Add multi-segment capability to BaseRasterWidget and children

src/spikeinterface/widgets/amplitudes.py

@@ -5,7 +5,7 @@
 
 from .rasters import BaseRasterWidget
 from .base import BaseWidget, to_attr
-from .utils import get_some_colors
+from .utils import get_some_colors, validate_segment_indices, get_segment_durations
 
 from spikeinterface.core.sortinganalyzer import SortingAnalyzer
 
@@ -25,8 +25,9 @@ class AmplitudesWidget(BaseRasterWidget):
     unit_colors : dict | None, default: None
         Dict of colors with unit ids as keys and colors as values. Colors can be any type accepted
         by matplotlib. If None, default colors are chosen using the `get_some_colors` function.
-    segment_index : int or None, default: None
-        The segment index (or None if mono-segment)
+    segment_indices : list of int or None, default: None
+        Segment index or indices to plot. If None and there are multiple segments, defaults to 0.
+        If list, spike trains and amplitudes are concatenated across the specified segments.
     max_spikes_per_unit : int or None, default: None
         Number of max spikes per unit to display. Use None for all spikes
     y_lim : tuple or None, default: None
@@ -51,70 +52,104 @@ def __init__(
         sorting_analyzer: SortingAnalyzer,
         unit_ids=None,
         unit_colors=None,
-        segment_index=None,
+        segment_indices=None,
         max_spikes_per_unit=None,
         y_lim=None,
         scatter_decimate=1,
         hide_unit_selector=False,
         plot_histograms=False,
         bins=None,
         plot_legend=True,
+        segment_index=None,
         backend=None,
         **backend_kwargs,
     ):
+        import warnings
+
+        # Handle deprecation of segment_index parameter
+        if segment_index is not None:
+            warnings.warn(
+                "The 'segment_index' parameter is deprecated and will be removed in a future version. "
+                "Use 'segment_indices' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            if segment_indices is None:
+                if isinstance(segment_index, int):
+                    segment_indices = [segment_index]
+                else:
+                    segment_indices = segment_index
 
         sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer)
-
         sorting = sorting_analyzer.sorting
         self.check_extensions(sorting_analyzer, "spike_amplitudes")
 
+        # Get amplitudes by segment
         amplitudes = sorting_analyzer.get_extension("spike_amplitudes").get_data(outputs="by_unit")
 
         if unit_ids is None:
             unit_ids = sorting.unit_ids
 
-        if sorting.get_num_segments() > 1:
-            if segment_index is None:
-                warn("More than one segment available! Using `segment_index = 0`.")
-                segment_index = 0
-        else:
-            segment_index = 0
+        # Handle segment_index input
+        segment_indices = validate_segment_indices(segment_indices, sorting)
+
+        # Check for SortingView backend
+        is_sortingview = backend == "sortingview"
+
+        # For SortingView, ensure we're only using a single segment
+        if is_sortingview and len(segment_indices) > 1:
+            warn("SortingView backend currently supports only single segment. Using first segment.")
+            segment_indices = [segment_indices[0]]
 
-        amplitudes_segment = amplitudes[segment_index]
-        total_duration = sorting_analyzer.get_num_samples(segment_index) / sorting_analyzer.sampling_frequency
+        # Create multi-segment data structure (dict of dicts)
+        spiketrains_by_segment = {}
+        amplitudes_by_segment = {}
 
-        all_spiketrains = {
-            unit_id: sorting.get_unit_spike_train(unit_id, segment_index=segment_index, return_times=True)
-            for unit_id in sorting.unit_ids
-        }
+        for idx in segment_indices:
+            amplitudes_segment = amplitudes[idx]
 
