Merge pull request nipreps#474 from oesteban/maint/drop-python2

MAINT: Pacify security patterns found by Codacy

Author: oesteban

niworkflows/interfaces/images.py

@@ -307,7 +307,8 @@ def _run_interface(self, runtime):
             out_name = fname
 
         transform = ornt_xfm.dot(conform_xfm)
-        assert np.allclose(orig_img.affine.dot(transform), target_affine)
+        if not np.allclose(orig_img.affine.dot(transform), target_affine):
+            raise ValueError("Original and target affines are not similar")
 
         mat_name = fname_presuffix(fname, suffix='.mat', newpath=runtime.cwd, use_ext=False)
         np.savetxt(mat_name, transform, fmt='%.08f')

niworkflows/interfaces/itk.py

@@ -109,7 +109,10 @@ def _run_interface(self, runtime):
         tmp_folder = TemporaryDirectory(prefix='tmp-', dir=runtime.cwd)
 
         xfms_list = _arrange_xfms(transforms, num_files, tmp_folder)
-        assert len(xfms_list) == num_files
+        if len(xfms_list) != num_files:
+            raise ValueError(
+                "Number of files and entries in the transforms list do not match"
+            )
 
         # Inputs are ready to run in parallel
         if num_threads < 1:

niworkflows/interfaces/masks.py

@@ -172,9 +172,10 @@ class ACompCorRPT(nrc.SegmentationRC, confounds.ACompCor):
     def _post_run_hook(self, runtime):
         ''' generates a report showing slices from each axis '''
 
-        assert len(self.inputs.mask_files) == 1, \
-            "ACompCorRPT only supports a single input mask. " \
-            "A list %s was found." % self.inputs.mask_files
+        if len(self.inputs.mask_files) != 1:
+            raise ValueError(
+                "ACompCorRPT only supports a single input mask. "
+                "A list %s was found." % self.inputs.mask_files)
         self._anat_file = self.inputs.realigned_file
         self._mask_file = self.inputs.mask_files[0]
         self._seg_files = self.inputs.mask_files
@@ -205,9 +206,11 @@ def _post_run_hook(self, runtime):
 
         high_variance_masks = self.aggregate_outputs(runtime=runtime).high_variance_masks
 
-        assert not isinstance(high_variance_masks, list),\
-            "TCompCorRPT only supports a single output high variance mask. " \
-            "A list %s was found." % str(high_variance_masks)
+        if isinstance(high_variance_masks, list):
+            raise ValueError(
+                "TCompCorRPT only supports a single output high variance mask. "
+                "A list %s was found." % high_variance_masks
+            )
         self._anat_file = self.inputs.realigned_file
         self._mask_file = high_variance_masks
         self._seg_files = [high_variance_masks]

niworkflows/interfaces/utils.py

@@ -724,7 +724,8 @@ def _run_interface(self, runtime):
         with open(self.inputs.join_file) as ifh:
             join = ifh.read().splitlines(keepends=False)
 
-        assert len(data) == len(join)
+        if len(data) != len(join):
+            raise ValueError("Number of columns in datasets do not match")
 
         merged = []
         for d, j in zip(data, join):

niworkflows/reports/core.py

@@ -378,7 +378,7 @@ def generate_report(self):
 
         env = jinja2.Environment(
             loader=jinja2.FileSystemLoader(searchpath=str(self.template_path.parent)),
-            trim_blocks=True, lstrip_blocks=True
+            trim_blocks=True, lstrip_blocks=True, autoescape=False
         )
         report_tpl = env.get_template(self.template_path.name)
         report_render = report_tpl.render(sections=self.sections, errors=self.errors,

niworkflows/utils/images.py

@@ -1,3 +1,4 @@
+"""Utilities to manipulate images."""
 import nibabel as nb
 import numpy as np
 
@@ -72,23 +73,29 @@ def overwrite_header(img, fname):
 
     ondisk = nb.load(fname, mmap=False)
 
