Merge branch 'dev'

Jenkinsfile

@@ -14,7 +14,9 @@ pipeline {
             environment {
               PATH = "$HOME/miniconda/bin:$PATH"
               }
-            when { changeset "environment.yml" }
+            when { 
+              changeset "requirements.txt"
+            }
             steps {
               echo 'Building Conda environment... ${BRANCH_NAME}'
               sh 'ls'
@@ -26,7 +28,9 @@ pipeline {
             environment {
               PATH = "$HOME/miniconda3/bin:$PATH"
               }
-            when { changeset "environment.yml" }
+            when {
+              changeset "requirements.txt"
+            }
             steps {
               echo 'Building Conda environment...' + 'env.BRANCH_NAME'
               sh 'ls'
@@ -114,6 +118,7 @@ pipeline {
                  cd test
                  ln -s /mnt/data/ci/data_ci_linux ./data
                  taskset -c 0-21 pytest \
+                    --junitxml=./test-reports/instantation_linux.xml \
                     --verbose \
                     --working_directory=$WORK_DIR_LINUX \
                     --disable-warnings \
@@ -124,6 +129,11 @@ pipeline {
                  conda deactivate
                  '''
             }
+            post {
+              always {
+                junit 'test/test-reports/*.xml'
+              }
+            }  
           }
           stage('Instantiate Mac') {
             agent { label 'macos' }
@@ -143,11 +153,20 @@ pipeline {
                  module load clinica.all
                  cd test
                  ln -s /Volumes/data/data_ci ./data
-                 pytest --verbose --disable-warnings -k 'test_instantiate'
+                 pytest \
+                    --verbose \
+                    --junitxml=./test-reports/instantation_mac.xml \
+                    --disable-warnings \
+                    -k 'test_instantiate'
                  module purge
                  conda deactivate
                  '''
             }
+            post {
+              always {
+                junit 'test/test-reports/*.xml'
+              }
+            }  
           }
         }
       }

clinica/VERSION

@@ -1 +1 @@
-0.3.4
+0.3.5

clinica/cmdline.py

@@ -200,6 +200,7 @@ def execute():
     from clinica.pipelines.t1_volume_existing_template.t1_volume_existing_template_cli import T1VolumeExistingTemplateCLI
     from clinica.pipelines.t1_volume_parcellation.t1_volume_parcellation_cli import T1VolumeParcellationCLI
     from clinica.pipelines.t1_linear.t1_linear_cli import T1LinearCLI
+    from clinica.pipelines.deeplearning_prepare_data.deeplearning_prepare_data_cli import DeepLearningPrepareDataCLI
     from clinica.pipelines.dwi_preprocessing_using_phasediff_fieldmap.dwi_preprocessing_using_phasediff_fieldmap_cli import DwiPreprocessingUsingPhaseDiffFieldmapCli
     from clinica.pipelines.dwi_preprocessing_using_t1.dwi_preprocessing_using_t1_cli import DwiPreprocessingUsingT1Cli
     from clinica.pipelines.dwi_dti.dwi_dti_cli import DwiDtiCli
@@ -230,6 +231,7 @@ def execute():
         PETVolumeCLI(),
         PetSurfaceCLI(),
         # PetSurfaceLongitudinalCLI(),
+        DeepLearningPrepareDataCLI(),
         SpatialSVMCLI(),
         StatisticsSurfaceCLI(),
         StatisticsVolumeCLI(),

clinica/iotools/converters/nifd_to_bids/nifd_to_bids.py

@@ -16,7 +16,6 @@
 
 def convert_images(path_to_dataset, bids_dir, path_to_clinical):
     # Conversion of the entire dataset in BIDS
-
     '''Scans available files in the path_to_dataset,
     identifies the patients that have images described by the json file,
     converts the image with the highest quality for each category'''

clinica/iotools/utils/data_handling.py

@@ -545,7 +545,6 @@ def create_subs_sess_list(input_dir, output_dir,
 
 
 def center_nifti_origin(input_image, output_image):
-
     """
 
