QuantumDamage/026-new.ipynb

## 026-new.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport requests\nimport pandas as pd\nimport numpy as np\nimport json",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import time",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "r = requests.get('http://api.gios.gov.pl/pjp-api/rest/station/findAll')\nallStations = pd.io.json.json_normalize(r.json())",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pollutants = [\"PM2.5\", \"PM10\", \"C6H6\", \"NO2\", \"SO2\", \"O3\", \"CO\"]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def get_sensors(Id):\n    stationId = Id\n    r = requests.get('http://api.gios.gov.pl/pjp-api/rest/station/sensors/' + str(stationId))\n    \n    sensors = pd.io.json.json_normalize(r.json())\n    return sensors[[\"param.paramCode\",\"id\"]].set_index(\"param.paramCode\").to_dict(orient = \"index\")",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "allStations[\"gegrLat\"] = allStations[\"gegrLat\"].astype(\"float\")\nallStations[\"gegrLon\"] = allStations[\"gegrLon\"].astype(\"float\")",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%time\nallStations[\"sensors\"] = allStations[\"id\"].apply(get_sensors)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "CPU times: user 2.63 s, sys: 78.6 ms, total: 2.71 s\nWall time: 30.6 s\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "allStations2 = pd.concat([allStations, allStations['sensors'].str.join('|').str.get_dummies()], axis=1, sort=False)\nallStations2[pollutants].sum()",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "PM2.5     63\nPM10     136\nC6H6      53\nNO2      143\nSO2      125\nO3       101\nCO        76\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#points = allStations2[allStations2[\"PM2.5\"] == 1][[\"gegrLat\",\"gegrLon\"]].values\n#points = allStations2[allStations2[\"PM10\"] == 1][[\"gegrLat\",\"gegrLon\"]].values\n#points = allStations2[allStations2[\"C6H6\"] == 1][[\"gegrLat\",\"gegrLon\"]].values\n#points = allStations2[allStations2[\"NO2\"] == 1][[\"gegrLat\",\"gegrLon\"]].values\n#points = allStations2[allStations2[\"SO2\"] == 1][[\"gegrLat\",\"gegrLon\"]].values\n#points = allStations2[allStations2[\"O3\"] == 1][[\"gegrLat\",\"gegrLon\"]].values\npoints = allStations2[allStations2[\"CO\"] == 1][[\"gegrLat\",\"gegrLon\"]].values",
      "execution_count": 247,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lat_max = points[:,0].max()\nlat_min = points[:,0].min()\nlon_max = points[:,1].max()\nlon_min = points[:,1].min()\n\n\nlat_max, lat_min, lon_max, lon_min",
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 248,
          "data": {
            "text/plain": "(54.560836, 49.293564, 23.00075, 15.008175)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "points[:3]",
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 249,
          "data": {
            "text/plain": "array([[51.129378, 17.02925 ],\n       [51.086225, 17.012689],\n       [51.204503, 16.180513]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pyproj",
      "execution_count": 250,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lats = points[:,0]\nlons = points[:,1]\nalts = np.zeros(lats.shape)\n\necef = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84')\nlla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84')    \nx, y, z = pyproj.transform(lla, ecef, lons, lats, alts, radians=False)\nprint (x,y,z)",
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[3834981.24111618 3838893.67152115 3845707.07698888 3880125.48107186\n 3872262.52205653 3877925.16465747 3869998.10037573 3648359.23263694\n 3647154.80693829 3728533.84607212 3728920.49190347 3722897.81394391\n 3731001.58156403 3753682.85310983 3787033.20858048 3664260.24882533\n 3873170.5212753  3806545.80940342 3805405.93095525 3763724.43736442\n 3450486.08474459 3557118.43220582 3730348.13294337 3730108.54919302\n 3762225.55378914 3717803.39344389 3670131.16526164 3725235.41587773\n 3852091.10293227 3863690.32165227 3863751.31990053 3886227.04965217\n 3888876.97829728 3898843.36419296 3815087.29044785 3813269.70408825\n 3913510.60551212 3895978.13103253 3773809.17820207 3793679.14987059\n 3748093.16836347 3537353.22557138 3529381.5169706  3857314.03095435\n 3853265.89279632 3833759.93441245 3470148.53516383 3530013.89842869\n 3525480.33330244 3515128.81531242 3533764.32709501 3524447.13710031\n 3528015.66447893 3552114.27467214 3563467.01256625 3549153.25587762\n 3531427.4764622  3560685.15323363 3859961.94099799 3917387.04406171\n 3655508.32434349 3658835.79074094 3658774.32716318 3718895.48091976\n 3692207.78136076 3658265.00903596 3666154.53697835 3693261.88955179\n 3734014.87444186 3796586.58259952 3761532.84923129 3793288.05499396\n 3828769.97191697 3644002.21176281 3664433.76951843 3600604.44777872] [1174612.55152773 1174597.28851616 1115862.95622844 1132399.34134647\n 1038161.80280697 1094828.51170148 1056936.03483108 1184572.43468005\n 1184730.38948361 1322524.8811893  1316884.85003308 1312590.08220956\n 1316449.90728658 1343788.05532954 1337243.62338769 1414742.31653423\n 1276173.97189856 1538761.6518523  1537843.5237447  1529244.0774935\n 1415687.88097743 1292498.52317143 1137600.55602892 1131678.79259489\n 