def test_mismatch_source_target_level_dimensionality(self):
source_bounds = np.zeros((3, 4, 2))
target_bounds = np.zeros((4, 2))
data = np.zeros((3, 4))
msg = "Expecting source and target levels dimensionality"
with self.assertRaisesRegex(ValueError, msg):
bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds, data)
def test_unexpected_bounds_shape(self):
# Expecting bounds of size 2 (that is the upper and lower).
# The source levels should reflect the shape of the data.
source_bounds = np.zeros((3, 4, 4))
target_bounds = np.zeros((4, 4, 4))
data = np.zeros((3, 4))
msg = r"Unexpected source and target bounds shape. shape\[-1\] != 2"
with self.assertRaisesRegex(ValueError, msg):
bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds,
data,
axis=0)
def test_mismatch_between_source_levels_source_data(self):
# The source levels should reflect the shape of the data.
source_bounds = np.zeros((2, 4, 2))
target_bounds = np.zeros((2, 4, 2))
data = np.zeros((3, 4))
msg = ("The provided data is not of compatible shape with the "
r"provided source bounds. \('-', 3, 4\) != \(2, 4\)")
with self.assertRaisesRegex(ValueError, msg):
bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds,
data,
axis=0)
def test_mismatch_source_target_level_shape(self):
# The source and target levels should have identical shape, other than
# the axis of interpolation.
source_bounds = np.zeros((3, 4, 2))
target_bounds = np.zeros((2, 5, 2))
data = np.zeros((3, 4))
msg = ("Expecting the shape of the source and target levels except "
"the axis of interpolation to be identical. "
r"\('-', 4, 2\) != \(2, 5, 2\)")
with self.assertRaisesRegex(ValueError, msg):
bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds,
data,
axis=0)
def test_target_double_resolution(self):
target_bounds = self.gen_bounds(0, 6.5, 0.5)
res = bounded_vinterp.interpolate_conservative(target_bounds,
self.bounds,
self.data,
axis=1)
target_data = np.ones((4, 12)) * 0.5
assert_array_equal(res, target_data)
def test_target_half_resolution(self):
target_bounds = self.gen_bounds(0, 7, 2)
res = bounded_vinterp.interpolate_conservative(target_bounds,
self.bounds,
self.data,
axis=1)
target_data = np.ones((4, 3)) * 2
assert_array_equal(res, target_data)
def test_target_extends_above_source(self):
# |-|-|-|-|-|-|-| - Source
# |-|-|-|-|-|-|-|-| - Target
source_bounds = self.gen_bounds(0, 7, 1)
target_bounds = self.gen_bounds(0, 8, 1)
res = bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds,
self.data,
axis=1)
target_data = np.ones((4, 7))
target_data[:, -1] = np.nan
assert_array_equal(res, target_data)
def test_no_broadcasting(self):
# In the case of no broadcasting, transposed and reshaped array of form
# [broadcasting_dims, axis_interpolation, z_varying] should end up
# [1, axis_interpolation, 1].
data = self.data[0]
target_bounds = self.gen_bounds(0, 7, 2)
res = bounded_vinterp.interpolate_conservative(target_bounds,
self.bounds,
data,
axis=0)
target_data = np.ones((3)) * 2
assert_array_equal(res, target_data)
def test_not_conservative(self):
# Where the target does not cover the full extent of the source.
# |-|-|-|-|-|-| - Source
# |-|-|-|-| - Target
def gen_bounds(start, stop, step):
bounds = np.vstack([
np.arange(start, stop - step, step),
np.arange(start + step, stop, step),
])
bounds = bounds.transpose((1, 0))
return bounds.copy()
source_bounds = gen_bounds(0, 7, 1)
target_bounds = gen_bounds(1, 6, 1)
data = np.ones((4, 6))
msg = "Weights calculation yields a less than conservative result."
with self.assertRaisesRegex(ValueError, msg):
bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds,
data,
axis=1)
def test_target_half_resolution_alt_axis(self):
# Ensure results as expected with an alternative axis of interpolation.
data = self.data.transpose((0, 2, 1, 3))
bounds = self.bounds.transpose((1, 0, 2, 3))
target_bounds = self.gen_bounds(0, 7, 2)
target_bounds = target_bounds.transpose((1, 0, 2, 3))
res = bounded_vinterp.interpolate_conservative(target_bounds,
bounds,
data,
axis=2)
target_data = np.ones((2, 4, 3, 3)) * 2
assert_array_equal(res, target_data)
def test_target_extends_above_source_non_equally_spaced_coords(self):
# |--|--|-------|| - Source
# |-|-|-|-|-|-|-|-| - Target
source_bounds = np.array([[0, 1.5], [1.5, 2], [2, 6], [6, 6.5]])
target_bounds = self.gen_bounds(0, 8, 1)
data = np.ones((4, 4))
res = bounded_vinterp.interpolate_conservative(target_bounds,
source_bounds,
data,
axis=1)
target_data = np.array(
[1 / 1.5, 1 + ((1 / 3.0) / 1), 0.25, 0.25, 0.25, 0.25, 1.0])[None]
target_data = np.repeat(target_data, 4, 0)
assert_array_equal(res, target_data)
def test_source_with_nans(self):
# In the case of no broadcasting, transposed and reshaped array of form
# [broadcasting_dims, axis_interpolation, z_varying] should end up
# [1, axis_interpolation, 1].
data = self.data[0]
data[0] = data[-2:] = np.nan
target_bounds = self.gen_bounds(0, 6.5, 0.5)
res = bounded_vinterp.interpolate_conservative(target_bounds,
self.bounds,
data,
axis=0)
target_data = np.ones((12)) * 0.5
target_data[:2] = np.nan
target_data[-4:] = np.nan
assert_array_equal(res, target_data)
