def test_1d_linear_interpolation_basic(self):
"""Interpolation library works for a 1D linear function - basic test
"""
# Define pixel centers along each direction
x = [1.0, 2.0, 4.0]
# Define array with corresponding values
A = numpy.zeros((len(x)))
# Define values for each xas a linear function
for i in range(len(x)):
A[i] = linear_function(x[i], 0)
# Test first that original points are reproduced correctly
for i, xi in enumerate(x):
val = interpolate1d(x, A, [xi], mode='linear')[0]
ref = linear_function(xi, 0)
assert numpy.allclose(val, ref, rtol=1e-12, atol=1e-12)
# Then test that genuinly interpolated points are correct
xis = numpy.linspace(x[0], x[-1], 10)
points = xis
vals = interpolate1d(x, A, points, mode='linear')
refs = linear_function(points, 0)
assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)
def test_1d_constant_interpolation_basic(self):
"""Interpolation library works for 1D piecewise constant function
"""
# Define pixel centers along each direction
x = numpy.array([1.0, 2.0, 4.0])
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x)))
# Define values for each x, y pair as a linear function
for i in range(len(x)):
A[i] = linear_function(x[i], 0)
# Then test that interpolated points are always assigned value of
# closest neighbour
xis = numpy.linspace(x[0], x[-1], 10)
points = xis
vals = interpolate1d(x, A, points, mode='constant')
# Find upper neighbours for each interpolation point
xi = points[:]
idx = numpy.searchsorted(x, xi, side='left')
# Get the neighbours for each interpolation point
x0 = x[idx - 1]
x1 = x[idx]
z0 = A[idx - 1]
z1 = A[idx]
# Location coefficients
alpha = (xi - x0) / (x1 - x0)
refs = numpy.zeros(len(vals))
for i in range(len(refs)):
if alpha[i] < 0.5:
refs[i] = z0[i]
if alpha[i] >= 0.5:
refs[i] = z1[i]
assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)
