{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autofig Size Modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import autofig\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#autofig.inline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,0.1,11)\n",
    "y = np.full_like(x, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## s as float vs array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "style = {'marker': 'o', 'linestyle': 'none', 'xpad': 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When setting the size (via the 's' dimension) as a float, that value is used directly according to the size-mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47fc251a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autofig.reset()\n",
    "autofig.plot(x, y, s=0.05, **style)\n",
    "autofig.plot(x, y+10, s=0.1, **style)\n",
    "mplfig = autofig.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, when sending an array (or list), the values are mapped according to smap *before* being scaled according to the size-mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0Hb18+XK6d+8e17x//etf6d+/f0pyJSvlN4yLiEj10q1bN55++umY2995552lilxVo0InIiLlPPTQQ4wdO5baFfTyYGY8+OCDjBs3Lo3J4qdCJyIiET344IO89tprdOnSpdy0k046icmTJzN27FgsEydu46BCJyIiET366KNceeWV/CPC4xhyc3O54YYbuP322yN2+lyVqNCJiEg5Y8eO5amnnqq03W9/+1seeOCBNCRKnAqdiIiUsn37dh577LGY2z///POsW7cuhYmSo0InIiKlTJo0iYMHD8bc3t2ZMGFCChMlR4VORERKmT59elrmSRcVOhERKeXbb+N/xGgi86SLCp2IiJTSoEGDtMyTLip0IiJSSo8EHpGeyDzpokInIiKl/OQnP0nLPOmiQiciIqWcf/759OzZM+b2J598MldccUUKEyVHhU5ERMp57bXX6BDDc6qOOeYY3nrrrQr7xMw0FToRESmnTZs2LFy4kKuuuipiETMz+vfvz6JFi+jYsWMGEsZOhU5ERCJq1aoVU6dOZdy4cbRv354GDRpQv3592rZtyzPPPMPrr79OVlZWQss2s/5mttbMcs1sVITp9c1senj6YjPLCo+vZ2a/N7PVZvaxmV1U2bpU6EREJKLVq1dz8sknc8cdd7B+/XoOHDjAwYMH+eqrrxg1ahRZWVnMnz8/7uWaWW1gPDAA6Axca2adyzQbAex095OA54Gx4fH/BuDupwOXAr8wswprmQqdiIiU8/nnn9OnTx++/PLLqG2+/fZb+vXrR05OTryL7wHkunueu+cD04CBZdoMBCaHX78K9LXQ84A6A3MB3P1bYBdQ4ZPMVehERKScu+++m+3bt1fabu/evdx6663xLr4tsKnE+83hcRHbuHsh8B3QHPgYGGhmdcysPdAdOK6ilanQiYhIKXl5ecyZMyfm9suWLWPJkiVlR7cws5wSw8gS0yI9qdXLvI/WZhKhwpgD/ApYABRWlK9ORRNFROT/n5kzZ8b9MNVp06aV7R1lm7tHO6S4mdJ7Ye2Ar6O02WxmdYCmwA53d+CeQ43MbAHwRUXZtEcnIiKlbN26Ne55tm3bFk/zpcDJZtbezOoBQ4FZZdrMAoaHXw8G5rm7m1lDM2sEYGaXAoXu/klFK9MenYiIlHLEEUekdB53LzSzO4A5QG1gkruvMbMngRx3nwW8BEwxs1xgB6FiCNAKmGNmxcBXwPWVrU+FTkRESunduzdPP/103PPEw91nA7PLjHu0xOsDwNUR5tsAdIpnXTp0KSIipfTt25dOnWKvJa1atWLw4MEpTJQcFToRESnFzHjiiSdibv/www9Tr169FCZKjgqdiIiUM2TIEH75y18Sukc7utGjR3PnnXemKVViVOhERCSie+65h/fff59BgwaV6tj5UIfOs2fPZsyYMRlMGJukLkYxs3uAWwjdxLcauCl8AlFERGqAXr16cdppp3H66aezaNEi3J0zzzyTe++9l2OPPTbT8WKS8B6dmbUF7gKy3b0LoUtEh1Y8l4iIVBeFhYXcc889tGvXjieffJL/+Z//4d133+W5557jhBNOYMSIEezfvz/TMSuV7O0FdYAjzKwAaEj5O9tFRKQaKi4u5tprr+XVV1+NOL2goIBJkyaxfv163nnnnZp5MYq7fwU8B2wEtgDfufv/lG1nZiMP9XWWyN32IiKSflOmTIla5Er629/+xrhx49KQKHHJHLpsRugxCu2BNkAjM7uubDt3n+ju2e6e3bJly8STiohI2owfPz7mthMmTIi7b8x0Suaqy0uA9e6+1d0LgNeB84KJJSIimZKXl8fSpUvjah/h6QVVRjKFbiPQM9zBpgF9gU+DiSUiIpmSyGmmqnxqKplzdIsJPfV1OaFbC2oBEwPKJSIiGdK4ceO0zJMuSd0w7u6Pufsp7t7F3a9394NBBRMRkcw49dRTycrKirl9s2bN6NmzZ+oCJUk9o4iISCm1atXixz/+ccztb7zxxoQe7ZMuKnQiIlLO7bffzhlnnFFpu6ysLEaNGpWGRIlToRMRkXKaNGnCu+++y7nnnhu1TZcuXfjb3/5Gq1at0pgsfnrwqoiIRNSqVSsWLFjA3LlzmTBhAqtWrcLd6dSpEz/+8Y+57LLLqFWr6u8vqdCJiEiF+vbtS9u2bVm2bBnuTpcuXTjrrLMyHStmKnQiIhLVRx99xCOPPML7779fanyPHj144okn6N+/f2aCxaHq73OKiEhGvPXWW/Tt27dckQNYsmQJl19+OX/4wx/SHyxOKnQiIlLO9u3bGTp0KPn5+VHbFBcXc8stt5CXl5fGZPFToRMRkXImTZrE3r17K21XUFDAf//3f6chUeJU6EREpJxp06bF3Hbq1KkpTJI8FToRESln27ZtKWmbCSp0IiJSTtOmTVPSNhNU6EREpJwf/ehHMbe96qqrUpgkeSp0IiJSzsiRI6lbt26l7cyM2267LQ2JEqdCJyIi5bRp04aJEycSeq52dGPHjqVLly5pSpUYFToREYnoxhtv5LXXXqNTp07lpmVlZfHyyy/zwAMPZCBZfNQFmIiIRDVo0CAGDRrEvHnzSvV12b9//2rRoTOo0ImISAz69OnDGWecgbvTvHnzalPkQIcuRUSkAvv27eMXv/gFHTt2pGXLlrRq1YqsrCyefvppdu3alel4MVGhExGRiHbt2kXv3r25//77yc3NPTx+06ZNPPLII/Ts2ZOvv/46gwljo0InIiIRjRgxgpycnKjT165dyzXXXJPGRIlRoRMRkXLy8vL4y1/+Umm7+fPns2TJkjQkSpwKnYiIlDNjxgyKi4tjaqtOnUVEpNqJp6Pm7du3pzBJ8lToRESknJYtW8bctkWLFilMkjwVOhERKWfIkCEx3ys3bNiwFKdJjgqdiIiUk5WVxaBBgyptd+GFF5KdnZ2GRIlToRMRkYheeuklzj777KjTTz31VKZPn57GRIlRoRMRkYiaNm3K+++/z7hx4zjllFMOj8/KyuKZZ55h4cKFHHvssQkt28z6m9laM8s1s1ERptc3s+nh6YvNLCs8vq6ZTTaz1Wb2qZmNrmxd6utSRESiatiwIXfddRd33XUXu3btwt056qijKn18T0XMrDYwHrgU2AwsNbNZ7v5JiWYjgJ3ufpKZDQXGAkOAq4H67n66mTUEPjGzqe6+Idr6tEcnIiIxOeqoo2jWrFlSRS6sB5Dr7nnung9MAwaWaTMQmBx+/SrQ10IrdqCRmdUBjgDygd0VrUyFTkREKvT5559zzz33kJ2dTbdu3bj11ltZtWpVZbO1MLOcEsPIEtPaAptKvN8cHkekNu5eCHwHNCdU9PYCW4CNwHPuvqOiIEkdujSzo4AXgS6EquzN7r4wmWWKiEjV8fzzz3Pffffh7ofHrVixghdeeIHRo0czZsyYaLNuc/dol2NG2iX0GNv0AIqANkAz4O9m9p6750ULkuwe3TjgHXc/BTgT+DTJ5YmISBXx+uuvc++995YqciU988wzTJgwIZFFbwaOK/G+HVD2MQiH24QPUzYFdgDDCNWdAnf/FpgPVHh/Q8