def test_interpn_fill_value():
def value_func_3d(x, y, z):
return 2 * x + 3 * y - z
x = np.linspace(0, 5, 10)
y = np.linspace(0, 5, 20)
z = np.linspace(0, 5, 30)
points = (x, y, z)
values = value_func_3d(*np.meshgrid(*points, indexing="ij"))
point = np.array([5.21, 3.12, 1.15])
value = np.interpn(
points, values, point,
method="bspline",
bounds_error=False,
fill_value=-17
)
assert value == pytest.approx(-17)
value = np.interpn(
points, values, point,
method="bspline",
bounds_error=False,
)
assert np.isnan(value)
value = np.interpn(
points, values, point,
method="bspline",
bounds_error=None,
fill_value=None
)
assert value == pytest.approx(value_func_3d(5, 3.12, 1.15))
def test_interpn_bounds_error_multiple_samples():
def value_func_3d(x, y, z):
return 2 * x + 3 * y - z
x = np.linspace(0, 5, 10)
y = np.linspace(0, 5, 20)
z = np.linspace(0, 5, 30)
points = (x, y, z)
values = value_func_3d(*np.meshgrid(*points, indexing="ij"))
point = np.array([
[2.21, 3.12, 1.15],
[3.42, 5.81, 2.43]
])
with pytest.raises(ValueError):
value = np.interpn(
points, values, point
)
### CasADi test
point = cas.DM(point)
with pytest.raises(ValueError):
value = np.interpn(
points, values, point
)
def test_interpn_linear_multiple_samples():
### NumPy test
def value_func_3d(x, y, z):
return 2 * x + 3 * y - z
x = np.linspace(0, 5, 10)
y = np.linspace(0, 5, 20)
z = np.linspace(0, 5, 30)
points = (x, y, z)
values = value_func_3d(*np.meshgrid(*points, indexing="ij"))
point = np.array([
[2.21, 3.12, 1.15],
[3.42, 0.81, 2.43]
])
value = np.interpn(
points, values, point
)
assert np.all(
value == pytest.approx(
value_func_3d(
*[
point[:, i] for i in range(point.shape[1])
]
)
)
)
assert len(value) == 2
### CasADi test
point = cas.DM(point)
value = np.interpn(
points, values, point
)
value_actual = value_func_3d(
*[
np.array(point[:, i]) for i in range(point.shape[1])
]
)
for i in range(len(value)):
assert value[i] == pytest.approx(float(value_actual[i]))
assert value.shape == (2,)
def __call__(self, x):
if isinstance(self.x_data_coordinates, dict):
x = np.stack(
tuple(x[k] for k, v in self.x_data_coordinates.items()))
return np.interpn(
points=self.x_data_coordinates_values,
values=self.y_data_structured,
xi=x,
method=self.method,
bounds_error=
False, # Can't be set true if general MX-type inputs are to be expected.
fill_value=self.fill_value)
def test_interpn_linear():
### NumPy test
def value_func_3d(x, y, z):
return 2 * x + 3 * y - z
x = np.linspace(0, 5, 10)
y = np.linspace(0, 5, 20)
z = np.linspace(0, 5, 30)
points = (x, y, z)
values = value_func_3d(*np.meshgrid(*points, indexing="ij"))
point = np.array([2.21, 3.12, 1.15])
value = np.interpn(
points, values, point
)
assert value == pytest.approx(value_func_3d(*point))
### CasADi test
point = cas.DM(point)
value = np.interpn(
points, values, point
)
assert value == pytest.approx(float(value_func_3d(point[0], point[1], point[2])))
def __call__(self, x):
if isinstance(self.x_data_coordinates, dict):
def get_shape(value):
try:
return value.shape
except AttributeError:
return tuple()
shape = np.broadcast_shapes(
*[get_shape(v) for v in x.values()]
)
shape_for_reshaping = (int(np.product(shape)),)
def reshape(value):
try:
return np.reshape(value, shape_for_reshaping)
except ValueError:
if isinstance(value, int) or isinstance(value, float) or value.shape == tuple() or np.product(
value.shape) == 1:
return value * np.ones(shape_for_reshaping)
raise ValueError("Could not reshape value of one of the inputs!")
x = np.stack(tuple(
reshape(x[k])
for k, v in self.x_data_coordinates.items()
))
output = np.interpn(
points=self.x_data_coordinates_values,
values=self.y_data_structured,
xi=x,
method=self.method,
bounds_error=False, # Can't be set true if general MX-type inputs are to be expected.
fill_value=self.fill_value
)
try:
return np.reshape(output, shape)
except UnboundLocalError:
return output
def test_interpn_bspline_casadi():
"""
The bspline method should interpolate seperable cubic multidimensional polynomials exactly.
"""
def func(x, y, z): # Sphere function
return x ** 3 + y ** 3 + z ** 3
x = np.linspace(-5, 5, 10)
y = np.linspace(-5, 5, 20)
z = np.linspace(-5, 5, 30)
points = (x, y, z)
values = func(
*np.meshgrid(*points, indexing="ij")
)
point = np.array([0.4, 0.5, 0.6])
value = np.interpn(
points, values, point, method="bspline"
)
assert value == pytest.approx(func(*point))