From 3b8256413119da30ed4fc3cc606170dcac324b36 Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Wed, 28 Dec 2016 09:03:23 +0100 Subject: [PATCH] Added tests + fixed pep8 --- wafo/kdetools/kdetools.py | 1 - wafo/kdetools/tests/test_gridding.py | 152 ++++++++++++++++++++++++++ wafo/kdetools/tests/test_kernels.py | 158 +++++++++++++++++++++++++++ 3 files changed, 310 insertions(+), 1 deletion(-) create mode 100644 wafo/kdetools/tests/test_gridding.py create mode 100644 wafo/kdetools/tests/test_kernels.py diff --git a/wafo/kdetools/kdetools.py b/wafo/kdetools/kdetools.py index 6109357..e38df5c 100644 --- a/wafo/kdetools/kdetools.py +++ b/wafo/kdetools/kdetools.py @@ -605,7 +605,6 @@ class KDE(_KDE): t = np.trapz(f, x) """ - def __init__(self, data, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=512): super(KDE, self).__init__(data, kernel, xmin, xmax) diff --git a/wafo/kdetools/tests/test_gridding.py b/wafo/kdetools/tests/test_gridding.py new file mode 100644 index 0000000..5ab5539 --- /dev/null +++ b/wafo/kdetools/tests/test_gridding.py @@ -0,0 +1,152 @@ +''' +Created on 23. des. 2016 + +@author: pab +''' +from __future__ import division +import unittest +import numpy as np +from numpy.testing import assert_allclose +from numpy import array +import wafo.kdetools.gridding as wkg + + +class TestKdeTools(unittest.TestCase): + + def setUp(self): + + # N = 20 + # data = np.random.rayleigh(1, size=(N,)) + self.data = array([0.75355792, 0.72779194, 0.94149169, 0.07841119, + 2.32291887, 1.10419995, 0.77055114, 0.60288273, + 1.36883635, 1.74754326, 1.09547561, 1.01671133, + 0.73211143, 0.61891719, 0.75903487, 1.8919469, + 0.72433808, 1.92973094, 0.44749838, 1.36508452]) + self.x = np.linspace(0, max(self.data) + 1, 10) + + def test_gridcount_1D(self): + data, x = self.data, self.x + dx = x[1] - x[0] + c = wkg.gridcount(data, x) + assert_allclose(c, [0.78762626, 1.77520717, 7.99190087, 4.04054449, + 1.67156643, 2.38228499, 1.05933195, 0.29153785, 0., + 0.]) + t = np.trapz(c / dx / len(data), x) + assert_allclose(t, 0.9803093435140049) + + def test_gridcount_2D(self): + 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(np.ravel(data)) + 1, 5) + dx = x[1] - x[0] + X = np.vstack((x, x)) + c = wkg.gridcount(data, X) + assert_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.]], 1e-5) + + t = np.trapz(np.trapz(c / (dx**2 * N), x), x) + assert_allclose(t, 0.9011618785736376) + + def test_gridcount_3D(self): + 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(np.ravel(data)) + 1, 3) + dx = x[1] - x[0] + X = np.vstack((x, x, x)) + c = wkg.gridcount(data, X) + assert_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, 0.0], + [8.53886762e-02, 3.73415131e-01, 0.0], + [4.16196568e-04, 1.62218824e-02, 0.0]]]) + + t = np.trapz(np.trapz(np.trapz(c / dx**3 / N, x), x), x) + assert_allclose(t, 0.5164999727560187) + + def test_gridcount_4D(self): + + 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(np.ravel(data)) + 1, 3) + dx = x[1] - x[0] + X = np.vstack((x, x, x, x)) + c = wkg.gridcount(data, X) + assert_allclose(c, + [[[[1.77163904e-01, 1.87720108e-01, 0.0], + [5.72573585e-01, 6.09557834e-01, 