diff --git a/pyproject.toml b/pyproject.toml
index a43ab63c8e..fc09ad9198 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ authors = [
 ]
 description = "Python toolkit for analysis, visualization, and comparison of spike sorting output"
 readme = "README.md"
-requires-python = ">=3.9,<4.0"
+requires-python = ">=3.9,<3.13"  # Only numpy 2.1 supported on python 3.13 for windows. We need to wait for fix on neo
 classifiers = [
     "Programming Language :: Python :: 3 :: Only",
     "License :: OSI Approved :: MIT License",
diff --git a/src/spikeinterface/core/analyzer_extension_core.py b/src/spikeinterface/core/analyzer_extension_core.py
index 1d3501c4d0..447bbe562e 100644
--- a/src/spikeinterface/core/analyzer_extension_core.py
+++ b/src/spikeinterface/core/analyzer_extension_core.py
@@ -22,21 +22,23 @@
 
 class ComputeRandomSpikes(AnalyzerExtension):
     """
-    AnalyzerExtension that select some random spikes.
+    AnalyzerExtension that select somes random spikes.
+    This allows for a subsampling of spikes for further calculations and is important
+    for managing that amount of memory and speed of computation in the analyzer.
 
     This will be used by the `waveforms`/`templates` extensions.
 
-    This internally use `random_spikes_selection()` parameters are the same.
+    This internally uses `random_spikes_selection()` parameters.
 
     Parameters
     ----------
-    method: "uniform" | "all", default: "uniform"
+    method : "uniform" | "all", default: "uniform"
         The method to select the spikes
-    max_spikes_per_unit: int, default: 500
+    max_spikes_per_unit : int, default: 500
         The maximum number of spikes per unit, ignored if method="all"
-    margin_size: int, default: None
+    margin_size : int, default: None
         A margin on each border of segments to avoid border spikes, ignored if method="all"
-    seed: int or None, default: None
+    seed : int or None, default: None
         A seed for the random generator, ignored if method="all"
 
     Returns
@@ -104,7 +106,7 @@ def get_random_spikes(self):
         return self._some_spikes
 
     def get_selected_indices_in_spike_train(self, unit_id, segment_index):
-        # usefull for Waveforms extractor backwars compatibility
+        # useful for WaveformExtractor backwards compatibility
         # In Waveforms extractor "selected_spikes" was a dict (key: unit_id) of list (segment_index) of indices of spikes in spiketrain
         sorting = self.sorting_analyzer.sorting
         random_spikes_indices = self.data["random_spikes_indices"]
@@ -133,16 +135,16 @@ class ComputeWaveforms(AnalyzerExtension):
 
     Parameters
     ----------
-    ms_before: float, default: 1.0
+    ms_before : float, default: 1.0
         The number of ms to extract before the spike events
-    ms_after: float, default: 2.0
+    ms_after : float, default: 2.0
         The number of ms to extract after the spike events
-    dtype: None | dtype, default: None
+    dtype : None | dtype, default: None
         The dtype of the waveforms. If None, the dtype of the recording is used.
 
     Returns
     -------
-    waveforms: np.ndarray
+    waveforms : np.ndarray
         Array with computed waveforms with shape (num_random_spikes, num_samples, num_channels)
     """
 
@@ -380,7 +382,12 @@ def _set_params(self, ms_before: float = 1.0, ms_after: float = 2.0, operators=N
         assert isinstance(operators, list)
         for operator in operators:
             if isinstance(operator, str):
-                assert operator in ("average", "std", "median", "mad")
+                if operator not in ("average", "std", "median", "mad"):
+                    error_msg = (
+                        f"You have entered an operator {operator} in your `operators` argument which is "
+                        f"not supported. Please use any of ['average', 'std', 'median', 'mad'] instead."
+                    )
+                    raise ValueError(error_msg)
             else:
                 assert isinstance(operator, (list, tuple))
                 assert len(operator) == 2
@@ -405,9 +412,13 @@ def _run(self, verbose=False, **job_kwargs):
             self._compute_and_append_from_waveforms(self.params["operators"])
 
