Merge branch 'main' of github.com:SpikeInterface/spikeinterface into …

…reset-times

src/spikeinterface/core/core_tools.py

@@ -201,7 +201,7 @@ def is_dict_extractor(d: dict) -> bool:
 extractor_dict_element = namedtuple(typename="extractor_dict_element", field_names=["value", "name", "access_path"])
 
 
-def extractor_dict_iterator(extractor_dict: dict) -> Generator[extractor_dict_element]:
+def extractor_dict_iterator(extractor_dict: dict) -> Generator[extractor_dict_element, None, None]:
     """
     Iterator for recursive traversal of a dictionary.
     This function explores the dictionary recursively and yields the path to each value along with the value itself.

src/spikeinterface/core/recording_tools.py

@@ -69,7 +69,7 @@ def _init_binary_worker(recording, file_path_dict, dtype, byte_offest, cast_unsi
 def write_binary_recording(
     recording: "BaseRecording",
     file_paths: list[Path | str] | Path | str,
-    dtype: np.ndtype = None,
+    dtype: np.typing.DTypeLike = None,
     add_file_extension: bool = True,
     byte_offset: int = 0,
     auto_cast_uint: bool = True,

src/spikeinterface/generation/drift_tools.py

@@ -40,9 +40,13 @@ def interpolate_templates(templates_array, source_locations, dest_locations, int
     source_locations = np.asarray(source_locations)
     dest_locations = np.asarray(dest_locations)
     if dest_locations.ndim == 2:
-        new_shape = templates_array.shape
+        new_shape = (*templates_array.shape[:2], len(dest_locations))
     elif dest_locations.ndim == 3:
-        new_shape = (dest_locations.shape[0],) + templates_array.shape
+        new_shape = (
+            dest_locations.shape[0],
+            *templates_array.shape[:2],
+            dest_locations.shape[1],
+        )
     else:
         raise ValueError(f"Incorrect dimensions for dest_locations: {dest_locations.ndim}. Dimensions can be 2 or 3. ")
 

src/spikeinterface/postprocessing/template_metrics.py

@@ -8,11 +8,9 @@
 
 import numpy as np
 import warnings
-from typing import Optional
 from copy import deepcopy
 
 from ..core.sortinganalyzer import register_result_extension, AnalyzerExtension
-from ..core import ChannelSparsity
 from ..core.template_tools import get_template_extremum_channel
 from ..core.template_tools import get_dense_templates_array
 
@@ -238,13 +236,17 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, **job
 
                 for metric_name in metrics_single_channel:
                     func = _metric_name_to_func[metric_name]
-                    value = func(
-                        template_upsampled,
-                        sampling_frequency=sampling_frequency_up,
-                        trough_idx=trough_idx,
-                        peak_idx=peak_idx,
-                        **self.params["metrics_kwargs"],
-                    )
+                    try:
+                        value = func(
+                            template_upsampled,
+                            sampling_frequency=sampling_frequency_up,
+                            trough_idx=trough_idx,
+                            peak_idx=peak_idx,
+                            **self.params["metrics_kwargs"],
+                        )
+                    except Exception as e:
+                        warnings.warn(f"Error computing metric {metric_name} for unit {unit_id}: {e}")
+                        value = np.nan
                     template_metrics.at[index, metric_name] = value
 
             # compute metrics multi_channel
@@ -274,12 +276,16 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, **job
                     sampling_frequency_up = sampling_frequency
 
                 func = _metric_name_to_func[metric_name]
-                value = func(
-                    template_upsampled,
-                    channel_locations=channel_locations_sparse,
-                    sampling_frequency=sampling_frequency_up,
-                    **self.params["metrics_kwargs"],
-                )
+                try:
+                    value = func(
+                        template_upsampled,
+                        channel_locations=channel_locations_sparse,
+                        sampling_frequency=sampling_frequency_up,
+                        **self.params["metrics_kwargs"],
+                    )
+                except Exception as e:
+                    warnings.warn(f"Error computing metric {metric_name} for unit {unit_id}: {e}")
+                    value = np.nan
                 template_metrics.at[index, metric_name] = value
         return template_metrics
 

src/spikeinterface/qualitymetrics/quality_metric_calculator.py

@@ -164,7 +164,10 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, **job
         pc_metric_names = [k for k in metric_names if k in _possible_pc_metric_names]
         if len(pc_metric_names) > 0 and not self.params["skip_pc_metrics"]:
             if not sorting_analyzer.has_extension("principal_components"):
-                raise ValueError("waveform_principal_component must be provied")
+                raise ValueError(
+                    "To compute principal components base metrics, the principal components "
+                    "extension must be computed first."
+                )
             pc_metrics = compute_pc_metrics(
                 sorting_analyzer,
                 unit_ids=non_empty_unit_ids,