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add metadata from new columns and make every module fit to use this. add stringence of local measures as background of plot, add vaccination status to plots, mute unreasonable warnings

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fordprefect
2020-12-17 13:18:19 +01:00
parent e0d6508682
commit 22c65b1b68
10 changed files with 111 additions and 60 deletions
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23
all_countries.py
View File

@@ -6,6 +6,8 @@ from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import numpy as np
import time as time_module
import pickle
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
basename="all_"
# manual y adjustments for new cases
@@ -28,10 +30,11 @@ def plot(data, countries, pop, **kwargs):
if loc == "International":
continue
name = basename+loc
time, new_cases, new_deaths, total_cases, total_deaths = data[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']
fig, ax1 = pp.subplots(num=name, figsize=figsize)
ax2 = ax1.twinx()
ax2.plot(time, np.array(total_deaths)*10, label="Total deaths (x10)", marker="", linestyle="--", color="green")
ax2.plot(time, total_cases, label=f"Total cases", marker="", linestyle="-", color="blue")
@@ -42,6 +45,10 @@ def plot(data, countries, pop, **kwargs):
ax1.plot(time, new_cases, label="raw new cases", color="grey", linestyle="-")
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)
if not np.isnan(total_vaccinations[-1]):
print(f"{loc} has vaccines, adding to plot")
ax2.plot(time, np.array(total_vaccinations), label=f"Total vaccinations", marker="", linestyle="-.", color="crimson")
# fix lower bound of plot
for ax in (ax1, ax2):
axis = ax.axis()
@@ -50,6 +57,12 @@ def plot(data, countries, pop, **kwargs):
axis = [axis[0], axis[1], axis[2], corr[loc]]
ax.axis([axis[0], axis[1], -1, axis[3]])
# fix population
#try:
# print(loc, pop[loc]['pop'] - metadata[loc]['population'])
#except:
# pop[loc]['pop'] = metadata[loc]['population']
# if we know population: plot 500 new cases / 1million inhabitants as a rough measure for comparison
# also set color for infection level indicator
infection_level_indicator = "grey"
@@ -82,6 +95,12 @@ def plot(data, countries, pop, **kwargs):
print(f"=====> population unknown for {loc}, skipping plot enhancements")
# stringency of countermeasures as background contourf
axbounds = ax1.axis()
ax1.contourf(time, [-1, 1e10], [stringency_index]*2, cmap="Greys", alpha=0.3, levels=99)
ax1.axis(axbounds)
# disabled for now: put second total-cases-per-million-inhabitants axis besides total-cases-axis
if loc in pop and False:
# according to https://matplotlib.org/3.2.2/gallery/axisartist/demo_parasite_axes.html
@@ -107,6 +126,8 @@ def plot(data, countries, pop, **kwargs):
if loc in pop:
#pp.title(f"{loc}", population = "+f"{pop[loc]['pop']:,}".replace(",","."))
title += ", population = "+f"{pop[loc]['pop']:,}".replace(",",".")
if not np.isnan(total_vaccinations[-1]):
title += ", vac rate: "+f"{total_vaccinations[-1]/pop[loc]['pop']*100:1.3f}%"
ax1.set_title(title)
fig.tight_layout()
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)

2
basics.py
View File

@@ -14,7 +14,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
# total cases
tc.plot(time, total_cases, label=f"{loc}", marker=".")

