{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SunPy and Astropy\n",
    "## Open Development in Astrophysics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open Source\n",
    "\n",
    "* Publically Accessible Code\n",
    "* Freedoms to run and modify the code (explicitly set by a licence)\n",
    "* Free as in zero cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open Development\n",
    "\n",
    "Where the development of the code is done in the open.\n",
    "\n",
    "Including:\n",
    "\n",
    "* Bug reports\n",
    "* Feature Requests\n",
    "* Commit logs\n",
    "* Code review\n",
    "\n",
    "**Enables anyone to contribute**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![a](sunpyorg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./sunpy_header.png)\n",
    "![](./sunpy_contributors.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](cadair_contribution.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](astropy_header.png)\n",
    "![](numpy_header.png)\n",
    "![](skimage_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Involved!!\n",
    "\n",
    "* https://sunpy.org/contribute.html\n",
    "* http://www.astropy.org/contribute.html\n",
    "* http://yt-project.org/\n",
    "* https://numfocus.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demos!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Astropy\n",
    "\n",
    "## Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy.units as u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fits.open(\"/home/stuart/sunpy/data/hsi_obssumm_20121127_043.2.fits\").info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "sc = SkyCoord.from_name(\"M32\")\n",
    "sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.transform_to(\"galactic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams[\"figure.figsize\"] = [9, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.modeling.models import Gaussian1D\n",
    "from astropy.modeling import fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "g1 = Gaussian1D(1, 0, 0.2)\n",
    "g2 = Gaussian1D(2.5, 0.5, 0.1)\n",
    "x = np.linspace(-1, 1, 200)\n",
    "y = g1(x) + g2(x) + np.random.normal(0., 0.2, x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x, y, \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = Gaussian1D(amplitude=1, mean=0, stddev=0.1) + Gaussian1D(amplitude=2, mean=0.5, stddev=0.1)\n",
    "fitter = fitting.SLSQPLSQFitter()\n",
    "gg = fitter(m2, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x, y, \"o\")\n",
    "plt.plot(x, gg(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SunPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sunpy.data.sample import AIA_171_ROLL_IMAGE\n",
    "import sunpy.map\n",
    "\n",
    "m = sunpy.map.Map(AIA_171_ROLL_IMAGE)\n",
    "m.peek()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Astropy and SunPy 😍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_point = SkyCoord(200*u.arcsec, 300*u.arcsec, frame=m.coordinate_frame)\n",
    "a_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = m.plot()\n",
    "ax = plt.gca()\n",
    "ax.plot_coord(a_point, \"o\", markersize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeSeries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sunpy.data.sample import GOES_XRS_TIMESERIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sunpy.timeseries import TimeSeries\n",
    "goes = TimeSeries(GOES_XRS_TIMESERIES)\n",
    "goes.peek()"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}