Merge branches 'feature/weighted_gradient_combination' and 'main' of …

…github.com:mesoscope/cellpack into feature/weighted_gradient_combination

.github/workflows/ci.yml

@@ -45,7 +45,7 @@ jobs:
       run: |
         pytest --cov cellpack/tests/
     - name: Upload codecov
-      uses: codecov/codecov-action@v3
+      uses: codecov/codecov-action@v4
 
   lint:
     runs-on: ubuntu-latest

cellpack/autopack/Analysis.py

@@ -828,12 +828,17 @@ def update_distance_distribution_dictionaries(
 
         get_angles = False
         if ingr.packing_mode == "gradient" and self.env.use_gradient:
-            if not isinstance(ingr.gradient, list):
+            if isinstance(ingr.gradient, list):
+                if len(ingr.gradient) > 1 or len(ingr.gradient) == 0:
+                    self.center = center
+                else:
+                    self.center = center = self.env.gradients[
+                        ingr.gradient[0]
+                    ].mode_settings.get("center", center)
+            else:
                 self.center = center = self.env.gradients[
                     ingr.gradient
                 ].mode_settings.get("center", center)
-            else:
-                self.center = center
             get_angles = True
 
         # get angles wrt gradient

cellpack/autopack/Environment.py

@@ -1700,28 +1700,9 @@ def getPointToDrop(
                 # get the most probable point using the gradient
                 # use the gradient weighted map and get mot probabl point
                 self.log.info("pick point from gradients %d", (len(allIngrPts)))
-                if isinstance(ingr.gradient, list) and len(ingr.gradient) > 1:
-                    if not hasattr(ingr, "combined_weight"):
-                        gradient_list = []
-                        gradient_weights = []
-                        for (gradient_name, gradient), gradient_weight in zip(
-                            self.gradients.items(), ingr.gradient_weights
-                        ):
-                            if gradient_name in ingr.gradient:
-                                gradient_list.append(gradient)
-                                gradient_weights.append(gradient_weight)
-
-                        combined_weight = Gradient.get_combined_gradient_weight(
-                            gradient_list, gradient_weights
-                        )
-                        ingr.combined_weight = combined_weight
-
-                    ptInd = Gradient.pick_point_from_weight(
-                        ingr.combined_weight, allIngrPts
-                    )
-
-                else:
-                    ptInd = self.gradients[ingr.gradient].pickPoint(allIngrPts)
+                ptInd = Gradient.pick_point_for_ingredient(
+                    ingr, allIngrPts, self.gradients
+                )
             else:
                 # pick a point randomly among free points
                 # random or uniform?

cellpack/autopack/Gradient.py

@@ -101,7 +101,7 @@ def scale_between_0_and_1(values):
         return (values - min_value) / (max_value - min_value)
 
     @staticmethod
-    def get_combined_gradient_weight(gradient_list, gradient_weights=None):
+    def get_combined_gradient_weight(gradient_list):
         """
         Combine the gradient weights
 
@@ -119,7 +119,7 @@ def get_combined_gradient_weight(gradient_list, gradient_weights=None):
         for i in range(len(gradient_list)):
             weight_list[i] = Gradient.scale_between_0_and_1(gradient_list[i].weight)
 
-        combined_weight = numpy.average(weight_list, axis=0, weights=gradient_weights)
+        combined_weight = numpy.mean(weight_list, axis=0)
         combined_weight = Gradient.scale_between_0_and_1(combined_weight)
 
         return combined_weight
@@ -143,15 +143,59 @@ def pick_point_from_weight(weight, points):
             the index of the picked point
         """
         weights_to_use = numpy.take(weight, points)
-        weights_to_use[numpy.isnan(weights_to_use)] = 0
         weights_to_use = Gradient.scale_between_0_and_1(weights_to_use)
+        weights_to_use[numpy.isnan(weights_to_use)] = 0
 
         point_probabilities = weights_to_use / numpy.sum(weights_to_use)
 
         point = numpy.random.choice(points, p=point_probabilities)
 
