import numpy as np
import matplotlib.pyplot as plt
import fract
%matplotlib tk
N = 10
hs = [0.25, 0.75]
for H in hs:
z = fract.fbm2D_spectral(H,N)
plt.imshow(z)
# plt.title(r"H = %g, %i x %i points"%(H, 2**N, 2**N))
plt.savefig(("spectr%g.png"%H), bbox_inches='tight' )
plt.show()
# autocorr_size = max(xwid, ywid)/2
# 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)