How to use invariant_2 method in hypothesis

Best Python code snippet using hypothesis

statics2.py

Source:statics2.py

1# Created by YongHua | 12 April 2021 23:102# Email: yht1e20@soton.ac.uk3import numpy as np4from math import pi, sin, cos, sqrt5def stress_transformation(exx, eyy, exy, theta):6    """Stress transformation for a 2-D stress tensor matrix.7    Takes four input parameters:8    `exx` >>> the exx stress component.9    `eyy` >>> the eyy stress component.10    `exy` >>> the exy stress component.11    `theta` >>> (in degrees) which is the angle of the stress element to be rotated.12    Returns `exx'`, `eyy'` and `exy'`13    * You can choose to include or omit prefixes e.g. MPa which is 1E6, as always take14      care of units and magnitudes!15    """16    # Convert to radians for the sin and cos functions17    thetar = theta / 180 * pi18    rotation_matrix = np.array([19        [cos(thetar)**2, sin(thetar)**2, sin(2*thetar)],20        [sin(thetar)**2, cos(thetar)**2, -sin(2*thetar)],21        [-cos(thetar)*sin(thetar), cos(thetar)*sin(thetar), cos(2*thetar)]22    ])23    old_stress = np.array([exx, eyy, exy])24    new_stress = np.dot(rotation_matrix, old_stress)25    return new_stress26def find_principle(exx, eyy, exy):27    """Finds the principle values `eI` and `eII` of a 2-D stress element.28    Takes three input parameters: `exx`, `eyy` and `exy`. Returns a list of29    principal stresses"""30    invariant_1 = exx + eyy  # trace31    invariant_2 = exx * eyy - exy ** 2  # determinant32    eI = 0.5 * (invariant_1 + sqrt(invariant_1 ** 2 - 4 * invariant_2))33    eII = 0.5 * (invariant_1 - sqrt(invariant_1 ** 2 - 4 * invariant_2))34    return [eI, eII]  # returns a list35def find_maxshear(exx, eyy, exy):36    """Find the maximum shear stress `exy` in 2-D stress element subject to37    planar rotational transformation. Numerical implementation of Mohr's circle.38    Takes three input parameters: `exx`, `eyy` and `exy`. Returns a list of39    coordinates of maximum shear stresses, in the form [centre, Â±radius]"""40    eI, eII = find_principle(exx, eyy, exy)41    centre = (eI + eII) / 2  # centre of Mohr's circle, 0.5tr(e)42    radius = abs(eI - centre)  # radius of Mohr's circle43    assert abs(eI - centre) == abs(eII - centre)44    maxshear = [[centre, radius], [centre, -radius]]45    return maxshear46if __name__ == '__main__':47    # example48    help(stress_transformation)49    print(stress_transformation(40, 860, 375, 30))50    help(find_principle)51    print(find_principle(-25, 75, -56))52    help(find_maxshear)...

