noebook to create boardings_by_segment_am.geojson

the produced file is used for acceptance criteria summaries

notebooks/create-boardings-by-segment-am-geojson.ipynb

@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import openmatrix as omx\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "import os\n",
+    "import json\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scenario 12 is AM period \n",
+    "# emme_tsegs.shp files are exported manually from EMME for time periods. \n",
+    "# transit_segments.csv is also exported manually from EMME. Added \"caps\" and \"capt\" fields to the data table before exporting. \n",
+    "\n",
+    "data_dir = \"V:\\\\projects\\\\MTC\\\\transit_path_builder_calib\\\\TM2.2.1.1\\\\output_summaries\\\\Scenario_12\"\n",
+    "segment_shp = gpd.read_file(os.path.join(data_dir, \"emme_tsegs.shp\"))\n",
+    "cap_df = pd.read_csv(os.path.join(data_dir, \"transit_segments.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap_df = cap_df [[\"Line\", \"From\", \"To\", \"capt\", \"caps\"]]\n",
+    "segment_shp = segment_shp[[\"SEG_ID\", \"LINE_ID\", \"INODE\", \"JNODE\", \"VOLTR\", \"geometry\"]]\n",
+    "out_df = segment_shp.merge(cap_df, left_on=[\"LINE_ID\", \"INODE\", \"JNODE\"], right_on = [\"Line\", \"From\", \"To\"], how=\"left\")\n",
+    "out_df = out_df.drop_duplicates(subset=['SEG_ID'])\n",
+    "out_df = out_df [[\"LINE_ID\", \"INODE\", \"JNODE\",\"VOLTR\", \"caps\", \"capt\", \"geometry\"]]\n",
+    "out_df.to_file(os.path.join(data_dir, \"boardings_by_segment_am.geojson\"), driver='GeoJSON')\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.6 ('tm2py_acceptance')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "79c5570345f898d1e567408541a5b012def5e8a07e15510a7e9419fb9933423e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}