Python-coding/openweather_map_pcolormesh_api_uk.py at main · kcj-coding/Python-coding · GitHub

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import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from scipy.interpolate import griddata
import numpy as np
import sklearn as sk
import xlsxwriter as xlw
import os
import html
import re

#################### configure lats/lons of interest ###########################
api_key = "" # api key for openweathermap.org

lat1 = 45 # positive number means North, negative number means South
lat2 = 75 # positive number means North, negative number means South

lon1 = -20 # positive number means East, negative number means West
lon2 = 10 # positive number means East, negative number means West

# the lat2 - lat1 and lon2 - lon1 range should be the same
if (lat2-lat1) == (lon2-lon1):
    print("Lat2-Lat1 and Lon2-Lon1 match... continuing")
else:
    raise ValueError("Lat2-Lat1 and Lon2-Lon1 do not match. Please make them equal")

increment = 1 # to increase lat/lon co-ordinates by this amount
################################################################################

# create a dataframe of the latitude points of interest to query for weather stations
lats = pd.DataFrame(data={'lat':np.arange(lat1,lat2,increment)}) # increment in 0.5 steps

# create a numpy array of the longitude points of interest
lon_range=[]
rngr = int((lon2-lon1)/increment)

for i in range(0,rngr,1):
    lon_rng = np.arange((lon1+i),(lon2+i),increment) # increment in 0.5 steps
    lon_range.append(lon_rng)

# configuring df
d1 = pd.DataFrame(data={'data':[0]})
d2 = pd.DataFrame(data={'data':[0]})

# for each longitude array, join the latitude entries
for each in lon_range:
    lon_df = pd.DataFrame(data={'lon':each})
    d1 = pd.concat([lats,lon_df],axis=1)
    d2 = pd.concat([d2,d1])

# remove things we do not need
d2 = d2[~pd.isnull(d2['lat'])]
d2 = d2[d2['lat']<=90] # filter to maximum latitude
d2 = d2[d2['lat']>=-90] # filter for minimum latitude
d2 = d2[d2['lon']<=180] # filter for maximum longitude
d2 = d2[d2['lon']>=-180] # filter for minimum longitude
d2 = d2.drop(columns=['data'])
d2 = d2.reset_index()


# configuring df
dff = pd.DataFrame(data={'name':['xyz']})
################################################################################

# make an api call for each lat/long combination as created earlier and store results in a dataframe
for ii in range(0,len(d2),1):

        city = "https://api.openweathermap.org/data/2.5/weather?lat="+str(d2['lat'][ii])+"&lon="+str(d2['lon'][ii])+"&apikey="+api_key+"&units=imperial"
        city = pd.read_json(city, lines=True)
        co_ords = pd.DataFrame([city.coord[0]])
        name = city.name
        wind = pd.DataFrame([city.wind[0]])
        df = pd.concat([co_ords,name,wind],axis=1)
        dff = pd.concat([dff,df])

dff = dff[dff['name']!='xyz']
dff = dff.reset_index()
################################################################################

# graph definitions
def base_map_plot(sdata,bar,title):
    # 1. Draw the map background
    fig = plt.figure(figsize=(10, 7))
    m = Basemap(projection='lcc', resolution='h',
                lat_0=54, lon_0=-1,
                width=0.1E7, height=0.1E7)
    #m.shadedrelief()
    m.drawmapboundary(fill_color='#99ffff') # or hex colour
    m.fillcontinents(color='#FFFFFF', lake_color='#99ffff')
    m.drawcoastlines(color='#FFFFFF')
    m.drawcountries(color='#FFFFFF')
    #m.drawstates(color='gray')
    m.drawparallels(np.arange(-90, 100, 1))#, linewidth=0.5, dashes=[4, 2], labels=[1,0,0,1], color='r', zorder=0 )
    m.drawmeridians(np.arange(-100,100,1))#, linewidth=0.5, dashes=[4, 2], labels=[1,0,0,1], color='r', zorder=0 )
    # 2. scatter data
    # and size reflecting area
    m.pcolormesh(xi, yi, zi,latlon=True,
              cmap="jet")#, zorder=2)#, alpha=0.5)

    # 3. create colorbar and legend
    m.drawparallels(range(-90, 100, 1), linewidth=0.5, dashes=[4, 2], labels=[1,0,0,1], color='r', zorder=0 )
    m.drawmeridians(range(-100,100,1), linewidth=0.5, dashes=[4, 2], labels=[1,0,0,1], color='r', zorder=0 )
    if bar==True:
        plt.colorbar(label="Wind speed/mph")
    plt.title(title)
################################################################################

# apply lat/lon checkers
X_dat = dff['lon']
Y_dat = dff['lat']
Z_dat = dff['speed']

# convert from pandas to numpy array
X, Y, Z = np.array([]), np.array([]), np.array([])

for iii in range(len(X_dat)):
    X = np.append(X, X_dat[iii])
    Y = np.append(Y, Y_dat[iii])
    Z = np.append(Z, Z_dat[iii])

# create x-y points to be used in heatmap
xi = np.linspace((X.min()), (X.max()), len(dff['lon']))
yi = np.linspace((Y.min()), (Y.max()), len(dff['lon']))

# Interpolate for plotting
zi = griddata((X, Y), Z, (xi[None,:], yi[:,None]), method='cubic')

# graph the data
base_map_plot(sdata=1,bar=True,
              title="Wind speed UK 06/02/2023")

#save plot
plt.savefig('C:\\Users\\kelvi\\Desktop\\wxr_map11_uk.png', dpi=400, bbox_inches='tight')