[Numpy-svn] r8299 - in trunk/numpy/lib: . tests
numpy-svn at scipy.org
numpy-svn at scipy.org
Sun Mar 21 21:45:40 EDT 2010
Author: pierregm
Date: 2010-03-21 20:45:40 -0500 (Sun, 21 Mar 2010)
New Revision: 8299
Modified:
trunk/numpy/lib/function_base.py
trunk/numpy/lib/tests/test_function_base.py
Log:
* Use putmask instead of fancy indexing in _nanop (bug #1421)
Modified: trunk/numpy/lib/function_base.py
===================================================================
--- trunk/numpy/lib/function_base.py 2010-03-21 22:30:39 UTC (rev 8298)
+++ trunk/numpy/lib/function_base.py 2010-03-22 01:45:40 UTC (rev 8299)
@@ -1282,7 +1282,10 @@
# We only need to take care of NaN's in floating point arrays
if not np.issubdtype(y.dtype, int):
- y[mask] = fill
+ # y[mask] = fill
+ # We can't use fancy indexing here as it'll mess w/ MaskedArrays
+ # Instead, let's fill the array directly...
+ np.putmask(y, mask, fill)
res = op(y, axis=axis)
mask_all_along_axis = mask.all(axis=axis)
Modified: trunk/numpy/lib/tests/test_function_base.py
===================================================================
--- trunk/numpy/lib/tests/test_function_base.py 2010-03-21 22:30:39 UTC (rev 8298)
+++ trunk/numpy/lib/tests/test_function_base.py 2010-03-22 01:45:40 UTC (rev 8299)
@@ -10,70 +10,70 @@
class TestAny(TestCase):
def test_basic(self):
- y1 = [0,0,1,0]
- y2 = [0,0,0,0]
- y3 = [1,0,1,0]
+ y1 = [0, 0, 1, 0]
+ y2 = [0, 0, 0, 0]
+ y3 = [1, 0, 1, 0]
assert(any(y1))
assert(any(y3))
assert(not any(y2))
def test_nd(self):
- y1 = [[0,0,0],[0,1,0],[1,1,0]]
+ y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
assert(any(y1))
- assert_array_equal(sometrue(y1,axis=0),[1,1,0])
- assert_array_equal(sometrue(y1,axis=1),[0,1,1])
+ assert_array_equal(sometrue(y1, axis=0), [1, 1, 0])
+ assert_array_equal(sometrue(y1, axis=1), [0, 1, 1])
class TestAll(TestCase):
def test_basic(self):
- y1 = [0,1,1,0]
- y2 = [0,0,0,0]
- y3 = [1,1,1,1]
+ y1 = [0, 1, 1, 0]
+ y2 = [0, 0, 0, 0]
+ y3 = [1, 1, 1, 1]
assert(not all(y1))
assert(all(y3))
assert(not all(y2))
assert(all(~array(y2)))
def test_nd(self):
- y1 = [[0,0,1],[0,1,1],[1,1,1]]
+ y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
assert(not all(y1))
- assert_array_equal(alltrue(y1,axis=0),[0,0,1])
- assert_array_equal(alltrue(y1,axis=1),[0,0,1])
+ assert_array_equal(alltrue(y1, axis=0), [0, 0, 1])
+ assert_array_equal(alltrue(y1, axis=1), [0, 0, 1])
class TestAverage(TestCase):
def test_basic(self):
- y1 = array([1,2,3])
- assert(average(y1,axis=0) == 2.)
- y2 = array([1.,2.,3.])
- assert(average(y2,axis=0) == 2.)
- y3 = [0.,0.,0.]
- assert(average(y3,axis=0) == 0.)
+ y1 = array([1, 2, 3])
+ assert(average(y1, axis=0) == 2.)
+ y2 = array([1., 2., 3.])
+ assert(average(y2, axis=0) == 2.)
+ y3 = [0., 0., 0.]
+ assert(average(y3, axis=0) == 0.)
- y4 = ones((4,4))
- y4[0,1] = 0
- y4[1,0] = 2
+ y4 = ones((4, 4))
+ y4[0, 1] = 0
+ y4[1, 0] = 2
assert_almost_equal(y4.mean(0), average(y4, 0))
assert_almost_equal(y4.mean(1), average(y4, 1))
- y5 = rand(5,5)
+ y5 = rand(5, 5)
assert_almost_equal(y5.mean(0), average(y5, 0))
assert_almost_equal(y5.mean(1), average(y5, 1))
- y6 = matrix(rand(5,5))
- assert_array_equal(y6.mean(0), average(y6,0))
+ y6 = matrix(rand(5, 5))
+ assert_array_equal(y6.mean(0), average(y6, 0))
def test_weights(self):
y = arange(10)
w = arange(10)
- assert_almost_equal(average(y, weights=w), (arange(10)**2).sum()*1./arange(10).sum())
+ assert_almost_equal(average(y, weights=w), (arange(10) ** 2).sum()*1. / arange(10).sum())
- y1 = array([[1,2,3],[4,5,6]])
- w0 = [1,2]
- actual = average(y1,weights=w0,axis=0)
- desired = array([3.,4.,5.])
+ y1 = array([[1, 2, 3], [4, 5, 6]])
+ w0 = [1, 2]
+ actual = average(y1, weights=w0, axis=0)
+ desired = array([3., 4., 5.])
assert_almost_equal(actual, desired)
- w1 = [0,0,1]
+ w1 = [0, 0, 1]
desired = array([3., 6.])
assert_almost_equal(average(y1, weights=w1, axis=1), desired)
@@ -82,7 +82,7 @@
# 2D Case
- w2 = [[0,0,1],[0,0,2]]
+ w2 = [[0, 0, 1], [0, 0, 2]]
desired = array([3., 6.])
assert_array_equal(average(y1, weights=w2, axis=1), desired)
@@ -90,345 +90,345 @@
def test_returned(self):
- y = array([[1,2,3],[4,5,6]])
+ y = array([[1, 2, 3], [4, 5, 6]])
# No weights
avg, scl = average(y, returned=True)
assert_equal(scl, 6.)
avg, scl = average(y, 0, returned=True)
- assert_array_equal(scl, array([2.,2.,2.]))
+ assert_array_equal(scl, array([2., 2., 2.]))
avg, scl = average(y, 1, returned=True)
- assert_array_equal(scl, array([3.,3.]))
+ assert_array_equal(scl, array([3., 3.]))
# With weights
- w0 = [1,2]
+ w0 = [1, 2]
avg, scl = average(y, weights=w0, axis=0, returned=True)
assert_array_equal(scl, array([3., 3., 3.]))
- w1 = [1,2,3]
+ w1 = [1, 2, 3]
avg, scl = average(y, weights=w1, axis=1, returned=True)
assert_array_equal(scl, array([6., 6.]))
