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Algebra-FUNAlgebra-FUN
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‎16-mcm-b/NetFlow.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from ShortPath import Floyd,inf\n",
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"import numpy as np \n",
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"import pandas as pd \n",
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"import networkx as nx \n",
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"from matplotlib import pyplot as plt "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def netflow(OD, T0, C, n=100, alpha=1, beta=.5):\n",
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" m = len(T0)\n",
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"\n",
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" # 初始化\n",
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" T = np.array(T0)\n",
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" Q = np.zeros_like(T0)\n",
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"\n",
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" def updateQ(COD, P):\n",
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" P = np.array(P)\n",
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" for i in range(m):\n",
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" for j in range(m):\n",
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" path = P[i, j]\n",
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" for k in range(len(path)-1):\n",
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" s, t = path[k], path[k+1]\n",
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" Q[s, t] += COD[i, j]\n",
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"\n",
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" def updateT():\n",
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" return T0*(1+alpha*((Q/C)**beta))\n",
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"\n",
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" for i in range(n):\n",
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" _, P = Floyd(T)\n",
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" updateQ(OD/n, P)\n",
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" T = updateT()\n",
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"\n",
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" return Q, T, T/T0 - 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def evalue(Q, T, J):\n",
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" RT = T[(T != inf)]\n",
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" QT = (Q*T).flatten()\n",
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" QT = QT[~np.isnan(QT)]\n",
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" J = J.flatten()\n",
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" J = J[~np.isnan(J)]\n",
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" return {\n",
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" '平均道路通行时间': np.mean(RT),\n",
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" '加权路网通行时耗': np.sum(QT),\n",
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" '路网拥堵指数': np.mean(J)\n",
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" }"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def run():\n",
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" Q, T, J = netflow(OD, T0, C)\n",
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" print('Q=', Q)\n",
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" print('J=', J)\n",
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" eval0 = evalue(Q, T, J)\n",
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" print('evalue=', eval0)\n",
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" return Q, T, J"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw_graph(cmap=plt.cm.RdYlGn):\n",
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" g = nx.Graph()\n",
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" colors = []\n",
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" for i in range(m):\n",
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" for j in range(m):\n",
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" if T0[i,j] < inf:\n",
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" g.add_edge(i,j,weight=T0[i,j])\n",
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" colors.append(-np.log(J[i,j]))\n",
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" options = {\n",
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" \"node_color\": \"#A0CBE2\",\n",
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" \"edge_color\": colors,\n",
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" \"width\": 4,\n",
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" \"edge_cmap\": cmap,\n",
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" \"with_labels\": True\n",
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" }\n",
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" nx.draw_kamada_kawai(g,**options)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "增加道路1-4之前:\nQ= [[ 0. 510. 690. 0.]\n [ 0. 0. 0. 310.]\n [ 0. 0. 0. 890.]\n [ 0. 0. 0. 0.]]\nJ= [[ nan 1.00995049 2.62678511 nan]\n [ nan nan nan 0.78740079]\n [ nan nan nan 2.98328678]\n [ nan nan nan nan]]\nevalue= {'平均道路通行时间': 3.801193611955694, '加权路网通行时耗': 9205.944948398836, '路网拥堵指数': 1.8518557916463467}\n增加道路1-4之后：\nQ= [[ 0. 240. 300. 660.]\n [ 0. 0. 0. 40.]\n [ 0. 0. 0. 500.]\n [ 0. 0. 0. 0.]]\nJ= [[ nan 0.69282032 1.73205081 2.96647939]\n [ nan nan nan 0.28284271]\n [ nan nan nan 2.23606798]\n [ nan nan nan nan]]\nevalue= {'平均道路通行时间': 3.5738327896660804, '加权路网通行时耗': 7279.645003961635, '路网拥堵指数': 1.5820522430818205}\n"
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},
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{
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"output_type": "error",
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"ename": "AttributeError",
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"evalue": "module 'matplotlib.cm' has no attribute 'PiYlGn'",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-12-7b2be1b639b0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m122\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'after join the network of community:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0mdraw_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPiYlGn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.cm' has no attribute 'PiYlGn'"
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]
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}
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],
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"source": [
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"T0 = np.array([\n",
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" [inf, 2, 1, inf],\n",
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" [inf, inf, inf, 2],\n",
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" [inf, inf, inf, 1],\n",
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" [inf, inf, inf, inf]\n",
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" ])\n",
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"OD = np.array([\n",
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" [0, 200, 0, 1000],\n",
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" [0, 0, 0, 0],\n",
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" [0, 0, 0, 200],\n",
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" [0, 0, 0, 0]\n",
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" ])\n",
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"C = np.array([\n",
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" [1, 500, 100, 1],\n",
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" [1, 1, 1, 500],\n",
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" [1, 1, 1, 100],\n",
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" [1, 1, 1, 1]\n",
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" ])\n",
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"m = len(T0)\n",
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"\n",
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"print('增加道路1-4之前:')\n",
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"Q, T, J = run()\n",
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"plt.subplot(121)\n",
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"plt.title('before join the network of community:')\n",
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"draw_graph()\n",
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"\n",
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"print('增加道路1-4之后：')\n",
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"T0[0, 3] = 1.5\n",
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"C[0, 3] = 75\n",
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"Q, T, J = run()\n",
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"plt.subplot(122)\n",
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"plt.title('after join the network of community:')\n",
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"draw_graph(plt.cm.YlGn)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python36864bit3f53e4684f024fc68f038ddcedf7a612",
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"display_name": "Python 3.6.8 64-bit"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎16-mcm-b/NetFlow.py‎

