pubplotlib 0.4.1

Make publication friendly plots easy!

PubPlotLib 🎨

PubPlotLib is a Python library built on top of Matplotlib that simplifies the creation of publication-quality figures. It provides pre-configured styles and sizes for major scientific journals, allowing you to focus on your data, not the boilerplate code for plotting.

Key Features

• Style-Ready Figures: Automatically create figures with the correct dimensions and styles for journals, slides, posters, and more (A&A, ApJ, etc).
• Simple API: Use pubplotlib.figure and pubplotlib.subplots as drop-in replacements for their Matplotlib counterparts.
• Flexible Sizing: Easily switch between single-column and double-column layouts and control the aspect ratio.
• Smart Formatting: Apply sensible defaults for axis tick formatters that avoid scientific notation for numbers like 1.0 (i.e., no more $10^0$).
• Customizable Ticks: Fine-tune the appearance of major and minor ticks with a single function call.
• Extensible: Add your own custom styles and sizes with ease.

Installation

Note: PubPlotLib requires Matplotlib version 3.2 or newer for style support.

You can install PubPlotLib via pip:

pip install pubplotlib

Or install the latest version from GitHub cloning the reposity on your machine:

git clone https://github.com/pier-astro/PubPlotLib.git
cd PubPlotLib
pip install .

Quick Start

Creating a journal-styled figure is as simple as importing pubplotlib and using its subplots function.

import matplotlib.pyplot as plt
import pubplotlib as pplt

# Set your target style (can be also done per-figure)
pplt.style.use('apj')

# Create a figure using pubplotlib's wrapper
fig, ax = pplt.subplots()
ax.plot(...)

# Customize ticks and formatters for a professional look
pplt.set_ticks(ax)
pplt.set_formatter(ax)

plt.show()

You can set a style globally for all figures using pplt.style.use('style_name'), or specify a style directly inside plots by passing the style argument to pplt.subplots() or pplt.figure().

Usage

Figure Sizing

PubPlotLib makes it easy to create figures that fit perfectly into your manuscript's columns.

Single and Double Column Figures

Use the twocols=True argument to create a figure with the width of a double column.

fig, ax = pplt.subplots(style='aanda', twocols=True)
ax.plot(...)

Custom Aspect Ratio

Control the figure's height using the height_ratio argument. The height will be width * height_ratio. If not provided, it defaults to the golden ratio.

# Create a wide, short figure
fig, ax = pplt.subplots(twocols=True, height_ratio=0.3)
ax.plot(...)

Using Matplotlib's Default Figure and Subplots with Custom Sizing

We’ve deliberately hidden the figsize keyword in our API—because that’s the devil that always messes up your image size when rendered in a paper! Instead, we set the figure width for you, and the height_ratio lets you decide how tall your image should be.

A couple of important notes:

1. Don’t exceed the figure canvas!
   Adding extra axes or text outside the Matplotlib limits (beyond (1, 1)) will enlarge the image and ruin our careful setup for correct font sizes. If you need more axes, create a subgrid within your canvas not going outsiede the default.
2. Using LaTeX?
   Don’t specify the width or height in \includegraphics! The figure is already sized perfectly. If you override this, you’ll corrupt the font sizes.
   Use a plain approach like:

   \begin{figure}
     \centering
     \includegraphics{image.pdf} % No [width=\textwidth] or similar!
     \caption{Hello World!}
   \end{figure}
   

If you still desire evil and you want to customize your plot, you can always use the default Matplotlib figure and subplots functions and specify the figure size yourself.
To help, we provide the onecol and twocol attributes so you can shape your figsize:

journal = pplt.style.get()
print("One column width:", journal.onecol)
print("Two column width:", journal.twocol)

fig, ax = plt.subplots(figsize=(journal.onecol, journal.onecol * 0.5))
ax.plot(...)
plt.show()

And for the truly bold:
PubPlotLib exposes the Golden Ratio as pplt.golden, so you can size your figures like Leonardo da Vinci would have—just use figsize=(journal.onecol, journal.onecol/pplt.golden) (which, by the way, is our default)!

