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src/spikeinterface/comparison/groundtruthstudy.py

@@ -45,7 +45,7 @@ class GroundTruthStudy:
 
     Parameters
     ----------
-    study_folder : srt | Path
+    study_folder : str | Path
         Path to folder containing `GroundTruthStudy`
     """
 

src/spikeinterface/comparison/multicomparisons.py

@@ -43,9 +43,9 @@ class MultiSortingComparison(BaseMultiComparison, MixinSpikeTrainComparison):
             - "intersection" : spike trains are the intersection between the spike trains of the
                best matching two sorters
     verbose : bool, default: False
-        if True, output is verbose
+        If True, output is verbose
     do_matching : bool, default: True
-        if True, SOMETHING HAPPENS.
+        If True, the comparison is done when the `MultiSortingComparison` is initialized
 
     Returns
     -------
@@ -320,15 +320,15 @@ class MultiTemplateComparison(BaseMultiComparison, MixinTemplateComparison):
     chance_score : float, default: 0.3
         Minimum agreement score to for a possible match
     verbose : bool, default: False
-        if True, output is verbose
+        If True, output is verbose
     do_matching : bool, default: True
-        if True, IT DOES SOMETHING
+        If True, the comparison is done when the `MultiSortingComparison` is initialized
     support : "dense" | "union" | "intersection", default: "union"
         The support to compute the similarity matrix.
     num_shifts : int, default: 0
         Number of shifts to use to shift templates to maximize similarity.
     similarity_method : "cosine" | "l1" | "l2", default: "cosine"
-         Method for the similarity matrix.
+        Method for the similarity matrix.
 
     Returns
     -------

src/spikeinterface/comparison/paircomparisons.py

@@ -566,7 +566,7 @@ def count_false_positive_units(self, redundant_score=None):
 
         Parameters
         ----------
-        redundant_score : float, default: None
+        redundant_score : float | None, default: None
             The agreement score below which tested units
             are counted as "false positive"" (and not "redundant").
         """
@@ -747,7 +747,7 @@ class TemplateComparison(BasePairComparison, MixinTemplateComparison):
         List of units from sorting_analyzer_2 to compare.
     name1 : str, default: "sess1"
         Name of first session.
-    name2 : str, default: "sess1"
+    name2 : str, default: "sess2"
         Name of second session.
     similarity_method : "cosine" | "l1" | "l2", default: "cosine"
         Method for the similarity matrix.

src/spikeinterface/core/generate.py

@@ -44,7 +44,7 @@ def generate_recording(
         The number of channels in the recording.
     sampling_frequency : float, default: 30000. (in Hz)
         The sampling frequency of the recording, default: 30000.
-    durations : List[float], default: [5.0, 2.5]
+    durations : list[float], default: [5.0, 2.5]
         The duration in seconds of each segment in the recording.
         The number of segments is determined by the length of this list.
     set_probe : bool, default: True
@@ -1295,8 +1295,8 @@ def generate_recording_by_size(
         The seed for np.random.default_rng.
     strategy : "tile_pregenerated"| "on_the_fly", default: "tile_pregenerated"
         The strategy of generating noise chunk:
-        * "tile_pregenerated": pregenerate a noise chunk of noise_block_size sample and repeat it very fast and cusume only one noise block.
-        * "on_the_fly": generate on the fly a new noise block by combining seed + noise block index no memory preallocation but a bit more computaion (random)
+        * "tile_pregenerated": pregenerate a noise chunk of `noise_block_size` samples and repeat it quickly consuming only one noise block.
+        * "on_the_fly": generate on the fly a new noise block by combining seed + noise block index. No memory preallocation but a bit more computaion (random)
 
     Returns
     -------
@@ -2062,9 +2062,9 @@ def generate_ground_truth_recording(
 
     Parameters
     ----------
-    durations : list of float, default: [10.]
+    durations : list[float], default: [10.]
         Durations in seconds for all segments.
-    sampling_frequency : float, default: 25000
+    sampling_frequency : float, default: 25000.0
         Sampling frequency.
     num_channels : int, default: 4
         Number of channels, not used when probe is given.
@@ -2085,7 +2085,7 @@ def generate_ground_truth_recording(
             * (num_units, num_samples, num_channels, upsample_factor): case with oversample template to introduce jitter.
     ms_before : float, default: 1.5
         Cut out in ms before spike peak.
-    ms_after : float, default: 3
+    ms_after : float, default: 3.0
         Cut out in ms after spike peak.
     upsample_factor : None or int, default: None
         A upsampling factor used only when templates are not provided.

src/spikeinterface/generation/hybrid_tools.py

@@ -183,7 +183,7 @@ def scale_template_to_range(
         The minimum amplitude of the output templates after scaling.
     max_amplitude : float
         The maximum amplitude of the output templates after scaling.
-    amplitude_function : "ptp" | "min" | "max", default: "pip"
+    amplitude_function : "ptp" | "min" | "max", default: "ptp"
         The function to use to compute the amplitude of the templates. Can be "ptp", "min" or "max".
 
     Returns

src/spikeinterface/postprocessing/template_similarity.py

@@ -17,8 +17,8 @@ class ComputeTemplateSimilarity(AnalyzerExtension):
     ----------
     sorting_analyzer : SortingAnalyzer
         The SortingAnalyzer object
-    method : str, default: "cosine"
-        The method to compute the similarity. Can be in ["cosine", "l2", "l1"]
+    method : "cosine" | "l1" | "l2", default: "cosine"
+        The method to compute the similarity.
         In case of "l1" or "l2", the formula used is:
         - similarity = 1 - norm(T_1 - T_2)/(norm(T_1) + norm(T_2)).
         In case of cosine it is:

src/spikeinterface/sortingcomponents/matching/main.py

@@ -16,7 +16,7 @@ def find_spikes_from_templates(
     ----------
     recording : RecordingExtractor
         The recording extractor object
-    method : "naive" | "tridesclous" | "circus" | "circus-omp" | "wobble"
+    method : "naive" | "tridesclous" | "circus" | "circus-omp" | "wobble", default: "naive"
         Which method to use for template matching
     method_kwargs : dict, optional
         Keyword arguments for the chosen method