def test_raster_cts():
"""
Tests instantiation of a RasterCTS and implementation of one transition,
with a callback function.
"""
# Set up a small grid with no events scheduled
mg = RasterModelGrid((4, 4))
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
node_state_grid = mg.add_ones("node", "node_state_map", dtype=int)
node_state_grid[6] = 0
ns_dict = {0: "black", 1: "white"}
xn_list = []
xn_list.append(
Transition((1, 0, 0), (0, 1, 0), 0.1, "", True, callback_function))
pd = mg.add_zeros("node", "property_data", dtype=int)
pd[5] = 50
ca = RasterCTS(mg,
ns_dict,
xn_list,
node_state_grid,
prop_data=pd,
prop_reset_value=0)
# Test the data structures
assert ca.num_link_states == 4, "wrong number of link states"
assert ca.prop_data[5] == 50, "error in property data"
assert ca.num_node_states == 2, "error in num_node_states"
assert ca.link_orientation[-1] == 0, "error in link orientation array"
assert ca.link_state_dict[(1, 0, 0)] == 2, "error in link state dict"
assert ca.n_trn[2] == 1, "error in n_trn"
assert ca.node_pair[1] == (0, 1, 0), "error in cell_pair list"
assert len(ca.priority_queue._queue) == 1, "event queue has wrong size"
assert ca.next_trn_id.size == 24, "wrong size next_trn_id"
assert ca.trn_id.shape == (4, 1), "wrong size for trn_to"
assert ca.trn_id[2][0] == 0, "wrong value in trn_to"
assert ca.trn_to[0] == 1, "wrong trn_to state"
assert ca.trn_rate[0] == 0.1, "wrong trn rate"
assert ca.trn_propswap[0] == 1, "wrong trn propswap"
assert ca.trn_prop_update_fn == callback_function, "wrong prop upd"
# Manipulate the data in the event queue for testing:
# pop the scheduled event off the queue
(event_time, index, event_link) = ca.priority_queue.pop()
assert ca.priority_queue._queue == [], "event queue should now be empty but is not"
# engineer an event
ca.priority_queue.push(8, 1.0)
ca.next_update[8] = 1.0
ca.next_trn_id[8] = 0
# run the CA
ca.run(2.0)
# some more tests.
# Is current time advancing correctly? (should only go to 1.0, not 2.0)
# Did the two nodes (5 and 6) correctly exchange states?
# Did the property ID and data arrays get updated? Note that the "propswap"
# should switch propids between nodes 5 and 6, and the callback function
# should increase the value of prop_data in the "swap" node from 50 to 150.
assert ca.current_time == 1.0, "current time incorrect"
assert ca.node_state[5] == 0, "error in node state 5"
assert ca.node_state[6] == 1, "error in node state 6"
def main():
# INITIALIZE
# User-defined parameters
nr = 100 # number of rows in grid
nc = 64 # number of columns in grid
plot_interval = 0.5 # time interval for plotting, sec
run_duration = 200.0 # 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 = RasterCTS(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 = 200 # number of rows in grid
nc = 200 # number of columns in grid
plot_interval = 0.05 # time interval for plotting (unscaled)
run_duration = 5.0 # duration of run (unscaled)
report_interval = 10.0 # report interval, in real-time seconds
frac_spacing = 10 # average fracture spacing, nodes
outfilename = 'wx' # name for netCDF files
# 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
# Counter for output files
time_slice = 0
# 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 : 'rock', 1 : 'saprolite' }
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid.
# (Note use of numpy's uint8 data type. This saves memory AND allows us
# to write output to a netCDF3 file; netCDF3 does not handle the default
# 64-bit integer type)
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=np.uint8)
node_state_grid[:] = make_frac_grid(frac_spacing, model_grid=mg)
# Create the CA model
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Set up the color map
rock_color = (0.8, 0.8, 0.8)
sap_color = (0.4, 0.2, 0)
clist = [rock_color, sap_color]
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()
# Output the initial grid to file
write_netcdf((outfilename+str(time_slice)+'.nc'), mg,
#format='NETCDF3_64BIT',
names='node_state_map')
# 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()
# Output the current grid to a netCDF file
time_slice += 1
write_netcdf((outfilename+str(time_slice)+'.nc'), mg,
#format='NETCDF3_64BIT',
names='node_state_map')
# FINALIZE
# Plot
ca_plotter.finalize()
def test_raster_cts():
"""
Tests instantiation of a RasterCTS and implementation of one transition,
with a callback function.
