diff --git a/bids/README.md b/bids/README.md
deleted file mode 100644
index 13b4f47..0000000
--- a/bids/README.md
+++ /dev/null
@@ -1,235 +0,0 @@
-This script exports imaging data from the NeuroSpin archive into the 
-BIDS format ([Brain Imaging Data Structure](http://bids.neuroimaging.io)).
-The BIDS format has been selected because it is simple, easy to share
-and supported by lots of software. We are focused on MRI data but other
-modalities can be added (diffusion imaging, behavioral, ...). For a full
-description, please consult the
-[BIDS specifications](http://bids.neuroimaging.io/bids_spec1.0.0.pdf).
-
-This script import data, but also:
-* all files such as README, CHANGES, dataset_descrption.json ...
-* deface anatomical data if you need
-* use the Bids-validator
-
-# Dependencies (can simply pip install packages)
-
-        pip install pandas --user
-        pip install mne --user
-        pip install mne-bids --user
-        pip install pydeface --user
-
-# Installation and usage:
-## With the installation of the module
-You can install the unicog repository and use `neurospin_to_bids.py` from anywhere.
-To install the module, please tape:
-
-        cd <path_to_download_to>
-        git clone https://github.com/neurospin/unicog.git
-        python setup.py install --user
-
-and use as follow:
-
-        neurospin_to_bids.py -h
-
-## Without the installation of the module
-If you don't want install the module, you can just use the `neurospin_to_bids.py` script as a python script.
-Simply download the unicog repository:
-
-        cd <path_to_download_to>
-        git clone https://github.com/neurospin/unicog.git
-
-and use as follow for instance:
-                                        
-        python <path_to_download_to>/unicog/bids/neurospin_to_bids.py -h
-
-
-# Preparation of data
-## Basic importation
-**To import data from NeuroSpin, you have to be connected to the NeuroSpin network.** 
-The information about subjects and data to import are stored into a **exp_info** directory. For instance:
-
-        ./exp_info
-        ├── participants.tsv
-
-
-See a small example at [https://github.com/neurospin/unicog/tree/master/bids/test_dataset/](https://github.com/neurospin/unicog/tree/master/bids/test_dataset/)
-
-Also [https://github.com/neurospin/unicog/tree/master/unicogfmri/localizer_pypreprocess/scripts/exp_info](https://github.com/neurospin/unicog/tree/master/unicogfmri/localizer_pypreprocess/scripts/exp_info)
-
-
-## Advanced importation
-The importaiton of events are also possible if the *_events.tsv files are correctly set up.
-Here is an example:
-
-        ./exp_info
-        ├── participants.tsv
-        └── recorded_events
-            ├── export_events.py
-            ├── sub-01
-            │   └── ses-01
-            │       └── func
-            │           └── sub-01_ses-01_task-loc_events.tsv
-            └── sub-02
-                └── func
-                    └── sub-02_task-loc_events.tsv
-
-# Importation of data
-Now we set all files into **exp_info** directory, you can launch the importation:
-
-        cd <path_where_is_exp_info>
-        python neurospin_to_bids.py
-
-* The `<path_where_is_exp_info` folder must contain an `exp_info` subfolder containing the participants.tsv file. For more details on the content and structure of these files, read the Additional information section.
-* The `neurospin_to_bids.py` script will export files from the NeuroSpin archive based on the information contained in the **exp_info** directory. The script when used as a bash command accept three optional arguments:
-    * **-root_path**: specifies the target folder - by default the current directory.
-    * **-dataset_name**: the folder name to export the dataset to, by default subfolder `bids_dataset` of the target folder.
-    * **-dry-run**: True/False - this mode will test the importaiton without to import data. A list of possible importation and warnings will be displayed.
-
-If instead we were to specify the target folder (the one containing an
-`exp_info` subfolder) and a name for the BIDS dataset subfolder, we would
-run the command as follows:
-
-        neurospin_to_bids.py -root_path some_path -dataset_name my_dataset
-
-To read the script documentation you can write:
-
-        neurospin_to_bids.py -h
-
-# Additionnal information
-
-## Basic BIDS organization
-For anatomical and functional data, the bids nomenclature corresponds to the following organisation of files.
-
-Anatomical:
-
-        sub­-<participant_label>/
-            [ses-<session_label>/]
-                anat/
-                    sub­-<participant_label>[_ses-<session_label>]_T1w.nii[.gz]
-
-Functional:
-
-        sub­-<participant_label>/
-            [ses-<session_label>/]
-                func/
-                    sub­-<participant_label>[_ses-<session_label>]_task-­<task_label>[_run-­<run_label>]_bold.nii[.gz]
-
-As seen by the examples, if you have a session level, a `ses-<session_label>`
-subfolder is added under the `sub­-<participant_label>` folder and it would
-then be the one to contain the modality folders (here, `anat` or `func`).
-Moreover it should also form part of the file names.
-
-The run level `run-<run_label>` is optional if there is only one functional
-run for a particular task.
-
-There are plenty more optional fields to include in the file names depending
-on your needs. For more details on that please check directly the
-[BIDS specifications](http://bids.neuroimaging.io/bids_spec1.0.0.pdf).
-
-Fmap:
-This script has an implementation of **case 4: Multiple phase encoded directions** of the BIDS specification.
-
-        sub­-<participant_label>/
-            [ses-<session_label>/]
-                fmap/
-                    sub­<participant_label>[_ses-<session_label>][_acq-<label>]_dir-<label>[_run-<index>]_epi.nii[.gz]
-                    sub­<participant_label>[_ses-<session_label>][_acq-<label>]_dir-<label>[_run-<index>]_epi.json
-					
-					
-
-## Files in the `exp_info` folder specifying the data download
-
-Only one file is mandatory in the `exp_info` directory: `participants.tsv`.
-Note that this file is not part of the BIDS standard. It is defined to contain
-the minimum information needed to simplify creating a BIDS dataset with the
-data from the NeuroSpin server.
-
-### Participants.tsv
-
-Contains information about the participants and their acquisitions.
-When there are multiple sessions per subject (with different acquisition
-dates), then the _session_label_ column is mandatory. 
