Merge pull request #163 from rwblair/issue155

Generate reports at end of run_workflow.

fmriprep/interfaces/__init__.py

@@ -3,4 +3,5 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 from fmriprep.interfaces.bids import ReadSidecarJSON, DerivativesDataSink, BIDSDataGrabber
+from fmriprep.interfaces.images import ImageDataSink
 from fmriprep.interfaces.utils import FormatHMCParam, IntraModalMerge

fmriprep/interfaces/bids.py

@@ -7,17 +7,18 @@
 # @Date:   2016-06-03 09:35:13
 # @Last Modified by:   oesteban
 # @Last Modified time: 2016-10-05 15:03:25
-from lockfile import LockFile
 import os
 import os.path as op
 import pkg_resources as pkgr
 import re
-from shutil import copy
 import simplejson as json
+from shutil import copy
 
 from nipype import logging
-from nipype.interfaces.base import (traits, isdefined, TraitedSpec, BaseInterface,
-                                    BaseInterfaceInputSpec, File, InputMultiPath, OutputMultiPath)
+from nipype.interfaces.base import (
+    traits, isdefined, TraitedSpec, BaseInterface, BaseInterfaceInputSpec, 
+    File, InputMultiPath, OutputMultiPath
+)
 
 from fmriprep.utils.misc import collect_bids_data, make_folder
 

fmriprep/interfaces/images.py

@@ -0,0 +1,88 @@
+import json
+import re
+import os
+import os.path as op
+from shutil import copy
+
+from nipype import logging
+from nipype.interfaces.base import (
+    traits, isdefined, TraitedSpec, BaseInterface, BaseInterfaceInputSpec, 
+    File, InputMultiPath, OutputMultiPath, traits
+)
+
+from fmriprep.interfaces.bids import _splitext 
+from fmriprep.utils.misc import make_folder
+
+class ImageDataSinkInputSpec(BaseInterfaceInputSpec):
+    base_directory = traits.Directory(
+        desc='Path to the base directory for storing data.')
+    in_file = traits.Str(desc='the image to be saved')
+    base_file = traits.Str(desc='the input func file')
+    overlay_file = traits.Str(desc='the input func file')
+    origin_file = traits.Str(desc='File from the dataset that image is primarily derived from')
+
+class ImageDataSinkOutputSpec(TraitedSpec):
+    out_file = OutputMultiPath(File(exists=True, desc='written file path'))
+
+class ImageDataSink(BaseInterface):
+    input_spec = ImageDataSinkInputSpec
+    output_spec = ImageDataSinkOutputSpec
+    _always_run = True
+
+    def __init__(self, **inputs):
+        self._results = {'out_file': []}
+        super(ImageDataSink, self).__init__(**inputs)
+
+    def _run_interface(self, runtime):
+        origin_fname, _ = _splitext(self.inputs.origin_file)
+
+        image_inputs = {}
+        if isdefined(self.inputs.base_file):
+            image_inputs['base_file'] = self.inputs.base_file
+        if isdefined(self.inputs.overlay_file):
+            image_inputs['overlay_file'] = self.inputs.overlay_file
+        if isdefined(self.inputs.origin_file):
+            image_inputs['origin_file'] = self.inputs.overlay_file
+
+        m = re.search(
+            '^(?P<subject_id>sub-[a-zA-Z0-9]+)(_(?P<ses_id>ses-[a-zA-Z0-9]+))?'
+            '(_(?P<task_id>task-[a-zA-Z0-9]+))?(_(?P<acq_id>acq-[a-zA-Z0-9]+))?'
+            '(_(?P<rec_id>rec-[a-zA-Z0-9]+))?(_(?P<run_id>run-[a-zA-Z0-9]+))?',
+            origin_fname
+        )
+
+        base_directory = os.getcwd()
+        if isdefined(self.inputs.base_directory):
+            base_directory = op.abspath(self.inputs.base_directory)
+
+        out_path = 'images/{subject_id}'.format(**m.groupdict())
+
+        out_path = op.join(base_directory, out_path)
+
+        make_folder(out_path)
+
+        _, out_filename = op.split(self.inputs.in_file)
+
+        #  test incoming origin file for these identifiers, if they exist
+        #  we want to fold them into out filename
+        group_keys = ['ses_id', 'task_id', 'acq_id', 'rec_id', 'run_id']
+        if [x for x in group_keys if m.groupdict().get(x)]:
+            out_filename, ext = _splitext(out_filename)
+            out_filename = '{}_{}.{}'.format(out_filename, origin_fname, ext)
+
+        out_file = op.join(out_path, out_filename)
+
+
+        self._results['out_file'].append(out_file)
+        copy(self.inputs.in_file, out_file)
+        json_fname, _ = _splitext(out_filename)
+
+        json_out_filename = '{}.{}'.format(json_fname, 'json')
+        json_out_file = op.join(out_path, json_out_filename)
+        with open(json_out_file, 'w') as fp:
+            json.dump(image_inputs, fp)
+
+        return runtime
+
+    def _list_outputs(self):
+        return self._results

