使用Python 程序來生成可視化圖像，具有明確的表達性。Matplotlib 里的常用類的包含關系為 Figure -> Axes -> (Line2D, Text, etc.)一個Figure對象可以包含多個子圖(Axes)，在matplotlib中用Axes對象表示一個繪圖區域，可以理解為子圖。
demo1:曲線圖和直線圖
#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt t = np.arange(0.0, 1.01, 0.01) s = np.sin(2*2*np.pi*t) plt.fill(t, s*np.exp(-5*t), 'r') plt.grid(True) #保存為PDF格式，也可保存為PNG等圖形格式 plt.savefig('test.png') plt.show()
import numpy as np import matplotlib.pyplot as plt x=np.linspace(0,10,1000) y=np.sin(x) z=np.cos(x**2) plt.figure(figsize=(8,4)) plt.plot(x,y,label='$sin(x)$',color='red',linewidth=2) plt.plot(x,z,'g--',label='$cos(x^2)$',lw=3) plt.xlabel('Time(s)') plt.ylabel('volt') plt.title('First python firgure') plt.ylim(-1.2,1.2) plt.legend() plt.savefig("test.png") plt.show()
import numpy as np import matplotlib.pyplot as plt # A class that will downsample the data and recompute when zoomed. class DataDisplayDownsampler(object): def __init__(self, xdata, ydata): self.origYData = ydata self.origXData = xdata self.ratio = 5 self.delta = xdata[-1] - xdata[0] def downsample(self, xstart, xend): # Very simple downsampling that takes the points within the range # and picks every Nth point mask = (self.origXData > xstart) & (self.origXData < xend) xdata = self.origXData[mask] xdata = xdata[::self.ratio] ydata = self.origYData[mask] ydata = ydata[::self.ratio] return xdata, ydata def update(self, ax): # Update the line lims = ax.viewLim if np.abs(lims.width - self.delta) > 1e-8: self.delta = lims.width xstart, xend = lims.intervalx self.line.set_data(*self.downsample(xstart, xend)) ax.figure.canvas.draw_idle() # Create a signal xdata = np.linspace(16, 365, 365-16) ydata = np.sin(2*np.pi*xdata/153) + np.cos(2*np.pi*xdata/127) d = DataDisplayDownsampler(xdata, ydata) fig, ax = plt.subplots() # Hook up the line d.line, = ax.plot(xdata, ydata, 'o-') ax.set_autoscale_on(False) # Otherwise, infinite loop # Connect for changing the view limits ax.callbacks.connect('xlim_changed', d.update) plt.savefig("test.png") plt.show()
含有鼠標線
import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import Cursor t = np.arange(0.0, 2.0, 0.01) s1 = np.sin(2 * np.pi * t) plt.plot(t, s1) cursor = Cursor(plt.gca(), horizOn=True, color='r', lw=1) plt.savefig("test.png") plt.show()
import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import MultiCursor t = np.arange(0.0, 2.0, 0.01) s1 = np.sin(2*np.pi*t) s2 = np.sin(4*np.pi*t) fig = plt.figure() ax1 = fig.add_subplot(211) ax1.plot(t, s1) ax2 = fig.add_subplot(212, sharex=ax1) ax2.plot(t, s2) multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1) plt.show()
#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt plt.figure(1) # 創建圖表1 plt.figure(2) # 創建圖表2 ax1 = plt.subplot(211) # 在圖表2中創建子圖1 ax2 = plt.subplot(212) # 在圖表2中創建子圖2 x = np.linspace(0, 3, 100) for i in xrange(5): plt.figure(1) #❶ # 選擇圖表1 plt.plot(x, np.exp(i*x/3)) plt.sca(ax1) #❷ # 選擇圖表2的子圖1 plt.plot(x, np.sin(i*x)) plt.sca(ax2) # 選擇圖表2的子圖2 plt.plot(x, np.cos(i*x)) plt.savefig("test.png") plt.show()
