def save_many_microscopically_optimal(
metropolis_state, final_sweep, order_5_damping, order_6_damping, initial_sweep=0, save_every_n_sweeps=10
):
"""
Runs a monte carlo simulation using the metropolis state (a class
instance of a descendent class of metropolis).
Performs final_sweep - initial_sweep sweeps. Every
save_every_n_sweeps, checks to see if the microscopically optimal
conditions are met. If they are, saves a sphere. Otherwise
continues sweeping.
Might be susceptible to crashes.
Progress file is unnecessary.
"""
# Reset the current sweep
metropolis_state.reset_current_sweep(initial_sweep)
# Calculate the current time
start_time = make_time_string()
# Start the sweeps
while metropolis_state.get_current_sweep() < final_sweep:
metropolis_state.sweep(save_every_n_sweeps)
v_count = st.vertex_count_selection_optimal(order_5_damping, order_6_damping, sd.vertex.instances.values())
if v_count.is_close_enough():
fname = metropolis_state.make_file_name_v4(final_sweep, start_time, order_5_damping, order_6_damping)
write_sphere_to_file(fname + output_suffix)
write_statistics_file(metropolis_state, v_count, fname + STATISTICS_SUFFIX)
# Print a happy message
print "All done! :)"
def stop_at_microscopically_optimal(
metropolis_state, final_sweep, order_5_damping, order_6_damping, initial_sweep=0, save_every_n_sweeps=10
):
"""
Runs a monte carlo simulation using the metropolis state (a class
instance of a descendent class of metropolis).
Saves to a single file, and stops when the sphere is close
microscopically ideal, as defined by the functions in the
vertex_count class and subclasses.
We write a progress file anyway, despite there being no final sweep.
initial_sweep and final sweep do nothing. They're in the
parameters list for consistency.
"""
# Pretend final sweep. For the progress file.
final_sweep = 0
# Reset the current sweep so that we start from 0.
metropolis_state.reset_current_sweep(initial_sweep)
# Calculate the file name
fname = metropolis_state.make_file_name_v3(order_5_damping, order_6_damping)
# Keep track of the number of vertexes of each order
v_count = st.vertex_count_selection_optimal(order_5_damping, order_6_damping, sd.vertex.instances.values())
# Immediately save a file.
write_sphere_to_file(fname + output_suffix)
write_progress_file(metropolis_state, final_sweep, save_every_n_sweeps, fname + tracking_suffix)
write_statistics_file(metropolis_state, v_count, fname + STATISTICS_SUFFIX)
while not v_count.is_close_enough():
metropolis_state.sweep(save_every_n_sweeps)
v_count = st.vertex_count_selection_optimal(order_5_damping, order_6_damping, sd.vertex.instances.values())
write_sphere_to_file(fname + output_suffix)
write_statistics_file(metropolis_state, v_count, fname + STATISTICS_SUFFIX)
write_progress_file(metropolis_state, final_sweep, save_every_n_sweeps, fname + tracking_suffix)
# Print a happy message
print "All done! :)"