-        all_amplitudes = amplitudes_segment
+            # Initialize for this segment
+            spiketrains_by_segment[idx] = {}
+            amplitudes_by_segment[idx] = {}
+
+            for unit_id in unit_ids:
+                # Get spike times for this unit in this segment
+                spike_times = sorting.get_unit_spike_train(unit_id, segment_index=idx, return_times=True)
+                amps = amplitudes_segment[unit_id]
+
+                # Store data in dict of dicts format
+                spiketrains_by_segment[idx][unit_id] = spike_times
+                amplitudes_by_segment[idx][unit_id] = amps
+
+        # Apply max_spikes_per_unit limit if specified
         if max_spikes_per_unit is not None:
-            spiketrains_to_plot = dict()
-            amplitudes_to_plot = dict()
-            for unit, st in all_spiketrains.items():
-                amps = all_amplitudes[unit]
-                if len(st) > max_spikes_per_unit:
-                    random_idxs = np.random.choice(len(st), size=max_spikes_per_unit, replace=False)
-                    spiketrains_to_plot[unit] = st[random_idxs]
-                    amplitudes_to_plot[unit] = amps[random_idxs]
-                else:
-                    spiketrains_to_plot[unit] = st
-                    amplitudes_to_plot[unit] = amps
-        else:
-            spiketrains_to_plot = all_spiketrains
-            amplitudes_to_plot = all_amplitudes
+            for idx in segment_indices:
+                for unit_id in unit_ids:
+                    st = spiketrains_by_segment[idx][unit_id]
+                    amps = amplitudes_by_segment[idx][unit_id]
+                    if len(st) > max_spikes_per_unit:
+                        # Scale down the number of spikes proportionally per segment
+                        # to ensure we have max_spikes_per_unit total after concatenation
+                        segment_count = len(segment_indices)
+                        segment_max = max(1, max_spikes_per_unit // segment_count)
+
+                        if len(st) > segment_max:
+                            random_idxs = np.random.choice(len(st), size=segment_max, replace=False)
+                            spiketrains_by_segment[idx][unit_id] = st[random_idxs]
+                            amplitudes_by_segment[idx][unit_id] = amps[random_idxs]
 
         if plot_histograms and bins is None:
             bins = 100
 
+        # Calculate durations for all segments for x-axis limits
+        durations = get_segment_durations(sorting, segment_indices)
+
+        # Build the plot data with the full dict of dicts structure
         plot_data = dict(
-            spike_train_data=spiketrains_to_plot,
-            y_axis_data=amplitudes_to_plot,
             unit_colors=unit_colors,
             plot_histograms=plot_histograms,
             bins=bins,
-            total_duration=total_duration,
+            durations=durations,
             unit_ids=unit_ids,
             hide_unit_selector=hide_unit_selector,
             plot_legend=plot_legend,
@@ -123,6 +158,17 @@ def __init__(
             scatter_decimate=scatter_decimate,
         )
 
+        # If using SortingView, extract just the first segment's data as flat dicts
+        if is_sortingview:
+            first_segment = segment_indices[0]
+            plot_data["spike_train_data"] = spiketrains_by_segment[first_segment]
+            plot_data["y_axis_data"] = amplitudes_by_segment[first_segment]
+        else:
+            # Otherwise use the full dict of dicts structure with all segments
+            plot_data["spike_train_data"] = spiketrains_by_segment
+            plot_data["y_axis_data"] = amplitudes_by_segment
+            plot_data["segment_indices"] = segment_indices
+
         BaseRasterWidget.__init__(self, **plot_data, backend=backend, **backend_kwargs)
 
     def plot_sortingview(self, data_plot, **backend_kwargs):
@@ -143,7 +189,10 @@ def plot_sortingview(self, data_plot, **backend_kwargs):
         ]
 
         self.view = vv.SpikeAmplitudes(
-            start_time_sec=0, end_time_sec=dp.total_duration, plots=sa_items, hide_unit_selector=dp.hide_unit_selector
+            start_time_sec=0,
+            end_time_sec=np.sum(dp.durations),
+            plots=sa_items,
+            hide_unit_selector=dp.hide_unit_selector,
         )
 
