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https://github.com/vale981/bandfit
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also fit the other peak height
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260aa1d302
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1 changed files with 17 additions and 7 deletions
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@ -133,18 +133,24 @@ def detect_bands_fixed_k(
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col /= col[guess_1]
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col /= col[guess_1]
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(e_1, e_2, γ, _), cov = sc.optimize.curve_fit(
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(e_1, e_2, γ, *_), cov = sc.optimize.curve_fit(
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double_lorentzian,
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double_lorentzian,
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e_axis,
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e_axis,
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col,
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col,
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(guess_1, guess_2, γ, 1),
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(guess_1, guess_2, γ, 1, 1),
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bounds=(
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bounds=(
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(max(guess_1 - γ, 0), max(guess_2 - γ, 0), 0.5, 0.3),
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(max(guess_1 - γ, 0), max(guess_2 - γ, 0), 0.5, 0.1, 0.1),
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(min(guess_1 + γ, col.size), min(guess_2 + γ, col.size), col.size, 1 / 0.3),
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(
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min(guess_1 + γ, col.size),
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min(guess_2 + γ, col.size),
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col.size,
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2,
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2,
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),
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),
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),
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)
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)
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σ_1, σ_2, _, _ = np.sqrt(np.diag(cov))
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σ_1, σ_2, *_ = np.sqrt(np.diag(cov))
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(e_1, σ_1), (e_2, σ_2) = np.sort(((e_1, σ_1), (e_2, σ_2)), axis=0)
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(e_1, σ_1), (e_2, σ_2) = np.sort(((e_1, σ_1), (e_2, σ_2)), axis=0)
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@ -155,13 +161,17 @@ def detect_bands_fixed_k(
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return e_1, e_2, σ_1, σ_2
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return e_1, e_2, σ_1, σ_2
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def detect_bands(data, γ=20, min_height=0.5):
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def detect_bands(data, *args, **kwargs):
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""" """
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bands = []
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bands = []
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if "last_separation" in kwargs:
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del kwargs["last_separation"]
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e_1, e_2 = 0, 0
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e_1, e_2 = 0, 0
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for k in range(data.shape[1]):
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for k in range(data.shape[1]):
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e_1, e_2, *σ = detect_bands_fixed_k(
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e_1, e_2, *σ = detect_bands_fixed_k(
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k, data, γ, last_separation=abs(e_2 - e_1), min_height=min_height
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k, data, *args, **kwargs, last_separation=abs(e_2 - e_1)
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)
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)
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bands.append((e_1, e_2, *σ))
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bands.append((e_1, e_2, *σ))
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