#!/usr/bin/env python3

#

# Usage: ./update_plots.py

# Updates plots from the Plotly section so they show the latest data.

from pathlib import Path

from datetime import date, time, datetime, timedelta

import pandas as pd

from plotly.express import line

import plotly.graph_objects as go

import re

def main():

# print('Updating covid deaths...')

# update_covid_deaths()

print('Updating covid cases...')

update_confirmed_cases()

def update_covid_deaths():

def update_readme(date_treshold):

lines = read_file('../README.md')

out = [re.sub("df.date < '\d{4}-\d{2}-\d{2}'", f"df.date < '{date_treshold}'", line)

for line in lines]

write_to_file('../README.md', out)

covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv',

usecols=['iso_code', 'date', 'total_deaths', 'population'])

continents = pd.read_csv('https://datahub.io/JohnSnowLabs/country-and-continent-codes-' + \

'list/r/country-and-continent-codes-list-csv.csv',

usecols=['Three_Letter_Country_Code', 'Continent_Name'])

df = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code')

df = df.groupby(['Continent_Name', 'date']).sum().reset_index()

df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population

date_treshold = str(date.today() - timedelta(days=2))

df = df[('2020-03-14' < df.date) & (df.date < date_treshold)]

df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns')

f = line(df, x='Date', y='Total Deaths per Million', color='Continent')

f.update_layout(margin=dict(t=24, b=0), paper_bgcolor='rgba(0, 0, 0, 0)')

update_file('covid_deaths.js', f)

update_readme(date_treshold)

def update_confirmed_cases():

def main():

df = wrangle_data(*scrape_data())

f = get_figure(df)

update_file('covid_cases.js', f)

def scrape_data():

def scrape_yahoo(id_):

BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/'

now = int(datetime.now().timestamp())

url = f'{BASE_URL}{id_}?period1=1579651200&period2={now}&interval=1d&events=history'

return pd.read_csv(url, usecols=['Date', 'Close']).set_index('Date').Close

covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv',

usecols=['date', 'total_cases'])

covid = covid.groupby('date').sum()

dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')]

dow.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin'

return covid, dow, gold, bitcoin

def wrangle_data(covid, dow, gold, bitcoin):

df = pd.concat([dow, gold, bitcoin], axis=1)

df = df.sort_index().interpolate()

df = df.rolling(10, min_periods=1, center=True).mean()

df = df.loc['2020-02-23':].iloc[:-2]

df = (df / df.iloc[0]) * 100

return pd.concat([covid, df], axis=1, join='inner')

def get_figure(df):

def get_trace(col_name):

return go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis='y2')

traces = [get_trace(col_name) for col_name in df.columns[1:]]

traces.append(go.Scatter(x=df.index, y=df.total_cases, name='Total Cases', yaxis='y1'))

figure = go.Figure()

figure.add_traces(traces)

figure.update_layout(

yaxis1=dict(title='Total Cases', rangemode='tozero'),

yaxis2=dict(title='%', rangemode='tozero', overlaying='y', side='right'),

legend=dict(x=1.1),

margin=dict(t=24, b=0),

paper_bgcolor='rgba(0, 0, 0, 0)'

)

return figure

main()

def update_file(filename, figure):

lines = read_file(filename)

f_json = figure.to_json(pretty=True).replace('\n', '\n ')

out = lines[:6] + [f' {f_json}\n', ' )\n', '};\n']

write_to_file(filename, out)

###

## UTIL

#

def read_file(filename):

with open(filename, encoding='utf-8') as file:

return file.readlines()

def write_to_file(filename, lines):

with open(filename, 'w', encoding='utf-8') as file:

file.writelines(lines)

if __name__ == '__main__':

main()