The starting IPython notebook for Bayesianism Python seminar at eScience

clean-start.ipynb

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   "cell_type": "code",
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   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "from matplotlib import rcParams\n",
    "rcParams[\"savefig.dpi\"] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "import emcee                         # http://dfm.io/emcee\n",
    "import corner                        # https://github.com/dfm/corner.py\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "print(\"emcee:\", emcee.__version__)\n",
    "print(\"corner:\", corner.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generate some fake data:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "def mean_model(theta, x):\n",
    "    return theta[0] * x + theta[1]"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "np.random.seed(1234)  # live coding\n",
    "\n",
    "theta_true = np.array([1.234, -0.01, 2*np.log(0.9)])  # m, b, ln(s^2)\n",
    "\n",
    "x = np.sort(np.random.uniform(-5, 5, 20))\n",
    "yerr = np.random.uniform(0.3, 0.5, len(x))\n",
    "y = mean_model(theta_true, x)\n",
    "y += np.sqrt(yerr**2 + np.exp(theta_true[2])) * np.random.randn(len(y))\n",
    "\n",
    "x0 = np.linspace(-5, 5, 500)\n",
    "\n",
    "pl.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n",
    "pl.plot(x0, mean_model(theta_true, x0), \"g\", lw=1.25)\n",
    "pl.xlabel(\"x\")\n",
    "pl.ylabel(\"y\")\n",
    "pl.xlim(-5.1, 5.1)\n",
    "pl.ylim(-5.8, 5.8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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