How to use the clinica.utils.exceptions.ClinicaException function in clinica

from clinica.iotools.utils.data_handling import check_volume_location_in_world_coordinate_system
        from clinica.utils.inputs import clinica_file_reader
        from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
        from clinica.utils.input_files import T1W_NII

        # Reading BIDS
        # ============
        read_node = npe.Node(name="read_node",
                             interface=nutil.IdentityInterface(fields=['bids_images'],
                                                               mandatory_inputs=True))
        try:
            read_node.inputs.bids_images = clinica_file_reader(self.subjects,
                                                               self.sessions,
                                                               self.bids_directory,
                                                               T1W_NII)
        except ClinicaException as e:
            error_str_final = 'Clinica faced error(s) while trying to read files in your CAPS directory.\n'
            error_str_final += str(e)
            raise ClinicaBIDSError(error_str_final)

        check_volume_location_in_world_coordinate_system(read_node.inputs.bids_images, self.bids_directory)

        self.connect([
            (read_node, self.input_node, [('bids_images', 'input_images')])
        ])

def get_processed_images(caps_directory, subjects, sessions):
        """Extract processed image IDs in `caps_directory` based on `subjects`_`sessions`.

        Todo:
            [ ] Implement this static method in all pipelines
            [ ] Make it abstract to force overload in future pipelines
        """
        from clinica.utils.exceptions import ClinicaException
        import datetime
        from colorama import Fore
        from clinica.utils.stream import cprint
        now = datetime.datetime.now().strftime('%H:%M:%S')
        cprint('\n%s[%s] Pipeline finished with errors.%s\n' % (Fore.RED, now, Fore.RESET))
        cprint('%sCAPS outputs were not found for some image(s):%s' % (Fore.RED, Fore.RESET))
        raise ClinicaException('Implementation on which image(s) failed will appear soon.')

interface=nutil.IdentityInterface(fields=self.get_input_fields(),
                                                                          mandatory_inputs=True))
        all_errors = []
        d_input = []
        for tissue_number in self.parameters['dartel_tissues']:
            try:
                current_file = clinica_file_reader(self.subjects,
                                                   self.sessions,
                                                   self.caps_directory,
                                                   {'pattern': 't1/spm/segmentation/dartel_input/*_*_T1w_segm-'
                                                               + tissue_names[tissue_number] + '_dartelinput.nii*',
                                                    'description': 'Dartel input for tissue ' + tissue_names[tissue_number]
                                                                   + ' from T1w MRI',
                                                    'needed_pipeline': 't1-volume-tissue-segmentation'})
                d_input.append(current_file)
            except ClinicaException as e:
                all_errors.append(e)

        # Raise all errors if some happened
        if len(all_errors) > 0:
            error_message = 'Clinica faced errors while trying to read files in your BIDS or CAPS directories.\n'
            for msg in all_errors:
                error_message += str(msg)
            raise RuntimeError(error_message)

        # d_input is a list of size len(self.parameters['dartel_tissues'])
        #     Each element of this list is a list of size len(self.subjects)
        read_parameters_node.inputs.dartel_inputs = d_input

        self.connect([
            (read_parameters_node, self.input_node, [('dartel_inputs', 'dartel_input_images')])
        ])

+ '/*_pet_space-Ixi549Space_suvr-pons_pet.nii.gz',
                                                       'description': pet_type + ' PET in Ixi549 space',
                                                       'needed_pipeline': 'pet-volume'})
                except ClinicaException as e:
                    all_errors.append(e)

            elif no_pvc.lower() == 'false':
                try:
                    input_image = clinica_file_reader(self.subjects,
                                                      self.sessions,
                                                      self.caps_directory,
                                                      {'pattern': 'pet/preprocessing/group-' + self.parameters['group_id']
                                                                  + '_pet_space-Ixi549Space_pvc-rbv_suvr-pons_pet.nii.gz',
                                                       'description': pet_type + ' PET partial volume corrected (RBV) in Ixi549 space',
                                                       'needed_pipeline': 'pet-volume with PVC'})
                except ClinicaException as e:
                    all_errors.append(e)

            else:
                raise ValueError(no_pvc + ' is not a valid keyword for -no_pvc'
                                 + ':only True or False are accepted (string)')
        else:
            raise ValueError('Image type ' + image_type + ' unknown')
        try:
            dartel_input = clinica_group_reader(self.caps_directory,
                                                {'pattern': 'group-' + self.parameters['group_id'] + '_template.nii*',
                                                 'description': 'template file of group ' + self.parameters['group_id'],
                                                 'needed_pipeline': 't1-volume or t1-volume-create-dartel'})
        except ClinicaException as e:
            all_errors.append(e)

        # Raise all errors if some happened

def custom_traceback(exc_type, exc_value, exc_traceback):
    import traceback
    import math
    from colorama import Fore
    from clinica.utils.exceptions import ClinicaException
    from clinica.utils.stream import cprint

    if issubclass(exc_type, ClinicaException):
        cprint(exc_value)
    elif issubclass(exc_type, KeyboardInterrupt):
        cprint('\n%s[Error] Program interrupted by the user. Clinica will now exit...%s' %
               (Fore.RED, Fore.RESET))
    else:
        cprint(Fore.RED + '\n' + '*' * 23 + '\n*** Clinica crashed ***\n' + '*' * 23 + '\n' + Fore.RESET)
        cprint('%sException type:%s %s' % (Fore.YELLOW, Fore.RESET, exc_type.__name__))
        cprint('%sException value:%s %s' % (Fore.YELLOW, Fore.RESET, exc_value))
        cprint('Below are displayed information that were gathered when Clinica crashed. This will help to understand '
               'what happened if you transfer those information to the Clinica development team.\n')

