Fix for #573: Change serialization format for rendering in IntelliJ IDEA

core/generated-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/impl/io/writeJson.kt

@@ -0,0 +1,212 @@
+package org.jetbrains.kotlinx.dataframe.impl.io
+
+import com.beust.klaxon.JsonArray
+import com.beust.klaxon.JsonObject
+import com.beust.klaxon.KlaxonJson
+import org.jetbrains.kotlinx.dataframe.AnyCol
+import org.jetbrains.kotlinx.dataframe.AnyFrame
+import org.jetbrains.kotlinx.dataframe.ColumnsContainer
+import org.jetbrains.kotlinx.dataframe.DataColumn
+import org.jetbrains.kotlinx.dataframe.api.indices
+import org.jetbrains.kotlinx.dataframe.api.isList
+import org.jetbrains.kotlinx.dataframe.api.name
+import org.jetbrains.kotlinx.dataframe.api.rows
+import org.jetbrains.kotlinx.dataframe.api.toDataFrame
+import org.jetbrains.kotlinx.dataframe.columns.ColumnGroup
+import org.jetbrains.kotlinx.dataframe.columns.ColumnKind
+import org.jetbrains.kotlinx.dataframe.columns.FrameColumn
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.COLUMNS
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.DATA
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.KIND
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.KOTLIN_DATAFRAME
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.METADATA
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.NCOL
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.NROW
+import org.jetbrains.kotlinx.dataframe.impl.io.SerializationKeys.VERSION
+import org.jetbrains.kotlinx.dataframe.io.arrayColumnName
+import org.jetbrains.kotlinx.dataframe.io.valueColumnName
+import org.jetbrains.kotlinx.dataframe.ncol
+import org.jetbrains.kotlinx.dataframe.nrow
+import org.jetbrains.kotlinx.dataframe.typeClass
+
+internal fun KlaxonJson.encodeRow(frame: ColumnsContainer<*>, index: Int): JsonObject? {
+    val values = frame.columns().map { col ->
+        when (col) {
+            is ColumnGroup<*> -> encodeRow(col, index)
+            is FrameColumn<*> -> encodeFrame(col[index])
+            else -> encodeValue(col, index)
+        }.let { col.name to it }
+    }
+    if (values.isEmpty()) return null
+    return obj(values)
+}
+
+internal object SerializationKeys {
+    const val DATA = "data"
+    const val METADATA = "metadata"
+    const val KIND = "kind"
+    const val NCOL = "ncol"
+    const val NROW = "nrow"
+    const val VERSION = "\$version"
+    const val COLUMNS = "columns"
+    const val KOTLIN_DATAFRAME = "kotlin_dataframe"
+}
+
+internal const val SERIALIZATION_VERSION = "2.0.0"
+
+internal fun KlaxonJson.encodeRowWithMetadata(
+    frame: ColumnsContainer<*>,
+    index: Int,
+    rowLimit: Int? = null
+): JsonObject? {
+    val values = frame.columns().map { col ->
+        when (col) {
+            is ColumnGroup<*> -> obj(
+                DATA to encodeRowWithMetadata(col, index, rowLimit),
+                METADATA to obj(KIND to ColumnKind.Group.toString())
+            )
+
+            is FrameColumn<*> -> {
+                val data = if (rowLimit == null) encodeFrameWithMetadata(col[index])
+                else encodeFrameWithMetadata(col[index].rows().take(rowLimit).toDataFrame(), rowLimit)
+                obj(
+                    DATA to data,
+                    METADATA to obj(
+                        KIND to ColumnKind.Frame.toString(),
+                        NCOL to col[index].ncol,
+                        NROW to col[index].nrow
+                    )
+                )
+            }
+
+            else -> encodeValue(col, index)
+        }.let { col.name to it }
+    }
+    if (values.isEmpty()) return null
+    return obj(values)
+}
+
+private val valueTypes =
+    setOf(Boolean::class, Double::class, Int::class, Float::class, Long::class, Short::class, Byte::class)
+
+internal fun KlaxonJson.encodeValue(col: AnyCol, index: Int): Any? = when {
+    col.isList() -> col[index]?.let { array(it as List<*>) } ?: array()
+    col.typeClass in valueTypes -> {
+        val v = col[index]
+        if ((v is Double && v.isNaN()) || (v is Float && v.isNaN())) {
+            v.toString()
+        } else v
+    }
+
