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mirror of https://github.com/gsi-upm/soil synced 2024-11-14 15:32:29 +00:00
soil/examples/NewsSpread.ipynb
J. Fernando Sánchez f811ee18c5 WIP
2022-10-06 15:49:19 +02:00

533 lines
236 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:41.843Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import soil\n",
"import networkx as nx\n",
" \n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"# To display plots in the notebook\n",
"%pylab inline\n",
"\n",
"from soil import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# News Spreading example with SOIL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example we three different kinds of models, which we combine in five types of simulation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 288K\r\n",
"drwxr-xr-x 7 j users 4.0K May 23 12:48 .\r\n",
"drwxr-xr-x 15 j users 20K May 7 18:59 ..\r\n",
"-rw-r--r-- 1 j users 451 Oct 17 2017 complete.yml\r\n",
"drwxr-xr-x 2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n",
"drwxr-xr-x 2 j users 4.0K Oct 17 2017 long_running\r\n",
"-rw-r--r-- 1 j users 1.2K May 23 12:49 .nbgrader.log\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 newsspread\r\n",
"-rw-r--r-- 1 j users 225K May 4 11:23 NewsSpread.ipynb\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:21 rabbits\r\n",
"-rw-r--r-- 1 j users 42 Jul 3 2017 torvalds.edgelist\r\n",
"-rw-r--r-- 1 j users 245 Oct 13 2017 torvalds.yml\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 tutorial\r\n"
]
}
],
"source": [
"!ls "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.440Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_dumb\r\n",
"network_agents:\r\n",
"- agent_class: DumbViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_class: DumbViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_half_herd\r\n",
"network_agents:\r\n",
"- agent_class: DumbViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_class: DumbViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"- agent_class: HerdViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_class: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_herd\r\n",
"network_agents:\r\n",
"- agent_class: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_class: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
" prob_neighbor_cure: 0.1\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_wise_herd\r\n",
"network_agents:\r\n",
"- agent_class: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_class: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
