I'm trying to write a simple program to traverse all the referenced code starting from a given method using scalameta.
I was able to follow the calls but could not resolve method references.
analyzeme/src/main/scala/codelab/FindMe.scala
package codelab
object FindMe {
def main(args: Array[String]): Unit = {
val x = someRecognizeableName(1, 2)
val y = List(1, 2, 3)
y.foldLeft(0)(someRecognizeableName)
}
def someRecognizeableName(a: Int, b: Int): Int = a + b
}
Generated and loaded semanticdb for FindMe.scala and checking the usages of someRecognizeableName method.
I can see the first call in the db.names list:
[87..108): someRecognizeableName => _root_.codelab.FindMe.someRecognizeableName(Int,Int).
The second one, though, when I don't call the method, just pass the reference is showing up as this:
[159..180): someRecognizeableName => local2_src_main_scala_codelab_FindMe_scala
So when I try to follow references startin from main, I don't get a fully qualified name of the someRecognizeableName reference in the second case.
Question: Is there a way to get a fully qualified name from semanticdb for that reference?
Full source to reproduce the above
run instructions:
analyzeme $ sbt compile
analyzer $ sbt "run ../analyzeme"
analyzeme/src/main/scala/codelab/FindMe.scala
package codelab
object FindMe {
def main(args: Array[String]): Unit = {
val x = someRecognizeableName(1, 2)
val y = List(1, 2, 3)
y.foldLeft(0)(someRecognizeableName)
}
def someRecognizeableName(a: Int, b: Int): Int = a + b
}
analyzer/src/main/scala/Main.scala
import org.langmeta.io.{Classpath, Sourcepath}
import scala.meta._
object Main {
def main(args: Array[String]): Unit = {
println(s"Loading from [${ args(0) }]")
println()
val cp = Classpath(s"${ args(0) }/target/scala-2.12/classes")
val sp = Sourcepath(s"${ args(0) }/src/main/scala")
val db = Database.load(cp, sp)
println("* names:")
db.names foreach println
println()
println("* symbols:")
db.symbols foreach println
println()
println("* synthetics:")
db.synthetics foreach println
println()
println("* messages:")
db.messages foreach println
println()
}
}
analyzeme/build.sbt
name := "analyzee"
version := "0.1"
scalaVersion := "2.12.4"
addCompilerPlugin("org.scalameta" % "semanticdb-scalac" % "3.4.0" cross CrossVersion.full)
scalacOptions += "-Yrangepos"
analyzer/build.sbt
name := "analyzer"
version := "0.1"
scalaVersion := "2.12.4"
libraryDependencies += "org.scalameta" %% "scalameta" % "3.4.0"
libraryDependencies += "org.scalameta" %% "contrib" % "3.4.0"
package codelab
object FindMe {
def main(args: Array[String]): Unit = {
val x = someRecognizeableName(1, 2)
y.foldLeft(0)(someRecognizeableName)
// same as
y.foldLeft(0){ a, b => someRecognizeableName(a, b) }
}
I debug the code and found at the second case, the compiler passed an anonymous symbol which is not accessible from the current semanticdb, it maybe should comes in the syhthetics partion but I can't find it inside.
So I guess the compiler generated anonymous is missing in the current semanticdb.
Related
i am writing a macro to get annotations from a 'Class'
inline def getAnnotations(clazz: Class[?]): Seq[Any] = ${ getAnnotationsImpl('clazz) }
def getAnnotationsImpl(expr: Expr[Class[?]])(using Quotes): Expr[Seq[Any]] =
import quotes.reflect.*
val cls = expr.valueOrError // error: value value is not a member of quoted.Expr[Class[?]]
val tpe = TypeRepr.typeConstructorOf(cls)
val annotations = tpe.typeSymbol.annotations.map(_.asExpr)
Expr.ofSeq(annotations)
but i get an error when i get class value from expr parameter
#main def test(): Unit =
val cls = getCls
val annotations = getAnnotations(cls)
def getCls: Class[?] = Class.forName("Foo")
is it possible to get annotations of a Class at compile time by this macro ?!
