def load_seperated_simulations(folder_name, plot_settings):
folder_dir = 'save/{}'.format(folder_name)
dir_list = all_folders_in_dir_with(folder_dir, 'sim')
attrs_lists_all_sims_critical = []
attrs_lists_all_sims_sub_critical = []
for dir in dir_list:
sim_name = dir[(dir.rfind('save/') + 5):]
isings_list = load_isings_specific_path('{}/isings'.format(dir))
if plot_settings['attr'] == 'norm_avg_energy' or plot_settings[
'attr'] == 'norm_food_and_ts_avg_energy':
settings = load_settings(sim_name)
calc_normalized_fitness(isings_list, plot_settings, settings)
isings_list_seperated = seperate_isolated_populations(isings_list)
isings_list_critical = isings_list_seperated[0]
isings_list_sub_critical = isings_list_seperated[1]
attrs_list_critical = [
attribute_from_isings(isings_crit, plot_settings['attr'])
for isings_crit in isings_list_critical
]
attrs_list_sub_critical = [
attribute_from_isings(isings_sub, plot_settings['attr'])
for isings_sub in isings_list_sub_critical
]
attrs_lists_all_sims_critical.append(attrs_list_critical)
attrs_lists_all_sims_sub_critical.append(attrs_list_sub_critical)
return attrs_lists_all_sims_critical, attrs_lists_all_sims_sub_critical
def load_data(sim_name, folder_name, dynamic_range_folder_includes, plot_settings):
sim_dir = 'save/{}'.format(sim_name)
attrs_list_each_food_num_all = []
attrs_list_each_food_num_critical = []
attrs_list_each_food_num_sub_critical = []
food_num_list = []
dir_list = all_folders_in_dir_with('{}/repeated_generations'.format(sim_dir), dynamic_range_folder_includes)
for dir in dir_list:
if plot_settings['compress_save_isings']:
isings_list = load_isings_specific_path_decompress(dir)
else:
isings_list = load_isings_specific_path(dir)
if plot_settings['only_copied']:
isings_list = [choose_copied_isings(isings) for isings in isings_list]
if plot_settings['attr'] == 'norm_avg_energy' or plot_settings['attr'] == 'norm_food_and_ts_avg_energy':
settings = load_settings(sim_name)
calc_normalized_fitness(isings_list, plot_settings, settings)
# MERGING INDIVIDUALS OF ALL REPEATS WITH SIMILAR SETTINGS INTO ONE LIST:
isings = make_2d_list_1d(isings_list)
# isings_populations_seperated = seperate_isolated_populations([isings])
# isings_critical = isings_populations_seperated[0][0]
# isings_sub_critical = isings_populations_seperated[1][0]
attrs_list_each_food_num_all.append(attribute_from_isings(isings, plot_settings['attr']))
# attrs_list_each_food_num_critical.append(attribute_from_isings(isings_critical, attr))
# attrs_list_each_food_num_sub_critical.append(attribute_from_isings(isings_sub_critical, attr))
food_num_list.append(get_int_end_of_str(dir))
return attrs_list_each_food_num_all, food_num_list
def load_attrs(folder_name, plot_settings):
folder_dir = 'save/{}'.format(folder_name)
dir_list = all_folders_in_dir_with(folder_dir, 'sim')
attrs_list_all_sims = []
settings_list = []
for dir in dir_list:
sim_name = dir[(dir.rfind('save/')+5):]
settings = load_settings(sim_name)
if plot_settings['only_plot_certain_generations']:
load_generations = np.arange(plot_settings['lowest_and_highest_generations_to_be_plotted'][0],
plot_settings['lowest_and_highest_generations_to_be_plotted'][1]+1)
isings_list = load_isings_from_list(sim_name, load_generations, decompress=plot_settings['decompress'])
else:
isings_list = load_isings_specific_path('{}/isings'.format(dir), decompress=plot_settings['decompress'])
if plot_settings['only_copied']:
isings_list = [choose_copied_isings(isings) for isings in isings_list]
if plot_settings['attr'] == 'norm_avg_energy' or plot_settings['attr'] == 'norm_food_and_ts_avg_energy':
calc_normalized_fitness(isings_list, plot_settings, settings)
isings_list = below_threshold_nan(isings_list, settings)
attrs_list = [attribute_from_isings(isings, plot_settings['attr']) if isings is not None else np.nan
for isings in isings_list]
attrs_list_all_sims.append(attrs_list)
del isings_list
# settings_list.append(load_settings(dir))
return attrs_list_all_sims
def load_simulations_single_population(folder_name, plot_settings):
'''
Untested!!
