def main():
# INITIALIZE
# User-defined parameters
nr = 80 # number of rows in grid
nc = 50 # number of columns in grid
plot_interval = 0.5 # time interval for plotting, sec
run_duration = 0.0 # duration of run, sec
report_interval = 10.0 # 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)
# Make the boundaries be walls
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
ns_dict = { 0 : 'fluid', 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)
# Initialize the node-state array: here, the initial condition is a pile of
# resting grains at the bottom of a container.
bottom_rows = where(mg.node_y<0.1*nr)[0]
node_state_grid[bottom_rows] = 1
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = RasterLCA(mg, ns_dict, xn_list, node_state_grid)
grain = '#5F594D'
fluid = '#D0E4F2'
clist = [fluid,grain]
my_cmap = matplotlib.colors.ListedColormap(clist)
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca, cmap=my_cmap)
# Plot the initial grid
ca_plotter.update_plot()
# RUN
current_time = 0.0
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)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 128
nc = 128
fracture_spacing = 10 # fracture spacing, cell widths
plot_interval = 0.25
run_duration = 4.0
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)
# 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 : 'rock', 1 : 'saprolite' }
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)
# Initialize the node-state array as a "fracture grid" in which randomly
# oriented fractures are represented as lines of saprolite embedded in
# bedrock.
node_state_grid[:] = make_frac_grid(fracture_spacing, model_grid=mg)
# Create the CA model
ca = RasterLCA(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
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
# 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 = 128
nc = 128
fracture_spacing = 10 # fracture spacing, cell widths
plot_interval = 0.25
run_duration = 4.0
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)
# 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: 'rock', 1: 'saprolite'}
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)
# Initialize the node-state array as a "fracture grid" in which randomly
# oriented fractures are represented as lines of saprolite embedded in
# bedrock.
node_state_grid[:] = make_frac_grid(fracture_spacing, model_grid=mg)
# Create the CA model
ca = RasterLCA(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]),
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
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
# 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]),
print
# FINALIZE
# Plot
ca_plotter.finalize()
