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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Inferring Grid Metrics\n",
+ "Modern general circulation models (GCMs) are usually constructed on different rectangular grids, commonly known as [five Arakawa grids](https://en.wikipedia.org/wiki/Arakawa_grids). The `xgcm` package aim to provide a set of unified core interfaces for users to make grid-awared calculations such as finite difference `Grid.derivative`, interpolation `Grid.interp` and average `Grid.average` on these different grids. These interfaces are also the building blocks for more complicated calculations such as higher-order derivatives. Although `xgcm` tends to hide as many details of a grid as possible, **users are still encouraged to know their grid specifics** before using `xgcm`.\n",
+ "\n",
+ "The most important concept in `xgcm` is the metrics which would be required for those core interfaces. Each grid point has three kinds of fundamental metrics associated with it: **1D distance, 2D area and 3D volume** metrics.\n",
+ "\n",
+ "The metrics are necessary for grid-awared calculation because they are in SI unit (m, m2 and m3 for distance, area, and volume respectively) while the raw grid coordinates may not (e.g., lat/lon coordinates). Users who want to use `xgcm`'s interface may need to provide certain metrics explicitly.\n",
+ "\n",
+ "**What does 'inferring Grid metrics' mean ?**\n",
+ "\n",
+ "Standard GCM outputs generally contains certain kinds of the metrics, usually 1D distance metrics (e.g., `dx`, `dy`, `dz`). However, some are missing for certain grid-awared operations. One needs to reconstructing/inferring missing metrics using exsiting ones. Hence **inferring Grid metrics** means:\n",
+ "\n",
+ "1. First define the grid with available metrics;\n",
+ "2. Reconstruct missing metrics from existing output using `grid.interp`;\n",
+ "3. Redefine grid with those new metrics.\n",
+ "\n",
+ "If you see `KeyError: \"Unable to find any combinations of metrics for array dims {...} and axes (...)\"`, then some metrics are probably missing in your `xgcm.Grid`. This notebook will give some examples on how to provide required metrics, mostly based on a dataset output from [MITgcm](https://mitgcm.readthedocs.io/en/latest/). Sometimes missing grid metrics can also be inferred (interpolated/reconstruct) from existing coordinates of the data.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## 1. Examples in the horizontal plane\n",
+ "First we load the example dataset (global ocean simulation at 4-degree resolution by MITgcm). This is an [Arakawa C-grid](https://mitgcm.readthedocs.io/en/latest/algorithm/horiz-grid.html) dataset. We need four kinds of variables: `UVEL` defined at U-point (`YC`, `XG`), `VVEL` at V-point (`YG`, `XC`), `DIVG` at tracer point (`YC`, `XC`) and `VORT` at zeta(vorticity)-point (`YG`, `XG`). We drop out all irrelevant variables and then get the divergence and vorticity using [the method here](https://xgcm.readthedocs.io/en/latest/example_mitgcm.html)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Dimensions: (XC: 90, XG: 90, YC: 40, YG: 40)\n",
+ "Coordinates:\n",
+ " maskC (YC, XC) bool ...\n",
+ " dyC (YG, XC) float32 ...\n",
+ " hFacC (YC, XC) float32 ...\n",
+ " rA (YC, XC) float32 ...\n",
+ " hFacS (YG, XC) float32 ...\n",
+ " Depth (YC, XC) float32 ...\n",
+ " * YG (YG) float32 -80.0 -76.0 -72.0 -68.0 -64.0 ... 64.0 68.0 72.0 76.0\n",
+ " Z float32 -25.0\n",
+ " PHrefC float32 ...\n",
+ " dyG (YC, XG) float32 ...\n",
+ " rAw (YC, XG) float32 ...\n",
+ " drF float32 ...\n",
+ " * YC (YC) float32 -78.0 -74.0 -70.0 -66.0 -62.0 ... 66.0 70.0 74.0 78.0\n",
+ " dxG (YG, XC) float32 ...\n",
+ " * XG (XG) float32 0.0 4.0 8.0 12.0 16.0 ... 344.0 348.0 352.0 356.0\n",
+ " maskW (YC, XG) bool ...\n",
+ " rAs (YG, XC) float32 ...\n",
+ " rAz (YG, XG) float32 ...\n",
+ " maskS (YG, XC) bool ...\n",
+ " dxC (YC, XG) float32 ...\n",
+ " hFacW (YC, XG) float32 ...\n",
+ " * XC (XC) float32 2.0 6.0 10.0 14.0 18.0 ... 346.0 350.0 354.0 358.0\n",
+ "Data variables:\n",
+ " UVEL (YC, XG) float32 ...\n",
+ " VVEL (YG, XC) float32 ...\n",
