{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "UNAM_GuestLecture_MCMCExamples.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oO3P0l82BTS"
      },
      "source": [
        "# Introduction\n",
        "This notebook is intended to demonstrate some simple applications of MCMC in astronomy using python, and will also set out some problems for attendees to solve. Please bear in mind that that these examples and problems all implicitly or explicitly assume Bayesian approaches, so I recommend you remind yourselves of the basic principles first.\n",
        "\n",
        "The text and graphical elements of this work are licensed under Attribution 4.0 International. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The code elements of this notebook are licensed under the MIT license (text at end).\n",
        "\n",
        "# Some resources\n",
        "You can find the slides for the lectures here. In addition to the slides, here is a reminder of some general resources that will be useful:\n",
        "\n",
        "\n",
        "*   David Hogg's \"lecture notes\"\n",
        "    * [Fitting a model to data](https://arxiv.org/abs/1008.4686)\n",
        "    * [Probability calculus for inference](https://arxiv.org/abs/1205.4446)\n",
        "    * [Using Markov Chain Monte Carlo](https://arxiv.org/abs/1710.06068)\n",
        "    * [Products of multivariate Gaussians in Bayesian inferences](https://arxiv.org/abs/2005.14199)\n",
        "*   [*emcee* tutorials](https://emcee.readthedocs.io/en/stable/tutorials/line/)\n",
        "*   [The MCMC interactive gallery](https://chi-feng.github.io/mcmc-demo/app.html) is a great way to visualise how various MCMC implementations explore parameter space\n",
        "* A [list of python MCMC packages](https://github.com/Gabriel-p/pythonMCMC)\n",
        "* Johannes Buchner's [minimal statistics checklist and learning material](https://astrost.at/istics/minimal-statistics-checklist.html)\n",
        "* Andrae et al (2010) a and b - discussing Error Estimation (key for defining Credible Intervals) and how *not* to use chi-squared statistics.\n",
        "\n",
        "\n",
        "# This problem set\n",
        "This will cover a number of examples, including\n",
        "1.   Basics of Colab\n",
        "2.   Why MCMC?\n",
        "3.   Some MCMC implementations in python\n",
        "4.   Interpreting the output of MCMC\n",
        "\n",
        "It will then go on to describe a set of problems with differing levels of difficulty and work to do.  These include\n",
        "1.   Fitting a line to data:\n",
        "     1.   with underestimated uncertainties;\n",
        "     2.   with uncertainties on both dependent and independent variabiles;\n",
        "     3.   with intrinsic scatter;\n",
        "     4.   in higher dimensions;\n",
        "     5.   with non-Gaussian likelihoods;\n",
        "2.   Checking convergence\n",
        "3.   Posterior predictive checks\n",
        "4.   Nuisance parameters\n",
        "5.   Prior sensitivity\n",
        "\n",
        "For the most part, these problems are setup in *emcee* but you are encouraged to try out other python packages based on other MCMC approaches.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jA0pawm4aki"
      },
      "source": [
        "# A quick reminder on Colab\n",
        "\n",
        "Colab is a very convenient, free cloud service from Google which allows you to run Jupyter notebooks with limited compute resources. Notebooks let you combine code and text. This is a text cell, and the following is a code cell that shows how to do a few simple but important python calls, including importing and installing packages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptivhJoa17VD",
        "outputId": "f20b6a56-0441-4433-fe41-bec1af4f02ed"
      },
      "source": [
        "import numpy as np #numpy is installed by default\n",
        "#But you can also install packages that you need using pip\n",
        "!pip install emcee\n",
        "import emcee\n",
        "#And if you want, you can create a try:...except:... to only attempt the install if absolutely necessary\n",
        "try:\n",
        "  import dynesty\n",
        "except ImportError:\n",
        "  !pip install dynesty\n",
        "  import dynesty\n",
        "\n",
        "#then it works just like python\n",
        "a = 1\n",
        "print(a)\n",
        "b = np.arange(10)\n",
        "c = a + b\n",
        "print(c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting emcee\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/97/f4/00151f5f843088337c6a53edd6cbb2df340f1044d23080c662f95219cc3f/emcee-3.0.2-py2.py3-none-any.whl (41kB)\n",
            "\r\u001b[K     |███████▉                        | 10kB 14.8MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 20kB 20.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 30kB 10.5MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 40kB 8.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 2.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from emcee) (1.19.5)\n",
            "Installing collected packages: emcee\n",
            "Successfully installed emcee-3.0.2\n",
            "Collecting dynesty\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/94/e6/dc4369009259a0a113b3f91e223be9229f71d8350aca6ec8fe978982d2f9/dynesty-1.0.1-py2.py3-none-any.whl (86kB)\n",
            "\u001b[K     |████████████████████████████████| 92kB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from dynesty) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from dynesty) (1.15.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from dynesty) (1.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from dynesty) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dynesty) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dynesty) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dynesty) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dynesty) (0.10.0)\n",
            "Installing collected packages: dynesty\n",
            "Successfully installed dynesty-1.0.1\n",
            "1\n",
            "[ 1  2  3  4  5  6  7  8  9 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihZgViEm-fgs"
      },
      "source": [
        "# Why MCMC?\n",
        "When we derive quantities from data or models we are interested not just in a value, but in some understanding of the probability distributions of the quantity. Often, this is parametrised as a value and an uncertainty to describe a *credible region*, but this is only a good description for independent, mono-modal, approximately-Gaussian distributions. \n",
        "\n",
        "However, in real astronomical applications these criteria are often violated. Model parameters are often correlated, and their distributions may not be Gaussian or mono-modal, or even analytically evaluable. In these cases, classical approaches will fail, but often *without telling us that*!\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kn-autE7-jqs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGxFsBTo-kCC"
      },
      "source": [
        "# Some examples of MCMC packages in python that are used in astronomy\n",
        "\n",
        "There are broadly two classes of packages that exist in python. The first support \"black box\" likelihoods, i.e. you write a function that connects the (potentially physical) model that you want to use with the data, similar to scipy.minimise or MPFITFUN. The second consists of probabilistic programming languages, which are more difficult to specify the model for but support a wider range of more complex MCMC (and other inference) methods, such as Hamiltonian Monte Carlo.\n",
        "\n",
        "## Black box methods\n",
        "emcee is particularly popular - simple to use, but capable of handling a wide variety of problems, including those where you just plug in a function to the likelihood. \n",
        "\n",
        "dynesty\n",
        "\n",
        "ultranest\n",
        "\n",
        "## Probabilistic programming\n",
        "PyMC3 is probably the most widely used. The third incarnation of the PyMC family provides a particular advantage by supporting theano and Tensorflow ML libraries. \n",
        "\n",
        "pyro-ppl targets similar approaches as PyMC3 but is part of the Torch ML ecosystem. It also supports a variety of approaches such as Variational Inference.\n",
        "\n",
        "STAN - one of the methods of choice for complex models with large numbers of parameters. Look out for this one for Hierarchical Models.\n",
        "\n",
        "JAGS?\n",
        "\n",
        "## A quick example\n",
        "\n",
