Merge pull request #3493 from chrishalcrow/report_without_waveforms

Allow `export_report` to run without waveforms

src/spikeinterface/exporters/report.py

@@ -20,10 +20,10 @@ def export_report(
     **job_kwargs,
 ):
     """
-    Exports a SI spike sorting report. The report includes summary figures of the spike sorting output
-    (e.g. amplitude distributions, unit localization and depth VS amplitude) as well as unit-specific reports,
-    that include waveforms, templates, template maps, ISI distributions, and more.
-
+    Exports a SI spike sorting report. The report includes summary figures of the spike sorting output.
+    What is plotted depends on what has been calculated. Unit locations and unit waveforms are always included.
+    Unit waveform densities, correlograms and spike amplitudes are plotted if `waveforms`, `correlograms`,
+    and `spike_amplitudes` have been computed for the given `sorting_analyzer`.
 
     Parameters
     ----------

src/spikeinterface/widgets/autocorrelograms.py

@@ -9,7 +9,13 @@ class AutoCorrelogramsWidget(CrossCorrelogramsWidget):
     # the doc is copied form CrossCorrelogramsWidget
 
     def __init__(self, *args, **kargs):
-        CrossCorrelogramsWidget.__init__(self, *args, **kargs)
+        _ = kargs.pop("min_similarity_for_correlograms", 0.0)
+        CrossCorrelogramsWidget.__init__(
+            self,
+            *args,
+            **kargs,
+            min_similarity_for_correlograms=None,
+        )
 
     def plot_matplotlib(self, data_plot, **backend_kwargs):
         import matplotlib.pyplot as plt

src/spikeinterface/widgets/crosscorrelograms.py

@@ -21,7 +21,8 @@ class CrossCorrelogramsWidget(BaseWidget):
         List of unit ids
     min_similarity_for_correlograms : float, default: 0.2
         For sortingview backend. Threshold for computing pair-wise cross-correlograms.
-        If template similarity between two units is below this threshold, the cross-correlogram is not displayed
+        If template similarity between two units is below this threshold, the cross-correlogram is not displayed.
+        For auto-correlograms plot, this is automatically set to None.
     window_ms : float, default: 100.0
         Window for CCGs in ms. If correlograms are already computed (e.g. with SortingAnalyzer),
         this argument is ignored

src/spikeinterface/widgets/unit_summary.py

@@ -107,82 +107,90 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
         # and use custum grid spec
         fig = self.figure
         nrows = 2
-        ncols = 3
-        if sorting_analyzer.has_extension("correlograms") or sorting_analyzer.has_extension("spike_amplitudes"):
+        ncols = 2
+        if sorting_analyzer.has_extension("correlograms"):
+            ncols += 1
+        if sorting_analyzer.has_extension("waveforms"):
             ncols += 1
         if sorting_analyzer.has_extension("spike_amplitudes"):
             nrows += 1
         gs = fig.add_gridspec(nrows, ncols)
+        col_counter = 0
 
