\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%useLatestDescriptors\n",
"%use lets-plot\n",
"%use dataframe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "aggressive-identifier",
"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()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "informational-payroll",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot() + geomLabel(x=0, y=0, label=\"Lorem ipsum\", size=14) + ggsize(500, 200)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "individual-congress",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot() + geomLabel( \n",
" x = 0, y = 0,\n",
" label = \"Lorem ipsum\",\n",
" size = 14,\n",
" fill = \"#edf8e9\",\n",
" color = \"#238b45\",\n",
" fontface = \"bold\",\n",
" labelPadding = 1.0,\n",
" labelR = 0.5,\n",
" labelSize = 2.0\n",
") + ggsize(500, 200)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "super-employer",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val families = listOf(\n",
" \"Arial\",\n",
" \"Calibri\",\n",
" \"Garamond\",\n",
" \"Geneva\",\n",
" \"Georgia\",\n",
" \"Helvetica\",\n",
" \"Lucida Grande\",\n",
" \"Rockwell\",\n",
" \"Times New Roman\",\n",
" \"Verdana\",\n",
" \"sans-serif\",\n",
" \"serif\",\n",
" \"monospace\"\n",
")\n",
"\n",
"letsPlot() + geomLabel(size = 10, labelPadding = 0, labelR = 0) {\n",
" y = IntArray(families.size) { it };\n",
" label = families;\n",
" family = families\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "shaped-internet",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val justifications = listOf(0, 0.5, 1)\n",
"val angles = listOf(0, 45, 90)\n",
"\n",
"val values = justifications.flatMap { hjust ->\n",
" justifications.flatMap { vjust ->\n",
" angles.map { angle ->\n",
" Triple(hjust, vjust, angle)\n",
" }\n",
" }\n",
"}\n",
"\n",
"val data = mapOf(\n",
" \"hjust\" to values.map { it.first },\n",
" \"vjust\" to values.map { it.second },\n",
" \"angle\" to values.map { it.third }\n",
")\n",
"\n",
"letsPlot(data) { x = \"hjust\"; y = \"vjust\" } +\n",
" geomPoint(size = 3) +\n",
" geomLabel(label = \"Text\", size = 9) { hjust = \"hjust\"; vjust = \"vjust\"; angle = \"angle\" } +\n",
" facetGrid(y = \"angle\", yFormat = \"{d}°\") +\n",
" scaleXContinuous(breaks = listOf(0, 0.5, 1), expand = listOf(0.1)) +\n",
" scaleYContinuous(breaks = listOf(0, 0.5, 1), expand = listOf(0.0, 0.5)) +\n",
" themeClassic() + theme(panelBorder = elementRect(size = 1))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "under-petersburg",
"metadata": {},
"outputs": [
{
"data": {
"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\"}]}",
"text/html": [
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"\n",
"
DataFrame: rowsCount = 3, columnsCount = 9
\n",
" \n",
" \n",
" "
]
},
"execution_count": 7,
"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": 8,
"id": "extensive-democrat",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val p = letsPlot(mpg.head(30).toMap()) { \n",
" x = \"vehicle weight (lbs.)\"\n",
" y = \"miles per gallon\"\n",
" label = \"vehicle name\"\n",
"}\n",
"\n",
"p + geomLabel()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "intermediate-bridal",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geomLabel(color = \"white\", fontface = \"bold\") {\n",
" fill = asDiscrete(\"number of cylinders\", order = 1)\n",
"} + ggsize(800,400)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "continent-nudist",
"metadata": {},
"outputs": [],
"source": [
"// Initialize Lets-Plot GeoTools extension. \n",
"%use lets-plot-gt(gt=\"[23,)\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "speaking-massage",
"metadata": {},
"outputs": [],
"source": [
"@file:DependsOn(\"org.geotools:gt-shapefile:[23,)\")\n",
"@file:DependsOn(\"org.geotools:gt-cql:[23,)\")\n",
"\n",
"import org.geotools.data.shapefile.ShapefileDataStoreFactory\n",
"import org.geotools.data.simple.SimpleFeatureCollection\n",
"import java.net.URL\n",
"\n",
"import org.geotools.filter.text.cql2.CQL\n",
"\n",
"\n",
"val factory = ShapefileDataStoreFactory()\n",
"\n",
"val worldFeatures : SimpleFeatureCollection = with(\"naturalearth_lowres\") {\n",
" val url = \"https://raw.githubusercontent.com/JetBrains/lets-plot-kotlin/master/docs/examples/shp/${this}/${this}.shp\"\n",
" factory.createDataStore(URL(url)).featureSource.features\n",
"}\n",
"val europe = worldFeatures.subCollection(CQL.toFilter(\"continent = 'Europe'\"))\n",
"\n",
"val cityFeatures : SimpleFeatureCollection = with(\"naturalearth_cities\") {\n",
" val url = \"https://raw.githubusercontent.com/JetBrains/lets-plot-kotlin/master/docs/examples/shp/${this}/${this}.shp\"\n",
" factory.createDataStore(URL(url)).featureSource.features\n",
"}\n",
"val cities = cityFeatures.toSpatialDataset()\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "agricultural-deployment",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot() + \n",
" geomMap(map = europe.toSpatialDataset(), fill=\"#e5f5e0\") +\n",
" geomPoint(data = cities, color = \"#224717\", size = 3) +\n",
" geomLabel(data = cities, hjust = 0, vjust = 1, color = \"#224717\") { label = \"name\" } +\n",
" coordMap(xlim = -10.5 to 44.0, ylim = 36.0 to 60.5) +\n",
" theme(axis=\"blank\", panelGrid=\"blank\")"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}