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 most functions of this library with that of matplotlib and
vice versa. For more detailed description see introduction
Installation
This package can be installed using pip from pypi using following command
pip install easy_mpl
API
Usage
For a wide range of usage examples see gallery of examples
plot
import numpy as np
from easy_mpl import plot
x = np.random.randint(2, 10, 10)
plot(x, '--o', color=np.array([35, 81, 53]) / 256.0,
ax_kws=dict(xlabel="Days", ylabel="Values"))
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)
ax, _ = scatter(x, y, colorbar=True)
assert isinstance(ax, plt.Axes)
imshow
import numpy as np
from easy_mpl import imshow
data = np.random.random((4, 10))
imshow(data, cmap="YlGn",
xticklabels=[f"Feature {i}" for i in range(data.shape[1])],
grid_params={'color': 'w', 'linewidth': 2}, annotate=True,
colorbar=True)
bar_chart
from easy_mpl import bar_chart
bar_chart(
[1,2,3,4,4,5,3,2,5],
['a','b','c','d','e','f','g','h','i'],
bar_labels=[11, 23, 12,43, 123, 12, 43, 234, 23],
cmap="GnBu",
sort=True)
hist
import numpy as np
from easy_mpl import hist
data = np.random.randn(1000)
hist(data, bins = 100)
lollipop_plot
import numpy as np
from easy_mpl import lollipop_plot
y = np.random.randint(0, 10, size=10)
lollipop_plot(y, sort=True, title="sort")
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)
regplot
import numpy as np
from easy_mpl import regplot
rng = np.random.default_rng(313)
x = rng.uniform(0, 10, size=100)
y = x + rng.normal(size=100)
regplot(x, y, line_color='black')
ridge
import numpy as np
from easy_mpl import ridge
data = np.random.random((100, 3))
ridge(data)
pie
from easy_mpl import pie
explode = (0, 0.1, 0, 0, 0)
pie(fractions=[0.2, 0.3, 0.15, 0.25, 0.1], explode=explode)
contour
import numpy as np
from easy_mpl import contour
_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)
circular_bar_plot
import numpy as np
from easy_mpl import circular_bar_plot
data = np.random.random(50, )
circular_bar_plot(data, text_kws={"fontsize": 16})
parallel_coordinates
import numpy as np
import pandas as pd
from easy_mpl import parallel_coordinates
ynames = ['P1', 'P2', 'P3', 'P4', 'P5'] # feature/column names
N1, N2, N3 = 10, 5, 8
N = N1 + N2 + N3
categories_ = ['a', 'b', 'c', 'd', 'e', 'f']
y1 = np.random.uniform(0, 10, N) + 7
y2 = np.sin(np.random.uniform(0, np.pi, N))
y3 = np.random.binomial(300, 1 / 10, N)
y4 = np.random.binomial(200, 1 / 3, N)
y5 = np.random.uniform(0, 800, N)
# combine all arrays into a pandas DataFrame
data_df = pd.DataFrame(np.column_stack((y1, y2, y3, y4, y5)),
columns=ynames)
# using continuous values for categories
parallel_coordinates(data_df, names=ynames,
categories=np.random.randint(0, 5, N))
spider_plot
import pandas as pd
from easy_mpl import spider_plot
df = pd.DataFrame.from_dict(
{'summer': {'a': -0.2, 'b': 0.1, 'c': 0.0, 'd': 0.1, 'e': 0.2, 'f': 0.3},
'winter': {'a': -0.3, 'b': 0.1, 'c': 0.0, 'd': 0.2, 'e': 0.15, 'f': 0.25},
'automn': {'a': -0.1, 'b': 0.3, 'c': 0.15, 'd': 0.24, 'e': 0.18, 'f': 0.2}})
spider_plot(df, xtick_kws={'size': 13}, frame="polygon",
color=['b', 'r', 'g', 'm'],
fill_color=['b', 'r', 'g', 'm'])
taylor_plot
import numpy as np
from easy_mpl import taylor_plot
np.random.seed(313)
observations = {
'site1': np.random.normal(20, 40, 10),
'site2': np.random.normal(20, 40, 10),
'site3': np.random.normal(20, 40, 10),
'site4': np.random.normal(20, 40, 10),
}
simulations = {
"site1": {"LSTM": np.random.normal(20, 40, 10),
"CNN": np.random.normal(20, 40, 10),
"TCN": np.random.normal(20, 40, 10),
"CNN-LSTM": np.random.normal(20, 40, 10)},
"site2": {"LSTM": np.random.normal(20, 40, 10),
"CNN": np.random.normal(20, 40, 10),
"TCN": np.random.normal(20, 40, 10),
"CNN-LSTM": np.random.normal(20, 40, 10)},
"site3": {"LSTM": np.random.normal(20, 40, 10),
"CNN": np.random.normal(20, 40, 10),
"TCN": np.random.normal(20, 40, 10),
"CNN-LSTM": np.random.normal(20, 40, 10)},
"site4": {"LSTM": np.random.normal(20, 40, 10),
"CNN": np.random.normal(20, 40, 10),
"TCN": np.random.normal(20, 40, 10),
"CNN-LSTM": np.random.normal(20, 40, 10)},
}
# define positions of subplots
rects = dict(site1=221, site2=222, site3=223, site4=224)
taylor_plot(observations=observations,
simulations=simulations,
axis_locs=rects,
plot_bias=True,
cont_kws={'colors': 'blue', 'linewidths': 1.0, 'linestyles': 'dotted'},
grid_kws={'axis': 'x', 'color': 'g', 'lw': 1.0},
title="mutiple subplots")
boxplot
import pandas as pd
from easy_mpl import boxplot
from easy_mpl.utils import _rescale
f = "https://raw.githubusercontent.com/AtrCheema/AI4Water/master/ai4water/datasets/arg_busan.csv"
df = pd.read_csv(f, index_col='index').iloc[:, 0:10]
for col in df.columns:
df[col] = _rescale(df[col])
boxplot(df,
fill_color="GnBu",
notch=True,
patch_artist=True,
medianprops={"color": "black"})
violin_plot
import matplotlib.pyplot as plt
import pandas as pd
from easy_mpl import violin_plot
from easy_mpl.utils import _rescale
f = "https://raw.githubusercontent.com/AtrCheema/AI4Water/master/ai4water/datasets/arg_busan.csv"
df = pd.read_csv(f, index_col='index').iloc[:, 0:10]
for col in df.columns:
df[col] = _rescale(df[col])
axes = violin_plot(df, show=False)
axes.set_facecolor("#fbf9f4")
plt.tight_layout()
plt.show()
Code Structure
