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analyse_season_jump.py
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import numpy as np
from automatic_plot_helper import load_isings_from_list
from automatic_plot_helper import detect_all_isings
from automatic_plot_helper import load_isings
import matplotlib.pylab as plt
from os import makedirs, path
import seaborn as sns
import os
import pandas as pd
import copy
from matplotlib.lines import Line2D
def extract_attr(isings_list, attr):
val_list = []
for isings in isings_list:
for I in isings:
exec('val_list.append(I.{})'.format(attr))
return val_list
def load_trained_vals(trained_sim_name, attr, n_last_gens = 100):
load_gens_trained = detect_all_isings(trained_sim_name)
load_gens_trained = load_gens_trained[-(n_last_gens):]
trained_isings_list = load_isings_from_list(trained_sim_name, load_gens_trained)
trained_vals = extract_attr(trained_isings_list, attr)
# trained_avg = np.avg(trained_vals)
# trained_std = np.std(trained_vals)
return trained_vals
# def load_switched_vals(switched_sim_name, attr, n_last_gens = 100):
# # load_gens_switched = detect_all_isings(switched_sim_name)
# # load_gens_switched = load_gens_switched[-(n_last_gens + 1):]
# # switched
# #switched_isings_list = load_isings(switched_sim_name)
#
# switched_vals = extract_attr(switched_isings_list, attr)
# # switched_avg = np.avg(switched_vals)
# # switched_std = np.std(switched_vals)
# return switched_vals
def load_plot_data(trained_sim_names, switched_sim_names, attr):
'''
:param trained_sim_names: names of one parameter set
:param switched_sim_names: names of one parameter set
:return: all values of sets concatenated
'''
all_trained_vals = []
for trained_sim_name in trained_sim_names:
all_trained_vals.append(load_trained_vals(trained_sim_name, attr))
# trained_avg = np.avg(all_trained_vals)
# trained_std = np.std(all_trained_vals)
all_switched_vals = []
for switched_sim_name in switched_sim_names:
#all_switched_vals.append(load_switched_vals(switched_sim_name, attr))
all_switched_vals.append(load_trained_vals(switched_sim_name, attr))
# switched_avg = np.avg(all_switched_vals)
# switched_std = np.std(all_switched_vals)
return all_trained_vals, all_switched_vals
# def plot(trained_sets, switched_sets):
# trained_avgs = []
# trained_stds = []
# switched_avgs = []
# switched_stds = []
# for trained_set, switched_set in zip(trained_sets):
# all_trained_vals, all_switched_vals = calc_plot_data(trained_set, switched_set)
# trained_avgs.append(np.avg(all_trained_vals))
# trained_stds.append(np.std(all_trained_vals))
def add_folder_name(sets, folder):
new_sets = []
for set in sets:
new_set = []
for sim_name in set:
new_set.append(folder + sim_name)
new_sets.append(new_set)
return new_sets
def create_DF(all_data, labels, sims_per_label=4):
columns = []
for label in labels:
for i in range(sims_per_label):
columns.append(label + str(i))
all_data = np.array([np.array(data) for data in all_data])
all_data = np.asarray(all_data)
all_data = np.stack(all_data, axis=0)
df = pd.DataFrame(all_data, index=columns)
df = df.transpose()
return df, columns
def create_violin_colors(color_list, repeat = 4):
out_color_list = []
for color in color_list:
for i in range(repeat):
out_color_list.append(color)
return out_color_list
def plot(trained_sets, switched_sets, attr, labels, new_order_labels, trained_folder=None, switched_folder=None,
