{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gridded Data with Xarray\n", "\n", "[Xarray](https://docs.xarray.dev/en/stable/) is a very powerful tool for exploring multi-dimensional (esp. geospatial) data in a way that is efficient and robust to making coding mistakes. Pandas provided us with a way to look at tabular data, Xarray takes this further and provides a framework for N-dimensional data. One of the coolest Xarray features, is the [integration with Dask](https://docs.xarray.dev/en/stable/user-guide/dask.html). This will allow us to easily parallelize our Xarray analysis!\n", "\n", "When using Xarray, our data will be stored in `DataArrays` which are collected together into a `DataSet`. A nice example of this is in the context of a climate model:\n", "\n", "1) DataSet - contains all possible coordinates on the model grid and provides a list of all model variables (DataArrays)\n", "\n", "2) DataArray - an individual model variable (e.g., sea surface temperature), the variable's coordinates on the model grid, and any additional meta data about that specific variable\n", "\n", "In the graphic below, you can see that `temperature` and `precipitation` are both variables with coordinates `lat` and `lon`. In this simple example temperature and precip each have 3 dimensions. Not only does the DataSet store all of this information, it also relates these two variables to each other by understanding that they share coordinates `lat` and `lon`. We will use some test data to inspect this further. Any NetCDF can be read into an Xarray dataset. Many popular models facilitate reading raw output directly into Xarray." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "\n", "# load a sample dataset from the xarray library \n", "ds = xr.tutorial.load_dataset(\"air_temperature\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TIP** If you realize you need to use Pandas for a particular problem, never fear! You can convert between Pandas and Xarray with a single line of code." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, time: 10, lon: 53)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2013-01-03T06:00:00\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "Data variables:\n",
       "    air      (lat, time, lon) float32 241.2 242.5 243.5 ... 296.9 296.8 297.1
" ], "text/plain": [ "\n", "Dimensions: (lat: 25, time: 10, lon: 53)\n", "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2013-01-03T06:00:00\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", "Data variables:\n", " air (lat, time, lon) float32 241.2 242.5 243.5 ... 296.9 296.8 297.1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = ds.isel(time=slice(10)).to_dataframe() # to dataframe from xarray\n", "df.to_xarray() # to xarray from dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspecting our DataSet" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" ], "text/plain": [ "\n", "Dimensions: (lat: 25, time: 2920, lon: 53)\n", "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Data variables:\n", " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", "Attributes:\n", " Conventions: COARDS\n", " title: 4x daily NMC reanalysis (1948)\n", " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", " platform: Model\n", " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that our variable `air` has 3 dimensions (lat, lon, time) defined by coordinates of the same name. We can inspect each of those coordinates to see their ranges." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "    standard_name:  longitude\n",
       "    long_name:      Longitude\n",
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       "    axis:           X
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<xarray.DataArray 'time' (time: 2920)>\n",
       "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
       "       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n",
       "       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      Time
" ], "text/plain": [ "\n", "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", " dtype='datetime64[ns]')\n", "Coordinates:\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Attributes:\n", " standard_name: time\n", " long_name: Time" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Attributes (`attrs`) provide additional meta-data in the form of strings." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Conventions': 'COARDS',\n", " 'title': '4x daily NMC reanalysis (1948)',\n", " 'description': 'Data is from NMC initialized reanalysis\\n(4x/day). These are the 0.9950 sigma level values.',\n", " 'platform': 'Model',\n", " 'references': 'http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.attrs" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'standard_name': 'longitude',\n", " 'long_name': 'Longitude',\n", " 'units': 'degrees_east',\n", " 'axis': 'X'}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.lon.attrs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Selecting (Slicing) Sub-sets of Data with Labels\n", "\n", "Xarray is great for doing reproducible science! Instead of having to remember or hard-code the order and size of your data dimensions, you can select subsets of data by their names and values. isel() is used to select by index, and sel() is used to select by dimension label and value." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
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       "        238.59999],\n",
       "       [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
       "        239.29999],\n",
       "       [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
       "        241.7    ],\n",
       "       ...,\n",
       "       [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
       "        294.69998],\n",
       "       [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
       "        295.19998],\n",
       "       [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
       "        296.6    ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "    time     datetime64[ns] 2013-01-01\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]
" ], "text/plain": [ "\n", "array([[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n", " 238.59999],\n", " [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n", " 239.29999],\n", " [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n", " 241.7 ],\n", " ...,\n", " [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n", " 294.69998],\n", " [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n", " 295.19998],\n", " [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n", " 296.6 ]], dtype=float32)\n", "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " time datetime64[ns] 2013-01-01\n", "Attributes:\n", " long_name: 4xDaily Air temperature at sigma level 995\n", " units: degK\n", " precision: 2\n", " GRIB_id: 11\n", " GRIB_name: TMP\n", " var_desc: Air temperature\n", " dataset: NMC Reanalysis\n", " level_desc: Surface\n", " statistic: Individual Obs\n", " parent_stat: Other\n", " actual_range: [185.16 322.1 ]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select the first day\n", "ds.air.isel(time=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "       [244.29999, 242.2    , 242.09999, ..., 293.     , 294.19998,\n",
