def test_run_oriented_raster():
"""Test running with a small grid, 2 states, 4 transition types."""
# Create an OrientedRaster with a 3x5 raster grid. Test model has 2 node
# states and 4 transition types.
grid = RasterModelGrid((3, 5))
nsd = {0: "zero", 1: "one"}
trn_list = []
trn_list.append(Transition((0, 1, 0), (1, 0, 0), 1.0))
trn_list.append(Transition((1, 0, 0), (0, 1, 0), 2.0))
trn_list.append(Transition((0, 1, 1), (1, 0, 1), 3.0))
trn_list.append(Transition((0, 1, 1), (1, 1, 1), 4.0))
ins = np.arange(15) % 2 # makes a checkerboard pattern
cts = OrientedRasterCTS(grid, nsd, trn_list, ins)
# Run to 1st transition, at ~0.12
cts.run(0.15)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0])
# Run to 2nd transition, at ~0.19
cts.run(0.2)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0])
# Run to 3rd transition, at ~0.265
cts.run(0.27)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0])
# Run to 4th transition, at ~0.276 (transition is ignored)
cts.run(0.28)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0])
# Run to 5th transition, at ~0.461 (ignored)
cts.run(0.5)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0])
# Run to 6th transition, at ~0.648
cts.run(0.65)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0])
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()
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 = 30.0 # 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', 2: 'crystal'}
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 moving particles at the bottom of a container. Seed crystal is
bottom_rows = where(mg.node_y < 0.1 * nr)[0]
node_state_grid[bottom_rows] = 1
seed_crystal_rows = where(mg.node_y > 0.5 * nr)[0]
node_state_grid[seed_crystal_rows] = 2
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = OrientedRasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Hex colors
grain = '#5F594D'
fluid = '#D0E4F2'
crystal = '#000000'
clist = [fluid, grain, crystal]
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()
# Calculate concentration profile
c = zeros(nr)
for r in range(nr):
c[r] = mean(node_state_grid[r * nc:(r + 1) * nc])
figure(2)
plot(c, range(nr), 'o')
show()
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 = 20.0 # 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 = OrientedRasterCTS(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()
# Calculate concentration profile
c = zeros(nr)
for r in range(nr):
c[r] = mean(node_state_grid[r*nc:(r+1)*nc])
figure(2)
plot(c, range(nr), 'o')
show()
def main():
# INITIALIZE
# User-defined parameters
nr = 80
nc = 80
plot_interval = 2
run_duration = 200
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)
# 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
middle_rows = where(bitwise_and(mg.node_y > 0.45 * nr, mg.node_y < 0.55 * nr))[0]
node_state_grid[middle_rows] = 1
# Create the CA model
ca = OrientedRasterCTS(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 test_run_oriented_raster():
"""Test running with a small grid, 2 states, 4 transition types."""
# Create an OrientedRaster with a 3x5 raster grid. Test model has 2 node
# states and 4 transition types.
grid = RasterModelGrid((3, 5))
nsd = {0 : 'zero', 1 : 'one'}
trn_list = []
trn_list.append(Transition((0, 1, 0), (1, 0, 0), 1.0))
trn_list.append(Transition((1, 0, 0), (0, 1, 0), 2.0))
trn_list.append(Transition((0, 1, 1), (1, 0, 1), 3.0))
trn_list.append(Transition((0, 1, 1), (1, 1, 1), 4.0))
ins = np.arange(15) % 2 # makes a checkerboard pattern
cts = OrientedRasterCTS(grid, nsd, trn_list, ins)
# Run to 1st transition, at ~0.12
cts.run(0.15)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0])
# Run to 2nd transition, at ~0.19
cts.run(0.2)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0])
# Run to 3rd transition, at ~0.265
cts.run(0.27)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0])
# Run to 4th transition, at ~0.276 (transition is ignored)
cts.run(0.28)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0])
# Run to 5th transition, at ~0.461 (ignored)
cts.run(0.5)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0])
# Run to 6th transition, at ~0.648
cts.run(0.65)
assert_array_equal(cts.node_state,
[0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0])
# 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()
#CMS says: supply new grains at the left side
node_state_grid[left_cols] = 1
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
print 'Model complete; close figure for concentration profiles'
ca_plotter.finalize()
plt.close(1)
#plt.savefig('final_grain_config.png')
# Calculate concentration profiles
c50 = np.zeros(nr)