     Put the origin of the coordinate system at the center of the image

clinica/pipelines/deeplearning_prepare_data/deeplearning_prepare_data_cli.py

@@ -0,0 +1,117 @@
+# coding: utf8
+
+
+import clinica.engine as ce
+from colorama import Fore
+
+class DeepLearningPrepareDataCLI(ce.CmdParser):
+
+    def define_name(self):
+        """Define the sub-command name to run this pipeline."""
+        self._name = 'deeplearning-prepare-data'
+
+    def define_description(self):
+        """Define a description of this pipeline."""
+        self._description = ('Prepare data generated Clinica for PyTorch with Tensor extraction:\n'
+                             'http://clinica.run/doc/Pipelines/DeepLearning_PrepareData/')
+
+    def define_options(self):
+        """Define the sub-command arguments."""
+        from clinica.engine.cmdparser import PIPELINE_CATEGORIES
+
+        # Clinica compulsory arguments (e.g. BIDS, CAPS, group_id...)
+        # Most of the time, you will want to read your pipeline inputs into
+        # a BIDS and/or CAPS directory. If your pipeline does not require BIDS input,
+        # simply remove the two lines involving the BIDS directory.
+        clinica_comp = self._args.add_argument_group(PIPELINE_CATEGORIES['CLINICA_COMPULSORY'])
+        clinica_comp.add_argument("caps_directory",
+                                  help='Path to the CAPS directory.')
+        clinica_comp.add_argument("extract_method",
+                help='''Format of the extracted features. Three options:
+                'image' to convert to PyTorch tensor the complete 3D image,
+                'patch' to extract 3D volumetric patches and 
+                'slice' to extract 2D slices from the image.
+                By default the features are extracted from the cropped image.''',
+                choices=['image', 'slice', 'patch'], default='image'
+                )
+
+        optional = self._args.add_argument_group(PIPELINE_CATEGORIES['OPTIONAL'])
+        optional.add_argument('-uui', '--use_uncropped_image',
+                              help='''Use the uncropped image instead of the
+                              cropped image generated by t1-linear.''',
+                              default=False, action="store_true"
+                              )
+
+        optional_patch = self._args.add_argument_group(
+            "%sPipeline options if you chose ‘patch’ extraction%s" % (Fore.BLUE, Fore.RESET)
+        )
+        optional_patch.add_argument(
+                '-ps', '--patch_size',
+                help='''Patch size (default: --patch_size 50).''',
+                type=int, default=50
+                )
+        optional_patch.add_argument(
+                '-ss', '--stride_size',
+                help='''Stride size (default: --stride_size 50).''',
+                type=int, default=50
+                )
+
+        optional_slice = self._args.add_argument_group(
+            "%sPipeline options if you chose ‘slice’ extraction%s" % (Fore.BLUE, Fore.RESET)
+        )
+        optional_slice.add_argument(
+                '-sd', '--slice_direction',
+                help='''Slice direction. Three options:
+                '0' -> Sagittal plane,
+                '1' -> Coronal plane or
+                '2' -> Axial plane
+                (default: sagittal plane i.e. --slice_direction 0)''',
+                type=int, default=0
+                )
+        optional_slice.add_argument(
+                '-sm', '--slice_mode',
+                help='''Slice mode. Two options: 'rgb' to save the slice in
+                three identical channels, ‘single’ to save the slice in a
+                single channel (default: --slice_mode rgb).''',
+                choices=['rgb', 'single'], default='rgb'
+                )
+
+        # Clinica standard arguments (e.g. --n_procs)
+        self.add_clinica_standard_arguments()
+
+    def run_command(self, args):
+        """Run the pipeline with defined args."""
+        from networkx import Graph
+        from .deeplearning_prepare_data_pipeline import DeepLearningPrepareData
+        from clinica.utils.ux import print_end_pipeline, print_crash_files_and_exit
+
+        parameters = {
+            # Add your own pipeline parameters here to use them inside your
+            # pipeline. See the file `deeplearning_prepare_data_pipeline.py` to
+            # see an example of use.
+            'extract_method': args.extract_method,
+            'patch_size': args.patch_size,
+            'stride_size': args.stride_size,
+            'slice_direction': args.slice_direction,
+            'slice_mode': args.slice_mode,
+            'use_uncropped_image': args.use_uncropped_image,
+        }
+
+        pipeline = DeepLearningPrepareData(
+            caps_directory=self.absolute_path(args.caps_directory),
+            tsv_file=self.absolute_path(args.subjects_sessions_tsv),
+            base_dir=self.absolute_path(args.working_directory),
+            parameters=parameters,
+            name=self.name
+        )
+
+        if args.n_procs:
+            exec_pipeline = pipeline.run(plugin='MultiProc',
+                                         plugin_args={'n_procs': args.n_procs})
+        else:
+            exec_pipeline = pipeline.run()
+
+        if isinstance(exec_pipeline, Graph):
+            print_end_pipeline(self.name, pipeline.base_dir, pipeline.base_dir_was_specified)
+        else:
+            print_crash_files_and_exit(args.logname, pipeline.base_dir)