Update again.

aslprep/interfaces/plotting.py

@@ -12,9 +12,8 @@
 )
 from nipype.utils.filemanip import fname_presuffix
 from niworkflows.utils.timeseries import _cifti_timeseries, _nifti_timeseries
-from niworkflows.viz.plots import fMRIPlot
 
-from aslprep.utils.plotting import CBFPlot, CBFtsPlot
+from aslprep.utils.plotting import CBFPlot, CBFtsPlot, fMRIPlot
 
 
 class _ASLSummaryInputSpec(BaseInterfaceInputSpec):
@@ -119,8 +118,6 @@ def _run_interface(self, runtime):
             units=units,
             nskip=self.inputs.drop_trs,
             paired_carpet=has_cifti,
-            # The main change from fMRIPrep's usage is that detrend is False for ASL.
-            detrend=False,
         ).plot()
         fig.savefig(self._results["out_file"], bbox_inches="tight")
         return runtime

aslprep/utils/plotting.py

@@ -12,7 +12,6 @@
 from nilearn._utils.niimg import _safe_get_data, load_niimg
 from nilearn._utils.niimg_conversions import check_niimg_4d
 from niworkflows import NIWORKFLOWS_LOG
-from niworkflows.interfaces.plotting import _get_tr
 from niworkflows.viz.plots import confoundplot, spikesplot
 from niworkflows.viz.utils import (
     compose_view,
@@ -24,92 +23,6 @@
 from svgutils.transform import SVGFigure
 
 
-class ASLPlot:
-    """Generates the ASL Summary Plot."""
-
-    __slots__ = ("func_file", "mask_data", "tr", "seg_data", "confounds", "spikes")
-
-    def __init__(
-        self,
-        func_file,
-        mask_file=None,
-        data=None,
-        confounds_file=None,
-        seg_file=None,
-        tr=None,
-        usecols=None,
-        units=None,
-        vlines=None,
-        spikes_files=None,
-    ):
-        func_img = nb.load(func_file)
-        self.func_file = func_file
-        self.tr = tr or _get_tr(func_img)
-        self.mask_data = None
-        self.seg_data = None
-
-        if not isinstance(func_img, nb.Cifti2Image):
-            self.mask_data = nb.fileslice.strided_scalar(func_img.shape[:3], np.uint8(1))
-            if mask_file:
-                self.mask_data = np.asanyarray(nb.load(mask_file).dataobj).astype("uint8")
-            if seg_file:
-                self.seg_data = np.asanyarray(nb.load(seg_file).dataobj)
-
-        if units is None:
-            units = {}
-        if vlines is None:
-            vlines = {}
-        self.confounds = {}
-        if data is None and confounds_file:
-            data = pd.read_csv(confounds_file, sep=r"[\t\s]+", usecols=usecols, index_col=False)
-
-        if data is not None:
-            for name in data.columns.ravel():
-                self.confounds[name] = {
-                    "values": data[[name]].values.ravel().tolist(),
-                    "units": units.get(name),
-                    "cutoff": vlines.get(name),
-                }
-
-        self.spikes = []
-        if spikes_files:
-            self.spikes.extend((np.loadtxt(sp_file), None, False) for sp_file in spikes_files)
-
-    def plot(self, figure=None):
-        """Generate the plot."""
-        sns.set_style("whitegrid")
-        sns.set_context("paper", font_scale=0.8)
-
-        if figure is None:
-            figure = plt.gcf()
-
-        nconfounds = len(self.confounds)
-        nspikes = len(self.spikes)
-        nrows = 1 + nconfounds + nspikes
-
-        # Create grid
-        grid = mgs.GridSpec(
-            nrows, 1, wspace=0.0, hspace=0.05, height_ratios=[1] * (nrows - 1) + [5]
-        )
-
-        grid_id = 0
-        for tsz, name, iszs in self.spikes:
-            spikesplot(tsz, title=name, outer_gs=grid[grid_id], tr=self.tr, zscored=iszs)
-            grid_id += 1
-
-        if self.confounds:
-            palette = color_palette("husl", nconfounds)
-
-        for i, (name, kwargs) in enumerate(self.confounds.items()):
-            tseries = kwargs.pop("values")
-            confoundplot(tseries, grid[grid_id], tr=self.tr, color=palette[i], name=name, **kwargs)
-            grid_id += 1
-
-        plot_carpet(self.func_file, atlaslabels=self.seg_data, subplot=grid[-1], tr=self.tr)
-        # spikesplot_cb([0.7, 0.78, 0.2, 0.008])
-        return figure
-
-
 class CBFtsPlot(object):
     """Generate the CBF time series Summary Plot."""
 