0.0], + [3.48549923e-03, 4.05931870e-02, 0.0]], + [[1.83770124e-01, 2.56357594e-01, 0.0], + [4.35845892e-01, 6.14958970e-01, 0.0], + [3.07662204e-03, 3.58312786e-02, 0.0]], + [[0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0]]], + [[[3.41883175e-01, 5.97977973e-01, 0.0], + [5.72071865e-01, 8.58566538e-01, 0.0], + [3.46939323e-03, 4.04056116e-02, 0.0]], + [[3.58861043e-01, 6.28962785e-01, 0.0], + [8.80697705e-01, 1.47373158e+00, 0.0], + [2.22868504e-01, 1.18008528e-01, 0.0]], + [[2.91835067e-03, 2.60268355e-02, 0.0], + [3.63686503e-02, 1.07959459e-01, 0.0], + [1.88555613e-02, 7.06358976e-03, 0.0]]], + [[[3.13810608e-03, 2.11731327e-02, 0.0], + [6.71606255e-03, 4.53139824e-02, 0.0], + [0.0, 0.0, 0.0]], + [[7.05946179e-03, 5.44614852e-02, 0.0], + [1.09099593e-01, 1.95935584e-01, 0.0], + [6.61257395e-02, 2.47717418e-02, 0.0]], + [[6.38695629e-04, 5.69610302e-03, 0.0], + [1.00358265e-02, 2.44053065e-02, 0.0], + [5.67244468e-03, 2.12498697e-03, 0.0]]]]) + + t = np.trapz(np.trapz(np.trapz(np.trapz(c / dx**4 / N, x), x), x), x) + assert_allclose(t, 0.21183518274521254) + + +if __name__ == "__main__": + # import sys;sys.argv = ['', 'Test.testName'] + unittest.main() diff --git a/wafo/kdetools/tests/test_kernels.py b/wafo/kdetools/tests/test_kernels.py new file mode 100644 index 0000000..24ad65b --- /dev/null +++ b/wafo/kdetools/tests/test_kernels.py @@ -0,0 +1,158 @@ +''' +Created on 23. des. 2016 + +@author: pab +''' +from __future__ import division +import unittest +import numpy as np +from numpy.testing import assert_allclose +from numpy import inf +import wafo.kdetools.kernels as wkk + + +class TestKernels(unittest.TestCase): + def setUp(self): + self.names = ['epanechnikov', 'biweight', 'triweight', 'logistic', + 'p1epanechnikov', 'p1biweight', 'p1triweight', + 'triangular', 'gaussian', 'rectangular', 'laplace'] + + def test_stats(self): + truth = { + 'biweight': (0.14285714285714285, 0.7142857142857143, 22.5), + 'logistic': (3.289868133696453, 1./6, 0.023809523809523808), + 'p1biweight': (0.14285714285714285, 0.7142857142857143, 22.5), + 'triangular': (0.16666666666666666, 0.6666666666666666, inf), + 'gaussian': (1, 0.28209479177387814, 0.21157109383040862), + 'epanechnikov': (0.2, 0.6, inf), + 'triweight': (0.1111111111111111, 0.8158508158508159, inf), + 'p1triweight': (0.1111111111111111, 0.8158508158508159, inf), + 'p1epanechnikov': (0.2, 0.6, inf), + 'rectangular': (0.3333333333333333, 0.5, inf), + 'laplace': (2, 0.25, inf)} + for name in self.names: + kernel = wkk.Kernel(name) + assert_allclose(kernel.stats(), truth[name]) + # truth[name] = kernel.stats() + # print(truth) + + def test_norm_factors_1d(self): + truth = { + 'biweight': 1.0666666666666667, 'logistic': 1.0, + 'p1biweight': 1.0666666666666667, 'triangular': 1.0, + 'gaussian': 2.5066282746310002, 'epanechnikov': 1.3333333333333333, + 'triweight': 0.91428571428571426, 'laplace': 2, + 'p1triweight': 0.91428571428571426, + 'p1epanechnikov': 1.3333333333333333, 'rectangular': 2.0} + for name in self.names: + kernel = wkk.Kernel(name) + assert_allclose(kernel.norm_factor(d=1, n=20), truth[name]) + # truth[name] = kernel.norm_factor(d=1, n=20) + + def test_effective_support(self): + truth = {'biweight': (-1.0, 1.0), 'logistic': (-7.0, 7.0), + 'p1biweight': (-1.0, 1.0), 'triangular': (-1.0, 1.0), + 'gaussian': (-4.0, 4.0), 'epanechnikov': (-1.0, 1.0), + 'triweight': (-1.0, 1.0), 'p1triweight': (-1.0, 1.0), + 'p1epanechnikov': (-1.0, 1.0), 'rectangular': (-1.0, 1.0), + 'laplace': (-7.0, 7.0)} + for name in self.names: + kernel = wkk.Kernel(name) + assert_allclose(kernel.effective_support(), truth[name]) + # truth[name] = kernel.effective_support() + # print(truth) + # self.assertTrue(False) + + def test_that_kernel_is_a_pdf(self): + + for name in self.names: + kernel = wkk.Kernel(name) + xmin, xmax = kernel.effective_support() + x = np.linspace(xmin, xmax, 4*1024+1) + m0 = kernel.norm_factor(d=1, n=1) + pdf = kernel(x)/m0 + # print(name) + # print(pdf[0], pdf[-1]) + # print(np.trapz(pdf, x) - 1) + assert_allclose(np.trapz(pdf, x), 1, 1e-2) + # self.assertTrue(False) + + +class TestSmoothing(unittest.TestCase): + def setUp(self): + self.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]]) + self.gauss = wkk.Kernel('gaussian') + + def test_hns(self): + hs = self.gauss.hns(self.data) + assert_allclose(hs, [0.18154437, 0.36207987, 0.37396219]) + + def test_hos(self): + hs = self.gauss.hos(self.data) + assert_allclose(hs, [0.195209, 0.3893332, 0.40210988]) + + def test_hms(self): + hs = self.gauss.hmns(self.data) + assert_allclose(hs, [[3.25196193e-01, -2.68892467e-02, 3.18932448e-04], + [-2.68892467e-02, 3.91283306e-01, 2.38654678e-02], + [3.18932448e-04, 2.38654678e-02, 4.05123874e-01]]) + hs = self.gauss.hmns(self.data[0]) + assert_allclose(hs, self.gauss.hns(self.data[0])) + + hs = wkk.Kernel('epan').hmns(self.data) + assert_allclose(hs, + [[8.363847e-01, -6.915749e-02, 8.202747e-04], + [-6.915749e-02, 1.006357e+00, 6.138052e-02], + [8.202747e-04, 6.138052e-02, 1.041954e+00]], + rtol=1e-5) + hs = wkk.Kernel('biwe').hmns(self.data[:2]) + assert_allclose(hs, [[0.868428, -0.071705], + [-0.071705, 1.04685]], rtol=1e-5) + hs = wkk.Kernel('triwe').hmns(self.data[:2]) + assert_allclose(hs, [[0.975375, -0.080535], + [-0.080535, 1.17577]], rtol=1e-5) + self.assertRaises(NotImplementedError, + wkk.Kernel('biwe').hmns, self.data) + self.assertRaises(NotImplementedError, + wkk.Kernel('triwe').hmns, self.data) + self.assertRaises(NotImplementedError, + wkk.Kernel('triangular').hmns, self.data) + + def test_hscv(self): + hs = self.gauss.hscv(self.data) + assert_allclose(hs, [0.1656318800590673, 0.3273938258112911, + 0.31072126996412214]) + + def test_hstt(self): + hs = self.gauss.hstt(self.data) + assert_allclose(hs, [0.18099075, 0.50409881, 0.11018912]) + + def test_hste(self): + hs = self.gauss.hste(self.data) + assert_allclose(hs, [0.17035204677390572, 0.29851960273788863, + 0.186685349741972]) + + def test_hldpi(self): + hs = self.gauss.hldpi(self.data) + assert_allclose(hs, [0.1732289, 0.33159097, 0.3107633]) + + def test_hisj(self): + hs = self.gauss.hisj(self.data) + assert_allclose(hs, [0.29542502, 0.74277133, 0.51899114]) + + +if __name__ == "__main__": + #import sys;sys.argv = ['', 'Test.testName'] + unittest.main()