def realign_from_photodiode(self, debug=False, verbose=True):
if verbose:
print(
'---> Realigning data with respect to photodiode signal [...] '
)
if debug:
from datavyz import ges as ge
success = True
# extract parameters
dt = 1. / self.metadata['NIdaq-acquisition-frequency']
tlim, tnew = [0, self.Screen.photodiode.t[-1]], 0
t0 = self.metadata['time_start'][0]
length = self.metadata['presentation-duration'] + self.metadata[
'presentation-interstim-period']
npulses = int(self.metadata['presentation-duration'])
self.metadata['time_start_realigned'] = []
Nepisodes = np.sum(self.metadata['time_start'] < tlim[1])
for i in range(Nepisodes):
cond = (self.Screen.photodiode.t >=
t0 - .3) & (self.Screen.photodiode.t <= t0 + length)
try:
tnew, integral, threshold = find_onset_time(
self.Screen.photodiode.t[cond] - t0,
self.Screen.photodiode.val[cond], npulses)
if debug and ((i < 3) or (i > Nepisodes - 3)):
ge.plot(self.Screen.photodiode.t[cond],
self.Screen.photodiode.val[cond])
ge.plot(self.Screen.photodiode.t[cond],
Y=[integral, integral * 0 + threshold])
ge.show()
except Exception:
success = False # one exception is enough to make it fail
t0 += tnew
self.metadata['time_start_realigned'].append(t0)
t0 += length
if verbose:
if success:
print('[ok] --> succesfully realigned')
else:
print('[X] --> realignement failed')
if success:
self.metadata['time_start_realigned'] = np.array(
self.metadata['time_start_realigned'])
self.metadata['time_stop_realigned'] = self.metadata['time_start_realigned']+\
self.metadata['presentation-duration']
else:
self.metadata['time_start_realigned'] = np.array([])
self.metadata['time_stop_realigned'] = np.array([])
return success
# ge.title(AX[i], "a=%.2f, k=%.2f, $\gamma$=%.2f" % (residual.x[0], residual.x[1], residual.x[2]), color=getattr(ge, color), size='small')
ge.title(AX[i],
"k=%.2f, $\gamma$=%.2f" % (residual.x[0], residual.x[1]),
color=getattr(ge, color),
size='small')
ge.scatter(lum,
array,
ax=AX[i],
color=getattr(ge, color),
label='data',
ms=3)
ge.plot(lum,
func(lum, residual.x),
ax=AX[i],
lw=3,
alpha=.5,
color=getattr(ge, color),
label='fit')
ge.annotate(AX[i],
'$\lambda$=%s' % LAMBDA[color], (0.5, .1),
color=getattr(ge, color))
ge.set_plot(AX[i],
xlabel='(computer) luminosity',
xticks=[0, 0.5, 1],
yticks=[0, 0.5, 1],
ylabel='measured I (norm.)')
fig.savefig('doc/gamma-correction-%s.png' % correc)
ge.show()
ax.plot(np.arange(10) * 10,
np.exp(np.random.randn(10)),
'o',
ms=2,
label='line' + str(i + 1))
for i in range(2):
ax.plot(np.arange(10) * 10,
np.exp(np.random.randn(10)),
'-',
label='line' + str(i + 1))
ge.legend(ax, ncol=2)
ge.plot(Y=Y,
xlabel='time',
ylabel='y-value',
colormap=ge.copper,
lw=1.,
ax=ax)
ge.bar_legend(fig,
X=np.arange(5),
inset={'rect': [.3, .8, .3, .05]},
colormap=ge.copper,
orientation='horizontal',
label='Trial ID',
no_ticks=True)
ge.bar_legend(fig, bounds=[1e-3, 10], scale='log', label='scale')
ge.show()
---> to be used with the "-Debug-" button of the GUI
"""
from datavyz import ges as ge
args.imgfolder = os.path.join(args.datafolder, 'FaceCamera-imgs')
args.data = np.load(os.path.join(args.datafolder, 'facemotion.npy'),
allow_pickle=True).item()
load_folder(args)
set_ROI_area(args, roi_coords=args.data['ROI'])
DATA = load_ROI_data(args, iframe1=0, iframe2=1000)
frames, motion = compute_motion(args,
time_subsampling=1,
with_ProgressBar=True)
ge.plot(frames, motion)
ge.show()
else:
if os.path.isfile(os.path.join(args.datafolder, 'facemotion.npy')):
print('Processing face motion for "%s" [...]' %
os.path.join(args.datafolder, 'facemotion.npy'))
args.imgfolder = os.path.join(args.datafolder, 'FaceCamera-imgs')
args.data = np.load(os.path.join(args.datafolder,
'facemotion.npy'),
allow_pickle=True).item()
load_folder(args)
set_ROI_area(args, roi_coords=args.data['ROI'])
frames, motion = compute_motion(
# 'NSYN':np.array(NSYN)})
if __name__=='__main__':
import sys, os
from model import Model
from analyz.workflow.batch_run import GridSimulation
from analyz.IO.npz import load_dict
if (len(sys.argv)>1) and (sys.argv[1]=='syn-input'):
index = int(sys.argv[2])
sim = GridSimulation(os.path.join('data', 'syn-input', 'spike-threshold-grid.npz'))
sim.update_dict_from_GRID_and_index(index, Model) # update Model parameters
if (Model['Fexc_bg']==0) and (Model['seed']>0):
pass
elif (Model['qNMDA']==0) and (Model['Deltax0']>0):
pass # no NMDA and Zinc-modulation, meaningless ...