@@ -462,6 +375,112 @@ def confoundplotx(
     return ax_ts, gs
 
 
+class fMRIPlot:
+    """Generates the fMRI Summary Plot."""
+
+    __slots__ = (
+        "timeseries",
+        "segments",
+        "tr",
+        "confounds",
+        "spikes",
+        "nskip",
+        "sort_carpet",
+        "paired_carpet",
+    )
+
+    def __init__(
+        self,
+        timeseries,
+        segments,
+        confounds=None,
+        conf_file=None,
+        tr=None,
+        usecols=None,
+        units=None,
+        vlines=None,
+        spikes_files=None,
+        nskip=0,
+        sort_carpet=True,
+        paired_carpet=False,
+    ):
+        self.timeseries = timeseries
+        self.segments = segments
+        self.tr = tr
+        self.nskip = nskip
+        self.sort_carpet = sort_carpet
+        self.paired_carpet = paired_carpet
+
+        if units is None:
+            units = {}
+        if vlines is None:
+            vlines = {}
+        self.confounds = {}
+        if confounds is None and conf_file:
+            confounds = pd.read_csv(conf_file, sep=r"[\t\s]+", usecols=usecols, index_col=False)
+
+        if confounds is not None:
+            for name in confounds.columns:
+                self.confounds[name] = {
+                    "values": confounds[[name]].values.squeeze().tolist(),
+                    "units": units.get(name),
+                    "cutoff": vlines.get(name),
+                }
+
+        self.spikes = []
+        if spikes_files:
+            for sp_file in spikes_files:
+                self.spikes.append((np.loadtxt(sp_file), None, False))
+
+    def plot(self, figure=None):
+        """Main plotter"""
+        import seaborn as sns
+        from niworkflows.viz.plots import plot_carpet
+
+        sns.set_style("whitegrid")
+        sns.set_context("paper", font_scale=0.8)
+
+        if figure is None:
+            figure = plt.gcf()
+
+        nconfounds = len(self.confounds)
+        nspikes = len(self.spikes)
+        nrows = 1 + nconfounds + nspikes
+
+        # Create grid
+        grid = mgs.GridSpec(
+            nrows, 1, wspace=0.0, hspace=0.05, height_ratios=[1] * (nrows - 1) + [5]
+        )
+
+        grid_id = 0
+        for tsz, name, iszs in self.spikes:
+            spikesplot(tsz, title=name, outer_gs=grid[grid_id], tr=self.tr, zscored=iszs)
+            grid_id += 1
+
+        if self.confounds:
+            from seaborn import color_palette
+
+            palette = color_palette("husl", nconfounds)
+
+        for i, (name, kwargs) in enumerate(self.confounds.items()):
+            tseries = kwargs.pop("values")
+            confoundplot(tseries, grid[grid_id], tr=self.tr, color=palette[i], name=name, **kwargs)
+            grid_id += 1
+
+        plot_carpet(
+            self.timeseries,
+            segments=self.segments,
+            subplot=grid[-1],
+            tr=self.tr,
+            sort_rows=self.sort_carpet,
+            drop_trs=self.nskip,
+            cmap="paired" if self.paired_carpet else None,
+            # This is the only modification we need for ASLPrep
+            detrend=False,
+        )
+        return figure
+
+
 def plot_carpet(
     func,
     atlaslabels=None,