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