Gridsplines

Introduction

Project contains a finite difference solver for heat transfer and diffusion problems at one or two dimensional grids. The grid can represent orthogonal or cyllindric coordinate spaces.
There is also a simple polynomial spline library, which contains cubic Lagrange, cubic Hermite and monotone cubic Hermite polynomial splines.

To add this library into your scala project,
add the following lines into build.sbt file:

val commonResolvers = Seq(
  "Sonatype OSS Snapshots" at "https://oss.sonatype.org/content/repositories/snapshots",
  "Sonatype OSS Releases" at  "https://oss.sonatype.org/content/repositories/releases"
)
resolvers ++= commonResolvers
libraryDependencies += "com.github.daniil-timofeev" %% "gridsplines" % "0.2.0-SNAPSHOT"

Splines

Lagrange splines

…

Monotonic splines

Monotonic Hermite splines is useful for two dimensional dataset interpolation, where exremum points are already known.
Spline will convert each of two adjacent points into a cubic polynomial equation, and extremum values will be only at spline construction points. Let’s construct monotonic spline with folowing vapor physical properties data, which is temperature dependent.

import piecewise._
val temperatures = 100.0 to 200.0 by 10.0 toList
// in kg/m3
val density =
  List(0.598, 0.826, 1.121, 1.496, 1.966,
    2.547, 3.258, 4.122, 5.156, 6.397, 7.862)
val points = temperatures zip density
...
val spline = Spline.m1Hermite3(points)
// spline: piecewise.Spline[piecewise.M1Hermite3] = 
// Spline(
//  [100.0, 110.0): 0,0000*(x-100,0000000)^3 -0,0003*(x-100,0000000)^2  +0,0228*(x-100,0000000) +0,5980
//  [110.0120.0) :0,0000*(x-110,0000000)^3 +0,0003*(x-110,0000000)^2  +0,0262*(x-110,0000000) +0,8260
//  [120.0130.0) :0,0000*(x-120,0000000)^3 +0,0003*(x-120,0000000)^2  +0,0335*(x-120,0000000) +1,1210
//  [130.0140.0) :0,0000*(x-130,0000000)^3 +0,0004*(x-130,0000000)^2  +0,0422*(x-130,0000000) +1,4960
//  [140.0, 150.0): 0,0000*(x-140,0000000)^3 +0,0005*(x-140,0000000)^2  +0,0526*(x-140,0000000) +1,9660
//  [150.0160.0) :0,0000*(x-150,0000000)^3 +0,0005*(x-150,0000000)^2  +0,0646*(x-150,0000000) +2,5470
//  [160.0, 170.0): 0,0000*(x-160,0000000)^3 +0,0007*(x-160,0000000)^2  +0,0787*(x-160,0000000) +3,2580
//  [170.0180.0) :0,0000*(x-170,0000000)^3 +0,0007*(x-170,0000000)^2  +0,0949*(x-170,0000000) +4,1220
//  [180.0190.0) :0,0000*(x-180,0000000)^3 +0,0010*(x-180,0000000)^2  +0,1138*(x-180,0000000) +5,1560
//  [190.0, 200.0): -0,0001*(x-190,0000000)^3 +0,0022*(x-190,0000000)^2  +0,1353*(x-190,0000000) +6,3970
// )
spline(110)
// res0: Double = 0.826
spline(150)
// res1: Double = 2.547
spline(135)
// res2: Double = 1.718125

Spline values at construction point will stay without changes, while values of other point will be something between them.
It’s also possible to calculate derivative and antiderivative at any point of spline interval.

spline.der(185)
// res3: Double = 0.1238875000000001
spline.integral(185)
// res4: Double = 27.242786458333335

and calculate an area under the part of spline

spline.area(150, 185)
// res5: Double = 139.148203125

If you pass argument out of the spline interval, an error will be occured.
If this case is possible in your program, use spline.applyOption() method instead. You can also convert this spline using .asUniSpline method, and then you get interval bound value, when passing an argument out of the interval bounds:

spline.applyOption(250)
// res6: Option[Double] = None
spline.asUniSpline.applyOption(250)
// res7: Option[Double] = Some(7.862000000000001)

WIP