Python ma.arange方法代码示例

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_sem(self):
        # example from stats.sem doc
        a = np.arange(20).reshape(5,4)
        am = np.ma.array(a)
        r = stats.sem(a,ddof=1)
        rm = stats.mstats.sem(am, ddof=1)

        assert_allclose(r, 2.82842712, atol=1e-5)
        assert_allclose(rm, 2.82842712, atol=1e-5)

        for n in self.get_n():
            x, y, xm, ym = self.generate_xy_sample(n)
            assert_almost_equal(stats.mstats.sem(xm, axis=None, ddof=0),
                                stats.sem(x, axis=None, ddof=0), decimal=13)
            assert_almost_equal(stats.mstats.sem(ym, axis=None, ddof=0),
                                stats.sem(y, axis=None, ddof=0), decimal=13)
            assert_almost_equal(stats.mstats.sem(xm, axis=None, ddof=1),
                                stats.sem(x, axis=None, ddof=1), decimal=13)
            assert_almost_equal(stats.mstats.sem(ym, axis=None, ddof=1),
                                stats.sem(y, axis=None, ddof=1), decimal=13) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
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]) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def sen_seasonal_slopes(x):
    x = ma.array(x, subok=True, copy=False, ndmin=2)
    (n,_) = x.shape
    # Get list of slopes per season
    szn_slopes = ma.vstack([(x[i+1:]-x[i])/np.arange(1,n-i)[:,None]
                            for i in range(n)])
    szn_medslopes = ma.median(szn_slopes, axis=0)
    medslope = ma.median(szn_slopes, axis=None)
    return szn_medslopes, medslope 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def sen_seasonal_slopes(x):
    x = ma.array(x, subok=True, copy=False, ndmin=2)
    (n,_) = x.shape
    # Get list of slopes per season
    szn_slopes = ma.vstack([(x[i+1:]-x[i])/np.arange(1,n-i)[:,None]
                            for i in range(n)])
    szn_medslopes = ma.median(szn_slopes, axis=0)
    medslope = ma.median(szn_slopes, axis=None)
    return szn_medslopes, medslope


#####--------------------------------------------------------------------------
#---- --- Inferential statistics ---
#####-------------------------------------------------------------------------- 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def _kolmog1(x,n):
    if x <= 0:
        return 0
    if x >= 1:
        return 1
    j = np.arange(np.floor(n*(1-x))+1)
    return 1 - x * np.sum(np.exp(np.log(misc.comb(n,j))
                                       + (n-j) * np.log(1-x-j/float(n))
                                       + (j-1) * np.log(x+j/float(n)))) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_trim(self):
        "Tests trimming"
        a = ma.arange(10)
        assert_equal(mstats.trim(a), [0,1,2,3,4,5,6,7,8,9])
        a = ma.arange(10)
        assert_equal(mstats.trim(a,(2,8)), [None,None,2,3,4,5,6,7,8,None])
        a = ma.arange(10)
        assert_equal(mstats.trim(a,limits=(2,8),inclusive=(False,False)),
                     [None,None,None,3,4,5,6,7,None,None])
        a = ma.arange(10)
        assert_equal(mstats.trim(a,limits=(0.1,0.2),relative=True),
                     [None,1,2,3,4,5,6,7,None,None])
        #
        a = ma.arange(12)
        a[[0,-1]] = a[5] = masked
        assert_equal(mstats.trim(a,(2,8)),
                     [None,None,2,3,4,None,6,7,8,None,None,None])
        #
        x = ma.arange(100).reshape(10,10)
        trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=None)
        assert_equal(trimx._mask.ravel(),[1]*10+[0]*70+[1]*20)
        trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=0)
        assert_equal(trimx._mask.ravel(),[1]*10+[0]*70+[1]*20)
        trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=-1)
        assert_equal(trimx._mask.T.ravel(),[1]*10+[0]*70+[1]*20)
        #
        x = ma.arange(110).reshape(11,10)
        x[1] = masked
        trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=None)
        assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
        trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=0)
        assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20)
        trimx = mstats.trim(x.T,(0.1,0.2),relative=True,axis=-1)
        assert_equal(trimx.T._mask.ravel(),[1]*20+[0]*70+[1]*20) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_trim_old(self):
        "Tests trimming."
        x = ma.arange(100)
        assert_equal(mstats.trimboth(x).count(), 60)
        assert_equal(mstats.trimtail(x,tail='r').count(), 80)
        x[50:70] = masked
        trimx = mstats.trimboth(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(mstats.trimboth(x).count(), 60)
        assert_equal(mstats.trimtail(x).count(), 80) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_percentile(self):
        x = np.arange(8) * 0.5
        assert_equal(mstats.scoreatpercentile(x, 0), 0.)
        assert_equal(mstats.scoreatpercentile(x, 100), 3.5)
        assert_equal(mstats.scoreatpercentile(x, 50), 1.75) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_plotting_positions():
    """Regression test for #1256"""
    pos = mstats.plotting_positions(np.arange(3), 0, 0)
    assert_array_almost_equal(pos.data, np.array([0.25, 0.5, 0.75])) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_kendalltau(self):
        # Tests some computations of Kendall's tau
        x = ma.fix_invalid([5.05, 6.75, 3.21, 2.66,np.nan])
        y = ma.fix_invalid([1.65, 26.5, -5.93, 7.96, np.nan])
        z = ma.fix_invalid([1.65, 2.64, 2.64, 6.95, np.nan])
        assert_almost_equal(np.asarray(mstats.kendalltau(x,y)),
                            [+0.3333333,0.4969059])
        assert_almost_equal(np.asarray(mstats.kendalltau(x,z)),
                            [-0.5477226,0.2785987])
        #
        x = ma.fix_invalid([0, 0, 0, 0,20,20, 0,60, 0,20,
                            10,10, 0,40, 0,20, 0, 0, 0, 0, 0, np.nan])
        y = ma.fix_invalid([0,80,80,80,10,33,60, 0,67,27,
                            25,80,80,80,80,80,80, 0,10,45, np.nan, 0])
        result = mstats.kendalltau(x,y)
        assert_almost_equal(np.asarray(result), [-0.1585188, 0.4128009])
        # make sure internal variable use correct precision with
        # larger arrays
        x = np.arange(2000, dtype=float)
        x = ma.masked_greater(x, 1995)
        y = np.arange(2000, dtype=float)
        y = np.concatenate((y[1000:], y[:1000]))
        assert_(np.isfinite(mstats.kendalltau(x,y)[1]))

        # test for namedtuple attributes
        res = mstats.kendalltau(x, y)
        attributes = ('correlation', 'pvalue')
        check_named_results(res, attributes, ma=True) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_trim(self):
        a = ma.arange(10)
        assert_equal(mstats.trim(a), [0,1,2,3,4,5,6,7,8,9])
        a = ma.arange(10)
        assert_equal(mstats.trim(a,(2,8)), [None,None,2,3,4,5,6,7,8,None])
        a = ma.arange(10)
        assert_equal(mstats.trim(a,limits=(2,8),inclusive=(False,False)),
                     [None,None,None,3,4,5,6,7,None,None])
        a = ma.arange(10)
        assert_equal(mstats.trim(a,limits=(0.1,0.2),relative=True),
                     [None,1,2,3,4,5,6,7,None,None])

        a = ma.arange(12)
        a[[0,-1]] = a[5] = masked
        assert_equal(mstats.trim(a, (2,8)),
                     [None, None, 2, 3, 4, None, 6, 7, 8, None, None, None])

        x = ma.arange(100).reshape(10, 10)
        expected = [1]*10 + [0]*70 + [1]*20
        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=None)
        assert_equal(trimx._mask.ravel(), expected)
        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=0)
        assert_equal(trimx._mask.ravel(), expected)
        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=-1)
        assert_equal(trimx._mask.T.ravel(), expected)

        # same as above, but with an extra masked row inserted
        x = ma.arange(110).reshape(11, 10)
        x[1] = masked
        expected = [1]*20 + [0]*70 + [1]*20
        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=None)
        assert_equal(trimx._mask.ravel(), expected)
        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=0)
        assert_equal(trimx._mask.ravel(), expected)
        trimx = mstats.trim(x.T, (0.1,0.2), relative=True, axis=-1)
        assert_equal(trimx.T._mask.ravel(), expected) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_trim_old(self):
        x = ma.arange(100)
        assert_equal(mstats.trimboth(x).count(), 60)
        assert_equal(mstats.trimtail(x,tail='r').count(), 80)
        x[50:70] = masked
        trimx = mstats.trimboth(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(mstats.trimboth(x).count(), 60)
        assert_equal(mstats.trimtail(x).count(), 80) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_plotting_positions():
    # Regression test for #1256
    pos = mstats.plotting_positions(np.arange(3), 0, 0)
    assert_array_almost_equal(pos.data, np.array([0.25, 0.5, 0.75])) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_describe_result_attributes(self):
        actual = mstats.describe(np.arange(5))
        attributes = ('nobs', 'minmax', 'mean', 'variance', 'skewness',
                      'kurtosis')
        check_named_results(actual, attributes, ma=True) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import arange [as 别名]
def test_trimboth(self):
        a = np.arange(20)
        b = stats.trimboth(a, 0.1)
        bm = stats.mstats.trimboth(a, 0.1)
        assert_allclose(np.sort(b), bm.data[~bm.mask])