matplotlib-柱状图-bar

#encoding:utf-8
"""
柱状图，bar，参数解析
（plt.barh：水平柱状图，plt.bar:竖直柱状图）
width:柱的宽度
facecolor：柱状图里填充的颜色
edgecolor：边框的颜色，默认不加边框
"""
import matplotlib.pyplot as plt
import numpy as np
import mat4py
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"C:\\WINDOWS\\Fonts\\simhei.ttf")

#添加errorbar,
def AddErrorbar(x,mean_s,std_s):
    plt.errorbar(x,mean_s, yerr=std_s,linewidth=1.5,fmt=" ",ecolor='k',capsize=3.5)

# 求两组数据均值加方差的最大值，以此来确定与 ylim
def Mean_Add_Std(data_s1_mean,single_para_inn_std,single_para_lie_mean,single_para_lie_std):
    max_mean_std_1 = np.max(data_s1_mean) + np.max(single_para_inn_std)
    max_mean_std_2 = np.max(single_para_lie_mean) + np.max(single_para_lie_std)
    if max_mean_std_1 >= max_mean_std_2:
        max_mean_std = max_mean_std_1
    else:
        max_mean_std = max_mean_std_2
    return max_mean_std

def LoadData(single_para_inn_mean, single_para_inn_std,single_para_lie_mean,single_para_lie_std,data_mark_s):#
    width = 0.4
    mean_size = np.size(single_para_inn_mean)
    # 添加bar
    x1 = np.arange(mean_size)
    plt.bar(x1, single_para_inn_mean,width=width,
             color='#3a2efe') #0339f8   #55A868
    # plt.show()
    x2 = x1 + width
    plt.bar(x2, single_para_lie_mean, width=width,facecolor='#f36196', edgecolor='')
    xticks_label = ['Cw', 'Lw', 'r']#('δ', 'θ', 'α', 'β')  #  '$E_{glob}$'
    xticks_position=x1+ width/2
    plt.xticks(xticks_position, data_mark_s, fontproperties='Times New Roman',
               fontsize=6)
    plt.yticks(fontsize=10)

    # 求两组数据均值加方差的最大值，以此来确定与 ylim
    max_mean_std = Mean_Add_Std(single_para_inn_mean, single_para_inn_std, single_para_lie_mean, single_para_lie_std)
    plt.ylim(0,max_mean_std+max_mean_std/8)
    plt.legend(('Innocent', 'Guilty'), loc='upper right', ncol=2, fontsize=8, frameon=False)  # bbox_to_anchor=(1.4,0.0)

    #添加errorbar
    AddErrorbar(x1, single_para_inn_mean, single_para_inn_std)
    AddErrorbar(x2, single_para_lie_mean, single_para_lie_std)

#主函数
if __name__== '__main__':

    # 数据文件夹路径
    path_1 = '.\\fscore_feature'

    frequency_name = ['Delta', 'Theta', 'Alpha', 'Beta']  # 四个频段
    frequency_name_num = np.size(frequency_name)  # 频段数
    fig = plt.figure(figsize=(16, 14))  # (3.5,10)

    for i in range(frequency_name_num):
        plt.subplot(2,2,i+1)
        # fig.subplots_adjust(left=0.139, bottom=0.1, right=0.99, top=0.98, wspace=0.25, hspace=0.1)
        fig.subplots_adjust(left=0.02, bottom=0.1, right=0.99, top=0.98, wspace=0.05, hspace=0.1)
        # 保存均值与标准差
        inn_single_para_mean = []
        inn_single_para_std = []
        lie_single_para_mean = []
        lie_single_para_std = []
        single_frequency_mark = []
        inn_lie = ['inn', 'lie']
        # 数据路径
        # inn
        path_2 =path_1+'\\'+inn_lie[0]+'_'+'fscore_feature'+str(i+1)+'.mat'
        # 打印正在处理的数据
        print('Processing ',path_2)
        # 导入数据
        data = mat4py.loadmat(path_2)
        StructName = inn_lie[0] + '_' + 'fscore_feature'
        data_single_frequency = np.transpose(data[StructName])
        row = np.size(data_single_frequency,axis=0)
        print('Frequency_name: %s, Feature Numbers: %d '%(frequency_name[i],row))
        col = np.size(data_single_frequency[0])

        if i == 0:
            row =4
        if i == 1:
            row = 5
        if i == 2:
            row = 7
        if i == 3:
            row = 7

        for j in range(row):
            # 参数循环，求 mean ,std
            single_frequency_para = data_single_frequency[j]
            inn_single_para_mean.append(np.mean(single_frequency_para))
            inn_single_para_std.append(np.std(single_frequency_para))

        # lie
        path_2 = path_1 + '\\' + inn_lie[1] + '_' + 'fscore_feature' + str(i + 1) + '.mat'
        # 打印正在处理的数据
        print('Processing ',path_2)
        # 导入数据
        data = mat4py.loadmat(path_2)
        StructName = inn_lie[1] + '_' + 'fscore_feature'
        data_single_frequency = np.transpose(data[StructName])

        for j in range(row):
            # 参数循环，求 mean ,std
            single_frequency_para = data_single_frequency[j]
            lie_single_para_mean.append(np.mean(single_frequency_para))
            lie_single_para_std.append(np.std(single_frequency_para))
        # 导入数据 xlable names
        data_mark_s = mat4py.loadmat(path_1+ '\\' +'fscore_feature_label' + str(i + 1) + '.mat')
        data_mark_s = data_mark_s['fscore_feature_label']
        # 导入数据,mean，std, 绘制bar
        LoadData(inn_single_para_mean, inn_single_para_std, lie_single_para_mean, lie_single_para_std,data_mark_s)

        # 保存图片
    # plt.savefig('.\\figure_png\\'  +frequency_name[0]+frequency_name[1]+frequency_name[2]+frequency_name[3] + '.png',dpi=600)  # 循环保存图片
    plt.show()