auto_load=False, yscale='linear', ylim=None, save_addition='', xlim=None):
#for some reason auto_load stopped working, did not look for bug yet
if not trained_folder is None:
trained_sets = add_folder_name(trained_sets, trained_folder)
switched_sets = add_folder_name(switched_sets, switched_folder)
# -----PLot concetinated data
# trained_sets = [j for sub in trained_sets for j in sub]
# switched_sets = [j for sub in switched_sets for j in sub]
npz_name = 'save/{}figs/{}_boxplot.npz'.format(switched_folder, attr)
if path.isfile(npz_name) and auto_load:
txt = 'Loading: ' + npz_name
print(txt)
data = np.load(npz_name)
all_data = data['all_data']
data = data['data']
else:
data = []
all_data = []
for trained_set, switched_set in zip(trained_sets, switched_sets):
trained_vals, switched_vals = load_plot_data(trained_set, switched_set, attr)
for trained_single_sim in trained_vals:
all_data.append(trained_single_sim)
for switched_single_sim in switched_vals:
all_data.append(switched_single_sim)
trained_vals_concat = [j for sub in trained_vals for j in sub]
switched_vals_concat = [j for sub in switched_vals for j in sub]
data.append(trained_vals_concat)
data.append(switched_vals_concat)
# plt.boxplot(data)
# plt.xticks(np.arange(1, len(labels) + 1), labels, rotation='vertical')
# plt.show()
savefolder = 'save/{}figs/{}_'.format(switched_folder, attr)
if not path.exists(savefolder):
makedirs(savefolder)
np.savez(npz_name, all_data=all_data, data=data)
df, names = create_DF(all_data, labels)
df = reorder_df(df, new_order_labels)
all_data_reordered = df_to_nested_list(df)
# plt.figure(figsize=(25, 5))
# chart = sns.violinplot(data=df, width=0.8, inner='quartile', scale='width', linewidth=0.01) # inner='quartile'
# chart.set_xticklabels(chart.get_xticklabels(), rotation=70)
# df.mean().plot(style='*')
# plt.savefig('{}violin_df_neworder{}.png'.format(savefolder, save_addition), dpi=300, bbox_inches='tight')
# plt.show()
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22',
'#17becf']
violin_colors = create_violin_colors(colors)
# LEGEND
legend_elements = [Line2D([0], [0], marker='_', color='black', label='mean', markerfacecolor='g', markersize=10)]
plt.figure(figsize=(25, 10))
chart = sns.violinplot(data=df, width=0.8, inner='quartile', scale='width', linewidth=0.05,
palette=violin_colors) # inner='quartile'
df.mean().plot(style='_', c='black', ms=30)
chart.set_xticklabels(chart.get_xticklabels(), rotation=70)
plt.yscale(yscale)
plt.gca().set_ylim(top=20)
plt.legend(handles=legend_elements)
plt.savefig('{}violin_df{}.png'.format(savefolder, save_addition), dpi=300, bbox_inches='tight')
plt.show()
fig, ax = plt.subplots()
col_i = 0
for i, d in enumerate(all_data_reordered):
color = colors[col_i]
noisy_x = i * np.ones((1, len(d))) + np.random.random(size=len(d)) * 0.5
ax.scatter(noisy_x[0, :], d, alpha=0.6, s=0.01, c=color)
if (i + 1) % 4 == 0:
col_i += 1
mean_series = df.mean()
mean_series.plot(style='_', c='black', ms=7)
ax.set_xticks(np.arange(32))
ax.set_yscale(yscale)
#plt.ylabel('median energy')
plt.ylabel(attr)
plt.xticks(np.arange(1, len(new_order_labels) * 4 + 1, 4), new_order_labels, rotation=70)
plt.legend(handles=legend_elements)
plt.savefig('{}scatter{}.png'.format(savefolder, save_addition), dpi=300, bbox_inches='tight')
plt.show()
