black formatting (DeepLabCut#1605)

deeplabcut/create_project/add.py

@@ -79,9 +79,7 @@ def add_new_videos(config, videos, copy_videos=False, coords=None):
                 os.symlink(src, dst)
 
     if copy_videos:
-        videos = (
-            destinations
-        )  # in this case the *new* location should be added to the config file
+        videos = destinations  # in this case the *new* location should be added to the config file
     # adds the video list to the config.yaml file
     for idx, video in enumerate(videos):
         try:

deeplabcut/create_project/new.py

@@ -158,18 +158,19 @@ def create_new_project(
             except OSError:
                 try:
                     import subprocess
+
                     subprocess.check_call("mklink %s %s" % (dst, src), shell=True)
                 except OSError:
-                    print("Symlink creation impossible (exFat architecture?): "
-                          "cutting/pasting the video instead.")
+                    print(
+                        "Symlink creation impossible (exFat architecture?): "
+                        "cutting/pasting the video instead."
+                    )
                     shutil.move(os.fspath(src), os.fspath(dst))
                     print("{} moved to {}".format(src, dst))
             videos = destinations
 
     if copy_videos == True:
-        videos = (
-            destinations
-        )  # in this case the *new* location should be added to the config file
+        videos = destinations  # in this case the *new* location should be added to the config file
 
     # adds the video list to the config.yaml file
     video_sets = {}
@@ -240,9 +241,7 @@ def create_new_project(
     cfg_file["y2"] = 624
     cfg_file[
         "batch_size"
-    ] = (
-        8
-    )  # batch size during inference (video - analysis); see https://www.biorxiv.org/content/early/2018/10/30/457242
+    ] = 8  # batch size during inference (video - analysis); see https://www.biorxiv.org/content/early/2018/10/30/457242
     cfg_file["corner2move2"] = (50, 50)
     cfg_file["move2corner"] = True
     cfg_file["skeleton_color"] = "black"

deeplabcut/generate_training_dataset/frame_extraction.py

@@ -393,7 +393,9 @@ def extract_frames(
         elif any(has_failed):
             print("Although most frames were extracted, some were invalid.")
         else:
-            print("Frames were successfully extracted, for the videos listed in the config.yaml file.")
+            print(
+                "Frames were successfully extracted, for the videos listed in the config.yaml file."
+            )
         print(
             "\nYou can now label the frames using the function 'label_frames' "
             "(Note, you should label frames extracted from diverse videos (and many videos; we do not recommend training on single videos!))."

deeplabcut/generate_training_dataset/multiple_individuals_trainingsetmanipulation.py

@@ -31,10 +31,7 @@
 
 
 def format_multianimal_training_data(
-    df,
-    train_inds,
-    project_path,
-    n_decimals=2,
+    df, train_inds, project_path, n_decimals=2,
 ):
     train_data = []
     nrows = df.shape[0]
@@ -45,9 +42,7 @@ def format_multianimal_training_data(
     mask_single = individuals.str.contains("single")
     n_animals = n_individuals - 1 if np.any(mask_single) else n_individuals
     array = np.full(
-        (nrows, n_individuals, n_bodyparts, 3),
-        fill_value=np.nan,
-        dtype=np.float32
+        (nrows, n_individuals, n_bodyparts, 3), fill_value=np.nan, dtype=np.float32
     )
     array[..., 0] = np.arange(n_bodyparts)
     temp = df.to_numpy()
@@ -56,9 +51,7 @@ def format_multianimal_training_data(
     array[:, :n_animals, :n_multibodyparts, 1:] = temp_multi
     if n_animals != n_individuals:  # There is a unique individual
         n_uniquebodyparts = n_bodyparts - n_multibodyparts
-        temp_single = np.reshape(
-            temp[:, mask_single], (nrows, 1, n_uniquebodyparts, 2)
-        )
+        temp_single = np.reshape(temp[:, mask_single], (nrows, 1, n_uniquebodyparts, 2))
         array[:, -1:, -n_uniquebodyparts:, 1:] = temp_single
     array = np.round(array, decimals=n_decimals)
     for i in tqdm(train_inds):
@@ -177,8 +170,10 @@ def create_multianimaltraining_dataset(
         raise ValueError("Crop size must be a tuple of two integers (width, height).")
 
