From 547d888f114228bd0a897c91d38d7dc3bceb35d8 Mon Sep 17 00:00:00 2001 From: Remi Gau Date: Thu, 14 Dec 2023 20:18:51 +0100 Subject: [PATCH] bidsify output --- giga_connectome/postprocess.py | 28 ++++++-- giga_connectome/tests/test_cli.py | 1 - giga_connectome/utils.py | 67 +++++++++++++++++- giga_connectome/workflow.py | 21 +++--- ...taset_desc-100Parcels7Networks_dseg.nii.gz | Bin 0 -> 14393 bytes ...taset_desc-200Parcels7Networks_dseg.nii.gz | Bin 0 -> 15568 bytes ...taset_desc-300Parcels7Networks_dseg.nii.gz | Bin 0 -> 16557 bytes ...taset_desc-400Parcels7Networks_dseg.nii.gz | Bin 0 -> 17671 bytes ...taset_desc-500Parcels7Networks_dseg.nii.gz | Bin 0 -> 18424 bytes ...taset_desc-600Parcels7Networks_dseg.nii.gz | Bin 0 -> 19207 bytes ...taset_desc-800Parcels7Networks_dseg.nii.gz | Bin 0 -> 20160 bytes ...es-dataset_label-GM_desc-group_mask.nii.gz | Bin 0 -> 8614 bytes 12 files changed, 98 insertions(+), 19 deletions(-) create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-100Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-200Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-300Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-400Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-500Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-600Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_atlas-Schaefer20187Networks_res-dataset_desc-800Parcels7Networks_dseg.nii.gz create mode 100644 work/groupmasks/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-dataset_label-GM_desc-group_mask.nii.gz diff --git a/giga_connectome/postprocess.py b/giga_connectome/postprocess.py index 6cf4d8a..6744b06 100644 --- a/giga_connectome/postprocess.py +++ b/giga_connectome/postprocess.py @@ -74,8 +74,9 @@ def run_postprocessing_dataset( Smoothing kernel size, passed to nilearn masker. output_path: - Full path to output file, named in the following format: - output_dir / atlas-_desc-.h5 + Full path to output directory. + Will be used to name the output file in the following format: + [sub-][_ses-_]atlas-_desc-_relmat.h5 analysis_level : str Level of analysis, either "participant" or "group". @@ -105,6 +106,16 @@ def run_postprocessing_dataset( ) # parse file name subject, session, specifier = utils.parse_bids_name(img.path) + + connectome_path = output_path / subject + if session: + connectome_path = connectome_path / session + filename = utils.output_filename( + Path(img.filename).stem, atlas, strategy["name"] + ) + connectome_path = connectome_path / "func" / filename + connectome_path = utils.check_path(connectome_path, verbose=True) + for desc, masker in atlas_maskers.items(): attribute_name = f"{subject}_{specifier}_atlas-{atlas}_desc-{desc}" if not denoised_img: @@ -126,8 +137,8 @@ def run_postprocessing_dataset( connectomes[desc].append(correlation_matrix) # dump to h5 - flag = _set_file_flag(output_path) - with h5py.File(output_path, flag) as f: + flag = _set_file_flag(connectome_path) + with h5py.File(connectome_path, flag) as f: group = _fetch_h5_group(f, subject, session) timeseries_dset = group.create_dataset( f"{attribute_name}_timeseries", data=time_series_atlas @@ -141,11 +152,18 @@ def run_postprocessing_dataset( if analysis_level == "group": print("create group connectome") + connectome_path = ( + output_path + / "group" + / utils.output_filename("", atlas, strategy["name"]) + ) + connectome_path = utils.check_path(connectome_path, verbose=True) + print(connectome_path) for desc in connectomes: average_connectome = np.mean( np.array(connectomes[desc]), axis=0 ).astype(np.float32) - with h5py.File(output_path, "a") as f: + with h5py.File(connectome_path, "a") as f: f.create_dataset( f"atlas-{atlas}_desc-{desc}_connectome", data=average_connectome, diff --git a/giga_connectome/tests/test_cli.py b/giga_connectome/tests/test_cli.py index 969506f..710ed08 100644 --- a/giga_connectome/tests/test_cli.py +++ b/giga_connectome/tests/test_cli.py @@ -31,7 +31,6 @@ def test_help(capsys): @pytest.mark.smoke def test_smoke(tmp_path, capsys): - bids_dir = resource_filename( "giga_connectome", "data/test_data/ds000017-fmriprep22.0.1-downsampled-nosurface", diff --git a/giga_connectome/utils.py b/giga_connectome/utils.py index cc5f801..7376c9e 100644 --- a/giga_connectome/utils.py +++ b/giga_connectome/utils.py @@ -1,9 +1,15 @@ -from typing import List, Tuple, Union +from __future__ import annotations + +from typing import List, Tuple, Union, Any +import json from pathlib import Path + from nilearn.interfaces.bids import parse_bids_filename from bids.layout import Query from bids import BIDSLayout +from giga_connectome import __version__ + def get_bids_images( subjects: List[str], @@ -184,6 +190,8 @@ def check_path(path: Path, verbose=True): ext = path.suffix path_parent = path.parent + path_parent.mkdir(parents=True, exist_ok=True) + if path.exists(): similar_paths = [ str(p).replace(ext, "") @@ -199,3 +207,60 @@ def check_path(path: Path, verbose=True): if verbose: print(f"Specified path already exists, using {path} instead.") return path + + +def create_ds_description(output_dir: Path) -> None: + """Create a dataset_description.json file.""" + ds_desc: dict[str, Any] = { + "BIDSVersion": "1.9.0", + "License": None, + "Name": None, + "ReferencesAndLinks": [], + "Authors": [ + "Foo", + "Bar", + ], + "DatasetDOI": None, + "DatasetType": "derivative", + "GeneratedBy": [ + { + "Name": "giga_connectome", + "Version": __version__, + "CodeURL": "https://github.com/SIMEXP/giga_connectome.git", + } + ], + "HowToAcknowledge": ( + "Please refer to our repository: " + "https://github.com/SIMEXP/giga_connectome.git." + ), + } + with open(output_dir / "dataset_description.json", "w") as f: + json.dump(ds_desc, f, indent=4) + + +def create_sidecar(output_path: Path) -> None: + """Create a JSON sidecar for the connectivity data.""" + metadata: dict[str, Any] = { + "NodeFiles": "REQUIRED", + "Measure": "Pearson correlation", + "MeasureDescription": "Pearson correlation", + "Weighted": "REQUIRED", + "Directed": False, + "ValidDiagonal": True, + "StorageFormat": "Full", + "NonNegative": "", + "Code": "https://github.com/SIMEXP/giga_connectome.git", + } + with open(output_path, "w") as f: + json.dump(metadata, f, indent=4) + + +def output_filename(source_file: str, atlas: str, strategy: str) -> str: + root = source_file.split("_")[:-1] + root = "_".join(root) + if len(root) > 0: + root += "_" + return ( + f"{root}atlas-{atlas}_meas-PearsonCorrelation_desc-{strategy}" + "_relmat.h5" + ) diff --git a/giga_connectome/workflow.py b/giga_connectome/workflow.py index 4f1a59d..0b3fd55 100644 --- a/giga_connectome/workflow.py +++ b/giga_connectome/workflow.py @@ -40,6 +40,12 @@ def workflow(args): ) print("Indexing BIDS directory") + utils.create_ds_description(output_dir) + utils.create_sidecar( + output_dir + / f"meas-PearsonCorrelation_desc-{args.denoise_strategy}_relmat.json" + ) + # create subject ts and connectomes # refactor the two cases into one @@ -51,11 +57,7 @@ def workflow(args): group_mask, resampled_atlases = generate_gm_mask_atlas( working_dir, atlas, template, subj_data["mask"] ) - connectome_path = output_dir / ( - f"sub-{subject}_atlas-{atlas['name']}" - f"_desc-{strategy['name']}.h5" - ) - connectome_path = utils.check_path(connectome_path, verbose=True) + print("Generate subject level connectomes") run_postprocessing_dataset( strategy, @@ -64,7 +66,7 @@ def workflow(args): group_mask, standardize, smoothing_fwhm, - connectome_path, + output_dir, analysis_level, calculate_average_correlation, ) @@ -77,11 +79,6 @@ def workflow(args): group_mask, resampled_atlases = generate_gm_mask_atlas( working_dir, atlas, template, subj_data["mask"] ) - connectome_path = ( - output_dir / f"atlas-{atlas['name']}_desc-{strategy['name']}.h5" - ) - connectome_path = utils.check_path(connectome_path, verbose=True) - print(connectome_path) print("Generate subject level connectomes") run_postprocessing_dataset( strategy, @@ -90,7 +87,7 @@ def workflow(args): group_mask, standardize, smoothing_fwhm, - connectome_path, + output_dir, analysis_level, calculate_average_correlation, ) diff --git 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