Remove duplicate function

clinica/iotools/bids_utils.py

@@ -465,146 +465,6 @@ def create_participants_df(
     return participant_df
 
 
-def create_sessions_dict_oasis(
-    clinical_data_dir: Path,
-    bids_dir: Path,
-    study_name: StudyName,
-    clinical_specifications_folder: Path,
-    bids_ids: list[str],
-    name_column_ids: str,
-    subj_to_remove: Optional[list[str]] = None,
-    participants_df: Optional[pd.DataFrame] = None,
-) -> dict:
-    """Extract the information regarding the sessions and store them in a dictionary (session M00 only).
-
-    Parameters
-    ----------
-    clinical_data_dir : Path
-        The path to the input folder.
-
-    bids_dir : Path
-        The path to the BIDS directory.
-
-    study_name : StudyName
-        The name of the study (Ex: ADNI).
-
-    clinical_specifications_folder : Path
-        The path to the clinical file.
-
-    bids_ids : list of str
-        The list of bids ids.
-
-    name_column_ids : str
-        The name of the column where the subject ids are stored.
-
-    subj_to_remove : list of str, optional
-        The list of subject IDs to remove.
-
-    participants_df : pd.DataFrame, optional
-        A pandas dataframe that contains the participants data (required for OASIS3 only).
-
-    Returns
-    -------
-    dict :
-        Session dict.
-    """
-    import numpy as np
-
-    from clinica.utils.stream import cprint
-
-    subj_to_remove = subj_to_remove or []
-    location = f"{study_name.value} location"
-    sessions = pd.read_csv(clinical_specifications_folder / "sessions.tsv", sep="\t")
-    sessions_fields = sessions[study_name.value]
-    field_location = sessions[location]
-    sessions_fields_bids = sessions["BIDS CLINICA"]
-    fields_dataset = []
-    fields_bids = []
-    sessions_dict = {}
-
-    for i in range(0, len(sessions_fields)):
-        if not pd.isnull(sessions_fields[i]):
-            fields_bids.append(sessions_fields_bids[i])
-            fields_dataset.append(sessions_fields[i])
-
-    for i in range(0, len(sessions_fields)):
-        # If the i-th field is available
-        if not pd.isnull(sessions_fields[i]):
-            # Load the file
-            tmp = field_location[i].split("/")
-            location = tmp[0]
-            if len(tmp) > 1:
-                sheet = tmp[1]
-            else:
-                sheet = 0
-
-            file_to_read_path = clinical_data_dir / location
-            file_ext = os.path.splitext(location)[1]
-            if file_ext == ".xlsx":
-                file_to_read = pd.read_excel(file_to_read_path, sheet_name=sheet)
-            elif file_ext == ".csv":
-                file_to_read = pd.read_csv(file_to_read_path)
-            else:
-                raise ValueError(
-                    f"Unknown file extension {file_ext}. Expecting either .xlsx or .csv."
-                )
-
-            for r in range(0, len(file_to_read.values)):
-                # Extracts the subject ids columns from the dataframe
-                subj_id = file_to_read.iloc[r][name_column_ids]
-                if hasattr(subj_id, "dtype"):
-                    if subj_id.dtype == np.int64:
-                        subj_id = str(subj_id)
-                # Removes all the - from
-                subj_id_alpha = str(subj_id[0:3] + "IS" + subj_id[3] + subj_id[5:9])
-
-                # Extract the corresponding BIDS id and create the output file if doesn't exist
-                subj_bids = [s for s in bids_ids if subj_id_alpha in s]
-                if len(subj_bids) == 0:
-                    # If the subject is not an excluded one
-                    if subj_id not in subj_to_remove:
-                        cprint(
-                            f"{sessions_fields[i]} for {subj_id} not found in the BIDS converted.",
-                            "info",
-                        )
-                else:
-                    subj_bids = subj_bids[0]
-                    subj_dir = bids_dir / subj_bids
-                    session_names = get_bids_sess_list(subj_dir)
-                    for s in session_names:
-                        s_name = s.replace("ses-", "")
-                        if study_name == StudyName.OASIS3:
-                            row = file_to_read[
-                                file_to_read["MR ID"].str.startswith(subj_id)
-                                & file_to_read["MR ID"].str.endswith(s_name)
-                            ].iloc[0]
-                        else:
-                            row = file_to_read.iloc[r]
-                        if subj_bids not in sessions_dict:
-                            sessions_dict.update({subj_bids: {}})
-                        if s_name not in sessions_dict[subj_bids].keys():
-                            sessions_dict[subj_bids].update({s_name: {"session_id": s}})
-                        (sessions_dict[subj_bids][s_name]).update(
-                            {sessions_fields_bids[i]: row[sessions_fields[i]]}
-                        )
-                        # Calculate the difference in months for OASIS3 only
-                        if (
-                            study_name == StudyName.OASIS3
-                            and sessions_fields_bids[i] == "age"
-                        ):
-                            diff_years = (
-                                float(sessions_dict[subj_bids][s_name]["age"])
-                                - participants_df[
-                                    participants_df["participant_id"] == subj_bids
-                                ]["age_bl"]
-                            )
-                            (sessions_dict[subj_bids][s_name]).update(
-                                {"diff_months": round(float(diff_years) * 12)}
-                            )
-
-    return sessions_dict
-
-
 def create_scans_dict(
     clinical_data_dir: Path,
     study_name: StudyName,