Python numeric.where方法代码示例

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def allequal (a, b, fill_value=1):
    """
        True if all entries of  a and b are equal, using
        fill_value as a truth value where either or both are masked.
    """
    m = mask_or(getmask(a), getmask(b))
    if m is nomask:
        x = filled(a)
        y = filled(b)
        d = umath.equal(x, y)
        return fromnumeric.alltrue(fromnumeric.ravel(d))
    elif fill_value:
        x = filled(a)
        y = filled(b)
        d = umath.equal(x, y)
        dm = array(d, mask=m, copy=0)
        return fromnumeric.alltrue(fromnumeric.ravel(filled(dm, 1)))
    else:
        return 0 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_values (data, value, rtol=1.e-5, atol=1.e-8, copy=1):
    """
       masked_values(data, value, rtol=1.e-5, atol=1.e-8)
       Create a masked array; mask is nomask if possible.
       If copy==0, and otherwise possible, result
       may share data values with original array.
       Let d = filled(data, value). Returns d
       masked where abs(data-value)<= atol + rtol * abs(value)
       if d is of a floating point type. Otherwise returns
       masked_object(d, value, copy)
    """
    abs = umath.absolute
    d = filled(data, value)
    if issubclass(d.dtype.type, numeric.floating):
        m = umath.less_equal(abs(d-value), atol+rtol*abs(value))
        m = make_mask(m, flag=1)
        return array(d, mask = m, copy=copy,
                      fill_value=value)
    else:
        return masked_object(d, value, copy=copy) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def power (a, b, third=None):
    "a**b"
    if third is not None:
        raise MAError("3-argument power not supported.")
    ma = getmask(a)
    mb = getmask(b)
    m = mask_or(ma, mb)
    fa = filled(a, 1)
    fb = filled(b, 1)
    if fb.dtype.char in typecodes["Integer"]:
        return masked_array(umath.power(fa, fb), m)
    md = make_mask(umath.less(fa, 0), flag=1)
    m = mask_or(m, md)
    if m is nomask:
        return masked_array(umath.power(fa, fb))
    else:
        fa = numeric.where(m, 1, fa)
        return masked_array(umath.power(fa, fb), m) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def where (condition, x, y):
    """where(condition, x, y) is x where condition is nonzero, y otherwise.
       condition must be convertible to an integer array.
       Answer is always the shape of condition.
       The type depends on x and y. It is integer if both x and y are
       the value masked.
    """
    fc = filled(not_equal(condition, 0), 0)
    xv = filled(x)
    xm = getmask(x)
    yv = filled(y)
    ym = getmask(y)
    d = numeric.choose(fc, (yv, xv))
    md = numeric.choose(fc, (ym, xm))
    m = getmask(condition)
    m = make_mask(mask_or(m, md), copy=0, flag=1)
    return masked_array(d, m) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def sort (x, axis = -1, fill_value=None):
    """If x does not have a mask, return a masked array formed from the
       result of numeric.sort(x, axis).
       Otherwise, fill x with fill_value. Sort it.
       Set a mask where the result is equal to fill_value.
       Note that this may have unintended consequences if the data contains the
       fill value at a non-masked site.

       If fill_value is not given the default fill value for x's type will be
       used.
    """
    if fill_value is None:
        fill_value = default_fill_value (x)
    d = filled(x, fill_value)
    s = fromnumeric.sort(d, axis)
    if getmask(x) is nomask:
        return masked_array(s)
    return masked_values(s, fill_value, copy=0) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def fix(x, y=None):
    """
    Round to nearest integer towards zero.

    Round an array of floats element-wise to nearest integer towards zero.
    The rounded values are returned as floats.

    Parameters
    ----------
    x : array_like
        An array of floats to be rounded
    y : ndarray, optional
        Output array

    Returns
    -------
    out : ndarray of floats
        The array of rounded numbers

    See Also
    --------
    trunc, floor, ceil
    around : Round to given number of decimals

    Examples
    --------
    >>> np.fix(3.14)
    3.0
    >>> np.fix(3)
    3.0
    >>> np.fix([2.1, 2.9, -2.1, -2.9])
    array([ 2.,  2., -2., -2.])

    """
    x = nx.asanyarray(x)
    y1 = nx.floor(x)
    y2 = nx.ceil(x)
    if y is None:
        y = nx.asanyarray(y1)
    y[...] = nx.where(x >= 0, y1, y2)
    return y 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def common_fill_value (a, b):
    "The common fill_value of a and b, if there is one, or None"
    t1 = get_fill_value(a)
    t2 = get_fill_value(b)
    if t1 == t2: return t1
    return None

# Domain functions return 1 where the argument(s) are not in the domain. 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def __init__(self, y1, y2):
        "domain_check_interval(a,b)(x) = true where x < a or y > b"
        self.y1 = y1
        self.y2 = y2 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def __call__(self, a, b):
        "Execute the call behavior."
        ma = getmask(a)
        mb = getmask(b)
        d1 = filled(a, self.fillx)
        d2 = filled(b, self.filly)
        t = self.domain(d1, d2)

        if fromnumeric.sometrue(t, None):
            d2 = where(t, self.filly, d2)
            mb = mask_or(mb, t)
        m = mask_or(ma, mb)
        result =  self.f(d1, d2)
        return masked_array(result, m) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_where(condition, x, copy=1):
    """Return x as an array masked where condition is true.
       Also masked where x or condition masked.
    """
    cm = filled(condition, 1)
    m = mask_or(getmask(x), cm)
    return array(filled(x), copy=copy, mask=m) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_greater(x, value, copy=1):
    "masked_greater(x, value) = x masked where x > value"
    return masked_where(greater(x, value), x, copy) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_greater_equal(x, value, copy=1):
    "masked_greater_equal(x, value) = x masked where x >= value"
    return masked_where(greater_equal(x, value), x, copy) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_less_equal(x, value, copy=1):
    "masked_less_equal(x, value) = x masked where x <= value"
    return masked_where(less_equal(x, value), x, copy) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_not_equal(x, value, copy=1):
    "masked_not_equal(x, value) = x masked where x != value"
    d = filled(x, 0)
    c = umath.not_equal(d, value)
    m = mask_or(c, getmask(x))
    return array(d, mask=m, copy=copy) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import where [as 别名]
def masked_equal(x, value, copy=1):
    """masked_equal(x, value) = x masked where x == value
       For floating point consider masked_values(x, value) instead.
    """
    d = filled(x, 0)
    c = umath.equal(d, value)
    m = mask_or(c, getmask(x))
    return array(d, mask=m, copy=copy)