From 696758293d34c849f7a8035d9f68bcbe722ad185 Mon Sep 17 00:00:00 2001 From: MiniUFO Date: Mon, 11 Jan 2021 13:31:20 +0800 Subject: [PATCH 1/3] add inferring metrics notebook --- 06_inferring_metrics.ipynb | 706 +++++++++++++++++++++++++++++++++++++ 1 file changed, 706 insertions(+) create mode 100644 06_inferring_metrics.ipynb diff --git a/06_inferring_metrics.ipynb b/06_inferring_metrics.ipynb new file mode 100644 index 0000000..24979dc --- /dev/null +++ b/06_inferring_metrics.ipynb @@ -0,0 +1,706 @@ +{ + "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 ... 2.920357e-07 1.4720649e-06\n", + " VORT (YG, XG) float32 0.0 0.0 0.0 ... 1.05465695e-07 -3.8749278e-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", + "from xgcm import Grid\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('../datasets/mitgcm_example_dataset_v2.nc').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": 3, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ('X',)\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\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", + "\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 1172\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 1173\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-> 1174\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 1175\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 1176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ('X',)\"" + ] + } + ], + "source": [ + "# 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": 4, + "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": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YC'} and axes ('Y',)\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\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", + "\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 1172\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 1173\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-> 1174\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 1175\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 1176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YC'} and axes ('Y',)\"" + ] + } + ], + "source": [ + "# 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": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " weighted means vs unweighted means\n", + " UVEL: 4.094420e-03 8.637649e-03\n", + " VVEL: 4.158888e-03 5.595285e-03\n", + " DIVG: 2.389647e-10 5.964250e-08\n", + " VORT: 1.626647e-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)\n", + "ds['rAs'] = ds['rAs'].where(maskS)\n", + "ds['rA' ] = ds['rA' ].where(maskC)\n", + "ds['rAz'] = ds['rAz'].where(maskZ)\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": 7, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\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", + "\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 1259\u001b[0m \"\"\"\n\u001b[0;32m 1260\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1261\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 1262\u001b[0m \u001b[0mweighted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mda\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1263\u001b[0m \u001b[1;31m# TODO: We should integrate xarray.weighted once available.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"" + ] + } + ], + "source": [ + "# 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: 1.626594e-11 -1.907488e-09\n" + ] + } + ], + "source": [ + "ds.coords['rAzNew'] = (ds.dxV * ds.dyU).where(maskZ)\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 (1.169156e-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 -290.0 ... -3575.0 -4190.0 -4855.0\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 -220.0 ... -3280.0 -3870.0 -4510.0\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('../datasets/mitgcm_example_dataset_v2.nc').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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MMG0P7Ej9zLYwdXyhn0o9qO5uqjrwCjPapoKDM6ebwQEuNOpC3BljbU21jwCktdlxYbZj2c+sDBpSC14Trvn6qe1Jq6Tj2jGU0qKKLnGZUpdnfKp+6UrKbPQ6p5iTlXrdC/sbKifPfgxT+neMRyLvxCL6VLR+OVT4v6hN164Zpn4ZzJDWzzAMxxSf2p5EKeamoBZNp94pcSAqFeF4F4VzEnxAlZo356JT2cOU/bDUJzjqm5gKTlDrHV9Gl4WV49qZ8pt1Im9F10drmLdDbAp5eA/iKRMRhzXe6db8wXCzNoZUg9oU76qTLiLTvcP3UmwqezBn0K2U1EjZuK1x4s7Pkih5R2UNebxdg5V6xzwyke7U464h51lx71EnBAOxR74gREMP3F5fcOFhPLz6ZW7b7vrjqzrDC+qVdAO3Ov5qQm8WrFSXI1Sn7oe1s35Kv1/C8NDyNdUyrVytbRkLf0RuhveCBGcitVPhr9WGDPGp/ZEc4U84PIhW8jmg6mQakqqeBZzl6pFnAe+xRrlhtE+8UW7MHjqdqikia4GP4dpKF6nq+bHtz8AtCTocOEFVvxLZdhLw9/7rB1X1styGGIbRFxRpKnsGfRwGLgfWAA8Ar1LVP/htZwGnAFPA6ap6VbM6ReRi4Ehc3/1vgTeo6g46wP66DcPoOmWR1NdcQUTKInLNTNthGEb/kVcffSjJTwIvAA4FXi0ih8ay/Ql4A/DFWNmlwDm48F5HAeeIyF4dH0wTW00jDcNolx7r4ynAg6p6MPBR4AJf9lCcz4jDgLXAp7yGNavzb1T1iap6OE5339HZkVvD3DCMLlPC9XamvbKiqtfO5hjmqjoF7BKRxTNti2EY/UMzjczAUcAmVf29qo4DVwDrohlU9Q+q+gsaVyU8H7haVbeq6oPA1bgH0J5gGmkYRrv0Wh/99zBT6CvAc8St21gHXKGqe1T1DmCTry+1TlV9GMCXH6ULAd1sKrthGF2lYB41x4BfisjVwM6QqKqnz5xJ04+ijPt1OpN7/PrIyCUQlh2G9cIhlMxAda1x/dq6aNmwpntHWNsd6gxh0kL4rrA+0H+fiKwbGvTrAkOZakQNqV8n2IxQdiq2pjIQWkDjCStAdkzV2kdLIun7zHN/wTvG3fYQvzykQ219fik2WhDOZdhfCNOza6JmV3XNdGytaHzgId5Dn9hjH1/an5SnBdV6q3a598HqOm6/1jTHkEE4Tw/sqi19Cutwx8M14X+86prZsJ8Q+k7rr08AlfCD1++vozWUzTWyVdSK5cCdke+bcSPgWUgquzxj2bwUXiNLSFXjIOIvw99FEpYPln3UGL/Urbpc0pcbjdSxlw9lFQ+tFSfo4PbxyUhaqKe+CRCWXIZVcuH+rERu/vhlW4rlCWXj90eST45q6MmmRwCl8V3Vz0tG5sXqde8jKWvbF/n11NGQY7UQnPW6GA8JmRSxPv5XUYllaieMWFai5zwcxvBAiLgzmFCikaQ15ixy19l8/6Ptnghr/t3mQe+DYCDyA8XPWSB+quL/V+0wDfpYzeOdOW7DrSZdDlwfKxv0MbVOEbkUeCHwa+Bv048sG9Ywb4ew1tu/B9GshO+DkZBnk+NA7eYJhJs1XHMaW3vsvoT1ybGn1LA+OIT8CuHSygk3ZlqIr+oac0nNH/4kqmvbQ0it4ARparzR5hTG/BPO+JTb77i/fcNzZ0ndU2Xd+nzPtzdtBeCVBzshflhdocpgWJefbw10L2kIdRdDYw46tkTWz+/dIjhY+LMI/8PlqRRfACSspU9bW59Qtnq5VfKvvy+QR81v+pdhzCrm0KqROUkTjUyNWuFJKpm1GdBJ2byYRs4RitPfPvfopzXm0HN9TMuTlp7UVVytU1VP9tPdPw68Cri0iX0tsYa5YRhdpUR7U9b7GVW9TERGgQNV9baZtscwjNlPhxq5GVgZ+b4CuLuNss+Klb02ryFZMI00DKMdpkEfQ57NIjIALAa2tijbtE5VnRKRLwHvpcOGua0xNwyjq4RpSJ2uMe8HROTFwM+Ab/vvTxKRDTNrlWEYs5lmGpmBG4BVInKQiAzhnBVl1ZyrgOeJyF7e6dvzfFrPMI00DKMdpkEfNwAn+c+vAL6nbm3aBuAEERkWkYOAVcBP0+oUx8HOZhHgxcBvOjp4bMTcMIwuIxRqKvv7cY5BrgVQ1Z95QTcMw0ikE430ayLfgWtQl4FLVPUWETkX2KiqG0TkycDXgL2AF4vIP6jqYaq6VUQ+gHvQBDhXVbd2ejwteD+mkYZhZKTX+ghcDHxORDbhRspP8GVvEZEv49aKTwJv9w4sSamzBFwmIou82T8H3przsKukNsxFZHWzgqp6U6c7Nwxj7iEUyvnbpKpuizm26fWazVlHCWG+96eR5MCrHLsegkOi4GQrOAyabOIxJ75pqur0qH5D0qUX9+0RnM4F52Dxh4Do71l1FBYclfnjCk7XgieGcLiasP+JlMMKjnTm+0qDk7o9kzX/DpOV4JCsVsm80Zo94ViqJkecQYVagqO64KgoPlWuEnN+1Ixwblo5bGpGK3mI+rOaauHcKs7S0dpjzbxBdwZ2D/nrzZ+HJCeDMH1TCDvVSFW9Ergylva+yOcbcNMtk8peAlySe+ftU3iNLJXqnZMNBe9a3seOxvwEBd9D4q/Ikr9vR8q1K3ThkLvOd03U+9qZivkXqjnrimpavaaMTwXHmu5+qWjQI19H5MZodPLl0yU4f6t/b+YEraatodaUzBFfOCXvA2mvYe8gLxzf1ISvob5Zs/8C931PREfC8ZZjtlePJd3krjrvyCoBSf9HIaUiTolLLW6pqPO3UN2CIe/czVca/n/j/5fRQ86qkUnO/rIyDfo4BhyfUvY84LyMdVaAp+U2NIVmI+b/1GSbAs/usi2zG5GaeHqRCI689oSnn+G9ag9v3jFXcFA24q/myZTLulIeRGKCKpXJxLyBsH8t1R6RqnV4517VOiS4ykxzAiaRp5VSct6wj4mx+iqGF9S27dlRt22Rf+JuuMdizscmHvH4qq0D9/8OgNX7LwKgfPt1ACxdsi8A4/u78IFBkJrd//Fz2pQWjtsyVeGPp8EJXMq5XDCY/fE2/ElWHQpOhT/kqQanftW/ubTfO4G2zlUTRGrePAvAr0TkRKAsIquA04HrZtgmw8jlNd2YHkwjTSP7kcJ0t89B+sn5W8H0sYHUhrmqHjudhswFOpm+241GUUd1dKlRFm2kt0O0E2Jy31UdjcbkoguN8hkjpdE/Y4hQHpplNvWOdwJnA3uAf8NNdfrAjFpkGMbsxjTSNNIwjGSKpY8NNJvK/lfNCqrqV7tvjmEY/Y4AUi6GqKrqLuBsEbnAfdXtM22TYRizG9NIwzCMZIqkj0k0m8r+4ibbFLCGuWEYjZSK09vpnSxdAiz037cBb1TVG2fUMMMwZi+mkaaRhmEkUyB9TKLZVPaTp9MQwzDmBiJQbmPtfJ9zMfA2Vf1vABF5Oi6G5eEzapVhGLMW00jTSMMwkimYPjbQsktCRPYTkYtF5Fv++6EickrvTTMMo1+RsqS+5hjbwwMngKr+D2BTNQ3DaEpB9BFMIw3DaJMC6WMDWeKY/yuud/Ns//23wJdwvaCGYRh1iAjlocL0dv5URD6Lc2qkwKuAa0O4yaKElSyXaqFX4qFnooSkeHiWEDZt3G+PRsiqaH1om1aEoklhekJaCJcT3puFyQmhhVpFfwm93HncSMZDn0XDxk340GnxwwlZQnikqWqAjcZwZvHfIhxLWs98llA3eaLZZC2SxxVp7XevlQ4hmwZjoZs0xdlps+NOK5MH08hiaWSJSIg0olriI/yUh1qUd9fegsj03il1j+8Lp9x1VLu268tW7/XIpV2OaVo8bGS5GuSl/n5QavdwPExaqKPkQ8DVIgL5Y0xyUltNa3FvRSIPVSMOTbl/i2oEmlJyc2bAK7JGzn/Q2ZmK6tpqt6XYec8UeazFKYyGSwth8kK1IwNBH6c/FFwSBdPHBrI0zJep6pdF5CyoBm+falVobiEgpZqwVIUmiFntjggPlOXBEaAWW1F87MWBwdG6OqgknMqcXrY1cveG+JdpYlXLF/9QH36tHi9wQ/N8Rvd9KuKJvRwLl7bXcP25qv5pePEub7u7mndyyXKgds4eudiHp9OD3PYF+7j9xZ64SxHj4wJWjQfa6qEqyznP47m9Rb3zB7Or15i/uBYO+zKVaHDR8C/Z5E8wapY/lmb5WtWRXrkUaRrSk/z7ObH0p1LEsJKGYbTGNBJMI/uOYoxXzk36KVxawfSxgSwN850isje+P0ZEjgG29dSqPmWqex3qfctUznBpoVEOLqa70QaZulOnEaEwU44srKRhGG1jGmkYhpFMgfQxiSwN83cDG4DHiMiPgH2AV/TUKsMw+hYRCjMNyXdangM8Hdd5+T/Auar6wIwaZhjGrMU00jTSMIxkiqSPSbRsmKvqTSLyTOCxuJkst6nqRM8tMwyjLxERyoOFCXVxBfBD4OX++2twPjieO2MWGYYxqzGNNI00DCOZguljA1m8sr8dWKCqt6jqr4AFIvK23ptWZ8PxInKLiFRE5MjYtrNEZJOI3CYiz4+kr/Vpm0TkzEj6QSLyExH5nYh8SUSae90wDKM9BKRcSn3NMZaq6gdU9Q7/+iCwZKaNMgxjFtNEI+cgppGGYWSnWPrYQJajfLOqPhS+qOqDwJt7Z1IivwL+CtfrWkVEDgVOAA4D1gKfEpGyiJSBTwIvAA4FXu3zAlwAfFRVVwEPAhb6zTC6iQjloVLqa47xfRE5QURK/vVK4JszbZRhGLOYJho5BzGNNAwjO8XSxwayHGVJIjETfKN3WkeZVfVWVb0tYdM64ApV3aOqdwCbgKP8a5Oq/l5Vx3FTqdb543g28BVf/jLgpb0/AsMoDmF9UNprjvEW4Iu4SF9Ba94tIttF5OHpMMBmFBlGf9FMI+cgM6qRpo+G0V90qo9p929k+7C/fzf5+/lRkW3tasIXfPqvROQSEenYe3UW529XAV8Wkc/gHHecBny70x13ieXA9ZHvm30awJ2x9KOBvYGHVHUyIX8dInIqcCrAypUru2iyYcxtpEChLlR14UzbQG1G0WejibEZRQcA14jIIX7zJ4HjcBp4g4hsUNVfU5tRdIXX/FOAT7cyQJC6OL2t8SEsGuLn1sebhvzxo/PEE086grTY1iFOa1K89LS8aYR9hOeO6PK6ig9SFLdt3O84pJeSwgS3iNjQUazZ/EUb6EY84XKG6BQ1R795dti9IzaNnFZmXB9B6oKntAzfGseHNx2OaOySYXf9hGhAUy3qbKZt4b6Ixyavfk+qLxYLXSqx+OXBnhDXXBIUOR7eNSVEa13o1rQwrq3SI6cnflzxuOG9ptXuGq6PSpN/s4xhbaNxzONXSjCnG47Qu3EuO9HHyIzppPs3cArwoKoeLCIn4O7rV+XUhC8Ar/V5vgi8iUyakE6WhvkZuAbqW3G/33eAizrZaRIicg3wiIRNZ6vqN9KKJaQpyRqkTfI3JqquB9YDrFm92gKhGUZWChTqQkSekZSuqj9MSu8FqnqrtyW+qTqjCLhDRMKMIvAziny5MKPoVtyMohN9nsuA99Phn4wxM7RqlBsziGnktGmk6aNh9Fsc8470sTpjGmr3LxBtmK/D3bvgZlB/ws+obksTgF+r6pVVs0V+CqzIa3ggi1f2CvAZ/+oZqprHQ+dmIDqcvQK4239OSt8CLBGRAT9qHs3f3D4pRXr3fE+h79UaSLh+pmKjGYHQm6il1n0ioZdQNNZbFushC3Zo0oNYtbewklhWM/a2RZkqDwNQ8j2hU02GjGRyj/sQYpOXfC9Y6OVd5PpiJhPqGBp7EICdiw8EYNB3c4YRtCwjJF0l7VxFf59wjbRbRwZWPOh0ZWLosW4fSfHeW9Ufvx4ixK/r3IhQGszS5zcneG/k8whOxG/EPcDNND2bUQQ2q8gwcmMaORs00vTRMGYjzfVxmYhsjHxf7wdSA8tJvn9JyqOqkyKyDXd/t6sJEZNlEHgd8K40w7PS7/8MG4AvishHcNMOVgE/xY2MrxKRg4C7cFMTTlRVFZHv4+KwXwGcBKSNxhuGkQMRoTzU79KSDVV9cfS7iKwEPtTt/X+hYOwAACAASURBVMy2GUVQP6to9eo1NqvIMDJiGtldjTR9NIy5Qwt93KKqR6ZtJNt9mpanXU2I8ingh6r6301sy0Rf/DOIyMuAjwP7AN8UkZ+p6vNV9RYR+TJuisIk8HZVN4wrIu/ArY8vA5eo6i2+ujOAK0Tkg8DNwMXTfDiGMbfxoS4KymbgCd2udLbPKDIMow1MI7uqkaaPhjGH6Ewfm93X8TybRWQAWAxsbVE2tU4ROQfXPn1LXqOjpDbMReRzqvo6EXmXqn6sGzvLi6p+DfhayrbzgPMS0q8ErkxI/z21NQOGYXQbEUqDHTum7AtE5OPUek5LwJOAn6fkXZqhyko0PGWH2Iwiw5iNmEY2aKTpo2EYQKf6eAMJ928szwbc/ftj3P38PX9/t6UJzlR5E/B84Dl+6XfHNBsxXyMijwTeKCKXExviV9Wt3TDAMIy5hfOo2ReTcbpBdK3TJPBvqvqjlLx3+1cz5whl4MB2DLAZRYbRX5hGJmqk6aNhGB3po18z3nD/isi5wEZV3YC7bz/nnbttxTW0yakJnwH+CPzYO5j8qqqem8t4T7Mj/wwuLNqjcY46omKpPt0wDKOBTqZp+jWIl+PWDFZwzj1mdNZOGqp6WRvZb1XVI5plEJGbc9hgM4oMo88oylT2NjTS9NEwDKAzfUy6f1X1fZHPY8DxKWXb1YSu97CmVqiqFwIXisinVfWt3d6xYRhzE+nc4/Ak8LeqepOILARuFJGrY3Eo+5GndCmPYRh9TBc0ci5i+mgYRuH1MUu4tLeKyBOB/+WTfqiqv+itWYZh9C0lodSBx2FVvQe4x3/e7uPHLqc+DmXf4XtpARCRvXDORAYi22+K5jEMY47SoUbORUwfDcMACq+PLY9cRE7HxWL8qk/6goisV9WP99QywzD6llLzaUit4lBWEZFHAUcAP+macV2gE+eYIvIB4A3A7dScIikzH9c3PwLSbGVojLLPXKoGHHHfK9oYVUibLjntDXn2WGrnBHiGymllGtOnKvXnZsSXlRz77SWlLpiTp4pw7UR/h/ipkYTrqxXao/PbQiObIiJrgY/h1jtepKrnx7YP45YDrQEeAF6lqn/wenorcJvPer2qnpbbkOY25tLIOauPHVx74qM1lSPX4lDIE90JUMmwm/g9KtX0+g1ZLv3qcYmzUUs5GlTS4l5I2J52X8bPc4VwDmvpaVo9XVKa+Vpo5kus1TmLMTI6ytju3a5oSp48/2FxunUOO9HHfifLHfQm4GhV3QkgIhfgPNlZw9wwjAYyTENqFYcy1LMA+A/gr1X14W7Z1yU6cY75SuAxqjreSwMNw5iddDJVU0TKwCeB43DhfW4QkQ2xpT6nAA+q6sEicgJwAfAqv+12VX1Sfuszk1cjTR8No8uERnk/YFPZWyPAVOT7FPk6tPsbKTX00E36Tq+y737UJr1gGnq34nmq6ZVIXt/Dp7GycZN8mbTtiftpsCv9pwy9evE8ldAlK2UAUgd9gMrgSP3+fNFJ3wM8MeUS5mtcNEbR4YUADFdNd3nDmWqrN7eXJJ3bjOd7oI1OwYl9D3F1hB7p0EOdo4uyWYl4L337lXcuqiIyiGuUf0FVv9oq/wzQiXPMXwFLgPt7Zp1hGLOXzjTyKGCTd0SGiFwBrKN+qc864P3+81eAT8j0T63Iq5Gmj4ZRZKxh3pJLgZ+ISPBq+VIsRIRhGCmICKVyuZPygtOYW1X1I10zrIt06Bzz/wI3i8ivgD2ROl/STRsNw5idtNDIVkt9lgN3Rr5vBo6O1VHN48MHbQP29tsO8t7NHwb+XlX/O+dhNKUDjTR9NIwC0+kzZL+TxfnbR0TkWuDpuB7Pk1W17ZAVhmEUBKFTxx1PA14H/FJEfubT/s6Hq5hV5HSOeRluaukvqU0AMQyjKDTXyFZLfZJGvuNTw9Ly3AMcqKoPiMga4Osiclgvlwrl0EjTR8MoMp0/Q/Y1mY5cVW8CbuqxLYZhzAE6XR+kqv9DnyyXyekcc4sfTTIMo4B0qJGbcR7LAyuAu1PybBaRAWAxsFXders9AKp6o4jcDhwCbKRH5NBI00fDKDC2xtwwDKObFEtU8zjHvFFE/i+wgfqpmtb5aRhFoDONvAFYJSIHAXcBJwAnxvJsAE7CadErgO+pqorIPrgG+pSIPBpYBfw+ryEZaVcjTR8No8gU6xmygeIeuWEYvUEEKRVmfVAe55hH+PdjImn9HQ7IMIzsdKCRfs34O4CrcOHSLlHVW0TkXGCjqm7A+ej4nIhsArbiGu8AzwDOFZFJnFad1iKCRDdoVyNNHw2jyBTrGbKBTA1zH/JilapeIyKjwICqbu+taYZh9CcCA4MzbcR00bZzTFU9tudWGYYxi+lMI72/jStjae+LfB4Djk8o9x+4aBfTSVsaafpoGEWnUM+QDbSMhyQib8aF2/isT1oBfL2XRhmG0ceIIINDqa+5hPcafzJuVOpBnHPMf07KKyIvalVfljyGYfQ5TTRyrpFVI00fDcMACqWPSWQZMX87Lm7mTwBU9Xcism9PrTIMo38RoEDTkNpwjvlhEbmL5tM4/xH4r64YZhjG7MQ0MgnTR8MwCqePcbI0zPeo6rgLLQzew2c8NIdhGAbgPGpKgachNeE+oFVc9t9NhyGzBYk9gpfjCYD22b9NwiGkMljKnvmFH/9x9fOPznw2Q+XkCW+VaTphpXYOdBoI9tQdv9Zv0zzBHnwd3Txc08hE5rQ+agcXUFLZckw7wmUvGR7P0+7dPCbGbZPWE3F7gqToXtW8yOagEaVqu6aXlnk78uiytD6X4fy3qn9kdJSx3buB2rXTyV9FL89Zp/ooImuBj+F8cFykqufHtg8DlwNrgAeAV6nqH/y2s4BTcL4wTlfVq5rV6f19/DXwGGAfVd2S23BPlob5D0Tk74BRETkOeBvwn0kZRaRVLEwB7lHVQ9oz0zCM/sEeOpNQ1WfNtA2GYcwGTCPjmD4aRu8IjfL+IL8+ikgZ+CRwHC5s5A0iskFVfx3JdgrwoKoeLCInABcArxKRQ3GOMg8DDgCuEZHQXk2r80e4WTzX5jI4gSwN8zP9QfwSeAtwpar+S0re21X1iJRtAIjIze2ZOPOoSF2vYOhlCp1NU5XGbqeGzqTQ86UVAIbuuQWAXfv/BQADkZ6xUN9Aq94yX1d1F6pU/J7TOrPa6bVNyxvvqY321MXLhGMJPZPlyoT/7o5tYNBNV6kwj9L4rrqyu9VtK1d7gh2DlXFXpjSc+Rha9SZG7W4nbzx/2v6ynstmTJTrjzdPSIVmhxbflrtDVKRQ05DMOaZhGG1hGmkaaRhGMp3p41HAJlX9vatKrgDWAdGG+Trg/f7zV4BPiJsWvg64QlX3AHf4qBZH+XyJdarqzT4tr70NZHm2f6eqfgyoNsZF5F0+Lc7LM9SXJY+Rg0r+plTXyDVdxzO8aGkXLTFmDO+4owh455inAktxU5lWAJ8BnjOTdhmGMYsxjTSNNAwjmeb6uExENka+r1fV9ZHvy4E7I983A0fH6qjm8eEntwF7+/TrY2WX+8+t6uwaWRrmJ+Hm1Ud5Q0IaoTchICKLovtQ1a3xPIZhzDGkUKEuzDmmYRjtYRppGmkYRjLN9XGLqh7ZrHRCWnzEMC1PWnrS9OWeOXNJnSstIq8Wkf8EDhKRDZHX93GL5VMRkbeIyH3AL4Ab/WtjszIt6vuwiPxGRH4hIl8TkSWRbWeJyCYRuU1Enh9JX+vTNonImZH0g0TkJyLyOxH5kogUo9vaMKYNQUrl1NccY4+qjocvWZxjisg8Efk/IvIv/vsqCwNkGEUiXSPnIG1ppOmjYRSdjvRxM7Ay8n0FcHdaHq9Hi3HhHNPKZqmzazRbxHwd8E/Ab/x7eP0tsLZFve8BDlPVR6nqQf716A7svBp4gqoeDvwWOAsgtlB/LfApESlHFv+/ADgUeLXPC26R/0dVdRUupuYpHdhlGEYc71Ez7TXHiDvH/HdSnGNGuBTYAzzFf98MfLB3JhqGMatoopFzkHY10vTRMIpMZ/p4A7DKD8IO4dqIG2J5NuBmgwO8AvieqqpPP0FEhkXkIGAV8NOMdXaN1Ia5qv5RVa9V1aeo6g8ir5tUdbJFvbcDu1rkyYyqfieyz+txvRUQWaivqncAYaF+dfG/76m9AljnF/c/G7fYH+Ay4KXdstMwDGrTkNJec4szgT9T7xzz7BZlHqOqHwImAFR1Nx342jMMo89oppFzj3Y10vTRMIpMB/ro24rvAK4CbgW+rKq3iMi5IvISn+1iYG/v3O3dOI1CVW8BvoxzFPdt4O2qOpVWpzNVTheRzbh26S9E5KJOD7/lGnMROQb4OPB4YAgXw22nqi5qUuws4DoR+Qmu5xMAVT29M3MBeCPwJf+53YX6ewMPRRr50fx1iMipOIclrFy5MimLYRgJSIEcG9Gec8zAuPdMrD7/Y4jopGEYcxvTyKYaafpoGAWmU31U1SuBK2Np74t8HgOOTyl7HnBeljp9+oXAhbmNTaB19Hr4BPBq4HfAKPAmXEO9GZ8FvodrNN8YeaUiIteIyK8SXusiec4GJoEvhKSEqpot4M/iFMAlqq5X1SNV9chl++zTzHTDMOrwoS7SXnOLkxLS3tCizDm43tiVIvIF4LvA/85rgPngMIx+o4lGzj3a1UjTR8MoNIXSxwYyhUJW1U0iUlbVKeBSEbmuRZFJVX13O4ao6nObbReRk4AXAc/xawGg+YL8pPQtwBIRGfCj5j1dwG8YhcSvD5rLiMirgRPxzjEjmxbSwjmmql4tIjcBx+A6C9+lqls6MOdq4Cwf9uMC3IylM2I+OA4ArhGRQ3yZTwLH4TT0BhHZoKq/puaD4woR+QzOB8enO7DNMIw4ppGpGmn6aBgFpwD62IwsDfNdvlfwZyLyIeAeYH6LMt/3U8H/k/qp7FvzGCkia4EzgGeqanTt+gbgiyLyEZywhoX6gl+oD9yFE98TVVXFeZV/BW7d+UnAN/LYZBhGGgKlTH1+/cx1OC1chnOKGdiOi0aRioi8DOds5Jv++xIReamqfj2PIar6ncjX63H6BhEfHMAdfj3VUX7bphC6UkSCD45bcT44TvR5LgPejz14GkaXMY1MK2T6aBhFpxD6mEqWI38dbsr7O4C/wY1Ev7xFmSBcZ0XSFMjrmf0TwDBwtfPfxvWqeppf0B8W6k/iF+oDiEhYqF8GLgkL9XEN/CtE5IPAzTgnAIZhdBGVLKtk+hdV/SPwR2qeg9vhHFX9WqSuh0TkHCDXg2eMafHBYRhGZ5hGpmL6aBgFZ67rYzNaNsy9uAKMAf+QpVJVPagToxLqO7jJtnYX6v+eWq+oYRjdRqQwa4FyOsdM+sdpqsUicg3wiIRNZ6vqN3yerD44kvbflg8Ov786B5lhgZHk8J+sTSO/5yNqRy/qb7a/vGWa2Xn1u57KcR+rrSJL219pmhxY5zne6aDUxLCKP8HN8qTR1WvINLKZRs5Jfew2U5XkaznPtR3oRMOrdaQUlsgNFPJIBzdVu2XLpUa7puN/obqvyHnp5LjjZK1rZHSUsd2768vEfqs8ZvXkHBZIH5PI4pX9abjpOo+M5k+KSy4iq1X1phb1tcxjGEb/ooCWCzMN6RO4pTL/DhwJvB5I7Uj0bPTLbz6JO13vpIVzzNnog0NV1wPrAVavXjONjzjF5UdnPnumTTC6gGlkU400fTQ6ItoINRz9dD4Kpo8NZJkrcDHwEeDpwJMjryQuFZG9RGRp2os5MHVcxL9wr5IIJZHqd8H1zpVLgkrJTckIhcqDUB6kMrKQyshChrffy/D2e0Er1degTjKok6hI4quCe22bLLFtslQtV9IpBsZ3MDC+o7fHr5q5l656bvzha2nAv8poix6xgZIwUBIGBQYFBqgwQAWkBFJqy474OexHwjUVzmkeKqrVUSOg7rrrGiLV3yjxNcdQ1U1A2ce7vBQ4tkWRdwLjuCmV/46bjfT2vPuP+OB4SYIPjhNEZNj72wg+OG7A++Dw/kNOADb4B9bggwPMB4dh9IZmGjkHaVMjTR8No8gUTB/jZOmS2Kaq38pY32Jcz2azVsOfM9ZltElpon96xIy5TKEcd7TtHFNVdwJnNsvTJuaDwzD6CtPItMymj4ZRdAqljw1kOfLvi8iHga9S72G9YTq6qj6qe6YZhtGvaHFEtW3nmCKyDy4u72HASEhX1VzzlM0Hh2H0H6aRyZg+GoZRIH1sIMuRH+3fj4ykKS5shGEYRj1h3UIByOMcE+d86Eu4NY+n4aZE2kwiwygKppHNMH00jCJTIH1MIotX9lbrJQ3DMOooSm9nO84xI+ytqheLyLtU9QfAD0TkB7211DCM2YRpZKpGmj4aRsEpij4mkXrkIvJaVf28iLw7abuqfiSlnAArVPXOpO2GYcxxRKA4HjUvxk3PvBGYylhmwr/fIyJ/ifPsu6IHthmGMRsxjWyG6aNhFJli6WMDzY48OOdY2E6Fqqoi8nVgTW6rDMPoY6Qw3jNpzzlm4IMishj4W1x830W4B1fDMAqBaWQTTB8No9AUSh8bSD1yVf2sf/+HpFeLeq8XkbSQaoZhzHFqYfEaX1kQkbUicpuIbBKRbnro7TbfF5EPi8hTRGR1eCVlFJEL/MdRVd2mqr9S1WNVdY2qbphGmw3DmGF6qY8+BNiX/PafiMijItvO8um3icjzu3ZA6WTSSNNHwzACs00f0+r0YRV/IiK/83UOdXTgNJ/KfmGzgqp6epPNxwKnicgfgJ248GmqqofnMdIwjD5CBFrEqG9eXMrAJ4HjgM3ADSKyQVV/3SULu0k7zjFfKCJ/D5yFi89rGEYR6UAjM+rjKcCDqnqwiJwAXAC8SkQOxcXlPgw4ALhGRA4JYcJ6RFaNNH00DGPW6aMvk1bnBcBHVfUKEfmMr/vTuYz3NOt+uNG/Pw04FOclE+D4yLY0XtCJUYZh9DPSqeOOo4BNPjQNInIFsA4Xb3ZW0aZzzG8DW4D5IvJwJD10XC7qqnGGYcxSOtLILPq4DudwDeArwCe8/591wBWquge4Q0Q2+fp+nNeYVrShkaaPhmEwC/WRpDpF5FZcB+OJPs9lvt7eNMxV9TJvwBuAY1V1wn//DPCdZpWq6h9F5OnAKlW91MelXNCJoYZh9BHN1wctE5GNke/rVXV95PtyIOo8cjO1UZdZQR7nmKr6XuC9IvINVV3XcyMNw5i9pGtkN/SxmkdVJ0VkG7C3T78+VnZ5+8a3pl2NNH00DKPK7NPHpDr3Bh5S1cmE/LnJ0iVxAM4B3Fb/fYFPS0VEzsFNW3oscCkwCHweN/puGMYcRhEqNI1BuUVVj2yyPamwdmZV18nlHNNPs5rfMqNhGHOWFhrZDX1MyzOd2tq2Rpo+GoYxC/UxqZegZ3qapWF+PnCziHzff38mtSkAabwMOAK4CUBV7xaRth5gDcPoV5Qp7UibNgMrI99X4ELmzBqizjHbLDclIrtEZLGqbuuNdYZhzG460sgs+hjybBaRAWAxbnBl2rQ1j0aaPhqGMUv1MSl9C7BERAb8qHlX9LRpw9zPub8G+Ba1qQBnquq9Leod92HT1NdjPaCGURAUqHTWZ3gDsEpEDgLuwjnjOLF5kemlQ+eYY8AvReRqnHPMLGX6hs76ZBrZutvNEnN/R7DXSPtOYe7d6UIjP2L+YKb80nTCR+8I+42ew217an655o3W0h8ac+lLYudjpmzvB0qz5OR0qJFZ9HEDcBJu7fgrgO/5Z7INwBdF5CO4mY+rgJ/mtqQJHWjknNTHLLrY6vKM1hGu5fGpin93G+cNluq2Z6ESM67kBwJ7dbtIt/8kMuxLZ8m9D422pJ2PkK8b52tkdJSx3bsT91+1I+H/J41e/oSzUB8lqU5f5vu+jit8nd/IbbmnacM8xCRX1TVt7uzLIvJZXE/Cm4E3Ahd1YOeMIqqIasPFPOC/SsU99JWiayLUf/ZpwZGBek+D947UOl8OmPxz/f4m3M1DuflD5PCAq3uPwrA4ca4M+ic39d/F7a/kZ1dkucHbFbBo/nj9Yb/VtyA0ldrDprbwvhimtIjPp5QT95WHpGNNE8MG28Pv7c911DYa8ub3Uh6Y8ko1QKVu/1E7G2zX+rwh586Ks2deuVZ2YHyHyzMw7N5zOt/QiK25yrs1P+8ArgLKwCWqekvuCntDJ84xv+lfhmEUkE40Mk0fReRcYKMPLXYx8DnvvGgr7kESn+/LOEdIk8Dbe+iRPa9Gmj4aRpcJjfJ+YDbqY5Nn0jOAK0Tkg8DNvu6OyPLkfb2IPFlVb8haqar+PxE5DngYt878fap6dV4j5zqD+x1EtAk+Pr4zNW8aI/PmM7arezblJU+v5OjISGL6Aj88tGv3WEc2GdOMwlSHfSaqeiVwZVfs6QEdOse8TERGgQNV9bZe22oYxiyjQ41M0kdVfV/k8xiuAZxU9jzgvPx7z0ZejTR9NIyCMwv1Me2Z1HtqPyqe3glNXSd7jgV+LCK3i8gvROSXIvKLZgVE5AJVvVpV36uq71HVq0Xkgu6YbBjGbEdVU19zjOAcM5DFOeaLgZ/hwgMhIk/yU6gMwygIBdFHaFMjTR8NwyiQPjaQZcQ8T0zy43DD+/F64mmGYcwxlM5HzPuIPM4x34/rYb0WQFV/5tcuGYZRAEwjm2rk+zF9NIzCUjB9bKBlw9zHJH8i8L980n+r6s+T8orIW4G3AY+OjaovBH7UqbGGYcx+VDtbY94vdOAcc1JVt0n9so+5f8IMwwBMI1topOmjYRSYouhjGi2nsovIu4AvAPv61+dF5J0p2b8IvBjn8e7FkdcaVX1tXiNF5AN+Gv3PROQ7InKATxcRuVBENvntqyNlThKR3/nXSZH0NX46/iZfdva4ajSMOUKlyWuuoG5e1ddV9V5V/YZ/tWqUA/xKRE4EyiKySkQ+DlzXW2sNw5hNzHV9hNwaafpoGAWnCPqYRpY15qcAR6vq+/zi+WOANydlVNVtqvoHVX01Lh7cBK6nc4GIHNiBnR9W1cNV9UnAfwFhEf8LcO7sVwGnAp8GEJGlwDm4HtqjgHNEZC9f5tM+byi3tgO7DMOIETxqpr3mGNeLyJPbLPNO4DBgD64zcxvwrm4bZhjG7KSZRs5B2tVI00fDKDAF08cGsqwxFyAaTmPKp6UXcG7l3w/cR62TQ4HD2zcRVPXhyNf51KY1rQMu972y14vIEhHZH3gWcLWqbvX2XA2sFZFrgUWq+mOffjnwUtw0K8MwukSB1gcdC7xFRP6Ii7kruIGiZlr3l6p6NnB2SBCR44F/76mlhmHMGkwjUzXS9NEwCk6B9LGBLA3zS4GfiMjXcIK6jtZx2v4aeKyqPtChfVVE5Dzg9bje02N98nLgzki2zT6tWfrmhPSk/Z2KG1ln5cqVSVkMw0hAUaYK4j2TfM4xz6LxITMpzTCMOYhpZFNMHw2jwBRMHxvI4vztI36k+ek+6WRVvblFsTtxDejMiMg1wCMSNp3t1yWdDZwtImcB78BNVU8audcc6Y2JquuB9QBrVq8u7hViGO2iznlHEWjTOeYLgBcCy0XkwsimRcBkby01DGPWYBrZoJGmj4ZhAIXSxySyjJiDm76u/pVl/f3vgWtF5Ju4dUKAa+SnFVDV52a05YvAN3EN881AdDh7BXC3T39WLP1an74iIb9hGF3CrQ+aaSumB+8c883AV33S50Vkvap+PCH73cBG4CXAjZH07cDf9NRQwzBmDaaRiRpp+mgYRqH0MYmWDfOIqP4HbsS52YNn4E/+NeRfHSEiq1T1d/7rS4Df+M8bgHeIyBU4R2/bVPUeEbkK+MeIw7fnAWep6lYR2S4ixwA/wU2Nb3YchmHkoEDTkIJzzJ0AInIB8GMSdMWPEv1cRL6oqhPTa6ZhGLMJ08h6jTR9NAwjUCB9bCDLiHnmB8+Aqv6Dzzs/lOuQ80XksbjR+j8Cp/n0K3FTnzYBu4CT/f63isgHgBt8vnODIzjgrcC/AqM4p2/m+M0wuogCleKIatvOMYGjROT9wCNxGhycIT06lwFO69bh9PF+4A2qercPBfkxnEbu8uk3+TInAX/vq/igql7m09dQ08crgXd555qGYXQJ08imGtlVfQTTSMPoJwqmjw30yiv7U3AO4hYAB/r1RW9R1bflMVJVX56SrsDbU7ZdAlySkL4ReEIeOwzDaI0qTBTHpWYe55gX46Zm3ki9tublw6r6fwBE5HRcOMnTqA8neTQuVOTRkXCSR+L+A28UkQ2q+iC1cJLX4x4612Kdl4bRVUwjm2pkt/URTCMNo28omD420K5XdnDhxVo9eP4z8HzcVHNU9eci8ozcVhqG0TcoykSlGAuEcjrH3KaqXXuQs3CShtFfmEY21ciu6qO3wTTSMPqEIuljEu16ZReyPXiiqne6WUJVutXzaRjGbEYL57ijXeeY3xeRD+OcIUWdY96U14DpDifp95k7pGSYplaS5MlX0eS95w3wwK6aU+ZWZQPRmXCPmD/IvTtry1ZbFJ1xovYtGSnz0Fj//H1Gf6ulo8mPGGOT7jYZGSgB3fk9/uz3u8+8rD5t09k5UbuNd4y7cz9/0Nm6YKjccf2mkU3puj7C7Ay5m/W6T5rVG8qOlKX6/vB4hQGf3s7lNRCzQ6V+vzOtlxoxQLo4xXmmjyugMUPCMYb3+PYkWp2XkdFRxnbvbm5HShXNppVrNY9795djy//n5ob0Th/9bJgvAY8C/gC80s+EiedraylLWr0i8jjcAPdqXJSx/9fKxizO344Bbomsu1koIker6k+aFLtTRJ4KqIgMAacDt7baV78hFf8Aou4Kil6GGsujpfpTvXColFrvroEFAIxU/MOYuLzh5gzX++hkdPn+qCs75TYO+btjQF0dFXEPE6Xk6HCJtLrRw+aS5nholPTjb8ga+5MIIjEQ/nok/UGp1TEkbW8lgloqI5XaMVci+y9P7amlDwzz4Lj78pLXqAAAIABJREFUvHRwwpcdqNtHnj+Z6nUXrovItRWvT/y1yZTb/8DgiHsvCbsnkpWvWp/mU0aFwvR25nSOebR/PzKSpsCzm+xnVoWThPqQkqtXr+npvLMVSxdUP+/Y1fzhIo1HL1vIrt1j3TJpWtl/yfzUtN1j/XlMRcY0sqlGtq2Pfj+zSiOnUx8D+y6e37LxNZcZGR1t+Fzk8xGln85Dj/XxTOC7qnq+iJzpv58RzZBzKUtavVtxbeCXZjUwS9fyp3Et/cDOhLQ4p+EcaoTexe+Qshbc6B6hUW70npF5rf8AR0dGeGisG74PHQvmuT+aPTvGu1Lf0oXzYinuQb9TAS/Y+qA8zjGPTdvWpIyFkzSMOYJpZLpG5tFHX8400jDmAD3Wx3XU7u3LcPf1GbE8z6f9pSyJ9arq/cD9IvKXWQ3M5Pwt6nFSVSsi0rScqm4BXpPVCMMw5g6KFsmjZmbnmCLyWlX9vIi8O2m7qn4klwEWTtIw+grTyEaN7JU++rpNIw2jT2ihj8tEZGPk+3o/OyUr+6nqPQD+Xt83IU+epSxZ6s1Elob5770Xy0/7728Dft+sgIhchpt3/5D/vhfwT6r6xryGGobRH7hpSIV56GzHOWaYj7ywyzZYOEnD6CNMIxM1slf6CKaRhtE3tNDHLap6ZNpGaL6sJaMJXVnul5csDfPTgAtxi+AV+C7eoUUTDg+NcgC/AP6I3FYahtE3uGlIxVg/2Y5zTFX9rH//hy7bYOEkDaOPMI1s1Mhe6aOv0zTSMPqETvWx2bIWEblPRPb3o9r7A/cnZMuzlCVLvZnI4pX9fuCENustichewdOdX0jfuatUwzBmPUUaDcrpHNMwjAJjGmkaaRhGMj3Wxw3AScD5/v0bCXnyLGXJUm8metVY/ifgOhH5Cu4cvxI4r0f7MgxjNqFKpSAPneRzjmkYRpExjTSNNAwjmd7q4/nAl0XkFOBPwPEAInIkcJqqvinnUpa0eh8BbAQWARUR+WvgUFV9OM3AnjTMVfVyvzj/2bipS3+lqr/uxb4Mw5hdFGk0iBzOMQ3DKDamkaaRhmEk00t9VNUHgOckpG8E3hT53tZSlib13kv99PeW9EwcfUPcGuOGUTCKtH6SNpxjpnkbDnTiddgwjP7BNLJRI00fDcOAwuljA6kNcxNJwzDyoEBxQvS25RyzF96GDcPoM0wjEzXS9NEwjKLpYwPNRsxNJA3DaBtFmagUo7ezHeeYvfA2bBhG/9ErjfSOdr8EPAr4A/DK4IQ3lu8kXEMZ4IOqeplPvxbYH9jttz3Pa1xusmqk6aNhGFCsZ8gkUhvmJpKGYeRBFcYniyuqaYjIhc22q+rp02WLYRgzRw818kzgu6p6voic6b+fEc3gG+/nAEfiBqduFJENkQb8a/w6ymnF9NEwDLBnyJZrzEVkBDgFOAwYCemq+sYe2mUYRp+iClPFcWzUDjfOtAGGYcw8PdTIddTi716Gi717RizP84Grg5dhEbkaWAv8Wy8MagPTR8MwCv8MmcX52+eA3+DE/FzgNcCtvTTKMIz+RdGe9XaKyIeBFwPjwO3Ayar6UE921mXCdNGAiMxX1Z0zZY9hGDNDC41c5qPaBNar6vqMVe+nqvcAqOo9IrJvQp7lwJ2R75t9WuBSEZkC/gM3zX1anpBNHw3DgN4+Q/YDWRrmB6vq8SKyTlUvE5Ev4oKvG4ZhNNDjaUhXA2ep6qSIXACcReOIUM/pxDmmiDwFuBhYABwoIk8E3qKqb+uulYZhzEZaaOQWVT0ybaOIXAM8ImHT2Rl3L0km+ffXqOpdIrIQ1zB/HXB5xnrrd5JTI00fDaPY2FT21kz494dE5AnAvTjHIoZhGA30chqSqn4n8vV64BU92VFrOnGO+c+4GUgbAFT15yLyjK5YZRjGrKcTjVTV56ZtE5H7RGR/P1q+P5DkuG0ztenu4GLsXuvrvsu/b/eDMEeRs2FOfo00fTSMAmNT2VuzXkT2wnnw3IDrxXxfT60yDKNvyTANqZOpmlHeiPNAPO106hxTVe8UqRu4murMov5i2x53fSweLgFQqj8XdGPybCWhkjF/XQ6W6/dX9vuXpLFEIxNbd7tLWEQIs58n/MNV7HQzVBbGp5Q/75oEYMlIuWH7dBKulXDFjAwIY5PJF2E4psFSfht7OFVzA3AScL5//0ZCnquAf/TPdQDPA84SkQFgiapuEZFB4EXANXkN6UQj55o+CkqJhOvJJ2kL4cmjS/H9Je1DUoQ2pFdLRLKFekKeVrYbjaSd99T8lcjlL6W6bVnP/8joKGO7d9ftP142XlUwM/r/POUTByN5JyKHM5B00bSJTWVvgape5D/+EHh0b82ZnShCBaEUu5i1VH/6pDJZ+6wVVEpoeaiuTLjB5g3U31xJVMQ9rIR7oiqWkf1kpXpfxf4IqgKhkZvA77chD7GyIftUzZ74OUmjHTEP+w/PSUEQxIdTUMqpZVob4o87InahbMUfYfiDE9VEu3dO1M7dkP+9AYYz7D7Pbxm/purqi9k+JQP+3Z2jBbdeW8t8xNqm+wnXX9v2KexpLqq5p2qq6jd8nrOBSeALuYzsEjmdY94pIk8FVESGgNMxvx3GHGLF0gUAbNu5OzXP0oXz2P7Ajq7t85F7u33u2j3Wlfr2WTTPvcfSmx1TVjJoZF7OB74sIqcAfwKOBxCRI4HTVPVNqrpVRD4A3ODLnOvT5gNX+UZ5Gdco/5dODcqhkaaPsxRrhPc3I6OjANUGerdYOG+0+rkbdfdQH/uCLF7Z/xH4UHCw5HtZ/1ZV/755ye4jIu8BPgzs43t1BfgY8EJgF/AGVb3J502L07kG+FdgFLgSeFevnJuMzJuf6yJdssA9ELT7gLF04bzUbbvHuvOw0oo8Dc1+YnSk+lxRFbntE7tS8++/ZH5i+nT9HnkIx5X3AbfTaUjNpmpC9d5+EfCc6XJM1IQ8zjFPw+nWcty00u8Ab++hjYZhzCJ6NVVTVR8AnpOQvhF4U+T7JcAlsTw7gTVdN6p9jTR9NIwCY1PZW/MCVf278EVVHxSRF1Jr9E4LIrISOA7XC1y1DVjlX0cDnwaObhGn89PAqbj1qVfiwoR8a7qOwzDmOooyPtUzr+xrcc7enqmq6T0i00fbzjFVdQvu4dQwjALSS42chbSlkaaPhlFsCqaPDbSeTw1lEanOyhWRUbLN0u02HwX+N/ULF9YBl6vjemCJd3hSjdPpG+NXA2v9tkWq+mM/0nY58NLpPQzDmNsEj5pprw75BM6p0NUi8jMR+UznFndE3DnmYlo4xxSRy0RkSeT7XiJySbMyhmHMHZpp5BykLY00fTSMYlMwfWwgy4j554HvisiluEbxG4HLmhfpLiLyEuAu750zuiktHmez9M0J6Un7PBU3ss7KlSs7PALDKA6qyvhkb3z1qOrBPak4P3mcYx4ejb3uZyEd0UMbDcOYRfRSI2ch7Wqk6aNhFJhe6qOfUf0lXOfgH4BX+gHceL62lkOn1Ssir6EW0ncH8FZV/XkzG7M4f/uQiPwSt25JgA+oatfjmLeIzfl3OM+hDcUS0jRHemOi8xK9HmD16jXFXexgGG2iFGd9UE7nmCUR2Sv8GXhBz+Y10TCMvsc0simmj4ZRYHqsj2cC31XV80XkTP/9jGiGnMuh0+q9A7f08kEReQGuXXl0MwMziZ2qfoser8NOc/gkIn8BHASE0fIVwE0ichRuxDs6nL0CuJv0OJ2b/ed4fsMwukSlQB41czrH/CfgOhH5Ck70Xwmc13NjDcOYFZhGNtVI00fDKDA91sd11NqHl+HahmfE8lSXQwOISFgOfS1+ObRPD8uhv5VWr6peF6n3eurboImkrjEXkf/x79tF5OHIa7uIPNyq4m6hqr9U1X1V9VGq+ihc43q1qt6Lmxb1enEcA2xT1XtwjkWe59cm7YUbbb/Kb9suIsd4j+6vJznOp2EYOXHTkHq2xny28YL4tEtclIhUVPVy4OXAfcCfgb9S1c/11ErDMGYNzTRyDtKWRpo+GkaxaaGPy0RkY+R1apvV7+fbgvj3fRPy5FkOnaXeU8gwyJ06Yq6qT/fvC1tVMoNciRP4TbhwaScDpMXp9J/fSm19QM9nAhhGESnKNE28c0xV3QPZnWOq6q+BX/faOMMwZiemkemYPhpGsWmij1tU9chmZVssjc5Cx8uhE2w6Ftcwf3qrvFnimH9OVV/XKm268KPm4bOSEt8yKU6nT98IPKFX9hlG0VGFybk58pPEjDvHNAyjvzCNNI00DCOZTvUxbWk0gIjcJyL7q+o9PlLX/QnZ8iyHTq1XRA4HLsLNHnqglf1ZwqUdFv0iIgPAmgzlDMMoIKrK1FQl9TWXUNUP4dY/Ph6nlR/wadOOiLxHRFRElvnvIiIXisgmEfmFiKyO5D1JRH7nXydF0teIyC99mQslFgbDMIzOaaaRcw3TSMMw2qHH+rgBCPfzSSQvZ86zHDqxXhE5EPgq8DpV/W0WA1NHzEXkLJw39NHImnIBxvHeyg3DMJLQ4kzTnBbnmK0QkZXAccCfIskvAFb519E4b6JH5/Q4ahhGFzGNnF5MIw2jf+ihPp4PfFlETsFpwfEAInIkcJqqvinncujEenGhIf9/e/ceLldVn3H8+yYBEhK55AJiSOViLFIFhAgoVCgiIrYEHrGoPAJyU4u1WhVRW5RSH6FYeYoigojBFgkStFJEYohExBIg5RpASsRUo1wNl4RLIDm//rHXkM3Jnpk955w5M3vm/TzPfs6eNXv2rDVnzntm71lr7SnAN9I5vLXNuuI3GmP+ZUlnARdFxHHl22xm/SwC1q3t7Q+dkm6MiH0lreLlY4xENspms1Gu0jnAKbz87O9s4LtpyM9iSVukLlb70/qMo2Y2QpyRzkgzK9bOfExdyd9WUL4EOCF3u6Xh0A32e0J+v2U0HGMeEQOSdm1lh2bW54Ke7JKZ102TY0o6FPh9RNQuKVnTaGbRVmccLXrek8i+OWLGjBn1NjOzwZyRo6oTGel8NBuiPsjHRsqMMV8s6U1tr4mZ9YggBuovvUTSBpfxKSobgee5TtLSgmU22UyjpxU9rKCs0cyiLc04GhEXRsSsiJg1deq0Ms0wM6BRRvaafs3IfD5Omzq1bDPMrI/ysUjTWdmBvwA+LGk58AzruyHt0s6KmVk1RX+d7RyVyTHrzTIq6Q3A9kDtm6Btgdsk7Un2bU7+q5raDKJDmXHUzEaIM9IZaWbF+iwfN1DmG/N3AjsABwB/Bfxl+mlmVmhg7UDdpRdI+mwaO7mLpKfTsgp4hOJZPtsiIu6OiK0iYrt0KckVwO4R8TDZLKFHp5mH9waeSrOKDmXGUTMbQb2cj+CMNLOh6/V8bKTpN+YR8X+S9gVmRsR3JE0DJrW/amZWRbVLXfSyikyOeQ1wCLAMeBb4IMAQZxw1sxHijOwazkizLtMP+dhI0wNzSbVLRvwp8B1gI+A/gH3aWzUzq6rog0ztxskx0zdCtfUATq6zXUszjjYlaOVqvpMnjGXlc+vqDmAfiPX3jKmz48HFGlj3sttjAJV8Iz7zYrbdhHGq+5yDiyIa31/GujRmbuyY6l0KeW2q+2PPrn2pbJOxxZ3wXliXbZt/yZqd3V/x9IsblM0cP75pvYbye6h9EFrTQm4N9+rVzsjO6FhGNqDBYdIGhc9RexOqTOfZ4n0MLos2X9a9tv9WX7NG2w91n2XkX49m+9faNdljxm1Sev/tqHNtn7X/n1q3PovHrVkNwMDEKSP+vHn9kI/1lBljfjjwRuA2gIj4g6SOz7I5mkQwJveRot4fQoyp/3K+9JiSAQisf842ZPYGbcjVq2l4DA7isRsP/XlbsNHgD8bp9R5WMDX4fYwhGCic66U7NGp37b1T+7y/0Vjx/LrWXqcxQ3zjZZe66JtUXSzpTRFxa/NNLW/6lhNZ9exzTbcbGPQ+n7TpBACee/75ho8rOiif/IpNC7d9/ulnm9bDmpu+5cSX3d58Yva7Wrmq+PV99ZTiw/MHHl01shUbQbU2NXv/NeKMtHYZPyF7fz7/XPNsbfT4vKHuy9Yb/Hkt/zo3en2Lfh/NHtNJw33/Qd/l4wbKHJi/EBEhKQAkTWz2AFuv0Zu03h9cmf1lyp8fmTDom4bR+qMeiT/STtp0QvNvaKZtVvxhv5H1v4/m+x8pW0zaFN54cPufqL+6IXlyTDNrjTPSGWlmxforHzdQ5sD8+5IuALaQdCJwHPCt9lbLzKoqoG8ua0E2OaaZWWnOSDOzYn2WjxsoM/nbVyS9HXiabJz5aRGxoO01M7Nq6qNuSJ4c08xa5ox0RppZsT7KxyJlJn/7BHCFD8bNrIyIYKBPuiF5ckwza5Uz0hlpZsX6KR+LlOnKvhkwX9JKYC4wLyIeaW+1zKzKBvqnG1LfT45pZq1zRpqZFeujfNxAma7spwOnS9oFOBL4uaQVEXFg22tnZtUTwcDaFzpdi9HiyTHNrDXOSDOzYv2Vjxsof+0ueBR4GPgjsFV7qmNmVRcRDLz4Qt2lxwyeHPM6PDmmmTXQKCOHQ9JkSQskPZB+bllnu2slPSnp6kHl20u6OT3+cknlr4VanzPSzEprVz5CSxl5TNrmAUnH5Mr3kHS3pGWSzpWyC9XX26+k2ZLuknSHpCVpvo2Gmh6YS/qIpEXAQmAqcKIvc2Fm9QUxsK7u0ksi4ivAPOBK1k+O+bXO1srMulv9jBymU4GFETGT7DPbqXW2Oxv4QEH5WcA56fFPAMcPt0LOSDNrTdvyEUpkpKTJwBeAvYA9gS/kDuDPB04CZqaldg3ievtdCOwaEbuRXdXsomYVLDPG/NXAxyPijhLbmlm/66NuSJ4c08xa1r6MnA3sn9YvARYBn9nw6WOhpP3zZembnwOA9+ce/0WyD6JD5ow0s5a09zNkmYx8B7AgIlYCSFoAHJy+pN4sIm5K5d8FDgN+Um+/EbE6t9+JZFeDa6jMGPN6Z1zNzDYQfXRgjifHNLMWNcnIqZKW5G5fGBEXltz11hHxUHqOhyS1MuxwCvBkRKxNt1cA01t4fD3OSDMrrY35COUycjrwu9ztWhZOT+uDyxvuV9LhwJfJhoG/q1kFy3xj3nGSvgicCDyWij4XEdek+z5L1t1qHfCxiJifyg8G/g0YC1wUEWem8u3J/jlMJpsl9AMR0TdHEWbtFwz0WJf1ejw5ppm1rmFGPh4Rs+rdKek64JUFd31+mJVSQdmwp0Z2RppZa4aejzAiGVkvC4eUkRHxQ+CHkt4KnAE0zL5KHJgn56SxSi+RtDPwXuDPgFcB10l6bbr7PODtZGc0bpV0VUTcy/oxVHMlfZPsoH5YXbXMbL0++8a8xpNjmlkpw8nIRge0kh6RtE36xmYbslwq63GyCdrGpW/NtwX+MKRKFnNGmllTw/0MOQIZuYL13dIhy8JFqXzbQeW1jGy634i4QdKOkqZGxOP16tjKrOzdaDYwNyLWRMRvgGVkA/X3BJZFxIPp2/C5wOzcGKp56fGXkI0PMLORMgqzskv6lKSQNHVEdjj0enhyTDNrTftmHb4KqM0gfAzwo/JVigCuB44YyuPrcUaaWUvaOCs75TJyPnCQpC3TpG8HAfNTV/VVkvZOx5NH5x5fuF9Jr8nN3L47sDHZycm6qvSN+UclHQ0sAT4ZEU+Q9e1fnNsm399/8PiAvWhhDJWkk8hm3mPGjBkj1Qaz3hcDbf3GXNIMst4wv23bk5TnyTHNrDXty8gzyS5PdjxZPr4HQNIs4MMRcUK6/QtgJ2CSpBXA8WkY4GeAuZL+Gbgd+PYI1MkZaWbltfczZNOMjIiVks4Abk2P+afaRHDAR4A5wASySd9+0mi/wLuBoyW9CDwHHJlOgtbVNQfmTcYEnE/WLz/Sz38lm3a+Xn//op4ALY0PSJMJXAiwx+67D3uclVm/CCAGBtr