def _setup_taste_map(self):
rec_dirs = self.recording_dirs
rec_labels = self.rec_labels
tastants = []
for rd in rec_dirs:
dat = load_dataset(rd)
tmp = dat.dig_in_mapping
tastants.extend(tmp['name'].to_list())
tastants = np.unique(tastants)
taste_map = {}
for rl, rd in rec_labels.items():
dat = load_dataset(rd)
din = dat.dig_in_mapping
for t in tastants:
if taste_map.get(t) is None:
taste_map[t] = {}
tmp = din['channel'][din['name'] == t]
if not tmp.empty:
taste_map[t][rl] = tmp.values[0]
self.taste_map = taste_map
def sort_spikes(self, electrode=None, shell=False):
if electrode is None:
electrode = userIO.get_user_input('Electrode #: ', shell=shell)
if electrode is None or not electrode.isnumeric():
return
electrode = int(electrode)
rec_dirs = list(self.rec_labels.values())
for rd in rec_dirs:
dat = load_dataset(rd)
if not dat.process_status['cleanup_clustering']:
dat.cleanup_clustering()
dat.process_status['sort_units'] = True
sorter = bclust.SpikeSorter(rec_dirs, electrode, shell=shell)
if not shell:
root, sorter_GUI = ssg.launch_sorter_GUI(sorter)
return root, sorter_GUI
else:
print('No shell UI yet')
return
def get_response_change(unit_name,
rec1,
unit1,
din1,
rec2,
unit2,
din2,
bin_size=250,
bin_step=25,
norm_func=None):
'''Uses the spike arrays to compute the change in
firing rate of the response to the tastant.
Parameters
----------
unit_name : str, name of held unit
rec1 : str, path to recording directory 1
unit1: str, name of unit in rec1
din1 : int, number of din to use from rec1
rec2 : str, path to recording directory 2
unit2: str, name of unit in rec2
din2 : int, number of din to use from rec2
bin_size : int, default=250
width of bins in units of time vector saved in hf5 spike_trains
usually ms
bin_step : int, default=25
step size to take from one bin to the next in same units (usually ms)
norm_func: function (optional)
function with which to normalize the firing rates before getting difference
must take inputs (time_vector, firing_rate_array) where time_vector is
1D numpy.array and firing_rate_array is a Trial x Time numpy.array
Must return a numpy.array with same size as firing rate array
Returns
-------
difference_of_means : numpy.array
SEM : numpy.array, standard error of the mean difference
'''
# Get metadata
dat1 = load_dataset(rec1)
dat2 = load_dataset(rec2)
# Get data from hf5 files
time1, spikes1 = dio.h5io.get_spike_data(rec1, unit1, din1)
time2, spikes2 = dio.h5io.get_spike_data(rec2, unit2, din2)
# Get Firing Rates
bin_time1, fr1 = sas.get_binned_firing_rate(time1, spikes1, bin_size,
bin_step)
bin_time2, fr2 = sas.get_binned_firing_rate(time2, spike2, bin_size,
bin_step)
if not np.array_equal(bin_time1, bin_time2):
raise ValueError('Time of spike trains is not aligned')
# Normalize firing rates
if norm_func:
fr1 = norm_func(bin_time1, fr1)
fr2 = norm_fun(bin_time2, fr2)
difference_of_mean, SEM = sas.get_mean_difference(fr1, fr2, axis=0)
return difference_of_mean, SEM, bin_time1
def cluster_spikes(self,
data_quality=None,
multi_process=True,
n_cores=None,
custom_params=None,
umap=False):
'''Write clustering parameters to file and
Run blech_process on each electrode using GNU parallel
Parameters
----------
data_quality : {'clean', 'noisy', None (default)}
set if you want to change the data quality parameters for cutoff
and spike detection before running clustering. These parameters are
automatically set as "clean" during initial parameter setup
accept_params : bool, False (default)
set to True in order to skip popup confirmation of parameters when
running
'''
clustering_params = None
if custom_params:
clustering_params = custom_params
elif data_quality:
tmp = dio.params.load_params('clustering_params',
self.root_dir,
default_keyword=data_quality)
if tmp:
clustering_params = tmp
else:
raise ValueError('%s is not a valid data_quality preset. Must '
'be "clean" or "noisy" or None.')
