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Update notebooks for eta file creations (#85)
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* updated generate_eta_files

* remove notebook, add script

* lint

---------

Co-authored-by: mlee03 <[email protected]>
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@mlee03
mlee03 and mlee03 committed May 10, 2024
1 parent fa6097e commit 74ccf31
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399 changes: 399 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(
[
3.000000e02,
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 =
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