feat(handlers.project): call Datalad's create() and save() methods (08d1adf7) · Commits · HIP / DataHIPy

datahipy/handlers/project.py

+31 −0

Original line number	Diff line number	Diff line
		@@ -10,6 +10,8 @@ import pandas as pd
		from pathlib import Path
		from sre_constants import SUCCESS

		import datalad.api

		from datahipy.bids.dataset import create_empty_bids_dataset
		from datahipy.bids.dataset import get_bidsdataset_content

		@@ -48,6 +50,12 @@ def initialize_project_structure(
		for folder in DOCUMENTS_FOLDERS:
		os.makedirs(project_dir / "documents" / folder, exist_ok=True)

		# Initialize the project dataset as a Datalad-managed dataset
		datalad.api.create(
		dataset=str(project_dir.absolute()),
		cfg_proc=['text2git']
		)

		# Create initial project README.md file
		with open(project_dir / "README.md", "w") as f:
		f.writelines([f"# {project_title}\n\n", f"{project_description}"])
		@@ -102,6 +110,12 @@ def create_project(input_data: str, output_file: str):
		)
		with open(output_file, "w") as f:
		json.dump(dataset_content, f, indent=4)
		# Save the state of the dataset with Datalad
		datalad.api.save(
		dataset=str(project_dir.absolute()),
		message='Initial dataset state of collaborative project',
		recursive=False # Do not save the nested Datalad-BIDS dataset
		)
		print(SUCCESS)


		@@ -157,6 +171,13 @@ def import_subject(input_data: str, output_file: str):
		)
		with open(output_file, "w") as f:
		json.dump(dataset_content, f, indent=4)
		# Save dataset state with Datalad
		save_msg = f'Import subject {input_data["participantId"]} from {input_data["sourceDatasetPath"]}'
		datalad.api.save(
		dataset=input_data["targetDatasetPath"],
		message=save_msg,
		recursive=True
		)
		print(SUCCESS)


		@@ -229,4 +250,14 @@ def import_document(input_data: str):
		os.remove(target_document_path)
		# Copy document from source to target
		shutil.copyfile(source_document_path, target_document_path)
		# Save dataset state with Datalad
		save_msg = (
		f'Add document {input_data["sourceDocumentAbsPath"]} '
		f"in project\'s documents/ folder"
		)
		datalad.api.save(
		dataset=input_data["targetDatasetPath"],
		message=save_msg,
		recursive=True
		)
		print(SUCCESS)