distance_posterior_and_estimators.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coryn Bailer-Jones, MPIA Heidelberg (https://mpia.de/homes/calj)\n",
    "\n",
    "Reference: [Bailer-Jones et al. 2018](https://arxiv.org/abs/1804.10121). \n",
    "\n",
    "Enter the parallax and parallax uncertainty below, as well as either the prior length scale (rlen) or the Galactic longitude and latitude of the star. If the latter are specified the prior length scale is computed from the Galactic model, as done in the paper and for the GDR2 catalogue. (If both are specified, rlen takes precedence.)\n",
    "\n",
    "The code will write various statistics to the screen and plot the posterior (black or red line) and prior (green line).\n",
    "* If the prior is unimodal the posterior is plotted in black.\n",
    "* If the prior is bimodal the posterior is plotted in red.\n",
    "* The green line may not be visible if the posterior and prior are near identical or if the posterior is very narrow and tall compared to the prior (the latter can then be constant at near zero density over the range of the plot)\n",
    "The solid vertical line is the distance estimator, and the dashed vertical lines are the lower and upper bounds of the (approximately) 68% confidence interval. \n",
    "When result_flag=1 these are the mode and HDI (highest density interval)\n",
    "When result_flag=2 these are the median and ETI (equal-tailed interval)\n",
    "\n",
    "If the resulting plot has a bad range, or even fails to plot, adjust the plotting range using rplotlo,rplothi (specified below). For the purpose of plotting (only) the posterior is normalized (numerically) over the range specified by rlo,rhi below. These must extend over the full range of the posterior! But if the posterior is *much* narrower than this range, the normalization may fail.\n",
    "\n",
    "When result_flag=2 and modality_flag=2 (distribution is bimodal), the code uses MCMC to compute the quantiles (progress of this is written to the screen). An additional plot is produced in this case showing a histogram of the MCMC samples compared to the actual posterior, so you can judge how likely the quantiles are a suitable summary of the posterior.\n",
    "\n",
    "If result_flag=0 the code could not compute either estimator and the reason for this will be reported. The plotting may also. See the paper for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Galactic coordinates to compute prior length scale\n",
      "\n",
      "w[mas] wsd[mas] wsd/w  glon[deg] glat[deg]\n",
      "1 0.2 0.2   -20 45 \n",
      "\n",
      "rest[pc] rlo[pc] rhi[pc] rlen[pc] result_flag modality_flag\n",
      "1036.717 836.065 1356.531 904.6903 1 1 \n",
      "\n",
      "HDI: probability contained, #steps to find: 0.6872477 48 \n"
     ]
    },
    {
     "data": {
      "image/png": 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lcCBAgQIEBg+wISpO0baoEAAQLbFTgr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1GDZubb0zJ5fbLeSiXXVTsJvGE7iLnFvJ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\neEPFFs+kpwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Input data\n",
    "# Specify either l,b or rlen. Set other to NA. rlen takes precedence.\n",
    "w    <- 1 # parallax in mas (corrected for any zeropoint offset; +0.029mas in the catalogue)\n",
    "wsd  <- abs(0.2*w) # parallax uncertainty in mas\n",
    "glon <- 340 # Galactic longitude in degrees (0 to 360)\n",
    "glat <-  45 # Galactic latitude (-90 to +90)\n",
    "rlen <-  NA # length scale in pc\n",
    "# Plotting parameters in pc\n",
    "# rlo,rhi are range for computing normalization of posterior\n",
    "# rplotlo, rplothi are plotting range (computed automatically if set to NA)\n",
    "rlo <- 0\n",
    "rhi <- 1e5\n",
    "rplotlo <- NA\n",
    "rplothi <- NA\n",
    "\n",
    "source(\"distest_single.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}