black

pyproject.toml

@@ -28,3 +28,6 @@ where = ["src"]
 [tool.pytest.ini_options]
 pythonpath = [".", "src"]
 testpaths = ["tests"]
+
+[tool.black]
+line-length = 119

src/vame/__init__.py

@@ -3,6 +3,7 @@
 sys.dont_write_bytecode = True
 
 from vame.initialize_project import init_new_project
+from vame.preprocessing.preprocessing import preprocessing
 from vame.model import create_trainset
 from vame.model import train_model
 from vame.model import evaluate_model

src/vame/analysis/community_analysis.py

@@ -250,12 +250,7 @@ def get_motif_labels(
         file_labels = np.load(
             os.path.join(
                 path_to_dir,
-                str(n_clusters)
-                + "_"
-                + segmentation_algorithm
-                + "_label_"
-                + session
-                + ".npy",
+                str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
             )
         )
         shape = len(file_labels)
@@ -276,12 +271,7 @@ def get_motif_labels(
         file_labels = np.load(
             os.path.join(
                 path_to_dir,
-                str(n_clusters)
-                + "_"
-                + segmentation_algorithm
-                + "_label_"
-                + session
-                + ".npy",
+                str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
             )
         )[:min_frames]
         community_label.extend(file_labels)
@@ -390,11 +380,7 @@ def create_cohort_community_bag(
                     add = input("Extend list or add in the end? (ext/end)")
                     if add == "ext":
                         motif_idx = int(input("Which motif number? "))
-                        list_idx = int(
-                            input(
-                                "At which position in the list? (pythonic indexing starts at 0) "
-                            )
-                        )
+                        list_idx = int(input("At which position in the list? (pythonic indexing starts at 0) "))
                         community_bag[list_idx].append(motif_idx)
                     if add == "end":
                         motif_idx = int(input("Which motif number? "))
@@ -440,9 +426,7 @@ def get_cohort_community_labels(
         for j in range(len(clust)):
             find_clust = np.where(motif_labels == clust[j])[0]
             community_labels[find_clust] = i
-    community_labels = np.int64(
-        scipy.signal.medfilt(community_labels, median_filter_size)
-    )
+    community_labels = np.int64(scipy.signal.medfilt(community_labels, median_filter_size))
     community_labels_all.append(community_labels)
     return community_labels_all
 
@@ -468,12 +452,7 @@ def save_cohort_community_labels_per_file(
         file_labels = np.load(
             os.path.join(
                 path_to_dir,
-                str(n_clusters)
-                + "_"
-                + segmentation_algorithm
-                + "_label_"
-                + session
-                + ".npy",
+                str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
             )
         )
         community_labels = get_cohort_community_labels(
@@ -640,9 +619,7 @@ def community(
                 ),
                 cohort_community_bag,
             )
-            with open(
-                os.path.join(path_to_dir, "hierarchy" + ".pkl"), "wb"
-            ) as fp:  # Pickling
+            with open(os.path.join(path_to_dir, "hierarchy" + ".pkl"), "wb") as fp:  # Pickling
                 pickle.dump(cohort_community_bag, fp)
 
             # Added by Luiz - 11/10/2024
@@ -659,9 +636,7 @@ def community(
 
         # # Work in Progress - cohort is False
         else:
-            raise NotImplementedError(
-                "Community analysis for cohort=False is not supported yet."
-            )
+            raise NotImplementedError("Community analysis for cohort=False is not supported yet.")
         #     labels = get_labels(cfg, files, model_name, n_clusters, parametrization)
         #     transition_matrices = compute_transition_matrices(
         #         files,

src/vame/analysis/generative_functions.py

@@ -333,12 +333,7 @@ def generative_model(
                     os.path.join(
                         path_to_file,
                         "",
-                        str(n_clusters)
-                        + "_"
-                        + segmentation_algorithm
-                        + "_label_"
-                        + session
-                        + ".npy",
+                        str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
                     )
                 )
                 return random_generative_samples_motif(

src/vame/analysis/gif_creator.py

@@ -102,9 +102,7 @@ def create_video(
         frame = frames[i]
         ax2.imshow(frame, cmap=cmap_reversed)
         # ax2.set_title("Motif %d,\n Community: %s" % (lbl, motifs[lbl]), fontsize=10)
-        fig.savefig(
-            os.path.join(path_to_file, "gif_frames", session + "gif_%d.png") % i
-        )
+        fig.savefig(os.path.join(path_to_file, "gif_frames", session + "gif_%d.png") % i)
 
 
 def gif(
@@ -205,9 +203,7 @@ def gif(
                 random_state=cfg["random_state"],
             )
 
