Chapter 30: MatPlotLib / matplotlib

library(reticulate)

## Warning: package 'reticulate' was built under R version 4.2.3

# conda_list()
use_condaenv(condaenv = 'sandbox-3.9')

## install Seaborn
# conda_install("r-reticulate", "seaborn")

## import Seaborn (it will be automatically discovered in "r-reticulate")
seaborn <- import("seaborn")

tikzplotlib^[16.6]: Python^[15] matplotlib^[30] export to TikZ^[16] .tex

30.1 Timothy H. Wu

巫孟叡

API = application programming interface
- functional^[30.1.1]
- object-oriented^[30.1.2]
  - figure
  - axes
  - subplot

https://matplotlib.org/stable/tutorials/introductory/quick_start.html

Fig. 30.1: matplotlib figure anatomy

https://pbpython.com/effective-matplotlib.html

Fig. 30.2: matplotlib subplot anatomy

https://tex.stackexchange.com/questions/84847/can-i-use-webp-images-in-latex

You probably need to convert the image to png.

30.1.1 funcitonal API

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
plt.title('Sales figure')
plt.xlabel('Month')
plt.ylabel('Unit')
plt.plot(a1, a2) # this doesn't actually show the plot.
# plt.show() This is automatically called for Jupyter notebook.

To plot a scatterplot, call scatter() instead of plot().

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
plt.title('Sales figure')
plt.xlabel('Month')
plt.ylabel('Unit')
plt.scatter(a1, a2) # instead of plot.plot(), use scatter() to show scatter plot

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
plt.title('Sales figure')
plt.xlabel('Month')
plt.ylabel('Unit')
plt.plot(a1, a2)
plt.plot(a2, a1)

The behavior of the functional API is stateful. What’s stateful? An example is when you read a text file. When you open() a text file to read, the library read the next line every time you call readline(). It rememember where you left off, despite the fact that you do not give it the position to read from. This behavior of the library is called stateful. The way we’ve used Matplotlib is also stateful. And everytime, plot.show() is called (and it automatically gets called on cell ends), some state about plots is reset. We can see that here:

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
plt.title('Sales figure')
plt.xlabel('Month')
plt.ylabel('Unit')
plt.plot(a1, a2)

plt.plot(a2, a1)
plt.ylabel('Unit')

It makes two graphs instead of one. Also note that ylabel() was called after plot(), and it is still shown before plot.show() but Sales Figure plot title and other labels don’t show up on this graph. Because every time plot.show() is called, things are reset. This is a stateful API we’re using. The functional APIs are used when you plot Matplotlib by calling on pyplot module level API (module level functions).

30.1.2 object-oriented API

In object-orinted API, we’re getting two type of objects. One is Figure, the other one is Axes. Figure is the canvas of the plot. In English, axes is the plural form of axis. We’re talking about the axis in x axis and y axis. Since one plot consists of both axis, in MatPlotlib the object that represents one plot is called Axes. Since it’s an object. We’ll call it “a” axes.

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
print("fig is of type:", type(fig))

## fig is of type: <class 'matplotlib.figure.Figure'>

ax1 = fig.add_axes([0, 0, 1, 1]) # [left, bottom, width, height]
print("ax1 is of type:", type(ax1))

## ax1 is of type: <class 'matplotlib.axes._axes.Axes'>

# ax1.plot(a1, a2)

Call figure() to get a Figure type

Call add_axes() to get a ax1 type ax1 = fig.add_axes([0, 0, 1, 1]) # [left, bottom, width, height]

The list given to add_axes() is the rectangular region of where to show the plot:

Bottom left corner at x=0, y=0, width and height of both 1, 1

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
# print("fig is of type:", type(fig))
ax1 = fig.add_axes([0, 0, 1, 1]) # [left, bottom, width, height]
# print("ax1 is of type:", type(ax1))
ax1.plot(a1, a2)

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
ax1 = fig.add_axes([0, 0, 1, 1])
ax1.plot(a1, a2)
ax2 = fig.add_axes([0.1, 0.5, .4, .4])
ax2.plot(a2, a1)

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)
ax.set_xlabel('Time')
ax.set_ylabel('Unit')
ax.set_title('Sales figure')
# alternatively: 
# ax.set(xlabel='Time', ylabel='Unit', title='Sales figure')

