Main data structures¶
 node_state : 1d array of int (x number of nodes in grid)
 Nodebased grid of nodestate codes. This is the grid of cell (sic) states.
 link_state_dict : dictionary
 Keys are 3element tuples that represent the cellstate pairs and orientation code for each possible link type; values are the corresponding linkstate codes. Allows you to look up the linkstate code corresponding to a particular pair of adjacent nodes with a particular orientation.
 node_pair : list (x number of possible link states)
 List of 3element tuples representing all the various link states. Allows you to look up the node states and orientation corresponding to a particular linkstate ID.
 priority_queue : PriorityQueue object containing event records
 Queue containing all future transition events, sorted by time of occurrence (from soonest to latest).
 next_update : 1d array (x number of 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 with each event 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.
 link_orientation : 1d array of int8 (x number of links)
 Orientation code for each link.
 link_state : 1d array of int (x number of links)
 State code for each link.
 n_trn : 1d array of int (x number of possible link states)
 Number of transitions (“trn” stands for “transition”) from a given link state.
 trn_to : 1d array of ints (x # transitions)
 Stores the linkstate code(s) to which a particular transition ID can transition.
 trn_rate : 1d array of floats (# transitions)
 Rate associated with each linkstate transition.
Created GT Sep 2014, starting from link_cap.py.

class
CAPlotter
(ca, cmap=None, **kwds)[source]¶ Bases:
object
Handle display of a CellLabCTS grid.
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
Examples
>>> from landlab import RasterModelGrid, HexModelGrid >>> from landlab.ca.celllab_cts import Transition >>> from landlab.ca.raster_cts import RasterCTS >>> import numpy as np >>> grid = RasterModelGrid((3, 5)) >>> nsd = {0 : 'zero', 1 : 'one'} >>> trn_list = [] >>> trn_list.append(Transition((0, 1, 0), (1, 1, 0), 1.0)) >>> ins = np.arange(15) % 2 >>> ca = RasterCTS(grid, nsd, trn_list, ins) >>> cap = CAPlotter(ca) >>> cap.gridtype 'rast' >>> cap._cmap.name 'jet'
>>> from landlab.ca.hex_cts import HexCTS >>> import matplotlib >>> grid = HexModelGrid((3, 3)) >>> ins = np.zeros(grid.number_of_nodes, dtype=int) >>> ca = HexCTS(grid, nsd, trn_list, ins) >>> cap = CAPlotter(ca, cmap=matplotlib.cm.pink) >>> cap.gridtype 'hex' >>> cap._cmap.name 'pink'
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

__init__
(ca, cmap=None, **kwds)[source]¶ 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

class
CellLabCTSModel
(model_grid, node_state_dict, transition_list, initial_node_states, prop_data=None, prop_reset_value=None, seed=0)[source]¶ Bases:
object
Linktype (or doublettype) cellular automaton model.
A CellLabCTSModel implements a linktype (or doublettype) 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.
Parameters:  model_grid (Landlab ModelGrid object) – Reference to the model’s grid
 node_state_dict (dict) – Keys are nodestate 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 nodestate grid
 prop_data (array (x number of nodes in grid), optional) – Array of properties associated with each node/cell
 prop_reset_value (number or object, optional) – Default or initial value for a node/cell property (e.g., 0.0). Must be same type as prop_data.
Initialize the CA model.
Parameters:  model_grid (Landlab ModelGrid object) – Reference to the model’s grid
 node_state_dict (dict) – Keys are nodestate 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 nodestate grid
 prop_data (array (x number of nodes in grid), optional) – Array of properties associated with each node/cell
 prop_reset_value (number or object, optional) – Default or initial value for a node/cell property (e.g., 0.0). Must be same type as prop_data.
 seed (int, optional) – Seed for random number generation.

__init__
(model_grid, node_state_dict, transition_list, initial_node_states, prop_data=None, prop_reset_value=None, seed=0)[source]¶ Initialize the CA model.
Parameters:  model_grid (Landlab ModelGrid object) – Reference to the model’s grid
 node_state_dict (dict) – Keys are nodestate 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 nodestate grid
 prop_data (array (x number of nodes in grid), optional) – Array of properties associated with each node/cell
 prop_reset_value (number or object, optional) – Default or initial value for a node/cell property (e.g., 0.0). Must be same type as prop_data.
 seed (int, optional) – Seed for random number generation.

assign_link_states_from_node_types
()[source]¶ Assign linkstate code for each link.
Takes lists/arrays of “tail” and “head” node IDs for each link, and a dictionary that associates pairs of node states (represented as a 3element tuple, comprising the TAIL state, FROM state, and orientation) to link states.
creates:
self.link_state
: 1D numpy array

create_link_state_dict_and_pair_list
()[source]¶ Create a dict of linkstate to nodestate.
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 3element 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.
Notes
Performance note: making self.node_pair a tuple does not appear to change time to lookup values in update_node_states. Changing it to a 2D array of int actually slows it down.

