python 实现在一张图中绘制一个小的子图方法
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:
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()
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