From fab7821577e9c4cfde50ad0889cd42bc51492335 Mon Sep 17 00:00:00 2001 From: mlee03 Date: Fri, 10 May 2024 12:07:58 -0400 Subject: [PATCH] lint --- examples/generate_eta_files.py | 465 +++++++++++++++++++++++++++------ 1 file changed, 381 insertions(+), 84 deletions(-) diff --git a/examples/generate_eta_files.py b/examples/generate_eta_files.py index aedfd172..1fb4d5ee 100755 --- a/examples/generate_eta_files.py +++ b/examples/generate_eta_files.py @@ -1,9 +1,10 @@ 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 +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 @@ -11,92 +12,388 @@ """ # 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}) +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([1.00000000e+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}) +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 = +# km =