fr/fr_env/lib/python3.8/site-packages/matplotlib/tests/test_triangulation.py

1166 lines
45 KiB
Python
Raw Normal View History

2021-02-17 12:26:31 +05:30
import numpy as np
from numpy.testing import (
assert_array_equal, assert_array_almost_equal, assert_array_less)
import numpy.ma.testutils as matest
import pytest
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
from matplotlib.path import Path
from matplotlib.testing.decorators import image_comparison
def test_delaunay():
# No duplicate points, regular grid.
nx = 5
ny = 4
x, y = np.meshgrid(np.linspace(0.0, 1.0, nx), np.linspace(0.0, 1.0, ny))
x = x.ravel()
y = y.ravel()
npoints = nx*ny
ntriangles = 2 * (nx-1) * (ny-1)
nedges = 3*nx*ny - 2*nx - 2*ny + 1
# Create delaunay triangulation.
triang = mtri.Triangulation(x, y)
# The tests in the remainder of this function should be passed by any
# triangulation that does not contain duplicate points.
# Points - floating point.
assert_array_almost_equal(triang.x, x)
assert_array_almost_equal(triang.y, y)
# Triangles - integers.
assert len(triang.triangles) == ntriangles
assert np.min(triang.triangles) == 0
assert np.max(triang.triangles) == npoints-1
# Edges - integers.
assert len(triang.edges) == nedges
assert np.min(triang.edges) == 0
assert np.max(triang.edges) == npoints-1
# Neighbors - integers.
# Check that neighbors calculated by C++ triangulation class are the same
# as those returned from delaunay routine.
neighbors = triang.neighbors
triang._neighbors = None
assert_array_equal(triang.neighbors, neighbors)
# Is each point used in at least one triangle?
assert_array_equal(np.unique(triang.triangles), np.arange(npoints))
def test_delaunay_duplicate_points():
npoints = 10
duplicate = 7
duplicate_of = 3
np.random.seed(23)
x = np.random.random(npoints)
y = np.random.random(npoints)
x[duplicate] = x[duplicate_of]
y[duplicate] = y[duplicate_of]
# Create delaunay triangulation.
triang = mtri.Triangulation(x, y)
# Duplicate points should be ignored, so the index of the duplicate points
# should not appear in any triangle.
assert_array_equal(np.unique(triang.triangles),
np.delete(np.arange(npoints), duplicate))
def test_delaunay_points_in_line():
# Cannot triangulate points that are all in a straight line, but check
# that delaunay code fails gracefully.
x = np.linspace(0.0, 10.0, 11)
y = np.linspace(0.0, 10.0, 11)
with pytest.raises(RuntimeError):
mtri.Triangulation(x, y)
# Add an extra point not on the line and the triangulation is OK.
x = np.append(x, 2.0)
y = np.append(y, 8.0)
mtri.Triangulation(x, y)
@pytest.mark.parametrize('x, y', [
# Triangulation should raise a ValueError if passed less than 3 points.
([], []),
([1], [5]),
([1, 2], [5, 6]),
# Triangulation should also raise a ValueError if passed duplicate points
# such that there are less than 3 unique points.
([1, 2, 1], [5, 6, 5]),
([1, 2, 2], [5, 6, 6]),
([1, 1, 1, 2, 1, 2], [5, 5, 5, 6, 5, 6]),
])
def test_delaunay_insufficient_points(x, y):
with pytest.raises(ValueError):
mtri.Triangulation(x, y)
def test_delaunay_robust():
# Fails when mtri.Triangulation uses matplotlib.delaunay, works when using
# qhull.
tri_points = np.array([
[0.8660254037844384, -0.5000000000000004],
[0.7577722283113836, -0.5000000000000004],
[0.6495190528383288, -0.5000000000000003],
[0.5412658773652739, -0.5000000000000003],
[0.811898816047911, -0.40625000000000044],
[0.7036456405748561, -0.4062500000000004],
[0.5953924651018013, -0.40625000000000033]])
test_points = np.asarray([
[0.58, -0.46],
[0.65, -0.46],
[0.65, -0.42],
[0.7, -0.48],
[0.7, -0.44],
[0.75, -0.44],
[0.8, -0.48]])
# Utility function that indicates if a triangle defined by 3 points
# (xtri, ytri) contains the test point xy. Avoid calling with a point that
# lies on or very near to an edge of the triangle.
def tri_contains_point(xtri, ytri, xy):
tri_points = np.vstack((xtri, ytri)).T
return Path(tri_points).contains_point(xy)
# Utility function that returns how many triangles of the specified
# triangulation contain the test point xy. Avoid calling with a point that
# lies on or very near to an edge of any triangle in the triangulation.
def tris_contain_point(triang, xy):
return sum(tri_contains_point(triang.x[tri], triang.y[tri], xy)
for tri in triang.triangles)
# Using matplotlib.delaunay, an invalid triangulation is created with
# overlapping triangles; qhull is OK.
triang = mtri.Triangulation(tri_points[:, 0], tri_points[:, 1])
for test_point in test_points:
assert tris_contain_point(triang, test_point) == 1
# If ignore the first point of tri_points, matplotlib.delaunay throws a
# KeyError when calculating the convex hull; qhull is OK.
triang = mtri.Triangulation(tri_points[1:, 0], tri_points[1:, 1])
@image_comparison(['tripcolor1.png'])
def test_tripcolor():
x = np.asarray([0, 0.5, 1, 0, 0.5, 1, 0, 0.5, 1, 0.75])
y = np.asarray([0, 0, 0, 0.5, 0.5, 0.5, 1, 1, 1, 0.75])
triangles = np.asarray([
[0, 1, 3], [1, 4, 3],
[1, 2, 4], [2, 5, 4],
[3, 4, 6], [4, 7, 6],
[4, 5, 9], [7, 4, 9], [8, 7, 9], [5, 8, 9]])
# Triangulation with same number of points and triangles.
triang = mtri.Triangulation(x, y, triangles)
Cpoints = x + 0.5*y
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
Cfaces = 0.5*xmid + ymid
plt.subplot(121)
plt.tripcolor(triang, Cpoints, edgecolors='k')
plt.title('point colors')
plt.subplot(122)
plt.tripcolor(triang, facecolors=Cfaces, edgecolors='k')
plt.title('facecolors')
def test_no_modify():
