Adding aspect ratio to REV

porespy/metrics/_funcs.py

@@ -4,7 +4,7 @@
 from scipy import fft as sp_ft
 from skimage.measure import regionprops
 from deprecated import deprecated
-from porespy.tools import extend_slice
+from porespy.tools import extend_slice, extend_slice_2
 from porespy.tools import _check_for_singleton_axes
 from porespy.tools import Results
 from porespy import settings
@@ -13,8 +13,7 @@
 from numba import njit
 tqdm = get_tqdm()
 
-
-def representative_elementary_volume(im, npoints=1000):
+def representative_elementary_volume_porosity(im, AR=([1,1,1]), npoints=1000):
     r"""
     Calculates the porosity of an image as a function subdomain size.
 
@@ -25,6 +24,9 @@ def representative_elementary_volume(im, npoints=1000):
     ----------
     im : ndarray
         The image of the porous material
+    AR : (,3) array
+        This is the aspect ratio of the subdomain. Each element is the ratio to the x-axis.
+        AR = ([x:x.y:x,z:x]). The default is 1:1:1 to get a cubic shape.
     npoints : int
         The number of randomly located and sized boxes to sample.  The default
         is 1000.
@@ -38,30 +40,12 @@ def representative_elementary_volume(im, npoints=1000):
             The total volume of each cubic subdomain tested
         'porosity'
             The porosity of each subdomain tested
-
-        These attributes can be conveniently plotted by passing the Results
-        object to matplotlib's ``plot`` function using the
-        \* notation: ``plt.plot(\*result, 'b.')``.  The resulting plot is
-        similar to the sketch given by Bachmat and Bear [1]_
-
-    Notes
-    -----
-    This function is frustratingly slow.  Profiling indicates that all the time
-    is spent on scipy's ``sum`` function which is needed to sum the number of
-    void voxels (1's) in each subdomain.
-
     References
     ----------
     .. [1] Bachmat and Bear. On the Concept and Size of a Representative
     Elementary Volume (Rev), Advances in Transport Phenomena in Porous Media
     (1987)
 
-    Examples
-    --------
-    `Click here
-    <https://porespy.org/examples/metrics/reference/representative_elementary_volume.html>`_
-    to view online example.
-
     """
     # TODO: this function is a prime target for parallelization since the
     # ``npoints`` are calculated independenlty.
@@ -76,7 +60,7 @@ def representative_elementary_volume(im, npoints=1000):
     for i in tqdm(np.arange(0, N), **settings.tqdm):
         s = slices[i]
         p = pads[i]
-        new_s = extend_slice(s, shape=im.shape, pad=p)
+        new_s = extend_slice_2(s, AR, shape=im.shape, pad=p)
         temp = im[new_s]
         Vp = np.sum(temp)
         Vt = np.size(temp)
@@ -87,7 +71,6 @@ def representative_elementary_volume(im, npoints=1000):
     profile.porosity = porosity
     return profile
 
-
 def porosity(im):
     r"""
     Calculates the porosity of an image assuming 1's are void space and 0's
@@ -646,12 +629,8 @@ def _radial_profile(autocorr, bins, pf=None, voxel_size=1):
     else:
         raise Exception('Image dimensions must be 2 or 3')
     if np.max(bins) > np.max(dt):
-        msg = (
-            'Bins specified distances exceeding maximum radial distance for'
-            ' image size. Radial distance cannot exceed distance from center'
-            ' of image to corner.'
-        )
-        raise Exception(msg)
+        raise Exception('Bins specified distances exceeding maximum radial distance for image size. \n'
+                        'Radial distance cannot exceed distance from center of image to corner.')
 
     bin_size = bins[1:] - bins[:-1]
     radial_sum = _get_radial_sum(dt, bins, bin_size, autocorr)
@@ -670,7 +649,6 @@ def _radial_profile(autocorr, bins, pf=None, voxel_size=1):
     tpcf.relfreq = h.relfreq
     return tpcf
 
-
 @njit(parallel=True)
 def _get_radial_sum(dt, bins, bin_size, autocorr):
     radial_sum = np.zeros_like(bins[:-1])
@@ -679,7 +657,6 @@ def _get_radial_sum(dt, bins, bin_size, autocorr):
         radial_sum[i] = np.sum(np.ravel(autocorr)[np.ravel(mask)]) / np.sum(mask)
     return radial_sum
 
-
 def two_point_correlation(im, voxel_size=1, bins=100):
     r"""
     Calculate the two-point correlation function using Fourier transforms
@@ -690,14 +667,12 @@ def two_point_correlation(im, voxel_size=1, bins=100):
         The image of the void space on which the 2-point correlation is
         desired, in which the phase of interest is labelled as True
     voxel_size : scalar
-        The size of a voxel side in preferred units.  The default is 1, so
-        the user can apply the scaling to the returned results after the
-        fact.
+        The size of a voxel side in preferred units.  The default is 1, so the
+        user can apply the scaling to the returned results after the fact.
     bins : scalar or array_like
-        Either an array of bin sizes to use, or the number of bins that
-        should be automatically generated that span the data range. The
-        maximum value of the bins, if passed as an array, cannot exceed
-        the distance from the center of the image to the corner.
+        Either an array of bin sizes to use, or the number of bins that should
+        be automatically generated that span the data range. The maximum value of the bins,
+        if passed as an array, cannot exceed the distance from the center of the image to the corner
 
     Returns
     -------

porespy/tools/_funcs.py

@@ -17,6 +17,7 @@
     'align_image_with_openpnm',
     'bbox_to_slices',
     'extend_slice',
+    'extend_slice_2',
     'extract_cylinder',
     'extract_subsection',
     'extract_regions',
@@ -599,6 +600,26 @@ def extend_slice(slices, shape, pad=1):
         a.append(slice(start, stop, None))
     return tuple(a)
 
+def extend_slice_2(slices, AR,shape, pad=1):
+    shape = np.array(shape)
+    pad = np.array(pad).astype(int)*(shape > 0)
+    a = []
+    for i, s in enumerate(slices):
+        if i!=0:#looking at y & z-coord slice
+            start = 0
+            stop = shape[i]
+            start = max(s.start - round(AR[i]*pad[i]), 0)#reduce the extension by aspect ratio(AR) of y/z
+            stop = min(s.stop + round(AR[i]*pad[i]), shape[i])
+            a.append(slice(start, stop, None))
+        else:
+            start = 0
+            stop = shape[i]
+            start = max(s.start - pad[i], 0)
+            stop = min(s.stop + pad[i], shape[i])
+            a.append(slice(start, stop, None))
+    return tuple(a)
+
+
 
 def randomize_colors(im, keep_vals=[0]):
     r'''