# encoding: utf-8
"""
Common implementation of the Projection class, to be sub-classed by
backend-specific Projection classes.

:copyright: Copyright 2006-2022 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
"""


from functools import reduce
import numpy as np
import logging
import operator
from copy import deepcopy
from warnings import warn
from pyNN import recording, errors, models, core, descriptions
from pyNN.parameters import ParameterSpace, LazyArray
from pyNN.space import Space
from pyNN.standardmodels import StandardSynapseType
from pyNN.connectors import Connector
from .populations import BasePopulation, Assembly

logger = logging.getLogger("PyNN")
deprecated = core.deprecated


class Projection(object):
    """
    A container for all the connections of a given type (same synapse type and
    plasticity mechanisms) between two populations, together with methods to
    set the parameters of those connections, including the parameters of
    plasticity mechanisms.

    Arguments:
        `presynaptic_neurons` and `postsynaptic_neurons`:
            Population, PopulationView or Assembly objects.
        `source`:
            string specifying which attribute of the presynaptic cell signals
            action potentials. This is only needed for multicompartmental cells
            with branching axons or dendrodendritic synapses. All standard cells
            have a single source, and this is the default.
        `receptor_type`:
            string specifying which synaptic receptor_type type on the postsynaptic cell to connect
            to. For standard cells, this can be 'excitatory' or 'inhibitory'.
            For non-standard cells, it could be 'NMDA', etc. If receptor_type is not
            given, the default values of 'excitatory' is used.
        `connector`:
            a Connector object, encapsulating the algorithm to use for
            connecting the neurons.
        `synapse_type`:
            a SynapseType object specifying which synaptic connection
            mechanisms to use.
        `space`:
            TO DOCUMENT
    """
    _nProj = 0
    MULTI_SYNAPSE_OPERATIONS = {
        'last': lambda a, b: b,
        'first': lambda a, b: a,
        'sum': operator.iadd,
        'min': min,
        'max': max
    }

    def __init__(self, presynaptic_neurons, postsynaptic_neurons, connector,
                 synapse_type=None, source=None, receptor_type=None,
                 space=Space(), label=None):
        """
        Create a new projection, connecting the pre- and post-synaptic neurons.
        """
        if not hasattr(self, "_simulator"):
            errmsg = "`common.Projection` should not be instantiated directly. " \
                     "You should import Projection from a PyNN backend module, " \
                     "e.g. pyNN.nest or pyNN.neuron"
            raise Exception(errmsg)
        for prefix, pop in zip(("pre", "post"),
                               (presynaptic_neurons, postsynaptic_neurons)):
            if not isinstance(pop, (BasePopulation, Assembly)):
                raise errors.ConnectionError(
                    "%ssynaptic_neurons must be a Population, PopulationView or Assembly, not a %s" % (prefix, type(pop)))

        if isinstance(postsynaptic_neurons, Assembly):
            if not postsynaptic_neurons._homogeneous_synapses:
                raise errors.ConnectionError(
                    'Projection to an Assembly object can be made only with homogeneous synapses types')

        self.pre = presynaptic_neurons    # } these really
        self.source = source              # } should be
        self.post = postsynaptic_neurons  # } read-only
        self.label = label
        self.space = space
        if not isinstance(connector, Connector):
            raise TypeError(
                "The connector argument should be an instance of a subclass of Connector. "
                f"The argument provided was of type '{type(connector).__name__}'."
            )
        self._connector = connector

        self.synapse_type = synapse_type or self._static_synapse_class()
        if not isinstance(self.synapse_type, models.BaseSynapseType):
            raise TypeError(
                "The synapse_type argument should be an instance of a subclass of BaseSynapseType. "
                f"The argument provided was of type '{type(synapse_type).__name__}'"
            )