5FOcApzAC3+YMlyfHNLNWtSsjI+KPwNsKypcAJ+Ru/3mdxz9I1uNwJOvkjDSz0tr5GbKFjLwYuLjOdq9vYb9nkQ2hLq1rDszLTgQi6VvA1enmCiD/dXa+v39RebvHUJlZ8/FBQ55VU9KhwO8j4s7UO8jMrFr6cx4OM7Pm+jwfu+bAvJHagPp083BgaVq/CviepK+STf42E7iF7JvxmWkG9t+TTRD3/ogISbUxVHMZoTFUZrZeRLCu8Vig4cw6/Dmy8T5mZpVUIiPNzPpSv+djJQ7MgX+RtBtZD4flwIcAIuIeSd8H7gXWAidHxDoASR8lG8A/Frg4Iu5J+2rHGCozq4kghnG5tHq9ZyS9AdgeqH1bvi1wm6Q9I+LhIT+hmdloGmZGmpn1rD7Px0ocmEfEBxrc9yXgSwXl1wDXFJSP+BgqM8trTzekiLib3GV2JC0HZjW67ISZWffp766aZmb19Xc+qsnkcAZIWgXc3+l6tMFUsnH3vcRtGnmvjohpZTeWdC1Znet5PCIOHm6lfGDePXo0Izv9d9cOvdgm6Hy7RjIjRyQfrXtIegx4ht772+v031279GK7Otkm52MLfGBegqQljcbEVlUvtsttMht9vfgedZuqo1fbZb2jF9+jvdgm6M129WKbelXRZcXMzMzMzMzMbJT4wNzMzMzMzMysg3xgXk6payxXUC+2y20yG329+B51m6qjV9tlvaMX36O92CbozXb1Ypt6kseYm5mZmZmZmXWQvzE3MzMzMzMz6yAfmJuZmZmZmZl1kA/Mm5B0sKT7JS2TdGqn61OWpIslPSppaa5ssqQFkh5IP7dM5ZJ0bmrjXZJ271zN65M0Q9L1ku6TdI+kv0vlVW/XeEm3SLoztev0VL69pJtTuy6XtHEq3yTdXpbu366T9bf+VdV8BGdkKu/6djkfrcqckd3FGemM7HY+MG9A0ljgPOCdwM7A+yTt3NlalTYHOHhQ2anAwoiYCSxMtyFr38y0nAScP0p1bNVa4JMR8Tpgb+Dk9PuoervWAAdExK7AbsDBkvYGzgLOSe16Ajg+bX888EREvAY4J21nNqoqno/gjIRqtMv5aJXkjOxKzkhnZFfzgXljewLLIuLBiHgBmAvM7nCdSomIG4CVg4pnA5ek9UuAw3Ll343MYmALSduMTk3Li4iHIuK2tL4KuA+YTvXbFRGxOt3cKC0BHADMS+WD21Vr7zzgbZI0StU1q6lsPoIzMq13fbucj1Zhzsgu44wEnJFdzQfmjU0Hfpe7vSKVVdXWEfEQZOEEbJXKK9fO1PXmjcDN9EC7JI2VdAfwKLAA+DXwZESsTZvk6/5Su9L9TwFTRrfGZtX5+2pB5bOkppcy0vloFVWJv68WVTpL8pyRzshu5APzxorOIPXi9eUq1U5Jk4ArgY9HxNONNi0o68p2RcS6iNgN2JbsLPvrijZLPyvTLutp/fQ+rFRbey0jnY9WUf30XqxUW52Rde+zDvOBeWMrgBm529sCf+hQXUbCI7UuOOnno6m8Mu2UtBFZmF4aET9IxZVvV01EPAksIhv7tIWkcemufN1fale6f3M27G5m1m6V+/sqofJZ0ssZ6Xy0iqnU31dJlc8SZ6Qzspv5wLyxW4GZaWbDjYH3Ald1uE7DcRVwTFo/BvhRrvzoNPvk3sBTtS493SSNgfk2cF9EfDV3V9XbNU3SFml9AnAg2bin64Ej0maD21Vr7xHAzyLCZztttPVaPkL1s6TnMtL5aBXmjOwyzkjAGdndIsJLgwU4BPhfsvEan+90fVqo92XAQ8CLZGfHjicbQ7IQeCD9nJy2FdnMob8G7gZmdbr+ddq0L1l3m7uAO9JySA+0axfg9tSupcBpqXwH4BZgGXAFsEkqH59uL0v379DpNnjpz6Wq+Zjq7oysQLucj16qvDgju2txRjoju31R+iWZmZmZmZmZWQe4K7uZmZmZmZlZB/nA3MzMzMzMzKyDfGBuZmZmZmZm1kE+MDczMzMzMzPrIB+Ym5mZmZmZmXWQD8zNzMzMzMzMOsgH5hUjaXUb9nmopFPT+mGSdh7CPhZJmtXi9vdLOrTgvu0kLW21DlUl6VhJr8rdvlTSSklHdLJeZlXjfOw9zkezkeF87D3Ox97jA3MjIq6KiDPTzcOAloN1iI6KiKva+QSSxrZz/yPkWOClYI2Io4C2vi5mVo7zseOOxflo1pWcjx13LM7HnuID84pS5mxJSyXdLenIVL5/Ops4T9Kv0tkzpfsOSWU3SjpX0tWp/FhJX5f0FuBQ4GxJd0jaMX8mU9JUScvT+gRJcyXdJelyYEKubgdJuknSbZKukDSpRHv2kHSnpJuAk3PlY1M7b03P9aFUPkbSNyTdI+lqSdfUzhBKWi7pNEk3Au9J7bhW0v9I+oWkndJ20yRdmfZ9q6R9Uvl+qf13SLpd0isa1PvTubqdniv/z/R890g6KdeWObnf2SdSnWcBl6bnm1DvucysHOej89HMijkfnY/WxSLCS4UWYHX6+W5gATAW2Br4LbANsD/wFLAt2YmXm4B9gfHA74Dt0+MvA65O68cCX0/rc4Ajcs+3CJiV1qcCy9P63wMXp/VdgLVkATEVuAGYmO77DHBaQTte2m+6fRewX1o/G1ia1k8C/iGtbwIsAbYHjgCuSW18JfBErd7AcuCU3L4XAjPT+l7Az9L694B90/qfAPel9f8C9knrk4BxdX4XBwEXAkr1uBp4a7pvcvo5AVgKTAH2ABbkHr9F0WtR9Hvw4sVL88X56Hz04sVL8eJ8dD566f5lHFZV+wKXRcQ64BFJPwfeBDwN3BIRKwAk3QFsB6wGHoyI36THX0YWWkP1VuBcgIi4S9JdqXxvsq5Mv0wnWjcmC/e6JG1OFjI/T0X/DrwzrR8E7KL142U2B2aStf+KiBgAHpZ0/aDdXp72PQl4C3BFqg9kAQ1wILBzrnyzdHbzl8BXJV0K/KD2WhY4KC23p9uTUt1uAD4m6fBUPiOV3w/sIOlrwI+BnzZ6XcxsyJyPzkczK+Y5+o8CAAAC9klEQVR8dD5al/KBeXWpwX1rcuvryH7PjbZvZC3rhzyMH3Rf1KnXgoh4XwvPoTr7qt33txEx/2WF0rua7POZ9HMM8GRE7FawzRjgzRHx3KDyMyX9GDgEWCzpwIj4VZ26fTkiLhhUt/3JQvvNEfGspEXA+Ih4QtKuwDvIulv9NXBck3aYWeucj405H836l/OxMeejdYzHmFfXDcCRadzJNLIzkLc02P5XZGfbtku3j6yz3SogPyZmOVkXGsi6/+Sf/ygASa8n644EsBjYR9Jr0n2bSnpto4ZExJPAU5L2TUVH5e6eD3xE0kZpf6+VNBG4EXh3Giu0NVkXrKJ9Pw38RtJ70uOVwg2yM44frW0rabf0c8eIuDsiziLr+rRTnarPB45LZ1WRNF3SVmRnZZ9IoboT2VlgJE0FxkTElcA/Arun/Qx+zc1seJyPzkczK+Z8dD5al/KBeXX9kGxczZ3Az8jGxDxcb+N0Vu9vgGuVTWrxCNlYosHmAp9WNmnFjsBXyILtv8nG/9ScD0xKXZBOIYV6RDxGNubosnTfYuoHU94HgfOUTd6RPwN5EXAvcJuyS2BcQHYG90pgBdn4mwuAm+u0B7KgPl7SncA9wOxU/jFglrKJN+4FPpzKP65sgo07U11+UrTTiPgp2TijmyTdDcwjC8hrgXGp/Wek1wBgOrAodQ+bA3w2lc8BvilP3mE2UpyPzkczK+Z8dD5al1JEvR4g1mskTYqI1ZIEnAc8EBHndKgui4BPRcSSYeyj1p4pZMG+T6N/LlUiaQ7Z5CrzOl0Xs37gfKwO56PZ6HI+Vofzsdr8jXl/OTGdbbuHrLvMBU22b6eVwBxJhw5jH1en9vwCOKOHQvVSYD/g+U7XxayPOB8rwPlo1hHOxwpwPlafvzE3a0LSG8hm+sxbExF7daI+ZmbdwvloZlbM+Wit8oG5mZmZmZmZWQe5K7uZmZmZmZlZB/nA3MzMzMzMzKyDfGBuZmZmZmZm1kE+MDczMzMzMzProP8H6t6HOdzdAgMAAAAASUVORK5CYII=\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": [ + { + "ename": "KeyError", + "evalue": "\"Unable to find any combinations of metrics for array dims {'Zl', 'XC'} and axes ('Z',)\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\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", + "\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 1172\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 1173\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-> 1174\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 1175\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 1176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'Zl', 'XC'} and axes ('Z',)\"" + ] + } + ], + "source": [ + "# 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": "3.7.6" + }, + "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 +} From c9fd4b1373e22451e7e1005a8db4c748b4e230cb Mon Sep 17 00:00:00 2001 From: MiniUFO Date: Wed, 13 Jan 2021 10:39:26 +0800 Subject: [PATCH 2/3] download sample dataset from zenodo --- 06_inferring_metrics.ipynb | 79 ++++++++++++++++++++++++-------------- 1 file changed, 51 insertions(+), 28 deletions(-) diff --git a/06_inferring_metrics.ipynb b/06_inferring_metrics.ipynb index 24979dc..88ea41b 100644 --- a/06_inferring_metrics.ipynb +++ b/06_inferring_metrics.ipynb @@ -64,8 +64,8 @@ "Data variables:\n", " UVEL (YC, XG) float32 ...\n", " VVEL (YG, XC) float32 ...\n", - " DIVG (YC, XC) float32 0.0 0.0 0.0 ... 2.920357e-07 1.4720649e-06\n", - " VORT (YG, XG) float32 0.0 0.0 0.0 ... 1.05465695e-07 -3.8749278e-08\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", @@ -76,11 +76,19 @@ ], "source": [ "import xarray as xr\n", + "import urllib.request\n", + "import shutil\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('../datasets/mitgcm_example_dataset_v2.nc').isel(dict(Z=0,time=0)) \\\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", @@ -135,7 +143,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -202,8 +210,8 @@ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\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", - "\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 1172\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 1173\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-> 1174\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 1175\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 1176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\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 1365\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 1366\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-> 1367\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 1368\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 1369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\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 1049\u001b[0m raise KeyError(\n\u001b[0;32m 1050\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[1;32m-> 1051\u001b[1;33m \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\u001b[0m\u001b[0;32m 1052\u001b[0m )\n\u001b[0;32m 1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ('X',)\"" ] } @@ -237,7 +245,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -299,8 +307,8 @@ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\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", - "\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 1172\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 1173\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-> 1174\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 1175\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 1176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\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 1365\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 1366\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-> 1367\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 1368\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 1369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\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 1049\u001b[0m raise KeyError(\n\u001b[0;32m 1050\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[1;32m-> 1051\u001b[1;33m \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\u001b[0m\u001b[0;32m 1052\u001b[0m )\n\u001b[0;32m 1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YC'} and axes ('Y',)\"" ] } @@ -350,11 +358,15 @@ "name": "stdout", "output_type": "stream", "text": [ + "test grid.average\n", + "test grid.average\n", + "test grid.average\n", + "test grid.average\n", " weighted means vs unweighted means\n", - " UVEL: 4.094420e-03 8.637649e-03\n", - " VVEL: 4.158888e-03 5.595285e-03\n", - " DIVG: 2.389647e-10 5.964250e-08\n", - " VORT: 1.626647e-11 -1.907488e-09\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" ] } ], @@ -405,6 +417,16 @@ "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test grid.average\n", + "test grid.average\n", + "test grid.average\n", + "test grid.average\n" + ] + }, { "ename": "KeyError", "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"", @@ -413,8 +435,8 @@ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\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 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\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 1262\u001b[0m \u001b[0mweighted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mda\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1263\u001b[0m \u001b[1;31m# TODO: We should integrate xarray.weighted once available.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\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 1015\u001b[0m raise KeyError(\n\u001b[0;32m 1016\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[1;32m-> 1017\u001b[1;33m \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\u001b[0m\u001b[0;32m 1018\u001b[0m )\n\u001b[0;32m 1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\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 1442\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'test grid.average'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1443\u001b[0m \u001b[0mda\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0mweight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\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 1049\u001b[0m raise KeyError(\n\u001b[0;32m 1050\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[1;32m-> 1051\u001b[1;33m \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\u001b[0m\u001b[0;32m 1052\u001b[0m )\n\u001b[0;32m 1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"" ] } @@ -449,15 +471,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "test grid.average\n", " weighted mean vs unweighted mean\n", - " VORT: 1.626594e-11 -1.907488e-09\n" + " VORT: 1.626682e-11 -1.907488e-09\n" ] } ], @@ -497,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -510,12 +533,12 @@ " maskC (Z, XC) bool ...\n", " hFacC (Z, XC) float32 ...\n", " Depth (XC) float32 ...