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    "# New `Scale` Functions with Parameter `aesthetic`\n",
    "\n",
    "- `scale_identity(aesthetic, *, ...)`\n",
    "- `scale_manual(aesthetic, values, *, ...)`\n",
    "- `scale_continuous(aesthetic, *, ...)`\n",
    "- `scale_gradient(aesthetic, *, ...)`\n",
    "- `scale_gradient2(aesthetic, *, ...)`\n",
    "- `scale_gradientn(aesthetic, *, ...)`\n",
    "- `scale_hue(aesthetic, *, ...)`\n",
    "- `scale_discrete(aesthetic, *, ...)`\n",
    "- `scale_grey(aesthetic, *, ...)`\n",
    "- `scale_brewer(aesthetic, *, ...)`\n",
    "- `scale_viridis(aesthetic, *, ...)`\n",
    "- `scale_cmapmpl(aesthetic, *, ...)`\n",
    "\n",
    "Comparing to familiar \"scale\" functions like `scale_color_gradient()` etc., the new set of functions\n",
    "adds more flexibility by allowing specifying an aesthetic or a list of aesthetics the scale is working with.\n",
    "\n",
    "For example, you can use just one function call to setup the same color palette for both, stroke and fill colors on plot:\n",
    "\n",
    "`scale_brewer(['color', 'fill'], palette='Set2')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:39:23.570926Z",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:39:23.574825Z",
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    "LetsPlot.setup_html()"
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  {
   "cell_type": "code",
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    "execution": {
     "iopub.execute_input": "2025-11-05T13:39:23.589646Z",
     "iopub.status.busy": "2025-11-05T13:39:23.589503Z",
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     "shell.execute_reply": "2025-11-05T13:39:23.677092Z"
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   "source": [
    "mpg_df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Plot with Default Colors"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:39:23.678748Z",
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     "shell.execute_reply": "2025-11-05T13:39:23.733359Z"
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"1\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   if (renderImmediately) {\n",
       "       renderPlot();\n",
       "   }\n",
       "   \n",
       "   if (!renderImmediately || responsive) {\n",
       "       // Set up observer for initial sizing or continuous monitoring\n",
       "       var observer = new ResizeObserver(function(entries) {\n",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x78bc87f96a40>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = ggplot(mpg_df, aes(as_discrete('drv', order=-1), 'hwy')) + \\\n",
    "    geom_violin(aes(color='drv', fill='drv'), alpha=.5, size=2)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Setup a Brewer Palette"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.1. Old School: for Each Aesthetic Separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:39:23.734506Z",
     "iopub.status.busy": "2025-11-05T13:39:23.734425Z",
     "iopub.status.idle": "2025-11-05T13:39:23.755537Z",
     "shell.execute_reply": "2025-11-05T13:39:23.755350Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"pT40mk\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "   \n",
       "   (function() {\n",
       "   // ----------\n",
       "   \n",
       "   const forceImmediateRender = false;\n",
       "   const responsive = false;\n",
       "   \n",
       "   let sizing = {\n",
       "       width_mode: \"MIN\",\n",
       "       height_mode: \"SCALED\",\n",
       "       width: null, \n",
       "       height: null \n",
       "   };\n",
       "   \n",
       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
       "   if (preferredWidth !== undefined) {\n",
       "       sizing = {\n",
       "           width_mode: 'FIXED',\n",
       "           height_mode: 'SCALED',\n",
       "           width: parseFloat(preferredWidth)\n",
       "       };\n",
       "   }\n",
       "   \n",
       "   const containerDiv = document.getElementById(\"pT40mk\");\n",
       "   let fig = null;\n",
       "   \n",
       "   function renderPlot() {\n",
       "       if (fig === null) {\n",
       "           const plotSpec = {\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"drv\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
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       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "}],\n",
       "\"mapping_annotations\":[{\n",
       "\"parameters\":{\n",
       "\"label\":\"drv\",\n",
       "\"order\":-1.0\n",
       "},\n",
       "\"aes\":\"x\",\n",
       "\"annotation\":\"as_discrete\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"color\",\n",
       "\"palette\":\"Set2\",\n",
       "\"scale_mapper_kind\":\"color_brewer\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"palette\":\"Set2\",\n",
       "\"scale_mapper_kind\":\"color_brewer\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"violin\",\n",
       "\"mapping\":{\n",
       "\"color\":\"drv\",\n",
       "\"fill\":\"drv\"\n",
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       "\"data_meta\":{\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"2\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   if (renderImmediately) {\n",
       "       renderPlot();\n",
       "   }\n",
       "   \n",
       "   if (!renderImmediately || responsive) {\n",
       "       // Set up observer for initial sizing or continuous monitoring\n",
       "       var observer = new ResizeObserver(function(entries) {\n",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x78bc849671f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + scale_color_brewer(palette='Set2') + scale_fill_brewer(palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.2. New: for Both Aesthetics at Once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-11-05T13:39:23.756595Z",
     "iopub.status.busy": "2025-11-05T13:39:23.756521Z",
     "iopub.status.idle": "2025-11-05T13:39:23.777869Z",
     "shell.execute_reply": "2025-11-05T13:39:23.777683Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   const preferredWidth = document.body.dataset.letsPlotPreferredWidth;\n",
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       "       sizing = {\n",
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       "   \n",
       "   function renderPlot() {\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"3\"\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
       "   }\n",
       "   \n",
       "   const renderImmediately = \n",
       "       forceImmediateRender || (\n",
       "           sizing.width_mode === 'FIXED' && \n",
       "           (sizing.height_mode === 'FIXED' || sizing.height_mode === 'SCALED')\n",
       "       );\n",
       "   \n",
       "   if (renderImmediately) {\n",
       "       renderPlot();\n",
       "   }\n",
       "   \n",
       "   if (!renderImmediately || responsive) {\n",
       "       // Set up observer for initial sizing or continuous monitoring\n",
       "       var observer = new ResizeObserver(function(entries) {\n",
       "           for (let entry of entries) {\n",
       "               if (entry.contentBoxSize && \n",
       "                   entry.contentBoxSize[0].inlineSize > 0) {\n",
       "                   if (!responsive && observer) {\n",
       "                       observer.disconnect();\n",
       "                       observer = null;\n",
       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
       "                       break;\n",
       "                   }\n",
       "               }\n",
       "           }\n",
       "       });\n",
       "       \n",
       "       observer.observe(containerDiv);\n",
       "   }\n",
       "   \n",
       "   // ----------\n",
       "   })();\n",
       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x78bc87f97a30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + scale_brewer(['color', 'fill'], palette='Set2')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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