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final_plot.py
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final_plot.py
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import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import seaborn as sns
sns.set()
import chart_studio
import chart_studio.plotly as py
import chart_studio.tools as tls
def convert_numeric(df, date_column):
cols=[i for i in df.columns if i not in [date_column]]
for col in cols:
df[col]=pd.to_numeric(df[col])
df[date_column] = pd.to_datetime(df[date_column])
return df
def graph_plot(x, y, tag, ax):
# plt.figure(tag[0])
if(tag[1]=='plot'):
ax.plot(x, y, tag[2], label=tag[3])
elif(tag[1]=='semilogy'):
ax.semilogy(x, y, tag[2], label=tag[3])
if(tag[3]!=None):
ax.legend()
plt.xticks(rotation=45)
plt.ylabel(tag[4])
'''
files required
tag = [figure_no, plot/semilogy, color+linestyle, label, ylabel]
'''
files = ['Bangladesh_90', 'Bangladesh_30']
population = ['53M', '18M', 'HR (53M)', 'HR (18M)']
marker = ['k-', 'k-.', 'g-', 'g-.', 'r-', 'r-.', 'b-', 'b-.', 'm-', 'm-.']
linestyle = ['-', '-.']
plot_data_semilog = ['confirmed', 'recovered', 'deaths', 'active']
plot_data_label_semilog = ['Confirmed', 'Recovered', 'Death', 'Active']
plot_data_plot = ['cfr_recovered', 'cfr_confirmed']
plot_data_label_plot = ['Case Fatality Rate (in %)', 'Case Fatality Rate (in %)']
plot_data_plot_sim = ['r0', 're']
plot_data_label_plot_sim = ['$R_0$', 'Reproduction Number', '$R_e$']
reproduction_legend = ['$R_0$', '$R_e$']
df_rt_1 = pd.read_csv('data/rt_1.csv')
df_rt_1 = convert_numeric(df_rt_1, 'Date')
df_rt_2 = pd.read_csv('data/rt_2.csv')
df_rt_2 = convert_numeric(df_rt_2, 'Date')
# print(df_rt)
df_doublings_1 = pd.read_csv('data/doublingtimes_1.csv')
df_doublings_1 = convert_numeric(df_doublings_1, 'date')
df_doublings_2 = pd.read_csv('data/doublingtimes_2.csv')
df_doublings_2 = convert_numeric(df_doublings_2, 'date')
df_isolation = pd.read_csv('data/isolation.csv')
df_isolation = convert_numeric(df_isolation, 'date')
### mobility https://www.google.com/covid19/mobility/
df_mobility = pd.read_csv('data/google_mobility.csv')
df_mobility = df_mobility[df_mobility['country_region_code']=='BD']
df_mobility = df_mobility[['date','retail_and_recreation_percent_change_from_baseline','grocery_and_pharmacy_percent_change_from_baseline','parks_percent_change_from_baseline','transit_stations_percent_change_from_baseline','workplaces_percent_change_from_baseline']]
df_mobility_mean = df_mobility[['retail_and_recreation_percent_change_from_baseline','grocery_and_pharmacy_percent_change_from_baseline','parks_percent_change_from_baseline','transit_stations_percent_change_from_baseline','workplaces_percent_change_from_baseline']]
df_mobility['mean'] = df_mobility_mean.mean(axis=1)
# print(df_mobility.head())
df_mobility = convert_numeric(df_mobility, 'date')
# df_mobility.plot(x='date')
df_mobility.to_csv('data/mobility.csv', index=False)
data = {'Cases': [ 10.2, 54.6, 28.4, 6.7 ],
'Deaths': [ 2.86, 11.57, 48.49, 37.08 ],
'label': ['1-20', '21-40', '41-60', '60+']
}
df = pd.DataFrame(data)
### Hospitals beds: https://public.tableau.com/profile/masud.parvez7954#!/vizhome/Logistics_15857300704580/facility-1
hospital_beds = 7034
