#     subject_working_dir = os.path.join(working_dir, subject_id)
#     subject_ds_dir = os.path.join(ds_dir, subject_id)

## #### ## indent here

test_dir = '/scr/adenauer2/Franz/LeiCA_NKI_test/' # '/Users/franzliem/Desktop/mattest/'
subject_working_dir = test_dir + 'wd'
subject_ds_dir = test_dir + 'ds'
#time_series_file = '/Users/franzliem/Desktop/LeiCA/0144314/residual_filt_norm_warp.nii.gz'
time_series_file = '/scr/adenauer2/Franz/LeiCA_NKI/results/0129973/rsfMRI_preprocessing/epis_MNI_3mm/03_denoised_BP_tNorm/TR_645/residual_filt_norm_warp.nii.gz'

# INPUT PARAMETERS for pipeline

bp_freq_list = [(None,None), (0.01, 0.1)]


parcellations_dict = {}
parcellations_dict['msdl'] = {'nii_path': os.path.join(template_dir, 'parcellations/msdl_atlas/MSDL_rois/msdl_rois.nii'),
                          'is_probabilistic': True}

extraction_methods_list = ['correlation', 'sparse_inverse_covariance']

calc_con_mats.connectivity_matrix_wf(time_series_file,
                                     working_dir=subject_working_dir,
                                     ds_dir=subject_ds_dir,
                                     parcellations_dict=parcellations_dict,
                                     extraction_methods_list=extraction_methods_list,
                                     bp_freq_list=bp_freq_list,
                                     use_n_procs=use_n_procs,
                                     plugin_name=plugin_name)
parcellations_dict = {}
# parcellations_dict['msdl'] = {
#     'nii_path': os.path.join(template_dir, 'parcellations/msdl_atlas/MSDL_rois/msdl_rois.nii.gz'),
#     'is_probabilistic': True}
# parcellations_dict['craddock_205'] = {
#     'nii_path': os.path.join(template_dir, 'parcellations/craddock_2012/scorr_mean_single_resolution/scorr_mean_parc_n_21_k_205_rois.nii.gz'),
#     'is_probabilistic': False}
parcellations_dict['gordon'] = {
    'nii_path': os.path.join(template_dir, 'parcellations/Gordon_2014_Parcels/Parcels_MNI_111_sorted.nii.gz'),
    'is_probabilistic': False}


extraction_methods_list = ['correlation', 'sparse_inverse_covariance']


# fixme
# ignore warning from np.rank
import warnings

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    calc_con_mats.connectivity_matrix_wf(subjects_list=full_subjects_list,
                                         preprocessed_data_dir=preprocessed_data_dir,
                                         working_dir=working_dir,
                                         ds_dir=ds_dir,
                                         parcellations_dict=parcellations_dict,
                                         extraction_methods_list=extraction_methods_list,
                                         bp_freq_list=bp_freq_list,
                                         use_n_procs=use_n_procs,
                                         plugin_name=plugin_name)