{
"cells": [
{
"cell_type": "markdown",
"id": "rational-surgery",
"metadata": {},
"source": [
"# Using Exotic Map Projections: `useCRS=\"provided\"`\n",
"\n",
"Value \"provided\" tells Lets-Plot that the input `SpatialDataset` already contains coordinates in the desired CRS and should not be reprojected any further.\n",
"\n",
"This is a workaround for the case when the desired CRS is not naturally supported by Lets-Plot.\n",
"\n",
"> Note: at the moment, Lets-Plot only supports **WGS84** CRS and the Mercator projection.\n",
"\n",
"For example, if you try to plot a `SpatialDataset` which uses the **Conus Albers** CRS (EPSG:5070), then Lets-Plot will return an error:\n",
"\n",
"> Geometry must use WGS84 coordinate reference system but was: NAD83 / Conus Albers.\n",
"\n",
"Let's see how can we still plot a map in the **Conus Albers** projection in Lets-Plot.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "relative-skiing",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%useLatestDescriptors\n",
"%use lets-plot"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "mighty-pollution",
"metadata": {},
"outputs": [],
"source": [
"%use lets-plot-gt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "hydraulic-spyware",
"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": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LetsPlot.getInfo()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "initial-resident",
"metadata": {},
"outputs": [],
"source": [
"LetsPlot.theme = themeBW()"
]
},
{
"cell_type": "markdown",
"id": "round-peter",
"metadata": {},
"source": [
"#### 1. Load North America `FeatureCollection` from \"natural earth\" Shapefiles\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "hollow-chapel",
"metadata": {},
"outputs": [],
"source": [
"@file:DependsOn(\"org.geotools:gt-shapefile:[23,)\")\n",
"@file:DependsOn(\"org.geotools:gt-cql:[23,)\")\n",
"@file:DependsOn(\"org.geotools:gt-epsg-hsql:[23,)\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "suspected-therapist",
"metadata": {},
"outputs": [],
"source": [
"import org.geotools.data.shapefile.ShapefileDataStoreFactory\n",
"import org.geotools.data.simple.SimpleFeatureCollection\n",
"import org.geotools.filter.text.cql2.CQL\n",
"import java.net.URL\n",
"\n",
"val factory = ShapefileDataStoreFactory()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dying-spending",
"metadata": {},
"outputs": [],
"source": [
"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"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "brilliant-sixth",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Oceania, Africa, North America, Asia, South America, Europe, Seven seas (open ocean), Antarctica]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worldFeatures.toSpatialDataset()[\"continent\"]?.distinct()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cellular-manitoba",
"metadata": {},
"outputs": [],
"source": [
"// Obtain North America feature collection.\n",
"val northAmFc = worldFeatures.subCollection(\n",
" CQL.toFilter(\"continent = 'North America'\")\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "optional-jones",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GCS_WGS_1984"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Convert \"North America\" Feature Collection to a SpatialDataset.\n",
"// Use 10 decimals to encode floating point numbers (this is the default).\n",
"val northAmSds = northAmFc.toSpatialDataset(10)\n",
"northAmSds.crs"
]
},
{
"cell_type": "markdown",
"id": "distant-conflict",
"metadata": {},
"source": [
"#### 2. A Map of North America in the *Mercator* Projection\n",
"\n",
"The Mercator projection is a map projection used by Lets-Plot by default when plotting a `SpatialDataset`.\n",
"\n",
"Note that `northAmSds` dataset uses **WGS84** (or *GCS_WGS_1984*) CRS which is naitively supported by Lets-Plot."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "worthy-korea",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot() + geomMap(map = northAmSds, fill = \"blue\", color = \"white\", size = 1)"
]
},
{
"cell_type": "markdown",
"id": "proud-capture",
"metadata": {},
"source": [
"#### 3. Reproject North America Feature Collection Using the *Conus Albers* Projection\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "sized-trading",
"metadata": {},
"outputs": [],
"source": [
"import org.geotools.referencing.CRS\n",
"import org.geotools.data.store.ReprojectingFeatureCollection"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "numeric-stevens",
"metadata": {},
"outputs": [],
"source": [
"val сonusAlbers = CRS.decode(\"EPSG:5070\", true)\n",
"\n",
"val northAmAlbersFc = ReprojectingFeatureCollection(northAmFc, сonusAlbers)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "likely-tokyo",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NAD83 / Conus Albers"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val northAmAlbersSds = northAmAlbersFc.toSpatialDataset(10)\n",
"northAmAlbersSds.crs"
]
},
{
"cell_type": "markdown",
"id": "comic-result",
"metadata": {},
"source": [
"#### 4. A Map of North America in the *Conus Albers* Projection\n"
]
},
{
"cell_type": "markdown",
"id": "infrared-orbit",
"metadata": {},
"source": [
"##### 4.1. Lets-Plot doesn't Support the *Conus Albers* Projection"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "scenic-nebraska",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"java.lang.IllegalArgumentException: Geometry must use WGS84 coordinate reference system but was: NAD83 / Conus Albers.\n"
]
}
],
"source": [
"// This attempt to create a map will fail with the error shown below.\n",
"\n",
"// letsPlot() + geomMap(map = northAmAlbertsSds)\n",
"\n",
"val p = letsPlot() + geomMap(map = northAmAlbersSds)\n",
"\n",
"try {\n",
" p.toSpec()\n",
"} catch(e:Exception) {\n",
" println(e)\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "labeled-plant",
"metadata": {},
"source": [
"##### 4.2. A Map in the Conus Albers Projection with the Help of the `useCRS = \"provided\"` parameter"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "hispanic-psychiatry",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot() + \n",
" geomMap(map = northAmAlbersSds, useCRS = \"provided\", fill = \"blue\", color = \"white\", size = 1) +\n",
" theme(axis = \"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",
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