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doc/how_to/drift_with_lfp.rst

@@ -36,7 +36,7 @@ For each patient, the dataset contains two recording : a high pass (AP -
 
     from pathlib import Path
     import matplotlib.pyplot as plt
-    
+
     import spikeinterface.full as si
     from spikeinterface.sortingcomponents.motion import estimate_motion
 
@@ -57,7 +57,7 @@ read the spikeglx file
 
 .. parsed-literal::
 
-    SpikeGLXRecordingExtractor: 384 channels - 2.5kHz - 1 segments - 2,183,292 samples 
+    SpikeGLXRecordingExtractor: 384 channels - 2.5kHz - 1 segments - 2,183,292 samples
                                 873.32s (14.56 minutes) - int16 dtype - 1.56 GiB
 
 
@@ -87,24 +87,24 @@ eyes ont the traces plotted with the map mode.
         raw_rec,
         freq_min=0.5,
         freq_max=250,
-    
+
         margin_ms=1500.,
         filter_order=3,
         dtype="float32",
         add_reflect_padding=True,
     )
     lfprec = si.phase_shift(lfprec)
     lfprec = si.resample(lfprec, resample_rate=250, margin_ms=1000)
-    
+
     lfprec = si.directional_derivative(lfprec, order=2, edge_order=1)
     lfprec = si.average_across_direction(lfprec)
-    
+
     print(lfprec)
 
 
 .. parsed-literal::
 
-    AverageAcrossDirectionRecording: 192 channels - 0.2kHz - 1 segments - 218,329 samples 
+    AverageAcrossDirectionRecording: 192 channels - 0.2kHz - 1 segments - 218,329 samples
                                      873.32s (14.56 minutes) - float32 dtype - 159.91 MiB
 
 
@@ -185,5 +185,3 @@ This motion match the LFP signal above.
 
 
 .. image:: drift_with_lfp_files/drift_with_lfp_12_1.png
-
-

doc/how_to/index.rst

@@ -14,4 +14,4 @@ Guides on how to solve specific, short problems in SpikeInterface. Learn how to.
     process_by_channel_group
     load_your_data_into_sorting
     benchmark_with_hybrid_recordings
-    drift_with_lfp
+    drift_with_lfp

examples/how_to/drift_with_lfp.py

@@ -108,6 +108,3 @@
 si.plot_motion(motion, mode='line', ax=ax)
 ax.set_xlim(400, 420)
 ax.set_ylim(800, 1300)
-
-
-

examples/tutorials/sortingcomponents/plot_1_estimate_motion.py

@@ -19,15 +19,15 @@
 from spikeinterface.widgets import plot_motion, plot_motion_info, plot_probe_map
 
 # %%
-# First, let's simulate a drifting recording using the 
+# First, let's simulate a drifting recording using the
 # :code:`spikeinterface.generation module`.
-# 
+#
 # Here the simulated recording has a small zigzag motion along the 'y' axis of the probe.
 
 static_recording, drifting_recording, sorting = generate_drifting_recording(
     num_units=200,
     duration=300.,
-    probe_name='Neuropixel-128', 
+    probe_name='Neuropixel-128',
     generate_displacement_vector_kwargs=dict(
         displacement_sampling_frequency=5.0,
         drift_start_um=[0, 20],
@@ -50,12 +50,12 @@
 
 # %%
 # Here we will use the high level function :code:`correct_motion()`
-# 
+#
 # Internally, this function is doing all steps of the motion detection:
 #  1. **activity profile** : detect peaks and localize them along time and depth
 #  2. **motion inference**: estimate the drift motion
 #  3. **motion interpolation**: interpolate traces using the estimated motion
-# 
+#
 # All steps have an use several methods with many parameters. This is why we can use
 # 'preset' which combine methods and related parameters.
 #
@@ -70,30 +70,30 @@
 
 # %%
 # The function return a recording 'corrected'
-# 
+#
 # A new recording is return, this recording will interpolate motion corrected traces
 # when calling get_traces()
 
 print(recording_corrected)
 
 # %%
 # Optionally the function also return the `Motion` object itself
-# 
+#
 
 print(motion)
 
