diff --git a/Analysis/0405_unprotected_left/0405_unprotected_left.ipynb b/Analysis/0405_unprotected_left/0405_unprotected_left.ipynb index 2a2fb7b95..833d8045e 100644 --- a/Analysis/0405_unprotected_left/0405_unprotected_left.ipynb +++ b/Analysis/0405_unprotected_left/0405_unprotected_left.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "이동류정보 불러오는 중: 100%|██████████| 17280/17280 [00:15<00:00, 1138.40it/s]\n" + "이동류정보 불러오는 중: 100%|██████████| 17280/17280 [00:13<00:00, 1252.17it/s]\n" ] } ], @@ -72,6 +72,1497 @@ "self.make_matching()" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['북', '북동', '동', '남동', '남', '남서', '서', '북서']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.directions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'북': array([6.123234e-17, 1.000000e+00]),\n", + " '북동': array([0.70710678, 0.70710678]),\n", + " '동': array([1., 0.]),\n", + " '남동': array([ 0.70710678, -0.70710678]),\n", + " '남': array([ 6.123234e-17, -1.000000e+00]),\n", + " '남서': array([-0.70710678, -0.70710678]),\n", + " '서': array([-1.0000000e+00, -1.2246468e-16]),\n", + " '북서': array([-0.70710678, 0.70710678])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# dictionary that maps dire(북) to dir(np.array([0,1]))\n", + "self.dire2dir = dict()\n", + "theta = np.pi/2\n", + "for dire in self.directions:\n", + " self.dire2dir[dire] = np.array([np.cos(theta), np.sin(theta)])\n", + " theta -= np.pi/4\n", + "self.dire2dir" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(10315.19, 11460.86),\n", + " (10314.85, 11439.86),\n", + " (10314.54, 11439.86),\n", + " (10314.9, 11460.86)]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "node_id = 'i5'\n", + "node = self.net.getNode(node_id)\n", + "inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges\n", + "out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges\n", + "inc_edge = inc_edges[0]\n", + "loc_inc_edge = inc_edge.getShape()[-1]\n", + "node.getShape()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2-2. 비보호우회전(g)을 배정했습니다.\n" + ] + } + ], + "source": [ + "self.initialize_state()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c30\n", + "571542116_01\n", + "571542116_02.96\n", + "i0\n", + "-571500487_01\n", + "-571542797_02\n", + "571510153_02\n", + "571545870_02\n", + "i1\n", + "-571542810_01\n", + "571542797_02.99\n", + "571543469_02\n", + "i2\n", + "-571542809_01\n", + "571542107_02\n", + "571542811_02\n", + "i3\n", + "-571500475_01\n", + "571540303_02.21\n", + "571540304_02\n", + "571556450_02\n", + "i6\n", + "-571542115_01\n", + "571500535_02.18\n", + "571500585_02\n", + "571511538_02.121\n", + "i7\n", + "-571511538_02\n", + "571542071_02\n", + "571542073_01\n", + "i8\n", + "-571500569_01\n", + "571500583_01\n", + "571500617_02\n", + "571500618_02\n", + "i9\n", + "571510152_01\n", + "571510152_02\n", + "u00\n", + "571500487_01\n", + "571500487_02\n", + "u20\n", + "571542810_01.51\n", + "571542811_01\n", + "u30\n", + "571556450_01\n", + "571556452_01\n", + "u31\n", + "571500475_01\n", + "571500475_02\n", + "u32\n", + "571540303_01\n", + "571540303_02\n", + "u60\n", + "571500535_01\n", + "571500535_02\n" + ] + } + ], + "source": [ + "for node_id in self.node_ids:\n", + " print(node_id)\n", + " node = self.net.getNode(node_id)\n", + " inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges\n", + " out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges\n", + " inc_edge_ids = [edge.getID() for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edge ids\n", + " out_edge_ids = [edge.getID() for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edge ids\n", + " # print(node)\n", + " for inc_edge_id in inc_edge_ids:\n", + " print(inc_edge_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inter_nomove_noinc_dirout_dirinc_edgeout_edgenode_id
01751동남571545870_02571542797_02i0
11752서동571510153_02571545870_01i0
21753남서-571542797_02571510153_01i0
31754북남-571500487_01571542797_02i0
41755서북571510153_02571500487_01i0
........................
7121021북서571511538_02.121571500535_01i6
7221021서남571500535_02.18571500585_01i6
7321021남동571500585_02571542115_01i6
7421021동북-571542115_01571511538_01i6
752105서북571500535_02-571500535_02u60
\n", + "

76 rows × 7 columns

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" + ], + "text/plain": [ + " inter_no move_no inc_dir out_dir inc_edge out_edge node_id\n", + "0 175 1 동 남 571545870_02 571542797_02 i0\n", + "1 175 2 서 동 571510153_02 571545870_01 i0\n", + "2 175 3 남 서 -571542797_02 571510153_01 i0\n", + "3 175 4 북 남 -571500487_01 571542797_02 i0\n", + "4 175 5 서 북 571510153_02 571500487_01 i0\n", + ".. ... ... ... ... ... ... ...\n", + "71 210 21 북 서 571511538_02.121 571500535_01 i6\n", + "72 210 21 서 남 571500535_02.18 571500585_01 i6\n", + "73 210 21 남 동 571500585_02 571542115_01 i6\n", + "74 210 21 동 북 -571542115_01 571511538_01 i6\n", + "75 210 5 서 북 571500535_02 -571500535_02 u60\n", + "\n", + "[76 rows x 7 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.matching" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['북', '북동', '동', '남동', '남', '남서', '서', '북서']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.directions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'-571542797_02'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(self.node_id2inc_edge2dir['i0'])[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'-571500487_01': array([-0.00936558, -0.99995614]),\n", + " '-571542797_02': array([0.00318883, 0.99999492]),\n", + " '571510153_02': array([0.99927006, 0.03820147]),\n", + " '571545870_02': array([-0.99951221, 0.03123044])}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.node_id2inc_edge2dir['i0']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'북': array([6.123234e-17, 1.000000e+00]),\n", + " '북동': array([0.70710678, 0.70710678]),\n", + " '동': array([1., 0.]),\n", + " '남동': array([ 0.70710678, -0.70710678]),\n", + " '남': array([ 6.123234e-17, -1.000000e+00]),\n", + " '남서': array([-0.70710678, -0.70710678]),\n", + " '서': array([-1.0000000e+00, -1.2246468e-16]),\n", + " '북서': array([-0.70710678, 0.70710678])}" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.dire2dir" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'-571500487_01': array([-0.00936558, -0.99995614]),\n", + " '-571542797_02': array([0.00318883, 0.99999492]),\n", + " '571510153_02': array([0.99927006, 0.03820147]),\n", + " '571545870_02': array([-0.99951221, 0.03123044])}" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + 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"\n", + "0\n", + "북\n", + "[6.123234e-17 1.000000e+00]\n", + "[0.00318883 0.99999492]\n", + "0.9999949156539597\n", + "\n", + "[0.70710678 0.70710678]\n", + "[0.00318883 0.99999492]\n", + "0.7093580325944496\n", + "\n", + "[1. 