Jupyter Integration

GoFigr provides first-class support for Jupyter notebooks, capturing figures with complete execution context.

What Gets Captured

When you create a figure in Jupyter with GoFigr enabled:

Element	Captured
Figure image	✅
Cell source code	✅
Cell execution order	✅
Variable values	✅
Notebook metadata	✅
Kernel info	✅

Setup

Load the GoFigr extension in your first cell:

%load_ext gofigr

That’s it! GoFigr will:

• Use your default workspace from gfconfig
• Create an analysis named after your notebook
• Automatically capture all figures

Supported Environments

Environment	Status
JupyterLab	✅ Full support
Jupyter Notebook (Classic)	✅ Full support
VS Code Notebooks	✅ Full support
Google Colab	✅ Supported (with API key)
Databricks	✅ Supported (with configuration)

Example Workflow

# Cell 1: Setup
%load_ext gofigr

import pandas as pd
import matplotlib.pyplot as plt

# Cell 2: Load data
df = pd.read_csv("experiment_results.csv")
print(f"Loaded {len(df)} rows")

# Cell 3: Visualize (automatically captured!)
plt.figure(figsize=(10, 6))
plt.scatter(df['x'], df['y'], c=df['category'], cmap='viridis')
plt.colorbar(label='Category')
plt.title("Experiment Results")
plt.xlabel("X Measurement")
plt.ylabel("Y Measurement")

The figure in Cell 3 is captured along with:

• The plotting code from Cell 3
• The data loading context from Cell 2
• The notebook’s execution state

Custom Configuration

For more control over workspace/analysis selection:

%load_ext gofigr

from gofigr.jupyter import configure, FindByName

configure(
    workspace=FindByName("My Workspace"),
    analysis=FindByName("Data Analysis", create=True),
    auto_publish=True,
    default_metadata={'study': 'Trial 1'}
)

Data Asset Tracking

Track the data files used in your analysis:

%load_ext gofigr

# Use gf.read_csv instead of pd.read_csv
df = gf.read_csv('data/experiment.csv')

# The DataFrame is linked to the tracked asset
# Figures created from this data will be linked to the data version

When you publish a figure, GoFigr automatically tracks which data assets were used, ensuring complete reproducibility.

QR Codes and Revision IDs

Each published figure displays:

• A QR code linking to the figure in GoFigr
• A unique revision ID for tracking

This allows anyone viewing your notebook to instantly access the full context in GoFigr.

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Git Import for Existing Notebooks

Already have notebooks in a Git repository? Import them directly without re-running:

1. Go to GoFigr → Import → Git Repository
2. Connect your GitHub/GitLab account
3. Select the repository and branches
4. GoFigr extracts all figures from all commits

See Git Repository Import for details.