docs/examples/jupyter-notebooks/colors_viridis.ipynb

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    "The `viridis` color maps are designed to be perceptually-uniform, both in regular form and also when converted to black-and-white.\n",
    "\n",
    "More info: https://bids.github.io/colormap/"
   ]
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       ":root[theme=\"dark\"], :root [data-jp-theme-light=\"false\"], .dataframe_dark{\n",
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    "%useLatestDescriptors\n",
    "%use lets-plot\n",
    "%use dataframe"
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   "cell_type": "code",
   "execution_count": 2,
   "id": "committed-longitude",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lets-Plot Kotlin API v.4.4.2. Frontend: Notebook with dynamically loaded JS. Lets-Plot JS v.4.0.0."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LetsPlot.getInfo()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "reliable-rates",
   "metadata": {},
   "outputs": [
    {
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      "application/kotlindataframe+json": "{\"nrow\":3,\"ncol\":9,\"columns\":[\"miles per gallon\",\"number of cylinders\",\"engine displacement (cu. inches)\",\"engine horsepower\",\"vehicle weight (lbs.)\",\"time to accelerate (sec.)\",\"model year\",\"origin of car\",\"vehicle name\"],\"kotlin_dataframe\":[{\"miles per gallon\":18.0,\"number of cylinders\":8,\"engine displacement (cu. inches)\":307.0,\"engine horsepower\":130,\"vehicle weight (lbs.)\":3504,\"time to accelerate (sec.)\":12.0,\"model year\":70,\"origin of car\":\"US\",\"vehicle name\":\"chevrolet chevelle malibu\"},{\"miles per gallon\":15.0,\"number of cylinders\":8,\"engine displacement (cu. inches)\":350.0,\"engine horsepower\":165,\"vehicle weight (lbs.)\":3693,\"time to accelerate (sec.)\":11.5,\"model year\":70,\"origin of car\":\"US\",\"vehicle name\":\"buick skylark 320\"},{\"miles per gallon\":18.0,\"number of cylinders\":8,\"engine displacement (cu. inches)\":318.0,\"engine horsepower\":150,\"vehicle weight (lbs.)\":3436,\"time to accelerate (sec.)\":11.0,\"model year\":70,\"origin of car\":\"US\",\"vehicle name\":\"plymouth satellite\"}]}",
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       "    height: 8px;\n",
       "    margin-right: 3px;\n",
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       "\n",
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       "\n",
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       "            \n",
       "<table class=\"dataframe\" id=\"df_33554432\"></table>\n",
       "\n",
       "<p class=\"dataframe_description\">DataFrame: rowsCount = 3, columnsCount = 9</p>\n",
       "        </body>\n",
       "        <script>\n",
       "            \n",
       "/*<!--*/\n",
       "call_DataFrame(function() { DataFrame.addTable({ cols: [{ name: \"<span title=\\\"miles per gallon: Double\\\">miles per gallon</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">18.0</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">15.0</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">18.0</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"number of cylinders: Int\\\">number of cylinders</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">8</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">8</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">8</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"engine displacement (cu. inches): Double\\\">engine displacement (cu. inches)</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">307.0</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">350.0</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">318.0</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"engine horsepower: Int\\\">engine horsepower</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">130</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">165</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">150</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"vehicle weight (lbs.): Int\\\">vehicle weight (lbs.)</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">3504</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">3693</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">3436</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"time to accelerate (sec.): Double\\\">time to accelerate (sec.)</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">12.0</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">11.5</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">11.0</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"model year: Int\\\">model year</span>\", children: [], rightAlign: true, values: [\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">70</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">70</span></span>\",\"<span class=\\\"formatted\\\" title=\\\"\\\"><span class=\\\"numbers\\\">70</span></span>\"] }, \n",
       "{ name: \"<span title=\\\"origin of car: String\\\">origin of car</span>\", children: [], rightAlign: false, values: [\"US\",\"US\",\"US\"] }, \n",
       "{ name: \"<span title=\\\"vehicle name: String\\\">vehicle name</span>\", children: [], rightAlign: false, values: [\"chevrolet chevelle malibu\",\"buick skylark 320\",\"plymouth satellite\"] }, \n",
       "], id: 33554432, rootId: 33554432, totalRows: 3 } ) });\n",
       "/*-->*/\n",
       "\n",
       "call_DataFrame(function() { DataFrame.renderTable(33554432) });\n",
       "\n",
       "\n",
       "        </script>\n",
       "        </html>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val mpg = DataFrame.readCSV(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg2.csv\")\n",
    "mpg.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "affected-testament",
   "metadata": {},
   "outputs": [],
   "source": [
    "import org.jetbrains.letsPlot.intern.Plot\n",
    "\n",
    "fun pair(p0:Plot, p1:Plot):Figure {\n",
    "    return gggrid(\n",
    "            listOf(p0, p1),\n",
    "            ncol = 2,\n",
    "            cellWidth = 500,\n",
    "            cellHeight = 300,\n",
    "            vGap = 0,\n",
    "            fit = true\n",
    "        )\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "numeric-silver",
   "metadata": {},
   "outputs": [],
   "source": [
    "val pC = letsPlot(mpg.toMap()) + \n",
    " geomPoint(size = 7) {x = \"vehicle weight (lbs.)\"; y = \"miles per gallon\"; color = \"miles per gallon\"} + \n",
    " ggtitle(\"Continuous data\") + labs(color = \"MPG\")\n",
    "\n",
    "val pD = letsPlot(mpg.toMap()) + geomBar{x = \"origin of car\"; fill = \"origin of car\"} +\n",
    " ggtitle(\"Discrete data\") + labs(fill = \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pretty-cemetery",
   "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,
   "id": "cathedral-shark",
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(),\n",
    "     pC + scaleColorViridis(end = 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "mineral-albert",
   "metadata": {},
   "outputs": [
    {
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       "\"height\":300\n",
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       "\"x\":500,\n",
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       "\"aesthetic\":\"fill\",\n",
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       "           var plotContainer = document.getElementById(\"BzFejQ\");\n",
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       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pD + scaleFillViridis(),\n",
    "     pD + scaleFillViridis(begin = 0.3, end = 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "looking-sample",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   <div id=\"VgGEZx\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"aesthetic\":\"fill\",\n",
