samples/KotlinSample01.ipynb

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       "Ok"
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   "source": [
    "@file:DependsOn(\"de.erichseifert.gral:gral-core:0.11\")"
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       "Ok"
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   "source": [
    "import de.erichseifert.gral.graphics.Drawable\n",
    "import de.erichseifert.gral.io.plots.DrawableWriterFactory\n",
    "import java.io.ByteArrayOutputStream\n",
    "\n",
    "fun<T: Drawable> T.toSvg(sizeX: Double, sizeY: Double): Pair<String, Any> {\n",
    "    val writer = DrawableWriterFactory.getInstance().get(\"image/svg+xml\")\n",
    "\n",
    "    val buf = ByteArrayOutputStream()\n",
    "\n",
    "    writer.write(this, buf, sizeX, sizeY)\n",
    "\n",
    "    return writer.mimeType to buf\n",
    "}"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import de.erichseifert.gral.data.DataSeries\n",
    "import de.erichseifert.gral.data.DataTable\n",
    "import de.erichseifert.gral.data.filters.Convolution\n",
    "import de.erichseifert.gral.data.filters.Filter\n",
    "import de.erichseifert.gral.data.filters.Kernel\n",
    "import de.erichseifert.gral.data.filters.Median\n",
    "import de.erichseifert.gral.graphics.Insets2D\n",
    "import de.erichseifert.gral.graphics.Orientation\n",
    "import de.erichseifert.gral.plots.XYPlot\n",
    "import de.erichseifert.gral.plots.lines.DefaultLineRenderer2D\n",
    "import de.erichseifert.gral.plots.points.PointRenderer\n",
    "import de.erichseifert.gral.util.GraphicsUtils\n",
    "import java.awt.Color\n",
    "import java.util.*\n",
    "\n",
    "// ported from Gral's ConvolutionExample.java\n",
    "fun convolutionExample(): XYPlot {\n",
    "\n",
    "    val SAMPLE_COUNT = 200\n",
    "    val KERNEL_VARIANCE = 5.0\n",
    "    val COLOR1 = Color(55, 170, 200)\n",
    "    val COLOR2 = Color(200, 80, 75)\n",
    "\n",
    "    // Generate 200 data points\n",
    "    val data = DataTable(java.lang.Double::class.java, java.lang.Double::class.java).apply {\n",
    "        val r = Random()\n",
    "        for (i in 0..SAMPLE_COUNT - 1) {\n",
    "            val x = i.toDouble() / 2.0 / Math.PI\n",
    "            val yError = Math.sqrt(3.0 * 0.1) * r.nextGaussian()\n",
    "            val y = 10.0 * Math.sin(x / 5.0) + yError * yError * yError\n",
    "            add(x, y)\n",
    "        }\n",
    "    }\n",
    "    val ds = DataSeries(\"Data\", data, 0, 1)\n",
    "\n",
    "    // Create a smoothed data series from a binomial (near-gaussian) convolution filter\n",
    "    val kernelLowpass = Kernel.getBinomial(KERNEL_VARIANCE).normalize()\n",
    "    val dataLowpass = Convolution(data, kernelLowpass, Filter.Mode.REPEAT, 1)\n",
    "    val dsLowpass = DataSeries(\"Lowpass\", dataLowpass, 0, 1)\n",
    "\n",
    "    // Create a derived data series from a binomial convolution filter\n",
    "    val kernelHighpass = Kernel.getBinomial(KERNEL_VARIANCE).normalize().negate().add(Kernel(1.0))\n",
    "    val dataHighpass = Convolution(data, kernelHighpass, Filter.Mode.REPEAT, 1)\n",
    "    val dsHighpass = DataSeries(\"Highpass\", dataHighpass, 0, 1)\n",
    "\n",
    "    // Create a new data series that calculates the moving average using a custom convolution kernel\n",
    "    val kernelMovingAverageSize = Math.round(4.0 * KERNEL_VARIANCE).toInt()\n",
    "    val kernelMovingAverage = Kernel.getUniform(kernelMovingAverageSize, kernelMovingAverageSize - 1, 1.0).normalize()\n",
    "    val dataMovingAverage = Convolution(data, kernelMovingAverage, Filter.Mode.OMIT, 1)\n",
    "    val dsMovingAverage = DataSeries(\"Moving Average\", dataMovingAverage, 0, 1)\n",
    "\n",
    "    // Create a new data series that calculates the moving median\n",
    "    val kernelMovingMedianSize = Math.round(4.0 * KERNEL_VARIANCE).toInt()\n",
    "    val dataMovingMedian = Median(data, kernelMovingMedianSize, kernelMovingMedianSize - 1, Filter.Mode.OMIT, 1)\n",
    "    val dsMovingMedian = DataSeries(\"Moving Median\", dataMovingMedian, 0, 1)\n",
    "\n",
    "    fun XYPlot.formatLine(series: DataSeries, color: Color) {\n",
    "        setPointRenderers(series, null as List<PointRenderer>?)\n",
    "        val line = DefaultLineRenderer2D().apply { setColor(color) }\n",
    "        setLineRenderers(series, line)\n",
    "    }\n",
    "\n",
    "    // Create a new xy-plot\n",
    "    return XYPlot(ds, dsLowpass, dsHighpass, dsMovingAverage, dsMovingMedian).apply {\n",
    "\n",
    "        // Format plot\n",
    "        insets = Insets2D.Double(20.0, 40.0, 40.0, 40.0)\n",
    "        isLegendVisible = true\n",
    "\n",
    "        // Format legend\n",
    "        legend.orientation = Orientation.HORIZONTAL\n",
    "        legend.alignmentY = 1.0\n",
    "\n",
    "        // Format data series as lines of different colors\n",
    "        formatLine(ds, Color.BLACK)\n",
    "        formatLine(dsLowpass, COLOR1)\n",
    "        formatLine(dsHighpass, GraphicsUtils.deriveDarker(COLOR1))\n",
    "        formatLine(dsMovingAverage, COLOR2)\n",
    "        formatLine(dsMovingMedian, GraphicsUtils.deriveDarker(COLOR2))\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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     "execution_count": 4,
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     "output_type": "execute_result"
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   "source": [
    "resultOf(convolutionExample().toSvg(1000.0, 500.0))"
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   "cell_type": "code",
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