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Fixed some tests

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master
pbrod 9 years ago
parent 7cc503f0ba
commit ead204e4fd
3 changed files with 107 additions and 115 deletions
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2
wafo/integrate_oscillating.py
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@ -266,7 +266,7 @@ class QuadOsc(object):
# Successive bisection of intervals
nq = len(xq)
n = nq
for k in xrange(1, max_iter):
for k in range(1, max_iter):
n += nq*2**k
hh = hh / 2

2
wafo/tests/test_integrate_oscillating.py
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@ -351,7 +351,7 @@ class LevinQuadrature(unittest.TestCase):
zdg = zeros_dg(beta)
ab = (a, ) + zdg + (b, )
true_val, _err = mp.quadts(ftot, ab, maxdegree=9, error=True)
if False:
import matplotlib.pyplot as plt
t = np.linspace(a, b, 5*513)
plt.subplot(2, 1, 1)

182
wafo/tests/test_kdetools.py
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@ -58,8 +58,8 @@ def test1_TKDE1D():
array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318,
0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882])
>>> np.trapz(f, x)
0.94787730659349068
>>> np.allclose(np.trapz(f, x), 0.94787730659349068)
True
h1 = plb.plot(x, f) # 1D probability density plot
'''
@ -78,12 +78,12 @@ def test1_KDE1D():
>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
>>> kde = wk.KDE(data, hs=0.5)
>>> f = kde(x)
>>> f
array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 ,
0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821])
>>> np.trapz(f, x)
0.92576174424281876
>>> np.allclose(f, [ 0.2039735 , 0.40252503, 0.54595078, 0.52219649,
... 0.3906213, 0.26381501, 0.16407362, 0.08270612, 0.02991145,
... 0.00720821])
True
>>> np.allclose(np.trapz(f, x), 0.92576174424281876)
True
h1 = plb.plot(x, f) # 1D probability density plot
'''
@ -103,12 +103,12 @@ def test2_KDE1D():
>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
>>> kde = wk.KDE(data, hs=0.5)
>>> f = kde(x)
>>> f
array([ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
>>> np.trapz(f, x)
0.97323338046725172
>>> np.allclose(f,
... [ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
... 0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
True
>>> np.allclose(np.trapz(f, x), 0.97323338046725172)
True
h1 = plb.plot(x, f) # 1D probability density plot
'''
@ -127,12 +127,12 @@ def test1a_KDE1D():
>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
>>> kde = wk.KDE(data, hs=0.5, alpha=0.5)
>>> f = kde(x)
>>> f
array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073,
0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164])
>>> np.trapz(f, x)
0.92938023659047952
>>> np.allclose(f,
... [ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073,
... 0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164])
True
>>> np.allclose(np.trapz(f, x), 0.92938023659047952)
True
h1 = plb.plot(x, f) # 1D probability density plot
'''
@ -152,12 +152,12 @@ def test2a_KDE1D():
>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
>>> kde = wk.KDE(data, hs=0.5, alpha=0.5)
>>> f = kde(x)
>>> f
array([ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
>>> np.trapz(f, x)
0.97323338046725172
>>> np.allclose(f,
... [ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
