A wrapper for automating common matplotlib tasks
Project description
plit
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 PRFAQ for more information.
Install
pip install plitlib
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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