         self.url = handle_display_and_url(self, self.view, **backend_kwargs)

src/spikeinterface/widgets/motion.py

@@ -117,14 +117,14 @@ class DriftRasterMapWidget(BaseRasterWidget):
         "spike_locations" extension computed.
     direction : "x" or "y", default: "y"
         The direction to display. "y" is the depth direction.
-    segment_index : int, default: None
-        The segment index to display.
     recording : RecordingExtractor | None, default: None
         The recording extractor object (only used to get "real" times).
-    segment_index : int, default: 0
-        The segment index to display.
     sampling_frequency : float, default: None
         The sampling frequency (needed if recording is None).
+    segment_indices : list of int or None, default: None
+        The segment index or indices to display. If None and there's only one segment, it's used.
+        If None and there are multiple segments, you must specify which to use.
+        If a list of indices is provided, peaks and locations are concatenated across the segments.
     depth_lim : tuple or None, default: None
         The min and max depth to display, if None (min and max of the recording).
     scatter_decimate : int, default: None
@@ -149,25 +149,46 @@ def __init__(
         direction: str = "y",
         recording: BaseRecording | None = None,
         sampling_frequency: float | None = None,
-        segment_index: int | None = None,
+        segment_indices: list[int] | None = None,
         depth_lim: tuple[float, float] | None = None,
         color_amplitude: bool = True,
         scatter_decimate: int | None = None,
         cmap: str = "inferno",
         color: str = "Gray",
         clim: tuple[float, float] | None = None,
         alpha: float = 1,
+        segment_index: int | list[int] | None = None,
         backend: str | None = None,
         **backend_kwargs,
     ):
+        import warnings
+        from matplotlib.pyplot import colormaps
+        from matplotlib.colors import Normalize
+
+        # Handle deprecation of segment_index parameter
+        if segment_index is not None:
+            warnings.warn(
+                "The 'segment_index' parameter is deprecated and will be removed in a future version. "
+                "Use 'segment_indices' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            if segment_indices is None:
+                if isinstance(segment_index, int):
+                    segment_indices = [segment_index]
+                else:
+                    segment_indices = segment_index
+
         assert peaks is not None or sorting_analyzer is not None
+
         if peaks is not None:
             assert peak_locations is not None
             if recording is None:
                 assert sampling_frequency is not None, "If recording is None, you must provide the sampling frequency"
             else:
                 sampling_frequency = recording.sampling_frequency
             peak_amplitudes = peaks["amplitude"]
+
         if sorting_analyzer is not None:
             if sorting_analyzer.has_recording():
                 recording = sorting_analyzer.recording
@@ -190,29 +211,56 @@ def __init__(
             else:
                 peak_amplitudes = None
 
-        if segment_index is None:
-            assert (
-                len(np.unique(peaks["segment_index"])) == 1
-            ), "segment_index must be specified if there are multiple segments"
-            segment_index = 0
-        else:
-            peak_mask = peaks["segment_index"] == segment_index
-            peaks = peaks[peak_mask]
-            peak_locations = peak_locations[peak_mask]
-            if peak_amplitudes is not None:
-                peak_amplitudes = peak_amplitudes[peak_mask]
-
-        from matplotlib.pyplot import colormaps
+        unique_segments = np.unique(peaks["segment_index"])
 
