ENH: Miscellaneous improvements to the Reports

niworkflows/reports/core.py

@@ -1,11 +1,9 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """
-Reports builder for BIDS-Apps
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Reports builder for BIDS-Apps.
 
+Generalizes report generation across BIDS-Apps
 
 """
 from pathlib import Path
@@ -20,21 +18,25 @@
 
 
 PLURAL_SUFFIX = defaultdict(str('s').format, [('echo', 'es')])
-SVG_SNIPPET = """\
+SVG_SNIPPET = ["""\
 <object class="svg-reportlet" type="image/svg+xml" data="./{0}">
 Problem loading figure {0}. If the link below works, please try \
 reloading the report in your browser.</object>
 </div>
 <div class="elem-filename">
     Get figure file: <a href="./{0}" target="_blank">{0}</a>
 </div>
-"""
+""", """\
+<img class="svg-reportlet" src="./{0}" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./{0}" target="_blank">{0}</a>
+</div>
+"""]
 
 
 class Element(object):
-    """
-    Just a basic component of a report
-    """
+    """Just a basic component of a report"""
 
     def __init__(self, name, title=None):
         self.name = name
@@ -77,6 +79,15 @@ class Reportlet(Element):
     >>> r.name
     'datatype-anat_desc-reconall'
 
+    >>> r.components[0][0].startswith('<img')
+    True
+
+    >>> r = Reportlet(bl, out_figs, config={
+    ...     'title': 'Some Title', 'bids': {'datatype': 'anat', 'desc': 'reconall'},
+    ...     'description': 'Some description', 'static': False})
+    >>> r.name
+    'datatype-anat_desc-reconall'
+
     >>> r.components[0][0].startswith('<object')
     True
 
@@ -145,7 +156,23 @@ def __init__(self, layout, out_dir, config=None):
                 out_file = out_dir / linked_svg
                 out_file.parent.mkdir(parents=True, exist_ok=True)
                 copyfile(src, out_file, copy=True, use_hardlink=True)
-                contents = SVG_SNIPPET.format(linked_svg)
+                is_static = config.get('static', True)
+                contents = SVG_SNIPPET[is_static].format(linked_svg)
+
+                # Our current implementations of dynamic reportlets do this themselves,
+                # however I'll leave the code here since this is potentially something we
+                # will want to transfer from every figure generator to this location.
+                # The following code misses setting preserveAspecRatio="xMidYMid meet"
+                # if not is_static:
+                #     # Remove height and width attributes from initial <svg> tag
+                #     svglines = out_file.read_text().splitlines()
+                #     expr = re.compile(r' (height|width)=["\'][0-9]+(\.[0-9]*)?[a-z]*["\']')
+                #     for l, line in enumerate(svglines[:6]):
+                #         if line.strip().startswith('<svg'):
+                #             newline = expr.sub('', line)
+                #             svglines[l] = newline
+                #             out_file.write_text('\n'.join(svglines))
+                #             break
 
             if contents:
                 self.components.append((contents, desc_text))
@@ -155,9 +182,7 @@ def is_empty(self):
 
 
 class SubReport(Element):
-    """
-    SubReports are sections within a Report
-    """
+    """SubReports are sections within a Report."""
 
     def __init__(self, name, isnested=False, reportlets=None, title=''):
         self.name = name
@@ -195,7 +220,7 @@ class Report(object):
     >>> robj.generate_report()
     0
     >>> len((testdir / 'out' / 'fmriprep' / 'sub-01.html').read_text())
-    20862
+    19352
 