0.]\n", + "[0.00318883 0.99999492]\n", + "0.0031888346194069504\n", + "\n", + "[ 0.70710678 -0.70710678]\n", + "[0.00318883 0.99999492]\n", + "-0.7048483394275195\n", + "\n", + "[ 6.123234e-17 -1.000000e+00]\n", + "[0.00318883 0.99999492]\n", + "-0.9999949156539597\n", + "\n", + "[-0.70710678 -0.70710678]\n", + "[0.00318883 0.99999492]\n", + "-0.7093580325944496\n", + "\n", + "[-1.0000000e+00 -1.2246468e-16]\n", + "[0.00318883 0.99999492]\n", + "-0.0031888346194070727\n", + "\n", + "[-0.70710678 0.70710678]\n", + "[0.00318883 0.99999492]\n", + "0.7048483394275193\n", + "\n", + "2\n", + "동\n", + "[6.123234e-17 1.000000e+00]\n", + "[0.99927006 0.03820147]\n", + "0.03820147100728745\n", + "\n", + "[0.70710678 0.70710678]\n", + "[0.99927006 0.03820147]\n", + 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0.70710678 -0.70710678]\n", + "[ 0.99992218 -0.01247524]\n", + "0.7158730815742801\n", + "\n", + "[ 6.123234e-17 -1.000000e+00]\n", + "[ 0.99992218 -0.01247524]\n", + "0.012475239731228834\n", + "\n", + "[-0.70710678 -0.70710678]\n", + "[ 0.99992218 -0.01247524]\n", + "-0.6982304283525205\n", + "\n", + "[-1.0000000e+00 -1.2246468e-16]\n", + "[ 0.99992218 -0.01247524]\n", + "-0.999922181168939\n", + "\n", + "[-0.70710678 0.70710678]\n", + "[ 0.99992218 -0.01247524]\n", + "-0.7158730815742802\n", + "\n", + "2\n", + "동\n", + "[6.123234e-17 1.000000e+00]\n", + "[ 0.99986806 -0.01624397]\n", + "-0.016243967759571678\n", + "\n", + "[0.70710678 0.70710678]\n", + "[ 0.99986806 -0.01624397]\n", + "0.6955272643837823\n", + "\n", + "[1. 0.]\n", + "[ 0.99986806 -0.01624397]\n", + "0.999868058051374\n", + "\n", + "[ 0.70710678 -0.70710678]\n", + "[ 0.99986806 -0.01624397]\n", + "0.71849970389612\n", + "\n", + "[ 6.123234e-17 -1.000000e+00]\n", + "[ 0.99986806 -0.01624397]\n", + "0.016243967759571803\n", + "\n", + "[-0.70710678 -0.70710678]\n", + "[ 0.99986806 -0.01624397]\n", + "-0.6955272643837822\n", + "\n", + "[-1.0000000e+00 -1.2246468e-16]\n", + "[ 0.99986806 -0.01624397]\n", + "-0.999868058051374\n", + "\n", + "[-0.70710678 0.70710678]\n", + "[ 0.99986806 -0.01624397]\n", + "-0.7184997038961201\n", + "\n", + "6\n", + "서\n", + "[6.123234e-17 1.000000e+00]\n", + "[-0.99986809 0.01624217]\n", + "0.016242171657398802\n", + "\n", + "[0.70710678 0.70710678]\n", + "[-0.99986809 0.01624217]\n", + "-0.6955285550518142\n", + "\n", + "[1. 0.]\n", + "[-0.99986809 0.01624217]\n", + "-0.9998680872294363\n", + "\n", + "[ 0.70710678 -0.70710678]\n", + "[-0.99986809 0.01624217]\n", + "-0.7184984544920996\n", + "\n", + "[ 6.123234e-17 -1.000000e+00]\n", + "[-0.99986809 0.01624217]\n", + "-0.016242171657398927\n", + "\n", + "[-0.70710678 -0.70710678]\n", + "[-0.99986809 0.01624217]\n", + "0.6955285550518141\n", + "\n", + "[-1.0000000e+00 -1.2246468e-16]\n", + "[-0.99986809 0.01624217]\n", + "0.9998680872294363\n", + "\n", + "[-0.70710678 0.70710678]\n", + "[-0.99986809 0.01624217]\n", + "0.7184984544920997\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'-571500487_01': '남',\n", + " '-571542797_02': '북',\n", + " '571510153_02': '동',\n", + " '571545870_02': '서'}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# dictionary that maps dire(북) to dir(np.array([0,1]))\n", + "self.dire2dir = dict()\n", + "theta = np.pi/2\n", + "for dire in self.directions:\n", + " self.dire2dir[dire] = np.array([np.cos(theta), np.sin(theta)])\n", + " theta -= np.pi/4\n", + "\n", + "# dictionary that maps node_id and inc_edge_id to direction(북)\n", + "self.node_id2inc_edge2dire = dict()\n", + "for node_id in self.parent_ids:\n", + " inc_edge2dire = dict()\n", + " node = self.net.getNode(node_id)\n", + " inc_edges = [inc_edge.getID() for inc_edge in node.getIncoming()]\n", + " out_edges = [out_edge.getID() for out_edge in node.getOutgoing()]\n", + "\n", + " for inc_edge in inc_edges:\n", + " inc_dir_true = self.node_id2inc_edge2dir[node_id][inc_edge]\n", + " inc_dirs = [self.dire2dir[inc_dire] for inc_dire in self.directions]\n", + " inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax()\n", + " print(inc_index)\n", + " print(self.directions[inc_index])\n", + " for inc_dir in inc_dirs:\n", + " print(inc_dir)\n", + " print(inc_dir_true)\n", + " print(np.dot(inc_dir, inc_dir_true))\n", + " print()\n", + " inc_edge2dire[inc_edge] = self.directions[inc_index]\n", + " self.node_id2inc_edge2dire[node_id] = inc_edge2dire\n", + "self.node_id2inc_edge2dire['i0']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 8, @@ -447,7 +1938,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -461,7 +1952,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -471,98 +1962,18 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 122, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "i0\n", - "u00\n", - "i1\n", - "i1\n", - "i1\n", - "i1\n", - "i1\n", - "i1\n", - "i1\n", - "i1\n", - "i2\n", - "i2\n", - "i2\n", - "i2\n", - "i2\n", - "i2\n", - "i2\n", - "i2\n", - "i2\n", - "u20\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "i3\n", - "u30\n", - "u31\n", - "u32\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i8\n", - "i9\n", - "i9\n", - "i9\n", - "i9\n", - "i7\n", - "i7\n", - "i7\n", - "i7\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "i6\n", - "u60\n" + "ename": "KeyError", + "evalue": "'i0'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[122], line 17\u001b[0m\n\u001b[0;32m 15\u001b[0m out_edge \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnet\u001b[38;5;241m.