Python numpy.inexact() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def nan_to_num(array, fill_value = 0.0, copy = True):
    """
    Replace NaNs with another fill value. 

    Parameters
    ----------
    array : array_like
        Input data.
    fill_value : float, optional
        NaNs will be replaced by ``fill_value``. Default is 0.0, in keeping
        with ``numpy.nan_to_num``.
    copy : bool, optional
        Whether to create a copy of `array` (True) or to replace values
        in-place (False). The in-place operation only occurs if
        casting to an array does not require a copy.
    
    Returns
    -------
    out : ndarray
        Array without NaNs. If ``array`` was not of floating or complearray type,
        ``array`` is returned unchanged.
    
    Notes
    -----
    Contrary to ``numpy.nan_to_num``, this functions does not handle
    infinite values.

    See Also
    --------
    numpy.nan_to_num : replace NaNs and Infs with zeroes.
    """
    array = np.array(array, subok = True, copy = copy)
    dtype = array.dtype.type

    # Non-inexact types do not have NaNs
    if not np.issubdtype(dtype, np.inexact):
        return array
    
    iscomplex = np.issubdtype(dtype, np.complexfloating)
    dest = (array.real, array.imag) if iscomplex else (array,)
    for d in dest:
        np.copyto(d, fill_value, where = np.isnan(d))
    return array 

Example 2

def _replace_nan(a, val):
    """
    If `a` is of inexact type, make a copy of `a`, replace NaNs with
    the `val` value, and return the copy together with a boolean mask
    marking the locations where NaNs were present. If `a` is not of
    inexact type, do nothing and return `a` together with a mask of None.

    Note that scalars will end up as array scalars, which is important
    for using the result as the value of the out argument in some
    operations.

    Parameters
    ----------
    a : array-like
        Input array.
    val : float
        NaN values are set to val before doing the operation.

    Returns
    -------
    y : ndarray
        If `a` is of inexact type, return a copy of `a` with the NaNs
        replaced by the fill value, otherwise return `a`.
    mask: {bool, None}
        If `a` is of inexact type, return a boolean mask marking locations of
        NaNs, otherwise return None.

    """
    is_new = not isinstance(a, np.ndarray)
    if is_new:
        a = np.array(a)
    if not issubclass(a.dtype.type, np.inexact):
        return a, None
    if not is_new:
        # need copy
        a = np.array(a, subok=True)

    mask = np.isnan(a)
    np.copyto(a, val, where=mask)
    return a, mask 

Example 3

def _divide_by_count(a, b, out=None):
    """
    Compute a/b ignoring invalid results. If `a` is an array the division
    is done in place. If `a` is a scalar, then its type is preserved in the
    output. If out is None, then then a is used instead so that the
    division is in place. Note that this is only called with `a` an inexact
    type.

    Parameters
    ----------
    a : {ndarray, numpy scalar}
        Numerator. Expected to be of inexact type but not checked.
    b : {ndarray, numpy scalar}
        Denominator.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    Returns
    -------
    ret : {ndarray, numpy scalar}
        The return value is a/b. If `a` was an ndarray the division is done
        in place. If `a` is a numpy scalar, the division preserves its type.

    """
    with np.errstate(invalid='ignore'):
        if isinstance(a, np.ndarray):
            if out is None:
                return np.divide(a, b, out=a, casting='unsafe')
            else:
                return np.divide(a, b, out=out, casting='unsafe')
        else:
            if out is None:
                return a.dtype.type(a / b)
            else:
                # This is questionable, but currently a numpy scalar can
                # be output to a zero dimensional array.
                return np.divide(a, b, out=out, casting='unsafe') 

Example 4

def _replace_nan(a, val):
    """
    If `a` is of inexact type, make a copy of `a`, replace NaNs with
    the `val` value, and return the copy together with a boolean mask
    marking the locations where NaNs were present. If `a` is not of
    inexact type, do nothing and return `a` together with a mask of None.

    Note that scalars will end up as array scalars, which is important
    for using the result as the value of the out argument in some
    operations.

    Parameters
    ----------
    a : array-like
        Input array.
    val : float
        NaN values are set to val before doing the operation.

