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JiaweiZhuang/autogenerate_1D.ipynb

Created August 29, 2018 14:44
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Test xgcm generate_grid_ds on 1D grid
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autogenerate_1D.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`pip install --upgrade git+https://github.com/xgcm/xgcm`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import xarray as xr\n",
"from xgcm import Grid\n",
"from xgcm.autogenerate import generate_grid_ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Uniform 1D grid"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 20, 30, 40, 50, 60, 70])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lon = np.arange(10, 80, 10)\n",
"lon"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lon: 7)\n",
"Coordinates:\n",
" * lon (lon) int64 10 20 30 40 50 60 70\n",
"Data variables:\n",
" dummy (lon) int64 0 0 0 0 0 0 0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.DataArray(np.zeros_like(lon), dims=['lon'], coords={'lon': lon}).to_dataset(name='dummy')\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lon: 7, lon_outer: 8)\n",
"Coordinates:\n",
" * lon (lon) int64 10 20 30 40 50 60 70\n",
" * lon_outer (lon_outer) float64 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0\n",
"Data variables:\n",
" dummy (lon) int64 0 0 0 0 0 0 0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_grid_ds(ds, {'X':'lon'}, position=('center', 'outer'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lon: 7, lon_outer: 8)\n",
"Coordinates:\n",
" * lon (lon) int64 10 20 30 40 50 60 70\n",
" * lon_outer (lon_outer) float64 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0\n",
"Data variables:\n",
" dummy (lon) int64 0 0 0 0 0 0 0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# what's the use of the dict key in the second argument? looks like arbitrary key is allowed.\n",
"generate_grid_ds(ds, {'ABC':'lon'}, position=('center', 'outer'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Non-uniform 1D grid"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-90., -80., -60., -40., -20., 0., 20., 40., 60., 80., 90.])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lat_b = np.linspace(-100, 100, 11).clip(-90, 90)\n",
"lat_b"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-85., -70., -50., -30., -10., 10., 30., 50., 70., 85.])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lat = 0.5*(lat_b[1:] + lat_b[:-1])\n",
"lat"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 10)\n",
"Coordinates:\n",
" * lat (lat) float64 -85.0 -70.0 -50.0 -30.0 -10.0 10.0 30.0 50.0 70.0 ...\n",
"Data variables:\n",
" dummy (lat) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds2 = xr.DataArray(np.zeros_like(lat), dims=['lat'], coords={'lat': lat}).to_dataset(name='dummy')\n",
"ds2"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 10, lat_outer: 11)\n",
"Coordinates:\n",
" * lat (lat) float64 -85.0 -70.0 -50.0 -30.0 -10.0 10.0 30.0 50.0 ...\n",
" * lat_outer (lat_outer) float64 -92.5 -77.5 -60.0 -40.0 -20.0 0.0 20.0 ...\n",
"Data variables:\n",
" dummy (lat) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds2_with_bnd = generate_grid_ds(ds2, {'Y':'lat'}, position=('center', 'outer'))\n",
"ds2_with_bnd"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-92.5, -77.5, -60. , -40. , -20. , 0. , 20. , 40. , 60. ,\n",
" 77.5, 92.5])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds2_with_bnd['lat_outer'].values\n",
"# this is wrong at the two end points"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-90., -80., -60., -40., -20., 0., 20., 40., 60., 80., 90.])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lat_b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A simple testing"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def test_bound_1d(center, bnd):\n",
" # np.testing.assert_array_equal(center, 0.5*(bnd[1:] + bnd[:-1]))\n",
" np.testing.assert_almost_equal(center, 0.5*(bnd[1:] + bnd[:-1]), decimal=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"test_bound_1d(lat, lat_b)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 1 decimals\n\n(mismatch 20.0%)\n x: array([-85., -70., -50., -30., -10., 10., 30., 50., 70., 85.])\n y: array([-85. , -68.8, -50. , -30. , -10. , 10. , 30. , 50. , 68.8,\n 85. ])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-9a01779cc3b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_bound_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds2_with_bnd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat_outer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-21-b4af415add7c>\u001b[0m in \u001b[0;36mtest_bound_1d\u001b[0;34m(center, bnd)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_bound_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbnd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# np.testing.assert_array_equal(center, 0.5*(bnd[1:] + bnd[:-1]))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbnd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbnd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_almost_equal\u001b[0;34m(actual, desired, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 566\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesired\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 568\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0massert_array_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 569\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 570\u001b[0m \u001b[0;31m# If one of desired/actual is not finite, handle it specially here:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_array_almost_equal\u001b[0;34m(x, y, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 962\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\n\u001b[1;32m 963\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Arrays are not almost equal to %d decimals'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m precision=decimal)\n\u001b[0m\u001b[1;32m 965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Research/Computing/miniconda3/envs/sci/lib/python3.6/site-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m names=('x', 'y'), precision=precision)\n\u001b[0;32m--> 780\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 781\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 1 decimals\n\n(mismatch 20.0%)\n x: array([-85., -70., -50., -30., -10., 10., 30., 50., 70., 85.])\n y: array([-85. , -68.8, -50. , -30. , -10. , 10. , 30. , 50. , 68.8,\n 85. ])"
]
}
],
"source": [
"test_bound_1d(lat, ds2_with_bnd['lat_outer'].values)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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