Merge pull request #193 from chrisfilo/enh/aroma_rpt

[RTM] ICA AROM supports

niworkflows/interfaces/segmentation.py

@@ -115,3 +115,39 @@ def _post_run_hook(self, runtime):
         self._melodic_dir = outputs.out_dir
 
         NIWORKFLOWS_LOG.info('Generating report for MELODIC')
+
+
+class ICA_AROMAInputSpecRPT(nrc.ReportCapableInputSpec,
+                            fsl.aroma.ICA_AROMAInputSpec):
+    out_report = File(
+        'ica_aroma_reportlet.svg', usedefault=True, desc='Filename for the visual'
+                                                         ' report generated '
+                                                         'by Nipype.')
+    report_mask = File(desc='Mask used to draw the outline on the reportlet. '
+                            'If not set the mask will be derived from the data.')
+
+class ICA_AROMAOutputSpecRPT(nrc.ReportCapableOutputSpec,
+                             fsl.aroma.ICA_AROMAOutputSpec):
+    pass
+
+
+class ICA_AROMARPT(nrc.ReportCapableInterface, fsl.ICA_AROMA):
+    input_spec = ICA_AROMAInputSpecRPT
+    output_spec = ICA_AROMAOutputSpecRPT
+
+    def _generate_report(self):
+        from niworkflows.viz.utils import plot_melodic_components
+        plot_melodic_components(melodic_dir=self.inputs.melodic_dir,
+                                in_file=self.inputs.in_file,
+                                out_file=self.inputs.out_report,
+                                compress=self.inputs.compress_report,
+                                report_mask=self.inputs.report_mask,
+                                noise_components_file=self._noise_components_file
+                                )
+
+    def _post_run_hook(self, runtime):
+        outputs = self.aggregate_outputs(runtime=runtime)
+        self._noise_components_file = os.path.join(outputs.out_dir,
+                                                   "classified_motion_ICs.txt")
+
+        NIWORKFLOWS_LOG.info('Generating report for ICA AROMA')

niworkflows/viz/utils.py

@@ -448,18 +448,24 @@ def compose_view(bg_svgs, fg_svgs, ref=0, out_file='report.svg'):
     out_file = op.abspath(out_file)
     fig.save(out_file)
 
+    # Post processing
+    with open(out_file, 'r' if PY3 else 'rb') as f:
+        svg = f.read().split('\n')
+
+    # Remove <?xml... line
+    if svg[0].startswith("<?xml"):
+        svg = svg[1:]
+
     # Add styles for the flicker animation
     if fg_svgs:
-        with open(out_file, 'r' if PY3 else 'rb') as f:
-            svg = f.read().split('\n')
-
         svg.insert(2, """<style type="text/css">
 @keyframes flickerAnimation%s { 0%% {opacity: 1;} 100%% { opacity: 0; }}
 .foreground-svg { animation: 1s ease-in-out 0s alternate none infinite paused flickerAnimation%s;}
 .foreground-svg:hover { animation-play-state: running;}
 </style>""" % tuple([uuid4()] * 2))
-        with open(out_file, 'w' if PY3 else 'wb') as f:
-            f.write('\n'.join(svg))
+
+    with open(out_file, 'w' if PY3 else 'wb') as f:
+        f.write('\n'.join(svg))
     return out_file
 
 
@@ -488,7 +494,8 @@ def transform_to_2d(data, max_axis):
 
 def plot_melodic_components(melodic_dir, in_file, tr=None,
                             out_file='melodic_reportlet.svg',
-                            compress='auto', report_mask=None):
+                            compress='auto', report_mask=None,
+                            noise_components_file=None):
     from nilearn.plotting import plot_glass_brain
     from nilearn.image import index_img, iter_img
     import nibabel as nb
@@ -497,6 +504,7 @@ 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()
@@ -546,13 +554,27 @@ def plot_melodic_components(melodic_dir, in_file, tr=None,
                   width_ratios=[1, 1, 1, 4, 0.001, 1, 1, 1, 4, ],
                   height_ratios=[1.1, 1] * n_rows)
 
+    if noise_components_file:
+        with open(noise_components_file) as cf:
+            noise_components = [int(c) for c in cf.read().split(",")]
+
     for i, img in enumerate(
             iter_img(os.path.join(melodic_dir, "melodic_IC.nii.gz"))):
 
         col = i % 2
         row = int(i / 2)
         l_row = row * 2
 
+        if noise_components_file:
+            if (i + 1) in noise_components:
+                color_title = color_time = color_power = 'r'
+            else:
+                color_title = color_time = color_power = 'g'
+        else:
+            color_title = 'k'
+            color_time = current_palette[0]
+            color_power = current_palette[1]
+
         data = img.get_data()
         for j in range(3):
             ax1 = fig.add_subplot(gs[l_row:l_row + 2, j + col * 5])
@@ -568,29 +590,33 @@ def plot_melodic_components(melodic_dir, in_file, tr=None,
                     "C%d: Tot. var. expl. %.2g%%" % (i + 1, stats[i, 1]), x=0,
                     y=1.18, fontsize=7,
                     horizontalalignment='left',
-                    verticalalignment='top')
+                    verticalalignment='top',
+                    color=color_title)
 
         ax2 = fig.add_subplot(gs[l_row, 3 + col * 5])
         ax3 = fig.add_subplot(gs[l_row + 1, 3 + col * 5])
 
         ax2.plot(np.arange(len(timeseries[:, i])) * tr, timeseries[:, i],
-                 linewidth=min(200 / len(timeseries[:, i]), 1.0))
+                 linewidth=min(200 / len(timeseries[:, i]), 1.0),
+                 color=color_time)
         ax2.set_xlim([0, len(timeseries[:, i]) * tr])
         ax2.axes.get_yaxis().set_visible(False)
         ax2.autoscale_view('tight')
         ax2.tick_params(axis='both', which='major', pad=0)
         sns.despine(left=True, bottom=True)
-        zed = [tick.label.set_fontsize(6) for tick in
-               ax2.xaxis.get_major_ticks()]
+        for tick in ax2.xaxis.get_major_ticks():
+            tick.label.set_fontsize(6)
+            tick.label.set_color(color_time)
 
-        ax3.plot(f[0:], power[0:, i], color=current_palette[1],
+        ax3.plot(f[0:], power[0:, i], color=color_power,
                  linewidth=min(100 / len(power[0:, i]), 1.0))
         ax3.set_xlim([f[0], f.max()])
         ax3.axes.get_yaxis().set_visible(False)
         ax3.autoscale_view('tight')
         ax3.tick_params(axis='both', which='major', pad=0)
-        zed = [tick.label.set_fontsize(6) for tick in
-               ax3.xaxis.get_major_ticks()]
+        for tick in ax3.xaxis.get_major_ticks():
+            tick.label.set_fontsize(6)
+            tick.label.set_color(color_power)
         sns.despine(left=True, bottom=True)
 
     plt.subplots_adjust(hspace=0.5)