCeYtzOphsAjXBO5VHsk+4eeS/ATa+zJrSVLVV3N0wUzQHL/vOGZMHJtHAlWBnWQn8XNOx/eRzseU/NKU5qgh9kDmrbXOK2SL+Dvi75L7jyk0f6M1TwRnJYVgDkSZDRUsE9y7tHnUsT7LFLNu25IimGzIXUN5y4YbZoisUCcRYJzrXFIfP3fJay4Z7QXDHEtmixnBIAJyqbrkUiJ9jrIVlNGWoCxHFTMdMzYzfbxJlFjC946HpFPEJDyGdzezYJvIsJIyZli4tb2Wb6CMJxSndRk+IiJ50M7GmCIVTSOI0tgyRMzRaDIyZNUh54mIermsG9qabD+esQnZkqiksnticpUqmA1Dhdc9BTazkMEox9XxQjEh1j5/jJSnAKiCw6hSRourrcx6kaf1/7PbevtrU4aEUFIai8i2mW+VreCRBV3H9EltlKd4vnw3mzZpX0JE3hY+Hiqsvvk/o504joZX7kJ7V0oFj+GrfNZgbKmklTP6fxGRepdbXlPPLAyURrfHEnUgnYKMhD72pZtz2mABfnssT4KLwKLRNI7MzDNUsCdTgUHUDf/HXqQZeGrKpuIw0tYJXUhrjovKedFn3JkyRIym5fwtEfenWr4fsHAQDKKAgICAgICjFZEclsSMAe0RDCLg+Oyk9GXTyol4MmYzVJkMbD9Ts4UJ6XhdgLRlaohRXis17OrnRBSTLShnbDs2w8wMwfmYqxRGU1S8dEATzEPElRCZIPb9RM3WSjCyIGunkemhNKYkREhwpg2dxnVOIsukndZt84usWrM22X4217zukaOch8is+kmeRF8JI5bmytF+bgppiohUZ+z5MnmjYYYi5rXxORS3cab1yTg+Oc7FvCjJh3XbPEVcyWYYUtAn1/nYJ++gZo1t9uftmCUvm8Q2jPRyXCeeC9kiRmap/Q7CMVtyy40Nz7VWTt/GqdrDJqmklowEZB+O/SpvIK8ppDQ+OBGf8Xjaj3qQt8sho4mI1FhYFg6xRtqaRu00MrGUuJgTLAvv+N44OSIZJPW75fyGeYUs0uYJmwzX4JQeG3nGOXcoa+cVMu3PlZsRsL56kF2Q3E2EbMOTFPeFwOEq3RHQHsEgAp6r9UhPLS1LwEcP55rGERO53le20s0D06uT7SGEsNcxARlDiWHm9Qwo5ch+zxVqL2KNsHHU1aEx44oma36PkWMmMWPV+T1m4DZV7VvH0ewvA6mtGllNQVe4h38SqPfuODabUR+cjDg+Rp+xJlJ3f3MynZ60Y85n3ZMUJ1b6ExljpgpZSxtSdru3376BKLE1YpmgiuzVHIWSdKbgl1DmC7n5v0sCm72tZCsHq0+bgAVRfTKTEz6jJXJuHnQfhNM46sCnyhXy39zf/MCXZLLh8QViNJ6N9HIbTHwnVrvhd+OoN6cMVY9yx3Eo+4lGX/wBE3uqFAfsg5FtFR40HR/DXoSIMhqaNpih3VFQdgYGEaPQ6HdTqrujzyrxfp8RtKvU69z/1CM2FUcjLv48sdLOA5uL1kha3mWNoHMHn0y2uagtxBdtAnUMC7iQnNPMdt1XiTlgXiMYRAEBAQEBAUcrglP1nCEYRA4wj08paq4E+rBieGnBFp97aMYWzKt6pCojDdExm3LS9rRliLhaqjqW8pORXcUMZ2wCRq54dijnbruyMmwQWSF+vgxSVR7Ly91IvJgx9b88pULICmVwLiOpPcm2ob957SbBfGWQb2go7S50ZJgZylf7ULWeSRqVc6mjnIGaLygndeA4mU6i4KhP4Xsq8R4Ow6gvMzzKa/R79ZTaUP2ZnDlkkyj/oTQGc3G6pJwDZnQOoI8D2u/NZeSmqtpm5PV9jlukSpzEHWbL9uC1Ihzq6eyscoy23luvpEbixhON56onoKQ27scfWUuuKobQ1ENTv4s05gT7U5QaHKnJwE5Xmr/hbrBhJbT1sT6EYZHIIPF7e2bsfFmu2jZR3p5k91NxothnLCu0F2N6fNie+I61dl58w/BDLeMjE+RjiEw0W7u6bocVkTh/Ywf0/YCOEAwioCppqUpGvdRH4qiIbkg+A2k7Y1zUZ39Y/3fiTGe/Q3HSRBoOz1aGnG1PzLfWdxERGYzlth7ISd3g7JkKgFFfJsJNRGQqNkRoqNDgoEFHo4njzsXpg+nzU/JIX+yPk0oxnp2p6ROU7lhTbXXveLJtjJWn9tmJsIJJUxgZhPNVdc0Sg8juq8KXopNIEpffCot7phEFRyPImX7aE2Wm/GR4nDYZp33SjLhttLbfUxFRvpe6aeM1Znggdxuzn4ZPJ5KZGl8bA9FXzNTlx8XotDQSbXIxkcFYKaUZFYbPUifFc9M+I9icl8/fjH14Mi+a65RquB84VbMWkZb0qSvERokxjET072gAtdGmqnYOYsZ3k1KDhtlExbbdN2PnmELOGiipSqtRqhQsXI/ioJ0/ihl3dN1MPe/cb9Dw5tSYGwQforlDqGUWEBAQEBAQsOgRGCJgaWZaejNpVassE68OJhvcZ/nxcwuWiugZ+EWy/e8TL0u2u2N2Z/OMreJLangp8vUwP9E4HDxNvTPmByLVTCh2B0tQwwypxIdYVjPqiyzTcpTmMNFs02CIyDhNQw9gviMyRGa/Sn7GPuDQPYi6Cr879ECyvbFwvIiITJTtMSanILspSsFd3ykfO1irSvZkiBqdrAyb42ZpjxRypkR56lb4FtiiJELMF03mYTkI42TrS3bIvumQm3GxCD7pxieTuaK7OohCU8xRw8EG+ZgPDxugS5wYmsl9Ub2JL1V+otYBcJy+a01JLBcfp8YYBd6LjEM6nd3GkbBROd17nMUVI0Yp0CXRRm62qE6nalScL8fMENlX5u3ah4r0M1VEoiHRas7x+yNqZYwDv8v0Msv6VGOCuD6O8h9wIF/RY+ePlw89nWzvrSMZpIOmm8INy+AamHmsk+SPhw2RHKIP0WEbyYJHMIiA52oD0l3LyMsL1t+lP9X8Ycwgs/T2Oou7WqF+LWpYfXzpw8l2Of5uemCL87h7GnRAsD/mPsxRudjxYzt8bVS9tIY7SouS03hcO4xG0nhkdfqMR6jejeKups2UMojc2a65PYRaayYEf0e1P9m3DPWJ6OczieMMwcfpjO5mwsb7u4+zbWEQdeT/E1+/PCLP6nUaT+6EjYSRQlJKAmt76FlSievFq/6yLT2nZdr7ZDSfxKUH1XoMn9Sm+uYs0sb/p10yRtW+A9+jdtJXCp8r6bGD4pxOGRIh5D43kgwj1eqtx1aRh1nftltiszs5KHyPUWboL8375ehPJZzEsWl7VqZh2MQFjmkszJTd8hmltjqun0uypq8QM4/XZ2jF28F29Tbnzpku1oa0X+xC5BulfdYqM8ZPT7bs/LzqWGCGTNULE0EyCwgICAgICFj0CAwRMFbvkZlaVr5XHUz2mdxBF3TZBIFLsQKhlNbAeqqKyvYDcfPtdbdEVFQShd2vHKXFRLsx/w8SHwqcp+FI3SeWIRqMpTImJVO12DyrnlXZ3fY4cd+DYHzIIK3I7bXHbiDSC32bc++GXkP2R5dJQW4njKk/zkw3mMcxqpC+oFukPPqN2a9qnbGCOWU8j/OoWf0qB2xeRiZxpHyDSLrEi9XLmDgcsGe1T6KZ2jBIIv6cOKYPH5uhHFc9yR3N7gNOxug4xbSHgUl7WB/CHMfHIB1QkJCqxZJy7m+beNHjSE0nbVXh3pHLiFBJIR3JNUVE6nl3QkkzFjJPmJqk1gNZcALMSxFzhYu2wtukVEFUGMvhoHk1ao12I5uUwgOQ5u+SMl7cR3Y72Ktpe7zdK2yE7Fi/3WaCyK44cKbmqV+2D1lNjWRW6YBhPGxoiJ/V7fT7AR0hGERAOcpJOsrKAJIBGoPhaWjKNEpymCBz+BENYJIai31RGA4/hm36/Bybs3LdGXlrJGyNJwq4CKhUALmUNRyWid3uppyV1OFBgkgWQVVGmt2exITVH6n4YhEROT47nmxjTlcuGDnVX/OxG4Uhxfpx9E/agTaD6emW7V0lazw1MJnumbI+Asv77HWsOQwzSnQ0jmjk0KjiBJ4oRGp+pBFk9yrfIleouidEXzfiy9luJgw/7TL4LDERJLNWq0ikNrnmlN/K/pOha3h8knzRLy6jSSVVVHKSx6fH7O/AGNMGJz9w7fPc25r7viS7lH+QJxUD/ZNgyNFQMvcAUfJ+PyTW8cq3DqrhuYc0KFTffN7ScbZrVQcQDxAnKoAGTyb2OWKG+QbTUTAyD0ZQDdJd+tmmpdQ15j5e5b5jku0HzrU/iBcN2tpnQ3GlgBlIapTLOQ8Y46hcnztxJUSZzR2CZBYQEBAQEBCw6BEYIiAtkaJKRUSGs00JaCRrGYwSVgxjWG6zzRhWTr+uLBcRkcmGZS2eLtv6YJSZTs1Z5+IxLLgG4+V+Fccex4oml2qlkUVExhz5hFizjN+bhCPhqEpO2bpyqsCTlowOna19eYiME/ZjM7YEymv7rRM6+yBY48ygG1F5zHGiaHgshTOg4QtxXpKZyLOcxXXMqPplreUMfAFpqYyHfSCNYC6Nr1K9x0FZsTTmu74cNzgcE/apEhYxW+QhMLwO1mq3azHqWaBSQnSuYpXs5nGOrnvamFw7vj58kl7dcb94rqRAVZImd39JnTQ6LTPtjUeCI1IOtohkjGJlVZSZe1CmDz5LCApTiTtVDbyMuhAtQ1aXDj+IbA7lctiFg2XxBS/USnC2nrTbhZgZyvGZpv/1Tru9/TE7506tsTfh/OOeaDkeWeO0tLLKaa/2+wIgOFXPGYJBBORSNcml9IvXJEQcQ4j7ALh5GkEDKfjmoM2xsaRUiayxQ5zfZaPPnq65o8jyUauewYSJfOmzeKsqvBq/+DF3KMmM2a4p4zFqzdRJo8TFSDYmVaTExQzbpr9lWZtp2+e/RGOMhpLp44Klm5N9v3rOGlickGkgKhvCMbn5kjEykk4Vq4xp/4aH3leRairMvE2sOuU6R8JBH1izigVAKZ8RbNPOTUH5G3mkOafx4zGw0o39Gyi+pIu+vmlcRLEBq4wnX38ch8vniP3SqPKNgzKTozv6DWnryX0HdBLGKO7XbeyoVAYew9EYDJTRsA4TBLJ6r6+p59dQdf2wLchkj8VEJtP626Aczag1RrURjS6mAogNMxa7xZgxnUp+D6S51bZRLZ47aQTVMJ82oHd2Rc1VQydJWw8bgkE0ZwiSWUBAQEBAQMCiR2CIHFgCZmM0dn4eRDLDKpYgexGeMQr78pHy8cn2K7ueEhGR52vWkfrEvK2H9mDFJmxkjbCcMIItFX9ed37eA+/YCTApDYzJsD5kLcgmpYWrN/tokN0xpTl2NmwOITJV09ADno+sQyPhksxe2vNMsn183vLcO2v2OHREPz7XdIpcW9iR7DtztY0EfGDrscm2cs4kCxbfO+7jtVHMkbhX/WZ1qxbSjLpiqFSOiVUgYZhHiKtORqHR2drD0hhGpN5lv5ebdIR/iT8yy9X2gKQxtvdFXR3IYlVdaO73bTsYJ0/dM3ElgpwF49isnI+RayxVdX9ROVjPHo/Mkp4wPnUqaKNYq0TuwrOZdbcFGaNYvGpvs31hgk7XOLbDiVvEzU6qCEOMuYFnvcGkpSlum8G19isiksnX0RbXCbXMokxzPqoM2O+Vlti2tSE7vxUGbEDIsl6WLGoOYBqBM2SAurOM7G0+DDVXVMQLhcAQzRmCQQQcl90jPbmMVCCs9KSaPwZGdO1kIVJVVNVGRJns1CIiTyKM34DGRzFFY8b6GemoqqZBRomLmaDHYGyxP5fvTgkGE40ZJi4j2IeRrWj48Hs6pB8vZ0cx2GORAJN97Ibhw3D8X8ysTbZX5XbHx7DXcXW3DTV5OGeNLYbMF+lzFE96fiPIgtm4K/BPMhEyjYbbYFLSBqUvRwJFta/mfil5jRWX4eIxntp9zyeNqQi2mruN037yzcdtJDGVBNEXxeVTLg7yHaBC3+P/Veg+krI2Mr6D7x/pGl7oHj+qiMf0SHZJf9hWz4rHoMyUXP5aOG9HZmwRfY/M7U/nMDY8E6reGN3l+AzF8pgOwHPLaynlw2e3q2c2HZ7SaJuGb9JA0c6FRdRDK2Tt9r5qcx6driF6je4KqIE2U2+2qXjSObwgCGH3c4ZgEAUEBAQEBBylCGH3c4dgEAG96bL0ptNyX2lNsm9NbldLO7IglK2438W2sBJ8CQwS2aLjczYJ4vO1wWTbRKWdkLcSER2bh1PWK3LKw9IYBouO1pTUFAuCNkvSNuzEnGMuhQgy5O6hoyyd04vg7HfWmuMgy8QxTaLOEGuqrXA4YZPh6oeDey6LMTlrHxw8SKebUh8s+dFJeQqFuDslqyiZwf01L5MTowzpIDeBlTzkE2e0Vcr9uS8xY7tz9EV0EcpJ27Wi9VyDBhiKLJyVD0Sa89eQixlE3IDsFBjGIqbPNtfDn3PJI+k5HMRFRKJYisqUwTIpT3Vuu6m7TMWE4JGdcg5PUqB0VMRZyowN+9Tx7Ad15N+SSgbtY0mSAQS4+akOnjHDDGnJGlIg2FyyPuWavXdjcRuOg+V8pmt2njKsYHUuEzMGzBmCQQSUo6xko7RTZhqHlMWQ8zykoB4YOZRyTH+M1sor3x3746KRYAwHHpM+NSfmtyfbTPrIqC/CGCg9GMfuBrJCQ6KjbDgOA8UYeip6DcYfjb6dkR0/jcV9seS4t2b7pXy2DMVul2VoBLW+uRjyz/QFNIhyyEpLqtxMip34EGkZzG4bqYzFWpkgkqkAGFmmoswSycwdZdaJEeH8HCH/jD5TtceYzNC85LiLw4AvzQFlemZ/HaxWkzpknsuRUr4xvotz6C8sc3z669RRM8s3Jmek2oGCdcjobxZLhzrIjP5QuM+MPqNhG7/U+bxRAs24SyvOMtJah6wyoOPzzBQWYMh23YgXVaksLx6yRWO/z2BLm+PgXLIwZphmgwYPo3IbDmu7AR+hCvxE8+kjUdw1+BDNFYJBFBAQEBAQcLSiER1gNILj+wEdIRhEQCnKSibKqHw3W6tLRERkGOUpyPRwdeFjSgxDROZmtGbDIsgEkeXoZomQOA8RmZYnqsuSbbJTdO6mJGYYFhcDJiLyWHV5sk2paihtpS9TWoRJJjn+KvI17UVNsmmEsfTG0lYuhZpruDYcHx3HzyhYRmxzdag5NshyfYgEH+qAIwAAMAxJREFUpJMz5T9S5a4aZyRpeG91YkawSPF+lh+Iah4mqF1kltrnjj7ywhyHOV8mKU/YpsohOt267atlppygOxlTG6jLryLm4l0d5B7yOpy7jkH4aszs399Ys1M1MjDYJqOTRFK5nwPlOK6oR8+wDUPEKLQGnxU3W6Rgxs1rB3Yn49HgdFRja98qUSjYpBpvQo3fa34hogO2Km+D3x8izuhsnRyO4/BQp2SYM7oOSXOfr3gekI4fgPTBUqQBRzWCQQQ8U10qXZWslGHYGJlJRWPB2KHxxBcyDRQDJi2kYUOJiMZWP/ozxymhcCvbMrGhr3irQcOT0JFGEKPCnoSh5AIlxKGsNVAKnozT++Jsab3g5nfBqGLYPaPuRMbtmMrNMY2g9hvx4iW2j4e224gzQXdGEvMlWaOURho+m2ml4TlhK8WESfqUoeQ4oPK18RhSvm3z0sE+VdvLYzjwJWbeq+q94PHjaBtZ5oGKqpI2hgij7iK3AeNLWply7TvcC2VeU5UEMWpt04kRRPjSFsQdqYg0ny+Q54QTSdKbfgEGP4y+rDP5IZ7vjOtzUTdBGeBGKqYrFm8YJDMaRzTeXP6BaY8PUTai31Xrg8HFYRGRZf0oIL2y2JTwy3VPptMXAkEymzMEgyggICAgIOCoxSEaRAebg2IRIhhEgCnd0ZexzIyhRsfrPck+Uq9bypY9eXFxW7JdzLSuILgCYZ6iatT+Ngxmmg7PpcjNCrFv1iqjXGTYot3IWcRzrWI1VUK5akpYSzKTLfson/HabK9aWZDtB7LNY04inxOj0MiCkSWjsmGuGe/LErBTWThSc5Xoksm4WlQJ4HxlPEjZxw7UdKrm8jilsuOhTQ0ygfmuWrRSPvGwRQ72hjIJ88louQNd0JnWsWJX8pp44LpMHiZLMxvc795uC59PdczMKSZOeWl7+nNcau94POU/VHdJG49XPh2VFcPlbpMALFQqpb6I/sjepFqa+KRAMl906NZRkM3/+YxlS26mKqUc9xG1FpcOaeQR1ICyHBF+IxxeRGdrI2HRQVwlf3RvkxXuitmgwYJlgnqziALG9/7f9rUiIlKbalUAAuY/gkEE5KQh+VRd+lHgySQ5HEUIPH2FLuj9dbK9LGPD03c7Qsd3N+zLm0YQ5TVGn7mySDM0vuS5fUUYEZMqOi4Tfw4fKLwdc2rmsqjDQNkRR7nxGnWnMDlgkmUW6WcrS+yYYkOoD30w4mwChtLW+lCyPYKw+7O7nhQRkTv3nZrso3G3q2SvdVfB+mK5qHKXkSQyOzv1/oUhFnHNwBhjGHGDE3zD8YLyyT9qeJ5xmAgxKksFjGnGLW0of6FMax+68Kn70O4w8w7a+i6pMe6UUeWWybyGVBujSl1fnz9R0sBzQYi0u02qVm/pQ/toUQpqc0E8Y/LeL/7Be2cew5TnmWAG7pzbiSxRu2njswYaPmCmbxaONdehxkMgGpLSvszYTipcTPQ2f9tdRTunuXyMZoNGjnEb2F2yPo+lnHtu7c03T7xWnUODKEhmc4ZgEAUEBAQEBBytaERySLJXiDLrGMEgAh4rD0shl5Ozup9O9hmnXubdoSMvo6NcrJCIlosMKBcNgllKex58U6us5JHXWGuNq58+sE9jiPoyqKsoNDhb43zJHE3HeYt21ph4sk9cmFIlQuzKb02hmeyS