Python array.array() Examples

def __xor__(self, other):                                      
        '''
        Take a bitwise 'XOR' of the bit vector on which the method is invoked with
        the argument bit vector.  Return the result as a new bit vector.  If the two
        bit vectors are not of the same size, pad the shorter one with zeros from the
        left.
        '''
        if self.size < other.size:                                  
            bv1 = self._resize_pad_from_left(other.size - self.size)
            bv2 = other                                             
        elif self.size > other.size:                                
            bv1 = self                                              
            bv2 = other._resize_pad_from_left(self.size - other.size)
        else:                                                        
            bv1 = self                                               
            bv2 = other                                             
        res = BitVector( size = bv1.size )                          
        lpb = map(operator.__xor__, bv1.vector, bv2.vector)         
        res.vector = array.array( 'H', lpb )                        
        return res 

def __and__(self, other):                                       
        '''
        Take a bitwise 'AND' of the bit vector on which the method is invoked with
        the argument bit vector.  Return the result as a new bit vector.  If the two
        bit vectors are not of the same size, pad the shorter one with zeros from the
        left.
        '''      
        if self.size < other.size:                                  
            bv1 = self._resize_pad_from_left(other.size - self.size)
            bv2 = other                                             
        elif self.size > other.size:                                
            bv1 = self                                              
            bv2 = other._resize_pad_from_left(self.size - other.size)
        else:                                                        
            bv1 = self                                               
            bv2 = other                                             
        res = BitVector( size = bv1.size )                          
        lpb = map(operator.__and__, bv1.vector, bv2.vector)         
        res.vector = array.array( 'H', lpb )                        
        return res 

def __or__(self, other):                                        
        '''
        Take a bitwise 'OR' of the bit vector on which the method is invoked with the
        argument bit vector.  Return the result as a new bit vector.  If the two bit
        vectors are not of the same size, pad the shorter one with zero's from the
        left.
        '''
        if self.size < other.size:                                  
            bv1 = self._resize_pad_from_left(other.size - self.size)
            bv2 = other                                             
        elif self.size > other.size:                                
            bv1 = self                                              
            bv2 = other._resize_pad_from_left(self.size - other.size)
        else:                                                       
            bv1 = self                                              
            bv2 = other                                             
        res = BitVector( size = bv1.size )                          
        lpb = map(operator.__or__, bv1.vector, bv2.vector)          
        res.vector = array.array( 'H', lpb )                        
        return res 

def __neg__(self):
        """x.__neg__() <=> -x

        Numerical negative, element-wise.

        Example
        -------
        >>> a = mx.sym.Variable('a')
        >>> a
        <Symbol a>
        >>> -a
        <Symbol _mulscalar0>
        >>> a_neg = a.__neg__()
        >>> c = a_neg*b
        >>> ex = c.eval(ctx=mx.cpu(), a=mx.nd.ones([2,3]), b=mx.nd.ones([2,3]))
        >>> ex[0].asnumpy()
        array([[-1., -1., -1.],
               [-1., -1., -1.]], dtype=float32)
        """
        return self.__mul__(-1.0) 

def __rmod__(self, other):
        """x.__rmod__(y) <=> y%x

        Only `NDArray` is supported for now.

        Example
        -------
        >>> x = mx.nd.ones((2,3))*3
        >>> y = mx.nd.ones((2,3))
        >>> x.__rmod__(y).asnumpy()
        array([[ 1.,  1.,  1.,
               [ 1.,  1.,  1., dtype=float32)
        """
        if isinstance(other, Number):
            return _internal._RModScalar(self, scalar=other)
        else:
            raise TypeError('type %s not supported' % str(type(other))) 

def __rsub__(self, other):
        """x.__rsub__(y) <=> y-x

        Only `NDArray` is supported for now.

        Example
        -------
        >>> x = mx.nd.ones((2,3))*3
        >>> y = mx.nd.ones((2,3))
        >>> x.__rsub__(y).asnumpy()
        array([[-2., -2., -2.],
               [-2., -2., -2.]], dtype=float32)
        """
        if isinstance(other, Number):
            return _internal._RMinusScalar(self, scalar=other)
        else:
            raise TypeError('type %s not supported' % str(type(other))) 

def set_value(self, *args, **kwargs):                            
        '''
        You can call set_value() to change the bit pattern associated with
        a previously constructed bitvector object:

            bv = BitVector(intVal = 7, size =16)
            print(bv)                              # 0000000000000111
            bv.set_value(intVal = 45)
            print(bv)                              # 101101

        You can think of this method as carrying out an in-place resetting
        of the bit array in a bitvector. The method does not return
        anything.  The allowable modes for changing the internally stored
        bit array for a bitvector are the same as for the constructor.
        '''
        self.__init__( *args, **kwargs )                             

    # For backward compatibility: 

def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
    """Return a new handle with specified shape and context.

