remove unneeded code

bandfit/bandfit.py

@@ -29,135 +29,12 @@ def g(k: np.ndarray, a: float, b: float, c: float, d: float) -> np.ndarray:
     return a + b * np.exp(-1j * k) + c * np.exp(1j * k) + d * np.exp(-2j * k)
 
 
-def hamiltonian(k: float, *args) -> np.ndarray:
-    """
-    The long range SSH Hamiltonian at momentum ``k`` for the parameter
-    values ``a,b,c,d``. See :any:`g`.
-    """
-
-    g_value = g(np.array(k), *args)[0]
-    return np.array([[0, g_value], [g_value.conjugate(), 0]])
-
-
 def energy(*args) -> np.ndarray:
     """The upper band energy for momentum ``k``. See :any:`g` for the parameters."""
 
     return np.abs(g(*args))
 
 
-def smear_band(
-    a: float,
-    b: float,
-    c: float,
-    d: float,
-    resolution: tuple[float, float] = (84, 249),
-    y_scaling: float = 1.1,
-    x_scaling: float = 1,
-    σ_x: float = 20,
-    σ_y: float = 1,
-    x_shift: float = 0,
-    y_shift: float = 0,
-) -> np.ndarray:
-    """
-    Get the
-    See :any:`g` for the parameters.
-    """
-
-    x_res = 1000
-    y_res = 100
-    ks = np.linspace(-np.pi, np.pi, x_res) * x_scaling + 2 * np.pi * x_shift
-    band_1d = energy(ks, a, b, c, d)
-
-    max_energy = np.max(band_1d) * y_scaling
-
-    band_2d = np.zeros((y_res, x_res))
-
-    for k, energy_k in enumerate(band_1d):
-        energy_bin = y_res - round((energy_k / max_energy) * (y_res - 1 / 2))
-        if energy_bin >= y_res or energy_bin <= 0:
-            continue
-
-        band_2d[energy_bin, k] = 1
-
-    band_2d = sc.ndimage.gaussian_filter(band_2d, sigma=(σ_y, σ_x))
-
-    for i in range(band_2d.shape[0]):
-        if band_2d[i].max() > 0:
-            band_2d[i] /= band_2d[i].max()
-
-    band_2d = np.concatenate([band_2d, band_2d[::-1, :]])
-
-    return resize(band_2d, resolution)
-
-
-def optimize():
-    d = load_data("../data/t_t_0.5_c_2.0_d_0.0_pc_1.0_pd_1.0.txt")
-    d = load_data("../data/t_t_1.0_c_0.75_d_0.2_pc_1.0_pd_1.0.txt")
-
-    plt.plot()
-    return
-
-    def target(x):
-        a, c, dd = x
-        test = smear_band(
-            a, a, c, dd, x_scaling=1, y_scaling=1.04, σ_y=0.3, resolution=d.shape
-        )
-
-        return np.linalg.norm(test - d) ** 8
-
-    return sc.optimize.minimize(
-        target,
-        (1, 0.75, 0.2),
-        method="nelder-mead",
-    )
-
-
-def mean_index(data):
-    size = data.size
-    return round(np.sum(data * (np.arange(size) + 1)) / np.sum(data) - 1)
-
-
-def detect_band(data: np.ndarray):
-    data = skeletonize(hessian(data), method="lee")
-    data = (data > 0).astype(float)
-    k_0 = data.shape[1] // 2
-
-    radius = 10
-    e = np.argmax(data[:radius, k_0].T)
-    points = [e]
-
-    print(e)
-    res = np.zeros_like(data)
-    for k in range(k_0 + 1, data.shape[1]):
-        min_index = max(0, e - radius)
-        max_index = min(min_index + radius, data.shape[1] - 1)
-
-        weights = []
-        for next_e in range(min_index, max_index + 1):
-            weights.append(get_weight(data, radius, k, next_e, 0, 3))
-
-        print(weights)
-        idx = np.argmax(weights)
-
-        res[e + idx, k] = 2
-
-    return res
-
-
-def get_weight(data, radius, k, e, weight, depth):
-    if depth <= 0 or k < 0 or k >= data.shape[1]:
-        return 0
-
-    weight += data[e, k]
-    min_index = max(0, e - radius)
-    max_index = min(min_index + radius, data.shape[1] - 1)
-
-    for next_e in range(min_index, max_index + 1):
-        weight += get_weight(data, radius, k + 1, next_e, weight, depth - 1)
-
-    return weight
-
-
 def lorentzian(x, x0, γ):
     return γ**2 / ((x - x0) ** 2 + γ**2)
 
@@ -166,10 +43,6 @@ def double_lorentzian(x, x0_1, x0_2, γ, r=1):
     return lorentzian(x, x0_1, γ) + r * lorentzian(x, x0_2, γ)
 
 
-def refine_band_fit(k, e0, data):
-    pass
-
-
 def detect_bands_fixed_k(
     k, data, γ, last_separation=0, min_height=0.5, separation_continuity=1 / 2
 ):