plotspec 0.2.0

Record and reproduce matplotlib figures

plotspec

Record and reproduce matplotlib figures.

plotspec captures matplotlib plotting calls and saves them as human-readable YAML "recipes" that can be used to reproduce figures exactly.

Features

• Drop-in replacement: Use mpr.subplots() instead of plt.subplots()
• Automatic tracking: All plotting calls are recorded automatically
• YAML recipes: Human-readable format for figure specifications
• Efficient storage: Large arrays saved to separate .npy files
• One-line reproduction: fig, ax = ps.reproduce("figure.yaml")
• Selective replay: Reproduce only specific plotting calls

Installation

pip install plotspec

Quick Start

Recording a Figure

import plotspec as ps
import numpy as np

# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create figure (drop-in replacement for plt.subplots)
fig, ax = ps.subplots()

# Plot as usual - calls are recorded automatically
ax.plot(x, y, color='red', linewidth=2, id='sine_wave')
ax.scatter(x[::10], y[::10], s=50, color='blue', id='peaks')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.set_title('Sine Wave Example')
ax.legend()

# Save the recipe
mpr.save(fig, 'sine_wave.yaml')

Reproducing a Figure

import plotspec as ps
import matplotlib.pyplot as plt

# Reproduce the entire figure
fig, ax = ps.reproduce('sine_wave.yaml')
plt.show()

# Or reproduce only specific calls
fig, ax = ps.reproduce('sine_wave.yaml', calls=['sine_wave'])
plt.show()

Inspecting a Recipe

import plotspec as ps

# Get recipe information without reproducing
info = ps.info('sine_wave.yaml')
print(f"Created: {info['created']}")
print(f"Figure size: {info['figsize']}")
print(f"Number of calls: {len(info['calls'])}")

for call in info['calls']:
    print(f"  - {call['id']}: {call['function']}()")

Recipe Format

Recipes are saved as YAML files:

plotspec: "1.0"
id: fig_a1b2c3d4
created: "2025-12-21T14:30:00"
matplotlib_version: "3.8.0"

figure:
  figsize: [10, 6]
  dpi: 100

axes:
  ax_0_0:
    calls:
      - id: sine_wave
        function: plot
        args:
          - name: x
            data: sine_wave_data/sine_wave_x.npz
          - name: y
            data: sine_wave_data/sine_wave_y.npz
        kwargs:
          color: red
          linewidth: 2

    decorations:
      - id: set_xlabel_000
        function: set_xlabel
        args:
          - name: arg0
            data: "Time (s)"
        kwargs: {}

Advanced Usage

Custom Call IDs

Use the id parameter to give meaningful names to your plots:

ax.plot(x, y, color='red', id='experimental_data')
ax.plot(x, y_fit, color='blue', id='fitted_curve')

Multiple Subplots

fig, axes = ps.subplots(2, 2, figsize=(12, 10))
axes[0][0].plot(x, y1, id='top_left')
axes[0][1].scatter(x, y2, id='top_right')
axes[1][0].bar(categories, values, id='bottom_left')
axes[1][1].hist(data, id='bottom_right')
mpr.save(fig, 'multi_panel.yaml')

Temporarily Disable Recording

fig, ax = ps.subplots()

# Recorded
ax.plot(x, y, id='main_data')

# Not recorded
with ax.no_record():
    ax.axhline(0, color='gray', linestyle='--')

# Recorded again
ax.scatter(x_points, y_points, id='highlights')

Why plotspec?

Scientific Reproducibility

Share the exact specification of your figures alongside your papers. Reviewers and readers can reproduce your figures exactly.

Version Control Friendly

YAML recipes are human-readable and diff-friendly, making it easy to track changes to figures in git.

Data Separation

Large arrays are automatically saved to efficient .npz files, keeping recipes small and readable while preserving full data fidelity.

Collaboration

Share figure "recipes" with collaborators who can modify and re-run them with their own data or styling preferences.

API Reference

Main Functions

• mpr.subplots(nrows=1, ncols=1, **kwargs) - Create recording-enabled figure
• mpr.save(fig, path) - Save figure recipe to YAML
• mpr.reproduce(path, calls=None) - Reproduce figure from recipe
• mpr.info(path) - Get recipe information
• mpr.load(path) - Load recipe as FigureRecord object

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.