+ " DIVG (YC, XC) float32 0.0 0.0 0.0 0.0 ... -6.82e-07 2.92e-07 1.472e-06\n",
+ " VORT (YG, XG) float32 0.0 0.0 0.0 ... -4.634e-08 1.055e-07 -3.875e-08\n",
+ "Attributes:\n",
+ " Conventions: CF-1.6\n",
+ " title: netCDF wrapper of MITgcm MDS binary data\n",
+ " source: MITgcm\n",
+ " history: Created by calling `open_mdsdataset(extra_metadata=None, ll...\n"
+ ]
+ }
+ ],
+ "source": [
+ "import xarray as xr\n",
+ "import urllib.request\n",
+ "import shutil\n",
+ "import expectexception\n",
+ "from xgcm import Grid\n",
+ "\n",
+ "# download the data\n",
+ "url = 'https://zenodo.org/record/4421428/files/'\n",
+ "file_name = 'mitgcm_example_dataset_v2.nc'\n",
+ "#with urllib.request.urlopen(url + file_name) as response, open(file_name, 'wb') as out_file:\n",
+ "# shutil.copyfileobj(response, out_file)\n",
+ " \n",
+ "# open example dataset, select the horizontal slices\n",
+ "# drop some variables so that we only focus on UVEL and VVEL in the horizontal plane\n",
+ "ds = xr.open_dataset(file_name).isel(dict(Z=0,time=0)) \\\n",
+ " .drop_vars(['Eta','PH','SALT','THETA','WVEL','Zl','time','iter'])\n",
+ "\n",
+ "# Provide no metrics, only allow those operations that\n",
+ "# do not need metrics e.g., Grid.diff, Grid.interp...\n",
+ "grid = Grid(ds, periodic='X')\n",
+ "\n",
+ "# Velocities should be explicitly weighted by metrics to get correct fluxes\n",
+ "ds['DIVG'] = (grid.diff(ds.UVEL * ds.dyG * ds.hFacW * ds.drF, 'X') +\n",
+ " grid.diff(ds.VVEL * ds.dxG * ds.hFacS * ds.drF, 'Y', boundary='extend')) / ds.rA\n",
+ "\n",
+ "ds['VORT'] = (grid.diff(ds.VVEL * ds.dyC, 'X') -\n",
+ " grid.diff(ds.UVEL * ds.dxC, 'Y', boundary='extend')) / ds.rAz\n",
+ "\n",
+ "print(ds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.1 1D metrics\n",
+ "Now the `ds` dataset contains a complete set of (four different) variables:\n",
+ "\n",
+ "| variable names | grid point | dimensions |\n",
+ "| :---: | ----: | :---: |\n",
+ "| UVEL | u-point | (YC, XG) |\n",
+ "| VVEL | v-point | (YG, XC) |\n",
+ "| DIVG | tracer-point | (YC, XC) |\n",
+ "| VORT | zeta-point | (YG, XG) |\n",
+ "\n",
+ "To calculate the gradient of these variables using `Grid.derivative`, we need to provide necessary metrics，which is clearly shown in this figure:\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the dataset `ds` does not contain all the necessary 1D metrics, we can infer them **APPROXIMATELY** by interpolation.\n",
+ "
\n",
+ "\n",
+ "*Note*: Interpolation of the metrics could lead to errors if the grid is very non-regular (e.g. near the poles) and it is always preferred to recover the metric from the model output if available.\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "UVEL = ds.UVEL\n",
+ "VVEL = ds.VVEL\n",
+ "DIVG = ds.DIVG\n",
+ "VORT = ds.VORT\n",
+ "\n",
+ "# this is the missing 1D metrics, we infer\n",
+ "# them approximately by interpolation!\n",
+ "ds.coords['dxF'] = grid.interp(ds.dxC, 'X')\n",
+ "ds.coords['dxV'] = grid.interp(ds.dxG, 'X')\n",
+ "\n",
+ "# provide all 1D X-metrics\n",
+ "metrics = {\n",
+ " ('X',): ['dxC', 'dxG', 'dxF', 'dxV']} # 1D delta-X metrics\n",
+ "\n",
+ "# pass in the metrics\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "# no need to provide boundary condition because 'X' is periodic\n",
+ "duveldx = grid.derivative(UVEL, 'X') # need `dxF`\n",
+ "dvveldx = grid.derivative(VVEL, 'X') # need `dxV`\n",
+ "ddivgdx = grid.derivative(DIVG, 'X') # need `dxC`\n",
+ "dvortdx = grid.derivative(VORT, 'X') # need `dxG`\n",
+ "\n",
+ "fig, ax = plt.subplots(2, 2, figsize=(13, 6))\n",
+ "\n",
+ "duveldx.plot(ax=ax[0,0])\n",
+ "dvveldx.plot(ax=ax[0,1])\n",
+ "ddivgdx.plot(ax=ax[1,0])\n",
+ "dvortdx.plot(ax=ax[1,1])\n",
+ "\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we do not provide, for example `dxV`, to the `Grid` object, it will raise an error like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n",
