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visualize-natural-heat-capacity-generational
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#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from os import path, makedirs
# beta-1/sim-20180511-163319
# beta-10/sim-20180512-105824
# beta-0-1/sim-20180512-105719
# TODO: make these scripts take these as params
loadfiles = ['beta_experiment/beta-1/sim-20180511-163319',
'beta_experiment/beta-10/sim-20180512-105824',
'beta_experiment/beta-0-1/sim-20180512-105719']
saveFig = False
for loadfile in loadfiles:
folder = 'save/' + loadfile
# iter_gen = np.arange(0, 2000, 250)
# iter_gen = np.append(iter_gen, 1999)
# iter_gen = [0, 252, 504, 756, 1002, 1254, 1500, 1752, 1998]
# iter_gen = [0, 1, 2, 5, 10, 20, 50, 100, 250, 1000, 1999,
# 2250, 2500, 2750, 3000, 3250, 3500, 3750, 3999]
iter_gen = [0, 3999]
R = 5
Nbetas = 101
betas = 10 ** np.linspace(-1, 1, Nbetas)
numAgents = 50
size = 10
C = np.zeros((R, numAgents, Nbetas, len(iter_gen)))
Sm = np.zeros((R, numAgents, Nbetas, len(iter_gen)))
Sp = np.zeros((R, numAgents, Nbetas, len(iter_gen)))
F = np.zeros((R, numAgents, Nbetas, len(iter_gen)))
print('Loading data...')
for ii, iter in enumerate(iter_gen):
for bind in np.arange(1, 101):
filename = folder + '/C-S-natural/C-S-natural_' + str(iter) + '/C-S-size_' + str(size) + '-Nbetas_' + \
str(Nbetas) + '-bind_' + str(bind) + '.npz'
data = np.load(filename)
C[:, :, bind, ii] = data['C']
Sm[:, :, bind, ii] = data['Sm']
Sp[:, :, bind, ii] = data['Sp']
F[:, :, bind, ii] = data['F']
print('Done.')
plt.rc('text', usetex=True)
font = {'family': 'serif', 'size': 28, 'serif': ['computer modern roman']}
plt.rc('font', **font)
plt.rc('legend', **{'fontsize': 20})
b = 0.8
alpha = 0.05
print('Generating figures...')
for ii, iter in enumerate(iter_gen):
fig, ax = plt.subplots(2, 2, figsize=(11, 10), sharex='col')
fig.text(0.51, 0.035, r'$\beta$', ha='center', fontsize=28)
# fig.text(0.005, 0.5, r'$C/N$', va='center', rotation='vertical', fontsize=28)
title = 'Specific Heat and Susceptibility\n Generation: ' + str(iter)
ax[0, 0].set_ylabel(r'$C/N$')
ax[0, 1].set_ylabel(r'$\chi_M$')
ax[1, 0].set_ylabel(r'$\chi_p$')
ax[1, 1].set_ylabel(r'$Fitness$')
fig.suptitle(title)
# CHANGE THIS TO CUSTOMIZE HEIGHT OF PLOT
# upperbound = 1.5 * np.max(np.mean(np.mean(C[:, :, :-40, :], axis=0), axis=0))
# upperbound = np.max(np.mean(np.mean(C, axis=0)), axis=0)
# upperbound = 0.4
label = iter
for numOrg in range(numAgents):
# c = np.dot(np.random.random(), [1, 1, 1])
# ax[0, 0].scatter(betas, np.mean(C[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[0, 1].scatter(betas, np.mean(Sm[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 0].scatter(betas, np.mean(Sp[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 1].scatter(betas, np.mean(F[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
# for rep in range(R):
# ax[0, 0].scatter(betas, np.multiply( betas**2, C[rep, numOrg, 1:, ii]),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[0, 1].scatter(betas, Sm[rep, numOrg, 1:, ii],
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 0].scatter(betas, Sp[rep, numOrg, 1:, ii],
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 1].scatter(betas, F[rep, numOrg, 1:, ii],
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
for rep in range(R):
ax[0, 0].scatter(betas[1:-1], C[rep, numOrg, 1:-1, ii],
color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
ax[0, 1].scatter(betas[1:-1], Sm[rep, numOrg, 1:-1, ii],
color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
ax[1, 0].scatter(betas[1:-1], Sp[rep, numOrg, 1:-1, ii],
color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
ax[1, 1].scatter(betas[1:-1], F[rep, numOrg, 1:-1, ii],
color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
# for numOrg in range(numAgents):
# c = np.dot(np.random.random(), [1, 1, 1])
# ax[0, 0].scatter(betas,np.mean(C[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[0, 1].scatter(betas, np.log10( np.mean(Sm[:, numOrg, :, ii], axis=0)),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 0].scatter(betas, np.log10( np.mean(Sp[:, numOrg, :, ii], axis=0)),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 1].scatter(betas, np.mean(F[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
# for numOrg in range(numAgents):
# c = np.dot(np.random.random(), [1, 1, 1])
# ax[0, 0].scatter(betas, np.log10(np.mean(C[:, numOrg, :, ii], axis=0)),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[0, 1].scatter(betas, np.log10( np.multiply(np.mean(Sm[:, numOrg, :, ii], axis=0), betas)),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 0].scatter(betas, np.log10( np.mean(Sp[:, numOrg, :, ii], axis=0)),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 1].scatter(betas, np.mean(F[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
# for numOrg in range(numAgents):
# c = np.dot(np.random.random(), [1, 1, 1])
# ax[0, 0].scatter(betas,
# np.log10(
# np.multiply( np.mean(C[:, numOrg, :, ii], axis=0), np.power(betas, 2))
# ),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[0, 1].scatter(betas,
# np.log10(
# np.mean(Sm[:, numOrg, :, ii], axis=0)),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 0].scatter(betas, np.log10(
# np.multiply(
# np.mean(Sp[:, numOrg, :, ii], axis=0),
# betas
# )
# ),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
#
# ax[1, 1].scatter(betas, np.mean(F[:, numOrg, :, ii], axis=0),
# color=[0, 0, 0], s=30, alpha=alpha, marker='x', label=label)
# ax[0, 0].set_xscale("log")
xticks = [0.1, 0.5, 1, 2, 10]
for i in np.arange(0, 2):
for j in np.arange(0, 2):
ax[i, j].set_xscale("log", nonposx='clip')
# ax[i, j].set_yscale("log", nonposy='clip')
ax[i, j].set_xticks(xticks)
ax[i, j].get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
# plt.axis([0.1, 10, 0, upperbound])
# leg = plt.legend(loc=2, title='Generation')
#
# for lh in leg.legendHandles:
# lh.set_alpha(1)
# lh.set_sizes(30)
savefolder = folder + '/figs/C-S-natural/'
savefilename = savefolder + 'C-S-natural-size_' + str(size) + '-Nbetas_' + \
str(Nbetas) + '-gen_' + str(iter) + '.png'
if not path.exists(savefolder):
makedirs(savefolder)
plt.tight_layout()
# plt.show()
if saveFig:
plt.savefig(savefilename, bbox_inches='tight')
savemsg = 'Saving ' + savefilename
print(savemsg)
# plt.close()
# plt.clf()
plt.show()
# plt.pause(0.1)