def sampleYes(array, N):
"""Sample without replacement N points from an array of XY coordinates.
Args:
array: 2D numpy array of XY points.
N: Integer number of points to sample without replacement from
input array.
Returns:
Tuple of (sampled points, unsampled points).
"""
# array is a Mx2 array of X,Y points
m, n = array.shape
allidx = np.arange(0, m)
sampleidx = np.random.choice(allidx, size=N, replace=False)
nosampleidx = np.setxor1d(allidx, sampleidx)
sampled = array[sampleidx, :]
notsampled = array[nosampleidx, :]
return (sampled, notsampled)
python类setxor1d()的实例源码
def test_boolean_spheres_overlap():
r"""Test to make sure that boolean objects (spheres, overlap)
behave the way we expect.
Test overlapping spheres.
"""
ds = fake_amr_ds()
sp1 = ds.sphere([0.45, 0.45, 0.45], 0.15)
sp2 = ds.sphere([0.55, 0.55, 0.55], 0.15)
# Get indices of both.
i1 = sp1["index","morton_index"]
i2 = sp2["index","morton_index"]
# Make some booleans
bo1 = sp1 & sp2
bo2 = sp1 - sp2
bo3 = sp1 | sp2
bo4 = ds.union([sp1, sp2])
bo5 = ds.intersection([sp1, sp2])
# Now make sure the indices also behave as we expect.
lens = np.intersect1d(i1, i2)
apple = np.setdiff1d(i1, i2)
both = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, lens)
assert_array_equal(b2, apple)
assert_array_equal(b3, both)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = sp1 ^ sp2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_regions_overlap():
r"""Test to make sure that boolean objects (regions, overlap)
behave the way we expect.
Test overlapping regions.
"""
ds = fake_amr_ds()
re1 = ds.region([0.55]*3, [0.5]*3, [0.6]*3)
re2 = ds.region([0.6]*3, [0.55]*3, [0.65]*3)
# Get indices of both.
i1 = re1["index","morton_index"]
i2 = re2["index","morton_index"]
# Make some booleans
bo1 = re1 & re2
bo2 = re1 - re2
bo3 = re1 | re2
bo4 = ds.union([re1, re2])
bo5 = ds.intersection([re1, re2])
# Now make sure the indices also behave as we expect.
cube = np.intersect1d(i1, i2)
bite_cube = np.setdiff1d(i1, i2)
both = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, cube)
assert_array_equal(b2, bite_cube)
assert_array_equal(b3, both)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = re1 ^ re2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_ellipsoids_overlap():
r"""Test to make sure that boolean objects (ellipsoids, overlap)
behave the way we expect.
Test overlapping ellipsoids.
"""
ds = fake_amr_ds()
ell1 = ds.ellipsoid([0.45]*3, 0.05, 0.05, 0.05, np.array([0.1]*3), 0.1)
ell2 = ds.ellipsoid([0.55]*3, 0.05, 0.05, 0.05, np.array([0.1]*3), 0.1)
# Get indices of both.
i1 = ell1["index","morton_index"]
i2 = ell2["index","morton_index"]
# Make some booleans
bo1 = ell1 & ell2
bo2 = ell1 - ell2
bo3 = ell1 | ell2
bo4 = ds.union([ell1, ell2])
bo5 = ds.intersection([ell1, ell2])
# Now make sure the indices also behave as we expect.
overlap = np.intersect1d(i1, i2)
diff = np.setdiff1d(i1, i2)
both = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, overlap)
assert_array_equal(b2, diff)
assert_array_equal(b3, both)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = ell1 ^ ell2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_slices_overlap():
r"""Test to make sure that boolean objects (slices, overlap)
behave the way we expect.
Test overlapping slices.
"""
ds = fake_amr_ds()
sl1 = ds.r[:,:,0.25]
sl2 = ds.r[:,0.75,:]
# Get indices of both.
i1 = sl1["index","morton_index"]
i2 = sl2["index","morton_index"]
# Make some booleans
bo1 = sl1 & sl2
bo2 = sl1 - sl2
bo3 = sl1 | sl2
bo4 = ds.union([sl1, sl2])
bo5 = ds.intersection([sl1, sl2])
# Now make sure the indices also behave as we expect.
line = np.intersect1d(i1, i2)
orig = np.setdiff1d(i1, i2)
both = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, line)
assert_array_equal(b2, orig)
assert_array_equal(b3, both)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = sl1 ^ sl2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def sampleNo(xvar, yvar, N, avoididx):
"""
Sample from pixels in mesh, excluding yes pixels and already sampled no
pixels.