" prob_neighbor_cure: 0.1\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_wise\r\n",
"network_agents:\r\n",
"- agent_class: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_class: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n"
]
}
],
"source": [
"!cat newsspread/NewsSpread.yml"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.879Z"
}
},
"outputs": [
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-bae848826594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m copy=copy)\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
]
}
],
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:45.458Z"
}
},
"outputs": [],
"source": [
"evodumb['mean'].plot(yerr=evodumb['std'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:26:19.361423Z",
"start_time": "2017-11-01T14:25:57.017418+01:00"
}
},
"outputs": [],
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);\n",
"evohalfherd = analysis.read_data('soil_output/Sim_half_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
"evoherd = analysis.read_data('soil_output/Sim_all_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
"evoherdwise = analysis.read_data('soil_output/Sim_wise_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
"evowise = analysis.read_data('soil_output/Sim_all_wise/', group=True, process=analysis.get_count, keys=['id'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:26:20.461665Z",
"start_time": "2017-11-01T14:26:19.363815+01:00"
}
},
"outputs": [
{
"data": {
"image/png": 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fRCRAFPoiIgGi0BcRCRCFvohIgCj0RUQCRKEvIhIgCn0RkQBR6IuIBIhCX0Qk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\niEiAKPRFRALk/wN2rsCB356VQwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa48c26df98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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5UoCI9EhmZialpaWMHj26x80jpfucc5SWlrZ7P0BXdRr6zrlVJK+nB5iWZHkH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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa48c26d668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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GUoa54Y8xqW5Me+ZYd6M1IrrtY6v1LiJ9VP8I+lX7D32QKf8zNwPT\nE1NdGV+ky+GedTzkXuWCesYYty4iJrj1FxHpId0S9I0xXwf+APiAJ6y193XJgf3BfZ3L694U4Fs+\npRoe4x5kik5yY96/fq97SjUl5/Bpf0VEQkCXR0FjjA/4L+B8IB9Yaox53Vq7NuCDNKX99T/I1Eba\n34hY11ofcbb3INNYt0wepkRhIiLt6I6m72Rgs7V2K4Ax5nngUqD9oF+1P/C0v5nj3FOqSUMV3EVE\nOqk7gv5g4KsWf+cDpxx2j33bAk/7KyIiR6w7gn5bM1nbQwoZMxuYDTBySBb8dHNgaX9FROSIdUf/\nSD4wpMXf2cCugwtZax+z1k6y1k5KzhykgC8i0gO6I+gvBUYbY4YbYyKBa4DXu+FzRESkk7q8e8da\nW2+MuQ14Bzdk8ylr7Zqu/hwREem8bhm4bq19C1DKSBGRXkZjHkVEQoiCvohICFHQFxEJIQr6IiIh\nxFh7yHNTPV8JY8qADcGuRzdKB/YGuxLdqD+fX38+N9D59XVjrLUJndmht6Sd3GCtnRTsSnQXY8wy\nnV/f1J/PDXR+fZ0xZlln91H3johICFHQFxEJIb0l6D8W7Ap0M51f39Wfzw10fn1dp8+vV9zIFRGR\nntFbWvoiItIDFPRFREJI0IO+MebrxpgNxpjNxpi5wa5PVzLG5BljvjTGrDySoVW9jTHmKWNMoTFm\ndYt1qcaY94wxm7xlSjDreDTaOb97jDE7ve9wpTHmG8Gs49Ewxgwxxiw2xqwzxqwxxvyHt77Pf4eH\nObd+8f0ZY6KNMZ8ZY77wzu8X3vrhxph/ed/dC146+8MfK5h9+t4k6htpMYk6cG2nJlHvxYwxecAk\na22/eDjEGHMmUA48a60d7627Hyix1t7nXbRTrLV3BbOeR6qd87sHKLfWPhDMunUFY0wWkGWtXWGM\nSQCWA5cBM+jj3+Fhzu0q+sH3Z4wxQJy1ttwYEwF8BPwHcAfwirX2eWPMn4EvrLXzDnesYLf0/ZOo\nW2trgaZJ1KUXstZ+CJQctPpS4Bnv/TO4f2h9Ujvn129Ya3dba1d478uAdbg5rfv8d3iYc+sXrFPu\n/RnhvSxwLrDQWx/QdxfsoN/WJOr95ovCfSnvGmOWe3MC90cDrLW7wf3DAzKDXJ/ucJsxZpXX/dPn\nuj7aYozJAU4A/kU/+w4POjfoJ9+fMcZnjFkJFALvAVuA/dbaeq9IQPEz2EE/oEnU+7CvWWtPBC4C\nbvW6D6RvmQeMBCYCu4HfB7c6R88YEw+8DHzfWnsg2PXpSm2cW7/5/qy1Ddbaibh5xycDY9sq1tFx\ngh30A5pEva+y1u7yloXAq7gvqr8p8PpTm/pVC4Ncny5lrS3w/rE1Ao/Tx79Drz/4ZWCBtfYVb3W/\n+A7bOrf+9v0BWGv3A0uAU4FkY0xTDrWA4mewg36/nUTdGBPn3VDCGBMHXACsPvxefdLrwHTv/XTg\nb0GsS5drCoaeb9GHv0PvZuCTwDpr7YMtNvX577C9c+sv358xJsMYk+y9jwHOw923WAxM84oF9N0F\n/YlcbwjVwzRPov7roFaoixhjRuBa9+CymT7X18/NGPM/wNm4dLUFwN3Aa8CLwFBgB3CltbZP3gxt\n5/zOxnUNWCAPuKWp/7uvMcacDvwf8CXQ6K3+Ka7vu09/h4c5t2vpB9+fMSYXd6PWh2usv2it/aUX\nZ54HUoHPgRustTWHPVawg76IiPScYHfviIhID1LQFxEJIQr6IiIhREFfRCSEKOiLiIQQBX0JCcaY\nZGPMd49gv592R31EgkVDNiUkePlY3mzKntmJ/cqttfHdUimRIFBLX0LFfcBIL6f67w7eaIzJMsZ8\n6G1fbYw5wxhzHxDjrVvglbvBy2u+0hjzqJceHGNMuTHm98aYFcaYRcaYjJ49PZHAqKUvIaGjlr4x\n5k4g2lr7ay+Qx1pry1q29I0xY4H7gcuttXXGmP8GPrXWPmuMsbinIRcYY34OZFprb+uJcxPpjPCO\ni4iEhKXAU17SrtestSvbKDMVOAlY6lK9EENzcrJG4AXv/V+BVw7ZW6QXUPeOCP4JVM4EdgLzjTE3\ntVHMAM9Yayd6rzHW2nvaO2Q3VVXkqCjoS6goAxLa22iMGQYUWmsfx2VrPNHbVOe1/gEWAdOMMZne\nPqnefuD+LTVlO7wON52dSK+j7h0JCdbaYmPMP42b9Pzv1tofHVTkbOBHxpg63Dy5TS39x4BVxpgV\n1trrjTH/DzcbWhhQB9wKbAcqgOOMMcuBUuDq7j8rkc7TjVyRLqChndJXqHtHRCSEqKUvIcUYMwGY\nf9DqGmvtKcGoj0hPU9AXEQkh6t4REQkhCvoiIiFEQV9EJIQo6IuIhBAFfRGREPL/AYkczLOQ2u6K\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460c6c320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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jRr766itKS0uZOnVq5FmE/ki3rEFfKTUodZZa+dZbbwW6l1o5WnSa5VdeeeWQ\n5Rs3bmTNmjWcfvrpgPWylVGjRlFXV8eePXs4//zzASvDZjzvvPMOq1evjlxd1NTUsHnzZt5//30u\nu+yySJPVqaeeGrNdON2yBn2l1MDpoEbeV/oitXK0eGmWoxljOProo/nkk09i5tfWdu1lMu2lbH7z\nzTc7LHtfp1vWNn2l1KDUX6mV2zNlyhTKysoiQd/v97N27VoyMzMZM2YMr776KmD1+GlsbDwk3XJ7\nKZtPPPFElixZQjAYZN++fYdk6ezrdMsa9JVSg1Y4tfLBbrzxRurr6ykuLuaBBx7oVWrl9qSkpPDy\nyy9z++23c8wxxzBjxgw+/vhjAJ5//nkWLVpEcXExX//619m/fz/FxcW4XC6OOeYYHn74Yb7zne8w\