Switched all filtering to Gaussian filtering

docs/source/modules.rst

@@ -13,13 +13,13 @@ This module will label units as belonging to a certain category if they meet som
 
 This module takes as a key the name of the category, and as a value a dictionary containing the criteria. Each criterion return a value for each unit, and a minimum and/or maximum can be set.
 
-For some parameters, you can specify the parameters for :code:`wvf_extraction` and :code:`filter` (parameters given to the SpikeInterface method :code:`core.extract_waveforms` and :code:`preprocessing.filter` respectively).
+For some parameters, you can specify the parameters for :code:`wvf_extraction` (parameters given to the SpikeInterface method :code:`core.extract_waveforms`) and :code:`filter` (a list :code:`[freq_min, freq_max]` for Gaussian bandpass filtering).
 
 - :code:`firing_rate`: returns the mean firing rate of the unit (in Hz).
 - :code:`contamination`: returns the estimated contamination of the unit (between 0 and 1; 0 being pure). The :code:`refractory_period = [censored_period, refractory_period]` has to be set (in ms).
 - :code:`amplitude`: returns the mean amplitude of the unit's template (in µV if the recording object can be scaled to µV). Optional parameters can be set to the function :code:`spikeinterface.core.get_template_extremum_amplitude`.
 - :code:`SNR`: returns the signal-to-noise ratio of the unit. Optional parameters can be set to the function :code:`spikeinterface.qualitymetrics.compute_snrs`
-- :code:`amplitude_std`: returns the standard deviation in the spike amplitudes for the unit. Optional parameters can be set to the function :code:`spikeinterface.postprocessing.compute_spike_amplitudes`
+- :code:`sd_ratio`: returns the standard deviation in the spike amplitudes for the unit divided by the standard deviation on the same channel. Optional parameters can be set to the function :code:`spikeinterface.postprocessing.compute_spike_amplitudes` as a dictionary with key :code:`spike_amplitudes_kwargs`. Optional parameters can be set to the function :code:`spikeinterface.qualitymetrics.compute_sd_ratio` as a dictionary with key :code:`sd_ratio_kwargs`.
 - :code:`ISI_portion`: Returns the fraction (between 0 and 1) of inter-spike intervals inside a time range. the :code:`range = [min_t, max_t]` has to be set (in ms).
 
 
@@ -58,11 +58,7 @@ Here is an example for categorizing complex-spikes from more regular spikes (cer
 						"ms_after": 1.0,
 						"max_spikes_per_unit": 500
 					},
-					"filter": {  // Parameters for spikeinterface.preprocessing.filter
-						"band": [150, 7_000],
-						"filter_order": 2,
-						"ftype": "bessel"
-					}
+					"filter": [150, 7_000] // Gaussian bandpass filter with cutoffs at 150 and 7,000 Hz.
 				}
 			}
 		}
@@ -140,8 +136,8 @@ Example of units removal
 			"firing_rate": {
 				"min": 1.0
 			},
-			"amplitude_std": {
-				"max": 80.0
+			"sd_ratio": {
+				"max": 2.0
 			}
 		}
 

params_examples/params_cerebellar_cortex[beta].json

@@ -108,11 +108,7 @@
 				},
 				"spikes": {
 					"amplitude": {
-						"filter": {
-							"band": [450, 9000],
-							"filter_order": 2,
-							"ftype": "bessel"
-						},
+						"filter": [450, 9000],
 						"min": 20,
 						"wvf_extraction": {
 							"ms_before": 2.0,
@@ -175,11 +171,7 @@
 						"ms_before": 1.5,
 						"ms_after": 3.5,
 						"max_spikes_per_unit": 2000,
-						"filter": {
-							"band": [150, 3000],
-							"filter_order": 2,
-							"ftype": "bessel"
-						}
+						"filter": [150, 3000]
 					},
 					"auto_merge_params": {
 						"minimum_spikes": 300,
@@ -199,11 +191,7 @@
 						"ms_before": 1.2,
 						"ms_after": 1.2,
 						"max_spikes_per_unit": 2000,
-						"filter": {
-							"band": [300, 6000],
-							"filter_order": 2,
-							"ftype": "bessel"
-						}
+						"filter": [300, 6000]
 					},
 					"auto_merge_params": {
 						"bin_ms": 0.05,

src/lussac/core/module.py

@@ -157,7 +157,7 @@ def tmp_folder(self) -> pathlib.Path:
 	def run(self, params: dict[str, Any]) -> si.BaseSorting:
 		...
 
