Geopandas_from_contourf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing how to get a GeoPandas dataframe of the areas of CWV>55 from contourf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic object is [GeoPandas dataframes](https://geopandas.org/getting_started/introduction.html): \n",
    "\n",
    "![geopandas dataframe tableau](https://geopandas.org/_images/dataframe.svg \"GeoPandas dataframe schema\")\n",
    "\n",
    "The `geometry` column contains shapely objects (polygons which may have holes), areas where CWV>55. Data columns can be anything -- area, rainrate from another dataset, or the ID tags of  active CCVLs (coast-crossing vapor lakes), of which each holey polygon is a part. A given polygon can be tagged as part of multiple CCVLs (having a notnull() entry in multiple data columns), because physically it may be destined to split into parts that will each separately cross the coast. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Jun  9 18:53:30 MDT 2021\n"
     ]
    }
   ],
   "source": [
    "!date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely import geometry\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import numpy as np\n",
    "import fiona             # a read-write library for shapefiles\n",
    "import os,json           # json just a convenient way to dump some numbers (contour levels)\n",
    "#from descartes.patch import PolygonPatch\n",
    "\n",
    "from glob import glob\n",
    "import xarray as xr\n",
    "#import dask.array as da\n",
    "\n",
    "import geopandas as gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = glob('MERRA2*.nc') #+glob('*2015*')+glob('*2016*')+glob('*2017*')+glob('*2018*') # selecting 2014-2018\n",
    "cwv_data = xr.open_mfdataset(files) # merging data files in one\n",
    "\n",
    "cwv_WEIO = cwv_data.sel(lat=slice(-30,30),lon=slice(30,90))\n",
    "lon = cwv_WEIO.lon\n",
    "lat = cwv_WEIO.lat\n",
    "tqv = cwv_WEIO.TQV[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2014-05-15T00:00:00.000000000'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(cwv_WEIO.time[0].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the contourf plot, storing the resulting ContourSet in cs\n",
    "plt.figure(figsize=[10,5])\n",
    "# Nlevels=3\n",
    "#levels = [ 30., 40., 50., 60.]\n",
    "#levels = [45., 50., 55., 60.]\n",
    "levels = [45., 999.]\n",
    "cs = plt.contourf(lon,lat,tqv,levels,cmap='gist_heat')\n",
    "plt.title('contour figure with levels '+ str(levels))\n",
    "plt.colorbar()\n",
    "\n",
    "# create lookup table for levels\n",
    "lvl_lookup = dict(zip(cs.collections, cs.levels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contour path:  0\n",
      "   ncp:  0\n",
      "contour path:  1\n",
      "   ncp:  0\n",
      "contour path:  2\n",
      "   ncp:  0\n",
      "   ncp:  1\n",
      "   ncp:  2\n",
      "   ncp:  3\n",
      "   ncp:  4\n",
      "   ncp:  5\n",
      "contour path:  3\n",
      "   ncp:  0\n",
      "contour path:  4\n",
      "   ncp:  0\n",
      "contour path:  5\n",
      "   ncp:  0\n",
      "contour path:  6\n",
      "   ncp:  0\n",
      "contour path:  7\n",
      "   ncp:  0\n",
      "contour path:  8\n",
      "   ncp:  0\n",
      "contour path:  9\n",
      "   ncp:  0\n",
      "contour path:  10\n",
      "   ncp:  0\n",
      "contour path:  11\n",
      "   ncp:  0\n",
      "contour path:  12\n",
      "   ncp:  0\n",
      "contour path:  13\n",
      "   ncp:  0\n"
     ]
    }
   ],
   "source": [
    "# loop over collections (and polygons in each collection), store in polylist \n",
    "levels, polylist  = [], []\n",
    "i=0\n",
    "for col in cs.collections:\n",
    "    z=lvl_lookup[col] # the value of this level\n",
    "    for contour_path in col.get_paths():\n",
    "        print('contour path: ',i); i = i+1\n",
    "        # create the polygon for this level\n",
    "        for ncp,cp in enumerate(contour_path.to_polygons()):\n",
    "            print('   ncp: ', ncp)\n",
    "            lons = np.array(cp)[:,0]\n",
    "            lats = np.array(cp)[:,1]\n",
    "            new_shape = geometry.Polygon([(i[0], i[1]) for i in zip(lons,lats)])            \n",
    "            if ncp == 0:\n",
    "                poly = new_shape # first shape\n",
    "            else:\n",
    "                poly = poly.difference(new_shape) # Remove the holes\n",
    "#        PolyList.append({'poly':poly}) #,'props':{'z': z}})\n",
    "        polylist.append(poly) #,'props':{'z': z}})\n",
    "\n",
    "        levels.append(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>date</th>\n",
