{ "cells": [ { "cell_type": "markdown", "id": "ea250b77-a92e-4946-9b2b-fefcae6f16d2", "metadata": {}, "source": [ "# Advanced Stimulus Options\n", "\n", "In this tutorial, we will explain additional options for loading and generating advanced current-clamp wave forms, applying voltage clamps to cells during simulation, imporyting experimental sweeps from the Allen Cell-Types Database and adding extracellular stimulation to your simulated network.\n", "\n", "**Note** - scripts and files for running this tutorial can be found in the directory [04_opt_advanced_inputs/](https://github.com/AllenInstitute/bmtk/tree/develop/docs/tutorial/04_opt_advanced_inputs)" ] }, { "cell_type": "markdown", "id": "71f5e6f7-a04e-4c31-98a2-026cdfb6655a", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## 1. Example: Advanced IClamp Options " ] }, { "cell_type": "markdown", "id": "eb8682bf-5e2a-44ee-8d8d-9d73edd62f9b", "metadata": {}, "source": [ "By default you can insert a simple step current into a cell in your simulation by adding a corresponding statement to the \"inputs\" section of your SONATA configuration file. For example, say we want to insert a 0.100 nA injection at t=500 ms, for the duration 1000 ms into the soma of our first cell:\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp\": {\n", " \"input_type\": \"clamp\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\"\n", " \"node_id\": 0\n", " },\n", " \"amp\": 0.1000,\n", " \"delay\": 500.0,\n", " \"duration\": 1000.0,\n", " \"section_name\": \"soma\",\n", " \"section_index\": 0,\n", " \"section_dist\": 0.5\n", " }\n", "}\n", "```\n", "* **input_type** and **module** should always be set to values `clamp` and `IClamp`, respectively.\n", "* The **node_set** specifies what cell(s) we want to apply clamp to. In this case only cell with node-id #0 in the \"net\" population of cells.\n", "* **amp** is the amplitude of the clamp to apply, in nA.\n", "* **delay** and **duration** tell us when to start the clamp and how long to apply it for, both in ms. In this case it will be a current from 500 ms to 1500 ms after start of simulation.\n", "* **section_name**, **section_index**, and **section_dist** tells us exactly where on the cell to place the clamp. In this case at the center of the soma. (*If you leave section_index and section_dist blank BMTK will attempt to find a random available place where to insert the clamp*)\n", "
\n", " NOTE: For PoinNet users section_name, section_index and section_dist are not applicable and can be removed.\n", "
\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "192123e0-d03d-4388-a6dd-afc5e47163d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-05-05 10:30:21,328 [INFO] Created log file\n", "2024-05-05 10:30:21,425 [INFO] Building cells.\n", "2024-05-05 10:30:22,432 [INFO] Building recurrent connections\n", "2024-05-05 10:30:22,441 [INFO] Running simulation for 2000.000 ms with the time step 0.100 ms\n", "2024-05-05 10:30:22,442 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-05-05 10:30:22,443 [INFO] Block save every 5000 steps\n", "2024-05-05 10:30:23,460 [INFO] step:5000 t_sim:500.00 ms\n", "2024-05-05 10:30:24,561 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-05-05 10:30:25,690 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-05-05 10:30:26,812 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-05-05 10:30:26,836 [INFO] Simulation completed in 4.394 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.current_clamp.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 9, "id": "34290b9c-9e00-4773-9714-ecf597d0390e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "_ = plot_traces(config_file='config.current_clamp.json', node_ids=range(5), report_name='membrane_potential')" ] }, { "cell_type": "markdown", "id": "1d3d422c-8ec2-43cb-a4b7-0201d062624f", "metadata": {}, "source": [ "### Selecting multiple cells\n", "\n", "In the above cells #1-4 have a flat trace since we only applied the current to cell #0. If you want to apply the same current to a specific population of cells you can use the **node_set** option to filter according to any available attributes. For example, say we want to apply the current cells in our network that have **model_type=biophysical**:\n", "\n", "```json\n", "\"current_clamp\": {\n", " \"input_type\": \"current_clamp\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"model_type\": \"biophysical\"\n", " },\n", " \"amp\": 0.1000,\n", " \"delay\": 500.0,\n", " \"duration\": 1000.0,\n", " \"section_name\": \"soma\"\n", "}\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "id": "41309ea0-4432-4c78-88bc-872b26ebde4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-05-05 10:38:22,112 [INFO] Created log file\n", "Mechanisms already loaded from path: ./components/mechanisms. Aborting.\n", "2024-05-05 10:38:22,130 [INFO] Building cells.\n", "2024-05-05 10:38:23,128 [INFO] Building recurrent connections\n", "2024-05-05 10:38:23,141 [INFO] Running simulation for 2000.000 ms with the time step 0.100 ms\n", "2024-05-05 10:38:23,142 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-05-05 10:38:23,143 [INFO] Block save every 5000 steps\n", "2024-05-05 10:38:24,566 [INFO] step:5000 t_sim:500.00 ms\n", "2024-05-05 10:38:25,905 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-05-05 10:38:27,381 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-05-05 10:38:28,682 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-05-05 10:38:28,700 [INFO] Simulation completed in 5.559 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.current_clamp_allcells.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 11, "id": "7472d4d5-754e-4179-b6ce-20816ba48272", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "_ = plot_traces(config_file='config.current_clamp.json', node_ids=range(5), report_name='membrane_potential')" ] }, { "cell_type": "markdown", "id": "40176a68-188b-4f2f-9a99-2fa8f9cf4112", "metadata": {}, "source": [ "### Selecting different placement\n", "\n", "Besides the `soma`, most morphologically detailed models will also give you options to place a clamp at the `axon`, `dend` (dendrites), `basal` (the basal dendrites), or the `apic` (the apical dendrites). However unlike the `soma`, these other sections may be split into hundreds of different **section_index** (aka branches and compartments). If you do not know the specific section index for where you want to place the clamp you can specify a range (in microns from the soma) of where you want BMTK to place it randomly.\n", "\n", "For example if we want to place the current at a random location on the apical or basal dendrites that is atleast 500 um away from the soma\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp_apic\": {\n", " \"input_type\": \"current_clamp\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"node_id\": [0]\n", " },\n", " \"amp\": 0.1000,\n", " \"delay\": 500.0,\n", " \"duration\": 1000.0,\n", " \"section_name\": [\"dend\", \"apic\"],\n", " \"section_index\": [500.0, 1.0e20]\n", " }\n", "}\n", "```\n", "* We are trying to find any section that is (500, $\\infty$) away from the soma. However python doesn't handle infinity values very well so we just use a distance, 1e20, that we know will cover all feasible segments\n", "\n", "
\n", " WARNING: As mentioned before this won't work on point-neurons or neurons with only a soma. Some morphologically detailed models may not have an \"apic\" section (or possibly other types of section too) and would thus fail if you attempt to insert an injection into such a section.\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "id": "46e4eeaa-03c5-4b3e-b5ca-f87816907ffb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-05-05 11:49:01,014 [INFO] Created log file\n", "2024-05-05 11:49:01,122 [INFO] Building cells.\n", "2024-05-05 11:49:02,108 [INFO] Building recurrent connections\n", "2024-05-05 11:49:02,125 [INFO] Running simulation for 2000.000 ms with the time step 0.100 ms\n", "2024-05-05 11:49:02,127 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-05-05 11:49:02,128 [INFO] Block save every 5000 steps\n", "2024-05-05 11:49:03,210 [INFO] step:5000 t_sim:500.00 ms\n", "2024-05-05 11:49:04,225 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-05-05 11:49:05,562 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-05-05 11:49:06,625 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-05-05 11:49:06,635 [INFO] Simulation completed in 4.51 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.current_clamp_apic.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 2, "id": "6b89f09d-781b-412d-8331-d13f6d0842e0", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "_ = plot_traces(config_file='config.current_clamp_apic.json', node_ids=range(5), report_name='membrane_potential')" ] }, { "cell_type": "markdown", "id": "ef02573a-673e-413b-acaa-59674e22c18b", "metadata": {}, "source": [ "### Adding multiple Clamps\n", "\n", "If you need to add multiple current-clamps (whether they overlap in time or not) to you simulation you can easily do it by adding multiple sections to the \"inputs\" sections:\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp_1\": {\n", " \"input_type\": \"current_clamp\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"node_id\": [0]\n", " },\n", " \"amp\": 0.1500,\n", " \"delay\": 500.0,\n", " \"section_name\": \"soma\"\n", " },\n", " \"current_clamp_2\": {\n", " \"input_type\": \"current_clamp\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"node_id\": [0]\n", " },\n", " \"amp\": 0.1750,\n", " \"delay\": 1500.0,\n", " \"duration\": 500.0,\n", " \"section_name\": \"soma\"\n", " },\n", " \"current_clamp_3\": {\n", " \"input_type\": \"current_clamp\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"node_id\": [0]\n", " },\n", " \"amp\": 0.2000,\n", " \"delay\": 2500.0,\n", " \"duration\": 500.0,\n", " \"section_name\": \"soma\"\n", " }\n", "}\n", "```\n", "\n", "#### As a list\n", "\n", "However if you are applying clamps to the same **node_set** and **section** it is also possible to set **amp**, **duration**, and **delay** as a list. The main difference is that we must set the **module** to value `list`.\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp_list\": {\n", " \"input_type\": \"current_clamp\",\n", " \"module\": \"list\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"model_type\": \"biophysical\"\n", " },\n", " \"amp\": [0.1500, 0.1750, 0.2000],\n", " \"delay\": [500.0, 1500.0, 2500.0],\n", " \"duration\": [500.0, 500.0, 500.0]\n", " \"section_name\": \"soma\"\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "markdown", "id": "601677ed-863b-4fbb-bad6-245dd0dec2e9", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## 2. Example: Complex current-clamp waveforms " ] }, { "cell_type": "markdown", "id": "04a33110-4561-4fe8-a07e-6e62a22e2833", "metadata": {}, "source": [ "In the above example we inserted a current clamp as single or a series of step-currents. However you may want to use another input waveform like a wave or a gradual current which, if needed to be implemented using the above approach, would be very unfeasbile. Luckily, BMTK also allows us to save our current function into a csv file which can contain many individual points determining how the current will change at each step of time.\n", "\n", "This is done in much the same way as in the example above with two exceptions:\n", "1. The **module** should be set to value `csv`.\n", "2. Instead of specifying the **amp**, **delay**, or **duration**; instead you specify the **file** path to the csv file.\n", "\n", "For example to have a sequences of current clamps in the above example we would add the following to our configuration file:\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp_series\": {\n", " \"input_type\": \"csv\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"model_type\": \"biophysical\"\n", " },\n", " \"file\": \"$INPUT_DIR/iclamp_inputs_series.csv\",\n", " \"section_name\": \"soma\"\n", " }\n", "}\n", "```\n", "\n", "Where our file has the following format, each row specifying the time **time-stamp**, where 0 represents beginning of simulation, at which the **amp** value is changes" ] }, { "cell_type": "code", "execution_count": 1, "id": "1c2ad04a-3fb5-4a60-a9c4-1b422bec6ae9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " timestamps amps\n", "0 500.0 0.150\n", "1 1000.0 0.000\n", "2 1500.0 0.175\n", "3 2000.0 0.000\n", "4 2500.0 0.200\n", "5 3000.0 0.000" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "pd.read_csv('inputs/iclamp_inputs_series.csv', sep=' ')" ] }, { "cell_type": "markdown", "id": "f98fbb94-da36-404d-85cc-9a83f9154ff5", "metadata": {}, "source": [ "Also note that unlike with the above example there is no **delay**, thus to turn off current between blocks we must reset amp to 0.0" ] }, { "cell_type": "code", "execution_count": 5, "id": "0b86ea3a-23bf-40dd-ab82-a6b8063f98a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-05-05 12:32:49,833 [INFO] Created log file\n", "Mechanisms already loaded from path: ./components/mechanisms. Aborting.\n", "2024-05-05 12:32:49,851 [INFO] Building cells.\n", "2024-05-05 12:32:50,585 [INFO] Building recurrent connections\n", "2024-05-05 12:32:50,609 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n", "2024-05-05 12:32:50,614 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-05-05 12:32:50,615 [INFO] Block save every 5000 steps\n", "2024-05-05 12:32:52,358 [INFO] step:5000 t_sim:500.00 ms\n", "2024-05-05 12:32:54,016 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-05-05 12:32:55,620 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-05-05 12:32:57,227 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-05-05 12:32:58,778 [INFO] step:25000 t_sim:2500.00 ms\n", "2024-05-05 12:33:00,376 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-05-05 12:33:00,394 [INFO] Simulation completed in 9.785 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.csv_series.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 6, "id": "f92332a6-7e16-448a-a5ba-40f46bbce18c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "_ = plot_traces(config_file='config.csv_series.json', node_ids=range(5), report_name='membrane_potential')" ] }, { "cell_type": "markdown", "id": "f8b062be-38f7-4cba-a5b0-211b126836a6", "metadata": {}, "source": [ "For a more complex example, we will use numpy to create current-clamp trace that has a damped wave. We'll save it to file *inputs/iclamp_damped_wave.csv*" ] }, { "cell_type": "code", "execution_count": 9, "id": "3db0e427-b833-4437-81da-28567a27b9e4", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "start_time = 0.0\n", "stop_time=3000.0\n", "dt=0.1\n", "\n", "times_secs = np.arange(start_time, stop_time, step=dt*200)/1000\n", "amps_na = np.sin(times_secs*3*np.pi)*np.exp(-times_secs)\n", "pd.DataFrame({\n", " 'timestamps': np.round(times_secs*1000, decimals=1),\n", " 'amps': amps_na*.1\n", "}).to_csv('inputs/iclamp_damped_wave.csv', sep=' ', index=False)\n" ] }, { "cell_type": "markdown", "id": "35c329cc-a5f7-4c79-be44-710db2de0a48", "metadata": {}, "source": [ "Then modify the config as such\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp_wave\": {\n", " \"input_type\": \"csv\",\n", " \"module\": \"IClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"model_type\": \"biophysical\"\n", " },\n", " \"file\": \"$INPUT_DIR/iclamp_damped_wave.csv\",\n", " \"section_name\": \"soma\"\n", " }\n", "}\n", "```\n", "\n", "Then run the simulation and plot the membrane potential to see that indeed, the cell is acting as we would expect when injecting a damped wave into the soma" ] }, { "cell_type": "code", "execution_count": 10, "id": "6691c13a-e60b-472a-a47d-faddf35ad1b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-05-05 13:05:33,058 [INFO] Created log file\n", "Mechanisms already loaded from path: ./components/mechanisms. Aborting.\n", "2024-05-05 13:05:33,072 [INFO] Building cells.\n", "2024-05-05 13:05:34,069 [INFO] Building recurrent connections\n", "2024-05-05 13:05:34,085 [WARNING] IClampMod: Stimulus of ./inputs/iclamp_damped_wave.csv does not end with a 0.0, attempting to set turn off at time 3000.0.\n", "2024-05-05 13:05:34,093 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n", "2024-05-05 13:05:34,098 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-05-05 13:05:34,099 [INFO] Block save every 5000 steps\n", "2024-05-05 13:05:35,856 [INFO] step:5000 t_sim:500.00 ms\n", "2024-05-05 13:05:37,875 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-05-05 13:05:39,645 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-05-05 13:05:41,639 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-05-05 13:05:43,769 [INFO] step:25000 t_sim:2500.00 ms\n", "2024-05-05 13:05:45,737 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-05-05 13:05:45,757 [INFO] Simulation completed in 11.66 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.csv_wave.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 11, "id": "4177b173-1914-417d-bd63-b50f913a73b7", "metadata": {}, "outputs": [ { "data": { "image/png": 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4t+QLIURmZqZQKpVi8eLFTstLUn9xP8vHjx8X7dq1EzqdTgBwqm/06NEiIiLCaYgGIm8kE4KT0RCRdzp79ixq1KiBadOmlfgsDeUbNGgQTpw4gR07dkjSvslkQvXq1TFmzBi8/PLLktRAVFzsU0REVIaNHz8ee/fuLfEUK66yYMECqFQqj0xKS3S/2KeIiDzOZrPdtcO1N962XxpFRETAaDRK1v7QoUMZiKjUYCgiIo9LT08vsrPxrcaPH+80qSgRkbuxTxEReZzRaMTOnTvvuE3NmjXv+Q44IqJ7wVBEREREBHa0JiIiIgJQTvoU2e12XLhwAX5+fpyMkIiIqJQQQuDGjRuoUqUK5HL3n8cpF6HowoULJZq4kIiIiLxHeno6qlWr5vZ2ykUo8vPzA5B/UO9lqHsiIiLyvJycHISHhzu+x92tXISigktm/v7+DEVERESljKe6vrCjNREREREYioiIiIgAMBQRERERASgnfYqIiIhKK5vNBovFInUZbqFSqaBQKKQuw4GhiIiIyAsJIZCRkYHr169LXYpbBQYGIiwszCvGEWQoIiIi8kIFgSgkJAR6vd4rQoMrCSGQl5eHrKwsAEDlypUlroihiIiIyOvYbDZHIKpYsaLU5biNTqcDAGRlZSEkJETyS2nsaE1ERORlCvoQ6fV6iStxv4L36A39phiKiIiIvFRZu2RWFG96jwxFRERERGAoIiIiIgLAUEREREQu9tlnn6F69erQarWIjY3Fnj17pC6pWBiKPOTG9eu4UcbHmiAiIvruu+8wcuRIjB8/Hvv370eTJk2QkJDguPXemzEUeUBuTja+GbUN347cDLPRKHU5REREbjN9+nQMHjwYAwYMQHR0NObMmQO9Xo/58+dLXdpdcZwiDzi6+3dYVX4AgAtnT6F6vYYSV0RERKWJEAIGi02StnUqRbHvEDObzUhKSsLYsWMdy+RyOeLj45GYmOiuEl2GoYiIiMjLGSw2RL+9TpK2j05KgF5dvLhw+fJl2Gw2hIaGOi0PDQ3F8ePH3VGeS/HyGRERERF4psjj7Da71CUQEVEpo1MpcHRSgmRtF1elSpWgUCiQmZnptDwzMxNhYWGuLs3lGIo8QC73ntE6iYio9JHJZMW+hCUltVqNmJgYbNq0Cb169QIA2O12bNq0CcOHD5e2uGLw/iNMREREpcbIkSPRr18/tGjRAi1btsTHH3+M3NxcDBgwQOrS7oqhiIiIiFzmySefxKVLl/D2228jIyMDTZs2xdq1awt1vvZGDEUeIJP9cz3WbpfmlkoiIiJPGT58eKm4XPZvvPuMiIiICAxFRERERAC8IBRNmTIFDzzwAPz8/BASEoJevXohNTXVaRuj0Yhhw4ahYsWK8PX1Re/evQvd7ldq8PIZERGRV5I8FG3btg3Dhg3DH3/8gQ0bNsBisaBLly7Izc11bDNixAj8+uuv+P7777Ft2zZcuHABjz76qIRVl4xcLvlhJiIioruQvKP12rVrnZ4vXLgQISEhSEpKQrt27ZCdnY158+bhm2++QadOnQAACxYsQP369fHHH3/gP//5jxRll4jdbocjf8qLPwgWEREReY7XncLIzs4GAFSoUAEAkJSUBIvFgvj4eMc29erVQ0RExG0nlzOZTMjJyXF6EBEREd2JV4Uiu92OV155Ba1bt0bDhvkzyWdkZECtViMwMNBp29DQUGRkZBS5nylTpiAgIMDxCA8Pd3fpd+R0+Yx9ioiIiLySV4WiYcOGISUlBcuWLbuv/YwdOxbZ2dmOR3p6uosqJCIiorJK8j5FBYYPH45Vq1Zh+/btqFatmmN5WFgYzGYzrl+/7nS26E6Ty2k0Gmg0GneXTERERGWI5GeKhBAYPnw4VqxYgc2bN6NGjRpO62NiYqBSqbBp0ybHstTUVKSlpSEuLs7T5d43u80udQlERERUBMlD0bBhw7BkyRJ888038PPzQ0ZGBjIyMmAwGAAAAQEBGDRoEEaOHIktW7YgKSkJAwYMQFxcXKm48wwAZArJDzMREZFHbN++HT169ECVKlUgk8nw888/S11SsUn+bf35558jOzsbHTp0QOXKlR2P7777zrHNjBkz8PDDD6N3795o164dwsLC8NNPP0lYNRERERUlNzcXTZo0wWeffSZ1KSUmeZ8iIcRdt9Fqtfjss89K5QEmIiIqT7p164Zu3bpJXcY9kTwUlQcyuQxAfvizFyMEEhERORECsORJ07ZKD8hk0rTtYQxFRERE3s6SB7xXRZq237gAqH2kadvDJO9TVN7Iy0naJiIiKm14psgTZHIAHMmaiIjukUqff8ZGqrbLCYYiTxD/jE3EPkVERFRiMlm5uYQlJYYiIiIicpmbN2/i1KlTjudnzpxBcnIyKlSogIiICAkruzuGIiIiInKZffv2oWPHjo7nI0eOBAD069cPCxculKiq4mEo8oRb+hQJm1XaWoiIiNyoQ4cOxRqD0Bvx7jMiIiIiMBQRERERAWAo8gi5TCF1CURERHQXDEUeZrOVzuusREREZR1DEREREREYioiIiIgAMBR5hELB+c6IiIi8HUMRERERERiKiIiIiAAwFBEREREBYCjyOLuwSV0CERGRW0yZMgUPPPAA/Pz8EBISgl69eiE1NVXqsoqNoYiIiIhcYtu2bRg2bBj++OMPbNiwARaLBV26dEFubq7UpRULJ4T1AJlCCcAkdRlERERutXbtWqfnCxcuREhICJKSktCuXTuJqio+hiJPE3apKyAiolJGCAGD1SBJ2zqlDjLZvQ0tk52dDQCoUKGCK0tyG4YiIiIiL2ewGhD7Tawkbe9+Zjf0Kn2JX2e32/HKK6+gdevWaNiwoRsqcz2GIiIiInK5YcOGISUlBTt37pS6lGJjKPIA+T2ediQiIgLyL2Htfma3ZG2X1PDhw7Fq1Sps374d1apVc0NV7sFQRERE5OVkMtk9XcLyNCEE/u///g8rVqzA1q1bUaNGDalLKhGGIiIiInKJYcOG4ZtvvsHKlSvh5+eHjIwMAEBAQAB0upKfcfI0jlPkAXIFDzMREZV9n3/+ObKzs9GhQwdUrlzZ8fjuu++kLq1YeKaIiIiIXEIIIXUJ94WnMDxM2Ev3B4aIiKisYijyALuNAzYSERF5O4YiIiIiIjAUeZzgWSMiIiKvxFBEREREBIYiIiIiIgAMRUREREQAGIqIiIiIAJSiUPTZZ5+hevXq0Gq1iI2NxZ49e6QuiYiIiMqQUhGKvvvuO4wcORLjx4/H/v370aRJEyQkJCArK0vq0oiIiKiMKBWhaPr06Rg8eDAGDBiA6OhozJkzB3q9HvPnz5e6tBKz23lLPhERlU2ff/45GjduDH9/f/j7+yMuLg5r1qyRuqxi8/pQZDabkZSUhPj4eMcyuVyO+Ph4JCYmFvkak8mEnJwcpwcRERG5V7Vq1TB16lQkJSVh37596NSpE3r27IkjR45IXVqxeH0ounz5Mmw2G0JDQ52Wh4aGIiMjo8jXTJkyBQEBAY5HeHi4J0olIiIq13r06IGHHnoIderUQd26dTF58mT4+vrijz/+kLq0YlFKXYA7jB07FiNHjnQ8z8nJYTAiIqJSSwgBYTBI0rZMp4NMJivx62w2G77//nvk5uYiLi7ODZW5nteHokqVKkGhUCAzM9NpeWZmJsLCwop8jUajgUaj8UR5xWIXwvFvIWwSVkJERKWRMBiQ2jxGkraj9idBptcXe/vDhw8jLi4ORqMRvr6+WLFiBaKjo91Yoet4/eUztVqNmJgYbNq0ybHMbrdj06ZNpSZ5EhERlRdRUVFITk7G7t278eKLL6Jfv344evSo1GUVi9efKQKAkSNHol+/fmjRogVatmyJjz/+GLm5uRgwYIDUpREREbmdTKdD1P4kydouCbVajdq1awMAYmJisHfvXsycORNz5851R3kuVSpC0ZNPPolLly7h7bffRkZGBpo2bYq1a9cW6nxNRERUFslkshJdwvImdrsdJpNJ6jKKpVSEIgAYPnw4hg8fLnUZREREdBtjx45Ft27dEBERgRs3buCbb77B1q1bsW7dOqlLK5ZSE4qIiIjIu2VlZaFv3764ePEiAgIC0LhxY6xbtw4PPvig1KUVC0MRERERucS8efOkLuG+eP3dZ2WN3S7uvhERERF5HEMRERERERiKiIiIiAAwFBEREREBYCjyOCHsUpdARERERWAo8jC7jaGIiIjIGzEUeYCwWaUugYiIiO6CocjDhJ1nioiIiLwRQxERERERGIqIiIiIADAUERERkZtMnToVMpkMr7zyitSlFAtDkadxmg8iIioH9u7di7lz56Jx48ZSl1JsDEUeZmdHayIiKuNu3ryJPn364Msvv0RQUJDU5RSbUuoCiIiI6M6EELCapfmjWqmWQyaTleg1w4YNQ/fu3REfH493333XTZW5HkORhwnwTBEREZWM1WzHFy9vk6TtITPbQ6VRFHv7ZcuWYf/+/di7d68bq3IPhiIiIiJyifT0dLz88svYsGEDtFqt1OWUGEORhwkbO1oTEVHJKNVyDJnZXrK2iyspKQlZWVlo3ry5Y5nNZsP27dvx6aefwmQyQaEo/lknT2Mo8jABm9QlEBFRKSOTyUp0CUsqnTt3xuHDh52WDRgwAPXq1cPo0aO9OhABDEVERETkIn5+fmjYsKHTMh8fH1SsWLHQcm/EW/I9wHbLJTPBcYqIiIi8Es8UERERkdts3bpV6hKKjWeKPIxnioiIiLwTQxERERERGIo8jtN8EBEReSeGIk8TvHxGRETkjRiKiIiIvJQoB39Ie9N7ZCjyMMHLZ0REdBcqlQoAkJeXJ3El7lfwHgves5R4Sz4REZGXUSgUCAwMRFZWFgBAr9eXeKZ6byeEQF5eHrKyshAYGOgVo10zFHmYF50lJCIiLxYWFgYAjmBUVgUGBjreq9QYioiIiLyQTCZD5cqVERISAovFInU5bqFSqbziDFEBhiIPE3ZOCEtERMWnUCi8KjiUZexo7QF28U8Q8qZe9kRERPQPhiIiIiIiMBR5HOc+IyIi8k4MRURERESQMBSdPXsWgwYNQo0aNaDT6VCrVi2MHz8eZrPZabtDhw6hbdu20Gq1CA8PxwcffCBRxa7BPkVERETeSbK7z44fPw673Y65c+eidu3aSElJweDBg5Gbm4sPP/wQAJCTk4MuXbogPj4ec+bMweHDhzFw4EAEBgZiyJAhUpV+XziiNRERkXeSLBR17doVXbt2dTyvWbMmUlNT8fnnnztC0dKlS2E2mzF//nyo1Wo0aNAAycnJmD59eukKRYJBiIiIyNt5VZ+i7OxsVKhQwfE8MTER7dq1g1qtdixLSEhAamoqrl27dtv9mEwm5OTkOD28BS+fEREReSevCUWnTp3CrFmz8MILLziWZWRkIDQ01Gm7gucZGRm33deUKVMQEBDgeISHh7unaCIiIiozXB6KxowZA5lMdsfH8ePHnV5z/vx5dO3aFY8//jgGDx583zWMHTsW2dnZjkd6evp979NVeKaIiIjIO7m8T9GoUaPQv3//O25Ts2ZNx78vXLiAjh07olWrVvjiiy+ctgsLC0NmZqbTsoLnd5o8TqPRQKPRlLByIiIiKs9cHoqCg4MRHBxcrG3Pnz+Pjh07IiYmBgsWLIBc7nziKi4uDuPGjYPFYoFKpQIAbNiwAVFRUQgKCnJ16W7jNGAjzxQRERF5Jcn6FJ0/fx4dOnRAREQEPvzwQ1y6dAkZGRlOfYWeeeYZqNVqDBo0CEeOHMF3332HmTNnYuTIkVKVfd94+YyIiMg7SXZL/oYNG3Dq1CmcOnUK1apVc1pXEBwCAgKwfv16DBs2DDExMahUqRLefvvt0nU7PhEREZUKkoWi/v3737XvEQA0btwYO3bscH9BHsK5z4iIiLyT19yST0RERCQlhiJP4+jWREREXomhyAOEjUGIiIjI2zEUeRj7FBEREXknhiIPE2AoIiIi8kYMRURERERgKPI49rMmIiLyTgxFHmC3MwkRERF5O4YiT+M0H0RERF6JocjTGIqIiIi8EkMRERERERiKPI635BMREXknhiIPEMImdQlERER0FwxFHibYp4iIiMgrMRQRERERgaHI4zj3GRERkXdiKPI0ZiIiIiKvxFBEREREBIYij7DfcslMcPIzIiIir8RQRERERASGIs/jLflEREReiaHIw5iJiIiIvBNDEREREREYijzCqXM1TxURERF5JYYiIiIiIjAUeR5PFBEREXklhiIiIiIiMBR5nGCfIiIiIq/EUORxDEVERETeiKHIA+w2Tu1BRETk7RiKPIxXz4iIiLwTQxERERERGIo8z85TRURERN6IoYiIiIgIDEUeIez/dLQWvPuMiIjIKzEUeRozERERkVdiKCIiIiKCl4Qik8mEpk2bQiaTITk52WndoUOH0LZtW2i1WoSHh+ODDz6QpkhX4T35REREXskrQtHrr7+OKlWqFFqek5ODLl26IDIyEklJSZg2bRomTJiAL774QoIqiYiIqCxTSl3AmjVrsH79evz4449Ys2aN07qlS5fCbDZj/vz5UKvVaNCgAZKTkzF9+nQMGTJEoorvwS234fNEERERkXeS9ExRZmYmBg8ejMWLF0Ov1xdan5iYiHbt2kGtVjuWJSQkIDU1FdeuXbvtfk0mE3JycpweRERERHciWSgSQqB///4YOnQoWrRoUeQ2GRkZCA0NdVpW8DwjI+O2+54yZQoCAgIcj/DwcNcVTkRERGWSy0PRmDFjIJPJ7vg4fvw4Zs2ahRs3bmDs2LGuLgFjx45Fdna245Genu7yNu4Zr58RERF5JZf3KRo1ahT69+9/x21q1qyJzZs3IzExERqNxmldixYt0KdPHyxatAhhYWHIzMx0Wl/wPCws7Lb712g0hfZLREREdCcuD0XBwcEIDg6+63affPIJ3n33XcfzCxcuICEhAd999x1iY2MBAHFxcRg3bhwsFgtUKhUAYMOGDYiKikJQUJCrS/cIwTNFREREXkmyu88iIiKcnvv6+gIAatWqhWrVqgEAnnnmGUycOBGDBg3C6NGjkZKSgpkzZ2LGjBker/d+2G+Z5oOIiIi8k+S35N9JQEAA1q9fj2HDhiEmJgaVKlXC22+/Xbpux/83nigiIiLySl4TiqpXr17kpaXGjRtjx44dElTkHrx6RkRE5J28YkRrIiIiIqkxFHkcTxURERF5I4YiDxBgR2siIiJvx1DkaexURERE5JUYijyNmYiIiMgrMRR5GDMRERGRd2Io8jR2LyIiIvJKDEUeIGz/nB8SPFdERETklRiKPEDA9s+/7QxFRERE3oihyAOcghAzERERkVdiKPIAm/WfM0UMRURERN6JocgTbhmbiJfPiIiIvBNDkQfYbbzljIiIyNsxFHmAsPPyGRERkbdjKPIApzNFDEVEREReiaHIA+y2W27J59xnREREXomhyAOcgpCQSVcIERER3RZDkQcIOy+fEREReTuGIg+49UwRMxEREZF3YijyAGHj3WdERETejqHIAzjNBxERkfdjKPKAW2/JZyYiIiLyTgxFHmA1mx3/ljEVEREReSWGIg+wma3/POEt+URERF6JocgD7JZbBm+UsA4iIiK6PYYiD7BbbjlTxLlhiYiIvBJDkQfYLExCRERE3o6hyAOEldN8EBEReTuGIg+wW259xlBERETkjRiKPECYeJiJiIi8Hb+tPUDYVLc84ZkiIiIib8RQ5AEyu0bqEoiIiOguGIo8QNj9//k3zxQRERF5JYYiD7ArKjj+zUhERETknRiK3Cw3JxsWVdA/CzikNRERkVdiKHKz31d8DyG/paM1zxURERF5JYYiN7uY/KfTc8FQRERE5JUkD0W//fYbYmNjodPpEBQUhF69ejmtT0tLQ/fu3aHX6xESEoLXXnsNVqu16J15IevVAKfnMna0JiIi8kpKKRv/8ccfMXjwYLz33nvo1KkTrFYrUlJSHOttNhu6d++OsLAw7Nq1CxcvXkTfvn2hUqnw3nvvSVh58Rhyc2FV1gMAaA2nYNTVlrgiIiIiuh3JQpHVasXLL7+MadOmYdCgQY7l0dHRjn+vX78eR48excaNGxEaGoqmTZvinXfewejRozFhwgSo1WopSi+2X2bMgFXVCgprHqA8D6A2L58RERF5Kckun+3fvx/nz5+HXC5Hs2bNULlyZXTr1s3pTFFiYiIaNWqE0NBQx7KEhATk5OTgyJEjt923yWRCTk6O00MKN0/kXzpT2/ZDJrcD4OUzIiIibyVZKPrzz/wOyBMmTMCbb76JVatWISgoCB06dMDVq1cBABkZGU6BCIDjeUZGxm33PWXKFAQEBDge4eHhbnoXt7d52WIY9Q0AYUedHvXguBefmYiIiMgruTwUjRkzBjKZ7I6P48ePw27PP3Mybtw49O7dGzExMViwYAFkMhm+//77+6ph7NixyM7OdjzS09Nd8dZK5OzacwAAreEI2j76hCMMcURrIiIi7+TyPkWjRo1C//7977hNzZo1cfHiRQDOfYg0Gg1q1qyJtLQ0AEBYWBj27Nnj9NrMzEzHutvRaDTQaKSbb+zUwX0wqWIAAAENDX8vzT9TJOOpIiIiIq/k8lAUHByM4ODgu24XExMDjUaD1NRUtGnTBgBgsVhw9uxZREZGAgDi4uIwefJkZGVlISQkBACwYcMG+Pv7O4Upb7Nj9grYFZ2hMfyFXiNG5S/8+5wczxQRERF5J8nuPvP398fQoUMxfvx4hIeHIzIyEtOmTQMAPP744wCALl26IDo6Gs899xw++OADZGRk4M0338SwYcMkPRN0J1np52C2tgRUgLpSKpQqldN6mYyhiIiIyBtJOk7RtGnToFQq8dxzz8FgMCA2NhabN29GUFD+XGEKhQKrVq3Ciy++iLi4OPj4+KBfv36YNGmSlGXf0W9T58KqiofadBmPTH3tnxVyAdgBIRTSFUdERES3JWkoUqlU+PDDD/Hhhx/edpvIyEisXr3ag1Xdu+wrl2ExxABqQOV7AH6BTzjWyRX5oUjiQ05ERES3Ifk0H2XJz+9Mh0UdBJX5OnqMG+60Tq4u6FSkKuKVREREJDWGIhfJzcmGObsxAECl2YeKYVWd1jtCERiKiIiIvBFDkYv8NHkazJoQKCw3kfD6gELrlZr8y2ZC5t1TkxAREZVXDEUuYLVYYMqoCwBQy/egSo06hbZR6v4OQzKeKSIiIvJGDEUu8MN7U2DSVYPcZkS74b2K3Eal1wLgmSIiIiJvxVDkArl/5o+urbHtQe0mLYrcxq9SRQCATaH3WF1ERERUfAxF9+nXz2bCqKsNmd2C5n3b3Ha7ms2aAwBsSj2y0s95qjwiIiIqJoai+3Rpb/7cZhpTMpp2iL/tdtVqRUFuMwMATuzfc9vtiIiISBoMRffh1OEDMKmaAgAqNr9zXyGlSgWl5RoA4EJKqmP5t+MnYl6/D5C8daPb6iQiIqK74/DK92HH3O9hV8RDY/wLD780/K7bK8R5AKG4edYIQ24uvn1lKgyKjoAO2LvoMvZ/Mw6B9QLR7YWXoPPxcf8bICIiIgeeKbpHZqMR1tz8wRqVvscLTfxaFLn/FQCAQdEJ80ftzg9EBfvTVIJB3hkXT8Tg2+Ez3FM0ERER3RZD0T368b2p+YM1WvPQZVThwRqL0vl//aEyX3M8l9tM8PPdhLZ9FfBRboTGeBEAYJE3wqUL6W6pm4iIiIrGUHSP8s7m34avFnuLHKyxKOF1otB2aBj0so3QYRNin1ag74eT0bhVe/T/9D08/lECVKarsKr88Mub891ZPhEREf0L+xTdg83ffg2jvi4gbGj0VGyJXlu/ZWvUb9m6yHUBFSvBL+IIrma2hVHdFt+8NR7PvDPRFSUTERHRXfBM0T04u/E0AEBrOIoHHnzIpft+euJ46Ew7AQDZGXFYOYv9i4iIiDyBoaiEDLm5sIomAABt+BW3tPHkzJehzTsCu0KNzAORuHH9ulvaISIion8wFJXQui/mwKIOhMKah4deecktbfj4B6DbxK6Q2W2wqAORuPIHt7RDRERE/2AoKqGrR7IBACpLKoKCw9zWTpUadaAx5g/y+NdOTgtCRETkbgxFJZB95TKs9qYAAFXIVbe3pwr9+xZ9W3NcyTjv9vaIiIjKM4aiElg5dQYs6kAozdnoOWaE29vrOWYEVOZrsKoD8OvkT93eHhERUXnGUFQC5qyaAACVIgkBFSu5vb2AipWg0iXlt30zhgM6EhERuRFDUTFtWroQJl0NyOw2tBjwoMfa7T7mBajM12DRVMCvE+Z4rF0iIqLyhqGomM5t/hMAoDEeReNW7T3Wbkh4JLQV9wMATKINkrdu9FjbRERE5QlDUTGYjUZY7fmTv2qqXvZ4+8+8NwEaQxrsCg0Ortjs8faJiIjKA4aiYlg993NYNBWgsBqQ8H9DPN6+UqWCXJ4GALDm6DzePhERUXnAUFQMVw/nz2yvshxHcJVwSWrQhdsBADbUh9VikaQGIiKisoyh6C6sFgtstvoAAFWw+8cmup34oYMgt5lg0VTAqs9mSVYHERFRWcVQdBerPv0EZk0o5DYT4of1l6yO4CrhUJsPAgCuJJskq4OIiKisYii6iysHzQAAtfkgqtSoI2ktgY3yf1xmVRNcOHNS0lqIiIjKGoaiO8g4dxoWZf5dZ4ENpT9UPV8eAbUpC3aFFuunL5C6HCIiojJF+m96L7Z+1nzYlDqoTZfR4//+J3U5UKpUUPocAgBYbjZBbk62xBURERGVHQxFd2DJqgIAUCgPQ63VSlxNvm6vDYLSchNmTTBWTJ4mdTnlgtloxB9rfsHeDaulLoWIiNxIKXUB3ipp0xoYdVEAgKiHm0pbzC3CImtBJVsEKzrAdLGC1OWUWT/P+AhXk22wy6rArA6GkPsCAJKXfgMFzkBVKQ//HTvCI3PgERGRZzAU3cahH3cAsnhoDafQuqfnB2y8E301BQwZgJCHSF1KmfPHml9w5NuTMOqbAUWMk2nWhgEIg+Em8P3IX1C1rRndnh/q8TqJiMj1ePnsNuw38wdplPmclbaQIkR36QAAMGmrICVxp7TFlCGLXx2H5B8V+YFI2KE17kbFKjvRtNs1DPwoFgkv+iMoeBt05h2Q20ww6arjz311Ma/vx9i58nupyyciovvEM0VFMBuNMGtqAACqt60ncTWFNW7VHnvmLoJJF44DP65Bw7g2UpdU6i0c/gZyrfGAEtAaTqNaByBhwFinbWo3aYHaTVoAyL+8mvz1fhi1TWHUN8axny+hWlQKqtdrKEH1RETkCjxTVIRfZ30Cu0INmd2C2O49pS6nSHLVaQCAOYt9Wu7Xwv8bmx+IAGjNO/DM7GeQMGDwHV8T07kbBi16FfXa/QWlORtmTTA2TtmDQ79v8UTJRETkBpKGohMnTqBnz56oVKkS/P390aZNG2zZ4vylkpaWhu7du0Ov1yMkJASvvfYarFarW+u6ftgPAKA174OPf4Bb27pXVVpVAwCYNfWRfjJV4mpKr0Uvv4Fcy4MAAJ15O/rNfQM6H59iv75zn/4Ij8uAwnITJl11/DHvCtYt+NJd5RIRkRtJGooefvhhWK1WbN68GUlJSWjSpAkefvhhZGRkAABsNhu6d+8Os9mMXbt2YdGiRVi4cCHefvttt9X044cfwKiPgsxuRcNnmritnfsV/9wAqI2ZsCvU2PLF11KXUyotmzQJN42dAOSfIeo7dxyUKlWJ9/PQ4BcR3T0XamMmLOoKOPN7FSRv3ejqcomIyM0kC0WXL1/GyZMnMWbMGDRu3Bh16tTB1KlTkZeXh5SUFADA+vXrcfToUSxZsgRNmzZFt27d8M477+Czzz6D2Wx2S13XD+XfcqS17EHLLg+7pQ1XUKpUUCiPAQAsmbwLraQ2L1uM62kxgEwOrWE3+s19454CUYF2jz6NjqPqQWM8D5tSh6T5B2G1WFxYMRERuZtkoahixYqIiorC119/jdzcXFitVsydOxchISGIiYkBACQmJqJRo0YIDQ11vC4hIQE5OTk4cuSIy2vaufJ7GHX1AQB1etR1+f5dLaJDTQCASVsfpw4fkLia0iM3Jxt/rrHAptRBaziJx6YPvq9AVKB2o2YIbXkdMrsNRn0zfP3SRBdUS0REniJZKJLJZNi4cSMOHDgAPz8/aLVaTJ8+HWvXrkVQUBAAICMjwykQAXA8L7jEVhSTyYScnBynR3GcWHkk/8xB3lG0ffSJe3xnnhP/bH9ojOch5Er8Pp+3hBfXd69/AJOuOhTWPDTrV8OlAzD2eOn/oNdsBgAYFJ2waMQbLts3ERG5l8tD0ZgxYyCTye74OH78OIQQGDZsGEJCQrBjxw7s2bMHvXr1Qo8ePXDx4sX7qmHKlCkICAhwPMLD88ccutNcYWajEVbkT/6qi7x0X+17klx9HABgvVJV4kpKh41LFsJobwcA0PntQvNOXVzeRv9ZU6CzbgUA5OZ2xIoZH7q8DSIicj2Xh6JRo0bh2LFjd3zUrFkTmzdvxqpVq7Bs2TK0bt0azZs3x+zZs6HT6bBo0SIAQFhYGDIzM532X/A8LCzstjWMHTsW2dnZjkd6ejoAYOOC288sv37hV7CoA6GwGtDtfy/e72HwmDpd8zuDG3V1eRfaXeTmZOPsBgEhV0Gbl4I+H7jv8lbfz9+CNu8AhFyBq8n+7F9ERFQKuDwUBQcHo169end8qNVq5OXl5Rcgdy5BLpfDbrcDAOLi4nD48GFkZWU51m/YsAH+/v6Ijo6+bQ0ajQb+/v5ODwDIOW687WuykvIvx6nMaQgKvn3g8jZtH30CKvNVQKbA7p9+krocr7Z89Psw6SKhsOYhZmC0S/oR3Y5SpUKDp+tAbjPDqKuNpa+Pd1tbRETkGpL1KYqLi0NQUBD69euHgwcP4sSJE3jttddw5swZdO/eHQDQpUsXREdH47nnnsPBgwexbt06vPnmmxg2bBg0Gk2J27TK6sOQm1to+c8zZ8Aoy7+kIvNNv783JgGF7SwAIOdknrSFeLFDu7bBZGkNAND57kLTDvFub/M/3f4LjWIHACDvZltsXrbY7W0SEdG9kywUVapUCWvXrsXNmzfRqVMntGjRAjt37sTKlSvRpEn+JSGFQoFVq1ZBoVAgLi4Ozz77LPr27YtJkybdU5sWdSBWzZzptOx40m5kHYyEkCuhzUvGs9PdNwaSuygrXAUA2C01Ja7Ee+37YidsSh00hnN4eqrnfsZPfvA6tHknYFdo8OdaGbLSz3msbSIiKhlJ5z5r0aIF1q1bd8dtIiMjsXr1ape1eSPV+S3v+mQbLLoWUBsvostb3aHWal3Wlqc0fTQeO5cCJl0kDu3ahsat2ktdkldJ2rQGRnVLAEClplc9+jP28Q/AAy9EIXHedZi0VbBq/EIMnM9LaURE3qjczX1mUjdG2omjAIBVcz6FQdcCEHaEPXAZ4XWiJK7u3jRp2wkaQxoA4MD3dw6Z5dHBJXsh5ApoDSfRa8Qoj7ffuHVHBEQcAgCYFC1xYv8ej9dARER3V65CUcG0GL9Oz8C8vnNwLjm/s7bWuB89hr0scXX3R67OnyDWeqWyxJV4l01LF8KgjgMA+EddkayOx8a9AY0hHXaFBjtnrZWsDiIiuj1JL595mlJ3DAL5/W6M+n9GrFYGXpeoItep3rkujm0FTNp6OLF/D+o2byl1SV7h7LpcQK+ANu8QHn9DuoEUlSoVAuqdR9a5cBjUcVg151M8PHS4ZPWURRnnTmPTF4tguiiDsAZByCvCJveDwpYDmciBTH4TUN+Af20/dOz7HCqGcWwvInImE0IIqYtwt5ycHAQEBGD/ru34Y34u7HIlfNSbYMvVQiYUeGjC0wiLrCV1mfftq36LYNKFw1ezEf1mvid1OZJbMeNDXEhtDpndisbds9Gm5+NSl4T5/T6EQdccGuNFPDK1A7+Y75PVYsGqTz/BlWQBs7oh7Ap1sV4nt5mhNqegchsfPDS49IxLRlTeFHx/Z2dnO4bXcadydaaoVoMmsDy+HYAVLbtMkbocl5PrTgAIh/VauNSleIWrB7SAHtCa96JNz3FSlwMAaPNyO2z59AJM2spY+eZcDPzq3u6kJODnGR/hSnIAjLpmQP48zlCbLkMhTkDumwddmB6+wUG4kXkFpmtG2HJlEKYKsClqwKIOhFHXHGeSgK92zoM27Cyeevdtt45dRUTer1ydKfJU0pRK4q8rsP+3AEDY0WmQDvVbtpa6JMmsW/AlTu2uBQgbmj6Ug9Y9e0tdksPSceNx/Up7yOw2/Ocp4ZapRsqyE/v3YMfMbTDq8ieOltvMUFsOICzOHwkDh9w12FgtFqydNxdZiUYYNY0h5Pl/G2rzUlCvd1Wv+qwQlXee/v4uVx2ty7q4Ho9AY/gLkMmxf0X57sx7fnv+/HlaQ4rXfcn1mTwRWsNpCLkCyd/slLqcUmXZxEnY8un5/EAk7NAZE9GqrxqDFo5F9xeGFetMj1KlwsNDh2PgolfRpr8GOutWyOw2GPUNcfA3X8wf/BauZJz3wLshIm/DUFTGyGUXAACmyxIXIqFTB/fBpGwBAPCLKjyCuTfQVssfOd0ki0NKIoPR3ZiNRswf9DauXGwDqzoAamMGIhofwcCF49Ckbad73m/jVu0x8KtJqNfxIrR5qRByFQyKjlgxegMSf/vZdW+AiEoFhqIyRu6XAwAQ5moSVyKd7bN+gV2hhcb4Fx59fbTU5RTpyQlvQWM4B7tCg91zfpe6HK924/p1LHnhUxhUHQAAOtNO9HqvrUuH0ej0dF/0m/c8AgK2QGm5CZMuAgdXqLBoxBuczJeoHGEoKmNqtG8IADBpqpfLKSWOJ+2GSfYfAIC+2mmv7TirVKkQ0iIbEHYYdQ/g+6llr+O/K1y7lIHl/1sIgy7/LkI/300YuOBtBFdx/c0ESpUKz77/Dhr+1wKt4TRsSh1uGuKx6PmPipwzkYjKHoaiMqZ1z8ehMl+DkKuwZVH5m4B015xV+WeJDGl4YvybUpdzR//9v1egNScCAK4fr47sK+X4mmcRMs6dxk+jfoRR3xgyuwVBlRPR98PJbm+3dc/eeGb2M9DZNwHCBqOuJb55cZ5jJHwiKrvK1S355YFSpYLCfgIWxOLm6TJ/Y6GTa5cyYLHHAApAG+a9Z4lu1Xn0I1j3wSmYtaFYMfEj9P+EZ4wA4OzxFGyasgtGfX3IbSZUqnkAj4/13JxxOh8fDPxiMpaOG4/sS61g1DfE+vcOoP4jx7yu435pZ7VYcPLAHpzauw83s67DcsMEYQNkCgAKGZQaJQIjw1CvVWvUbNBY6nKpjGMoKoP0kWYYMwGbLBpWi6VUhANXWPX+LFhVnaEyX0PPsZ6f4+xeVK/XEGrNN7CKeFhz6sNsNJbKSYld6cT+Pdg+8whMutpQWPMQ1ugker0szWjkfSZPxK+zZ+HivqowaSsj5ZebuJ45G92HvCRJPWWB2WjE6rmf4+qRKxDGUFjU1WFT+gBoVPQLbgDXLgNnki5DYf0VKstFyJTnoa0CNOvRpVwPPUKux3GKyqArGeex/K1DsCs0iGx6tNxMJzGv76cw6qOhs23GwC/flbqcYjt1cB82zsqETalDYNAW9JnyjtQlSSb9ZCrWvfs7TLrqUFpuIPw/F7xixOn9m9dj/6LzMOkiobDmIrjeMfR+9XWpyyo1zEYjVs+ZjauHbsCKhrCogwptozJfg9yWA5nIA2BDfu8OBYRMA5uyAqyqov/vVhszIMcZhP4noNz8X1eecERrum8Vw6pCbV4Go64ZsvZlSV2ORyRtWgOjLgoAUPPB+hJXUzK1m7TAdtlbMKAj8jIa4tKFdLd0JPZ2htxcrH9nA0z6aCgtN1G70zV07iN9IAKA5p26oFK1o9gweReMuprIPNEUi18dh+c80MepNEs7cRQbP14Cm6EpzJqmwN+zsCisBqjMxyH3v4LghlUQ1+uRu055c+HMSRzeuhUZh87Ads0HdlSHSVsZZm0YgDCcSwa+6r8UMsUJ1IivhU5P93X326MyiGeKyqivXxuHGzc6Q2O8gOcXPit1OW43b9B4GFXtoc07iUFfvyB1OSWWfjIVa6YcgUUdCJ1tCwZ+Wf7OFs0f8iYM8k6Q20yo1vSES2+5d5VLF9Lxy9jl+VOLAPBRbUD/WewH9m9/rPkFR74/AIu8BWzK/DlYFFYDVJYj0EXkImHYEJfM+3fq4D7s+eFXGNP8nEYnh7BBazwEn1q56PHKy/DxD7jvtkganv7+Zigqo/48cghrZ2ZCyBVo3OUy2j76hNQluc3Z4ylY++E52JQ6BAVvwzPvTJS6pHuyZOxbyL7WETK7Bc165CGuxyNSl+QxC4e/gVxrPAB4/YTGZqMRS156HwZ1W0DY4avd7NX1etKh37dg31e/w6iKhZArAABq40WoA47godEvuvUM6KnDB5C4+EeYM8Jg1Ec7lqtNl6GpeBA9x45AQMVKbmu/vLh0IR1Hd+1A1smzMFzJhd0kYLfIAKsCEDIAgAAgk9shU9ogVwMKvQK+IYEIq1cHDeLawS8wsNjtMRS5QXkMRQAwr+9sGPX1oJdtxIDPy+5/2vOHjoMBnaExXkTfOb1LbUdlq8WCRYO+hFFfD1rDXgxa5J0DT7pawVxwAKC1bEO/OW96/c0BVosFXw9+HwZtKwCADpswcE75vZR26UI6Vr3zOUyWuL87TQPavFT41L6ER19/3eO/k6u//BwZuy7BIm8Kq8oXAKA050ClSEKnUU+ier2GHq2nNLJaLNi2fCn+2nMCthwthC0YVmVlWNX3+R0q7FBZrkNhzQIUV6DwMSCwVkW07PVfVKlRp9DmDEVuUF5D0YKX3kCePR7avCMY9PX/SV2OW+TmZGPpK5tgUQfCz2cT+n5Uur+Yfp09C2mHGkBmt6D1c6r7msKiNNi8bDFSN1WEXaGF1vQ7+n0xxusDUQGrxYKvh77zz0jblm3oWwoCnat9+/YE3PirkaPztMaQhqBGWV7RET3j3GmsnjwfFmsLWNX5l9CUlhtQKfYg7vmHeOfaLawWC3av/gWnNifBej0IVkWd2wYguc0EpeUa5PZsAAbI5GZAZoaQ5ccJGQDYFRBCDQgtINPBLg+ARRUAIb/N74ewQ226BLlIh9zvGkIaVUO7J/tAyOUMRa5WXkPRylkz8NeRJlBabmDQnIfK5H/W3749AVez2kFpuYGnPmhdJk6Pz+v3JYy6WtCafsegBW9JXY7b3Lh+HctfXg6jrja0eSfwxCdPlei0urdY8OIbyBP5l/501q3o+/lbZfJ37d+SNq1B8teHYNQ9AABQma5CE7gfj00c7XV9eG5cv46VUz6CIbP+3x2zAZndAq01ER1GPVpuxz86uGMzDq7cBMslX9jktWHRVHRaL7eZoTadgUyZCUWAGRXqVEb9tm1QvV7De/qMm41GHN+/G3/uSUL2ucuw5aggLBVhU1aDRR1YaHuZ3QLbjT/xv2UvMRS5UnkNRdcuZWDZG8mwK9SoE/cnuvR7XuqSXG5e35kw6htBZ96OgfMnSF2OS/zwwVRk/tkSMrsVDbpcRvvHnpG6JJczG41Y/MJMGHUPQG4zoWkvE+K695K6rHu2YPgbyPu7T5TOuAe9P3q+TAT0opiNRiwdOQlGSzvYFfm3k+nM29Hj3UFef9dkbk42fpz0AcyXasOkiwSQ3wFcbd+HqJ4Ny8XAnDt+Wo5TG5Jhz6sJo66m0zqZ3QqN6Sxk6nQE1PVDp/59ERQc5pG6UhJ34siGLchNN0GYwv4ev0oPgzkXry34L0ORK5XXUAQA8/rOglHfoEz2K0o/mYpfp52DkCtRr20aOvfpL3VJLlPwc9MaTqDfV4PK3JmH+c+/BYOyIyBsqFh5F56a4LnRqt1l4ctvINfYEZApoM1LwaMfPeGxLxRP+X3ljzj+4yUY9XUBAFrDKYTEmtDjpdJ1ed5qseCHKVNw88+aMGmrAPjnzFGLQe3RuHVHiSt0rcRfV+DYr/tgN0fBpHWeLFxjOAe58ix8a6rQ7tmnERZZS6IqnZmNRuxZ9yuObd+LIdOnMRS5UnkORf/0KzqOQV+XrVF4F414AzcN8VCbMjF4wdNSl+NSv6/8EYdW+cCuUJeJvlK3+u7dd3E5/T+ATA5f3Ub0m1F2wvqyiZNw7a+WsCvU0OYdQq8PHnfJredSM+TmYtmrk2G05p8dkttM0Gl34NnpE0t1YDcbjVjx4Ye4eaIijPr8cc5kdgu05gOo2r4iEgYMlrjCe3c8aTcS56+E/ea/zggJG7SGk1AE/IWmvTuhaYd46YosBna0doPyHIo2L1uMY1urQma3oMdr1RFeJ0rqklzCarFg4fNLYdJFlNk7fwruqlOZr+HJae3LxOWYjHOn8cuEA7BoKkBn2oWBC7x70t57sXzyZFw+1wJCroLWcBrtRzZD7SYtpC7rnh3atQ175xyCUd8AAKA1nEDUoxXRpufjElfmOlaLBT9OmYqbpys7BQht3nH41LqMx94YWyrC36mD+/D7wp9gvVIZJm3dfzo1Czu0hpNQVkxH6/6PlqrPI0ORG5TnUGS1WLBgyA8wa0LhH7AZz71feqa/uJMVMz7EhdTmkNvM6Dg0CPViYqUuyeWuZJzHD2/sgVUdAD/fTR6ZId6dDLm5+ObFuTDqm0JlvopeEx9ASHik1GW5xQ/vT8Hlk41hU+qgMV5Ekz5BeODBh6Quq0TMRiO+eXUSjMZWsCn1kNvM0PtsR59ppfvs0N2snDUDl/fZYdQ2AWRyAIDGkA5VUCoSRgzymstLBdJPpmLrl4thzqgEkzb6nwEskX8noML3BJo97v1nhG6H03yQSylVKigUxwCEwnyxgtTluMzVZAWgA9SWJNSLGSd1OW5RMawqVMp9sKIzTFea4NqljFLdR+Xb//sQRn1byOwWVIz+EyHhj0ldkts8NnosNnw9D2e2BsKkrYwD315DTtbCUtPvLXnrRiTNPwajPh5Q5n+5Vm1rRLfny86lztvp+X8jAACJv/2Moz8ehFnxAEy6cJiM4VjxbirU5p+gC7+JhOGuGZX7Xhzb8zv2Ll8NS1YFmDUNYFd0AvT56zTGvyBXn0Bk+9ql5vPmTXimqBxY/eXnOJMUBbnNjEferON1f+mU1M6V3+Pg6iBAJkf99n+V6TmOMs6dxi8TD8KiDoQemzCglF4mLBitG0CZOmN5N3vWr8LBb3Jg1oZBbjPCt8Iur37vVosFS18fD8ON/LNDMrsFOvVWPPX+m9D5+EhdniRO7N+D37/8GVZjY5g1IY7lcpsRavMR6CJuumzaktu5dikDWxYvxvVj2bCba8CkjXScxQIAtSkTCsUxRHSoifhn+7utDinw8pkblPdQlH8J7SeYNcEICNqCZ0v5LOzzn38bBmUHaPOOYtDXZX9W7IIO5XKbGQ26XUO7R0tXp/Jje37Hti+uwabUQ2fZioHzJkldkkcdT9qN3z9JhlGXP1qvt45ldGL/Huz4eI9jigyNIQ2RnW14sO8giSvzDlaLBWu+mI1LSddgRUNY1P+cec+f4PYk5PpLCKgbiLZPPXXPwxNYLRYc2LoBp3bsgeGiDcJUFWZNdcfwBwU0hr8gV51CSIsQdB30gtd9nlyFl8/I5ZQqFRSyYwCCYbrgJ3U598WQmwurvQkAQFM5U+JqPKPPBxOxaNAXMOrr4+TKG2jVw1Jq/gM0G43Y9ele2PSNoTGk4/GZI6UuyePqxcQifFY9fDfiIxhUHWBQdsCi5z9B+1c7oXajZlKXh9ycbPzw1vsw3ngAVn005DYztIodeGLW6143CKOUlCqVY5Jiq8WC3+Z8issHcmBDQ1jUQbAp8weAzDsBfD/hONTmbZDZrwDK65DrTVBoZVBqlZAp5JDJAKvJCpvRBrsJECYVhNUfAkGwKSv9PTVJO0ABx2UxpSUHSstpKAIvo1HPdojpXHbPkEuJZ4rKiYKOyQrLTTw/p1up+VL9tx8//AAZp1pAYc3D4+82KxO3OxfHH2t+wYGfVLArNAiqtA3PvFs6Jr2dN3AijOq2kNmtCG+aWurGs3G1W8cyUpmvIqTROfQaMUqyetYt+BLpW1Uw6SIAAGpjJiLa3yzVt6J7mtloxNov5+DK0SzYc4NhU0YWOTpziQgbNKYMyMQFKPyvI6J1NNr1fqrU/r99P3j5zA0YioDsK5exdGwShFyFJt2ulNrbaecPGg+Dqj20hiQMWvSa1OV41LxB42FUtYfG8Bf6zO7t9X08Fr3yBm4a8+948fPbhL7TSmd/KFf7ZdbHyNxfGWZNMCBs0Nm245Epwzzaif7Qrm3Y+8VOGDWxgEwOhTUXGnUiek38X6nuzO8NrBYL9m9ZhzN7kmHIzIMtVwPYfAGhhZBpkT8zmBwymAFhAmRmyOS5kOmMUAepEFQ9DLE9/ltu/uC7G4YiN2Aoylcwp5aPZiP6zyx9d5FcyTiPH8btg1Xlh8CgLehTyvtGldStfXN8lBvR/1Pv/RluXb4UxzZUhF2hhs62GQO/9N7OxVJIP5mK9e+uhFGXP16M2pgB38iTePzNN9x6NuDUwX3YPmslTLI42BX5M9dr85LRYnDTMj/5MJVOnv7+lt99EyorZJpzAADbtZC7bOmdVr3/Gawqv/wxbsa+KnU5Hle/ZWtotbsAAEZjayT+ukLiiopmyM3F6VXX/x7V+TienVX2Bmi8X+F1otDvqxEICNoCpSX/7rSrmW2xaNA8/Dp7lsvbO3VwH+Y//zY2zsqEQdkRdoUWGsNZhNXeh0Ffj2QgIvobQ1E5EhpTGQBg1tRF9pXLEldTMlaLBZZr9QEAKs3+ctsB9Mn3x0FrOA2bUoejP1xBbk621CU5MRuN+OalT2DU14fcZkaNh/yg1mqlLssrKVUqPDvlHXR+JRI6yzbI7BYY9XWRdqgB5vX9BMsmToIhN/ee92+1WPDzx9Mxv/972PBpFgzKDn8PJnkegUFb0P+r59D71ddd+I6ISj9ePitHzEYjFry0Fla1P4LDd+GJcaXnL/jlkyfjUnoc5DYTEl6pipoNGktdkmQSf/sZB1eoYFPqoJdvxIDZ3nMZrWC4BAg7AitsK3eXOO/HnvWrcHjpERi1MY4xaFSmK1DKU+AfpUPHvs/dtZ9J+slU/P7Nd8g9J2AXdWHWhDrWaYx/QVf1NB5/ayyDKpUavCWf3Eat1UJpPwIr4nDjhELqckrkRqovoAc01r2o2aCb1OVIKq57L5xY/wZumuJhMrdCSuJONIxrI3VZ+Hb8xPxABMBXvxl9pnhPWCsNWnZ5GC27PIyty5fi9NpTsMibw6KpCAvaw/An8N3bR6E2b4JMZOZ3zpXZALkNwqYG7P4Q8iCYNKGArA3w95A2cpsJavMhBDaUoecrI8rl3UtEJcFQVM4E1JfDeAawqBoh+8rlUjHJ6LE9v8OkzR9QLjI+QuJqvMMT743D0pd+gEkXjt2fJ6Fei1hJv/AO7tiMG+mNADWgM+1EvzkMRPeqwxN90OEJ4NKFdKydORfmDD9YFVGwqv1h0lYDUM35Bf/6satNl6EQp6AKzUOH559DeJ3y/UcEUUm4rU/R5MmT0apVK+j1egQGBha5TVpaGrp37w69Xo+QkBC89tprsFqtTtts3boVzZs3h0ajQe3atbFw4UJ3lVwudB/+PyjN2bAp9Vgz6zOpyymWPxb9BiFXQGM4y7l8/qbz8UF4B/Pf/VAaYcmI8ZLVcjxpN/bOOw+LOhBqYyZ6TuEIyK4QXCUcz73/LgYtGo1Bc7ujZW8jgoK3QS/bCJ1tM7SWbdCZd0Bn2wIfzUYEh+/CA4/mYfCCJzBw4Rt47v13EV4nSuq3QVSquO1MkdlsxuOPP464uDjMmzev0HqbzYbu3bsjLCwMu3btwsWLF9G3b1+oVCq8917+X5lnzpxB9+7dMXToUCxduhSbNm3C888/j8qVKyMhIcFdpZdpOh8fqMRhWNEGuWdKR78C+40qgB5Q+JySuhSvkjBgMBbsfQN59njkmTvip+nT8OhIz47ddOrwAez8JBUmXTWozNcR/Yia46u4gVKlwgMPPoQHHnxI6lKIyjS3nSmaOHEiRowYgUaNGhW5fv369Th69CiWLFmCpk2bolu3bnjnnXfw2WefwWw2AwDmzJmDGjVq4KOPPkL9+vUxfPhwPPbYY5gxY4a7yi4XAhrkhyGLKho3rl+Xtpi72LthNYy6/L92I1rXlbga7/PczInQGg5AyBW4dKQO9m5Y7bG2zUYjtk3bCZOuGpTmHNTtakDrnr091j4RkatJdkt+YmIiGjVqhNDQf+6OSEhIQE5ODo4cOeLYJj4+3ul1CQkJSExM9GitZc1DLw6D0nIDNqUP1s39XOpy7ujwt3sAmQLavBO8dFYEpUqF7pN6QWP4C1aVPw4uycKlC+lub9dqsWDpy+/AqG8Auc2M8LiL6PBEH7e3S0TkTpKFooyMDKdABMDxPCMj447b5OTkwGAw3HbfJpMJOTk5Tg/6h87HB0prKgAg5+Ttj6PUzh5PgVn+AABAX6N8TP56L8Iia6HBE/5QWm7CpIvAL2O/gdVicVt7N65fx6LnpyNP1hkAoJXvwEODX3Rbe0REnlKiUDRmzBjIZLI7Po4fP+6uWottypQpCAgIcDzCw8OlLsnrKCtcBwDYLTWlLeQOtsz85u/B5i6i99jRUpfj1eK690KF2imAsMGoewCLXnjPLcEoNycby19eAKPuAUDYoJdvxHOfTnB5O0REUihRKBo1ahSOHTt2x0fNmsX7kg0LC0NmpvNf/wXPw8LC7riNv78/dDrdbfc9duxYZGdnOx7p6e6/nFDaxD7bExA2mHQR+H3lj1KXU0huTjYspvx5oVRBRzjYXDE8PvYN+Gi3AACM6rb4esgUlwajrPRzWPZ/c2HUNYHMbkHFKokYMPs9jn1DRGVGiUJRcHAw6tWrd8eHWq0u1r7i4uJw+PBhZGVlOZZt2LAB/v7+iI6OdmyzadMmp9dt2LABcXFxd9y3RqOBv7+/04Oc1YuJhdaQfzfXibVJEldT2C8ffQyLOhBKyw38943/SV1OqdF/5nvw1WwEhB0GTRt8PeR9mI3G+97v5mWL8ctb2/MnMBV2+AftxFPj33ZBxURE3sNtfYrS0tKQnJyMtLQ02Gw2JCcnIzk5GTdv3gQAdOnSBdHR0Xjuuedw8OBBrFu3Dm+++SaGDRsGjUYDABg6dCj+/PNPvP766zh+/Dhmz56N5cuXY8SIEe4qu1xRBOSfQbPl1ZG4ksLyzuTPbaYSBxAUHCZxNaVLv5nvwc9n89/BqBUWD/kSx5N23/P+vnlrPFI3VYJJWxVKcw5CauzFs+9z+g4iKnvcNvdZ//79sWjRokLLt2zZgg4dOgAAzp07hxdffBFbt26Fj48P+vXrh6lTp0Kp/Gf4pK1bt2LEiBE4evQoqlWrhrfeegv9+/cvUS2c+6xoh3Ztw86FZgi5AlFt0hD/bH+pSwIAXMk4j+/eToGQq1A79jQSBgyWuqRSafHoN3HjWlsIuQoq83X4VTuEx98aV+zLXZu//Rpn1lyDUZ8/rIY2LxUPDI1G41bt3Vk2EZGDp7+/OSFsOTev78cw6htDa96BQfOlGxX5Vl+/Og43bnaGynwNA+f+l31W7sO6BV8ibZsPzNr8s23avGPwrXsNDw0fDr8iRpq3WizY/uMynF17DgbNf/InJhU26MyJeHzG/4p8DRGRu3BCWPIon5rZMGYAVlkzXLuUIfmlKrPRCNPlaEALKNX7oVRxMMD7kTBgMDI6ncbqSV/DqGgFo74+jH8BS1/dCZXlKGTqHAAyAIAw+8KmqA2LuiqgzR+VWpt3CLUeDkaHJ9h/iIjKPp4pKudyc7Kx9JWNsKiDEBS8Dc+8M1HSepaMfgvZ2R2hsOah66vVUb1eQ0nrKUt2/LQcJ35NhVXeGFaV3223k9kt0BhPwaf2FTz1NsMQEUmHZ4rIo3z8A6AUR2BBGxjSpL3t3Ww0wpgZBWgBtWw3qtd7WNJ6ypq2jz6Bto8ChtxcrPtiDq6m5AA2DQA7hExAJrPBt5YKHfo+i5Bwzi1IROUPQxHBr64ShnOAVVkfhtxc6Hx8JKnjh8lTYNK2h8Kahw4jHpekhvJA5+ODXiNGSV0GEZHXkWyaD/IeXV98CUrLTVhV/vjlo+mS1WE4FwIAUIu9qNmgsWR1EBFR+cRQRPALDITKngwAuHlKL0kNib+ugFFfHxB21O/VVJIaiIiofGMoIgBA5db5Z2lM2oY4sX+Px9s/tmYvAEBrPI24Ho94vH0iIiKGIgIAdHt+KDSGNAi5Cju/WOnRti+cOQmLsSUAQOF/zqNtExERFWAoIgd1pZMAAKs5BtcuZXis3bXvfQ2r2h9q0yU8MmGkx9olIiK6FUMROfR8YwRU5muwqAPxy3ufeKTN5K0bYUIrAIAuJAUBFSt5pF0iIqJ/Yygih4CKlaBU7wcAWK/W9Eib+xfuhl2hgdbwJ556lwMFEhGRdBiKyElMn86AsMGoq4nN337t1raSt26EUf0AAKBC0+uc44yIiCTFUEROmrTtBK3hCADg7Lo0t7a1f+k2CLkSWsMpPDLiVbe2RUREdDcMRVSIf7QBAGBUP4CDOza7pY30k6mw2poCAFQVz7qlDSIiopJgKKJCHhn1KrSG0xByFfYt2OGWNta/txwWdRDUpsvo8vIgt7RBRERUEgxFVIhSpYJvrfxb8s2KWOzdsNql+//hg6kwaloDAALrnEJYZC2X7p+IiOheMBRRkXqPHQOt4RTsCjUOLTnssv1eupCOa8dqAAC0xkQ8/sYbLts3ERHR/WAooiIpVSqExpkBAEbdA/h19iyX7PfXSXNg1gRDZb6GB9/gdB5EROQ9GIroth4eOhxaQ/6cZFm7NbBaLPe1v0sX0mGxxAIAtBX2I6Ju9H3XSERE5CoMRXRHTfs2htxmhlFXG9+MnXDP+zHk5uKXMT/AqvKF0nIDj7z5muuKJCIicgGGIrqjmM7doJHl34GWd70V/ljzS4n3YbVY8O2wGTDqm0Bmt6Bi3aPwCwx0caVERET3h6GI7urJD1+H1vAnbEodjizLwpWM8yV6/eKXx8OgbQUIOwIq7sRjo8e6qVIiIqJ7x1BEd+XjH4B6jwZBYc2DUVcTP49eBrPRWKzXrl/0FQzWDgAAvWIz+kx5x42VEhER3TuGIiqW1j17o2Ltw5DZrTDqmmHxC58gNyf7tttbLRYsfnUc/twe8vdUHifw3MyJHqyYiIioZBiKqNgeHzMW/kE7ILPbYNS1wLLhS7D9p28LbZeVfg6LBs1Czs3OsKl8oTGkIfqJME74SkREXk0mhBBSF+FuOTk5CAgIQHZ2Nvz9/aUup9T7dvxEXL8QC7tCDQDQ5p2ETPMXIGQQFj/YFDVhUQdBZrdAix14/INX2bGaiIhKzNPf3wxFdE82LlmIc+tzYdRFAbLCJxxldgsqRe7DE+PGSVAdERGVBQxFbsBQ5D5Jm9bg0PKdsJnqA8IKue4kNBUVqN+5DZp36iJ1eUREVIoxFLkBQxEREVHp4+nvb3a0JiIiIgJDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEwI2haPLkyWjVqhX0ej0Ci5j36uDBg3j66acRHh4OnU6H+vXrY+bMmYW227p1K5o3bw6NRoPatWtj4cKF7iqZiIiIyjG3hSKz2YzHH38cL774YpHrk5KSEBISgiVLluDIkSMYN24cxo4di08//dSxzZkzZ9C9e3d07NgRycnJeOWVV/D8889j3bp17iqbiIiIyim3T/OxcOFCvPLKK7h+/fpdtx02bBiOHTuGzZs3AwBGjx6N3377DSkpKY5tnnrqKVy/fh1r164tdg2c5oOIiKj0KdfTfGRnZ6NChQqO54mJiYiPj3faJiEhAYmJiZ4ujYiIiMo4pdQFFNi1axe+++47/Pbbb45lGRkZCA0NddouNDQUOTk5MBgM0Ol0Re7LZDLBZDI5nufk5LinaCIiIiozSnSmaMyYMZDJZHd8HD9+vMRFpKSkoGfPnhg/fjy6dOlS4tf/25QpUxAQEOB4hIeH3/c+iYiIqGwr0ZmiUaNGoX///nfcpmbNmiUq4OjRo+jcuTOGDBmCN99802ldWFgYMjMznZZlZmbC39//tmeJAGDs2LEYOXKk43l2djYiIiJ4xoiIiKgUKfjednP3Z4cShaLg4GAEBwe7rPEjR46gU6dO6NevHyZPnlxofVxcHFavXu20bMOGDYiLi7vjfjUaDTQajeP55cuXAYBnjIiIiEqhK1euICAgwO3tuK1PUVpaGq5evYq0tDTYbDYkJycDAGrXrg1fX1+kpKSgU6dOSEhIwMiRI5GRkQEAUCgUjuA1dOhQfPrpp3j99dcxcOBAbN68GcuXL3fqd1QcBZ2309LSPHJQy6qcnByEh4cjPT2dd/HdJx5L1+GxdA0eR9fhsXSdgis9t96E5U5uC0Vvv/02Fi1a5HjerFkzAMCWLVvQoUMH/PDDD7h06RKWLFmCJUuWOLaLjIzE2bNnAQA1atTAb7/9hhEjRmDmzJmoVq0avvrqKyQkJJSoFrk8v+tUQEAAP6Au4O/vz+PoIjyWrsNj6Ro8jq7DY+k6Bd/j7ub2cYq8Accpcg0eR9fhsXQdHkvX4HF0HR5L1ynX4xQRERERSaVchCKNRoPx48c7db6mkuNxdB0eS9fhsXQNHkfX4bF0HU8fy3Jx+YyIiIjobsrFmSIiIiKiu2EoIiIiIgJDEREREREAhiIiIiIiAOUgFH322WeoXr06tFotYmNjsWfPHqlL8ioTJkwoNKlvvXr1HOuNRiOGDRuGihUrwtfXF7179y40H11aWhq6d+8OvV6PkJAQvPbaa7BarZ5+Kx63fft29OjRA1WqVIFMJsPPP//stF4IgbfffhuVK1eGTqdDfHw8Tp486bTN1atX0adPH/j7+yMwMBCDBg3CzZs3nbY5dOgQ2rZtC61Wi/DwcHzwwQfufmsed7dj2b9//0Kf065duzptw2OZPxn2Aw88AD8/P4SEhKBXr15ITU112sZVv9Nbt25F8+bNodFoULt2bSxcuNDdb8+jinMsO3ToUOhzOXToUKdtyvux/Pzzz9G4cWPHQJZxcXFYs2aNY73XfR5FGbZs2TKhVqvF/PnzxZEjR8TgwYNFYGCgyMzMlLo0rzF+/HjRoEEDcfHiRcfj0qVLjvVDhw4V4eHhYtOmTWLfvn3iP//5j2jVqpVjvdVqFQ0bNhTx8fHiwIEDYvXq1aJSpUpi7NixUrwdj1q9erUYN26c+OmnnwQAsWLFCqf1U6dOFQEBAeLnn38WBw8eFP/9739FjRo1hMFgcGzTtWtX0aRJE/HHH3+IHTt2iNq1a4unn37asT47O1uEhoaKPn36iJSUFPHtt98KnU4n5s6d66m36RF3O5b9+vUTXbt2dfqcXr161WkbHkshEhISxIIFC0RKSopITk4WDz30kIiIiBA3b950bOOK3+k///xT6PV6MXLkSHH06FExa9YsoVAoxNq1az36ft2pOMeyffv2YvDgwU6fy+zsbMd6HkshfvnlF/Hbb7+JEydOiNTUVPHGG28IlUolUlJShBDe93ks06GoZcuWYtiwYY7nNptNVKlSRUyZMkXCqrzL+PHjRZMmTYpcd/36daFSqcT333/vWHbs2DEBQCQmJgoh8r/M5HK5yMjIcGzz+eefC39/f2Eymdxauzf59xe53W4XYWFhYtq0aY5l169fFxqNRnz77bdCCCGOHj0qAIi9e/c6tlmzZo2QyWTi/PnzQgghZs+eLYKCgpyO5ejRo0VUVJSb35F0bheKevbsedvX8FgWLSsrSwAQ27ZtE0K47nf69ddfFw0aNHBq68knnxQJCQnufkuS+fexFCI/FL388su3fQ2PZdGCgoLEV1995ZWfxzJ7+cxsNiMpKQnx8fGOZXK5HPHx8UhMTJSwMu9z8uRJVKlSBTVr1kSfPn2QlpYGAEhKSoLFYnE6hvXq1UNERITjGCYmJqJRo0YIDQ11bJOQkICcnBwcOXLEs2/Ei5w5cwYZGRlOxy4gIACxsbFOxy4wMBAtWrRwbBMfHw+5XI7du3c7tmnXrh3UarVjm4SEBKSmpuLatWseejfeYevWrQgJCUFUVBRefPFFXLlyxbGOx7Jo2dnZAP6ZFNtVv9OJiYlO+yjYpiz/3/rvY1lg6dKlqFSpEho2bIixY8ciLy/PsY7H0pnNZsOyZcuQm5uLuLg4r/w8um1CWKldvnwZNpvN6UACQGhoKI4fPy5RVd4nNjYWCxcuRFRUFC5evIiJEyeibdu2SElJQUZGBtRqNQIDA51eExoaioyMDABARkZGkce4YF15VfDeizo2tx67kJAQp/VKpRIVKlRw2qZGjRqF9lGwLigoyC31e5uuXbvi0UcfRY0aNXD69Gm88cYb6NatGxITE6FQKHgsi2C32/HKK6+gdevWaNiwIQC47Hf6dtvk5OTAYDBAp9O54y1JpqhjCQDPPPMMIiMjUaVKFRw6dAijR49GamoqfvrpJwA8lgUOHz6MuLg4GI1G+Pr6YsWKFYiOjkZycrLXfR7LbCii4unWrZvj340bN0ZsbCwiIyOxfPnyMvHLSGXDU0895fh3o0aN0LhxY9SqVQtbt25F586dJazMew0bNgwpKSnYuXOn1KWUerc7lkOGDHH8u1GjRqhcuTI6d+6M06dPo1atWp4u02tFRUUhOTkZ2dnZ+OGHH9CvXz9s27ZN6rKKVGYvn1WqVAkKhaJQL/bMzEyEhYVJVJX3CwwMRN26dXHq1CmEhYXBbDbj+vXrTtvcegzDwsKKPMYF68qrgvd+p89fWFgYsrKynNZbrVZcvXqVx/cuatasiUqVKuHUqVMAeCz/bfjw4Vi1ahW2bNmCatWqOZa76nf6dtv4+/uXuT+mbncsixIbGwsATp9LHktArVajdu3aiImJwZQpU9CkSRPMnDnTKz+PZTYUqdVqxMTEYNOmTY5ldrsdmzZtQlxcnISVebebN2/i9OnTqFy5MmJiYqBSqZyOYWpqKtLS0hzHMC4uDocPH3b6QtqwYQP8/f0RHR3t8fq9RY0aNRAWFuZ07HJycrB7926nY3f9+nUkJSU5ttm8eTPsdrvjP9e4uDhs374dFovFsc2GDRsQFRVV5i73lMRff/2FK1euoHLlygB4LAsIITB8+HCsWLECmzdvLnS50FW/03FxcU77KNimLP3ferdjWZTk5GQAcPpc8lgWZrfbYTKZvPPzWPJ+46XHsmXLhEajEQsXLhRHjx4VQ4YMEYGBgU692Mu7UaNGia1bt4ozZ86I33//XcTHx4tKlSqJrKwsIUT+7ZIRERFi8+bNYt++fSIuLk7ExcU5Xl9wu2SXLl1EcnKyWLt2rQgODi4Xt+TfuHFDHDhwQBw4cEAAENOnTxcHDhwQ586dE0Lk35IfGBgoVq5cKQ4dOiR69uxZ5C35zZo1E7t37xY7d+4UderUcbqN/Pr16yI0NFQ899xzIiUlRSxbtkzo9foydRu5EHc+ljdu3BCvvvqqSExMFGfOnBEbN24UzZs3F3Xq1BFGo9GxDx5LIV588UUREBAgtm7d6nSbeF5enmMbV/xOF9wC/dprr4ljx46Jzz77rEzdRi7E3Y/lqVOnxKRJk8S+ffvEmTNnxMqVK0XNmjVFu3btHPvgsRRizJgxYtu2beLMmTPi0KFDYsyYMUImk4n169cLIbzv81imQ5EQQsyaNUtEREQItVotWrZsKf744w+pS/IqTz75pKhcubJQq9WiatWq4sknnxSnTp1yrDcYDOKll14SQUFBQq/Xi0ceeURcvHjRaR9nz54V3bp1EzqdTlSqVEmMGjVKWCwWT78Vj9uyZYsAUOjRr18/IUT+bflvvfWWCA0NFRqNRnTu3FmkpqY67ePKlSvi6aefFr6+vsLf318MGDBA3Lhxw2mbgwcPijZt2giNRiOqVq0qpk6d6qm36DF3OpZ5eXmiS5cuIjg4WKhUKhEZGSkGDx5c6I8bHktR5DEEIBYsWODYxlW/01u2bBFNmzYVarVa1KxZ06mNsuBuxzItLU20a9dOVKhQQWg0GlG7dm3x2muvOY1TJASP5cCBA0VkZKRQq9UiODhYdO7c2RGIhPC+z6NMCCFKfn6JiIiIqGwps32KiIiIiEqCoYiIiIgIDEVEREREABiKiIiIiAAwFBEREREBYCgiIiIiAsBQRERERASAoYiIiIgIAEMREbnB1q1bIZPJCk306CmbNm1C/fr1YbPZ3NbGf/7zH/z4449u2z8ReR5HtCai+9KhQwc0bdoUH3/8sWOZ2WzG1atXERoaCplM5vGaYmJiMHLkSPTp08dtbaxatQojRoxAamoq5HL+fUlUFvA3mYhcTq1WIywsTJJAtHPnTpw+fRq9e/d2azvdunXDjRs3sGbNGre2Q0Sew1BERPesf//+2LZtG2bOnAmZTAaZTIazZ88Wuny2cOFCBAYGYtWqVYiKioJer8djjz2GvLw8LFq0CNWrV0dQUBD+97//OV3yMplMePXVV1G1alX4+PggNjYWW7duvWNNy5Ytw4MPPgitVutYNmHCBDRt2hTz589HREQEfH198dJLL8Fms+GDDz5AWFgYQkJCMHnyZMdrhBCYMGECIiIioNFoUKVKFfzvf/9zrFcoFHjooYewbNky1xxMIpKcUuoCiKj0mjlzJk6cOIGGDRti0qRJAIDg4GCcPXu20LZ5eXn45JNPsGzZMty4cQOPPvooHnnkEQQGBmL16tX4888/0bt3b7Ru3RpPPvkkAGD48OE4evQoli1bhipVqmDFihXo2rUrDh8+jDp16hRZ044dO/DMM88UWn769GmsWbMGa9euxenTp/HYY4/hzz//RN26dbFt2zbs2rULAwcORHx8PGJjY/Hjjz9ixowZWLZsGRo0aICMjAwcPHjQaZ8tW7bE1KlT7/MoEpG3YCgionsWEBAAtVoNvV6PsLCwO25rsVjw+eefo1atWgCAxx57DIsXL0ZmZiZ8fX0RHR2Njh07YsuWLXjyySeRlpaGBQsWIC0tDVWqVAEAvPrqq1i7di0WLFiA9957r8h2zp0759j+Vna7HfPnz4efn5+jrdTUVKxevRpyuRxRUVF4//33sWXLFsTGxiItLQ1hYWGIj4+HSqVCREQEWrZs6bTPKlWqID09HXa7nf2KiMoA/hYTkUfo9XpHIAKA0NBQVK9eHb6+vk7LsrKyAACHDx+GzWZD3bp14evr63hs27YNp0+fvm07BoPB6dJZgerVq8PPz8+prejoaKcwc2v7jz/+OAwGA2rWrInBgwdjxYoVsFqtTvvU6XSw2+0wmUwlPBpE5I14poiIPEKlUjk9l8lkRS6z2+0AgJs3b0KhUCApKQkKhcJpu1uD1L9VqlQJ165du+/2w8PDkZqaio0bN2LDhg146aWXMG3aNGzbts3xuqtXr8LHxwc6ne5Ob52ISgmGIiK6L2q12i3jATVr1gw2mw1ZWVlo27ZtiV539OhRl9Sg0+nQo0cP9OjRA8OGDUO9evVw+PBhNG/eHACQkpKCZs2auaQtIpIeQxER3Zfq1atj9+7dOHv2LHx9fVGhQgWX7Ldu3bro06cP+vbti48++gjNmjXDpUuXsGnTJjRu3Bjdu3cv8nUJCQlYtGjRfbe/cOFC2Gw2xMbGQq/XY8mSJdDpdIiMjHRss2PHDnTp0uW+2yIi78A+RUR0X1599VUoFApER0cjODgYaWlpLtv3ggUL0LdvX4waNQpRUVHo1asX9u7di4iIiNu+pk+fPjhy5AhSU1Pvq+3AwEB8+eWXaN26NRo3boyNGzfi119/RcWKFQEA58+fx65duzBgwID7aoeIvAdHtCaiMue1115DTk4O5s6d67Y2Ro8ejWvXruGLL75wWxtE5Fk8U0REZc64ceMQGRnp6DTtDiEhIXjnnXfctn8i8jyeKSIiIiICzxQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAQD+H9yLW2TdHstTAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "_ = plot_traces(config_file='config.csv_wave.json', node_ids=range(5), report_name='membrane_potential')" ] }, { "cell_type": "markdown", "id": "8ece4be3-20a9-4fc1-a77f-5e31fb04b9d4", "metadata": {}, "source": [ "## 3. Example: Voltage Clamps " ] }, { "cell_type": "markdown", "id": "c5f4cad8-afb6-4446-b7c9-7ccbb12161a7", "metadata": {}, "source": [ "Besides the current clamp, we can use a voltage clamp to set one or more cells to have a constant membrane voltage during some period of the simulation. And like the IClamp we can dynamically insert or remove a voltage clamp during any given simulation by simply editing our SONATA configuration json file. However, note that not all models allow us to insert a voltage clamp - in particular only models with conductance based mechanics allow voltage clamps (so for example we cannot clamp an integrate-and-fire neuron).\n", "\n", "In the network built using the file *build_network.voltage_clamp.py* we have a simple network of two cells, with cell #1 synapsing onto cell #2. As we might do with a simple in-vitro experiment we want to clamp only one cell and see the results it has on both cells. And we want to use a simple single-electrode clamp (eg `SEClamp`) which is the simplest way to fix the voltage of one or more cells. To do so we add the following to the SONATA configuration **\"inputs**\" section:\n", "\n", "```json\n", " \"inputs\": {\n", " \"se_clamp_1\": {\n", " \"input_type\": \"clamp\",\n", " \"module\": \"SEClamp\",\n", " \"node_set\": {\n", " \"population\": \"net\", \n", " \"node_ids\": [0]\n", " },\n", " \"amp\": 20.0,\n", " \"duration\": 1000.0,\n", " \"delay\": 1000.0,\n", " \"section_name\": \"soma\",\n", " \"section_index\": 0,\n", " \"section_dist\": 0.5\n", " }\n", " }\n", "```\n", "\n", "* We must set **input_type** to `clamp` and **module** to 'SEClamp` to tell BMTK which type of stimuli that is being added.\n", "* For the **node_set** we make sure to only attach a clamp to cell with `node_id=0`. (eg our source cell only, not the second cell in the network).\n", "* We must specify the onset/**delay** of the clamp (in ms), the **duration** (ms) and the **amp** (mV) at which we want to hold our cell. In this case we will set the cell such that between times 1000 to 2000 ms it will be clamped to a membrane voltage of 20.0 mV.\n", "* As with current-clamp we have options **section_name**, **section_index** and **section_dist** to determine where to place the clamp within the cell. If not specified BMTK will place it by default in the center of the soma.\n", "* We can specify the **resistance** in nOhms - by default it will be set to 0.0.\n" ] }, { "cell_type": "markdown", "id": "2057ec57-7361-4c50-89d2-4c7adacfe669", "metadata": {}, "source": [ "