else:
print('Running:', sim.params_filename(index))
t, v = run_single_trial(Model)
np.savez(os.path.join('data', 'syn-input', sim.params_filename(index)), **{'t':t, 'v':v})
else:
Model['Fexc_bg'] = 2.
t, v = run_single_trial(Model)
from datavyz import ges as ge
ge.plot(t, v)
ge.show()
'V0NMDA':1./0.08,# [mV]
'Mg_NMDA':1.,# mM
}
if sys.argv[-1]=='plot':
from analyz.IO.npz import load_dict
from datavyz import ges as ge
data = load_dict('data.npz')
fig, AX = ge.figure(axes=(1,3), figsize=(2.,1.))
ge.plot(data['t'], Y=[data['Vsoma'], data['Vdend']],
LABELS=['soma', 'dend'], COLORS=['k', ge.blue], ax=AX[0],
axes_args={'ylabel':'Vm (mV)', 'xlim':[0, data['t'][-1]]})
ge.plot(data['t'], Y=[data['Casoma'], data['Cadend']],
COLORS=['k', ge.blue], ax=AX[1],
axes_args={'ylabel':'[Ca$^{2+}$] (nM)', 'xlabel':'time (ms)', 'xlim':[0, data['t'][-1]]})
ge.scatter(data['Espike_times'], data['Espike_IDs'], color=ge.green,ax=AX[2],no_set=True,ms=3)
ge.scatter(data['Ispike_times'], data['Espike_IDs'].max()*1.2+data['Ispike_IDs'], color=ge.red,
ax=AX[2], no_set=True, ms=3)
ge.set_plot(AX[2], [], xlim=[0, data['t'][-1]])
fig.savefig('/home/yann/Desktop/trace.svg')
ge.show()
else:
nrn.defaultclock.dt = 0.025*nrn.ms
return pca
if __name__=='__main__':
from sklearn.datasets import load_breast_cancer
from datavyz import ges
raw, data = load_breast_cancer(), {}
for i, key in enumerate(raw['feature_names']):
data[key] = np.log10(raw['data'][i])
ges.features_plot(data, features=list(data.keys())[:6])
ges.cross_correl_plot(data, features=list(data.keys())[:6])
pca = PCA(data, n_components = len(data)) # desired_var_explained=0.9)
ges.plot(100.*pca.explained_variance_ratio_,
m='o', ms=4, xlabel='component #', ylabel='% var. expl.')
ges.parallel_plot(pca.components_,
fig_args=dict(figsize=(3,1)))
ges.show()
# recording and running
if active:
M = ntwk.StateMonitor(neuron, ('v', 'InternalCalcium'), record=[0, synapses_loc[0]])
else:
M = ntwk.StateMonitor(neuron, ('v'), record=[0, synapses_loc[0]])
S = ntwk.StateMonitor(ES, ('X', 'gAMPA', 'gE_post', 'bZn'), record=[0])
# # Run simulation
print('running simulation [...]')
ntwk.run(tstop*ntwk.ms)
from datavyz import ges as ge
fig, AX = ge.figure(axes=(1,2),figsize=(2,1))
AX[0].plot(np.array(M.t/ntwk.ms), np.array(M.v/ntwk.mV)[0,:], label='soma')
ge.plot(np.array(M.t/ntwk.ms), np.array(M.v/ntwk.mV)[1,:], label='dend', ax=AX[0])
if active:
ge.plot(np.array(M.t/ntwk.ms), np.array(M.InternalCalcium/ntwk.nM)[1,:],
label='dend', ax=AX[1], axes_args={'ylabel':'[Ca2+] (nM)', 'xlabel':'time (ms)'})
else:
AX[1].plot(np.array(M.t/ntwk.ms), np.array(S.gAMPA/ntwk.nS)[0,:], ':', color=ge.orange, label='gAMPA')
AX[1].plot(np.array(M.t/ntwk.ms), np.array(S.gE_post/ntwk.nS)[0,:]-np.array(S.gAMPA/ntwk.nS)[0,:], color=ge.orange, label='gNMDA')
ge.legend(AX[0])
ge.legend(AX[1])
ge.show()
else:
pass
mf = FastMeanField(Model, tstop=6., dt=1e-2, tau=5e-2)
if sys.argv[-1]=='sim':
print('building the TF sim. (based on the COEFFS)')
mf.simulate_TF_func(300,
coeffs_location='../configs/Network_Modulation_2020/COEFFS_pyrExc.npy',
tf_sim_file='tf_sim_points.npz',
Iinj_lim=[0, 200.], # in pA
Exc_lim=[0.01,50000],
Inh_lim=[0.01,50000],
with_Vm_functions=True,
sampling='log')
mf.build_TF_func(tf_sim_file='tf_sim_points.npz')
X, mVm, sVm = mf.run_single_connectivity_sim(mf.ecMatrix, verbose=True)
from datavyz import ges as ge
fig, AX = ge.figure(figsize=(3,1), axes=(1,5))
COLORS=[ge.g, ge.b, ge.r, ge.purple]
for i, label in enumerate(Model['REC_POPS']):
AX[-1].plot(1e3*mf.t, 1e-2+X[i,:], lw=4, color=COLORS[i], alpha=.5)
ge.plot(1e3*mf.t, 1e3*mVm[i,:], sy=1e3*sVm[i,:], ax=AX[i], color='k')
AX[i].set_ylim([-72,-45])
ge.show()
# # benchmark
