#! /usr/env/python
"""
Landlab's Continuous-Time Stochastic (CTS) cellular automata modeling package.

Overview
--------

A CellLab CTS model implements a particular type of cellular
automaton (CA): a continuous-time stochastic CA. The approach is based on that
of Narteau et al. (2002, 2009) and Rozier and Narteau (2014). Like a normal
CA, the domain consists of a lattice of cells, each of which has a discrete
state. Unlike a conventional CA, the updating process is stochastic, and takes
place in continuous rather than discrete time. Any given pair (or "doublet")
of adjacent cell states has a certain specified probability of transition to a 
different pair of states. The transition probability is given in the form of an
average *transition rate*, :math:\lambda (with dimensions of 1/T); the actual 
time of transition is a random variable drawn from an exponential probability 
distribution with mean :math:1/\lambda

Subclasses
----------

Landlab provides for several different lattice and connection types:
    - RasterCTS: regular raster grid with transitions between horizontal and
        vertical cell pairs
    - OrientedRasterCTS: like a RasterLCA, but different transition rates can
        be assigned to vertical and horizontal pairs. This property of
        orientation can be used, for example, to implement rules representing
        gravitational attraction, or flow of a fluid with a particular direction.
    - RasterD8CTS: like a RasterLCA, but includes diagonal as well as vertical
        and horizontal cell pairs.
    - OrientedRasterD8CTS: as above but orientation also matters.
    - HexCTS: hexagonal grid
    - OrientedHexCTS: hexagonal grid, with transition rates allowed to vary
        according to orientation.
        
Encoding of "states"
--------------------
As in any traditional cellular automaton model, a LandlabCellularAutomaton
contains a grid of cells ("nodes" in Landlab parlance), each of which is has a
discrete state. States are represented by integers (0, 1, ... N). 

In addition, every active link has an *orientation code* and a *link state code*. 
The orientation code represents the orientation of the link in space: is it
"vertical" (aligned with the y axis), "horizontal" (aligned with x), or in some
other orientation? The number of possible orientations depends on the subclass.
The base class has only one orientation code (0) (meaning "orientation doesn't
matter), but this is overridden in some of the subclasses. For example, the
OrientedRasterLCA has two orientation codes (0 and 1, for vertical and
horizontal), while the OrientedHexLCA has three (representing the three axes
in a hex-cell / triagonal grid).

Each active link also has a *link state code*. The *state* of a link refers to
its particular combination of nodes and its orientation. For example, link 
state 1 refers to a link in which the tail-node has state 0, the head-node has
state 1, and the orientation code is 0. The number of possible link states is
equal to R N^2, where R is the number of orientations (1 to 3, depending on the 
subclass) and N is the number of possible node states. The simplest possible
Landlab CA model would have just one orientation code and two possible cell
states, so that there are four unique link states. These would be represented by
the tuples of (tail-node state, head-node state, orientation) as follows:
    
    link state 0 = (0, 0, 0)
    link state 1 = (0, 1, 0)
    link state 2 = (1, 0, 0)
    link state 3 = (1, 1, 0)
        
Main data structures
--------------------
node_state : 1d array (x number of nodes in grid)
    Node-based grid of node-state codes. This is the grid of cell (sic) states.
    
link_state_dict : dictionary
    Keys are 3-element tuples that represent the cell-state pairs and 
    orientation code for each possible link type; values are the corresponding
    link-state codes. Allows you to look up the link-state code corresponding
    to a particular pair of adjacent nodes with a particular orientation.

cell_pair : list (x number of possible link states)
    List of 3-element tuples representing all the various link states. Allows
    you to look up the node states and orientation corresponding to a particular 
    link-state ID.
    
event_queue : heap of Event objects
    Queue containing all future transition events, sorted by time of occurrence
    (from soonest to latest).
    
next_update : 1d array (x number of active links)
    Time (in the future) at which the link will undergo its next transition.
    You might notice that the update time for every scheduled transition is also
    stored in each Event object in the event queue. Why store it twice? Because
    a scheduled event might be invalidated after the event has been scheduled
    (because another transition has changed one of a link's two nodes, for
    example). The way to tell whether a scheduled event is still valid is to
    compare its time with the corresponding transition time in the *next_update*
    array. If they are different, the event is discarded.

active_link_orientation : 1d array of ints (x number of active links)
    Orientation code for each link.
    
link_state : 1d array of ints (x number of active links)
    State code for each link.

n_xn : 1d array of ints (x number of possible link states)
    Number of transitions ("xn" stands for "transition") from a given link
    state.

xn_to : 2d array of ints (# possible link states x max. # transitions)
    Stores the link-state code(s) to which a particular link state can 
    transition. "max. # transitions" means the maximum number of transitions
    from a single state. For example, if each link state is associated with one
    and only one transition, then the maximum is 1, but if there is at least
    one link state that can have either of two different transitions, then the
    maximum would be two.

xn_rate : 2d array of floats (# possible link states x max. # transitions)
    Rate associated with each link-state transition.


