Python ma.where方法代碼示例

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def skew(a, axis=0, bias=True):
    a, axis = _chk_asarray(a,axis)
    n = a.count(axis)
    m2 = moment(a, 2, axis)
    m3 = moment(a, 3, axis)
    olderr = np.seterr(all='ignore')
    try:
        vals = ma.where(m2 == 0, 0, m3 / m2**1.5)
    finally:
        np.seterr(**olderr)

    if not bias:
        can_correct = (n > 2) & (m2 > 0)
        if can_correct.any():
            m2 = np.extract(can_correct, m2)
            m3 = np.extract(can_correct, m3)
            nval = ma.sqrt((n-1.0)*n)/(n-2.0)*m3/m2**1.5
            np.place(vals, can_correct, nval)
    return vals 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def kurtosis(a, axis=0, fisher=True, bias=True):
    a, axis = _chk_asarray(a, axis)
    m2 = moment(a,2,axis)
    m4 = moment(a,4,axis)
    olderr = np.seterr(all='ignore')
    try:
        vals = ma.where(m2 == 0, 0, m4 / m2**2.0)
    finally:
        np.seterr(**olderr)

    if not bias:
        n = a.count(axis)
        can_correct = (n > 3) & (m2 is not ma.masked and m2 > 0)
        if can_correct.any():
            n = np.extract(can_correct, n)
            m2 = np.extract(can_correct, m2)
            m4 = np.extract(can_correct, m4)
            nval = 1.0/(n-2)/(n-3)*((n*n-1.0)*m4/m2**2.0-3*(n-1)**2.0)
            np.place(vals, can_correct, nval+3.0)
    if fisher:
        return vals - 3
    else:
        return vals 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def skewtest(a, axis=0):
    a, axis = _chk_asarray(a, axis)
    if axis is None:
        a = a.ravel()
        axis = 0
    b2 = skew(a,axis)
    n = a.count(axis)
    if np.min(n) < 8:
        warnings.warn(
            "skewtest only valid for n>=8 ... continuing anyway, n=%i" %
            np.min(n))
    y = b2 * ma.sqrt(((n+1)*(n+3)) / (6.0*(n-2)))
    beta2 = (3.0*(n*n+27*n-70)*(n+1)*(n+3)) / ((n-2.0)*(n+5)*(n+7)*(n+9))
    W2 = -1 + ma.sqrt(2*(beta2-1))
    delta = 1/ma.sqrt(0.5*ma.log(W2))
    alpha = ma.sqrt(2.0/(W2-1))
    y = ma.where(y == 0, 1, y)
    Z = delta*ma.log(y/alpha + ma.sqrt((y/alpha)**2+1))
    return Z, (1.0 - stats.zprob(Z))*2 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def f_oneway(*args):
    """
Performs a 1-way ANOVA, returning an F-value and probability given
any number of groups.  From Heiman, pp.394-7.

Usage:   f_oneway (*args)    where *args is 2 or more arrays, one per
                                  treatment group
Returns: f-value, probability
"""
    # Construct a single array of arguments: each row is a group
    data = argstoarray(*args)
    ngroups = len(data)
    ntot = data.count()
    sstot = (data**2).sum() - (data.sum())**2/float(ntot)
    ssbg = (data.count(-1) * (data.mean(-1)-data.mean())**2).sum()
    sswg = sstot-ssbg
    dfbg = ngroups-1
    dfwg = ntot - ngroups
    msb = ssbg/float(dfbg)
    msw = sswg/float(dfwg)
    f = msb/msw
    prob = stats.fprob(dfbg,dfwg,f)
    return f, prob 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def argstoarray(*args):
    """
    Constructs a 2D array from a group of sequences.

    Sequences are filled with missing values to match the length of the longest
    sequence.

    Parameters
    ----------
    args : sequences
        Group of sequences.

    Returns
    -------
    argstoarray : MaskedArray
        A ( `m` x `n` ) masked array, where `m` is the number of arguments and
        `n` the length of the longest argument.

    Notes
    -----
    `numpy.ma.row_stack` has identical behavior, but is called with a sequence
    of sequences.

