Merge pull request #2158 from DradeAW/wvf_extension_typo

`WaveformExtractor.is_extension` --> `has_extension`

src/spikeinterface/core/waveform_extractor.py

@@ -506,7 +506,7 @@ def get_recording_property(self, key) -> np.ndarray:
     def get_sorting_property(self, key) -> np.ndarray:
         return self.sorting.get_property(key)
 
-    def get_extension_class(self, extension_name):
+    def get_extension_class(self, extension_name: str):
         """
         Get extension class from name and check if registered.
 
@@ -525,7 +525,7 @@ def get_extension_class(self, extension_name):
         ext_class = extensions_dict[extension_name]
         return ext_class
 
-    def is_extension(self, extension_name) -> bool:
+    def has_extension(self, extension_name: str) -> bool:
         """
         Check if the extension exists in memory or in the folder.
 
@@ -556,7 +556,15 @@ def is_extension(self, extension_name) -> bool:
                     and "params" in self._waveforms_root[extension_name].attrs.keys()
                 )
 
-    def load_extension(self, extension_name):
+    def is_extension(self, extension_name) -> bool:
+        warn(
+            "WaveformExtractor.is_extension is deprecated and will be removed in version 0.102.0! Use `has_extension` instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        return self.has_extension(extension_name)
+
+    def load_extension(self, extension_name: str):
         """
         Load an extension from its name.
         The module of the extension must be loaded and registered.
@@ -572,7 +580,7 @@ def load_extension(self, extension_name):
             The loaded instance of the extension
         """
         if self.folder is not None and extension_name not in self._loaded_extensions:
-            if self.is_extension(extension_name):
+            if self.has_extension(extension_name):
                 ext_class = self.get_extension_class(extension_name)
                 ext = ext_class.load(self.folder, self)
         if extension_name not in self._loaded_extensions:
@@ -588,7 +596,7 @@ def delete_extension(self, extension_name) -> None:
         extension_name: str
             The extension name.
         """
-        assert self.is_extension(extension_name), f"The extension {extension_name} is not available"
+        assert self.has_extension(extension_name), f"The extension {extension_name} is not available"
         del self._loaded_extensions[extension_name]
         if self.folder is not None and (self.folder / extension_name).is_dir():
             shutil.rmtree(self.folder / extension_name)
@@ -610,7 +618,7 @@ def get_available_extension_names(self):
         """
         extension_names_in_folder = []
         for extension_class in self.extensions:
-            if self.is_extension(extension_class.extension_name):
+            if self.has_extension(extension_class.extension_name):
                 extension_names_in_folder.append(extension_class.extension_name)
         return extension_names_in_folder
 

src/spikeinterface/exporters/report.py

@@ -51,7 +51,7 @@ def export_report(
     unit_ids = sorting.unit_ids
 
     # load or compute spike_amplitudes
-    if we.is_extension("spike_amplitudes"):
+    if we.has_extension("spike_amplitudes"):
         spike_amplitudes = we.load_extension("spike_amplitudes").get_data(outputs="by_unit")
     elif force_computation:
         spike_amplitudes = compute_spike_amplitudes(we, peak_sign=peak_sign, outputs="by_unit", **job_kwargs)
@@ -62,7 +62,7 @@ def export_report(
         )
 
     # load or compute quality_metrics
-    if we.is_extension("quality_metrics"):
+    if we.has_extension("quality_metrics"):
         metrics = we.load_extension("quality_metrics").get_data()
     elif force_computation:
         metrics = compute_quality_metrics(we)
@@ -73,7 +73,7 @@ def export_report(
         )
 
     # load or compute correlograms
-    if we.is_extension("correlograms"):
+    if we.has_extension("correlograms"):
         correlograms, bins = we.load_extension("correlograms").get_data()
     elif force_computation:
         correlograms, bins = compute_correlograms(we, window_ms=100.0, bin_ms=1.0)
@@ -84,7 +84,7 @@ def export_report(
         )
 
     # pre-compute unit locations if not done
-    if not we.is_extension("unit_locations"):
+    if not we.has_extension("unit_locations"):
         unit_locations = compute_unit_locations(we)
 
     output_folder = Path(output_folder).absolute()

src/spikeinterface/exporters/to_phy.py

@@ -196,7 +196,7 @@ def export_to_phy(
         templates[unit_ind, :, :][:, : len(chan_inds)] = template
         templates_ind[unit_ind, : len(chan_inds)] = chan_inds
 