         return point
 
+    @staticmethod
+    def pick_point_for_ingredient(ingr, allIngrPts, all_gradients):
+        """
+        Picks a point for an ingredient according to the gradient
+
+        Parameters
+        ----------
+        ingr: Ingredient
+            the ingredient object
+
+        allIngrPts: numpy.ndarray
+            list of grid point indices
+
+        all_gradients: dict
+            dictionary of all gradient objects
+
+        Returns
+        ----------
+        int
+            the index of the picked point
+        """
+        if isinstance(ingr.gradient, list):
+            if len(ingr.gradient) > 1:
+                if not hasattr(ingr, "combined_weight"):
+                    gradient_list = [
+                        gradient
+                        for gradient_name, gradient in all_gradients.items()
+                        if gradient_name in ingr.gradient
+                    ]
+                    combined_weight = Gradient.get_combined_gradient_weight(
+                        gradient_list
+                    )
+                    ingr.combined_weight = combined_weight
+
+                ptInd = Gradient.pick_point_from_weight(
+                    ingr.combined_weight, allIngrPts
+                )
+            else:
+                ptInd = all_gradients[ingr.gradient[0]].pickPoint(allIngrPts)
+        else:
+            ptInd = all_gradients[ingr.gradient].pickPoint(allIngrPts)
+
+        return ptInd
+
     def get_center(self):
         """get the center of the gradient grid"""
         center = [0.0, 0.0, 0.0]

cellpack/autopack/loaders/recipe_loader.py

@@ -132,16 +132,6 @@ def _sanitize_format_version(recipe_data):
             format_version = recipe_data["format_version"]
         return format_version
 
-    def get_only_recipe_metadata(self):
-        recipe_meta_data = {
-            "format_version": self.recipe_data["format_version"],
-            "version": self.recipe_data["version"],
-            "name": self.recipe_data["name"],
-            "bounding_box": self.recipe_data["bounding_box"],
-            "composition": {},
-        }
-        return recipe_meta_data
-
     def _migrate_version(self, old_recipe):
         converted = False
         if old_recipe["format_version"] == "1.0":

cellpack/autopack/upy/simularium/simularium_helper.py

@@ -363,7 +363,10 @@ def add_grid_data_to_scene(self, incoming_name, positions, values, radius=0.5):
 
         positions, values = self.sort_values(positions, values)
 
-        colormap = matplotlib.cm.Reds(values)
+        normalized_values = (values - np.min(values)) / (
+            np.max(values) - np.min(values)
+        )
+        colormap = matplotlib.cm.Reds(normalized_values)
 
         for index, value in enumerate(values):
             name = f"{incoming_name}#{value:.3f}"

cellpack/bin/upload.py

@@ -8,6 +8,25 @@
 from cellpack.autopack.loaders.recipe_loader import RecipeLoader
 
 
+def get_recipe_metadata(loader):
+    """
+    Extracts and returns essential metadata from a recipe for uploading
+    """
+    try:
+        recipe_meta_data = {
+            "format_version": loader.recipe_data["format_version"],
+            "version": loader.recipe_data["version"],
+            "name": loader.recipe_data["name"],
+            "bounding_box": loader.recipe_data["bounding_box"],
+            "composition": {},
+        }
+        if "grid_file_path" in loader.recipe_data:
+            recipe_meta_data["grid_file_path"] = loader.recipe_data["grid_file_path"]
+        return recipe_meta_data
+    except KeyError as e:
+        sys.exit(f"Recipe metadata is missing. {e}")
+
+
 def upload(
     recipe_path,
     db_id=DATABASE_IDS.FIREBASE,
@@ -23,7 +42,7 @@ def upload(
         if FirebaseHandler._initialized:
             recipe_loader = RecipeLoader(recipe_path)
             recipe_full_data = recipe_loader._read(resolve_inheritance=False)
-            recipe_meta_data = recipe_loader.get_only_recipe_metadata()
+            recipe_meta_data = get_recipe_metadata(recipe_loader)
             recipe_db_handler = DBUploader(db_handler)
             recipe_db_handler.upload_recipe(recipe_meta_data, recipe_full_data)
         else: