from copy import copy
import inspect
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import animation
from tqdm import tqdm
from porespy import settings
__all__ = [
'set_mpl_style',
'satn_to_movie',
'satn_to_panels',
'prep_for_imshow',
]
[docs]
def set_mpl_style(): # pragma: no cover
r"""
Prettifies matplotlib's output by adjusting fonts, markersize etc.
"""
sfont = 12
mfont = 12
lfont = 12
image_props = {'interpolation': 'none',
'cmap': 'viridis',
'origin': 'lower'}
line_props = {'linewidth': 2,
'markersize': 8,
'markerfacecolor': 'w'}
font_props = {'size': sfont}
axes_props = {'titlesize': lfont,
'labelsize': mfont,
'linewidth': 2,
'labelpad': 8}
xtick_props = {'labelsize': sfont,
'top': True,
'direction': 'in',
'major.size': 6,
'major.width': 2}
ytick_props = {'labelsize': sfont,
'right': True,
'direction': 'in',
'major.size': 6,
'major.width': 2}
legend_props = {'fontsize': mfont,
'frameon': False}
figure_props = {'titlesize': sfont,
'autolayout': True}
plt.rc('font', **font_props)
plt.rc('lines', **line_props)
plt.rc('axes', **axes_props)
plt.rc('xtick', **xtick_props)
plt.rc('ytick', **ytick_props)
plt.rc('legend', **legend_props)
plt.rc('figure', **figure_props)
plt.rc('image', **image_props)
import matplotlib_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('retina')
[docs]
def satn_to_movie(
im,
satn,
cmap='viridis',
c_under='grey',
c_over='white',
v_under=1e-3,
v_over=1.0,
fps=10,
repeat=True,
):
r"""
Converts a saturation map into an animation that can be saved
Parameters
----------
im : ndarray
The boolean image of the porous media with ``True`` values indicating
the void space
satn : ndaray
The saturation map such as that produced by an invasion or drainage
algorithm.
cmap : str
The name of the matplotlib color map to use. These are listed on
matplotlib's website
`here <https://matplotlib.org/stable/gallery/color/colormap_reference.html>`_
c_under, c_over : str
The color to insert for values that are less than `v_under`
(greater than `v_over`). The string value of colors are given on
matplotlib's website
`here <https://matplotlib.org/stable/gallery/color/named_colors.html>`_
v_under, v_over : scalar
The values in ``satn`` that should be considered the lower and upper
threshold, beyond which the colors given in `c_under` and `c_over`
are used.
fps : int
The frames per second to use when generating the movie. A higher
number gives a shorter and faster-paced movie.
repeat : bool
If ``True`` the produced animation will rerun repeatedly until
stopped or closed.
Notes
-----
To save animation as a file use:
``ani.save('image_based_ip.gif', writer='imagemagick', fps=3)``
Examples
--------
`Click here
<https://porespy.org/examples/visualization/reference/satn_to_movie.html>`_
to view online example.
"""
# Define nice color map
cmap = copy(getattr(plt.cm, cmap))
cmap.set_over(color=c_over)
cmap.set_under(color=c_under)
# Reduce inv_satn image to limited number of values to speed-up movie
target = np.around(satn, decimals=3)
seq = np.zeros_like(target) # Empty image to place frame
movie = [] # List to append each frame
fig, ax = plt.subplots(1, 1)
steps = np.unique(target)[1:]
desc = inspect.currentframe().f_code.co_name # Get current func name
with tqdm(steps, desc=desc, **settings.tqdm) as pbar:
for v in steps:
pbar.update()
seq += v*(target == v)
seq[~im] = target.max() + 10
frame1 = ax.imshow(seq, vmin=v_under, vmax=v_over,
animated=True, cmap=cmap, origin='lower',
interpolation='none')
movie.append([frame1])
ani = animation.ArtistAnimation(fig, movie, interval=int(1000/fps),
blit=True, repeat=repeat,
repeat_delay=1.0)
return ani
[docs]
def satn_to_panels(
satn,
im,
bins=None,
axis=0,
slice=None,
**kwargs,
):
r"""
Produces a set of images with each panel containing one saturation
Parameters
----------
satn : ndarray
An image with each voxel indicating the global saturation at which
it was invaded. 0 indicates solid and -1 indicates uninvaded.
im : ndarray
A boolean image with ``True`` values indicating the void voxels and
``False`` for solid.
bins : int
Indicates for which saturations images should be made. If an ``int``
then a list of equally space values between 0 and 1 is generated.
If ``None`` (default) than all saturation values in the image are used.
axis : int, optional
If the image is 3D, a 2D image is extracted at the specified
``slice`` taken along this axis. If the image is 2D this is ignored.
slice : int, optional
If the image is 3D, a 2D image is extracted from this slice
along the given ``axis``. If ``None``, then a slice at the mid-point
of the axis is returned. If 2D this is ignored.
**kwargs : various
Additional keyword arguments are sent to the ``imshow`` function,
such as ``interpolation``.
Returns
-------
fig, ax : Matplotlib figure and axis objects
The same things as returned by ``plt.subplots``
Examples
--------
`Click here
<https://porespy.org/examples/visualization/reference/satn_to_panels.html>`_
to view online example.
"""
def factors(n):
return sorted(list(set(
factor for i in range(1, int(n**0.5) + 1) if n % i == 0
for factor in (i, n//i)
)))
if bins is None:
Ps = np.unique(satn)
elif isinstance(bins, int):
Ps = np.linspace(0, 1, bins+1)[1:]
Ps = Ps[Ps > 0]
f = factors(len(Ps))
if len(Ps) < 4:
m = 1
n = len(Ps)
elif len(f) % 2 == 0:
m, n = f[int(len(f)/2-1)], f[int(len(f)/2)]
else:
m = f[int(len(f)/2)]
n = m
fig, ax = plt.subplots(m, n)
ax = np.atleast_2d(ax)
temp_old = np.zeros_like(im)
for i, p in enumerate(Ps):
temp = (satn <= p)*(satn > 0)
im_data = prep_for_imshow(im=temp*2.0 - temp_old*1.0, mask=im,
axis=axis, slice=slice)
im_data.pop('vmax')
[im_data.pop(i) for i in kwargs]
ax[i // n][i % n].imshow(**im_data, vmax=2, **kwargs)
ax[i // n][i % n].set_title(
str(np.around(temp.sum(dtype=np.int64)/im.sum(dtype=np.int64),
decimals=5)))
temp_old = np.copy(temp)
return fig, ax
[docs]
def prep_for_imshow(
im,
mask=None,
axis=0,
slice=None,
):
r"""
Adjusts the range of greyscale values in an image to improve visualization
by ``matplotlib.pyplot.imshow``
Parameters
----------
im : ndimage
The image to show. If ``im`` includes ``+inf`` or ``-inf`` values,
they are converted to 1 above or below the minimum and maximum finite
values in ``im``, respectively.
mask : ndimage, optional
An image of the porous material with ``True`` indicating voxels of
interest. The ``False`` voxels are excluded from the ``vmax`` and
``vmin`` calculation.
axis : int, optional
If the image is 3D, a 2D image is returned with the specified
``slice`` taken along this axis (default = 0). If ``None`` then a 3D
image is returned. If the image is 2D this is ignored.
slice : int, optional
If ``im`` is 3D, a 2D image is be returned showing this slice
along the given ``axis``. If ``None``, then a slice at the mid-point
of the axis is returned. If ``axis`` is ``None`` or the image is 2D
this is ignored.
Returns
-------
kwargs : dict
A python dicionary designed to be passed directly to
``matplotlib.pyplot.imshow`` using the "\*\*kwargs" features (i.e.
``plt.imshow(\*\*data)``). It contains the following key-value pairs:
=============== =======================================================
key value
=============== =======================================================
'X' The adjusted image with ``+inf`` replaced by
``vmax + 1``, and all solid voxels replacd by
``np.nan`` to show as white in ``imshow``
'vmax' The maximum of ``values`` not including ``+inf`` or
values in ``False`` voxels in ``mask``.
'vmin' The minimum of ``values`` not including ``-inf`` or
values in ``False`` voxels in ``mask``.
'interpolation' Set to 'none' to avoid artifacts in ``imshow``
'origin' Set to 'lower' to put (0, 0) on the bottom-left corner
=============== =======================================================
Notes
-----
If any of the *extra* items are unwanted they can be removed with
``del data['interpolation']`` or ``data.pop('interpolation')``.
Examples
--------
`Click here
<https://porespy.org/examples/visualization/reference/prep_for_imshow.html>`_
to view online example.
"""
# If 3D, fetch 2D slice immediately to save memory
if (im.ndim == 3) and (axis is not None):
if slice is None:
slice = int(im.shape[axis]/2)
# Rotate image to put given axis first, then slice
im = np.swapaxes(im, 0, axis)[slice, ...]
if mask is not None: # Rotate mask as well, then slice
mask = np.swapaxes(mask, 0, axis)[slice, ...]
im = im.astype(float)
if mask is None:
mask = np.ones_like(im, dtype=bool)
vmax = np.amax((im*(im < np.inf))[mask])
im[(im == np.inf)] = vmax + 1
vmin = np.amin((im*(im > -np.inf))[mask])
im[(im == -np.inf)] = vmin - 1
d = {
'X': im,
'vmin': vmin,
'vmax': vmax,
'interpolation': 'none',
'origin': 'lower',
}
return d