{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69244d82",
   "metadata": {},
   "source": [
    "# Facets, Axis Guides, and Text Legends\n",
    "\n",
    "This page covers the plotnine-extra APIs added or tightened for the 0.3.1\n",
    "release: extended facets, per-panel position scales, ggh4x-style axis helpers,\n",
    "and text-only legends.\n",
    "\n",
    "The examples use explicit imports from `plotnine` and `plotnine_extra`, so each\n",
    "feature's package is visible in the code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e0f9e15",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Guide helpers can be added to a plot or passed to position scales. Extended\n",
    "facets such as `facet_wrap2()` and `facet_manual()` read those guide\n",
    "specifications while drawing axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "522db648",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-13T10:41:16.141752Z",
     "iopub.status.busy": "2026-07-13T10:41:16.141463Z",
     "iopub.status.idle": "2026-07-13T10:41:26.633608Z",
     "shell.execute_reply": "2026-07-13T10:41:26.626521Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from plotnine import (\n",
    "    aes,\n",
    "    geom_col,\n",
    "    geom_point,\n",
    "    ggplot,\n",
    "    guides,\n",
    "    labs,\n",
    "    scale_x_continuous,\n",
    "    theme_minimal,\n",
    ")\n",
    "from plotnine_extra import (\n",
    "    facet_manual,\n",
    "    facet_wrap2,\n",
    "    guide_axis_manual,\n",
    "    guide_axis_nested,\n",
    "    guide_stringlegend,\n",
    "    scale_x_facet,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9fc84c",
   "metadata": {},
   "source": [
    "## Axis guides and per-panel scales\n",
    "\n",
    "`guide_axis_manual()` sets breaks, labels, and label styling for a position\n",
    "axis. `scale_x_facet()` and `scale_y_facet()` target individual facet panels\n",
    "with selector expressions evaluated against the facet layout.\n",
    "\n",
    "If more than one selector matches a panel, the first selector wins. Use\n",
    "`scales=\"free_x\"` or `scales=\"free_y\"` when a facet needs independent panel\n",
    "scales to replace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b8fc88b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-13T10:41:26.638498Z",
     "iopub.status.busy": "2026-07-13T10:41:26.637959Z",
     "iopub.status.idle": "2026-07-13T10:41:27.715245Z",
     "shell.execute_reply": "2026-07-13T10:41:27.714470Z"
    }
   },
   "outputs": [
    {
     "data": {
