def test_wavelet_fbm(fbm_file):
for fname in fbm_file:
with open(fname, 'rb') as f:
X = np.load(f)
j2 = 7
wt_coefs, _, j2_eff, _ = wavelet_analysis(X, p_exp=None, j2=j2)
hmin = estimate_hmin(wt_coefs, j1=1, j2_eff=j2_eff, weighted=True)[0]
hmin = hmin.min()
gamint = 0.0 if hmin >= 0 else -hmin + 0.1
wavelet_analysis(X, p_exp=np.inf, j2=j2)
wavelet_analysis(X, p_exp=2, j2=j2, gamint=gamint)
def test_mfa_mrw(mrw_file):
with open('tests/mrw_config.json', 'rb') as f:
config_list = json.load(f)
for i, fname in enumerate(mrw_file):
with open(fname, 'rb') as f:
X = np.load(f)
if config_list[i]['H'] == 0.01:
continue
j2 = np.log2(X.shape[0]) - 3
wt_coefs, _, j2_eff, _ = wavelet_analysis(X, p_exp=None, j2=j2)
hmin = estimate_hmin(wt_coefs, j1=1, j2_eff=j2_eff, weighted=True)[0]
hmin = hmin.min()
gamint = 0.0 if hmin >= 0 else -hmin + 0.1
q = np.array([-2, -1, 0, 1, 2])
dwt, lwt = mf_analysis_full(X,
j1=3,
j2=j2_eff,
gamint=gamint,
p_exp=np.inf,
n_cumul=3,
q=q)
assert abs(dwt.structure.H.mean() - config_list[i]['H']) < 0.1
assert abs(lwt.cumulants.log_cumulants[1, :].mean() +
(config_list[i]['lam']**2)) < 0.025
mf_analysis_full(X,
j1=3,
j2=j2_eff,
gamint=gamint,
p_exp=2,
n_cumul=3,
q=q)
assert abs(lwt.cumulants.log_cumulants[1, :].mean() +
(config_list[i]['lam']**2)) < 0.025
def test_mfa_fbm(fbm_file):
with open('tests/fbm_config.json', 'rb') as f:
config_list = json.load(f)
for i, fname in enumerate(fbm_file):
with open(fname, 'rb') as f:
X = np.load(f)
j2 = np.log2(config_list[i]['shape']) - 3
wt_coefs, _, j2_eff, _ = wavelet_analysis(X, p_exp=None, j2=j2)
hmin = estimate_hmin(wt_coefs, j1=1, j2_eff=j2_eff, weighted=True)[0]
hmin = hmin.min()
gamint = 0.0 if hmin >= 0 else -hmin + 0.1
q = np.array([-2, -1, 0, 1, 2])
dwt, lwt = mf_analysis_full(X,
j1=3,
j2=j2_eff,
gamint=gamint,
p_exp=np.inf,
n_cumul=3,
q=q)
if config_list[i]['H'] != 0.01:
assert abs(dwt.structure.H.mean() - config_list[i]['H']) < 0.1,\
print(f'{dwt.structure.H.mean()=}, {config_list[i]["H"]=}, '
f'{gamint=}')
assert abs(lwt.cumulants.log_cumulants[1, :].mean()) < 0.01
_, lwt = mf_analysis_full(X,
j1=3,
j2=j2_eff,
gamint=gamint,
p_exp=2,
n_cumul=3,
q=q)
assert abs(lwt.cumulants.log_cumulants[1, :].mean()) < 0.01