213 lines
9.3 KiB
Python
213 lines
9.3 KiB
Python
#!/usr/bin/python
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import requests
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import csv
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import datetime
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import os
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import matplotlib.pyplot as pp
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import numpy as np
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import sys
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import importlib
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import time
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sys.path.append(".")
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#### config
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# countries of interest
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countries = ["Germany", "Italy", "India", "Japan", "Brazil", "Iran", "United States", "World", "United Kingdom", "Sweden"]
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# enabled plots
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plots = ["basics", "death_per_case",
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#"normalized_to_first_death", "delay_from_china", "delay_from_usa", "normalized_to_ten_cases", "percent_increase",
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"doubling_time",
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"all_countries",
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]
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metadata_fields = [
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"iso_code",
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"continent",
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"location",
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"population",
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"population_density",
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"median_age",
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"aged_65_older",
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"aged_70_older",
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"gdp_per_capita",
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"extreme_poverty",
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"cardiovasc_death_rate",
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"diabetes_prevalence",
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"female_smokers",
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"male_smokers",
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"handwashing_facilities",
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"hospital_beds_per_thousand",
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"life_expectancy",
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"human_development_index",
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"tests_units",
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]
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### manual data
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# population: sourced ECDC data
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from population_repository import pop
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###
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def toint(a):
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try:
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return int(a)
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except:
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return np.nan
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def tofloat(a):
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try:
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return float(a)
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except:
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return np.nan
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def addmeta(field, value):
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pass
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def get_data():
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"""fetch data from remote, cache locally and reorganize internal data
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not beautiful (at all), but effective!!"""
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tries = 10
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delay = 10
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dataurl = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
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date = datetime.date.today()
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datafile = f"data/{date}-full-data.csv"
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if not os.path.isfile(datafile):
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for n in range(tries):
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try:
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r = requests.get(dataurl)
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except:
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print(f"==> download failed, retrying after {delay}s up to another {tries-n} times…")
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time.sleep(delay)
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continue
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break
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with open(datafile, "wb") as f:
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f.write(r.content)
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else:
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print(f"file found: {datafile}")
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# processing
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data = {}
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metadata = {}
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with open(datafile, "r") as f:
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reader = csv.reader(f)
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for row in reader:
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if len(row) == 6:
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date,location,new_cases,new_deaths,total_cases,total_deaths = row
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elif len(row) == 10:
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date,location,new_cases,new_deaths,total_cases,total_deaths,weekly_cases,weekly_deaths,biweekly_cases,biweekly_deaths = row
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elif len(row) == 50:
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iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,new_tests,total_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,stringency_index,population,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index = row
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elif len(row) == 52:
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iso_code,continent,location,date,total_cases,new_cases,new_cases_smoothed,total_deaths,new_deaths,new_deaths_smoothed,total_cases_per_million,new_cases_per_million,new_cases_smoothed_per_million,total_deaths_per_million,new_deaths_per_million,new_deaths_smoothed_per_million,reproduction_rate,icu_patients,icu_patients_per_million,hosp_patients,hosp_patients_per_million,weekly_icu_admissions,weekly_icu_admissions_per_million,weekly_hosp_admissions,weekly_hosp_admissions_per_million,new_tests,total_tests,total_tests_per_thousand,new_tests_per_thousand,new_tests_smoothed,new_tests_smoothed_per_thousand,positive_rate,tests_per_case,tests_units,total_vaccinations,total_vaccinations_per_hundred,stringency_index,population,population_density,median_age,aged_65_older,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,hospital_beds_per_thousand,life_expectancy,human_development_index = row
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else:
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print(f"WARNING! Table format changed, new header:\n{row})")
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exit(1)
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# break loop if header
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if location=="location":