fig1, ax1 = plt.subplots(1)
fig2, ax2 = plt.subplots(1)
for i in range(len(files)):
### Real data
df_1 = pd.read_csv('data/'+files[i]+'_1.csv', encoding='UTF-8')
df_1 = convert_numeric(df_1, 'days')
### Simulated data
df_2 = pd.read_csv('data/'+files[i]+'_2.csv', encoding='UTF-8')
df_2 = convert_numeric(df_2, 'days_sim')
### cases
if (i<1):
tag = [11, 'semilogy', 'kx', None, None]
graph_plot(df_1['days'], df_1['active'], tag, ax1) # active cases
tag = [11, 'semilogy', 'mx', None, None]
graph_plot(df_1['days'], df_1['confirmed'].diff(), tag, ax1) # growth cases
tag = [11, 'semilogy', 'gx', None, None]
graph_plot(df_1['days'], df_1['confirmed'], tag, ax1) # confirmed cases
tag = [11, 'semilogy', 'rx', None, None]
graph_plot(df_1['days'], df_1['deaths'], tag, ax1) # death cases
# ax1.axhline(hospital_beds, color='black', linestyle='dotted')
# ax1.text('2020-03-01', hospital_beds*1.2, 'Covid Beds', size=10)
# tag = [11, 'semilogy', 'rx', None, None]
# graph_plot(df_1['days'], df_1['deaths'], tag, ax1) # death cases
# ax1.axhline(hospital_beds, color='black', linestyle='dotted')
# ax1.text('2020-03-01', hospital_beds*1.2, 'Covid Beds', size=10)
# tag = [11, 'semilogy', 'gx', None, None]
# graph_plot(home_isolation_date, home_isolation, tag)
# graph_plot(df_isolation['date'], df_isolation['isolation'], tag)
# plt.axvline('2020-05-25', color='maroon', alpha=0.5, linestyle='--')
# plt.text('2020-05-26', 1e5, 'Regional\nLockdown', color='maroon', size=10)
# plt.axvline('2020-05-05', color='green', alpha=0.5, linestyle='--')
# plt.text('2020-05-06', 1e5, 'Lockdown\nRelaxed', color='green', size=10)
# plt.axvline('2020-04-09', color='black', alpha=0.5, linestyle='--')
# plt.text('2020-04-10', 1e5, 'Lockdown\nStarted', size=10)
### active cases
tag = [11, 'semilogy', marker[i], 'A ('+population[i]+')', None]
graph_plot(df_2['days_sim'], df_2['active_sim'], tag, ax1)
### hospital required
tag = [11, 'semilogy', marker[i+2], 'HR ('+population[i]+')', None]
graph_plot(df_2['days_sim'], df_2['active_sim']*16/100, tag, ax1)
### Death Cases
tag = [11, 'semilogy', marker[i+4], 'D ('+population[i]+')', 'Number of Cases']
graph_plot(df_2['days_sim'], df_2['deaths_sim'], tag, ax1)
# ### susceptible Cases
# tag = [11, 'semilogy', marker[i+6], 'S ('+population[i]+')', 'Number of Cases']
# graph_plot(df_2['days_sim'], df_2['susceptible_sim'], tag)
### growth cases
tag = [11, 'semilogy', marker[i+8], 'G ('+population[i]+')', 'Number of Cases']
graph_plot(df_2['days_sim'], df_2['confirmed_sim'].diff(), tag, ax1)
g_max = df_2['confirmed_sim'].diff().max()
ax1.axhline(g_max, color='m', linestyle=':')
ax1.text('2020-03-01', g_max*1.2, '$G_{max}$: '+str(int(g_max/1e3))+'K', size=10)
# print(int(df_2['confirmed_sim'][df_2['days_sim']=='2020-09-01'].values[0]))
ax1.set_ylim(bottom=1, top=1e6)
ax1.set_xlim(xmin='2020-03-01', xmax='2021-04-01')
# plt.xlim(xmin='2020-03-01')
ax1.legend(ncol=2, fancybox=True, framealpha=0.2)
### reproduction numbers
if (i<1):