\n", "⚠️ WARNING: Currently SEClamp can only handle simple single square-wave block. It does not support advance features like specified in the IClamp.\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "id": "96eecab3-7360-41fc-972f-ab37947adaf4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-06-24 17:25:45,034 [INFO] Created log file\n", "2024-06-24 17:25:45,088 [INFO] Building cells.\n", "2024-06-24 17:25:45,224 [INFO] Building recurrent connections\n", "2024-06-24 17:25:45,235 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n", "2024-06-24 17:25:45,236 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-06-24 17:25:45,237 [INFO] Block save every 5000 steps\n", "2024-06-24 17:25:45,618 [INFO] step:5000 t_sim:500.00 ms\n", "2024-06-24 17:25:45,980 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-06-24 17:25:46,394 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-06-24 17:25:46,824 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-06-24 17:25:47,233 [INFO] step:25000 t_sim:2500.00 ms\n", "2024-06-24 17:25:47,617 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-06-24 17:25:47,631 [INFO] Simulation completed in 2.396 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.voltage_clamp.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "markdown", "id": "edfb2be7-967a-436f-a02a-ce90b2a5ddde", "metadata": {}, "source": [ "Now we can plot the membrane potential for the cell with the attached clamp (node-id 0) as-well-as the downstream cell in our network (node-id 1)" ] }, { "cell_type": "code", "execution_count": 4, "id": "6c8174cd-c04a-42b5-98e5-920335826998", "metadata": {}, "outputs": [ { "data": { "image/png": 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l5eW0nVzN++6774pevXoJg8Eg+vTpI9asWeP4PNbVkkPyhRAiNzdXeHl5iffff9+pvTX1t/SzfPz4cTFixAhhNBoFAKf65s+fL6Kjo51O0UCkRJIQvBgNESnT2bNn0b17dyxfvrzVvTRkN3PmTJw4cQJ79uyRZf0WiwXdunXDggUL8Pjjj8tSA1FLcUwREVEHtmjRInz33XetvsSKu6xZswY6nc4jF6UlulEcU0REHme1WpsdcK3Ew/bbo+joaFRUVMi2/kceeYSBiNoNhiIi8rjz58+7HGxc16JFi5wuKkpE1NY4poiIPK6iogJff/11k/PExsZe9xFwRETXg6GIiIiICBxoTURERARAJWOKbDYbLl68CD8/P16MkIiIqJ0QQqC4uBiRkZHQaNq+H0cVoejixYutunAhERERKcf58+fRtWvXNl+PKkKRn58fAPtGvZ5T3RMREZHnmc1mREVFOb7H25oqQlHtLjN/f3+GIiIionbGU0NfONCaiIiICAxFRERERAAYioiIiIgAqGRMERERUXtltVpRVVUldxltQqfTQavVyl2GA0MRERGRAgkhkJOTg8LCQrlLaVOBgYGIiIhQxHkEGYqIiIgUqDYQhYWFwWQyKSI0uJMQAmVlZcjLywMAdO7cWeaKGIqIiIgUx2q1OgJRp06d5C6nzRiNRgBAXl4ewsLCZN+VxoHWREREClM7hshkMslcSdurfY9KGDfFUERERKRQHW2XmStKeo8MRURERERgKCIiIiICwFBEREREbrZy5Up069YN3t7eSExMxP79++UuqUUYioiIiMhtPvzwQ8yZMweLFi3CDz/8gEGDBiElJcVx6L2SMRQRERGR26xYsQKzZs3CjBkzEB8fj7fffhsmkwnvvfee3KU1i+cpIiIiUjghBMqrrLKs26jTtvgIscrKShw4cAALFy50tGk0GiQnJyMtLa2tSnQbhiIiIiKFK6+yIv75rbKs+9iSFJj0LYsLV65cgdVqRXh4uFN7eHg4jh8/3hbluRV3nxERERGBPUVERESKZ9RpcWxJimzrbqmQkBBotVrk5uY6tefm5iIiIsLdpbkdQxEREZHCSZLU4l1YctLr9UhISMD27dsxadIkAIDNZsP27dsxe/ZseYtrAeVvYSIiImo35syZg2nTpmHIkCEYOnQo/vKXv6C0tBQzZsyQu7RmMRQRERGR20yZMgWXL1/G888/j5ycHAwePBhbtmxpMPhaiRiKiIiIyK1mz57dLnaX1cejz4iIiIjAUEREREQEQK2hqLIM+ORR4Pj/5K6EiIiIFEKdoSjtTeDQv4D198tdCRERESmEOkNR8SW5KyAiIiKFUWcoIiIiIqqHoYiIiIgICgtFy5YtgyRJeOKJJxxtFRUVSE1NRadOneDr64vJkyc3uKZK60k3+HoiIiLqaBQTir777jusXr0aAwcOdGp/8skn8fnnn2PDhg3YvXs3Ll68iLvvvlumKomIiKijUkQoKikpwdSpU/HOO+8gKCjI0V5UVIR3330XK1aswB133IGEhASsWbMGe/fuxbfffitjxURERNTRKCIUpaamYsKECUhOTnZqP3DgAKqqqpza+/Tpg+joaKSlpTW6PIvFArPZ7HRzInH3GRERUVv46quvMHHiRERGRkKSJHz66adyl9Risoei9evX44cffsDSpUsbTMvJyYFer0dgYKBTe3h4OHJychpd5tKlSxEQEOC4RUVFubtsIiIicqG0tBSDBg3CypUr5S6l1WS9IOz58+fx+OOPY9u2bfD29nbbchcuXIg5c+Y4npvNZgYjIiIiDxg/fjzGjx8vdxnXRdZQdODAAeTl5eHmm292tFmtVnz11Vd48803sXXrVlRWVqKwsNCptyg3NxcRERGNLtdgMMBgMDSxZu4+I3ltPHwR566WIXV0T7lLIaL2QAigqkyedetMqhl2ImsoGjNmDI4cOeLUNmPGDPTp0wfz589HVFQUdDodtm/fjsmTJwMAMjMzkZ2djaSkJDlKJnKL2f86CABI6tEJN0cHNTM3EaleVRnwp0h51v2Hi4DeR551e5isocjPzw/9+/d3avPx8UGnTp0c7TNnzsScOXMQHBwMf39//O53v0NSUhJuvfVWOUomcqv8kkq5SyAiohqyhqKWeO2116DRaDB58mRYLBakpKRg1apVcpdF5BZC7gKIqH3Qmew9NnKtWyUUF4p27drl9Nzb2xsrV6507yh2lewbJSKiDkKSVLMLS06KC0VEaiIE+4qIqGMpKSnBqVOnHM+zsrKQnp6O4OBgREdHy1hZ8xiKiIiIyG2+//57jB492vG89hQ506ZNw9q1a2WqqmVUGoq4+4yUgf1ERNTRjBo1qt32gst+RmsiIiIiJWAoIpJRO/1jioioQ1JnKOLRZ0RERFSPOkMRkWKwq4iISCkYioiIiBSqvQ5Ybg0lvUeGIiIZKeh3AREpiE6nAwCUlcl0EVgPqn2Pte9ZTio9JJ+IiEi5tFotAgMDkZeXBwAwmUyQOth4WCEEysrKkJeXh8DAQGi1WrlLYigikhM7ioioMREREQDgCEYdVWBgoOO9yk2loahjpW0iIup4JElC586dERYWhqqqKrnLaRM6nU4RPUS1VBqKiJSBY4qIqDlarVZRwaEj40BrIiIiIjAUEclKcFQREZFiqDMUdbAR/ERERHTj1BmKiBSCY4qIiJSDoYiIiIgIqg1F3H1GysCOIiIi5VBpKCIiIiJyxlBEREREBLWGIh59RgqhpKtDExGpnTpDEREREVE9DEVEREREYCgiIiIiAsBQRCQrDikiIlIOhiIiIiIiqDUU8egzUgheEJaISDnUGYqIiIiI6mEoIpIRxxQRESkHQxGRjBiKiIiUg6GISEbMREREysFQRCQjG7uKiIgUg6GISE7MREREiqHSUMRD8kkZeEg+EZFyqDQUESmDjZmIiEgxZA9FS5cuxS233AI/Pz+EhYVh0qRJyMzMdJqnoqICqamp6NSpE3x9fTF58mTk5ubKVDGR+3BIERGRcsgeinbv3o3U1FR8++232LZtG6qqqjB27FiUlpY65nnyySfx+eefY8OGDdi9ezcuXryIu++++/pXyjNak0JwoDURkXJ4yV3Ali1bnJ6vXbsWYWFhOHDgAEaMGIGioiK8++67+Ne//oU77rgDALBmzRr07dsX3377LW699VY5yiZyC0YiIiLlkL2nqL6ioiIAQHBwMADgwIEDqKqqQnJysmOePn36IDo6GmlpaS6XYbFYYDabnW5ESiTYU0REpBiKCkU2mw1PPPEEhg0bhv79+wMAcnJyoNfrERgY6DRveHg4cnJyXC5n6dKlCAgIcNyioqLqzcHdZ6QMzERERMqhqFCUmpqKjIwMrF+//oaWs3DhQhQVFTlu58+fd1OFRO7FniIiIuWQfUxRrdmzZ2Pjxo346quv0LVrV0d7REQEKisrUVhY6NRblJubi4iICJfLMhgMMBgMbV0y0Q3jIflERMohe0+REAKzZ8/GJ598gh07dqB79+5O0xMSEqDT6bB9+3ZHW2ZmJrKzs5GUlHR9K+XRZ6QQzERERMohe09Ramoq/vWvf+Gzzz6Dn5+fY5xQQEAAjEYjAgICMHPmTMyZMwfBwcHw9/fH7373OyQlJfHIM2r3uPuMiEg5ZA9Fb731FgBg1KhRTu1r1qzB9OnTAQCvvfYaNBoNJk+eDIvFgpSUFKxatcrDlRIREVFHJnsoaslfyt7e3li5ciVWrlzpgYqIPIcnbyQiUg7ZxxTJg2OKSBmYiYiIlEOloYhIGZiJiIiUg6GISEbcfUZEpBzqDEU8JJ8UgpmIiEg51BmKiBSCh+QTESkHQxGRjJiJiIiUQ6WhiLvPSBl4mQ8iIuVQZyjimCJSCMHjz4iIFEOdoYhIIbj7jIhIOVQaithTRMrAgdZERMqhzlDE3WekEIxERETKoc5QxJ4iUgievJGISDlkvyCsLNhTRArBTKRcNptAlc2GKquA1SZgswnYhIBVCNhsqLmvmSZqptngeN5cuxD2gfZCoOaxfXeq/R5A7TTAeV5c2+3a1DLQ4DXOz+G0LrsgHz3G94+ATqvSv5dJ9dQZithTRArBQ/KbV2W1oaSiGsUV1Si2VKGkohplVVZYqqwor7KiosqG8korKqqtqKi0oqK65nnVtceVVhuqqm2ottlQaRWOx1VWgco6j6uqbU5BSI1ev3cw7hrcRe4yiGShzlDEniJSCLUcki+EQImlGgWlVcgvq0RBaSXya281zwvKKu3Bp6IaJZaaEFRRBUu1Te7ynUgSoJUkaDSS/V6C/XHtc429zTGPRoKmZr7ax1qNBEkCJNTe2xesqXksSVLNvX0eONrrvKbOYzR4jfNz1FmPq2UAwA/ZBbhUVIGC0kpPb1IixVBnKGJPESlEVXX7D0U2m0BesQU/FZQhx1yBnKIK5JorkGO2ILeoAjlm+/MbDTcmvRa+Bi/4envBpNfCqNPCW6eFwUsLo14Lby+N/V5Xe9PA26t2Hg10XhrotRK8NPbHOq0EnVZTc2v8sZdGYw88NWFH6qB/VM3+1w/YePiS3GUQyUqdoahj/k6jdqii2ip3CS1iswn8VFCOU5eLce5qGbLzy5B9tQzn8stwPr+sxYHHqNMi2EePYB89gnz0CDbpau71CPTRw9/bC37eXvDz1sHXUPPYoIOPQQsvjnMhojamzlDEVEQKUVGlvFCUa67AsYtmnMgtxoncEpzMK8bJ3BKUN1GrViOhc4A3Ogd4I9zfGxH+3oiofRxgfx7ia4BRr/XgOyEiah11hqIO2v1N7Y/coSi/tBKHfyrE4Z+Kam6FyCu2uJxXr9UgNtQH3Tr5IKaTCVHBJsR0MiE62ITIQCOPWOog2v8OXaLrp85QxJ4iUoiKKs8OIr5QWI79WVex70w+9mfl48yV0gbzaCSgZ5gveof71dx80SvcDzHBJu7CIqIOTZ2hiD1FpBBt3VNUWFaJr05ewVcnLuPbM1fxU0F5g3liQ30wsEsABnYNxMCuAegXGcDdXESkSuoMRewpIoVoapzO9RBCIOOCGbsy87DrxGUczC5wOheSViOhf5cA3No9GEO7B2NIt2AEGHVurYHap456VB1Ra6gzFPGHnxTCXF51w8uw2QQOni/A/w7nYEvGJVwsqnCaHhfuh1FxoRjWMwQJMUHwMajzx56IqDkq/e3IUETKUFB2faFICIFDPxXh04MXsCUjBznma0HIpNfi9p4hGBUXhpFxoegSaHRXuUREHZo6QxF7ikgh8lt59uBSSzU+S7+ID/adw9GLZke7r8ELyX3D8LMBnTGidyi8dRwTRNeH1+MjNVNnKGJPEbUzx3PM+ODbbHxy8AJKLNUAAL2XBuP7R2DiwEjc3iuEQYiI6AapMxSxp4gUpKLK6jLQVFRZsTnjEj74NhvfnytwtHfrZMLUxBj8KqErgnz0niyVOjD+ViRSayjijz/JzEevRWml/cizrCul6NvZ3zEt60op/rXvHDYc+AmFNWOOtBoJY+PD8ZtbY5AU2wkaDT/DRETups5QxJ4iklndw5+PXjSjZ5gvvjyWiw/2ZePrU1cc0yIDvHHf0Gjcc0sUwv295SiViEg11BmK2FNECrJ0049YuulHXK0ZdC1JwKjeoZiaGIPRfcKgZa8QeRDHWZOaqTMUsaeIFKQ2DIX46nHPkCjcNzQaUcEmmasiIlIfdYYi9hSRQnz48K3IulKKLkFG3BrbiRdVJdnwb0Ui1YYiImWICPBGYmwnucsgIiIA6vyzlH8SERERUT3qDEVEROSS4CmtScXaTShauXIlunXrBm9vbyQmJmL//v3XvzD2FBEREVE97SIUffjhh5gzZw4WLVqEH374AYMGDUJKSgry8vKuc4kMRUREROSsXYSiFStWYNasWZgxYwbi4+Px9ttvw2Qy4b333ru+BbKniIjICX8rErWDUFRZWYkDBw4gOTnZ0abRaJCcnIy0tLTrXCp//ImIiMiZ4g/Jv3LlCqxWK8LDw53aw8PDcfz4cZevsVgssFgsjudms9l5hro9RUKw54iIiIiU31N0PZYuXYqAgADHLSoqqt4c9UIRERERqZ7iQ1FISAi0Wi1yc3Od2nNzcxEREeHyNQsXLkRRUZHjdv78eecZnHqGGIqIiIioHYQivV6PhIQEbN++3dFms9mwfft2JCUluXyNwWCAv7+/080Ze4qIiOqSOIyASPljigBgzpw5mDZtGoYMGYKhQ4fiL3/5C0pLSzFjxozrWyB7ikhmLk+Ql5MBmC8Cvcd6viAiImofoWjKlCm4fPkynn/+eeTk5GDw4MHYsmVLg8HXLceeIlIGqe5n8e1h9vuHtgLRt8pTEBGRirWLUAQAs2fPxuzZs92zMPYUkZKd+4ahiGTDvxNJzRQ/pqhtsKeIFKzkes/UTkREN0KdoYg9RaRkpVfkroCISJXUGYrYU0RKVlkidwWkQjz2jEitoYg9RaRkFoYiIiI5qDMUsaeIlMxibn4eojYi+IciqZg6QxF7ikjJLMVyV0BEpEoqDUV13ratWr46iFzhmCIiIlmoMxRpddceV1fKVweRK+wpIjlwpDWRSkNRXdUVcldA5IyfSSIiWTAUVVvkroCISDF47AmpGUMR/yonIiIiMBSxp4iUiX+uExF5HENRdbncFRDZaQ3XHleWylcHqZLEkdZEDEXsKSLF0BmvPWYoIiLyOIaiKvYUkULUPakoz1VERORxDEVWnqeIFKLuOCKGIpIJR7ORmjEU8egzUiJeFJaIyOMYihiKSIk4poiIyOMYijjQmhSj7u4zXuqDPEviwWdEDEXsKSJFYk8REZHHMRSxp4hk0OxgVo4pIpnwvKGkZgxF7CkiJWJPERGRxzEUsaeIZOQ0jqPuX+gcU0RE5HEMRewpIiViTxF5GMdZEzEUsaeIlIljioiIPI6hiD1FpBg8ozXJT/Cc1qRiDEXsKSIlYigiIvI4hiL2FJEScUwREZHHMRSxp4iUou4JYjimiIjI4xiK2FNESsSeIvIwXuaDiKGIPUWkTDxPEcmEZ7QmNVNnKKr7U8+eIlIi7j4jIvI4dYaiuthTRIpRJ6zbqoDqSvlKISJSIYYi9hSRUvGwfCIij2IoYk8RKRVDEXmQxAt9EDEUsaeIFKP+CFeOKyIi8ijZQtHZs2cxc+ZMdO/eHUajET169MCiRYtQWek8juLw4cMYPnw4vL29ERUVhZdfftm9hVgrAZvVvcskcgcelk9E5FFecq34+PHjsNlsWL16NXr27ImMjAzMmjULpaWleOWVVwAAZrMZY8eORXJyMt5++20cOXIEDz30EAIDA/Hwww+7r5iqcsDg677lEd0Iva9915nFLHclRESqIlsoGjduHMaNG+d4Hhsbi8zMTLz11luOUPTBBx+gsrIS7733HvR6Pfr164f09HSsWLHCzaGojKGIFKBm95l3AEMREZEMFDWmqKioCMHBwY7naWlpGDFiBPR6vaMtJSUFmZmZKCgocN+Kq8rctyyiG+UdYL+vKJK3DlIVntGaSEGh6NSpU3jjjTfw29/+1tGWk5OD8PBwp/lqn+fk5DS6LIvFArPZ7HRrUlX59RdO5G7egfZ7hiIiIo9yeyhasGABJElq8nb8+HGn11y4cAHjxo3Dr3/9a8yaNeuGa1i6dCkCAgIct6ioqKZfwJ4iUhJjoP2+vFDOKkilBK/zQSrm9jFFc+fOxfTp05ucJzY21vH44sWLGD16NG677Tb87W9/c5ovIiICubm5Tm21zyMiIhpd/sKFCzFnzhzHc7PZ3HQwqmQoIgUQdcYUAewpIiLyMLeHotDQUISGhrZo3gsXLmD06NFISEjAmjVroNE4d1wlJSXhmWeeQVVVFXQ6HQBg27ZtiIuLQ1BQUKPLNRgMMBgMLS+au89ISRiKiIhkIduYogsXLmDUqFGIjo7GK6+8gsuXLyMnJ8dprND9998PvV6PmTNn4ujRo/jwww/x+uuvO/UCtUaj3cLcfUYe1uQeCoYiIiJZyHZI/rZt23Dq1CmcOnUKXbt2dZpWG14CAgLwxRdfIDU1FQkJCQgJCcHzzz/v3sPxAfYUkULU7j4LtN8zFJEH8egzIhlD0fTp05sdewQAAwcOxJ49e9q2GPYUkZKwp4hkxHHWpGaKOSTfExr9YWcoIiVhKCIikoWqQlGjuPuMlKT2kHyGIiIij2IoAthTRMpQ/5D86nKg2iJfPUREKqOqUNTornKep4iUxOB/7TF7i8hjONKaSFWhqFGWYrkrILpGowUMHFdE8uA4a1IzhiKAXzykEHW+jjjYmojI4xiKAMDSzAVjiTzNEYoKZS2DiEhNVBWKrp3Rul4HMf8aJ0WR2FNERCQDVYWiBqSat88vHlICwd1nJB+e0ZpI7aGo9ouHu89IaRiKiIg8TlWhqMFRFbWHPleYeW57UhaGIpIJfxWSmqkqFDVQ+8UjrEBlqby1ENWSOKaIiEgO6g5FOhMgae2PuQuNZOdiTFF5oSyVEBGpkapCUYNuYUkCvOvsQiNSCsf1zwrlrIKISFVUFYpc4m4KUhwJ8A2zPyzOkbcUUg0efEbEUMRQRMpRtyvTv6v9vuiCPLWQagle6INUTFWhyOUPuzHIfl9e4NliiJoS0MV+bynitfmIiDxEVaHIJVMn+33ZVXnrIKolSYDB79pFYYt+krceIiKV8JK7ANkZg+33DEXUFmw2wFZdc6sCbFbAVo0w5MMqVUJbeBaw6ezt9Xsyg7sDl9KBy8eBsL5Nr0cIoKrcfhRlRRFQWQJUW67drHUeV1fY6xE2+3qFteZe1Hlce29znk/YanbziSbu4aLdVVtz92h8eqPTGmyYxreX6wmtmLexRXi6jtYsu/H38tv8MkzWW9D5kBE46+1ijmZGHTV7Suz2/vrmFq/0+tvp68uqmlmue6kqFLn8HVHbU1Se79FaqBWEuPZlbq28dquufVxl/9KvfVxtaaK9quZ5ncd12+sEF9iq7e2O51U191YX06pdv7aRL6HdWgBaAP9wNbXml0PkTfZQdOEHoN8v7W0lecCZXUB2mr0HqegCUJJjD0K2andveVKRaADRGgDFNTciJbB4doybqkKRSyb2FF0Xmw2oKrOf9LKqFKis+7gUqKqwh5jqCnsPhuNxBVBd7mK6pU57zfO6r+tgKoUWNmig1xug0Wrt58vSaIGoxGtHnkUnAQfWAIf+DUDYw1DOkaYXLGnsBw/ofQEvA6A12O+9DIBWD3h5A156QKOzz6upXbfGfu/UVvO8bpukqfmLTnJxj0baa+6BJl5b974V8zW2Hqdt4uov0Eb+Km1pb8ENL7Mt6mzpMl3P+89957Ar8zImDuyMuwZ3qTe1mS+mZnvT1P765hav9PplfH1JGbDskWaW7z4MRY4xRR28p6jaYh+wazHbz8lU+9hSfO1WG3IqS50fO56X2MNPVc1NLlq9881L37DNqV1nDwe1j70M9dp1dV7nZQ8MGq+am9Y+ve5zp+leNa9p4uY0XQdoNBj43GZUVNmw5/HRiAo2uX6ffScCX4QCpZeBvW9ca48YCMSOBEJ6A/6RgF9nwDuwJgz58MqedF2OHz+CL23Z6NepF9Cnt9zlENmZzQAYijxEunb0mZJDkc1WE2YK7UfJldfeF9QLOXWDTr12q6WNipPsZwbX+wB6k72HQmcCdN6Al/HavZcB0Blreiq8602vbXMxvf7rtLoO9aXf5FvRm4AHPwO+fs3+3ruPBGJHAb6hniqPiEhVVB6K4Pmjz6orgbIr9r/+S6/Yb2VXroWc2sDjCEAF9vEiwuae9et9a45s8q+5r3NzhJuam64m5OhNzo/1PoCudh5jhwopihPeD5j8d7mrICJSBYai2jFF5fn2/ZrX+wVvKQHMFwHzhZr7mseO8HPZHn5u5CSRXkZ7z5YxyH4ZCGOQPdx4+zuHHKfn9cKPRnv96yciIurAVBWKmjz6zFppHzNj8Gt8AWX5QO5RIP80cPU0kH8GyM+yHwVkaUXYkbSATwhgCrHf+4TYTw1QG3SMQfYxInUDkHegfZcSEVEbkGoGX/N81qRmqgpFLulM9sG2Vot9F1rdUFR6FTj7FXD2G+DcN0DesaaXZQiwD3yte/MNqwk/oTW3EHvA0fC8mURERErCUCRJ9t6i4ov2UBTUDTBfAv43Fzj5hf3cM3UFdQM69QKCY4FOPez3gTGAf+eme5mIiIhI0RiKAHtvTvFF+xXJLx0GVg+/Ni20L9B9OBAzzH7jkT9EREQdkqpCUaNXfw7oaj9zcOF5YP3919oH3Qf88m2P1EZERETyUufAlvojrv1rzt569BPndgYiIlIJnlmDSK2hqL6AmlB0/ttrbU9kyFMLEZGcbvSSFUTtmKpCUaM/6/71rvMTMwwIjGrzeoiIiEg5VBWKGhUa5/x86gZ56iAiIiLZMBQBQFg/+2H1ADDhVfvlK4iIiEhVVHb0WSM0GmDa58CVE0DPZE+WRESkCBxnTaSQniKLxYLBgwdDkiSkp6c7TTt8+DCGDx8Ob29vREVF4eWXX26bIoJigF538hAMIlI1DrMmNVNEKHr66acRGRnZoN1sNmPs2LGIiYnBgQMHsHz5cixevBh/+9vfZKiSiIiIOjLZd59t3rwZX3zxBT7++GNs3rzZadoHH3yAyspKvPfee9Dr9ejXrx/S09OxYsUKPPzww61el+ChpkRERNQIWXuKcnNzMWvWLLz//vswmUwNpqelpWHEiBHQ6/WOtpSUFGRmZqKgoKDR5VosFpjNZqcbERERUVNkC0VCCEyfPh2PPPIIhgwZ4nKenJwchIeHO7XVPs/JyWl02UuXLkVAQIDjFhXFcw4RERFR09weihYsWABJkpq8HT9+HG+88QaKi4uxcOFCd5eAhQsXoqioyHE7f/48AA4gJCJqjFRzkAlHGZCauX1M0dy5czF9+vQm54mNjcWOHTuQlpYGg8HgNG3IkCGYOnUq1q1bh4iICOTm5jpNr30eERHR6PINBkOD5RIRERE1xe2hKDQ0FKGhoc3O99e//hV//OMfHc8vXryIlJQUfPjhh0hMTAQAJCUl4ZlnnkFVVRV0Oh0AYNu2bYiLi0NQUJC7SyfyGP41TkSkPLIdfRYdHe303NfXFwDQo0cPdO3aFQBw//3344UXXsDMmTMxf/58ZGRk4PXXX8drr712XevkFxERERE1RvZD8psSEBCAL774AqmpqUhISEBISAief/756zocn0iJJJ4slIhIMRQTirp16+byPEIDBw7Enj17ZKiIiIiI1EQRZ7T2mPqZi3+lExE5ETxOl1RMXaGIiIiIqBEMRURERERQWShitzARERE1RlWhiIiIiKgxDEVEROQ47oTncyM1U2ko4k89EREROVNVKOJfQERERNQYVYUiIiIiosYwFBERERFBZaGIe8+IiFyTYB9pzd+TpGaqCkVEREREjWEoIiIiIoLaQhEPPyMiIqJGqCsUERERETWCoYiIiHhGayKoLBTxZ52IiIgao6pQRERERNQYhiIiIiIiqCwUcV85ERERNUZVoYhIKZjPiYiUh6GIiIhqLvIBCEZ2UjFVhSL+sJPSSM3PQkREHqKqUERERETUGIYiIiIiIqgtFDXYe8adF0RERGSnrlBEREQuSddGWhOpFkMRERERERiKiIiIiACoLBSxV5iIiIgao6pQ5MDrfRAREVE96gxFRETkEv9kJDVTVShiBxERkWuSxFOUEKkqFBERERE1hqGIiIiICCoLRbwgLBERETVG9lD0v//9D4mJiTAajQgKCsKkSZOcpmdnZ2PChAkwmUwICwvDvHnzUF1dLU+xREQdnODgS1IxLzlX/vHHH2PWrFn405/+hDvuuAPV1dXIyMhwTLdarZgwYQIiIiKwd+9eXLp0CQ8++CB0Oh3+9Kc/yVg5EVHHwmHWRDKGourqajz++ONYvnw5Zs6c6WiPj493PP7iiy9w7NgxfPnllwgPD8fgwYPx4osvYv78+Vi8eDH0en2r1sk/gIiIiKgxsu0+++GHH3DhwgVoNBrcdNNN6Ny5M8aPH+/UU5SWloYBAwYgPDzc0ZaSkgKz2YyjR482umyLxQKz2ex0IyKi5l0oLJe7BCLZyBaKzpw5AwBYvHgxnn32WWzcuBFBQUEYNWoU8vPzAQA5OTlOgQiA43lOTk6jy166dCkCAgIct6ioqDZ6F0REHcumIzn47w8/yV0GkSzcHooWLFgASZKavB0/fhw2mw0A8Mwzz2Dy5MlISEjAmjVrIEkSNmzYcEM1LFy4EEVFRY7b+fPnAfBMrURELfHcpxnNz0TUAbl9TNHcuXMxffr0JueJjY3FpUuXADiPITIYDIiNjUV2djYAICIiAvv373d6bW5urmNaYwwGAwwGw/WUT0SkTnVGWldabfLVQSQjt4ei0NBQhIaGNjtfQkICDAYDMjMzcfvttwMAqqqqcPbsWcTExAAAkpKS8NJLLyEvLw9hYWEAgG3btsHf398pTBG1O+y2JAWrsvIDSuok29Fn/v7+eOSRR7Bo0SJERUUhJiYGy5cvBwD8+te/BgCMHTsW8fHxeOCBB/Dyyy8jJycHzz77LFJTU6+rJ4jn3yAiahkhBK+HRqoj63mKli9fDi8vLzzwwAMoLy9HYmIiduzYgaCgIACAVqvFxo0b8eijjyIpKQk+Pj6YNm0alixZImfZRG7D7xxSiup6vUPm8moEmHQyVUMkD1lDkU6nwyuvvIJXXnml0XliYmKwadMmD1ZFRKQ+ZZVWp+d5xRUMRaQ6sl/mw5O494yIyLWySufLJ+UVW2SqhEg+qgpFRETkWnm9nqJcc4VMlRDJh6GIiIhgqXY+DJ89RaRG6g5FHOVKRAQAsFSzp4hI3aGIiIgAsKeICGAoIiIiAJYq51B02cxQROqj0lDEw9CIiOqy2px/L+YWc/cZqY+qQhGjEBGRa7Z65yzJM1t4FQBSHVWFIiIicq1+KCqvsqLYUt3I3EQdE0MREREh1O/a9ST9DPaLHeRxXBGpjKpCkeAONCIil/44aQBujg7Emum3IMzfHpB4WD6pjapCERERudYzzBf/fWwYRvcJQ5cgEwDgQkG5zFUReRZDEREROYkKMgIAzheUyVwJkWepKhTxQAoiouZ1rekpOp/PUETqoqpQREREzYsKtvcU/cTdZ6QyDEVEROTE0VPE3WekMqoKRdx7RkTUvNoxRblmS4MLxRJ1ZKoKRfVPY09ERA0F++hh1GkB8Ag0UhdVhSKOtCYiap4kSRxXRKqkqlBkZSgiImqR2nFF2TwCjVREXaGIu89IIXh2dVK6mE72UHTuaqnMlRB5jqpCkc0mdwVERO1DbIgPAODMZYYiUg91hSLuPiOFkSDJXQKRS7GhvgCArCsMRaQeqgpFHFNERNQy3Wt6irLzy1BlZTc7qYOqQpGNY4qIiFokwt8b3joNqm2Cl/sg1VBZKJK7AiKi9kGjkdA9hLvQSF3UFYrYUURE1GK1g60ZikgtVBaKmIqIiFqqdlzRaR6BRiqhqlDEgdZERC0XG1rbU1QicyVEnqHOUMRwRETUrNrD8k/lMRSROqgqFAmGISKiFusd7gtJAq6UVOJKiUXucojanKpCEU+1QUTUcia9F6KD7Zf7yMwplrkaoranslDEniIiotboE+EHADjOUEQqoKpQVF5ZLXcJRETtSlyEPwDg+CWzzJUQtT1VhaK/bj8pdwlERO1K35qeosxc9hRRx6eqUJRbbEFhWaXcZRARtRtxNaHoRG4xhyBQhydrKDpx4gTuuusuhISEwN/fH7fffjt27tzpNE92djYmTJgAk8mEsLAwzJs3D9XV17cbTAjg2zNX3VE6EZEqxHTygbdOg4oqG7J5DTTq4GQNRT//+c9RXV2NHTt24MCBAxg0aBB+/vOfIycnBwBgtVoxYcIEVFZWYu/evVi3bh3Wrl2L559//rrXmXaaoYiIqKW0Gglx4fbeoowLRTJXQ9S2ZAtFV65cwcmTJ7FgwQIMHDgQvXr1wrJly1BWVoaMjAwAwBdffIFjx47hn//8JwYPHozx48fjxRdfxMqVK1FZeX27wdLYU0RE1CoDuwYCAA7/VChrHURtTbZQ1KlTJ8TFxeEf//gHSktLUV1djdWrVyMsLAwJCQkAgLS0NAwYMADh4eGO16WkpMBsNuPo0aONLttiscBsNjvdap3ILYHZwqPQiIhaalBUIADg0Hn2FFHHJlsokiQJX375JQ4ePAg/Pz94e3tjxYoV2LJlC4KCggAAOTk5ToEIgON57S42V5YuXYqAgADHLSoqCoD97KwAcIpHURARtdjgqAAAwJELRajmWXCpA3N7KFqwYAEkSWrydvz4cQghkJqairCwMOzZswf79+/HpEmTMHHiRFy6dOmGali4cCGKiooct/PnzwMAxkXYr99zIpfX8SEiaqnYEF/4GrxQXmXFqcv8/Ukdl5e7Fzh37lxMnz69yXliY2OxY8cObNy4EQUFBfD3t58cbNWqVdi2bRvWrVuHBQsWICIiAvv373d6bW5uLgAgIiKi0eUbDAYYDIYG7WOk7/EmRuAEe4qIiFpMo5EwoEsA0s5cxaHzhehTc0JHoo7G7aEoNDQUoaGhzc5XVmY/tFOjce6s0mg0sNns3bNJSUl46aWXkJeXh7CwMADAtm3b4O/vj/j4+FbX1u3yDui1o3C52ALoW/1yIrfhtYmpvRkUFYi0M1eRfr4QU26JlrscojYh25iipKQkBAUFYdq0aTh06BBOnDiBefPmISsrCxMmTAAAjB07FvHx8XjggQdw6NAhbN26Fc8++yxSU1Nd9gQ1R3s1E/dGF7j7rRARdXgJMfaxnvuz8mWuhKjtyBaKQkJCsGXLFpSUlOCOO+7AkCFD8PXXX+Ozzz7DoEGDAABarRYbN26EVqtFUlISfvOb3+DBBx/EkiVLrnu9U/VfuestEN0wSZK7AqKWGdotGJIEnL5cirziCrnLIWoTbt991hpDhgzB1q1bm5wnJiYGmzZtcts6e+VuhkGaAgCwWG1ofX8TEZH6BJh0iO/sj6MXzfj2TD5+MShS7pKI3E5V1z6DXyQ0FYW41+cgAOBKsUXmgoiI2o9bYzsB4OWSqONSVygaPBUAMKTSfkTb5WILBEe8EhG1CEMRdXTqCkU3PwB4GR1PyyqtyLhgbuIFRERUa2i3YGgk4MzlUlwoLJe7HCK3U1coMgUDN/3GqWn9d9kyFUNE1L4EmHS4Odp+FNqO43kyV0PkfuoKRQCQ9JjjYYLmJD7+4ScUll3fxWWJiNTmjr72c8bt+DFX5kqI3E99oSg4FujUCwBQoTGiosqGD/axt4iIqCXG9LFff/Kb01dRVsmLa1PHor5QBAAPbQEG3IMjQ18GAKzbexYVVVaZiyIiUr7e4b7oGmREZbUNX5+8Inc5RG6lzlDkEwJMfgdD75yCyABv5BVb8I+0s3JXRUSkeJIkYWy8/dqTnx++sYt3EymNOkNRDb2XBk/c2RsAsHLnaRSVV8lcERGR8k26yX7ixm3HclBi4S406jhUHYoAYPLNXdErzBdF5VV4bdsJucshIlK8AV0CEBvig4oqG7Zm5MhdDpHbqD4UaTUSnp8YDwBYl3YWB7N5wVgioqZIkoRJN3UBAPz34E8yV0PkPqoPRQAwvFco7r6pC4QA5v3nMErZHUxE1KS7b+4CjQR8c+oqTuYWy10OkVswFNV49ufxCPMz4FReCRb+9wgv/0FE1ISuQSbcGW8/PH8dD1ShDoKhqEawjx5v3n8ztBoJ/+/QRazadVrukoiIFG36bd0BAB8fuICCUp4El9o/hqI6hnYPxnMT+gIAlm/NxLq9Z+UtiIhIwW6NDUZ8Z3+UV1nxtz1n5C6H6IYxFNUzfVh3/P6OngCARf/vKF79IpO70oiIXJAkCXNqTmuy9puzyCuukLkiohvDUOTCk3f2xu9qgtEbO05h1j8O4GqJReaqiIiUZ0zfMAyKCkR5lZWnNaF2j6HIBUmSMHdsHP48eQB0Wglf/piLlL98hY++Ow+rjb1GRES1JEnCszXDDv69/zy+O5svc0VE14+hqAlTbonGZ6m3o3e4L66UVOLpjw9j/Otf4cPvsnmtNCKiGrd0C8a9t0QBABZ8fJgXiqV2i6GoGfGR/vj8d7fj2Ql94e/thRO5JZj/8REMfelLzPkoHVuP5qCojJcHISJ1WzC+D8L8DDh9uRR/4GlNqJ2ShAo+uWazGQEBASgqKoK/v/91L6eorAoffp+Nf6Sdw08F5U7TeoX5YmDXQMSG+qBHqA+6BpkQ6mdAsI8eOq182VMIAatNoNpW/95mv7c20m4TsDV4nc3F/AI2ISCEgE0Atpp71Htun36tTQjAZqv7vAXz12kTQsBmq5lmXx0EBGr+Od77tWnObbUNAvZlO15fZ35RZ8ZrbaLOsq61want2jKd1lFnvftrdjHs+8MYhPt7u/F/nEg++7Pycd8738JqE5g/rg8eHdVD7pKonXPX93dLMRRdB6tN4MC5AmzJyMHOzDxkXSltcv5Akw6+Bi8YdVoY9Vp4e2lh0Gmg1UgAAAn2