Merge branch 'main' of https://github.com/mesoscope/cellpack into fea…

…ture/markdown-class

cellpack/autopack/DBRecipeHandler.py

@@ -218,15 +218,14 @@ def check_and_replace_references(
                         # replace nested objs in comp["regions"]
                         if DataDoc.is_key(region_item):
                             update_field_path = f"regions.{region_name}"
+                            update_data = {
+                                "index": update_field_path,
+                                "name": region_item,
+                            }
                             if self.name in references_to_update:
-                                references_to_update[self.name].update(
-                                    {"index": update_field_path, "name": region_item}
-                                )
+                                references_to_update[self.name].append(update_data)
                             else:
-                                references_to_update[self.name] = {
-                                    "index": update_field_path,
-                                    "name": region_item,
-                                }
+                                references_to_update[self.name] = [update_data]
                         elif not db.is_reference(region_item["object"]):
                             obj_name = region_item["object"]
                             region_item["object"] = objects_to_path_map.get(obj_name)
@@ -554,7 +553,8 @@ def upload_compositions(self, compositions, recipe_to_save, recipe_data):
                 "inherit": self.comp_to_path_map[comp_name]["path"]
             }
             if comp_name in references_to_update:
-                references_to_update[comp_name].update({"comp_id": doc_id})
+                for inner_data in references_to_update[comp_name]:
+                    inner_data["comp_id"] = doc_id
         return references_to_update
 
     def _get_recipe_id(self, recipe_data):
@@ -586,15 +586,14 @@ def upload_collections(self, recipe_meta_data, recipe_data):
         # update nested comp in composition
         if references_to_update:
             for comp_name in references_to_update:
-                inner_data = references_to_update[comp_name]
-                comp_id = inner_data["comp_id"]
-                index = inner_data["index"]
-                name = inner_data["name"]
-
-                item_id = self.comp_to_path_map[name]["id"]
-                CompositionDoc.update_reference(
-                    self.db, comp_id, item_id, index, name, update_in_array=True
-                )
+                for inner_data in references_to_update[comp_name]:
+                    comp_id = inner_data["comp_id"]
+                    index = inner_data["index"]
+                    name = inner_data["name"]
+                    item_id = self.comp_to_path_map[name]["id"]
+                    CompositionDoc.update_reference(
+                        self.db, comp_id, item_id, index, name, update_in_array=True
+                    )
         return recipe_to_save
 
     def upload_recipe(self, recipe_meta_data, recipe_data):

cellpack/autopack/Environment.py

@@ -974,13 +974,8 @@ def create_ingredient(self, recipe, arguments):
         ingredient_type = arguments["type"]
         ingredient_class = ingredient.get_ingredient_class(ingredient_type)
         ingr = ingredient_class(**arguments)
-        if (
-            "gradient" in arguments
-            and arguments["gradient"] != ""
-            and arguments["gradient"] != "None"
-        ):
-            ingr.gradient = arguments["gradient"]
-            # TODO: allow ingrdients to have multiple gradients
+        if "gradient" in arguments:
+            ingr = Gradient.update_ingredient_gradient(ingr, arguments)
         if "results" in arguments:
             ingr.results = arguments["results"]
         ingr.initialize_mesh(self.mesh_store)

cellpack/autopack/Gradient.py

@@ -91,6 +91,18 @@ def __init__(self, gradient_data):
 
         self.function = self.defaultFunction  # lambda ?
 
