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

# In[3]:


##Install and import all the required packages in Pythonpackages for the Python Project.
import geoplot as gplt
import geopandas as gpd


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import geoplot.crs as gcrs
import pandas as pd
import pathlib
import matplotlib.pyplot as plt
import mapclassify as mc
import numpy as np
import imageio


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get_ipython().run_line_magic('matplotlib', 'inline')


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# Read spatial data as shapefile- Karnataka Districts
Kar = gpd.read_file("Spatialdata/Karnataka_dist_final.shp")
Kar.head()


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# Read CSV File COVID19 dataset(9th March to 20th July, 2020)
covid_19 = pd.read_csv("9March_20July_Covid19_Karnataka.csv")
covid_19.head()


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# To know the data type of csv reading through pandas
print(type(covid_19)) 


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#Transpose of the csv file, Where Date will be rows and district names will be columns

covid_19_transposed = covid_19.T

covid_19_transposed.head()


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# Check is there any mismatch in District Name in both the files

for items in covid_19['Dist_Name'].to_list():
    Districts = Kar['Dist_Name'].to_list()
    
    if items in Districts:
        pass
    else:
        print(items +' is not in list')


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# Merge operation for Covid_19 csv file and shapefile

Kar_covid19 = Kar.merge(covid_19, left_on="Dist_Name", right_on="Dist_Name")
Kar_covid19.head()


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# Saving updated shapefile as new shapefile in project folder
Kar_covid19.to_file('C:/Users/rahis/Desktop/Project/Kar_covid.shp')


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# Basic plot,
Kar_covid19.plot()


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# Read updated shapefile to view its attribute table 
Kar_covid= gpd.read_file("Kar_covid.shp")
Kar_covid.head()


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# Individual district plot
Kar_covid19[Kar_covid19.Dist_Name =="Bidar"].plot()


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# to know the datatype of a shapefile
print(type(Kar_covid)) 


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# Plot by Dist_name, Random colors
Kar_covid.plot(column='Dist_Name')


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## Standard Map layout with legend

ax = Kar_covid.plot(column = Kar_covid.Dist_Name,
            cmap = 'OrRd',
            figsize =(25,14),
            legend = True,
            #legend_kwds ={'label': "COVID-19 cases", 'orientation' : "vertical"},
            edgecolor = 'black')
            #linewidth = 0.8) 


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#Plot Dist name with an accurate legend
fig, ax = plt.subplots(1, 1)

Kar_covid.plot(column='Dist_Name', ax=ax, legend=True)


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#Plot Dist name with an accurate legend
fig, ax = plt.subplots(1, 1)

Kar_covid.plot(column='9/03/2020', ax=ax, legend=True)


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# Plot Covid active cases per day with an accurate legend

from mpl_toolkits.axes_grid1 import make_axes_locatable


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fig, ax = plt.subplots(1, 1)

divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.2)
Kar_covid.plot(column='20/07/2020', ax=ax, legend=True,  
               legend_kwds={'label': "COVID 19 by Districs",
                           'orientation': "vertical"}, cax=cax)


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# Horizontal legend 
fig, ax = plt.subplots(1, 1)

Kar_covid.plot(column='20/07/2020', ax=ax,
               legend=True,
               legend_kwds={'label': "COVID 19 by Districs",
                           'orientation': "horizontal"})


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#Choosing colors using cmap
Kar_covid.plot(column='Dist_Name', cmap='OrRd')


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#Boundary file
Kar_covid.boundary.plot()


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Kar_covid.plot(column='20/07/2020', cmap='OrRd', scheme='quantiles')


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#  Resize figure and adjusting Map layout the axes
ax = Kar_covid.plot(column =('9/03/2020'),
                 cmap = 'Reds',
                 figsize =(40,14),
                 legend = True,
                 edgecolor = 'black',
                 linewidth = 0.5)
ax.set_title ('Total Active COVID19 Cases in Karnataka : '+ ('9/03/2020'), fontdict= 
                 {'fontsize': 15}, pad = 12.5)


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ax = Kar_covid.plot(column =('20/07/2020'),
                 cmap = 'Reds',
                 figsize =(40,14),
                 legend = True,
                 edgecolor = 'black',
                 linewidth = 0.5)
ax.set_title ('Total Active COVID19 Cases in Karnataka : '+ ('20/07/2020'), fontdict= 
                 {'fontsize': 15}, pad = 12.5)

#Removing the axes

ax.set_axis_off()


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import io
import PIL


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# Dates in the form of LIST
for dates in Kar_covid.columns.to_list()[6:139]:
    print(dates)


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# Creating GIF file
image_frames = []

for dates in Kar_covid.columns.to_list()[6:10]:
    ax = Kar_covid.plot(column = dates,
                 cmap = 'OrRd',
                 figsize =(14,14),
                 legend = True,
                 legend_kwds ={'label' : "COVID-19 Active cases", 'orientation' : "vertical"},
                 edgecolor = 'black',
                 linewidth = 0.8)     
    ax.set_title ('Total Active COVID19 Cases in Karnataka : '+ dates, fontdict= {'fontsize': 22}, pad = 12.5)
    ax.set_axis_off()


# In[38]:


# Adding all the images into an empty list and creating a GIF file as Dynamic Map
image_frames = []

for dates in Kar_covid.columns.to_list()[6:139]:
    ax = Kar_covid.plot(column = dates,
                 cmap = 'OrRd',
                 figsize =(14,14),
                 legend = True,
                 legend_kwds ={'label' : "COVID-19 Active cases", 'orientation' : "vertical"},
                 edgecolor = 'black',
                 linewidth = 0.8)     
    ax.set_title ('Total Active COVID19 Cases in Karnataka : '+ dates, fontdict= {'fontsize': 22}, pad = 12.5)
    ax.set_axis_off()

    img = ax.get_figure()
    
    f = io.BytesIO()
    img.savefig(f, format = 'png', bbox_inches = 'tight')
    f.seek(0)
    image_frames.append(PIL.Image.open(f))

image_frames[0].save('Dynamic COVID19 Map Karnataka.gif', format = 'GIF',
                     append_images = image_frames[1:], 
                     save_all = True, duration = 300,
                     loop = 3)
f.close()