def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 0.8
f = 1.0
plot_interval = 1.0
run_duration = 200.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.4 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr,
nc,
1.0,
orientation='vertical',
reorient_links=True)
# Close the grid boundaries
#hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = {
0: 'empty',
1: 'moving up',
2: 'moving right and up',
3: 'moving right and down',
4: 'moving down',
5: 'moving left and down',
6: 'moving left and up',
7: 'rest',
8: 'wall'
}
xn_list = setup_transition_list(g, f)
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if random.random() < p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, 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=plot_every_transition,
plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 21
nc = 21
plot_interval = 0.5
run_duration = 25.0
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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Close the grid boundaries
hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent the disease status of a population.
ns_dict = { 0 : 'fluid', 1 : 'grain' }
xn_list = setup_transition_list()
# Create data and initialize values. We start with the 3 middle columns full
# of grains, and the others empty.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
middle = 0.25*(nc-1)*sqrt(3)
is_middle_cols = logical_and(hmg.node_x<middle+1., hmg.node_x>middle-1.)
node_state_grid[where(is_middle_cols)[0]] = 1
# Create the CA model
ca = OrientedHexCTS(hmg, 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=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 = 41
nc = 61
plot_interval = 1.0
run_duration = 100.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.1 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr,
nc,
1.0,
orientation='vertical',
reorient_links=True)
# Close the grid boundaries
#hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = {
0: 'empty',
1: 'moving up',
2: 'moving right and up',
3: 'moving right and down',
4: 'moving down',
5: 'moving left and down',
6: 'moving left and up',
7: 'rest',
8: 'wall'
}
xn_list = setup_transition_list()
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid', dtype=int)
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if random.random() < p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, 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()
# Create an array to store the numbers of states at each plot interval
nstates = zeros((9, int(run_duration / plot_interval)))
k = 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()
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=plot_every_transition,
plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# Record numbers in each state
nstates[:, k] = bincount(node_state_grid)
k += 1
# FINALIZE
# Plot
ca_plotter.finalize()
# Display the numbers of each state
fig, ax = subplots()
for i in range(1, 8):
plot(arange(plot_interval, run_duration + plot_interval,
plot_interval),
nstates[i, :],
label=ns_dict[i])
ax.legend()
xlabel('Time')
ylabel('Number of particles in state')
title('Particle distribution by state')
axis([0, run_duration, 0, 2 * nstates[7, 0]])
show()
def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 0.05
plot_interval = 1.0
run_duration = 100.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.1 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Close the grid boundaries
#hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = { 0 : 'empty',
1 : 'moving up',
2 : 'moving right and up',
3 : 'moving right and down',
4 : 'moving down',
5 : 'moving left and down',
6 : 'moving left and up',
7 : 'rest',
8 : 'wall'}
xn_list = setup_transition_list(g)
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if random.random()<p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, 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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 0.8
f = 1.0
silo_y0 = 30.0
silo_opening_half_width = 6
plot_interval = 1.0
run_duration = 80.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.4 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = { 0 : 'empty',
1 : 'moving up',
2 : 'moving right and up',
3 : 'moving right and down',
4 : 'moving down',
5 : 'moving left and down',
6 : 'moving left and up',
7 : 'rest',
8 : 'wall'}
xn_list = setup_transition_list(g, f)
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Place wall particles to form the base of the silo, initially closed
tan30deg = numpy.tan(numpy.pi/6.)