1225934.97154371 1227317.37018783 1105252.82106777 1138896.22524444\n 1343794.15924031 1331843.9700902  1313249.79108354 1301530.71585396\n 1315520.00218967 1346180.48603049 1322392.01946512 1322707.74853301\n 1320013.29919989 1300215.89200165 1421014.83676227 1477367.58073867\n 1429805.97769607 1321582.37953798 1243649.08987623 1398322.67347345\n 1406549.49940701 1471072.78963634 1473044.27058953 1190401.42652329\n 1190395.09649848 1175690.71415472 1186345.00320858 1184719.03278554\n 1188698.38289105 1089158.43672374 1156181.042619   1224351.44754335\n 1130160.2333425  1169521.21821657 1365175.83580079 1422722.33996583\n 1403563.99166453 1309164.76918026 1310736.29918642 1438543.66474253\n 1401915.54170287 1421687.81150889 1261454.20268044 1535022.63885726\n 1016515.82350689 1054232.67078382 1016520.370713   1110492.83082193\n 1035075.23207977 1227249.14607899 1266010.52495935 1223091.32129925] [4942589.94277523 4939575.73538475 4947830.70833963 4917313.16401395\n 4944056.67543508 4927478.38813731 4941866.06975731 5078687.07319759\n 5079509.69402966 4986184.64047599 4987380.06131803 4992970.73089642\n 4985947.92596846 4961734.76607408 4938262.67411145 5008321.46406459\n 4887694.55861997 4864521.82645145 4865695.94432173 4900466.95150336\n 5156684.26313614 5116692.67467985 5029958.92868002 5031462.05772431\n 4985564.95746307 5018221.19068099 5080866.41645833 5033430.25122983\n 4886239.37594206 4880389.08929584 4885343.74971212 4870728.98839341\n 4864891.54543614 4848609.20335363 4920755.11173066 4922070.06290721\n 4844016.13864845 4863334.44283947 4925062.22040401 4893326.09833642\n 4942011.94659277 5122920.09683039 5147702.95909205 4866910.36853432\n 4867740.78522753 4864093.82062319 5127538.2816625  5159762.71451956\n 5162842.1099513  5173189.60334699 5158140.4567091  5164839.2907402\n 5161509.95704815 5166906.79473928 5144628.67477505 5138787.35562949\n 5172242.62457141 5143546.66909151 4874167.28304756 4811914.55221999\n 5017789.36759296 5040674.56052299 5040313.19364276 4961365.37843122\n 4991487.9411873  5010720.68347238 5047472.02213916 4951657.15690398\n 5052958.01145233 4998675.54643944 5032642.84324139 4989053.26927921\n 4978227.40608754 5071723.87732819 5047580.08909741 5103410.42734767]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "converted = pd.DataFrame({\"x\":x,\"y\":y,\"z\":z})",
      "execution_count": 252,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "converted.shape",
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 253,
          "data": {
            "text/plain": "(76, 3)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from scipy.spatial import voronoi_plot_2d\nfrom scipy.spatial import Voronoi\n\nvor = Voronoi(converted)",
      "execution_count": 254,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vertices_xyz = pd.DataFrame(vor.vertices, columns = [\"x\", \"y\", \"z\"])",
      "execution_count": 255,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#import matplotlib.pyplot as plt\n#voronoi_plot_2d(vor)\n#plt.show()\n",
      "execution_count": 256,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lons_ver, lats_ver, alts_ver = pyproj.transform(ecef,lla, vertices_xyz[\"x\"].values,\n                                                vertices_xyz[\"y\"].values, \n                                                vertices_xyz[\"z\"].values, radians=False)\n",
      "execution_count": 257,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vertices_xyz.head()",
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 258,
          "data": {
            "text/plain": "              x             y              z\n0  -9574.456414  -2795.087146  -46614.811354\n1 -83905.932240 -29653.491721 -147825.476361\n2   6753.273057   2557.749623  -24393.929931\n3  19187.311667   7662.561331   -6631.947835\n4   9168.679595   3798.431266  -20994.936650",
            "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>x</th>\n      <th>y</th>\n      <th>z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-9574.456414</td>\n      <td>-2795.087146</td>\n      <td>-46614.811354</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-83905.932240</td>\n      <td>-29653.491721</td>\n      <td>-147825.476361</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6753.273057</td>\n      <td>2557.749623</td>\n      <td>-24393.929931</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>19187.311667</td>\n      <td>7662.561331</td>\n      <td>-6631.947835</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9168.679595</td>\n      <td>3798.431266</td>\n      <td>-20994.936650</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vertices = pd.DataFrame()\nvertices[\"Lat\"] = lats_ver\nvertices[\"Lon\"] = lons_ver",
      "execution_count": 259,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vertices[(vertices[\"Lat\"] < lat_max) & (vertices[\"Lat\"] > lat_min)]",
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 260,
          "data": {
            "text/plain": "           Lat        Lon\n38   50.401710  20.565719\n56   52.105918  20.116088\n60   50.943521  21.597556\n66   50.619931  19.925491\n85   50.621107  19.902210\n136  54.299764  19.046350\n149  52.446123  16.000309\n158  50.619295  19.917425\n164  53.627330  17.969675\n165  54.395755  18.126882\n167  54.395218  18.129956\n181  51.047320  18.100943\n244  51.545597  15.778879",
