def initialize_plotting(self):
"""
Creates a CA plotter object, sets its colormap, and plots the initial
model state.
"""
# Set up some plotting information
grain = "#5F594D"
bleached_grain = "#CC0000"
fluid = "#D0E4F2"
clist = [fluid, bleached_grain, grain]
my_cmap = matplotlib.colors.ListedColormap(clist)
# Create a CAPlotter object for handling screen display
self.ca_plotter = CAPlotter(self, cmap=my_cmap)
# Plot the initial grid
self.ca_plotter.update_plot()
# Make a colormap for use in showing the bleaching of each grain
clist = [
(0.0, (1.0, 1.0, 1.0)),
(0.49, (0.8, 0.8, 0.8)),
(1.0, (0.0, 0.0, 0.0)),
]
self.cmap_for_osl = matplotlib.colors.LinearSegmentedColormap.from_list(
"osl_cmap", clist)
def main():
# INITIALIZE
# User-defined parameters
nr = 80
nc = 41
plot_interval = 0.25
run_duration = 5.0
report_interval = 5.0 # report interval, in real-time seconds
infection_rate = 3.0
outfilename = 'sirmodel'+str(int(infection_rate))+'ir'
# 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
time_slice = 0
# Create a grid
hmg = HexModelGrid(nr, nc, 1.0)
# Set up the states and pair transitions.
# Transition data here represent the disease status of a population.
ns_dict = { 0 : 'susceptible', 1 : 'infectious', 2: 'recovered' }
xn_list = setup_transition_list(infection_rate)
# Create data and initialize values
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
wid = nc-1.0
ht = (nr-1.0)*0.866
is_middle_rows = logical_and(hmg.node_y>=0.4*ht, hmg.node_y<=0.5*ht)
is_middle_cols = logical_and(hmg.node_x>=0.4*wid, hmg.node_x<=0.6*wid)
middle_area = where(logical_and(is_middle_rows, is_middle_cols))[0]
node_state_grid[middle_area] = 1
node_state_grid[0] = 2 # to force full color range, set lower left to 'recovered'
# Create the CA model
ca = HexCTS(hmg, ns_dict, xn_list, node_state_grid)
# Set up the color map
import matplotlib
susceptible_color = (0.5, 0.5, 0.5) # gray
infectious_color = (0.05, 0.0, 0.0) # dark red
recovered_color = (0.95, 0.95, 1.0) # white w/ faint blue
clist = [susceptible_color, infectious_color, recovered_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()
pylab.axis('off')
savename = outfilename+'0'
pylab.savefig(savename+'.pdf', format='pdf')
# 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) #True, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
pylab.axis('off')
time_slice += 1
savename = outfilename+str(time_slice)
pylab.savefig(savename+'.pdf', format='pdf')
# 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 = 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 = 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 = 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 = 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 = 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()
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()
