Python numpy.core.numeric.arange() Examples

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

def __getslice__(self, i, j):
        return _nx.arange(i, j) 

def __getslice__(self, i, j):
        return _nx.arange(i, j) 

def __getslice__(self, i, j):
        return _nx.arange(i, j) 

def __getslice__(self, i, j):
        res = _nx.arange(i, j)
        return self._retval(res) 

def __getslice__(self, i, j):
        res = _nx.arange(i, j)
        return self._retval(res) 

def __getslice__(self, i, j):
        res = _nx.arange(i, j)
        return self._retval(res) 

def put (self, values):
        """Set the non-masked entries of self to filled(values).
           No change to mask
        """
        iota = numeric.arange(self.size)
        d = self._data
        if self._mask is nomask:
            ind = iota
        else:
            ind = fromnumeric.compress(1 - self._mask, iota)
        d[ind] =  filled(values).astype(d.dtype) 

def __getslice__(self, i, j):
        res = _nx.arange(i, j)
        return self._retval(res) 

def __getslice__(self, i, j):
        return _nx.arange(i, j) 

def __getslice__(self, i, j):
        return _nx.arange(i, j) 

def __getslice__(self, i, j):
        res = _nx.arange(i, j)
        return self._retval(res) 

def __getslice__(self, i, j):
        return _nx.arange(i, j) 

def __getslice__(self, i, j):
        res = _nx.arange(i, j)
        return self._retval(res) 

def hsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays horizontally (column-wise).

    Please refer to the `split` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [ 12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [ 10.,  11.],
           [ 14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.],
           [  4.,   5.,   6.],
           [  8.,   9.,  10.],
           [ 12.,  13.,  14.]]),
     array([[  3.],
           [  7.],
           [ 11.],
           [ 15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[ 0.,  1.]],
           [[ 4.,  5.]]]),
     array([[[ 2.,  3.]],
           [[ 6.,  7.]]])]

    """
    if _nx.ndim(ary) == 0:
        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
    if ary.ndim > 1:
        return split(ary, indices_or_sections, 1)
    else:
        return split(ary, indices_or_sections, 0) 

def vsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays vertically (row-wise).

    Please refer to the ``split`` documentation.  ``vsplit`` is equivalent
    to ``split`` with `axis=0` (default), the array is always split along the
    first axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.vsplit(x, 2)
    [array([[ 0.,  1.,  2.,  3.],
           [ 4.,  5.,  6.,  7.]]),
     array([[  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])]
    >>> np.vsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.]]),
     array([[ 12.,  13.,  14.,  15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the first axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.vsplit(x, 2)
    [array([[[ 0.,  1.],
            [ 2.,  3.]]]),
     array([[[ 4.,  5.],
            [ 6.,  7.]]])]

    """
    if _nx.ndim(ary) < 2:
        raise ValueError('vsplit only works on arrays of 2 or more dimensions')
    return split(ary, indices_or_sections, 0) 

def hsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays horizontally (column-wise).

    Please refer to the `split` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [ 12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [ 10.,  11.],
           [ 14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.],
           [  4.,   5.,   6.],
           [  8.,   9.,  10.],
           [ 12.,  13.,  14.]]),
     array([[  3.],
           [  7.],
           [ 11.],
           [ 15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[ 0.,  1.]],
           [[ 4.,  5.]]]),
     array([[[ 2.,  3.]],
           [[ 6.,  7.]]])]

    """
    if _nx.ndim(ary) == 0:
        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
    if ary.ndim > 1:
        return split(ary, indices_or_sections, 1)
    else:
        return split(ary, indices_or_sections, 0) 

def array_split(ary, indices_or_sections, axis=0):
    """
    Split an array into multiple sub-arrays.

    Please refer to the ``split`` documentation.  The only difference
    between these functions is that ``array_split`` allows
    `indices_or_sections` to be an integer that does *not* equally
    divide the axis. For an array of length l that should be split
    into n sections, it returns l % n sub-arrays of size l//n + 1
    and the rest of size l//n.

    See Also
    --------
    split : Split array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

    >>> x = np.arange(7.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.]), array([ 5.,  6.])]