"""
# Set up a small grid with no events scheduled
mg = RasterModelGrid(4, 4, 1.0)
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
node_state_grid = mg.add_ones('node', 'node_state_map', dtype=int)
node_state_grid[6] = 0
ns_dict = { 0 : 'black', 1 : 'white' }
xn_list = []
xn_list.append( Transition((1,0,0), (0,1,0), 0.1, '', True, callback_function))
pd = mg.add_zeros('node', 'property_data', dtype=int)
pd[5] = 50
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid, prop_data=pd,
prop_reset_value=0)
# Test the data structures
assert (ca.xn_to.size==4), 'wrong size for xn_to'
assert (ca.xn_to.shape==(4, 1)), 'wrong size for xn_to'
assert (ca.xn_to[2][0]==1), 'wrong value in xn_to'
assert (len(ca.event_queue)==1), 'event queue has wrong size'
assert (ca.num_link_states==4), 'wrong number of link states'
assert (ca.prop_data[5]==50), 'error in property data'
assert (ca.xn_rate[2][0]==0.1), 'error in transition rate array'
assert (ca._active_links_at_node[1][6]==8), 'error in active link array'
assert (ca.num_node_states==2), 'error in num_node_states'
assert (ca.link_orientation[-1]==0), 'error in link orientation array'
assert (ca.link_state_dict[(1, 0, 0)]==2), 'error in link state dict'
assert (ca.n_xn[2]==1), 'error in n_xn'
assert (ca.node_pair[1]==(0, 1, 0)), 'error in cell_pair list'
# Manipulate the data in the event queue for testing:
# pop the scheduled event off the queue
ev = heappop(ca.event_queue)
assert (ca.event_queue==[]), 'event queue should now be empty but is not'
# engineer an event
ev.time = 1.0
ev.link = 8
ev.xn_to = 1
ev.propswap = True
ev.prop_update_fn = callback_function
ca.next_update[8] = 1.0
# push it onto the event queue
heappush(ca.event_queue, ev)
# run the CA
ca.run(2.0)
# some more tests.
# Is current time advancing correctly? (should only go to 1.0, not 2.0)
# Did the two nodes (5 and 6) correctly exchange states?
# Did the property ID and data arrays get updated? Note that the "propswap"
# should switch propids between nodes 5 and 6, and the callback function
# should increase the value of prop_data in the "swap" node from 50 to 150.
assert (ca.current_time==1.0), 'current time incorrect'
assert (ca.node_state[5]==0), 'error in node state 5'
assert (ca.node_state[6]==1), 'error in node state 6'
def main():
# INITIALIZE
# User-defined parameters
nr = 100 # number of rows in grid
nc = 64 # number of columns in grid
plot_interval = 0.5 # time interval for plotting, sec
run_duration = 200.0 # 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 = RasterCTS(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 = 5 # number of rows in grid
nc = 5 # number of columns in grid
plot_interval = 10.0 # time interval for plotting, sec
run_duration = 10.0 # 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: "black", 1: "white"}
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)
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# 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=True,
plotter=ca_plotter,
)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
print("ok, here are the keys")
print(ca.__dict__.keys())
def main():
# INITIALIZE
# User-defined parameters
nr = 5 # number of rows in grid
nc = 5 # number of columns in grid
plot_interval = 10.0 # time interval for plotting, sec
run_duration = 10.0 # 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: 'black', 1: 'white'}
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)
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# 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=True, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
print('ok, here are the keys')
print(ca.__dict__.keys())
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 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 simulation time '+str(current_time)+' \
('+str(int(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()
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 100 # number of rows in grid
nc = 64 # number of columns in grid
plot_interval = 1.0 # time interval for plotting, sec -- each plot is 10^7 years
run_duration = 100.0 # 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 array to record He count through time
he = zeros(int(run_duration/plot_interval) + 1)
time_step_count = 1
# 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 : 'empty',
1 : 'filled lattice',
2 : 'helium',
3 : 'uranium238right',
4 : 'uranium238left',
5 : 'uranium235right',
6 : 'uranium235left',
7 : 'thorium232right',
8 : 'thorium232left'}
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.