#-*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt plt.figure(1) # 創建圖表1 plt.figure(2) # 創建圖表2 ax1 = plt.subplot(211) # 在圖表2中創建子圖1 ax2 = plt.subplot(212) # 在圖表2中創建子圖2 x = np.linspace(0, 3, 100) for i in xrange(5): plt.figure(1) #❶ # 選擇圖表1 plt.plot(x, np.exp(i*x/3)) plt.sca(ax1) #❷ # 選擇圖表2的子圖1 plt.plot(x, np.sin(i*x)) plt.sca(ax2) # 選擇圖表2的子圖2 plt.plot(x, np.cos(i*x)) plt.show()
含有公式：
import numpy as np import matplotlib.pyplot as plt t = np.arange(0.0, 2.0, 0.01) s = np.sin(2*np.pi*t) plt.plot(t,s) plt.title(r'$\alpha_i > \beta_i$', fontsize=20) plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20) plt.text(0.6, 0.6, r'$\mathcal{A}\mathrm{sin}(2 \omega t)$', fontsize=20) plt.xlabel('time (s)') plt.ylabel('volts (mV)') plt.show()
################################# #-*- coding: utf-8 -*- # File name :5.py # Author :kangkangliang # File desc : # Mail :liangkangkang@yahoo.com # Create time :2017-06-11 ################################# #!/usr/bin/env python import os import numpy as np import matplotlib.pyplot as plt import sys x = [1,2,3,4,5,6,7,8,9,10] y = [1,2,3,4,5,6,7,8,9,10] plt.plot(x,y) plt.text(2, 8, r"$ \mu \alpha \tau \pi \lambda \omega \tau \lambda \iota \beta $",fontsize=20); plt.text(2, 6, r"$ \lim_{x \rightarrow 0} \frac{1}{x} $",fontsize=20); plt.text(2, 4, r"$ a \ \leq \ b \ \leq \ c \ \Rightarrow \ a \ \leq \ c$",fontsize=20); plt.text(2, 2, r"$ \sum_{i=1}^{\infty}\ x_i^2$",fontsize=20); plt.text(4, 8, r"$ \sin(0) = \cos(\frac{\pi}{2})$",fontsize=20); plt.text(4, 6, r"$ \sqrt[3]{x} = \sqrt{y}$",fontsize=20); plt.text(4, 4, r"$ \neg (a \wedge b) \Leftrightarrow \neg a \vee \neg b$",fontsize=20); plt.text(4, 2, r"$ \int_a^b f(x)dx$",fontsize=20); plt.xlabel('time (s)') plt.ylabel('volts (mV)') plt.show()
import numpy as np import pylab as pl data = np.loadtxt('testdata.txt') # plot the first column as x, and second column as y pl.plot(data[:,0], data[:,1], 'ro') pl.xlabel('x') pl.ylabel('y') pl.xlim(0.0, 10.) pl.show()
import numpy as np import matplotlib.pyplot as plt x1 = [1, 2, 3, 4, 5]# Make x, y arrays for each graph y1 = [1, 4, 9, 16, 25] x2 = [1, 2, 4, 6, 8] y2 = [2, 4, 8, 12, 16] pltot1 = plt.plot(x1, y1, 'r')# use pylab to plot x and y : Give your plots names pltot2 = plt.plot(x2, y2, 'go') plt.title('Plot of y vs. x')# give plot a title plt.xlabel('x axis')# make axis labels plt.ylabel('y axis') plt.xlim(0.0, 9.0)# set axis limits plt.ylim(0.0, 30.) # plt.legend([plot1, plot2], ('red line', 'green circles'), 'best', numpoints=1)# make legend plt.legend(('red line','green circles')) plt.show()# show the plot on the screen
import numpy as np import pylab as plt x1 = [1, 2, 3, 4, 5]# Make x, y arrays for each graph y1 = [1, 4, 9, 16, 25] x2 = [1, 2, 4, 6, 8] y2 = [2, 4, 8, 12, 16] plt.plot(x1, y1, 'r')# use pylab to plot x and y plt.plot(x2, y2, 'g') plt.title('Plot of y vs. x')# give plot a title plt.xlabel('x axis')# make axis labels plt.ylabel('y axis') plt.xlim(0.0, 9.0)# set axis limits plt.ylim(0.0, 30.) plt.show()# show the plot on the screen
