def _epoch_name_handler(rec_or_sig, epoch_names):
'''
helper function to transform heterogeneous inputs of epochs names (epoch names, list of epochs names, keywords) into
the corresponding list of epoch names.
:param rec_or_sig: nems recording of signal object
:param epoch_names: epoch name (str), regexp, list of epoch names, 'single', 'pair'. keywords 'single' and 'pair'
correspond to all single vocalization, and pair of stim_num prb vocalization pairs.
:return: a list with the apropiate epoch names as found in signal.epoch.name
'''
if epoch_names == 'single': # get eps matching 'voc_x' where x is a positive integer
reg_ex = r'\Avoc_\d'
epoch_names = nep.epoch_names_matching(rec_or_sig.epochs, (reg_ex))
elif epoch_names == 'pair': # get eps matching 'Cx_Py' where x and y are positive integers
reg_ex = r'\AC\d_P\d'
epoch_names = nep.epoch_names_matching(rec_or_sig.epochs, (reg_ex))
elif isinstance(epoch_names, str): # get eps matching the specified regexp
reg_ex = epoch_names
epoch_names = nep.epoch_names_matching(rec_or_sig.epochs, (reg_ex))
elif isinstance(epoch_names, list): # uses epoch_names as a list of epoch names.
ep_intersection = set(epoch_names).intersection(set(rec_or_sig.epochs.name.unique()))
if len(ep_intersection) == 0:
raise AttributeError("specified eps are not contained in sig")
pass
if len(epoch_names) == 0:
raise AttributeError("no eps match regex '{}'".format(reg_ex))
return epoch_names
def r_ceiling_test(result, fullrec, pred_name='pred', resp_name='resp', N=100):
"""
Compute noise-corrected correlation coefficient based on single-trial
correlations in the actual response.
"""
epoch_regex = '^STIM_'
epochs_to_extract = ep.epoch_names_matching(result[resp_name].epochs,
epoch_regex)
folded_resp = result[resp_name].extract_epochs(epochs_to_extract)
epochs_to_extract = ep.epoch_names_matching(result[pred_name].epochs,
epoch_regex)
folded_pred = result[pred_name].extract_epochs(epochs_to_extract)
resp = fullrec[resp_name].rasterize()
X = np.array([])
Y = np.array([])
for k, d in folded_resp.items():
if np.sum(np.isfinite(d)) > 0:
x = resp.extract_epoch(k)
X = np.concatenate((X, x.flatten()))
p = folded_pred[k]
if p.shape[0] < x.shape[0]:
p = np.tile(p, (x.shape[0], 1, 1))
Y = np.concatenate((Y, p.flatten()))
# exclude nan values of X or Y
gidx = (np.isfinite(X) & np.isfinite(Y))
X = X[gidx]
Y = Y[gidx]
sx = X
mx = X
fit_alpha, fit_loc, fit_beta = stats.gamma.fit(X)
mu = [
np.reshape(
stats.gamma.rvs(fit_alpha + sx,
loc=fit_loc,
scale=fit_beta / (1 + fit_beta)), (1, -1))
for a in range(10)
]
mu = np.concatenate(mu)
mu[mu > np.max(X)] = np.max(X)
xc_set = [np.corrcoef(mu[i, :], X)[0, 1] for i in range(10)]
log.info("Simulated r_single: %.3f +/- %.3f", np.mean(xc_set),
np.std(xc_set) / np.sqrt(10))
xc_act = np.corrcoef(Y, X)[0, 1]
log.info("actual r_single: %.03f", xc_act)
# weighted average based on number of samples in each epoch
rnorm = xc_act / np.mean(xc_set)
return rnorm
def compute_snr_multi(resp, frac_total=True):
epochs = resp.epochs
stim_epochs = ep.epoch_names_matching(epochs, 'STIM_')
resp_dict = resp.extract_epochs(stim_epochs)
chan_count = resp.shape[0]
snr = np.zeros(chan_count)
for cidx in range(chan_count):
per_stim_snrs = []
for stim, r in resp_dict.items():
repcount = r.shape[0]
if repcount > 2:
for j in range(repcount):
_r = r[:, cidx, :]
products = np.dot(_r, _r.T)
per_rep_snrs = []
for i in range(repcount):
total_power = products[i, i]
signal_powers = np.delete(products[i], i)
if frac_total:
rep_snr = np.nanmean(signal_powers) / total_power
else:
rep_snr = np.nanmean(signal_powers /
(total_power - signal_powers))
per_rep_snrs.append(rep_snr)
per_stim_snrs.append(np.nanmean(per_rep_snrs))
snr[cidx] = np.nanmean(per_stim_snrs)
#print(resp.chans[cidx], snr[cidx])
return snr
def est_halved(half=1, seed_idx=0, random_seed=1234, **ctx):
'''
Use only one half of estimation data, for comparing a model to itself.
'''
est = ctx['est']
epochs = est['stim'].epochs
stims = np.array(ep.epoch_names_matching(epochs, 'STIM_'))
indices = np.linspace(0, len(stims) - 1, len(stims), dtype=np.int)
st0 = np.random.get_state()
random_seed += seed_idx
np.random.seed(random_seed)
set1_idx = np.random.choice(indices, round(len(stims) / 2), replace=False)
np.random.set_state(st0)
mask = np.zeros_like(stims, np.bool)
mask[set1_idx] = True
set1_stims = stims[mask].tolist()
set2_stims = stims[~mask].tolist()
est1, est2 = est.split_by_epochs(set1_stims, set2_stims)
if half == 1:
est = est1
else:
est = est2
return {'est': est}
def compute_snr(resp, frac_total=True):
epochs = resp.epochs
stim_epochs = ep.epoch_names_matching(epochs, 'STIM_')
resp_dict = resp.extract_epochs(stim_epochs)
per_stim_snrs = []
for stim, resp in resp_dict.items():
resp = resp.squeeze()
if resp.ndim == 1:
# Only one stim rep, have to add back in axis for number of reps
resp = np.expand_dims(resp, 0)
products = np.dot(resp, resp.T)
per_rep_snrs = []
for i, _ in enumerate(resp):
total_power = products[i, i]
signal_powers = np.delete(products[i], i)
if frac_total:
rep_snr = np.nanmean(signal_powers) / total_power
else:
rep_snr = np.nanmean(signal_powers /
(total_power - signal_powers))
per_rep_snrs.append(rep_snr)
per_stim_snrs.append(np.nanmean(per_rep_snrs))
# if np.sum(np.isnan(per_stim_snrs)) == len(per_stim_snrs):
# import pdb; pdb.set_trace()
return np.nanmean(per_stim_snrs)
def generate_average_sig(signal_to_average,
new_signalname='respavg',
epoch_regex='^STIM_'):
'''
Returns a signal with a new signal created by replacing every epoch
matched in "epoch_regex" with the average of every occurrence in that
epoch. This is often used to make a response average signal that
is the same length as the original signal_to_average, usually for plotting.
Optional arguments:
signal_to_average The signal from which you want to create an
average signal. It will not be modified.
new_signalname The name of the new, average signal.
epoch_regex A regex to match which epochs to average across.