By the way, eval for Class[_] doesn't work even in Scala 2 macros: c.eval(c.Expr[Class[_]](clazz)) produces
java.lang.ClassCastException:
scala.reflect.internal.Types$ClassNoArgsTypeRef cannot be cast to java.lang.Class.
Class[_] is too runtimy thing. How can you extract its value from its tree ( Expr is a wrapper over tree)?
If you already have a Class[?] you should use Java reflection rather than Scala 3 macros (with Tasty reflection).
Actually, you can try to evaluate a tree from its source code (hacking multi-staging programming and implementing our own eval instead of forbidden staging.run). It's a little similar to context.eval in Scala 2 macros (but we evaluate from a source code rather than from a tree).
import scala.quoted.*
object Macro {
inline def getAnnotations(clazz: Class[?]): Seq[Any] = ${getAnnotationsImpl('clazz)}
def getAnnotationsImpl(expr: Expr[Class[?]])(using Quotes): Expr[Seq[Any]] = {
import quotes.reflect.*
val str = expr.asTerm.pos.sourceCode.getOrElse(
report.errorAndAbort(s"No source code for ${expr.show}")
)
val cls = Eval[Class[?]](str)
val tpe = TypeRepr.typeConstructorOf(cls)
val annotations = tpe.typeSymbol.annotations.map(_.asExpr)
Expr.ofSeq(annotations)
}
}
import dotty.tools.dotc.core.Contexts.Context
import dotty.tools.dotc.{Driver, util}
import dotty.tools.io.{VirtualDirectory, VirtualFile}
import java.net.URLClassLoader
import java.nio.charset.StandardCharsets
import dotty.tools.repl.AbstractFileClassLoader
object Eval {
def apply[A](str: String): A = {
val content =
s"""
|package $$generated
|
|object $$Generated {
| def run = $str
|}""".stripMargin
val sourceFile = util.SourceFile(
VirtualFile(
name = "$Generated.scala",
content = content.getBytes(StandardCharsets.UTF_8)),
codec = scala.io.Codec.UTF8
)
val files = this.getClass.getClassLoader.asInstanceOf[URLClassLoader].getURLs
val depClassLoader = new URLClassLoader(files, null)
val classpathString = files.mkString(":")
val outputDir = VirtualDirectory("output")
class DriverImpl extends Driver {
private val compileCtx0 = initCtx.fresh
val compileCtx = compileCtx0.fresh
.setSetting(
compileCtx0.settings.classpath,
classpathString
).setSetting(
compileCtx0.settings.outputDir,
outputDir
)
val compiler = newCompiler(using compileCtx)
}
val driver = new DriverImpl
given Context = driver.compileCtx
val run = driver.compiler.newRun
run.compileSources(List(sourceFile))
val classLoader = AbstractFileClassLoader(outputDir, depClassLoader)
val clazz = Class.forName("$generated.$Generated$", true, classLoader)
val module = clazz.getField("MODULE$").get(null)
val method = module.getClass.getMethod("run")
method.invoke(module).asInstanceOf[A]
}
}
package mypackage
import scala.annotation.experimental
#experimental
class Foo
Macro.getAnnotations(Class.forName("mypackage.Foo")))
// new scala.annotation.internal.SourceFile("/path/to/src/main/scala/mypackage/Foo.scala"), new scala.annotation.experimental()
scalaVersion := "3.1.3"
libraryDependencies += scalaOrganization.value %% "scala3-compiler" % scalaVersion.value
How to compile and execute scala code at run-time in Scala3?
