{
"cells": [
{
"cell_type": "markdown",
"id": "247ef883-735e-4946-ab86-dbbd02108ca3",
"metadata": {},
"source": [
"# Identifying Which Observations Utilized Fiber Offsets\n",
"\n",
"## Learning Goals\n",
"By the end of this tutorial, you will:\n",
"- Know how to identify for which visits of a target the fiber was offset\n",
"- Understand some of the structure of a allVisit file\n",
"\n",
"## Introduction\n",
"SDSS V offsets fibers from the center of bright sources in order to avoid saturation and bleeding any light onto the spectrum of targets that are neighbors on the chip. While this allows us to observe brighter targets than we normally would during operations, this can cause issues in the e.g. spectrophotometry of the resulting spectrum.\n",
"\n",
"In this short tutorial we will demonstrate how you can identify which targets were observed with the fibers offset. Additionally, we will show some plots that demonstrate the degree of offseting as a function of the magnitude of the target.\n",
"\n",
"## Imports\n",
"This tutorial requires three commonly used python packages, and an SDSS specific package\n",
"- _numpy_ for doing large array operations\n",
"- _matplotlib_ for plotting\n",
"- _astropy_ for reading fits table\n",
"\n",
"Missing packages can be installed via `pip install numpy matplotlib astropy`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "25c1bc4a-0083-4842-8e4a-df7e66a14fcf",
"metadata": {},
"outputs": [],
"source": [
"from astropy.table import Table\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"plt.rcParams.update({'figure.facecolor': 'white',\n",
" 'savefig.facecolor': 'white',\n",
" 'font.size': 24,\n",
" 'xtick.labelsize': 24,\n",
" 'ytick.labelsize': 24,\n",
" 'errorbar.capsize': 2})"
]
},
{
"cell_type": "markdown",
"id": "eab8a5e5-8f35-4f25-84f3-12ac262df1d4",
"metadata": {},
"source": [
"### Loading data from a fits file\n",
"\n",
"First, let us open the mwmAllVisit file from the SAS. The offset information is only stored at the visit level, so this is why we must look at the mwmAllVisit file.\n",
"\n",
"**Where**: you can download the data --> https://dr19.sdss.org/sas/dr19/spectro/astra/0.6.0/summary/mwmAllVisit-0.6.0.fits.gz\n",
"\n",
"**OR** if you are running this notebook on sciServer, the summary file is available at /home/idies/workspace/sdss_sas/dr19/spectro/astra/0.6.0/summary/\n",
"\n",
"Below, the default loadpath is set to sciServer. If you download it locally, change loadpath as needed, e.g. \"/Users/myuser/Downloads/\"\n",
"\n",
"Next, we'll use `astropy.fits` to load the file and choose the extension we want. In this case, BOSS data is in extension 1, but we want APOGEE data in extension 2."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "654fe7bd-39b9-44de-a4b4-00d89b1d3e5d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"filename = 'mwmAllVisit-0.6.0.fits.gz'\n",
"localpath = '/home/idies/workspace/sdss_sas/dr19/spectro/astra/0.6.0/summary/'\n",
"allvisit = Table.read(localpath + filename, format='fits', hdu=1)"
]
},
{
"cell_type": "markdown",
"id": "28ddfa83-8dcd-44dc-9a70-44b91cffc88f",
"metadata": {},
"source": [
"For SDSS V all offsets are done in the positive Right Ascension direction. These offset values for the visit are stored in the column `delta_ra`. This column is an array of floats, where each index is for an exposure during that visit. To identify all visits that had an offset, we simply do the following to get the visits (rows in the mwmAllVisit) and exposures (columns in the `delta_ra` array):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bbfcfb63-e01b-4f56-a8e6-e2a732ac888b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"visits, exp = np.where(allvisit['delta_ra'] > 0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7a769631-54b5-4437-bdc4-687ef6fec5a9",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"
Table length=3938\n",
"
\n",
"
sdss_id
delta_ra
\n",
"
int64
float32[18]
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"
89859144
1.115352 .. --
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65983508
1.872786 .. --
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88441661
1.145294 .. --
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88443495
2.859497 .. --
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88443495
2.326175 .. --
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88443495
2.650124 .. --
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88443495
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88443495
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...
...
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65928737
2.199074 .. --
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65928737
2.199074 .. --
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65928737
2.199074 .. --
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65928737
2.020514 .. --
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65928737
2.020514 .. --
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65928737
2.020514 .. --
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65928737
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65927758
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],
"text/plain": [
"
\n",
"sdss_id delta_ra \n",
" int64 float32[18] \n",
"-------- --------------\n",
"89859144 1.115352 .. --\n",
"65983508 1.872786 .. --\n",
"88441661 1.145294 .. --\n",
"88443495 2.859497 .. --\n",
"88443495 2.326175 .. --\n",
"88443495 2.650124 .. --\n",
"88443495 2.650124 .. --\n",
"88443495 2.650124 .. --\n",
"88443495 2.439237 .. --\n",
" ... ...\n",
"65928737 2.199074 .. --\n",
"65928737 2.199074 .. --\n",
"65928737 2.199074 .. --\n",
"65928737 2.020514 .. --\n",
"65928737 2.020514 .. --\n",
"65928737 2.020514 .. --\n",
"65928737 2.020514 .. --\n",
"65927758 2.162212 .. --\n",
"65927758 1.98216 .. --\n",
"65927758 1.98216 .. --"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"allvisit[['sdss_id', 'delta_ra']][visits]"
]
},
{
"cell_type": "markdown",
"id": "0f4fcacb-792d-4a2d-ad07-01b9e8d518d7",
"metadata": {},
"source": [
"Now, let us plot the offset used for the first exposure of these visits as a function of the Gaia G magnitude of the source."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "25cf6746-d8f7-43db-bcde-7bc210903a6c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"delta_ra_0 = np.array(allvisit['delta_ra'][visits])[:, 0]\n",
"Gmag = np.array(allvisit['g_mag'][visits])\n",
"\n",
"plt.figure(figsize=(7, 7))\n",
"plt.scatter(Gmag, delta_ra_0, marker='.', c='k')\n",
"plt.grid()\n",
"plt.xlabel('G Magnitude')\n",
"plt.ylabel('Offset (arcseconds)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "dbda6fcc-346b-4a37-aedf-b836fceeace1",
"metadata": {},
"source": [
"Here we can see that the offset function is more complicated than a simple function of one magnitude. For DR19, we have been testing different functional forms of the offset function. The results of these commissioning tests are shown above.\n",
"\n",
"For DR19, there is no science data that utilizes this offset capability, only the results from these comissioning tests. In future data release, offsetting of fibers from science targets will be allowed for normal science operations. The offset function will change based on the lunation and design mode (restrictions on the observing conditions for the visit), and there is a color dependence as well. The full details of this can be found in [Medan et al. (submitted)](https://arxiv.org/abs/2506.15475) and, in the future, should be considered when evaluating these data."
]
},
{
"cell_type": "markdown",
"id": "c9eadd94-4f6d-436f-b0e1-4e3712decf92",
"metadata": {},
"source": [
"### About this notebook\n",
"\n",
"This notebook was created to illustrate how to identify targets that have been observed with offset fibers.\n",
"\n",
"**Authors**: Ilija Medan\n",
"\n",
"**Last Update**: 2025 June 23\n",
"\n",
"If you use `astropy` for published research, please cite the\n",
"authors. Follow this link for more information about citing `astropy`:\n",
"\n",
"* [Citing `astropy`](https://www.astropy.org/acknowledging.html)\n",
"\n",
"And of course please [cite](https://sdss.org/collaboration/citing-sdss/) SDSS when you use our data."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f3ed6e8-7e38-4e46-9ccd-5b3fac755f1d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}