#! /usr/env/python
"""Simple raster Landlab cellular automaton.
Simple raster Landlab cellular automaton, with
cell-pair transitions that depend on orientation (vertical or horizontal)
This file defines the OrientedRasterCTS class, which is a sub-class of
CellLabCTSModel that implements a simple, oriented, raster-grid
CA. Like its parent class, OrientedRasterCTS implements a continuous-time,
stochastic, pair-based CA.
Created GT Sep 2014
"""
import numpy as np
from ..grid import RasterModelGrid
from .celllab_cts import CellLabCTSModel
[docs]
class OrientedRasterCTS(CellLabCTSModel):
"""Oriented raster CellLab-CTS model.
RasterCTS constructor: sets number of orientations to 2 and calls
base-class constructor.
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 : 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 (default 0)
Seed for random number generator
Examples
--------
>>> from landlab import RasterModelGrid
>>> from landlab.ca.celllab_cts import Transition
>>> from landlab.ca.oriented_raster_cts import OrientedRasterCTS
>>> mg = RasterModelGrid((3, 4))
>>> nsd = {0: "yes", 1: "no"}
>>> xnlist = []
>>> xnlist.append(Transition((0, 1, 0), (1, 1, 0), 1.0, "frogging"))
>>> nsg = mg.add_zeros("node", "node_state_grid")
>>> orcts = OrientedRasterCTS(mg, nsd, xnlist, nsg)
"""
[docs]
def __init__(
self,
model_grid,
node_state_dict,
transition_list,
initial_node_states,
prop_data=None,
prop_reset_value=None,
seed=0,
):
"""RasterCTS constructor: sets number of orientations to 2 and calls
base-class constructor.
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 : number or object, optional
Default or initial value for a node/cell property (e.g., 0.0).
Must be same type as *prop_data*.
"""
# Make sure caller has sent the right grid type
if not isinstance(model_grid, RasterModelGrid):
raise TypeError("model_grid must be a Landlab RasterModelGrid")
# Define the number of distinct cell-pair orientations: here just 1,
# because RasterLCA represents a non-oriented CA model.
self.number_of_orientations = 2
# Call the LandlabCellularAutomaton constructor to do the rest of
# the initialization
super().__init__(
model_grid,
node_state_dict,
transition_list,
initial_node_states,
prop_data,
prop_reset_value,
seed,
)
[docs]
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).
Notes
-----
**Creates**:
* ``self.active_link_orientation``: 1D numpy array of ints
Array of orientation codes for each cell pair (link)
This overrides the method of the same name in landlab_ca.py.
"""
# Create array for the orientation of each active link
self.link_orientation = np.zeros(self.grid.number_of_links, dtype=np.int8)
# Set its value according to the different in y coordinate between each
# link's TO and FROM nodes (the numpy "astype" method turns the
# resulting array into integer format)
dy = (
self.grid.node_y[self.grid.node_at_link_head]
- self.grid.node_y[self.grid.node_at_link_tail]
)
self.link_orientation = dy.astype(np.int8)