def onselect(vmin, vmax):
global result, selected_index
ax.cla()
selected_index = np.where((X >= vmin) & (X <= vmax))
optimizer = Optimize_powerlaw(
args=(X[selected_index], Y[selected_index]),
parameters=[0.1, 2.])
result = optimizer.fitting()
print "beta = {}, D = {}".format(beta, result['D'])
ax.loglog(X, Y, ls='', marker='.', label=r'$\beta = %2.2f$' % beta,
alpha=0.5)
ax.loglog(X[selected_index], optimizer.fitted(X[selected_index]),
ls='-', marker='', color='k',
label=r'$D = %2.2f$' % result['D'])
ax.legend(loc='best')
ax.set_title('frames = {}, beta = {}'.format(frames, beta))
def mass_in_r_for_one_beta(beta, num_of_strings, L, frames, plot=True,
optimize=False, save_image=False, save_data=False):
print "beta = %2.2f" % beta
r = None
rs = []
Ms = []
for s in tqdm(range(num_of_strings)):
r, M = get_mass_in_r_for_one_string(L, frames, beta, r)
rs.append(r)
Ms.append(M)
r = np.average(np.array(rs), axis=0)
M = np.average(np.array(Ms), axis=0)
if save_data:
save_data.save("results/data/mass_in_r/beta=%2.2f_" % beta,
num_of_strings=num_of_strings,
beta=beta, L=L, frames=frames, r=r, M=M)
if plot or save_image:
fig, ax = plt.subplots()
ax.loglog(r, M)
ax.set_xlabel('Radius $r$ from the center of gravity')
ax.set_ylabel('Mass in a circle with radius $r$')
ax.set_title('$r$ vs. $M(r)$')
if optimize:
index_stop = len(r) - 5
optimizer = Optimize_powerlaw(args=(r[:index_stop],
M[:index_stop]),
parameters=[0., 2.])
result = optimizer.fitting()
print "D = %f" % result['D']
ax.loglog(r[:index_stop], optimizer.fitted(r[:index_stop]), lw=2,
label='D = %f' % result['D'])
ax.legend(loc='best')
if save_image:
result_image_path = "results/img/mass_in_r/beta=%2.2f" % beta
result_image_path += "_" + time.strftime("%y%m%d_%H%M%S")
result_image_path += ".png"
plt.savefig(result_image_path)
plt.close()
print "[saved] " + result_image_path
else:
plt.show()
def onselect(vmin, vmax):
global result, selected_index, ln, text
if globals().has_key('ln') and ln:
ln.remove()
text.remove()
selected_index = np.where((steps >= vmin) & (steps <= vmax))
optimizer = Optimize_powerlaw(
args=(steps[selected_index], R[selected_index]),
parameters=[1., 0.5])
result = optimizer.fitting()
D = 1. / result['D']
print "beta = {}, D = {}".format(beta, D)
optimizer.c = result['c'] + 0.15
X = steps[selected_index]
Y = optimizer.fitted(X)
ln, = ax.loglog(X, Y, ls='-', marker='', color='k')
text = ax.text((X[0] + X[-1]) / 2., (Y[0] + Y[-1]) / 2.,
r'$D = %2.2f$' % D,
ha='center', va='bottom',
rotation=np.arctan(result['D']) * (180 / np.pi))
def onselect(vmin, vmax):
global result, selected_index, ln, text, D
if globals().has_key('ln') and ln:
ln.remove()
text.remove()
selected_index = np.where((r >= vmin) & (r <= vmax))
optimizer = Optimize_powerlaw(
args=(r[selected_index], M[selected_index]),
parameters=[1., 0.5])
result = optimizer.fitting()
D = result['D']
print "beta = {}, D = {}".format(beta, D)
optimizer.c = result['c'] + 1.
X = r[selected_index]
Y = optimizer.fitted(X)
ln, = ax.loglog(X, Y, ls='-', marker='', color='k')
text = ax.text((X[0] + X[-1]) / 2., (Y[0] + Y[-1]) / 2.,
r'$D = %2.2f$' % D,
ha='center', va='bottom',
rotation=np.arctan(result['D']) * (180 / np.pi))
def main():
N_r = 100
fig, ax = plt.subplots()
main = Count_in_r()
r, M = count_point_in_r(main, main.strings[0], N_r)
index_stop = len(r) - 10
ax.loglog(r, M)
optimizer = Optimize_powerlaw(args=(r[:index_stop],
M[:index_stop]),
parameters=[0., 2.])
