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< h1 > Pyladoc< a class = "headerlink" href = "#pyladoc" title = "Link to this heading" > #< / a > < / h1 >
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< section id = "description" >
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< h2 > Description< a class = "headerlink" href = "#description" title = "Link to this heading" > #< / a > < / h2 >
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< p > Pyladoc is a Python package for programmatically generating HTML and
PDF/LaTeX output. This package specifically targets applications where reports
or results with Pandas tables and Matplotlib figures are generated
to be displayed as a website and as a PDF document without involving any manual
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formatting steps.< / p >
< p > This package focuses on the “Document in Code” approach for cases
where a lot of calculations and data handling is done but not a lot of
document text needs to be displayed. The multiline string capability of Python
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handles this very well. In comparison to “Code in Document” templates,
Python tools support this approach out of the box—similar to docstrings.< / p >
< p > LaTeX is used as the backend for PDF generation. There are excellent engines for
rendering HTML to PDF, but even if there is no requirement for
accurate typesetting, placing programmatically generated content of variable
composition and element sizes on fixed-size pages without manual intervention
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is a hard problem where LaTeX is superior.< / p >
< / section >
< section id = "example-outputs" >
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< h2 > Example outputs< a class = "headerlink" href = "#example-outputs" title = "Link to this heading" > #< / a > < / h2 >
< p > < a class = "reference external" href = "https://raw.githubusercontent.com/Nonannet/pyladoc/refs/heads/main/tests/out/test_latex_render1.pdf" > < img alt = "example output" src = "_images/output_example.png" / > < / a > < / p >
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< ul class = "simple" >
< li > < p > HTML: < a class = "reference external" href = "https://html-preview.github.io/?url=https://github.com/Nonannet/pyladoc/blob/main/tests/out/test_html_render1.html" > test_html_render1.html< / a > (< a class = "reference external" href = "https://github.com/Nonannet/pyladoc/blob/main/tests/out/test_html_render1.html" > code< / a > )< / p > < / li >
< li > < p > PDF: < a class = "reference external" href = "https://raw.githubusercontent.com/Nonannet/pyladoc/refs/heads/main/tests/out/test_latex_render1.pdf" > test_latex_render1.pdf< / a > (< a class = "reference external" href = "https://github.com/Nonannet/pyladoc/blob/main/tests/out/test_html_render1.tex" > code< / a > )< / p > < / li >
< / ul >
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< p > The documents are generated by the script < a class = "reference download internal" download = "" href = "_downloads/93efa674efb48f743263074a28a5345b/test_rendering_example1_doc.py" > < span class = "xref download myst" > tests/test_rendering_example1_doc.py< / span > < / a > .< / p >
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< section id = "supported-primitives" >
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< h3 > Supported primitives< a class = "headerlink" href = "#supported-primitives" title = "Link to this heading" > #< / a > < / h3 >
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< ul class = "simple" >
< li > < p > Text (can be Markdown or HTML formatted)< / p > < / li >
< li > < p > Headings< / p > < / li >
< li > < p > Tables (Pandas, Markdown or HTML)< / p > < / li >
< li > < p > Matplotlib figures< / p > < / li >
< li > < p > LaTeX equations (block or inline)< / p > < / li >
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< li > < p > Named references for figures, tables, and equations< / p > < / li >
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< / ul >
< / section >
< section id = "key-features" >
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< h3 > Key Features< a class = "headerlink" href = "#key-features" title = "Link to this heading" > #< / a > < / h3 >
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< ul class = "simple" >
< li > < p > HTML and PDF/LaTeX rendering of the same document< / p > < / li >
< li > < p > Single file output including figures< / p > < / li >
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< li > < p > Figure and equation embedding in HTML by inline SVG, SVG in Base64, or PNG in Base64< / p > < / li >
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< li > < p > Figure embedding in LaTeX as PGF/TikZ< / p > < / li >
< li > < p > Tested on Linux and Windows< / p > < / li >
< / ul >
< / section >
< section id = "usage-scenarios" >
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< h3 > Usage Scenarios< a class = "headerlink" href = "#usage-scenarios" title = "Link to this heading" > #< / a > < / h3 >
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< ul class = "simple" >
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< li > < p > Web services< / p > < / li >
