{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colors Viridis\n",
    "\n",
    "The `viridis` color maps are designed to be perceptually-uniform,\n",
    "both in regular form and also when converted to black-and-white.\n",
    "\n",
    "More info: [https://bids.github.io/colormap/](https://bids.github.io/colormap/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:07.889706Z",
     "iopub.status.busy": "2026-01-27T17:08:07.889619Z",
     "iopub.status.idle": "2026-01-27T17:08:07.892484Z",
     "shell.execute_reply": "2026-01-27T17:08:07.892202Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:07.893353Z",
     "iopub.status.busy": "2026-01-27T17:08:07.893280Z",
     "iopub.status.idle": "2026-01-27T17:08:07.894809Z",
     "shell.execute_reply": "2026-01-27T17:08:07.894615Z"
    }
   },
   "outputs": [
    {
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      "text/html": [
       "\n",
       "        <div id=\"2KUEZ4\"></div>\n",
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       "            (function() {\n",
       "                var script = document.createElement(\"script\");\n",
       "                script.type = \"text/javascript\";\n",
       "                script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.8.2/js-package/distr/lets-plot.min.js\";\n",
       "                script.onload = function() {\n",
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       "                    var div = document.createElement(\"div\");\n",
       "                    div.style.color = 'darkred';\n",
       "                    div.textContent = 'Error loading Lets-Plot JS';\n",
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       "                var e = document.getElementById(\"2KUEZ4\");\n",
       "                e.appendChild(script);\n",
       "            })()\n",
       "        </script>\n",
       "        "
      ]
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     "output_type": "display_data"
    }
   ],
   "source": [
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:07.908064Z",
     "iopub.status.busy": "2026-01-27T17:08:07.907990Z",
     "iopub.status.idle": "2026-01-27T17:08:08.019711Z",
     "shell.execute_reply": "2026-01-27T17:08:08.019276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>miles per gallon</th>\n",
       "      <th>number of cylinders</th>\n",
       "      <th>engine displacement (cu. inches)</th>\n",
       "      <th>engine horsepower</th>\n",
       "      <th>vehicle weight (lbs.)</th>\n",
       "      <th>time to accelerate (sec.)</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin of car</th>\n",
       "      <th>vehicle name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   miles per gallon  number of cylinders  engine displacement (cu. inches)  \\\n",
       "0              18.0                    8                             307.0   \n",
       "1              15.0                    8                             350.0   \n",
       "2              18.0                    8                             318.0   \n",
       "\n",
       "   engine horsepower  vehicle weight (lbs.)  time to accelerate (sec.)  \\\n",
       "0                130                   3504                       12.0   \n",
       "1                165                   3693                       11.5   \n",
       "2                150                   3436                       11.0   \n",
       "\n",
       "   model year origin of car               vehicle name  \n",
       "0          70            US  chevrolet chevelle malibu  \n",
       "1          70            US          buick skylark 320  \n",
       "2          70            US         plymouth satellite  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.read_csv (\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg2.csv\")\n",
    "mpg.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.020788Z",
     "iopub.status.busy": "2026-01-27T17:08:08.020712Z",
     "iopub.status.idle": "2026-01-27T17:08:08.022100Z",
     "shell.execute_reply": "2026-01-27T17:08:08.021890Z"
    }
   },
   "outputs": [],
   "source": [
    "def pair(p0, p1):\n",
    "    return gggrid([p0, p1]) + ggsize(1000, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.023048Z",
     "iopub.status.busy": "2026-01-27T17:08:08.022979Z",
     "iopub.status.idle": "2026-01-27T17:08:08.047107Z",
     "shell.execute_reply": "2026-01-27T17:08:08.046864Z"
    }
   },
   "outputs": [],
   "source": [
    "p_c = (ggplot(mpg) + \n",
    " geom_point(aes(\"vehicle weight (lbs.)\", \"miles per gallon\", color=\"miles per gallon\"), size=7) + \n",