################################# #-*- coding: utf-8 -*- # File name :demo.py # Author :kangkangliang # File desc : # Mail :liangkangkang@yahoo.com # Create time :2017-06-11 ################################# #!/usr/bin/env python import os import numpy as np import matplotlib.pyplot as plt import sys x = np.arange(0.0, 10.0, 1.0) y1 = x*2 y2 = x + 2 y3 = x*3 # color rgbyck # b blue # r green # r red # c cyan # m magenta # y yellow # k black # white # 散點圖 plt.plot(x,y1,'or') # 折線圖 plt.plot(x,y2) # 虛線 plt.plot(x,y3,'--') plt.show()
import matplotlib.pyplot as plt X1 = range(0, 50) Y1 = [num**2 for num in X1] # y = x^2 X2 = [0, 1] Y2 = [0, 1] # y = x Fig = plt.figure(figsize=(8,4)) # Create a `figure' instance Ax = Fig.add_subplot(111) # Create a `axes' instance in the figure Ax.plot(X1, Y1, X2, Y2) # Create a Line2D instance in the axes Fig.show() Fig.savefig("test.png"
import numpy as np import pylab as pl x = [1, 2, 3, 4, 5]# Make an array of x values y = [1, 4, 9, 16, 25]# Make an array of y values for each x value pl.plot(x, y)# use pylab to plot x and y pl.show()# show the plot on the screen
#!/usr/bin/env python import matplotlib.pyplot as plt plt.plot([10, 20, 30]) plt.xlabel('times') plt.ylabel('numbers') plt.savefig("test.png") plt.show()
import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties font = FontProperties() plt.figure() plt.title('this is title') plt.xlabel('x label') plt.ylabel('y label') plt.axis([0, 25, 0, 25]) plt.grid(True) x = [[1],[2],[3],[4],[5],[6]] y = [[1],[2.1],[2.9],[4.2],[5.1],[5.8]] plt.plot(x, y, 'k.') plt.savefig('test.png') plt.show()
from math import pi from numpy import cos, sin from matplotlib import pyplot as plt if __name__ == '__main__': '''''plot data margin''' angles_circle = [i*pi/180 for i in range(0,360)] #i先轉換成double #angles_circle = [i/np.pi for i in np.arange(0,360)] # <=> # angles_circle = [i/180*pi for i in np.arange(0,360)] X x = cos(angles_circle) y = sin(angles_circle) plt.plot(x, y, 'r') plt.axis('equal') plt.axis('scaled') plt.savefig("test.png") plt.show()
import numpy as np import matplotlib.pyplot as plt N = 50 x = np.random.rand(N) y = np.random.rand(N) area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses color = 2 * np.pi * np.random.rand(N) plt.scatter(x, y, s=area, c=color, alpha=0.5, cmap=plt.cm.hsv) plt.savefig("test.png") plt.show()
多個圖：
import numpy as np import matplotlib.pyplot as plt t = np.arange(-1, 2, .01) s = np.sin(2 * np.pi * t) plt.plot(t,s) # draw a thick red hline at y=0 that spans the xrange l = plt.axhline(linewidth=4, color='r') plt.axis([-1, 2, -1, 2]) plt.show() plt.close() # draw a default hline at y=1 that spans the xrange plt.plot(t,s) l = plt.axhline(y=1, color='b') plt.axis([-1, 2, -1, 2]) plt.show() plt.close() # draw a thick blue vline at x=0 that spans