matplotlib graphics window
Drawing objects (drawing window)
- Subgraph;
- Scale locator;
- Scale grid line;
- Semi logarithmic coordinates;
1. Drawing objects (drawing window)
matplotlib API for creating windows
# Manually create the matplotlib window plt.figure( title='title', # Window title bar text figsize=(4, 3), # Window size (tuple) dpi=120, # Pixel density facecolor='' # Chart background color ) plt.show()
The plt.figure method can build a new window. If the window with title='AAA 'has been built, PLT will not create a new window, but set the window with title='AAA' as the window of the current operation.
import matplotlib.pyplot as plt plt.figure('A figure',facecolor='gray') # facecolor sets the window or palette to grayscale plt.plot([0,1],[1,2]) plt.figure('B figure',facecolor='lightgray') # facecolor sets the window or palette to light gray plt.plot([1,2],[2,1]) # If the title in figure has been created, no new window will be created # Instead, the old window (with the same window title) is set as the current window plt.figure('A figure',facecolor='gray') # Subsequent operations are also performed for 'A figure' plt.plot([1,2],[2,1]) plt.show()
It can be seen from the displayed drawing results that a straight line with (1,2) and (2,1) as the endpoint is added in the A figure window.
2. Sets the parameters of the current window
From the image drawn above, we find that it lacks the title description, the scale value of the coordinate axis is too small and unclear, and there is no necessary text annotation of the coordinate axis. Therefore, the following methods are introduced:
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Title of chart: plt.title()
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Axis text: plt.xlabel() and plt.ylabel()
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Tick mark parameter: plt.tick_params
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Chart gridlines: plt.grid()
# Set the chart title, which is displayed above the chart plt.title(title, fontsize=12) # Sets the text for the horizontal axis plt.xlabel(x_label_str, fontsize=12) # Sets the text for the vertical axis plt.ylabel(y_label_str, fontsize=12) # Set the scale parameter and labelsize set the scale font size plt.tick_params(labelsize=8) # Set chart grid linestyle sets the style of the gridlines """ 1. - or solid Thick line 2. -- or dashed Dotted line 3. -. or dashdot Dotted line 4. : or dotted Point line """ plt.grid(linestyle='') # Set the compact layout and display the chart related parameters in the window plt.tight_layout()
Case: sinusoidal function y = s i n x y=sinx y=sinx as an example, test the relevant parameters of the window;
import matplotlib.pyplot as plt import numpy as np # Create a new window titled sinx figure plt.figure('Sinx figure') xdata = np.linspace(-2 * np.pi, 2 * np.pi, 100) ydata = np.sin(xdata) plt.plot(xdata, ydata, linewidth=2, alpha=0.9, color='dodgerblue') plt.title(r'$y=sin(x)$', fontsize=18) plt.xlabel('time', fontsize=16) plt.ylabel('price', fontsize=16) plt.grid(linestyle=':') plt.tick_params(labelsize=14) plt.tight_layout() plt.savefig('sinx3.jpg') plt.show()
What if you need to display Chinese fonts in the image drawn by matplotlib?
In the matplotlib drawing function, the display of Chinese Fonts is not supported by default, so you need to set it manually.
- Global font settings
import matplotlib.pyplot as plt # Global font settings ## Step 1: replace the default sans serif font plt.rcParams['font.sans-serif'] = ['KaiTi'] ## Step 2: solve the problem of displaying the negative sign of the negative number of the coordinate axis plt.rcParams['axes.unicode_minus'] = False xdata = np.linspace(0, 100, 1000) ydata = -(xdata - 50) ** 2 + 5050 plt.plot(xdata, ydata, linewidth=2, alpha=0.9, color='dodgerblue') plt.title("Relationship between speed and traffic flow",fontsize=16) plt.xlabel("x axis",fontsize=16) plt.ylabel("y axis",fontsize=16) plt.savefig('fig2.png') plt.show()
- Local font settings
import matplotlib.pyplot as plt # Local font setting. The font of title and coordinate axis can be set separately plt.xlabel("x axis", fontproperties="SimSun") plt.ylabel('y axis', fontproperties="SimSun") plt.title("title", fontproperties="SimHei") plt.show()
English names of some commonly used Chinese fonts (of course, the font style of the local system can also be used):
Chinese fontEnglish nameChinese fontEnglish nameSong typefaceSimSunChinese regular scriptSTKaitiBlackbodySimHeiChinese Song typefaceSTSongMicrosoft YaHei Microsoft YaHeiChinese imitation Song DynastySTFangsongRegular scriptKaiTiMicrosoft JhengHei Microsoft JhengHeiImitation Song DynastyFangSongFine bright bodyMingLiU3. Subgraph layout
API related to drawing matrix layout (common): plot.subplot (rows, cols, Num), where rows represents the number of rows, cols represents the number of columns, and num represents the number of subgraphs.
