Add unsaved files

src/spikeinterface/core/base.py

@@ -269,8 +269,8 @@ def copy_metadata(
             If True, only the main annotations/properties are copied.
         ids: list
             List of ids to copy the metadata to. If None, all ids are copied.
-        skip_properties: list
-            List of properties to skip. Default is None.
+        skip_properties: list, default: None
+            List of properties to skip
         """
 
         if ids is None:

src/spikeinterface/extractors/neoextractors/openephys.py

@@ -331,7 +331,7 @@ def read_openephys(folder_path, **kwargs):
 
 def read_openephys_event(folder_path, block_index=None):
     """
-    Read Open Ephys events from 'binary' format.
+    Read Open Ephys events from "binary" format.
 
     Parameters
     ----------

src/spikeinterface/extractors/neuropixels_utils.py

@@ -111,7 +111,7 @@ def synchronize_neuropixel_streams(recording_ref, recording_other):
 
     Method used :
       1. detect pulse times on both streams.
-      2. make a linear regression from 'other' to 'ref'.
+      2. make a linear regression from "other" to "ref".
           The slope is nclose to 1 and corresponds to the sample rate correction
           The intercept is close to 0 and corresponds to the delta time start
 

src/spikeinterface/sorters/launcher.py

@@ -339,7 +339,7 @@ def run_sorters(
             * "raise" : raise error if subfolder exists
             * "overwrite" : delete and force recompute
             * "keep" : do not compute again if f=subfolder exists and log is OK
-    engine: "loop" | "joblib" | "dask', default: "loop"
+    engine: "loop" | "joblib" | "dask", default: "loop"
         Which engine to use to run sorter.
     engine_kwargs: dict
         This contains kwargs specific to the launcher engine:

src/spikeinterface/sortingcomponents/motion_estimation.py

@@ -226,10 +226,10 @@ class DecentralizedRegistration:
     error_sigma: float, default: 0.2
         In case weight_scale="exp" this controls the sigma of the exponential.
     conv_engine: "numpy" or "torch" or None, default: None
-        In case of pairwise_displacement_method="conv', what library to use to compute
+        In case of pairwise_displacement_method="conv", what library to use to compute
         the underlying correlation
     torch_device=None
-        In case of conv_engine='torch', you can control which device (cpu or gpu)
+        In case of conv_engine="torch", you can control which device (cpu or gpu)
     batch_size: int
         Size of batch for the convolution. Increasing this will speed things up dramatically
         on GPUs and sometimes on CPU as well.
@@ -241,10 +241,10 @@ class DecentralizedRegistration:
         When not None the parwise discplament matrix is computed in a small time horizon.
         In short only pair of bins close in time.
         So the pariwaise matrix is super sparse and have values only the diagonal.
-    convergence_method: 'lsmr', 'lsqr_robust', 'gradient_descent'
+    convergence_method: "lsmr" | "lsqr_robust" | "gradient_descent", default: "lsqr_robust"
         Which method to use to compute the global displacement vector from the pairwise matrix.
     robust_regression_sigma: float
-        Use for convergence_method='lsqr_robust' for iterative selection of the regression.
+        Use for convergence_method="lsqr_robust" for iterative selection of the regression.
     temporal_prior : bool, default: True
         Ensures continuity across time, unless there is evidence in the recording for jumps.
     spatial_prior : bool, default: False
@@ -259,7 +259,7 @@ class DecentralizedRegistration:
          - "time" : the displacement at a given time (in seconds) is subtracted
          - "mode_search" : an attempt is made to guess the mode. needs work.
     lsqr_robust_n_iter: int
-        Number of iteration for convergence_method='lsqr_robust'.
+        Number of iteration for convergence_method="lsqr_robust".
     """
 
     @classmethod

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -53,8 +53,8 @@ def detect_peaks(
 ):
     """Peak detection based on threshold crossing in term of k x MAD.
 
-    In 'by_channel' : peak are detected in each channel independently
-    In 'locally_exclusive' : a single best peak is taken from a set of neighboring channels
+    In "by_channel" : peak are detected in each channel independently
+    In "locally_exclusive" : a single best peak is taken from a set of neighboring channels
 
     Parameters
     ----------
@@ -357,7 +357,7 @@ def compute(self, traces, start_frame, end_frame, segment_index, max_margin):
 
 
 class DetectPeakByChannel(PeakDetectorWrapper):
-    """Detect peaks using the 'by channel' method."""
+    """Detect peaks using the "by channel" method."""
 
     name = "by_channel"
     engine = "numpy"
@@ -439,7 +439,7 @@ def detect_peaks(cls, traces, peak_sign, abs_threholds, exclude_sweep_size):
 
 
 class DetectPeakByChannelTorch(PeakDetectorWrapper):
-    """Detect peaks using the 'by channel' method with pytorch."""
+    """Detect peaks using the "by channel" method with pytorch."""
 
     name = "by_channel_torch"
     engine = "torch"
@@ -505,7 +505,7 @@ def detect_peaks(cls, traces, peak_sign, abs_threholds, exclude_sweep_size, devi
 
 
 class DetectPeakLocallyExclusive(PeakDetectorWrapper):
-    """Detect peaks using the 'locally exclusive' method."""
+    """Detect peaks using the "locally exclusive" method."""
 
     name = "locally_exclusive"
     engine = "numba"
@@ -581,7 +581,7 @@ def detect_peaks(cls, traces, peak_sign, abs_threholds, exclude_sweep_size, neig
 
 
 class DetectPeakLocallyExclusiveTorch(PeakDetectorWrapper):
-    """Detect peaks using the 'locally exclusive' method with pytorch."""
+    """Detect peaks using the "locally exclusive" method with pytorch."""
 
     name = "locally_exclusive_torch"
     engine = "torch"

src/spikeinterface/widgets/base.py

@@ -6,7 +6,7 @@
 
 def get_default_plotter_backend():
     """Return the default backend for spikeinterface widgets.
-    The default backend is 'matplotlib' at init.
+    The default backend is "matplotlib" at init.
     It can be be globally set with `set_default_plotter_backend(backend)`
     """
 
@@ -123,7 +123,7 @@ def __init__(self, d):
         Helper function that transform a dict into
         an object where attributes are the keys of the dict
 
-        d = {'a': 1, 'b': 'yep'}
+        d = {"a": 1, "b": "yep"}
         o = to_attr(d)
         print(o.a, o.b)
         """