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-rw-r--r--numpy/random/__init__.py2
-rw-r--r--numpy/random/setup.py8
-rw-r--r--numpy/random/tests/test_random.py62
3 files changed, 36 insertions, 36 deletions
diff --git a/numpy/random/__init__.py b/numpy/random/__init__.py
index 80723cec6..614b25a1c 100644
--- a/numpy/random/__init__.py
+++ b/numpy/random/__init__.py
@@ -100,7 +100,7 @@ with warnings.catch_warnings():
# Some aliases:
ranf = random = sample = random_sample
-__all__.extend(['ranf','random','sample'])
+__all__.extend(['ranf', 'random', 'sample'])
def __RandomState_ctor():
"""Return a RandomState instance.
diff --git a/numpy/random/setup.py b/numpy/random/setup.py
index 917623bba..c7e792f2f 100644
--- a/numpy/random/setup.py
+++ b/numpy/random/setup.py
@@ -19,7 +19,7 @@ def needs_mingw_ftime_workaround():
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration, get_mathlibs
- config = Configuration('random',parent_package,top_path)
+ config = Configuration('random', parent_package, top_path)
def generate_libraries(ext, build_dir):
config_cmd = config.get_config_cmd()
@@ -41,9 +41,9 @@ def configuration(parent_package='',top_path=None):
['mtrand.c', 'randomkit.c', 'initarray.c',
'distributions.c']]+[generate_libraries],
libraries=libs,
- depends = [join('mtrand','*.h'),
- join('mtrand','*.pyx'),
- join('mtrand','*.pxi'),
+ depends = [join('mtrand', '*.h'),
+ join('mtrand', '*.pyx'),
+ join('mtrand', '*.pxi'),
],
define_macros = defs,
)
diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py
index 6959c45dc..4ae7cfeb1 100644
--- a/numpy/random/tests/test_random.py
+++ b/numpy/random/tests/test_random.py
@@ -26,8 +26,8 @@ class TestMultinomial(TestCase):
random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
def test_int_negative_interval(self):
- assert_( -5 <= random.randint(-5,-1) < -1)
- x = random.randint(-5,-1,5)
+ assert_( -5 <= random.randint(-5, -1) < -1)
+ x = random.randint(-5, -1, 5)
assert_(np.all(-5 <= x))
assert_(np.all(x < -1))
@@ -94,20 +94,20 @@ class TestRandomDist(TestCase):
actual = np.random.rand(3, 2)
desired = np.array([[ 0.61879477158567997, 0.59162362775974664],
[ 0.88868358904449662, 0.89165480011560816],
- [ 0.4575674820298663 , 0.7781880808593471 ]])
+ [ 0.4575674820298663, 0.7781880808593471 ]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
def test_randn(self):
np.random.seed(self.seed)
actual = np.random.randn(3, 2)
desired = np.array([[ 1.34016345771863121, 1.73759122771936081],
- [ 1.498988344300628 , -0.2286433324536169 ],
- [ 2.031033998682787 , 2.17032494605655257]])
+ [ 1.498988344300628, -0.2286433324536169 ],
+ [ 2.031033998682787, 2.17032494605655257]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
def test_randint(self):
np.random.seed(self.seed)
- actual = np.random.randint(-99, 99, size=(3,2))
+ actual = np.random.randint(-99, 99, size=(3, 2))
desired = np.array([[ 31, 3],
[-52, 41],
[-48, -66]])
@@ -115,7 +115,7 @@ class TestRandomDist(TestCase):
def test_random_integers(self):
np.random.seed(self.seed)
- actual = np.random.random_integers(-99, 99, size=(3,2))
+ actual = np.random.random_integers(-99, 99, size=(3, 2))
desired = np.array([[ 31, 3],
[-52, 41],
[-48, -66]])
@@ -126,7 +126,7 @@ class TestRandomDist(TestCase):
actual = np.random.random_sample((3, 2))
desired = np.array([[ 0.61879477158567997, 0.59162362775974664],
[ 0.88868358904449662, 0.89165480011560816],
- [ 0.4575674820298663 , 0.7781880808593471 ]])
+ [ 0.4575674820298663, 0.7781880808593471 ]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
def test_choice_uniform_replace(self):
@@ -164,25 +164,25 @@ class TestRandomDist(TestCase):
sample = np.random.choice
assert_raises(ValueError, sample, -1, 3)
assert_raises(ValueError, sample, 3., 3)
- assert_raises(ValueError, sample, [[1,2],[3,4]], 3)
+ assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
assert_raises(ValueError, sample, [], 3)
- assert_raises(ValueError, sample, [1,2,3,4], 3,
- p=[[0.25,0.25],[0.25,0.25]])
- assert_raises(ValueError, sample, [1,2], 3, p=[0.4,0.4,0.2])
- assert_raises(ValueError, sample, [1,2], 3, p=[1.1,-0.1])
- assert_raises(ValueError, sample, [1,2], 3, p=[0.4,0.4])
- assert_raises(ValueError, sample, [1,2,3], 4, replace=False)
- assert_raises(ValueError, sample, [1,2,3], 2, replace=False,
- p=[1,0,0])
+ assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+ p=[[0.25, 0.25], [0.25, 0.25]])
+ assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+ assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+ assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+ assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+ assert_raises(ValueError, sample, [1, 2, 3], 2, replace=False,