5UY4jjKcwu8cOxnHtv29uvdXzXFU7LGX5myOphf12YSaT25fmmwf02WvkwveCBXIboxGMa2zOXuP2UeDUppbVbNMlafaua4h4pHSZvUlIpIuuSOEeGzmDzXEoZeBQRfenETGt9vHcPnIFtf4POPw1S8TF1vU7nOR9i8MTwkMJUPB+VjVKos9gNMlZsOk47C7NlqkmBl8td5s08hDCi9BHu6iTorvqZppsZyYdjNIdApnpXqXdJcpt3/ZVrvhpF1p/Vwlf+xCdBrq8kWqth9K54w35wQmIe3CQTKepKx1MrQmiSR+1zNVRAyD4jK//Vrd65EeMI8RDCJgXfcW6e7JJPXLRER2xz4qq5BBuuiRlrSPkJ0An6w2X8jjMEgYjeWKBBPRRoRBDseYwg/11xUbSUUDixFgGYc/FOueEfTjoaRnUgEwoo7Q/kR24mJEnAnTL3tqpxEn99oiuA/vG0m2x2rNc9hVgQzZba9HLwoy0r9nqoLw/0LntDeNoL3T1pg1vgg+2U1HwVATw95q7IfkY/qZtbrhMbCSft1+HK7w+pY28eVTkg6P4ai/Nru/ZEw+46+TxWq7NgcQ1taRP5JHMmv33Y5KIsT9RWk65uBzhujTOFKpAPiwxJ/TAFM132hFemQ1YxCxPhztqKz7GeJ9ycQJMVlYNl2BMQ6jsDAOA6uAxULN7GsZWtwAhhLkuMx4q7FYgeQuCIotFuy8w+hQnphJmcFFD1MFMMK0EPsbZTLud8ALgqixn8mhw+8HdIRgEAUEBAQEBBytCD5Ec4ZgEAGFVFWKqYaSpVz5hCiT7UTJjG6wLTvBwhiHaCYRpCTlKx1RhXQ0GldZHkR5DbIuZLCYh4gMkXEGJyvU5ynzQTKDTuRGPmMJEYKlR1xSoYhlmTh+tuV1ynXZ/Y9P2aXftlIzgq1Ut2Mbzu5Ntl/TY53dv999SrJdd6Tc90WZcVldQkJHVQKg1NyfgWTmq52mHbqlZTvlKd0R+RLsuSQnRtUwQizvaOuDJyLNHzmG3eZcUq37Zn9Ap19XnTQfO9FRTjzHS6Ajmewg3x1aPmsdINkfNSZ1v/A9ym5k4xxSmpHRmuPItHweH8hummsTuR4g8V8DR/6nFFlD1nybsWPK0FG6hKCQoeaX8zYNmzQwHVGWrXW52aJad7O/dBlyf8n+VtOqJqCdb1K4voYhqtVbpbFmH7a/bKrR0vYFR/AhmjMEgwjY1+iSRiOj5CBT38vnuzOcsb9m+u7kMYvtjn+IvuSJxG5kmX68tCLZNhmsq4h+SHukNkZnVKLWHy4jy5gxm35PKhGlQ9qiPEgDhhiDsUh/IZNygHO+kcBEREp5dz23boTCGumQ4bHPV49Jtlnn7bTldvvZffZ8XVD+PzR8UJ+sXmt9KTWQqToNil2F8VfdRo4JzY+opdCnw+c/4wmVTpp28Ot2uUxFHmmskz7Md6MDNGDUMc25eyLLiIMO11cH9/QRH18dg0YBEzDypUNr0GUHOfx5RIRlvFTIvOTRSZsoOF4nhsTzmInPEW9SmQaH/WIdBpYzCabn2qhrgOuUwXPfM9rsEEnslQRHec2bXiGWC6l6Vo5BNCcezkoVdeii1rQczDbPYs2FPCLYYkMp6iCrdcD8QzCIAgICAgICjlYEyWzOEAwioBJlJBtlJIfl+ZSDyZmC4zCjuEqo48VyF5SGDMjAkJFiRBcdrw2jRMaELAgdsDn+sYZlZoxTN5mlsoepIlvEvEUGlMY6kc84PsMukXmaqlnPSjqf76ra8+1Cssvd5eZ5LS9Yhk4nu7Qr2z9b+aNk+/9s/r1kO3I4RJPFm4YD9vSM3a5N2W3jr5jpsveNTphknLIo78HoM5OfKPKGXQGKjnE4A2Nfrce9is1Mw3nX5XjNhb6j1IKIJgBcSf1UW3zsKzmhisiZUyGFWPNRBO7dh7VMh68v7/523thu6TRSf3j6cNUQY74sj8O8am/6wM3ltaZTtS+JZLKr7v485blfKlmoYXcg7aY88o5J4iiinbArx8SMDWqgMRJTRXl6KtSb33/KsU9E/4anq83ffq06h0ZGJIdoEB22kSx4BIMIWJvbJb25tPQwIWL8W8gpPx8mPrSX8O7SbyTb33n+zGT7xQPNBIWv6NuS7KPhQ4mLhshABv5CsVHCz2nYMPqMBVHZxkhblOv6U1a0Z5Zp+vQw3N1k7u7G3ELDhgVdmShxKG0j34yhREOrK2P9mpic8jd7H0u2//n535LZYJFFnwzJa7a0y45jT7lp2NKHi5Em+2bctdjy/dbwMsaPK5mwiJ7HKKmyHlNkGilJzX7uL1BKycYVgs+BoCkyAwtfRrXWPpStA7vYJ6slPdOQYr00j7yjpKMDsWY855j4Mrndsg6sOK3HaPFlk1bFVh1JEL1GhpII3UZOu+918iAmxo8nCs1XG00Vjo2vg7pTXgPMvd9MnZTJqM7XigzBb/2eOkYOqTD4rHuyXaeL9C1qHTiNIEppU+Xm3FWvBCtjISIYRMC22oB01zLKkdqE4D9cPjbZR2NmiSd8/qIVjybbxolYh6+72QyGu9NQMn4/OeYVAvtDMHP0pGKzWkPlycYMwgCjUTKJiqi5+A06iZmLTBDPiwYdr01fXLqDzBmNkpMLz7eelIgMFawxU4iXlWXX7Chu3ykRkRVdllF6Yncze3ZXoZUBE5nFHMH/oKvocUR3QL1oPDmObOmODhiiduDKVtwvNnVsGmaeF1cCOml7Ft7GyFH9+twt2jhm64KoaFs/DC8jT6g9e27nJtKAgzPH1KDPj2uoHfgkKSdsGDPp2P+Hn9PR2p//yWFA0UjKM7EUmnr6S4wjD9ulclkxjQOO2YgNswjrDsUEwXlaFYBlNZzE98mOX6XFAlvEoAX6CLlSZ6jofyySqnFblx/hC4Ygmc0ZgkEUEBAQEBBwtKLREPEE0HT+/YBOEAwi4NT8LunLp1X009aYq71/35pkH2WaM3qeTbbX5G125IxKoNhcAjG6i+wP65NlsJzOOGQwHptSEBmREpgjskKUvgzI6FBeq3hSDxhZbRnqilGqaqhs1whVR99JYdbIslOU2hj5xiSSa7rGkm0j6fF67KhYX6bpgjv8f2neMnrFfJPyiDzRZIwcK3ZbSS/toA7Yh2+bES+RM8GiL1bdAzYxK+EDjO7KlBzj8LA/rqzWIrMYoJT6r/k5yQdFwbBRax/+zN5uucgZqUZ4gpZ8tcyMj4vPr8WXtdobCej6IlgLRWpSPqMEVDTZM91yl08i1FKa4yYRVHaRPZuMmNOfqEqGC+wkC8SSOYqzT1Me9DFLqggxMqGY46hM1oRnt4oKNTdJnZ6bNTxI3jZgniAYRMC+KCMSpaUPTsmPxFIZnYI5j1PqGYaRMApHY2MkTDl8cUREJlHyg3mICpCU1uSaxtb9M8cn+36jYLM4q2zX+NXSWDHGT0OV63D/xBuesHvjDD6GWFmeF7NPF/DW5PiMLw2lNn5va9UWgqUxtraws2WcHNsT09aRvS5u2eL1/ZuS7fEVzeu+eWJZsm/bXtw3TJrMS8uMt6aQpEom7DGIiIbD54eFYCPXC6zlC+7dthM09ZQQqaMAbNrki4mcTf0ymOul4yomKuLPa+QoBeLzH/f5LynJKd16LsrI8AxD7Xc5mfvUB48Pjjj7cMua3g4pEUatH6vjdWC8Jd+jI7XH9yjy+UYlRusBPpvMqh13l4YDNo0dbpcHPD5V5v7j+eazzt8fffgyWYfFzN+wx3qux+dLX8MXHEEymzMEgyggICAgIOBoRTCI5gzBIAKG0g3pS4t8Z58tKProdLNGWA11w3pz7hpYVSxd+pEB2rAtrvB1EZHj85b58CVvnI49D8/qegr9uqPCfNFWJhy/yuW45xGgY3Ydq8ApSFjJODzeuGSF6GBtrgc/p4P17c+enWy/esXmZPuk4raWY/C8y0h7MFodTLbp+M7M3Kbe2Yv6UKcONYrGSlbG2z1hz9sVlcIVI2U3b5bpNkhl1LLUbnuyYCdApBijxrzJHQETfcbvIfjPy9K4pC+fXOdN+qikHoeTecrN6TSYIRpNTKZk5eBMmclHgzmi4PT7pP3LxckieSLVOpGtpF1SZFVXztMhpc/4mnkd7dV95lhbZboo1/75pnzG/kwfalrExWZ26gzb8FxiklmnDYCTeQHRZyzKTKkyHohKlhpi1RclgkEE/KrSLz2VjLy0+HTLZ8yHQ8NG+9c4dGls04eHfTASjKAUZSLRKmmkl8cxRmuDyfaLkJ+Ikp4xhOjbQ8msriI14D+Dsebiioz8nOH16njQWFz+SyobNoy4Y3us9Lizwki6lThm07DZVrH+Ro/usZJZNm0Npd8desAeE/doea55nPGqvf6revYk22t6rc/SlqKV8XZPW+OoEqfwpwRWq7gNonSGk7C0tFHGE40M+Ed4I7aMDVFtfaHP3ta+Nq0OKIzM8fnGcBismWmaKCPCXdPWOz5X0VT1koaBwJepy8CiHFPPuV/uvtDy5Jp6DASvQdHuZdqJjewxoMwxld+QR73xuqHFJ6Q+94TueyMPXfmQPIalKizs8HfSUYMH5k9k7j9zGaVRKqReQob5LAwiqoyO3186426bGEpzaS+F0h1zhmAQBQQEBAQEHKWIooZEh1Cx/lC+u9gQDCIH+tJWJzijsFVERLZmLENAR15fYkBGiBk2RUWCoSipK5N1c78dRya+Vb7IMhZ3JVs0iWQeq/JjLf1W8AiQqcrBjdhVy4xMi5b57JjIRDF3k2GGfFLbRNWyScPFveiPkXTNMf2/0ROSfbvHLZu0q6e3pa2IyFjK7n9xLME9Nm1rxhEV5DgazFsWbKJsr2nCEKkCsbaPhqqPBAdmtVqNmRmPI7hCO3bB4Zzc7I9Op56uY7aIWX8bHjaG43BFn/mcpztxRTWSWcTCnMxl04GDdb3Y/IAMkS8CSydsJLPR6sGs+mgoasnZx2GBiqRrZXc6Wf87z53DTHvqpTkkruY4XAdxt/XCwXap7ly108RzzzlkMETRDH5/Wfsw11yklAqMgOzmiAhtlOfw1RlFh8byBB+ijhEMIiCTakgmlZJpvkDjEPbtVSvN0MjoRihECdXW+zOtyfu4j1IVJakph7QkYg0KGkE0pChhPQ//mcem7Ms+3d/8YTBknrIbDYcqKk3ST6fheKXR+Jt2yHwis0qLxP1xH6PMjG+PiMhEzV7r/qy9fqNxiH0JCRPrkKryKJ+x15N80vh59eAeciLk8fqwvW3KRqJVqq0OHl6fE19ovplwVXSMuwsvEp8Od5g0bHzviyZpy4zZPD3eehpVrlP0GGbqtDxRa673oy5JwfFRTuRb0+gxdle6Yg9SL7hNM6fsw31MpOgxfHyGkgveMHnVqNUgc5Zc2Q+cRXyzngN6/JD8Yfz7H0c74yjlu1ye8D9d/qP5v1c6pYELg0jZOBnHRQ1YlAgGUUBAQEBAwNGK6BB9iAJD1DGCQQT0pKrSk6rL7oZ1mv3l9GoREdkyYyUzSiksLuqr/2X2D+fGk311yGtMPujLC2Tgi1RjVNjpRSaLRARVLJUxESRLdzBqjcTAYMaWzDDyGM+PrNVUyu7vhkzmSt44jSqNZNp6c5aNGa/Y8fUi5Mncg1IFek3JLlGf2GPv19mDlmUaq1vJzDjEv7jb5nPaPGMZNTJcvLdr+qzjdS0+l50T7jIqjBaLEL0VIfV/Otfsm7KbVFAWggxL1rOcTryZU+62TKSI1T0fJ0PSqeAuSlUeGcwZUeaTOFw5ZPSwk/bK9cFTZJZsRgPna6U7+3m2BKYT0WdtmQ1H4kkRzXApB2AXw+JhjTqSltpAOb4DivBoU+7kYMfhc5j2Op+7Emn6Uic12l8oc93JgCpWk2OaoaSHRjGrqs/FE4UYU0uN0hy+OhsNf9hmJwg+RB0jGERAXVJSl5QK2TbSDF/MWc/DSTnmVNTj2hm/hClP0V+HEWn0EaKxYvxu6JdTSlljgFXr+fJmJuqkD4SIUwrcUbVSUBdmmHQR4fHxm4YGzljNGgM5jxFEmDZ9kBApQ9LwmUjZ67SrYg3HYhyHOzJoZcot49bA6sq5DUdem5x4nGnafG8l/Jp2lZpj2pOx9yrK2bY1SGq+l46RzzJ5fI+FVuGHpPJvKrnI7MQ+RKc10B8eCfWyysaPb82eiqQ9vjs+g8dY0mzLxN4NnxGBLtplqlY+S8yYqWSk2NcGL2Alk0VuPcb9UveE/3sknciRZTrlOYYyFjqQ2pJr7TE4OjJsXG0OsFhsEnbviyyjUULDN9N6HVRtMmS7TnvSRqjnyVE7T9kyjD4r2/4ylGvN74th9yqSDd+Lp6z6XPoQBcwZwl0NCAgICAg4WhEkszlDMIiAsXqPlOoZebYylOzLxA/iElRaZ64dyk+Mtqqq7eZl/mVpJNlHJ+JjsrZv5jWiPGZYpB4H4yMiko/sUr5dvTMyNyz/wW2CrFR/urVSPaUlJj6kg7ipxSZi2RYT9SYishXX/FE4gvdm7flWQC9M15p9r0TOoq4T7Zi6s3a7iOUeI+xMXqYT8/a8nyotTbaZ6r8brBXZomy8XO0p2M+nxJYkYU4ijVY2QCWOY2JGFd3VZnLz0A90PvaGqsWPU95eUqlCCeSqmdXHdVIi0wAfg9FJ+WYcMgDx5VVJAX21vRgFRxYmbk/ndRN5JqKZCkaiiUMCijy1zngVI98fJpJKRUZFsz9u7vclVfQdxwEvW0QHage7085hmt9je18iSF/eqwbGYe4dWUhfOT9vnTqz3/Ncsa3yNpjm9Ui1tGVCUj7fpspSRGbyBUbUaEh0CJJZCLvvHMEgAnbU+6WrllVRU6f0NKUvJmYsw4doOwqKbs/ZbRouplgpX8yMWupO82Vr2zCiyxglU3i2VcJEfI9GGv17TM00GjA0WvIp96+cBqBLBuP4afioqDUc0xiDz5Stn48y7qCD7Kva/oaL9k1tos+ma3l8Dz5cNXdqBMqaBrxXvOY5WCIq6Saj6uLjcBFWr3ucUgiXmwm/p16w3PZ84JAflD8R5TX42tCvyXRHAyaLy1V3B0AqmMuU8rzklE8SZQtXFBT3ZTxGiefFa17U9CvyGRw0iHjMet4RZea4Xq0DbzV4Uh7ZKF13H7ttRVHe+g5SNHgzWLf7nk8Sc8ia7e5na3/N/5CBZFbyTHzPVwPPNPX4o6mycjCIKLVGjjULn/sa8uZWe5sH53MVsHAQDKKAgICAgICjFUEymzMEgwh4eOpYKaRyMoWl8N5cc3lAmWQ5NIWTuqzc4kuwaGQkRnGRdVFRXJ7UdaZvnecHt4+JD/H8k6Uxx+cxyspDlWO2iQjJOBkmivl62B+vUxqJ9baVBpPtQrw8K2CZNlG3yzDWjavhOI9N2qr0/XE9uSKKHHG7D5Fq0w3LItGRuidmtnId0NFKvkR7U/usijGzjEfkkE9ExOkwnIUzdsUjtSlJxFF2w9uYuVsy7m1XJXVCOTB78hAl5KmH7aDEBTVUr9gdxIxiFjzJAlOOhDYs/earnVbtQkRfu/w+HmdhhaiV5egkV5AvGqttW8+hVd2wNgyLi12bfZyG422hvgfmq+FTil2ym8PRWkS0fOl5ZhNS2BMfob7ni4iLz6tupwnFCmH6cOaBesHRiA7tgMEg6hjBIAKeLw1KLpNX4c9d8YtwZZctwDqMBIxDGVs4lFIbI8QycTRYySNPMRQ87VkJVNOtt4oh+hlxSzr0IVIGVIycRyZzpQ0QEZmMii37CBp9FRxvZX482TayG2W0frEG2HjGzkY7SvbabJuwkuRE7LOztMsakzSeju+26QZovPEeGUNotGblUIIGG5NI1jFu42fERJBVRpmxrpnnDZ+Kx6EKTlbdL2klleQcb2T6CvGADPlnJmra1LG+kPa8XFIeI0g3cnzu8//wGF6JjQ5bnXlOvS936CON+KIxiWMDjZVx5LNZG462PDTH0UYi8howHiPNG2ZuZE2fz4zPh8ghsXXiN+SNLEwkM1xfjKmhCrq6x+ryZfJdJ29ds3rr5+p7MAaqvRirMgCb//t8mZRvUSn+oOS54QHzGsEgCggICAgIOFoRReKnIzv9fkAnCAYR0JWpSD4j0gMuf03XLhEROQ4JDvOqlIVdKYyD6SHDYhyXdfV3y8MyMqtC52KwN+a7zFlEB+A6loCU7nKOqDWyV646ZbPBsRp5jM7VLodpEc0ikWExTthsy2N0QZ4iW1fHsm46TshYyttHeG/JMkuZIXudqi6vSWA1GL9eUBE5MEQZXF8ycL2xdJdBhBglM12J3JPszSERUeuJ8nQMdrdxMgoeyayBpXCK2oZJZuipe6ZyvfhYJJMXxsMsqLxGjD7Kte7PUIF2rOh5vOa40T5yOFUzIaV3/K0vD9/xfE7LTgdxdRBsevIoeV9i0az/Z40v8uUTIpKcVfyee3w+dsfFMqmILgwwcU6XWcyWg02kbKVyAXm2E2mUfgKea0OWkdGOybMACdeXjLOe5CGSOUPUiHSiyAP9fjCIOsaCMYi++MUvyt/+7d/Ktm3b5LTTTpObb75Zzj///APq46Se7VLszakw+Bflt4uISDfCtWkMbK4MJ9t8qS/L2gzWJiEjkyfSaKl7jCDCGC4qsaCnraqThhm87lhl+LIx02dmMDWNNs1jliJ7rjSweN6MYOO1MYYQDYsVOeuXtV2sNJaFoZGFLNWdr7Z8TqPk0X02dF9nE4f/T7xdxfXaUbHy2ZKcfQ76HLXpRETO7GtmBX9u34Dzc85FLOiaLbS+kTltZbsh183gZ5r16TfxMcbtM6Yiy3ptf4y2ahRoMDgGQuPIJ+8A5hGKXC8+0f4YrnBmEZFsrEKrlyevIx57lejRMSYuWFKel4rPyHGJIvqF7pFNnNKSu2m96Ita239EnI5acxxPRFINdx8ueSnyGJw+JAagx3jyRZm5DFEaQURhwm3k1HDNnLfUY4BnkPTRZQDqa+cekzHu6hX35y8IooYcGkMUwu47RSfFp496fPOb35SrrrpKrr32Wrn//vvl/PPPl0suuUSeeeaZIz20gICAgICAgHmABcEQfe5zn5P3vOc98id/8iciInLzzTfLf/3Xf8mXvvQlufHGGzvu5zcK26W7mFF5aQxM1XsRkSfLy5Ptf3jggmR75RIrvbx6xeZkeyhmnHJKArOXvgC2qOJwfBaxzBGZG5YCYb6hEhIDUmYyLBMZHV2R3l0iYkpaE9BkVLkRlOvwSHA5JTM2V2pMpFfCebMtK98Tg8WZuA+76iNDxJxEzBs1WrPsUz67p6Xf8/oeT7YfK61MtslmNRw5iZZ0WRZtxySyGVIlA2OTdixt06xJkAdDVPUsvRFlZpio3AQkRkgV9BtXC8YMGaJmez6CdXyeKbvpE5ejtE8aU6twnwwWf5dRaGQRlCO4r9aaC8yjo8pTuNu4FuVtnYw94/A5O/vqbnnRrsq8L6pO9eHY10EuI9XcwXz5Is7ayW4+WbbSB4mT8mmbKDnV1uNYnnZcG5dcKqJ/R+a3MZekS5DM5g7z3iCqVCqyceNGueaaa9T+iy++WO6++27nd8rlspTLdradmGjKNX3pGelJZ5RRMh4Xen28ZCUYyj8vW2ULqY50jSfbxyELs0kGSIMj46FAadjQaDKGEMPh6UNUqbv9kwTGkZGw9OcWvsgxGjzm2rgyT4vo8/LVMjPYh/plNGxowGTxZu0r2ntmMlH7ovLoBzZVswbd9pqVto7PNe/R1tpgso8RhFtS1vAlXLLlecc8keyj39MDW49NtpmGgMaPecdx3spm7OdljxEkjlpPKiSZoez4nq8cVxSPj3W3fAnv1IvGZQB08sLwRM9Vjon7gL3qlYh8/kRG0fHIJ3X6E9VoFKI/V/HRTowgx0tYdeExjjoJpU9+Gr4UDvze/l3nZjVu359q7rjnfN5qNFo9Umtyvq7UDzLrvD1TickJ20n0oqpx5qobx+PBoKNENxNn/phLH6Igmc0d5r1BtGvXLqnX67JixQq1f8WKFTI66i5FceONN8onP/nJlv3T+5q/KvqUTDea+0plu+woQ5uvTlkGo1y3bWYadnYwbAUdn30GURVtIHlLI/7FZ9KtTMvs45U9An4pdrgoeXwL0pjRIpX5mkyU6QsO3cw9JJ4XuQO8XikaRMjYXC3Z61ubsrNQNarEx8P1woutgizTFRgOM1lkwa41xzpdQykOJDQpTSP3UMbtYF2LrxPPlc9EYxq+RzBy6gjNdxlEiqhAlW6fQWSGVGcVbj4/M7jW/BoNpUq8zZB/hjjTAOPjy5BoR6i6l8Hw+YTH23zpdNSfc9WPpnxpcj8NIp6Lw4H5YA0iNaZDMIjajemgcRgMIpazqCvL0n0ccy7qtjieJbZtOWb8U4uY8dtTVqPBZ7mNQcTfBbNnm2eyXm7+rueCfalJ1c/gdfr9gI4w7w0ig9QsKjmKopZ9Bh/72Mfk6quvTv5+7rnn5NRTT5X1v/mEs33AwsHtzr3bPK03e/a3w/8c5PcCAgLmEyYnJ2VgwB1QcajI5/MyPDwsPx39/iH3NTw8LPm8x3s9IMG8N4iWLl0qmUymhQ3asWNHC2tkUCgUpFCwMkpvb6888sgjcuqpp8rWrVulv7/f+b35jomJCVm1alU4x3mMhX5+IuEcFwIW+vlFUSSTk5MyMjLSvvFBolgsypYtW6RSOfSQtnw+L8VisX3DRY55bxDl83lZt26dbNiwQX7/938/2b9hwwb5vd/7vY76SKfTcuyxTV+P/v7+BfkDJsI5zn8s9PMTCee4ELCQz++FYoaIYrEYDJk5xLw3iERErr76alm/fr2cffbZct5558ktt9wizzzzjFxxxRVHemgBAQEBAQEB8wALwiB629veJrt375ZPfepTsm3bNjn99NPl+9//vqxZs+ZIDy0gICAgICBgHmBBGEQiIu9973vlve9970F/v1AoyCc+8QnlW7TQEM5x/mOhn59IOMeFgIV+fgELE6koZG0KCAgICAgIWORYEKU7AgICAgICAgIOBcEgCggICAgICFj0CAZRQEBAQEBAwKJHMIgCAgICAgICFj2CQRTji1/8oqxdu1aKxaKsW7dOfvKTnxzpIR0UbrzxRnn5y18ufX19snz5cnnTm94kjz76qGoTRZFcd911MjIyIl1dXXLhhRfKww8/fIRGfOi48cYbJZVKyVVXXZXsWwjn+Nxzz8m73vUuWbJkiXR3d8tLX/pS2bhxY/L5fD7HWq0mH//4x2Xt2rXS1dUlJ5xwgnzqU5+SBopZzbfz+/GPfyxvfOMbZWRkRFKplHznO99Rn3dyPuVyWd7//vfL0qVLpaenRy699FJ59tln5WjB/s6xWq3KRz/6UTnjjDOkp6dHRkZG5LLLLpPnn39e9XG0n2PAIkYUEN1+++1RLpeLbr311uiRRx6Jrrzyyqinpyd6+umnj/TQDhive93roq985SvRQw89FG3atCl6/etfH61evTrat29f0uamm26K+vr6om9961vRgw8+GL3tbW+LVq5cGU1MTBzBkR8c7r333uj444+PXvKSl0RXXnllsn++n+PY2Fi0Zs2a6I/+6I+ie+65J9qyZUt0xx13RI8//njSZj6f4/XXXx8tWbIk+o//+I9oy5Yt0b/9279Fvb290c0335y0mW/n9/3vfz+69tpro29961uRiET//u//rj7v5HyuuOKK6Nhjj402bNgQ3XfffdGrX/3q6Mwzz4xqtdocn40b+zvH8fHx6KKLLoq++c1vRr/+9a+jn/3sZ9E555wTrVu3TvVxtJ9jwOJFMIiiKHrFK14RXXHFFWrfySefHF1zzTVHaESHDzt27IhEJLrrrruiKIqiRqMRDQ8PRzfddFPSplQqRQMDA9E//uM/HqlhHhQmJyejE088MdqwYUN0wQUXJAbRQjjHj370o9GrXvUq7+fz/Rxf//rXR3/8x3+s9r35zW+O3vWud0VRNP/Pb7ax0Mn5jI+PR7lcLrr99tuTNs8991yUTqejH/zgB3M29k7hMvpm4957741EJFlczrdzDFhcWPSSWaVSkY0bN8rFF1+s9l988cVy9913H6FRHT7s3btXRESGhoZERGTLli0yOjqqzrdQKMgFF1ww7873L//yL+X1r3+9XHTRRWr/QjjH7373u3L22WfLH/zBH8jy5cvlrLPOkltvvTX5fL6f46te9Sr57//+b3nsscdEROSXv/yl/PSnP5Xf/d3fFZH5f36z0cn5bNy4UarVqmozMjIip59++rw8Z5Hm/JNKpWRwcFBEFuY5BiwcLJhM1QeLXbt2Sb1elxUrVqj9K1askNHR0SM0qsODKIrk6quvlle96lVy+umni4gk5+Q636effnrOx3iwuP322+W+++6T//3f/235bCGc45NPPilf+tKX5Oqrr5a//uu/lnvvvVc+8IEPSKFQkMsuu2zen+NHP/pR2bt3r5x88smSyWSkXq/Lpz/9aXnHO94hIgvjHhKdnM/o6Kjk83k55phjWtrMx7moVCrJNddcI+985zuTAq8L7RwDFhYWvUFkkEql1N9RFLXsm2943/veJw888ID89Kc/bflsPp/v1q1b5corr5Qf/vCH+60EPZ/PsdFoyNlnny033HCDiIicddZZ8vDDD8uXvvQlueyyy5J28/Ucv/nNb8ptt90m3/jGN+S0006TTZs2yVVXXSUjIyNy+eWXJ+3m6/n5cDDnMx/PuVqtytvf/nZpNBryxS9+sW37+XiOAQsPi14yW7p0qWQymZbVyY4dO1pWc/MJ73//++W73/2u3HnnnXLccccl+4eHh0VE5vX5bty4UXbs2CHr1q2TbDYr2WxW7rrrLvn7v/97yWazyXnM53NcuXKlnHrqqWrfKaecIs8884yIzP/7+Fd/9VdyzTXXyNvf/nY544wzZP369fLBD35QbrzxRhGZ/+c3G52cz/DwsFQqFdmzZ4+3zXxAtVqVt771rbJlyxbZsGFDwg6JLJxzDFiYWPQGUT6fl3Xr1smGDRvU/g0bNsgrX/nKIzSqg0cURfK+971Pvv3tb8v//M//yNq1a9Xna9euleHhYXW+lUpF7rrrrnlzvq997WvlwQcflE2bNiX/zj77bPnDP/xD2bRpk5xwwgnz/hx/8zd/syVdwmOPPSZr1qwRkfl/H6enpyWd1tNPJpNJwu7n+/nNRifns27dOsnlcqrNtm3b5KGHHpo352yMoc2bN8sdd9whS5YsUZ8vhHMMWMA4Ut7cRxNM2P2Xv/zl6JFHHomuuuqqqKenJ3rqqaeO9NAOGH/xF38RDQwMRD/60Y+ibdu2Jf+mp6eTNjfddFM0MDAQffvb344efPDB6B3veMdRHc7cCRhlFkXz/xzvvffeKJvNRp/+9KejzZs3R1//+tej7u7u6LbbbkvazOdzvPzyy6Njjz02Cbv/9re/HS1dujT6yEc+krSZb+c3OTkZ3X///dH9998fiUj0uc99Lrr//vuTCKtOzueKK66IjjvuuOiOO+6I7rvvvug1r3nNURWSvr9zrFar0aWXXhodd9xx0aZNm9T8Uy6Xkz6O9nMMWLwIBlGML3zhC9GaNWuifD4fvexlL0vC1OcbRMT57ytf+UrSptFoRJ/4xCei4eHhqFAoRL/1W78VPfjgg0du0IcBsw2ihXCO3/ve96LTTz89KhQK0cknnxzdcsst6vP5fI4TExPRlVdeGa1evToqFovRCSecEF177bXqxTnfzu/OO+90/vYuv/zyKIo6O5+ZmZnofe97XzQ0NBR1dXVFb3jDG6JnnnnmCJyNG/s7xy1btnjnnzvvvDPp42g/x4DFi1QURdHc8VEBAQEBAQEBAUcfFr0PUUBAQEBAQEBAMIgCAgICAgICFj2CQRQQEBAQEBCw6BEMooCAgICAgIBFj2AQBQQEBAQEBCx6BIMoICAgICAgYNEjGEQBAQEBAQEBix7BIAoICAgICAhY9AgGUUBAQEBAQMCiRzCIAgICAgICAhY9gkEUELDIceGFF8oHPvAB+chHPiJDQ0MyPDws1113nYiI/OhHP5J8Pi8/+clPkvaf/exnZenSpbJt27YjNOKAgICAw49gEAUEBMjXvvY16enpkXvuuUc+85nPyKc+9SnZsGGDXHjhhXLVVVfJ+vXrZe/evfLLX/5Srr32Wrn11ltl5cqVR3rYAQEBAYcNobhrQMAix4UXXij1el2xQK94xSvkNa95jdx0001SqVTk3HPPlRNPPFEefvhhOe+88+TWW289giMOCAgIOPzIHukBBAQEHHm85CUvUX+vXLlSduzYISIi+XxebrvtNnnJS14ia9askZtvvvkIjDAgICDghUWQzAICAiSXy6m/U6mUNBqN5O+7775bRETGxsZkbGxsTscWEBAQMBcIBlFAQMB+8cQTT8gHP/hBufXWW+Xcc8+Vyy67TBlLAQEBAQsBwSAKCAjwol6vy/r16+Xiiy+Wd7/73fKVr3xFHnroIfnsZz97pIcWEBAQcFgRDKKAgAAvPv3pT8tTTz0lt9xyi4iIDA8Pyz//8z/Lxz/+cdm0adORHVxAQEDAYUSIMgsICAgICAhY9AgMUUBAQEBAQMCiRzCIAgICAgICAhY9gkEUEBAQEBAQsOgRDKKAgICAgICARY9gEAUEBAQEBAQsegSDKCAgICAgIGDRIxhEAQEBAQEBAYsewSAKCAgICAgIWPQIBlFAQEBAQEDAokcwiAICAgICAgIWPYJBFBAQEBAQELDoEQyigICAgICAgEWP/w/Gk89SmIu4WgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "expt.topog.depth.plot()" ] @@ -601,7 +757,6 @@ "metadata": {}, "outputs": [], "source": [ - "## FOR GLORYS: \n", "expt.ocean_forcing(\n", " tmpdir, ## Path to ocean foring files\n", " {\"time\":\"time\",\n", @@ -624,9 +779,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 6 (optional): Select number of processors \n", + "## Step 6 Run the FRE tools\n", "\n", - "This is just a wrapper for check_mask FRE tool. Choose the number of processors in the X and Y directions respectively. Having run `.bathymetry()`, you already have one set up for 10x10 by default." + "This is just a wrapper for the FRE tools needed to make the mosaics and masks for the experiment. The only thing you need to tell it is the processor layout. In this case we're saying that we want a 10 by 10 grid of 100 processors. " ] }, { @@ -635,40 +790,14 @@ "metadata": {}, "outputs": [], "source": [ - "expt.processor_mask((10,10))" + "expt.FRE_tools((10,10))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 7: Regrid the runoff \n", - "\n", - " This step will be removed in a future update when this functionality is added to rest of pipeline. Currently it calls a function from the legacy regional_model_scripts file. Just execute cell to give your domain runoff from JRA in 1991. Rivers do the same thing every year right?\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from regional_model_scripts import regrid_runoff\n", - "runoff_path = \"/g/data/ik11/inputs/JRA-55/RYF/v1-3/RYF.runoff_all.1990_1991.nc\" ## Can change to match your year\n", - "\n", - "regrid_runoff(inputdir + \"ocean_mask.nc\",\n", - " inputdir + \"hgrid.nc\",\n", - " runoff_path,\n", - " inputdir + \"runoff_regrid.nc\",\n", - " np.array(xextent) - np.array([180,180]),\n", - " yextent)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 8: Modify the default input directory to make a (hopefully) runnable configuration out of the box\n", + "## Step 7: Modify the default input directory to make a (hopefully) runnable configuration out of the box\n", "\n", "This cell just copies a default run directory and modifies it to match your configuration.\n", "\n" @@ -680,7 +809,7 @@ "metadata": {}, "outputs": [], "source": [ - "subprocess.run(f\"cp default_rundir/jra_surface/* {rundir} -r\",shell = True)\n", + "subprocess.run(f\"cp default_rundir/era5_surface/* {rundir} -r\",shell = True)\n", "# subprocess.run(f\"cp default_rundir/era5_surface/* {rundir} -r\",shell = True)\n", "subprocess.run(f\"ln -s {inputdir} {rundir}/inputdir\",shell=True)\n", "\n", @@ -778,21 +907,12 @@ "inputfile.close()\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BONUS! Want to use ERA5 surface forcing instead?\n", - "\n", - "This is WIP and not well tested but thought I'd include it" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SET UP ERA5 forcing:\n", - "Here we take the ERA forcing as it already exists on Gadi. For NCI users, you need access to the rt group. ERA5 - specific functions provided cut out the region of interest and fix up the metadata ready for MOM6.