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< li > < p > Report generation for lab equipment< / p > < / li >
< / ul >
< / section >
< / section >
< section id = "installation" >
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< h2 > Installation< a class = "headerlink" href = "#installation" title = "Link to this heading" > #< / a > < / h2 >
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< p > It can be installed with pip:< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > pip< span class = "w" > < / span > install< span class = "w" > < / span > pyladoc
< / pre > < / div >
< / div >
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< p > As well as with conda:< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > conda< span class = "w" > < / span > install< span class = "w" > < / span > conda-forge::pyladoc
< / pre > < / div >
< / div >
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< / section >
< section id = "dependencies" >
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< h2 > Dependencies< a class = "headerlink" href = "#dependencies" title = "Link to this heading" > #< / a > < / h2 >
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< p > Pyladoc depends on the markdown package.< / p >
< p > Optional dependencies are:< / p >
< ul class = "simple" >
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< li > < p > Matplotlib Python package for rendering LaTeX equations for HTML output< / p > < / li >
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< li > < p > LaTeX for exporting to PDF or exporting Matplotlib figures to LaTeX (PGF/TikZ rendering)< / p > < / li >
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< li > < p > Pandas and Jinja2 for rendering Pandas tables< / p > < / li >
< li > < p > Matplotlib for rendering Matplotlib figures (obviously)< / p > < / li >
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< / ul >
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< p > For the included template, the < code class = "docutils literal notranslate" > < span class = "pre" > miktex< / span > < / code > LaTeX distribution works on Windows
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and the following LaTeX setup works on Ubuntu (both tested in CI):< / p >
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< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > sudo< span class = "w" > < / span > apt-get< span class = "w" > < / span > update
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sudo< span class = "w" > < / span > apt-get< span class = "w" > < / span > install< span class = "w" > < / span > -y< span class = "w" > < / span > texlive-latex-extra< span class = "w" > < / span > texlive-fonts-recommended< span class = "w" > < / span > lmodern< span class = "w" > < / span > texlive-xetex< span class = "w" > < / span > texlive-science
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< / pre > < / div >
< / div >
< / section >
< section id = "usage" >
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< h2 > Usage< a class = "headerlink" href = "#usage" title = "Link to this heading" > #< / a > < / h2 >
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< p > It is easy to use, as the following example code shows:< / p >
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< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "w" > < / span > < span class = "nn" > pyladoc< / span >
< span class = "kn" > import< / span > < span class = "w" > < / span > < span class = "nn" > pandas< / span > < span class = "w" > < / span > < span class = "k" > as< / span > < span class = "w" > < / span > < span class = "nn" > pd< / span >
< span class = "n" > doc< / span > < span class = "o" > =< / span > < span class = "n" > pyladoc< / span > < span class = "o" > .< / span > < span class = "n" > DocumentWriter< / span > < span class = "p" > ()< / span >
< span class = "n" > doc< / span > < span class = "o" > .< / span > < span class = "n" > add_markdown< / span > < span class = "p" > (< / span > < span class = "s2" > " " " < / span >
< span class = "s2" > # Example< / span >
< span class = "s2" > This is inline LaTeX: $$< / span > < span class = "se" > \\< / span > < span class = "s2" > lambda$$< / span >
< span class = "s2" > This is a LaTeX block with a number:< / span >
< span class = "s2" > $$< / span >
< span class = "s2" > < / span > < span class = "se" > \\< / span > < span class = "s2" > label{eq:test1}< / span >
< span class = "s2" > < / span > < span class = "se" > \\< / span > < span class = "s2" > lambda_{< / span > < span class = "se" > \t< / span > < span class = "s2" > ext< / span > < span class = "si" > {mix}< / span > < span class = "s2" > } = < / span > < span class = "se" > \\< / span > < span class = "s2" > sum_{i=1}^< / span > < span class = "si" > {n}< / span > < span class = "s2" > < / span > < span class = "se" > \\< / span > < span class = "s2" > frac{x_i < / span > < span class = "se" > \\< / span > < span class = "s2" > lambda_i}{< / span > < span class = "se" > \\< / span > < span class = "s2" > sum_{j=1}^< / span > < span class = "si" > {n}< / span > < span class = "s2" > x_j < / span > < span class = "se" > \\< / span > < span class = "s2" > Phi_< / span > < span class = "si" > {ij}< / span > < span class = "s2" > }< / span >
