def __init__(self,
model=None,
stimulus=None,
eye_movement=None,
graph_env_key='manuscript'):
self.ge = graph_env(graph_env_key)
# need to find screen parameters
if stimulus is not None:
self.stimulus = stimulus
self.SCREEN = stimulus.SCREEN
elif model is not None:
self.model = model
self.SCREEN = model.SCREEN
elif eye_movement is not None:
self.eye_movement = eye_movement
self.SCREEN = eye_movement.SCREEN
else:
print('need to find screen parameters')
ax.add_patch(cij)
ax.set_ylim([-1.5, 1.5])
ax.set_xlim([-1.5, 1.5])
ax.axis('equal')
ax.axis('off')
return fig, ax
if __name__ == '__main__':
from datavyz.main import graph_env
ge = graph_env('screen')
POPS = {
'Pyr': {
'Ncell': 4000,
'color': ge.green
},
'oscillExc': {
'Ncell': 500,
'color': ge.blue
},
'PV': {
'Ncell': 500,
'color': ge.red
},
'SST': {
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.main import graph_env
ge = graph_env('manuscript')
y = np.exp(np.random.randn(100))
fig, ax = ge.plot(y, xlabel='time', ylabel='y-value')
sax = ge.inset(ax, rect=[.5,.8,.5,.4])
ge.hist(y, bins=10, ax=sax, axes_args={'spines':[]}, xlabel='y-value')
fig.savefig('docs/inset.svg')
ge.show()
import sys, pathlib, os
sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
import numpy as np
import neural_network_dynamics.main as ntwk
from analyz.processing.signanalysis import gaussian_smoothing as smooth
from analyz.IO.npz import load_dict
from datavyz.main import graph_env
ge = graph_env()
COLORS = [ge.g, ge.b, ge.r, ge.purple]
from model import Model, REC_POPS, AFF_POPS
from Umodel import Umodel
if sys.argv[-1] == 'plot':
######################
## ----- Plot ----- ##
######################
## load file
print('plotting "data/draft_data.h5" [...]')
data = ntwk.load_dict_from_hdf5('data/draft_data.h5')
# ## plot
fig, _ = ntwk.activity_plots(data, smooth_population_activity=10)
ntwk.show()
elif sys.argv[-1] == 'mf':
for x, y, sy1, sy2, c in zip(X, Y, sY1, sY2, COLORS):
ax.fill_between(x,
y - sy1,
y + sy2,
color=c,
lw=0,
alpha=alpha_std)
elif (sY is not None):
for x, y, sy, c in zip(X, Y, sY, COLORS):
ax.fill_between(x, y - sy, y + sy, color=c, lw=0, alpha=alpha_std)
if __name__ == '__main__':
from datavyz.main import graph_env
ge = graph_env('manuscript')
geS, geM = graph_env('screen'), graph_env('manuscript')
# ge.plot(Y=3*np.random.randn(4,10),
# sY=np.random.randn(4,10),
# ls=':', m='o', ms=0.1, lw=0.4,
# xlabel='x-label (X)', ylabel='y-label (Y)')
tstop, dt = 10, 1e-2
t = np.arange(int(tstop / dt)) * dt
x = np.random.randn(len(t)) * 10. - 70.
for tt in np.cumsum(np.random.exponential(tstop / 10., 10)):
x[np.argmin(np.abs(tt - t))] = 10.
for ge in [geS, geM]:
fig, ax = ge.plot(t,
from datavyz.main import graph_env
graph_env_manuscript = graph_env('manuscript')
graph_env_screen = graph_env('screen')
graph_env_notebook = graph_env('notebook')
graph_env_dark_notebook = graph_env('dark_notebook')
ge = graph_env_manuscript
ges = graph_env_screen
gen = graph_env_notebook
gedn = graph_env_dark_notebook
from datavyz.nrn_morpho import nrnvyz
