# Source code for porespy.visualization._views

```import numpy as np
import scipy.ndimage as spim
import matplotlib.pyplot as plt
# from mpl_toolkits.mplot3d.art3d import Poly3DCollection

__all__ = [
'show_3D',
'show_planes',
'sem',
'xray',
]

[docs]def show_3D(im):  # pragma: no cover
r"""
Rotates a 3D image and creates an angled view for rough 2D visualization.

Because it rotates the image it can be slow for large images, so is
mostly meant for rough checking of small prototype images.

Parameters
----------
im : ndarray
The 3D array to be viewed from an angle

Returns
-------
image : ndarray
A 2D view of the given 3D image

Notes
-----
Although this is meant to be *quick* it can still take on the order of
minutes to render very large images.  It uses ``scipy.ndimage.rotate``
with no interpolation to view the 3D image from an angle, then casts the
result into a 2D projection.

Examples
--------
<https://porespy.org/examples/visualization/reference/show_3D.html>`_
to view online example.

"""
im = ~np.copy(im)
if im.ndim < 3:
raise Exception('show_3D only applies to 3D images')
im = spim.rotate(input=im, angle=22.5, axes=[0, 1], order=0)
im = spim.rotate(input=im, angle=45, axes=[2, 1], order=0)
im = spim.rotate(input=im, angle=-17, axes=[0, 1], order=0, reshape=False)
view = view.max() - view
f = view.max()/5
view[view == view.max()] = -f
view = (view + f)**2
return view

[docs]def show_planes(im, spacing=10):  # pragma: no cover
r"""
Create a quick montage showing a 3D image in all three directions

Parameters
----------
im : ndarray
A 3D image of the porous material
spacing : int (optional, default=10)
Controls the amount of space to put between each panel

Returns
-------
image : ndarray
A 2D array containing the views.  This single image can be viewed using
``matplotlib.pyplot.imshow``.

Examples
--------
<https://porespy.org/examples/visualization/reference/show_planes.html>`_
to view online example.

"""
s = spacing
if np.squeeze(im.ndim) < 3:
raise Exception('This view is only necessary for 3D images')
x, y, z = (np.array(im.shape)/2).astype(int)
im_xy = im[:, :, z]
im_xz = im[:, y, :]
im_yz = np.rot90(im[x, :, :])

new_x = im_xy.shape[0] + im_yz.shape[0] + s

new_y = im_xy.shape[1] + im_xz.shape[1] + s

new_im = np.zeros([new_x + 2*s, new_y + 2*s], dtype=im.dtype)

# Add xy image to upper left corner
new_im[s:im_xy.shape[0] + s,
s:im_xy.shape[1] + s] = im_xy
# Add xz image to lower left coner
x_off = im_xy.shape[0] + 2*s
y_off = im_xy.shape[1] + 2*s
new_im[s:s + im_xz.shape[0],
y_off:y_off + im_xz.shape[1]] = im_xz
new_im[x_off:x_off + im_yz.shape[0],
s:s + im_yz.shape[1]] = im_yz

return new_im

[docs]def sem(im, axis=0):  # pragma: no cover
r"""
Simulates an SEM image looking into the porous material.

Features are colored according to their depth into the image, so
darker features are further away.

Parameters
----------
im : array_like
ndarray of the porous material with the solid phase marked as 1 or
True
axis : int
Specifes the axis along which the camera will point.

Returns
-------
image : ndarray
A 2D greyscale image suitable for use in matplotlib's ``imshow``
function.

Examples
--------
<https://porespy.org/examples/visualization/reference/sem.html>`_
to view online example.

"""
im = np.array(~im, dtype=int)
if axis == 1:
im = np.transpose(im, axes=[1, 0, 2])
if axis == 2:
im = np.transpose(im, axes=[2, 1, 0])
t = im.shape[0]
depth = np.reshape(np.arange(0, t), [t, 1, 1])
im = im*depth
im = np.amax(im, axis=0)
return im

[docs]def xray(im, axis=0):  # pragma: no cover
r"""
Simulates an X-ray radiograph looking through the porous material.

The resulting image is colored according to the amount of attenuation an
X-ray would experience, so regions with more solid will appear darker.

Parameters
----------
im : array_like
ndarray of the porous material with the solid phase marked as 1 or
True

axis : int
Specifes the axis along which the camera will point.

Returns
-------
image : ndarray
A 2D greyscale image suitable for use in matplotlib\'s ```imshow```
function.

Examples
--------