Taking the MTB tire size argument to Jupyter

tires.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Offroad bicycle tires diameters\n",
    "\n",
    "## 26ers don't roll\n",
    "\n",
    "## 29ers are for XC nerds\n",
    "\n",
    "## 650b was an industry conspiracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy\n",
    "import pandas\n",
    "import seaborn\n",
    "seaborn.set(style='ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the approximate circumferences\n",
    "\n",
    "From: http://www.bikecalc.com/wheel_size_math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>26er</th>\n",
       "      <th>650b</th>\n",
       "      <th>700c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.75 in</th>\n",
       "      <td>647.760618</td>\n",
       "      <td>672.588790</td>\n",
       "      <td>710.785976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00 in</th>\n",
       "      <td>660.493014</td>\n",
       "      <td>685.321185</td>\n",
       "      <td>723.518371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.35 in</th>\n",
       "      <td>678.318367</td>\n",
       "      <td>703.464848</td>\n",
       "      <td>738.797246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               26er        650b        700c\n",
       "1.75 in  647.760618  672.588790  710.785976\n",
       "2.00 in  660.493014  685.321185  723.518371\n",
       "2.35 in  678.318367  703.464848  738.797246"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circum = pandas.DataFrame({\n",
    "    '700c': [2233, 2273, 2321],\n",
    "    '650b': [2113, 2153, 2210],\n",
    "    '26er': [2035, 2075, 2131],\n",
    "}, index=['1.75 in', '2.00 in', '2.35 in'])\n",
    "\n",
    "diameter = circum / numpy.pi\n",
    "\n",
    "diameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the heights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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DFRsbK5PJVO4SvQAAoOrNmTNHycnJZdrdTel455131KVLFz388MPaunWrtm3b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\nGBphBQAAGBphBQAAGBphBQAAGNr/A+ahVAJmFcZoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8b8c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = diameter.plot.bar(legend=False)\n",
    "\n",
    "ax.set_ylim(bottom=0, top=800)\n",
    "ax.set_xlabel('Nominal Tire Width')\n",
    "ax.set_ylabel('Esimated Tire Diameter (mm)')\n",
    "ax.legend(bbox_to_anchor=(1.1, 0.5))\n",
    "seaborn.despine(ax=ax, offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the difference compared next size down (so 26er = N/A)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tW7ZMQUFBMplMxd7fsWNHidslJiaWqwAAAHBz/Pz8tGLookof056UlBQ1bNjQ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f83c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (\n",
    "    diameter\n",
    "        .diff(axis='columns')\n",
    "        .dropna(axis='columns')\n",
    "        .plot\n",
    "        .bar(legend=False)\n",
    ")\n",
    "\n",
    "ax.set_ylim(bottom=0, top=40)\n",
    "ax.set_xlabel('Nominal Tire Width')\n",
    "ax.set_ylabel('Increase in Tire Diameter from\\nnext smallest size (mm)')\n",
    "ax.legend(bbox_to_anchor=(1.1, 0.5))\n",
    "seaborn.despine(ax=ax, offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the difference compared to the original MTB wheels (so 26er = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x8be1c18>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WrVLt2rUVEBCgt956S5KUn59v3e7h4aH09HS7/cTGxiouLq5MNQAAAOOwGWIyMzM1dOjQ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\nAAAYEiEGAAAYEiEGAAAYEiEGAAAY0v8Hrz0YTcRi5ZQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa49c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (\n",
    "    diameter\n",
    "        .sub(diameter['26er'], axis='rows')\n",
    "        .drop('26er', axis='columns')\n",
    "        .plot\n",
    "        .bar(legend=False)\n",
    ")\n",
    "ax.set_ylim(bottom=0, top=70)\n",
    "seaborn.despine(ax=ax, offset=10, trim=True)\n",
    "ax.legend(bbox_to_anchor=(1.1, 0.5))\n",
    "ax.set_xlabel('Nominal Tire Width')\n",
    "ax.set_ylabel('Difference in Tire Diameter\\ncompared to 26ers (mm)')"
   ]
  }
 ],
 "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": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}