oberbichler / hyperjet Goto Github PK

Hello,

I am trying to follow your quickstart.md but I find this error. could you please help me?

I have installed with

>> pip install git+https://github.com/oberbichler/HyperJet

then

In [2]: import hyperjet as hj

In [3]: x, y = hj.Jet.variables([3, 6])
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-1bbea4a89a88> in <module>
----> 1 x, y = hj.Jet.variables([3, 6])

AttributeError: module 'hyperjet' has no attribute 'Jet'

Thanks

Have you tried what happens if one takes the square root of 0.0?

>>> import hyperjet as hj
>>> a = hj.Jet(0.0, [1])
>>> np.sqrt(a)
__main__:1: RuntimeWarning: divide by zero encountered in sqrt
Jet<0.000000>

I'm not sure if this Warning is caused by the division by 0.0 inside the Jet implementation or somewhere in numpy.

Armin

Hi,

I'm always looking for alternative backends beside casADi for my constitutive material definition python package (matADi, ideas on alternative backends here) and I really like hyperjet, thanks 🏅! Given the example below, do you have any other ideas to speed things up? My use case is to evaluate gradients and hessian of a strain energy function for hyperelastic solids within finite element analyses (using FElupe).

import numpy as np
from hyperjet import DDScalar

def det(A):
    return (
        A[0] * A[1] * A[2] + 2 * A[3] * A[4] * A[5]
        - A[1] * A[5] ** 2 - A[0] * A[4] **2 - A[2] * A[3] ** 2
    )

def trace(A):
    return A[:3].sum()

def neo_hooke(C):
    return det(C) ** (-1 / 3) * trace(C) - 3

def tensor(x):
    return np.array(DDScalar.variables(x.ravel())).reshape(*x.shape)

# init 100,000 random right Cauchy-Green deformation tensors
rCG = (np.random.rand(100000, 6) - 0.5) / 5
rCG[:, :3] += 1
    
from joblib import Parallel, delayed

hessian = lambda fun, x: fun(tensor(x)).hm()
A = Parallel(n_jobs=16, verbose=1, batch_size=1000)(
    delayed(hessian)(neo_hooke, C) for C in rCG#
)

gives

[Parallel(n_jobs=16)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done 18032 tasks      | elapsed:    0.9s
[Parallel(n_jobs=16)]: Done 97760 tasks      | elapsed:    3.0s
[Parallel(n_jobs=16)]: Done 100000 out of 100000 | elapsed:    3.1s finished

which is very ok but I'd like to know if there is room for improvement. Not in using joblib, but some best-practise tips for hyperjet instead.