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

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SpikeInterface · Feb 5, 2024 · ecb7e3e · ecb7e3e
1 parent 91dfd38
commit ecb7e3e
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Showing 3 changed files with 21 additions and 34 deletions.
diff --git a/src/spikeinterface/comparison/groundtruthstudy.py b/src/spikeinterface/comparison/groundtruthstudy.py
@@ -23,7 +23,7 @@
 
 
 # This is to separate names when the key are tuples when saving folders
-# _key_separator = "_##_"
+# _key_separator = "_##_"
 _key_separator = "_-°°-_"
 
 

diff --git a/src/spikeinterface/sorters/internal/tridesclous2.py b/src/spikeinterface/sorters/internal/tridesclous2.py
@@ -47,14 +47,13 @@ class Tridesclous2Sorter(ComponentsBasedSorter):
             "threshold_diff": 1.5,
         },
         "templates": {
-            "ms_before": 2.,
-            "ms_after": 3.,
-            "max_spikes_per_unit" : 400,
+            "ms_before": 2.0,
+            "ms_after": 3.0,
+            "max_spikes_per_unit": 400,
             # "peak_shift_ms": 0.2,
         },
         # "matching": {"method": "tridesclous", "method_kwargs": {"peak_shift_ms": 0.2, "radius_um": 100.0}},
         "matching": {"method": "circus-omp-svd", "method_kwargs": {}},
-
         "job_kwargs": {"n_jobs": -1},
         "save_array": True,
     }
@@ -63,16 +62,16 @@ class Tridesclous2Sorter(ComponentsBasedSorter):
         "apply_preprocessing": "Apply internal preprocessing or not",
         "cache_preprocessing": "A dict contaning how to cache the preprocessed recording. mode='memory' | 'folder | 'zarr' ",
         "waveforms": "A dictonary containing waveforms params: ms_before, ms_after, radius_um",
-        "filtering": "A dictonary containing filtering params: freq_min, freq_max", 
-        "detection": "A dictonary containing detection params: peak_sign, detect_threshold, exclude_sweep_ms, radius_um", 
-        "selection": "A dictonary containing selection params: n_peaks_per_channel, min_n_peaks", 
-        "svd": "A dictonary containing svd params: n_components", 
+        "filtering": "A dictonary containing filtering params: freq_min, freq_max",
+        "detection": "A dictonary containing detection params: peak_sign, detect_threshold, exclude_sweep_ms, radius_um",
+        "selection": "A dictonary containing selection params: n_peaks_per_channel, min_n_peaks",
+        "svd": "A dictonary containing svd params: n_components",
         "clustering": "A dictonary containing clustering params: split_radius_um, merge_radius_um",
         "templates": "A dictonary containing waveforms params for peeler: ms_before, ms_after",
         "matching": "A dictonary containing matching params for matching: peak_shift_ms, radius_um",
         "job_kwargs": "A dictionary containing job kwargs",
         "save_array": "Save or not intermediate arrays",
-     }
+    }
 
     handle_multi_segment = True
 
@@ -118,10 +117,12 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             recording = common_reference(recording)
             recording = zscore(recording, dtype="float32")
             # recording = whiten(recording, dtype="float32")
-            
+
             # used only if "folder" or "zarr"
             cache_folder = sorter_output_folder / "cache_preprocessing"
-            recording = cache_preprocessing(recording, folder=cache_folder, **job_kwargs,  **params["cache_preprocessing"])
+            recording = cache_preprocessing(
+                recording, folder=cache_folder, **job_kwargs, **params["cache_preprocessing"]
+            )
 
             noise_levels = np.ones(num_chans, dtype="float32")
         else:
@@ -243,7 +244,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
 
         merge_radius_um = params["clustering"]["merge_radius_um"]
         threshold_diff = params["clustering"]["threshold_diff"]
-
 
         post_merge_label, peak_shifts = merge_clusters(
             peaks,
@@ -297,17 +297,11 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         )
         sorting_temp = sorting_temp.save(folder=sorter_output_folder / "sorting_temp")
 
-        we = extract_waveforms(
-            recording,
-            sorting_temp,
-            sorter_output_folder / "waveforms_temp",
-            **params["templates"])
+        we = extract_waveforms(recording, sorting_temp, sorter_output_folder / "waveforms_temp", **params["templates"])
 
         # snrs = compute_snrs(we, peak_sign=params["detection"]["peak_sign"], peak_mode="extremum")
         # print(snrs)
 
-
-
         # matching_params = params["matching"].copy()
         # matching_params["waveform_extractor"] = we
         # matching_params["noise_levels"] = noise_levels

diff --git a/src/spikeinterface/sortingcomponents/clustering/merge.py b/src/spikeinterface/sortingcomponents/clustering/merge.py
@@ -660,10 +660,10 @@ def merge(
         for shift in range(-num_shift, num_shift + 1):
             temp0 = template0[num_shift : num_samples - num_shift, :]
             temp1 = template1[num_shift + shift : num_samples - num_shift + shift, :]
-            #d = np.mean(np.abs(temp0 - temp1)) / (norm)
+            # d = np.mean(np.abs(temp0 - temp1)) / (norm)
             # d = np.max(np.abs(temp0 - temp1)) / (norm)
             diff_per_channel = np.abs(temp0 - temp1) / norm
-            
+
             diff_max = np.max(diff_per_channel, axis=0)
 
             # diff = np.max(diff_per_channel)
@@ -675,7 +675,6 @@ def merge(
             # d = np.mean(diff_by_channel)
             # all_shift_diff.append(d)
         normed_diff = np.min(all_shift_diff)
-
 
         is_merge = normed_diff < threshold_diff
 
@@ -687,7 +686,6 @@ def merge(
         else:
             final_shift = 0
             merge_value = np.nan
-
 
         # print('merge_value', merge_value, 'final_shift', final_shift, 'is_merge', is_merge)
 
@@ -696,7 +694,7 @@ def merge(
         # if DEBUG and ( 0. < normed_diff < .4):
         # if 0.5 < normed_diff < 4:
         if DEBUG and is_merge:
-        # if DEBUG:
+            # if DEBUG:
 
             import matplotlib.pyplot as plt
 
@@ -711,8 +709,6 @@ def merge(
             m0 = temp0.T.flatten()
             m1 = temp1.T.flatten()
 
-
-
             ax = axs[0]
             ax.plot(m0, color="C0", label=f"{label0} {inds0.size}")
             ax.plot(m1, color="C1", label=f"{label1} {inds1.size}")
@@ -724,16 +720,15 @@ def merge(
 
             ax = axs[1]
 
-            #~ temp0 = template0[num_shift : num_samples - num_shift, :]
-            #~ temp1 = template1[num_shift + shift : num_samples - num_shift + shift, :]
+            # ~ temp0 = template0[num_shift : num_samples - num_shift, :]
+            # ~ temp1 = template1[num_shift + shift : num_samples - num_shift + shift, :]
             ax.plot(np.abs(m0 - m1))
             # ax.axhline(norm, ls='--', color='k')
             ax = axs[2]
             ax.plot(diff_per_channel.T.flatten())
-            ax.axhline(threshold_diff, ls='--')
+            ax.axhline(threshold_diff, ls="--")
             ax.axhline(normed_diff)
-
-
+
             # ax.axhline(normed_diff, ls='-', color='b')
             # ax.plot(norm, ls='--')
             # ax.plot(diff_by_channel)
@@ -744,8 +739,6 @@ def merge(
 
             # ax.set_title(f"{norm=:.3f}")
 
-
-
             plt.show()
 
         return is_merge, label0, label1, final_shift, merge_value