'''
# 1. Fold matrix over all stimuli, returning a dict where keys are stimuli
# and each value in the dictionary is (reps X cell X bins)
epochs_to_extract = ep.epoch_names_matching(signal_to_average.epochs,
epoch_regex)
folded_matrices = signal_to_average.extract_epochs(epochs_to_extract)
# 2. Average over all reps of each stim and save into dict called psth.
per_stim_psth = dict()
for k in folded_matrices.keys():
per_stim_psth[k] = np.nanmean(folded_matrices[k], axis=0)
# 3. Invert the folding to unwrap the psth into a predicted spike_dict by
# replacing all epochs in the signal with their average (psth)
respavg = signal_to_average.replace_epochs(per_stim_psth)
respavg.name = new_signalname
return respavg
def strf_tensor(mfilename,cellid,plot=False,linalg=False,real=False):
'''
Creates matrices used as features and labels of tensor
:param mfilename: File with your data in it
:param cellid: Name of cell
:param fs: sampling frequency, default 1000
:param linalg: if True, will perform and display strf generated from inputs using linear algebra as check
:param real: if True, will run full tor_tuning function to give actual strf output for given input
:return: matrix of stimulus, with time delay & matrix of response
'''
fs=1000
rec = nb.baphy_load_recording_file(mfilename=mfilename, cellid=cellid, fs=fs, stim=False)
globalparams, exptparams, exptevents = nio.baphy_parm_read(mfilename)
signal = rec['resp'].rasterize(fs=fs)
epoch_regex = "^STIM_TORC_.*" # pick all epochs that have STIM_TORC_...
epochs_to_extract = ep.epoch_names_matching(signal.epochs, epoch_regex) # find those epochs
r = signal.extract_epochs(
epochs_to_extract) # extract them, r.keys() yields names of TORCS that can be looked through as dic r['name']...can be np.squeeze(0, np.mean(
all_arr = list()
for val in r.values():
fval = np.swapaxes(np.squeeze(val), 0, 1)
all_arr.append(fval)
stacked = np.stack(all_arr,axis=2)
TorcObject = exptparams["TrialObject"][1]["ReferenceHandle"][1]
PreStimbin = int(TorcObject['PreStimSilence'] * fs)
PostStimbin = int(TorcObject['PostStimSilence'] * fs)
numbin = stacked.shape[0]
stacked = stacked[PreStimbin:(numbin - PostStimbin), :, :]
stimall,avgResp,Params = strf_input_gen(stacked,TorcObject,exptparams,fs)
fitH = model_strf(stimall,avgResp)
if plot == True:
#bf = strf_plot_prepare(fitH,Params)
[_,_] = strfplot(fitH, Params['lfreq'], Params['basep'],smooth=1, noct=Params['octaves'])
plt.title('%s - Linear Regression' % (os.path.basename(mfilename)), fontweight='bold')
if linalg == True:
#based on idea that H = Y*Xtranspose*inverse of correlation matrix
X = stimall
XT = np.swapaxes(stimall, 0, 1)
Y = np.swapaxes(np.expand_dims(avgResp, axis=1), 0, 1)
C = np.dot(X, XT)
Cinv = np.linalg.pinv(C)
H = np.reshape((np.dot(np.dot(Y, XT), Cinv)),(15,25),order='F')
#bfl = strf_plot_prepare(H,Params)
[_,_] = strfplot(H, Params['lfreq'], Params['basep'], 1, Params['octaves'])
plt.title('%s - Control' % (os.path.basename(mfilename)), fontweight='bold')
if real == True:
_ = tor_tuning(mfilename,cellid,plot=True)
return fitH
def generate_stim_from_epochs(rec,
new_signal_name='stim',
epoch_regex='^STIM_',
epoch_shift=0,
epoch2_regex=None,
epoch2_shift=0,
epoch2_shuffle=False,
onsets_only=True):
rec = rec.copy()
resp = rec['resp'].rasterize()
epochs_to_extract = ep.epoch_names_matching(resp.epochs, epoch_regex)
sigs = []
for e in epochs_to_extract:
log.info('Adding to %s: %s with shift = %d', new_signal_name, e,
epoch_shift)
s = resp.epoch_to_signal(e, onsets_only=onsets_only, shift=epoch_shift)
if epoch_shift:
s.chans[0] = "{}{:+d}".format(s.chans[0], epoch_shift)
sigs.append(s)
if epoch2_regex is not None:
epochs_to_extract = ep.epoch_names_matching(resp.epochs, epoch2_regex)
for e in epochs_to_extract:
log.info('Adding to %s: %s with shift = %d', new_signal_name, e,
epoch2_shift)
s = resp.epoch_to_signal(e,
onsets_only=onsets_only,
shift=epoch2_shift)
if epoch2_shuffle:
log.info('Shuffling %s', e)
s = s.shuffle_time()
s.chans[0] = "{}_shf".format(s.chans[0])
if epoch_shift:
s.chans[0] = "{}{:+d}".format(s.chans[0], epoch2_shift)
sigs.append(s)
stim = sigs[0].concatenate_channels(sigs)
stim.name = new_signal_name
# add_signal operates in place
rec.add_signal(stim)
return rec
def all_nwb_epochs(resp,epoch_regex):
#return epoch data as array - helpful if recording includes multiple units?
epoch_list=ep.epoch_names_matching(resp.epochs, epoch_regex)
epoch_dict=resp.extract_epochs(epoch_list)
all_epochs=[epoch_dict[key] for key in epoch_dict.keys()]
e_size=np.max([i.shape[2] for i in all_epochs])
#merge everything together - resulting array has shape trials*stimsxunitsxsamples
all_epochs=np.concatenate([np.resize(i,(50,len(resp.chans),e_size)) for i in all_epochs],0)
return all_epochs
def psth_per_file(rec):
raise NotImplementedError
resp = rec['resp'].rasterize()
file_epochs = ep.epoch_names_matching(resp.epochs, "^FILE_")
epoch_regex = "^STIM_"
stim_epochs = ep.epoch_names_matching(resp.epochs, epoch_regex)
r = []
max_rep_id = np.zeros(len(file_epochs))
for f in file_epochs:
r.append(resp.as_matrix(stim_epochs, overlapping_epoch=f) * resp.fs)
repcount = np.sum(np.isfinite(r[:, :, 0, 0]), axis=1)
max_rep_id, = np.where(repcount == np.max(repcount))
t = np.arange(r.shape[-1]) / resp.fs
plt.figure()
ax = plt.subplot(3, 1, 1)
nplt.plot_spectrogram(s[max_rep_id[-1], 0, :, :],
fs=stim.fs,
ax=ax,
title="cell {} - stim".format(cellid))
ax = plt.subplot(3, 1, 2)
nplt.raster(t, r[max_rep_id[-1], :, 0, :], ax=ax, title='raster')
ax = plt.subplot(3, 1, 3)
nplt.psth_from_raster(t,
r[max_rep_id[-1], :, 0, :],
ax=ax,
title='raster',
ylabel='spk/s')
plt.tight_layout()
def _split_signal(signal):
# finds in epochs the transition between one experiment and the next
if isinstance(signal, PointProcess):
pass
elif isinstance(signal, RasterizedSignal):
raise NotImplementedError('signal must be a PointPorcess')
elif isinstance(signal, TiledSignal):
raise NotImplementedError('signal must be a PointPorcess')
else:
raise ValueError('First argument must be a NEMS signal')
epochs = signal.epochs
epoch_names = nep.epoch_names_matching(signal.epochs, '\AFILE_[a-zA-Z]{3}\d{3}[a-z]\d{2}_[ap]_CPN\Z')
if len(epoch_names) == 0:
raise ValueError('Epochs do not contain files matching CPN experiments.')
file_epochs = epochs.loc[epochs.name.isin(epoch_names), :]
sub_signals = dict()
trip_counter = 0
perm_counter = 0
for ff, (_, file) in enumerate(file_epochs.iterrows()):
# extract relevant epochs and data
sub_epochs = epochs.loc[(epochs.start >= file.start) & (epochs.end <= file.end), :].copy()
sub_epochs[['start', 'end']] = sub_epochs[['start', 'end']] - file.start
sub_data = {cell: spikes[np.logical_and(spikes >= file.start, spikes < file.end)] - file.start
for cell, spikes in signal._data.copy().items()}
meta = signal.meta.copy()
meta['rawid'] = [meta['rawid'][ff]]
sub_signal = signal._modified_copy(data=sub_data, epochs=sub_epochs, meta=meta)
# checks names of epochs to define triples or permutation
# keeps track of number of trip of perm experiments
# names the signal with the experiment type and number in case of repeated trip and/or perm
exp_type = _detect_type(sub_epochs)
if exp_type == 'perm':
exp_type = f'{exp_type}{perm_counter}'
perm_counter += 1
elif exp_type == 'trip':
exp_type = f'{exp_type}{trip_counter}'
trip_counter += 1
else:
raise ValueError('not Permutations or Triplets')
sub_signals[exp_type] = sub_signal
return sub_signals
def generate_psth_from_est_for_both_est_and_val(est,
val,
epoch_regex='^STIM_'):
'''
Estimates a PSTH from the EST set, and returns two signals based on the
est and val, in which each repetition of a stim uses the EST PSTH?
subtract spont rate based on pre-stim silence for ALL estimation data.
