Merge pull request #70 from fractal-analytics-platform/misc_fixes

Misc of minor fixes

Author: lorenzocerrone

pixi.lock

@@ -1,7 +1,7 @@
 [project]
 authors = [{ name = "lorenzo", email = "[email protected]" }]
 dependencies = [
-    "streamlit>=1.50.0, <1.51.0",
+    "streamlit>=1.51.0, <1.52.0",
     "ngio==0.4.2",
     "plotly",
     "matplotlib",

pyproject.toml

@@ -70,6 +70,9 @@ def heat_map_component(
         df_piv = df_piv.median(numeric_only=True)
     elif aggregation == "Counts":
         df_piv = df_piv.count()
+    elif aggregation is None:
+        st.error("Please select one aggregation")
+        st.stop()
     else:
         raise ValueError(f"Unknown aggregation: {aggregation}")
 

src/fractal_feature_explorer/pages/explore_page/_heat_map_plot.py

@@ -56,13 +56,13 @@ def view_point(point: int, feature_df: pl.DataFrame) -> None:
 
     channels = container.channel_labels
     if len(channels) > 1:
-        channel = st.selectbox(
+        channel_sel = st.selectbox(
             label="Select channel",
             options=channels,
             index=0,
             help="Select the channel to display",
         )
-        channel = channels.index(channel)
+        channel = channels.index(channel_sel)
     else:
         channel = 0
 
@@ -97,7 +97,7 @@ def view_point(point: int, feature_df: pl.DataFrame) -> None:
             label="Zoom Factor",
             min_value=0.5,
             max_value=10.0,
-            value=1.5,
+            value=5.0,
             step=0.1,
             help="Zoom factor for the image",
         )
@@ -108,7 +108,28 @@ def view_point(point: int, feature_df: pl.DataFrame) -> None:
             index=0,
             help="Select the level to display",
         )
-
+        
+        image_scaling = st.selectbox(
+            label="Image Scaling",
+            options=["Metadata", "Min-Max", "Custom"],
+            index=0,
+            help="Select the image scaling",
+        )
+        
+        vis_meta = image.channels_meta.channels[channel].channel_visualisation
+        if image_scaling == "Custom":
+            vis_meta = image.channels_meta.channels[channel].channel_visualisation
+            max_value = float(max(vis_meta.end * 1.5, 255.))
+            min_intensity, max_intensity = st.slider(
+                label="Intensity Range",
+                min_value=0.,
+                max_value=max_value,
+                value=(float(vis_meta.start), float(vis_meta.end)),
+            )
+        else:
+            min_intensity = vis_meta.start
+            max_intensity = vis_meta.end
+        
     try:
         image = get_single_label_image(
             image_url=point_dict["image_url"],
@@ -120,8 +141,12 @@ def view_point(point: int, feature_df: pl.DataFrame) -> None:
             t_slice=t_slice,
             show_label=show_label,
             zoom_factor=zoom_factor,
+            scaling_mode=image_scaling,
+            min_intensity=min_intensity,
+            max_intensity=max_intensity,
         )
-        st.image(image, use_container_width=True)
+        
+        st.image(image, width=500)
     except Exception as e:
         logger.error(f"Error opening image: {e}")
         st.error("Error opening image")

src/fractal_feature_explorer/pages/filters_page/_scatter_filter.py

@@ -355,6 +355,27 @@ def _get_label_array(
     return label_array
 
 
+def scale_image_intensity(
+    image_array: np.ndarray,
+    scaling_mode: str = "Metadata",
+    min_intensity: int | float = 0,
+    max_intensity: int | float = 255,
+) -> np.ndarray:
+    """
+    Scale the image intensity based on the selected scaling mode.
+    """
+    if scaling_mode not in ["Metadata", "Min-Max", "Custom"]:
+        raise ValueError(f"Invalid scaling mode: {scaling_mode}")
+    
+    if scaling_mode != "Min-Max":
+        image_array = np.clip(image_array, min_intensity, max_intensity)
+        
+    min_intensity = image_array.min()
+    max_intensity = image_array.max()
+    image_array = (image_array - min_intensity) / (max_intensity - min_intensity)
+    return image_array * 255
+
+
 def get_single_label_image(
     image_url: str,
     ref_label: str,
@@ -365,6 +386,9 @@ def get_single_label_image(
     level_path: str = "0",
     show_label: bool = True,
     zoom_factor: float = 1,
+    scaling_mode: str = "Metadata",
+    min_intensity: int | float = 0,
+    max_intensity: int | float = 255,
 ) -> np.ndarray:
     """
     Get the region of interest from the image url
@@ -382,7 +406,12 @@ def get_single_label_image(
         fractal_token=fractal_token,
     )
     image_array = image_array.squeeze()
-    image_array = np.clip(image_array, 0, 255)
+    image_array = scale_image_intensity(
+        image_array,
+        scaling_mode=scaling_mode,
+        min_intensity=min_intensity,
+        max_intensity=max_intensity,
+    ).astype(np.uint8)
 
     image_rgba = np.empty(
         (image_array.shape[0], image_array.shape[1], 4), dtype=np.uint8