.space.kts

@@ -34,7 +34,7 @@ job("Publish") {
             api.space().projects.automation.deployments.start(
                 project = api.projectIdentifier(),
                 targetIdentifier = TargetIdentifier.Key(projectName),
-                version = version + revisionSuffix,
+                version = version+revisionSuffix,
                 // automatically update deployment status based on the status of a job
                 syncWithAutomationJob = true
             )

CHANGELOG.md

@@ -77,8 +77,7 @@
 - Major refactor of tensors (only minor API changes)
 - Kotlin 1.8.20
 - `LazyStructure` `deffered` -> `async` to comply with coroutines code style
-- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added
-  to `DoubleTensorAlgebra`.
+- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added to `DoubleTensorAlgebra`.
 - Multik went MPP
 
 ### Removed
@@ -237,11 +236,9 @@
 - MST to JVM bytecode translator (https://github.com/mipt-npm/kmath/pull/94)
 - FloatBuffer (specialized MutableBuffer over FloatArray)
 - FlaggedBuffer to associate primitive numbers buffer with flags (to mark values infinite or missing, etc.)
-- Specialized builder functions for all primitive buffers
-  like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
+- Specialized builder functions for all primitive buffers like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
 - Interface `NumericAlgebra` where `number` operation is available to convert numbers to algebraic elements
-- Inverse trigonometric functions support in
-  ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
+- Inverse trigonometric functions support in ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
 - New space extensions: `average` and `averageWith`
 - Local coding conventions
 - Geometric Domains API in `kmath-core`
@@ -254,8 +251,7 @@
 - `readAsMemory` now has `throws IOException` in JVM signature.
 - Several functions taking functional types were made `inline`.
 - Several functions taking functional types now have `callsInPlace` contracts.
-- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor
-  optimizations
+- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations
 - `power(T, Int)` extension function has preconditions and supports `Field<T>`
 - Memory objects have more preconditions (overflow checking)
 - `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114)

README.md

@@ -25,8 +25,7 @@ experience could be achieved with [kmath-for-real](/kmath-for-real) extension mo
 
 # Goal
 
-* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
-  Wasm).
+* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and Wasm).
 * Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
 * Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
 
@@ -56,181 +55,150 @@ module definitions below. The module stability could have the following levels:
 
 ## Modules
 
-### [attributes-kt](attributes-kt)
 
+### [attributes-kt](attributes-kt)
 > An API and basic implementation for arranging objects in a continuous memory block.
 >
 > **Maturity**: DEVELOPMENT
 
 ### [benchmarks](benchmarks)
-
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [examples](examples)
-
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-ast](kmath-ast)
-
 >
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
-> - [expression-language](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and
-    its parser
-> - [mst-jvm-codegen](kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode
-    compiler
+> - [expression-language](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser
+> - [mst-jvm-codegen](kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
 > - [mst-js-codegen](kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
-> - [rendering](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST
-    rendering
+> - [rendering](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
+
 
 ### [kmath-commons](kmath-commons)
-
 > Commons math binding for kmath
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-complex](kmath-complex)
-
 > Complex numbers and quaternions.
 >
 > **Maturity**: PROTOTYPE
 >
 > **Features:**
 > - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex numbers operations
-> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their
-    composition
+> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their composition
+
 
 ### [kmath-core](kmath-core)
-
 > Core classes, algebra definitions, basic linear algebra
 >
 > **Maturity**: DEVELOPMENT
 >
 > **Features:**
-> - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like
-    rings, spaces and fields.
-> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures
-    and operations on them.
-> - [linear](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra
-    operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix
-    inversion and LU decomposition.
+> - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
+> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
+> - [linear](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
 > - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
-> - [expressions](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical
-    expression once, users will be able to apply different types of
-    objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high
-    performance calculations to code generation.
+> - [expressions](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of 
+objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
+performance calculations to code generation.
 > - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
-> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic
-    differentiation
+> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
+
 
 ### [kmath-coroutines](kmath-coroutines)
-
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-dimensions](kmath-dimensions)
-
 > A proof of concept module for adding type-safe dimensions to structures
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-ejml](kmath-ejml)
-
 >
 > **Maturity**: PROTOTYPE
 >
 > **Features:**
 > - [ejml-vector](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations.
 > - [ejml-matrix](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation.
-> - [ejml-linear-space](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace
-    implementations.
+> - [ejml-linear-space](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations.
+
 
 ### [kmath-for-real](kmath-for-real)
-
 > Extension module that should be used to achieve numpy-like behavior.
-> All operations are specialized to work with `Double` numbers without declaring algebraic contexts.
-> One can still use generic algebras though.
+All operations are specialized to work with `Double` numbers without declaring algebraic contexts.
+One can still use generic algebras though.
 >
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
-> - [DoubleVector](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like
-    operations for Buffers/Points
-> - [DoubleMatrix](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like
-    operations for 2d real structures
+> - [DoubleVector](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for Buffers/Points
+> - [DoubleMatrix](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures
 > - [grids](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators
 
-### [kmath-functions](kmath-functions)
 
+### [kmath-functions](kmath-functions)
 > Functions, integration and interpolation
 >
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
-> - [piecewise](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt) : Piecewise
-    functions.
-> - [polynomials](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Polynomial
-    functions.
-> - [linear interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt) :
-    Linear XY interpolator.
-> - [spline interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) :
-    Cubic spline XY interpolator.
+> - [piecewise](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt) : Piecewise functions.
+> - [polynomials](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Polynomial functions.
+> - [linear interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt) : Linear XY interpolator.
+> - [spline interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) : Cubic spline XY interpolator.
 > - [integration](kmath-functions/#) : Univariate and multivariate quadratures
 
-### [kmath-geometry](kmath-geometry)
 
+### [kmath-geometry](kmath-geometry)
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-histograms](kmath-histograms)
-
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-jafama](kmath-jafama)
-
 > Jafama integration module
 >
 > **Maturity**: DEPRECATED
 >
 > **Features:**
-> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations
-    based on Jafama
+> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
+
 
 ### [kmath-jupyter](kmath-jupyter)
-
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-kotlingrad](kmath-kotlingrad)
-
 > Kotlin∇ integration module
 >
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
-> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) :
-    MST based DifferentiableExpression.
-> - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) :
-    Conversions between Kotlin∇'s SFun and MST
+> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression.
+> - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) : Conversions between Kotlin∇'s SFun and MST
+
 
 ### [kmath-memory](kmath-memory)
-
 > An API and basic implementation for arranging objects in a continuous memory block.
 >
 > **Maturity**: DEVELOPMENT
 
 ### [kmath-multik](kmath-multik)
-
 > JetBrains Multik connector
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-nd4j](kmath-nd4j)
-
 > ND4J NDStructure implementation and according NDAlgebra classes
 >
 > **Maturity**: DEPRECATED
@@ -240,52 +208,45 @@ module definitions below. The module stability could have the following levels:
 > - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long
 > - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
 
-### [kmath-optimization](kmath-optimization)
 
+### [kmath-optimization](kmath-optimization)
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-stat](kmath-stat)
-
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-symja](kmath-symja)
-
 > Symja integration module
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-tensorflow](kmath-tensorflow)
-
 > Google tensorflow connector
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-tensors](kmath-tensors)
-
 >
 > **Maturity**: PROTOTYPE
 >
 > **Features:**
-> - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic
-    linear algebra operations on tensors (plus, dot, etc.)
-> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt) :
-    Basic linear algebra operations implemented with broadcasting.
-> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) :
-    Advanced linear algebra operations like LU decomposition, SVD, etc.
+> - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
+> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
+> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
+
 
 ### [kmath-viktor](kmath-viktor)
-
 > Binding for https://github.com/JetBrains-Research/viktor
 >
 > **Maturity**: DEVELOPMENT
 
 ### [test-utils](test-utils)
-
 >
 > **Maturity**: EXPERIMENTAL
 
+
 ## Multi-platform support
 
 KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
@@ -296,19 +257,16 @@ feedback are also welcome.
 
 ## Performance
 
-Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
-achieve both
+Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to achieve both
 performance and flexibility.
 
 We expect to focus on creating a convenient universal API first and then work on increasing performance for specific
 cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
-native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
-better than SciPy.
+native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
 
 ## Requirements
 
-KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or
-Oracle GraalVM for execution to get better performance.
+KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or Oracle GraalVM for execution to get better performance.
 
 ### Repositories
 
@@ -331,7 +289,4 @@ dependencies {
 ## Contributing
 
 The project requires a lot of additional work. The most important thing we need is feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-marked
-with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-label.
+required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues marked with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) label.

attributes-kt/build.gradle.kts

@@ -3,7 +3,7 @@ plugins {
     `maven-publish`
 }
 
-version = rootProject.extra.get("attributesVersion").toString()
+version = "0.1.0"
 
 kscience {
     jvm()

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt

@@ -30,28 +30,20 @@ public interface Attributes {
     override fun hashCode(): Int
 
     public companion object {
-        public val EMPTY: Attributes = object : Attributes {
-            override val content: Map<out Attribute<*>, Any?> get() = emptyMap()
-
-            override fun toString(): String = "Attributes.EMPTY"
-
-            override fun equals(other: Any?): Boolean = (other as? Attributes)?.isEmpty() ?: false
-
-            override fun hashCode(): Int = Unit.hashCode()
-        }
+        public val EMPTY: Attributes = AttributesImpl(emptyMap())
 
         public fun equals(a1: Attributes, a2: Attributes): Boolean =
             a1.keys == a2.keys && a1.keys.all { a1[it] == a2[it] }
     }
 }
 
-internal class MapAttributes(override val content: Map<out Attribute<*>, Any?>) : Attributes {
+internal class AttributesImpl(override val content: Map<out Attribute<*>, Any?>) : Attributes {
     override fun toString(): String = "Attributes(value=${content.entries})"
     override fun equals(other: Any?): Boolean = other is Attributes && Attributes.equals(this, other)
     override fun hashCode(): Int = content.hashCode()
 }
 
-public fun Attributes.isEmpty(): Boolean = keys.isEmpty()
+public fun Attributes.isEmpty(): Boolean = content.isEmpty()
 
 /**
  * Get attribute value or default
@@ -83,7 +75,7 @@ public inline fun <reified A : FlagAttribute> Attributes.hasFlag(): Boolean =
 public fun <T, A : Attribute<T>> Attributes.withAttribute(
     attribute: A,
     attrValue: T,
-): Attributes = MapAttributes(content + (attribute to attrValue))
+): Attributes = AttributesImpl(content + (attribute to attrValue))
 
 public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attributes =
     withAttribute(attribute, Unit)
@@ -91,15 +83,15 @@ public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attribu
 /**
  * Create a new [Attributes] by modifying the current one
  */
-public fun <O> Attributes.modified(block: AttributesBuilder<O>.() -> Unit): Attributes = Attributes<O> {
-    putAll(this@modified)
+public fun <T> Attributes.modify(block: AttributesBuilder<T>.() -> Unit): Attributes = Attributes<T> {
+    from(this@modify)
     block()
 }
 
 /**
  * Create new [Attributes] by removing [attribute] key
  */
-public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = MapAttributes(content.minus(attribute))
+public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = AttributesImpl(content.minus(attribute))
 
 /**
  * Add an element to a [SetAttribute]
@@ -109,7 +101,7 @@ public fun <T, A : SetAttribute<T>> Attributes.withAttributeElement(
     attrValue: T,
 ): Attributes {
     val currentSet: Set<T> = get(attribute) ?: emptySet()
-    return MapAttributes(
+    return AttributesImpl(
         content + (attribute to (currentSet + attrValue))
     )
 }
@@ -122,7 +114,7 @@ public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
     attrValue: T,
 ): Attributes {
     val currentSet: Set<T> = get(attribute) ?: emptySet()
-    return MapAttributes(content + (attribute to (currentSet - attrValue)))
+    return AttributesImpl(content + (attribute to (currentSet - attrValue)))
 }
 
 /**
@@ -131,13 +123,13 @@ public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
 public fun <T, A : Attribute<T>> Attributes(
     attribute: A,
     attrValue: T,
-): Attributes = MapAttributes(mapOf(attribute to attrValue))
+): Attributes = AttributesImpl(mapOf(attribute to attrValue))
 
 /**
  * Create Attributes with a single [Unit] valued attribute
  */
 public fun <A : Attribute<Unit>> Attributes(
     attribute: A,
-): Attributes = MapAttributes(mapOf(attribute to Unit))
+): Attributes = AttributesImpl(mapOf(attribute to Unit))
 
-public operator fun Attributes.plus(other: Attributes): Attributes = MapAttributes(content + other.content)
+public operator fun Attributes.plus(other: Attributes): Attributes = AttributesImpl(content + other.content)

