def mark_dead_channels(self, dead_channels=None, shell=False):
'''Plots small piece of raw traces and a metric to help identify dead
channels. Once user marks channels as dead a new column is added to
electrode mapping
Parameters
----------
dead_channels : list of int, optional
if this is specified then nothing is plotted, those channels are
simply marked as dead
shell : bool, optional
'''
if dead_channels is None:
em = self.electrode_mapping.copy()
fig, ax = datplt.plot_traces_and_outliers(self.h5_file)
save_file = os.path.join(self.data_dir, 'Electrode_Traces.png')
fig.savefig(save_file)
subprocess.call(['xdg-open', save_file])
choice = userIO.select_from_list('Select dead channels:',
em.Electrode.to_list(),
'Dead Channel Selection',
multi_select=True,
shell=shell)
plt.close('all')
plt.ioff()
dead_channels = list(map(int, choice))
em['dead'] = False
em.loc[dead_channels, 'dead'] = True
self.electrode_mapping = em
return dead_channels
def get_h5_filename(file_dir, shell=False):
'''Return the name of the h5 file found in file_dir.
Asks for selection if multiple found
Parameters
----------
file_dir : str, path to recording directory
Returns
-------
str
filename of h5 file in directory (not full path), None if no file found
'''
file_list = os.listdir(file_dir)
h5_files = [f for f in file_list if f.endswith('.h5')]
if len(h5_files) > 1:
choice = userIO.select_from_list('Choose which h5 file to load',
h5_files, 'Multiple h5 stores found',
shell=shell)
if choice is None:
return None
else:
h5_files = [choice]
elif len(h5_files) == 0:
return None
return h5_files[0]
def __init__(self, exp_dir=None, shell=False):
'''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 exp_dir is None:
exp_dir = eg.diropenbox('Select Experiment Directory',
'Experiment Directory')
if exp_dir is None or exp_dir == '':
return
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)]
order_dict = dict.fromkeys(file_dirs, 0)
tmp = userIO.dictIO(order_dict, shell=shell)
order_dict = tmp.fill_dict(prompt=('Set order of recordings (1-%i)\n'
'Leave blank to delete directory'
' from list'))
if order_dict is None:
return
file_dirs = [k for k, v in order_dict.items()
if v is not None and v != 0]
file_dirs = sorted(file_dirs, key=order_dict.get)
file_dirs = [os.path.join(exp_dir, x) for x in file_dirs]
file_dirs = [x[:-1] if x.endswith('/') else x
for x in file_dirs]
self.recording_dirs = file_dirs
self.experiment_dir = exp_dir
self.shell = shell
dat = dataset.load_dataset(file_dirs[0])
em = dat.electrode_mapping.copy()
ingc = userIO.select_from_list('Select all eletrodes confirmed in GC',
em['Electrode'],
multi_select=True, shell=shell)
ingc = list(map(int, ingc))
em['Area'] = np.where(em['Electrode'].isin(ingc), 'GC', 'Other')
self.electrode_mapping = em
self.save_file = os.path.join(exp_dir, '%s_experiment.p'
% os.path.basename(exp_dir))
def get_recording_filetype(file_dir, shell=False):
'''Check Intan recording directory to determine type of recording and thus
extraction method to use. Asks user to confirm, and manually correct if
incorrect
Parameters
----------
file_dir : str, recording directory to check
Returns
-------
str : file_type of recording
'''
file_list = os.listdir(file_dir)
file_type = None
for k, v in support_rec_types.items():
regex = re.compile(v)
if any([True for x in file_list if regex.match(x) is not None]):
file_type = k
if file_type is None:
msg = '\n '.join([
'unsupported recording type. Supported types are:',
*list(support_rec_types.keys())
])
else:
msg = '\"' + file_type + '\"'
query = 'Detected recording type is %s \nIs this correct?: ' % msg
q = userIO.ask_user(query, choices=['Yes', 'No'], shell=shell)
if q == 0:
return file_type
else:
choice = userIO.select_from_list('Select correct recording type',
list(support_rec_types.keys()),
'Select Recording Type',
shell=shell)
choice = list(support_rec_types.keys())[choice]
return choice
def palatability_identity_calculations(rec_dir, pal_ranks=None,
unit_type=None, params=None,
shell=False):
dat = dataset.load_dataset(rec_dir)
dim = dat.dig_in_mapping
if pal_ranks is None:
dim = get_palatability_ranks(dim, shell=shell)
elif 'palatability_rank' in dim.columns:
