def init_input_node(pet_nii):
import datetime
import nibabel as nib
from colorama import Fore
from clinica.utils.filemanip import get_subject_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(pet_nii)
# Check that the PET file is a 3D volume
img = nib.load(pet_nii)
if len(img.shape) == 4:
now = datetime.datetime.now().strftime("%H:%M:%S")
error_msg = (
f"{Fore.RED}[{now}] Error: Clinica does not handle 4D volumes "
f"for {image_id.replace('_', ' | ')}{Fore.RESET}")
cprint(error_msg)
raise NotImplementedError(error_msg)
# Print begin message
print_begin_image(image_id)
return pet_nii
def init_input_node(t1w, recon_all_args, output_dir):
"""Initialize the pipeline.
This function will:
- Extract <image_id> (e.g. sub-CLNC01_ses-M00) T1w filename;
- Check FOV of T1w;
- Create SUBJECTS_DIR for recon-all (otherwise, the command won't run);
- Print begin execution message.
"""
import os
import errno
from clinica.utils.filemanip import get_subject_id
from clinica.utils.freesurfer import check_flags
from clinica.utils.ux import print_begin_image
# Extract <image_id>
image_id = get_subject_id(t1w)
# Check flags for T1w
flags = check_flags(t1w, recon_all_args)
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
subjects_dir = os.path.join(output_dir, image_id)
try:
os.makedirs(subjects_dir)
except OSError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
print_begin_image(image_id, ['ReconAllArgs'], [flags])
return image_id, t1w, flags, subjects_dir
def init_input_node(t1w, dwi, bvec, bval, dwi_json):
"""Initialize the pipeline."""
from clinica.utils.dwi import bids_dir_to_fsl_dir, check_dwi_volume
from clinica.utils.filemanip import extract_metadata_from_json, get_subject_id
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(t1w)
# Check that the number of DWI, bvec & bval are the same
check_dwi_volume(dwi, bvec, bval)
# Read metadata from DWI JSON file:
[total_readout_time, phase_encoding_direction] = extract_metadata_from_json(
dwi_json, ["TotalReadoutTime", "PhaseEncodingDirection"]
)
phase_encoding_direction = bids_dir_to_fsl_dir(phase_encoding_direction)
# Print begin message
print_begin_image(
image_id,
["TotalReadoutTime", "PhaseEncodingDirection"],
[str(total_readout_time), phase_encoding_direction],
)
return (
image_id,
t1w,
dwi,
bvec,
bval,
total_readout_time,
phase_encoding_direction,
)
def init_input_node(t1w, recon_all_args, output_dir):
"""Initialize the pipeline.
This function will:
- Extract <image_id> (e.g. sub-CLNC01_ses-M00) T1w filename;
- Check FOV of T1w;
- Create SUBJECTS_DIR for recon-all (otherwise, the command won't run);
- Print begin execution message.
"""
import os
from clinica.utils.filemanip import get_subject_id
from clinica.utils.freesurfer import check_flags
from clinica.utils.ux import print_begin_image
# Extract <image_id>
image_id = get_subject_id(t1w)
# Check flags for T1w
flags = check_flags(t1w, recon_all_args)
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
subjects_dir = os.path.join(output_dir, image_id)
os.makedirs(subjects_dir, exist_ok=True)
print_begin_image(image_id, ["ReconAllArgs"], [flags])
return image_id, t1w, flags, subjects_dir
def init_input_node(pet):
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(pet)
print_begin_image(image_id)
return pet
def init_input_node(t1w):
"""Extract "sub-<participant_id>_ses-<session_label>" from <t1w> and print begin message."""