(compile time of the code expanding macros is the runtime of macros)
Actually, there is even a way to evaluate a tree itself (not its source code). Such functionality exists in Scala 3 compiler but is deliberately blocked because of phase consistency principle. So this to work, the code expanding macros should be compiled with a compiler patched
https://github.com/DmytroMitin/dotty-patched
scalaVersion := "3.2.1"
libraryDependencies += scalaOrganization.value %% "scala3-staging" % scalaVersion.value
// custom Scala settings
managedScalaInstance := false
ivyConfigurations += Configurations.ScalaTool
libraryDependencies ++= Seq(
scalaOrganization.value % "scala-library" % "2.13.10",
scalaOrganization.value %% "scala3-library" % "3.2.1",
"com.github.dmytromitin" %% "scala3-compiler-patched-assembly" % "3.2.1" % "scala-tool"
)
import scala.quoted.{Expr, Quotes, staging, quotes}
object Macro {
inline def getAnnotations(clazz: Class[?]): Seq[String] = ${impl('clazz)}
def impl(expr: Expr[Class[?]])(using Quotes): Expr[Seq[String]] = {
import quotes.reflect.*
given staging.Compiler = staging.Compiler.make(this.getClass.getClassLoader)
val tpe = staging.run[Any](expr).asInstanceOf[TypeRepr]
val annotations = Expr(tpe.typeSymbol.annotations.map(_.asExpr.show))
report.info(s"annotations=${annotations.show}")
annotations
}
}
Normally, for expr: Expr[A] staging.run(expr) returns a value of type A. But Class is specific. For expr: Expr[Class[_]] inside macros it returns a value of type dotty.tools.dotc.core.Types.CachedAppliedType <: TypeRepr. That's why I had to cast.
In Scala 2 this also would be c.eval(c.Expr[Any](/*c.untypecheck*/(clazz))).asInstanceOf[Type].typeSymbol.annotations because for Class[_] c.eval returns scala.reflect.internal.Types$ClassNoArgsTypeRef <: Type.
https://github.com/scala/bug/issues/12680
I would like to get groups attribute as Seq[Int] from the given mongodb Document. How to do it? The method getList catches a runtime exception, and I would like to understand and fix it.
n: Document((_id,BsonObjectId{value=613645d689898b7d4ac2b1b2}), (groups,BsonArray{values=[BsonInt32{value=2}, BsonInt32{value=3}]}))
I tried this way that compiles, but I get the runtime error "Caused by: java.lang.ClassCastException: List element cannot be cast to scala.Int$"
val groups = n.getList("groups", Int.getClass)
Some sbt library dependencies:
scalaVersion := "2.12.14"
libraryDependencies += "org.mongodb.scala" %% "mongo-scala-driver" % "4.3.1"
Setup code:
val collection = db.getCollection("mylist")
Await.result(collection.drop.toFuture, Duration.Inf)
val groupsIn = Seq[Int](2, 3)
val doc = Document("groups" -> groupsIn)
Await.result(collection.insertOne(doc).toFuture, Duration.Inf)
println("see mongosh to verify that a Seq[Int] has been added")
val result = Await.result(collection.find.toFuture, Duration.Inf)
for(n <- result) {
println("n: " + n)
val groups = n.getList("groups", Int.getClass)
println("groups: " + groups)
}
Comments: result is of type Seq[Document], n is of type Document.
The getList hover-on description in VSCODE:
def getList[T](key: Any, clazz: Class[T]): java.util.List[T]
Gets the list value of the given key, casting the list elements to the given Class. This is useful to avoid having casts in client code, though the effect is the same.
With the help of sarveshseri and Gael J, the solution is reached:
import collection.JavaConverters._
val groups = n.getList("groups", classOf[Integer]).asScala.toSeq.map(p => p.toInt)
Good day,
I have been struggling for a few days to save a custom transformer that is part of a large pipeline of stages. I have a transformer that is completely defined by its params. I have an estimator which in it's fit method will generate a matrix and then set the transformer parameters accordingly so that I can use DefaultParamsReadable and DefaultParamsReadable to take advantage of the serialisation/deserialisation already present in util.ReadWrite.scala.
My summarised code is as follows (includes important aspects):
...
import org.apache.spark.ml.util._
...