# plt.boxplot(data, showmeans=True)
# plt.xticks(np.arange(1, len(labels) + 1), labels, rotation='vertical')
# plt.ylabel(attr)
# plt.savefig('{}boxplot.png'.format(savefolder), dpi=200, bbox_inches='tight')
# plt.show()
# plt.boxplot(all_data_reordered, showmeans=True)
# plt.xticks(np.arange(1, len(new_order_labels)*4 + 1, 4), new_order_labels, rotation='vertical')
# plt.ylabel(attr)
# plt.savefig('{}boxplot_all.png'.format(savefolder), dpi=200, bbox_inches='tight')
# plt.show()
plt.figure(figsize=(20, 5))
plt.violinplot(all_data_reordered, showmeans=True, showextrema=False, widths=0.8)
plt.xticks(np.arange(1, len(new_order_labels)*4 + 1, 4), new_order_labels, rotation=70)
plt.yscale(yscale)
#plt.ylabel('median energy')
plt.ylabel(attr)
plt.ylim(ylim)
plt.xlim(xlim)
plt.savefig('{}violin_all{}.png'.format(savefolder, save_addition), dpi=300, bbox_inches='tight')
plt.show()
# fig, ax = plt.subplots()
# for i, s in enumerate(df):
# d = df[s].tolist()
# noisy_x = i * np.ones((1, len(d))) + np.random.random(size=len(d)) * 0.5
# ax.scatter(noisy_x[0, :], d, alpha=0.2, s=0.1)
# ax.set_xticks(np.arange(32))
# ax.set_yscale('log')
#
# plt.xticks(np.arange(1, len(labels) * 4 + 1, 4), labels, rotation=70)
# plt.savefig('{}scatter{}.png'.format(savefolder, save_addition), dpi=300, bbox_inches='tight')
# plt.show()
def df_to_nested_list(df):
df = copy.deepcopy(df)
out_list = []
for col in df:
out_list.append(df[col].tolist())
return out_list
def which(trained_sim, switched_sets, two_dim = True, trained_also_simulated_again = True):
if trained_also_simulated_again:
start = trained_sim.find('-l_sim')+4
end = start + 19
trained_sim = trained_sim[start:end]
if two_dim:
switched_sets_1D = [j for sub in switched_sets for j in sub]
else:
switched_sets_1D = switched_sets
for switched_sim in switched_sets_1D:
if trained_sim in switched_sim:
return switched_sim
#raise FileNotFoundError('No switched simulation found for the trained simulation {}'.format(trained_sim))
def sort_switched_sets(trained_sets, switched_sets, two_dim = True):
'''Sort switched sets according to trained sets'''
sorted_switched_sets = []
for trained_set in trained_sets:
sorted_switched_set = []
for trained_sim in trained_set:
acc_switched_sim = which(trained_sim, switched_sets, two_dim)
sorted_switched_set.append(acc_switched_sim)
sorted_switched_sets.append(sorted_switched_set)
return sorted_switched_sets
def load_switched_sets_sorted(switched_folder, trained_sets):
directory_list = [f.path for f in os.scandir('save/{}'.format(switched_folder)) if f.is_dir()]
switched_set = []
for sim_name in directory_list:
sim_name = sim_name.split('/')[-1]
if 'sim-' in sim_name:
switched_set.append(sim_name)
return sort_switched_sets(trained_sets, switched_set, two_dim = False)
def sort_sets(order_list, sets):
'''sort sets according to order given in order list.
:param order_list: list of ints, where each int gives place in new order
'''
ordered_sets = [x for _, x in sorted(zip(order_list, sets))]
#ordered_labels = [x for _, x in sorted(zip(order_list, labels))]
return ordered_sets
def reorder_df(df, new_order_labels):
old_cols = df.columns.tolist()
new_cols = []
for new_label in new_order_labels:
for old_col in old_cols:
if new_label in old_col:
new_cols.append(old_col)
df_new = copy.deepcopy(df)
df_new = df_new[new_cols]
return df_new
if __name__ == '__main__':
#Sort labels according to order of trained sets!!!!!