'''
resp_est = est['resp'].rasterize()
resp_val = val['resp'].rasterize()
# compute PSTH response and spont rate during those valid trials
prestimsilence = resp_est.extract_epoch('PreStimSilence')
if len(prestimsilence.shape) == 3:
spont_rate = np.nanmean(prestimsilence, axis=(0, 2))
else:
spont_rate = np.nanmean(prestimsilence)
epochs_to_extract = ep.epoch_names_matching(resp_est.epochs, epoch_regex)
folded_matrices = resp_est.extract_epochs(epochs_to_extract,
mask=est['mask'])
# 2. Average over all reps of each stim and save into dict called psth.
per_stim_psth = dict()
for k in folded_matrices.keys():
per_stim_psth[k] = np.nanmean(folded_matrices[k], axis=0) - \
spont_rate[:, np.newaxis]
# 3. Invert the folding to unwrap the psth into a predicted spike_dict by
# replacing all epochs in the signal with their average (psth)
respavg_est = resp_est.replace_epochs(per_stim_psth)
respavg_est.name = 'psth'
# add signal to the est recording
est.add_signal(respavg_est)
respavg_val = resp_val.replace_epochs(per_stim_psth)
respavg_val.name = 'psth'
# add signal to the val recording
val.add_signal(respavg_val)
return est, val
def spike_dev_by_pupil(rec,rras):
#mean dev from psth per trial and mean pupil pertrial accross all epochs - return df of correlation coeff
#will return data for multiple units if
epochs=rec.epochs
if rras is None:
rras=rec['resp'].rasterize()
#extract all epochs for pupil
epoch_regex="^natural_scene"
epoch_list=ep.epoch_names_matching(epochs, epoch_regex)
pupil_epochs=rec['pupil'].extract_epochs(epoch_list)
pupil_epochs=[pupil_epochs[key] for key in pupil_epochs.keys()]
e_size=np.max([i.shape[2] for i in pupil_epochs])
pupil_epochs=np.squeeze(np.concatenate([np.resize(i,(50,1,e_size)) for i in pupil_epochs],0),1)
#mean pupil over time for each stim,trial
meanpupil=np.nanmean(pupil_epochs,1).reshape(119,50)
#extract all natural_scene epochs then merge dict into array along trial num axis
epoch_dict=rras.extract_epochs(epoch_list)
all_epochs=[epoch_dict[key] for key in epoch_dict.keys()]
e_size=np.max([i.shape[2] for i in all_epochs])
#merge everything together - resulting array has shape trials*stimsxunitsxsamples
all_epochs=np.concatenate([np.resize(i,(50,len(rras.chans),e_size)) for i in all_epochs],0)
from scipy.stats import pearsonr
#iterate over cells in signal
respdev_dict={}
for i in range(all_epochs.shape[1]):
chan=all_epochs[:,i,:].reshape(119,50,-1)
meanresp_pertrial=np.nanmean(chan,1) #avg accross trials for each epoch
respdev_dict[rras.chans[i]]=np.nanmean((chan-np.expand_dims(meanresp_pertrial,1)),2)
pupilnan=(~np.isnan(meanpupil))
dev_corr = {}
#df of pupil, resp dev correlation
for key in respdev_dict.keys():
resp = respdev_dict[key]
corr_index=((~np.isnan(resp))&pupilnan) #remove nans for correlation
(coeff,pval) = pearsonr(meanpupil[corr_index],resp[corr_index])
dev_corr[key]={'coeff':coeff,'pval':pval}
dev_corr=pd.DataFrame.from_dict(dev_corr)
return dev_corr
def stim_resp_per_epoch(rec):
stim = copy.deepcopy(rec['stim'].as_continuous())
resp = copy.deepcopy(rec['resp'].as_continuous())
fs = rec['stim'].fs
epochs = rec.epochs
stim_epochs = ep.epoch_names_matching(epochs, 'STIM_')
pre_silence = _silence_duration(epochs, 'PreStimSilence')
post_silence = _silence_duration(epochs, 'PostStimSilence')
stims = []
resps = []
for s in stim_epochs:
row = epochs[epochs.name == s]
start = int((row['start'].values[0] + pre_silence)*fs)
end = int((row['end'].values[0] - post_silence)*fs)
stims.append(stim[:, start:end])
resps.append(resp[start:end])
return stims, resps
def mean_sd_per_stim_by_cellid(cellid, batch, loadkey='ozgf.fs100.ch18',
max_db_scale=65, pre_log_floor=1,
stims_to_skip=[]):
rec_path = xwrap.generate_recording_uri(cellid, batch, loadkey=loadkey)
rec = nems.recording.load_recording(rec_path)
stim = copy.deepcopy(rec['stim'].as_continuous())
fs = rec['stim'].fs
epochs = rec.epochs
stim_epochs = ep.epoch_names_matching(epochs, 'STIM_')
stim_epochs = [s for s in stim_epochs if s not in stims_to_skip]
pre_silence = silence_duration(epochs, 'PreStimSilence')
post_silence = silence_duration(epochs, 'PostStimSilence')
results = {}
for s in stim_epochs:
row = epochs[epochs.name == s]
start = int((row['start'].values[0] + pre_silence)*fs)
end = int((row['end'].values[0] - post_silence)*fs)
results[s] = spectrogram_mean_sd(stim[:, start:end],
max_db_scale=max_db_scale,
pre_log_floor=pre_log_floor)
return results
def compute_snr(resp, frac_total=True):
epochs = resp.epochs
stim_epochs = ep.epoch_names_matching(epochs, 'STIM_')
resp_dict = resp.extract_epochs(stim_epochs)
per_stim_snrs = []
for stim, resp in resp_dict.items():
resp = resp.squeeze()
products = np.dot(resp, resp.T)
per_rep_snrs = []
for i, _ in enumerate(resp):
total_power = products[i, i]
signal_powers = np.delete(products[i], i)
if frac_total:
rep_snr = np.nanmean(signal_powers) / total_power
else:
rep_snr = np.nanmean(signal_powers /
(total_power - signal_powers))
per_rep_snrs.append(rep_snr)
per_stim_snrs.append(np.nanmean(per_rep_snrs))
return np.nanmean(per_stim_snrs)
def nwb_resp_psth(rec,epoch_regex):
#intended to give similar output generate_psth_from_resp from preprocessing model, but works better w/ structure
#of neuropixels data?
