[pre-commit.ci] auto fixes from pre-commit.com hooks

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src/spikeinterface/sortingcomponents/clustering/random_projections.py

@@ -36,14 +36,14 @@ class RandomProjectionClustering:
             "cluster_selection_method": "leaf",
         },
         "cleaning_kwargs": {},
-        "waveforms" : {"ms_before" : 2, "ms_after" : 2, "max_spikes_per_unit": 100},
+        "waveforms": {"ms_before": 2, "ms_after": 2, "max_spikes_per_unit": 100},
         "radius_um": 100,
         "selection_method": "closest_to_centroid",
         "nb_projections": 10,
         "ms_before": 1,
         "ms_after": 1,
         "random_seed": 42,
-        "smoothing_kwargs" : {"window_length_ms" : 1},
+        "smoothing_kwargs": {"window_length_ms": 1},
         "shared_memory": True,
         "tmp_folder": None,
         "job_kwargs": {"n_jobs": os.cpu_count(), "chunk_memory": "100M", "verbose": True, "progress_bar": True},
@@ -84,40 +84,46 @@ def main_function(cls, recording, peaks, params):
 
         ### Then we extract the SVD features
         node0 = PeakRetriever(recording, peaks)
-        node1 = ExtractDenseWaveforms(recording, parents=[node0], return_output=False,
-                                    ms_before=params['ms_before'],
-                                    ms_after=params['ms_after']
+        node1 = ExtractDenseWaveforms(
+            recording, parents=[node0], return_output=False, ms_before=params["ms_before"], ms_after=params["ms_after"]
         )
 
-        node2 = SavGolDenoiser(recording, parents=[node0, node1], return_output=False, **params['smoothing_kwargs'])
+        node2 = SavGolDenoiser(recording, parents=[node0, node1], return_output=False, **params["smoothing_kwargs"])
 
         projections = np.random.randn(num_chans, d["nb_projections"])
         projections -= projections.mean(0)
         projections /= projections.std(0)
 
-        nbefore = int(params['ms_before'] * fs / 1000)
-        nafter = int(params['ms_after'] * fs / 1000)
+        nbefore = int(params["ms_before"] * fs / 1000)
+        nafter = int(params["ms_after"] * fs / 1000)
         nsamples = nbefore + nafter
 
         import scipy
+
         x = np.random.randn(100, nsamples, num_chans).astype(np.float32)
         x = scipy.signal.savgol_filter(x, node2.window_length, node2.order, axis=1)
 
         ptps = np.ptp(x, axis=1)
         a, b = np.histogram(ptps.flatten(), np.linspace(0, 100, 1000))
-        ydata = np.cumsum(a)/a.sum()
+        ydata = np.cumsum(a) / a.sum()
         xdata = b[1:]
 
         from scipy.optimize import curve_fit
-        def sigmoid(x, L ,x0, k, b):
-            y = L / (1 + np.exp(-k*(x-x0))) + b
-            return (y)
 
-        p0 = [max(ydata), np.median(xdata), 1, min(ydata)] # this is an mandatory initial guess
+        def sigmoid(x, L, x0, k, b):
+            y = L / (1 + np.exp(-k * (x - x0))) + b
+            return y
+
+        p0 = [max(ydata), np.median(xdata), 1, min(ydata)]  # this is an mandatory initial guess
         popt, pcov = curve_fit(sigmoid, xdata, ydata, p0)
 
-        node3 = RandomProjectionsFeature(recording, parents=[node0, node2], return_output=True,
-                                    projections=projections, radius_um=params['radius_um'])
+        node3 = RandomProjectionsFeature(
+            recording,
+            parents=[node0, node2],
+            return_output=True,
+            projections=projections,
+            radius_um=params["radius_um"],
+        )
 
         pipeline_nodes = [node0, node1, node2, node3]
 
@@ -136,7 +142,7 @@ def sigmoid(x, L ,x0, k, b):
 
         all_indices = np.arange(0, peak_labels.size)
 
-        max_spikes = params['waveforms']["max_spikes_per_unit"]
+        max_spikes = params["waveforms"]["max_spikes_per_unit"]
         selection_method = params["selection_method"]
 
         for unit_ind in labels:

src/spikeinterface/sortingcomponents/features_from_peaks.py

@@ -185,7 +185,7 @@ def __init__(
         parents=None,
         projections=None,
         sigmoid=None,
-        radius_um=None
+        radius_um=None,
     ):
         PipelineNode.__init__(self, recording, return_output=return_output, parents=parents)
 
@@ -203,12 +203,12 @@ def get_dtype(self):
 
     def _sigmoid(self, x):
         L, x0, k, b = self.sigmoid
-        y = L / (1 + np.exp(-k*(x-x0))) + b
+        y = L / (1 + np.exp(-k * (x - x0))) + b
         return y
 
     def compute(self, traces, peaks, waveforms):
         all_projections = np.zeros((peaks.size, self.projections.shape[1]), dtype=self._dtype)
-        
+
         for main_chan in np.unique(peaks["channel_index"]):
             (idx,) = np.nonzero(peaks["channel_index"] == main_chan)
             (chan_inds,) = np.nonzero(self.neighbours_mask[main_chan])
@@ -221,7 +221,7 @@ def compute(self, traces, peaks, waveforms):
             denom = np.sum(wf_ptp, axis=1)
             mask = denom != 0
             all_projections[idx[mask]] = np.dot(wf_ptp[mask], local_projections) / (denom[mask][:, np.newaxis])
-        
+
         return all_projections