new plot: doubling time

coronavis.py

@@ -14,7 +14,7 @@ sys.path.append(".")
 # countries of interest
 countries = ["Germany", "Italy", "Spain", "China", "Japan", "South Korea", "Iran", "United States", "World"]
 # enabled plots
-plots = ["basics", "death_per_case", "normalized_to_first_death", "delay_from_china", "delay_from_usa", "normalized_to_ten_cases", "percent_increase"]
+plots = ["basics", "death_per_case", "normalized_to_first_death", "delay_from_china", "delay_from_usa", "normalized_to_ten_cases", "percent_increase", "doubling_time"]
 ###
 def get_data():
     """fetch data from remote, cache locally and reorganize internal data

doubling_time.py

@@ -0,0 +1,34 @@
+"""
+Plot doubling time
+"""
+import matplotlib.pyplot as pp
+import numpy as np
+name="doubling_time"
+
+def moving_average(x, w):
+    return np.convolve(x, np.ones(w), 'valid') / w
+
+def plot(data, countries):
+    figsize = (10,5)
+    for loc in data:
+        if loc not in countries:
+            continue
+        time, new_cases, new_deaths, total_cases, total_deaths = data[loc]
+        
+        pp.figure(name, figsize=figsize)
+        window_size = 3
+        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]
+        new_time_axis = time[int(window_size/2):-int(window_size/2)]
+        pp.plot(new_time_axis[50:], thalf[50:], label=f"{loc}", marker=".")
+
+    pp.yscale("log")
+    pp.ylabel("doubling time [days]")
+    #pp.xlabel("daye reported ten cases")
+    #pp.xticks(rotation=45)
+    pp.legend(frameon=False)
+    pp.grid(which="both", axis="y")
+    pp.tight_layout()
+
+    pp.savefig(name+".png")