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Original file line numberDiff line numberDiff line change
@@ -65,6 +65,8 @@ def evalue(Q, T, J):
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'路网拥堵指数': np.mean(np.log(J))
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}
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print('增加道路1-4之前:')
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Q, T, J = netflow(OD, T0, C)
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print('Q=', Q)

‎16-mcm-b/NetFlow2.py‎

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# To add a new cell, type '# %%'
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# To add a new markdown cell, type '# %% [markdown]'
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# %%
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from ShortPath import Floyd,inf
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import numpy as np
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import pandas as pd
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import networkx as nx
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from matplotlib import pyplot as plt
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import matplotlib as mpl
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# %%
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def netflow(OD, T0, C, n=100, alpha=1, beta=.5):
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m = len(T0)
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# 初始化
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T = np.array(T0)
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Q = np.zeros_like(T0)
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def updateQ(COD, P):
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P = np.array(P)
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for i in range(m):
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for j in range(m):
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path = P[i, j]
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for k in range(len(path)-1):
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s, t = path[k], path[k+1]
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Q[s, t] += COD[i, j]
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def updateT():
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return T0*(1+alpha*((Q/C)**beta))
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for i in range(n):
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_, P = Floyd(T)
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updateQ(OD/n, P)
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T = updateT()
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return Q, T, T/T0 - 1
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# %%
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def evalue(Q, T, J):
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RT = T[(T != inf)]
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QT = (Q*T).flatten()
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QT = QT[~np.isnan(QT)]
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J = J.flatten()
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J = J[~np.isnan(J)]
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return {
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'平均道路通行时间': np.mean(RT),
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'加权路网通行时耗': np.sum(QT),
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'路网拥堵指数': np.mean(J)
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}
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# %%
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def run():
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Q, T, J = netflow(OD, T0, C)
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print('Q=', Q)
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print('J=', J)
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eval0 = evalue(Q, T, J)
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print('evalue=', eval0)
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return Q, T, J
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# %%
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def draw_graph(cmap=plt.cm.RdYlGn):
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g = nx.Graph()
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colors = []
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for i in range(m):
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for j in range(m):
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if T0[i,j] < inf:
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g.add_edge(i,j,weight=T0[i,j])
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colors.append(-np.log(J[i,j]))
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options = {
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"node_color": "#A0CBE2",
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"edge_color": colors,
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"width": 4,
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"edge_cmap": cmap,
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"with_labels": True
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}
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nx.draw_kamada_kawai(g,**options)
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# %%
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T0 = np.array([
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[inf, 2, 1, inf],
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[inf, inf, inf, 2],
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[inf, inf, inf, 1],
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[inf, inf, inf, inf]
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])
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OD = np.array([
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[0, 200, 0, 1000],
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[0, 0, 0, 0],
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[0, 0, 0, 200],
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[0, 0, 0, 0]
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])
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C = np.array([
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[1, 500, 100, 1],
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[1, 1, 1, 500],
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[1, 1, 1, 100],
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[1, 1, 1, 1]
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])
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m = len(T0)
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print('增加道路1-4之前:')
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Q, T, J = run()
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plt.subplot(121)
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plt.title('before join the network of community:')
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draw_graph()
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print('增加道路1-4之后：')
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T0[0, 3] = 1.5
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C[0, 3] = 75
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Q, T, J = run()
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plt.subplot(122)
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plt.title('after join the network of community:')
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draw_graph(plt.cm.YlGn)
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plt.show()
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# %%
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