Source code for pacman.model.routing_info.base_key_and_mask

# Copyright (c) 2017-2019 The University of Manchester
#
# 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/>.

import numpy
from pacman.exceptions import PacmanConfigurationException


class BaseKeyAndMask(object):
    """ A Key and Mask to be used for routing.
    """

    __slots__ = [
        # The routing key
        "_base_key",

        # The routing mask
        "_mask"
    ]

    def __init__(self, base_key, mask):
        """
        :param int base_key: The routing key
        :param int mask: The routing mask
        :raise PacmanConfigurationException:
            If key & mask != key i.e. the key is not valid for the given mask
        """
        self._base_key = base_key
        self._mask = mask

        if base_key & mask != base_key:
            raise PacmanConfigurationException(
                "This routing info is invalid as the mask {} and key {} "
                "together alters the key. This is deemed to be a error from "
                "SpiNNaker's point of view and therefore please rectify and "
                "try again".format(hex(base_key), hex(mask)))

    @property
    def key(self):
        """ The base key

        :rtype: int
        """
        return self._base_key

    @property
    def key_combo(self):
        """ The key combined with the mask

        :rtype: int
        """
        return self._base_key & self._mask

    @property
    def mask(self):
        """ The mask

        :rtype: int
        """
        return self._mask

    def __eq__(self, key_and_mask):
        if not isinstance(key_and_mask, BaseKeyAndMask):
            return False
        return (self._base_key == key_and_mask.key and
                self._mask == key_and_mask.mask)

    def __ne__(self, other):
        return not self.__eq__(other)

    def __repr__(self):
        return "KeyAndMask:{}:{}".format(hex(self._base_key), hex(self._mask))

    def __str__(self):
        return self.__repr__()

    def __hash__(self):
        return self.__repr__().__hash__()

    @property
    def n_keys(self):
        """ The total number of keys that can be generated given the mask

        :rtype: int
        """
        # converts mask into array of bit representation
        unwrapped_mask = numpy.unpackbits(
            numpy.asarray([self._mask], dtype=">u4").view(dtype="uint8"))

        # how many zeros are in the bit representation array
        zeros = numpy.where(unwrapped_mask == 0)[0]

        # number of keys available from this mask size
        return 2 ** len(zeros)

[docs] def get_keys(self, key_array=None, offset=0, n_keys=None): """ Get the ordered list of keys that the combination allows :param ~numpy.ndarray(int) key_array: Optional array into which the returned keys will be placed :param int offset: Optional offset into the array at which to start placing keys :param int n_keys: Optional limit on the number of keys returned. If less than this number of keys are available, only the keys available will be added :return: A tuple of an array of keys and the number of keys added to the array :rtype: tuple(~numpy.ndarray(int), int) """ # Get the position of the zeros in the mask - assume 32-bits unwrapped_mask = numpy.unpackbits( numpy.asarray([self._mask], dtype=">u4").view(dtype="uint8")) zeros = numpy.where(unwrapped_mask == 0)[0] # If there are no zeros, there is only one key in the range, so # return that if len(zeros) == 0: if key_array is None: key_array = numpy.zeros(1, dtype=">u4") key_array[offset] = self._base_key return key_array, 1 # We now know how many values there are - 2^len(zeros) max_n_keys = 2 ** len(zeros) if key_array is not None and len(key_array) < max_n_keys: max_n_keys = len(key_array) if n_keys is None or n_keys > max_n_keys: n_keys = max_n_keys if key_array is None: key_array = numpy.zeros(n_keys, dtype=">u4") # Create a list of 2^len(zeros) keys unwrapped_key = numpy.unpackbits( numpy.asarray([self._base_key], dtype=">u4").view(dtype="uint8")) # for each key, create its key with the idea of a neuron ID being # continuous and live at an offset position from the bottom of # the key for value in range(n_keys): key = numpy.copy(unwrapped_key) unwrapped_value = numpy.unpackbits( numpy.asarray([value], dtype=">u4") .view(dtype="uint8"))[-len(zeros):] key[zeros] = unwrapped_value key_array[value + offset] = \ numpy.packbits(key).view(dtype=">u4")[0].item() return key_array, n_keys