Merge pull request #44 from nipreps/maint/add-mriqc-reportlets

MAINT: Migration of reportlets and interfaces from MRIQC

nireports/interfaces/viz.py

@@ -0,0 +1,199 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2023 The NiPreps Developers <[email protected]>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+# STATEMENT OF CHANGES: This file was ported carrying over full git history from MRIQC,
+# another NiPreps project licensed under the Apache-2.0 terms, and has been changed since.
+# The original file this work derives from is found at:
+# https://github.com/nipreps/mriqc/blob/1ffd4c8d1a20b44ebfea648a7b12bb32a425d4ec/
+# mriqc/interfaces/viz.py
+"""Visualization interfaces."""
+from pathlib import Path
+
+import numpy as np
+from nipype.interfaces.base import (
+    BaseInterfaceInputSpec,
+    File,
+    SimpleInterface,
+    TraitedSpec,
+    isdefined,
+    traits,
+)
+
+from nireports.reportlets.mriqc.utils import plot_mosaic, plot_segmentation, plot_spikes
+
+
+class PlotContoursInputSpec(BaseInterfaceInputSpec):
+    in_file = File(exists=True, mandatory=True, desc="File to be plotted")
+    in_contours = File(exists=True, mandatory=True, desc="file to pick the contours from")
+    cut_coords = traits.Int(8, usedefault=True, desc="number of slices")
+    levels = traits.List([0.5], traits.Float, usedefault=True, desc="add a contour per level")
+    colors = traits.List(
+        ["r"],
+        traits.Str,
+        usedefault=True,
+        desc="colors to be used for contours",
+    )
+    display_mode = traits.Enum(
+        "ortho",
+        "x",
+        "y",
+        "z",
+        "yx",
+        "xz",
+        "yz",
+        usedefault=True,
+        desc="visualization mode",
+    )
+    saturate = traits.Bool(False, usedefault=True, desc="saturate background")
+    out_file = traits.File(exists=False, desc="output file name")
+    vmin = traits.Float(desc="minimum intensity")
+    vmax = traits.Float(desc="maximum intensity")
+
+
+class PlotContoursOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc="output svg file")
+
+
+class PlotContours(SimpleInterface):
+    """Plot contours"""
+
+    input_spec = PlotContoursInputSpec
+    output_spec = PlotContoursOutputSpec
+
+    def _run_interface(self, runtime):
+        in_file_ref = Path(self.inputs.in_file)
+
+        if isdefined(self.inputs.out_file):
+            in_file_ref = Path(self.inputs.out_file)
+
+        fname = in_file_ref.name.rstrip("".join(in_file_ref.suffixes))
+        out_file = (Path(runtime.cwd) / ("plot_%s_contours.svg" % fname)).resolve()
+        self._results["out_file"] = str(out_file)
+
+        vmax = None if not isdefined(self.inputs.vmax) else self.inputs.vmax
+        vmin = None if not isdefined(self.inputs.vmin) else self.inputs.vmin
+
+        plot_segmentation(
+            self.inputs.in_file,
+            self.inputs.in_contours,
+            out_file=str(out_file),
+            cut_coords=self.inputs.cut_coords,
+            display_mode=self.inputs.display_mode,
+            levels=self.inputs.levels,
+            colors=self.inputs.colors,
+            saturate=self.inputs.saturate,
+            vmin=vmin,
+            vmax=vmax,
+        )
+
+        return runtime
+
+
+class PlotBaseInputSpec(BaseInterfaceInputSpec):
+    in_file = File(exists=True, mandatory=True, desc="File to be plotted")
+    title = traits.Str(desc="a title string for the plot")
+    annotate = traits.Bool(True, usedefault=True, desc="annotate left/right")
+    figsize = traits.Tuple(
+        (11.69, 8.27),
+        traits.Float,
+        traits.Float,
+        usedefault=True,
+        desc="Figure size",
+    )
+    dpi = traits.Int(300, usedefault=True, desc="Desired DPI of figure")
+    out_file = File("mosaic.svg", usedefault=True, desc="output file name")
+    cmap = traits.Str("Greys_r", usedefault=True)
+
+
+class PlotMosaicInputSpec(PlotBaseInputSpec):
+    bbox_mask_file = File(exists=True, desc="brain mask")
+    only_noise = traits.Bool(False, desc="plot only noise")
+
+
+class PlotMosaicOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc="output pdf file")
+
+
+class PlotMosaic(SimpleInterface):
+
+    """
+    Plots slices of a 3D volume into a pdf file
+    """
+
+    input_spec = PlotMosaicInputSpec
+    output_spec = PlotMosaicOutputSpec
+
+    def _run_interface(self, runtime):
+        mask = None
+        if isdefined(self.inputs.bbox_mask_file):
+            mask = self.inputs.bbox_mask_file
+
+        title = None
+        if isdefined(self.inputs.title):
+            title = self.inputs.title
+
+        plot_mosaic(
+            self.inputs.in_file,
+            out_file=self.inputs.out_file,
+            title=title,
+            only_plot_noise=self.inputs.only_noise,
+            bbox_mask_file=mask,
+            cmap=self.inputs.cmap,
+            annotate=self.inputs.annotate,
+        )
+        self._results["out_file"] = str((Path(runtime.cwd) / self.inputs.out_file).resolve())
+        return runtime
+
+
+class PlotSpikesInputSpec(PlotBaseInputSpec):
+    in_spikes = File(exists=True, mandatory=True, desc="tsv file of spikes")
+    in_fft = File(exists=True, mandatory=True, desc="nifti file with the 4D FFT")
+
+
+class PlotSpikesOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc="output svg file")
+
+
+class PlotSpikes(SimpleInterface):
+    """Plot slices of a dataset with spikes."""
+
+    input_spec = PlotSpikesInputSpec
+    output_spec = PlotSpikesOutputSpec
+
+    def _run_interface(self, runtime):
+        out_file = str((Path(runtime.cwd) / self.inputs.out_file).resolve())
+        self._results["out_file"] = out_file
+
+        spikes_list = np.loadtxt(self.inputs.in_spikes, dtype=int).tolist()
+        # No spikes
+        if not spikes_list:
+            Path(out_file).write_text("<p>No high-frequency spikes were found in this dataset</p>")
+            return runtime
+
+        spikes_list = [tuple(i) for i in np.atleast_2d(spikes_list).tolist()]
+        plot_spikes(
+            self.inputs.in_file,
+            self.inputs.in_fft,
+            spikes_list,
+            out_file=out_file,
+        )
+        return runtime