# Get electrodes, throw out 'dead' electrodes
em = self.electrode_mapping
if 'dead' in em.columns:
electrodes = em.Electrode[em['dead'] == False].tolist()
else:
electrodes = em.Electrode.tolist()
# Setup progress bar
pbar = tqdm(total=len(electrodes))
def update_pbar(ans):
pbar.update()
# get clustering params
rec_dirs = list(self.rec_labels.values())
if clustering_params is None:
dat = load_dataset(rec_dirs[0])
clustering_params = dat.clustering_params.copy()
print('\nRunning Blech Clust\n-------------------')
print('Parameters\n%s' % pt.print_dict(clustering_params))
# Write clustering params to recording directories & check for spike detection
spike_detect = True
for rd in rec_dirs:
dat = load_dataset(rd)
if dat.process_status['spike_detection'] == False:
raise FileNotFoundError(
'Spike detection has not been run on %s' % rd)
dat.clustering_params = clustering_params
wt.write_params_to_json('clustering_params', rd, clustering_params)
# dat.save()
# Run clustering
if not umap:
clust_objs = [
bclust.BlechClust(rec_dirs, x, params=clustering_params)
for x in electrodes
]
else:
clust_objs = [
bclust.BlechClust(rec_dirs,
x,
params=clustering_params,
data_transform=bclust.UMAP_METRICS,
n_pc=5) for x in electrodes
]
if multi_process:
if n_cores is None or n_cores > multiprocessing.cpu_count():
n_cores = multiprocessing.cpu_count() - 1
pool = multiprocessing.get_context('spawn').Pool(n_cores)
for x in clust_objs:
pool.apply_async(x.run, callback=update_pbar)
pool.close()
pool.join()
else:
for x in clust_objs:
res = x.run()
update_pbar(res)
pbar.close()
for rd in rec_dirs:
dat = load_dataset(rd)
dat.process_status['spike_clustering'] = True
dat.process_status['cleanup_clustering'] = False
# dat.save()
# self.save()
print('Clustering Complete\n------------------')
def __init__(self,
exp_dir=None,
exp_name=None,
shell=False,
order_dict=None):
'''Setup for analysis across recording sessions
Parameters
----------
exp_dir : str (optional)
path to directory containing all recording directories
if None (default) is passed then a popup to choose file
will come up
shell : bool (optional)
True to use command-line interface for user input
False (default) for GUI
'''
if 'SSH_CONNECTION' in os.environ:
shell = True
super().__init__('experiment',
root_dir=exp_dir,
data_name=exp_name,
shell=shell)
fd = [os.path.join(exp_dir, x) for x in os.listdir(exp_dir)]
file_dirs = [
x for x in fd
if (os.path.isdir(x) and dio.h5io.get_h5_filename(x) is not None)
]
if len(file_dirs) == 0:
q = userIO.ask_user(
'No recording directories with h5 files found '
'in experiment directory\nContinue creating'
'empty experiment?',
shell=shell)
if q == 0:
return
self.recording_dirs = file_dirs
self._order_dirs(shell=shell, order_dict=order_dict)
rec_names = [os.path.basename(x) for x in self.recording_dirs]
el_map = None
rec_labels = {}
for rd in self.recording_dirs:
dat = load_dataset(rd)
if dat is None:
raise FileNotFoundError('No dataset.p object found for in %s' %
rd)
elif el_map is None:
el_map = dat.electrode_mapping.copy()
rec_labels[dat.data_name] = rd
self.rec_labels = rec_labels
self.electrode_mapping = el_map
self._setup_taste_map()
save_dir = os.path.join(self.root_dir, '%s_analysis' % self.data_name)
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
self.analysis_dir = save_dir
self.save()
def palatability_identity_calculations(rec_dir, pal_ranks=None,
params=None, shell=False):
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)
dat = load_dataset(rec_dir)
dim = dat.dig_in_mapping
if 'palatability_rank' in dim.columns:
pass
elif pal_ranks is None:
dim = get_palatability_ranks(dim, shell=shell)
else:
dim['palatability_rank'] = dim['name'].map(pal_ranks)
dim = dim.dropna(subset=['palatability_rank'])
dim = dim[dim['palatability_rank'] > 0]
dim = dim.reset_index(drop=True)
num_tastes = len(dim)
taste_names = dim.name.to_list()
trial_list = dat.dig_in_trials.copy()
