docs/source/getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started\n",
    "\n",
    "`uravu` enables the use of powerful Bayesian modelling in Python, building on the capabilities of `emcee` and `dynesty`, for the understanding of some data. \n",
    "To show how this can be used, we will create some synthetic data using `numpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from uravu.relationship import Relationship\n",
    "from uravu import plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 9, 10)\n",
    "y = np.linspace(0, 18, 10) + 2\n",
    "y += y * np.random.randn((10)) * 0.025\n",
    "y_err = np.ones((10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting this data, shows it appears to be a straight line relationship, with some gradient and intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x, y, y_err, marker='o', ls='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `uravu` to model a straight line function for the data, for more information about the requirements of the input function check out the [Input functions](./input_functions.html) tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def straight_line(x, m, c):\n",
    "    \"\"\"\n",
    "    A straight line.\n",
    "    \n",
    "    Args:\n",
    "        x (array_like): Abscissa data.\n",
    "        m (float): Gradient.\n",
    "        c (float): Intercept.\n",
    "    \n",
    "    Returns:\n",
    "        (array_like): Ordinate data.\n",
    "    \"\"\"\n",
    "    return m * x + c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function, x data, y data, and y error can then be brought together in the `Relationship` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "modeller = Relationship(straight_line, x, y, ordinate_error=y_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Relationship` class enables substantial functionality and is at the core of `uravu`, you can learn about this functionality in the [tutorials](./tutorials.html). \n",
    "Quickly, we will show the maximisation of the likelihood (there is more about this in the [first tutorial](max_likelihood.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "modeller.max_likelihood('mini')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The string `'mini'` indicates that the standard [scipy.optimize.minimize()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) function should be used.\n",
    "\n",
    "The built in `plotting` library can be used to make publication quality plot of the `Relationship`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax = plotting.plot_relationship(modeller, axes=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hope you enjoy using `uravu`, feel free to contribute on [Github](https://github.com/arm61/uravu), and tell your friends and colleagues!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}