建筑阴影计算.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import python.singleShadow as singleShadow\n",
    "from suncalc import get_position,get_times\n",
    "from shapely.geometry import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bdshadow(bd,height,sunPosition):\n",
    "    '''\n",
    "    输入建筑物Polygon与高度,时间，输出阴影Polygon\n",
    "    '''\n",
    "    wall = pd.DataFrame(list(bd.exterior.coords),columns=['x1','y1'])\n",
    "    wall['x2'] = wall['x1'].shift(-1)\n",
    "    wall['y2'] = wall['y1'].shift(-1)\n",
    "    wall['height']=height\n",
    "    wall = wall.iloc[:-1]\n",
    "    wall['geometry'] = wall.apply(lambda r:Polygon(singleShadow.calSunShadow([[r['x1'],r['y1']],[r['x2'],r['y2']]],r['height'],sunPosition)),axis = 1)\n",
    "    shadow = gpd.GeoSeries(list(wall['geometry'])+[bd]).unary_union\n",
    "    return shadow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "building = gpd.read_file(r'data/bd_demo.json')\n",
    "#给定经纬度\n",
    "lon1,lat1,lon2,lat2 = list(building.bounds.mean())\n",
    "lon = (lon1+lon2)/2\n",
    "lat = (lat1+lat2)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    }
   ],
   "source": [
    "#建筑转换坐标系\n",
    "building.crs = {'init':'epsg:4326'}\n",
    "building = building.to_crs(epsg = 3857)\n",
    "testbuilding = building.iloc[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#时间\n",
    "date = pd.to_datetime('2015-01-01 02:45:33.959797119')\n",
    "#正午时间，获取太阳位置\n",
    "sunPosition = get_position(date, lon, lat)\n",
    "buildingshadow = testbuilding.copy()\n",
    "buildingshadow['geometry'] = testbuilding.apply(lambda r:bdshadow(r['geometry'],r['height'],sunPosition),axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(1,(5,5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "buildingshadow.plot(ax = ax,facecolor = (0,0,0,0.3))\n",
    "testbuilding.plot(ax = ax,edgecolor = 'k')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}