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
'green': ga,
'red': ra,
'blue': ba
}
}
lum = np.linspace(0, 1, len(gb))
def func(lum, coefs):
# return coefs[0]+coefs[1]*lum**coefs[2]
return coefs[0] * lum**coefs[1]
for correc in ['before', 'after']:
fig, AX = ge.figure(axes=(3, 1))
for i, color in enumerate(['blue', 'green', 'red']):
array = calib[correc][color]
array = (array - np.min(array)) / (np.max(array) - np.min(array))
def to_minimize(coefs):
return np.sum(np.abs(array - func(lum, coefs))**2)
residual = minimize(to_minimize, [1, 1], bounds=[(0.5, 2), (0.1, 3.)])
print('For %s and %s, gamma=' % (correc, color), residual.x[1])
# 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]),
from neural_network_dynamics import main as ntwk # based on Brian2
if (args.filename == '') and (args.directory == ''):
print("""
please provide as arguments either a filename of a morphology or a directory containing some
FOR EXAMPLE:
python nrn_morpho.py --filename ./neural_network_dynamics/single_cell_integration/morphologies/Jiang_et_al_2015/L5pyr-j140408b.CNG.swc
OR:
python nrn_morpho.py --directory ./neural_network_dynamics/single_cell_integration/morphologies/Jiang_et_al_2015/
""")
elif args.directory != '':
file_list = [
fn for fn in os.listdir(args.directory) if fn.endswith('.swc')
]
fig, AX = ge.figure(figsize=(.6, .6),
axes=(int(len(file_list) / 8) + 1, 8))
n = 0
for fn, ax in zip(file_list, ge.flat(AX)):
morpho = ntwk.Morphology.from_swc_file(
os.path.join(args.directory, fn))
SEGMENTS = ntwk.morpho_analysis.compute_segments(morpho)
colors = [
ge.green if comp_type == 'axon' else ge.red
for comp_type in SEGMENTS['comp_type']
]
plot_nrn_shape(ge, SEGMENTS, colors=colors, ax=ax)
ge.title(ax, fn.split('-')[0], bold=True, style='italic', size='')
n += 1
while n < len(ge.flat(AX)):
ge.flat(AX)[n].axis('off')
# ncol=ncol,
# # numpoints=1,
# # scatterpoints=1,
# columnspacing=columnspacing,
# handletextpad=handletextpad,
# handlelength=handlelength,
# title=title,
# fontsize=fontsize)
if __name__ == '__main__':
from datavyz import ges as ge
Y = [np.exp(np.random.randn(100)) for i in range(4)]
fig, ax = ge.figure(right=5.) # with extended right space
for i in range(2):
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)
os.path.join(args.imgfolder, args.FILES[0])),
k=3)
init_fit_area(args,
ellipse=args.data['ROIellipse'],
reflectors=(args.data['reflectors']
if 'reflectors' in args.data else None))
args.cframe = 10000
preprocess(args, with_reinit=False)
fit = perform_fit(args,
saturation=args.data['ROIsaturation'],
verbose=True,
N_iterations=0)[0]
fig, ax = ge.figure(figsize=(1.4, 2), left=0, bottom=0, right=0, top=0)
ax.plot(*ellipse_coords(*fit), 'r')
ax.plot([fit[0]], [fit[1]], 'ro')
ge.image(args.img_fit, ax=ax)
ge.show()
fit = perform_fit(args,
saturation=args.data['ROIsaturation'],
verbose=True,
N_iterations=8,
do_xy=True)[0]
fig, ax = ge.figure(figsize=(1.4, 2), left=0, bottom=0, right=0, top=0)
ax.plot(*ellipse_coords(*fit), 'r')
ax.plot([fit[0]], [fit[1]], 'ro')
ax.plot(*args.xy.T, 'r')
ge.image(args.img_fit, ax=ax)
loc_syn0 = LOCs[1]
except BaseException:
pass
NSYNs = [2, 4, 6, 8, 10, 12, 14]
if sys.argv[-1] == 'syn-demo':
from datavyz import nrnvyz, ges as ge
_, neuron, SEGMENTS = initialize_sim(Model)
vis = nrnvyz(SEGMENTS, ge=ge)
fig, AX = ge.figure(axes=(len(NSYNs), 1),
figsize=(.8, 1.2),
wspace=0,
left=0,
top=0.3,
bottom=0,
right=0)
for nsyn, ax in zip(NSYNs, AX):
ge.title(ax, '$N_{syn}$=%i' % nsyn, size='xx-small')
vis.plot_segments(SEGMENTS['comp_type'] != 'axon',
bar_scale_args=dict(Ybar=50,
Xbar_label='50um',
Xbar=50,
Ybar_label='',
loc='top-right',
xyLoc=(-110, 90),
size='xx-small'),
ax=ax)
vis.add_dots(ax, loc_syn0 + np.arange(nsyn), 10, ge.orange)
viridis, viridis_r, copper, copper_r, cool, jet, PiYG, binary,
binary_r, bone, Pastel1, Pastel2, Paired, Accent, Dark2, Set1,
Set2, Set3, tab10, tab20, tab20b, tab20c
]:
setattr(cls, color.name, color)
cls.cmaps.append(color)
# then some linear colormaps
for (c1, c2) in zip(
['blue', 'red', 'blue', 'green', 'red', 'green', 'orange'],
['red', 'blue', 'orange', 'red', 'green', 'orange', 'green']):
setattr(cls, '%s_to_%s' % (c1, c2),
get_linear_colormap(getattr(cls, c1), getattr(cls, c2)))
cls.get_linear_colormap = get_linear_colormap
if __name__ == '__main__':
import numpy as np
from datavyz import ges as ge
colors = ['tab:blue', 'tab:red']
fig, ax = ge.figure()
for i, c in enumerate(ge.colors[:3]):
ge.scatter([i], [i], color=c, ax=ax, no_set=True)
fig, ax = ge.figure()
for i, x in enumerate(np.linspace(0, 1, 50)):
ax.scatter([i], [x], color=ge.get_linear_colormap(ge.blue, ge.red)(x))
ge.show()
'cMg': 1., # mM
'etaMg': 0.33, # 1/mM
'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:
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()
ON_EXC_EVENT.format(**Model))
# 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()
top=0,
right=0))
vis.add_dots(ax, PROX, 50, ge.orange)
vis.add_dots(ax, DIST, 50, ge.orange)
ge.show()
elif args.protocol == 'syn-number':
tstop, t0_stim = 300, 20
if args.plot:
data = load_dict(
os.path.join('data', 'branco-syn-number-%s.npz' % suffix))
fig, AX = ge.figure(axes=(1, 2),
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,
# 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()
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
