scour.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb4ab1bd",
   "metadata": {},
   "source": [
    "### demonstration of using scour to simplify SVG files for editing in Inkscape\n",
    "\n",
    "- scour repository: https://github.com/scour-project/scour  \n",
    "- docs: https://github.com/scour-project/scour/wiki/Documentation  \n",
    "- install using conda: conda install -c conda-forge scour  \n",
    "\n",
    "_if interested, scour can be used directly in a notebook following guidance here:_  \n",
    " https://stackoverflow.com/questions/50891715/run-python-scour-svg-optimizer-from-script-in-stead-of-the-cli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73bdab86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2c0f346a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scourfig(file):\n",
    "    # simple function to call the scour library to simplify SVGs made with matplotlib  \n",
    "    # using \"maximum scrubbing\" as described in the project's github repo\n",
    "    os.system('cp '+file+' temp.svg')\n",
    "    os.system('scour -i temp.svg -o '+file+' --enable-viewboxing --enable-id-stripping \\\n",
    "        --enable-comment-stripping --shorten-ids --indent=none')\n",
    "    os.remove('temp.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da5a7879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7febf87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iris.sepal_length, iris.sepal_width, 'o')\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Sepal Width')\n",
    "plt.savefig('iris.svg')\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abe5eee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scour processed file \"temp.svg\" in 56 ms: 19809/36336 bytes new/orig -> 54.5%\n"
     ]
    }
   ],
   "source": [
    "scourfig('iris.svg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}