trained_folder = '4th_run_same_season/'
switched_folder = '4th_run_switched_season/'
#attr = 'avg_energy'
#attr = 'avg_velocity'
attr = 'food'
#labels in order of trained sets
labels = ['b10 summer', 'b10 switched to winter', 'b10 winter', 'b10 switched to summer',
'b1 summer', 'b1 switched to winter', 'b1 winter', 'b1 switched to summer']
#labels in wanted plot order
new_order_labels = ['b1 summer', 'b1 switched to summer', 'b10 summer', 'b10 switched to summer', 'b1 winter',
'b1 switched to winter', 'b10 winter', 'b10 switched to winter']
#To list all files in a certain folder in windows command shell: dir /B > filelist.txt
trained_sets = [['sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_100_-n_1_-r_200_-ser_-f_100_-n_1same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_100_-n_2_-r_200_-ser_-f_100_-n_2same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_100_-n_3_-r_200_-ser_-f_100_-n_3same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_100_-n_4_-r_200_-ser_-f_100_-n_4same'],
['sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_10_-n_1_-r_200_-ser_-f_10_-n_1same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_10_-n_2_-r_200_-ser_-f_10_-n_2same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_10_-n_3_-r_200_-ser_-f_10_-n_3same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_10_-f_10_-n_4_-r_200_-ser_-f_10_-n_4same'],
['sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_100_-n_1_-r_200_-ser_-f_100_-n_1same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_100_-n_2_-r_200_-ser_-f_100_-n_2same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_100_-n_3_-r_200_-ser_-f_100_-n_3same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_100_-n_4_-r_200_-ser_-f_100_-n_4same'],
['sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_10_-n_1_-r_200_-ser_-f_10_-n_1same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_10_-n_2_-r_200_-ser_-f_10_-n_2same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_10_-n_3_-r_200_-ser_-f_10_-n_3same',
'sim-20200214-180611-l_sim-20200209-124814-ser_-b_1_-f_10_-n_4_-r_200_-ser_-f_10_-n_4same']]
# trained_sets = [['sim-20200121-213309-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200121-213313-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200121-213321-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200121-213347-ser_-cfg_2000_100_-b_1_-nmb_-a_200_1999_2190',],
# ['sim-20200121-213356-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200121-213400-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200121-213403-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200121-213424-ser_-cfg_2000_100_-b_10_-nmb_-a_200_1999_2190'],
# ['sim-20200121-213437-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200121-213441-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200121-213446-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200121-213458-ser_-f_10_-cfg_2000_100_-b_1_-nmb_-a_200_1999_2190'],
# ['sim-20200121-213512-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200121-213520-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200121-213524-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200121-213537-ser_-f_10_-cfg_2000_100_-b_10_-nmb_-a_200_1999_2190'
# ]]
switched_sets = load_switched_sets_sorted(switched_folder, trained_sets)
# new_order_trained_sets = [,,0]
# new_order_switched_sets = [0,,1 ]
# switched_sets =
# switched_sets = [['sim-20200130-205401-ser_-b_1_-f_10_-r_200_-li_1999_-a_5_-l_sim-20200121-213347-ser_-cfg_2000_100_-b_1_-nmb_-a_200_1999_2190',
# 'sim-20200130-205401-ser_-b_1_-f_10_-r_200_-li_1999_-l_sim-20200121-213309-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200130-205401-ser_-b_1_-f_10_-r_200_-li_1999_-l_sim-20200121-213313-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200130-205401-ser_-b_1_-f_10_-r_200_-li_1999_-l_sim-20200121-213321-ser_-cfg_2000_100_-b_1_-nmb'],
# ['sim-20200130-205401-ser_-f_100_-b_10_-r_200_-li_1999_-a_5_-l_sim-20200121-213537-ser_-f_10_-cfg_2000_100_-b_10_-nmb_-a_200_1999_2190',
