Advanced Usage

Professional Tick and Formatter Setup

PubPlotLib provides utilities for professional-looking axes.

Custom Tick Settings

import pubplotlib as pplt
import matplotlib.pyplot as plt

fig, ax = pplt.subplots()

# Apply custom tick settings
pplt.set_ticks(
    ax,
    direction='in',    # 'in' or 'out'
    top=True,          # Show ticks on top
    right=True,        # Show ticks on right
    major_length=3.5,  # Major tick length in pt
    minor_length=1.75, # Minor tick length in pt
)

Smart Axis Formatters

Fix scientific notation issues (e.g., “10^0” becoming “1”):

import pubplotlib as pplt
import matplotlib.pyplot as plt
import numpy as np

fig, ax = pplt.subplots()

# Log-log plot
x = np.logspace(0, 3, 100)
y = 10**(np.log10(x) * 0.5)
ax.loglog(x, y)

# Fix axis formatting
pplt.set_formatter(ax)  # Applies to both axes

# Or apply to specific axes
pplt.set_formatter(ax, axis='x')
pplt.set_formatter(ax, axis='y')

Batch Formatting

Apply formatting to all axes in a figure at once:

import pubplotlib as pplt
import matplotlib.pyplot as plt

fig, axes = pplt.subplots(nrows=2, ncols=2)

# Apply to all axes
pplt.set_ticks()        # Uses current figure
pplt.set_formatter()    # Uses current figure

# Or apply to specific axes
pplt.set_ticks(axes)
pplt.set_formatter(axes)

Working with Multiple Figures

Switching Styles Between Figures

import pubplotlib as pplt

# Set global style for first figure
pplt.style.use('aanda')
fig1, ax1 = pplt.subplots()
ax1.plot([1, 2, 3], [1, 4, 9])

# Create figure with different local style
fig2, ax2 = pplt.subplots(style='apj')
ax2.plot([1, 2, 3], [9, 4, 1])

# First figure still uses 'aanda'
# Global style is still 'aanda' (not changed)

Creating Subplots with Different Styles

Each call to subplots() or figure() can use its own style:

import pubplotlib as pplt

# Global style
pplt.style.use('aanda')

# Figure 1: uses global style
fig1, ax1 = pplt.subplots()

# Figure 2: uses local style
fig2, ax2 = pplt.subplots(style='presentation')

# Figure 3: uses global style again
fig3, ax3 = pplt.subplots()

Working with LaTeX

Enabling LaTeX Rendering

For better typography when using LaTeX:

Edit your custom .mplstyle file and add:

text.usetex: True
text.latex.preamble: \usepackage{amsmath}\usepackage{amssymb}

Register the custom style:

import pubplotlib as pplt

my_style = pplt.Style(
    name='aanda_latex',
    onecol=3.54,
    twocol=7.25,
    mplstyle='aanda_latex.mplstyle'
)
my_style.register(overwrite=True)

Use it:

pplt.style.use('aanda_latex')
fig, ax = pplt.subplots()

Requirements: You need a working LaTeX installation on your system.

Writing Math Expressions

import pplt
import matplotlib.pyplot as plt

pplt.style.use('aanda')
fig, ax = pplt.subplots()

x = [1, 2, 3, 4, 5]
y = [i**2 for i in x]
ax.plot(x, y)

# Math expressions work with or without LaTeX
ax.set_xlabel(r'$x$ (m)')
ax.set_ylabel(r'$y = x^2$ (m$^2$)')
ax.set_title(r'Quadratic Relationship')

plt.show()

Saving Figures

Best Practices

import pubplotlib as pplt
import matplotlib.pyplot as plt

fig, ax = pplt.subplots(style='aanda')
ax.plot([1, 2, 3], [1, 4, 9])

# Save as PDF (recommended for papers)
plt.savefig('figure.pdf', bbox_inches='tight', dpi=300)

# Save as PNG (good for presentations)
plt.savefig('figure.png', bbox_inches='tight', dpi=300)

Common Save Options

plt.savefig(
    'figure.pdf',
    bbox_inches='tight',  # Remove excess whitespace
    dpi=300,              # Resolution (300+ for print)
    transparent=False,    # Background color
    facecolor='white'     # Background color
)

Accessing Style Registry

Get All Style Information

import pubplotlib as pplt

for style_name in pplt.style.available():
    s = pplt.style.get(style_name)
    print(f"{style_name}:")
    print(f"  One-column: {s.onecol}\"")
    print(f"  Two-column: {s.twocol}\"")
    if s.mplstyle:
        print(f"  Style file: {s.mplstyle}")

Directly Accessing Style Objects

import pubplotlib as pplt
from pubplotlib.stylebuilder import user_style_dir, builtin_yaml_filename

# User styles are stored in
print(f"User styles: {user_style_dir}")

# Built-in styles config is at
print(f"Built-in config: {builtin_yaml_filename}")

Troubleshooting

Figures Look Different Than Expected

Make sure the correct style is set: pplt.style.current()
Check that fonts are available: from pubplotlib.stylebuilder import check_fonts; check_fonts()
Verify matplotlib version: import matplotlib; print(matplotlib.__version__)

Text Not Rendering Correctly

For LaTeX: ensure a LaTeX distribution is installed
Clear Matplotlib font cache: rm -rf ~/.cache/matplotlib
Rebuild: python -c "import matplotlib.font_manager; matplotlib.font_manager._load_fontmanager(try_read_cache=False)"

Inconsistent Figure Sizes

Don’t override figsize in calls
Don’t add elements outside the (0,1) canvas bounds
Use pplt.figure() or pplt.subplots() instead of plt.figure()