{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This page is available as an executable or viewable Jupyter Notebook:\n",
"
\n",
" \n",
" ![](\"https://mybinder.org/badge_logo.svg\")
\n",
"\n",
" \n",
" ![](\"https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.png\")
\n",
"\n",
"
\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%use lets-plot\n",
"%use numpy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import org.jetbrains.numkt.core.*\n",
"import org.jetbrains.numkt.math.*\n",
"import org.jetbrains.numkt.random.Random\n",
"import org.jetbrains.numkt.*"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"val cov0 = array(listOf(listOf(1, -.8),\n",
" listOf(-.8, 1)))\n",
"val cov1 = array(listOf(listOf(1, .8),\n",
" listOf(.8, 1)))\n",
"val cov2 = array(listOf(listOf(10, .1),\n",
" listOf(.1, .1)))\n",
"\n",
"val n = intArrayOf(400)\n",
"val xy0 = Random.multivariateNormal(mean=array(listOf(-2,0)), \n",
" cov=cov0, \n",
" size=n,\n",
" checkValid=\"raise\",\n",
" tol=1e-8\n",
" ).transpose()\n",
"val xy1 = Random.multivariateNormal(mean=array(listOf(2,0)), \n",
" cov=cov1, \n",
" size=n,\n",
" checkValid=\"raise\",\n",
" tol=1e-8\n",
" ).transpose()\n",
"val xy2 = Random.multivariateNormal(mean=array(listOf(0,1)), \n",
" cov=cov2, \n",
" size=n,\n",
" checkValid=\"raise\",\n",
" tol=1e-8\n",
" ).transpose()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"val data = mapOf(\n",
" \"x\" to concatenate(xy0[0], xy1[0], xy2[0]).toList(),\n",
" \"y\" to concatenate(xy0[1], xy1[1], xy2[1]).toList()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val p = ggplot(data) {x=\"x\"; y=\"y\"} + ggsize(600,300) +\n",
" geom_point(color=\"black\", alpha=.1)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Basic density \n",
"p + geom_density2d(color=\"red\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Set contour color by level\n",
"// - change defailt position and size of colorbar\n",
"p + geom_density2d {color=\"..level..\"} +\n",
" scale_color_gradient(low=\"dark_green\", high=\"yellow\", guide=guide_colorbar(barHeight=10, barWidth=300)) +\n",
" theme().legendPosition_bottom()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filling contours by level"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val p1 = ggplot(data) {x=\"x\"; y=\"y\"} + ggsize(600,300)\n",
"\n",
"// Filled polygons are not always working well - note missing polygons in the middle. \n",
"p1 + geom_polygon(stat=Stat.density2d()) {fill=\"..level..\"} + coord_fixed()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// 'density2df' is not dependent on poligons order and works a lot better\n",
"p1 + geom_density2df {fill=\"..level..\"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### geom_bin2d is another way to plot density"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p1 + geom_bin2d()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Adjust the tile size - make them square and bigger.\n",
"// Show density instead of count.\n",
"p1 + geom_bin2d(binWidth=1 to 1) {fill=\"..density..\"}"
]
}
],
"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",
"pygments_lexer": "kotlin",
"version": "1.4.20-dev-2342"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": []
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}