add distribution plot functions

src/napatrackmater/Trackmate.py

@@ -960,7 +960,7 @@ def _master_track_computer(self, track, track_id, t_start=None, t_end=None):
 
         all_source_ids, all_target_ids = self._generate_generations(track)
         root_root, root_splits, root_leaf = self._create_generations(all_source_ids)
-
+        self._iterate_split_down(root_root, root_leaf, root_splits)
         # Determine if a track has divisions or none
         number_dividing = len(root_splits)
         if number_dividing > 0:

src/napatrackmater/Trackvector.py

@@ -20,7 +20,9 @@
 from statsmodels.tsa.stattools import acf, ccf
 from scipy.stats import norm, anderson
 from kapoorlabs_lightning.lightning_trainer import MitosisInception
-
+from natsort import natsorted
+import seaborn as sns
+from tqdm import tqdm
 
 logger = logging.getLogger(__name__)
 
@@ -521,11 +523,23 @@ def plot_mitosis_times(self, full_dataframe, save_path=""):
         counts = dividing_counts.values
         data = {"Time": times, "Count": counts}
         df = pd.DataFrame(data)
-        np.save(save_path + "_counts.npy", df.to_numpy())
+        np.save(save_path + "counts.npy", df.to_numpy())
 
         max_number_dividing = full_dataframe["Number_Dividing"].max()
         min_number_dividing = full_dataframe["Number_Dividing"].min()
-        excluded_keys = ["Track ID", "t", "z", "y", "x"]
+        excluded_keys = [
+            "Track ID",
+            "t",
+            "z",
+            "y",
+            "x",
+            "Unnamed: 0",
+            "Unnamed",
+            "Track Duration",
+            "Generation ID",
+            "TrackMate Track ID",
+            "Tracklet Number ID",
+        ]
         for i in range(
             min_number_dividing.astype(int), max_number_dividing.astype(int) + 1
         ):
@@ -534,12 +548,13 @@ def plot_mitosis_times(self, full_dataframe, save_path=""):
                     data = full_dataframe[column][
                         full_dataframe["Number_Dividing"].astype(int) == i
                     ]
+
                     np.save(
-                        f"{save_path}_{column}_Number_Dividing_{i}.npy", data.to_numpy()
+                        f"{save_path}{column}Number_Dividing_{i}.npy", data.to_numpy()
                     )
 
         all_split_data = []
-        for split_id in self.split_cell_ids:
+        for split_id in tqdm(self.split_cell_ids, desc="Cell split IDs"):
             spot_properties = self.unique_spot_properties[split_id]
             track_id = spot_properties[self.trackid_key]
             unique_id = spot_properties[self.uniqueid_key]
@@ -592,7 +607,7 @@ def plot_mitosis_times(self, full_dataframe, save_path=""):
 
             all_split_data.append(data)
 
-        np.save(f"{save_path}_data_at_mitosis_time.npy", all_split_data)
+        np.save(f"{save_path}data_at_mitosis_time.npy", all_split_data)
 
     def get_shape_dynamic_feature_dataframe(self):
 
@@ -3157,6 +3172,14 @@ def extract_number_from_string(string):
     return numbers
 
 
+def extract_number_dividing(file_name):
+    number_dividing = -1  # Default value if the pattern isn't found
+    match = re.search(r"Number_Dividing_(\d+)\.npy", file_name)
+    if match:
+        number_dividing = int(match.group(1))
+    return number_dividing
+
+
 def cross_correlation_class(tracks_dataframe, cell_type_label=None):
 