+ "\u001b[0;32m 8\u001b[0m \u001b[1;31m# no need to provide boundary condition because 'X' is periodic\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 9\u001b[0m \u001b[0mduveldx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mUVEL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'X'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dxF`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m---> 10\u001b[1;33m \u001b[0mdvveldx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVVEL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'X'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dxV`, raise error here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0mddivgdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDIVG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'X'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dxC`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 12\u001b[0m \u001b[0mdvortdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVORT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'X'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dxG`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mderivative\u001b[1;34m(self, da, axis, **kwargs)\u001b[0m\n",
+ "\u001b[0;32m 1660\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1661\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mda\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1662\u001b[1;33m \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_metric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[0;32m 1663\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mget_metric\u001b[1;34m(self, array, axes)\u001b[0m\n",
+ "\u001b[0;32m 1339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1340\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetric_vars\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1341\u001b[1;33m raise KeyError(\n",
+ "\u001b[0m\u001b[0;32m 1342\u001b[0m \u001b[1;34m\"Unable to find any combinations of metrics for \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1343\u001b[0m \u001b[1;34m\"array dims %r and axes %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marray_dims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ('X',)\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%expect_exception KeyError\n",
+ "\n",
+ "# do not provide `dxV`.\n",
+ "metrics = {\n",
+ " ('X',) : ['dxC', 'dxG', 'dxF']} # 1D delta-X metrics, missing 'dxV'\n",
+ "\n",
+ "# pass in the metrics\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "# no need to provide boundary condition because 'X' is periodic\n",
+ "duveldx = grid.derivative(UVEL, 'X') # need `dxF`\n",
+ "dvveldx = grid.derivative(VVEL, 'X') # need `dxV`, raise error here\n",
+ "ddivgdx = grid.derivative(DIVG, 'X') # need `dxC`\n",
+ "dvortdx = grid.derivative(VORT, 'X') # need `dxG`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similar for the y-derivative:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# this is the missing 1D metrics along Y, we infer\n",
+ "# them approximately by interpolation!\n",
+ "ds.coords['dyF'] = grid.interp(ds.dyC, 'Y', boundary='extend')\n",
+ "ds.coords['dyU'] = grid.interp(ds.dyG, 'Y', boundary='extend')\n",
+ "\n",
+ "# provide all 1D y-metrics\n",
+ "metrics = {\n",
+ " ('Y',) : ['dyC', 'dyG', 'dyF', 'dyU']} # 1D delta-Y metrics\n",
+ "\n",
+ "# pass in the metrics\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "# need Y-boundary condition\n",
+ "duveldy = grid.derivative(UVEL, 'Y', boundary='extend') # need `dyU`\n",
+ "dvveldy = grid.derivative(VVEL, 'Y', boundary='extend') # need `dyF`\n",
+ "ddivgdy = grid.derivative(DIVG, 'Y', boundary='extend') # need `dyC`\n",
+ "dvortdy = grid.derivative(VORT, 'Y', boundary='extend') # need `dyG`\n",
+ "\n",
+ "fig, ax = plt.subplots(2, 2, figsize=(13, 6))\n",
+ "\n",
+ "duveldy.plot(ax=ax[0,0])\n",
+ "dvveldy.plot(ax=ax[0,1])\n",
+ "ddivgdy.plot(ax=ax[1,0])\n",
+ "dvortdy.plot(ax=ax[1,1])\n",
+ "\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we remove `dyG`, it also raise similar error as that in the x-direction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n",