        self.receptor_type = receptor_type
        if self.receptor_type in ("default", None):
            self._guess_receptor_type()
        if self.receptor_type not in postsynaptic_neurons.receptor_types:
            valid_types = postsynaptic_neurons.receptor_types
            assert len(valid_types) > 0
            errmsg = "User gave receptor_types=%s, receptor_types must be one of: '%s'"
            raise errors.ConnectionError(errmsg % (self.receptor_type, "', '".join(valid_types)))

        if label is None:
            if self.pre.label and self.post.label:
                self.label = u"%s→%s" % (self.pre.label, self.post.label)
        self.initial_values = {}
        self.annotations = {}
        Projection._nProj += 1

    def _guess_receptor_type(self):
        """
        If the receptor_type is not specified, we follow the convention that the first element
        in the list of available post-synaptic receptor types is the default for excitatory
        synapses and the second element is the default for inhibitory synapses.
        """
        if len(self.post.receptor_types) > 1:
            ps = deepcopy(self.synapse_type.parameter_space)
            ps = self._handle_distance_expressions(ps)
            weights = ps["weight"]
            if weights.shape is None:
                weights.shape = self.shape
            try:
                wl = weights[self.pre.size - 1, self.post.size - 1]
                if wl >= 0:
                    self.receptor_type = self.post.receptor_types[0]
                else:
                    self.receptor_type = self.post.receptor_types[1]
            except TypeError:  # for example, if using a native RNG with no Python interface
                warn("Unable to guess receptor type")
                self.receptor_type = self.post.receptor_types[0]
        else:
            self.receptor_type = self.post.receptor_types[0]

    def __len__(self):
        """Return the total number of local connections."""
        raise NotImplementedError

    def size(self, gather=True):
        """
        Return the total number of connections.
            - only local connections, if gather is False,
            - all connections, if gather is True (default)
        """
        if gather and self._simulator.state.num_processes > 1:
            n = len(self)
            return recording.mpi_sum(n)
        else:
            return len(self)

    @property
    def shape(self):
        return (self.pre.size, self.post.size)

    def __repr__(self):
        return 'Projection("%s")' % self.label

    def __getitem__(self, i):
        """Return the /i/th connection within the Projection."""
        raise NotImplementedError

    def __iter__(self):
        """Return an iterator over all connections on the local MPI node."""
        for i in range(len(self)):
            yield self[i]

    # --- Methods for setting connection parameters ---------------------------

    def set(self, **attributes):
        """
        Set connection attributes for all connections on the local MPI node.

        Attribute names may be 'weight', 'delay', or the name of any parameter
        of a synapse dynamics model (e.g. 'U' for TsodyksMarkramSynapse).

        Each attribute value may be:
            (1) a single number
            (2) a RandomDistribution object
            (3) a 2D array with the same dimensions as the connectivity matrix
                (as returned by `get(format='array')`
            (4) a mapping function, which accepts a single float argument (the
                distance between pre- and post-synaptic cells) and returns a single value.

        Weights should be in nA for current-based and µS for conductance-based
        synapses. Delays should be in milliseconds.

        Note that where a projection contains multiple connections between a given pair
        of neurons, all these connections will be set to the same value.
        """
        # should perhaps add a "distribute" argument, for symmetry with "gather" in get()

        # Note: we have removed the option:
        #      "a list/1D array of the same length as the number of local connections"
        # because it was proving tricky to implement and was holding up the release.
        # The plan is to add this option back at a later date.

        attributes = self._value_list_to_array(attributes)
        parameter_space = ParameterSpace(attributes,
                                         self.synapse_type.get_schema(),
                                         (self.pre.size, self.post.size))
        parameter_space = self._handle_distance_expressions(parameter_space)
        if isinstance(self.synapse_type, StandardSynapseType):
            parameter_space = self.synapse_type.translate(parameter_space)
        self._set_attributes(parameter_space)

    def initialize(self, **initial_values):
        """
        Set initial values of state variables of synaptic plasticity models.

