plt.rcParams['legend.numpoints'] = 1
plt.rcParams['legend.fontsize'] = 6
plt.rcParams['legend.borderaxespad'] = 1
plt.rcParams['lines.linewidth'] = 0.5
plt.rcParams['lines.markersize'] = 3
keep_last_percent = 50 # percentage to keep starting from the end
gamma_dot = 1e-4
files = glob.glob('stress.dat.*')
files.sort(lambda u, v: cmp(int(u[u.rindex('.') + 1:]), int(v[v.rindex('.') + 1:])))
print files
pxz = []
for file in files:
if('norm' in file):
pxz_ = pypolymer.get_data(file, 5)
else:
pxz_ = pypolymer.get_data(file, 2)
pxz.extend(pxz_)
# ignore early data
start = len(pxz) * (1.0 - float(keep_last_percent) / 100)
pxz = pxz[int(start):]
eta = np.array(pxz) / -gamma_dot
print 'ave=', np.mean(eta), 'std=', np.std(eta)
pxz = np.array(pxz) / -gamma_dot
pxz = pxz.tolist()
block_pre = filter(lambda u: u <= len(pxz), [2**i for i in range(25)])
# g1 or g3
g = 'g1'
# window parameters
windows = 40
# monomers
if('100' in fin): monomers = 100
if('200' in fin): monomers = 200
if('400' in fin): monomers = 400
if('800' in fin): monomers = 800
if('1600' in fin): monomers = 1600
# read data
t = pypolymer.get_data(fin, 0)
g1 = pypolymer.get_data(fin, 1)
g3 = pypolymer.get_data(fin, 3)
total_points = len(t)
print 'first line = ', t[0], g1[0], g3[0]
print 'last line = ', t[-1], g1[-1], g3[-1]
print 'total points = ', total_points
bounds = np.logspace(2, 8, num=windows)
intervals = bounds.size - 1
x_ = t
y_ = g3 if(g == 'g3') else g1
# get correct starting index and trim list if necessary
f = open('msid_' + str(M_rings) + '_' + str(M_linear) + '_N' + str(monomers) + '.dat', 'w')
for m in range(1, monomers):
f.write('%d %10.5f\n' % (m, aveDistsq[m]))
f.close()
if(mpl):
plt.rcParams['xtick.labelsize'] = 8
plt.rcParams['ytick.labelsize'] = 8
plt.rcParams['legend.numpoints'] = 1
plt.rcParams['text.usetex'] = True
plt.rcParams['axes.linewidth'] = 0.5
fin = '/people/thnfs/homes/halvers/research/contaminants/r2ij_limiting_distribution/dist_best_combined.dat'
x = pypolymer.get_data(fin, 0)
y = pypolymer.get_data(fin, 1)
w = 8.0 / 2.54
h = (3.0 / 4.0) * w
fig = plt.figure(1, figsize=(w, h))
plt.semilogx(x, y, 'r-', label='ideal')
plt.semilogx(range(0, monomers), aveDistsq, 'b-')
plt.ylim(0, 3.5)
plt.xlabel(r'$s$', fontsize=10)
plt.ylabel(r'$[d(s)]^2/s$', fontsize=10)
plt.title(r'$M_{\mathrm{rings}} = 200$, $M_{\mathrm{linear}} = 6$, $N = 200$, $\phi_{\mathrm{linear}} = 0.03$', ha='center', fontsize=8)
lg = plt.legend(loc='lower left', prop={'size':8}, borderaxespad=1, handletextpad=0.5)
lg.get_frame().set_linewidth(0.25)
for i in range(0, m_):
b_ = half(b_)
return b_
if 1:
gamma_dot = 1e-6
files = glob.glob('stress.dat.*')
files.sort(lambda u, v: cmp(int(u[u.rindex('.') + 1:]),
int(v[v.rindex('.') + 1:])))
print files
pxz = []
for file in files:
if ('norm' in file):
pxz_ = pypolymer.get_data(file, 5)
else:
pxz_ = pypolymer.get_data(file, 2)
pxz.extend(pxz_)
eta = np.array(pxz) / -gamma_dot
print 'ave=', np.mean(eta), 'std=', np.std(eta)
f = open('blocks.dat', 'w')
ct = 0
sg = []
sg_err = []
for M in range(50):
z = block(eta, M)
Lp = len(z)
if (Lp < 2): break
ave = np.mean(z)