       "        295.79   ],\n",
       "       [246.79999, 242.39   , 243.7    , ..., 292.29   , 293.     ,\n",
       "        295.     ],\n",
       "       ...,\n",
       "       [235.98999, 241.89   , 251.29   , ..., 296.29   , 297.69   ,\n",
       "        298.29   ],\n",
       "       [237.09   , 239.89   , 250.29   , ..., 296.49   , 297.88998,\n",
       "        298.29   ],\n",
       "       [238.89   , 238.59   , 246.59   , ..., 297.19   , 297.69   ,\n",
       "        298.09   ]], dtype=float32)\n",
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       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "    lon      float32 220.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
       "    statistic:     Individual Obs\n",
       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]
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       "         270.6    ],\n",
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       "         269.69   ],\n",
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       "Attributes:\n",
       "    long_name:     4xDaily Air temperature at sigma level 995\n",
       "    units:         degK\n",
       "    precision:     2\n",
       "    GRIB_id:       11\n",
       "    GRIB_name:     TMP\n",
       "    var_desc:      Air temperature\n",
       "    dataset:       NMC Reanalysis\n",
       "    level_desc:    Surface\n",
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       "    parent_stat:   Other\n",
       "    actual_range:  [185.16 322.1 ]
" ], "text/plain": [ "\n", "array([[[273.69998, 273.6 , 273.79 , ..., 273. , 275.5 ,\n", " 276. ],\n", " [274.79 , 275.19998, 275.6 , ..., 270.19998, 272.79 ,\n", " 274.9 ],\n", " [275.9 , 276.9 , 276.9 , ..., 271.1 , 271.6 ,\n", " 272.79 ],\n", " ...,\n", " [295.4 , 295.69998, 295.79 , ..., 290.19998, 290. ,\n", " 289.9 ],\n", " [297. , 296.69998, 296.1 , ..., 290.79 , 290.9 ,\n", " 290.69998],\n", " [296.6 , 296.19998, 296.4 , ..., 292. , 292.1 ,\n", " 291.79 ]],\n", "\n", " [[272.1 , 272.69998, 273.19998, ..., 270.19998, 272.79 ,\n", " 273.6 ],\n", " [274. , 274.4 , 275.1 , ..., 267. , 270.29 ,\n", " 272.5 ],\n", " [275.6 , 276.1 , 276.29 , ..., 267.79 , 269.19998,\n", " 270.6 ],\n", "...\n", " [290.88998, 291.49 , 293.19 , ..., 291.69 , 291.38998,\n", " 290.79 ],\n", " [291.59 , 291.69 , 293.59 , ..., 292.79 , 292.59 ,\n", " 292.59 ],\n", " [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.59 ,\n", " 295.09 ]],\n", "\n", " [[272.59 , 271.99 , 272.19 , ..., 274.19 , 275.38998,\n", " 273.88998],\n", " [274.29 , 274.49 , 275.59 , ..., 269.38998, 272.88998,\n", " 274.69 ],\n", " [276.79 , 277.49 , 277.99 , ..., 264.59 , 266.88998,\n", " 269.69 ],\n", " ...,\n", " [291.49 , 291.38998, 292.38998, ..., 291.59 , 291.19 ,\n", " 290.99 ],\n", " [292.88998, 292.09 , 292.99 , ..., 293.49 , 292.88998,\n", " 292.88998],\n", " [293.79 , 293.69 , 295.09 , ..., 295.38998, 294.79 ,\n", " 294.79 ]]], dtype=float32)\n", "Coordinates:\n", " * lat (lat) float32 60.0 57.5 55.0 52.5 50.0 ... 30.0 27.5 25.0 22.5 20.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 232.5 235.0 237.5 240.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Attributes:\n", " long_name: 4xDaily Air temperature at sigma level 995\n", " units: degK\n", " precision: 2\n", " GRIB_id: 11\n", " GRIB_name: TMP\n", " var_desc: Air temperature\n", " dataset: NMC Reanalysis\n", " level_desc: Surface\n", " statistic: Individual Obs\n", " parent_stat: Other\n", " actual_range: [185.16 322.1 ]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extract a sub-region of data by selecting slices of latitude and longitude\n", "ds.air.sel(lat=slice(60, 20), lon=slice(200, 240)) # keep in mind that latitude is decreasing -- the slice is from 60 to 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpolating (or Resampling) Data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# interpolate the air temperature to a new longitude (or new set of longitudes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting in Xarray (so easy!)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.air.isel(time=0).plot()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import cmocean.cm as cmo\n", "\n", "# Let's use a non-default colormap\n", "ds.air.isel(time=0).plot(cmap=cmo.thermal)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.air.sel(lon=220.0,method='nearest').plot(y='lat',x='time',cmap=cmo.thermal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistics in Xarray" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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k6f59N1aVn376SXh7ewsDAwMhl8vFqFGjRHx8vFqbKVOmCFNT02rbXLBgwQPvRGloqOPdXFXHW9tr5cqVatvNz88Xr7/+urC3txcGBgbC29tbbNiwodr+q+4s+/d5zMjIEJMnTxZWVlbCyMhI9OrVS4SFhdXpmGq7G0sIIRISEsTgwYOFkZGRsLKyElOnThXbt29XuxurSmRkpBg+fLiwsrIS+vr6wsnJSQwfPlxs2rRJrd3WrVuFl5eXMDAwEK1btxaLFy8Wr7/+urC0tFRrB0CEhITUWNfly5fF888/L5ycnIS+vr6wtbUVffr0ER999JFau4KCAvHee+8JDw8P1c+Yl5eXmDVrlsjIyBBCCLFz504xdOhQ4eTkJAwMDIRCoRDDhg0TR44cqdP5u9e2bdvEgAEDhIWFhTA0NBSurq7iySefFAcOHFC1SU1NFU888YSwtLQU5ubmYsiQISIuLq7av8Pbb78tfH19haWlpTA0NBRt2rQRs2bNEjdu3FC1USqV4oUXXhC2trZCJpPV+LPBu7GoociEEKJp4xURUcNwc3NDv3798PPPP0NHR0dtMG1TKC0thY+PD5ycnLB///4m3bc2Ky8vhxACbdu2haenZ527Colqw1vPiahZW716NfT19fH66683+r6mTp2KjRs3IjIyEr/++isCAwNx7tw5vPnmm42+75ake/fu0NfXr9P0DER1wTE7RNRs/fHHH6rHbtT0OIyGlp+fjzlz5iA7Oxv6+vro1q0bdu/erRrjpGmqrpDU5mFn3m5s69evx507dwCg2nggoofBbiwiIi3l5uZ236sjdZ2ygKi545UdIiItde+Vr5rUZcoCIm3AKztERESk1ThAmYiIiLQau7EAVFRUIC0tDebm5g/94EEiIiJqWkII5Ofnw9HR8b5TTzDsoPJ5Ri4uLlKXQURERA8hJSXlvg9NZtjBP4P0UlJSYGFhIXE1REREVBd5eXlwcXF54GB7hh3883RiCwsLhh0iIqJm5kFDUDhAmYiIiLQaww4RERFpNYYdIiIi0moMO0RERKTVGHaIiIhIqzHsEBERkVZj2CEiIiKtxrBDREREWo1hh4iIiLQaww4RERFpNYYdIiIi0moMO0RERKTVGHYaUVZeMX6PTkV5hZC6FCIiohaLYacRfXPoAuZsOoshXx/GvvgMCMHQQ0RE1NQYdhqRq7UJ5Mb6SM4qwMtrojHmu6M4dvGm1GURERG1KDLByw3Iy8uDXC5Hbm4uLCwsGnTbuUWl+PHwRayIuoKi0nIAwKPtbPBmUAd4OcsbdF9EREQtSV2/vxl20Lhhp0pWfjH+79AFrD9xDWV3x/AM93LAG4Ht8YitWaPsk4iISJsx7NRDU4SdKtdu3sH/DiRh25nrEALQ1ZFhvK8zXh/YDg5y40bdNxERkTZh2KmHpgw7Vc5n5OGLfYk4cC4LAGCgp4PgPm54td8jsDQ1aJIaiIiImjOGnXqQIuxUOXXlFj7bm4i/rtwCAJgb6uGlgDZ43t8dpoZ6TVoLERFRc8KwUw9Shh0AEEIgIikbn+1NxLn0PACAjZkBpg1oi0k9W8NQT7fJayIiItJ0DDv1IHXYqVJRIbAzNh1f7k/E1Zt3AADOlsZ4Y3B7jPJxgq6OTLLaiIiINA3DTj1oStipUlpegV9PpmDpwWRk5SsBAO3tzDA70AODOtox9BAREYFhp140LexUKSopxy9Hr2BZxAXkFZcBAJxaGWOinwvG+7nAzsJI4gqJiIikw7BTD5oadqrk3inFD4cvYt2Ja8gtKgVQecv6Yx0UeKpHawS0t+XVHiIianEYdupB08NOleLScuyJS8eGEymqu7cAwFFuhAl+rTHez5lz9RARUYvBsFMPzSXs3OtCVj42/JWCzX+n4vadyqs9OjLgsQ4KTOrRGv09FLzaQ0REWo1hpx6aY9ipUlxajn3xGVh/4hpOXP7nao+D3AgT/Fww3tcFjq14tYeIiLQPw049NOewc68LWQX49eQ1/B6dipx7rvYM8Ki62mMLPV0+6J6IiLQDw049aEvYqaIsK8e++ExsOHENxy7dVC23tzDCeD8XTPBzgROv9hARUTNX1+9vSf/MDw0NhZ+fH8zNzaFQKDB69GgkJiaqtcnMzERwcDAcHR1hYmKCIUOGIDk5Wa1N//79IZPJ1F4TJ05sykPRKIZ6uhjZxREbXuqFQ7P74eWANrAyNUBGXjGWHkyG/6eH8OzPJ/DryWvIKSyRulwiIqJGJemVnSFDhmDixInw8/NDWVkZ3n33XcTGxiIhIQGmpqYQQqBPnz7Q19fHl19+CQsLC3z11VfYu3evqg1QGXbat2+PDz74QLVtY2NjyOXyOtWhbVd2aqIsK0dYQibWn7iGoxf/udqjqyNDn0esMczLAUGd7WHFh5ASEVEz0Sy7sbKzs6FQKBAZGYmAgAAkJSXBw8MDcXFx6Ny5MwCgvLwcCoUCn376