# 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):
print(H)
for j in range(0,stat_n):
print(" ",j)
z2d = fract.fbm2D_spectral(H, N)
try:
res1 = fract.dfa_H(z2d, data=False)
print(" ",res1)
except Exception as ex:
res1 = np.nan
print("Exception ", ex)
results[H_i].append(res1)
np_res = np.array(results)
if first_time == False:
disk_np_res = np.hstack(( disk_np_res, np_res ))
elif first_time == True:
disk_np_res = np_res
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
print("Exception ", ex)
results[H_i].append(detect_H1)
results[H_i].append(detect_H2)
for (i, H) in enumerate(np.arange(start, stop=stop, step=step)):
# H = start + i*step
stat_res = np.zeros(stat_n)
print("H", H)
for j in range(1, stat_n):
# generate
# Random.seed!(729 + i*34);
data = fract.fbm2D(H, N=N)
data_plane, _ = np.mgrid[0:data.shape[0], 0:data.shape[1]]
# data = data + data_plane/5.0
# dfa
h_detected, c, scales, flucts = fract.dfa_H(data,
approx="quadratic",
estimator="rms")
stat_res[j] = h_detected
# print(" ", j)
av_h_detected = np.mean(stat_res)
std_h_detected = np.std(stat_res)
print(" av. H detected =", av_h_detected)
newrow = np.transpose([H, av_h_detected, std_h_detected])
res[i, :] = newrow
# print(res)
plt.errorbar(res[:, 0], res[:, 1], yerr=res[:, 2])
plt.xlabel("generation H")
plt.ylabel("detected H")
print("data_H", data_H)
for (i, contour_H) in enumerate(np.arange(start, stop=stop, step=step)):
stat_res = np.zeros(stat_n)
# print("contour_H", contour_H)
for j in range(1, stat_n):
# generate
data = fract.fbm2D(data_H, N=N)
data = cut_profile(data, contour_H)
# plt.imshow(data)
# plt.show()
# dfa
try:
h_detected, c, scales, flucts = fract.dfa_H(data)
stat_res[j] = h_detected
except:
stat_res[j] = np.nan
av_h_detected = np.mean(stat_res[~np.isnan(stat_res)])
std_h_detected = np.std(stat_res[~np.isnan(stat_res)])
# print(" av. H detected =", av_h_detected)
newrow = np.transpose([contour_H, av_h_detected, std_h_detected])
res[i, :] = newrow
# full square
stat_res_fullsq = np.zeros(stat_n)
for j in range(1, stat_n):
# generate
def dfa_H_discard(*args, **kwargs):
try:
dfa_H, dfa_c, scales3, flucts3 = fract.dfa_H(*args, **kwargs)
return dfa_H
except:
return np.nan
print("fourier_H", fourier_H)
plt.figure()
plt.scatter(freqs, powers)
plt.plot(freqs,
fract.power_law(freqs, freq_exp, f_c),
color="red",
label="fourier")
plt.xscale("log")
plt.yscale("log")
plt.legend()
plt.show()
#### dfa
dfa_H, dfa_c, scales3, flucts3 = fract.dfa_H(npy_points,
messages=False,
min_nonzero=0.99)
print("dfa_H", dfa_H)
### first branch
dfa_H2, c2, pcov = fract.autoseeded_weighted_power_law_fit(
scales3[:17], flucts3[:17], flucts3[:17])
print("dfa_H2", dfa_H2)
plt.figure()
plt.scatter(scales3, flucts3)
plt.plot(scales3,
fract.power_law(scales3, dfa_H, dfa_c),
color="springgreen",
label="dfa: H =" + str(dfa_H))
plt.plot(scales3,
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)
res1 = 0
res2 = 0
try:
res1 = fract.dfa_H(z2d1, data=False)
print(" ",res1)
except Exception as ex:
res1 = np.nan
print("Exception ", ex)
try:
res2 = fract.dfa_H(z2d2, data=False)
# print(" ",res2)
except Exception as ex:
res2 = np.nan
print("Exception ", ex)
results[H_i].append(res1)
results[H_i].append(res2)
# mixed scale
# small
z2d = fract.fbm2D(0.6, N=9)
# large
z2d += fract.fbm2D(0.1, N=9)
## fourier
fourier_H, freq_exp, f_c, freqs, powers = fract.fourier_H(np.copy(z2d),
fill_with_mean=True,
images=True,
corr=True)
print("fourier_H", fourier_H)
#### dfa
dfa_H, c, scales3, flucts3 = fract.dfa_H(z2d, messages=False, min_nonzero=0.99)
print("dfa_H", dfa_H)
# fourier plot
freq_exp_2, fc2, pcov = fract.autoseeded_weighted_power_law_fit(
freqs[:20], powers[:20], powers[:20])
plt.figure()
plt.scatter(freqs, powers)
plt.plot(freqs,
fract.power_law(freqs, freq_exp, f_c),
color="red",
label="fourier: H =" + str(fract.freq_exp_to_H(freq_exp)))
plt.plot(freqs,
fract.power_law(freqs, freq_exp_2, fc2),
import numpy as np
import matplotlib.pyplot as plt
import fract
from fract import *
N = 10
H = 0.8
z = fbm2D_exact(H, N)
gen_params = fract.save_fbm2D_exact_generator_params(H, N)
for j in range(5):
z2d1, z2d2 = fract.fbm2D_exact_from_generator(*gen_params)
try:
res1 = fract.dfa_H(z2d1)
print(" ", res1)
except Exception as ex:
res1 = np.nan
print("Exception ", ex)
try:
res2 = fract.dfa_H(z2d2)
# print(" ",res2)
except Exception as ex:
res2 = np.nan
print("Exception ", ex)