        Values passed to initialize() may be:
            (1) single numeric values (all neurons set to the same value)
            (2) RandomDistribution objects
            (3) a 2D array with the same dimensions as the connectivity matrix
                (as returned by `get(format='array')`
            (4) a mapping function, which accepts a single float argument (the
                distance between pre- and post-synaptic cells) and returns a single value.

        Values should be expressed in the standard PyNN units (i.e. millivolts,
        nanoamps, milliseconds, microsiemens, nanofarads, event per second).

        Example::

            prj.initialize(u=-70.0)
        """
        for variable, value in initial_values.items():
            logger.debug("In Projection '%s', initialising %s to %s" %
                         (self.label, variable, value))
            initial_value = LazyArray(value, shape=(self.size,), dtype=float)
            self._set_initial_value_array(variable, initial_value)
            self.initial_values[variable] = initial_value

    def _value_list_to_array(self, attributes):
        """Convert a list of connection parameters/attributes to a 2D array."""
        connection_mask = ~np.isnan(self.get('weight', format='array', gather='all'))
        for name, value in attributes.items():
            if isinstance(value, list) or (isinstance(value, np.ndarray) and value.ndim == 1):
                array_value = np.nan * np.ones(self.shape)
                array_value[connection_mask] = value
                attributes[name] = array_value
        return attributes

    def _handle_distance_expressions(self, parameter_space):
        # also index-based expressions
        for name, map in parameter_space.items():
            if callable(map.base_value):
                if isinstance(map.base_value, core.IndexBasedExpression):
                    map.base_value.projection = self
                    parameter_space[name] = map
                else:
                    # Assumes map is a function of distance
                    position_generators = (self.pre.position_generator,
                                           self.post.position_generator)
                    distance_map = LazyArray(self.space.distance_generator(*position_generators),
                                             shape=self.shape)
                    parameter_space[name] = map(distance_map)
        return parameter_space

    @deprecated("set(weight=w)")
    def setWeights(self, w):
        self.set(weight=w)

    @deprecated("set(weight=rand_distr)")
    def randomizeWeights(self, rand_distr):
        self.set(weight=rand_distr)

    @deprecated("set(delay=d)")
    def setDelays(self, d):
        self.set(delay=d)

    @deprecated("set(delay=rand_distr)")
    def randomizeDelays(self, rand_distr):
        self.set(delay=rand_distr)

    @deprecated("set(parameter_name=value)")
    def setSynapseDynamics(self, parameter_name, value):
        self.set(parameter_name=value)

    @deprecated("set(name=rand_distr)")
    def randomizeSynapseDynamics(self, parameter_name, rand_distr):
        self.set(parameter_name=rand_distr)

    # --- Methods for writing/reading information to/from file. ---------------

    def get(self, attribute_names, format, gather=True, with_address=True,
            multiple_synapses='sum'):
        """
        Get the values of a given attribute (weight or delay) for all
        connections in this Projection.

        `attribute_names`:
            name of the attributes whose values are wanted, or a list of such
            names.
        `format`:
            "list" or "array".
        `gather`:
            If True, node 0 gets connection information from all MPI nodes,
            other nodes get information only from connections that exist in this node.
            If 'all', all nodes will receive connection information from all other nodes.
            If False, all nodes get only information about local connections.

        With list format, returns a list of tuples. By default, each tuple
        contains the indices of the pre- and post-synaptic cell followed by
        the attribute values in the order given in `attribute_names`.
        Example::

            >>> prj.get(["weight", "delay"], format="list")[:5]
            [(0.0, 0.0, 0.3401892507507171, 0.1),
             (0.0, 1.0, 0.7990713166233654, 0.30000000000000004),
             (0.0, 2.0, 0.6180841812877726, 0.5),
             (0.0, 3.0, 0.6758149775627305, 0.7000000000000001),
             (0.0, 4.0, 0.7166906726862953, 0.9)]

        If `with_address` is set to False, then the tuples will contain only the
        attribute values, not the cell indices.

