Python numpy.core.numeric.where() Examples

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) 

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) 

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 

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) 

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) 

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 

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 

def __call__ (self, a, b=None):
        "Execute the call behavior."
        if b is None:
            m = getmask(a)
            if m is nomask:
                d = amin(filled(a).ravel())
                return d
            ac = a.compressed()
            if len(ac) == 0:
                return masked
            else:
                return amin(ac.raw_data())
        else:
            return where(less(a, b), a, b) 

def putmask(a, mask, values):
    "putmask(a, mask, values) sets a where mask is true."
    if mask is nomask:
        return
    numeric.putmask(a.raw_data(), mask, values)
    m = getmask(a)
    if m is nomask: return
    a.unshare_mask()
    numeric.putmask(a.raw_mask(), mask, 0) 

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) 

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) 

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) 

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) 

def masked_greater(x, value, copy=1):
    "masked_greater(x, value) = x masked where x > value"
    return masked_where(greater(x, value), x, copy) 

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) 

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) 

def __init__ (self, aufunc, fill=0, domain=None):
        """ masked_unary_operation(aufunc, fill=0, domain=None)
            aufunc(fill) must be defined
            self(x) returns aufunc(x)
            with masked values where domain(x) is true or getmask(x) is true.
        """
        self.f = aufunc
        self.fill = fill
        self.domain = domain
        self.__doc__ = getattr(aufunc, "__doc__", str(aufunc))
        self.__name__ = getattr(aufunc, "__name__", str(aufunc))
        ufunc_domain[aufunc] = domain
        ufunc_fills[aufunc] = fill, 

def __init__(self, critical_value):
        "domain_greater_equal(v)(x) = true where x < v"
        self.critical_value = critical_value 

def __init__(self, eps):
        "domain_tan(eps) = true where abs(cos(x)) < eps)"
        self.eps = eps 

def __init__(self, y1, y2):
        "domain_check_interval(a,b)(x) = true where x < a or y > b"
        self.y1 = y1
        self.y2 = y2 

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. 

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 

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 

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 

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 

def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y 

def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y 

def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y 

def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y 

def isposinf(x, y=None):
    """
    Test element-wise for positive infinity, return result as bool array.

    Parameters
    ----------
    x : array_like
        The input array.
    y : array_like, optional
        A boolean array with the same shape as `x` to store the result.

    Returns
    -------
    y : ndarray
        A boolean array with the same dimensions as the input.
        If second argument is not supplied then a boolean array is returned
        with values True where the corresponding element of the input is
        positive infinity and values False where the element of the input is
        not positive infinity.

        If a second argument is supplied the result is stored there. If the
        type of that array is a numeric type the result is represented as zeros
        and ones, if the type is boolean then as False and True.
        The return value `y` is then a reference to that array.

    See Also
    --------
    isinf, isneginf, isfinite, isnan

    Notes
    -----
    Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
    (IEEE 754).

    Errors result if the second argument is also supplied when `x` is a
    scalar input, or if first and second arguments have different shapes.

    Examples
    --------
    >>> np.isposinf(np.PINF)
    array(True, dtype=bool)
    >>> np.isposinf(np.inf)
    array(True, dtype=bool)
    >>> np.isposinf(np.NINF)
    array(False, dtype=bool)
    >>> np.isposinf([-np.inf, 0., np.inf])
    array([False, False,  True], dtype=bool)

    >>> x = np.array([-np.inf, 0., np.inf])
    >>> y = np.array([2, 2, 2])
    >>> np.isposinf(x, y)
    array([0, 0, 1])
    >>> y
    array([0, 0, 1])

    """
    if y is None:
        x = nx.asarray(x)
        y = nx.empty(x.shape, dtype=nx.bool_)
    nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
    return y