 # %%
 # This motion can be plotted, in our case the motion has been estimated as non-rigid
 # so we can use the use the `mode='map'` to check the motion across depth.
-# 
+#
 
 plot_motion(motion, mode='line')
 plot_motion(motion, mode='map')
 
 
 # %%
 # The dict `motion_info` can be used for more plotting.
-# Here we can appreciate of the two top axes the raster of peaks depth vs times before and 
+# Here we can appreciate of the two top axes the raster of peaks depth vs times before and
 # after correction.
 
 fig = plt.figure()

src/spikeinterface/preprocessing/motion.py

@@ -20,7 +20,7 @@
             peak_sign="neg",
             detect_threshold=8.0,
             exclude_sweep_ms=0.8,
-            radius_um=80.,
+            radius_um=80.0,
         ),
         "select_kwargs": dict(),
         "localize_peaks_kwargs": dict(
@@ -76,7 +76,7 @@
             peak_sign="neg",
             detect_threshold=8.0,
             exclude_sweep_ms=0.8,
-            radius_um=80.,
+            radius_um=80.0,
         ),
         "select_kwargs": dict(),
         "localize_peaks_kwargs": dict(
@@ -196,7 +196,6 @@
 }
 
 
-
 def correct_motion(
     recording,
     preset="nonrigid_accurate",
@@ -398,16 +397,15 @@ def correct_motion(
 
     if not output_motion and not output_motion_info:
         return recording_corrected
-    
-    out = (recording_corrected, )
+
+    out = (recording_corrected,)
     if output_motion:
-        out += (motion, )
+        out += (motion,)
     if output_motion_info:
-        out += (motion_info, )
+        out += (motion_info,)
     return out
 
 
-
 _doc_presets = "\n"
 for k, v in motion_options_preset.items():
     if k == "":

src/spikeinterface/sortingcomponents/motion/__init__.py

@@ -1,5 +1,9 @@
 from .motion_utils import Motion
 from .motion_estimation import estimate_motion
-from .motion_interpolation import (correct_motion_on_peaks, interpolate_motion_on_traces,
-                                   InterpolateMotionRecording, interpolate_motion)
+from .motion_interpolation import (
+    correct_motion_on_peaks,
+    interpolate_motion_on_traces,
+    InterpolateMotionRecording,
+    interpolate_motion,
+)
 from .motion_cleaner import clean_motion_vector

src/spikeinterface/sortingcomponents/motion/decentralized.py

@@ -111,8 +111,8 @@ def run(
         bin_um=1.0,
         hist_margin_um=20.0,
         bin_s=1.0,
-        histogram_depth_smooth_um=1.,
-        histogram_time_smooth_s=1.,
+        histogram_depth_smooth_um=1.0,
+        histogram_time_smooth_s=1.0,
         pairwise_displacement_method="conv",
         max_displacement_um=100.0,
         weight_scale="linear",
@@ -135,7 +135,6 @@ def run(
         lsqr_robust_n_iter=20,
         weight_with_amplitude=False,
     ):
-
 
         dim = ["x", "y", "z"].index(direction)
         contact_depth = recording.get_channel_locations()[:, dim]
@@ -153,7 +152,7 @@ def run(
             win_step_um=win_step_um,
             win_scale_um=win_scale_um,
             win_margin_um=win_margin_um,
-            zero_threshold=None
+            zero_threshold=None,
         )
 
         # make 2D histogram raster
@@ -322,6 +321,7 @@ def compute_pairwise_displacement(
         try:
             import torch
             import torch.nn.functional as F
+
             conv_engine = "torch"
         except ImportError:
             conv_engine = "numpy"
@@ -430,7 +430,6 @@ def compute_pairwise_displacement(
     return pairwise_displacement, pairwise_displacement_weight
 
 
-
 _possible_convergence_method = ("lsmr", "gradient_descent", "lsqr_robust")
 
 
@@ -687,9 +686,6 @@ def jac(p):
     return np.squeeze(displacement)
 
 
-
-
-
 # normxcorr1d is now implemented in dredge
 # we keep the old version here but this will be removed soon
 
@@ -809,4 +805,4 @@ def jac(p):
 #         # get rid of NaNs in zero-variance areas
 #         corr[~npx.isfinite(corr)] = 0
 
-#     return corr
+#     return corr