pass
else:
dim['palatability_rank'] = dim['name'].map(pal_ranks)
dim = dim.dropna(subset=['palatability_rank'])
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']
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 = {'window_size': 250, 'window_step': 25,
'num_comparison_bins': 5, 'comparison_bin_size': 250,
'discrim_p': 0.01, 'pal_deduce_start_time': 700,
'pal_deduce_end_time': 1200}
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' %
dp.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.dig_in * 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.dig_in
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 _, r1 in dim.iterrows():
for _, r2 in dim.iterrows():
t1 = r1.dig_in
t2 = r2.dig_in
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, t1, t2] = 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()
def get_CAR_groups(num_groups, electrode_mapping, shell=False):
'''Returns a dict containing standard params for common average referencing
Each dict field with fields, num groups, car_electrodes
Can set num_groups to an integer or as unilateral or bilateral
Settings as unilateral or bilateral will automatically assign channels to
groups, setting to a number will allow choice of channels for each group
unilateral: 1 CAR group, all channels on port
bilateral: 2 CAR groups, [0-7,24-31] & [8-23], assumes same port for both
Parameters
----------
num_groups : int or 'bilateral', number of CAR groups, bilateral
autmatically assigns the first and last 8 electrodes to group 1 and the
middle 16 to group 2
electrode_mapping : pandas.DataFrame, mapping electrode numbers to port and channel,
has columns: 'Electrode', 'Port' and 'Channel'
Returns
-------
num_groups : int, number of CAR groups
car_electrodes : list of lists of ints, list with a list of electrodes for
each CAR group
Throws
------
ValueError : if num_groups is not a valid int (>0) or 'bilateral'
'''
if num_groups == 'bilateral32':
num_groups = 2
implant_type = 'bilateral32'
elif isinstance(num_groups, int) and num_groups > 0:
implant_type = None
else:
raise ValueError(
'Num groups must be an integer >0 or a string bilateral')
electrodes = electrode_mapping['Electrode'].tolist()
car_electrodes = []
if implant_type == 'bilateral32':
g1 = electrodes[:8]
g1.extend(electrodes[-8:])
g2 = electrodes[8:-8]
car_electrodes = [g1, g2]
elif num_groups == 1:
car_electrodes.append(electrodes)
else:
select_list = []
for idx, row in electrode_mapping.iterrows():
select_list.append(', '.join([str(x) for x in row]))
for i in range(num_groups):
tmp = userIO.select_from_list('Choose CAR electrodes for group %i'
': [Electrode, Port, Channel]' % i,
select_list,
title='Group %i Electrodes' % i,
multi_select=True,
shell=shell)
if tmp is None:
raise ValueError('Must select electrodes for CAR groups')
car_electrodes.append([int(x.split(',')[0]) for x in tmp])
if 'dead' in electrode_mapping.columns:
dead_ch = electrode_mapping['Electrode'][electrode_mapping['dead']]
dead_ch = dead_ch.to_list()
for group in car_electrodes:
for dc in dead_ch:
if dc in group:
group.remove(dc)
return num_groups, car_electrodes
def initParams(self, data_quality='clean', emg_port=None,
emg_channels=None, shell=False, dig_in_names=None,
dig_out_names=None,
spike_array_params=None,
psth_params=None,
confirm_all=False):
'''
Initializes basic default analysis parameters that can be customized
before running processing methods
Can provide data_quality as 'clean' or 'noisy' to preset some
parameters that are useful for the different types. Best practice is to
run as clean (default) and to re-run as noisy if you notice that a lot
of electrodes are cutoff early
'''
# Get parameters from info.rhd
file_dir = self.data_dir
rec_info = dio.rawIO.read_rec_info(file_dir, shell)
ports = rec_info.pop('ports')
channels = rec_info.pop('channels')
sampling_rate = rec_info['amplifier_sampling_rate']
self.rec_info = rec_info
self.sampling_rate = sampling_rate
# Get default parameters for blech_clust
clustering_params = deepcopy(dio.params.clustering_params)
data_params = deepcopy(dio.params.data_params[data_quality])
bandpass_params = deepcopy(dio.params.bandpass_params)
