def make_state_signal(rec,
state_signals=['pupil'],
permute_signals=[],
new_signalname='state',
**context):
rec = preproc.make_state_signal(rec,
state_signals=state_signals,
permute_signals=permute_signals,
new_signalname=new_signalname)
return {'rec': rec}
def make_state_signal(rec,
state_signals=['pupil'],
permute_signals=[],
new_signalname='state',
**context):
rec = preproc.make_state_signal(rec,
state_signals=state_signals,
permute_signals=permute_signals,
new_signalname=new_signalname)
rec = preproc.remove_invalid_segments(rec)
# rec = preproc.nan_invalid_segments(rec)
return {'rec': rec}
def load_tbp_recording(siteid, batch, **options):
options['resp'] = True
options['pupil'] = options.get('pupil', True)
options['rasterfs'] = options.get('rasterfs', 50)
manager = BAPHYExperiment(siteid=siteid, batch=batch)
rec = manager.get_recording(**options)
rec['resp'] = rec['resp'].rasterize()
onsetsec = 0.1
offsetsec = 0.4
onset = int(onsetsec * options['rasterfs'])
offset = int(offsetsec * options['rasterfs'])
# PCA on trial averaged responses
ref_stims, sounds, all_sounds = get_sound_labels(rec)
"""
try:
targets = [f for f in rec['resp'].epochs.name.unique() if 'TAR_' in f]
catch = [f for f in rec['resp'].epochs.name.unique() if 'CAT_' in f]
sounds = targets + catch
ref_stims = [x for x in rec['resp'].epochs.name.unique() if 'STIM_' in x]
idx = np.argsort([int(s.split('_')[-1]) for s in ref_stims])
ref_stims = np.array(ref_stims)[idx].tolist()
sounds=sort_targets(sounds)
except:
ref_stims=['REFERENCE']
sounds = ['TARGET']
all_sounds = ref_stims + sounds
"""
rall = rec.and_mask(['ACTIVE_EXPERIMENT', 'PASSIVE_EXPERIMENT'])
# can't simply extract evoked for refs because can be longer/shorted if it came after target
# and / or if it was the last stim.So, masking prestim / postim doesn't work.Do it manually
d = rall['resp'].extract_epochs(all_sounds, mask=rall['mask'])
d = {
k: v[~np.isnan(v[:, :, onset:offset].sum(axis=(1, 2))), :, :]
for (k, v) in d.items()
}
d = {k: v[:, :, onset:offset] for (k, v) in d.items()}
Rall_u = np.vstack([d[k].sum(axis=2).mean(axis=0) for k in d.keys()])
pca = PCA(n_components=2)
pca.fit(Rall_u)
pc_axes = pca.components_
# project onto first PC and plot trial psth
rec['pc1'] = rec['resp']._modified_copy(rec['resp']._data.T.dot(
pc_axes.T).T[[0], :])
rec['pc2'] = rec['resp']._modified_copy(rec['resp']._data.T.dot(
pc_axes.T).T[[1], :])
#rec = make_state_signal(rec, state_signals=['pupil','active'], permute_signals=['pupil'])
rec = make_state_signal(rec, state_signals=['pupil', 'active'])
return rec
# ----------------------------------------------------------------------------
# DATA LOADING
# GOAL: Get your data loaded into memory as a Recording object
logging.info('Loading data...')
# Method #1: Load the data from a local directory
rec = Recording.load(os.path.join(signals_dir, 'TAR010c-18-1.tgz'))
# Method #2: Load the data from baphy using the (incomplete, TODO) HTTP API:
#URL = "http://potoroo:3004/baphy/271/bbl086b-11-1?rasterfs=200"
#rec = Recording.load_url(URL)
logging.info('Generating state signal...')
rec = preproc.make_state_signal(rec, ['pupil'], [''], 'state')
# ----------------------------------------------------------------------------
# INITIALIZE MODELSPEC
# GOAL: Define the model that you wish to test
logging.info('Initializing modelspec...')
# Method #1: create from "shorthand" keyword string
#modelspec = nems.initializers.from_keywords('pup_wcg18x1_fir15x1_lvl1_dexp1')
modelspec = nems.initializers.from_keywords('wcgNx2_fir15x2_lvl1_stategain2')
#modelspec = nems.initializers.from_keywords('wcgNx2_fir15x2_lvl1')
#modelspec = nems.initializers.from_keywords('pup_wcg18x2_fir15x2_lvl1_stategain2')
# ----------------------------------------------------------------------------
#recording.get_demo_recordings(signals_dir)
#rec = recording.load_recording(recording_uri)
nwb_filepath = Path('/auto/users/tomlinsa/code/allen/data/session_759883607/session_759883607.nwb')
rec=from_nwb_pupil(nwb_filepath,'neuropixel',with_pupil=True,fs=40)
rec=rec[cellid]
#try out allen data
# ----------------------------------------------------------------------------
# PREPROCESSING
# create a new signal that will be used to modulates the output of the linear
# predicted response
logging.info('Generating state signal...')
