9f009780 eb00916 b1 9f02dd08 <fdt_fixup_tp_vendor_id> 这段是我在lst文件里面看见的,怎么理解
时间: 2024-04-06 11:30:07 浏览: 86
这是一个类似于函数调用的语句,它在LST文件中表示对名为 `fdt_fixup_tp_vendor_id` 的函数的调用。具体来说,这个函数接受三个参数:`9f009780`、`eb00916b` 和 `9f02dd08`。
这里的参数都是以十六进制表示的内存地址或数值。可以猜测这个函数的作用是修复设备树中的某个属性或者值。但是需要进一步查看代码才能确定函数的具体作用和实现方式。
需要注意的是,LST文件并不是一种通用的编程语言,而是一种特定领域的数据格式。因此,这里的函数调用语句只是一种表示方式,具体的实现方式需要参考具体应用场景的代码。
相关问题
用pandas函数代替下列语句:def generate_time_series(start,end,t1,t2): current = start end_item = end time_delta = datetime.timedelta(days=t1,hours=t2) time_series = [current] while current < end_item: next1 = current + time_delta time_series.append(next1) current = next1 return time_series time_hour=generate_time_series(data1.loc[0]['FDT_TIME'],data1.loc[len(data1)-1]['FDT_TIME'],0,1) time_require=[time_hour[160],time_hour[162]] data1_require=data1.drop(data1[(data1['FDT_TIME']<time_require[0])].index) data1_require=data1_require.drop(data1_require[(data1_require['FDT_TIME']>time_require[1])].index) data1_require=data1_require[['FINT_SPEED']]
可以使用pandas.date_range()函数替换generate_time_series()函数来生成时间序列,用pandas.DataFrame.loc[]函数替换data1的切片操作,用pandas.DataFrame.drop()函数替换删除操作,代码如下:
import pandas as pd
import datetime
start = data1.loc[0]['FDT_TIME']
end = data1.loc[len(data1)-1]['FDT_TIME']
time_delta = datetime.timedelta(days=0, hours=1)
time_hour = pd.date_range(start=start, end=end, freq=time_delta)
time_require = [time_hour[160], time_hour[162]]
data1_require = data1.loc[(data1['FDT_TIME'] >= time_require[0]) & (data1['FDT_TIME'] <= time_require[1])]
data1_require = data1_require[['FINT_SPEED']]
用pandas中的date_range函数代替下列语句:def generate_time_series(start,end,t1,t2): current = start end_item = end time_delta = datetime.timedelta(days=t1,hours=t2) time_series = [current] while current < end_item: next1 = current + time_delta time_series.append(next1) current = next1 return time_series time_hour=generate_time_series(data1.loc[0]['FDT_TIME'],data1.loc[len(data1)-1]['FDT_TIME'],0,1) time_require=[time_hour[160],time_hour[162]] data1_require=data1.drop(data1[(data1['FDT_TIME']<time_require[0])].index) data1_require=data1_require.drop(data1_require[(data1_require['FDT_TIME']>time_require[1])].index) data1_require=data1_require[['FINT_SPEED']]
可以使用pandas的date_range函数来代替generate_time_series函数,具体代码如下:
import pandas as pd
import datetime
start = data1.loc[0]['FDT_TIME']
end = data1.loc[len(data1)-1]['FDT_TIME']
time_delta = datetime.timedelta(days=0, hours=1)
time_series = pd.date_range(start=start, end=end, freq=time_delta)
time_require=[time_series[160], time_series[162]]
data1_require = data1[(data1['FDT_TIME']>=time_require[0])&(data1['FDT_TIME']<=time_require[1])]
data1_require = data1_require[['FINT_SPEED']]