reox

import SimpleITK as sitk
import numpy as np

def construct_gradU_numpy(img: sitk.Image) -> np.array:
    """Constructs the deformation gradient using numpy (potentially slow)"""
    u = sitk.GetArrayViewFromImage(img)
    dx, dy, dz = img.GetSpacing()
    # Have to calculate the gradient row vise, as np.gradient can only use scalar values.

    # NOTE: The components of the vector are in x,y,z. However, the image itself is in z,y,x!

reox

import numpy as np

# 2nd order identity tensor
I = np.eye(3)

# Lamé Constants
lam_0 = 456
mu_0 = 123

print(f"G = {mu_0}")

reox

# Recommended to run in a clean environment

export EMAIL="builder@localhost"

apt update
apt dist-upgrade -y
apt install -y git git-buildpackage --no-install-recommends

git config --global user.name "Bob the Builder"
git config --global user.email "$EMAIL"

reox

# This programmable filter can be used on an unstructured grid to display the 
# numerical value of the cell type (i.e., tet, hex, wedge, ...)
output.CellData.append(inputs[0].CellTypes, 'Cell_type')

reox

"""
Fun with stiffness matrices!

The plotting routine is inspired by https://github.com/coudertlab/elate
The Voigt-Reuss-Hill Averaging was directly copied from there.

Note the following relationships of stresses and strains:
sigma_ij ... stress tensor
epsilon_ij ... strain tensor

reox

import matplotlib.pyplot as plt
# do plotting:
#plt.dosomething(...)

# pil_kwargs can be used to pass things to PIL.
# the save function has **params which then passes the kwargs to the file format save function, for example:
# See https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html#saving-tiff-images
# LZW compression is called tiff_lzw in pil...
plt.savefig('foobaz.tiff', pad_inches=0.05, bbox_inches='tight', pil_kwargs=dict(compression='tiff_lzw'))

reox

import numpy as np

# Numpy array is xyz
img = np.zeros((10,20,30))

a = slice(None)  # all items, same as ':'

faces = {
  'top': (-1, a, a),
  'bottom': (0, a, a),

reox

import numpy as np

A = np.array([[2,1,2.5], [1,3,1], [2.5,1,4]])

# Eigenvalues and -vectors have the property such that: Ax = lx
# x ... Eigenvectors
# l ... Eigenvalues
l, x = np.linalg.eig(A)

# it follows:

reox

[
	{
		"ColorSpace": "RGB",
		"DefaultMap": true,
		"Name": "Turbo",
		"RGBPoints": [
			0.0,
			0.18823529411764706,
			0.07058823529411765,
			0.23137254901960785,

reox

# What you would normaly do:
def some_divison():
  flag = True

  val = 23
  divisor = 42

  if flag:
    return val / divisor