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AspectJ:如何替换现有注释(AspectJ: How to replace an existing annotation)

使用AspectJ,如何替换现有注释?

我有以下代码

declare @method : @Test * *(..) : @Test(timeout=10);

这会在每个测试方法上生成以下错误:

... already has an annotation of type org.junit.Test, cannot add a second
instance [Xlint:elementAlreadyAnnotated]

当然,错误是有道理的,但是语法是什么,“从拥有它的所有方法中删除@Test注释。然后将其替换为@Test(timeout=10)


Using AspectJ, how do you replace an existing annotation?

I have the following code

declare @method : @Test * *(..) : @Test(timeout=10);

Which generates the following error on every test method:

... already has an annotation of type org.junit.Test, cannot add a second
instance [Xlint:elementAlreadyAnnotated]

Of course, the error makes sense but what is the syntax to say, "remove the @Test annotation from all methods that have it. Then replace it with @Test(timeout=10)"


原文:https://stackoverflow.com/questions/4106187
更新时间:2022-01-14 12:01

最满意答案

它不一定是最漂亮的,但在过去我做过类似的事情

df = pd.read_csv("vd.csv", sep="\s+")
d2 = pd.melt(df, id_vars="day")
d2["sample"] = d2.groupby(["variable", "day"])["day"].rank("first")
d3 = d2.pivot_table(index=["variable", "sample"], columns="day")

这使

>>> d3
                    value                                        
day                     1         2         3         4         5
variable sample                                                  
var1     1      -0.001285 -0.001331 -0.001362 -0.001394 -0.001467
         2      -0.001310 -0.001360 -0.001372 -0.001431 -0.001491
var2     1       0.001381  0.001375  0.001444  0.001437  0.001433
         2       0.001377  0.001430  0.001441  0.001434  0.001432
var3     1       1.022781  1.022477  1.022280  1.022017  1.021749
         2       1.022626  1.022385  1.022161  1.021908  1.021602

[6 rows x 5 columns]

(虽然老实说,我认为安迪的方式更加流畅。我会把它留在这里,因为过去在更难的情况下融化修改枢轴模式对我来说非常有用。)


It's not necessarily the prettiest, but in the past I've done things like

df = pd.read_csv("vd.csv", sep="\s+")
d2 = pd.melt(df, id_vars="day")
d2["sample"] = d2.groupby(["variable", "day"])["day"].rank("first")
d3 = d2.pivot_table(index=["variable", "sample"], columns="day")

which gives

>>> d3
                    value                                        
day                     1         2         3         4         5
variable sample                                                  
var1     1      -0.001285 -0.001331 -0.001362 -0.001394 -0.001467
         2      -0.001310 -0.001360 -0.001372 -0.001431 -0.001491
var2     1       0.001381  0.001375  0.001444  0.001437  0.001433
         2       0.001377  0.001430  0.001441  0.001434  0.001432
var3     1       1.022781  1.022477  1.022280  1.022017  1.021749
         2       1.022626  1.022385  1.022161  1.021908  1.021602

[6 rows x 5 columns]

(Although to be honest, I think Andy's way is slicker. I'll leave this here though because the melt-modify-pivot pattern has proved pretty useful for me in the past in harder cases.)

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