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import warnings
from numpy.testing import *
import numpy.lib
from numpy.lib import *
from numpy.core import *
from numpy import matrix, asmatrix
class TestAny(TestCase):
def test_basic(self):
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]]
assert(any(y1))
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]
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]]
assert(not all(y1))
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.)
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)
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))
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())
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]
desired = array([3., 6.])
assert_almost_equal(average(y1, weights=w1, axis=1), desired)
# This should raise an error. Can we test for that ?
# assert_equal(average(y1, weights=w1), 9./2.)
# 2D Case
w2 = [[0,0,1],[0,0,2]]
desired = array([3., 6.])
assert_array_equal(average(y1, weights=w2, axis=1), desired)
assert_equal(average(y1, weights=w2), 5.)
def test_returned(self):
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.]))
avg, scl = average(y, 1, returned=True)
assert_array_equal(scl, array([3.,3.]))
# With weights
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]
avg, scl = average(y, weights=w1, axis=1, returned=True)
assert_array_equal(scl, array([6., 6.]))
w2 = [[0,0,1],[1,2,3]]
avg, scl = average(y, weights=w2, axis=1, returned=True)
assert_array_equal(scl, array([1.,6.]))
class TestSelect(TestCase):
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]
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))
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])
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])
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])
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])
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))
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)
if ctype in ['1', 'b']:
self.failUnlessRaises(ArithmeticError, prod, a)
self.failUnlessRaises(ArithmeticError, prod, a2, 1)
self.failUnlessRaises(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))
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)
if ctype in ['1', 'b']:
self.failUnlessRaises(ArithmeticError, cumprod, a)
self.failUnlessRaises(ArithmeticError, cumprod, a2, 1)
self.failUnlessRaises(ArithmeticError, cumprod, a)
else:
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],
[ 5, 30, 210, 1890],
[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)
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)
class TestGradient(TestCase):
def test_basic(self):
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.]))
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]
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)
class TestTrimZeros(TestCase):
""" only testing for integer splits.
"""
def test_basic(self):
a= array([0,0,1,2,3,4,0])
res = trim_zeros(a)
assert_array_equal(res,array([1,2,3,4]))
def test_leading_skip(self):
a= array([0,0,1,0,2,3,4,0])
res = trim_zeros(a)
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])
res = trim_zeros(a)
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])
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])
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)
class TestVectorize(TestCase):
def test_simple(self):
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])
def test_scalar(self):
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])
def test_large(self):
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))
def test_reverse(self):
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)
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])
#check that unwrap maintans continuity
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)
#check known value
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)
#check known value
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)
#check known value
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)
#check known value
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)
#check integral of normal equals 1
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[0] /= 2
wx[-1] /= 2
wy = ones_like(y) * (y[1]-y[0])
wy[0] /= 2
wy[-1] /= 2
wz = ones_like(z) * (z[1]-z[0])
wz[0] /= 2
wz[-1] /= 2
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)
# n-d `x`
r = trapz(q, x=x[:,None,None], axis=0)
assert_almost_equal(r, qx)
r = trapz(q, x=y[None,:,None], axis=1)
assert_almost_equal(r, qy)
r = trapz(q, x=z[None,None,:], axis=2)
assert_almost_equal(r, qz)
# 1-d `x`
r = trapz(q, x=x, axis=0)
assert_almost_equal(r, qx)
r = trapz(q, x=y, axis=1)
assert_almost_equal(r, qy)
r = trapz(q, x=z, axis=2)
assert_almost_equal(r, qz)
class TestSinc(TestCase):
def test_simple(self):
assert(sinc(0)==1)
w=sinc(linspace(-1,1,100))
#check symmetry
assert_array_almost_equal(w,flipud(w),7)
class TestHistogram(TestCase):
def setUp(self):
warnings.simplefilter('ignore', DeprecationWarning)
def tearDown(self):
warnings.resetwarnings()
def test_simple_old(self):
n=100
v=rand(n)
(a,b)=histogram(v, new=False)
#check if the sum of the bins equals the number of samples
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), new=False)
assert_array_equal(a, 10)
def test_simple(self):
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)
#check that the bin counts are evenly spaced when the data is from a
# linear function
(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])
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))
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))
assert_almost_equal(area, 1)
def test_outliers(self):
# Check that outliers are not tallied
a = arange(10)+.5
# Lower outliers
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)
# Normalization
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)
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)
assert(issubdtype(h.dtype, int))
h,b = histogram(a, normed=True)
assert(issubdtype(h.dtype, float))
h,b = histogram(a, weights=ones(10, int))
assert(issubdtype(h.dtype, int))
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)
assert_array_almost_equal(na, nwa)