# Test that Triangulation does not modify triangles array passed to it.
triangles = np.array([[3, 2, 0], [3, 1, 0]], dtype=np.int32)
points = np.array([(0, 0), (0, 1.1), (1, 0), (1, 1)])
old_triangles = triangles.copy()
mtri.Triangulation(points[:, 0], points[:, 1], triangles).edges
assert_array_equal(old_triangles, triangles)
def test_trifinder():
# Test points within triangles of masked triangulation.
x, y = np.meshgrid(np.arange(4), np.arange(4))
x = x.ravel()
y = y.ravel()
triangles = [[0, 1, 4], [1, 5, 4], [1, 2, 5], [2, 6, 5], [2, 3, 6],
[3, 7, 6], [4, 5, 8], [5, 9, 8], [5, 6, 9], [6, 10, 9],
[6, 7, 10], [7, 11, 10], [8, 9, 12], [9, 13, 12], [9, 10, 13],
[10, 14, 13], [10, 11, 14], [11, 15, 14]]
mask = np.zeros(len(triangles))
mask[8:10] = 1
triang = mtri.Triangulation(x, y, triangles, mask)
trifinder = triang.get_trifinder()
xs = [0.25, 1.25, 2.25, 3.25]
ys = [0.25, 1.25, 2.25, 3.25]
xs, ys = np.meshgrid(xs, ys)
xs = xs.ravel()
ys = ys.ravel()
tris = trifinder(xs, ys)
assert_array_equal(tris, [0, 2, 4, -1, 6, -1, 10, -1,
12, 14, 16, -1, -1, -1, -1, -1])
tris = trifinder(xs-0.5, ys-0.5)
assert_array_equal(tris, [-1, -1, -1, -1, -1, 1, 3, 5,
-1, 7, -1, 11, -1, 13, 15, 17])
# Test points exactly on boundary edges of masked triangulation.
xs = [0.5, 1.5, 2.5, 0.5, 1.5, 2.5, 1.5, 1.5, 0.0, 1.0, 2.0, 3.0]
ys = [0.0, 0.0, 0.0, 3.0, 3.0, 3.0, 1.0, 2.0, 1.5, 1.5, 1.5, 1.5]
tris = trifinder(xs, ys)
assert_array_equal(tris, [0, 2, 4, 13, 15, 17, 3, 14, 6, 7, 10, 11])
# Test points exactly on boundary corners of masked triangulation.
xs = [0.0, 3.0]
ys = [0.0, 3.0]
tris = trifinder(xs, ys)
assert_array_equal(tris, [0, 17])
#
# Test triangles with horizontal colinear points. These are not valid
# triangulations, but we try to deal with the simplest violations.
#
# If +ve, triangulation is OK, if -ve triangulation invalid,
# if zero have colinear points but should pass tests anyway.
delta = 0.0
x = [1.5, 0, 1, 2, 3, 1.5, 1.5]
y = [-1, 0, 0, 0, 0, delta, 1]
triangles = [[0, 2, 1], [0, 3, 2], [0, 4, 3], [1, 2, 5], [2, 3, 5],
[3, 4, 5], [1, 5, 6], [4, 6, 5]]
triang = mtri.Triangulation(x, y, triangles)
trifinder = triang.get_trifinder()
xs = [-0.1, 0.4, 0.9, 1.4, 1.9, 2.4, 2.9]
ys = [-0.1, 0.1]
xs, ys = np.meshgrid(xs, ys)
tris = trifinder(xs, ys)
assert_array_equal(tris, [[-1, 0, 0, 1, 1, 2, -1],
[-1, 6, 6, 6, 7, 7, -1]])
#
# Test triangles with vertical colinear points. These are not valid
# triangulations, but we try to deal with the simplest violations.
#
# If +ve, triangulation is OK, if -ve triangulation invalid,
# if zero have colinear points but should pass tests anyway.
delta = 0.0
x = [-1, -delta, 0, 0, 0, 0, 1]
y = [1.5, 1.5, 0, 1, 2, 3, 1.5]
triangles = [[0, 1, 2], [0, 1, 5], [1, 2, 3], [1, 3, 4], [1, 4, 5],
[2, 6, 3], [3, 6, 4], [4, 6, 5]]
triang = mtri.Triangulation(x, y, triangles)
trifinder = triang.get_trifinder()
xs = [-0.1, 0.1]
ys = [-0.1, 0.4, 0.9, 1.4, 1.9, 2.4, 2.9]
xs, ys = np.meshgrid(xs, ys)
tris = trifinder(xs, ys)
assert_array_equal(tris, [[-1, -1], [0, 5], [0, 5], [0, 6], [1, 6], [1, 7],
[-1, -1]])
# Test that changing triangulation by setting a mask causes the trifinder
# to be reinitialised.
x = [0, 1, 0, 1]
y = [0, 0, 1, 1]
triangles = [[0, 1, 2], [1, 3, 2]]
triang = mtri.Triangulation(x, y, triangles)
trifinder = triang.get_trifinder()
xs = [-0.2, 0.2, 0.8, 1.2]
ys = [0.5, 0.5, 0.5, 0.5]
tris = trifinder(xs, ys)
assert_array_equal(tris, [-1, 0, 1, -1])
triang.set_mask([1, 0])
assert trifinder == triang.get_trifinder()
tris = trifinder(xs, ys)
assert_array_equal(tris, [-1, -1, 1, -1])
def test_triinterp():
# Test points within triangles of masked triangulation.
x, y = np.meshgrid(np.arange(4), np.arange(4))
x = x.ravel()
y = y.ravel()
z = 1.23*x - 4.79*y
triangles = [[0, 1, 4], [1, 5, 4], [1, 2, 5], [2, 6, 5], [2, 3, 6],
[3, 7, 6], [4, 5, 8], [5, 9, 8], [5, 6, 9], [6, 10, 9],
[6, 7, 10], [7, 11, 10], [8, 9, 12], [9, 13, 12], [9, 10, 13],
[10, 14, 13], [10, 11, 14], [11, 15, 14]]
mask = np.zeros(len(triangles))
mask[8:10] = 1
triang = mtri.Triangulation(x, y, triangles, mask)
linear_interp = mtri.LinearTriInterpolator(triang, z)
cubic_min_E = mtri.CubicTriInterpolator(triang, z)
cubic_geom = mtri.CubicTriInterpolator(triang, z, kind='geom')
xs = np.linspace(0.25, 2.75, 6)
ys = [0.25, 0.75, 2.25, 2.75]
xs, ys = np.meshgrid(xs, ys) # Testing arrays with array.ndim = 2
for interp in (linear_interp, cubic_min_E, cubic_geom):
zs = interp(xs, ys)
assert_array_almost_equal(zs, (1.23*xs - 4.79*ys))