spike_snapshot = deepcopy(dio.params.spike_snapshot)
if spike_array_params is None:
spike_array_params = deepcopy(dio.params.spike_array_params)
if psth_params is None:
psth_params = deepcopy(dio.params.psth_params)
# Ask for emg port & channels
if emg_port is None and not shell:
q = eg.ynbox('Do you have an EMG?', 'EMG')
if q:
emg_port = userIO.select_from_list('Select EMG Port:',
ports, 'EMG Port',
shell=shell)
emg_channels = userIO.select_from_list(
'Select EMG Channels:',
[y for x, y in
zip(ports, channels)
if x == emg_port],
title='EMG Channels',
multi_select=True, shell=shell)
elif emg_port is None and shell:
print('\nNo EMG port given.\n')
electrode_mapping, emg_mapping = dio.params.flatten_channels(
ports,
channels,
emg_port=emg_port,
emg_channels=emg_channels)
self.electrode_mapping = electrode_mapping
self.emg_mapping = emg_mapping
# Get digital input names and spike array parameters
if rec_info.get('dig_in'):
if dig_in_names is None:
dig_in_names = dict.fromkeys(['dig_in_%i' % x
for x in rec_info['dig_in']])
name_filler = userIO.dictIO(dig_in_names, shell=shell)
dig_in_names = name_filler.fill_dict('Enter names for '
'digital inputs:')
if dig_in_names is None or \
any([x is None for x in dig_in_names.values()]):
raise ValueError('Must name all dig_ins')
dig_in_names = list(dig_in_names.values())
if spike_array_params['laser_channels'] is None:
laser_dict = dict.fromkeys(dig_in_names, False)
laser_filler = userIO.dictIO(laser_dict, shell=shell)
laser_dict = laser_filler.fill_dict('Select any lasers:')
if laser_dict is None:
laser_channels = []
else:
laser_channels = [i for i, v
in zip(rec_info['dig_in'],
laser_dict.values()) if v]
spike_array_params['laser_channels'] = laser_channels
else:
laser_dict = dict.fromkeys(dig_in_names, False)
for lc in spike_array_params['laser_channels']:
laser_dict[dig_in_names[lc]] = True
if spike_array_params['dig_ins_to_use'] is None:
di = [x for x in rec_info['dig_in']
if x not in laser_channels]
dn = [dig_in_names[x] for x in di]
spike_dig_dict = dict.fromkeys(dn, True)
filler = userIO.dictIO(spike_dig_dict, shell=shell)
spike_dig_dict = filler.fill_dict('Select digital inputs '
'to use for making spike'
' arrays:')
if spike_dig_dict is None:
spike_dig_ins = []
else:
spike_dig_ins = [x for x, y in
zip(di, spike_dig_dict.values())
if y]
spike_array_params['dig_ins_to_use'] = spike_dig_ins
dim = pd.DataFrame([(x, y) for x, y in zip(rec_info['dig_in'],
dig_in_names)],
columns=['dig_in', 'name'])
dim['laser'] = dim['name'].apply(lambda x: laser_dict.get(x))
self.dig_in_mapping = dim.copy()
# Get digital output names
if rec_info.get('dig_out'):
if dig_out_names is None:
dig_out_names = dict.fromkeys(['dig_out_%i' % x
for x in rec_info['dig_out']])
name_filler = userIO.dictIO(dig_out_names, shell=shell)
dig_out_names = name_filler.fill_dict('Enter names for '
'digital outputs:')
if dig_out_names is None or \
any([x is None for x in dig_out_names.values()]):
raise ValueError('Must name all dig_outs')
dig_out_names = list(dig_out_names.values())
self.dig_out_mapping = pd.DataFrame([(x, y) for x, y in
zip(rec_info['dig_out'],
dig_out_names)],
columns=['dig_out', 'name'])
# Store clustering parameters
self.clust_params = {'file_dir': file_dir,
'data_quality': data_quality,
'sampling_rate': sampling_rate,
'clustering_params': clustering_params,
'data_params': data_params,
'bandpass_params': bandpass_params,
'spike_snapshot': spike_snapshot}
# Store and confirm spike array parameters
spike_array_params['sampling_rate'] = sampling_rate
self.spike_array_params = spike_array_params
self.psth_params = psth_params
if not confirm_all:
prompt = ('\n----------\nSpike Array Parameters\n----------\n'
+ dp.print_dict(spike_array_params) +
'\nAre these parameters good?')
q_idx = userIO.ask_user(prompt, ('Yes', 'Edit'), shell=shell)
if q_idx == 1:
self.edit_spike_array_parameters(shell=shell)
# Edit and store psth parameters
prompt = ('\n----------\nPSTH Parameters\n----------\n'
+ dp.print_dict(psth_params) +
'\nAre these parameters good?')
q_idx = userIO.ask_user(prompt, ('Yes', 'Edit'), shell=shell)
if q_idx == 1:
self.edit_psth_parameters(shell=shell)
self.save()