From 7d1c800490dfb7ce19cf2f6cee687db83c5db6cb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=2E=20Fernando=20S=C3=A1nchez?= Date: Wed, 1 Nov 2017 14:44:46 +0100 Subject: [PATCH] Parallelism and granular exporting options * Graphs are not saved by default (not backwards compatible) * Modified newsspread examples * More granular options to save results (exporting to CSV and GEXF are now optional) * Updated tutorial to include exporting options * Removed references from environment to simulation * Added parallelism to simulations (can be turned off with a flag or argument). --- examples/newsspread/NewsSpread.ipynb | 194 ++++++++++++++++----------- examples/newsspread/NewsSpread.yml | 6 +- examples/newsspread/newsspread.py | 50 +++---- soil/__init__.py | 20 ++- soil/agents/DrawingAgent.py | 2 +- soil/agents/__init__.py | 130 ++++++++++++++++-- soil/environment.py | 57 ++++++-- soil/simulation.py | 171 +++++++++-------------- soil/utils.py | 18 +-- 9 files changed, 399 insertions(+), 249 deletions(-) diff --git a/examples/newsspread/NewsSpread.ipynb b/examples/newsspread/NewsSpread.ipynb index 5f2a6b3..09b8263 100644 --- a/examples/newsspread/NewsSpread.ipynb +++ b/examples/newsspread/NewsSpread.ipynb @@ -2,23 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:01.378353Z", - "start_time": "2017-10-19T17:54:00.685043+02:00" + "end_time": "2017-11-01T13:25:49.702468Z", + "start_time": "2017-11-01T14:25:47.710337+01:00" }, "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "outputs": [], "source": [ "import soil\n", "import networkx as nx\n", @@ -51,8 +43,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:02.678166Z", - "start_time": "2017-10-19T17:54:02.508949+02:00" + "end_time": "2017-11-01T13:25:51.869688Z", + "start_time": "2017-11-01T14:25:51.737060+01:00" } }, "outputs": [ @@ -130,7 +122,7 @@ "- agent_type: HerdViewer\r\n", " state:\r\n", " has_tv: true\r\n", - " id: infected\r\n", + " id: neutral\r\n", " weight: 1\r\n", "- agent_type: HerdViewer\r\n", " state:\r\n", @@ -157,7 +149,7 @@ "- agent_type: HerdViewer\r\n", " state:\r\n", " has_tv: true\r\n", - " id: infected\r\n", + " id: neutral\r\n", " weight: 1\r\n", "- agent_type: WiseViewer\r\n", " state:\r\n", @@ -183,7 +175,7 @@ "- agent_type: WiseViewer\r\n", " state:\r\n", " has_tv: true\r\n", - " id: infected\r\n", + " id: neutral\r\n", " weight: 1\r\n", "- agent_type: WiseViewer\r\n", " state:\r\n", @@ -210,8 +202,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:07.617556Z", - "start_time": "2017-10-19T17:54:03.481983+02:00" + "end_time": "2017-11-01T13:25:56.792431Z", + "start_time": "2017-11-01T14:25:52.447422+01:00" } }, "outputs": [], @@ -224,15 +216,15 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:07.832237Z", - "start_time": "2017-10-19T17:54:07.620220+02:00" + "end_time": "2017-11-01T13:25:57.015612Z", + "start_time": "2017-11-01T14:25:56.794756+01:00" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -241,9 +233,9 @@ }, { "data": { - "image/png": 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LJSIDbPz48WzdupXS0tKBLkrgZWRkMH78+F7Zlq7IFZGEwuEwkyZNGuhiSC/T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\n2be7sd4P+qI8IgNFXTYlEPz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fRCRAFPoiIgGi0BcRCRCFvohIgCj0RUQCRKEvIhIgCn0RkQBR6IuIBIhCX0Qk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\niEiAKPRFRALk/wN2rsCB356VQwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -259,10 +251,9 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:28.017296Z", - "start_time": "2017-10-19T17:54:07.834047+02:00" - }, - "collapsed": true + "end_time": "2017-11-01T13:26:19.361423Z", + "start_time": "2017-11-01T14:25:57.017418+01:00" + } }, "outputs": [], "source": [ @@ -270,7 +261,7 @@ "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'])" + "evowise = analysis.read_data('soil_output/Sim_all_wise/', group=True, process=analysis.get_count, keys=['id'])" ] }, { @@ -278,16 +269,16 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:28.963822Z", - "start_time": "2017-10-19T17:54:28.020292+02:00" + "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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LJSIDbPz48WzdupXS0tKBLkrgZWRkMH78+F7Zlq7IFZGEwuEwkyZNGuhiSC/T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\n2be7sd4P+qI8IgNFXTYlEPz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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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vv/wyS5YswWw2M2nSJL755htiYmL47LPPeOKJJ5g7dy533HEHM2fOZOzYsRQX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\n8e8f9qK15q5BbTFbNRabzf5u1VhtNsw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Sui3veDDO9xZ/jtcf4sQpWWytaWFF2b5XG2UlxTI5M4Gt1S143A7e+rqSwjGJ5KTF4erl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\nj2XblrFhj/V0po11G9nV0vn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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -341,19 +332,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:29.248966Z", - "start_time": "2017-10-19T17:54:28.966025+02:00" - } + "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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2ug8k+Ash3IJSiuAAqyOhXaeK+hZ2ldaxs7SGXaV17CqtpbSmGQ1M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\nYII=\n", 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114hu/+V1zJozj7y8PMaMGcPTTz/d5DwP3G8f7rrrLk499VTy8vI45ZRTWLNm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\nuK3Qf4reexyHkXXiT6FzTvDpD+mdqcl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -362,44 +354,27 @@ ], "source": [ "diff = evodumb['mean']-evohalfherd['mean']\n", - "diff.plot(yerr=evodumb['std']+evohalfherd['std']);" + "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": 8, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2017-10-19T15:54:29.688734Z", - "start_time": "2017-10-19T17:54:29.251456+02:00" + "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\nxMREbrjhBl544YWqfmPGjGHx4sUALFy4kLFjx9Y5j4kTJzJ//nwKCwsB2L9/P4cPHwbg8ssvZ//+\n/dXGj4yM5Ac/+AFPPPFEVb9x48ZVVV1csWIFycnJxMfH17rMyy+/nCVLllQtJzs7m927dwe72iIi\nInIGUqAm0vqtBQYaY/oaY6KAqUBQrTcaYzoZY6K9/5OBi4CtzZbSJnDPPfdUa/3xd7/7HX/+859J\nS0vjxRdf5Mknn6xz+gkTJnDTTTdx4YUXct555zFlyhQKCgqoqKhgx44dJCYm1pjm29/+NmVlZVXd\nc+fOZd26daSlpTFnzhz+8pe/1LnMwYMH89BDDzFhwgTS0tK48sorOXDgQAPXXESam6ozikhLYgJV\n4RGR1sUY81XgCSAcmG+t/aUx5kFgnbX2dWPMSOCfQCegGDhorU01xowBnsE1MhIGPGGtfSHwUk4a\nMWKEXbduXbV+n332Geecc06TrtfptHnzZubPn8/jjz8e6qQ0ida+P0SaSmXg9dLMC0M67j/uGLPe\nWjui3pFFRDx6R02kDbDWvgW85dfvPp//1+KqRPpP9yFwXrMnsBU499xz20yQJiIiIq2fqj6KiIhI\nq6IqiiJyJlCgJiIiIiIi0sIoUBMREREREWlhFKiJiIiIiIi0MArUREREJOT03pmISHUK1ERERERE\nRFoYBWoi0mYUFRVxySWXUF5eTmZmJlOmTAk43vjx4/H/DlxzeuKJJzh+/HiDp5s+fTpLliwBYOrU\nqWzfvr2pkyYiIiItlAI1EWkz5s+fz+TJkwkPD6d79+5VQU6o1RWolZeXBzWPO++8k0cffbQpkyXS\nrFSVUUSkcfTBaxFpcg+8sYWtmflNOs/B3eO5/5rUOsdZuHAhixYtAiAjI4NJkyaxefNmioqKmDFj\nBlu3buWcc86hqKio3uWNHz+e0aNHs3z5cnJzc3nhhRe4+OKLKS8vZ86cOaxYsYKSkhLuuusuZs6c\nyYoVK3jsscd48803AZg1axYjRowgPz+fzMxMLr30UpKTk1m+fDkdOnTghz/8IW+//Ta/+c1vWLZs\nGW+88QZFRUWMGTOGZ555BmNMtfRcfPHFTJ8+nbKyMiIiVHSLiIi0dXqiJiJtwokTJ9i5cycpKSk1\nhj399NO0a9eO9PR07r33XtavXx/UPMvKylizZg1PPPEEDzzwAAAvvPACHTt2ZO3ataxdu5bnnnuO\nXbt21TqAtr0eAAAgAElEQVSP2bNn0717d5YvX87y5csBOHbsGOeeey4ff/wxY8eOZdasWaxdu7Yq\nqKwM9nyFhYUxYMAAPvnkk6DSLtIc9JTs1JSUBffkXETEl27LikiTq+/JV3M4evQoCQkJAYetXLmS\n2bNnA5CWlkZaWlpQ85w8eTIAw4cPJyMjA4ClS5eSnp5eVa0yLy+P7du3ExUVFXRaw8PDuf7666u6\nly9fzqOPPsrx48fJzs4mNTWVa665psZ0Xbp0ITMzk+HDhwe9LBE5/fKKSvloZxarth9l1Y6j7Dp6\nLNRJEpFWSIGaiLQJsbGxFBcX1zrcvyphMKKjowEXWJWVlQFgreX3v/89EydOrDbuqlWrqKioqOqu\nKy0xMTGEh4dXjfe9732PdevW0atXL+bOnVvrtMXFxcTGxjZ4PUSkeZ0oq2DT3lxWbT/C+zuO8sne\nXCostIsKZ3TfRMorLLtDnUgRaXVU9VFE2oROnTpRXl4eMMgZN24cCxcuBGDz5s2kp6dXDbvllltY\ns2ZN0MuZOHEiTz/9NKWlpQBs27aNY8eO0adPH7Zu3UpJSQl5eXm8++67VdPExcVRUFAQcH6V6U1O\nTqawsLDOBlC2bdtGaurpf1opItWVlVdwrKSMg3nF3LZgLUMeXMoNz6zmqeU7ALjr0gG8dPsFbLpv\nAn+eMYpuHWNCnGIRaY30RE1E2owJEyawatUqrrjiimr977zzTmbMmEFaWhpDhgxh1KhRVcPS09Pp\n1q1b0Mv4zne+Q0ZGBsOGDcNaS+fOnXnttdfo1asXN9xwA2lpaQwcOJChQ4dWTXP77bdz9dVX061b\nt6r31ColJCTw3e9+l/POO4+UlBRGjhwZcLmHDh0iNja2QWkVCUblO2cvzbwwxClpuY4UlLBxTw4b\n9uSycU8O6fvyKCp1752FhRkmD+vB2AGdubB/Eh1jI0OcWhFpKxSoiUibMWvWLB5//HGuuOIKUlJS\n2Lx5M+CqRS5evLjG+Pn5+QwcOJBevXrVGLZixYqq/5OTk6veUQsLC2PevHnMmzevxjSPPvpowCb0\n7777bu6+++6q7sLCwmrDH3roIR566KEa0y1YsKDq/0WLFjFz5swa44hI06qwlvR9uWzY7QVme3PY\nm+1aio0MNwzu3pEbR/Zi1fYjdIiO4LVZY0OcYhFpqxSoiUibMXToUC699FLKy8ur3gGrS3x8PC+/\n/PJpSFnjJSQkMG3atFAnQ6TNsNZypLCEbQcL+eJQAV8czGdzZh7HT5TzX099AMBZ8dEM692JWy5I\nYWjvBM7t0ZGYSFe2qPVLEWluCtREpE257bbbQp2EZjFjxoxQJ0FaEVVnrK6sooJ1Gdl8caiAbQcL\nvMCsgJzjpVXjJLWPItwYzoqL5ueTUhnaO4HuCWq8R0RCR4GaiIiItAl5x0vZfriA7YcL2X6okO2H\nC9i4J5cT5RVM+ZMLXttHhTOoaxwTU7tydtc4zj4rjkFd40juEF0V4H4tTe+CikjoKVATERGRVqWs\nvILjJ8p58aPd7DjkBWaHCzlSUFI1TkxkGAO6dCAuJoLYqHAe+K9Uzu4aR4+E2FP6XIeIyOmmQE1E\nRKQVOJOrM5aVu++UvbftCCu3HeGTfXkA/Py1zXSIjmBAlw6MH9SZgWd1YGCXOAZ06UCPhFjCwkzV\ndrv8nLNCuQoiIg2mQE1ERCREzuTgqz6ZuUWs3HaE97YdYdWOoxQUlxFmYFjvTvRMiKV9dAQLbhtJ\n1/gYPSETkTZJH7wWkTajqKiISy65hPLycjIzM5kyZUrA8caPH8+6devqnNd9993HO++8U+c4JSUl\nXHHFFQwZMoSXXnqpQWnNyMhg0aJFDZoGYPr06VUfxZ46dSrbt29v8DxEWqKKCkvu8VIeenMrVz7+\nHmMeWcacVz9l095cvnZeN/548zA2/nwCS+4cQ49OsSS0i6RbR1VjFJG2S0/URNoAY8xVwJNAOPC8\ntfYRv+HjgCeANGCqtXaJz7BbgZ95nQ9Za/9yelLd9ObPn8/kyZMJDw+ne/fuVQHNqXjwwQfrHWfj\nxo2UlpayadOmBs+/MlC76aabTiV5gPuQ96OPPspzzz13yvMQCYXCkjI+P5DP1gP5bNnv/m7OzMNa\n2JV1jNF9E7lxZC/GDerMwC4dFIyJyBlJgZpIK2eMCQf+AFwJ7APWGmNet9Zu9RltDzAd+JHftInA\n/cAIwALrvWlzGpWof8+Bg582ahY1dD0Prn6kzlEWLlxY9ZQqIyODSZMmsXnzZoqKipgxYwZbt27l\nnHPOoaioqN7FTZ8+nUmTJjFlyhRSUlK49dZbeeONNygtLeXll18mMTGRb33rWxw5coQhQ4bwyiuv\nkJubyw9/+EMKCwtJTk5mwYIFdOvWjR07dnDHHXdw5MgRwsPDefnll5kzZw6fffYZQ4YM4dZbb2X2\n7NnMmTOHFStWUFJSwl133cXMmTOx1nL33XezbNky+vbti7W2Ko0XX3wx06dPp6ysjIgIFectgaoy\n1nQ4v5gtB/LZmun9DuSTkXWMyqzcqV0kqd070jU+hviYCF67ayyxUfV/B1FEpK3TmV2k9RsF7LDW\n7gQwxiwGvg5UBWrW2gxvWIXftBOB/1hrs73h/wGuAv7e/MluWidOnGDnzp2kpKTUGPb000/Trl07\n0tPTSU9PZ9iwYQ2ef3JyMhs2bOCPf/wjjz32GM8//zzPP/88jz32GG+++SalpaVMmzaNf/3rX3Tu\n3JmXXnqJe++9l/nz53PzzTczZ84crrvuOoqLi6moqOCRRx6pmhbg2WefpWPHjqxdu5aSkhIuuugi\nJkyYwMaNG/niiy/49NNPOXToEIMHD676VlxYWBgDBgzgk08+Yfjw4Y3afiJNIb+4lPS9eWzam8MX\nhwo4VlLGqHnvVg3vndiOwd3imTy0B4O7x5PavSNnxUdjzMlGPxSkiYg4CtREWr8ewF6f7n3A6EZM\n26PRKarnyVdzOHr0KAkJCQGHrVy5ktmzZwOQlpZGWlpag+c/efJkAIYPH86rr75aY/gXX3zB5s2b\nufLKKwEoLy+nW7duFBQUsH//fq677joAYmJiAs5/6dKlpKenV1XXzMvLY/v27axcuZJvfvObVdU5\nL7vssmrTdenShczMTAVqctqVlVew7VAhG/fmsGlPLpv25rLjSGHVk7KYyDA6xkZy16UDGNwtnnO6\nxxMfExnaRIuItCIK1ERav0Avb9gA/Ro1rTHmduB2gN69ewc5+9MnNjaW4uLiWoc39h2X6OhoAMLD\nwykrK6sx3FpLamoqq1evrtY/Pz8/qPlba/n973/PxIkTq/V/66236kx7cXExsbGxQS1DTo2qMzpH\nCkrIPnaCwpIybnhmNZ/uy6OotBxw1ReH9u7ENed3Z2jvBNJ6JnD7X12DPTMu6hvKZIuItFpq9VGk\n9dsH9PLp7glkNvW01tpnrbUjrLUjOnfufEoJbU6dOnWivLw8YLA2btw4Fi5cCMDmzZtJT0+vGnbL\nLbewZs2aRi//7LPP5siRI1WBWmlpKVu2bCE+Pp6ePXvy2muvAa6lyOPHjxMXF0dBQUHV9BMnTuTp\np5+mtLQUgG3btnHs2DHGjRvH4sWLKS8v58CBAyxfvrzacrdt20Zqamqj0y/iq6y8gi2Zeby4OoMf\nvLSJcY8uZ+Qv32H74UIO5hVTUlbBjSN78eTUIbz34/Fs+PmVzJ8+ktmXD+TigZ3pGKsnZyIijaUn\naiKt31pgoDGmL7AfmAoE25Tg28A8Y0wnr3sC8NOmT+LpMWHCBFatWsUVV1xRrf+dd97JjBkzSEtL\nY8iQIYwaNapqWHp6Ot26dWv0sqOioliyZAmzZ88mLy+PsrIyvv/975OamsqLL77IzJkzue+++4iM\njOTll18mLS2NiIgIzj//fKZPn85///d/k5GRwbBhw7DW0rlzZ1577TWuu+46li1bxnnnncegQYO4\n5JJLqpZ56NAhYmNjmyT9cmYrK69g+ReH2bA7h/W7c/hkby7HTrinZZ3johneuxPTLujDPzfuo31U\nBC/fOSbEKRYRafsUqIm0ctbaMmPMLFzQFQ7Mt9ZuMcY8CKyz1r5ujBkJ/BPoBFxjjHnAWptqrc02\nxvwCF+wBPFjZsEhrNGvWLB5//HGuuOIKUlJS2Lx5M+CqRS5evLjG+Pn5+QwcOJBevXrVGLZgwYKq\n/zMyMqr+HzFiBCtWrADc99jGjx9fNWzIkCGsXLmyxrwGDhzIsmXLavR/9913q3XPmzePefPm1Rjv\nqaeeqtEPYNGiRcycOTPgMKnbmVyd0VrLnuzjrMvIYd3ubNK9Kowz/ryW8DDDOd3imDK8J8P6dHIf\nl+508ltl73x2KMSpFxE5cyhQE2kDrLVvAW/59bvP5/+1uGqNgaadD8xv1gSeJkOHDuXSSy+lvLyc\n8PD6W46Lj4/n5ZdfPg0pax4JCQlMmzYt1MmQFq60vIItmfmsy8hm/e4c1mbkcLSwBIC4mAiiI8JI\n6hDFr6ecz/m9OtIuSpcGTe2lmRfyjztCnQoRaW1UGotIm1LZdP2ZYMaMGaFOgrRAx0rKyD1eSkFx\nKVOfXc2mvbkUl7ovc/TsFMvFA5MZ3qcTI1MSGdilA9987iMALuyfFMpki4iIHwVqItJkrLWNbl1R\nGs/3o9jS9hWdKGf97hxW7zzKRzuz+WRvLmUVLg8kdYhm6sjejExJZERKJ86KD/x5CBERaXkUqIlI\nk4iJiSErK4ukpCQFayFkrSUrK6vW77W1ZWfKe2fFpeVs2JPDR19msXpnFpv25lJabgkPM5zXoyPf\nHdePZZ8dIi4mkiVq9KPZtPV8JiKhp0BNRJpEz5492bdvH0eOHAl1Us54MTEx9OwZ8JVEaWXyi0vZ\neeQYO48Usjf7OAUlZaQ9sJQTZRWEGTi3R0duu6gvF/RPYkSfTsR5H5TesDsnxCkXEZHGUqAmIk0i\nMjKSvn31YVuRhrLWsuuoC8Z2HjnGzqOFfHnkGDuPHKtq9KNSu6hwbrmgDxf2T2Jk30TiY/S9sqak\np2Qi0pIoUBMRkRarLVZnzD52go93ZvHhl1mk78ujuLScSx9bUTW8U7tI+nXuwKVnd6Zf5w7069ye\n/p3b89NXPyXMGH42aXDoEi8iIqeNAjUREZFmlFdUyppd2Xz45VFWf5nF5wcLAPd0LCrCkNAuhh9c\nOYj+ndvTL7kDndpHBZxPmN79PCVtKcgXkTOLAjUREZEmVF5hWf7FYVZ/mcXqL7PYkplHhYXoiDBG\npHTiRxMGcWH/JNJ6JvCt5z8G4IYRNT+6LiIiZzYFaiIiclq1teqMh/KLWZeRw7rd2Wzen8exE+XM\n+PNaosLDGNI7gbsvG8iF/ZMY2juB6Ij6P8Qu9WsreUdEpC4K1ERERIJUUWHZcaSQtRnZrM/IYe3u\nbPZmFwEQExlGZHgY3TvG8OtvnM+w3p2IjVJgJiIip0aBmoiISC2KS8vJLy6loLiM2xasZf3uHPKK\nSgFI7hDNiD6duPXCFEakJJLaPb6qKuNFA5JDmexWSU/JRESqU6AmIiKN1laqMxYUl7J+dw5rM7JZ\nuyuHTftyOVFWAUBMZDhfPa8rw/skMjKlE70T2+nj7iIi0mwUqImIyBnrSEEJ6zKy+XhXNmszsvns\nQD4VFiLCDKk9OnLrhX1474sjdIiJ4NXvXRTq5LYqrT1oFxEJNQVqIiJyxjhcUMzqL7PYefQYBcWl\njPzlO4B7v2xor07cfdlARvVNZGjvBNpFuVNk+r7VoUyyiIicoRSoiYhIm5V97AQf7XTN5K/emcWO\nw4UAhBtDXEwEP7hiECP7JnJu945ERYSFOLUtn56SiYicPgrURESkhtb6zllZeQVLtxxktRec+X5c\nemRKIlOG92RM/yQeenMrxhhmXtI/xCkWEREJTIGaiIi0WsWl5azNyOaDHVlV3zC7/cX1VR+X/vHE\ns7mgXxJpPTsSGX7yiZkaATmptQXjIiJnCgVqIiLSapRXWD7dn8cHO47ywY6jrNudw4myCiLCDLGR\n4fRIiOHxG4YwRB+XFhGRVk6BmojIGaI1Vme01lJcWsFfV2ewavtRPtqZRX5xGQDndIvnlgv6cNHA\nZEalJHLbgrUAjO6XFMIUtwytaR+LiEhgCtRERKTFOFZSRvq+PDbuzWHTnlw27s2ltNySvj+Pnp1i\n+ep53bhoQDIX9k8iuUN0qJMrIiLSbBSoiYi0Yq3xKVml8grLjsOFbNqbw6a9uWzck8u2QwVUWDe8\nb3J74mMiiY+J4C+3jaZ3UrvQJlhEROQ0UqAmIiKnxdHCEjbuyWVv9nEKS8o4/4GlFJa4aowdYyM5\nv1cCE1O7MqR3AkN6JtCpfVRVIKogrXUG4yIicuoUqImISJM7UVbB1gP5bNyTw8Y9uWzcm8Pe7KKq\n4e2iwrl+WE+G9EpgSO8E+ia1JyxMLTGKiIhUUqAm0gYYY64CngTCgeettY/4DY8G/goMB7KAG621\nGcaYFOAz4Atv1I+stXecrnRL21FSVk5hcRm/eHMrG/fksDkznxNlFQB0jY9haO8Epl3Qh6G9O/Gr\nf39OeJjhF9eeG+JUh56ekomISG0UqIm0csaYcOAPwJXAPmCtMeZ1a+1Wn9G+DeRYawcYY6YCvwJu\n9IZ9aa0dcloTLXVq6e+dWeveLft4VzZrM7JZuyubzLxiAPbm7CatZ0emj0lhqPe0rFvH2GrTh+vJ\nmYiISL0UqIm0fqOAHdbanQDGmMXA1wHfQO3rwFzv/yXAU0Zf/JUglZZXsCUzn7W7slmTkc26jGxy\njpcC0CUumpF9E4ncl0dcTAT/vOuiah+WPtO01OBaRERaHwVqIq1fD2CvT/c+YHRt41hry4wxeUDl\nx6b6GmM2AvnAz6y17zdzeqUFs9ZyuKCE3OMnKCwp5+bnP2LjnlyOnygHICWpHVeccxYj+yYyKiWR\nPkntMMZUPQU8k4M0ERGRpqRATaT1C/RkzAY5zgGgt7U2yxgzHHjNGJNqrc2vsRBjbgduB+jdu3cj\nkywtQXmFZdfRY2w9kM+WzDy2Zubz2YF8jhaeqBonPjaSG0b0YmRKIiNTOtElPiaEKQ4NPSUTEZFQ\nUKAm0vrtA3r5dPcEMmsZZ58xJgLoCGRbay1QAmCtXW+M+RIYBKzzX4i19lngWYARI0b4B4JSj1C/\nd1ZRYTl+oozC4jLu/eenbD2Qz+cHCigqdU/KIsMNg86K47KvdGFwt3heWreXdlERvHLnmJCkV0RE\n5EynQE2k9VsLDDTG9AX2A1OBm/zGeR24FVgNTAGWWWutMaYzLmArN8b0AwYCO09f0qW5lHnvla3Z\nlc3Hu7JYm5FDXpF7r+zosUwGd4vnm6N6M7h7PKnd4+nfuQNRESerLf5788FQJV1ERERQoCbS6nnv\nnM0C3sY1zz/fWrvFGPMgsM5a+zrwAvCiMWYHkI0L5gDGAQ8aY8qAcuAOa2326V8LaaySsnLS9+Wx\nZlc2H+3MYsPuHI5575X1S27P1ed2Zc2ubOJiInjtros409uSUXVGERFp6RSoibQB1tq3gLf8+t3n\n838x8I0A070CvNLsCWyjQl2dcU/WcTJzi8gtKuW8uUurvlt29llxXD+8J6O8Bj8q3yurTO+ZHqSJ\niIi0BgrURERakczcIv43/QBvpmfyyb48ANpFhTPtgj6M7pvIyJREOrWPCnEqQ0NPyUREpC1RoCYi\n0sIdLijm358e5I1PMlm3OweA83p05KdXf4V/f3qA6Mhwfj5pcIhTKSIiIk1JgZqIiCfUVRl9lZZX\nsOjjPbzxSSYf78qiwsJXusbxowmDmJTWnZTk9gAs+/xwiFPavFrCvhAREQkFBWoiIi1ASVk5n+zN\n4+OdWXx2IJ/84jI27MmlX+f2zLpsINekdWPgWXGhTqaIiIicJgrURERCoLi0nE17c/l4p9dK454c\nSsoqMAZiI8Pp1jGG528dweBu8Wr8Q0RE5AykQE1E2rSWUp2xosJSUFLGb/+zjY92ZrFxby4nvMBs\ncLd4bh7dhwv6JTKqbyIzX1wPQGr3jiFNc3MJ9b4QERFpDRSoiYg0g8KSMjbszuHjXVms2ZXNut05\nWGDboQJSu3fklgv6cEG/JEamJNKxXWSok9toCr5ERESalgI1EZEmkHe8lLUZ2azJyObjnVlszsyn\nvMISHmY4r0dHunaMIS4mgiV3jiE+pvUHZiIiItK8FKiJiJyC7GMnyD52gvziUq5+8n0+P5iPtRAV\nHsaQXgl8b3x/RvVNZFjvTrSPjqiqgtlagjQ9IRMREQktBWoiIkEoLCljza4sPtyRxYdfZrH1QD4A\nYQYGdonjh1cMYlTfRM7vlUBMZHiIUxuYgi8REZHWQ4GaiLQ6p6OBkOLScjbsyWH1l1l8sOMon+zL\no7zCEhURxog+nfjRhEG89ekB2kdH8LfvjG62dIiIiMiZSYGaiAjuA9MFxaXkF5dx8/MfsS7DNZcf\nHmZI69mROy/pz5j+SQzr06nqidn724+GONUiIiLSVilQE5EzUll5BZsz81n9ZRard2axLiOb4yfK\nAegQHcG3LujDmP5JjOqbSFwLfq9M1RlFRETaJgVqInJGKK+wbM3MZ/XOo6z+Mou1GTkUlpQBMLBL\nB6YM78mHX2YRHxPBq9+7KMSpFRERkTOdAjURaRGa472zvdnHOZhfTF5RKUMeXEpBsQvM+nVuz9eH\ndOfC/kmM7ptE57joamkQERERCTUFaiLSZpSWV7B+dw7LPz/Mss8Ps/1wIQDREWFMHtaDC/olcWG/\nJLrEx4Q4pSIiIiJ1U6AmIq1aVmEJK744wrIvDrNy2xEKisuIDDeM6pvI1FG9eX3TfmIiw3l4clqo\nkxo0vXcmIiIiCtREpFUpK6+gsKSMvOOlXPuHD/hkXy7WQue4aK4+tyuXfaULYwd2pkO0K96WbjkY\n4hSLiIiINJwCNRFp0crKK9iSmc9HO7P4aGf1RkCSOkTx/csHcdlXupDaPZ6wMBPi1NZOT8lERESk\nIRSoiUizOZUGQsrKK9h6wDWb7x+Y9fcaAfl4ZxbxsZFqnVFERETaLAVqIhJSxaXlpO/LIzO3iILi\nMoY++B8KSk62zvhfQ7pzYb8kRvdLpEucawSkpbTOqKdkIiIi0lwUqInIaWOtZV9OERv25LBxTy4b\n9uSwNTOfsgoLQExkGJOH9eSCfklc0DdRrTOKiIjIGUuBmog0m4oKy7ETZTzz3pds2JPDhj25HCko\nASA2Mpzze3Xk9nH9GNa7E39csYPI8DDmXXdeiFMtIiIiEnoK1ETaAGPMVcCTQDjwvLX2Eb/h0cBf\ngeFAFnCjtTbDG/ZT4NtAOTDbWvv2qaajvMKyeX8eq3Yc5YMdR1m3JwdrYeuBz+mT1I6xA5IZ1juB\nob078ZWucUSEh1VN+9z7O091sU1GVRlFRESkpVCgJtLKGWPCgT8AVwL