# Test points outside triangulation.
xs = [-0.25, 1.25, 1.75, 3.25]
ys = xs
xs, ys = np.meshgrid(xs, ys)
for interp in (linear_interp, cubic_min_E, cubic_geom):
zs = linear_interp(xs, ys)
assert_array_equal(zs.mask, [[True]*4]*4)
# Test mixed configuration (outside / inside).
xs = np.linspace(0.25, 1.75, 6)
ys = [0.25, 0.75, 1.25, 1.75]
xs, ys = np.meshgrid(xs, ys)
for interp in (linear_interp, cubic_min_E, cubic_geom):
zs = interp(xs, ys)
matest.assert_array_almost_equal(zs, (1.23*xs - 4.79*ys))
mask = (xs >= 1) * (xs <= 2) * (ys >= 1) * (ys <= 2)
assert_array_equal(zs.mask, mask)
# 2nd order patch test: on a grid with an 'arbitrary shaped' triangle,
# patch test shall be exact for quadratic functions and cubic
# interpolator if *kind* = user
(a, b, c) = (1.23, -4.79, 0.6)
def quad(x, y):
return a*(x-0.5)**2 + b*(y-0.5)**2 + c*x*y
def gradient_quad(x, y):
return (2*a*(x-0.5) + c*y, 2*b*(y-0.5) + c*x)
x = np.array([0.2, 0.33367, 0.669, 0., 1., 1., 0.])
y = np.array([0.3, 0.80755, 0.4335, 0., 0., 1., 1.])
triangles = np.array([[0, 1, 2], [3, 0, 4], [4, 0, 2], [4, 2, 5],
[1, 5, 2], [6, 5, 1], [6, 1, 0], [6, 0, 3]])
triang = mtri.Triangulation(x, y, triangles)
z = quad(x, y)
dz = gradient_quad(x, y)
# test points for 2nd order patch test
xs = np.linspace(0., 1., 5)
ys = np.linspace(0., 1., 5)
xs, ys = np.meshgrid(xs, ys)
cubic_user = mtri.CubicTriInterpolator(triang, z, kind='user', dz=dz)
interp_zs = cubic_user(xs, ys)
assert_array_almost_equal(interp_zs, quad(xs, ys))
(interp_dzsdx, interp_dzsdy) = cubic_user.gradient(x, y)
(dzsdx, dzsdy) = gradient_quad(x, y)
assert_array_almost_equal(interp_dzsdx, dzsdx)
assert_array_almost_equal(interp_dzsdy, dzsdy)
# Cubic improvement: cubic interpolation shall perform better than linear
# on a sufficiently dense mesh for a quadratic function.
n = 11
x, y = np.meshgrid(np.linspace(0., 1., n+1), np.linspace(0., 1., n+1))
x = x.ravel()
y = y.ravel()
z = quad(x, y)
triang = mtri.Triangulation(x, y, triangles=meshgrid_triangles(n+1))
xs, ys = np.meshgrid(np.linspace(0.1, 0.9, 5), np.linspace(0.1, 0.9, 5))
xs = xs.ravel()
ys = ys.ravel()
linear_interp = mtri.LinearTriInterpolator(triang, z)
cubic_min_E = mtri.CubicTriInterpolator(triang, z)
cubic_geom = mtri.CubicTriInterpolator(triang, z, kind='geom')
zs = quad(xs, ys)
diff_lin = np.abs(linear_interp(xs, ys) - zs)
for interp in (cubic_min_E, cubic_geom):
diff_cubic = np.abs(interp(xs, ys) - zs)
assert np.max(diff_lin) >= 10 * np.max(diff_cubic)
assert (np.dot(diff_lin, diff_lin) >=
100 * np.dot(diff_cubic, diff_cubic))
def test_triinterpcubic_C1_continuity():
# Below the 4 tests which demonstrate C1 continuity of the
# TriCubicInterpolator (testing the cubic shape functions on arbitrary
# triangle):
#
# 1) Testing continuity of function & derivatives at corner for all 9
# shape functions. Testing also function values at same location.
# 2) Testing C1 continuity along each edge (as gradient is polynomial of
# 2nd order, it is sufficient to test at the middle).
# 3) Testing C1 continuity at triangle barycenter (where the 3 subtriangles
# meet)
# 4) Testing C1 continuity at median 1/3 points (midside between 2
# subtriangles)
# Utility test function check_continuity
def check_continuity(interpolator, loc, values=None):
"""
Checks the continuity of interpolator (and its derivatives) near
location loc. Can check the value at loc itself if *values* is
provided.
*interpolator* TriInterpolator
*loc* location to test (x0, y0)
*values* (optional) array [z0, dzx0, dzy0] to check the value at *loc*
"""
n_star = 24 # Number of continuity points in a boundary of loc
epsilon = 1.e-10 # Distance for loc boundary
k = 100. # Continuity coefficient
(loc_x, loc_y) = loc
star_x = loc_x + epsilon*np.cos(np.linspace(0., 2*np.pi, n_star))
star_y = loc_y + epsilon*np.sin(np.linspace(0., 2*np.pi, n_star))
z = interpolator([loc_x], [loc_y])[0]
(dzx, dzy) = interpolator.gradient([loc_x], [loc_y])
if values is not None:
assert_array_almost_equal(z, values[0])
assert_array_almost_equal(dzx[0], values[1])
assert_array_almost_equal(dzy[0], values[2])
diff_z = interpolator(star_x, star_y) - z
(tab_dzx, tab_dzy) = interpolator.gradient(star_x, star_y)
diff_dzx = tab_dzx - dzx
diff_dzy = tab_dzy - dzy
assert_array_less(diff_z, epsilon*k)
assert_array_less(diff_dzx, epsilon*k)
assert_array_less(diff_dzy, epsilon*k)
# Drawing arbitrary triangle (a, b, c) inside a unit square.
(ax, ay) = (0.2, 0.3)
(bx, by) = (0.33367, 0.80755)
(cx, cy) = (0.669, 0.4335)
x = np.array([ax, bx, cx, 0., 1., 1., 0.])
