def test_res_equals_tres(self): """Check ds_freq() output to known values.""" a = ds.ds_freq(osr=128, f0=0.0, quadrature=True) b = np.diff(a) res = (0.00190595677588, 0.00510204081633, 0.207803148686, 0.00491921819577) tres = (a.mean(), b.mean(), a.std(), b.std()) self.assertTrue(np.allclose(res, tres, atol=1e-8, rtol=1e-5))
def testds_freq2(self): """Test ds_freq() with quadrature=True""" ref = np.array((-0.500000000000000, -0.489898989898990, -0.479797979797980, -0.469696969696970, -0.459595959595960, -0.449494949494950, -0.439393939393939, -0.429292929292929, -0.419191919191919, -0.409090909090909, -0.398989898989899, -0.388888888888889, -0.378787878787879, -0.368686868686869, -0.358585858585859, -0.348484848484849, -0.338383838383838, -0.328282828282828, -0.318181818181818, -0.308080808080808, -0.297979797979798, -0.287878787878788, -0.277777777777778, -0.267676767676768, -0.257575757575758, -0.247474747474747, -0.237373737373737, -0.227272727272727, -0.217171717171717, -0.207070707070707, -0.196969696969697, -0.186868686868687, -0.176767676767677, -0.166666666666667, -0.156565656565657, -0.151250000000000, -0.150303030303030, -0.149356060606061, -0.148409090909091, -0.147462121212121, -0.146515151515152, -0.145568181818182, -0.144621212121212, -0.143674242424242, -0.142727272727273, -0.141780303030303, -0.140833333333333, -0.139886363636364, -0.138939393939394, -0.137992424242424, -0.137045454545455, -0.136098484848485, -0.135151515151515, -0.134204545454545, -0.133257575757576, -0.132310606060606, -0.131363636363636, -0.130416666666667, -0.129469696969697, -0.128522727272727, -0.127575757575758, -0.126628787878788, -0.125681818181818, -0.124734848484848, -0.123787878787879, -0.122840909090909, -0.121893939393939, -0.120946969696970, -0.120000000000000, -0.119053030303030, -0.118106060606061, -0.117159090909091, -0.116212121212121, -0.115265151515152, -0.114318181818182, -0.113371212121212, -0.112424242424242, -0.111477272727273, -0.110530303030303, -0.109583333333333, -0.108636363636364, -0.107689393939394, -0.106742424242424, -0.105795454545455, -0.104848484848485, -0.103901515151515, -0.102954545454545, -0.102007575757576, -0.101060606060606, -0.100113636363636, -0.0991666666666667, -0.0982196969696970, -0.0972727272727273, -0.0963257575757576, -0.0953787878787879, -0.0944318181818182, -0.0934848484848485, -0.0925378787878788, -0.0915909090909091, -0.0906439393939394, -0.0896969696969697, -0.0887500000000000, -0.0878030303030303, -0.0868560606060606, -0.0859090909090909, -0.0849621212121212, -0.0840151515151515, -0.0830681818181818, -0.0821212121212121, -0.0811742424242424, -0.0802272727272727, -0.0792803030303030, -0.0783333333333333, -0.0773863636363636, -0.0764393939393939, -0.0754924242424242, -0.0745454545454545, -0.0735984848484848, -0.0726515151515152, -0.0717045454545455, -0.0707575757575758, -0.0698106060606061, -0.0688636363636364, -0.0679166666666667, -0.0669696969696970, -0.0660227272727273, -0.0650757575757576, -0.0641287878787879, -0.0631818181818182, -0.0622348484848485, -0.0612878787878788, -0.0603409090909091, -0.0593939393939394, -0.0584469696969697, -0.0575000000000000, -0.0555555555555556, -0.0454545454545455, -0.0353535353535354, -0.0252525252525253, -0.0151515151515151, -0.00505050505050503, 0.00505050505050508, 0.0151515151515151, 0.0252525252525253, 0.0353535353535354, 0.0454545454545454, 0.0555555555555556, 0.0656565656565656, 0.0757575757575758, 0.0858585858585859, 0.0887500000000000, 0.0896969696969697, 0.0906439393939394, 0.0915909090909091, 0.0925378787878788, 0.0934848484848485, 0.0944318181818182, 0.0953787878787879, 0.0963257575757576, 0.0972727272727273, 0.0982196969696970, 0.0991666666666667, 0.100113636363636, 0.101060606060606, 0.102007575757576, 0.102954545454545, 0.103901515151515, 0.104848484848485, 0.105795454545455, 0.106742424242424, 0.107689393939394, 0.108636363636364, 0.109583333333333, 0.110530303030303, 0.111477272727273, 0.112424242424242, 0.113371212121212, 0.114318181818182, 0.115265151515152, 0.116212121212121, 0.117159090909091, 0.118106060606061, 0.119053030303030, 0.120000000000000, 0.120946969696970, 0.121893939393939, 0.122840909090909, 0.123787878787879, 0.124734848484848, 0.125681818181818, 0.126628787878788, 0.127575757575758, 0.128522727272727, 0.129469696969697, 0.130416666666667, 0.131363636363636, 0.132310606060606, 0.133257575757576, 0.134204545454545, 0.135151515151515, 0.136098484848485, 0.137045454545455, 0.137992424242424, 0.138939393939394, 0.139886363636364, 0.140833333333333, 0.141780303030303, 0.142727272727273, 0.143674242424242, 0.144621212121212, 0.145568181818182, 0.146515151515152, 0.147462121212121, 0.148409090909091, 0.149356060606061, 0.150303030303030, 0.151250000000000, 0.152196969696970, 0.153143939393939, 0.154090909090909, 0.155037878787879, 0.155984848484848, 0.156931818181818, 0.157878787878788, 0.158825757575758, 0.159772727272727, 0.160719696969697, 0.161666666666667, 0.162613636363636, 0.163560606060606, 0.164507575757576, 0.165454545454545, 0.166401515151515, 0.167348484848485, 0.168295454545455, 0.169242424242424, 0.170189393939394, 0.171136363636364, 0.172083333333333, 0.173030303030303, 0.173977272727273, 0.174924242424242, 0.175871212121212, 0.176818181818182, 0.177765151515152, 0.178712121212121, 0.179659090909091, 0.180606060606061, 0.181553030303030, 0.182500000000000, 0.186868686868687, 0.196969696969697, 0.207070707070707, 0.217171717171717, 0.227272727272727, 0.237373737373737, 0.247474747474748, 0.257575757575758, 0.267676767676768, 0.277777777777778, 0.287878787878788, 0.297979797979798, 0.308080808080808, 0.318181818181818, 0.328282828282828, 0.338383838383838, 0.348484848484849, 0.358585858585859, 0.368686868686869, 0.378787878787879, 0.388888888888889, 0.398989898989899, 0.409090909090909, 0.419191919191919, 0.429292929292929, 0.439393939393939, 0.449494949494950, 0.459595959595960, 0.469696969696970, 0.479797979797980, 0.489898989898990, 0.500000000000000)) res = ds.ds_freq(32, 0.12, True) assert np.allclose(res, ref, atol=1e-8, rtol=1e-5)