7gLXGmNettVt9Rvs2kGOt\nHWCMmQr8CrjRGDMYmAqkAt2Bd4wxg6y15cEs21rL7qzjVYHZh19mkVdUCsA53eI5Ky6G+JgIFt1+\nAckdoptsnUVERETaOgVqIq3fKGCHtXYngDFmMfB1wDdQ+zow1/t/CfCUMcZ4/Rdba0uAXcaYHd78\n6mytI7eolJ8sSWfVjqPszy0CoHvHGCamnsVFA5IZ0z+ZznHRbJk3FoohucOqelfivqwfe//VP25D\n6CmZiIiItEYK1ERavx7AXp/ufcDo2sax1pYZY/KAJK//R37T9qhvge1yvmDk5rn0P2sMCRdezsjU\nQaQktcPFfs2vuYI6ERERkZZCgZpI6xcoOrJBjhPMtG4GxtwO3A6Q2r0910evwRx6Fw49BJ+nQb/x\n7tf7QoiMDTbtpyS1W8dmnb+IiIhIqClQE2n99gG9fLp7Apm1jLPPGBMBdASyg5wWAGvts8CzACNG\njLDmfz6CzI2wc4X7rf4jfPAkhEdD79HQ71JSE8MgqkMTrGIj/Plr7u+M/w1tOkREREQaQIGaSOu3\nFhhojOkL7Mc1DnKT3zivA7fi3j2bAiyz1lpjzOvAImPM47jGRAYCa4JaangE9Brpfpf8GEoKYc/q\nk4Hbuw+48UwY/OUa6DkKeo2CniOhXWKN2ekpmYiIiMhJCtREWjnvnbNZwNu45vnnW2u3GGMeBNZZ\na18HXgBe9BoLycYFc3jj/QPX8EgZcFewLT7WEN0BBl7pfgCFh2HBJCjJh+I8WPVbqJx10gAvcBvp\n/nY559Q3gIiIiEgbpEBNpA2w1r4FvOXX7z6f/4uBb9Qy7S+BXzZ5ojp0gfad3W/G/8KJY66q5N41\nsG8tbF8Knyxy40bFQVgYRMfDrvfdU7fImCZPUr1UTVJERERaCAVqInJ6RLWHlLHuB2At5OyCvWth\n3xrY9HfI2wt/meTec+s58uT4oQrcREREREJEgZqIhIYxkNjP/c6/EQ5/DhVlMPYHkPE+ZKyC934F\n7z3iArdeo04GbrbCvfsmIiIi0kYpUBOR5tPQKoRhEXD2Ve4HUJTrGijJWOWCtxWPUPVVgZh4+PAp\nGHQVJA9o6pQHR1UlRUREpJkoUBORlis2Ac6+2v3gZOD25g+hOBeW3ut+if1g4EQYNAH6XAQR0aFN\nt4iIiEgjKVATkdajMnD78CnXfe0fXaMk296G9X+Gj592323rNx4GTnC/lkJP30RERKQBFKiJSOvV\nqQ+M+q77nTgOu1bC9rdh21L4/E03TlR7iE2Ene+599wiY0Ob5mAoqBMRETnjKVATkbYhqt3J99us\nhcNb3ZO2Vb91rUn+9b8gPMp9ty1lLPS92LUmqWqSIiIi0gIpUBORtscYOCvV/Xa861qTvPiHrkGS\nXe/Dykdda5IRMS5Y6zsOUi5Wa5IiIiLSYihQE5GWoTmr+YVFwKCJ7gcnGyXZ9T5krITl8wDrgrTo\neHj/N9B3PHQ7H8JVTIqIiMjppysQETnz+LcmeTwbdn8Ib/0IivPg3QeBB13Q1uci6HeJe+rW+RwI\n09nlfUsAABgQSURBVBM3ERERaX4K1ERE2iXCOZPgo6dd9zcWuCdtu7zftn974yW7gK3yZ62rZhlK\nanhERESkTVKgJiLir0NnOPd69wPI3eOqSe56zwVuW151/cOj3Ye31//FNVCS2C/0gZuIiIi0CQrU\nRKT1Od1PjxJ6w9Cb3c9ayNoBO1fAiofd+25vzHbjdTgL+oxx1SX7XASdv9Kyqkrq6ZuIiEiroUBN\nRKQhjIHkge635TUXuF3zBOxe5d5zy/gAtvzTjRub6AVuY6Ck0H3TrbVQUCciIhJSCtRERBrDGOg8\nyP1G3OYCt5wMF7Tt/tAFcJUf3zbhsPAGV00y5SLoqlYlRUREJDBdIYiINCVjILGv+w292fXL2w8v\nXgvF+ZC9E7a/7fpHxUHvC7zAbSx0G9J6Azc9gRMREWlSrfSKQEQkSC0hcOjYA9p3cb8Z/wsFB2H3\nB5CxylWVfOd+N15UB+g1GvL2QmynltGqpIiIiISEAjURkdMtrmv1ViULD1cP3HJ3u99TI73xJkPn\ns0Ob5qakp28iIiL1UqAmIhJqHbpA6nXuB/D8lXA8ywV07/0K3nsEuqS6gO3cye4zACIiItKmKVAT\nEanUUp7whEdBXDeY/qarJrn1X7D5VVj2C/frPhRSJ58M7NoyPX0TEZEzlAI1EZGWLK4rjJ7pfrl7\nYetrsPkV+M/P3S86DmKTYP96tSIpIiLShuiMLiLSWiT0gjF3u1/2TveU7f3fQG4GPHeZ1xjJKO/b\nbWOhxzCIiA51qk8fPX0TEZE2RIGaiMipCmVAkNgPxv0IvlwOZSfgwjtPfrtt2UNunPBo6DnSfbOt\nzxioKIew8NClWURERIKmQE2kFTPGJAIvASlABnCDtTYnwHi3Aj/zOh+y1v7F678C6AYUecMmWGsP\nN2+qpclFRJ1saATgeDbsWe0Fbh/Ayl+DrQAMRLWHN74PXc+DbudDl8EQ1S6kyQ8JPX0TEZEWToGa\nSOs2B3jXWvuIMWaO1/0T3xG8YO5+YARggfXGmNd9ArqbrbXrTmei/3979x/lVV3ncfz5nhlg5DcI\nyMgwokJq+YNi8kdqkVL229qltt/YqbW2Oqc97bb93m3bLCpbO1tbholRWWplZbZlSrJi/gIJgfwt\nEYwiiCQCGqB89o/PnZ2RZpCBmfl+73eej3M+537v59775XO958D35edzPx/1saFj4ehX5wJ5oe11\nt8EvPgQ7tsIfroTbL8nHog4OngoTj8/hbeJx+bMkSaoog5pUbmcDM4vPC4BF7BHUgLOAa1NKmwEi\n4lrgFcAP+6eJqrjGkTBtFoyZkvfPuTovqv3wSli/Im/X3QqrftxxTf3g3Pu2+Ctw2Kl5psmB9L6b\nJEkVZlCTyu2QlNJ6gJTS+oiY0MU5k4B1nfbbirp2l0TE08BPyMMiU1d/UEScC5wL0NLS0httH1iq\naYhdBIxuyaW91w3ykMkNq3J4+91XYcc2WPjZfKyhESbNgJZT8vtuk0/MM04OBA6TlCRVgEFNqnIR\ncR0wsYtDn9zXr+iirj2MvS2l9GBEjCAHtXcA3+3qS1JK84B5AK2trV2GOZXc0LFw+ItzuedXue5N\nC4r33W6GtTfBjRfA4vMh6vMwycNelMvTu6B+UGXbL0lSDTGoSVUupTSru2MRsSEimoretCagq4lA\n2ugYHgnQTB4iSUrpwWK7NSJ+AJxIN0FNA9SwcXDMa3OB/I5b25JiopKbYel8uOUb+VhDI1x5bp5p\nsrkVDjl24IU3e98kSb3EoCaV21XAHGBusf15F+dcA3w+IsYU+y8HPh4RDcDolNKmiBgEvAa4rh/a\nrGdTzT/yh4yAI8/IBeCpHfDQ8hzQdmyF1YtgxeX5WENjfrdt0owivL0QRk3q9qslSVIHg5pUbnOB\nKyLi3cBa4I0AEdEKvC+l9J6U0uaI+A9gSXHNZ4u6YcA1RUirJ4e0i/r/FlRqDUOg5SQY1Zz3z7ka\ntrTlXre2pXl72zy4+ev5+IhD4ekdMHhE7pVrmj4wlweQJOlZGNSkEkspPQqc2UX9UuA9nfbnA/P3\nOGc7MKOv26gBJgJGT86lfV23p3bAw6uK8LYE7v4FPPEoXPLK/K7bIc/NvW2TWvP24KlQV1fZ++gv\nDpWUJHXDoCZJ6lsNQ6B5Ri68L4eTp3fC6R/OvW4PLoWVP87vuwEMGQWTXpBD2xObYcjIijZfkqRK\nMKhJUpmVtSemfjAc9cpcAHbvhk335tDWVpTF50PanY/Peykc8RI4YiZMPhkGNVaq5ZVj75skDSgG\nNUlS5dXVwYSjc3n+23Pdzu1w8Vnwly25V+6mr+XlARoaYfJJObQdMROaToC6+sq1XZKkPmBQkyRV\np8HDoHFULu/6ZZ5V8k8355klVy+Chf+eS+OovPb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"text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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0/OuZwLwzthapAzSFIh6n/EEOg4BdwEfGmDFnWc0AH1pro8t/dbXW\nTqlsl04qVeSiKMDFHR0FGlT2ojGmPbDfWvsOZR3t+pa/VFx+VQ7wIzDCGNO8fJsm5dtB2c/FiY5w\nIyl73JVInaMpFHE71toDxpifTNnDiv9trf39aasMBn5vjCmm7JmYJ67ApwOpxpjV1tpRxpj/puwJ\nSz5AMfAwsB04BvQ0xqwCcoHbnX9UItWnNzFFTqPbDcVdaApFRMRN6Qpc3JYxpjfw0WmLC6218a6o\nR6S2KcBFRNyUplBERNyUAlxExE0pwEVE3JQCXETETSnARUTc1P8BFstpdoNudu0AAAAASUVORK5C\nYII=\n", - "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -407,9 +382,78 @@ } ], "source": [ - "evohalfherd['std'].plot()\n", - "evodumb['std'].plot()" + "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": [ + "" + ] + }, + "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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\n3OWJIjKoWLtAEVknIosvtCP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hskm0v6z7ufKwNpEMeHgBO378Eae8W9A/4VckPXMN+7avq5P9rnn/afqd/o7E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\nT3LClzTnNN6xN9VH98YY43F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adNUvv179yuPXrf6yjYSh0uihoaE0a9bsset3dnbmxIkT/O9//2P27NksXryY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\nq6opwHQgIkuaCGCye/wV0F2cV5wjgOmqmqyqe4AYoLOqHlH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+ "text/plain": [ + "" + ] + }, + "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": { diff --git a/examples/newsspread/NewsSpread.yml b/examples/newsspread/NewsSpread.yml index 6713d04..787a46b 100644 --- a/examples/newsspread/NewsSpread.yml +++ b/examples/newsspread/NewsSpread.yml @@ -68,7 +68,7 @@ network_agents: - agent_type: HerdViewer state: has_tv: true - id: infected + id: neutral weight: 1 - agent_type: HerdViewer state: @@ -95,7 +95,7 @@ network_agents: - agent_type: HerdViewer state: has_tv: true - id: infected + id: neutral weight: 1 - agent_type: WiseViewer state: @@ -121,7 +121,7 @@ network_agents: - agent_type: WiseViewer state: has_tv: true - id: infected + id: neutral weight: 1 - agent_type: WiseViewer state: diff --git a/examples/newsspread/newsspread.py b/examples/newsspread/newsspread.py index cdb3ee0..8934245 100644 --- a/examples/newsspread/newsspread.py +++ b/examples/newsspread/newsspread.py @@ -1,5 +1,4 @@ -from soil.agents import BaseAgent,FSM, state, default_state -import random +from soil.agents import FSM, state, default_state, prob import logging @@ -10,70 +9,73 @@ class DumbViewer(FSM): ''' defaults = { 'prob_neighbor_spread': 0.5, - 'prob_neighbor_cure': 0.25, + 'prob_tv_spread': 0.1, } @default_state @state def neutral(self): - r = random.random() - if self['has_tv'] and r < self.env['prob_tv_spread']: - self.infect() - return + if self['has_tv']: + if prob(self.env['prob_tv_spread']): + self.set_state(self.infected) @state def infected(self): for neighbor in self.get_neighboring_agents(state_id=self.neutral.id): - prob_infect = self.env['prob_neighbor_spread'] - r = random.random() - if r < prob_infect: - self.set_state(self.infected.id) + if prob(self.env['prob_neighbor_spread']): neighbor.infect() - return def infect(self): self.set_state(self.infected) + class HerdViewer(DumbViewer): ''' A viewer whose probability of infection depends on the state of its neighbors. ''' level = logging.DEBUG - + def infect(self): infected = self.count_neighboring_agents(state_id=self.infected.id) total = self.count_neighboring_agents() prob_infect = self.env['prob_neighbor_spread'] * infected/total self.debug('prob_infect', prob_infect) - r = random.random() - if r < prob_infect: + if prob(prob_infect): self.set_state(self.infected.id) + class WiseViewer(HerdViewer): ''' A viewer that can change its mind. ''' + + defaults = { + 'prob_neighbor_spread': 0.5, + 'prob_neighbor_cure': 0.25, + 'prob_tv_spread': 0.1, + } + @state def cured(self): prob_cure = self.env['prob_neighbor_cure'] for neighbor in self.get_neighboring_agents(state_id=self.infected.id): - r = random.random() - if r < prob_cure: + if prob(prob_cure): try: neighbor.cure() except AttributeError: self.debug('Viewer {} cannot be cured'.format(neighbor.id)) - return def cure(self): self.set_state(self.cured.id) @state def infected(self): - prob_cure = self.env['prob_neighbor_cure'] - r = random.random() - if r < prob_cure: - self.cure() - return - return super().infected() + cured = max(self.count_neighboring_agents(self.cured.id), + 1.0) + infected = max(self.count_neighboring_agents(self.infected.id), + 1.0) + prob_cure = self.env['prob_neighbor_cure'] * (cured/infected) + if prob(prob_cure): + return self.cure() + return self.set_state(super().infected) diff --git a/soil/__init__.py b/soil/__init__.py index a0db181..80572f0 100644 --- a/soil/__init__.py +++ b/soil/__init__.py @@ -35,8 +35,14 @@ def main(): help='Do not store the results of the simulation.') parser.add_argument('--pdb', action='store_true', help='Use a pdb console in case of exception.') - parser.add_argument('--output', '-o', type=str, + parser.add_argument('--graph', '-g', action='store_true', + help='Dump GEXF graph. Defaults to false.') + parser.add_argument('--csv', action='store_true', + help='Dump history in CSV format. Defaults to false.') + parser.add_argument('--output', '-o', type=str, default="soil_output", help='folder to write results to. It defaults to the current directory.') + parser.add_argument('--synchronous', action='store_true', + help='Run trials serially and synchronously instead of in parallel. Defaults to false.') args = parser.parse_args() @@ -47,7 +53,17 @@ def main(): logging.info('Loading config file: {}'.format(args.file, args.output)) try: - simulation.run_from_config(args.file, dump=(not args.dry_run), results_dir=args.output) + dump = [] + if not args.dry_run: + if args.csv: + dump.append('csv') + if args.graph: + dump.append('gexf') + simulation.run_from_config(args.file, + dry_run=args.dry_run, + dump=dump, + parallel=(not args.synchronous), + results_dir=args.output) except Exception as ex: if args.pdb: pdb.post_mortem() diff --git a/soil/agents/DrawingAgent.py b/soil/agents/DrawingAgent.py index ccf0aa8..cd41534 100644 --- a/soil/agents/DrawingAgent.py +++ b/soil/agents/DrawingAgent.py @@ -15,4 +15,4 @@ class DrawingAgent(BaseAgent): # Outside effects f = plt.figure() nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111)) - f.savefig(os.path.join(self.env.sim().dir_path, "graph-"+str(self.env.now)+".png")) + f.savefig(os.path.join(self.env.get_path(), "graph-"+str(self.env.now)+".png")) diff --git a/soil/agents/__init__.py b/soil/agents/__init__.py index 4d88d42..42697fd 100644 --- a/soil/agents/__init__.py +++ b/soil/agents/__init__.py @@ -12,9 +12,9 @@ from copy import deepcopy from functools import partial import json - from functools import wraps +from .. import utils agent_types = {} @@ -41,7 +41,7 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent): super().