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A wrapper for automating common matplotlib tasks

Project description


plit is a Matplotlib wrapper that automates the undifferentiated heavy-lifting of writing boilerplate code while maintaining the power and feel of Matplotlib.

There are two components to plit:

  • Wrappers around core chart types for standard line, scatter, histograms, and bar charts.
  • Templates that are built from these primatives for specific analytic tasks.

Here is an example chart created with plit:

See the docs for more information.

Note: if you are looking for the project that manages a set of git repositories, that project has been renamed to glit (GitHub, PyPi).


pip install plit

Quick Start

The best place to get started is the wrappers. There are three main wrappers included in plit. The naming is consistent with matplotlib. They work with multi-series by default.

  • plot: for line and scatter charts.
  • hist: for histograms.
  • bar: for bar charts.

Create a line chart

Create a line and scatter chart using the plot function.

import numpy as np
x = [np.arange(10)]
y = [np.random.random(size=(10,1)) for _ in range(4)]

from plit import plot

plot(x, y, list("ABCD"), 'X', 'Y');

Create a scatter chart

By simply changing the marker_type='o' you switch from line to scatter chart.

from plit import plot

x = [np.random.random(size=(10,1)) for _ in range(4)]
plot(x, y, list("ABCD"), 'X', 'Y', marker_type='o')

Create a histogram

Create a histogram using the hist function.

from plit import hist

x = [np.random.normal(size=(100,1)), np.random.gamma(shape=1, size=(100,1)) - 2]
hist(x, list("AB"), 'X', title='Histogram', bins=20)

Create a bar chart

Create a grouped bar chart with the bar function.

from plit import bar

x = [f"Group {i+1}"for i in range(6)]
y = [np.random.random(size=(6)) for _ in range(2)]
bar(x, y, list("AB"),'X', 'Y', colors=list("kb"), title='Bar Chart')

Example notebooks

The best way to go deeper is to look at the examples notebooks:

  • quick-start notebook gives an overview of core functionality including creating core chart types.
  • plit-vs-matplotlib shows the difference between matplotlib and plit with a simple example.
  • creating-templates-file demonstrates how to use partial functions to simplify and streamline your visualization workflow.
  • accuracy-vs-coverage shows an illustrative example using a template created for visualizing accuracy and coverage.
  • precision-vs-recall shows an illustrative example using a template created for choosing a threshold using precision and recall.
  • softmax-calibration shows an illustrative example using a template created for evaluating the calibration for softmax output.

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