maint: replace ported sources with shim imports and deprecation warnings

Resolves: #787.

Author: oesteban

niworkflows/interfaces/plotting.py

@@ -21,229 +21,17 @@
 #     https://www.nipreps.org/community/licensing/
 #
 """Visualization tools."""
-import numpy as np
-import nibabel as nb
-
-from nipype.utils.filemanip import fname_presuffix
-from nipype.interfaces.base import (
-    File,
-    BaseInterfaceInputSpec,
-    TraitedSpec,
-    SimpleInterface,
-    traits,
-    isdefined,
-)
-from niworkflows.utils.timeseries import _cifti_timeseries, _nifti_timeseries
-from niworkflows.viz.plots import (
-    fMRIPlot,
-    compcor_variance_plot,
-    confounds_correlation_plot,
+import warnings
+from nireports.interfaces import (
+    CompCorVariancePlot,
+    ConfoundsCorrelationPlot,
+    FMRISummary,
 )
 
+__all__ = (
+    "CompCorVariancePlot",
+    "ConfoundsCorrelationPlot",
+    "FMRISummary",
+)
 
-class _FMRISummaryInputSpec(BaseInterfaceInputSpec):
-    in_func = File(exists=True, mandatory=True, desc="")
-    in_spikes_bg = File(exists=True, desc="")
-    fd = File(exists=True, desc="")
-    dvars = File(exists=True, desc="")
-    outliers = File(exists=True, desc="")
-    in_segm = File(exists=True, desc="")
-    tr = traits.Either(None, traits.Float, usedefault=True, desc="the TR")
-    fd_thres = traits.Float(0.2, usedefault=True, desc="")
-    drop_trs = traits.Int(0, usedefault=True, desc="dummy scans")
-
-
-class _FMRISummaryOutputSpec(TraitedSpec):
-    out_file = File(exists=True, desc="written file path")
-
-
-class FMRISummary(SimpleInterface):
-    """Prepare an fMRI summary plot for the report."""
-
-    input_spec = _FMRISummaryInputSpec
-    output_spec = _FMRISummaryOutputSpec
-
-    def _run_interface(self, runtime):
-        import pandas as pd
-
-        self._results["out_file"] = fname_presuffix(
-            self.inputs.in_func,
-            suffix="_fmriplot.svg",
-            use_ext=False,
-            newpath=runtime.cwd,
-        )
-
-        dataframe = pd.DataFrame({
-            "outliers": np.loadtxt(self.inputs.outliers, usecols=[0]).tolist(),
-            # Pick non-standardize dvars (col 1)
-            # First timepoint is NaN (difference)
-            "DVARS": [np.nan]
-            + np.loadtxt(self.inputs.dvars, skiprows=1, usecols=[1]).tolist(),
-            # First timepoint is zero (reference volume)
-            "FD": [0.0]
-            + np.loadtxt(self.inputs.fd, skiprows=1, usecols=[0]).tolist(),
-        }) if (
-            isdefined(self.inputs.outliers)
-            and isdefined(self.inputs.dvars)
-            and isdefined(self.inputs.fd)
-        ) else None
-
-        input_data = nb.load(self.inputs.in_func)
-        seg_file = self.inputs.in_segm if isdefined(self.inputs.in_segm) else None
-        dataset, segments = (
-            _cifti_timeseries(input_data)
-            if isinstance(input_data, nb.Cifti2Image) else
-            _nifti_timeseries(input_data, seg_file)
-        )
-
-        fig = fMRIPlot(
-            dataset,
-            segments=segments,
-            spikes_files=(
-                [self.inputs.in_spikes_bg]
-                if isdefined(self.inputs.in_spikes_bg) else None
-            ),
-            tr=(
-                self.inputs.tr if isdefined(self.inputs.tr) else
-                _get_tr(input_data)
-            ),
-            confounds=dataframe,
-            units={"outliers": "%", "FD": "mm"},
-            vlines={"FD": [self.inputs.fd_thres]},
-            nskip=self.inputs.drop_trs,
-        ).plot()
-        fig.savefig(self._results["out_file"], bbox_inches="tight")
-        return runtime
-
-
-class _CompCorVariancePlotInputSpec(BaseInterfaceInputSpec):
-    metadata_files = traits.List(
-        File(exists=True),
-        mandatory=True,
-        desc="List of files containing component " "metadata",
-    )
-    metadata_sources = traits.List(
-        traits.Str,
-        desc="List of names of decompositions "
-        "(e.g., aCompCor, tCompCor) yielding "
-        "the arguments in `metadata_files`",
-    )
