nifty_utilities.py 6.16 KB
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# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
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#
# Copyright(C) 2013-2017 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
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from builtins import next, range
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import numpy as np
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from itertools import product
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from functools import reduce
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def get_slice_list(shape, axes):
    """
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    Helper function which generates slice list(s) to traverse over all
    combinations of axes, other than the selected axes.
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    Parameters
    ----------
    shape: tuple
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        Shape of the data array to traverse over.
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    axes: tuple
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        Axes which should not be iterated over.
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    Yields
    -------
    list
        The next list of indices and/or slice objects for each dimension.

    Raises
    ------
    ValueError
        If shape is empty.
    ValueError
        If axes(axis) does not match shape.
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    """
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    if not shape:
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        raise ValueError("shape cannot be None.")
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    if axes:
        if not all(axis < len(shape) for axis in axes):
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            raise ValueError("axes(axis) does not match shape.")
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        axes_select = [0 if x in axes else 1 for x, y in enumerate(shape)]
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        axes_iterables = \
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            [list(range(y)) for x, y in enumerate(shape) if x not in axes]
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        for index in product(*axes_iterables):
            it_iter = iter(index)
            slice_list = [
                next(it_iter)
                if axis else slice(None, None) for axis in axes_select
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                ]
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            yield slice_list
    else:
        yield [slice(None, None)]
        return
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def cast_axis_to_tuple(axis, length=None):
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    if axis is None:
        return None
    try:
        axis = tuple(int(item) for item in axis)
    except(TypeError):
        if np.isscalar(axis):
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            axis = (int(axis),)
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        else:
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            raise TypeError("Could not convert axis-input to tuple of ints")
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    if length is not None:
        # shift negative indices to positive ones
        axis = tuple(item if (item >= 0) else (item + length) for item in axis)
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        # Deactivated this, in order to allow for the ComposedOperator
        # remove duplicate entries
        # axis = tuple(set(axis))
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        # assert that all entries are elements in [0, length]
        for elem in axis:
            assert (0 <= elem < length)
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    return axis


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def parse_domain(domain):
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    from .domain_object import DomainObject
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    if domain is None:
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        return ()
    if isinstance(domain, DomainObject):
        return (domain,)
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    if not isinstance(domain, tuple):
        domain = tuple(domain)
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    for d in domain:
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        if not isinstance(d, DomainObject):
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            raise TypeError(
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                "Given object contains something that is not an "
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                "instance of DomainObject-class.")
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    return domain
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def bincount_axis(obj, minlength=None, weights=None, axis=None):
    if minlength is not None:
        length = max(np.amax(obj) + 1, minlength)
    else:
        length = np.amax(obj) + 1

    if obj.shape == ():
        raise ValueError("object of too small depth for desired array")
    data = obj

    # if present, parse the axis keyword and transpose/reorder self.data
    # such that all affected axes follow each other. Only if they are in a
    # sequence flattening will be possible
    if axis is not None:
        # do the reordering
        ndim = len(obj.shape)
        axis = sorted(cast_axis_to_tuple(axis, length=ndim))
        reordering = [x for x in range(ndim) if x not in axis]
        reordering += axis

        data = np.transpose(data, reordering)
        if weights is not None:
            weights = np.transpose(weights, reordering)

        reord_axis = list(range(ndim-len(axis), ndim))

        # semi-flatten the dimensions in `axis`, i.e. after reordering
        # the last ones.
        semi_flat_dim = reduce(lambda x, y: x*y,
                               data.shape[ndim-len(reord_axis):])
        flat_shape = data.shape[:ndim-len(reord_axis)] + (semi_flat_dim, )
    else:
        flat_shape = (reduce(lambda x, y: x*y, data.shape), )

    data = np.ascontiguousarray(data.reshape(flat_shape))
    if weights is not None:
        weights = np.ascontiguousarray(
                            weights.reshape(flat_shape))

    # compute the local bincount results
    # -> prepare the local result array
    if weights is None:
        result_dtype = np.int
    else:
        result_dtype = np.float
    local_counts = np.empty(flat_shape[:-1] + (length, ),
                            dtype=result_dtype)
    # iterate over all entries in the surviving axes and compute the local
    # bincounts
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    for slice_list in get_slice_list(flat_shape,
                                     axes=(len(flat_shape)-1, )):
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        if weights is not None:
            current_weights = weights[slice_list]
        else:
            current_weights = None
        local_counts[slice_list] = np.bincount(
                                        data[slice_list],
                                        weights=current_weights,
                                        minlength=length)

    # restore the original ordering
    # place the bincount stuff at the location of the first `axis` entry
    if axis is not None:
        # axis has been sorted above
        insert_position = axis[0]
        new_ndim = len(local_counts.shape)
        return_order = (list(range(0, insert_position)) +
                        [new_ndim-1, ] +
                        list(range(insert_position, new_ndim-1)))
        local_counts = np.ascontiguousarray(
                            local_counts.transpose(return_order))
    return local_counts