Created GT Sep 2014, starting from link_ca.py.
"""
from __future__ import print_function

from heapq import heappush
from heapq import heappop
import landlab
import numpy
import pylab as plt

_NEVER = 1e50

_DEBUG = False

_TEST = False


class Transition():
    """
    Represents a transition from one state ("from_state") to another 
    ("to_state") at a link. The transition probability is represented by a rate 
    parameter "rate", with dimensions of 1/T. The probability distribution of 
    time until the transition event occurs is exponentional with mean 1/rate. 
    The optional name parameter allows the caller to assign a name to any given 
    transition.
    
    Note that from_state and to_state can now be either integer IDs for the
    standardised ordering of the link states (as before), or tuples explicitly
    describing the node state at each end, and the orientation.
    Orientation is 0: horizontal, L-R; 1: vertical, bottom-top.
    For such a tuple, order is (left/bottom, right/top, orientation).
    """
    def __init__(self, from_state, to_state, rate, name=None, 
                 swap_properties=False, prop_update_fn=None):
        """
        Transition() constructor sets 3 required properties and 2 optional
        properties for a transition from one cell pair to another.
        
        Parameters
        ----------
        from_state : int
            Code for the starting state of the cell pair (link)
        to_state : int
            Code for the new state of the cell pair (link)
        rate : float
            Average rate at which this transition occurs (dimension of 1/time)
        name : string (optional)
            Name for this transition
        swap_properties : bool (optional)
            Flag: should properties be exchanged between the two cells?
        """
        self.from_state = from_state
        self.to_state = to_state
        self.rate = rate
        self.name = name
        self.swap_properties = swap_properties
        self.prop_update_fn = prop_update_fn
        
        
        
class Event():
    """
    Represents a transition event at a link. The transition occurs at a given
    link and a given time, and it involves a transition into the state xn_to
    (an integer code representing the new link state; "xn" is shorthand for
    "transition").
    
    The class overrides the __lt__ (less than operator) method so that when
    Event() objects are placed in a PriorityQueue, the earliest event is
    given the highest priority (i.e., placed at the top of the queue).
    
    Examples
    --------
    >>> from landlab.components.cellular_automata.landlab_ca import Event
    >>> e1 = Event( 10.0, 1, 2)
    >>> e2 = Event( 2.0, 3, 1)
    >>> e1 < e2
    False
    >>> e2 < e1
    True
    """
    def __init__(self, time, link, xn_to, propswap=False, prop_update_fn=None):
        """
        Event() constructor sets 3 required properties and one optional
        property.
        
        Parameters
        ----------
        time : float
            Time at which the event is scheduled to occur
        link : int
            ID of the link at which event occurs
        xn_to : int
            New state to which this cell pair (link) will transition
        propswap : bool (optional)
            Flag: does this event involve an exchange of properties between
            the two cells?
        """
        self.time = time
        self.link = link
        self.xn_to = xn_to
        self.propswap = propswap
        self.prop_update_fn = prop_update_fn
        
        
    def __lt__(self, other):
        """
        Overridden less-than operator: returns true if the event on the left
        has an earlier scheduled time than the event on the right
        """
        return self.time < other.time
            
            
            
class CAPlotter():
    """
    A CAPlotter is an object that handles display of a CellLab-CTS grid.
    """
    def __init__(self, ca, cmap=None):
        """
        CAPlotter() constructor keeps a reference to the CA model, and
        optionally a colormap to be used with plots.
        
