X[i,
j * m2 + k] = legendre.legval2d(f_lines_1_rf[i, 0],
f_lines_1_rf[i, 1], c)
wav_sol_1 = np.linalg.lstsq(X,
f_lines_1_rf[:, 0] * f_lines_1_rf[:, 3],
rcond=-1)
wav_sol2d_1 = wav_sol_1[0].reshape(m1, m2)
fitwv1 = np.array([
legendre.legval2d(f_lines_1_rf[i, 0], f_lines_1_rf[i, 1],
wav_sol2d_1) / f_lines_1_rf[i, 0]
for i in range(len(f_lines_1_rf))
])
res1 = fitwv1 - f_lines_1_rf[:, 3]
rms1 = rmsd(fitwv1, f_lines_1_rf[:, 3])
if rms1 < 3.0e-3:
print(rms1, len(f_lines_1_rf))
break
else:
mask1 = abs(res1) > 3.0 * rms1
f_lines_1_rf = f_lines_1_rf[~mask1]
f_lines_1_mask = np.array(
[i in f_lines_1_rf[:, 3] for i in f_lines_1[:, 3]])
f_lines_2 = deepcopy(f_lines_1)
f_lines_2_mask = deepcopy(f_lines_1_mask)
f_lines_3 = deepcopy(f_lines_1)
f_lines_3_mask = deepcopy(f_lines_1_mask)
# obtain the parameters of the selected lines by super-Gaussian fit
]],
axis=0)
# 0order, 1n, 2wav, 3flag, 4-8popt
except:
z1 = np.append(z1, [[
order + order_init, line_order[i, 0], line_order[i, 1],
0.0, *np.zeros(shape=(5), dtype=float)
]],
axis=0)
mask1 = (z1[:, 3] == 1)
x1 = z1[mask1, 1]
y1 = z1[mask1, 5]
popt1 = np.polyfit(x1, y1, 10)
res1 = y1 - np.poly1d(popt1)(x1)
rms1 = rmsd(y1, np.poly1d(popt1)(x1))
mask2 = abs(res1) > 3.0 * rms1
x2 = x1[~mask2]
y2 = y1[~mask2]
popt_nx = np.polyfit(x2, y2, 10)
line_order = np.vstack(
(line_order.T, np.poly1d(popt_nx)(line_order[:, 0]))).T
# 0n, 1wav, 2line_center_(ThAr_solution), 3line_center_(Gaussian_solution)
# plt.figure()
# plt.plot(x1, y1 - np.poly1d(popt_nx)(x1), 'rs')
# plt.plot(x2, y2 - np.poly1d(popt_nx)(x2), 'ks')
# plt.show()
for i in range(len(chunk) - 1):
X[i,
j * m2 + k] = legendre.legval2d(f_lines_1_rf[i, 0],
f_lines_1_rf[i, 1], c)
wav_sol_1 = np.linalg.lstsq(X,
f_lines_1_rf[:, 0] * f_lines_1_rf[:, 3],
rcond=-1)
wav_sol2d_1 = wav_sol_1[0].reshape(m1, m2)
fitwv1 = np.array([
legendre.legval2d(f_lines_1_rf[i, 0], f_lines_1_rf[i, 1],
wav_sol2d_1) / f_lines_1_rf[i, 0]
for i in range(len(f_lines_1_rf))
])
res1 = fitwv1 - f_lines_1_rf[:, 3]
rms1 = rmsd(fitwv1, f_lines_1_rf[:, 3])
if rms1 < 3.0e-3:
print(rms1, len(f_lines_1_rf))
break
else:
mask1 = abs(res1) > 3.0 * rms1
f_lines_1_rf = f_lines_1_rf[~mask1]
f_lines_1_mask = np.array(
[i in f_lines_1_rf[:, 3] for i in f_lines_1[:, 3]])
f_lines_2 = deepcopy(f_lines_1)
f_lines_2_mask = deepcopy(f_lines_1_mask)
f_lines_3 = deepcopy(f_lines_1)
f_lines_3_mask = deepcopy(f_lines_1_mask)
# obtain the parameters of the selected lines by super-Gaussian fit