freeplot 0.0.3

a Python data visualization library based on matplotlib

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

1. import

from freeplot.base import FreePlot

2. 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 supported:

# fp[:, 0]

3. 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')

4. 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))

5. set title

fp.set_title(y=0.98) # for all axes

6. save your fig

fp.savefig('test.pdf')

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

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, index=(0, 0), style='bright')

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. In addition, it needs long time to throughly complete the plotting under 'latex' environment. You shall use the following to close it,

fp.set_style('no-latex')

or

plt.style.use('no-latex')

Tips

1. For lineplot, barplot ..., you can directly use matplotlib.axes._axes.Axes as index, e.g.:

   fp.lineplot(x, y, index=fp[0, 0])
   

2. You may find some interesting implementations in freeplot.zoo, such as tsne, roc_curve ...

3. You can use a list of styles:

   fp.set_style(['bright', 'high-vis'])
   

4. It would be better to use

   fp.legend
   

   to set legend instance instead of

   fp[0, 0].legend
   

5. You can find all available styles by

   fp.styles
   

6. You can scale x|y-axis by

   fp.set_scale(value='symlog', index=(0, 0), axis='x')
   fp.set_scale(value='symlog', index=(0, 0), axis='y')