        Parameters
        ----------
        ca : LandlabCellularAutomaton object
            Reference to a CA model
        cmap : Matplotlib colormap (optional)
            Colormap to be used in plotting
        """
        import matplotlib
        
        # Set the colormap; default to matplotlib's "jet" colormap
        if cmap is None:
            self._cmap = matplotlib.cm.jet
        else:
            self._cmap = cmap
            
        # Keep a reference to the CA model
        self.ca = ca
        
        # Initialize the plot and remember the grid type
        plt.ion()
        plt.figure(1)
        if type(ca.grid) is landlab.grid.hex.HexModelGrid:
            self.gridtype = 'hex'
        else:
            self.gridtype = 'rast'


    def update_plot(self):
        """
        Plots the current node state grid.
        """
        plt.clf()
        if self.gridtype=='rast':
            nsr = self.ca.grid.node_vector_to_raster(self.ca.node_state)
            plt.imshow(nsr, interpolation='None', origin='lower', cmap=self._cmap)
        else:
            self.ca.grid.hexplot(self.ca.node_state, color_map=self._cmap)
        
        plt.draw()
        plt.pause(0.001)
        
        
    def finalize(self):
        """
        Wraps up plotting by switching off interactive model and showing the
        plot.
        """        
        plt.ioff()
        plt.show()
        
        
        
class CellLabCTSModel(object):
    """
    A CellLabCTSModel implements a link-type (or doublet-type) cellular
    automaton model. A link connects a pair of cells. Each cell has a state
    (represented by an integer code), and each link also has a state that is
    determined by the states of the cell pair.
    """
    def __init__(self, model_grid, node_state_dict, transition_list,
                 initial_node_states, prop_data=None, prop_reset_value=None):
        """
        LandlabCellularAutomaton() constructor initializes the CA model.
        
        Parameters
        ----------
        model_grid : Landlab ModelGrid object
            Reference to the model's grid
        node_state_dict : dict
            Keys are node-state codes, values are the names associated with
            these codes
        transition_list : list of Transition objects
            List of all possible transitions in the model
        initial_node_states : array of ints (x number of nodes in grid)
            Starting values for node-state grid
        prop_data : array (x number of nodes in grid) (optional)
            Array of properties associated with each node/cell
        prop_reset_value : (scalar; same type as entries in prop_data) (optional)
            Default or initial value for a node/cell property (e.g., 0.0)
        """
        # Are we calling this from a subclass __init__? If so, then the 
        # variable self.number_of_orientations should already be defined.
        try:
            self.number_of_orientations == 1
        except AttributeError:  # if self.number_of_orientations not already defined
            self.number_of_orientations = 1
        
        # Keep a copy of the model grid; remember how many active links in it
        self.grid = model_grid
        ###self.node_active_links = self.grid.node_activelinks()
        self.node_active_links = self.grid.node_activelinks2()

        # Set up the initial node-state grid
        self.set_node_state_grid(initial_node_states)
        
        # Current simulation time starts out at zero
        self.current_time = 0.0
        
        # Figure out how many states there are, and make sure the input data
        # are self consistent.
        #   There are 2 x (N^2) link states, where N is the number of node 
        # states. For example, if there are just two node states, 0 and 1, then 
        # the possible oriented link pairs are listed below:
        #   0-0 0-1 1-0 1-1  0 0 1 1
        #                    0 1 0 1
        self.num_node_states = len(node_state_dict)
        self.num_link_states = self.number_of_orientations*self.num_node_states**2
        
        assert (type(transition_list) is list), 'transition_list must be a list!'
        assert (transition_list), \
               'Transition list must contain at least one transition'
        last_type = None
        for t in transition_list:
            try:
                assert (t.from_state < self.num_link_states), \
                    'Transition from_state out of range'
                assert (t.to_state < self.num_link_states), \
                    'Transition to_state out of range'
                this_type = int
            # TODO: make orientation optional for cases where self.number_of_orientations = 1
            except: #added to allow from and to states to be tuples, not just ids
                assert type(t.from_state) == tuple, 'Transition from_state out of range'
                assert type(t.to_state) == tuple, 'Transition to_state out of range'
                for i in t.from_state[:-1]:
                    assert (i < self.num_node_states), \
                    'Transition from_state out of range'
                for i in t.to_state[:-1]:
                    assert (i < self.num_node_states), \
                    'Transition to_state out of range'
                assert t.from_state[-1] < self.number_of_orientations, \
                    'Encoding for orientation in from_state must be < number of orientations.'
                assert t.to_state[-1] < self.number_of_orientations, \
                    'Encoding for orientation in to_state must be < number of orientations.'
                this_type = tuple
            assert last_type==this_type or last_type==None, \
                'All transition types must be either int IDs, or all tuples.'
            #this test to ensure all entries are either IDs, or tuples, not mixed
            last_type=this_type
        
        # Create priority queue for events and next_update array for links
        self.event_queue = []
        self.next_update = self.grid.add_zeros('link', 'next_update_time')
    