-	def extract_waveforms(self, sorting: si.BaseSorting | None = None, sub_folder: str | None = None, filter: dict[str, Any] | None = None, **params) -> si.WaveformExtractor:
+	def extract_waveforms(self, sorting: si.BaseSorting | None = None, sub_folder: str | None = None, filter: list[float, float] | None = None, **params) -> si.WaveformExtractor:
 		"""
 		Creates the WaveformExtractor object and returns it.
 
@@ -168,8 +168,8 @@ def extract_waveforms(self, sorting: si.BaseSorting | None = None, sub_folder: s
 			The sub-folder where to save the waveforms.
 		@param params
 			The parameters for the waveform extractor.
-		@param filter: dict | None
-			The filter to apply to the recording.
+		@param filter: list[float, float] | None
+			The cutoff frequencies for the Gaussian bandpass filter to apply to the recording.
 		@return wvf_extractor: WaveformExtractor
 			The waveform extractor object.
 		"""
@@ -180,7 +180,7 @@ def extract_waveforms(self, sorting: si.BaseSorting | None = None, sub_folder: s
 
 		recording = self.recording
 		if filter is not None:
-			recording = spre.filter(recording, **filter)
+			recording = spre.gaussian_bandpass_filter(recording, *filter)
 
 		sorting = self.sorting if sorting is None else sorting
 		return si.extract_waveforms(recording, sorting, folder_path, allow_unfiltered=True, **params)
@@ -250,22 +250,22 @@ def get_units_attribute(self, attribute: str, params: dict) -> dict:
 			'firing_rate': {},
 			'contamination': {},
 			'amplitude': {
+				'wvf_extraction': {'ms_before': 1.0, 'ms_after': 1.0, 'max_spikes_per_unit': 500},
 				'peak_sign': "both",
 				'mode': "extremum",
-				'wvf_extraction': {'ms_before': 1.0, 'ms_after': 1.0, 'max_spikes_per_unit': 500},
-				'filter': {'band': [100, 9_000], 'filter_order': 2, 'ftype': "bessel"}
+				'filter': [100, 9_000]
 			},
 			'SNR': {
+				'wvf_extraction': {'ms_before': 1.0, 'ms_after': 1.0, 'max_spikes_per_unit': 500},
 				'peak_sign': "both",
 				'mode': "extremum",
-				'wvf_extraction': {'ms_before': 1.0, 'ms_after': 1.0, 'max_spikes_per_unit': 500},
-				'filter': {'band': [100, 9_000], 'filter_order': 2, 'ftype': "bessel"}
+				'filter': [100, 9_000]
 			},
-			'amplitude_std': {
-				'peak_sign': "both",
-				'return_scaled': True,
+			'sd_ratio': {
 				'wvf_extraction': {'ms_before': 1.0, 'ms_after': 1.0, 'max_spikes_per_unit': 500},
-				'filter': {'band': [100, 9_000], 'filter_order': 2, 'ftype': "bessel"}
+				'spike_amplitudes_kwargs': {'peak_sign': "both"},
+				'sd_ratio_kwargs': {},
+				'filter': [100, 9_000]
 			},
 			'ISI_portion': {}
 		}
@@ -277,7 +277,7 @@ def get_units_attribute(self, attribute: str, params: dict) -> dict:
 		recording = self.data.recording
 		sorting = self.sorting
 		if 'filter' in params:
-			recording = spre.filter(recording, **params['filter'])
+			recording = spre.gaussian_bandpass_filter(recording, *params['filter'])
 
 		wvf_extractor = self.extract_waveforms(sub_folder=attribute, **params['wvf_extraction']) if 'wvf_extraction' in params \
 						else si.WaveformExtractor(recording, sorting, allow_unfiltered=True)
@@ -303,11 +303,10 @@ def get_units_attribute(self, attribute: str, params: dict) -> dict:
 				SNRs = sqm.compute_snrs(wvf_extractor, **params)
 				return SNRs
 
