hiro/bandfit
1
0
Fork 0
mirror of https://github.com/vale981/bandfit synced 2025-03-04 17:11:38 -05:00
Code Issues Projects Releases Packages Wiki Activity Actions

cleverer ic scanning

Browse source
This commit is contained in:
Valentin Boettcher 2023-03-21 12:43:51 -04:00
parent ad0b307da3
commit 8d92ff908c
No known key found for this signature in database
GPG key ID: E034E12B7AF56ACE
1 changed files with 9 additions and 7 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

16
bandfit/bandfit.py
View file

@ -250,7 +250,7 @@ def plot_data_with_bands(data, bands):
# return sc.optimize.curve_fit(double_lorentzian, e_axis, col, (0, 10, 0, 3))
def candidate(k, a, c, d, δb, k_scale, k_shift):
def candidate(k, c, d, a, δb, k_scale, k_shift):
k = np.asarray(k[: k.size // 2]) * k_scale + k_shift
energies = energy(k, a, a + δb * a, c, d)
# energies /= energies.max()
@ -258,7 +258,7 @@ def candidate(k, a, c, d, δb, k_scale, k_shift):
return np.hstack([energies, energies])
def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=3):
def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=5):
bands_normalized = bands.copy()
bands_normalized[:, :2] -= np.sum(bands_normalized[:, :2], axis=1).mean() / 2
@ -270,12 +270,12 @@ def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=3):
plt.plot(ks, bands_normalized[:, 0])
plt.plot(ks, bands_normalized[:, 1])
bounds = np.array([(0.1, 0, -10, -0.5, 0.9, -0.5), (10, 10, 10, 0.5, 1.1, 0.5)])
Δ_bounds = bounds[1, :3] - bounds[0, :3]
bounds = np.array([(-10, -10, 0.1, -0.5, 0.9, -0.5), (10, 10, 10, 0.5, 1.1, 0.5)])
Δ_bounds = bounds[1, :2] - bounds[0, :2]
ics = np.tile(np.linspace(0, 1, ic_scan_steps), (3, 1))
ics = np.tile(np.linspace(0, 1, ic_scan_steps), (2, 1))
ics *= Δ_bounds[:, None]
ics += bounds[0, :3][:, None]
ics += bounds[0, :2][:, None]
min_δb = np.inf
for ic in itertools.product(*ics):
@ -283,7 +283,7 @@ def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=3):
candidate,
np.hstack([ks, ks]),
np.hstack([bands_normalized[:, 0], bands_normalized[:, 1]]),
(*ic, 0, 1, 0),
(*ic, 1, 0, 1, 0),
sigma=np.hstack([bands_normalized[:, 2], bands_normalized[:, 3]]),
bounds=bounds,
full_output=True,
@ -296,11 +296,13 @@ def fit_to_bands(bands, a=1, δb=0, c=10, d=10, ic_scan_steps=3):
abs(p[3]) < min_δb
and np.sqrt(np.sum(np.diag(cov_))) / np.linalg.norm(p) < 0.1
):
print(ic)
print("hey", p, p[3], min_δb)
(a, c, d, δb, k_scale, k_shift) = p
min_δb = abs(δb)
cov = cov_
plt.plot(ks, candidate(np.hstack([ks, ks]), *p)[: bands.shape[0]])
b = a + δb * a