+    @staticmethod
+    def update_ingredient_gradient(ingr, arguments):
+        """
+        Update the ingredient gradient
+        """
+        ingr.gradient = arguments["gradient"]
+        ingr.gradient_weights = None
+        if "gradient_weights" in arguments:
+            ingr.gradient_weights = arguments["gradient_weights"]
+
+        return ingr
+
     @staticmethod
     def scale_between_0_and_1(values):
         """
@@ -101,7 +113,7 @@ def scale_between_0_and_1(values):
         return (values - min_value) / (max_value - min_value)
 
     @staticmethod
-    def get_combined_gradient_weight(gradient_list):
+    def get_combined_gradient_weight(gradient_list, gradient_weights=None):
         """
         Combine the gradient weights
 
@@ -115,11 +127,13 @@ def get_combined_gradient_weight(gradient_list):
         numpy.ndarray
             the combined gradient weight
         """
+        assert len(gradient_list) > 1, "Need at least two gradients to combine"
+
         weight_list = numpy.zeros((len(gradient_list), len(gradient_list[0].weight)))
         for i in range(len(gradient_list)):
             weight_list[i] = Gradient.scale_between_0_and_1(gradient_list[i].weight)
 
-        combined_weight = numpy.mean(weight_list, axis=0)
+        combined_weight = numpy.average(weight_list, axis=0, weights=gradient_weights)
         combined_weight = Gradient.scale_between_0_and_1(combined_weight)
 
         return combined_weight
@@ -143,8 +157,8 @@ def pick_point_from_weight(weight, points):
             the index of the picked point
         """
         weights_to_use = numpy.take(weight, points)
-        weights_to_use = Gradient.scale_between_0_and_1(weights_to_use)
         weights_to_use[numpy.isnan(weights_to_use)] = 0
+        weights_to_use = Gradient.scale_between_0_and_1(weights_to_use)
 
         point_probabilities = weights_to_use / numpy.sum(weights_to_use)
 
@@ -176,13 +190,16 @@ def pick_point_for_ingredient(ingr, allIngrPts, all_gradients):
         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
-                    ]
+                    gradient_list = []
+                    for gradient_name in ingr.gradient:
+                        if gradient_name not in all_gradients:
+                            raise ValueError(
+                                f"Gradient {gradient_name} not found in gradient list"
+                            )
+                        gradient_list.append(all_gradients[gradient_name])
+
                     combined_weight = Gradient.get_combined_gradient_weight(
-                        gradient_list
+                        gradient_list, ingr.gradient_weights
                     )
                     ingr.combined_weight = combined_weight
 

cellpack/autopack/ingredient/Ingredient.py

@@ -161,6 +161,7 @@ class Ingredient(Agent):
         "distance_function",
         "force_random",
         "gradient",
+        "gradient_weights",
         "is_attractor",
         "max_jitter",
         "molarity",
@@ -199,6 +200,7 @@ def __init__(
         distance_function=None,
         force_random=False,  # avoid any binding
         gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         molarity=0.0,
@@ -231,6 +233,7 @@ def __init__(
             distance_function=distance_function,
             force_random=force_random,
             gradient=gradient,
+            gradient_weights=gradient_weights,
             is_attractor=is_attractor,
             overwrite_distance_function=overwrite_distance_function,
             packing_mode=packing_mode,
@@ -407,6 +410,38 @@ def validate_ingredient_info(ingredient_info):
                 ingredient_info["size_options"]
             )
 
+        # check if gradient information is entered correctly
+        if "gradient" in ingredient_info:
+            if not isinstance(ingredient_info["gradient"], (list, str)):
+                raise Exception(
+                    (
+                        f"Invalid gradient: {ingredient_info['gradient']} "
+                        f"for ingredient {ingredient_info['name']}"
+                    )
+                )
+            if (
+                ingredient_info["gradient"] == ""
+                or ingredient_info["gradient"] == "None"
+            ):
+                raise Exception(
+                    f"Missing gradient for ingredient {ingredient_info['name']}"
+                )
+
+            # if multiple gradients are provided with weights, check if weights are correct
+            if isinstance(ingredient_info["gradient"], list):
+                if "gradient_weights" in ingredient_info:
+                    # check if gradient_weights are missing
+                    if not isinstance(ingredient_info["gradient_weights"], list):
+                        raise Exception(
+                            f"Invalid gradient weights for ingredient {ingredient_info['name']}"
+                        )
+                    if len(ingredient_info["gradient"]) != len(
+                        ingredient_info["gradient_weights"]
+                    ):
+                        raise Exception(
+                            f"Missing gradient weights for ingredient {ingredient_info['name']}"
+                        )
+
         return ingredient_info
 
