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remove notebook, add script
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mlee03 authored and mlee03 committed May 8, 2024
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102 changes: 102 additions & 0 deletions examples/generate_eta_files.py
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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([ 3.000000e+02, 6.467159e+02, 1.045222e+03, 1.469188e+03, 1.897829e+03,
2.325385e+03, 2.754396e+03, 3.191294e+03, 3.648332e+03, 4.135675e+03,
4.668282e+03, 5.247940e+03, 5.876271e+03, 6.554716e+03, 7.284521e+03,
8.066738e+03, 8.902188e+03, 9.791482e+03, 1.073499e+04, 1.162625e+04,
1.237212e+04, 1.299041e+04, 1.349629e+04, 1.390277e+04, 1.422098e+04,
1.446058e+04, 1.462993e+04, 1.473633e+04, 1.478617e+04, 1.478511e+04,
1.473812e+04, 1.464966e+04, 1.452370e+04, 1.436382e+04, 1.417324e+04,
1.395491e+04, 1.371148e+04, 1.344540e+04, 1.315890e+04, 1.285407e+04,
1.253280e+04, 1.219685e+04, 1.184788e+04, 1.148739e+04, 1.111682e+04,
1.073748e+04, 1.035062e+04, 9.957395e+03, 9.558875e+03, 9.156069e+03,
8.749922e+03, 8.341315e+03, 7.931065e+03, 7.519942e+03, 7.108648e+03,
6.698281e+03, 6.290007e+03, 5.884984e+03, 5.484372e+03, 5.089319e+03,
4.700960e+03, 4.320421e+03, 3.948807e+03, 3.587201e+03, 3.236666e+03,
2.898237e+03, 2.572912e+03, 2.261667e+03, 1.965424e+03, 1.685079e+03,
1.421479e+03, 1.175419e+03, 9.476516e+02, 7.388688e+02, 5.497130e+02,
3.807626e+02, 2.325417e+02, 1.054810e+02, -8.381903e-04, 0.000000e+00]) )
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., 0.00106595, 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. ]) )
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([1.00000000e+00, 1.75000000e+00, 2.75000000e+00, 4.09999990e+00,
5.98951054e+00, 8.62932968e+00, 1.22572632e+01, 1.71510906e+01,
2.36545467e+01, 3.21627693e+01, 4.31310921e+01, 5.71100426e+01,
7.46595764e+01, 9.64470978e+01, 1.23169769e+02, 1.55601318e+02,
1.94594009e+02, 2.41047531e+02, 2.95873840e+02, 3.60046967e+02,
4.34604828e+02, 5.20628723e+02, 6.19154846e+02, 7.31296021e+02,
8.58240906e+02, 1.00106561e+03, 1.16092859e+03, 1.33903992e+03,
1.53650012e+03, 1.75448938e+03, 1.99417834e+03, 2.25667407e+03,
2.54317139e+03, 2.85476392e+03, 3.19258569e+03, 3.55775366e+03,
3.95135107e+03, 4.37428662e+03, 4.82711084e+03, 5.31022168e+03,
5.82387793e+03, 6.36904248e+03, 6.94875244e+03, 7.56691992e+03,
8.22634277e+03, 8.93120996e+03, 9.68446191e+03, 1.04822725e+04,
1.13182793e+04, 1.21840771e+04, 1.30655674e+04, 1.39532207e+04,
1.48307285e+04, 1.56872617e+04, 1.65080645e+04, 1.72810996e+04,
1.79942988e+04, 1.86363223e+04, 1.91961797e+04, 1.96640723e+04,
2.00301914e+04, 2.02853691e+04, 2.04215254e+04, 2.04300684e+04,
2.03028730e+04, 2.00323711e+04, 1.96110664e+04, 1.90313848e+04,
1.82866426e+04, 1.73777930e+04, 1.63224639e+04, 1.51444033e+04,
1.38725674e+04, 1.25404785e+04, 1.11834170e+04, 9.83532715e+03,
8.52630664e+03, 7.28224512e+03, 6.12326074e+03, 5.06350684e+03,
4.11124902e+03, 3.27000122e+03, 2.53922729e+03, 1.91530762e+03,
1.39244995e+03, 9.63134766e+02, 6.20599365e+02, 3.57989502e+02,
1.69421387e+02, 5.10314941e+01, 2.48413086e+00, 0.00000000e+00]))
bk = xr.DataArray( dims=["km1"], attrs=dict(units="None", _FillValue=False),
data = np.array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 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, 1.00000000e+00]))
coefficients=xr.Dataset(data_vars={"ak":ak, "bk":bk})
coefficients.to_netcdf("eta91.nc")

#km =
164 changes: 0 additions & 164 deletions examples/notebooks/generate_eta_files.ipynb
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