__init__(**kwargs) if not hasattr(self, 'level'): self.level = logging.DEBUG - self.logger = logging.getLogger('Agent-{}'.format(self.id)) + self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name, self.id)) self.logger.setLevel(self.level) @@ -140,20 +140,24 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent): def state(func): + ''' + A state function should return either a state id, or a tuple (state_id, when) + The default value for state_id is the current state id. + The default value for when is the interval defined in the nevironment. + ''' @wraps(func) def func_wrapper(self): - when = None next_state = func(self) + when = None + if next_state is None: + return when try: next_state, when = next_state - except TypeError: + except (ValueError, TypeError): pass if next_state: - try: - self.state['id'] = next_state.id - except AttributeError: - raise ValueError('State id %s is not valid.' % next_state) + self.set_state(next_state) return when func_wrapper.id = func.__name__ @@ -212,6 +216,116 @@ class FSM(BaseAgent, metaclass=MetaFSM): if state not in self.states: raise ValueError('{} is not a valid state'.format(state)) self.state['id'] = state + return state + + +def prob(prob=1): + ''' + A true/False uniform distribution with a given probability. + To be used like this: + + .. code-block:: python + + if prob(0.3): + do_something() + + ''' + r = random.random() + return r < prob + + +def calculate_distribution(network_agents=None, + agent_type=None): + ''' + Calculate the threshold values (thresholds for a uniform distribution) + of an agent distribution given the weights of each agent type. + + The input has this form: :: + + [ + {'agent_type': 'agent_type_1', + 'weight': 0.2, + 'state': { + 'id': 0 + } + }, + {'agent_type': 'agent_type_2', + 'weight': 0.8, + 'state': { + 'id': 1 + } + } + ] + + In this example, 20% of the nodes will be marked as type + 'agent_type_1'. + ''' + if network_agents: + network_agents = deepcopy(network_agents) + elif agent_type: + network_agents = [{'agent_type': agent_type}] + else: + return [] + + # Calculate the thresholds + total = sum(x.get('weight', 1) for x in network_agents) + acc = 0 + for v in network_agents: + upper = acc + (v.get('weight', 1)/total) + v['threshold'] = [acc, upper] + acc = upper + return network_agents + + +def _serialize_distribution(network_agents): + d = _convert_agent_types(network_agents, + to_string=True) + ''' + When serializing an agent distribution, remove the thresholds, in order + to avoid cluttering the YAML definition file. + ''' + for v in d: + if 'threshold' in v: + del v['threshold'] + return d + + +def _validate_states(states, topology): + '''Validate states to avoid ignoring states during initialization''' + states = states or [] + if isinstance(states, dict): + for x in states: + assert x in topology.node + else: + assert len(states) <= len(topology) + return states + + +def _convert_agent_types(ind, to_string=False): + '''Convenience method to allow specifying agents by class or class name.''' + d = deepcopy(ind) + for v in d: + agent_type = v['agent_type'] + if to_string and not isinstance(agent_type, str): + v['agent_type'] = str(agent_type.__name__) + elif not to_string and isinstance(agent_type, str): + v['agent_type'] = agent_types[agent_type] + return d + + +def _agent_from_distribution(distribution, value=-1): + """Used in the initialization of agents given an agent distribution.""" + if value < 0: + value = random.random() + for d in distribution: + threshold = d['threshold'] + if value >= threshold[0] and value < threshold[1]: + state = {} + if 'state' in d: + state = deepcopy(d['state']) + return d['agent_type'], state + + raise Exception('Distribution for value {} not found in: {}'.format(value, distribution)) from .BassModel import * diff --git a/soil/environment.py b/soil/environment.py index 0a9e62b..c2f38f5 100644 --- a/soil/environment.py +++ b/soil/environment.py @@ -1,16 +1,16 @@ import os import sqlite3 import time -import weakref import csv import random import simpy from copy import deepcopy +from networkx.readwrite import json_graph import networkx as nx import nxsim -from . import utils +from . import utils, agents class SoilEnvironment(nxsim.NetworkEnvironment): @@ -22,21 +22,18 @@ class SoilEnvironment(nxsim.NetworkEnvironment): default_state=None, interval=1, seed=None, - dump=False, - simulation=None, + dry_run=False, + dir_path=None, *args, **kwargs): self.name = name or 'UnnamedEnvironment' if isinstance(states, list): states = dict(enumerate(states)) self.states = deepcopy(states) if states else {} self.default_state = deepcopy(default_state) or {} - self.sim = weakref.ref(simulation) - if 'topology' not in kwargs and simulation: - kwargs['topology'] = self.sim().topology.copy() super().__init__(*args, **kwargs) self._env_agents = {} + self.dry_run = dry_run self.interval = interval - self.dump = dump # Add environment agents first, so their events get # executed before network agents self['SEED'] = seed or time.time() @@ -44,10 +41,11 @@ class SoilEnvironment(nxsim.NetworkEnvironment): self.process(self.save_state()) self.environment_agents = environment_agents or [] self.network_agents = network_agents or [] - if self.dump: - self._db_path = os.path.join(self.get_path(), '{}.db.sqlite'.format(self.name)) - else: + self.dir_path = dir_path + if self.dry_run: self._db_path = ":memory:" + else: + self._db_path = os.path.join(self.get_path(), '{}.db.sqlite'.format(self.name)) self.create_db(self._db_path) def create_db(self, db_path=None): @@ -93,7 +91,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment): for ix in self.G.nodes(): i = ix node = self.G.node[i] - agent, state = utils.agent_from_distribution(network_agents) + agent, state = agents._agent_from_distribution(network_agents) self.set_agent(i, agent_type=agent, state=state) def