-    try:
-        assert isinstance(ondisk.header, img.header_class)
-        # Check that the data block should be the same size
-        assert ondisk.get_data_dtype() == img.get_data_dtype()
-        assert img.header.get_data_shape() == ondisk.shape
-        # At the same offset from the start of the file
-        assert img.header['vox_offset'] == ondisk.dataobj.offset
-        # With the same scale factors
-        assert np.allclose(img.header['scl_slope'], ondisk.dataobj.slope, equal_nan=True)
-        assert np.allclose(img.header['scl_inter'], ondisk.dataobj.inter, equal_nan=True)
-    except AssertionError as e:
-        raise ValueError("Cannot write header without compromising data") from e
-    else:
-        data = np.asarray(dataobj.get_unscaled())
-        img._dataobj = data  # Allow old dataobj to be garbage collected
-        del ondisk, img, dataobj  # Drop everything we don't need, to be safe
-        unsafe_write_nifti_header_and_data(fname, header, data)
+    errmsg = "Cannot overwrite header (reason: {}).".format
+    if not isinstance(ondisk.header, img.header_class):
+        raise ValueError(errmsg("inconsistent header objects"))
+
+    if (
+        ondisk.get_data_dtype() != img.get_data_dtype()
+        or img.header.get_data_shape() != ondisk.shape
+    ):
+        raise ValueError(errmsg("data blocks are not the same size"))
+
+    if img.header['vox_offset'] != ondisk.dataobj.offset:
+        raise ValueError(errmsg("change in offset from start of file"))
+
+    if (
+        not np.allclose(img.header['scl_slope'], ondisk.dataobj.slope, equal_nan=True)
+        or not np.allclose(img.header['scl_inter'], ondisk.dataobj.inter, equal_nan=True)
+    ):
+        raise ValueError(errmsg("change in scale factors"))
+
+    data = np.asarray(dataobj.get_unscaled())
+    img._dataobj = data  # Allow old dataobj to be garbage collected
+    del ondisk, img, dataobj  # Drop everything we don't need, to be safe
+    unsafe_write_nifti_header_and_data(fname, header, data)
 
 
 def update_header_fields(fname, **kwargs):

niworkflows/viz/utils.py

@@ -1,30 +1,27 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Helper tools for visualization purposes"""
-import os.path as op
+from pathlib import Path
 from shutil import which
 import subprocess
 import base64
 import re
-from sys import version_info
 from uuid import uuid4
 from io import StringIO
 
 import numpy as np
 import nibabel as nb
 
-from lxml import etree
 from nilearn import image as nlimage
 from nilearn.plotting import plot_anat
-from svgutils.transform import SVGFigure
+from svgutils.transform import fromstring, SVGFigure, GroupElement
 from seaborn import color_palette
 
 from nipype.utils import filemanip
 from .. import NIWORKFLOWS_LOG
 
 
 SVGNS = "http://www.w3.org/2000/svg"
-PY3 = version_info[0] > 2
 
 
 def robust_set_limits(data, plot_params, percentiles=(15, 99.8)):
@@ -229,19 +226,9 @@ def plot_segs(image_nii, seg_niis, out_file, bbox_nii=None, masked=False,
         plot_params['cut_coords'] = cuts[d]
         svg = _plot_anat_with_contours(image_nii, segs=seg_niis, compress=compress,
                                        **plot_params)
-
         # Find and replace the figure_1 id.
-        try:
-            xml_data = etree.fromstring(svg)
-        except etree.XMLSyntaxError as e:
-            NIWORKFLOWS_LOG.info(e)
-            return
-        find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % SVGNS)
-        find_text(xml_data)[0].set('id', 'segmentation-%s-%s' % (d, uuid4()))
-
-        svg_fig = SVGFigure()
-        svg_fig.root = xml_data
-        out_files.append(svg_fig)
+        svg = svg.replace('figure_1', 'segmentation-%s-%s' % (d, uuid4()), 1)
+        out_files.append(fromstring(svg))
 
     return out_files
 
@@ -333,17 +320,8 @@ def plot_registration(anat_nii, div_id, plot_params=None,
         display.close()
 
         # Find and replace the figure_1 id.
-        try:
-            xml_data = etree.fromstring(svg)
-        except etree.XMLSyntaxError as e:
-            NIWORKFLOWS_LOG.info(e)
-            return
-        find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % SVGNS)
-        find_text(xml_data)[0].set('id', '%s-%s-%s' % (div_id, mode, uuid4()))
-
-        svg_fig = SVGFigure()
-        svg_fig.root = xml_data
-        out_files.append(svg_fig)
+        svg = svg.replace('figure_1', '%s-%s-%s' % (div_id, mode, uuid4()), 1)
+        out_files.append(fromstring(svg))
 
     return out_files
 
@@ -353,7 +331,6 @@ def compose_view(bg_svgs, fg_svgs, ref=0, out_file='report.svg'):
     Composes the input svgs into one standalone svg and inserts
     the CSS code for the flickering animation
     """
-    import svgutils.transform as svgt
 
     if fg_svgs is None:
         fg_svgs = []
@@ -380,7 +357,7 @@ def compose_view(bg_svgs, fg_svgs, ref=0, out_file='report.svg'):
 