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56dSpU2pqavLnP/+5UZ+7POqN1I14Zs0bmDcvNX+5rfEKAgBWqCXPGwAAoCECwJXYaKON0r9//waXd+nSJbvttluS5K233mrU56594pG1bqP8xmspT53SCNUAACvTkucNAADQEAFgI1j87X5tbW2jtVmeMT11r7zYKG3VrmTyEACg+TTFeQMAAKyIALARvPTSS0mSvn37NlqbtS+MSJYYQHyt2npuLW4jBgAaVVOcNwAAwIqYBGQtPfnkkxk1alSSZP/991/p+pMmTVqldttNnJC2a1XZEma/n0nvvJNUVjZWi7BeWjw4f11d3Sofi7Chc9w0jsX7cXXOG+zvdYtjgeakv9Fc9LV1U69evVq6BApIALgWpkyZkssvvzxJsueee9aP6bMiiz9gV2rhwrUpbRnlhQtSbtNokSKs91b5WATqOW7WzuqeN9jf6y7vDc1Jf6O56GtQbALANTR79uycd955mTlzZnr16pXTTjttlbarqFjFu67btFmL6pZVatM2pVV9biioJU9qVvlYhA2c46ZxzJ49O5deeulqnTfY3+sWxwLNSX+juehrsOEQAK6BefPm5ZxzzsnYsWPTtWvXnHvuudl4441XadtVvZS3dvMtUjPi6bUp8wOdu6TXZps1TltNqFwup6amJlVVVSmVSi1dDv9SpPdl0qRJqaurS0VFRSEuqy/Se1MkRXtfinbctIR58+blJz/5yWqfNzTl/i5aP20OjoU1o6+tGf1tzehvq09fWzP6GusjEf9qWrBgQc4999y8/vrr6dSpU84777wm+aCsGLhrUtU4+Wzlx/ZslHYAgNWz+Lxh9OjRTXreAAAAKyIAXA0LFizIeeedl5dffjkdOnTIueeem80337xJnqu0UYdUDNhl7RuqqEjlHnutfTsAwGpZ8ryhffv2TXreAAAAKyIAXEXV1dW54IILMnLkyLRv3z5nn312+vXr16TPWbnXoLVuo2KHASl16rz2xQAAq+zD5w3f/OY3m/y8AQAAGiIAXAU1NTX5yU9+kueeey5t27bNj370o2yzzTZN/rwVm/dN5eAD1ryBTl1SdehRjVcQALBSyztvEP4BANCSBIArUVtbmwsvvDDPPPNMWrdunf/6r//KRz/60WZ7/soDD07FmtzCu3HHtDrx31Pq2KnxiwIAlqulzxsAAGB5zAK8Eq+++moef/zxJItm+rnwwgtXuP61117bqM9fqqhI1ZGfS23Xbqm97+9JdfXKt+nbL62OPT6lLl0btRYAYMUaOm9YPMPihzX2eQMAACyPAHAlyuVy/c/V1dWZMWNGs9dQKpVS9clPpfLje6f2H0+n7olHU5767tIrVbVKxcBdU/mJfVKxed9mrxEAWDfOGwAA4MMEgCux00475Y477mjpMpIkpXbtU7XP4JT33i/ldydn2ri3U66uTtq1S/dtt0+pXbuWLhEANmgNnTdMmjQpvXr1aoGKAABAALheKpVKKfXslZryB7cUCf8AAAAAWB6TgAAAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAITAAIAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAITAAIAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAIrlcvlcksXAQAAAAA0DVcAAgAAAECBCQABAAAAoMAEgAAAAABQYAJAAAAAACgwASAAAAAAFJgAEABgPfHgq++mdPxNKR1/U0uXAgDAeqSqpQtgkbcuvjLVM2Zl08M/nU47f7TF6qieMStvXXxlkmSr009Mq84dW6wWaAwPPvhgfvnLX650vd/97nfp2FF/p/hmz56dV155JaNHj86YMWMyevTozJw5M0ly1llnZYcddlhpG08//XSGDx+eMWPGZP78+enevXt22223HH744Y4jCqVcLuehhx7KAw88kNGjR2f27Nlp3bp1evbsmV122SWHHnpounXr1tJlsh6YPXt2XnrppYwaNSpvvfVWRo0aVf/Ze/7552ennXZaaRtPPPFE/va3v+Wtt97KggUL0r179+y+++4ZNmyYz17qrWlfq62tzfPPP58RI0bk1VdfzcSJEzN//vx06NAh/fv3z+DBg7PffvulosI1RLC+EgCuI966+MrM++c7ab9lnxYPAF8/55IkyRZfOloASGGUSqUVnhw7mWFD8eyzz65SKN6QK664InfffXeSRcdNmzZtMnHixNx555155JFHctZZZ6VPnz6NVS60mIULF+aCCy7IiBEj6h9r37595s+fn7Fjx2bs2LH5+9//njPPPDMDBgxowUpZHzz11FO55JJL1nj7X//617nrrruSfPDZO2HChPz5z3/OQw89lPPPPz+bb755Y5XLemxN+9qvfvWrDB8+vP73ysrKtGnTJjNnzsxzzz2X5557Lvfee2/+67/+K+3atWvMkoFmIgAENgjdu3fP5Zdf3tJlwDqhc+fO6d+/f/r3759NN900v/jFL1Zpu3vvvTd33313SqVSPve5z2Xo0KFp06ZNxo4dm1/84hcZN25cfvrTn+aiiy5KVZVTDNZvN998c334d+yxx+bggw/OxhtvnNra2owcOTK//vWvM3HixPzv//5vfve736VNmzYtXDHrui5dumSrrbbKRz7ykWy22Wa56KKLVmm7u+++O3fddVdKpVK+8IUv5LDDDkubNm0yZsyYXHTRRfnnP/+ZH//4x7nsssvSqlWrJn4VrA/WpK/V1NSkS5cu2X///bPXXnulf//+qaioyKxZs3LHHXfk1ltvzYsvvpjLLrss3/nOd5rhVQCNzdk5AGxABg0alMGDB9f/PmfOnFXarqamJjfffHOS5MADD8yRRx5Zv2zLLbfMGWeckf/8z//MpEmTct999+XAAw9s1LqhuT344INJkiFDhuTYY4+tf7yysjK77LJLvvvd7+Y//uM/MnPmzLz88svZddddW6hS1geDBw/O/vvvX//77NmzV2m76urq/P73v0+S/Nu//Vs++9nP1i/r169ffvjDH+ZrX/taJk6cmHvuuSf/9m//1riFs95Z07520EEH5dRTT03r1q2Xerxjx4457rjjUlFRkRtvvDGPPPJIvvSlL6VHjx6NWjfQ9ASATaRcLmf89X/K29f8MbNeeDXVM2alqmOHtOnRNZ0/tlN6Hfap9B42NK+dfXH9LbdJ8twJ38lzJ3zwjUq7vr3z6bGP1v8+7YkRmXDb3zLt8RGZN