def main():
# INITIALIZE
# User-defined parameters
nr = 10
nc = 10
plot_interval = 0.25
run_duration = 40.0
report_interval = 5.0 # report interval, in real-time seconds
# Remember the clock time, and calculate when we next want to report
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
# Transition data here represent a body of fractured rock, with rock
# represented by nodes with state 0, and saprolite (weathered rock)
# represented by nodes with state 1. Node pairs (links) with 0-1 or 1-0
# can undergo a transition to 1-1, representing chemical weathering of the
# rock.
ns_dict = { 0 : 'air', 1 : 'particle' }
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=int)
node_state_grid[where(mg.node_y>nr-3)[0]] = 1
# Create the CA model
ca = OrientedRasterCTS(mg, ns_dict, xn_list, node_state_grid)
#ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Debug output if needed
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print('{0:.0f}'.format(ca.node_state[n]), end=' ')
print()
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca)
# Plot the initial grid
ca_plotter.update_plot()
# RUN
current_time = 0.0
updated = False
while current_time < run_duration:
# Once in a while, print out simulation and real time to let the user
# know that the sim is running ok
current_real_time = time.time()
if current_real_time >= next_report:
print('Current sim time',current_time,'(',100*current_time/run_duration,'%)')
next_report = current_real_time + report_interval
# Run the model forward in time until the next output step
ca.run(current_time+plot_interval, ca.node_state,
plot_each_transition=False) #, plotter=ca_plotter)
current_time += plot_interval
# Add a bunch of particles
if current_time > run_duration/2. and not updated:
print('updating...')
node_state_grid[where(ca.grid.node_y>(nc/2.0))[0]] = 1
ca.update_link_states_and_transitions(current_time)
updated = True
# Plot the current grid
ca_plotter.update_plot()
# for debugging
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print('{0:.0f}'.format(ca.node_state[n]), end=' ')
print()
# FINALIZE
# Plot
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 10
nc = 10
plot_interval = 0.25
run_duration = 40.0
report_interval = 5.0 # report interval, in real-time seconds
# Remember the clock time, and calculate when we next want to report
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
# Transition data here represent a body of fractured rock, with rock
# represented by nodes with state 0, and saprolite (weathered rock)
# represented by nodes with state 1. Node pairs (links) with 0-1 or 1-0
# can undergo a transition to 1-1, representing chemical weathering of the
# rock.
ns_dict = {0: 'air', 1: 'particle'}
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=int)
node_state_grid[where(mg.node_y > nr - 3)[0]] = 1
# Create the CA model
ca = OrientedRasterCTS(mg, ns_dict, xn_list, node_state_grid)
#ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Debug output if needed
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print('{0:.0f}'.format(ca.node_state[n]), end=' ')
print()
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca)
# Plot the initial grid
ca_plotter.update_plot()
# RUN
current_time = 0.0
updated = False
while current_time < run_duration:
# Once in a while, print out simulation and real time to let the user
# know that the sim is running ok
current_real_time = time.time()
if current_real_time >= next_report:
print('Current sim time', current_time, '(',
100 * current_time / run_duration, '%)')
next_report = current_real_time + report_interval
# Run the model forward in time until the next output step
ca.run(current_time + plot_interval,
ca.node_state,
plot_each_transition=False) #, plotter=ca_plotter)
current_time += plot_interval
# Add a bunch of particles
if current_time > run_duration / 2. and not updated:
print('updating...')
node_state_grid[where(ca.grid.node_y > (nc / 2.0))[0]] = 1
ca.update_link_states_and_transitions(current_time)
updated = True
# Plot the current grid
ca_plotter.update_plot()
# for debugging
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print('{0:.0f}'.format(ca.node_state[n]), end=' ')
print()
# FINALIZE
# Plot
ca_plotter.finalize()