KFzsyOBHoBLwG4e767V4+HE4mISKV+9rOfVdrm2WefjblPzBKWAiebWXszqwcMBWaVaTMLGB5+PRiY56GKuxG42EIaAT2BzypaWTJ7dB2ArcDvzWyFmb0YXmkpZjby0DHarVu3JrE6ERFJl3Xr1rF06dJK261fv55FixbFtezwObc7gDmEjgTOcPc1ZvakmV0RbvYS0NzMcoF7gUO3IIwHGgP/IFQwf+/uFZ4wTOYcXR2gG3Cnuy82s3HhII+U+YEmAhMBsrOzI+//iohIlbJjR4XXdyTc9hB3nw3MLjPu0RKvDxC6laDsfN9HGl+RZPboNgOb3X1x+P2rhAqfiIhUc61bt465baI3jadLwoXO3f8JbDKzTuFRfYFPKphFRESqieOOO46LL7640nann3463bt3T0OixCV71eWdwCtmtgo4C4h6namIiFQvjz76KHXqRD/DZWY89thjaUyUmKQKnbuvdPdsdz/D3a90951BBRMRkczq3bs3U6dOpVGjctcZUr9+fSZMmMBVV12VgWTxUV+XIiIS1eDBg7n00kuZPHkyH374Ie5Ojx49GDFiRJV/4OohKnQiIlKhpk2bHu7YuTpSX5ciIlKjaY9OREQqNW/ePObOnUtxcTEXXHABAwYMoFat6rGvpEInIiJR5ebm8qMf/YjVq1eXGn/iiScyc+ZMunbtmqFksase5VhERNJu586dXHzxxeWKHIS6CLv00kvZtGlThDmrFhU6ERGJ6MUXX6ywkG3fvp3/+q//SmOixKjQiYhIRFOnTq20zZ/+9Kc0JEmOCp2IiEQUS2fNiXTonG4qdCIiElH79u0rbdOhQ4c0JEmOCp2IiER0yy23BNIm01ToREQkomuuuYY+ffpEnd61a1dGjhyZxkSJUaETEZGI6taty1tvvcVtt91WqmPn+vXrc8MNNzBv3ryIHT5XNbphXEREomrYsCHjx49nzJgxLFq0CHcnOzu72nToDCp0IiISg6ZNm9KvX79Mx0iIDl2KiEiNpj06ERGp1IEDB1iyZAnFxcVkZ2fTuHHjTEeKmfboREQkquLiYp588knatm1L79696dOnD23atOH++++noKAg0/Fioj06ERGJ6u677+bXv/51qXF79uzhF7/4BZs2bWL69OkZShY77dGJiEhE69evZ/z48VGnz5gxg6VLl6YxUWJU6EREJKJp06ZRXFxcYRt16iwiItXWzp07A2mTaSp0IiISUefOnQNpk2kqdCIiEtGQIUNo1qxZ1On169fnpptuSmOixKjQiYhIREcccQSvvPIKDRo0KDetdu3avPTSS7Rs2TIDyeKjQiciIlENGDCAZcuWMXLkSI477jjatm3Lddddx6JFi/jXf/3XTMeLie6jExGRCnXu3JkXXngh0zESpj06ERGp0VToRESkRlOhExGRGk2FTkREKvTxxx8zZMgQGjZsSIMGDfjhD3/IggULMh0rZip0IiIS1YIFCzjvvPOYMWMG+/fv5+DBg7z11ltcdNFFvP3225mOF5OkC52Z1TazFWb2VhCBRESk6rj77rvZt29fufEFBQXcddddlfaFWRUEsUf3U+DTAJYjIiJVSG5uboVPJ8jLy2PhwoVpTJSYpAqdmbUDLgdeDCaOiIhUFbt27aq0zXfffZeGJMlJdo/uV8CDQNR9VzMbaWY5ZpazdevWJFcnIiLp0qlTJxo3bhx1eu3atTnzzDPTmCgxCRc6M/sX4Ft3X1ZRO3ef6O7Z7p5dHfpEExGRkCZNmjBixIio06+55hratm2b0LLNrL+ZrTWzXDMbFWF6fTObHp6+2MyywuP/1cxWlhiKzeysitaVzB7d+cAVZrYBmAZcbGZ/TGJ5IiJSxYwdO5bBgweXG3/JJZck3C2YmdUGxgMDgM7AtWZW9nk/I4Cd7n4S8DwwFsDdX3H3s9z9LOB6YIO7r6xofQn3denuo4HR4dAXAfe7+3WJLk9ERKqe+vXrM3PmTD7++GPefPNNioqK6NevHz179kxmsT2AXHfPAzCzacBA4JMSbQYCj4dfvwr8xszM3b1Em2uBqZWtTJ06i4hIpc4888x4z8e1MLOcEu8nuvvE8Ou2wKYS0zYD55SZ/3Abdy80s++A5sC2Em2GECqIFQqk0Ln7+8D7QSxLRERqhG3unh1lmkUY5/G0MbNzgH3u/o/KgqhnFBERSbfNwHEl3rcDvo7WxszqAE2BHSWmDyWGw5agQiciIum3FDjZzNqbWT1CRWtWmTazgOHh14OBeYfOz5lZLeBqQhdCVkrn6EREpEL5+fnMmDGDP//5zxQVFXH55Zdz3XXXccQRRyS0vPA5tzuAOUBtYJK7rzGzJ4Ecd58FvARMMbNcQntyQ0ssohew+dDFLJWx0hewpFZ2drbn5ORU3lBERGJiZssqOBcWl0jf0QcOHGDAgAG8//77pcZ37dqVefPmcdRRR6U8V7J06FJERKIaP358uSIHsGLFCsaMGZP+QAlQoRMRkaj++Mfo/YC88soraUySOBU6ERGJavfu3VGnVYcOnUGFTkREKtCrV6+o03r37p3GJIlToRMRkajuv/+8s9XUAAAP8klEQVR+GjZsWG58nTp1GD16dAYSxU+FTkREojrttNN47733OP/88w+P69atG2+//TYXXHBBBpPFTvfRiYhIhc4991w++ugjvvnmG4qKimjTpk2mI8VFhU5ERGJyzDHHZDpCQnToUkREajQVOhERqdFU6EREpEbTOToREanU1q1b+ctf/kJhYSE//OEPadeuXaYjxUx7dCIiUqGXX36Zdu3aMXLkSG677TaysrJ47rnnMh0rZip0IiIS1YYNG7jlllvIz88/PK6oqIgHHniA6vI0GhU6ERGJ6k9/+hNFRUURp02ZMiXNaRKjQiciIlF9//33CU2rSlToREQkqv79+0edNmDAgDQmSZwKnYiIRNWrVy+GDRtWbny/fv248sorM5Aofrq9QEREKjRlyhQuv/xyZsyYQWFhIYMGDeL666+nTp3qUUKqR0oREcmYWrVqMWzYsIh7dtWBDl2KiEiNpkInIiI1mgqdiIjUaCp0IiJSo6nQiYhITDZt2sSGDRsyHSNuKnQiIlKhr776ij59+nD88cfTvn17evTowdq1azMdK2YqdCIiUqEhQ4bw/vvvH36/dOlSrrjiCtw9c6HioEInIiJRffHFF8yfP7/c+M8//5wFCxZkIFH8Ei50Znacmf3NzD41szVm9tMgg4mISOYdOHAg6rT9+/enMUniktmjKwTuc/dTgZ7A7WbWOZhYIiJSFXTp0oWOHTuWG9+yZUsuvPDCDCSKX8KFzt23uPvy8Os9wKdA26CCiYhI5pkZr7zyCscee+zhcc2aNWPq1KnUr18/g8liF0hfl2aWBXQFFkeYNhIYCXD88ccHsToREUmj7OxsNmzYwHvvvUdhYSGXXHIJDRs2zHSsmCVd6MysMfAacLe77y473d0nAhMBsrOzq8clOiIiUkq9evW47LLLMh0jIUlddWlmdQkVuVfc/fVgIomIiAQnmasuDXgJ+NTdfxlcJBERkeAks0d3PnA9cLGZrQwP1XO/VkREaqyEz9G5+0eABZhFREQkcOoZRUREajQVOhERqdT48eM5/fTTOeWUU3jmmWcoLi7OdKSYBXIfnYiI1Fy//e1vueOOOw6//4//+A8OHjzI448/nrlQcdAenYiIVGjixIkxjYuHmfU3s7VmlmtmoyJMr29m08PTF4c7Jjk07QwzWxjuZ3m1mTWoaF0qdCIiUqGioqKYxsXKzGoD44EBQGfg2gh9JY8Adrr7ScDzwNjwvHWAPwK3uvtpwEVAQUXrU6ETEZEKDR8+vNy4G2+8MZlF9gBy3T3P3fOBacDAMm0GApPDr18F+obv3/4BsMrdPwZw9+3uXmHV1Tk6ERGp0H333cfBgwd54YUXKCgoYPjw4Tz11FPJLLItsKnE+83AOdHauHuhmX0HNAc6Am5mc4CWwDR3f7ailanQiYhIhcyMhx56iIceeiie2VqYWU6J9xPDfR9D5Huwy/aFHK1NHeAC4GxgHzDXzJa5+9xoQVToREQkFba5e3aUaZuB40q8bwd8HaXN5vB5uabAjvD4D9x9G4CZzQa6AVELnc7RiYhIui0FTjaz9mZWDxgKzCrTZhZw6OTgYGCeuzswBzjDzBqGC2Bv4JOKVqY9OhERSavwObc7CBWt2sAkd19jZk8COe4+i9BDA6aYWS6hPbmh4Xl3mtkvCRVLB2a7+9sVrU+FTkRE0s7dZwOzy4x7tMTrA8DVUeb9I6FbDGKiQ5ciIlKjqdCJiEiNpkOXIiJSqXXr1jFx4kQKCgoYMWIEp512WqYjxUyFTkREKrRx40ays7PZtWsXABMmTGDx4sWcfvrpGU4WGx26FBGRCk2ZMuVwkQPYv38/v/vd7zKYKD4qdCIiUqHatWuXG1enTvU5IKhCJyIiFRo+fDitWrU6/L5Jkyb8+Mc/zmCi+FSfkiwiIhlx7LHHsnz5cl5++WXy8/MZPnw4HTp0yHSsmKnQiYhIpdq2bRtvp85Vhg5diohIjaZCJyIiNZoKnYiI1GgqdCIiUqOp0ImISI2mQiciIjWabi8QEZFKuTvz5s2joKCAvn37Urdu3UxHipn26EREpELuzpAhQ7jkkksYMGAAAwYMoLCwMNOxYqZCJyIiFVq7di0zZ848/H7u3LnMnz8/g4nio0InIiIVatCgQblxDRs2zECSxKjQiYhIhbKysnj00UcPv//JT37C2WefncFE8Umq0JlZfzNba2a5ZjYqqFAiIlK1PPHEE2zZsoVNmzbx29/+NtNx4pJwoTOz2sB4YADQGbjWzDpXNE9+fn6iqwvM6tWrWb16daZjsH79ehYtWpTpGHz77bd8+OGHFBQUZDTH999/zwcffMDu3bszmqOoqIi///3v/POf/8xoDoAlS5awbt26TMfgk08+YeXKlZmOwcaNG5k/fz7untEc27dv54MPPuDgwYMZzbF//34++OCDtK6zdevWtGvXLq3rDIS7JzQA5wJzSrwfDYyuaB4z85UrV3qmzJw5083MzcxnzpyZsRwrV670Bg0aOOCPP/54xnJs27bNW7du7YBfddVVGcvh7t69e3cH/LTTTvOCgoKM5bjhhhsc8ObNm/uWLVsyluPnP/+5A163bl1ftGhRxnK8/fbbXqtWLQd88uTJGcvx2WefeaNGjRzwBx54IGM5du/e7ccff7wD3r9//4zlcHe/8MILHXAgxxP8Hi87dO/ePbB8QeZKdkjm0GVbYFOJ95vD40oxs5FmlmNmOe6e0b8MFy5cePgHz+Te1IoVKzhw4MDhTJmyfv36w3sumcxx4MABli1bBsCaNWsyule3YMECIPRX++eff57xHAUFBeTk5GQsx+LFiykuLgYy+xlZtWoVe/fuBf5v22TCV199xcaNG4HMbo9Mf4dVO4lWSOBq4MUS768Hfl3RPEceeaTv2bMnkL8WEpGXl+c9evTwHj16eF5eXsZy7N692wcMGOCnnnqqf/TRRxnLUVxc7LfccoufeOKJPmXKlIzlcHf/2c9+5h06dPDHHnssozlmzpzpJ510kl933XVeWFiYsRxLly71Ll26+CWXXOI7duzIWI5Nmzb5eeed5926dfPPPvssYzn27dvnAwcO9E6dOvl7772XsRzu7nfeeaefeOKJPnHixIzmGDdunHfo0EF7dDEMFsoTPzM7F3jc3fuF348OF85nos2TnZ3tmfzrVESkpjGzZe6eHcSygvyODjJXspI5dLkUONnM2ptZPWAoMCuYWCIiIsFIuK9Ldy80szuAOUBtYJK7rwksmYiISACS6tTZ3WcDswPKIiIiVVhRURHuTp061et5AOoZRUREKvX222/TtGlTGjVqxJQpUzIdJy4qdCIiUqlRo0axd+9e8vPzue+++zIdJy4qdCIiUqkWLVocft2yZcsMJolf9TrQKiIiGfH73/+ee++9l4MHD/Lss89mOk5cVOhERKRSWVlZvP7665mOkRAduhQRkRpNhU5ERGo0FToREanRVOhERKRGU6ETEZEaTYVORERqNBU6ERFJOzPrb2ZrzSzXzEZFmF7fzKaHpy82s6zw+Cwz229mK8PDhMrWpfvoREQkrcysNjAeuBTYDCw1s1nu/kmJZiOAne5+kpkNBcYCQ8LT1rn7WbGuT3t0IiJSqZ07dzJkyBAGDhzI119/neziegC57p7n7vnANGBgmTYDgcnh168Cfc3MElmZ9uhERKRSv/nNb5gxYwYAxx13HL/5zW+SWVxbYFOJ95uBc6K1CT//9DugeXhaezNbAewGHnb3v1e0MnP3ZMLGxcz2AGvTtsKqrwWwLdMhqhhtk9K0PUrT9iivk7s3CWJBZrYV+DKIZQGdKP19P9HdJ4bXczXQz91vCb+/Hujh7neWyLIm3GZz+P06QnuC3wON3X27mXUH/gKc5u67owVJ9x7dWnfPTvM6qywzy9H2KE3bpDRtj9K0Pcozs5ygluXu6XoswWbguBLv2wFlj4cearPZzOoATYEdHto7Owjg7svCBbAjEHU76BydiIik21LgZDNrb2b1gKHArDJtZgHDw68HA/Pc3c2sZfhiFsysA3AykFfRynSOTkRE0ip8zu0OYA5QG5jk7mvM7Ekgx91nAS8BU8wsF9hBqBgC9AKeNLNCoAi41d13VLS+dBe6iWleX1Wn7VGetklp2h6laXuUVy23ibvPBmaXGfdoidcHgKsjzPca8Fo860rrxSgiIiLppnN0IiJSowVS6BLtyiU8bXR4/Foz6xdEnqogie5tLjWzZWa2OvzvxenOngrJfEbC0483s+/N7P50ZU6lJH9nzjCzhWa2Jvw5aZDO7KmSxO9MXTObHN4Wn5rZ6HRnT4UYtkcvM1tuZoVmNrjMtOFm9kV4GF523v933D2pgdCJxHVAB6Ae8DHQuUyb24AJ4ddDgenh153D7esD7cPLqZ1spkwPSW6TrkCb8OsuwFeZ/nkyuT1KTH8NmAncn+mfJ8OfjzrAKuDM8Pvm+p1hGDAt/LohsAHIyvTPlIbtkQWcAfwBGFxi/NGErkI8GmgWft0s0z9TJocg9uiS6cplYPgDetDd1wO54eVVdwlvE3df4e6H7idZAzQws/ppSZ06SXX3Y2ZXEvplXZOmvKmWzPb4AbDK3T8GcPft7l6UptyplMw2caBR+F6rI4B8Qj1mVGeVbg933+Duq4DiMvP2A9519x3uvhN4F+ifjtBVVRCFLlJXLm2jtXH3QuBQVy6xzFsdJbNNSroKWOHuB1OUM10S3h5m1gj4d+CJNORMl2Q+Hx0BN7M54cNWD6Yhbzoks01eBfYCW4CNwHNeyeXm1UAy34019Xs1YUHcXhCpk82yl3JGaxPLvNVRMtskNNHsNEK9df8gwFyZksz2eAJ43t2/T7A/16oome1RB7gAOBvYB8w1s2XuPjfYiGmXzDbpQeh+qjaEDtX93czec/cKbyKu4pL5bqyp36sJC2KPLp6uXCjZlUuM81ZHyWwTzKwd8GfgBndfl/K0qZfM9jgHeNbMNgB3A/8RvtG0Okv2d+YDd9/m7vsI3YfULeWJUy+ZbTIMeMfdC9z9W2A+UN27CUvmu7Gmfq8mLIhCl3BXLuHxQ8NXU7Un1JXLkgAyZVoy3dscBbwNjHb3+WlLnFoJbw93v9Dds9w9C/gVMMbdk+o2vQpI5ndmDnCGmTUMf9n3Bj6h+ktmm2wELraQRkBP4LM05U6VWLZHNHOAH5hZMzNrRuio0JwU5awegriiBbgM+JzQVUIPhcc9CVwRft2A0BVzuYQKWYcS8z4Unm8tMCDTV+cENSS6TYCHCZ1vWFliaJXpnyeTn5ESy3icGnDVZbLbA7iO0IU5/wCezfTPkultAjQOj19DqOg/kOmfJU3b42xCe297ge3AmhLz3hzeTrnATZn+WTI9qGcUERGp0dQzioiI1GgqdCIiUqOp0ImISI2mQiciIjWaCp2IiNRoKnQiIlKjqdCJiEiNpkInIiI12v8C/JpBh+F12YsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f480f80f150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autofig.reset()\n",
    "autofig.plot(x, y, s=np.full_like(x, 0.05), **style)\n",
    "autofig.plot(x, y+10, s=np.full_like(x, 0.1), **style)\n",
    "mplfig = autofig.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here even though we're sending the same 0.05 and 0.1 as above, are rescaled according to the default smap of 0.01 to 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.01, 0.05)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autofig.gcf().axes.ss[0].smap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This smap scaling can of course be changed by manually sending a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47fc156710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autofig.reset()\n",
    "autofig.plot(x, y, s=np.full_like(x, 0.05), smap=(0.05,0.1), **style)\n",
    "autofig.plot(x, y+10, s=np.full_like(x, 0.1), smap=(0.05,0.1), **style)\n",
    "mplfig = autofig.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## smode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values in the 's' dimension (after mapping, if applicable) are then rescaled onto matplotlib's pixel scale based on the provided value of 'smode'.  There are a number of accepted values for smode:\n",