25iFr77Xio7tE+ngdunz3GHZ4vjj0qpsnKZeqY++GQe++Sik9fDymMy7cnnMurC32baY//IgnffS/9vHJ+Zz7+S9x56qn6be/rtu1Qbmx9/VHa9+sJG20cANL81vd39xRdfzIwZM1IqlXLooYcus7xHjx7Ze++9c//99+eRRx4pTAD45qT3c9HfX899r0zO+OnzUllRylabdMgRu/bO6Qduk07tl/6H0uD/vj8PvTYlZx2+Q3502A755X2jcuUjo/P6xPfTqqoiu/frmh8csn0+uX3PBp/z3Vnzc87tL+cvz03Iu+/PzyYbt81BA3rlR4etfHxGGs/06dOTJB/5yEeWu7xfv36prKxMbW1t5s+f35ylsR6qXM75+aoYOXJkpk+fnlKptNQXL4ttsskmGTRoUO655548+OCDAkDWuK9tu+22K1y+//7758Ybb0ySjBo1SgAI6yEBYBMZcfy3Mv66P9X/XtVx49TOmZvZ02Zk9uujM/XBp9J72NBUdWifNj27Z8GUaUldXao6bpzKdh/cQtKmx9IDSz+y11H1P1d22CgV7dqketqMTH3giUx94IlM/OPd2eP236RiBbdevXPTnfnHcf+Rck1NqjptXB8Ytu7aOa27d83CqdMW/d69a0qVH/xDsVWnjddupwCw3nrppZeSJH369En37t2Xu87AgQNz//33580338z8+fPTtm3b5iyx0V3x0Oh89Zp/pLq2LknSvnVlFlTX5YW3Z+SFt2fkmsfG5p7vDM5WPTsss21NbTmHXvxI/vrCxLSqrEibVhWZObc69748OQ+8+m5u+8beOWzX3sts9/rEWRn83w9k0sxFgVK71pWZNmdhfvvg6Nw+4p1ccLSx5ppLz549M378+IwaNWq5y8eMGZPa2tpUVFSkX79+zVwdG4qRI0cmSTbffPMGA5dddtkl99xzT15//fVCfPayblpyLO3a2toWrARYUwLAJvDeI08vCv8qKrLD/34/fU/6bFp16phyuZyFU97L1AefzJR7Fl3V95FvfyUf+fZXMnzLfTLvn+9kp0t+lC2+dHSDbfc67FPZ/ItHptugPdKme9ckSfXMWRl/w5/zyhk/zeS/3p+3fn5Ftv7OKQ228dzJZ6TXYQdkxwvPTPst+6Supibzx09K+y37ZO7Y8fVX/u33zJ/TfksDuVMMs2bNyve+971MmDAhSdK1a9d89KMfzUEHHZQtttiihauDdd/48eOTZIWDzC9eVi6XM2HChPTv379ZamsKd70wIV++6pm0a1WZHx22Y07er396dW6Xmtq6PD16Wr55w4g8O2Z6jvzFo3nu3ANTUVFaavtf3j8qpSQ3nfqJHL5b77SuqszIt2fkC795Mi+Nn5mvXfuPHLzzpqlc4orM6pq6DLvs8UyaOT99urbLFSfukU/t2DOlUikPvvpuTrzi6Xzrxuebd0dswA488MBcccUVuf/++9OrV68MHTq0fgzAF198Mb/61a+SLLotc9NNN23haimqcePGJUn69u3b4DqLl5XL5YwfP77Bq1ZhbSz+IjBZcX8E1l0CwCYw7YlFA0Zv8ql98pH/PLn+8VKplDabdE/vzx6c3p89eI3a3vP23y7zWKtOHdPv1P+Xqk4bZ8Rx/5Gxv7x+hQFgp4HbZ/ebL0/pX//oqKiqEvRReAsWLMjYsWPrZ3CcOHFiJk6cmAceeCCf//znl3tLI/CBGTNmJFk0sHhDunbtWv/z4tsn10e1dXX5xnUjUi4n15/y8RzxsQ/+RlZVVmSvrbvn7m/vlx3P/HtGjpuZ20e8kyM/tvTf0elzFuaRM4dkn20+uGJnwBadc/XJe+RjZ9+Td6bPy2NvTM2g7TapX37TU2/nxfEzU1lRyp3/sW8GbvHBvh68/Sb527cGZeAP727CV86SDj744EyZMiV/+ctf8vvf/z6///3v6/+G1NXVpXfv3jnllFMydOjQli6VAps2bdGdOUt+vn5YUT57WXfV1tbmD3/4Q5JFtwqbcRrWTwLAJlDVcdGtsgumTEu5rq4+aGtqvYYOSZLMHTs+8yZMTrvNlj++0FbfOrnZaoKW1qVLlwwbNix77rlnNttss1RVVaWmpiavvfZa/vCHP+TNN9/M9ddfn65du2afffZp6XJhnbV4jLMVzXS65MDh8+bNa/KamspDr03J6ClzFo3197Hlf0HWtUObHDRg01z58JgMf2nSMgHgvtv0WCr8W2y3fl3Tp2u7jJ82Ly+9M3OpAPDWZxddZTl04KZLhX+LbbtpxwzbffNc//g/1+blsYoqKytzwgknZLPNNssVV1yR6urqzJ07t375ggUL6sPANR1bE1ZmVT57l1y2ZB+FxnLddddl1KhRqaqqyle+8pWWLgdYQwLAJtBj/71S0bp1Zo54KY/u97n0/fIx6TFkr7Trs/a3h9TV1GTcNbdlwi13ZeYLr6Z62szULVy4zHrzVxAAdv2EWerYcAwcODADBw5c6rGqqqrsuOOOOeecc3LWWWflzTffzA033JC99trLP+KAPP7m1CTJ+Olz0+u0Pze43uz5NUmSt99b9h/cu/dv+ErJ3l0WBYDT51Qv9fg/xi660mfwEqHgh31y+00EgM1kxowZueCCC/Laa69l8ODBOfzww7PZZptlxowZ+cc//pHrr78+11xzTUaPHp3vfOc7K28QYD10zz335I9//GOS5Pjjj8/WW2/dwhUBa0oA2AQ6bN0vA37947z49bMy7dFnM+3RZ5Mkbftsmk0+vW+2OOHodNtn99Vut2b2nDxx4PGZ9vg/6h+raNtmqck6Fkxe9I+W2jkNf/vXpkfDtxDAhqSqqirHHntszj333Lz33nsZO3bsej1mGTSlxYPKL1iwoMF1Fi7xhVS7du2avKamMvFfE3AsqK7L5Jkrn9117sKaZR7buG2rBtdv22rR5FuLJxdZbMr7i/Zt7y4N77sVLaNx/fznP89rr72W/fffP9/85jfrH188HuDmm2+eH/7wh3nkkUcyZMiQ7Lbbbi1YLUW1Kp+9Sy5r3759k9fEhuORRx7J5ZdfniQ56qijcthhh7VwRcDaEAA2kb4nDEuvoZ/MOzf/NVMfeDLTHns288dPzNtX3py3r7w5W371uAz85Xmr1ebr5/0i0x7/R0qtWmXHn/0gmx75mbTr3at+ebm2NndU/WvQ33LD7ZTWcGp4KKIlB8qePHmyABAa0KVLl4wZM2aF40stHqtq8frrq9q6RX9ED9ypV/7+7f1auBpawrhx4/Lcc88lSQ4//PDlrjNgwID0798/b731Vp566ikBIE2ia9euGT169FKfrx9WlM9e1i1PPvlkLrrootTV1WXo0KE5/vjjW7okYC25160Jtdmke/p//fjscduv8plJz2Tw839Nn/93RJJk7K+uz+S7Hlit9ibccleSZJvvfzX9v/GlpcK/JJn/r6v/AKCx9emzaIy7xTNSLs/imYJLpVI222yzZqmrKfTsuOiKm+Xd2tuUemy8aByvCTMaHj/xnenr79iK65Ml+3mvXr0aXK9nz0XDrUyePLnJa2LDtHiyhbfffrvBdRYvK5VK9Z/VsDaeeeaZ/PSnP01tbW0OOOAA4/5BQQgAm1GngR/NbtdelI47bZskmfrgk/XLFk/KUS43fOnevPGTkiSddx+w3OVTH3hi7YusKNX/uKJaoChGjRpV//MmmzQ87hZs6Hbccccki0K+9957b7nrPP/880mSrbfeuv62tfXRXlt3S5K8NnFW3po8u9med7ctFw3R8dBrUxpc58FX322ucjZopdIH50NTpjT8fkyduujLV7dd0lQGDFh03v/222/X97cPW3y16rbbbrtef/aybhgxYkR+8pOfpKamJvvtt1++/vWvL/WZCKy/BIBNYHmTciypou2ib/jrFnywXlXHDkmSmhmzGtyuVadFswvPfu2tZZbVzpufN86/fLVrXeY5/jWDcZJUr6AWWB+sLMSuqanJTTfdlGTRLTb9+vVrjrJgvbTjjjumc+fOKZfLueOOO5ZZPnXq1Dz22GNJkn333be5y2tUQ7bvmb7d26dcTv7j98+lrq7hz5LqmrrMnl/d4PLVMWz3RVfu3PnChLw4bsYyy9+c9H5ueWZ8ozwXK7bk34O//e1vy13nzTffrP8SaZtttmmWutjwDBgwIF26dEm5XM6f/vSnZZZPmTIlDz/8cJJk8ODBzVwdRTNy5MhccMEFqa6uzl577ZXTTz/dBHlQII7mJjDyaz/Ks8eelol/Hp6F02bUP77wvel59Yc/y4xnRiZJNjlocP2yjjsuOnGc8Me/p3rm8oO3Hp/aJ0nyxvmXZ9Jf70+5btHg4TOffyWPf+r/ZeG7y78iY3W06twxbf91a/G4a/+Ycm3tWrcJLWXKlCn5wQ9+kHvvvTfvvvvBVTO1tbV55ZVXcs455+T1119PknzhC19wgsMGY9asWfX/zZ79wRVuc+fOXWpZTc0Hk1tUVVXls5/9bJLk73//e26//fb6ST/++c9/5n/+53+yYMGC9OrVK/vvv3/zvqBG1qqqIr/84m6pKJXyl+cn5MALH8qTo6bWB4F1deW88s7M/OTOV7PN9+7K82/PaJTn/eweW2SnPp1SU1vO0IseyT0vTar/IuPh197NZy58KG1a+ZxqDr169cquu+6aJPnrX/+aq666qn78ywULFuTxxx/PBRdckLq6umy00UbrfZ+neTT02TtnzpwGP3tbtWqVz3/+80mSO++8M7feemv9pB9jxozJeeedl/nz52fTTTfNpz71qeZ9Qayz1qSvvfrqq/nxj3+chQsXZo899si3v/3tVBo7HgqlVHafZ6Mb8aVvZ9w1t9X/XrVxh6RUSs2s9+sf6/e1/5cBl51b//vUB5/MY0M+n5TLKVVVpc0m3VJqVZV2fTbNvo/ekiSZM/rtPLznEVk4ddFAvxWtW6eiTevUvD87le3aZo/bf5MnDlw0OOveD/wh3Qd/fOn2P3lskuSw8pgV1v/aOZfk9bMvXvQcbdukzSbdklIpmx19UHa88My12DPQvN599918/etfr/+9VatWadu2bebNm1d/wlNZWZnjjjsuQ4cObakyodktDvJW5qyzzsoOO+yw1GO/+93vMnz48CSLjp/WrVtn3rxF49J16tQpZ511VmHGoPrDE//MSVc+k3kLF30Z1qZVRTq0qcqseTVLzeD76JlDsvc2PZIkg//7/jz02pScdfgOOfuIHZfb7orWeX3irAz+7wcy6V+zD7dvXZlSqZQ5C2qyScc2ueDoATn5ymeSJOVrPtfor5kPTJ8+PT/84Q+XGnutXbt2mT9/fn0w2759+5xxxhnZeeedW6hK1ieHHnroKq13/vnnZ6eddlrqsV//+te5665F44FXVlamTZs2mTt30TilnTt3zvnnn18/XiCsSV8788wz8+KLLyZJNt544xWGf0cccUSOOOKItS8UaFZmAW4C2/7wG+m0yw6Zev/jef+1t7Jg4pTUzpuftr17pcueO6fvyZ9LzyWu/kuS7oM/nj3//Nu89fMrM/P5VzJ/0pSkrm6pdTbqv0UGPX17XvvRRZky/JEsnD4rVR07pNeh+2fr75+ajjs0zu0n2/7wG6naeKOMv/72zH5jTOa9PSFJsnBqwzM/wrqoc+fOOeGEE/L666/nn//8Z2bNmpW5c+emdevW6dOnTz760Y/m05/+9Ho9WQE0t5NPPjkDBgzI3XffnTFjxtRf9fexj30shx9+eDp27NjSJTaaYz/RN4O27ZHL7h2Vv784MaOnzMmMudXp1K5Vtum1cfbauluO3K1PffjXGLbdtGOeP+/AnHP7S/nL8xPy7qwF6dmxTb7wiS3yo8N2yJvNOCbhhq5Lly656KKLMnz48DzxxBMZO3Zs5s6dm7Zt26Znz