A3d/ref tuts #7
A3d/ref tuts #7
Conversation
miguelglezb
requested review from
GabriellaQuattropani,
PabloCorcho and
paranoya
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The tutorial for cubing should go on the 2-science-data/ dir
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Perhaps there should be two (or more) tutorials: one for basic stuff (explaining how to read a datacube, what are wavelength, intensity, and variance, how to make simple plots...), and another one on creating a cube from a set of RSS, which may contain advanced stuff such as registration or ADR.
In any case, why do we use a standard star instead of a galaxy?
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@PabloCorcho noted.
@paranoya is there a fits file in the repository data that contains a galaxy?
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "datacube_shape = (std_star_rss[0].wavelength.size, 40, 60)\n", | ||
| "ref_position = (std_star_rss[0].wavelength[0], np.mean(std_star_rss[0].info['fib_ra']), np.mean(std_star_rss[0].info['fib_dec'])) # (deg, deg)\n", | ||
| "spatial_pixel_size = 1.0 << u.arcsec\n", | ||
| "spectral_pixel_size = std_star_rss[0].wavelength[1] - std_star_rss[0].wavelength[0] # (angstrom)\n", | ||
| "\n", | ||
| "print(f\"Creating a WCS with\\n position: {ref_position}\\n Spatial pixel size: {spatial_pixel_size}\\n Spectral pixel size: {spectral_pixel_size}\")\n", | ||
| "\n", | ||
| "wcs = build_wcs(datacube_shape=datacube_shape,\n", | ||
| " reference_position=ref_position,\n", | ||
| " spatial_pix_size=spatial_pixel_size,\n", | ||
| " spectra_pix_size=spectral_pixel_size,\n", | ||
| " )" | ||
| ] | ||
| }, |
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### `rss_intensity`" |
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It might be worth explaining the meaning of this variable
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "pos_com = find_centroid_in_dc(cube, centroider='com', com_power=1.)\n", | ||
| "pos_com_3 = find_centroid_in_dc(cube, centroider='com', com_power=3.)\n", | ||
| "pos_gauss = find_centroid_in_dc(cube, centroider='gauss')" | ||
| ] | ||
| }, | ||
| { |
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Create a new section where the use of this method is explained?
| "source": [ | ||
| "interpolator = CubeInterpolator(rss_set=std_star_rss)\n", | ||
| "cube = interpolator.build_cube()\n", | ||
| "white_image = np.nanmean(cube.intensity, axis=0)" |
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Since cubes have a method cube.get_white_image it might be worth using that here instead
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "print(f\"Center of mass of the data cube: \\n\\n {cube.get_centre_of_mass()}\")" |
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Here I would explain that the com is per wavelength unit
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "With `science_fibres` we can find the indexes of the fibres with non-bad pixels:" |
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Science fibres are those that target the object of interest, sky fibres are the ones located in sky blanks to estimate the contribution of the sky emission. In the case of KOALA, there's no default sky fibres.
| "Now we select the wavelenght range. **Make sure to use astropy quantities and not simple float values.** \n", | ||
| "\n", | ||
| "In this case we will use the same elements of the RSS wavelengh data to define the range." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "rss_sample.get_integrated_fibres(wavelength_range=[6000,7000])" |
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You say the user has to make sure of using quantities, but then you pass a list of ints (which also works because we do not enforce the use of units here)
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I would remove this file and let the user create it on the fly
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Shouldn't the output be .gitignore'd?
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "RSS metada is stored as a `DataContainerHistory` object" |
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Add link to the relevant tutorial?
Refactored tutorials made to follow refactored pykoala commits from Dec 2024 to April 2025. This MR includes preliminary version of cubing tutorial