set_agent(self, agent_id, agent_type, state=None): @@ -200,7 +198,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment): return self[key] if key in self else default def get_path(self, dir_path=None): - dir_path = dir_path or self.sim().dir_path + dir_path = dir_path or self.dir_path if not os.path.exists(dir_path): os.makedirs(dir_path) return dir_path @@ -227,6 +225,19 @@ class SoilEnvironment(nxsim.NetworkEnvironment): self.name+".gexf") nx.write_gexf(G, graph_path, version="1.2draft") + def dump(self, dir_path=None, formats=None): + if not formats: + return + functions = { + 'csv': self.dump_csv, + 'gexf': self.dump_gexf + } + for f in formats: + if f in functions: + functions[f](dir_path) + else: + raise ValueError('Unknown format: {}'.format(f)) + def state_to_tuples(self, now=None): if now is None: now = self.now @@ -289,3 +300,23 @@ class SoilEnvironment(nxsim.NetworkEnvironment): G.add_node(agent.id, **attributes) return G + + def __getstate__(self): + state = self.__dict__.copy() + state['G'] = json_graph.node_link_data(self.G) + state['network_agents'] = agents.serialize_distribution(self.network_agents) + state['environment_agents'] = agents._convert_agent_types(self.environment_agents, + to_string=True) + del state['_queue'] + import inspect + for k, v in state.items(): + if inspect.isgeneratorfunction(v): + print(k, v, type(v)) + return state + + def __setstate__(self, state): + self.__dict__ = state + self.G = json_graph.node_link_graph(state['G']) + self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents)) + self.environment_agents = self._convert_agent_types(self.environment_agents) + return state diff --git a/soil/simulation.py b/soil/simulation.py index bb345f2..f1b7acf 100644 --- a/soil/simulation.py +++ b/soil/simulation.py @@ -5,14 +5,14 @@ import sys import yaml import networkx as nx from networkx.readwrite import json_graph - -from copy import deepcopy +from multiprocessing import Pool +from functools import partial import pickle from nxsim import NetworkSimulation -from . import agents, utils, environment, basestring +from . import utils, environment, basestring, agents from .utils import logger @@ -46,7 +46,7 @@ class SoilSimulation(NetworkSimulation): """ def __init__(self, name=None, topology=None, network_params=None, network_agents=None, agent_type=None, states=None, - default_state=None, interval=1, dump=False, + default_state=None, interval=1, dump=None, dry_run=False, dir_path=None, num_trials=1, max_time=100, agent_module=None, load_module=None, seed=None, environment_agents=None, environment_params=None): @@ -57,7 +57,6 @@ class SoilSimulation(NetworkSimulation): elif isinstance(topology, basestring) or isinstance(topology, dict): topology = json_graph.node_link_graph(topology) - self.load_module = load_module self.topology = nx.Graph(topology) self.network_params = network_params @@ -69,94 +68,64 @@ class SoilSimulation(NetworkSimulation): self.interval = interval self.seed = str(seed) or str(time.time()) self.dump = dump + self.dry_run = dry_run self.environment_params = environment_params or {} if load_module: - path = sys.path + [self.dir_path] + path = sys.path + [self.dir_path, os.getcwd()] f, fp, desc = imp.find_module(load_module, path) imp.load_module('soil.agents.custom', f, fp, desc) environment_agents = environment_agents or [] - self.environment_agents = self._convert_agent_types(environment_agents) + self.environment_agents = agents._convert_agent_types(environment_agents) - distro = self.calculate_distribution(network_agents, - agent_type) - self.network_agents = self._convert_agent_types(distro) + distro = agents.calculate_distribution(network_agents, + agent_type) + self.network_agents = agents._convert_agent_types(distro) - self.states = self.validate_states(states, - self.topology) + self.states = agents._validate_states(states, + self.topology) - def calculate_distribution(self, - network_agents=None, - agent_type=None): - if network_agents: - network_agents = deepcopy(network_agents) - elif agent_type: - network_agents = [{'agent_type': agent_type}] - else: - return [] + def run_simulation(self, *args, **kwargs): + return self.run(*args, **kwargs) - # Calculate the thresholds - total = sum(x.get('weight', 1) for x in network_agents) - acc = 0 - for v in network_agents: - upper = acc + (v.get('weight', 1)/total) - v['threshold'] = [acc, upper] - acc = upper - return network_agents + def run(self, *args, **kwargs): + return list(self.run_simulation_gen(*args, **kwargs)) - def serialize_distribution(self): - d = self._convert_agent_types(self.network_agents, - to_string=True) - for v in d: - if 'threshold' in v: - del v['threshold'] - return d - - def _convert_agent_types(self, ind, to_string=False): - d = deepcopy(ind) - for v in d: - agent_type = v['agent_type'] - if to_string and not isinstance(agent_type, str): - v['agent_type'] = str(agent_type.