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_begin_image
subject_id = get_subject_id(t1w)
print_begin_image(subject_id)
return subject_id, t1w
def init_input_node(caps_dir, participant_id, session_id, long_id, output_dir):
"""Initialize the pipeline."""
import os
import errno
import datetime
import platform
from tempfile import mkdtemp
from colorama import Fore
from clinica.utils.longitudinal import read_sessions
from clinica.utils.ux import print_begin_image
from clinica.utils.stream import cprint
# Extract <image_id>
image_id = '{0}_{1}_{2}'.format(participant_id, session_id, long_id)
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
if platform.system().lower().startswith('darwin'):
# Special case: On macOS, 'recon-all -long' can failed if the $SUBJECTS_DIR is too long
# To circumvent this issue, we create a sym link in $(TMP) so that $SUBJECTS_DIR is a short path
subjects_dir = mkdtemp()
now = datetime.datetime.now().strftime('%H:%M:%S')
cprint(
'%s[%s] Needs to create a $SUBJECTS_DIR folder in %s for %s (macOS case). %s'
% (Fore.YELLOW, now, subjects_dir, image_id.replace(
'_', ' | '), Fore.RESET))
else:
subjects_dir = os.path.join(output_dir, image_id)
try:
os.makedirs(subjects_dir)
except OSError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
# Create symbolic link containing cross-sectional segmentation(s) in SUBJECTS_DIR so that recon-all can run
for s_id in read_sessions(caps_dir, participant_id, long_id):
cross_sectional_path = os.path.join(caps_dir, 'subjects',
participant_id, s_id, 't1',
'freesurfer_cross_sectional',
participant_id + '_' + s_id)
os.symlink(cross_sectional_path,
os.path.join(subjects_dir, participant_id + '_' + s_id))
# Create symbolic links containing unbiased template in SUBJECTS_DIR so that recon-all can run
template_path = os.path.join(caps_dir, 'subjects', participant_id, long_id,
'freesurfer_unbiased_template',
participant_id + '_' + long_id)
os.symlink(template_path,
os.path.join(subjects_dir, participant_id + '_' + long_id))
print_begin_image(image_id)
return subjects_dir
def init_input_node(parameters, base_dir, subjects_visits_tsv):
"""Initialize the pipeline.
This function will:
- Create `surfstat_results_dir` in `base_dir`/<group_id> for SurfStat;
- Save pipeline parameters in JSON file;
- Copy TSV file with covariates;
- Print begin execution message.
"""
import os
import errno
import json
import shutil
from clinica.utils.ux import print_begin_image
from clinica.pipelines.statistics_surface.statistics_surface_utils import create_glm_info_dictionary
group_id = 'group-' + parameters['group_label']
# Create surfstat_results_dir for SurfStat
surfstat_results_dir = os.path.join(base_dir, group_id)
try:
os.makedirs(surfstat_results_dir)
except OSError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
# Save pipeline parameters in JSON file
glm_dict = create_glm_info_dictionary(subjects_visits_tsv, parameters)
json_filename = os.path.join(surfstat_results_dir, group_id + '_glm.json')
with open(json_filename, 'w') as json_file:
json.dump(glm_dict, json_file, indent=4)
# Copy TSV file with covariates
tsv_filename = os.path.join(surfstat_results_dir,
group_id + '_covariates.tsv')
shutil.copyfile(subjects_visits_tsv, tsv_filename)
# Print begin message
list_keys = [
'AnalysisType', 'Covariates', 'Contrast', 'FWHM', 'ClusterThreshold'
]
list_values = [
parameters['glm_type'], parameters['covariates'],
parameters['contrast'],
str(parameters['full_width_at_half_maximum']),
str(parameters['cluster_threshold'])
]
group_id = 'group-' + parameters['group_label']
print_begin_image(group_id, list_keys, list_values)
return parameters['group_label'], surfstat_results_dir
def init_input_node(parameters, base_dir, subjects_visits_tsv):
"""Initialize the pipeline.
This function will:
- Create `surfstat_results_dir` in `base_dir`/<group_id> for SurfStat;
- Save pipeline parameters in JSON file;
- Copy TSV file with covariates;
- Print begin execution message.
