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vl-convert-python
is a dependency-free Python package for converting Vega-Lite chart specifications into static images (SVG or PNG) or Vega chart specifications.
Since an Altair chart can generate Vega-Lite, this package can be used to easily create static images from Altair charts.
vl-convert-python
can be installed using pip with
$ pip install vl-convert-python
The vl-convert-python
package provides a series of conversion functions under the vl_convert
module.
The vegalite_to_svg
and vegalite_to_png
functions can be used to convert Vega-Lite specifications to static SVG and PNG images respectively. The vegalite_to_vega
function can be used to convert a Vega-Lite specification to a Vega specification.
import vl_convert as vlc
import json
vl_spec = r"""
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"data": {"url": "https://raw.githubusercontent.com/vega/vega-datasets/next/data/movies.json"},
"mark": "circle",
"encoding": {
"x": {
"bin": {"maxbins": 10},
"field": "IMDB Rating"
},
"y": {
"bin": {"maxbins": 10},
"field": "Rotten Tomatoes Rating"
},
"size": {"aggregate": "count"}
}
}
"""
# Create SVG image string and then write to a file
svg_str = vlc.vegalite_to_svg(vl_spec=vl_spec)
with open("chart.svg", "wt") as f:
f.write(svg_str)
# Create PNG image data and then write to a file
png_data = vlc.vegalite_to_png(vl_spec=vl_spec, scale=2)
with open("chart.png", "wb") as f:
f.write(png_data)
# Create low-level Vega representation of chart and write to file
vg_spec = vlc.vegalite_to_vega(vl_spec)
with open("chart.vg.json", "wt") as f:
json.dump(vg_spec, f)
The Altair visualization library provides a Pythonic API for generating Vega-Lite visualizations. As such, vl-convert-python
can be used to convert Altair charts to PNG, SVG, or Vega. The vegalite_*
functions support an optional vl_version
argument that can be used to specify the particular version of the Vega-Lite JavaScript library to use. Version 4.2 of the Altair package uses Vega-Lite version 4.17, so this is the version that should be specified when converting Altair charts.
import altair as alt
from vega_datasets import data
import vl_convert as vlc
import json
source = data.barley()
chart = alt.Chart(source).mark_bar().encode(
x='sum(yield)',
y='variety',
color='site'
)
# Create SVG image string and then write to a file
svg_str = vlc.vegalite_to_svg(chart.to_json(), vl_version="4.17")
with open("altair_chart.svg", "wt") as f:
f.write(svg_str)
# Create PNG image data and then write to a file
png_data = vlc.vegalite_to_png(chart.to_json(), vl_version="4.17", scale=2)
with open("altair_chart.png", "wb") as f:
f.write(png_data)
# Create low-level Vega representation of chart and write to file
vg_spec = vlc.vegalite_to_vega(chart.to_json(), vl_version="4.17")
with open("altair_chart.vg.json", "wt") as f:
json.dump(vg_spec, f)
This crate uses PyO3 to wrap the vl-convert-rs
Rust crate as a Python library. The vl-convert-rs
crate is a self-contained Rust library for converting Vega-Lite visualization specifications into various formats. The conversions are performed using the Vega-Lite and Vega JavaScript libraries running in a v8 JavaScript runtime provided by the deno_runtime
crate. Font metrics and SVG-to-PNG conversions are provided by the resvg
crate.
Of note, vl-convert-python
is fully self-contained and has no dependency on an external web browser or Node.js runtime.
Create development conda environment
$ conda create -n vl-convert-dev -c conda-forge python=3.10 deno maturin altair pytest black black-jupyter scikit-image
Activate environment and pip install remaining dependencies
$ conda activate vl-convert-dev
$ pip install pypdfium2
Change to Python package directory
$ cd vl-convert-python
Build Rust python package with maturin in develop mode
$ maturin develop --release
Run tests
$ pytest tests
FAQs
Convert Vega-Lite chart specifications to SVG, PNG, or Vega
We found that vl-convert-python demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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