__name__) - elif not to_string and isinstance(agent_type, str): - v['agent_type'] = agents.agent_types[agent_type] - return d - - def validate_states(self, states, topology): - states = states or [] - # Validate states to avoid ignoring states during - # initialization - if isinstance(states, dict): - for x in states: - assert x in self.topology.node - else: - assert len(states) <= len(self.topology) - return states - - def run_simulation(self): - return self.run() - - def run(self): - return list(self.run_simulation_gen()) - - def run_simulation_gen(self, *args, **kwargs): + def run_simulation_gen(self, *args, parallel=False, **kwargs): + p = Pool() with utils.timer('simulation'): - for i in range(self.num_trials): - res = self.run_trial(i) - if self.dump: - res.dump_gexf(self.dir_path) - res.dump_csv(self.dir_path) - yield res - - if self.dump: + if parallel: + func = partial(self.run_trial, return_env=not parallel) + for i in p.imap_unordered(func, range(self.num_trials)): + yield i + else: + for i in range(self.num_trials): + yield self.run_trial(i) + if not self.dry_run: logger.info('Dumping results to {}'.format(self.dir_path)) self.dump_pickle(self.dir_path) self.dump_yaml(self.dir_path) else: logger.info('NOT dumping results') - def run_trial(self, trial_id=0, dump=False, dir_path=None): + def get_env(self, trial_id=0, dump=False, dir_path=None): + env_name = '{}_trial_{}'.format(self.name, trial_id) + env = environment.SoilEnvironment(name=env_name, + topology=self.topology.copy(), + seed=self.seed+env_name, + initial_time=0, + dry_run=self.dry_run, + interval=self.interval, + network_agents=self.network_agents, + states=self.states, + default_state=self.default_state, + environment_agents=self.environment_agents, + dir_path=dir_path or self.dir_path, + **self.environment_params) + return env + + def run_trial(self, trial_id=0, dump=False, dir_path=None, until=None, return_env=False): """Run a single trial of the simulation Parameters @@ -164,25 +133,16 @@ class SoilSimulation(NetworkSimulation): trial_id : int """ # Set-up trial environment and graph - logger.info('Trial: {}'.format(trial_id)) - env_name = '{}_trial_{}'.format(self.name, trial_id) - env = environment.SoilEnvironment(name=env_name, - topology=self.topology.copy(), - seed=self.seed+env_name, - initial_time=0, - dump=self.dump, - interval=self.interval, - network_agents=self.network_agents, - states=self.states, - default_state=self.default_state, - environment_agents=self.environment_agents, - simulation=self, - **self.environment_params) + until = until or self.max_time + env = self.get_env(trial_id=trial_id, dump=dump, dir_path=dir_path) # Set up agents on nodes - logger.info('\tRunning') - with utils.timer('trial'): - env.run(until=self.max_time) - return env + with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)): + env.run(until) + if self.dump and not self.dry_run: + with utils.timer('Dumping simulation {} trial {}'.format(self.name, trial_id)): + env.dump(dir_path, formats=self.dump) + if return_env: + return env def to_dict(self): return self.__getstate__() @@ -213,16 +173,16 @@ class SoilSimulation(NetworkSimulation): def __getstate__(self): state = self.__dict__.copy() state['topology'] = json_graph.node_link_data(self.topology) - state['network_agents'] = self.serialize_distribution() - state['environment_agents'] = self._convert_agent_types(self.environment_agents, - to_string=True) + state['network_agents'] = agents._serialize_distribution(self.network_agents) + state['environment_agents'] = agents._convert_agent_types(self.environment_agents, + to_string=True) return state def __setstate__(self, state): self.__dict__ = state self.topology = json_graph.node_link_graph(state['topology']) - self.network_agents = self._convert_agent_types(self.network_agents) - self.environment_agents = self._convert_agent_types(self.environment_agents) + self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents)) + self.environment_agents = agents._convert_agent_types(self.environment_agents) return state @@ -235,21 +195,18 @@ def from_config(config, G=None): return sim -def run_from_config(*configs, dump=True, results_dir=None, timestamp=False): - if not results_dir: - results_dir = 'soil_output' +def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False, **kwargs): for config_def in configs: for config, cpath in utils.load_config(config_def): name = config.get('name', 'unnamed') logger.info("Using config(s): {name}".format(name=name)) - sim = SoilSimulation(**config) if timestamp: - sim_folder = '{}_{}'.format(sim.name, + sim_folder = '{}_{}'.format(name, time.strftime("%Y-%m-%d_%H:%M:%S")) else: - sim_folder = sim.name - sim.dir_path = os.path.join(results_dir, sim_folder) - sim.dump = dump - logger.info('Dumping results to {} : {}'.format(sim.dir_path, dump)) - results = sim.run_simulation() + sim_folder = name + dir_path = os.path.join(results_dir, sim_folder) + sim = SoilSimulation(dir_path=dir_path, dump=dump, **config) + logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump)) + results = sim.run_simulation(**kwargs) diff --git a/soil/utils.py b/soil/utils.py index 0d31ff1..147d0ca 100644 --- a/soil/utils.py +++ b/soil/utils.py @@ -11,7 +11,7 @@ import networkx as nx from contextlib import contextmanager -logger = logging.getLogger(__name__) +logger = logging.getLogger('soil') logger.setLevel(logging.INFO) @@ -62,6 +62,7 @@ def load_config(config): @contextmanager def timer(name='task', pre="", function=logger.info, to_object=None): start = time() + function('{}Starting {} at {}.'.format(pre, name, start)) yield start end = time() function('{}Finished {} in {} seconds'.format(pre, name, str(end-start))) @@ -70,21 +71,6 @@ def timer(name='task', pre="", function=logger.info, to_object=None): to_object.end = end -def agent_from_distribution(distribution, value=-1): - """Find the agent """ - if value < 0: - value = random() - for d in distribution: - threshold = d['threshold'] - if value >= threshold[0] and value < threshold[1]: - state = {} - if 'state' in d: - state = deepcopy(d['state']) - return d['agent_type'], state - - raise Exception('Distribution for value {} not found in: {}'.format(value, distribution)) - - def repr(v): if isinstance(v, bool): v = "true" if v else ""