// trait to implement in Estimator and Transformer for params
trait NBParams extends Params {
final val featuresCol= new Param[String](this, "featuresCol", "The input column")
setDefault(featuresCol, "_tfIdfOut")
final val labelCol = new Param[String](this, "labelCol", "The labels column")
setDefault(labelCol, "P_Root_Code_Index")
final val predictionsCol = new Param[String](this, "predictionsCol", "The output column")
setDefault(predictionsCol, "NBOutput")
final val ratioMatrix = new Param[DenseMatrix](this, "ratioMatrix", "The transformation matrix")
def getfeaturesCol: String = $(featuresCol)
def getlabelCol: String = $(labelCol)
def getPredictionCol: String = $(predictionsCol)
def getRatioMatrix: DenseMatrix = $(ratioMatrix)
}
// Estimator
class CustomNaiveBayes(override val uid: String, val alpha: Double)
extends Estimator[CustomNaiveBayesModel] with NBParams with DefaultParamsWritable {
def copy(extra: ParamMap): CustomNaiveBayes = {
defaultCopy(extra)
}
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
def setLabelCol(value: String): this.type = set(labelCol, value)
def setPredictionCol(value: String): this.type = set(predictionsCol, value)
def setRatioMatrix(value: DenseMatrix): this.type = set(ratioMatrix, value)
override def transformSchema(schema: StructType): StructType = {...}
override def fit(ds: Dataset[_]): CustomNaiveBayesModel = {
...
val model = new CustomNaiveBayesModel(uid)
model
.setRatioMatrix(ratioMatrix)
.setFeaturesCol($(featuresCol))
.setLabelCol($(labelCol))
.setPredictionCol($(predictionsCol))
}
}
// companion object for Estimator
object CustomNaiveBayes extends DefaultParamsReadable[CustomNaiveBayes]{
override def load(path: String): CustomNaiveBayes = super.load(path)
}
// Transformer
class CustomNaiveBayesModel(override val uid: String)
extends Model[CustomNaiveBayesModel] with NBParams with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("customnaivebayes"))
def copy(extra: ParamMap): CustomNaiveBayesModel = {defaultCopy(extra)}
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
def setLabelCol(value: String): this.type = set(labelCol, value)
def setPredictionCol(value: String): this.type = set(predictionsCol, value)
def setRatioMatrix(value: DenseMatrix): this.type = set(ratioMatrix, value)
override def transformSchema(schema: StructType): StructType = {...}
}
def transform(dataset: Dataset[_]): DataFrame = {...}
}
// companion object for Transformer
object CustomNaiveBayesModel extends DefaultParamsReadable[CustomNaiveBayesModel]
When I add this Model as part of a pipeline and fit the pipeline, all runs ok. When I save the pipeline, there are no errors. However, when I attempt to load the pipeline in I get the following error:
NoSuchMethodException: $line3b380bcad77e4e84ae25a6bfb1f3ec0d45.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$$$6fa979eb27fa6bf89c6b6d1b271932c$$$$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$CustomNaiveBayesModel.read()
To save the pipeline, which includes a number of other transformers related to NLP pre-processing, I run
fittedModelRootCode.write.save("path")
and to then load it (where the failure occurs) I run
import org.apache.spark.ml.PipelineModel
val fittedModelRootCode = PipelineModel.load("path")
The model itself appears to be working well but I cannot afford to retrain the model on a dataset every time I wish to use it. Does anyone have any ideas why even with the companion object, the read() method appears to be unavailable?
Notes:
I am running on Databricks Runtime 8.3 (Spark 3.1.1, Scala 2.12)
My model is in a separate package so is external to Spark
I have reproduced this based on a number of existing examples all of which appear to work fine so I am unsure why my code is failing
I am aware there is a Naive Bayes model available in Spark ML, however, I have been tasked with making a large number of customizations so it is not worth modifying the existing version (plus I would like to learn how to get this right)
Any help would be greatly appreciated.
Since you extend the CustomNaiveBayesModel companion object by DefaultParamsReadable, I think you should use the companion object CustomNaiveBayesModel for loading the model. Here I write some code for saving and loading models and it works properly:
import org.apache.spark.SparkConf
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.sql.SparkSession
import path.to.CustomNaiveBayesModel
object SavingModelApp extends App {
val spark: SparkSession = SparkSession.builder().config(
new SparkConf()
.setMaster("local[*]")
.setAppName("Test app")
.set("spark.driver.host", "localhost")
.set("spark.ui.enabled", "false")
).getOrCreate()
val training = spark.createDataFrame(Seq(
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0)
)).toDF("id", "text", "label")
val fittedModelRootCode: PipelineModel = new Pipeline().setStages(Array(new CustomNaiveBayesModel())).fit(training)
fittedModelRootCode.write.save("path/to/model")
val mod = PipelineModel.load("path/to/model")
}
I think your mistake is using PipelineModel.load for loading the concrete model.
