{
"cells": [
{
"cell_type": "markdown",
"id": "employed-rebate",
"metadata": {},
"source": [
"# Convenience Methods for Exporting Plot to a File or File-Like Object\n",
"\n",
"You can export figure created by `ggplot()` or `gggrid()` functions
\n",
"to a vector or raster format using the following methods: \n",
"- `to_svg(path)`\n",
"- `to_html(path, iframe)`\n",
"- `to_png(path, scale)`\n",
"- `to_pdf(path, scale)`\n",
"\n",
"To save plot to a file on disc, specify the file' pathname in `path`.
\n",
"To stream plot image to a file-like object, supply such object in the `path` parameter instead of a pathname.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "arranged-meter",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import io\n",
"from IPython import display\n",
"from lets_plot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c38745bd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "remarkable-toolbox",
"metadata": {},
"outputs": [],
"source": [
"data = {'x': np.random.normal(size=100)}\n",
"p = ggplot(data, aes(x='x')) + geom_histogram()"
]
},
{
"cell_type": "markdown",
"id": "9a70ad45-7707-40ae-a261-d0544b77668e",
"metadata": {},
"source": [
"#### 1. Saving to a File\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d0df0736-f108-4aff-93e3-55a0a0077edc",
"metadata": {},
"outputs": [],
"source": [
"path = p.to_svg('lets-plot-images/hist.svg')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "002b80c6-32b0-4df9-8895-243550e03f3b",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"display.SVG(path)"
]
},
{
"cell_type": "markdown",
"id": "8c3b3b37",
"metadata": {},
"source": [
"#### 2. Wrighting to a Stream of In-Memory Bytes\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "db9b95de-0615-409d-8b54-3bc042e53c1b",
"metadata": {},
"outputs": [],
"source": [
"stream = io.BytesIO()\n",
"\n",
"p.to_svg(stream)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "14bb91c9-95ab-433d-a513-f5e69774e20d",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"display.SVG(stream.getvalue())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}