result = optimizer.fitting()
print "D = %f" % result['D']
ax.loglog(r[:index_stop], optimizer.fitted(r[:index_stop]), lw=2,
label='D = %f' % result['D'])
ax.set_xlabel('Radius $r$ from the center of gravity')
ax.set_ylabel('Mass in a circle with radius $r$')
ax.set_title('$r$ vs. $M(r)$')
ax.legend(loc='best')
plt.show()
def main():
N_r = 100
fig, ax = plt.subplots()
main = Count_in_r()
r, M = count_point_in_r(main, main.strings[0], N_r)
index_stop = len(r) - 10
ax.loglog(r, M)
optimizer = Optimize_powerlaw(args=(r[:index_stop], M[:index_stop]),
parameters=[0., 2.])
result = optimizer.fitting()
print "D = %f" % result['D']
ax.loglog(r[:index_stop],
optimizer.fitted(r[:index_stop]),
lw=2,
label='D = %f' % result['D'])
ax.set_xlabel('Radius $r$ from the center of gravity')
ax.set_ylabel('Mass in a circle with radius $r$')
ax.set_title('$r$ vs. $M(r)$')
ax.legend(loc='best')
plt.show()
def onselect(vmin, vmax):
global result, selected_index
ax.cla()
selected_index = np.where((X >= vmin) & (X <= vmax))
optimizer = Optimize_powerlaw(args=(X[selected_index],
Y[selected_index]),
parameters=[0.1, 2.])
result = optimizer.fitting()
print "beta = {}, D = {}".format(beta, result['D'])
ax.loglog(X,
Y,
ls='',
marker='.',
label=r'$\beta = %2.2f$' % beta,
alpha=0.5)
ax.loglog(X[selected_index],
optimizer.fitted(X[selected_index]),
ls='-',
marker='',
color='k',
label=r'$D = %2.2f$' % result['D'])
ax.legend(loc='best')
ax.set_title('frames = {}, beta = {}'.format(frames, beta))
res = []
for _r in r:
res.append(len(np.where(dist < _r)[0]))
return r, np.array(res)
if __name__ == '__main__':
N_r = 100
fig, ax = plt.subplots()
main = Count_in_r()
main.execute()
r, res = count_point_in_r(main, N_r)
ax.loglog(r, res)
optimizer = Optimize_powerlaw(args=(r[:-20], res[:-20]),
parameters=[0., 2.])
result = optimizer.fitting()
print "D = %f" % result['D']
ax.loglog(r[:-20],
optimizer.fitted(r[:-20]),
lw=2,
label='D = %f' % result['D'])
ax.set_xlabel('Radius $r$ from the center of gravity')
ax.set_ylabel('Mass in a circle with radius $r$')
ax.set_title('$r$ vs. $M(r)$ on Eden model')
ax.legend(loc='best')
plt.show()
plot=False,
frames=1000,
beta=0.,
pre_function=count_bend)
pairs, bend1, bend2 = np.array(main.pre_func_res).T
steps = range(len(pairs))
# fig, ax = plt.subplots()
# ax.plot(steps, pairs, label="straight")
# ax.plot(steps, bend1, label="bend1")
# ax.plot(steps, bend2, label="bend2")
# ax.set_xlabel("Step")
# ax.set_ylabel("Number of pairs")
# ax.legend(loc="best")
# plt.show()
# pairsがべき乗則に従いそう。
# フィッティングを行う。
optimizer = Optimize_powerlaw(args=(steps, pairs), parameters=[0., 1.])
result = optimizer.fitting()
print "D = %f" % result['D']
# # D ≒ 1.0
fig, ax = plt.subplots()
ax.plot(steps, pairs, label="straight")
ax.plot(steps, optimizer.fitted(steps), label='D = %f' % result['D'])
ax.set_xlabel("Step")
ax.set_ylabel("Number of pairs")
ax.legend(loc="best")
plt.show()
R = np.sqrt(np.sum((x - X) ** 2 + (y - Y) ** 2) / N)
r = np.logspace(2, int(np.log2(R)) + 1, num=N_r, base=2.)
dist = np.sqrt((x - X) ** 2 + (y - Y) ** 2)
res = []
for _r in r:
res.append(len(np.where(dist < _r)[0]))
return r, np.array(res)
if __name__ == '__main__':
N_r = 100
fig, ax = plt.subplots()
main = Count_in_r()
main.execute()
r, res = count_point_in_r(main, N_r)
ax.loglog(r, res)
optimizer = Optimize_powerlaw(args=(r[:-20], res[:-20]), parameters=[0., 2.])