     """
 

niworkflows/reports/fmriprep.yml

@@ -20,6 +20,7 @@ sections:
     description: Results of nonlinear alignment of the T1w reference one or more template
       space(s). Hover on the panels with the mouse pointer to transition between both
       spaces.
+    static: false
     subtitle: Spatial normalization of the anatomical T1w reference
   - bids: {datatype: anat, desc: reconall, suffix: T1w}
     caption: Surfaces (white and pial) reconstructed with FreeSurfer (<code>recon-all</code>)
@@ -29,7 +30,7 @@ sections:
   ordering: session
   reportlets:
   - bids: {datatype: fmap, desc: brain, suffix: mask}
-    caption: Brain extraction of the magnitude image from the fieldmap
+    caption: Brain extraction of the magnitude image from the fieldmap.
     subtitle: Skull stripped magnitude image
 - name: Functional
   ordering: session,task,run
@@ -39,72 +40,72 @@ sections:
   - bids: {datatype: func, desc: magnitude, suffix: bold}
     caption: Results of affine coregistration between the magnitude image of the fieldmap
       and the reference EPI image
+    static: false
     subtitle: Fieldmap to EPI registration
   - bids: {datatype: func, desc: fieldmap, suffix: bold}
-    caption: Overlaid on the reference EPI image
+    caption: Overlaid on the reference EPI image.
+    static: false
     subtitle: Fieldmap
   - bids: {datatype: func, desc: sdc, suffix: bold}
     caption: Results of performing susceptibility distortion correction (SDC) on the
       EPI
+    static: false
     subtitle: Susceptibility distortion correction
   - bids: {datatype: func, desc: forcedsyn, suffix: bold}
     caption: The dataset contained some fieldmap information, but the argument <code>--force-syn</code>
       was used. The higher-priority SDC method was used. Here, we show the results
       of performing SyN-based SDC on the EPI for comparison.
+    static: false
     subtitle: Experimental fieldmap-less susceptibility distortion correction
-  - bids: {datatype: func, desc: rois, suffix: bold}
-    caption: Brain mask calculated on the BOLD signal (red contour), along with the
-      masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative
-      CSF and white-matter mask for extracting physiological and movement confounds.
-      <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels
-      within a heavily-eroded brain-mask.
-    subtitle: ROIs in BOLD space
-  - bids:
-      datatype: func
-      desc: '[at]compcor'
-      extensions: [.html]
-      suffix: bold
-  - bids: {datatype: func, desc: '[at]compcorvar', suffix: bold}
-    caption: The cumulative variance explained by the first k components of the
-      <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>.
-      The number of components that must be included in the model in order to
-      explain some fraction of variance in the decomposition mask can be used
-      as a feature selection criterion for confound regression.
-  - bids: {datatype: func, desc: 'confoundcorr', suffix: bold}
-    caption: |
-      Left: Heatmap summarizing the correlation structure among confound variables.
-      (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
-      Right: magnitude of the correlation between each confound time series and the
-      mean global signal. Strong correlations might be indicative of partial volume
-      effects and can inform decisions about feature orthogonalization prior to 
-      confound regression.
-    subtitle: Correlations among nuisance regressors
   - bids: {datatype: func, desc: flirtnobbr, suffix: bold}
     caption: FSL <code>flirt</code> was used to generate transformations from EPI
       space to T1 Space - BBR refinement rejected. Note that Nearest Neighbor interpolation
       is used in the reportlets in order to highlight potential spin-history and other
       artifacts, whereas final images are resampled using Lanczos interpolation.
-    subtitle: EPI to T1 registration
+    static: false
+    subtitle: Alignment of functional and anatomical MRI data (volume based)
   - bids: {datatype: func, desc: coreg, suffix: bold}
     caption: <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
       from EPI space to T1 Space - <code>bbregister</code> refinement rejected. Note
       that Nearest Neighbor interpolation is used in the reportlets in order to highlight
       potential spin-history and other artifacts, whereas final images are resampled
       using Lanczos interpolation.
-    subtitle: EPI to T1 registration
+    static: false
+    subtitle: Alignment of functional and anatomical MRI data (volume based)
   - bids: {datatype: func, desc: flirtbbr, suffix: bold}
     caption: FSL <code>flirt</code> was used to generate transformations from EPI-space
       to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain
       tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation
       is used in the reportlets in order to highlight potential spin-history and other
       artifacts, whereas final images are resampled using Lanczos interpolation.
-    subtitle: EPI to T1 registration
+    static: false
+    subtitle: Alignment of functional and anatomical MRI data (surface driven)
   - bids: {datatype: func, desc: bbregister, suffix: bold}
     caption: <code>bbregister</code> was used to generate transformations from EPI-space
       to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets
       in order to highlight potential spin-history and other artifacts, whereas final
       images are resampled using Lanczos interpolation.
-    subtitle: EPI to T1 registration
+    static: false
+    subtitle: Alignment of functional and anatomical MRI data (surface driven)
+  - bids: {datatype: func, desc: rois, suffix: bold}
+    caption: Brain mask calculated on the BOLD signal (red contour), along with the
+      masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative
+      CSF and white-matter mask for extracting physiological and movement confounds.
+      <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels
+      within a heavily-eroded brain-mask.
+    subtitle: Brain mask and (temporal/anatomical) CompCor ROIs
+  - bids:
+      datatype: func
+      desc: '[at]compcor'
+      extensions: [.html]
+      suffix: bold
+  - bids: {datatype: func, desc: 'compcorvar', suffix: bold}
+    caption: The cumulative variance explained by the first k components of the
+      <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>.
+      The number of components that must be included in the model in order to
+      explain some fraction of variance in the decomposition mask can be used
+      as a feature selection criterion for confound regression.
+    subtitle: Variance explained by t/aCompCor components
   - bids: {datatype: func, desc: carpetplot, suffix: bold}
     caption: Summary statistics are plotted, which may reveal trends or artifacts
       in the BOLD data. Global signals calculated within the whole-brain (GS), within
@@ -116,6 +117,15 @@ sections:
       and white matter and CSF (red), indicated by the color map on the left-hand
       side.
     subtitle: BOLD Summary
+  - bids: {datatype: func, desc: 'confoundcorr', suffix: bold}
+    caption: |
+      Left: Heatmap summarizing the correlation structure among confound variables.
+      (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+      Right: magnitude of the correlation between each confound time series and the
+      mean global signal. Strong correlations might be indicative of partial volume
+      effects and can inform decisions about feature orthogonalization prior to
+      confound regression.
+    subtitle: Correlations among nuisance regressors
   - bids: {datatype: func, desc: aroma, suffix: bold}
     caption: |
       Maps created with maximum intensity projection (glass brain) with a