Axis Styling: Better Ticks and Formatters

PubPlotLib makes it easy to create professional-looking axes with minimal effort:

• Customizable Ticks:
  Use pplt.set_ticks() to control tick direction, length, and which sides show ticks.

  fig, ax = pplt.subplots()
  ax.plot(...)
  # Show ticks on all sides, pointing inwards
  pplt.set_ticks(ax, direction='in', top=True, right=True)
  

  You can run pplt.set_ticks() without arguments to apply the default setup to all axes in the current figure.
  If you want to apply it only to specific axes, pass the axis or a list of axes.
  Note: For colorbars, a blind setup may affect the figure layout, so specify only the actual plot axes.

• Smart Axis Formatters:
  Logarithmic axes in Matplotlib often format the number 1 as $10^0$.
  Use pplt.set_formatter() to automatically format axis labels for clarity—numbers like 1 will show as 1, not $10^0$.

  fig, ax = pplt.subplots()
  ax.loglog(x, 10**(y*4))
  # Apply the formatter to both axes
  pplt.set_formatter(ax) # No more "10^0"!
  

  You can also run pplt.set_formatter() without arguments to apply the default formatter to all axes in the current figure.
  For advanced use, PubPlotLib provides improved versions of Matplotlib's formatters:

  • pplt.formatter.ScalarFormatter
  • pplt.formatter.LogFormatterSciNotation
    Use these directly for custom formatting needs.

These functions help you avoid tedious manual axis formatting and ensure your figures look clean and publication-ready!

Managing Styles

PubPlotLib comes with built-in support for several styles (journals, slides, posters, etc).

List Available Styles

See which styles are available out-of-the-box:

print(pplt.available_styles())

Adding a New Style

You can easily define and register your own style.

1. Create a Style object: Specify its name and column widths in inches. You can optionally link a .mplstyle file.

   # Assumes 'my_style.mplstyle' exists in your working directory
   my_style = pplt.Style(
       name="my_style",
       onecol=3.5,          # 3.5 inches wide
       twocol=7.1,          # 7.1 inches wide
       mplstyle="my_style.mplstyle"
   )
   

2. Register the style: This makes it available globally by copying the style file into your home directory and adding it to the configuration.

   my_style.register(overwrite=True)
   

Now you can use it like any other style:

fig, ax = pplt.subplots(style="my_style")

Removing a Style

You can remove a custom style you've added.

pplt.stylebuilder.remove_style("my_style")

Checking Fonts

Some styles require specific fonts (e.g., Times, ...). To check if these fonts are available to Matplotlib, run:

from pubplotlib.stylebuilder import check_fonts
check_fonts()  # Prints a summary

If any fonts are missing, install them and update the Matplotlib font cache:

python -c "import matplotlib.font_manager; matplotlib.font_manager._get_fontconfig_fonts(); matplotlib.font_manager._load_fontmanager(try_read_cache=False)"

Or simply delete the cache and let Matplotlib rebuild it automatically:

rm -rf ~/.cache/matplotlib

We provide several fonts in the fonts folder of the repository—check there if you need them!

LaTeX Font Rendering Engines

By default, PubPlotLib styles do not use LaTeX for font rendering, since requiring a LaTeX compiler can break the library for users who do not have one installed.

However, for some journals (e.g., A&A), LaTeX is used for typesetting, so matching font metrics and kerning is important (i.e. nicer). To enable LaTeX rendering in Matplotlib, edit your .mplstyle file and add:

text.usetex: True
text.latex.preamble: \usepackage{amsmath}\usepackage{amssymb}  # For more math symbols

After editing, you can override the default journal style and save it locally.
For example, to use your custom style with the same name as the default:

import pubplotlib as pplt

# Register your custom style (assumes you have edited 'aanda.mplstyle')
my_style = pplt.Style(
    name="aanda",
    onecol=3.54,  # A&A one-column width in inches
    twocol=7.25,  # A&A two-column width in inches
    mplstyle="aanda.mplstyle"
)
my_style.register(overwrite=True)

# Now use your updated style
fig, ax = pplt.subplots(style="aanda")
ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

Note:

• You must have a working LaTeX installation for text.usetex: True to work.
• If you want to revert to the default style, remove your custom style using:

  pplt.stylebuilder.remove_style("aanda")
  

Contributing

Contributions are welcome! If you'd like to add a new style, fix a bug, or suggest an improvement, please open an issue or submit a pull request on our GitHub repository.