def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
# ''' TODO [Soza2004] add range of cases in addition to min/max
# E = [8.2,8.85]
# v = [0.4552, 0.461]
# '''
model = Linear.Generic("Linear MRI registration -min [Soza2004]", 0,
8.2 * kPa, 0.4552)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0, 0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "Reg-min-Soza2004"
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
stretches = model.stretches
model = Linear.Generic("Linear MRI registration -max [Soza2004]", 0,
8.85 * kPa, 0.461)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Reg-max-Soza2004")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
PlotMaterial.Groups("MRI Registration", stretches, data_l, data_W, data_dW,
data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M
def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
# [castellanoSmith2003]
model = Linear.Generic("Linear sim abnormal dev --WM-- [castellanoSmith2003]", 0, 4.0 * kPa, 0.495)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0,0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "Abnormal-WM-castellanoSmith2003"
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
stretches = model.stretches
# model.Plot()
# [Sase2015]
model = Linear.Generic("Linear sim abnormal dev --GM-- [castellanoSmith2003]", 0, 8.0 * kPa, 0.495)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0,0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Abnormal-GM-castellanoSmith2003")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
# model.Plot()
PlotMaterial.Groups("Simulation Abnormal Developments", stretches, data_l, data_W, data_dW, data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M
def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
# [Dequidt2015]
model = Linear.Generic("Linear brain surgery simulator [Dequidt2015]", 0, 2.1 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0,0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "SurgSim-Dequidt2015"
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
stretches = model.stretches
# model.Plot()
# [Sase2015]
model = Linear.Generic("Linear brain surgery simulator [Sase2015]", 0, 1.0 * kPa, 0.4)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0,0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("SurgSim-Sase2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# model.Plot()
PlotMaterial.Groups("Brain Surgery Simulation", stretches, data_l, data_W, data_dW, data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M
def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
# [Yousefi2013]
# E +- 0.25
# v +- 0.145
model = Linear.Generic(
"Linear pathological region growing --brain-- [Yousefi2013]", 0,
3.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0, 0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "Pathos-Yousefi2013"
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
stretches = model.stretches
# [Prastawa2009]
model = Linear.Generic(
"Linear pathological region growing --brain-- [Prastawa2009]", 0,
0.694 * kPa, 0.4)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Pathos-Prastawa2009")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# model = Linear.Generic("Linear pathological region growing --falx-- [Prastawa2009]", 0, 200.0 * kPa, 0.4)
# model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Pathos-falx-Prastawa2009")
# data_C.append(c)
# data_A.append(Common.AB)
# data_M.append(Common.LIN)
# [Kyriacou1999]
# TODO MICCAI exclusion
# model = Nonlinear.Mihai2015a("Neo-Hookean --WM-- [Kyriacou1999]", 3.0 * kPa)
# model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.8,1.2])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Pathos-WM-Kyriacou1999")
# data_C.append(c)
# data_A.append(Common.WM)
# data_M.append(Common.HYP)
# TODO MICCAI exclusion
# model = Nonlinear.Mihai2015a("Neo-Hookean --GM-- [Kyriacou1999]", 30.0 * kPa)
# model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.8,1.2])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Pathos-GM-Kyriacou1999")
# data_C.append(c)
# data_A.append(Common.GM)
# data_M.append(Common.HYP)
# [6]
model = Nonlinear.Mihai2015a("Neo-Hookean --Tumour-- [Kyriacou1999]",
30.0 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.8, 1.2])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Pathos-TUMOUR-Kyriacou1999")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.HYP)
