proc = np.cumsum(seq,axis=1);
return proc
if __name__ == "__main__":
n_dims = 1;
N_samples = 1000;
seq1 = genrand_seq(n_dims,N_samples);
seq1 = gen_proc(seq1);
seq2 = genrand_seq(n_dims,N_samples);
seq2 = gen_proc(seq2);
plt.plot(np.transpose(seq1))
cdf = df.gen_cdf(seq1[0],100,1)
pdf = df.gen_pdf(seq1[0],100,1)
rho = [-0.95,-0.5, 0, 0.5 ,0.95]
rho = np.array(rho);
rho2 = np.sqrt(1-np.power(rho,2))
for i in range(5):
corr_seq = rho[i]*seq1 + rho2[i]*seq2;
#corr_seq = np.transpose(corr_seq);
q = plt.figure()
plt.plot(corr_seq,seq1,'o');
title = 'Correlation Coefficient - ' + str(rho[i])
q.suptitle(title)
plt.show()
def gen_rand_seq(N):
rand_seq = np.random.rand(N);
return rand_seq
if __name__ == "__main__":
N = 1000000;
lambda_poisson = .7;
delta = 1;
p = lambda_poisson*delta;
threshold = 1-p;
seq = gen_rand_seq(N);
loc_seq = threshold_loc(seq,threshold);
(ia_times,n_arrivals) = calc_interarrival_times(loc_seq);
x_val = np.arange(n_arrivals);
#dist_hist = np.histogram(ia_times,10);
(cdf_freq_val,cdf_xvals) = df.gen_cdf(ia_times,10,1);
(pdf_val,pdf_xval) = df.gen_pdf(ia_times,10,1);
#plt.plot(cdf_xvals,cdf_freq_val,'o')
#plt.figure()
#plt.plot(pdf_val,pdf_xval)
plt.show()
#plt.figure
#plt.plot(dist_hist[0])
#plt.figure
#plt.plot(x_val,ia_times)
