plot cumulative indicators + plot data by age and vaccine status (multiprocessed)

drees.py

@@ -2,16 +2,24 @@ import argparse
 import json
 import logging
 import os
+from datetime import datetime as dt
 from enum import Enum
-from typing import Any, Dict, List, Optional, OrderedDict
+from functools import partial
+from multiprocessing import Pool
+from typing import Any, Dict, List, Optional, OrderedDict, Tuple
 
+import matplotlib
+import numpy as np
 import requests
 from matplotlib import dates as md
 from matplotlib import pyplot as plt
+from numba import njit
 
 FORMAT = "%(asctime)s - %(levelname)s - %(message)s"
 logging.basicConfig(format=FORMAT, level=logging.INFO)
 
+DATE_FORMAT = "%Y-%m-%d"
+
 DATA_URL = "https://data.drees.solidarites-sante.gouv.fr/api/records/1.0/search/?dataset=covid-19-resultats-par-age-issus-des-appariements-entre-si-vic-si-dep-et-vac-si&q=&rows=-1&facet=date&facet=vac_statut&facet=age"
 DATA_REPOSITORY = "data"
 OUTPUT_REPOSITORY = "output"
@@ -21,7 +29,6 @@ class Field(str, Enum):
     HC = "hc"
     SC = "sc"
     DC = "dc"
-    EFF = "effectif"
 
 
 class VacStatus(str, Enum):
@@ -36,7 +43,7 @@ class VacStatus(str, Enum):
 
 class AgeGroup(str, Enum):
     VERY_YOUNG = "[0,19]"
-    YONG = "[20,39]"
+    YOUNG = "[20,39]"
     MID_OLD = "[40,59]"
     OLD = "[60,79]"
     VERY_OLD = "[80;+]"
@@ -69,7 +76,7 @@ def get_data(
     return json.load(open(file_path, "rb"))
 
 
-def group_by_age_date(data: Dict[str, Any], fields: List[str]) -> Dict[str, Any]:
+def group_by_age_date(data: Dict[str, Any], fields: List[str]) -> Dict[dt, Any]:
     """
     Group the original dictionnary into a more readable one
     'date': {
@@ -83,10 +90,10 @@ def group_by_age_date(data: Dict[str, Any], fields: List[str]) -> Dict[str, Any]
         }
     }
     """
-    dic_data_grouped: Dict[str, Any] = OrderedDict()
+    dic_data_grouped: Dict[dt, Any] = OrderedDict()
     for row in data["records"]:
         row_fields = row["fields"]
-        date = row_fields["date"]
+        date = dt.strptime(row_fields["date"], DATE_FORMAT)
         age = row_fields["age"]
         vac_status = row_fields["vac_statut"]
         if date not in dic_data_grouped:
@@ -100,37 +107,136 @@ def group_by_age_date(data: Dict[str, Any], fields: List[str]) -> Dict[str, Any]
     return dic_data_grouped
 
