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

import os

from glob import glob

from PIL import Image

import numpy as np

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# Utility function to retrieve data from a single country
def get_country_df(world_df, country_name):
    
    # Some countries have several Provinces/States, must aggregate
    country_df = world_df[world_df['Country/Region'] == country_name] \
        .groupby(["Country/Region", "Date"]) \
        .sum() \
        .sort_values(by='Date')
    
    # Restore columns

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# Imports for performing ML analysis
from sklearn.linear_model import LinearRegression

from scipy.optimize import curve_fit

from datetime import timedelta

# Set range of data to build model
# It might make sense to skip part of the initial points, when the exponential trend was still not evident
START_DATE = datetime(2020, 2, 23).date()

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# Estimate a reasonable value for Y tick given the maximum number of cases
Y_GRID_TICK = 10**int(np.log10(country_df['Confirmed'].max()))/2

ax = plt.gca()

sns.set_style("whitegrid", {'grid.linestyle': ':'})
ax.xaxis.set_major_locator(ticker.MultipleLocator(4))

# Plot 
country_df.plot(x='Date', y=["Confirmed", "Deaths", "Recovered"], figsize=(18,9), ax=ax, marker='o')

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# Save the country's name in a variable, so that it can be easily changed to show data
# from another country.
country_name = "Italy"

# Get data for a single country, sorted by date
country_df = world_df[world_df['Country/Region'] == country_name].sort_values(by='Date')

# Compute daily increase, in absolute number
country_df['Increase'] = country_df['Confirmed'].diff()

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from datetime import datetime

csv_files = glob.glob(DATA_DIR + "/*.csv")

# Auxiliary function to extract date from file name
def extract_date(file_name):
    date_str = os.path.basename(file_name)[:-4]
    
    date = datetime.strptime(date_str, '%m-%d-%Y').date()
    

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git clone https://github.com/CSSEGISandData/COVID-19
cd COVID-19
conda create --name jupyter python=3 matplotlib seaborn \
    pandas scikit-learn
conda activate jupyter
conda install -c conda-forge jupyterlab
mkdir notebooks && cd notebooks
jupyter lab

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# Basic imports for path
import os, sys, glob

# Data loading and 
import pandas as pd
import numpy as np

# Plotting
import seaborn as sns
import matplotlib.pyplot as plt

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"""Module to parse dataset."""

# Imports.
import os
from os.path import join
import glob

import pandas as pd
from PIL import Image
import numpy as np