# calculates response realiability and select only good cells to improve analysis
r_vals, goodcells = signal_reliability(sig,
r'\ASTIM_*',
threshold=meta['reliability'])
goodcells = goodcells.tolist()
# plots PSTHs of all probes after silence
# fig, axes = cplot.hybrid(sig, epoch_names=r'\AC0_P\d\Z', channels=goodcells)
# plots PSHTs of individual best probe after all contexts
# fig, axes = cplot.hybrid(sig, epoch_names=r'\AC\d_P3\Z', channels=goodcells)
# takes an example probe
full_array, invalid_cp, valid_cp, all_contexts, all_probes = \
tp.make_full_array(sig, channels=goodcells, smooth_window=meta['smoothing_window'])
# get a specific probe after a set of different transitions
trialR = full_array[:, 1:, :, :,
100:] # excludes silence as context, only includes response to probe
all_probes.pop(0)
# reorders dimentions from Context x Probe x Trial x Neuron x Time to Trial x Neuron x Context x Probe x Time
trialR, R, _ = cdPCA.format_raster(trialR)
Tr, N, C, P, T = trialR.shape
n_components = N if N < 10 else 10
# initializes model
dpca = dPCA.dPCA(labels='cpt',
fig, axes = cplot.hybrid(sig, epoch_names='C0_P9', channels=goodcells) # for good cells, all the relevant probes after silence fig, axes = cplot.hybrid(sig, epoch_names=r'\AC0_P([679]|10)\Z', channels=goodcells) fig, axes = cplot.hybrid(sig, epoch_names=r'\AC0_P([679]|10)\Z', channels=best_cell) # best cell, best probe, all the contexts fig, axes = cplot.hybrid(sig, epoch_names=r'\AC(\d|10)_P6\Z', channels=goodcells) fig, axes = cplot.hybrid(sig, epoch_names=r'\AC(\d|10)_P6\Z', channels=best_cell) ######################################################################################################################## # organizes relevant data in array with dimensions Context x Probe x Repetition x Unit x Time full_array, bad_cpp, good_cpp, context_names, probe_names = tp.make_full_array(sig, 'CPN') # now calculate pairwise difference between context types valid_probes = [6, 7, 9, 10] context_transitions = ['silence', 'continuous', 'similar', 'sharp'] diff_arr = src.metrics.distance.pairwise_PSHT_distance(valid_probes, context_transitions, full_array, context_names, probe_names, ) ######################################################################################################################## # plot the PSTHs of a probe given two contexts transitions, compares, p = 7 ct1 = 'continuous' ct2 = 'sharp' cell = cellorder.index(best_cell) arr1 = tp._extract_triplets_sub_arr(p, ct1, full_array, context_names, probe_names) # shape Rep x Unit x Time