# Check weights are properly applied.
v = linspace(0,10,10)
w = concatenate((zeros(5), ones(5)))
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)
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)
# 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.))
# Check that H has the correct shape.
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)
# 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]]])
assert_array_equal(H, answer)
Z = zeros((5,5,5))
Z[range(5), range(5), range(5)] = 1.
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)
for b in bins:
H, edges = histogramdd(r, b)
assert(H.shape == b)
def test_shape_4d(self):
# All possible permutations for bins of different lengths in 4D.
bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
(5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
(7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
(4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
(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)
for b in bins:
H, edges = histogramdd(r, b)
assert(H.shape == b)
def test_weights(self):
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)
assert_array_equal(w_hist, n_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)
hist, edges = histogramdd(x, bins=2)
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 = ['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]))
class TestCheckFinite(TestCase):
def test_simple(self):
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]]])
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]]))
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 nanmin([nan, nan]) is nan
def test_nanargmin(self):
assert_almost_equal(nanargmin(self.A), 1)
assert_almost_equal(nanargmin(self.A,0),
array([[1, 0, 0],
[0, 2, 2],
[0, 0, 2]]))
assert_almost_equal(nanargmin(self.A,1),
array([[1, 0, 0],
[0, 1, 2],
[0, 2, 1]]))
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]]))
def test_nanmin_allnan_on_axis(self):
assert_array_equal(isnan(nanmin([[nan]*2]*3, axis=1)),
[True, True, True])
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]])
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],
[ 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. ]]))
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])
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]])
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]]))
class TestKaiser(TestCase):
def test_simple(self):
assert_almost_equal(kaiser(0, 1.0), array([]))
assert isfinite(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]])
assert_almost_equal(msort(A),
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])
assert all(X == array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
[1, 2, 3]]))
assert all(Y == array([[4, 4, 4],
[5, 5, 5],
[6, 6, 6],
[7, 7, 7]]))
class TestPiecewise(TestCase):
def test_simple(self):
# Condition is single bool list
x = piecewise([0, 0], [True, False], [1])
assert_array_equal(x, [1, 0])
# List of conditions: single bool list
x = piecewise([0, 0], [[True, False]], [1])
assert_array_equal(x, [1, 0])
# Conditions is single bool array
x = piecewise([0, 0], array([True, False]), [1])
assert_array_equal(x, [1, 0])
# Condition is single int array
x = piecewise([0, 0], array([1, 0]), [1])
assert_array_equal(x, [1, 0])
# List of conditions: int array
x = piecewise([0, 0], [array([1, 0])], [1])
assert_array_equal(x, [1, 0])
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])
assert_array_equal(x, [3, 4])
def test_default(self):
# No value specified for x[1], should be 0
x = piecewise([1, 2], [True, False], [2])
assert_array_equal(x, [2, 0])
# Should set x[1] to 3
x = piecewise([1, 2], [True, False], [2, 3])
assert_array_equal(x, [2, 3])
def test_0d(self):
x = array(3)
y = piecewise(x, x>3, [4, 0])
assert y.ndim == 0
assert y == 0
def compare_results(res,desired):
for i in range(len(desired)):
assert_array_equal(res[i],desired[i])
if __name__ == "__main__":
run_module_suite()
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