    Returns
    -------
    y : ndarray
        If `a` is of inexact type, return a copy of `a` with the NaNs
        replaced by the fill value, otherwise return `a`.
    mask: {bool, None}
        If `a` is of inexact type, return a boolean mask marking locations of
        NaNs, otherwise return None.

    """
    is_new = not isinstance(a, np.ndarray)
    if is_new:
        a = np.array(a)
    if not issubclass(a.dtype.type, np.inexact):
        return a, None
    if not is_new:
        # need copy
        a = np.array(a, subok=True)

    mask = np.isnan(a)
    np.copyto(a, val, where=mask)
    return a, mask 

Example 5

def _divide_by_count(a, b, out=None):
    """
    Compute a/b ignoring invalid results. If `a` is an array the division
    is done in place. If `a` is a scalar, then its type is preserved in the
    output. If out is None, then then a is used instead so that the
    division is in place. Note that this is only called with `a` an inexact
    type.

    Parameters
    ----------
    a : {ndarray, numpy scalar}
        Numerator. Expected to be of inexact type but not checked.
    b : {ndarray, numpy scalar}
        Denominator.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    Returns
    -------
    ret : {ndarray, numpy scalar}
        The return value is a/b. If `a` was an ndarray the division is done
        in place. If `a` is a numpy scalar, the division preserves its type.

    """
    with np.errstate(invalid='ignore'):
        if isinstance(a, np.ndarray):
            if out is None:
                return np.divide(a, b, out=a, casting='unsafe')
            else:
                return np.divide(a, b, out=out, casting='unsafe')
        else:
            if out is None:
                return a.dtype.type(a / b)
            else:
                # This is questionable, but currently a numpy scalar can
                # be output to a zero dimensional array.
                return np.divide(a, b, out=out, casting='unsafe') 

Example 6

def _replace_nan(a, val):
    """
    If `a` is of inexact type, make a copy of `a`, replace NaNs with
    the `val` value, and return the copy together with a boolean mask
    marking the locations where NaNs were present. If `a` is not of
    inexact type, do nothing and return `a` together with a mask of None.

    Note that scalars will end up as array scalars, which is important
    for using the result as the value of the out argument in some
    operations.

    Parameters
    ----------
    a : array-like
        Input array.
    val : float
        NaN values are set to val before doing the operation.

    Returns
    -------
    y : ndarray
        If `a` is of inexact type, return a copy of `a` with the NaNs
        replaced by the fill value, otherwise return `a`.
    mask: {bool, None}
        If `a` is of inexact type, return a boolean mask marking locations of
        NaNs, otherwise return None.

    """
    is_new = not isinstance(a, np.ndarray)
    if is_new:
        a = np.array(a)
    if not issubclass(a.dtype.type, np.inexact):
        return a, None
    if not is_new:
        # need copy
        a = np.array(a, subok=True)

    mask = np.isnan(a)
    np.copyto(a, val, where=mask)
    return a, mask 

Example 7

def _divide_by_count(a, b, out=None):
    """
    Compute a/b ignoring invalid results. If `a` is an array the division
    is done in place. If `a` is a scalar, then its type is preserved in the
    output. If out is None, then then a is used instead so that the
    division is in place. Note that this is only called with `a` an inexact
    type.

    Parameters
    ----------
    a : {ndarray, numpy scalar}
        Numerator. Expected to be of inexact type but not checked.
    b : {ndarray, numpy scalar}
        Denominator.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    Returns
    -------
    ret : {ndarray, numpy scalar}
        The return value is a/b. If `a` was an ndarray the division is done
        in place. If `a` is a numpy scalar, the division preserves its type.