-            latent_vector = np.load(
-                os.path.join(path_to_file, "", "latent_vector_" + session + ".npy")
-            )
+            latent_vector = np.load(os.path.join(path_to_file, "", "latent_vector_" + session + ".npy"))
 
             num_points = cfg["num_points"]
             if num_points > latent_vector.shape[0]:
@@ -228,12 +224,7 @@ def gif(
             umap_label = np.load(
                 os.path.join(
                     path_to_file,
-                    str(n_clusters)
-                    + "_"
-                    + segmentation_algorithm
-                    + "_label_"
-                    + session
-                    + ".npy",
+                    str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
                 )
             )
         elif label == "community":

src/vame/analysis/pose_segmentation.py

@@ -82,15 +82,9 @@ def embedd_latent_vectors(
                 data_sample_np = data[:, i : temp_win + i].T
                 data_sample_np = np.reshape(data_sample_np, (1, temp_win, num_features))
                 if use_gpu:
-                    h_n = model.encoder(
-                        torch.from_numpy(data_sample_np)
-                        .type("torch.FloatTensor")
-                        .cuda()
-                    )
+                    h_n = model.encoder(torch.from_numpy(data_sample_np).type("torch.FloatTensor").cuda())
                 else:
-                    h_n = model.encoder(
-                        torch.from_numpy(data_sample_np).type("torch.FloatTensor").to()
-                    )
+                    h_n = model.encoder(torch.from_numpy(data_sample_np).type("torch.FloatTensor").to())
                 mu, _, _ = model.lmbda(h_n)
                 latent_vector_list.append(mu.cpu().data.numpy())
 
@@ -406,9 +400,7 @@ def segment_session(
                     )
 
             else:
-                logger.info(
-                    f"\nSegmentation with {n_clusters} k-means clusters already exists for model {model_name}"
-                )
+                logger.info(f"\nSegmentation with {n_clusters} k-means clusters already exists for model {model_name}")
 
                 if os.path.exists(
                     os.path.join(

src/vame/analysis/tree_hierarchy.py

@@ -42,9 +42,7 @@ def hierarchy_pos(
         raise TypeError("cannot use hierarchy_pos on a graph that is not a tree")
     if root is None:
         if isinstance(G, nx.DiGraph):
-            root = next(
-                iter(nx.topological_sort(G))
-            )  # allows back compatibility with nx version 1.11
+            root = next(iter(nx.topological_sort(G)))  # allows back compatibility with nx version 1.11
         else:
             root = random.choice(list(G.nodes))
 
@@ -121,9 +119,7 @@ def merge_func(
         for i in range(n_clusters):
             for j in range(n_clusters):
                 try:
-                    cost = motif_norm[i] + motif_norm[j] / np.abs(
-                        transition_matrix[i, j] + transition_matrix[j, i]
-                    )
+                    cost = motif_norm[i] + motif_norm[j] / np.abs(transition_matrix[i, j] + transition_matrix[j, i])
                 except ZeroDivisionError:
                     print(
                         "Error: Transition probabilities between motif "

src/vame/analysis/umap.py

@@ -328,12 +328,7 @@ def visualization(
                     os.path.join(
                         path_to_file,
                         "",
-                        str(n_clusters)
-                        + "_"
-                        + segmentation_algorithm
-                        + "_label_"
-                        + session
-                        + ".npy",
+                        str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
                     )
                 )
                 output_figure = umap_label_vis(

