plast_observer.py
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	import numpy as np
	import matplotlib.pyplot as plt
	import sys
	sys.path.append("./build-clang-5.0-dbg/language_bindings/python/")
	from numpy.linalg import norm
	import muSpectre as µ



	def hyper_curve(tau_y0 = 200e6, h0 = 10e9, max_shear = 5e-2):
	bulk_m = 175e9;
	shear_m = 120e9;

	bulk_m = .833
	shear_m = .386
	Young = 9bulk_mshear_m/(3*bulk_m + shear_m)
	Poisson = Young/(2*shear_m) - 1
	print("E = {}, ν = {}".format(Young, Poisson))

	tolerance=1e-12
	dim=3
	resolution = [1,1, 1]
	lengths = [1, 1, 1]
	formulation = µ.Formulation.finite_strain

	cell = µ.Cell(resolution,
	lengths,
	formulation)
	dim = len(lengths)

	mat = µ.material.MaterialHyperElastoPlastic1_3d.make(
	cell, "hyper-elasto-plastic", Young, Poisson, tau_y0, h0)

	for pixel in cell:
	mat.add_pixel(pixel)

	cell.initialise()

	tau = list()
	sigma_xx=list()
	gammas=list()
	#tau_inc = list()
	#gamma_dot = list()
	shear_incr = 5e-3
	np_load = int(max_shear/shear_incr)
	F = np.eye(dim)

	for step in range(np_load):
	F[0,1] += shear_incr
	gammas.append(F[0,1])

	stress, tangent = cell.evaluate_stress_tangent(F.T.reshape(-1))
	mat.save_history_variables()
	tau.append(stress[dim])
	print("stress =\n", stress)
	sigma_xx.append(stress[0])
	gammas = np.array(gammas).reshape(-1)
	tau = np.array(tau)

	return gammas, tau, sigma_xx

	gammas, tau, sigma_xx = hyper_curve(tau_y0 = .006, h0 = .008*2, max_shear = 5e-2)
	plt.plot(gammas, tau, '+-', label=r"$\tau$ default")
	#plt.plot(gammas, sigma_xx, label=r"$\sigma_{{xx}}$ default")
	pass
	plt.legend(loc='best')
	plt.xlabel('ε₁₂')
	plt.ylabel('γ₁₂');

	plt.show()