more matplotlib

src/spikeinterface/sortingcomponents/benchmark/benchmark_clustering.py

@@ -6,24 +6,13 @@
 from spikeinterface.widgets import (
     plot_probe_map,
     plot_agreement_matrix,
-    plot_comparison_collision_by_similarity,
-    plot_unit_templates,
-    plot_unit_waveforms,
 )
-from spikeinterface.comparison.comparisontools import make_matching_events
 
-import matplotlib.patches as mpatches
 
-# from spikeinterface.postprocessing import get_template_extremum_channel
-from spikeinterface.core import get_noise_levels
-
-import pylab as plt
 import numpy as np
 
 
 from .benchmark_tools import BenchmarkStudy, Benchmark
-from spikeinterface.core.basesorting import minimum_spike_dtype
-from spikeinterface.core.basesorting import minimum_spike_dtype
 from spikeinterface.core.sortinganalyzer import create_sorting_analyzer
 from spikeinterface.core.template_tools import get_template_extremum_channel
 
@@ -180,6 +169,7 @@ def plot_unit_counts(self, case_keys=None, figsize=None, **extra_kwargs):
     def plot_agreements(self, case_keys=None, figsize=(15, 15)):
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         fig, axs = plt.subplots(ncols=len(case_keys), nrows=1, figsize=figsize, squeeze=False)
 
@@ -193,6 +183,7 @@ def plot_agreements(self, case_keys=None, figsize=(15, 15)):
     def plot_performances_vs_snr(self, case_keys=None, figsize=(15, 15)):
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         fig, axs = plt.subplots(ncols=1, nrows=3, figsize=figsize)
 
@@ -218,6 +209,7 @@ def plot_error_metrics(self, metric="cosine", case_keys=None, figsize=(15, 5)):
 
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         fig, axs = plt.subplots(ncols=len(case_keys), nrows=1, figsize=figsize, squeeze=False)
 
@@ -254,6 +246,7 @@ def plot_metrics_vs_snr(self, metric="agreement", case_keys=None, figsize=(15, 5
 
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         fig, axs = plt.subplots(ncols=len(case_keys), nrows=1, figsize=figsize, squeeze=False)
 
@@ -308,6 +301,7 @@ def plot_metrics_vs_depth_and_snr(self, metric="agreement", case_keys=None, figs
 
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         fig, axs = plt.subplots(ncols=len(case_keys), nrows=1, figsize=figsize, squeeze=False)
 
@@ -365,6 +359,7 @@ def plot_metrics_vs_depth_and_snr(self, metric="agreement", case_keys=None, figs
         return fig
 
     def plot_unit_losses(self, case_before, case_after, metric="agreement", figsize=None):
+        import pylab as plt
 
         fig, axs = plt.subplots(ncols=1, nrows=3, figsize=figsize)
 
@@ -407,6 +402,7 @@ def plot_comparison_clustering(
 
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         num_methods = len(case_keys)
         fig, axs = plt.subplots(ncols=num_methods, nrows=num_methods, figsize=(10, 10))
@@ -442,6 +438,8 @@ def plot_comparison_clustering(
                         ax.set_xticks([])
                     if i == num_methods - 1 and j == num_methods - 1:
                         patches = []
+                        import matplotlib.patches as mpatches
+
                         for color, name in zip(colors, performance_names):
                             patches.append(mpatches.Patch(color=color, label=name))
                         ax.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0)
@@ -460,6 +458,7 @@ def plot_comparison_clustering(
     def plot_some_over_merged(self, case_keys=None, overmerged_score=0.05, max_units=5, figsize=None):
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         figs = []
         for count, key in enumerate(case_keys):
@@ -498,6 +497,7 @@ def plot_some_over_merged(self, case_keys=None, overmerged_score=0.05, max_units
     def plot_some_over_splited(self, case_keys=None, oversplit_score=0.05, max_units=5, figsize=None):
         if case_keys is None:
             case_keys = list(self.cases.keys())
+        import pylab as plt
 
         figs = []
         for count, key in enumerate(case_keys):

src/spikeinterface/sortingcomponents/benchmark/benchmark_motion_interpolation.py

@@ -13,9 +13,6 @@
 from spikeinterface.sortingcomponents.benchmark.benchmark_tools import Benchmark, BenchmarkStudy, _simpleaxis
 
 
-import matplotlib.pyplot as plt
-
-
 class MotionInterpolationBenchmark(Benchmark):
     def __init__(
         self,
@@ -128,6 +125,7 @@ def plot_sorting_accuracy(
         ax=None,
         axes=None,
     ):
+        import matplotlib.pyplot as plt
 
         if case_keys is None:
             case_keys = list(self.cases.keys())
@@ -139,6 +137,7 @@ def plot_sorting_accuracy(
 
         if mode == "ordered_accuracy":
             if ax is None:
+
                 fig, ax = plt.subplots(figsize=figsize)
             else:
                 fig = ax.figure