    """
    with np.errstate(invalid='ignore'):
        if isinstance(a, np.ndarray):
            if out is None:
                return np.divide(a, b, out=a, casting='unsafe')
            else:
                return np.divide(a, b, out=out, casting='unsafe')
        else:
            if out is None:
                return a.dtype.type(a / b)
            else:
                # This is questionable, but currently a numpy scalar can
                # be output to a zero dimensional array.
                return np.divide(a, b, out=out, casting='unsafe') 

Example 8

def minimum_dtype(x, dtype=np.bool_):
        """returns the "most basic" dtype which represents `x` properly, which
        provides at least the same value range as the specified dtype."""

        def check_type(x, dtype):
            try:
                converted = dtype.type(x)
            except (ValueError, OverflowError):
                return False
            # False if some overflow has happened
            return converted == x or math.isnan(x)

        def type_loop(x, dtype, dtype_dict, default=None):
            while True:
                try:
                    dtype = np.dtype(dtype_dict[dtype.name])
                    if check_type(x, dtype):
                        return np.dtype(dtype)
                except KeyError:
                    if default is not None:
                        return np.dtype(default)
                    raise ValueError("Can not determine dtype of %r" % x)

        dtype = np.dtype(dtype)
        if check_type(x, dtype):
            return dtype

        if np.issubdtype(dtype, np.inexact):
            return type_loop(x, dtype, _next_float_dtype)
        else:
            return type_loop(x, dtype, _next_int_dtype, default=np.float32) 

Example 9

def _replace_nan(a, val):
    """
    If `a` is of inexact type, make a copy of `a`, replace NaNs with
    the `val` value, and return the copy together with a boolean mask
    marking the locations where NaNs were present. If `a` is not of
    inexact type, do nothing and return `a` together with a mask of None.

    Note that scalars will end up as array scalars, which is important
    for using the result as the value of the out argument in some
    operations.

    Parameters
    ----------
    a : array-like
        Input array.
    val : float
        NaN values are set to val before doing the operation.

    Returns
    -------
    y : ndarray
        If `a` is of inexact type, return a copy of `a` with the NaNs
        replaced by the fill value, otherwise return `a`.
    mask: {bool, None}
        If `a` is of inexact type, return a boolean mask marking locations of
        NaNs, otherwise return None.

    """
    is_new = not isinstance(a, np.ndarray)
    if is_new:
        a = np.array(a)
    if not issubclass(a.dtype.type, np.inexact):
        return a, None
    if not is_new:
        # need copy
        a = np.array(a, subok=True)

    mask = np.isnan(a)
    np.copyto(a, val, where=mask)
    return a, mask 

Example 10

def _divide_by_count(a, b, out=None):
    """
    Compute a/b ignoring invalid results. If `a` is an array the division
    is done in place. If `a` is a scalar, then its type is preserved in the
    output. If out is None, then then a is used instead so that the
    division is in place. Note that this is only called with `a` an inexact
    type.

    Parameters
    ----------
    a : {ndarray, numpy scalar}
        Numerator. Expected to be of inexact type but not checked.
    b : {ndarray, numpy scalar}
        Denominator.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    Returns
    -------
    ret : {ndarray, numpy scalar}
        The return value is a/b. If `a` was an ndarray the division is done
        in place. If `a` is a numpy scalar, the division preserves its type.

    """
    with np.errstate(invalid='ignore'):
        if isinstance(a, np.ndarray):
            if out is None:
                return np.divide(a, b, out=a, casting='unsafe')
            else:
                return np.divide(a, b, out=out, casting='unsafe')
        else:
            if out is None:
                return a.dtype.type(a / b)
            else:
                # This is questionable, but currently a numpy scalar can
                # be output to a zero dimensional array.
                return np.divide(a, b, out=out, casting='unsafe') 

Example 11

def _replace_nan(a, val):
    """
    If `a` is of inexact type, make a copy of `a`, replace NaNs with
    the `val` value, and return the copy together with a boolean mask
    marking the locations where NaNs were present. If `a` is not of
    inexact type, do nothing and return `a` together with a mask of None.

    Note that scalars will end up as array scalars, which is important
    for using the result as the value of the out argument in some
    operations.

    Parameters
    ----------
    a : array-like
        Input array.
    val : float
        NaN values are set to val before doing the operation.

    Returns
    -------
    y : ndarray
        If `a` is of inexact type, return a copy of `a` with the NaNs
        replaced by the fill value, otherwise return `a`.
    mask: {bool, None}
        If `a` is of inexact type, return a boolean mask marking locations of
        NaNs, otherwise return None.