result = optimizer.fitting()
print "D = %f" % result['D']
ax.loglog(r[:-20], optimizer.fitted(r[:-20]), lw=2,
label='D = %f' % result['D'])
ax.set_xlabel('Radius $r$ from the center of gravity')
ax.set_ylabel('Mass in a circle with radius $r$')
ax.set_title('$r$ vs. $M(r)$ on Eden model')
ax.legend(loc='best')
plt.show()
Ds = []
for result_data_path in result_data_paths:
data = np.load(result_data_path)
beta = data['beta']
num_of_strings = data['num_of_strings']
L = data['L']
frames = data['frames']
r = data['r']
M = data['M']
# ax.loglog(r, M, label=r'$\beta = %2.2f$' % beta)
index_stop = len(r) - 20
optimizer = Optimize_powerlaw(args=(r[:index_stop], M[:index_stop]),
parameters=[0.1, 2.])
result = optimizer.fitting()
print "D = %f" % result['D']
# ax.loglog(r[:index_stop], optimizer.fitted(r[:index_stop]), lw=2,
# label='D = %f' % result['D'])
Ds.append(result['D'])
ax.plot(range(11), Ds)
ax.set_xlabel('Radius $r$ from the center of gravity')
ax.set_ylabel('Mass in a circle with radius $r$')
ax.set_title('$r$ vs. $M(r)$')
# ax.legend(loc='best')
result_image_path = "results/img/mass_in_r/raw"
result_image_path += "_" + current_time
result_image_path += ".png"
if __name__ == '__main__':
main = Main(Lx=60, Ly=60, size=[3,] * 1, plot=False, frames=1000,
beta=0.,
pre_function=count_bend)
pairs, bend1, bend2 = np.array(main.pre_func_res).T
steps = range(len(pairs))
# fig, ax = plt.subplots()
# ax.plot(steps, pairs, label="straight")
# ax.plot(steps, bend1, label="bend1")
# ax.plot(steps, bend2, label="bend2")
# ax.set_xlabel("Step")
# ax.set_ylabel("Number of pairs")
# ax.legend(loc="best")
# plt.show()
# pairsがべき乗則に従いそう。
# フィッティングを行う。
optimizer = Optimize_powerlaw(args=(steps, pairs), parameters=[0., 1.])
result = optimizer.fitting()
print "D = %f" % result['D']
# # D ≒ 1.0
fig, ax = plt.subplots()
ax.plot(steps, pairs, label="straight")
ax.plot(steps, optimizer.fitted(steps), label='D = %f' % result['D'])
ax.set_xlabel("Step")
ax.set_ylabel("Number of pairs")
ax.legend(loc="best")
plt.show()
X = np.average(self.lattice_X[pos])
Y = np.average(self.lattice_Y[pos])
r = np.sqrt(np.sum((self.lattice_X[pos] - X) ** 2
+ (self.lattice_Y[pos] - Y) ** 2) / N)
return r
if __name__ == '__main__':
fig, ax = plt.subplots()
main = Radius()
main.execute()
radius_of_rotation = main.post_func_res
steps = np.arange((len(radius_of_rotation)))
ax.loglog(steps, radius_of_rotation)
c_start = 100
optimizer = Optimize_powerlaw(args=(steps[c_start:],
radius_of_rotation[c_start:]),
parameters=[0., 0.5])
result = optimizer.fitting()
ax.loglog(steps[c_start:], optimizer.fitted(steps[c_start:]), lw=2,
label='D = %f' % result['D'])
ax.set_xlabel('Steps N')
ax.set_ylabel('Radius of rotation')
ax.set_title('Raidus of rotation')
ax.legend(loc='best')
plt.show()
np.sum((self.lattice_X[pos] - X)**2 + (self.lattice_Y[pos] - Y)**2) /
N)
return r
if __name__ == '__main__':
fig, ax = plt.subplots()
main = Radius()
main.execute()
radius_of_rotation = main.post_func_res
steps = np.arange((len(radius_of_rotation)))
ax.loglog(steps, radius_of_rotation)
c_start = 100
optimizer = Optimize_powerlaw(args=(steps[c_start:],
radius_of_rotation[c_start:]),
parameters=[0., 0.5])
result = optimizer.fitting()
ax.loglog(steps[c_start:],
optimizer.fitted(steps[c_start:]),
lw=2,
label='D = %f' % result['D'])
ax.set_xlabel('Steps N')
ax.set_ylabel('Radius of rotation')
ax.set_title('Raidus of rotation')
ax.legend(loc='best')
plt.show()