def test_2d_constant_distribution(self):
"""
various new domains with a constant underlying distribution
2d
"""
# old size
m = 8
n = 11
# new size
p = 5
q = 14
new_bounds = [ (0., 1., -1.5, 1.7),
(0., 1., -1.5, 0.7),
(0., 1., -1.5, -0.7),
(-1., 1.5, -1.5, 1.7),
(-1., 0.5, -1., 0.5),
(0.1, 0.6, 0.1, 0.5),
(0.01, 0.02, -10.0, 20.7)]
for (a, b, c, d) in new_bounds:
# bin edges
x_old = np.linspace(0., 1., m + 1)
y_old = np.linspace(-0.5, 1.5, n + 1)
x_new = np.linspace(a, b, p + 1)
y_new = np.linspace(c, d, q + 1)
# constant spline
z_old = np.ones((m + 1, n + 1))
mms_spline = BoundedRectBivariateSpline(x_old, y_old, z_old, s=0.)
z_old = np.zeros((m,n))
for i in range(m):
for j in range(n):
z_old[i, j] = mms_spline.integral(x_old[i], x_old[i + 1],
y_old[j], y_old[j + 1])
z_new_mms = np.zeros((p, q))
for i in range(p):
for j in range(q):
z_new_mms[i, j] = mms_spline.integral(x_new[i],
x_new[i + 1],
y_new[j],
y_new[j + 1])
# rebin
z_new = rebin.rebin2d(x_old, y_old, z_old, x_new, y_new)
assert_allclose(z_new, z_new_mms)
def test_2d_same(self):
"""
x1, y1 == x2, y2 implies z1 == z2
2d
"""
# old size
m = 20
n = 30
# bin edges
x_old = np.linspace(0., 1., m + 1)
y_old = np.linspace(-0.5, 1.5, n + 1)
z_old = np.random.random((m, n))
# rebin
z_new = rebin.rebin2d(x_old, y_old, z_old, x_old, y_old)
assert_allclose(z_old, z_new)
