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daily_updater.py
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daily_updater.py
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import pandas as pd
import pygal
from pygal.style import DefaultStyle
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from iso3166 import countries
from pygal.style import RotateStyle
paths={'cw':'static/cases_worldwide.svg',
'ci':'static/cases_india.svg',
'pr':'static/prediction_india.svg',
'ttpos':'static/test_to_positive_stacked.svg',
'mf':'static/male_female.svg',
'sttst':'static/state_wise_testing_radar.svg',
'world':'static/world_map.svg',
'guj':'static/guj.svg',
'guj2':'static/guj2.svg',
'ic':'static/ind_cases.svg',
'ir':'static/ind_recovered.svg',
'id':'static/ind_dead.svg',
'piei':'static/piechart_india.svg',
'pieg':'static/piechart_guj.svg'
}
def plot_cases_worldwide(df,path):
start_date = df.head(1).date.values[0]
end_date = df.tail(1).date.values[0]
daterange = pd.date_range(start_date, end_date)
dates = []
cases = []
deaths = []
for single_date in daterange:
d = single_date.strftime("%Y-%m-%d")
dates.append(d)
temp = df[df['date'] == d]
case = temp['total_cases'].values[0]
death = temp['total_deaths'].values[0]
cases.append(case)
deaths.append(death)
line_chart = pygal.Line(fill=True,show_legend=True,show_x_labels=True, show_y_labels=True, style=DefaultStyle)
line_chart.add('Cases', cases, show_dots=True,dots_size=8)
line_chart.add('Deaths', deaths, show_dots=True,dots_size=8)
line_chart.x_labels = map(str, dates)
line_chart.render_to_file(path)
def plot_cases_india(df,path):
dates = []
cases = []
deaths = []
recovered=[]
for i in range(len(df)):
dates.append(df.iloc[i].values[0])
cases.append(df.iloc[i].values[2])
deaths.append(df.iloc[i].values[6])
recovered.append(df.iloc[i].values[4])
line_chart = pygal.Line(fill=True,show_legend=True, show_x_labels=True, show_y_labels=False, style=DefaultStyle)
line_chart.add('Cases', cases, show_dots=True,dots_size=8)
line_chart.add('Recovered', recovered, show_dots=True,dots_size=8)
line_chart.add('Deaths', deaths, show_dots=True,dots_size=8)
line_chart.x_labels = map(str, dates)
line_chart.render_to_file(path)
return cases,deaths,recovered
def plot_prediction_india(cases,deaths,path):
dates=[i+1 for i in range(len(cases))]
X = np.array(dates[-10:])
Y = np.log(cases[-10:])
Z = np.log(deaths[-10:])
X = X[:, np.newaxis]
lin_reg_cases= LinearRegression()
lin_reg_cases.fit(X, Y)
lin_reg_deaths = LinearRegression()
lin_reg_deaths.fit(X, Z)
line_chart = pygal.Line(fill=False, show_legend=False, show_x_labels=True, show_y_labels=False, style=DefaultStyle)
line_chart.x_labels = map(str, ['Yesterday','Today','Tommorow','Day-After'])
line_chart.add('Cases',[np.exp(lin_reg_cases.predict([[dates[-2]]])[0]),
np.exp(lin_reg_cases.predict([[dates[-1]]])[0]),
np.exp(lin_reg_cases.predict([[dates[-1]+1]])[0]),
np.exp(lin_reg_cases.predict([[dates[-1]+2]])[0])],show_dots=True,dots_size=15)
line_chart.add('Deaths',
[np.exp(lin_reg_deaths.predict([[dates[-2]]])[0]),
np.exp(lin_reg_deaths.predict([[dates[-1]]])[0]),
np.exp(lin_reg_deaths.predict([[dates[-1] + 1]])[0]),
np.exp(lin_reg_deaths.predict([[dates[-1] + 2]])[0])], show_dots=True,dots_size=15)
line_chart.render_to_file(path)
def testing_findings(df,df2,path):
tests = []
cases = []
states = []
for state in df.State.unique():
temp = df[df['State'] == state]
if len(temp)>2:
if temp.tail(1).isnull().values[0][2]:
temp = temp.iloc[-2]
else:
temp = temp.iloc[-1]
states.append(state)
tests.append(temp.values[2])
cases.append(temp.values[3])
ratio=[]
for i in range(len(cases)):
if tests[i] != np.nan and cases[i] !=np.nan:
ratio.append(int(100*tests[i]/cases[i]))
line_chart = pygal.Bar()
line_chart.title = 'Statewise calculation'
line_chart.x_labels = map(str, states)
line_chart.add('Test',tests)
line_chart.add('Cases',cases)
line_chart.add('Test Positive Per 100',ratio)
line_chart.render_table(style=True)
def plot_male_female(df,path):
g=df.Gender.value_counts()
Male=g.values[0]
Female = g.values[1]
tot=Male+Female
