a Python data visualization library based on matplotlib
Project description
FreePlot is a Python data visualization library based on matplotlib. It provides some simple implements according to my preference. Matplotlib is powerful yet not easy to draw what you want due to its complicated arguments. I feel FreePlot is more friendly, especially for papers.
Installation
pip install freeplot
Quick Recipe
- import
Mostly FreePlot module is enough.
from freeplot.base import FreePlot
- create a new container
fp = FreePlot(shape=(2, 2), figsize=(5, 5), titles=('a', 'b', 'c', 'd'), sharey=True)
shape: the arrangement of axes, 2 x 2, a total 4 axes in this case; figsize: (width, height), 500 x 500 in this case; titles: the title for each ax; sharey: axes will share the y axis if true.
the container can be used in a ndarray-style, e.g.:
# fp[0, 0], fp[0, 1]
You can also use title:
# fp['a']
But the slice operation is not support:
# fp[:, 0]
- plotting
I implement some methods for plotting such as lineplot, scatterplot ...
fp.lineplot(x=[1, 2, 3], y=[4, 5, 6], index=(0, 0), label='test')
- set xlabel, ylabel
use
fp.set_label('X', axis='x', index=(0, 0))
fp.set_label('Y', axis='y', index=(0, 0))
or
fp.set(xlabel='X', ylabel='Y', index=(0, 0))
- set title
fp.set_title(y=0.98) # for all axes
- save your fig
fp.savefig('test.pdf')
FreePlot will create a file named media and save the fig there, namely './media/test.pdf' in this case.
Example
Line, Scatter, Bar, Heatmap
Let's do a little complicate plotting.
import numpy as np
import pandas as pd
from freeplot.base import FreePlot
titles = ('Line', 'Scatter', 'Bar', 'Heatmap')
fp = FreePlot(shape=(2, 2), figsize=(5, 5), titles=titles, sharey=False)
fp.set_style('no-latex')
# Line
x = np.linspace(0, 2, 10)
y1 = x ** 0.5
y2 = x ** 2
fp.lineplot(x, y1, index=(0, 0), style='linemarker', label='sqrt(2)')
fp.lineplot(x, y2, index=(0, 0), style='linemarker', label='pow(2)')
fp[0, 0].legend()
# scatter
x = np.random.randn(100)
y = np.random.randn(100)
fp.scatterplot(x, y, index='Scatter', style='scatter')
fp.set_label('X', index=(0, 1), axis='x')
fp.set(ylabel='Y', index=(0, 1))
# bar
A = [1., 2., 3.]
B = [2., 3., 4.]
T = ['One', 'Two', 'Three'] * 2
Hue = ['A'] * len(A) + ['B'] * len(B)
data = pd.DataFrame(
{
"T": T,
"val": A + B,
"category": Hue
}
)
fp.barplot(x='T', y='val', hue='category', data=data, index=(1, 0), auto_fmt=True)
# Heatmap
row_labels = ('c', 'u', 't', 'e')
col_labels = ('l', 'r', 'i', 'g')
data = np.random.rand(4, 4)
df = pd.DataFrame(data, index=col_labels, columns=row_labels)
fp.heatmap(df, index='Heatmap', annot=True, fmt=".4f", cbar=False, linewidth=0.5)
# set titles
fp.set_title(y=0.98)
# savefig
fp.savefig('demo.png')
# fp.show()
Radar
EE
import numpy as np
from freeplot.base import FreePlot
from freeplot.zoo import pre_radar, pos_radar
labels = (
"brightness", "fog", "gaussian_blur", "glass_blur", "jpeg_compression",
"motion_blur", "saturate, snow", "speckle_noise", "contrast", "elastic_transform", "frost",
"gaussian_noise", "impulse_noise", "pixelate", "shot_noise", "spatter", "zoom_blur", "transform", "flowSong"
)
theta = pre_radar(len(labels), frame="polygon")
# shape: 1, 1; figsize: 4, 4;
fp = FreePlot((1, 1), (4, 4), dpi=100, titles=["RADAR"], projection="radar")
fp.set_style('no-latex')
data = {
"A": np.random.rand(len(labels)),
'B': np.random.rand(len(labels)),
'C': np.random.rand(len(labels))
}
pos_radar(data, labels, fp)
fp[0, 0].legend()
fp.savefig("radar.png", tight_layout=True)
Violin
import numpy as np
import matplotlib.pyplot as plt
from freeplot.base import FreePlot
fp = FreePlot((1, 1), (5, 5))
# note that each element is a group of data ...
all_data = [np.random.normal(0, std, 100) for std in range(5, 10)]
fp.violinplot(x=None, y=all_data, index=(0, 0))
fp.savefig('violin.png')
Inset_axes
from freeplot.base import FreePlot
fp = FreePlot((1, 1), (5, 4))
fp.lineplot([1, 2, 3], [4, 5, 6], label='a')
fp.lineplot([1, 2, 3], [3, 5, 7], label='b')
axins, patch, lines = fp.inset_axes(
xlims=(1.9, 2.1),
ylims=(4.9, 5.1),
bounds=(0.1, 0.7, 0.2, 0.2),
index=(0, 0),
style='line' # !!!
)
fp.lineplot([1, 2, 3], [4, 5, 6], index=axins)
fp.lineplot([1, 2, 3], [3, 5, 7], index=axins)
fp.savefig('inset.png')
Latex
Because we adopt the 'science' as the basic style, which use latex in default, it will raise error if you don't have Latex on you computer. You shall call
fp.set_style('no-latex')
or
plt.style.use('no-latex')
to circumvent this problem. However, once you call this, please don't use
fp.show()
or
plt.show()
any more. You shall use savefig directly.
Tips
-
For lineplot, barplot ..., you can directly use matplotlib.axes._axes.Axes as index, e.g.:
fp.lineplot(x, y, index=fp[0, 0])
-
You may find some interesting implementations in freeplot.zoo, such as tsne, roc_curve ...
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