# Finalize plot
title = f'Covidgevallen per 100k per week t/m {date_end.strftime("%Y-%m-%d")}'
ax.set_title(f'{title}\nGemeentes met {pop_range[0]/1e3:.0f}k-{pop_range[1]/1e3:.0f}k inwoners.')
fig.canvas.set_window_title(title)
df_renamed = dfm_wc[['HHsize', 'Maandelijks%', 'WeeklyPer100k']].copy()
df_renamed.rename(columns={'HHsize': 'HHgrootte',
'Maandelijks%':'Religieus%',
'WeeklyPer100k':'CovidGevallen'}, inplace=True)
df_renamed['LogReligieus'] = np.log10(df_renamed['Religieus%'])
corr = df_renamed.corr()
print(f'Correlation matrix:\n{np.around(corr, 2)}')
fig.show()
return dfm_wc, date_end
if __name__ == '__main__':
nlcs.reset_plots()
nlcs.init_data(autoupdate=True)
plot_hhsize_relig_cases(pop_range=(0, 100e3))
plot_hhsize_relig_cases(ref_date='2020-11-20', pop_range=(0, 100e3))
# plot_trends_by_religious_visits(ndays=80, maxpop=30e3)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Get DoW corrections for daily case numbers.
Created on Tue Dec 29 20:00:21 2020
@hk_nien
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nlcovidstats as nlcs
if __name__ == '__main__':
nlcs.init_data()
plt.close('all')
get_dow_correction((-100, -1), True)