the upper quadrant of the yrange plt.plot(t,s) l = plt.axvline(x=0, ymin=0, linewidth=4, color='b') plt.axis([-1, 2, -1, 2]) plt.show() plt.close() # draw a default hline at y=.5 that spans the the middle half of the axes plt.plot(t,s) l = plt.axhline(y=.5, xmin=0.25, xmax=0.75) plt.axis([-1, 2, -1, 2]) plt.show() plt.close() plt.plot(t,s) p = plt.axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5) p = plt.axvspan(1.25, 1.55, facecolor='g', alpha=0.5) plt.axis([-1, 2, -1, 2]) # plt.show() # plt.savefig('test.png')
對數圖：
import numpy as np import matplotlib.pyplot as plt w = np.linspace(0.1, 1000, 1000) p = np.abs(1/(1+0.1j*w)) plt.subplot(221) plt.plot(w, p, linewidth=2) plt.ylim(0,1.5) plt.subplot(222) plt.semilogx(w, p, linewidth=2) plt.ylim(0,1.5) plt.subplot(223) plt.semilogy(w, p, linewidth=2) plt.ylim(0,1.5) plt.subplot(224) plt.loglog(w, p, linewidth=2) plt.ylim(0,1.5) plt.savefig("test.png") plt.show()
import matplotlib.pyplot as plt import matplotlib.pylab as pylab import scipy.io import numpy as np # params={ # 'axes.labelsize': '35', # 'xtick.labelsize':'27', # 'ytick.labelsize':'27', # 'lines.linewidth':2 , # 'legend.fontsize': '27', # 'figure.figsize' : '12, 9' # set figure size # } # pylab.rcParams.update(params) #set figure parameter line_styles=['ro-','b^-','gs-','ro--','b^--','gs--'] #set line style #We give the coordinate date directly to give an example. x1 = [-20,-15,-10,-5,0,0,5,10,15,20] y1 = [0,0.04,0.1,0.21,0.39,0.74,0.78,0.80,0.82,0.85] y2 = [0,0.014,0.03,0.16,0.37,0.78,0.81,0.83,0.86,0.92] y3 = [0,0.001,0.02,0.14,0.34,0.77,0.82,0.85,0.90,0.96] y4 = [0,0,0.02,0.12,0.32,0.77,0.83,0.87,0.93,0.98] y5 = [0,0,0.02,0.11,0.32,0.77,0.82,0.90,0.95,1] plt.plot(x1,y1,'bo-',label='m=2, p=10%',markersize=20) # in 'bo-', b is blue, o is O marker, - is solid line and so on plt.plot(x1,y2,'gv-',label='m=4, p=10%',markersize=20) plt.plot(x1,y3,'ys-',label='m=6, p=10%',markersize=20) plt.plot(x1,y4,'ch-',label='m=8, p=10%',markersize=20) plt.plot(x1,y5,'mD-',label='m=10, p=10%',markersize=20) fig1 = plt.figure(1) axes = plt.subplot(111) #axes = plt.gca() axes.set_yticks([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]) axes.grid(True) # add grid plt.legend(loc="lower right") #set legend location plt.ylabel('Percentage') # set ystick label plt.xlabel('Difference') # set xstck label plt.savefig('test.png',dpi = 1000,bbox_inches='tight') plt.show()
demo2:餅圖
#!/usr/bin/env python # -*- coding: utf-8 -*- from pylab import * # make a square figure and axes figure(1, figsize=(6,6)) ax = axes([0.1, 0.1, 0.8, 0.8]) labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' fracs = [15,30,45, 10] explode=(0, 0.05, 0, 0) pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True) title('Raining Hogs and Dogs', bbox={'facecolor':'0.8', 'pad':5}) savefig('pie.png') show()
import matplotlib.pyplot as plt for idx,color in enumerate('rgbyck'): plt.subplot(321+idx,axisbg=color) plt.savefig("test.png") plt.show()