trial_list = trial_list[[True if x in taste_names else False
for x in trial_list.name]]
num_trials = trial_list.groupby('channel').count()['name'].unique()
if len(num_trials) > 1:
raise ValueError('Unequal number of trials for tastes to used')
else:
num_trials = num_trials[0]
dim['num_trials'] = num_trials
# Get which units to use
unit_table = h5io.get_unit_table(rec_dir)
unit_types = ['Single', 'Multi', 'All', 'Custom']
unit_type = params.get('unit_type')
if unit_type is None:
q = userIO.ask_user('Which units do you want to use for taste '
'discrimination and palatability analysis?',
choices=unit_types,
shell=shell)
unit_type = unit_types[q]
if unit_type == 'Single':
chosen_units = unit_table.loc[unit_table['single_unit'],
'unit_num'].to_list()
elif unit_type == 'Multi':
chosen_units = unit_table.loc[unit_table['single_unit'] == False,
'unit_num'].to_list()
elif unit_type == 'All':
chosen_units = unit_table['unit_num'].to_list()
else:
selection = userIO.select_from_list('Select units to use:',
unit_table['unit_num'],
'Select Units',
multi_select=True)
chosen_units = list(map(int, selection))
num_units = len(chosen_units)
unit_table = unit_table.loc[chosen_units]
# Enter Parameters
if params is None or params.keys() != default_pal_id_params.keys():
params = default_pal_id_params.copy()
params = userIO.confirm_parameter_dict(params,
('Palatability/Identity '
'Calculation Parameters'
'\nTimes in ms'), shell=shell)
win_size = params['window_size']
win_step = params['window_step']
print('Running palatability/identity calculations with parameters:\n%s' %
pt.print_dict(params))
with tables.open_file(dat.h5_file, 'r+') as hf5:
trains_dig_in = hf5.list_nodes('/spike_trains')
time = trains_dig_in[0].array_time[:]
bin_times = np.arange(time[0], time[-1] - win_size + win_step,
win_step)
num_bins = len(bin_times)
palatability = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=int)
identity = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=int)
unscaled_response = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=np.dtype('float64'))
response = np.empty((num_bins, num_units, num_tastes*num_trials),
dtype=np.dtype('float64'))
laser = np.empty((num_bins, num_units, num_tastes*num_trials, 2),
dtype=float)
# Fill arrays with data
print('Filling data arrays...')
onesies = np.ones((num_bins, num_units, num_trials))
for i, row in dim.iterrows():
idx = range(num_trials*i, num_trials*(i+1))
palatability[:, :, idx] = row.palatability_rank * onesies
identity[:, :, idx] = row.channel * onesies
for j, u in enumerate(chosen_units):
for k,t in enumerate(bin_times):
t_idx = np.where((time >= t) & (time <= t+win_size))[0]
unscaled_response[k, j, idx] = \
np.mean(trains_dig_in[i].spike_array[:, u, t_idx],
axis=1)
try:
lasers[k, j, idx] = \
np.vstack((trains_dig_in[i].laser_durations[:],
trains_dig_in[i].laser_onset_lag[:]))
except:
laser[k, j, idx] = np.zeros((num_trials, 2))
# Scaling was not done, so:
response = unscaled_response.copy()
# Make ancillary_analysis node and put in arrays
if '/ancillary_analysis' in hf5:
hf5.remove_node('/ancillary_analysis', recursive=True)
hf5.create_group('/', 'ancillary_analysis')
hf5.create_array('/ancillary_analysis', 'palatability', palatability)
hf5.create_array('/ancillary_analysis', 'identity', identity)
hf5.create_array('/ancillary_analysis', 'laser', laser)
hf5.create_array('/ancillary_analysis', 'scaled_neural_response',
response)
hf5.create_array('/ancillary_analysis', 'window_params',
np.array([win_size, win_step]))
hf5.create_array('/ancillary_analysis', 'bin_times', bin_times)
hf5.create_array('/ancillary_analysis', 'unscaled_neural_response',
unscaled_response)
# for backwards compatibility
hf5.create_array('/ancillary_analysis', 'params',
np.array([win_size, win_step]))
hf5.create_array('/ancillary_analysis', 'pre_stim', np.array(time[0]))
hf5.flush()
# Get unique laser (duration, lag) combinations
print('Organizing trial data...')
