# pylint: disable-msg=W0611, W0612, W0511,R0201 """Tests suite for maskedArray statistics. :author: Pierre Gerard-Marchant :contact: pierregm_at_uga_dot_edu """ from __future__ import division, print_function, absolute_import __author__ = "Pierre GF Gerard-Marchant ($Author: backtopop $)" import numpy as np import numpy.ma as ma import scipy.stats.mstats as ms #import scipy.stats.mmorestats as mms from numpy.testing import TestCase, run_module_suite, assert_equal, \ assert_almost_equal, assert_ class TestMisc(TestCase): def __init__(self, *args, **kwargs): TestCase.__init__(self, *args, **kwargs) def test_mjci(self): "Tests the Marits-Jarrett estimator" data = ma.array([77, 87, 88,114,151,210,219,246,253,262, 296,299,306,376,428,515,666,1310,2611]) assert_almost_equal(ms.mjci(data),[55.76819,45.84028,198.87875],5) def test_trimmedmeanci(self): "Tests the confidence intervals of the trimmed mean." data = ma.array([545,555,558,572,575,576,578,580, 594,605,635,651,653,661,666]) assert_almost_equal(ms.trimmed_mean(data,0.2), 596.2, 1) assert_equal(np.round(ms.trimmed_mean_ci(data,(0.2,0.2)),1), [561.8, 630.6]) def test_idealfourths(self): "Tests ideal-fourths" test = np.arange(100) assert_almost_equal(np.asarray(ms.idealfourths(test)), [24.416667,74.583333],6) test_2D = test.repeat(3).reshape(-1,3) assert_almost_equal(ms.idealfourths(test_2D, axis=0), [[24.416667,24.416667,24.416667], [74.583333,74.583333,74.583333]],6) assert_almost_equal(ms.idealfourths(test_2D, axis=1), test.repeat(2).reshape(-1,2)) test = [0,0] _result = ms.idealfourths(test) assert_(np.isnan(_result).all()) #.............................................................................. class TestQuantiles(TestCase): def __init__(self, *args, **kwargs): TestCase.__init__(self, *args, **kwargs) def test_hdquantiles(self): data = [0.706560797,0.727229578,0.990399276,0.927065621,0.158953014, 0.887764025,0.239407086,0.349638551,0.972791145,0.149789972, 0.936947700,0.132359948,0.046041972,0.641675031,0.945530547, 0.224218684,0.771450991,0.820257774,0.336458052,0.589113496, 0.509736129,0.696838829,0.491323573,0.622767425,0.775189248, 0.641461450,0.118455200,0.773029450,0.319280007,0.752229111, 0.047841438,0.466295911,0.583850781,0.840581845,0.550086491, 0.466470062,0.504765074,0.226855960,0.362641207,0.891620942, 0.127898691,0.490094097,0.044882048,0.041441695,0.317976349, 0.504135618,0.567353033,0.434617473,0.636243375,0.231803616, 0.230154113,0.160011327,0.819464108,0.854706985,0.438809221, 0.487427267,0.786907310,0.408367937,0.405534192,0.250444460, 0.995309248,0.144389588,0.739947527,0.953543606,0.680051621, 0.388382017,0.863530727,0.006514031,0.118007779,0.924024803, 0.384236354,0.893687694,0.626534881,0.473051932,0.750134705, 0.241843555,0.432947602,0.689538104,0.136934797,0.150206859, 0.474335206,0.907775349,0.525869295,0.189184225,0.854284286, 0.831089744,0.251637345,0.587038213,0.254475554,0.237781276, 0.827928620,0.480283781,0.594514455,0.213641488,0.024194386, 0.536668589,0.699497811,0.892804071,0.093835427,0.731107772] # assert_almost_equal(ms.hdquantiles(data,[0., 1.]), [0.006514031, 0.995309248]) hdq = ms.hdquantiles(data,[0.25, 0.5, 0.75]) assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,]) hdq = ms.hdquantiles_sd(data,[0.25, 0.5, 0.75]) assert_almost_equal(hdq, [0.03786954, 0.03805389, 0.03800152,], 4) # data = np.array(data).reshape(10,10) hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0) assert_almost_equal(hdq[:,0], ms.hdquantiles(data[:,0],[0.25,0.5,0.75])) assert_almost_equal(hdq[:,-1], ms.hdquantiles(data[:,-1],[0.25,0.5,0.75])) hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0,var=True) assert_almost_equal(hdq[...,0], ms.hdquantiles(data[:,0],[0.25,0.5,0.75],var=True)) assert_almost_equal(hdq[...,-1], ms.hdquantiles(data[:,-1],[0.25,0.5,0.75], var=True)) ############################################################################### if __name__ == "__main__": run_module_suite()