from scipy.ndimage import convolve
import matplotlib.pylab as plt
from tqdm import tqdm
from saveobject import save_obj
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
dT = 0.05
T = np.arange(dT, 6, dT)
betas = 1 / T
mus = np.logspace(-4.0, 0, num=10)
size = 4**4
samples = 1024
fig, axs = plt.subplots(3, 2, figsize=(6.3, 4.2))
values = {}
values["betas"] = betas
values["mu"] = mus
for beta in tqdm(betas):
for mu in mus:
dist = IsingDist(size, beta, mu)
dist.sample(samples)
dist.calcEV()
values[(beta, mu)] = [dist.energy, dist.magnet]
del dist
save_obj(values, "values")
B = np.arange(b1, b2, res)
B = B[B != 0]
B = 1 / B
M = np.array([0])
ensemble = Ensemble(N, B, M, steps, repeat, False)
ensemble.getStats()
beta = ensemble.beta
mu = ensemble.mu
stats = ensemble.stats
save_obj(stats, "stats8")
# keys = ["energy","magnetization","population","entropy"]
# def calcStats(size,beta,mu,steps,times):
# global keys
# stats = {}
# arr = {}
# for key in keys:
# arr[key]=[]
# for i in range(times):
# model = ising(size,beta,mu)
# model.evolve(steps)
# for key in keys:
# prop=model.thermofuncs(key)
# arr[key].append(prop)
# del model
def pickle(self): save_obj(self, self.name)