unique_lasers = np.vstack(list({tuple(row) for row in laser[0, 0, :, :]}))
unique_lasers = unique_lasers[unique_lasers[:, 1].argsort(), :]
num_conditions = unique_lasers.shape[0]
trials = []
for row in unique_lasers:
tmp_trials = [j for j in range(num_trials * num_tastes)
if np.array_equal(laser[0, 0, j, :], row)]
trials.append(tmp_trials)
trials_per_condition = [len(x) for x in trials]
if not all(x == trials_per_condition[0] for x in trials_per_condition):
raise ValueError('Different number of trials for each laser condition')
trials_per_condition = int(trials_per_condition[0] / num_tastes) #assumes same number of trials per taste per condition
print('Detected:\n %i tastes\n %i laser conditions\n'
' %i trials per condition per taste' %
(num_tastes, num_conditions, trials_per_condition))
trials = np.array(trials)
# Store laser conditions and indices of trials per condition in trial x
# taste space
hf5.create_array('/ancillary_analysis', 'trials', trials)
hf5.create_array('/ancillary_analysis', 'laser_combination_d_l',
unique_lasers)
hf5.flush()
# Taste Similarity Calculation
neural_response_laser = np.empty((num_conditions, num_bins,
num_tastes, num_units,
trials_per_condition),
dtype=np.dtype('float64'))
taste_cosine_similarity = np.empty((num_conditions, num_bins,
num_tastes, num_tastes),
dtype=np.dtype('float64'))
taste_euclidean_distance = np.empty((num_conditions, num_bins,
num_tastes, num_tastes),
dtype=np.dtype('float64'))
# Re-format neural responses from bin x unit x (trial*taste) to
# laser_condition x bin x taste x unit x trial
print('Reformatting data arrays...')
for i, trial in enumerate(trials):
for j, _ in enumerate(bin_times):
for k, _ in dim.iterrows():
idx = np.where((trial >= num_trials*k) &
(trial < num_trials*(k+1)))[0]
neural_response_laser[i, j, k, :, :] = \
response[j, :, trial[idx]].T
# Compute taste cosine similarity and euclidean distances
print('Computing taste cosine similarity and euclidean distances...')
for i, _ in enumerate(trials):
for j, _ in enumerate(bin_times):
for k, _ in dim.iterrows():
for l, _ in dim.iterrows():
taste_cosine_similarity[i, j, k, l] = \
np.mean(cosine_similarity(
neural_response_laser[i, j, k, :, :].T,
neural_response_laser[i, j, l, :, :].T))
taste_euclidean_distance[i, j, k, l] = \
np.mean(cdist(
neural_response_laser[i, j, k, :, :].T,
neural_response_laser[i, j, l, :, :].T,
metric='euclidean'))
hf5.create_array('/ancillary_analysis', 'taste_cosine_similarity',
taste_cosine_similarity)
hf5.create_array('/ancillary_analysis', 'taste_euclidean_distance',
taste_euclidean_distance)
hf5.flush()
# Taste Responsiveness calculations
bin_params = [params['num_comparison_bins'],
params['comparison_bin_size']]
discrim_p = params['discrim_p']
responsive_neurons = []
discriminating_neurons = []
taste_responsiveness = np.zeros((bin_params[0], num_units, 2))
new_bin_times = np.arange(0, np.prod(bin_params), bin_params[1])
baseline = np.where(bin_times < 0)[0]
print('Computing taste responsiveness and taste discrimination...')