    " ggtitle(\"Continuous data\") + labs(color=\"MPG\"))\n",
    "\n",
    "p_d = (ggplot(mpg) + geom_bar(aes(\"origin of car\", fill=\"origin of car\")) +\n",
    " ggtitle(\"Discrete data\") + labs(fill=\"\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The \"viridis\" colormap is the default option.\n",
    "\n",
    "Adjust scales with `begin`, `end`, `alpha` and `direction` parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.048405Z",
     "iopub.status.busy": "2026-01-27T17:08:08.048293Z",
     "iopub.status.idle": "2026-01-27T17:08:08.059886Z",
     "shell.execute_reply": "2026-01-27T17:08:08.059666Z"
    }
   },
   "outputs": [
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       "\"kind\":\"subplots\",\n",
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       "\"figures\":[{\n",
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       "\"vehicle weight (lbs.)\":[3504.0,3693.0,3436.0,3433.0,3449.0,4341.0,4354.0,4312.0,4425.0,3850.0,3563.0,3609.0,3761.0,3086.0,2372.0,2833.0,2774.0,2587.0,2130.0,1835.0,2672.0,2430.0,2375.0,2234.0,2648.0,4615.0,4376.0,4382.0,4732.0,2130.0,2264.0,2228.0,2634.0,3439.0,3329.0,3302.0,3288.0,4209.0,4464.0,4154.0,4096.0,4955.0,4746.0,5140.0,2962.0,2408.0,3282.0,3139.0,2220.0,2123.0,2074.0,2065.0,1773.0,1613.0,1834.0,1955.0,2278.0,2126.0,2254.0,2408.0,2226.0,4274.0,4385.0,4135.0,4129.0,3672.0,4633.0,4502.0,4456.0,4422.0,2330.0,3892.0,4098.0,4294.0,4077.0,2933.0,2511.0,2979.0,2189.0,2395.0,2288.0,2506.0,2164.0,2100.0,4100.0,3672.0,3988.0,4042.0,3777.0,4952.0,4464.0,4363.0,4237.0,4735.0,4951.0,3821.0,3121.0,3278.0,2945.0,3021.0,2904.0,1950.0,4997.0,4906.0,4654.0,4499.0,2789.0,2279.0,2401.0,2379.0,2124.0,2310.0,2472.0,2265.0,4082.0,4278.0,1867.0,2158.0,2582.0,2868.0,3399.0,2660.0,2807.0,3664.0,3102.0,2901.0,3336.0,1950.0,2451.0,1836.0,2542.0,3781.0,3632.0,3613.0,4141.0,4699.0,4457.0,4638.0,4257.0,2219.0,1963.0,2300.0,1649.0,2003.0,2125.0,2108.0,2246.0,2489.0,2391.0,2000.0,3264.0,3459.0,3432.0,3158.0,4668.0,4440.0,4498.0,4657.0,3907.0,3897.0,3730.0,3785.0,3039.0,3221.0,3169.0,2171.0,2639.0,2914.0,2592.0,2702.0,2223.0,2545.0,2984.0,1937.0,3211.0,2694.0,2957.0,2945.0,2671.0,1795.0,2464.0,2220.0,2572.0,2255.0,2202.0,4215.0,4190.0,3962.0,4215.0,3233.0,3353.0,3012.0,3085.0,2035.0,2164.0,1937.0,1795.0,3651.0,3574.0,3645.0,3193.0,1825.0,1990.0,2155.0,2565.0,3150.0,3940.0,3270.0,2930.0,3820.0,4380.0,4055.0,3870.0,3755.0,2045.0,2155.0,1825.0,2300.0,1945.0,3880.0,4060.0,4140.0,4295.0,3520.0,3425.0,3630.0,3525.0,4220.0,4165.0,4325.0,4335.0,1940.0,2740.0,2265.0,2755.0,2051.0,2075.0,1985.0,2190.0,2815.0,2600.0,2720.0,1985.0,1800.0,1985.0,2070.0,1800.0,3365.0,3735.0,3570.0,3535.0,3155.0,2965.0,2720.0,3430.0,3210.0,3380.0,3070.0,3620.0,3410.0,3425.0,3445.0,3205.0,4080.0,2155.0,2560.0,2300.0,2230.0,2515.0,2745.0,2855.0,2405.0,2830.0,3140.0,2795.0,3410.0,1990.0,2135.0,3245.0,2990.0,2890.0,3265.0,3360.0,3840.0,3725.0,3955.0,3830.0,4360.0,4054.0,3605.0,3940.0,1925.0,1975.0,1915.0,2670.0,3530.0,3900.0,3190.0,3420.0,2200.0,2150.0,2020.0,2130.0,2670.0,2595.0,2700.0,2556.0,2144.0,1968.0,2120.0,2019.0,2678.0,2870.0,3003.0,3381.0,2188.0,2711.0,2542.0,2434.0,2265.0,2110.0,2800.0,2110.0,2085.0,2335.0,2950.0,3250.0,1850.0,2145.0,1845.0,2910.0,2420.0,2500.0,2290.0,2490.0,2635.0,2620.0,2725.0,2385.0,1755.0,1875.0,1760.0,2065.0,1975.0,2050.0,1985.0,2215.0,2045.0,2380.0,2190.0,2210.0,2350.0,2615.0,2635.0,3230.0,3160.0,2900.0,2930.0,3415.0,3725.0,3060.0,3465.0,2605.0,2640.0,2395.0,2575.0,2525.0,2735.0,2865.0,1980.0,2025.0,1970.0,2125.0,2125.0,2160.0,2205.0,2245.0,1965.0,1965.0,1995.0,2945.0,3015.0,2585.0,2835.0,2665.0,2370.0,2950.0,2790.0,2130.0,2295.0,2625.0,2720.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"miles per gallon\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"number of cylinders\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"engine displacement (cu. inches)\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"engine horsepower\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"vehicle weight (lbs.)\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"time to accelerate (sec.)\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"model year\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"origin of car\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"vehicle name\"\n",
       "}]\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Continuous data\"\n",
       "},\n",
       "\"guides\":{\n",
       "\"color\":{\n",
       "\"title\":\"MPG\"\n",
       "}\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