\n", + "Here we assume you've already got ERA5 data stored somewhere on your system. For NCI users, you need access to the rt group. ERA5 - specific functions provided cut out the region of interest and fix up the metadata ready for MOM6.\n", "\n", "For this example, we are forcing for the entire year of 2015 so we just generate a single forcing file with 2015's data.\n", "\n", @@ -871,9 +991,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:analysis3]", + "display_name": "Python [conda env:analysis3-23.04] *", "language": "python", - "name": "conda-env-analysis3-py" + "name": "conda-env-analysis3-23.04-py" }, "language_info": { "codemirror_mode": { @@ -885,7 +1005,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 7a39c406..2a12193f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,13 +7,14 @@ authors = [ ] dynamic = ["version"] dependencies = [ + "bottleneck", "dask[array]", "dask[distributed]", + "netCDF4", "numpy>=1.17.0", "scipy>=1.2.0", - "netCDF4", "xarray", - "xesmf", + "xesmf>=0.8", ] [build-system] diff --git a/regional_mom6/default_rundir/MOM_input b/regional_mom6/default_rundir/MOM_input deleted file mode 100755 index ead7171a..00000000 --- a/regional_mom6/default_rundir/MOM_input +++ /dev/null @@ -1,287 +0,0 @@ -! modified from OM4_025.JRA and ESMG's Arctic5 - -! === module MOM === -USE_REGRIDDING = True -! diffuse interface heights with KHTH -THICKNESSDIFFUSE = False -! do thickness diffusion before dynamics -THICKNESSDIFFUSE_FIRST = True -! baroclinic timestep -DT = 900.0 -!DT = 360.0 - -! thermodynamic and tracer advection timestep -!DT_THERM = 3600.0 -DT_THERM = 1800.0 -!DT_THERM = 900.0 -! thermo timesteps can be longer than coupling timestep -THERMO_SPANS_COUPLING = True -! form frazil ice (accumulated heat deficit is returned -! in the surface state) -FRAZIL = True - -! limit salinity to positive values -BOUND_SALINITY = True -! heat capacity of seawater -C_P = 3992.0 - -! check for excessive SSH, SST and SSS -CHECK_BAD_SURFACE_VALS = True - -! don't write geometry/vertical grid files -WRITE_GEOM = 1 - -! for debug: save initial conditions -SAVE_INITIAL_CONDS = True -RESTART_CHECKSUMS_REQUIRED = False - -! === module MOM_domains === -NIGLOBAL = 210 -NJGLOBAL = 271 - -LAYOUT = 10,10 -IO_LAYOUT = 1,1 - -MASKTABLE = "mask_table.2.10x10" - -! === module MOM_verticalGrid === -NK = 75 - -! === module MOM_fixed_initialization === -INPUTDIR = "INPUT" - -DEPRESS_INITIAL_SURFACE = True -SURFACE_HEIGHT_IC_FILE = "forcing/init_eta.nc" -SURFACE_HEIGHT_IC_VAR = "eta_t" - -VELOCITY_CONFIG = "file" -VELOCITY_FILE = "forcing/init_vel.nc" - -! === module MOM_grid_init === -GRID_CONFIG = "mosaic" -GRID_FILE = "hgrid.nc" -TOPO_CONFIG = "file" -TOPO_FILE = "topog.nc" - -REENTRANT_X = False - -MINIMUM_DEPTH = 9.5 -MAXIMUM_DEPTH = 6000.0 - -! === module MOM_open_boundary === -OBC_NUMBER_OF_SEGMENTS = 4 -OBC_FREESLIP_VORTICITY = True -OBC_FREESLIP_STRAIN = True -#OBC_COMPUTED_VORTICITY = True -OBC_ZERO_BIHARMONIC = True -!RAMP_OBCS = True -BRUSHCUTTER_MODE = True ! read data on supergrid - -! segment 1: southern boundary -OBC_SEGMENT_001 = "J=0,I=0:N,FLATHER,ORLANSKI,NUDGED" -OBC_SEGMENT_001_VELOCITY_NUDGING_TIMESCALES = .3, 360.0 ! inflow and outflow timescales -OBC_SEGMENT_001_DATA = "U=file:forcing/forcing_obc_segment_001.nc(u),V=file:forcing/forcing_obc_segment_001.nc(v),SSH=file:forcing/forcing_obc_segment_001.nc(eta),TEMP=file:forcing/forcing_obc_segment_001.nc(temp),SALT=file:forcing/forcing_obc_segment_001.nc(salt)" - -! segment 2: northern boundary -OBC_SEGMENT_002 = "J=N,I=N:0,FLATHER,ORLANSKI,NUDGED" -OBC_SEGMENT_002_VELOCITY_NUDGING_TIMESCALES = .3, 360.0 ! inflow and outflow timescales -OBC_SEGMENT_002_DATA = "U=file:forcing/forcing_obc_segment_002.nc(u),V=file:forcing/forcing_obc_segment_002.nc(v),SSH=file:forcing/forcing_obc_segment_002.nc(eta),TEMP=file:forcing/forcing_obc_segment_002.nc(temp),SALT=file:forcing/forcing_obc_segment_002.nc(salt)" - -! segment 3: western boundary -OBC_SEGMENT_003 = "I=0,J=N:0,FLATHER,ORLANSKI,NUDGED" -OBC_SEGMENT_003_VELOCITY_NUDGING_TIMESCALES = .3, 360.0 ! inflow and outflow timescales -OBC_SEGMENT_003_DATA = "U=file:forcing/forcing_obc_segment_003.nc(u),V=file:forcing/forcing_obc_segment_003.nc(v),SSH=file:forcing/forcing_obc_segment_003.nc(eta),TEMP=file:forcing/forcing_obc_segment_003.nc(temp),SALT=file:forcing/forcing_obc_segment_003.nc(salt)" - -! segment 4: eastern boundary -OBC_SEGMENT_004 = "I=N,J=0:N,FLATHER,ORLANSKI,NUDGED" -OBC_SEGMENT_004_VELOCITY_NUDGING_TIMESCALES = .3, 360.0 ! inflow and outflow timescales -OBC_SEGMENT_004_DATA = "U=file:forcing/forcing_obc_segment_004.nc(u),V=file:forcing/forcing_obc_segment_004.nc(v),SSH=file:forcing/forcing_obc_segment_004.nc(eta),TEMP=file:forcing/forcing_obc_segment_004.nc(temp),SALT=file:forcing/forcing_obc_segment_004.nc(salt)" - -OBC_TRACER_RESERVOIR_LENGTH_SCALE_OUT = 30000 -OBC_TRACER_RESERVOIR_LENGTH_SCALE_IN = 3000 - -! sponges -SPONGE = False - -! === module MOM_coord_initialization === -! don't define layers in ALE mode -COORD_CONFIG = "ALE" - -REGRIDDING_COORDINATE_MODE = "ZSTAR" -! use high-order scheme at boundary cells for remapping, -! instead of just piecewise-constant -BOUNDARY_EXTRAPOLATION = True - -! read target densities from file, set minimum interface depth -! from profile: minimum dz=2, 4000m total thickness, profile is dz^4.5 -ALE_COORDINATE_CONFIG = "FILE:vcoord.nc,interfaces=zi" - -REMAPPING_SCHEME = "PPM_H4" - -! === module MOM_state_initialization === -! initialise states from the nested Z-space file -INIT_LAYERS_FROM_Z_FILE = True -! if we've pre-interpolated the initial conditions, -! we only have to perform remapping -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = True - -! === module MOM_initialize_layers_from_Z === -! read "temp" and "salt" from a z-space file -TEMP_SALT_Z_INIT_FILE = "forcing/init_tracers.nc" -Z_INIT_FILE_PTEMP_VAR = "temp" -! remap straight to the model coordinate -Z_INIT_ALE_REMAPPING = True - -! === module MOM_set_visc === -CHANNEL_DRAG = True -! turbulent Prandtl number for shear instability -PRANDTL_TURB = 1.25 -HBBL = 10.0 -DRAG_BG_VEL = 0.1 -BBL_USE_EOS = True -BBL_THICK_MIN = 0.1 -KV = 1.0E-04 -KV_BBL_MIN = 0.0 -KV_TBL_MIN = 0.0 - -! === module MOM_continuity_PPM === -ETA_TOLERANCE = 1.0E-06 -ETA_TOLERANCE_AUX = 0.001 - -! === module MOM_CoriolisAdv === -CORIOLIS_SCHEME = "SADOURNY75_ENSTRO" -BOUND_CORIOLIS = True - -! === module MOM_PressureForce_AFV === -MASS_WEIGHT_IN_PRESSURE_GRADIENT = True - -! === module MOM_hor_visc === -LAPLACIAN = True -AH_VEL_SCALE = 0.01 -SMAGORINSKY_AH = True -SMAG_BI_CONST = 0.06 - -! === module MOM_vert_friction === -U_TRUNC_FILE = "U_velocity_truncations" -V_TRUNC_FILE = "V_velocity_truncations" -HMIX_FIXED = 0.5 -! maximum velocity before truncation -MAXVEL = 6.0 - -! === module MOM_barotropic === -BOUND_BT_CORRECTION = True -BT_PROJECT_VELOCITY = True -DYNAMIC_SURFACE_PRESSURE = True -BEBT = 0.2 -DTBT = -0.9 - -! === module MOM_thickness_diffuse === -! maximum value of local diffusive CFL ratio -! permitted for thickness diffusivity -KHTH_MAX_CFL = 0.1 - -! === module MOM_mixed_layer_restrat === -MIXEDLAYER_RESTRAT = True -! proportional coefficient to ratio of deformation radius -! to the dominant lengthscale of submesoscale mixed layer -! instabilities, times the minimum of the ratio of -! mesoscale EKE to large-scale geostrophic KE, or 1 + the -! square of the grid spacing over the deformation radius -FOX_KEMPER_ML_RESTRAT_COEF = 1.0 -! frontal-length scale used to calculate -! upscaling of buoyancy gradients -MLE_FRONT_LENGTH = 500.0 -MLE_USE_PBL_MLD = True -! 