Collision paper spike sorting performance
Spike sorting performance against spike collisions (figure 2-3-5)¶
In this notebook, we describe how to generate the figures for all the sudies, i.e. for all rate and correlation levels, in a systematic manner. However, while by default the figures were saved as .pdf, here we will modify the ranges of rates and correlations to display only a single figures. Feel free to modify the scripts in order to display only a single figures
In [1]:
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
from matplotlib import gridspec
import MEArec as mr
import spikeinterface.full as si
In [2]:
study_base_folder = Path('../data/study/')
Plot global spike sorting performance (Figure 2)¶
In [1]:
res = {}
rate_levels = [5]
corr_levels = [0]
for rate_level in rate_levels:
for corr_level in corr_levels:
fig = plt.figure(figsize=(15,5))
gs = gridspec.GridSpec(2, 3, figure=fig)
study_folder = study_base_folder / f'20cells_5noise_{corr_level}corr_{rate_level}rate_Neuronexus-32'
study = si.GroundTruthStudy(study_folder)
study.run_comparisons(exhaustive_gt=True)
ax_1 = plt.subplot(gs[0, 0])
ax_2 = plt.subplot(gs[0, 1:])
ax_3 = plt.subplot(gs[1, 1:])
ax_4 = plt.subplot(gs[1, 0])
for ax in [ax_1, ax_2, ax_3, ax_4]:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax_2.tick_params(labelbottom=False)
ax_2.set_xlabel('')
si.plot_gt_study_run_times(study, ax=ax_1)
si.plot_gt_study_unit_counts(study, ax=ax_2)
si.plot_gt_study_performances_averages(study, ax=ax_3)
si.plot_gt_study_performances_by_template_similarity(study, ax=ax_4)
plt.tight_layout()
Plot collision recall as function of the lags (Figure 3)¶
In [2]:
for rate_level in rate_levels:
for corr_level in corr_levels:
study_folder = f'../data/study/20cells_5noise_{corr_level}corr_{rate_level}rate_Neuronexus-32'
res[(rate_level, corr_level)] = si.CollisionGTStudy(study_folder)
res[(rate_level, corr_level)].run_comparisons(exhaustive_gt=True, collision_lag=2, nbins=11)
for rec_name in res[(rate_level, corr_level)].rec_names:
res[(rate_level, corr_level)].compute_waveforms(rec_name)
si.plot_study_comparison_collision_by_similarity(res[(rate_level, corr_level)],
show_legend=False, ylim=(0.4, 1))
plt.tight_layout()
Plot collision recall as function of the lag and/or cosine similarity (supplementary figures)¶
In [3]:
gs = gridspec.GridSpec(len(rate_levels), len(corr_levels))
for i, rate_level in enumerate(rate_levels):
for j, corr_level in enumerate(corr_levels):
ax = plt.subplot(gs[i, j])
if i == 0 and j == 0:
show_legend = True
else:
show_legend = False
si.plot_study_comparison_collision_by_similarity_range(res[(rate_level, corr_level)], show_legend=show_legend, similarity_range=[0.5, 1], ax=ax, ylim=(0.3, 1))
ax.set_title(f'Rate {rate_level} Hz, Corr {corr_level}' )
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
if rate_level != rate_levels[-1]:
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
else:
ax.set_xlabel('lags (ms)')
if corr_level != corr_levels[0]:
ax.tick_params(labelleft=False)
ax.set_ylabel('')
else:
ax.set_ylabel('collision accuracy')
In [4]:
gs = gridspec.GridSpec(len(rate_levels), len(corr_levels))
for i, rate_level in enumerate(rate_levels):
for j, corr_level in enumerate(corr_levels):
ax = plt.subplot(gs[i, j])
if i == 0 and j == 0:
show_legend = True
else:
show_legend = False
si.plot_study_comparison_collision_by_similarity_ranges(res[(rate_level, corr_level)], show_legend=show_legend, ax=ax)
ax.set_title(f'Rate {rate_level} Hz, Corr {corr_level}' )
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
if rate_level != rate_levels[-1]:
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
else:
ax.set_xlabel('similarity')
if corr_level != corr_levels[0]:
ax.tick_params(labelleft=False)
ax.set_ylabel('')
else:
ax.set_ylabel('collision accuracy')
Plot average collision recall over multiple conditions, as function of the lags (Figure 5)¶
In [9]:
rate_levels = [5,10,15]
corr_levels = [0, 0.1, 0.2]
gs = gridspec.GridSpec(1, 2)
ax = plt.subplot(gs[0, 0])
curves = {}
for i, rate_level in enumerate(rate_levels):
for j, corr_level in enumerate(corr_levels):
study_folder = f'../data/study/20cells_5noise_{corr_level}corr_{rate_level}rate_Neuronexus-32'
res[(rate_level, corr_level)] = si.CollisionGTStudy(study_folder)
res[(rate_level, corr_level)].run_comparisons(exhaustive_gt=True, collision_lag=2, nbins=11)
res[(rate_level, corr_level)].precompute_scores_by_similarities()
for sorter_name in res[(rate_level, corr_level)].sorter_names:
data = res[(rate_level, corr_level)].get_mean_over_similarity_range([0.5, 1], sorter_name)
if not sorter_name in curves:
curves[sorter_name] = [data]
else:
curves[sorter_name] += [data]
lags = res[(rate_level, corr_level)].get_lags()
for sorter_name in res[(rate_level, corr_level)].sorter_names:
curves[sorter_name] = np.array(curves[sorter_name])
mean_sorter = curves[sorter_name].mean(0)
std_sorter = curves[sorter_name].std(0)
ax.plot(lags[:-1] + (lags[1]-lags[0]) / 2, mean_sorter, label=sorter_name)
ax.fill_between(lags[:-1] + (lags[1]-lags[0]) / 2, mean_sorter-std_sorter,mean_sorter+std_sorter, alpha=0.2)
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel('lag (ms)')
ax.set_ylabel('collision accuracy')
Out[9]:
Plotting the average collision recall over multiple conditions, as function of the similarity¶
In [5]:
rate_levels = [5,10,15]
corr_levels = [0, 0.1, 0.2]
res = {}
gs = gridspec.GridSpec(1, 2)
ax = plt.subplot(gs[0, 0])
curves = {}
similarity_ranges = np.linspace(-0.4, 1, 8)
for i, rate_level in enumerate(rate_levels):
for j, corr_level in enumerate(corr_levels):
study_folder = f'../data/study/20cells_5noise_{corr_level}corr_{rate_level}rate_Neuronexus-32'
res[(rate_level, corr_level)] = si.CollisionGTStudy(study_folder)
res[(rate_level, corr_level)].run_comparisons(exhaustive_gt=True, collision_lag=2, nbins=11)
res[(rate_level, corr_level)].precompute_scores_by_similarities()
for sorter_name in res[(rate_level, corr_level)].sorter_names:
all_similarities = res[(rate_level, corr_level)].all_similarities[sorter_name]
all_recall_scores = res[(rate_level, corr_level)].all_recall_scores[sorter_name]
order = np.argsort(all_similarities)
all_similarities = all_similarities[order]
all_recall_scores = all_recall_scores[order, :]
mean_recall_scores = []
std_recall_scores = []
for k in range(similarity_ranges.size - 1):
cmin, cmax = similarity_ranges[k], similarity_ranges[k + 1]
amin, amax = np.searchsorted(all_similarities, [cmin, cmax])
value = np.mean(all_recall_scores[amin:amax])
mean_recall_scores += [np.nan_to_num(value)]
xaxis = np.diff(similarity_ranges)/2 + similarity_ranges[:-1]
data = mean_recall_scores
if not sorter_name in curves:
curves[sorter_name] = [data]
else:
curves[sorter_name] += [data]
for sorter_name in res[(rate_level, corr_level)].sorter_names:
curves[sorter_name] = np.array(curves[sorter_name])
mean_sorter = curves[sorter_name].mean(0)
std_sorter = curves[sorter_name].std(0)
ax.plot(xaxis, mean_sorter, label=sorter_name)
ax.fill_between(xaxis, mean_sorter-std_sorter,mean_sorter+std_sorter, alpha=0.2)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel('cosine similarity')
#ax.set_ylabel('collision accuracy')
#ax.set_yticks([])
plt.tight_layout()