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       "\"scale_mapper_kind\":\"color_cmap\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_d + scale_fill_viridis(alpha=0.4),\n",
    "     p_d + scale_fill_viridis(begin=0.3, end=0.8, direction=-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supported colormaps\n",
    "\n",
    "Use the `option` parameter to select a colormap you like:\n",
    "\n",
    "- \"magma\" (or \"A\"),\n",
    "- \"inferno\" (or \"B\")\n",
    "- \"plasma\" (or \"C\")\n",
    "- \"viridis\" (or \"D\") - the default\n",
    "- \"cividis\" (or \"E\")\n",
    "- \"turbo\"\n",
    "- \"twilight\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Magma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.079870Z",
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     "iopub.status.idle": "2026-01-27T17:08:08.087436Z",
     "shell.execute_reply": "2026-01-27T17:08:08.087249Z"
    }
   },
   "outputs": [
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       "\"column\":\"vehicle weight (lbs.)\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"time to accelerate (sec.)\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"model year\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"origin of car\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"vehicle name\"\n",
       "}]\n",
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       "\"text\":\"Discrete data\"\n",
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       "\"fill\":{\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"option\":\"magma\",\n",
       "\"scale_mapper_kind\":\"color_cmap\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[245.0,79.0,68.0],\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"spec_id\":\"8\"\n",
       "}]\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.subplots.SupPlotsSpec at 0x7b6e1b161420>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"A\"),\n",
    "     p_d + scale_fill_viridis(option=\"magma\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inferno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.088584Z",
     "iopub.status.busy": "2026-01-27T17:08:08.088515Z",
     "iopub.status.idle": "2026-01-27T17:08:08.095905Z",
     "shell.execute_reply": "2026-01-27T17:08:08.095702Z"
    }
   },
   "outputs": [
    {
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       "\"figures\":[{\n",
       "\"data\":{\n",
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       "\"vehicle weight (lbs.)\":[3504.0,3693.0,3436.0,3433.0,3449.0,4341.0,4354.0,4312.0,4425.0,3850.0,3563.0,3609.0,3761.0,3086.0,2372.0,2833.0,2774.0,2587.0,2130.0,1835.0,2672.0,2430.0,2375.0,2234.0,2648.0,4615.0,4376.0,4382.0,4732.0,2130.0,2264.0,2228.0,2634.0,3439.0,3329.0,3302.0,3288.0,4209.0,4464.0,4154.0,4096.0,4955.0,4746.0,5140.0,2962.0,2408.0,3282.0,3139.0,2220.0,2123.0,2074.0,2065.0,1773.0,1613.0,1834.0,1955.0,2278.0,2126.0,2254.0,2408.0,2226.0,4274.0,4385.0,4135.0,4129.0,3672.0,4633.0,4502.0,4456.0,4422.0,2330.0,3892.0,4098.0,4294.0,4077.0,2933.0,2511.0,2979.0,2189.0,2395.0,2288.0,2506.0,2164.0,2100.0,4100.0,3672.0,3988.0,4042.0,3777.0,4952.0,4464.0,4363.0,4237.0,4735.0,4951.0,3821.0,3121.0,3278.0,2945.0,3021.0,2904.0,1950.0,4997.0,4906.0,4654.0,4499.0,2789.0,2279.0,2401.0,2379.0,2124.0,2310.0,2472.0,2265.0,4082.0,4278.0,1867.0,2158.0,2582.0,2868.0,3399.0,2660.0,2807.0,3664.0,3102.0,2901.0,3336.0,1950.0,2451.0,1836.0,2542.0,3781.0,3632.0,3613.0,4141.0,4699.0,4457.0,4638.0,4257.0,2219.0,1963.0,2300.0,1649.0,2003.0,2125.0,2108.0,2246.0,2489.0,2391.0,2000.0,3264.0,3459.0,3432.0,3158.0,4668.0,4440.0,4498.0,4657.0,3907.0,3897.0,3730.0,3785.0,3039.0,3221.0,3169.0,2171.0,2639.0,2914.0,2592.0,2702.0,2223.0,2545.0,2984.0,1937.0,3211.0,2694.0,2957.0,2945.0,2671.0,1795.0,2464.0,2220.0,2572.0,2255.0,2202.0,4215.0,4190.0,3962.0,4215.0,3233.0,3353.0,3012.0,3085.0,2035.0,2164.0,1937.0,1795.0,3651.0,3574.0,3645.0,3193.0,1825.0,1990.0,2155.0,2565.0,3150.0,3940.0,3270.0,2930.0,3820.0,4380.0,4055.0,3870.0,3755.0,2045.0,2155.0,1825.0,2300.0,1945.0,3880.0,4060.0,4140.0,4295.0,3520.0,3425.0,3630.0,3525.0,4220.0,4165.0,4325.0,4335.0,1940.0,2740.0,2265.0,2755.0,2051.0,2075.0,1985.0,2190.0,2815.0,2600.0,2720.0,1985.0,1800.0,1985.0,2070.0,1800.0,3365.0,3735.0,3570.0,3535.0,3155.0,2965.0,2720.0,3430.0,3210.0,3380.0,3070.0,3620.0,3410.0,3425.0,3445.0,3205.0,4080.0,2155.0,2560.0,2300.0,2230.0,2515.0,2745.0,2855.0,2405.0,2830.0,3140.0,2795.0,3410.0,1990.0,2135.0,3245.0,2990.0,2890.0,3265.0,3360.0,3840.0,3725.0,3955.0,3830.0,4360.0,4054.0,3605.0,3940.0,1925.0,1975.0,1915.0,2670.0,3530.0,3900.0,3190.0,3420.0,2200.0,2150.0,2020.0,2130.0,2670.0,2595.0,2700.0,2556.0,2144.0,1968.0,2120.0,2019.0,2678.0,2870.0,3003.0,3381.0,2188.0,2711.0,2542.0,2434.0,2265.0,2110.0,2800.0,2110.0,2085.0,2335.0,2950.0,3250.0,1850.0,2145.0,1845.0,2910.0,2420.0,2500.0,2290.0,2490.0,2635.0,2620.0,2725.0,2385.0,1755.0,1875.0,1760.0,2065.0,1975.0,2050.0,1985.0,2215.0,2045.0,2380.0,2190.0,2210.0,2350.0,2615.0,2635.0,3230.0,3160.0,2900.0,2930.0,3415.0,3725.0,3060.0,3465.0,2605.0,2640.0,2395.0,2575.0,2525.0,2735.0,2865.0,1980.0,2025.0,1970.0,2125.0,2125.0,2160.0,2205.0,2245.0,1965.0,1965.0,1995.0,2945.0,3015.0,2585.0,2835.0,2665.0,2370.0,2950.0,2790.0,2130.0,2295.0,2625.0,2720.0]\n",