fmriprep/run_workflow.py

@@ -17,6 +17,7 @@
 from argparse import RawTextHelpFormatter
 from multiprocessing import cpu_count
 
+
 def main():
     """Entry point"""
     from fmriprep import __version__
@@ -70,6 +71,7 @@ def create_workflow(opts):
     import logging
     from nipype import config as ncfg
     from fmriprep.utils import make_folder
+    from fmriprep.viz.reports import run_reports
     from fmriprep.workflows import base as fwb
     from fmriprep.workflows.base import base_workflow_enumerator
 
@@ -143,5 +145,7 @@ def create_workflow(opts):
     if opts.write_graph:
         preproc_wf.write_graph()
 
+    run_reports(settings['output_dir'])
+
 if __name__ == '__main__':
     main()

fmriprep/utils/misc.py

@@ -11,6 +11,9 @@
 
 INPUTS_SPEC = {'fieldmaps': [], 'func': [], 't1': [], 'sbref': []}
 
+def _first(inlist):
+    return sorted(inlist)[0]
+
 def make_folder(folder):
     try:
         os.makedirs(folder)

fmriprep/viz/config.json

@@ -30,10 +30,34 @@
         "elements":
         [
             {
-                "name": "sbref_to_t1",
-                "file_pattern": "sbref_to_t1",
+                "name": "t1_seg_native",
+                "file_pattern": "t1_seg_native",
                 "title": "Sbref to T1",
                 "description": "Corrected sbref image registred into t1 space."
+            },
+            {
+                "name": "t1_to_mni_overlay",
+                "file_pattern": "t1_to_mni_overlay",
+                "title": "T1 to MNI",
+                "description": "Corrected anatomical T1 image registered into MNI space"
+            },
+            {
+                "name": "mean_epi_overlay",
+                "file_pattern": "mean_epi_overlay",
+                "title": "EPI",
+                "description": " "
+            },
+            {
+                "name": "corrected_EPI",
+                "file_pattern": "corrected_EPI",
+                "title": "Corrected EPI",
+                "description": " "
+            },
+            {
+                "name": "mean_epi_to_sbref",
+                "file_pattern": "mean_epi_to_sbref",
+                "title": "Mean EPI to SBRef",
+                "description": " "
             }
         ]
     }

fmriprep/viz/report.tpl

@@ -15,10 +15,14 @@
 {% for sub_report in sub_reports %}
     <h2>{{ sub_report.name }}</h2>
     {% for elem in sub_report.elements %}
-        {{ elem.name }}
+        {% if elem.files_contents %}
+        <h4>{{ elem.name }}<h4/>
+        <br>
         {% for image in elem.files_contents %}
-            {{ image }}
+            {{ image.1 }}<br>
+            {{ image.0 }}
         {% endfor %}
+        {% endif %}
     {% endfor %}
 {% endfor %}
 

fmriprep/viz/reports.py

@@ -4,7 +4,6 @@
 import json
 import re
 import os
-from os.path import join, exists, basename, dirname
 
 import jinja2
 from pkg_resources import resource_filename as pkgrf
@@ -18,6 +17,7 @@ def __init__(self, name, file_pattern, title, description):
         self.description = description
         self.files = []
         self.files_contents = []
+        self.files_subjects = []
 
 
 class SubReport(object):
@@ -38,6 +38,7 @@ def generate_sub_report(self, report):
         sub_report_render = sub_report_tpl.render
         return sub_report_render
 
