From 7fc42cfbdba2b83522076b08a4ba9301e366d2b3 Mon Sep 17 00:00:00 2001 From: Frank Malatino Date: Tue, 27 Aug 2024 14:37:40 -0400 Subject: [PATCH] Adding back in generate_eta_files.py as it is needed for other tests, adding file generation call to workflow --- .github/workflows/main_unit_tests.yaml | 7 + examples/generate_eta_files.py | 399 +++++++++++++++++++++++++ 2 files changed, 406 insertions(+) create mode 100755 examples/generate_eta_files.py diff --git a/.github/workflows/main_unit_tests.yaml b/.github/workflows/main_unit_tests.yaml index 4bdd95c6..f5f6739b 100644 --- a/.github/workflows/main_unit_tests.yaml +++ b/.github/workflows/main_unit_tests.yaml @@ -48,6 +48,13 @@ jobs: pip3 install --upgrade pip setuptools wheel pip3 install -r requirements_dev.txt -c constraints.txt + - name: prepare input files + run: | + cd ${GITHUB_WORKSPACE}/pace + mkdir tests/main/input + python3 examples/generate_eta_files.py + mv *eta*.nc tests/main/input + - name: run tests run: | cd ${GITHUB_WORKSPACE}/pace diff --git a/examples/generate_eta_files.py b/examples/generate_eta_files.py new file mode 100755 index 00000000..1fb4d5ee --- /dev/null +++ b/examples/generate_eta_files.py @@ -0,0 +1,399 @@ +import numpy as np +import xarray as xr + + +""" +This notebook uses the python xarray module +to create an eta_file containing ak and bk coefficients +for km=79 and km=91. The coefficients are written out to +eta79.nc and eta91.nc netcdf files respectively + +To run this script: `python3 ./generate_eta_files.py` +""" + +# km = 79 +ak = xr.DataArray( + dims=["km1"], + attrs=dict(units="Pa", _FillValue=False), + data=np.array( + [ + 3.000000e02, + 6.467159e02, + 1.045222e03, + 1.469188e03, + 1.897829e03, + 2.325385e03, + 2.754396e03, + 3.191294e03, + 3.648332e03, + 4.135675e03, + 4.668282e03, + 5.247940e03, + 5.876271e03, + 6.554716e03, + 7.284521e03, + 8.066738e03, + 8.902188e03, + 9.791482e03, + 1.073499e04, + 1.162625e04, + 1.237212e04, + 1.299041e04, + 1.349629e04, + 1.390277e04, + 1.422098e04, + 1.446058e04, + 1.462993e04, + 1.473633e04, + 1.478617e04, + 1.478511e04, + 1.473812e04, + 1.464966e04, + 1.452370e04, + 1.436382e04, + 1.417324e04, + 1.395491e04, + 1.371148e04, + 1.344540e04, + 1.315890e04, + 1.285407e04, + 1.253280e04, + 1.219685e04, + 1.184788e04, + 1.148739e04, + 1.111682e04, + 1.073748e04, + 1.035062e04, + 9.957395e03, + 9.558875e03, + 9.156069e03, + 8.749922e03, + 8.341315e03, + 7.931065e03, + 7.519942e03, + 7.108648e03, + 6.698281e03, + 6.290007e03, + 5.884984e03, + 5.484372e03, + 5.089319e03, + 4.700960e03, + 4.320421e03, + 3.948807e03, + 3.587201e03, + 3.236666e03, + 2.898237e03, + 2.572912e03, + 2.261667e03, + 1.965424e03, + 1.685079e03, + 1.421479e03, + 1.175419e03, + 9.476516e02, + 7.388688e02, + 5.497130e02, + 3.807626e02, + 2.325417e02, + 1.054810e02, + -8.381903e-04, + 0.000000e00, + ] + ), +) +bk = xr.DataArray( + dims=["km1"], + attrs=dict(units="None", _FillValue=False), + data=np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.00106595, + 0.00412866, + 0.00900663, + 0.01554263, + 0.02359921, + 0.03305481, + 0.0438012, + 0.05574095, + 0.06878554, + 0.08285347, + 0.09786981, + 0.1137643, + 0.130471, + 0.1479275, + 0.1660746, + 0.1848558, + 0.2042166, + 0.2241053, + 0.2444716, + 0.2652672, + 0.286445, + 0.3079604, + 0.3297701, + 0.351832, + 0.3741062, + 0.3965532, + 0.4191364, + 0.4418194, + 