newrec=rec.copy()
resp=newrec['resp'].rasterize()
#epoch_regex="^natural_scene"
#extract all natural_scene epochs then merge dict and avg - add new signal
epochs_to_extract = ep.epoch_names_matching(resp.epochs, epoch_regex)
#epoch_dict=resp.extract_epochs(epochs.loc[epochs.name.str.contains('natural_scene'),'name'])
epoch_dict=resp.extract_epochs(epochs_to_extract)
#no pre/post stim silence but spontaneous intervals work instead? - or use psth w/out mean subtracted
spont = resp.extract_epoch('spontaneous')
spont_rate=np.nanmean(spont)
per_stim_psth_spont = {}
per_stim_psth = {}
for k, v in epoch_dict.items():
per_stim_psth_spont[k] = np.nanmean(v, axis=0)
per_stim_psth[k] = np.nanmean(v, axis=0) - spont_rate
respavg = resp.replace_epochs(per_stim_psth)
respavg.name = 'psth'
respavg_data = respavg.as_continuous().copy()
respavg_with_spont = resp.replace_epochs(per_stim_psth_spont)
respavg_with_spont.name = 'psth_sp'
respavg_spont_data = respavg_with_spont.as_continuous().copy()
respavg = respavg._modified_copy(respavg_data)
respavg_with_spont = respavg_with_spont._modified_copy(respavg_spont_data)
newrec.add_signal(respavg)
newrec.add_signal(respavg_with_spont)
return newrec
def test_plots():
recording.get_demo_recordings(name=recording_file)
rec = recording.load_recording(uri)
resp = rec['resp'].rasterize()
stim = rec['stim'].rasterize()
epoch_regex = "^STIM_"
stim_epochs = ep.epoch_names_matching(resp.epochs, epoch_regex)
r = resp.as_matrix(stim_epochs) * resp.fs
s = stim.as_matrix(stim_epochs)
repcount = np.sum(np.isfinite(r[:, :, 0, 0]), axis=1)
max_rep_id, = np.where(repcount == np.max(repcount))
t = np.arange(r.shape[-1]) / resp.fs
plt.figure()
ax = plt.subplot(3, 1, 1)
nplt.plot_spectrogram(s[max_rep_id[-1], 0, :, :],
fs=stim.fs,
ax=ax,
title="cell {} - stim".format(cellid))
ax = plt.subplot(3, 1, 2)
nplt.raster(t, r[max_rep_id[-1], :, 0, :], ax=ax, title='raster')
ax = plt.subplot(3, 1, 3)
nplt.psth_from_raster(t,
r[max_rep_id[-1], :, 0, :],
ax=ax,
title='raster',
ylabel='spk/s')
plt.tight_layout()
'st.pca.pup+r1'
]
resp_modelname = f"psth.fs4.pup-ld-hrc-psthfr.z-pca.cc1.no.p-{states[-1]}-plgsm.p2-aev-rd.resp"+\
"_stategain.2xR.x1,3-spred-lvnorm.4xR.so.x2-inoise.4xR.x3"+\
"_tfinit.xx0.n.lr1e4.cont.et4.i20-lvnoise.r4-aev-ccnorm.md.t1.f0.ss3"
modelnames = [resp_modelname] + [modelname_base.format(s) for s in states]
xf, ctx = load_model_xform(cellid=cellid,
batch=batch,
modelname=modelnames[-1])
val = ctx['val'].copy()
resp = val['resp'].rasterize()
epoch_regex = "^STIM_"
epochs = ep.epoch_names_matching(resp.epochs, regex_str=epoch_regex)
input_name = 'pred0'
pred0 = val[input_name].extract_epochs(epochs, mask=val['mask'])
pred = val['pred'].extract_epochs(epochs, mask=val['mask'])
resp = val['resp'].extract_epochs(epochs, mask=val['mask'])
pupil = val['pupil'].extract_epochs(epochs, mask=val['mask'])
pmedian = np.nanmedian(val['pupil'].as_continuous())
epochs = list(resp.keys())
epochs
#
#ncells, nreps, nstim, nbins = X.shape
e1 = epochs[0]
def generate_psth_from_resp(rec, epoch_regex='^STIM_', smooth_resp=False):
'''
Estimates a PSTH from all responses to each regex match in a recording
subtract spont rate based on pre-stim silence for ALL estimation data.
if rec['mask'] exists, uses rec['mask'] == True to determine valid epochs
'''
newrec = rec.copy()
resp = newrec['resp'].rasterize()
# compute spont rate during valid (non-masked) trials
if 'mask' in newrec.signals.keys():
prestimsilence = resp.extract_epoch('PreStimSilence',
mask=newrec['mask'])
else:
prestimsilence = resp.extract_epoch('PreStimSilence')
if len(prestimsilence.shape) == 3:
spont_rate = np.nanmean(prestimsilence, axis=(0, 2))
else:
spont_rate = np.nanmean(prestimsilence)
idx = resp.get_epoch_indices('PreStimSilence')
prebins = idx[0][1] - idx[0][0]
idx = resp.get_epoch_indices('PostStimSilence')
postbins = idx[0][1] - idx[0][0]
# compute PSTH response during valid trials
if type(epoch_regex) == list:
epochs_to_extract = []
for rx in epoch_regex:
eps = ep.epoch_names_matching(resp.epochs, rx)
epochs_to_extract += eps
elif type(epoch_regex) == str:
epochs_to_extract = ep.epoch_names_matching(resp.epochs, epoch_regex)
if 'mask' in newrec.signals.keys():
folded_matrices = resp.extract_epochs(epochs_to_extract,
mask=newrec['mask'])
else:
folded_matrices = resp.extract_epochs(epochs_to_extract)
# 2. Average over all reps of each stim and save into dict called psth.
per_stim_psth = dict()
per_stim_psth_spont = dict()
for k, v in folded_matrices.items():
if smooth_resp:
# replace each epoch (pre, during, post) with average
v[:, :, :prebins] = np.nanmean(v[:, :, :prebins],
axis=2,
keepdims=True)
v[:, :,
prebins:(prebins + 2)] = np.nanmean(v[:, :,
prebins:(prebins + 2)],
axis=2,
keepdims=True)
v[:, :,
(prebins + 2):-postbins] = np.nanmean(v[:, :,
(prebins + 2):-postbins],
axis=2,
keepdims=True)
v[:, :, -postbins:(-postbins + 2)] = np.nanmean(
v[:, :, -postbins:(-postbins + 2)], axis=2, keepdims=True)
v[:, :, (-postbins + 2):] = np.nanmean(v[:, :, (-postbins + 2):],
axis=2,
keepdims=True)
per_stim_psth[k] = np.nanmean(v, axis=0) - spont_rate[:, np.newaxis]
per_stim_psth_spont[k] = np.nanmean(v, axis=0)
folded_matrices[k] = v
# 3. Invert the folding to unwrap the psth into a predicted spike_dict by
# replacing all epochs in the signal with their average (psth)
respavg = resp.replace_epochs(per_stim_psth)
respavg_with_spont = resp.replace_epochs(per_stim_psth_spont)
respavg.name = 'psth'
respavg_with_spont.name = 'psth_sp'
# Fill in a all non-masked periods with 0 (presumably, these are spont
# periods not contained within stimulus epochs), or spont rate (for the signal
# containing spont rate)
respavg_data = respavg.as_continuous().copy()
respavg_spont_data = respavg_with_spont.as_continuous().copy()
if 'mask' in newrec.signals.keys():
mask_data = newrec['mask']._data
else:
mask_data = np.ones(respavg_data.shape).astype(np.bool)
spont_periods = ((np.isnan(respavg_data)) & (mask_data == True))
respavg_data[:, spont_periods[0, :]] = 0
# respavg_spont_data[:, spont_periods[0,:]] = spont_rate[:, np.newaxis]
respavg = respavg._modified_copy(respavg_data)
respavg_with_spont = respavg_with_spont._modified_copy(respavg_spont_data)
# add the new signals to the recording
newrec.add_signal(respavg)
newrec.add_signal(respavg_with_spont)
if smooth_resp:
log.info('Replacing resp with smoothed resp')
resp = resp.replace_epochs(folded_matrices, mask=newrec['mask'])
newrec.add_signal(resp)
return newrec
def mask_all_but_correct_references(rec,
balance_rep_count=False,
include_incorrect=False):
"""
Specialized function for removing incorrect trials from data
collected using baphy during behavior.
