add new plot: normalized to 100 cases (when in most countries the exponential spread lifted off)

normalized_to_ten_cases.py

@@ -0,0 +1,33 @@
+"""
+Plot total cases timeshiftet to first death
+"""
+import matplotlib.pyplot as pp
+import numpy as np
+name="normalized_to_100_cases"
+
+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)
+        day_of_100_cases = np.argwhere(np.array(total_cases) > 99)[0][0]
+        new_time_axis = np.arange(len(time)) - day_of_100_cases
+        pp.plot(new_time_axis, np.array(total_cases), label=f"{loc}", marker=".")
+
+    pp.yscale("log")
+    pp.xticks(rotation=45)
+    pp.legend(frameon=False)
+    axis = pp.axis()
+    pp.axis([-2, axis[1], 80, axis[3]])
+
+    pos = [(10, 5e5), (30, 5e5), (50, 5e5), (70, 1e3)]
+    for i, n in enumerate([1,2,3,7]):
+        pp.plot([0, axis[1]], [100, np.exp(np.log(2) / n * axis[1])], color="grey", linestyle="--")
+        pp.text(pos[i][0], pos[i][1], f"doubling every\n{n} days", fontsize=8)
+
+    pp.tight_layout()
+
+    pp.savefig(name+".png")