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pypy / lib_pypy / numpypy / core / numeric.py

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__all__ = ['asanyarray', 'base_repr',
           'array_repr', 'array_str', 'set_string_function',
           'array_equal', 'asarray', 'outer', 'identity']

from _numpypy import array, ndarray, int_, float_, bool_, flexible #, complex_# , longlong
from _numpypy import concatenate
from .fromnumeric import any
import math
import sys
import multiarray
from numpypy.core.arrayprint import array2string

newaxis = None

# XXX this file to be reviewed
def seterr(**args):
    return args

def asanyarray(a, dtype=None, order=None):
    """
    Convert the input to an ndarray, but pass ndarray subclasses through.

    Parameters
    ----------
    a : array_like
        Input data, in any form that can be converted to an array.  This
        includes scalars, lists, lists of tuples, tuples, tuples of tuples,
        tuples of lists, and ndarrays.
    dtype : data-type, optional
        By default, the data-type is inferred from the input data.
    order : {'C', 'F'}, optional
        Whether to use row-major ('C') or column-major ('F') memory
        representation.  Defaults to 'C'.

    Returns
    -------
    out : ndarray or an ndarray subclass
        Array interpretation of `a`.  If `a` is an ndarray or a subclass
        of ndarray, it is returned as-is and no copy is performed.

    See Also
    --------
    asarray : Similar function which always returns ndarrays.
    ascontiguousarray : Convert input to a contiguous array.
    asfarray : Convert input to a floating point ndarray.
    asfortranarray : Convert input to an ndarray with column-major
                     memory order.
    asarray_chkfinite : Similar function which checks input for NaNs and
                        Infs.
    fromiter : Create an array from an iterator.
    fromfunction : Construct an array by executing a function on grid
                   positions.

    Examples
    --------
    Convert a list into an array:

    >>> a = [1, 2]
    >>> np.asanyarray(a)
    array([1, 2])

    Instances of `ndarray` subclasses are passed through as-is:

    >>> a = np.matrix([1, 2])
    >>> np.asanyarray(a) is a
    True

    """
    return array(a, dtype, copy=False, order=order, subok=True)

def base_repr(number, base=2, padding=0):
    """
    Return a string representation of a number in the given base system.

    Parameters
    ----------
    number : int
        The value to convert. Only positive values are handled.
    base : int, optional
        Convert `number` to the `base` number system. The valid range is 2-36,
        the default value is 2.
    padding : int, optional
        Number of zeros padded on the left. Default is 0 (no padding).

    Returns
    -------
    out : str
        String representation of `number` in `base` system.

    See Also
    --------
    binary_repr : Faster version of `base_repr` for base 2.

    Examples
    --------
    >>> np.base_repr(5)
    '101'
    >>> np.base_repr(6, 5)
    '11'
    >>> np.base_repr(7, base=5, padding=3)
    '00012'

    >>> np.base_repr(10, base=16)
    'A'
    >>> np.base_repr(32, base=16)
    '20'

    """
    digits = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
    if base > len(digits):
        raise ValueError("Bases greater than 36 not handled in base_repr.")

    num = abs(number)
    res = []
    while num:
        res.append(digits[num % base])
        num //= base
    if padding:
        res.append('0' * padding)
    if number < 0:
        res.append('-')
    return ''.join(reversed(res or '0'))

_typelessdata = [int_, float_]#, complex_]
# XXX
#if issubclass(intc, int):
#    _typelessdata.append(intc)

#if issubclass(longlong, int):
#    _typelessdata.append(longlong)

def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
    """
    Return the string representation of an array.

    Parameters
    ----------
    arr : ndarray
        Input array.
    max_line_width : int, optional
        The maximum number of columns the string should span. Newline
        characters split the string appropriately after array elements.
    precision : int, optional
        Floating point precision. Default is the current printing precision
        (usually 8), which can be altered using `set_printoptions`.
    suppress_small : bool, optional
        Represent very small numbers as zero, default is False. Very small
        is defined by `precision`, if the precision is 8 then
        numbers smaller than 5e-9 are represented as zero.

    Returns
    -------
    string : str
      The string representation of an array.

    See Also
    --------
    array_str, array2string, set_printoptions

    Examples
    --------
    >>> np.array_repr(np.array([1,2]))
    'array([1, 2])'
    >>> np.array_repr(np.ma.array([0.]))
    'MaskedArray([ 0.])'
    >>> np.array_repr(np.array([], np.int32))
    'array([], dtype=int32)'

    >>> x = np.array([1e-6, 4e-7, 2, 3])
    >>> np.array_repr(x, precision=6, suppress_small=True)
    'array([ 0.000001,  0.      ,  2.      ,  3.      ])'