niworkflows/viz/utils.py

@@ -6,7 +6,6 @@
 
 import os
 import os.path as op
-from pathlib import Path
 import subprocess
 import base64
 import re
@@ -560,8 +559,6 @@ def plot_melodic_components(melodic_dir, in_file, tr=None,
     import seaborn as sns
     from matplotlib.gridspec import GridSpec
     import os
-    import re
-    from io import StringIO
     sns.set_style("white")
     current_palette = sns.color_palette()
     in_nii = nb.load(in_file)
@@ -702,19 +699,6 @@ def plot_melodic_components(melodic_dir, in_file, tr=None,
         sns.despine(left=True, bottom=True)
 
     plt.subplots_adjust(hspace=0.5)
-
-    image_buf = StringIO()
-    fig.savefig(image_buf, dpi=300, format='svg', transparent=True,
+    fig.savefig(out_file, dpi=300, format='svg', transparent=True,
                 bbox_inches='tight', pad_inches=0.01)
     fig.clf()
-    image_svg = image_buf.getvalue()
-
-    if compress is True or compress == 'auto':
-        image_svg = svg_compress(image_svg, compress)
-    image_svg = re.sub(' height="[0-9]+[a-z]*"', '', image_svg, count=1)
-    image_svg = re.sub(' width="[0-9]+[a-z]*"', '', image_svg, count=1)
-    image_svg = re.sub(' viewBox',
-                       ' preseveAspectRation="xMidYMid meet" viewBox',
-                       image_svg, count=1)
-
-    Path(out_file).write_text(image_svg)