fix conflicts

Author: alejoe91

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 25.12.0
+    rev: 26.1.0
     hooks:
     -   id: black
         files: ^src/

src/spikeinterface/benchmark/benchmark_sorter.py

@@ -8,7 +8,6 @@
 from spikeinterface.sorters import run_sorter
 from spikeinterface.comparison import compare_sorter_to_ground_truth
 
-
 # TODO later integrate CollisionGTComparison optionally in this class.
 
 

src/spikeinterface/benchmark/benchmark_sorter_without_gt.py

@@ -10,7 +10,6 @@
 
 from spikeinterface.benchmark import analyse_residual
 
-
 # TODO later integrate CollisionGTComparison optionally in this class.
 
 

src/spikeinterface/benchmark/tests/test_residual_analysis.py

@@ -7,7 +7,6 @@
 
 from spikeinterface.benchmark import analyse_residual
 
-
 job_kwargs = dict(n_jobs=-1, progress_bar=True)
 
 

src/spikeinterface/comparison/tests/test_multisortingcomparison.py

@@ -9,7 +9,6 @@
 from spikeinterface.extractors import NumpySorting
 from spikeinterface.comparison import compare_multiple_sorters, MultiSortingComparison
 
-
 ON_GITHUB = bool(os.getenv("GITHUB_ACTIONS"))
 
 

src/spikeinterface/comparison/tests/test_templatecomparison.py

@@ -5,7 +5,6 @@
 from spikeinterface.core import create_sorting_analyzer, generate_ground_truth_recording
 from spikeinterface.comparison import compare_templates, compare_multiple_templates
 
-
 # def setup_module():
 #     if test_dir.is_dir():
 #         shutil.rmtree(test_dir)

src/spikeinterface/core/analyzer_extension_core.py

@@ -961,18 +961,28 @@ def get_metric_column_descriptions(cls, metric_names=None):
                     )
         return metric_column_descriptions
 
-    def _cast_metrics(self, metrics_df):
-        metric_dtypes = {}
-        for m in self.metric_list:
-            metric_dtypes.update(m.metric_columns)
-
-        for col in metrics_df.columns:
-            if col in metric_dtypes:
-                try:
-                    metrics_df[col] = metrics_df[col].astype(metric_dtypes[col])
-                except Exception as e:
-                    print(f"Error casting column {col}: {e}")
-        return metrics_df
+    @classmethod
+    def get_optional_dependencies(cls, **params):
+        metric_names = params.get("metric_names", None)
+        if metric_names is None:
+            metric_names = [m.metric_name for m in cls.metric_list]
+        else:
+            for metric_name in metric_names:
+                if metric_name not in [m.metric_name for m in cls.metric_list]:
+                    raise ValueError(
+                        f"Metric {metric_name} not in available metrics {[m.metric_name for m in cls.metric_list]}"
+                    )
+        metric_depend_on = set()
+        for metric_name in metric_names:
+            metric = [m for m in cls.metric_list if m.metric_name == metric_name][0]
+            for dep in metric.depend_on:
+                if "|" in dep:
+                    dep_options = dep.split("|")
+                    metric_depend_on.update(dep_options)
+                else:
+                    metric_depend_on.add(dep)
+        depend_on = list(cls.depend_on) + list(metric_depend_on)
+        return depend_on
 
     def _set_params(
         self,
@@ -994,6 +1004,8 @@ def _set_params(
             If None, default parameters for all metrics are used.
         delete_existing_metrics : bool, default: False
             If True, existing metrics in the extension will be deleted before computing new ones.
+        metrics_to_compute : list[str] | None
+            List of metric names to compute. If None, all metrics in `metric_names` are computed.
         other_params : dict
             Additional parameters for metric computation.
 
@@ -1208,15 +1220,18 @@ def _get_data(self):
         # convert to correct dtype
         return self.data["metrics"]
 
-    def set_data(self, ext_data_name, data):
-        import pandas as pd
+    def _cast_metrics(self, metrics_df):
+        metric_dtypes = {}
+        for m in self.metric_list:
+            metric_dtypes.update(m.metric_columns)
 
-        if ext_data_name != "metrics":
-            return
-        if not isinstance(data, pd.DataFrame):
-            return
-        metrics = self._cast_metrics(data)
-        self.data[ext_data_name] = metrics
+        for col in metrics_df.columns:
+            if col in metric_dtypes:
+                try:
+                    metrics_df[col] = metrics_df[col].astype(metric_dtypes[col])
+                except Exception as e:
+                    print(f"Error casting column {col}: {e}")
+        return metrics_df
 
     def _select_extension_data(self, unit_ids: list[int | str]):
         """
@@ -1331,6 +1346,16 @@ def _split_extension_data(
         new_data = dict(metrics=metrics)
         return new_data
 
+    def set_data(self, ext_data_name, data):
+        import pandas as pd
+
+        if ext_data_name != "metrics":
+            return
+        if not isinstance(data, pd.DataFrame):
+            return
+        metrics = self._cast_metrics(data)
+        self.data[ext_data_name] = metrics
+
 
 class BaseSpikeVectorExtension(AnalyzerExtension):
     """

src/spikeinterface/core/base.py

@@ -27,7 +27,6 @@
 )
 from .job_tools import _shared_job_kwargs_doc
 
-
 # base dtypes used throughout spikeinterface
 base_peak_dtype = [
     ("sample_index", "int64"),

src/spikeinterface/core/baserecordingsnippets.py

@@ -237,7 +237,7 @@ def _set_probes(self, probe_or_probegroup, group_mode="auto", in_place=False):
         probes_info = []
         for probe in probegroup.probes:
             probes_info.append(probe.annotations)
-        self.annotate(probes_info=probes_info)
+        sub_recording.annotate(probes_info=probes_info)
 
         return sub_recording
 
@@ -264,6 +264,12 @@ def get_probegroup(self):
                 probegroup.add_probe(probe)
         else:
             probegroup = ProbeGroup.from_numpy(arr)
+
+            if "probes_info" in self.get_annotation_keys():
+                probes_info = self.get_annotation("probes_info")
+                for probe, probe_info in zip(probegroup.probes, probes_info):
+                    probe.annotations = probe_info
+
             for probe_index, probe in enumerate(probegroup.probes):
                 contour = self.get_annotation(f"probe_{probe_index}_planar_contour")
                 if contour is not None:

src/spikeinterface/core/job_tools.py

@@ -18,7 +18,6 @@
 
 from spikeinterface.core.core_tools import convert_string_to_bytes, convert_bytes_to_str, convert_seconds_to_str
 
-
 _shared_job_kwargs_doc = """**job_kwargs : keyword arguments for parallel processing:
     * chunk_duration or chunk_size or chunk_memory or total_memory
         - chunk_size : int