scottk/oopt-gnpy
1
0
Fork 0
mirror of https://github.com/Telecominfraproject/oopt-gnpy.git synced 2025-10-30 17:47:50 +00:00
Code Issues Packages Projects Releases 10 Wiki Activity

Merge pull request #165 from ojnas/fix-broken-links

Browse Source 
fix broken links in readme
This commit is contained in:
James
2018-11-19 18:16:46 -05:00
committed by GitHub
parent 1908d7e29a adbe283c83
commit 4b20afd599
1 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
README.rst
View File

@@ -181,7 +181,7 @@ can be added and existing ones removed. Three different noise models are availab
1. `'type_def': 'variable_gain'` is a simplified model simulating a 2-coil EDFA with internal, input and output VOAs. The NF vs gain response is calculated accordingly based on the input parameters: `nf_min`, `nf_max`, and `gain_flatmax`. It is not a simple interpolation but a 2-stage NF calculation. 1. `'type_def': 'variable_gain'` is a simplified model simulating a 2-coil EDFA with internal, input and output VOAs. The NF vs gain response is calculated accordingly based on the input parameters: `nf_min`, `nf_max`, and `gain_flatmax`. It is not a simple interpolation but a 2-stage NF calculation.
2. `'type_def': 'fixed_gain'` is a fixed gain model. `NF == Cte == nf0` if `gain_min < gain < gain_flatmax` 2. `'type_def': 'fixed_gain'` is a fixed gain model. `NF == Cte == nf0` if `gain_min < gain < gain_flatmax`
3. `'type_def': None` is an advanced model. A detailed json configuration file is required (by default `examples/advanced_config_from.json <examples/advanced_config_from.json>`_.) It uses a 3rd order polynomial where NF = f(gain), NF_ripple = f(frequency), gain_ripple = f(frequency), N-array dgt = f(frequency). Compared to the previous models, NF ripple and gain ripple are modelled. 3. `'type_def': None` is an advanced model. A detailed json configuration file is required (by default `examples/std_medium_gain_advanced_config.json <examples/std_medium_gain_advanced_config.json>`_.) It uses a 3rd order polynomial where NF = f(gain), NF_ripple = f(frequency), gain_ripple = f(frequency), N-array dgt = f(frequency). Compared to the previous models, NF ripple and gain ripple are modelled.
For all amplifier models: For all amplifier models:
@@ -203,7 +203,7 @@ For all amplifier models:
| | | Excel template topology files.) | | | | Excel template topology files.) |
+----------------------+-----------+-----------------------------------------+ +----------------------+-----------+-----------------------------------------+
The fiber library currently describes SSMF but additional fiber types can be entered by the user following the same model: The fiber library currently describes SSMF and NZDF but additional fiber types can be entered by the user following the same model:
+----------------------+-----------+-----------------------------------------+ +----------------------+-----------+-----------------------------------------+
| field | type | description | | field | type | description |
@@ -490,8 +490,8 @@ library. The program computes performances for the list of services (accepts
json or excel format) using the same spectrum propagation modules as json or excel format) using the same spectrum propagation modules as
transmission_main_example.py. Explanation on the Excel template is provided in transmission_main_example.py. Explanation on the Excel template is provided in
the `Excel_userguide.rst <Excel_userguide.rst#service-sheet>`_. Template for the `Excel_userguide.rst <Excel_userguide.rst#service-sheet>`_. Template for
the json format can be found here: `service_template.json the json format can be found here: `service-template.json
<service_template.json>`_. <service-template.json>`_.
Contributing Contributing
------------ ------------