# [Takizawa1994]
# model = Linear.Generic("Linear pathological region growing --CSF-- [Takizawa1994]", 0, 1.0 * kPa, 0.47)
# model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Pathos-CSF-Takizawa1994")
# data_C.append(c)
# data_A.append(Common.AB)
# data_M.append(Common.LIN)
# model = Linear.Generic("Linear pathological region growing --falx-- [Takizawa1994]", 0, 100.0 * kPa, 0.47)
# model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.9,1.1])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Pathos-falx-Takizawa1994")
# data_C.append(c)
# data_A.append(Common.AB)
# data_M.append(Common.LIN)
model = Linear.Generic(
"Linear pathological region growing --WM-- [Takizawa1994]", 0,
4.0 * kPa, 0.47)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Pathos-WM-Takizawa1994")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.LIN)
model = Linear.Generic(
"Linear pathological region growing --GM-- [Takizawa1994]", 0,
8.0 * kPa, 0.47)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Pathos-GM-Takizawa1994")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.LIN)
# [Dumpuri2006,Chen2011]
model = Linear.Generic(
"Linear pathological region growing --tumour-- [Dumpuri2006,Chen2011]",
0, 100.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Pathos-Tumour-Dumpuri2006")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
model = Linear.Generic(
"Linear brain-shift compensation for ablation --tumour-- [Miller2013]",
0, 9.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-tumour-Miller2013")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
model = Linear.Generic(
"Linear brain-shift compensation for ablation --tumour-- [Morin2016]",
0, 10.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-tumour-Morin2016")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
PlotMaterial.Groups("Pathological Region Growing", stretches, data_l,
data_W, data_dW, data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M
def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
''' [Mihai2017] [38] '''
# TODO check - too flat and fitting is bad
# TODO Recent papers to add
model = Nonlinear.Mihai2017a("Ogden 1-term [Mihai2017]", -0.0939 * kPa,
-4.0250)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.8, 1.2])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0, 0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "Hyperelastic-Ogden1-Mihai2017"
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
stretches = model.stretches
model = Nonlinear.Mihai2017b(
"3-term Mooney-Rivlin: Ogden 1-term + MR 2-term [Mihai2017]",
0.0653 * kPa, 7.1813, -3.8201 * kPa, 3.5376 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.8, 1.2])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR3-Mihai2017")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - negative
# model = Nonlinear.Mihai2017c("3-term Ogden [Mihai2017]", 3, [-5.5090 * kPa, 2.9269 * kPa, 1.4653 * kPa], [2, -2, 4])
# model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.8,1.2])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Hyperelastic-Ogden3-Mihai2017")
# data_C.append(c)
# data_A.append(Common.NH)
# data_M.append(Common.HYP)
''' [Mihai2015] '''
# model = Nonlinear.Mihai2015a("Neo-Hookean [Mihai2015]", 333.28 * kPa)
model = Nonlinear.Mihai2015a("Neo-Hookean [Mihai2015]", 0.33328 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-NH-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - prediction is poor
# model = Nonlinear.Mihai2015b("Mooney-Rivlin [Mihai2015]", 0.28 * kPa, 333.0 * kPa)
model = Nonlinear.Mihai2015b("Mooney-Rivlin [Mihai2015]", 0.28 * kPa,
0.333 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - fit crashes
# model = Nonlinear.Mihai2015c("Fung [Mihai2015]", 166.64 * kPa, 2.4974)
model = Nonlinear.Mihai2015c("Fung [Mihai2015]", 0.16664 * kPa, 2.4974)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Fung-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check fit nan
# model = Nonlinear.Mihai2015d("Gent [Mihai2015]", 333.28 * kPa, 0.9918)
model = Nonlinear.Mihai2015d("Gent [Mihai2015]", 0.33328 * kPa, 0.9918)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Gent-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - change optimisation params
# model = Nonlinear.Mihai2015e("Ogden 3-term [Mihai2015]", 3, [-3543 * kPa, -2723 * kPa, 654 * kPa], [1, -1, 2])
model = Nonlinear.Mihai2015e("Ogden 3-term [Mihai2015]", 3,
[-3.543 * kPa, -2.723 * kPa, 0.654 * kPa],
[1, -1, 2])
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.01, 5])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden3-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - strain curve starts from megative values (consider only narrower window)
# model = Nonlinear.Mihai2015e("Ogden 4-term [Mihai2015]", 4, [-5877 * kPa, -5043 * kPa, 1161 * kPa, 501 * kPa], [1, -1, 2, -2])
model = Nonlinear.Mihai2015e(
"Ogden 4-term [Mihai2015]", 4,
[-5.877 * kPa, -5.043 * kPa, 1.161 * kPa, 0.501 * kPa], [1, -1, 2, -2])
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden4-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - consider only narrower window
# model = Nonlinear.Mihai2015e("Ogden 5-term [Mihai2015]", 5, [-34399 * kPa, -18718 * kPa, 14509 * kPa, 2947 * kPa, -2349 * kPa], [1, -1, 2, -2, 3])
model = Nonlinear.Mihai2015e("Ogden 5-term [Mihai2015]", 5, [
-34.399 * kPa, -18.718 * kPa, 14.509 * kPa, 2.947 * kPa, -2.349 * kPa
], [1, -1, 2, -2, 3])
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden5-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# model = Nonlinear.Mihai2015e("Ogden 6-term [Mihai2015]", 6, [1189 * kPa, 16855 * kPa, 1444 * kPa, -10108 * kPa, -458 * kPa, 1889 * kPa], [1, -1, 2, -2, 3, -3])
model = Nonlinear.Mihai2015e("Ogden 6-term [Mihai2015]", 6, [
1.189 * kPa, 16.855 * kPa, 1.444 * kPa, -10.108 * kPa, -0.458 * kPa,
1.889 * kPa
], [1, -1, 2, -2, 3, -3])
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden6-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# model = Nonlinear.Mihai2015e("Ogden 7-term [Mihai2015]", 7, [-187150 * kPa, -91970 * kPa, 109290 * kPa, 23200 * kPa, -33290 * kPa, -2290 * kPa, 4100 * kPa], [1, -1, 2, -2, 3, -3, 4])
model = Nonlinear.Mihai2015e("Ogden 7-term [Mihai2015]", 7, [
-187.150 * kPa, -91.970 * kPa, 109.290 * kPa, 23.200 * kPa,
-33.290 * kPa, -2.290 * kPa, 4.100 * kPa
], [1, -1, 2, -2, 3, -3, 4])
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden7-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# TODO check - consider only window
# model = Nonlinear.Mihai2015e("Ogden 8-term [Mihai2015]", 8, [-639530 * kPa, -544840 * kPa, 322660 * kPa, 237040 * kPa, -88640 * kPa, -57830 * kPa, 10150 * kPa, 6080 * kPa], [1, -1, 2, -2, 3, -3, 4, -4])
model = Nonlinear.Mihai2015e("Ogden 8-term [Mihai2015]", 8, [
-639.530 * kPa, -544.840 * kPa, 322.660 * kPa, 237.040 * kPa,
-88.640 * kPa, -57.830 * kPa, 10.150 * kPa, 6.080 * kPa
], [1, -1, 2, -2, 3, -3, 4, -4])
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.6, 1.4])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden8-Mihai2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
''' [Laksari2012] from [Morin2017] '''
# TODO c10=[+-0.06], c01=[+-0.05], c11=[+-0.01], K=[+-0.26]
# model = Nonlinear.Laksari2012("Mooney Rivlin 3-term + Ogden 2-param [Laksari2012]", -1.34 * kPa, 1.83 * kPa, 0.29 * kPa, 46, 100, 0.49) # compressible
model = Nonlinear.Laksari2012(
"Mooney Rivlin 3-term + Ogden 2-param [Laksari2012]", -1.01 * kPa,
1.49 * kPa, 0.19 * kPa, 2.38, 100, 0.49) # incompressible
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.7, 1.3])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR3-Laksari2012")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
''' [Schiavone2009] from [Morin2017] '''
# TODO MICCAI assumption for tension
model = Nonlinear.Schiavone2009("Mooney Rivlin 2-term [Schiavone2009]",
0.24 * kPa, 3.42 * kPa)
model.Compute()
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [1.0,1.45])
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.55, 1.45])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR2-Schiavone2009")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
''' [Miller2002] '''
model = Nonlinear.Miller2002("Ogden mod 1-term [Miller2002]", 0.842 * kPa,
-4.7)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.7, 1.3])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-OgdenMod1-Miller2002")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.HYP)
