initial visualization

coronavis.py

@@ -0,0 +1,82 @@
+#!/usr/bin/python
+
+import requests
+import csv
+import datetime
+import os
+import matplotlib.pyplot as pp
+import numpy as np
+
+
+dataurl = "https://covid.ourworldindata.org/data/full_data.csv"
+date = datetime.date.today()
+
+datafile = f"{date}-full-data.csv"
+
+if not os.path.isfile(datafile):
+    r = requests.get(dataurl)
+    with open(datafile, "wb") as f:
+        f.write(r.content)
+else:
+    print(f"file found: {datafile}")
+
+# processing
+data = {}
+with open(datafile, "r") as f:
+    reader = csv.reader(f)
+    for row in reader:
+        date,location,new_cases,new_deaths,total_cases,total_deaths = row
+        if location=="location":
+            # table header
+            continue
+        if location not in data:
+            data[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])
+
+
+data2 = {}
+
+# reorganize data
+for loc in data:
+    time = []
+    new_cases, new_deaths, total_cases, total_deaths = [], [], [], []
+    for entry in data[loc]:
+        t_, new_cases_, new_deaths_, total_cases_, total_deaths_ = 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]
+
+# countries of interest
+countries = ["Germany", "Italy", "Spain", "Denmark", "China", "Japan", "South Korea", "Iran", "Belgium", "World"]
+
+# plotting
+for loc in data2:
+    if loc not in countries:
+        continue
+    time, new_cases, new_deaths, total_cases, total_deaths = data2[loc]
+    
+    # total cases
+    pp.figure("total_cases")
+    pp.plot(time, total_cases, label=f"{loc}")
+
+    # death/case
+    pp.figure("death_per_case")
+    pp.plot(time, np.array(total_deaths)/np.array(total_cases), label=f"{loc}")
+# figure postprocessing
+for fig in ["total_cases", "death_per_case"]:
+    pp.figure(fig)
+    pp.yscale("log")
+    pp.xticks(rotation=90)
+    pp.legend(frameon=False)
+    pp.tight_layout()
+
+    pp.savefig(f"{fig}.png")