demo3:柱狀圖
import scipy.io import numpy as np import matplotlib.pylab as pylab import matplotlib.pyplot as plt import matplotlib.ticker as mtick # params={ # 'axes.labelsize': '35', # 'xtick.labelsize':'27', # 'ytick.labelsize':'27', # 'lines.linewidth':2 , # 'legend.fontsize': '27', # 'figure.figsize' : '24, 9' # } # pylab.rcParams.update(params) y1 = [9.79,7.25,7.24,4.78,4.20] y2 = [5.88,4.55,4.25,3.78,3.92] y3 = [4.69,4.04,3.84,3.85,4.0] y4 = [4.45,3.96,3.82,3.80,3.79] y5 = [3.82,3.89,3.89,3.78,3.77] ind = np.arange(5) # the x locations for the groups width = 0.15 plt.bar(ind,y1,width,color = 'blue',label = 'm=2') plt.bar(ind+width,y2,width,color = 'g',label = 'm=4') # ind+width adjusts the left start location of the bar. plt.bar(ind+2*width,y3,width,color = 'c',label = 'm=6') plt.bar(ind+3*width,y4,width,color = 'r',label = 'm=8') plt.bar(ind+4*width,y5,width,color = 'm',label = 'm=10') plt.xticks(np.arange(5) + 2.5*width, ('10%','15%','20%','25%','30%')) plt.xlabel('Sample percentage') plt.ylabel('Error rate') fmt = '%.0f%%' # Format you want the ticks, e.g. '40%' xticks = mtick.FormatStrFormatter(fmt) # Set the formatter axes = plt.gca() # get current axes axes.yaxis.set_major_formatter(xticks) # set % format to ystick. axes.grid(True) plt.legend(loc="upper right") plt.savefig('test.png', format='png',dpi = 1000,bbox_inches='tight') plt.show()
import numpy as np import matplotlib.pyplot as plt # make an array of random numbers with a gaussian distribution with # mean = 5.0 # rms = 3.0 # number of points = 1000 data = np.random.normal(5.0, 3.0, 1000) # make a histogram of the data array plt.hist(data) # make plot labels plt.xlabel('data') plt.show()
去掉中間的黑線
#-*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt # make an array of random numbers with a gaussian distribution with # mean = 5.0 # rms = 3.0 # number of points = 1000 data = np.random.normal(5.0, 3.0, 1000) # make a histogram of the data array # pl.hist(data) plt.hist(data,histtype='stepfilled') # make plot labels plt.xlabel('data') plt.show()
#-*- coding: utf-8 -*- import numpy as np import pylab as pl # make an array of random numbers with a gaussian distribution with # mean = 5.0 # rms = 3.0 # number of points = 1000 data = np.random.normal(5.0, 3.0, 1000) # make a histogram of the data array bins = np.arange(-5., 16., 1.) #浮點數版本的range pl.hist(data, bins, histtype='stepfilled') # pl.hist(data) # make plot labels pl.xlabel('data') pl.show()
demo4:網狀圖
import networkx as nx import pylab as plt g = nx.Graph() g.add_edge(1,2,weight = 4) g.add_edge(1,3,weight = 7) g.add_edge(1,4,weight = 8) g.add_edge(1,5,weight = 3) g.add_edge(1,9,weight = 3) g.add_edge(1,6,weight = 6) g.add_edge(6,7,weight = 7) g.add_edge(6,8,weight = 7) g.add_edge(6,9,weight = 6) g.add_edge(9,10,weight = 7) g.add_edge(9,11,weight = 6) fixed_pos = {1:(1,1),2:(0.7,2.2),3:(0,1.8),4:(1.6,2.3),5:(2,0.8),6:(-0.6,-0.6),7:(-1.3,0.8), 8:(-1.5,-1), 