     # Compose the views panel: total size is the width of
     # any element (used the first here) and the sum of heights
-    fig = svgt.SVGFigure(width, heights[:nsvgs].sum())
+    fig = SVGFigure(width, heights[:nsvgs].sum())
 
     yoffset = 0
     for i, r in enumerate(roots):
@@ -393,21 +370,20 @@ def compose_view(bg_svgs, fg_svgs, ref=0, out_file='report.svg'):
     # Group background and foreground panels in two groups
     if fg_svgs:
         newroots = [
-            svgt.GroupElement(roots[:nsvgs], {'class': 'background-svg'}),
-            svgt.GroupElement(roots[nsvgs:], {'class': 'foreground-svg'})
+            GroupElement(roots[:nsvgs], {'class': 'background-svg'}),
+            GroupElement(roots[nsvgs:], {'class': 'foreground-svg'})
         ]
     else:
         newroots = roots
     fig.append(newroots)
     fig.root.attrib.pop("width")
     fig.root.attrib.pop("height")
     fig.root.set("preserveAspectRatio", "xMidYMid meet")
-    out_file = op.abspath(out_file)
-    fig.save(out_file)
+    out_file = Path(out_file).absolute()
+    fig.save(str(out_file))
 
     # Post processing
-    with open(out_file, 'r' if PY3 else 'rb') as f:
-        svg = f.read().split('\n')
+    svg = out_file.read_text().splitlines()
 
     # Remove <?xml... line
     if svg[0].startswith("<?xml"):
@@ -422,9 +398,8 @@ def compose_view(bg_svgs, fg_svgs, ref=0, out_file='report.svg'):
 .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))
-    return out_file
+    out_file.write_text("\n".join(svg))
+    return str(out_file)
 
 
 def transform_to_2d(data, max_axis):
@@ -459,32 +434,31 @@ def plot_melodic_components(melodic_dir, in_file, tr=None,
     from functional MRI data.
 
     Parameters
-
-        melodic_dir : str
-            Path pointing to the outputs of MELODIC
-        in_file :  str
-            Path pointing to the reference fMRI dataset. This file
-            will be used to extract the TR value, if the ``tr`` argument
-            is not set. This file will be used to calculate a mask
-            if ``report_mask`` is not provided.
-        tr : float
-            Repetition time in seconds
-        out_file : str
-            Path where the resulting SVG file will be stored
-        compress : ``'auto'`` or bool
-            Whether SVG should be compressed. If ``'auto'``, compression
-            will be executed if dependencies are installed (SVGO)
-        report_mask : str
-            Path to a brain mask corresponding to ``in_file``
-        noise_components_file : str
-            A CSV file listing the indexes of components classified as noise
-            by some manual or automated (e.g. ICA-AROMA) procedure. If a
-            ``noise_components_file`` is provided, then components will be
-            plotted with red/green colors (correspondingly to whether they
-            are in the file -noise components, red-, or not -signal, green-).
-            When all or none of the components are in the file, a warning
-            is printed at the top.
-
+    ----------
+    melodic_dir : str
+        Path pointing to the outputs of MELODIC
+    in_file :  str
+        Path pointing to the reference fMRI dataset. This file
+        will be used to extract the TR value, if the ``tr`` argument
+        is not set. This file will be used to calculate a mask
+        if ``report_mask`` is not provided.
+    tr : float
+        Repetition time in seconds
+    out_file : str
+        Path where the resulting SVG file will be stored
+    compress : ``'auto'`` or bool
+        Whether SVG should be compressed. If ``'auto'``, compression
+        will be executed if dependencies are installed (SVGO)
+    report_mask : str
+        Path to a brain mask corresponding to ``in_file``
+    noise_components_file : str
+        A CSV file listing the indexes of components classified as noise
+        by some manual or automated (e.g. ICA-AROMA) procedure. If a
+        ``noise_components_file`` is provided, then components will be
+        plotted with red/green colors (correspondingly to whether they
+        are in the file -noise components, red-, or not -signal, green-).
+        When all or none of the components are in the file, a warning
+        is printed at the top.
 
     """
     from nilearn.image import index_img, iter_img