add date range filter to integrated_alerts titiler algorithm

app/routes/titiler/algorithms.py

@@ -1,5 +1,8 @@
 # from datetime import date
+from datetime import date
+
 import numpy as np
+from dateutil.relativedelta import relativedelta
 from pydantic import Field
 from rio_tiler.models import ImageData
 from titiler.core.algorithm import BaseAlgorithm
@@ -11,11 +14,21 @@ class IntegratedAlerts(BaseAlgorithm):
     title: str = "Integrated Deforestation Alerts"
     description: str = "Decode and vizualize alerts"
 
-    # START_DATE: np.datetime64 = np.datetime64("2014-12-31").astype(int)
+    START_DATE: str = "2014-12-31"  # start of record
 
     # Parameters
-    start_date: int = Field(None, description="start date of alert")
-    end_date: int = Field(None, description="end date of alert")
+    default_start_date: str = (date.today() - relativedelta(days=90)).strftime(
+        "%Y-%m-%d"
+    )
+    start_date: str = Field(
+        default_start_date,
+        description="start date of alert in YYYY-MM-DD format.",
+    )
+
+    default_end_date: str = date.today().strftime("%Y-%m-%d")
+    end_date: str = Field(
+        default_end_date, description="end date of alert in YYYY-MM-DD format."
+    )
 
     # metadata
     input_nbands: int = 2
@@ -26,24 +39,31 @@ def __call__(self, img: ImageData) -> ImageData:
         """Encode Integrated alerts to RGBA."""
         mask = img.array.mask[0].astype(int)
         data = img.data[0]
+        alert_date = data % 10000  # in days since 2014-12-31
 
-        # Will add this in next iteration
-        # if self.start_date:
-        #     start_mask = data % 10000 >= (
-        #         np.datetime64(self.start_date).astype(int) - self.START_DATE
-        #     )
-        #     mask = mask * start_mask
-        # if self.end_date:
-        #     end_mask = data % 10000 <= (
-        #         np.datetime64(self.end_date).astype(int) - self.START_DATE
-        #     )
-        #     mask = mask * end_mask
+        intensity = np.where(mask == 0, img.data[1], 0)
+        if self.start_date:
+            start_mask = alert_date >= (
+                np.datetime64(self.start_date).astype(int)
+                - np.datetime64(self.START_DATE).astype(int)
+            )
+            intensity = np.where(start_mask.astype(int) == 1, img.data[1], 0)
+        if self.end_date:
+            end_mask = alert_date <= (
+                np.datetime64(self.end_date).astype(int)
+                - np.datetime64(self.START_DATE).astype(int)
+            )
+            intensity = np.where(start_mask.astype(int) == 1, img.data[1], 0)
 
         r = np.where(data > 0, 228, 0)
         g = np.where(data > 0, 102, 0)
         b = np.where(data > 0, 153, 0)
 
-        intensity = np.where(mask == 0, img.data[1], 0)
+        intensity = np.where(
+            ((mask == 0) & (start_mask.astype(int) == 1) & (end_mask.astype(int) == 1)),
+            img.data[1],
+            0,
+        )
         intensity = np.minimum(255, intensity * 50)
         data = np.stack([r, g, b, intensity]).astype(self.output_dtype)
         data = np.ma.MaskedArray(data, mask=False)