Plotnine/scale_x_continuous.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:09:52.978088Z",
     "iopub.status.busy": "2021-03-04T13:09:52.974346Z",
     "iopub.status.idle": "2021-03-04T13:09:57.372200Z",
     "shell.execute_reply": "2021-03-04T13:09:57.370134Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from plotnine import *\n",
    "from mizani.transforms import trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guitar Neck ###\n",
    "*Using a transformed x-axis to visualise guitar chords*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The x-axis is transformed to resemble the narrowing width of frets on a 25.5 inch Strat. To do that\n",
    "we create custom transformation.\n",
    "\n",
    "The key parts of *any* transform object are the `transform` and `inverse` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-03-04T13:09:57.393795Z",
     "iopub.status.busy": "2021-03-04T13:09:57.390236Z",
     "iopub.status.idle": "2021-03-04T13:09:57.400041Z",
     "shell.execute_reply": "2021-03-04T13:09:57.398492Z"
    }
   },
   "outputs": [],
   "source": [
    "class frets_trans(trans):\n",
    "    \"\"\"\n",
    "    Frets Transformation\n",
    "    \"\"\"\n",
    "    number_of_frets = 23               # Including fret 0\n",
    "    domain = (0, number_of_frets-1)\n",
    "    \n",
    "    @staticmethod\n",
    "    def transform(x):\n",
    "        x = np.asarray(x)\n",
    "        return 25.5 - (25.5 / (2 ** (x/12)))\n",
    "    \n",
    "    @staticmethod\n",
    "    def inverse(x):\n",
    "        x = np.asarray(x)\n",
    "        return 12 * np.log2(25.5/(25.5-x))\n",
    "    \n",
    "    @classmethod\n",
    "    def breaks_(cls, limits):\n",
    "        # Fixed major breaks\n",
    "        return cls.domain\n",
    "    \n",
    "    @classmethod\n",
    "    def minor_breaks(cls, major, limits):\n",
    "        # The major breaks as passed to this method are in transformed space.\n",
    "        # The minor breaks are calculated in data space to reveal the \n",
    "        # non-linearity of the scale.\n",
    "        _major = cls.inverse(major)\n",
    "        minor = cls.transform(np.linspace(*_major, cls.number_of_frets))\n",
    "        return minor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above transform is different from most in that, breaks and minor breaks do not change. This is common of very specialized scales. It can also be a key requirement when creating graphics for demontration purposes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some chord Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-03-04T13:09:57.423912Z",
     "iopub.status.busy": "2021-03-04T13:09:57.413022Z",
     "iopub.status.idle": "2021-03-04T13:09:57.431027Z",
     "shell.execute_reply": "2021-03-04T13:09:57.429052Z"
    }
   },
   "outputs": [],
   "source": [
    "# Notes: the 0 fret is an open strum, all other frets are played half-way between fret bars.\n",
    "# The strings are 1:low E, 2: A, 3: D, 4: G, 5: B, 6: E\n",
    "c_chord = pd.DataFrame({\n",
    "    'Fret':   [0, 2.5, 1.5, 0, 0.5, 0],\n",
    "    'String': [1, 2, 3, 4, 5, 6]\n",
    "})\n",
    "\n",
    "# Sequence based on the number of notes in the chord\n",
    "c_chord['Sequence'] = list(range(1, 1+len(c_chord['Fret'])))\n",
    "\n",
    "# Standard markings for a Stratocaster\n",
    "markings = pd.DataFrame({\n",
    "    'Fret':   [2.5, 4.5, 6.5, 8.5, 11.5, 11.5, 14.5, 16.5, 18.5, 20.5],\n",
    "    'String': [3.5, 3.5, 3.5, 3.5, 2, 5, 3.5, 3.5, 3.5, 3.5]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the chord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T13:09:57.505111Z",
     "iopub.status.busy": "2021-03-04T13:09:57.468857Z",
     "iopub.status.idle": "2021-03-04T13:09:59.488591Z",
     "shell.execute_reply": "2021-03-04T13:09:59.486980Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look and feel of the graphic\n",
    "neck_color = '#FFDDCC'\n",
    "fret_color = '#998888'\n",
    "string_color = '#AA9944'\n",
    "\n",
    "neck_theme = theme(\n",
    "    figure_size=(10, 2),\n",
    "    panel_background=element_rect(fill=neck_color),\n",
    "    panel_grid_major_y=element_line(color=string_color, size=2.2),\n",
    "    panel_grid_major_x=element_line(color=fret_color, size=2.2),\n",
    "    panel_grid_minor_x=element_line(color=fret_color, size=1)\n",
    ")\n",
    "\n",
    "# Gallery Plot\n",
    "\n",
    "(ggplot(c_chord, aes('Fret', 'String'))\n",
    " + geom_path(aes(color='Sequence'), size=3)\n",
    " + geom_point(aes(color='Sequence'), fill='#FFFFFF', size=3)\n",
    " + geom_point(data=markings, fill='#000000', size=4)\n",
    " + scale_x_continuous(trans=frets_trans)\n",
    " + scale_y_continuous(breaks=range(0, 7), minor_breaks=[])\n",
    " + guides(color=False)\n",
    " + neck_theme\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Credit**: This example was motivated by [Jonathan Vitale](https://github.com/jonvitale) who wanted to create graphics for a guitar scale trainer."
   ]
  }
 ],
 "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": "3.8.3"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}