 
-def plot(dic_data_grouped: Dict[str, Any], age: str, vac_status: str) -> None:
+@njit
+def cumulate_array(array: np.ndarray) -> np.ndarray:
+    cumulate = list()
+    sum: float = 0
+    for item in array:
+        sum += item
+        cumulate.append(sum)
+    return np.array(cumulate)
+
+
+def get_plot_fig(
+    grid: Optional[bool] = True, date_format: Optional[str] = DATE_FORMAT
+) -> plt.figure:
     """
-    Plot data by vaccine status and age
+    return pyplot fig, ax to plot data over range period with date formatting
     """
-    x: List[str] = list()
-    hc: List[float] = list()
-    sc: List[float] = list()
-    dc: List[float] = list()
     fig, ax = plt.subplots()
+    ax.grid(grid)
+    date_formatter = md.DateFormatter(date_format)
+    ax.xaxis.set_major_locator(md.AutoDateLocator())
+    ax.xaxis.set_major_formatter(date_formatter)
+    fig.autofmt_xdate()
+    return fig
+
+
+def save_and_close_fig(
+    fig: plt.figure, output_path: str, has_legend: Optional[bool] = True
+):
+    if has_legend:
+        plt.legend()
+    plt.savefig(output_path)
+    plt.close(fig)
+
+
+def get_cumulative_field_by_age(
+    dic_data_grouped: Dict[dt, Any], age: str, field: Field
+) -> Tuple[np.ndarray, List[dt]]:
+    """
+    cumulate field values over data period
+    """
+    dcs: List[int] = list()
+    dates: List[dt] = list()
     for date, dic_age_grouped in dic_data_grouped.items():
-        print(Field.HC.value)
-        hc.append(dic_age_grouped[age][vac_status][Field.HC.value])
-        sc.append(dic_age_grouped[age][vac_status][Field.SC.value])
-        dc.append(dic_age_grouped[age][vac_status][Field.DC.value])
-        x.append(date)
-    plt.plot(x, hc, label="hospitalisation")
-    plt.plot(x, sc, label="soin_critique")
-    plt.plot(x, dc, label="deces")
+        if (dic_age := dic_age_grouped.get(age)) is None:
+            logging.error(f"{age} not found in grouped ages")
+            continue
+        for dic_vac_status in dic_age.values():
+            if (field_value := dic_vac_status[field.value]) is not None:
+                dcs.append(field_value)
+                dates.append(date)
+    np_dcs = np.array(dcs)
+    np_cumulate = cumulate_array(np_dcs)
+    return np_cumulate, dates
+
+
+def get_values_by_age_vac_field(
+    dic_data_grouped: Dict[dt, Any], age: AgeGroup, vac_status: VacStatus, field: Field
+) -> Tuple[List[dt], List[float]]:
+    """
+    get deep field data by age, vaccine status and field
+    """
+    dates: List[dt] = list()
+    fields: List[float] = list()
+    for date, dic_age_grouped in dic_data_grouped.items():
+        if (dic_vac_status := dic_age_grouped.get(age.value)) is not None:
+            if (dic_field := dic_vac_status.get(vac_status.value)) is not None:
+                if (field_value := dic_field.get(field.value)) is not None:
+                    fields.append(field_value)
+                    dates.append(date)
+    return dates, fields
+
+
+def plot_cumulative_field(dic_data_grouped: Dict[dt, Any], field: Field) -> None:
+    fig = get_plot_fig()
+
+    for age_group in AgeGroup:
+        deaths, dates = get_cumulative_field_by_age(
+            dic_data_grouped, age_group.value, field
+        )
+        plt.plot(dates, deaths, label=age_group.value)
+
+    plt.title(
+        f"nombre de {field.value} cumulé par age (status vaccinal non pris en compte)"
+    )
+    plt.xlabel("date")
+    save_and_close_fig(
+        fig, os.path.join(OUTPUT_REPOSITORY, f"cumulative_{field.value}.pdf")
+    )
+
+
+def plot_data(
+    dic_data_grouped: Dict[dt, Any], age: AgeGroup, vac_status: VacStatus, field: Field
+) -> None:
+    """
+    Plot data by vaccine status, age and field
+    """
+    fig = get_plot_fig()
+
+    dates, fields = get_values_by_age_vac_field(
+        dic_data_grouped, age, vac_status, field
+    )
+
+    plt.plot(dates, fields, label=f"{field.value}")
     plt.xlabel("date")
     plt.ylabel("nombre")
     plt.title(f"{age}ans - {vac_status}")
-    ax.grid(True)
-    ax.xaxis.set_major_locator(md.MonthLocator())
-    fig.autofmt_xdate()
-    plt.legend()
-    plt.savefig(os.path.join(OUTPUT_REPOSITORY, f"{age}_{vac_status}.pdf"))
+
+    save_and_close_fig(
+        fig, os.path.join(OUTPUT_REPOSITORY, f"{age}_{vac_status}_{field}.pdf")
+    )
+
+
+def build_data_pool_args() -> List[Tuple[AgeGroup, VacStatus, Field]]:
+    """
+    build tuple arguments to plot all data on multiprocess
+    """
+    pool_args: List[Tuple[AgeGroup, VacStatus, Field]] = list()
+    for age_group in AgeGroup:
+        for vac_status in VacStatus:
+            for field in Field:
+                pool_args.append((age_group, vac_status, field))
+    return pool_args
 
 
 if __name__ == "__main__":
     """
-    This script aims to plot DRESS data with vaccine status and ages grouped
+    This script aims to plot DRESS data
+    Plots availables :
+        - cumulative deaths by age
+        - indicators by vaccine status and age
     Main indicators are :
         - hospitalisations
         - criticals
@@ -153,8 +259,14 @@ if __name__ == "__main__":
     dic_data: Dict[str, Any] = get_data(
         file_path=os.path.join(DATA_REPOSITORY, "dress.json"), refresh=args.refresh
     )
-    dic_data_grouped: Dict[str, Any] = group_by_age_date(
+    dic_data_grouped: Dict[dt, Any] = group_by_age_date(
         dic_data, [x.value for x in Field]
     )
-    plot(dic_data_grouped, AgeGroup.YONG, VacStatus.NC.value)
-    plot(dic_data_grouped, AgeGroup.YONG, VacStatus.CM3MSR.value)
+
+    plot_data_pool_args = build_data_pool_args()
+    f = partial(plot_data, dic_data_grouped)
+    with Pool() as pool:
+        pool.starmap(f, plot_data_pool_args)
+
+    for field in Field:
+        plot_cumulative_field(dic_data_grouped, field)