# Commits

committed fe9dc13

# lib_pypy/numpypy/core/numeric.py

`     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, maskna=None, ownmaskna=False):`
`+    """`
`+    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'.`
`+   maskna : bool or None, optional`
`+        If this is set to True, it forces the array to have an NA mask.`
`+        If this is set to False, it forces the array to not have an NA`
`+        mask.`
`+    ownmaskna : bool, optional`
`+        If this is set to True, forces the array to have a mask which`
`+        it owns.`
`+`
`+    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,`
`+                            maskna=maskna, ownmaskna=ownmaskna)`
`+`
` set_string_function(array_str, 0)`
` set_string_function(array_repr, 1)`
` `
` False_ = bool_(False)`
` True_ = bool_(True)`
` e = math.e`
`-pi = math.pi`
`+pi = math.pi`