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'''
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Created on 20. nov. 2010
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@author: pab
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'''
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import numpy as np # @UnusedImport
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from numpy import array # @UnusedImport
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import wafo.kdetools as wk # @UnusedImport
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# import pylab as plb
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def test0_KDE1D():
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'''
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>>> data = array([0.75355792, 0.72779194, 0.94149169, 0.07841119,
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... 2.32291887, 1.10419995, 0.77055114, 0.60288273,
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... 1.36883635, 1.74754326, 1.09547561, 1.01671133,
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... 0.73211143, 0.61891719, 0.75903487, 1.8919469,
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... 0.72433808, 1.92973094, 0.44749838, 1.36508452])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
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>>> import wafo.kdetools as wk
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>>> kde = wk.KDE(data, hs=0.5, alpha=0.5)
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>>> kde0 = wk.KDE(data, hs=0.5, alpha=0.0, inc=16)
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>>> kde0.eval_grid(x)
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array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 ,
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0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821])
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>>> kde0.eval_grid_fast(x)
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array([ 0.20729484, 0.39865044, 0.53716945, 0.5169322 , 0.39060223,
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0.26441126, 0.16388801, 0.08388527, 0.03227164, 0.00883579])
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>>> f = kde0.eval_grid_fast(); f
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array([ 0.06807544, 0.12949095, 0.21985421, 0.33178031, 0.44334874,
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0.52429234, 0.55140336, 0.52221323, 0.45500674, 0.3752208 ,
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0.30046799, 0.235667 , 0.17854402, 0.12721305, 0.08301993,
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0.04862324])
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>>> np.allclose(np.trapz(f,kde0.args), array([ 0.96716261]))
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True
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'''
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def test1_TKDE1D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(N,))
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>>> data = array([0.75355792, 0.72779194, 0.94149169, 0.07841119,
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... 2.32291887, 1.10419995, 0.77055114, 0.60288273,
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... 1.36883635, 1.74754326, 1.09547561, 1.01671133,
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... 0.73211143, 0.61891719, 0.75903487, 1.8919469,
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... 0.72433808, 1.92973094, 0.44749838, 1.36508452])
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>>> x = np.linspace(0.01, max(data.ravel()) + 1, 10)
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>>> kde = wk.TKDE(data, hs=0.5, L2=0.5)
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>>> f = kde(x)
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>>> f
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array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318,
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0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882])
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>>> np.allclose(np.trapz(f, x), 0.94787730659349068)
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True
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h1 = plb.plot(x, f) # 1D probability density plot
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'''
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def test1_KDE1D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(N,))
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>>> data = array([0.75355792, 0.72779194, 0.94149169, 0.07841119,
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... 2.32291887, 1.10419995, 0.77055114, 0.60288273,
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... 1.36883635, 1.74754326, 1.09547561, 1.01671133,
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... 0.73211143, 0.61891719, 0.75903487, 1.8919469,
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... 0.72433808, 1.92973094, 0.44749838, 1.36508452])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
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>>> kde = wk.KDE(data, hs=0.5)
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>>> f = kde(x)
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>>> np.allclose(f, [ 0.2039735 , 0.40252503, 0.54595078, 0.52219649,
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... 0.3906213, 0.26381501, 0.16407362, 0.08270612, 0.02991145,
