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# %%
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import pandas as pd
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import requests
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import matplotlib.pyplot as plt
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from matplotlib import rc
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import json
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from scipy import interpolate
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import numpy as np
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# %% Use LaTeX
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rc('text', usetex=True)
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# %% Function to convert sizes in cm for figure size
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def cm2inch(*tupl):
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inch = 2.54
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if isinstance(tupl[0], tuple):
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return tuple(i/inch for i in tupl[0])
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else:
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return tuple(i/inch for i in tupl)
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# %%
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r = requests.get('https://disease.sh/v3/covid-19/historical/fr?lastdays=all')
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x = r.json()
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#%%
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df = pd.DataFrame(x['timeline'])
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df.index = pd.to_datetime(df.index)
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#df = df.reset_index()
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# %%
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df['daily_deaths'] = df['deaths'].diff().abs() # dirty trick to prevent negative outliers
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df['daily_deaths_avg'] = df['daily_deaths'].rolling(7).mean()
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df['death_change'] = df['daily_deaths_avg'].diff()
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#df.fillna(value=0, inplace=True)
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# %%
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df = df.resample('4H').asfreq()
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# %%
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df = df.interpolate(method='spline', order=5)
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# %%
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# df['daily_deaths_avg'] = df['daily_deaths'].rolling(14).mean()
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# df['death_change'] = df['daily_deaths_avg'].diff()
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# %%
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fig, ax = plt.subplots(figsize=cm2inch(15,15))
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ax.plot(df['death_change'], df['daily_deaths_avg'], lw=1)
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#plt.plot(df['death_change'], df['daily_deaths_avg'], 'ob') # dots for debugging
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ax.axvline(x=0, c='black', lw=1, ls=':')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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plt.show()
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#%%
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#df.plot()
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#df.plot(x='death_change', y='tornado')
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