Export Group Level maps as BIDS datalad dataset

narps_open/utils/export/__main__.py

@@ -0,0 +1,82 @@
+#!/usr/bin/python
+# coding: utf-8
+
+""" A command line tool for the narps_open.utils.export module """
+
+import os
+from os.path import join, exists, basename
+import shutil
+from argparse import ArgumentParser
+
+from narps_open.utils.configuration import Configuration
+from narps_open.pipelines import get_implemented_pipelines
+from narps_open.runner import PipelineRunner
+
+
+# Derived Class Cleaner or only add method
+class PipelineExporter(PipelineRunner):
+    def get_export_filename(i):
+        """Get NARPS compliant filename based on the position in the fixed list files
+
+        """
+        hypothesis = str(i // 2 + 1) # 2 files per hypothesis starting at 1
+        suffix = "" if i%2 == 0 else "un" #even thresholded odd unthresholded
+        export_filename = f"hypo_{hypothesis}_{suffix}thresh.nii.gz"
+        return export_filename
+
+def get_export_filename(i):
+    """Get NARPS compliant filename based on the position in the fixed list files
+
+    """
+    hypothesis = str(i // 2 + 1)  # 2 files per hypothesis starting at 1
+    suffix = "" if i % 2 == 0 else "un"  # even thresholded odd unthresholded
+    export_filename = f"hypo_{hypothesis}_{suffix}thresh.nii.gz"
+    return export_filename
+
+
+def main():
+    """ Entry-point for the command line tool narps_open_export """
+
+    # Parse arguments
+    parser = ArgumentParser(description = 'Export group level NARPS reproduced results into BIDS format')
+    parser.add_argument('-t', '--team', type = str, required = True,
+        help = 'the team ID', choices = get_implemented_pipelines())
+    parser.add_argument('-n', '--nsubjects', type = int, required = True,
+        help='the number of subjects to be selected')
+    arguments = parser.parse_args()
+
+    # Initialize pipeline
+    runner = PipelineRunner(arguments.team)
+    runner.pipeline.directories.dataset_dir = Configuration()['directories']['dataset']
+    runner.pipeline.directories.results_dir = Configuration()['directories']['reproduced_results']
+    runner.pipeline.directories.set_output_dir_with_team_id(arguments.team)
+    runner.pipeline.directories.set_working_dir_with_team_id(arguments.team)
+    # should I use export files with team_id
+    runner.nb_subjects = arguments.nsubjects
+
+
+    # Retrieve the paths to the reproduced files
+    reproduced_files = runner.pipeline.get_hypotheses_outputs()
+    # Not statisfactory !
+    # TODO: a group of minimun XXXXX subjects can be exported
+    # TODO have a hash based on the exact subjects list
+    # TODO: full dataset should be the default case
+
+    export_dir = join(runner.pipeline.directories.results_dir, "group-level", runner.pipeline.team_id, f"nbsub-{str(len(runner.nb_subjects))}")
+    if not exists(export_dir):
+        os.makedirs(export_dir)
+    for i, file in enumerate(reproduced_files):
+        export_file = join(export_dir, get_export_filename(i))
+        # TODO use datalad instead (installation will be messier)
+        shutil.copy2(file, export_file)
+
+
+
+
+
+
+
+
+
+if __name__ == '__main__':
+    main()

setup.py

@@ -69,6 +69,7 @@
     entry_points = {
         'console_scripts': [
             'narps_open_runner = narps_open.runner:main',
+            'narps_open_exporter = narps_open.utils.export.__main__:main',
             'narps_open_tester = narps_open.tester:main',
             'narps_open_status = narps_open.utils.status:main',
             'narps_open_correlations = narps_open.utils.correlation.__main__:main',