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@ -12,14 +12,6 @@ import numpy as np
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plt.rc('text', usetex=True)
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plt.rc('font', family='serif')
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# matplotlib.use("pgf")
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# matplotlib.rcParams.update({
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# "pgf.texsystem": "pdflatex",
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# 'font.family': 'serif',
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# 'text.usetex': True,
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# 'pgf.rcfonts': False,
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# })
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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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@ -28,50 +20,63 @@ def cm2inch(*tupl):
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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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# %% Countries
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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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countries = {
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'cn': {'name': 'China'},
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'it': {'name': 'Italy'},
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'fr': {'name': 'France'},
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'de': {'name': 'Germany'},
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'us': {'name': 'USA'},
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'uk': {'name': 'UK'}
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}
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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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# %% Data
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for country_code, country_data in countries.items():
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request_url = 'https://disease.sh/v3/covid-19/historical/' + country_code + '?lastdays=all'
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r = requests.get(request_url)
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x = r.json()
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df = pd.DataFrame(x['timeline'])
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df.index = pd.to_datetime(df.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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# Process data
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df['daily_deaths'] = df['deaths'].diff().abs() # .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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# %%
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df = df.resample('4H').asfreq()
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# Smoothing
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df = df.resample('4H').asfreq()
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df = df.interpolate(method='spline', order=5)
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# %%
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df = df.interpolate(method='spline', order=5)
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country_data['dataframe'] = df
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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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# %% Plotting
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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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for country_code, country_data in countries.items():
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df = country_data['dataframe']
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line, = ax.plot(df['death_change'], df['daily_deaths_avg'], lw=0.5, label=country_data['name'])
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#plt.plot(df['death_change'], df['daily_deaths_avg'], 'ob') # dots for debugging
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df['month'] = df.index.month
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df['month_change'] = df['month'].diff()
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dates = df['month_change'] == 1
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for index, row in df[dates].iterrows():
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date_text = row.name.strftime(format='%d %b')
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ax.annotate(date_text,
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(row['death_change'], row['daily_deaths_avg']),
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color=line.get_color())
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plt.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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ax.legend(loc='upper center', ncol=3, bbox_to_anchor=(0.5,1.15))
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plt.savefig('tornado_plot.pdf')
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# %%
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#df.plot(x='death_change', y='daily_deaths_avg')
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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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plt.show()
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#plt.savefig('tornado_plot.pdf')
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# %%
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