and create a save_gnpy_json for specific gnpy exports.
because save_json is used as dependency in other projects
Fix example and test file.
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I9af07a13510658dece0685a3bce7589efd57e259
Here the Fiber behaviour change because pch = signal + ase + nli is
considered instead of just signal. This slightly changes the
propagation results as more power is considered to be injected in the
fiber span. All differences in tests are below 0.1 dB, with a few
exceptions slightly higher.
Change-Id: I5b2227ee036a26a11e13f3169e16868d70f0c457
Instead of copying the CLI script in API code, use functions shared
by CLI and API
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I3f9b30b8700b68237d0e80768db015d8dec3deb5
gnpy currently uses the same parameter for tx output power and span
input power: this prevents from modelling low tx power effect.
This patch introduces a new tx-cannel-power and uses it to
propagate in ROADM.
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: Id3ac75e2cb617b513bdb38b51a52e05d15af46f5
Prepare for the next step, to be able to handle lists of candidate assignment
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I2bd78606ce4502f68efb60f85892df5f76d52bb5
The nonlinear coefficient can be expressed at the reference frequency and will be scaled in frequency using the scaling rule of the effective area
Change-Id: Id103b227472702776bda17ab0a2a120ecfbf7473
Previous check was made on reference channel computation.
Now we use the actual total input power to compute the actual gain.
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I3e0db72fdb030a49e2b06cdcfb442b5e642c1777
We have a test which compares the raw output of GNPy against a fixed
expected output. That comparison of course chokes when forward slashes
and backslashes are used, which breaks the test suite on Windows. Let's
try to solve this by always using forward slashes if possible. The way
to go is via pathlib's as_posix(), but that one can possibly return an
absolute path -- which cannot work in a test suite, obviously. So one
can workaround that via calling a Path.relative_to(), but that one
chokes on paths which require at least one "path up" component (`..`).
I posted a patch which use brute force here, but Jonas is right, better
just don't print that output in the test suite in the first place.
Change-Id: I762ddb58a2042120c7b20414152a06a3ed72048d
Bug: https://github.com/Telecominfraproject/oopt-gnpy/issues/358
The term "GSNR" is well established by now. I think it's time we start
using it in our own result outputs instead of alternatives like "total
SNR" or just "SNR".
Signed-off-by: Jonas Mårtensson <jonas.martensson@ri.se>
Change-Id: I1fc65f6db1e3b2d7cfe974875174132fe5b28d3b
This is an old pull request rebased and restricted to only raising warning.
The initial work also limited gain, which is finally not a desired behaviour:
an advanced user might want to have this high gain.
the only impact on test is that it raises warnings on almost all amplifiers
on the mesh_topology_exampleV2.xls: indeed all of them are set to low_gain
but without gain specified and the result of autodesign results in higher gains
than supported by this amplifier variety.
This may be confusing for users to see these warnings on an example from gnpy
so I will push a new commit changing the amp types to avoid this.
The alternative would be to push the warnings into the logger, so they
remain invisible, but I think that the example change makes more sense.
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: Idf0c67137b5b466b07ddc7817f53a82f92a21a5b
I would like to create a package for distribution to PIP, and this seems
like the path of least resistance.
This is, apparently, the way for shippign arbitrary data with Python
[1]. I've at least tried to make it user-firendly via adding a simple
utility which just prints out whatever that data path is.
[1] https://python-packaging.readthedocs.io/en/latest/non-code-files.html
Change-Id: I220ecad84b1d57d01e3f98f15befc700bd97c0b8
