Plotting functions for the Genetic Perturbation Platform's R&D group at the Broad institute.
Project description
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.
History
0.1.0 (2020-06-16)
Project start
0.2.1 (2020-06-20)
First release on pypi
0.2.2 (2020-06-25)
Automatic integration with pypi
0.3.1 (2020-07-12)
Add plt.gca to add_correlation and label_points
Update docs to include notebook references
0.3.2 (2020-07-12)
Added minimum version for requirements
0.3.3 (2020-07-12)
Added version for dev requirements
0.3.4 (2020-07-12)
Added python version >= 3.6
Project details
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