720 days -MLE_MLD_DECAY_TIME = 2.592E+06 - -! === module MOM_diabatic_driver === -ENERGETICS_SFC_PBL = True -EPBL_IS_ADDITIVE = False - -! === module MOM_set_diffusivity === -! add BBL mixing diffusivity, instead of taking maximum -BBL_MIXING_AS_MAX = False -! non-coord dependent BBL diffusivity -USE_LOTW_BBL_DIFFUSIVITY = True -! use estimate of Kd/TKE for arbitrary vertical coordinate -SIMPLE_TKE_TO_KD = True -! latitude-dependent scaling for near-surface background -! diffusivity -HENYEY_IGW_BACKGROUND = True -!N2_FLOOR_IOMEGA2 = 0.0 -KD = 1.5E-05 -KD_MIN = 2.0E-06 -KD_MAX = 0.1 -INT_TIDE_DISSIPATION = False -!INT_TIDE_PROFILE = "POLZIN_09" -!INT_TIDE_DECAY_SCALE = 300.3003003003003 -! topographic wavenumber, 2pi/10km -!KAPPA_ITIDES = 6.28319E-04 -!KAPPA_H2_FACTOR = 0.84 -! maximum internal tide energy source available -! to mix above the BBL -!TKE_ITIDE_MAX = 0.1 - -! === MOM_kappa_shear === -USE_JACKSON_PARAM = True -!MAX_RINO_INT = 25 - -! === module MOM_energetic_PBL === -EPBL_MSTAR_SCHEME = "OM4" -MSTAR = 0.0 -MIX_LEN_EXPONENT = 1.0 -MSTAR_CAP = 10.0 -MSTAR_CONV_ADJ = 0.667 -MSTAR2_COEF1 = 0.29 -MSTAR2_COEF2 = 0.152 -NSTAR = 0.06 -TKE_DECAY = 0.01 -ML_OMEGA_FRAC = 0.001 -USE_MLD_ITERATION = True -EPBL_TRANSITION_SCALE = 0.01 -USE_LA_LI2016 = True -EPBL_LANGMUIR_SCHEME = "ADDITIVE" -LT_ENHANCE_COEF = 0.044 -LT_ENHANCE_EXP = -1.5 -LT_MOD_LAC1 = 0.0 -LT_MOD_LAC4 = 0.0 -LT_MOD_LAC5 = 0.22 -!EPBL_USTAR_MIN = 1.45842E-18 - -! === module MOM_trace_advect === -TRACER_ADVECTION_SCHEME = "PPM:H3" - -! === module MOM_tracer_hor_diff === -! ensure diffusive equivalent of CFL limit isn't -! violated -CHECK_DIFFUSIVE_CFL = True - -! === module ocean_model_init === -! time between saves of energies and other global -! integrals -TIMEUNIT = 86400 -ENERGYSAVEDAYS = 1.0 - -! === module MOM_surface_forcing === -! maximum surface pressure that can be exerted -! by atmosphere and floating sea-ice -MAX_P_SURF = 0.0 -CD_TIDES = 0.0018 -GUST_CONST = 0.0 -USE_RIGID_SEA_ICE = True -SEA_ICE_RIGID_MASS = 100.0 - -! === module MOM_sum_output === -MAXTRUNC = 100000 - -REPORT_UNUSED_PARAMS = True -!FATAL_UNUSED_PARAMS = True - -NUM_DIAG_COORDS = 1 -DIAG_COORDS = "z Z ZSTAR" -DIAG_COORD_DEF_Z = "FILE:vcoord.nc,interfaces=zi" -! drho (arg 5) is a bit magic and has a big impact on -! the tail of the coord... - -BAD_VAL_SST_MIN = -3.0 diff --git a/regional_mom6/default_rundir/MOM_override b/regional_mom6/default_rundir/MOM_override deleted file mode 100644 index b4be647e..00000000 --- a/regional_mom6/default_rundir/MOM_override +++ /dev/null @@ -1,7 +0,0 @@ -#override OBC_SEGMENT_001_VELOCITY_NUDGING_TIMESCALES = .3, 300.0 ! inflow and outflow timescales -#override OBC_SEGMENT_001_VELOCITY_NUDGING_TIMESCALES = .3, 300.0 ! inflow and outflow timescales -#override OBC_SEGMENT_003_VELOCITY_NUDGING_TIMESCALES = .3, 300.0 ! inflow and outflow timescales -#override OBC_SEGMENT_004_VELOCITY_NUDGING_TIMESCALES = .3, 300.0 ! inflow and outflow timescales - -#override DT = 300 -#override DT_THERM = 600 \ No newline at end of file diff --git a/regional_mom6/default_rundir/SIS_input b/regional_mom6/default_rundir/SIS_input deleted file mode 100755 index 37231a83..00000000 --- a/regional_mom6/default_rundir/SIS_input +++ /dev/null @@ -1,66 +0,0 @@ -CGRID_ICE_DYNAMICS = True -DT_ICE_DYNAMICS = 180.0 -! fixed bulk salinity of sea ice -ICE_BULK_SALINITY = 0.0 -! salinity of sea ice as fraction of salinity -! of sea water from which it formed -ICE_RELATIVE_SALINITY = 0.1 -! cosine of solar zenith angle for first radiation step -CONSTANT_COSZEN_IC = 0.0 -! don't call iceberg module -DO_ICEBERGS = False -! time between writes of global ice diags and conservation -ICE_STATS_INTERVAL = 0.25 - -NIGLOBAL = 80 -NJGLOBAL = 129 -LAYOUT = 10,10 -MASKTABLE = "mask_table.18.10x10" - -GRID_CONFIG = "mosaic" -GRID_FILE = "hgrid.nc" -INPUTDIR = "INPUT" - -TOPO_FILE = "topog.nc" - -MAXIMUM_DEPTH = 6000.0 - -WRITE_GEOM = 0 - -! rotation rate of earth -OMEGA = 7.292E-05 - -! tuning params for radiative properties -ICE_DELTA_EDD_R_ICE = 1.0 -ICE_DELTA_EDD_R_SNOW = 1.0 -ICE_DELTA_EDD_R_POND = 1.0 - -CP_ICE = 2100.0 -CP_SEAWATER = 3992.0 -CP_BRINE = 3992.0 - -! subcycling timestep for rheology -DT_RHEOLOGY = 50.0 - -U_TRUNC_FILE = "SIS_U_truncations" -V_TRUNC_FILE = "SIS_V_truncations" - -SIS_THICKNESS_ADVECTION_SCHEME = "PCM" -SIS_CONTINUITY_SCHEME = "PCM" - -! use previous calculations of ice-top surface -! skin temp for tsurf -EULERIAN_TSURF = True -! redistribute after advection -RECATEGORIZE_ICE = True - -SIS_TRACER_ADVECTION_SCHEME = "PPM:H3" - -MAXTRUNC = 200 -DOCUMENT_FILE = "SIS_parameter_doc" - -REPORT_UNUSED_PARAMS = True -FATAL_UNUSED_PARAMS = True - -! from overrides: -ADD_DIURNAL_SW = False diff --git a/regional_mom6/default_rundir/config.yaml b/regional_mom6/default_rundir/config.yaml deleted file mode 100755 index a6b79176..00000000 --- a/regional_mom6/default_rundir/config.yaml +++ /dev/null @@ -1,29 +0,0 @@ -project: x77 -queue: normal -walltime: 02:00:00 -jobname: mom6_GIPPSLAND -ncpus: 82 -jobfs: 10GB - -shortpath: /scratch/x77 - -model: mom6 -input: - - /scratch/v45/ab8992/mom6/regional_configs/gippsland -# - /g/data/ua8/JRA55-do/RYF/v1-3/ - - /g/data/ik11/inputs/JRA-55/RYF/v1-3/ -# release exe -exe: /g/data/x77/ahg157/exes/MOM6_SIS2/symmetric_FMS2-e7d09b7 -# debug exe -#exe: /g/data/x77/ahg157/exes/MOM6_SIS2/symmetric_FMS2-9bc3419a - -collate: false -#runlog: true -storage: - gdata: - - ua8 - - x77 - - ik11 - -mpi: - module: openmpi/4.1.2 diff --git a/regional_mom6/default_rundir/data_table b/regional_mom6/default_rundir/data_table deleted file mode 100755 index d82980fd..00000000 --- a/regional_mom6/default_rundir/data_table +++ /dev/null @@ -1,21 +0,0 @@ -"ATM", "p_surf", "psl", "./INPUT/RYF.slp.1990_1991.nc", "bilinear", 1.0 -"ATM", "p_bot", "psl", "./INPUT/RYF.slp.1990_1991.nc", "bilinear", 1.0 -"ATM", "t_bot", "tas_10m", "./INPUT/RYF.t_10.1990_1991.nc", "bilinear", 1.0 -"ATM", "sphum_bot", "huss_10m", "./INPUT/RYF.q_10.1990_1991.nc", "bilinear", 1.0 -"ATM", "u_bot", "uas_10m", "./INPUT/RYF.u_10.1990_1991.nc", "bicubic", 1.0 -"ATM", "v_bot", "vas_10m", "./INPUT/RYF.v_10.1990_1991.nc", "bicubic", 1.0 -"ATM", "z_bot", "", "", "bilinear", 10.0 -"ATM", "gust", "", "", "bilinear", 1.0e-4 -"ICE", "lw_flux_dn", "rlds", "./INPUT/RYF.rlds.1990_1991.nc", "bilinear", 1.0 -"ICE", "sw_flux_vis_dir_dn", "rsds", "./INPUT/RYF.rsds.1990_1991.nc", "bilinear", 0.285 -"ICE", "sw_flux_vis_dif_dn", "rsds", "./INPUT/RYF.rsds.1990_1991.nc", "bilinear", 0.285 -"ICE", "sw_flux_nir_dir_dn", "rsds", "./INPUT/RYF.rsds.1990_1991.nc", "bilinear", 0.215 -"ICE", "sw_flux_nir_dif_dn", "rsds", "./INPUT/RYF.rsds.1990_1991.nc", "bilinear", 0.215 -"ICE", "lprec", "prrn", "./INPUT/RYF.rain.1990_1991.nc", "bilinear", 1.0 -"ICE", "fprec", "prsn", "./INPUT/RYF.snow.1990_1991.nc", "bilinear", 1.0 -"ICE", "runoff", "friver", "./INPUT/runoff_regrid.nc", "none", 1.0 -"ICE", "dhdt", "", "", "none", 80.0 -"ICE", "dedt", "", "", "none", 2.0e-6 -"ICE", "drdt", "", "", "none", 10.0 -"LND", "rough_mom", "", "", "none", 0.01 -"LND", "rough_heat", "", "", "none", 0.1 diff --git a/regional_mom6/default_rundir/diag_table b/regional_mom6/default_rundir/diag_table deleted file mode 100755 index d6d95b72..00000000 --- a/regional_mom6/default_rundir/diag_table +++ /dev/null @@ -1,90 +0,0 @@ -eac -1991 1 1 0 0 0 - -"ocean_daily", 1, "days", 1, "days", "time" -"ocean_month", 1, "months", 1, "days", "time" -"ocean_month_z", 1, "months", 1, "days", "time" -# "ocean_month_rho2", 1, "months", 1, "days", "time" -"ocean_static", -1, "months", 1, "days", "time" # ocean_static is a protected name. Do not change this line. -"ice_daily", 1, "days", 1, "days", "time" -"ice_month", 1, "months", 1, "days", "time" - -# Ocean grid-cell volume -"ocean_model_z", "volcello", "volcello", "ocean_month_z", "all", "mean", "none",2 -# "ocean_model_rho2", "volcello", "volcello", "ocean_month_rho2", "all", "mean", "none",2 - -# Sea surface height above geoid -"ocean_model", "zos", "zos", "ocean_daily", "all", "mean", "none",2 -# Sea Surface Temperature -"ocean_model", "tos", "tos", "ocean_daily", "all", "mean", "none",2 -# Sea Surface Salinity -"ocean_model", "sos", "sos", "ocean_daily", "all", "mean", "none",2 -# Sea Water Potential Temperature at Sea Floor -"ocean_model", "tob", "tob", "ocean_daily", "all", "mean", "none",2 -# Sea Water Salinity at Sea Floor -"ocean_model", "sob", "sob", "ocean_daily", "all", "mean", "none",2 -# Mixed layer depth (delta rho = 0.03) -"ocean_model", "mlotst", "mlotst", "ocean_daily", "all", "mean", "none",2 -# Sea Surface Speed -"ocean_model", "speed", "speed", "ocean_daily", "all", "mean", "none",2 - -# (umo,vmo) =net mass transport from residual mean velocity (model resolved + SGS) -# "ocean_model_rho2","umo", "umo", "ocean_month_rho2", "all", "mean", "none",2 -# "ocean_model_rho2","vmo", "vmo", "ocean_month_rho2", "all", "mean", "none",2 -# "ocean_model_rho2", "h", "hmo", "ocean_month_rho2", "all", "mean", "none", 2 - -# Zonal/Meridional velocity -"ocean_model_z","uo", "uo", "ocean_month_z", "all", "mean", "none",2 -"ocean_model_z","vo", "vo", "ocean_month_z", "all", "mean", "none",2 -# Potential Temperature -"ocean_model_z", "thetao", "thetao", "ocean_month_z", "all", "mean", "none",2 # if use pre-TEOS10 -# Salinity -"ocean_model_z", "so", "so", "ocean_month_z", "all", "mean", "none",2 -# Potential Density referenced to 2000 dbar -"ocean_model_z", "rhopot2", "rhopot2", "ocean_month_z", "all", "mean", "none", 2 - -# Ocean Mass X/Y Transport Vertical Sum -"ocean_model", "umo_2d", "umo_2d", "ocean_month", "all", "mean", "none",2 -"ocean_model", "vmo_2d", "vmo_2d", "ocean_month", "all", "mean", "none",2 - -# Zonal/Meridional surface stress from ocean interactions with atmos and ice -"ocean_model", "tauuo", "tauuo", "ocean_month", "all", "mean", "none",2 -"ocean_model", "tauvo", "tauvo", "ocean_month", "all", "mean", "none",2 -# Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments -"ocean_model", "hfds", "hfds", "ocean_month", "all", "mean", "none",2 -# Net surface water flux (precip+melt+lrunoff+ice calving-evap) -"ocean_model", "wfo", "wfo", "ocean_month", "all", "mean", "none",2 -"ocean_model", "friver", "friver", "ocean_month", "all", "mean", "none",2 - -# Surface area of tracer (T) cells -"ocean_model", "areacello", "areacello", "ocean_static", "all", "none", "none", 2 -# Depth of the ocean at tracer points -"ocean_model", "deptho", "deptho", "ocean_static", "all", "none", "none", 2 -"ocean_model", "Coriolis", "Coriolis", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolon", "geolon", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolat", "geolat", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolon_c", "geolon_c", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolat_c", "geolat_c", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolon_u", "geolon_u", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolat_u", "geolat_u", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolon_v", "geolon_v", "ocean_static", "all", "none", "none", 2 -"ocean_model", "geolat_v", "geolat_v", "ocean_static", "all", "none", "none", 2 -"ocean_model", "wet", "wet", "ocean_static", "all", "none", "none", 2 -"ocean_model", "wet_c", "wet_c", "ocean_static", "all", "none", "none", 2 -"ocean_model", "wet_u", "wet_u", "ocean_static", "all", "none", "none", 2 -"ocean_model", "wet_v", "wet_v", "ocean_static", "all", "none", "none", 2 -"ocean_model", "dxt", "dxt", "ocean_static", "all", "none", "none", 2 -"ocean_model", "dyt", "dyt", "ocean_static", "all", "none", "none", 2 -"ocean_model", "dxCu", "dxCu", "ocean_static", "all", "none", "none", 2 -"ocean_model", "dyCu", "dyCu", "ocean_static", "all", "none", "none", 2 -"ocean_model", "dxCv", "dxCv", "ocean_static", "all", "none", "none", 2 -"ocean_model", "dyCv", "dyCv", "ocean_static", "all", "none", "none", 2 -"ocean_model", "areacello_cu","areacello_cu","ocean_static", "all", "none", "none", 2 -"ocean_model", "areacello_cv","areacello_cv","ocean_static", "all", "none", "none", 2 -"ocean_model", "areacello_bu","areacello_bu","ocean_static", "all", "none", "none", 2 - - -"ice_model", "siconc", "siconc", "ice_month", "all", "mean", "none", 2 -"ice_model", "siconc", "siconc", "ice_daily", "all", "mean", "none", 2 -"ice_model", "sithick", "sithick", "ice_month", "all", "mean", "none", 2 -"ice_model", "sithick", "sithick", "ice_daily", "all", "mean", "none", 2 diff --git a/regional_mom6/default_rundir/env.yaml b/regional_mom6/default_rundir/env.yaml deleted file mode 100755 index dfe1ea31..00000000 --- a/regional_mom6/default_rundir/env.yaml +++ /dev/null @@ -1,93 +0,0 @@ -CPATH: /apps/openmpi/4.1.2/include -CPATH_modshare: /apps/openmpi/4.1.2/include:1 -CPLUS_INCLUDE_PATH: /apps/openmpi/4.1.2/include -CPLUS_INCLUDE_PATH_modshare: /apps/openmpi/4.1.2/include:1 -C_INCLUDE_PATH: /apps/openmpi/4.1.2/include -C_INCLUDE_PATH_modshare: /apps/openmpi/4.1.2/include:1 -ENVIRONMENT: BATCH -FPATH: /apps/openmpi/4.1.2/include -FPATH_modshare: /apps/openmpi/4.1.2/include:1 -GIT_CONFIG_NOGLOBAL: 'yes' -HCOLL_ENABLE_MCAST: '0' -HOME: /home/149/ab8992 -LC_CTYPE: C.UTF-8 -LD_LIBRARY_PATH: /apps/openmpi/4.1.2/lib:/apps/openmpi/4.1.2/lib/profilers -LD_LIBRARY_PATH_modshare: /apps/openmpi/4.1.2/lib:1:/apps/openmpi/4.1.2/lib/profilers:1 -LD_RUN_PATH: /apps/openmpi/4.1.2/lib:/apps/openmpi/4.1.2/lib/profilers -LD_RUN_PATH_modshare: /apps/openmpi/4.1.2/lib:1:/apps/openmpi/4.1.2/lib/profilers:1 -LIBRARY_PATH: /apps/openmpi/4.1.2/lib:/apps/openmpi/4.1.2/lib/profilers -LIBRARY_PATH_modshare: /apps/openmpi/4.1.2/lib:1:/apps/openmpi/4.1.2/lib/profilers:1 -LOADEDMODULES: openmpi/4.1.2:pbs -LOADEDMODULES_modshare: pbs:1:openmpi/4.1.2:1 -LOGNAME: ab8992 -MANPATH: /opt/pbs/default/share/man:/apps/openmpi/4.1.2/share/man -MANPATH_modshare: /apps/openmpi/4.1.2/share/man:1:/opt/pbs/default/share/man:1 -MODULEPATH: /g/data/hh5/public/modules:/etc/scl/modulefiles:/opt/Modules/modulefiles:/opt/Modules/v4.3.0/modulefiles:/apps/Modules/modulefiles -MODULESHOME: /opt/Modules/v4.3.0 -MODULES_CMD: /opt/Modules/v4.3.0/libexec/modulecmd.tcl -MODULES_LMCONFLICT: openmpi/4.1.2&mpi&lam&mpich&openmpi&intel-mpi&o/wrappers&o/yes-wrappers&o/use-wrappers&o/enable-wrappers&o/with-wrappers&o/no-wrappers&o/not-wrappers&o/disable-wrappers&o/without-wrappers&o/ld_library_path&o/yes-ld_library_path&o/use-ld_library_path&o/enable-ld_library_path&o/with-ld_library_path&o/no-ld_library_path&o/not-ld_library_path&o/disable-ld_library_path&o/without-ld_library_path&o/ld_run_path&o/yes-ld_run_path&o/use-ld_run_path&o/enable-ld_run_path&o/with-ld_run_path&o/no-ld_run_path&o/not-ld_run_path&o/disable-ld_run_path&o/without-ld_run_path&o/show-debug&o/yes-show-debug&o/use-show-debug&o/enable-show-debug&o/with-show-debug&o/no-show-debug&o/not-show-debug&o/disable-show-debug&o/without-show-debug&o/append-paths&o/yes-append-paths&o/use-append-paths&o/enable-append-paths&o/with-append-paths&o/no-append-paths&o/not-append-paths&o/disable-append-paths&o/without-append-paths&o/library_path&o/yes-library_path&o/use-library_path&o/enable-library_path&o/with-library_path&o/no-library_path&o/not-library_path&o/disable-library_path&o/without-library_path&o/packaged-envvars&o/yes-packaged-envvars&o/use-packaged-envvars&o/enable-packaged-envvars&o/with-packaged-envvars&o/no-packaged-envvars&o/not-packaged-envvars&o/disable-packaged-envvars&o/without-packaged-envvars -MODULES_LMCONFLICT_modshare: openmpi/4.1.2&mpi&lam&mpich&openmpi&intel-mpi&o/wrappers&o/yes-wrappers&o/use-wrappers&o/enable-wrappers&o/with-wrappers&o/no-wrappers&o/not-wrappers&o/disable-wrappers&o/without-wrappers&o/ld_library_path&o/yes-ld_library_path&o/use-ld_library_path&o/enable-ld_library_path&o/with-ld_library_path&o/no-ld_library_path&o/not-ld_library_path&o/disable-ld_library_path&o/without-ld_library_path&o/ld_run_path&o/yes-ld_run_path&o/use-ld_run_path&o/enable-ld_run_path&o/with-ld_run_path&o/no-ld_run_path&o/not-ld_run_path&o/disable-ld_run_path&o/without-ld_run_path&o/show-debug&o/yes-show-debug&o/use-show-debug&o/enable-show-debug&o/with-show-debug&o/no-show-debug&o/not-show-debug&o/disable-show-debug&o/without-show-debug&o/append-paths&o/yes-append-paths&o/use-append-paths&o/enable-append-paths&o/with-append-paths&o/no-append-paths&o/not-append-paths&o/disable-append-paths&o/without-append-paths&o/library_path&o/yes-library_path&o/use-library_path&o/enable-library_path&o/with-library_path&o/no-library_path&o/not-library_path&o/disable-library_path&o/without-library_path&o/packaged-envvars&o/yes-packaged-envvars&o/use-packaged-envvars&o/enable-packaged-envvars&o/with-packaged-envvars&o/no-packaged-envvars&o/not-packaged-envvars&o/disable-packaged-envvars&o/without-packaged-envvars:1 -MODULE_VERSION: v4.3.0 -MODULE_VERSION_STACK: v4.3.0 -NCPUS: '48' -OMPI_BASE: /apps/openmpi/4.1.2 -OMPI_MCA_orte_tmpdir_base: /jobfs/57505323.gadi-pbs -OMPI_ROOT: /apps/openmpi/4.1.2 -OMPI_VERSION: 4.1.2 -OMP_NUM_THREADS: '48' -OPENMPI_BASE: /apps/openmpi/4.1.2 -OPENMPI_ROOT: /apps/openmpi/4.1.2 -OPENMPI_VERSION: 4.1.2 -PATH: /apps/openmpi/wrapper/fortran:/apps/openmpi/wrapper:/apps/openmpi/4.1.2/bin:/bin:/usr/bin:/opt/pbs/default/bin -PATH_modshare: /apps/openmpi/4.1.2/bin:1:/bin:1:/apps/openmpi/wrapper/fortran:1:/usr/bin:1:/opt/pbs/default/bin:1:/apps/openmpi/wrapper:1 -PAYU_FORCE: 'True' -PAYU_PATH: /g/data3/hh5/public/apps/miniconda3/envs/analysis3-22.04/bin -PBS_ENVIRONMENT: PBS_BATCH -PBS_JOBCOOKIE: 4D421325409555406510687B28164628 -PBS_JOBDIR: /home/149/ab8992 -PBS_JOBFS: /jobfs/57505323.gadi-pbs -PBS_JOBID: 57505323.gadi-pbs -PBS_JOBNAME: mom6_GIPPSLAND -PBS_MOMPORT: '15003' -PBS_NCI_FS_GDATA1: '0' -PBS_NCI_FS_GDATA1A: '0' -PBS_NCI_FS_GDATA1B: '0' -PBS_NCI_FS_GDATA2: '0' -PBS_NCI_FS_GDATA3: '0' -PBS_NCI_FS_GDATA4: '0' -PBS_NCI_HT: '0' -PBS_NCI_IMAGE: '' -PBS_NCI_JOBFS: 10gb -PBS_NCI_LAUNCH_COMPATIBILITY: '0' -PBS_NCI_NCPUS_PER_NODE: '48' -PBS_NCI_NCPUS_PER_NUMA: '12' -PBS_NCI_NUMA_PER_NODE: '4' -PBS_NCI_STORAGE: gdata/hh5+gdata/ik11+gdata/x77+scratch/v45+scratch/x77+gdata/ua8 -PBS_NCI_WD: '1' -PBS_NCPUS: '96' -PBS_NGPUS: '0' -PBS_NNODES: '2' -PBS_NODEFILE: /local/spool/pbs/aux/57505323.gadi-pbs -PBS_NODENUM: '0' -PBS_O_HOME: /home/149/ab8992 -PBS_O_HOST: gadi-login-06.gadi.nci.org.au -PBS_O_LANG: en_AU.UTF-8 -PBS_O_LOGNAME: ab8992 -PBS_O_MAIL: /var/spool/mail/ab8992 -PBS_O_PATH: /home/149/ab8992/tools/topogtools:/home/149/ab8992/tools/access-om2/tools:/g/data3/hh5/public/apps/miniconda3/envs/analysis3-22.04/bin:/g/data3/hh5/public/apps/miniconda3/condabin:/apps/ncview/2.1.7/bin:/home/149/ab8992/.local/bin:/home/149/ab8992/bin:/opt/pbs/default/bin:/opt/nci/bin:/opt/bin:/opt/Modules/v4.3.0/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin -PBS_O_QUEUE: normal -PBS_O_SHELL: /bin/bash -PBS_O_SYSTEM: Linux -PBS_O_TZ: :/etc/localtime -PBS_O_WORKDIR: /home/149/ab8992/libraries/gippsland_rundir -PBS_QUEUE: normal-exec -PBS_TASKNUM: '1' -PBS_VMEM: '412316860416' -PROJECT: x77 -SHELL: /opt/bin/nfsh -TMPDIR: /jobfs/57505323.gadi-pbs -USER: ab8992 -VT_MAX_FLUSHES: '0' -VT_PFORM_LDIR: /jobfs/57505323.gadi-pbs -_LMFILES_: /apps/Modules/modulefiles/openmpi/4.1.2:/opt/Modules/modulefiles/pbs -_LMFILES__modshare: /apps/Modules/modulefiles/openmpi/4.1.2:1:/opt/Modules/modulefiles/pbs:1 diff --git a/regional_mom6/default_rundir/field_table b/regional_mom6/default_rundir/field_table deleted file mode 100755 index 54f36b3b..00000000 --- a/regional_mom6/default_rundir/field_table +++ /dev/null @@ -1,2 +0,0 @@ -"TRACER", "atmos_mod", "sphum" / -"TRACER", "land_mod", "sphum" / \ No newline at end of file diff --git a/regional_mom6/default_rundir/input.nml b/regional_mom6/default_rundir/input.nml deleted file mode 100755 index 1641422a..00000000 --- a/regional_mom6/default_rundir/input.nml +++ /dev/null @@ -1,83 +0,0 @@ -&MOM_input_nml - output_directory = '.', - input_filename = 'n', - restart_input_dir = 'INPUT', - restart_output_dir = 'RESTART', - parameter_filename = 'MOM_input', 'MOM_override' -/ - -&SIS_input_nml - output_directory = '.', - input_filename = 'n', - restart_input_dir = 'INPUT', - restart_output_dir = 'RESTART', - parameter_filename = 'SIS_input' -/ - -&fms_nml - domains_stack_size = 1600000, - stack_size = 0, - clock_grain = 'LOOP' -/ - -&fms2_io_nml - netcdf_default_format = "netcdf4" - shuffle = .true., - deflate = 5 -/ - -&coupler_nml - months = 0, - days = 5, - hours = 0, - current_date = 1991,1,1,0,0,0, - calendar = 'NOLEAP', - dt_cpld = 3600, - dt_atmos = 3600, - do_atmos = .false., - do_land = .false., - do_ice = .true., - do_ocean = .true., - do_flux = .true., - atmos_npes = 0, - concurrent = .false., - use_lag_fluxes = .false., - check_stocks = 0 -/ - -&flux_exchange_nml - debug_stocks = .false., - divert_stocks_report = .true., - do_area_weighted_flux = .false. -/ - -&ice_albedo_nml - t_range = 10. -/ - -&monin_obukhov_nml - neutral = .true. -/ - -&ocean_albedo_nml - ocean_albedo_option = 2 -/ - -&ocean_rough_nml - rough_scheme = 'beljaars' -/ - -&sat_vapor_pres_nml - construct_table_wrt_liq = .true., - construct_table_wrt_liq_and_ice = .true. -/ - -&surface_flux_nml - ncar_ocean_flux = .true., - raoult_sat_vap = .true. -/ - -&xgrid_nml - make_exchange_reproduce = .false., - interp_method = 'second_order' -/ diff --git a/regional_mom6/default_rundir/job.yaml b/regional_mom6/default_rundir/job.yaml deleted file mode 100755 index 788f8c53..00000000 --- a/regional_mom6/default_rundir/job.yaml +++ /dev/null @@ -1,9 +0,0 @@ -PAYU_CONTROL_DIR: /home/149/ab8992/libraries/gippsland_rundir -PAYU_CURRENT_RUN: 3 -PAYU_FINISH_TIME: '2022-09-09T14:20:28.453581' -PAYU_JOB_STATUS: 1 -PAYU_N_RUNS: 1 -PAYU_PATH: /g/data3/hh5/public/apps/miniconda3/envs/analysis3-22.04/bin -PAYU_RUN_ID: 39390be2ad0c5ac30dbc2ffef615c954ab984620 -PAYU_START_TIME: '2022-09-09T14:03:22.114145' -PAYU_WALLTIME: 1026.339436 s diff --git a/regional_mom6/regional_mom6.py b/regional_mom6/regional_mom6.py index 50dc4f3f..af7fce73 100644 --- a/regional_mom6/regional_mom6.py +++ b/regional_mom6/regional_mom6.py @@ -120,7 +120,7 @@ def nicer_slicer(data, xextent, xcoords, buffer=2): num_xpoints = ( int(data[x].shape[0] * (mp_target - xextent[0])) // 360 + buffer * 2 - ) ## The extra 8 is a buffer region + ) data = new_data.isel( { @@ -596,7 +596,6 @@ def ocean_forcing( ## Do initial condition ## pull out the initial velocity on MOM5's Bgrid - ic_raw = xr.open_dataset(path + "/ic_unprocessed") if varnames["time"] in ic_raw.dims: @@ -797,7 +796,16 @@ def ocean_forcing( vel_out = vel_out.interp({"zl": self.vgrid.zl.values}) print("Saving outputs... ", end="") - vel_out.fillna(0).drop("time").to_netcdf( + + ## Remove time IF it exists. Users may already have done so for us + if "time" in vel_out.dims: + vel_out = vel_out.isel(time=0).drop("time") + if "time" in tracers_out.dims: + tracers_out = tracers_out.isel(time=0).drop("time") + if "time" in eta_out.dims: + eta_out = eta_out.isel(time=0).drop("time") + + vel_out.fillna(0).to_netcdf( self.mom_input_dir + "forcing/init_vel.nc", mode="w", encoding={ @@ -806,7 +814,7 @@ def ocean_forcing( }, ) - tracers_out.drop("time").to_netcdf( + tracers_out.to_netcdf( self.mom_input_dir + "forcing/init_tracers.nc", mode="w", encoding={ @@ -817,7 +825,7 @@ def ocean_forcing( "salt": {"_FillValue": -1e20, "missing_value": -1e20}, }, ) - eta_out.drop("time").to_netcdf( + eta_out.to_netcdf( self.mom_input_dir + "forcing/init_eta.nc", mode="w", encoding={ @@ -863,6 +871,7 @@ def bathymetry( minimum_layers=3, maketopog=True, positivedown=False, + chunks="auto", ): """Cuts out and interpolates chosen bathymetry, then fills inland lakes. @@ -886,39 +895,67 @@ def bathymetry( make topography (if true), or read an existing file. positivedown (Optional[bool]): If true, assumes that bathymetry vertical coordinate is positive down. + chunks (Optional Dict[str, str]): Chunking scheme for bathymetry, eg {"lon": 100, "lat": 100}. Use lat/lon rather than the coordinate names in the input file. """ if maketopog == True: - bathy = xr.open_dataset( - bathy_path, chunks={varnames["xh"]: 500, varnames["yh"]: 500} - )[varnames["elevation"]] + if chunks != "auto": + chunks = {varnames["xh"]: chunks["lon"], varnames["yh"]: chunks["lat"]} + + bathy = xr.open_dataset(bathy_path, chunks=chunks)[varnames["elevation"]] bathy = bathy.sel( - {varnames["yh"]: slice(self.yextent[0] - 0.1, self.yextent[1] + 0.1)} + { + varnames["yh"]: slice(self.yextent[0] - 1, self.yextent[1] + 1) + } #! Hardcoded 1 degree buffer around bathymetry selection. TODO: automatically select buffer ).astype("float") - bathy = nicer_slicer( - bathy, np.array(self.xextent) + np.array([-0.1, 0.1]), varnames["xh"] + ## Here need to make a decision as to whether to slice 'normally' or with nicer slicer for 360 degree domain. + + horizontal_resolution = bathy[varnames["xh"]][1] - bathy[varnames["xh"]][0] + horizontal_extent = ( + bathy[varnames["xh"]][-1] + - bathy[varnames["xh"]][0] + + horizontal_resolution ) + if np.isclose(horizontal_extent, 360): + ## Assume that we're dealing with a global grid, in which case we use nicer slicer + bathy = nicer_slicer( + bathy, + np.array(self.xextent) + + np.array( + [-0.1, 0.1] + ), #! Hardcoded 0.1 degree buffer around bathymetry selection. TODO: automatically select buffer + varnames["xh"], + ) + else: + ## Otherwise just slice normally + bathy = bathy.sel( + { + varnames["xh"]: slice(self.xextent[0] - 1, self.xextent[1] + 1) + } #! Hardcoded 1 degree buffer around bathymetry selection. TODO: automatically select buffer + ) + bathy.attrs[ "missing_value" ] = -1e20 # This is what FRE tools expects I guess? - - bathy = xr.Dataset({"elevation": bathy}) - - bathy.lon.attrs["units"] = "degrees_east" - bathy.lat.attrs["units"] = "degrees_north" - bathy.lon.attrs["_FillValue"] = 1e20 - bathy.elevation.attrs["_FillValue"] = 1e20 - bathy.elevation.attrs["units"] = "m" - bathy.elevation.attrs["standard_name"] = "height_above_reference_ellipsoid" - bathy.elevation.attrs["long_name"] = "Elevation relative to sea level" - bathy.elevation.attrs["coordinates"] = "lon lat" - - bathy.to_netcdf( + bathyout = xr.Dataset({"elevation": bathy}) + bathy.close() + + bathyout = bathyout.rename({varnames["xh"]: "lon", varnames["yh"]: "lat"}) + bathyout.lon.attrs["units"] = "degrees_east" + bathyout.lat.attrs["units"] = "degrees_north" + bathyout.elevation.attrs["_FillValue"] = -1e20 + bathyout.elevation.attrs["units"] = "m" + bathyout.elevation.attrs[ + "standard_name" + ] = "height_above_reference_ellipsoid" + bathyout.elevation.attrs["long_name"] = "Elevation relative to sea level" + bathyout.elevation.attrs["coordinates"] = "lon lat" + bathyout.to_netcdf( f"{self.mom_input_dir}bathy_original.nc", mode="w", engine="netcdf4" ) @@ -934,31 +971,60 @@ def bathymetry( ), } ) + tgrid = xr.Dataset( + data_vars={ + "elevation": ( + ["lat", "lon"], + np.zeros( + self.hgrid.x.isel( + nxp=slice(1, None, 2), nyp=slice(1, None, 2) + ).shape + ), + ) + }, + coords={ + "lon": ( + ["lon"], + self.hgrid.x.isel(nxp=slice(1, None, 2), nyp=1).values, + ), + "lat": ( + ["lat"], + self.hgrid.y.isel(nxp=1, nyp=slice(1, None, 2)).values, + ), + }, + ) + + # rewrite chunks to use lat/lon now for use with xesmf + if chunks != "auto": + chunks = {"lon": chunks[varnames["xh"]], "lat": chunks[varnames["yh"]]} + tgrid = tgrid.chunk(chunks) tgrid.lon.attrs["units"] = "degrees_east" tgrid.lon.attrs["_FillValue"] = 1e20 tgrid.lat.attrs["units"] = "degrees_north" - # tgrid.to_netcdf(f"{self.mom_input_dir}tgrid.nc", mode = "w",engine='netcdf4') tgrid.to_netcdf( f"{self.mom_input_dir}topog_raw.nc", mode="w", engine="netcdf4" ) + tgrid.close() - #! Hardcoded for whole node notebook. - # topog_raw file is the 'target' grid used for gridgen. This is then overweitten by the second ESMF function (needs a blank netcdf to overwrite as the output) - - if ( - subprocess.run( - "mpirun ESMF_Regrid -s bathy_original.nc -d topog_raw.nc -m bilinear --src_var elevation --dst_var elevation --netcdf4 --src_regional --dst_regional", - shell=True, - cwd=self.mom_input_dir, - ).returncode - != 0 - ): - raise RuntimeError( - "Regridding of bathymetry failed! This is probably because mpirun was initialised earlier by xesmf doing some other regridding. Try restarting the kernel, then calling .bathymetry() before any other methods." - ) + ## Replace subprocess run with regular regridder + print( + "Starting to regrid bathymetry. If this process hangs you might be better off calling ESMF directly from a terminal with appropriate computational resources using \n\n mpirun ESMF_Regrid -s bathy_original.nc -d topog_raw.nc -m bilinear --src_var elevation --dst_var elevation --netcdf4 --src_regional --dst_regional\n\nThis is better for larger domains.