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Diffstat (limited to 'numpy/ma/tests/test_mstats.py')
| -rw-r--r-- | numpy/ma/tests/test_mstats.py | 174 |
1 files changed, 174 insertions, 0 deletions
diff --git a/numpy/ma/tests/test_mstats.py b/numpy/ma/tests/test_mstats.py new file mode 100644 index 000000000..e4657a58f --- /dev/null +++ b/numpy/ma/tests/test_mstats.py @@ -0,0 +1,174 @@ +# 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 3473 2007-10-29 15:18:13Z jarrod.millman $ +""" +__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)" +__version__ = '1.0' +__revision__ = "$Revision: 3473 $" +__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $' + +import numpy + +import numpy.ma +from numpy.ma import masked, masked_array + +import numpy.ma.testutils +from numpy.ma.testutils import * + +from numpy.ma.mstats import * + +#.............................................................................. +class TestQuantiles(NumpyTestCase): + "Base test class for MaskedArrays." + def __init__(self, *args, **kwds): + NumpyTestCase.__init__(self, *args, **kwds) + self.a = numpy.ma.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 = numpy.ma.resize(a, (100,100)) + assert_almost_equal(mquantiles(b), [25.45, 50.5, 75.55]) + assert_almost_equal(mquantiles(b, axis=0), numpy.ma.resize(a,(3,100))) + assert_almost_equal(mquantiles(b, axis=1), + numpy.ma.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 = numpy.ma.resize(a, (100,100)) + assert_almost_equal(mquantiles(b), [25., 50., 75.]) + assert_almost_equal(mquantiles(b, axis=0), numpy.ma.resize(a,(3,100))) + assert_almost_equal(mquantiles(b, axis=1), + numpy.ma.resize([24.9, 50., 75.1], (100,3))) + +class TestMedian(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 = numpy.ma.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 = numpy.ma.arange(24).reshape(4,3,2) + x[x%5==0] = masked + assert_equal(mmedian(x,0), [[12,10],[8,9],[16,17]]) + +#.............................................................................. +class TestTrimming(NumpyTestCase): + # + def __init__(self, *args, **kwds): + NumpyTestCase.__init__(self, *args, **kwds) + # + def test_trim(self): + "Tests trimming." + x = numpy.ma.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 TestMisc(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() |