Python

GoFigr's Python package provides seamless figure capture for all major visualization libraries.

Supported Libraries

Library	Auto-capture	Manual capture
Matplotlib	✅	✅
Seaborn	✅	✅
Plotly	✅	✅
Altair	✅	✅
Bokeh	✅	✅

Jupyter Notebooks

Auto-Configured Setup (Simplest)

Just load the extension:

%load_ext gofigr

That's it! GoFigr will:

• Automatically use your default workspace from gfconfig

• Create or use an analysis named after your notebook

• Enable auto-publish (automatically captures all figures)

All figures you create will be automatically published:

%load_ext gofigr

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Create a simple plot
df = pd.DataFrame({'x': np.random.randn(100), 'y': np.random.randn(100)})
plt.scatter(df['x'], df['y'])
plt.title('Random Scatter Plot')

# This figure is automatically published!

Custom Configuration

For more control, use the configure() function:

%load_ext gofigr

from gofigr.jupyter import configure, FindByName, ApiId, NotebookName

configure(
    workspace=FindByName("Primary Workspace", create=False),
    analysis=FindByName("My Analysis", create=True),
    auto_publish=True,
    default_metadata={
        'requested_by': "Alyssa",
        'study': 'Pivotal Trial 1'
    }
)

Configuration Options

Option	Description
workspace	FindByName("Name"), ApiId("uuid"), or None (use default)
analysis	FindByName("Name", create=True), NotebookName(), or ApiId("uuid")
auto_publish	If True, all figures are automatically published
default_metadata	Dictionary of metadata to store with each revision
api_key	Override API key (if not using default from gfconfig)

Manual Publishing

If you set auto_publish=False, manually publish figures:

%load_ext gofigr

from gofigr.jupyter import configure, FindByName, publish

configure(auto_publish=False, analysis=FindByName("My Analysis", create=True))

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.title('Manual Publish Example')

# Manually publish
publish(fig=plt.gcf(), target=FindByName("My Figure", create=True))

---

Standalone Scripts

Use the Publisher class for scripts outside Jupyter:

import matplotlib.pyplot as plt
from gofigr.publisher import Publisher

# Initialize the publisher
pub = Publisher(workspace="My Workspace", analysis="Script Analysis")

# Create a figure
plt.plot([1, 2, 3], [1, 4, 9])
plt.title('Quadratic Function')

# Publish the figure
pub.publish(plt.gcf())

Complete Script Example

import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from gofigr.publisher import Publisher

# Setup GoFigr Publisher
pub = Publisher(workspace="My Workspace", analysis="Penguin Analysis")

# Load data
penguins = sns.load_dataset("penguins")

# Create and publish a Seaborn plot
sns.scatterplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", hue="species")
plt.title("Penguin Measurements (Seaborn)")
pub.publish(plt.gcf())

# Create and publish a Plotly plot
fig = px.scatter(penguins, x="flipper_length_mm", y="bill_length_mm", 
                 color="species", title="Penguin Measurements (Plotly)")
pub.publish(fig)

---

Asset Tracking

GoFigr can automatically track data files used in your analyses.

Using Tracked Data Reading

Use gf.read_csv() instead of pd.read_csv() to automatically track data:

%load_ext gofigr

# gf is automatically available after loading the extension
df = gf.read_csv('data/penguins.csv')

# The DataFrame is now linked to the tracked asset
print(df.attrs.get('_gofigr_revision'))  # Shows the asset revision ID

Supported Reading Methods

Method	File Type
gf.read_csv()	CSV files
gf.read_excel()	Excel files
gf.read_json()	JSON files
gf.read_parquet()	Parquet files
gf.read_feather()	Feather files
gf.read_pickle()	Pickle files

All methods accept the same parameters as their pandas counterparts.

Manual Asset Syncing

# Sync a file without reading it
gf.sync.sync('data/penguins.csv')

# Or use as a context manager
with gf.sync.open('data/raw_data.txt', 'r') as f:
    content = f.read()