{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a BoxPlot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First we import magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import Magics.macro as magics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DRagsKkJlMZ9NoCIEAACwDhYsAAAAyViwAAAAJIuYBYtKhUpF2UqFipCK0AQqQuYz89ko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\nAJDs/wMAogFpN3sI+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "map = magics.mmap(subpage_map_projection = \"cartesian\",\n",
    "                  subpage_x_axis_type = 'date',\n",
    "                  subpage_y_axis_type = 'regular',\n",
    "                  subpage_x_date_min = '2016-11-03 00:00',\n",
    "                  subpage_x_date_max = '2016-11-13 12:00',\n",
    "                  subpage_y_min = 10.,\n",
    "                  subpage_y_max = 40.,\n",
    "                  subpage_y_position = 5.)\n",
    "\n",
    "horizontal_axis = magics.maxis(axis_orientation = \"horizontal\",\n",
    "                               axis_type = 'date',\n",
    "                               axis_grid = \"on\",\n",
    "                               axis_grid_line_style = \"solid\",\n",
    "                               axis_grid_thickness = 1,\n",
    "                               axis_grid_colour = \"grey\",\n",
    "                               axis_minor_tick ='on',\n",
    "                               axis_minor_grid ='on',\n",
    "                               axis_minor_grid_line_style = \"dot\",\n",
    "                               axis_minor_grid_colour = \"grey\",\n",
    "                               axis_title = 'on',\n",
    "                               axis_title_text = \"Time...\",\n",
    "                               )\n",
    "vertical_axis = magics.maxis(axis_orientation = \"vertical\",\n",
    "                               axis_grid = \"on\",\n",
    "                               axis_grid_line_style = \"solid\",\n",
    "                               axis_grid_thickness = 1,\n",
    "                               axis_grid_colour = \"grey\",\n",
    "                            )\n",
    "title = magics.mtext(text_lines=['Setting up a Box Plot'])\n",
    "magics.plot(map, horizontal_axis, vertical_axis, title)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the input data .. \n",
    "In this example we are using a 10 days temperature forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "steps= ['2016-11-03 06:00', '2016-11-03 12:00', '2016-11-03 18:00', \n",
    "        '2016-11-04 00:00', '2016-11-04 06:00', '2016-11-04 12:00', '2016-11-04 18:00', \n",
    "        '2016-11-05 00:00', '2016-11-05 06:00', '2016-11-05 12:00', '2016-11-05 18:00', \n",
    "        '2016-11-06 00:00', '2016-11-06 06:00', '2016-11-06 12:00', '2016-11-06 18:00', \n",
    "        '2016-11-07 00:00', '2016-11-07 06:00', '2016-11-07 12:00', '2016-11-07 18:00', \n",
    "        '2016-11-08 00:00', '2016-11-08 06:00', '2016-11-08 12:00', '2016-11-08 18:00', \n",
    "        '2016-11-09 00:00', '2016-11-09 06:00', '2016-11-09 12:00', '2016-11-09 18:00', \n",
    "        '2016-11-10 00:00', '2016-11-10 06:00', '2016-11-10 12:00', '2016-11-10 18:00', \n",
    "        '2016-11-11 00:00', '2016-11-11 06:00', '2016-11-11 12:00', '2016-11-11 18:00', \n",
    "        '2016-11-12 00:00', '2016-11-12 06:00', '2016-11-12 12:00', '2016-11-12 18:00', \n",
    "        '2016-11-13 00:00']\n",
    "\n",
    "min = numpy.array([ 20.11,  22.13,  26.13,  22.93,  20.19,  23.61,  28.19,  23.66,\n",
    "        21.63,  25.32,  29.07,  23.35,  22.26,  24.88,  28.81,  23.48,\n",
    "        20.83,  22.85,  26.6 ,  21.36,  19.44,  22.81,  25.14,  20.95,\n",
    "        20.12,  22.74,  23.64,  20.62,  19.52,  22.31,  27.21,  21.16,\n",
    "        20.55,  23.7 ,  25.92,  21.48,  20.99,  22.06,  23.51,  20.74])\n",
    "max = numpy.array([ 21.87,  25.78,  30.05,  24.85,  22.42,  27.4 ,  32.44,  26.14,\n",
    "        23.29,  27.8 ,  34.48,  27.21,  24.19,  28.01,  33.23,  27.4 ,\n",
    "        23.6 ,  27.64,  34.16,  26.57,  22.8 ,  27.02,  35.19,  26.19,\n",
    "        22.7 ,  26.97,  32.82,  25.82,  22.55,  27.33,  33.13,  26.61,\n",
    "        23.21,  27.79,  34.48,  28.86,  25.17,  29.95,  34.86,  28.17])\n",
    "seventyfive = [ 21.05,  24.83,  28.67,  24.32,  21.99,  26.12,  30.73,  25.27,\n",
    "        22.83,  27.49,  32.76,  26.43,  23.32,  26.93,  32.07,  25.99,\n",
    "        22.63,  26.83,  32.  ,  25.56,  22.21,  25.99,  31.17,  24.97,\n",
    "        22.  ,  26.16,  30.79,  24.68,  21.44,  26.05,  31.51,  25.37,\n",
    "        22.23,  26.93,  32.31,  25.92,  22.9 ,  27.67,  31.92,  25.75]\n",
    "twentyfive = [ 20.45,  23.53,  27.29,  23.54,  21.29,  24.86,  29.28,  24.42,\n",
    "        22.4 ,  26.59,  31.12,  25.42,  22.7 ,  26.09,  30.51,  25.11,\n",
    "        21.91,  25.93,  29.79,  24.16,  21.57,  25.04,  28.95,  23.51,\n",
    "        21.11,  25.05,  28.59,  22.86,  20.83,  24.96,  29.7 ,  23.38,\n",
    "        21.27,  25.32,  29.15,  23.73,  21.73,  25.26,  27.33,  23.05]\n",
    "median = [ 20.87,  24.16,  27.86,  23.75,  21.78,  25.31,  30.02,  24.81,\n",
    "        22.58,  27.13,  31.76,  25.74,  23.08,  26.58,  31.54,  25.5 ,\n",
    "        22.3 ,  26.35,  31.07,  24.95,  21.9 ,  25.5 ,  29.79,  24.35,\n",
    "        21.48,  25.66,  29.74,  23.95,  21.11,  25.58,  30.3 ,  24.39,\n",
    "        21.73,  26.18,  31.14,  24.72,  22.43,  26.75,  29.92,  24.19]\n",
    "\n",
    "\n",
    "\n",
    "boxplot = magics.mboxplot(boxplot_date_positions=steps,\n",
    "        boxplot_minimum_values = min,\n",
    "        boxplot_maximum_values = max,\n",
    "        boxplot_box_upper_values = seventyfive,\n",
    "        boxplot_box_lower_values = twentyfive,\n",
    "        boxplot_median_values = median,\n",
    "        boxplot_box_width = 0.5,\n",
    "        boxplot_box_colour = \"navy\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Then we redefine the projection to take into account the values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KuDsypAj5kCLkQ4pQRkgR8pH7c0mJ5NpBsYUUofyQIuRDilDG3dEgRSgIKUI+pAhlhBQh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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map = magics.mmap(subpage_map_projection = \"cartesian\",\n",
    "                  subpage_x_axis_type = 'date',\n",
    "                  subpage_y_axis_type = 'regular',\n",
    "                  subpage_x_date_min = steps[0],\n",
    "                  subpage_x_date_max = steps[-1],\n",
    "                  subpage_y_min = min.min(),\n",
    "                  subpage_y_max = max.max(),\n",
    "                  subpage_y_position = 5.)\n",
    "magics.plot(map, horizontal_axis, vertical_axis, boxplot, title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
