def test_vectorize_2d_lazy():
# Test vectorization in 1d without data type --> lazy vectorization
arr = np.empty((2, 5), dtype='int')
arr[0] = ([-3, -2, -1, 0, 1])
arr[1] = ([-1, 0, 1, 2, 3])
mg = sparse_meshgrid([-3, -2, -1, 0, 1], [-1, 0, 1, 2, 3])
val_1 = (-1, 1)
val_2 = (2, 1)
@vectorize
def simple_func(x):
return 0 if x[0] < 0 and x[1] > 0 else 1
true_result_arr = [1, 1, 0, 1, 1]
true_result_mg = [[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [1, 1, 0, 0, 0],
[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]
# Out of place
out = simple_func(arr)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5, )
assert all_equal(out, true_result_arr)
out = simple_func(mg)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5, 5)
assert all_equal(out, true_result_mg)
assert simple_func(val_1) == 0
assert simple_func(val_2) == 1
def test_vectorize_callable_class():
# Test vectorization in 1d without data type --> lazy vectorization
arr = [[-2, -1, 0, 1, 2]]
mg = [[-3, -2, -1, 0, 1]]
val_1 = -1
val_2 = 2
# Class with __call__ method
class CallableClass(object):
def __call__(self, x):
return 0 if x < 0 else 1
vectorized_call = vectorize(CallableClass())
true_result_arr = [0, 0, 1, 1, 1]
true_result_mg = [0, 0, 0, 1, 1]
# Out-of-place
out = vectorized_call(arr)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5,)
assert all_equal(out, true_result_arr)
out = vectorized_call(mg)
assert isinstance(out, np.ndarray)
assert out.shape == (5,)
assert is_int_dtype(out.dtype)
assert all_equal(out, true_result_mg)
assert vectorized_call(val_1) == 0
assert vectorized_call(val_2) == 1
def test_vectorize_1d_lazy():
# Test vectorization in 1d without data type --> lazy vectorization
arr = (np.arange(5) - 2)[None, :]
mg = sparse_meshgrid(np.arange(5) - 3)
val_1 = -1
val_2 = 2
@vectorize
def simple_func(x):
return 0 if x < 0 else 1
true_result_arr = [0, 0, 1, 1, 1]
true_result_mg = [0, 0, 0, 1, 1]
# Out-of-place
out = simple_func(arr)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5,)
assert all_equal(out, true_result_arr)
out = simple_func(mg)
assert isinstance(out, np.ndarray)
assert out.shape == (5,)
assert is_int_dtype(out.dtype)
assert all_equal(out, true_result_mg)
assert simple_func(val_1) == 0
assert simple_func(val_2) == 1
def test_vectorize_1d_lazy():
# Test vectorization in 1d without data type --> lazy vectorization
arr = (np.arange(5) - 2)[None, :]
mg = sparse_meshgrid(np.arange(5) - 3)
val_1 = -1
val_2 = 2
@vectorize
def simple_func(x):
return 0 if x < 0 else 1
true_result_arr = [0, 0, 1, 1, 1]
true_result_mg = [0, 0, 0, 1, 1]
# Out-of-place
out = simple_func(arr)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5, )
assert all_equal(out, true_result_arr)
out = simple_func(mg)
assert isinstance(out, np.ndarray)
assert out.shape == (5, )
assert is_int_dtype(out.dtype)
assert all_equal(out, true_result_mg)
assert simple_func(val_1) == 0
assert simple_func(val_2) == 1
def __init__(self, space):
"""Initialize an instance.
Parameters
----------
space : `FnBase`
The domain of the operator.
"""
if not isinstance(space, LinearSpace):
raise TypeError('`space` {!r} not a `LinearSpace`'.format(space))
if _is_integer_only_ufunc(name) and not is_int_dtype(space.dtype):
raise ValueError("ufunc '{}' only defined with integral dtype"
"".format(name))
if nargin == 1:
domain = space
else:
domain = ProductSpace(space, nargin)
if nargout == 1:
range = space
else:
range = ProductSpace(space, nargout)
linear = name in LINEAR_UFUNCS
Operator.__init__(self, domain=domain, range=range, linear=linear)
def __init__(self, space):
"""Initialize an instance.
Parameters
----------
space : `FnBase`
The domain of the operator.
"""
if not isinstance(space, LinearSpace):
raise TypeError('`space` {!r} not a `LinearSpace`'.format(space))
if _is_integer_only_ufunc(name) and not is_int_dtype(space.dtype):
raise ValueError("ufunc '{}' only defined with integral dtype"
"".format(name))
if nargin == 1:
domain = space
else:
domain = ProductSpace(space, nargin)
if nargout == 1:
range = space
else:
range = ProductSpace(space, nargout)
linear = name in LINEAR_UFUNCS
Operator.__init__(self, domain=domain, range=range, linear=linear)
def test_vectorize_2d_lazy():
# Test vectorization in 1d without data type --> lazy vectorization
arr = np.empty((2, 5), dtype='int')
arr[0] = ([-3, -2, -1, 0, 1])
arr[1] = ([-1, 0, 1, 2, 3])
mg = sparse_meshgrid([-3, -2, -1, 0, 1], [-1, 0, 1, 2, 3])
val_1 = (-1, 1)
val_2 = (2, 1)
@vectorize
def simple_func(x):
return 0 if x[0] < 0 and x[1] > 0 else 1
true_result_arr = [1, 1, 0, 1, 1]
true_result_mg = [[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]]
# Out-of-place
out = simple_func(arr)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5,)
assert all_equal(out, true_result_arr)
out = simple_func(mg)
assert isinstance(out, np.ndarray)
assert is_int_dtype(out.dtype)
assert out.shape == (5, 5)
assert all_equal(out, true_result_mg)
assert simple_func(val_1) == 0
assert simple_func(val_2) == 1
def contains_all(self, array):
"""Test if `array` is an array of integers."""
dtype = getattr(array, 'dtype', None)
if dtype is None:
dtype = np.result_type(*array)
return is_int_dtype(dtype)
def contains_all(self, array):
"""Test if `array` is an array of integers."""
dtype = getattr(array, 'dtype', None)
if dtype is None:
dtype = np.result_type(*array)
return is_int_dtype(dtype)
def contains_all(self, other):
"""Return ``True`` if ``other`` is a sequence of integers."""
dtype = getattr(other, 'dtype', None)
if dtype is None:
dtype = np.result_type(*other)
return is_int_dtype(dtype)