    """
    if len(args) == 1 and not isinstance(args[0], ndarray):
        output = ma.asarray(args[0])
        if output.ndim != 2:
            raise ValueError("The input should be 2D")
    else:
        n = len(args)
        m = max([len(k) for k in args])
        output = ma.array(np.empty((n,m), dtype=float), mask=True)
        for (k,v) in enumerate(args):
            output[k,:len(v)] = v

    output[np.logical_not(np.isfinite(output._data))] = masked
    return output 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def _betai(a, b, x):
    x = np.asanyarray(x)
    x = ma.where(x < 1.0, x, 1.0)  # if x > 1 then return 1.0
    return special.betainc(a, b, x) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def normaltest(a, axis=0):
    """
    Tests whether a sample differs from a normal distribution.

    Parameters
    ----------
    a : array_like
        The array containing the data to be tested.
    axis : int or None, optional
        Axis along which to compute test. Default is 0. If None,
        compute over the whole array `a`.

    Returns
    -------
    statistic : float or array
        ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
        ``k`` is the z-score returned by `kurtosistest`.
    pvalue : float or array
       A 2-sided chi squared probability for the hypothesis test.

    Notes
    -----
    For more details about `normaltest`, see `stats.normaltest`.

    """
    a, axis = _chk_asarray(a, axis)
    s, _ = skewtest(a, axis)
    k, _ = kurtosistest(a, axis)
    k2 = s*s + k*k

    return NormaltestResult(k2, distributions.chi2.sf(k2, 2)) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def _mask_non_positives(a):
    """
    Return a Numpy masked array where all non-positive values are
    masked.  If there are no non-positive values, the original array
    is returned.
    """
    mask = a <= 0.0
    if mask.any():
        return ma.MaskedArray(a, mask=mask)
    return a 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def transform_non_affine(self, a):
        sign = np.sign(a)
        masked = ma.masked_inside(a,
                                  -self.linthresh,
                                  self.linthresh,
                                  copy=False)
        log = sign * self.linthresh * (
            self._linscale_adj +
            ma.log(np.abs(masked) / self.linthresh) / self._log_base)
        if masked.mask.any():
            return ma.where(masked.mask, a * self._linscale_adj, log)
        else:
            return log 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def argstoarray(*args):
    """
    Constructs a 2D array from a group of sequences.

    Sequences are filled with missing values to match the length of the longest
    sequence.

    Parameters
    ----------
    args : sequences
        Group of sequences.

    Returns
    -------
    argstoarray : MaskedArray
        A ( `m` x `n` ) masked array, where `m` is the number of arguments and
        `n` the length of the longest argument.

    Notes
    -----
    numpy.ma.row_stack has identical behavior, but is called with a sequence of
    sequences.

    """
    if len(args) == 1 and not isinstance(args[0], ndarray):
        output = ma.asarray(args[0])
        if output.ndim != 2:
            raise ValueError("The input should be 2D")
    else:
        n = len(args)
        m = max([len(k) for k in args])
        output = ma.array(np.empty((n,m), dtype=float), mask=True)
        for (k,v) in enumerate(args):
            output[k,:len(v)] = v
    output[np.logical_not(np.isfinite(output._data))] = masked
    return output


#####--------------------------------------------------------------------------
#---- --- Ranking ---
#####-------------------------------------------------------------------------- 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def betai(a, b, x):
    x = np.asanyarray(x)
    x = ma.where(x < 1.0, x, 1.0)  # if x > 1 then return 1.0
    return special.betainc(a, b, x) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def trimboth(data, proportiontocut=0.2, inclusive=(True,True), axis=None):
    """
    Trims the smallest and largest data values.

    Trims the `data` by masking the ``int(proportiontocut * n)`` smallest and
    ``int(proportiontocut * n)`` largest values of data along the given axis,
    where n is the number of unmasked values before trimming.

    Parameters
    ----------
    data : ndarray
        Data to trim.
    proportiontocut : float, optional
        Percentage of trimming (as a float between 0 and 1).
        If n is the number of unmasked values before trimming, the number of
        values after trimming is ``(1 - 2*proportiontocut) * n``.
        Default is 0.2.
    inclusive : {(bool, bool) tuple}, optional
        Tuple indicating whether the number of data being masked on each side
        should be rounded (True) or truncated (False).
    axis : int, optional
        Axis along which to perform the trimming.
        If None, the input array is first flattened.