TODO: Migrate to nems_lbhb and/or make a more generic version
"""
newrec = rec.copy()
newrec['resp'] = newrec['resp'].rasterize()
if 'stim' in newrec.signals.keys():
newrec['stim'] = newrec['stim'].rasterize()
resp = newrec['resp']
if balance_rep_count:
epoch_regex = "^STIM_"
epochs_to_extract = ep.epoch_names_matching(resp.epochs, epoch_regex)
p = resp.get_epoch_indices("PASSIVE_EXPERIMENT")
a = resp.get_epoch_indices("HIT_TRIAL")
epoch_list = []
for s in epochs_to_extract:
e = resp.get_epoch_indices(s)
pe = ep.epoch_intersection(e, p)
ae = ep.epoch_intersection(e, a)
if len(pe) > len(ae):
epoch_list.extend(ae)
subset = np.round(np.linspace(0, len(pe),
len(ae) + 1)).astype(int)
for i in subset[:-1]:
epoch_list.append(pe[i])
else:
subset = np.round(np.linspace(0, len(ae),
len(pe) + 1)).astype(int)
for i in subset[:-1]:
epoch_list.append(ae[i])
epoch_list.extend(pe)
newrec = newrec.create_mask(epoch_list)
elif include_incorrect:
log.info('INCLUDING ALL TRIALS (CORRECT AND INCORRECT)')
newrec = newrec.and_mask(['REFERENCE'])
else:
newrec = newrec.and_mask(['PASSIVE_EXPERIMENT', 'HIT_TRIAL'])
newrec = newrec.and_mask(['REFERENCE'])
# figure out if some actives should be masked out
# t = ep.epoch_names_matching(resp.epochs, "^TAR_")
# tm = [tt[:-2] for tt in t] # trim last digits
# active_epochs = resp.get_epoch_indices("ACTIVE_EXPERIMENT")
# if len(set(tm)) > 1 and len(active_epochs) > 1:
# print('Multiple targets: ', tm)
# files = ep.epoch_names_matching(resp.epochs, "^FILE_")
# keep_files = files
# e = active_epochs[1]
# for i,f in enumerate(files):
# fi = resp.get_epoch_indices(f)
# if any(ep.epoch_contains([e], fi, 'both')):
# keep_files = files[:i]
#
# print('Print keeping files: ', keep_files)
# newrec = newrec.and_mask(keep_files)
if 'state' in newrec.signals:
b_states = [
'far', 'hit', 'lick', 'puretone_trials', 'easy_trials',
'hard_trials'
]
trec = newrec.copy()
trec = trec.and_mask(['ACTIVE_EXPERIMENT'])
st = trec['state'].as_continuous().copy()
str = trec['state_raw'].as_continuous().copy()
mask = trec['mask'].as_continuous()[0, :]
for s in trec['state'].chans:
if s in b_states:
i = trec['state'].chans.index(s)
m = np.nanmean(st[i, mask])
sd = np.nanstd(st[i, mask])
# print("{} {}: m={}, std={}".format(s, i, m, sd))
# print(np.sum(mask))
st[i, mask] -= m
st[i, mask] /= sd
str[i, mask] -= m
str[i, mask] /= sd
newrec['state'] = newrec['state']._modified_copy(st)
newrec['state_raw'] = newrec['state_raw']._modified_copy(str)
return newrec
def average_away_epoch_occurrences(rec, epoch_regex='^STIM_'):
'''
Returns a recording with _all_ signals averaged across epochs that
match epoch_regex, shortening them so that each epoch occurs only
once in the new signals. i.e. unlike 'add_average_sig', the new
recording will have signals 3x shorter if there are 3 occurrences of
every epoch.
This has advantages:
1. Averaging the value of a signal (such as a response) in different
occurrences will make it behave more like a linear variable with
gaussian noise, which is advantageous in many circumstances.
2. There will be less computation needed because the signal is shorter.
It also has disadvantages:
1. Stateful filters (FIR, IIR) will be subtly wrong near epoch boundaries
2. Any ordering of epochs is essentially lost, unless all epochs appear
in a perfectly repeated order.
To avoid accidentally averaging away differences in responses to stimuli
that are based on behavioral state, you may need to create new epochs
(based on stimulus and behaviorial state, for example) and then match
the epoch_regex to those.
'''
# Create new recording
newrec = rec.copy()
counter = 0
# iterate through each signal
for signal_name, signal_to_average in rec.signals.items():
# TODO: for TiledSignals, there is a much simpler way to do this!
# 0. rasterize
signal_to_average = signal_to_average.rasterize()
# 1. Find matching epochs
epochs_to_extract = ep.epoch_names_matching(rec.epochs, epoch_regex)
# 2. Fold over all stimuli, returning a dict where keys are stimuli
# and each value in the dictionary is (reps X cell X bins)
folded_matrices = signal_to_average.extract_epochs(epochs_to_extract)
# force a standard list of sorted keys for all signals
if counter == 0:
sorted_keys = list(folded_matrices.keys())
sorted_keys.sort()
counter += 1
# 3. Average over all occurrences of each epoch, and append to data
fs = signal_to_average.fs
data = np.zeros([signal_to_average.nchans, 0])
current_time = 0
epochs = None
for k in sorted_keys:
# Supress warnings about all-nan matrices
with warnings.catch_warnings():
warnings.simplefilter("ignore")
per_stim_psth = np.nanmean(folded_matrices[k], axis=0)
data = np.concatenate((data, per_stim_psth), axis=1)
epoch = current_time + np.array([[0, per_stim_psth.shape[1] / fs]])
df = pd.DataFrame(np.tile(epoch, [2, 1]), columns=['start', 'end'])
df['name'] = k
df.at[1, 'name'] = 'TRIAL'
if epochs is not None:
epochs = epochs.append(df, ignore_index=True)
else:
epochs = df
current_time = epoch[0, 1]
#print("{0} epoch: {1}-{2}".format(k,epoch[0,0],epoch[0,1]))
avg_signal = signal.RasterizedSignal(
fs=fs,
data=data,
name=signal_to_average.name,
recording=signal_to_average.recording,
chans=signal_to_average.chans,
epochs=epochs,
meta=signal_to_average.meta)
newrec.add_signal(avg_signal)
return newrec
def generate_psth_from_est_for_both_est_and_val(est, val):
'''
Estimates a PSTH from the EST set, and returns two signals based on the
est and val, in which each repetition of a stim uses the EST PSTH?
subtract spont rate based on pre-stim silence for ALL estimation data.
'''
epoch_regex = '^STIM_'
resp_est = est['resp'].copy()
resp_val = val['resp']
# find all valid references in est data-- passive or correct trials
ref_phase = resp_est.epoch_to_signal('REFERENCE')
active_phase = resp_est.epoch_to_signal('ACTIVE_EXPERIMENT')
correct_phase = resp_est.epoch_to_signal('HIT_TRIAL')
valid_phase = np.logical_and(
ref_phase.as_continuous(),
np.logical_or(np.logical_not(active_phase.as_continuous()),
correct_phase.as_continuous()))
ref_phase = ref_phase._modified_copy(valid_phase)
resp_est = resp_est.nan_mask(ref_phase.as_continuous())
# compute PSTH response and spont rate during those valid trials
prestimsilence = resp_est.extract_epoch('PreStimSilence')
if len(prestimsilence.shape) == 3:
spont_rate = np.nanmean(prestimsilence, axis=(0, 2))
else:
spont_rate = np.nanmean(prestimsilence)
epochs_to_extract = ep.epoch_names_matching(resp_est.epochs, epoch_regex)
folded_matrices = resp_est.extract_epochs(epochs_to_extract)
