for source, position in itt.product(range(20), range(positions)):
probe = NTI_epoch_name(duration, source, position)
fourway_name = f'191014_{site}_P{probe}_fourway_analysis'
analysis_parameters = '_'.join(
['{}-{}'.format(key, str(val)) for key, val in meta.items()])
analysis_name = 'NTI_LDA_dprime'
nway = make_cache(
function=dPrime_from_NIT_site,
func_args={
'site': site,
'duration': duration,
'source': source,
'position': position,
'meta': meta
},
classobj_name=fourway_name,
cache_folder=
f'/home/mateo/mycache/{analysis_name}/{analysis_parameters}',
recache=False)
real, shuffled, simulated, transitions, contexts = get_cache(
nway) # get_cache(twoway)
if real is None:
continue
trans_pairs = itt.combinations(transitions, 2)
trans_pairs = [sorted(tp) for tp in trans_pairs
bad_sites = list()
df = list()
# for site, probe in zip(['AMT029a', 'ley070a'],[5,2]):
for site, probe in itt.product(all_sites, all_probes):
try:
LDA_anal_name = f'191014_{site}_P{probe}_fourway_analysis'
LDA_anal = make_cache(
function=fourway_analysis,
func_args={
'site': site,
'probe': probe,
'meta': meta
},
classobj_name=LDA_anal_name,
cache_folder=
f'/home/mateo/mycache/{analysis_name}/{analysis_parameters}')
# LDA_real, LDA_shuffled, LDA_simulated = get_cache(LDA_anal)
except:
bad_sites.append(f"{site}_P{probe}_LDA")
continue
try:
dPCA_anal_name = f'191015_{site}_P{probe}_fourway_analysis'
dPCA_anal = make_cache(
function=dPCA_fourway_analysis,
signal_name = f'190709_{site}_P{probe}_ct-marg'
func_args = {
'transitions_array': trans_arr,
'probe_names': [probe],
'context_transitions': trans_pair,
'probe_order': [probe],
'trans_order': meta['transitions'],
'shuffle_num': meta['nonparam_shuffle'],
'trial_combinations': True
}
shuffled_dispersion_time = make_cache(
function=ndisp.transition_pair_comparison_by_trials,
func_args=func_args,
classobj_name=signal_name,
recache=False,
cache_folder=
f'/home/mateo/mycache/{analysis_name}/{analysis_parameters}')
real, trans_shuffle, trial_shuffle = get_cache(
shuffled_dispersion_time)
# pvalue = (msd_floor > obs_msd).sum() / shuffle_n
pvalues = np.sum(
(real < trans_shuffle), axis=0) / meta['nonparam_shuffle']
for pval_threshold in [0.05, 0.01, 0.001]:
signif = pvalues < pval_threshold
total_sig = np.sum(signif)
try:
last_sig = np.max(np.argwhere(signif))
'voc_2': 'Greens',
'voc_3': 'Reds',
'voc_4': 'Purples'
}
sites = load.get_site_ids(310)
pop_recs = col.defaultdict(dict)
for (site_name, cells), modelname in itt.product(sites.items(), all_models):
print('#####\nreconstituting site {} with model {}\n '.format(
site_name, modelname))
recons_args = {'batch': 310, 'cellid_list': cells, 'modelname': modelname}
recons_cache = ccache.make_cache(
crec.reconsitute_rec,
func_args=recons_args,
classobj_name='reconstitution',
recache=False,
cache_folder='/home/mateo/mycache/reconstitute_recs',
use_hash=True)
reconstituted_recording = ccache.get_cache(recons_cache)
# rasterizes and takes the PSTH of full length signals (containing multiple contex probes)
reconstituted_recording.signals = {
key: val.rasterize()
for key, val in reconstituted_recording.signals.items()
}
reconstituted_recording = npre.average_away_epoch_occurrences(
reconstituted_recording)
pop_recs[site_name][modelname] = reconstituted_recording