#rec = preproc.make_state_signal(rec, ['active','pupil'], [''], 'state')
rec = preproc.make_state_signal(rec, state_signals=['pupil'],
permute_signals=[''], new_signalname='state')
#rec = preproc.make_state_signal(rec, state_signals=['pupil'],
# permute_signals=['pupil'], new_signalname='state') #shuffled pupil
# mask out data from incorrect trials
#rec = preproc.mask_all_but_correct_references(rec)#not for allen?
# calculate a PSTH response for each stimulus, save to a new signal 'psth'
#epoch_regex="^STIM_"
epoch_regex="^natural_scene" #only natural scene stimulus
#rec = preproc.generate_psth_from_resp(rec, epoch_regex=epoch_regex, smooth_resp=False)
rec=nwb_resp_psth(rec,epoch_regex) #should return similar results as above for allen data
# ----------------------------------------------------------------------------
# INSPECT THE DATA
recording.get_demo_recordings(signals_dir)
rec = recording.load_recording(recording_uri)
# Method #2: Load the data from baphy using the (incomplete, TODO) HTTP API:
#URL = "http://potoroo:3004/baphy/271/bbl086b-11-1?rasterfs=200"
#rec = Recording.load_url(URL)
# ----------------------------------------------------------------------------
# PREPROCESSING
# create a new signal that will be used to modulate the output of the linear
# predicted response
logging.info('Generating state signal...')
#rec = preproc.make_state_signal(rec, ['active','pupil_bs','pupil_ev'], [''], 'state')
#rec = preproc.make_state_signal(rec, ['active','pupil'], [''], 'state')
rec = preproc.make_state_signal(rec, ['pupil', 'each_file'], [''], 'state')
# mask out data from incorrect trials
rec = preproc.mask_all_but_correct_references(rec)
# calculate a PSTH response for each stimulus, save to a new signal 'psth'
epoch_regex = "^STIM_"
rec = preproc.generate_psth_from_resp(rec, epoch_regex, smooth_resp=False)
# ----------------------------------------------------------------------------
# INSPECT THE DATA
resp = rec['resp'].rasterize()
epochs = resp.epochs
epoch_regex = "^STIM_"
epoch_list = ep.epoch_names_matching(epochs, epoch_regex)
def model_per_time_wrapper(cellid,
batch=307,
loader="psth.fs20.pup-ld-",
fitter="_jk.nf20-basic",
basemodel="-ref-psthfr_stategain.S",
state_list=None,
plot_halves=True,
colors=None):
"""
batch = 307 # A1 SUA and MUA
batch = 309 # IC SUA and MUA
alternatives:
basemodels = ["-ref-psthfr.s_stategain.S",
"-ref-psthfr.s_sdexp.S",
"-ref.a-psthfr.s_sdexp.S"]
state_list = ['st.pup0.hlf0','st.pup0.hlf','st.pup.hlf0','st.pup.hlf']
state_list = ['st.pup0.far0.hit0.hlf0','st.pup0.far0.hit0.hlf',
'st.pup.far.hit.hlf0','st.pup.far.hit.hlf']
state_list = ['st.pup0.fil0','st.pup0.fil','st.pup.fil0','st.pup.fil']
"""
# pup vs. active/passive
if state_list is None:
state_list = [
'st.pup0.hlf0', 'st.pup0.hlf', 'st.pup.hlf0', 'st.pup.hlf'
]
#state_list = ['st.pup0.far0.hit0.hlf0','st.pup0.far0.hit0.hlf',
# 'st.pup.far.hit.hlf0','st.pup.far.hit.hlf']
#state_list = ['st.pup0.fil0','st.pup0.fil','st.pup.fil0','st.pup.fil']
modelnames = []
contexts = []
for i, s in enumerate(state_list):
modelnames.append(loader + s + basemodel + fitter)
xf, ctx = xhelp.load_model_xform(cellid,
batch,
modelnames[i],
eval_model=False)
ctx, l = xforms.evaluate(xf, ctx, start=0, stop=-2)
ctx['val'] = preproc.make_state_signal(ctx['val'],
state_signals=['each_half'],
new_signalname='state_f')
contexts.append(ctx)
#import pdb;
#pdb.set_trace()
plt.figure()
#if ('hlf' in state_list[0]) or ('fil' in state_list[0]):
if plot_halves:
files_only = True
else:
files_only = False
for i, ctx in enumerate(contexts):
rec = ctx['val'].apply_mask()
modelspec = ctx['modelspec']
epoch = "REFERENCE"
rec = ms.evaluate(rec, modelspec)
if i == len(contexts) - 1:
ax = plt.subplot(len(contexts) + 1, 1, 1)
nplt.state_vars_timeseries(rec, modelspec, ax=ax)
ax.set_title('{} {}'.format(cellid, modelnames[-1]))
ax = plt.subplot(len(contexts) + 1, 1, 2 + i)
nplt.state_vars_psth_all(rec,
epoch,
psth_name='resp',
psth_name2='pred',
state_sig='state_f',
colors=colors,
channel=None,
decimate_by=1,
ax=ax,
files_only=files_only,
modelspec=modelspec)
ax.set_ylabel(state_list[i])
ax.set_xticks([])
