pass
else:
batch = 289
# get decoding results
loader = decoding.DecodingResults()
fn = os.path.join(DPRIME_DIR, site, modelname + '_TDR.pickle')
results = loader.load_results(fn,
cache_path=CACHE_PATH,
recache=recache)
df = results.numeric_results.loc[results.evoked_stimulus_pairs]
df['noiseAlign'] = results.slice_array_results(
'cos_dU_evec_test', results.evoked_stimulus_pairs, 2,
idx=(0, 0))[0]
X, sp_bins, X_pup, pup_mask, epochs = decoding.load_site(
site=site, batch=batch, return_epoch_list=True)
ncells = X.shape[0]
nreps = X.shape[1]
nstim = X.shape[2]
nbins = X.shape[3]
sp_bins = sp_bins.reshape(1, sp_bins.shape[1], nstim * nbins)
nstim = nstim * nbins
# =========================== generate a list of stim pairs ==========================
# these are the indices of the decoding results dataframes
all_combos = list(combinations(range(nstim), 2))
spont_bins = np.argwhere(sp_bins[0, 0, :])
spont_combos = [
c for c in all_combos
if (c[0] in spont_bins) & (c[1] in spont_bins)
]
regress_pupil = False
el_only = False
sim1 = False
sim2 = False
njacks = 10 # generate one random est / val set
jk_set = 2 # which jack set to use for plotting
combo = None #(56, 58) # if None, just using first evoked/evoked combo
site = 'TAR010c'
batch = 289
site = 'AMT020a'
batch = 331
# ================================= load recording ==================================
X, sp_bins, X_pup, pup_mask = decoding.load_site(site=site,
batch=batch,
sim_first_order=sim1,
sim_second_order=sim2,
regress_pupil=regress_pupil,
verbose=False)
ncells = X.shape[0]
nreps = X.shape[1]
nstim = X.shape[2]
nbins = X.shape[3]
X = X.reshape(ncells, nreps, nstim * nbins)
sp_bins = sp_bins.reshape(1, sp_bins.shape[1], nstim * nbins)
# mask pupil per stimulus, rather than overall
pup_mask = pup_mask.reshape(1, nreps, nstim * nbins)
nstim = nstim * nbins
# =========================== generate list of est/val sets ==========================
est, val, p_est, p_val = nat_preproc.get_est_val_sets(X,
beta2 = lv_results[site + str(batch)]['beta2']
else:
beta1 = None
beta2 = None
else:
beta1 = None
beta2 = None
# ================================= load recording ==================================
X, sp_bins, X_pup, pup_mask, epochs = decoding.load_site(
site=site,
batch=batch,
pca_ops=pca_ops,
regress_pupil=regress_pupil,
gain_only=gain_only,
dc_only=dc_only,
use_xforms=use_xforms,
return_epoch_list=True,
exclude_low_fr=exclude_low_fr,
threshold=threshold,
mask_movement=movement_mask,
use_old_cpn=use_old_cpn)
ncells = X.shape[0]
nreps_raw = X.shape[1]
nstim = X.shape[2]
nbins = X.shape[3]
sp_bins = sp_bins.reshape(1, sp_bins.shape[1], nstim * nbins)
nstim = nstim * nbins
# =========================== generate a list of stim pairs ==========================
all_combos = list(combinations(range(nstim), 2))
res = loader.load_results(fn, cache_path=None, recache=recache)
df = res.numeric_results.loc[pd.IndexSlice[res.evoked_stimulus_pairs, ndim], :]
df['delta_dprime'] = (df['bp_dp'] - df['sp_dp']) / (df['bp_dp'] + df['sp_dp'])
df['raw_delta'] = (df['bp_dp'] - df['sp_dp'])
stims = (1, 8, 9)
stims = (0, 1, 9)
#stims = (5, 6, 9) # pretty good example, but numerically doesn't hold up
#stims = np.sort(np.random.choice(range(12), 3, replace=False))
combos = ['_'.join([str(i) for i in x]) for x in list(combinations(stims, 2))]
#stims = (2, 8, 11)
# ====================== Get PC data ======================
xf_model = None
X_raw, sp_bins, X_pup, pup_mask, epochs, spont_raw = decoding.load_site(
site=site,
batch=batch,
xforms_modelname=None,
mask_movement=(.25, 1),
special=True)
if xf_model is not None:
X, _, _, _, _, _ = decoding.load_site(site=site,
batch=batch,
xforms_modelname=xf_model,
special=True)
else:
X = X_raw.copy()
ncells = X_raw.shape[0]
nreps = X_raw.shape[1]
nstim = X_raw.shape[2]
nbins = X_raw.shape[3]
sp_bins = sp_bins.reshape(1, sp_bins.shape[1], nstim * nbins)
nstim = nstim * nbins
if 'mask' not in rec.signals:
rec = rec.create_mask(True)
rec['mask'] = rec['mask']._modified_copy(rec['mask']._data.astype(bool))
rec = rec.and_mask(['PreStimSilence', 'PostStimSilence'], invert=True)
if batch != 331:
nspont_bins = rec['resp'].extract_epoch('PreStimSilence').shape[-1]
else:
nspont_bins = 0
else:
nspont_bins = 0
rec = rec.apply_mask(reset_epochs=True)
# get the pupil range for each stimulus pair, then save the mean of this for the site
use_xforms = regression_method2
X, sp_bins, X_pup, pup_mask = load_site(site=site,
batch=batch,
regress_pupil=pupil_regress,
use_xforms=use_xforms)
ncells = X.shape[0]
nreps_raw = X.shape[1]
nstim = X.shape[2]
nbins = X.shape[3]
sp_bins = sp_bins.reshape(1, sp_bins.shape[1], nstim * nbins)
nstim = nstim * nbins
X_pup = X_pup.reshape(1, nreps_raw, nstim)
pup_mask = pup_mask.reshape(1, nreps_raw, nstim)
pupil_range = get_pupil_range(X_pup, pup_mask)
mean_pupil_range = pupil_range['range'].iloc[:-1].mean()
log.info("Mask large and small pupil")
# handle using xforms pupil mask
rec_bp = rec.copy()