30.1.2.1 Configure the figure size and DPI

Get image size for the figure object. 6 by 4 is the default.

fig.get_size_inches()

import matplotlib.pyplot as plt

fig = plt.figure()
fig.get_size_inches()

## array([7., 5.])

fig = plt.figure(figsize=(2, 4))

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure(figsize=(2, 4))
# you can also set after getting the figure
# fig.set_size_inches((12, 2))
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)

fig.set_size_inches((4, 2))

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
# you can also set after getting the figure
fig.set_size_inches((4, 2))
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)

DPI = dots per inch

Get image DPI for the figure object. 100 is the default here.

fig.get_dpi()

import matplotlib.pyplot as plt

fig = plt.figure()
fig.get_dpi()

## 100.0

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
# you can also set after getting the figure
fig.set_size_inches((12, 2))
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)
plt.title('Sales figure')
fig.get_dpi()

## 100.0

30.1.2.2 subplot

import matplotlib.pyplot as plt

# Subplots handles add_axes for you according to the number of rows and columns
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(6, 3))
# axes is a numpy array, you can use it like using a list.
print(axes)

## [[<Axes: > <Axes: >]
##  [<Axes: > <Axes: >]]

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
# Subplots handles add_axes for you according to the number of rows and columns
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(6, 3))
# axes is a numpy array, you can use it like using a list.
# print(axes)
axes[0][0].plot(a1, a2)
axes[0][1].plot(a1, a2)
axes[1][0].plot(a2, a1)
axes[1][1].plot(a2, a1)

30.1.2.3 color and linestyle

Color

https://matplotlib.org/stable/tutorials/colors/colors.html

line-style (ls)

https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html

marker

https://matplotlib.org/stable/api/markers_api.html

linewidth (lw)

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
# Subplots handles add_axes for you according to the number of rows and columns
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 3))
# axes is a numpy array
axes[0].plot(a1, a2, color='green', linestyle='-.')
axes[0].plot(a2, a1, color=(1, 0, 0.5), marker='v', markersize=20)
axes[1].plot(a1, a2, linestyle='dotted')
axes[1].plot(a2, a1, linestyle=(0, (3, 10, 1, 10)))

30.1.2.4 other inputs

NumPy array
Pandas series

30.1.2.5 legend

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 30)
y = x * x
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
line1 = ax.plot(x, y, label="Competitor")
line2 = ax.plot(x, y**2, label="Us")
ax.legend()

30.1.2.6 customize style

predefined styles

import matplotlib.pyplot as plt

print(plt.style.available)

## ['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
plt.style.use('ggplot')
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)

restore default style

import matplotlib.pyplot as plt

plt.style.use('default') # But strangely enough figure size gets changed still

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (6, 4)
plt.rcParams["figure.dpi"] = 100

30.1.2.7 save to file

savefig saves image to file. We also set the bbox_inches parameter to tight to make sure the image doesn’t get out of the image bound.

save to .png

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)
fig.savefig('out.png', bbox_inches = 'tight')

save to .pdf

import matplotlib.pyplot as plt

a1 = list(range(1, 7)) # [1, 2, 3, 4, 5, 6]
a2 = [1, 4, 9, 16, 25, 36]
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.plot(a1, a2)
fig.savefig('out.pdf', bbox_inches = 'tight')

30.1.3 Seaborn

Kimberly Fessel

Fig. 30.3: seaborn plot type

Seaborn
- figure-level plot
- axes-level plot

R or RStudio run Python with installing packages or modules by using reticulate, R package

and directly using Anaconda conda environment for convenience, instead of virtualenv

https://rstudio.github.io/reticulate/articles/python_packages.html

# library(reticulate)
# conda_list()
# use_condaenv(condaenv = 'sandbox-3.9')

## install Seaborn
# conda_install("r-reticulate", "seaborn")

## import Seaborn (it will be automatically discovered in "r-reticulate")
# seaborn <- import("seaborn")

30.1.3.1 basic

import matplotlib.pyplot as plt  # need it sometimes
import seaborn as sns

sns.set_theme() # set the default theme

import seaborn as sns

x = [3, 5, 1.2, 8.7, 6.4]
y = [8.2, 4.3, 7, 9.6, 4]
sns.scatterplot(x=x, y=y)

import seaborn as sns

x = [3, 5, 1.2, 8.7, 6.4]
y = [8.2, 4.3, 7, 9.6, 4]
sns.lineplot(x=x, y=y)