# model = Nonlinear.Mihai2015e("Ogden 3-term Rubber", 3, [6300 * kPa, 1.2 * kPa, -10 * kPa], [1.3/2.0, 5.0/2.0, -2.0/2.0])
# model.Compute()
# # l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.8,1.2])
# l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.001, 5])
# data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
# data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
# data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
# data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
# data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
# data_P = np.concatenate((data_P, P.reshape(1,p)), axis=0)
# data_offset.append(offset)
# data_Y.append("Hyperelastic-Ogden3-Mihai2015")
# data_C.append(c)
# data_A.append(Common.NH)
# data_M.append(Common.HYP)
''' ############################################################################################# '''
''' Budday NH [53]'''
model = Nonlinear.Budday2017a("Neo-Hookean CC [Budday2017]", 0.99 * kPa,
0.29 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-NH-CC-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017a("Neo-Hookean CR [Budday2017]", 1.75 * kPa,
0.52 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-NH-CR-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
# [55]
model = Nonlinear.Budday2017a("Neo-Hookean BG [Budday2017]", 1.37 * kPa,
0.57 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-NH-BG-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017a("Neo-Hookean C [Budday2017]", 2.80 * kPa,
1.22 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-NH-C-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
''' Budday MR [57]'''
model = Nonlinear.Budday2017b("Mooney-Rivlin CC [Budday2017]", 0.92 * kPa,
0.0 * kPa, 0.29 * kPa, 0.13 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR-CC-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017b("Mooney-Rivlin CR [Budday2017]", 1.62 * kPa,
0.0 * kPa, 0.52 * kPa, 0.26 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR-CR-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
# [59]
model = Nonlinear.Budday2017b("Mooney-Rivlin BG [Budday2017]", 1.27 * kPa,
0.0 * kPa, 0.57 * kPa, 0.29 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR-BG-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017b("Mooney-Rivlin C [Budday2017]", 2.59 * kPa,
0.0 * kPa, 1.22 * kPa, 0.61 * kPa)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-MR-C-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
''' Budday DMR [61] '''
model = Nonlinear.Budday2017c("Demiray CC [Budday2017]", 0.55 * kPa, 28.4,
0.28 * kPa, 1.29)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-DMR-CC-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017c("Demiray CR [Budday2017]", 1.05 * kPa, 24.6,
0.48 * kPa, 4.37)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-DMR-CR-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
# [63]
model = Nonlinear.Budday2017c("Demiray BG [Budday2017]", 0.97 * kPa, 17.1,
0.52 * kPa, 6.03)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-DMR-BG-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017c("Demiray C [Budday2017]", 1.90 * kPa, 18.8,
0.91 * kPa, 16.7)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-DMR-C-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
''' Budday Gnt [65] '''
model = Nonlinear.Budday2017d("Gent CC [Budday2017]", 0.62 * kPa, 0.06,
0.29 * kPa, 85.0)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Gent-CC-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017d("Gent CR [Budday2017]", 1.16 * kPa, 0.06,
0.48 * kPa, 0.23)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Gent-CR-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
# [67]
model = Nonlinear.Budday2017d("Gent BG [Budday2017]", 1.01 * kPa, 0.08,
0.52 * kPa, 0.18)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Gent-BG-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
model = Nonlinear.Budday2017d("Gent C [Budday2017]", 2.01 * kPa, 0.08,
0.92 * kPa, 0.08)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Gent-C-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
''' Budday Ogden [69] '''
# model = Nonlinear.Budday2017e("Mod 1-term Ogden CC [Budday2017]", 0.47 * kPa, -11.4, 0.33*kPa, -25.6)
model = Nonlinear.Budday2017e("Mod 1-term Ogden CC [Budday2017]",
0.43 * kPa, -22.8, 0.35 * kPa, -26.6)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden1-CC-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
# model = Nonlinear.Budday2017e("Mod 1-term Ogden CR [Budday2017]", 0.86 * kPa, -19.9, 0.58 * kPa, -29.2)
model = Nonlinear.Budday2017e("Mod 1-term Ogden CR [Budday2017]",