121
coronavis.py
View File

@@ -20,11 +20,46 @@ plots = ["basics", "death_per_case",
"doubling_time",
"all_countries",
]
metadata_fields = [
"iso_code",
"continent",
"location",
"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",
]
### manual data
# population: sourced ECDC data
from population_repository import pop
###
def toint(a):
try:
return int(a)
except:
return np.nan
def tofloat(a):
try:
return float(a)
except:
return np.nan
def addmeta(field, value):
pass
def get_data():
"""fetch data from remote, cache locally and reorganize internal data
not beautiful (at all), but effective!!"""
@@ -55,6 +90,7 @@ def get_data():
# processing
data = {}
metadata = {}
with open(datafile, "r") as f:
reader = csv.reader(f)
for row in reader:
@@ -64,80 +100,61 @@ def get_data():
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
elif len(row) == 52:
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
else:
print(f"WARNING! Table format changed, new header:\n{row})")
exit(1)
# break loop if header
if location=="location":
# table header
header = row
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")
total_cases = tofloat(total_cases)
new_cases = tofloat(new_cases)
total_deaths = tofloat(total_deaths)
new_deaths = tofloat(new_deaths)
total_vaccinations = tofloat(total_vaccinations)
stringency_index = tofloat(stringency_index)
if location not in data:
data[location] = []
metadata[location] = {}
year, month, day = date.split("-")
def toint(a):
try:
return int(a)
except:
return np.nan
data[location].append([datetime.date(int(year), int(month), int(day)), new_cases, new_deaths, total_cases, total_deaths])
data[location].append([datetime.date(int(year), int(month), int(day)), new_cases, new_deaths, total_cases, total_deaths, total_vaccinations, stringency_index])
# catch all data fields
#dfields = {field: row[n] for n, field in enumerate(header)}
# add metadata
for n, field in enumerate(header):
if field in metadata_fields:
if field not in metadata[location]:
metadata[location][field] = row[n]
else:
if metadata[location][field] != row[n]:
print(f"{location}: {field} seems not to be a constant ({metadata[location][field]} vs {row[n]})")
# reorganize data
data2 = {}
for loc in data:
time = []
new_cases, new_deaths, total_cases, total_deaths = [], [], [], []
new_cases, new_deaths, total_cases, total_deaths, total_vaccinations, stringency_index = [], [], [], [], [], []
for entry in data[loc]:
t_, new_cases_, new_deaths_, total_cases_, total_deaths_ = entry
t_, new_cases_, new_deaths_, total_cases_, total_deaths_, total_vaccinations_, stringency_index_ = entry
time.append(t_)
new_cases.append(toint(new_cases_))
new_deaths.append(toint(new_deaths_))
total_cases.append(toint(total_cases_))
total_deaths.append(toint(total_deaths_))
data2[loc] = [time, new_cases, new_deaths, total_cases, total_deaths]
return data2
total_vaccinations.append(toint(total_vaccinations_))
stringency_index.append(toint(stringency_index_))
data2[loc] = {'time': time, 'new_cases': new_cases, 'new_deaths': new_deaths, 'total_cases': total_cases, 'total_deaths': total_deaths, 'total_vaccinations': total_vaccinations, "stringency_index": stringency_index}
return data2, metadata
data = get_data()
data, metadata = get_data()
for plot in plots:
i = importlib.import_module(plot)
i.plot(data, countries, pop)
i.plot(data, countries, pop, metadata=metadata)

2
death_per_case.py
View File

@@ -10,7 +10,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
# death/case
pp.figure(name, figsize=figsize)

2
delay_from_usa.py
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@@ -10,7 +10,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
pp.figure(name, figsize=figsize)
day_of_above_hundred_cases = np.argwhere(np.array(total_cases) > 100)[0][0]

4
doubling_time.py
View File

@@ -3,6 +3,8 @@ Plot doubling time
"""
import matplotlib.pyplot as pp
import numpy as np
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
name="doubling_time"
def moving_average(x, w):
@@ -13,7 +15,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
pp.figure(name, figsize=figsize)
window_size = 7

11
index.html
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@@ -56,6 +56,17 @@ Aktuelle Daten aus Deutschland mit vielen Hintergründen finden sich im <a href=
<summary><h2>Ausgewählte Länder</h2></summary>
<b>Achtung! Die Datenqualität variiert zwischen den Ländern enorm, damit sind einzelne Länder auch nur schwer miteinander zu vergleichen!</b>
<br>
Di Plots werden immer komplexer, daher hier eine kurze Zusammenfassung der sichtbaren Informationen:
<ol>
<li>gemeldete Neuinfektion: Rohdaten (grau durchgezogen) und 7-Tage-Mittel (orange) (linke Achse)</li>
<li>Absolute Zahl gemeldeter Infektionen (blau) (rechte Achse)</li>
<li>gemeldete neue Todesfälle: Rohdaten (grau gestrichelt) und 7-Tage-Mittel (schwarz gestrichelt) - der besseren Sichtbarkeit halber mit 10 multipliziert! (linke Achse)</li>
<li>Absolute Zahl gemeldeter Todesfälle (grün gestrichelt) - der besseren Sichtbarkeit halber mit 10 multipliziert! (rechte Achse)</li>
<li>Einschätzung der Strenge der Maßnahmen zur Einordnung der Gegenmaßnahmen als Hintergrundschattierung (<a href=https://ourworldindata.org/grapher/covid-stringency-index>Erklärung (EN)</a>, weiß kann entweder "keine Daten" oder "keine Einschränkungen" bedeuten)</li>
<li>Zahl der verabreichten Impfungen (dunkelrot Strichpunkt-Linie) (rechte Achse)</li>
<li>Referenzen für die Inzidenz (Anzahl der neuen Fälle pro 1Mio Einwohner pro Woche): 5, 50 und 500</li>
<br>
<br>
<details open>
<summary>Deutschland und europäische Nachbarn</summary>

2
normalized_to_first_death.py
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@@ -10,7 +10,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
pp.figure(name, figsize=figsize)
day_of_first_death = np.argwhere(np.array(total_deaths) > 0)[0][0]

2
normalized_to_ten_cases.py
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@@ -10,7 +10,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
pp.figure(name, figsize=figsize)
day_of_100_cases = np.argwhere(np.array(total_cases) > 99)[0][0]

2
percent_increase.py
View File

@@ -13,7 +13,7 @@ def plot(data, countries, pop, **kwargs):
for loc in data:
if loc not in countries:
continue
time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
# moving average of relative new cases with a window size of 3 days
window_size = 3