    """
    if arr.size > 0 or arr.shape==(0,):
        lst = array2string(arr, max_line_width, precision, suppress_small,
                           ', ', "array(")
    else: # show zero-length shape unless it is (0,)
        lst = "[], shape=%s" % (repr(arr.shape),)

    if arr.__class__ is not ndarray:
        cName= arr.__class__.__name__
    else:
        cName = "array"

    skipdtype = (arr.dtype.type in _typelessdata) and arr.size > 0

    # XXX pypy lacks support
    if 0 and arr.flags.maskna:
        whichna = isna(arr)
        # If nothing is NA, explicitly signal the NA-mask
        if not any(whichna):
            lst += ", maskna=True"
        # If everything is NA, can't skip the dtype
        if skipdtype and all(whichna):
            skipdtype = False

    if skipdtype:
        return "%s(%s)" % (cName, lst)
    else:
        typename = arr.dtype.name
        # Quote typename in the output if it is "complex".
        if typename and not (typename[0].isalpha() and typename.isalnum()):
            typename = "'%s'" % typename

        lf = ''
        if issubclass(arr.dtype.type, flexible):
            if arr.dtype.names:
                typename = "%s" % str(arr.dtype)
            else:
                typename = "'%s'" % str(arr.dtype)
            lf = '\n'+' '*len("array(")
        return cName + "(%s, %sdtype=%s)" % (lst, lf, typename)

def array_str(a, max_line_width=None, precision=None, suppress_small=None):
    """
    Return a string representation of the data in an array.

    The data in the array is returned as a single string.  This function is
    similar to `array_repr`, the difference being that `array_repr` also
    returns information on the kind of array and its data type.

    Parameters
    ----------
    a : ndarray
        Input array.
    max_line_width : int, optional
        Inserts newlines if text is longer than `max_line_width`.  The
        default is, indirectly, 75.
    precision : int, optional
        Floating point precision.  Default is the current printing precision
        (usually 8), which can be altered using `set_printoptions`.
    suppress_small : bool, optional
        Represent numbers "very close" to zero as zero; default is False.
        Very close is defined by precision: if the precision is 8, e.g.,
        numbers smaller (in absolute value) than 5e-9 are represented as
        zero.

    See Also
    --------
    array2string, array_repr, set_printoptions

    Examples
    --------
    >>> np.array_str(np.arange(3))
    '[0 1 2]'

    """
    return array2string(a, max_line_width, precision, suppress_small, ' ', "", str)

def set_string_function(f, repr=True):
    """
    Set a Python function to be used when pretty printing arrays.

    Parameters
    ----------
    f : function or None
        Function to be used to pretty print arrays. The function should expect
        a single array argument and return a string of the representation of
        the array. If None, the function is reset to the default NumPy function
        to print arrays.
    repr : bool, optional
        If True (default), the function for pretty printing (``__repr__``)
        is set, if False the function that returns the default string
        representation (``__str__``) is set.

    See Also
    --------
    set_printoptions, get_printoptions

    Examples
    --------
    >>> def pprint(arr):
    ...     return 'HA! - What are you going to do now?'
    ...
    >>> np.set_string_function(pprint)
    >>> a = np.arange(10)
    >>> a
    HA! - What are you going to do now?
    >>> print a
    [0 1 2 3 4 5 6 7 8 9]

    We can reset the function to the default:

    >>> np.set_string_function(None)
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

    `repr` affects either pretty printing or normal string representation.
    Note that ``__repr__`` is still affected by setting ``__str__``
    because the width of each array element in the returned string becomes
    equal to the length of the result of ``__str__()``.

    >>> x = np.arange(4)
    >>> np.set_string_function(lambda x:'random', repr=False)
    >>> x.__str__()
    'random'
    >>> x.__repr__()
    'array([     0,      1,      2,      3])'

    """
    if f is None:
        if repr:
            return multiarray.set_string_function(array_repr, 1)
        else:
            return multiarray.set_string_function(array_str, 0)
    else:
        return multiarray.set_string_function(f, repr)

def array_equal(a1, a2):
    """
    True if two arrays have the same shape and elements, False otherwise.

    Parameters
    ----------
    a1, a2 : array_like
        Input arrays.

    Returns
    -------
    b : bool
        Returns True if the arrays are equal.

    See Also
    --------
    allclose: Returns True if two arrays are element-wise equal within a
              tolerance.
    array_equiv: Returns True if input arrays are shape consistent and all
                 elements equal.

    Examples
    --------
    >>> np.array_equal([1, 2], [1, 2])
    True
    >>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
    True
    >>> np.array_equal([1, 2], [1, 2, 3])
    False
    >>> np.array_equal([1, 2], [1, 4])
    False

    """
    try:
        a1, a2 = asarray(a1), asarray(a2)
    except:
        return False
    if a1.shape != a2.shape:
        return False
    return bool((a1 == a2).all())

def asarray(a, dtype=None, order=None):
    """
    Convert the input to an array.

    Parameters
    ----------
    a : array_like
        Input data, in any form that can be converted to an array.  This
        includes lists, lists of tuples, tuples, tuples of tuples, tuples
        of lists and ndarrays.
    dtype : data-type, optional
        By default, the data-type is inferred from the input data.
    order : {'C', 'F'}, optional
        Whether to use row-major ('C') or column-major ('F' for FORTRAN)
        memory representation.  Defaults to 'C'.

