feat: only use last three years for median calculation

Author: Gigaszi

ohsome_quality_api/indicators/user_activity/indicator.py

@@ -69,13 +69,14 @@ def calculate(self):
             return
         else:
             self.result.description = ""
+        self.result.value = int(median(self.bin_total.users_abs[1:37]))
         label_description = self.templates.label_description[self.result.label]
         self.result.description += Template(
             self.templates.result_description
         ).substitute(
-            median_users=f"{int(median(self.bin_total.users_abs))}",
-            from_timestamp=self.bin_total.timestamps[-1].strftime("%b %Y"),
-            to_timestamp=self.bin_total.timestamps[0].strftime("%b %Y"),
+            median_users=self.result.value,
+            from_timestamp=self.bin_total.timestamps[37].strftime("%b %Y"),
+            to_timestamp=self.bin_total.timestamps[1].strftime("%b %Y"),
         )
         self.result.description += "\n" + label_description
 
@@ -96,22 +97,23 @@ def create_figure(self):
         values_for_mean = values[1:]
         for i in range(len(values_for_mean) + 1):
             if i == 0:
+                weighted_avg.append(None)
                 continue
-            start = max(0, i - window + 1)
+            start = max(1, i - window + 1)
             window_vals = values_for_mean[start : i + 1]
             window_weights = weights[-len(window_vals) :]
             avg = np.dot(window_vals, window_weights) / window_weights.sum()
             weighted_avg.append(avg)
 
         # regression trend line for the last 36 months
-        if len(values) >= 36:
-            x = np.arange(len(values))
-            x_last = x[:36]
-            y_last = np.array(values[:36])
+        if len(weighted_avg) >= 36:
+            x = np.arange(len(weighted_avg))
+            x_last = x[1:37]
+            y_last = np.array(weighted_avg[1:37])
 
             coeffs = np.polyfit(x_last, y_last, 1)
             trend_y = np.polyval(coeffs, x_last)
-            trend_timestamps = timestamps[:36]
+            trend_timestamps = timestamps[1:37]
         else:
             trend_timestamps = []
             trend_y = []

tests/approvals/integrationtests/indicators/test_user_activity.py-TestFigure-test_create_figure.approved.json

@@ -2,6 +2,10 @@
     "data": [
         {
             "customdata": [
+                [
+                    0,
+                    "Oct 2025"
+                ],
                 [
                     0,
                     "Sep 2025"
@@ -874,6 +878,7 @@
             "name": "Users per Month",
             "type": "bar",
             "x": [
+                "2025-10-01T00:00:00Z",
                 "2025-09-01T00:00:00Z",
                 "2025-08-01T00:00:00Z",
                 "2025-07-01T00:00:00Z",
@@ -1092,6 +1097,7 @@
                 "2007-10-01T00:00:00Z"
             ],
             "y": [
+                0,
                 0,
                 20,
                 21,
@@ -1320,6 +1326,7 @@
             "name": "12-Month Weighted Avg",
             "type": "scatter",
             "x": [
+                "2025-10-01T00:00:00Z",
                 "2025-09-01T00:00:00Z",
                 "2025-08-01T00:00:00Z",
                 "2025-07-01T00:00:00Z",
@@ -1538,6 +1545,8 @@
                 "2007-10-01T00:00:00Z"
             ],
             "y": [
+                null,
+                20.0,
                 20.52173913043478,
                 18.87878787878788,
                 22.333333333333332,
@@ -1804,7 +1813,7 @@
                 "2022-10-01T00:00:00Z"
             ],
             "y": {
-                "bdata": "EX7Mzj9hMUBCwwq6RrQxQHIISaVNBzJAo02HkFRaMkDTksV7W60yQATYA2diADNANB1CUmlTM0BlYoA9cKYzQJanvih3+TNAxuz8E35MNED3MTv/hJ80QCd3eeqL8jRAWLy31ZJFNUCIAfbAmZg1QLlGNKyg6zVA6otyl6c+NkAa0bCCrpE2QEoW72215DZAe1stWbw3N0CsoGtEw4o3QNzlqS/K3TdADSvoGtEwOEA+cCYG2IM4QG61ZPHe1jhAnvqi3OUpOUDPP+HH7Hw5QACFH7PzzzlAMMpdnvoiOkBhD5yJAXY6QJJU2nQIyTpAwpkYYA8cO0Dy3lZLFm87QCMklTYdwjtAVGnTISQVPECErhENK2g8QLTzT/gxuzxA",
+                "bdata": "9I414T0CM0AMyyZ6aUAzQCQHGBOVfjNAPEMJrMC8M0BUf/pE7PozQGy7690XOTRAhPfcdkN3NECcM84Pb7U0QLRvv6ia8zRAzKuwQcYxNUDk56Ha8W81QPwjk3MdrjVAFGCEDEnsNUAsnHWldCo2QETYZj6gaDZAXBRY18umNkB0UElw9+Q2QIyMOgkjIzdApMgrok5hN0C8BB07ep83QNVADtSl3TdA7Xz/bNEbOEAFufAF/Vk4QB314Z4omDhANTHTN1TWOEBNbcTQfxQ5QGWptWmrUjlAfeWmAteQOUCVIZibAs85QK1diTQuDTpAxZl6zVlLOkDd1WtmhYk6QPURXf+wxzpADU5OmNwFO0Alij8xCEQ7QD3GMMozgjtA",
                 "dtype": "f8"
             }
         }