KV544QUAlWHHx8cHX3/99UPttyWEnXtduVGIXbHp2BWTjoS7z+ICKoNPrzZWquBjY2YoYZVERET31yy6sf4tNzcXAGBlZQUAUCorH5VgZPTPTMG6urowMDBAVFSU2mfXrVsHGxsbdO7cGXPmzEF+fn6t+1EqlcjLy1N7tSRuNqYIGdAWu2c8ivA5/TE3yAOeThYorxD488JNvLs1Dj0+PoBJPx7HmuNXkX33kRVERETNkcZc2RFCYNSoUcjJycGRI0cAAKWlpWjXrh169OiBH374Aaampvjqq68wb948BAYGYt++fQCA5cuXw93dHfb29oiLi8O8efPQtm1bhIWF1bivhQsXYtGiRdWWt5QrO7W5erMQu2MzsCcuHTGpuarlMhnQw80Kw70dMKSzPRR8TAUREWmAZteNFRISgl27diEqKgrOzs6q5dHR0Zg6dSrOnj0LXV1dDBo0CDo6lRekdu/eXeO2oqOj4evri+joaHTr1q3aeqVSqbpqBFSeLBcXlxYfdu6VcusOdsemY3dcBs6m3FYtl8kAP1crDPOyxxBPB9jLGXyIiEgazSrsTJ8+Hdu2bcPhw4fh7u5eY5vc3FyUlJTA1tYWPXv2hK+vL7799tsa2wohYGhoiDVr1mDChAkP3H9LG7NTX6k5d7AnNgO749Jx+tpttXW+rpYY4mmPoM72cLEykaZAIiJqkZpF2BFCYPr06di6dSsiIiLQrl27B34mOTkZHTp0wJ49exAYGFhjm7i4OHh5eakGOj8Iw07dXb9dhD2x6dgTl4Hoqzlq6zrYmyOwsz0CO9mhs6MFZDI+roKIiBpPswg7r732GtavX4/t27fDw8NDtVwul8PYuHLSu02bNsHW1hatW7dGbGwsZsyYge7du2Pz5s0AgIsXL2LdunUYNmwYbGxskJCQgNmzZ8PY2BgnT56Erq7uA+tg2Hk46blF2BuXgf3xmfjryi2UV/zzo+TUyhiDO9khsLMderhZceZmIiJqcM0i7NT2l//KlSsRHBwMAFi6dCk+//xzZGZmwsHBAZMnT8b8+fNhYFA5H0xKSgqeeeYZxMXFoaCgAC4uLhg+fDgWLFiguqvrQRh2/rucwhIcOp+F/QkZiEzKRnFphWpdKxN9PNZBgcBO9ghobwMTAz0JKyUiIm3RLMKOpmDYaVhFJeWIunAD++MzcOBcpuo5XQBgqKeDR9vZIrCzHQZ2UMCac/kQEdFDYtipB4adxlNWXoHoqznYn5CJffEZSM0pUq3TkQG+blYI7GTHAc5ERFRvDDv1wLDTNIQQOJ+Rj/3xmdifkIH4NPXJHDvYm6Nr61Zob2eO9nbmaGdnBlszQw50JiKiGjHs1APDjjRSbt3BgXOZNQ5wrtLKRP9u+DGrDECKyv9m9xcRETHs1APDjvRyCksQdeEGEjPykZiZj+TMfFy9dQe1/XTamBmogk87O3N42JujvcIcchP9pi2ciIgkw7BTDww7mqm4tBwXsgqQnJWPxIwCJGfmIykrHym3imr9jMLcEO3tzNHB3hxDvezRrbUlu8GIiLQUw049MOw0L3dKynAhqwBJmQVIysxHUmY+kjMLcP129RDkZm2Csd2cMaarEwdAExFpGYademDY0Q4FyjIk3w0+xy/dxN74DNwpKVet79XGCmO7OWOYlwPMDDnXDxFRc8ewUw8MO9qpUFmGvXEZ2Px3Ko5duqka/2Osr4shnvYY280JfR6xga4Ou7mIiJojhp16YNjRftdvF2Hb6evYHJ2KSzcKVcvtLYwwuqsTnuzuhLYKcwkrJCKi+mLYqQeGnZZDCIEzKbex+e9U/HE2HblF/8zu3MVZjrHdnDGyiyMsTQ0krJKIiOqCYaceGHZaJmVZOQ6dy8Lmv1MRkZiNsrvz/OjryjDAQ4EnujtjgIcCBnp8iCkRkSZi2KkHhh26UaDEjjNp2Px3qtrMzpYm+ni8iyMe7+KI7q0tocPxPUREGoNhpx4Yduhe5zPysOXv69h6+jqy85Wq5fYWRhjm5YDh3g7o6tKKwYeISGIMO/XAsEM1KSuvQNSFG9hxNg1h8ZnIV5ap1jnKjTDc2wHDvR3RxVnOiQuJiCTAsFMPDDv0IMqychxOuoFdMWkIS8hE4T3z9zhbGmO4twNGeDnC08mCwYeIqIkw7NQDww7VR3FpOSISs7ErNh0Hz2WqTVzoam2C4Xe7ujo5MPgQETUmhp16YNihh1VUUo7wxCzsiknHwfOZKC6tUK1rY2N6t6vLAR525gw+REQNjGGnHhh2qCHcKSnDwXOVwSc8MQvKsn+CT1uFGYZ7OWCYlwPa25kx+BARNQCGnXpg2KGGVqAsw8FzmdgZk47IxGyUlP8TfFpbmWBwJzsM7mQHX1dL6OlyHh8ioofBsFMPDDvUmPKKS3EgIRO7YtJx5MINlNxzxcfSRB8DOigQ2MkOj7azhSkfUEpEVGcMO/XAsENNpVBZhiPJ2difkIlD57Nw+84/j6sw0NOBf1sbDO5kh4EdFVCYG0lYKRGR5mPYqQeGHZJCWXkFTl3NQVhCJsISMnHt1h219T4urTC4kx0CO9mhrYLjfIiI/o1hpx4YdkhqQggkZRYgLCEDYQmZOJuaq7bezbpqnI89urtaQpezNxMRMezUB8MOaZrMvGLVFZ9jF2+qDXC2MjXAYx0UeKpna3RrbSlhlURE0mLYqQeGHdJkBcoyHE7KRtjdcT65Rf+M8xnX3RlvDe0AGzNDCSskIpIGw049MOxQc1FaXoGTV27h9+hUbPn7OgDAwkgPc4I88HRPV3ZvEVGLwrBTDww71BxFX83B+9vjEJ+WBwDo7GiBD0Z5orsru7aIqGVg2KkHhh1qrsorBNafuIrP9yUir7jyqezjfZ3x1pAOsGbXFhFpubp+f3PqVqJmTFdHhmd7u+HQnP4Y190ZAPDbqVQM+CICa45dQXlFi/9bhoiIV3YAXtkh7RF9NQfzt8UhIb2ya8vTqbJri3dtEZE2YjdWPTDskDYprxBYd7drK59dW0SkxdiNRdRC6erIMLm3G8Jr6to6fpVdW0TU4kgadkJDQ+Hn5wdzc3MoFAqMHj0aiYmJam0yMzMRHBwMR0dHmJiYYMiQIUhOTlZro1QqMX36dNjY2MDU1BQjR45EampqUx4KkcaxMTPE5+O6YPOrvdHJwQJ5xWWYvy0Oo76Nwt/XcqQuj4ioyUgadiIjIxESEoLjx48jLCwMZWVlCAwMRGFhIYDKKfRHjx6NS5cuYfv27Th9+jRcXV0xaNAgVRsAmDlzJrZu3YqNGzciKioKBQUFGDFiBMrLy6U6NCKN0d3VCjum9cWikZ1hbqSHuOt5GPvdUbz1ewxuFiilLo+IqNFp1Jid7OxsKBQKREZGIiAgAElJSfDw8EBcXBw6d+4MACgvL4dCocCnn36KF154Abm5ubC1tcWaNWswYcIEAEBaWhpcXFywe/duBAUFPXC/HLNDLUV2vhKf7j2P36Mrr3zKjfUxJ8gDT/VozQkJiajZaZZjdnJzKx9+aGVlBaCyewoAjIyMVG10dXVhYGCAqKgoAEB0dDRKS0sRGBioauPo6AhPT08cPXq0xv0olUrk5eWpvYhaAltzQ3wxrgt+f6U3OjpYILeoFPO3xWHYkiM4dD4TGvS3DxFRg9GYsCOEwBtvvAF/f394enoCADp06ABXV1fMmzcPOTk5KCkpweLFi5GRkYH09HQAQEZGBgwMDGBpqX5rrZ2dHTIyMmrcV2hoKORyuerl4uLSuAdHpGF83azwx92uLQsjPSRm5uP5X05hwo/HOZ6HiLSOxoSdadOmISYmBhs2bFAt09fXx+bNm5GUlAQrKyuYmJggIiICQ4cOha6u7n23J4SATFbzZfl58+YhNzdX9UpJSWnQYyFqDvR0dTCljxuOvPkYXu7XBgZ6Ovjr8i2M/e4oXl0bjYvZBVKXSETUIDQi7EyfPh07duxAeHg4nJ2d1dZ1794dZ86cwe3bt5Geno69e/fi5s2bcHd3BwDY29ujpKQEOTnqf41mZWXBzs6uxv0ZGhrCwsJC7UXUUslN9DFvaEdE3L1VXUcG7InLQOD/DuPdrbHIyiuWukQiov9E0rAjhMC0adOwZcsWHDp0SBVgaiKXy2Fra4vk5GScOnUKo0aNAlAZhvT19REWFqZqm56ejri4OPTp06fRj4FIWzi2Msbn47pgz4wADOyguDs54TX0+zwCX+5PRH5xqdQlEhE9FEnvxnrttdewfv16bN++HR4eHqrlcrkcxsbGAIBNmzbB1tYWrVu3RmxsLGbMmIHu3btj8+bNqvavvvoqdu7ciV9++QVWVlaYM2cObt68iejo6Ad2dwG8G4uoJicu3cTivedx+tptAICVqQGmP9YWT/d0hYGeRlwUJqIWrlk8LqK2MTUrV65EcHAwAGDp0qX4/PPPkZmZCQcHB0yePBnz58+HgYGBqn1xcTHmzp2L9evXo6ioCAMHDsR3331X54HHDDtENRNCYF98Bj7bm4hLNyrntmptZYLZge3xuLcjdHi7OhFJqFmEHU3BsEN0f6XlFfjtVAq+PpCM7PzKKSE8nSzw9pCO8G9nI3F1RNRSMezUA8MOUd3cKSnDiqjL+D7yEgqUlQ8ZfbSdDd4a0gGeTnKJqyOiloZhpx4Ydojq52aBEv8XfgFrj19FaXnlr5BRPo6YE+gBFysTiasjopaCYaceGHaIHs61m3fwZVgitp9JAwAY6Opg3rAOCO7jVuuYPCKihtIsHxdBRM1La2sTLJnYFTun+8O/rQ1Kyiuw6I8EvLbub+TxVnUi0hAMO0T0n3k6ybFmag8sfLwT9HVl2BOXgce/iULc9VypSyMiYtghooYhk8kQ3Ncdm17pA6dWxrh68w7GLjuKdSeu8gGjRCQphh0ialA+Lq2w63V/DOqoQElZBd7dGoeZv55B4d27t4iImhrDDhE1uFYmBlg+2RfvDOsAXR0Ztp9Jw8j/i0JiRr7UpRFRC8SwQ0SNQiaT4aWAR/DrS71gb2GEi9mFGPVtFDadSpG6NCJqYRh2iKhR+bpZYdfr/ghob4vi0grM/T0GczedRVFJudSlEVELwbBDRI3O2swQvwT7YU5ge+jIgE3RqRj97Z+4mF0gdWlE1AIw7BBRk9DRkWHaY+2w9oWesDEzRGJmPkZ+E4XtZ65LXRoRaTmGHSJqUn0escHuGf7o1cYKhSXlmLHxDN7dGoviUnZrEVHjYNghoianMDfCuhd6YfpjbSGTAetOXMMTy47i6s1CqUsjIi3EsENEktDVkWF2oAd+ea4HrEwNEJ+WhxFLo7A3Ll3q0ohIyzDsEJGk+rW3xa7X/eHraol8ZRleWfs3Fv0Rj5KyCqlLIyItwbBDRJJzkBtjw0u98HK/NgCAlX9ewbgfjiHl1h2JKyMibcCwQ0QaQV9XB/OGdsRPk30hN9bH2ZTbGL70CPbHZ0hdGhE1cww7RKRRBnWyw87p/vBxaYW84jK8tCYaH/yRwG4tInpoDDtEpHFcrEzw28u98eKj7gCAFX9exrjvj7Jbi4geCsMOEWkkAz0dvDu8E5ZXdWul5mL40iPYx24tIqonhh0i0miDO9lh1+v/dGu9vCaad2sRUb0w7BCRxnO2VO/WWvnnFXZrEVGdMewQUbNQ1a310z3dWsOWHsHeOHZrEdH9MewQUbMyqJMdds94FF1bt0J+cRleWRuNhTvioSzjs7WIqGYMO0TU7Di1MsZvL/fGywGVkxD+cvQKnlx2DNdusluLiKpj2CGiZklfVwfzhnXEimBftDLRR+z1yru19sTy2VpEpI5hh4iatcc62GH364+iW+tWyFeW4dV1f2PB9jh2axGRCsMOETV7jq2M8evLvVXP1lp17CqeXHYMV28WSlwZEWkChh0i0gpVz9a6t1trxNIo7Ga3FlGLx7BDRFqlqluru6sl8pVleG3d35i76SxSczh4mailYtghIq3j2MoYG1/qhVf6PQIA2BSdiv6fR+DtzTG8Y4uoBZI07ISGhsLPzw/m5uZQKBQYPXo0EhMT1doUFBRg2rRpcHZ2hrGxMTp27Ihly5aptenfvz9kMpnaa+LEiU15KESkYfR1dfD20A7Y9Epv9G1rjbIKgY0nUzDgywjM2XQWl29wPA9RSyETQgipdj5kyBBMnDgRfn5+KCsrw7vvvovY2FgkJCTA1NQUAPDiiy8iPDwcP/30E9zc3LB//3689tpr2Lx5M0aNGgWgMuy0b98eH3zwgWrbxsbGkMvldaojLy8Pcrkcubm5sLCwaPgDJSLJnbpyC0sPXcDhpGwAgI4MGOXjhJABbdFWYSZxdUT0MOr6/S1p2Pm37OxsKBQKREZGIiAgAADg6emJCRMmYP78+ap23bt3x7Bhw/Dhhx8CqAw7Pj4++Prrrx9qvww7RC3H6Ws5+ObQBRw6nwUAkMmA4V4OmP5YO3jYm0tcHRHVR12/vzVqzE5ubi4AwMrKSrXM398fO3bswPXr1yGEQHh4OJKSkhAUFKT22XXr1sHGxgadO3fGnDlzkJ+fX+t+lEol8vLy1F5E1DJ0bW2JFcF++GOaPwZ3soMQwM6YdAR9fRivro1GQhp/HxBpG425siOEwKhRo5CTk4MjR46olpeUlODFF1/E6tWroaenBx0dHfz000949tlnVW2WL18Od3d32NvbIy4uDvPmzUPbtm0RFhZW474WLlyIRYsWVVvOKztELU9CWh7+LzwZu2P/eaDo4E52eP2xdvByrltXOBFJo9l1Y4WEhGDXrl2IioqCs7OzavkXX3yB5cuX44svvoCrqysOHz6MefPmYevWrRg0aFCN24qOjoavry+io6PRrVu3auuVSiWUSqXqfV5eHlxcXBh2iFqwxIx8/F/4BeyMSUPVb8XHOigw/bG26NraUtriiKhGzSrsTJ8+Hdu2bcPhw4fh7u6uWl5UVAS5XI6tW7di+PDhquUvvPACUlNTsXfv3hq3J4SAoaEh1qxZgwkTJjxw/xyzQ0RVLmQV4NvwC9h+5joq7v52DGhvi9cfawtfN6v7f5iImlSzGLMjhMC0adOwZcsWHDp0SC3oAEBpaSlKS0uho6Nepq6uLioqKmrdbnx8PEpLS+Hg4NAodROR9mqrMMP/Jvjg4Oz+eLK7M3R1ZDiclI0nvz+Gp5YfR0zqbalLJKJ6kvTKzmuvvYb169dj+/bt8PDwUC2Xy+UwNjYGUHmn1Y0bN/B///d/cHV1RWRkJF599VV89dVXePXVV3Hx4kWsW7cOw4YNg42NDRISEjB79mwYGxvj5MmT0NXVfWAdvLJDRLW5dvMOlkVewKZTqSirENDVkSFkQFtMf6wt9HU16h4PohanWXRjyWSyGpevXLkSwcHBAICMjAzMmzcP+/fvx61bt+Dq6oqXXnoJs2bNgkwmQ0pKCp555hnExcWhoKAALi4uGD58OBYsWKB2V9f9MOwQ0YOk5tzB4j3nsTOm8llbnk4W+N94H7Sz4+3qRFJpFmFHUzDsEFFd7Tibhvnb4pBbVAoDPR28GeSB5/u6Q0en5j/eiKjxNIsxO0REzc3ILo7YPysA/T1sUVJWgY92ncOk5ceRcovP3CLSVAw7RET1ZGdhhJXBfvh4jCdMDHRx4vItDF1yBL+dTAEvlhNpHoYdIqKHIJPJ8HRPV+yZ8Sh8XS1RoCzDm5tj8OLqU8jOVz54A0TUZBh2iIj+A1drU/z6cm+8PbQDDHR1cOBcFoK+Pow9selSl0ZEdzHsEBH9R7o6MrzS7xFsn9YXHezNcauwBK+u+xtv/HoGuUWlUpdH1OIx7BARNZCODhbYMc0fIQMegY4M2HL6OoZ8fRhRyTekLo2oRWPYISJqQAZ6Opgb1AGbXukDN2sTpOcW45mfT2DB9jgUlZRLXR5Ri8SwQ0TUCLq7WmL3jEfxbC9XAMCqY1cxfOkRnL6WI3FlRC0Pww4RUSMxMdDDh6M9ser5HrCzMMSlG4V4YtlRfLk/ESVltT/fj4gaFsMOEVEj69feFvtn9sNoH0dUCOCbQxcw5rs/cfzSTc7LQ9QE+LgI8HERRNR0dsWk491tsbh9p/IurW6tW+G1/m3xWAcFHzlBVE98NlY9MOwQUVPKyivGkoPJ2BSdqurO8rAzx6v9H8EIbwfo8WnqRHXCsFMPDDtEJIWs/GL8HHUZ645fQ4GyDADgYmWMlwIewbjuzjDS15W4QiLNxrBTDww7RCSl3KJSrDl2BSv+vIJbhSUAABszQ0z1d8czvVrD3Ehf4gqJNBPDTj0w7BCRJigqKcevJ69h+ZHLuH67CABgbqSHKb3d8FxfN1ibGUpcIZFmYdipB4YdItIkpeUV2H4mDd9HXsSFrAIAgJG+Dib6tcaLAW3g1MpY4gqJNAPDTj0w7BCRJqqoENifkIllERdwNjUXAKCnI8MoHye82r8N2irMJa6QSFoMO/XAsENEmkwIgaMXb+K7iAv488JNAIBMBgR2ssNr/duii0sraQskkgjDTj0w7BBRc3Em5TaWRVzAvvhM1TJvZznMjfTU2v37N3tNv+kFqi90szbFK/0egZuNaYPUS9SYGHbqgWGHiJqbC1n5WBZxCdvPXEdZRcP+GtfTkWGCnwteH9gOdhZGDbptoobEsFMPDDtE1Fxdv12E6Ks51R47IZNVn425pvmZ721WXiGw7fR1hCdmA6gcFD2ljxte7fcIWpkYNGTZRA2CYaceGHaIiP7x1+Vb+GzveZy6WvmEdnMjPbwc0AbP9XWHqaHeAz5N1HQYduqBYYeISJ0QAuGJWfhsbyLOZ+QDAGzMDDBtQFtM6tkahnqc3Zmkx7BTDww7REQ1q6gQ+CMmDV+FJeHqzTsAAGdLY8wa1B6juzpBlw8vJQkx7NQDww4R0f2Vllfgt1MpWHIgGVn5SgBAezszzA70QGAnuxrHCBE1NoademDYISKqm6KScqw6dgXLIi4it6gUAODj0gpvDvFAn0dsJK6OWhqGnXpg2CEiqp/colIsP3wJP0ddRlFpOQDg0XY2mBvkAW/nVtIWRy0Gw049MOwQET2crPxifHvoAtb/dQ2l5ZVfJ0M97TE70ANtFWYSV0fajmGnHhh2iIj+m5Rbd/C/A0nYevo6hAB0ZMCcIA+81r+t1KWRFqvr97dOE9ZERERaysXKBF+N98G+mQEI7GSHCgH8LywJt++USF0aEcMOERE1nPZ25vhxsi86OligtFxgT1yG1CURMewQEVHDG+XjCADYfua6xJUQSRx2QkND4efnB3NzcygUCowePRqJiYlqbQoKCjBt2jQ4OzvD2NgYHTt2xLJly9TaKJVKTJ8+HTY2NjA1NcXIkSORmpralIdCRET3eLxLZdg5cfkWMnKLJa6GWjpJw05kZCRCQkJw/PhxhIWFoaysDIGBgSgsLFS1mTVrFvbu3Yu1a9fi3LlzmDVrFqZPn47t27er2sycORNbt27Fxo0bERUVhYKCAowYMQLl5eVSHBYRUYvn1MoYfm6WEALYGZMmdTnUwmnU3VjZ2dlQKBSIjIxEQEAAAMDT0xMTJkzA/PnzVe26d++OYcOG4cMPP0Rubi5sbW2xZs0aTJgwAQCQlpYGFxcX7N69G0FBQQ/cL+/GIiJqeGuOX8X8bXHwcpLjj+n+UpdDWqhZ3o2Vm5sLALCyslIt8/f3x44dO3D9+vXKB9OFhyMpKUkVYqKjo1FaWorAwEDVZxwdHeHp6YmjR4/WuB+lUom8vDy1FxERNazhXg7Q05Eh9nouLmUXSF0OtWAaE3aEEHjjjTfg7+8PT09P1fKlS5eiU6dOcHZ2hoGBAYYMGYLvvvsO/v6VfyVkZGTAwMAAlpaWatuzs7NDRkbNdwGEhoZCLperXi4uLo13YERELZSVqQEebVf5CIkdZ9mVRdLRmLAzbdo0xMTEYMOGDWrLly5diuPHj2PHjh2Ijo7Gl19+iddeew0HDhy47/aEELU+mG7evHnIzc1VvVJSUhrsOIiI6B8j796VteNMGjRo1AS1MHpSFwAA06dPx44dO3D48GE4OzurlhcVFeGdd97B1q1bMXz4cACAt7c3zpw5gy+++AKDBg2Cvb09SkpKkJOTo3Z1JysrC3369Klxf4aGhjA0NGzcgyIiIgzuZA8j/VhculGIuOt58HKWS10StUCSXtkRQmDatGnYsmULDh06BHd3d7X1paWlKC0thY6Oepm6urqoqKgAUDlYWV9fH2FhYar16enpiIuLqzXsEBFR0zAz1MOgjnYAgB1nOecOSUPSKzshISFYv349tm/fDnNzc9UYG7lcDmNjY1hYWKBfv36YO3cujI2N4erqisjISKxevRpfffWVqu3UqVMxe/ZsWFtbw8rKCnPmzIGXlxcGDRok5eERERGAkV0csTMmHTvOpuHtoR2hq1PzEAOixiLpree1jalZuXIlgoODAVQOQJ43bx7279+PW7duwdXVFS+99BJmzZql+nxxcTHmzp2L9evXo6ioCAMHDsR3331X54HHvPWciKjxKMvK4ffRAeQVl2HDi73Q+xFrqUsiLcGnntcDww4RUeN6e3MMNp5MwaQeLggd6y11OaQlmuU8O0REpJ2q7sraHZuBkrIKiauhloZhh4iIGl1Pd2sozA2RW1SKw0nZUpdDLQzDDhERNTpdHZnq4aDbOcEgNTGGHSIiahKj7nZlhSVkoFBZJnE11JLUO+yUl5cjMjISOTk5jVEPERFpKS8nOdxtTFFcWoED5zKlLodakHqHHV1dXQQFBeH27duNUA4REWkrmeyerqwz7MqipvNQ3VheXl64dOlSQ9dCRERabuTdsHM4KRu3CkskroZaiocKOx9//DHmzJmDnTt3Ij09HXl5eWovIiKimrRVmMHTyQJlFQK7Y9OlLodaiId6XMSQIUMAACNHjlSbBbnqSePl5eUNUx0REWmdkV0cEXc9DzvOpuGZXq5Sl0MtwEOFnfDw8Iaug4iIWojHuzgidM95/HX5FtJuF8GxlbHUJZGWe6iw069fv4aug4iIWggHuTF6uFnhxOVb+ONsGl7u94jUJZGWq3PYiYmJgaenJ3R0dBATE3Pftt7efO4JERHVbpSPE05cvoXtZxh2qPHVOez4+PggIyMDCoUCPj4+kMlkqOkZohyzQ0REDzLU0x7vb49DQnoeLmTlo63CXOqSSIvVOexcvnwZtra2qv8mIiJ6WJamBujX3hYHz2dhx5k0vBHoIXVJpMXqHHZcXV2r/XdCQgKuXbuGkpJ/5kqQyWRqbYmIiGoy0scRB89nYfvZNMwa3F7t7l6ihvRQA5QvXbqEMWPGIDY2Vq07q+oHld1YRET0IIM72cFYXxdXb97B2dRc+Li0krok0lIPNangjBkz4O7ujszMTJiYmCAuLg6HDx+Gr68vIiIiGrhEIiLSRiYGehjcyQ4AsIOPj6BG9FBh59ixY/jggw9ga2sLHR0d6Orqwt/fH6GhoXj99dcbukYiItJSVU9C/yMmDeUV1W96IWoIDxV2ysvLYWZmBgCwsbFBWlplInd1dUViYmLDVUdERFrt0Xa2aGWij+x8JY5fuil1OaSlHirseHp6quba6dmzJz777DP8+eef+OCDD9CmTZsGLZCIiLSXgZ4Ohno6AGBXFjWehwo77733HioqKgAAH330Ea5evYpHH30Uu3fvxtKlSxu0QCIi0m5VXVm749KhLOMNLtTwHupurKCgINV/t2nTBgkJCbh16xYsLS156yAREdVLDzcr2FsYISOvGBGJ2QjqbC91SaRlHurKTk2srKwYdIiIqN50dGQYeffqDruyqDE0WNghIiJ6WCO7VIadA+cyUaAsk7ga0jYMO0REJLnOjhZoY2sKZVkF9sdnSF0OaRmGHSIikpxMJsOoLk4AgO3syqIGxrBDREQaoWrcTtSFG7hZoJS4GtImDDtERKQR3G1M4e0sR3mFwO7YdKnLIS3CsENERBqjaqAyu7KoITHsEBGRxni8iyNkMuDU1Ryk3LojdTmkJRh2iIhIY9hZGKGXuzWAyoeDEjUEScNOaGgo/Pz8YG5uDoVCgdGjR1d7kKhMJqvx9fnnn6va9O/fv9r6iRMnNvXhEBFRAxjFCQapgUkadiIjIxESEoLjx48jLCwMZWVlCAwMRGFhoapNenq62mvFihWQyWR44okn1Lb14osvqrX74YcfmvpwiIioAQz1dIC+rgznM/KRmJEvdTmkBR7q2VgNZe/evWrvV65cCYVCgejoaAQEBAAA7O3Vn5Gyfft2DBgwoNrT1U1MTKq1JSKi5kduoo/+HgqEJWRix9nrmGvfQeqSqJnTqDE7ubm5ACqfs1WTzMxM7Nq1C1OnTq22bt26dbCxsUHnzp0xZ84c5OfzrwEiouaq6q6sHWfTIISQuBpq7iS9snMvIQTeeOMN+Pv7w9PTs8Y2q1atgrm5OcaOHau2/Omnn4a7uzvs7e0RFxeHefPm4ezZswgLC6txO0qlEkrlPxNW5eXlNdyBEBHRfzaoox1MDHSRcqsIp1Nuo1trS6lLomZMY8LOtGnTEBMTg6ioqFrbrFixAk8//TSMjIzUlr/44ouq//b09ES7du3g6+uLv//+G926dau2ndDQUCxatKjhiiciogZlbKCLoM722Hr6OnacSWPYof9EI7qxpk+fjh07diA8PBzOzs41tjly5AgSExPxwgsvPHB73bp1g76+PpKTk2tcP2/ePOTm5qpeKSkp/6l+IiJqeFWPj9gZk4ay8gqJq6HmTNIrO0IITJ8+HVu3bkVERATc3d1rbfvzzz+je/fu6NKlywO3Gx8fj9LSUjg4ONS43tDQEIaGhg9dNxERNT7/tjawNNHHjYISHL14EwHtbaUuiZopSa/shISEYO3atVi/fj3Mzc2RkZGBjIwMFBUVqbXLy8vDpk2baryqc/HiRXzwwQc4deoUrly5gt27d2PcuHHo2rUr+vbt21SHQkREDUxfVwfDvSv/aN3yd6rE1VBzJmnYWbZsGXJzc9G/f384ODioXr/++qtau40bN0IIgUmTJlXbhoGBAQ4ePIigoCB4eHjg9ddfR2BgIA4cOABdXd2mOhQiImoE47q7AAB2xaYjK69Y4mqouZIJ3tOHvLw8yOVy5ObmwsLCQupyiIjoHk8uO4pTV3MwbUBbzAnykLoc0iB1/f7WiAHKREREtXnh0crxnGtPXEVRSbnE1VBzxLBDREQabXAne7hYGeP2nVJsOc2xO1R/DDtERKTRdHVkeK5P5dWdn6Muo6KixY++oHpi2CEiIo033s8F5oZ6uJRdiIikLKnLoWaGYYeIiDSemaEeJvaovDPr56jLEldDzQ3DDhERNQtT+rhBV0eGPy/cREIan2lIdcewQ0REzYKzpQmGeNoDAFb8yas7VHcMO0RE1Gy84F85UHn7meucZJDqjGGHiIiaja6tLdHd1RKl5QJrjl+VuhxqJhh2iIioWZl69+rO2uNXUVzKSQbpwRh2iIioWQnsZAdnS2Pk3CnFlr+vS10ONQMMO0RE1Kzo6eoguI8bgMqBypxkkB6EYYeIiJqdCX4uMDPUw4WsAkQmZ0tdDmk4hh0iImp2zI30McHv7iSDR3gbOt0fww4RETVLwX3coCMDoi7cwPkMTjJItWPYISKiZsnFygRDPR0A8OoO3R/DDhERNVvPqyYZTEN2vlLiakhTMewQEVGz1d3VEl1bt0JJeQUnGaRaMewQEVGzVjXJ4DpOMki1YNghIqJmbUhnezi1MsbNwhJsO81JBqk6hh0iImrW9HR18FxfNwDAz1GXIQQnGSR1DDtERNTsjfdzgamBLpKzCnA4+YbU5ZCGYdghIqJmz8JIHxP8WgMAfjpySeJqSNMw7BARkVZ4rm/lJINHkm8gMSNf6nJIgzDsEBGRVnCxMkFQZ3sAwIooTjJI/2DYISIirVF1G/rWM9dxo4CTDFIlhh0iItIa3V0t0cWlFUrKKrCWkwzSXQw7RESkNWQyGV64e3VnzTFOMkiVGHaIiEirDPW0h6PcCDcLS7DjTJrU5ZAGYNghIiKtoqerg+C7kwz+FHWJkwwSww4REWmfCX6tYWKgi6TMAkRd4CSDLR3DDhERaR25sT7G+7oAAH46wtvQWzpJw05oaCj8/Pxgbm4OhUKB0aNHIzExUa2NTCar8fX555+r2iiVSkyfPh02NjYwNTXFyJEjkZqa2tSHQ0REGuS5vm6QyYDIpGwkZ3KSwZZM0rATGRmJkJAQHD9+HGFhYSgrK0NgYCAKCwtVbdLT09VeK1asgEwmwxNPPKFqM3PmTGzduhUbN25EVFQUCgoKMGLECJSXcxQ+EVFL5WptisBOdgCAFX/y6k5LJhMaNHIrOzsbCoUCkZGRCAgIqLHN6NGjkZ+fj4MHDwIAcnNzYWtrizVr1mDChAkAgLS0NLi4uGD37t0ICgp64H7z8vIgl8uRm5sLCwuLhjsgIiKS1MkrtzDu+2Mw0NPBsbcfg7WZodQlUQOq6/e3Ro3Zyc3NBQBYWVnVuD4zMxO7du3C1KlTVcuio6NRWlqKwMBA1TJHR0d4enri6NGjNW5HqVQiLy9P7UVERNrH19US3s5ylJRVYN2Ja/95e3dKyhB9NQdrjl3BmuNXUV6hMdcL6D70pC6gihACb7zxBvz9/eHp6Vljm1WrVsHc3Bxjx45VLcvIyICBgQEsLS3V2trZ2SEjI6PG7YSGhmLRokUNVzwREWkkmUyGqf7umLHxDFYfu4KXAtrASF+3Tp/NLSpFQloe4tNyEXc9F/FpebiYXYB7801eUSlCBrRtpOqpoWhM2Jk2bRpiYmIQFRVVa5sVK1bg6aefhpGR0QO3J4SATCarcd28efPwxhtvqN7n5eXBxcWl/kUTEZHGG+blgMV7ziM9txg7zqap7tK6140CJeLT8u6GmlzEXc/DtVt3atyejZkh3KxNcOpqDv4XlgT/tjbo4tKqkY+C/guNCDvTp0/Hjh07cPjwYTg7O9fY5siRI0hMTMSvv/6qttze3h4lJSXIyclRu7qTlZWFPn361LgtQ0NDGBqy35aIqCXQ19XBlD5uWLznPFZEXUbftjaIv3ulpirYZOQV1/hZp1bG8HSyQGdHOTydLODpKIfCwghCCEzbcBq7YtIx89cz2DndH6aGGvGVSjWQdICyEALTp0/H1q1bERERgXbt2tXaNjg4GHFxcTh16pTa8qoBymvXrsX48eMBVN7B5ezszAHKREQEAMi9U4peoQdRVMuzsmQywN3GtDLUOFrA00mOTg4WsDQ1uO82hyw5jPTcYkz0c8HiJ7wbq3yqRV2/vyWNoSEhIVi/fj22b98Oc3Nz1RgbuVwOY2NjVbu8vDxs2rQJX375ZbVtyOVyTJ06FbNnz4a1tTWsrKwwZ84ceHl5YdCgQU12LEREpLnkJvqY0scN30dehK6ODO0UZvB0kqPz3WDT0cECZvW8MiM30cdX433w1E/HsfFkCvp72GKIp0MjHQH9F5Je2altTM3KlSsRHBysev/jjz9i5syZSE9Ph1wur9a+uLgYc+fOxfr161FUVISBAwfiu+++q/M4HF7ZISLSfuUVAim37sBeblTnQcp18ene81gWcRGtTPSxd0YA7OUPHldKDaOu398aNc+OVBh2iIjoYZWUVeCJZUcRez0XfdtaY83zPaGjU/Mf89SwmuU8O0RERM2NgZ4Ovp7oA2N9Xfx54SZ+juJszZqGYYeIiOg/esTWDO8/3gkA8Nm+84i7nitxRXQvhh0iIqIGMNHPBYGd7FBaLjBj42kUlfD5jJqCYYeIiKgByGQyLH7CGwpzQ1zMLsTHuxOkLonuYtghIiJqIFamBvhyfBcAwNrj13AgIVPiighg2CEiImpQj7azxQv+7gCANzfHICu/5tmZqekw7BARETWwuUM80NHBArcKSzBnUwwq+HR0STHsEBERNTBDPV0snegDQz0dHE7KxqpjV6QuqUVj2CEiImoE7ezM8d7wjgCA0D3ncT4jT+KKWi6GHSIiokbyTC9XPNZBgZKyCszYcAbFtTyIlBoXww4REVEjkclk+OxJb9iYGSAxMx+f7j0vdUktEsMOERFRI7IxM8Tn4ypvR1/55xVEJGZJXFHLw7BDRETUyAZ4KBDcxw0AMGdTDG4WKKUtqIVh2CEiImoCbw/tgPZ2ZrhRoMRbm2MgBG9HbyoMO0RERE3ASF8XSyZ2hYGuDg6cy8K6E9ekLqnFYNghIiJqIh0dLPDW0A4AgI92JeBCVr7EFbUMDDtERERN6Lk+bni0nQ2KSyvw+oYzUJbxdvTGxrBDRETUhHR0ZPhyXBdYmugjIT0PX+5PkrokrcewQ0RE1MQUFkb49AlvAMCPhy8hKvmGxBVpN4YdIiIiCQR2tsdTPVsDAGZvOoP84lKJK9JeDDtEREQSeW94R7hZmyAzT4mvDyRLXY7WYtghIiKSiImBHhaO7AwA+OXoFT4stJEw7BAREUmov4cCQzrbo7xC4P1t8ZxssBEw7BAREUns/cc7wVhfF39duYWtp69LXY7WYdghIiKSmGMrY7w+sB0A4JPd55BbxMHKDYlhh4iISANM9XfHI7amuFFQgq/2J0pdjlZh2CEiItIABno6+GCUJwBgzfGriLueK3FF2oNhh4iISEP0bWuDEd4OqBDA/O1xqKjgYOWGwLBDRESkQd4b3gmmBro4fe02fo9OlbocrcCwQ0REpEHs5UaYNbg9AGDx3vO4fadE4oqaP4YdIiIiDTOljxva25nhVmEJPtvHwcr/FcMOERGRhtHX1cGHdwcrb/jrGs6m3Ja2oGZO0rATGhoKPz8/mJubQ6FQYPTo0UhMrJ5gz507h5EjR0Iul8Pc3By9evXCtWvXVOv79+8PmUym9po4cWJTHgoREVGD6tnGGmO6OkHcHaxczsHKD03SsBMZGYmQkBAcP34cYWFhKCsrQ2BgIAoLC1VtLl68CH9/f3To0AERERE4e/Ys5s+fDyMjI7Vtvfjii0hPT1e9fvjhh6Y+HCIiogY1b1gHmBvqISY1FxtPXnvwB6hGMqFBD+HIzs6GQqFAZGQkAgICAAATJ06Evr4+1qxZU+vn+vfvDx8fH3z99dcPtd+8vDzI5XLk5ubCwsLiobZBRETUGFb+eRmL/kiA3Fgfh2b3g7WZodQlaYy6fn9r1Jid3NzKCZSsrKwAABUVFdi1axfat2+PoKAgKBQK9OzZE9u2bav22XXr1sHGxgadO3fGnDlzkJ+fX+t+lEol8vLy1F5ERESa6NlerujkYIHcolJ8tpeDlR+GxoQdIQTeeOMN+Pv7w9OzclBWVlYWCgoKsHjxYgwZMgT79+/HmDFjMHbsWERGRqo++/TTT2PDhg2IiIjA/PnzsXnzZowdO7bWfYWGhkIul6teLi4ujX58RERED0NPVwcfju4MAPj1VAqir+ZIXFHzozHdWCEhIdi1axeioqLg7OwMAEhLS4OTkxMmTZqE9evXq9qOHDkSpqam2LBhQ43bio6Ohq+vL6Kjo9GtW7dq65VKJZRKpep9Xl4eXFxc2I1FREQaa+6ms9gUnYpODhbYMa0v9HQ15nqFZJpVN9b06dOxY8cOhIeHq4IOANjY2EBPTw+dOnVSa9+xY0e1u7H+rVu3btDX10dycnKN6w0NDWFhYaH2IiIi0mRvD+0ACyM9JKTnYd0JDlauD0nDjhAC06ZNw5YtW3Do0CG4u7urrTcwMICfn1+129GTkpLg6upa63bj4+NRWloKBweHRqmbiIioqVmbGWLukA4AgC/2JyI7X/mAT1AVPSl3HhISgvXr12P79u0wNzdHRkYGAEAul8PY2BgAMHfuXEyYMAEBAQEYMGAA9u7diz/++AMREREAKm9NX7duHYYNGwYbGxskJCRg9uzZ6Nq1K/r27SvVoRERETW4p3q0xqZTKYhJzUXonnP4aryP1CU1C5KO2ZHJZDUuX7lyJYKDg1XvV6xYgdDQUKSmpsLDwwOLFi3CqFGjAAApKSl45plnEBcXh4KCAri4uGD48OFYsGCB6q6uB+Gt50RE1FycTbmN0d/9CSGA317ujR7udfuu00Z1/f7WmAHKUmLYISKi5mTellhs+OsaPOzMsfN1f+i30MHKzWqAMhEREdXdm0EesDTRR2JmPlYdvSJ1ORqPYYeIiKiZsTQ1wFt3Byt/fSAZmXnFElek2Rh2iIiImqHxvi7o2roVCpRl+HjXOanL0WgMO0RERM2Qjo4MH47yhI4M2HE2DUcv3JC6JI3FsENERNRMeTrJ8Uyvynnn5m+PQ0lZhcQVaSaGHSIiomZsdqAHrE0NcDG7ECv+vCx1ORqJYYeIiKgZkxvrY96wjgCAJQeSkXa7SOKKNA/DDhERUTP3RDcn+LlZoqi0HO9ujUV5RYufQk8Nww4REVEzJ5PJ8OFoTxjo6iA8MRsLd8SDcwb/g2GHiIhIC3Swt8D/JvhAJgPWHL+KZZEXpS5JYzDsEBERaYnh3g6YP7wTAOCzvYnY8neqxBVpBoYdIiIiLfK8vzteCmgDAHjz9xgcSc6WuCLpMewQERFpmbeHdMDjXRxRViHwyppoxF3PlbokSTHsEBERaRkdHRm+GOeN3m2sUVhSjud+OYmUW3ekLksyDDtERERayFBPFz9M7o4O9ubIzldiysq/kFNYInVZkmDYISIi0lIWRvr45bkecJQb4VJ2IV5YfQrFpeVSl9XkGHaIiIi0mL3cCL883wMWRnqIvpqDGRtPt7hJBxl2iIiItFx7O3Msn+wLA10d7IvPxKI/Wtakgww7RERELUDPNtaqSQdXH7uK7yMvSV1Sk2HYISIiaiGGezvg/RGVkw5+uvd8i5l0kGGHiIioBXmub8ubdJBhh4iIqIV5e0gHjGxBkw4y7BAREbUwOjoyfN6CJh1k2CEiImqBWtKkgww7RERELVRLmXSQYYeIiKgFs5cbYZWWTzrIsENERNTCtbMzx09T/GCgp52TDjLsEBEREXq4W+FrLZ10kGGHiIiIAADDvNQnHdx2+rrEFTUMhh0iIiJSUZt0cHMM/r6WI3FF/x3DDhEREal5e0gHBHayQ0lZBV5aHY3rt4ukLuk/YdghIiIiNTo6Mvxvgg86OljgRoESL646hTslZVKX9dAkDTuhoaHw8/ODubk5FAoFRo8ejcTExGrtzp07h5EjR0Iul8Pc3By9evXCtWvXVOuVSiWmT58OGxsbmJqaYuTIkUhNbRkPNyMiImoMpoZ6+GmKL2zMDJCQnoc3fj2LimZ6S7qkYScyMhIhISE4fvw4wsLCUFZWhsDAQBQWFqraXLx4Ef7+/ujQoQMiIiJw9uxZzJ8/H0ZGRqo2M2fOxNatW7Fx40ZERUWhoKAAI0aMQHm59k2MRERE1FScWhnjh2d9YaCrg73xGfjfgSSpS3ooMqFBN9JnZ2dDoVAgMjISAQEBAICJEydCX18fa9asqfEzubm5sLW1xZo1azBhwgQAQFpaGlxcXLB7924EBQU9cL95eXmQy+XIzc2FhYVFwx0QERGRFtgcnYrZm84CAJZM9MEoHyeJK6pU1+9vjRqzk5tb+dRVKysrAEBFRQV27dqF9u3bIygoCAqFAj179sS2bdtUn4mOjkZpaSkCAwNVyxwdHeHp6YmjR4/WuB+lUom8vDy1FxEREdXsie7OeLlf5R1ac3+PwZmU29IWVE8aE3aEEHjjjTfg7+8PT09PAEBWVhYKCgqwePFiDBkyBPv378eYMWMwduxYREZGAgAyMjJgYGAAS0tLte3Z2dkhIyOjxn2FhoZCLperXi4uLo17cERERM3cm0EdMKijAiVlFXhx9Smk5zafO7Q0JuxMmzYNMTEx2LBhg2pZRUUFAGDUqFGYNWsWfHx88Pbbb2PEiBH4/vvv77s9IQRkMlmN6+bNm4fc3FzVKyUlpeEOhIiISAvp6sjw9cSuqqekv7j6FIpKmsfYWI0IO9OnT8eOHTsQHh4OZ2dn1XIbGxvo6emhU6dOau07duyouhvL3t4eJSUlyMlRn/QoKysLdnZ2Ne7P0NAQFhYWai8iIiK6PzNDPSyf7AsrUwPEXc/DnE3N4w4tScOOEALTpk3Dli1bcOjQIbi7u6utNzAwgJ+fX7Xb0ZOSkuDq6goA6N69O/T19REWFqZan56ejri4OPTp06fxD4KIiKgFcbEywQ/Pdoe+rgy7YtOx5GCy1CU9kJ6UOw8JCcH69euxfft2mJubq8bYyOVyGBsbAwDmzp2LCRMmICAgAAMGDMDevXvxxx9/ICIiQtV26tSpmD17NqytrWFlZYU5c+bAy8sLgwYNkurQiIiItJafmxU+HuOFN3+PwZKDyWhnZ4YR3o5Sl1UrSW89r21MzcqVKxEcHKx6v2LFCoSGhiI1NRUeHh5YtGgRRo0apVpfXFyMuXPnYv369SgqKsLAgQPx3Xff1XngMW89JyIiqr+PdyVg+ZHLMNTTwaZXesPbuVWT7r+u398aNc+OVBh2iIiI6q+8QuDF1adw6HwW7CwMsT3EH/Zyowd/sIE0y3l2iIiIqPnQ1ZFhyUQftFOYITNPiZfWnEJxqebdocWwQ0RERA/N3EgfP0/xg6WJPmJSczH39xhoWqcRww4RERH9J62tTbDsme7Q05Hhj7Np+ObQBalLUsOwQ0RERP9ZrzbW+Gh05RMQvgpLwp7YdIkr+gfDDhERETWIiT1a4/m+lXPmzfrtDOKu50pcUSWGHSIiImow7wzrgID2tigurXyGVlZesdQlMewQERFRw9HT1cH/PdUVj9iaIj23GC+tiZb8Di2GHSIiImpQFnfv0Gploo8zKbfx1mZp79Bi2CEiIqIG52Zjiu+e7gY9HRm2n0nDssiLktXCsENERESNos8jNlg0qjNszAzRq421ZHVI+iBQIiIi0m5P93TFCC9HyE30JauBV3aIiIioUUkZdACGHSIiItJyDDtERESk1Rh2iIiISKsx7BAREZFWY9ghIiIircawQ0RERFqNYYeIiIi0GsMOERERaTWGHSIiItJqDDtERESk1Rh2iIiISKsx7BAREZFWY9ghIiIiraYndQGaQAgBAMjLy5O4EiIiIqqrqu/tqu/x2jDsAMjPzwcAuLi4SFwJERER1Vd+fj7kcnmt62XiQXGoBaioqEBaWhrMzc0hk8mkLqfR5eXlwcXFBSkpKbCwsJC6HI3Gc1V3PFd1x3NVPzxfddfSzpUQAvn5+XB0dISOTu0jc3hlB4COjg6cnZ2lLqPJWVhYtIj/GRoCz1Xd8VzVHc9V/fB81V1LOlf3u6JThQOUiYiISKsx7BAREZFWY9hpgQwNDbFgwQIYGhpKXYrG47mqO56ruuO5qh+er7rjuaoZBygTERGRVuOVHSIiItJqDDtERESk1Rh2iIiISKsx7BAREZFWY9jRUqGhofDz84O5uTkUCgVGjx6NxMREtTZCCCxcuBCOjo4wNjZG//79ER8fL1HF0lm2bBm8vb1Vk3D17t0be/bsUa3neapdaGgoZDIZZs6cqVrG8/WPhQsXQiaTqb3s7e1V63mu1F2/fh3PPPMMrK2tYWJiAh8fH0RHR6vW83xVcnNzq/ZzJZPJEBISAoDnqSYMO1oqMjISISEhOH78OMLCwlBWVobAwEAUFhaq2nz22Wf46quv8H//9384efIk7O3tMXjwYNWzwloKZ2dnLF68GKdOncKpU6fw2GOPYdSoUapfDjxPNTt58iR+/PFHeHt7