def initialize(self,
grid_size=(5, 5),
report_interval=5.0,
grid_orientation="vertical",
node_layout="rect",
show_plots=False,
cts_type="oriented_hex",
run_duration=1.0,
output_interval=1.0e99,
plot_every_transition=False,
**kwds):
# Remember the clock time, and calculate when we next want to report
# progress.
self.current_real_time = time.time()
self.next_report = self.current_real_time + report_interval
self.report_interval = report_interval
# Interval for output
self.output_interval = output_interval
# Duration for run
self.run_duration = run_duration
# Create a grid
self.create_grid_and_node_state_field(grid_size[0], grid_size[1],
grid_orientation, node_layout,
cts_type)
# Create the node-state dictionary
ns_dict = self.node_state_dictionary()
# Initialize values of the node-state grid
nsg = self.initialize_node_state_grid()
# Create the transition list
xn_list = self.transition_list()
# Create the CA object
if cts_type == "raster":
from landlab.ca.raster_cts import RasterCTS
self.ca = RasterCTS(self.grid, ns_dict, xn_list, nsg)
elif cts_type == "oriented_raster":
from landlab.ca.oriented_raster_cts import OrientedRasterCTS
self.ca = OrientedRasterCTS(self.grid, ns_dict, xn_list, nsg)
elif cts_type == "hex":
from landlab.ca.hex_cts import HexCTS
self.ca = HexCTS(self.grid, ns_dict, xn_list, nsg)
else:
from landlab.ca.oriented_hex_cts import OrientedHexCTS
self.ca = OrientedHexCTS(self.grid, ns_dict, xn_list, nsg)
# Initialize graphics
self._show_plots = show_plots
if show_plots:
self.initialize_plotting(**kwds)
def test_transitions_as_ids():
"""Test passing from-state and to-state IDs instead of tuples """
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition(2, 3, 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
cts = HexCTS(mg, nsd, xnlist, nsg)
assert cts.num_link_states == 4, "wrong number of transitions"
def test_hex_cts():
"""Tests instantiation of a HexCTS() object"""
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition((0, 1, 0), (1, 1, 0), 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
hcts = HexCTS(mg, nsd, xnlist, nsg)
assert hcts.num_link_states == 4
assert_array_equal(hcts.link_orientation, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def test_hex_cts():
"""Tests instantiation of a HexCTS() object"""
mg = HexModelGrid(3, 2, 1.0, orientation='vertical', reorient_links=True)
nsd = {0 : 'zero', 1 : 'one'}
xnlist = []
xnlist.append(Transition((0,1,0), (1,1,0), 1.0, 'transitioning'))
nsg = mg.add_zeros('node', 'node_state_grid')
hcts = HexCTS(mg, nsd, xnlist, nsg)
assert_equal(hcts.num_link_states, 4)
assert_array_equal(hcts.link_orientation, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def initialize(self,
grid_size=(5, 5),
report_interval=5.0,
grid_orientation='vertical',
grid_shape='rect',
show_plots=False,
cts_type='oriented_hex',
run_duration=1.0,
output_interval=1.0e99,
plot_every_transition=False,
initial_state_grid=None,
prop_data=None,
prop_reset_value=None,
**kwds):
"""Initialize CTSModel."""
# Remember the clock time, and calculate when we next want to report
# progress.
self.current_real_time = time.time()
self.next_report = self.current_real_time + report_interval
self.report_interval = report_interval
# Interval for output
self.output_interval = output_interval
# Duration for run
self.run_duration = run_duration
# Create a grid
self.create_grid_and_node_state_field(grid_size[0], grid_size[1],
grid_orientation, grid_shape,
cts_type)
# If prop_data is a string, we assume it is a field name
if isinstance(prop_data, string_types):
prop_data = self.grid.add_zeros('node', prop_data)
# Create the node-state dictionary
ns_dict = self.node_state_dictionary()
# Initialize values of the node-state grid
if initial_state_grid is None:
nsg = self.initialize_node_state_grid()
else:
try:
nsg = initial_state_grid
self.grid.at_node['node_state'][:] = nsg
except:
#TODO: use new Messaging capability
print('If initial_state_grid given, must be array of int')
raise
# Create the transition list
xn_list = self.transition_list()
# Create the CA object
if cts_type == 'raster':
from landlab.ca.raster_cts import RasterCTS
self.ca = RasterCTS(self.grid, ns_dict, xn_list, nsg, prop_data,
prop_reset_value)
elif cts_type == 'oriented_raster':
from landlab.ca.oriented_raster_cts import OrientedRasterCTS
self.ca = OrientedRasterCTS(self.grid, ns_dict, xn_list, nsg,
prop_data, prop_reset_value)
elif cts_type == 'hex':
from landlab.ca.hex_cts import HexCTS
self.ca = HexCTS(self.grid, ns_dict, xn_list, nsg, prop_data,
prop_reset_value)
else:
from landlab.ca.oriented_hex_cts import OrientedHexCTS
self.ca = OrientedHexCTS(self.grid, ns_dict, xn_list, nsg,
prop_data, prop_reset_value)
# Initialize graphics
self._show_plots = show_plots
if show_plots == True:
self.initialize_plotting(**kwds)
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 = 8.0
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 = (0.5, 0.0, 0.0) # dark red
recovered_color = (0.0, 0.0, 1.0) # 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 = 80
nc = 41
plot_interval = 0.25
run_duration = 5.0
report_interval = 5.0 # report interval, in real-time seconds
infection_rate = 8.0
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 = (0.5, 0.0, 0.0) # dark red
recovered_color = (0.0, 0.0, 1.0) # 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()
