def __eq__(self, other, rtol=1.0e-5, atol=1.0e-8):
"""Override '==' to allow comparison with other raster objecs
Input
other: Raster instance to compare to
rtol, atol: Relative and absolute tolerance.
See numpy.allclose for details
"""
# Check type
if not isinstance(other, Raster):
msg = ('Raster instance cannot be compared to %s'
' as its type is %s ' % (str(other), type(other)))
raise TypeError(msg)
# Check projection
if self.projection != other.projection:
return False
# Check geotransform
if self.get_geotransform() != other.get_geotransform():
return False
# Check data
if not nanallclose(self.get_data(),
other.get_data(),
rtol=rtol, atol=atol):
return False
# Check keywords
if self.keywords != other.keywords:
return False
# Raster layers are identical up to the specified tolerance
return True
def test_linear_interpolation_nan_array(self):
"""Interpolation library works (linear mode) with grid points being NaN
"""
# Define pixel centers along each direction
x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
y = [4.0, 5.0, 7.0, 9.0, 11.0, 13.0]
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x), len(y)))
# Define values for each x, y pair as a linear function
for i in range(len(x)):
for j in range(len(y)):
A[i, j] = linear_function(x[i], y[j])
A[2, 3] = numpy.nan # (x=2.0, y=9.0): NaN
# Then test that interpolated points can contain NaN
xis = numpy.linspace(x[0], x[-1], 12)
etas = numpy.linspace(y[0], y[-1], 10)
points = combine_coordinates(xis, etas)
vals = interpolate2d(x, y, A, points, mode='linear')
refs = linear_function(points[:, 0], points[:, 1])
# Set reference result with expected NaNs and compare
for i, (xi, eta) in enumerate(points):
if (1.0 < xi <= 3.0) and (7.0 < eta <= 11.0):
refs[i] = numpy.nan
assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
def __eq__(self, other, rtol=1.0e-5, atol=1.0e-8):
"""Override '==' to allow comparison with other raster objecs
Input
other: Raster instance to compare to
rtol, atol: Relative and absolute tolerance.
See numpy.allclose for details
"""
# Check type
if not isinstance(other, Raster):
msg = ('Raster instance cannot be compared to %s'
' as its type is %s ' % (str(other), type(other)))
raise TypeError(msg)
# Check projection
if self.projection != other.projection:
return False
# Check geotransform
if self.get_geotransform() != other.get_geotransform():
return False
# Check data
if not nanallclose(
self.get_data(), other.get_data(), rtol=rtol, atol=atol):
return False
# Check keywords
if self.keywords != other.keywords:
return False
# Raster layers are identical up to the specified tolerance
return True
def test_linear_interpolation_nan_points(self):
"""Interpolation library works with interpolation points being NaN
This is was the reason for bug reported in:
https://github.com/AIFDR/riab/issues/155
"""
# Define pixel centers along each direction
x = [1.0, 2.0, 4.0]
y = [5.0, 9.0]
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x), len(y)))
# Define values for each x, y pair as a linear function
for i in range(len(x)):
for j in range(len(y)):
A[i, j] = linear_function(x[i], y[j])
# Then test that interpolated points can contain NaN
xis = numpy.linspace(x[0], x[-1], 10)
etas = numpy.linspace(y[0], y[-1], 10)
xis[6:7] = numpy.nan
etas[3] = numpy.nan
points = combine_coordinates(xis, etas)
vals = interpolate2d(x, y, A, points, mode='linear')
refs = linear_function(points[:, 0], points[:, 1])
assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
def test_linear_interpolation_nan_array(self):
"""Interpolation library works (linear mode) with grid points being NaN
"""
# Define pixel centers along each direction
x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
y = [4.0, 5.0, 7.0, 9.0, 11.0, 13.0]
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x), len(y)))
# Define values for each x, y pair as a linear function
for i in range(len(x)):
for j in range(len(y)):
A[i, j] = linear_function(x[i], y[j])
A[2, 3] = numpy.nan # (x=2.0, y=9.0): NaN
# Then test that interpolated points can contain NaN
xis = numpy.linspace(x[0], x[-1], 12)
etas = numpy.linspace(y[0], y[-1], 10)
points = combine_coordinates(xis, etas)
vals = interpolate2d(x, y, A, points, mode='linear')
refs = linear_function(points[:, 0], points[:, 1])
# Set reference result with expected NaNs and compare
for i, (xi, eta) in enumerate(points):
if (1.0 < xi <= 3.0) and (7.0 < eta <= 11.0):
refs[i] = numpy.nan
assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
def test_linear_interpolation_nan_points(self):
"""Interpolation library works with interpolation points being NaN
This is was the reason for bug reported in:
https://github.com/AIFDR/riab/issues/155
"""
# Define pixel centers along each direction
x = [1.0, 2.0, 4.0]
y = [5.0, 9.0]
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x), len(y)))
# Define values for each x, y pair as a linear function
for i in range(len(x)):
for j in range(len(y)):
A[i, j] = linear_function(x[i], y[j])
# Then test that interpolated points can contain NaN
xis = numpy.linspace(x[0], x[-1], 10)
etas = numpy.linspace(y[0], y[-1], 10)
xis[6:7] = numpy.nan
etas[3] = numpy.nan
points = combine_coordinates(xis, etas)
vals = interpolate2d(x, y, A, points, mode='linear')
refs = linear_function(points[:, 0], points[:, 1])
assert nanallclose(vals, refs, rtol=1e-12, atol=1e-12)
def test_interpolation_random_array_and_nan(self):
"""Interpolation library (constant and linear) works with NaN
"""
# Define pixel centers along each direction
x = numpy.arange(20) * 1.0
y = numpy.arange(25) * 1.0
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x), len(y)))
# Define arbitrary values for each x, y pair
numpy.random.seed(17)
A = numpy.random.random((len(x), len(y))) * 10
# Create islands of NaN
A[5, 13] = numpy.nan
A[6, 14] = A[6, 18] = numpy.nan
A[7, 14:18] = numpy.nan
A[8, 13:18] = numpy.nan
A[9, 12:19] = numpy.nan
A[10, 14:17] = numpy.nan
A[11, 15] = numpy.nan
A[15, 5:6] = numpy.nan
# Creat interpolation points
xis = numpy.linspace(x[0], x[-1], 39) # Hit all mid points
etas = numpy.linspace(y[0], y[-1], 73) # Hit thirds
points = combine_coordinates(xis, etas)
for mode in ['linear', 'constant']:
vals = interpolate2d(x, y, A, points, mode=mode)
# Calculate reference result with expected NaNs and compare
i = j = 0
for k, (xi, eta) in enumerate(points):
# Find indices of nearest higher value in x and y
i = numpy.searchsorted(x, xi)
j = numpy.searchsorted(y, eta)
if i > 0 and j > 0:
# Get four neigbours
A00 = A[i - 1, j - 1]
A01 = A[i - 1, j]
A10 = A[i, j - 1]
A11 = A[i, j]
if numpy.allclose(xi, x[i]):
alpha = 1.0
else:
alpha = 0.5
if numpy.allclose(eta, y[j]):
beta = 1.0
else:
beta = eta - y[j - 1]
if mode == 'linear':
if numpy.any(numpy.isnan([A00, A01, A10, A11])):
ref = numpy.nan
else:
ref = (A00 * (1 - alpha) * (1 - beta) + A01 *
(1 - alpha) * beta + A10 * alpha *
(1 - beta) + A11 * alpha * beta)
elif mode == 'constant':
assert alpha >= 0.5 # Only case in this test
if beta < 0.5:
ref = A10
else:
ref = A11
else:
msg = 'Unknown mode: %s' % mode
raise Exception(msg)
#print i, j, xi, eta, alpha, beta, vals[k], ref
assert nanallclose(vals[k], ref, rtol=1e-12, atol=1e-12)
def test_interpolation_random_array_and_nan(self):
"""Interpolation library (constant and linear) works with NaN
"""
# Define pixel centers along each direction
x = numpy.arange(20) * 1.0
y = numpy.arange(25) * 1.0
# Define ny by nx array with corresponding values
A = numpy.zeros((len(x), len(y)))
# Define arbitrary values for each x, y pair
numpy.random.seed(17)
A = numpy.random.random((len(x), len(y))) * 10
# Create islands of NaN
A[5, 13] = numpy.nan
A[6, 14] = A[6, 18] = numpy.nan
A[7, 14:18] = numpy.nan
A[8, 13:18] = numpy.nan
A[9, 12:19] = numpy.nan
A[10, 14:17] = numpy.nan
A[11, 15] = numpy.nan
A[15, 5:6] = numpy.nan
# Creat interpolation points
xis = numpy.linspace(x[0], x[-1], 39) # Hit all mid points
etas = numpy.linspace(y[0], y[-1], 73) # Hit thirds
points = combine_coordinates(xis, etas)
for mode in ['linear', 'constant']:
vals = interpolate2d(x, y, A, points, mode=mode)
# Calculate reference result with expected NaNs and compare
i = j = 0
for k, (xi, eta) in enumerate(points):
# Find indices of nearest higher value in x and y
i = numpy.searchsorted(x, xi)
j = numpy.searchsorted(y, eta)
if i > 0 and j > 0:
# Get four neigbours
A00 = A[i - 1, j - 1]
A01 = A[i - 1, j]
A10 = A[i, j - 1]
A11 = A[i, j]
if numpy.allclose(xi, x[i]):
alpha = 1.0
else:
alpha = 0.5
if numpy.allclose(eta, y[j]):
beta = 1.0
else:
beta = eta - y[j - 1]
if mode == 'linear':
if numpy.any(numpy.isnan([A00, A01, A10, A11])):
ref = numpy.nan
else:
ref = (A00 * (1 - alpha) * (1 - beta) +
A01 * (1 - alpha) * beta +
A10 * alpha * (1 - beta) +
A11 * alpha * beta)
elif mode == 'constant':
assert alpha >= 0.5 # Only case in this test
if beta < 0.5:
ref = A10
else:
ref = A11
else:
msg = 'Unknown mode: %s' % mode
raise Exception(msg)
#print i, j, xi, eta, alpha, beta, vals[k], ref
assert nanallclose(vals[k], ref, rtol=1e-12, atol=1e-12)