        frames = traceback.extract_tb(exc_traceback)
        framewidth = int(math.ceil(math.log(len(frames)) / math.log(10)))
        filewidth = 0
        linewidth = 0
        functionwidth = 0

from colorama import Fore
    from clinica.utils.exceptions import ClinicaException

    if not os.path.isfile(tsv_file):
        raise ClinicaException(
            "\n%s[Error] The TSV file you gave is not a file.%s\n"
            "\n%sError explanations:%s\n"
            " - Clinica expected the following path to be a file: %s%s%s\n"
            " - If you gave relative path, did you run Clinica on the good folder?" %
            (Fore.RED, Fore.RESET,
             Fore.YELLOW, Fore.RESET,
             Fore.BLUE, tsv_file, Fore.RESET)
        )
    ss_df = pd.io.parsers.read_csv(tsv_file, sep='\t')
    if 'participant_id' not in list(ss_df.columns.values):
        raise ClinicaException(
            "\n%s[Error] The TSV file does not contain participant_id column (path: %s)%s" %
            (Fore.RED, tsv_file, Fore.RESET)
        )
    if 'session_id' not in list(ss_df.columns.values):
        raise ClinicaException(
            "\n%s[Error] The TSV file does not contain session_id column (path: %s)%s" %
            (Fore.RED, tsv_file, Fore.RESET)
        )
    participants = list(ss_df.participant_id)
    sessions = list(ss_df.session_id)

    # Remove potential whitespace in participant_id or session_id
    return [sub.strip(' ') for sub in participants], [ses.strip(' ') for ses in sessions]

"""


class ClinicaException(Exception):
    """ Base class for Clinica exceptions """


class ClinicaMissingDependencyError(ClinicaException):
    """ Base class for Clinica dependencies errors """


class ClinicaBIDSError(ClinicaException):
    """ Base class for BIDS errors """


class ClinicaCAPSError(ClinicaException):
    """ Base class for CAPS errors """

args.feature_label = 'RD'
            elif args.feature_type == 'dti_projection_ad':
                args.custom_file = '@subject/@session/noddi/postprocessing/dti-register-vertex-fsaverage/cortex-projection/@subject_@session_OnFsaverage_fwhm-@fwhm_measure-ad_hemi-@hemi.mgh'
                args.feature_label = 'AD'
            else:
                raise ClinicaException('Feature type ' + args.feature_type + ' not recognized. Use --custom_file '
                                       'to specify your own files (without --feature_type).')
        elif args.feature_type is None:
            if args.custom_file is None:
                cprint('No feature type selected: using cortical thickness as default value')
                args.custom_file = '@subject/@session/t1/freesurfer_cross_sectional/@subject_@session/surf/@hemi.thickness.fwhm@fwhm.fsaverage.mgh'
                args.feature_label = 'ct'
            else:
                cprint('Using custom features.')
                if args.feature_label is None:
                    raise ClinicaException('You must specify a --feature_label when using the --custom_files flag.')

        # Check if the group label has been existed, if yes, give an error to the users
        # Note(AR): if the user wants to compare Cortical Thickness measure with PET measure
        # using the group_id, Clinica won't allow it.
        # TODO: Modify this behaviour
        if os.path.exists(os.path.join(os.path.abspath(self.absolute_path(args.caps_directory)), 'groups', 'group-' + args.group_id)):
            error_message = 'group_id: ' + args.group_id + ' already exists, please choose another one or delete ' \
                            'the existing folder and also the working directory and rerun the pipeline'
            raise ClinicaException(error_message)

        pipeline = StatisticsSurface(
            caps_directory=self.absolute_path(args.caps_directory),
            tsv_file=self.absolute_path(args.subject_visits_with_covariates_tsv),
            base_dir=self.absolute_path(args.working_directory),
        )
        pipeline.parameters = {

# coding: utf8

"""
Clinica exceptions
"""


class ClinicaException(Exception):
    """ Base class for Clinica exceptions """


class ClinicaMissingDependencyError(ClinicaException):
    """ Base class for Clinica dependencies errors """


class ClinicaBIDSError(ClinicaException):
    """ Base class for BIDS errors """


class ClinicaCAPSError(ClinicaException):
    """ Base class for CAPS errors """

from clinica.utils.dwi import check_dwi_volume
        from clinica.utils.inputs import clinica_file_reader
        import clinica.pipelines.dwi_preprocessing_using_phasediff_fieldmap.dwi_preprocessing_using_phasediff_fieldmap_utils as utils
        from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
        import clinica.utils.input_files as input_files
        from clinica.utils.stream import cprint

        all_errors = []

        # DWI
        try:
            dwi_bids = clinica_file_reader(self.subjects,
                                           self.sessions,
                                           self.bids_directory,
                                           input_files.DWI_NII)
        except ClinicaException as e:
            all_errors.append(e)

        # DWI json
        try:
            dwi_json = clinica_file_reader(self.subjects,
                                           self.sessions,
                                           self.bids_directory,
                                           input_files.DWI_JSON)

            # Create list_eff_echo_spacings and list_enc_directions
            list_eff_echo_spacings = []
            list_enc_directions = []
            for json in dwi_json:
                [eff_echo_spacing, enc_direction] = utils.parameters_from_dwi_metadata(json)
                list_eff_echo_spacings.append(eff_echo_spacing)
                list_enc_directions.append(enc_direction)