Args:
xvar: Numpy array of centers of all columns in mesh.
yvar: Numpy array of centers of all rows in mesh.
N: Number of no pixels to sample.
avoididx: 1D array of indices from mesh that should NOT be sampled
from. Initially this will be the array of indices where the yes
pixels are.
Returns:
Randomly chosen list of tuples of (x,y) coordinate points that are
outside polygons.
"""
# flattened array of all indices in mesh
allidx = np.arange(0, len(xvar) * len(yvar))
noidx = np.setxor1d(allidx, avoididx) # allidx - avoididx
nosampleidx = np.random.choice(noidx, size=N, replace=False)
newavoididx = np.sort(np.hstack((avoididx, nosampleidx)))
rowidx, colidx = np.unravel_index(nosampleidx, (len(yvar), len(xvar)))
samples = []
for row, col in zip(rowidx, colidx):
xp = xvar[col]
yp = yvar[row]
samples.append((xp, yp))
return (samples, newavoididx)
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
def test_boolean_spheres_no_overlap():
r"""Test to make sure that boolean objects (spheres, no overlap)
behave the way we expect.
Test non-overlapping spheres. This also checks that the original spheres
don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
sp1 = ds.sphere([0.25, 0.25, 0.25], 0.15)
sp2 = ds.sphere([0.75, 0.75, 0.75], 0.15)
# Store the original indices
i1 = sp1["index","morton_index"]
i1.sort()
i2 = sp2["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = sp1 & sp2
bo2 = sp1 - sp2
bo3 = sp1 | sp2 # also works with +
bo4 = ds.union([sp1, sp2])
bo5 = ds.intersection([sp1, sp2])
# This makes sure the original containers didn't change.
new_i1 = sp1["index","morton_index"]
new_i1.sort()
new_i2 = sp2["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1)
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1)
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
assert_array_equal(b4, ii)
bo6 = sp1 ^ sp2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_regions_no_overlap():
r"""Test to make sure that boolean objects (regions, no overlap)
behave the way we expect.
Test non-overlapping regions. This also checks that the original regions
don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
re1 = ds.region([0.25]*3, [0.2]*3, [0.3]*3)
re2 = ds.region([0.65]*3, [0.6]*3, [0.7]*3)
# Store the original indices
i1 = re1["index","morton_index"]
i1.sort()
i2 = re2["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = re1 & re2
bo2 = re1 - re2
bo3 = re1 | re2
bo4 = ds.union([re1, re2])
bo5 = ds.intersection([re1, re2])
# This makes sure the original containers didn't change.
new_i1 = re1["index","morton_index"]
new_i1.sort()
new_i2 = re2["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1)
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1 )
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
bo6 = re1 ^ re2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_cylinders_no_overlap():
r"""Test to make sure that boolean objects (cylinders, no overlap)
behave the way we expect.
Test non-overlapping cylinders. This also checks that the original cylinders
don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
cyl1 = ds.disk([0.25]*3, [1, 0, 0], 0.1, 0.1)
cyl2 = ds.disk([0.75]*3, [1, 0, 0], 0.1, 0.1)
# Store the original indices
i1 = cyl1["index","morton_index"]
i1.sort()
i2 = cyl2["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = cyl1 & cyl2
bo2 = cyl1 - cyl2
bo3 = cyl1 | cyl2
bo4 = ds.union([cyl1, cyl2])
bo5 = ds.intersection([cyl1, cyl2])
# This makes sure the original containers didn't change.
new_i1 = cyl1["index","morton_index"]
new_i1.sort()
new_i2 = cyl2["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1)
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1)
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
bo6 = cyl1 ^ cyl2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_ellipsoids_no_overlap():
r"""Test to make sure that boolean objects (ellipsoids, no overlap)
behave the way we expect.