< span class = "s2" > $$< / span >
< span class = "s2" > This is an example table. The table @table:pandas_example shows some random data.< / span >
< span class = "s2" > " " " < / span > < span class = "p" > )< / span >
< span class = "n" > some_data< / span > < span class = "o" > =< / span > < span class = "p" > {< / span >
< span class = "s1" > ' Row1' < / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "s2" > " Line1" < / span > < span class = "p" > ,< / span > < span class = "s2" > " Line2" < / span > < span class = "p" > ,< / span > < span class = "s2" > " Line3" < / span > < span class = "p" > ],< / span >
< span class = "s1" > ' Row2' < / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "mi" > 120< / span > < span class = "p" > ,< / span > < span class = "mi" > 100< / span > < span class = "p" > ,< / span > < span class = "mi" > 110< / span > < span class = "p" > ],< / span >
< span class = "s1" > ' Row3' < / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "s1" > ' 12 g/km' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' > 150 g/km' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' 110 g/km' < / span > < span class = "p" > ]< / span >
< span class = "p" > }< / span >
< span class = "n" > df< / span > < span class = "o" > =< / span > < span class = "n" > pd< / span > < span class = "o" > .< / span > < span class = "n" > DataFrame< / span > < span class = "p" > (< / span > < span class = "n" > some_data< / span > < span class = "p" > )< / span >
< span class = "n" > doc< / span > < span class = "o" > .< / span > < span class = "n" > add_table< / span > < span class = "p" > (< / span > < span class = "n" > df< / span > < span class = "p" > ,< / span > < span class = "s1" > ' This is a pandas example table' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' pandas_example' < / span > < span class = "p" > )< / span >
< span class = "n" > html_code< / span > < span class = "o" > =< / span > < span class = "n" > doc< / span > < span class = "o" > .< / span > < span class = "n" > to_html< / span > < span class = "p" > ()< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > html_code< / span > < span class = "p" > )< / span >
< span class = "n" > doc< / span > < span class = "o" > .< / span > < span class = "n" > to_pdf< / span > < span class = "p" > (< / span > < span class = "s1" > ' test.pdf' < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / section >
< section id = "contributing" >
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< h2 > Contributing< a class = "headerlink" href = "#contributing" title = "Link to this heading" > #< / a > < / h2 >
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< p > Contributions are welcome; please open an issue or submit a pull request on GitHub.< / p >
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< h2 > Developer Guide< a class = "headerlink" href = "#developer-guide" title = "Link to this heading" > #< / a > < / h2 >
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< p > To get started with developing the < code class = "docutils literal notranslate" > < span class = "pre" > pyladoc< / span > < / code > package, follow these steps.< / p >
< p > First, clone the repository to your local machine using Git:< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > git< span class = "w" > < / span > clone< span class = "w" > < / span > https://github.com/Nonannet/pyladoc.git
< span class = "nb" > cd< / span > < span class = "w" > < / span > pyladoc
< / pre > < / div >
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< p > It’ < / p >
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< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > python< span class = "w" > < / span > -m< span class = "w" > < / span > venv< span class = "w" > < / span > .venv
< span class = "nb" > source< / span > < span class = "w" > < / span > .venv/bin/activate< span class = "w" > < / span > < span class = "c1" > # On Windows use `.venv\Scripts\activate`< / span >
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< / pre > < / div >
< / div >
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< p > Install the package and development dependencies while keeping files in the
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current directory:< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > pip< span class = "w" > < / span > install< span class = "w" > < / span > -e< span class = "w" > < / span > .< span class = "o" > [< / span > dev< span class = "o" > ]< / span >
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< p > Ensure that everything is set up correctly by running the tests:< / p >
< div class = "highlight-bash notranslate" > < div class = "highlight" > < pre > < span > < / span > pytest
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< h2 > License< a class = "headerlink" href = "#license" title = "Link to this heading" > #< / a > < / h2 >
< p > This project is licensed under the MIT License - see the < a class = "reference internal" href = "LICENSE.html" > < span class = "doc std std-doc" > LICENSE< / span > < / a > file for details.< / p >
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