+ p=[1, 0, 0])
def test_choice_return_shape(self):
- p = [0.1,0.9]
+ p = [0.1, 0.9]
# Check scalar
assert_(np.isscalar(np.random.choice(2, replace=True)))
assert_(np.isscalar(np.random.choice(2, replace=False)))
assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))
assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))
- assert_(np.isscalar(np.random.choice([1,2], replace=True)))
+ assert_(np.isscalar(np.random.choice([1, 2], replace=True)))
assert_(np.random.choice([None], replace=True) is None)
a = np.array([1, 2])
arr = np.empty(1, dtype=object)
@@ -195,7 +195,7 @@ class TestRandomDist(TestCase):
assert_(not np.isscalar(np.random.choice(2, s, replace=False)))
assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))
assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))
- assert_(not np.isscalar(np.random.choice([1,2], s, replace=True)))
+ assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))
assert_(np.random.choice([None], s, replace=True).ndim == 0)
a = np.array([1, 2])
arr = np.empty(1, dtype=object)
@@ -203,7 +203,7 @@ class TestRandomDist(TestCase):
assert_(np.random.choice(arr, s, replace=True).item() is a)
# Check multi dimensional array
- s = (2,3)
+ s = (2, 3)
p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
assert_(np.random.choice(6, s, replace=True).shape, s)
assert_(np.random.choice(6, s, replace=False).shape, s)
@@ -224,7 +224,7 @@ class TestRandomDist(TestCase):
lambda x: [(i, i) for i in x],
lambda x: np.asarray([(i, i) for i in x])]:
np.random.seed(self.seed)
- alist = conv([1,2,3,4,5,6,7,8,9,0])
+ alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
np.random.shuffle(alist)
actual = alist
desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
@@ -251,7 +251,7 @@ class TestRandomDist(TestCase):
actual = np.random.chisquare(50, size=(3, 2))
desired = np.array([[ 63.87858175501090585, 68.68407748911370447],
[ 65.77116116901505904, 47.09686762438974483],
- [ 72.3828403199695174 , 74.18408615260374006]])
+ [ 72.3828403199695174, 74.18408615260374006]])
np.testing.assert_array_almost_equal(actual, desired, decimal=13)
def test_dirichlet(self):
@@ -302,7 +302,7 @@ class TestRandomDist(TestCase):
np.random.seed(self.seed)
actual = np.random.gumbel(loc = .123456789, scale = 2.0, size = (3, 2))
desired = np.array([[ 0.19591898743416816, 0.34405539668096674],
- [-1.4492522252274278 , -1.47374816298446865],
+ [-1.4492522252274278, -1.47374816298446865],
[ 1.10651090478803416, -0.69535848626236174]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
@@ -378,7 +378,7 @@ class TestRandomDist(TestCase):
def test_multivariate_normal(self):
np.random.seed(self.seed)
mean= (.123456789, 10)
- cov = [[1,0],[1,0]]
+ cov = [[1, 0], [1, 0]]
size = (3, 2)
actual = np.random.multivariate_normal(mean, cov, size)
desired = np.array([[[ -1.47027513018564449, 10. ],
@@ -418,7 +418,7 @@ class TestRandomDist(TestCase):
np.random.seed(self.seed)
actual = np.random.normal(loc = .123456789, scale = 2.0, size = (3, 2))
desired = np.array([[ 2.80378370443726244, 3.59863924443872163],
- [ 3.121433477601256 , -0.33382987590723379],
+ [ 3.121433477601256, -0.33382987590723379],
[ 4.18552478636557357, 4.46410668111310471]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
@@ -463,7 +463,7 @@ class TestRandomDist(TestCase):
def test_rayleigh(self):
np.random.seed(self.seed)
actual = np.random.rayleigh(scale = 10, size = (3, 2))
- desired = np.array([[ 13.8882496494248393 , 13.383318339044731 ],
+ desired = np.array([[ 13.8882496494248393, 13.383318339044731 ],
[ 20.95413364294492098, 21.08285015800712614],
[ 11.06066537006854311, 17.35468505778271009]])
np.testing.assert_array_almost_equal(actual, desired, decimal=14)
@@ -480,8 +480,8 @@ class TestRandomDist(TestCase):
np.random.seed(self.seed)
actual = np.random.standard_exponential(size = (3, 2))
desired = np.array([[ 0.96441739162374596, 0.89556604882105506],
- [ 2.1953785836319808 , 2.22243285392490542],
- [ 0.6116915921431676 , 1.50592546727413201]])
+ [ 2.1953785836319808, 2.22243285392490542],
+ [ 0.6116915921431676, 1.50592546727413201]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_gamma(self):
@@ -496,8 +496,8 @@ class TestRandomDist(TestCase):
np.random.seed(self.seed)
actual = np.random.standard_normal(size = (3, 2))
desired = np.array([[ 1.34016345771863121, 1.73759122771936081],
- [ 1.498988344300628 , -0.2286433324536169 ],
- [ 2.031033998682787 , 2.17032494605655257]])
+ [ 1.498988344300628, -0.2286433324536169 ],
+ [ 2.031033998682787, 2.17032494605655257]])
np.testing.assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_t(self):
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