### R0
# plt.axhline(final_r0, color='black', linestyle='-', label='$R_0$')
# plt.fill_between(df_2['days_sim'], final_r0+0.25, final_r0-0.25, color='k', alpha=0.2)
# plt.text('2020-11-15', 3.8, '$R_0$', rotation=0, color='k')
# plt.axvline('2020-05-25', color='maroon', alpha=0.5, label = '$L_1$')
# plt.axvline('2020-05-05', color='green', alpha=0.5, label = '$L_2$')
# plt.axvline('2020-04-09', color='black', alpha=0.5, label = '$L_3$')
# % Lockdown: 26th March, 2020: https://bdnews24.com/bangladesh/2020/03/25/bangladesh-in-virtual-lockdown-as-coronavirus-fight-flares
# % Reopen Garments: 26 April, 2020: https://bdnews24.com/bangladesh/2020/04/26/hundreds-of-clothing-factory-workers-dash-for-dhaka-again-in-dark-hours-amid-lockdown
# % Shopping Mall reopen: 10th May, 2020: https://bdnews24.com/business/2020/05/04/shopping-malls-to-reopen-ahead-of-eid
# % Restarting economy: 30th May, 2020: https://bdnews24.com/bangladesh/2020/05/29/bangladesh-set-to-step-into-coronavirus-new-normal-with-a-lot-at-stake
### mobility
tag = [12, 'plot', 'k-', 'Negative Mobility (x10)', None]
graph_plot(df_mobility['date'], -df_mobility['mean']/10, tag, ax2)
tag = [12, 'plot', 'k.', None, None]
graph_plot(df_mobility['date'], -df_mobility['mean']/10, tag, ax2)
### growth
tag = [12, 'plot', 'b-', 'Daily Growth (x1K)', None]
graph_plot(df_1['days'], df_1['confirmed'].diff()/1000, tag, ax2)
tag = [12, 'plot', 'b.', None, None]
graph_plot(df_1['days'], df_1['confirmed'].diff()/1000, tag, ax2)
### doublingtimes
# tag = [12, 'plot', 'g-', 'Doubling Time (x10)', None]
# graph_plot(df_doublings_1['date'], df_doublings_1['doublingtimes']/10, tag, ax2)
# tag = [12, 'plot', 'g.', None, None]
# graph_plot(df_doublings_1['date'], df_doublings_1['doublingtimes']/10, tag, ax2)
### lock down
text_position = 6.5
line_max = 7
ax2.vlines(x='2020-03-26', ymin=0, ymax=line_max, color='black', alpha=0.5)
ax2.text('2020-03-27', text_position, '$L_1$', size=10)
ax2.vlines('2020-04-26', ymin=0, ymax=line_max, color='k', alpha=0.5)
ax2.text('2020-04-27', text_position, '$L_2$', color='black', size=10)
ax2.vlines('2020-05-10', color='k', alpha=0.5, ymin=0, ymax=line_max)
ax2.text('2020-05-11', text_position, '$L_3$', color='black', size=10)
ax2.vlines('2020-05-30', color='k', alpha=0.5, ymin=0, ymax=line_max)
ax2.text('2020-05-31', text_position, '$L_4$', color='black', size=10)
### Eid ul Adha
ax2.vlines('2020-07-31', color='k', alpha=0.5, ymin=0, ymax=line_max)
ax2.text('2020-08-01', text_position, '$L_5$', color='black', size=10)
### durga puja
ax2.vlines('2020-10-26', color='k', alpha=0.5, ymin=0, ymax=line_max-3)
ax2.text('2020-10-27', text_position-3, '$L_6$', color='black', size=10)
### Paid vaccine
ax2.vlines('2020-06-30', color='g', alpha=0.5, ymin=0, ymax=line_max)
ax2.text('2020-07-01', text_position, '$G_1$', color='g', size=10)
### Winter
ax2.vlines('2020-11-15', color='r', alpha=0.5, ymin=0, ymax=line_max-3)
ax2.text('2020-11-16', text_position-3, 'Winter', color='r', size=10)
### Rt
tag = [12, 'plot', 'r-', '$R_t$', None]
graph_plot(df_rt_1['Date'], df_rt_1['ML'], tag, ax2) # active cases
tag = [12, 'plot', 'r.', None, None]
graph_plot(df_rt_1['Date'], df_rt_1['ML'], tag, ax2) # active cases
test = ax2.fill_between(df_rt_1['Date'], df_rt_1['Low_90'], df_rt_1['High_90'], alpha=0.5, color='r')
