Improve export to Phy property handling

src/spikeinterface/exporters/tests/test_export_to_phy.py

@@ -91,6 +91,69 @@ def test_export_to_phy_by_property(sorting_analyzer_with_group_for_export, creat
     assert template_inds.shape == (sorting_analyzer.unit_ids.size, 4)
 
 
+def test_export_to_phy_metrics(sorting_analyzer_sparse_for_export, create_cache_folder):
+    cache_folder = create_cache_folder
+
+    sorting_analyzer = sorting_analyzer_sparse_for_export
+
+    # quality metrics are computed already
+    qm = sorting_analyzer.get_extension("quality_metrics").get_data()
+    output_folder = cache_folder / "phy_output_qm"
+    export_to_phy(
+        sorting_analyzer,
+        output_folder,
+        compute_pc_features=True,
+        compute_amplitudes=True,
+        n_jobs=1,
+        chunk_size=10000,
+        progress_bar=True,
+        add_quality_metrics=True,
+    )
+    for col_name in qm.columns:
+        assert (output_folder / f"cluster_{col_name}.tsv").is_file()
+
+    # quality metrics are computed already
+    tm_ext = sorting_analyzer.compute("template_metrics")
+    tm = tm_ext.get_data()
+    output_folder = cache_folder / "phy_output_tm_not_qm"
+    export_to_phy(
+        sorting_analyzer,
+        output_folder,
+        compute_pc_features=True,
+        compute_amplitudes=True,
+        n_jobs=1,
+        chunk_size=10000,
+        progress_bar=True,
+        add_quality_metrics=False,
+        add_template_metrics=True,
+    )
+    for col_name in tm.columns:
+        assert (output_folder / f"cluster_{col_name}.tsv").is_file()
+    for col_name in qm.columns:
+        assert not (output_folder / f"cluster_{col_name}.tsv").is_file()
+
+    # custom metrics
+    sorting_analyzer.sorting.set_property("custom_metric", np.random.rand(sorting_analyzer.unit_ids.size))
+    output_folder = cache_folder / "phy_output_custom"
+    export_to_phy(
+        sorting_analyzer,
+        output_folder,
+        compute_pc_features=True,
+        compute_amplitudes=True,
+        n_jobs=1,
+        chunk_size=10000,
+        progress_bar=True,
+        add_quality_metrics=False,
+        add_template_metrics=False,
+        additional_properties=["custom_metric"],
+    )
+    assert (output_folder / "cluster_custom_metric.tsv").is_file()
+    for col_name in tm.columns:
+        assert not (output_folder / f"cluster_{col_name}.tsv").is_file()
+    for col_name in qm.columns:
+        assert not (output_folder / f"cluster_{col_name}.tsv").is_file()
+
+
 if __name__ == "__main__":
     sorting_analyzer_sparse = make_sorting_analyzer(sparse=True)
     sorting_analyzer_group = make_sorting_analyzer(sparse=False, with_group=True)

src/spikeinterface/exporters/to_phy.py

@@ -25,8 +25,10 @@ def export_to_phy(
     sparsity: Optional[ChannelSparsity] = None,
     copy_binary: bool = True,
     remove_if_exists: bool = False,
-    peak_sign: Literal["both", "neg", "pos"] = "neg",
     template_mode: str = "average",
+    add_quality_metrics: bool = True,
+    add_template_metrics: bool = True,
+    additional_properties: list | None = None,
     dtype: Optional[npt.DTypeLike] = None,
     verbose: bool = True,
     use_relative_path: bool = False,
@@ -51,10 +53,14 @@ def export_to_phy(
         If True, the recording is copied and saved in the phy "output_folder"
     remove_if_exists : bool, default: False
         If True and "output_folder" exists, it is removed and overwritten
-    peak_sign : "neg" | "pos" | "both", default: "neg"
-        Used by compute_spike_amplitudes
     template_mode : str, default: "average"
         Parameter "mode" to be given to SortingAnalyzer.get_template()
+    add_quality_metrics : bool, default: True
+        If True, quality metrics (if computed) are saved as Phy tsv and will appear in the ClusterView.
+    add_template_metrics : bool, default: True
+        If True, template metrics (if computed) are saved as Phy tsv and will appear in the ClusterView.
+    additional_properties : list | None, default: None
+        List of additional properties to be saved as Phy tsv and will appear in the ClusterView.
     dtype : dtype or None, default: None
         Dtype to save binary data
     verbose : bool, default: True
@@ -244,7 +250,7 @@ def export_to_phy(
     channel_group = pd.DataFrame({"cluster_id": [i for i in range(len(unit_ids))], "channel_group": unit_groups})
     channel_group.to_csv(output_folder / "cluster_channel_group.tsv", sep="\t", index=False)
 
-    if sorting_analyzer.has_extension("quality_metrics"):
+    if sorting_analyzer.has_extension("quality_metrics") and add_quality_metrics:
         qm_data = sorting_analyzer.get_extension("quality_metrics").get_data()
         for column_name in qm_data.columns:
             # already computed by phy
@@ -253,6 +259,19 @@ def export_to_phy(
                     {"cluster_id": [i for i in range(len(unit_ids))], column_name: qm_data[column_name].values}
                 )
                 metric.to_csv(output_folder / f"cluster_{column_name}.tsv", sep="\t", index=False)
+    if sorting_analyzer.has_extension("template_metrics") and add_template_metrics:
+        tm_data = sorting_analyzer.get_extension("template_metrics").get_data()
+        for column_name in tm_data.columns:
+            metric = pd.DataFrame(
+                {"cluster_id": [i for i in range(len(unit_ids))], column_name: tm_data[column_name].values}
+            )
+            metric.to_csv(output_folder / f"cluster_{column_name}.tsv", sep="\t", index=False)
+    if additional_properties is not None:
+        for prop_name in additional_properties:
+            prop_data = sorting.get_property(prop_name)
+            if prop_data is not None:
+                prop = pd.DataFrame({"cluster_id": [i for i in range(len(unit_ids))], prop_name: prop_data})
+                prop.to_csv(output_folder / f"cluster_{prop_name}.tsv", sep="\t", index=False)
 
     if verbose:
         print("Run:\nphy template-gui ", str(output_folder / "params.py"))