+
 class Report(object):
 
     def __init__(self, path, config, out_dir, out_filename='report.html'):
@@ -48,8 +49,11 @@ def __init__(self, path, config, out_dir, out_filename='report.html'):
         self.out_filename = out_filename
 
     def _load_config(self, config):
-        if isinstance(config, str):
+        try:
             config = json.load(open(config, 'r'))
+        except Exception as e:
+            print(e)
+            return
 
         for e in config['sub_reports']:
             sub_report = SubReport(**e)
@@ -60,15 +64,15 @@ def _load_config(self, config):
     def index(self):
         for root, directories, filenames in os.walk(self.root):
             for f in filenames:
-                f = join(root, f)
+                f = os.path.join(root, f)
                 for sub_report in self.sub_reports:
                     for element in sub_report.elements:
                         if element.file_pattern.search(f) and f.split('.')[-1] == 'svg':
                             element.files.append(f)
                             with open(f) as fp:
                                 content = fp.read()
                                 content = '\n'.join(content.split('\n')[1:])
-                                element.files_contents.append(content)
+                                element.files_contents.append((f, content))
 
     def generate_report(self):
         searchpath = pkgrf('fmriprep', '/')
@@ -81,3 +85,19 @@ def generate_report(self):
         with open(os.path.join(self.out_dir, self.out_filename), 'w') as fp:
             fp.write(report_render)
         return report_render
+
+def run_reports(out_dir):
+    path = os.path.join(out_dir, 'images/')
+    config = pkgrf('fmriprep', 'viz/config.json')
+
+    for root, _, _ in os.walk(path):
+        #  relies on the fact that os.walk does not return a trailing /
+        dir = root.split('/')[-1]
+        try:
+            subject = re.search('^(?P<subject_id>sub-[a-zA-Z0-9]+)$', dir).group()
+            out_filename='{}{}'.format(subject, '.html')
+            report = Report(path, config, out_dir, out_filename)
+            report.generate_report()
+        except AttributeError:
+            continue
+

fmriprep/workflows/anatomical.py

@@ -13,18 +13,19 @@
 import pkg_resources as pkgr
 
 from nipype.interfaces import ants
-from nipype.interfaces import fsl
 from nipype.interfaces import freesurfer as fs
+from nipype.interfaces import fsl
 from nipype.interfaces import io as nio
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
 
-from niworkflows.anat.skullstrip import afni_wf as skullstrip_wf
 from niworkflows.anat.mni import RobustMNINormalization
+from niworkflows.anat.skullstrip import afni_wf as skullstrip_wf
 from niworkflows.common import reorient as mri_reorient_wf
-
-from fmriprep.interfaces import DerivativesDataSink, IntraModalMerge
 from niworkflows.data import get_mni_template
+
+from fmriprep.interfaces import (DerivativesDataSink, IntraModalMerge,
+    ImageDataSink)
 from fmriprep.viz import stripped_brain_overlay, anatomical_overlay
 
 
@@ -111,6 +112,11 @@ def t1w_preprocessing(name='t1w_preprocessing', settings=None):
     )
     t1_stripped_overlay.inputs.out_file = 't1_stripped_overlay.svg'
 
+    t1_stripped_overlay_ds = pe.Node(
+        ImageDataSink(base_directory=settings['output_dir']),
+        name='T1StrippedOverlayDS'
+    )
+
     #  t1 segmentation mapped onto t1 before registration into mni space
     t1_seg_native = pe.Node(
         niu.Function(
@@ -122,6 +128,11 @@ def t1w_preprocessing(name='t1w_preprocessing', settings=None):
     )
     t1_seg_native.inputs.out_file = 't1_seg_native.svg'
 