    """
    try:
        Ntotal = ary.shape[axis]
    except AttributeError:
        Ntotal = len(ary)
    try:
        # handle array case.
        Nsections = len(indices_or_sections) + 1
        div_points = [0] + list(indices_or_sections) + [Ntotal]
    except TypeError:
        # indices_or_sections is a scalar, not an array.
        Nsections = int(indices_or_sections)
        if Nsections <= 0:
            raise ValueError('number sections must be larger than 0.')
        Neach_section, extras = divmod(Ntotal, Nsections)
        section_sizes = ([0] +
                         extras * [Neach_section+1] +
                         (Nsections-extras) * [Neach_section])
        div_points = _nx.array(section_sizes, dtype=_nx.intp).cumsum()

    sub_arys = []
    sary = _nx.swapaxes(ary, axis, 0)
    for i in range(Nsections):
        st = div_points[i]
        end = div_points[i + 1]
        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

    return sub_arys 

def __getitem__(self, key):
        try:
            size = []
            typ = int
            for k in range(len(key)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    size.append(int(abs(step)))
                    typ = float
                else:
                    size.append(
                        int(math.ceil((key[k].stop - start)/(step*1.0))))
                if (isinstance(step, float) or
                        isinstance(start, float) or
                        isinstance(key[k].stop, float)):
                    typ = float
            if self.sparse:
                nn = [_nx.arange(_x, dtype=_t)
                        for _x, _t in zip(size, (typ,)*len(size))]
            else:
                nn = _nx.indices(size, typ)
            for k in range(len(size)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    step = int(abs(step))
                    if step != 1:
                        step = (key[k].stop - start)/float(step-1)
                nn[k] = (nn[k]*step+start)
            if self.sparse:
                slobj = [_nx.newaxis]*len(size)
                for k in range(len(size)):
                    slobj[k] = slice(None, None)
                    nn[k] = nn[k][tuple(slobj)]
                    slobj[k] = _nx.newaxis
            return nn
        except (IndexError, TypeError):
            step = key.step
            stop = key.stop
            start = key.start
            if start is None:
                start = 0
            if isinstance(step, complex):
                step = abs(step)
                length = int(step)
                if step != 1:
                    step = (key.stop-start)/float(step-1)
                stop = key.stop + step
                return _nx.arange(0, length, 1, float)*step + start
            else:
                return _nx.arange(start, stop, step) 

def __getitem__(self, key):
        try:
            size = []
            typ = int
            for k in range(len(key)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    size.append(int(abs(step)))
                    typ = float
                else:
                    size.append(
                        int(math.ceil((key[k].stop - start)/(step*1.0))))
                if (isinstance(step, float) or
                        isinstance(start, float) or
                        isinstance(key[k].stop, float)):
                    typ = float
            if self.sparse:
                nn = [_nx.arange(_x, dtype=_t)
                        for _x, _t in zip(size, (typ,)*len(size))]
            else:
                nn = _nx.indices(size, typ)
            for k in range(len(size)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    step = int(abs(step))
                    if step != 1:
                        step = (key[k].stop - start)/float(step-1)
                nn[k] = (nn[k]*step+start)
            if self.sparse:
                slobj = [_nx.newaxis]*len(size)
                for k in range(len(size)):
                    slobj[k] = slice(None, None)
                    nn[k] = nn[k][tuple(slobj)]
                    slobj[k] = _nx.newaxis
            return nn
        except (IndexError, TypeError):
            step = key.step
            stop = key.stop
            start = key.start
            if start is None:
                start = 0
            if isinstance(step, complex):
                step = abs(step)
                length = int(step)
                if step != 1:
                    step = (key.stop-start)/float(step-1)
                stop = key.stop + step
                return _nx.arange(0, length, 1, float)*step + start
            else:
                return _nx.arange(start, stop, step) 

def __getitem__(self, key):
        try:
            size = []
            typ = int
            for k in range(len(key)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    size.append(int(abs(step)))
                    typ = float
                else:
                    size.append(
                        int(math.ceil((key[k].stop - start)/(step*1.0))))
                if (isinstance(step, float) or
                        isinstance(start, float) or
                        isinstance(key[k].stop, float)):
                    typ = float
            if self.sparse:
                nn = [_nx.arange(_x, dtype=_t)
                        for _x, _t in zip(size, (typ,)*len(size))]
            else:
                nn = _nx.indices(size, typ)
            for k in range(len(size)):
                step = key[k].step
                start = key[k].start
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    step = int(abs(step))
                    if step != 1:
                        step = (key[k].stop - start)/float(step-1)
                nn[k] = (nn[k]*step+start)
            if self.sparse:
                slobj = [_nx.newaxis]*len(size)
                for k in range(len(size)):
                    slobj[k] = slice(None, None)
                    nn[k] = nn[k][slobj]
                    slobj[k] = _nx.newaxis
            return nn
        except (IndexError, TypeError):
            step = key.step
            stop = key.stop
            start = key.start
            if start is None:
                start = 0
            if isinstance(step, complex):
                step = abs(step)
                length = int(step)
                if step != 1:
                    step = (key.stop-start)/float(step-1)
                stop = key.stop + step
                return _nx.arange(0, length, 1, float)*step + start
            else:
                return _nx.arange(start, stop, step)