     if cell_type_label is not None:
@@ -3374,3 +3397,125 @@ def cross_correlation_class(tracks_dataframe, cell_type_label=None):
         N_gen_dynamic_test_dict,
         N_gen_shape_test_dict,
     )
+
+
+def plot_at_mitosis_time(matrix_directory, save_dir, dataset_name, channel):
+
+    files = os.listdir(matrix_directory)
+
+    sorted_files = natsorted(
+        [
+            file
+            for file in files
+            if file.endswith(".npy") and "data_at_mitosis_time" in file
+        ],
+        key=lambda x: (
+            "_Dynamic Cluster" in x,
+            "_Shape Dynamic Cluster" in x,
+            "_Shape Cluster" in x,
+            x,
+        ),
+    )
+
+    excluded_keys = [
+        "Track ID",
+        "t",
+        "z",
+        "y",
+        "x",
+        "Unnamed: 0",
+        "Unnamed",
+        "Track Duration",
+        "Generation ID",
+        "TrackMate Track ID",
+        "Tracklet Number ID",
+        "Tracklet_ID",
+        "Unique_ID",
+        "Track_ID",
+    ]
+
+    for file_name in sorted_files:
+
+        all_split_data = np.load(
+            os.path.join(matrix_directory, file_name), allow_pickle=True
+        )
+
+        grouped_data = {}
+        for data_dict in all_split_data:
+            number_times_divided = data_dict.get("Number Times Divided")
+            for key, value in data_dict.items():
+                if key not in excluded_keys:
+                    if key not in grouped_data:
+                        grouped_data[key] = {number_times_divided: [value]}
+                    else:
+                        if number_times_divided not in grouped_data[key]:
+                            grouped_data[key][number_times_divided] = [value]
+                        else:
+                            grouped_data[key][number_times_divided].append(value)
+
+        for property_name, property_data in grouped_data.items():
+            plt.figure(figsize=(10, 6))
+            for number_times_divided, values in property_data.items():
+                sns.histplot(
+                    values,
+                    label=f"Number Times Divided {number_times_divided}",
+                    kde=True,
+                    bins=20,
+                    edgecolor="black",
+                    alpha=0.5,
+                )
+            plt.xlabel("Property Values")
+            plt.ylabel("Frequency")
+            plt.title(f"{property_name}")
+
+            fig_name = (
+                f"{dataset_name}_{channel}_{property_name}_at_mitosis_distribution.png"
+            )
+            plt.savefig(os.path.join(save_dir, fig_name), dpi=300, bbox_inches="tight")
+            plt.show()
+
+
+def plot_histograms_for_groups(matrix_directory, save_dir, dataset_name, channel):
+
+    files = os.listdir(matrix_directory)
+    sorted_files = natsorted(
+        [
+            file
+            for file in files
+            if file.endswith(".npy")
+            and "Number_Dividing" in file
+            and "Number_DividingNumber_Dividing"
+            and "DividingNumber_Dividing" not in file
+        ]
+    )
+    groups = set()
+    print(sorted_files)
+    for file_name in sorted_files:
+        group_name = file_name.split("Number_Dividing")[0]
+
+        groups.add(group_name)
+
+    for group_name in groups:
+        plt.figure(figsize=(8, 6))
+        group_files = [file for file in sorted_files if group_name in file]
+
+        for file_name in group_files:
+            file_path = os.path.join(matrix_directory, file_name)
+            number_dividing = extract_number_dividing(file_name)
+
+            data = np.load(file_path)
+            sns.histplot(
+                data, alpha=0.5, kde=True, label=f"Number_Dividing: {number_dividing}"
+            )
+
+        plt.xlabel("Value")
+        plt.ylabel("Counts")
+        simplified_group_name = re.search(r"__(.*?)_Number_Dividing", group_name)
+        simplified_group_name = (
+            simplified_group_name.group(1) if simplified_group_name else group_name
+        )
+        plt.title(f"{simplified_group_name}")
+        plt.legend()
+        fig_name = f"{dataset_name}_{channel}_{group_name}_all_distribution.png"
+        plt.savefig(os.path.join(save_dir, fig_name), dpi=300, bbox_inches="tight")
+        plt.show()

src/napatrackmater/_version.py

@@ -1,2 +1,2 @@
-__version__ = version = "5.3.2"
-__version_tuple__ = version_tuple = (5, 3, 2)
+__version__ = version = "5.3.3"
+__version_tuple__ = version_tuple = (5, 3, 3)