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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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