def main():
""" Main *boilerplate* function to start simulation """
# Now let's make use of logging
logger = logging.getLogger()
# Create folders for data and plots
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.hdf5')
# Create an environment
env = Environment(trajectory='cellular_automata',
multiproc=True,
ncores=4,
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
# extract the trajectory
traj = env.traj
traj.par.ncells = Parameter('ncells', 400, 'Number of cells')
traj.par.steps = Parameter('steps', 250, 'Number of timesteps')
traj.par.rule_number = Parameter('rule_number', 30, 'The ca rule')
traj.par.initial_name = Parameter('initial_name', 'random',
'The type of initial state')
traj.par.seed = Parameter('seed', 100042, 'RNG Seed')
# Explore
exp_dict = {
'rule_number': [10, 30, 90, 110, 184],
'initial_name': ['single', 'random'],
}
# # You can uncomment the ``exp_dict`` below to see that changing the
# # exploration scheme is now really easy:
# exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
# 'ncells' : [100, 200, 300],
# 'seed': [333444555, 123456]}
exp_dict = cartesian_product(exp_dict)
traj.f_explore(exp_dict)
# Run the simulation
logger.info('Starting Simulation')
env.run(wrap_automaton)
# Load all data
traj.f_load(load_data=2)
logger.info('Printing data')
for idx, run_name in enumerate(traj.f_iter_runs()):
# Plot all patterns
filename = os.path.join(folder, make_filename(traj))
plot_pattern(traj.crun.pattern, traj.rule_number, filename)
progressbar(idx, len(traj), logger=logger)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
""" Main *boilerplate* function to start simulation """
# Now let's make use of logging
logger = logging.getLogger()
# Create folders for data and plots
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.hdf5')
# Create an environment
env = Environment(trajectory='cellular_automata',
multiproc=True,
ncores=4,
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
# extract the trajectory
traj = env.traj
traj.v_lazy_adding = True
traj.par.ncells = 400, 'Number of cells'
traj.par.steps = 250, 'Number of timesteps'
traj.par.rule_number = 30, 'The ca rule'
traj.par.initial_name = 'random', 'The type of initial state'
traj.par.seed = 100042, 'RNG Seed'
# Explore
exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
'initial_name' : ['single', 'random'],}
# # You can uncomment the ``exp_dict`` below to see that changing the
# # exploration scheme is now really easy:
# exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
# 'ncells' : [100, 200, 300],
# 'seed': [333444555, 123456]}
exp_dict = cartesian_product(exp_dict)
traj.f_explore(exp_dict)
# Run the simulation
logger.info('Starting Simulation')
env.run(wrap_automaton)
# Load all data
traj.f_load(load_data=2)
logger.info('Printing data')
for idx, run_name in enumerate(traj.f_iter_runs()):
# Plot all patterns
filename = os.path.join(folder, make_filename(traj))
plot_pattern(traj.crun.pattern, traj.rule_number, filename)
progressbar(idx, len(traj), logger=logger)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
""" Main simulation function """
rules_to_test = [10, 30, 90, 110, 184] # rules we want to explore:
steps = 250 # cell iterations
ncells = 400 # number of cells
seed = 100042 # RNG seed
initial_states = ['single', 'random'] # Initial states we want to explore
# create a folder for the plots and the data
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_original')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.p')
print('Computing all patterns')
all_patterns = [] # list containing the simulation results
for idx, rule_number in enumerate(rules_to_test):
# iterate over all rules
for initial_name in initial_states:
# iterate over the initial states
# make the initial state
initial_state = make_initial_state(initial_name, ncells, seed=seed)
# simulate the automaton
pattern = cellular_automaton_1D(initial_state, rule_number, steps)
# keep the resulting pattern
all_patterns.append((rule_number, initial_name, pattern))
# Print a progressbar, because I am always impatient
# (ok that's already from pypet, but it's really handy!)
progressbar(idx, len(rules_to_test), reprint=True)
# Store all patterns to disk
with open(filename, 'wb') as file:
pickle.dump(all_patterns, file=file)
# Finally print all patterns
print('Plotting all patterns')
for idx, pattern_tuple in enumerate(all_patterns):
rule_number, initial_name, pattern = pattern_tuple
# Plot the pattern
filename = os.path.join(
folder, 'rule_%s_%s.png' % (str(rule_number), initial_name))
plot_pattern(pattern, rule_number, filename)
progressbar(idx, len(all_patterns), reprint=True)
def main():
""" Main simulation function """
rules_to_test = [10, 30, 90, 110, 184] # rules we want to explore:
steps = 250 # cell iterations
ncells = 400 # number of cells
seed = 100042 # RNG seed
initial_states = ['single', 'random'] # Initial states we want to explore
# create a folder for the plots and the data
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_original')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.p')
print('Computing all patterns')
all_patterns = [] # list containing the simulation results
for idx, rule_number in enumerate(rules_to_test):
# iterate over all rules
for initial_name in initial_states:
# iterate over the initial states
# make the initial state
initial_state = make_initial_state(initial_name, ncells, seed=seed)
# simulate the automaton
pattern = cellular_automaton_1D(initial_state, rule_number, steps)
# keep the resulting pattern
all_patterns.append((rule_number, initial_name, pattern))
# Print a progressbar, because I am always impatient
# (ok that's already from pypet, but it's really handy!)
progressbar(idx, len(rules_to_test), reprint=True)
# Store all patterns to disk
with open(filename, 'wb') as file:
pickle.dump(all_patterns, file=file)
# Finally print all patterns
print('Plotting all patterns')
for idx, pattern_tuple in enumerate(all_patterns):
rule_number, initial_name, pattern = pattern_tuple
# Plot the pattern
filename = os.path.join(folder, 'rule_%s_%s.png' % (str(rule_number), initial_name))
plot_pattern(pattern, rule_number, filename)
progressbar(idx, len(all_patterns), reprint=True)
def test_progress():
print('\n\n')
total = 100
for irun in range(total):
time.sleep(0.1)
progressbar(irun, total)