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... 0.00720821])
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True
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>>> np.allclose(np.trapz(f, x), 0.92576174424281876)
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True
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h1 = plb.plot(x, f) # 1D probability density plot
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'''
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def test2_KDE1D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(N,))
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>>> data = array([ 0.75355792, 0.72779194, 0.94149169, 0.07841119,
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... 2.32291887, 1.10419995, 0.77055114, 0.60288273, 1.36883635,
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... 1.74754326, 1.09547561, 1.01671133, 0.73211143, 0.61891719,
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... 0.75903487, 1.8919469, 0.72433808, 1.92973094, 0.44749838,
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... 1.36508452])
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>>> data = np.asarray([1,2])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
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>>> kde = wk.KDE(data, hs=0.5)
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>>> f = kde(x)
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>>> np.allclose(f,
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... [ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
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... 0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
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True
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>>> np.allclose(np.trapz(f, x), 0.97323338046725172)
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True
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h1 = plb.plot(x, f) # 1D probability density plot
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'''
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def test1a_KDE1D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(N,))
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>>> data = array([
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... 0.75355792, 0.72779194, 0.94149169, 0.07841119, 2.32291887,
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... 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326,
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... 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487,
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... 1.8919469 , 0.72433808, 1.92973094, 0.44749838, 1.36508452])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
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>>> kde = wk.KDE(data, hs=0.5, alpha=0.5)
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>>> f = kde(x)
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>>> np.allclose(f,
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... [ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073,
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... 0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164])
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True
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>>> np.allclose(np.trapz(f, x), 0.92938023659047952)
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True
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h1 = plb.plot(x, f) # 1D probability density plot
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'''
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def test2a_KDE1D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(N,))
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>>> data = array([
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... 0.75355792, 0.72779194, 0.94149169, 0.07841119, 2.32291887,
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... 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326,
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... 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487,
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... 1.8919469 , 0.72433808, 1.92973094, 0.44749838, 1.36508452])
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>>> data = np.asarray([1,2])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
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>>> kde = wk.KDE(data, hs=0.5, alpha=0.5)
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>>> f = kde(x)
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>>> np.allclose(f,
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... [ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
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... 0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
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True
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>>> np.allclose(np.trapz(f, x), 0.97323338046725172)
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True
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h1 = plb.plot(x, f) # 1D probability density plot
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'''
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def test_KDE2D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(2, N))
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>>> data = array([[
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... 0.38103275, 0.35083136, 0.90024207, 1.88230239, 0.96815399,
