notebooks/SDSS_filters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SDSS filters\n",
    "\n",
    "http://www.astroml.org/examples/datasets/plot_sdss_filters.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111830898>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c7NVLtWHN6i3eq7e4q7dWFb+BgYFJj2/atAlnn302Lr74YvzTP/3TeCD98Ic/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\nELAcF1u2V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef4b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Author: Jake VanderPlas\n",
    "# License: BSD\n",
    "#   The figure produced by this code is published in the textbook\n",
    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
    "#   For more information, see http://astroML.github.com\n",
    "#   To report a bug or issue, use the following forum:\n",
    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
    "from matplotlib import pyplot as plt\n",
    "from astroML.datasets import fetch_sdss_filter, fetch_vega_spectrum\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n",
    "# Note that with usetex=True, fonts are rendered with LaTeX.  This may\n",
    "# result in an error if LaTeX is not installed on your system.  In that case,\n",
    "# you can set usetex to False.\n",
    "from astroML.plotting import setup_text_plots\n",
    "# setup_text_plots(fontsize=8, usetex=True)\n",
    "setup_text_plots(fontsize=16, usetex=True)\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Set up figure and axes\n",
    "# fig = plt.figure(figsize=(5, 3.75))\n",
    "fig = plt.figure(figsize=(10, 7.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#----------------------------------------------------------------------\n",
    "# Fetch and plot the Vega spectrum\n",
    "spec = fetch_vega_spectrum()\n",
    "lam = spec[0]\n",
    "spectrum = spec[1] / 2.1 / spec[1].max()\n",
    "ax.plot(lam, spectrum, '-k')\n",
    "\n",
    "#------------------------------------------------------------\n",
    "# Fetch and plot the five filters\n",
    "text_kwargs = dict(ha='center', va='center', alpha=0.5, fontsize=14)\n",
    "\n",
    "for f, c, loc in zip('ugriz', 'bgrmk', [3500, 4600, 6100, 7500, 8800]):\n",
    "    data = fetch_sdss_filter(f)\n",
    "    ax.fill(data[0], data[1], ec=c, fc=c, alpha=0.4)\n",
    "    ax.text(loc, 0.02, f, color=c, **text_kwargs)\n",
    "\n",
    "ax.set_xlim(3000, 11000)\n",
    "\n",
    "ax.set_title('SDSS Filters and Reference Spectrum')\n",
    "ax.set_xlabel('Wavelength (Angstroms)')\n",
    "ax.set_ylabel('normalized flux / filter transmission')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Altair version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"28158031-9d46-4936-a52a-8f145ebb421d\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#28158031-9d46-4936-a52a-8f145ebb421d"
     },
     "output_type": "display_data"
    },
    {
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\"spectrum\": 0.03537057494658366, \"u\": 0.0, \"wavelength\": 10855.71142284569, \"z\": 0.0006971543086172387}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.0344916616204559, \"u\": 0.0, \"wavelength\": 10871.743486973948, \"z\": 0.0006330260521042073}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.03350038417284673, \"u\": 0.0, \"wavelength\": 10887.775551102204, \"z\": 0.0006}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.032324699082633655, \"u\": 0.0, \"wavelength\": 10903.80761523046, \"z\": 0.0006}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.03137137392236402, \"u\": 0.0, \"wavelength\": 10919.839679358716, \"z\": 0.0006}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.031172543153946224, \"u\": 0.0, \"wavelength\": 10935.871743486972, \"z\": 0.000576513026052111}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.031244562318931978, \"u\": 0.0, \"wavelength\": 10951.903807615228, \"z\": 0.0005123847695390868}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.03159512289616051, \"u\": 0.0, \"wavelength\": 10967.935871743486, \"z\": 0.0005}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.03213490798311751, \"u\": 0.0, \"wavelength\": 10983.967935871744, \"z\": 0.0004841282565130241}, {\"g\": 0.0, \"i\": 0.0, \"r\": 0.0, \"spectrum\": 0.03272217551149945, \"u\": 0.0, \"wavelength\": 11000.0, \"z\": 0.00042}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
       "var selector = \"#28158031-9d46-4936-a52a-8f145ebb421d\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#28158031-9d46-4936-a52a-8f145ebb421d"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#28158031-9d46-4936-a52a-8f145ebb421d"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from astroML.datasets import fetch_sdss_filter, fetch_vega_spectrum\n",
    "\n",
    "from altair import Chart, LayeredChart, Color, Scale, Y, X, Axis\n",
    "\n",
    "#------------------------------------------\n",
    "# Set up a DataFrame with the data\n",
    "#\n",
    "# Since all transmission curves and the spectrum have different \n",
    "# wavelength sampling and number of points, interpolation is needed.\n",
    "\n",
    "fdict = {}\n",
    "wavelength = np.linspace(3000, 11000, 500)\n",
    "fdict['wavelength'] = wavelength\n",
    "for filt in 'ugriz':\n",
    "    fdata = fetch_sdss_filter(filt)\n",
    "    fdict[filt] = np.interp(wavelength, fdata[0], fdata[1])\n",
    "\n",
    "# Fetch the Vega spectrum, scale and interpolate it \n",
    "spec = fetch_vega_spectrum()\n",
    "spectrum = spec[1] / 2.1 / spec[1].max()\n",
    "fdict['spectrum'] = np.interp(wavelength, spec[0], spectrum)\n",
    "\n",
    "# Create the DataFrame from the dictionnary\n",
    "data = pd.DataFrame(fdict)\n",
    "\n",
    "#------------------------------------------\n",
    "# Set up a LayeredChart to plot the data\n",
    "\n",
    "chart = LayeredChart(data)\n",
    "        \n",
    "# Plot the filters\n",
    "filter_colors = ['blue', 'green', 'red', 'magenta', 'black']\n",
    "\n",
    "for filt, color in zip('ugriz', filter_colors):\n",
    "    chart += Chart().mark_area(color=color, opacity=0.4).encode(\n",
    "        X('wavelength', scale=Scale(zero=False), axis=Axis(title='Wavelength [Angstroms]', format='f')), \n",
    "        Y('%s:Q' % filt, axis=Axis(title='normalized flux / filter transmission', format='f')))\n",
    "\n",
    "# Plot the Vega spectrum on top of it\n",
    "# The axis properties are conserved so there is no need to write them twice\n",
    "chart += Chart().mark_line(color='black').encode(x='wavelength', y='spectrum:Q')\n",
    "        \n",
    "chart.display()"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}