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A3d/ref tuts #7

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6bd407f
Added Table of Contents
miguelglezb Nov 21, 2024
35cd4ab
Added HistoryRecords methods in table of contents
miguelglezb Nov 22, 2024
1f4a096
Added RSS tutorial and renamed history log tutorial
miguelglezb Dec 11, 2024
6f88e87
Refactored tutorials to comply with refactored pykoala version
miguelglezb Apr 15, 2025
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286 changes: 286 additions & 0 deletions tutorials/0-introduction/data_containers_cubing.ipynb
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{
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@PabloCorcho PabloCorcho Apr 15, 2025

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The tutorial for cubing should go on the 2-science-data/ dir

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@paranoya paranoya Apr 16, 2025

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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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@miguelglezb miguelglezb May 6, 2025

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@PabloCorcho noted.

@paranoya is there a fits file in the repository data that contains a galaxy?

"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyKOALA Cubing "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of contents:\n",
"\n",
"1. [Importing class](#importing-class)\n",
"2. [Organising data](#organising-data)\n",
"3. [KOALA cubing](#KOALA-cubing)\n",
" - [`Cubing` methods](#cubing-methods)\n",
" - [Rows and columns](#rows-and-columns)\n",
" - [`rss_intensity`](#rss_intensity)\n",
" - [`rss_variance`](#rss_variance)\n",
" - [`get_centre_of_mass`](#get_centre_of_mass)\n",
" - [`get_integrated_light_frac`](#get_integrated_light_frac)\n",
"4. [Plotting cube data](#plotting-cube-data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing class"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"from astropy import units as u\n",
"from pykoala.corrections.astrometry import AstrometryCorrection\n",
"\n",
"# Pykoala modules\n",
"\n",
"from pykoala.cubing import build_wcs, CubeInterpolator, CubeStacking\n",
"from pykoala.instruments.koala_ifu import koala_rss\n",
"from pykoala.corrections.astrometry import find_centroid_in_dc\n",
"from pykoala.corrections.astrometry import AstrometryCorrection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Organising data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# List of RSS objects\n",
"\n",
"std_star_rss = []\n",
"data_path = '../data/koala/'\n",
"grating = '385R'\n",
"\n",
"for i in [28, 29, 30]:\n",
" filename = os.path.join(data_path, grating, f\"27feb200{i}red.fits\")\n",
" rss = koala_rss(filename)\n",
" std_star_rss.append(rss)\n",
"\n",
"star_name = rss.info['name'].split(' ')[0]\n",
"print(\"Star name: \", star_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"astrom_corr = AstrometryCorrection()\n",
"\n",
"offsets, fig = astrom_corr.register_centroids(std_star_rss, object_name=star_name,\n",
" qc_plot=True, centroider='gauss')\n",
"for offset in offsets:\n",
" print(\"Offset (ra, dec) in arcsec: \", offset[0].to('arcsec'), offset[1].to('arcsec'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `KOALA` cubing"
]
},
{
"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",
" )"
]
Comment on lines +111 to +123
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@PabloCorcho PabloCorcho Apr 15, 2025

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Here we could also suggest the use of build_wcs_from_rss

},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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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@PabloCorcho PabloCorcho Apr 15, 2025

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Since cubes have a method cube.get_white_image it might be worth using that here instead

]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Cubing` methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Rows and columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"Number of spaxel columns: {cube.n_cols}\")\n",
"print(f\"Number of spaxel rows: {cube.n_rows}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `rss_intensity`"
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@PabloCorcho PabloCorcho Apr 15, 2025

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It might be worth explaining the meaning of this variable

]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"Intensity data: \\n\\n {cube.rss_intensity}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `rss_variance`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"Intensity data: \\n\\n {cube.rss_variance}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `get_centre_of_mass`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"Center of mass of the data cube: \\n\\n {cube.get_centre_of_mass()}\")"
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@PabloCorcho PabloCorcho Apr 15, 2025

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Here I would explain that the com is per wavelength unit

]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting cube data"
]
},
{
"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",
" )"
]
},
Comment on lines +215 to +234
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@PabloCorcho PabloCorcho Apr 15, 2025

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Duplicated cell?

{
"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')"
]
},
{
Comment on lines +236 to +247
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@PabloCorcho PabloCorcho Apr 15, 2025

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Create a new section where the use of this method is explained?

"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection=wcs.celestial)\n",
"mappable = ax.imshow(np.log10(white_image.value), vmin=-2)\n",
"fig.set_size_inches(18.5, 10.5)\n",
"plt.colorbar(mappable)\n",
"ax.scatter(pos_com.ra, pos_com.dec, marker='*', ec='r', transform=ax.get_transform('world'))\n",
"ax.scatter(pos_com_3.ra, pos_com_3.dec, marker='*', ec='lime', transform=ax.get_transform('world'))\n",
"ax.scatter(pos_gauss.ra, pos_gauss.dec, marker='+', ec='k', transform=ax.get_transform('world'))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv_koala",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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