-        if color_amplitude:
-            amps = peak_amplitudes
+        if segment_indices is None:
+            if len(unique_segments) == 1:
+                segment_indices = [int(unique_segments[0])]
+            else:
+                raise ValueError("segment_indices must be specified if there are multiple segments")
+
+        if not isinstance(segment_indices, list):
+            raise ValueError("segment_indices must be a list of ints")
+
+        # Validate all segment indices exist in the data
+        for idx in segment_indices:
+            if idx not in unique_segments:
+                raise ValueError(f"segment_index {idx} not found in peaks data")
+
+        # Filter data for the selected segments
+        # Note: For simplicity, we'll filter all data first, then construct dict of dicts
+        segment_mask = np.isin(peaks["segment_index"], segment_indices)
+        filtered_peaks = peaks[segment_mask]
+        filtered_locations = peak_locations[segment_mask]
+        if peak_amplitudes is not None:
+            filtered_amplitudes = peak_amplitudes[segment_mask]
+
+        # Create dict of dicts structure for the base class
+        spike_train_data = {}
+        y_axis_data = {}
+
+        # Process each segment separately
+        for seg_idx in segment_indices:
+            segment_mask = filtered_peaks["segment_index"] == seg_idx
+            segment_peaks = filtered_peaks[segment_mask]
+            segment_locations = filtered_locations[segment_mask]
+
+            # Convert peak times to seconds
+            spike_times = segment_peaks["sample_index"] / sampling_frequency
+
+            # Store in dict of dicts format (using 0 as the "unit" id)
+            spike_train_data[seg_idx] = {0: spike_times}
+            y_axis_data[seg_idx] = {0: segment_locations[direction]}
+
+        if color_amplitude and peak_amplitudes is not None:
+            amps = filtered_amplitudes
             amps_abs = np.abs(amps)
             q_95 = np.quantile(amps_abs, 0.95)
-            cmap = colormaps[cmap]
+            cmap_obj = colormaps[cmap]
             if clim is None:
                 amps = amps_abs
                 amps /= q_95
-                c = cmap(amps)
+                c = cmap_obj(amps)
             else:
                 from matplotlib.colors import Normalize
 
@@ -226,18 +274,30 @@ def __init__(
         else:
             color_kwargs = dict(color=color, c=None, alpha=alpha)
 
-        # convert data into format that `BaseRasterWidget` can take it in
-        spike_train_data = {0: peaks["sample_index"] / sampling_frequency}
-        y_axis_data = {0: peak_locations[direction]}
+        # Calculate segment durations for x-axis limits
+        if recording is not None:
+            durations = [recording.get_duration(seg_idx) for seg_idx in segment_indices]
+        else:
+            # Find boundaries between segments using searchsorted
+            segment_boundaries = [
+                np.searchsorted(filtered_peaks["segment_index"], [seg_idx, seg_idx + 1]) for seg_idx in segment_indices
+            ]
+
+            # Calculate durations from max sample in each segment
+            durations = [
+                (filtered_peaks["sample_index"][end - 1] + 1) / sampling_frequency for (_, end) in segment_boundaries
+            ]
 
         plot_data = dict(
             spike_train_data=spike_train_data,
             y_axis_data=y_axis_data,
+            segment_indices=segment_indices,
             y_lim=depth_lim,
             color_kwargs=color_kwargs,
             scatter_decimate=scatter_decimate,
             title="Peak depth",
             y_label="Depth [um]",
+            durations=durations,
         )
 
         BaseRasterWidget.__init__(self, **plot_data, backend=backend, **backend_kwargs)
@@ -370,10 +430,10 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
             dp.recording,
         )
 
-        commpon_drift_map_kwargs = dict(
+        common_drift_map_kwargs = dict(
             direction=dp.motion.direction,
             recording=dp.recording,
-            segment_index=dp.segment_index,
+            segment_indices=[dp.segment_index],
             depth_lim=dp.depth_lim,
             scatter_decimate=dp.scatter_decimate,
             color_amplitude=dp.color_amplitude,
@@ -390,15 +450,15 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
             dp.peak_locations,
             ax=ax0,
             immediate_plot=True,
-            **commpon_drift_map_kwargs,
+            **common_drift_map_kwargs,
         )
 
         _ = DriftRasterMapWidget(
             dp.peaks,
             corrected_location,
             ax=ax1,
             immediate_plot=True,
-            **commpon_drift_map_kwargs,
+            **common_drift_map_kwargs,
         )
 
         ax2.plot(temporal_bins_s, displacement, alpha=0.2, color="black")