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If we want to run a new data set, this requires building a new `LinearInterpolation` and `ODESystem` followed by running `structural_simplify`, all of which takes time. Therefore, to run serveral pieces of data it's better to re-use an `ODESystem`. The next couple methods will demonstrate how to do this.
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If we want to run a new data set, this requires building a new `LinearInterpolation` and `ODESystem` followed by running `structural_simplify`, all of which takes time. Therefore, to run several pieces of data it's better to re-use an `ODESystem`. The next couple methods will demonstrate how to do this.
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## Custom Component with External Data
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@@ -105,7 +105,7 @@ Additional code could be added to resolve this issue, for example by using a `Re
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## `SampledData` Component
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To resolve the issues presented above, the `ModelingToolkitStandardLibrary.Blocks.SampledData` component can be used which allows for a resusable `ODESystem` and self contained data which ensures a solution which remains valid for it's lifetime. Now it's possible to also parallize the call to `solve()`.
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To resolve the issues presented above, the `ModelingToolkitStandardLibrary.Blocks.SampledData` component can be used which allows for a resusable `ODESystem` and self contained data which ensures a solution which remains valid for it's lifetime. Now it's possible to also parallelize the call to `solve()`.
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```julia
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functionSystem(; name)
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end
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```
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Note, in the above example, we can build the system with an empty `SampledData` component, only setting the expected data type: `@named src = SampledData(Float64)`. It's also possible to initialize the component with real sampled data: `@named src = SampledData(data, dt)`. Additionally note that before running an `ODEProblem` using the `SampledData` component, one must be careful about the parameter vector Type. The `SampledData` component contains a `buffer` parameter of type `Parameter`, therefore we must generate the problem using `tofloat=false`. This will initially give a parameter vector of type `Vector{Any}` with a mix of numbers and `Parameter` type. We can convert the vector to a uniform `Parameter` type by running `p = Parameter.(p)`. This will wrap all the single values in a `Parameter` which will be mathmatically equivalent to a `Number`.
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Note, in the above example, we can build the system with an empty `SampledData` component, only setting the expected data type: `@named src = SampledData(Float64)`. It's also possible to initialize the component with real sampled data: `@named src = SampledData(data, dt)`. Additionally note that before running an `ODEProblem` using the `SampledData` component, one must be careful about the parameter vector Type. The `SampledData` component contains a `buffer` parameter of type `Parameter`, therefore we must generate the problem using `tofloat=false`. This will initially give a parameter vector of type `Vector{Any}` with a mix of numbers and `Parameter` type. We can convert the vector to a uniform `Parameter` type by running `p = Parameter.(p)`. This will wrap all the single values in a `Parameter` which will be mathematically equivalent to a `Number`.
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