# 2. Average over all reps of each stim and save into dict called psth.
per_stim_psth = dict()
for k in folded_matrices.keys():
per_stim_psth[k] = np.nanmean(folded_matrices[k],
axis=0) - spont_rate[:, np.newaxis]
# 3. Invert the folding to unwrap the psth into a predicted spike_dict by
# replacing all epochs in the signal with their average (psth)
respavg_est = resp_est.replace_epochs(per_stim_psth)
respavg_est.name = 'stim' # TODO: SVD suggests rename 2018-03-08
# mark invalid phases as nan
respavg_est = respavg_est.nan_mask(ref_phase.as_continuous())
# add signal to the recording
est.add_signal(respavg_est)
respavg_val = resp_val.replace_epochs(per_stim_psth)
respavg_val.name = 'stim' # TODO: SVD suggests rename 2018-03-08
ref_phase = val['resp'].epoch_to_signal('REFERENCE')
active_phase = val['resp'].epoch_to_signal('ACTIVE_EXPERIMENT')
correct_phase = val['resp'].epoch_to_signal('HIT_TRIAL')
valid_phase = np.logical_and(
ref_phase.as_continuous(),
np.logical_or(np.logical_not(active_phase.as_continuous()),
correct_phase.as_continuous()))
ref_phase = ref_phase._modified_copy(valid_phase)
respavg_val = respavg_val.nan_mask(ref_phase.as_continuous())
# add signal to the recording
val.add_signal(respavg_val)
return est, val
#est, val = estimation, validation data sets est, val = rec.split_using_epoch_occurrence_counts(epoch_regex="^STIM_") est = average_away_epoch_occurrences(est, epoch_regex="^STIM_") val = average_away_epoch_occurrences(val, epoch_regex="^STIM_") # get matrices for fitting: X_est = est['stim'].apply_mask().as_continuous() # frequency x time Y_est = est['resp'].apply_mask().as_continuous() # neuron x time # get matrices for testing model predictions: X_val = val['stim'].apply_mask().as_continuous() Y_val = val['resp'].apply_mask().as_continuous() # find a stimulus to display epoch_regex = '^STIM_' epochs_to_extract = ep.epoch_names_matching(val.epochs, epoch_regex) epoch = epochs_to_extract[0] plt.figure() ax = plt.subplot(3, 1, 1) nplt.spectrogram_from_epoch(val['stim'], epoch, ax=ax, time_offset=2) ax = plt.subplot(3, 1, 2) nplt.timeseries_from_epoch([val['resp']], epoch, ax=ax) raster = rec['resp'].extract_epoch(epoch) ax = plt.subplot(3, 1, 3) plt.imshow(raster[:, 0, :]) plt.tight_layout()
cells_sets = dict()
for cellid in first_cell:
# cellid='bbl099g-04-1'
try:
ctx=baphy_load_wrapper(cellid=cellid, batch=batch, loadkey=loadkey, siteid=None)
rec=load_recording(ctx['recording_uri_list'][0])
except:
print('could not load site for cell {}'.format(cellid))
bad_cells.append(cellid)
continue
sig = rec['resp'].rasterize()
#wav_names = ne.epoch_names_matching(sig.epochs, r'^STIM_00') # for validation set only
wav_names = ne.epoch_names_matching(sig.epochs, r'^STIM_')
epre = sig.get_epoch_indices('PreStimSilence')
epost = sig.get_epoch_indices('PostStimSilence')
prebins = epre[0][1] - epre[0][0]
postbins = epost[0][1] - epost[0][0]
mtx_dict = sig.extract_epochs(wav_names)
# orderse the response of all the cells in the site to all the sounds in a 3d array of shape Sound x Cells x Time
# then normalizes the response of each cell across all stimuli
shape = [len(mtx_dict.keys()),
list(mtx_dict.values())[0].shape[1],
np.max([val.shape[2] for val in mtx_dict.values()])]
site_arr = np.empty(shape); site_arr[:] = np.nan
for ss, (sound, response) in enumerate(mtx_dict.items()):
PSTH = np.mean(response, axis=0)
site_arr[ss, :, :] = PSTH
import nems.plots.api as nplt
from nems.recording import Recording
# specify directories for loading data and fitted modelspec
signals_dir = '../signals'
#signals_dir = '/home/jacob/auto/data/batch271_fs100_ozgf18/'
modelspecs_dir = '../modelspecs'
# load the data
rec = Recording.load(os.path.join(signals_dir, 'TAR010c-18-1'))
# Add a new signal, respavg, to the recording, in 4 steps
# 1. Fold matrix over all stimuli, returning a dictionary where keys are stimuli
# and each value in the dictionary is (reps X cell X bins)
epochs_to_extract = ep.epoch_names_matching(rec.epochs, '^STIM_')
folded_matrix = rec['resp'].extract_epochs(epochs_to_extract)
# 2. Average over all reps of each stim and save into dict called psth.
per_stim_psth = dict()
for k in folded_matrix.keys():
per_stim_psth[k] = np.nanmean(folded_matrix[k], axis=0)
# 3. Invert the folding to unwrap the psth back out into a predicted spike_dict by
# simply replacing all epochs in the signal with their psth
respavg = rec['resp'].replace_epochs(per_stim_psth)
respavg.name = 'respavg'
# 4. Now add the signal to the recording
rec.add_signal(respavg)
def r_ceiling(result, fullrec, pred_name='pred', resp_name='resp', N=100):
"""
parameter:
result : recording
validation data containing resp_name and pred_name signals
fullrec : orginal recording that isn't averaged across reps
N : int
number of random single trial pairs to test
returns:
rnorm: nparray
corrected ceiling measure for each response channel (ie,
there should be support for multiple neural channels)
Compute noise-corrected correlation coefficient based on single-trial
correlations in the actual response. Based on method in
Hsu and Theusnissen (2004) Network.
SVD revised 2018-08-30 to hopefully make more stable. Instead of computing
average single-trial corr from separate per-stimulus measurements, now
concatenates one rep of each validation stimulus into a long vector for
calculating a corr coeff across all stimuli. Still repeats this for a
bunch of pairs to get a good estimate of correlation between single trials
"""
epoch_regex = '^STIM_'
epochs_to_extract = ep.epoch_names_matching(result[resp_name].epochs,
epoch_regex)
folded_resp = result[resp_name].extract_epochs(epochs_to_extract)
epochs_to_extract = ep.epoch_names_matching(result[pred_name].epochs,
epoch_regex)
folded_pred = result[pred_name].extract_epochs(epochs_to_extract)
resp = fullrec[resp_name].rasterize()
chancount = fullrec[resp_name].shape[0]
rnorm = np.zeros(chancount)
for chanidx in range(chancount):
Xall = []
p = []
reps = []
preps = []
for k, d in folded_resp.items():
if np.sum(np.isfinite(d)) > 0:
Xall.append(resp.extract_epoch(k)[:, chanidx, :])
p.append(folded_pred[k][:, chanidx, :])
reps.append(Xall[-1].shape[0])
preps.append(p[-1].shape[0])
if Xall == []:
return 0
minreps = np.min(reps)
X = [x[:minreps, :] for x in Xall]
X = np.concatenate(X, axis=1)
minpreps = np.min(preps)
p = [p0[:minpreps, :] for p0 in p]
p = np.concatenate(p, axis=1)
if minreps > 1:
rac = _r_single(X, N)
repcount = X.shape[0]
rs = np.zeros(repcount)
for nn in range(repcount):
X1 = X[nn, :]
X2 = p[0, :]
# remove all nans from pred and resp
ff = np.isfinite(X1) & np.isfinite(X2)
X1 = X1[ff]
X2 = X2[ff]
if (np.sum(ff) == 0) or \
(np.sum(X1) == 0) or \
(len(np.unique(X2)) == 1):
rs[nn] = 0
else:
rs[nn] = np.corrcoef(X1, X2)[0, 1]
rnorm[chanidx] = np.mean(rs) / np.sqrt(rac)
else:
rnorm[chanidx] = 0
return rnorm
def make_state_signal(rec,
state_signals=['pupil'],
permute_signals=[],
new_signalname='state'):
"""
generate state signal for stategain.S/sdexp.S models
valid state signals include (incomplete list):
pupil, pupil_ev, pupil_bs, pupil_psd
active, each_file, each_passive, each_half
far, hit, lick, p_x_a
TODO: Migrate to nems_lbhb or make a more generic version
"""
newrec = rec.copy()
resp = newrec['resp'].rasterize()
# normalize mean/std of pupil trace if being used
if ('pupil' in state_signals) or ('pupil_ev' in state_signals) or \