for i, t in enumerate(new_bin_times):
places = np.where((bin_times >= t) &
(bin_times <= t+bin_params[1]))[0]
for j, u in enumerate(chosen_units):
# Check taste responsiveness
f, p = f_oneway(np.mean(response[places, j, :], axis=0),
np.mean(response[baseline, j, :], axis=0))
if np.isnan(f):
f = 0.0
p = 1.0
if p <= discrim_p and u not in responsive_neurons:
responsive_neurons.append(u)
taste_responsiveness[i, j, 0] = 1
# Check taste discrimination
taste_idx = [np.arange(num_trials*k, num_trials*(k+1))
for k in range(num_tastes)]
taste_responses = [np.mean(response[places, j, :][:, k], axis=0)
for k in taste_idx]
f, p = f_oneway(*taste_responses)
if np.isnan(f):
f = 0.0
p = 1.0
if p <= discrim_p and u not in discriminating_neurons:
discriminating_neurons.append(u)
responsive_neurons = np.sort(responsive_neurons)
discriminating_neurons = np.sort(discriminating_neurons)
# Write taste responsive and taste discriminating units to text file
save_file = os.path.join(rec_dir, 'discriminative_responsive_neurons.txt')
with open(save_file, 'w') as f:
print('Taste discriminative neurons', file=f)
for u in discriminating_neurons:
print(u, file=f)
print('Taste responsive neurons', file=f)
for u in responsive_neurons:
print(u, file=f)
hf5.create_array('/ancillary_analysis', 'taste_disciminating_neurons',
discriminating_neurons)
hf5.create_array('/ancillary_analysis', 'taste_responsive_neurons',
responsive_neurons)
hf5.create_array('/ancillary_analysis', 'taste_responsiveness',
taste_responsiveness)
hf5.flush()
# Get time course of taste discrimibility
print('Getting taste discrimination time course...')
p_discrim = np.empty((num_conditions, num_bins, num_tastes, num_tastes,
num_units), dtype=np.dtype('float64'))
for i in range(num_conditions):
for j, t in enumerate(bin_times):
for k in range(num_tastes):
for l in range(num_tastes):
for m in range(num_units):
_, p = ttest_ind(neural_response_laser[i, j, k, m, :],
neural_response_laser[i, j, l, m, :],
equal_var = False)
if np.isnan(p):
p = 1.0
p_discrim[i, j, k, l, m] = p
hf5.create_array('/ancillary_analysis', 'p_discriminability',
p_discrim)
hf5.flush()
# Palatability Rank Order calculation (if > 2 tastes)
t_start = params['pal_deduce_start_time']
t_end = params['pal_deduce_end_time']
if num_tastes > 2:
print('Deducing palatability rank order...')
palatability_rank_order_deduction(rec_dir, neural_response_laser,
unique_lasers,
bin_times, [t_start, t_end])
# Palatability calculation
r_spearman = np.zeros((num_conditions, num_bins, num_units))
p_spearman = np.ones((num_conditions, num_bins, num_units))
r_pearson = np.zeros((num_conditions, num_bins, num_units))
p_pearson = np.ones((num_conditions, num_bins, num_units))
f_identity = np.ones((num_conditions, num_bins, num_units))
p_identity = np.ones((num_conditions, num_bins, num_units))
lda_palatability = np.zeros((num_conditions, num_bins))
lda_identity = np.zeros((num_conditions, num_bins))
r_isotonic = np.zeros((num_conditions, num_bins, num_units))
id_pal_regress = np.zeros((num_conditions, num_bins, num_units, 2))
pairwise_identity = np.zeros((num_conditions, num_bins, num_tastes, num_tastes))
print('Computing palatability metrics...')