+    t1_seg_native_ds = pe.Node(
+        ImageDataSink(base_directory=settings['output_dir']),
+        name='T1SegNativeDS'
+    )
+
     #  The T1-to-MNI will be plotted using the segmentation.
     #  That's why we transform it first
     seg_2_mni = pe.Node(
@@ -144,6 +155,13 @@ def t1w_preprocessing(name='t1w_preprocessing', settings=None):
     t1_2_mni_overlay.inputs.overlay_file = op.join(get_mni_template(),
                                                    'MNI152_T1_1mm.nii.gz')
 
+    t1_2_mni_overlay_ds = pe.Node(
+        ImageDataSink(base_directory=settings['output_dir']),
+        name='T12MNIOverlayDS'
+    )
+    t1_2_mni_overlay_ds.inputs.overlay_file = op.join(get_mni_template(),
+                                                   'MNI152_T1_1mm.nii.gz')
+
     datasink = pe.Node(
         interface=nio.DataSink(
             base_directory=op.join(settings['output_dir'], 'images')
@@ -155,15 +173,27 @@ def t1w_preprocessing(name='t1w_preprocessing', settings=None):
     workflow.connect([
         (inu_n4, t1_stripped_overlay, [('output_image', 'overlay_file')]),
         (asw, t1_stripped_overlay, [('outputnode.out_mask', 'in_file')]),
-        (t1_stripped_overlay, datasink, [('out_file', '@t1_stripped_overlay')]),
+        (t1_stripped_overlay, t1_stripped_overlay_ds, [('out_file', 'in_file')]),
+        (inu_n4, t1_stripped_overlay_ds, [('output_image', 'base_file')]),
+        (asw, t1_stripped_overlay_ds, [('outputnode.out_mask', 'overlay_file')]),
+        (inputnode, t1_stripped_overlay_ds, [('t1w', 'origin_file')]),
+
         (t1_seg, seg_2_mni, [('tissue_class_map', 'input_image')]),
         (t1_2_mni, seg_2_mni, [('forward_transforms', 'transforms'),
                                ('forward_invert_flags', 'invert_transform_flags')]),
+
         (seg_2_mni, t1_2_mni_overlay, [('output_image', 'in_file')]),
         (t1_2_mni_overlay, datasink, [('out_file', '@t1_2_mni_overlay')]),
+        (seg_2_mni, t1_2_mni_overlay_ds, [('output_image', 'base_file')]),
+        (t1_2_mni_overlay, t1_2_mni_overlay_ds, [('out_file', 'in_file')]),
+        (inputnode, t1_2_mni_overlay_ds, [('t1w', 'origin_file')]),
+
         (t1_seg, t1_seg_native, [('tissue_class_map', 'in_file')]),
         (inu_n4, t1_seg_native, [('output_image', 'overlay_file')]),
-        (t1_seg_native, datasink, [('out_file', '@t1_seg_native')])
+        (t1_seg, t1_seg_native_ds, [('tissue_class_map', 'base_file')]),
+        (inu_n4, t1_seg_native_ds, [('output_image', 'overlay_file')]),
+        (inputnode, t1_seg_native_ds, [('t1w', 'origin_file')]),
+        (t1_seg_native, t1_seg_native_ds, [('out_file', 'in_file')])
     ])
 
     # Write corrected file in the designated output dir

fmriprep/workflows/base.py

@@ -92,6 +92,7 @@ def wf_ds054_type(subject_data, settings, name='fMRI_prep'):
         (bidssrc, t1w_pre, [('t1w', 'inputnode.t1w')]),
         (bidssrc, fmap_est, [('fmap', 'inputnode.input_images')]),
         (bidssrc, sbref_pre, [('sbref', 'inputnode.sbref')]),
+        (bidssrc, sbref_t1, [('sbref', 'inputnode.sbref')]),
         (fmap_est, sbref_pre, [('outputnode.fmap', 'inputnode.fmap'),
                                ('outputnode.fmap_ref', 'inputnode.fmap_ref'),
                                ('outputnode.fmap_mask', 'inputnode.fmap_mask')]),