x_data.append(time.time())

This line of code appends the current time (in seconds since January 1, 1970) to a list called `x_data`.

This is often used in data collection or plotting applications, where time is an important variable. By appending the current time to `x_data` at regular intervals, we can track how data changes over time.

For example, if we were collecting temperature data every minute, we could use `x_data` to keep track of when each temperature reading was taken. We could then plot the temperature over time using `x_data` as the x-axis.

相关问题

解释下这段代码watch_time_record = openpyxl.load_workbook("eye_movement_record_43.xlsx") #这里要改文件名，最后头要改储存文件名 images_workbook = openpyxl.load_workbook("images.xlsx") fix_record = watch_time_record["FIX"] images_info = images_workbook["Sheet1"] # watch_time_record.close() # images_workbook.close() # 注意openpyxl读取excel的时候，行从1开始，列仍从0开始 out_data_workbook = ow.Workbook(write_only=True) out_data_sheet = out_data_workbook.create_sheet("watch_time_stat_fix") # out_data_sheet.append(["start_time", "end_time", "duration", "coordinate_x", "coordinate_y", "pupil_size"]) out_data_sheet.append( ["trail_index", "img", "trail_start_time", "trail_end_time", "object1_duration", "object1_times", "object2_duration", "object2_times"])

这段代码的作用是使用openpyxl库导入Excel文件，并在其中读取指定的工作表信息，同时也创建了一个新的Excel文件，以待写入数据。

具体解释如下：

1. `watch_time_record = openpyxl.load_workbook("eye_movement_record_43.xlsx")`：使用`openpyxl`库中的`load_workbook()`函数读取名为`eye_movement_record_43.xlsx`的Excel文件，并将其存储到`watch_time_record`变量中，以便后续读取工作表信息。

2. `images_workbook = openpyxl.load_workbook("images.xlsx")`：同样使用`openpyxl`库中的`load_workbook()`函数读取名为`images.xlsx`的Excel文件，并将其存储到`images_workbook`变量中，以便后续读取工作表信息。

3. `fix_record = watch_time_record["FIX"]`：使用`watch_time_record`变量中打开的Excel文件中的`"FIX"`工作表，并将其存储到`fix_record`变量中，以便后续读取信息。

4. `images_info = images_workbook["Sheet1"]`：使用`images_workbook`变量中打开的Excel文件中的`"Sheet1"`工作表，并将其存储到`images_info`变量中，以便后续读取信息。

5. `out_data_workbook = ow.Workbook(write_only=True)`：创建一个新的Excel文件，并将其存储到`out_data_workbook`变量中，以待写入数据。

6. `out_data_sheet = out_data_workbook.create_sheet("watch_time_stat_fix")`：在`out_data_workbook`变量中创建一个名为`"watch_time_stat_fix"`的工作表，并将其存储到`out_data_sheet`变量中，以待写入数据。

7. `out_data_sheet.append(["trail_index", "img", "trail_start_time", "trail_end_time", "object1_duration", "object1_times", "object2_duration", "object2_times"])`：向`out_data_sheet`工作表中添加一行数据，该行包含了多个列名，用于在后续的数据写入中进行参考。

data.fillna(method='ffill', inplace=True) date_history,data_history = pd.DataFrame(data.iloc[:, 0]) data_history = pd.DataFrame(data.iloc[:, 1]) date_history = np.array(date_history) data_history = [x for item in np.array(data_history).tolist() for x in item] # 缺失值处理 history_time_list = [] for date in date_history: date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') #将字符串转为 datetime 对象 history_time_list.append(date_obj) start_time = history_time_list[0] # 起始时间 end_time = history_time_list[-1] # 结束时间 delta = datetime.timedelta(minutes=15) #时间间隔为15分钟 time_new_list = [] current_time = start_time while current_time <= end_time: time_new_list.append(current_time) current_time += delta # 缺失位置记录 code_list = [] for i in range(len(time_new_list)): code_list = code_list history_time_list = history_time_list while (time_new_list[i] - history_time_list[i]) != datetime.timedelta(minutes=0): history_time_list.insert(i, time_new_list[i]) code_list.append(i) for i in code_list: data_history.insert(i, data_history[i - 1]) # 输出补充好之后的数据 data = pd.DataFrame({'date': time_new_list, 'load': data_history}) return data 代码优化

以下是对代码的优化：

data.fillna(method='ffill', inplace=True) 
date_history, data_history = data.iloc[:, 0], data.iloc[:, 1:].values.flatten()
date_history = np.array([datetime.datetime.strptime(date, '%Y/%m/%d %H:%M') for date in date_history])
start_time, end_time = date_history[0], date_history[-1]
delta = datetime.timedelta(minutes=15)
time_new_list = []
current_time = start_time
while current_time <= end_time:
    time_new_list.append(current_time)
    current_time += delta
code_list = [i for i, date in enumerate(date_history) if date not in time_new_list]
for i in code_list:
    data_history = np.insert(data_history, i, data_history[i - 1])
data = pd.DataFrame({'date': time_new_list, 'load': data_history})
return data

代码优化的主要思路是：

1. 将第二列数据展平成一维数组，避免后续操作需要用到的循环。
2. 将日期字符串转换为 datetime 对象。
3. 使用列表推导式生成时间序列。
4. 使用列表推导式生成缺失位置列表。
5. 使用 NumPy 的 `insert()` 方法在数据中插入缺失值。
6. 最后将补充好的数据转换为 DataFrame 返回。

这样可以使代码更加简洁、高效，并且减少不必要的循环。