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributesBuilder.kt

@@ -6,18 +6,19 @@
 package space.kscience.attributes
 
 /**
- * A builder for [Attributes].
- * The builder is not thread safe
+ * A safe builder for [Attributes]
  *
  * @param O type marker of an owner object, for which these attributes are made
  */
-public class AttributesBuilder<out O> internal constructor() : Attributes {
+public class AttributesBuilder<out O> internal constructor(
+    private val map: MutableMap<Attribute<*>, Any?>,
+) : Attributes {
 
-    private val map = mutableMapOf<Attribute<*>, Any?>()
+    public constructor() : this(mutableMapOf())
 
-    override fun toString(): String = "Attributes(value=${map.entries})"
+    override fun toString(): String = "Attributes(value=${content.entries})"
     override fun equals(other: Any?): Boolean = other is Attributes && Attributes.equals(this, other)
-    override fun hashCode(): Int = map.hashCode()
+    override fun hashCode(): Int = content.hashCode()
 
     override val content: Map<out Attribute<*>, Any?> get() = map
 
@@ -33,18 +34,13 @@ public class AttributesBuilder<out O> internal constructor() : Attributes {
         set(this, value)
     }
 
-    public infix fun <V> Attribute<V>.put(value: V?) {
-        set(this, value)
-    }
-
-    /**
-     * Put all attributes for given [attributes]
-     */
-    public fun putAll(attributes: Attributes) {
+    public fun from(attributes: Attributes) {
         map.putAll(attributes.content)
     }
 
-    public infix fun <V> SetAttribute<V>.add(attrValue: V) {
+    public fun <V> SetAttribute<V>.add(
+        attrValue: V,
+    ) {
         val currentSet: Set<V> = get(this) ?: emptySet()
         map[this] = currentSet + attrValue
     }
@@ -52,17 +48,15 @@ public class AttributesBuilder<out O> internal constructor() : Attributes {
     /**
      * Remove an element from [SetAttribute]
      */
-    public infix fun <V> SetAttribute<V>.remove(attrValue: V) {
+    public fun <V> SetAttribute<V>.remove(
+        attrValue: V,
+    ) {
         val currentSet: Set<V> = get(this) ?: emptySet()
         map[this] = currentSet - attrValue
     }
 
-    public fun build(): Attributes = MapAttributes(map)
+    public fun build(): Attributes = AttributesImpl(map)
 }
 
-/**
- * Create [Attributes] with a given [builder]
- * @param O the type for which attributes are built. The type is used only during compilation phase for static extension dispatch
- */
-public fun <O> Attributes(builder: AttributesBuilder<O>.() -> Unit): Attributes =
+public inline fun <O> Attributes(builder: AttributesBuilder<O>.() -> Unit): Attributes =
     AttributesBuilder<O>().apply(builder).build()

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/PolymorphicAttribute.kt

@@ -21,14 +21,11 @@ public abstract class PolymorphicAttribute<T>(public val type: SafeType<T>) : At
 /**
  * Get a polymorphic attribute using attribute factory
  */
-@UnstableAttributesAPI
-public operator fun <T> Attributes.get(attributeKeyBuilder: () -> PolymorphicAttribute<T>): T? =
-    get(attributeKeyBuilder())
+public operator fun <T> Attributes.get(attributeKeyBuilder: () -> PolymorphicAttribute<T>): T? = get(attributeKeyBuilder())
 
 /**
  * Set a polymorphic attribute using its factory
  */
-@UnstableAttributesAPI
 public operator fun <O, T> AttributesBuilder<O>.set(attributeKeyBuilder: () -> PolymorphicAttribute<T>, value: T) {
     set(attributeKeyBuilder(), value)
 }

benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

@@ -94,7 +94,6 @@ class ExpressionsInterpretersBenchmark {
         }
 
         private val mst = node.toExpression(Float64Field)
-
         @OptIn(UnstableKMathAPI::class)
         private val wasm = node.wasmCompileToExpression(Float64Field)
         private val estree = node.estreeCompileToExpression(Float64Field)

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt

@@ -67,7 +67,7 @@ internal class BigIntBenchmark {
 
     @Benchmark
     fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmLargeNumber * kmLargeNumber)
+        blackhole.consume(kmLargeNumber*kmLargeNumber)
     }
 
     @Benchmark
@@ -77,7 +77,7 @@ internal class BigIntBenchmark {
 
     @Benchmark
     fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmLargeNumber * jvmLargeNumber)
+        blackhole.consume(jvmLargeNumber*jvmLargeNumber)
     }
 
     @Benchmark

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt

@@ -75,6 +75,6 @@ internal class BufferBenchmark {
 
     private companion object {
         private const val size = 100
-        private val reversedIndices = IntArray(size) { it }.apply { reverse() }
+        private val reversedIndices = IntArray(size){it}.apply { reverse() }
     }
 }

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/IntegrationBenchmark.kt

@@ -24,7 +24,7 @@ internal class IntegrationBenchmark {
     fun doubleIntegration(blackhole: Blackhole) {
         val res = Double.algebra.gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
             //sin(1 / x)
-            1 / x
+            1/x
         }.value
         blackhole.consume(res)
     }
@@ -33,7 +33,7 @@ internal class IntegrationBenchmark {
     fun complexIntegration(blackhole: Blackhole) = with(Complex.algebra) {
         val res = gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
 //            sin(1 / x) + i * cos(1 / x)
-            1 / x - i / x
+            1/x - i/x
         }.value
         blackhole.consume(res)
     }

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt

@@ -13,6 +13,8 @@ import space.kscience.kmath.jafama.JafamaDoubleField
 import space.kscience.kmath.jafama.StrictJafamaDoubleField
 import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.invoke
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.random.Random
 
 @State(Scope.Benchmark)
@@ -34,6 +36,7 @@ internal class JafamaBenchmark {
 }
 
 private inline fun invokeBenchmarks(blackhole: Blackhole, expr: (Double) -> Double) {
+    contract { callsInPlace(expr, InvocationKind.AT_LEAST_ONCE) }
     val rng = Random(0)
     repeat(1000000) { blackhole.consume(expr(rng.nextDouble())) }
 }

build.gradle.kts

@@ -6,8 +6,6 @@ plugins {
     id("org.jetbrains.kotlinx.kover") version "0.7.6"
 }
 
-val attributesVersion by extra("0.1.0")
-
 allprojects {
     repositories {
         maven("https://repo.kotlin.link")
@@ -65,7 +63,7 @@ ksciencePublish {
         useApache2Licence()
         useSPCTeam()
     }
-    repository("spc", "https://maven.sciprog.center/kscience")
+    repository("spc","https://maven.sciprog.center/kscience")
     sonatype("https://oss.sonatype.org")
 }
 

buildSrc/build.gradle.kts

@@ -24,8 +24,8 @@ dependencies {
     implementation("com.fasterxml.jackson.module:jackson-module-kotlin:2.14.+")
 }
 
-kotlin {
-    jvmToolchain {
+kotlin{
+    jvmToolchain{
         languageVersion.set(JavaLanguageVersion.of(11))
     }
     sourceSets.all {

buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/addBenchmarkProperties.kt

@@ -63,8 +63,7 @@ fun Project.addBenchmarkProperties() {
                     if (resDirectory == null || !(resDirectory.resolve("jvm.json")).exists()) {
                         "> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
                     } else {
-                        val reports: List<JmhReport> =
-                            jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
+                        val reports: List<JmhReport> = jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
 
                         buildString {
                             appendLine("<details>")
@@ -77,20 +76,16 @@ fun Project.addBenchmarkProperties() {
                             appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
                             appendLine()
                             appendLine("```")
-                            appendLine(
-                                "${first.jvm} ${
-                                    first.jvmArgs.joinToString(" ")
-                                }"
-                            )
+                            appendLine("${first.jvm} ${
+                                first.jvmArgs.joinToString(" ")
+                            }")
                             appendLine("```")
 
-                            appendLine(
-                                "* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
-                                    noun(first.warmupIterations, "iteration", "iterations")
-                                } by ${first.warmupTime} and ${first.measurementIterations} measurement ${
-                                    noun(first.measurementIterations, "iteration", "iterations")
-                                } by ${first.measurementTime}."
-                            )
+                            appendLine("* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
+                                noun(first.warmupIterations, "iteration", "iterations")
+                            } by ${first.warmupTime} and ${first.measurementIterations} measurement ${
+                                noun(first.measurementIterations, "iteration", "iterations")
+                            } by ${first.measurementTime}.")
 
                             appendLine()
                             appendLine("| Benchmark | Score |")

docs/buffers.md

@@ -17,4 +17,4 @@ own `MemoryBuffer.create()` factory).
 ## Buffer performance
 
 One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers
-instead .
+instead  .

docs/codestyle.md

@@ -1,27 +1,20 @@
 # Coding Conventions
 
-Generally, KMath code follows
-general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of
-small changes and clarifications.
+Generally, KMath code follows general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of small changes and clarifications.
 
 ## Utility Class Naming
 
-Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe
-its contents.
+Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe its contents.
 
-The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that
-file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and
-aggregators with a small letter seems to be a good way to visually separate those files.
+The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and aggregators with a small letter seems to be a good way to visually separate those files.
 
 This convention could be changed in future in a non-breaking way.
 
 ## Private Variable Naming
 
-Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public
-read-only value with the same meaning.
+Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public read-only value with the same meaning.
 
-This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and
-private versions draw up the same entity. It is allowed only for private variables.
+This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and private versions draw up the same entity. It is allowed only for private variables.
 
 This convention could be changed in future in a non-breaking way.
 
@@ -31,5 +24,4 @@ Use one-liners when they occupy single code window line both for functions and p
 `val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be 
 cleanly separated.
 
-There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook
-one-lines seem to better show that the property or function is easily calculated.
+There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook one-lines seem to better show that the property or function is easily calculated.

docs/expressions.md

@@ -1,19 +1,16 @@
 # Expressions
 
-Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical
-expressions.
+Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical expressions.
 
 The potential use-cases for it (so far) are following:
 
 * lazy evaluation (in general simple lambda is better, but there are some border cases);
 * automatic differentiation in single-dimension and in multiple dimensions;
 * generation of mathematical syntax trees with subsequent code generation for other languages;
-* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with
-  Symja's `IExpr`—integration, simplification, and more);
+* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with Symja's `IExpr`—integration, simplification, and more);
 * visualization with `kmath-jupyter`.
 
-The workhorse of this API is `Expression` interface, which exposes
-single `operator fun invoke(arguments: Map<Symbol, T>): T`
+The workhorse of this API is `Expression` interface, which exposes single `operator fun invoke(arguments: Map<Symbol, T>): T`
 method. `ExpressionAlgebra` is used to generate expressions and introduce variables.
 
 Currently there are two implementations:
@@ -21,4 +18,4 @@ Currently there are two implementations:
 * Generic `ExpressionField` in `kmath-core` which allows construction of custom lazy expressions
 
 * Auto-differentiation expression in `kmath-commons` module allows using full power of `DerivativeStructure` 
-  from commons-math. **TODO: add example**
+from commons-math. **TODO: add example**

docs/linear.md

@@ -1,12 +1,8 @@
 ## Basic linear algebra layout
 
-KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases
-declared in context classes, and are not the members of classes that store data. This allows more flexible approach to
-maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of
-data structures.
+KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases declared in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
 
-The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products
-of matrices and vectors:
+The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products of matrices and vectors:
 
 ```kotlin
 import space.kscience.kmath.linear.*
@@ -32,5 +28,4 @@ LinearSpace.Companion.real {
 ## Backends overview
 
 ### EJML
-
 ### Commons Math

docs/nd-structure.md

@@ -8,7 +8,6 @@ One of the most sought after features of mathematical libraries is the high-perf
 structures. In `kmath` performance depends on which particular context was used for operation.
 
 Let us consider following contexts:
-
 ```kotlin
     // automatically build context most suited for given type.
     val autoField = NDField.auto(DoubleField, dim, dim)
@@ -17,7 +16,6 @@ Let us consider following contexts:
     //A generic boxing field. It should be used for objects, not primitives.
     val genericField = NDField.buffered(DoubleField, dim, dim)
 ```
-
 Now let us perform several tests and see, which implementation is best suited for each case:
 
 ## Test case
@@ -26,9 +24,7 @@ To test performance we will take 2d-structures with `dim = 1000` and add a struc
 to it `n = 1000` times.
 
 ## Specialized
-
 The code to run this looks like:
-
 ```kotlin
     specializedField.run {
         var res: NDBuffer<Double> = one
@@ -37,16 +33,13 @@ The code to run this looks like:
         }
     }
 ```
-
 The performance of this code is the best of all tests since it inlines all operations and is specialized for operation
 with doubles. We will measure everything else relative to this one, so time for this test will be `1x` (real time
 on my computer is about 4.5 seconds). The only problem with this approach is that it requires specifying type
 from the beginning. Everyone does so anyway, so it is the recommended approach.
 
 ## Automatic
-
 Let's do the same with automatic field inference:
-
 ```kotlin
     autoField.run {
         var res = one
@@ -55,16 +48,13 @@ Let's do the same with automatic field inference:
         }
     }
 ```
-
 Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just
 returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure.
 
 ## Lazy
-
 Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand
 using coroutines to parallelize computations.
 When one calls
-
 ```kotlin
     lazyField.run {
         var res = one
@@ -73,14 +63,12 @@ When one calls
         }
     }
 ```
-
 The result will be calculated almost immediately but the result will be empty. To get the full result
 structure one needs to call all its elements. In this case computation overhead will be huge. So this field never
 should be used if one expects to use the full result structure. Though if one wants only small fraction, it could
 save a lot of time.
 
 This field still could be used with reasonable performance if call code is changed:
-
 ```kotlin
     lazyField.run {
         val res = one.map {
@@ -94,13 +82,10 @@ This field still could be used with reasonable performance if call code is chang
         res.elements().forEach { it.second }
     }
 ```
-
 In this case it completes in about `4x-5x` time due to boxing.
 
 ## Boxing
-
 The boxing field produced by
-
 ```kotlin
     genericField.run {
         var res: NDBuffer<Double> = one
@@ -109,22 +94,18 @@ The boxing field produced by
         }
     }
 ```
-
 is the slowest one, because it requires boxing and unboxing the `double` on each operation. It takes about
 `15x` time (**TODO: there seems to be a problem here, it should be slow, but not that slow**). This field should
 never be used for primitives.
 
 ## Element operation
-
 Let us also check the speed for direct operations on elements:
-
 ```kotlin
     var res = genericField.one
     repeat(n) {
         res += 1.0
     }
 ```
-
 One would expect to be at least as slow as field operation, but in fact, this one takes only `2x` time to complete.
 It happens, because in this particular case it does not use actual `NDField` but instead calculated directly
 via extension function.
@@ -133,7 +114,6 @@ via extension function.
 
 Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to
 work completely without frame of reference. In this case, simple numpy code:
-
 ```python
 import numpy as np
 