"""
import json
import os
import shutil
from clinica.pipelines.statistics_surface.statistics_surface_utils import (
create_glm_info_dictionary, )
from clinica.utils.ux import print_begin_image
group_id = "group-" + parameters["group_label"]
# Create surfstat_results_dir for SurfStat
surfstat_results_dir = os.path.join(base_dir, group_id)
os.makedirs(surfstat_results_dir, exist_ok=True)
# Save pipeline parameters in JSON file
glm_dict = create_glm_info_dictionary(subjects_visits_tsv, parameters)
json_filename = os.path.join(surfstat_results_dir, group_id + "_glm.json")
with open(json_filename, "w") as json_file:
json.dump(glm_dict, json_file, indent=4)
# Copy TSV file with covariates
tsv_filename = os.path.join(surfstat_results_dir,
group_id + "_covariates.tsv")
shutil.copyfile(subjects_visits_tsv, tsv_filename)
# Print begin message
list_keys = [
"AnalysisType", "Covariates", "Contrast", "FWHM", "ClusterThreshold"
]
list_values = [
parameters["glm_type"],
parameters["covariates"],
parameters["contrast"],
str(parameters["full_width_at_half_maximum"]),
str(parameters["cluster_threshold"]),
]
group_id = "group-" + parameters["group_label"]
print_begin_image(group_id, list_keys, list_values)
return parameters["group_label"], surfstat_results_dir
def init_input_node(pet_nii):
import nibabel as nib
from clinica.utils.filemanip import get_subject_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(pet_nii)
# Check that the PET file is a 3D volume
img = nib.load(pet_nii)
if len(img.shape) == 4:
error_msg = f"Clinica does not handle 4D volumes for {image_id.replace('_', ' | ')}"
cprint(error_msg, lvl="error")
raise NotImplementedError(error_msg)
# Print begin message
print_begin_image(image_id)
return pet_nii
def init_input_node(dwi, bvec, bval, dwi_json, fmap_magnitude, fmap_phasediff,
fmap_phasediff_json):
"""Initialize pipeline (read JSON, check files and print begin message)."""
import datetime
import nibabel as nib
from clinica.utils.dwi import bids_dir_to_fsl_dir, check_dwi_volume
from clinica.utils.filemanip import extract_metadata_from_json, get_subject_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(dwi)
# Check that the number of DWI, bvec & bval are the same
try:
check_dwi_volume(dwi, bvec, bval)
except ValueError as e:
now = datetime.datetime.now().strftime("%H:%M:%S")
error_msg = (
f"[{now}] Error: Number of DWIs, b-vals and b-vecs mismatch for {image_id.replace('_', ' | ')}"
)
cprint(error_msg, lvl="error")
raise ValueError(e)
# Check that PhaseDiff and magnitude1 have the same header
# Otherwise, FSL in FugueExtrapolationFromMask will crash
img_phasediff = nib.load(fmap_phasediff)
img_magnitude = nib.load(fmap_magnitude)
if img_phasediff.shape != img_magnitude.shape:
now = datetime.datetime.now().strftime("%H:%M:%S")
error_msg = (
f"[{now}] Error: Headers of PhaseDiff and Magnitude1 are not the same "
f"for {image_id.replace('_', ' | ')} ({img_phasediff.shape} vs {img_magnitude.shape})"
)
cprint(error_msg, lvl="error")
raise NotImplementedError(error_msg)
# Read metadata from DWI JSON file:
[total_readout_time,
phase_encoding_direction] = extract_metadata_from_json(
dwi_json, ["TotalReadoutTime", "PhaseEncodingDirection"])
phase_encoding_direction = bids_dir_to_fsl_dir(phase_encoding_direction)
# Read metadata from PhaseDiff JSON file:
[echo_time1,
echo_time2] = extract_metadata_from_json(fmap_phasediff_json,
["EchoTime1", "EchoTime2"])
delta_echo_time = abs(echo_time2 - echo_time1)
# Print begin message
print_begin_image(
image_id,
["TotalReadoutTime", "PhaseEncodingDirection", "DeltaEchoTime"],
[
str(total_readout_time), phase_encoding_direction,
str(delta_echo_time)
],
)
return (
image_id,
dwi,
bvec,
bval,
total_readout_time,
phase_encoding_direction,
fmap_magnitude,
fmap_phasediff,
delta_echo_time,
)
def build_input_node(self):
"""Build and connect an input node to the pipeline."""
import os
import sys
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from clinica.utils.exceptions import ClinicaException
from clinica.utils.input_files import t1_volume_dartel_input_tissue
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.stream import cprint
from clinica.utils.ux import (
print_begin_image,
print_groups_in_caps_directory,
print_images_to_process,
)
representative_output = os.path.join(
self.caps_directory,
"groups",
f"group-{self.parameters['group_label']}",
"t1",
f"group-{self.parameters['group_label']}_template.nii.gz",
)
if os.path.exists(representative_output):
cprint(
msg=
(f"DARTEL template for {self.parameters['group_label']} already exists. "
"Currently, Clinica does not propose to overwrite outputs for this pipeline."
),
lvl="warning",
)
print_groups_in_caps_directory(self.caps_directory)
sys.exit(0)
# Check that there is at least 2 subjects
if len(self.subjects) <= 1:
raise ClinicaException(
"This pipeline needs at least 2 images to create DARTEL "
f"template but Clinica only found {len(self.subjects)}.")
read_parameters_node = npe.Node(
name="LoadingCLIArguments",
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,
t1_volume_dartel_input_tissue(tissue_number),
)
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
if len(self.subjects):
print_images_to_process(self.subjects, self.sessions)
cprint(
"Computational time for DARTEL creation will depend on the number of images."
)
print_begin_image(f"group-{self.parameters['group_label']}")
# fmt: off
self.connect([(read_parameters_node, self.input_node,
[("dartel_inputs", "dartel_input_images")])])
def init_input_node(caps_dir, participant_id, list_session_ids, output_dir):
"""Initialize the pipeline.
This function will create folders and symbolic links in SUBJECTS_DIR env variable for upcoming run of recon-all.
Note (@alexis-g-icm):
There currently (as of 22 Feb 2019) is a bug in FreeSurfer recon-all -base, which in some cases (e.g., only one
time point), will crash as it's trying to write lines too long for the shell to handle. This is caused by
the path to FreeSurfer SUBJECT_DIR being too long itself.
The current function works around this issue by checking if there only is one session associated to a subject, and
in that case, putting the SUBJECT_DIR inside the system temporary folder so that its path is as short as possible.
"""
import os
from tempfile import mkdtemp
from clinica.compat import errno
from clinica.utils.longitudinal import get_long_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract <image_id>
long_id = get_long_id(list_session_ids)
image_id = f"{participant_id}_{long_id}"
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
if len(list_session_ids) == 1:
# Special case: When only one time point is used, 'recon-all -base' can failed
# if the $SUBJECTS_DIR is too long ('Word too long.' error).
# To circumvent this issue, we create a sym link in $(TMP) so that $SUBJECTS_DIR is a short path
subjects_dir = mkdtemp()
cprint(
msg=(
f"{image_id.replace('_', ' | ')} has only one time point. "
f"Needs to create a $SUBJECTS_DIR folder in {subjects_dir}"
),
lvl="warning",
)
else:
subjects_dir = os.path.join(output_dir, image_id)
os.makedirs(subjects_dir, exist_ok=True)
# Create symbolic links containing cross-sectional segmentation(s) in SUBJECTS_DIR so that recon-all can run
for session_id in list_session_ids:
cross_sectional_path = os.path.join(
caps_dir,
"subjects",
participant_id,
session_id,
"t1",
"freesurfer_cross_sectional",
f"{participant_id}_{session_id}",
)
try:
os.symlink(
cross_sectional_path,
os.path.join(subjects_dir, f"{participant_id}_{session_id}"),
)
except FileExistsError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
# Prepare arguments for recon-all.
flags = ""
for session_id in list_session_ids:
flags += f" -tp {participant_id}_{session_id}"
print_begin_image(image_id)
return image_id, subjects_dir, flags
def init_input_node(caps_dir, participant_id, list_session_ids, output_dir):
"""Initialize the pipeline.
This function will create folders and symbolic links in SUBJECTS_DIR env variable for upcoming run of recon-all.
Note (@alexis-g-icm):
There currently (as of 22 Feb 2019) is a bug in FreeSurfer recon-all -base, which in some cases (e.g., only one
time point), will crash as it's trying to write lines too long for the shell to handle. This is caused by
the path to FreeSurfer SUBJECT_DIR being too long itself.
The current function works around this issue by checking if there only is one session associated to a subject, and
in that case, putting the SUBJECT_DIR inside the system temporary folder so that its path is as short as possible.
"""
import os
import errno
import datetime
from tempfile import mkdtemp
from colorama import Fore
from clinica.utils.stream import cprint
from clinica.utils.longitudinal import get_long_id
from clinica.utils.ux import print_begin_image
# Extract <image_id>
long_id = get_long_id(list_session_ids)
image_id = participant_id + '_' + long_id
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
if len(list_session_ids) == 1:
# Special case: When only one time point is used, 'recon-all -base' can failed
# if the $SUBJECTS_DIR is too long ('Word too long.' error).
# To circumvent this issue, we create a sym link in $(TMP) so that $SUBJECTS_DIR is a short path
subjects_dir = mkdtemp()
now = datetime.datetime.now().strftime('%H:%M:%S')
cprint(
'%s[%s] %s has only one time point. Needs to create a $SUBJECTS_DIR folder in %s%s'
% (Fore.YELLOW, now, image_id.replace(
'_', ' | '), subjects_dir, Fore.RESET))
else:
subjects_dir = os.path.join(output_dir, image_id)
try:
os.makedirs(subjects_dir)
except OSError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
# Create symbolic links containing cross-sectional segmentation(s) in SUBJECTS_DIR so that recon-all can run
for session_id in list_session_ids:
cross_sectional_path = os.path.join(caps_dir, 'subjects',
participant_id, session_id, 't1',
'freesurfer_cross_sectional',
participant_id + '_' + session_id)
try:
os.symlink(
cross_sectional_path,
os.path.join(subjects_dir, participant_id + '_' + session_id))
except FileExistsError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
# Prepare arguments for recon-all.
flags = ""
for session_id in list_session_ids:
flags += " -tp " + participant_id + "_" + session_id
print_begin_image(image_id)
return image_id, subjects_dir, flags
def build_input_node(self):
"""Build and connect an input node to the pipeline."""
from colorama import Fore
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from clinica.utils.exceptions import ClinicaException
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import (t1_volume_template_tpm_in_mni,
pet_volume_normalized_suvr_pet)
from clinica.utils.stream import cprint
from clinica.utils.ux import print_images_to_process, print_begin_image
all_errors = []
if self.parameters['orig_input_data'] == 'pet-volume':
if not (self.parameters["acq_label"]
and self.parameters["suvr_reference_region"]):
raise ValueError(
f"Missing value(s) in parameters from pet-volume pipeline. Given values:\n"
f"- acq_label: {self.parameters['acq_label']}\n"
f"- suvr_reference_region: {self.parameters['suvr_reference_region']}\n"
f"- use_pvc_data: {self.parameters['use_pvc_data']}\n")
self.parameters['measure_label'] = self.parameters['acq_label']
information_dict = pet_volume_normalized_suvr_pet(
acq_label=self.parameters["acq_label"],
group_label=self.parameters["group_label_dartel"],
suvr_reference_region=self.parameters["suvr_reference_region"],
use_brainmasked_image=True,
use_pvc_data=self.parameters["use_pvc_data"],
fwhm=self.parameters['full_width_at_half_maximum'])
elif self.parameters['orig_input_data'] == 't1-volume':
self.parameters['measure_label'] = 'graymatter'
information_dict = t1_volume_template_tpm_in_mni(
self.parameters['group_label_dartel'], 0, True)
elif self.parameters['orig_input_data'] == 'custom-pipeline':
if self.parameters['custom_file'] is None:
raise ClinicaException(
f"{Fore.RED}Custom pipeline was selected but no 'custom_file' was specified.{Fore.RESET}"
)
# If custom file are grabbed, information of fwhm is irrelevant and should not appear on final filenames
self.parameters['full_width_at_half_maximum'] = None
information_dict = {
'pattern': self.parameters['custom_file'],
'description': 'custom file provided by user'
}
else:
raise ValueError(
f"Input data {self.parameters['orig_input_data']} unknown.")
try:
input_files = clinica_file_reader(self.subjects, self.sessions,
self.caps_directory,
information_dict)
except ClinicaException as e:
all_errors.append(e)
if len(all_errors) > 0:
error_message = 'Clinica faced errors while trying to read files in your CAPS directories.\n'
for msg in all_errors:
error_message += str(msg)
raise ClinicaException(error_message)
read_parameters_node = npe.Node(name="LoadingCLIArguments",
interface=nutil.IdentityInterface(
fields=self.get_input_fields(),
mandatory_inputs=True),
synchronize=True)
read_parameters_node.inputs.input_files = input_files
if len(self.subjects):
print_images_to_process(self.subjects, self.sessions)
cprint(
'The pipeline will last a few minutes. Images generated by SPM will popup during the pipeline.'
)
print_begin_image(f"group-{self.parameters['group_label']}")
self.connect([(read_parameters_node, self.input_node,
[('input_files', 'input_files')])])
def build_input_node(self):
"""Build and connect an input node to the pipeline."""
import os
import sys
from colorama import Fore
import nipype.pipeline.engine as npe
import nipype.interfaces.utility as nutil
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import t1_volume_dartel_input_tissue
from clinica.utils.exceptions import ClinicaException
from clinica.utils.stream import cprint
from clinica.utils.ux import print_groups_in_caps_directory, print_images_to_process, print_begin_image
representative_output = os.path.join(
self.caps_directory, 'groups',
'group-' + self.parameters['group_label'], 't1',
'group-' + self.parameters['group_label'] + '_template.nii.gz')
if os.path.exists(representative_output):
cprint(
"%sDARTEL template for %s already exists. Currently, Clinica does not propose to overwrite outputs "
"for this pipeline.%s" %
(Fore.YELLOW, self.parameters['group_label'], Fore.RESET))
print_groups_in_caps_directory(self.caps_directory)
sys.exit(0)
# Check that there is at least 2 subjects
if len(self.subjects) <= 1:
raise ClinicaException(
'%sThis pipeline needs at least 2 images to create DARTEL template but '
'Clinica only found %s.%s' %
(Fore.RED, len(self.subjects), Fore.RESET))
read_parameters_node = npe.Node(name="LoadingCLIArguments",
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,
t1_volume_dartel_input_tissue(tissue_number))
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
if len(self.subjects):
print_images_to_process(self.subjects, self.sessions)
cprint(
'Computational time for DARTEL creation will depend on the number of images.'
)
print_begin_image('group-' + self.parameters['group_label'])
self.connect([(read_parameters_node, self.input_node,
[('dartel_inputs', 'dartel_input_images')])])
def init_input_node(caps_dir, participant_id, session_id, long_id, output_dir):
"""Initialize the pipeline."""
import os
import platform
from tempfile import mkdtemp
from clinica.utils.longitudinal import read_sessions
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract <image_id>
image_id = "{0}_{1}_{2}".format(participant_id, session_id, long_id)
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
if platform.system().lower().startswith("darwin"):
# Special case: On macOS, 'recon-all -long' can failed if the $SUBJECTS_DIR is too long
# To circumvent this issue, we create a sym link in $(TMP) so that $SUBJECTS_DIR is a short path
subjects_dir = mkdtemp()
cprint(
msg=
(f"Needs to create a $SUBJECTS_DIR folder "
f"in {subjects_dir} for {image_id.replace('_', ' | ')} (macOS case)."
),
lvl="warning",
)
else:
subjects_dir = os.path.join(output_dir, image_id)
os.makedirs(subjects_dir, exist_ok=True)
# Create symbolic link containing cross-sectional segmentation(s) in SUBJECTS_DIR so that recon-all can run
for s_id in read_sessions(caps_dir, participant_id, long_id):
cross_sectional_path = os.path.join(
caps_dir,
"subjects",
participant_id,
s_id,
"t1",
"freesurfer_cross_sectional",
f"{participant_id}_{s_id}",
)
os.symlink(
cross_sectional_path,
os.path.join(subjects_dir, f"{participant_id}_{s_id}"),
)
# Create symbolic links containing unbiased template in SUBJECTS_DIR so that recon-all can run
template_path = os.path.join(
caps_dir,
"subjects",
participant_id,
long_id,
"freesurfer_unbiased_template",
f"{participant_id}_{long_id}",
)
os.symlink(template_path,
os.path.join(subjects_dir, f"{participant_id}_{long_id}"))
print_begin_image(image_id)
return subjects_dir
def print_begin_pipeline(in_bids_or_caps_file: str) -> None:
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_begin_image
print_begin_image(get_subject_id(in_bids_or_caps_file))
def build_input_node(self):
"""Build and connect an input node to the pipeline."""
import os
from colorama import Fore
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from clinica.iotools.utils.data_handling import check_volume_location_in_world_coordinate_system
from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
from clinica.utils.filemanip import extract_subjects_sessions_from_filename, save_participants_sessions
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import T1W_NII
from clinica.utils.stream import cprint
from clinica.utils.ux import print_images_to_process, print_begin_image
gic = '*'
if self.parameters['group_id_caps'] is not None:
gic = self.parameters['group_id_caps']
all_errors = []
if self.parameters['feature_type'] == 'fdg':
try:
input_files = clinica_file_reader(
self.subjects, self.sessions, self.caps_directory, {
'pattern':
'*_pet_space-Ixi549Space_suvr-pons_mask-brain_fwhm-' +
str(self.parameters['full_width_at_half_maximum']) +
'mm_pet.nii*',
'description':
'pons normalized FDG PET image in MNI space (brain masked)',
'needed_pipeline':
'pet-volume'
})
except ClinicaException as e:
all_errors.append(e)
elif self.parameters['feature_type'] == 'graymatter':
try:
input_files = clinica_file_reader(
self.subjects, self.sessions, self.caps_directory, {
'pattern':
't1/spm/dartel/group-' + gic +
'/*_T1w_segm-graymatter_space-Ixi549Space_modulated-on_fwhm-'
+ str(self.parameters['full_width_at_half_maximum']) +
'mm_probability.nii.*',
'description':
'probability map of gray matter segmentation based on T1w image in MNI space',
'needed_pipeline':
't1-volume or t1-volume-existing-template'
})
except ClinicaException as e:
all_errors.append(e)
else:
if not self.parameters['custom_files']:
raise ClinicaException(
Fore.RED +
'[Error] You did not specify the --custom_files flag in the command line for the feature type '
+ Fore.Blue + self.parameters['feature_type'] + Fore.RED +
'! Clinica can\'t ' +
'know what file to use in your analysis ! Type: \n\t' +
Fore.BLUE + 'clinica run statistics-volume\n' + Fore.RED +
' to have help on how to use the command line.' +
Fore.RESET)
try:
# If custom file are grabbed, information of fwhm is irrelevant and should not appear on final filenames
self.parameters['full_width_at_half_maximum'] = None
input_files = clinica_file_reader(
self.subjects, self.sessions, self.caps_directory, {
'pattern': self.parameters['custom_files'],
'description': 'custom file provided by user'
})
except ClinicaException as e:
all_errors.append(e)
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 ClinicaException(error_message)
read_parameters_node = npe.Node(name="LoadingCLIArguments",
interface=nutil.IdentityInterface(
fields=self.get_input_fields(),
mandatory_inputs=True),
synchronize=True)
read_parameters_node.inputs.input_files = input_files
if len(self.subjects):
print_images_to_process(self.subjects, self.sessions)
cprint(
'The pipeline will last a few minutes. Images generated by SPM will popup during the pipeline.'
)
print_begin_image('group-' + self.parameters['group_id'])
self.connect([(read_parameters_node, self.input_node,
[('input_files', 'input_files')])])