        # Assign link types from node types
        self.create_link_state_dict_and_pair_list()

        #DEJH adds: convert transition_list to IDs if necessary
        #This is the new part that allows Transition from_ and to_ types 
        #to be specified either as ints, or as tuples.
        transition_list_as_ID = transition_list[:]
        if type(transition_list[0].from_state) == tuple:
            #(then they all are..., because of the assertions in __init__)
            for i in range(len(transition_list)):
                transition_list_as_ID[i].from_state = self.link_state_dict[transition_list[i].from_state]
                transition_list_as_ID[i].to_state = self.link_state_dict[transition_list[i].to_state]
    
        # Set up the information needed to determine the orientation of links
        # in the lattice. The default method just creates an array of zeros (all
        # orientations considered the same), but this will be overridden
        # in subclasses that do use orientation.
        self.setup_array_of_orientation_codes()
        
        # Using the grid of node states, figure out all the link states
        self.assign_link_states_from_node_types()
    
        # Create transition data for links
        self.setup_transition_data(transition_list_as_ID)

        # Put the various transitions on the event queue
        self.push_transitions_to_event_queue()
        
        # In order to keep track of cell "properties", we create an array of
        # indices that refer to locations in the caller's code where properties
        # are tracked.
        self.propid = numpy.arange(self.grid.number_of_nodes)
        if prop_data is None:
            self.prop_data = numpy.zeros(self.grid.number_of_nodes)
            self.prop_reset_value = 0.0
        else:
            self.prop_data = prop_data
            self.prop_reset_value = prop_reset_value
        

    def set_node_state_grid(self, node_states):
        """
        Sets the grid of node-state codes to node_states. Also checks
        to make sure node_states is in the proper format, which is to
        say, it's a Numpy array of the same length as the number of nodes in 
        the grid.
        
        Parameters
        ----------
        node_states : 1D array of ints (x number of nodes in grid)
        
        Creates
        -------
        self.node_state : 1D array of ints (x number of nodes in grid)
        	The node-state array
        	
        Notes
        -----
        The node-state array is attached to the grid as a field with the name 'node_state'.
        """
        assert (type(node_states) is numpy.ndarray), \
               'initial_node_states must be a Numpy array'
        assert (len(node_states)==self.grid.number_of_nodes), \
               'length of initial_node_states must equal number of nodes in grid'
        self.grid.at_node['node_state'] = node_states
        self.node_state = node_states
        
                 
    def create_link_state_dict_and_pair_list(self):
        """
        Creates a dictionary that can be used as a lookup table to find out 
        which link state corresponds to a particular pair of node states. The 
        dictionary keys are 3-element tuples, each of which represents the state
        of the TAIL node, the HEAD node, and the orientation of the link. The 
        values are integer codes representing the link state numbers.
        """
        self.link_state_dict = {}
        self.cell_pair = []
        k=0
        for orientation in range(self.number_of_orientations):
            for tail_state in range(self.num_node_states):
                for head_state in range(self.num_node_states):
                    self.link_state_dict[(tail_state,head_state,orientation)] = k
                    k+=1
                    self.cell_pair.append((tail_state,head_state,orientation))
    
        if False and _DEBUG:
            print()
            print('create_link_state_dict_and_pair_list(): dict is:')
            print((self.link_state_dict))
            print('  and the pair list is:')
            print((self.cell_pair))
            
            
    def setup_array_of_orientation_codes(self):
        """
        Creates and configures an array that contain the orientation code for 
        each active link (and corresponding cell pair).
        
        Parameters
        ----------
        (none)
        
        Returns
        -------
        (none)
        
        Creates
        -------
        self.active_link_orientation : 1D numpy array
        
        Notes
        -----
        The setup varies depending on the type of LCA. The default is 
        non-oriented, in which case we just have an array of zeros. Subclasses
        will override this method to handle lattices in which orientation 
        matters (for example, vertical vs. horizontal in an OrientedRasterLCA).
        """
        self.link_orientation = numpy.zeros(self.grid.number_of_links, dtype=int)
    
    
    def assign_link_states_from_node_types(self):
        """
        Assigns a link-state code for each link, and returns a list of these.
        
