Bokeh vs. NumPy vs. pdfChip vs. test_coverage Comparison Chart

Bokeh makes it simple to create common plots, but also can handle custom or specialized use-cases. Plots, dashboards, and apps can be published in web pages or Jupyter notebooks. Python has an incredible ecosystem of powerful analytics tools: NumPy, Scipy, Pandas, Dask, Scikit-Learn, OpenCV, and more. With a wide array of widgets, plot tools, and UI events that can trigger real Python callbacks, the Bokeh server is the bridge that lets you connect these tools to rich, interactive visualizations in the browser. Microscopium is a project maintained by researchers at Monash University. It allows researchers to discover new gene or drug functions by exploring large image datasets with Bokeh’s interactive tools. Panel is a tool for polished data presentation that utilizes the Bokeh server. It is created and supported by Anaconda. Panel makes it simple to create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.

pdfChip is a command-line application for the creation of high-quality PDFs from HTML (taking full advantage of CSS and JavaScript). It supports all HTML features but extends to support CMYK, spot color, XMP metadata, PDF standards, SVG, MathML, barcodes, and more. Capable of generating single and multi-page PDF documents it is ideally suited for high-volume dynamic PDF generation. pdfChip is a command-line application for the creation of high-quality PDFs from HTML, taking full advantage of CSS and JavaScript. pdfChip is based on WebKit technology, it renders your designs quickly and flawlessly into PDF. It allows to creation of fully compliant PDF/X files (for print) or PDF/A files (for archival), in the smallest output PDF size possible. Furthermore, it’s possible to use the exact same HTML and CSS code on the web as well as for PDF generation. pdfChip has a low entry level, as the command-line interface is clear and easy-to-learn.