""" Plot detailed figures for each country """ import matplotlib.pyplot as pp from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes import numpy as np import time as time_module import pickle import logging logging.getLogger().setLevel(logging.CRITICAL) import os def plot(data, countries, pop, metadata, **kwargs): figsize = (10,5) for loc in data: if loc == "International": continue if loc == "World": continue path = "img/"+f"{loc}".replace(" ", "_").replace("'", "").replace("/", "") if not os.path.isdir(path): os.mkdir(path) if not os.path.isfile(path+"/index.html") or True: # TODO enable html file generation with open(path+"/index.html", "w") as f: f.write(f""" CoVid19 in {loc}

Details zu {loc}

Zurück

Landesspezifische Kennzahlen
ISO Code{metadata[loc]["iso_code"]}
Continent{metadata[loc]["continent"]}
Country name{metadata[loc]["location"]}
Population{metadata[loc]["population"]}
Population density{metadata[loc]["population_density"]}
Median age{metadata[loc]["median_age"]}
% older than 65{metadata[loc]["aged_65_older"]}
% older than 70{metadata[loc]["aged_70_older"]}
GDP per capita{metadata[loc]["gdp_per_capita"]}
Extreme poverty{metadata[loc]["extreme_poverty"]}
Cardiovascular death rate{metadata[loc]["cardiovasc_death_rate"]}
Diabetes prevalence{metadata[loc]["diabetes_prevalence"]}
female smokers (%){metadata[loc]["female_smokers"]}
male smokers (%){metadata[loc]["male_smokers"]}
Handwashing facilities{metadata[loc]["handwashing_facilities"]}
hospital beds per thousand inhabitants{metadata[loc]["hospital_beds_per_thousand"]}
life expectancy{metadata[loc]["life_expectancy"]}
human development index{metadata[loc]["human_development_index"]}
Übersicht
Krankenhaussituation
Testsituation


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""") # extract data time = data[loc]['time'] new_cases = data[loc]['new_cases'] new_deaths = data[loc]['new_deaths'] total_cases = data[loc]['total_cases'] total_deaths = data[loc]['total_deaths'] total_vaccinations = data[loc]['total_vaccinations'] stringency_index = data[loc]['stringency_index'] reproduction_rate = data[loc]['reproduction_rate'] icu_patients = data[loc]['icu_patients'] hosp_patients = data[loc]['hosp_patients'] weekly_icu_admissions = data[loc]['weekly_icu_admissions'] weekly_hosp_admissions = data[loc]['weekly_hosp_admissions'] new_tests = data[loc]['new_tests'] total_tests = data[loc]['total_tests'] positive_rate = data[loc]['positive_rate'] tests_per_case = data[loc]['tests_per_case'] tests_units = data[loc]['tests_units'] # plots ############### hospital occupation if True: fig, ax1 = pp.subplots(num=loc+"_hospitalization", figsize=figsize) ax2 = ax1.twinx() icu_map = ~np.isnan(weekly_icu_admissions) hosp_map = ~np.isnan(weekly_hosp_admissions) if not np.isnan(icu_patients).all() : ax2.plot(time, icu_patients, color="blue", label="ICU patients") if not np.isnan(hosp_patients).all() : ax2.plot(time, hosp_patients, color="green", label="Hospitalized patients") if not np.isnan(weekly_icu_admissions).all() : ax1.plot(np.array(time)[icu_map], np.array(weekly_icu_admissions)[icu_map], color="blue", linestyle="--", label="weekly ICU admissions") if not np.isnan(weekly_hosp_admissions).all() : ax1.plot(np.array(time)[hosp_map], np.array(weekly_hosp_admissions)[hosp_map], color="green", linestyle="--", label="weekly hospital admissions") axbounds = ax2.axis() if metadata[loc]["hospital_beds_per_thousand"] == "" or metadata[loc]["population"] == "": pass else: hospital_beds = float(metadata[loc]["hospital_beds_per_thousand"]) * float(metadata[loc]["population"]) / 1000 ax2.plot([axbounds[0], axbounds[1]], [hospital_beds]*2, color="k", label=f"total hospital beds: {hospital_beds:1.0f}") ax2.axis(axbounds) ax1.set_ylabel("admissions") ax2.set_ylabel("patients") fig.legend(frameon=False, loc="upper left", bbox_to_anchor=(0,1), bbox_transform=ax1.transAxes) ax1.set_title(f"Hospital situation in {loc}") pp.text(0.002,0.005, f"plot generated {time_module.strftime('%Y-%m-%d %H:%M')}, CC-by-sa-nc, origin: dukun.de/corona, datasource: ourworldindata.org/coronavirus-source-data", color="dimgrey", fontsize=8, transform=fig.transFigure) pp.savefig(path+"/hospitals.png") pp.close(fig) ############## test situation if True: fig, ax1 = pp.subplots(num=loc+"_tests", figsize=figsize) ax2 = ax1.twinx() if set(tests_units) == {''}: testunit = "unknown" elif len(set(tests_units).difference({''})) == 1: testunit = set(tests_units).difference({''}) .pop() else: testunit = "changing" print(loc, set(tests_units)) if np.isnan(new_tests).all() and not np.isnan(total_tests).all(): ttest_map = ~np.isnan(total_tests) total_tests = np.array(total_tests) new_tests = (total_tests[ttest_map][1:] - total_tests[ttest_map][:-1])/7. ax1.plot(np.array(time)[ttest_map][1:], new_tests, color="blue", linestyle="-", linewidth=2, label="new tests") elif not np.isnan(new_tests).all() : ntest_map = ~np.isnan(new_tests) ax1.plot(np.array(time)[ntest_map], np.array(new_tests)[ntest_map]/7, color="grey", linestyle="--", label="new tests") ax1.plot(np.array(time)[ntest_map][3:-3], np.convolve(np.array(new_tests)[ntest_map], np.ones((7,))/7, mode="valid")/7, color="blue", linewidth=2, label="new tests 7day mean") if not np.isnan(positive_rate).all(): prate_map = ~np.isnan(positive_rate) ax2.plot(np.array(time)[prate_map], np.array(positive_rate)[prate_map]*100, color="black", linestyle="-", linewidth=2, label="positive rate (%)") ax1.set_ylabel(f"tests (unit: {testunit})") ax2.set_ylabel("positive rate") fig.legend(frameon=False, loc="upper left", bbox_to_anchor=(0,1), bbox_transform=ax1.transAxes) ax1.set_title(f"Test situation in {loc}") pp.text(0.002,0.005, f"plot generated {time_module.strftime('%Y-%m-%d %H:%M')}, CC-by-sa-nc, origin: dukun.de/corona, datasource: ourworldindata.org/coronavirus-source-data", color="dimgrey", fontsize=8, transform=fig.transFigure) pp.savefig(path+"/tests.png") pp.close(fig) if __name__ == "__main__": import pickle with open("20201221-data-metadata.dmp", "rb") as f: data, metadata = pickle.load(f) plot(data, [], {}, metadata=metadata)