#odd_rows = where(mg.node_y<0.1*nr)[0]
node_state_grid[:] = arange(nr*nc, dtype=int) % 2
for r in range(0, nr, 2):
for c in range(nc):
n = r*nc + c
node_state_grid[n] = 1 - node_state_grid[n]
print(node_state_grid)
node_state_grid[0]=8
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
filled = '#cdcdc1'
empty = '#ffffff'
helium = '#7fffd4'
uranium238right = '#ff1493' #pink
uranium238left = '#ff1493' #pink
uranium235right = '#8a2be2' #purple
uranium235left = '#8a2be2' #purple
thorium232right = '#f08080' #coral
thorium232left = '#f08080' #coral
clist = [empty, filled, helium, uranium238right, uranium238left, uranium235right, uranium235left, thorium232right, thorium232left]
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=True)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
print bincount(node_state_grid)
he[time_step_count] = bincount(node_state_grid)[2]
time_step_count += 1
#plt.plot(bincount(node_state_grid))
#plt.ylabel('number of particles')
# FINALIZE
# Plot
ca_plotter.finalize()
plt.figure(2)
plt.plot(he)
plt.ylabel('Number of Helium Atoms')
plt.xlabel('Run')
plt.title('Number of helium particles present in model')
plt.show()
def test_raster_cts():
"""
Tests instantiation of a RasterCTS and implementation of one transition,
with a callback function.
"""
# Set up a small grid with no events scheduled
mg = RasterModelGrid((4, 4))
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
node_state_grid = mg.add_ones("node", "node_state_map", dtype=int)
node_state_grid[6] = 0
ns_dict = {0: "black", 1: "white"}
xn_list = []
xn_list.append(Transition((1, 0, 0), (0, 1, 0), 0.1, "", True, callback_function))
pd = mg.add_zeros("node", "property_data", dtype=int)
pd[5] = 50
ca = RasterCTS(
mg, ns_dict, xn_list, node_state_grid, prop_data=pd, prop_reset_value=0
)
# Test the data structures
assert ca.num_link_states == 4, "wrong number of link states"
assert ca.prop_data[5] == 50, "error in property data"
assert ca.num_node_states == 2, "error in num_node_states"
assert ca.link_orientation[-1] == 0, "error in link orientation array"
assert ca.link_state_dict[(1, 0, 0)] == 2, "error in link state dict"
assert ca.n_trn[2] == 1, "error in n_trn"
assert ca.node_pair[1] == (0, 1, 0), "error in cell_pair list"
assert len(ca.priority_queue._queue) == 1, "event queue has wrong size"
assert ca.next_trn_id.size == 24, "wrong size next_trn_id"
assert ca.trn_id.shape == (4, 1), "wrong size for trn_to"
assert ca.trn_id[2][0] == 0, "wrong value in trn_to"
assert ca.trn_to[0] == 1, "wrong trn_to state"
assert ca.trn_rate[0] == 0.1, "wrong trn rate"
assert ca.trn_propswap[0] == 1, "wrong trn propswap"
assert ca.trn_prop_update_fn == callback_function, "wrong prop upd"
# Manipulate the data in the event queue for testing:
# pop the scheduled event off the queue
(event_time, index, event_link) = ca.priority_queue.pop()
assert ca.priority_queue._queue == [], "event queue should now be empty but is not"
# engineer an event
ca.priority_queue.push(8, 1.0)
ca.next_update[8] = 1.0
ca.next_trn_id[8] = 0
# run the CA
ca.run(2.0)
# some more tests.
# Is current time advancing correctly? (should only go to 1.0, not 2.0)
# Did the two nodes (5 and 6) correctly exchange states?
# Did the property ID and data arrays get updated? Note that the "propswap"
# should switch propids between nodes 5 and 6, and the callback function
# should increase the value of prop_data in the "swap" node from 50 to 150.
assert ca.current_time == 1.0, "current time incorrect"
assert ca.node_state[5] == 0, "error in node state 5"
assert ca.node_state[6] == 1, "error in node state 6"
def test_user_guide_example():
# 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 = 1.0 # 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(shape=(nr, nc), xy_spacing=1.0)
# Make the boundaries be walls
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Create a node-state dictionary
ns_dict = {0: "fluid", 1: "particle"}
# Create the transition list
xn_list = setup_transition_list()
# Create an array containing the initial node-state values
# 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 = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Set up plotting
# Set up colors for plotting
# (commented out for CI testing)
# grain = '#5F594D'
# fluid = '#D0E4F2'
# clist = [fluid,grain]
# my_cmap = matplotlib.colors.ListedColormap(clist)
# Create a CAPlotter object for handling screen display
# (commented out for CI testing)
# ca_plotter = CAPlotter(ca, cmap=my_cmap)
# Plot the initial grid (commented out for CI testing)
# ca_plotter.update_plot()
# RUN
current_time = 0.0
while current_time < run_duration:
# Once in a while, print out simulation 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 simulation time " + str(current_time) + " \
(" + str(int(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
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 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 simulation time ' + str(current_time) + ' \
(' + str(int(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()
ca_plotter.finalize()
