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matplotlib intro

You can make plots with any tool you like. But we may help you a little with the open source matplotlib library for scientific ploting, which runs with Python.

References:

Get ready

You need to install Python3.x

Check the existence of Python3 by typing python3 --version in your terminal (Powershell for Windows). Otherwise, we recommend to use conda to manage your Python environments:

install miniconda (Linux, macOS or Windows).
create a new environment: conda create --name myenv python=3.x
activating the environment: conda activate myenv

You need to install matplotlib & numpy

pip or conda install matplotlib

pip or conda install numpy

Then you should be able import this library.
import matplotlib.pyplot as plt

You need a code editor
We recommend to use JupyterLab, a web-based interactive coding application with excution results shown on-fly.
Modern IDE like VsCode, Atom are worth trying.

hello world for matplotlib

let's plot a sine function with Matplotlib:

Import numpy package to numerically calculate array and matplotlib to plot.
Construct the x and y axis value of the point.
Call plot() to draw a line that connects all the points
Save the figure and show it on screen

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 3*np.pi, 0.1)   # array for x axis
y = np.sin(x)                    # array for y axis
plt.plot(x, y)                   # plot x vs y
plt.savefig('sine.png')          # save to file
plt.show()                       # show on screen

hello world for matplotlib

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 3*np.pi, 0.1)   # array for x axis
y = np.sin(x)                    # array for y axis
plt.plot(x, y)                   # plot x vs y
plt.savefig('sine.png')          # save to file
plt.show()                       # show on screen

🤔Is this figure complete?

hello world for matplotlib

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 3*np.pi, 0.1)   # array for x axis
y = np.sin(x)                    # array for y axis
plt.plot(x, y, label = 'sin(x)')
plt.xlabel('physical quantity on x axis (unit)')
plt.ylabel('physical quantity on y axis (unit)')
plt.legend()                     # create legend
plt.savefig('sine.png')          # save to file
plt.show()                       # show on screen

Elements of a figure

©️matplotlib cheatsheet

make reader-friendly figure: step 1

include at least x,y labels
figure resolution (dpi)
fontsize

make reader-friendly figure: step 1

No ugly blank border

plt.figure(figsize = (7,5),facecolor= 'yellow')   # create a new figure
plt.xlabel('physical quantity on x axis (unit)', fontsize = 12)
plt.ylabel('physical quantity on y axis (unit)', fontsize = 12)
plt.legend(fontsize = 12)       # create legend
plt.title('title for this figure',fontsize = 15)
plt.savefig('sine_highQ.png', dpi = 288, bbox_inches = 'tight')

Line plot: color

You can use 'color' argument to assign the color of the line.

color='r': red
color='k': black
for more color string, see matplotlib official website

plt.plot(x, y, color = 'r', label = "color = red")
plt.plot(x, y+1, color = 'k', label = 'color = \'k\'')

Line plot: linestyle

You can use 'linestyle' argument to change the look of the line.

linestyle='-': (default) solid line
linestyle='--': dashed line
linestyle=':': dotted line
linestyle='': do not plot line

plt.plot(x, y, linestyle='--', label="linestyle='--'")
plt.plot(x, y+1, linestyle=':', label='linestyle=\':\'')
plt.plot(x, y+2, linestyle='', marker='.', label='linestyle=\'\', marker=\'.\'')

Line plot: marker

You can use 'marker' argument to label the data point on the line.

marker='.': dot
marker='+': +
marker='o': larger dot
marker='': (default) no marker
Try argument 'markersize', 'markeredgecolor', 'markerfacecolor', 'markeredgewidth' yourself!

plt.plot(x, y, color='b', marker='.', label='marker=\'.\'')
plt.plot(x, y+1, color='b', marker='+', label='marker=\'+\'')
plt.plot(x, y+2, color='b', marker='o', label='marker=\'o\'')

Line plot: linewidth and transparency

You can use 'linewidth' argument to adjust the width of the line.
You can use 'alpha' argument to adjust the transparency of the line.

plt.plot(x, y, linewidth=4, label='linewidth=4')
plt.plot(x, y+1, 'k', label = 'solid black')
plt.plot(x, y+1, linewidth=10, alpha = 0.5, label='linewidth=10, alpha=0.5')

Towards the complex: object-oriented class

Let's look at these lines:

fig = plt.figure(figsize=(5,4),facecolor= 'white')
ax = fig.add_axes([0,0,1,1])
line, = ax.plot(x,y)
line.set_color('red')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('title')
fig.savefig('line_object.png')

note, there is a "," in line, = ax.plot(x,y)

why? ax.plot() returns a list of line objects.

fig, ax, line are respectively objects of figure, axes and line plots.

add_axes(), plot() and set_color() are respectively attributes / functions of these objects.

🤔why object?

review: Elements of a figure

©️matplotlib cheatsheet

Now you can change attributes of every object on this figure!