        "Here we have an example of using emcee to compute a black box likelihood and do some MCMC on it. In this case, the data are some observations of cold dust, and the model is the modified blackbody function. The data are assumed to be drawn from Normal distributions, so that the likelihood function is the familiar Gaussian likelihood\n",
        "$\\log \\mathcal{L} \\propto -\\frac{1}{2}\\sum\\limits_{i}^{} \\frac{\\left(y_{i,\\mathrm{obs}} - y_{i,\\mathrm{mod}}\\right)^2}{\\sigma_i^2} $ where constant terms have been dropped for brevity.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfw9Q60S-o4X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2f3854e-eb5c-405c-fd54-8474124dc77b"
      },
      "source": [
        "#For starters, we define the model itself - the process by which we think the observed data might have been generated\n",
        "try:\n",
        "  from astropy import constants as const\n",
        "except ImportError:\n",
        "  !pip install astropy\n",
        "  from astropy import constants as const\n",
        "from astropy import units as u\n",
        "from astropy.modeling import blackbody\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\"BlackBody provides the same capabilities\")\n",
        "warnings.filterwarnings(\"ignore\", message=\"invalid value encountered in double_scalars\")\n",
        "\n",
        "#Here's some data: These are observations of the flux of XXX from XXXXX\n",
        "x = np.array([70., 160., 250., 350., 500., 450., 850.]) #Wavelengths in micron\n",
        "y = np.array([0.06291731942186395, 0.4737297147415956, \n",
        "              0.6390202295454346, 0.6397258752453974, \n",
        "              0.5666783622052882, 0.5923100333782477, \n",
        "              0.4048189704746445])#np.array([10, 12, 9, 7, 4, 3, 1]) #Fluxes in Jy\n",
        "yerr = np.array([0.010727567289237351, 0.07215265086492663, \n",
        "                 0.06050432065824665, 0.10415758267647844, \n",
        "                 0.052355096848173376, 0.21872154153894846, \n",
        "                 0.09148243225657898])#np.array([2, 2, 2, 2, 2, 2, 1]) #Uncertainties on the fluxes\n",
        "\n",
        "normWave = 160. #wavelength (in micron) at which dust opacity is normalised\n",
        "sigmaNormWave = 0.3 #opacity/mass-absorption coefficient of the dust at normWave\n",
        "dist = 100*u.pc.to(u.m) #Distance to the star\n",
        "\n",
        "def model(M, T, beta, lam):\n",
        "  #convert lambda to nu\n",
        "  freq = const.c.value / (lam*1e-6)\n",
        "\n",
        "  bb = blackbody.blackbody_nu(freq,T).to(u.Jy / u.sr).value\n",
        "  bb = bb / dist**2\n",
        "  bb = bb * 10**(M)*const.M_sun.value * sigmaNormWave * ((lam / normWave)**beta)\n",
        "  return bb\n",
        "\n",
        "\n",
        "def lnprior(theta):\n",
        "  #This is a flat prior - probability is uniform inside a given range, and zero outside\n",
        "  if -10 < theta[0] < 10 and 3 < theta[1] < 100 and -3 < theta[2] < 3:\n",
        "    return 0\n",
        "  return -np.inf\n",
        "\n",
        "def lnlike(theta, x, y, yerr):\n",
        "  #This function calculates the likelihood P(D|M) for our model.\n",
        "  M = theta[0]\n",
        "  T = theta[1]\n",
        "  beta = theta[2]\n",
        "  flux = model(M, T, beta, x)\n",
        "  return-0.5* np.sum((y - flux)**2 /yerr**2)\n",
        "\n",
        "def lnprob(theta, x, y, yerr):\n",
        "  lp = lnprior(theta)\n",
        "  if lp == -np.inf:\n",
        "    return lp\n",
        "  return lp + lnlike(theta, x, y, yerr)\n",
        "\n",
        "#Now we have a model, and we have defined our posterior, we can set up a sampler and get things moving.\n",
        "ndim=3 #Three dimensions for this problem - M, T and beta\n",
        "nwalkers=100 #emcee is an affine-invariant ensemble sampler. This means it uses several chains.\n",
        "             # the number of walkers (=chains) must be even, and should be as large as possible (at least double ndim)\n",
        "\n",
        "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(x, y, yerr))\n",
        "\n",
        "steps = 500\n",
        "pos = [[0, 10, 0] + np.random.randn(ndim) for i in range(nwalkers)]\n",
        "sampler.run_mcmc(pos,steps)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State([[-5.16339785 37.33419629  1.30695901]\n",
              " [-2.59961377 10.20297476 -1.61078975]\n",
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              " [-5.05697922 35.01734187  1.3055148 ]\n",
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              " [-4.90375145 32.58420678  1.12192067]\n",
              " [-4.78558854 28.65450719  1.02503916]\n",
              " [-4.97196945 33.59845374  1.11726413]\n",
              " [-4.96934388 32.66724892  1.23416903]\n",
              " [-5.09468348 36.22831064  1.24303056]\n",
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              " [-5.04550021 34.87578547  1.2845992 ]\n",
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              " [-4.8092595  28.78432792  0.9196836 ]\n",
              " [-4.65484913 27.05504069  0.7389104 ]\n",
              " [-4.79289723 29.62417717  0.90702099]\n",
              " [-5.0013846  34.7203613   1.10058622]\n",
              " [-4.86852095 31.85397197  0.98170585]\n",
              " [-4.7211644  28.81012893  0.78586776]\n",
              " [-4.91002914 31.81813345  1.07702574]\n",
              " [-5.01716803 32.92595926  1.29381799]\n",
              " [-4.99347942 34.04205157  1.21310748]\n",
              " [-4.88209062 31.3650749   1.03888058]\n",
              " [-4.87697513 32.47795455  0.93415692]\n",
              " [-4.7176062  28.73077906  0.92373943]\n",
              " [-2.1451286   9.09218067 -2.25852549]\n",
              " [-4.95454851 33.52540274  1.06503812]\n",
              " [-2.96730117 11.45586521 -1.07000971]\n",
              " [-5.12886675 38.18408374  1.35514323]\n",
              " [-4.65098733 25.83244773  0.94222793]\n",
              " [-1.51945849  6.99048248 -2.23929381]\n",
              " [-4.81432709 30.06955132  0.85458513]], log_prob=[ -2.83433487 -28.13007573  -0.64145559  -2.64806949  -2.25861548\n",
              "  -1.35775359  -0.53106585  -1.52943768  -2.18162428  -0.97849001\n",
              "  -0.50967371  -0.96953583  -0.84151162  -2.37577237  -0.43922479\n",
              "  -1.40293134  -0.44000532  -0.60293939  -1.19277202 -32.47603966\n",
              "  -4.24713102  -0.96876463  -0.23351011  -0.12121032  -0.74286577\n",
              " -25.52508762  -3.17050633  -4.45268615  -0.21059838  -0.86773049\n",
              "  -3.84976898  -0.94118202  -1.44096809  -0.65048076  -0.33588233\n",
              "  -0.69034024  -1.23862783  -3.12992106  -3.12624346  -2.29938879\n",
              "  -3.75156982  -0.7500532   -1.50809117 -28.46153487  -1.68470523\n",
              "  -0.46150135  -0.64084802  -2.67788561  -0.85875017  -0.26737894\n",
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              "  -1.81044162  -0.68789045  -0.76514985  -1.53203534  -0.99319806\n",
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              "       3270870571, 2779884771, 4218818313, 1120943332], dtype=uint32), 569, 0, 0.0))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzAUNkRk-pQc"
      },
      "source": [
        "# Interpreting the output\n",
        "MCMC gives us *samples* from the posterior PDF of the model parameters. These samples are generated in *chains* such that consecutive points may be correlated. Therefore, the first thing to check is whether the chains are long enough to give enough independent samples from the distribution. \n",
        "\n",
        "One way of visualising this is the *trace* of the chains. The chains should rapidly converge to one region of parameter space. Comparing multiple chains (emcee's ensemble-sampling approach is particularly useful here) allows us to check that everything is consistent."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5fX716c-uJr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "e2e2bd62-3551-4028-f6f7-a91ea0db6492"
      },
      "source": [
        "#get the chains from the sampler\n",
        "samples = sampler.get_chain()\n",
        "flat_samples = sampler.get_chain(flat=True)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#trace plots first\n",
        "\n",
        "fig, axes = plt.subplots(3, figsize=(10, 7), sharex=True)\n",
        "fig.patch.set_facecolor('white')\n",
        "labels = [\"M\", \"T\", r\"$\\beta$\"]\n",
        "for i in range(ndim):\n",
        "    ax = axes[i]\n",
        "    ax.plot(samples[:, :, i], \"k\", alpha=0.3)\n",
        "    ax.set_xlim(0, len(samples))\n",
        "    ax.set_ylabel(labels[i])\n",
        "    ax.yaxis.set_label_coords(-0.1, 0.5)\n",
        "\n",