-        if sorting_analyzer.has_extension("unit_locations"):
-            ax1 = fig.add_subplot(gs[:2, 0])
-            # UnitLocationsPlotter().do_plot(dp.plot_data_unit_locations, ax=ax1)
-            w = UnitLocationsWidget(
-                sorting_analyzer,
-                unit_ids=[unit_id],
-                unit_colors=unit_colors,
-                plot_legend=False,
-                backend="matplotlib",
-                ax=ax1,
-                **unitlocationswidget_kwargs,
-            )
-
-            unit_locations = sorting_analyzer.get_extension("unit_locations").get_data(outputs="by_unit")
-            unit_location = unit_locations[unit_id]
-            x, y = unit_location[0], unit_location[1]
-            ax1.set_xlim(x - 80, x + 80)
-            ax1.set_ylim(y - 250, y + 250)
-            ax1.set_xticks([])
-            ax1.set_xlabel(None)
-            ax1.set_ylabel(None)
-
-        ax2 = fig.add_subplot(gs[:2, 1])
-        w = UnitWaveformsWidget(
+        # Unit locations and unit waveform plots are always generated
+        ax_unit_locations = fig.add_subplot(gs[:2, col_counter])
+        _ = UnitLocationsWidget(
+            sorting_analyzer,
+            unit_ids=[unit_id],
+            unit_colors=unit_colors,
+            plot_legend=False,
+            backend="matplotlib",
+            ax=ax_unit_locations,
+            **unitlocationswidget_kwargs,
+        )
+        col_counter += 1
+
+        unit_locations = sorting_analyzer.get_extension("unit_locations").get_data(outputs="by_unit")
+        unit_location = unit_locations[unit_id]
+        x, y = unit_location[0], unit_location[1]
+        ax_unit_locations.set_xlim(x - 80, x + 80)
+        ax_unit_locations.set_ylim(y - 250, y + 250)
+        ax_unit_locations.set_xticks([])
+        ax_unit_locations.set_xlabel(None)
+        ax_unit_locations.set_ylabel(None)
+
+        ax_unit_waveforms = fig.add_subplot(gs[:2, col_counter])
+        _ = UnitWaveformsWidget(
             sorting_analyzer,
             unit_ids=[unit_id],
             unit_colors=unit_colors,
             plot_templates=True,
+            plot_waveforms=sorting_analyzer.has_extension("waveforms"),
             same_axis=True,
             plot_legend=False,
             sparsity=sparsity,
             backend="matplotlib",
-            ax=ax2,
+            ax=ax_unit_waveforms,
             **unitwaveformswidget_kwargs,
         )
+        col_counter += 1
 
-        ax2.set_title(None)
+        ax_unit_waveforms.set_title(None)
 
-        ax3 = fig.add_subplot(gs[:2, 2])
-        UnitWaveformDensityMapWidget(
-            sorting_analyzer,
-            unit_ids=[unit_id],
-            unit_colors=unit_colors,
-            use_max_channel=True,
-            same_axis=False,
-            backend="matplotlib",
-            ax=ax3,
-            **unitwaveformdensitymapwidget_kwargs,
-        )
-        ax3.set_ylabel(None)
+        if sorting_analyzer.has_extension("waveforms"):
+            ax_waveform_density = fig.add_subplot(gs[:2, col_counter])
+            UnitWaveformDensityMapWidget(
+                sorting_analyzer,
+                unit_ids=[unit_id],
+                unit_colors=unit_colors,
+                use_max_channel=True,
+                same_axis=False,
+                backend="matplotlib",
+                ax=ax_waveform_density,
+                **unitwaveformdensitymapwidget_kwargs,
+            )
+            col_counter += 1
+            ax_waveform_density.set_ylabel(None)
 
         if sorting_analyzer.has_extension("correlograms"):
-            ax4 = fig.add_subplot(gs[:2, 3])
+            ax_correlograms = fig.add_subplot(gs[:2, col_counter])
             AutoCorrelogramsWidget(
                 sorting_analyzer,
                 unit_ids=[unit_id],
                 unit_colors=unit_colors,
                 backend="matplotlib",
-                ax=ax4,
+                ax=ax_correlograms,
                 **autocorrelogramswidget_kwargs,
             )
+            col_counter += 1
 
-            ax4.set_title(None)
-            ax4.set_yticks([])
+            ax_correlograms.set_title(None)
+            ax_correlograms.set_yticks([])
 
         if sorting_analyzer.has_extension("spike_amplitudes"):
-            ax5 = fig.add_subplot(gs[2, :3])
-            ax6 = fig.add_subplot(gs[2, 3])
-            axes = np.array([ax5, ax6])
+            ax_spike_amps = fig.add_subplot(gs[2, : col_counter - 1])
+            ax_amps_distribution = fig.add_subplot(gs[2, col_counter - 1])
+            axes = np.array([ax_spike_amps, ax_amps_distribution])
             AmplitudesWidget(
                 sorting_analyzer,
                 unit_ids=[unit_id],