    """
    is_new = not isinstance(a, np.ndarray)
    if is_new:
        a = np.array(a)
    if not issubclass(a.dtype.type, np.inexact):
        return a, None
    if not is_new:
        # need copy
        a = np.array(a, subok=True)

    mask = np.isnan(a)
    np.copyto(a, val, where=mask)
    return a, mask 

Example 12

def _divide_by_count(a, b, out=None):
    """
    Compute a/b ignoring invalid results. If `a` is an array the division
    is done in place. If `a` is a scalar, then its type is preserved in the
    output. If out is None, then then a is used instead so that the
    division is in place. Note that this is only called with `a` an inexact
    type.

    Parameters
    ----------
    a : {ndarray, numpy scalar}
        Numerator. Expected to be of inexact type but not checked.
    b : {ndarray, numpy scalar}
        Denominator.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    Returns
    -------
    ret : {ndarray, numpy scalar}
        The return value is a/b. If `a` was an ndarray the division is done
        in place. If `a` is a numpy scalar, the division preserves its type.

    """
    with np.errstate(invalid='ignore', divide='ignore'):
        if isinstance(a, np.ndarray):
            if out is None:
                return np.divide(a, b, out=a, casting='unsafe')
            else:
                return np.divide(a, b, out=out, casting='unsafe')
        else:
            if out is None:
                return a.dtype.type(a / b)
            else:
                # This is questionable, but currently a numpy scalar can
                # be output to a zero dimensional array.
                return np.divide(a, b, out=out, casting='unsafe') 

Example 13

def _median(a, axis=None, out=None, overwrite_input=False):
    # can't be reasonably be implemented in terms of percentile as we have to
    # call mean to not break astropy
    a = np.asanyarray(a)

    # Set the partition indexes
    if axis is None:
        sz = a.size
    else:
        sz = a.shape[axis]
    if sz % 2 == 0:
        szh = sz // 2
        kth = [szh - 1, szh]
    else:
        kth = [(sz - 1) // 2]
    # Check if the array contains any nan's
    if np.issubdtype(a.dtype, np.inexact):
        kth.append(-1)

    if overwrite_input:
        if axis is None:
            part = a.ravel()
            part.partition(kth)
        else:
            a.partition(kth, axis=axis)
            part = a
    else:
        part = partition(a, kth, axis=axis)

    if part.shape == ():
        # make 0-D arrays work
        return part.item()
    if axis is None:
        axis = 0

    indexer = [slice(None)] * part.ndim
    index = part.shape[axis] // 2
    if part.shape[axis] % 2 == 1:
        # index with slice to allow mean (below) to work
        indexer[axis] = slice(index, index+1)
    else:
        indexer[axis] = slice(index-1, index+1)

    # Check if the array contains any nan's
    if np.issubdtype(a.dtype, np.inexact) and sz > 0:
        # warn and return nans like mean would
        rout = mean(part[indexer], axis=axis, out=out)
        return np.lib.utils._median_nancheck(part, rout, axis, out)
    else:
        # if there are no nans
        # Use mean in odd and even case to coerce data type
        # and check, use out array.
        return mean(part[indexer], axis=axis, out=out) 

Example 14

def object_to_json(o, level=0, indent=4, space=" ", newline="\n"):
    ret = ""
    if isinstance(o, dict):
        ret += "{" + newline
        comma = ""
        for k,v in o.iteritems():
            ret += comma
            comma = ",\n"
            ret += space * indent * level
            ret += '"' + str(k) + '":' + space
            ret += object_to_json(v, level + 1)