- w2 = [[0,0,1],[1,2,3]]
+ w2 = [[0, 0, 1], [1, 2, 3]]
avg, scl = average(y, weights=w2, axis=1, returned=True)
- assert_array_equal(scl, array([1.,6.]))
+ assert_array_equal(scl, array([1., 6.]))
class TestSelect(TestCase):
- def _select(self,cond,values,default=0):
+ def _select(self, cond, values, default=0):
output = []
for m in range(len(cond)):
- output += [V[m] for V,C in zip(values,cond) if C[m]] or [default]
+ output += [V[m] for V, C in zip(values, cond) if C[m]] or [default]
return output
def test_basic(self):
- choices = [array([1,2,3]),
- array([4,5,6]),
- array([7,8,9])]
- conditions = [array([0,0,0]),
- array([0,1,0]),
- array([0,0,1])]
- assert_array_equal(select(conditions,choices,default=15),
- self._select(conditions,choices,default=15))
+ choices = [array([1, 2, 3]),
+ array([4, 5, 6]),
+ array([7, 8, 9])]
+ conditions = [array([0, 0, 0]),
+ array([0, 1, 0]),
+ array([0, 0, 1])]
+ assert_array_equal(select(conditions, choices, default=15),
+ self._select(conditions, choices, default=15))
- assert_equal(len(choices),3)
- assert_equal(len(conditions),3)
+ assert_equal(len(choices), 3)
+ assert_equal(len(conditions), 3)
class TestInsert(TestCase):
def test_basic(self):
- a = [1,2,3]
- assert_equal(insert(a,0,1), [1,1,2,3])
- assert_equal(insert(a,3,1), [1,2,3,1])
- assert_equal(insert(a,[1,1,1],[1,2,3]), [1,1,2,3,2,3])
+ a = [1, 2, 3]
+ assert_equal(insert(a, 0, 1), [1, 1, 2, 3])
+ assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
+ assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
class TestAmax(TestCase):
def test_basic(self):
- a = [3,4,5,10,-3,-5,6.0]
- assert_equal(amax(a),10.0)
- b = [[3,6.0, 9.0],
- [4,10.0,5.0],
- [8,3.0,2.0]]
- assert_equal(amax(b,axis=0),[8.0,10.0,9.0])
- assert_equal(amax(b,axis=1),[9.0,10.0,8.0])
+ a = [3, 4, 5, 10, -3, -5, 6.0]
+ assert_equal(amax(a), 10.0)
+ b = [[3, 6.0, 9.0],
+ [4, 10.0, 5.0],
+ [8, 3.0, 2.0]]
+ assert_equal(amax(b, axis=0), [8.0, 10.0, 9.0])
+ assert_equal(amax(b, axis=1), [9.0, 10.0, 8.0])
class TestAmin(TestCase):
def test_basic(self):
- a = [3,4,5,10,-3,-5,6.0]
- assert_equal(amin(a),-5.0)
- b = [[3,6.0, 9.0],
- [4,10.0,5.0],
- [8,3.0,2.0]]
- assert_equal(amin(b,axis=0),[3.0,3.0,2.0])
- assert_equal(amin(b,axis=1),[3.0,4.0,2.0])
+ a = [3, 4, 5, 10, -3, -5, 6.0]
+ assert_equal(amin(a), -5.0)
+ b = [[3, 6.0, 9.0],
+ [4, 10.0, 5.0],
+ [8, 3.0, 2.0]]
+ assert_equal(amin(b, axis=0), [3.0, 3.0, 2.0])
+ assert_equal(amin(b, axis=1), [3.0, 4.0, 2.0])
class TestPtp(TestCase):
def test_basic(self):
- a = [3,4,5,10,-3,-5,6.0]
- assert_equal(ptp(a,axis=0),15.0)
- b = [[3,6.0, 9.0],
- [4,10.0,5.0],
- [8,3.0,2.0]]
- assert_equal(ptp(b,axis=0),[5.0,7.0,7.0])
- assert_equal(ptp(b,axis=-1),[6.0,6.0,6.0])
+ a = [3, 4, 5, 10, -3, -5, 6.0]
+ assert_equal(ptp(a, axis=0), 15.0)
+ b = [[3, 6.0, 9.0],
+ [4, 10.0, 5.0],
+ [8, 3.0, 2.0]]
+ assert_equal(ptp(b, axis=0), [5.0, 7.0, 7.0])
+ assert_equal(ptp(b, axis= -1), [6.0, 6.0, 6.0])
class TestCumsum(TestCase):
def test_basic(self):
- ba = [1,2,10,11,6,5,4]
- ba2 = [[1,2,3,4],[5,6,7,9],[10,3,4,5]]
- for ctype in [int8,uint8,int16,uint16,int32,uint32,
- float32,float64,complex64,complex128]:
- a = array(ba,ctype)
- a2 = array(ba2,ctype)
- assert_array_equal(cumsum(a,axis=0), array([1,3,13,24,30,35,39],ctype))
- assert_array_equal(cumsum(a2,axis=0), array([[1,2,3,4],[6,8,10,13],
- [16,11,14,18]],ctype))
- assert_array_equal(cumsum(a2,axis=1),
- array([[1,3,6,10],
- [5,11,18,27],
- [10,13,17,22]],ctype))
+ ba = [1, 2, 10, 11, 6, 5, 4]
+ ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
+ for ctype in [int8, uint8, int16, uint16, int32, uint32,
+ float32, float64, complex64, complex128]:
+ a = array(ba, ctype)
+ a2 = array(ba2, ctype)
+ assert_array_equal(cumsum(a, axis=0), array([1, 3, 13, 24, 30, 35, 39], ctype))
+ assert_array_equal(cumsum(a2, axis=0), array([[1, 2, 3, 4], [6, 8, 10, 13],
+ [16, 11, 14, 18]], ctype))
+ assert_array_equal(cumsum(a2, axis=1),
+ array([[1, 3, 6, 10],
+ [5, 11, 18, 27],
+ [10, 13, 17, 22]], ctype))
class TestProd(TestCase):
def test_basic(self):
- ba = [1,2,10,11,6,5,4]
- ba2 = [[1,2,3,4],[5,6,7,9],[10,3,4,5]]
- for ctype in [int16,uint16,int32,uint32,
- float32,float64,complex64,complex128]:
- a = array(ba,ctype)
- a2 = array(ba2,ctype)
+ ba = [1, 2, 10, 11, 6, 5, 4]
+ ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
+ for ctype in [int16, uint16, int32, uint32,
+ float32, float64, complex64, complex128]:
+ a = array(ba, ctype)
