zi, ei, pi = Bcpnn.compute_traces(pre_trace, tau_dict['tau_zi'], tau_dict['tau_ei'], tau_dict['tau_pi'], eps=dt/tau_dict['tau_pi'])
pre_id = my_conns[0][0]
post_id = my_conns[0][1]
# print the seperately computed traces
np.savetxt('debug_zi_trace_%d.dat' % pre_id, zi)
np.savetxt('debug_ei_trace_%d.dat' % pre_id, ei)
np.savetxt('debug_pi_trace_%d.dat' % pre_id, pi)
zj, ej, pj = Bcpnn.compute_traces(post_trace, tau_dict['tau_zj'], tau_dict['tau_ej'], tau_dict['tau_pj'], eps=dt/tau_dict['tau_pj'])
np.savetxt('debug_zj_trace_%d.dat' % post_id, zj)
np.savetxt('debug_ej_trace_%d.dat' % post_id, ej)
np.savetxt('debug_pj_trace_%d.dat' % post_id, pj)
pij, w_ij, bias = Bcpnn.compute_pij(zi, zj, pi, pj, tau_dict['tau_eij'], tau_dict['tau_pij'])
print 'debug w_ij', pre_id, post_id, w_ij
w_ij_trace, bias, pij, eij = Bcpnn.compute_pij(zi, zj, pi, pj, tau_dict['tau_eij'], tau_dict['tau_pij'], get_traces=True)
print 'debug w_ij_trace[-1]', w_ij_trace[-1]
print 'debug compare:'
print 'zi zi_debug', zi[-1], zi_debug[-1], zi[-1] == zi_debug[-1], np.all(zi == zi_debug)
print 'zj zj_debug', zj[-1], zj_debug[-1], zj[-1] == zj_debug[-1], np.all(zj == zj_debug)
print 'ei ei_debug', ei[-1], ei_debug[-1], ei[-1] == ei_debug[-1], np.all(ei == ei_debug)
print 'ej ej_debug', ej[-1], ej_debug[-1], ej[-1] == ej_debug[-1], np.all(ej == ej_debug)
print 'pi pi_debug', pi[-1], pi_debug[-1], pi[-1] == pi_debug[-1], np.all(pi == pi_debug)
print 'pj pj_debug', pj[-1], pj_debug[-1], pj[-1] == pj_debug[-1], np.all(pj == pj_debug)
print 'bias, bias_j_debug[-1]', bias[-1], bias_j_debug[-1]
assert np.all(bias[-1] == bias_j_debug[-1])
trace_plotter.plot_all(my_conns[0][0], my_conns[0][1], fig=None, text=text, output_fn=output_fig_fn, show=True)
output_fn = params["etrace_fn_base"] + "%d.dat" % pre_id
np.savetxt(output_fn, ei)
output_fn = params["etrace_fn_base"] + "%d.dat" % post_id
np.savetxt(output_fn, ej)
output_fn = params["etrace_fn_base"] + "%d_%d.dat" % (pre_id, post_id)
np.savetxt(output_fn, eij)
output_fn = params["ptrace_fn_base"] + "%d.dat" % pre_id
np.savetxt(output_fn, pi)
output_fn = params["ptrace_fn_base"] + "%d.dat" % post_id
np.savetxt(output_fn, pj)
output_fn = params["ptrace_fn_base"] + "%d_%d.dat" % (pre_id, post_id)
np.savetxt(output_fn, pij)
text = ""
text += "tau_z = %d\n" % tau_z
text += "tau_e = %d\n" % tau_e
text += "tau_p = %d\n" % tau_p
text += "x_stim(t) = (%.1f, %.1f) + (%.1f, %.1f) * t\n" % (mp[0], mp[1], mp[2], mp[3])
output_fn = params["figures_folder"] + "bcpnn_%d.png" % (i)
fig = None
fig = plotter.plot_all(pre_id, post_id, fig, text, show=False, output_fn=output_fn)
# fig.canvas.draw()
input_fn = params["figures_folder"] + "bcpnn_%d.png"
output_fn = params["movie_folder"] + "bcpnn_traces.avi"
fps = 0.5
import make_movie as mm
mm.avconv(input_fn, output_fn, fps)