def log_sample(x, y, base=2):
ind = np.array(range(x.size))
xl = x[(np.mod(np.log(ind) / np.log(base), 1)) == 0]
yl = y[(np.mod(np.log(ind) / np.log(base), 1)) == 0]
return (xl, yl)
if __name__ == '__main__':
print("generating")
N = 8
gen_H = np.float64(0.1)
contour_H = np.float64(0.7)
z2d = fract.fbm2D(gen_H, N)
z2d = fract.cut_profile(z2d, contour_H)
imm = np.copy(z2d)
imm[imm == 0.0] = np.nan
plt.imshow(imm, interpolation="None")
plt.show()
freqs, powers = fract.fourier_1(z2d, images=False, corr=False)
freq_exp, c = fract.linear_log_fit(freqs[1:], powers[1:])
detect_H = fract.freq_exp_to_H(freq_exp)
freqs = freqs[2:]
powers = powers[2:]
sigmas = powers
# don't overwrite...
results = [ [] for h in hs]
means = np.zeros(hs.shape)
stdevs = np.zeros(hs.shape)
for H_i, H in enumerate(hs):
gen_params = fract.save_fbm2D_exact_generator_params(H,N)
print(H)
for j in range(0,stat_n//2):
print(" ",j)
z2d1, z2d2 = fract.fbm2D_exact_from_generator(*gen_params)
z2d1 = fract.cut_profile(z2d1, contour_H)
z2d2 = fract.cut_profile(z2d2, contour_H)
res1 = 0
res2 = 0
try:
detect_H1 = fract.dfa_H(z2d1, data=False)
print(" ",detect_H1)
except Exception as ex:
res1 = np.nan
print("Exception ", ex)
try:
detect_H2 = fract.dfa_H(z2d2, data=False)
# print(" ",res2)
except Exception as ex:
res2 = np.nan
# r_out = np.arange(0.0, np.float64(autocorr_size), 1.0)
# autocorr_out = np.empty_like(r_out)
# libautocorr.autocorr(z2d.ravel(), xwid, ywid, autocorr_out, autocorr_size)
# return r_out, autocorr_out
# if __name__ == "__main__":
# z2d = fract.fbm2D_spectral(H=0.6, N=9)
# z2d = fract.cut_profile(z2d, 0.99)
# res = autocorr_1(z2d)
z2d = fract.fbm2D_spectral(H=0.6, N=8)
z2d = fract.cut_profile(z2d, 0.99)
xwid, ywid = z2d.shape
autocorr_size = min(xwid, ywid) // 2
r_out = np.arange(0.0, np.float64(autocorr_size), 1.0)
autocorr_out = np.zeros(r_out.shape, dtype=float)
count_out = np.zeros(r_out.shape, dtype=int)
libautocorr.autocorr(z2d.ravel(), xwid, ywid, autocorr_out, count_out,
autocorr_size)
autocorr_out = autocorr_out * 10000 / count_out
plt.scatter(r_out, autocorr_out)