+ a2 = array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, prod, a)
self.assertRaises(ArithmeticError, prod, a2, 1)
self.assertRaises(ArithmeticError, prod, a)
else:
- assert_equal(prod(a,axis=0),26400)
- assert_array_equal(prod(a2,axis=0),
- array([50,36,84,180],ctype))
- assert_array_equal(prod(a2,axis=-1),array([24, 1890, 600],ctype))
+ assert_equal(prod(a, axis=0), 26400)
+ assert_array_equal(prod(a2, axis=0),
+ array([50, 36, 84, 180], ctype))
+ assert_array_equal(prod(a2, axis= -1), array([24, 1890, 600], ctype))
class TestCumprod(TestCase):
def test_basic(self):
- ba = [1,2,10,11,6,5,4]
- ba2 = [[1,2,3,4],[5,6,7,9],[10,3,4,5]]
- for ctype in [int16,uint16,int32,uint32,
- float32,float64,complex64,complex128]:
- a = array(ba,ctype)
- a2 = array(ba2,ctype)
+ ba = [1, 2, 10, 11, 6, 5, 4]
+ ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
+ for ctype in [int16, uint16, int32, uint32,
+ float32, float64, complex64, complex128]:
+ a = array(ba, ctype)
+ a2 = array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, cumprod, a)
self.assertRaises(ArithmeticError, cumprod, a2, 1)
self.assertRaises(ArithmeticError, cumprod, a)
else:
- assert_array_equal(cumprod(a,axis=-1),
+ assert_array_equal(cumprod(a, axis= -1),
array([1, 2, 20, 220,
- 1320, 6600, 26400],ctype))
- assert_array_equal(cumprod(a2,axis=0),
- array([[ 1, 2, 3, 4],
- [ 5, 12, 21, 36],
- [50, 36, 84, 180]],ctype))
- assert_array_equal(cumprod(a2,axis=-1),
- array([[ 1, 2, 6, 24],
+ 1320, 6600, 26400], ctype))
+ assert_array_equal(cumprod(a2, axis=0),
+ array([[ 1, 2, 3, 4],
+ [ 5, 12, 21, 36],
+ [50, 36, 84, 180]], ctype))
+ assert_array_equal(cumprod(a2, axis= -1),
+ array([[ 1, 2, 6, 24],
[ 5, 30, 210, 1890],
- [10, 30, 120, 600]],ctype))
+ [10, 30, 120, 600]], ctype))
class TestDiff(TestCase):
def test_basic(self):
- x = [1,4,6,7,12]
- out = array([3,2,1,5])
- out2 = array([-1,-1,4])
- out3 = array([0,5])
- assert_array_equal(diff(x),out)
- assert_array_equal(diff(x,n=2),out2)
- assert_array_equal(diff(x,n=3),out3)
+ x = [1, 4, 6, 7, 12]
+ out = array([3, 2, 1, 5])
+ out2 = array([-1, -1, 4])
+ out3 = array([0, 5])
+ assert_array_equal(diff(x), out)
+ assert_array_equal(diff(x, n=2), out2)
+ assert_array_equal(diff(x, n=3), out3)
def test_nd(self):
- x = 20*rand(10,20,30)
- out1 = x[:,:,1:] - x[:,:,:-1]
- out2 = out1[:,:,1:] - out1[:,:,:-1]
- out3 = x[1:,:,:] - x[:-1,:,:]
- out4 = out3[1:,:,:] - out3[:-1,:,:]
- assert_array_equal(diff(x),out1)
- assert_array_equal(diff(x,n=2),out2)
- assert_array_equal(diff(x,axis=0),out3)
- assert_array_equal(diff(x,n=2,axis=0),out4)
+ x = 20 * rand(10, 20, 30)
+ out1 = x[:, :, 1:] - x[:, :, :-1]
+ out2 = out1[:, :, 1:] - out1[:, :, :-1]
+ out3 = x[1:, :, :] - x[:-1, :, :]
+ out4 = out3[1:, :, :] - out3[:-1, :, :]
+ assert_array_equal(diff(x), out1)
+ assert_array_equal(diff(x, n=2), out2)
+ assert_array_equal(diff(x, axis=0), out3)
+ assert_array_equal(diff(x, n=2, axis=0), out4)
class TestGradient(TestCase):
def test_basic(self):
- x = array([[1,1],[3,4]])
- dx = [array([[2.,3.],[2.,3.]]),
- array([[0.,0.],[1.,1.]])]
+ x = array([[1, 1], [3, 4]])
+ dx = [array([[2., 3.], [2., 3.]]),
+ array([[0., 0.], [1., 1.]])]
assert_array_equal(gradient(x), dx)
def test_badargs(self):
# for 2D array, gradient can take 0,1, or 2 extra args
- x = array([[1,1],[3,4]])
- assert_raises(SyntaxError, gradient, x, array([1.,1.]),
- array([1.,1.]), array([1.,1.]))
+ x = array([[1, 1], [3, 4]])
+ assert_raises(SyntaxError, gradient, x, array([1., 1.]),
+ array([1., 1.]), array([1., 1.]))
def test_masked(self):
# Make sure that gradient supports subclasses like masked arrays
- x = np.ma.array([[1,1],[3,4]])
+ x = np.ma.array([[1, 1], [3, 4]])
assert_equal(type(gradient(x)[0]), type(x))
class TestAngle(TestCase):
def test_basic(self):
- x = [1+3j,sqrt(2)/2.0+1j*sqrt(2)/2,1,1j,-1,-1j,1-3j,-1+3j]
+ x = [1 + 3j, sqrt(2) / 2.0 + 1j * sqrt(2) / 2, 1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
y = angle(x)
- yo = [arctan(3.0/1.0),arctan(1.0),0,pi/2,pi,-pi/2.0,
- -arctan(3.0/1.0),pi-arctan(3.0/1.0)]
- z = angle(x,deg=1)
- zo = array(yo)*180/pi
- assert_array_almost_equal(y,yo,11)
- assert_array_almost_equal(z,zo,11)
+ yo = [arctan(3.0 / 1.0), arctan(1.0), 0, pi / 2, pi, -pi / 2.0,
+ - arctan(3.0 / 1.0), pi - arctan(3.0 / 1.0)]
+ z = angle(x, deg=1)
+ zo = array(yo) * 180 / pi
+ assert_array_almost_equal(y, yo, 11)
+ assert_array_almost_equal(z, zo, 11)
class TestTrimZeros(TestCase):
""" only testing for integer splits.
