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fix ordering and clean up

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Valentin Boettcher 2023-03-21 12:54:25 -04:00
parent 1662e9221b
commit 60f1394f34
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GPG key ID: E034E12B7AF56ACE
1 changed files with 30 additions and 20 deletions
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bandfit/bandfit.py
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@ -258,7 +258,13 @@ def candidate(k, c, d, a, δb, k_scale, k_shift):
return np.hstack([energies, energies]) return np.hstack([energies, energies])
def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=5, c_d_order=0): def fit_to_bands(
bands,
bounds=[(-10, -10, 0.1, -0.5, 0.9, -0.5), (10, 10, 10, 0.5, 1.1, 0.5)],
ic_scan_steps=5,
c_d_order=0,
debug_plots=False,
):
bands_normalized = bands.copy() bands_normalized = bands.copy()
bands_normalized[:, :2] -= np.sum(bands_normalized[:, :2], axis=1).mean() / 2 bands_normalized[:, :2] -= np.sum(bands_normalized[:, :2], axis=1).mean() / 2
@ -267,10 +273,11 @@ def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=5, c_d_order=0):
ks = np.linspace(-np.pi, np.pi, bands_normalized.shape[0]) ks = np.linspace(-np.pi, np.pi, bands_normalized.shape[0])
plt.plot(ks, bands_normalized[:, 0]) if debug_plots:
plt.plot(ks, bands_normalized[:, 1]) plt.plot(ks, bands_normalized[:, 0])
plt.plot(ks, bands_normalized[:, 1])
bounds = np.array([(-10, -10, 0.1, -0.5, 0.9, -0.5), (10, 10, 10, 0.5, 1.1, 0.5)]) bounds = np.array(bounds)
Δ_bounds = bounds[1, :2] - bounds[0, :2] Δ_bounds = bounds[1, :2] - bounds[0, :2]
ics = np.tile(np.linspace(0, 1, ic_scan_steps), (2, 1)) ics = np.tile(np.linspace(0, 1, ic_scan_steps), (2, 1))
@ -278,14 +285,11 @@ def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=5, c_d_order=0):
ics += bounds[0, :2][:, None] ics += bounds[0, :2][:, None]
min_δb = np.inf min_δb = np.inf
(c, d, a, δb, k_scale, k_shift) = np.zeros(6)
cov = np.zeros(6)
for ic in itertools.product(*ics): for ic in itertools.product(*ics):
if c_d_order == 1 and ic[0] > ic[1]: p, current_cov, _, _, success = sc.optimize.curve_fit(
continue
if c_d_order == -1 and ic[0] < ic[1]:
continue
p, cov_, _, _, success = sc.optimize.curve_fit(
candidate, candidate,
np.hstack([ks, ks]), np.hstack([ks, ks]),
np.hstack([bands_normalized[:, 0], bands_normalized[:, 1]]), np.hstack([bands_normalized[:, 0], bands_normalized[:, 1]]),
@ -298,23 +302,29 @@ def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=5, c_d_order=0):
if success < 1 or success > 4: if success < 1 or success > 4:
continue continue
if c_d_order == 1 and p[0] > p[1]:
continue
if c_d_order == -1 and p[0] < p[1]:
continue
if ( if (
abs(p[3]) < min_δb abs(p[3]) < min_δb
and np.sqrt(np.sum(np.diag(cov_))) / np.linalg.norm(p) < 0.1 and np.sqrt(np.sum(np.diag(current_cov))) / np.linalg.norm(p) < 0.1
): ):
print(ic) (c, d, a, δb, k_scale, k_shift) = p
print("hey", p, p[3], min_δb)
(a, c, d, δb, k_scale, k_shift) = p
min_δb = abs(δb) min_δb = abs(δb)
cov = cov_ cov = current_cov
plt.plot(ks, candidate(np.hstack([ks, ks]), *p)[: bands.shape[0]]) if debug_plots:
plt.plot(ks, candidate(np.hstack([ks, ks]), *p)[: bands.shape[0]])
b = a + δb * a b = a + δb * a
σ = np.sqrt(np.diag(cov)) σ_c, σ_d, σ_a, σ_δb, σ_k_scale, σ_k_shift = np.sqrt(np.diag(cov))
σ[1] = np.sqrt((σ[0] * (1 + δb)) ** 2 + (a * σ[1]) ** 2)
σ_b = np.sqrt((σ_a * (1 + δb)) ** 2 + (a * σ_δb) ** 2)
σ = np.array((σ_a, σ_b, σ_c, σ_d, σ_k_scale, σ_k_shift))
scale = 1 / a scale = 1 / a