wip: port initial mriqc viz code identified by @celprov

nireports/mriqc/interfaces/viz.py

@@ -0,0 +1,203 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2021 The NiPreps Developers <nipreps@gmail.com>
+#
+# 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/
+#
+"""Visualization interfaces."""
+from io import open  # pylint: disable=W0622
+from pathlib import Path
+
+import numpy as np
+from mriqc.viz.utils import plot_mosaic, plot_segmentation, plot_spikes
+from nipype.interfaces.base import (
+    BaseInterfaceInputSpec,
+    File,
+    SimpleInterface,
+    TraitedSpec,
+    isdefined,
+    traits,
+)
+
+
+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:
+            with open(out_file, "w") as f:
+                f.write("<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/mriqc/reports/__init__.py

@@ -0,0 +1,100 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2021 The NiPreps Developers <nipreps@gmail.com>
+#
+# 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/
+#
+"""
+Generate individual and group reports.
+
+In order to ease the screening process of individual images, MRIQC
+generates individual reports with mosaic views of a number of cutting planes and
+supporting information (for example, segmentation contours). The most straightforward
+use-case is the visualization of those images flagged as low-quality by the classifier.
+
+After the extraction of :abbr:`IQMs (image quality metrics)` in all the images of our sample,
+a group report is generated. The group report shows a scatter plot for each of the
+:abbr:`IQMs (image quality metrics)`, so it is particularly easy to identify the cases that
+are outliers for each metric. The plots are interactive, such that clicking on any particular
+sample opens the corresponding individual report of that case. Examples of group and individual
+reports for the ABIDE dataset are available online at `mriqc.org <http://mriqc.org>`_.
+
+.. toctree::
+    :maxdepth: 3
+
+    reports/group
+    reports/smri
+    reports/bold
+
+mriqc.reports package
+=====================
+
+Submodules
+----------
+
+.. automodule:: mriqc.reports.group
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: mriqc.reports.individual
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: mriqc.reports.utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+"""
+from copy import deepcopy
+
+from .group import gen_html as group_html
+from .individual import individual_html
+
+REPORT_TITLES = {
+    "bold": [
+        ("BOLD average", "bold-avg"),
+        ("Standard deviation map", "std-map"),
+        ("FMRI summary plot", "fmri-summary"),
+        ("Zoomed-in BOLD average", "zoomed-avg"),
+        ("Background noise", "bg-noise"),
+        ("Calculated brain mask", "brain-msk"),
+        ("Approximate spatial normalization", "normalization"),
+    ],
+    "T1w": [
+        ("Zoomed-in (brain mask)", "zoomed-view"),
+        ("Background noise", "bg-noise"),
+        ("Approximate spatial normalization", "normalization"),
+        ("Brain mask", "brain-msk"),
+        ("Brain tissue segmentation", "brain-seg"),
+        ("Artifacts in background", "bg-arts"),
+        ("Head outline", "head-msk"),
+        ('"Hat" mask', "hat-msk"),
+        ("Distribution of the noise in the background", "qi2-fitting"),
+    ],
+}
+
+REPORT_TITLES["T2w"] = deepcopy(REPORT_TITLES["T1w"])
+
+__all__ = [
+    "individual_html",
+    "group_html",
+]