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 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)
# 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.node_active_links[1][6] == 16, "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 = 16
ev.xn_to = 1
ev.propswap = True
ev.prop_update_fn = callback_function
ca.next_update[16] = 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 = 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 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 = 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 = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Set up colors for plotting
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 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()
# 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()