\n", - " * Z (Z) float32 -25.0 -85.0 -170.0 -290.0 ... -3575.0 -4190.0 -4855.0\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 -220.0 ... -3280.0 -3870.0 -4510.0\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", @@ -533,7 +556,7 @@ ], "source": [ "# re-open example dataset, select the x-z slices\n", - "ds = xr.open_dataset('../datasets/mitgcm_example_dataset_v2.nc').isel(dict(YC=10,YG=10,time=0)) \\\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", @@ -551,12 +574,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -599,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -609,9 +632,9 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\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", - 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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", + "#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", @@ -199,24 +200,43 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ('X',)\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\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 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**kwargs)\u001b[0m\n\u001b[0;32m 1365\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 1366\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-> 1367\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 1368\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 1369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\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 1049\u001b[0m raise KeyError(\n\u001b[0;32m 1050\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[1;32m-> 1051\u001b[1;33m \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\u001b[0m\u001b[0;32m 1052\u001b[0m )\n\u001b[0;32m 1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ('X',)\"" + "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", @@ -240,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -294,26 +314,43 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { - "ename": "KeyError", - "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YC'} and axes ('Y',)\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\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", - "\u001b[1;32m~\\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 1365\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 1366\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-> 1367\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 1368\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 1369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\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 1049\u001b[0m raise KeyError(\n\u001b[0;32m 1050\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[1;32m-> 1051\u001b[1;33m \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 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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 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"execution_count": 12, "metadata": { "scrolled": false }, @@ -358,10 +395,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "test grid.average\n", - "test grid.average\n", - "test grid.average\n", - "test grid.average\n", " weighted means vs unweighted means\n", " UVEL: 4.094419e-03 8.637649e-03\n", " VVEL: 4.158882e-03 5.595285e-03\n", @@ -379,10 +412,10 @@ "maskZ = (VORT != 0)\n", "\n", "# maskout metrics\n", - "ds['rAw'] = ds['rAw'].where(maskW)\n", - "ds['rAs'] = ds['rAs'].where(maskS)\n", - "ds['rA' ] = ds['rA' ].where(maskC)\n", - "ds['rAz'] = ds['rAz'].where(maskZ)\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", @@ -414,34 +447,41 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "test grid.average\n", - "test grid.average\n", - "test grid.average\n", - "test grid.average\n" - ] - }, - { - "ename": "KeyError", - "evalue": "\"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\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# 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\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\u001b[0m\u001b[0;32m 1052\u001b[0m )\n\u001b[0;32m 1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'XG', 'YG'} and axes ['X', 'Y']\"" + "\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 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\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", @@ -471,21 +511,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "test grid.average\n", " weighted mean vs unweighted mean\n", " VORT: 1.626682e-11 -1.907488e-09\n" ] } ], "source": [ - "ds.coords['rAzNew'] = (ds.dxV * ds.dyU).where(maskZ)\n", + "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", @@ -501,7 +540,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that the new weighted mean (1.169156e-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", + "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", @@ -520,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -574,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -622,24 +661,42 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"Unable to find any combinations of metrics for array dims {'Zl', 'XC'} and axes ('Z',)\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m 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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", - "\u001b[1;32m~\\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 1365\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 1366\u001b[0m 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\u001b[0;36mget_metric\u001b[1;34m(self, array, axes)\u001b[0m\n\u001b[0;32m 1049\u001b[0m raise KeyError(\n\u001b[0;32m 1050\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[1;32m-> 1051\u001b[1;33m \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\u001b[0m\u001b[0;32m 1052\u001b[0m )\n\u001b[0;32m 1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: \"Unable to find any combinations of metrics for array dims {'Zl', 'XC'} and axes ('Z',)\"" + "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 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\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", @@ -692,7 +749,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.5" }, "varInspector": { "cols": {