    Returns
    -------
    out : ndarray
        Array interpretation of `a`.  No copy is performed if the input
        is already an ndarray.  If `a` is a subclass of ndarray, a base
        class ndarray is returned.

    See Also
    --------
    asanyarray : Similar function which passes through subclasses.
    ascontiguousarray : Convert input to a contiguous array.
    asfarray : Convert input to a floating point ndarray.
    asfortranarray : Convert input to an ndarray with column-major
                     memory order.
    asarray_chkfinite : Similar function which checks input for NaNs and Infs.
    fromiter : Create an array from an iterator.
    fromfunction : Construct an array by executing a function on grid
                   positions.

    Examples
    --------
    Convert a list into an array:

    >>> a = [1, 2]
    >>> np.asarray(a)
    array([1, 2])

    Existing arrays are not copied:

    >>> a = np.array([1, 2])
    >>> np.asarray(a) is a
    True

    If `dtype` is set, array is copied only if dtype does not match:

    >>> a = np.array([1, 2], dtype=np.float32)
    >>> np.asarray(a, dtype=np.float32) is a
    True
    >>> np.asarray(a, dtype=np.float64) is a
    False

    Contrary to `asanyarray`, ndarray subclasses are not passed through:

    >>> issubclass(np.matrix, np.ndarray)
    True
    >>> a = np.matrix([[1, 2]])
    >>> np.asarray(a) is a
    False
    >>> np.asanyarray(a) is a
    True

    """
    return array(a, dtype, copy=False, order=order)

set_string_function(array_str, 0)
set_string_function(array_repr, 1)

little_endian = (sys.byteorder == 'little')

Inf = inf = infty = Infinity = PINF = float('inf')
NINF = float('-inf')
PZERO = 0.0
NZERO = -0.0
nan = NaN = NAN = float('nan')
False_ = bool_(False)
True_ = bool_(True)
e = math.e
pi = math.pi

def outer(a,b):
    """
    Compute the outer product of two vectors.

    Given two vectors, ``a = [a0, a1, ..., aM]`` and
    ``b = [b0, b1, ..., bN]``,
    the outer product [1]_ is::

      [[a0*b0  a0*b1 ... a0*bN ]
       [a1*b0    .
       [ ...          .
       [aM*b0            aM*bN ]]

    Parameters
    ----------
    a, b : array_like, shape (M,), (N,)
        First and second input vectors.  Inputs are flattened if they
        are not already 1-dimensional.

    Returns
    -------
    out : ndarray, shape (M, N)
        ``out[i, j] = a[i] * b[j]``

    See also
    --------
    inner, einsum

    References
    ----------
    .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd
             ed., Baltimore, MD, Johns Hopkins University Press, 1996,
             pg. 8.

    Examples
    --------
    Make a (*very* coarse) grid for computing a Mandelbrot set:

    >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
    >>> rl
    array([[-2., -1.,  0.,  1.,  2.],
           [-2., -1.,  0.,  1.,  2.],
           [-2., -1.,  0.,  1.,  2.],
           [-2., -1.,  0.,  1.,  2.],
           [-2., -1.,  0.,  1.,  2.]])
    >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
    >>> im
    array([[ 0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j,  0.+2.j],
           [ 0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j,  0.+1.j],
           [ 0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
           [ 0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j,  0.-1.j],
           [ 0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j,  0.-2.j]])
    >>> grid = rl + im
    >>> grid
    array([[-2.+2.j, -1.+2.j,  0.+2.j,  1.+2.j,  2.+2.j],
           [-2.+1.j, -1.+1.j,  0.+1.j,  1.+1.j,  2.+1.j],
           [-2.+0.j, -1.+0.j,  0.+0.j,  1.+0.j,  2.+0.j],
           [-2.-1.j, -1.-1.j,  0.-1.j,  1.-1.j,  2.-1.j],
           [-2.-2.j, -1.-2.j,  0.-2.j,  1.-2.j,  2.-2.j]])

    An example using a "vector" of letters:

    >>> x = np.array(['a', 'b', 'c'], dtype=object)
    >>> np.outer(x, [1, 2, 3])
    array([[a, aa, aaa],
           [b, bb, bbb],
           [c, cc, ccc]], dtype=object)

    """
    a = asarray(a)
    b = asarray(b)
    return a.ravel()[:,newaxis]*b.ravel()[newaxis,:]

def identity(n, dtype=None):
    """
    Return the identity array.

    The identity array is a square array with ones on
    the main diagonal.

    Parameters
    ----------
    n : int
        Number of rows (and columns) in `n` x `n` output.
    dtype : data-type, optional
        Data-type of the output.  Defaults to ``float``.

    Returns
    -------
    out : ndarray
        `n` x `n` array with its main diagonal set to one,
        and all other elements 0.

    Examples
    --------
    >>> np.identity(3)
    array([[ 1.,  0.,  0.],
           [ 0.,  1.,  0.],
           [ 0.,  0.,  1.]])

    """
    from numpy import eye
    return eye(n, dtype=dtype)