push_transitions_to_event_queue
()[source]¶ 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.
Examples
>>> from landlab import RasterModelGrid >>> from landlab.ca.celllab_cts import Transition >>> from landlab.ca.oriented_raster_cts import OrientedRasterCTS >>> import numpy as np >>> grid = RasterModelGrid((3, 5)) >>> nsd = {0 : 'zero', 1 : 'one'} >>> trn_list = [] >>> trn_list.append(Transition((0, 1, 0), (1, 0, 0), 1.0)) >>> trn_list.append(Transition((1, 0, 0), (0, 1, 0), 2.0)) >>> trn_list.append(Transition((0, 1, 1), (1, 0, 1), 3.0)) >>> trn_list.append(Transition((0, 1, 1), (1, 1, 1), 4.0)) >>> ins = np.arange(15) % 2 >>> cts = OrientedRasterCTS(grid, nsd, trn_list, ins) >>> ev0 = cts.priority_queue._queue[0] >>> np.round(100 * ev0[0]) 12.0 >>> ev0[2] # this is the link ID 16 >>> ev6 = cts.priority_queue._queue[6] >>> np.round(100 * ev6[0]) 27.0 >>> ev6[2] # this is the link ID 6 >>> cts.next_trn_id[ev0[2]] # ID of the transition to occur at this link 3 >>> cts.next_trn_id[cts.grid.active_links] array([1, 2, 1, 1, 0, 1, 0, 2, 1, 3])

run
(run_to, node_state_grid=None, plot_each_transition=False, plotter=None)[source]¶ Run the model forward for a specified period of time.
Parameters:  run_to (float) – Time to run to, starting from self.current_time
 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
Examples
>>> from landlab import RasterModelGrid >>> from landlab.ca.celllab_cts import Transition >>> from landlab.ca.oriented_raster_cts import OrientedRasterCTS >>> import numpy as np >>> grid = RasterModelGrid((3, 5)) >>> nsd = {0 : 'zero', 1 : 'one'} >>> trn_list = [] >>> trn_list.append(Transition((0, 1, 0), (1, 0, 0), 1.0)) >>> trn_list.append(Transition((1, 0, 0), (0, 1, 0), 2.0)) >>> trn_list.append(Transition((0, 1, 1), (1, 0, 1), 3.0)) >>> trn_list.append(Transition((0, 1, 1), (1, 1, 1), 4.0)) >>> ins = np.arange(15) % 2 >>> cts = OrientedRasterCTS(grid, nsd, trn_list, ins)

set_node_state_grid
(node_states)[source]¶ Set the grid of nodestate codes to node_states.
Sets the grid of nodestate 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.
Creates:
 self.node_state : 1D array of ints (x number of nodes in grid) The nodestate array
Parameters: node_states (1D array of ints (x number of nodes in grid)) – Notes
The nodestate array is attached to the grid as a field with the name ‘node_state’.

setup_array_of_orientation_codes
()[source]¶ Create array of active link orientation codes.
Creates and configures an array that contain the orientation code for each active link (and corresponding cell pair).
creates:
self.link_orientation
: 1D numpy array
Notes
The setup varies depending on the type of LCA. The default is nonoriented, 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).

update_component_data
(new_node_state_array)[source]¶ Update all component data.
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.
Examples
>>> from landlab import RasterModelGrid >>> from landlab.ca.celllab_cts import Transition >>> from landlab.ca.raster_cts import RasterCTS >>> import numpy as np >>> grid = RasterModelGrid((3, 5)) >>> nsd = {0 : 'zero', 1 : 'one'} >>> trn_list = [] >>> trn_list.append(Transition((0, 1, 0), (1, 1, 0), 1.0)) >>> ins = np.zeros(15, dtype=np.int) >>> ca = RasterCTS(grid, nsd, trn_list, ins) >>> list(ca.node_state[6:9]) [0, 0, 0] >>> list(ca.link_state[9:13]) [0, 0, 0, 0] >>> len(ca.priority_queue._queue) # there are no transitions 0 >>> nns = np.arange(15) % 2 # make a new nodestate grid... >>> ca.update_component_data(nns) # ...and assign it >>> list(ca.node_state[6:9]) [0, 1, 0] >>> list(ca.link_state[9:13]) [2, 1, 2, 1] >>> len(ca.priority_queue._queue) # now there are 5 transitions 5

update_link_state_new
(link, new_link_state, current_time)[source]¶ 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

class
Transition
(from_state, to_state, rate, name=None, swap_properties=False, prop_update_fn=None)[source]¶ Bases:
object
A transition from one state to another.
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, LR; 1: vertical, bottomtop. For such a tuple, order is (left/bottom, right/top, orientation).
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?
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?

__init__
(from_state, to_state, rate, name=None, swap_properties=False, prop_update_fn=None)[source]¶ 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?