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... 0.57392873, 1.63367908, 1.20944125, 2.03887811, 0.81789145,
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... 0.69302049, 1.40856592, 0.92156032, 2.14791432, 2.04373821,
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... 0.69800708, 0.58428735, 1.59128776, 2.05771405, 0.87021964],
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... [1.44080694, 0.39973751, 1.331243 , 2.48895822, 1.18894158,
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... 1.40526085, 1.01967897, 0.81196474, 1.37978932, 2.03334689,
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... 0.870329 , 1.25106862, 0.5346619 , 0.47541236, 1.51930093,
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... 0.58861519, 1.19780448, 0.81548296, 1.56859488, 1.60653533]])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 3)
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>>> kde = wk.KDE(data, hs=0.5, alpha=0.5)
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>>> kde0 = wk.KDE(data, hs=0.5, alpha=0.0, inc=16)
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>>> np.allclose(kde0.eval_grid(x, x),
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... [[ 3.27260963e-02, 4.21654678e-02, 5.85338634e-04],
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... [ 6.78845466e-02, 1.42195839e-01, 1.41676003e-03],
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... [ 1.39466746e-04, 4.26983850e-03, 2.52736185e-05]])
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True
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>>> np.allclose(kde0.eval_grid_fast(x, x),
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... [[ 0.04435061, 0.06433531, 0.00413538],
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... [ 0.07218297, 0.12358196, 0.00928889],
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... [ 0.00161333, 0.00794858, 0.00058748]])
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True
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'''
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def test_smooth_params():
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'''
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>>> data = np.array([[
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... 0.932896 , 0.89522635, 0.80636346, 1.32283371, 0.27125435,
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... 1.91666304, 2.30736635, 1.13662384, 1.73071287, 1.06061127,
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... 0.99598512, 2.16396591, 1.23458213, 1.12406686, 1.16930431,
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... 0.73700592, 1.21135139, 0.46671506, 1.3530304 , 0.91419104],
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... [ 0.62759088, 0.23988169, 2.04909823, 0.93766571, 1.19343762,
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... 1.94954931, 0.84687514, 0.49284897, 1.05066204, 1.89088505,
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... 0.840738 , 1.02901457, 1.0758625 , 1.76357967, 0.45792897,
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... 1.54488066, 0.17644313, 1.6798871 , 0.72583514, 2.22087245],
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... [ 1.69496432, 0.81791905, 0.82534709, 0.71642389, 0.89294732,
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... 1.66888649, 0.69036947, 0.99961448, 0.30657267, 0.98798713,
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... 0.83298728, 1.83334948, 1.90144186, 1.25781913, 0.07122458,
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... 2.42340852, 2.41342037, 0.87233305, 1.17537114, 1.69505988]])
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>>> gauss = wk.Kernel('gaussian')
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>>> gauss.hns(data)
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array([ 0.18154437, 0.36207987, 0.37396219])
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>>> gauss.hos(data)
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array([ 0.195209 , 0.3893332 , 0.40210988])
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>>> gauss.hmns(data)
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array([[ 3.25196193e-01, -2.68892467e-02, 3.18932448e-04],
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[ -2.68892467e-02, 3.91283306e-01, 2.38654678e-02],
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[ 3.18932448e-04, 2.38654678e-02, 4.05123874e-01]])
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>>> gauss.hscv(data)
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array([ 0.16858959, 0.32739383, 0.3046287 ])
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>>> gauss.hstt(data)
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array([ 0.18099075, 0.50409881, 0.11018912])
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>>> gauss.hste(data)
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array([ 0.16750009, 0.29059113, 0.17994255])
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>>> gauss.hldpi(data)
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array([ 0.1732289 , 0.33159097, 0.3107633 ])
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>>> np.allclose(gauss.hisj(data),
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... array([ 0.29542502, 0.74277133, 0.51899114]))
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True
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'''
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def test_gridcount_1D():
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'''
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N = 20
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data = np.random.rayleigh(1, size=(N,))
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>>> data = array([
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... 0.75355792, 0.72779194, 0.94149169, 0.07841119, 2.32291887,
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... 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326,
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... 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487,
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... 1.8919469 , 0.72433808, 1.92973094, 0.44749838, 1.36508452])
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>>> x = np.linspace(0, max(data.ravel()) + 1, 10)
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>>> dx = x[1] - x[0]
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>>> c = wk.gridcount(data, x)
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>>> np.allclose(c,
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... [ 0.78762626, 1.77520717, 7.99190087, 4.04054449, 1.67156643,
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... 2.38228499, 1.05933195, 0.29153785, 0. , 0. ])
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True
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|
|
|
|
|
|
|
|
|
h = plb.plot(x, c, '.') # 1D histogram
|
|
|
|
|
|
|
|
|
|
h1 = plb.plot(x, c / dx / N) # 1D probability density plot
|
|
|
|
|
t = np.trapz(c / dx / N, x)
|
|
|
|
|
print(t)
|
|
|
|
|
'''
|
|
|
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|
|
|
|
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|
|
|
|
|
def test_gridcount_2D():
|
|
|
|
|
'''
|
|
|
|
|
N = 20
|
|
|
|
|
data = np.random.rayleigh(1, size=(2, N))
|
|
|
|
|
>>> data = array([[
|
|
|
|
|
... 0.38103275, 0.35083136, 0.90024207, 1.88230239, 0.96815399,
|
|
|
|
|
... 0.57392873, 1.63367908, 1.20944125, 2.03887811, 0.81789145,
|
|
|
|
|
... 0.69302049, 1.40856592, 0.92156032, 2.14791432, 2.04373821,
|
|
|
|
|
... 0.69800708, 0.58428735, 1.59128776, 2.05771405, 0.87021964],
|
|
|
|
|
... [ 1.44080694, 0.39973751, 1.331243 , 2.48895822, 1.18894158,
|
|
|
|
|
... 1.40526085, 1.01967897, 0.81196474, 1.37978932, 2.03334689,
|
|
|
|
|
... 0.870329 , 1.25106862, 0.5346619 , 0.47541236, 1.51930093,
|
|
|
|
|
... 0.58861519, 1.19780448, 0.81548296, 1.56859488, 1.60653533]])
|
|
|
|
|
|
|
|
|
|
>>> x = np.linspace(0, max(data.ravel()) + 1, 5)
|
|
|
|
|
>>> dx = x[1] - x[0]
|
|
|
|
|
>>> X = np.vstack((x, x))
|
|
|
|
|
>>> c = wk.gridcount(data, X)
|
|
|
|
|
>>> 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
|
|
|
|
|
|
|
|
|
|
h1 = plb.plot(x, c / dx / N) # 1D probability density plot
|
|
|
|
|
t = np.trapz(c / dx / N, x)
|
|
|
|
|
print(t)
|
|
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_gridcount_3D():
|
|
|
|
|
'''
|
|
|
|
|
N = 20
|
|
|
|
|
data = np.random.rayleigh(1, size=(3, N))
|
|
|
|
|
>>> data = np.array([[
|
|
|
|
|
... 0.932896 , 0.89522635, 0.80636346, 1.32283371, 0.27125435,
|
|
|
|
|
... 1.91666304, 2.30736635, 1.13662384, 1.73071287, 1.06061127,
|
|
|
|
|
... 0.99598512, 2.16396591, 1.23458213, 1.12406686, 1.16930431,
|
|
|
|
|
... 0.73700592, 1.21135139, 0.46671506, 1.3530304 , 0.91419104],
|
|
|
|
|
... [ 0.62759088, 0.23988169, 2.04909823, 0.93766571, 1.19343762,
|
|
|
|
|
... 1.94954931, 0.84687514, 0.49284897, 1.05066204, 1.89088505,
|
|
|
|
|
... 0.840738 , 1.02901457, 1.0758625 , 1.76357967, 0.45792897,
|
|
|
|
|
... 1.54488066, 0.17644313, 1.6798871 , 0.72583514, 2.22087245],
|
|
|
|
|
... [ 1.69496432, 0.81791905, 0.82534709, 0.71642389, 0.89294732,
|
|
|
|
|
... 1.66888649, 0.69036947, 0.99961448, 0.30657267, 0.98798713,
|
|
|
|
|
... 0.83298728, 1.83334948, 1.90144186, 1.25781913, 0.07122458,
|
|
|
|
|
... 2.42340852, 2.41342037, 0.87233305, 1.17537114, 1.69505988]])
|
|
|
|
|
|
|
|
|
|
>>> x = np.linspace(0, max(data.ravel()) + 1, 3)
|
|
|
|
|
>>> dx = x[1] - x[0]
|
|
|
|
|
>>> X = np.vstack((x, x, x))
|
|
|
|
|
>>> c = wk.gridcount(data, X)
|
|
|
|
|
>>> 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
|
|
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_gridcount_4D():
|
|
|
|
|
'''
|
|
|
|
|
N = 20
|
|
|
|
|
data = np.random.rayleigh(1, size=(2, N))
|
|
|
|
|
>>> data = array([[
|
|
|
|
|
... 0.38103275, 0.35083136, 0.90024207, 1.88230239, 0.96815399,
|
|
|
|
|
... 0.57392873, 1.63367908, 1.20944125, 2.03887811, 0.81789145],
|
|
|
|
|
... [ 0.69302049, 1.40856592, 0.92156032, 2.14791432, 2.04373821,
|
|
|
|
|
... 0.69800708, 0.58428735, 1.59128776, 2.05771405, 0.87021964],
|
|
|
|
|
... [ 1.44080694, 0.39973751, 1.331243 , 2.48895822, 1.18894158,
|
|
|
|
|
... 1.40526085, 1.01967897, 0.81196474, 1.37978932, 2.03334689],
|
|
|
|
|
... [ 0.870329 , 1.25106862, 0.5346619 , 0.47541236, 1.51930093,
|
|
|
|
|
... 0.58861519, 1.19780448, 0.81548296, 1.56859488, 1.60653533]])
|
|
|
|
|
|
|
|
|
|
>>> x = np.linspace(0, max(data.ravel()) + 1, 3)
|
|
|
|
|
>>> dx = x[1] - x[0]
|
|
|
|
|
>>> X = np.vstack((x, x, x, x))
|
|
|
|
|
>>> c = wk.gridcount(data, X)
|
|
|
|
|
>>> 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
|
|
|
|
|
|
|
|
|
|
h1 = plb.plot(x, c / dx / N) # 1D probability density plot
|
|
|
|
|
t = np.trapz(x, c / dx / N)
|
|
|
|
|
print(t)
|
|
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_docstrings():
|
|
|
|
|
import doctest
|
|
|
|
|
print('Testing docstrings in %s' % __file__)
|
|
|
|
|
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
test_docstrings()
|