         else:
-            for operator in self.params["operators"]:
-                if operator not in ("average", "std"):
-                    raise ValueError(f"Computing templates with operators {operator} needs the 'waveforms' extension")
+            bad_operator_list = [
+                operator for operator in self.params["operators"] if operator not in ("average", "std")
+            ]
+            if len(bad_operator_list) > 0:
+                raise ValueError(
+                    f"Computing templates with operators {bad_operator_list} requires the 'waveforms' extension"
+                )
 
             recording = self.sorting_analyzer.recording
             sorting = self.sorting_analyzer.sorting
@@ -441,7 +452,7 @@ def _run(self, verbose=False, **job_kwargs):
 
     def _compute_and_append_from_waveforms(self, operators):
         if not self.sorting_analyzer.has_extension("waveforms"):
-            raise ValueError(f"Computing templates with operators {operators} needs the 'waveforms' extension")
+            raise ValueError(f"Computing templates with operators {operators} requires the 'waveforms' extension")
 
         unit_ids = self.sorting_analyzer.unit_ids
         channel_ids = self.sorting_analyzer.channel_ids
@@ -466,7 +477,7 @@ def _compute_and_append_from_waveforms(self, operators):
 
         assert self.sorting_analyzer.has_extension(
             "random_spikes"
-        ), "compute templates requires the random_spikes extension. You can run sorting_analyzer.get_random_spikes()"
+        ), "compute 'templates' requires the random_spikes extension. You can run sorting_analyzer.compute('random_spikes')"
         some_spikes = self.sorting_analyzer.get_extension("random_spikes").get_random_spikes()
         for unit_index, unit_id in enumerate(unit_ids):
             spike_mask = some_spikes["unit_index"] == unit_index
@@ -549,9 +560,17 @@ def _get_data(self, operator="average", percentile=None, outputs="numpy"):
         if operator != "percentile":
             key = operator
         else:
-            assert percentile is not None, "You must provide percentile=..."
+            assert percentile is not None, "You must provide percentile=... if `operator=percentile`"
             key = f"percentile_{percentile}"
 
+        if key not in self.data.keys():
+            error_msg = (
+                f"You have entered `operator={key}`, but the only operators calculated are "
+                f"{list(self.data.keys())}. Please use one of these as your `operator` in the "
+                f"`get_data` function."
+            )
+            raise ValueError(error_msg)
+
         templates_array = self.data[key]
 
         if outputs == "numpy":
@@ -566,7 +585,7 @@ def _get_data(self, operator="average", percentile=None, outputs="numpy"):
                 probe=self.sorting_analyzer.get_probe(),
             )
         else:
-            raise ValueError("outputs must be numpy or Templates")
+            raise ValueError("outputs must be `numpy` or `Templates`")
 
     def get_templates(self, unit_ids=None, operator="average", percentile=None, save=True, outputs="numpy"):
         """
@@ -576,26 +595,26 @@ def get_templates(self, unit_ids=None, operator="average", percentile=None, save
 
         Parameters
         ----------
-        unit_ids: list or None
+        unit_ids : list or None
             Unit ids to retrieve waveforms for
-        operator: "average" | "median" | "std" | "percentile", default: "average"
+        operator : "average" | "median" | "std" | "percentile", default: "average"
             The operator to compute the templates
-        percentile: float, default: None
+        percentile : float, default: None
             Percentile to use for operator="percentile"
-        save: bool, default True
+        save : bool, default: True
             In case, the operator is not computed yet it can be saved to folder or zarr
-        outputs: "numpy" | "Templates"
+        outputs : "numpy" | "Templates", default: "numpy"
             Whether to return a numpy array or a Templates object
 
         Returns
         -------
-        templates: np.array
+        templates : np.array | Templates
             The returned templates (num_units, num_samples, num_channels)
         """
         if operator != "percentile":
             key = operator
         else:
-            assert percentile is not None, "You must provide percentile=..."
+            assert percentile is not None, "You must provide percentile=... if `operator='percentile'`"
             key = f"pencentile_{percentile}"
 
         if key in self.data:
@@ -632,7 +651,7 @@ def get_templates(self, unit_ids=None, operator="average", percentile=None, save
                 is_scaled=self.sorting_analyzer.return_scaled,
             )
         else:
-            raise ValueError("outputs must be numpy or Templates")
+            raise ValueError("`outputs` must be 'numpy' or 'Templates'")
 
     def get_unit_template(self, unit_id, operator="average"):
         """
@@ -642,7 +661,7 @@ def get_unit_template(self, unit_id, operator="average"):
         ----------
         unit_id: str | int
             Unit id to retrieve waveforms for
-        operator: str
+        operator: str, default: "average"
              The operator to compute the templates
 
