InterpolationFileTest

Tests time-based interpolation using data from a CSV file.

This component reads data from a specified CSV file and performs linear interpolation, using the current simulation time as the input. The interpolated value is made available through the output variable y. The component utilizes a dataset structure that identifies dependent and independent variables from the CSV file, allowing for flexible time-series data interpolation.

Usage

InterpolationFileTest()

Parameters:

Name	Description	Units	Default value
dataset	Dataset configuration specifying the CSV file path and variable mappings	–	DyadDataset("data/interp_square_data.csv", dependent_vars=["data"], independent_var="ts")

Variables

Name	Description	Units
y	Output variable that holds the interpolated value	–

Behavior

$$\begin{array}{rl}\mathtt{interp}\mathtt{.}\mathtt{u}\left(t\right)& =t\\ \mathtt{interp}\mathtt{.}\mathtt{y}\left(t\right)& =y\left(t\right)\\ \mathtt{interp}\mathtt{.}\mathtt{y}\left(t\right)& =\mathtt{interp}\mathtt{.}\mathtt{interpolator}\left(\mathtt{interp}\mathtt{.}\mathtt{u}\left(t\right)\right)\end{array}$$

Source

# Tests time-based interpolation using data from a CSV file.
#
# This component reads data from a specified CSV file and performs linear interpolation, using the
# current simulation time as the input. The interpolated value is made available through the output
# variable `y`. The component utilizes a dataset structure that identifies dependent and independent
# variables from the CSV file, allowing for flexible time-series data interpolation.
test component InterpolationFileTest
  # Interpolation object that performs the actual interpolation calculation
  interp = Interpolation(interpolation_type=LinearInterpolation, dataset=dataset)
  # Dataset configuration specifying the CSV file path and variable mappings
  structural parameter dataset::DyadDataset = DyadDataset("data/interp_square_data.csv", dependent_vars=["data"], independent_var="ts")
  # Output variable that holds the interpolated value
  variable y::Real
relations
  interp.u = time
  interp.y = y
metadata {"Dyad": {"tests": {"case1": {"stop": 0.1, "expect": {"signals": ["y"]}}}}}
end

Flattened Source

# Tests time-based interpolation using data from a CSV file.
#
# This component reads data from a specified CSV file and performs linear interpolation, using the
# current simulation time as the input. The interpolated value is made available through the output
# variable `y`. The component utilizes a dataset structure that identifies dependent and independent
# variables from the CSV file, allowing for flexible time-series data interpolation.
test component InterpolationFileTest
  # Interpolation object that performs the actual interpolation calculation
  interp = Interpolation(interpolation_type=LinearInterpolation, dataset=dataset)
  # Dataset configuration specifying the CSV file path and variable mappings
  structural parameter dataset::DyadDataset = DyadDataset("data/interp_square_data.csv", dependent_vars=["data"], independent_var="ts")
  # Output variable that holds the interpolated value
  variable y::Real
relations
  interp.u = time
  interp.y = y
metadata {"Dyad": {"tests": {"case1": {"stop": 0.1, "expect": {"signals": ["y"]}}}}}
end

Test Cases

This is setup code, that must be run before each test case.

using BlockComponents
using ModelingToolkit, OrdinaryDiffEqDefault
using Plots
using CSV, DataFrames

snapshotsdir = joinpath(dirname(dirname(pathof(BlockComponents))), "test", "snapshots")

"/home/actions-runner-10/.julia/packages/BlockComponents/77kIK/test/snapshots"

Test Case case1

@mtkbuild model_case1 = InterpolationFileTest()
u0_case1 = []
prob_case1 = ODEProblem(model_case1, u0_case1, (0, 0.1))
sol_case1 = solve(prob_case1)

retcode: Success
Interpolation: 1st order linear
t: 2-element Vector{Float64}:
 0.0
 0.1
u: 2-element Vector{Vector{Float64}}:
 []
 []

df_case1 = DataFrame(:t => sol_case1[:t], :actual => sol_case1[model_case1.y])
dfr_case1 = try CSV.read(joinpath(snapshotsdir, "InterpolationFileTest_case1_sig0.ref"), DataFrame); catch e; nothing; end
plt = plot(sol_case1, idxs=[model_case1.y], width=2, label="Actual value of y")
if !isnothing(dfr_case1)
  scatter!(plt, dfr_case1.t, dfr_case1.expected, mc=:red, ms=3, label="Expected value of y")
end

plt

Related

• Examples

• Experiments

• Analyses

• Tests

  • Interpolation