+    else -> col[index]?.toString()
+}
+
+internal fun KlaxonJson.encodeFrameWithMetadata(frame: AnyFrame, rowLimit: Int? = null): JsonArray<*> {
+    val valueColumn = frame.extractValueColumn()
+    val arrayColumn = frame.extractArrayColumn()
+
+    val arraysAreFrames = arrayColumn?.kind() == ColumnKind.Frame
+
+    val data = frame.indices().map { rowIndex ->
+        valueColumn
+            ?.get(rowIndex)
+            ?: arrayColumn?.get(rowIndex)
+                ?.let {
+                    if (arraysAreFrames) encodeFrameWithMetadata(it as AnyFrame, rowLimit) else null
+                }
+            ?: encodeRowWithMetadata(frame, rowIndex, rowLimit)
+    }
+
+    return array(data)
+}
+
+internal fun AnyFrame.extractValueColumn(): DataColumn<*>? {
+    val allColumns = columns()
+
+    return allColumns.filter { it.name.startsWith(valueColumnName) }
+        .takeIf { isPossibleToFindUnnamedColumns }
+        ?.maxByOrNull { it.name }?.let { valueCol ->
+            if (valueCol.kind() != org.jetbrains.kotlinx.dataframe.columns.ColumnKind.Value) { // check that value in this column is not null only when other values are null
+                null
+            } else {
+                // check that value in this column is not null only when other values are null
+                val isValidValueColumn = rows().all { row ->
+                    if (valueCol[row] != null) {
+                        allColumns.all { col ->
+                            if (col.name != valueCol.name) col[row] == null
+                            else true
+                        }
+                    } else true
+                }
+                if (isValidValueColumn) valueCol
+                else null
+            }
+        }
+}
+
+// if there is only 1 column, then `isValidValueColumn` always true.
+// But at the same time, we shouldn't treat dataFrameOf("value")(1,2,3) like unnamed column
+// because it was created by user.
+internal val AnyFrame.isPossibleToFindUnnamedColumns: Boolean
+    get() = columns().size != 1
+
+internal fun AnyFrame.extractArrayColumn(): DataColumn<*>? {
+    val allColumns = columns()
+
+    return columns().filter { it.name.startsWith(arrayColumnName) }
+        .takeIf { isPossibleToFindUnnamedColumns }
+        ?.maxByOrNull { it.name }?.let { arrayCol ->
+            if (arrayCol.kind() == org.jetbrains.kotlinx.dataframe.columns.ColumnKind.Group) null
+            else {
+                // check that value in this column is not null only when other values are null
+                val isValidArrayColumn = rows().all { row ->
+                    if (arrayCol[row] != null) {
+                        allColumns.all { col ->
+                            if (col.name != arrayCol.name) col[row] == null
+                            else true
+                        }
+                    } else true
+                }
+                if (isValidArrayColumn) arrayCol
+                else null
+            }
+        }
+}
+
+internal fun KlaxonJson.encodeFrame(frame: AnyFrame): JsonArray<*> {
+    val valueColumn = frame.extractValueColumn()
+    val arrayColumn = frame.extractArrayColumn()
+
+    val arraysAreFrames = arrayColumn?.kind() == ColumnKind.Frame
+
+    val data = frame.indices().map { rowIndex ->
+        valueColumn
+            ?.get(rowIndex)
+            ?: arrayColumn?.get(rowIndex)
+                ?.let {
+                    if (arraysAreFrames) encodeFrame(it as AnyFrame) else null
+                }
+            ?: encodeRow(frame, rowIndex)
+    }
+
+    return array(data)
+}
+
+internal fun KlaxonJson.encodeDataFrameWithMetadata(
+    frame: AnyFrame,
+    rowLimit: Int,
+    nestedRowLimit: Int? = null,
+): JsonObject {
+    return obj(
+        VERSION to SERIALIZATION_VERSION,
+        METADATA to obj(
+            COLUMNS to frame.columnNames(),
+            NROW to frame.rowsCount(),
+            NCOL to frame.columnsCount()
+        ),
+        KOTLIN_DATAFRAME to encodeFrameWithMetadata(
+            frame.rows().take(rowLimit).toDataFrame(),
+            rowLimit = nestedRowLimit
+        ),
+    )
+}