# The chart background color is set to bright gray plt.figure('subplot Layout', facecolor='lightgray') """ plt.subplot(rows, cols, num) rows: Number of rows cols: Number of columns num: number """ # Operate the subgraph numbered 5 in the matrix with 3 rows and 3 columns plt.subplot(3, 3, 5) # 1 2 3 # 4 5 6 # 7 8 9 plt.subplot(335) # Abbreviation
Case: draw a nine palace lattice matrix subgraph, and write a number in each subgraph.
import numpy as np import matplotlib.pyplot as plt # Create a window plt.figure('subplot', facecolor='lightgray') for i in range(1, 10): plt.subplot(3, 3, i) # Select subgraph plt.text(0.5, 0.5, str(i), ha='center', va='center',size=36,alpha=0.6) plt.xticks([]) plt.yticks([]) plt.tight_layout() plt.show()
Through figure.add_aubplot() draws subgraphs
import numpy as np import pandas as pd import matplotlib.pyplot as plt # Create a canvas or window fig = plt.figure(figsize=(10, 8)) # Sub Figure 1 x = np.linspace(1, 10,100) ax1 = fig.add_subplot(221) ax1.plot(x, x ** 2) # Subgraph 2 ax2 = fig.add_subplot(222) ax2.scatter(x, np.random.rand(100), s=10) ax2.set_xlim(0, 12) ax2.set_ylim(0, 1.2) ax2.spines['top'].set_color('none') # Indicates that the top axis is not displayed ax2.spines['right'].set_color('none') # Sub Figure 3 ax3 = fig.add_subplot(223) ax3.plot(x, np.log(x)) # How to add axis dimensions and titles to the subgraphs # Operate the window parameters of the subgraph through ax3 ax3.set_title(r"$y=log(x)$") ax3.set_xlabel("time") ax3.set_ylabel("price") ax3.legend() # Sub Figure 4 ax4 = fig.add_subplot(224) ax4.plot(x, np.sin(x)) plt.show()
The subgraph is drawn through plt.subplots(). The type of the value returned by subplots() is tuple, which contains two elements: the first is a canvas or window, and the second is the list of subgraphs;
import numpy as np import pandas as pd import matplotlib.pyplot as plt fig, axes = plt.subplots(2, 2) # Line chart x1 = np.arange(1, 100) y1 = x1 ** 2 axes[0][0].plot(x1, x1 ** 2) # Scatter diagram x2 = np.arange(0, 10) y2 = np.random.rand(10) axes[0][1].scatter(x2, y2) # Pie chart x3 = [15,30,45,10] # The cumulative sum is 100 axes[1][0].pie(x3, labels=list('ABCD'), autopct='%.0f', explode=[0,0.05,0,0]) # Bar chart x4 = ['A', 'B', 'C', 'D', 'E'] y4 = [25, 15, 35, 30, 20] axes[1][1].bar(x4, y4, color='b') plt.show()
4. Save the drawn image
Image storage formats supported by matplotlib: eps, jpeg, jpg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff.