# removese the flat response of BRT057b todo solve the bug
del (pop_recs['BRT057b'])
modelname = 'resp'
site = 'AMT031a'
start = 0
end = 200
t1 = (start / 100) - 1
t2 = (end / 100) - 1
##
signal_name = '190431_{}_{}'.format(site, modelname)
func_args = {'signal': sig, 'probe_names': prbs, 'context_names': ctxs, 'shuffle_num': 1000,
'trial_combinations': False}
shuffled_dispersion_time = cch.make_cache(function=cdisp.signal_single_trial_dispersion_pooled_shuffled,
func_args=func_args,
classobj_name=signal_name, recache=False,
cache_folder='/home/mateo/mycache/shuffled_euclideans')
real, distribution = cch.get_cache(shuffled_dispersion_time)
fig, ax = plt.subplots()
line = real[start:end]
shade = distribution[:, start:end]
shade = np.mean(shade, axis=0) + np.std(shade, axis=0) * 2
t = np.linspace(t1, t2, len(line))
ax.plot(t, line , label='{}'.format(site), color='C0')
ax.fill_between(t, -shade , shade , alpha=0.5, color='C0')
ax.axvline(0, color='black', linestyle='--')
# ax.legend(prop={'size': 15})
ax.set_xlabel('time (s)', fontsize=18)
voc_color = {'voc_{}'.format(cc): 'C{}'.format(cc) for cc in range(5)}
voc_cmpat = {'voc_0': 'Blues', 'voc_1': 'Oranges', 'voc_2': 'Greens', 'voc_3': 'Reds', 'voc_4': 'Purples'}
sites = load.get_site_ids(310)
################################################
# get and reconstitute single cell recordings into population recording
pop_recs = coll.defaultdict(dict)
for (site_name, cells), modelname in itt.product(sites.items(), all_models):
print('#####\nreconstituting site {} with model {}\n '.format(site_name, modelname))
recons_args = {'batch':310, 'cellid_list':cells, 'modelname': modelname}
recons_cache = ccache.make_cache(crec.reconsitute_rec, func_args=recons_args, classobj_name='reconstitution',
recache=False, cache_folder='/home/mateo/mycache/reconstitute_recs',
use_hash=True)
reconstituted_recording = ccache.get_cache(recons_cache)
pop_recs[site_name][modelname] = reconstituted_recording
# if pop_recs['BRT057b']:
# del(pop_recs['BRT057b'])
################################################
# reorders in dictionary of signals, including only the response and the prediction of each mode
# reformats the epochs
pop_sigs = coll.defaultdict(dict)
for site_key, model_recs in pop_recs.items():
for modelname, rec in model_recs.items():
cpp_rec = cep.set_recording_subepochs(rec, set_pairs=True)
pop_sigs[site_key][modelname] = cpp_rec['pred'].rasterize()
this_site_shuffled = list()
this_site_pvalues = list()
for pp, probe in enumerate(all_probes):
# single cell analysis
object_name = f'200221_{site}_P{probe}_single_cell_dprime'
analysis_parameters = '_'.join(
['{}-{}'.format(key, str(val)) for key, val in meta.items()])
analysis_name = 'CPN_singel_cell_dprime'
cache_folder = pl.Path('C:\\', 'users', 'mateo', 'mycache',
analysis_name, analysis_parameters)
SC_cache = make_cache(function=cell_dprime,
func_args={
'site': site,
'probe': probe,
'meta': meta
},
classobj_name=object_name,
cache_folder=cache_folder,
recache=dprime_recache)
dprime, shuf_dprime, cell_names, trans_pairs = get_cache(SC_cache)
this_site_reals.append(dprime)
this_site_shuffled.append(shuf_dprime)
# single tailed p value base on the montecarlo shuffling
SC_pvalues = np.sum(
(shuf_dprime >= dprime), axis=0) / meta['montecarlo']
this_site_pvalues.append(SC_pvalues)