\u001b[39mgetEdge(out_edge)\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m conn \u001b[38;5;129;01min\u001b[39;00m inc_edge\u001b[38;5;241m.\u001b[39mgetConnections(out_edge):\n\u001b[1;32m---> 17\u001b[0m \u001b[43mnode2allocated_conns\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnode_id\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mappend(conn)\n\u001b[0;32m 18\u001b[0m index \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetTLLinkIndex()\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "\u001b[1;31mKeyError\u001b[0m: 'i0'" ] } ], @@ -583,7 +1994,7 @@ " inc_edge = self.net.getEdge(inc_edge)\n", " out_edge = self.net.getEdge(out_edge)\n", " for conn in inc_edge.getConnections(out_edge):\n", - " # node2allocated_conns[node_id].append(conn)\n", + " node2allocated_conns[node_id].append(conn)\n", " index = conn.getTLLinkIndex()\n", " if index >= 0:\n", " state[index] = 'G'\n", diff --git a/Results/sn_1704416700.add.xml b/Results/sn_1704416700.add.xml index e9fb412ec..f83fa06f5 100644 --- a/Results/sn_1704416700.add.xml +++ b/Results/sn_1704416700.add.xml @@ -1,62 +1,59 @@ - - - - + + + + - - + + - - + + + + + + + + - - - - - + + - - + + + + + + + + - - + + - - - - - + + + + + + + + - - - - - - - - - - - - - - - - - + + - + - + - + @@ -68,10 +65,10 @@ - + - + @@ -83,17 +80,12 @@ - - - - - - - + + - + @@ -124,12 +116,9 @@ - - - - + @@ -172,120 +161,112 @@ - - - - + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - - - - + + - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - - - - + + - + @@ -328,72 +309,66 @@ - - - - - - + + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - - - + @@ -412,12 +387,9 @@ - - - - + @@ -454,263 +426,244 @@ - - - - - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - + - - - - - + + - - + + - - + + - - + + - - + + - - + + - + + + + - - + + - - + + + + + - - + + + + + - + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - - - - + + - - + + - + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - - - - + + - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - - - - + + \ No newline at end of file diff --git a/Scripts/__pycache__/preprocess_daily.cpython-38.pyc b/Scripts/__pycache__/preprocess_daily.cpython-38.pyc index 5ac8e54f1..33f5057cc 100644 Binary files a/Scripts/__pycache__/preprocess_daily.cpython-38.pyc and b/Scripts/__pycache__/preprocess_daily.cpython-38.pyc differ diff --git a/Scripts/preprocess_daily.py b/Scripts/preprocess_daily.py index d78ed5f33..3aaf26195 100644 --- a/Scripts/preprocess_daily.py +++ b/Scripts/preprocess_daily.py @@ -432,7 +432,7 @@ class DailyPreprocessor(): self.child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()) self.uturn_ids = sorted(self.uturn.child_id.unique()) self.coord_ids = sorted(self.coord.child_id.unique()) - + # ids ids = {'node_ids' : self.node_ids, 'parent_ids': self.parent_ids, @@ -443,13 +443,12 @@ class DailyPreprocessor(): with open(os.path.join(self.path_intermediates, 'ids.json'), 'w') as file: json.dump(ids, file) - ch2pa = {} # child to parent for child_id in self.child_ids: parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0] sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no] ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id - directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향 + self.directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향 # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여 cmatches = [] @@ -468,18 +467,18 @@ class DailyPreprocessor(): cmatch[['inc_edge', 'out_edge']] = np.nan # 보행신호시/좌회전시 진입/진출방향 - ind = directions.index(direction) - inc_dire_pedes = directions[(ind + 2) % len(directions)] - out_dire_pedes = directions[(ind - 2) % len(directions)] + ind = self.directions.index(direction) + inc_dire_pedes = self.directions[(ind + 2) % len(self.directions)] + out_dire_pedes = self.directions[(ind - 2) % len(self.directions)] inc_dire_right = direction - out_dire_right = directions[(ind + 2) % len(directions)] + out_dire_right = self.directions[(ind + 2) % len(self.directions)] # 보행신호시/좌회전시 조건 pedes_exists = (cmatch.inc_dir==inc_dire_pedes) & (cmatch.out_dir==out_dire_pedes) right_exists = (cmatch.inc_dir==inc_dire_right) & (cmatch.out_dir==out_dire_right) # 보행신호시/좌회전시 진입/진출 엣지id 배정 - ind = directions.index(direction) + ind = self.directions.index(direction) if condition == "보행신호시": cmatch.loc[pedes_exists, ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] elif condition == "좌회전시": diff --git a/Scripts/preprocess_daily_0408_backup.py b/Scripts/preprocess_daily_0408_backup.py new file mode 100644 index 000000000..a5aa4a5ae --- /dev/null +++ b/Scripts/preprocess_daily_0408_backup.py @@ -0,0 +1,818 @@ +# python .\Scripts\preprocess_daily.py +import pandas as pd +import numpy as np +import os, sys, copy +import json +import sumolib, traci +from tqdm import tqdm + +class DailyPreprocessor(): + def __init__(self): + # 루트폴더 지정 + self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + with open(os.path.join(self.path_root, 'Scripts', 'config.json'), 'r') as config_file: + config = json.load(config_file) + # 주요 폴더 경로 지정 + self.paths = config['paths'] + self.path_data = os.path.join(self.path_root, *self.paths['data']) + self.path_intermediates = os.path.join(self.path_root, *self.paths['intermediates']) + self.path_results = os.path.join(self.path_root, *self.paths['results']) + self.path_tables = os.path.join(self.path_root, *self.paths['tables']) + self.path_networks = os.path.join(self.path_root, *self.paths['networks']) + self.path_scripts = os.path.join(self.path_root, *self.paths['scripts']) + + # 이슈사항 목록 + self.issues = [] + + # 1. 