"""
def test_basic(self):
- a= array([0,0,1,2,3,4,0])
+ a = array([0, 0, 1, 2, 3, 4, 0])
res = trim_zeros(a)
- assert_array_equal(res,array([1,2,3,4]))
+ assert_array_equal(res, array([1, 2, 3, 4]))
def test_leading_skip(self):
- a= array([0,0,1,0,2,3,4,0])
+ a = array([0, 0, 1, 0, 2, 3, 4, 0])
res = trim_zeros(a)
- assert_array_equal(res,array([1,0,2,3,4]))
+ assert_array_equal(res, array([1, 0, 2, 3, 4]))
def test_trailing_skip(self):
- a= array([0,0,1,0,2,3,0,4,0])
+ a = array([0, 0, 1, 0, 2, 3, 0, 4, 0])
res = trim_zeros(a)
- assert_array_equal(res,array([1,0,2,3,0,4]))
+ assert_array_equal(res, array([1, 0, 2, 3, 0, 4]))
class TestExtins(TestCase):
def test_basic(self):
- a = array([1,3,2,1,2,3,3])
- b = extract(a>1,a)
- assert_array_equal(b,[3,2,2,3,3])
+ a = array([1, 3, 2, 1, 2, 3, 3])
+ b = extract(a > 1, a)
+ assert_array_equal(b, [3, 2, 2, 3, 3])
def test_place(self):
- a = array([1,4,3,2,5,8,7])
- place(a,[0,1,0,1,0,1,0],[2,4,6])
- assert_array_equal(a,[1,2,3,4,5,6,7])
+ a = array([1, 4, 3, 2, 5, 8, 7])
+ place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
+ assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
def test_both(self):
a = rand(10)
mask = a > 0.5
ac = a.copy()
c = extract(mask, a)
- place(a,mask,0)
- place(a,mask,c)
- assert_array_equal(a,ac)
+ place(a, mask, 0)
+ place(a, mask, c)
+ assert_array_equal(a, ac)
class TestVectorize(TestCase):
def test_simple(self):
- def addsubtract(a,b):
+ def addsubtract(a, b):
if a > b:
return a - b
else:
return a + b
f = vectorize(addsubtract)
- r = f([0,3,6,9],[1,3,5,7])
- assert_array_equal(r,[1,6,1,2])
+ r = f([0, 3, 6, 9], [1, 3, 5, 7])
+ assert_array_equal(r, [1, 6, 1, 2])
def test_scalar(self):
- def addsubtract(a,b):
+ def addsubtract(a, b):
if a > b:
return a - b
else:
return a + b
f = vectorize(addsubtract)
- r = f([0,3,6,9],5)
- assert_array_equal(r,[5,8,1,4])
+ r = f([0, 3, 6, 9], 5)
+ assert_array_equal(r, [5, 8, 1, 4])
def test_large(self):
- x = linspace(-3,2,10000)
+ x = linspace(-3, 2, 10000)
f = vectorize(lambda x: x)
y = f(x)
assert_array_equal(y, x)
class TestDigitize(TestCase):
def test_forward(self):
- x = arange(-6,5)
- bins = arange(-5,5)
- assert_array_equal(digitize(x,bins),arange(11))
+ x = arange(-6, 5)
+ bins = arange(-5, 5)
+ assert_array_equal(digitize(x, bins), arange(11))
def test_reverse(self):
- x = arange(5,-6,-1)
- bins = arange(5,-5,-1)
- assert_array_equal(digitize(x,bins),arange(11))
+ x = arange(5, -6, -1)
+ bins = arange(5, -5, -1)
+ assert_array_equal(digitize(x, bins), arange(11))
def test_random(self):
x = rand(10)
bin = linspace(x.min(), x.max(), 10)
- assert all(digitize(x,bin) != 0)
+ assert all(digitize(x, bin) != 0)
class TestUnwrap(TestCase):
def test_simple(self):
#check that unwrap removes jumps greather that 2*pi
- assert_array_equal(unwrap([1,1+2*pi]),[1,1])
+ assert_array_equal(unwrap([1, 1 + 2 * pi]), [1, 1])
#check that unwrap maintans continuity
- assert(all(diff(unwrap(rand(10)*100))<pi))
+ assert(all(diff(unwrap(rand(10) * 100)) < pi))
class TestFilterwindows(TestCase):
def test_hanning(self):
#check symmetry
- w=hanning(10)
- assert_array_almost_equal(w,flipud(w),7)
+ w = hanning(10)
+ assert_array_almost_equal(w, flipud(w), 7)
#check known value
- assert_almost_equal(sum(w,axis=0),4.500,4)
+ assert_almost_equal(sum(w, axis=0), 4.500, 4)
def test_hamming(self):
#check symmetry
- w=hamming(10)
- assert_array_almost_equal(w,flipud(w),7)
+ w = hamming(10)
+ assert_array_almost_equal(w, flipud(w), 7)
#check known value
- assert_almost_equal(sum(w,axis=0),4.9400,4)
+ assert_almost_equal(sum(w, axis=0), 4.9400, 4)
def test_bartlett(self):
#check symmetry
- w=bartlett(10)
- assert_array_almost_equal(w,flipud(w),7)
+ w = bartlett(10)
+ assert_array_almost_equal(w, flipud(w), 7)
#check known value
- assert_almost_equal(sum(w,axis=0),4.4444,4)
+ assert_almost_equal(sum(w, axis=0), 4.4444, 4)
def test_blackman(self):
#check symmetry
- w=blackman(10)
- assert_array_almost_equal(w,flipud(w),7)
+ w = blackman(10)
+ assert_array_almost_equal(w, flipud(w), 7)
#check known value
- assert_almost_equal(sum(w,axis=0),3.7800,4)
+ assert_almost_equal(sum(w, axis=0), 3.7800, 4)
class TestTrapz(TestCase):
def test_simple(self):
- r=trapz(exp(-1.0/2*(arange(-10,10,.1))**2)/sqrt(2*pi),dx=0.1)
+ r = trapz(exp(-1.0 / 2 * (arange(-10, 10, .1)) ** 2) / sqrt(2 * pi), dx=0.1)
#check integral of normal equals 1
- assert_almost_equal(sum(r,axis=0),1,7)
+ assert_almost_equal(sum(r, axis=0), 1, 7)
def test_ndim(self):
x = linspace(0, 1, 3)
y = linspace(0, 2, 8)
z = linspace(0, 3, 13)
- wx = ones_like(x) * (x[1]-x[0])
+ wx = ones_like(x) * (x[1] - x[0])
wx[0] /= 2
wx[-1] /= 2
- wy = ones_like(y) * (y[1]-y[0])
+ wy = ones_like(y) * (y[1] - y[0])
wy[0] /= 2
wy[-1] /= 2
- wz = ones_like(z) * (z[1]-z[0])
+ wz = ones_like(z) * (z[1] - z[0])
wz[0] /= 2
wz[-1] /= 2
- q = x[:,None,None] + y[None,:,None] + z[None,None,:]
+ q = x[:, None, None] + y[None, :, None] + z[None, None, :]
- qx = (q*wx[:,None,None]).sum(axis=0)
- qy = (q*wy[None,:,None]).sum(axis=1)
- qz = (q*wz[None,None,:]).sum(axis=2)
+ qx = (q * wx[:, None, None]).sum(axis=0)
+ qy = (q * wy[None, :, None]).sum(axis=1)
+ qz = (q * wz[None, None, :]).sum(axis=2)
# n-d `x`
- r = trapz(q, x=x[:,None,None], axis=0)