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       "\"column\":\"miles per gallon\"\n",
       "},{\n",
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       "\"column\":\"number of cylinders\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"engine displacement (cu. inches)\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"engine horsepower\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"vehicle weight (lbs.)\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
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       "\"text\":\"Continuous data\"\n",
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       "\"data\":{\n",
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       "\"geom\":\"bar\",\n",
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       "\"data_meta\":{\n",
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       "\"data\":{\n",
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       "}\n",
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       "\"metainfo_list\":[],\n",
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       "   \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x7b6e1b1789a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"B\"),\n",
    "     p_d + scale_fill_viridis(option=\"inferno\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plasma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.097067Z",
     "iopub.status.busy": "2026-01-27T17:08:08.096996Z",
     "iopub.status.idle": "2026-01-27T17:08:08.104882Z",
     "shell.execute_reply": "2026-01-27T17:08:08.104664Z"
    }
   },
   "outputs": [
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       "\"spec_id\":\"11\"\n",
       "},{\n",
       "\"data\":{\n",
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       "\"mapping\":{\n",
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       "\"data_meta\":{\n",
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       "},{\n",
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       "\"column\":\"time to accelerate (sec.)\"\n",
       "},{\n",
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       "},{\n",
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       "\"column\":\"vehicle name\"\n",
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       "\"text\":\"Discrete data\"\n",
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       "\"guides\":{\n",
       "\"fill\":{\n",
       "\"title\":\"\"\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
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       "\"option\":\"plasma\",\n",
       "\"scale_mapper_kind\":\"color_cmap\"\n",
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       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
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       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[245.0,79.0,68.0],\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
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       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
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       "       );\n",
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       "   if (renderImmediately) {\n",
       "       renderPlot();\n",
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       "   if (!renderImmediately || responsive) {\n",
       "       // Set up observer for initial sizing or continuous monitoring\n",
       "       var observer = new ResizeObserver(function(entries) {\n",
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       "                   }\n",
       "                   renderPlot();\n",
       "                   if (!responsive) {\n",
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       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x7b6e1b1940a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"C\"),\n",
    "     p_d + scale_fill_viridis(option=\"plasma\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Viridis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.105903Z",