-
-
-        participant_id	NIP		infos_participant		session_label	acq_date	acq_label	location	to_import
-        sub-01		tr070015	{"sex":"F", "age":"45"}		01		2010-06-28		trio		[['2','anat','T1w'],['9','func','task-loc_std_run-01_bold'],('10','func','task-loc_std_run-02_bold']]
-        sub-02		ap100009	{"sex":"M", "age":"35"}				2010-07-01		trio		[['2','anat','T1w'],['9','func','task-loc_std_bold']]
-
-The _NIP_ column will only be used to identify subjects in the NeuroSpin
-database and will not be included in any way on the BIDS dataset to ensure
-proper de-identification. Moreover we recommend you to not use the NIP as
-the _participant_label_ to avoid the need of future de-identification of
-the BIDS dataset before publication.
-
-The _participant_label_ and _session_label_ are taken from this file to
-create the folders and file names in the BIDS dataset, every other column
-will be added to a new `participants.tsv` file included under the
-`bids_dataset` top folder.
-
-#### User case with 2 sessions the same day with the same participant
-For instance, if a participant undergoes an examen in the morning and in the afternoon, 
-you have to complete the NIP with the number of session. The nip level in Neurospin
-is labelled as follow : '<nip>-<exman-number>-<automatic-number>' 
-The examen number is automatically incremented for each new examen. Don't mange about
-the automatic number. 
-
-Here is an example for the `participants.tsv` file:
-
-
-        participant_id  NIP             infos_participant               session_label   acq_date	acq_label        location        to_import
-        sub-01          tt989898_6405   {"sex":"F", "age":"45"}         01              2010-06-28	      trio            [['2','anat','T1w'],['9','func','task-loc_std_run-01_bold']]
-        sub-01          tt989898_6405                                	02 		        2010-06-28	      trio            [['9','func','task-loc_std_bold']]
-
-#### User case for fmap importation (Multiple phase encoded directions)
-
-Here is an example for the `participants.tsv` file:
-
-
-        participant_id  NIP             infos_participant               session_label   acq_date	acq_label        location        to_import
-        sub-01          tt989898_6405   {"sex":"F", "age":"45"}         01              2010-06-28	      prisma            [['24','anat','T1w'],['13','func','task-number_dir-ap_run-01_bold'],['14','func','task-number_dir-ap_run-01_sbref'],['5','fmap','dir-ap_epi',{'intendedFor':'/fmri/sub-301_task-number_dir-ap_run-01_bold'}]]
-
-#### User case for adding a field into the json file
-Here we are adding the IndendedFor field into the **fmap/sub-301_dir-ap_epi.json**. This field is not mandatory, but recommended. It seems 
-if you use fmriprep, this field is not directly read and fmriprep use the **PhaseEncodingDirection" information which give by the scanner.
-
-		participant_id  NIP     infos_participant       session_label   acq_date        acq_label       location        to_import
-		sub-301 jj140402        "{""sex"":""F"", ""age"":""6""}"                2020-01-15          prisma  [['24','anat','T1w'],['13','func','task-number_dir-ap_run-01_bold'),['5','fmap','sub-301_dir-ap_epi',{'IntendedFor':'/func/task-number_dir-ap_run-01_bold'}]]
-
-
-
-# Importation of events
-The importation of events can not be automatic because very often the events have to extract from log files depending on your stimulation presentation program (expyriment for python).
-Here we propose a possible solution, but we are truly free to import yourself into the bids_datset repository the events.
-The events for functional runs will be automatically copied in the BIDS dataset if the files are available in a `recorded_events` folder that already respect the bids structure. Which means that files would have the same fields as the bold.nii files in its file name but its final name part would be events.tsv instead, for example:
-
-    <data_root>/exp_info/recorded_events/sub-<sub_label>[/ses-<ses_label>]/func/sub-*_<task>_events.tsv
-
-Here is an example of `sub-*_<task>_events.tsv` following the BIDS standard:
-
-        onset   duration   trial_type
-        0.0     1          computation_video
-        2.4     1          computation_video
-        8.7     1          h_checkerboard
-        11.4    1          r_hand_audio
-        15.0    1          sentence_audio
-
-the onset, duration and trial_type columns are the only mandatory ones. onset and duration fields should be expressed in second. Other information can be added to events.tsv files such as ​response_time or other arbitrary additional columns respecting subject anonimity. See the [BIDS specification](http://bids.neuroimaging.io/bids_spec1.0.0.pdf).
-
-
-# Deface
-If you want to import data and share them with other laboratories or on an open server, you have to anonymize them. For that, the bids importation remove all fields in the header containing specific
-information such as "Patient's name" and the script of importation will propose to deface anatomical data. The pydeface python is used to propose this step.
-If you need to deface, ensure that pydeface is installed on your workstation.
-
-Please see [https://github.com/poldracklab/pydeface](https://github.com/poldracklab/pydeface)
-
-# Summary of importation
-## Files imported and warnings
-A summary will be displayed at the end of importation into the terminal. The summart is also saved into ./report/download_report_*.csv file. This file is not in the bids_dataset repository because it is not part of BIDS.
-
-## BIDS validation
-If you are selected the bids validation option, the summary is saved in ./report/report_bids_valisation.txt .
-
-# Notes
-* Note 1: if the importation has been interrupted or partially then, then launch again the script. The last partially downloaded data folder will be redownloaded from scratch.
-* Note 2: the .tsv extension means "tabulation separated values", so each value must be separated by a tabulation and not commas, spaces or dots. If files in `exp_info` are not tsv, most likely the `neurospin_to_bids.py` script will fail. Please make sure your files comply with your favorite text editor.
diff --git a/bids/__init__.py b/bids/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/bids/neurospin_to_bids.py b/bids/neurospin_to_bids.py
deleted file mode 100755
index eee5d0a..0000000
--- a/bids/neurospin_to_bids.py
+++ /dev/null
@@ -1,907 +0,0 @@
-#! /usr/bin/env python3
-
-import argparse
-import glob
-import json
-import os
-import re
-import shutil
-import subprocess
-import time
-from ast import literal_eval
-from collections import OrderedDict
-from itertools import combinations
-from pathlib import Path
-
-import mne
-import pandas as pd
-import pydeface.utils as pdu
-import yaml
-from bids_validator import BIDSValidator
-from mne_bids import make_dataset_description, write_raw_bids
-from pkg_resources import DistributionNotFound, Requirement, resource_filename
-
-NEUROSPIN_DATABASES = {
-    'prisma': '/neurospin/acquisition/database/Prisma_fit',
-    'trio': '/neurospin/acquisition/database/TrioTim',
-    '7T': '/neurospin/acquisition/database/Investigational_Device_7T',
-    'meg': '/neurospin/acquisition/neuromag/data',
-}
-
-
-class Bcolors:
-    """Colors to improve print statements' readability
-
-    Example:
-        `print(f"{Bcolors.OKBLUE}Hello World!{Bcolors.ENDC}")`
-    """
-    HEADER = '\033[95m'
-    OKBLUE = '\033[94m'
-    OKGREEN = '\033[92m'
-    WARNING = '\033[93m'
-    FAIL = '\033[91m'
-    ENDC = '\033[0m'
-    BOLD = '\033[1m'
-    UNDERLINE = '\033[4m'
-
-
-def yes_no(question: str, default: str = None) -> bool:
-    """A simple yes/no prompt
-
-    Args:
-        question (str): The question to be answered.
