CensorAndScale for continious

app.py

@@ -159,13 +159,13 @@ def analyze_burn(body: AnaylzeBurnPOSTBody, sftp_client: SFTPClient = Depends(ge
         logger.log_text(f"Error: {e}")
         return f"Error: {e}", 400
 
-@app.get("/map/{fire_event_name}/{burn_metric}", response_class=HTMLResponse)
-def serve_map(request: Request, fire_event_name: str, burn_metric: str, manifest: dict = Depends(get_manifest)):
+@app.get("/map/{affiliation}/{fire_event_name}/{burn_metric}", response_class=HTMLResponse)
+def serve_map(request: Request, fire_event_name: str, burn_metric: str, affiliation: str, manifest: dict = Depends(get_manifest)):
     mapbox_token = get_mapbox_secret()
 
     tileserver_endpoint = 'https://tf-rest-burn-severity-ohi6r6qs2a-uc.a.run.app'
     # tileserver_endpoint = 'http://localhost:5050'
-    cog_url = f"https://burn-severity-backend.s3.us-east-2.amazonaws.com/public/{fire_event_name}/{burn_metric}.tif"
+    cog_url = f"https://burn-severity-backend.s3.us-east-2.amazonaws.com/public/{affiliation}/{fire_event_name}/{burn_metric}.tif"
     cog_tileserver_url_prefix = tileserver_endpoint + f"/cog/tiles/WebMercatorQuad/{{z}}/{{x}}/{{y}}.png?url={cog_url}&nodata=-99&return_mask=true"
 
     fire_metadata = manifest[fire_event_name]

src/lib/burn_severity.py

@@ -104,25 +104,3 @@ def classify_burn(array, thresholds):
         reclass = xr.where((array < threshold) & (reclass.isnull()), value, reclass)
 
     return reclass
-
-def is_s3_url_valid(url):
-    """
-    This function checks if an S3 URL is valid
-    """
-    s3 = boto3.client('s3')
-    s3.meta.events.register('choose-signer.s3.*', disable_signing)
-
-    bucket_name = url.split('/')[2]
-    key = '/'.join(url.split('/')[3:])
-    try:
-        response = s3.list_objects_v2(Bucket=bucket_name, Prefix=key)
-        for obj in response.get('Contents', []):
-            if obj['Key'] == key:
-                return True
-        return False
-    except NoCredentialsError:
-        print("No AWS credentials found")
-        return False
-    except Exception as e:
-        print(f"Invalid S3 URL: {url}. Exception: {str(e)}")
-        return False

src/lib/titiler_algorithms.py

@@ -3,6 +3,22 @@
 from rio_tiler.models import ImageData
 import numpy as np
 
+def convert_to_rgb(classified: np.ndarray, mask: np.ndarray) -> np.ndarray:
+    # Convert to a red rgb image, from grayscale
+    r_channel = np.full_like(classified, 255)
+    classified_rgb = np.stack(
+        [r_channel, classified, classified],
+        axis=0
+    )
+    rgb_mask = np.stack(
+        [mask, mask, mask],
+        axis=0
+    ).squeeze()
+
+    final_img = np.ma.MaskedArray(classified_rgb, mask=rgb_mask)
+    return final_img
+
+
 class Classify(BaseAlgorithm):
 
     # Parameters
@@ -18,17 +34,7 @@ def __call__(self, img: ImageData) -> ImageData:
 
         classified = np.select(threshold_checks, png_int_values).astype(np.uint8)
 
-        # Convert to a red rgb image, from grayscale
-        r_channel = np.full_like(classified, 255)
-        classified_rgb = np.stack(
-            [r_channel, classified, classified],
-            axis=0
-        )
-        rgb_mask = np.stack(
-            [mask, mask, mask],
-            axis=0
-        ).squeeze()
-        final_img = np.ma.MaskedArray(classified_rgb, mask=rgb_mask)
+        final_img = convert_to_rgb(classified, mask)
 
         # Create output ImageData
         return ImageData(
@@ -38,7 +44,35 @@ def __call__(self, img: ImageData) -> ImageData:
             bounds=img.bounds,
         )
 
+class CensorAndScale(BaseAlgorithm):
+    # Parameters
+    min: float
+    max: float
+
+    def __call__(self, img: ImageData) -> ImageData:
+        # Create masks for values below min and above max
+        mask_below = img.data < self.min
+        mask_above = img.data > self.max
+
+        # Create a mask for NaN values or values equal to -99
+        mask_transparent = (np.isnan(img.data)) | (img.data == -99)
+
+        # Set values below min to 0 and above max to 255
+        img.data[mask_below] = 0
+        img.data[mask_above] = 255
 
+        # Scale values between min and max to 0-255
+        mask_middle = ~mask_below & ~mask_above
+        img.data[mask_middle] = ((img.data[mask_middle] - self.min) / (self.max - self.min)) * 255
+
+        final_img = convert_to_rgb(img.data, mask_transparent)
+
+        return ImageData(
+            final_img,
+            assets=img.assets,
+            crs=img.crs,
+            bounds=img.bounds,
+        )
 
 algorithms = default_algorithms.register(
     {

src/util/sftp.py

@@ -8,7 +8,7 @@
 import datetime
 import rasterio
 from rasterio.enums import Resampling
-
+import geopandas as gpd
 from google.cloud import logging as cloud_logging
 
 class SFTPClient:
@@ -148,6 +148,17 @@ def upload_cogs(self, metrics_stack, fire_event_name, prefire_date_range, postfi
                     source_local_path=local_cog_path,
                     remote_path=f"{affiliation}/{fire_event_name}/{band_name}.tif",
                 )
+
+            # Upload the difference between dNBR and RBR
+            local_cog_path = os.path.join(tmpdir, f"pct_change_dnbr_rbr.tif")
+            pct_change = (metrics_stack.sel(burn_metric="rbr") - metrics_stack.sel(burn_metric="dnbr")) / \
+                metrics_stack.sel(burn_metric="dnbr") * 100
+            pct_change.rio.to_raster(local_cog_path, driver="GTiff")
+            self.upload(
+                source_local_path=local_cog_path,
+                remote_path=f"{affiliation}/{fire_event_name}/pct_change_dnbr_rbr.tif",
+            )
+
 
     def update_manifest(self, fire_event_name, bounds, prefire_date_range, postfire_date_range, affiliation):
         with tempfile.TemporaryDirectory() as tmpdir: