Does the recording's duration affect the quality of spike sorting?

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path

import spikeinterface as si
import spikeinterface.extractors as se
import spikeinterface.sorters as ss
import spikeinterface.widgets as sw

from spikeinterface.comparison import GroundTruthStudy

# clone and install MATLAB sorters

# kilosort2
!git clone https://github.com/MouseLand/Kilosort2.git
kilosort2_path = './Kilosort2'
ss.Kilosort2Sorter.set_kilosort2_path(kilosort2_path)

# kilosort
!git clone https://github.com/cortex-lab/KiloSort.git
kilosort_path = './KiloSort'
ss.KilosortSorter.set_kilosort_path(kilosort_path)

# ironclust
!git clone https://github.com/flatironinstitute/ironclust.git
ironclust_path = './ironclust'
ss.IronclustSorter.set_ironclust_path(ironclust_path)

%matplotlib inline

# some matplotlib hack to prettify figure
SMALL_SIZE = 12
MEDIUM_SIZE = 14
BIGGER_SIZE = 16

plt.rc('font', size=SMALL_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize
plt.rc('figure', figsize=(10.0, 8.0))  # figsize
plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title

def clear_axes(ax):
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)    

si.print_spikeinterface_version()
ss.print_sorter_versions()

# Change this path to point to where you downloaded the dataset
p = Path('/home/samuel/Documents/DataSpikeSorting/mearec/')
study_folder = p / 'study_mearec_neuronexus_several_durations/'

mearec_filename = p / 'recordings_10cells_Neuronexus-32_1800.0_10.0uV_2020-02-28.h5'

rec  = se.MEArecRecordingExtractor(mearec_filename, locs_2d=True)
gt_sorting = se.MEArecSortingExtractor(mearec_filename)

fs = rec.get_sampling_frequency()

gt_dict = {}
durations = [60, 300, 600, 1800]
for duration in durations:
    sub_rec = se.SubRecordingExtractor(rec, start_frame=0, end_frame=int(duration*fs))
    sub_sorting = se.SubSortingExtractor(gt_sorting, start_frame=0, end_frame=int(duration*fs))
    gt_dict[f'rec{duration}'] = (sub_rec, sub_sorting)
study = GroundTruthStudy.create(study_folder, gt_dict)

sorter_list = ['herdingspikes', 'ironclust', 'kilosort2',  'kilosort',
               'mountainsort4', 'spykingcircus', 'tridesclous'] 

study = GroundTruthStudy(study_folder)
sorter_params = {} 
study.run_sorters(sorter_list, sorter_params=sorter_params, mode='keep', verbose=True)

study = GroundTruthStudy(study_folder)
snr = study.get_units_snr(rec_name='rec1800')
snr

fig, ax = plt.subplots()
ax.hist(snr['snr'].values, bins=10)
ax.set_xlabel('GT units SNR')

# this copy sorting is necessary to copy results from sorter
# into a centralize folder with all results
study.copy_sortings()

# this run all comparison sto GT
study.run_comparisons(exhaustive_gt=True, match_score=0.1, overmerged_score=0.2)

# this retrieve results
comparisons = study.comparisons
dataframes = study.aggregate_dataframes()

run_times = dataframes['run_times']
run_times

# insert durations
run_times['duration']  = run_times['rec_name'].apply(lambda s: float(s.replace('rec', '')))

g = sns.catplot(data=run_times, x='duration', y='run_time', hue='sorter_name', kind='bar')

perf = dataframes['perf_by_units']
perf

# insert durations
perf['duration']  = perf['rec_name'].apply(lambda s: float(s.replace('rec', '')))

g = sns.catplot(data=perf, x='duration', y='accuracy', hue='sorter_name', kind='bar')

count_units = dataframes['count_units']
count_units

# insert durations
count_units['duration']  = count_units['rec_name'].apply(lambda s: float(s.replace('rec', '')))

g = sns.catplot(data=count_units, x='duration', y='num_well_detected', hue='sorter_name', kind='bar')

g = sns.catplot(data=count_units, x='duration', y='num_false_positive', hue='sorter_name', kind='bar')

# same as previous but with other limits
g = sns.catplot(data=count_units, x='duration', y='num_false_positive', hue='sorter_name', kind='bar')
g.fig.axes[0].set_ylim(0, 10)

g = sns.catplot(data=count_units, x='duration', y='num_redundant', hue='sorter_name', kind='bar')