30.1.3.2 data frame

sns.load_dataset

import seaborn as sns

tips = sns.load_dataset("tips")
tips

##      total_bill   tip     sex smoker   day    time  size
## 0         16.99  1.01  Female     No   Sun  Dinner     2
## 1         10.34  1.66    Male     No   Sun  Dinner     3
## 2         21.01  3.50    Male     No   Sun  Dinner     3
## 3         23.68  3.31    Male     No   Sun  Dinner     2
## 4         24.59  3.61  Female     No   Sun  Dinner     4
## ..          ...   ...     ...    ...   ...     ...   ...
## 239       29.03  5.92    Male     No   Sat  Dinner     3
## 240       27.18  2.00  Female    Yes   Sat  Dinner     2
## 241       22.67  2.00    Male    Yes   Sat  Dinner     2
## 242       17.82  1.75    Male     No   Sat  Dinner     2
## 243       18.78  3.00  Female     No  Thur  Dinner     2
## 
## [244 rows x 7 columns]

data

import seaborn as sns

tips = sns.load_dataset("tips")
sns.scatterplot(data=tips, 
                x='total_bill', 
                y='tip')

hue

import seaborn as sns

tips = sns.load_dataset("tips")
sns.scatterplot(data=tips, 
                x='total_bill', 
                y='tip', 
                hue='smoker')

style

import seaborn as sns

tips = sns.load_dataset("tips")
sns.scatterplot(data=tips, 
                x='total_bill', 
                y='tip', 
                hue='smoker', 
                style='time')

size

import seaborn as sns

tips = sns.load_dataset("tips")
sns.scatterplot(data=tips, 
                x='total_bill', 
                y='tip', 
                hue='smoker', 
                style='time', 
                size='size')

Legend is covering up the graph, it’s getting out of hand. Let’s tune the range of x

sns.scatterplot() actually returns something that resembles Matplotlib axis. So we use a Matplotlib axis function:

ax = sns.scatterplot(...)

https://stackoverflow.com/questions/26597116/seaborn-plots-not-showing-up

import matplotlib.pyplot as plt
import seaborn as sns

tips = sns.load_dataset("tips")
ax = sns.scatterplot(data=tips, 
                x='total_bill', 
                y='tip', 
                hue='smoker', 
                style='time', 
                size='size')
ax.set_xlim((0, 70))
# alternatively:
# ax.set(xlim=(0, 70))
plt.show()

## (0.0, 70.0)

30.1.3.3 axis-level plot and figure-level plot

sns.relplot

Fig. 16.23: seaborn plot type

import seaborn as sns

tips = sns.load_dataset("tips")
sns.relplot(kind='scatter', 
            data=tips, 
            x='total_bill', 
            y='tip')

import seaborn as sns

tips = sns.load_dataset("tips")
sns.relplot(kind='line', 
            data=tips, 
            x='total_bill', 
            y='tip')

With figure-level plot, we can draw more than one plot (one axes).

Here we specify that different sex be on different column by specifying col=sex.

col=sex

import seaborn as sns

tips = sns.load_dataset("tips")
sns.relplot(kind='scatter',
            data=tips,
            x='total_bill', 
            y='tip', 
            hue='smoker', 
            style='time', 
            size='size',
            col='sex')

col=sex row=smoker

import seaborn as sns

tips = sns.load_dataset("tips")
sns.relplot(kind='scatter',
            data=tips,
            x='total_bill', 
            y='tip', 
            style='time', 
            hue='size',
            row='smoker', 
            col='sex')

30.1.3.4 accessing fgure and axes objects

Recall that Seaborn uses Matplotlib to draw the graphics. So underneath the Seaborn libray, you can still access Matplotlib’s figure object and axes objects if necessary. The call to figure-level plot returns an object.

import seaborn as sns

tips = sns.load_dataset("tips")
g = sns.relplot(kind='scatter',
                data=tips,
                x='total_bill', 
                y='tip', 
                style='time', 
                hue='size',
                row='smoker', 
                col='sex')
print(type(g))
print(type(g.fig)) # g.fig gets you the Figure
g.fig