데이터 불러오기 + def load_data(self): + print('1. 데이터를 로드합니다.') + self.load_networks() + self.load_tables() + self.check_networks() + self.check_tables() + + # 1-1. 네트워크 불러오기 + def load_networks(self): + self.net = sumolib.net.readNet(os.path.join(self.path_networks, 'sn.net.xml')) + print("1-1. 네트워크가 로드되었습니다.") + + # 1-2. 테이블 불러오기 + def load_tables(self): + # 모든 컬럼에 대하여 데이터타입 지정 + loading_dtype = { + 'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int', + 'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str', + 'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str', + 'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float', + 'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str' + } + for alph in ['A', 'B']: + for j in range(1,9): + loading_dtype[f'angle_{alph}{j}'] = 'str' + loading_dtype[f'dura_{alph}{j}'] = 'int' + + # 테이블 불러오기 + self.inter_info = pd.read_csv(os.path.join(self.path_tables, 'inter_info.csv'), dtype=loading_dtype) + self.angle = pd.read_csv(os.path.join(self.path_tables, 'angle.csv'), dtype=loading_dtype) + self.plan = pd.read_csv(os.path.join(self.path_tables, 'plan.csv'), dtype=loading_dtype) + self.inter_node = pd.read_csv(os.path.join(self.path_tables, 'inter_node.csv'), dtype=loading_dtype) + self.uturn = pd.read_csv(os.path.join(self.path_tables, 'child_uturn.csv'), dtype=loading_dtype) + self.coord = pd.read_csv(os.path.join(self.path_tables, 'child_coord.csv'), dtype=loading_dtype) + self.nema = pd.read_csv(os.path.join(self.path_tables, 'nema.csv'), encoding='cp949', dtype=loading_dtype) + + # 교차로목록, 노드목록 정의 + self.inter_nos = [int(x) for x in sorted(self.inter_info.inter_no.unique())] + self.node_ids = sorted(self.inter_node.node_id.unique()) + + print("1-2. 테이블들이 로드되었습니다.") + + # 1-3. 네트워크 무결성 검사 + def check_networks(self): + # https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections + if 'SUMO_HOME' in os.environ: + tools = os.path.join(os.environ['SUMO_HOME'], 'tools') + if tools not in sys.path: + sys.path.append(tools) + else: + raise EnvironmentError("please declare environment variable 'SUMO_HOME'") + traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_networks, 'sn.net.xml')]) + nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light'] + for node in nodes: + node_id = node.getID() + from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0]) + from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id)) + if from_xml != from_traci: + sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'} + self.issues.append(sub) + traci.close() + print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.") + + # 1-4. 테이블 무결성 검사 + def check_tables(self): + self.check_plan() + self.check_inter_info() + self.check_angle() + print("1-4. 테이블들의 무결성 검사를 완료했습니다.") + pass + + # 1-4-1. 신호계획(plan) 검사 + def check_plan(self): + # 1-4-1-1. inter_no 검사 + # self.plan.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.plan.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-1-1. plan의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-1-2. 시작시각 검사 + # self.plan.loc[0, 'start_hour'] = 27 # 에러 발생을 위한 코드 + for _, row in self.plan.iterrows(): + start_hour = row.start_hour + start_minute = row.start_minute + if not (0 <= start_hour <= 23) or not (0 <= start_minute <= 59): + msg = f"1-4-1-2. plan에 잘못된 형식의 start_time이 존재합니다: {start_hour, start_minute}" + self.issues.append(msg) + + # 1-4-1-3. 현시시간 검사 + # self.plan.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드 + durations = self.plan[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]] + valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1) + invalid_inter_nos = sorted(self.plan[~ valid_indices].inter_no.unique()) + if invalid_inter_nos: + msg = f"1-4-1-3. plan에 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}" + self.issues.append(msg) + + # 1-4-1-4. 주기 일관성 검사 + # self.plan.loc[0, 'cycle'] = 50 # 에러 발생을 위한 코드 + inconsistent_cycle = self.plan.groupby(['inter_no', 'start_hour', 'start_minute'])['cycle'].nunique().gt(1) + if inconsistent_cycle.any(): + inc_inter_no, start_hour, start_minute = inconsistent_cycle[inconsistent_cycle].index[0] + msg = f"1-4-1-4. 한 프로그램에 서로 다른 주기가 존재합니다. inter_no:{inc_inter_no}, start_hour:{start_minute}, start_hour:{start_minute}일 때, cycle이 유일하게 결정되지 않습니다." + self.issues.append(msg) + + # 1-4-1-5. 현시시간 / 주기 검사 + # self.plan.loc[0, 'duration'] = 10 # 에러 발생을 위한 코드 + right_duration = True + for (inter_no, start_hour, start_minute), group in self.plan.groupby(['inter_no', 'start_hour', 'start_minute']): + A_sum = group[[f'dura_A{j}' for j in range(1, 9)]].iloc[0].sum() + B_sum = group[[f'dura_B{j}' for j in range(1, 9)]].iloc[0].sum() + # A_sum = group[group['ring_type']=='A']['duration'].sum() + # B_sum = group[group['ring_type']=='B']['duration'].sum() + cycle = group['cycle'].unique()[0] + if not (A_sum == B_sum == cycle): + right_duration = False + inc_inter_no = inter_no + if not right_duration: + msg = f"1-4-1-5. inter_no:{inc_inter_no}, A링현시시간의 합과 B링현시시간의 합이 일치하지 않거나, 현시시간의 합과 주기가 일치하지 않습니다." + self.issues.append(msg) + + # 1-4-2. 교차로정보(inter_info) 검사 + def check_inter_info(self): + # 1-4-2-1. inter_lat, inter_lon 적절성 검사 + # self.inter_info.loc[0, 'inter_lat'] = 38.0 # 에러 발생을 위한 코드 + self.max_lon, self.min_lon = 127.3, 127.0 + self.max_lat, self.min_lat = 37.5, 37.2 + for _, row in self.inter_info.iterrows(): + latbool = self.min_lat <= row['inter_lat'] <= self.max_lat + lonbool = self.min_lon <= row['inter_lon'] <= self.max_lon + if not(latbool and lonbool): + msg = f"1-4-2-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : {row['inter_no']}" + self.issues.append(msg) + + # 1-4-3. 