diff --git a/all_countries.py b/all_countries.py index 59aadfa..9c551fc 100644 --- a/all_countries.py +++ b/all_countries.py @@ -23,7 +23,7 @@ corr = {"Chile": 10000, "Spain": 30000, } -def plot(data, countries, pop, **kwargs): +def plot(data, countries, pop, metadata={}, **kwargs): figsize = (10,5) vaccs = [] @@ -33,7 +33,7 @@ def plot(data, countries, pop, **kwargs): if loc == "International": continue name = basename+loc - time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations, stringency_index = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations'], data[loc]['stringency_index'] + time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations, stringency_index, new_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations'], data[loc]['stringency_index'], data[loc]['new_vaccinations'] fig, ax1 = pp.subplots(num=name, figsize=figsize) @@ -49,16 +49,25 @@ def plot(data, countries, pop, **kwargs): ax1.plot(time[3:-3], np.convolve(new_cases, np.ones((7,))/7, mode="valid"), label="new cases 7day mean", color="orange", linestyle="-", linewidth=2) # plot vaccinations - if not np.isnan(total_vaccinations).all() : - print(f"{loc} has vaccines, adding to plot") - - # fix data: not all countries report daily - for n in range(1, len(total_vaccinations)): - if np.isnan(total_vaccinations[n]) and not np.isnan(total_vaccinations[n-1]): - total_vaccinations[n] = total_vaccinations[n-1] + if not np.isnan(total_vaccinations).all(): + # notify of new vaccine programs + if np.isnan(total_vaccinations[-2]): + print(f"{loc} starts vaccinating, adding to plot") ax2.plot(time, np.array(total_vaccinations), label=f"Total vaccinations", marker="", linestyle="-.", color="crimson") + if False: + # plot detailed vaccination plot for all_countries.py + rfig, rax = pp.subplots(1,1,num=f"{loc}_vacc") + rax2 = rax.twinx() + rax.plot(time, new_vaccinations, linestyle="--", color="green", label="new vac") + rax2.plot(time, total_vaccinations, linestyle="-.", color="red", label="total vac") + rax.set_ylabel("new vaccinatios") + rax2.set_ylabel("total vaccinations") + rax.legend(frameon=False, loc=2) + + rfig.savefig("img/"+f"{loc}".replace(" ", "_").replace("'", "").replace("/", "") + "/vaccs.png") + # fix lower bound of plot for ax in (ax1, ax2): axis = ax.axis() @@ -154,10 +163,17 @@ def plot(data, countries, pop, **kwargs): with open("index.html.head", "r") as g: f.write(g.read()) # table header - f.write("\n") + f.write("
LandImpfungenImpfrate
\n") # data for loc, tvac, rvac in vaccs: - f.write(f"\n".replace(".", ",")) + line = f"".replace(".", ",") + if "vaccines" in metadata[loc]: + line += f"\n" + else: + line += f"\n" + f.write(line) # table footer f.write("
LandImpfungenImpfrateImpfstoffe
{loc}" + f"{tvac:,d}".replace(",",".") + f"{rvac:3.3f}%
{loc}" + \ + f"{tvac:,d}".replace(",",".") + \ + f"{rvac:3.3f}%{metadata[loc]['vaccines']}
-
\n") # site footer diff --git a/coronavis.py b/coronavis.py index 815e105..0dd5a77 100644 --- a/coronavis.py +++ b/coronavis.py @@ -94,6 +94,7 @@ def get_data(): metadata = {} with open(datafile, "r") as f: reader = csv.reader(f) + for row in reader: if len(row) == 6: date,location,new_cases,new_deaths,total_cases,total_deaths = row @@ -105,6 +106,8 @@ def get_data(): 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 elif len(row) == 54: 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, new_vaccinations, total_vaccinations_per_hundred, new_vaccinations_per_million, 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 + elif len(row) == 55: + 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, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, new_vaccinations_smoothed_per_million, 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 else: print(f"WARNING! Table format changed, length now {len(row)}, new header:\n{row})") exit(1) @@ -130,6 +133,7 @@ def get_data(): total_tests = tofloat(total_tests) positive_rate = tofloat(positive_rate) tests_per_case = tofloat(tests_per_case) + new_vaccinations = tofloat(new_vaccinations) tests_units = tests_units if location not in data: @@ -141,7 +145,8 @@ def get_data(): 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, tests_units,] + total_tests, positive_rate, tests_per_case, tests_units, + new_vaccinations,] ) @@ -155,6 +160,21 @@ def get_data(): else: if metadata[location][field] != row[n]: print(f"{location}: {field} seems not to be a constant ({metadata[location][field]} vs {row[n]})") + + ### End of csv reading loop + +# get data about vaccines + vaccinesurl = "https://github.com/owid/covid-19-data/raw/master/public/data/vaccinations/locations.csv" + vacraw = requests.get(vaccinesurl).content.decode("UTF8").split('\n')[1:-1] + vacreader = csv.reader(vacraw) + vaccines_country_dict = {} + + for row in vacreader: + land = row[0] + vaccines = row[2] + vaccines_country_dict[land] = vaccines + del(vaccinesurl, vacraw, vacreader) + # reorganize data data2 = {} for loc in data: @@ -170,8 +190,9 @@ def get_data(): positive_rate = [] tests_per_case = [] tests_units = [] + new_vaccinations = [] for entry in data[loc]: - 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_, tests_units_ = entry + 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_, tests_units_, new_vaccinations_ = entry time.append(t_) new_cases.append(toint(new_cases_)) @@ -189,7 +210,21 @@ def get_data(): total_tests.append(toint(total_tests_)) positive_rate.append(positive_rate_) tests_per_case.append(tests_per_case_) + new_vaccinations.append(toint(new_vaccinations_)) tests_units.append(tests_units_) + + ### data tweaking and fixing goes here + + # fix vaccination data: not all countries report daily vaccinations + for n in range(1, len(total_vaccinations)): + if np.isnan(total_vaccinations[n]) and not np.isnan(total_vaccinations[n-1]): + total_vaccinations[n] = total_vaccinations[n-1] + + + ### + + + # collecting data data2[loc] = {'time': time, 'new_cases': new_cases, 'new_deaths': new_deaths, @@ -207,11 +242,28 @@ def get_data(): 'positive_rate': positive_rate, 'tests_per_case': tests_per_case, 'tests_units': tests_units, + 'new_vaccinations': new_vaccinations, } + # add vaccine info to metadata + if loc in vaccines_country_dict: + metadata[loc]['vaccines'] = vaccines_country_dict[loc] + # cast population to int + if loc != "International": + try: metadata[loc]['population'] = int(float(metadata[loc]['population'])) + except: metadata[loc][loc]['population'] = np.nan + return data2, metadata data, metadata = get_data() +## dump data instead of plotting +if False: + print("dumping data, no plotting") + import pickle + with open("data.dump", "wb") as f: + pickle.dump([data, metadata], f) + exit() + for plot in plots: i = importlib.import_module(plot) i.plot(data, countries, pop, metadata=metadata) diff --git a/country_details.py b/country_details.py index 1ca3310..6475d69 100644 --- a/country_details.py +++ b/country_details.py @@ -9,6 +9,9 @@ import pickle import logging logging.getLogger().setLevel(logging.CRITICAL) import os +import datetime +import locale +locale.setlocale(locale.LC_ALL, "de_DE.utf8") def plot(data, countries, pop, metadata, **kwargs): figsize = (10,5) @@ -24,7 +27,31 @@ def plot(data, countries, pop, metadata, **kwargs): if not os.path.isdir(path): os.mkdir(path) - if not os.path.isfile(path+"/index.html") or True: # TODO enable html file generation + # is this country vaccinating? + is_vaccinating = True if data[loc]['total_vaccinations'][-1] > 0 else False + if is_vaccinating: + first_vac_report = np.argwhere(np.isnan(data[loc]['total_vaccinations']))[-1][0] + 1 + vac_text = f"Impfstart{data[loc]['time'][first_vac_report]}" + try: + vac_rate = data[loc]['total_vaccinations'][-1]/metadata[loc]['population']*100 + vac_text+= f"Impfrate{vac_rate:1.3f} %" + except: + pass + try: + immune_rate = (data[loc]['total_vaccinations'][-1] + data[loc]['total_cases'][-1])/metadata[loc]['population']*100 + vac_text += f"Immunrate{immune_rate:1.3f} %" + except: + pass + try: + vaccines = metadata[loc]['vaccines'] if 'vaccines' in metadata[loc] else "unknown" + vac_text += f"Impfstoffe{vaccines}" + except: + pass + total_cases = data[loc]['total_cases'][-1] + total_deaths = data[loc]['total_deaths'][-1] + today = datetime.datetime.now().strftime("%d.%m.%Y") + + if True: #not os.path.isfile(path+"/index.html") or False: # TODO enable html file generation with open(path+"/index.html", "w") as f: f.write(f""" @@ -42,6 +69,7 @@ def plot(data, countries, pop, metadata, **kwargs): Zurück