-        default (bool, optional): Default answer to `question`.
-                                  Defaults to None.
-
-    Raises:
-        ValueError: Raise `ValueError` when default answer is not
-                    `yes` or `no`.
-
-    Returns:
-        bool: Boolean answer to the yes/no question.
-    """
-    valid = {"yes": True, "y": True, "no": False, "n": False}
-    if default is None:
-        prompt = " [y/n] "
-    elif default == "yes":
-        prompt = " [Y/n] "
-    elif default == "no":
-        prompt = " [y/N] "
-    else:
-        raise ValueError(f"invalid default answer: '{default}'")
-
-    while True:
-        choice = input(question + prompt).lower()
-        if default is not None and choice == '':
-            return valid[default]
-        elif choice in valid:
-            return valid[choice]
-        else:
-            print("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n")
-
-
-def file_manager_default_file(main_path,
-                              filter_list,
-                              file_tag,
-                              file_type='*',
-                              allow_other_fields=True):
-    """Path to the most specific file with respect to optional filters.
-
-    Each filter is a list [key, value]. Like [sub, 01] or [ses, 02].
-
-    Following BIDS standard files can be of the form
-    [key-value_]...[key-value_]file_tag.file_type.
-    """
-    filters = []
-    for n in list(reversed(range(1, len(filter_list) + 1))):
-        filters += combinations(filter_list, n)
-    filters += [[]]
-    for filt in filters:
-        found = get_bids_files(main_path,
-                               sub_folder=False,
-                               file_type=file_type,
-                               file_tag=file_tag,
-                               filters=filt,
-                               allow_other_fields=allow_other_fields)
-        if found:
-            return found[0]
-    return None
-
-
-def file_reference(img_path):
-    reference = {}
-    reference['file_path'] = img_path
-    reference['file_basename'] = os.path.basename(img_path)
-    parts = reference['file_basename'].split('_')
-    tag, typ = parts[-1].split('.', 1)
-    reference['file_tag'] = tag
-    reference['file_type'] = typ
-    reference['file_fields'] = ''
-    reference['fields_ordered'] = []
-    for part in parts[:-1]:
-        reference['file_fields'] += part + '_'
-        field, value = part.split('-')
-        reference['fields_ordered'].append(field)
-        reference[field] = value
-    return reference
-
-
-def get_bids_files(main_path,
-                   file_tag='*',
-                   file_type='*',
-                   sub_id='*',
-                   file_folder='*',
-                   filters=[],
-                   ref=False,
-                   sub_folder=True,
-                   allow_other_fields=True):
-    """Return files following bids spec
-
-    Filters are of the form (key, value). Only one filter per key allowed.
-    A file for which a filter do not apply will be discarded.
-    """
-    if sub_folder:
-        files = os.path.join(main_path, 'sub-*', 'ses-*')
-        if glob.glob(files):
-            files = os.path.join(
-                main_path, 'sub-%s' % sub_id, 'ses-*', file_folder,
-                'sub-%s*_%s.%s' % (sub_id, file_tag, file_type))
-        else:
-            files = os.path.join(
-                main_path, 'sub-%s' % sub_id, file_folder,
-                'sub-%s*_%s.%s' % (sub_id, file_tag, file_type))
-    else:
-        files = os.path.join(main_path, '*%s.%s' % (file_tag, file_type))
-
-    files = glob.glob(files)
-    files.sort()
-    if filters:
-        if not allow_other_fields:
-            files = [
-                file_ for file_ in files
-                if len(os.path.basename(file_).split('_')) <= len(filters) + 1
-            ]
-        files = [file_reference(file_) for file_ in files]
-        for key, value in filters:
-            files = [
-                file_ for file_ in files
-                if (key in file_ and file_[key] == value)
-            ]
-    else:
-        files = [file_reference(file_) for file_ in files]
-
-    if ref:
-        return files
-    else:
-        return [ref_file['file_path'] for ref_file in files]
-
-
-def bids_copy_events(behav_path='exp_info/recorded_events',
-                     data_root_path='',
-                     dataset_name=None):
-    dataset_name, data_path = get_bids_default_path(data_root_path,
-                                                    dataset_name)
-    #print(os.path.join(data_root_path, behav_path, 'sub-*', 'ses-*'))
-    if glob.glob(os.path.join(data_root_path, behav_path, 'sub-*', 'ses-*')):
-        sub_folders = glob.glob(
-            os.path.join(behav_path, 'sub-*', 'ses-*', 'func'))
-    else:
-        #print(os.path.join(data_root_path, behav_path,'sub-*', 'func'))
-        sub_folders = glob.glob(
-            os.path.join(data_root_path, behav_path, 'sub-*', 'func'))
-
-    # raise warning if no folder is found in recorded events
-    if not sub_folders:
-        print(
-            f'{Bcolors.WARNING}BIDS IMPORTATION WARNING: NO EVENTS FILE{Bcolors.ENDC}'
-        )
-    else:
-        for sub_folder in sub_folders:
-            #file_path = sub_folder.replace(behav_path + '/', '')
-            file_path = sub_folder
-            for file_name in os.listdir(os.path.join(sub_folder)):
-
-                # dest_directory = os.path.join(data_path, file_path)
-                # if not os.path.exists(dest_directory):
-                #     os.makedirs(dest_directory)
-
-                file_ext = []
-                last = ''
-                root, last = os.path.split(sub_folder)
-                while last != 'recorded_events':
-                    if last == '':
-                        break
-                    file_ext.append(last)
-                    sub_folder = root
-                    root, last = os.path.split(sub_folder)
-
-                list_tmp = []
-                elements_path = [[item, '/'] for item in reversed(file_ext)]
-                elements_path = [(list_tmp.append(item[0]),
-                                  list_tmp.append(item[1]))
-                                 for item in elements_path]
-                ext = ''.join(list_tmp)
-                shutil.copyfile(os.path.join(file_path, file_name),
-                                os.path.join(data_path, ext, file_name))
-
-
-def get_bids_path(data_root_path='',
-                  subject_id='01',
-                  folder='',
-                  session_id=None):
-    if session_id is None:
-        session_id = ''
-    else:
-        session_id = 'ses-' + session_id
-    return os.path.join(data_root_path, 'sub-' + subject_id, session_id, folder)
-
-
-def get_bids_file_descriptor(subject_id,
-                             task_id=None,
-                             session_id=None,
-                             acq_label=None,
-                             dir_label=None,
-                             rec_id=None,
-                             run_id=None,
-                             run_dir=None,
-                             file_tag=None,
-                             file_type=None):
-    """ Creates a filename descriptor following BIDS.