\n\n" + ) + + # If we have a domain large enough for chunks, we'll run regridder with parallel=True + parallel = True + if len(tgrid.chunks) != 2: + parallel = False + regridder = xe.Regridder(bathyout, tgrid, "bilinear", parallel=parallel) + + topog = regridder(bathyout) + topog.to_netcdf( + f"{self.mom_input_dir}topog_raw.nc", mode="w", engine="netcdf4" + ) ## reopen topography to modify + print("Reading in regridded bathymetry to fix up metadata...", end="") topog = xr.open_dataset(self.mom_input_dir + "topog_raw.nc", engine="netcdf4") ## Ensure correct encoding @@ -1123,7 +1189,7 @@ def bathymetry( subprocess.run( "mv topog_deseas.nc topog.nc", shell=True, cwd=self.mom_input_dir ) - + print("done.") self.topog = topog return @@ -1299,9 +1365,10 @@ def brushcut(self, ryf=False): ).set_coords(["lat", "lon"]) if self.grid == "A": + rawseg = rawseg.rename({self.x: "lon", self.y: "lat"}) ## In this case velocities and tracers all on same points regridder = xe.Regridder( - rawseg[self.u].rename({self.x: "lon", self.y: "lat"}), + rawseg[self.u], interp_grid, "bilinear", locstream_out=True, @@ -1309,6 +1376,7 @@ def brushcut(self, ryf=False): filename=self.outfolder + f"weights/bilinear_velocity_weights_{self.orientation}.nc", ) + segment_out = xr.merge( [ regridder( @@ -1344,9 +1412,19 @@ def brushcut(self, ryf=False): segment_out = xr.merge( [ - regridder_velocity(rawseg[[self.u, self.v]]), + regridder_velocity( + rawseg[[self.u, self.v]].rename( + {self.xq: "lon", self.yq: "lat"} + ) + ), regridder_tracer( - rawseg[[self.eta] + [self.tracers[i] for i in self.tracers]] + rawseg[ + [self.eta.rename({self.xh: "lon", self.yh: "lat"})] + + [ + self.tracers[i].rename({self.xh: "lon", self.yh: "lat"}) + for i in self.tracers + ] + ] ), ] ) diff --git a/tests/test_expt_class.py b/tests/test_expt_class.py new file mode 100644 index 00000000..e45cc7c4 --- /dev/null +++ b/tests/test_expt_class.py @@ -0,0 +1,278 @@ +import numpy as np +import pytest +from regional_mom6 import experiment +import xarray as xr + + +@pytest.mark.parametrize( + ( + "xextent", + "yextent", + "daterange", + "resolution", + "vlayers", + "dz_ratio", + "depth", + "mom_run_dir", + "mom_input_dir", + "toolpath", + "gridtype", + ), + [ + ( + [-5, 5], + [0, 10], + ["2003-01-01 00:00:00", "2003-01-01 00:00:00"], + 0.1, + 5, + 1, + 1000, + "rundir/", + "inputdir/", + "toolpath", + "even_spacing", + ), + ], +) +def test_bathymetry( + xextent, + yextent, + daterange, + resolution, + vlayers, + dz_ratio, + depth, + mom_run_dir, + mom_input_dir, + toolpath, + gridtype, + tmp_path, +): + expt = experiment( + xextent, + yextent, + daterange, + resolution, + vlayers, + dz_ratio, + depth, + mom_run_dir, + mom_input_dir, + toolpath, + gridtype, + ) + + ## Generate some bathymetry to test on + + bathy_file = tmp_path / "bathy.nc" + + bathy = np.random.random((100, 100)) * (-100) + bathy = xr.DataArray( + bathy, + dims=["lata", "lona"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[0] - 5, xextent[1] + 5, 100), + }, + ) + bathy.name = "elevation" + bathy.to_netcdf(bathy_file) + bathy.close() + + # Now use this bathymetry as input in `expt.bathymetry()` + expt.bathymetry( + str(bathy_file), + {"xh": "lona", "yh": "lata", "elevation": "elevation"}, + minimum_layers=1, + chunks={"lat": 10, "lon": 10}, + ) + + bathy_file.unlink() + + +@pytest.mark.parametrize( + ( + "xextent", + "yextent", + "daterange", + "resolution", + "vlayers", + "dz_ratio", + "depth", + "mom_run_dir", + "mom_input_dir", + "toolpath", + "gridtype", + ), + [ + ( + [-5, 5], + [0, 10], + ["2003-01-01 00:00:00", "2003-01-01 00:00:00"], + 0.1, + 5, + 1, + 1000, + "rundir/", + "inputdir/", + "toolpath", + "even_spacing", + ), + ], +) +def test_ocean_forcing( + xextent, + yextent, + daterange, + resolution, + vlayers, + dz_ratio, + depth, + mom_run_dir, + mom_input_dir, + toolpath, + gridtype, + tmp_path, +): + expt = experiment( + xextent, + yextent, + daterange, + resolution, + vlayers, + dz_ratio, + depth, + mom_run_dir, + mom_input_dir, + toolpath, + gridtype, + ) + + ## Generate some initial condition to test on + + # initial condition includes, temp, salt, eta, u, v + initial_cond = xr.Dataset( + { + "temp": xr.DataArray( + np.random.random((100, 100, 10)), + dims=["lata", "lona", "deepness"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[0] - 5, xextent[1] + 5, 100), + "deepness": np.linspace(0, 1000, 10), + }, + ), + "eta": xr.DataArray( + np.random.random((100, 100)), + dims=["lata", "lona"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[0] - 5, xextent[1] + 5, 100), + }, + ), + "salt": xr.DataArray( + np.random.random((100, 100, 10)), + dims=["lata", "lona", "deepness"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[0] - 5, xextent[1] + 5, 100), + "deepness": np.linspace(0, 1000, 10), + }, + ), + "u": xr.DataArray( + np.random.random((100, 100, 10)), + dims=["lata", "lona", "deepness"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[0] - 5, xextent[1] + 5, 100), + "deepness": np.linspace(0, 1000, 10), + }, + ), + "v": xr.DataArray( + np.random.random((100, 100, 10)), + dims=["lata", "lona", "deepness"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[0] - 5, xextent[1] + 5, 100), + "deepness": np.linspace(0, 1000, 10), + }, + ), + } + ) + + # Generate boundary forcing + + eastern_boundary = xr.Dataset( + { + "temp": xr.DataArray( + np.random.random((100, 5, 10, 10)), + dims=["lata", "lona", "deepness", "time"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[1] - 0.5, xextent[1] + 0.5, 5), + "deepness": np.linspace(0, 1000, 10), + "time": np.linspace(0, 1000, 10), + }, + ), + "eta": xr.DataArray( + np.random.random((100, 5, 10)), + dims=["lata", "lona", "time"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[1] - 0.5, xextent[1] + 0.5, 5), + "time": np.linspace(0, 1000, 10), + }, + ), + "salt": xr.DataArray( + np.random.random((100, 5, 10, 10)), + dims=["lata", "lona", "deepness", "time"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[1] - 0.5, xextent[1] + 0.5, 5), + "deepness": np.linspace(0, 1000, 10), + "time": np.linspace(0, 1000, 10), + }, + ), + "u": xr.DataArray( + np.random.random((100, 5, 10, 10)), + dims=["lata", "lona", "deepness", "time"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[1] - 0.5, xextent[1] + 0.5, 5), + "deepness": np.linspace(0, 1000, 10), + "time": np.linspace(0, 1000, 10), + }, + ), + "v": xr.DataArray( + np.random.random((100, 5, 10, 10)), + dims=["lata", "lona", "deepness", "time"], + coords={ + "lata": np.linspace(yextent[0] - 5, yextent[1] + 5, 100), + "lona": np.linspace(xextent[1] - 0.5, xextent[1] + 0.5, 5), + "deepness": np.linspace(0, 1000, 10), + "time": np.linspace(0, 1000, 10), + }, + ), + } + ) + + eastern_boundary.to_netcdf(tmp_path / "east_unprocessed") + initial_cond.to_netcdf(tmp_path / "ic_unprocessed") + eastern_boundary.close() + initial_cond.close() + + expt.ocean_forcing( + str(tmp_path), + { + "x": "lona", + "y": "lata", + "time": "time", + "eta": "eta", + "zl": "deepness", + "u": "u", + "v": "v", + "tracers": {"temp": "temp", "salt": "salt"}, + }, + boundaries=["east"], + gridtype="A", + )
\n", - " Comm: tcp://127.0.0.1:38117\n", + " Comm: tcp://127.0.0.1:33149\n", " \n", " Total threads: 4\n", @@ -276,7 +280,7 @@ "
\n", - " Dashboard: /proxy/34757/status\n", + " Dashboard: /proxy/40965/status\n", " \n", " Memory: 16.00 GiB\n", @@ -284,13 +288,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:40081\n", + " Nanny: tcp://127.0.0.1:42157\n", "
\n", - " Local directory: /jobfs/87881662.gadi-pbs/dask-worker-space/worker-p950f4nl\n", + " Local directory: /jobfs/95589519.gadi-pbs/dask-scratch-space/worker-ubamd8wz\n", "