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@jmatt
Last active April 13, 2018 23:03
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LSST EPO JTM Jupyter and Skyviewer Technical Presentation and Demo
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Jupyter: Skyviewer, Notebooks and Widgets\n",
"\n",
"[Jupyter](http://jupyter.org/) is a an open-source, open-standards and service for interactive computing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Skyviewer\n",
"\n",
"We investigated and created a skyviewer in fall of 2017. It was created with Aladin. It can be visualized using Aladin-lite or any HiPS viewer. It can be used within a Jupyter notebook through a widget."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f6ce388e630e4db1a7973f5fb0f02e45",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Aladin(fov=135.0, options=['allow_full_zoomout', 'coo_frame', 'fov', 'full_screen', 'log', 'overlay_survey', '…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Example of a Skyviewer within a Jupyter notebook.\n",
"import ipyaladin as ipyal\n",
"aladin= ipyal.Aladin(target='15 26 20.534 -57 02 14.74', fov=135, survey='P/Mellinger/color')\n",
"aladin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Actuals on creating the HiPS\n",
"\n",
"Create HiPS for individual bands from FITS images. If additional bands are combined, then create single or n-color HiPS. Finally, create a RGB HiPS from those bands.\n",
"\n",
"1. COSMOS (2 sq.deg.) Reality ~3.5 sq. deg.\n",
"1. 16.7 GB RGB PNGs only.\n",
"1. Order 3-12\n",
"1. Approximately 16 hours using 8 vCPU and 16 GB RAM.\n",
"\n",
"I created a [poster amd related paper](https://zenodo.org/record/1094804) for ADASS 2017."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "956b0e240f05427ab1a084f72bbbfc30",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Aladin(fov=0.16, options=['allow_full_zoomout', 'coo_frame', 'fov', 'full_screen', 'log', 'overlay_survey', 'o…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Example of a Skyviewer within a Jupyter notebook.\n",
"import ipyaladin as ipyal\n",
"aladin= ipyal.Aladin(target='10 01 14.498 +02 17 10.55', fov=0.16, survey='P/LSSTHSCCOSMOS/color')\n",
"aladin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### HSC HiPS Details\n",
"\n",
"Res=12 NSide=4096 0.84 arcmin mean spacing of pixels. Pixel angular resolution 100.6 mas.\n",
"\n",
"As an aside: one notable difference between image formats and fits is the precision available. PNG specifically and JPEG if you choose to use integers. Meanwhile our FITS uses floats. Qualilty options when creating images should be used to EPO's benefit to avoid sharing too precise image data, indepdent of the resolution used."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jupyter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Extends DM's implementation.\n",
"* Batteries included. It's just an interactive website.\n",
"* Users are isolated and can't easily abuse the system or each other.\n",
"* Services are local. Results are remote."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Widgets\n",
"\n",
"I'll demo what we have created up to this point and where we plan to go."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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"version": "3.6.4"
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