ENH: Make number of ICA components plotted in reports dynamic

mne/preprocessing/ica.py

@@ -2579,6 +2579,7 @@ def plot_sources(
         picks=None,
         start=None,
         stop=None,
+        n_channels=None,
         title=None,
         show=True,
         block=False,
@@ -2600,6 +2601,7 @@ def plot_sources(
             picks=picks,
             start=start,
             stop=stop,
+            n_channels=n_channels,
             title=title,
             show=show,
             block=block,

mne/report/report.py

@@ -2058,10 +2058,12 @@ def _add_ica_components(self, *, ica, picks, image_format, section, tags, replac
             )
 
     def _add_ica_sources(
-        self, *, ica, inst, picks, image_format, section, tags, replace
+        self, *, ica, inst, picks, n_channels, image_format, section, tags, replace
     ):
         with use_browser_backend("matplotlib"):
-            fig = ica.plot_sources(inst=inst, picks=picks, show=False)
+            fig = ica.plot_sources(
+                inst=inst, picks=picks, n_channels=n_channels, show=False
+            )
         self._add_figure(
             fig=fig,
             title="Sources",
@@ -2079,6 +2081,7 @@ def _add_ica(
         ica,
         inst,
         picks,
+        n_channels,
         ecg_evoked,
         eog_evoked,
         ecg_scores,
@@ -2234,6 +2237,7 @@ def _add_ica(
                 ica=ica,
                 inst=inst,
                 picks=picks,
+                n_channels=n_channels,
                 image_format=image_format,
                 section=section,
                 tags=tags,
@@ -2248,6 +2252,7 @@ def add_ica(
         *,
         inst,
         picks=None,
+        n_channels=None,
         ecg_evoked=None,
         eog_evoked=None,
         ecg_scores=None,
@@ -2271,6 +2276,8 @@ def add_ica(
             ``None``.
         %(picks_ica)s This only affects the behavior of the component
             topography and properties plots.
+        n_channels : int | None
+            The number of ICA components to plot. If None, defaults to 20.
         ecg_evoked, eog_evoked : path-line | mne.Evoked | None
             Evoked signal based on ECG and EOG epochs, respectively. If passed,
             will be used to visualize the effects of artifact rejection.
@@ -2298,6 +2305,7 @@ def add_ica(
             ica=ica,
             inst=inst,
             picks=picks,
+            n_channels=n_channels,
             ecg_evoked=ecg_evoked,
             eog_evoked=eog_evoked,
             ecg_scores=ecg_scores,

mne/viz/ica.py

@@ -40,6 +40,7 @@ def plot_ica_sources(
     picks=None,
     start=None,
     stop=None,
+    n_channels=None,
     title=None,
     show=True,
     block=False,
@@ -70,6 +71,7 @@ def plot_ica_sources(
     inst : instance of Raw, Epochs or Evoked
         The object to plot the sources from.
     %(picks_ica)s
+     : int | None
     start, stop : float | int | None
        If ``inst`` is a `~mne.io.Raw` or an `~mne.Evoked` object, the first and
        last time point (in seconds) of the data to plot. If ``inst`` is a
@@ -78,6 +80,8 @@ def plot_ica_sources(
        `~mne.Evoked`, ``None`` refers to the beginning and end of the evoked
        signal. If ``inst`` is an `~mne.Epochs` object, specifies the index of
        the first and last epoch to show.
+    n_channels : int | None
+        The number of ICA components to plot. If None, defaults to 20.
     title : str | None
         The window title. If None a default is provided.
     show : bool
@@ -142,6 +146,7 @@ def plot_ica_sources(
             exclude,
             start=start,
             stop=stop,
+            n_channels=n_channels,
             show=show,
             title=title,
             block=block,
@@ -1292,6 +1297,7 @@ def _plot_sources(
     psd_args,
     theme=None,
     overview_mode=None,
+    n_channels=20,
     splash=True,
 ):
     """Plot the ICA components as a RawArray or EpochsArray."""
@@ -1348,7 +1354,9 @@ def _plot_sources(
         data = np.append(data, eog_ecg_data, axis=0)
     picks = np.concatenate((picks, ica.n_components_ + np.arange(len(extra_picks))))
     ch_order = np.arange(len(picks))
-    n_channels = min([20, len(picks)])
+    if n_channels is None:
+        n_channels = 20
+    n_channels = min([n_channels, len(picks)])
     ch_names_picked = [ch_names[x] for x in picks]
 
     # create info