def scaling_above():
critical = []
for i in range(2, 260):
model = RelaxModel(0.5, i, i * i + 4000)
model.start()
critical.append(model.critical_z())
print(model.critical_z())
def write_in_ava_size():
avalanche_size = []
for i in range(len(length)):
model = RelaxModel(0.5, length[i], time)
model.start()
avalanche_size.append(model.ava_size_list.copy())
with open('avalanche_size.txt', 'wb') as fp:
pickle.dump(avalanche_size, fp)
def ave_tc(self):
for i in range(len(self.lengths_of_system)):
ave_tc = 0.0
model = RelaxModel(0.5, self.lengths_of_system[i], self.times)
model.start()
model.cross_over_time()
for j in range(len(model.ave_slop)):
self.ave_tc = self.ave_tc + model.ave_slop[i]*(i+1)
self.ave_tc_list.append(ave_tc)
def ave_height_f():
for i in range(len(length)):
model = RelaxModel(0.5,length[i],times)
model.start()
useful_height = model.heights[cross_over_time[i]+1:]
ave = sum(useful_height)/len(useful_height)
ave_height.append(ave)
print('done 1')
return ave_height
def sigma_f():
for i in range(len(length)):
model = RelaxModel(0.5, length[i], times)
model.start()
useful_height = model.heights[cross_over_time[i] + 1:]
ave = sum(useful_height) / len(useful_height)
hh = [x * x for x in useful_height]
sigma = (sum(hh) / len(useful_height) - ave**2)**(0.5)
sigma_h.append((sigma))
print(sigma_h)
return sigma_h
def ave_tc(number_of_piles):
lengths_of_system = [4, 8, 16, 32, 64, 128, 256]
all_tc_list = []
times = 100000
for i in range(len(lengths_of_system)):
ave_tc_list = []
for j in range(number_of_piles):
model = RelaxModel(0.5, lengths_of_system[i], times)
model.start()
model.cross_over_time()
ave_tc_list.append(model.t_c)
all_tc_list.append(sum(ave_tc_list) / len(ave_tc_list))
return all_tc_list
def h_tilde(M):
length = [4, 8]
times = 100000
h_tilde = []
for i in range(len(length)):
h_data_base = [0] * times
for j in range(M):
model = RelaxModel(0.5, length[i], times)
model.start()
h_data_base = list(map(add, h_data_base, model.heights))
h_data_base = [x / M for x in h_data_base]
h_data_base = [round(i, 3) for i in h_data_base]
h_tilde.append(h_data_base)
return h_tilde
def plot_ave_slop(length,times):
data_base = []
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for i in range(len(length)):
model = RelaxModel(0.5, length[i], times)
model.start()
data_base.append(model.ave_slop)
for i in range(len(data_base)):
ax.plot(range(times), data_base[i], label='L=%a' % (length[i]), linewidth=0.5)
ax.set_xscale('log')
plt.legend()
plt.xlabel('Grains driven into system')
plt.ylabel('Height of system')
plt.show()
def height_prob_f():
for i in range(len(length)):
media = []
model = RelaxModel(0.5, length[i], times)
model.start()
useful_height = model.heights[cross_over_time[i]+1:]
maximum = max(useful_height)+1
minimum = min(useful_height)
for j in range(minimum,maximum):
P = useful_height.count(j)/len(useful_height)
media.append(P)
Prob.append(media.copy())
maxmin.append(range(minimum, maximum))
print('done all')
return Prob,maxmin
def plot_figure_data(self):
for i in range(len(self.lengths_of_system)):
model = RelaxModel(0.5,self.lengths_of_system[i],self.times)
model.start()
self.data_base_height.append(model.heights)
from Oslo_model import RelaxModel ''' # Question 1 ------------------------------------------------------------------------- ''' model_16 = RelaxModel(0.5, 16, 100000) model_16.start() print(sum(model_16.heights) / len(model_16.heights)) model_32 = RelaxModel(0.5, 32, 100000) model_32.start() print(sum(model_32.heights) / len(model_32.heights))