+ "\u001b[0;32m 10\u001b[0m \u001b[0mdvveldy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVVEL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'extend'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dyF`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 11\u001b[0m \u001b[0mddivgdy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDIVG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'extend'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dyC`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m---> 12\u001b[1;33m \u001b[0mdvortdy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVORT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'extend'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need `dyG`, raise error here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mderivative\u001b[1;34m(self, da, axis, **kwargs)\u001b[0m\n",
+ "\u001b[0;32m 1660\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1661\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mda\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1662\u001b[1;33m \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_metric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[0;32m 1663\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mget_metric\u001b[1;34m(self, array, axes)\u001b[0m\n",
+ "\u001b[0;32m 1339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1340\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetric_vars\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1341\u001b[1;33m raise KeyError(\n",
+ "\u001b[0m\u001b[0;32m 1342\u001b[0m \u001b[1;34m\"Unable to find any combinations of metrics for \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1343\u001b[0m \u001b[1;34m\"array dims %r and axes %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marray_dims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'YC', 'XG'} and axes ('Y',)\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%expect_exception KeyError\n",
+ "\n",
+ "# do not provide `dyG`.\n",
+ "metrics = {\n",
+ " ('Y',) : ['dyC', 'dyF', 'dyU']} # 1D delta-Y metrics, missing `dyG`\n",
+ "\n",
+ "# pass in the metrics\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "# need Y-boundary condition\n",
+ "duveldy = grid.derivative(UVEL, 'Y', boundary='extend') # need `dyU`\n",
+ "dvveldy = grid.derivative(VVEL, 'Y', boundary='extend') # need `dyF`\n",
+ "ddivgdy = grid.derivative(DIVG, 'Y', boundary='extend') # need `dyC`\n",
+ "dvortdy = grid.derivative(VORT, 'Y', boundary='extend') # need `dyG`, raise error here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.2 2D metrics\n",
+ "Suppose that we need to calculate the area-weighted mean of a variable over the global, we need to choose proper 2D metrics. Four kinds of variables need for four 2D metrics, according to the above [staggered-grid figure](https://mitgcm.readthedocs.io/en/latest/_images/hgrid-abcd.svg):\n",
+ "\n",
+ "| variable names | grid point | dimensions | 2D metrics |\n",
+ "| :---: | ----: | :---: | :---: |\n",
+ "| UVEL | u-point | (YC, XG) | rAw |\n",
+ "| VVEL | v-point | (YG, XC) | rAs |\n",
+ "| DIVG | tracer-point | (YC, XC) | rA |\n",
+ "| VORT | zeta-point | (YG, XG) | rAz |\n",
+ "\n",
+ "Here we just calculate the area-weighted mean using `xgcm.average` function, and compare it with non-weighted mean.\n",
+ "\n",
+ "Calculating global mean of different variables are:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " weighted means vs unweighted means\n",
+ " UVEL: 4.094419e-03 8.637649e-03\n",
+ " VVEL: 4.158882e-03 5.595285e-03\n",
+ " DIVG: 2.389945e-10 5.964250e-08\n",
+ " VORT: 1.626648e-11 -1.907488e-09\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Notice that right now we need this mask to properly maskout\n",
+ "# undefined values in both data and metrics\n",
+ "maskW = (UVEL != 0)\n",
+ "maskS = (VVEL != 0)\n",
+ "maskC = (DIVG != 0)\n",
+ "maskZ = (VORT != 0)\n",
+ "\n",
+ "# maskout metrics\n",
+ "ds['rAw'] = ds['rAw'].where(maskW, other=0)\n",
+ "ds['rAs'] = ds['rAs'].where(maskS, other=0)\n",
+ "ds['rA' ] = ds['rA' ].where(maskC, other=0)\n",
+ "ds['rAz'] = ds['rAz'].where(maskZ, other=0)\n",
+ "\n",
+ "metrics = {('X', 'Y'): ['rAw','rAs','rA','rAz']} # 2D area metrics\n",
+ "\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "uave_wei = grid.average(UVEL, ['X', 'Y']).values # need rAw\n",