Test non-overlapping ellipsoids. This also checks that the original
ellipsoids don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
ell1 = ds.ellipsoid([0.25]*3, 0.05, 0.05, 0.05, np.array([0.1]*3), 0.1)
ell2 = ds.ellipsoid([0.75]*3, 0.05, 0.05, 0.05, np.array([0.1]*3), 0.1)
# Store the original indices
i1 = ell1["index","morton_index"]
i1.sort()
i2 = ell2["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = ell1 & ell2
bo2 = ell1 - ell2
bo3 = ell1 | ell2
bo4 = ds.union([ell1, ell2])
bo5 = ds.intersection([ell1, ell2])
# This makes sure the original containers didn't change.
new_i1 = ell1["index","morton_index"]
new_i1.sort()
new_i2 = ell2["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1 )
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1)
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
bo6 = ell1 ^ ell2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_mix_periodicity():
r"""Test that a hybrid boolean region behaves as we expect.
This also tests nested logic and that periodicity works.
"""
ds = fake_amr_ds()
re = ds.region([0.5]*3, [0.0]*3, [1]*3) # whole thing
sp = ds.sphere([0.95]*3, 0.3) # wraps around
cyl = ds.disk([0.05]*3, [1,1,1], 0.1, 0.4) # wraps around
# Get original indices
rei = re["index","morton_index"]
spi = sp["index","morton_index"]
cyli = cyl["index","morton_index"]
# Make some booleans
# whole box minux spherical bites at corners
bo1 = re - sp
# sphere plus cylinder
bo2 = sp | cyl
# a jumble, the region minus the sp+cyl
bo3 = re - (sp | cyl)
# Now make sure the indices also behave as we expect.
bo4 = ds.union([re, sp, cyl])
bo5 = ds.intersection([re, sp, cyl])
expect = np.setdiff1d(rei, spi)
ii = bo1["index","morton_index"]
ii.sort()
assert_array_equal(expect, ii)
#
expect = np.union1d(spi, cyli)
ii = bo2["index","morton_index"]
ii.sort()
assert_array_equal(expect, ii)
#
expect = np.union1d(spi, cyli)
expect = np.setdiff1d(rei, expect)
ii = bo3["index","morton_index"]
ii.sort()
assert_array_equal(expect, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
ii = np.union1d(np.union1d(rei, cyli), spi)
ii.sort()
assert_array_equal(ii, b4)
ii = np.intersect1d(np.intersect1d(rei, cyli), spi)
ii.sort()
assert_array_equal(ii, b5)
bo6 = (re ^ sp) ^ cyl
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(np.setxor1d(rei, spi), cyli))
def test_boolean_ray_region_no_overlap():
r"""Test to make sure that boolean objects (ray, region, no overlap)
behave the way we expect.
Test non-overlapping ray and region. This also checks that the original
objects don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
re = ds.box([0.25]*3, [0.75]*3)
ra = ds.ray([0.1]*3, [0.1, 0.1, 0.9])
# Store the original indices
i1 = re["index","morton_index"]
i1.sort()
i2 = ra["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = re & ra
bo2 = re - ra
bo3 = re | ra
bo4 = ds.union([re, ra])
bo5 = ds.intersection([re, ra])
# This makes sure the original containers didn't change.
new_i1 = re["index","morton_index"]
new_i1.sort()
new_i2 = ra["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1)
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1 )
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
bo6 = re ^ ra
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_ray_region_overlap():
r"""Test to make sure that boolean objects (ray, region, overlap)
behave the way we expect.
Test overlapping ray and region. This also checks that the original
objects don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
re = ds.box([0.25]*3, [0.75]*3)
ra = ds.ray([0]*3, [1]*3)
# Get indices of both.
i1 = re["index","morton_index"]
i2 = ra["index","morton_index"]
# Make some booleans
bo1 = re & ra
bo2 = re - ra
bo3 = re | ra
bo4 = ds.union([re, ra])
bo5 = ds.intersection([re, ra])
# Now make sure the indices also behave as we expect.
short_line = np.intersect1d(i1, i2)
cube_minus_line = np.setdiff1d(i1, i2)
both = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, short_line)
assert_array_equal(b2, cube_minus_line)
assert_array_equal(b3, both)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = re ^ ra
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_rays_overlap():
r"""Test to make sure that boolean objects (rays, overlap)
behave the way we expect.
Test non-overlapping rays.