💡To modify the attribute, you usually use set_?()

subplots

sometimes we want to plot sultiple subfigures in one figure, this is often done by fig.add_subplots() or plt.subplots() . Now we create a figure with 2 times 3 panels:

fig, axes = plt.subplots(2, 3)    # create subplot
((ax11, ax12, ax13), (ax21, ax22, ax23)) = axes
ax11.plot(x, y)                 # plot on each axes
ax12.plot(x, y+1)
ax13.plot(x, y+2)
ax21.plot(x, y+3)
ax22.plot(x, y+4)
ax23.plot(x, y+5)

The line fig, axes = plt.subplots(2, 3) returns a Figure and a 2x3 array of Axes objects

💡You can also use plt.subplots() when you only want to plot one subplot!

share the same axis

Add argument sharey=True to the subplots() so that every subplot share the same y axis property (like range, scale ...)

fig, axes = plt.subplots(2, 3, sharey=True)
((ax11, ax12, ax13), (ax21, ax22, ax23)) = axes
ax11.plot(x, y)
ax12.plot(x, y+1)
ax13.plot(x, y+2)
ax21.plot(x, y+3)
ax22.plot(x, y+4)
ax23.plot(x, y+5)

💡The same can be done to x-axis with sharex=True

subplots of different layout

add argument width_ratios=[the ratio you allocate] / height_ratios to the subplots() to set the width fraction of each subplot.

fig, axes = plt.subplots(2, 3, width_ratios=[1, 2, 3], height_ratios=[2, 1])
((ax11, ax12, ax13), (ax21, ax22, ax23)) = axes
ax11.plot(x, y)
ax12.plot(x, y+1)
ax13.plot(x, y+2)
ax21.plot(x, y+3)
ax22.plot(x, y+4)
ax23.plot(x, y+5)

💡try plt.subplots_adjust(wsapce =, hspace=,) to adjust the relative position of subplots.

🎯 You are encouraged to investigate more complex layout with functions like G = gridspec(rows,cols,…), ax.inset_axes(extent)... You can find them on the cheatsheet.

a figure with two axis

Sometimes we want to plot 2 quantity with the same invariant in one plot, this can be done with ax.twinx().

fig, ax = plt.subplots(1,1,figsize=(5,4))
ax.plot(x, y)
ax.set_ylabel('label for left y axis')
ax2 = ax.twinx()
ax2.plot(x, -y, color='r', label='plot on right y axis')
ax2.tick_params(labelcolor='r', axis='y')    # red axis label
ax2.set_ylabel('label for right y axis',color='r')
plt.legend()

💡And, ax.twiny() is also available

make reader-friendly figure: step2

©️helong

aspect ratio

Aspect ratio determines the length ratio of unit y axis and x axis.

plt.plot(xcircle, ycircle)
ax = plt.gca()
ax.set_aspect('equal')

Logarithmic axis

sometimes the best scale is not linear

logarithmic axis

ax.set_xscale('log')
ax.set_yscale('log')

💡 Here I use ax.annotate() to put descriptions on the plot.

axis range

sometimes you focus on different scales

ax.set_xlim([0, 1])
ax.set_ylim([0, 1])

Beyond line plots

read data

You may need this in the homework. You can use readline() to read in .txt file.

with open('solar_system_planet.txt', 'r') as file:
    # Read the first line to get the headers
    headers = file.readline().strip().split(',')
    data = []
    for line in file:
        # Read the rest of the lines for data
        row_data = line.strip().split(',')
        # Append the row data to the data list
        data.append(row_data)
print("Headers:", headers)
print("Data:", data)

split the string at a delimiter: string.split(',')
remove something at the beginning or the end: string.strip(' ')
💡 readline() is useful to skip the lines you don't need.
💡 Check the dataset for the proper delimiter ("|", "\t", " ")
💡 This way we read data all as string, you need to transfer them to float.

Use Pandas for .txt/.csv/.xlsx

The library pandas read in data as python dictionary.
You can easily access different quantities by their "position" or "name".

Use Pandas for .txt/.csv/.xlsx

🎯let's try to reproduce the Mass-Radius diagram (data) of solar system objects.

import pandas as pd
data = pd.read_csv('solar_system_planet.txt',delimiter=',')
radius = data['radius']
mass = data['mass']
text = data['object']
fig, ax = plt.subplots(1,1,figsize=(6,4))
ax.scatter(radius,mass,color='tab:blue',s=100,alpha= 0.5)
ax.set_xlabel('radius ($R_\oplus$)')
ax.set_ylabel('mass ($M_\oplus$)')
for i in range(len(text)):
    if (text[i] == 'Venus') or (text[i] == 'Uranus'):
        ax.annotate(text[i], (radius[i], mass[i]), textcoords="offset points", xytext=(0,-12), ha='center')
    else:
        ax.annotate(text[i], (radius[i], mass[i]), textcoords="offset points", xytext=(0,12), ha='center')
ax.set_yscale('log')
ax.set_xscale('log')