        ret += newline + space * indent * (level - 1) + "}"
    elif isinstance(o, basestring):
        ret += '"' + o + '"'
    elif isinstance(o, list):
        ret += "[" + ", ".join([object_to_json(e, level+1) for e in o]) + "]"
    elif isinstance(o, bool):
        ret += "true" if o else "false"
    elif isinstance(o, (int, long)):
        ret += str(o)
    elif isinstance(o, datetime):
        ret += o.strftime('%Y-%m-%d %H:%M:%S.%f')
    elif isinstance(o, bson.ObjectId):
        ret += 'ObjectId(%s)' % o
    elif isinstance(o, float):
        ret += '%.7g' % o
    elif isinstance(o, numpy.ndarray) and numpy.issubdtype(o.dtype, numpy.integer):
        ret += "[" + ','.join(map(str, o.flatten().tolist())) + "]"
    elif isinstance(o, numpy.ndarray) and numpy.issubdtype(o.dtype, numpy.inexact):
        ret += "[" + ','.join(map(lambda x: '%.7g' % x, o.flatten().tolist())) + "]"
    elif isinstance(o, bson.timestamp.Timestamp):
        ret += o.as_datetime().strftime('%Y-%m-%d %H:%M:%S.%f')
    elif o is None:
        ret += 'null'
    else:
        raise TypeError("Unknown type '%s' for json serialization" % str(type(o)))
    return ret 

Example 15

def _replace_nan(a, val):
    """
    If `a` is of inexact type, make a copy of `a`, replace NaNs with
    the `val` value, and return the copy together with a boolean mask
    marking the locations where NaNs were present. If `a` is not of
    inexact type, do nothing and return `a` together with a mask of None.

    Note that scalars will end up as array scalars, which is important
    for using the result as the value of the out argument in some
    operations.

    Parameters
    ----------
    a : array-like
        Input array.
    val : float
        NaN values are set to val before doing the operation.

    Returns
    -------
    y : ndarray
        If `a` is of inexact type, return a copy of `a` with the NaNs
        replaced by the fill value, otherwise return `a`.
    mask: {bool, None}
        If `a` is of inexact type, return a boolean mask marking locations of
        NaNs, otherwise return None.

    """
    is_new = not isinstance(a, np.ndarray)
    if is_new:
        a = np.array(a)
    if not issubclass(a.dtype.type, np.inexact):
        return a, None
    if not is_new:
        # need copy
        a = np.array(a, subok=True)

    mask = np.isnan(a)
    np.copyto(a, val, where=mask)
    return a, mask 

Example 16

def _divide_by_count(a, b, out=None):
    """
    Compute a/b ignoring invalid results. If `a` is an array the division
    is done in place. If `a` is a scalar, then its type is preserved in the
    output. If out is None, then then a is used instead so that the
    division is in place. Note that this is only called with `a` an inexact
    type.

    Parameters
    ----------
    a : {ndarray, numpy scalar}
        Numerator. Expected to be of inexact type but not checked.
    b : {ndarray, numpy scalar}
        Denominator.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``; if provided, it must have the same shape as the
        expected output, but the type will be cast if necessary.

    Returns
    -------
    ret : {ndarray, numpy scalar}
        The return value is a/b. If `a` was an ndarray the division is done
        in place. If `a` is a numpy scalar, the division preserves its type.

    """
    with np.errstate(invalid='ignore'):
        if isinstance(a, np.ndarray):
            if out is None:
                return np.divide(a, b, out=a, casting='unsafe')
            else:
                return np.divide(a, b, out=out, casting='unsafe')
        else:
            if out is None:
                return a.dtype.type(a / b)
            else:
                # This is questionable, but currently a numpy scalar can
                # be output to a zero dimensional array.
                return np.divide(a, b, out=out, casting='unsafe') 

Example 17

def to_json(o, level=0):
    INDENT = 2
    SPACE = " "
    NEWLINE = "\n"
    ret = ""
    if isinstance(o, dict):
        ret += "{" + NEWLINE
        comma = ""
        for k in sorted(o.keys()):
            v = o[k]
            ret += comma
            comma = ",\n"
            ret += SPACE * INDENT * (level+1)
            ret += '"' + str(k) + '":' + SPACE
            ret += to_json(v, level + 1)

        ret += NEWLINE + SPACE * INDENT * level + "}"
    elif isinstance(o, str):
        ret += '"' + o + '"'
    elif isinstance(o, list) or isinstance(o, tuple):
        ret += "[" + ",".join([to_json(e, level+1) for e in o]) + "]"
    elif isinstance(o, bool):
        ret += "true" if o else "false"
    elif isinstance(o, int):
        ret += str(o)
    elif isinstance(o, float):
        ret += '%.7g' % o
    elif isinstance(o, np.ndarray) and np.issubdtype(o.dtype, np.integer):
        ret += "[" + ','.join(map(str, o.flatten().tolist())) + "]"
    elif isinstance(o, np.ndarray) and np.issubdtype(o.dtype, np.inexact):
        ret += "[" + \
            ','.join(map(lambda x: '%.7g' % x, o.flatten().tolist())) + "]"
    elif o is None:
        ret += 'null'
    elif hasattr(o, '__class__'):
        ret += '"' + o.__class__.__name__ + '"'
    else:
        raise TypeError(
            "Unknown type '%s' for json serialization" % str(type(o)))
    return ret