        "axes[-1].set_xlabel(\"step number\");\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jB8vrbEebQBN"
      },
      "source": [
        "We can also check the autocorrelation time (in terms of number of samples) for the chains. This gives us some idea of whether we have enough independent samples. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5x1v8BNmboy2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a479542-375e-4765-8aa9-758f48f88f6f"
      },
      "source": [
        "tau = sampler.get_autocorr_time(quiet=True)\n",
        "print(tau)\n",
        "taus = [tau]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 10;\n",
            "tau: [53.740919   60.21386764 52.32871848]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53.740919   60.21386764 52.32871848]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkTwolY1bpHA"
      },
      "source": [
        "But there are some caveats. For example, the autocorrelation time can appear short if we don't compute long enough MCMC chains to properly capture the autocorrelation behaviour"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpYrp4BddT2t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "659883c8-067a-4b47-fd9a-6e094837a622"
      },
      "source": [
        "#THIS STEP TAKES SOME TIME!\n",
        "\n",
        "for i in range(9): #add more steps in chunks of 500 until we have 5000 steps, updating the autocorrelation time each 500 steps\n",
        "  sampler.run_mcmc(sampler.get_last_sample(),steps)\n",
        "  tau = sampler.get_autocorr_time(quiet=True)\n",
        "  print(tau)\n",
        "  taus.append(tau)\n",
        "#sampler.run_mcmc(pos,steps*20)\n",
        "#tau = sampler.get_autocorr_time()\n",
        "#print(tau)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 20;\n",
            "tau: [77.82650339 87.49607554 74.17671962]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77.82650339 87.49607554 74.17671962]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 30;\n",
            "tau: [ 88.73763569 100.18156755  84.53482057]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[ 88.73763569 100.18156755  84.53482057]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 40;\n",
            "tau: [100.90695924 111.56671025  97.01241239]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[100.90695924 111.56671025  97.01241239]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 50;\n",
            "tau: [109.37784564 120.66070582 105.40928281]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[109.37784564 120.66070582 105.40928281]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 60;\n",
            "tau: [112.56434392 122.54044816 107.50204054]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[112.56434392 122.54044816 107.50204054]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 70;\n",
            "tau: [113.75784858 120.41632901 108.24475886]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[113.75784858 120.41632901 108.24475886]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 80;\n",
            "tau: [116.38720756 122.05175838 110.86709607]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[116.38720756 122.05175838 110.86709607]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 90;\n",
            "tau: [117.5765648  122.10685881 111.74749679]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[117.5765648  122.10685881 111.74749679]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 100;\n",
            "tau: [117.33458206 120.09862047 110.69451591]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[117.33458206 120.09862047 110.69451591]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fBWiIxaO-y0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6200301-ee5c-419f-c2c2-60f934456c53"
      },
      "source": [
        "#Optional code cell to keep computing a longer chain to see when we really have enough samples\n",
        "\n",
        "for i in range(10): #add more steps in chunks of 500 until we have 10000 steps, updating the autocorrelation time each 500 steps\n",
        "  sampler.run_mcmc(sampler.get_last_sample(),steps)\n",
        "  tau = sampler.get_autocorr_time(quiet=True)\n",
        "  print(tau)\n",
        "  taus.append(tau)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 110;\n",
            "tau: [116.95557518 118.1088806  110.19448669]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[116.95557518 118.1088806  110.19448669]\n",
            "[116.59460128 116.43830357 109.72712516]\n",
            "[116.31429336 114.69615913 108.94802887]\n",
            "[116.07312741 113.64818047 108.45818625]\n",
            "[115.44338027 111.98932252 107.30522473]\n",
            "[114.98110425 110.70432266 106.30264437]\n",
            "[114.72915609 109.17832637 105.86950338]\n",
            "[114.47772431 108.15902574 105.35521773]\n",
            "[113.64458115 106.53438615 103.89148772]\n",
            "[112.98210784 105.11702441 102.99900236]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0HWdVk_2YYh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "adfed4d8-097a-4cf3-cec8-a1e85150e2f4"
      },
      "source": [
        "taus = np.array(taus)\n",
        "xs = (np.arange(20) + 1) * steps\n",
        "figt, axest = plt.subplots(3, figsize =(10, 7), sharey = True)\n",
        "figt.patch.set_facecolor('white')\n",
        "for i in range(ndim):\n",
        "  ax = axest[i]\n",
        "  ax.plot(xs, taus[:,0], label=r\"$\\tau$\")\n",
        "  ax.plot(xs, xs/50, label=\"nsteps/50\")\n",
        "  ax.set_ylabel(r\"$\\tau$\" + labels[i])\n",
        "ax.legend(loc=\"lower right\")\n",
        "ax.set_xlabel(\"Step\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Step')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unzUW_oMEjrl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "0fe7bdfc-6417-4e42-f001-42e38aaa5438"
      },
      "source": [
        "samples = sampler.get_chain()\n",
        "\n",
        "fig2, axes2 = plt.subplots(3, figsize=(10, 7), sharex=True)\n",
        "labels = [\"M\", \"T\", r\"$\\beta$\"]\n",
        "for i in range(ndim):\n",
        "    ax = axes2[i]\n",
        "    ax.plot(samples[:, :, i], \"k\", alpha=0.3)\n",
        "    ax.set_xlim(0, len(samples))\n",
        "    ax.set_ylabel(labels[i])\n",
        "    ax.yaxis.set_label_coords(-0.1, 0.5)\n",
        "\n",
        "axes2[-1].set_xlabel(\"step number\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qCuYTQ4cSzn"
      },
      "source": [
        "If we trust these numbers, we can now discard the first chunk as burn in, and thin the chains to minimise correlation and flatten them (combine the chains from different walkers) for further analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8FS10AdcTGo"
      },
      "source": [
        "flat_samples = sampler.get_chain(discard = 6000, thin = 90, flat = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_o8p37UdSlM"
      },
      "source": [
        "Once we're sure we've computed enough samples we can check whether our model actually reproduces our data. We're Bayesians, so we don't have any sort of absolute measure of goodness of fit, only relative ones, so we can only do this through visualisation.\n",
        "\n",
        "Hence, we use a technique called *posterior predictive checks*, whereby we draw samples from the posterior and evaluate the model and compare it to the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOLxXMcrgUVN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "beee4c69-0406-46dc-fcbb-70498ff20ec3"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\"BlackBody provides the same capabilities\")\n",
        "\n",
        "inds = np.random.randint(len(flat_samples), size=100)\n",
        "plot_x = np.logspace(np.log10(50), np.log10(1000), 30)\n",
        "for ind in inds:\n",
        "    sample = flat_samples[ind]\n",
        "    plt.plot(plot_x, model(sample[0], sample[1], sample[2], plot_x), \"C1\", alpha=0.1)\n",
        "plt.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n",
        "#plt.plot(x0, m_true * x0 + b_true, \"k\", label=\"truth\")\n",
        "#plt.legend(fontsize=14)\n",
        "plt.xlim(60, 1000)\n",
        "plt.ylim(0.01, 1)\n",
        "plt.xscale('log')\n",
        "plt.yscale('log')\n",
        "plt.xlabel(r\"$\\lambda$ [$\\mu$m]\")\n",
        "plt.ylabel(r\"$F_\\nu$ [Jy]\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlB_xtbDi0MJ"
      },
      "source": [
        "Now, we're sure that we have enough samples, that our model is representative of our data and we understand how big the impact of the choice of prior is, we can see what the posterior distributions of the model can tell us. The most straightfoward thing to do is to get point estimates and their credible intervals. These are determined from the 1D distribution of the posterior after marginalising out all other parameters, which is straightforward with MCMC samples:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Hi9voSzkEFo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "outputId": "e82d7bda-f1d3-42f6-8f84-704be8ac377b"