     if crop_sampling not in ("uniform", "keypoints", "density", "hybrid"):
-            raise ValueError(f"Invalid sampling {crop_sampling}. Must be "
-                             f"either 'uniform', 'keypoints', 'density', or 'hybrid.")
+        raise ValueError(
+            f"Invalid sampling {crop_sampling}. Must be "
+            f"either 'uniform', 'keypoints', 'density', or 'hybrid."
+        )
 
     # Loading metadata from config file:
     cfg = auxiliaryfunctions.read_config(config)
@@ -204,7 +199,7 @@ def create_multianimaltraining_dataset(
     ### dlcr101_ms5/dlcr152_ms5: backbone resnet101/152 + multi-fusion & multi-stage module
     if all(net in net_type for net in ("dlcr", "_ms5")):
         num_layers = re.findall("dlcr([0-9]*)", net_type)[0]
-        if num_layers == '':
+        if num_layers == "":
             num_layers = 50
         net_type = "resnet_{}".format(num_layers)
         multi_stage = True
@@ -219,19 +214,16 @@ def create_multianimaltraining_dataset(
     if paf_graph is None:  # Automatically form a complete PAF graph
         partaffinityfield_graph = [
             list(edge) for edge in combinations(range(len(multianimalbodyparts)), 2)
-    ]
+        ]
     else:
         # Ignore possible connections between 'multi' and 'unique' body parts;
         # one can never be too careful...
-        to_ignore = auxfun_multianimal.filter_unwanted_paf_connections(
-            cfg, paf_graph
-        )
+        to_ignore = auxfun_multianimal.filter_unwanted_paf_connections(cfg, paf_graph)
         partaffinityfield_graph = [
             edge for i, edge in enumerate(paf_graph) if i not in to_ignore
         ]
         auxfun_multianimal.validate_paf_graph(cfg, partaffinityfield_graph)
 
-
     print("Utilizing the following graph:", partaffinityfield_graph)
     # Disable the prediction of PAFs if the graph is empty
     partaffinityfield_predict = bool(partaffinityfield_graph)
@@ -253,12 +245,8 @@ def create_multianimaltraining_dataset(
         splits = []
         for shuffle in Shuffles:  # Creating shuffles starting from 1
             for train_frac in cfg["TrainingFraction"]:
-                train_inds, test_inds = SplitTrials(
-                    range(len(Data)), train_frac
-                )
-                splits.append(
-                    (train_frac, shuffle, (train_inds, test_inds))
-                )
+                train_inds, test_inds = SplitTrials(range(len(Data)), train_frac)
+                splits.append((train_frac, shuffle, (train_inds, test_inds)))
     else:
         if len(trainIndices) != len(testIndices) != len(Shuffles):
             raise ValueError(
@@ -280,9 +268,7 @@ def create_multianimaltraining_dataset(
             train_inds = train_inds[train_inds != -1]
             test_inds = np.asarray(test_inds)
             test_inds = test_inds[test_inds != -1]
-            splits.append(
-                (trainFraction, Shuffles[shuffle], (train_inds, test_inds))
-            )
+            splits.append((trainFraction, Shuffles[shuffle], (train_inds, test_inds)))
 
     for trainFraction, shuffle, (trainIndices, testIndices) in splits:
         ####################################################
@@ -297,10 +283,7 @@ def create_multianimaltraining_dataset(
 
