Merge remote-tracking branch 'upstream/master' into enh/bids_filter

Author: effigies

docs/faq.rst

@@ -133,7 +133,6 @@ Below are some benchmark data that have been computed on a high performance clus
 CPUs and 64 GB of physical memory:
 
 .. figure:: _static/fmriprep_benchmark.svg
-    :scale: 100%
 
 **Compute Time**: time in hours to complete the preprocessing for all subjects. **Physical Memory**: the maximum of RAM usage
 used across all fMRIPrep processes as reported by the HCP job manager. **Virtual Memory**: the maximum of virtual memory used

docs/outputs.rst

@@ -453,7 +453,6 @@ Some of the estimated confounds are plotted with a "carpet" visualization of the
 An example of these plots follows:
 
 .. figure:: _static/sub-01_task-mixedgamblestask_run-01_bold_carpetplot.svg
-    :scale: 100%
 
     The figure shows on top several confounds estimated for the BOLD series:
     global signals ('GlobalSignal', 'WM', 'GM'), standardized DVARS ('stdDVARS'),
@@ -474,7 +473,6 @@ option ``--return-all-components``.
 component, ordered by descending singular value.
 
 .. figure:: _static/sub-01_task-rest_compcor.svg
-    :scale: 100%
 
     The figure displays the cumulative variance explained by components for each
     of four CompCor decompositions (left to right: anatomical CSF mask, anatomical
@@ -491,7 +489,6 @@ This can be used to guide selection of a confound model or to assess the extent
 to which tissue-specific regressors correlate with global signal.
 
 .. figure:: _static/sub-01_task-mixedgamblestask_run-01_confounds_correlation.svg
-    :scale: 100%
 
     The left-hand panel shows the matrix of correlations among selected confound
     time series as a heat-map.

docs/workflows.rst

@@ -119,15 +119,13 @@ brain extraction workflow:
 An example of brain extraction is shown below:
 
 .. figure:: _static/brainextraction_t1.svg
-    :scale: 100%
 
     Brain extraction
 
 
 Once the brain mask is computed, FSL ``fast`` is utilized for brain tissue segmentation.
 
 .. figure:: _static/segmentation.svg
-    :scale: 100%
 
     Brain tissue segmentation.
 
@@ -139,7 +137,6 @@ be set to resample the preprocessed data onto the final output spaces.
 
 
 .. figure:: _static/T1MNINormalization.svg
-    :scale: 100%
 
     Animation showing spatial normalization of T1w onto the ``MNI152NLin2009cAsym`` template.
 
@@ -245,7 +242,6 @@ details.
 Reconstructed white and pial surfaces are included in the report.
 
 .. figure:: _static/reconall.svg
-    :scale: 100%
 
     Surface reconstruction (FreeSurfer)
 
@@ -361,7 +357,6 @@ workflow <bold_hmc>` and the :ref:`registration workflow to map
 BOLD series into the T1w image of the same subject <bold_reg>`.
 
 .. figure:: _static/brainextraction.svg
-    :scale: 100%
 
     Calculation of a brain mask from the BOLD series.
 
@@ -426,7 +421,6 @@ Susceptibility Distortion Correction (SDC)
 :mod:`fmriprep.workflows.fieldmap.base.init_sdc_wf`
 
 .. figure:: _static/unwarping.svg
-    :scale: 100%
 
     Applying susceptibility-derived distortion correction, based on
     fieldmap estimation.
@@ -483,7 +477,6 @@ of each run and the reconstructed subject using the gray/white matter boundary
 (FreeSurfer's ``?h.white`` surfaces) is calculated by the ``bbregister`` routine.
 
 .. figure:: _static/EPIT1Normalization.svg
-    :scale: 100%
 
     Animation showing :abbr:`EPI (echo-planar imaging)` to T1w registration (FreeSurfer ``bbregister``)
 
@@ -631,7 +624,6 @@ melodic's estimation of components.
 A visualization of the AROMA component classification is also included in the HTML reports.
 
 .. figure:: _static/aroma.svg
-    :scale: 100%
 
     Maps created with maximum intensity projection (glass brain) with a black
     brain outline.

fmriprep/cli/run.py

@@ -332,6 +332,7 @@ def main():
     log_level = int(max(25 - 5 * opts.verbose_count, logging.DEBUG))
     # Set logging
     logger.setLevel(log_level)
+    logger.addHandler(logging.StreamHandler())
     nlogging.getLogger('nipype.workflow').setLevel(log_level)
     nlogging.getLogger('nipype.interface').setLevel(log_level)
     nlogging.getLogger('nipype.utils').setLevel(log_level)
@@ -509,7 +510,7 @@ def build_workflow(opts, retval):
 
     if opts.clean_workdir:
         from niworkflows.utils.misc import clean_directory
-        build_log.log("Clearing previous fMRIPrep working directory: %s" % work_dir)
+        build_log.info("Clearing previous fMRIPrep working directory: %s" % work_dir)
         if not clean_directory(work_dir):
             build_log.warning("Could not clear all contents of working directory: %s" % work_dir)
 

fmriprep/workflows/base.py

@@ -537,7 +537,9 @@ def init_single_subject_wf(
     inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
                         name='inputnode')
 
-    bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data, anat_only=anat_only),
+    bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
+                                      anat_only=anat_only,
+                                      subject_id=subject_id),
                       name='bidssrc')
 
     bids_info = pe.Node(BIDSInfo(