+ r = trapz(q, x=x[:, None, None], axis=0)
assert_almost_equal(r, qx)
- r = trapz(q, x=y[None,:,None], axis=1)
+ r = trapz(q, x=y[None, :, None], axis=1)
assert_almost_equal(r, qy)
- r = trapz(q, x=z[None,None,:], axis=2)
+ r = trapz(q, x=z[None, None, :], axis=2)
assert_almost_equal(r, qz)
# 1-d `x`
@@ -442,10 +442,10 @@
class TestSinc(TestCase):
def test_simple(self):
- assert(sinc(0)==1)
- w=sinc(linspace(-1,1,100))
+ assert(sinc(0) == 1)
+ w = sinc(linspace(-1, 1, 100))
#check symmetry
- assert_array_almost_equal(w,flipud(w),7)
+ assert_array_almost_equal(w, flipud(w), 7)
class TestHistogram(TestCase):
def setUp(self):
@@ -455,139 +455,139 @@
pass
def test_simple(self):
- n=100
- v=rand(n)
- (a,b)=histogram(v)
+ n = 100
+ v = rand(n)
+ (a, b) = histogram(v)
#check if the sum of the bins equals the number of samples
- assert_equal(sum(a,axis=0), n)
+ assert_equal(sum(a, axis=0), n)
#check that the bin counts are evenly spaced when the data is from a
# linear function
- (a,b)=histogram(linspace(0,10,100))
+ (a, b) = histogram(linspace(0, 10, 100))
assert_array_equal(a, 10)
def test_one_bin(self):
# Ticket 632
- hist,edges = histogram([1,2,3,4],[1,2])
- assert_array_equal(hist,[2, ])
- assert_array_equal(edges,[1,2])
+ hist, edges = histogram([1, 2, 3, 4], [1, 2])
+ assert_array_equal(hist, [2, ])
+ assert_array_equal(edges, [1, 2])
def test_normed(self):
# Check that the integral of the density equals 1.
n = 100
v = rand(n)
- a,b = histogram(v, normed=True)
- area = sum(a*diff(b))
+ a, b = histogram(v, normed=True)
+ area = sum(a * diff(b))
assert_almost_equal(area, 1)
# Check with non constant bin width
- v = rand(n)*10
- bins = [0,1,5, 9, 10]
- a,b = histogram(v, bins, normed=True)
- area = sum(a*diff(b))
+ v = rand(n) * 10
+ bins = [0, 1, 5, 9, 10]
+ a, b = histogram(v, bins, normed=True)
+ area = sum(a * diff(b))
assert_almost_equal(area, 1)
def test_outliers(self):
# Check that outliers are not tallied
- a = arange(10)+.5
+ a = arange(10) + .5
# Lower outliers
- h,b = histogram(a, range=[0,9])
- assert_equal(h.sum(),9)
+ h, b = histogram(a, range=[0, 9])
+ assert_equal(h.sum(), 9)
# Upper outliers
- h,b = histogram(a, range=[1,10])
- assert_equal(h.sum(),9)
+ h, b = histogram(a, range=[1, 10])
+ assert_equal(h.sum(), 9)
# Normalization
- h,b = histogram(a, range=[1,9], normed=True)
- assert_equal((h*diff(b)).sum(),1)
+ h, b = histogram(a, range=[1, 9], normed=True)
+ assert_equal((h * diff(b)).sum(), 1)
# Weights
- w = arange(10)+.5
- h,b = histogram(a, range=[1,9], weights=w, normed=True)
- assert_equal((h*diff(b)).sum(),1)
+ w = arange(10) + .5
+ h, b = histogram(a, range=[1, 9], weights=w, normed=True)
+ assert_equal((h * diff(b)).sum(), 1)
- h,b = histogram(a, bins=8, range=[1,9], weights=w)
+ h, b = histogram(a, bins=8, range=[1, 9], weights=w)
assert_equal(h, w[1:-1])
def test_type(self):
# Check the type of the returned histogram
- a = arange(10)+.5
- h,b = histogram(a)
+ a = arange(10) + .5
+ h, b = histogram(a)
assert(issubdtype(h.dtype, int))
- h,b = histogram(a, normed=True)
+ h, b = histogram(a, normed=True)
assert(issubdtype(h.dtype, float))
- h,b = histogram(a, weights=ones(10, int))
+ h, b = histogram(a, weights=ones(10, int))
assert(issubdtype(h.dtype, int))
- h,b = histogram(a, weights=ones(10, float))
+ h, b = histogram(a, weights=ones(10, float))
assert(issubdtype(h.dtype, float))
def test_weights(self):
v = rand(100)
- w = ones(100)*5
- a,b = histogram(v)
- na,nb = histogram(v, normed=True)
- wa,wb = histogram(v, weights=w)
- nwa,nwb = histogram(v, weights=w, normed=True)
- assert_array_almost_equal(a*5, wa)
+ w = ones(100) * 5
+ a, b = histogram(v)
+ na, nb = histogram(v, normed=True)
+ wa, wb = histogram(v, weights=w)
+ nwa, nwb = histogram(v, weights=w, normed=True)
+ assert_array_almost_equal(a * 5, wa)
assert_array_almost_equal(na, nwa)
# Check weights are properly applied.
- v = linspace(0,10,10)
+ v = linspace(0, 10, 10)
w = concatenate((zeros(5), ones(5)))
- wa,wb = histogram(v, bins=arange(11),weights=w)
+ wa, wb = histogram(v, bins=arange(11), weights=w)
assert_array_almost_equal(wa, w)
# Check with integer weights
- wa, wb = histogram([1,2,2,4], bins=4, weights=[4,3,2,1])
- assert_array_equal(wa, [4,5,0,1])
- wa, wb = histogram([1,2,2,4], bins=4, weights=[4,3,2,1], normed=True)
- assert_array_equal(wa, array([4,5,0,1])/10./3.*4)
+ wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
+ assert_array_equal(wa, [4, 5, 0, 1])
+ wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True)
+ assert_array_equal(wa, array([4, 5, 0, 1]) / 10. / 3. * 4)
class TestHistogramdd(TestCase):
def test_simple(self):
x = array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], \
[.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]])
- H, edges = histogramdd(x, (2,3,3), range = [[-1,1], [0,3], [0,3]])
- answer = asarray([[[0,1,0], [0,0,1], [1,0,0]], [[0,1,0], [0,0,1],
- [0,0,1]]])
- assert_array_equal(H,answer)
+ H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]])
+ answer = asarray([[[0, 1, 0], [0, 0, 1], [1, 0, 0]], [[0, 1, 0], [0, 0, 1],
+ [0, 0, 1]]])
+ assert_array_equal(H, answer)
# Check normalization
- ed = [[-2,0,2], [0,1,2,3], [0,1,2,3]]
- H, edges = histogramdd(x, bins = ed, normed = True)
- assert(all(H == answer/12.))
+ ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
+ H, edges = histogramdd(x, bins=ed, normed=True)
+ assert(all(H == answer / 12.))
# Check that H has the correct shape.
- H, edges = histogramdd(x, (2,3,4), range = [[-1,1], [0,3], [0,4]],
+ H, edges = histogramdd(x, (2, 3, 4), range=[[-1, 1], [0, 3], [0, 4]],
normed=True)
- answer = asarray([[[0,1,0,0], [0,0,1,0], [1,0,0,0]], [[0,1,0,0],
- [0,0,1,0], [0,0,1,0]]])
- assert_array_almost_equal(H, answer/6., 4)
+ answer = asarray([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]], [[0, 1, 0, 0],
+ [0, 0, 1, 0], [0, 0, 1, 0]]])
+ assert_array_almost_equal(H, answer / 6., 4)
# Check that a sequence of arrays is accepted and H has the correct
# shape.
- z = [squeeze(y) for y in split(x,3,axis=1)]
- H, edges = histogramdd(z, bins=(4,3,2),range=[[-2,2], [0,3], [0,2]])
- answer = asarray([[[0,0],[0,0],[0,0]],
- [[0,1], [0,0], [1,0]],
- [[0,1], [0,0],[0,0]],
- [[0,0],[0,0],[0,0]]])
+ z = [squeeze(y) for y in split(x, 3, axis=1)]
+ H, edges = histogramdd(z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
+ answer = asarray([[[0, 0], [0, 0], [0, 0]],
+ [[0, 1], [0, 0], [1, 0]],
+ [[0, 1], [0, 0], [0, 0]],
+ [[0, 0], [0, 0], [0, 0]]])
assert_array_equal(H, answer)
- Z = zeros((5,5,5))
+ Z = zeros((5, 5, 5))
Z[range(5), range(5), range(5)] = 1.
- H,edges = histogramdd([arange(5), arange(5), arange(5)], 5)
+ H, edges = histogramdd([arange(5), arange(5), arange(5)], 5)
assert_array_equal(H, Z)
def test_shape_3d(self):
# All possible permutations for bins of different lengths in 3D.
bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
(4, 5, 6))
- r = rand(10,3)
+ r = rand(10, 3)
for b in bins:
H, edges = histogramdd(r, b)
assert(H.shape == b)
@@ -601,199 +601,208 @@
(6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
(5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))
- r = rand(10,4)
+ r = rand(10, 4)
for b in bins:
H, edges = histogramdd(r, b)
assert(H.shape == b)
def test_weights(self):
- v = rand(100,2)
+ v = rand(100, 2)
hist, edges = histogramdd(v)
n_hist, edges = histogramdd(v, normed=True)
w_hist, edges = histogramdd(v, weights=ones(100))
assert_array_equal(w_hist, hist)
- w_hist, edges = histogramdd(v, weights=ones(100)*2, normed=True)
+ w_hist, edges = histogramdd(v, weights=ones(100) * 2, normed=True)
assert_array_equal(w_hist, n_hist)
- w_hist, edges = histogramdd(v, weights=ones(100, int)*2)
- assert_array_equal(w_hist, 2*hist)
+ w_hist, edges = histogramdd(v, weights=ones(100, int) * 2)
+ assert_array_equal(w_hist, 2 * hist)
def test_identical_samples(self):
- x = zeros((10,2),int)
+ x = zeros((10, 2), int)
hist, edges = histogramdd(x, bins=2)
- assert_array_equal(edges[0],array([-0.5, 0. , 0.5]))
+ assert_array_equal(edges[0], array([-0.5, 0. , 0.5]))
class TestUnique(TestCase):
def test_simple(self):
- x = array([4,3,2,1,1,2,3,4, 0])
- assert(all(unique(x) == [0,1,2,3,4]))
- assert(unique(array([1,1,1,1,1])) == array([1]))
+ x = array([4, 3, 2, 1, 1, 2, 3, 4, 0])
+ assert(all(unique(x) == [0, 1, 2, 3, 4]))
+ assert(unique(array([1, 1, 1, 1, 1])) == array([1]))
x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
- assert(all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
- x = array([5+6j, 1+1j, 1+10j, 10, 5+6j])
- assert(all(unique(x) == [1+1j, 1+10j, 5+6j, 10]))
+ assert(all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
+ x = array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
+ assert(all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))
class TestCheckFinite(TestCase):
def test_simple(self):
- a = [1,2,3]
- b = [1,2,inf]
- c = [1,2,nan]
+ a = [1, 2, 3]
+ b = [1, 2, inf]
+ c = [1, 2, nan]
numpy.lib.asarray_chkfinite(a)
assert_raises(ValueError, numpy.lib.asarray_chkfinite, b)
assert_raises(ValueError, numpy.lib.asarray_chkfinite, c)
class TestNaNFuncts(TestCase):
def setUp(self):
- self.A = array([[[ nan, 0.01319214, 0.01620964],
- [ 0.11704017, nan, 0.75157887],
- [ 0.28333658, 0.1630199 , nan ]],
- [[ 0.59541557, nan, 0.37910852],
- [ nan, 0.87964135, nan ],
- [ 0.70543747, nan, 0.34306596]],
- [[ 0.72687499, 0.91084584, nan ],
- [ 0.84386844, 0.38944762, 0.23913896],
- [ nan, 0.37068164, 0.33850425]]])
+ self.A = array([[[ nan, 0.01319214, 0.01620964],
+ [ 0.11704017, nan, 0.75157887],
+ [ 0.28333658, 0.1630199 , nan ]],
+ [[ 0.59541557, nan, 0.37910852],
+ [ nan, 0.87964135, nan ],
+ [ 0.70543747, nan, 0.34306596]],
+ [[ 0.72687499, 0.91084584, nan ],
+ [ 0.84386844, 0.38944762, 0.23913896],
+ [ nan, 0.37068164, 0.33850425]]])
def test_nansum(self):
assert_almost_equal(nansum(self.A), 8.0664079100000006)
- assert_almost_equal(nansum(self.A,0),
- array([[ 1.32229056, 0.92403798, 0.39531816],
