def get_infection_data() -> Infections:
"""
Retrieve infections (confirmed cases, deaths and recoveries) by country and date.
Data is cached for 24 hours
:return: A dictionary of countries with a list of dates and counts
"""
config = read_config()
endpoint = config["endpoints"]["infections"]
text = requests.get(endpoint).text
infection_data = json.loads(text)
logger.info(
f"retrieved infection data with {len(infection_data)} countries")
return infection_data
def test_read_config():
config = read_config()
config_has_content = bool(config)
config_is_dict = isinstance(config, dict)
assert config_has_content and config_is_dict, "malformed config"
"""Statistical models"""
from datetime import datetime
from typing import Tuple
from typing import Union, List
import numpy as np
from sklearn.linear_model import LinearRegression
from covid19 import types as t
from covid19.utils import read_config
config = read_config()
def fit_infection_trend(kind: t.InfectionStatus,
infection_data: t.Infections) -> Tuple[list, list]:
"""
Fit a linear model to the log of infection data
:param kind: Infection status (deaths, confirmed, recovered)
:param infection_data: infection data
:return: A tuple of unique days (from zero) and predicted infections for that day
"""
linear_model = LinearRegression()
predictors, response = [], []
for country, content in infection_data.items():
if country not in config["exclude"]["trend"]:
counts = [day[kind.value] for day in content]
days = [day for day in range(len(counts))]
predictors.extend(days)
response.extend(np.log10(counts))
trend_days = sorted(list(set(predictors)))
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
from decouple import config
from flask import Flask
from covid19 import callbacks as cb, plots as p
from covid19.data import get_infection_data, filter_infection_data
from covid19.stats import build_summary_stats
from covid19.types import InfectionStatus
from covid19.utils import get_app_dir, read_config
logger = getLogger(__name__)
default_infection_status = InfectionStatus.CONFIRMED.value
config_file = read_config()
app_dir = get_app_dir()
with open(str(app_dir / "assets" / "header.md")) as header_file:
header_md = header_file.read()
raw_infection_data = get_infection_data()
infection_data = filter_infection_data(raw_infection_data)
summary_data = build_summary_stats(infection_data)
graphic = p.Graphic(infection_data)
plots = graphic.figures[default_infection_status]
def generate_stats_panel() -> list:
"""Build a div with top level statistics"""
panel_list = []