+ "vave_wei = grid.average(VVEL, ['X', 'Y']).values # need rAs\n",
+ "dave_wei = grid.average(DIVG, ['X', 'Y']).values # need rA\n",
+ "zave_wei = grid.average(VORT, ['X', 'Y']).values # need rAz\n",
+ "\n",
+ "uave = UVEL.where(maskW).mean(['YC','XG'], skipna=True).values\n",
+ "vave = VVEL.where(maskS).mean(['YG','XC'], skipna=True).values\n",
+ "dave = DIVG.where(maskC).mean(['YC','XC'], skipna=True).values\n",
+ "zave = VORT.where(maskZ).mean(['YG','XG'], skipna=True).values\n",
+ "\n",
+ "print(' weighted means vs unweighted means')\n",
+ "print(' UVEL: {0:.6e} {1:.6e}'.format(uave_wei, uave))\n",
+ "print(' VVEL: {0:.6e} {1:.6e}'.format(vave_wei, vave))\n",
+ "print(' DIVG: {0:.6e} {1:.6e}'.format(dave_wei, dave))\n",
+ "print(' VORT: {0:.6e} {1:.6e}'.format(zave_wei, zave))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that the surface integral of divergence and vorticity over the global should be zero, which indicates the better results of the weighted average. If we drop out `rAz`, it will raise similar error as before:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n",
+ "\u001b[0;32m 7\u001b[0m \u001b[0mvave_wei\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVVEL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'X'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;31m# need rAs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 8\u001b[0m \u001b[0mdave_wei\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDIVG\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'X'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;31m# need rA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m----> 9\u001b[1;33m \u001b[0mzave_wei\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVORT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'X'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;31m# need rAz, raise error here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36maverage\u001b[1;34m(self, da, axis, **kwargs)\u001b[0m\n",
+ "\u001b[0;32m 1734\u001b[0m \u001b[0mThe\u001b[0m \u001b[0maveraged\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1735\u001b[0m \"\"\"\n",
+ "\u001b[1;32m-> 1736\u001b[1;33m \u001b[0mweight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_metric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mda\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[0;32m 1737\u001b[0m \u001b[0mweighted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mda\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweighted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1738\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mget_metric\u001b[1;34m(self, array, axes)\u001b[0m\n",
+ "\u001b[0;32m 1339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1340\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetric_vars\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1341\u001b[1;33m raise KeyError(\n",
+ "\u001b[0m\u001b[0;32m 1342\u001b[0m \u001b[1;34m\"Unable to find any combinations of metrics for \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1343\u001b[0m \u001b[1;34m\"array dims %r and axes %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marray_dims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%expect_exception KeyError\n",
+ "\n",
+ "# do not provide `rAz`\n",
+ "metrics = {('X', 'Y'): ['rAw','rAs','rA']} # 2D area metrics missing `rAz`\n",
+ "\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "uave_wei = grid.average(UVEL, ['X', 'Y']).values # need rAw\n",
+ "vave_wei = grid.average(VVEL, ['X', 'Y']).values # need rAs\n",
+ "dave_wei = grid.average(DIVG, ['X', 'Y']).values # need rA\n",
+ "zave_wei = grid.average(VORT, ['X', 'Y']).values # need rAz, raise error here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If the model does not provide the 2D metrics, we can also infer them **APPROXIMATELY** using eight 1D metrics (`dxC`, `dxF`, `dxG`, `dxV`, `dyC`, `dyF`, `dyG`, `dyU`) as:\n",
+ "\n",
+ "```python\n",
+ "rAw = ds.dxC * ds.dyG\n",
+ "rAs = ds.dxG * ds.dyC\n",
+ "rA = ds.dxF * ds.dyF\n",
+ "rAz = ds.dxV * ds.dyU\n",
+ "```\n",
+ "\n",