ax2.set_ylabel('Magnitude')
ax2.set_xlim(xmin='2020-03-15', xmax='2020-08-01')
ax2.legend(ncol=2, framealpha=0.2, loc='upper right')
ax2.set_ylim(bottom=0, top=7.5)
if(i==0):
last_data_date = (df_1['days'].iloc[len(df_1)-1])
last_data_index = len(df_1)-1
save_df = df_2[['days_sim', 'confirmed_sim', 'recovered_sim', 'deaths_sim']].iloc[last_data_index:last_data_index+14]
save_df.columns = ['Date', 'Confirmed Cases', 'Recovered Cases', 'Deaths']
save_df[['Confirmed Cases', 'Recovered Cases', 'Deaths']] = save_df[['Confirmed Cases', 'Recovered Cases', 'Deaths']].astype(int)
rt_array = df_rt_2[df_rt_2['Date']>=last_data_date]['ML'].iloc[0:14].to_numpy()
doubling_array = df_doublings_2[df_doublings_2['date']>=last_data_date]['doublingtimes'].iloc[0:14].round().to_numpy()
save_df['Rt'] = rt_array
save_df['DT'] = doubling_array
save_df.to_csv('data/forcasting.csv', index=False)
### CFR
if (i>0):
plt.figure()
tag = [13, 'plot', 'kx', None, None]
graph_plot(df_1['days'], df_1['cfr_recovered'], tag, plt) # active cases
tag = [13, 'plot', 'gx', None, None]
graph_plot(df_1['days'], df_1['cfr_confirmed'], tag, plt) # active cases
tag = [13, 'plot', marker[0], 'CFR$_{adj}$', None]
graph_plot(df_2['days_sim'], df_2['cfr_recovered_sim'], tag, plt) # active cases
tag = [13, 'plot', marker[i+1], 'CFR', 'Case Fatality Rate (in %)']
graph_plot(df_2['days_sim'], df_2['cfr_confirmed_sim'], tag, plt) # active cases
for k in range(len(df['label'])):
tag = [13, 'plot', marker[i+2+k], 'Age: '+df['label'][k], 'Case Fatality Rate (in %)']
graph_plot(df_2['days_sim'], df_2['cfr_confirmed_sim']*df['Deaths'][k]/df['Cases'][k], tag, plt) # active cases
plt.xlim(xmin='2020-05-01', xmax='2021-10-01')
plt.ylim(0,15)
plt.legend(ncol=3, loc='upper center', fancybox=True, framealpha=0.2)
### Reproduction number
# final_r0 = df_2['r0'][len(df_2['r0'])-1]
# tag = [14, 'plot', marker[i+4], '$R_e$ ('+population[i]+')', 'Reproduction Number']
# graph_plot(df_2['days_sim'], final_r0*df_2['susceptible_sim']/df_2['susceptible_sim'][0], tag) # active cases
# plt.fill_between(df_2['days_sim'], final_r0*df_2['susceptible_sim']/df_2['susceptible_sim'][0]+0.5, final_r0*df_2['susceptible_sim']/df_2['susceptible_sim'][0]-0.5, color='r', alpha=0.2)
if (i==1):
# df_2['r0'] = df_2['r0']
plt.figure()
df_2['re'] = df_2['r0']*df_2['susceptible_sim']/(df_2['susceptible_sim'][0])
tag = [12, 'plot', 'b-', '$R_0(t)=\\beta(t)/\gamma(t)$', None]
graph_plot(df_2['days_sim'], df_2['r0'], tag, plt) # active cases
# print(final_r0)
plt.axhline(1, color='k')
tag = [12, 'plot', 'g-', '$R_e(t)=R_0(t) S(t)/S(0)$', None]
graph_plot(df_2['days_sim'], df_2['re'], tag, plt) # active cases
tag = [14, 'plot', 'r-', '$R_t=gln(I_{t+1}/I_t) + 1$', None]
graph_plot(df_rt_2['Date'], df_rt_2['ML'], tag, plt) # active cases
# tag = [14, 'plot', 'r.', None, None]
# graph_plot(df_rt['Date'], df_rt['ML'], tag, plt) # active cases
# plt.fill_between(df_rt['Date'], df_rt['Low_90'], df_rt['High_90'], color='black', alpha=0.2)
# # plt.axhline(final_r0, color='black', linestyle='-', label='$R_0$')
# # plt.fill_between(df_2['days_sim'], final_r0+0.5, final_r0-0.5, color='k', alpha=0.2)
# tag = [14, 'plot', 'g.', None, None]
# graph_plot(df_1['days'], df_1['confirmed'].diff()/1000, tag, plt)
# tag = [14, 'plot', 'g-', 'Confirmed (x1K)', None]
# graph_plot(df_1['days'], df_1['confirmed'].diff()/1000, tag, plt)
plt.xlim(xmin='2020-04-01', xmax='2021-05-01')