방위각정보(inter_info) 검사 + def check_angle(self): + # 1-4-3-1. inter_no 검사 + # self.angle.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드 + missing_inter_nos = set(self.angle.inter_no) - set(self.inter_nos) + if missing_inter_nos: + msg = f"1-4-3-1. angle의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}" + self.issues.append(msg) + + # 1-4-3-2. 각도 코드 검사 + angle_codes = self.angle[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]].values.flatten() + angle_codes = [code for code in angle_codes if not pd.isna(code) and code != 'stop'] + of_length_6 = [len(code)==6 for code in angle_codes] + if not all(of_length_6): + msg = f"1-4-3-2. 여섯자리가 아닌 각도코드가 존재합니다." + self.issues.append(msg) + angle_codes = [[code[:3],code[3:]] for code in angle_codes] + angle_codes = [int(item) for sublist in angle_codes for item in sublist] + angle_codes = [0<=code<360 for code in angle_codes] + if not all(angle_codes): + msg = f"1-4-3-2. 0과 359 사이의 값을 벗어나는 방위각이 존재합니다." + self.issues.append(msg) + + # 2. 중간산출물 만들기 + def get_intermediates(self): + print('2. 중간산출물을 생성합니다.') + self.get_matches() + self.initialize_state() + self.assign_signals() + self.get_node2num_cycles() + + # 2-1 매칭테이블 생성 + def get_matches(self): + self.make_match1() + self.make_match2() + self.make_match3() + self.make_match4() + self.make_match5() + self.make_match6() + self.make_matching() + print('2-1. 매칭 테이블들을 생성했습니다.') + + # 2-1-1 + def make_match1(self): + ''' + 신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다. + '../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다. + + return : 통합된 이동류정보 + - 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보 + + match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다. + ''' + # [이동류번호] 불러오기 (약 1분의 소요시간) + path_move = os.path.join(self.path_tables, 'move') + csv_moves = os.listdir(path_move) + moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중')] + df = pd.concat(moves).reset_index(drop=True) + self.match1 = [] + for i, group in df.groupby(['inter_no', 'phas_A', 'phas_B']): + inter_no, phas_A, phas_B = i + pairs_array = np.array(group[['move_A', 'move_B']]) + unique_pairs, counts = np.unique(pairs_array, axis=0, return_counts=True) + frequent_pair = unique_pairs[np.argmax(counts)] + self.match1.append(pd.DataFrame({'inter_no':[inter_no], 'phas_A':[phas_A], 'phas_B':[phas_B], + 'move_A':[frequent_pair[0]], 'move_B':[frequent_pair[1]]})) + self.match1 = pd.concat(self.match1).reset_index(drop=True) + self.match1.to_csv(os.path.join(self.path_intermediates, 'match1.csv')) + + # 2-1-2 + def make_match2(self): + ''' + match1을 계층화함. + - match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + ''' + # 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no') + matchA = self.match1[['inter_no', 'phas_A', 'move_A']].copy() + matchA.columns = ['inter_no', 'phase_no', 'move_no'] + matchA['ring_type'] = 'A' + matchB = self.match1[['inter_no', 'phas_B', 'move_B']].copy() + matchB.columns = ['inter_no', 'phase_no', 'move_no'] + matchB['ring_type'] = 'B' + self.match2 = pd.concat([matchA, matchB]).drop_duplicates() + self.match2 = self.match2[['inter_no', 'phase_no', 'ring_type', 'move_no']] + self.match2 = self.match2.sort_values(by=list(self.match2.columns)) + + # 2-1-3 + def make_match3(self): + ''' + 각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함. + - match2의 컬럼 : inter_no, phase_no, ring_type, move_no + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + + nema : + - 컬럼 : move_no, inc_dir, out_dir + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + # nema 정보 불러오기 및 병합 + self.match3 = pd.merge(self.match2, self.nema, how='left', on='move_no').drop_duplicates() + + # 2-1-4 + def make_match4(self): + ''' + 방위각 정보를 매칭시켜 추가함. + - match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + + angle_original : + - 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8) + - 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블 + - 이동류번호 : 1 ~ 16, 17, 18, 21 + - 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서 + ''' + + # 계층화 + angles = [] + for i, row in self.angle.iterrows(): + angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] + new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()}) + angles.append(new) + angles = pd.concat(angles) + angles = angles.dropna().reset_index(drop=True) + + # 병합 + six_chars = angles.angle_code.apply(lambda x:len(x)==6) + angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3]) + angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:]) + angles = angles.drop('angle_code', axis=1) + self.match4 = pd.merge(self.match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'], + right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates() + + # 2-1-5 + def make_match5(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로). + - match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle + - match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) net + - 성남시 정자동 부근의 샘플 네트워크 + (2) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (3) inter_info + - 교차로 정보. 