0.4645682, + 0.48735, + 0.5101338, + 0.5328897, + 0.5555894, + 0.5782067, + 0.6007158, + 0.6230936, + 0.6452944, + 0.6672683, + 0.6889648, + 0.7103333, + 0.7313231, + 0.7518838, + 0.7719651, + 0.7915173, + 0.8104913, + 0.828839, + 0.846513, + 0.8634676, + 0.8796583, + 0.8950421, + 0.9095779, + 0.9232264, + 0.9359506, + 0.9477157, + 0.9584892, + 0.9682413, + 0.9769447, + 0.9845753, + 0.9911126, + 0.9965372, + 1.0, + ] + ), +) +coefficients = xr.Dataset(data_vars={"ak": ak, "bk": bk}) +coefficients.to_netcdf("eta79.nc") + + +# km = 91 +ak = xr.DataArray( + dims=["km1"], + attrs=dict(units="Pa", _FillValue=False), + data=np.array( + [ + 1.00000000e00, + 1.75000000e00, + 2.75000000e00, + 4.09999990e00, + 5.98951054e00, + 8.62932968e00, + 1.22572632e01, + 1.71510906e01, + 2.36545467e01, + 3.21627693e01, + 4.31310921e01, + 5.71100426e01, + 7.46595764e01, + 9.64470978e01, + 1.23169769e02, + 1.55601318e02, + 1.94594009e02, + 2.41047531e02, + 2.95873840e02, + 3.60046967e02, + 4.34604828e02, + 5.20628723e02, + 6.19154846e02, + 7.31296021e02, + 8.58240906e02, + 1.00106561e03, + 1.16092859e03, + 1.33903992e03, + 1.53650012e03, + 1.75448938e03, + 1.99417834e03, + 2.25667407e03, + 2.54317139e03, + 2.85476392e03, + 3.19258569e03, + 3.55775366e03, + 3.95135107e03, + 4.37428662e03, + 4.82711084e03, + 5.31022168e03, + 5.82387793e03, + 6.36904248e03, + 6.94875244e03, + 7.56691992e03, + 8.22634277e03, + 8.93120996e03, + 9.68446191e03, + 1.04822725e04, + 1.13182793e04, + 1.21840771e04, + 1.30655674e04, + 1.39532207e04, + 1.48307285e04, + 1.56872617e04, + 1.65080645e04, + 1.72810996e04, + 1.79942988e04, + 1.86363223e04, + 1.91961797e04, + 1.96640723e04, + 2.00301914e04, + 2.02853691e04, + 2.04215254e04, + 2.04300684e04, + 2.03028730e04, + 2.00323711e04, + 1.96110664e04, + 1.90313848e04, + 1.82866426e04, + 1.73777930e04, + 1.63224639e04, + 1.51444033e04, + 1.38725674e04, + 1.25404785e04, + 1.11834170e04, + 9.83532715e03, + 8.52630664e03, + 7.28224512e03, + 6.12326074e03, + 5.06350684e03, + 4.11124902e03, + 3.27000122e03, + 2.53922729e03, + 1.91530762e03, + 1.39244995e03, + 9.63134766e02, + 6.20599365e02, + 3.57989502e02, + 1.69421387e02, + 5.10314941e01, + 2.48413086e00, + 0.00000000e00, + ] + ), +) +bk = xr.DataArray( + dims=["km1"], + attrs=dict(units="None", _FillValue=False), + data=np.array( + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.50123992e-06, + 2.81484008e-05, + 9.38666999e-05, + 2.28561999e-04, + 5.12343016e-04, + 1.04712998e-03, + 1.95625005e-03, + 3.42317997e-03, + 5.58632007e-03, + 8.65428988e-03, + 1.27844000e-02, + 1.81719996e-02, + 2.49934997e-02, + 3.34198996e-02, + 4.36249003e-02, + 5.57769015e-02, + 7.00351968e-02, + 8.65636021e-02, + 1.05520003e-01, + 1.27051994e-01, + 1.51319996e-01, + 1.78477004e-01, + 2.08675995e-01, + 2.42069006e-01, + 2.78813988e-01, + 3.19043010e-01, + 3.62558991e-01, + 4.08596009e-01, + 4.56384987e-01, + 5.05111992e-01, + 5.53902984e-01, + 6.01903021e-01, + 6.48333013e-01, + 6.92534983e-01, + 7.33981013e-01, + 7.72292018e-01, + 8.07236016e-01, + 8.38724971e-01, + 8.66774976e-01, + 8.91497016e-01, + 9.13065016e-01, + 9.31702971e-01, + 9.47658002e-01, + 9.61175978e-01, + 9.72495019e-01, + 9.81844008e-01, + 9.89410996e-01, + 9.95342016e-01, + 1.00000000e00, + ] + ), +) +coefficients = xr.Dataset(data_vars={"ak": ak, "bk": bk}) +coefficients.to_netcdf("eta91.nc") + +# km =