How to use the clinica.bids.converters.adni_utils.replace_sequence_chars function in clinica

else:  # scan already selected above
        sequence = scan.Sequence[:scan.Sequence.find('N3') - 2]
        original = False

    if not qc_passed:
        if scan.Sequence == 'MP-RAGE':
            original_img_seq = 'MPR'
        else:  # 'MP-RAGE REPEAT'
            original_img_seq = 'MPR-R'

        processing_seq = qc_prev_sequence[qc_prev_sequence.find(';'):qc_prev_sequence.find('N3') - 2]
        sequence = original_img_seq + processing_seq
        # print sequence

    sequence = replace_sequence_chars(sequence)

    qc = mri_quality_subj[mri_quality_subj.LONIUID == 'S' + str(scan.SeriesID)]
    if qc.shape[0] > 0 and qc.iloc[0].PASS != 1:
        # TODO - LOG THIS
        print 'QC found but NOT passed, NONONONO'
        print 'Subject ' + subject_id + ' - Series: ' + str(scan.SeriesID) + ' - Study: ' + str(scan.StudyID)

    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),
            'Field_Strength': scan.MagStrength,
            'Original': original}

& (subject_pet_meta['Scan Date'] == qc_visit.EXAMDATE)]
                if original_pet_meta.shape[0] < 1:
                    # TODO Log somewhere subjects with problems
                    print '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'] == '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_fdg_col)
            pet_fdg_df = pet_fdg_df.append(row_to_append, ignore_index=True)

    images = pet_fdg_df
    # count = 0
    # total = images.shape[0]
    is_dicom = []
    image_folders = []

lambda x: ((x.lower().find('mprage') > -1) | (x.lower().find('mp-rage') > -1) | (
        x.lower().find('mp rage') > -1)) & (x.find('2') < 0)))

    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
        print 'NO MPRAGE Meta2: ' + 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),
            'Field_Strength': scan.MagStrength,
            'Original': True}

def dti_image(subject_id, timepoint, visit_str, ida_meta_scans, mri_qc_subj, enhanced):

    from clinica.bids.converters.adni_utils import replace_sequence_chars

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

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

    sequence = sel_scan.Sequence
    sequence = replace_sequence_chars(sequence)

    image_dict = {'Subject_ID': subject_id,
                  'VISCODE': timepoint,
                  'Visit': visit_str,
                  'Sequence': sequence,
                  'Scan_Date': sel_scan['Scan Date'],
                  'Study_ID': str(int(sel_scan.LONISID)),
                  'Series_ID': str(int(sel_scan.LONIUID)),
                  'Image_ID': str(int(sel_scan.IMAGEUID)),
                  'Field_Strength': sel_scan.MagStrength,
                  'Scanner': sel_scan.Scanner,
                  'Enhanced': enhanced}

    return image_dict

& (subject_pet_meta['Scan Date'] == qc_visit.EXAMDATE)]
                if original_pet_meta.shape[0] < 1:
                    # TODO Log somewhere subjects with problems
                    print '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)

    images = pet_av45_df
    # count = 0
    # total = images.shape[0]
    is_dicom = []
    image_folders = []