ndepUZs2axbRp07jhhhsIBAKcf/75TJ48menTp3PjjTdy0kknRb6zP9Ita2plpVRcmlq5/3WUbllT\nKyulhr3hnFo5nv5It6w3cpVSg9pwTa0cT3+kW9aavlJKJREN+koplUQ6DfoiMlZElonIehFZKyLf\ns+fnisjfRGSzPcyx54uILBKRLSKyWkS6/6icUkqpPtGVmn4AuM0YcxRwHHCziEwF7gCWGmMmA0vt\naYCzgcn2ZyHwaMJLrZRSqkc6DfrGmH3GmM/t8TpgPTAaOBd41l7tWeA8e/xc4Dlj+SeQLSJ91+lU\nKTVsJTK1cn9JT08HoKysjLPOOmuAS3OobrXpi0gRMBP4FBhhjNkH1okBKLRXGw3sjtqsxJ538L4W\nisgKEVlRVlbW/ZIrpYa9RKZW7o2udhmNVlBQwKhRo/joo4/6oEQ91+WgLyLpwP8C3zfGdJR8Il5S\niUOeADPGPGGMmWOMmVNQUNDVYiilkkgiUyvHS4f83nvv8a1vfSuyzi233MIzzzwDWOmX7733Xr7x\njW/w0ksvsXXrVs466yxmz57NCSecwIYNGwDYvn07X/va15g7dy7/9V//FfOd5513XiRdxGDRpX76\nIuLGCviLjTHhdHSlIjLKGLPPbr4JJ4UuAcZGbT4G2JuoAiul+t8vlv+CDZUbErrPI3OP5PZ5t7e7\nPNGpleOlQ969e3eH23g8nkgaiPnz5/PYY48xefJkPv30U2666Sbeffddvve973HjjTeyYMECfvvb\n38ZsP2fOHH7yk590Wrb+1JXeOwI8Caw3xjwUteh1IPzo2NXAa1HzF9i9eI4DasLNQEop1VWdpVa+\n8sorga4nDvBeAAAZTUlEQVSlVo6XDjktLa3TMoTfnlVfX8/HH3/MRRddxIwZM7jhhhsiL1T56KOP\nuOyyywC46qqrYrYPp0keTLpS0z8euAr4SkRW2fN+BNwPvCgi1wO7gIvsZW8C3wS2AI1A3z9ippTq\nUx3VyPtKIlMrt5djzOVyEQqFItMHf5/P5wMgFAqRnZ3NqlWriKe9svR1muSe6ErvnQ+NMWKMKTbG\nzLA/bxpjKowx840xk+1hpb2+McbcbIyZZIyZbowZHLfUlVJDSiJTK7eXDnn8+PGsW7eOlpYWampq\nWLp0adyyZGZmMmHChEheH2MMX375JQDHH388S5YsATik/X7Tpk2R+xCDhT6Rq5QatBKZWjleOuSx\nY8dy8cUXU1xczBVXXMHMmTPbLcvixYt58sknOeaYYzj66KN57TWrRfvXv/41v/3tb5k7dy41NTUx\n2yxbtoxzzjmnp4ffJzS1slIqLk2t3Hsnnngir732Gjk5Ob3el6ZWVkoNe0M5tXJZWRk/+MEPEhLw\nE0lTKyulBrWhmlq5oKCA8847r/MV+5nW9JVS7RoMzb8qsb+DBn2lVFwej4eKigoN/APMGENFRQUe\njych+9PmHaVUXGPGjKGkpATNjTXwPB4PY8aMSci+NOgrpeJyu91MmDBhoIuhEkybd5RSKolo0FdK\nqSSiQV8ppZKIBn2llEoiGvSVUiqJaNBXSqkkokFfKaWSiAZ9pZRKIhr0lVIqiWjQV0qpJKJBXyml\nkogGfaWUSiIa9JVSKolo0FdKqSSiQV8ppZKIBn2llEoiGvSVUiqJaNBXSqkkokFfKaWSiAZ9pZRK\nIhr0lVIqiWjQV0qpJKJBXymlkkinQV9EnhKRAyKyJmperoj8TUQ228Mce76IyCIR2SIiq0VkVl8W\nXimlVPd0pab/DHDWQfPuAJYaYyYDS+1pgLOByfZnIfBoYoqplFIqEToN+saY94HKg2afCzxrjz8L\nnBc1/zlj+SeQLSKjElVYpZRSvdPTNv0Rxph9APaw0J4/GtgdtV6JPe8QIrJQRFaIyIqysrIeFkMp\npVR3JPpGrsSZZ+KtaIx5whgzxxgzp6CgIMHFUEopFU9Pg35puNnGHh6w55cAY6PWGwPs7XnxlFJK\nJVJPg/7rwNX2+NXAa1HzF9i9eI4DasLNQEoppQaeq7MVRORPwMlAvoiUAHcB9wMvisj1wC7gInv1\nN4FvAluARuDaPiizUkoNT0+fYw2v/b+ur9tNnQZ9Y8xl7SyaH2ddA9zco5IopdRw1J1A3g86DfpK\nKaWiDEQQD7RCaz20NrR9mqp7tCsN+kqp4aknTSWJDuShIARbIRSAkpXQUgPNtdBcAy211nj0cP9X\nYILwm3l2cLcDfcifsCJp0FdKDR0D3VRijBXIQwHY9yXUl0HDAag/AA1l9tCerj8AjRVEeq3//tT4\n+0zNtD6eTGtdhxsKj4SUdEjxRX3SY8eX/hT4qNuHoEFfKTWw+iOQh4JRtesa+xNV467eZQXy129t\nq1231Nvj9fa4XfMOB/HHT4z9DpcX0gutT84EGDsPfIXw1cvgcMGZP2sL7uFhSgY4ojpRhv8WFz/X\n+TF9tKhHfwoN+kqpznU3MCcikBtjNY1Et2O31FkBfP1f7CaROitoR5pI6trGS9dYgfy+0Xaw7oQ4\nYNNbbTXq1AwrgKdMbJtO8cHql8DhhNPutpb7Cuz10kHiPJ+682NreMSZPf9bJJAGfaWSVV/WsI2x\n2qard1m16aZqaK62hzVR49VwYK0VyB8/0Q7ujW21ahOMv/8XroyddqdZQTk10xp6MsHtBXHC9Ius\naU+W9UkNj0fN+9MVVsDuyt9i5yfWcOq/9u5v1FvX/h9cFy8JQsc06Cs1nCQykAdaoakKmiqtQB0K\nwKo/RjWPhJtIqtumw80nTVXWPh6Z3s7OxQq23myrNi8uSB9pt1entdW23VHjKWnwjwetWvb5j8YG\neae7/b/FWfd1fqzxauiJ0p3foh/uVWjQV2qw620gN8YKxo2VVgBvtD+1e6xA/sYP2oJ7Y6VVA2+q\njN8k8uqNbeMpGbE15szDwHOUFYg3/tVqxz7xNvBkW8Hdk9U2Ht2WHT6+K17s/Fg+e8oajmzvZNIP\nBkl/+57SoK/UQOhpIPc3WwG5qart0xg1XbHZCuRPf9MO7hXW+qFA+/tc+2dIywVvDmSMghFHW+Pe\nXEjLscbff8gK4hc/09ZE4uwgfBxYbw1nLeje8Q2UIR7Iu0ODvlKJ0tNAHgxY3fzq9kHd/rZPvT3c\n+4XVT/tnIyHQ1P5+HG4rz63DburIm2T1IEnLhbQ8O4jntU3/+UYrkF/3ZudlDNewcyd079i6oq+a\nP5IokHeHBn2l2tObZpWgHxrKrb7bh3zKoXSt1Zb9y8nWvEMykIvd/W8EuFLA4YPii9tq5OGaeGQ8\nx2r3fuZbXS9zvHbwRNGAO2hp0FfJpTuB3BirWaR8i/0kZVTf7pg+3/Zw/2or2P+iqO1G5sGcKVbf\n7ZDfCuZTzraaVDJGWMN0e+graGs+CZf5zP/u9eHH0FrzkHbtWz3LZ6lBXw193a2Rh4JQud1+anK/\nNazbD/WlbZ+6UmsZwG9mx9+PONoesvFkWfNS0uDoC6yg7ctv68cdnk7NtHqKhMv8rz17wKZdGpyH\ntHAgf/qsp/vsOzToq8GpJ00rxljdB2v3Qe1eq3dK3T5rWLvX+pRvsmrvi2bEbisOqwaeXggZI63e\nIVv/YTWBnHxHW3/ucIBPzbS6EsZ7mvKcB3t37AfTQD6kJSqQG2No8DdQ1VJFTUsNNS01PdqPBn3V\nf3oTyA9pH4+aDjer3HcY+BsP3Yev0OpOmDPBanZxpsBJ/2H1Cw8H+bQ8q/93vPIec0nPjrcjGsiH\nrEQE8UAoQG1rbSR4V7dUU9NSw/6G/QRMgHs+uYealhqqmquobqmOfAId9cLqIg36qnd6EshDQasr\nYSQ5VZykVeEeKz8taD/DoDfHajYB6yZm8SVWcM8cBZmjrfH0kVbb+cHlnXnlofvrLQ3kQ1ZPArkx\nhqZAE7WttdanpTYyXtNSE5m3rWYbgVCAy964zArurTXUtdZ1uO93d71LTmoOWalZjM8czzGpx5Cd\nmk2Ox5qXk5rDb774DWtY0+1j1aCvEsff1NY2Hm+493OrRv7TfDChQ7d3pra1gbtSwJEOM6+IahPP\naxtPy2vrfdKdJy+7Q4P4kNbdQG6MoaKpgqrmKqpaqqhsrqSyuZKq5qrIsKqlijXlawiYALP+MKvD\nmrcgZKRk0BJswSlOslKzGJc5juzUbLJSs8hKzYqMh4c/+uBHOMXJM2c/02l5n1nb+TrxaNBXh4pX\new8Frfbx6l1Rn51Q+pX1uP7Px1k9XA7mcFk9UtJHgMtjtYXPWhCbqMpXCOkFbTc5o8tw2l2JPTYN\n5ENaVwO5MYZgKEjABFhTvibSTFLZXEl1S7UVwKPmldSVEDABTn7x5Lj7C9eucz25eFweXA4X5046\nl8zUTDJT7M9B4+nudBziiJT5sdMf6/T4XI6uh+Snz3qaZ3imy+tHvqPbW6jhyd/UdvOzvhQCLfDq\nzVZgr9kNNSWHPtWZPhJCIavHyrQLrW6H6SOjhiOtvuQHP25/6o8TW3YN5ENaZ4HcH/RbgbqlitrW\nWgKhAC9ufJHa1lqqm63mkuqWampbaiNt4zWtNZFa+GX/F/vGV5e4yPZkk52aTa4nlyNyjqDB34DL\n4eKao68h15NLjieHHI8V5LNTs2OCcbi835/9/YT/Lfqy106YBv1k8NTZVhA/416o2QO1JVaAjx5v\nrDh0u61LIXscjJkL075tjWePg+zxVpu529MWyL/5QGLLrIF8SOsokLcEW6hoqqC8qZyKpgrKGssI\nmAAPfPYA1c1WcI8MW6pp8Dccso+f/vOnAHicnpimkknZk6zplCze3vE2LoeL2+bcFgnw2Z5sMtwZ\nyEEJ1sLlvfyoyxP9p+iXQN4dYr3LfGDNmTPHrFixYqCLMbREN8EE/VYNvXoXVO08tAmmds+h23tz\n7JudoyHLvumZOcYa/9vd4EqF6/7avXKoYauzIB7dA6WmpYZHVj6CP+TnpLEnRQJ8ZXMlFU0V1Pnj\n38T0urzkpOaQ7cmOHdo3MLNTs3l89eO4xMWiUxeRlZqFx+XpUZmHCxFZaYyZ051ttKY/2BnTlhEx\n/KnZA+Ubrdr7w9OsedE3RsVhBfPscTDhJNjxgdWe/s1fQtYYK8Cn+Nr/Tre36