    """
    return trimr(data, limits=(proportiontocut,proportiontocut),
                 inclusive=inclusive, axis=axis)

#.............................................................................. 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def friedmanchisquare(*args):
    """Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA.
    This function calculates the Friedman Chi-square test for repeated measures
    and returns the result, along with the associated probability value.

    Each input is considered a given group. Ideally, the number of treatments
    among each group should be equal. If this is not the case, only the first
    n treatments are taken into account, where n is the number of treatments
    of the smallest group.
    If a group has some missing values, the corresponding treatments are masked
    in the other groups.
    The test statistic is corrected for ties.

    Masked values in one group are propagated to the other groups.

    Returns: chi-square statistic, associated p-value
    """
    data = argstoarray(*args).astype(float)
    k = len(data)
    if k < 3:
        raise ValueError("Less than 3 groups (%i): " % k +
                         "the Friedman test is NOT appropriate.")
    ranked = ma.masked_values(rankdata(data, axis=0), 0)
    if ranked._mask is not nomask:
        ranked = ma.mask_cols(ranked)
        ranked = ranked.compressed().reshape(k,-1).view(ndarray)
    else:
        ranked = ranked._data
    (k,n) = ranked.shape
    # Ties correction
    repeats = np.array([find_repeats(_) for _ in ranked.T], dtype=object)
    ties = repeats[repeats.nonzero()].reshape(-1,2)[:,-1].astype(int)
    tie_correction = 1 - (ties**3-ties).sum()/float(n*(k**3-k))
    #
    ssbg = np.sum((ranked.sum(-1) - n*(k+1)/2.)**2)
    chisq = ssbg * 12./(n*k*(k+1)) * 1./tie_correction
    return chisq, stats.chisqprob(chisq,k-1)

#-############################################################################-# 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def trimboth(data, proportiontocut=0.2, inclusive=(True,True), axis=None):
    """
    Trims the smallest and largest data values.

    Trims the `data` by masking the ``int(proportiontocut * n)`` smallest and
    ``int(proportiontocut * n)`` largest values of data along the given axis,
    where n is the number of unmasked values before trimming.

    Parameters
    ----------
    data : ndarray
        Data to trim.
    proportiontocut : float, optional
        Percentage of trimming (as a float between 0 and 1).
        If n is the number of unmasked values before trimming, the number of
        values after trimming is ``(1 - 2*proportiontocut) * n``.
        Default is 0.2.
    inclusive : {(bool, bool) tuple}, optional
        Tuple indicating whether the number of data being masked on each side
        should be rounded (True) or truncated (False).
    axis : int, optional
        Axis along which to perform the trimming.
        If None, the input array is first flattened.

    """
    return trimr(data, limits=(proportiontocut,proportiontocut),
                 inclusive=inclusive, axis=axis) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import where [as 別名]
def f_oneway(*args):
    """
    Performs a 1-way ANOVA, returning an F-value and probability given
    any number of groups.  From Heiman, pp.394-7.

    Usage: ``f_oneway(*args)``, where ``*args`` is 2 or more arrays,
    one per treatment group.

    Returns
    -------
    statistic : float
        The computed F-value of the test.
    pvalue : float
        The associated p-value from the F-distribution.

    """
    # Construct a single array of arguments: each row is a group
    data = argstoarray(*args)
    ngroups = len(data)
    ntot = data.count()
    sstot = (data**2).sum() - (data.sum())**2/float(ntot)
    ssbg = (data.count(-1) * (data.mean(-1)-data.mean())**2).sum()
    sswg = sstot-ssbg
    dfbg = ngroups-1
    dfwg = ntot - ngroups
    msb = ssbg/float(dfbg)
    msw = sswg/float(dfwg)
    f = msb/msw
    prob = special.fdtrc(dfbg, dfwg, f)  # equivalent to stats.f.sf

    return F_onewayResult(f, prob)