plotly.py relies on Orca, which is a custom headless Electron application that uses the Chromium browser engine built into Electron to perform image rendering.Bokeh, Altair, bqplot, and ipyvolume rely on the Selenium Python library to control a system web browser such as Firefox or Chrome/Chromium to perform image rendering.There are three main approaches that are currently in use among Python web-based visualization libraries: To accomplish this, the Python portion of the visualization library needs programmatic access to a web browser's rendering engine. from Python) without displaying it in a browser and without user interaction. The difficulty arises when trying to export an image programmatically (e.g. When the figure is displayed in a browser window, it's relatively straight-forward for a visualization library to provide an export-image button because it has full access to the browser for rendering. Similar to how Matplotlib relies on various backends to display figures, web-based visualization libraries rely on a web browser rendering engine to display figures. color the pixels) on their own, instead they delegate this work to web technologies like SVG, Canvas, WebGL, etc. The core difficulty is that web-based visualization libraries don't actually render plots (i.e. It's a problem that library developers have struggled with for years, and it has delayed the adoption of these libraries among scientific communities that rely on print-based publications for sharing their research. Plotly.js, Vega-Lite, etc.) is a complex problem. raster images like PNGs or vector images like SVGs) from web-based visualization libraries (e.g. transform ( fig, format = "png" ))Īs simple as it sounds, programmatically generating static images (e.g. Figure ( data = )]) with open ( "figure.png", "wb" ) as f : f. from import PlotlyScope import aph_objects as go scope = PlotlyScope ( plotlyjs = "", # plotlyjs="/path/to/local/plotly.js", ) fig = go. When the plotly Python library uses Kaleido (as in the example above), it provides the path to its own local offline copy of plotly.js and so no internet connection is required. Note: This particular example uses an online copy of the plotly JavaScript library from a CDN location, so it will not work without an internet connection. ![]() Here is an example of exporting a Plotly figure using the low-level Kaleido API: The kaleido Python package provides a low-level Python API that is designed to be used by high-level plotting libraries like Plotly. See the plotly static image export documentation for more information. Then, open figure.png in the current working directory. write_image ( "figure.png", engine = "kaleido" ) iris (), x = "sepal_length", y = "sepal_width", color = "species" ) fig. For example: import plotly.express as px fig = px. Versions 4.9 and above of the Plotly Python library will automatically use kaleido for static image export when kaleido is installed. Use Kaleido to export plotly.py figures as static images Releases of the core kaleido C++ executable are attached as assets to GitHub releases at. $ conda install -c conda-forge python-kaleido The kaleido package can be installed from PyPI using pip. As such, the focus is on providing a programmatic-friendly, rather than user-friendly, API. The primary focus of Kaleido (at least initially) is to serve as a dependency of web-based visualization libraries like plotly.py. ![]() ![]() The project's initial focus is on the export of plotly.js images from Python for use by plotly.py, but it is designed to be relatively straight-forward to extend to other web-based visualization libraries, and other programming languages. png, svg, pdf, etc.) for web-based visualization libraries, with a particular focus on eliminating external dependencies. ![]() Kaleido is a cross-platform library for generating static images (e.g.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |