def main(sim_names, load_isings_list=True):
settings = load_settings(sim_names[0])
if load_isings_list:
isings_lists = [load_isings(sim_name) for sim_name in sim_names]
plot_anything_auto(sim_names, ['Beta', 'avg_velocity'],
settings,
isings_lists=isings_lists,
autoLoad=False)
def main(sim_name, load_isings_list=True, final=False):
'''
final defines whether this is the final/ last generation of simulation is plotted
'''
settings = load_settings(sim_name)
colors = ['red', 'blue', 'green']
if load_isings_list:
isings_list = load_isings(sim_name)
isings_list_dict = seperate_isolated_populations(isings_list)
isings_list_dict_fittest = fittest_in_isolated_populations(
isings_list_dict)
# isings_list_fittest = [sorted(isings, key=operator.attrgetter('avg_energy'), reverse=True)[:20] for isings in isings_list]
# isings_list = load_isings_from_list(sim_name, [0])
plot_vars = ['avg_energy', 'Beta', 'avg_velocity', 'food']
plot_var_tuples = [('generation', 'avg_energy'),
('generation', 'avg_velocity'), ('generation', 'food'),
('generation', 'Beta'), ('Beta', 'avg_energy'),
('Beta', 'avg_velocity'),
('avg_energy', 'avg_velocity'), ('avg_energy', 'food')]
try:
if settings['speciation']:
append_species_stuff = [('species', 'shared_fitness'),
('species', 'avg_energy'),
('generation', 'species')]
for tup in append_species_stuff:
plot_var_tuples.append(tup)
except KeyError:
print('Older version, when speciation was not implemented')
# Try plotting norm_avg_energy in case dataset already has I.time_steps
try:
for isings in isings_list:
for I in isings:
I.norm_avg_energy = I.avg_energy / I.time_steps
plot_vars.append('norm_avg_energy')
plot_var_tuples.append(('generation', 'norm_avg_energy'))
if settings['random_food_seasons']:
for isings in isings_list:
for I in isings:
I.norm_food_and_ts_avg_energy = I.norm_avg_energy / I.food_in_env
plot_vars.append('norm_food_and_ts_avg_energy')
plot_var_tuples.append(
('generation', 'norm_food_and_ts_avg_energy'))
except Exception:
print(
'Could not calculate norm_avg_energy (Do isings lack attribute I.time_steps?)'
)
try:
for plot_var in plot_vars:
plot_avg_attr_generational_isolated.main(sim_name,
isings_list_dict_fittest,
plot_var,
name_extension='fittest',
colors=colors)
except Exception:
print('Could not create generational plots')
try:
plot_scatter_auto(sim_name,
settings,
plot_var_tuples,
isings_list,
colors,
autoLoad=False)
except Exception:
print('Could not create scatter plot')
del isings_list
del settings
# # food = np.divide(food, 6)
# x_pars_list[ii, :] = x_pars
#
# if not path.exists(folder2):
# makedirs(folder2)
#np.savez(fname2, FOOD=x_pars_list)
if __name__ == '__main__':
#loadfile = sys.argv[1]
#plot_var = sys.argv[2] #plot_var = 'v'
loadfile = 'sim-20200103-170556-ser_-s_-b_1_-ie_2_-a_0_500_1000_1500_1999'
plot_var_x = 'avg_energy'
plot_var_y = 'avg_velocity' #'food'
isings_list = load_isings(loadfile)
settings = load_settings(loadfile)
#TODO: add something that detetcts .npz file and skips loading isings in that case
main(loadfile,
settings,
isings_list,
plot_var_x,
plot_var_y,
autoLoad=False,
x_lim=(-1, 20),
y_lim=(-0.1, 0.8),
alpha=0.05)
#TODO: Evt. PCA oder decision trees um herauszufinden welche eigenschaften wichtig sind fuer hohe avg energy?
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use('Agg') #For server use
from automatic_plot_helper import detect_all_isings
from automatic_plot_helper import load_settings
from automatic_plot_helper import load_isings
def how_many_below_min_velocity(settings, isings):
amount = 0
for I in isings:
if I.v
def plot(sim_name, settings, isings_list):
folder = 'save/' + sim_name
folder2 = folder + '/figs/' + 'individuals_below_min_velocity' + '/'
if __name__ == '__main__':
sim_name =
isings_list = load_isings(sim_name)
settings = load_settings(sim_name)
plot(sim_name, settings, isings_list)
def main(sim_name, load_isings_list=True, final=False):
'''
final defines whether this is the final/ last generation of simulation is plotted
'''
for i_type in ('pred', 'prey'):
settings = load_settings(sim_name)
if load_isings_list:
isings_list = load_isings(sim_name, i_type)
#try:
if i_type == 'pred':
plot_var_list = ['Beta', 'avg_velocity', 'food']
plot_var_tuples = [('generation', 'avg_energy'),
('generation', 'avg_velocity'),
('generation', 'food')]
elif i_type == 'prey':
plot_var_list = ['Beta', 'avg_velocity', 'times_killed']
plot_var_tuples = [('generation', 'avg_energy'),
('generation', 'avg_velocity'),
('generation', 'times_killed')]
try:
plot_anything_auto(sim_name,
plot_var_list,
settings,
i_type,
isings_list=isings_list,
autoLoad=False)
except Exception:
print('Could not create generational plots')
#plot_var_tuples = [('Beta', 'avg_velocity'), ('avg_energy', 'avg_velocity'), ('avg_energy', 'food')]
try:
plot_scatter_auto(sim_name,
settings,
plot_var_tuples,
isings_list,
i_type,
autoLoad=False)
except Exception:
print('Could not create scatter plot')
try:
if i_type == 'pred':
plot_anythingXY_scatter_food_velocity_optimized.main(
sim_name,
settings,
isings_list,
'avg_velocity',
'food',
i_type,
s=0.8,
alpha=0.05,
autoLoad=False)
elif i_type == 'prey':
plot_anythingXY_scatter_food_velocity_optimized.main(
sim_name,
settings,
isings_list,
'avg_velocity',
'avg_energy',
i_type,
s=0.8,
alpha=0.05,
autoLoad=False)
except Exception:
print('Could not create food velocity scatter plot')
if final:
pass
# try:
# if settings['cores'] != 0:
# compute_and_plot_heat_capacity_automatic.main(sim_name, settings, i_type)
# except Exception:
# print('Could not compute and plot heat capacity')
# Trying to fix memory leak:
del isings_list
del settings
startstr = 'Loading simulation:' + filename
print(startstr)
try:
isings = pickle.load(open(filename, 'rb'))
except Exception:
print("Error while loading %s. Skipped file" % filename)
#Leads to the previous datapoint being drawn twice!!
food = []
for i, I in enumerate(isings):
exec('food.append(I.%s)' % plot_var)
# food = np.divide(food, 6)
FOOD[ii, :] = food
if not path.exists(folder2):
makedirs(folder2)
np.savez(fname2, FOOD=FOOD)
return FOOD
if __name__ == '__main__':
loadfile = sys.argv[1]
plot_var = sys.argv[2] #plot_var = 'v'
isings_list = [load_isings(loadfile)]
main(loadfile, plot_var, isings_lists=isings_list)
def main(sim_name, only_top_isings=None, load_isings_list=True, final=False, subfolder=None):
'''
final defines whether this is the final/ last generation of simulation is plotted
'''
# if subfolder != None:
# sim_name = sim_name + subfolder
settings = load_settings(sim_name)
if load_isings_list:
if only_top_isings is not None:
isings_list = load_top_isings(sim_name, only_top_isings)
settings['pop_size'] = only_top_isings
save_txt_path = 'save/{}/figs/'.format(sim_name)
if not os.path.exists(save_txt_path):
os.makedirs(save_txt_path)