qy3n+VLXuXNnpKenq16xsbGqdTxX/8jJyUHfvn2hr6+PPXv2ICEhAV9++SVatWqlasPzVenkyZNqP1NhYWEAgHHjxgHgeaqRoBYhKytLABCRkZFCCCEqKiqEvb29WLx4sapNcXGxkMvl4vvvv5eqTI1haWkpfvrpJ56nWuTn54t27dqJsLAw0a9fPzFjxgwhBH+u/m3BggWiS5cuNa7juVL31ltvCX9//1rX83zVbsaMGeKRRx4RFRUVPE+14JWdFiI3NxcAYGVlBQC4fPkyMjIyEBgYqGpjaGiIfv364ejRo5LUqAnKy8uxceNGFBYWonfv3jxPtQgJCcHw4cMxaNAgteU8X9UlJyfD0dER7u7umDhxIi5dugSA5+rfduzYAV9fX4wbNw4KhQJdu3bF8uXLVet5vmpWUlKCtWvX4vnnn4dMJuN5qgXDTgsghMAbb7wBf39/eHp6AgAyMjIAAHZ2dmpt7ezsVOtaktjYWJiZmcHQ0BCvvPIKtm7dik6dOvE81WDjxo34+++/ERoaWm0dz5e6nj17YvXq1di3bx+WL1+OjIwM9OnTBzdv3uS5+pdLly5h2bJlaNeuHfbt24dXXnkFr7/+OlavXg2AP1u12bZtG27fvo3g4GAAPE+14VPPW4Bp06YhJiYGUVFR1dbJZDK190KIastaAg8PD5w5cwa3b9/G5s2bMWXKFERGRqrW8zxVSklJwYwZM7B//34YGRnV2o7nq9LQoUNV/+3l5YXevXvjkUcewapVq9CrVy8APFdVKioq4Ovri08++QQA0LVrV8THx2PZsmWYPHmyqh3Pl7qff/4ZQ4cOhaOjo9pynid1vLKj5aZPn44dO3YgPDwczs7OquVVd4T8O+lnZWVV+4ugJTAwMEDbtm3h6+uL0NBQdOnSBUuWLOF5+pfo6GhkZWWhe/fu0NPTg56eHiIjI7F06VLo6empzgnPV81MTU3h5eWF5ORk/mz9i4ODAzp16qS2rGPHjrh27RoA/s6qydWrV3HgwAG88MILqmU8TzVj2NFSQghMmzYNW7ZswaFDh+Du7q623t3dHfb29qpR/EBl329kZCT69OnT1OVqHCEElEolz9O/DBw4ELGxsThz5ozq5evri6effhpnzpxBmzZteL7uQ6lU4ty5c3BwcODP1r/07du32vQYSUlJcHV1BcDfWTVZuXIlFAoFhg8frlrG81QLyYZGU6N69dVXhVwuFxERESI9PV31unPnjqrN4sWLhVwuF1u2bBGxsbFi0qRJwsHBQeTl5UlYedObN2+eOHz4sLh8+bKIiYkR77zzjtDR0RH79+8XQvA8Pci9d2MJwfN1r9mzZ4uIiAhx6dIlcfz4cTFixAhhbm4urly5IoTgubrXX3/9JfT09MTHH38skpOTxbp164SJiYlYu3atqg3P1z/Ky8tF69atxVtvvVVtHc9TdQw7WgpAja+VK1eq2lRUVIgFCxYIe3t7YWhoKAICAkRsbKx0RUvk+eefF66ursLAwEDY2tqKgQMHqoKOEDxPD/LvsMPz9Y8JEyYIBwcHoa+vLxwdHcXYsWNFfHy8aj3Plbo//vhDeHp6CkNDQ9GhQwfx448/qq3n+frHvn37BACRmJhYbR3PU3UyIYSQ8MISERERUaPimB0iIiLSagw7REREpNUYdoiIiEirMewQERGRVmPYISIiIq3GsENERERajWGHiIiItBrDDlEL4Obmhq+//rpRtt2/f3/MnDnzP21DJpNBJpOhVatW9223cOFC+Pj4/Kd9UXVXrlyBTCbDmTNnam0TERGh+ncaPXp0k9VG1BAYdog0SHBw8H/6Ivnll19qDAwnT57ESy+9pHovk8mwbdu2h95PY1i5ciWSkpKkLkPrPezPWJ8+fZCeno7x48c3fFFEjUxP6gKIqPHZ2tpKXcIDtWrVCgqFQuoyIIRAeXk59PS069djeXk5ZDLZQ3/ewMAA9vb2MDY2hlKpbMDKiBofr+wQNSNfffUVvLy8YGpqChcXF7z22msoKCgAUNnN8NxzzyE3N1fV3bBw4UIA6t1Ybm5uAIAxY8ZAJpOp3tf0F//MmTPRv39/1fvCwkJMnjwZZmZmcHBwwJdfflmtxpKSErz55ptwcnKCqakpevbsiYiIiIc63sWLF8POzg7m5uaYOnUqiouLq7VZuXIlOnbsCCMjI3To0AHfffed2vqjR4/Cx8cHRkZG8PX1xbZt29S6bKq6Z/bt2wdfX18YGhriyJEjEELgs88+Q5s2bWBsbIwuXbrg999/V9t2QkIChg0bBjMzM9jZ2eHZZ5/FjRs3VOt///13eHl5wdjYGNbW1hg0aBAKCwsfeNxV/xZffPEFHBwcYG1tjZCQEJSWlqra5OTkYPLkybC0tISJiQmGDh2K5ORk1fqqq3w7d+5Ep06dYGhoiOeeew6rVq3C9u3bVT8j9/7bXLp0CQMGDICJiQm6dOmCY8eOPbBWouaAYYeoGdHR0cHSpUsRFxeHVatW4dChQ3jzzTcBVHYzfP3117CwsEB6ejrS09MxZ86cats4efIkgMqQkJ6ernpfF3PnzkV4eDi2bt2K/fv3IyIiAtHR0WptnnvuOfz555/YuHEjYmJiMG7cOAwZMkTti7gufvvtNyxYsAAff/wxTp06BQcHh2pBZvny5Xj33Xfx8ccf49y5c/jkk08wf/58rFq1CgCQn5+Pxx9/HF5eXvj777/x4Ycf4q233qpxf2+++SZCQ0Nx7tw5eHt747333sPKlSuxbNkyxMfHY9asWXjmmWcQGRkJAEhPT0e/fv3g4+ODU6dOYe/evcjMzFR186Snp2PSpEl4/vnnce7cOURERGDs2LGo6+MIw8PDcfHiRYSHh2PVqlX45Zdf8Msvv6jWBwcH49SpU9ixYweOHTsGIQSGDRumFoju3LmD0NBQ/PTTT4iPj8fSpUsxfvx4DBkyRPUz0qdPH1X7d999F3PmzMGZM2fQvn17TJo0CWVlZXWql0ijSfkUUiJSN2XKFDFq1Kg6t//tt9+EtbW16v3KlSuFXC6v1s7V1VX873//U70HILZu3frAfc+YMUP069dPCCFEfn6+MDAwEBs3blStv3nzpjA2NlY99fzChQtCJpOJ69evq21n4MCBYt68ebUeR0319O7dW7zyyitqy3r27Cm6dOmieu/i4iLWr1+v1ubDDz8UvXv3FkIIsWzZMmFtbS2KiopU65cvXy4AiNOnTwshhAgPDxcAxLZt21RtCgoKhJGRkTh69KjatqdOnSomTZokhBBi/vz5IjAwUG19SkqK6knU0dHRAoC4cuVKrcddmylTpghXV1dRVlamWjZu3DgxYcIEIYQQSUlJAoD4888/Vetv3LghjI2NxW+//SaEqPxZACDOnDlTbdv//ne+fPmyACB++ukn1bL4+HgBQJw7d+6BnyfSdNrVKU2k5cLDw/HJJ58gISEBeXl5KCsrQ3FxMQoLC2Fqatqo+7548SJKSkrQu3dv1TIrKyt4eHio3v/9998QQqB9+/Zqn1UqlbC2tq7X/s6dO4dXXnlFbVnv3r0RHh4OAMjOzkZKSgqmTp2KF198UdWmrKwMcrkcAJCYmAhvb28YGRmp1vfo0aPG/fn6+qr+OyEhAcXFxRg8eLBam5KSEnTt2hUAEB0djfDwcJiZmVXb1sWLFxEYGIiBAwfCy8sLQUFBCAwMxJNPPglLS8s6HX/nzp2hq6ureu/g4IDY2FgAledGT08PPXv2VK23traGh4cHzp07p1pmYGAAb2/vOu0PgFpbBwcHAEBWVhY6dOhQ520QaSKGHaJm4urVqxg2bBheeeUVfPjhh7CyskJUVBSmTp2q1nXxsHR0dKp1sdy73X+vq0lFRQV0dXURHR2t9kUNoMZQ8F9UVFQAqOzKuvdLH4Bq30KIaoNyazuOe8Ni1bZ37doFJycntXaGhoaqNo8//jg+/fTTattycHCArq4uwsLCcPToUezfvx/ffPMN3n33XZw4cQLu7u4PPD59fX219zKZTFVXbcfw7+M1Njau16Dke/dZ9bmqfRI1ZxyzQ9RMnDp1CmVlZfjyyy/Rq1cvtG/fHmlpaWptDAwMUF5e/sBt6evrV2tna2uL9PR0tWX3zrvStm1b6Ovr4/jx46plOTk5areLd+3aFeXl5cjKykLbtm3VXvb29vU5XHTs2FFtXwDU3tvZ2cHJyQmXLl2qtq+qMNGhQwfExMSo3T106tSpB+67akDvtWvXqm3bxcUFANCtWzfEx8fDzc2tWpuq4CSTydC3b18sWrQIp0+fhoGBAbZu3Vqv81BbfWVlZThx4oRq2c2bN5GUlISOHTve97N1/Rkh0iYMO0QaJjc3F2fOnFF7Xbt2DY888gjKysrwzTff4NKlS1izZg2+//57tc+6ubmhoKAABw8exI0bN3Dnzp0a9+Hm5oaDBw8iIyMDOTk5AIDHHnsMp06dwurVq5GcnIwFCxYgLi5O9RkzMzNMnToVc+fOxcGDBxEXF4fg4GDo6Pzza6R9+/Z4+umnMXnyZGzZsgWXL1/GyZMn8emnn2L37t31Og8zZszAihUrsGLFCiQlJWHBggWIj49Xa7Nw4UKEhoZiyZIlSEpKQmxsLFauXImvvvoKAPDUU0+hoqICL730Es6dO4d9+/bhiy++AID7XvEwNzfHnDlzMGvWLKxatQoXL17E6dOn8e2336oGP4eEhODWrVuYNGkS/vrrL1y6dAn79+/H888/j/Lycpw4cQKffPIJTp06hWvXrmHLli3Izs5+YBipi3bt2mHUqFF48cUXERUVhbNnz+KZZ56Bk5MTRo0add/Purm5ISYmBomJibhx40aDXBUk0ngSjhcion+ZMmWKAFDtNWXKFCGEEF999ZVwcHAQxsbGIigoSKxevVoAEDk5OaptvPLKK8La2loAEAsWLBBCVB+gvGPHDtG2bVuhp6cnXF1dVcvff/99YWdnJ+RyuZg1a5aYNm2aaoCyEJWDlJ955hlhYmIi7OzsxGeffSb69eunGqAshBAlJSXi/fffF25ubkJfX1/Y29uLMWPGiJiYmFqPGzUMUBZCiI8//ljY2NgIMzMzMWXKFPHmm2+qDVAWQoh169YJHx8fYWBgICwtLUVAQIDYsmWLav2ff/4pvL29hYGBgejevbtYv369ACDOnz8vhPhngPK951AIISoqKsSSJUuEh4eH0NfXF7a2tiIoKEhERkaq2iQlJYkxY8aIVq1aCWNjY9GhQwcxc+ZMUVFRIRISEkRQUJCwtbUVhoaGon379uKbb76p9Rzc60GDxYUQ4tatW+LZZ58Vcrlc9fOQlJSkWl/bYPWsrCwxePBgYWZmJgCI8PBw1QDlqkHbQgiRk5OjWv+g2og0nUyIOt4HSUTUSGQyGbZu3dokjyFYt26daj4iY2PjRt+ftgkODsbt27c1bgZuovthNxYRaYRJkybB2dm5wbe7evVqREVF4fLly9i2bRveeustjB8/nkGnno4cOQIzMzOsW7dO6lKI6o13YxGR5KomHPz3HVwNISMjA++//z4yMjLg4OCAcePG4eOPP27w/dTH/e5M27NnDx599NEmrKZufH19VQPWG/rOOqLGxm4sIqImduHChVrXOTk58aoTUQNj2CEiIiKtxjE7REREpNUYdoiIiEirMewQERGRVmPYISIiIq3GsENERERajWGHiIiItBrDDhEREWk1hh0iIiLSav8PQLjlU8AF6RsAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.air.sel(lon=220.0,method='nearest').mean(dim='time').plot()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "del ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A More Exciting Xarray Example\n", "\n", "Let's look at a more interesting and realistic example:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "ds = xr.open_dataset('TPOSE6_Daily_2012_surface.nc') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What about something harder? Gradients? Integrals? ... Enter `xgcm`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate Divergence with xgcm\n", "\n", "[xgcm](https://xgcm.readthedocs.io/en/latest/index.html) is a Python package that is built around Xarray. With it you create a `Grid` object which can then be used to perform more complicated mathematical operations, while remaining aware of complex grid geometries. The `Grid` is aware of the different coordinates and their relationships to each other in space. This can be really useful for model output or observations where not all variables are on the same points in space. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Y Axis (periodic, boundary=None):\n", " * center YC --> outer\n", " * outer YG --> center\n", "X Axis (periodic, boundary=None):\n", " * center XC --> outer\n", " * outer XG --> center\n", "Z Axis (not periodic, boundary=None):\n", " * center Z\n", "T Axis (not periodic, boundary=None):\n", " * center time" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xgcm \n", "\n", "# create the grid object from our dataset\n", "grid = xgcm.Grid(ds, periodic=['X','Y'])\n", "grid" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'UVEL' (time: 366, Z: 1, YC: 84, XG: 241)>\n",
