easy-mpl 0.11.0

one stop shop for matplotlib plots

Matplotlib is great library which offers huge flexibility due to its object oriented programming style. However, most of the times, we the users don't need that much flexibiliy and just want to get things done as quickly as possible. For example why should I write at least three lines to plot a simple array with legend when same can be done in one line and my purpose is just to view the array. Why I can't simply do plot(data) or imshow(img). This motivation gave birth to this library. easy_mpl stands for easy maplotlib. The purpose of this is to ease the use of matplotlib while keeping the flexibility of object oriented programming paradigm of matplotlib intact. Using these one liners will save the time and will not hurt. Moreover, you can swap every function of this library with that of matplotlib and vice versa.

Installation

This package can be installed using pip from pypi using following command

pip install easy_mpl

Usage

plot

from easy_mpl import plot
import numpy as np
plot(np.random.random(100))
# use x and y
plot(np.arange(100), np.random.random(100))
# use x and y with marker style
plot(np.arange(100), np.random.random(100), '.')

plot(np.random.random(100), '.')
# use cutom marker
plot(np.random.random(100), '--*')

plot(np.random.random(100), '--*', label='label')
# log transform y-axis
plot(np.random.random(100), '--*', logy=True, label='label')

bar_chart

from easy_mpl import bar_chart
bar_chart([1,2,3,4,4,5,3,2,5])
# specifying labels
bar_chart([3,4,2,5,10], ['a', 'b', 'c', 'd', 'e'])
# sorting the data
bar_chart([1,2,3,4,4,5,3,2,5], sort=True)

regplot

import numpy as np
from easy_mpl import regplot
x_, y_ = np.random.random(100), np.random.random(100)
regplot(x_, y_)

imshow

import numpy as np
from easy_mpl import imshow
x = np.random.random((10, 5))
imshow(x, annotate=True)
# show colorbar
imshow(x, colorbar=True)

hist

from easy_mpl import hist
import numpy as np
hist(np.random.random((10, 1)))

pie

from easy_mpl import pie
import numpy as np
pie(np.random.randint(0, 3, 100))
# or by directly providing fractions
pie([0.2, 0.3, 0.1, 0.4])

scatter

import numpy as np
from easy_mpl import scatter
import matplotlib.pyplot as plt
x = np.random.random(100)
y = np.random.random(100)
scatter(x, y, show=False)
# show colorbar
scatter(x, y, colorbar=True, show=False)
# retrieve axes for further processing
ax, _ = scatter(x, y, show=False)
assert isinstance(ax, plt.Axes)

contour

from easy_mpl import contour
import numpy as np
x = np.random.uniform(-2, 2, 200)
y = np.random.uniform(-2, 2, 200)
z = x * np.exp(-x**2 - y**2)
contour(x, y, z, fill_between=True, show_points=True)
# show contour labels
contour(x, y, z, label_contours=True, show_points=True)

dumbbell_plot

import numpy as np
from easy_mpl import dumbbell_plot
st = np.random.randint(1, 5, 10)
en = np.random.randint(11, 20, 10)
dumbbell_plot(st, en)
# modify line color
dumbbell_plot(st, en, line_kws={'color':"black"})

ridge

import numpy as np
from easy_mpl import ridge
data_ = np.random.random((100, 3))
ridge(data_)
# specifying colormap
ridge(data_, cmap="Blues")
# using pandas DataFrame
import pandas as pd
ridge(pd.DataFrame(data_))