refactor

coronavis.py

@@ -6,23 +6,35 @@ import datetime
 import os
 import matplotlib.pyplot as pp
 import numpy as np
+import sys
+import importlib
+sys.path.append(".")
 
+#### config
+# countries of interest
+countries = ["Germany", "Italy", "Spain", "Denmark", "China", "Japan", "South Korea", "Iran", "Belgium", "World"]
+# enabled plots
+plots = ["total_cases", "death_per_case", "normalized_to_first_death"]
+###
+def get_data():
+    """fetch data from remote, cache locally and reorganize internal data
+    not beautiful (at all), but effective!!"""
 
-dataurl = "https://covid.ourworldindata.org/data/full_data.csv"
+    dataurl = "https://covid.ourworldindata.org/data/full_data.csv"
-date = datetime.date.today()
+    date = datetime.date.today()
 
-datafile = f"{date}-full-data.csv"
+    datafile = f"{date}-full-data.csv"
 
-if not os.path.isfile(datafile):
+    if not os.path.isfile(datafile):
         r = requests.get(dataurl)
         with open(datafile, "wb") as f:
             f.write(r.content)
-else:
+    else:
         print(f"file found: {datafile}")
 
-# processing
+    # processing
-data = {}
+    data = {}
-with open(datafile, "r") as f:
+    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
@@ -38,12 +50,9 @@ with open(datafile, "r") as f:
                 except:
                     return np.nan
             data[location].append([datetime.date(int(year), int(month), int(day)), new_cases, new_deaths, total_cases, total_deaths])
-
+    # reorganize data
-
+    data2 = {}
-data2 = {}
+    for loc in data:
-
-# reorganize data
-for loc in data:
         time = []
         new_cases, new_deaths, total_cases, total_deaths = [], [], [], []
         for entry in data[loc]:
@@ -54,29 +63,13 @@ for loc in data:
             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
 
-# countries of interest
+data = get_data()
-countries = ["Germany", "Italy", "Spain", "Denmark", "China", "Japan", "South Korea", "Iran", "Belgium", "World"]
 
-# plotting
+for plot in plots:
-for loc in data2:
+    #try:
-    if loc not in countries:
+    i = importlib.import_module(plot)
-        continue
+    i.plot(data, countries)
-    time, new_cases, new_deaths, total_cases, total_deaths = data2[loc]
+    #except:
-    
+    #    print(f"plotting {plot} failed")
-    # 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")

death_per_case.py

@@ -0,0 +1,23 @@
+"""
+Plot total deaths per total cases of countries over time on log scale
+"""
+import matplotlib.pyplot as pp
+import numpy as np
+name="death_per_case"
+
+def plot(data, countries):
+    for loc in data:
+        if loc not in countries:
+            continue
+        time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
+        
+        # death/case
+        pp.figure(name)
+        pp.plot(time, np.array(total_deaths)/np.array(total_cases), label=f"{loc}")
+
+    pp.yscale("log")
+    pp.xticks(rotation=90)
+    pp.legend(frameon=False)
+    pp.tight_layout()
+
+    pp.savefig(name+".png")

normalized_to_first_death.py

@@ -0,0 +1,24 @@
+"""
+Plot total cases timeshiftet to first death
+"""
+import matplotlib.pyplot as pp
+import numpy as np
+name="normalized_to_first_death"
+
+def plot(data, countries):
+    for loc in data:
+        if loc not in countries:
+            continue
+        time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
+        
+        pp.figure(name)
+        day_of_first_death = np.argwhere(np.array(total_deaths) > 0)[0][0]
+        new_time_axis = np.arange(len(time)) - day_of_first_death
+        pp.plot(new_time_axis, np.array(total_cases), label=f"{loc}")
+
+    pp.yscale("log")
+    pp.xticks(rotation=90)
+    pp.legend(frameon=False)
+    pp.tight_layout()
+
+    pp.savefig(name+".png")

total_cases.py

@@ -0,0 +1,23 @@
+"""
+Plot total cases of countries over time on log scale
+"""
+import matplotlib.pyplot as pp
+name="total_cases"
+
+def plot(data, countries):
+
+    for loc in data:
+        if loc not in countries:
+            continue
+        time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
+        
+        # total cases
+        pp.figure(name)
+        pp.plot(time, total_cases, label=f"{loc}")
+
+    pp.yscale("log")
+    pp.xticks(rotation=90)
+    pp.legend(frameon=False)
+    pp.tight_layout()
+
+    pp.savefig(name+".png")