0.85 * kPa, -20.5, 0.61 * kPa, -30.5)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden1-CR-Budday2017")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.HYP)
# [71]
# model = Nonlinear.Budday2017e("Mod 1-term Ogden BG [Budday2017]", 0.84 * kPa, -12.5, 0.64 * kPa, -30.0)
model = Nonlinear.Budday2017e("Mod 1-term Ogden BG [Budday2017]",
0.83 * kPa, -15.5, 0.65 * kPa, -32.5)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden1-BG-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
# model = Nonlinear.Budday2017e("Mod 1-term Ogden C [Budday2017]", 1.63 * kPa, -16.5, 1.16 * kPa, -38.9)
model = Nonlinear.Budday2017e("Mod 1-term Ogden C [Budday2017]",
1.61 * kPa, -16.6, 1.20 * kPa, -43.6)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("Hyperelastic-Ogden1-C-Budday2017")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.HYP)
PlotMaterial.Groups("Hyperelastic Nonlinear", stretches, data_l, data_W,
data_dW, data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M
def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
# [Kruse1999] add individual cases in addition to this average
# age = [18, 20, 26, 45, 52, 52, 62, 79]
# WM: G = [6.5, 26.5, 12.0, 8.5, 12.0, 18.0, 16.0, 14.0]
# GM: G = [5.0, 12.0, 5.5, 5.0, 6.0, 7.0, 7.5, 3.0]
model = Linear.Generic("Linear MRE WM [Kruse1999]", 14.6 * kPa, 42.34 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0,0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "MRE-WM-Kruse1999"
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.MRE)
stretches = model.stretches
model = Linear.Generic("Linear MRE GM [Kruse1999]", 6.43 * kPa, 18.65 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0,0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-GM-Kruse1999")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.MRE)
# [Uffmann2004] Need to add models under standard deviation
model = Linear.Generic("Linear MRE WM [Uffmann2004]", 15.2 * kPa, 44.08 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-WM-Uffmann2004")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.MRE)
model = Linear.Generic("Linear MRE GM [Uffmann2004]", 12.9 * kPa, 37.41 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-GM-Uffmann2004")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.MRE)
# [McCracken2005] Need to add models under standard deviation
model = Linear.Generic("Linear MRE WM [McCracken2005]", 10.7 * kPa, 31.03 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-WM-McCracken2005")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.MRE)
model = Linear.Generic("Linear MRE GM [McCracken2005]", 5.3 * kPa, 15.37 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-GM-McCracken2005")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.MRE)
# [Green2006]
model = Linear.Generic("Linear MRE WM [Green2006]", 2.1 * kPa, 6.09 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-WM-Green2006")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.MRE)
model = Linear.Generic("Linear MRE GM [Green2006]", 2.8 * kPa, 8.12 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-GM-Green2006")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.MRE)
# [Kruse2008]
# TODO CI=[12.3,14.8]
model = Linear.Generic("Linear MRE WM [Kruse2008]", 13.6 * kPa, 39.44 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-WM-Kruse2008")
data_C.append(c)
data_A.append(Common.WM)
data_M.append(Common.MRE)
# TODO This case was added to include a MRE abnormality (100 times G)
model = Linear.Generic("Linear MRE abnormal WM [Kruse2008]", 1360 * kPa, 3944 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95, 1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-abnormal-WM-Kruse2008")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.MRE)
# TODO CI=[4.76,5.66]
model = Linear.Generic("Linear MRE GM [Kruse2008]", 5.22 * kPa, 15.14 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-GM-Kruse2008")
data_C.append(c)
data_A.append(Common.GM)
data_M.append(Common.MRE)
# TODO G=[1.7,6.1] over gray and white matter
model = Linear.Generic("Linear MRE [Hamhaber2007]", 3.5 * kPa, 10.15 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial, [0.95,1.05])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("MRE-Hamhaber2007")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.MRE)
PlotMaterial.Groups("MR Elastography", stretches, data_l, data_W, data_dW, data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M