y = np.array([ay, by, cy, 0., 0., 1., 1.])
triangles = np.array([[0, 1, 2], [3, 0, 4], [4, 0, 2], [4, 2, 5],
[1, 5, 2], [6, 5, 1], [6, 1, 0], [6, 0, 3]])
triang = mtri.Triangulation(x, y, triangles)
for idof in range(9):
z = np.zeros(7, dtype=np.float64)
dzx = np.zeros(7, dtype=np.float64)
dzy = np.zeros(7, dtype=np.float64)
values = np.zeros([3, 3], dtype=np.float64)
case = idof//3
values[case, idof % 3] = 1.0
if case == 0:
z[idof] = 1.0
elif case == 1:
dzx[idof % 3] = 1.0
elif case == 2:
dzy[idof % 3] = 1.0
interp = mtri.CubicTriInterpolator(triang, z, kind='user',
dz=(dzx, dzy))
# Test 1) Checking values and continuity at nodes
check_continuity(interp, (ax, ay), values[:, 0])
check_continuity(interp, (bx, by), values[:, 1])
check_continuity(interp, (cx, cy), values[:, 2])
# Test 2) Checking continuity at midside nodes
check_continuity(interp, ((ax+bx)*0.5, (ay+by)*0.5))
check_continuity(interp, ((ax+cx)*0.5, (ay+cy)*0.5))
check_continuity(interp, ((cx+bx)*0.5, (cy+by)*0.5))
# Test 3) Checking continuity at barycenter
check_continuity(interp, ((ax+bx+cx)/3., (ay+by+cy)/3.))
# Test 4) Checking continuity at median 1/3-point
check_continuity(interp, ((4.*ax+bx+cx)/6., (4.*ay+by+cy)/6.))
check_continuity(interp, ((ax+4.*bx+cx)/6., (ay+4.*by+cy)/6.))
check_continuity(interp, ((ax+bx+4.*cx)/6., (ay+by+4.*cy)/6.))
def test_triinterpcubic_cg_solver():
# Now 3 basic tests of the Sparse CG solver, used for
# TriCubicInterpolator with *kind* = 'min_E'
# 1) A commonly used test involves a 2d Poisson matrix.
def poisson_sparse_matrix(n, m):
"""
Return the sparse, (n*m, n*m) matrix in coo format resulting from the
discretisation of the 2-dimensional Poisson equation according to a
finite difference numerical scheme on a uniform (n, m) grid.
"""
l = m*n
rows = np.concatenate([
np.arange(l, dtype=np.int32),
np.arange(l-1, dtype=np.int32), np.arange(1, l, dtype=np.int32),
np.arange(l-n, dtype=np.int32), np.arange(n, l, dtype=np.int32)])
cols = np.concatenate([
np.arange(l, dtype=np.int32),
np.arange(1, l, dtype=np.int32), np.arange(l-1, dtype=np.int32),
np.arange(n, l, dtype=np.int32), np.arange(l-n, dtype=np.int32)])
vals = np.concatenate([
4*np.ones(l, dtype=np.float64),
-np.ones(l-1, dtype=np.float64), -np.ones(l-1, dtype=np.float64),
-np.ones(l-n, dtype=np.float64), -np.ones(l-n, dtype=np.float64)])
# In fact +1 and -1 diags have some zeros
vals[l:2*l-1][m-1::m] = 0.
vals[2*l-1:3*l-2][m-1::m] = 0.
return vals, rows, cols, (n*m, n*m)
# Instantiating a sparse Poisson matrix of size 48 x 48:
(n, m) = (12, 4)
mat = mtri.triinterpolate._Sparse_Matrix_coo(*poisson_sparse_matrix(n, m))
mat.compress_csc()
mat_dense = mat.to_dense()
# Testing a sparse solve for all 48 basis vector
for itest in range(n*m):
b = np.zeros(n*m, dtype=np.float64)
b[itest] = 1.
x, _ = mtri.triinterpolate._cg(A=mat, b=b, x0=np.zeros(n*m),
tol=1.e-10)
assert_array_almost_equal(np.dot(mat_dense, x), b)
# 2) Same matrix with inserting 2 rows - cols with null diag terms
# (but still linked with the rest of the matrix by extra-diag terms)
(i_zero, j_zero) = (12, 49)
vals, rows, cols, _ = poisson_sparse_matrix(n, m)
rows = rows + 1*(rows >= i_zero) + 1*(rows >= j_zero)
cols = cols + 1*(cols >= i_zero) + 1*(cols >= j_zero)
# adding extra-diag terms
rows = np.concatenate([rows, [i_zero, i_zero-1, j_zero, j_zero-1]])
cols = np.concatenate([cols, [i_zero-1, i_zero, j_zero-1, j_zero]])
vals = np.concatenate([vals, [1., 1., 1., 1.]])
mat = mtri.triinterpolate._Sparse_Matrix_coo(vals, rows, cols,
(n*m + 2, n*m + 2))
mat.compress_csc()
mat_dense = mat.to_dense()
# Testing a sparse solve for all 50 basis vec
for itest in range(n*m + 2):
b = np.zeros(n*m + 2, dtype=np.float64)
b[itest] = 1.
x, _ = mtri.triinterpolate._cg(A=mat, b=b, x0=np.ones(n*m + 2),
tol=1.e-10)
assert_array_almost_equal(np.dot(mat_dense, x), b)
# 3) Now a simple test that summation of duplicate (i.e. with same rows,
# same cols) entries occurs when compressed.
vals = np.ones(17, dtype=np.float64)
rows = np.array([0, 1, 2, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1],
dtype=np.int32)
cols = np.array([0, 1, 2, 1, 1, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
dtype=np.int32)
dim = (3, 3)
mat = mtri.triinterpolate._Sparse_Matrix_coo(vals, rows, cols, dim)
mat.compress_csc()
mat_dense = mat.to_dense()
assert_array_almost_equal(mat_dense, np.array([
[1., 2., 0.], [2., 1., 5.], [0., 5., 1.]], dtype=np.float64))
def test_triinterpcubic_geom_weights():
# Tests to check computation of weights for _DOF_estimator_geom:
# The weight sum per triangle can be 1. (in case all angles < 90 degrees)
# or (2*w_i) where w_i = 1-alpha_i/np.pi is the weight of apex i; alpha_i
# is the apex angle > 90 degrees.
(ax, ay) = (0., 1.687)
x = np.array([ax, 0.5*ax, 0., 1.])