/fJSzfyShGuhoyaEVNULMfWfV9ts9tdYrwUWa00gImUy6bX4/tlkmPRecpdC5Dbvfp2FFzceAwC8OKk/Hrg1RuaKqD1jKGoDbb1Rr5RYcOBcATJzipF1pRRnrpTiQkE58kstUGom0UiAl8YezLw0ErRa+71Gqvu8znSn+5p2rX1+rUaCRroW7DSSBI3G/lwj2adpJAlSzf2159ce177eMV3TsvklCZBQew/Hc9Q8tt87B87aEFr3ORrMI117fd3lOrVJjmmuXgdX89SpNS7CD7Ghvu78byVShJe3HHecAHf26J6Yc2dvaDRSM68iaoihqA14eqPWstkECsurcKXEghJLNSqqrKiosqK80obyKqvTrhjU232j1UjQaZ1DSf3nXlrngGJv17gOMdprz7WSxF9QRNRmhBB49YsTeHPnKQDA8F4hePlXA9E5wChzZdTeMBS1AblCERGRmm34/jye+ywDFVU2+Oi1eHhED8wc3h2+Bu4yppZhKGoDDEVERPI4fbkET204hIPZhQAAP4MX7r65C36VEIX+Xfwdu7KJXGEoagMMRURE8rHZBDZlXMKKL07gTJ0DU8L9DRjeKxSDowIxqGsgeoX7wlunlbFSUhqGojbAUEREJD+bTWDv6av49/5s7MzMQ1llw5PghvkZENPJhHB/bwSZ9Ajy0SOo5gheb50W3jotDF4ax7225gARjaaRgzaa6Imq++1XZ4Rnvfa68zt/XTpPcz2l8WW1YN31TutR2+Z0mg84n34EdaYJca1mV6cucX0qkmunLWm4Xtfrqr/85k5XYnOcksV+lHTtaVastoaPS0vMmDfxZo99f3PHLhEReYRGI+H2XiG4vVcILNVWfHsmH99l5ePQT4U4/FMRisqrkFdsQV4xrzVJdjZLmUfXx1BEREQeZ/DSYmTvUIzsHQrA3jtQVF6F7PwynLtahislFhSUVqKgrAr5ZZUodRzBa4Ol2gZLzdG8LT3p67WTbsD5tBn16qo7xslpWr0Z6z51ek1L52uwXtevcnVqEJenFnFx2g/H6UjqP6+3LNSZ1tiyUHd6I8u6tp46r3VxmhRH717d06w4evqunZZFI0moKi/BX+E5DEVERCQ7SZIQaNIj0KTHwK6BcpdDCmE2mz0ainjtMyIiIiIwFBEREREBYCgiIiIiAsBQRERERASgDUPRSy+9hNtuuw0mkwmBgYEu58nOzsaECRNgMpkQFhaGefPmobq62mmeXbt24eabb4bBYEDPnj2xdu3atiqZiIiIVKzNQlFlZSV+/etf49FHH3U53Wq1YsKECaisrMTevXuxbt06rF27Fs8//7xjnqysLEyYMAGjR49Geno6nnjiCfzf//0ftm7d2lZlExERkUq1+Rmt165diyeeeAKFhYVO7Zs3b8bPf/5zXLx4EeHh4QCAt99+G/Pnz8fly5eh1+sxf/58/O9//0NGRobjdffeey8KCwuxZcuWFtfAM1oTERG1P57+/pZtTFFaWhoGDBjgCEQAkJKSArPZjKNHjzrmSU5OdnpdSkoK0tLSPForERERdXyynbwxJyfHKRABcDzPyclpch6z2Yzy8nIYjUaXy7ZYLLBYrp0m3mw2u7N0IiIi6oBa1VO0YMEC+6m9m7gdP368rWptsaVLlyIgIMBxi4qKkrskIiIiUrhW9RTNnTsX06dPb3Ke2NjYFi0rIiIC+/fvd2rLzc11TKu9r22rO4+/v3+jvUQAsHDhQsyZM8fx3Gw2MxgRERFRk1oVikJDQxEaGuqWFSclJeGll15CXl4ewsLCAADbtm2Dv78/4uPjHfNs2rTJ6XXbtm1DUlJSk8s2GAwwGAxuqZOIiIjUoc0GWmdnZyM9PR3Z2dmwWq1IT09Heno6SkpKAABjx45FfHw8HnjgARw6dAhbt27Fs88+i9TUVEegeeSRR3DmzBk8/fTTOH78OFatWoWPPvoITz75ZFuVTURERCrVZofkT58+HevWrWvQvnPnTowaNQoAcO7cOTz66KPYtWsXfHx8MG3aNCxbtgxeXtc6sHbt2oUnn3wSx44dQ9euXfHcc881uwuvPh6ST0RE1P54+vu7zc9TpARFRUUIDAzE+fPnGYqIiIjaidoxwYWFhQgICGjz9cl2SL4nXb16FQA42JqIiKgdunr1KkORuwQHBwOwj3PyxEbtqGoTO3vcbhy3pftwW7oHt6P7cFu6T1FREaKjox3f421NFaFIo7GPJw8ICOAH1A38/f25Hd2E29J9uC3dg9vRfbgt3af2e7zN1+ORtRAREREpHEMREREREVQSigwGAxYtWsQTOt4gbkf34bZ0H25L9+B2dB9uS/fx9LZUxSH5RERERM1RRU8RERERUXMYioiIiIjAUEREREQEgKGIiIiICIAKQtHKlSvRrVs3eHt7IzExEfv375e7JEVZvHgxJElyuvXp08cxvaKiAqmpqejUqRN8fX0xefJk5ObmOi0jOzsbEyZMgMlkQlhYGObNm4fq6mpPvxWP++qrrzBx4kRERkZCkiR8+umnTtOFEHj++efRuXNnGI1GJCcn4+TJk07z5OfnY+rUqfD390dgYCBmzpyJkpISp3kOHz6M4cOHw9vbG1FRUXj55Zfb+q15XHPbcvr06Q0+p+PGjXOah9sSWLp0KW655Rb4+fkhLCwMkyZNQmZmptM87vqZ3rVrF26++WYYDAb07NkTa9eubeu351Et2ZajRo1q8Ll85JFHnOZR+7Z86623MHDgQMeJLJOSkrB582bHdMV9HkUHtn79eqHX68V7770njh49KmbNmiUCAwNFbm6u3KUpxqJFi0S/fv3EpUuXHLfLly87pj/yyCMiKipKbN++XXz//ffi1ltvFbfddptjenV1tejfv79ITk4WBw8eFJs2bRIhISFi4cKFcrwdj9q0aZN45plnxH//+18BQHzyySdO05ctWyYCAgLEp59+Kg4dOiR+8YtfiO7du4vy8nLHPOPGjRODBg0S3377rdizZ4/o2bOnuO+++xzTi4qKRHh4uJg6darIyMgQ//73v4XRaBSrV6/21Nv0iOa25bRp08S4ceOcPqf5+flO83BbCpGSkiLWrFkjMjIyRHp6uvjZz34moqOjRUlJiWMed/xMnzlzRphMJjFnzhxx7Ngx8cYbbwitViu2bNni0ffbllqyLUeOHClmzZrl9LksKipyTOe2FOL//b//J/73v/+JEydOiMzMTPGHP/xB6HQ6kZGRIYRQ3uexQ4eioUOHitTUVMdzq9UqIiMjxdKlS2WsSlkWLVokBg0a5HJaYWGh0Ol0YsOGDY62H3/8UQAQaWlpQgj7l5lGoxE5OTmOed566y3h7+8vLBZLm9auJPW/yG02m4iIiBDLly93tBUWFgqDwSD+/e9/CyGEOHbsmAAgvvvuO8c8mzdvFpIkiQsXLgghhFi1apUICgpy2pbz588XcXFxbfyO5NNYKLrrrrsafQ23pWt5eXkCgNi9e7cQwn0/008//bTo16+f07qmTJkiUlJS2votyab+thTCHooef/zxRl/DbelaUFCQ+Pvf/67Iz2OH3X1WWVmJAwcOIDk52dGm0WiQnJyMtLQ0GStTnpMnTyIyMhKxsbGYOnUqsrOzAQAHDhxAVVWV0zbs06cPoqOjHdswLS0NAwYMQHh4uGOelJQUmM1mHD161LNvREGysrKQk5PjtO0CAgKQmJjotO0CAwMxZMgQxzzJycnQaDTYt2+fY54RI0ZAr9c75klJSUFmZiYKCgo89G6UYdeuXQgLC0NcXBweffRRXL161TGN29K1oqIiANcuiu2un+m0tDSnZdTO05F/t9bflrU++OADhISEoH///li4cCHKysoc07gtnVmtVqxfvx6lpaVISkpS5Oexw14Q9sqVK7BarU4bEgDCw8Nx/PhxmapSnsTERKxduxZxcXG4dOkSXnjhBQwfPhwZGRnIycmBXq9HYGCg02vCw8ORk5MDAMjJyXG5jWunqVXte3e1bepuu7CwMKfpXl5eCA4Odpqne/fuDZZROy0oKKhN6leacePG4e6770b37t1x+vRp/OEPf8D48eORlpYGrVbLbemCzWbDE088gWHDhqF///4A4Laf6cbmMZvNKC8vh9FobIu3JBtX2xIA7r//fsTExCAyMhKHDx/G/PnzkZmZif/+978AuC1rHTlyBElJSaioqICvry8++eQTxMfHIz09XXGfxw4biqhlxo8f73g8cOBAJCYmIiYmBh999FGH+GGkjuHee+91PB4wYAAGDhyIHj16YNeuXRgzZoyMlSlXamoqMjIy8PXXX8tdSrvX2LZ8+OGHHY8HDBiAzp07Y8yYMTh9+jR69Ojh6TIVKy4uDunp6SgqKsJ//vMfTJs2Dbt375a7LJc67O6zkJAQaLXaBqPYc3NzERERIVNVyhcYGIjevXvj1KlTiIiIQGVlJQoLC53mqbsNIyIiXG7j2mlqVfvem/r8RUREIC8vz2l6dXU18vPzuX2bERsbi5CQEJw6dQoAt2V9s2fPxsaNG7Fz50507drV0e6un+nG5vH39+9wf0w1ti1dSUxMBACnzyW3JaDX69GzZ08kJCRg6dKlGDRoEF5//XVFfh47bCjS6/VISEjA9u3bHW02mw3bt29HUlKSjJUpW0lJCU6fPo3OnTsjISEBOp3OaRtmZmYiOzvbsQ2TkpJw5MgRpy+kbdu2wd/fH/Hx8R6vXym6d++OiIgIp21nNpuxb98+p21XWFiIAwcOOObZsWMHbDab45drUlISvvrqK1RVVTnm2bZtG+Li4jrc7p7W+Omnn3D16lV07twZALdlLSEEZs+ejU8++QQ7duxosLvQXT/TSUlJTsuonacj/W5tblu6kp6eDgBOn0tuy4ZsNhssFosyP4+tHzfefqxfv14YDAaxdu1acezYMfHwww+LwMBAp1Hsajd37lyxa9cukZWVJb755huRnJwsQkJCRF5enhDCfrhkdHS02LFjh/j+++9FUlKSSEpKcry+9nDJsWPHivT0dLFlyxYRGhqqikPyi4uLxcGDB8XBgwcFALFixQpx8OBBce7cOSGE/ZD8wMBA8dlnn4nDhw+Lu+66y+Uh+TfddJPYt2+f+Prrr0WvXr2cDiMvLCwU4eHh4oEHHhAZGRli/fr1wmQydajDyIVoelsWFxeLp556SqSlpYmsrCzx5Zdfiptvvln06tVLVFRUOJbBbSnEo48+KgICAsSuXbucDhMvKytzzOOOn+naQ6DnzZsnfvzxR7Fy5coOdRi5EM1vy1OnToklS5aI77//XmRlZYnPPvtMxMbGihEjRjiWwW0pxIIFC8Tu3btFVlaWOHz4sFiwYIGQJEl88cUXQgjlfR47dCgSQog33nhDREdHC71eL4YOHSq+/fZbuUtSlClTpojOnTsLvV4vunTpIqZMmSJOnTrlmF5eXi4ee+wxERQUJEwmk/jlL38pLl265LSMs2fPivHjxwuj0ShCQkLE3LlzRVVVlaffisft3LlTAGhwmzZtmhDCflj+c889J8LDw4XBYBBjxowRmZmZTsu4evWquO+++4Svr6/w9/cXM2bMEMXFxU7zHDp0SNx+++3CYDCILl26iGXLlnnqLXpMU9uyrKxMjB07VoSGhgqdTidiYmLErFmzGvxxw20pXG5DAGLNmjWOedz1M71z504xePBgodfrRWxsrNM6OoLmtmV2drYYMWKECA4OFgaDQfTs2VPMmzfP6TxFQnBbPvTQQyImJkbo9XoRGhoqxowZ4whEQijv8ygJIUTr+5eIiIiIOpYOO6aIiIiIqDUYioiIiIjAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRURvYtWsXJElqcKFHT9m+fTv69u0Lq9XaZuu49dZb8fHHH7fZ8onI83hGayK6IaNGjcLgwYPxl7/8xdFWWVmJ/Px8hIeHQ5Ikj9eUkJCAOXPmYOrUqW22jo0bN+LJJ59EZmYmNBr+fUnUEfAnmYjcTq/XIyIiQpZA9PXXX+P06dOYPHlym65n/PjxKC4uxubNm9t0PUTkOQxFRHTdpk+fjt27d+P111+HJEmQJAlnz55tsPts7dq1CAwMxMaNGxEXFweTyYRf/epXKCsrw7p169CtWzcEBQXh97//vdMuL4vFgqeeegpdunSBj48PEhMTsWvXriZrWr9+Pe688054e3s72hYvXozBgwfjvffeQ3R0NHx9ffHYY4/BarXi5ZdfRkREBMLCwvDSSy85XiOEwOLFixEdHQ2DwYDIyEj8/ve/d0zXarX42c9+hvXr17tnYxKR7LzkLoCI2q/XX38dJ06cQP/+/bFkyRIAQGhoKM6ePdtg3rKyMvz1r3/F+vXrUVxcjLvvvhu//OUvERgYiE2bNuHMmTOYPHkyhg0bhilTpgAAZs+ejWPHjmH9+vWIjIzEJ598gnHjxuHIkSPo1auXy5r27NmD+++/v0H76dOnsXnzZmzZsgWnT5/Gr371K5w5cwa9e/fG7t27sXfvXjz00ENITk5GYmIiPv74Y7z22mtYv349+vXrh5ycHBw6dMhpmUOHDsWyZctucCsSkVIwFBHRdQsICIBer4fJZEJEREST81ZVVeGtt95Cjx49AAC/+tWv8P777yM3Nxe+vr6Ij4/H6NGjsXPnTkyZMgXZ2dlYs2YNsrOzERkZCQB46qmnsGXLFqxZswZ/+tOfXK7n3LlzjvnrstlseO+99+Dn5+dYV2ZmJjZt2gSNRoO4uDj8+c9/xs6dO5GYmIjs7GxEREQgOTkZOp0O0dHRGDp0qNMyIyMjcf78edhsNo4rIuoA+FNMRB5hMpkcgQgAwsPD0a1bN/j6+jq15eXlAQCOHDkCq9WK3r17w9fX13HbvXs3Tp8+3eh6ysvLnXad1erWrRv8/Pyc1hUfH+8UZuqu/9e//jXKy8sRGxuLWbNm4ZNPPkF1dbXTMo1GI2w2GywWSyu3BhEpEXuKiMgjdDqd03NJkly22Ww2AEBJSQm0Wi0OHDgArVbrNF/dIFVfSEgICgoKbnj9UVFRyMzMxJdffolt27bhsccew/Lly7F7927H6/Lz8+Hj4wOj0djUWyeidoKhiIhuiF6vb5PzAd10002wWq3Iy8vD8OHDW/W6Y8eOuaUGo9GIiRMnYuLEiUhNTUWfPn1w5MgR3HzzzQCAjIwM3HTTTW5ZFxHJj6GIiG5It27dsG/fPpw9exa+vr4IDg52y3J79+6NqVOn4sEHH8Srr76Km266CZcvX8b27dsxcOBATJgwweXrUlJSsG7duhte/9q1a2G1WpGYmAiTyYR//vOfMBqNiImJccyzZ88ejB079obXRUTKwDFFRHRDnnrqKWi1WsTHxyM0NBTZ2dluW/aaNWvw4IMPYu7cuYiLi8OkSZPw3XffITo6utHXTJ06FUePHkVmZuYNrTswMBDvvPMOhg0bhoEDB+LLL7/E559/jk6dOgEALly4gL1792LGjBk3tB4iUg6e0ZqIOpx58+bBbDZj9erVbbaO+fPno6CgAH/729/abB1E5FnsKSKiDueZZ55BTEyMY9B0WwgLC8OLL77YZssnIs9jTxERERER2FNEREREBIChiIiIiAgAQxERERERAIYiIiIiIgAMRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBAD4/wWX6kV6Vj2kAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "\n", "_ = plot_traces(config_file='config.voltage_clamp.json', report_name='membrane_potential', population='net', node_ids=[0, 1])" ] }, { "cell_type": "markdown", "id": "15e5748c-1900-4701-a0f9-17eb27ab7311", "metadata": {}, "source": [ "## 4. Example: Using sweep data from the Allen Cell-Types Database " ] }, { "cell_type": "markdown", "id": "71d64192-60e2-4dc3-91cb-6ebf4a9cb3b8", "metadata": {}, "source": [ "When creating the [Cell-Type Database at the Allen Insitute](https://celltypes.brain-map.org/data), a wide range of clamping stimuli were used in the process of optimizing the parameters for the thousands of human and mouse cell models. BMTK allows the option to download and import these stimuli directly into single and multi-cell simulations (for both BioNet and PointNet). This allows users to apply the same conditions for their own models, or as just a way to use much more advanced and realistic current-clamping stimuli that would be difficult to rebuild from scratch." ] }, { "cell_type": "markdown", "id": "65fea49d-b3cf-44ef-9a67-a23afb061710", "metadata": {}, "source": [ "### Step 1: Downloading the data\n", "\n", "First step is to determine which experimental conditions we want to replicate and which stimulus types to use. Different cells in the Allen Cell Types Database were optimized and tested against different types of current clamps. For example if we look at the electrophysiology page for [cell 488683425](https://celltypes.brain-map.org/experiment/electrophysiology/488683425) we can see we have stimulus types including `Long square`, `Noise`, `Ramp`, `Short Square` and `Square`. And for each types there are different *sweeps* where amplitudes and times were varied (or not) between trials:\n", "\n", "
\n", "
\n", " \n", "
\n", "
\n", "\n", "If you click on the \"`Download Data`\" button it will download an ephys nwb file containing all the *sweep* available for said experiment. You just need to put it in a directory that can be accessed by BMTK. If you need a more programtic way to download multiple such files you can use the AllenSDK, and BMTK also includes helper functions to download the data. You can run the following in a command-line:\n", "```bash\n", "$ python -m bmtk.utils.cell_types_db download_ephys_data --specimen-id=488683425 --download-dir=ecephys_inputs\n", "```\n", "\n", "Or through the notebook" ] }, { "cell_type": "code", "execution_count": 1, "id": "c8217ae3-8eee-481e-a28d-f2fcb9371cbb", "metadata": {}, "outputs": [], "source": [ "from bmtk.utils.cell_types_db import download_ephys_data\n", "\n", "download_ephys_data(\n", " specimen_id=488683425,\n", " download_dir='ephys_inputs'\n", ")" ] }, { "cell_type": "markdown", "id": "8eedfa8e-79e1-4623-af03-b947ab5a7c60", "metadata": {}, "source": [ "Due to the size of the files and available internet speeds it may take some time to fully download the nwb file(s). But when it does it will save the file into the local *ephy_inputs/* directory." ] }, { "cell_type": "markdown", "id": "01684297-31ef-4902-acc3-3d68a5bf6152", "metadata": {}, "source": [ "### (Optional) Step 2: Downloading cell model data\n", "\n", "The downloaded ephys data can be used with any cell-type (even cells that are not from the Allen Cell-Types Database). However for testing and replication purposes you may want to run the downloaded ephys data on the original cell. If you click on `Select neuronal model` button on the page it will give you an option to download a possible GLIF Model (which can be run using PointNet) or a Biophysical (run using BioNet) model. This will download a zip file containing the *fit_parameters.json* which you can use as the **dynamics_params** in you SONATA network. Also for Biophysical models it will contain the file *reconstruction.swc* which is the **morphology** swc file for the specific cell. You can move these file(s) into their appropriate *components/* directory and rebuild/modify the SONATA network file so that **dynamics_params** and **morphology** points to the correct paths.\n", "\n", "Alternatively you can use BMTK to download, move and rename the files for you. In the command line you can run:\n", "```bash\n", "$ python -m bmtk.utils.cell_types_db download_model --specimen-id=488683425 --model-dir=\"components\" --model-type=\"Biophysical - all active\"\n", "```\n", "\n", "or in python\n", "```python\n", "model_dir = download_model(\n", " specimen_id=488683425,\n", " model_dir='components',\n", " model_type='Biophysical - all active'\n", ")\n", "```\n" ] }, { "cell_type": "markdown", "id": "2bd6923b-ca11-4f27-b409-01333c08de24", "metadata": {}, "source": [ "### Step 3: Set the configuration\n", "\n", "To use the downloaded ephys.nwb file we just need to create an \"inputs sections with **module** set to `iclamp` and **input_type** set to `allen`. Use the **file** option to point to the path of our downloaded nwb file. And finally we must select our **sweep** number. In this case we'll use sweep #35 which is just a 1 second long-square current.\n", "\n", "```json\n", "\"inputs\": {\n", " \"current_clamp_allen\": {\n", " \"module\": \"IClamp\",\n", " \"input_type\": \"allen\",\n", " \"node_set\": \"all\",\n", " \"file\": \"${INPUT_DIR}/488683423_ephys.nwb\",\n", " \"sweep_id\": \"35\"\n", " }\n", "}\n", "```\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "c4edd959-9003-41ac-b2b4-4e6c1c3418a6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-05-05 14:54:45,142 [INFO] Created log file\n", "2024-05-05 14:54:45,243 [INFO] Building cells.\n", "2024-05-05 14:54:45,490 [INFO] Building recurrent connections\n", "2024-05-05 14:54:45,857 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n", "2024-05-05 14:54:45,858 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-05-05 14:54:45,859 [INFO] Block save every 5000 steps\n", "2024-05-05 14:54:53,078 [INFO] step:5000 t_sim:500.00 ms\n", "2024-05-05 14:55:00,473 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-05-05 14:55:07,069 [INFO] step:15000 t_sim:1500.00 ms\n", "2024-05-05 14:55:13,562 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-05-05 14:55:20,164 [INFO] step:25000 t_sim:2500.00 ms\n", "2024-05-05 14:55:26,899 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-05-05 14:55:26,917 [INFO] Simulation completed in 41.06 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.cell_types.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 4, "id": "37cb859a-6e33-4f34-88e4-ff8c2e0b50c4", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "_ = plot_traces(config_file='config.cell_types.json', node_ids=[0], report_name='membrane_potential')" ] }, { "cell_type": "markdown", "id": "7b037df1-25bf-4cc0-aa8d-cc6a9abd3628", "metadata": {}, "source": [ "## 5. Example: Extracellular Stimulation " ] }, { "cell_type": "markdown", "id": "560a29e1-14eb-4e40-95ac-49a91725ed15", "metadata": {}, "source": [ "
\n", "⚠️ NOTE: This feature will only work in BioNet using morphologically detailed cells.\n", "
" ] }, { "cell_type": "markdown", "id": "e2771c93-0e51-45d9-9f13-c44a23a52d3e", "metadata": {}, "source": [ "The *BioNet* simulator has optional input stimulus - an electric field impulse, thus allowing you to simulate simulation of, e.g., deep brain stimulation. One or more stimulating electrodes (or a mesh) can be readily inserted and removed into most networks and used along side other stimuli through simple changes to the SONATA config. However, take note that this can only be applied to cells with biophysical conductances, such as morphologically detailed \"biophysical\" cells or even simple single-compartment \"soma\" cells, but won't work for integrate-and-fire type neurons.\n", "\n", "When extracellular stimulation(s) are inserted into a simulation, the effect the generated filed has on the extracellular membrane of each cell compartment will depend on the geometric relationship between the cell and the electrode(s). Thus it is important that we specify the **x**, **y**, **z** attributes for each cell, plus make sure the cell is rotated properly (as specified by attributes **rotation_angle_x**, **rotation_angle_y**, **rotation_angle_z**). Once we have such a network (see *build_network.xstim.py*) we can go ahead and insert an extracellular stimulation into a simulation by adding to the **\"inputs\"** section of the SONATA config like such:\n", "\n", "``` json\n", " \"inputs\": {\n", " \"Extracellular_Stim\": {\n", " \"module\": \"xstim\",\n", " \"input_type\": \"lfp\",\n", " \"node_set\": {\n", " \"model_type\": \"biophysical\"\n", " },\n", " \"positions_file\": \"components/stimulations/xstim_coords.csv\",\n", " \"resistance\": 300.0,\n", " \"waveform\": {\n", " \"shape\": \"sin\",\n", " \"del\": 1000.0,\n", " \"amp\": 0.100,\n", " \"dur\": 2000.0,\n", " \"freq\": 8.0,\n", " \"phase\": 0.0\n", " }\n", " }\n", " }\n", "```\n", "* You must set **module** to \"xstim\" and **input_type** to \"lfp\" to signal to bmtk the mod of stimulus to apply\n", "* The **node_set** is used to filter out which subset of cells in the network to apply the xstim. In this case we want to apply it to all \"biophysical\" type cells (in this example all cells are \"biophysical\", but in mixed networks it's important to keep track of which cells are biophysical and which are not - otherwise bmtk can fail when applying stimlus to \"point-neuron\" type cells).\n", "* Set the extracellular resistance between the electrode(s) and the cells to 300 nOhms.\n", "* The **waveform** is set to generate a sin-wave shaped stimuli, starting at 1000 ms (**del**) that will run for 2000 ms (**dur**) with a maximum amplitude of .100 nA (**amp**). \n", "* Lastly the **positions_file** is used to specify the coordinates and rotations (if using a mesh) of the electrodes. It should be a space-separated file with each row being the location of a different electrode. In our case we are only applying a single electrode\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "cb948bb9-33ac-4842-b3cd-ccaffb10f1f6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ippos_xpos_ypos_zrotation_xrotation_yrotation_z