The plt.savefig() function is used to save the drawn image. The parameters are as follows:
- filename: the name or path to save the picture file; required;
- Figsize: save the size of the output image, in inches, for example: figsize=[8, 8];
- dpi: the resolution of image saving (the higher the resolution, the clearer the image). The unit is pixel. Generally, it is set as dpi=600;
import numpy as np import matplotlib.pyplot as plt x = np.linspace(-np.pi, np.pi, 1000) sinx = np.sin(x) cosx = 1/2 * np.cos(x) plt.plot(x, sinx, linestyle='-.', linewidth=2, color='dodgerblue', label=r'$y=sin(x)$') plt.plot(x, cosx, linestyle='--', linewidth=2, color='orangered', label=r'$y=\fraccos(x)$') plt.legend(loc='upper left') # Adjust the dpi resolution if the image is not clear enough plt.savefig('figC.jpg', figsize=[8, 8], dpi=600) plt.show()
5. Scale locator
API related to scale locator:
# Gets the current axis ax = plt.gca() # Sets the major scale locator for the horizontal axis ax.xaxis.set_major_locator(plt.NullLocator(1)) # Set the sub scale locator of the horizontal coordinate axis as a multi-point locator with an interval of 0.1 ax.xaxis.set_minor_locator(plt.MultipleLocator(0,1))
Case: draw a number axis;
import matplotlib.pyplot as plt plt.figure(figsize=(8, 6), dpi=100) # Gets the current axis plt.xlim(0, 10) ax = plt.gca() # Hide all axes except the bottom axis ax.spines['left'].set_color('none') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') # Adjust the bottom coordinate axis to the center of the sub drawing ax.spines['bottom'].set_position(('data',0)) plt.yticks([]) # Sets the locator for the major scale of the horizontal axis ax.xaxis.set_major_locator(plt.MultipleLocator(1)) # Set the sub scaler of the horizontal coordinate axis as a multipoint locator with an interval of 0.1 ax.xaxis.set_minor_locator(plt.MultipleLocator(0.2)) # Mark the scale locator class name used plt.text(5, 0.3,s='MultipleLocator', ha='center', size=14) plt.show()
6. Scale grid line
API for drawing scale gridlines
ax = plt.gca() # Draw scale gridlines ax.grid( which='', # Set major / minor. The default is major axis='', # x / y / both, the default is both linewidth=1, # line width linestyle='' # Line type color='', # Color, gray by default alpha=0.5 # transparency )
import matplotlib.pyplot as plt import numpy as np plt.figure('Grid Line',figsize=(8, 6)) x = np.linspace(-2 * np.pi, 2 * np.pi, 100) y = np.cos(x) plt.plot(x, y, linestyle='--', color='dodgerblue',linewidth=1.5) plt.title(r"$y=cos(x)$",fontsize=16) plt.xlabel("x", fontsize=16) plt.ylabel("y", fontsize=16) plt.grid(linestyle=":") # Use the default parameter major/both plt.show()
Comprehensive case: draw the curve [1, 10, 100, 1000, 100, 10, 1], then set the scale grid line and test the parameters of the scale grid line.
import matplotlib.pyplot as plt plt.figure('Grid Line',figsize=(8, 6)) ax = plt.gca() # Modify the scale locator for x and y ax.xaxis.set_major_locator(plt.MultipleLocator(1)) ax.xaxis.set_minor_locator(plt.MultipleLocator(0.2)) ax.yaxis.set_major_locator(plt.MultipleLocator(250)) ax.yaxis.set_minor_locator(plt.MultipleLocator(50)) # Draw gridlines ax.grid(which='major', axis='both', color='orangered',linewidth=0.5) ax.grid(which='minor', axis='both', color='orangered',linewidth=0.1) # draw a curve y = [1, 10, 100, 1000, 100, 10, 1] plt.plot(y, 'o-', color='dodgerblue',linewidth=0.75) plt.savefig("fig1.png") plt.show()
**Semilogarithmic coordinates: * * y axis will increase exponentially, i.e.: plt.semilogy().
Draw the second subgraph based on semi logarithmic coordinates to represent the curve: [1, 10, 100, 1000, 100, 10, 1].
import matplotlib.pyplot as plt plt.figure('Grid Line',figsize=(8, 6)) ax = plt.gca() # Modify the scale locator for x and y ax.xaxis.set_major_locator(plt.MultipleLocator(1)) ax.xaxis.set_minor_locator(plt.MultipleLocator(0.2)) ax.yaxis.set_major_locator(plt.MultipleLocator(250)) ax.yaxis.set_minor_locator(plt.MultipleLocator(50)) ax.grid(which='major', axis='both', color='orangered',linewidth=0.5) ax.grid(which='minor', axis='both', color='orangered',linewidth=0.1) # draw a curve x = [1, 2, 3, 4, 5, 6, 7] y = [1, 10, 100, 1000, 100, 10, 1] plt.semilogy(x, y, 'o-', color='dodgerblue',linewidth=0.75) plt.savefig("fig3.png") plt.show()
