Python matplotib 画图
使用Python 程序来生成可视化图像,具有明确的表达性。Matplotlib 里的常用类的包含关系为 Figure -> Axes -> (Line2D, Text, etc.)
一个Figure对象可以包含多个子图(Axes),在matplotlib中用Axes对象表示一个绘图区域,可以理解为子图。
demo1:曲线图和直线图
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#!/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) #保存为PDF格式,也可保存为PNG等图形格式 plt.savefig('test.png') plt.show()
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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()
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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()
含有鼠标线
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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()
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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()
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#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt plt.figure(1) # 创建图表1 plt.figure(2) # 创建图表2 ax1 = plt.subplot(211) # 在图表2中创建子图1 ax2 = plt.subplot(212) # 在图表2中创建子图2 x = np.linspace(0, 3, 100) for i in xrange(5): plt.figure(1) #❶ # 选择图表1 plt.plot(x, np.exp(i*x/3)) plt.sca(ax1) #❷ # 选择图表2的子图1 plt.plot(x, np.sin(i*x)) plt.sca(ax2) # 选择图表2的子图2 plt.plot(x, np.cos(i*x)) plt.savefig("test.png") plt.show()
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#-*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt plt.figure(1) # 创建图表1 plt.figure(2) # 创建图表2 ax1 = plt.subplot(211) # 在图表2中创建子图1 ax2 = plt.subplot(212) # 在图表2中创建子图2 x = np.linspace(0, 3, 100) for i in xrange(5): plt.figure(1) #❶ # 选择图表1 plt.plot(x, np.exp(i*x/3)) plt.sca(ax1) #❷ # 选择图表2的子图1 plt.plot(x, np.sin(i*x)) plt.sca(ax2) # 选择图表2的子图2 plt.plot(x, np.cos(i*x)) plt.show()
含有公式:
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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()
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################################# #-*- 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()
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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()
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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
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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
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################################# #-*- 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 # 散点图 plt.plot(x,y1,'or') # 折线图 plt.plot(x,y2) # 虚线 plt.plot(x,y3,'--') plt.show()
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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"
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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
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#!/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()
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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()
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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先转换成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()
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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()
多个图:
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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')
对数图:
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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()
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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:饼图
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#!/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()
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import matplotlib.pyplot as plt for idx,color in enumerate('rgbyck'): plt.subplot(321+idx,axisbg=color) plt.savefig("test.png") plt.show()
demo3:柱状图
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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()
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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()
去掉中间的黑线
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#-*- 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()
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#-*- 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.) #浮点数版本的range pl.hist(data, bins, histtype='stepfilled') # pl.hist(data) # make plot labels pl.xlabel('data') pl.show()
demo4:网状图
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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()
从文件中读取数据来画图
cat testdata.txt
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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
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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()
写入数据到文件
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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()
参考:
http://hyry.dip.jp/tech/book/page/scipy/matplotlib_fast_plot.html
http://reverland.org/python/2012/09/07/matplotlib-tutorial/
matplotlib中文翻译
http://hyry.dip.jp/tech/book/page/scipy/matplotlib.html