       "[7409304 values with dtype=float32]\n",
       "Coordinates: (12/13)\n",
       "    iter     (time) int64 ...\n",
       "  * time     (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n",
       "  * YC       (YC) float64 -3.917 -3.75 -3.583 -3.417 ... 9.417 9.583 9.75 9.917\n",
       "  * XG       (XG) float64 210.0 210.2 210.3 210.5 ... 249.5 249.7 249.8 250.0\n",
       "  * Z        (Z) float64 -1.0\n",
       "    dyG      (YC, XG) float32 ...\n",
       "    ...       ...\n",
       "    rAw      (YC, XG) float32 ...\n",
       "    drF      (Z) float32 ...\n",
       "    PHrefC   (Z) float32 ...\n",
       "    hFacW    (Z, YC, XG) float32 ...\n",
       "    maskW    (Z, YC, XG) bool ...\n",
       "    rhoRef   (Z) float32 ...\n",
       "Attributes:\n",
       "    standard_name:  UVEL\n",
       "    long_name:      Zonal Component of Velocity (m/s)\n",
       "    units:          m/s\n",
       "    mate:           VVEL
" ], "text/plain": [ "\n", "[7409304 values with dtype=float32]\n", "Coordinates: (12/13)\n", " iter (time) int64 ...\n", " * time (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n", " * YC (YC) float64 -3.917 -3.75 -3.583 -3.417 ... 9.417 9.583 9.75 9.917\n", " * XG (XG) float64 210.0 210.2 210.3 210.5 ... 249.5 249.7 249.8 250.0\n", " * Z (Z) float64 -1.0\n", " dyG (YC, XG) float32 ...\n", " ... ...\n", " rAw (YC, XG) float32 ...\n", " drF (Z) float32 ...\n", " PHrefC (Z) float32 ...\n", " hFacW (Z, YC, XG) float32 ...\n", " maskW (Z, YC, XG) bool ...\n", " rhoRef (Z) float32 ...\n", "Attributes:\n", " standard_name: UVEL\n", " long_name: Zonal Component of Velocity (m/s)\n", " units: m/s\n", " mate: VVEL" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# inspect some of our variables \n", "ds.UVEL # zonal velocity" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'VVEL' (time: 366, Z: 1, YG: 85, XC: 240)>\n",
       "[7466400 values with dtype=float32]\n",
       "Coordinates: (12/13)\n",
       "    iter     (time) int64 ...\n",
       "  * time     (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n",
       "  * Z        (Z) float64 -1.0\n",
       "    drF      (Z) float32 ...\n",
       "    PHrefC   (Z) float32 ...\n",
       "    rhoRef   (Z) float32 ...\n",
       "    ...       ...\n",
       "  * YG       (YG) float64 -4.0 -3.833 -3.667 -3.5 ... 9.5 9.667 9.833 10.0\n",
       "    dxG      (YG, XC) float32 ...\n",
       "    dyC      (YG, XC) float32 ...\n",
       "    rAs      (YG, XC) float32 ...\n",
       "    hFacS    (Z, YG, XC) float32 ...\n",
       "    maskS    (Z, YG, XC) bool ...\n",
       "Attributes:\n",
       "    standard_name:  VVEL\n",
       "    long_name:      Meridional Component of Velocity (m/s)\n",
       "    units:          m/s\n",
       "    mate:           UVEL
" ], "text/plain": [ "\n", "[7466400 values with dtype=float32]\n", "Coordinates: (12/13)\n", " iter (time) int64 ...\n", " * time (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n", " * Z (Z) float64 -1.0\n", " drF (Z) float32 ...\n", " PHrefC (Z) float32 ...\n", " rhoRef (Z) float32 ...\n", " ... ...\n", " * YG (YG) float64 -4.0 -3.833 -3.667 -3.5 ... 9.5 9.667 9.833 10.0\n", " dxG (YG, XC) float32 ...\n", " dyC (YG, XC) float32 ...\n", " rAs (YG, XC) float32 ...\n", " hFacS (Z, YG, XC) float32 ...\n", " maskS (Z, YG, XC) bool ...\n", "Attributes:\n", " standard_name: VVEL\n", " long_name: Meridional Component of Velocity (m/s)\n", " units: m/s\n", " mate: UVEL" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.VVEL # meridional velocity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that zonal and meridional velocity are computed on different grid points (XG, YC) v (XC, YG). This is not uncommon, but it means we can't just add/subtract/multiply the velocities because they are not located at the same points in space. How would we handle this without xgcm?" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# interpolate zonal velocity to XC " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would have to do this for meridional velocity too." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# interpolate meridional velocity to YC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will have to decide how to handle the boundaries and implement that as well, otherwise these new variables will have different sizes... if we switch to a model or data format where the names of coordinates are slightly different, we will have to rewrite all of our code. Instead we can use our Grid object. If, in the future, the naming/structure of the grid changes then we will update the grid to reflect the correct relationships." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('time', 'Z', 'YC', 'XC')" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "('time', 'Z', 'YC', 'XC')" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# interpolate U along the X axis: this will interpolate XG to the other X cordinate XC\n", "U = grid.interp(ds.UVEL,'X')\n", "# interpolate V along the Y axis: this will interpolate YG to the other Y cordinate YC\n", "V = grid.interp(ds.VVEL,'Y')\n", "\n", "display(U.dims)\n", "display(V.dims)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even better, the Grid object will automatically handle the interpolation for us. Further reducing the complexity of the code and the opportunities for making mistakes." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('time', 'Z', 'YC', 'XG')" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "('time', 'Z', 'YG', 'XC')" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "('time', 'Z', 'YC', 'XC')" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's say we don't do the interpolations, we compute the quantities of interest directly on their original coordinates.\n", "u_transport = ds.UVEL * ds.dyG * ds.hFacW * ds.drF\n", "v_transport = ds.VVEL * ds.dxG * ds.hFacS * ds.drF\n", "display(u_transport.dims)\n", "display(v_transport.dims)\n", "\n", "# The Grid object will automatically handle the coordinate transformations for us!\n", "div_uv = (grid.diff(u_transport, 'X') + grid.diff(v_transport, 'Y')) / ds.rA # calculate the divergence of the flow\n", "display(div_uv.dims)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import cmocean.cm as cmo\n", "\n", "# plot divergence at the surface\n", "div_uv.isel(time=0,Z=0).plot(cmap=cmo.balance, robust=True, \n", " cbar_kwargs={'label': 'Divergence (1/s)'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell above shows that we can take the divergence of the flow, while accounting for the grid geometry, in only 3 lines of code! This framework is robust to making mistakes due to coding errors or misunderstanding of the grid itself. For example:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# multiply ds.VVEL by ds.dxC instead of ds.dxG and see what happens\n", "v_transport = ds.VVEL * ds.dxC * ds.hFacS * ds.drF\n", "print(v_transport)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Xarray will not blindly combine variables of the same size when it knows they are not on the same points in space. The result here is a nonsensical thing with two X dimensions, and you will fail to analyze/plot the results because of this, forcing you to see your error. That being said, nothing is fool-proof. When we use Xarray and xgcm we cannot prevent mistakes entirely. Instead we aim to spend more time on errors such as \"our question is not formulated properly\" rather than \"the third dimension of my data isn't the size I expected it to be, which of my indices was wrong?