       "      <th>CWV</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((75.00000 -29.81981, 75.09288 -30.000...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.989391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((73.12500 -21.75559, 73.75000 -21.788...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.809003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((61.25000 -15.53873, 61.17523 -15.500...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>811.612962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((53.12500 -11.05794, 53.18909 -11.000...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.013447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((52.50000 -10.68851, 52.78975 -10.500...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.113020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POLYGON ((36.25000 3.43496, 36.87500 3.34424, ...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.162476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POLYGON ((43.12500 5.91484, 43.48023 6.00000, ...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.511349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POLYGON ((43.75000 11.69578, 44.37500 11.91763...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.693805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>POLYGON ((35.62500 12.29523, 36.25000 12.16750...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.794567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>POLYGON ((40.62500 13.47382, 40.65782 13.50000...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.295348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>POLYGON ((85.62500 15.78527, 86.17431 16.00000...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13.153657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>POLYGON ((42.50000 16.63613, 42.58098 17.00000...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.046041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>POLYGON ((71.87500 25.92514, 72.50000 25.70106...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.577435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>POLYGON ((68.12500 26.92679, 68.31088 27.00000...</td>\n",
       "      <td>2014-05-15T00:00:00.000000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.096365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             geometry  \\\n",
       "0   POLYGON ((75.00000 -29.81981, 75.09288 -30.000...   \n",
       "1   POLYGON ((73.12500 -21.75559, 73.75000 -21.788...   \n",
       "2   POLYGON ((61.25000 -15.53873, 61.17523 -15.500...   \n",
       "3   POLYGON ((53.12500 -11.05794, 53.18909 -11.000...   \n",
       "4   POLYGON ((52.50000 -10.68851, 52.78975 -10.500...   \n",
       "5   POLYGON ((36.25000 3.43496, 36.87500 3.34424, ...   \n",
       "6   POLYGON ((43.12500 5.91484, 43.48023 6.00000, ...   \n",
       "7   POLYGON ((43.75000 11.69578, 44.37500 11.91763...   \n",
       "8   POLYGON ((35.62500 12.29523, 36.25000 12.16750...   \n",
       "9   POLYGON ((40.62500 13.47382, 40.65782 13.50000...   \n",
       "10  POLYGON ((85.62500 15.78527, 86.17431 16.00000...   \n",
       "11  POLYGON ((42.50000 16.63613, 42.58098 17.00000...   \n",
       "12  POLYGON ((71.87500 25.92514, 72.50000 25.70106...   \n",
       "13  POLYGON ((68.12500 26.92679, 68.31088 27.00000...   \n",
       "\n",
       "                             date   CWV        area  \n",
       "0   2014-05-15T00:00:00.000000000  45.0    5.989391  \n",
       "1   2014-05-15T00:00:00.000000000  45.0    2.809003  \n",
       "2   2014-05-15T00:00:00.000000000  45.0  811.612962  \n",
       "3   2014-05-15T00:00:00.000000000  45.0    0.013447  \n",
       "4   2014-05-15T00:00:00.000000000  45.0    0.113020  \n",
       "5   2014-05-15T00:00:00.000000000  45.0   19.162476  \n",
       "6   2014-05-15T00:00:00.000000000  45.0    0.511349  \n",
       "7   2014-05-15T00:00:00.000000000  45.0    5.693805  \n",
       "8   2014-05-15T00:00:00.000000000  45.0    0.794567  \n",
       "9   2014-05-15T00:00:00.000000000  45.0    1.295348  \n",
       "10  2014-05-15T00:00:00.000000000  45.0   13.153657  \n",
       "11  2014-05-15T00:00:00.000000000  45.0    0.046041  \n",
       "12  2014-05-15T00:00:00.000000000  45.0    1.577435  \n",
       "13  2014-05-15T00:00:00.000000000  45.0    0.096365  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = GeoDataFrame(geometry=polylist_nodict)\n",
    "gdf['date']=str(cwv_WEIO.time[0].values)\n",
    "gdf['CWV']=levels\n",
    "gdf['area']=gdf.area\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.area.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    POLYGON ((61.25000 -15.53873, 61.17523 -15.500...\n",
      "Name: geometry, dtype: geometry\n"
     ]
    }
   ],
   "source": [
    "bigone = gdf[2:3].geometry\n",
    "print(bigone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf[(gdf.CWV == 45)][:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x174d6bc70>"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigboy = polylist_nodict[2:3][0]\n",
    "bigboy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x174a4bc40>"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# learn about cutting holes \n",
    "interior = [(80,-5), (85,-5), (87,3), (80,0)]\n",
    "bigboy.difference(geometry.Polygon(interior))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = gp.datasets.get_path('naturalearth_lowres')\n",
    "mapdf = gp.read_file(path)\n",
    "mapdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['naturalearth_cities', 'naturalearth_lowres', 'nybb']"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.datasets.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = gp.datasets.get_path('nybb') # New York boroughs \n",
    "mapdf = gp.read_file(path)\n",
    "mapdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}