+ "This is only exact in Cartesian coordinates. Here we add `rAz` back by **INFERRING** it as `rAzNew = ds.dxV * ds.dyU`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " weighted mean vs unweighted mean\n",
+ " VORT: 1.626682e-11 -1.907488e-09\n"
+ ]
+ }
+ ],
+ "source": [
+ "ds.coords['rAzNew'] = (ds.dxV * ds.dyU).where(maskZ, other=0)\n",
+ "\n",
+ "metrics = {('X', 'Y'): ['rAw','rAs','rA', 'rAzNew']} # provided with new rAz\n",
+ "\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "zave_wei = grid.average(VORT, ['X', 'Y']).values # need rAzNew\n",
+ "\n",
+ "print(' weighted mean vs unweighted mean')\n",
+ "print(' VORT: {0:.6e} {1:.6e}'.format(zave_wei, zave))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that the new weighted mean (1.626682e-11) here is not the same as previous one (1.626647e-11). But it is small enough as compared to unweighted mean. This indicates that it is an **approximation** of spherical area fractions by small rectangles and direct model-output 2D metrics are always prefered if available.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## 2. Examples in the x-z plane\n",
+ "For a simulation carried out in 2D x-z plane, here is a figure showing the vertical metrics:\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we only need the variables of `UVEL`, `WVEL` and `THETA`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Dimensions: (XC: 90, XG: 90, Z: 15, Zl: 15)\n",
+ "Coordinates:\n",
+ " maskC (Z, XC) bool ...\n",
+ " hFacC (Z, XC) float32 ...\n",
+ " Depth (XC) float32 ...\n",
+ " * Z (Z) float32 -25.0 -85.0 -170.0 ... -3.575e+03 -4.19e+03 -4.855e+03\n",
+ " drF (Z) float32 ...\n",
+ " dxG (XC) float32 ...\n",
+ " * XG (XG) float32 0.0 4.0 8.0 12.0 16.0 ... 344.0 348.0 352.0 356.0\n",
+ " maskW (Z, XG) bool ...\n",
+ " * Zl (Zl) float32 0.0 -50.0 -120.0 ... -3.28e+03 -3.87e+03 -4.51e+03\n",
+ " dxC (XG) float32 ...\n",
+ " hFacW (Z, XG) float32 ...\n",
+ " * XC (XC) float32 2.0 6.0 10.0 14.0 18.0 ... 346.0 350.0 354.0 358.0\n",
+ "Data variables:\n",
+ " UVEL (Z, XG) float32 ...\n",
+ " WVEL (Zl, XC) float32 ...\n",
+ " THETA (Z, XC) float32 ...\n",
+ "Attributes:\n",
+ " Conventions: CF-1.6\n",
+ " title: netCDF wrapper of MITgcm MDS binary data\n",
+ " source: MITgcm\n",
+ " history: Created by calling `open_mdsdataset(extra_metadata=None, ll...\n"
+ ]
+ }
+ ],
+ "source": [
+ "# re-open example dataset, select the x-z slices\n",
+ "ds = xr.open_dataset(file_name).isel(dict(YC=10,YG=10,time=0)) \\\n",
+ " .drop_vars(['Eta','PH','SALT','VVEL','time','YG','iter','rA','rAw',\n",
+ " 'rAs','rAz','PHrefC','dyC','dyG','YC','maskS','hFacS'])\n",
+ "\n",
+ "grid = Grid(ds, periodic='X') # simple grid without metrics\n",
+ "\n",
+ "print(ds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here three variables, `UVEL`, `WVEL` and `THETA`, are respectively defined on U-point, W-point, and tracer-point. To calculate the vertical derivatives using `Grid.derivative`, one need proper 1D vertical metrics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# this is only an approximation! Use model outputs if available. \n",
+ "ds['drG'] = ds.hFacC * ds.drF\n",
+ "ds['drC'] = grid.interp(ds.drG, 'Z', boundary='extend')\n",
+ "ds['drW'] = grid.interp(ds.drC, 'X', boundary='extend')\n",
+ "\n",
+ "metrics = {('Z',) : ['drC','drG','drW']} # 1D vertical metrics\n",
+ "\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "dwdz = grid.derivative(ds.WVEL , 'Z', boundary='extend') # need drG\n",
+ "dTdz = grid.derivative(ds.THETA, 'Z', boundary='extend') # need drC\n",
+ "dudz = grid.derivative(ds.UVEL , 'Z', boundary='extend') # need drW\n",
+ "\n",
+ "fig, ax = plt.subplots(1, 3, figsize=(14, 3))\n",
+ "\n",
+ "dwdz.plot(ax=ax[0])\n",
+ "dTdz.plot(ax=ax[1])\n",
+ "dudz.plot(ax=ax[2])\n",
+ "\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we remove drC, it also raise similar error as that in the x-direction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n",
