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Loop through Cartesian product
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@lalalavi
lalalavi committed May 7, 2024
1 parent d95020d commit cd877a5
Showing 1 changed file with 103 additions and 99 deletions.
202 changes: 103 additions & 99 deletions tests/workflows/test_consistency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

import os
import shutil
from itertools import product
from multiprocessing import cpu_count
from pathlib import Path

Expand All @@ -21,9 +22,31 @@
from .datasets import Dataset, datasets
from .spec import make_spec


@pytest.mark.parametrize("dataset", datasets)
def test_extraction(dataset: Dataset, tmp_path: Path, pcc_mask: Path):
counfound_comb = [
# None,
["a_comp_cor_0[0-4]"],
["(trans|rot)_[xyz]"],
[
"(trans|rot)_[xyz]",
"(trans|rot)_[xyz]_derivative1",
"(trans|rot)_[xyz]_power2",
"(trans|rot)_[xyz]_derivative1_power2",
],
[
"(trans|rot)_[xyz]",
"(trans|rot)_[xyz]_derivative1",
"(trans|rot)_[xyz]_power2",
"(trans|rot)_[xyz]_derivative1_power2",
"global_signal",
],
["global_signal"],
]

# Need to add the option to do it with none


@pytest.mark.parametrize("dataset,confound_args", list(product(datasets, counfound_comb)))
def test_extraction(dataset: Dataset, confound_args: list[str], tmp_path: Path, pcc_mask: Path):
"""
Run preprocessing and feature extraction for each of the four participants,
coming from our list of datasets, then compare features to those acquired in
Expand All @@ -44,100 +67,81 @@ def test_extraction(dataset: Dataset, tmp_path: Path, pcc_mask: Path):

dataset_file = dataset.download(tmp_path)

# Need to add the option to do it with none

counfound_comb = [
# None,
["a_comp_cor_0[0-4]"],
["(trans|rot)_[xyz]"],
[
"(trans|rot)_[xyz]",
"(trans|rot)_[xyz]_derivative1",
"(trans|rot)_[xyz]_power2",
"(trans|rot)_[xyz]_derivative1_power2",
],
[
"(trans|rot)_[xyz]",
"(trans|rot)_[xyz]_derivative1",
"(trans|rot)_[xyz]_power2",
"(trans|rot)_[xyz]_derivative1_power2",
"global_signal",
],
["global_signal"],
]

# Iterate with an index to use in naming
for index, c in enumerate(counfound_comb, start=1):
spec = make_spec(dataset_files=[dataset_file], pcc_mask=pcc_mask, confounds_chosen=c, ica_aroma=False)
config.nipype.omp_nthreads = cpu_count()

confound_folder_name = f"comb{index}" # Generate a folder name depending on confound
specific_workdir = tmp_path / confound_folder_name
os.makedirs(specific_workdir, exist_ok=True)
save_spec(spec, workdir=specific_workdir)

workflow = init_workflow(specific_workdir)
graphs = init_execgraph(specific_workdir, workflow)
parser = build_parser()
opts = parser.parse_args(args=list())
opts.graphs = graphs
opts.nipype_run_plugin = "Simple"
opts.debug = True

raw_data = Path(specific_workdir) / "rawdata"
has_sessions = any(raw_data.glob("sub-*/ses-*"))

if has_sessions:
(bold_path,) = specific_workdir.glob("rawdata/sub-*/ses-*/func/*_bold.nii.gz")
else:
(bold_path,) = specific_workdir.glob("rawdata/sub-*/func/*_bold.nii.gz")

# Reorient the BOLD image and overwrite the original file
# reorient_image(bold_path, bold_path) # Input = output path because we are overwriting

bold_image = nib.nifti1.load(bold_path)

run_stage_run(opts)

if has_sessions:
(preproc_path,) = specific_workdir.glob("derivatives/halfpipe/sub-*/ses-*/func/*_bold.nii.gz")
else:
(preproc_path,) = specific_workdir.glob("derivatives/halfpipe/sub-*/func/*_bold.nii.gz")
preproc_image = nib.nifti1.load(preproc_path)

# Calculate extra TSNR and store on the fly
tsnr = TSNR()
tsnr.inputs.in_file = preproc_path
tsnr_hp = tsnr.run()
current_tsnr_halfpipe = tsnr_hp.outputs.out_file
output_dir = os.path.dirname(preproc_path) # Extracting directory from the file path
os.makedirs(output_dir, exist_ok=True)
output_file_path = os.path.join(output_dir, "tsnr_halfpipe.nii.gz") # Define the full path for new TSNR
shutil.copy(current_tsnr_halfpipe, output_file_path) # Ccopy into new location

############ Baseline check ##########
assert bold_image.shape[3] == preproc_image.shape[3]

# ############ Consistency checks ##########
# setup_test_resources()
# zip_path = get_resource("halfpipe122_baseline.zip") # this will be done 1 time per dataset, split test?

# with ZipFile(zip_path) as zip_file:
# zip_file.extractall(tmp_path)

# baseline_path = tmp_path / "halfpipe122_baseline"
# assert isinstance(baseline_path, Path), "Baseline path did not return a Path object."
# assert any(baseline_path.iterdir()), "The extracted directory is empty."

# # Establish paths for all relevant files for comparison
# base_paths = [baseline_path / path for path in dataset.osf_paths]
# current_paths = [tmp_path / path for path in dataset.consistency_paths]
# base_tsnr, base_fc, base_reho, base_seed, base_falff, base_alff, base_dual = base_paths
# current_tsnr, current_fc, current_reho, current_seed, current_falff, current_alff, current_dual = current_paths
# #! add base_tsnr_halfpipe
# #! add current_tsnr_halfpipe

# # threshold = 0.3 # Example threshold?
# fc_fig, mean_abs_diff = compare_fcs(base_fc, current_fc)

# # assert mean_abs_diff < threshold, "Mean absolute difference is too high"
spec = make_spec(dataset_files=[dataset_file], pcc_mask=pcc_mask, confounds_chosen=confound_args, ica_aroma=True)
config.nipype.omp_nthreads = cpu_count()

ica_bool = spec.settings[0]["ica_aroma"] # Retrieve state from spec
confound_folder_name = f"{ica_bool}_{counfound_comb.index(confound_args)}"

specific_workdir = tmp_path / confound_folder_name
os.makedirs(specific_workdir, exist_ok=True)
save_spec(spec, workdir=specific_workdir)

workflow = init_workflow(specific_workdir)
graphs = init_execgraph(specific_workdir, workflow)
parser = build_parser()
opts = parser.parse_args(args=list())
opts.graphs = graphs
opts.nipype_run_plugin = "Simple"
opts.debug = True

raw_data = Path(specific_workdir) / "rawdata"
has_sessions = any(raw_data.glob("sub-*/ses-*"))

if has_sessions:
(bold_path,) = specific_workdir.glob("rawdata/sub-*/ses-*/func/*_bold.nii.gz")
else:
(bold_path,) = specific_workdir.glob("rawdata/sub-*/func/*_bold.nii.gz")

# Reorient the BOLD image and overwrite the original file
# reorient_image(bold_path, bold_path) # Input = output path because we are overwriting

bold_image = nib.nifti1.load(bold_path)

run_stage_run(opts)

if has_sessions:
(preproc_path,) = specific_workdir.glob("derivatives/halfpipe/sub-*/ses-*/func/*_bold.nii.gz")
else:
(preproc_path,) = specific_workdir.glob("derivatives/halfpipe/sub-*/func/*_bold.nii.gz")
preproc_image = nib.nifti1.load(preproc_path)

# Calculate extra TSNR and store on the fly
tsnr = TSNR()
tsnr.inputs.in_file = preproc_path
tsnr_hp = tsnr.run()
current_tsnr_halfpipe = tsnr_hp.outputs.out_file
output_dir = os.path.dirname(preproc_path) # Extracting directory from the file path
os.makedirs(output_dir, exist_ok=True)
output_file_path = os.path.join(output_dir, "tsnr_halfpipe.nii.gz") # Define the full path for new TSNR
shutil.copy(current_tsnr_halfpipe, output_file_path) # Ccopy into new location

############ Baseline check ##########
assert bold_image.shape[3] == preproc_image.shape[3]

# ############ Consistency checks ##########
# setup_test_resources()
# zip_path = get_resource("halfpipe122_baseline.zip") # this will be done 1 time per dataset, split test?

# with ZipFile(zip_path) as zip_file:
# zip_file.extractall(tmp_path)

# baseline_path = tmp_path / "halfpipe122_baseline"
# assert isinstance(baseline_path, Path), "Baseline path did not return a Path object."
# assert any(baseline_path.iterdir()), "The extracted directory is empty."

# # Establish paths for all relevant files for comparison
# base_paths = [baseline_path / path for path in dataset.osf_paths]
# current_paths = [tmp_path / path for path in dataset.consistency_paths]
# base_tsnr, base_fc, base_reho, base_seed, base_falff, base_alff, base_dual = base_paths
# current_tsnr, current_fc, current_reho, current_seed, current_falff, current_alff, current_dual = current_paths
# #! add base_tsnr_halfpipe
# #! add current_tsnr_halfpipe

# # threshold = 0.3 # Example threshold?
# fc_fig, mean_abs_diff = compare_fcs(base_fc, current_fc)

# # assert mean_abs_diff < threshold, "Mean absolute difference is too high"

#! create zip file?

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