... 0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
True
>>> np.allclose(np.trapz(f, x), 0.97323338046725172)
True
h1 = plb.plot(x, f) # 1D probability density plot
'''
@ -183,15 +183,16 @@ def test_KDE2D():
>>> kde0 = wk.KDE(data, hs=0.5, alpha=0.0, inc=16)
>>> kde0.eval_grid(x, x)
array([[ 3.27260963e-02, 4.21654678e-02, 5.85338634e-04],
[ 6.78845466e-02, 1.42195839e-01, 1.41676003e-03],
[ 1.39466746e-04, 4.26983850e-03, 2.52736185e-05]])
>>> kde0.eval_grid_fast(x, x)
array([[ 0.04435061, 0.06433531, 0.00413538],
[ 0.07218297, 0.12358196, 0.00928889],
[ 0.00161333, 0.00794858, 0.00058748]])
>>> np.allclose(kde0.eval_grid(x, x),
... [[ 3.27260963e-02, 4.21654678e-02, 5.85338634e-04],
... [ 6.78845466e-02, 1.42195839e-01, 1.41676003e-03],
... [ 1.39466746e-04, 4.26983850e-03, 2.52736185e-05]])
True
>>> np.allclose(kde0.eval_grid_fast(x, x),
... [[ 0.04435061, 0.06433531, 0.00413538],
... [ 0.07218297, 0.12358196, 0.00928889],
... [ 0.00161333, 0.00794858, 0.00058748]])
True
'''
@ -251,9 +252,10 @@ def test_gridcount_1D():
>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
>>> dx = x[1] - x[0]
>>> c = wk.gridcount(data, x)
>>> c
array([ 0.78762626, 1.77520717, 7.99190087, 4.04054449, 1.67156643,
2.38228499, 1.05933195, 0.29153785, 0. , 0. ])
>>> np.allclose(c,
... [ 0.78762626, 1.77520717, 7.99190087, 4.04054449, 1.67156643,
... 2.38228499, 1.05933195, 0.29153785, 0. , 0. ])
True
h = plb.plot(x, c, '.') # 1D histogram
@ -281,12 +283,13 @@ def test_gridcount_2D():
>>> dx = x[1] - x[0]
>>> X = np.vstack((x, x))
>>> c = wk.gridcount(data, X)
>>> c
array([[ 0.38922806, 0.8987982 , 0.34676493, 0.21042807, 0. ],
[ 1.15012203, 5.16513541, 3.19250588, 0.55420752, 0. ],
[ 0.74293418, 3.42517219, 1.97923195, 0.76076621, 0. ],
[ 0.02063536, 0.31054405, 0.71865964, 0.13486633, 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
>>> np.allclose(c,
... [[ 0.38922806, 0.8987982 , 0.34676493, 0.21042807, 0. ],
... [ 1.15012203, 5.16513541, 3.19250588, 0.55420752, 0. ],
... [ 0.74293418, 3.42517219, 1.97923195, 0.76076621, 0. ],
... [ 0.02063536, 0.31054405, 0.71865964, 0.13486633, 0. ],
... [ 0. , 0. , 0. , 0. , 0. ]])
True
h = plb.plot(x, c, '.') # 1D histogram
@ -318,19 +321,17 @@ def test_gridcount_3D():
>>> dx = x[1] - x[0]
>>> X = np.vstack((x, x, x))
>>> c = wk.gridcount(data, X)
>>> c
array([[[ 8.74229894e-01, 1.27910940e+00, 1.42033973e-01],
[ 1.94778915e+00, 2.59536282e+00, 3.28213680e-01],
[ 1.08429416e-01, 1.69571495e-01, 7.48896775e-03]],
<BLANKLINE>
[[ 1.44969128e+00, 2.58396370e+00, 2.45459949e-01],
[ 2.28951650e+00, 4.49653348e+00, 2.73167915e-01],
[ 1.10905565e-01, 3.18733817e-01, 1.12880816e-02]],
<BLANKLINE>
[[ 7.49265424e-02, 2.18142488e-01, 0.00000000e+00],
[ 8.53886762e-02, 3.73415131e-01, 0.00000000e+00],
[ 4.16196568e-04, 1.62218824e-02, 0.00000000e+00]]])
>>> np.allclose(c,
... [[[ 8.74229894e-01, 1.27910940e+00, 1.42033973e-01],
... [ 1.94778915e+00, 2.59536282e+00, 3.28213680e-01],
... [ 1.08429416e-01, 1.69571495e-01, 7.48896775e-03]],
... [[ 1.44969128e+00, 2.58396370e+00, 2.45459949e-01],
... [ 2.28951650e+00, 4.49653348e+00, 2.73167915e-01],
... [ 1.10905565e-01, 3.18733817e-01, 1.12880816e-02]],
... [[ 7.49265424e-02, 2.18142488e-01, 0.00000000e+00],
... [ 8.53886762e-02, 3.73415131e-01, 0.00000000e+00],
... [ 4.16196568e-04, 1.62218824e-02, 0.00000000e+00]]])
True
'''
@ -352,44 +353,35 @@ def test_gridcount_4D():
>>> dx = x[1] - x[0]
>>> X = np.vstack((x, x, x, x))
>>> c = wk.gridcount(data, X)
>>> c
array([[[[ 1.77163904e-01, 1.87720108e-01, 0.00000000e+00],
[ 5.72573585e-01, 6.09557834e-01, 0.00000000e+00],
[ 3.48549923e-03, 4.05931870e-02, 0.00000000e+00]],
<BLANKLINE>
[[ 1.83770124e-01, 2.56357594e-01, 0.00000000e+00],
[ 4.35845892e-01, 6.14958970e-01, 0.00000000e+00],
[ 3.07662204e-03, 3.58312786e-02, 0.00000000e+00]],
<BLANKLINE>
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]],
<BLANKLINE>
<BLANKLINE>
[[[ 3.41883175e-01, 5.97977973e-01, 0.00000000e+00],
[ 5.72071865e-01, 8.58566538e-01, 0.00000000e+00],
[ 3.46939323e-03, 4.04056116e-02, 0.00000000e+00]],
<BLANKLINE>
[[ 3.58861043e-01, 6.28962785e-01, 0.00000000e+00],
[ 8.80697705e-01, 1.47373158e+00, 0.00000000e+00],
[ 2.22868504e-01, 1.18008528e-01, 0.00000000e+00]],
<BLANKLINE>
[[ 2.91835067e-03, 2.60268355e-02, 0.00000000e+00],
[ 3.63686503e-02, 1.07959459e-01, 0.00000000e+00],
[ 1.88555613e-02, 7.06358976e-03, 0.00000000e+00]]],
<BLANKLINE>
<BLANKLINE>
[[[ 3.13810608e-03, 2.11731327e-02, 0.00000000e+00],
[ 6.71606255e-03, 4.53139824e-02, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
<BLANKLINE>
[[ 7.05946179e-03, 5.44614852e-02, 0.00000000e+00],
[ 1.09099593e-01, 1.95935584e-01, 0.00000000e+00],
[ 6.61257395e-02, 2.47717418e-02, 0.00000000e+00]],
<BLANKLINE>
[[ 6.38695629e-04, 5.69610302e-03, 0.00000000e+00],
[ 1.00358265e-02, 2.44053065e-02, 0.00000000e+00],
[ 5.67244468e-03, 2.12498697e-03, 0.00000000e+00]]]])
>>> np.allclose(c,
... [[[[ 1.77163904e-01, 1.87720108e-01, 0.00000000e+00],
... [ 5.72573585e-01, 6.09557834e-01, 0.00000000e+00],
... [ 3.48549923e-03, 4.05931870e-02, 0.00000000e+00]],
... [[ 1.83770124e-01, 2.56357594e-01, 0.00000000e+00],
... [ 4.35845892e-01, 6.14958970e-01, 0.00000000e+00],
... [ 3.07662204e-03, 3.58312786e-02, 0.00000000e+00]],
... [[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
... [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
... [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]],
... [[[ 3.41883175e-01, 5.97977973e-01, 0.00000000e+00],
... [ 5.72071865e-01, 8.58566538e-01, 0.00000000e+00],
... [ 3.46939323e-03, 4.04056116e-02, 0.00000000e+00]],
... [[ 3.58861043e-01, 6.28962785e-01, 0.00000000e+00],
... [ 8.80697705e-01, 1.47373158e+00, 0.00000000e+00],
... [ 2.22868504e-01, 1.18008528e-01, 0.00000000e+00]],
... [[ 2.91835067e-03, 2.60268355e-02, 0.00000000e+00],
... [ 3.63686503e-02, 1.07959459e-01, 0.00000000e+00],
... [ 1.88555613e-02, 7.06358976e-03, 0.00000000e+00]]],
... [[[ 3.13810608e-03, 2.11731327e-02, 0.00000000e+00],
... [ 6.71606255e-03, 4.53139824e-02, 0.00000000e+00],
... [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
... [[ 7.05946179e-03, 5.44614852e-02, 0.00000000e+00],
... [ 1.09099593e-01, 1.95935584e-01, 0.00000000e+00],
... [ 6.61257395e-02, 2.47717418e-02, 0.00000000e+00]],
... [[ 6.38695629e-04, 5.69610302e-03, 0.00000000e+00],
... [ 1.00358265e-02, 2.44053065e-02, 0.00000000e+00],
... [ 5.67244468e-03, 2.12498697e-03, 0.00000000e+00]]]])
True
h = plb.plot(x, c, '.') # 1D histogram

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