여기에서는 위도와 경도가 쓰임. + - 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no + + 진입엣지id, 진출엣지id를 얻는 과정 : + - match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함. + * 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음 + - 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장. + * inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge + - inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄. + - 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄. + - 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함. + ''' + + # parent node만 가져옴. + inter_node1 = self.inter_node[self.inter_node.inter_type == 'parent'].drop('inter_type', axis=1) + inter_info1 = self.inter_info[['inter_no', 'inter_lat', 'inter_lon']] + inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'], + right_on=['inter_no']).drop_duplicates() + + self.inter2node = dict(zip(inter['inter_no'], inter['node_id'])) + + # node_id, inc/out_edge가 주어질 때 해당되는 방향벡터를 매칭하는 딕셔너리 + self.node_id2inc_edge2dir = dict() + self.node_id2out_edge2dir = dict() + + # 진입진출ID 매칭 + self.match5 = self.match4.copy() + for index, row in self.match5.iterrows(): + node_id = self.inter2node[row.inter_no] + node = self.net.getNode(node_id) + self.node_id2inc_edge2dir[node_id] = dict() + self.node_id2out_edge2dir[node_id] = dict() + # 교차로의 모든 (from / to) edges + inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges + out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges + # 교차로의 모든 (from / to) directions + inc_dirs = [] + for inc_edge in inc_edges: + start = inc_edge.getShape()[-2] + end = inc_edge.getShape()[-1] + inc_dir = np.array(end) - np.array(start) + inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5 + inc_dirs.append(inc_dir) + self.node_id2inc_edge2dir[node_id][inc_edge.getID()] = inc_dir + out_dirs = [] + self.out_edge2dir = dict() + for out_edge in out_edges: + start = out_edge.getShape()[0] + end = out_edge.getShape()[1] + out_dir = np.array(end) - np.array(start) + out_dir = out_dir / (out_dir ** 2).sum() ** 0.5 + out_dirs.append(out_dir) + self.out_edge2dir[out_edge] = out_dir + self.node_id2out_edge2dir[node_id][out_edge.getID()] = out_dir + if not pd.isna(row.inc_angle): + # 진입각, 진출각 불러오기 + inc_angle = int(row.inc_angle) + out_angle = int(row.out_angle) + # 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환 + inc_angle = (-90 - inc_angle) % 360 + inc_angle = inc_angle * np.pi / 180. + inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)]) + out_angle = (90 - out_angle) % 360 + out_angle = out_angle * np.pi / 180. + out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)]) + # 매칭 엣지 반환 + inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax() + out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax() + inc_edge_id = inc_edges[inc_index].getID() + out_edge_id = out_edges[out_index].getID() + self.match5.at[index, 'inc_edge'] = inc_edge_id + self.match5.at[index, 'out_edge'] = out_edge_id + self.match5['node_id'] = self.match5['inter_no'].map(self.inter2node) + self.match5 = self.match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True) + + # 정북기준 시계방향으로 8방향 + self.directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] + + + # 2-1-6 + def make_match6(self): + ''' + 진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로). + - match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id + + 사용된 데이터 : + (1) inter_node + - 교차로번호와 노드id를 매칭시키는 테이블. + - parent/child 정보도 포함되어 있음 + - 컬럼 : inter_no, node_id, inter_type + (2) uturn (유턴정보) + - 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 유턴교차로id + - direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향) + - condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나 + - inc_edge, out_edge : 유턴에 대한 진입진출엣지 + (3) coord (연동교차로정보) + - 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge + - parent_id, child_id : 주교차로id, 연동교차로id + - 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지 + + 설명 : + - match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음. + 여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함. + 유턴교차로 : + - directions를 정북기준 시계방향의 8방위로 정함. + - 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음. + - 예) 진입방향(direction)이 '북'일 때, + - 직진 : (북, 남) + * 남 : directions[(ind + 4) % len(directions)] + - 좌회전 : (북, 동) + * 동 : directions[(ind + 2) % len(directions)] + - 보행 : (서, 동) + * 서 : directions[(ind - 2) % len(directions)] + - uturn의 각 행을 순회하면서 아래 과정을 반복함 + - match5에서 parent_id에 해당하는 행들을 가져옴(cmatch). + - condition 별로 진입방향, 진출방향을 정함. + - 상술한 directions를 활용하여 정함. + - (진입방향, 진출방향)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함. + - ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + - 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함. + + 연동교차로 : + - 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음. + - 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음. + - 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음. + + match6 : + - 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄. + ''' + + self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no'])) + + self.parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique()) + self.child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()) + self.uturn_ids = sorted(self.uturn.child_id.unique()) + self.coord_ids = sorted(self.coord.child_id.unique()) + + # ids + ids = {'node_ids' : self.node_ids, + 'parent_ids': self.parent_ids, + 'child_ids' : self.child_ids, + 'uturn_ids' : self.uturn_ids, + 'coord_ids' : self.coord_ids, + 'inter_nos' : self.inter_nos} + with open(os.path.join(self.path_intermediates, 'ids.json'), 'w') as file: + json.dump(ids, file) + + ch2pa = {} # child to parent + for child_id in self.child_ids: + parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0] + sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no] + ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id + + # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여 + cmatches = [] + for _, row in self.uturn.iterrows(): + child_id = row.child_id + parent_id = row.parent_id + direction = row.direction + condition = row.condition + inc_edge_id = row.inc_edge + out_edge_id = row.out_edge + + # match5에서 부모노드id에 해당하는 행들을 가져옴 (cmatch) + cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node + cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True) + cmatch['node_id'] = child_id + cmatch[['inc_edge', 'out_edge']] = np.nan + + # 보행신호시/좌회전시 진입/진출방향 + ind = self.directions.index(direction) + inc_dire_pedes = self.directions[(ind + 2) % len(self.directions)] + out_dire_pedes = self.directions[(ind - 2) % len(self.directions)] + inc_dire_right = direction + out_dire_right = self.directions[(ind + 2) % len(self.directions)] + + # 보행신호시/좌회전시 조건 + pedes_exists = (cmatch.inc_dir==inc_dire_pedes) & (cmatch.out_dir==out_dire_pedes) + right_exists = (cmatch.inc_dir==inc_dire_right) & (cmatch.out_dir==out_dire_right) + + # 보행신호시/좌회전시 진입/진출 엣지id 배정 + ind = self.directions.index(direction) + if condition == "보행신호시": + cmatch.loc[pedes_exists, ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + elif condition == "좌회전시": + cmatch.loc[right_exists, ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + + # 신호없음이동류발생시/보행신호이동류발생시 조건 + all_redsigns = cmatch.move_no == 18 + crosswalk_on = cmatch.move_no == 17 + + # 만약 어떤 유턴신호도 배정되지 않았다면 + # 좌회전시 → 보행신호시 → 보행신호이동류발생시 → 신호없음이동류발생시 순으로 진입/진출 엣지id 배정 + uturn_not_assigned = cmatch[['inc_edge','out_edge']].isna().any(axis=1).all() + if uturn_not_assigned: + # 좌회전시 + if right_exists.any(): + cmatch.loc[right_exists, ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 보행신호시 + elif pedes_exists.any(): + cmatch.loc[pedes_exists, ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 보행신호이동류(17) 발생시 + elif crosswalk_on.any(): + cmatch.loc[crosswalk_on & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + # 신호없음이동류(18) 발생시 + elif all_redsigns.any(): + cmatch.loc[all_redsigns & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id] + cmatches.append(cmatch) + + # 각 연등교차로(coordination node)에 대하여 (inc_edge_id, out_edge_id) 부여 + self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter) + self.coord = self.coord.rename(columns={'child_id':'node_id'}) + self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan + self.coord['move_no'] = 20 + self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']] + + # display(coord) + cmatches = pd.concat(cmatches) + self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type']) + # self.match6.to_csv(os.path.join(self.path_intermediates, 'match6.csv')) + + + # 2-1-7 + def make_matching(self): + ''' + 이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다. + 모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다. + - matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id + + 설명 : + - 필요한 리스트, 딕셔너리 등을 정의 + (1) 가능한 (진입방향, 진출방향) 목록 [리스트] + (2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리] + (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리] + (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리] + (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리] + - matching은 빈 리스트로 지정. + - 모든 노드id에 대하여 다음 과정을 반복 + - 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복 + - (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄 + - 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append + ''' + + self.match7 = self.match6.copy() + self.match7 = self.match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']] + + # parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique()) + # child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()) + + # (1) 가능한 (진입방향, 진출방향) 목록 + flows = self.nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist() + # (2) 각 교차로별 방향 목록 : pdires (possible directions) + pdires = {} + for node_id in self.parent_ids: + dires = self.match7[self.match7.node_id == node_id][['inc_dir','out_dir']].values.flatten() + dires = {dire for dire in dires if type(dire)==str} + pdires[node_id] = dires + # (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) + inc2id = {} + for node_id in self.parent_ids: + for inc_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dir==inc_dir)] + inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0] + # (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) + out2id = {} + for node_id in self.parent_ids: + for out_dir in pdires[node_id]: + df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dir==out_dir)] + out2id[(node_id, out_dir)] = df.out_edge.iloc[0] + # (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) + pflow = {} + for node_id in self.parent_ids: + pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])] + # (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching + # node2inter = dict(zip(self.match7['node_id'], self.match7['inter_no'])) + dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow + self.matching = [] + for node_id in self.parent_ids: + inter_no = self.node2inter[node_id] + # 좌회전과 직진(1 ~ 16) + for (inc_dir, out_dir) in pflow[node_id]: + move_no = self.nema[(self.nema.inc_dir==inc_dir) & (self.nema.out_dir==out_dir)].move_no.iloc[0] + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + # 보행신호(17), 전적색(18) + new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18], + 'inc_dir':[None]*2, 'out_dir':[None]*2, + 'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2}) + self.matching.append(new_row) + # 신호우회전(21) + for d in range(len(dires_right)-1): + inc_dir = dires_right[d] + out_dir = dires_right[d+1] + if {inc_dir, out_dir}.issubset(pdires[node_id]): + inc_edge = inc2id[(node_id, inc_dir)] + out_edge = out2id[(node_id, out_dir)] + new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21], + 'inc_dir':[inc_dir], 'out_dir':[out_dir], + 'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]}) + self.matching.append(new_row) + self.matching.append(self.match7[self.match7.node_id.isin(self.child_ids)]) + self.matching = pd.concat(self.matching) + self.matching = self.matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True) + self.matching['move_no'] = self.matching['move_no'].astype(int) + # self.matching.to_csv(os.path.join(self.path_intermediates, 'matching.csv')) + + # 2-2 신호 초기화 + def initialize_state(self): + ''' + 비보호우회전신호 (g) 배정 + + input : + (1) net : 네트워크 + (2) nodes : 노드 목록 + (3) histids : 모든 교차로에 대한 시작유닉스 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지 + + output : node2init + - 각 노드를 초기화된 신호로 맵핑하는 딕셔너리 + - 초기화된 신호란, 우회전을 g로 나머지는 r로 지정한 신호를 말함. + ''' + self.nodes = [self.net.getNode(node_id) for node_id in self.node_ids] + self.node2init = {} + # 모든 노드들을 순회 + for node in self.nodes: + node_id = node.getID() + # 모든 connection + conns = [(c.getJunctionIndex(), c) for c in node.getConnections()] + conns = [c for c in conns if c[0] >= 0] + conns = sorted(conns, key=lambda x: x[0]) + state = [] + # i번째 connection : ci + for i, ci in conns: + if ci.getTLLinkIndex() < 0: + continue + are_foes = False + # j번째 connection : cj + # 합류지점이 다르면서 상충되는 cj가 존재하면 are_foes = True (r) + # 그외의 경우에는 are_foes = False (g) + for j, cj in conns: + # ci, cj의 합류지점이 같으면 통과 + if ci.getTo() == cj.getTo(): + continue + # ci, cj가 상충되면 are_foes를 True로 지정. + if node.areFoes(i, j): + are_foes = True + break + state.append('r' if are_foes else 'g') + self.node2init[node_id] = state + + # 어떤 연결과도 상충이 일어나지는 않지만, 신호가 부여되어 있는 경우에는 r을 부여 + for _, row in self.matching.iterrows(): + node_id = row.node_id + move_no = row.move_no + inc_edge = row.inc_edge + out_edge = row.out_edge + if move_no != 21: + inc_edge = self.net.getEdge(inc_edge) + out_edge = self.net.getEdge(out_edge) + for conn in inc_edge.getConnections(out_edge): + index = conn.getTLLinkIndex() + if index >= 0: + self.node2init[node_id][index] = 'r' + + # 연등교차로 + for _, row in self.coord.iterrows(): + node_id = row.node_id + inc_edge = row.inc_edge + out_edge = row.out_edge + if not (pd.isna(inc_edge) and pd.isna(out_edge)): + inc_edge = self.net.getEdge(inc_edge) + out_edge = self.net.getEdge(out_edge) + for conn in inc_edge.getConnections(out_edge): + index = conn.getTLLinkIndex() + if index >= 0: + self.node2init[node_id][index] = 'r' + # 유턴교차로 + for _, row in self.uturn.iterrows(): + node_id = row.child_id + inc_edge = row.inc_edge + out_edge = row.out_edge + if not (pd.isna(inc_edge) and pd.isna(out_edge)): + inc_edge = self.net.getEdge(inc_edge) + out_edge = self.net.getEdge(out_edge) + for conn in inc_edge.getConnections(out_edge): + index = conn.getTLLinkIndex() + if index >= 0: + self.node2init[node_id][index] = 'r' + + # json 파일로 저장 + with open(os.path.join(self.path_intermediates, 'node2init.json'), 'w') as file: + json.dump(self.node2init, file) + + print('2-2. 비보호우회전(g)을 배정했습니다.') + + # 2-3 신호배정 + def assign_signals(self): + # assign signals on matching + self.matching['init_state'] = self.matching['node_id'].map(self.node2init) + self.matching['state'] = self.matching['init_state'].map(lambda x:''.join(x)) + # matching의 각 행을 순회 + for row in self.matching.itertuples(index=True): + node_id = row.node_id + move_no = row.move_no + inc_edge = row.inc_edge + out_edge = row.out_edge + state = copy.deepcopy(self.node2init)[node_id] + + if move_no != 21: + inc_edge = self.net.getEdge(inc_edge) + out_edge = self.net.getEdge(out_edge) + for conn in inc_edge.getConnections(out_edge): + index = conn.getTLLinkIndex() + if index >= 0: + state[index] = 'G' + self.matching.at[row.Index, 'state'] = ''.join(state) + + self.matching = self.matching.dropna(subset='state') + self.matching = self.matching.reset_index(drop=True) + self.matching = self.matching[['inter_no', 'node_id', 'move_no', 'state']] + + # assign signals on match6 + + self.match6 = self.match6.reset_index(drop=True) + self.match6['init_state'] = self.match6['node_id'].map(self.node2init) + self.match6['state'] = self.match6['init_state'].map(lambda x:''.join(x)) + + # match6의 각 행을 순회 + for i, row in self.match6.iterrows(): + node_id = row.node_id + move_no = row.move_no + inc_edge = row.inc_edge + out_edge = row.out_edge + state = copy.deepcopy(self.node2init)[node_id] + if (pd.isna(inc_edge)) or (pd.isna(out_edge)): + continue + if (move_no != 21): + # print(i, node_id, move_no, ''.join(state)) + inc_edge = self.net.getEdge(inc_edge) + out_edge = self.net.getEdge(out_edge) + for conn in inc_edge.getConnections(out_edge): + index = conn.getTLLinkIndex() + if index >= 0: + state[index] = 'G' + # print(i, node_id, move_no, index, ''.join(state)) + self.match6.at[i, 'state'] = ''.join(state) + + self.match6 = self.match6.dropna(subset='state') + self.match6 = self.match6.reset_index(drop=True) + self.match6 = self.match6[['inter_no', 'node_id', 'phase_no', 'ring_type', 'move_no', 'state']] + self.match6.to_csv(os.path.join(self.path_intermediates, 'match6.csv')) + self.matching.to_csv(os.path.join(self.path_intermediates, 'matching.csv')) + print('2-3. 직진 및 좌회전(G)을 배정했습니다.') + + uid2uindex = {} + for uid in self.uturn_ids: + states = self.match6[self.match6.node_id==uid].state.unique() + for state in states: + if 'G' in state: + index = state.index('G') + uid2uindex[uid] = index + break + + # json 파일로 저장 + with open(os.path.join(self.path_intermediates, 'uid2uindex.json'), 'w') as file: + json.dump(uid2uindex, file) + + # 2-4 node2num_cycles : A dictionary that maps a node_id to the number of cycles + def get_node2num_cycles(self): + Aplan = self.plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']] + grouped = Aplan.groupby('inter_no') + df = grouped.agg({'cycle': 'min'}).reset_index() + df = df.rename(columns={'cycle': 'min_cycle'}) + df['num_cycle'] = 300 // df['min_cycle'] + 2 + inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle'])) + node2num_cycles = {node_id : inter2num_cycles[self.node2inter[node_id]] for node_id in self.node_ids} + with open(os.path.join(self.path_intermediates,'node2num_cycles.json'), 'w') as file: + json.dump(node2num_cycles, file, indent=4) + print("2-2. node2num_cycles.json를 저장했습니다.") + + # 3. 이슈사항 저장 + def write_issues(self): + print('3. 이슈사항을 저장합니다.') + path_issues = os.path.join(self.path_results, "issues_preprocess_daily.txt") + with open(path_issues, "w", encoding="utf-8") as file: + for item in self.issues: + file.write(item + "\n") + if self.issues: + print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :") + for review in self.issues: + print(review) + + def main(self): + # 1. 데이터 불러오기 + self.load_data() + # 2. 중간산출물 만들기 + self.get_intermediates() + # 3. 이슈사항 저장 + self.write_issues() + +if __name__ == '__main__': + self = DailyPreprocessor() + self.main()