Source code for spynnaker8.models.connectors.from_list_connector

# Copyright (c) 2017-2021 The University of Manchester
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# This program is free software: you can redistribute it and/or modify
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from spynnaker.pyNN.models.neural_projections.connectors import (
    FromListConnector as _BaseClass)
from spynnaker.pyNN.utilities.utility_calls import moved_in_v6


[docs]class FromListConnector(_BaseClass): """ Make connections according to a list. .. deprecated:: 6.0 Use :py:class:`spynnaker.pyNN.models.neural_projections.connectors.FromListConnector` instead. """ __slots__ = [] def __init__( self, conn_list, safe=True, verbose=False, column_names=None, callback=None): """ :param conn_list: a list of tuples, one tuple for each connection. Each tuple should contain: `(pre_idx, post_idx, p1, p2, ..., pn)` where `pre_idx` is the index (i.e. order in the Population, not the ID) of the presynaptic neuron, `post_idx` is the index of the postsynaptic neuron, and `p1`, `p2`, etc. are the synaptic parameters (e.g., weight, delay, plasticity parameters). :type conn_list: list(tuple(int,int,...)) or ~numpy.ndarray :param bool safe: if True, check that weights and delays have valid values. If False, this check is skipped. :param bool verbose: Whether to output extra information about the connectivity to a CSV file :param column_names: the names of the parameters `p1`, `p2`, etc. If not provided, it is assumed the parameters are `weight, delay` (for backwards compatibility). :type column_names: tuple(str) or list(str) or None :param callable callback: if given, a callable that display a progress bar on the terminal. .. note:: Not supported by sPyNNaker. """ moved_in_v6("spynnaker8.models.connectors.FromListConnector", "spynnaker.pyNN.models.neural_projections.connectors" ".FromListConnector") _BaseClass.__init__( self, conn_list=conn_list, safe=safe, verbose=verbose, column_names=column_names, callback=callback)