examples/example_17_wrapping_an_existing_project/original.py
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():
    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 ( ) : 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 )
Main simulation function
Main simulation function