removed the requirement of dataframe properties to be mutable for isOpen to be true in DataSchemas in Jupyter

Author: Jolanrensen

core/src/main/kotlin/org/jetbrains/kotlinx/dataframe/codeGen/ReplCodeGenerator.kt

@@ -6,21 +6,18 @@ import org.jetbrains.kotlinx.dataframe.codeGen.CodeWithConverter
 import org.jetbrains.kotlinx.dataframe.impl.codeGen.ReplCodeGeneratorImpl
 import org.jetbrains.kotlinx.jupyter.api.Code
 import kotlin.reflect.KClass
-import kotlin.reflect.KMutableProperty
 import kotlin.reflect.KProperty
 
 internal interface ReplCodeGenerator {
 
     fun process(
         df: AnyFrame,
         property: KProperty<*>? = null,
-        isMutable: Boolean = property is KMutableProperty?,
     ): CodeWithConverter
 
     fun process(
         row: AnyRow,
         property: KProperty<*>? = null,
-        isMutable: Boolean = property is KMutableProperty?,
     ): CodeWithConverter
 
     fun process(markerClass: KClass<*>): Code

core/src/main/kotlin/org/jetbrains/kotlinx/dataframe/impl/codeGen/ReplCodeGeneratorImpl.kt

@@ -16,7 +16,6 @@ import org.jetbrains.kotlinx.dataframe.codeGen.MarkersExtractor
 import org.jetbrains.kotlinx.dataframe.schema.DataFrameSchema
 import org.jetbrains.kotlinx.jupyter.api.Code
 import kotlin.reflect.KClass
-import kotlin.reflect.KMutableProperty
 import kotlin.reflect.KProperty
 import kotlin.reflect.KType
 import kotlin.reflect.KVisibility
@@ -45,25 +44,22 @@ internal class ReplCodeGeneratorImpl : ReplCodeGenerator {
             else -> null
         }
 
-    override fun process(row: AnyRow, property: KProperty<*>?, isMutable: Boolean) =
-        process(row.df(), property, isMutable)
+    override fun process(row: AnyRow, property: KProperty<*>?): CodeWithConverter = process(row.df(), property)
 
-    override fun process(df: AnyFrame, property: KProperty<*>?, isMutable: Boolean): CodeWithConverter {
+    override fun process(df: AnyFrame, property: KProperty<*>?): CodeWithConverter {
         var targetSchema = df.schema()
-        var isMutable = isMutable
 
         if (property != null) {
             val wasProcessedBefore = property in registeredProperties
             registeredProperties.add(property)
-            isMutable = property is KMutableProperty
 
             // maybe property is already properly typed, let's do some checks
             val currentMarker = getMarkerClass(property.returnType)
                 ?.takeIf { it.findAnnotation<DataSchema>() != null }
                 ?.let { registeredMarkers[it] ?: MarkersExtractor.get(it) }
             if (currentMarker != null) {
-                // if property is mutable, we need to make sure that its marker type is open in order to let derived data frames be assignable to it
-                if (!isMutable || currentMarker.isOpen) {
+                // we need to make sure that the property's marker type is open in order to let derived data frames be assignable to it
+                if (currentMarker.isOpen) {
                     val columnSchema = currentMarker.schema
                     // for mutable properties we do strong typing only at the first processing, after that we allow its type to be more general than actual data frame type
                     if (wasProcessedBefore || columnSchema == targetSchema) {
@@ -79,7 +75,7 @@ internal class ReplCodeGeneratorImpl : ReplCodeGenerator {
             }
         }
 
-        return generate(schema = targetSchema, name = markerInterfacePrefix, isOpen = isMutable)
+        return generate(schema = targetSchema, name = markerInterfacePrefix, isOpen = true)
     }
 
     fun generate(

core/src/test/kotlin/org/jetbrains/kotlinx/dataframe/codeGen/ReplCodeGenTests.kt

@@ -68,7 +68,7 @@ class ReplCodeGenTests : BaseTest() {
     @Test
     fun `process derived markers`() {
         val repl = ReplCodeGenerator.create()
-        val code = repl.process(df, isMutable = true).declarations
+        val code = repl.process(df).declarations
 
         val marker = ReplCodeGeneratorImpl.markerInterfacePrefix
         val markerFull = Test1._DataFrameType::class.qualifiedName!!
@@ -100,10 +100,10 @@ class ReplCodeGenTests : BaseTest() {
         code2 shouldBe ""
 
         val df3 = typed.filter { city != null }
-        val code3 = repl.process(df3, isMutable = false).declarations
+        val code3 = repl.process(df3).declarations
         val marker3 = marker + "1"
         val expected3 = """
-            @DataSchema(isOpen = false)
+            @DataSchema
             interface $marker3 : $markerFull
             
             val $dfName<$marker3>.city: $dataCol<$stringName> @JvmName("${marker3}_city") get() = this["city"] as $dataCol<$stringName>
@@ -118,10 +118,10 @@ class ReplCodeGenTests : BaseTest() {
         code4 shouldBe ""
 
         val df5 = typed.filter { weight != null }
-        val code5 = repl.process(df5, isMutable = false).declarations
+        val code5 = repl.process(df5).declarations
         val marker5 = marker + "2"
         val expected5 = """
-            @DataSchema(isOpen = false)
+            @DataSchema
             interface $marker5 : $markerFull
             
             val $dfName<$marker5>.weight: $dataCol<$intName> @JvmName("${marker5}_weight") get() = this["weight"] as $dataCol<$intName>
@@ -144,12 +144,12 @@ class ReplCodeGenTests : BaseTest() {
         repl.process<Test2._DataFrameType1>() shouldBe ""
 
         val expected = """
-            @DataSchema(isOpen = false)
+            @DataSchema
             interface ${Test2._DataFrameType2::class.simpleName!!} : ${Test2._DataFrameType::class.qualifiedName}, ${Test2._DataFrameType1::class.qualifiedName}
             
         """.trimIndent()
 
-        val code = repl.process(typed, isMutable = false).declarations.trimIndent()
+        val code = repl.process(typed).declarations.trimIndent()
         code shouldBe expected
     }
 
@@ -163,7 +163,7 @@ class ReplCodeGenTests : BaseTest() {
 
         val marker = Test2._DataFrameType2::class.simpleName!!
         val expected = """
-            @DataSchema(isOpen = false)
+            @DataSchema
             interface $marker : ${Test2._DataFrameType::class.qualifiedName}
             
             val $dfName<$marker>.city: $dataCol<$stringName?> @JvmName("${marker}_city") get() = this["city"] as $dataCol<$stringName?>
@@ -176,17 +176,17 @@ class ReplCodeGenTests : BaseTest() {
             val $dfRowName<$marker?>.weight: $intName? @JvmName("Nullable${marker}_weight") get() = this["weight"] as $intName?
         """.trimIndent()
 
-        val code = repl.process(typed, isMutable = false).declarations.trimIndent()
+        val code = repl.process(typed).declarations.trimIndent()
         code shouldBe expected
     }
 
     @Test
-    fun `process overriden property`() {
+    fun `process overridden property`() {
         val repl = ReplCodeGenerator.create()
         repl.process<Test3.A>()
         repl.process<Test3.B>()
         repl.process<Test3.C>()
-        val c = repl.process(Test3.df, Test3::df)
+        val c = repl.process(Test3.df, Test3::df) // TODO this now generates stuff
         c.declarations.shouldBeEmpty()
     }