def Plot(self):
# Plot
PlotMaterial.Functions(self.name, self.stretches, self.W_v, self.dW_v, self.H_v, self.sigma_v)
def Process():
# output
data_l = np.zeros((1, s))
data_W = np.zeros((1, s))
data_dW = np.zeros((1, s))
data_H = np.zeros((1, s))
data_sigma = np.zeros((1, s))
data_P = np.zeros((1, p), dtype=bool)
data_offset = []
data_Y = [""]
data_C = []
data_A = []
data_M = []
# [Clatz2005]
model = Linear.Generic(
"Linear brain-shift compensation for ablation [Clatz2005]", 0,
0.694 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l[0, :] = l
data_W[0, :] = W
data_dW[0, :] = dW[0]
data_H[0, :] = H[0, 0]
data_sigma[0, :] = sigma[0]
data_P[0, :] = P
data_offset.append(offset)
data_Y[0] = "BrainShift-Clatz2005"
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
stretches = model.stretches
# [Wittek2009]
model = Linear.Generic(
"Linear brain-shift compensation for ablation [Wittek2009]", 0,
2.5 * kPa, 0.49)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-Wittek2009")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# [Dumpuri2006,Chen2011]
model = Linear.Generic(
"Linear brain-shift compensation for ablation [Dumpuri2006,Chen2011]",
0, 2.1 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-Dumpuri2006")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# [Vigneron2012]
model = Linear.Generic(
"Linear brain-shift compensation for ablation [Vigneron2012]", 0,
3.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_Y.append("BrainShift-Vigneron2012")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# [deLorenzo2012]
# TODO E too high (may remove, no reference 27 within paper)
model = Linear.Generic(
"Linear brain-shift compensation for ablation [deLorenzo2012]", 0,
66.7 * kPa, 0.48)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-deLorenzo2012")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# [Bucki2012]
model = Linear.Generic(
"Linear brain-shift compensation for ablation [Bucki2012]", 0,
0.694 * kPa, 0.4)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-Bucki2012")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
model = Linear.Generic(
"Linear brain-shift compensation for ablation --ventricles-- [Bucki2012]",
0, 0.01 * kPa, 0.05)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-ventricles-Bucki2012")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
# [Miller2013]
model = Linear.Generic(
"Linear brain-shift compensation for ablation --normal-- [Miller2013]",
0, 3.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_Y.append("BrainShift-Miller2013")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# [Mohammadi2015]
model = Linear.Generic(
"Linear brain-shift compensation for ablation [Mohammadi2015]", 0,
0.7 * kPa, 0.42)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-Mohammadi2015")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
model = Linear.Generic(
"Linear brain-shift compensation for ablation --ventricles-- [Mohammadi2015]",
0, 0.015 * kPa, 0.05)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-ventricles-Mohammadi2015")
data_C.append(c)
data_A.append(Common.AB)
data_M.append(Common.LIN)
# [Morin2016]
model = Linear.Generic(
"Linear brain-shift compensation for ablation --normal-- [Morin2016]",
0, 1.5 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-Morin2016")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
# [Clatz2003]
model = Linear.Generic(
"Linear brain-shift compensation for Parkinson [Clatz2003]", 0,
2.0 * kPa, 0.45)
model.Compute()
l, W, dW, H, sigma, P, offset = model.Evaluate(MechanicalTests.Uniaxial,
[0.9, 1.1])
data_l = np.concatenate((data_l, l.reshape(1, s)), axis=0)
data_W = np.concatenate((data_W, W.reshape(1, s)), axis=0)
data_dW = np.concatenate((data_dW, dW[0].reshape(1, s)), axis=0)
data_H = np.concatenate((data_H, H[0, 0].reshape(1, s)), axis=0)
data_sigma = np.concatenate((data_sigma, sigma[0].reshape(1, s)), axis=0)
data_P = np.concatenate((data_P, P.reshape(1, p)), axis=0)
data_offset.append(offset)
data_Y.append("BrainShift-Clatz2003")
data_C.append(c)
data_A.append(Common.NH)
data_M.append(Common.LIN)
PlotMaterial.Groups("Brain-shift Compensation", stretches, data_l, data_W,
data_dW, data_H, data_sigma, data_Y)
return data_l, data_W, data_dW, data_H, data_sigma, data_P, data_offset, data_Y, data_C, data_A, data_M