+XTYD9kHRwUjTE0\nBhojbd3Rw5L6EgKhAD947wcx3QxrW2tp6iAf0Btb3yDPm0eeN48puVPI81jj+d78yPh9n96H2+Hm\n2bOf7bTMf9rwJwBG+EZ0uu5wDva9oUF/oLXUW+3lNbvhr7dbgXzCCda88ANFB/+ncrisl0O4UmH8\n8VHNLuMgx256ic6rEq6NTzqla2XSQD5khQP5k2c+SV1rXaR/dzhIVze3TW+t3krABPj269+ONKf4\nO3gBt8vhYmv1VrJSsxiVPoojc4+M6YkS3cxy7yf3djmQpzpTu3x8Gsh7T4N+XwqF4MkzIdgM3/i+\nHdztT/VuK9A3Vx+63fYPrNr4qGIrN0ukCcaupacXwrP2W3sueLzzcmgQH3S60/Rw7VvXEjIhHjzp\nQavrYEsVlU2VkW6F0V0K11WsIxAKMPP5mYTidYsFHOIgKyWLpkATToeT0emjmZY/zWpGide84snm\n1qW3IiJdDroayAcvDfq9YYz1IFH1zrb285j29N0QbLHWffk6a5iaCVljrWaWsfMge2zb9Ns/sZ4W\n7UqqWw3kg057gTz6IZ7wQztVzVUETZDn1z1Pvb+e+tZ6GvwNkfF6vzVd11pHeVM5QRPk1JdOPeQ7\nHeKIBOtcby5elxeXw8X5k88nOzU7ph94eDwjJSOmK+GiUzvP/3Pwjc/OaCAfvPRGbmeMsZpYKjZD\n+Wao2AJfvgCBZsDYwyhp+bHNLRveAKcHLvy9FdjDibnUoBcviIdMiLrWukh/78rmykjTyJINSwiE\nAkwvmE5tS22k62BNS02HzSZg3cRMd6fjc/vISMnA5/aR7k4nPSWdj/d+jEtcXDftupiuhLmeXDJT\nMnFGpY9IhpuXqk1PbuRq0A978iyrr/rx/58d3MNBfitEdxlz+6wXVbg8MONyq/ti9ng7yI/t+Aap\nGnDXvnUtxhgeOeUR6vx1kVp1bWttZLyutY661jre3PYmQRNkUvakSICvaakh2M7Luh04cDlcTMia\nEGnfzkzJbGvvTskiMzWTrJQsfrXiVzgdTh497VF8bl+HD+VoIFft0aDfGWOsB4/KNlrZFss328NN\nB3VrFCuI50+GvMmQf7g9nGzlOu/LlyirDgVDQStIR+U5uX/5/QRDQS6aclHc5pHoYUVTBSHit3VH\n87q8BEIBnOJkat7USB/vnNScSG07Mm63e9/095sADc6q/2jQDwsF4fenWxkXiy+2g/tGa9hS27Ze\nSroVyPOPgF3/BJcXLnoacidaDx6phLr6r1cTMiF+fsLPI+3XDf6GmCAdHr61/S2CJsjh2YdT11pH\nbWstda111PvjvKz7IJGmEbt5JHr44Z4PcYqTq6ZeRUZKBukp6WS47WFKBhnuDHwpPtwOt9aw1aCX\nfEE/GICqHVC2Hso2wIEN1rB8c9sNVLBq5/lHRH0mQ8EUrbW3wxhDa6iVRn8jjYFGmvxNNAas8fC8\nRn8jTYEm/rj+jwRNkJPGnERjoJEGf0PsNv5GGgINNPobO23XBuvGpM/toyXQgtPh5Kjco2JymmSk\nZBwy/OVnv8TpcPL46Y/jc/twiKPT71FqOBg0QV9EzgJ+DTiB3xtj7u9o/U6DvjFW88sfLoTWBqvX\nS7zgnjUOCo+0AvrGt8CdBte8Yb34YpjzB/00BhppDjTTFGiKfMLTjYHGuE0e8ZpDaltrO//CKA4c\nZHuySXOlkeZOiwx9bh9elzcy/db2t3A6nHx3+ndjblb6Utpq5l6Xt9s9RZRKVoMi6IuIE9gEnA6U\nAJ8Blxlj1rW3TUzQDwasm6j7Vlsvx9j/lfVpqmzbIBLc7U/hkZA/BVLTE3osPeEP+WkNttIcaLaG\nQWvYEmyhJdgSGY+e5w/5I+MtgZa28ejPQfN31+0mZEJ4nB6aAk0ETNdfruByuKxmjOieIuFmEHc6\n7+1+D6fDagI5OJAfPPQ4PTG9R5RS/WewpGGYB2wxxmyzC7UEOBdoN+jX1e5h6cuXQfUOqC5pe2mG\nww3ZY2DScVYPmZzxhLLGEHS5rdSpJkjIhAg27iK0c0fbtAlGlgdNMJJiNWRCkfFgyFoWCAUi2wVC\ngcj0wesFQ0H8IX9kvCXUEje4t9ezo6sEId2dToozBY/LYw2dbUOf24fH5aGyuRKHODir6Cw8Lg9e\nlxevyxsz7nV68brt+U5PpN06xZHSYW36x8clOAumUmrQ6IugPxrYHTVdAhx78EoishBYCOAp8vD9\nhjXgBgqyD1qzHhrWWJ84ecO6yyUunA4nTnG2De2Py2EtczlcbdP2euHtUiQFp8NJqiOVVGcqqS57\nGPXxuDyR8eigHZ6OXnbwUNujlVJ9qS+Cfrwq5CFtSMaYJ4AnAKYVTzEvf+ulLt9UdYoTh8OBA0dk\n3ClOHOLAIbHjkcBtz9P2YqVUMuuLoF8CjI2aHgPs7WgDT0oGU/KO7IOiKKWUitYXbQmfAZNFZIKI\npACXAq/3wfcopZTqpoTX9I0xARG5BXgbq8vmU8aYtYn+HqWUUt3XJ1k2jTFvAl1IFamUUqo/aVcR\npZRKIhr0lVIqiWjQV0qpJKJBXymlksigyLIpInXAxoEuRx/KB8oHuhB9aDgf33A+NtDjG+qmGGMy\nurPBYHlH7sbuJg0aSkRkhR7f0DScjw30+IY6Eel2Tnpt3lFKqSSiQV8ppZLIYAn6Twx0AfqYHt/Q\nNZyPDfT4hrpuH9+guJGrlFKqfwyWmr5SSql+oEFfKaWSyIAHfRE5S0Q2isgWEbljoMuTSCKyQ0S+\nEpFVPelaNdiIyFMickBE1kTNyxWRv4nIZnuYM5Bl7I12ju9uEdlj/4arROSbA1nG3hCRsSKyTETW\ni8haEfmePX/I/4YdHNuw+P1ExCMiy0XkS/v47rHnTxCRT+3f7gU7nX3H+xrINv2evER9KBGRHcAc\nY8yweDhERE4E6oHnjDHT7HkPAJXGmPvtk3aOMeb2gSxnT7VzfHcD9caYBweybIkgIqOAUcaYz0Uk\nA1gJnAdcwxD/DTs4tosZBr+fWK/88xlj6kXEDXwIfA/4AfCKMWaJiDwGfGmMebSjfQ10TT/yEnVj\nTCsQfom6GoSMMe8DlQfNPhd41h5/Fus/2pDUzvENG8aYfcaYz+3xOmA91juth/xv2MGxDQvGUm9P\nuu2PAU4FXrbnd+m3G+igH+8l6sPmh8L6Ud4RkZX2i+CHoxHGmH1g/ccDCge4PH3hFhFZbTf/DLmm\nj3hEpAiYCXzKMPsNDzo2GCa/n4g4RWQVcAD4G7AVqDbGBOxVuhQ/Bzrod+kl6kPY8caYWcDZwM12\n84EaWh4FJgEzgH3Arwa2OL0nIunA/wLfN8bUDnR5EinOsQ2b388YEzTGzMB67/g84Kh4q3W2n4EO\n+t1+ifpQYozZaw8PAH/G+qGGm1K7PTXcrnpggMuTUMaYUvs/Wwj4fwzx39BuD/5fYLEx5hV79rD4\nDeMd23D7/QCMMdXAe8BxQLaIhHOodSl+DnTQH7YvURcRn31DCRHxAWcAazreakh6HbjaHr8aeG0A\ny5Jw4WBoO58h/BvaNwOfBNYbYx6KWjTkf8P2jm24/H4iUiAi2fa4FzgN677FMuBCe7Uu/XYD/kSu\n3YXqEdpeov7fA1qgBBGRiVi1e7Cymf5xqB+biPwJOBkrXW0pcBfwKvAiMA7YBVxkjBmSN0PbOb6T\nsZoGDLADuCHc/j3UiMg3gA+Ar4CQPftHWG3fQ/o37ODYLmMY/H4iUox1o9aJVVl/0Rhzrx1nlgC5\nwBfAlcaYlg73NdBBXymlVP8Z6OYdpZRS/UiDvlJKJREN+koplUQ06CulVBLRoK+UUklEg75KCiKS\nLSI39WC7H/VFeZQaKNplUyUFOx/LG+Hsmd3Yrt4Yk94nhVJqAGhNXyWL+4FJdk71Xx68UERGicj7\n9vI1InKCiNwPeO15i+31rrTzmq8Skcft9OCISL2I/EpEPheRpSJS0L+Hp1TXaE1fJYXOavoichvg\nMcb8tx3I04wxddE1fRE5CngAuMAY4xeR3wH/NMY8JyIG62nIxSJyJ1BojLmlP45Nqe5wdb6KUknh\nM+ApO2nXq8aYVXHWmQ/MBj6zUr3gpS05WQh4wR7/A/DKIVsrNQho845SRF6gciKwB3heRBbEWU2A\nZ40xM+zPFGPM3e3tso+KqlSvaNBXyaIOyGhvoYiMBw4YY/4fVrbGWfYiv137B1gKXCgihfY2ufZ2\nYP1fCmc7vBzrdXZKDTravKOSgjGmQkQ+Euul5381xvzwoFVOBn4oIn6s9+SGa/pPAKtF5HNjzBUi\n8hOst6E5AD9wM7ATaACOFpGVQA1wSd8flVLdpzdylUoA7dqphgpt3lFKqSSiNX2VVERkOvD8QbNb\njDHHDkR5lOpvGvSVUiqJaPOOUkolEQ36SimVRDToK6VUEtGgr5RSSUSDvlJKJZH/H8TUIKfNIlUq\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460d184e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Sui3veDDO9xZ/jtcf4sQpWWytaWFF2b5XG2UlxTI5M4Gt1S143A7e+rqSwjGJ5KTF4erl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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa46017c0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in [evodumb, evohalfherd, evoherd, evoherdwise, evowise]:\n",
" i['mean'].plot(yerr=i['std'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:27:56.226662Z",