- [ 0.96090861, 1.26908897, 0.99071783],
- [ 0.98877405, 0.53370154, 0.68157021]]))
- assert_almost_equal(nansum(self.A,1),
- array([[ 0.40037675, 0.17621204, 0.76778851],
- [ 1.30085304, 0.87964135, 0.72217448],
- [ 1.57074343, 1.6709751 , 0.57764321]]))
- assert_almost_equal(nansum(self.A,2),
- array([[ 0.02940178, 0.86861904, 0.44635648],
- [ 0.97452409, 0.87964135, 1.04850343],
- [ 1.63772083, 1.47245502, 0.70918589]]))
+ assert_almost_equal(nansum(self.A, 0),
+ array([[ 1.32229056, 0.92403798, 0.39531816],
+ [ 0.96090861, 1.26908897, 0.99071783],
+ [ 0.98877405, 0.53370154, 0.68157021]]))
+ assert_almost_equal(nansum(self.A, 1),
+ array([[ 0.40037675, 0.17621204, 0.76778851],
+ [ 1.30085304, 0.87964135, 0.72217448],
+ [ 1.57074343, 1.6709751 , 0.57764321]]))
+ assert_almost_equal(nansum(self.A, 2),
+ array([[ 0.02940178, 0.86861904, 0.44635648],
+ [ 0.97452409, 0.87964135, 1.04850343],
+ [ 1.63772083, 1.47245502, 0.70918589]]))
def test_nanmin(self):
assert_almost_equal(nanmin(self.A), 0.01319214)
- assert_almost_equal(nanmin(self.A,0),
- array([[ 0.59541557, 0.01319214, 0.01620964],
- [ 0.11704017, 0.38944762, 0.23913896],
- [ 0.28333658, 0.1630199 , 0.33850425]]))
- assert_almost_equal(nanmin(self.A,1),
- array([[ 0.11704017, 0.01319214, 0.01620964],
- [ 0.59541557, 0.87964135, 0.34306596],
- [ 0.72687499, 0.37068164, 0.23913896]]))
- assert_almost_equal(nanmin(self.A,2),
- array([[ 0.01319214, 0.11704017, 0.1630199 ],
- [ 0.37910852, 0.87964135, 0.34306596],
- [ 0.72687499, 0.23913896, 0.33850425]]))
+ assert_almost_equal(nanmin(self.A, 0),
+ array([[ 0.59541557, 0.01319214, 0.01620964],
+ [ 0.11704017, 0.38944762, 0.23913896],
+ [ 0.28333658, 0.1630199 , 0.33850425]]))
+ assert_almost_equal(nanmin(self.A, 1),
+ array([[ 0.11704017, 0.01319214, 0.01620964],
+ [ 0.59541557, 0.87964135, 0.34306596],
+ [ 0.72687499, 0.37068164, 0.23913896]]))
+ assert_almost_equal(nanmin(self.A, 2),
+ array([[ 0.01319214, 0.11704017, 0.1630199 ],
+ [ 0.37910852, 0.87964135, 0.34306596],
+ [ 0.72687499, 0.23913896, 0.33850425]]))
assert nanmin([nan, nan]) is nan
def test_nanargmin(self):
assert_almost_equal(nanargmin(self.A), 1)
- assert_almost_equal(nanargmin(self.A,0),
+ assert_almost_equal(nanargmin(self.A, 0),
array([[1, 0, 0],
[0, 2, 2],
[0, 0, 2]]))
- assert_almost_equal(nanargmin(self.A,1),
+ assert_almost_equal(nanargmin(self.A, 1),
array([[1, 0, 0],
[0, 1, 2],
[0, 2, 1]]))
- assert_almost_equal(nanargmin(self.A,2),
+ assert_almost_equal(nanargmin(self.A, 2),
array([[1, 0, 1],
[2, 1, 2],
[0, 2, 2]]))
def test_nanmax(self):
assert_almost_equal(nanmax(self.A), 0.91084584000000002)
- assert_almost_equal(nanmax(self.A,0),
- array([[ 0.72687499, 0.91084584, 0.37910852],
- [ 0.84386844, 0.87964135, 0.75157887],
- [ 0.70543747, 0.37068164, 0.34306596]]))
- assert_almost_equal(nanmax(self.A,1),
- array([[ 0.28333658, 0.1630199 , 0.75157887],
- [ 0.70543747, 0.87964135, 0.37910852],
- [ 0.84386844, 0.91084584, 0.33850425]]))
- assert_almost_equal(nanmax(self.A,2),
- array([[ 0.01620964, 0.75157887, 0.28333658],
- [ 0.59541557, 0.87964135, 0.70543747],
- [ 0.91084584, 0.84386844, 0.37068164]]))
+ assert_almost_equal(nanmax(self.A, 0),
+ array([[ 0.72687499, 0.91084584, 0.37910852],
+ [ 0.84386844, 0.87964135, 0.75157887],
+ [ 0.70543747, 0.37068164, 0.34306596]]))
+ assert_almost_equal(nanmax(self.A, 1),
+ array([[ 0.28333658, 0.1630199 , 0.75157887],
+ [ 0.70543747, 0.87964135, 0.37910852],
+ [ 0.84386844, 0.91084584, 0.33850425]]))
+ assert_almost_equal(nanmax(self.A, 2),
+ array([[ 0.01620964, 0.75157887, 0.28333658],
+ [ 0.59541557, 0.87964135, 0.70543747],
+ [ 0.91084584, 0.84386844, 0.37068164]]))
def test_nanmin_allnan_on_axis(self):
- assert_array_equal(isnan(nanmin([[nan]*2]*3, axis=1)),
+ assert_array_equal(isnan(nanmin([[nan] * 2] * 3, axis=1)),
[True, True, True])
+ def test_nanmin_masked(self):
+ a = np.ma.fix_invalid([[2, 1, 3, nan], [5, 2, 3, nan]])
+ ctrl_mask = a._mask.copy()
+ test = np.nanmin(a, axis=1)
+ assert_equal(test, [1, 2])
+ assert_equal(a._mask, ctrl_mask)
+ assert_equal(np.isinf(a), np.zeros((2, 4), dtype=bool))
+
+
class TestCorrCoef(TestCase):
def test_simple(self):
- A = array([[ 0.15391142, 0.18045767, 0.14197213],
- [ 0.70461506, 0.96474128, 0.27906989],
- [ 0.9297531 , 0.32296769, 0.19267156]])
- B = array([[ 0.10377691, 0.5417086 , 0.49807457],
- [ 0.82872117, 0.77801674, 0.39226705],
- [ 0.9314666 , 0.66800209, 0.03538394]])
+ A = array([[ 0.15391142, 0.18045767, 0.14197213],
+ [ 0.70461506, 0.96474128, 0.27906989],
+ [ 0.9297531 , 0.32296769, 0.19267156]])
+ B = array([[ 0.10377691, 0.5417086 , 0.49807457],
+ [ 0.82872117, 0.77801674, 0.39226705],
