examples/legend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11682e048>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4klQIA1+SCmHgS1IhDHxJKoSBL0mFMPAlqRAGviQVwsCXpEIY+JJUCANfkgph4EtSIQx8SSqEgS9JhTDwJakQBr4kFcLAl6RCGPiSVAgDX5IKYeBLUiEMfEkqhIEvSYUw8CWpEAa+JBXCwJekQhj4klQIA1+SCmHgS1IhDHxJKoSBL0mFMPAlqRAGviQVwsCXpEIY+JJUiIYCPyLOjYh1EbE+Iq7exfq3RMTt9fWPR8SxlVcqSRqSAQM/Ig4EFgPnATOBhRExs1+zy4FXMvPfgP8CvlJ1oZKkoWnkCP8UYH1mPpeZbwC3AQv6tVkAfL8+vRx4R0REdWVKkobqoAbaHAW80Ge+C3jb7tpk5raIeBU4AvhL30YRsQhYVJ99PSKeGUzR+6Fx9OurgtkXO9kXO9kXO00f7BsbCfzKZOZSYClARHRmZnsz9z9S2Rc72Rc72Rc72Rc7RUTnYN/byCmdjcCUPvOT68t22SYiDgIOAzYNtihJUvUaCfxVwLSImBoRo4CLgRX92qwAPlSfvhD4VWZmdWVKkoZqwFM69XPynwDuBw4ElmXm6oi4HujMzBXA94AfRsR64K/UPhQGsnQIde9v7Iud7Iud7Iud7IudBt0X4YG4JJXBJ20lqRAGviQVYtgD32EZdmqgL66KiDUR8XRE/DIijmlFnc0wUF/0aXdBRGRE7Le35DXSFxHxgfrvxuqIuLXZNTZLA38jR0fEgxHx2/rfyfmtqHO4RcSyiHh5d88qRc2N9X56OiLmNrThzBy2F7WLvH8A/hUYBTwFzOzX5kpgSX36YuD24aypVa8G++LfgUPq01eU3Bf1docCDwErgfZW193C34tpwG+Bf6nPH9nqulvYF0uBK+rTM4ENra57mPriTGAu8Mxu1p8P/BwI4FTg8Ua2O9xH+A7LsNOAfZGZD2bmlvrsSmrPPOyPGvm9ALiB2rhMvc0srska6YuPAosz8xWAzHy5yTU2SyN9kcDY+vRhwJ+aWF/TZOZD1O543J0FwA+yZiVweERMGmi7wx34uxqW4ajdtcnMbcA/hmXY3zTSF31dTu0TfH80YF/Uv6JOycyfNbOwFmjk9+I44LiIeCQiVkbEuU2rrrka6YvrgEsiogu4D/hkc0obcfY2T4AmD62gxkTEJUA7cFara2mFiDgA+CZwWYtLGSkOonZaZz61b30PRcRbM/N/W1lUiywEbs7Mb0TEadSe/5mVmdtbXdi+YLiP8B2WYadG+oKIeCdwDdCRma83qbZmG6gvDgVmAb+OiA3UzlGu2E8v3Dbye9EFrMjMrZn5R+D31D4A9jeN9MXlwB0AmfkY0EZtYLXSNJQn/Q134Dssw04D9kVEnAR8l1rY76/naWGAvsjMVzNzXGYem5nHUrue0ZGZgx40agRr5G/kLmpH90TEOGqneJ5rYo3N0khfPA+8AyAiZlAL/O6mVjkyrAAurd+tcyrwama+ONCbhvWUTg7fsAz7nAb74mvAGODO+nXr5zOzo2VFD5MG+6IIDfbF/cA5EbEG+Dvwuczc774FN9gXnwFuiohPU7uAe9n+eIAYET+m9iE/rn694lrgYIDMXELt+sX5wHpgC/Dhhra7H/aVJGkXfNJWkgph4EtSIQx8SSqEgS9JhTDwJakQBr4kFcLAl6RC/D8YimMKOgV+UwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a patch of color\n",
    "fig,ax = plt.subplots()\n",
    "ax.legend(handles=[mpatches.Patch(color=i) for i in 'rgb'],labels=['red','green','blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1166ac0f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line2D\n",
    "fig,ax = plt.subplots()\n",
    "ax.legend(handles=[mlines.Line2D([],[],marker='v',linestyle='--',color=i) for i in 'rgb'],labels=['red','green','blue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# further explanation of loc and bbox_to_anchors argument.\n",
    "\n",
    "1. there's a bounding box and a legend box \n",
    "2. when bbox_to_anchor is a tuple of length 4 (x0,y0,width,height), the x0,y0 always means the lower left of the bounding box. Then the loc determine how the legend box will align with the bounding box. for instance, upper left means the upper left of the bounding box and the upper left of the legend box are overlapping with each other.\n",
    "3. when bbox_to_anchor is a tuple of length 2 (x0,y0), the bounding box is by default a dot (width and height = 0), so it seems like the loc means the \"loc\" of the legend box itself to the (x0,y0) we just specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.94347372, 0.70198675],\n",
       "       [1.07004182, 0.92273731]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How to retrieve the width and height of the legend box\n",
    "fig,ax = plt.subplots()\n",
    "leg1 = ax.legend(handles=[mpatches.Patch(color=i) for i in 'rgb'],labels=['r','g','b'],loc='upper left',bbox_to_anchor=(1,1))\n",
    "plt.pause(0.01)  # most importantly!!!!!! doesn't matter which matplotlib backend you are using\n",
    "frame = leg1.get_frame()  # rectangle, actually fancyboxpath\n",
    "x0 = frame.get_x()\n",
    "y0 = frame.get_y()\n",
    "x1 = x0 + frame.get_width()\n",
    "y1 = y0 + frame.get_height()\n",
    "ax.transAxes.inverted().transform([[x0,y0],[x1,y1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94347372, 0.70198675],\n",
       "       [1.07004182, 0.92273731]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternatively\n",
    "frame = leg1.get_frame()\n",
    "ax.transAxes.inverted().transform(frame.get_bbox().get_points())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}