57ZZZddcvDBB6dHj8Z7/6Eh//7v/56BAwfmrrvuyujRo+uv+ttjjz1y9NFHp1OnTi1dIuu5Ja8Lev/991ewZuq/+APWL64ABAAAAIACM5AMAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAITAAIAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAITAAIAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAbiFKptNR/xxxzzCpve8899yyz/dVXX910xUIzePDBB3P22Wfn7LPPztixY1u6HACAJlPV0gUAAC3j9ttvz/Tp09OlS5eVrnvllVc2Q0XQvB588MGcc845SZLBgwdnyy23bNmCAACaiCsAAWADU1W16Pu/BQsW5IYbbljp+tOnT8/tt9++1LYAAMD6QwAIABuYnj17Ztddd02SXHXVVStd//e//33mz5+fJBk6dGiT1gYAADQ+ASAAbIBOPPHEJMmIESMycuTIFa67+Pbf3XffPTvuuGOT1wYAADQuASAAbIA+//nPp23btkmSK664osH1Ro4cmREjRiT5IDRcHQ8//HC+8pWvZPvtt0/nzp3Ttm3bbL755jnqqKNy2223pVwur3D7mpqa3HPPPfnud7+b/fbbL5tuumlat26djTbaKFtuuWWGDRuWW2+9NXV1dSutZcGCBfnNb36Tgw46KL17907btm3Tvn37bLHFFtl1111z3HHH5eqrr87s2bOX2fZLX/pS/eQnK5ss4uqrr17hRCljx46tX/6lL30pSTJhwoT86Ec/yi677JJu3bottWxJtbW1ueGGGzJs2LBsueWW2WijjdKhQ4dsu+22+fKXv5xnn312tWt7/vnnc9JJJ6V///5p165d+vTpkyOOOCJPP/30Mtv/7W9/y2GHHZa+ffumTZs26dOnT0444YSMGTNmhc+7pOeffz7f/OY3M3DgwHTt2jVt2rTJZpttlqFDh+bKK69MTU3NCrdfXP/gwYOTJPPnz8+ll16aT3ziE+nWrVvatWuXj3zkI/nqV7/aYF1nn312SqVS/fh/SfLJT35ymYlujAkIABRGGQDYICQpJyn37t27XC6Xy8ccc0w5Sblbt27lBQsWLHeb0047rZyk3LZt2/L06dPLZ555Zn07V111VYPPNX369PLBBx9cv25D/w0aNKg8ZcqUBtv55Cc/udI2kpT32muv8qRJkxpsZ/To0eVtttlmldq65ZZbltn++OOPr18+ZsyYBp+nXC6Xr7rqqhXuozFjxtQvP/7448vDhw8vd+3adZk6jj/++KW2e/HFF8vbbbfdSuv/+te/Xq6pqVml2i6//PJyq1atlttORUVF+dprry2Xy+XywoULyyeddFKDz9mxY8fy008/vcL9Mn/+/PKJJ55YLpVKK6x/hx12KL/11lsNtrN4vf322688evTo8k477dRgWxtttFH5gQceWKaNs846a5X6Qt++fVf4mgAA1hdG8gaADdSJJ56YG2+8Me+9917uuOOOHH300UstX7hwYf0kIUceeWQ6d+68Su3OmjUre++9d1555ZUkydZbb51hw4Zl++23T+vWrTN69Oj84Q9/yMiRI/Pwww/ngAMOyJNPPll/ReKS5s6dm4022iiDBw/Obrvtln79+mXjjTfOnDlz8uqrr+aWW27JW2+9lccffzxHHHFEHn744eVOVHL00UfnjTfeSJJst912GTZsWPr27ZtOnTpl1qxZef311/Pwww8v96q3pjRq1KgcffTRef/993PUUUflgAMOSJcuXfL2228v9Tqee+657Lfffnn//feTJPvuu2+GDh2avn37pq6uLiNHjszVV1+dyZMn57LLLsvChQvzm9/8ZoXP/de//jW33XZbunXrlpNOOikDBgxIdXV17rrrrtx8882pq6vLiSeemE984hO59NJLc8UVV2SHHXbIcccdl379+mXatGm55ppr8tRTT2XWrFn5/Oc/n5dffjmtW7de5rlqamrymc98Jg8++GCSZLPNNssxxxyTAQMGpH379hk/fnz++Mc/5tFHH83LL7+cQYMG5bnnnkuPHj0arH/WrFkZOnRoXn311Xz605/OIYcckp49e2bixIm55pprMmLEiMyZMyfHHHNMXn311aVmuz7mmGOy884758Ybb8xNN92UJDnvvPOWucW9ffv2K34DAQDWFy2dQAIAzSMfugKwtra2vMUWW5STlA866KBl1r/55pvrt7n33nvL5XJ5la4AXHxlYZLy2Wefvdyr0Wpra8vf+ta36tc788wzl9vWPffcU54zZ06Dr6m6urr8ta99rb6d6667bpl1nnnmmfrlw4YNK9fW1jbY3tixY5d7hV9TXQGYf12ltnj/Ls+cOXPK/fv3Lycpt2/fvnzHHXcsd70ZM2YsdcXkPffcs8LakpR33XXX5V6Bec455yy1TqlUKn/5y19e5r2srq4uDxkypH7dm2++ebm1nXHGGfXrfPnLXy7Pmzdvuetdcskl9et94QtfWO46S9ZfVVW13Cs2q6ury5/5zGfq1/vZz3623LaWvBJweVcKAgAUhTEAAWADVVFRkRNOOCFJMnz48LzzzjtLLV88+ceWW26ZIUOGrFKbI0eOzI033pgkOemkk3LWWWelsrJyuc994YUXZu+9906SXHbZZVmwYMEy6x1wwAErvAqrqqoqF198cf1Ybddcc80y64waNar+5y996UupqGj49Kdv377NPu7bj3/84+y///4NLv/d736X0aNHJ0l+85vf5JBDDlnuep06dcott9ySjh07Jkl+9rOfrfB5W7dunVtvvTXdu3dfZtl3v/vdbLzxxkkWTRSzww475Je//OUy72VVVdVS4+j9/e9/X6atd999NxdffHGSRe/nb3/72+Ve7Zkkp512Wj7/+c8nSW688cZl+uSH/eAHP1jmytXFdf385z+v//1vf/vbCtsBACg6ASAAbMBOOOGElEql1NbW5tprr61//J133snw4cOTfDABxqpYMoD77ne/u9L1v/jFLyZJZs6cmaeeemp1Sq9XVVWVj3/840mSp59+epmJRTbaaKP6n//xj3+s0XM0lXbt2uXkk09e4TqL92nv3r3rw7GGdOvWLUOHDk2SPPjgg8sNVRc7+OCD069fv+Uua9u2bXbbbbf63//93/99ubdWJ8nHP/7xtGrVKknqb/te0k033ZT58+cnSb7zne+ssP4kOf7445MsmvDkvvvua3C9ioqKfPOb32xw+XbbbZc+ffokSV566aWVPi8AQJEZAxAANmB9+/bNkCFDct999+Wqq67K97///SSLZoutq6trcDbahjz88MNJFgVIr7zyynIDoSUteYXXK6+8kkGDBi2zzty5c3PTTTflL3/5S1588cVMnjw5s2fPXu4MwrNmzcqsWbPSqVOn+sf23nvvtG/fPnPnzs25556b9957L8cff3x23nnnVQ42m8ouu+ySDh06NLh81qxZef7555Mkm266ae64446Vtrk49Js/f37GjBmT7bbbbrnrLQ5NG9KrV6/6n/fYY48G16uqqkq3bt0yadKkTJ8+fZnli/tEkkyePDm33377Cp/3w32iIdtuu226du26wrb69OmT8ePHL7cuAIANiQAQADZwJ554Yu677768+eabeeSRR7Lvvvvm6quvTpIMGTIkffv2XeW2xo4dm2RR+HTEEUesVh3Tpk1b5rHHH388xxxzTMaNG7fK7Xw4AOzatWsuueSSnHLKKampqckll1ySSy65JN26dcsnPvGJ7LPPPvn0pz+dXXbZZbXqbQyLr1BryLhx41JXV5ckefbZZxtlny7WrVu3FW7bpk2b1V538ZV+S1rcJ5IPrvhcVSuqf3m3LjdU14quhAQA2BAIAAFgA7d4ht8ZM2bkqquuSl1dXf24eSeeeOJqtTVjxow1rmPhwoVL/T5mzJgceOCBmT17dpLkIx/5SD7zmc9km222Sffu3dO2bdv6K/guvfTSPPDAA0kW3Tr6YSeffHK22267nHfeebn33ntTV1eX9957L3feeWfuvPPOnHHGGdlpp53yP//zPznooIPW+DWsrnbt2q1w+drsz2TZfbqkFY2FuDbrflhj9oklrU1NAAAbGgEgAGzg2rZtm2OPPTa/+tWvcsstt2TmzJlJks6dO+fII49crbY6dOiQGTNmpGvXrnnvvffWqq4LLrigPvz73ve+l//+7/9u8JbdG264YaXt7bPPPrn77rszffr0PProo3niiSfyyCOP5Mknn0xNTU1efPHF/Nu//Vuuuuqq1brt+cOWF0CuqSVvDz7yyCNz2223NVrbzWXJ1zBr1qz6yUUAAGg+vjoFAOqv9Js9e3b++Mc/JkmOPfbYBmdrbcjiW1pnzJhRH96tqcWTkGyyySY5//zzVzhe35gxY1a53S5duuSQQw7JBRdckEceeSQTJkzI17/+9frl3/rWt1JdXb3UNkveDruiq9KSZOrUqatcy8r07t27/ufVuQ16XbLkbc7r62sAAFjfCQABgHzsYx/LgAEDlnrshBNOWO129ttvvyRJXV1d7r777rWqadKkSUmSfv36pbKyssH1Jk6cmBdeeGGNn6dHjx75xS9+kYEDByZZNO7cyy+/vNQ6Xbp0qf95yUkqlufxxx9f41o+rHv37tlhhx2SJCNGjMjkyZMbre3msrhPJMnf/va3FqxkWUveRry8SWUAAIpCAAgAJEn+8z//M3vuuWf23HPPHHnkkdl9991Xu40lJ3k499xzlzspxKraaKONkiRvvfXWCsOZc889NzU1NWv8PIv169ev/ucPt7c4hEuSe++9t8E2Xn/99dx1111rXcuSjj/++CSLbi3+0Y9+1KhtN4djjjmm/grKiy66qFGvkFxbS96ePGfOnBasBACgaQkAAYAki4KmJ598Mk8++eQajzW3xx57ZNiwYUmSkSNH5rDDDsuUKVMaXL9cLuexxx7Lt7/97WWWLQ4gp06dmp/97GfL3f5nP/tZfv3rX6+wphtuuCFXXHHFCgOeN954I/fdd1+SRWMibrvttkst/9SnPpWqqkVDJ19++eX1k6Qs6Z133slRRx3VKGHkkr72ta9lyy23TJL89re/zfe+971lblFe0sKFC3PzzTfn8ssvb9Q61lSfPn1y2mmnJUkmTJiQAw88MKNHj17hNi+88EJOOeWUJq9tydB3xIgRTf58AAAtxSQgAECjuuKKK/LGG2/khRdeyPDhw7PlllvmqKOOysc//vH06NEj1dXVmTx5ckaOHJl7770348ePz1ZbbZULL7xwqXZOP/30+nEAv/Od7+SBBx7IZz7zmfTs2TNvv/12br755jzzzDPZdNNNs9NOO9Wv+2FvvvlmzjnnnJx22mk54IADsvvuu2eLLbZIu3btMmXKlDz99NO59dZb6wPC0047bZmJKnr16pUvfvGLufLKKzNz5szsscce+epXv5oBAwZkwYIFeeaZZ3LNNddk7ty5+dznPpebbrqp0fZn+/btc8cdd2TQoEGZMWNGfvrTn+b666/P0UcfnYEDB6Zjx46ZO3duxo0blxEjRuTee+/NrFmzctJJJzVaDWvrggsuqO8PI0aMyHbbbZdDDz00++67bzbddNPU1dVl6tSpeemll/LAAw/kjTfeSGVlZX7zm980aV2DBg1K69ats3Dhwvzv//5vkmTgwIH1Vyy2a9duqVuYAQDWVwJAAKBRbbzxxnn00Udz6qmn5vrrr8/cuXNz3XXX5brrrmtwmyUniljsoIMOytlnn52zzz47SXLXXXctc3tt375988c//jGXXnppg20vnjxk7ty5ueOOO3LHHXc0uN6pp56aCy64YLnLL