compute-natural-heat-capacity-generational

#!/usr/bin/env python

from embodied_ising import ising
import numpy as np
from sys import argv
from os import path, makedirs
from embodied_ising import TimeEvolve2
import matplotlib.pyplot as plt

import pickle
import glob

# --- COMPUTE HEAT CAPACITY -------------------------------------------------------+

if len(argv) < 3:
    print("Usage: " + argv[0] + " <sim> + <bind> + <gen>")
    # loadfile = 'sim-20180131-145412'
    # bind = 0
    # iterNum = 0
loadfile = str(argv[1])
bind = int(argv[2])
iterNum = int(argv[3])

# --- CONSTANTS ----------------------------------------------------------------+
settings = {}

# ENVIRONMENT SETTINGS
settings['pop_size'] = 50  # number of organisms
settings['numKill'] = int(settings['pop_size'] / 1.66)
settings['food_num'] = 100  # number of food particles
settings['food_radius'] = 0.03
settings['org_radius'] = 0.05

# SIMULATION SETTINGS
settings['TimeSteps'] = 4000  # number of timesteps per iteration

# number of system-wide spin updates per unit of time  (multiplies computation time)
settings['thermalTime'] = 10

# settings['evolution_toggle'] = False  # only toggles for CriticalLearning
# settings['evolution_rate'] = 1  # number of iterations to skip to kill/mate (gives more time to eat before evolution)

settings['dt'] = 0.2  # kinetic time step      (dt)
settings['r_max'] = 720
settings['dr_max'] = 90  # max rotational speed      (degrees per second)
settings['v_max'] = 0.5  # max velocity              (units per second)
settings['dv_max'] = 0.05  # max acceleration (+/-)    (units per second^2)

settings['x_min'] = -4.0  # arena eastern border
settings['x_max'] = 4.0  # arena western border
settings['y_min'] = -4.0  # arena southern border
settings['y_max'] = 4.0  # arena northern border

settings['plotLive'] = True  # live updates of figures


# -----------------------------------------------------------------------------

# animation settings
settings['plot'] = False
settings['diagnostics'] = False
settings['frameRate'] = 2
settings['save_data'] = False # for saving figures, keep off

savefile = True
R = 1

Nbetas = 101
betas = 10 ** np.linspace(-1, 1, Nbetas)

BetaFactor = betas[bind]
loadstr = 'save/' + loadfile +  '/isings/gen[' + str(iterNum) + ']-isings.pickle'

# print(iterNum)

isings = pickle.load(open(loadstr, 'rb'))
size = isings[0].size # get size from first agent
numAgents = len(isings)

C = np.zeros((R, numAgents))
Sm = np.zeros((R, numAgents))
Sp = np.zeros((R, numAgents))
F = np.zeros((R, numAgents))

for rep in range(R):

    T = 2000

    isings_new = TimeEvolve2(isings, BetaFactor, settings, T)
    if settings['plot'] == True:
        plt.clf()
    # NOTE: The thermalize time in the settings plays a role in the thermalization of the NN each time step.


    for orgNum, I in enumerate(isings_new):
        C[rep, orgNum] = (I.E2m - I.Em ** 2) / size
        Sm[rep, orgNum] = (I.m2 - I.m ** 2) / size
        #TODO: relative positions instead of absolute
        Sp[rep, orgNum] = np.linalg.norm((I.p2m - np.power(I.pm, 2)))
        F[rep, orgNum] = I.fitness / float(T)

# print(np.mean(C, 0))
folder = 'save/' + loadfile + '/C-S-natural_' + str(iterNum) + '/'
file = 'C-S-size_' + str(size) + '-Nbetas_' + \
       str(Nbetas) + '-bind_' + str(bind)
filename = folder + file

if not path.exists(folder):
    makedirs(folder)

if savefile:
    np.savez(filename, C=C, Sm=Sm, Sp=Sp, F=F)
# savestr = 'Saving: ./.../' + file
# print(savestr)