# 'sim-20200130-205401-ser_-f_100_-b_10_-r_200_-li_1999_-l_sim-20200121-213512-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200130-205401-ser_-f_100_-b_10_-r_200_-li_1999_-l_sim-20200121-213520-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200130-205401-ser_-f_100_-b_10_-r_200_-li_1999_-l_sim-20200121-213524-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200130-205401-ser_-f_100_-b_1_-r_200_-li_1999_-a_5_-l_sim-20200121-213458-ser_-f_10_-cfg_2000_100_-b_1_-nmb_-a_200_1999_2190'],
# ['sim-20200130-205401-ser_-f_100_-b_1_-r_200_-li_1999_-l_sim-20200121-213437-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200130-205401-ser_-f_100_-b_1_-r_200_-li_1999_-l_sim-20200121-213441-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200130-205401-ser_-f_100_-b_1_-r_200_-li_1999_-l_sim-20200121-213446-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200130-205401-ser_-f_10_-b_10_-r_200_-li_1999_-a_5_-l_sim-20200121-213424-ser_-cfg_2000_100_-b_10_-nmb_-a_200_1999_2190'],
# ['sim-20200130-205401-ser_-f_10_-b_10_-r_200_-li_1999_-l_sim-20200121-213356-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200130-205401-ser_-f_10_-b_10_-r_200_-li_1999_-l_sim-20200121-213400-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200130-205401-ser_-f_10_-b_10_-r_200_-li_1999_-l_sim-20200121-213403-ser_-cfg_2000_100_-b_10_-nmb']]
switched_sets = sort_switched_sets(trained_sets, switched_sets)
# switched_sets = [[
#
# 'sim-20200129-212115-ser_-b_1_-f_10_-r_200_-li_1999_-a_5_-l_sim-20200121-213347-ser_-cfg_2000_100_-b_1_-nmb_-a_200_1999_2190',
# 'sim-20200129-212115-ser_-b_1_-f_10_-r_200_-li_1999_-l_sim-20200121-213309-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200129-212115-ser_-b_1_-f_10_-r_200_-li_1999_-l_sim-20200121-213313-ser_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200129-212115-ser_-b_1_-f_10_-r_200_-li_1999_-l_sim-20200121-213321-ser_-cfg_2000_100_-b_1_-nmb'],
# ['sim-20200129-212115-ser_-f_100_-b_10_-r_200_-li_1999_-a_5_-l_sim-20200121-213537-ser_-f_10_-cfg_2000_100_-b_10_-nmb_-a_200_1999_2190',
# 'sim-20200129-212115-ser_-f_100_-b_10_-r_200_-li_1999_-l_sim-20200121-213512-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200129-212115-ser_-f_100_-b_10_-r_200_-li_1999_-l_sim-20200121-213520-ser_-f_10_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200129-212115-ser_-f_100_-b_10_-r_200_-li_1999_-l_sim-20200121-213524-ser_-f_10_-cfg_2000_100_-b_10_-nmb'],
# ['sim-20200129-212115-ser_-f_100_-b_1_-r_200_-li_1999_-a_5_-l_sim-20200121-213424-ser_-cfg_2000_100_-b_10_-nmb_-a_200_1999_2190',
# 'sim-20200129-212115-ser_-f_100_-b_1_-r_200_-li_1999_-l_sim-20200121-213403-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200129-212115-ser_-f_100_-b_1_-r_200_-li_1999_-l_sim-20200121-213437-ser_-f_10_-cfg_2000_100_-b_1_-nmb',
# 'sim-20200129-212115-ser_-f_100_-b_1_-r_200_-li_1999_-l_sim-20200121-213441-ser_-f_10_-cfg_2000_100_-b_1_-nmb'],
# ['sim-20200129-212115-ser_-f_10_-b_10_-r_200_-li_1999_-l_sim-20200121-213356-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200129-212115-ser_-f_10_-b_10_-r_200_-li_1999_-l_sim-20200121-213400-ser_-cfg_2000_100_-b_10_-nmb',
# 'sim-20200129-212115-ser_-f_10_-b_10_-r_200_-li_1999_-l_sim-20200121-213403-ser_-cfg_2000_100_-b_10_-nmb',]]
plot(trained_sets, switched_sets, attr, labels, new_order_labels, trained_folder, switched_folder, yscale='linear', ylim=None,
save_addition='_nolog', xlim=None, auto_load=False)
#yscale='symlog'
# def analyse(trained_sim_name, switched_sim_name, n_last_gens = 100):
# load_gens_trained = detect_all_isings(trained_sim_name)
# load_gens_trained = load_gens_trained[-(n_last_gens+1)]
# trained_isings_list = load_isings_from_list(trained_sim_name, load_gens_trained)
# switched_isings_list = load_isings(switched_sim_name)
# trained_vals = extract_attr(trained_isings_list, 'avg_energy')
# switched_vals = extract_attr(switched_isings_list, 'avg_energy')
# trained_avg = np.avg(trained_vals)
# switched_avg = np.avg(switched_vals)