            "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>Lat</th>\n      <th>Lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>38</th>\n      <td>50.401710</td>\n      <td>20.565719</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>52.105918</td>\n      <td>20.116088</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>50.943521</td>\n      <td>21.597556</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>50.619931</td>\n      <td>19.925491</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>50.621107</td>\n      <td>19.902210</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>54.299764</td>\n      <td>19.046350</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>52.446123</td>\n      <td>16.000309</td>\n    </tr>\n    <tr>\n      <th>158</th>\n      <td>50.619295</td>\n      <td>19.917425</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>53.627330</td>\n      <td>17.969675</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>54.395755</td>\n      <td>18.126882</td>\n    </tr>\n    <tr>\n      <th>167</th>\n      <td>54.395218</td>\n      <td>18.129956</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>51.047320</td>\n      <td>18.100943</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>51.545597</td>\n      <td>15.778879</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "proper_vertices = pd.DataFrame(vertices[(vertices[\"Lon\"] < lon_max) & \n         (vertices[\"Lon\"] > lon_min) & \n         (vertices[\"Lat\"] < lat_max) & \n         (vertices[\"Lat\"] > lat_min)], copy = True)",
      "execution_count": 261,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import geopy.distance",
      "execution_count": 262,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for index, row in proper_vertices.iterrows():\n    list_of_dists = []\n    for point in points:\n        #list_of_dists.append(np.sqrt(np.square(row[\"Lat\"] - point[0]) + np.square(row[\"Lon\"] - point[0])))\n        coords_1 = (row[\"Lat\"], row[\"Lon\"])\n        coords_2 = (point[0], point[1])\n        #print(coords_1,coords_2)\n        list_of_dists.append(geopy.distance.vincenty(coords_1, coords_2).km)\n    #print(list_of_dists)\n    proper_vertices.at[index, 'dist'] = min(list_of_dists)",
      "execution_count": 263,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "proper_vertices.sort_values(\"dist\").tail(n=1)",
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 264,
          "data": {
            "text/plain": "          Lat        Lon       dist\n181  51.04732  18.100943  75.639616",
            "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>Lat</th>\n      <th>Lon</th>\n      <th>dist</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>181</th>\n      <td>51.04732</td>\n      <td>18.100943</td>\n      <td>75.639616</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import folium\nfrom folium.plugins import MarkerCluster",
      "execution_count": 265,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "boulder_coords = [lat_min + (lat_max - lat_min)/2, lon_min + (lon_max - lon_min)/2]",
      "execution_count": 266,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "my_map = folium.Map(location = boulder_coords)\nmy_map.fit_bounds(bounds=[(lat_min, lon_min), (lat_max, lon_max)])",
      "execution_count": 267,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for point in points:\n    folium.Marker(point).add_to(my_map)",
      "execution_count": 268,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from geopy.geocoders import Nominatim\ngeolocator = Nominatim(user_agent=\"jakbadacdane.pl\")",
      "execution_count": 269,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for index, row in proper_vertices.sort_values(\"dist\", ascending=False).iterrows():\n    location = geolocator.reverse(\"{}, {}\".format(row[\"Lat\"], row[\"Lon\"]), timeout = 30)\n    #print(location.raw)\n    #print(row[\"Lat\"], row[\"Lon\"])\n    \n    if location.raw == {'error': 'Unable to geocode'}:\n        print(\"Out of space\")\n        continue\n    \n    try:\n        is_poland = location.raw[\"address\"][\"country\"] == \"RP\"\n    except KeyError:\n        print(location)\n        break\n        \n    if is_poland:\n        print(location)\n        folium.Circle(radius=row[\"dist\"]*1000, location=[row[\"Lat\"], row[\"Lon\"]], fill = True).add_to(my_map)\n        break\n    else:\n        print(location.raw[\"address\"][\"country\"])",
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "stream",
          "text": "1336O, Skałągi, gmina Wołczyn, powiat kluczborski, opolskie, 46-262, RP\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#for index, row in proper_vertices.sort_values(\"dist\").tail(n=1).iterrows():\n#    #print(row[\"dist\"]*1000)\n#    folium.Circle(radius=row[\"dist\"]*1000, location=[row[\"Lat\"], row[\"Lon\"]], fill = True).add_to(my_map)",
      "execution_count": 271,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "my_map",
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 272,
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