plt.ylim(0, 5)
plt.ylabel('Magnitude')
plt.show()
# import chart_studio
# import chart_studio.plotly as py
# import chart_studio.tools as tls
# import plotly.express as px
# import plotly.graph_objects as go
# from password import *
# username, api_key = get_plotly_api()
# chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
# figure_1 = go.Figure(data=[
# go.Scatter(
# x=df_2['days_sim'],
# y=df_2['confirmed_sim'],
# name='Estimated Confirm Cases'
# ),
# go.Scatter(
# x=df_1['days'],
# y=df_1['confirmed'],
# mode='markers',
# # marker=dict(color="Black"),
# name='Real Confirmed Cases'
# ),
# go.Scatter(
# x=df_2['days_sim'],
# y=df_2['confirmed_sim'].diff(),
# name='Estimated Daily Confirm Cases'
# ),
# go.Scatter(
# x=df_1['days'],
# y=df_1['confirmed'].diff(),
# mode='markers',
# # marker=dict(color="Black"),
# name='Real Daily Confirmed Cases'
# ),
# go.Scatter(
# x=df_2['days_sim'],
# y=df_2['recovered_sim'],
# name='Estimated Recovered Cases'
# ),
# go.Scatter(
# x=df_1['days'],
# y=df_1['recovered'],
# mode='markers',
# name='Real Recovered Cases'
# ),
# go.Scatter(
# x=df_2['days_sim'],
# y=df_2['deaths_sim'],
# name='Estimated Deaths Cases'
# ),
# go.Scatter(
# x=df_1['days'],
# y=df_1['deaths'],
# mode='markers',
# name='Real Deaths Cases'
# ),
# go.Scatter(
# x=df_2['days_sim'],
# y=df_2['active_sim'],
# name='Estimated Active Cases'
# ),
# go.Scatter(
# x=df_1['days'],
# y=df_1['active'],
# mode='markers',
# name='Real Active Cases'
# )
# ])
# figure_1.update_layout(
# yaxis_type='log',
# )
# figure_1.update_xaxes(range=['2020-04-01', '2021-01-01'])
# figure_2 = go.Figure(data=[
# go.Scatter(
# x=df_rt_2['Date'],
# y=df_rt_2['ML'],
# name='Predicted Rt'
# ),
# go.Scatter(
# x=df_doublings_2['date'],
# y=df_doublings_2['doublingtimes']/10,
# name='Predicted Doubling Times (x10)'
# ),
# go.Scatter(
# x=df_rt_2['Date'],
# y=df_rt_2['High_90'],
# fill=None,
# name='Predicted High 90'
# ),
# go.Scatter(
# x=df_rt_2['Date'],
# y=df_rt_2['Low_90'],
# fill='tonexty',
# name='Predicted Low 90'
# ),
# go.Scatter(
# x=df_rt_1['Date'],
# y=df_rt_1['ML'],
# name='Real Rt'
# ),
# go.Scatter(
# x=df_doublings_1['date'],
# y=df_doublings_1['doublingtimes']/10,
# name='Real Doubling Times (x10)'
# ),
# go.Scatter(
# x=df_rt_1['Date'],
# y=df_rt_1['High_90'],
# fill=None,
# name='Real High 90'
# ),
# go.Scatter(
# x=df_rt_1['Date'],
# y=df_rt_1['Low_90'],
# fill='tonexty',
# name='Real Low 90'
# ),
# go.Scatter(
# x=df_mobility['date'],
# y=-df_mobility['mean']/10,
# name='Drop in Mobility (x10)'
# )
# ])
# figure_2.update_xaxes(range=['2020-04-01', '2021-01-01'])
# figure_2.update_yaxes(range=[-0.1, 8.0])
# figure_2.update_layout(shapes=[
# dict(
# type= 'line',
# xref= 'x', x0= '2020-04-01', x1= '2021-01-01',
# yref= 'y', y0= 1, y1= 1,
# )
# ])
# py.plot(figure_1, filename='figure 1')
# py.plot(figure_2, filename='figure 2')
plt.show()