My environment:
scalaVersion := "2.12.6"
scalacOptions := Seq(
"-encoding", "UTF-8", "-target:jvm-1.8", "-deprecation",
"-feature", "-unchecked", "-language:implicitConversions", "-language:postfixOps")
libraryDependencies += "org.apache.spark" %% "spark-core" % "3.1.1",
libraryDependencies += "org.apache.spark" %% "spark-sql" % "3.1.1"
libraryDependencies += "org.apache.spark" %% "spark-mllib" % "3.1.1"
I have a script in scala, when I run it in Zeppelin works well, but when I try compile with sbt, it doesnt work. I believe is something related to the versions but Im not being able to identify.
Those three ways returns the same error:
val catMap = catDF.rdd.map((row: Row) => (row.getAs[String](1)->row.getAs[Integer](0))).collect.toMap
val catMap = catDF.select($"description", $"id".cast("int")).as[(String, Int)].collect.toMap
val catMap = catDF.rdd.map((row: Row) => (row.getAs[String](1)->row.getAs[Integer](0))).collectAsMap()
Returning an error: "value rdd is not a member of Unit"
val bizCat = bizCatRDD.rdd.map(t => (t.getAs[String](0),catMap(t.getAs[String](1)))).toDF
Returning an error: "value toDF is not a member of org.apache.spark.rdd.RDD[U]"
Scala version: 2.12
Sbt Version: 1.3.13
UPDATE:
The whole class is:
package importer
import org.apache.spark.sql.{Row, SaveMode, SparkSession}
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import udf.functions._
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.Column
object BusinessImporter extends Importer{
def importa(spark: SparkSession, inputDir: String): Unit = {
import spark.implicits._
val bizDF = spark.read.json(inputDir).cache
// categories
val explode_categories = bizDF.withColumn("categories", explode(split(col("categories"), ",")))
val sort_categories = explode_categories.select(col("categories").as("description"))
.distinct
.coalesce(1)
.orderBy(asc("categories"))
// Create sequence column
val windowSpec = Window.orderBy("description")
val categories_with_sequence = sort_categories.withColumn("id",row_number.over(windowSpec))
val categories = categories_with_sequence.select("id","description")
val catDF = categories.write.insertInto("categories")
// business categories
//val catMap = catDF.rdd.map((row: Row) => (row.getAs[String](1)->row.getAs[Integer](0))).collect.toMap
//val catMap = catDF.select($"description", $"id".cast("int")).as[(String, Int)].collect.toMap
val catMap = catDF.rdd.map((row: Row) => (row.getAs[String](1)->row.getAs[Integer](0))).collectAsMap()
val auxbizCatRDD = bizDF.withColumn("categories", explode(split(col("categories"), ",")))
val bizCatRDD = auxbizCatRDD.select("business_id","categories")
val bizCat = bizCatRDD.rdd.map(t => (t.getAs[String](0),catMap(t.getAs[String](1)))).toDF
bizCat.write.insertInto("business_category")
// Business
val businessDF = bizDF.select("business_id","categories","city","address","latitude","longitude","name","is_open","review_count","stars","state")
businessDF.coalesce(1).write.insertInto("business")
// Hours
val bizHoursDF = bizDF.select("business_id","hours.Sunday","hours.Monday","hours.Tuesday","hours.Wednesday","hours.Thursday","hours.Friday","hours.Saturday")
val bizHoursDF_structs = bizHoursDF
.withColumn("Sunday",struct(
split(col("Sunday"),"-").getItem(0).as("Open"),
split(col("Sunday"),"-").getItem(1).as("Close")))
.withColumn("Monday",struct(
split(col("Monday"),"-").getItem(0).as("Open"),
