""" 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