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       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
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       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"end\":0.8,\n",
       "\"begin\":0.3,\n",
       "\"direction\":-1.0\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
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       "}]\n",
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       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pD + scaleFillViridis(alpha = 0.4),\n",
    "     pD + scaleFillViridis(begin = 0.3, end = 0.8, direction = -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-hearts",
   "metadata": {},
   "source": [
    "### Supported colormaps\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",
   "id": "nervous-zealand",
   "metadata": {},
   "source": [
    "#### Magma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adjustable-business",
   "metadata": {},
   "outputs": [
    {
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       "\"mapping\":{\n",
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       "\"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",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"color\",\n",
       "\"name\":\"MPG\"\n",
       "},{\n",
       "\"aesthetic\":\"color\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"A\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"vehicle weight (lbs.)\",\n",
       "\"y\":\"miles per gallon\",\n",
       "\"color\":\"miles per gallon\"\n",
       "},\n",
       "\"stat\":\"identity\",\n",
       "\"size\":7.0,\n",
       "\"position\":\"identity\",\n",
       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
       "\"feature_spec\":{\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Discrete data\"\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"magma\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"m3Uwl2\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"A\"),\n",
    "     pD + scaleFillViridis(option = \"magma\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broken-tsunami",
   "metadata": {},
   "source": [
    "#### Inferno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "consistent-champion",
   "metadata": {},
   "outputs": [
    {
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       "\"mapping\":{\n",
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       "},{\n",
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       "\"scales\":[{\n",
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       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"CcFBIN\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"B\"),\n",
    "     pD + scaleFillViridis(option = \"inferno\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explicit-cabinet",
   "metadata": {},
   "source": [
    "#### Plasma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "painted-retailer",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "},\n",
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       "\"aesthetic\":\"color\",\n",
       "\"name\":\"MPG\"\n",
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       "\"scale_mapper_kind\":\"color_cmap\",\n",
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       "\"size\":7.0,\n",
       "\"position\":\"identity\",\n",
       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
       "\"feature_spec\":{\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Discrete data\"\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"plasma\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"3BCRoq\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"C\"),\n",
    "     pD + scaleFillViridis(option = \"plasma\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quick-reviewer",
   "metadata": {},
   "source": [
    "#### Viridis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "curious-hearts",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\"name\":\"MPG\"\n",
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       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
       "\"feature_spec\":{\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Discrete data\"\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"viridis\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"PFO2hA\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"D\"),\n",
    "     pD + scaleFillViridis(option = \"viridis\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cathedral-recipe",
   "metadata": {},
   "source": [
    "#### Cividis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "german-ownership",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
       "\"feature_spec\":{\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Discrete data\"\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"cividis\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"iRP6Zs\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"E\"),\n",
    "     pD + scaleFillViridis(option = \"cividis\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indoor-iraqi",
   "metadata": {},
   "source": [
    "#### Turbo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pointed-criminal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"color\",\n",
       "\"name\":\"MPG\"\n",
       "},{\n",
       "\"aesthetic\":\"color\",\n",
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       "\"stat\":\"identity\",\n",
       "\"size\":7.0,\n",
       "\"position\":\"identity\",\n",
       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
       "\"feature_spec\":{\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Discrete data\"\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"turbo\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"RA6BrC\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"turbo\", direction = -1),\n",
    "     pD + scaleFillViridis(option = \"turbo\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-notice",
   "metadata": {},
   "source": [
    "#### Twilight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "prospective-generation",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"color\",\n",
       "\"name\":\"MPG\"\n",
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       "\"scale_mapper_kind\":\"color_cmap\",\n",
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       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"vehicle weight (lbs.)\",\n",
       "\"y\":\"miles per gallon\",\n",
       "\"color\":\"miles per gallon\"\n",
       "},\n",
       "\"stat\":\"identity\",\n",
       "\"size\":7.0,\n",
       "\"position\":\"identity\",\n",
       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "},{\n",
       "\"x\":500,\n",
       "\"width\":500,\n",
       "\"y\":0,\n",
       "\"feature_spec\":{\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Discrete data\"\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"scale_mapper_kind\":\"color_cmap\",\n",
       "\"option\":\"twilight\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"mapping\":{\n",
       "\"x\":\"origin of car\",\n",
       "\"fill\":\"origin of car\"\n",
       "},\n",
       "\"stat\":\"count\",\n",
       "\"position\":\"stack\",\n",
       "\"geom\":\"bar\",\n",
       "\"data\":{\n",
       "\"origin of car\":[\"US\",\"Asia\",\"Europe\"],\n",
       "\"..count..\":[245.0,79.0,68.0]\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"height\":300\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"t755Gq\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair(pC + scaleColorViridis(option = \"twilight\"),\n",
    "     pD + scaleFillViridis(option = \"twilight\"))"
   ]
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