def show_residuals_over_trials(self, graph=None, ax=None, threshold=100):
if graph is None:
from datavyz import ges as graph
if ax is None:
# fig, ax = graph.figure(axes=(1,3), wspace=.2,
# left=.7, bottom=.7, figsize=(.7,1.))
fig, ax = graph.figure(left=.7, bottom=.7, figsize=(.7,1.))
else:
fig = None
result = self.result
if self.new_result is not None:
RESULTS = [self.result, self.new_result]
else:
RESULTS = [self.result]
for res in RESULTS:
indices = np.argsort(res['residuals'])
x = res['residuals'][indices]
full_x = np.concatenate([np.arange(1, len(indices)+1)[x<=threshold],
[len(indices)+1, res['Ntot']]])
full_y = np.concatenate([x[x<=threshold], [threshold, threshold]])
ax.plot(np.log10(full_x), np.log10(full_y), clip_on=False)
graph.set_plot(ax,
# yticks=np.log10([1, 2, 5, 10, 20]),
# yticks_labels=['1', '2', '5', '10', '>20'],
# yminor_ticks=np.log10([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20]),
# ylim=[1, np.log10(threshold)],
xminor_ticks=np.log10(np.concatenate([[i*10**k for i in range(1,10)]\
for k in range(0,7)])),
xlim=[np.log10(result['Ntot']), -1.],
xticks=np.arange(7),
xticks_labels=['1','','','$10^3$','','','$10^6$'],
xlabel='config #',
ylabel=r'fit $\chi^2$ (norm.) ', ylabelpad=-7)
graph.annotate(ax, '4 pop. model', (.4, 1.2), ha='center')
return fig, ax
# 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()
fig1, ax, _ = matrix(ge,
z,
aspect='equal',
xlabel='x-label (X)',
ylabel='y-label (Y)')
x, y, z = np.array(x).flatten(),\
np.array(y).flatten(),\
np.array(z).flatten()*np.random.randn(len(z.flatten()))
index = (x < 15) & (y < x)
# np.random.shuffle(index)
x, y, z = x[index], y[index], z[index]
fig2, ax, acb = twoD_plot(ge,
x,
y,
z,
vmin=-7,
vmax=7,
bar_legend_args={
'label': 'color',
'ticks': [-7, 0, 7],
'ticks_labels': ['-7', '0', '>7'],
'color_discretization': 20
})
ge.set_plot(ax, xlabel='x-label (X)', ylabel='y-label (Y)')
fig2.savefig('docs/surface-plot.png')
ge.show()
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
# calcium dynamics following: HighVoltageActivationCalciumCurrent + LowThresholdCalciumCurrent
# Equation_String = nrn.CalciumConcentrationDynamics(contributing_currents='0*mA/cm**2',
Equation_String = nrn.CalciumConcentrationDynamics(contributing_currents='IHVACa+IT',
name='CaDynamics').insert(nrn.Equation_String)
# intrinsic currents
CURRENTS = [nrn.PassiveCurrent(name='Pas'),
nrn.PotassiumChannelCurrent(name='K'),
return data
if __name__ == '__main__':
import sys
if len(sys.argv) > 1:
fn = sys.argv[1]
else:
fn = '/home/yann/DATA/2020_09_11/13-40-10/pupil-data.npy'
from datavyz import ges as ge
data = np.load(fn, allow_pickle=True).item()
fig, AX = ge.figure(axes=(1, 6), figsize=(3, 1), hspace=0.1)
for i, key in enumerate(['cx', 'cy', 'sx', 'sy', 'residual']):
AX[i].plot(data['times'], data[key], label='fit')
data = replace_outliers(data)
for i, key in enumerate(['cx', 'cy', 'sx', 'sy', 'residual']):
AX[i].plot(data['times'], data[key + '-corrected'], label='corrected')
ge.set_plot(AX[i],
xlim=[data['times'][0], data['times'][-1]],
ylabel=key)
ge.show()
figsize=(.7, 1),
right=4.,
hspace=0.1,
bottom=0.1)
ge.title(AX[0],
'%i to %i synapses (by 2)' % (N_PULSES[0], N_PULSES[-1]))
for synapses_loc, label, ax in zip([PROX, DIST], ['prox', 'dist'],
AX):
for i in range(len(data['v-%s' % label])):
ax.plot(data['t'],
data['v-%s' % label][i],
label=label,
color='k',
lw=1)
ge.set_plot(ax, [], ylim=[-76, -45], xlim=[0, tstop])
ge.draw_bar_scales(ax,
Xbar=50.,
Xbar_label='50ms',
Ybar=5.,
Ybar_label='5mV',
loc='top-right')
ge.show()
else:
from single_cell_sim import *
if args.chelated_zinc:
Model['Deltax0'] = 0. # removing Zinc modulation !!
data = {'t': [], 'v-prox': [], 'v-dist': []}
# axes_args={'spines':['left', 'bottom'], 'xlabel':'my-x value', 'ylabel':'my-y value'})
# save_on_desktop(fig, figname='2.svg')
# show()
# mg = graphs('ggplot_npotebook')
# mg = graphs()
# mg.hist(np.random.randn(100), xlabel='ksjdfh')
from datavyz import ges as ge
# ge.boxplot(np.exp(np.random.randn(100)),
# axes_args=dict(xticks=[1],
# xticks_labels=['data']))
fig_lf, AX = ge.figure(axes_extents=[[[3, 1]], [[1, 2], [1, 2], [1, 2]]],
figsize=(1., .5),
wspace=3.,
hspace=2.)
for ax in [item for sublist in AX for item in sublist]:
ge.top_left_letter(ax, 'a')
# _, ax, _ = ge.figure(with_bar_legend=True)
AX[1][0].hist(np.random.randn(100))
fig, ax = ge.figure()
ax.hist(np.random.randn(100))
ge.panel_label(ax, 'a')
ge.annotate(ax, 'blabla', (0.7, 0.8), italic=True)
ge.set_plot(ax)
ge.show()
ge.show()