Cleaning up finished tutorials and updating README files

.gitignore

@@ -163,3 +163,10 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+# Output of tutorials
+tutorials/0-introduction/output/*
+
+# Local files
+tutorials/0-introduction/plot_check.py
+tutorials/data/koala/385R/27feb20030red.fits_miguel.fits

tutorials/0-introduction/README.md

@@ -4,14 +4,7 @@ This directory contains a set of tutorials aiming to get users familiarised with
 
 ## Contents
 
-### A guide through DataContainers
-
-The tutorial `data_containers.ipynb` goes through the multiple aspects of `DataContainers`.
-
-### Corrections
-
-The tutorial `corrections.ipynb` explains the current structure of the different corrections that can be applied to `DataContainers`.
 
 ### I/O
 
-The tutorial `io.ipynb` provies examples on how informatino (i.e. data, logs, etc) is handled in `pykoala`.
+The tutorial `io.ipynb` provies examples on how information (i.e. data, logs, etc) is handled in `pykoala`.

tutorials/2-science-data/README.md

@@ -4,3 +4,11 @@ This directory contains a set of tutorials on how to process scientific frames t
 
 ## Contents
 
+
+### RSS
+
+The tutorial `rss_registration.ipynb`, shows how to read/write from/to FITS files with the PyKOALA headers.
+
+### Cubing
+
+The tutorial `cubing.ipynb`, contains the information to convert RSS data into cubes and how to extract the information.  

tutorials/2-science-data/cubing.ipynb

@@ -0,0 +1,323 @@
+{
+ "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)"
+   ]
+  },
+  {
+   "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 (star)\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": [
+    "# List of RSS objects (galaxy)\n",
+    "\n",
+    "std_galaxy_rss = []\n",
+    "\n",
+    "for i in [34, 35, 36]:\n",
+    "    filename = os.path.join(data_path, grating, f\"27feb200{i}red.fits\")\n",
+    "    rss = koala_rss(filename)\n",
+    "    std_galaxy_rss.append(rss)\n",
+    "\n",
+    "galaxy_name = rss.info['name'].split(' ')[0]\n",
+    "print(\"Galaxy name: \", galaxy_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",
+    "            )"
+   ]
+  },
+  {
+   "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)"
+   ]
+  },
+  {
+   "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`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The spectra data (counts per pixel per second) is stored in this attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Intensity data: \\n\\n {cube.rss_intensity}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the wavelength data we can plot raw spectra:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(2, sharex=True)\n",
+    "fig.suptitle('Raw spectra examples')\n",
+    "ax1.plot(cube.wavelength, cube.rss_intensity[0])\n",
+    "ax2.plot(cube.wavelength, cube.rss_intensity[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `rss_variance`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Variance of the intensity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Variance data: \\n\\n {cube.rss_variance}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `get_centre_of_mass`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The center of mass per wavelength unit is obtained with this method:"
+   ]
+  },
+  {
+   "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()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "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')"
+   ]
+  },
+  {
+   "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
+}