from nems.recording import load_recording
modelname = 'dprime_jk10_zscore_nclvz_fixtdr2'
recache = False
site = 'TAR010c' #'DRX008b.e1:64' #'TAR010c' #'DRX007a.e65:128'
batch = 289
for site in HIGHR_SITES:
if 'BOL' in site:
batch = 294
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]
pr = pupil_range
get_mean = lambda x: (pr[pr.stim == x[0]]['range'] + pr[pr.stim == x[1]][
'range']) / 2
pr_range = combos.apply(get_mean)
tdr_results['mean_pupil_range'] = pr_range.values
# convert to correct dtypes
tdr_results = decoding.cast_dtypes(tdr_results)
if do_PCA:
pca_results = decoding.cast_dtypes(pca_results)
if do_pls:
pls_results = decoding.cast_dtypes(pls_results)
# collapse over results to save disk space by packing into "DecodingResults object"
log.info("Compressing results into DecodingResults object... ")
tdr_results = decoding.DecodingResults(tdr_results, pupil_range=pupil_range)
if do_PCA:
pca_results = decoding.DecodingResults(pca_results,
pupil_range=pupil_range)
if do_pls:
pls_results = decoding.DecodingResults(pls_results,
pupil_range=pupil_range)
if meta is not None:
if 'mask_bins' in meta.keys():
tdr_results.meta['mask_bins'] = meta['mask_bins']
# save results
modelname = modelname.replace('*', '_')
log.info("Saving results to {}".format(os.path.join(path, str(batch))))
