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Commit: 2fac2e0
In the linear theory code when evolving a single mode, we intend to recover the perturbations introduced in the real and imaginary parts by taking FTs of the input distribution function. However, the accuracy of the value recovered is dependant on the resolution considered. This seems to hamper the order of convergence.
For instance, if the input perturbations for the real and imaginary parts are (0.01, 0.02), we find that for N=32 we recover (0.0079, 0.0208) and N = 128, we recover (0.0095, 0.0202).
Current Workaround : The perturbations are setup using the user defined parameters params.pert_real and params.pert_imag. Doing so leads to the expected convergence behaviour
The text was updated successfully, but these errors were encountered:
Commit: 2fac2e0
In the linear theory code when evolving a single mode, we intend to recover the perturbations introduced in the real and imaginary parts by taking FTs of the input distribution function. However, the accuracy of the value recovered is dependant on the resolution considered. This seems to hamper the order of convergence.
For instance, if the input perturbations for the real and imaginary parts are (0.01, 0.02), we find that for N=32 we recover (0.0079, 0.0208) and N = 128, we recover (0.0095, 0.0202).
Current Workaround : The perturbations are setup using the user defined parameters
params.pert_real
andparams.pert_imag
. Doing so leads to the expected convergence behaviourThe text was updated successfully, but these errors were encountered: