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* style: use Black everywhere

* style: minor touchup from review
This commit is contained in:
Henry Schreiner
2020-10-16 16:38:13 -04:00
committed by GitHub
parent 2e31e466dc
commit c50f90eca6
38 changed files with 1653 additions and 941 deletions
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147
tests/test_numpy_vectorize.py
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@@ -17,28 +17,40 @@ def test_vectorize(capture):
assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
with capture:
assert np.allclose(f(np.array([1, 3]), np.array([2, 4]), 3), [6, 36])
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=3)
my_func(x:int=3, y:float=4, z:float=3)
"""
)
with capture:
a = np.array([[1, 2], [3, 4]], order='F')
b = np.array([[10, 20], [30, 40]], order='F')
a = np.array([[1, 2], [3, 4]], order="F")
b = np.array([[10, 20], [30, 40]], order="F")
c = 3
result = f(a, b, c)
assert np.allclose(result, a * b * c)
assert result.flags.f_contiguous
# All inputs are F order and full or singletons, so we the result is in col-major order:
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=10, z:float=3)
my_func(x:int=3, y:float=30, z:float=3)
my_func(x:int=2, y:float=20, z:float=3)
my_func(x:int=4, y:float=40, z:float=3)
"""
)
with capture:
a, b, c = np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3
a, b, c = (
np.array([[1, 3, 5], [7, 9, 11]]),
np.array([[2, 4, 6], [8, 10, 12]]),
3,
)
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=3)
my_func(x:int=3, y:float=4, z:float=3)
my_func(x:int=5, y:float=6, z:float=3)
@@ -46,10 +58,13 @@ def test_vectorize(capture):
my_func(x:int=9, y:float=10, z:float=3)
my_func(x:int=11, y:float=12, z:float=3)
"""
)
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=2, y:float=3, z:float=2)
my_func(x:int=3, y:float=4, z:float=2)
@@ -57,10 +72,13 @@ def test_vectorize(capture):
my_func(x:int=5, y:float=3, z:float=2)
my_func(x:int=6, y:float=4, z:float=2)
"""
)
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=2, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
@@ -68,10 +86,17 @@ def test_vectorize(capture):
my_func(x:int=5, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
)
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]], order='F'), np.array([[2], [3]]), 2
a, b, c = (
np.array([[1, 2, 3], [4, 5, 6]], order="F"),
np.array([[2], [3]]),
2,
)
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=2, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
@@ -79,36 +104,53 @@ def test_vectorize(capture):
my_func(x:int=5, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
)
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]])[::, ::2], np.array([[2], [3]]), 2
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
my_func(x:int=4, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
)
with capture:
a, b, c = np.array([[1, 2, 3], [4, 5, 6]], order='F')[::, ::2], np.array([[2], [3]]), 2
a, b, c = (
np.array([[1, 2, 3], [4, 5, 6]], order="F")[::, ::2],
np.array([[2], [3]]),
2,
)
assert np.allclose(f(a, b, c), a * b * c)
assert capture == """
assert (
capture
== """
my_func(x:int=1, y:float=2, z:float=2)
my_func(x:int=3, y:float=2, z:float=2)
my_func(x:int=4, y:float=3, z:float=2)
my_func(x:int=6, y:float=3, z:float=2)
"""
)
def test_type_selection():
assert m.selective_func(np.array([1], dtype=np.int32)) == "Int branch taken."
assert m.selective_func(np.array([1.0], dtype=np.float32)) == "Float branch taken."
assert m.selective_func(np.array([1.0j], dtype=np.complex64)) == "Complex float branch taken."
assert (
m.selective_func(np.array([1.0j], dtype=np.complex64))
== "Complex float branch taken."
)
def test_docs(doc):
assert doc(m.vectorized_func) == """
assert (
doc(m.vectorized_func)
== """
vectorized_func(arg0: numpy.ndarray[numpy.int32], arg1: numpy.ndarray[numpy.float32], arg2: numpy.ndarray[numpy.float64]) -> object
""" # noqa: E501 line too long
)
def test_trivial_broadcasting():
@@ -116,16 +158,24 @@ def test_trivial_broadcasting():
assert vectorized_is_trivial(1, 2, 3) == trivial.c_trivial
assert vectorized_is_trivial(np.array(1), np.array(2), 3) == trivial.c_trivial
assert vectorized_is_trivial(np.array([1, 3]), np.array([2, 4]), 3) == trivial.c_trivial
assert (
vectorized_is_trivial(np.array([1, 3]), np.array([2, 4]), 3)
== trivial.c_trivial
)
assert trivial.c_trivial == vectorized_is_trivial(
np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3)
assert vectorized_is_trivial(
np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2) == trivial.non_trivial
assert vectorized_is_trivial(
np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2) == trivial.non_trivial
z1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype='int32')
z2 = np.array(z1, dtype='float32')
z3 = np.array(z1, dtype='float64')
np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3
)
assert (
vectorized_is_trivial(np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2)
== trivial.non_trivial
)
assert (
vectorized_is_trivial(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2)
== trivial.non_trivial
)
z1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype="int32")
z2 = np.array(z1, dtype="float32")
z3 = np.array(z1, dtype="float64")
assert vectorized_is_trivial(z1, z2, z3) == trivial.c_trivial
assert vectorized_is_trivial(1, z2, z3) == trivial.c_trivial
assert vectorized_is_trivial(z1, 1, z3) == trivial.c_trivial
@@ -135,7 +185,7 @@ def test_trivial_broadcasting():
assert vectorized_is_trivial(1, 1, z3[::2, ::2]) == trivial.non_trivial
assert vectorized_is_trivial(z1, 1, z3[1::4, 1::4]) == trivial.c_trivial
y1 = np.array(z1, order='F')
y1 = np.array(z1, order="F")
y2 = np.array(y1)
y3 = np.array(y1)
assert vectorized_is_trivial(y1, y2, y3) == trivial.f_trivial
@@ -156,30 +206,41 @@ def test_trivial_broadcasting():
def test_passthrough_arguments(doc):
assert doc(m.vec_passthrough) == (
"vec_passthrough(" + ", ".join([
"arg0: float",
"arg1: numpy.ndarray[numpy.float64]",
"arg2: numpy.ndarray[numpy.float64]",
"arg3: numpy.ndarray[numpy.int32]",
"arg4: int",
"arg5: m.numpy_vectorize.NonPODClass",
"arg6: numpy.ndarray[numpy.float64]"]) + ") -> object")
"vec_passthrough("
+ ", ".join(
[
"arg0: float",
"arg1: numpy.ndarray[numpy.float64]",
"arg2: numpy.ndarray[numpy.float64]",
"arg3: numpy.ndarray[numpy.int32]",
"arg4: int",
"arg5: m.numpy_vectorize.NonPODClass",
"arg6: numpy.ndarray[numpy.float64]",
]
)
+ ") -> object"
)
b = np.array([[10, 20, 30]], dtype='float64')
b = np.array([[10, 20, 30]], dtype="float64")
c = np.array([100, 200]) # NOT a vectorized argument
d = np.array([[1000], [2000], [3000]], dtype='int')
g = np.array([[1000000, 2000000, 3000000]], dtype='int') # requires casting
d = np.array([[1000], [2000], [3000]], dtype="int")
g = np.array([[1000000, 2000000, 3000000]], dtype="int") # requires casting
assert np.all(
m.vec_passthrough(1, b, c, d, 10000, m.NonPODClass(100000), g) ==
np.array([[1111111, 2111121, 3111131],
[1112111, 2112121, 3112131],
[1113111, 2113121, 3113131]]))
m.vec_passthrough(1, b, c, d, 10000, m.NonPODClass(100000), g)
== np.array(
[
[1111111, 2111121, 3111131],
[1112111, 2112121, 3112131],
[1113111, 2113121, 3113131],
]
)
)
def test_method_vectorization():
o = m.VectorizeTestClass(3)
x = np.array([1, 2], dtype='int')
y = np.array([[10], [20]], dtype='float32')
x = np.array([1, 2], dtype="int")
y = np.array([[10], [20]], dtype="float32")
assert np.all(o.method(x, y) == [[14, 15], [24, 25]])
@@ -188,7 +249,7 @@ def test_array_collapse():
assert not isinstance(m.vectorized_func(np.array(1), 2, 3), np.ndarray)
z = m.vectorized_func([1], 2, 3)
assert isinstance(z, np.ndarray)
assert z.shape == (1, )
assert z.shape == (1,)
z = m.vectorized_func(1, [[[2]]], 3)
assert isinstance(z, np.ndarray)
assert z.shape == (1, 1, 1)