dataframe-json/org.jetbrains.kotlinx.dataframe.impl.io

Package-level declarations

Types

AnyNameValueProperty

internal interface AnyNameValueProperty : NameValueProperty<Any?>

BufferedImageEncoder

internal class BufferedImageEncoder(options: Base64ImageEncodingOptions) : CustomEncoder

DataframeConvertableEncoder

internal class DataframeConvertableEncoder(encoders: List<CustomEncoder>, rowLimit: Int? = null) : CustomEncoder

SerializationKeys

internal object SerializationKeys

Properties

isPossibleToFindUnnamedColumns

internal val AnyFrame.isPossibleToFindUnnamedColumns: Boolean

SERIALIZATION_VERSION

internal const val SERIALIZATION_VERSION: String

Functions

encodeDataFrameWithMetadata

internal fun encodeDataFrameWithMetadata(frame: AnyFrame, rowLimit: Int, nestedRowLimit: Int? = null, customEncoders: List<CustomEncoder> = emptyList(), isFormatted: Boolean = false): JsonObject

encodeFrame

internal fun encodeFrame(frame: AnyFrame): JsonArray

Encodes each row of frame as the JSON record it was read from: the value of the unnamed "value" column, else the array of the unnamed "array" column, else an object of the remaining columns, else — when the row holds no values at all — null.

encodeFrameNoDynamicNestedTables

internal fun encodeFrameNoDynamicNestedTables(df: AnyFrame, limit: Int, isFormatted: Boolean): JsonObject

encodeFrameWithMetadata

internal fun encodeFrameWithMetadata(frame: AnyFrame, rowLimit: Int? = null, customEncoders: List<CustomEncoder> = emptyList()): JsonArray

Unlike encodeFrame, this is the wire format of the Kotlin Notebook plugin, described in docs/serialization_format.md: every row has to be an object with one entry per column of frame. So the unnamed value/array columns are encoded as regular columns here, instead of as the JSON record they were read from.

encodeGzip

internal fun ByteArray.encodeGzip(): ByteArray

encodeRow

internal fun encodeRow(frame: ColumnsContainer<*>, index: Int): JsonObject

encodeRowWithMetadata

internal fun encodeRowWithMetadata(frame: ColumnsContainer<*>, index: Int, rowLimit: Int? = null, customEncoders: List<CustomEncoder> = emptyList()): JsonElement?

encodeValue

internal fun encodeValue(col: AnyCol, index: Int, customEncoders: List<CustomEncoder> = emptyList()): JsonElement

extractArrayColumn

internal fun AnyFrame.extractArrayColumn(): DataColumn<*>?

extractValueColumn

internal fun AnyFrame.extractValueColumn(): DataColumn<*>?

fromJsonListAnyColumns

internal fun fromJsonListAnyColumns(records: List<*>, unifyNumbers: Boolean, isLenient: Boolean, keyValuePaths: List<JsonPath> = emptyList(), header: List<String> = emptyList(), jsonPath: JsonPath = JsonPath()): AnyFrame

Json to DataFrame converter that creates Any columns. A.k.a. TypeClashTactic.ANY_COLUMNS.

fromJsonListArrayAndValueColumns

internal fun fromJsonListArrayAndValueColumns(records: List<*>, unifyNumbers: Boolean, isLenient: Boolean, keyValuePaths: List<JsonPath> = emptyList(), header: List<String> = emptyList(), jsonPath: JsonPath = JsonPath()): AnyFrame

Json to DataFrame converter that creates allows creates value and array accessors instead of Any columns. A.k.a. TypeClashTactic.ARRAY_AND_VALUE_COLUMNS.

readJsonImpl

internal fun readJsonImpl(parsed: Any?, unifyNumbers: Boolean, header: List<String>, isLenient: Boolean, keyValuePaths: List<JsonPath> = emptyList(), typeClashTactic: JSON.TypeClashTactic = ARRAY_AND_VALUE_COLUMNS): DataFrame<*>

resizeKeepingAspectRatio

internal fun BufferedImage.resizeKeepingAspectRatio(maxSize: Int, resultImageType: Int = BufferedImage.TYPE_INT_ARGB, interpolation: Any = RenderingHints.VALUE_INTERPOLATION_NEAREST_NEIGHBOR, renderingQuality: Any = RenderingHints.VALUE_RENDER_QUALITY, antialiasing: Any = RenderingHints.VALUE_ANTIALIAS_ON, observer: ImageObserver? = null): BufferedImage

toBase64

internal fun ByteArray.toBase64(): String