@@ -141,9 +121,7 @@ res = np.ones((1000,1000))
 for i in range(1000):
     res = res + 1.0
 ```
-
-gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think
-it is
+gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think it is
 because better memory management). Of course if one writes `res += 1.0`, the performance will be different,
 but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are
 available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping

docs/polynomials.md

@@ -1,54 +1,27 @@
 # Polynomials and Rational Functions
 
-KMath provides a way to work with uni- and multivariate polynomials and rational functions. It includes full support of
-arithmetic operations of integers, **constants** (elements of ring polynomials are build over), variables (for certain
-multivariate implementations), polynomials and rational functions encapsulated in so-called **polynomial space** and *
-*rational function space** and some other utilities such as algebraic differentiation and substitution.
+KMath provides a way to work with uni- and multivariate polynomials and rational functions. It includes full support of arithmetic operations of integers, **constants** (elements of ring polynomials are build over), variables (for certain multivariate implementations), polynomials and rational functions encapsulated in so-called **polynomial space** and **rational function space** and some other utilities such as algebraic differentiation and substitution.
 
 ## Concrete realizations
 
 There are 3 approaches to represent polynomials:
+1. For univariate polynomials one can represent and store polynomial as a list of coefficients for each power of the variable. I.e. polynomial  $a_0 + \dots + a_n x^n $ can be represented as a finite sequence $(a_0; \dots; a_n)$. (Compare to sequential definition of polynomials.)
+2. For multivariate polynomials one can represent and store polynomial as a matching (in programming it is called "map" or "dictionary", in math it is called [functional relation](https://en.wikipedia.org/wiki/Binary_relation#Special_types_of_binary_relations)) of each "**term signature**" (that describes what variables and in what powers appear in the term) with corresponding coefficient of the term. But there are 2 possible approaches of term signature representation:
+   1. One can number all the variables, so term signature can be represented as a sequence describing powers of the variables. I.e. signature of term  $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural or zero  $d_i $) can be represented as a finite sequence  $(d_0; \dots; d_n)$.
+   2. One can represent variables as objects ("**labels**"), so term signature can be also represented as a matching of each appeared variable with its power in the term. I.e. signature of term  $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural non-zero  $d_i $) can be represented as a finite matching  $(x_0 \to d_1; \dots; x_n \to d_n)$.
 
-1. For univariate polynomials one can represent and store polynomial as a list of coefficients for each power of the
-   variable. I.e. polynomial $a_0 + \dots + a_n x^n $ can be represented as a finite sequence $(a_0; \dots; a_n)$. (
-   Compare to sequential definition of polynomials.)
-2. For multivariate polynomials one can represent and store polynomial as a matching (in programming it is called "map"
-   or "dictionary", in math it is
-   called [functional relation](https://en.wikipedia.org/wiki/Binary_relation#Special_types_of_binary_relations)) of
-   each "**term signature**" (that describes what variables and in what powers appear in the term) with corresponding
-   coefficient of the term. But there are 2 possible approaches of term signature representation:
-    1. One can number all the variables, so term signature can be represented as a sequence describing powers of the
-       variables. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural or zero $d_i $) can be
-       represented as a finite sequence $(d_0; \dots; d_n)$.
-    2. One can represent variables as objects ("**labels**"), so term signature can be also represented as a matching of
-       each appeared variable with its power in the term. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for
-       natural non-zero $d_i $) can be represented as a finite matching $(x_0 \to d_1; \dots; x_n \to d_n)$.
-
-All that three approaches are implemented by "list", "numbered", and "labeled" versions of polynomials and polynomial
-spaces respectively. Whereas all rational functions are represented as fractions with corresponding polynomial numerator
-and denominator, and rational functions' spaces are implemented in the same way as usual field of rational numbers (or
-more precisely, as any field of fractions over integral domain) should be implemented.
+All that three approaches are implemented by "list", "numbered", and "labeled" versions of polynomials and polynomial spaces respectively. Whereas all rational functions are represented as fractions with corresponding polynomial numerator and denominator, and rational functions' spaces are implemented in the same way as usual field of rational numbers (or more precisely, as any field of fractions over integral domain) should be implemented.
 
 So here are a bit of details. Let `C` by type of constants. Then:
+1. `ListPolynomial`, `ListPolynomialSpace`, `ListRationalFunction` and `ListRationalFunctionSpace` implement the first scenario. `ListPolynomial` stores polynomial  $a_0 + \dots + a_n x^n $ as a coefficients list `listOf(a_0, ..., a_n)` (of type `List<C>`).
   
-1. `ListPolynomial`, `ListPolynomialSpace`, `ListRationalFunction` and `ListRationalFunctionSpace` implement the first
-   scenario. `ListPolynomial` stores polynomial $a_0 + \dots + a_n x^n $ as a coefficients
-   list `listOf(a_0, ..., a_n)` (of type `List<C>`).
-
-   They also have variation `ScalableListPolynomialSpace` that replaces former polynomials and
-   implements `ScaleOperations`.
-2. `NumberedPolynomial`, `NumberedPolynomialSpace`, `NumberedRationalFunction` and `NumberedRationalFunctionSpace`
-   implement second scenario. `NumberedPolynomial` stores polynomials as structures of type `Map<List<UInt>, C>`.
-   Signatures are stored as `List<UInt>`. To prevent ambiguity signatures should not end with zeros.
-3. `LabeledPolynomial`, `LabeledPolynomialSpace`, `LabeledRationalFunction` and `LabeledRationalFunctionSpace` implement
-   third scenario using common `Symbol` as variable type. `LabeledPolynomial` stores polynomials as structures of
-   type `Map<Map<Symbol, UInt>, C>`. Signatures are stored as `Map<Symbol, UInt>`. To prevent ambiguity each signature
-   should not map any variable to zero.
+   They also have variation `ScalableListPolynomialSpace` that replaces former polynomials and implements `ScaleOperations`.
+2. `NumberedPolynomial`, `NumberedPolynomialSpace`, `NumberedRationalFunction` and `NumberedRationalFunctionSpace` implement second scenario. `NumberedPolynomial` stores polynomials as structures of type `Map<List<UInt>, C>`. Signatures are stored as `List<UInt>`. To prevent ambiguity signatures should not end with zeros.
+3. `LabeledPolynomial`, `LabeledPolynomialSpace`, `LabeledRationalFunction` and `LabeledRationalFunctionSpace` implement third scenario using common `Symbol` as variable type. `LabeledPolynomial` stores polynomials as structures of type `Map<Map<Symbol, UInt>, C>`. Signatures are stored as `Map<Symbol, UInt>`. To prevent ambiguity each signature should not map any variable to zero.
 
 ### Example: `ListPolynomial`
 
-For example, polynomial $2 - 3x + x^2 $ (with `Int` coefficients) is represented
-
+For example, polynomial  $2 - 3x + x^2 $ (with `Int` coefficients) is represented
 ```kotlin
 val polynomial: ListPolynomial<Int> = ListPolynomial(listOf(2, -3, 1))
 // or
@@ -56,7 +29,6 @@ val polynomial: ListPolynomial<Int> = ListPolynomial(2, -3, 1)
 ```
 
 All algebraic operations can be used in corresponding space:
-
 ```kotlin
 val computationResult = Int.algebra.listPolynomialSpace {
    ListPolynomial(2, -3, 1) + ListPolynomial(0, 6) == ListPolynomial(2, 3, 1)
@@ -69,8 +41,7 @@ For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functio
 
 ### Example: `NumberedPolynomial`
 
-For example, polynomial $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
-
+For example, polynomial  $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
 ```kotlin
 val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
    mapOf(
@@ -88,7 +59,6 @@ val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
 ```
 
