Add interpolation functions

examples/postprocessing/interpolation.jl

@@ -1,6 +1,7 @@
 # Example for using interpolation
 #######################################################################################
 using TrixiParticles
+using Plots
 
 trixi_include(@__MODULE__, joinpath(examples_dir(), "fluid", "rectangular_tank_2d.jl"))
 
@@ -34,6 +35,7 @@ result_endpoint = interpolate_line([1.0, -0.05], [1.0, 1.0], 10, semi, fluid_sys
 walldistance = [coord[2] for coord in result.coord]
 walldistance_endpoint = [coord[2] for coord in result_endpoint.coord]
 
+# Alternatively, to using Plots.jl one can also use PythonPlot which uses matplotlib
 # using PythonPlot
 
 # figure()
@@ -48,8 +50,6 @@ walldistance_endpoint = [coord[2] for coord in result_endpoint.coord]
 
 # plotshow()
 
-using Plots
-
 p = plot(walldistance, result.density, marker=:circle, color=:blue, markerstrokecolor=:blue,
          linewidth=2, label="With Endpoint")
 

src/general/interpolation.jl

@@ -30,7 +30,7 @@ but does not include these points.
     - This function is particularly useful for analyzing gradients or creating visualizations
       along a specified line in the SPH simulation domain.
     - The interpolation accuracy is subject to the density of particles and the chosen smoothing length.
-    - With `cut_off_bnd` is used a density-based estimation of the surface is used which is not as
+    - With `cut_off_bnd`, a density-based estimation of the surface is used which is not as
       accurate as a real surface reconstruction.
 
 # Examples
@@ -96,7 +96,7 @@ results = interpolate_point(points, semi, ref_system, sol)
     - This function is particularly useful for analyzing gradients or creating visualizations
       along a specified line in the SPH simulation domain.
     - The interpolation accuracy is subject to the density of particles and the chosen smoothing length.
-    - With `cut_off_bnd` is used a density-based estimation of the surface is used which is not as
+    - With `cut_off_bnd`, a density-based estimation of the surface is used which is not as
     accurate as a real surface reconstruction.
 """
 function interpolate_point(points_coords::AbstractArray{<:AbstractArray}, semi, ref_system,

test/general/interpolation.jl

@@ -1,3 +1,4 @@
+include("../test_util.jl")
 @testset verbose=true "SPH Interpolation" begin
     function compare_interpolation_result(actual, expected; tolerance=1e-8)
         @test length(actual.density) == length(expected.density)
@@ -79,8 +80,8 @@
         smoothing_kernel = SchoenbergCubicSplineKernel{2}()
         sound_speed = 10 * sqrt(9.81 * 0.9)
 
-        state_equation = StateEquationCole(sound_speed, 7, 1000, 100000.0,
-                                           background_pressure=100000.0,
+        state_equation = StateEquationCole(; sound_speed, reference_density=1000.0,
+                                           exponent=7, background_pressure=100000.0,
                                            clip_negative_pressure=false)
 
         fluid = rectangular_patch(particle_spacing, (nx, ny), seed=1,
@@ -581,8 +582,8 @@
         smoothing_kernel = SchoenbergCubicSplineKernel{3}()
         sound_speed = 10 * sqrt(9.81 * 0.9)
 
-        state_equation = StateEquationCole(sound_speed, 7, 1000, 100000.0,
-                                           background_pressure=100000.0,
+        state_equation = StateEquationCole(; sound_speed, reference_density=1000.0,
+                                           exponent=7, background_pressure=100000.0,
                                            clip_negative_pressure=false)
 
         fluid = rectangular_patch(particle_spacing, (nx, ny, nz), seed=1,