-    if waveform_extractor.is_extension("similarity"):
+    if waveform_extractor.has_extension("similarity"):
         tmc = waveform_extractor.load_extension("similarity")
         template_similarity = tmc.get_data()
     else:
@@ -219,7 +219,7 @@ def export_to_phy(
     np.save(str(output_folder / "channel_groups.npy"), channel_groups)
 
     if compute_amplitudes:
-        if waveform_extractor.is_extension("spike_amplitudes"):
+        if waveform_extractor.has_extension("spike_amplitudes"):
             sac = waveform_extractor.load_extension("spike_amplitudes")
             amplitudes = sac.get_data(outputs="concatenated")
         else:
@@ -231,7 +231,7 @@ def export_to_phy(
         np.save(str(output_folder / "amplitudes.npy"), amplitudes)
 
     if compute_pc_features:
-        if waveform_extractor.is_extension("principal_components"):
+        if waveform_extractor.has_extension("principal_components"):
             pc = waveform_extractor.load_extension("principal_components")
         else:
             pc = compute_principal_components(
@@ -264,7 +264,7 @@ 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 waveform_extractor.is_extension("quality_metrics"):
+    if waveform_extractor.has_extension("quality_metrics"):
         qm = waveform_extractor.load_extension("quality_metrics")
         qm_data = qm.get_data()
         for column_name in qm_data.columns:

src/spikeinterface/postprocessing/principal_component.py

@@ -750,7 +750,7 @@ def compute_principal_components(
     >>> pc.run_for_all_spikes(file_path="all_pca_projections.npy")
     """
 
-    if load_if_exists and waveform_extractor.is_extension(WaveformPrincipalComponent.extension_name):
+    if load_if_exists and waveform_extractor.has_extension(WaveformPrincipalComponent.extension_name):
         pc = waveform_extractor.load_extension(WaveformPrincipalComponent.extension_name)
     else:
         pc = WaveformPrincipalComponent.create(waveform_extractor)

src/spikeinterface/postprocessing/tests/common_extension_tests.py

@@ -143,7 +143,7 @@ def _test_extension_folder(self, we, in_memory=False):
 
             # reload as an extension from we
             assert self.extension_class.extension_name in we.get_available_extension_names()
-            assert we.is_extension(self.extension_class.extension_name)
+            assert we.has_extension(self.extension_class.extension_name)
             ext = we.load_extension(self.extension_class.extension_name)
             assert isinstance(ext, self.extension_class)
             for ext_name in self.extension_data_names:

src/spikeinterface/postprocessing/tests/test_principal_component.py

@@ -135,7 +135,7 @@ def test_project_new(self):
         from sklearn.decomposition import IncrementalPCA
 
         we = self.we1
-        if we.is_extension("principal_components"):
+        if we.has_extension("principal_components"):
             we.delete_extension("principal_components")
         we_cp = we.select_units(we.unit_ids, self.cache_folder / "toy_waveforms_1seg_cp")
 

src/spikeinterface/qualitymetrics/misc_metrics.py

@@ -201,7 +201,7 @@ def compute_snrs(
     snrs : dict
         Computed signal to noise ratio for each unit.
     """
-    if waveform_extractor.is_extension("noise_levels"):
+    if waveform_extractor.has_extension("noise_levels"):
         noise_levels = waveform_extractor.load_extension("noise_levels").get_data()
     else:
         if random_chunk_kwargs_dict is None:
@@ -687,7 +687,7 @@ def compute_amplitude_cv_metrics(
     if unit_ids is None:
         unit_ids = sorting.unit_ids
 
-    if waveform_extractor.is_extension(amplitude_extension):
+    if waveform_extractor.has_extension(amplitude_extension):
         sac = waveform_extractor.load_extension(amplitude_extension)
         amps = sac.get_data(outputs="concatenated")
         if amplitude_extension == "spike_amplitudes":
@@ -803,7 +803,7 @@ def compute_amplitude_cutoffs(
 
     spike_amplitudes = None
     invert_amplitudes = False
-    if waveform_extractor.is_extension("spike_amplitudes"):
+    if waveform_extractor.has_extension("spike_amplitudes"):
         amp_calculator = waveform_extractor.load_extension("spike_amplitudes")
         spike_amplitudes = amp_calculator.get_data(outputs="by_unit")
         if amp_calculator._params["peak_sign"] == "pos":
@@ -881,7 +881,7 @@ def compute_amplitude_medians(waveform_extractor, peak_sign="neg", unit_ids=None
     extremum_channels_ids = get_template_extremum_channel(waveform_extractor, peak_sign=peak_sign)
 
     spike_amplitudes = None
-    if waveform_extractor.is_extension("spike_amplitudes"):
+    if waveform_extractor.has_extension("spike_amplitudes"):
         amp_calculator = waveform_extractor.load_extension("spike_amplitudes")
         spike_amplitudes = amp_calculator.get_data(outputs="by_unit")
 