y = np.array([ay, -ay, 0., 0.])
z = np.zeros(4, dtype=np.float64)
triangles = [[0, 2, 3], [1, 3, 2]]
sum_w = np.zeros([4, 2]) # 4 possibilities; 2 triangles
for theta in np.linspace(0., 2*np.pi, 14): # rotating the figure...
x_rot = np.cos(theta)*x + np.sin(theta)*y
y_rot = -np.sin(theta)*x + np.cos(theta)*y
triang = mtri.Triangulation(x_rot, y_rot, triangles)
cubic_geom = mtri.CubicTriInterpolator(triang, z, kind='geom')
dof_estimator = mtri.triinterpolate._DOF_estimator_geom(cubic_geom)
weights = dof_estimator.compute_geom_weights()
# Testing for the 4 possibilities...
sum_w[0, :] = np.sum(weights, 1) - 1
for itri in range(3):
sum_w[itri+1, :] = np.sum(weights, 1) - 2*weights[:, itri]
assert_array_almost_equal(np.min(np.abs(sum_w), axis=0),
np.array([0., 0.], dtype=np.float64))
def test_triinterp_colinear():
# Tests interpolating inside a triangulation with horizontal colinear
# points (refer also to the tests :func:`test_trifinder` ).
#
# These are not valid triangulations, but we try to deal with the
# simplest violations (i. e. those handled by default TriFinder).
#
# Note that the LinearTriInterpolator and the CubicTriInterpolator with
# kind='min_E' or 'geom' still pass a linear patch test.
# We also test interpolation inside a flat triangle, by forcing
# *tri_index* in a call to :meth:`_interpolate_multikeys`.
# If +ve, triangulation is OK, if -ve triangulation invalid,
# if zero have colinear points but should pass tests anyway.
delta = 0.
x0 = np.array([1.5, 0, 1, 2, 3, 1.5, 1.5])
y0 = np.array([-1, 0, 0, 0, 0, delta, 1])
# We test different affine transformations of the initial figure; to
# avoid issues related to round-off errors we only use integer
# coefficients (otherwise the Triangulation might become invalid even with
# delta == 0).
transformations = [[1, 0], [0, 1], [1, 1], [1, 2], [-2, -1], [-2, 1]]
for transformation in transformations:
x_rot = transformation[0]*x0 + transformation[1]*y0
y_rot = -transformation[1]*x0 + transformation[0]*y0
(x, y) = (x_rot, y_rot)
z = 1.23*x - 4.79*y
triangles = [[0, 2, 1], [0, 3, 2], [0, 4, 3], [1, 2, 5], [2, 3, 5],
[3, 4, 5], [1, 5, 6], [4, 6, 5]]
triang = mtri.Triangulation(x, y, triangles)
xs = np.linspace(np.min(triang.x), np.max(triang.x), 20)
ys = np.linspace(np.min(triang.y), np.max(triang.y), 20)
xs, ys = np.meshgrid(xs, ys)
xs = xs.ravel()
ys = ys.ravel()
mask_out = (triang.get_trifinder()(xs, ys) == -1)
zs_target = np.ma.array(1.23*xs - 4.79*ys, mask=mask_out)
linear_interp = mtri.LinearTriInterpolator(triang, z)
cubic_min_E = mtri.CubicTriInterpolator(triang, z)
cubic_geom = mtri.CubicTriInterpolator(triang, z, kind='geom')
for interp in (linear_interp, cubic_min_E, cubic_geom):
zs = interp(xs, ys)
assert_array_almost_equal(zs_target, zs)
# Testing interpolation inside the flat triangle number 4: [2, 3, 5]
# by imposing *tri_index* in a call to :meth:`_interpolate_multikeys`
itri = 4
pt1 = triang.triangles[itri, 0]
pt2 = triang.triangles[itri, 1]
xs = np.linspace(triang.x[pt1], triang.x[pt2], 10)
ys = np.linspace(triang.y[pt1], triang.y[pt2], 10)
zs_target = 1.23*xs - 4.79*ys
for interp in (linear_interp, cubic_min_E, cubic_geom):
zs, = interp._interpolate_multikeys(
xs, ys, tri_index=itri*np.ones(10, dtype=np.int32))
assert_array_almost_equal(zs_target, zs)
def test_triinterp_transformations():
# 1) Testing that the interpolation scheme is invariant by rotation of the
# whole figure.
# Note: This test is non-trivial for a CubicTriInterpolator with
# kind='min_E'. It does fail for a non-isotropic stiffness matrix E of
# :class:`_ReducedHCT_Element` (tested with E=np.diag([1., 1., 1.])), and
# provides a good test for :meth:`get_Kff_and_Ff`of the same class.
#
# 2) Also testing that the interpolation scheme is invariant by expansion
# of the whole figure along one axis.
n_angles = 20
n_radii = 10
min_radius = 0.15
def z(x, y):
r1 = np.hypot(0.5 - x, 0.5 - y)
theta1 = np.arctan2(0.5 - x, 0.5 - y)
r2 = np.hypot(-x - 0.2, -y - 0.2)
theta2 = np.arctan2(-x - 0.2, -y - 0.2)
z = -(2*(np.exp((r1/10)**2)-1)*30. * np.cos(7.*theta1) +
(np.exp((r2/10)**2)-1)*30. * np.cos(11.*theta2) +
0.7*(x**2 + y**2))
return (np.max(z)-z)/(np.max(z)-np.min(z))
