Fix/risk score

common/management/commands/update_country_data.py

@@ -17,16 +17,15 @@ def handle(self, *args, **options):
                 independent = data["independent"]
                 is_deprecated = data["is_deprecated"]
                 record_type = data["record_type"]
-                print(record_type)
                 iso3 = data["iso3"]
                 if iso3:
                     country = Country.objects.filter(iso3=iso3.lower())
                     if country.exists():
-                        country = country.last()
-                        country.independent = independent
-                        country.is_deprecated = is_deprecated
-                        country.record_type = record_type
-                        country.save(update_fields=["independent", "is_deprecated", "record_type"])
+                        for country_item in country:
+                            country_item.independent = independent
+                            country_item.is_deprecated = is_deprecated
+                            country_item.record_type = record_type
+                            country_item.save(update_fields=["independent", "is_deprecated", "record_type"])
             cluster_country_name = [
                 "West Africa Country Cluster",
                 "Tunis Country Cluster",

imminent/management/commands/import_gwis_data.py

@@ -17,7 +17,7 @@ def handle(self, *args, **kwargs):
         # Get all the countries with iso3 codes that are not deprecated and independent
         country_iso3 = list(Country.objects.filter(is_deprecated=False, independent=True).values_list("iso3", flat=True))
 
-        for year in range(2024, 2025):
+        for year in range(2003, 2026):
             for iso3 in country_iso3:
                 if iso3:
                     self.import_monthly_data(iso3, year)
@@ -41,7 +41,7 @@ def import_monthly_data(self, iso3, year):
             self.create_gwis_entry(iso3, month, year, GWIS.DSRTYPE.MONTHLY)
 
     def import_cumulative_data(self, iso3, year):
-        url = f"https://api2.effis.emergency.copernicus.eu/statistics/v2/dsr/cumulative?country={iso3.upper()}&year={year}"
+        url = f"https://api2.effis.emergency.copernicus.eu/statistics/v2/dsr/monthly?country={iso3.upper()}&year={year}"
         response = requests.get(url, verify=False)
 
         if response.status_code != 200:
@@ -55,7 +55,7 @@ def import_cumulative_data(self, iso3, year):
         cumulative_response = response_data.get("dsrcumulative", [])
 
         for month in cumulative_response:
-            self.create_gwis_entry(iso3, month, year, GWIS.DSRTYPE.CUMULATIVE)
+            self.create_gwis_entry(iso3, month, year, GWIS.DSRTYPE.CUMMULATIVE)
 
     def create_gwis_entry(self, iso3, data, year, dsr_type):
         country = Country.objects.filter(iso3=iso3).first()

seasonal/management/commands/aggregate_gwis_data.py

@@ -42,7 +42,7 @@ def handle(self, *args, **options):
         new_df = new_df.replace(np.nan, None)
         country_groups = new_df.groupby("country")
         for country_name, group in country_groups:
-            gwis_data = {"country": Country.objects.filter(name__icontains=country_name).first()}
+            gwis_data = {"country": Country.objects.filter(name=country_name).first()}
             for month in month_mapping.values():
                 gwis_data[month] = (
                     group.loc[group["month"] == month, "dsr_avg"].values[0] if month in group["month"].values else None

seasonal/management/commands/create_hazard_information.py

@@ -48,7 +48,7 @@ def handle(self, **options):
                     map_hazard_type = HazadTypeList[1]
                 elif hazard_type == "DR":
                     map_hazard_type = HazadTypeList[2]
-                if map_hazard_type:
+                if not map_hazard_type:
                     continue
 
                 url = f"https://thinkhazard.org/en/report/{country_id}/{hazard_type}.json"

seasonal/management/commands/create_risk_score.py

@@ -54,7 +54,7 @@ def handle(self, **options):
         df1 = inform_score_dataframe
         df1["ISO3"] = df1["ISO3"].str.upper()
         # latest inform score data
-        df2 = pd.read_excel(file1, sheet_name="INFORM Risk 2024 (a-z)", skiprows=(0, 2), usecols="A, B, S, AE", engine="openpyxl")
+        df2 = pd.read_excel(file1, sheet_name="INFORM Risk 2025 (a-z)", skiprows=(0, 2), usecols="A, B, S, AE", engine="openpyxl")
         df2.rename(
             {"VULNERABILITY": "Vulnerability", "LACK OF COPING CAPACITY": "LCC"},
             axis=1,
@@ -118,7 +118,7 @@ def handle(self, **options):
             )
             risk_score_data = {
                 "country": Country.objects.filter(
-                    iso3__icontains=row["ISO3"],
+                    name=row["name"],
                     iso3__isnull=False,
                     record_type__isnull=False,
                     record_type=Country.CountryType.COUNTRY,
@@ -141,4 +141,5 @@ def handle(self, **options):
                 "population_in_thousands": row["Population_in_thousands"],
                 "vulnerability": row["Vulnerability"],
             }
-            RiskScore.objects.create(**risk_score_data)
+            if risk_score_data["country"]:
+                RiskScore.objects.create(**risk_score_data)

seasonal/scripts/fetch_inform_data.py

@@ -15,7 +15,7 @@ def get_maximum_rows(*, sheet_object):
 def fetch_inform_data(file):
     # NOTE: set `data_only=True` to read cell value instead of formula
     workbook = openpyxl.load_workbook(file, data_only=True)
-    worksheet = workbook.get_sheet_by_name("INFORM Risk 2022 (a-z)")
+    worksheet = workbook.get_sheet_by_name("INFORM Risk 2025 (a-z)")
     max_rows = get_maximum_rows(sheet_object=worksheet)
     # Iterate the loop to read the cell values
     data_all = []

seasonal/scripts/fetch_inform_seasonal.py

@@ -26,7 +26,7 @@ def fetch_inform_seasonal(file):
     workbook = openpyxl.load_workbook(file)
     worksheet = workbook.get_sheet_by_name("Sheet1")
     max_rows = get_maximum_rows(sheet_object=worksheet)
-    hazard_list = [HazardType.CYCLONE, HazardType.DROUGHT, HazardType.FLOOD]
+    hazard_list = [HazardType.CYCLONE.value, HazardType.DROUGHT.value, HazardType.FLOOD.value]
     for i in range(2, max_rows + 1):
         iso3 = worksheet.cell(row=i, column=1).value
         hazard_type = parse_hazard_type(worksheet.cell(row=i, column=2).value)