f = open(save_txt_path + "Only_first_{}_fittest_individuals_have_been_plotted.txt".format(only_top_isings), "w+")
f.close()
else:
isings_list = load_isings(sim_name, wait_for_memory=True)
isings_list_fittest = [sorted(isings, key=operator.attrgetter('avg_energy'), reverse=True)[:20] for isings in isings_list]
#isings_list = load_isings_from_list(sim_name, [0])
plot_vars = ['Beta', 'avg_velocity', 'food']
plot_var_tuples = [('generation', 'avg_energy'), ('generation', 'avg_velocity'), ('generation', 'food'),
('generation', 'Beta'), ('Beta', 'avg_energy'), ('Beta', 'avg_velocity'),
('avg_energy', 'avg_velocity'), ('avg_energy', 'food')]
try:
if settings['speciation']:
append_species_stuff = [('species', 'shared_fitness'), ('species', 'avg_energy'), ('generation', 'species')]
for tup in append_species_stuff:
plot_var_tuples.append(tup)
except KeyError:
print('Older version, when speciation was not implemented')
# Try plotting norm_avg_energy in case dataset already has I.time_steps
try:
for isings in isings_list:
for I in isings:
I.norm_avg_energy = I.avg_energy / I.time_steps
plot_vars.append('norm_avg_energy')
plot_var_tuples.append(('generation', 'norm_avg_energy'))
if settings['random_food_seasons']:
for isings in isings_list:
for I in isings:
I.norm_food_and_ts_avg_energy = I.norm_avg_energy / I.food_in_env
plot_vars.append('norm_food_and_ts_avg_energy')
plot_var_tuples.append(('generation', 'norm_food_and_ts_avg_energy'))
except Exception:
print('Could not calculate norm_avg_energy (Do isings lack attribute I.time_steps?)')
# try:
plot_anything_auto(sim_name, plot_vars, settings, isings_list=isings_list_fittest, autoLoad=False)
# except Exception:
# print('Could not create generational plots')
try:
plot_scatter_auto(sim_name, settings, plot_var_tuples, isings_list, autoLoad=False)
except Exception:
print('Could not create scatter plot')
try:
plot_anythingXY_scatter_food_velocity_optimized.main(sim_name, settings, isings_list, 'avg_velocity', 'food', s=0.8,
alpha=0.05, autoLoad=False)
except Exception:
print('Could not create food velocity scatter plot')
if final:
pass
# try:
# if settings['cores'] != 0:
# compute_and_plot_heat_capacity_automatic.main(sim_name, settings)
#
# except Exception:
# print('Could not compute and plot heat capacity')
# Trying to fix memory leak:
del isings_list
del settings
markersize=5,
alpha=0.75))
plt.scatter(x_axis, y_axis, c=colors[i], alpha=0.15)
if attr == 'norm_food_and_ts_avg_energy':
plt.ylim(0, 0.0002)
# plt.legend(loc="lower right", bbox_to_anchor=(0.95, 0.05), handles=legend_elements)
plt.legend(handles=legend_elements)
plt.xlabel('Generation')
plt.ylabel(attr)
plt.savefig(savefilename, dpi=300, bbox_inches='tight')
if __name__ == '__main__':
sim_name = 'sim-20201005-115242-g_4000_-t_2000_-rand_seas_-rec_c_1000_-c_props_100_50_-2_2_100_40_-iso_-ref_1000_-c_4_-a_1000_1001_10002_2000_3998_3999_-no_trace_-n_different_betas_rand_seas2_TEST_COPY_2_DYNAMIC_RANGE' #'sim-20200714-190003-g_100_-t_5_-iso_-n_test'
isings_list = load_isings(sim_name, wait_for_memory=False)
#isings_list = load_isings_from_list(sim_name, np.arange(100))
isings_list_dict = seperate_isolated_populations(isings_list)
isings_list_dict = fittest_in_isolated_populations(isings_list_dict)
settings = load_settings(sim_name)
if True:
for isings in isings_list:
for I in isings:
I.norm_avg_energy = I.avg_energy / I.time_steps
if settings['random_food_seasons']:
for isings in isings_list:
for I in isings:
I.norm_food_and_ts_avg_energy = I.norm_avg_energy / I.food_in_env