    Empty handle is only used to hold results.

    Returns
    -------
    handle
        A new empty `NDArray` handle.
    """
    hdl = NDArrayHandle()
    check_call(_LIB.MXNDArrayCreateEx(
        c_array_buf(mx_uint, native_array('I', shape)),
        mx_uint(len(shape)),
        ctypes.c_int(ctx.device_typeid),
        ctypes.c_int(ctx.device_id),
        ctypes.c_int(int(delay_alloc)),
        ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
        ctypes.byref(hdl)))
    return hdl 

def rint(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`rint`.

        The arguments are the same as for :py:func:`rint`, with
        this array as data.
        """
        return op.rint(self, *args, **kwargs) 

def log1p(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`log1p`.

        The arguments are the same as for :py:func:`log1p`, with
        this array as data.
        """
        return op.log1p(self, *args, **kwargs) 

def fix(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`fix`.

        The arguments are the same as for :py:func:`fix`, with
        this array as data.
        """
        return op.fix(self, *args, **kwargs) 

def floor(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`floor`.

        The arguments are the same as for :py:func:`floor`, with
        this array as data.
        """
        return op.floor(self, *args, **kwargs) 

def ceil(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`ceil`.

        The arguments are the same as for :py:func:`ceil`, with
        this array as data.
        """
        return op.ceil(self, *args, **kwargs) 

def sin(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`sin`.

        The arguments are the same as for :py:func:`sin`, with
        this array as data.
        """
        return op.sin(self, *args, **kwargs) 

def cos(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`cos`.

        The arguments are the same as for :py:func:`cos`, with
        this array as data.
        """
        return op.cos(self, *args, **kwargs) 

def expm1(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`expm1`.

        The arguments are the same as for :py:func:`expm1`, with
        this array as data.
        """
        return op.expm1(self, *args, **kwargs) 

def log2(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`log2`.

        The arguments are the same as for :py:func:`log2`, with
        this array as data.
        """
        return op.log2(self, *args, **kwargs) 

def log(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`log`.

        The arguments are the same as for :py:func:`log`, with
        this array as data.
        """
        return op.log(self, *args, **kwargs) 

def radians(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`radians`.

        The arguments are the same as for :py:func:`radians`, with
        this array as data.
        """
        return op.radians(self, *args, **kwargs) 

def round(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`round`.

        The arguments are the same as for :py:func:`round`, with
        this array as data.
        """
        return op.round(self, *args, **kwargs) 

def min(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`min`.

        The arguments are the same as for :py:func:`min`, with
        this array as data.
        """
        return op.min(self, *args, **kwargs) 

def max(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`max`.

        The arguments are the same as for :py:func:`max`, with
        this array as data.
        """
        return op.max(self, *args, **kwargs) 

def mean(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`mean`.

        The arguments are the same as for :py:func:`mean`, with
        this array as data.
        """
        return op.mean(self, *args, **kwargs) 

def nanprod(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`nanprod`.

        The arguments are the same as for :py:func:`nanprod`, with
        this array as data.
        """
        return op.nanprod(self, *args, **kwargs) 

def prod(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`prod`.

        The arguments are the same as for :py:func:`prod`, with
        this array as data.
        """
        return op.prod(self, *args, **kwargs) 

def sum(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`sum`.

        The arguments are the same as for :py:func:`sum`, with
        this array as data.
        """
        return op.sum(self, *args, **kwargs) 

def diag(self, k=0, **kwargs):
        """Convenience fluent method for :py:func:`diag`.

        The arguments are the same as for :py:func:`diag`, with
        this array as data.
        """
        return op.diag(self, k, **kwargs) 

def space_to_depth(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`space_to_depth`.

        The arguments are the same as for :py:func:`space_to_depth`, with
        this array as data.
        """
        return op.space_to_depth(self, *args, **kwargs) 

def depth_to_space(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`depth_to_space`.

        The arguments are the same as for :py:func:`depth_to_space`, with
        this array as data.
        """
        return op.depth_to_space(self, *args, **kwargs) 

def flip(self, *args, **kwargs):
        """Convenience fluent method for :py:func:`flip`.

        The arguments are the same as for :py:func:`flip`, with
        this array as data.
        """
        return op.flip(self, *args, **kwargs)