var special_die_info = dataModel.getWorkValue("special_die_info_customize1") GUI.msgbox({detail:_.toString(special_die_info)}); if (!_.isEmpty(special_die_info)) { special_die_info = JSON.parse(special_die_info) Bridge_Die_Mount_Step_Quantity = _.map(special_die_info, 'die_fullname') Bridge_Die_Type_uantity = _.uniq(_.map(special_die_info, 'die_name')) //插入行 _.forEach(special_die_info, function (v) { excel.copyRows(4); excel.insertRow(4); }) excel.setRangeValue("E2", Bridge_Die_Mount_Step_Quantity.length); excel.setRangeValue("H2", Bridge_Die_Type_uantity.length); const EXPORT_KEYS = ["die_size_x_mm", "die_size_y_mm", "die_angle", "die_count", "cavity_size_x", "cavity_size_y"]; for (var stkIndex = 0; stkIndex < special_die_info.length; stkIndex++) { var dataMap = special_die_info[stkIndex]; for (var headerIndex = 0; headerIndex < EXPORT_KEYS.length; headerIndex++) { var row = stkIndex + 4; var col = headerIndex + 9; var cell = excel.getCell(row, col); var range = excel.getCellAddress(cell); excel.setRangeValue('C' + row, _.toString(dataMap['die_fullname'])); excel.setRangeValue('F' + row, _.toString(dataMap['die_name'])); excel.setRangeValue(range, _.toString(dataMap[EXPORT_KEYS[headerIndex]])); } } excel.deleteRows(4 + special_die_info.length); }
时间: 2024-04-05 21:34:12 浏览: 22
这段代码看起来是使用 JavaScript 语言编写的,它的作用是从一个名为 "special_die_info_customize1" 的数据模型中获取数据,并将其转换为 JSON 格式。如果获取到的数据不为空,它会对数据进行解析,并将特定的数据按照一定的格式插入到一个 Excel 表格中。同时,它还会删除插入的行,以保证表格的正确性。
相关问题
pd.read_csv
pd.read_csv is a function provided by the pandas library in Python. It is used to read a CSV (Comma Separated Values) file and create a DataFrame object from it. The function takes the file path as input and returns a DataFrame object. It has several parameters that can be used to customize the behavior of the function, such as delimiter, header, encoding, and more. Here's an example of how to use pd.read_csv:
```
import pandas as pd
# read the CSV file and create a DataFrame object
df = pd.read_csv('data.csv')
# display the first few rows of the DataFrame
print(df.head())
```
In this example, the data.csv file is read using pd.read_csv and a DataFrame object is created. The head() method is then called on the DataFrame to display the first few rows of the data.
left_axis.tick_params
Left_axis.tick_params is a method used to modify the appearance of the ticks and tick labels on the y-axis (left side) of a matplotlib plot. This method takes several arguments to customize the tick parameters, such as:
- axis: specify which axis to modify (e.g. 'y' for the y-axis)
- which: specify which ticks to modify (e.g. 'both' for both major and minor ticks)
- direction: specify the direction of the ticks ('in', 'out', or 'inout')
- length: specify the length of the ticks in points
- width: specify the width of the ticks in points
- color: specify the color of the ticks
- labelsize: specify the font size of the tick labels
For example, to make the y-axis ticks longer and thicker with red color and larger tick labels, we can use the following code:
```
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
left_axis = ax.yaxis
left_axis.tick_params(axis='y', length=10, width=2, color='red', labelsize=12)
```
This will modify the appearance of the ticks and tick labels on the y-axis of the plot.
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