texer 0.5.2

Generate LaTeX tables and figures with glom-style specs

texer

Generate LaTeX tables and figures (PGFPlots) with Python using a glom-style spec system.

Installation

pip install texer

Quick Start

Tables

from texer import Table, Tabular, Row, Ref, Iter, Format, evaluate

# Define structure with specs
table = Table(
    Tabular(
        columns="lcc",
        header=Row("Experiment", "Result", "Error"),
        rows=Iter(
            Ref("experiments"),
            template=Row(
                Ref("name"),
                Format(Ref("result"), ".3f"),
                Format(Ref("error"), ".1%"),
            )
        ),
        toprule=True,
        bottomrule=True,
    ),
    caption=Ref("table_title"),
    label="tab:results",
)

# Provide data
data = {
    "table_title": "Experimental Results",
    "experiments": [
        {"name": "Trial A", "result": 3.14159, "error": 0.023},
        {"name": "Trial B", "result": 2.71828, "error": 0.015},
    ]
}

print(evaluate(table, data))

Plots

from texer import PGFPlot, Axis, AddPlot, Coordinates, Ref, Iter, evaluate

plot = PGFPlot(
    Axis(
        xlabel=Ref("x_label"),
        ylabel=Ref("y_label"),
        grid=True,
        plots=[
            AddPlot(
                color="blue",
                mark="*",
                coords=Coordinates(
                    Iter(Ref("measurements"), x=Ref("time"), y=Ref("value"))
                ),
            )
        ],
        legend=[Ref("series_name")],
    )
)

data = {
    "x_label": "Time (hours)",
    "y_label": "Temperature (°C)",
    "series_name": "Sensor 1",
    "measurements": [
        {"time": 0, "value": 20.5},
        {"time": 1, "value": 22.3},
        {"time": 2, "value": 25.1},
    ]
}

print(evaluate(plot, data))

Saving and Compiling

Save to file and compile to PDF directly:

from texer import Table, Tabular, Row, evaluate

table = Table(
    Tabular(columns="lc", rows=[Row("Name", "Value")]),
    caption="Results",
)

# Save to .tex file
evaluate(table, output_file="table.tex")

# Save with preamble for standalone compilation
evaluate(table, output_file="table.tex", with_preamble=True)

# Compile directly to PDF
pdf_path = evaluate(table, output_file="table.tex", compile=True)

Cycle Lists

PGFPlots cycle lists allow you to define a sequence of styles that are automatically applied to successive \addplot commands. When using cycle lists, AddPlot automatically generates \addplot+ (instead of \addplot) when no explicit styling is provided, allowing PGFPlots to pick the next style from the cycle list:

from texer import PGFPlot, Axis, AddPlot, Coordinates

# Using a predefined cycle list
plot = PGFPlot(
    Axis(
        cycle_list_name="color list",
        plots=[
            # These generate \addplot+ to use cycle list styles
            AddPlot(coords=Coordinates([(0, 0), (1, 1), (2, 4)])),
            AddPlot(coords=Coordinates([(0, 1), (1, 2), (2, 3)])),
        ],
    )
)

# Custom cycle list with style dictionaries
plot = PGFPlot(
    Axis(
        cycle_list=[
            {"color": "blue", "mark": "*", "line width": "2pt"},
            {"color": "red", "mark": "square*", "line width": "2pt"},
            {"color": "green", "mark": "triangle*", "line width": "2pt"},
        ],
        plots=[
            # Automatically uses \addplot+ to apply cycle list styles
            AddPlot(coords=Coordinates([(0, 1), (1, 2), (2, 4)])),
            AddPlot(coords=Coordinates([(0, 2), (1, 3), (2, 5)])),
        ],
    )
)

# Simple color cycle
plot = PGFPlot(
    Axis(
        cycle_list=["blue", "red", "green"],
        plots=[
            AddPlot(coords=Coordinates([(0, 0), (1, 1)])),
            AddPlot(coords=Coordinates([(0, 1), (1, 2)])),
        ],
    )
)

# Override cycle list with explicit styling
plot = PGFPlot(
    Axis(
        cycle_list=["blue", "red", "green"],
        plots=[
            # This uses the cycle list (generates \addplot+)
            AddPlot(coords=Coordinates([(0, 0), (1, 1)])),
            # This overrides with explicit styling (generates \addplot)
            AddPlot(color="purple", mark="x", coords=Coordinates([(0, 1), (1, 2)])),
        ],
    )
)

Documentation

For complete documentation, visit: Documentation Site

Or build the docs locally:

pip install -e ".[docs]"
mkdocs serve

Then open http://127.0.0.1:8000

Key Features

• Data-driven: Separate structure from data
• Type-safe: Full type hints and mypy support
• Glom-style specs: Familiar pattern for data extraction
• LaTeX best practices: Automatic escaping, booktabs tables
• NumPy integration: Direct support for NumPy arrays
• PDF compilation: Built-in compile=True option in evaluate()

Core Concepts

texer uses specs to describe how to extract and transform data:

• Ref("path") - Access data by path (e.g., Ref("user.name"))
• Iter(source, template=...) - Loop over collections
• Format(value, ".2f") - Format values
• Cond(test, if_true, if_false) - Conditional logic
• Raw(r"\textbf{bold}") - Unescaped LaTeX

See the Core Concepts guide for details.

LaTeX Requirements

For PDF compilation, you need a LaTeX distribution:

• Ubuntu/Debian: sudo apt-get install texlive-latex-base texlive-pictures
• macOS: brew install --cask mactex
• Windows: MiKTeX or TeX Live

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Type checking
mypy src

License

MIT