 All algebraic operations can be used in corresponding space:
-
 ```kotlin
 val computationResult = Int.algebra.numberedPolynomialSpace {
    NumberedPolynomial(
@@ -113,8 +83,7 @@ For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functio
 
 ### Example: `LabeledPolynomial`
 
-For example, polynomial $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
-
+For example, polynomial  $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
 ```kotlin
 val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
    mapOf(
@@ -132,7 +101,6 @@ val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
 ```
 
 All algebraic operations can be used in corresponding space:
-
 ```kotlin
 val computationResult = Int.algebra.labeledPolynomialSpace {
    LabeledPolynomial(
@@ -182,42 +150,23 @@ classDiagram
    PolynomialSpaceOfFractions <|-- MultivariatePolynomialSpaceOfFractions
 ```
 
-There are implemented `Polynomial` and `RationalFunction` interfaces as abstractions of polynomials and rational
-functions respectively (although, there is not a lot of logic in them) and `PolynomialSpace`
-and `RationalFunctionSpace` (that implement `Ring` interface) as abstractions of polynomials' and rational functions'
-spaces respectively. More precisely, that means they allow to declare common logic of interaction with such objects and
-spaces:
-
+There are implemented `Polynomial` and `RationalFunction` interfaces as abstractions of polynomials and rational functions respectively (although, there is not a lot of logic in them) and `PolynomialSpace` and `RationalFunctionSpace` (that implement `Ring` interface) as abstractions of polynomials' and rational functions' spaces respectively. More precisely, that means they allow to declare common logic of interaction with such objects and spaces:
 - `Polynomial` does not provide any logic. It is marker interface.
 - `RationalFunction` provides numerator and denominator of rational function and destructuring declaration for them.
-- `PolynomialSpace` provides all possible arithmetic interactions of integers, constants (of type `C`), and
-  polynomials (of type `P`) like addition, subtraction, multiplication, and some others and common properties like
-  degree of polynomial.
-- `RationalFunctionSpace` provides the same as `PolynomialSpace` but also for rational functions: all possible
-  arithmetic interactions of integers, constants (of type `C`), polynomials (of type `P`), and rational functions (of
-  type `R`) like addition, subtraction, multiplication, division (in some cases), and some others and common properties
-  like degree of polynomial.
+- `PolynomialSpace` provides all possible arithmetic interactions of integers, constants (of type `C`), and polynomials (of type `P`) like addition, subtraction, multiplication, and some others and common properties like degree of polynomial.
+- `RationalFunctionSpace` provides the same as `PolynomialSpace` but also for rational functions: all possible arithmetic interactions of integers, constants (of type `C`), polynomials (of type `P`), and rational functions (of type `R`) like addition, subtraction, multiplication, division (in some cases), and some others and common properties like degree of polynomial.
 
-Then to add abstraction of similar behaviour with variables (in multivariate case) there are
-implemented `MultivariatePolynomialSpace` and `MultivariateRationalFunctionSpace`. They just include variables (of
-type `V`) in the interactions of the entities.
+Then to add abstraction of similar behaviour with variables (in multivariate case) there are implemented `MultivariatePolynomialSpace` and `MultivariateRationalFunctionSpace`. They just include variables (of type `V`) in the interactions of the entities.
 
 Also, to remove boilerplates there were provided helping subinterfaces and abstract subclasses:
-
-- `PolynomialSpaceOverRing` allows to replace implementation of interactions of integers and constants with
-  implementations from provided ring over constants (of type `A: Ring<C>`).
+- `PolynomialSpaceOverRing` allows to replace implementation of interactions of integers and constants with implementations from provided ring over constants (of type `A: Ring<C>`).
 - `RationalFunctionSpaceOverRing` &mdash; the same but for `RationalFunctionSpace`.
-- `RationalFunctionSpaceOverPolynomialSpace` &mdash; the same but "the inheritance" includes interactions with
-  polynomials from provided `PolynomialSpace`.
-- `PolynomialSpaceOfFractions` is actually abstract subclass of `RationalFunctionSpace` that implements all fractions
-  boilerplates with provided (`protected`) constructor of rational functions by polynomial numerator and denominator.
-- `MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace` and `MultivariatePolynomialSpaceOfFractions`
-  &mdash; the same stories of operators inheritance and fractions boilerplates respectively but in multivariate case.
+- `RationalFunctionSpaceOverPolynomialSpace` &mdash; the same but "the inheritance" includes interactions with polynomials from provided `PolynomialSpace`.
+- `PolynomialSpaceOfFractions` is actually abstract subclass of `RationalFunctionSpace` that implements all fractions boilerplates with provided (`protected`) constructor of rational functions by polynomial numerator and denominator.
+- `MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace` and `MultivariatePolynomialSpaceOfFractions` &mdash; the same stories of operators inheritance and fractions boilerplates respectively but in multivariate case.
 
 ## Utilities
 
-For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common
-utilities as:
-
+For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common utilities as:
 1. differentiation and anti-differentiation,
 2. substitution, invocation and functional representation.

docs/templates/ARTIFACT-TEMPLATE.md

@@ -3,7 +3,6 @@
 The Maven coordinates of this project are `${group}:${name}:${version}`.
 
 **Gradle:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

docs/templates/README-TEMPLATE.md

@@ -25,8 +25,7 @@ experience could be achieved with [kmath-for-real](/kmath-for-real) extension mo
 
 # Goal
 
-* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
-  Wasm).
+* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and Wasm).
 * Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
 * Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
 
@@ -68,19 +67,16 @@ feedback are also welcome.
 
 ## Performance
 
-Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
-achieve both
+Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to achieve both
 performance and flexibility.
 
 We expect to focus on creating a convenient universal API first and then work on increasing performance for specific
 cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
-native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
-better than SciPy.
+native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
 
 ## Requirements
 
-KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or
-Oracle GraalVM for execution to get better performance.
+KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or Oracle GraalVM for execution to get better performance.
 
 ### Repositories
 
@@ -103,7 +99,4 @@ dependencies {
 ## Contributing
 
 The project requires a lot of additional work. The most important thing we need is feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-marked
-with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-label.
+required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues marked with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) label.

examples/build.gradle.kts

@@ -67,8 +67,8 @@ kotlin {
 }
 
 tasks.withType<KotlinJvmCompile> {
-    compilerOptions {
-        freeCompilerArgs.addAll("-Xjvm-default=all", "-Xopt-in=kotlin.RequiresOptIn", "-Xlambdas=indy")
+    kotlinOptions {
+        freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn" + "-Xlambdas=indy"
     }
 }
 

examples/src/main/kotlin/space/kscience/kmath/expressions/autodiff.kt

@@ -6,7 +6,6 @@
 package space.kscience.kmath.expressions
 
 import space.kscience.kmath.UnstableKMathAPI
-
 // Only kmath-core is needed.
 
 // Let's declare some variables
@@ -52,7 +51,7 @@ fun main() {
     // >>> 0.0
 
     // But in case you forgot to specify bound symbol's value, exception is thrown:
-    println(runCatching { someExpression(z to 4.0) })
+    println( runCatching { someExpression(z to 4.0) } )
     // >>> Failure(java.lang.IllegalStateException: Symbol 'x' is not supported in ...)
 
     // The reason is that the expression is evaluated lazily,

examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt

@@ -77,7 +77,7 @@ suspend fun main() {
     val result = chi2.optimizeWith(
         CMOptimizer,
         mapOf(a to 1.5, b to 0.9, c to 1.0),
-    ) {
+    ){
         FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
     }
 

examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt

@@ -8,6 +8,7 @@ package space.kscience.kmath.operations
 import space.kscience.kmath.commons.linear.CMLinearSpace
 import space.kscience.kmath.linear.matrix
 import space.kscience.kmath.nd.Float64BufferND
+import space.kscience.kmath.nd.ShapeND
 import space.kscience.kmath.nd.Structure2D
 import space.kscience.kmath.nd.mutableStructureND
 import space.kscience.kmath.nd.ndAlgebra

examples/src/main/kotlin/space/kscience/kmath/series/analyzeDif.kt

@@ -44,10 +44,10 @@ fun main() = with(Double.seriesAlgebra()) {
 
     Plotly.page {
         h1 { +"This is my plot" }
-        p {
+        p{
             +"Kolmogorov-smirnov test for s1 and s2: ${kmTest.value}"
         }
-        plot {
+        plot{
             plotSeries("s1", s1)
             plotSeries("s2", s2)
             plotSeries("s3", s3)

examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt

@@ -53,10 +53,7 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
         return BufferND(strides, array.asBuffer())
     }
 
-    override fun mutableStructureND(
-        shape: ShapeND,
-        initializer: DoubleField.(IntArray) -> Double,
-    ): MutableBufferND<Double> {
+    override fun mutableStructureND(shape: ShapeND, initializer: DoubleField.(IntArray) -> Double): MutableBufferND<Double> {
         val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
             val index = strides.index(offset)
             DoubleField.initializer(index)

examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt

@@ -12,7 +12,7 @@ import space.kscience.kmath.operations.withSize
 
 inline fun <reified R : Any> MutableBuffer.Companion.same(
     n: Int,
-    value: R,
+    value: R
 ): MutableBuffer<R> = MutableBuffer(n) { value }
 
 

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt

@@ -31,7 +31,7 @@ fun main() {
 
     val exampleNumber = 1
 
-    var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
+    var y_hat =  funcDifficultForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {
@@ -51,8 +51,7 @@ fun main() {
     val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-2, 11.0, 9.0, 1.0)
 //    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-3, 11.0, 9.0, 1.0)
 
-    val inputData = LMInput(
-        ::funcDifficultForLm,
+    val inputData = LMInput(::funcDifficultForLm,
         p_init.as2D(),
         t,
         y_dat,
@@ -65,8 +64,7 @@ fun main() {
         doubleArrayOf(opts[6], opts[7], opts[8]),
         opts[9].toInt(),
         10,
-        1
-    )
+        1)
 
     val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
 
@@ -78,7 +76,7 @@ fun main() {
     println()
 
     println("Y true and y received:")
-    var y_hat_after = funcDifficultForLm(t_example, result.resultParameters, exampleNumber)
+    var y_hat_after =  funcDifficultForLm(t_example, result.resultParameters, exampleNumber)
     for (i in 0 until y_hat.shape.component1()) {
         val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
         val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticEasyTest.kt

@@ -18,8 +18,7 @@ import kotlin.math.roundToInt
 
 fun main() {
     val startedData = getStartDataForFuncEasy()
-    val inputData = LMInput(
-        ::funcEasyForLm,
+    val inputData = LMInput(::funcEasyForLm,
         DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
         startedData.t,
         startedData.y_dat,
@@ -32,8 +31,7 @@ fun main() {
         doubleArrayOf(startedData.opts[6], startedData.opts[7], startedData.opts[8]),
         startedData.opts[9].toInt(),
         10,
-        startedData.example_number
-    )
+        startedData.example_number)
 
     val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
 
@@ -45,7 +43,7 @@ fun main() {
     println()
 
     println("Y true and y received:")
-    var y_hat_after = funcDifficultForLm(startedData.t, result.resultParameters, startedData.example_number)
+    var y_hat_after =  funcDifficultForLm(startedData.t, result.resultParameters, startedData.example_number)
     for (i in 0 until startedData.y_dat.shape.component1()) {
         val x = (startedData.y_dat[i, 0] * 10000).roundToInt() / 10000.0
         val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt

@@ -15,7 +15,6 @@ import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.LMInput
 import space.kscience.kmath.tensors.core.levenbergMarquardt
 import kotlin.math.roundToInt
-
 fun main() {
     val NData = 100
     var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
@@ -31,7 +30,7 @@ fun main() {
 
     val exampleNumber = 1
 
-    var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
+    var y_hat =  funcMiddleForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {
@@ -50,8 +49,7 @@ fun main() {
     p_min = p_min.div(1.0 / 50.0)
     val opts = doubleArrayOf(3.0, 7000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
 
-    val inputData = LMInput(
-        ::funcMiddleForLm,
+    val inputData = LMInput(::funcMiddleForLm,
         p_init.as2D(),
         t,
         y_dat,
@@ -64,8 +62,7 @@ fun main() {
         doubleArrayOf(opts[6], opts[7], opts[8]),
         opts[9].toInt(),
         10,
-        1
-    )
+        1)
 
     val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
 
@@ -77,7 +74,7 @@ fun main() {
     println()
 
 
-    var y_hat_after = funcMiddleForLm(t_example, result.resultParameters, exampleNumber)
+    var y_hat_after =  funcMiddleForLm(t_example, result.resultParameters, exampleNumber)
     for (i in 0 until y_hat.shape.component1()) {
         val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
         val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt

@@ -6,23 +6,18 @@
 package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
 
 import kotlinx.coroutines.delay
-import kotlinx.coroutines.flow.Flow
-import kotlinx.coroutines.flow.flow
-import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.component1
+import kotlinx.coroutines.flow.*
+import space.kscience.kmath.nd.*
 import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.LMInput
 import space.kscience.kmath.tensors.core.levenbergMarquardt
 import kotlin.random.Random
+import kotlin.reflect.KFunction3
 
-fun streamLm(
-    lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
-    startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int,
-): Flow<MutableStructure2D<Double>> = flow {
+fun streamLm(lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
+             startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int): Flow<MutableStructure2D<Double>> = flow{
 
     var example_number = startData.example_number
     var p_init = startData.p_init
@@ -37,8 +32,7 @@ fun streamLm(
     var steps = numberOfLaunches
     val isEndless = (steps <= 0)
 
-    val inputData = LMInput(
-        lm_func,
+    val inputData = LMInput(lm_func,
         p_init,
         t,
         y_dat,
@@ -51,8 +45,7 @@ fun streamLm(
         doubleArrayOf(opts[6], opts[7], opts[8]),
         opts[9].toInt(),
         10,
-        example_number
-    )
+        example_number)
 
     while (isEndless || steps > 0) {
         val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
@@ -64,7 +57,7 @@ fun streamLm(
     }
 }
 
-fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double> {
+fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double>{
     val n = y_dat.shape.component1()
     val y_dat_new = zeros(ShapeND(intArrayOf(n, 1))).as2D()
     for (i in 0 until n) {

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt

@@ -5,15 +5,14 @@
 
 package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
 
-import space.kscience.kmath.nd.component1
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
-import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncDifficult
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.tensors.LevenbergMarquardt.*
 import kotlin.math.roundToInt
 
-suspend fun main() {
+suspend fun main(){
     val startData = getStartDataForFuncDifficult()
     // Создание потока:
-    val lmFlow = streamLm(::funcDifficultForLm, startData, 0, 100)
+    val lmFlow = streamLm(::funcDifficultForLm, startData, 0,  100)
     var initialTime = System.currentTimeMillis()
     var lastTime: Long
     val launches = mutableListOf<Long>()

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt

@@ -18,7 +18,7 @@ import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
 import space.kscience.kmath.tensors.core.asDoubleTensor
 
-public data class StartDataLm(
+public data class StartDataLm (
     var lm_matx_y_dat: MutableStructure2D<Double>,
     var example_number: Int,
     var p_init: MutableStructure2D<Double>,
@@ -29,14 +29,10 @@ public data class StartDataLm(
     var p_min: MutableStructure2D<Double>,
     var p_max: MutableStructure2D<Double>,
     var consts: MutableStructure2D<Double>,
-    var opts: DoubleArray,
+    var opts: DoubleArray
 )
 
-fun funcEasyForLm(
-    t: MutableStructure2D<Double>,
-    p: MutableStructure2D<Double>,
-    exampleNumber: Int,
-): MutableStructure2D<Double> {
+fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
     val m = t.shape.component1()
     var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
 
@@ -44,13 +40,15 @@ fun funcEasyForLm(
         y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
             DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
         )
-    } else if (exampleNumber == 2) {
+    }
+    else if (exampleNumber == 2) {
         val mt = t.max()
         y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
                 (t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
                 (t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
                 (t.times(1.0 / mt)).pow(4).times(p[3, 0])
-    } else if (exampleNumber == 3) {
+    }
+    else if (exampleNumber == 3) {
         y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
             .times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
     }
@@ -58,40 +56,32 @@ fun funcEasyForLm(
     return y_hat.as2D()
 }
 
-fun funcMiddleForLm(
-    t: MutableStructure2D<Double>,
-    p: MutableStructure2D<Double>,
-    exampleNumber: Int,
-): MutableStructure2D<Double> {
+fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
     val m = t.shape.component1()
-    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
+    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
 
     val mt = t.max()
-    for (i in 0 until p.shape.component1()) {
+    for(i in 0 until p.shape.component1()){
         y_hat += (t.times(1.0 / mt)).times(p[i, 0])
     }
 
-    for (i in 0 until 5) {
+    for(i in 0 until 5){
         y_hat = funcEasyForLm(y_hat.as2D(), p, exampleNumber).asDoubleTensor()
     }
 
     return y_hat.as2D()
 }
 
-fun funcDifficultForLm(
-    t: MutableStructure2D<Double>,
-    p: MutableStructure2D<Double>,
-    exampleNumber: Int,
-): MutableStructure2D<Double> {
+fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
     val m = t.shape.component1()
-    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
+    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
 
     val mt = t.max()
-    for (i in 0 until p.shape.component1()) {
-        y_hat = y_hat.plus((t.times(1.0 / mt)).times(p[i, 0]))
+    for(i in 0 until p.shape.component1()){
+        y_hat = y_hat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
     }
 
-    for (i in 0 until 4) {
+    for(i in 0 until 4){
         y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
     }
 
@@ -99,7 +89,7 @@ fun funcDifficultForLm(
 }
 
 
-fun getStartDataForFuncDifficult(): StartDataLm {
+fun getStartDataForFuncDifficult(): StartDataLm  {
     val NData = 200
     var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
     for (i in 0 until NData) {
@@ -114,7 +104,7 @@ fun getStartDataForFuncDifficult(): StartDataLm {
 
     val exampleNumber = 1
 
-    var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
+    var y_hat =  funcDifficultForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {
@@ -139,7 +129,7 @@ fun getStartDataForFuncDifficult(): StartDataLm {
     return StartDataLm(y_dat, 1, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
 }
 
-fun getStartDataForFuncMiddle(): StartDataLm {
+fun getStartDataForFuncMiddle(): StartDataLm  {
     val NData = 100
     var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
     for (i in 0 until NData) {
@@ -154,7 +144,7 @@ fun getStartDataForFuncMiddle(): StartDataLm {
 
     val exampleNumber = 1
 
-    var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
+    var y_hat =  funcMiddleForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {

gradle.properties

@@ -5,10 +5,13 @@
 kotlin.code.style=official
 kotlin.mpp.stability.nowarn=true
 kotlin.native.ignoreDisabledTargets=true
+
 org.gradle.configureondemand=true
 org.gradle.jvmargs=-Xmx4096m
+
 org.gradle.parallel=true
 org.gradle.workers.max=4
+
 toolsVersion=0.15.2-kotlin-1.9.22
 #kotlin.experimental.tryK2=true
 #kscience.wasm.disabled=true

gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +1,5 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-8.7-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.6-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

kmath-ast/README.md

@@ -2,17 +2,17 @@
 
 Extensions to MST API: transformations, dynamic compilation and visualization.
 
-- [expression-language](src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser
-- [mst-jvm-codegen](src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
-- [mst-js-codegen](src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
-- [rendering](src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
+ - [expression-language](src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser
+ - [mst-jvm-codegen](src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
+ - [mst-js-codegen](src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
+ - [rendering](src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
+
 
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-ast:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")
@@ -26,27 +26,21 @@ dependencies {
 
 ## Parsing expressions
 
-In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more
-specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
+In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
 
 Supported literals:
-
 1. Constants and variables (consist of latin letters, digits and underscores, can't start with digit): `x`, `_Abc2`.
 2. Numbers: `123`, `1.02`, `1e10`, `1e-10`, `1.0e+3`—all parsed either as `kotlin.Long` or `kotlin.Double`.
 
 Supported binary operators (from the highest precedence to the lowest one):
-
 1. `^`
 2. `*`, `/`
 3. `+`, `-`
 
 Supported unary operator:
-
 1. `-`, e. g. `-x`
 
-Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't
-start with digit. Examples:
-
+Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't start with digit. Examples:
 1. `sin(x)`
 2. `add(x, y)`
 
@@ -111,15 +105,12 @@ public final class CompiledExpression_-386104628_0 implements DoubleExpression {
 }
 ```
 
-Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class
-files to program's working directory, so they can be reviewed manually.
+Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class files to program's working directory, so they can be reviewed manually.
 
 #### Limitations
 
-- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class
-  loading overhead.
-- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not
-  support class loaders.
+- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class loading overhead.
+- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not support class loaders.
 
 ### On JS
 
@@ -197,8 +188,7 @@ public fun main() {
 
 Result LaTeX:
 
-$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)
-}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
+$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
 
 Result MathML (can be used with MathJax or other renderers):
 

kmath-ast/build.gradle.kts

@@ -2,7 +2,7 @@ plugins {
     id("space.kscience.gradle.mpp")
 }
 
-kscience {
+kscience{
     jvm()
     js()
     native()
@@ -22,7 +22,7 @@ kscience {
         implementation(npm("js-base64", "3.6.1"))
     }
 
-    dependencies(jvmMain) {
+    dependencies(jvmMain){
         implementation("org.ow2.asm:asm-commons:9.2")
     }
 
@@ -31,7 +31,7 @@ kscience {
 kotlin {
     js {
         nodejs {
-            testTask {
+            testTask{
                 useMocha().timeout = "0"
             }
         }

kmath-ast/docs/README-TEMPLATE.md

@@ -8,27 +8,21 @@ ${artifact}
 
 ## Parsing expressions
 
-In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more
-specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
+In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
 
 Supported literals:
-
 1. Constants and variables (consist of latin letters, digits and underscores, can't start with digit): `x`, `_Abc2`.
 2. Numbers: `123`, `1.02`, `1e10`, `1e-10`, `1.0e+3`—all parsed either as `kotlin.Long` or `kotlin.Double`.
 
 Supported binary operators (from the highest precedence to the lowest one):
-
 1. `^`
 2. `*`, `/`
 3. `+`, `-`
 
 Supported unary operator:
-
 1. `-`, e. g. `-x`
 
-Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't
-start with digit. Examples:
-
+Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't start with digit. Examples:
 1. `sin(x)`
 2. `add(x, y)`
 