9:(0.5,-1.5), 10:(1.7,-0.8), 11:(1.5,-2.3)} #set fixed layout location #pos=nx.spring_layout(g) # or you can use other layout set in the module nx.draw_networkx_nodes(g,pos = fixed_pos,nodelist=[1,2,3,4,5], node_color = 'g',node_size = 600) nx.draw_networkx_edges(g,pos = fixed_pos,edgelist=[(1,2),(1,3),(1,4),(1,5),(1,9)],edge_color='g',width = [4.0,4.0,4.0,4.0,4.0],label = [1,2,3,4,5],node_size = 600) nx.draw_networkx_nodes(g,pos = fixed_pos,nodelist=[6,7,8], node_color = 'r',node_size = 600) nx.draw_networkx_edges(g,pos = fixed_pos,edgelist=[(6,7),(6,8),(1,6)],width = [4.0,4.0,4.0],edge_color='r',node_size = 600) nx.draw_networkx_nodes(g,pos = fixed_pos,nodelist=[9,10,11], node_color = 'b',node_size = 600) nx.draw_networkx_edges(g,pos = fixed_pos,edgelist=[(6,9),(9,10),(9,11)],width = [4.0,4.0,4.0],edge_color='b',node_size = 600) plt.text(fixed_pos[1][0],fixed_pos[1][1]+0.2, s = '1',fontsize = 40) plt.text(fixed_pos[2][0],fixed_pos[2][1]+0.2, s = '2',fontsize = 40) plt.text(fixed_pos[3][0],fixed_pos[3][1]+0.2, s = '3',fontsize = 40) plt.text(fixed_pos[4][0],fixed_pos[4][1]+0.2, s = '4',fontsize = 40) plt.text(fixed_pos[5][0],fixed_pos[5][1]+0.2, s = '5',fontsize = 40) plt.text(fixed_pos[6][0],fixed_pos[6][1]+0.2, s = '6',fontsize = 40) plt.text(fixed_pos[7][0],fixed_pos[7][1]+0.2, s = '7',fontsize = 40) plt.text(fixed_pos[8][0],fixed_pos[8][1]+0.2, s = '8',fontsize = 40) plt.text(fixed_pos[9][0],fixed_pos[9][1]+0.2, s = '9',fontsize = 40) plt.text(fixed_pos[10][0],fixed_pos[10][1]+0.2, s = '10',fontsize = 40) plt.text(fixed_pos[11][0],fixed_pos[11][1]+0.2, s = '11',fontsize = 40) plt.show()
從文件中讀取數據來畫圖
cat testdata.txt
0.0 0.0 1.0 1.0 2.0 4.0 3.0 9.0 4.0 16.0 5.0 25.0 6.0 36.0 7.0 49.0 8.0 64.0 9.0 81.0
import numpy as np import matplotlib.pyplot as plt # Use numpy to load the data contained in the file # testdata.txt into a 2-D array called data data = np.loadtxt('testdata.txt') # plot the first column as x, and second column as y plt.plot(data[:,0], data[:,1], 'ro') plt.xlabel('x') plt.ylabel('y') plt.xlim(0.0, 10.) plt.show()
寫入數據到文件
import numpy as np # Let's make 2 arrays (x, y) which we will write to a file # x is an array containing numbers 0 to 10, with intervals of 1 x = np.arange(0.0, 10., 1.) # y is an array containing the values in x, squared y = x*x print 'x = ', x print 'y = ', y # Now open a file to write the data to # 'w' means open for 'writing' file = open('testdata.txt', 'w') # loop over each line you want to write to file for i in range(len(x)): # make a string for each line you want to write # 'str()' means you are converting the quantity in brackets to a string type txt = str(x[i]) + '\t' + str(y[i]) + ' \n' # write the txt to the file file.write(txt) # Close your file file.close()
參考：
http://hyry.dip.jp/tech/book/page/scipy/matplotlib_fast_plot.html
http://reverland.org/python/2012/09/07/matplotlib-tutorial/
matplotlib中文翻譯
http://hyry.dip.jp/tech/book/page/scipy/matplotlib.html