('pupil_bs' in state_signals):
# normalize min-max
p = newrec["pupil"].as_continuous().copy()
# p[p < np.nanmax(p)/5] = np.nanmax(p)/5
p -= np.nanmean(p)
p /= np.nanstd(p)
newrec["pupil"] = newrec["pupil"]._modified_copy(p)
if ('pupil_psd') in state_signals:
pup = newrec['pupil'].as_continuous().copy()
fs = newrec['pupil'].fs
# get spectrogram of pupil
nperseg = int(60 * fs)
noverlap = nperseg - 1
f, time, Sxx = ss.spectrogram(pup.squeeze(),
fs=fs,
nperseg=nperseg,
noverlap=noverlap)
max_chan = 4 # (np.abs(f - 0.1)).argmin()
# Keep only first five channels of spectrogram
#f = interpolate.interp1d(np.arange(0, Sxx.shape[1]), Sxx[:max_chan, :], axis=1)
#newspec = f(np.linspace(0, Sxx.shape[-1]-1, pup.shape[-1]))
pad1 = np.ones((max_chan, int(nperseg / 2))) * Sxx[:max_chan, [0]]
pad2 = np.ones((max_chan, int(nperseg / 2 - 1))) * Sxx[:max_chan, [-1]]
newspec = np.concatenate((pad1, Sxx[:max_chan, :], pad2), axis=1)
# = np.concatenate((Sxx[:max_chan, :], np.tile(Sxx[:max_chan,-1][:, np.newaxis], [1, noverlap])), axis=1)
newspec -= np.nanmean(newspec, axis=1, keepdims=True)
newspec /= np.nanstd(newspec, axis=1, keepdims=True)
spec_signal = newrec['pupil']._modified_copy(newspec)
spec_signal.name = 'pupil_psd'
chan_names = []
for chan in range(0, newspec.shape[0]):
chan_names.append('puppsd{0}'.format(chan))
spec_signal.chans = chan_names
newrec.add_signal(spec_signal)
if ('pupil_ev' in state_signals) or ('pupil_bs' in state_signals):
# generate separate pupil baseline and evoked signals
prestimsilence = newrec["pupil"].extract_epoch('PreStimSilence')
spont_bins = prestimsilence.shape[2]
pupil_trial = newrec["pupil"].extract_epoch('TRIAL')
pupil_bs = np.zeros(pupil_trial.shape)
for ii in range(pupil_trial.shape[0]):
pupil_bs[ii, :, :] = np.mean(pupil_trial[ii, :, :spont_bins])
pupil_ev = pupil_trial - pupil_bs
newrec['pupil_ev'] = newrec["pupil"].replace_epoch('TRIAL', pupil_ev)
newrec['pupil_ev'].chans = ['pupil_ev']
newrec['pupil_bs'] = newrec["pupil"].replace_epoch('TRIAL', pupil_bs)
newrec['pupil_bs'].chans = ['pupil_bs']
if ('each_passive' in state_signals):
file_epochs = ep.epoch_names_matching(resp.epochs, "^FILE_")
pset = []
found_passive1 = False
for f in file_epochs:
# test if passive expt
epoch_indices = ep.epoch_intersection(
resp.get_epoch_indices(f),
resp.get_epoch_indices('PASSIVE_EXPERIMENT'))
if epoch_indices.size:
if not (found_passive1):
# skip first passive
found_passive1 = True
else:
pset.append(f)
newrec[f] = resp.epoch_to_signal(f)
state_signals.remove('each_passive')
state_signals.extend(pset)
if 'each_passive' in permute_signals:
permute_signals.remove('each_passive')
permute_signals.extend(pset)
if ('each_file' in state_signals):
file_epochs = ep.epoch_names_matching(resp.epochs, "^FILE_")
trial_indices = resp.get_epoch_indices('TRIAL')
passive_indices = resp.get_epoch_indices('PASSIVE_EXPERIMENT')
pset = []
pcount = 0
acount = 0
for f in file_epochs:
# test if passive expt
f_indices = resp.get_epoch_indices(f)
epoch_indices = ep.epoch_intersection(f_indices, passive_indices)
if epoch_indices.size:
# this is a passive file
name1 = "PASSIVE_{}".format(pcount)
pcount += 1
if pcount == 1:
acount = 1 # reset acount for actives after first passive
else:
# use first passive part A as baseline - don't model
pset.append(name1)
newrec[name1] = resp.epoch_to_signal(name1,
indices=f_indices)
else:
name1 = "ACTIVE_{}".format(acount)
pset.append(name1)
newrec[name1] = resp.epoch_to_signal(name1, indices=f_indices)
if pcount == 0:
acount -= 1
else:
acount += 1
# test if passive expt
# epoch_indices = ep.epoch_intersection(
# resp.get_epoch_indices(f),
# resp.get_epoch_indices('PASSIVE_EXPERIMENT'))
# if epoch_indices.size and not(found_passive1):
# # skip first passive
# found_passive1 = True
# else:
# pset.append(f)
# newrec[f] = resp.epoch_to_signal(f)
state_signals.remove('each_file')
state_signals.extend(pset)
if 'each_file' in permute_signals:
permute_signals.remove('each_file')
permute_signals.extend(pset)
if ('each_half' in state_signals):
file_epochs = ep.epoch_names_matching(resp.epochs, "^FILE_")
trial_indices = resp.get_epoch_indices('TRIAL')
passive_indices = resp.get_epoch_indices('PASSIVE_EXPERIMENT')
pset = []
pcount = 0
acount = 0
for f in file_epochs:
# test if passive expt
f_indices = resp.get_epoch_indices(f)
epoch_indices = ep.epoch_intersection(f_indices, passive_indices)
trial_intersect = ep.epoch_intersection(f_indices, trial_indices)
#trial_count = trial_intersect.shape[0]
#_split = int(trial_count/2)
_t1 = trial_intersect[0, 0]
_t2 = trial_intersect[-1, 1]
_split = int((_t1 + _t2) / 2)
epoch1 = np.array([[_t1, _split]])
epoch2 = np.array([[_split, _t2]])
if epoch_indices.size:
# this is a passive file
name1 = "PASSIVE_{}_{}".format(pcount, 'A')
name2 = "PASSIVE_{}_{}".format(pcount, 'B')
pcount += 1
if pcount == 1:
acount = 1 # reset acount for actives after first passive
else:
# don't model PASSIVE_0 A -- baseline
pset.append(name1)
newrec[name1] = resp.epoch_to_signal(name1, indices=epoch1)
# do include part B
pset.append(name2)
newrec[name2] = resp.epoch_to_signal(name2, indices=epoch2)
else:
name1 = "ACTIVE_{}_{}".format(acount, 'A')
name2 = "ACTIVE_{}_{}".format(acount, 'B')
pset.append(name1)
newrec[name1] = resp.epoch_to_signal(name1, indices=epoch1)
pset.append(name2)
newrec[name2] = resp.epoch_to_signal(name2, indices=epoch2)
if pcount == 0:
acount -= 1
else:
acount += 1
state_signals.remove('each_half')
state_signals.extend(pset)
if 'each_half' in permute_signals:
permute_signals.remove('each_half')
permute_signals.extend(pset)
# generate task state signals
if 'pas' in state_signals:
fpre = (resp.epochs['name'] == "PRE_PASSIVE")
fpost = (resp.epochs['name'] == "POST_PASSIVE")
INCLUDE_PRE_POST = (np.sum(fpre) > 0) & (np.sum(fpost) > 0)
if INCLUDE_PRE_POST:
# only include pre-passive if post-passive also exists
# otherwise the regression gets screwed up
newrec['pre_passive'] = resp.epoch_to_signal('PRE_PASSIVE')
else:
# place-holder, all zeros
newrec['pre_passive'] = resp.epoch_to_signal('XXX')
newrec['pre_passive'].chans = ['PRE_PASSIVE']
if 'puretone_trials' in state_signals:
newrec['puretone_trials'] = resp.epoch_to_signal('PURETONE_BEHAVIOR')
newrec['puretone_trials'].chans = ['puretone_trials']
if 'easy_trials' in state_signals:
newrec['easy_trials'] = resp.epoch_to_signal('EASY_BEHAVIOR')
newrec['easy_trials'].chans = ['easy_trials']
if 'hard_trials' in state_signals:
newrec['hard_trials'] = resp.epoch_to_signal('HARD_BEHAVIOR')
newrec['hard_trials'].chans = ['hard_trials']
if ('active' in state_signals) or ('far' in state_signals):
newrec['active'] = resp.epoch_to_signal('ACTIVE_EXPERIMENT')
newrec['active'].chans = ['active']
if (('hit_trials' in state_signals) or ('miss_trials' in state_signals)
or ('far' in state_signals) or ('hit' in state_signals)):
newrec['hit_trials'] = resp.epoch_to_signal('HIT_TRIAL')
newrec['miss_trials'] = resp.epoch_to_signal('MISS_TRIAL')
newrec['fa_trials'] = resp.epoch_to_signal('FA_TRIAL')
sm_len = 180 * newrec['resp'].fs
if 'far' in state_signals:
a = newrec['active'].as_continuous()
fa = newrec['fa_trials'].as_continuous().astype(float)
#c = np.concatenate((np.zeros((1,sm_len)), np.ones((1,sm_len+1))),
# axis=1)
c = np.ones((1, sm_len)) / sm_len
fa = convolve2d(fa, c, mode='same')
fa[a] -= 0.25 # np.nanmean(fa[a])
fa[np.logical_not(a)] = 0