# First create the x and y coordinates of the points.
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0 + n_angles, 2*np.pi + n_angles,
n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi/n_angles
x0 = (radii*np.cos(angles)).flatten()
y0 = (radii*np.sin(angles)).flatten()
triang0 = mtri.Triangulation(x0, y0) # Delaunay triangulation
z0 = z(x0, y0)
# Then create the test points
xs0 = np.linspace(-1., 1., 23)
ys0 = np.linspace(-1., 1., 23)
xs0, ys0 = np.meshgrid(xs0, ys0)
xs0 = xs0.ravel()
ys0 = ys0.ravel()
interp_z0 = {}
for i_angle in range(2):
# Rotating everything
theta = 2*np.pi / n_angles * i_angle
x = np.cos(theta)*x0 + np.sin(theta)*y0
y = -np.sin(theta)*x0 + np.cos(theta)*y0
xs = np.cos(theta)*xs0 + np.sin(theta)*ys0
ys = -np.sin(theta)*xs0 + np.cos(theta)*ys0
triang = mtri.Triangulation(x, y, triang0.triangles)
linear_interp = mtri.LinearTriInterpolator(triang, z0)
cubic_min_E = mtri.CubicTriInterpolator(triang, z0)
cubic_geom = mtri.CubicTriInterpolator(triang, z0, kind='geom')
dic_interp = {'lin': linear_interp,
'min_E': cubic_min_E,
'geom': cubic_geom}
# Testing that the interpolation is invariant by rotation...
for interp_key in ['lin', 'min_E', 'geom']:
interp = dic_interp[interp_key]
if i_angle == 0:
interp_z0[interp_key] = interp(xs0, ys0) # storage
else:
interpz = interp(xs, ys)
matest.assert_array_almost_equal(interpz,
interp_z0[interp_key])
scale_factor = 987654.3210
for scaled_axis in ('x', 'y'):
# Scaling everything (expansion along scaled_axis)
if scaled_axis == 'x':
x = scale_factor * x0
y = y0
xs = scale_factor * xs0
ys = ys0
else:
x = x0
y = scale_factor * y0
xs = xs0
ys = scale_factor * ys0
triang = mtri.Triangulation(x, y, triang0.triangles)
linear_interp = mtri.LinearTriInterpolator(triang, z0)
cubic_min_E = mtri.CubicTriInterpolator(triang, z0)
cubic_geom = mtri.CubicTriInterpolator(triang, z0, kind='geom')
dic_interp = {'lin': linear_interp,
'min_E': cubic_min_E,
'geom': cubic_geom}
# Test that the interpolation is invariant by expansion along 1 axis...
for interp_key in ['lin', 'min_E', 'geom']:
interpz = dic_interp[interp_key](xs, ys)
matest.assert_array_almost_equal(interpz, interp_z0[interp_key])
@image_comparison(['tri_smooth_contouring.png'], remove_text=True, tol=0.07)
def test_tri_smooth_contouring():
# Image comparison based on example tricontour_smooth_user.
n_angles = 20
n_radii = 10
min_radius = 0.15
def z(x, y):
r1 = np.hypot(0.5 - x, 0.5 - y)
theta1 = np.arctan2(0.5 - x, 0.5 - y)
r2 = np.hypot(-x - 0.2, -y - 0.2)
theta2 = np.arctan2(-x - 0.2, -y - 0.2)
z = -(2*(np.exp((r1/10)**2)-1)*30. * np.cos(7.*theta1) +
(np.exp((r2/10)**2)-1)*30. * np.cos(11.*theta2) +
0.7*(x**2 + y**2))
return (np.max(z)-z)/(np.max(z)-np.min(z))
# First create the x and y coordinates of the points.
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0 + n_angles, 2*np.pi + n_angles,
n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi/n_angles
x0 = (radii*np.cos(angles)).flatten()
y0 = (radii*np.sin(angles)).flatten()
triang0 = mtri.Triangulation(x0, y0) # Delaunay triangulation
z0 = z(x0, y0)
triang0.set_mask(np.hypot(x0[triang0.triangles].mean(axis=1),
y0[triang0.triangles].mean(axis=1))
< min_radius)
# Then the plot
refiner = mtri.UniformTriRefiner(triang0)
tri_refi, z_test_refi = refiner.refine_field(z0, subdiv=4)
levels = np.arange(0., 1., 0.025)
plt.triplot(triang0, lw=0.5, color='0.5')
plt.tricontour(tri_refi, z_test_refi, levels=levels, colors="black")
@image_comparison(['tri_smooth_gradient.png'], remove_text=True, tol=0.092)
def test_tri_smooth_gradient():
# Image comparison based on example trigradient_demo.
def dipole_potential(x, y):
"""An electric dipole potential V."""
r_sq = x**2 + y**2
theta = np.arctan2(y, x)
z = np.cos(theta)/r_sq
return (np.max(z)-z) / (np.max(z)-np.min(z))
# Creating a Triangulation
n_angles = 30
n_radii = 10
min_radius = 0.2
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi/n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
V = dipole_potential(x, y)
triang = mtri.Triangulation(x, y)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)
# Refine data - interpolates the electrical potential V
refiner = mtri.UniformTriRefiner(triang)
tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3)
# Computes the electrical field (Ex, Ey) as gradient of -V
tci = mtri.CubicTriInterpolator(triang, -V)
Ex, Ey = tci.gradient(triang.x, triang.y)
E_norm = np.hypot(Ex, Ey)
# Plot the triangulation, the potential iso-contours and the vector field
plt.figure()
plt.gca().set_aspect('equal')
plt.triplot(triang, color='0.8')
levels = np.arange(0., 1., 0.01)
cmap = cm.get_cmap(name='hot', lut=None)
plt.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
linewidths=[2.0, 1.0, 1.0, 1.0])
# Plots direction of the electrical vector field
plt.quiver(triang.x, triang.y, Ex/E_norm, Ey/E_norm,
units='xy', scale=10., zorder=3, color='blue',
width=0.007, headwidth=3., headlength=4.)
# We are leaving ax.use_sticky_margins as True, so the
# view limits are the contour data limits.
def test_tritools():
# Tests TriAnalyzer.scale_factors on masked triangulation
# Tests circle_ratios on equilateral and right-angled triangle.
x = np.array([0., 1., 0.5, 0., 2.])
y = np.array([0., 0., 0.5*np.sqrt(3.), -1., 1.])
triangles = np.array([[0, 1, 2], [0, 1, 3], [1, 2, 4]], dtype=np.int32)
mask = np.array([False, False, True], dtype=bool)
triang = mtri.Triangulation(x, y, triangles, mask=mask)
analyser = mtri.TriAnalyzer(triang)
assert_array_almost_equal(analyser.scale_factors,
np.array([1., 1./(1.+0.5*np.sqrt(3.))]))
assert_array_almost_equal(
analyser.circle_ratios(rescale=False),
np.ma.masked_array([0.5, 1./(1.+np.sqrt(2.)), np.nan], mask))
# Tests circle ratio of a flat triangle
x = np.array([0., 1., 2.])
y = np.array([1., 1.+3., 1.+6.])
triangles = np.array([[0, 1, 2]], dtype=np.int32)
triang = mtri.Triangulation(x, y, triangles)
analyser = mtri.TriAnalyzer(triang)
assert_array_almost_equal(analyser.circle_ratios(), np.array([0.]))
# Tests TriAnalyzer.get_flat_tri_mask
# Creates a triangulation of [-1, 1] x [-1, 1] with contiguous groups of
# 'flat' triangles at the 4 corners and at the center. Checks that only
# those at the borders are eliminated by TriAnalyzer.get_flat_tri_mask
n = 9
def power(x, a):
return np.abs(x)**a*np.sign(x)
x = np.linspace(-1., 1., n+1)
x, y = np.meshgrid(power(x, 2.), power(x, 0.25))
x = x.ravel()
y = y.ravel()
triang = mtri.Triangulation(x, y, triangles=meshgrid_triangles(n+1))
analyser = mtri.TriAnalyzer(triang)
mask_flat = analyser.get_flat_tri_mask(0.2)
verif_mask = np.zeros(162, dtype=bool)
corners_index = [0, 1, 2, 3, 14, 15, 16, 17, 18, 19, 34, 35, 126, 127,
142, 143, 144, 145, 146, 147, 158, 159, 160, 161]
verif_mask[corners_index] = True
assert_array_equal(mask_flat, verif_mask)
# Now including a hole (masked triangle) at the center. The center also
# shall be eliminated by get_flat_tri_mask.
mask = np.zeros(162, dtype=bool)
mask[80] = True
triang.set_mask(mask)
mask_flat = analyser.get_flat_tri_mask(0.2)
center_index = [44, 45, 62, 63, 78, 79, 80, 81, 82, 83, 98, 99, 116, 117]
verif_mask[center_index] = True
assert_array_equal(mask_flat, verif_mask)
def test_trirefine():
# Testing subdiv=2 refinement
n = 3
subdiv = 2
x = np.linspace(-1., 1., n+1)
x, y = np.meshgrid(x, x)
x = x.ravel()
y = y.ravel()
mask = np.zeros(2*n**2, dtype=bool)
mask[n**2:] = True
triang = mtri.Triangulation(x, y, triangles=meshgrid_triangles(n+1),
mask=mask)
refiner = mtri.UniformTriRefiner(triang)
refi_triang = refiner.refine_triangulation(subdiv=subdiv)
x_refi = refi_triang.x
y_refi = refi_triang.y
n_refi = n * subdiv**2
x_verif = np.linspace(-1., 1., n_refi+1)
x_verif, y_verif = np.meshgrid(x_verif, x_verif)
x_verif = x_verif.ravel()
y_verif = y_verif.ravel()
ind1d = np.in1d(np.around(x_verif*(2.5+y_verif), 8),
np.around(x_refi*(2.5+y_refi), 8))
assert_array_equal(ind1d, True)
# Testing the mask of the refined triangulation
refi_mask = refi_triang.mask
refi_tri_barycenter_x = np.sum(refi_triang.x[refi_triang.triangles],
axis=1) / 3.
refi_tri_barycenter_y = np.sum(refi_triang.y[refi_triang.triangles],
axis=1) / 3.
tri_finder = triang.get_trifinder()
refi_tri_indices = tri_finder(refi_tri_barycenter_x,
refi_tri_barycenter_y)
refi_tri_mask = triang.mask[refi_tri_indices]
assert_array_equal(refi_mask, refi_tri_mask)
# Testing that the numbering of triangles does not change the
# interpolation result.
x = np.asarray([0.0, 1.0, 0.0, 1.0])
y = np.asarray([0.0, 0.0, 1.0, 1.0])
triang = [mtri.Triangulation(x, y, [[0, 1, 3], [3, 2, 0]]),
mtri.Triangulation(x, y, [[0, 1, 3], [2, 0, 3]])]
z = np.hypot(x - 0.3, y - 0.4)
# Refining the 2 triangulations and reordering the points
xyz_data = []
for i in range(2):
refiner = mtri.UniformTriRefiner(triang[i])
refined_triang, refined_z = refiner.refine_field(z, subdiv=1)
xyz = np.dstack((refined_triang.x, refined_triang.y, refined_z))[0]
xyz = xyz[np.lexsort((xyz[:, 1], xyz[:, 0]))]
xyz_data += [xyz]
assert_array_almost_equal(xyz_data[0], xyz_data[1])
@pytest.mark.parametrize('interpolator',
[mtri.LinearTriInterpolator,
mtri.CubicTriInterpolator],
ids=['linear', 'cubic'])
def test_trirefine_masked(interpolator):
# Repeated points means we will have fewer triangles than points, and thus
# get masking.
x, y = np.mgrid[:2, :2]
x = np.repeat(x.flatten(), 2)
y = np.repeat(y.flatten(), 2)
z = np.zeros_like(x)
tri = mtri.Triangulation(x, y)
refiner = mtri.UniformTriRefiner(tri)
interp = interpolator(tri, z)
refiner.refine_field(z, triinterpolator=interp, subdiv=2)
def meshgrid_triangles(n):
"""
Return (2*(N-1)**2, 3) array of triangles to mesh (N, N)-point np.meshgrid.
"""
tri = []
for i in range(n-1):
for j in range(n-1):
a = i + j*n
b = (i+1) + j*n
c = i + (j+1)*n
d = (i+1) + (j+1)*n
tri += [[a, b, d], [a, d, c]]
return np.array(tri, dtype=np.int32)
def test_triplot_return():
# Check that triplot returns the artists it adds
ax = plt.figure().add_subplot()
triang = mtri.Triangulation(
[0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0],
triangles=[[0, 1, 3], [3, 2, 0]])
assert ax.triplot(triang, "b-") is not None, \
'triplot should return the artist it adds'
def test_trirefiner_fortran_contiguous_triangles():
# github issue 4180. Test requires two arrays of triangles that are
# identical except that one is C-contiguous and one is fortran-contiguous.
triangles1 = np.array([[2, 0, 3], [2, 1, 0]])
assert not np.isfortran(triangles1)
triangles2 = np.array(triangles1, copy=True, order='F')
assert np.isfortran(triangles2)
x = np.array([0.39, 0.59, 0.43, 0.32])
y = np.array([33.99, 34.01, 34.19, 34.18])
triang1 = mtri.Triangulation(x, y, triangles1)
triang2 = mtri.Triangulation(x, y, triangles2)
refiner1 = mtri.UniformTriRefiner(triang1)
refiner2 = mtri.UniformTriRefiner(triang2)
fine_triang1 = refiner1.refine_triangulation(subdiv=1)
fine_triang2 = refiner2.refine_triangulation(subdiv=1)
assert_array_equal(fine_triang1.triangles, fine_triang2.triangles)
def test_qhull_triangle_orientation():
# github issue 4437.
xi = np.linspace(-2, 2, 100)
x, y = map(np.ravel, np.meshgrid(xi, xi))
w = (x > y - 1) & (x < -1.95) & (y > -1.2)
x, y = x[w], y[w]
theta = np.radians(25)
x1 = x*np.cos(theta) - y*np.sin(theta)
y1 = x*np.sin(theta) + y*np.cos(theta)
# Calculate Delaunay triangulation using Qhull.
triang = mtri.Triangulation(x1, y1)
# Neighbors returned by Qhull.
qhull_neighbors = triang.neighbors
# Obtain neighbors using own C++ calculation.
triang._neighbors = None
own_neighbors = triang.neighbors
assert_array_equal(qhull_neighbors, own_neighbors)
def test_trianalyzer_mismatched_indices():
# github issue 4999.
x = np.array([0., 1., 0.5, 0., 2.])
y = np.array([0., 0., 0.5*np.sqrt(3.), -1., 1.])
triangles = np.array([[0, 1, 2], [0, 1, 3], [1, 2, 4]], dtype=np.int32)
mask = np.array([False, False, True], dtype=bool)
triang = mtri.Triangulation(x, y, triangles, mask=mask)
analyser = mtri.TriAnalyzer(triang)
# numpy >= 1.10 raises a VisibleDeprecationWarning in the following line
# prior to the fix.
analyser._get_compressed_triangulation()
def test_tricontourf_decreasing_levels():
# github issue 5477.
x = [0.0, 1.0, 1.0]
y = [0.0, 0.0, 1.0]
z = [0.2, 0.4, 0.6]
plt.figure()
with pytest.raises(ValueError):
plt.tricontourf(x, y, z, [1.0, 0.0])
def test_internal_cpp_api():
# Following github issue 8197.
from matplotlib import _tri # noqa: ensure lazy-loaded module *is* loaded.
# C++ Triangulation.
with pytest.raises(
TypeError,
match=r'function takes exactly 7 arguments \(0 given\)'):
mpl._tri.Triangulation()
with pytest.raises(
ValueError, match=r'x and y must be 1D arrays of the same length'):
mpl._tri.Triangulation([], [1], [[]], None, None, None, False)
x = [0, 1, 1]
y = [0, 0, 1]
with pytest.raises(
ValueError,
match=r'triangles must be a 2D array of shape \(\?,3\)'):
mpl._tri.Triangulation(x, y, [[0, 1]], None, None, None, False)
tris = [[0, 1, 2]]
with pytest.raises(
ValueError,
match=r'mask must be a 1D array with the same length as the '
r'triangles array'):
mpl._tri.Triangulation(x, y, tris, [0, 1], None, None, False)
with pytest.raises(
ValueError, match=r'edges must be a 2D array with shape \(\?,2\)'):
mpl._tri.Triangulation(x, y, tris, None, [[1]], None, False)
with pytest.raises(
ValueError,
match=r'neighbors must be a 2D array with the same shape as the '
r'triangles array'):
mpl._tri.Triangulation(x, y, tris, None, None, [[-1]], False)
triang = mpl._tri.Triangulation(x, y, tris, None, None, None, False)
with pytest.raises(
ValueError,
match=r'z array must have same length as triangulation x and y '
r'array'):
triang.calculate_plane_coefficients([])
with pytest.raises(
ValueError,
match=r'mask must be a 1D array with the same length as the '
r'triangles array'):
triang.set_mask([0, 1])
# C++ TriContourGenerator.
with pytest.raises(
TypeError,
match=r'function takes exactly 2 arguments \(0 given\)'):
mpl._tri.TriContourGenerator()
with pytest.raises(
ValueError,
match=r'z must be a 1D array with the same length as the x and y '
r'arrays'):
mpl._tri.TriContourGenerator(triang, [1])
z = [0, 1, 2]
tcg = mpl._tri.TriContourGenerator(triang, z)
with pytest.raises(
ValueError, match=r'filled contour levels must be increasing'):
tcg.create_filled_contour(1, 0)
# C++ TrapezoidMapTriFinder.
with pytest.raises(
TypeError, match=r'function takes exactly 1 argument \(0 given\)'):
mpl._tri.TrapezoidMapTriFinder()
trifinder = mpl._tri.TrapezoidMapTriFinder(triang)
with pytest.raises(
ValueError, match=r'x and y must be array-like with same shape'):
trifinder.find_many([0], [0, 1])
def test_qhull_large_offset():
# github issue 8682.
x = np.asarray([0, 1, 0, 1, 0.5])
y = np.asarray([0, 0, 1, 1, 0.5])
offset = 1e10
triang = mtri.Triangulation(x, y)
triang_offset = mtri.Triangulation(x + offset, y + offset)
assert len(triang.triangles) == len(triang_offset.triangles)
def test_tricontour_non_finite_z():
# github issue 10167.
x = [0, 1, 0, 1]
y = [0, 0, 1, 1]
triang = mtri.Triangulation(x, y)
plt.figure()
with pytest.raises(ValueError, match='z array must not contain non-finite '
'values within the triangulation'):
plt.tricontourf(triang, [0, 1, 2, np.inf])
with pytest.raises(ValueError, match='z array must not contain non-finite '
'values within the triangulation'):
plt.tricontourf(triang, [0, 1, 2, -np.inf])
with pytest.raises(ValueError, match='z array must not contain non-finite '
'values within the triangulation'):
plt.tricontourf(triang, [0, 1, 2, np.nan])
with pytest.raises(ValueError, match='z must not contain masked points '
'within the triangulation'):
plt.tricontourf(triang, np.ma.array([0, 1, 2, 3], mask=[1, 0, 0, 0]))