def load_with_parms(site, **kwargs):
# defaults
options = {'batch': 316,
'cellid': site,
'stimfmt': 'envelope',
'rasterfs': 100,
'runclass': 'CPN',
'stim': False,
'resp':True}
options.update(**kwargs)
manager = BAPHYExperiment(siteid=site, batch=options['batch'])
loaded_rec = manager.get_recording(recache=True, **options)
parameters = manager.get_baphy_exptparams()
# load_URI, _ = nb.baphy_load_recording_uri(**options)
# loaded_rec = recording.load_recording(load_URI)
CPN_rec = cpe.set_recording_subepochs(loaded_rec)
recordings = split_recording(CPN_rec)
return recordings, parameters
def load(site, boxload=True, **kwargs):
# defaults
options = {'batch': 316,
'cellid': site,
'stimfmt': 'envelope',
'rasterfs': 100,
'recache': False,
'runclass': 'CPN',
'stim': False}
options.update(**kwargs)
if boxload is True:
toname = options.copy()
del toname['recache']
filename = pl.Path(config['paths']['recording_cache']) / set_name(toname)
if not filename.parent.exists():
filename.parent.mkdir()
if filename.exists() and options['recache'] is False:
print('loading recording from box')
loaded_rec = jl.load(filename)
elif filename.exists() is False or options['recache'] is True:
load_URI, _ = nb.baphy_load_recording_uri(**options)
loaded_rec = recording.load_recording(load_URI)
print('cacheing recoring in box')
jl.dump(loaded_rec, filename)
else:
raise SystemError('WTF?')
elif boxload is False:
load_URI, _ = nb.baphy_load_recording_uri(**options)
loaded_rec = recording.load_recording(load_URI)
else:
raise ValueError('boxload must be boolean')
CPN_rec = cpe.set_recording_subepochs(loaded_rec)
recordings = split_recording(CPN_rec)
return recordings
from src.data import epochs as cpe, PCA as cpca
import joblib as jl
import matplotlib.pyplot as plt
import numpy as np
test_rec_path = '/home/mateo/context_probe_analysis/pickles/BRT037b'
loaded_rec = jl.load(test_rec_path)
rec = cpe.set_recording_subepochs(loaded_rec, set_pairs=True)
sig = rec['resp']
eps = sig.epochs
# normalize matrix
full_mat = sig.rasterize()._data
mean = np.mean(full_mat.T, axis=0)
full_mat = (full_mat.T - mean).T
folded_mat = sig.rasterize().extract_epoch('C0_P1')
sk_sig, pca_sig = cpca.signal_PCA(sig, center=True)
sk_rec, pca_rec = cpca.recording_PCA(rec, signal_names='all', inplace=False, center=True)
U = pca_sig.components_ # 2d array nComponents x nFeatures
V = sk_sig._data
U = pca_rec['resp'].components_
V = sk_rec['resp_PCs']._data
#### plotting
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'])
# reorders in dictionary of signals, including only the response and the prediction of each mode
# reformats the epochs
pop_sigs = col.defaultdict(dict)
for site_key, model_recs in pop_recs.items():
for modelname, rec in model_recs.items():
rec = cep.set_recording_subepochs(rec, set_pairs=False)
pop_sigs[site_key][modelname] = rec['pred'].rasterize()
pop_sigs[site_key]['resp'] = rec['resp'].rasterize()
# calculates the PCAs for each site, for each model prediction and response
PCA_stats = col.defaultdict(dict)
site_PCA = col.defaultdict(dict)
for site_key, site_models in pop_sigs.items():
for sig_key, signal in site_models.items():
PCA_sig, stats = cpca.signal_PCA(signal)
site_PCA[site_key][sig_key] = PCA_sig
PCA_stats[site_key][sig_key] = stats
# plots the variance explained by PCs for each site
for site_key, site_vals in PCA_stats.items():
fig, ax = plt.subplots()
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 = col.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()
pop_sigs[site_key]['resp'] = cpp_rec['resp'].rasterize()
del (pop_sigs['BRT057b'])
################################################
# iterates over each site id, and calculates the dispersion for predictions of each different model
site_disps = dict()
site_pvals = dict()
site_cnames = dict()
for disp_val in ['metric', 'pvalue']:
for site, recs in pop_recs.items():
# for multi core running
# site_IDs = list(pop_recs.keys())
# site = site_IDs[4]
Test = True
if Test == True:
# # sets automatic path finding
# this_script_dir = os.path.dirname(os.path.realpath(__file__))
# pickle_path = '{}/pickles'.format(this_script_dir)
# test_rec_path = os.path.normcase('{}/BRT037b'.format(pickle_path))
test_rec_path = '/home/mateo/code/context_probe_analysis/pickles/BRT037b'
loaded_rec = jl.load(test_rec_path)
else:
load_URI = nb.baphy_load_recording_uri(**options)
loaded_rec = recording.load_recording(load_URI)
# sets eps correpsondign to individual sounds as well as stim_num prb pairs
rec = cpe.set_recording_subepochs(loaded_rec)
sig = rec['resp']
eps = sig.epochs
# transforms the recording into its PCA equivalent
rec_pca, pca_stats = cpca.recording_PCA(rec, inplace=False, center=True)
sig_pca = rec_pca['resp_PCs']
plot = False
if plot == True:
# variance explained by PCs
for sig_name, pca in pca_stats.items():
fig, ax = plt.subplots()
