diff --git a/all_countries.py b/all_countries.py
index fac96c2..e6fc086 100644
--- a/all_countries.py
+++ b/all_countries.py
@@ -17,6 +17,7 @@ corr = {"Chile": 10000,
"Peru": 10000,
"Bolivia": 3000,
"Yemen": 220,
+ "Turkey": 100000,
}
def plot(data, countries, pop):
diff --git a/coronavis.py b/coronavis.py
index 5d8a362..9321048 100644
--- a/coronavis.py
+++ b/coronavis.py
@@ -32,7 +32,7 @@ def get_data():
tries = 10
delay = 10
- dataurl = "https://covid.ourworldindata.org/data/ecdc/full_data.csv"
+ dataurl = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
date = datetime.date.today()
datafile = f"{date}-full-data.csv"
@@ -62,13 +62,56 @@ def get_data():
date,location,new_cases,new_deaths,total_cases,total_deaths = row
elif len(row) == 10:
date,location,new_cases,new_deaths,total_cases,total_deaths,weekly_cases,weekly_deaths,biweekly_cases,biweekly_deaths = row
+ elif len(row) == 50:
+ 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
else:
print(f"WARNING! Table format changed, new header:\n{row})")
exit(1)
-
+ # break loop if header
if location=="location":
# table header
continue
+ # cast to num type
+ if isinstance(total_cases, str):
+ if total_cases in ('', ' ', '0.0'):
+ total_cases = 0
+ try:
+ total_cases = int(total_cases)
+ except:
+ try:
+ total_cases = float(total_cases)
+ except:
+ print("tc failed")
+ if isinstance(new_cases, str):
+ if new_cases in ('', ' ', '0.0'):
+ new_cases = 0
+ try:
+ new_cases = int(new_cases)
+ except:
+ try:
+ new_cases = float(new_cases)
+ except:
+ print("nc failed")
+ if isinstance(new_deaths, str):
+ if new_deaths in ('', ' ', '0.0'):
+ new_deaths = 0
+ try:
+ new_deaths = int(new_deaths)
+ except:
+ try:
+ new_deaths = float(new_deaths)
+ except:
+ print("nd failed")
+ if isinstance(total_deaths, str):
+ if total_deaths in ('', ' ', '0.0'):
+ total_deaths = 0
+ try:
+ total_deaths = int(total_deaths)
+ except:
+ try:
+ total_deaths = float(total_deaths)
+ except:
+ print("td failed")
if location not in data:
data[location] = []
year, month, day = date.split("-")
diff --git a/doubling_time.py b/doubling_time.py
index 5892fdb..b95ad3b 100644
--- a/doubling_time.py
+++ b/doubling_time.py
@@ -19,7 +19,10 @@ def plot(data, countries, pop):
window_size = 7
tchar = moving_average(np.array(new_cases),window_size)/moving_average(np.array(total_cases),window_size)
thalf = np.log(2)/tchar
- day_of_100_cases = np.argwhere(np.array(total_cases) > 99)[0][0]
+ try:
+ day_of_100_cases = np.argwhere(np.array(total_cases) > 99)[0][0]
+ except:
+ return
new_time_axis = time[int(window_size/2):-int(window_size/2)]
pp.plot(new_time_axis[50:], thalf[50:], label=f"{loc}", marker=".")
diff --git a/index.html b/index.html
index 7f69498..93562f6 100644
--- a/index.html
+++ b/index.html
@@ -25,6 +25,7 @@ Das wird aber ganz massiv durch die Testrate, Meldekette, politische Einflussnah
die einzelnen Länder 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.
Aktuelle Daten aus Deutschland mit vielen Hintergründen finden sich im Lagebericht des RKI.
@@ -61,7 +62,7 @@ Aktuelle Daten aus Deutschland mit vielen Hintergründen finden sich im Germany
Austria
Belgium
Czech Republic
Czech Republic
Denmark
France
Italy
Curacao
Cyprus
Czech Republic
Czech Republic
Democratic Republic of Congo
Denmark
Djibouti
Lithuania
Luxembourg
Macedonia
North Macedonia
Madagascar
Malawi
Malaysia
Sudan
Suriname
Swaziland
Swaziland
Sweden
Switzerland
Syria
