python 一张图中绘制一个小的子图

浅浅的花香味﹌ 2022-05-23 11:45 240阅读 0赞

有时候为了直观展现图的信息，可以在大图中添加小子图的方式进行数据分析，如下图所示：  
![这里写图片描述][70]

具体的代码如下：该图连接了数据库，当然重要的不是数据展示，而是添加子图的方法。

import matplotlib.pyplot as plt
    import MySQLdb as mdb
    import numpy as np
    from mpl_toolkits.axes_grid1.inset_locator import inset_axes
    from mpl_toolkits.axes_grid1.inset_locator import mark_inset
    
    
    def graph():
        # 连接数据库
        conn = mdb.connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='alibaba_trace', charset='utf8')
    
        # 如果使用事务引擎，可以设置自动提交事务，或者在每次操作完成后手动提交事务conn.commit()
        conn.autocommit(1)  # conn.autocommit(True)
    
        # 使用cursor()方法获取操作游标
        cursor = conn.cursor()
        # 因该模块底层其实是调用CAPI的，所以，需要先得到当前指向数据库的指针。
        try:
            cursor.execute("select machineID, count(id) from batch_instance where machineID != 0 group by machineID")
            records = cursor.fetchall()
            list_records = list(records)
    
        except:
            import traceback
            traceback.print_exc()
            # 发生错误时回滚
            conn.rollback()
        finally:
            # 关闭游标连接
            cursor.close()
            # 关闭数据库连接
            conn.close()
    
        res = []
        res[:] = map(list, list_records)
        machineID = [x[0] for x in res]
        instance_num = [x[1] for x in res]
        print(max(instance_num))
        print(min(instance_num))
    
    
        fig = plt.figure()
        ax1 = fig.add_subplot(1, 1, 1)
        # # cdf
        # hist, bin_edges = np.histogram(instance_num, bins=len(np.unique(instance_num)))
        # cdf = np.cumsum(hist / sum(hist))
        # ax1.plot(bin_edges[1:], cdf, color='red', ls='-')
        # ax1.set_xlabel("instance number per machine")
        # ax1.set_ylabel("portion of machine")
        # plt.savefig('../../imgs_mysql/cdf_of_machine_instance.png')
    
        # # 直方图
        ax1.hist(instance_num, normed=False, alpha=1.0, bins=100)
        ax1.set_xlabel('instance number per machine')
        ax1.set_ylabel('machine number')
        # cdf 要添加的子图
        axins = inset_axes(ax1, width=1.5, height=1.5, loc='upper left')
        # ax1 大图
        # width height分别为子图的宽和高
        # loc 为子图在大图ax1中的相对位置 相应的值有
        # upper left
        # lower left
        # lower right
        # right
        # center left
        # center right
        # lower center
        # upper center
        # center
        hist, bin_edges = np.histogram(instance_num, bins=len(np.unique(instance_num)))
        cdf = np.cumsum(hist / sum(hist))
        axins.plot(bin_edges[1:], cdf, color='red', ls='-')
        axins.set_yticks([])
        # axins.set_xlabel("instance number per machine")
        # axins.set_ylabel("portion of machine")
    
        plt.savefig("../../imgs_mysql/hist_of_machine_instance")
        plt.show()
    
    if __name__ == '__main__':
        graph()

[70]: /images/20220523/5ddf99d37ff843d5af31172abe0ddfdf.png