rampy1 = silo_y0-hmg.node_x*tan30deg
rampy2 = silo_y0-((nc*0.866-1.)-hmg.node_x)*tan30deg
rampy = numpy.maximum(rampy1, rampy2)
(ramp_nodes, ) = numpy.where(numpy.logical_and(hmg.node_y>rampy-0.5, \
hmg.node_y<rampy+0.5))
node_state_grid[ramp_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if hmg.node_y[i]>rampy[i] and random.random()<p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, 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
# Run with closed silo
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 '+str(current_time)+' ('+str(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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# Open the silo
xmid = nc*0.866*0.5
for i in range(hmg.number_of_nodes):
if node_state_grid[i]==8 and hmg.node_x[i]>(xmid-silo_opening_half_width) \
and hmg.node_x[i]<(xmid+silo_opening_half_width) \
and hmg.node_y[i]>0:
node_state_grid[i]=0
# Create the CA model
ca = OrientedHexCTS(hmg, 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()
# Re-run with open silo
current_time = 0.0
while current_time < 5*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 '+str(current_time)+' ('+str(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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 52
nc = 120
plot_interval = 1.0
run_duration = 100.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.1 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Close the grid boundaries
#hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = { 0 : 'empty',
1 : 'moving up',
2 : 'moving right and up',
3 : 'moving right and down',
4 : 'moving down',
5 : 'moving left and down',
6 : 'moving left and up',
7 : 'rest',
8 : 'wall'}
xn_list = setup_transition_list()
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid', dtype=int)
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if random.random()<p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, ns_dict, xn_list, node_state_grid)
# Set up a color map for plotting
import matplotlib
clist = [ (1.0, 1.0, 1.0), # empty = white
(1.0, 0.0, 0.0), # up = red
(1.0, 1.0, 0.0), # right-up = yellow
(0.0, 1.0, 0.0), # down-up = green
(0.0, 1.0, 1.0), # down = cyan
(0.0, 0.0, 1.0), # left-down = blue
(1.0, 0.0, 1.0), # left-up = magenta
(0.5, 0.5, 0.5), # resting = gray
(0.0, 0.0, 0.0) ] # wall = black
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()
# Create an array to store the numbers of states at each plot interval
nstates = zeros((9, int(run_duration/plot_interval)))
k = 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()
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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
axis('off')
# Record numbers in each state
nstates[:,k] = bincount(node_state_grid)
k += 1
# FINALIZE
# Plot
ca_plotter.finalize()
# Display the numbers of each state
fig, ax = subplots()
for i in range(1, 8):
plot(arange(plot_interval, run_duration+plot_interval, plot_interval), nstates[i,:], label=ns_dict[i], color=clist[i])
ax.legend()
xlabel('Time')
ylabel('Number of particles in state')
title('Particle distribution by state')
axis([0, run_duration, 0, 2*nstates[7,0]])
show()
def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 1.0
f = 0.7
silo_y0 = 30.0
silo_opening_half_width = 6
plot_interval = 10.0
run_duration = 240.0
report_interval = 300.0 # report interval, in real-time seconds
p_init = 0.4 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr,
nc,
1.0,
orientation='vertical',
shape='rect',
reorient_links=True)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = {
0: 'empty',
1: 'moving up',
2: 'moving right and up',
3: 'moving right and down',
4: 'moving down',
5: 'moving left and down',
6: 'moving left and up',
7: 'rest',
8: 'wall'
}
xn_list = setup_transition_list(g, f)
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Place wall particles to form the base of the silo, initially closed
tan30deg = numpy.tan(numpy.pi / 6.)
rampy1 = silo_y0 - hmg.node_x * tan30deg
rampy2 = silo_y0 - ((nc * 0.866 - 1.) - hmg.node_x) * tan30deg
rampy = numpy.maximum(rampy1, rampy2)
(ramp_nodes, ) = numpy.where(numpy.logical_and(hmg.node_y>rampy-0.5, \
hmg.node_y<rampy+0.5))
node_state_grid[ramp_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if hmg.node_y[i] > rampy[i] and random.random() < p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, ns_dict, xn_list, node_state_grid)
import matplotlib
rock = (0.0, 0.0, 0.0) #'#5F594D'
sed = (0.6, 0.6, 0.6) #'#A4874B'
#sky = '#CBD5E1'
#sky = '#85A5CC'
sky = (1.0, 1.0, 1.0) #'#D0E4F2'
mob = (0.3, 0.3, 0.3) #'#D98859'
#mob = '#DB764F'
#mob = '#FFFF00'
#sed = '#CAAE98'
#clist = [(0.5, 0.9, 0.9),mob, mob, mob, mob, mob, mob,'#CD6839',(0.3,0.3,0.3)]
clist = [sky, mob, mob, mob, mob, mob, mob, sed, rock]
my_cmap = matplotlib.colors.ListedColormap(clist)
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca, cmap=my_cmap)
k = 0
# Plot the initial grid
ca_plotter.update_plot()
# RUN
# Run with closed silo
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=plot_every_transition,
plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# Open the silo
xmid = nc * 0.866 * 0.5
for i in range(hmg.number_of_nodes):
if node_state_grid[i]==8 and hmg.node_x[i]>(xmid-silo_opening_half_width) \
and hmg.node_x[i]<(xmid+silo_opening_half_width) \
and hmg.node_y[i]>0 and hmg.node_y[i]<38.0:
node_state_grid[i] = 0
# Create the CA model
ca = OrientedHexCTS(hmg, 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()
# Re-run with open silo
savefig('silo' + str(k) + '.png')
k += 1
current_time = 0.0
while current_time < 5 * 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=plot_every_transition,
plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
savefig('silo' + str(k) + '.png')
k += 1
# FINALIZE
# Plot
ca_plotter.finalize()
