import numpy as np import numpy.ma as ma import scipy.stats.mstats as ms from numpy.testing import (assert_equal, assert_almost_equal, assert_, assert_allclose) def test_compare_medians_ms(): x = np.arange(7) y = x + 10 assert_almost_equal(ms.compare_medians_ms(x, y), 0) y2 = np.linspace(0, 1, num=10) assert_almost_equal(ms.compare_medians_ms(x, y2), 0.017116406778) def test_hdmedian(): # 1-D array x = ma.arange(11) assert_allclose(ms.hdmedian(x), 5, rtol=1e-14) x.mask = ma.make_mask(x) x.mask[:7] = False assert_allclose(ms.hdmedian(x), 3, rtol=1e-14) # Check that `var` keyword returns a value. TODO: check whether returned # value is actually correct. assert_(ms.hdmedian(x, var=True).size == 2) # 2-D array x2 = ma.arange(22).reshape((11, 2)) assert_allclose(ms.hdmedian(x2, axis=0), [10, 11]) x2.mask = ma.make_mask(x2) x2.mask[:7, :] = False assert_allclose(ms.hdmedian(x2, axis=0), [6, 7]) def test_rsh(): np.random.seed(132345) x = np.random.randn(100) res = ms.rsh(x) # Just a sanity check that the code runs and output shape is correct. # TODO: check that implementation is correct. assert_(res.shape == x.shape) # Check points keyword res = ms.rsh(x, points=[0, 1.]) assert_(res.size == 2) def test_mjci(): # 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_trimmed_mean_ci(): # 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(): # 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: 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] def test_hdquantiles(self): data = self.data 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,]) 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)) def test_hdquantiles_sd(self): # Standard deviation is a jackknife estimator, so we can check if # the efficient version (hdquantiles_sd) matches a rudimentary, # but clear version here. hd_std_errs = ms.hdquantiles_sd(self.data) # jacknnife standard error, Introduction to the Bootstrap Eq. 11.5 n = len(self.data) jdata = np.broadcast_to(self.data, (n, n)) jselector = np.logical_not(np.eye(n)) # leave out one sample each row jdata = jdata[jselector].reshape(n, n-1) jdist = ms.hdquantiles(jdata, axis=1) jdist_mean = np.mean(jdist, axis=0) jstd = ((n-1)/n * np.sum((jdist - jdist_mean)**2, axis=0))**.5 assert_almost_equal(hd_std_errs, jstd) # Test actual values for good measure assert_almost_equal(hd_std_errs, [0.0379258, 0.0380656, 0.0380013]) two_data_points = ms.hdquantiles_sd([1, 2]) assert_almost_equal(two_data_points, [0.5, 0.5, 0.5]) def test_mquantiles_cimj(self): # Only test that code runs, implementation not checked for correctness ci_lower, ci_upper = ms.mquantiles_cimj(self.data) assert_(ci_lower.size == ci_upper.size == 3)