      },
      "source": [
        "from IPython.display import display, Math\n",
        "\n",
        "for i in range(ndim):\n",
        "    mcmc = np.percentile(flat_samples[:, i], [16, 50, 84])\n",
        "    q = np.diff(mcmc)\n",
        "    txt = \"\\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}\"\n",
        "    txt = txt.format(mcmc[1], q[0], q[1], labels[i]).replace(\"$\", \"\")\n",
        "    display(Math(txt))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "$$\\mathrm{M} = -4.907_{-0.149}^{0.143}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "$$\\mathrm{T} = 32.012_{-2.761}^{3.183}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "$$\\mathrm{\\beta} = 1.053_{-0.202}^{0.200}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywb3owz5kEfp"
      },
      "source": [
        "Another common tool is the *triangle plot* which shows the 2D marginal distributions of the parameters, which we can explore to see if parameters are correlated. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22H-aSdVl2nJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 805
        },
        "outputId": "fa535f03-a35c-4080-c8c8-d73ec31dc74a"
      },
      "source": [
        "try:\n",
        "  import corner\n",
        "except ImportError:\n",
        "  !pip install corner\n",
        "  import corner\n",
        "\n",
        "#mask_samples = np.logical_and(flat_samples)\n",
        "fig = corner.corner(\n",
        "    flat_samples[flat_samples[:,2] > 0], labels=labels#, truths=[m_true, b_true, np.log(f_true)]\n",
        ");\n",
        "fig.patch.set_facecolor('white')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting corner\n",
            "  Downloading https://files.pythonhosted.org/packages/5a/ff/df5e34996aec8bc342c72714d1384e9af17259e6f60c2a63da2f53ba1631/corner-2.1.0-py2.py3-none-any.whl\n",
            "Collecting setuptools-scm\n",
            "  Downloading https://files.pythonhosted.org/packages/db/6e/2815f7c8561b088ccedc128681e64daac3d6b2e81a9918b007e244dad8b1/setuptools_scm-5.0.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.6/dist-packages (from corner) (0.36.2)\n",
            "Requirement already satisfied: matplotlib>=2.1 in /usr/local/lib/python3.6/dist-packages (from corner) (3.2.2)\n",
            "Requirement already satisfied: setuptools>=40.6.0 in /usr/local/lib/python3.6/dist-packages (from corner) (51.3.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (1.3.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (0.10.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib>=2.1->corner) (1.15.0)\n",
            "Installing collected packages: setuptools-scm, corner\n",
            "Successfully installed corner-2.1.0 setuptools-scm-5.0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 547.2x547.2 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cg1c3JE2l261"
      },
      "source": [
        "As we can see, all the parameters have correlations with the others. We can try to quantify some of those correlations by computing the numerical correlation matrix of the parameters, e.g."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCx57dg0Soo9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9f3712b-dd5f-4da3-942a-5410ebed3a89"
      },
      "source": [
        "cov = np.cov(flat_samples, rowvar=False)\n",
        "print(cov)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.1113995  -1.13965963 -0.12872335]\n",
            " [-1.13965963 14.71723925  1.34179401]\n",
            " [-0.12872335  1.34179401  0.15304716]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-UhhNB4gVLA"
      },
      "source": [
        "Which shows the large correlations between the parameters on the off-diagonal elements. However, this doesn't capture the whole picture, which is why the corner plot is useful. The correlation matrix can't tell us that the $T - \\beta$ and $M - T$ correlations are non-Gaussian, but we can see that by eye from the banana-shaped contours.\n",
        "\n",
        "We should also check that our choice of prior doesn't unnecessarily alter the results. This is known as *prior sensitivity testing*. Essentially, we must recompute the posterior to see what it would look like under the assumption of a different prior. Most of the time, this requires us to re-run the MCMC with the different prior. The code is included here to demonstrate (and provide a complete overview of the code in one place) but executing it is left as an exervise for the reader to compare the outputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R68NNCDViz5y"
      },
      "source": [
        "assert False #Ugly cludge to halt automatic execution when using Run all/ctrl+F9 to prevent next cell from executing."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MzYB78kSAVui",
        "outputId": "ee364d79-b97c-4412-a7b4-3cf53abe2b1a"
      },
      "source": [
        "import numpy as np #numpy is installed by default\n",
        "#But you can also install packages that you need using pip\n",
        "try:\n",
        "  import emcee\n",
        "except ImportError:\n",
        "  !pip install emcee\n",
        "  import emcee\n",
        "try:\n",
        "  from astropy import constants as const\n",
        "except ImportError:\n",
        "  !pip install astropy\n",
        "  from astropy import constants as const\n",
        "from astropy import units as u\n",
        "from astropy.modeling import blackbody\n",
        "try:\n",
        "  import corner\n",
        "except ImportError:\n",
        "  !pip install corner\n",
        "  import corner\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\"BlackBody provides the same capabilities\")\n",
        "warnings.filterwarnings(\"ignore\", message=\"invalid value encountered in double_scalars\")\n",
        "\n",
        "#Here's some data: These are observations of the flux of XXX from XXXXX\n",
        "x = np.array([70., 160., 250., 350., 500., 450., 850.]) #Wavelengths in micron\n",
        "y = np.array([0.06291731942186395, 0.4737297147415956, \n",
        "              0.6390202295454346, 0.6397258752453974, \n",
        "              0.5666783622052882, 0.5923100333782477, \n",
        "              0.4048189704746445])#np.array([10, 12, 9, 7, 4, 3, 1]) #Fluxes in Jy\n",
        "yerr = np.array([0.010727567289237351, 0.07215265086492663, \n",
        "                 0.06050432065824665, 0.10415758267647844, \n",
        "                 0.052355096848173376, 0.21872154153894846, \n",
        "                 0.09148243225657898])#np.array([2, 2, 2, 2, 2, 2, 1]) #Uncertainties on the fluxes\n",
        "\n",
        "normWave = 160. #wavelength (in micron) at which dust opacity is normalised\n",
        "sigmaNormWave = 0.3 #opacity/mass-absorption coefficient of the dust at normWave\n",
        "dist = 100*u.pc.to(u.m) #Distance to the star\n",
        "\n",
        "def model(M, T, beta, lam):\n",
        "  #convert lambda to nu\n",
        "  freq = const.c.value / (lam*1e-6)\n",
        "\n",
        "  bb = blackbody.blackbody_nu(freq,T).to(u.Jy / u.sr).value\n",
        "  bb = bb / dist**2\n",
        "  bb = bb * 10**(M)*const.M_sun.value * sigmaNormWave * ((lam / normWave)**beta)\n",
        "  return bb\n",
        "\n",
        "from scipy.stats import norm\n",
        "def lnprior(theta):\n",
        "  #This is our new prior - probability is limited to a given range where T is expected to be normally distributed, and zero outside\n",
        "  if -10 < theta[0] < 10 and 3 < theta[1] < 100 and -3 < theta[2] < 3:\n",
        "    return norm.logpdf(theta[1], 20, 10) #T prior has a mean of 20K with a standard deviation of 10K\n",
        "  return -np.inf\n",
        "\n",
        "def lnlike(theta, x, y, yerr):\n",
        "  #This function calculates the likelihood P(D|M) for our model.\n",
        "  M = theta[0]\n",
        "  T = theta[1]\n",
        "  beta = theta[2]\n",
        "  flux = model(M, T, beta, x)\n",
        "  return-0.5* np.sum((y - flux)**2 /yerr**2)\n",
        "\n",
        "def lnprob(theta, x, y, yerr):\n",
        "  lp = lnprior(theta)\n",
        "  if lp == -np.inf:\n",
        "    return lp\n",
        "  return lp + lnlike(theta, x, y, yerr)\n",
        "\n",
        "#Now we have a model, and we have defined our posterior, we can set up a sampler and get things moving.\n",
        "ndim=3 #Three dimensions for this problem - M, T and beta\n",
        "nwalkers=100 #emcee is an affine-invariant ensemble sampler. This means it uses several chains.\n",
