我有一个JS视图,我在其中创建一个sap.m.Table。它"列"绑定到JSONModel。它"项目"绑定到ODataModel。当我单击ColumnListItem中包含的Icon时,我想访问行数据和列名。
查看代码:
createContent : function(oController) {
var oTable = new sap.m.Table("table1", {
width: "auto",
noDataText: "Please add rows to be displayed!"
}).addStyleClass("sapUiResponsiveMargin");
oTable.bindAggregation("columns", "Periods>/periods", function(sId, oContext) {
var sColumnId = oContext.getObject().period;
return new sap.m.Column({
hAlign: "Center",
vAlign: "Middle",
header: new sap.m.Text({
text: sColumnId
})
});
});
oTable.bindItems("zStatus>/StatusSet", function(sId, oContext) {
var row = new sap.m.ColumnListItem(sId, {
type : "Inactive",
cells: [
new sap.ui.core.Icon({
src: "sap-icon://delete",
hoverColor: "red",
activeColor: "red",
press: [oController.onDeleteIconPress, oController]
}),
new sap.m.Text({
text: "{zStatus>Description}"
}),
new sap.ui.core.Icon(sId, {
src: {
path: "zStatus>Status1",
formatter: function(status) {
switch(status) {
case "R":
return "sap-icon://sys-cancel";
case "G":
return "sap-icon://sys-enter";
case "Y":
return "sap-icon://notification";
default:
return "sap-icon://sys-help";
}
}
},
size: "1.5em",
press: [oController.onStatusIconPress, oController]
}) ]
});
return oTable;
}
在我的控制器中,我创建了一个数组,然后是一个JSON模型" Periods"从它并将其设置为此视图。 Odata模型" zStatus"在清单文件中定义。
控制器代码:
onInit : function() {
// array aPeriods is populated first then
var oPeriodsModel = new sap.ui.model.json.JSONModel();
oPeriodsModel.setData({
periods : aPeriods
});
this.getView().setModel(oPeriodsModel, "Periods");
},
onStatusIconPress : function(oEvent) {
// I can get the row data on icon press
// Problem 2: but how do I get the column name?
// I wanted to attach the column name to icon as customData but I could
// not access model attached to columns inside bindItems method
}
答案 0 :(得分:1)
我设法自己解决了。
在createContent中创建一个数组。在bindAggregation列中填充列ID,然后在bindItems方法中使用此数组。 然后我可以将customData传递给图标。
这是代码 -
np.random.seed(123)
#1M df
N = 1000000
L2 = ['S','E','U',np.nan]
df = pd.DataFrame({'Group':np.random.randint(100000, size=N),
'Morph': np.random.choice(L2, N)})
#print (df)
In [46]: %timeit (df[~df['Group'].isin(df.loc[df['Morph'].isin(['U',np.nan]), 'Group'].unique())])
1 loop, best of 3: 372 ms per loop
In [47]: %timeit (df.groupby('Group').filter(lambda x: (~x.Morph.isin(['U',np.nan]).any() )))
1 loop, best of 3: 34.7 s per loop