split(col("Monday"),"-").getItem(1).as("Close")))
.withColumn("Tuesday",struct(
split(col("Tuesday"),"-").getItem(0).as("Open"),
split(col("Tuesday"),"-").getItem(1).as("Close")))
.withColumn("Wednesday",struct(
split(col("Wednesday"),"-").getItem(0).as("Open"),
split(col("Wednesday"),"-").getItem(1).as("Close")))
.withColumn("Thursday",struct(
split(col("Thursday"),"-").getItem(0).as("Open"),
split(col("Thursday"),"-").getItem(1).as("Close")))
.withColumn("Friday",struct(
split(col("Friday"),"-").getItem(0).as("Open"),
split(col("Friday"),"-").getItem(1).as("Close")))
.withColumn("Saturday",struct(
split(col("Saturday"),"-").getItem(0).as("Open"),
split(col("Saturday"),"-").getItem(1).as("Close")))
bizHoursDF_structs.coalesce(1).write.insertInto("business_hour")
}
def singleSpace(col: Column): Column = {
trim(regexp_replace(col, " +", " "))
}
}
sbt file:
name := "yelp-spark-processor"
version := "1.0"
scalaVersion := "2.12.12"
libraryDependencies += "org.apache.spark" % "spark-core_2.12" % "3.0.1"
libraryDependencies += "org.apache.spark" % "spark-sql_2.12" % "3.0.1"
libraryDependencies += "org.apache.spark" % "spark-hive_2.12" % "3.0.1"
Can someone pls give me some orientations about what is wrong?
Many Thanks
Xavy
The issue here is that in scala this line returns type Unit:
val catDF = categories.write.insertInto("categories")
Unit in scala is like void in java, it's returned by functions that don't return anything meaningful. So basically at this point catDF is not a dataframe and you can't treat it as such. So you probably want to keep using categories instead of catDF in the lines that follow.
Let it be the following hierarchy:
object X extends Y{
...
}
trait Y extends Z {
...
}
trait Z {
def run(): Unit
}
I parse the scala file containing the X and
I want to know if its parent or grandparent is Z.
I can check for parent as follows:
Given that x: Defn.Object is the X class I parsed,
x
.children.collect { case c: Template => c }
.flatMap(p => p.children.collectFirst { case c: Init => c }
will give Y.
Question: Any idea how I can get the parent of the parent of X (which is Z in the above example) ?
Loading Y (the same way I loaded X) and finding it's parent doesn't seem like a good idea, since the above is part of a scan procedure where among all files under src/main/scala I'm trying to find all classes which extend Z and implement run, so I don't see an easy and performant way to create a graph with all intermediate classes so as to load them in the right order and check for their parents.
It seems you want Scalameta to process your sources not syntactically but semantically. Then you need SemanticDB. Probably the most convenient way to work with SemanticDB is Scalafix
rules/src/main/scala/MyRule.scala
import scalafix.v1._
import scala.meta._
class MyRule extends SemanticRule("MyRule") {
override def isRewrite: Boolean = true
override def description: String = "My Rule"
override def fix(implicit doc: SemanticDocument): Patch = {
doc.tree.traverse {
case q"""..$mods object $ename extends ${template"""
{ ..$stats } with ..$inits { $self => ..$stats1 }"""}""" =>
val initsParents = inits.collect(_.symbol.info.map(_.signature) match {
case Some(ClassSignature(_, parents, _, _)) => parents
}).flatten
println(s"object: $ename, parents: $inits, grand-parents: $initsParents")
}
Patch.empty
}
}
in/src/main/scala/App.scala
object X extends Y{
override def run(): Unit = ???