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# table header
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header = row
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continue
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# cast to num type
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total_cases = tofloat(total_cases)
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new_cases = tofloat(new_cases)
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total_deaths = tofloat(total_deaths)
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new_deaths = tofloat(new_deaths)
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total_vaccinations = tofloat(total_vaccinations)
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stringency_index = tofloat(stringency_index)
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reproduction_rate = tofloat(reproduction_rate)
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icu_patients = tofloat(icu_patients)
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hosp_patients = tofloat(hosp_patients)
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weekly_icu_admissions = tofloat(weekly_icu_admissions)
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weekly_hosp_admissions = tofloat(weekly_hosp_admissions)
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new_tests = tofloat(new_tests)
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total_tests = tofloat(total_tests)
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positive_rate = tofloat(positive_rate)
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tests_per_case = tofloat(tests_per_case)
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if location not in data:
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data[location] = []
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metadata[location] = {}
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year, month, day = date.split("-")
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data[location].append(
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[datetime.date(int(year), int(month), int(day)),
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new_cases, new_deaths, total_cases, total_deaths, total_vaccinations,
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stringency_index, reproduction_rate, icu_patients, hosp_patients,
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weekly_icu_admissions, weekly_hosp_admissions, new_tests,
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total_tests, positive_rate, tests_per_case,]
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)
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# catch all data fields
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#dfields = {field: row[n] for n, field in enumerate(header)}
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# add metadata
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for n, field in enumerate(header):
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if field in metadata_fields:
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if field not in metadata[location]:
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metadata[location][field] = row[n]
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else:
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if metadata[location][field] != row[n]:
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print(f"{location}: {field} seems not to be a constant ({metadata[location][field]} vs {row[n]})")
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# reorganize data
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data2 = {}
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for loc in data:
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time = []
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new_cases, new_deaths, total_cases, total_deaths, total_vaccinations, stringency_index = [], [], [], [], [], []
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reproduction_rate = []
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icu_patients = []
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hosp_patients = []
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weekly_icu_admissions = []
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weekly_hosp_admissions = []
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new_tests = []
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total_tests = []
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positive_rate = []
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tests_per_case = []
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for entry in data[loc]:
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t_, new_cases_, new_deaths_, total_cases_, total_deaths_, total_vaccinations_, stringency_index_, reproduction_rate_, icu_patients_, hosp_patients_, weekly_icu_admissions_, weekly_hosp_admissions_, new_tests_, total_tests_, positive_rate_, tests_per_case = entry
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time.append(t_)
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new_cases.append(toint(new_cases_))
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new_deaths.append(toint(new_deaths_))
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total_cases.append(toint(total_cases_))
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total_deaths.append(toint(total_deaths_))
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total_vaccinations.append(toint(total_vaccinations_))
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stringency_index.append(toint(stringency_index_))
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reproduction_rate.append(toint(reproduction_rate_))
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icu_patients.append(toint(icu_patients_))
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hosp_patients.append(toint(hosp_patients_))
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weekly_icu_admissions.append(toint(weekly_icu_admissions_))
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weekly_hosp_admissions.append(toint(weekly_hosp_admissions_))
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new_tests.append(toint(new_tests_))
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total_tests.append(toint(total_tests_))
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positive_rate.append(toint(positive_rate_))
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tests_per_case.append(toint(tests_per_case_))
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data2[loc] = {'time': time,
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'new_cases': new_cases,
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'new_deaths': new_deaths,
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'total_cases': total_cases,
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'total_deaths': total_deaths,
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'total_vaccinations': total_vaccinations,
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'stringency_index': stringency_index,
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'reproduction_rate': reproduction_rate,
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'icu_patients': icu_patients,
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'hosp_patients': hosp_patients,
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'weekly_icu_admissions': weekly_icu_admissions,
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'weekly_hosp_admissions': weekly_hosp_admissions,
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'new_tests': new_tests,
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'total_tests': total_tests,
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'positive_rate': positive_rate,
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'tests_per_case': tests_per_case,
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}
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return data2, metadata
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data, metadata = get_data()
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for plot in plots:
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i = importlib.import_module(plot)
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i.plot(data, countries, pop, metadata=metadata)
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