+ "\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 6\u001b[0m \u001b[0mdwdz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWVEL\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'extend'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need drG\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdTdz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTHETA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'extend'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need drC, raise error here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[0mdudz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderivative\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUVEL\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'extend'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# need drW\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mderivative\u001b[1;34m(self, da, axis, **kwargs)\u001b[0m\n",
+ "\u001b[0;32m 1660\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1661\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mda\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1662\u001b[1;33m \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_metric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[0;32m 1663\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgcm\\grid.py\u001b[0m in \u001b[0;36mget_metric\u001b[1;34m(self, array, axes)\u001b[0m\n",
+ "\u001b[0;32m 1339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1340\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmetric_vars\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m-> 1341\u001b[1;33m raise KeyError(\n",
+ "\u001b[0m\u001b[0;32m 1342\u001b[0m \u001b[1;34m\"Unable to find any combinations of metrics for \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m 1343\u001b[0m \u001b[1;34m\"array dims %r and axes %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marray_dims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'Zl', 'XC'} and axes ('Z',)\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%expect_exception KeyError\n",
+ "\n",
+ "# remove `drC`\n",
+ "metrics = {('Z',) : ['drG','drW']} # 1D vertical metrics, missing `drC`\n",
+ "\n",
+ "grid = Grid(ds, periodic='X', metrics=metrics)\n",
+ "\n",
+ "dwdz = grid.derivative(ds.WVEL , 'Z', boundary='extend') # need drG\n",
+ "dTdz = grid.derivative(ds.THETA, 'Z', boundary='extend') # need drC, raise error here\n",
+ "dudz = grid.derivative(ds.UVEL , 'Z', boundary='extend') # need drW"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## 3. Summary\n",
+ "For the MITgcm output defined on the Arakawa C grid, four kinds of grid points are defined (u-point, v-point, tracer-point and zeta-point). As a result, 12 distance metrics, three sets of area metrics (4 per set), and four volume metrics can be summaried as:\n",
+ "\n",
+ "```python\n",
+ "metrics = {\n",
+ " ('X',): ['dxG', 'dxF', 'dxC', 'dxV'], # X distances\n",
+ " ('Y',): ['dyG', 'dyF', 'dyC', 'dyU'], # Y distances\n",
+ " ('Z',): ['drW', 'drS', 'drC', 'drF'], # Z distances\n",
+ " \n",
+ " ('X', 'Y'): ['rAw', 'rAs', 'rA', 'rAz'], # Areas in x-y plane\n",
+ " ('X', 'Z'): ['yAw', 'yAc', 'yA', 'yAz'], # Areas in x-z plane\n",
+ " ('Y', 'Z'): ['xAs', 'xAc', 'xA', 'xAz'], # Areas in y-z plane\n",
+ " \n",
+ " ('X', 'Y', 'Z'): ['volW', 'volS', 'volC', 'volZ']} # volume\n",
+ "```\n",
+ "\n",
+ "Note the two area metrics `yAz` and `xAz` are defined at y- and x-components of vorticity points, which may be not be frequently used. One may use this if they are interested in meridional overturning streamfunction or Walker circulation streamfunction."
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ },
+ "varInspector": {
+ "cols": {
+ "lenName": 16,
+ "lenType": 16,
+ "lenVar": 40
+ },
+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
+ ],
+ "window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
![](\"https://mitgcm.readthedocs.io/en/latest/_images/hgrid-abcd.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the dataset `ds` does not contain all the necessary 1D metrics, we can infer them **APPROXIMATELY** by interpolation.\n",
+ "![](\"https://mitgcm.readthedocs.io/en/latest/_images/vgrid-accur-center.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we only need the variables of `UVEL`, `WVEL` and `THETA`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "