idx = np.where(lineLabels == old_label)
lineLatex[idx] = new_label
# Data location
cube_address = fitsFolder / fileList[i]
objFolder = resultsFolder / obj
voxelFolder = resultsFolder / obj / 'voxel_data'
db_addresss = objFolder / f'{obj}_database.fits'
maskFits_address = objFolder / f'{obj}_masks.fits'
idx_j, idx_i = voxel_cords
wave, cube, header = import_muse_fits(cube_address)
flux_voxel = cube[:, idx_j, idx_i].data.data / 1000
flux_err = np.sqrt(cube[:, idx_j, idx_i].var.data) / 1000
voxel = lime.Spectrum(wave, flux_voxel, input_err=flux_err, redshift=z_objs[i])
# voxel.plot_spectrum(spec_label=f'{obj} voxel {idx_j}-{idx_i}', log_scale=True)
STANDARD_PLOT = {
'figure.figsize': (16, 5),
'axes.titlesize': 10,
'axes.labelsize': 6,
'legend.fontsize': 12,
'xtick.labelsize': 6,
'ytick.labelsize': 6
}
rcParams.update(STANDARD_PLOT)
fig = plt.figure(dpi=300)
gs = fig.add_gridspec(nrows=1, ncols=1)
gs_obj = gs[i].subgridspec(2,
return W, F, E
# file_address = Path('/home/vital/Dropbox/Astrophysics/Data/CGCG007_mike/CGCG007_redArm_v2.fits')
file_address = Path(
'/home/vital/Dropbox/Astrophysics/Data/CGCG007_mike/CGCG007_redArm_v3.fits'
)
# file_address = Path('/home/vital/Dropbox/Astrophysics/Data/CGCG007_mike/CGCG007_redArm_v4.fits')
ext = 15
with fits.open(file_address) as hdu_list:
wave_m = hdu_list[1].data[ext]
flux_m = hdu_list[2].data[ext]
err_m = hdu_list[3].data[ext]
spec_order = lime.Spectrum(wave_m, flux_m, err_m, redshift=0.004691)
spec_order.plot_spectrum(frame='rest')
line = 'H1_6563A_b'
lineWaves = np.array([6539, 6545, 6548, 6587, 6589, 6597])
fit_conf = {
'H1_6563A_b': 'H1_6563A-H1_6563A_w1-H1_6563A_w2-N2_6584A-N2_6548A',
'H1_6563A_w1_sigma': {
'expr': '>2*H1_6563A_sigma'
},
'H1_6563A_w2_sigma': {
'expr': '>3*H1_6563A_w1_sigma'
},
'H1_6563A_cont_slope': {
'vary': False
},
frame_idx = 0
with fits.open(file_address) as hdul:
data, header = hdul[frame_idx].data, hdul[frame_idx].header
w_min = header['CRVAL3']
dw = header['CD3_3'] # dw = 0.862936 INDEF (Wavelength interval per pixel)
pixels = header['NAXIS3'] # nw = 3801 number of output pixels
w_max = w_min + dw * pixels
wave = np.linspace(w_min, w_max, pixels, endpoint=False)
print(data.shape)
region_Halpha = np.array([7732.0, 7764.0])
idcsHalpha = np.searchsorted(wave, region_Halpha)
wave_Halpha, fluxHalpha = wave[idcsHalpha[0]:idcsHalpha[1]], data[
idcsHalpha[0]:idcsHalpha[1], :, :]
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot()
im = ax.imshow(fluxHalpha.sum(axis=1), aspect=0.5)
print(fluxHalpha.sum(axis=1).shape)
ax.update({'title': r'J0926 galaxy', 'xlabel': r'X', 'ylabel': r'Y'})
plt.show()
j_idx, i_idx = 10, 10
voxel_spec = lime.Spectrum(wave, data[:, j_idx, i_idx])
voxel_spec.plot_spectrum()
ext = f'coord-{coord_y}-{coord_x}'
n_lines = 0
n_voxels = idcs_voxels.shape[0]
for idx_voxel in np.arange(n_voxels):
idx_j, idx_i = idcs_voxels[idx_voxel]
voxel_dict = {}
local_mask = voxelFolder / f'{idx_j}-{idx_i}_mask_{obj}.txt'
local_lineslog = voxelFolder / f'{idx_j}-{idx_i}_lineslog_{obj}.txt'
grid_address_i = voxelFolder / f'{idx_j}-{idx_i}_LineGrid_{obj}.png'
# Voxel data
flux_voxel = cube[:, idx_j, idx_i]
voxel = lime.Spectrum(wave,
flux_voxel,
norm_flux=norm_flux,
crop_waves=(4515, 9500))
# lime.MaskInspector(mask_address, mask_df, wave, flux_voxel, norm_flux=norm_flux)
# Locate the line fluxes
peaks_table, matched_DF = voxel.match_line_mask(
mask_df,
noise_region,
line_type='absorption',
width_tol=6,
width_mode='fix')
if verbose:
voxel.plot_spectrum(spec_label=f'{obj} voxel {idx_j}-{idx_i}',
peaks_table=peaks_table,