# format object and its properties into printable dict 

Example 18

def check_dtype(dtype, func_str, a, n):
        if np.isscalar(a) or not a.shape:
            if func_str not in ("sum", "prod", "len"):
                raise ValueError("scalar inputs are supported only for 'sum', "
                                 "'prod' and 'len'")
            a_dtype = np.dtype(type(a))
        else:
            a_dtype = a.dtype

        if dtype is not None:
            # dtype set by the user
            # Careful here: np.bool != np.bool_ !
            if np.issubdtype(dtype, np.bool_) and \
                    not('all' in func_str or 'any' in func_str):
                raise TypeError("function %s requires a more complex datatype "
                                "than bool" % func_str)
            if not np.issubdtype(dtype, np.integer) and func_str in ('len', 'nanlen'):
                raise TypeError("function %s requires an integer datatype" % func_str)
            # TODO: Maybe have some more checks here
            return np.dtype(dtype)
        else:
            try:
                return np.dtype(_forced_types[func_str])
            except KeyError:
                if func_str in _forced_float_types:
                    if np.issubdtype(a_dtype, np.floating):
                        return a_dtype
                    else:
                        return np.dtype(np.float64)
                else:
                    if func_str == 'sum':
                        # Try to guess the minimally required int size
                        if np.issubdtype(a_dtype, np.int64):
                            # It's not getting bigger anymore
                            # TODO: strictly speaking it might need float
                            return np.dtype(np.int64)
                        elif np.issubdtype(a_dtype, np.integer):
                            maxval = np.iinfo(a_dtype).max * n
                            return minimum_dtype(maxval, a_dtype)
                        elif np.issubdtype(a_dtype, np.bool_):
                            return minimum_dtype(n, a_dtype)
                        else:
                            # floating, inexact, whatever
                            return a_dtype
                    elif func_str in _forced_same_type:
                        return a_dtype
                    else:
                        if isinstance(a_dtype, np.integer):
                            return np.dtype(np.int64)
                        else:
                            return a_dtype 

Example 19

def _median(a, axis=None, out=None, overwrite_input=False):
    if overwrite_input:
        if axis is None:
            asorted = a.ravel()
            asorted.sort()
        else:
            a.sort(axis=axis)
            asorted = a
    else:
        asorted = sort(a, axis=axis)

    if axis is None:
        axis = 0
    elif axis < 0:
        axis += asorted.ndim

    if asorted.ndim == 1:
        idx, odd = divmod(count(asorted), 2)
        return asorted[idx + odd - 1 : idx + 1].mean(out=out)

    counts = count(asorted, axis=axis)
    h = counts // 2

    # create indexing mesh grid for all but reduced axis
    axes_grid = [np.arange(x) for i, x in enumerate(asorted.shape)
                 if i != axis]
    ind = np.meshgrid(*axes_grid, sparse=True, indexing='ij')

    # insert indices of low and high median
    ind.insert(axis, np.maximum(0, h - 1))
    low = asorted[tuple(ind)]
    ind[axis] = h
    high = asorted[tuple(ind)]

    # duplicate high if odd number of elements so mean does nothing
    odd = counts % 2 == 1
    if asorted.ndim > 1:
        np.copyto(low, high, where=odd)
    elif odd:
        low = high

    if np.issubdtype(asorted.dtype, np.inexact):
        # avoid inf / x = masked
        s = np.ma.sum([low, high], axis=0, out=out)
        np.true_divide(s.data, 2., casting='unsafe', out=s.data)
    else:
        s = np.ma.mean([low, high], axis=0, out=out)
    return s 
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