        Takes lists/arrays of "tail" and "head" node IDs for each link, and a 
        dictionary that associates pairs of node states (represented as a 
        3-element tuple, comprising the TAIL state, FROM state, and orientation) 
        to link states.
        """
        self.link_state = numpy.zeros(self.grid.number_of_links, dtype=int)
    
        for i in self.grid.active_links:
            orientation = self.link_orientation[i]
            node_pair = (self.node_state[self.grid.link_fromnode[i]], \
                         self.node_state[self.grid.link_tonode[i]], \
                         orientation)
            self.link_state[i] = self.link_state_dict[node_pair]
                    
        if False and _DEBUG:
            print()
            print('assign_link_states_from_node_types(): the link state array is:')
            print((self.link_state))


    def setup_transition_data(self, xn_list):
        """
        Using the transition list and the number of link states, creates 
        three arrays that collectively contain data on state transitions:
            n_xn: for each link state, contains the number of transitions out of 
                  that state.
            xn_to: 2D array that records, for each link state and each
                   transition, the new state into which the link transitions.
            xn_rate: 2D array that records, for each link state and each
                     transition, the rate (1/time) of the transition.
            xn_propswap: 2D array that indicates, for each link state and each
                         transition, whether that transition is accompanied by
                         a "property" swap, in which the two cells exchange
                         properties (in order to represent a particle moving)
        """
        # First, create an array that stores the number of possible transitions
        # out of each state.
        self.n_xn = numpy.zeros(self.num_link_states, dtype=int)
        for xn in xn_list:
            self.n_xn[xn.from_state] += 1
        
        # Now, create arrays to hold the "to state" and transition rate for each
        # transition. These arrays are dimensioned N x M where N is the number
        # of states, and M is the maximum number of transitions from a single 
        # state (for example if state 3 could transition either to state 1 or 
        # state 4, and the other states only had one or zero possible 
        # transitions, then the maximum would be 2).
        max_transitions = numpy.max(self.n_xn)
        self.xn_to = numpy.zeros((self.num_link_states, max_transitions), dtype=int)
        self.xn_rate = numpy.zeros((self.num_link_states, max_transitions))
        self.xn_propswap = numpy.zeros((self.num_link_states, max_transitions), dtype=bool)
        self.xn_prop_update_fn = numpy.empty((self.num_link_states, max_transitions), dtype=object)
    
        # Populate the "to" and "rate" arrays
        self.n_xn[:] = 0  # reset this and then re-do (inefficient but should work)
        for xn in xn_list:
            from_state = xn.from_state
            self.xn_to[from_state][self.n_xn[from_state]] = xn.to_state
            self.xn_rate[from_state][self.n_xn[from_state]] = xn.rate
            self.xn_propswap[from_state][self.n_xn[from_state]] = xn.swap_properties
            self.xn_prop_update_fn[from_state][self.n_xn[from_state]] = xn.prop_update_fn
            self.n_xn[from_state] += 1
    
        if False and _DEBUG:
            print()
            print('setup_transition_data():')
            print(('  n_xn',self.n_xn))
            print(('  to:',self.xn_to))
            print(('  rate:',self.xn_rate))
            
            
    def current_link_state(self, link_id):
        """
        Used to determines whether the link state at link *link_id* has changed 
        due to an independent change in the node-state grid. Returns the 
        current state of the link based on the states of its two end nodes; this
        can be compared to the entry in self.link_state to determine whether the
        state has changed.
        
        Parameters
        ----------
        link_id : int
            ID of the active link to test
            
        Returns
        -------
        int
            New link state code
        
        Notes
        -----
        Vectorizing this might yield some speed.
        """
        
        # Find out the states of the two nodes, and the orientation
        ###tail_node_state = self.node_state[self.grid.activelink_fromnode[link_id]]
        ###head_node_state = self.node_state[self.grid.activelink_tonode[link_id]]
        ###orientation = self.active_link_orientation[link_id]
        tail_node_state = self.node_state[self.grid.link_fromnode[link_id]]
        head_node_state = self.node_state[self.grid.link_tonode[link_id]]
        orientation = self.link_orientation[link_id]
        
        # Return the corresponding state code.
        return self.link_state_dict[(tail_node_state,head_node_state,orientation)]
        
        
    def update_link_states_and_transitions(self, current_time):
        """
        Following an "external" change to the node state grid, updates link
        states where necessary and creates any needed events.
        
        Algorithm
        ---------
            FOR each active link:
                if the actual node pair is different from the link's code:
                    change the link state to be correct
                    schedule an event
        """
        for i in self.grid.active_links:
        ###for i in range(self.grid.number_of_active_links):
            current_state = self.current_link_state(i)
            if current_state!=self.link_state[i]:
                self.update_link_state(i, current_state, current_time)
                
    
    def get_next_event(self, link, current_state, current_time):
        """
        Returns the next event for link with ID "link", which is in state
        "current state".
    
