Python scientific drawing code sharing, python scientific drawing

Python scientific drawing code sharing, python scientific drawing

The matplotlib library is mainly used for Python plotting. Matplotlib is a Python 2D drawing library that generates publishing quality-level graphics in various hard copy formats and cross-platform interactive environments.

Here is an ebook for your reference :《Python Chart Drawing: getting started with matplotlib Drawing Library"

Specifically, it is the pylab and pyplot sub-databases. These two databases can meet the basic drawing requirements, and special charts such as bar charts and scatter plots are described separately below.

First, we will give the pylab artifacts: pylab. rcParams. update (params ). This function can adjust almost all attributes of a graph, including but not limited to: coordinate range, axes label font size, xtick, ytick label font size, graph width, and legend font size.

For specific parameters, see the official documentation: http://matplotlib.org/users/customizing.html

First, we will give an example of Python3 plotting.

import matplotlib.pyplot as pltimport matplotlib.pylab as pylabimport scipy.ioimport numpy as npparams={  '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 onplt.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 locationplt.ylabel('Percentage')  # set ystick labelplt.xlabel('Difference') # set xstck label plt.savefig('D:\\commonNeighbors_CDF_snapshots.eps',dpi = 1000,bbox_inches='tight')plt.show()

The display effect is as follows:

There is nothing to say about the code. Here we only talk about the plt. subplot (111) function.

Plt. subplot (111) and plt. subplot (, 1) are equivalent. This means that the area is divided into one row and one column. Currently, the first graph is drawn (sorted from row to column ).

Plt. subplot (211) means to divide the area into two rows and one column. Currently, the first graph is drawn (the first row and the first column ). And so on, as long as there are no more than 10, the comma can be omitted.

Python draws a bar chart. The Code is as follows.

import scipy.ioimport numpy as npimport matplotlib.pylab as pylabimport matplotlib.pyplot as pltimport matplotlib.ticker as mtickparams={  '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 groupswidth = 0.15plt.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 formatteraxes = plt.gca()  # get current axesaxes.yaxis.set_major_formatter(xticks) # set % format to ystick.axes.grid(True)plt.legend(loc="upper right")plt.savefig('D:\\errorRate.eps', format='eps',dpi = 1000,bbox_inches='tight')plt.show()

The result is as follows:

Scatter Plot, mainly the scatter function. Others are similar.

The networkx library is used to draw a network diagram. An example is provided below:

import networkx as nximport pylab as pltg = 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 modulenx.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()

The result is as follows:

Summary

The above is all about the Python science drawing code, and I hope it will help you. If you are interested, you can continue to refer to other related topics on this site. If you have any shortcomings, please leave a message. Thank you for your support!