006.180340.019.021130.00.00.0
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" ], "text/plain": [ " ip pos_x pos_y pos_z rotation_x rotation_y rotation_z\n", "0 0 6.18034 0.0 19.02113 0.0 0.0 0.0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "pd.read_csv('components/stimulations/xstim_coords.csv', sep=' ')" ] }, { "cell_type": "markdown", "id": "7772db17-16a0-4280-8d3e-c7af238e2747", "metadata": {}, "source": [ "Now we can run the simulation as normal and show the varying results in terms of changes to cell membrane potentials" ] }, { "cell_type": "code", "execution_count": 2, "id": "aeca0247-d98e-4f2b-bcd7-f61f1a992a90", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-06-24 14:27:32,061 [INFO] Created log file\n", "2024-06-24 14:27:32,109 [INFO] Building cells.\n", "2024-06-24 14:27:32,303 [INFO] Building recurrent connections\n", "2024-06-24 14:27:32,480 [INFO] Running simulation for 4000.000 ms with the time step 0.100 ms\n", "2024-06-24 14:27:32,481 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-06-24 14:27:32,482 [INFO] Block save every 10000 steps\n", "2024-06-24 14:27:40,453 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-06-24 14:27:48,758 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-06-24 14:27:56,865 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-06-24 14:28:04,825 [INFO] step:40000 t_sim:4000.00 ms\n", "2024-06-24 14:28:04,834 [INFO] Simulation completed in 32.35 seconds \n" ] } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.xstim_sin.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()" ] }, { "cell_type": "code", "execution_count": 4, "id": "e8741b5f-dee6-4313-b8f7-ce7493693e8c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.analyzer.compartment import plot_traces\n", "\n", "_ = plot_traces(config_file='config.xstim_sin.json', report_name='membrane_potential', population='bio')" ] }, { "cell_type": "markdown", "id": "845720d4-5c92-49b7-805e-7f4f8d5c9f84", "metadata": {}, "source": [ "### More advanced wave-forms" ] }, { "cell_type": "markdown", "id": "4274f41b-76fd-4890-99d8-0a85cb9e4737", "metadata": {}, "source": [ "In the above example we set it so the stimulus will generate a sine-wave at the electrode site. BMTK has a number of built-in shapes available, including a sine-wave and a direct-current (**waveform**: \"dc\")\n", "\n", "```json\n", " \"inputs\": {\n", " \"Extracellular_Stim\": {\n", " \"module\": \"xstim\",\n", " \"input_type\": \"lfp\",\n", " \"node_set\": {\n", " \"model_type\": \"biophysical\"\n", " },\n", " \"positions_file\": \"$STIM_DIR/xstim_coords.csv\",\n", " \"resistance\": 300.0,\n", " \"waveform\": {\n", " \"shape\": \"dc\",\n", " \"del\": 1000.0,\n", " \"amp\": 0.400,\n", " \"dur\": 1000.0\n", " }\n", " }\n", " },\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "id": "fb774bc5-c74c-40e9-941a-029099f0e3ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-06-24 14:33:33,694 [INFO] Created log file\n", "Mechanisms already loaded from path: ./components/mechanisms. Aborting.\n", "2024-06-24 14:33:33,718 [INFO] Building cells.\n", "2024-06-24 14:33:33,993 [INFO] Building recurrent connections\n", "2024-06-24 14:33:34,190 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n", "2024-06-24 14:33:34,191 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-06-24 14:33:34,193 [INFO] Block save every 10000 steps\n", "2024-06-24 14:33:49,082 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-06-24 14:34:03,980 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-06-24 14:34:19,004 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-06-24 14:34:19,019 [INFO] Simulation completed in 44.83 seconds \n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.xstim_dc.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()\n", "\n", "_ = plot_traces(config_file='config.xstim_dc.json', report_name='membrane_potential', population='bio')" ] }, { "cell_type": "markdown", "id": "84dfa32f-bf00-403f-91df-63666e739c47", "metadata": {}, "source": [ "Or alternatively, instead of having BMTK try to generate a shape for us, we can create the stimlus waveform ourselves. To do so we just need to create a space-separated csv file with columns **time** (ms) and **amplitude** (nA) and set the **waveform** attribute in our csv file to the path of the file.\n", "\n", "For example, the following lines of code will generate a slow ramping csv file that will go from -0.100 nA to 0.500 nA within a 3 second window, and save it under *./inputs/ramping_xstim.csv*" ] }, { "cell_type": "code", "execution_count": 12, "id": "ebeb4ca8-dbcd-4ca1-a351-db6b108d0ee1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "time = np.linspace(0.0, 3000.0, 1000)\n", "amplitude = time*0.0002-.100\n", "\n", "pd.DataFrame({\n", " 'time': time,\n", " 'amplitude': amplitude\n", "}).to_csv('inputs/ramping_xstim.csv', sep=' ', index=False)\n", "plt.plot(time, amplitude)" ] }, { "cell_type": "markdown", "id": "8389a2d5-ab46-45ca-ba45-cb69d8384be9", "metadata": {}, "source": [ "Then in our configuration file we just need to change the **waveform** attribute to point to the location of our customized file\n", "\n", "```json\n", " \"inputs\": {\n", " \"Extracellular_Stim\": {\n", " \"module\": \"xstim\",\n", " \"input_type\": \"lfp\",\n", " \"node_set\": {\n", " \"model_type\": \"biophysical\"\n", " },\n", " \"positions_file\": \"$STIM_DIR/xstim_coords.csv\",\n", " \"resistance\": 300.0,\n", " \"waveform\": \"inputs/ramping_xstim.csv\"\n", " }\n", " }\n", "```\n", "\n", "And run it like before" ] }, { "cell_type": "code", "execution_count": 15, "id": "939f38fc-5376-4dd4-a22e-31666b6e753c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-06-24 14:42:31,518 [INFO] Created log file\n", "Mechanisms already loaded from path: ./components/mechanisms. Aborting.\n", "2024-06-24 14:42:31,541 [INFO] Building cells.\n", "2024-06-24 14:42:31,826 [INFO] Building recurrent connections\n", "2024-06-24 14:42:32,029 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n", "2024-06-24 14:42:32,030 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n", "2024-06-24 14:42:32,031 [INFO] Block save every 10000 steps\n", "2024-06-24 14:42:55,386 [INFO] step:10000 t_sim:1000.00 ms\n", "2024-06-24 14:43:21,217 [INFO] step:20000 t_sim:2000.00 ms\n", "2024-06-24 14:43:50,595 [INFO] step:30000 t_sim:3000.00 ms\n", "2024-06-24 14:43:50,609 [INFO] Simulation completed in 78.58 seconds \n" ] }, { "data": { "image/png": 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s2LBB2abYz1yI4qUX/va3v4l99tlH6PV61XEqtu2jjz4qpkyZIkwmk5g+fbpYtmyZ8nks1JMyDUII0djYKPR6vXjiiSdUy3v6uensZ9vT43zttdeKmpoaVbkOoqFIIwQndCKi0WPbtm2ora3F0qVLe916RJLzzjsPGzduxLvvvjugr5vJZDBhwgRcd911uPzyywf0tYl6i2OwiIioV2666SZ8+OGHvZ6maG8tW7YMBoOhzxNNEw0kjsEiohEhl8t1O9h9KJZyGI5qamqQTqcH/HUvuugihisaNhiwiGhE2LFjR9GB3oVuuukmLFq0aGB2iIhGNY7BIqIRIZ1O47333utym4kTJ/b5TEYiot5gwCIiIiIqMQ5yJyIiIiqxUTsG6/e//z2WLl2KhoYGfO1rX8P999+PWbNmdfu4fD6PXbt2oby8nBONEhERDRNCCDQ1NWHMmDHQavu/fWlUdhE+88wzOOecc/DQQw9h9uzZuPfee/Hcc89hw4YN8Pv9XT52586dHSbWJSIiouFhx44dGDduXL+/zqgMWLNnz8bXv/51PPDAAwCkVqnq6mr89Kc/xXXXXdflY+PxOJxOJ3bs2NFhmggiIiIamhKJBKqrqxGLxeBwOPr99UZdF2E2m8XHH3+M66+/Xlmm1Woxb948rFy5stvHy92CdrudAYuIiGiYGajhPaMuYIVCIeRyOVRUVKiWV1RUYP369R22z2QyyGQyyv1EItHv+0hERETDG88i7MaSJUvgcDiUC8dfERERUXdGXcDyer3Q6XRobGxULW9sbERlZWWH7a+//nrE43HlsmPHjoHaVSIiIhqmRl0XodFoxMyZM/HGG2/glFNOASANcn/jjTdw6aWXdtjeZDLBZDL1+nVyuRxaW1v3dndpkBgMBuh0usHeDSIiGqZGXcACgKuuugoLFy7EIYccglmzZuHee+9Fc3Mzzj333L1+biEEGhoaEIvF9n5HaVA5nU5UVlay3hkREfXaqAxYp59+OoLBIG688UY0NDTgwAMPxKuvvtph4HtfyOHK7/fDarXyy3kYEkKgpaUFgUAAAFBVVTXIe0RERMPNqKyDtTcSiQQcDgfi8XiHMg25XA4bN26E3++Hx+MZpD2kUgmHwwgEApg6dSq7C4mIhrmuvr/7w6gb5N6f5DFXVqt1kPeESkH+OXIsHRER9RYDVj9gt+DIwJ8jERH1FQMWERERUYkxYNGotmjRIqVcBxERUakwYBERERGVGAMWDZpcLod8Pj/Yu0FERMNEZ4UPOvsuGcxCCQxYpHj11Vdx2GGHwel0wuPx4Fvf+ha+/PJLAMDcuXNx7bXXqrYPBoMwGAx45513AEgTY1999dUYO3YsysrKMHv2bKxYsULZfvny5XA6nfj73/+OffbZByaTCXV1dfjwww9x3HHHwev1wuFw4Mgjj8Qnn3yieq3169fjsMMOg9lsxj777IPXX38dGo0GL774orLNjh078P3vfx9OpxNutxsnn3wytm3bpqzP5XK46qqrlPd3zTXXDOovHxGNXkII5e9P4d+hXC4HQAoM8u22tjYIIZDL5ZDJZLp97nQ6jebmZtWyeDyOVCqlPI/8GoXy+Tyy2SxyuRwSiQTy+TySySTa2tp69d7k54jFYojH44hGo2hsbERLSwvC4XCHv7uxWAzr169HS0tLl88bj8fxxRdfIBAIYOfOnUgkEti5cyfq6+uxdu1a1XsEgJ07d2Lt2rWDdib4qCw0OpCEALr5zPQbqxXozYlwzc3NuOqqq3DAAQcgmUzixhtvxHe+8x2sXr0aZ511Fu68807cfvvtytl1zzzzDMaMGYPDDz8cAHDppZdi7dq1ePrppzFmzBi88MILWLBgAT7//HNMmTIFANDS0oI77rgDjzzyCDweD/x+P7Zs2YKFCxfi/vvvhxACd911F775zW9i06ZNKC8vRy6XwymnnIKamhqsWrUKTU1NWLx4sWrfW1tbMX/+fMyZMwfvvvsu9Ho9fv3rX2PBggX47LPPYDQacdddd2H58uV47LHHMGPGDNx111144YUXcMwxx5TmgBPRoGttbYVer+/yLGAhBJqammC1WqHX6xGPx2EwGGC1WpVg4XQ6VY/J5/PI5/PQ6/VIpVJIp9NwuVyqbeQ5bisqKpDNZqHT6dDW1oadO3cq/0DK6urqkE6nUVtbiy+//BIOhwN6vR6BQADV1dXKvLfjxo3Dzp074Xa7EY1GIYRAbW0tAoEA3G43Wlpa0NTUhIkTJyKRSMBms2Hz5s0AgNraWsTjcZSVlSnPZ7PZkEwm4ff7EQgEUFFRoRyHLVu2IJfLwWAwoLW1FVqtVnnP06ZNU71XjUaDfD4PrVarHJ9AIACz2YydO3cWPe7BYBCA9D0ghEBZWRkikYgSGrds2QKDwYDKykpks1mUlZUhHo/DarUiFAohlUoBgFIEuv2MKfJ7NJlMEEIgm80qPxf5WA0kFhrtpa4KlaXTaWzduhW1tbUwm80AgOZmwGYbjD0FkkmgrKzvjw+FQvD5fPj8889RUVGBMWPG4M0331QC1dy5c3HEEUfg9ttvR11dHSZOnIi6ujqMGTNGeY558+Zh1qxZ+M1vfoPly5fj3HPPxerVq/G1r32t09eV/7g99dRT+Na3voVXX30VJ510Enbs2KFMyP3666/juOOOwwsvvIBTTjkFTz75JH79619j3bp1yh/WbDYLp9OJF198EccffzzGjBmDK6+8Ej/72c8ASP8V1tbWYubMmaqWMFmxnycRda2pqQkGg0H5ncnn80in07BYLD0qfSKEQDQahcVigcViQT6fRzweh81mg8FgQC6XQ0tLC2w2GzQajRI63G43wuEwdu/eDa/Xi8rKStTX1wMAxo4dq3pcOBxGQ0MD9Ho9JkyYoASS/fbbD2vWrAEATJ06FUIItLa2wmazYdOmTchkMpgyZQo2bdoEAJgwYQISiQTS6TRqamqwfv16AMCkSZPw5ZdfQqPRwGKxKF/s1dXVSoCSX9NsNiOdTquOgRxsekN+TF8e2xMmkwkGgwFarRZNTU3K8a6srEQikQCAAQ8wvZVMJjFnzpwBKzTKFixSbNq0CTfeeCNWrVqFUCik/JLW1dVhv/32w/HHH48///nPOPzww7F161asXLkSDz/8MADg888/Ry6Xw9SpU1XPmclkVFXtjUYjDjjgANU2jY2N+OUvf4kVK1YgEAgofwjr6uoAABs2bEB1dbUSrgBg1qxZquf49NNPsXnzZpSXl6uWp9NpfPnll4jH49i9ezdmz56trNPr9TjkkEPYTUijVmtrK+LxOJxOJ/T6PV8Hzc3NEELAVvDfYTQaRSKRwLhx45SZDbLZLFpbW1H21X9y6XQa27dvByCFFQDYunUrUqkUxowZA7fbrbxuYSuTEEK5HY/HsWvXLuU5AoEAQqEQDAYDpk2bhi1btiCTycDv98Pr9Sohym63Y/fu3QD2/HMYjUYBSC1K27ZtQzqdht/vV7rP2tralFaO9nK5nDJEYtKkSUorS1NTk7JNOp1GJBLpsFy+LYRQhR25hUW+LiX5dfprXGsmk1F1T4bDYQDS9HBUHANWP7NapZakwXrt3jjppJMwfvx4/OlPf8KYMWOQz+ex3377KX+AzjrrLFx22WW4//778dRTT2H//ffH/vvvD0D6z0Cn0+Hjjz/uMK1M4R/pYv/FLly4EOFwGPfddx/Gjx8Pk8mEOXPmdPqHr5hkMomZM2fiz3/+c4d1Pp+vx89DNJps374d6XQayWQSEyZMACB9QW/duhUAMGPGDOX3WQ4yoVBImbd148aNAICJEyfCarUWHR8kd+tEo1G43W4kEgnU1dXB4XCguroara2t2LRpExwOB8aOHatsL5PDijyORn6NeDyu+uet/T9K7e/LrUTxeFwVJnuifQvT3uLJPaMDA1Y/02j2rptuoITDYWzYsAF/+tOflC7A9957T7XNySefjAsvvBCvvvoqnnrqKZxzzjnKuoMOOgi5XA6BQEB5fE+9//77ePDBB/HNb34TgPTfXSgUUtZPmzYNO3bsQGNjo/KH/cMPP1Q9x8EHH4xnnnkGfr+/06bfqqoqrFq1CkcccQQA6b/Xjz/+GAcffHCv9pdopJCDQ7Lgv8DCL/98Pt/hHyZ5gHShlpaWHk8RJo/DicfjqK6uRiQSQT6fRzQaxdixY3v9HnqCrdQ0GHgWIQEAXC4XPB4P/vjHP2Lz5s148803cdVVV6m2KSsrwymnnIIbbrgB69atwxlnnKGsmzp1Ks466yycc845+Otf/4qtW7fiv//9L5YsWYJ//vOfXb72lClT8MQTT2DdunVYtWoVzjrrLFgsFmX9cccdh0mTJmHhwoX47LPP8P777+OXv/wlgD3T2Zx11lnwer04+eST8e6772Lr1q1YsWIFLrvsMmXA5eWXX47bb78dL774ItavX4+f/OQnHQZJEhH1BUMctceARQCkAZJPP/00Pv74Y+y333648sorsXTp0g7bnXXWWfj0009x+OGHo6amRrVu2bJlOOecc7B48WJMmzYNp5xyCj788MMO27X36KOPIhqN4uCDD8YPf/hDXHbZZfD7/cp6nU6HF198EclkEl//+tdx/vnn4xe/+AUAKANprVYr3nnnHdTU1ODUU0/FjBkzcN555yGdTistWosXL8YPf/hDLFy4EHPmzEF5eTm+853v7NVxIyICSjt3KcPayMAuQlLMmzcPa9euVS1r/4t+wgkndPrLbzAYcMstt+CWW24pun7RokVYtGhRh+UHHXRQhy6/733ve6r706dPV3VZvv/++wCAyZMnK8sqKyvx+OOPF31tQBrUfu+99+Lee+/tdBuigSKEQFtbGwwGg2p5YRddPB6H2WyGwWBQjRtKJpOIx+PQaDTw+XxIpVLQarUwGAyIRqMwm80wm83I5/PK6fDtX5s62pvjwmNK7TFg0bDwwgsvwGazYcqUKdi8eTMuv/xyHHrooZg0adJg7xqRSj6fR1tbG4xGI5LJJJLJpFIXKR6PIxKJoKKiAplMBqFQCFarFZWVlbBarcjn8x3+yZHZbDZUVFSgtbVVOcMWgHIWW1fkweRNTU3wer2qMY6Fstksg0I3eHyopxiwaFhoamrCtddei7q6Oni9XsybNw933XXXYO8WjXJyxevy8nKlhWnTpk1obW2FRqNRvoybmppUZ9jJZ+QB0gDxLVu2KAUgOyOHtb4oLAvQvsK3bPv27apSA9Q7pewipJGBAYuGhXPOOUd11iLRQEilUkgkEtBqtUqXnsPhgNlsRjgcVipKA4DH40FbW5tSTqCwpaMn05v0NTyVSrFwtWHDBpSXlyv1qwC24HSGx4XaY8AiIoLUtafRaNDc3Ayj0Qi9Xq8UmizUWZecXHhxpGlqalKFr2g0Crvd3qGoLxGpMWD1A/4nMzLw5zg6yJPurl+/ngUge2j79u1KpfbhrlRde+wipPYYsEpIPhuopaVFVceJhid5Xq32Z3nRyBAOh5FKpZDJZDpUD6fuyXP2DXel+keK/5BRewxYJaTT6eB0OpVxGVarlf/VDENCCLS0tCAQCMDpdHaoZE0jgzxvHe29YtXdiUY7BqwSkyckLhz8SsOT0+lUTTBNI0MgEGCLVYkFg0Ho9fpez/E3FPCfYOovw++3YYjTaDSoqqqC3+9Xziai4cdgMLDlaoTiPz/9Y/fu3Rg3btxg70avsWuP+gsDVj/R6XT8giaiUWWknklJ1Beci5CIiEpiMLte27dE9bTrj12E1F8YsIiIaMRh1x8NNgYsIiIiohJjwCIioiFjMAt/DpVWr6GyH7R3OMidiEaFwnkCaehiuKCRggGLiEaF9evXD/YujGoMTjTajJguwm3btuG8885DbW0tLBYLJk2ahJtuugnZbFa1jUaj6XD5z3/+M4h7TkREMp7VRyPFiGnBkidqffjhhzF58mSsWbMGF1xwAZqbm/Hb3/5Wte3rr7+OfffdV7nv8XgGeneJiEa0nTt3DsvCo0SlMmIC1oIFC7BgwQLl/sSJE7Fhwwb84Q9/6BCwPB4Pp0AhIupHsViMAYtGtRHTRVhMPB6H2+3usPzb3/42/H4/DjvsMPz9738fhD0jIiKikWzEtGC1t3nzZtx///2q1iubzYa77roLhx56KLRaLZ5//nmccsopePHFF/Htb3+76PNkMhlkMhnlfiKR6Pd9JyKi3tNoNBxMT0PGkG/Buu6664oOTC+8tD87qL6+HgsWLMBpp52GCy64QFnu9Xpx1VVXYfbs2fj617+O22+/HWeffTaWLl3a6esvWbIEDodDuVRXV/fbeyUiGklyudyAvh7DFQ0lQ74Fa/HixVi0aFGX20ycOFG5vWvXLhx99NGYO3cu/vjHP3b7/LNnz8Zrr73W6frrr78eV111lXI/kUgwZBER9cBglsZg2KLBNuQDls/ng8/n69G29fX1OProozFz5kwsW7YMWm33DXSrV69GVVVVp+tNJhNMJlOP95eIiCQDHXLYRUhDyZAPWD1VX1+Po446CuPHj8dvf/tbBINBZZ18xuDjjz8Oo9GIgw46CADw17/+FY899hgeeeSRQdlnIiIaXhjgqKdGTMB67bXXsHnzZmzevLnDqcGFvxC/+tWvsH37duj1ekyfPh3PPPMMvve97w307hIREdEINmIC1qJFi7odq7Vw4UIsXLhwYHaIiIiIRq0hfxYhERER0XDDgEVERENWV3MTcjwUDWUMWERERAOIwXB0YMAiIqJhpzchZW8DDQMR9QUDFhERUTsMVbS3RsxZhERExWSzWSSTycHeDSIaZRiwiGhE27hx42DvAg0hXbVMsdWKSoldhERERHuJ4YzaY8AiIqIB01XZBaKRhAGLiIj63Whq4RlN75U6x4BFRERDRl9buBhqaKhhwCIiIiIqMQYsIiIaNCO9xYpjzkYvBiwiIhrRhksYo5GFAYuIiEacnoaqwhamvQliAzl1Dw0PDFhERDRkDYUuts4CEYMSdYUBi4iIiKjEGLCIiKjfDZXWnp7ux1DZXxq+GLCIiGjYE0IMie5EIhkDFhER9btsNjtkQlBn+8BWKyolBiwiIhrRShmcGMKopxiwiIhoQPQknPSmhaur5xuIIMSwRV1hwCIiogFRLJAMhS7DUugsbBV7fwxmowMDFhERDZq+ho3+CCkMPlRKDFhERDQgRkuAGS3vk7rGgEVERAOilMFjpHQt0sjFgEVERAOioaGh3557sFuN+vr6DIojFwMWERENiHg8PihBqC+v2ZPHlOK9DHYwpP7DgEVERANmKNSk6m2h0b3ZZwao0YsBi4iIBgwDB40WDFhERDRg2traBuR1+qM1iqg3RlTAmjBhAjQajepy++23q7b57LPPcPjhh8NsNqO6uhp33nnnIO0tEdHoM1ABayjjwPbRQT/YO1Bqt956Ky644ALlfnl5uXI7kUjg+OOPx7x58/DQQw/h888/x49+9CM4nU5ceOGFg7G7REQ0RJSqdSufzxe9XWxZa2trSV6Thp4RF7DKy8tRWVlZdN2f//xnZLNZPPbYYzAajdh3332xevVq3H333QxYRETDTGEg6k04Ktw2m80qt5ubm5XbiUSi6O14PK7cjkQiyu1gMFh0eSaT6fL1c7lcj/ebhpcR1UUIALfffjs8Hg8OOuggLF26VNUcvXLlShxxxBEwGo3Ksvnz52PDhg2IRqODsbtERKNWXV1dhy7D9uGj8H5hAMrn84jFYsr9cDisahmqr69XvY6sra0NyWRSub9r1y7l9u7du5XbhSGppaVFuV0YyArDUV9bv3Q6XZ8eR0PfiGrBuuyyy3DwwQfD7Xbjgw8+wPXXX4/du3fj7rvvBiAVuautrVU9pqKiQlnncrk6PGcmk1H9B1L4nwwREfVd+7+n69atU4WkdevWqdZv3bpVuZ3L5VStRoUtS4A6FHXVDafVapXXNBqNSoCyWq3Kc5SVlSnhrvC2w+FQXtflcin/qLvdbiWgmc1mpNPpTl/TYDCwFWuEGvItWNddd12HgevtL+vXrwcAXHXVVTjqqKNwwAEH4KKLLsJdd92F+++/v2gTbU8tWbIEDodDuVRXV5fqrRERUYH2LVbtFfY+aDQalJWVKfcdDodqW/mfZwCoqqpSbuv1ethsNuV+TU2NcnvcuHHKbb/fr9y22+3KbYvFotw2GAyq5y3ct67wTMbRYcgHrMWLF2PdunVdXiZOnFj0sbNnz0ZbWxu2bdsGAKisrERjY6NqG/l+Z+O2rr/+esTjceWyY8eO0r05Iuo3LS0tHX7faWibOHGiqidhn332gdlsVu5PnTpVua3X6zFmzBjlvsfjgVa75yuts1Ak/2Penc5CUG8fyzA1eg35LkKfzwefz9enx65evRparVb5T2TOnDn4xS9+gdbWVuU/j9deew3Tpk0r2j0IACaTCSaTqW87T0SDZteuXR26ZmhomT59OiKRCAKBAACpW66pqUlZr9Vquww0XYWXvqxjxXYqpSHfgtVTK1euxL333otPP/0UW7ZswZ///GdceeWVOPvss5XwdOaZZ8JoNOK8887DF198gWeeeQb33XcfrrrqqkHeeyIqJSEEw9UwUNit1lulqiXFgqTUX4Z8C1ZPmUwmPP3007j55puRyWRQW1uLK6+8UhWeHA4H/v3vf+OSSy7BzJkz4fV6ceONN7JEA9EIw0HDw0d3QaanQaf9dl118XXWhdfbUNXXcMbwNjqMmIB18MEH4z//+U+32x1wwAF49913B2CPiGigCSEQjUb36sQW6l86na7LANzXelbt7+9tiClVCGKYGr1GTMAiotFLCIFkMom2tjZVXSMaetqXJWgfivraEtWVwu3ady2WKpT15nkK950BbORiwCKiYUUIgdbWVrS2tqKpqQmZTAbZbJatVsNAX04Y6k3A6mnI6UkXIbsLaW8xYBHRkCSEQFtbG7LZLLLZLFpaWpBOp9Ha2soJg4epMWPGdGhh7GmLVTF9PYuwJ4/pbfAqXF5YLLXY9izjMDowYBHRkCCf+dfU1IRUKoVUKtVlkNLr9TAajbBYLMhms7Db7YjFYjCZTKppTmho0Ol0KCsr63WAKkUXYSGtVtuj8NRdSOrqsb0JUAxYIxcDFhENikwmg2g0qkxjkkqlVPO8yQwGgxKkLBYL9Ho9LBaLqqikTC7JUl5ejkwmg4aGhv59E9Rj8px77QNFYZApdr+vBT/bP0/h4zoLT3uzvLNtGLBGLwYsIhpwTU1NqKur6/DlotFoUF5ejrKyMlgsFpjN5qJBqjvl5eWwWCwMWEOIPP6qtwGrL0Gpq8dptVpVy2hnIaknt9mCRV1hwCKiARWNRlFfXw9Aqtxtt9shhIDRaITNZlNaOvaWXq9HTU0NGhoairaM0cCSf67tSzS0DyxdBazC292Ve+gsCLUPZr1ttepJC1ZPuheLbUsjCwMWEQ2YcDiM3bt3AwCcTifGjh1bsorcxdjtdqTTaWUqFho88s+5ty1Yna3TarU93rb94zoLSYWBrbPH93YbGr0YsIio3+XzeTQ0NCiDzz0eDyorK/s1XMkG4jWo79qHka7CSU+31Wq1qnWFXYIajUa1rie3Cx/f221o9GLAIqJ+lclksHPnTqRSKQCA3++Hz+dj8BmFinWXySc5yAqDSvvt259V2tlYKr1er1rX1dmonW3X29udBS8avRiwiKhf5PN5hEIhBINBCCGg1Woxbtw42O32wd41GiBlZWVobm5W7heGKZ1OByFEl61ShQFGr9d3CGOF9wtvtx/I3lnl+Pav0dsg1VnAIwIYsIioxIQQiMViCAQCypeezWbDmDFjYDQaB3x/+MU3eAwGg+p++5MNugpMPdm+MOAUrpOr/csKq/y3D2KF99k6RaXEgEVEJZHP5xGPxxEKhZQvNL1ej4qKCjidzkHrEmz/JU8Do7KyEk1NTcp9IYTSTSzr7f3CoCSEUN1vP1VSYTjrbDt5tgBZYdjqqtWLqCcYsIhor+RyOUQiEYTDYeXLSqvVwufzwePx9KmOVSnZ7XZEo9EOX9bUv2w2G4LBoGpZS0tLr+63/5ml02nldiaTUQWfwnXZbFa1jsGJBgMDFhH1mhACLS0tiEajiMfjyheVXq+Hx+OB2+0uWT2rvaXX6zFp0iR8+eWXDFkDKJvNduhO25uAlc/nVSGq8DbQsYuQaLAxYBFRj7W1tSEWiyESiai6YEwmE7xeLxwOx6C3WHWGE0QPrMLB7YAUiNrXkOqqhQpQhyaOpaPhhgGLiLrU1taGRCKBeDyu+tLUarVwOBxwuVywWCxDvuwCv6D7n9FoRC6XQy6XUz4rckFQOTwZDAa0trYqrUztK7L3dRJnoqGGAYuIOugsVAGAxWKBy+WCw+EYMt2APaHX63kGWD8rLy9HPB4HsKc1ymazIZFIKNvYbDZEo1HlfllZmWq91WpVfebMZrOqZat9javupswhGixDsy2fiAZcW1sbIpEItm7divXr12PXrl3KF53ZbEZFRQWmTJmCSZMmDakxVj3l8/kGexdGFJ1O16Gmmc1mU7VkarVa2Gy2DtsUslqtqse0bw0tKytTbV94X6PRwGw2qx5bqHCdPNk0IIW0QkO99ZWGJ7ZgEY1iXbVUmc1mOBwO2O121ZfTcOVwOGC1WrFx48bB3pURoba2VmmtAqSQUlZWphqD1/6+RqOB1WpVPY/ValVNbWO1WhGJRJRuQqvVinA4rNpefl2z2ax6fqvVqozr0uv1MBgMSuuX2WxWSjRYLBZVCQmj0ahaJz+HyWRSlstdm/Jzy61ohS1o7N6kQgxYRKPMaApVcsHJVCqlXKh3DAYDamtrkUgk0NDQoCwzmUyqlh85KBUuKy8v79A61b7l02w2q0KJxWJRjZcrDGQ6nU5V18xsNqu6B8vKypQwVth6JT+vHMwKA5bRaFR1ORqNRuVzYrFYVMFLDliFjzeZTMrZjzqdrsuTKQrDWHeTVdPwx4BFNAqMhlBVLEy1P3ON1Nq3vtTW1sJkMmHXrl1KGPF4PDAajaqWIjk4FYYnueuvMCzZbDZVYc/23YEGg6FD0GhfGLawO699i5XFYkEsFlPdL9w2mUyq7he7bTQaVdu1r/ReuF2xfezurNnCVq3C8MaANfAcDseAvh4DFtEI1ZNQ5XA4BmX6mlJoH6ZSqVTRMCWP07FYLMpF/qJraGiAXq9XdRcNZ3q9HhqNBnq9HkajUbkOhUKq4FBbW4uysjJks1mly1TuQgXUY5LkcVaFQUIOU+1DF4AOrUGFr9t+jJb8enLYkAO+fL/9eCyLxdKhS7Dws13YOmY2m9HW1qZ0EbZfJwcfeUxXMpmE1WqF1WpFS0sLtFqt6nej8LYc0DQaDWw2G5LJJAwGA+x2O8LhsHJ8kskkvF4votEo2tra4HQ6lYDl9XrR0NCgvH5zczMqKirQ2NgIjUYDr9eLYDAIl8uFZDKJ1tZW+P1+BAIB2Gw25HI5pFIpZVl5eTlaW1uRTqeVZW63WzmBwG63IxKJKOscDgcymYyyX6FQCH6/X3nN5uZmaLVamM1mNDU1we12IxwOw+fzIRQKKc9tsViQy+WUn1dzczMcDgdisRjcbjcikQg8Hg+i0SicTicSiQTsdjuSySTKysqQSqVgNpvR2toKg8GAXC6nzFMp/+zbj+uTf3by50Sn0yGfzyvduEajEdlsVjlBQv6ZDnSgZcAiGkHa2toQj8eRSCSKnv1nt9uHZahqa2vrEKY664ppH6ZMJlPRVoaysjJMmjRJmS5Fo9EglUopEwW3tbVBq9UilUrBaDQinU4rLT7yl0Cx4pbyF4N8X/4S0Gg0yhdBYVeavG86nQ4ajUZ1X6vVKsFAp9MpF/lxcitSd4O0m5ublX0tKytTvtQNBoMynsjtdquOTSwWU8IosKd1SKfTKQFCfh6TyaSEI5/Ph4aGBlRUVCg/Dzn4yYHK4XCgqakJXq9XeUxjYyOqqqoAQAkBlZWVAKB8mbvdbuj1emVsl8lkgsfjUYKGVquFx+NBOp1WWmQzmQx8Pp/yT4UcOquqqpTgUFZWBo1Gg4qKCmi1WmSzWXg8HuUL2uFwoKysDOl0GuXl5SgrK0Mmk4HdbleCnMPhUFrkXC4XhBDKPpeXlyOTycDpdEKv10On0ynvQQ6mcjCR98VkMsFsNithqq2tDRaLBVarFRaLRWmxtVgsKC8vh8lkQj6fRy6Xg8lkUt6/3+9Xfm4+nw8Gg0E5SUX+jGo0GuX4eb1e5TNV+FmWtwGg/Nzk+8V4PB4AgMvlAgA4nU7Vffm6v8ifV/lz6XA4VGerDgSN4Ii8XkkkEnA4HIjH4x3OoCEaDCMtVMkFKFOpFFpaWpBKpTpM8iszmUyqMNW+C4kkLS0t2LZtG3Q6HWpra1WfhUwmg1wupxrrJIeDsrIyVRddKpWCTqfr8Hg5NMiPzWazMBqNSvCTWxYLt5FbHgpfs6ug2N16ou4M9Pc3W7CIhqHu6lQNl1AlT9hbGKjaV/OWMUz1ndVqxYwZMwB0LElQbNydRqMpOl6lfRmEYo+XW18KtR/YLrfmtV/WFYYrGm4YsIiGiZEQqtra2pRWKfm62LgIvV6vBCm5S2S41d0aahhQiAYWAxbRENbW1oampibE43HVmU7A0A9VQgik02m0tLQol2JdfRqNpkOYMhgMDARENKwxYPVRJBLBzp07odfr4XK54HK5OlQH7k7hAEMiWS6XU1qq2oeqoVxSIZfLqcJUZ61TclefHKbkM7qIiEYSBqw+amhogM1mQzabRWNjIwKBAOx2O9xud4daL7JcLqd8aabTadWEp/IpwUajUfXfPMeYjA75fB5NTU2IxWJIJpOqWkJms1lpqRoqoUo+g6m5uVkJVIX1jmRarVY59Z1dfUQ0mjBg7QWDwQC/349wOIx0Oo14PI54PA6DwaDUGDKbzcjn8wgGgwiHw51OoyDPNp9Op5VTSeWpJ8rLy2G32zsU4KPhTQiB5uZmxGIxJBIJVWuPyWRSPkNDIVTJ3X2FgapYmQSj0agKVO2rfRMRjRYjJmCtWLECRx99dNF1//3vf/H1r38d27ZtQ21tbYf1K1euxDe+8Y1ev6bNZlO6B1OpFCKRCOLxOFpbWxEKhRAKhZTCafKXp/zFabValerIQgjkcjmlSJw8ALitrQ3JZBLJZBK7d+9GWVmZ8qXLVoDhK51OIxaLIRaLqUKKHMydTmeHaT4GWmGgkkNV+yKecgHPwkDFfwKIiCQjJmDNnTsXu3fvVi274YYb8MYbb+CQQw5RLX/99dex7777Kvflgmi9VTiw2GKxYOzYsUrxung8jqamJtWgXvn0ZYPBoBTfk8nL5KJz8unrTU1NaGpqQktLi/Jlt3v3btjtdng8ng6Vjmloam1tVUJVYVeaVqtVQlVnXcsDQQiBVCqlClTtx0/J3X1lZWVKdx+7sImIihsxActoNCpVfwHpC+1vf/sbfvrTn3b40vJ4PKpt+6rYf+vyF6bBYFC6+vR6vdJKlUgklOUmkwk2m02prFzYKiW3DpjNZvh8PmSzWcTjceULWu6ONJlMcLvdcDqdbNUaYuRxVdFoVDVYXaPRoLy8HE6nEzabbVBCitxClUwmuwxUcpgqKytjmCci6oURE7Da+/vf/45wOIxzzz23w7pvf/vbSKfTmDp1Kq655hp8+9vf7vR5MpmMqsWhsNR+Z4Eml8thx44dAKRuxJqaGmUaDrlFKp1OK88tz/5usViUsGWxWFQtXEajET6fDz6fDy0tLUp3ZCaTwe7du9HY2AiXy6VMzEqDJ5VKIRqNIh6Pq7rVrFYrnE7noHXxZrNZJVAlk8kOXX46nU4JU2VlZTy7j4hoL4zYgPXoo49i/vz5GDdunLLMZrPhrrvuwqGHHgqtVovnn38ep5xyCl588cVOQ9aSJUtwyy23FF3X2ZdkQ0ODMnFldXW10kIhj1OpqKhAW1ub0h2TTCaRzWaVatahUAiA1MJVOL5FnnpCvl9VVYVYLIZIJKIEtXA4DLvdDq/Xq5r6gvqXPF1NNBpVVSKXy3g4nc4BH6wuf8bkUJXNZlXr5RYqBioiotIb8nMRXnfddbjjjju63GbdunWYPn26cn/nzp0YP348nn32WXz3u9/t8rHnnHMOtm7dinfffbfo+mItWNXV1Vi5ciUOOuigDl+aqVQKX375JYA9M9b3hHzKezKZREtLS4cvQ0D6si4MXPJUIUIIJJNJhMNhVVeU1WqFx+OB3W7nF2c/kM8CjEQiaGpqUtU1s9vtShfgQB17eRxVU1MTkskkUqlUh23kFiqbzcYxVFSUEMD3vgfkcsALLwD800EjBecibGfx4sVYtGhRl9tMnDhRdX/ZsmXweDxddv3JZs+ejddee63T9YWzxLdX7IuzsbERAJRZ0XvKYDDA6XQqM47LU4rIg9vT6bQyVUphGQf5LC6LxYIxY8Ygl8shHA4jHo8rj5e7Fx0OB79QS6CtrQ3RaBSRSER1EoPZbIbL5YLD4eh10dm92ZdkMqmEqvbdfl2N8yMqJhoF/vpX6XZDA1BVNbj7QzRcDfmAJY876ikhBJYtW4ZzzjmnR6eMr169GlV9/AvSPmDJg4YBwO/39+k5ZXq9Hna7XUnZ+XxeKd8gX3K5nNKtKJPH0bjdbmWalWw2i/r6ejQ2NsLn88HlcjFo9ZIQQhn7lkgkVAVinU4nXC5X0Ylw+2M/0um0MpavfSuVVquFzWZDeXk5bDYbyyZQr+1Nn4YQwM03A/vuC3z/+yXbJaJhacgHrN568803sXXrVpx//vkd1j3++OMwGo046KCDAAB//etf8dhjj+GRRx7p02u1712VB6v3xzQmheNl5NeWx23J05Kk02nkcjk0NTWpHqvT6ZDP59HW1qYMiPd6vfB4PGzR6EYul0M0GkU0GlV1FVssFrjd7gFpFczn82hubkYikUBTU1OHAp9ms1kJVYNZ6oFGnt5+lFasAG69VbrNgEWj3YgLWI8++ijmzp2rGpNV6Fe/+hW2b98OvV6P6dOn45lnnsH3vve9Pr1W4Wnt+Xwe8XgcAOB2u/v0fL0h19QymUxKt6JcDV4OXPLkuu27jfL5PAKBAAKBAGw2G3w+H7+Y2yk8U7OwtcrhcMDtdvd7a5UclBOJBJLJpOqzJrdSyaGKrVRUSnvzZyAQKN1+EA13Iy5gPfXUU52uW7hwIRYuXFiy1yr80pO/BPV6fa/GXpVS4bxvsra2NlUrV/t6R3KleACqCXgtFoty1uJoIYRAIpFAKBRSdb0NVK2xbDarhKrm5mbVusIuY85RSUQ09I24gDWQCluGYrEYAMDpdA6pUKLX61FeXq6qEJ/NZtHS0qJ8kcuBq/14Lq1Wq4Qt+WIwGIbU+ysFuRswHA4rg9blMwH7u1q+XEA2kUh0GE9lMpmUUMUSCkREwwsDVh+tWlWGGTOkYCKXSQAwIKd+7o3CrkWXy6Xse2Njo6p+E7Bn7E9ha4pOp+sQuvR6/bD88pdrh8ViMSVk6nQ6uN1uuN3ufut6y2azSCQSiMfjHUKV1WqF3W5HeXn5kJjkmYiI+oYBq49SKa3SgpVKpZDP55XwMZzI07bYbDY0NzcjEAigpaVFWS9P4CvXA8vlcqpuRWBP6DKbzcr1UO1elGtXhcNh1ckAJpMJHo8HTqezX7rfugpV8iTedrt9wMo7jGb5PNDaKpUjCIWACROAVArIZgGTCdDpALtdugaAlhaANXt7Zgj+yhMNGv4176Ncbs8YLPmLuqysbEiGip7QaDTKwGk5aMlz1AGAy+XCuHHjlGKWLS0tSKfTypmL7UOXVqtVBS6LxQKTyTRox0c+CSEcDqta6mw2G7xeb7/87OS6ZbFYTBVaAemzInf/cZD6wDrqKGDVKilQdUanA2pqpG3q6wGvFxgzBnC7gblzpSBWVSWVJaivB046CbDZAKdTCnBbtgD77CMti8WAvazaQkTDEANWHwmxZwyW3IVms9kGc5dKpqysDLW1taqgJZcqcDqd8Pl8ypmS8pmL6XRaKRWRTqeRz+eVel2ywgms5eAlV6PvL21tbYhEIohEIkp5A41GA6fTCY/HA7PZXNLXy+fzSCaTiMViqurugNQaKLdUMVQNvOXLgXfeATqZtEEllwO2bt1zPxSSLoBUiqA9uTRBZ3w+wOORnsPvB771LSmgff45cMghwGGHAWPHSi1lyeSeVjQhgIFu1Bzac3sQDR8MWH2Uy2mQz+eVwo8ARtzcf3LQamlpQSAQUIJDLBZTglbhfIkyIQQymYwSuORruViqPBmyzGQydehi3Nuz9dLptDK+Sg45er1eGV9Vyq44uQhpLBZDIpFQnfxgNpuVCZ4ZqgZXkXnfB0wwKF0AKWStXbtn3WOPdf/42lrgiCOA2bOllrRMBkgkpJB29NGAViuFsmIfsXxeeu2Kio7rNm0CXnkFuPjiPY9lwCIqDQasPsrnpdaRTCaDfD4PrVY7YgclW61WTJgwoWjQcjgc8Pv9qvde2FIlk89eLAxcqVQKuVyuw3yPgDR1kPwc8qW7cV2dzcloNpvh9Xpht9tL2lqWzWYRjUYRi8VUU+bo9Xpl2qNSt5DR6LR1q3R5/PGebV9eLnVPtrXtCXbvvAMcfrh6u7lzpcDX2Ajcdpu0rLOA9dFHwJtvAlddNfCtakTDEX9N+iif16C1tVXpAuvPU/mHisKgFQwG0dTUhHg8jng8rmrRKqbw7EWHwwFACkRyna7C4NXa2qpcCgeiFwa3wotGo0EsFkM4HFYFNbnMQimLqObzeTQ1NSEajXYYcyZP8Dycx+KNRLEY8Omng70XA6upSboU+t3vOgYsudvzmWeKB6zC21//unRdXi61ePVVSwvwwAPAyScD06ZJy955B5gyZc+8h9u2AZWVAP8/oeGMAauPpDORWpUzwobb2YN7w2q1Yvz48UilUggEAmhqalJ1Hfr9fhiNxm6fR6PRwGAwwGAwqMpb5HI5ZSxX4UUeYN/+LLz2z+lwOLoMe32RTqeV1qrCLsCysjK4XK6St45R6cydC6xbN9h7MfgKM38oBBTWZC6c7KGwxm1BTWLF55/37nXffhv405+Au++Wxp/dcIN0+9prpQD35pvAscdK2woBfPghMGsWMHUqsGEDsGMHcOmlwBVXSN2hySTw8cfSuDXO9EVDGQNWH+XzGqV7CxhdAUtmsVgwfvz4ol2HLpcLPp+vR0GrPZ1Op5p3EVB3MTY1NSGZTHaYk0/eLhaLIR6Pq1q5TCaTMrarp61LuVwO8Xgc0WhUFer0ej1cLhdcLlef3h8NLIYryXPPAevXS119P/yhel0+L13uu0/qAixcDnTeqvXhh1LgOvdcKcAV+9U66ijpuq0NePpp4L331OvfeEN9/+mnpeuNG6Xr888H/v1v4O9/l177+OOBlSuBu+4CrrwS+Pa3pbIbr7wC7NwptcRddpl0Fmc0Kj1mAGYvI+qAAauPhIAyaBvAiB1/1ROFXYeNjY3KWYeFQasUA7zlwqCFZyZaLBaUl5dDq9Uik8l029ql0+mUsFV4XTjoPZ1OIxKJqAqQAlKXo8vlgs1mYxcgDUszZhRfXldXvDUolwM++QSYOXPPssZG6ToSkVqaAGDcOCn4dPVrsWVLz/ax/XPs2KG+v3KldP3oo1L4eukl6f6uXcB3vyuFvieflLqG5WCVyQD//S/w+99LrWcOhxQmv/1tYN99gYYGaczaCDkRnIYIBqw+yuelvwLyGWpsyZCCVvvyDpFIBNFotM9BK5fLKeOrsgWFixwOhzK+qr3C1i75kslkkM1mkcvlOpSPAKTgpdfrkcvlVC1jRqNRaa1iEVAabWprOy574QUgHJZqg8m++KL7gNXZ4Pm9+V+l/XN+/LF03dwMxON7lkcie8afNTUB++0H3HEH8POfS4GxqkoauN/aCvz0p9KyZ56RulKDQak1TAipIO0IO1mc+hG/Mfqo/diEUCikTJBsMBhG9XicwjpajY2NaGlpUYKW2+2G1+vtNmhls1mEw2FEo1HVNDYulwsej6fLxxcbUA9ILY5yK5d85mIqlUJbWxtyuZxqbFXhY5qbm5HL5ZQWL6PR2K+TPhMNdYXhqtD//rfnthB9C0/tH9Ob5yjctrPHbd6sHme2apV0Lf9f9cAD0vVNN0lBDJDKWVx6KfCvf0mFZf/yF+C116Tr7dulIDZ3rhQ8NRp2SZKEAauPPvnEinPOiUPOUYFAQLVeo9FAp9NBq9Uq1+0vGo2mx7fbLxsOXVTtC5a2tLQgHA4jEonA7XbD5/N1aBVqaWlBKBRCIpFQlhmNRng8Hrhcrr0KrvLk1WazGc3NzchkMqrWKrnUhkajQTabRVtbG9ra2jpUqQekMhJGo1EJXHKgG67zMg4UIaRBysGg1KoQiUhdU+Xle0oLyNd722AohDQGh/rfVVepx24BHQOW3NrU21+Pzn7lNZqua3b1tJ5XsX0E9gQuAPjgAylcAdLJAT/7mXT7ySelbkpAGt82fbp0O5UCFi2SWs3OPhtYuhT4wQ+kwJZKAfKQ3b6GUBoeGLD66K23HLjzTguuu64BNpsNOp0OqVQKra2tEEIoJQj6S2/CWV8CXamCnDwFT1lZmdKilUqllKDl8Xjg8XiU+QELx0yVlZXB6/X2acxTPi+Nu0il9lxaWvIIBJoQCCTQ1JRDJqNBJuOAEGbodDYIYUY2q0Frq/THNZPJI53OKZdsNo9MJodsFmhr03x1kYrOtrUBra1Z5HKtEEILIbRf7bMWgAaABvm8BkLI4/fQ5W1A+mLRaNTXxZb1ZF1frvvyGCGk457JAOm0dMlkpFPzQyEgEJB+Fj1hNncMXl3dBqSWg2hUamF5/PE9X4o08HI5dTjqaeBpv11nv/rtt+tLoGqvsGeis4H9hQpLYbz//p7bjzwidTE+8wywerV0/7bbgF/9SjqL8pVXpM/n//4njRm75hqpi/XII6UB/SefLB27XI5dksMZA9ZeeOopD04+OYZjj7UrU8cIIZDP55Uup8Lb8jq5AnxvbxdOuyKE6LRbq5T6EsrahzpAi0xGi3Rag3x+DFKpDBoa4mhqakMmk0ImU49MRoN02ohMxgyt1gqdzoa2NoMSjtJpdVjq7n67uqVf0QJwfHXpCe1XF1ZgLzWLRZo6xuWSvtSSyT21m+SarXJAkwtl0vDSfhhFf1SI7yoEdRXMOltXrCxF++WdbVOocPzXf/+75/YNN0jXF18s1foCgNNPlwLXQw9JlfpXrQJ+/GPprM9IRKr6/+MfSyHMZpNC2RVXSGdMjhnDUhVDGQNWH1VVZbF7twZ33VWJ+fP3/GbLXYP9MUZHDlydhbDehLVMJo+WFrllRyi302kNUikN0mkN0uk9wUi6rUEqJS3bc1taJ6/veFuD1tb2bfwWAM6SH5/O6HQCJlMeJpOA2SxgtWphs2lhNmtgsUC5mM3SJL5Go9Q9ZTB0fWm/jU6Xh0bThny+FblcK9rapOvW1iw0GqFqFQKEcluv18FoNMBo1MNkMsJo1MNgMEKn0wPQqlq25NPp2y/rbl0u13WLWamuAekYms0dL16vVAfJ7wcKKnB0kM1KQaswdHV3W+5R3rxZCmUbNvTzh4q6NWEC8Pzze+7Ln4/2fxrbdwG2v9/TFqf29raLsLcKH9tdg3vhtvX1e27L48H+/Gfpsw1I9b4iEfUcmtu2SYVjTzgBOOAA4OWXpZavRx6RzqSMRKTxYHIJC87SNTgYsProgQe244wz3Fi1yobXX2/Fqaf2/2u2tmoQjeqUsSvhsHQdj0u/jPKXjXy78CIvlwKVurDgQNLr8zCb9wQek6njfSkI5WE0StfF7hduV3i/2PO1/+Mi18La2+7T4q12emg0BlWXphACra2tysD6wkt3LZAGg0EZ32U0GpXxXgaD+jVGEqNRat3yePr+HNGo1Ip5553APfeUbt+o5xoagEMP3XNf/qi3D1DdBa7OyMG+8H7hr0Rn3X1Az1qwOgtbnbWalbKFrnA/IpGO63/3O+n6lVekC7CnCr7cSgYAv/mNdKbkk09Kvwdz5gCnnSbVHrv+eqkm2Zw5Xf/DQ33HgNVHY8a04qyzwli2zIfFi/U46qi9O3MknZb++960SapJU1+vvjQ07PmPppQ0mj0tOFar+rqz271ZZjLlkc3G0dISBpBW/piWl5fD7XYrBVpzuRySySRCoZAyr59Go0F5eflXU93ov2qBkyfZ1nx1X+4uFchmW3s07q2/u1Xlfe8qiOn1eqW0R2HrYuEZjXIwa21t7TDIHoBqcL1822g0cqA9pK5HQKp5dNRR0pgWGlzyr2ZvA1ZngSuXUweR9r/WfQlChes6ax3rbZAq9qvY3XOUKqz9/OfS9dlnS9cff7znLMl775Vafo87Tro+5BCpLMfrr0u/N8uWAZdfLrUGH3BA52eOUucYsPbCBRcE8dprDmzbZsS3vgX87W+Az9f1Y1papJoxn34KfPaZ9OHduFE61bcnv1QaDeB0SmFOvjidewb8ypf29+VL+0BkMvXPWSzZbFYpzZDL5aDTSWfpOZ1OeDyeDoVZ9Xo9TCYT3G43EokEAoEAMpkMEokEmpublcHw7bteW1tblQHzheUc3G433G43DAZDt12rPbndk+2KjZHrT9lsFtlsVjVfo0wOc3J3tVznS6/X96jlrti64eqkk6TaTU8+qe6yooElB5buAlVPW7TkLnFZW5t6267CV3f7CHQeynoyBmtvDcRryN3qr70mXcvdkwDw6qvS9dKl0rXfLw3CHzNGuv74Y+Cii6QB+kcdJR0flgnsiIdkL5SX5/H739dj4cJarFwpVUm+6CKpz9zlklqlduwAtm6VppP47DOphaqzXx6HQ5rwtLYWGDtW+jCPHStdqqqkLhOnc+gOahRCKKUYCsssGAwGpcxCd2PT5LkE7Xa7KmgFAgGEw2F4vV643W60trYiFAohHo8rwcZkMsHj8cDpdKrKOcjj4vqbfCJCKcJad7d7si+lPglib0926OlZq6UOcxoNcMop0u/lpEnS+JVnny3pS1APpNPSdfvA1P7j3NMWrL4GrPZdiZ11K/bkdqHOAllfWrAGawhHZwIB6R8UQOp2B6S5JAHgiCOkrsabbwYeewz4yU+kybsPPVT6B95gkLohGxul1rJoVApsowEDVh/JXwIzZrThgw+kfu0vvtgzI31X/H7ga1+Tml332Uea1HTqVKn1azg2FOTzecTjcYTDYaTlv6KQyix4PB6Ul5f3+kuzMGjF43EEAgFks1k0NjaiUZ6r4ytWqxVer7dPr1NKcjjo7yKznQU5uTSI3K3Y2tqq1PKSux73hvx6/a0/yo1otVo4HFrcdpsGmYwW55yjwZgxGtx/v/S798QT0tivtWulQfZUenIvd3dnF+5NwCpsRSl8XvnMVPlxhdsVPn/hdoW/Lj0Zm1XKqjwD0YJVKu+8I13feKN0fc010rU8hVEhk0kaG/nrXwP/+Q9w3XXS4PxTT5Xmmrz0UqkBwW4fmH3vbwxYfSS3Img0GsyYIdU6+ctfpK6Ideuk5leTSWp9mjBBat362tekS0XFoO56ybTvBgSk4yF3A5rN5r1+DTloaTQaNDY2qqbLAQC3243Kysp+DzVDSV+DnBzA5K7F9peeBDC5m1Gv16u6H+V96mt3bCH5fn92sY4fLwW5n/1MOo7f/a5UryyT0aGlRYemJh127DDCZNLgvfdMcDiA1auNCAS0qKvTIRqV6qBRz4XD0nX7ANtdPavO/mdqPwarrU3dul+4rjA4td+uMGwV7ltnLWCdBa/eBKzCxxX7NR5OAas35PI5v/yldC2HsFtvla7lkHbRRdLQmSuukOqLLVwofccefjiwYoXU7Z/LDf2K+QxYJaLXS5V6f/CDwd6T/tVdN6DT6SzZnH1yy1goFELmq99MjUYDi8WitNBEIhHE43H4fD643e5RFbR6S6PRwGAwwGAwoKzIaUO5XK7T8CWfeCC3hnX1/PJ0Ue2vOxt8L7fI7W23aXddsO1ryXUW5KxW6SL/I7T//tL1mWfu2aatDRBCg3BYj3BYB40G+OILC4QAdu40YvduAwIBA7ZsMSGf1yCRGKL9+gPs9tvVBTkBaVJmWT4vjVMtVBiOCmk06lDT2tp5F2H7lqnC7QrDVmHA6kmoKlzevpWscD/b6279aPfQQ9L1m29K13fcUXy7E08EvvwSOO88qSXtxhulk8JOPFEKc1ar9HkarLMkGbD20nAe/Nsb/dEN2JlcLodIJIJwOKx8mWu1WrjdbmUeQiEEYrEYAoEAWltb0dDQgFAopIzRYtDqPZ1OB4vFopzZWajwjEY5cBWGL3kGA3lZMRqNpkPoah/A+nus3N6EtWLb2O15jB8v3T/ggKaiQQ6QvohzOQ0aG/XIZrVIJrXYudOIYFCPWEyPujojkkkdNm40IRweuUWLrr9efV8IqUVC1tYmnUUty+f3DMZuz2pVn1ktzaaw537BnynVcq1WHaoKbxcWKO7JGKzCgFUY9gq3KfaRLnzcKPkK6Rf//Kd0LU9d9I9/qNdXVkpn4D/yiPTzmTlzYPePAWsv9WTA8XAmtxJFIpF+6waUtbW1IRwOIxwOK60Ler0eXq+3wwB5jUYDl8sFp9OJaDSKYDCoClo+n2+v5y6kPeRwZDQai7Z+yQGsMHwVXssBTK7/1Rk5dLW/FE6ivjdBvj8LARfqLJRNm9ZZoMt+db9JWZdK5ZFKSTXsAgEtdu7UIZHQYMcOPbZtMyIc1mHbNiNCoeEbyM44ow2ffLLna+i999rwzDN77ofDUvkaWeE0SxUV6ir/bW3q9YUTOhcGLL1eHYYKw05hKOssSHXWLdhZC1Z3AWukdgcOBQ0N0rU8X+RAY8DaSwMx6HegCSGU+QLjBXM+GAwGuN1uuFyuknUDAlDOCIxEIqozAr1eLxwOR5chSaPRwO12w+l0IhaLKUFr9+7dCAaDDFoDpDCAFSPX+Cps9SoMX3IXZOHtYrRabZcBTC5DMdgG9sxVuYZaHi0tArFYHrmcQCqVByBQXp7HySeX4fPPh96f+8IwBQDHHqu+f+CBWQDSZ8pszuOUU1oA2AAALlcz7rtPA0CarK+hIYpczqU8du3apLJtPN6ibGc2578KYtLnpLlZQJovVD3FTftWr2I6G7NVuLzY/+A9GTRPw9/Q+40bZkZSwMrn80gkEh0mXbZarfB4PLDb7SXtEs1kMgiFQojFYkqwMpvN8Pv9ve5ylLsQ5aAVCATQ1tbGoDVEaLXaLgNYsTMg2wcweW7P7lrB9Hp90RAmLx8qIawUCk940OulKYmKDfz92c+Ac84Z+P0r5ne/C2L58nJ88okZFRWt+PWvG3DDDZVoaJBa4g47rAnvvSfN3r1r157PSzqtxb//bVPuv/iiuiX1Jz/Zs87jacVvf7vnZ/zOOzHIAcvrTWLDBjPk4Pbxx7sAjAUArF5dr9zeuLERgDQQr7k5AkA6sJnMnuDW3LwnuKVSaQDmr5a3Qf56zef3BLhi2EU4cjFg7SW54vZwHosldwNGo1FlzJN89p7H4yk6JmdvpNNpBINBVeuY1WqFz+eDzWbbq2NZGLTkrkM5aMldh+3rZNHgKxyA35l8Pt9lAJO7IeWB+IX/JLSn0+mKhq/C+wPRAjVQnM6Oyx58EPjOd/ZMsSKbP18qOlk4+ByQ6hl10bjYqT/8QaoO/vzzwB//CFxwgQ9nnSXNt3fyyQbU1FRDCKm+0sUXC1x2WRkWLBB44w0NDjwwjyefzOJrXzMhl9PAbBb4wQ+yWL5cKlSs0wnkctLfi8Kxa+GwQXV/yZIxyu0PP7Qhldrz+/+Pf+w5OP/9754CyBs37nkPgcCeJikpVEkBKxZrhhywAoEmyAErEIgDkOZ6SiTSkOZf3SObzQGQPl/5/J5gRiOLRoz0QUQllkgk4HA4sHLlSths0i/ZjBkzhuUf48KzAeWPgV6vV6qgl7IbEABSqRQCgYCq8rjNZoPP5ys6rqcU8vm8KmgBUlen3KI1nIMxqcnFVQsDWLG6YD39k1fYHVksgBkMBqVExVCXy0mnwodC0gTAxx+/p+UkGAS+9S2pDtjddwNf/7o0jcqVV+55/LJl0nMUjmX5+telgq21tXuWzZsnlamRJzC+8UapAGVTE7BmjVRwsieHKxiUzgr75jelGSfuu08awLx0qTT2ato0afzT008D//qXFOIA6SzF666TblutUiDsSygEAINBoLVV2tl9923DF19Ifw8XLEjj1VelQHTyyc3429+kv12nnprAX/8qFXA68cQ4/vlPBwCgpiaDujr1zBVGYx7ZrBTyZsxIYd260v4TS51JAHAgHo/DPgDFthiweqlYwJoyZUqHqV+GqoHuBpTLOgSDQdV8ena7HT6fr+StY53pLGj5/X44nc5h8SVJe68whLUPX4XLetr1r9FoVF2P8nX7ZcNxuqFwGPjkE+DYY6UxSLt2AQcdJFX1/te/pJAmhFQwec0aqdL3WWcBK1dKgewHPwCOOaZ/9q2+Xtqnqipg926pcOWppwJHHw0ceaQU8p59VpoI+be/lc4mu/lmqb4SIG3z9tul3Se7fc8Zj9OnA+vXS7e1WoF8Xv2zN5kEMhlp2bRpOWzYMPz+QR+eGLCKuu222/DPf/4Tq1evhtFoRKx9+zWAuro6XHzxxXjrrbdgs9mwcOFCLFmyRNUSs2LFClx11VX44osvUF1djV/+8pdYtGhRj/ejWMCqra3ttxaYUmlra1POBhyIbkAhBJLJJILBIFoKCts4nU54vd6Snn3YG/l8HpFIBKFQiEGLOpXL5Yq2frUfE9ZTIyWIyd8WhbuYTkutROXlg7NPxeTzUgBrbpa6JufNk6YaO+88qaXugQeABQukKV4eeECqJv63vwHV1cDBB0u3Aan1rIue5pIwGjl7wMAZ2IA1bMZgZbNZnHbaaZgzZw4effTRDutzuRxOPPFEVFZW4oMPPsDu3btxzjnnwGAw4De/+Q0AYOvWrTjxxBNx0UUX4c9//jPeeOMNnH/++aiqqsL8+fP7vG+dFV4cCgrPBhyIbkAhBBKJBILBoFIvSy6p4PV6Ox3kPFC0Wq1SKysSiShnHdbX1yuD4Rm0SC7l0FXLtHxmZGH4ksd/Fd6Wx2l2d4Yk0DGIdRbKBqtrsthLms3SZSiRh1iWlakH98vz6QHSJMfRqNS69c1vArNnAz/8obRs61bg7LOByZOllrEZM6RwdvXV0mOPPRZ4443S7CvD1cg1bFqwZMuXL8cVV1zRoQXrlVdewbe+9S3s2rULFV+VYH7ooYdw7bXXIhgMwmg04tprr8U///lPrFmzRnncD37wA8RiMbwqTx/ejWItWFVVVfB4PKV5gyUgh5xwOKxqPbJYLEo3YKkHecuFP4PBoFJosn1x0KEon88jHA4jFAopLRJGoxF+v1+Zoodob7QPYp2Fsr60iPXkMtRbxYa6jRulli2DQepunDULGDdO6gI98kipeOUPfyhd77cf8PjjUmuZVquu00VDAVuw+mTlypXYf//9lXAFAPPnz8fFF1+ML774AgcddBBWrlyJefPmqR43f/58XHHFFZ0+b/tTwhNFygoPlRYsuQJ6JBJR/lPWaDSw2+3weDywWq0lf015bFMoFFJeU6fTKcGq1C1kpabVapVpduSuw2w2i507dyotWgxatDe6K1Ehk4NYsQDW1xYxoHirWFdhjNSmTt1zWx5AD0jj02SzZknzW0ajUqvX6adLY9F+9ztpYP7VVwOffQbcdBNwww3SY/bdF/jii4F5DzQ4hva3Xy80NDSowhUA5X7DV+VcO9smkUgglUoVHYe0ZMkS3HLLLV2+dk/+yPWnTCaDcDiMaDSqdAPKIcftdvdL61GxsUx6vR4ejwdut3vYnVWp0+mUoCVXk89kMgxaNGD6EsS6u8iV4nsaxqR6WnpV96Rc0qL9RN8MY3vIIayycs9ExlOm7Ome/OADqexFRYUUrMrKpLMvL78c+P73gf/+V5rO5dZbgZ//XHrM/vtLoYyGr0ENWNdddx3u6GwWx6+sW7cO06dPH6A96uj666/HVVddpdxPJBKorq5WbTMYAUseRB4Oh1Vn55nNZng8nm4roPeV3EpW2KVmMBiU6WyG+x9dnU4Hv98Pj8ejdB3KQSsQCHCMFg26ngYxYM9g/Z5c5Cl6uppPsv1+tG8B0+l0Re8Pl3IW/cVk2jN5+He+s2f5yy9L1wsXAvfcI203b57UvehyAUuWSCHtT3+SQtj550utYQcdJM3ZGA4P/HuhnhvUgLV48eJuz+CbOHFij56rsrIS//3vf1XLGhsblXXytbyscBu73d7pWXQmk6nbEgwDGbCEEIjH4wgGg6quy/Lycng8HpSVlfXLH7JcLqe07BSOVZJbdoZ7sGqvfdAKh8PIZrOor69XBa2R9r5pZOnJYH1gz9yJhYGrsFuy/QVAr8IYgG5D2GjuqtRopHAFAIccsmf5H/8oXR922J5l3/qWNAZszRrgww+BE06QWsJOPBF44QUpiJ14IvDUU4DPx3Fgg2lQA5bP54PP5yvJc82ZMwe33XYbAoEA/H4/AOC1116D3W7HPvvso2zzsvwvw1dee+01zJkzZ69eW64g3Z//oRUb66TVauFyueB2u/utDlexCZhH0yDwwqAlt9y1trZi165dqrMOR9sXAo0shZNg9ySM5XK5oq1j7ZfJ2wFQhbOe7E93IaywdWw0/f5NmyZdz54tXQDgpZek64sukgrCCgFccIFU2HX5culMRY9HKknx/e8D11wjdUGuXaue4JpKa9icRVhXV4dIJIK///3vWLp0Kd59910AwOTJk2Gz2ZDL5XDggQdizJgxuPPOO9HQ0IAf/vCHOP/881VlGvbbbz9ccskl+NGPfoQ333wTl112Gf75z3/2uExDsbMIAWDatGn9Ntap/VluOp0OHo8HHo+n38Y6tbW1KRMwy8HKZDLB7/eXvBjpcNLZ2DPOdUhUXD6fVwWv7oJZX76S5O7KwtDV1fVoP7MyHpfGga1dKw3Mt1ql4rDf/jZw7bVSwdYnnpBqdCWT0mVkYKHRohYtWoTHH3+8w/K33noLRx11FABg+/btuPjii7FixQqUlZVh4cKFuP322zsUGr3yyiuxdu1ajBs3DjfccEOfC43a7XYlfEycOLGkZ+l1Vnm8v8c6tba2KsGqcAJmn883qoNVe8V+Pnq9XqmxxaBF1HuFXZXFWsOKBbO+kFvsehPKRtvfvqYmqeuysRH4/HNpUP4f/gAcdxzwq18B3/iGFMLsduCr88iGAQasIa0wYPn9fqXO1NixY+Fyufb6+eV6UoFAQOkKHIhK49lsFqFQSHUmosVigc/nQ3l5+aj749JTnZWpkIPWcDubkmg4ad9dWaylrP11X7/y5C7Unoay0dBKlkhIY8fWrQNaWqQzJd99F6ipAR57DNhnH+D//T9pgH+74c+DhAFrSCsMWDU1NYhEIgAAr9erDKbvq5aWFuzevVuZI1Cv18Pv9/frpMTZbBbBYBCxWEz5w2O1WuHz+WCz2Ub8H4hSyefzSqFVBi2ioat9C1lPrvuifStZ+9vF1o3EUJZMSiHs7belwLVsmXQW5EMPAZMmAc88I3VRbt0qTXHUvxiwhrTCgDV9+nTs3LkTAGCz2TBhwoQ+PWcul0NDQwOi0SiAPcUvPR5Pv3U1ZTIZJVjJysrK4PP5+u1MxNGgWEX7gRgzR0T9Q24l600o25uv1Z4Esfa3h/PfayGkrsht26QgtmqVNAfkBx9Ig/P/9z9pnNiGDdLYsb3DSu7DRuGYq8KSCb3R1NSE+vp6ZRyP0+lERUVFv00tUyxY2Ww2JVjR3pHnXXQ6napyGoFAAKFQSAlaQ73CPRFJCs9o7AkhBIQQHYJXd7flUFZ45mVPFQ7y72mr2VAZJypnQ7l94pRTpOv25521tEhnSG7ZIk1b9N570vL33pOW/+c/UiD7qs1jSOBf+b1gMBhgtVrR0tKC1tZW5HK5HrdQ5PN57N69W2m1MhqNGDt2bL+FnGw2i0AgoApW5eXl8Pl8/TKFzmin0WjgdDrhcDiQSCQQCASUcBsOh5XJr4fqHI1E1DcajQYajabXE9vLZ1v2JpTJJ1nJNcl6u5897b4svAxWa5n8NfW1r0nXX1VfwoUX7tlGCKkAa3k58K9/AWPHAs89B4wZI9UImzlT6pocKL3uIjz//PNx9tlnK2fujTaFXYTf+MY3EAwGleKl48ePR3l5ebfPkc1mUVdXh/RXBUg8Hg8qKir65T8KeYyVHOQAqcXK7/czWA0geQLuYDCo/Nzl1i6v19vrP8ZERO27L3sa0PaGVqvtMoB1FtCGQjem/P09ZLsIg8EgFixYAJ/Phx/84Ac4++yz8TU5Uo5CNptNCVjNzc3dBqxkMokdO3YorV3V1dWqelqlwmA1tGg0GjgcDtjtdiSTSQSDQbS0tCiTczudTni9XpjN5sHeVSIaJnrbfQl0LIVRLKAVW1bYWpbP53s9g4kczLprIRtJ48t6HbD+9re/IRqN4rnnnsNTTz2Fu+++G9OnT8dZZ52FM888s88DvYcb+QNtNpuh0WgghEBTU1OnZxIKIRAOh5WJp81mM2pqakrectHa2qoEK7lxsqysDBUVFQxWQ4BGo0F5eTlsNhtaWloQDAaRTCYRi8UQi8Vgt9vh8/k6nbqJiGhvFFbt743CkhidhbJi60sdzHoSzobKGLO9Potw586d+L//+z889thj2LRpU4+nQhiu5CbGcDgMt9sNQCpw2tTUBACYOnVqh9CUz+dRX1+P+FenQDgcDowdO7akH4C2tjYEAoEOwcrv93Pw+hCXSqUQDAaRSCSUZfKJB1ardVj/B0dEo1v7YNaTcNaXgf7tFQtmLS0tmDFjxtDtIizU2tqKjz76CKtWrcK2bdtQIU8XPgoUNsk6nU4lYEUiEVUrVvvxVpWVlfB4PCX70szlcgiFQqq5Aq1WKyoqKhishgmLxYKamhqk02mEQiHEYjEkk0kkk0nWJCOiYa0v3ZhA8WDW04AGFG8xSw7wnD99ClhvvfUWnnrqKTz//PPI5/M49dRT8dJLL+GYY44p9f4NC4XjruQzxIxGI2KxGHbv3o18Pl/y8VbynHjBYFD5QFksFiVY8ct4+DGbzRg3bhz8fr9SVb+lpQXbt2/ndEVENKrsTTBrP8ZMvhSeRT8Qet1FOHbsWEQiESxYsABnnXUWTjrppG5nXx9JOjsLobCbUKPRQKvVqoJPTU1NSU7JLzaVjtFoREVFBb98Rxh5XshoNKq0ThqNRvh8vn6dNomIaCQa8mcR3nzzzTjttNPgdDr7YXeGL5fLpQQsuWlTrsju9Xr3+stQHkTf2NioFDUdiKl0aPAYDAZUVVXB5/MhHA4jEokgm82ivr4egUCg3yf+JiKivuNUOb3UWQIWQmDDhg1oa2tTJkg2m80l+fJrbm5GY2OjMrG0PMddf06lQ0NPLpdDJBJBOBxWTibhNDxERD0z5FuwqDiNRgO3241AIICmpib4/f69blVKp9NobGxUdT16PB74fD5+mY5COp1OmaMyGo0iFAqhtbVVmYaH1eGJiIYOBqwScrvdCIVCSKfTiMfjfe5GzWazaGxsVMo6yM/t8/n45UnQarXweDxwu92q+Q7lbkSHw8GipUREg4wBq4T0ej28Xi8CgQAaGhpgs9l6dQZEsVpWDocDfr9/VJ1IQD1TON9hYXV4uWgp55okIho8DFgl5vV6EYvFlMHINTU13XYVymeLRSIRJVjZbDZUVFSwojd1S64OX15erlSHb2pqUi6spUVENPA4yL2XejJILpVKYcuWLRBCoLy8HOPGjeswZkoIgZaWFkSjUcTjcSVYybWs+mN+Qho95KKlhZ8ts9kMr9cLh8PBoEVEo85AD3JnwOqlnv6AEokEduzYASEEtFotHA4HTCYThBDIZDJIJpOqaYXYykD9oVgtLYPBwBIPRDTqMGANcb35AbW0tKC+vl6pW9WeHLxcLhfHyVC/amtrU0o8yAVw5RIPbre719WSiYiGGwasIa63PyAhhDKvnNxiZTQaUVZWBqvVyhYEGlD5fF5V4gGQgj5LPBDRSMc6WCNM4QBkosHWvsSDXFaEJR6IiEqLAYtoFGpf4iEUCqG5uZklHoiISoQBi2gUa1/iIRQKIZFIsMQDEdFeYsAiIgDSmaw1NTXIZDIIhUKIxWJoaWnB9u3bYTKZ4PV64XQ6GbSIiHqAAYuIVEwmE8aOHQu/36+MzcpkMqivr0cgEIDH44HL5eJ8mEREXWDAIqKiDAYDKisr4fP5lBIPra2taGhoQDAYhNvthsfjYYkHIqIi+JeRiLqk0+ng8/ng8XgQi8UQCoWQzWYRDAYRCoWUEg9Go3Gwd5WIaMhgwCKiHtFqtXC73XC5XEgkEgiFQkilUohEIqoSD5w/k4iIAYuIekmj0cDhcMBut6O5uRmhUAjJZBLxeBzxeBw2mw1erxdlZWUcEE9EoxYDFhH1iUajgc1mg81mQyqVUiaXlmcusFgs8Hq9sNvtDFpENOoMm3labrvtNsydOxdWqxVOp7PD+k8//RRnnHEGqqurYbFYMGPGDNx3332qbVasWAGNRtPh0tDQMEDvgmhkslgsqK6uxtSpU+F2u6HRaJBKpbBjxw5s2rQJkUhEmWyaiGg0GDYtWNlsFqeddhrmzJmDRx99tMP6jz/+GH6/H08++SSqq6vxwQcf4MILL4ROp8Oll16q2nbDhg2qeYj8fn+/7z/RaGA0GjFmzBhViYdsNotdu3YpJR7cbjdLPBDRiDfsJntevnw5rrjiCsRisW63veSSS7Bu3Tq8+eabAKQWrKOPPhrRaLRoK1hPDPRkkUTDWS6XQzQaVUo8AHsGy3s8Hk4uTUQDZqC/v4dNF2FfxONxuN3uDssPPPBAVFVV4bjjjsP777/f5XNkMhkkEgnVhYh6RqfTwev1YurUqRg7dixMJhPy+TxCoRA2btyI+vp6ZDKZwd5NIqKSG7EB64MPPsAzzzyDCy+8UFlWVVWFhx56CM8//zyef/55VFdX46ijjsInn3zS6fMsWbIEDodDuVRXVw/E7hONKBqNBi6XC5MnT0ZNTQ2sViuEEIhGo9i0aRPq6urQ0tIy2LtJRFQyg9pFeN111+GOO+7ocpt169Zh+vTpyv2edBGuWbMGRx99NC6//HL88pe/7PL5jzzySNTU1OCJJ54ouj6Tyaj+w04kEqiurmYXIdFeamlpQTAYRFNTk7KMk0sTUX8Z6C7CQR3kvnjxYixatKjLbSZOnNir51y7di2OPfZYXHjhhd2GKwCYNWsW3nvvvU7Xm0wmmEymXu0DEXXParVi/PjxSKfTSomHwsmlfT4fHA4HgxYRDUuDGrB8Ph98Pl/Jnu+LL77AMcccg4ULF+K2227r0WNWr16Nqqqqku0DEfWO2WzGuHHjUFFRgVAohGg0ikwmg507d6KxsRFerxculwta7Ygd0UBEI9CwKdNQV1eHSCSCuro65HI5rF69GgAwefJk2Gw2rFmzBscccwzmz5+Pq666SqltJc+jBgD33nsvamtrse+++yKdTuORRx7Bm2++iX//+9+D9baI6CsGgwFVVVVKiQf5zMPdu3cjEAhwcmkiGlaGzV+qG2+8EY8//rhy/6CDDgIAvPXWWzjqqKPwl7/8BcFgEE8++SSefPJJZbvx48dj27ZtAKRaWosXL0Z9fT2sVisOOOAAvP766zj66KMH9L0QUed0Oh38fj+8Xi8nlyaiYWvY1cEabKyDRTSwhBBIJBIIBoNIp9PKck4uTUS9MaoGuRMRdaf95NLBYBDNzc2cXJqIhjQGLCIaFji5NBENJwxYRDTsyJNLywPio9GoMrm00WiE1+uF0+nkmYdENGgYsIho2DKZTKrJpcPhMCeXJqIhgQGLiIY9vV6PiooKeL1eRKNRhEIhtLW1obGxEcFgkJNLE9GAY8AiohFDnlza7XYjHo8jFAohk8kgFAohHA7D6XTC6/VydgYi6ncMWEQ04mi1WrhcLjidTjQ1NSEUCqGlpQXRaBTRaBR2ux1erxdWq3Wwd5WIRigGLCIasTQaDex2u1LiIRQKoampCYlEAolEAmVlZfD5fCzxQEQlx4BFRKNCWVkZysrKlMmlY7EYmpub0dzcDIvFAp/Ph/LycgYtIioJBiwiGlXkyaX9fr8yuXQqlUJdXR1MJhN8Ph8cDgeDFhHtFRaJIaJRyWg0YsyYMZg6dSq8Xi+0Wi0ymQx27tyJTZs2IRKJIJ/PD/ZuEtEwxRYsIhrVDAYDKisr4fP5itbSks9KZNFSIuoNBiwiIkglHvx+Pzwej6qWVkNDA4LBIDweDzweD4uWElGPMGARERUorKUVi8UQDAbR2tqKQCCAUCgEt9sNr9cLvZ5/Pomoc/wLQURUhFarhdvthsvlQjweRzAYVBUtdblc8Hq9MBqNg72rRDQEMWAREXVBo9HA6XTC4XCgqakJwWAQqVQKkUgEkUgETqcTPp+P1eGJSIUBi4ioB+SipeXl5WhubkYwGERzczNisRhisRjsdjt8Ph8sFstg7yoRDQEMWEREvaDRaGCz2WCz2dDS0oJgMKiqDm+z2ZTq8EQ0ejFgERH1kdVqxfjx45FOpxEMBhGPx5FMJpFMJmG1WuHz+WCz2Vi0lGgUYsAiItpLZrMZ1dXVSnX4WCyGlpYWbN++HWazGX6/n9PwEI0yDFhERCViMpkwduxYJWhFIhGk02lOw0M0CrE0MRFRiRkMBlRVVWHatGnw+XwdpuGJRqMQQgz2bhJRP2ILFhFRP9Hr9aioqIDX61VNw1NfX49AIACfzwen08lpeIhGIAYsIqJ+VjgNTyQSQSgUQmtrqzLfoc/ng8vlYtAiGkEYsIiIBohOp4PP51MFrba2NuzevVs1sTTnOyQa/hiwiIgGmFarLTrfYWNjI0KhECeWJhoBGLCIiAZJ4XyHctDKZrPKxNJy0OLE0kTDD39riYgGmUajgcvlgtPpVE0sHQwGEQ6H4Xa74fF4YDAYBntXiaiHGLCIiIaI9hNLBwIBpNNphEIhhMNhuFwueL1eGI3Gwd5VIuoGAxYR0RBTOLF0MplEIBBAKpVCJBJBNBqF0+mEz+dj0CIawhiwiIiGKI1Gg/LycthsNjQ3NyMYDKK5uRnRaFQVtEwm02DvKhG1M2yKrtx2222YO3curFYrnE5n0W00Gk2Hy9NPP63aZsWKFTj44INhMpkwefJkLF++vP93nohoL2g0GthsNtTW1qK2thY2mw0AEIvFsGnTJtTV1SGdTg/yXhJRoWETsLLZLE477TRcfPHFXW63bNky7N69W7mccsopyrqtW7fixBNPxNFHH43Vq1fjiiuuwPnnn49//etf/bz3RESlUVZWhgkTJmDSpEkoLy8HACQSCWzevBnbt29HKpUa5D0kImAYdRHecsstANBti5PT6URlZWXRdQ899BBqa2tx1113AQBmzJiB9957D/fccw/mz59f0v0lIupPFosF48ePRzqdRjAYRDweR1NTE5qammCz2eDz+VBWVjbYu0k0ag2bFqyeuuSSS+D1ejFr1iw89thjqglVV65ciXnz5qm2nz9/PlauXNnp82UyGSQSCdWFiGioMJvNqK6uxpQpU5ThE8lkElu3bsXWrVuRTCY5sTTRIBg2LVg9ceutt+KYY46B1WrFv//9b/zkJz9BMpnEZZddBgBoaGhARUWF6jEVFRVIJBJIpVKwWCwdnnPJkiVK6xkR0VBlMpkwbtw4+P1+BINBxGIxNDc3o7m5GVarFX6/H2VlZdBoNIO9q0SjwqC2YF133XVFB6YXXtavX9/j57vhhhtw6KGH4qCDDsK1116La665BkuXLt2rfbz++usRj8eVy44dO/bq+YiI+pPRaMTYsWMxZcoUuN1uaDQatLS0YNu2bdiyZQuamprYokU0AAa1BWvx4sVYtGhRl9tMnDixz88/e/Zs/OpXv0Imk4HJZEJlZSUaGxtV2zQ2NsJutxdtvQKk/wp5CjQRDTdGoxFjxoyBz+dDKBRCJBJBKpXC9u3bYbFY4PP5UF5ezhYton4yqAHL5/PB5/P12/OvXr0aLpdLCUhz5szByy+/rNrmtddew5w5c/ptH4iIBpPBYEBVVRW8Xq8qaNXV1cFsNsPv9zNoEfWDYTMGq66uDpFIBHV1dcjlcli9ejUAYPLkybDZbPjHP/6BxsZGfOMb34DZbMZrr72G3/zmN7j66quV57jooovwwAMP4JprrsGPfvQjvPnmm3j22Wfxz3/+c5DeFRHRwJCDVmGLVjqdRl1dHUwmE/x+P+x2O4MWUYloxDDpjF+0aBEef/zxDsvfeustHHXUUXj11Vdx/fXXY/PmzRBCYPLkybj44otxwQUXQKvdM9RsxYoVuPLKK7F27VqMGzcON9xwQ7fdlIUSiQQcDgfi8Tjsdnsp3hoR0YBra2tDOBxGOBxGPp8HIA2J8Pl8cDgcDFo04gz09/ewCVhDBQMWEY0kuVwO4XAYoVBICVpGoxE+nw9Op5NBi0aMgf7+HjZdhEREVHo6nQ5+vx8ej0dp0cpms6ivr0cwGGTQIuojBiwiIlIFrUgkglAopAStQCCgBK3CIRdE1DkGLCIiUuh0Ovh8PlXQam1txa5duxAMBuH1euFyuRi0iLrBgEVERB1otVp4vV643W5V0Nq9e7fSdcigRdQ5BiwiIupUYdCKRqMIBoNoa2tTgpa8jkGLSI0Bi4iIuqXVauHxeOByuRCLxRAMBtHa2oqGhgZV0NLpdIO9q0RDAgMWERH1mFarhdvtVoJWIBBAa2srGhsbEQqF4PF44PF4GLRo1GPAIiKiXtNoNHC5XHA6nUqLVjabRSAQQCgUgtfrZdCiUY0Bi4iI+qwwaMXjcQSDQWQyGSVoyS1aej2/bmh04SeeiIj2mkajgdPphMPhQCKRQCAQQCaTQTAYRDgchtvthtfrZdCiUYOfdCIiKhmNRgOHwwG73Y5EIoFgMIh0Oo1QKIRwOAyPx8OgRaMCP+FERFRyhUGrqakJgUBAFbTkFi2DwTDYu0rULxiwiIio32g0GtjtdpSXlyOZTCIQCCCVSiEcDiMSicDlcsHn8zFo0YjDgEVERP1Oo9GgvLwcNpsNzc3NCAQCaGlpQSQSQTQahcvlgtfrhdFoHOxdJSoJBiwiIhowGo0GNpsNZWVlRYOW0+mEz+dj0KJhjwGLiIgGnBy0Clu0mpubEY1GlRYtBi0azhiwiIhoUJWVlaG2trZo0JJbtEwm02DvJlGvMGAREdGQIAetlpYWBAIBJJNJxGIxxGIxBi0adhiwiIhoSLFarZgwYQKDFg1rDFhERDQkdRW0HA4H/H4/gxYNWQxYREQ0pMlBK5VKIRAIoKmpCfF4HPF4HA6HAz6fD2azebB3k0iFAYuIiIYFi8WC8ePHFw1adrsdfr+fQYuGDAYsIiIaVgqDVjAYRCKRUC4MWjRUMGAREdGwZLFYUFNTg3Q6jUAg0CFo+Xw+WCyWwd5NGqUYsIiIaFgzm82dBq3y8nL4/X4GLRpwDFhERDQiFAatYDCIeDyOpqYmNDU1MWjRgGPAIiKiEcVsNqO6uho+n69o0PL5fLBarYO9mzTCMWAREdGIJActv9+PQCCgClo2mw1+v59Bi/oNAxYREY1oJpNJCVrBYBCxWAzJZBLJZJJBi/oNAxYREY0KJpMJ48aNU7oOC4NWWVkZ/H4/ysrKBns3aYTQDvYOEBERDSQ5aE2dOhUulwsA0NzcjK1bt2Lr1q1obm4e5D2kkWDYBKzbbrsNc+fOhdVqhdPp7LB++fLl0Gg0RS+BQAAAsGLFiqLrGxoaBvjdEBHRYDMajRg7diyDFvWLYdNFmM1mcdppp2HOnDl49NFHO6w//fTTsWDBAtWyRYsWIZ1Ow+/3q5Zv2LABdrtdud9+PRERjR5y0CrsOpSDltVqVboONRrNYO8qDSPDJmDdcsstAKSWqmIsFouqvkkwGMSbb75ZNIz5/f6irWBERDR6FQatUCiEaDSKlpYWbNu2jUGLem3YdBH21v/7f/8PVqsV3/ve9zqsO/DAA1FVVYXjjjsO77//fpfPk8lkVFWBE4lEf+0yERENAUajEWPGjMHUqVPhdruh0WiUoLV161Y0NTVBCDHYu0lD3IgNWI8++ijOPPNMVatWVVUVHnroITz//PN4/vnnUV1djaOOOgqffPJJp8+zZMkSOBwO5VJdXT0Qu09ERIPMYDAoQcvj8ShBa/v27diyZQuDFnVJIwbx03Hdddfhjjvu6HKbdevWYfr06cr95cuX44orrkAsFuv0MStXrsTcuXPx0UcfYebMmV0+/5FHHomamho88cQTRddnMhlkMhnlfiKRQHV1NeLxuGocFxERjWytra0IhUKIRCJKsLJYLPD7/bDZbOw6HOISiQQcDseAfX8P6hisxYsXY9GiRV1uM3HixF4/7yOPPIIDDzyw23AFALNmzcJ7773X6XqTyQSTydTrfSAiopHFYDCgqqoKXq9XCVqpVArbt2+HxWKBz+dDeXk5gxYBGOSA5fP54PP5SvqcyWQSzz77LJYsWdKj7VevXo2qqqqS7gMREY1cctCSB8OHw2GkUinU1dXBbDbD7/czaNHwOYuwrq4OkUgEdXV1yOVyWL16NQBg8uTJsNlsynbPPPMM2tracPbZZ3d4jnvvvRe1tbXYd999kU6n8cgjj+DNN9/Ev//974F6G0RENELo9XpUVlaqWrTS6TSDFgEYRgHrxhtvxOOPP67cP+iggwAAb731Fo466ihl+aOPPopTTz21aBmGbDaLxYsXo76+HlarFQcccABef/11HH300f29+0RENEJ1F7R8Ph/sdjuD1igzqIPch6OBHiRHRETDS1tbG8LhMMLhMPL5PABpPK/f72fQGkSjapA7ERHRSKPX61FRUQGPx6MErUwmgx07djBojSIMWERERP1ADlpy12H7oOXz+eBwOBi0RqgRW2iUiIhoKNDpdKioqMC0adPg9/uh1WqRyWSwc+dObN68GbFYjAVLRyC2YBEREQ0AnU4Hv9/foetw586dCAQC8Pl8cDqdbNEaIRiwiIiIBlBh0IpEIgiFQshms6ivr0cwGGTQGiEYsIiIiAaBTqeDz+eD2+3uELQCgQD8fj+D1jDGgEVERDSIigWt1tZWJWjJLVpaLYdNDycMWEREREOAHLTkrsNgMIjW1lbs2rVL1XXIoDU8MGARERENIVqtFl6vt0OLFoPW8MKARURENAQVBq1oNNqhRcvr9cLlcjFoDVEMWEREREOYVquFx+OBy+VSBa3du3crLVoMWkMPAxYREdEwUCxotbW1MWgNUQxYREREwwiD1vDAgEVERDQMFQatWCzWoetQHr/FoDU4GLCIiIiGMa1WC7fbDafTqQpaDQ0NCIVCDFqDhAGLiIhoBGDQGloYsIiIiEaQroKWPEaLQav/MWARERGNQHLQksdoBQIBVdDyer3weDwMWv2EAYuIiGgE02g0cLlcSouWHLQaGxtVXYc6nW6wd3VEYcAiIiIaBdoHrWAwiGw2y6DVTxiwiIiIRhEGrYHBgEVERDQKdRe0PB4PPB4Pg1YfMWARERGNYoVBKx6PIxAIIJvNIhAIIBwOM2j1EQMWERERQaPRwOl0wuFwIB6PIxgMIpPJIBAIKF2HDFo9x4BFRERECgat0mDAIiIiog4Kg1YikUAgEFAFLY/HA6/Xy6DVCQYsIiIi6pRGo4HD4YDdblcFrWAwqBqjpdczUhTi0SAiIqJuMWj1Do8CERER9Vj7oBUMBpFOpxm02hnd756IiIj6pDBoNTU1IRAIqIKW2+2G1+sdtUFrdL5rIiIiKgmNRgO73Y7y8nJV0AqFQohEIqM2aA2LKbS3bduG8847D7W1tbBYLJg0aRJuuukmZLNZ1XafffYZDj/8cJjNZlRXV+POO+/s8FzPPfccpk+fDrPZjP333x8vv/zyQL0NIiKiEUsOWpMmTUJNTQ3MZjPy+TxCoRA2btyIhoYGtLW1DfZuDphhEbDWr1+PfD6Phx9+GF988QXuuecePPTQQ/j5z3+ubJNIJHD88cdj/Pjx+Pjjj7F06VLcfPPN+OMf/6hs88EHH+CMM87Aeeedh//973845ZRTcMopp2DNmjWD8baIiIhGnK6C1oYNG0ZN0NIIIcRg70RfLF26FH/4wx+wZcsWAMAf/vAH/OIXv0BDQwOMRiMA4LrrrsOLL76I9evXAwBOP/10NDc346WXXlKe5xvf+AYOPPBAPPTQQz163UQioRRfs9vtJX5XREREI4sQAslkEoFAAKlUCoAUwuSuQ4PBMCD7MdDf38OiBauYeDwOt9ut3F+5ciWOOOIIJVwBwPz587FhwwZEo1Flm3nz5qmeZ/78+Vi5cmWnr5PJZJBIJFQXIiIi6hmNRoPy8nJMnDgR48ePh8VigRAC4XAYGzduxO7du9Ha2jrYu1lywzJgbd68Gffffz9+/OMfK8saGhpQUVGh2k6+39DQ0OU28vpilixZAofDoVyqq6tL9TaIiIhGjdEWtAY1YF133XXQaDRdXuTuPVl9fT0WLFiA0047DRdccEG/7+P111+PeDyuXHbs2NHvr0lERDRSjZagNajnTC5evBiLFi3qcpuJEycqt3ft2oWjjz4ac+fOVQ1eB4DKyko0Njaqlsn3Kysru9xGXl+MyWSCyWTq9r0QERFRz8lBy2azobm5GYFAAC0tLQiHw4hEInC5XPD5fAM2RqvUBjVg+Xw++Hy+Hm1bX1+Po48+GjNnzsSyZcug1aob3+bMmYNf/OIXaG1tVX4Yr732GqZNmwaXy6Vs88Ybb+CKK65QHvfaa69hzpw5pXlDRERE1CsajQY2mw1lZWWqoBWJRBCNRodt0BoWY7Dq6+tx1FFHoaamBr/97W8RDAbR0NCgGjt15plnwmg04rzzzsMXX3yBZ555Bvfddx+uuuoqZZvLL78cr776Ku666y6sX78eN998Mz766CNceumlg/G2iIiI6Cty0KqtrcWECRNgtVohhEAkEsHGjRuxa9euDvUvh7JhUaZh+fLlOPfcc4uuK9z9zz77DJdccgk+/PBDeL1e/PSnP8W1116r2v65557DL3/5S2zbtg1TpkzBnXfeiW9+85s93heWaSAiIup/QghVixYghTCXywWv16uqGtATA/39PSwC1lDCgEVERDSwkskkgsEgmpubAUhBy+l0wufz9ThoDfT39+iaGIiIiIiGHZvNphoM39zcjGg0ilgs1uugNVAYsIiIiGhYKCsrQ21tbYegVTgYfqgELQYsIiIiGla6ClpOpxN+v3/QgxYDFhEREQ1LctBqaWlBIBBAMplELBZTdR0OVi1LBiwiIiIa1qxWKyZMmNBl0BpoDFhEREQ0InQVtHQ63YDuCwMWERERjSjFglY8Hh/QfWDAIiIiohGpMGht2bJlQF97WEyVQ0RERNRXVqsVNTU1A/qaDFhEREREJcaARURERFRiDFhEREREJcaARURERFRiDFhEREREJcaARURERFRiDFhEREREJcaARURERFRiDFhEREREJcaARURERFRiDFhEREREJcaARURERFRiDFhEREREJcaARURERFRi+sHegeFGCAEASCQSg7wnRERE1FPy97b8Pd7fGLB6KRwOAwCqq6sHeU+IiIiot8LhMBwOR7+/DgNWL7ndbgBAXV3dgPyARrJEIoHq6mrs2LEDdrt9sHdnWOOxLA0ex9LhsSwdHsvSiMfjqKmpUb7H+xsDVi9ptdKwNYfDwQ96idjtdh7LEuGxLA0ex9LhsSwdHsvSkL/H+/11BuRViIiIiEYRBiwiIiKiEmPA6iWTyYSbbroJJpNpsHdl2OOxLB0ey9LgcSwdHsvS4bEsjYE+jhoxUOcrEhEREY0SbMEiIiIiKjEGLCIiIqISY8AiIiIiKjEGLCIiIqISY8Dqpd///veYMGECzGYzZs+ejf/+97+DvUtDys033wyNRqO6TJ8+XVmfTqdxySWXwOPxwGaz4bvf/S4aGxtVz1FXV4cTTzwRVqsVfr8fP/vZz9DW1jbQb2XAvfPOOzjppJMwZswYaDQavPjii6r1QgjceOONqKqqgsViwbx587Bp0ybVNpFIBGeddRbsdjucTifOO+88JJNJ1TafffYZDj/8cJjNZlRXV+POO+/s77c2oLo7josWLerwGV2wYIFqGx5HYMmSJfj617+O8vJy+P1+nHLKKdiwYYNqm1L9Pq9YsQIHH3wwTCYTJk+ejOXLl/f32xtQPTmWRx11VIfP5UUXXaTahscS+MMf/oADDjhAKbo6Z84cvPLKK8r6IfWZFNRjTz/9tDAajeKxxx4TX3zxhbjggguE0+kUjY2Ng71rQ8ZNN90k9t13X7F7927lEgwGlfUXXXSRqK6uFm+88Yb46KOPxDe+8Q0xd+5cZX1bW5vYb7/9xLx588T//vc/8fLLLwuv1yuuv/76wXg7A+rll18Wv/jFL8Rf//pXAUC88MILqvW33367cDgc4sUXXxSffvqp+Pa3vy1qa2tFKpVStlmwYIH42te+Jv7zn/+Id999V0yePFmcccYZyvp4PC4qKirEWWedJdasWSP+7//+T1gsFvHwww8P1Nvsd90dx4ULF4oFCxaoPqORSES1DY+jEPPnzxfLli0Ta9asEatXrxbf/OY3RU1NjUgmk8o2pfh93rJli7BareKqq64Sa9euFffff7/Q6XTi1VdfHdD32596ciyPPPJIccEFF6g+l/F4XFnPYyn5+9//Lv75z3+KjRs3ig0bNoif//znwmAwiDVr1gghhtZnkgGrF2bNmiUuueQS5X4ulxNjxowRS5YsGcS9Glpuuukm8bWvfa3oulgsJgwGg3juueeUZevWrRMAxMqVK4UQ0pejVqsVDQ0NyjZ/+MMfhN1uF5lMpl/3fShpHwzy+byorKwUS5cuVZbFYjFhMpnE//3f/wkhhFi7dq0AID788ENlm1deeUVoNBpRX18vhBDiwQcfFC6XS3Usr732WjFt2rR+fkeDo7OAdfLJJ3f6GB7H4gKBgAAg3n77bSFE6X6fr7nmGrHvvvuqXuv0008X8+fP7++3NGjaH0shpIB1+eWXd/oYHsvOuVwu8cgjjwy5zyS7CHsom83i448/xrx585RlWq0W8+bNw8qVKwdxz4aeTZs2YcyYMZg4cSLOOuss1NXVAQA+/vhjtLa2qo7h9OnTUVNToxzDlStXYv/990dFRYWyzfz585FIJPDFF18M7BsZQrZu3YqGhgbVsXM4HJg9e7bq2DmdThxyyCHKNvPmzYNWq8WqVauUbY444ggYjUZlm/nz52PDhg2IRqMD9G4G34oVK+D3+zFt2jRcfPHFCIfDyjoex+Li8TiAPRPel+r3eeXKlarnkLcZyX9X2x9L2Z///Gd4vV7st99+uP7669HS0qKs47HsKJfL4emnn0ZzczPmzJkz5D6TnOy5h0KhEHK5nOqHAgAVFRVYv379IO3V0DN79mwsX74c06ZNw+7du3HLLbfg8MMPx5o1a9DQ0ACj0Qin06l6TEVFBRoaGgAADQ0NRY+xvG60kt97sWNTeOz8fr9qvV6vh9vtVm1TW1vb4TnkdS6Xq1/2fyhZsGABTj31VNTW1uLLL7/Ez3/+c5xwwglYuXIldDodj2MR+XweV1xxBQ499FDst99+AFCy3+fOtkkkEkilUrBYLP3xlgZNsWMJAGeeeSbGjx+PMWPG4LPPPsO1116LDRs24K9//SsAHstCn3/+OebMmYN0Og2bzYYXXngB++yzD1avXj2kPpMMWFRSJ5xwgnL7gAMOwOzZszF+/Hg8++yzI+aXm4a3H/zgB8rt/fffHwcccAAmTZqEFStW4Nhjjx3EPRu6LrnkEqxZswbvvffeYO/KsNfZsbzwwguV2/vvvz+qqqpw7LHH4ssvv8SkSZMGejeHtGnTpmH16tWIx+P4y1/+goULF+Ltt98e7N3qgF2EPeT1eqHT6TqcjdDY2IjKyspB2quhz+l0YurUqdi8eTMqKyuRzWYRi8VU2xQew8rKyqLHWF43WsnvvavPX2VlJQKBgGp9W1sbIpEIj28XJk6cCK/Xi82bNwPgcWzv0ksvxUsvvYS33noL48aNU5aX6ve5s23sdvuI+6ess2NZzOzZswFA9bnksZQYjUZMnjwZM2fOxJIlS/C1r30N991335D7TDJg9ZDRaMTMmTPxxhtvKMvy+TzeeOMNzJkzZxD3bGhLJpP48ssvUVVVhZkzZ8JgMKiO4YYNG1BXV6ccwzlz5uDzzz9XfcG99tprsNvt2GeffQZ8/4eK2tpaVFZWqo5dIpHAqlWrVMcuFovh448/VrZ58803kc/nlT/Wc+bMwTvvvIPW1lZlm9deew3Tpk0bcd1aPbVz506Ew2FUVVUB4HGUCSFw6aWX4oUXXsCbb77ZoUu0VL/Pc+bMUT2HvM1I+rva3bEsZvXq1QCg+lzyWBaXz+eRyWSG3meyb2P2R6enn35amEwmsXz5crF27Vpx4YUXCqfTqTobYbRbvHixWLFihdi6dat4//33xbx584TX6xWBQEAIIZ1CW1NTI958803x0UcfiTlz5og5c+Yoj5dPoT3++OPF6tWrxauvvip8Pt+oKNPQ1NQk/ve//4n//e9/AoC4++67xf/+9z+xfft2IYRUpsHpdIq//e1v4rPPPhMnn3xy0TINBx10kFi1apV47733xJQpU1TlBWKxmKioqBA//OEPxZo1a8TTTz8trFbriCov0NVxbGpqEldffbVYuXKl2Lp1q3j99dfFwQcfLKZMmSLS6bTyHDyOQlx88cXC4XCIFStWqEoHtLS0KNuU4vdZPiX+Zz/7mVi3bp34/e9/P+JKC3R3LDdv3ixuvfVW8dFHH4mtW7eKv/3tb2LixIniiCOOUJ6Dx1Jy3XXXibffflts3bpVfPbZZ+K6664TGo1G/Pvf/xZCDK3PJANWL91///2ipqZGGI1GMWvWLPGf//xnsHdpSDn99NNFVVWVMBqNYuzYseL0008XmzdvVtanUinxk5/8RLhcLmG1WsV3vvMdsXv3btVzbNu2TZxwwgnCYrEIr9crFi9eLFpbWwf6rQy4t956SwDocFm4cKEQQirVcMMNN4iKigphMpnEscceKzZs2KB6jnA4LM444wxhs9mE3W4X5557rmhqalJt8+mnn4rDDjtMmEwmMXbsWHH77bcP1FscEF0dx5aWFnH88ccLn88nDAaDGD9+vLjgggs6/JPE4yiKHkMAYtmyZco2pfp9fuutt8SBBx4ojEajmDhxouo1RoLujmVdXZ044ogjhNvtFiaTSUyePFn87Gc/U9XBEoLHUgghfvSjH4nx48cLo9EofD6fOPbYY5VwJcTQ+kxqhBCid21eRERERNQVjsEiIiIiKjEGLCIiIqISY8AiIiIiKjEGLCIiIqISY8AiIiIiKjEGLCIiIqISY8AiIiIiKjEGLCIiIqISY8AioiFvxYoV0Gg0HSZxHShvvPEGZsyYgVwu12+v8Y1vfAPPP/98vz0/EQ0sVnInoiHlqKOOwoEHHoh7771XWZbNZhGJRFBRUQGNRjPg+zRz5kxcddVVOOuss/rtNV566SVceeWV2LBhA7Ra/u9LNNzxt5iIhjyj0YjKyspBCVfvvfcevvzyS3z3u9/t19c54YQT0NTUhFdeeaVfX4eIBgYDFhENGYsWLcLbb7+N++67DxqNBhqNBtu2bevQRbh8+XI4nU689NJLmDZtGqxWK773ve+hpaUFjz/+OCZMmACXy4XLLrtM1a2XyWRw9dVXY+zYsSgrK8Ps2bOxYsWKLvfp6aefxnHHHQez2awsu/nmm3HggQfiscceQ01NDWw2G37yk58gl8vhzjvvRGVlJfx+P2677TblMUII3HzzzaipqYHJZMKYMWNw2WWXKet1Oh2++c1v4umnny7NwSSiQaUf7B0gIpLdd9992LhxI/bbbz/ceuutAACfz4dt27Z12LalpQW/+93v8PTTT6OpqQmnnnoqvvOd78DpdOLll1/Gli1b8N3vfheHHnooTj/9dADApZdeirVr1+Lpp5/GmDFj8MILL2DBggX4/PPPMWXKlKL79O677+LMM8/ssPzLL7/EK6+8gldffRVffvklvve972HLli2YOnUq3n77bXzwwQf40Y9+hHnz5mH27Nl4/vnncc899+Dpp5/Gvvvui4aGBnz66aeq55w1axZuv/32vTyKRDQUMGAR0ZDhcDhgNBphtVpRWVnZ5batra34wx/+gEmTJgEAvve97+GJJ55AY2MjbDYb9tlnHxx99NF46623cPrpp6Ourg7Lli1DXV0dxowZAwC4+uqr8eqrr2LZsmX4zW9+U/R1tm/frmxfKJ/P47HHHkN5ebnyWhs2bMDLL78MrVaLadOm4Y477sBbb72F2bNno66uDpWVlZg3bx4MBgNqamowa9Ys1XOOGTMGO3bsQD6f5zgsomGOv8FENCxZrVYlXAFARUUFJkyYAJvNploWCAQAAJ9//jlyuRymTp0Km82mXN5++218+eWXnb5OKpVSdQ/KJkyYgPLyctVr7bPPPqpgVPj6p512GlKpFCZOnIgLLrgAL7zwAtra2lTPabFYkM/nkclkenk0iGioYQsWEQ1LBoNBdV+j0RRdls/nAQDJZBI6nQ4ff/wxdDqdarvCUNae1+tFNBrd69evrq7Ghg0b8Prrr+O1117DT37yEyxduhRvv/228rhIJIKysjJYLJau3joRDQMMWEQ0pBiNxn6pN3XQQQchl8shEAjg8MMP79Xj1q5dW5J9sFgsOOmkk3DSSSfhkksuwfTp0/H555/j4IMPBgCsWbMGBx10UElei4gGFwMWEQ0pEyZMwKpVq7Bt2zbYbDa43e6SPO/UqVNx1lln4ZxzzsFdd92Fgw46CMFgEG+88QYOOOAAnHjiiUUfN3/+fDz++ON7/frLly9HLpfD7NmzYbVa8eSTT8JisWD8+PHKNu+++y6OP/74vX4tIhp8HINFREPK1VdfDZ1Oh3322Qc+nw91dXUle+5ly5bhnHPOweLFizFt2jSccsop+PDDD1FTU9PpY8466yx88cUX2LBhw169ttPpxJ/+9CcceuihOOCAA/D666/jH//4BzweDwCgvr4eH3zwAc4999y9eh0iGhpYyZ2IqBs/+9nPkEgk8PDDD/fba1x77bWIRqP44x//2G+vQUQDhy1YRETd+MUvfoHx48crA9b7g9/vx69+9at+e34iGlhswSIiIiIqMbZgEREREZUYAxYRERFRiTFgEREREZUYAxYRERFRiTFgEREREZUYAxYRERFRiTFgEREREZUYAxYRERFRiTFgEREREZXY/wfoSvBIcbYJHAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bmtk.simulator import bionet\n", "\n", "bionet.reset()\n", "conf = bionet.Config.from_json('config.xstim_custom.json')\n", "conf.build_env()\n", "\n", "graph = bionet.BioNetwork.from_config(conf)\n", "sim = bionet.BioSimulator.from_config(conf, network=graph)\n", "sim.run()\n", "\n", "_ = plot_traces(config_file='config.xstim_custom.json', report_name='membrane_potential', population='bio')" ] }, { "cell_type": "code", "execution_count": null, "id": "52c99a2e-c291-49ce-b924-6c896a6781b3", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }