Python ma.median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 median [as 别名]
def regridToCoarse(fine,fac,mode,missValue):
    nr,nc = np.shape(fine)
    coarse = np.zeros(nr/fac * nc / fac).reshape(nr/fac,nc/fac) + MV
    nr,nc = np.shape(coarse)
    for r in range(0,nr):
        for c in range(0,nc):
            ar = fine[r * fac : fac * (r+1),c * fac: fac * (c+1)]
            m = np.ma.masked_values(ar,missValue)
            if ma.count(m) == 0:
                coarse[r,c] = MV
            else:
                if mode == 'average':
                    coarse [r,c] = ma.average(m)
                elif mode == 'median': 
                    coarse [r,c] = ma.median(m)
                elif mode == 'sum':
                    coarse [r,c] = ma.sum(m)
                elif mode =='min':
                    coarse [r,c] = ma.min(m)
                elif mode == 'max':
                    coarse [r,c] = ma.max(m)
    return coarse 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [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 median [as 别名]
def stde_median(data, axis=None):
    """Returns the McKean-Schrader estimate of the standard error of the sample
    median along the given axis. masked values are discarded.

    Parameters
    ----------
    data : ndarray
        Data to trim.
    axis : {None,int}, optional
        Axis along which to perform the trimming.
        If None, the input array is first flattened.

    """
    def _stdemed_1D(data):
        data = np.sort(data.compressed())
        n = len(data)
        z = 2.5758293035489004
        k = int(np.round((n+1)/2. - z * np.sqrt(n/4.),0))
        return ((data[n-k] - data[k-1])/(2.*z))

    data = ma.array(data, copy=False, subok=True)
    if (axis is None):
        return _stdemed_1D(data)
    else:
        if data.ndim > 2:
            raise ValueError("Array 'data' must be at most two dimensional, "
                             "but got data.ndim = %d" % data.ndim)
        return ma.apply_along_axis(_stdemed_1D, axis, data) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def scoreatpercentile(data, per, limit=(), alphap=.4, betap=.4):
    """Calculate the score at the given 'per' percentile of the
    sequence a.  For example, the score at per=50 is the median.

    This function is a shortcut to mquantile

    """
    if (per < 0) or (per > 100.):
        raise ValueError("The percentile should be between 0. and 100. !"
                         " (got %s)" % per)

    return mquantiles(data, prob=[per/100.], alphap=alphap, betap=betap,
                      limit=limit, axis=0).squeeze() 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def compare_medians_ms(group_1, group_2, axis=None):
    """
    Compares the medians from two independent groups along the given axis.

    The comparison is performed using the McKean-Schrader estimate of the
    standard error of the medians.

    Parameters
    ----------
    group_1 : array_like
        First dataset.  Has to be of size >=7.
    group_2 : array_like
        Second dataset.  Has to be of size >=7.
    axis : int, optional
        Axis along which the medians are estimated. If None, the arrays are
        flattened.  If `axis` is not None, then `group_1` and `group_2`
        should have the same shape.

    Returns
    -------
    compare_medians_ms : {float, ndarray}
        If `axis` is None, then returns a float, otherwise returns a 1-D
        ndarray of floats with a length equal to the length of `group_1`
        along `axis`.

    """
    (med_1, med_2) = (ma.median(group_1,axis=axis), ma.median(group_2,axis=axis))
    (std_1, std_2) = (mstats.stde_median(group_1, axis=axis),
                      mstats.stde_median(group_2, axis=axis))
    W = np.abs(med_1 - med_2) / ma.sqrt(std_1**2 + std_2**2)
    return 1 - norm.cdf(W) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [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 median [as 别名]
def stde_median(data, axis=None):
    """Returns the McKean-Schrader estimate of the standard error of the sample
median along the given axis. masked values are discarded.

    Parameters
    ----------
        data : ndarray
            Data to trim.
        axis : {None,int}, optional
            Axis along which to perform the trimming.
            If None, the input array is first flattened.

    """
    def _stdemed_1D(data):
        data = np.sort(data.compressed())
        n = len(data)
        z = 2.5758293035489004
        k = int(np.round((n+1)/2. - z * np.sqrt(n/4.),0))
        return ((data[n-k] - data[k-1])/(2.*z))
    #
    data = ma.array(data, copy=False, subok=True)
    if (axis is None):
        return _stdemed_1D(data)
    else:
        if data.ndim > 2:
            raise ValueError("Array 'data' must be at most two dimensional, but got data.ndim = %d" % data.ndim)
        return ma.apply_along_axis(_stdemed_1D, axis, data)

#####--------------------------------------------------------------------------
#---- --- Normality Tests ---
#####-------------------------------------------------------------------------- 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def scoreatpercentile(data, per, limit=(), alphap=.4, betap=.4):
    """Calculate the score at the given 'per' percentile of the
    sequence a.  For example, the score at per=50 is the median.

    This function is a shortcut to mquantile

    """
    if (per < 0) or (per > 100.):
        raise ValueError("The percentile should be between 0. and 100. !"
                         " (got %s)" % per)
    return mquantiles(data, prob=[per/100.], alphap=alphap, betap=betap,
                      limit=limit, axis=0).squeeze() 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def compare_medians_ms(group_1, group_2, axis=None):
    """
    Compares the medians from two independent groups along the given axis.

    The comparison is performed using the McKean-Schrader estimate of the
    standard error of the medians.

    Parameters
    ----------
    group_1 : array_like
        First dataset.
    group_2 : array_like
        Second dataset.
    axis : int, optional
        Axis along which the medians are estimated. If None, the arrays are
        flattened.  If `axis` is not None, then `group_1` and `group_2`
        should have the same shape.

    Returns
    -------
    compare_medians_ms : {float, ndarray}
        If `axis` is None, then returns a float, otherwise returns a 1-D
        ndarray of floats with a length equal to the length of `group_1`
        along `axis`.

    """
    (med_1, med_2) = (ma.median(group_1,axis=axis), ma.median(group_2,axis=axis))
    (std_1, std_2) = (mstats.stde_median(group_1, axis=axis),
                      mstats.stde_median(group_2, axis=axis))
    W = np.abs(med_1 - med_2) / ma.sqrt(std_1**2 + std_2**2)
    return 1 - norm.cdf(W) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def test_median_axis_none_mask_none(set_random_seed):
    for i in range(25):
        size = np.random.randint(1, 10000)
        mean = np.random.uniform(-1000, 1000)
        sigma = np.random.uniform(0, 1000)
        a = np.random.normal(mean, sigma, size)
        expected = np.median(a.astype(np.float32))
        actual = stats.median(a)
        assert np.float32(expected) == actual 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def test_median_2d_axis_none_mask_none(set_random_seed):
    for i in range(5):
        size1 = np.random.randint(1, 300)
        size2 = np.random.randint(1, 300)
        mean = np.random.uniform(-1000, 1000)
        sigma = np.random.uniform(0, 1000)
        a = np.random.normal(mean, sigma, size=(size1, size2))
        expected = np.median(a.astype(np.float32))
        actual = stats.median(a)
        assert np.float32(expected) == actual 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def test_median_3d_axis_none_mask_none(set_random_seed):
    for i in range(5):
        size1 = np.random.randint(1, 50)
        size2 = np.random.randint(1, 50)
        size3 = np.random.randint(1, 50)
        mean = np.random.uniform(-1000, 1000)
        sigma = np.random.uniform(0, 1000)
        a = np.random.normal(mean, sigma, size=(size1, size2, size3))
        expected = np.median(a.astype(np.float32))
        actual = stats.median(a)
        assert np.float32(expected) == actual 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def test_median_2d_axis_0_mask_none(set_random_seed):
    for i in range(5):
        size1 = np.random.randint(1, 300)
        size2 = np.random.randint(1, 300)
        mean = np.random.uniform(-1000, 1000)
        sigma = np.random.uniform(0, 1000)
        a = np.random.normal(mean, sigma, size=(size1, size2))
        expected = np.median(a.astype(np.float32), axis=0)
        actual = stats.median(a, axis=0)
        np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def test_median_2d_axis_1_mask_none(set_random_seed):
    for i in range(5):
        size1 = np.random.randint(1, 300)
        size2 = np.random.randint(5, 300)
        mean = np.random.uniform(-1000, 1000)
        sigma = np.random.uniform(0, 1000)
        a = np.random.normal(mean, sigma, size=(size1, size2))
        expected = np.median(a.astype(np.float32), axis=1)
        actual = stats.median(a, axis=1)
        np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6) 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import median [as 别名]
def test_median_3d_axis_1_mask_none(set_random_seed):
    for i in range(5):
        size1 = np.random.randint(1, 50)
        size2 = np.random.randint(5, 50)
        size3 = np.random.randint(1, 50)
        mean = np.random.uniform(-1000, 1000)
        sigma = np.random.uniform(0, 1000)
        a = np.random.normal(mean, sigma, size=(size1, size2, size3))
        expected = np.median(a.astype(np.float32), axis=1)
        actual = stats.median(a, axis=1)
        np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)