From 74ccf31f65e138183c0b8cb9f4aaaa6c949e12c6 Mon Sep 17 00:00:00 2001 From: MiKyung Lee <58964324+mlee03@users.noreply.github.com> Date: Fri, 10 May 2024 13:39:12 -0400 Subject: [PATCH] Update notebooks for eta file creations (#85) * updated generate_eta_files * remove notebook, add script * lint --------- Co-authored-by: mlee03 --- examples/generate_eta_files.py | 399 ++++++++++++++++++ .../notebooks/generate_eta_file_netcdf.ipynb | 148 ------- .../notebooks/generate_eta_file_xarray.ipynb | 140 ------ 3 files changed, 399 insertions(+), 288 deletions(-) create mode 100755 examples/generate_eta_files.py delete mode 100644 examples/notebooks/generate_eta_file_netcdf.ipynb delete mode 100644 examples/notebooks/generate_eta_file_xarray.ipynb 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 = diff --git a/examples/notebooks/generate_eta_file_netcdf.ipynb b/examples/notebooks/generate_eta_file_netcdf.ipynb deleted file mode 100644 index bf9623c7..00000000 --- a/examples/notebooks/generate_eta_file_netcdf.ipynb +++ /dev/null @@ -1,148 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "2c056479", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c96fbff", - "metadata": {}, - "outputs": [], - "source": [ - "import netCDF4 as nc\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6827b1b5", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "This notebook uses the python netCDF4 module\n", - "to create an eta_file containg\n", - "ak and bk coefficients for km=79\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45d4a704", - "metadata": {}, - "outputs": [], - "source": [ - "#create a Dataset instance\n", - "coefficients = nc.Dataset(\"eta79.nc\", \"w\", format=\"NETCDF4\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b964a014", - "metadata": {}, - "outputs": [], - "source": [ - "#Set dimensionsion\n", - "km = coefficients.createDimension(\"km\", 80)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d51c395f", - "metadata": {}, - "outputs": [], - "source": [ - "#Create ak and bk variables\n", - "ak = coefficients.createVariable(\"ak\", np.float64, (\"km\"))\n", - "bk = coefficients.createVariable(\"bk\", np.float64, (\"km\"))\n", - "ak.units=\"\"\n", - "bk.units=\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6723352e", - "metadata": {}, - "outputs": [], - "source": [ - "#Assign and write out values of ak\n", - "ak[:] = np.array(\n", - " [ 3.000000e+02, 6.467159e+02, 1.045222e+03, 1.469188e+03, 1.897829e+03,\n", - " 2.325385e+03, 2.754396e+03, 3.191294e+03, 3.648332e+03, 4.135675e+03,\n", - " 4.668282e+03, 5.247940e+03, 5.876271e+03, 6.554716e+03, 7.284521e+03,\n", - " 8.066738e+03, 8.902188e+03, 9.791482e+03, 1.073499e+04, 1.162625e+04,\n", - " 1.237212e+04, 1.299041e+04, 1.349629e+04, 1.390277e+04, 1.422098e+04,\n", - " 1.446058e+04, 1.462993e+04, 1.473633e+04, 1.478617e+04, 1.478511e+04,\n", - " 1.473812e+04, 1.464966e+04, 1.452370e+04, 1.436382e+04, 1.417324e+04,\n", - " 1.395491e+04, 1.371148e+04, 1.344540e+04, 1.315890e+04, 1.285407e+04,\n", - " 1.253280e+04, 1.219685e+04, 1.184788e+04, 1.148739e+04, 1.111682e+04,\n", - " 1.073748e+04, 1.035062e+04, 9.957395e+03, 9.558875e+03, 9.156069e+03,\n", - " 8.749922e+03, 8.341315e+03, 7.931065e+03, 7.519942e+03, 7.108648e+03,\n", - " 6.698281e+03, 6.290007e+03, 5.884984e+03, 5.484372e+03, 5.089319e+03,\n", - " 4.700960e+03, 4.320421e+03, 3.948807e+03, 3.587201e+03, 3.236666e+03,\n", - " 2.898237e+03, 2.572912e+03, 2.261667e+03, 1.965424e+03, 1.685079e+03,\n", - " 1.421479e+03, 1.175419e+03, 9.476516e+02, 7.388688e+02, 5.497130e+02,\n", - " 3.807626e+02, 2.325417e+02, 1.054810e+02, -8.381903e-04, 0.000000e+00] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "195c9ef5", - "metadata": {}, - "outputs": [], - "source": [ - "#Assign and write out values of bk \n", - "bk[:] = np.array(\n", - " [ 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0.,\n", - " 0., 0.00106595, 0.00412866, 0.00900663, 0.01554263, 0.02359921,\n", - " 0.03305481, 0.0438012, 0.05574095, 0.06878554, 0.08285347, 0.09786981,\n", - " 0.1137643, 0.130471, 0.1479275, 0.1660746, 0.1848558, 0.2042166,\n", - " 0.2241053, 0.2444716, 0.2652672, 0.286445, 0.3079604, 0.3297701,\n", - " 0.351832, 0.3741062, 0.3965532, 0.4191364, 0.4418194, 0.4645682,\n", - " 0.48735, 0.5101338, 0.5328897, 0.5555894, 0.5782067, 0.6007158,\n", - " 0.6230936, 0.6452944, 0.6672683, 0.6889648, 0.7103333, 0.7313231,\n", - " 0.7518838, 