         Returns
@@ -701,13 +720,13 @@ def _set_params(self, **noise_level_params):
         return params
 
     def _select_extension_data(self, unit_ids):
-        # this do not depend on units
+        # this does not depend on units
         return self.data
 
     def _merge_extension_data(
         self, merge_unit_groups, new_unit_ids, new_sorting_analyzer, keep_mask=None, verbose=False, **job_kwargs
     ):
-        # this do not depend on units
+        # this does not depend on units
         return self.data.copy()
 
     def _run(self, verbose=False):
diff --git a/src/spikeinterface/exporters/report.py b/src/spikeinterface/exporters/report.py
index 3a4be9213a..ab08401382 100644
--- a/src/spikeinterface/exporters/report.py
+++ b/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
     ----------
diff --git a/src/spikeinterface/sorters/basesorter.py b/src/spikeinterface/sorters/basesorter.py
index 3502d27548..c59fa29c05 100644
--- a/src/spikeinterface/sorters/basesorter.py
+++ b/src/spikeinterface/sorters/basesorter.py
@@ -145,9 +145,10 @@ def initialize_folder(cls, recording, output_folder, verbose, remove_existing_fo
         elif recording.check_serializability("pickle"):
             recording.dump(output_folder / "spikeinterface_recording.pickle", relative_to=output_folder)
         else:
-            # TODO: deprecate and finally remove this after 0.100
-            d = {"warning": "The recording is not serializable to json"}
-            rec_file.write_text(json.dumps(d, indent=4), encoding="utf8")
+            raise RuntimeError(
+                "This recording is not serializable and so can not be sorted. Consider `recording.save()` to save a "
+                "compatible binary file."
+            )
 
         return output_folder
 
diff --git a/src/spikeinterface/widgets/autocorrelograms.py b/src/spikeinterface/widgets/autocorrelograms.py
index c8acd93dc2..c211a277f8 100644
--- a/src/spikeinterface/widgets/autocorrelograms.py
+++ b/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
diff --git a/src/spikeinterface/widgets/crosscorrelograms.py b/src/spikeinterface/widgets/crosscorrelograms.py
index cdb2041aa3..88dd803323 100644
--- a/src/spikeinterface/widgets/crosscorrelograms.py
+++ b/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
diff --git a/src/spikeinterface/widgets/traces.py b/src/spikeinterface/widgets/traces.py
index 86f2350a85..f5dadc780f 100644
--- a/src/spikeinterface/widgets/traces.py
+++ b/src/spikeinterface/widgets/traces.py
@@ -52,6 +52,8 @@ class TracesWidget(BaseWidget):
         If dict, keys should be the same as recording keys
     scale : float, default: 1
         Scale factor for the traces
+    vspacing_factor : float, default: 1.5
+        Vertical spacing between channels as a multiple of maximum channel amplitude
     with_colorbar : bool, default: True
         When mode is "map", a colorbar is added
     tile_size : int, default: 1500
@@ -82,6 +84,7 @@ def __init__(
         tile_size=1500,
         seconds_per_row=0.2,
         scale=1,
+        vspacing_factor=1.5,
         with_colorbar=True,
         add_legend=True,
         backend=None,
@@ -168,7 +171,7 @@ def __init__(
         traces0 = list_traces[0]
         mean_channel_std = np.mean(np.std(traces0, axis=0))
         max_channel_amp = np.max(np.max(np.abs(traces0), axis=0))
-        vspacing = max_channel_amp * 1.5
+        vspacing = max_channel_amp * vspacing_factor
 
         if rec0.get_channel_groups() is None:
             color_groups = False
diff --git a/src/spikeinterface/widgets/unit_summary.py b/src/spikeinterface/widgets/unit_summary.py
index 755e60ccbf..9466110110 100644
--- a/src/spikeinterface/widgets/unit_summary.py
+++ b/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],