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
}
rec_recache = False
region_map = dict(
zip([
'AMT028b', 'AMT029a', 'AMT030a', 'AMT031a', 'AMT032a', 'DRX008b',
'DRX021a', 'ley070a', 'ley072b'
], ['PEG', 'PEG', 'PEG', 'PEG', 'PEG', 'A1', 'A1', 'A1', 'A1']))
all_probes = [2, 3, 5, 6]
# load the calculated dprimes and montecarlo shuffling/simulations
# the loadede dictionary has 3 layers, analysis, value type and cell/site
batch_dprimes_file = pl.Path(
config['paths']['analysis_cache']) / 'batch_dprimes' / set_name(meta)
batch_dprimes = jl.load(batch_dprimes_file)
sites = set(batch_dprimes['dPCA']['dprime'].keys())
all_cells = set(batch_dprimes['SC']['dprime'].keys())
########################################################################################################################
# defines a significant threshold and transform the pvalues into boolean (significant vs nonsignificant)
threshold = 0.01
for analysis_name, mid_dict in batch_dprimes.items():
mid_dict['shuffled_significance'] = {
key: (val <= threshold)
for key, val in mid_dict['shuffled_pvalue'].items()
}
if analysis_name != 'SC':
mid_dict['simulated_significance'] = {
# transferable plotting parameters
plt.rcParams['svg.fonttype'] = 'none'
sup_title_size = 30
sub_title_size = 20
ax_lab_size = 15
ax_val_size = 11
full_screen = [19.2, 9.83]
sns.set_style("ticks")
########################################################################################################################
########################################################################################################################
# data frame containing all the important summary data, i.e. exponential decay fits for dprime and significance, for
# all combinations of transition pairs, and probes, for the means across probes, transitions pairs or for both, and
# for the single cell analysis or the dPCA projections
summary_DF_file = pl.Path(
config['paths']['analysis_cache']) / 'DF_summary' / set_name(meta)
print('loading cached summary DataFrame')
DF = jl.load(summary_DF_file)
########################################################################################################################
# SC
########################################################################################################################
# compare parameters between different probes or transitions pairs
analyses = ['SC', 'dPCA']
sources = ['dprime', 'significance']
parameters = ['tau', 'r0']
comparisons = ['probe', 'transition_pair']
good_thresh = 0.1
# transferable plotting parameters
plt.rcParams['svg.fonttype'] = 'none'
sup_title_size = 30
sub_title_size = 20
ax_lab_size = 15
ax_val_size = 11
full_screen = [19.2, 9.83]
sns.set_style("ticks")
########################################################################################################################
########################################################################################################################
# data frame containing all the important summary data, i.e. exponential decay fits for dprime and significance, for
# all combinations of transition pairs, and probes, for the means across probes, transitions pairs or for both, and
# for the single cell analysis or the dPCA projections
summary_DF_file = pl.Path(config['paths']['analysis_cache']) / 'DF_summary' / set_name(meta)
print('loading cached summary DataFrame')
DF = jl.load(summary_DF_file)
########################################################################################################################
# SC
########################################################################################################################
# compare tau between different probe means
ff_anal = DF.analysis == 'SC'
ff_probe = DF.probe != 'mean'
ff_trans = DF.transition_pair == 'mean'
ff_param = DF.parameter == 'tau'
ff_source = DF.source == 'significance'
ff_outliers = DF.value < 1000
filtered = DF.loc[ff_anal & ff_probe & ff_trans & ff_param & ff_source & ff_outliers,
barax.tick_params(axis='y', labelcolor=barkwargs['color'])
lineax.tick_params(axis='y', labelcolor=linekwargs['color'])
return barax, lineax
meta = {'reliability': 0.1, # r value
'smoothing_window': 0, # ms
'raster_fs': 30,
'transitions': ['silence', 'continuous', 'similar', 'sharp'],
'montecarlo': 1000,
'zscore': True,
'dprime_absolute': None}
# load the calculated dprimes and montecarlo shuffling/simulations
# the loadede dictionary has 3 layers, analysis, value type and cell/site
batch_dprimes_file = pl.Path(config['path']['analysis_cache']) / 'batch_dprimes' / set_name(meta)
batch_dprimes = jl.load(batch_dprimes_file)
########################################################################################################################
# set up the time bin labels in milliseconds, this is critical for plotting and calculating the tau
nbin = np.max([value.shape[-1] for value in batch_dprimes['SC']['dprime'].values()])
fs = meta['raster_fs']
times = np.linspace(0, nbin / fs, nbin, endpoint=False) * 1000
bar_width = 1 / fs * 1000
fig_root = 'single_cell_context_dprime'
def compare_plot(cell):
site = cell[0:7]
SC_hist = np.nanmean(batch_dprimes['SC']['shuffled_significance'][cell], axis=(0, 1))