@@ -93,15 +87,12 @@ public final class CompiledExpression_-386104628_0 implements DoubleExpression {
 }
 ```
 
-Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class
-files to program's working directory, so they can be reviewed manually.
+Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class files to program's working directory, so they can be reviewed manually.
 
 #### Limitations
 
-- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class
-  loading overhead.
-- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not
-  support class loaders.
+- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class loading overhead.
+- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not support class loaders.
 
 ### On JS
 
@@ -179,8 +170,7 @@ public fun main() {
 
 Result LaTeX:
 
-$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)
-}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
+$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
 
 Result MathML (can be used with MathJax or other renderers):
 

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt

@@ -68,7 +68,7 @@ public sealed interface TypedMst<T> : WithType<T> {
     ) : TypedMst<T> {
 
         init {
-            require(left.type == right.type) { "Left and right expressions must be of the same type" }
+            require(left.type==right.type){"Left and right expressions must be of the same type"}
         }
 
         override val type: SafeType<T> get() = left.type

kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt

@@ -426,13 +426,11 @@ public class InverseTrigonometricOperations(operations: Collection<String>?) : U
          * The default instance configured with [TrigonometricOperations.ACOS_OPERATION],
          * [TrigonometricOperations.ASIN_OPERATION], [TrigonometricOperations.ATAN_OPERATION].
          */
-        public val Default: InverseTrigonometricOperations = InverseTrigonometricOperations(
-            setOf(
-                TrigonometricOperations.ACOS_OPERATION,
-                TrigonometricOperations.ASIN_OPERATION,
-                TrigonometricOperations.ATAN_OPERATION,
-            )
-        )
+        public val Default: InverseTrigonometricOperations = InverseTrigonometricOperations(setOf(
+            TrigonometricOperations.ACOS_OPERATION,
+            TrigonometricOperations.ASIN_OPERATION,
+            TrigonometricOperations.ATAN_OPERATION,
+        ))
     }
 }
 
@@ -454,12 +452,10 @@ public class InverseHyperbolicOperations(operations: Collection<String>?) : Unar
          * The default instance configured with [ExponentialOperations.ACOSH_OPERATION],
          * [ExponentialOperations.ASINH_OPERATION], and [ExponentialOperations.ATANH_OPERATION].
          */
-        public val Default: InverseHyperbolicOperations = InverseHyperbolicOperations(
-            setOf(
-                ExponentialOperations.ACOSH_OPERATION,
-                ExponentialOperations.ASINH_OPERATION,
-                ExponentialOperations.ATANH_OPERATION,
-            )
-        )
+        public val Default: InverseHyperbolicOperations = InverseHyperbolicOperations(setOf(
+            ExponentialOperations.ACOSH_OPERATION,
+            ExponentialOperations.ASINH_OPERATION,
+            ExponentialOperations.ATANH_OPERATION,
+        ))
     }
 }

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt

@@ -17,8 +17,7 @@ internal class TestFeatures {
     fun printNumeric() {
         val num = object : Number() {
             override fun toByte(): Byte = throw UnsupportedOperationException()
-
-            //            override fun toChar(): Char = throw UnsupportedOperationException()
+//            override fun toChar(): Char = throw UnsupportedOperationException()
             override fun toDouble(): Double = throw UnsupportedOperationException()
             override fun toFloat(): Float = throw UnsupportedOperationException()
             override fun toInt(): Int = throw UnsupportedOperationException()

kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt

@@ -81,10 +81,8 @@ internal class TestMathML {
 
     @Test
     fun radicalWithIndex() =
-        testMathML(
-            RadicalWithIndexSyntax("", SymbolSyntax("x"), SymbolSyntax("y")),
-            "<mroot><mrow><mi>y</mi></mrow><mrow><mi>x</mi></mrow></mroot>"
-        )
+        testMathML(RadicalWithIndexSyntax("", SymbolSyntax("x"), SymbolSyntax("y")),
+            "<mroot><mrow><mi>y</mi></mrow><mrow><mi>x</mi></mrow></mroot>")
 
     @Test
     fun multiplication() {

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/binaryen/index.binaryen.kt

@@ -52,7 +52,7 @@ internal external fun createType(types: Array<Type>): Type
 
 internal external fun expandType(type: Type): Array<Type>
 
-internal external enum class ExpressionIds {
+internal external enum class  ExpressionIds {
     Invalid,
     Block,
     If,
@@ -1656,27 +1656,27 @@ internal open external class Module {
     open fun `if`(
         condition: ExpressionRef,
         ifTrue: ExpressionRef,
-        ifFalse: ExpressionRef = definedExternally,
+        ifFalse: ExpressionRef = definedExternally
     ): ExpressionRef
 
     open fun loop(label: String, body: ExpressionRef): ExpressionRef
     open fun br(
         label: String,
         condition: ExpressionRef = definedExternally,
-        value: ExpressionRef = definedExternally,
+        value: ExpressionRef = definedExternally
     ): ExpressionRef
 
     open fun br_if(
         label: String,
         condition: ExpressionRef = definedExternally,
-        value: ExpressionRef = definedExternally,
+        value: ExpressionRef = definedExternally
     ): ExpressionRef
 
     open fun switch(
         labels: Array<String>,
         defaultLabel: String,
         condition: ExpressionRef,
-        value: ExpressionRef = definedExternally,
+        value: ExpressionRef = definedExternally
     ): ExpressionRef
 
     open fun call(name: String, operands: Array<ExpressionRef>, returnType: Type): ExpressionRef
@@ -1685,14 +1685,14 @@ internal open external class Module {
         target: ExpressionRef,
         operands: Array<ExpressionRef>,
         params: Type,
-        results: Type,
+        results: Type
     ): ExpressionRef
 
     open fun return_call_indirect(
         target: ExpressionRef,
         operands: Array<ExpressionRef>,
         params: Type,
-        results: Type,
+        results: Type
     ): ExpressionRef
 
     open var local: `T$2`
@@ -1730,7 +1730,7 @@ internal open external class Module {
         condition: ExpressionRef,
         ifTrue: ExpressionRef,
         ifFalse: ExpressionRef,
-        type: Type = definedExternally,
+        type: Type = definedExternally
     ): ExpressionRef
 
     open fun drop(value: ExpressionRef): ExpressionRef
@@ -1754,7 +1754,7 @@ internal open external class Module {
         externalModuleName: String,
         externalBaseName: String,
         params: Type,
-        results: Type,
+        results: Type
     )
 
     open fun addTableImport(internalName: String, externalModuleName: String, externalBaseName: String)
@@ -1763,7 +1763,7 @@ internal open external class Module {
         internalName: String,
         externalModuleName: String,
         externalBaseName: String,
-        globalType: Type,
+        globalType: Type
     )
 
     open fun addEventImport(
@@ -1772,7 +1772,7 @@ internal open external class Module {
         externalBaseName: String,
         attribute: Number,
         params: Type,
-        results: Type,
+        results: Type
     )
 
     open fun addFunctionExport(internalName: String, externalName: String): ExportRef
@@ -1786,7 +1786,7 @@ internal open external class Module {
         initial: Number,
         maximum: Number,
         funcNames: Array<Number>,
-        offset: ExpressionRef = definedExternally,
+        offset: ExpressionRef = definedExternally
     )
 
     open fun getFunctionTable(): `T$26`
@@ -1796,7 +1796,7 @@ internal open external class Module {
         exportName: String? = definedExternally,
         segments: Array<MemorySegment>? = definedExternally,
         flags: Array<Number>? = definedExternally,
-        shared: Boolean = definedExternally,
+        shared: Boolean = definedExternally
     )
 
     open fun getNumMemorySegments(): Number
@@ -1827,7 +1827,7 @@ internal open external class Module {
         expr: ExpressionRef,
         fileIndex: Number,
         lineNumber: Number,
-        columnNumber: Number,
+        columnNumber: Number
     )
 
     open fun copyExpression(expr: ExpressionRef): ExpressionRef
@@ -2231,7 +2231,7 @@ internal open external class Relooper(module: Module) {
         from: RelooperBlockRef,
         to: RelooperBlockRef,
         indexes: Array<Number>,
-        code: ExpressionRef,
+        code: ExpressionRef
     )
 
     open fun renderAndDispose(entry: RelooperBlockRef, labelHelper: Number): ExpressionRef

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.extensions.kt

@@ -30,13 +30,12 @@ internal fun Identifier(name: String) = object : Identifier {
     override var name = name
 }
 
-internal fun FunctionExpression(id: Identifier?, params: Array<dynamic>, body: BlockStatement) =
-    object : FunctionExpression {
-        override var params = params
-        override var type = "FunctionExpression"
-        override var id: Identifier? = id
-        override var body = body
-    }
+internal fun FunctionExpression(id: Identifier?, params: Array<dynamic>, body: BlockStatement) = object : FunctionExpression {
+    override var params = params
+    override var type = "FunctionExpression"
+    override var id: Identifier? = id
+    override var body = body
+}
 
 internal fun BlockStatement(vararg body: dynamic) = object : BlockStatement {
     override var type = "BlockStatement"

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/tsstdlib/lib.es5.kt

@@ -91,6 +91,6 @@ internal typealias Extract<T, U> = Any
 internal external interface PromiseLike<T> {
     fun then(
         onfulfilled: ((value: T) -> Any?)? = definedExternally,
-        onrejected: ((reason: Any) -> Any?)? = definedExternally,
+        onrejected: ((reason: Any) -> Any?)? = definedExternally
     ): PromiseLike<dynamic /* TResult1 | TResult2 */>
 }

kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/webassembly/lib.dom.WebAssembly.module_dukat.kt

@@ -15,11 +15,11 @@
 
 package space.kscience.kmath.internal.webassembly
 
+import space.kscience.kmath.internal.tsstdlib.PromiseLike
 import org.khronos.webgl.ArrayBuffer
 import org.khronos.webgl.ArrayBufferView
 import org.khronos.webgl.Uint8Array
 import org.w3c.fetch.Response
-import space.kscience.kmath.internal.tsstdlib.PromiseLike
 import kotlin.js.Promise
 
 @Suppress("NESTED_CLASS_IN_EXTERNAL_INTERFACE")

kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt

@@ -91,7 +91,7 @@ public inline fun <reified T : Any> MST.compile(algebra: Algebra<T>, vararg argu
  * @author Iaroslav Postovalov
  */
 @UnstableKMathAPI
-public fun MST.compileToExpression(algebra: Int32Ring): IntExpression {
+public fun MST.compileToExpression(algebra: Int32Ring): IntExpression  {
     val typed = evaluateConstants(algebra)
 
     return if (typed is TypedMst.Constant) object : IntExpression {

kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/GenericAsmBuilder.kt

@@ -13,6 +13,9 @@ import space.kscience.kmath.expressions.*
 import java.lang.invoke.MethodHandles
 import java.lang.invoke.MethodType
 import java.nio.file.Paths
+import java.util.stream.Collectors.toMap
+import kotlin.contracts.InvocationKind
+import kotlin.contracts.contract
 import kotlin.io.path.writeBytes
 
 /**
@@ -280,6 +283,7 @@ internal class GenericAsmBuilder<T>(
     fun loadVariable(name: Symbol): Unit = invokeMethodVisitor.load(2 + argumentsLocals.indexOf(name), tType)
 
     inline fun buildCall(function: Function<T>, parameters: GenericAsmBuilder<T>.() -> Unit) {
+        contract { callsInPlace(parameters, InvocationKind.EXACTLY_ONCE) }
         val `interface` = function.javaClass.interfaces.first { Function::class.java in it.interfaces }
 
         val arity = `interface`.methods.find { it.name == "invoke" }?.parameterCount

kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/PrimitiveAsmBuilder.kt

@@ -332,7 +332,7 @@ internal sealed class PrimitiveAsmBuilder<T : Number, out E : Expression<T>>(
     private fun visitVariables(
         node: TypedMst<T>,
         arrayMode: Boolean,
-        alreadyLoaded: MutableList<Symbol> = mutableListOf(),
+        alreadyLoaded: MutableList<Symbol> = mutableListOf()
     ): Unit = when (node) {
         is TypedMst.Variable -> if (node.symbol !in alreadyLoaded) {
             alreadyLoaded += node.symbol

kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/codegenUtils.kt

@@ -8,6 +8,7 @@ package space.kscience.kmath.asm.internal
 import org.objectweb.asm.*
 import org.objectweb.asm.commons.InstructionAdapter
 import space.kscience.kmath.expressions.Expression
+import space.kscience.kmath.expressions.MST
 import kotlin.contracts.InvocationKind
 import kotlin.contracts.contract
 

kmath-commons/README.md

@@ -9,7 +9,6 @@ Commons math binding for kmath
 The Maven coordinates of this project are `space.kscience:kmath-commons:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt

@@ -35,13 +35,11 @@ public class CMGaussRuleIntegrator(
                 range.start,
                 range.endInclusive
             )
-
             GaussRule.LEGENDREHP -> factory.legendreHighPrecision(
                 numpoints,
                 range.start,
                 range.endInclusive
             )
-
             GaussRule.UNIFORM -> GaussIntegrator(
                 getUniformRule(
                     range.start,
@@ -82,7 +80,7 @@ public class CMGaussRuleIntegrator(
             type: GaussRule = GaussRule.LEGENDRE,
             function: (Double) -> Double,
         ): Double = CMGaussRuleIntegrator(numPoints, type).integrate(
-            UnivariateIntegrand({ IntegrationRange(range) }, function)
+            UnivariateIntegrand({IntegrationRange(range)},function)
         ).value
     }
 }

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt

@@ -48,11 +48,9 @@ public fun CMLinearSpace.inverse(
 
 
 public fun CMLinearSpace.solver(decomposition: CMDecomposition): LinearSolver<Double> = object : LinearSolver<Double> {
-    override fun solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> =
-        solver(a, decomposition).solve(b.toCM().origin).wrap()
+    override fun solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> = solver(a, decomposition).solve(b.toCM().origin).wrap()
 
-    override fun solve(a: Matrix<Double>, b: Point<Double>): Point<Double> =
-        solver(a, decomposition).solve(b.toCM().origin).toPoint()
+    override fun solve(a: Matrix<Double>, b: Point<Double>): Point<Double> = solver(a, decomposition).solve(b.toCM().origin).toPoint()
 
     override fun inverse(matrix: Matrix<Double>): Matrix<Double> = solver(matrix, decomposition).inverse.wrap()
 }

kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt

@@ -43,7 +43,7 @@ public object CMOptimizerData : SetAttribute<SymbolIndexer.() -> OptimizationDat
  * Specify Commons-maths optimization data.
  */
 public fun AttributesBuilder<FunctionOptimization<Double>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
-    CMOptimizerData add data
+    CMOptimizerData.add(data)
 }
 
 public fun AttributesBuilder<FunctionOptimization<Double>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {

kmath-commons/src/jvmTest/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt

@@ -73,7 +73,7 @@ internal class OptimizeTest {
         val result: FunctionOptimization<Double> = chi2.optimizeWith(
             CMOptimizer,
             mapOf(a to 1.5, b to 0.9, c to 1.0),
-        ) {
+        ){
             FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
         }
         println(result)

kmath-complex/README.md

@@ -2,15 +2,15 @@
 
 Complex and hypercomplex number systems in KMath.
 
-- [complex](src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex numbers operations
-- [quaternion](src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their composition
+ - [complex](src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex numbers operations
+ - [quaternion](src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their composition
+
 
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-complex:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt

@@ -148,8 +148,8 @@ public object ComplexField :
         exp(pow * ln(arg))
     }
 
-    public fun power(arg: Complex, pow: Complex): Complex = if (arg == zero || arg == (-0.0).toComplex()) {
-        if (pow == zero) {
+    public fun power(arg: Complex, pow: Complex): Complex = if(arg == zero || arg == (-0.0).toComplex()){
+        if(pow == zero){
             one
         } else {
             zero

kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt

@@ -19,8 +19,7 @@ import kotlin.contracts.contract
  */
 @OptIn(UnstableKMathAPI::class)
 public sealed class ComplexFieldOpsND : BufferedFieldOpsND<Complex, ComplexField>(ComplexField.bufferAlgebra),
-    ScaleOperations<StructureND<Complex>>, ExtendedFieldOps<StructureND<Complex>>,
-    PowerOperations<StructureND<Complex>> {
+    ScaleOperations<StructureND<Complex>>, ExtendedFieldOps<StructureND<Complex>>, PowerOperations<StructureND<Complex>> {
 
     @OptIn(PerformancePitfall::class)
     override fun StructureND<Complex>.toBufferND(): BufferND<Complex> = when (this) {
@@ -54,7 +53,7 @@ public sealed class ComplexFieldOpsND : BufferedFieldOpsND<Complex, ComplexField
     override fun atanh(arg: StructureND<Complex>): BufferND<Complex> = mapInline(arg.toBufferND()) { atanh(it) }
 
     override fun power(arg: StructureND<Complex>, pow: Number): StructureND<Complex> =
-        mapInline(arg.toBufferND()) { power(it, pow) }
+        mapInline(arg.toBufferND()) { power(it,pow) }
 
     public companion object : ComplexFieldOpsND()
 }

kmath-core/README.md

@@ -2,28 +2,23 @@
 
 The core interfaces of KMath.
 
-- [algebras](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces
-  and fields.
-- [nd](src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and
-  operations on them.
-- [linear](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums,
-  products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU
-  decomposition.
-- [buffers](src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
-- [expressions](src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression
-  once, users will be able to apply different types of
-  objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high
-  performance calculations to code generation.
-- [domains](src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
-- [autodiff](src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
-- [linear.parallel](#) : Parallel implementation for `LinearAlgebra`
+ - [algebras](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
+ - [nd](src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
+ - [linear](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
+ - [buffers](src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
+ - [expressions](src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of 
+objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
+performance calculations to code generation.
+ - [domains](src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
+ - [autodiff](src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
+ - [linear.parallel](#) : Parallel implementation for `LinearAlgebra`
+
 
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-core:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-core/build.gradle.kts

@@ -2,7 +2,7 @@ plugins {
     id("space.kscience.gradle.mpp")
 }
 
-kscience {
+kscience{
     jvm()
     js()
     native()
@@ -73,8 +73,8 @@ readme {
     ) { "Automatic differentiation" }
 
     feature(
-        id = "Parallel linear algebra"
-    ) {
+        id="Parallel linear algebra"
+    ){
         """
             Parallel implementation for `LinearAlgebra`
         """.trimIndent()

kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYErrorColumnarData.kt

@@ -27,7 +27,7 @@ public interface XYErrorColumnarData<T, out X : T, out Y : T> : XYColumnarData<T
 
     public companion object {
         public fun <T, X : T, Y : T> of(
-            x: Buffer<X>, y: Buffer<Y>, yErr: Buffer<Y>,
+            x: Buffer<X>, y: Buffer<Y>, yErr: Buffer<Y>
         ): XYErrorColumnarData<T, X, Y> {
             require(x.size == y.size) { "Buffer size mismatch. x buffer size is ${x.size}, y buffer size is ${y.size}" }
             require(y.size == yErr.size) { "Buffer size mismatch. y buffer size is ${x.size}, yErr buffer size is ${y.size}" }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MST.kt

@@ -58,7 +58,6 @@ public fun <T> MST.interpret(algebra: Algebra<T>, arguments: Map<Symbol, T>): T
             this.operation,
             algebra.number(this.value.value),
         )
-
         else -> algebra.unaryOperationFunction(this.operation)(this.value.interpret(algebra, arguments))
     }
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt

@@ -224,7 +224,11 @@ public inline fun <T : Any, F : Field<T>> SimpleAutoDiffField<T, F>.const(block:
 public fun <T : Any, F : Field<T>> F.simpleAutoDiff(
     bindings: Map<Symbol, T>,
     body: SimpleAutoDiffField<T, F>.() -> AutoDiffValue<T>,
-): DerivationResult<T> = SimpleAutoDiffField(this, bindings).differentiate(body)
+): DerivationResult<T> {
+    contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) }
+
+    return SimpleAutoDiffField(this, bindings).differentiate(body)
+}
 
 public fun <T : Any, F : Field<T>> F.simpleAutoDiff(
     vararg bindings: Pair<Symbol, T>,

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt

@@ -182,7 +182,7 @@ public interface LinearSpace<T, out A : Ring<T>> : MatrixScope<T> {
      * better use [StructureND.getOrComputeAttribute].
      */
     @UnstableKMathAPI
-    public fun <V : Any, A : StructureAttribute<V>> Matrix<T>.withComputedAttribute(
+    public fun <V : Any, A : StructureAttribute<V>> Matrix<T>.compute(
         attribute: A,
     ): Matrix<T>? {
         return if (attributes[attribute] != null) {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt

@@ -83,7 +83,7 @@ internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
 public fun <T : Comparable<T>> Field<T>.lup(
     matrix: Matrix<T>,
     checkSingular: (T) -> Boolean,
-): GenericLupDecomposition<T> {
+): GenericLupDecomposition<T>  {
     require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" }
     val m = matrix.colNum
     val pivot = IntArray(matrix.rowNum)
@@ -110,7 +110,7 @@ public fun <T : Comparable<T>> Field<T>.lup(
         // upper
         for (row in 0 until col) {
             var sum = lu[row, col]
-            for (i in 0 until row) {
+            for (i in 0 until row){
                 sum -= lu[row, i] * lu[i, col]
             }
             lu[row, col] = sum
@@ -122,7 +122,7 @@ public fun <T : Comparable<T>> Field<T>.lup(
 
         for (row in col until m) {
             var sum = lu[row, col]
-            for (i in 0 until col) {
+            for (i in 0 until col){
                 sum -= lu[row, i] * lu[i, col]
             }
             lu[row, col] = sum
@@ -226,7 +226,7 @@ private fun <T> Field<T>.solve(
 public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lupSolver(
     singularityCheck: (T) -> Boolean,
 ): LinearSolver<T> = object : LinearSolver<T> {
-    override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T> = elementAlgebra {
+    override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T>  = elementAlgebra{
         // Use existing decomposition if it is provided by matrix or linear space itself
         val decomposition = a.getOrComputeAttribute(LUP) ?: lup(a, singularityCheck)
         return solve(decomposition, b)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Int16RingND.kt

@@ -18,11 +18,12 @@ public sealed class Int16RingOpsND : BufferedRingOpsND<Short, Int16Ring>(Int16Ri
 
 @OptIn(UnstableKMathAPI::class)
 public class Int16RingND(
-    override val shape: ShapeND,
+    override val shape: ShapeND
 ) : Int16RingOpsND(), RingND<Short, Int16Ring>, NumbersAddOps<StructureND<Short>> {
 
     override fun number(value: Number): BufferND<Short> {
-        val short = value.toShort() // minimize conversions
+        val short
+        = value.toShort() // minimize conversions
         return structureND(shape) { short }
     }
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/IntRingND.kt

@@ -35,7 +35,7 @@ public sealed class IntRingOpsND : BufferedRingOpsND<Int, Int32Ring>(Int32Ring.b
 
 @OptIn(UnstableKMathAPI::class)
 public class IntRingND(
-    override val shape: ShapeND,
+    override val shape: ShapeND
 ) : IntRingOpsND(), RingND<Int, Int32Ring>, NumbersAddOps<StructureND<Int>> {
 
     override fun number(value: Number): BufferND<Int> {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/algebraNDExtentions.kt

@@ -14,13 +14,13 @@ import kotlin.jvm.JvmName
 public fun <T, A : Algebra<T>> AlgebraND<T, A>.structureND(
     shapeFirst: Int,
     vararg shapeRest: Int,
-    initializer: A.(IntArray) -> T,
+    initializer: A.(IntArray) -> T
 ): StructureND<T> = structureND(ShapeND(shapeFirst, *shapeRest), initializer)
 
 public fun <T, A : Algebra<T>> AlgebraND<T, A>.mutableStructureND(
     shapeFirst: Int,
     vararg shapeRest: Int,
-    initializer: A.(IntArray) -> T,
+    initializer: A.(IntArray) -> T
 ): MutableStructureND<T> = mutableStructureND(ShapeND(shapeFirst, *shapeRest), initializer)
 
 public fun <T, A : Group<T>> AlgebraND<T, A>.zero(shape: ShapeND): StructureND<T> = structureND(shape) { zero }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt

@@ -454,12 +454,10 @@ public fun String.parseBigInteger(): BigInt? {
             sign = +1
             1
         }
-
         '-' -> {
             sign = -1
             1
         }
-
         else -> {
             sign = +1
             0

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Float64BufferField.kt

@@ -33,7 +33,7 @@ public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Dou
         arg.map { it.pow(pow.toInt()) }
     } else {
         arg.map {
-            if (it < 0) throw IllegalArgumentException("Negative argument $it could not be raised to the fractional power")
+            if(it<0) throw IllegalArgumentException("Negative argument $it could not be raised to the fractional power")
             it.pow(pow.toDouble())
         }
     }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/OptionalOperations.kt

@@ -84,7 +84,7 @@ public expect fun Number.isInteger(): Boolean
  *
  * @param T the type of this structure element
  */
-public interface PowerOperations<T> : Algebra<T> {
+public interface PowerOperations<T>: Algebra<T> {
 
     /**
      * Raises [arg] to a power if possible (negative number could not be raised to a fractional power).

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/algebraExtensions.kt

@@ -99,10 +99,9 @@ public fun <T> Iterable<T>.sumWith(group: Group<T>): T = group.sum(this)
  * @param group tha algebra that provides addition
  * @param extractor the (inline) lambda function to extract value
  */
-public inline fun <T, R> Iterable<T>.sumWithGroupOf(group: Group<R>, extractor: (T) -> R): R =
-    this.fold(group.zero) { left: R, right: T ->
-        group.add(left, extractor(right))
-    }
+public inline fun <T, R> Iterable<T>.sumWithGroupOf(group: Group<R>, extractor: (T) -> R): R = this.fold(group.zero) { left: R, right: T ->
+    group.add(left, extractor(right))
+}
 
 /**
  * Returns the sum of all elements in the sequence in provided space.

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/integerFields.kt

@@ -34,7 +34,7 @@ public object Int16Field : Field<Int16>, Norm<Int16, Int16>, NumericAlgebra<Int1
     override fun multiply(left: Int16, right: Int16): Int16 = (left * right).toShort()
     override fun norm(arg: Int16): Int16 = abs(arg)
 
-    override fun scale(a: Int16, value: Double): Int16 = (a * value).roundToInt().toShort()
+    override fun scale(a: Int16, value: Double): Int16 = (a*value).roundToInt().toShort()
 
     override fun divide(left: Int16, right: Int16): Int16 = (left / right).toShort()
 
@@ -58,7 +58,7 @@ public object Int32Field : Field<Int32>, Norm<Int32, Int32>, NumericAlgebra<Int3
     override fun multiply(left: Int, right: Int): Int = left * right
     override fun norm(arg: Int): Int = abs(arg)
 
-    override fun scale(a: Int, value: Double): Int = (a * value).roundToInt()
+    override fun scale(a: Int, value: Double): Int = (a*value).roundToInt()
 
     override fun divide(left: Int, right: Int): Int = left / right
 
@@ -81,7 +81,7 @@ public object Int64Field : Field<Int64>, Norm<Int64, Int64>, NumericAlgebra<Int6
     override fun multiply(left: Int64, right: Int64): Int64 = left * right
     override fun norm(arg: Int64): Int64 = abs(arg)
 
-    override fun scale(a: Int64, value: Double): Int64 = (a * value).roundToLong()
+    override fun scale(a: Int64, value: Double): Int64 = (a*value).roundToLong()
 
     override fun divide(left: Int64, right: Int64): Int64 = left / right
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ArrayBuffer.kt

@@ -32,4 +32,4 @@ public value class ArrayBuffer<T>(internal val array: Array<T>) : MutableBuffer<
 /**
  * Returns an [ArrayBuffer] that wraps the original array.
  */
-public fun <T> Array<T>.asBuffer(): ArrayBuffer<T> = ArrayBuffer(this)
+public fun <T> Array<T>.asBuffer(): ArrayBuffer<T> = ArrayBuffer( this)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/FlaggedBuffer.kt

@@ -55,7 +55,7 @@ public fun FlaggedBuffer<*>.isMissing(index: Int): Boolean = hasFlag(index, Valu
  */
 public class FlaggedDoubleBuffer(
     public val values: DoubleArray,
-    public val flags: ByteArray,
+    public val flags: ByteArray
 ) : FlaggedBuffer<Double?>, Buffer<Double?> {
 
     init {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Float32Buffer.kt

@@ -37,8 +37,7 @@ public typealias FloatBuffer = Float32Buffer
  * The function [init] is called for each array element sequentially starting from the first one.
  * It should return the value for a buffer element given its index.
  */
-public inline fun Float32Buffer(size: Int, init: (Int) -> Float): Float32Buffer =
-    Float32Buffer(FloatArray(size) { init(it) })
+public inline fun Float32Buffer(size: Int, init: (Int) -> Float): Float32Buffer = Float32Buffer(FloatArray(size) { init(it) })
 
 /**
  * Returns a new [Float32Buffer] of given elements.

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt

@@ -14,7 +14,7 @@ import kotlin.reflect.typeOf
  *
  * @param T the type of elements contained in the buffer.
  */
-public interface MutableBuffer<T> : Buffer<T> {
+public interface MutableBuffer<T> : Buffer<T>{
 
     /**
      * Sets the array element at the specified [index] to the specified [value].
@@ -65,21 +65,20 @@ public interface MutableBuffer<T> : Buffer<T> {
 /**
  * Returns a shallow copy of the buffer.
  */
-public fun <T> Buffer<T>.copy(bufferFactory: BufferFactory<T>): Buffer<T> = if (this is ArrayBuffer) {
+public fun <T> Buffer<T>.copy(bufferFactory: BufferFactory<T>): Buffer<T> =if(this is ArrayBuffer){
     ArrayBuffer(array.copyOf())
-} else {
-    bufferFactory(size, ::get)
+}else{
+    bufferFactory(size,::get)
 }
 
 /**
  * Returns a mutable shallow copy of the buffer.
  */
-public fun <T> Buffer<T>.mutableCopy(bufferFactory: MutableBufferFactory<T>): MutableBuffer<T> =