s = newrec['fa_trials']._modified_copy(fa)
s.chans = ['far']
s.name = 'far'
newrec.add_signal(s)
if 'hit' in state_signals:
a = newrec['active'].as_continuous()
hr = newrec['hit_trials'].as_continuous().astype(float)
ms = newrec['miss_trials'].as_continuous().astype(float)
ht = hr - ms
c = np.ones((1, sm_len)) / sm_len
ht = convolve2d(ht, c, mode='same')
ht[a] -= 0.1 # np.nanmean(ht[a])
ht[np.logical_not(a)] = 0
s = newrec['hit_trials']._modified_copy(ht)
s.chans = ['hit']
s.name = 'hit'
newrec.add_signal(s)
if 'lick' in state_signals:
newrec['lick'] = resp.epoch_to_signal('LICK')
# pupil interactions
if ('p_x_a' in state_signals):
# normalize min-max
p = newrec["pupil"].as_continuous()
a = newrec["active"].as_continuous()
newrec["p_x_a"] = newrec["pupil"]._modified_copy(p * a)
newrec["p_x_a"].chans = ["p_x_a"]
if ('prw' in state_signals):
# add channel two of the resp to state and delete it from resp
if len(rec['resp'].chans) != 2:
raise ValueError("this is for pairwise fitting")
else:
ch2 = rec['resp'].chans[1]
ch1 = rec['resp'].chans[0]
newrec['prw'] = newrec['resp'].extract_channels([ch2]).rasterize()
newrec['resp'] = newrec['resp'].extract_channels([ch1]).rasterize()
if ('pup_x_prw' in state_signals):
# interaction term between pupil and the other cell
if 'prw' not in newrec.signals.keys():
raise ValueError("Must include prw alone before using interaction")
else:
pup = newrec['pupil']._data
prw = newrec['prw']._data
sig = newrec['pupil']._modified_copy(pup * prw)
sig.name = 'pup_x_prw'
sig.chans = ['pup_x_prw']
newrec.add_signal(sig)
for i, x in enumerate(state_signals):
if x in permute_signals:
# kludge: fix random seed to index of state signal in list
# this avoids using the same seed for each shuffled signal
# but also makes shuffling reproducible
newrec = concatenate_state_channel(
newrec,
newrec[x].shuffle_time(rand_seed=i, mask=newrec['mask']),
state_signal_name=new_signalname)
else:
newrec = concatenate_state_channel(
newrec, newrec[x], state_signal_name=new_signalname)
newrec = concatenate_state_channel(newrec,
newrec[x],
state_signal_name=new_signalname +
"_raw")
return newrec
def average_away_epoch_occurrences(recording, epoch_regex='^STIM_'):
'''
Returns a recording with _all_ signals averaged across epochs that
match epoch_regex, shortening them so that each epoch occurs only
once in the new signals. i.e. unlike 'add_average_sig', the new
recording will have signals 3x shorter if there are 3 occurrences of
every epoch.
This has advantages:
1. Averaging the value of a signal (such as a response) in different
occurrences will make it behave more like a linear variable with
gaussian noise, which is advantageous in many circumstances.
2. There will be less computation needed because the signal is shorter.
It also has disadvantages:
1. Stateful filters (FIR, IIR) will be subtly wrong near epoch boundaries
2. Any ordering of epochs is essentially lost, unless all epochs appear
in a perfectly repeated order.
To avoid accidentally averaging away differences in responses to stimuli
that are based on behavioral state, you may need to create new epochs
(based on stimulus and behaviorial state, for example) and then match
the epoch_regex to those.
'''
epochs = recording.epochs
epoch_names = sorted(set(ep.epoch_names_matching(epochs, epoch_regex)))
offset = 0
new_epochs = []
fs = recording[list(recording.signals.keys())[0]].fs
d = int(np.ceil(np.log10(fs)) + 1)
for epoch_name in epoch_names:
common_epochs = ep.find_common_epochs(epochs, epoch_name, d=d)
query = 'name == "{}"'.format(epoch_name)
end = common_epochs.query(query).iloc[0]['end']
common_epochs[['start', 'end']] += offset
offset += end
new_epochs.append(common_epochs)
new_epochs = pd.concat(new_epochs, ignore_index=True)
averaged_recording = recording.copy()
for signal_name, signal in recording.signals.items():
# TODO: this may be better done as a method in signal subclasses since
# some subclasses may have more efficient approaches (e.g.,
# TiledSignal)
# Extract all occurances of each epoch, returning a dict where keys are
# stimuli and each value in the dictionary is (reps X cell X bins)
epoch_data = signal.rasterize().extract_epochs(epoch_names)
# Average over all occurrences of each epoch
for epoch_name, epoch in epoch_data.items():
# TODO: fix empty matrix error. do epochs align properly?
if np.sum(np.isfinite(epoch)):
epoch_data[epoch_name] = np.nanmean(epoch, axis=0)
else:
epoch_data[epoch_name] = epoch[0, ...]
data = [epoch_data[epoch_name] for epoch_name in epoch_names]
data = np.concatenate(data, axis=-1)
if data.shape[-1] != round(signal.fs * offset):
raise ValueError('Misalignment issue in averaging signal')
averaged_signal = signal._modified_copy(data, epochs=new_epochs)
averaged_recording.add_signal(averaged_signal)
# # TODO: Eventually need a smarter check for this incase it's named
# # something else. Basically just want to preserve spike data.
# if signal.name == 'resp':
# spikes = signal.copy()
# spikes.name = signal.name + ' spikes'
# averaged_recording.add_signal(spikes)
return averaged_recording
def spo_dstrf_per_stream_condition(n_pc=2,
memory=12,
recname='val',
cellids=None,
**ctx):
rec = ctx[recname].apply_mask()
modelspec = ctx['modelspec']
print(ctx['modelspec'].meta['modelname'])
print(ctx['modelspec'].meta['cellid'])
if cellids is None:
# analyze all output channels
cellids = rec['resp'].chans
siteids = [c.split("-")[0] for c in cellids]
out_channel = list(np.arange(len(cellids)))
channel_count = len(out_channel)
# figure out epoch bounds
e = rec['resp'].epochs
enames = ep.epoch_names_matching(e, '^STIM_')
set_names = ['stream 1', 'stream 2', 'coh', 'inc']
esets = [['STIM_T+si464+null', 'STIM_T+si516+null'],
['STIM_T+null+si464', 'STIM_T+null+si516'],
['STIM_T+si464+si464', 'STIM_T+si516+si516'],
['STIM_T+si464+si516', 'STIM_T+si516+si464']]
index_sets = []
for _es in esets:
this_index = np.array([], dtype=int)
for e in _es:
x = rec['resp'].get_epoch_indices(e)
print(f'{e}: {x}')
this_index = np.concatenate(
(this_index, np.arange(x[0, 0], x[0, 1], dtype=int)))
index_sets.append(this_index)
pcs = [''] * 4
pc_mag = [''] * 4
for s in range(4):
index_range = index_sets[s]
# skip silent bins
stim_mag = rec['stim'].as_continuous().sum(axis=0)
stim_big = stim_mag > np.max(stim_mag) / 1000
index_range = index_range[(index_range > memory)
& stim_big[index_range.astype(int)]]
print(
f'Calculating dstrf for {channel_count} channels, {len(index_range)} timepoints, memory={memory}'
)
pcs[s], pc_mag[s] = dstrf_pca(modelspec,
rec,
pc_count=n_pc,
out_channel=out_channel,
index_range=index_range,
memory=memory)
f2, axs = plt.subplots(8, 10, figsize=(20, 12))
cmax = np.min([channel_count, 10])
for c in range(cmax):
cellid = cellids[c]
for s in range(4):
for i in range(2):
mm = np.max(np.abs(pcs[s][i, :, :, c]))
_p = pcs[s][i, :, :, c] * pc_mag[s][i, c] / pc_mag[s][0, c]
_p *= np.sign(_p.sum())
_row = s * 2 + i
_col = c
axs[_row, _col].imshow(_p,
aspect='auto',
origin='lower',
clim=[-mm, mm])
if i + s == 0:
axs[_row, _col].set_title(f'{cellid} {pc_mag[s][i,c]:.3f}',
fontsize=8)
else:
axs[_row, _col].set_title(f'{pc_mag[s][i,c]:.3f}',
fontsize=8)
if _col < 7:
axs[_row, _col].set_xticks([])
if c > 0:
axs[_row, _col].set_yticks([])
if (c == 0):
axs[_row, _col].set_ylabel(set_names[s])
ax_remove_box(axs[_row, _col])
return f2, pcs, pc_mag