-
-    subject_id refers to the subject label
-    task_id refers to the task label
-    run_id refers to run index
-    acq_label refers to acquisition parameters as a label
-    rec_id refers to reconstruction parameters as a label
-    """
-    if 'sub-' or 'sub' in subject_id:
-        descriptor = subject_id
-    else:
-        descriptor = 'sub-{0}'.format(subject_id)
-    if (session_id is not None) and (session_id is not pd.np.nan):
-        descriptor += '_ses-{0}'.format(session_id)
-    if (task_id is not None) and (task_id is not pd.np.nan):
-        descriptor += '_task-{0}'.format(task_id)
-    if (acq_label is not None) and (acq_label is not pd.np.nan):
-        descriptor += '_acq-{0}'.format(acq_label)
-    if (dir_label is not None) and (dir_label is not pd.np.nan):
-        descriptor += '_dir-{0}'.format(dir_label)
-    if (rec_id is not None) and (rec_id is not pd.np.nan):
-        descriptor += '_rec-{0}'.format(rec_id)
-    if (run_dir is not None) and (run_dir is not pd.np.nan):
-        descriptor += '_dir-{0}'.format(run_dir)
-    if (run_id is not None) and (run_id is not pd.np.nan):
-        descriptor += '_run-{0}'.format(run_id)
-    if (file_tag is not None) and (file_type is not None):
-        descriptor += '_{0}.{1}'.format(file_tag, file_type)
-    return descriptor
-
-
-def get_bids_default_path(data_root_path='', dataset_name=None):
-    """Default experiment raw dataset folder name"""
-    if dataset_name is None:
-        dataset_name = 'bids_dataset'
-    return (dataset_name, os.path.join(data_root_path, dataset_name))
-
-
-def bids_init_dataset(data_root_path='',
-                      dataset_name=None,
-                      dataset_description=dict(),
-                      readme='',
-                      changes=''):
-    """Create directories and files missing to follow bids.
-
-    Files and folders already created will be left untouched.
-    This is an utility to initialize all files that should be present
-    according to the standard. Particularly those that should be filled
-    manually like README files.
-
-    dataset_description.json : interactif mode to fill in. Or laer on if the
-    user wants. By default :
-    Name: dataset_name
-    BidsVersion: 1.0.0
-
-    README is quite free as a file
-
-    CHANGES follow CPAN standards
-
-    """
-
-    # CHECK DATASET RESPOSITORY
-    dataset_name, dataset_name_path = get_bids_default_path(
-        data_root_path, dataset_name)
-    if not os.path.exists(dataset_name_path):
-        os.makedirs(dataset_name_path)
-
-    # CHECK dataset_description.json FILE
-    description_file = os.path.exists(
-        os.path.join(dataset_name_path, 'dataset_description.json'))
-    overwrite_datadesc_file = True
-    if description_file:
-        overwrite_datadesc_file = yes_no(
-            '\nA dataset_description.json is already exising, do you want to overwrite?',
-            default="yes")
-    if overwrite_datadesc_file or not description_file:
-        data_descrip = yes_no(
-            '\nDo you want to create or overwrite the dataset_description.json?',
-            default="yes")
-        if data_descrip:
-            print(
-                '\nIf you do not know all information: pass and edit the file later.'
-            )
-            name = input("\nType the name of this BIDS dataset: ").capitalize()
-            authors = input("\nA list of authors like `a, b, c`: ").capitalize()
-            acknowledgements = input(
-                "\nA list of acknowledgements like `a, b, c`: ").capitalize()
-            how_to_acknowledge = input(
-                "\nEither a str describing how to  acknowledge this dataset OR a list of publications that should be cited: "
-            )
-            funding = input(
-                '\nList of sources of funding (e.g., grant numbers). Must be a list of strings or a single comma separated string like `a, b, c`: '
-            )
-            references_and_links = input(
-                "\nList of references to publication that contain information on the dataset, or links. Must be a list of strings or a single comma separated string like `a, b, c`: "
-            )
-            doi = input('\nThe DOI for the dataset: ')
-            make_dataset_description(dataset_name_path,
-                                     name=name,
-                                     data_license=None,
-                                     authors=authors,
-                                     acknowledgements=str(acknowledgements),
-                                     how_to_acknowledge=how_to_acknowledge,
-                                     funding=str(funding),
-                                     references_and_links=references_and_links,
-                                     doi=doi,
-                                     verbose=False)
-        else:
-            print(
-                "\nYou may update the README file later on. A README file by default has been created."
-            )
-            make_dataset_description(dataset_name_path, name=dataset_name)
-
-    # CHECK CHANGES FILE / TEXT FILE CPAN CONVENTION
-    changes_file = os.path.join(dataset_name_path, 'CHANGES')
-    changes_file_exist = os.path.exists(changes_file)
-    overwrite_changes_file = True
-    if changes_file_exist:
-        overwrite_changes_file = yes_no(
-            '\nA CHANGES file is already existing, do you want to overwrite?',
-            default="yes")
-
-    if overwrite_changes_file or not changes_file_exist:
-        changes = yes_no('\nDo you want to create/overwrite the CHANGES file?',
-                         default="yes")
-        if changes:
-            changes_input = input("Type your text: ")
-            with open(changes_file, 'w', encoding="utf-8") as fid:
-                fid.write(str(changes_input))
-
-    # CHECK README FILE / TEXT FILE
-    readme_file = os.path.join(os.path.join(dataset_name_path, 'README'))
-    readme_file_exist = os.path.exists(readme_file)
-    overwrite_readme_file = True
-    if readme_file_exist:
-        overwrite_readme_file = yes_no(
-            '\nA README file is already existing, do you want to overwrite?',
-            default="yes")
-
-    if overwrite_readme_file or not readme_file_exist:
-        readme = yes_no('\nDo you want to create/complete the README file?',
-                        default="yes")
-        if not readme:
-            readme_input = "TO BE COMPLETED BY THE USER"
-        else:
-            readme_input = input("Type your text: ")
-        with open(readme_file, 'w') as fid:
-            fid.write(readme_input)
-
-
-def bids_acquisition_download(data_root_path='',
-                              dataset_name=None,
-                              force_download=False,
-                              behav_path='exp_info/recorded_events',
-                              copy_events='n',
-                              deface=False,
-                              dry_run=False):
-    """Automatically download files from neurospin server to a BIDS dataset.