src/vame/analysis/videowriter.py

@@ -71,19 +71,12 @@ def create_cluster_videos(
         labels = np.load(
             os.path.join(
                 path_to_file,
-                str(n_clusters)
-                + "_"
-                + segmentation_algorithm
-                + "_label_"
-                + session
-                + ".npy",
+                str(n_clusters) + "_" + segmentation_algorithm + "_label_" + session + ".npy",
             )
         )
     if flag == "community":
         if cohort:
-            logger.info(
-                "Cohort community videos getting created for " + session + " ..."
-            )
+            logger.info("Cohort community videos getting created for " + session + " ...")
             labels = np.load(
                 os.path.join(
                     path_to_file,
@@ -109,9 +102,7 @@ def create_cluster_videos(
     )
     capture = cv.VideoCapture(video_file_path)
     if not capture.isOpened():
-        raise ValueError(
-            f"Video capture could not be opened. Ensure the video file is valid.\n {video_file_path}"
-        )
+        raise ValueError(f"Video capture could not be opened. Ensure the video file is valid.\n {video_file_path}")
     width = capture.get(cv.CAP_PROP_FRAME_WIDTH)
     height = capture.get(cv.CAP_PROP_FRAME_HEIGHT)
     fps = 25  # capture.get(cv.CAP_PROP_FPS)

src/vame/initialize_project/new.py

@@ -110,20 +110,14 @@ def init_new_project(
     for i in videos:
         # Check if it is a folder
         if os.path.isdir(i):
-            vids_in_dir = [
-                os.path.join(i, vp) for vp in os.listdir(i) if video_type in vp
-            ]
+            vids_in_dir = [os.path.join(i, vp) for vp in os.listdir(i) if video_type in vp]
             vids = vids + vids_in_dir
             if len(vids_in_dir) == 0:
                 logger.info(f"No videos found in {i}")
-                logger.info(
-                    f"Perhaps change the video_type, which is currently set to: {video_type}"
-                )
+                logger.info(f"Perhaps change the video_type, which is currently set to: {video_type}")
             else:
                 videos = vids
-                logger.info(
-                    f"{len(vids_in_dir)} videos from the directory {i} were added to the project."
-                )
+                logger.info(f"{len(vids_in_dir)} videos from the directory {i} were added to the project.")
         else:
             if os.path.isfile(i):
                 vids = vids + [i]
@@ -210,9 +204,7 @@ def init_new_project(
 
     unique_num_features = list(set(num_features_list))
     if len(unique_num_features) > 1:
-        raise ValueError(
-            "All pose estimation files must have the same number of features."
-        )
+        raise ValueError("All pose estimation files must have the same number of features.")
 
     if config_kwargs is None:
         config_kwargs = {}

src/vame/logging/logger.py

@@ -6,8 +6,7 @@
 
 class VameLogger:
     LOG_FORMAT = (
-        "%(asctime)-15s.%(msecs)d %(levelname)-5s --- [%(threadName)s]"
-        " %(name)-15s : %(lineno)d : %(message)s"
+        "%(asctime)-15s.%(msecs)d %(levelname)-5s --- [%(threadName)s]" " %(name)-15s : %(lineno)d : %(message)s"
     )
     LOG_DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
 
@@ -19,19 +18,15 @@ def __init__(
     ):
         self.log_level = log_level
         self.file_handler = None
-        logging.basicConfig(
-            level=log_level, format=self.LOG_FORMAT, datefmt=self.LOG_DATE_FORMAT
-        )
+        logging.basicConfig(level=log_level, format=self.LOG_FORMAT, datefmt=self.LOG_DATE_FORMAT)
         self.logger = logging.getLogger(f"{base_name}")
         if self.logger.hasHandlers():
             self.logger.handlers.clear()
 
         self.logger.setLevel(self.log_level)
         # Stream handler for logging to stdout
         stream_handler = logging.StreamHandler()
-        stream_handler.setFormatter(
-            logging.Formatter(self.LOG_FORMAT, self.LOG_DATE_FORMAT)
-        )
+        stream_handler.setFormatter(logging.Formatter(self.LOG_FORMAT, self.LOG_DATE_FORMAT))
         self.logger.addHandler(stream_handler)
         self.logger.propagate = False
 
@@ -56,9 +51,7 @@ def add_file_handler(self, file_path: str):
             f.write(f"{line_break}[LOGGING STARTED AT: {handler_datetime}]")
 
         self.file_handler = logging.FileHandler(file_path, mode="a")
-        self.file_handler.setFormatter(
-            logging.Formatter(self.LOG_FORMAT, self.LOG_DATE_FORMAT)
-        )
+        self.file_handler.setFormatter(logging.Formatter(self.LOG_FORMAT, self.LOG_DATE_FORMAT))
         self.logger.addHandler(self.file_handler)
 
     def remove_file_handler(self):