How to use the clinica.iotools.converters.adni_to_bids.adni_utils.replace_sequence_chars function in clinica

Returns:

    """
    from clinica.iotools.converters.adni_to_bids.adni_utils import replace_sequence_chars, select_image_qc

    mri_qc_subj.columns = [x.lower() for x in mri_qc_subj.columns]
    sel_image = select_image_qc(list(visit_mri_list.IMAGEUID), mri_qc_subj)
    if sel_image is None:
        return None

    sel_scan = visit_mri_list[visit_mri_list.IMAGEUID == sel_image].iloc[0]

    image_dict = {'Subject_ID': subject_id,
                  'VISCODE': timepoint,
                  'Visit': visit_str,
                  'Sequence': replace_sequence_chars(sel_scan.SEQUENCE),
                  'Scan_Date': sel_scan['SCANDATE'],
                  'Study_ID': str(int(sel_scan.STUDYID)),
                  'Series_ID': str(int(sel_scan.SERIESID)),
                  'Image_ID': str(int(sel_scan.IMAGEUID)),
                  'Field_Strength': sel_scan.MAGSTRENGTH}

    return image_dict

if len(filtered_mprage.MagStrength.unique()) > 1:
        filtered_mprage = filtered_mprage[filtered_mprage.MagStrength == preferred_field_strength]

    # Sort by Series ID in case there are several images, so we keep the one acquired first
    filtered_mprage = filtered_mprage.sort_values('SeriesID')

    scan = filtered_mprage.iloc[0]

    # Check if selected scan passes QC (if QC exists)
    if not check_qc(scan, subject_id, visit_str, mprage_meta_subj, mri_quality_subj):
        return None

    n3 = scan.Sequence.find('N3')
    # Sequence ends in 'N3' or in 'N3m'
    sequence = scan.Sequence[:n3 + 2 + int(scan.Sequence[n3 + 2] == 'm')]
    sequence = replace_sequence_chars(sequence)

    return {'Subject_ID': subject_id,
            'VISCODE': timepoint,
            'Visit': visit_str,
            'Sequence': sequence,
            'Scan_Date': scan.ScanDate,
            'Study_ID': str(scan.StudyID),
            'Series_ID': str(scan.SeriesID),
            'Image_ID': str(scan.ImageUID),
            'Field_Strength': scan.MagStrength,
            'Original': False}

Returns:

    """
    from clinica.iotools.converters.adni_to_bids.adni_utils import replace_sequence_chars, select_image_qc

    sel_image = select_image_qc(list(visit_mri_list.IMAGEUID), mri_qc_subj)
    if sel_image is None:
        return None

    sel_scan = visit_mri_list[visit_mri_list.IMAGEUID == sel_image].iloc[0]

    image_dict = {'Subject_ID': subject_id,
                  'VISCODE': timepoint,
                  'Visit': visit_str,
                  'Sequence': replace_sequence_chars(sel_scan.SEQUENCE),
                  'Scan_Date': sel_scan['SCANDATE'],
                  'Study_ID': str(int(sel_scan.STUDYID)),
                  'Series_ID': str(int(sel_scan.SERIESID)),
                  'Image_ID': str(int(sel_scan.IMAGEUID)),
                  'Field_Strength': sel_scan.MAGSTRENGTH}

    return image_dict

cond_spgr = ((mprage_meta_subj_orig.Visit == visit_str)
                 & mprage_meta_subj_orig.Sequence.map(
                    lambda x: (x.lower().find('spgr') > -1)
                    & (x.lower().find('acc') < 0)
                    )
                 )

    filtered_scan = mprage_meta_subj_orig[cond_mprage | cond_spgr]

    if filtered_scan.shape[0] < 1:
        # TODO - LOG THIS
        cprint('NO MPRAGE Meta: ' + subject_id + ' for visit ' + timepoint + ' - ' + visit_str)
        return None

    scan = select_scan_qc_adni2(filtered_scan, mayo_mri_qc_subj, preferred_field_strength)
    sequence = replace_sequence_chars(scan.Sequence)

    return {'Subject_ID': subject_id,
            'VISCODE': timepoint,
            'Visit': visit_str,
            'Sequence': sequence,
            'Scan_Date': scan.ScanDate,
            'Study_ID': str(scan.StudyID),
            'Series_ID': str(scan.SeriesID),
            'Image_ID': str(scan.ImageUID),
            'Field_Strength': scan.MagStrength,
            'Original': True}

& (subject_pet_meta['Scan Date'] == qc_visit.EXAMDATE)]
                if original_pet_meta.shape[0] < 1:
                    # TODO Log somewhere subjects with problems
                    cprint('NO Screening: Subject - ' + subj + ' for visit ' + qc_visit.VISCODE2)
                    continue
            original_image = original_pet_meta.iloc[0]
            averaged_pet_meta = subject_pet_meta[(subject_pet_meta['Sequence'] == 'AV45 Co-registered, Averaged') & (
                subject_pet_meta['Series ID'] == original_image['Series ID'])]
            if averaged_pet_meta.shape[0] < 1:
                sel_image = original_image
                original = True
            else:
                sel_image = averaged_pet_meta.iloc[0]
                original = False
            visit = sel_image.Visit
            sequence = replace_sequence_chars(sel_image.Sequence)
            date = sel_image['Scan Date']
            study_id = sel_image['Study ID']
            series_id = sel_image['Series ID']
            image_id = sel_image['Image ID']

            row_to_append = pd.DataFrame(
                [[subj, qc_visit.VISCODE2, str(visit), sequence, date, str(study_id), str(series_id), str(image_id),
                  original]],
                columns=pet_av45_col)
            pet_av45_df = pet_av45_df.append(row_to_append, ignore_index=True)

    # Exceptions
    # ==========
    conversion_errors = [  # Eq_1
                         ('128_S_2220', 'm48')]

# Co-registered and Averaged image with the same Series ID of the original image
            averaged_pet_meta = subject_pet_meta[(subject_pet_meta['Sequence'] == 'Co-registered, Averaged')
                                                 & (subject_pet_meta['Series ID'] == original_image['Series ID'])]

            # If an explicit Co-registered, Averaged image does not exist,
            # the original image is already in that preprocessing stage.

            if averaged_pet_meta.shape[0] < 1:
                sel_image = original_image
                original = True
            else:
                sel_image = averaged_pet_meta.iloc[0]
                original = False

            visit = sel_image.Visit
            sequence = replace_sequence_chars(sel_image.Sequence)
            date = sel_image['Scan Date']
            study_id = sel_image['Study ID']
            series_id = sel_image['Series ID']
            image_id = sel_image['Image ID']

            row_to_append = pd.DataFrame(
                [[qc_visit.Phase, subj, qc_visit.VISCODE2, str(visit), sequence, date, str(study_id), str(series_id),
                  str(image_id), original]],
                columns=pet_fdg_col)
            pet_fdg_df = pet_fdg_df.append(row_to_append, ignore_index=True)

    # Exceptions
    # ==========
    conversion_errors = [  # NONAME.nii
                         ('031_S_0294', 'bl'),
                         ('037_S_1421', 'm36'),