@@ -12,11 +12,8 @@
 from itertools import islice
 from pathlib import Path
 
-import numpy as np
 import pandas as pd
-from tqdm import tqdm
 
-import deeplabcut as dlc
 from deeplabcut.utils import auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import collect_video_paths
 
@@ -86,129 +83,6 @@ def convertcsv2h5(config: str | Path, userfeedback=True, scorer=None):
             print("Attention:", folder, "does not appear to have labeled data!")
 
 
-def adapt_labeled_data_to_new_project(
-    config_path: str | Path,
-    remove_old_bodyparts=False,
-    other_scorer=False,
-    userfeedback=False,
-):
-    """Given the config.yaml file, this function will convert the labels of an ancient
-    project to a new project. For this, the labeled data must be in the project folder,
-    under the labeled-data folder and with the same configuration as all deeplabcut
-    projects.
-
-    Args:
-        config_path (str): The path to the config.yaml file.
-        remove_old_bodyparts (bool): If True, old bodyparts not in the new project are
-            removed from the dataframe. Defaults to False.
-        other_scorer (bool): If True, the labels will be converted to the new scorer. Defaults to False.
-        userfeedback (bool): If true the user will be asked specifically
-            for each folder in labeled-data if the containing csv
-            shall be converted to hdf format. Defaults to True.
-    """
-    # Load the config file
-    cfg = dlc.auxiliaryfunctions.read_config(config_path)
-
-    # Get the Project path
-    project_path = cfg["project_path"]
-
-    # Get the bodyparts
-    bodyparts = cfg["multianimalbodyparts"]
-    print("New Bodyparts:", bodyparts)
-
-    # Iterate over each labeled data video
-
-    # Use tqdm for a progress bar
-    for video in tqdm.tqdm(cfg["video_sets"]):
-        print("Video:", video)
-
-        video_name = video.split("\\")[-1]
-        # discard the file extension
-        video_name = video_name.split(".")[0]
-        # Load the csv file
-        label_path = Path(project_path) / "labeled-data" / video_name
-        csv_files = [f.name for f in label_path.iterdir() if f.name.endswith(".csv")]
-        if not csv_files:
-            print("No csv file in the folder:", label_path)
-        else:
-            csv_path = str(label_path / csv_files[0])
-            df = pd.read_csv(csv_path, header=None)
-
-            # get the scorer
-            if other_scorer:
-                scorer = cfg["scorer"]
-                # Change the scorer in the dataframe
-                df.iloc[0, 3:] = pd.Series([scorer] * len(df.columns[3:]))
-
-            else:
-                scorer = df.iloc[0, 3]
-
-            # Get the individuals
-            individuals = np.unique(df.iloc[1, 3:])
-
-            # Get the old bodyparts
-            old_bodyparts = np.unique(df.iloc[2, 3:])
-            print("Old bodyparts:", old_bodyparts)
-
-            # Get the number of old bodyparts
-            num_of_old_bodyparts = len(old_bodyparts)
-
-            # Bodyparts to add
-            print("Bodyparts to add:", set(bodyparts) - set(old_bodyparts))
-
-            # If a bodypart is missing, add it to the dataframe
-            for index, bodypart in enumerate(bodyparts):
-                if bodypart not in old_bodyparts:
-                    num_of_old_bodyparts += 1
-                    for i, individual in enumerate(individuals):
-                        # create the columns for the bodypart, concatenate, the individual, the bodypart, and nan values
-                        x_column = pd.concat(
-                            [
-                                pd.Series(scorer),
-                                pd.Series(individual),
-                                pd.Series(bodypart),
-                                pd.Series("x"),
-                                pd.Series(np.nan, index=df.index),
-                            ],
-                            axis=0,
-                            ignore_index=True,
-                        )
-                        y_column = pd.concat(
-                            [
-                                pd.Series(scorer),
-                                pd.Series(individual),
-                                pd.Series(bodypart),
-                                pd.Series("y"),
-                                pd.Series(np.nan, index=df.index),
-                            ],
-                            axis=0,
-                            ignore_index=True,
-                        )
-                        # Insert the columns in the dataframe
-                        df.insert(
-                            i * 2 * num_of_old_bodyparts + index * 2 + 3,
-                            "insert_" + bodypart + "_x" + individual,
-                            x_column,
-                        )
-                        df.insert(
-                            i * 2 * num_of_old_bodyparts + index * 2 + 4,
-                            "insert" + bodypart + "_y" + individual,
-                            y_column,
-                        )
-
-            # If the old bodyparts are not in the new project, remove them
-            if remove_old_bodyparts:
-                for bodypart in old_bodyparts:
-                    if bodypart not in bodyparts:
-                        df = df.drop(df.columns[df.iloc[2, :] == bodypart], axis=1)
-
-            # Save the dataframe
-            df.to_csv(csv_path, index=False, header=False)
-
-    # Create/Update the h5 file
-    convertcsv2h5(config_path, userfeedback=userfeedback)
-
-
 # TODO: @deruyter92 2026-05-20: this function uses videotype instead of video_extensions.
 def analyze_videos_converth5_to_csv(video_folder, videotype=None, listofvideos=False):
     """By default the output poses (when running analyze_videos) are stored as