        Parameters
        ----------
        link : int
            ID of the link
        current_state : int
            Current state code for the link
        current_time : float
            Current time in simulation (i.e., time of event just processed)
            
        Returns
        -------
        Event object       
            The returned Event object contains the time, link ID, and type of
            the next transition event at this link.
        
        Notes
        -----        
        If there is only one potential transition out of the current state, a 
        time for the transition is selected at random from an exponential 
        distribution with rate parameter appropriate for this transition.
    
        If there are more than one potential transitions, a transition time is 
        chosen for each, and the smallest of these applied.
    
        Assumes that there is at least one potential transition from the current
        state.
        """
        assert (self.n_xn[current_state]>0), \
               'must have at least one potential transition'
    
        # Find next event time for each potential transition
        if self.n_xn[current_state]==1:
            xn_to = self.xn_to[current_state][0]
            propswap = self.xn_propswap[current_state][0]
            next_time = numpy.random.exponential(1.0/self.xn_rate[current_state][0])
            prop_update_fn = self.xn_prop_update_fn[current_state][0]
        else:
            next_time = _NEVER
            xn_to = None
            propswap = False
            for i in range(self.n_xn[current_state]):
                this_next = numpy.random.exponential(1.0/self.xn_rate[current_state][i])
                if this_next < next_time:
                    next_time = this_next
                    xn_to = self.xn_to[current_state][i]
                    propswap = self.xn_propswap[current_state][i]
                    prop_update_fn = self.xn_prop_update_fn[current_state][i]

        # Create and setup event, and return it
        my_event = Event(next_time+current_time, link, xn_to, propswap, prop_update_fn)
    
        if _DEBUG:
            print('get_next_event():')
            print(('  next_time:', my_event.time))
            print(('  link:', my_event.link))
            print(('  xn_to:', my_event.xn_to))
            print(('  propswap:', my_event.propswap))
    
        return my_event
    
    
    def push_transitions_to_event_queue(self):
        """
        Initializes the event queue by creating transition events for each
        cell pair that has one or more potential transitions and pushing these
        onto the queue. Also records scheduled transition times in the
        self.next_update array.
        """
        if _DEBUG:
            print(('push_transitions_to_event_queue():', self.num_link_states, self.n_xn))
            
        for i in self.grid.active_links:
        ###for i in range(self.grid.number_of_active_links):
        
            if self.n_xn[self.link_state[i]] > 0:
                event = self.get_next_event(i, self.link_state[i], 0.0)
                heappush(self.event_queue, event)
                self.next_update[i] = event.time
            
            else:
                self.next_update[i] = _NEVER
            
        if _DEBUG:
            print('  push_transitions_to_event_queue(): events in queue are now:')
            for e in self.event_queue:
                print('    next_time:',e.time,'link:',e.link,'xn_to:',e.xn_to)
            
            
    def update_node_states(self, tail_node, head_node, new_link_state):
        """
        Updates the states of the two nodes in the given link.
        
        Parameters
        ----------
        tail_node : int
            ID of the tail node of the link (cell pair) in question
        head_node : int
            ID of the head node of the link (cell pair) in question
        new_link_state : int
            Link state code for the new cell pair
            
        Returns
        -------
        (bool, bool)
            Flags indicating whether the tail node and head node, respectively,
            have changed state
        """
    
        # Remember the previous state of each node so we can detect whether the 
        # state has changed
        old_tail_node_state = self.node_state[tail_node]
        old_head_node_state = self.node_state[head_node]
    
        # Change to the new states
        if self.grid.node_status[tail_node]==landlab.grid.base.CORE_NODE:
            self.node_state[tail_node] = self.cell_pair[new_link_state][0]
        if self.grid.node_status[head_node]==landlab.grid.base.CORE_NODE:
            self.node_state[head_node] = self.cell_pair[new_link_state][1]
    
        if _DEBUG:
            print('update_node_states() for',tail_node,'and',head_node)
            print('  tail_node was',old_tail_node_state,'and is now',self.node_state[tail_node])
            print('  head_node was',old_head_node_state,'and is now',self.node_state[head_node])
    
        return self.node_state[tail_node]!=old_tail_node_state, \
               self.node_state[head_node]!=old_head_node_state
               

    def update_link_state(self, link, new_link_state, current_time):
        """
        Implements a link transition by updating the current state of the link
        and (if appropriate) choosing the next transition event and pushing it 
        on to the event queue.
    