-    if (this is ArrayBuffer) {
-        ArrayBuffer(array.copyOf())
-    } else {
-        bufferFactory(size, ::get)
-    }
+public fun <T> Buffer<T>.mutableCopy(bufferFactory: MutableBufferFactory<T>): MutableBuffer<T> =if(this is ArrayBuffer){
+    ArrayBuffer(array.copyOf())
+}else{
+    bufferFactory(size,::get)
+}
 
 
 /**

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt

@@ -21,14 +21,14 @@ fun <T : Any> assertMatrixEquals(expected: StructureND<T>, actual: StructureND<T
 class DoubleLUSolverTest {
 
     @Test
-    fun testInvertOne() = Double.algebra.linearSpace.run {
+    fun testInvertOne() = Double.algebra.linearSpace.run{
         val matrix = one(2, 2)
         val inverted = lupSolver().inverse(matrix)
         assertMatrixEquals(matrix, inverted)
     }
 
     @Test
-    fun testDecomposition() = with(Double.algebra.linearSpace) {
+    fun testDecomposition() = with(Double.algebra.linearSpace){
         val matrix = matrix(2, 2)(
             3.0, 1.0,
             2.0, 3.0
@@ -43,7 +43,7 @@ class DoubleLUSolverTest {
     }
 
     @Test
-    fun testInvert() = Double.algebra.linearSpace.run {
+    fun testInvert()  = Double.algebra.linearSpace.run{
         val matrix = matrix(2, 2)(
             3.0, 1.0,
             1.0, 3.0

kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt

@@ -50,7 +50,7 @@ class MatrixTest {
         infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
             var res = this
             repeat(power - 1) {
-                res = res dot this@pow
+                res =  res dot this@pow
             }
             return res
         }

kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/PermSortTest.kt

@@ -29,7 +29,7 @@ class PermSortTest {
      */
     @Test
     fun testOnEmptyBuffer() {
-        val emptyBuffer = Int32Buffer(0) { it }
+        val emptyBuffer = Int32Buffer(0) {it}
         var permutations = emptyBuffer.indicesSorted()
         assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
         permutations = emptyBuffer.indicesSortedDescending()
@@ -67,11 +67,7 @@ class PermSortTest {
         assertContentEquals(expected, permutations.map { platforms[it] }, "PermSort using custom ascending comparator")
 
         permutations = platforms.indicesSortedWith(compareByDescending { it.name.length })
-        assertContentEquals(
-            expected.reversed(),
-            permutations.map { platforms[it] },
-            "PermSort using custom descending comparator"
-        )
+        assertContentEquals(expected.reversed(), permutations.map { platforms[it] }, "PermSort using custom descending comparator")
     }
 
     private fun testPermutation(bufferSize: Int) {   
@@ -86,9 +82,9 @@ class PermSortTest {
         // Ensure no doublon is present in indices
         assertEquals(indices.toSet().size, indices.size)
 
-        for (i in 0 until (bufferSize - 1)) {
+        for (i in 0 until (bufferSize-1)) {
             val current = buffer[indices[i]]
-            val next = buffer[indices[i + 1]]
+            val next = buffer[indices[i+1]]
             assertTrue(current <= next, "Permutation indices not properly sorted")
         }
 
@@ -97,12 +93,12 @@ class PermSortTest {
         // Ensure no doublon is present in indices
         assertEquals(descIndices.toSet().size, descIndices.size)
 
-        for (i in 0 until (bufferSize - 1)) {
+        for (i in 0 until (bufferSize-1)) {
             val current = buffer[descIndices[i]]
-            val next = buffer[descIndices[i + 1]]
+            val next = buffer[descIndices[i+1]]
             assertTrue(current >= next, "Permutation indices not properly sorted in descending order")
         }
     }
 
-    private fun Random.buffer(size: Int) = Int32Buffer(size) { nextInt() }
+    private fun Random.buffer(size : Int) = Int32Buffer(size) { nextInt() }
 }

kmath-core/src/commonTest/kotlin/space/kscience/kmath/nd/NdOperationsTest.kt

@@ -18,7 +18,7 @@ class NdOperationsTest {
 
         println(StructureND.toString(structure))
 
-        val rolled = structure.roll(0, -1)
+        val rolled = structure.roll(0,-1)
 
         println(StructureND.toString(rolled))
 

kmath-core/src/commonTest/kotlin/space/kscience/kmath/nd/StridesTest.kt

@@ -12,10 +12,10 @@ class StridesTest {
     fun checkRowBasedStrides() {
         val strides = RowStrides(ShapeND(3, 3))
         var counter = 0
-        for (i in 0..2) {
-            for (j in 0..2) {
+        for(i in 0..2){
+            for(j in 0..2){
 //                print(strides.offset(intArrayOf(i,j)).toString() + "\t")
-                require(strides.offset(intArrayOf(i, j)) == counter)
+                require(strides.offset(intArrayOf(i,j)) == counter)
                 counter++
             }
             println()
@@ -26,10 +26,10 @@ class StridesTest {
     fun checkColumnBasedStrides() {
         val strides = ColumnStrides(ShapeND(3, 3))
         var counter = 0
-        for (i in 0..2) {
-            for (j in 0..2) {
+        for(i in 0..2){
+            for(j in 0..2){
 //                print(strides.offset(intArrayOf(i,j)).toString() + "\t")
-                require(strides.offset(intArrayOf(j, i)) == counter)
+                require(strides.offset(intArrayOf(j,i)) == counter)
                 counter++
             }
             println()

kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/BufferExpandedTest.kt

@@ -13,7 +13,7 @@ internal class BufferExpandedTest {
     private val buffer = (0..100).toList().asBuffer()
 
     @Test
-    fun shrink() {
+    fun shrink(){
         val view = buffer.slice(20..30)
         assertEquals(20, view[0])
         assertEquals(30, view[10])
@@ -21,10 +21,10 @@ internal class BufferExpandedTest {
     }
 
     @Test
-    fun expandNegative() {
-        val view: BufferView<Int> = buffer.expand(-20..113, 0)
-        assertEquals(0, view[4])
-        assertEquals(0, view[123])
+    fun expandNegative(){
+        val view: BufferView<Int> = buffer.expand(-20..113,0)
+        assertEquals(0,view[4])
+        assertEquals(0,view[123])
         assertEquals(100, view[120])
         assertFails { view[-2] }
         assertFails { view[134] }

kmath-core/src/jvmMain/kotlin/space/kscience/kmath/linear/Float64ParallelLinearSpace.kt

@@ -41,7 +41,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
     }
 
     override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Float64Buffer =
-        IntStream.range(0, size).parallel().mapToDouble { Float64Field.initializer(it) }.toArray().asBuffer()
+        IntStream.range(0, size).parallel().mapToDouble{ Float64Field.initializer(it) }.toArray().asBuffer()
 
     override fun Matrix<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
         asND().map { -it }.as2D()

kmath-core/src/jvmMain/kotlin/space/kscience/kmath/operations/isInteger.kt

@@ -8,5 +8,4 @@ package space.kscience.kmath.operations
 /**
  * Check if number is an integer
  */
-public actual fun Number.isInteger(): Boolean =
-    (this is Int) || (this is Long) || (this is Short) || (this.toDouble() % 1 == 0.0)
+public actual fun Number.isInteger(): Boolean = (this is Int) || (this is Long) || (this is Short) || (this.toDouble() % 1 == 0.0)

kmath-core/src/jvmMain/kotlin/space/kscience/kmath/structures/parallelMutableBuffer.kt

@@ -33,8 +33,7 @@ public fun <T> MutableBuffer.Companion.parallel(
     typeOf<Double>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
         .asBuffer() as MutableBuffer<T>
     //TODO add unsigned types
-    else -> IntStream.range(0, size).parallel().mapToObj { initializer(it) }.collect(Collectors.toList<T>())
-        .asMutableBuffer()
+    else -> IntStream.range(0, size).parallel().mapToObj { initializer(it) }.collect(Collectors.toList<T>()).asMutableBuffer()
 }
 
 public class ParallelBufferFactory<T>(override val type: SafeType<T>) : MutableBufferFactory<T> {

kmath-core/src/jvmTest/kotlin/space/kscience/kmath/linear/ParallelMatrixTest.kt

@@ -19,14 +19,14 @@ import kotlin.test.assertTrue
 class ParallelMatrixTest {
 
     @Test
-    fun testTranspose() = Float64Field.linearSpace.parallel {
+    fun testTranspose() =  Float64Field.linearSpace.parallel{
         val matrix = one(3, 3)
         val transposed = matrix.transposed()
         assertTrue { StructureND.contentEquals(matrix, transposed) }
     }
 
     @Test
-    fun testBuilder() = Float64Field.linearSpace.parallel {
+    fun testBuilder() = Float64Field.linearSpace.parallel{
         val matrix = matrix(2, 3)(
             1.0, 0.0, 0.0,
             0.0, 1.0, 2.0
@@ -36,7 +36,7 @@ class ParallelMatrixTest {
     }
 
     @Test
-    fun testMatrixExtension() = Float64Field.linearSpace.parallel {
+    fun testMatrixExtension() = Float64Field.linearSpace.parallel{
         val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
             when {
                 col == 0 -> .50
@@ -49,7 +49,7 @@ class ParallelMatrixTest {
         infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
             var res = this
             repeat(power - 1) {
-                res = res dot this@pow
+                res =  res dot this@pow
             }
             return res
         }

kmath-core/src/nativeMain/kotlin/space/kscience/kmath/operations/isInteger.kt

@@ -8,5 +8,4 @@ package space.kscience.kmath.operations
 /**
  * Check if number is an integer
  */
-public actual fun Number.isInteger(): Boolean =
-    (this is Int) || (this is Long) || (this is Short) || (this.toDouble() % 1 == 0.0)
+public actual fun Number.isInteger(): Boolean = (this is Int) || (this is Long) || (this is Short) || (this.toDouble() % 1 == 0.0)

kmath-coroutines/README.md

@@ -1,5 +1,7 @@
 # Module kmath-coroutines
 
+
+
 ## Usage
 
 ## Artifact:
@@ -7,7 +9,6 @@
 The Maven coordinates of this project are `space.kscience:kmath-coroutines:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/structures/LazyStructureND.kt

@@ -25,7 +25,6 @@ public class LazyStructureND<out T>(
     }
 
     public suspend fun await(index: IntArray): T = async(index).await()
-
     @PerformancePitfall
     override operator fun get(index: IntArray): T = runBlocking { async(index).await() }
 
@@ -49,13 +48,13 @@ public suspend fun <T> StructureND<T>.await(index: IntArray): T =
  * PENDING would benefit from KEEP-176
  */
 @OptIn(PerformancePitfall::class)
-public inline fun <T, R> StructureND<T>.mapAsyncIndexed(
+public inline fun <T, reified R> StructureND<T>.mapAsyncIndexed(
     scope: CoroutineScope,
     crossinline function: suspend (T, index: IntArray) -> R,
 ): LazyStructureND<R> = LazyStructureND(scope, shape) { index -> function(get(index), index) }
 
 @OptIn(PerformancePitfall::class)
-public inline fun <T, R> StructureND<T>.mapAsync(
+public inline fun <T, reified R> StructureND<T>.mapAsync(
     scope: CoroutineScope,
     crossinline function: suspend (T) -> R,
-): LazyStructureND<R> = LazyStructureND(scope, shape) { index -> function(get(index)) }
+): LazyStructureND<R> = LazyStructureND(scope,  shape) { index -> function(get(index)) }

kmath-dimensions/README.md

@@ -9,7 +9,6 @@ A proof of concept module for adding type-safe dimensions to structures
 The Maven coordinates of this project are `space.kscience:kmath-dimensions:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-dimensions/build.gradle.kts

@@ -2,13 +2,13 @@ plugins {
     id("space.kscience.gradle.mpp")
 }
 
-kscience {
+kscience{
     jvm()
     js()
     native()
     wasm()
 
-    dependencies {
+    dependencies{
         api(projects.kmathCore)
     }
 

kmath-ejml/README.md

@@ -2,16 +2,16 @@
 
 EJML based linear algebra implementation.
 
-- [ejml-vector](src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations.
-- [ejml-matrix](src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation.
-- [ejml-linear-space](src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations.
+ - [ejml-vector](src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations.
+ - [ejml-matrix](src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation.
+ - [ejml-linear-space](src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations.
+
 
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-ejml:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/implementations.kt

@@ -674,17 +674,15 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
 
         val raw: Any? = when (attribute) {
             Inverted -> {
-                val res = DMatrixRMaj(origin.numRows, origin.numCols)
-                CommonOps_DSCC.invert(origin, res)
+                val res = DMatrixRMaj(origin.numRows,origin.numCols)
+                CommonOps_DSCC.invert(origin,res)
                 res.wrapMatrix()
             }
 
             Determinant -> CommonOps_DSCC.det(origin)
 
             QR -> object : QRDecomposition<Double> {
-                val ejmlQr by lazy {
-                    DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
-                }
+                val ejmlQr by lazy { DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) } }
                 override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
                 override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
             }
@@ -897,17 +895,15 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
 
         val raw: Any? = when (attribute) {
             Inverted -> {
-                val res = FMatrixRMaj(origin.numRows, origin.numCols)
-                CommonOps_FSCC.invert(origin, res)
+                val res = FMatrixRMaj(origin.numRows,origin.numCols)
+                CommonOps_FSCC.invert(origin,res)
                 res.wrapMatrix()
             }
 
             Determinant -> CommonOps_FSCC.det(origin)
 
             QR -> object : QRDecomposition<Float32> {
-                val ejmlQr by lazy {
-                    DecompositionFactory_FSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
-                }
+                val ejmlQr by lazy { DecompositionFactory_FSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) } }
                 override val q: Matrix<Float32> get() = ejmlQr.getQ(null, false).wrapMatrix()
                 override val r: Matrix<Float32> get() = ejmlQr.getR(null, false).wrapMatrix()
             }

kmath-for-real/README.md

@@ -2,18 +2,16 @@
 
 Specialization of KMath APIs for Double numbers.
 
-- [DoubleVector](src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for
-  Buffers/Points
-- [DoubleMatrix](src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real
-  structures
-- [grids](src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators
+ - [DoubleVector](src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for Buffers/Points
+ - [DoubleMatrix](src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures
+ - [grids](src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators
+
 
 ## Artifact:
 
 The Maven coordinates of this project are `space.kscience:kmath-for-real:0.4.0-dev-3`.
 
 **Gradle Kotlin DSL:**
-
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt

@@ -48,7 +48,7 @@ public fun Sequence<DoubleArray>.toMatrix(): RealMatrix = toList().let {
 }
 
 public fun RealMatrix.repeatStackVertical(n: Int): RealMatrix =
-    VirtualMatrix(rowNum * n, colNum) { row, col ->
+    VirtualMatrix( rowNum * n, colNum) { row, col ->
         get(if (row == 0) 0 else row % rowNum, col)
     }
 

kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt

@@ -39,7 +39,7 @@ public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Double
         else -> return Float64Buffer(range.start)
     }
     val numberOfPoints = floor(normalizedRange.length / step).toInt() + 1
-    return Float64Buffer(numberOfPoints) { normalizedRange.start + step * it }
+    return Float64Buffer(numberOfPoints) { normalizedRange.start + step * it  }
 }
 
 /**