        "             # the number of walkers (=chains) must be even, and should be as large as possible (at least double ndim)\n",
        "\n",
        "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(x, y, yerr))\n",
        "\n",
        "steps = 500\n",
        "pos = [[0, 10, 0] + np.random.randn(ndim) for i in range(nwalkers)]\n",
        "sampler.run_mcmc(pos,steps)\n",
        "\n",
        "#get the chains from the sampler\n",
        "samples = sampler.get_chain()\n",
        "flat_samples = sampler.get_chain(flat=True)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#trace plots first\n",
        "\n",
        "fig, axes = plt.subplots(3, figsize=(10, 7), sharex=True)\n",
        "fig.patch.set_facecolor('white')\n",
        "labels = [\"M\", \"T\", r\"$\\beta$\"]\n",
        "for i in range(ndim):\n",
        "    ax = axes[i]\n",
        "    ax.plot(samples[:, :, i], \"k\", alpha=0.3)\n",
        "    ax.set_xlim(0, len(samples))\n",
        "    ax.set_ylabel(labels[i])\n",
        "    ax.yaxis.set_label_coords(-0.1, 0.5)\n",
        "\n",
        "axes[-1].set_xlabel(\"step number\");\n",
        "\n",
        "tau = sampler.get_autocorr_time(quiet=True)\n",
        "print(tau)\n",
        "taus = [tau]\n",
        "\n",
        "#THIS STEP TAKES SOME TIME!\n",
        "\n",
        "for i in range(9): #add more steps in chunks of 500 until we have 5000 steps, updating the autocorrelation time each 500 steps\n",
        "  sampler.run_mcmc(sampler.get_last_sample(),steps)\n",
        "  tau = sampler.get_autocorr_time(quiet=True)\n",
        "  print(tau)\n",
        "  taus.append(tau)\n",
        "\n",
        "#Optional code cell to keep computing a longer chain to see when we really have enough samples\n",
        "\n",
        "for i in range(10): #add more steps in chunks of 500 until we have 10000 steps, updating the autocorrelation time each 500 steps\n",
        "  sampler.run_mcmc(sampler.get_last_sample(),steps)\n",
        "  tau = sampler.get_autocorr_time(quiet=True)\n",
        "  print(tau)\n",
        "  taus.append(tau)\n",
        "\n",
        "taus = np.array(taus)\n",
        "xs = (np.arange(20) + 1) * steps\n",
        "figt, axest = plt.subplots(3, figsize =(10, 7), sharey = True)\n",
        "figt.patch.set_facecolor('white')\n",
        "for i in range(ndim):\n",
        "  ax = axest[i]\n",
        "  ax.plot(xs, taus[:,0], label=r\"$\\tau$\")\n",
        "  ax.plot(xs, xs/50, label=\"nsteps/50\")\n",
        "  ax.set_ylabel(r\"$\\tau$\" + labels[i])\n",
        "ax.legend(loc=\"lower right\")\n",
        "ax.set_xlabel(\"Step\")\n",
        "\n",
        "samples = sampler.get_chain()\n",
        "\n",
        "fig2, axes2 = plt.subplots(3, figsize=(10, 7), sharex=True)\n",
        "labels = [\"M\", \"T\", r\"$\\beta$\"]\n",
        "for i in range(ndim):\n",
        "    ax = axes2[i]\n",
        "    ax.plot(samples[:, :, i], \"k\", alpha=0.3)\n",
        "    ax.set_xlim(0, len(samples))\n",
        "    ax.set_ylabel(labels[i])\n",
        "    ax.yaxis.set_label_coords(-0.1, 0.5)\n",
        "\n",
        "axes2[-1].set_xlabel(\"step number\");\n",
        "\n",
        "flat_samples = sampler.get_chain(discard = 6000, thin = 90, flat = True)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\"BlackBody provides the same capabilities\")\n",
        "\n",
        "figpp = plt.figure()\n",
        "axpp = fig.add_subplot(111)\n",
        "inds = np.random.randint(len(flat_samples), size=100)\n",
        "plot_x = np.logspace(np.log10(50), np.log10(1000), 30)\n",
        "for ind in inds:\n",
        "    sample = flat_samples[ind]\n",
        "    axpp.plot(plot_x, model(sample[0], sample[1], sample[2], plot_x), \"C1\", alpha=0.1)\n",
        "axpp.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n",
        "#plt.plot(x0, m_true * x0 + b_true, \"k\", label=\"truth\")\n",
        "#plt.legend(fontsize=14)\n",
        "axpp.set_xlim(60, 1000)\n",
        "axpp.set_ylim(0.01, 1)\n",
        "axpp.set_xscale('log')\n",
        "axpp.set_yscale('log')\n",
        "axpp.set_xlabel(r\"$\\lambda$ [$\\mu$m]\")\n",
        "axpp.set_ylabel(r\"$F_\\nu$ [Jy]\");\n",
        "\n",
        "from IPython.display import display, Math\n",
        "\n",
        "for i in range(ndim):\n",
        "    mcmc = np.percentile(flat_samples[:, i], [16, 50, 84])\n",
        "    q = np.diff(mcmc)\n",
        "    txt = \"\\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}\"\n",
        "    txt = txt.format(mcmc[1], q[0], q[1], labels[i]).replace(\"$\", \"\")\n",
        "    display(Math(txt))\n",
        "\n",
        "#import corner\n",
        "\n",
        "#mask_samples = np.logical_and(flat_samples)\n",
        "fig = corner.corner(\n",
        "    flat_samples[flat_samples[:,2] > 0], labels=labels#, truths=[m_true, b_true, np.log(f_true)]\n",
        ");\n",
        "fig.patch.set_facecolor('white')\n",
        "\n",
        "cov = np.cov(flat_samples, rowvar=False)\n",
        "print(cov)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting corner\n",
            "  Downloading https://files.pythonhosted.org/packages/5a/ff/df5e34996aec8bc342c72714d1384e9af17259e6f60c2a63da2f53ba1631/corner-2.1.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: matplotlib>=2.1 in /usr/local/lib/python3.6/dist-packages (from corner) (3.2.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.6/dist-packages (from corner) (0.36.2)\n",
            "Collecting setuptools-scm\n",
            "  Downloading https://files.pythonhosted.org/packages/db/6e/2815f7c8561b088ccedc128681e64daac3d6b2e81a9918b007e244dad8b1/setuptools_scm-5.0.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: setuptools>=40.6.0 in /usr/local/lib/python3.6/dist-packages (from corner) (51.3.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (1.3.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (1.19.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1->corner) (2.8.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=2.1->corner) (1.15.0)\n",
            "Installing collected packages: setuptools-scm, corner\n",
            "Successfully installed corner-2.1.0 setuptools-scm-5.0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 10;\n",
            "tau: [53.93095347 59.89693994 55.1752793 ]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53.93095347 59.89693994 55.1752793 ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 20;\n",
            "tau: [77.13991361 88.15160123 80.33237379]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77.13991361 88.15160123 80.33237379]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 30;\n",
            "tau: [ 88.77278136 101.16666718  93.20304723]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[ 88.77278136 101.16666718  93.20304723]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 40;\n",
            "tau: [ 93.78253319 103.68951899  98.99468322]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[ 93.78253319 103.68951899  98.99468322]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 50;\n",
            "tau: [ 99.73767116 110.22727297 104.95262449]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[ 99.73767116 110.22727297 104.95262449]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 60;\n",
            "tau: [100.17486157 107.9926534  105.13326713]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[100.17486157 107.9926534  105.13326713]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 70;\n",
            "tau: [103.25924345 109.74936676 107.05659639]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[103.25924345 109.74936676 107.05659639]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 80;\n",
            "tau: [104.43787511 108.96077432 106.76371834]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[104.43787511 108.96077432 106.76371834]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 90;\n",
            "tau: [104.37422502 107.45812625 105.31688219]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[104.37422502 107.45812625 105.31688219]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:The chain is shorter than 50 times the integrated autocorrelation time for 3 parameter(s). Use this estimate with caution and run a longer chain!\n",
            "N/50 = 100;\n",
            "tau: [103.97769925 105.08424507 103.89204325]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[103.97769925 105.08424507 103.89204325]\n",
            "[103.77940473 103.77867378 103.478454  ]\n",
            "[102.96941703 101.64203097 101.76098557]\n",
            "[102.68053555 100.85019211 101.33119794]\n",
            "[102.53514987 100.25822262 100.88223736]\n",
            "[102.29889788  99.15652803 100.31488088]\n",