7roorz88st56qmnMn369GXWa9euXa666qrU1tY2agCYJDvttFOeffbZfOELX8hTTz2VCRMmrPQ1LzmBSEurqqrKnXfeme9///u55JJLUl1dndtuu22FV5our080tm7duuW73/1ufvzjH2f27Nk566yzllret2/fjB07tsnrAABoagJAAKDRdejQIddee23OOOOMXH311XnooYcyZsyYTJ8+Pa1bt06PHj2y7bbbZq+99spBBx2UPfbYY7ntnHXWWRk0aFAuvfTSPPHEE5k2bVo6d+6c/v3754gjjsgpp5ySzp07r7CWM888M5/85Cdz//335+mnn87rr7+eiRMnZuHChenQoUP69++fvffeOyeeeGJ22WWXBtvp1KlTHnroofz617/OH/7wh7z66qtZuHBhevfunQMPPDCnnXZatt1221x99dVrsecattVWW+XJJ5/M8OHDc8stt+Txxx/PhAkT8v7776d9+/bp3bt3PvrRj2a//fbLIYccstTtreuCVq1a5cILL8w3vvGNXHnllXnggQfy5ptvZtq0aamoqEi3bt2yzTbbZM8998yBBx6YQYMGNUtd5513XgYOHJirrroqzz//fKZOnbrSmZ4BANY3pbIpzwAAAACgsEwCAgAAAAAFJgAEAAAAgAITAAIAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAITAAIAAABAgQkAAQAAAKDABIAAAAAAUGACQAAAAAAoMAEgAAAAABSYABAAAAAACkwACAAAAAAFJgAEAAAAgAL7/5M8q960SLV6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<plotnine.ggplot.ggplot object at 0x7fd04da4ff00>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df = pd.DataFrame({\n",
    "    \"panel\": [\"full\", \"full\", \"full\", \"zoom\", \"zoom\", \"zoom\"],\n",
    "    \"x\": [0, 5, 10, 8, 10, 12],\n",
    "    \"y\": [2, 5, 4, 8, 10, 9],\n",
    "    \"group\": [\"A\", \"B\", \"C\", \"A\", \"B\", \"C\"],\n",
    "})\n",
    "\n",
    "(\n",
    "    ggplot(feature_df, aes(\"x\", \"y\", color=\"group\"))\n",
    "    + geom_point(size=3)\n",
    "    + facet_wrap2(\"panel\", scales=\"free_x\", axes=\"all\")\n",
    "    + scale_x_continuous(\n",
    "        guide=guide_axis_manual(\n",
    "            breaks=[0, 5, 10, 12],\n",
    "            labels=[\"start\", \"5\", \"10\", \"end\"],\n",
    "            label_colour=[\"#b00020\", \"#555555\", \"#555555\", \"#005ea8\"],\n",
    "        )\n",
    "    )\n",
    "    + scale_x_facet('panel == \"zoom\"', limits=(7, 13), breaks=[8, 10, 12])\n",
    "    + guides(color=guide_stringlegend(title=\"Group\"))\n",
    "    + labs(\n",
    "        title=\"Manual axis guide with a zoomed facet\",\n",
    "        x=\"Measurement\",\n",
    "        y=\"Response\",\n",
    "    )\n",
    "    + theme_minimal()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d701cb",
   "metadata": {},
   "source": [
    "## Manual facet designs\n",
    "\n",
    "`facet_manual()` uses a text design to place panels. Repeating a design label\n",
    "creates a spanning panel, and `#` or `NA` marks an empty cell. Repeated labels\n",
    "must form a rectangle; invalid designs raise a `ValueError` rather than silently\n",
    "dropping panels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce3288eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-13T10:41:27.719067Z",
     "iopub.status.busy": "2026-07-13T10:41:27.718791Z",
     "iopub.status.idle": "2026-07-13T10:41:28.132104Z",
     "shell.execute_reply": "2026-07-13T10:41:28.131355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manual_df = pd.DataFrame({\n",
    "    \"panel\": [\"A\", \"A\", \"B\", \"B\", \"C\", \"C\"],\n",