-
-    Download-database is based on NeuroSpin server conventions.
-    Options are 'prisma', 'trio' and custom path.
-    Prisma db_path = '/neurospin/acquisition/database/Prisma_fit'
-    Trio db_path = '/neurospin/acquisition/database/TrioTim'
-
-    The bids dataset is created if necessary before download with some
-    empy mandatory files to be filled like README in case they dont exist.
-
-    The download depends on the file '[sub-*_][ses-*_]download.csv' contained
-    in the folder 'exp_info'.
-
-    NIP and acq date of the subjects will be taken automatically from
-    exp_info/participants.tsv file that follows bids standard. The file will
-    be copied in the dataset folder without the NIP column for privacy.
-
-    Posible exceptions
-    1) exp_info directory not found
-    2) participants.tsv not found
-    3) download files not found
-    4) Acquisition directory in neurospin server not found
-    5) There is more than one acquisition directory (Have to ask manip for
-    extra digits for NIP, the NIP then would look like xxxxxxxx-ssss)
-    6) Event file corresponding to downloaded bold.nii not found
-    """
-
-    ### CHECK PATHS AND FILES
-
-    # exp_info path where is the participants.tsv
-    # print(data_root_path)
-    exp_info_path = os.path.join(data_root_path, 'exp_info')
-    if not os.path.exists(exp_info_path):
-        raise Exception('exp_info directory not found')
-    if not os.path.isfile(os.path.join(exp_info_path, 'participants.tsv')):
-        raise Exception('exp_info/participants.tsv not found')
-
-    # Determine target path with the name of dataset
-    dataset_name, target_root_path = get_bids_default_path(
-        data_root_path, dataset_name)
-
-    # Create dataset directories and files if necessary
-    bids_init_dataset(data_root_path, dataset_name)
-
-    # Manage the report and download information
-    download_report = ('download_report_' +
-                       time.strftime("%d-%b-%Y-%H:%M:%S", time.gmtime()) +
-                       '.csv')
-    report_path = os.path.join(data_root_path, 'report')
-    if not os.path.exists(report_path):
-        os.makedirs(report_path)
-    download_report = open(os.path.join(report_path, download_report), 'w')
-    #report_line = '%s,%s,%s\n' % ('subject_id', 'session_id', 'download_file')
-    #download_report.write(report_line)
-    list_imported = []
-    list_already_imported = []
-    list_warning = []
-
-    # Create a dataFrame to store participant information
-    #df_participant = pd.DataFrame()
-    #Dict for info participant
-    #list_all_participants = {}
-    dic_info_participants = OrderedDict()
-
-    # List for the bacth file for dc2nii_batch command
-    infiles_dcm2nii = []
-
-    # List for data to deface
-    files_for_pydeface = []
-
-    #Dict of descriptors to be added
-    dict_descriptors = {}
-
-    ### GETTING FOR INFORMATION TO DOWNLOAD
-
-    # Download command for each subject/session
-    # one line has the following information
-    # participant_id / NIP / infos_participant / session_label / acq_date / location / to_import
-
-    # Read the participants.tsv file for getting subjects/sessions to download
-    pop = pd.read_csv(os.path.join(exp_info_path, 'participants.tsv'),
-                      dtype=str,
-                      sep='\t',
-                      index_col=False)
-
-    #print(df_participant)
-
-    for row_idx, subject_info in pop.iterrows():
-        # Fill the partcipant information for the participants.tsv
-        if subject_info[0] in dic_info_participants:
-            existing_items = dic_info_participants[subject_info[0]]
-            dico_add = {}
-            info_participant = json.loads(subject_info['infos_participant'])
-            for k, v in info_participant.items():
-                if k not in existing_items:
-                    dico_add[k] = v
-            #fusion dicos
-            existing_items.update(dico_add)
-            dic_info_participants[subject_info[0]] = existing_items
-        else:
-            dic_info_participants[subject_info[0]] = json.loads(
-                subject_info['infos_participant'])
-
-        # Determine path to files in NeuroSpin server
-        download_database = subject_info['location']
-        if download_database in NEUROSPIN_DATABASES:
-            db_path = NEUROSPIN_DATABASES[download_database]
-        else:
-            db_path = download_database
-
-        # the row_idx for giving either participant_label or participant_id
-        subject_id = subject_info[0]
-
-        # sub_path = target_root_path + subject_id + ses_path
-        # Mange the optional filters
-        # optional_filters = [('sub', subject_id)]
-        # if session_id is not None:
-        #  optional_filters += [('ses', session_id)]
-        if 'session_label' in subject_info.index:
-            if subject_info['session_label'] is not pd.np.nan:
-                session_id = subject_info['session_label']
-            else:
-                session_id = None
-        if session_id is None:
-            ses_path = ''
-        else:
-            ses_path = 'ses-' + session_id
-
-        if subject_id.isnumeric():
-            int(subject_id)
-            subject_id = 'sub-{0}'.format(subject_id)
-        else:
-            if 'sub-' not in subject_id:
-                print(
-                    f'{Bcolors.WARNING}BIDS IMPORTATION WARNING: SUBJECT ID PROBABLY NOT CONFORM{Bcolors.ENDC}'
-                )
-
-        sub_path = os.path.join(target_root_path, subject_id, ses_path)
-        if not os.path.exists(sub_path):
-            os.makedirs(sub_path)
-
-        # Avoid redownloading subjects/sessions
-        if not force_download:
-            check_file = os.path.join(sub_path, 'downloaded')
-            if os.path.isfile(check_file):
-                continue
-
-        # DATE has to be transformed from BIDS to NeuroSpin server standard
-        # NeuroSpin standard is yyyymmdd -> Bids standard is YYYY-MM-DD
-        acq_date = subject_info['acq_date'].replace('-', '').replace('\n', '')
-
-        #acq_label
-        acq_label = subject_info['acq_label']
-
-        #dir_label
-        #dir_label = subject_info['dir_label']
-
-        # nip number
-        nip = subject_info['NIP']
-
-        # Get appropriate download file. As specific as possible
-        # specs_path = file_manager_default_file(exp_info_path,
-        #                                optional_filters, 'download',
-        #                               file_type='tsv',
-        #                               allow_other_fields=False)
-        #report_line = '%s,%s,%s\n' % (subject_id, session_id, specs_path)
-        #download_report.write(report_line)
-
-        #specs = pd.read_csv(specs_path, dtype=str, sep='\t', index_col=False)
-
-        # Retrieve list of list for seqs to import
-        # One tuple is configured as :(file_to_import;acq_folder;acq_name)
-        # value[0] : num of seq
-        # value[1] : modality
-        # value[2] : part of ht file_name
-        print("Scans for ", subject_info['NIP'])
-        print(subject_info['to_import'])
-        seqs_to_retrieve = literal_eval(subject_info['to_import'])
-        #Convert the first element id there is only one sequence, otherwise
-        #each value will be used as str and note tuple).