test.py

Source:test.py

1import torch2import numpy as np3import pickle as pkl4'''5train_mdata_, bb, invariant_1 = pkl.load(open('snapshot.pkl', 'rb'))6print(invariant.shape, invariant.dtype, type(invariant))7tnsr_features_ = train_mdata_._getTensorFeatures().cuda()8basis = bb.getBasisTensor()9bias  = bb.getBiasTensor()10sub_basis = basis[:, :, invariant]11sub_bias  = bias[:, invariant]12'''13np.random.seed(0)14invariant_2 = np.random.permutation(6452)15#dice = np.random.rand(6452)16#invariant_2[dice < 0.01] = 117#invariant_2[invariant_1] = True18print(torch.__version__)19print(np.sum(invariant_2))20invariant0 = invariant_2[:10]21invariant1 = invariant_2[:129]22tnsr_features_ = torch.rand(10808, 784).cuda()23basis = (torch.rand(1, 784, 6452) - 0.5).cuda()24bias  = (torch.rand(1, 6452) - 0.5).cuda()25sub_basis = basis[:, :, invariant0]26sub_bias  = bias[:, invariant0]27sub_basis_ = basis[:, :, invariant1]28sub_bias_  = bias[:, invariant1]29print('=============================')30print(tnsr_features_.dtype, tnsr_features_.shape)31print(basis.dtype, basis.shape)32print(bias.dtype, bias.shape)33print(sub_basis.dtype, sub_basis.shape)34print(sub_bias.dtype, sub_bias.shape)35hashval = torch.matmul(tnsr_features_, basis)36sub_hashval = hashval[:, :, invariant0]37sub_hashval_ = hashval[:, :, invariant1]38sub_config  = np.zeros(sub_hashval.shape)39sub_config[sub_hashval.cpu().numpy() > 0] = 140sub_basis = basis[:, :, invariant0]41sub_basis_ = basis[:, :, invariant1]42sub_bias  = bias[:, invariant0]43sub_bias_  = bias[:, invariant1]44new_sub_hashval = torch.matmul(tnsr_features_, sub_basis)45new_sub_hashval_ = torch.matmul(tnsr_features_, sub_basis_)46error = np.sum(np.abs(new_sub_hashval[:,:,:2].cpu().numpy() - new_sub_hashval_[:,:,:2].cpu().numpy()))47print("=================")48print('error:', error)49print("==================")50new_sub_config = np.zeros(new_sub_hashval.shape)51new_sub_config_ = np.zeros(new_sub_hashval_.shape)52new_sub_config[new_sub_hashval.cpu().numpy() > 0] = 153new_sub_config_[new_sub_hashval_.cpu().numpy() > 0] = 154diff1 = np.sum(np.abs(sub_hashval.cpu().numpy() - new_sub_hashval.cpu().numpy()))55print('diff1: ', diff1)56diff2 = np.sum(np.abs(sub_config - new_sub_config))57print('diff2: ', diff2)58diff_mat = np.abs(sub_hashval.cpu().numpy() - new_sub_hashval.cpu().numpy())59print(np.amax(diff_mat))60'''61print(np.amin(sub_hashval.numpy()), np.amax(sub_hashval.numpy()))62print(np.amin(new_sub_hashval.numpy()), np.amax(new_sub_hashval.numpy()))...

test_prec_np.py

Source:test_prec_np.py

1import numpy as np2import pickle as pkl3train_mdata_, bb, invariant = pkl.load(open('snapshot.pkl', 'rb'))4tnsr_features_ = train_mdata_._getTensorFeatures().squeeze().cpu().numpy()5basis = bb.getBasisTensor().squeeze().cpu().numpy()6bias  = bb.getBiasTensor().squeeze().cpu().numpy()7sub_basis = basis[:, invariant]8sub_bias  = bias[invariant]9print('num boundaries: ', sub_bias.shape)10'''11invariant_2 = np.zeros(6452, dtype='bool')12dice = np.random.rand(6452)13invariant_2[dice < 0.8] = True14#invariant_2[invariant_1] = True15print(np.sum(invariant_2))16invariant = invariant_217tnsr_features_ = np.random.rand(10808, 784)18basis = np.random.rand(784, 6452) - 0.519bias  = np.random.rand(6452) - 0.520sub_basis = basis[:, invariant]21sub_bias  = bias[invariant]22'''23print('=============================')24print(tnsr_features_.dtype, tnsr_features_.shape)25print(basis.dtype, basis.shape)26print(bias.dtype, bias.shape)27print(sub_basis.dtype, sub_basis.shape)28print(sub_bias.dtype, sub_bias.shape)29hashval = np.matmul(tnsr_features_, basis)30sub_hashval = hashval[:, invariant]31sub_config  = np.zeros(sub_hashval.shape)32sub_config[sub_hashval > 0] = 133sub_basis = basis[:, invariant]34sub_bias  = bias[invariant]35new_sub_hashval = np.matmul(tnsr_features_, sub_basis)36new_sub_config = np.zeros(new_sub_hashval.shape)37new_sub_config[new_sub_hashval > 0] = 138diff1 = np.sum(np.abs(sub_hashval - new_sub_hashval))39print('diff1: ', diff1)40diff2 = np.sum(np.abs(sub_config - new_sub_config))41print('diff2: ', diff2)42diff_mat = np.abs(sub_hashval - new_sub_hashval)43print(np.amax(diff_mat))44'''45print(np.amin(sub_hashval.numpy()), np.amax(sub_hashval.numpy()))46print(np.amin(new_sub_hashval.numpy()), np.amax(new_sub_hashval.numpy()))...

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