"start_time": "2017-11-01T14:27:55.987887+01:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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114hu/+V1zJozj7y8PMaMGcPTTz/d5DwP3G8f7rrrLk499VTy8vI45ZRTWLNm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\nMxGTJEmSpJaZiEmSJElSy/4PDTIOHCj38kMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa46019e748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = evodumb['mean']-evohalfherd['mean']\n",
"m = evodumb['max'].loc[0].sum()\n",
"diff = diff / m\n",
"diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
" title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 50% herd agents.');"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:28:05.554284Z",
"start_time": "2017-11-01T14:28:05.324360+01:00"
}
},
"outputs": [
{
"data": {
"image/png": 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ZGdZabr311hoBn4iIiEht9ERNpPXbB/Ty6e4JZAY7sbU20/u7E1gBDG3KxDWl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\np8HDoHFULu/6ZZ5V8k8355klVy+Chf+eS+OovPb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G7kop/WenQzXx/Lq7vxp6fuMjYnTx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\nSVKVMahJkiRJUpX5PyHeewauSb+8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa45f75d978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = evodumb['mean']-evoherd['mean']\n",
"m = evodumb['max'].loc[0].sum()\n",
"diff = diff / m\n",
"diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
" title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 100% herd agents.');"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:32:12.118102Z",
"start_time": "2017-11-01T14:32:11.479970+01:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa45f950e10>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Gp6SnpZLyjzvonbuRlW0fpe/o5+x+gjGVzBKD8Rn7d28j/6NhRBYcIqHXC/S7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\nMUVYYjDGGFPE/wN3GX2TzrxUPAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460d17c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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hskm0v6z7ufKwNpEMeHgBO378Eae8W9A/4VckPXMN+7avq5P9rnn/afqd/o7E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\n8/8U7UznepyQDQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa45f8fd4e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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L93rUxaNS8UxWq5QMqtNQUSlD7IrYxe/hv/NK41cY3mB4SZvzAHmdxNGriYTFpiAlWJiZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460444588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(evohalfherd['std']/m).plot()\n",
"(evodumb['std']/m).plot()\n",
"(evowise['std']/m).plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "30px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}