     "iopub.status.busy": "2026-01-27T17:08:08.105834Z",
     "iopub.status.idle": "2026-01-27T17:08:08.113616Z",
     "shell.execute_reply": "2026-01-27T17:08:08.113415Z"
    }
   },
   "outputs": [
    {
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       "\"data\":{\n",
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      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x7b6e1b1a7790>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"D\"),\n",
    "     p_d + scale_fill_viridis(option=\"viridis\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cividis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.114745Z",
     "iopub.status.busy": "2026-01-27T17:08:08.114675Z",
     "iopub.status.idle": "2026-01-27T17:08:08.122059Z",
     "shell.execute_reply": "2026-01-27T17:08:08.121855Z"
    }
   },
   "outputs": [
    {
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       "\"data\":{\n",
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       "\"data\":{\n",
       "\"..count..\":[245.0,79.0,68.0],\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"]\n",
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      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x7b6e1b1beec0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"E\"),\n",
    "     p_d + scale_fill_viridis(option=\"cividis\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Turbo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.123136Z",
     "iopub.status.busy": "2026-01-27T17:08:08.123064Z",
     "iopub.status.idle": "2026-01-27T17:08:08.130593Z",
     "shell.execute_reply": "2026-01-27T17:08:08.130392Z"
    }
   },
   "outputs": [
    {
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       "},\n",
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       "},\n",
       "\"guides\":{\n",
       "\"fill\":{\n",
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       "\"kind\":\"plot\",\n",
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       "\"data\":{\n",
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      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x7b6e1b1da710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"turbo\", direction=-1),\n",
    "     p_d + scale_fill_viridis(option=\"turbo\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Twilight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-27T17:08:08.131525Z",
     "iopub.status.busy": "2026-01-27T17:08:08.131457Z",
     "iopub.status.idle": "2026-01-27T17:08:08.139200Z",
     "shell.execute_reply": "2026-01-27T17:08:08.138975Z"
    }
   },
   "outputs": [
    {
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       "\"type\":\"int\",\n",
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       "\"ggtitle\":{\n",
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       "}\n",
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       "\"spec_id\":\"19\"\n",
       "},{\n",
       "\"data\":{\n",
       "},\n",
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       "},\n",
       "\"ggtitle\":{\n",
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       "},\n",
       "\"guides\":{\n",
       "\"fill\":{\n",
       "\"title\":\"\"\n",
       "}\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"option\":\"twilight\",\n",
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       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
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       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[245.0,79.0,68.0],\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
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       "}]\n",
       "};\n",
       "           window.letsPlotCall(function() { fig = LetsPlot.buildPlotFromProcessedSpecs(plotSpec, containerDiv, sizing); });\n",
       "       } else {\n",
       "           fig.updateView({});\n",
       "       }\n",
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       "   \n",
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       "       forceImmediateRender || (\n",
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       "   \n",
       "   if (renderImmediately) {\n",
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      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x7b6e1b1f1de0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(p_c + scale_color_viridis(option=\"twilight\"),\n",
    "     p_d + scale_fill_viridis(option=\"twilight\"))"
   ]
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