        With array format, returns a tuple of 2D NumPy arrays, one for each
        name in `attribute_names`. The array element X_ij contains the
        attribute value for the connection from the ith neuron in the pre-
        synaptic Population to the jth neuron in the post-synaptic Population,
        if a single such connection exists. If there are no such connections,
        X_ij will be NaN. Example::

            >>> weights, delays = prj.get(["weight", "delay"], format="array")
            >>> weights
            array([[ 0.66210438,         nan,  0.10744555,  0.54557088],
                   [ 0.3676134 ,         nan,  0.41463193,         nan],
                   [ 0.57434871,  0.4329354 ,  0.58482943,  0.42863916]])

        If there are multiple such connections, the action to take is
        controlled by the `multiple_synapses` argument, which must be one of
        {'last', 'first', 'sum', 'min', 'max'}.

        Values will be expressed in the standard PyNN units (i.e. millivolts,
        nanoamps, milliseconds, microsiemens, nanofarads, event per second).
        """
        if isinstance(attribute_names, str):
            attribute_names = (attribute_names,)
            return_single = True
        else:
            return_single = False
        if isinstance(self.synapse_type, StandardSynapseType):
            attribute_names = self.synapse_type.get_native_names(*attribute_names)
        if format == 'list':
            names = list(attribute_names)
            if with_address:
                names = ["presynaptic_index", "postsynaptic_index"] + names
            values = self._get_attributes_as_list(names)
            if gather and self._simulator.state.num_processes > 1:
                all_values = {self._simulator.state.mpi_rank: values}
                all_values = recording.gather_dict(all_values, all=(gather == 'all'))
                if gather == 'all' or self._simulator.state.mpi_rank == 0:
                    values = reduce(operator.add, all_values.values())
            if not with_address and return_single:
                values = [val[0] for val in values]
            return values
        elif format == 'array':
            if multiple_synapses not in Projection.MULTI_SYNAPSE_OPERATIONS:
                raise ValueError("`multiple_synapses` argument must be one of {}".format(
                    list(Projection.MULTI_SYNAPSE_OPERATIONS)))
            if gather and self._simulator.state.num_processes > 1:
                # Node 0 is the only one creating a full connection matrix, and returning it (saving memory)
                # Slaves nodes are returning list of connections, so this may be inconsistent...
                names = list(attribute_names)
                names = ["presynaptic_index", "postsynaptic_index"] + names
                values = self._get_attributes_as_list(names)
                all_values = {self._simulator.state.mpi_rank: values}
                all_values = recording.gather_dict(all_values, all=(gather == 'all'))
                if gather == 'all' or self._simulator.state.mpi_rank == 0:
                    tmp_values = reduce(operator.add, all_values.values())
                    values = self._get_attributes_as_arrays(attribute_names,
                                                            multiple_synapses=multiple_synapses)
                    tmp_values = np.array(tmp_values)
                    for i in range(len(values)):
                        values[i][tmp_values[:, 0].astype(
                            int), tmp_values[:, 1].astype(int)] = tmp_values[:, 2 + i]
            else:
                values = self._get_attributes_as_arrays(attribute_names,
                                                        multiple_synapses=multiple_synapses)
            if return_single:
                if gather == 'all' or self._simulator.state.mpi_rank == 0:
                    assert len(values) == 1, values
                return values[0]
            else:
                return values
        else:
            raise Exception("format must be 'list' or 'array'")

    def _get_attributes_as_list(self, names):
        return [c.as_tuple(*names) for c in self.connections]

    def _get_attributes_as_arrays(self, names, multiple_synapses='sum'):
        multi_synapse_operation = Projection.MULTI_SYNAPSE_OPERATIONS[multiple_synapses]
        all_values = []
        for attribute_name in names:
            values = np.nan * np.ones((self.pre.size, self.post.size))
            if attribute_name[-1] == "s":  # weights --> weight, delays --> delay
                attribute_name = attribute_name[:-1]
            for c in self.connections:
                value = getattr(c, attribute_name)
                addr = (c.presynaptic_index, c.postsynaptic_index)
                if np.isnan(values[addr]):
                    values[addr] = value
                else:
                    values[addr] = multi_synapse_operation(values[addr], value)
            all_values.append(values)
        return all_values