-        if isinstance(seqs_to_retrieve[0], str):
-            seqs_to_retrieve = [seqs_to_retrieve]
-
-        # download data, store information in batch files for anat/fmri
-        # download data for meg data
-        for value in seqs_to_retrieve:
-            #print(seqs_to_retrieve)
-            def get_value(key, text):
-                m = re.search(key + '-(.+?)_', text)
-                if m:
-                    return m.group(1)
-                else:
-                    return None
-
-            run_task = get_value('task', value[2])
-            acq_label = get_value('acq', value[2])
-            run_id = get_value('run', value[2])
-            run_dir = get_value('dir', value[2])
-            run_session = session_id
-
-            tag = value[2].split('_')[-1]
-
-            target_path = os.path.join(sub_path, value[1])
-            if not os.path.exists(target_path):
-                os.makedirs(target_path)
-
-            # MEG CASE
-            if value[1] == 'meg':
-                # Create subject path if necessary
-                meg_path = os.path.join(sub_path, 'meg')
-                if not os.path.exists(meg_path):
-                    os.makedirs(meg_path)
-
-                # Create the sub-emptyroom
-                #sub-emptyroom_path = os.path.join(data_root_path, 'sub_emptyroom')
-                #if not os.path.exists(sub-emptyroom_path):
-                #    os.makedirs(sub-emptyroom_path)
-
-                meg_file = os.path.join(db_path, nip, acq_date, value[0])
-                print(meg_file)
-                filename = get_bids_file_descriptor(subject_id,
-                                                    task_id=run_task,
-                                                    run_id=run_id,
-                                                    run_dir=run_dir,
-                                                    session_id=run_session,
-                                                    file_tag=tag,
-                                                    acq_label=acq_label,
-                                                    file_type='tif')
-                #output_path = os.path.join(target_path, filename)
-                #print(output_path)
-                #shutil.copyfile(meg_file, output_path)
-                raw = mne.io.read_raw_fif(meg_file, allow_maxshield=True)
-
-                write_raw_bids(raw, filename, target_path, overwrite=True)
-                # add event
-                # create json file
-                #copy the subject emptyroom
-
-            # ANAT and FUNC case
-            # todo: bad practices, to refactor for the sake of simplicity
-            elif (value[1] == 'anat') or (value[1] == 'func') or (
-                    value[1] == 'dwi') or (value[1] == 'fmap'):
-                download = True
-                dicom_paths = []
-                path_file_glob = ""
-                nip_dirs = glob.glob(
-                    os.path.join(db_path, str(acq_date),
-                                 str(nip) + '*'))
-                #print(os.path.join(db_path, str(acq_date), str(nip) + '*'))
-                if len(nip_dirs) < 1:
-                    list_warning.append(
-                        f"\n {Bcolors.WARNING}WARNING: No directory found for given NIP {nip} and SESSION {session_id}{Bcolors.ENDC}"
-                    )
-                    #print(message)
-                    #download_report.write(message)
-                    download = False
-                elif len(nip_dirs) > 1:
-                    list_warning.append(
-                        f"\n  {Bcolors.WARNING}WARNING: Multiple path for given NIP {nip} \
-                            SESSION {session_id} - please \
-                            mention the session of the subject for this date, \
-                            2 sessions for the same subject the same day are \
-                            possible{Bcolors.ENDC}")
-                    #print(message)
-                    #download_report.write(message)
-                    download = False
-                else:
-                    path_file_glob = os.path.join(
-                        nip_dirs[0], '{0:06d}_*'.format(int(value[0])))
-                    #print(path_file_glob)
-                    dicom_paths = glob.glob(path_file_glob)
-
-                if not dicom_paths and download:
-                    list_warning.append("\n WARNING: file not found " +
-                                        path_file_glob)
-                    #print(message)
-                    #download_report.write(message)
-                elif download:
-                    dicom_path = dicom_paths[0]
-                    list_imported.append("\n IMPORTATION OF " + dicom_path)
-                    #print(message)
-                    #download_report.write(message)
-                    # Expecting page 10 bids specification file name
-                    filename = get_bids_file_descriptor(subject_id,
-                                                        task_id=run_task,
-                                                        run_id=run_id,
-                                                        run_dir=run_dir,
-                                                        session_id=run_session,
-                                                        file_tag=tag,
-                                                        acq_label=acq_label,
-                                                        file_type='nii')
-
-                    if value[1] == 'anat' and deface:
-                        print("\n Deface with pydeface")
-                        files_for_pydeface.append(
-                            os.path.join(target_path, filename))
-
-                    # append list for preparing the batch importation
-                    file_to_convert = {
-                        'in_dir': dicom_path,
-                        'out_dir': target_path,
-                        'filename': os.path.splitext(filename)[0]
-                    }
-                    is_file_to_import = os.path.join(
-                        os.path.join(os.getcwd(), target_path, filename))
-
-                    if os.path.isfile(is_file_to_import):
-                        list_already_imported.append(
-                            f" ALREADY IMPORTED: {is_file_to_import}")
-                    else:
-                        infiles_dcm2nii.append(file_to_convert)
-
-                    # Add descriptor into the json file
-                    if run_task:
-                        filename_json = os.path.join(target_path,
