gpplot

Introduction

Plotting functions for the Genetic Perturbation Platform's R&D group at the Broad Institute. Extends matplotlib and seaborn functionality with extra plot types. Includes functions for easy styling and consistent color palettes.

• Free software: MIT license
• Documentation: https://gpplot.readthedocs.io.

Tutorial

To install gpplot, run this command in your terminal:

$ pip install gpplot

Import packages:

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gpplot

Set aesthetics for all plots using gpplot defaults:

gpplot.set_aesthetics()

Setup data:

nsamps = 20000
scatter_data = pd.DataFrame({'x': np.random.normal(size = nsamps)}, index = range(nsamps))
scatter_data['y'] = 2*scatter_data['x'] + np.random.normal(size = nsamps)

Create a point density plot and add a pearson correlation:

fig, ax = plt.subplots(figsize = (4,4))
ax = gpplot.point_densityplot(scatter_data, 'x', 'y', palette=gpplot.sequential_cmap())
ax = gpplot.add_correlation(scatter_data, 'x', 'y')

Label points in a scatterplot:

fig, ax = plt.subplots(figsize = (4,4))
mpg = sns.load_dataset('mpg')
ax = sns.scatterplot(data = mpg, x = 'weight', y = 'mpg', ax = ax)
label = ['hi 1200d', 'ford f250', 'chevy c20', 'oldsmobile omega']
gpplot.label_points(mpg, 'weight', 'mpg', label, 'name',
                    size = 12, style = 'italic')

TODO

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.