예제 #1
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def unravel_index(x,dims):
    """Convert a flat index into an index tuple for an array of given shape.

    e.g. for a 2x2 array, unravel_index(2,(2,2)) returns (1,0).

    Example usage:
      p = x.argmax()
      idx = unravel_index(p,x.shape)
      x[idx] == x.max()

    Note:  x.flat[p] == x.max()

      Thus, it may be easier to use flattened indexing than to re-map
      the index to a tuple.
    """
    if x > _nx.prod(dims)-1 or x < 0:
        raise ValueError("Invalid index, must be 0 <= x <= number of elements.")

    idx = _nx.empty_like(dims)

    # Take dimensions
    # [a,b,c,d]
    # Reverse and drop first element
    # [d,c,b]
    # Prepend [1]
    # [1,d,c,b]
    # Calculate cumulative product
    # [1,d,dc,dcb]
    # Reverse
    # [dcb,dc,d,1]
    dim_prod = _nx.cumprod([1] + list(dims)[:0:-1])[::-1]
    # Indices become [x/dcb % a, x/dc % b, x/d % c, x/1 % d]
    return tuple(x/dim_prod % dims)
예제 #2
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def unravel_index(x,dims):
    """Convert a flat index into an index tuple for an array of given shape.

    e.g. for a 2x2 array, unravel_index(2,(2,2)) returns (1,0).

    Example usage:
      p = x.argmax()
      idx = unravel_index(p,x.shape)
      x[idx] == x.max()

    Note:  x.flat[p] == x.max()

      Thus, it may be easier to use flattened indexing than to re-map
      the index to a tuple.
    """
    if x > _nx.prod(dims)-1 or x < 0:
        raise ValueError("Invalid index, must be 0 <= x <= number of elements.")

    idx = _nx.empty_like(dims)

    # Take dimensions
    # [a,b,c,d]
    # Reverse and drop first element
    # [d,c,b]
    # Prepend [1]
    # [1,d,c,b]
    # Calculate cumulative product
    # [1,d,dc,dcb]
    # Reverse
    # [dcb,dc,d,1]
    dim_prod = _nx.cumprod([1] + list(dims)[:0:-1])[::-1]
    # Indeces become [x/dcb % a, x/dc % b, x/d % c, x/1 % d]
    return tuple(x/dim_prod % dims)
예제 #3
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def unravel_index(x, dims):
    """
    Convert a flat index to an index tuple for an array of given shape.

    Parameters
    ----------
    x : int
        Flattened index.
    dims : tuple of ints
        Input shape, the shape of an array into which indexing is
        required.

    Returns
    -------
    idx : tuple of ints
        Tuple of the same shape as `dims`, containing the unraveled index.

    Notes
    -----
    In the Examples section, since ``arr.flat[x] == arr.max()`` it may be
    easier to use flattened indexing than to re-map the index to a tuple.

    Examples
    --------
    >>> arr = np.arange(20).reshape(5, 4)
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11],
           [12, 13, 14, 15],
           [16, 17, 18, 19]])
    >>> x = arr.argmax()
    >>> x
    19
    >>> dims = arr.shape
    >>> idx = np.unravel_index(x, dims)
    >>> idx
    (4, 3)
    >>> arr[idx] == arr.max()
    True

    """
    if x > _nx.prod(dims) - 1 or x < 0:
        raise ValueError("Invalid index, must be 0 <= x <= number of elements.")

    idx = _nx.empty_like(dims)

    # Take dimensions
    # [a,b,c,d]
    # Reverse and drop first element
    # [d,c,b]
    # Prepend [1]
    # [1,d,c,b]
    # Calculate cumulative product
    # [1,d,dc,dcb]
    # Reverse
    # [dcb,dc,d,1]
    dim_prod = _nx.cumprod([1] + list(dims)[:0:-1])[::-1]
    # Indices become [x/dcb % a, x/dc % b, x/d % c, x/1 % d]
    return tuple(x / dim_prod % dims)
예제 #4
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def i0(x):
    x = atleast_1d(x).copy()
    y = empty_like(x)
    ind = (x < 0)
    x[ind] = -x[ind]
    ind = (x <= 8.0)
    y[ind] = _i0_1(x[ind])
    ind2 = ~ind
    y[ind2] = _i0_2(x[ind2])
    return y.squeeze()
def i0(x):
    x = atleast_1d(x).copy()
    y = empty_like(x)
    ind = (x<0)
    x[ind] = -x[ind]
    ind = (x<=8.0)
    y[ind] = _i0_1(x[ind])
    ind2 = ~ind
    y[ind2] = _i0_2(x[ind2])
    return y.squeeze()
예제 #6
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def unravel_index(x, dims):
    """
    Convert a flat index into an index tuple for an array of given shape.

    Parameters
    ----------
    x : int
        Flattened index.
    dims : shape tuple
        Input shape.

    Notes
    -----
    In the Examples section, since ``arr.flat[x] == arr.max()`` it may be
    easier to use flattened indexing than to re-map the index to a tuple.

    Examples
    --------
    >>> arr = np.ones((5,4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11],
           [12, 13, 14, 15],
           [16, 17, 18, 19]])
    >>> x = arr.argmax()
    >>> x
    19
    >>> dims = arr.shape
    >>> idx = np.unravel_index(x, dims)
    >>> idx
    (4, 3)
    >>> arr[idx] == arr.max()
    True

    """
    if x > _nx.prod(dims) - 1 or x < 0:
        raise ValueError(
            "Invalid index, must be 0 <= x <= number of elements.")

    idx = _nx.empty_like(dims)

    # Take dimensions
    # [a,b,c,d]
    # Reverse and drop first element
    # [d,c,b]
    # Prepend [1]
    # [1,d,c,b]
    # Calculate cumulative product
    # [1,d,dc,dcb]
    # Reverse
    # [dcb,dc,d,1]
    dim_prod = _nx.cumprod([1] + list(dims)[:0:-1])[::-1]
    # Indices become [x/dcb % a, x/dc % b, x/d % c, x/1 % d]
    return tuple(x / dim_prod % dims)
예제 #7
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def masked_all_like(arr):
    """Returns an empty masked array of the same shape and dtype as the array `a`,
    where all the data are masked."""
    a = masked_array(numeric.empty_like(arr),
                     mask=numeric.ones(arr.shape, bool_))
    return a