nireports/reportlets/mriqc/svg.py

@@ -0,0 +1,110 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2023 The NiPreps Developers <[email protected]>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+# STATEMENT OF CHANGES: This file was ported carrying over full git history from MRIQC,
+# another NiPreps project licensed under the Apache-2.0 terms, and has been changed since.
+# The original file this work derives from is found at:
+# https://github.com/nipreps/mriqc/blob/1ffd4c8d1a20b44ebfea648a7b12bb32a425d4ec/
+# mriqc/viz/svg.py
+"""SVG handling utilities."""
+
+
+def svg2str(display_object, dpi=300):
+    """
+    Serializes a nilearn display object as a string
+    """
+    from io import StringIO
+
+    image_buf = StringIO()
+    display_object.frame_axes.figure.savefig(
+        image_buf, dpi=dpi, format="svg", facecolor="k", edgecolor="k"
+    )
+    image_buf.seek(0)
+    return image_buf.getvalue()
+
+
+def combine_svg(svg_list, axis="vertical"):
+    """
+    Composes the input svgs into one standalone svg
+    """
+    import numpy as np
+    import svgutils.transform as svgt
+
+    # Read all svg files and get roots
+    svgs = [svgt.fromstring(f.encode("utf-8")) for f in svg_list]
+    roots = [f.getroot() for f in svgs]
+
+    # Query the size of each
+    sizes = [(int(f.width[:-2]), int(f.height[:-2])) for f in svgs]
+
+    if axis == "vertical":
+        # Calculate the scale to fit all widths
+        scales = [1.0] * len(svgs)
+        if not all([width[0] == sizes[0][0] for width in sizes[1:]]):
+            ref_size = sizes[0]
+            for i, els in enumerate(sizes):
+                scales[i] = ref_size[0] / els[0]
+
+        newsizes = [tuple(size) for size in np.array(sizes) * np.array(scales)[..., np.newaxis]]
+        totalsize = [newsizes[0][0], np.sum(newsizes, axis=0)[1]]
+
+    elif axis == "horizontal":
+        # Calculate the scale to fit all heights
+        scales = [1.0] * len(svgs)
+        if not all([height[0] == sizes[0][1] for height in sizes[1:]]):
+            ref_size = sizes[0]
+            for i, els in enumerate(sizes):
+                scales[i] = ref_size[1] / els[1]
+
+        newsizes = [tuple(size) for size in np.array(sizes) * np.array(scales)[..., np.newaxis]]
+        totalsize = [np.sum(newsizes, axis=0)[0], newsizes[0][1]]
+
+    # Compose the views panel: total size is the width of
+    # any element (used the first here) and the sum of heights
+    fig = svgt.SVGFigure(totalsize[0], totalsize[1])
+
+    if axis == "vertical":
+        yoffset = 0
+        for i, r in enumerate(roots):
+            size = newsizes[i]
+            r.moveto(0, yoffset, scale=scales[i])
+            yoffset += size[1]
+            fig.append(r)
+    elif axis == "horizontal":
+        xoffset = 0
+        for i, r in enumerate(roots):
+            size = newsizes[i]
+            r.moveto(xoffset, 0, scale=scales[i])
+            xoffset += size[0]
+            fig.append(r)
+
+    return fig
+
+
+def extract_svg(display_object, dpi=300):
+    """
+    Removes the preamble of the svg files generated with nilearn
+    """
+    image_svg = svg2str(display_object, dpi)
+    start_idx = image_svg.find("<svg ")
+    end_idx = image_svg.rfind("</svg>")
+    return image_svg[start_idx:end_idx]