{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Sampler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from opal import load_dataset, filetype\n",
    "\n",
    "ds = load_dataset('sampler/', fname='turn180_182_samples_0.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the plotting style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opal.visualization.styles import load_style\n",
    "\n",
    "load_style('jupyter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do some plotting ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "train0 = np.arange(84).tolist()\n",
    "train1 = np.arange(168).tolist()\n",
    "train2 = np.arange(252).tolist()\n",
    "train3 = np.arange(336).tolist()\n",
    "\n",
    "plt = ds.plot_training_vs_validation(train0=train0,\n",
    "                                     train1=train1,\n",
    "                                     train2=train2,\n",
    "                                     train3=train3)#,\n",
    "                                     #nsamples=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "train0 = np.arange(84).tolist()\n",
    "train3 = np.arange(336).tolist()\n",
    "\n",
    "plt = ds.plot_auto_correlation(train0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = ds.plot_auto_correlation(train3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = ds.plot_auto_correlation(train3, percent=True)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}