        Parameters
        ----------
        link : int
            ID of the link to update
        new_link_state : int
            Code for the new state
        current_time : float
            Current time in simulation
        """
        if _DEBUG:
            print()
            print('update_link_state()')
            
        # If the link connects to a boundary, we might have a different state
        # than the one we planned
        fn = self.grid.link_fromnode[link]
        tn = self.grid.link_tonode[link]
        ###fn = self.grid.activelink_fromnode[link]
        ###tn = self.grid.activelink_tonode[link]
        if _DEBUG:
            print('fn',fn,'tn',tn,'fnstat',self.grid.node_status[fn],'tnstat',self.grid.node_status[tn])
        if self.grid.node_status[fn]!=landlab.grid.base.CORE_NODE or \
           self.grid.node_status[tn]!=landlab.grid.base.CORE_NODE:
            ###fns = self.node_state[self.grid.activelink_fromnode[link]]
            ###tns = self.node_state[self.grid.activelink_tonode[link]]
            ###orientation = self.active_link_orientation[link]
            fns = self.node_state[self.grid.link_fromnode[link]]
            tns = self.node_state[self.grid.link_tonode[link]]
            orientation = self.link_orientation[link]
            actual_pair = (fns,tns,orientation)
            new_link_state = self.link_state_dict[actual_pair]
            if _DEBUG:
                print('**Boundary: overriding new link state to',new_link_state)
            
        self.link_state[link] = new_link_state
        if self.n_xn[new_link_state] > 0:
            event = self.get_next_event(link, new_link_state, current_time)
            heappush(self.event_queue, event)
            self.next_update[link] = event.time
        else:
            self.next_update[link] = _NEVER
            
        if _DEBUG:
            print('  at link',link)
            print('  state changed to',self.link_state[link],self.cell_pair[self.link_state[link]])
            print('  update time now',self.next_update[link])
        
            
    def do_transition(self, event, current_time, plot_each_transition=False,
                      plotter=None):
        """
        Implements a state transition. 
        
        Parameters
        ----------
        event : Event object 
        	Event object containing the data for the current transition event
        current_time : float
        	Current time in simulation
        plot_each_transition : bool (optional)
        	True if caller wants to show a plot of the grid after this transition
        plotter : CAPlotter object
        	Sent if caller wants a plot after this transition
        
        Algorithm
        ---------
        First checks that the transition is still
        valid by comparing the link's next_update time with the corresponding update
        time in the event object.
        
        If the transition is valid, we:
            1) Update the states of the two nodes attached to the link
            2) Update the link's state, choose its next transition, and push it on
            the event queue.
            3) Update the states of the other links attached to the two nodes, 
            choose their next transitions, and push them on the event queue.         
        """
        if _DEBUG:
            print()
            print('do_transition() for link',event.link)

        # We'll process the event if its update time matches the one we have 
        # recorded for the link in question. If not, it means that the link has
        # changed state since the event was pushed onto the event queue, and in that
        # case we'll ignore it.
        if event.time == self.next_update[event.link]:
        
            if _DEBUG:
                print('  event time =',event.time)
            
            ###tail_node = self.grid.activelink_fromnode[event.link]
            ###head_node = self.grid.activelink_tonode[event.link]
            tail_node = self.grid.link_fromnode[event.link]
            head_node = self.grid.link_tonode[event.link]
            tail_changed, head_changed = self.update_node_states(tail_node, head_node, 
                                                          event.xn_to)
            self.update_link_state(event.link, event.xn_to, event.time)

            # Next, when the state of one of the link's nodes changes, we have to
            # update the states of the OTHER links attached to it. This could happen
            # to one or both nodes.
            if tail_changed:
                
                if _DEBUG:
                    print('    fromnode has changed state, so updating its links')
            
                for link in self.node_active_links[:,tail_node]:
                    
                    if _DEBUG:
                        print('f checking link',link)
                    if link!=-1 and link!=event.link:
                    
