#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import requests
import pandas as pd
import numpy as np
import json


# In[2]:


import time


# In[3]:


r = requests.get('http://api.gios.gov.pl/pjp-api/rest/station/findAll')
allStations = pd.io.json.json_normalize(r.json())


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pollutants = ["PM2.5", "PM10", "C6H6", "NO2", "SO2", "O3", "CO"]


# In[5]:


def get_sensors(Id):
    stationId = Id
    r = requests.get('http://api.gios.gov.pl/pjp-api/rest/station/sensors/' + str(stationId))
    
    sensors = pd.io.json.json_normalize(r.json())
    return sensors[["param.paramCode","id"]].set_index("param.paramCode").to_dict(orient = "index")


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allStations["gegrLat"] = allStations["gegrLat"].astype("float")
allStations["gegrLon"] = allStations["gegrLon"].astype("float")


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get_ipython().run_cell_magic('time', '', 'allStations["sensors"] = allStations["id"].apply(get_sensors)\n')


# In[8]:


allStations2 = pd.concat([allStations, allStations['sensors'].str.join('|').str.get_dummies()], axis=1, sort=False)
allStations2[pollutants].sum()


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#points = allStations2[allStations2["PM2.5"] == 1][["gegrLat","gegrLon"]].values
#points = allStations2[allStations2["PM10"] == 1][["gegrLat","gegrLon"]].values
#points = allStations2[allStations2["C6H6"] == 1][["gegrLat","gegrLon"]].values
#points = allStations2[allStations2["NO2"] == 1][["gegrLat","gegrLon"]].values
#points = allStations2[allStations2["SO2"] == 1][["gegrLat","gegrLon"]].values
#points = allStations2[allStations2["O3"] == 1][["gegrLat","gegrLon"]].values
points = allStations2[allStations2["CO"] == 1][["gegrLat","gegrLon"]].values


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points


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lat_max = points[:,0].max()
lat_min = points[:,0].min()
lon_max = points[:,1].max()
lon_min = points[:,1].min()


lat_max, lat_min, lon_max, lon_min


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from scipy.spatial import voronoi_plot_2d
from scipy.spatial import Voronoi

vor = Voronoi(points)


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vertices = pd.DataFrame(vor.vertices, columns = ["Lat", "Lon"])


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import matplotlib.pyplot as plt
voronoi_plot_2d(vor)
plt.show()


# In[15]:


vertices[(vertices["Lat"] < lat_max) & (vertices["Lat"] > lat_min)]


# In[16]:


proper_vertices = pd.DataFrame(vertices[(vertices["Lon"] < lon_max) & 
         (vertices["Lon"] > lon_min) & 
         (vertices["Lat"] < lat_max) & 
         (vertices["Lat"] > lat_min)], copy = True)


# In[17]:


import geopy.distance


# In[18]:


for index, row in proper_vertices.iterrows():
    list_of_dists = []
    for point in points:
        #list_of_dists.append(np.sqrt(np.square(row["Lat"] - point[0]) + np.square(row["Lon"] - point[0])))
        coords_1 = (row["Lat"], row["Lon"])
        coords_2 = (point[0], point[1])
        #print(coords_1,coords_2)
        list_of_dists.append(geopy.distance.vincenty(coords_1, coords_2).km)
    #print(list_of_dists)
    proper_vertices.at[index, 'dist'] = min(list_of_dists)


# In[19]:


proper_vertices.sort_values("dist").tail(n=1)


# In[20]:


import folium
from folium.plugins import MarkerCluster


# In[21]:


boulder_coords = [lat_min + (lat_max - lat_min)/2, lon_min + (lon_max - lon_min)/2]


# In[22]:


my_map = folium.Map(location = boulder_coords)
my_map.fit_bounds(bounds=[(lat_min, lon_min), (lat_max, lon_max)])


# In[23]:


for point in points:
    folium.Marker(point).add_to(my_map)


# In[24]:


from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="jakbadacdane.pl")


# In[25]:


for index, row in proper_vertices.sort_values("dist", ascending=False).iterrows():
    location = geolocator.reverse("{}, {}".format(row["Lat"], row["Lon"]), timeout = 30)
    #print(location.raw)
    #print(row["Lat"], row["Lon"])
    
    if location.raw == {'error': 'Unable to geocode'}:
        print("Out of space")
        continue
    
    try:
        is_poland = location.raw["address"]["country"] == "RP"
    except KeyError:
        print(location)
        break
        
    if is_poland:
        print(location)
        folium.Circle(radius=row["dist"]*1000, location=[row["Lat"], row["Lon"]], fill = True).add_to(my_map)
        break
    else:
        print(location.raw["address"]["country"])


# In[26]:


#for index, row in proper_vertices.sort_values("dist").tail(n=1).iterrows():
#    #print(row["dist"]*1000)
#    folium.Circle(radius=row["dist"]*1000, location=[row["Lat"], row["Lon"]], fill = True).add_to(my_map)


# In[27]:


my_map


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