            "[101.74820943  98.03414973  98.86069028]\n",
            "[100.91262556  96.16848623  97.49051524]\n",
            "[100.16645466  94.75587766  96.48047567]\n",
            "[99.79124359 93.54879268 95.56633627]\n",
            "[99.15365375 92.32139947 94.66355422]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "$$\\mathrm{M} = -4.862_{-0.137}^{0.138}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "$$\\mathrm{T} = 31.074_{-2.488}^{2.867}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "$$\\mathrm{\\beta} = 0.997_{-0.192}^{0.185}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[[ 0.04560353 -0.65990983 -0.05259447]\n",
            " [-0.65990983 10.70103294  0.76761611]\n",
            " [-0.05259447  0.76761611  0.06534863]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 547.2x547.2 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_UTn334I-uhf"
      },
      "source": [
        "# Some toy problems\n",
        "\n",
        "Previous sections will have used some example problem (I'm thinking a modified blackbody, as it has some correlated parameters and is clearly astrophysically relevant) to demonstrate at each stage, building it up with emcee. Now I will basically demonstrate how to do the same thing with a few other packages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c49fLh0l_Ao1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tVjbmQM_BBz"
      },
      "source": [
        "# Problem set\n",
        "\n",
        "## Fitting a line to data\n",
        "\n",
        "First, we define a set of data that will be used in the first few problems here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzPIIGJO_KYU"
      },
      "source": [
        "x = np.array([ 0.23397176,  0.11288574,  0.50961469,  0.79911787,  0.52566214,\n",
        "        1.50423103,  1.05759968,  1.93254129,  1.97368057,  2.46199348,\n",
        "        2.8082922 ,  2.48448987,  3.18557041,  3.2426218 ,  3.07491281,\n",
        "        3.97624142,  3.59475299,  4.52290351,  4.44632792,  4.9194819 ,\n",
        "        3.43477682,  4.33252443,  5.53803749,  5.9694292 ,  6.96247836,\n",
        "        5.87545248,  5.95249939,  7.55538808,  8.09503967,  5.57724702,\n",
        "        8.25834164,  7.75371055,  8.57417011,  7.16522482,  8.76276191,\n",
        "        8.25800109,  8.85517661, 10.62058448,  8.91969572,  8.21046701,\n",
        "        9.05865873, 12.40087501,  9.86730305, 10.08400622, 10.96808943,\n",
        "       11.3766884 , 14.01166776, 13.64927879, 12.91979521, 13.47962809])\n",
        "xerr = np.array([0.39707461, 0.25286896, 0.40876382, 0.33422734, 0.44015499,\n",
        "       0.23088603, 0.25107607, 0.39855484, 0.29186554, 0.55797678,\n",
        "       0.12010648, 0.42387588, 0.12751993, 0.38407238, 0.51137741,\n",
        "       0.17259736, 0.10992658, 0.12621551, 0.55812833, 0.10497772,\n",
        "       0.32221105, 0.44205587, 0.55420819, 0.51460545, 0.21683453,\n",
        "       0.29253898, 0.33786511, 0.42698222, 0.18027914, 0.1117489 ,\n",
        "       0.10372124, 0.3186137 , 0.35795788, 0.46816197, 0.19754305,\n",
        "       0.4101803 , 0.52614458, 0.31444343, 0.4971158 , 0.3011785 ,\n",
        "       0.16670916, 0.58577319, 0.3536859 , 0.35960165, 0.37355915,\n",
        "       0.34889201, 0.31134202, 0.36053818, 0.35934637, 0.59438089])\n",
        "y = np.array([  5.33159863,   4.54207063,   3.94714832,   5.07835022,\n",
        "         3.48107278,   5.75684067,   1.96142563,   5.37431185,\n",
        "        -0.36494822,   2.10337149,   2.98365343,   0.78280289,\n",
        "         3.13322895,   2.2306922 ,   1.07370918,   1.95343259,\n",
        "         0.74043931,   1.00071999,   0.96095849,   0.9630811 ,\n",
        "         1.16994485,   1.20598939,   0.09682621,   0.4290044 ,\n",
        "        -0.57584825,  -0.53772109,   0.98842269,  -0.76816703,\n",
        "        -0.49606409,  -0.60852032,  -1.82310651,  -1.69694637,\n",
        "        -0.6495982 ,  -2.90039355,  -3.63629452,  -2.69475927,\n",
        "        -3.88547614,  -4.95450567,  -4.08394741,  -3.75948239,\n",
        "        -1.76474007,  -0.2151749 ,  -5.24217571,   0.47526216,\n",
        "        -5.31799796,  -3.21582053, -12.99338521,  -7.43975556,\n",
        "        -5.7743399 ,  -2.21306606])\n",
        "yerr = np.array([0.42537167, 0.41059338, 0.48075352, 0.24533628, 0.13800163,\n",
        "       0.22955306, 0.16164972, 0.43055699, 0.12072778, 0.3339441 ,\n",
        "       0.51044773, 0.31123917, 0.47911244, 0.29986134, 0.46914146,\n",
        "       0.31812468, 0.25274154, 0.39277097, 0.34739   , 0.45935337,\n",
        "       0.32571458, 0.58467079, 0.15559012, 0.23166097, 0.35361529,\n",
        "       0.17283631, 0.50050884, 0.59724943, 0.13159362, 0.38846466,\n",
        "       0.5793877 , 0.17410052, 0.38810178, 0.30507875, 0.10496006,\n",
        "       0.16885583, 0.38571698, 0.40130093, 0.54612869, 0.15087449,\n",
        "       0.26459806, 0.22369835, 0.33098201, 0.47549512, 0.39043335,\n",
        "       0.53012881, 0.19095729, 0.27526493, 0.22856709, 0.39380301])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHX-dbxJD68F"
      },
      "source": [
        "This same dataset will be used for several different problems. First, assume that the uncertainty on x is negligible, but that the uncertainty on the y values has been underestimated by some amount. Try to infer both the slope and intercept of the line which generated this data, and the amount by which the uncertainty has been underestimated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5-CytHnEV7C"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QUMFfKCEWkO"
      },
      "source": [
        "Next, take the uncertainties at face value, but try to modify the likelihood function to include uncertainties on both the x and y values. This problem is covered in detail by Hogg."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTDGUh7wGAvB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nl3kJUNMGBCq"
      },
      "source": [
        "Now, a particularly common problem for astronomy. Let's say that the data are good, but the uncertainties reflect only the precision (that is, the statistical uncertainty on the measurement itself) but the values are drawn from some distribution where unknown variables perturb each source through physical mechanisms that are not included in our model. In this case, we say that the problem is affected by *intrinsic scatter*. Again, Hogg covers this in some detail, and it is widely applied to the M-sigma relation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZS-5te1G-kr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poGALu_hG-1_"
      },
      "source": [
        "Finally, an advanced problem. What if the distribution of each measurement is *not* gaussian? Assume that the y values are **count rates** and try to re-write the likelihood function for this case."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WckTmkl-HY_A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asmD7tGuHZSS"
      },
      "source": [
        "## Checking convergence\n",
        "\n",
        "The code cell below fits a model to some data - a spectrum interpreted with a single line. Make trace plots and calculate autocorrelation times for the chains and decide whether the MCMC has been run long enough yet. If not, how many steps does it need to run for?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1T0-1125HhEe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c866c4a9-8f35-4bd8-e71f-b48b724d9516"
      },
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "x = np.array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
        "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
        "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
        "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
        "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
        "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
        "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
        "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
        "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
        "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
        "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
        "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
        "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
        "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
        "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
        "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
        "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
        "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
        "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
        "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])\n",
        "y = np.array([0.60614598, 0.6153662 , 0.47419432, 0.64142881, 0.5404495 ,\n",
        "       0.65958241, 0.5388796 , 0.72837078, 0.56604171, 0.86397498,\n",
        "       0.4751028 , 0.4119352 , 0.53598582, 1.04707218, 0.83268879,\n",
        "       0.65939936, 0.71741511, 0.79977691, 0.76297566, 0.56573409,\n",
        "       0.68881211, 0.93441395, 1.08102487, 1.06692672, 0.91861211,\n",
        "       1.14857326, 0.98065209, 0.99522437, 0.98278417, 1.11751265,\n",
        "       0.79828341, 0.18980406, 1.11795621, 1.16912414, 1.08937796,\n",
        "       1.05315588, 0.95460981, 1.35636458, 1.5210485 , 1.70122012,\n",
        "       1.8312029 , 2.50786172, 2.80173261, 3.10481785, 2.62385979,\n",
        "       2.20492704, 1.6121115 , 1.64443925, 1.69162613, 0.96340852,\n",
        "       0.86319356, 0.61203334, 0.87827908, 0.73654364, 0.99289407,\n",
        "       0.74860198, 0.5520727 , 0.74797704, 0.60877654, 0.48734763,\n",
        "       0.41548919, 0.47134843, 0.65060695, 0.55424859, 0.53872972,\n",
        "       0.42004391, 0.50261679, 0.41800777, 0.14338189, 0.39985653,\n",
        "       0.34840721, 0.37024026, 0.57578898, 0.16391488, 0.36135355,\n",
        "       0.36556374, 0.72052741, 0.49379491, 0.58735107, 0.24443858,\n",
        "       0.44170883, 0.498692  , 0.51745757, 0.45058975, 0.25297367,\n",
        "       0.57483966, 0.29939929, 0.5941698 , 0.35649874, 0.56844186,\n",
        "       0.45872986, 0.36447514, 0.52903476, 0.51461255, 0.70671988,\n",
        "       0.36093269, 0.63303675, 0.76049668, 1.0671897 , 0.58650049])\n",
        "yerr = np.array([0.13571468, 0.17649925, 0.2427158 , 0.19674616, 0.1374289 ,\n",
        "       0.13734713, 0.23344185, 0.13494697, 0.16865474, 0.14877157,\n",
        "       0.1523842 , 0.21276768, 0.21248585, 0.23104686, 0.2186303 ,\n",
        "       0.16633297, 0.12466968, 0.19250511, 0.23896049, 0.24204692,\n",
        "       0.11889055, 0.20328089, 0.19548629, 0.1582987 , 0.18590566,\n",
        "       0.24759034, 0.23386036, 0.14216423, 0.18593009, 0.2357429 ,\n",
        "       0.21747414, 0.24222248, 0.17103163, 0.1992606 , 0.15450062,\n",
        "       0.22667307, 0.23436873, 0.11204839, 0.11259136, 0.11087712,\n",
        "       0.23634978, 0.17765576, 0.24790074, 0.15669243, 0.1050639 ,\n",
        "       0.11204431, 0.13651244, 0.14456857, 0.24083602, 0.21794055,\n",
        "       0.18097375, 0.13599461, 0.23872987, 0.10324854, 0.24533545,\n",
        "       0.12294229, 0.16149936, 0.10591347, 0.21025653, 0.18675912,\n",
        "       0.16242686, 0.16716389, 0.18856679, 0.11728497, 0.11803215,\n",
        "       0.15240304, 0.12481872, 0.17082965, 0.24074905, 0.13743163,\n",
        "       0.23039399, 0.21941084, 0.24978922, 0.19685753, 0.16923487,\n",
        "       0.22468505, 0.18220194, 0.10126559, 0.16738149, 0.13034307,\n",
        "       0.16072441, 0.12061447, 0.15581466, 0.18634505, 0.21928294,\n",
        "       0.12790317, 0.21768046, 0.15576875, 0.19392913, 0.10682687,\n",
        "       0.18502616, 0.17874781, 0.1710912 , 0.12937686, 0.20394657,\n",
        "       0.13313035, 0.10067853, 0.24100547, 0.18669291, 0.19211422])\n",
        "\n",
        "\n",
        "def model(x, centre, width, int_intens, baseline):\n",
        "  #A model for a spectral line described by a gaussian, normalised by integrated intensity\n",
        "  return int_intens*norm.pdf(x, centre, width) + baseline\n",
        "\n",
        "def lnlike(theta, x, y, yerr):\n",
        "  #This function calculates the likelihood P(D|M) for our model.\n",
        "  centre = theta[0]\n",
        "  width = theta[1]\n",
        "  int_intens = theta[2]\n",
        "  baseline = theta[3]\n",
        "  flux = model(x, centre, width, int_intens, baseline)\n",
        "  return-0.5* np.sum((y - flux)**2 /yerr**2)\n",
        "\n",
        "def lnprior(theta):\n",
        "  #This is a flat prior - probability is uniform inside a given range, and zero outside\n",
        "  if -10 < theta[0] < 10 and 0 < theta[1] < 100 and 0 < theta[2] < 3 and -10 < theta[3] < 10:\n",
        "    return 0\n",
        "  return -np.inf\n",
        "\n",
        "def lnprob(theta, x, y, yerr):\n",
        "  lp = lnprior(theta)\n",
        "  if lp == -np.inf:\n",
        "    return lp\n",
        "  return lp + lnlike(theta, x, y, yerr)\n",
        "\n",
        "\n",
        "ndim=4 #Three dimensions for this problem - M, T and beta\n",
        "nwalkers=100 #emcee is an affine-invariant ensemble sampler. This means it uses several chains.\n",
        "             # the number of walkers (=chains) must be even, and should be as large as possible (at least double ndim)\n",
        "\n",
        "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(x, y, yerr))\n",
        "\n",
        "steps = 500\n",
        "pos = [[0, 1, 0, 0] + np.random.randn(ndim) for i in range(nwalkers)]\n",
        "sampler.run_mcmc(pos,steps)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State([[ 4.30698245  0.37538387  1.99589112  0.60391623]\n",
              " [ 4.31029293  0.38675313  1.89884259  0.63245835]\n",
              " [ 4.29232002  0.32888891  1.86545952  0.65168914]\n",
              " [ 4.30123308  0.35909561  1.90436449  0.62064179]\n",
              " [ 4.30065495  0.35226575  1.88529494  0.59876063]\n",
              " [-7.77848653  1.15068747  0.11697991  0.86299199]\n",
              " [-1.01280783 -0.36686548 -2.19605223 -1.67595619]\n",
              " [ 4.27774108  0.33427478  1.93173168  0.603249  ]\n",
              " [ 4.30487786  0.36094901  1.90813135  0.60820885]\n",
              " [ 4.32900258  0.37325395  1.92563384  0.60026777]\n",
              " [-8.45164511  2.12173945  0.24681781  0.88323435]\n",
              " [ 4.29269656  0.36917022  1.85076217  0.60490078]\n",
              " [-5.03834859  1.75107521  1.52811445  0.85003628]\n",
              " [-7.79698775  4.45435657  1.93654094  0.83454301]\n",
              " [ 4.29656179  0.35108297  1.9661742   0.62807585]\n",
              " [ 4.29019574  0.36961875  1.84688643  0.60476372]\n",
              " [ 4.29033493  0.36642923  1.93626287  0.59556982]\n",
              " [ 4.28357678  0.36545818  1.8882496   0.62014856]\n",
              " [ 4.30431314  0.36706437  1.7453771   0.62740972]\n",
              " [-6.88067363  1.26205111  1.86053975  0.82943545]\n",
              " [ 4.29308992  0.36873815  1.91753152  0.63385289]\n",
              " [ 4.24463611  0.48069794  1.85403053  0.61375464]\n",
              " [ 4.29720647  0.3570369   1.80726243  0.63469497]\n",
              " [ 4.2984736   0.33289354  1.88760259  0.61853352]\n",
              " [-6.15603217  1.85037932  2.24444775  0.84879971]\n",
              " [ 4.29237059  0.3713555   1.91892216  0.6000411 ]\n",
              " [ 4.30103513  0.34998026  1.93227504  0.61951379]\n",
              " [ 4.28978805  0.35497565  1.9093127   0.60930236]\n",
              " [ 4.29303875  0.35251755  1.85687978  0.61166538]\n",
              " [ 4.29123664  0.38264272  1.93446284  0.61670212]\n",
              " [ 4.28264524  0.35835258  1.87468872  0.62525995]\n",
              " [ 4.29774382  0.36550161  1.93222984  0.58514086]\n",
              " [ 4.29237184  0.33834497  1.82491985  0.63182987]\n",
              " [-4.56716262  1.31446833  1.99654175  0.84985533]\n",
              " [ 3.98672911  0.40177149  1.66577923  0.64181087]\n",
              " [-8.18275166  0.60574041  0.24174816  0.85019993]\n",
              " [ 4.27904286  0.36160019  1.86970619  0.62799824]\n",
              " [ 4.30955431  0.33950465  1.73460387  0.64481218]\n",
              " [ 4.27545749  0.38191881  1.96577831  0.59638887]\n",
              " [ 4.29197694  0.37368101  1.81650494  0.63815565]\n",
              " [ 4.28572139  0.36700089  1.901489    0.62662611]\n",
              " [ 4.29019264  0.37295915  1.89005297  0.62973556]\n",
              " [ 4.29805821  0.36386575  1.88045027  0.61601955]\n",
              " [ 4.31818413  0.37723798  2.00999323  0.58450216]\n",
              " [ 4.28605302  0.36119334  1.98386542  0.60265931]\n",
              " [ 4.3070814   0.34056148  1.84591308  0.61818135]\n",
              " [ 4.28573624  0.32637187  1.87727044  0.58958619]\n",
              " [ 4.32285161  0.34025418  1.83290343  0.59217231]\n",
              " [ 4.2915566   0.36918531  1.90142892  0.63253189]\n",
              " [ 4.27940878  0.34229482  1.8631963   0.6385786 ]\n",
              " [ 4.31038512  0.37179799  1.96238226  0.58816467]\n",
              " [ 4.30292576  0.33286729  1.86050283  0.59737058]\n",
              " [-5.1150737   1.19895177  2.64268501  0.84064448]\n",
              " [-4.59922264  0.36683455  2.21436804  0.82918438]\n",
              " [-8.17167492  5.72746542  0.663644    0.83963529]\n",
              " [-8.74792905  0.81081197  2.89981278  0.83931428]\n",
              " [ 4.29375713  0.34918529  1.85775879  0.59934835]\n",
              " [ 4.31584937  0.38964639  1.92664269  0.59207577]\n",
              " [ 4.2997525   0.3601617   1.91992916  0.61288965]\n",
              " [ 4.27362986  0.37053718  1.91213684  0.61638898]\n",
              " [-7.2339732   0.60940593  0.93911854  0.85895033]\n",
              " [ 4.29600857  0.36641062  1.98715162  0.59676099]\n",
              " [ 4.29907793  0.36308392  1.91323592  0.61291791]\n",
              " [ 4.29918577  0.33974831  1.85269381  0.65082892]\n",
              " [ 4.31782936  0.35074163  1.90673674  0.61106495]\n",
              " [ 4.2948015   0.38592068  1.99567321  0.59203545]\n",
              " [ 4.3040264   0.3566441   1.91283965  0.62489297]\n",
              " [ 4.31543391  0.35070026  1.9002436   0.59518313]\n",
              " [ 4.27048139  0.33711844  1.94397427  0.56941585]\n",
              " [-7.37097543  0.67994004  2.79635881  0.83603549]\n",
              " [ 4.28907242  0.36620678  1.98861937  0.58930955]\n",
              " [ 4.29732193  0.36132114  1.97062736  0.60749138]\n",
              " [-9.22512077  3.8251311   1.03954608  0.83145685]\n",
              " [-4.83657666  0.02318099  0.97460607  0.82272648]\n",
              " [-5.06317786  0.4611184   2.55166673  0.83886304]\n",
              " [ 4.30264206  0.33702161  1.90068756  0.60665098]\n",
              " [ 4.32080469  0.36197474  1.96094847  0.58864229]\n",
              " [ 4.31871983  0.34699303  1.87206133  0.611696  ]\n",
              " [-7.67832061  0.46706553  1.85441265  0.84315052]\n",
              " [ 4.30910142  0.35909648  1.86631745  0.62316499]\n",
              " [ 4.32801232  0.33175946  1.82428402  0.65467412]\n",
              " [ 4.3064048   0.376189    1.95935689  0.62180912]\n",
              " [ 4.32195025  0.38384369  1.92724245  0.63008129]\n",
              " [ 4.29744089  0.35343015  1.86871692  0.63387686]\n",
              " [ 4.29676072  0.35769265  1.89363857  0.63785012]\n",
              " [ 4.31210584  0.32640119  1.78196453  0.64891502]\n",
              " [-7.15230709 11.63737756  0.93956472  0.81460894]\n",
              " [ 4.2999651   0.31741197  1.76845196  0.63770576]\n",
              " [-6.03250027  1.8907513   2.00772398  0.86783961]\n",
              " [ 4.30969745  0.35611893  1.95181883  0.61927757]\n",
              " [ 4.32197749  0.37299555  1.84071243  0.61667608]\n",
              " [ 4.28643035  0.34600314  1.92607904  0.63432027]\n",
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              " [ 4.3137241   0.3496364   1.85805135  0.60695297]\n",
              " [ 4.28348022  0.41066586  1.89655823  0.61453929]\n",
              " [ 4.29555975  0.35065536  1.84427982  0.64441176]\n",
              " [ 4.28741559  0.33162508  1.82264176  0.64292891]\n",
              " [ 4.29904525  0.34835311  1.90843114  0.60524444]\n",
              " [-8.64675257  4.357223    0.96642118  0.84079107]], log_prob=[ -85.32787492  -86.71614199  -88.68581598  -84.55796701  -85.03724448\n",
              " -658.48673117          -inf  -88.02682782  -84.53902583  -86.7598288\n",
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              " -658.03125185  -85.37869686  -86.9052406   -84.50289569  -86.86757245\n",
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              "  -86.1559196   -86.9405968   -86.02169189 -657.95476929  -84.81280894\n",
              "  -91.15881665  -85.30692061  -86.77236452  -85.05825297  -85.55341461\n",
              "  -88.05613817 -655.42867784  -87.57443178 -658.99591985  -85.82015751\n",
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              "  -91.24301002  -85.87832333  -86.64713576  -84.97789949 -658.30658209], blobs=None, random_state=('MT19937', array([1185594678,  991691327, 1253129389, 2901481988, 1639418262,\n",
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              "       2046071770, 2883875363,  464761964, 1210323171, 3621253043,\n",
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              "       2171527728, 1149132529, 1811390544, 2925392815, 1417050149,\n",
              "        465410277, 3829142322, 1658007162, 1026649268, 3568527544,\n",
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              "       1485717803, 1923556375, 1704793973, 3221944842, 2903865583,\n",
              "       1373693777, 3422716309, 3951157385, 3826368953, 1151724784,\n",
              "       3491551542, 2510323211, 2749635447, 2317214840, 4269941356,\n",
              "       2002059132, 2534645418,  894105298, 2944753806, 3521098660,\n",
              "       2019556745, 2291366992,  319018631, 2224681712, 1280238345,\n",
              "       4006477197, 3229489616, 3319293631, 1412178884,  802190987,\n",
              "       1720506585, 2610311905, 3581415116,  975154571, 2047503319,\n",
              "        304591682, 4257732913, 1199784328, 1271646612, 3328569057,\n",
              "       2111342410,  491099782,  773758467,  633848288, 3080961593,\n",
              "       3754975071, 1424564023,  724627768, 3604535237, 3286615788,\n",
              "       1404797601, 2808554352, 1990473163, 2109204951,    7572147,\n",
              "        676277993, 3800343775, 1929947184, 3181227071, 1066928799,\n",
              "       1233337883, 4210246985, 1434532315, 3260700591, 3554587476,\n",
              "       1746846975, 2291181545, 3465491653, 3951478141, 4070341341,\n",
              "       1342785305, 4016750378,  529328520, 1531019133], dtype=uint32), 169, 0, 0.0))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTkxVUOcWQsT"
      },
      "source": [
        "#Put your work here"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmqIk9-iHhUs"
      },
      "source": [
        "## Posterior predictive checks\n",
        "\n",
        "Using the example from the previous section, is the model appropriate for the data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AWqYnrJHmiz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79K0h6hVIW4Q"
      },
      "source": [
        "## Prior sensitivity tests\n",
        "\n",
        "Try varying the prior of the above model and see what difference  this makes to the fit. First, try changing the bounds of the flat prior. Then see if you can estimate any of the parameters ahead of time to impose non-flat priors on the parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzhk8FiYIWrc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3T-brc8Hm8v"
      },
      "source": [
        "## Nuisance parameters\n",
        "\n",
        "Go back to the modified blackbody example given above. In that case we assumed that the distance is a delta function (known exactly). In most cases, however, we have a measured distance with an uncertainty, possibly from parallax measurements. Attempt to re-write this example to assume that the distance can be constrained by a parallax of 10 +/- 1 milliarcseconds, and marginalise out this constraint to see what effect this has on the posterior distribution of the mass."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqweFwB1EWtN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_f43A5guEW6u"
      },
      "source": [
        "## Using other packages\n",
        "\n",
        "Now try to repeat some of the examples using other MCMC packages. In particular, if you used a black box code before, try probabilistic programming instead, or vice versa. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQ0K2iF3nMQj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_yss1XomaeY"
      },
      "source": [
        "\"\"\" Copyright 2021 Peter Scicluna\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy of \n",
        "this software and associated documentation files (the \"Software\"), to deal in \n",
        "the Software without restriction, including without limitation the rights to \n",
        "use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of \n",
        "the Software, and to permit persons to whom the Software is furnished to do so, \n",
        "subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all \n",
        "copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR \n",
        "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS \n",
        "FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR \n",
        "COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER \n",
        "IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN \n",
        "CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}