ordering dic_data by date (keys) + set to 0 'effectif' lower thant 0 + dotted line for graphs by fields and ages + black (#1)

drees.py

@@ -69,6 +69,7 @@ class VacStatus(DreesEnum):
     """
     WARN: Be careful, after refreshing dataset, some VacStatus can changed
     """
+
     NC = (0, "Non-vaccinés")
     PDR = (1, "Primo dose récente")
     PDE = (2, "Primo dose efficace")
@@ -180,6 +181,8 @@ def structure_data(data: Dict[str, Any]) -> Dict[dt, Any]:
         for quota in Quota:
             quota_name = quota.name.lower()
             dic_data[date][age][vac_status][quota_name] = row_fields[quota_name]
+    # order `dic_data` date keys in ascending order
+    dic_data = OrderedDict(sorted(dic_data.items(), key=lambda t: t[0]))
     logging.info("data restructured")
     return dic_data
 
@@ -203,10 +206,12 @@ def get_np_data(dic_data: Dict[dt, Any]) -> Tuple[np.ndarray, np.ndarray]:
                     idx_field = get_enum_field(field)
                     np_data[idx_date, idx_age, idx_vac, idx_field] = value
     logging.info("date and data generated")
-    np_date = np.array(sorted(np_date))
     date_start = np_date[0]
     date_end = np_date[len(np_date) - 1]
     logging.info(f"range period : {date_start} - {date_end}")
+    # set 'effectif' equals to 0 if effectif < 1 (0.04 means nothing...)
+    quota_mask = np_data[:, :, :, 3] < 1
+    np_data[quota_mask] = 0
     return np_data, np_date
 
 
@@ -470,12 +475,13 @@ def plot_fields_by_age_vac(
     fig, _ = get_plot_fig()
 
     for field in Field:
-        plt.plot(
-            np_date,
+        np_result = (
             10e6
             * np_data[:, age_group.value, vac_status.value, field.value]
-            / np_data[:, age_group.value, vac_status.value, Quota.EFFECTIF.value],
-            label=f"{field.label}",
+            / np_data[:, age_group.value, vac_status.value, Quota.EFFECTIF.value]
+        )
+        plt.plot(
+            np_date, np_result, label=f"{field.label}", linestyle="dotted", linewidth=2
         )
         plt.xlabel("Date")
         plt.ylabel("Cas par million de personnes")