     def reset(self):

cellpack/autopack/ingredient/agent.py

@@ -11,7 +11,8 @@ def __init__(
         distance_expression=None,
         distance_function=None,
         force_random=False,  # avoid any binding
-        gradient="",
+        gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         overwrite_distance_function=True,  # overWrite
         packing_mode="random",
@@ -42,6 +43,7 @@ def __init__(
         self.distance_expression = distance_expression
         self.overwrite_distance_function = overwrite_distance_function
         self.gradient = gradient
+        self.gradient_weights = gradient_weights
         self.cb = None
         self.radii = None
         self.recipe = None  # weak ref to recipe

cellpack/autopack/ingredient/grow.py

@@ -50,6 +50,7 @@ def __init__(
         cutoff_boundary=1.0,
         cutoff_surface=0.5,
         gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         length=10.0,

cellpack/autopack/ingredient/multi_cylinder.py

@@ -30,6 +30,7 @@ def __init__(
         distance_function=None,
         force_random=False,  # avoid any binding
         gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         molarity=0.0,

cellpack/autopack/ingredient/multi_sphere.py

@@ -23,6 +23,7 @@ def __init__(
         cutoff_boundary=None,
         cutoff_surface=None,
         gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         molarity=0.0,

cellpack/autopack/ingredient/single_cube.py

@@ -27,6 +27,7 @@ def __init__(
         distance_function=None,
         force_random=False,  # avoid any binding
         gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         molarity=0.0,

cellpack/autopack/ingredient/single_cylinder.py

@@ -30,7 +30,8 @@ def __init__(
         distance_expression=None,
         distance_function=None,
         force_random=False,  # avoid any binding
-        gradient="",
+        gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         molarity=0.0,

cellpack/autopack/ingredient/single_sphere.py

@@ -28,6 +28,7 @@ def __init__(
         distance_function=None,
         force_random=False,  # avoid any binding
         gradient=None,
+        gradient_weights=None,
         is_attractor=False,
         max_jitter=(1, 1, 1),
         molarity=0.0,

cellpack/tests/recipes/v2/test_combined_gradient.json

@@ -88,6 +88,10 @@
             "gradient": [
                 "X_gradient",
                 "Y_gradient"
+            ],
+            "gradient_weights": [
+                70,
+                30
             ]
         }
     },

cellpack/tests/test_db_uploader.py

@@ -104,7 +104,7 @@ def test_upload_compositions():
         "space": {"id": "test_id", "path": "firebase:composition/test_id"},
     }
     assert references_to_update == {
-        "space": {"comp_id": "test_id", "index": "regions.interior", "name": "A"}
+        "space": [{"comp_id": "test_id", "index": "regions.interior", "name": "A"}]
     }
 
 

cellpack/tests/test_ingredient.py

@@ -65,6 +65,44 @@
             },
             "Missing option 'min' for uniform distribution",
         ),
+        (
+            {
+                "name": "test",
+                "type": "single_sphere",
+                "count": 1,
+                "gradient": 3,
+            },
+            "Invalid gradient: 3 for ingredient test",
+        ),
+        (
+            {
+                "name": "test",
+                "type": "single_sphere",
+                "count": 1,
+                "gradient": "",
+            },
+            "Missing gradient for ingredient test",
+        ),
+        (
+            {
+                "name": "test",
+                "type": "single_sphere",
+                "count": 1,
+                "gradient": ["gradient_1", "gradient_2"],
+                "gradient_weights": 0.5,
+            },
+            "Invalid gradient weights for ingredient test",
+        ),
+        (
+            {
+                "name": "test",
+                "type": "single_sphere",
+                "count": 1,
+                "gradient": ["gradient_1", "gradient_2", "gradient_3"],
+                "gradient_weights": [0.5, 0.5],
+            },
+            "Missing gradient weights for ingredient test",
+        ),
     ],
 )
 def test_validate_ingredient_info(ingredient_info, output):