-                                                     filename[:-3] + 'json')
-                        dict_descriptors.update(
-                            {filename_json: {
-                                'TaskName': run_task
-                            }})
-
-                    if len(value) == 4:
-                        #print('value[3]', value[3] )
-                        filename_json = os.path.join(target_path,
-                                                     filename[:-3] + 'json')
-                        dict_descriptors.update({filename_json: value[3]})
-
-        #Importation and conversion of dicom files
-        dcm2nii_batch = dict(Options=dict(isGz='false',
-                                          isFlipY='false',
-                                          isVerbose='false',
-                                          isCreateBIDS='true',
-                                          isOnlySingleFile='false'),
-                             Files=infiles_dcm2nii)
-
-    dcm2nii_batch_file = os.path.join(exp_info_path, 'batch_dcm2nii.yaml')
-    with open(dcm2nii_batch_file, 'w') as f:
-        data = yaml.dump(dcm2nii_batch, f)
-
-    print(
-        "\n------------------------------------------------------------------------------------"
-    )
-    print(
-        "-------------------    SUMMARY OF IMPORTATION   --------------------------------------"
-    )
-    print(
-        "--------------------------------------------------------------------------------------\n"
-    )
-    for i in list_already_imported:
-        print(i)
-        download_report.write(i)
-    print(
-        "\n------------------------------------------------------------------------------------"
-    )
-    for i in list_imported:
-        print(i)
-        download_report.write(i)
-    print(
-        "\n------------------------------------------------------------------------------------"
-    )
-    for i in list_warning:
-        print(i)
-        download_report.write(i)
-    print(
-        "\n------------------------------------------------------------------------------------"
-    )
-    print(
-        "------------------------------------------------------------------------------------\n"
-    )
-    download_report.close()
-
-    if dry_run:
-        print("\n NO IMPORTATION, DRY-RUN OPTION IS TRUE \n")
-    else:
-        print('\n')
-        cmd = "dcm2niibatch %s" % (dcm2nii_batch_file)
-        subprocess.call(cmd, shell=True)
-
-        # loop for checking if downloaded are ok and create the downloaded files
-        #    done_file = open(os.path.join(sub_path, 'downloaded'), 'w')
-        #    done_file.close()
-
-        #Data to deface
-        #print(files_for_pydeface)
-        if files_for_pydeface:
-            try:
-                # warning: Isn't that too restrictive?
-                template = resource_filename(
-                    Requirement.parse("unicog"),
-                    "bids/template_deface/mean_reg2mean.nii.gz")
-                facemask = resource_filename(
-                    Requirement.parse("unicog"),
-                    "bids/template_deface/facemask.nii.gz")
-            except DistributionNotFound:
-                template = (
-                    "/neurospin/unicog/protocols/IRMf/Unicogfmri/BIDS/"
-                    "unicog-dev/bids/template_deface/mean_reg2mean.nii.gz")
-                facemask = ("/neurospin/unicog/protocols/IRMf/Unicogfmri/BIDS/"
-                            "unicog-dev/bids/template_deface/facemask.nii.gz")
-            os.environ['FSLDIR'] = "/i2bm/local/fsl/bin/"
-            os.environ['FSLOUTPUTTYPE'] = "NIFTI_PAIR"
-            os.environ['PATH'] = os.environ['FSLDIR'] + ":" + os.environ['PATH']
-
-            for file_to_deface in files_for_pydeface:
-                print(f"\nDeface with pydeface {file_to_deface}")
-                pdu.deface_image(infile=file_to_deface,
-                                 outfile=file_to_deface,
-                                 facemask=facemask,
-                                 template=template,
-                                 force=True)
-
-        # Create participants.tsv in dataset folder (take out NIP column)
-        participants_path = os.path.join(target_root_path, 'participants.tsv')
-        df_participant = pd.DataFrame.from_dict(dic_info_participants,
-                                                orient="index")
-        df_participant.to_csv(participants_path, sep='\t')
-
-        if dict_descriptors:
-            #print(dict_descriptors)
-            # Adding a new key value pair in a json file such as taskname
-            for k, v in dict_descriptors.items():
-                with open(k, 'r+') as json_file:
-                    for key, val in v.items():
-                        temp_json = json.load(json_file)
-                        temp_json[key] = val
-                        json_file.seek(0)
-                        json.dump(temp_json, json_file)
-                        json_file.truncate()
-
-        # Copy recorded event files
-        if copy_events == "y":
-            bids_copy_events(behav_path, data_root_path, dataset_name)
-
-        #Validate paths with BIDSValidator
-        #see also http://bids-standard.github.io/bids-validator/
-        validation_bids = yes_no('\nDo you want to use a bids validator?',
-                                 default=None)
-        if validation_bids:
-            bids_validation_report = os.path.join(report_path,
-                                                  "report_bids_valisation.txt")
-            if shutil.which('bids-validator'):
-                cmd = f"bids-validator {target_root_path} > {bids_validation_report}"
-                subprocess.call(cmd, shell=True)
-                cmd = f"cat < {bids_validation_report}"
-                subprocess.call(cmd, shell=True)
-                print(
-                    f'\n\nSee the summary of bids validator at {bids_validation_report}'
-                )
-            else:
-                validator = BIDSValidator()
-                os.chdir(target_root_path)
-                for file_to_test in Path('.').glob('./**/*'):
-                    if file_to_test.is_file():
-                        file_to_test = '/' + str(file_to_test)
-                        print(
-                            f'\nTest the following name of file : {file_to_test} with BIDSValidator'
-                        )
-                        print(validator.is_bids(file_to_test))
-
-    print('\n')
-
-