         # Make training file!
         data = format_multianimal_training_data(
-            Data,
-            trainIndices,
-            cfg["project_path"],
-            numdigits,
+            Data, trainIndices, cfg["project_path"], numdigits,
         )
 
         if len(trainIndices) > 0:
@@ -356,10 +339,7 @@ def create_multianimaltraining_dataset(
             )
             path_test_config = str(
                 os.path.join(
-                    cfg["project_path"],
-                    Path(modelfoldername),
-                    "test",
-                    "pose_cfg.yaml",
+                    cfg["project_path"], Path(modelfoldername), "test", "pose_cfg.yaml",
                 )
             )
             path_inference_config = str(
@@ -379,8 +359,7 @@ def create_multianimaltraining_dataset(
                 "num_joints": len(multianimalbodyparts)
                 + len(uniquebodyparts),  # cfg["uniquebodyparts"]),
                 "all_joints": [
-                    [i]
-                    for i in range(len(multianimalbodyparts) + len(uniquebodyparts))
+                    [i] for i in range(len(multianimalbodyparts) + len(uniquebodyparts))
                 ],  # cfg["uniquebodyparts"]))],
                 "all_joints_names": jointnames,
                 "init_weights": model_path,
@@ -444,9 +423,7 @@ def create_multianimaltraining_dataset(
                 dlcparent_path, "inference_cfg.yaml"
             )
             items2change = {
-                "minimalnumberofconnections": int(
-                    len(cfg["multianimalbodyparts"]) / 2
-                ),
+                "minimalnumberofconnections": int(len(cfg["multianimalbodyparts"]) / 2),
                 "topktoretain": len(cfg["individuals"])
                 + 1 * (len(cfg["uniquebodyparts"]) > 0),
                 "withid": cfg.get("identity", False),
@@ -463,10 +440,7 @@ def create_multianimaltraining_dataset(
 
 
 def convert_cropped_to_standard_dataset(
-    config_path,
-    recreate_datasets=True,
-    delete_crops=True,
-    back_up=True,
+    config_path, recreate_datasets=True, delete_crops=True, back_up=True,
 ):
     import pandas as pd
     import pickle
@@ -478,8 +452,10 @@ def convert_cropped_to_standard_dataset(
     videos_orig = cfg.pop("video_sets_original")
     is_cropped = cfg.pop("croppedtraining")
     if videos_orig is None or not is_cropped:
-        print("Labeled data do not appear to be cropped. "
-              "Project will remain unchanged...")
+        print(
+            "Labeled data do not appear to be cropped. "
+            "Project will remain unchanged..."
+        )
         return
 
     project_path = cfg["project_path"]
@@ -545,9 +521,7 @@ def strip_cropped_image_name(path):
 
     # Search a pose_config.yaml file to parse missing information
     pose_config_path = ""
-    for dirpath, _, filenames in os.walk(
-            os.path.join(project_path, "dlc-models")
-    ):
+    for dirpath, _, filenames in os.walk(os.path.join(project_path, "dlc-models")):
         for file in filenames:
             if file.endswith("pose_cfg.yaml"):
                 pose_config_path = os.path.join(dirpath, file)

deeplabcut/generate_training_dataset/trainingsetmanipulation.py

@@ -395,12 +395,12 @@ def _robust_path_split(path):
     sep = "\\" if "\\" in path else "/"
     splits = path.rsplit(sep, 1)
     if len(splits) == 1:
-        parent = '.'
+        parent = "."
         file = splits[0]
     elif len(splits) == 2:
         parent, file = splits
     else:
-        raise('Unknown filepath split for path {}'.format(path))
+        raise ("Unknown filepath split for path {}".format(path))
     filename, ext = os.path.splitext(file)
     return parent, filename, ext
 
@@ -425,10 +425,7 @@ def merge_annotateddatasets(cfg, trainingsetfolder_full):
             data = pd.read_hdf(file_path)
             AnnotationData.append(data)
         except FileNotFoundError:
-            print(
-                file_path,
-                " not found (perhaps not annotated)."
-            )
+            print(file_path, " not found (perhaps not annotated).")
 
