def plot_sim(tdiscard=100, tend=1000):
Data = np.load('data/pair_calib.npy', allow_pickle=True).item()
cond = (Data['t']>tdiscard) & (Data['t']<tend)
from datavyz import ge
fig, ax= ge.figure(figsize=(2,2))
for i in range(len(Data['alphaZn'])):
ge.plot(Data['t'][cond], np.array(Data['Current'][i]).mean(axis=0)[cond],
color=ge.viridis(i/len(Data['alphaZn'])), ax=ax, no_set=True)
ge.show()
def plot_sim(filename, ge,
omf_data=None,
Umodel_data=None,
mf_data=None):
## load file
data = ntwk.load_dict_from_hdf5(filename)
# ## plot
fig, AX = ntwk.activity_plots(data,
smooth_population_activity=10,
COLORS=COLORS,
pop_act_log_scale=True)
if Umodel_data is not None:
AX[2].plot(Umodel_data['t'],
Umodel_data['desired_Vm'], '-', lw=2, color='dimgrey', label='mean-field')
if mf_data is None:
mf = ntwk.FastMeanField(data, tstop=6., dt=2.5e-3, tau=20e-3)
mf.build_TF_func(tf_sim_file='neural_network_dynamics/theory/tf_sim_points.npz')
X, mVm, sVm = mf.run_single_connectivity_sim(mf.ecMatrix, verbose=True)
t = np.linspace(0, data['tstop'], X.shape[1])
for i, label in enumerate(data['REC_POPS']):
AX[-1].plot(t, 1e-2+X[i,:], '--', lw=1, color=COLORS[i])
ge.plot(t, 1e3*mVm[i,:], sy=1e3*sVm[i,:], color='k', lw=1, label='mean-field', ax=AX[2+i])
AX[2+i].plot(t, 1e3*(mVm[i,:]-sVm[i,:]), 'k-', lw=0.5)
AX[2+i].plot(t, 1e3*(mVm[i,:]+sVm[i,:]), 'k-', lw=0.5)
# AX[2].plot(mf_data['t'], mf_data['desired_Vm'], 'k--', lw=2, label='U-model')
# AX[2].legend(frameon=False, loc='best')
if omf_data is None:
try:
omf_data = load_dict(filename.replace('ntwk.h5', 'mf.npz'))
except FileNotFoundError:
pass
if omf_data is not None:
t = np.linspace(0, data['tstop'], len(omf_data['pyrExc']))
for i, label in enumerate(data['REC_POPS']):
AX[-1].plot(t, 1e-2+omf_data[label], '-', lw=4, color=COLORS[i], alpha=.5)
# AX[-1].plot(t, omf_data[label], 'k--')
figM, _, _ = plot_matrix(data)
ntwk.show()
def read_file(filename):
io = pynwb.NWBHDF5IO(filename, mode='r')
nwbfile = io.read()
from datavyz import ge
#
cond = (nwbfile.stimulus['protocol_id'].data[:] == 2)
ge.plot(
nwbfile.acquisition['episode_waveforms'].timestamps[:],
nwbfile.acquisition['episode_waveforms'].data[cond, :].mean(axis=0))
ge.show()
cond = (nwbfile.acquisition['running-speed'].data[:] != 666)
ge.hist(nwbfile.acquisition['running-speed'].data[:][cond])
ge.show()
io.close()
ge.annotate(ax,
'$\\gamma_{env}^{pyr}$', (1.1, .3),
color=COLORS[2],
ha='right',
size='large')
ge.set_plot(ax,
ylabel='depol. (mV)',
xticks=[0, 5, 10, 15],
yticks=[0, 10, 20],
ylim=[-1, 22],
xlabel='modulatory\ninput (Hz)',
title='U-model of cortical states ')
ge.annotate(fig, 'a', (0, 1), va='top', bold=True, size='x-large')
# ge.legend(ax)
return fig, ax
if __name__ == '__main__':
um = Umodel()
from datavyz import ge
fig, _ = um.illustration(ge)
t = np.linspace(0, 10, 1000)
Faff = 1.5 * t
ge.plot(um.predict_Vm(t, Faff), fig_args={'figsize': (3, 1)})
ge.show()
ntwk.defaultclock.dt = 0.025*ntwk.ms
ntwk.run(tstop*ntwk.ms)
# if not active:
# ntwk.run(tstop*ntwk.ms)
# else:
# adaptive_run(tstop, neuron, M)
# ntwk.run(tstop*ntwk.ms)
print('Runtime: %.2f s' % (time.time()-start))
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()
def show_response_bg_dep(FREE, CHELATED, AMPA=None,
ge=ge,
method='Integral',
BG_levels=None,
crossing=None,
xlim=None, ylim=None, yscale='lin'):
if BG_levels is None:
BG_levels = [R['bg_level'] for R in FREE]
CS = [CHELATED, FREE]
if AMPA is not None:
CS.append(AMPA)
fig, AX = ge.figure(axes=(1,3), figsize=(1,1.), hspace=0.3, right=1.5)
else:
fig, AX = ge.figure(axes=(1,2), figsize=(1,1.), hspace=0.3, right=1.5)
#fig2, ax2 = ge.figure(axes=(1,1), figsize=(1,1.), hspace=0.3, right=1.5)
for ibg, bg in enumerate(BG_levels):
if method[:5]=='delta':
AX[0].plot(FREE[ibg]['nstims'], FREE[ibg][method[5:]]-FREE[ibg][method[5:]][0],
lw=1, color=cmap(ibg/(len(BG_levels)-1)))
AX[1].plot(CHELATED[ibg]['nstims'], CHELATED[ibg][method[5:]]-CHELATED[ibg][method[5:]][0],
lw=1, color=cmap(ibg/(len(BG_levels)-1)))
else:
for C, ax in zip(CS, AX):
# data
try:
ge.scatter(C[ibg]['nstims'], C[ibg][method],
ax=ax, color=cmap(ibg/(len(BG_levels)-1)), no_set=True, lw=0.5, ms=2)
# fit
x, coefs = np.linspace(C[ibg]['nstims'][0], C[ibg]['nstims'][-1], 100), C[ibg][method+'-coeffs']
y = coefs[0]*sigmoid_func(x, coefs[1], coefs[2])
ge.plot(x, y, ax=ax, color=cmap(ibg/(len(BG_levels)-1)), no_set=True, lw=3, alpha=0.5)
except IndexError:
pass
# ADDING THE c50 position:
if crossing is not None:
try:
ix0 = min(np.argwhere(y>crossing).flatten())
ax.plot(np.ones(2)*x[ix0], [0,crossing], ':', lw=1, color=cmap(ibg/(len(BG_levels)-1)))
except ValueError:
pass
if crossing is not None:
for ax in AX[:2]:
ax.plot([x[0], x[-1]], crossing*np.ones(2), ':', lw=0.5, color=ge.dimgrey)
ge.annotate(AX[1], 'L23-L23\nprops', (0., .9), size='small', color='dimgrey', bold=True, va='top')
ge.annotate(AX[0], 'L4-L23\nprops', (0., .9), size='small', color=ge.green, bold=True, va='top')
if method=='Integral':
ylabel='PSP integral (mV.s)'+10*' '
if method=='Peak':
ylabel='max. $\delta$ $V_m$ (mV)'+10*' '
if method=='Freq':
ylabel='Spike Freq. (Hz) '+10*' '
if method=='Proba':
ylabel='Spike Probability '+20*' '
if method=='deltaFreq':
ylabel='$\delta$ Spike Freq. (Hz) '+10*' '
ge.set_plot(AX[0], ['left'], ylabel=ylabel, ylim=ylim)
if AMPA is None:
ge.set_plot(AX[0], ['left'], ylabel=ylabel, ylim=ylim, xlim=xlim, yscale=yscale)
ge.set_plot(AX[1], xlabel='$N_{syn}$', ylim=ylim, xlim=xlim, yscale=yscale)
else:
ge.set_plot(AX[0], ['left'], ylim=ylim, xlim=xlim, yscale=yscale)
ge.set_plot(AX[1], ['left'], ylabel=ylabel, ylim=ylim, xlim=xlim, yscale=yscale)
ge.set_plot(AX[2], xlabel='$N_{syn}$', ylim=ylim, xlim=xlim, yscale=yscale)
ge.annotate(AX[2], 'AMPA\nonly', (0., .9), size='small', color=ge.blue, bold=True, va='top')
#ge.set_plot(ax2, xlabel='$\\nu_{bg}$ (Hz)', ylabel='$c_{50}$ ($N_{syn}$)')
ge.bar_legend(fig,
