Final goal is to add fiber type dependency on the optimum optical power computation and remove the need for the reference span loss input. The current way is kept but a new one is introduced, that can be selected via a configuration in sim-params.
short story 1: as a user I am able to select an autodesign power optimisation by filling a value in sim-params.json. Only default available for now
- 'autodesign': 'default'
- in core.network.py: change def target_power() function with the autodesign selection .
- target_power may call different functions depending on autodesign choice. target_power -> default_target_power function.
This prepares th next step with the advanced function.
short story 2: creates the test
- create a test that computes the optimum power for one span with both default and advanced case, with expected results on advanced case.
short story 3: as a user I am able to select an advanced model to compute optimum optical power.
- create and advanced_target_power based on Andrea's paper that makes the test successful. Note that the eta function is available in core.bscience_utils
short story 4: as a user I don't want to tune a reference span loss
- change the algorithm to use PSD instead of power
Final goal is to add fiber type dependency on the optimum optical power computation and remove the need for the reference span loss input. The current way is kept but a new one is introduced, that can be selected via a configuration in sim-params.
short story 1: as a user I am able to select an autodesign power optimisation by filling a value in sim-params.json. Only default available for now
This prepares th next step with the advanced function.
short story 2: creates the test
short story 3: as a user I am able to select an advanced model to compute optimum optical power.
short story 4: as a user I don't want to tune a reference span loss