import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from porespy.tools import get_tqdm
__all__ = [
'bar',
'imshow',
'show_mesh',
'show_panels',
]
tqdm = get_tqdm()
def show_panels(im, rc=[3, 3], axis=0):
r"""
Show slices of a 3D image as a 2D array of panels.
Parameters
----------
im : ndarray
The 3D image to visualize
rc : list if ints
The number of rows and columns to create
axis : int
The axis along which to create the slices
Returns
-------
fig, ax : Matplotlib figure and axis handles
"""
from porespy.visualization import prep_for_imshow
i, j = rc
im = np.swapaxes(im, axis, 2)
slices = np.linspace(0, im.shape[2], i*j, endpoint=False).astype(int)
fig, ax = plt.subplots(i, j)
s = 0
for row in range(i):
for col in range(j):
temp = prep_for_imshow(im[..., slices[s]])
ax[row][col].imshow(**temp)
ax[row][col].text(
0, 1,
f"Slice {slices[s]}",
# ha="center", va="center",
bbox=dict(boxstyle="square,pad=0.3",
fc="white", ec="white", lw=1, alpha=0.75))
s += 1
return fig, ax
[docs]
def bar(results, h='pdf', **kwargs): # pragma: no cover
r"""
Convenience wrapper for matplotlib's ``bar``.
This automatically:
* fetches the ``bin_centers``
* fetches the bin heights from the specified ``h``
* sets the bin widths
* sets the edges to black
Parameters
----------
results : object
The objects returned by various functions in the
``porespy.metrics`` submodule, such as ``chord_length_distribution``.
h : str
The value to use for bin heights. The default is ``pdf``, but
``cdf`` is another option. Depending on the function the named-tuple
may have different options.
kwargs : keyword arguments
All other keyword arguments are passed to ``bar``, including
``edgecolor`` if you wish to overwrite the default black.
Returns
-------
fig: Matplotlib figure handle
Examples
--------
`Click here
<https://porespy.org/examples/visualization/reference/bar.html>`_
to view online example.
"""
if 'edgecolor' not in kwargs:
kwargs['edgecolor'] = 'k'
fig = plt.bar(x=results.bin_centers, height=getattr(results, h),
width=results.bin_widths, **kwargs)
xlab = [attr for attr in results.__dir__() if not attr.startswith('_')][0]
plt.xlabel(xlab)
plt.ylabel(h)
return fig
[docs]
def imshow(*im, ind=None, axis=None, **kwargs): # pragma: no cover
r"""
Convenience wrapper for matplotlib's ``imshow``.
This automatically:
* slices a 3D image in the middle of the last axis
* uses a masked array to make 0's white
* sets the origin to 'lower' so bottom-left corner is [0, 0]
* disables interpolation
Parameters
----------
im : ndarray
The 2D or 3D image (or images) to show. If 2D then all other
arguments are ignored.
ind : int
The slice to show if ``im`` is 3D. If not given then the middle of
the image is used.
axis : int
The axis to show if ``im`` is 3D. If not given, then the last
axis of the image is used, so an 'lower' slice is shown.
**kwargs
All other keyword arguments are passed to ``plt.imshow``
Note
----
``im`` can also be a series of unnamed arguments, in which case all
received images will be shown using ``subplot``.
Examples
--------
`Click here
<https://porespy.org/examples/visualization/reference/imshow.html>`_
to view online example.
"""
if 'origin' not in kwargs.keys():
kwargs['origin'] = 'lower'
if 'interpolation' not in kwargs.keys():
kwargs['interpolation'] = 'none'
if not isinstance(im, tuple):
im = tuple([im])
for i, image in enumerate(im):
if image.ndim == 3:
if axis is None:
axis = 2
if ind is None:
ind = int(image.shape[axis]/2)
image = image.take(indices=ind, axis=axis)
image = np.ma.array(image, mask=image == 0)
fig = plt.subplot(1, len(im), i+1)
plt.imshow(image, **kwargs)
return fig
[docs]
def show_mesh(mesh): # pragma: no cover
r"""
Visualizes the mesh of a region as obtained by ``get_mesh`` function in
the ``metrics`` submodule.
Parameters
----------
mesh : tuple
A mesh returned by ``skimage.measure.marching_cubes``
Returns
-------
fig : Matplotlib figure
A handle to a matplotlib 3D axis
Examples
--------
`Click here
<https://porespy.org/examples/visualization/reference/show_mesh.html>`_
to view online example.
"""
try:
verts = mesh.vertices
except AttributeError:
verts = mesh.verts
lim_max = np.amax(verts, axis=0)
lim_min = np.amin(verts, axis=0)
# Display resulting triangular mesh using Matplotlib.
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Fancy indexing: `verts[faces]` to generate a collection of triangles
mesh = Poly3DCollection(verts[mesh.faces])
mesh.set_edgecolor('k')
ax.add_collection3d(mesh)
ax.set_xlabel("x-axis")
ax.set_ylabel("y-axis")
ax.set_zlabel("z-axis")
ax.set_xlim(lim_min[0], lim_max[0])
ax.set_ylim(lim_min[1], lim_max[1])
ax.set_zlim(lim_min[2], lim_max[2])
return fig