for i, t in enumerate(trials):
for j in range(num_bins):
for k in range(num_units):
ranks = rankdata(response[j, k, t])
r_spearman[i, j, k], p_spearman[i, j, k] = \
spearmanr(ranks, palatability[j, k, t])
r_pearson[i, j, k], p_pearson[i, j, k] = \
pearsonr(response[j, k, t], palatability[j, k, t])
if np.isnan(r_spearman[i, j, k]):
r_spearman[i, j, k] = 0.0
p_spearman[i, j, k] = 1.0
if np.isnan(r_pearson[i, j, k]):
r_pearson[i, j, k] = 0.0
p_pearson[i, j, k] = 1.0
# Isotonic regression of firing against palatability
model = IsotonicRegression(increasing = 'auto')
model.fit(palatability[j, k, t], response[j, k, t])
r_isotonic[i, j, k] = model.score(palatability[j, k, t],
response[j, k, t])
# Multiple Regression of firing rate against palatability and identity
# Regress palatability on identity
tmp_id = identity[j, k, t].reshape(-1, 1)
tmp_pal = palatability[j, k, t].reshape(-1, 1)
tmp_resp = response[j, k, t].reshape(-1, 1)
model_pi = LinearRegression()
model_pi.fit(tmp_id, tmp_pal)
pi_residuals = tmp_pal - model_pi.predict(tmp_id)
# Regress identity on palatability
model_ip = LinearRegression()
model_ip.fit(tmp_pal, tmp_id)
ip_residuals = tmp_id - model_ip.predict(tmp_pal)
# Regress firing on identity
model_fi = LinearRegression()
model_fi.fit(tmp_id, tmp_resp)
fi_residuals = tmp_resp - model_fi.predict(tmp_id)
# Regress firing on palatability
model_fp = LinearRegression()
model_fp.fit(tmp_pal, tmp_resp)
fp_residuals = tmp_resp - model_fp.predict(tmp_pal)
# Get partial correlation coefficient of response with identity
idp_reg0, p = pearsonr(fp_residuals, ip_residuals)
if np.isnan(idp_reg0):
idp_reg0 = 0.0
idp_reg1, p = pearsonr(fi_residuals, pi_residuals)
if np.isnan(idp_reg1):
idp_reg1 = 0.0
id_pal_regress[i, j, k, 0] = idp_reg0
id_pal_regress[i, j, k, 1] = idp_reg1
# Identity Calculation
samples = []
for _, row in dim.iterrows():
taste = row.channel
samples.append([trial for trial in t
if identity[j, k, trial] == taste])
tmp_resp = [response[j, k, sample] for sample in samples]
f_identity[i, j, k], p_identity[i, j, k] = f_oneway(*tmp_resp)
if np.isnan(f_identity[i, j, k]):
f_identity[i, j, k] = 0.0
p_identity[i, j, k] = 1.0
# Linear Discriminant analysis for palatability
X = response[j, :, t]
Y = palatability[j, 0, t]
test_results = []
c_validator = LeavePOut(1)
for train, test in c_validator.split(X, Y):
model = LDA()
model.fit(X[train, :], Y[train])
tmp = np.mean(model.predict(X[test]) == Y[test])
test_results.append(tmp)
lda_palatability[i, j] = np.mean(test_results)
# Linear Discriminant analysis for identity
Y = identity[j, 0, t]
test_results = []
c_validator = LeavePOut(1)
for train, test in c_validator.split(X, Y):
model = LDA()
model.fit(X[train, :], Y[train])
tmp = np.mean(model.predict(X[test]) == Y[test])
test_results.append(tmp)
lda_identity[i, j] = np.mean(test_results)
# Pairwise Identity Calculation
for ti1, r1 in dim.iterrows():
for ti2, r2 in dim.iterrows():
t1 = r1.channel
t2 = r2.channel
tmp_trials = np.where((identity[j, 0, :] == t1) |
(identity[j, 0, :] == t2))[0]
idx = [trial for trial in t if trial in tmp_trials]
X = response[j, :, idx]
Y = identity[j, 0, idx]
test_results = []
c_validator = StratifiedShuffleSplit(n_splits=10,
test_size=0.25,
random_state=0)
for train, test in c_validator.split(X, Y):
model = GaussianNB()
model.fit(X[train, :], Y[train])
tmp_score = model.score(X[test, :], Y[test])
test_results.append(tmp_score)
pairwise_identity[i, j, ti1, ti2] = np.mean(test_results)
hf5.create_array('/ancillary_analysis', 'r_pearson', r_pearson)
hf5.create_array('/ancillary_analysis', 'r_spearman', r_spearman)
hf5.create_array('/ancillary_analysis', 'p_pearson', p_pearson)
hf5.create_array('/ancillary_analysis', 'p_spearman', p_spearman)
hf5.create_array('/ancillary_analysis', 'lda_palatability', lda_palatability)
hf5.create_array('/ancillary_analysis', 'lda_identity', lda_identity)
hf5.create_array('/ancillary_analysis', 'r_isotonic', r_isotonic)
hf5.create_array('/ancillary_analysis', 'id_pal_regress', id_pal_regress)
hf5.create_array('/ancillary_analysis', 'f_identity', f_identity)
hf5.create_array('/ancillary_analysis', 'p_identity', p_identity)
hf5.create_array('/ancillary_analysis', 'pairwise_NB_identity', pairwise_identity)
hf5.flush()
warnings.filterwarnings('default', category=UserWarning)
warnings.filterwarnings('default', category=RuntimeWarning)