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JolanrensenJolanrensen
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Made mri-like example for Multik inside dataframe
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  • examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/multik

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‎examples/idea-examples/unsupported-data-sources/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/multik/multikInsideDataFrame.kt

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package org.jetbrains.kotlinx.dataframe.examples.multik
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import kotlinx.datetime.LocalDate
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import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
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import org.jetbrains.kotlinx.dataframe.api.append
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import org.jetbrains.kotlinx.dataframe.api.cast
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import org.jetbrains.kotlinx.dataframe.api.mapToFrame
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import org.jetbrains.kotlinx.dataframe.api.print
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import org.jetbrains.kotlinx.dataframe.api.single
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import org.jetbrains.kotlinx.dataframe.api.toDataFrame
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import org.jetbrains.kotlinx.dataframe.io.toStandaloneHtml
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import org.jetbrains.kotlinx.multik.api.identity
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import org.jetbrains.kotlinx.multik.api.mk
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import org.jetbrains.kotlinx.multik.ndarray.data.D2Array
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import org.jetbrains.kotlinx.multik.ndarray.data.set
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import kotlin.math.cos
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import kotlin.math.sin
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import kotlin.math.tan
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@DataSchema
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data class Transformation(
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val type: TransformationType,
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val parameters: Map<String, Double>,
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val note: String,
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val matrix: D2Array<Double>,
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)
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enum class TransformationType {
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IDENTITY,
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TRANSLATION,
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SCALING,
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ROTATION,
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SHEARING,
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REFLECTION_ABOUT_ORIGIN,
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REFLECTION_ABOUT_X_AXIS,
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REFLECTION_ABOUT_Y_AXIS,
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}
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import org.jetbrains.kotlinx.multik.api.rand
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import org.jetbrains.kotlinx.multik.ndarray.data.D3Array
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import org.jetbrains.kotlinx.multik.ndarray.data.D4Array
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import java.time.Month.JULY
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/**
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* IDK yet about this one... TODO
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* DataFrames can store anything inside, including Multik ndarrays.
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* This can be useful for storing matrices for easier access later or to simply organize data read from other files.
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* For example, MRI data is often stored as 3D arrays and sometimes even 4D arrays.
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*/
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fun main() {
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// DataFrames can store anything inside, including Multik nd arrays.
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// This can be useful for storing matrices for easier access later,
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// such as affine transformations when making 2D graphics!
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// (https://en.wikipedia.org/wiki/Affine_transformation)
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// let's make a transformation sequence that rotates and scales an image in place.
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// It's currently 100x50, positioned with its left bottom corner at (x=10, y=0)
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val transformations = listOf(
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Transformation(
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type = TransformationType.TRANSLATION,
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parameters = mapOf("x" to -10.0, "y" to 0.0),
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note = "Translate so left-bottom touches origin",
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matrix = translationMatrixOf(x = -10.0, y = 0.0),
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),
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Transformation(
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type = TransformationType.SCALING,
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parameters = mapOf("w" to 2.0, "h" to 2.0),
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note = "Scale by x2",
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matrix = scaleMatrixOf(w = 2.0, h = 2.0),
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),
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Transformation(
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type = TransformationType.TRANSLATION,
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parameters = mapOf("x" to -100.0, "y" to -50.0),
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note = "Translate so the new image center is at the origin",
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matrix = translationMatrixOf(x = -100.0, y = -50.0),
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),
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Transformation(
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type = TransformationType.ROTATION,
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parameters = mapOf("angle" to 45.0),
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note = "Rotate by 45 degrees",
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matrix = rotationMatrixOf(angle = 45.0),
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),
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Transformation(
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type = TransformationType.TRANSLATION,
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parameters = mapOf("x" to 10.0 + 50.0, "y" to 0.0 + 25.0),
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note = "Translate back so the center is at the same original position",
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matrix = translationMatrixOf(x = 10.0 + 50.0, y = 0.0 + 25.0),
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),
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// imaginary list of patient data
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@Suppress("ktlint:standard:argument-list-wrapping")
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val metadata = listOf(
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MriMetadata(10012L, 25, "Healthy", LocalDate(2023, 1, 1)),
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MriMetadata(10013L, 45, "Tuberculosis", LocalDate(2023, 2, 15)),
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MriMetadata(10014L, 32, "Healthy", LocalDate(2023, 3, 22)),
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MriMetadata(10015L, 58, "Pneumonia", LocalDate(2023, 4, 8)),
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MriMetadata(10016L, 29, "Tuberculosis", LocalDate(2023, 5, 30)),
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MriMetadata(10017L, 42, "Healthy", LocalDate(2023, 6, 15)),
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MriMetadata(10018L, 37, "Healthy", LocalDate(2023, 7, 1)),
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MriMetadata(10019L, 55, "Healthy", LocalDate(2023, 8, 15)),
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MriMetadata(10020L, 28, "Healthy", LocalDate(2023, 9, 1)),
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MriMetadata(10021L, 44, "Healthy", LocalDate(2023, 10, 15)),
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MriMetadata(10022L, 31, "Healthy", LocalDate(2023, 11, 1)),
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).toDataFrame()
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transformations.print(borders = true)
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transformations.toStandaloneHtml().openInBrowser()
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}
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fun identityMatrix(): D2Array<Double> = mk.identity(3)
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// "reading" the results from "files"
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val results = metadata.mapToFrame {
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+patientId
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+age
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+diagnosis
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+scanDate
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"t1WeightedMri" from { readT1WeightedMri(patientId) }
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"fMriBoldSeries" from { readFMRiBoldSeries(patientId) }
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}.cast<MriResults>(verify = true)
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.append()
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/** Returns a 3x3 affine transformation matrix that translates by (x, y) */
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fun translationMatrixOf(x: Double = 0.0, y: Double = 0.0): D2Array<Double> =
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identityMatrix().apply {
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this[0, 2] = x
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this[1, 2] = y
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}
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results.print(borders = true)
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/** Returns a 3x3 affine transformation matrix that scales by (w, h) about the origin */
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fun scaleMatrixOf(w: Double = 1.0, h: Double = 1.0): D2Array<Double> =
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identityMatrix().apply {
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this[0, 0] = w
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this[1, 1] = h
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}
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// now when we want to check and visualize the T1-weighted MRI scan
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// for that one healthy patient in July, we can do:
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val scan = results
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.single { scanDate.month == JULY && diagnosis == "Healthy" }
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.t1WeightedMri
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/** Returns a 3x3 affine transformation matrix that rotates by [angle] degrees about the origin */
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fun rotationMatrixOf(angle: Double): D2Array<Double> {
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val cos = cos(angle)
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val sin = sin(angle)
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return identityMatrix().apply {
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this[0, 0] = cos
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this[0, 1] = -sin
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this[1, 0] = sin
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this[1, 1] = cos
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}
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// easy :)
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visualize(scan)
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}
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/** Returns a 3x3 affine transformation matrix that shears by [x] and [y] */
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fun shearingMatrixOf(x: Double = 0.0, y: Double = 0.0): D2Array<Double> =
112-
identityMatrix().apply {
113-
this[0, 1] = tan(x)
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this[1, 0] = tan(y)
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}
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@DataSchema
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data class MriMetadata(
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/** Unique patient ID. */
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val patientId: Long,
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/** Patient age. */
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val age: Int,
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/** Clinical diagnosis (e.g. "Healthy", "Tuberculosis") */
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val diagnosis: String,
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/** Date of the scan */
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val scanDate: LocalDate,
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)
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@DataSchema
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data class MriResults(
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/** Unique patient ID. */
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val patientId: Long,
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/** Patient age. */
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val age: Int,
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/** Clinical diagnosis (e.g. "Healthy", "Tuberculosis") */
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val diagnosis: String,
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/** Date of the scan */
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val scanDate: LocalDate,
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/**
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* T1-weighted anatomical MRI scan.
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*
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* Dimensions: (256 x 256 x 180)
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* - 256 width x 256 height
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* - 180 slices
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*/
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val t1WeightedMri: D3Array<Float>,
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/**
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* Blood oxygenation level-dependent (BOLD) time series from an fMRI scan.
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*
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* Dimensions: (64 x 64 x 30 x 200)
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* - 64 width x 64 height
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* - 30 slices
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* - 200 timepoints
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*/
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val fMriBoldSeries: D4Array<Float>,
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)
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/** Returns a 3x3 affine transformation matrix that reflects about the origin */
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fun reflectionAboutOriginMatrix(): D2Array<Double> =
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identityMatrix().apply {
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this[0, 0] = -1.0
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this[1, 1] = -1.0
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}
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fun readT1WeightedMri(id: Long): D3Array<Float> {
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// This should in practice, of course, read the actual data, but for this example we just return a dummy array
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return mk.rand(256, 256, 180)
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}
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/** Returns a 3x3 affine transformation matrix that reflects about the x-axis */
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fun reflectionAboutXAxisMatrix(): D2Array<Double> =
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identityMatrix().apply {
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this[1, 1] = -1.0
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}
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fun readFMRiBoldSeries(id: Long): D4Array<Float> {
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// This should in practice, of course, read the actual data, but for this example we just return a dummy array
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return mk.rand(64, 64, 30, 200)
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}
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/** Returns a 3x3 affine transformation matrix that reflects about the y-axis */
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fun reflectionAboutYAxisMatrix(): D2Array<Double> =
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identityMatrix().apply {
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this[0, 0] = -1.0
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}
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fun visualize(scan: D3Array<Float>) {
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// This would then actually visualize the scan
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}

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