{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A papermill example: Fitting a model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify default parameters\n",
    "\n",
    "This is a \"parameters\" cell, which defines default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Our default parameters\n",
    "# This cell has a \"parameters\" tag, means that it defines the parameters for use in the notebook\n",
    "start_date = \"2001-08-05\"\n",
    "stop_date = \"2016-01-01\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up our packages and create the data\n",
    "\n",
    "We'll run `plt.ioff()` so that we don't get double plots in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import papermill as pm\n",
    "plt.ioff()\n",
    "np.random.seed(1337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate some fake data by date\n",
    "dates = pd.date_range(\"2010-01-01\", \"2017-01-01\")\n",
    "data = pd.DataFrame(np.random.randn(len(dates)), index=dates, columns=['mydata'])\n",
    "data = data.rolling(100).mean()  # Smooth it so it looks purdy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose a subset of data to highlight\n",
    "\n",
    "Here we use the **start_date** and **stop_date** parameters, which are defined above by default, but can\n",
    "be overwritten at runtime by papermill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_highlight = data.loc[start_date: stop_date]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `pm.record()` function to keep track of how many records were included in the\n",
    "highlighted section. This lets us inspect this value after running the notebook with papermill.\n",
    "\n",
    "We also include a ValueError if we've got a but in the start/stop times, which will be captured\n",
    "and displayed by papermill if it's triggered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_records = len(data_highlight)\n",
    "pm.record('num_records', num_records)\n",
    "if num_records == 0:\n",
    "    raise ValueError(\"I have no data to highlight! Check that your dates are correct!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make our plot\n",
    "\n",
    "Below we'll generate a matplotlib figure with our highlighted dates. By calling `pm.display()`, papermill\n",
    "will store the figure to the key that we've specified (`highlight_dates_fig`). This will let us inspect the\n",
    "output later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(data.index, data['mydata'], c='k', alpha=.5)\n",
    "ax.plot(data_highlight.index, data_highlight['mydata'], c='r', lw=3)\n",
    "ax.set(title=\"Start: {}\\nStop: {}\".format(start_date, stop_date))\n",
    "pm.display('highlight_dates_fig', fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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