-    variance_thresholds = traits.Tuple(
-        traits.Float(0.5),
-        traits.Float(0.7),
-        traits.Float(0.9),
-        usedefault=True,
-        desc="Levels of explained variance to include in " "plot",
-    )
-    out_file = traits.Either(
-        None, File, value=None, usedefault=True, desc="Path to save plot"
-    )
-
-
-class _CompCorVariancePlotOutputSpec(TraitedSpec):
-    out_file = File(exists=True, desc="Path to saved plot")
-
-
-class CompCorVariancePlot(SimpleInterface):
-    """Plot the number of components necessary to explain the specified levels of variance."""
-
-    input_spec = _CompCorVariancePlotInputSpec
-    output_spec = _CompCorVariancePlotOutputSpec
-
-    def _run_interface(self, runtime):
-        if self.inputs.out_file is None:
-            self._results["out_file"] = fname_presuffix(
-                self.inputs.metadata_files[0],
-                suffix="_compcor.svg",
-                use_ext=False,
-                newpath=runtime.cwd,
-            )
-        else:
-            self._results["out_file"] = self.inputs.out_file
-        compcor_variance_plot(
-            metadata_files=self.inputs.metadata_files,
-            metadata_sources=self.inputs.metadata_sources,
-            output_file=self._results["out_file"],
-            varexp_thresh=self.inputs.variance_thresholds,
-        )
-        return runtime
-
-
-class _ConfoundsCorrelationPlotInputSpec(BaseInterfaceInputSpec):
-    confounds_file = File(
-        exists=True, mandatory=True, desc="File containing confound regressors"
-    )
-    out_file = traits.Either(
-        None, File, value=None, usedefault=True, desc="Path to save plot"
-    )
-    reference_column = traits.Str(
-        "global_signal",
-        usedefault=True,
-        desc="Column in the confound file for "
-        "which all correlation magnitudes "
-        "should be ranked and plotted",
-    )
-    columns = traits.List(
-        traits.Str,
-        desc="Filter out all regressors not found in this list."
-    )
-    max_dim = traits.Int(
-        20,
-        usedefault=True,
-        desc="Maximum number of regressors to include in "
-        "plot. Regressors with highest magnitude of "
-        "correlation with `reference_column` will be "
-        "selected.",
-    )
-
-
-class _ConfoundsCorrelationPlotOutputSpec(TraitedSpec):
-    out_file = File(exists=True, desc="Path to saved plot")
-
-
-class ConfoundsCorrelationPlot(SimpleInterface):
-    """Plot the correlation among confound regressors."""
-
-    input_spec = _ConfoundsCorrelationPlotInputSpec
-    output_spec = _ConfoundsCorrelationPlotOutputSpec
-
-    def _run_interface(self, runtime):
-        if self.inputs.out_file is None:
-            self._results["out_file"] = fname_presuffix(
-                self.inputs.confounds_file,
-                suffix="_confoundCorrelation.svg",
-                use_ext=False,
-                newpath=runtime.cwd,
-            )
-        else:
-            self._results["out_file"] = self.inputs.out_file
-        confounds_correlation_plot(
-            confounds_file=self.inputs.confounds_file,
-            columns=self.inputs.columns if isdefined(self.inputs.columns) else None,
-            max_dim=self.inputs.max_dim,
-            output_file=self._results["out_file"],
-            reference=self.inputs.reference_column,
-        )
-        return runtime
-
-
-def _get_tr(img):
-    """
-    Attempt to extract repetition time from NIfTI/CIFTI header
-
-    Examples
-    --------
-    >>> _get_tr(nb.load(Path(test_data) /
-    ...    'sub-ds205s03_task-functionallocalizer_run-01_bold_volreg.nii.gz'))
-    2.2
-    >>> _get_tr(nb.load(Path(test_data) /
-    ...    'sub-01_task-mixedgamblestask_run-02_space-fsLR_den-91k_bold.dtseries.nii'))
-    2.0
-
-    """
-
-    try:
-        return img.header.matrix.get_index_map(0).series_step
-    except AttributeError:
-        return img.header.get_zooms()[-1]
-    raise RuntimeError("Could not extract TR - unknown data structure type")
+warnings.warn("Please use nireports.interfaces", DeprecationWarning)