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The "openroadm" NF model only applies to inline amps

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Change-Id: I2971b97506c8d0a778b3007fd5bd092c3ba83601
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Jan Kundrát
2021-05-06 16:36:25 +02:00
parent 3d9d5d7a8d
commit fc82f43b89
3 changed files with 7 additions and 7 deletions
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2
docs/concepts.rst
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@@ -237,7 +237,7 @@ GNPy supports several different noise models with varying level of accuracy.
When in doubt, contact your vendor's technical support and ask them to :ref:`contribute their equipment descriptions<extending-edfa>` to GNPy. When in doubt, contact your vendor's technical support and ask them to :ref:`contribute their equipment descriptions<extending-edfa>` to GNPy.
The most accurate noise models describe the resulting NF of an EDFA as a third-degree polynomial. The most accurate noise models describe the resulting NF of an EDFA as a third-degree polynomial.
GNPy understands polynomials as a NF-yielding function of the :ref:`gain difference from the optimal gain<ext-nf-model-polynomial-NF>`, or as a function of the input power resulting in an :ref:`incremental OSNR as used in OpenROADM<ext-nf-model-polynomial-OSNR-OpenROADM>`. GNPy understands polynomials as a NF-yielding function of the :ref:`gain difference from the optimal gain<ext-nf-model-polynomial-NF>`, or as a function of the input power resulting in an incremental OSNR as used in :ref:`OpenROADM inline amplifiers<ext-nf-model-polynomial-OSNR-OpenROADM>`.
For scenarios where the vendor has not yet contributed an accurate EDFA NF description to GNPy, it is possible to approximate the characteristics via an operator-focused, min-max NF model. For scenarios where the vendor has not yet contributed an accurate EDFA NF description to GNPy, it is possible to approximate the characteristics via an operator-focused, min-max NF model.
.. _nf-model-min-max-NF: .. _nf-model-min-max-NF:

8
docs/extending.rst
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@@ -42,11 +42,11 @@ In that case, use:
.. _ext-nf-model-polynomial-OSNR-OpenROADM: .. _ext-nf-model-polynomial-OSNR-OpenROADM:
Polynomial OSNR (OpenROADM-style) Polynomial OSNR (OpenROADM-style for inline amplifier)
********************************* ******************************************************
This model is useful for amplifiers compliant to the OpenROADM specification for ILA. This model is useful for amplifiers compliant to the OpenROADM specification for ILA (an in-line amplifier).
In OpenROADM, amplifier performance is evaluated via its incremental OSNR, which is a function of the input power. The amplifier performance is evaluated via its incremental OSNR, which is a function of the input power.
.. math:: .. math::

4
gnpy/tools/json_io.py
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@@ -29,7 +29,7 @@ _logger = getLogger(__name__)
Model_vg = namedtuple('Model_vg', 'nf1 nf2 delta_p') Model_vg = namedtuple('Model_vg', 'nf1 nf2 delta_p')
Model_fg = namedtuple('Model_fg', 'nf0') Model_fg = namedtuple('Model_fg', 'nf0')
Model_openroadm = namedtuple('Model_openroadm', 'nf_coef') Model_openroadm_ila = namedtuple('Model_openroadm_ila', 'nf_coef')
Model_hybrid = namedtuple('Model_hybrid', 'nf_ram gain_ram edfa_variety') Model_hybrid = namedtuple('Model_hybrid', 'nf_ram gain_ram edfa_variety')
Model_dual_stage = namedtuple('Model_dual_stage', 'preamp_variety booster_variety') Model_dual_stage = namedtuple('Model_dual_stage', 'preamp_variety booster_variety')
@@ -202,7 +202,7 @@ class Amp(_JsonThing):
nf_coef = kwargs.pop('nf_coef') nf_coef = kwargs.pop('nf_coef')
except KeyError: # nf_coef is expected for openroadm amp except KeyError: # nf_coef is expected for openroadm amp
raise EquipmentConfigError(f'missing nf_coef input for amplifier: {type_variety} in equipment config') raise EquipmentConfigError(f'missing nf_coef input for amplifier: {type_variety} in equipment config')
nf_def = Model_openroadm(nf_coef) nf_def = Model_openroadm_ila(nf_coef)
elif type_def == 'dual_stage': elif type_def == 'dual_stage':
try: # nf_ram and gain_ram are expected for a hybrid amp try: # nf_ram and gain_ram are expected for a hybrid amp
preamp_variety = kwargs.pop('preamp_variety') preamp_variety = kwargs.pop('preamp_variety')