# pylint: disable-msg=W0611, W0612, W0511,R0201 """Tests suite for maskedArray statistics. :author: Pierre Gerard-Marchant :contact: pierregm_at_uga_dot_edu :version: $Id: test_mstats.py 3153 2007-07-09 15:38:26Z pierregm $ """ __author__ = "Pierre GF Gerard-Marchant ($Author: pierregm $)" __version__ = '1.0' __revision__ = "$Revision: 3153 $" __date__ = '$Date: 2007-07-09 08:38:26 -0700 (Mon, 09 Jul 2007) $' import numpy import maskedarray from maskedarray import masked, masked_array import maskedarray.testutils from maskedarray.testutils import * from maskedarray.mstats import * #.............................................................................. class test_quantiles(NumpyTestCase): "Base test class for MaskedArrays." def __init__(self, *args, **kwds): NumpyTestCase.__init__(self, *args, **kwds) self.a = maskedarray.arange(1,101) # def test_1d_nomask(self): "Test quantiles 1D - w/o mask." a = self.a assert_almost_equal(mquantiles(a, alphap=1., betap=1.), [25.75, 50.5, 75.25]) assert_almost_equal(mquantiles(a, alphap=0, betap=1.), [25., 50., 75.]) assert_almost_equal(mquantiles(a, alphap=0.5, betap=0.5), [25.5, 50.5, 75.5]) assert_almost_equal(mquantiles(a, alphap=0., betap=0.), [25.25, 50.5, 75.75]) assert_almost_equal(mquantiles(a, alphap=1./3, betap=1./3), [25.41666667, 50.5, 75.5833333]) assert_almost_equal(mquantiles(a, alphap=3./8, betap=3./8), [25.4375, 50.5, 75.5625]) assert_almost_equal(mquantiles(a), [25.45, 50.5, 75.55])# # def test_1d_mask(self): "Test quantiles 1D - w/ mask." a = self.a a[1::2] = masked assert_almost_equal(mquantiles(a, alphap=1., betap=1.), [25.5, 50.0, 74.5]) assert_almost_equal(mquantiles(a, alphap=0, betap=1.), [24., 49., 74.]) assert_almost_equal(mquantiles(a, alphap=0.5, betap=0.5), [25., 50., 75.]) assert_almost_equal(mquantiles(a, alphap=0., betap=0.), [24.5, 50.0, 75.5]) assert_almost_equal(mquantiles(a, alphap=1./3, betap=1./3), [24.833333, 50.0, 75.166666]) assert_almost_equal(mquantiles(a, alphap=3./8, betap=3./8), [24.875, 50., 75.125]) assert_almost_equal(mquantiles(a), [24.9, 50., 75.1]) # def test_2d_nomask(self): "Test quantiles 2D - w/o mask." a = self.a b = maskedarray.resize(a, (100,100)) assert_almost_equal(mquantiles(b), [25.45, 50.5, 75.55]) assert_almost_equal(mquantiles(b, axis=0), maskedarray.resize(a,(3,100))) assert_almost_equal(mquantiles(b, axis=1), maskedarray.resize([25.45, 50.5, 75.55], (100,3))) # def test_2d_mask(self): "Test quantiles 2D - w/ mask." a = self.a a[1::2] = masked b = maskedarray.resize(a, (100,100)) assert_almost_equal(mquantiles(b), [25., 50., 75.]) assert_almost_equal(mquantiles(b, axis=0), maskedarray.resize(a,(3,100))) assert_almost_equal(mquantiles(b, axis=1), maskedarray.resize([24.9, 50., 75.1], (100,3))) class test_median(NumpyTestCase): def __init__(self, *args, **kwds): NumpyTestCase.__init__(self, *args, **kwds) def test_2d(self): "Tests median w/ 2D" (n,p) = (101,30) x = masked_array(numpy.linspace(-1.,1.,n),) x[:10] = x[-10:] = masked z = masked_array(numpy.empty((n,p), dtype=numpy.float_)) z[:,0] = x[:] idx = numpy.arange(len(x)) for i in range(1,p): numpy.random.shuffle(idx) z[:,i] = x[idx] assert_equal(mmedian(z[:,0]), 0) assert_equal(mmedian(z), numpy.zeros((p,))) def test_3d(self): "Tests median w/ 3D" x = maskedarray.arange(24).reshape(3,4,2) x[x%3==0] = masked assert_equal(mmedian(x,0), [[12,9],[6,15],[12,9],[18,15]]) x.shape = (4,3,2) assert_equal(mmedian(x,0),[[99,10],[11,99],[13,14]]) x = maskedarray.arange(24).reshape(4,3,2) x[x%5==0] = masked assert_equal(mmedian(x,0), [[12,10],[8,9],[16,17]]) #.............................................................................. class test_trimming(NumpyTestCase): # def __init__(self, *args, **kwds): NumpyTestCase.__init__(self, *args, **kwds) # def test_trim(self): "Tests trimming." x = maskedarray.arange(100) assert_equal(trim_both(x).count(), 60) assert_equal(trim_tail(x,tail='r').count(), 80) x[50:70] = masked trimx = trim_both(x) assert_equal(trimx.count(), 48) assert_equal(trimx._mask, [1]*16 + [0]*34 + [1]*20 + [0]*14 + [1]*16) x._mask = nomask x.shape = (10,10) assert_equal(trim_both(x).count(), 60) assert_equal(trim_tail(x).count(), 80) # def test_trimmedmean(self): "Tests the trimmed mean." data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262, 296,299,306,376,428,515,666,1310,2611]) assert_almost_equal(trimmed_mean(data,0.1), 343, 0) assert_almost_equal(trimmed_mean(data,0.2), 283, 0) # def test_trimmed_stde(self): "Tests the trimmed mean standard error." data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262, 296,299,306,376,428,515,666,1310,2611]) assert_almost_equal(trimmed_stde(data,0.2), 56.1, 1) # def test_winsorization(self): "Tests the Winsorization of the data." data = masked_array([ 77, 87, 88,114,151,210,219,246,253,262, 296,299,306,376,428,515,666,1310,2611]) assert_almost_equal(winsorize(data).varu(), 21551.4, 1) data[5] = masked winsorized = winsorize(data) assert_equal(winsorized.mask, data.mask) #.............................................................................. class test_misc(NumpyTestCase): def __init__(self, *args, **kwds): NumpyTestCase.__init__(self, *args, **kwds) def check_cov(self): "Tests the cov function." x = masked_array([[1,2,3],[4,5,6]], mask=[[1,0,0],[0,0,0]]) c = cov(x[0]) assert_equal(c, (x[0].anom()**2).sum()) c = cov(x[1]) assert_equal(c, (x[1].anom()**2).sum()/2.) c = cov(x) assert_equal(c[1,0], (x[0].anom()*x[1].anom()).sum()) ############################################################################### #------------------------------------------------------------------------------ if __name__ == "__main__": NumpyTest().run()