Male=100*Male/tot
Female=100*Female/tot
pie_chart = pygal.Pie(inner_radius=.4)
pie_chart.title = 'Male vs Female affected becaues of COVID-19'
pie_chart.add('Male', Male)
pie_chart.add('Female', Female)
pie_chart.render_to_file(path)
def plot_world_map(df,path):
world_dict={}
for c in countries:
try:
world_dict[str(c.alpha2).lower()]=df[df['iso_code'] ==str(c.alpha3).upper()].iloc[-1][3]
except:
world_dict[str(c.alpha2).lower()]=0
worldmap_chart = pygal.maps.world.World(style=RotateStyle('#336699'))
worldmap_chart.force_uri_protocol = "http"
worldmap_chart.value_formatter = lambda x: "{:,}".format(x)
worldmap_chart.value_formatter = lambda y: "{:,}".format(y)
worldmap_chart.title = 'Covid 19 stats'
worldmap_chart.add('COVID-19', world_dict)
worldmap_chart.render_to_file(path)
def plot_guj_1(df):
t=df['Detected District'].value_counts()[:10]
line_chart = pygal.HorizontalBar()
line_chart.title = 'Cities with Highest Cases'
for i in range(len(t)):
line_chart.add(t.index[i],t[i])
line_chart.render_to_file(paths['guj'])
def plot_cases_rec_ded_state(df,ttt):
daterange = pd.date_range(df.Date[0], df.iloc[-1].values[0])
line_chart1 = pygal.Line()
line_chart2 = pygal.Line()
line_chart3 = pygal.Line()
line_chart3.title = 'Recovered'
line_chart2.title = 'Deaths'
line_chart1.title = 'Cases'
line_chart4 = pygal.Line(fill=True)
line_chart4.title = 'Gujarat COVID-19'
ttt = pd.read_csv('stats/state_wise.csv')
for i in range(len(df.columns) - 3):
dates = []
cases = []
recovered = []
deaths = []
state = df.columns[3 + i]
for single_date in daterange:
d = single_date.strftime("%d-%b-%y")
dates.append(d)
temp = df[df['Date'] == d]
case = temp[state].values[0]
death = temp[state].values[2]
rec = temp[state].values[1]
cases.append(case)
deaths.append(death)
recovered.append(rec)
line_chart3.add(ttt[ttt['State_code'] == state]['State'].values[0], recovered, dots_size=1)
line_chart2.add(ttt[ttt['State_code'] == state]['State'].values[0], deaths, dots_size=1)
line_chart1.add(ttt[ttt['State_code'] == state]['State'].values[0], cases, dots_size=1)
line_chart1.x_labels = map(str, dates)
line_chart2.x_labels = map(str, dates)
line_chart3.x_labels = map(str, dates)
if (state == 'GJ'):
line_chart4.add("Cases", cases)
line_chart4.add("Recovered", recovered)
line_chart4.add("Deaths", deaths)
line_chart4.x_labels = map(str, dates)
line_chart1.render_to_file(paths['ic'])
line_chart2.render_to_file(paths['id'])
line_chart3.render_to_file(paths['ir'])
line_chart4.render_to_file(paths['guj2'])
def plot_spread_pie(df,df_guj):
t=df['Type of transmission'].value_counts()
pie_chart = pygal.Pie()
pie_chart.title = 'Transmission type (TBD-To Be Decided)*(for Available data)'
pie_chart.add(t.index[0], t[0])
pie_chart.add(t.index[1], t[1])
pie_chart.add(t.index[2], t[2])
pie_chart.render_to_file(paths['piei'])
t = df_guj['Type of transmission'].value_counts()
pie_chart = pygal.Pie()
pie_chart.title = 'Transmission type (TBD-To Be Decided)*(for Available data)'
pie_chart.add(t.index[0], t[0])
pie_chart.add(t.index[1], t[1])
pie_chart.add(t.index[2], t[2])
pie_chart.render_to_file(paths['pieg'])
if __name__=='__main__':
raw_data=pd.read_csv('stats/raw_data.csv')
state_wise=pd.read_csv('stats/state_wise.csv')
state_wise_daily=pd.read_csv('stats/state_wise_daily.csv')
statewise_tested_numbers_data=pd.read_csv('stats/statewise_tested_numbers_data.csv')
case_time_series=pd.read_csv('stats/case_time_series.csv')
tested_numbers_icmr_data=pd.read_csv('stats/tested_numbers_icmr_data.csv')
world_cases=pd.read_csv('stats/world_cases.csv')
world_cases_all=pd.read_csv('stats/world_all_cases.csv')
guj=pd.read_csv('stats/gujarat.csv')
plot_cases_worldwide(world_cases,paths['cw'])
cases,deaths,recovered=plot_cases_india(case_time_series,paths['ci'])
plot_prediction_india(cases,deaths,paths['pr'])
#testing_findings(statewise_tested_numbers_data,state_wise,paths['ttpos'])
plot_male_female(raw_data,paths['mf'])
plot_world_map(world_cases_all,paths['world'])
plot_guj_1(guj)
plot_cases_rec_ded_state(state_wise_daily,state_wise)
plot_spread_pie(raw_data,guj)