Landesspezifische Kennzahlen

 +(Stand: {today}) @@ -61,12 +89,18 @@ def plot(data, countries, pop, metadata, **kwargs): + + +{vac_text if is_vaccinating else ''}
ISO Code{metadata[loc]["iso_code"]}
Continent{metadata[loc]["continent"]}
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"]}
Absolute bestätigte Infektionsfälle{total_cases}
Absolute bestätigte Todesfälle{total_deaths}
Übersicht
Krankenhaussituation
-
Testsituation
+
Testsituation +
+Testing dataset: Hasell, J., Mathieu, E., Beltekian, D. et al. A cross-country database of COVID-19 testing. Sci Data 7, 345 (2020). DOI:10.1038/s41597-020-00688-8
+
Impfsituation


Zurück
@@ -81,6 +115,7 @@ Ein Infoservice von dukun.de; Anregungen gern dukun.de; Anregungen gern sehr unterschiedlich sein!

-Die Daten stammen von hier und werden dort aus den WHO- und ECDC-Reports generiert. -Von den extrem reichhaltigen Daten dort verarbeite ich nur die Zahl der Neufälle. +Die Daten stammen von hier und werden dort aus verschiedensten Quellen aggregiert. +

Aktuelle Daten aus Deutschland mit vielen Hintergründen finden sich im Lagebericht des RKI.