    "\n",
    "* 'pt' - the mapped value is passed directly to matplotlib and will have the same size on the screen regardless of zoom/figuresize\n",
    "* [dimension(s)]:[figure/axes]:fixed - the mapped value is rescaled according to the size of the figure or axes in the given dimension/dimensions, but then remains fixed regardless of the zoom/figuresize\n",
    "* [dimension(s)]:[figure/axes]:current - the mapped value is rescaled according to the size of the figure or axes and is updated when zooming or resizing the figure based on the *current* axes limits.  This means when zooming in an interactive matplotlib window, the points will keep a fixed size on the screen but when resizing the figure, the points will react.\n",
    "* [dimension(s)]:[figure/axes]:original - the mapped value is rescaled according to the size of the figure or axes and is updated when zooming or resizing the figure based on the *original* axes limits.  This means when zooming in an interactive matplotlib window, the points will keep a fixed size *in data unit* and will therefore appear larger when zooming in.\n",
    "\n",
    "Where dimension(s) can be one of:\n",
    "* 'x'\n",
    "* 'y'\n",
    "* 'xy'\n",
    "\n",
    "By default, smode is 'xy:figure:fixed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "style1 = {'marker': 'o', 'linestyle': 'solid', 'xpad': 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'll plot s=0.1 for several different size modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47d8f7b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autofig.reset()\n",
    "autofig.plot(x, y, s=5, smode='pt', color='black', **style) \n",
    "autofig.plot(x, y-10, s=0.1, smode='x:figure:fixed', color='blue', **style)\n",
    "autofig.plot(x, y-20, s=0.1, smode='x:figure:current', color='red', **style)\n",
    "autofig.plot(x, y-30, s=0.1, smode='x:figure:original', color='green', **style)\n",
    "mplfig = autofig.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides 'pt', there is no obvious difference between 'fixed', 'current', and 'original' until resizing the figure interactively.  Here we can fake zooming in on the plot by setting the x-limits externally (in matplotlib instead of autofig)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}