+ [ 0.9314666 , 0.66800209, 0.03538394]])
assert_almost_equal(corrcoef(A),
- array([[ 1. , 0.9379533 , -0.04931983],
- [ 0.9379533 , 1. , 0.30007991],
- [-0.04931983, 0.30007991, 1. ]]))
- assert_almost_equal(corrcoef(A,B),
- array([[ 1. , 0.9379533 , -0.04931983,
- 0.30151751, 0.66318558, 0.51532523],
- [ 0.9379533 , 1. , 0.30007991,
- -0.04781421, 0.88157256, 0.78052386],
- [-0.04931983, 0.30007991, 1. ,
- -0.96717111, 0.71483595, 0.83053601],
+ array([[ 1. , 0.9379533 , -0.04931983],
+ [ 0.9379533 , 1. , 0.30007991],
+ [-0.04931983, 0.30007991, 1. ]]))
+ assert_almost_equal(corrcoef(A, B),
+ array([[ 1. , 0.9379533 , -0.04931983,
+ 0.30151751, 0.66318558, 0.51532523],
+ [ 0.9379533 , 1. , 0.30007991,
+ - 0.04781421, 0.88157256, 0.78052386],
+ [-0.04931983, 0.30007991, 1. ,
+ - 0.96717111, 0.71483595, 0.83053601],
[ 0.30151751, -0.04781421, -0.96717111,
1. , -0.51366032, -0.66173113],
- [ 0.66318558, 0.88157256, 0.71483595,
- -0.51366032, 1. , 0.98317823],
- [ 0.51532523, 0.78052386, 0.83053601,
- -0.66173113, 0.98317823, 1. ]]))
+ [ 0.66318558, 0.88157256, 0.71483595,
+ - 0.51366032, 1. , 0.98317823],
+ [ 0.51532523, 0.78052386, 0.83053601,
+ - 0.66173113, 0.98317823, 1. ]]))
class Test_i0(TestCase):
def test_simple(self):
assert_almost_equal(i0(0.5), array(1.0634833707413234))
- A = array([ 0.49842636, 0.6969809 , 0.22011976, 0.0155549])
+ A = array([ 0.49842636, 0.6969809 , 0.22011976, 0.0155549])
assert_almost_equal(i0(A),
- array([ 1.06307822, 1.12518299, 1.01214991, 1.00006049]))
- B = array([[ 0.827002 , 0.99959078],
- [ 0.89694769, 0.39298162],
- [ 0.37954418, 0.05206293],
- [ 0.36465447, 0.72446427],
- [ 0.48164949, 0.50324519]])
+ array([ 1.06307822, 1.12518299, 1.01214991, 1.00006049]))
+ B = array([[ 0.827002 , 0.99959078],
+ [ 0.89694769, 0.39298162],
+ [ 0.37954418, 0.05206293],
+ [ 0.36465447, 0.72446427],
+ [ 0.48164949, 0.50324519]])
assert_almost_equal(i0(B),
- array([[ 1.17843223, 1.26583466],
- [ 1.21147086, 1.0389829 ],
- [ 1.03633899, 1.00067775],
- [ 1.03352052, 1.13557954],
- [ 1.0588429 , 1.06432317]]))
+ array([[ 1.17843223, 1.26583466],
+ [ 1.21147086, 1.0389829 ],
+ [ 1.03633899, 1.00067775],
+ [ 1.03352052, 1.13557954],
+ [ 1.0588429 , 1.06432317]]))
class TestKaiser(TestCase):
def test_simple(self):
- assert_almost_equal(kaiser(0,1.0), array([]))
- assert isnan(kaiser(1,1.0))
- assert_almost_equal(kaiser(2,1.0), array([ 0.78984831, 0.78984831]))
- assert_almost_equal(kaiser(5,1.0),
- array([ 0.78984831, 0.94503323, 1. ,
- 0.94503323, 0.78984831]))
- assert_almost_equal(kaiser(5,1.56789),
- array([ 0.58285404, 0.88409679, 1. ,
- 0.88409679, 0.58285404]))
+ assert_almost_equal(kaiser(0, 1.0), array([]))
+ assert isnan(kaiser(1, 1.0))
+ assert_almost_equal(kaiser(2, 1.0), array([ 0.78984831, 0.78984831]))
+ assert_almost_equal(kaiser(5, 1.0),
+ array([ 0.78984831, 0.94503323, 1. ,
+ 0.94503323, 0.78984831]))
+ assert_almost_equal(kaiser(5, 1.56789),
+ array([ 0.58285404, 0.88409679, 1. ,
+ 0.88409679, 0.58285404]))
def test_int_beta(self):
kaiser(3, 4)
class TestMsort(TestCase):
def test_simple(self):
- A = array([[ 0.44567325, 0.79115165, 0.5490053 ],
- [ 0.36844147, 0.37325583, 0.96098397],
- [ 0.64864341, 0.52929049, 0.39172155]])
+ A = array([[ 0.44567325, 0.79115165, 0.5490053 ],
+ [ 0.36844147, 0.37325583, 0.96098397],
+ [ 0.64864341, 0.52929049, 0.39172155]])
assert_almost_equal(msort(A),
- array([[ 0.36844147, 0.37325583, 0.39172155],
- [ 0.44567325, 0.52929049, 0.5490053 ],
- [ 0.64864341, 0.79115165, 0.96098397]]))
+ array([[ 0.36844147, 0.37325583, 0.39172155],
+ [ 0.44567325, 0.52929049, 0.5490053 ],
+ [ 0.64864341, 0.79115165, 0.96098397]]))
class TestMeshgrid(TestCase):
def test_simple(self):
- [X, Y] = meshgrid([1,2,3], [4,5,6,7])
+ [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
assert all(X == array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
@@ -827,7 +836,7 @@
assert_array_equal(x, [1, 0])
- x = piecewise([0, 0], [[False, True]], [lambda x: -1])
+ x = piecewise([0, 0], [[False, True]], [lambda x:-1])
assert_array_equal(x, [0, -1])
x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
@@ -844,7 +853,7 @@
def test_0d(self):
x = array(3)
- y = piecewise(x, x>3, [4, 0])
+ y = piecewise(x, x > 3, [4, 0])
assert y.ndim == 0
assert y == 0
@@ -869,9 +878,9 @@
y = np.bincount(x, w)
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
-def compare_results(res,desired):
+def compare_results(res, desired):
for i in range(len(desired)):
- assert_array_equal(res[i],desired[i])
+ assert_array_equal(res[i], desired[i])
if __name__ == "__main__":
run_module_suite()
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