From 4d8f28d9d3a7efc628cffdfaa76175f45c7cf928 Mon Sep 17 00:00:00 2001 From: fordprefect Date: Thu, 23 Sep 2021 10:22:01 +0200 Subject: [PATCH] new header size, add total boosters to available data dict --- coronavis.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/coronavis.py b/coronavis.py index 629219a..dbe84f9 100644 --- a/coronavis.py +++ b/coronavis.py @@ -91,7 +91,7 @@ def get_data(): ## file archiving: include error log just in case… try: subprocess.run(["/usr/bin/cp", f"../{date}-full-data.csv", "data.csv"], cwd="/srv/http/dukun.de/corona/data/git") - subprocess.run(["/usr/bin/git", "commit", "-a", f"-m'{date}'"], cwd="/srv/http/dukun.de/corona/data/git") + subprocess.run(["/usr/bin/git", "commit", "-a", f"-m {date}"], cwd="/srv/http/dukun.de/corona/data/git") subprocess.run(["/usr/bin/git", "push"], cwd="/srv/http/dukun.de/corona/data/git") except Exception as e: print(f"File archiving failed with {e} - need for debugging here…") @@ -125,6 +125,10 @@ 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, total_tests, new_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, people_vaccinated, people_fully_vaccinated, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_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 elif len(row) == 60: 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, people_vaccinated, people_fully_vaccinated, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_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, excess_mortality = row + elif len(row) == 62: + 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, people_vaccinated, people_fully_vaccinated, total_boosters, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, total_boosters_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, excess_mortality = row + elif len(row) == 63: + 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, people_vaccinated, people_fully_vaccinated, total_boosters, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, total_boosters_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, excess_mortality_cumulative, excess_mortality = row else: print(f"WARNING! Table format changed, length now {len(row)}, new header:\n{row})") exit(1) @@ -153,6 +157,7 @@ def get_data(): tests_per_case = tofloat(tests_per_case) new_vaccinations = tofloat(new_vaccinations) tests_units = tests_units + total_boosters = tofloat(total_boosters) if location not in data: data[location] = [] @@ -164,7 +169,7 @@ def get_data(): 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, people_fully_vaccinated] + new_vaccinations, people_fully_vaccinated, total_boosters] ) @@ -210,8 +215,9 @@ def get_data(): tests_units = [] new_vaccinations = [] people_fully_vaccinated = [] + total_boosters = [] 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_, new_vaccinations_, people_fully_vaccinated_ = 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_, people_fully_vaccinated_, total_boosters_ = entry time.append(t_) new_cases.append(toint(new_cases_)) @@ -232,6 +238,7 @@ def get_data(): new_vaccinations.append(toint(new_vaccinations_)) tests_units.append(tests_units_) people_fully_vaccinated.append(people_fully_vaccinated_) + total_boosters.append(total_boosters_) ### data tweaking and fixing goes here @@ -268,6 +275,7 @@ def get_data(): 'tests_units': tests_units, 'new_vaccinations': new_vaccinations, 'people_fully_vaccinated': people_fully_vaccinated, + 'total_boosters': total_boosters, } # add vaccine info to metadata if loc in vaccines_country_dict: