now with scales and trasformations

paracoords2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.transforms as mtrans\n",
    "import mpl_toolkits.axes_grid1.inset_locator as inset\n",
    "\n",
    "import pandas\n",
    "import seaborn\n",
    "\n",
    "seaborn.set(style='ticks')\n",
    "%matplotlib inline\n",
    "\n",
    "def connect_spines(left_ax, right_ax, left_y, right_y, **line_kwds):\n",
    "    left_trans = mtrans.blended_transform_factory(left_ax.transData, left_ax.transAxes)\n",
    "    right_trans = mtrans.blended_transform_factory(right_ax.transData, right_ax.transAxes)\n",
    "\n",
    "    left_data_trans = left_ax.transScale + left_ax.transLimits\n",
    "    right_data_trans = right_ax.transScale + right_ax.transLimits\n",
    "    \n",
    "    left_pos = left_data_trans.transform((0, left_y))[1]\n",
    "    right_pos = right_data_trans.transform((0, right_y))[1]\n",
    "    \n",
    "    bbox = mtrans.Bbox.from_extents(0, left_pos, 0, right_pos)\n",
    "    right_bbox = mtrans.TransformedBbox(bbox, right_trans)\n",
    "    left_bbox = mtrans.TransformedBbox(bbox, left_trans)\n",
    "\n",
    "    connecter = inset.BboxConnector(left_bbox, right_bbox, loc1=3, loc2=2, **line_kwds)\n",
    "    connecter.set_clip_on(False)\n",
    "\n",
    "    left_ax.add_line(connecter)\n",
    "\n",
    "    return connecter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EfXVPxmkROp4oHdCoQhQZj8x8DfiemR1aO4v0FhWidKXMfBRYY2a7184ivUOFKF0rM28F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\ncETU+YKZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae33ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (left_ax, center_ax, right_ax) = plt.subplots(ncols=3, figsize=(5,5))\n",
    "\n",
    "left_ax.set_yscale('log')\n",
    "left_ax.set_ylim(0.1, 100)\n",
    "\n",
    "center_ax.set_ylim(3, 12)\n",
    "right_ax.set_ylim(-15, -10)\n",
    "\n",
    "_ = connect_spines(left_ax, center_ax, 5, 9)\n",
    "_ = connect_spines(center_ax, right_ax, 9, -14)\n",
    "\n",
    "seaborn.despine(fig=fig, bottom=True)\n",
    "for ax in fig.axes:\n",
    "    ax.set_xticks([])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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