Source code for nengo.node

import warnings

import numpy as np

import nengo.utils.numpy as npext
from nengo.base import NengoObject, ObjView
from nengo.exceptions import ValidationError
from nengo.params import Default, IntParam, Parameter
from nengo.processes import Process
from nengo.utils.compat import is_array_like
from nengo.utils.stdlib import checked_call

class OutputParam(Parameter):
    def __init__(self, name, default, optional=True, readonly=False):
        assert optional  # None has meaning (passthrough node)
        super(OutputParam, self).__init__(name, default, optional, readonly)

    def __set__(self, node, output):
        super(OutputParam, self).validate(node, output)

        size_in_set = node.size_in is not None
        node.size_in = node.size_in if size_in_set else 0

        # --- Validate and set the new size_out
        if output is None:
            if node.size_out is not None:
                warnings.warn("'Node.size_out' is being overwritten with "
                              "'Node.size_in' since 'Node.output=None'")
            node.size_out = node.size_in
        elif isinstance(output, Process):
            if not size_in_set:
                node.size_in = output.default_size_in
            if node.size_out is None:
                node.size_out = output.default_size_out
        elif callable(output):
            # We trust user's size_out if set, because calling output
            # may have unintended consequences (e.g., network communication)
            if node.size_out is None:
                result = self.validate_callable(node, output)
                node.size_out = 0 if result is None else result.size
        elif is_array_like(output):
            # Make into correctly shaped numpy array before validation
            output = npext.array(
                output, min_dims=1, copy=False, dtype=np.float64)
            self.validate_ndarray(node, output)
            if not np.all(np.isfinite(output)):
                raise ValidationError("Output value must be finite.",
                            , obj=node)
            node.size_out = output.size
            raise ValidationError("Invalid node output type %r" %
                        , obj=node)

        # --- Set output[node] = output

    def validate_callable(self, node, output):
        t, x = 0.0, np.zeros(node.size_in)
        args = (t, x) if node.size_in > 0 else (t,)
        result, invoked = checked_call(output, *args)
        if not invoked:
            msg = ("output function '%s' is expected to accept exactly "
                   "%d argument" % (output, len(args)))
            msg += (' (time, as a float)' if len(args) == 1 else
                    's (time, as a float and data, as a NumPy array)')
            raise ValidationError(msg,, obj=node)

        if result is not None:
            result = np.asarray(result)
            if len(result.shape) > 1:
                raise ValidationError("Node output must be a vector (got shape"
                                      " %s)" % (result.shape,),
                            , obj=node)
        return result

    def validate_ndarray(self, node, output):
        if len(output.shape) > 1:
            raise ValidationError("Node output must be a vector (got shape "
                                  "%s)" % (output.shape,),
                        , obj=node)
        if node.size_in != 0:
            raise ValidationError("output must be callable if size_in != 0",
                        , obj=node)
        if node.size_out is not None and node.size_out != output.size:
            raise ValidationError("Size of Node output (%d) does not match "
                                  "size_out (%d)"
                                  % (output.size, node.size_out),
                        , obj=node)

[docs]class Node(NengoObject): """Provide non-neural inputs to Nengo objects and process outputs. Nodes can accept input, and perform arbitrary computations for the purpose of controlling a Nengo simulation. Nodes are typically not part of a brain model per se, but serve to summarize the assumptions being made about sensory data or other environment variables that cannot be generated by a brain model alone. Nodes can also be used to test models by providing specific input signals to parts of the model, and can simplify the input/output interface of a `~nengo.Network` when used as a relay to/from its internal ensembles (see `~nengo.networks.EnsembleArray` for an example). Parameters ---------- output : callable, array_like, or None Function that transforms the Node inputs into outputs, a constant output value, or None to transmit signals unchanged. size_in : int, optional (Default: 0) The number of dimensions of the input data parameter. size_out : int, optional (Default: None) The size of the output signal. If None, it will be determined based on the values of ``output`` and ``size_in``. label : str, optional (Default: None) A name for the node. Used for debugging and visualization. seed : int, optional (Default: None) The seed used for random number generation. Note: no aspects of the node are random, so currently setting this seed has no effect. Attributes ---------- label : str The name of the node. output : callable, array_like, or None The given output. size_in : int The number of dimensions for incoming connection. size_out : int The number of output dimensions. """ probeable = ('output',) output = OutputParam('output', default=None) size_in = IntParam('size_in', default=None, low=0, optional=True) size_out = IntParam('size_out', default=None, low=0, optional=True) def __init__(self, output=Default, size_in=Default, size_out=Default, label=Default, seed=Default): if not (seed is Default or seed is None): raise NotImplementedError( "Changing the seed of a node has no effect") super(Node, self).__init__(label=label, seed=seed) self.size_in = size_in self.size_out = size_out self.output = output # Must be set after size_out; may modify size_out def __getitem__(self, key): return ObjView(self, key) def __len__(self): return self.size_out