}
trait Y extends Z {
}
trait Z {
def run(): Unit
}
Output of sbt out/compile
object: X, parents: List(Y), grand-parents: List(AnyRef, Z)
build.sbt
name := "scalafix-codegen"
inThisBuild(
List(
//scalaVersion := "2.13.2",
scalaVersion := "2.11.12",
addCompilerPlugin(scalafixSemanticdb),
scalacOptions ++= List(
"-Yrangepos"
)
)
)
lazy val rules = project
.settings(
libraryDependencies += "ch.epfl.scala" %% "scalafix-core" % "0.9.16",
organization := "com.example",
version := "0.1",
)
lazy val in = project
lazy val out = project
.settings(
sourceGenerators.in(Compile) += Def.taskDyn {
val root = baseDirectory.in(ThisBuild).value.toURI.toString
val from = sourceDirectory.in(in, Compile).value
val to = sourceManaged.in(Compile).value
val outFrom = from.toURI.toString.stripSuffix("/").stripPrefix(root)
val outTo = to.toURI.toString.stripSuffix("/").stripPrefix(root)
Def.task {
scalafix
.in(in, Compile)
.toTask(s" --rules=file:rules/src/main/scala/MyRule.scala --out-from=$outFrom --out-to=$outTo")
.value
(to ** "*.scala").get
}
}.taskValue
)
project/plugins.sbt
addSbtPlugin("ch.epfl.scala" % "sbt-scalafix" % "0.9.16")
Other examples:
https://github.com/olafurpg/scalafix-codegen (semantic)
https://github.com/DmytroMitin/scalafix-codegen (semantic)
https://github.com/DmytroMitin/scalameta-demo (syntactic)
Is it possible to using macro to modify the generated code of structural-typing instance invocation? (semantic)
Scala conditional compilation (syntactic)
Macro annotation to override toString of Scala function (syntactic)
How to merge multiple imports in scala? (syntactic)
You can avoid Scalafix but then you'll have to work with internals of SemanticDB manually
import scala.meta._
import scala.meta.interactive.InteractiveSemanticdb
import scala.meta.internal.semanticdb.{ClassSignature, Range, SymbolInformation, SymbolOccurrence, TypeRef}
val source: String =
"""object X extends Y{
| override def run(): Unit = ???
|}
|
|trait Y extends Z
|
|trait Z {
| def run(): Unit
|}""".stripMargin
val textDocument = InteractiveSemanticdb.toTextDocument(
InteractiveSemanticdb.newCompiler(List(
"-Yrangepos"
)),
source
)
implicit class TreeOps(tree: Tree) {
val occurence: Option[SymbolOccurrence] = {
val treeRange = Range(tree.pos.startLine, tree.pos.startColumn, tree.pos.endLine, tree.pos.endColumn)
textDocument.occurrences
.find(_.range.exists(occurrenceRange => treeRange == occurrenceRange))
}
val info: Option[SymbolInformation] = occurence.flatMap(_.symbol.info)
}
implicit class StringOps(symbol: String) {
val info: Option[SymbolInformation] = textDocument.symbols.find(_.symbol == symbol)
}
source.parse[Source].get.traverse {
case tree#q"""..$mods object $ename extends ${template"""
{ ..$stats } with ..$inits { $self => ..$stats1 }"""}""" =>
val initsParents = inits.collect(_.info.map(_.signature) match {
case Some(ClassSignature(_, parents, _, _)) =>
parents.collect {
case TypeRef(_, symbol, _) => symbol
}
}).flatten
println(s"object = $ename = ${ename.info.map(_.symbol)}, parents = $inits = ${inits.map(_.info.map(_.symbol))}, grand-parents = $initsParents")
}
Output:
object = X = Some(_empty_/X.), parents = List(Y) = List(Some(_empty_/Y#)), grand-parents = List(scala/AnyRef#, _empty_/Z#)
build.sbt
//scalaVersion := "2.13.3"
scalaVersion := "2.11.12"
lazy val scalametaV = "4.3.18"
libraryDependencies ++= Seq(
"org.scalameta" %% "scalameta" % scalametaV,
"org.scalameta" % "semanticdb-scalac" % scalametaV cross CrossVersion.full
)
Semanticdb code seems to be working in Scala 3
https://scastie.scala-lang.org/DmytroMitin/3QQwsDG2Rqm71qa6mMMkTw/36 [copy] (at Scastie -Dscala.usejavacp=true didn't help with object scala.runtime in compiler mirror not found, so I used Coursier to guarantee that scala-library is on path, locally it works without Coursier)