-if __name__ == "__main__":
-    # Parse arguments from console
-    parser = argparse.ArgumentParser(description='NeuroSpin to BIDS conversion')
-    parser.add_argument('-root_path',
-                        type=str,
-                        nargs=1,
-                        default=[''],
-                        help='directory containing exp_info to download to')
-    parser.add_argument('-dataset_name',
-                        type=str,
-                        nargs=1,
-                        default=['bids_dataset'],
-                        help='desired name for the dataset')
-    parser.add_argument(
-        '-copy_events',
-        type=str,
-        nargs=1,
-        default=['n'],
-        help='copy events from a directory with the same structure')
-    parser.add_argument('-neurospin_database',
-                        type=str,
-                        nargs=1,
-                        default=['prisma'],
-                        help='neurospin server to download from')
-    parser.add_argument('-dry-run',
-                        type=bool,
-                        nargs=1,
-                        default=[False],
-                        help='Test without importation of data')
-
-    # LOAD CONSOLE ARGUMENTS
-    args = parser.parse_args()
-    deface = yes_no('\nDo you want deface T1?', default=None)
-    bids_acquisition_download(data_root_path=args.root_path[0],
-                              dataset_name=args.dataset_name[0],
-                              force_download=False,
-                              behav_path='exp_info/recorded_events',
-                              copy_events=args.copy_events[0],
-                              deface=deface,
-                              dry_run=args.dry_run[0])
diff --git a/bids/template_deface/facemask.nii.gz b/bids/template_deface/facemask.nii.gz
deleted file mode 100644
index b6ecd59..0000000
Binary files a/bids/template_deface/facemask.nii.gz and /dev/null differ
diff --git a/bids/template_deface/mean_reg2mean.nii.gz b/bids/template_deface/mean_reg2mean.nii.gz
deleted file mode 100644
index af35865..0000000
Binary files a/bids/template_deface/mean_reg2mean.nii.gz and /dev/null differ
diff --git a/bids/test_dataset/example_1/participants.tsv b/bids/test_dataset/example_1/participants.tsv
deleted file mode 100644
index f8692cd..0000000
--- a/bids/test_dataset/example_1/participants.tsv
+++ /dev/null
@@ -1,3 +0,0 @@
-participant_id	NIP	infos_participant	session_label	acq_date	acq_label	location	to_import
-sub-01	tr070015	{"sex":"F", "age":"45"}	01	2010-06-28		trio	(('2','anat','T1w'),('9','func','task-loc_std_run-01_bold'),('9','func','task-loc_std_run-02_bold'))
-sub-02	ap100009	{"sex":"M", "age":"35"}		2010-07-01		trio	(('2','anat','T1w'),('9','func','task-loc_std_bold'))
diff --git a/bids/test_dataset/example_2/participants.tsv b/bids/test_dataset/example_2/participants.tsv
deleted file mode 100644
index 0b189a5..0000000
--- a/bids/test_dataset/example_2/participants.tsv
+++ /dev/null
@@ -1,3 +0,0 @@
-participant_id	NIP	infos_participant	session_label	acq_date	acq_label	location	to_import
-sub-301	jj140004	"{""sex"":""F"", ""age"":""6""}"		2019-01-15		prisma	(('24','anat','T1w'),('13','func','task-number_dir-ap_run-01_bold'),('14','func','task-number_run-01_sbref'),('5','fmap','dir-ap_epi',{'IntendedFor':'/fmri/sub-301_task-number_dir-ap_run-01_bold'}))
-sub-31	ju140333	"{""sex"":""F"", ""age"":""6""}"		2020-06-15		prisma	(('24','anat','T1w'),('13','func','task-number_dir-ap_run-01_bold'),('14','func','task-number_run-01_sbref'),('5','fmap','dir-ap_epi'))
diff --git a/bids/todo.txt b/bids/todo.txt
deleted file mode 100644
index f85e4ce..0000000
--- a/bids/todo.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-# TEST
-
-* test data description file by default
-* test README file
-* test CHANGES file
-* test the downloaded system / file_manager_default_file
-
-
-# CODE
-
-* use the optional_filters for getting session_id and sub-id ?
-* loop to check and the downloaded file
-
-# FUNCTIONS
-
-* function to check a field into json file, e.g. Repetition Time
-* function to chech unity ms
-* function to import onset .... information
-* function for updating the intended for field in fmap json file
-
-
-
-# NOTES
-
-        slice_times = pydicom.read_file(dicom_ref)[0x19, 0x1029].value
-        if (max(slice_times) > 1000):
-          print('****  BIDS IMPORTATION WARMING: SLICE TIMING SEEM TO BE IN MS CONVERSION IN Seconds IS DONE')
-          slice_times = [round((v*10**-3), 4  for v in slice_times]
-          dico_json['SliceTiming'] = slice_times
-          
-        TR = pydicom.read_file(dicom_ref).RepetitionTime
-        if (TR > 10):
-            print('****  BIDS IMPORTATION WARMING: REPETITION TIME SEEM TO BE IN MS, CONVERSION IN Seconds IS DONE')
-            TR = round((TR * 10**-3), 4)
diff --git a/setup.py b/setup.py
index 5cbb63d..7969e26 100644
--- a/setup.py
+++ b/setup.py
@@ -8,10 +8,6 @@
 from setuptools import setup, find_packages
 
 
-datafiles = {'unicog': ['bids/template_deface/facemask.nii.gz',
-                          'bids/template_deface/mean_reg2mean.nii.gz']}
-
-
 setup(
     name='unicog',
     distname='unicog',
@@ -22,16 +18,9 @@
     url='https://github.com/neurospin/unicog',
     packages=find_packages(),
     install_requires=[
-        'pydicom',
         'pandas',
         'mne',
-        'mne-bids',
-        'pydeface',
-        'PyYAML',
-        'bids-validator',
     ],
     python_requires='~=3.6',
-    package_data=datafiles,
-    scripts=['bids/neurospin_to_bids.py'],
     zip_safe=False
 )
diff --git a/unicogfmri/localizer_pypreprocess/README.md b/unicogfmri/localizer_pypreprocess/README.md
index 199758b..9a6c52f 100644
--- a/unicogfmri/localizer_pypreprocess/README.md
+++ b/unicogfmri/localizer_pypreprocess/README.md
@@ -52,14 +52,14 @@ Let us create a directory to run this tutorial, by issuing the following command
        cd $ROOTDIR
 
 ###  Data importation 
-See more information on the importation with BIDS [https://github.com/neurospin/unicog/tree/master/bids](https://github.com/neurospin/unicog/tree/master/bids).
+See more information on the importation with BIDS [https://github.com/neurospin/neurospin_to_bids](https://github.com/neurospin/neurospin_to_bids).
 
 Here we have an example with 14 subjects from the localizer project.
 
 Launch the importation:
 
       cd <where_is_the_exp_info_directory_including_the_participants.tsv>
-      python /tmp/unicog/bids/neurospin_to_bids.py
+      neurospin_to_bids.py
 
 
 ### Processing