"""
Created on Fri Feb 7 15:23:04 2020
@author: Ching Hoe Lee
"""
import numpy as np
import complexity as com
L = np.array([4, 8, 16, 32, 64, 128, 256])
attractor_t = np.array([40, 70, 300, 1000, 4000, 15000, 60000])
num_sample = 100000
avg_attractor_t_h_list = []
height_sample = []
sample = []
i = 0
while i <= len(L) - 1:
model = com.oslo(L[i])
model.height(attractor_t[i])
height_sample = []
for j in range(0, num_sample):
h = model.height(1)
height_sample.append(h)
avg_attractor_t_h = np.mean(height_sample)
avg_attractor_t_h_list.append(avg_attractor_t_h)
sample.append(height_sample)
i += 1
sample = np.asarray(sample)
std = np.std(sample, axis=1)
np.savetxt('taskiie.csv', sample, delimiter=',')
#task 2d
@author: Ching Hoe Lee
"""
from logbin230119 import logbin
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import complexity as com
L=np.array([4,8,16,32,64,128,256])
attractor_t=np.array([40,70,300,1000,4000,15000,60000])
num=10000
a=0
a_size_list=[]
list_x_3a=[]
list_y_3a=[]
while a <=6:
model_3a=com.oslo(L[a])
model_3a.iteration(attractor_t[a])
a_size=model_3a.avalanche_size(num)
a_size_list.append(a_size)
x_3a,y_3a=logbin(a_size, scale=1.5)
list_x_3a.append(x_3a)
list_x_3a.append(y_3a)
plt.plot(x_3a,y_3a,label=f'L={L[a]}')
a+=1
plt.loglog()
plt.xlabel('avalanche size')
plt.ylabel('probability')
plt.legend()
plt.savefig('task3a.png')
plt.show()
np.savetxt('avalanche_size.txt',a_size_list,delimiter=',')
Created on Wed Jan 22 11:43:16 2020
@author: Ching Hoe Lee
"""
import complexity as com
import numpy as np
import matplotlib.pyplot as plt
#t_xover=time when first grain added induce a loss in grain in the system
steps=300000
add=[1]*steps
L=np.array([4,8,16,32,64,128,256])
results=[]
for i in L:
model=com.oslo(i)
t_x_sample=[]
t_xocer_i=np.asarray(range(1,i+1))
for j in range(0,len(add)):
t_xover=np.multiply(model.all_slope(1),t_xocer_i)
t_x=np.sum(t_xover)
t_x_sample.append(t_x)
t_x_avg_L=np.mean(t_x_sample)
results.append(t_x_avg_L)
np.savetxt('task2b.txt',results,delimiter=',')
'''
for k in range(0, len(results)):
plt.plot(t, results[k], 'x', label=f'L={L[k]}')
"""
Created on Mon Feb 10 16:00:18 2020
@author: Ching Hoe Lee
"""
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import complexity as com
L=np.array([4,8,16,32,64,128,256])
num=np.array([80,140,600,2000,8000,30000,120000])
sample_2g=[]
k=0
while k<=6:
model=com.oslo(L[k])
height_sample_2g=[]
for j in range(0,num[k]):
h_2g=model.height(1)
height_sample_2g.append(h_2g)
sample_2g.append(height_sample_2g)
k+=1
np.savetxt('taskiig_one.csv',sample_2g[0])
np.savetxt('taskiig_two.csv',sample_2g[1])
np.savetxt('taskiig_three.csv',sample_2g[2])
np.savetxt('taskiig_four.csv',sample_2g[3])
np.savetxt('taskiig_five.csv',sample_2g[4])
np.savetxt('taskiig_six.csv',sample_2g[5])
np.savetxt('taskiig_seven.csv',sample_2g[6])
for i in sa