def open_fan_plot(self,
cell_specimen_id=None,
include_labels=False,
cell_index=None):
cell_index = self.row_from_cell_id(cell_specimen_id, cell_index)
df = self.mean_sweep_response[str(cell_index)]
st = self.data_set.get_stimulus_table('static_gratings')
mask = st.dropna(subset=['orientation']).index
data = df.values
cmin = self.response[0, 0, 0, cell_index, 0]
cmax = data.mean() + data.std() * 3
fp = cplots.FanPlotter.for_static_gratings()
fp.plot(r_data=st.spatial_frequency.ix[mask].values,
angle_data=st.orientation.ix[mask].values,
group_data=st.phase.ix[mask].values,
data=df.ix[mask].values,
clim=[cmin, cmax])
fp.show_axes(closed=False)
if include_labels:
fp.show_r_labels()
fp.show_angle_labels()
def open_star_plot(self, cell_specimen_id=None, include_labels=False, cell_index=None):
cell_index = self.row_from_cell_id(cell_specimen_id, cell_index)
df = self.mean_sweep_response[str(cell_index)]
st = self.data_set.get_stimulus_table('drifting_gratings')
mask = st.dropna(subset=['orientation']).index
data = df.values
cmin = self.response[0,0,cell_index,0]
cmax = max(cmin, data.mean() + data.std()*3)
fp = cplots.FanPlotter.for_drifting_gratings()
fp.plot(r_data=st.temporal_frequency.ix[mask].values,
angle_data=st.orientation.ix[mask].values,
data=df.ix[mask].values,
clim=[cmin, cmax])
fp.show_axes(closed=True)
if include_labels:
fp.show_r_labels()
fp.show_angle_labels()
lambda el: el[0] if el[1:] == el[:-1] else None)
df.set_index('network', inplace=True)
stats = pd.concat([stats, df])
stats = stats.applymap(np.array)
#stats[stats.isnull()] = np.NaN
stats.sort_index(inplace=True)
try:
stats['hidden_neurons_total'] = stats.pop(
'hidden_neurons').to_frame().applymap(np.sum)
stats['l2_norm_weighted'] = stats.pop('l2_norm') / stats.pop(
'hidden_neurons_total')
stats['l2'] = stats.pop('l2_norm_weighted')
except KeyError:
pass
stats.dropna(axis='columns', how='all', inplace=True)
#'no_pop_frac', 'no_thresh_frac', 'pop_abs_mis_95width',
# 'pop_abs_mis_median', 'rms_test', 'thresh_rel_mis_95width',
# 'thresh_rel_mis_median', 'l2_norm_weighted'
#print(stats.max())
#print(stats.min())
#print(stats.mean())
#print(stats.abs().mean())
del stats['pop_abs_mis_95width']
del stats['thresh_rel_mis_95width']
stats['rms'] = stats.pop('rms')
stats['thresh'] = stats.pop('thresh_rel_mis_median').abs().apply(np.max)
stats['no_thresh_frac'] = stats.pop('no_thresh_frac').apply(np.max)
stats['pop'] = (14 - stats.pop('pop_abs_mis_median').abs()).apply(np.max)
if 'wobble_tot' in stats.keys():
stats['wobble_tot'] = stats.pop('wobble_tot').apply(np.max)