                        ###this_link_fromnode = self.grid.activelink_fromnode[link]
                        ###this_link_tonode = self.grid.activelink_tonode[link]
                        ###orientation = self.active_link_orientation[link]
                        this_link_fromnode = self.grid.link_fromnode[link]
                        this_link_tonode = self.grid.link_tonode[link]
                        orientation = self.link_orientation[link]
                        current_pair = (self.node_state[this_link_fromnode], 
                                        self.node_state[this_link_tonode], orientation)
                        new_link_state = self.link_state_dict[current_pair]
                        self.update_link_state(link, new_link_state, event.time)

            if head_changed:
            
                if _DEBUG:
                    print('    tonode has changed state, so updating its links')
            
                for link in self.node_active_links[:,head_node]:
                
                    if _DEBUG:
                        print('t checking link',link)
                    if link!=-1 and link!=event.link:
                    
                        ###this_link_fromnode = self.grid.activelink_fromnode[link]
                        ###this_link_tonode = self.grid.activelink_tonode[link]
                        ###orientation = self.active_link_orientation[link]
                        this_link_fromnode = self.grid.link_fromnode[link]
                        this_link_tonode = self.grid.link_tonode[link]
                        orientation = self.link_orientation[link]
                        current_pair = (self.node_state[this_link_fromnode], 
                                        self.node_state[this_link_tonode], orientation)
                        new_link_state = self.link_state_dict[current_pair]
                        self.update_link_state(link, new_link_state, event.time)

		# If requested, display a plot of the grid
            if plot_each_transition and (plotter is not None):
                plotter.update_plot()
                
            # If this event involves an exchange of properties (i.e., the event involves
            # motion of an object that posses properties we want to track), implement
            # the swap.
            #   If the event requires a call to a user-defined callback function,
            # we handle that here too.
            if event.propswap:
                tmp = self.propid[tail_node]
                self.propid[tail_node] = self.propid[head_node]
                self.propid[head_node] = tmp
                if self.grid.node_status[tail_node]!=landlab.grid.base.CORE_NODE:
                    self.prop_data[self.propid[tail_node]] = self.prop_reset_value
                if self.grid.node_status[head_node]!=landlab.grid.base.CORE_NODE:
                    self.prop_data[self.propid[head_node]] = self.prop_reset_value
                if event.prop_update_fn is not None:
                    event.prop_update_fn(self, tail_node, head_node, event.time)
               
            if _DEBUG:
                n = self.grid.number_of_nodes
                for r in range(self.grid.number_of_node_rows):
                    for c in range(self.grid.number_of_node_columns):
                        n -= 1
                        print('{0:.0f}'.format(self.node_state[n]), end=' ')
                    print()
                if self.propid is not None:
                    print()
                    n = self.grid.number_of_nodes
                    for r in range(self.grid.number_of_node_rows):
                        for c in range(self.grid.number_of_node_columns):
                            n -= 1
                            print('{0:2.0f}'.format(self.propid[n]), end=' ')
                        print()

        elif _DEBUG:
            print('  event time is',event.time,'but update time is', \
                  self.next_update[event.link],'so event will be ignored')
                  
    
    def update_component_data(self, new_node_state_array):
        """
        Call this method to update all data held by the component, if, for
        example, another component or boundary conditions modify the node 
        statuses outside the component between run steps.
        
        This method updates all necessary properties, including both node and
        link states.
        
        *new_node_state_array* is the updated list of node states, which must
        still all be compatible with the state list originally supplied to
        this component.
        """
        self.set_node_state_grid(new_node_state_array)
        self.assign_link_states_from_node_types()
        self.push_transitions_to_event_queue()
        
                  
    def run(self, run_duration, node_state_grid=None, plot_each_transition=False,
            plotter=None):
        """
        Runs the model forward for a specified period of time.
        
        Parameters
        ----------
        run_duration : float
        	Length of time to run
        node_state_grid : 1D array of ints (x number of nodes) (optional)
        	Node states (if given, replaces model's current node state grid)
        plot_each_transition : bool (optional)
        	Option to display the grid after each transition
        plotter : CAPlotter object (optional)
        	Needed if caller wants to plot after every transition
        """
        if node_state_grid is not None:
            self.set_node_state_grid(node_state_grid)
    
        # Continue until we've run out of either time or events
        while self.current_time < run_duration and self.event_queue:
        
            if _DEBUG:
                print('Current Time = ', self.current_time)
        
            # Pick the next transition event from the event queue
            ev = heappop(self.event_queue)
        
            if _DEBUG:
                print('Event:',ev.time,ev.link,ev.xn_to)
        
            self.do_transition(ev, self.current_time, plot_each_transition, plotter)
            
            # Update current time
            self.current_time = ev.time

            
    
if __name__ == "__main__":
    import doctest
    doctest.testmod()