'''
folder_dir = 'save/{}'.format(folder_name)
dir_list = all_folders_in_dir_with(folder_dir, 'sim')
attrs_lists_sims = []
for sim_name in dir_list:
attrs_list = load_isings_attr(sim_name, plot_settings['attr'])
attrs_lists_sims.append(attrs_list)
return attrs_lists_sims
def load_all_sims_parallel_folder(folder_name, include_name, sim_num,
plot_settings):
folder_dir = 'save/{}'.format(folder_name)
dir_list = all_folders_in_dir_with(folder_dir, 'sim')
for dir in dir_list:
sim_name = dir[(dir.rfind('save/') + 5):]
sim_num_str = sim_name[(sim_name.rfind('Run_')):]
sim_num_str = sim_num_str[sim_num_str.rfind('Run_') + 4:]
if sim_num_str == str(sim_num):
ising = load_from_dynamic_range_data_one_sim(
sim_name, include_name, plot_settings)
return ising
def load_all_sims_parallel_folder(folder_name, df_each_sim, plot_settings):
folder_dir = 'save/{}'.format(folder_name)
dir_list = all_folders_in_dir_with(folder_dir, 'sim')
for dir in dir_list:
sim_name = dir[(dir.rfind('save/') + 5):]
sim_run_num_str = sim_name[(sim_name.rfind('Run_')):]
isings = load_from_dynamic_range_data_one_sim(sim_name, plot_settings)
if not isings is None:
avg_avg_energy = np.mean([I.avg_energy for I in isings])
# Fill data frame
df_each_sim[sim_run_num_str] = [
isings, avg_avg_energy, plot_settings['plot_varying_number']
]
return df_each_sim
def load_data(attr, sim_name):
sim_dir = 'save/{}'.format(sim_name)
attrs_list_each_food_num_all = []
attrs_list_each_food_num_critical = []
attrs_list_each_food_num_sub_critical = []
food_num_list = []
dir_list = all_folders_in_dir_with('{}/repeated_generations'.format(sim_dir), 'foods_dynamic_range_run')
for dir in dir_list:
isings_list = load_isings_specific_path(dir)
isings = make_2d_list_1d(isings_list)
isings_populations_seperated = seperate_isolated_populations([isings])
isings_critical = isings_populations_seperated[0][0]
isings_sub_critical = isings_populations_seperated[1][0]
attrs_list_each_food_num_all.append(attribute_from_isings(isings, attr))
attrs_list_each_food_num_critical.append(attribute_from_isings(isings_critical, attr))
attrs_list_each_food_num_sub_critical.append(attribute_from_isings(isings_sub_critical, attr))
food_num_list.append(get_int_end_of_str(dir))
return attrs_list_each_food_num_all, attrs_list_each_food_num_critical, attrs_list_each_food_num_sub_critical, food_num_list
def load_from_dynamic_range_data_one_sim(sim_name, plot_settings):
dir = 'save/{}/repeated_generations'.format(sim_name)
dir_list = all_folders_in_dir_with(dir, plot_settings['include_name'])
# plot_settings['plot_varying_number']
if plot_settings['plot_largest_varying_number']:
# find largest varying number
plot_settings['plot_varying_number'] = np.max(
[get_int_end_of_str(dir) for dir in dir_list])
elif plot_settings['plot_original_varying_number']:
plot_settings['plot_varying_number'] = plot_settings[
'original_varying_number']
# Find dirs that shall be plotted
dirs_to_plot = []
for dir in dir_list:
if get_int_end_of_str(dir) == plot_settings['plot_varying_number']:
dirs_to_plot.append(dir)
if len(dirs_to_plot) > 1:
print(
'Found more than one simulation in repeated generation folder! Choos ing first detected!'
)
if len(dirs_to_plot) == 0:
print(
'Did not find varying number (time step) {} in {}. Skip plotting this'
.format(plot_settings['plot_varying_number'], sim_name))
return None
else:
# TODO: Change loading all isings in this path as soon as we have more than one ising for speed
if plot_settings['compress_save_isings']:
isings = load_isings_specific_path_decompress(dirs_to_plot[0])[0]
else:
isings = load_isings_specific_path(dirs_to_plot[0])[0]
if plot_settings['only_copied_isings']:
isings = choose_copied_isings(isings)
plot_ind_nums = np.random.randint(0,
len(isings) - 1,
plot_settings['number_individuals'])
return [isings[i] for i in plot_ind_nums]