-			case "amplitude_std":  # Returns the standard deviation of the amplitude of spikes.
-				params = utils.filter_kwargs(params, spost.compute_spike_amplitudes)
-				amplitudes = spost.compute_spike_amplitudes(wvf_extractor, outputs='by_unit', **params)[0]
-				std_amplitudes = {unit_id: np.std(amp) for unit_id, amp in amplitudes.items()}
-				return std_amplitudes
+			case "sd_ratio":  # Returns the standard deviation of the amplitude of spikes divided by the standard deviation on the same channel.
+				_ = spost.compute_spike_amplitudes(wvf_extractor, **params['spike_amplitudes_kwargs'])
+				sd_ratio = sqm.compute_sd_ratio(wvf_extractor, **params['sd_ratio_kwargs'])
+				return sd_ratio
 
 			case "ISI_portion":  # Returns the portion of consecutive spikes that are between a certain range (in ms).
 				low, high = np.array(params['range']) * recording.sampling_frequency * 1e-3

src/lussac/modules/merge_units.py

@@ -27,11 +27,7 @@ def default_params(self) -> dict[str, Any]:
 				'ms_after': 1.5,
 				'max_spikes_per_unit': 2_000,
 				'sparse': False,
-				'filter': {
-					'band': [100, 9000],
-					'filter_order': 2,
-					'ftype': 'bessel'
-				}
+				'filter': [100, 9000]
 			},
 			'auto_merge_params': {
 				'bin_ms': 0.05,

tests/core/test_mono_sorting_module.py

@@ -18,9 +18,7 @@
 			"ms_after": 1.0,
 			"max_spikes_per_unit": 10
 		},
-		"filter": {
-			"band": [200, 5000]
-		},
+		"filter": [200, 5000],
 		"min": 20
 	},
 	"SNR": {
@@ -29,20 +27,16 @@
 			"ms_after": 1.0,
 			"max_spikes_per_unit": 10
 		},
-		"filter": {
-			"band": [300, 6000]
-		},
+		"filter": [300, 6000],
 		"min": 1.2
 	},
-	"amplitude_std": {
+	"sd_ratio": {
 		"wvf_extraction": {
 			"ms_before": 1.0,
 			"ms_after": 1.0,
 		},
-		"filter": {
-			"band": [200, 5000]
-		},
-		"max": 140
+		"filter": [200, 5000],
+		"max": 2.0
 	}
 }
 
@@ -142,9 +136,9 @@ def test_get_units_attribute(mono_sorting_data: MonoSortingData) -> None:
 	assert isinstance(SNRs, np.ndarray)
 	assert SNRs.shape == (num_units, )
 
-	amplitude_std = module.get_units_attribute_arr("amplitude_std", params['amplitude_std'])
-	assert isinstance(amplitude_std, np.ndarray)
-	assert amplitude_std.shape == (num_units, )
+	sd_ratio = module.get_units_attribute_arr("sd_ratio", params['sd_ratio'])
+	assert isinstance(sd_ratio, np.ndarray)
+	assert sd_ratio.shape == (num_units, )
 
 	with pytest.raises(ValueError):
 		module.get_units_attribute("test", {})

tests/modules/test_remove_bad_units.py

@@ -17,9 +17,7 @@
 			"ms_after": 1.0,
 			"max_spikes_per_unit": 10
 		},
-		"filter": {
-			"band": [200, 5000]
-		},
+		"filter": [200, 5000],
 		"min": 20
 	},
 	"SNR": {
@@ -28,20 +26,16 @@
 			"ms_after": 1.0,
 			"max_spikes_per_unit": 10
 		},
-		"filter": {
-			"band": [300, 6000]
-		},
+		"filter": [300, 6000],
 		"min": 1.2
 	},
-	"amplitude_std": {
+	"sd_ratio": {
 		"wvf_extraction": {
 			"ms_before": 1.0,
 			"ms_after": 1.0,
 		},
-		"filter": {
-			"band": [200, 5000]
-		},
-		"max": 140
+		"filter": [200, 5000],
+		"max": 2.0
 	}
 }