"""
ds = fake_amr_ds()
ra1 = ds.ray([0]*3, [1]*3)
ra2 = ds.ray([0]*3, [0.5]*3)
# Get indices of both.
i1 = ra1["index","morton_index"]
i1.sort()
i2 = ra2["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = ra1 & ra2
bo2 = ra1 - ra2
bo3 = ra1 | ra2
bo4 = ds.union([ra1, ra2])
bo5 = ds.intersection([ra1, ra2])
# Now make sure the indices also behave as we expect.
short_line = np.intersect1d(i1, i2)
short_line_b = np.setdiff1d(i1, i2)
full_line = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, short_line)
assert_array_equal(b2, short_line_b)
assert_array_equal(b3, full_line)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, i1)
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = ra1 ^ ra2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_slices_no_overlap():
r"""Test to make sure that boolean objects (slices, no overlap)
behave the way we expect.
Test non-overlapping slices. This also checks that the original regions
don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
sl1 = ds.r[:,:,0.25]
sl2 = ds.r[:,:,0.75]
# Store the original indices
i1 = sl1["index","morton_index"]
i1.sort()
i2 = sl2["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = sl1 & sl2
bo2 = sl1 - sl2
bo3 = sl1 | sl2
bo4 = ds.union([sl1, sl2])
bo5 = ds.intersection([sl1, sl2])
# This makes sure the original containers didn't change.
new_i1 = sl1["index","morton_index"]
new_i1.sort()
new_i2 = sl2["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1)
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1 )
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
bo6 = sl1 ^ sl2
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_ray_slice_no_overlap():
r"""Test to make sure that boolean objects (ray, slice, no overlap)
behave the way we expect.
Test non-overlapping ray and slice. This also checks that the original
regions don't change as part of constructing the booleans.
"""
ds = fake_amr_ds()
sl = ds.r[:,:,0.25]
ra = ds.ray([0]*3, [0, 1, 0])
# Store the original indices
i1 = sl["index","morton_index"]
i1.sort()
i2 = ra["index","morton_index"]
i2.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = sl & ra
bo2 = sl - ra
bo3 = sl | ra
bo4 = ds.union([sl, ra])
bo5 = ds.intersection([sl, ra])
# This makes sure the original containers didn't change.
new_i1 = sl["index","morton_index"]
new_i1.sort()
new_i2 = ra["index","morton_index"]
new_i2.sort()
assert_array_equal(new_i1, i1)
assert_array_equal(new_i2, i2)
# Now make sure the indices also behave as we expect.
empty = np.array([])
assert_array_equal(bo1["index","morton_index"], empty)
assert_array_equal(bo5["index","morton_index"], empty)
b2 = bo2["index","morton_index"]
b2.sort()
assert_array_equal(b2, i1 )
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b3, ii)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, b4)
bo6 = sl ^ ra
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def test_boolean_ray_slice_overlap():
r"""Test to make sure that boolean objects (rays and slices, overlap)
behave the way we expect.
Test overlapping rays and slices.
"""
ds = fake_amr_ds()
sl = ds.r[:,:,0.25]
ra = ds.ray([0, 0, 0.25], [0, 1, 0.25])
# Get indices of both.
i1 = sl["index","morton_index"]
i1.sort()
i2 = ra["index","morton_index"]
i1.sort()
ii = np.concatenate((i1, i2))
ii.sort()
# Make some booleans
bo1 = sl & ra
bo2 = sl - ra
bo3 = sl | ra
bo4 = ds.union([sl, ra])
bo5 = ds.intersection([sl, ra])
# Now make sure the indices also behave as we expect.
line = np.intersect1d(i1, i2)
sheet_minus_line = np.setdiff1d(i1, i2)
sheet = np.union1d(i1, i2)
b1 = bo1["index","morton_index"]
b1.sort()
b2 = bo2["index","morton_index"]
b2.sort()
b3 = bo3["index","morton_index"]
b3.sort()
assert_array_equal(b1, line)
assert_array_equal(b2, sheet_minus_line)
assert_array_equal(b3, sheet)
b4 = bo4["index","morton_index"]
b4.sort()
b5 = bo5["index","morton_index"]
b5.sort()
assert_array_equal(b3, i1)
assert_array_equal(b3, b4)
assert_array_equal(b1, b5)
bo6 = sl ^ ra
b6 = bo6["index", "morton_index"]
b6.sort()
assert_array_equal(b6, np.setxor1d(i1, i2))
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
