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_midpoint(H,N)
plt.imshow(z)
# plt.title(r"H = %g, %i x %i points"%(H, 2**N, 2**N))
plt.savefig(("midpoint%g.png"%H), bbox_inches='tight' )
plt.show()
field1 = field1[0:n_cut, 0:m_cut]
field2 = field2[0:n_cut, 0:m_cut]
else:
n_cut = int(n / (np.sqrt(2) * 2))
m_cut = int(m / (np.sqrt(2) * 2))
field1 = field1[0:n_cut, 0:m_cut]
field2 = field2[0:n_cut, 0:m_cut]
return (field1, field2)
# gen_params = fbm2D_exact(0.7, 512)
# f1, f2 = fbm2D_exact_from_generator(*gen_params)
# plt.imshow(f1)
# plt.imshow(f2)
exact_image = fbm2D_exact(H=0.7, n_out=2**9)
plt.figure()
plt.imshow((exact_image))
plt.title("exact image")
plt.show()
midpoint_image = fract.fbm2D_midpoint(H=0.7, N=9)
plt.figure()
plt.imshow((midpoint_image))
plt.title("midpoint image")
plt.show()
def pow_law(x, a, b):
return a * np.power(x, b)
# return a * x**(b)
def pow_law_jacobian(x, a, b):
return b * a * x**(b - 1)
if __name__ == '__main__':
print("generating")
N = 10
gen_H = np.float64(0.8)
z2d = fract.fbm2D_midpoint(gen_H, N)
plt.imshow(z2d, interpolation="None")
plt.show()
detect_H, freq_exp, c, freqs, powers = profile_fourier_from_z2d(
z2d, images=False, corr=True)
freqs = freqs[2:]
powers = powers[2:]
sigmas = powers
popt, pcov = scipy.optimize.curve_fit(pow_law,
freqs,
powers,
sigma=sigmas,