## <class 'seaborn.axisgrid.FacetGrid'>

## <class 'matplotlib.figure.Figure'>

30.1.3.5 distribution plot

sns.displot

30.1.3.5.1 histogram

import seaborn as sns

tips = sns.load_dataset("tips")
sns.displot(kind='hist',
            data=tips,
            x='total_bill')

bins

import seaborn as sns

tips = sns.load_dataset("tips")
sns.displot(kind='hist', bins=20,
            data=tips,
            x='total_bill')

30.1.3.5.2 ECDF = empirical cumulative distrutive function

import seaborn as sns

tips = sns.load_dataset("tips")
sns.displot(kind='ecdf',
            data=tips,
            x='total_bill')

30.1.3.5.3 KDE = kernel density estimation

import seaborn as sns

tips = sns.load_dataset("tips")
sns.displot(kind='kde',
            data=tips,
            x='total_bill')

histogram with KDE

import seaborn as sns

tips = sns.load_dataset("tips")
sns.displot(kind='hist', bins=20, kde='true',
            data=tips,
            x='total_bill')

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TikZ / tikzpicture with PGFplots axis by transforming Matplotlib-based Seaborn plot to .tex via Python package tikzplotlib

xlabel={total_bill}, changed to xlabel={total bill}, wihout _ in text

X ylabel={Count}, changed to ylabel={$\text{Count}$},: not necessoary to be changed, the problem is _ in xlabel name

Fig. 30.4: tikzplotlib

import seaborn as sns

tips = sns.load_dataset("tips")
sns.displot(kind='hist', bins=20, kde='true',
            data=tips,
            x='total_bill',
            hue='smoker',
            col='sex'
            )

Let’s cutomize the label and title.

import seaborn as sns

tips = sns.load_dataset("tips")
g = sns.displot(kind='hist', bins=20, kde='true',
                data=tips,
                x='total_bill',
                hue='smoker',
                col='sex'
                )
g.set(xlabel='Total bill')
g.axes[0][0].set(title='Male')
g.axes[0][1].set(title='Female')
g.fig

30.1.3.6 categorical plot

sns.catplot

import seaborn as sns

tips = sns.load_dataset("tips")
sns.catplot(kind='box', 
            data=tips, 
            x="day", hue="day",
            y="total_bill"
            )

import seaborn as sns

tips = sns.load_dataset("tips")
sns.catplot(kind='violin', 
            data=tips, 
            x="day", hue="day",
            y="total_bill",
            col='sex'
            )

30.1.3.7 joint plot

sns.jointplot

import matplotlib.pyplot as plt
import seaborn as sns

tips = sns.load_dataset("tips")
sns.jointplot(data=tips,
              x="total_bill",
              y="tip",
              hue='sex'
              )
plt.show()

## <seaborn.axisgrid.JointGrid object at 0x000001FD5044F550>

30.1.3.8 pair plot

sns.pairplot

import seaborn as sns

penguins = sns.load_dataset("penguins")
sns.pairplot(data=penguins,
             hue='species')

30.1.3.9 palette

Changing the color with Matplotlib’s color

A list of color: https://matplotlib.org/stable/tutorials/colors/colormaps.html

import seaborn as sns

penguins = sns.load_dataset("penguins")
sns.pairplot(data=penguins,
             hue='species',
             palette='Pastel1')

30.1.3.10 volcano plot

30.1.3.11 heatmap

30.2 export

30.2.1 .svg

https://stackoverflow.com/questions/24525111/how-can-i-get-the-output-of-a-matplotlib-plot-as-an-svg

import matplotlib.pyplot as plt
import seaborn as sns

penguins = sns.load_dataset("penguins")
sns.pairplot(data=penguins,
             hue='species')

plt.savefig("test.svg")
# plt.savefig("test.svg", dpi=1200)

30.2.2 .eps

https://stackoverflow.com/questions/16183462/saving-images-in-python-at-a-very-high-quality

The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.

import matplotlib.pyplot as plt
import seaborn as sns

penguins = sns.load_dataset("penguins")
sns.pairplot(data=penguins,
             hue='species')

plt.savefig("test.eps")
# plt.savefig("test.eps", dpi=1200)