    "    \"x\": [1, 2, 1, 2, 1, 2],\n",
    "    \"y\": [3, 5, 2, 4, 4, 6],\n",
    "})\n",
    "\n",
    "manual_plot = (\n",
    "    ggplot(manual_df, aes(\"x\", \"y\"))\n",
    "    + geom_point(size=3)\n",
    "    + facet_manual(\"panel\", design=\"AA\\nBC\", axes=\"all\")\n",
    "    + labs(\n",
    "        title=\"Manual facet design with a spanning panel\",\n",
    "        x=\"Step\",\n",
    "        y=\"Value\",\n",
    "    )\n",
    "    + theme_minimal()\n",
    ")\n",
    "\n",
    "manual_figure = manual_plot.draw()\n",
    "manual_figure.set_layout_engine(None)\n",
    "manual_figure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd27f11",
   "metadata": {},
   "source": [
    "## Nested axis labels\n",
    "\n",
    "`guide_axis_nested()` splits labels with a delimiter and draws a small nesting\n",
    "line. Use it for categorical axes where each label has a parent and child\n",
    "level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a60c1b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-13T10:41:28.135551Z",
     "iopub.status.busy": "2026-07-13T10:41:28.135271Z",
     "iopub.status.idle": "2026-07-13T10:41:28.473011Z",
     "shell.execute_reply": "2026-07-13T10:41:28.470026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<plotnine.ggplot.ggplot object at 0x7fd04dae3200>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested_df = pd.DataFrame({\n",
    "    \"stage\": [\"baseline_low\", \"baseline_high\", \"treated_low\", \"treated_high\"],\n",
    "    \"value\": [2, 4, 5, 7],\n",
    "})\n",
    "\n",
    "(\n",
    "    ggplot(nested_df, aes(\"stage\", \"value\"))\n",
    "    + geom_col(fill=\"#4c78a8\")\n",
    "    + guide_axis_nested(delim=\"_\")\n",
    "    + labs(\n",
    "        title=\"Nested axis labels\",\n",
    "        x=\"Stage and subgroup\",\n",
    "        y=\"Value\",\n",
    "    )\n",
    "    + theme_minimal()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0664d8d3",
   "metadata": {},
   "source": [
    "## Other guide helpers\n",
    "\n",
    "The same rendering path also supports:\n",
    "\n",
    "- `guide_axis_colour()` / `guide_axis_color()` for per-label colours\n",
    "- `guide_axis_minor()` for visible minor ticks and optional minor labels\n",
    "- `guide_axis_logticks()` for log-scale tick marks\n",
    "- `guide_axis_truncated()` for spine bounds from numeric or callable limits\n",
    "- `guide_axis_scalebar()` for data-coordinate scalebars\n",
    "- `guide_dendro()` for dendrograms aligned to tick order\n",
    "- `guide_stringlegend()` for colored text legends without key glyph boxes\n",
    "\n",
    "Where plotnine lacks an equivalent ggh4x hook, plotnine-extra documents partial\n",
    "support rather than exporting a silent no-op."
   ]
  }
 ],
 "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": "3.14.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}