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optimize bounds and variance filtering

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Valentin Boettcher 2023-03-21 14:51:06 -04:00
parent 9785bb5339
commit 9363e71898
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GPG key ID: E034E12B7AF56ACE
1 changed files with 9 additions and 5 deletions
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14
bandfit/bandfit.py
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@ -130,7 +130,7 @@ def candidate(k, c, d, a, δb, k_scale, k_shift):
def fit_to_bands(
bands,
bounds=[(-10, -10, 0.1, -0.5, 0.8, -0.5), (10, 10, 10, 0.5, 1.2, 0.5)],
bounds=[(-10, -10, 0.5, -0.5, 0.8, -0.5), (10, 10, 10, 0.5, 1.2, 0.5)],
ic_scan_steps=5,
c_d_order=0,
debug_plots=False,
@ -158,6 +158,7 @@ def fit_to_bands(
(c, d, a, δb, k_scale, k_shift) = np.zeros(6)
cov = np.zeros(6)
σs = []
for ic in itertools.product(*ics):
p, current_cov, _, _, success = sc.optimize.curve_fit(
candidate,
@ -178,14 +179,17 @@ def fit_to_bands(
if c_d_order == -1 and p[0] < p[1]:
continue
if (
abs(p[3]) < min_δb
and np.sqrt(np.sum(np.diag(current_cov))) / np.linalg.norm(p) < 0.1
σ_δb_current = np.sqrt(current_cov[3, 3])
σ_rel = np.sqrt(np.sum(np.diag(current_cov))) / np.linalg.norm(p)
if abs(p[3]) + σ_δb_current < min_δb and (
len(σs) == 0 or σ_rel <= np.min(σs) * 2
):
(c, d, a, δb, k_scale, k_shift) = p
min_δb = abs(δb)
min_δb = abs(δb) + σ_δb_current
cov = current_cov
σs.append(σ_rel)
if debug_plots:
plt.plot(ks, candidate(np.hstack([ks, ks]), *p)[: bands.shape[0]])