{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GGBunch\n",
"\n",
"*GGBunch* allows to show a collection of plots on one figure. Each plot in the collection can have arbitrary location and size. There is no automatic layout inside the bunch."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
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"data": {
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"%useLatestDescriptors\n",
"%use lets-plot\n",
"\n",
"@file:DependsOn(\"org.apache.commons:commons-math3:3.6.1\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import kotlin.random.Random\n",
"import org.apache.commons.math3.distribution.MultivariateNormalDistribution"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"val cov : Array = arrayOf(doubleArrayOf(1.0, 0.0),\n",
" doubleArrayOf(0.0, 1.0))\n",
"val means : DoubleArray = doubleArrayOf(0.0, 0.0)\n",
"val xy = MultivariateNormalDistribution(means, cov).sample(400)\n",
"\n",
"val xs = xy.map { it[0] }\n",
"val ys = xy.map { it[1] }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View this data as a scatter plot and as a histogram"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"data": {
"text/html": [
" \n",
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val p = letsPlot() + ggsize(600,200)\n",
"\n",
"val scatter = p + geomPoint(color=\"black\", alpha=.4) {x=xs; y=ys}\n",
"scatter"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val histogram = p + geomHistogram(fill=\"dark_magenta\") {x=xs}\n",
"histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combine both plots in one figure"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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"text/html": [
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"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Set scale X limits manually because of computed automatically\n",
"// the scale used by each plot would be slightly different\n",
"// and the stacked plots wouldn't be aligned.\n",
"val scale_x = scaleXContinuous(limits=-3.5 to 3.5)\n",
"GGBunch().addPlot(histogram + scale_x, 0, 0)\n",
" .addPlot(scatter + scale_x, 0, 200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adjust visuals of the bunch figure"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"val upper_theme = theme(axis=\"blank\", axisText=elementText(), axisTitleY=elementText(), panelGridMajorY=\"blank\")\n",
"val lower_theme = theme(axis=\"blank\", axisTextY=elementText(), axisTitle=elementText())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
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"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GGBunch().addPlot(histogram + upper_theme + scale_x, 0, 0)\n",
" .addPlot(scatter + lower_theme + scale_x, 0, 200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adjust plot sizes\n",
"\n",
"*addPlot()* method has two more (optional) parameters: *width* and *height*.\n",
"\n",
"This values will override plot size earlier defined via *ggsize()* function."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
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"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GGBunch().addPlot(histogram + upper_theme + scale_x, 0, 0, 600, 100)\n",
" .addPlot(scatter + lower_theme + scale_x, 0, 100, 600, 300)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Kotlin",
"language": "kotlin",
"name": "kotlin"
},
"language_info": {
"codemirror_mode": "text/x-kotlin",
"file_extension": ".kt",
"mimetype": "text/x-kotlin",
"name": "kotlin",
"nbconvert_exporter": "",
"pygments_lexer": "kotlin",
"version": "1.8.20"
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