X=[0]+BG_levels,
bounds=[-BG_levels[1]/2., BG_levels[-1]+BG_levels[1]/2],
inset=dict(rect=[.9,.3,.05, .5]),
label='$\\nu_{bg}$ (Hz)',
colormap=cmap)
return fig #, fig2
method='Nelder-Mead' # default method
if (bounds is not None):
res = minimize(to_minimize, coeffs,
method=method, bounds=bounds, options=options)
else:
res = minimize(to_minimize, coeffs,
method=method, options=options)
if verbose:
print(res)
return res
if __name__=='__main__':
import numpy as np
from datavyz import ge
def waveform(x, coeffs=[.3, .2, 2.]):
return coeffs[2]*(np.sign(x-coeffs[0])+1.)*np.exp(-(x-coeffs[0])/coeffs[1])
x = np.linspace(0, 1, 100)
y = np.random.randn(100)+waveform(x, [0.5, .2, 4.])
res = curve_fit(x, y, waveform)
ge.plot(x, Y=[y, waveform(x, res.x)])
ge.show()
parser.add_argument("--n_pulses", type=int, default=5)
parser.add_argument("--Tdiscard0", type=float, default=200)
parser.add_argument("--Tend", type=float, default=200)
parser.add_argument("-NaZn", "--NalphaZn", type=int, default=30)
parser.add_argument("-s", "--seed", help="#", type=int, default=1)
args = parser.parse_args()
if args.task=='demo':
data = run_single_sim(Model, args)
from datavyz import ge
ge.plot(data['t'], 1e3*data['Ic'])
ge.show()
elif args.task=='run':
Data = {'Current':[], 'alphaZn':np.linspace(0, args.alphaZnMax, args.NalphaZn)}
for args.alphaZn in Data['alphaZn']:
Data['Current'].append([])
for args.syn_location in np.random.choice(np.arange(len(LOCs)), args.N_syn_location):
data = run_single_sim(Model, args)
print(Model['alphaZn'])
Data['Current'][-1].append(data['Ic'])
Data['t'] = data['t']
np.save('data/pair_calib.npy', Data)
import numpy as np
from datavyz import ge
fig, ax = ge.plot(Y=np.random.randn(4, 10),
sY=np.random.randn(4, 10),
xlabel='xlabel (xunit)',
ylabel='ylabel (yunit)',
title='datavyz demo plot')
ge.savefig(fig, 'docs/demo.png')
ge.show()
# width = box.width
# height = box.height
# inax_position = ax.transAxes.transform(rect[0:2])
# transFigure = fig.transFigure.inverted()
# infig_position = transFigure.transform(inax_position)
# x = infig_position[0]
# y = infig_position[1]
# width *= rect[2]
# height *= rect[3] # <= Typo was here
# subax = fig.add_axes([x,y,width,height],facecolor=facecolor)
# # x_labelsize = subax.get_xticklabels()[0].get_size()
# # y_labelsize = subax.get_yticklabels()[0].get_size()
# # x_labelsize *= rect[2]**0.5
# # y_labelsize *= rect[3]**0.5
# # subax.xaxis.set_tick_params(labelsize=x_labelsize)
# # subax.yaxis.set_tick_params(labelsize=y_labelsize)
# return subax
if __name__ == '__main__':
from datavyz import ge
y = np.exp(np.random.randn(100))
fig, ax = ge.plot(y, xlabel='time', ylabel='y-value')
sax = ge.inset(ax, [.5, .8, .5, .4])
sax2 = ge.inset(fig, [0.1, 0.1, .3, .2])
ge.hist(y, bins=10, ax=sax, axes_args={'spines': []}, xlabel='y-value')
ge.hist(y, bins=10, ax=sax2, axes_args={'spines': []}, xlabel='y-value')
ge.savefig(fig, 'docs/inset.png')
ge.show()