     if not len(AnnotationData):
         print(
@@ -463,9 +460,7 @@ def merge_annotateddatasets(cfg, trainingsetfolder_full):
 
 
 def SplitTrials(
-    trialindex,
-    trainFraction=0.8,
-    enforce_train_fraction=False,
+    trialindex, trainFraction=0.8, enforce_train_fraction=False,
 ):
     """ Split a trial index into train and test sets. Also checks that the trainFraction is a two digit number between 0 an 1. The reason
     is that the folders contain the trainfraction as int(100*trainFraction).
@@ -488,8 +483,8 @@ def SplitTrials(
         train_fraction = round(trainFraction, 2)
         train_size = index_len * train_fraction
         shuffle = np.random.permutation(trialindex)
-        test_indices = shuffle[int(train_size):]
-        train_indices = shuffle[:int(train_size)]
+        test_indices = shuffle[int(train_size) :]
+        train_indices = shuffle[: int(train_size)]
         if enforce_train_fraction and not train_size.is_integer():
             train_indices, test_indices = pad_train_test_indices(
                 train_indices, test_indices, train_fraction,
@@ -510,8 +505,7 @@ def pad_train_test_indices(train_inds, test_inds, train_fraction):
     min_n_train = int(round(min_length_req * train_fraction))
     min_n_test = min_length_req - min_n_train
     mult = max(
-        math.ceil(n_train_inds / min_n_train),
-        math.ceil(n_test_inds / min_n_test),
+        math.ceil(n_train_inds / min_n_train), math.ceil(n_test_inds / min_n_test),
     )
     n_train = mult * min_n_train
     n_test = mult * min_n_test
@@ -576,8 +570,7 @@ def mergeandsplit(config, trainindex=0, uniform=True):
         Data = pd.read_hdf(fn + ".h5")
     except FileNotFoundError:
         Data = merge_annotateddatasets(
-            cfg,
-            Path(os.path.join(project_path, trainingsetfolder)),
+            cfg, Path(os.path.join(project_path, trainingsetfolder)),
         )
         if Data is None:
             return [], []
@@ -766,13 +759,13 @@ def create_training_dataset(
                 auxiliaryfunctions.edit_config(config, {"default_augmenter": "imgaug"})
                 augmenter_type = "imgaug"
         elif augmenter_type not in [
-                "default",
-                "scalecrop",
-                "imgaug",
-                "tensorpack",
-                "deterministic",
-            ]:
-                raise ValueError("Invalid augmenter type:", augmenter_type)
+            "default",
+            "scalecrop",
+            "imgaug",
+            "tensorpack",
+            "deterministic",
+        ]:
+            raise ValueError("Invalid augmenter type:", augmenter_type)
 
         # Loading the encoder (if necessary downloading from TF)
         dlcparent_path = auxiliaryfunctions.get_deeplabcut_path()

deeplabcut/pose_estimation_3d/camera_calibration.py

@@ -233,9 +233,7 @@ def calibrate_cameras(config, cbrow=8, cbcol=6, calibrate=False, alpha=0.4):
             )
 
             # Stereo Rectification
-            rectify_scale = (
-                alpha
-            )  # Free scaling parameter check this https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#fisheye-stereorectify
+            rectify_scale = alpha  # Free scaling parameter check this https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#fisheye-stereorectify
             R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(
                 cameraMatrix1,
                 distCoeffs1,

deeplabcut/pose_estimation_3d/triangulation.py

@@ -203,9 +203,7 @@ def triangulate(
                     )
                     stereo_file = auxiliaryfunctions.read_pickle(path_stereo_file)
                     cam_pair = str(cam_names[0] + "-" + cam_names[1])
-                    if_video_analyzed = (
-                        False
-                    )  # variable to keep track if the video was already analyzed
+                    if_video_analyzed = False  # variable to keep track if the video was already analyzed
                     # Check for the camera matrix
                     for k in metadata_["stereo_matrix"].keys():
                         if np.all(