Merge pull request #13 from neuroinfo-os/add_rki_data

Add rki data to map_csv

src/add_rki_to_map.py

+import os
+import pickle as pkl
+import pandas as pd
+
+data = pd.read_csv('../data/diseases/covid19.csv', sep=',', encoding='iso-8859-1', index_col=0)
+data.index = [pd.Timestamp(date) for date in data.index]
+
+with open('../data/counties/counties.pkl', 'rb') as f:
+    counties = pkl.load(f)
+
+shared_assets = '/p/project/covid19dynstat/shared_assets/'
+figures = os.path.join(shared_assets, 'figures/')
+dates = next(os.walk(figures))[1]
+
+# errors if the RKI based csv is out of date with the available folders
+for date in dates:
+    map_csv_path = os.path.join(figures, date, 'map.csv')   
+    
+    map_rki = data.loc[date.replace('_', '-')].values.astype('float64')
+    for (i, (key, _)) in enumerate(counties.items()):
+        n_people = counties[key]['demographics'][('total', 2018)]
+        map_rki[i] = map_rki[i] / n_people * 100000
+    
+    map_data = pd.read_csv(map_csv_path, index_col=0)
+    map_data["newInf100k_RKI"] = list(map_rki)
+
+    map_data.to_csv(map_csv_path)

src/plot_window_germany.py

@@ -121,17 +121,20 @@ def curves(start, n_weeks=3, model_i=35,save_plot=False):
      
     # relativize prediction mean.
     map_vals = prediction_mean.iloc[-10]
+    map_rki = data.iloc[-1].values.astype('float64')
     map_keys = []
-    ik= 0
-    for key, _ in counties.items():
+    # ik= 0
+    for (ik, (key, _)) in enumerate(counties.items()):
         n_people = counties[key]['demographics'][('total',2018)]
         map_vals[ik] = (map_vals[ik] / n_people) * 100000
-        ik = ik+1
+        map_rki[ik] = (map_rki[ik] / n_people) * 100000
+        # ik = ik+1
         map_keys.append(key)
 
     map_df = pd.DataFrame(index=None)
     map_df["countyID"] = map_keys
     map_df["newInf100k"] = list(map_vals)
+    map_df["newInf100k_RKI"] = list(map_rki)
     
     # plot the chloropleth map
     plot_counties(map_ax,