0.7719651, 0.7915173, 0.8104913, 0.828839, 0.846513,\n", - " 0.8634676, 0.8796583, 0.8950421, 0.9095779, 0.9232264, 0.9359506,\n", - " 0.9477157, 0.9584892, 0.9682413, 0.9769447, 0.9845753, 0.9911126,\n", - " 0.9965372, 1. ] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0f3bd9d", - "metadata": {}, - "outputs": [], - "source": [ - "#Close netcdf file\n", - "coefficients.close()" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "executable": "/usr/bin/env python3", - "main_language": "python", - "notebook_metadata_filter": "-all" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/generate_eta_file_xarray.ipynb b/examples/notebooks/generate_eta_file_xarray.ipynb deleted file mode 100644 index a47b09e6..00000000 --- a/examples/notebooks/generate_eta_file_xarray.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "6dc5fe4c", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81be9a15", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c74c6c07", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "This notebook uses the python xarray module\n", - "to create an eta_file containg\n", - "ak and bk coefficients for km=79\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f72c5d5b", - "metadata": {}, - "outputs": [], - "source": [ - "#Assign ak data\n", - "ak=np.array(\n", - " [ 3.000000e+02, 6.467159e+02, 1.045222e+03, 1.469188e+03, 1.897829e+03,\n", - " 2.325385e+03, 2.754396e+03, 3.191294e+03, 3.648332e+03, 4.135675e+03,\n", - " 4.668282e+03, 5.247940e+03, 5.876271e+03, 6.554716e+03, 7.284521e+03,\n", - " 8.066738e+03, 8.902188e+03, 9.791482e+03, 1.073499e+04, 1.162625e+04,\n", - " 1.237212e+04, 1.299041e+04, 1.349629e+04, 1.390277e+04, 1.422098e+04,\n", - " 1.446058e+04, 1.462993e+04, 1.473633e+04, 1.478617e+04, 1.478511e+04,\n", - " 1.473812e+04, 1.464966e+04, 1.452370e+04, 1.436382e+04, 1.417324e+04,\n", - " 1.395491e+04, 1.371148e+04, 1.344540e+04, 1.315890e+04, 1.285407e+04,\n", - " 1.253280e+04, 1.219685e+04, 1.184788e+04, 1.148739e+04, 1.111682e+04,\n", - " 1.073748e+04, 1.035062e+04, 9.957395e+03, 9.558875e+03, 9.156069e+03,\n", - " 8.749922e+03, 8.341315e+03, 7.931065e+03, 7.519942e+03, 7.108648e+03,\n", - " 6.698281e+03, 6.290007e+03, 5.884984e+03, 5.484372e+03, 5.089319e+03,\n", - " 4.700960e+03, 4.320421e+03, 3.948807e+03, 3.587201e+03, 3.236666e+03,\n", - " 2.898237e+03, 2.572912e+03, 2.261667e+03, 1.965424e+03, 1.685079e+03,\n", - " 1.421479e+03, 1.175419e+03, 9.476516e+02, 7.388688e+02, 5.497130e+02,\n", - " 3.807626e+02, 2.325417e+02, 1.054810e+02, -8.381903e-04, 0.000000e+00] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5b85c7e", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "#Assign bk data\n", - "bk=np.array(\n", - " [ 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0.,\n", - " 0., 0.00106595, 0.00412866, 0.00900663, 0.01554263, 0.02359921,\n", - " 0.03305481, 0.0438012, 0.05574095, 0.06878554, 0.08285347, 0.09786981,\n", - " 0.1137643, 0.130471, 0.1479275, 0.1660746, 0.1848558, 0.2042166,\n", - " 0.2241053, 0.2444716, 0.2652672, 0.286445, 0.3079604, 0.3297701,\n", - " 0.351832, 0.3741062, 0.3965532, 0.4191364, 0.4418194, 0.4645682,\n", - " 0.48735, 0.5101338, 0.5328897, 0.5555894, 0.5782067, 0.6007158,\n", - " 0.6230936, 0.6452944, 0.6672683, 0.6889648, 0.7103333, 0.7313231,\n", - " 0.7518838, 0.7719651, 0.7915173, 0.8104913, 0.828839, 0.846513,\n", - " 0.8634676, 0.8796583, 0.8950421, 0.9095779, 0.9232264, 0.9359506,\n", - " 0.9477157, 0.9584892, 0.9682413, 0.9769447, 0.9845753, 0.9911126,\n", - " 0.9965372, 1. ] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5450f7f", - "metadata": {}, - "outputs": [], - "source": [ - "#Create a Dataset instance\n", - "coefficients = xr.Dataset(\n", - " { \"ak\": ([\"km1\"], ak),\n", - " \"bk\": ([\"km1\"], bk) \n", - " })" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a0e5487", - "metadata": {}, - "outputs": [], - "source": [ - "#Set attributes for each variable\n", - "coefficients[\"ak\"].attrs[\"units\"]=\"\"\n", - "coefficients[\"bk\"].attrs[\"units\"]=\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "612b0134", - "metadata": {}, - "outputs": [], - "source": [ - "#Write netcdf file\n", - "coefficients.to_netcdf(\"eta79.nc\")" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "executable": "/usr/bin/env python3", - "main_language": "python", - "notebook_metadata_filter": "-all" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}