From c8b33c376f7cb5fccb1efea317b440e7656fd81e Mon Sep 17 00:00:00 2001
From: fordprefect <fordprefect@dukun.de>
Date: Fri, 27 Mar 2020 16:46:34 +0100
Subject: [PATCH] add delay from USA plot (leader in infection number as of
 today)

---
 delay_from_usa.py | 80 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 delay_from_usa.py

diff --git a/delay_from_usa.py b/delay_from_usa.py
new file mode 100644
index 0000000..1119e21
--- /dev/null
+++ b/delay_from_usa.py
@@ -0,0 +1,80 @@
+"""
+Plot delay of countries
+"""
+import matplotlib.pyplot as pp
+import numpy as np
+name="delay"
+
+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_above_hundred_cases = np.argwhere(np.array(total_cases) > 100)[0][0]
+        
+        new_time_axis = np.arange(len(time)) - day_of_above_hundred_cases
+        pp.plot(new_time_axis, np.array(total_cases), label=f"{loc} - {day_of_above_hundred_cases}", marker=".")
+
+    pp.yscale("log")
+    pp.xticks(rotation=45)
+    pp.legend(frameon=False)
+    pp.tight_layout()
+
+    pp.savefig(name+".png")
+
+    # plot delay
+    pp.clf()
+    delay = {}
+    tcases = [50, 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000] + list(np.arange(10000, np.max(data["United States"][3]), 2500))
+
+    time_us = data["United States"][0]
+    try:    
+        case_date_us = np.array([time_us[np.argwhere(np.array(data["United States"][3]) > cases)[0][0]] for cases in tcases])
+    except:
+        print("workaround delay_us:40")
+        case_date_us = []
+        usdat = np.array(data["United States"][3])
+        for cases in tcases:
+            try:
+                casepos = np.argwhere(usdat > cases)[0][0]
+                casetime = time_us[casepos]
+            except:
+                print(f"case date for {cases} not found")
+                casetime = np.nan
+                #continue
+            case_date_us.append(casetime)
+        case_date_us = np.array(case_date_us)
+            
+    total_cases_us = data["United States"][3]
+    for loc in data:
+        if loc not in countries:
+            continue
+        if loc in ["United States", "World"]:
+            continue
+        this_delay = []
+        for i, cases in enumerate(tcases):
+            try:
+                case_date = data[loc][0][np.argwhere(np.array(data[loc][3]) > cases)[0][0]]
+            except:
+                this_delay.append(np.nan)
+                continue
+            #case_date = np.array([time_us[np.argwhere(np.array(data[loc][3]) > cases)[0][0]] for cases in tcases])
+        
+            this_delay.append((case_date_us[i] - case_date).days)
+
+        delay[loc] = this_delay
+
+    sort = np.argsort(delay)
+    
+    for loc in delay:
+        pp.plot(tcases, delay[loc], label=loc, marker=".")
+    #pp.plot(np.arange(len(delay)), np.array(delay)[sort] - np.min(delay), marker="o", linestyle="")
+    #pp.xticks(ticks=np.arange(len(delay)), labels=np.array(d_loc)[sort])
+    pp.xticks(rotation=45)
+    pp.legend(frameon=False)
+    pp.xlabel("total cases")
+    pp.ylabel("delay to United States [days]")
+    pp.tight_layout()
+    pp.savefig(name+"_usa.png")