说明

  一般做完实验就是画图，实验结果多采用条形图，这里整理下自己的画图模板。图是否丰富是由数据的维度来支撑的，这里按数据维度的大小画出简单条形图与多维并列条形图，注释很详细，直接上代码。
  论文画图一般要求：

• 全英，标题首字母大写，实词大写
• 一般不为彩印，因此要以黑白填充为主

  画图总结，同系列其他文章请浏览：

• 【数据处理】 python 极速极简画图(黑白)——简单条形图、多维并列条形图
• 【数据处理】 python 极速极简画图——频数(率)分布直方图
• 【数据处理】 python 极速极简画图——二维连线、散点图
• 【数据处理】 python 极速极简画图——折线图

简单条形图

  此模板X和Y都是1维的，适用于在横坐标的数据集下，单个方法在单个问题上的表现。

import numpy as np
import matplotlib.pyplot as plt
from pylab import mpl

#如全英图使用：
mpl.rcParams['font.sans-serif']=['Times New Roman'] #设置字体

#如要显示出中文使用：
# plt.rcParams['font.sans-serif']=['SimHei']
# plt.rcParams['axes.unicode_minus'] = False

def draw(x_data,y_data,title,xytitle,is_showval):
    plt.figure()
    bar_width=0.3 #条的宽度
    plt.bar(x=x_data, height=y_data,color="w",edgecolor="k", alpha=0.8, width=bar_width,hatch="\\\\") #画图并填充
    if(is_showval): #选择是否显示数值
        for x, y in enumerate(y_data):
            plt.text(x, y, '%s' % y, ha='center', va='bottom')
    plt.title(title,fontsize = 14)
    plt.xlabel(xytitle[0],fontsize = 14) #横坐标标题
    plt.ylabel(xytitle[1],fontsize = 14) #纵坐标标题
    plt.grid(alpha=0.8,linestyle='-.',axis='y') #增加网格线,axis可以是'x','y','both',默认为both
    # plt.savefig(title+'.jpg', dpi=300) #高清保存
    plt.show()
    
ydata = [0.0060287,0.00605076,0.00569933,0.00567153,0.00595692] 
xdata = [r'$X_'+str(i+1)+'$' for i in range(0,len(ydata))]
xytitle = ['The Meaning of the X-axis /Unit','The Meaning of the Y-axis /Unit']
ptitle = 'The Title of the Picture'
is_showval = 1 #显示数值
draw(xdata,ydata,ptitle,xytitle,is_showval)

  结果展示，Unit表示单位：

多维并列条形图

  此模板X是1维的，Y是多维的，适用于在横坐标的数据集下，多个方法在单个问题上的表现。这里以五个方法为例，经测试，1~5个都是适用的，可以看出，简单条形图只是多维并列条形图的一个特例。

import numpy as np
import matplotlib.pyplot as plt
from pylab import mpl

#如全英图使用：
mpl.rcParams['font.sans-serif']=['Times New Roman'] #设置字体

#如要显示出中文使用：
# plt.rcParams['font.sans-serif']=['SimHei']
# plt.rcParams['axes.unicode_minus'] = False

def draw2(x_data,y_datas,ptitle,labels,xytitle,is_showval,loc):
    fig,ax = plt.subplots()
    bar_width=0.15 #不可随意更改，受图大小,及x、ydata维度限制
    hatch = ['\\\\\\','///','---','|||','***'] #填充
    x = np.arange(len(x_data))
    for i in range(0,len(y_datas)):
        plt.bar(x+i*bar_width,label = labels[i],height=y_datas[i],color="w",edgecolor="k", alpha=0.8, width=bar_width,hatch=hatch[i])
  
    if(is_showval):
        for i in range(0,len(y_datas)):
            for x, y in enumerate(y_datas[i]):
                plt.text(x, y, '%s' % y, ha='center', va='bottom')
                
    plt.xticks(np.arange(len(x_data))+(len(y_datas)/2-0.5)*bar_width,x_data) #横坐标
    plt.tick_params(labelsize=12) #刻度字体大小
    plt.legend(loc=loc,ncol=len(y_datas),columnspacing =0.05,borderpad=0.2) #label样式
    plt.title(ptitle,fontsize = 14)
    plt.xlabel(xytitle[0],fontsize = 14)
    plt.ylabel(xytitle[1],fontsize = 14)
    plt.grid(alpha=0.5,linestyle='-.')
    # plt.savefig(ptitle+'.jpg', dpi=300)
    plt.show()

ydata = np.array([
                  [0.0060287 ,0.01594343,0.06331985,0.02869123,0.0469503 ,0.07628051],
                  [0.00605076,0.01573297,0.06387128,0.02878903,0.04677676,0.07667354],
                  [0.00569933,0.01567336,0.06227836,0.02670255,0.04563465,0.07510068],
                  [0.00567153,0.01423082,0.06031738,0.02584667,0.04421975,0.07164518],
                  [0.00595692,0.0146612 ,0.06123959,0.02661324,0.04608986,0.07424363]
                 ])
xdata = [r'$X_'+str(i+1)+'$' for i in range(0,len(ydata[0]))]
labels = ['M1','M2','M3','M4','M5'] #Method方法
xytitle = ['The Meaning of the X-axis /Unit','The Meaning of the Y-axis /Unit']
ptitle = 'The Title of the Picture'
is_showval = 0 #显示数值
loc = "upper left" #labels显示的位置
draw2(xdata,ydata,ptitle,labels,xytitle,is_showval,loc)