@@ -974,7 +974,7 @@ def compute_drift_metrics(
     if unit_ids is None:
         unit_ids = sorting.unit_ids
 
-    if waveform_extractor.is_extension("spike_locations"):
+    if waveform_extractor.has_extension("spike_locations"):
         locs_calculator = waveform_extractor.load_extension("spike_locations")
         spike_locations = locs_calculator.get_data(outputs="concatenated")
         spike_locations_by_unit = locs_calculator.get_data(outputs="by_unit")

src/spikeinterface/qualitymetrics/quality_metric_calculator.py

@@ -42,14 +42,14 @@ def _set_params(
         if metric_names is None:
             metric_names = list(_misc_metric_name_to_func.keys())
             # if PC is available, PC metrics are automatically added to the list
-            if self.waveform_extractor.is_extension("principal_components"):
+            if self.waveform_extractor.has_extension("principal_components"):
                 # by default 'nearest_neightbor' is removed because too slow
                 pc_metrics = _possible_pc_metric_names.copy()
                 pc_metrics.remove("nn_isolation")
                 pc_metrics.remove("nn_noise_overlap")
                 metric_names += pc_metrics
             # if spike_locations are not available, drift is removed from the list
-            if not self.waveform_extractor.is_extension("spike_locations"):
+            if not self.waveform_extractor.has_extension("spike_locations"):
                 if "drift" in metric_names:
                     metric_names.remove("drift")
 
@@ -130,7 +130,7 @@ def _run(self, verbose, **job_kwargs):
         # metrics based on PCs
         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 self.waveform_extractor.is_extension("principal_components"):
+            if not self.waveform_extractor.has_extension("principal_components"):
                 raise ValueError("waveform_principal_component must be provied")
             pc_extension = self.waveform_extractor.load_extension("principal_components")
             pc_metrics = calculate_pc_metrics(
@@ -216,7 +216,7 @@ def compute_quality_metrics(
     metrics: pandas.DataFrame
         Data frame with the computed metrics
     """
-    if load_if_exists and waveform_extractor.is_extension(QualityMetricCalculator.extension_name):
+    if load_if_exists and waveform_extractor.has_extension(QualityMetricCalculator.extension_name):
         qmc = waveform_extractor.load_extension(QualityMetricCalculator.extension_name)
     else:
         qmc = QualityMetricCalculator(waveform_extractor)

src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py

@@ -88,7 +88,7 @@ def test_metrics(self):
         we = self.we_long
 
         # avoid NaNs
-        if we.is_extension("spike_amplitudes"):
+        if we.has_extension("spike_amplitudes"):
             we.delete_extension("spike_amplitudes")
 
         # without PC

src/spikeinterface/widgets/base.py

@@ -107,7 +107,7 @@ def check_extensions(waveform_extractor, extensions):
         error_msg = ""
         raise_error = False
         for extension in extensions:
-            if not waveform_extractor.is_extension(extension):
+            if not waveform_extractor.has_extension(extension):
                 raise_error = True
                 error_msg += (
                     f"The {extension} waveform extension is required for this widget. "

src/spikeinterface/widgets/unit_summary.py

@@ -80,13 +80,13 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
         fig = self.figure
         nrows = 2
         ncols = 3
-        if we.is_extension("correlograms") or we.is_extension("spike_amplitudes"):
+        if we.has_extension("correlograms") or we.has_extension("spike_amplitudes"):
             ncols += 1
-        if we.is_extension("spike_amplitudes"):
+        if we.has_extension("spike_amplitudes"):
             nrows += 1
         gs = fig.add_gridspec(nrows, ncols)
 
-        if we.is_extension("unit_locations"):
+        if we.has_extension("unit_locations"):
             ax1 = fig.add_subplot(gs[:2, 0])
             # UnitLocationsPlotter().do_plot(dp.plot_data_unit_locations, ax=ax1)
             w = UnitLocationsWidget(
@@ -129,7 +129,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
         )
         ax3.set_ylabel(None)
 
-        if we.is_extension("correlograms"):
+        if we.has_extension("correlograms"):
             ax4 = fig.add_subplot(gs[:2, 3])
             AutoCorrelogramsWidget(
                 we,
@@ -142,7 +142,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
             ax4.set_title(None)
             ax4.set_yticks([])
 
-        if we.is_extension("spike_amplitudes"):
+        if we.has_extension("spike_amplitudes"):
             ax5 = fig.add_subplot(gs[2, :3])
             ax6 = fig.add_subplot(gs[2, 3])
             axes = np.array([ax5, ax6])