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risk_map.py
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risk_map.py
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#!/usr/bin/env python
from helper import *
import geopandas as gpd
from matplotlib.colors import ListedColormap
import matplotlib as mpl
mpl.rcParams['figure.subplot.left'] = 0
mpl.rcParams['figure.subplot.right'] = 1
mpl.rcParams['figure.subplot.bottom'] = 0
mpl.rcParams['figure.subplot.top'] = 0.95
### https://www.worldbank.org/en/data/interactive/2016/11/10/bangladesh-poverty-maps
filepath = 'data/BD_District_WGS84.shp'
map_df = gpd.read_file(filepath)
# print(map_df.columns) Index(['COMMENTS', 'DIV_NAME', 'DIST_NAME', 'Div_BBS', 'Dist_BBS', 'geometry'], dtype='object')
map_df['district_index'] = map_df['DIST_NAME'].str.slice(stop=2)
map_df['risk'] = np.nan
replace_district_name = {
'CHITTAGONG': 'CHATTOGRAM' ,
'COX\'S BAZAR': 'COXS BAZAR' ,
'NETROKONA': 'NETRAKONA' ,
'CHAPAI': 'CHAPAINABABGANJ' ,
'BOGRA': 'BOGURA' ,
'JESSORE': 'JASHORE' ,
'MAULVIBAZAR': 'MOULVIBAZAR' ,
'JHENAIDAHA': 'JHENAIDAH' ,
'COMILLA': 'CUMILLA' ,
'BARISAL': 'BARISHAL'
}
map_df = map_df.replace({'DIST_NAME': replace_district_name})
# print(map_df.DIST_NAME.sort_values().unique())
# print(map_df.columns)
# ss.exit()
bd_population = df_owid['population'].iloc[-1]
# cases_type = 'combined'
df_risk = pd.read_csv('data/zone_risk_value.csv')
this_week = datadate
last_week = pd.to_datetime(datadate) - timedelta(days=7)
last_week = last_week.strftime('%Y-%m-%d')
predicted = pd.to_datetime(datadate) + timedelta(days=7)
predicted = predicted.strftime('%Y-%m-%d')
weeks = [last_week, this_week, predicted]
titles = ['Last Week', 'This Week', 'Next Week (Predicted)']
weeks = [this_week]
titles = ['This Week']
for district in districts:
iso13 = df_population.loc[df_population['Name']==district.capitalize(), 'Abbr.'].values[0]
map_df.loc[map_df['DIST_NAME'] == district.upper(), 'district_index'] = iso13
fig, axs = plt.subplots(1, len(weeks), figsize=(4*len(weeks), 6))
variable = 'risk'
i = 0
for week in weeks:
for district in districts:
map_df.loc[map_df['DIST_NAME'] == district.upper(), 'risk'] = df_risk.loc[df_risk.district == district, week].values[0]
print(week, map_df[['DIST_NAME', 'risk']])
if (i == len(weeks)-1):
condition = True
else:
condition = False
vmax = map_df[variable].max()
if(vmax > 24):
colors = ['#54b45f', '#ecd424', '#f88c51', '#c01a27']
labels = ['Trivial', 'Community Spread', 'Accelerated Spread', 'Tipping Point']
bins = [1, 9, 24]
# test_k_means(merged[variable], axs1[i])
elif(vmax > 9):
colors = ['#54b45f', '#ecd424', '#f88c51']
labels = ['Trivial', 'Community Spread', 'Accelerated Spread']
bins = [1, 9]
elif(vmax > 1):
colors = ['#54b45f', '#ecd424']
labels = ['Trivial', 'Community Spread']
bins = [1]
else:
colors = ['#54b45f']
labels = ['Trivial']
bins = [1]
map_df.plot(column=variable, linewidth=0.3, ax=axs, edgecolor='0.8',
cmap=ListedColormap(colors),
legend_kwds={'loc': 'upper right', 'ncol':1, 'fontsize':8, 'labels':labels, 'title':'Risk'},
legend=condition,
scheme='user_defined',
# missing_kwds={
# "color": "lightgrey",
# # "edgecolor": "red",
# "hatch": "///",
# "label": "Missing values",
# },
classification_kwds={'bins':bins}
)
axs.set_axis_off()
# axs[i].set_title('Risk on '+titles[i])
axs.set_title('Risk on '+weeks[i])
# create an annotation for the data source
for index,row in map_df.iterrows():
xy=row['geometry'].centroid.coords[:]
xytext=row['geometry'].centroid.coords[:]
axs.annotate(row['district_index'],xy=xy[0], xytext=xytext[0], horizontalalignment='center',verticalalignment='center', fontsize=7)
# print(week)
start_date = pd.to_datetime(week) - timedelta(days=6)
start_date = start_date.strftime('%Y-%m-%d')
# print(start_date, week)
test_rate_df = df_owid[(df_owid.date >= start_date) & (df_owid.date <= week)]
total_test = test_rate_df.new_tests.sum()
test_rate = total_test*1000/bd_population
# print(test_rate)
test_label_tr = 'Covid Testing Rate (in 1K population per week): {:.2f}%'.format(test_rate)
axs.text(88, 20.5, test_label_tr, fontsize=10)
test_label_rt = 'Reproduction Rate, $R_t$: {:.2f}'.format(test_rate_df[test_rate_df.date == week]['reproduction_rate'].iloc[-1])
axs.text(88, 20.9, test_label_rt, fontsize=10)
test_label_tpr = 'Test Positive Rate (Cases per 100 Test): {:.2f}%'.format(test_rate_df[test_rate_df.date == week]['positive_rate'].iloc[-1]*100)
axs.text(88, 20.7, test_label_tpr, fontsize=10)
test_label_cases = 'Total Cases: {:.0f}'.format(test_rate_df[test_rate_df.date == week]['total_cases'].iloc[-1])
axs.text(88, 21.1, test_label_cases, fontsize=10)
plt.show()