    @deprecated("get('weight', format, gather)")
    def getWeights(self, format='list', gather=True):
        return self.get('weight', format, gather, with_address=False)

    @deprecated("get('delay', format, gather)")
    def getDelays(self, format='list', gather=True):
        return self.get('delay', format, gather, with_address=False)

    @deprecated("get(parameter_name, format, gather)")
    def getSynapseDynamics(self, parameter_name, format='list', gather=True):
        return self.get(parameter_name, format, gather, with_address=False)

    def save(self, attribute_names, file, format='list', gather=True, with_address=True):
        """
        Print synaptic attributes (weights, delays, etc.) to file. In the array
        format, zeros are printed for non-existent connections.

        Values will be expressed in the standard PyNN units (i.e. millivolts,
        nanoamps, milliseconds, microsiemens, nanofarads, event per second).
        """
        if attribute_names in ('all', 'connections'):
            attribute_names = self.synapse_type.get_parameter_names()
        if isinstance(file, str):
            file = recording.files.StandardTextFile(file, mode='wb')
        all_values = self.get(attribute_names, format=format,
                              gather=gather, with_address=with_address)
        if format == 'array':
            all_values = [np.where(np.isnan(values), 0.0, values)
                          for values in all_values]
        if self._simulator.state.mpi_rank == 0:
            metadata = {"columns": attribute_names}
            if with_address:
                metadata["columns"] = ["i", "j"] + list(metadata["columns"])
            file.write(all_values, metadata)
            file.close()

    @deprecated("save('all', file, format='list', gather=gather)")
    def saveConnections(self, file, gather=True, compatible_output=True):
        self.save('all', file, format='list', gather=gather)

    @deprecated("save('weight', file, format, gather)")
    def printWeights(self, file, format='list', gather=True):
        self.save('weight', file, format, gather)

    @deprecated("save('delay', file, format, gather)")
    def printDelays(self, file, format='list', gather=True):
        """
        Print synaptic weights to file. In the array format, zeros are printed
        for non-existent connections.
        """
        self.save('delay', file, format, gather)

    @deprecated("np.histogram()")
    def weightHistogram(self, min=None, max=None, nbins=10):
        """
        Return a histogram of synaptic weights.
        If min and max are not given, the minimum and maximum weights are
        calculated automatically.
        """
        weights = np.array(self.get('weight', format='list', gather=True, with_address=False))
        if min is None:
            min = weights.min()
        if max is None:
            max = weights.max()
        bins = np.linspace(min, max, nbins + 1)
        return np.histogram(weights, bins)  # returns n, bins

    def annotate(self, **annotations):
        self.annotations.update(annotations)

    def describe(self, template='projection_default.txt', engine='default'):
        """
        Returns a human-readable description of the projection.

        The output may be customized by specifying a different template
        togther with an associated template engine (see ``pyNN.descriptions``).

        If template is None, then a dictionary containing the template context
        will be returned.
        """
        context = {
            "label": self.label,
            "pre": self.pre.describe(template=None),
            "post": self.post.describe(template=None),
            "source": self.source,
            "receptor_type": self.receptor_type,
            "size_local": len(self),
            "size": self.size(gather=True),
            "connector": self._connector.describe(template=None),
            "plasticity": None,
        }
        if self.synapse_type:
            context.update(plasticity=self.synapse_type.describe(template=None))
        return descriptions.render(engine, template, context)


class Connection(object):
    """
    Store an individual plastic connection and information about it. Provide an
    interface that allows access to the connection's weight, delay and other
    attributes.
    """
    pass