\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EOF decomposition (a.k.a. PCA) with xeofs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Xarray automatically sped up with Dask\n", "\n", "Let's load a larger dataset (~2.5GB). Here is where Xarray really shines! The data can be easily `chunked` into Dask arrays. If we spin up a Dask client with the 3 lines of code we learned earlier, our Xarray data analysis will be parallelized over the chunks. We can see the size of the chunks through inspection of the DataSet." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Without Dask" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Y Axis (periodic, boundary=None):\n", " * center YC --> outer\n", " * outer YG --> center\n", "Z Axis (not periodic, boundary=None):\n", " * center Z\n", "T Axis (not periodic, boundary=None):\n", " * center time\n", "X Axis (periodic, boundary=None):\n", " * center XC --> outer\n", " * outer XG --> center" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import xarray as xr\n", "import xgcm\n", "\n", "ds = xr.open_dataset('TPOSE6_Daily_2012.nc')\n", "grid = xgcm.Grid(ds, periodic=['X','Y'])\n", "grid" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 321 ms, sys: 585 ms, total: 907 ms\n", "Wall time: 926 ms\n" ] }, { "data": { "text/html": [ "
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<xarray.DataArray (time: 366, Z: 22, YC: 84, XC: 240)>\n",
       "array([[[[ 1.37244683e-06,  1.43047191e-06,  1.46951675e-06, ...,\n",
       "           6.62662956e-07,  1.10988776e-06,  1.41652595e-06],\n",
       "         [ 5.94653841e-07,  6.68495829e-07,  7.41130521e-07, ...,\n",
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       "         [-3.75415624e-07, -3.15581644e-07, -2.50060850e-07, ...,\n",
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       "         ...,\n",
       "         [ 8.62729678e-07,  7.14634893e-07,  3.88255700e-07, ...,\n",
       "          -3.11771328e-06, -2.89566060e-06, -2.37975678e-06],\n",
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       "         [-7.58210774e-07, -6.46966441e-07, -7.30312934e-07, ...,\n",
       "          -2.86520299e-06, -2.44957505e-06, -2.19216781e-06]],\n",
       "\n",
       "        [[ 1.34057188e-06,  1.40016880e-06,  1.44013302e-06, ...,\n",
       "           7.09427923e-07,  1.17261857e-06,  1.48308425e-06],\n",
       "         [ 5.64541381e-07,  6.34911260e-07,  7.06026526e-07, ...,\n",
       "           1.25195004e-06,  1.92384437e-06,  2.14392071e-06],\n",
       "         [-4.11960059e-07, -3.54990362e-07, -2.90499941e-07, ...,\n",
       "           1.67858138e-06,  1.79416759e-06,  2.04223534e-06],\n",
       "...\n",
       "          -8.71693283e-06, -5.95823076e-06, -6.15254976e-06],\n",
       "         [-6.71484213e-06, -4.20082051e-06, -2.26667885e-06, ...,\n",
       "           3.52229357e-09,  1.22546305e-06, -1.09015275e-06],\n",
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       "           9.23616972e-06,  7.55049496e-06,  6.20488800e-06]],\n",
       "\n",
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       "         [-5.18033232e-07, -3.21178746e-08,  5.64866127e-07, ...,\n",
       "           2.97049564e-06,  3.35908271e-06,  3.85124167e-06],\n",
       "         [ 2.11237398e-06,  2.65563722e-06,  3.24382063e-06, ...,\n",
       "           9.82252459e-07,  1.40929524e-06,  8.60606008e-07],\n",
       "         ...,\n",
       "         [-4.70574696e-06, -3.26171971e-06, -2.07992093e-06, ...,\n",
       "          -8.10987694e-06, -5.83119663e-06, -5.26532494e-06],\n",
       "         [-5.91146409e-06, -3.32312925e-06, -2.49081700e-06, ...,\n",
       "          -2.19475260e-06, -7.33080412e-07, -2.52351583e-06],\n",
       "         [-2.57763872e-06, -3.84479017e-06, -2.77097206e-06, ...,\n",
       "           6.42505483e-06,  4.72095462e-06,  3.74518891e-06]]]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n",
       "  * YC       (YC) float64 -3.917 -3.75 -3.583 -3.417 ... 9.417 9.583 9.75 9.917\n",
       "  * Z        (Z) float64 -1.0 -3.0 -5.0 -7.0 -9.0 ... -68.5 -76.0 -85.0 -95.0\n",
       "  * XC       (XC) float64 210.1 210.2 210.4 210.6 ... 249.4 249.6 249.8 249.9\n",
       "    rA       (YC, XC) float32 3.425e+08 3.425e+08 ... 3.382e+08 3.382e+08\n",
       "    Depth    (YC, XC) float32 4.65e+03 4.491e+03 ... 3.44e+03 3.665e+03\n",
       "    dxF      (YC, XC) float32 1.849e+04 1.849e+04 ... 1.825e+04 1.825e+04\n",
       "    dyF      (YC, XC) float32 1.853e+04 1.853e+04 ... 1.853e+04 1.853e+04
" ], "text/plain": [ "\n", "array([[[[ 1.37244683e-06, 1.43047191e-06, 1.46951675e-06, ...,\n", " 6.62662956e-07, 1.10988776e-06, 1.41652595e-06],\n", " [ 5.94653841e-07, 6.68495829e-07, 7.41130521e-07, ...,\n", " 1.22283927e-06, 1.87492367e-06, 2.07832477e-06],\n", " [-3.75415624e-07, -3.15581644e-07, -2.50060850e-07, ...,\n", " 1.70110923e-06, 1.76351773e-06, 1.99446072e-06],\n", " ...,\n", " [ 8.62729678e-07, 7.14634893e-07, 3.88255700e-07, ...,\n", " -3.11771328e-06, -2.89566060e-06, -2.37975678e-06],\n", " [ 8.57911502e-08, -4.61088696e-08, -3.47979068e-07, ...,\n", " -3.19846913e-06, -2.62534672e-06, -2.25218719e-06],\n", " [-7.58210774e-07, -6.46966441e-07, -7.30312934e-07, ...,\n", " -2.86520299e-06, -2.44957505e-06, -2.19216781e-06]],\n", "\n", " [[ 1.34057188e-06, 1.40016880e-06, 1.44013302e-06, ...,\n", " 7.09427923e-07, 1.17261857e-06, 1.48308425e-06],\n", " [ 5.64541381e-07, 6.34911260e-07, 7.06026526e-07, ...,\n", " 1.25195004e-06, 1.92384437e-06, 2.14392071e-06],\n", " [-4.11960059e-07, -3.54990362e-07, -2.90499941e-07, ...,\n", " 1.67858138e-06, 1.79416759e-06, 2.04223534e-06],\n", "...\n", " -8.71693283e-06, -5.95823076e-06, -6.15254976e-06],\n", " [-6.71484213e-06, -4.20082051e-06, -2.26667885e-06, ...,\n", " 3.52229357e-09, 1.22546305e-06, -1.09015275e-06],\n", " [-2.41673092e-06, -2.31488502e-06, -1.91019740e-06, ...,\n", " 9.23616972e-06, 7.55049496e-06, 6.20488800e-06]],\n", "\n", " [[-1.69971406e-06, -1.02415493e-06, -4.37987836e-07, ...,\n", " 7.77255536e-07, 9.61086997e-08, -7.09721562e-08],\n", " [-5.18033232e-07, -3.21178746e-08, 5.64866127e-07, ...,\n", " 2.97049564e-06, 3.35908271e-06, 3.85124167e-06],\n", " [ 2.11237398e-06, 2.65563722e-06, 3.24382063e-06, ...,\n", " 9.82252459e-07, 1.40929524e-06, 8.60606008e-07],\n", " ...,\n", " [-4.70574696e-06, -3.26171971e-06, -2.07992093e-06, ...,\n", " -8.10987694e-06, -5.83119663e-06, -5.26532494e-06],\n", " [-5.91146409e-06, -3.32312925e-06, -2.49081700e-06, ...,\n", " -2.19475260e-06, -7.33080412e-07, -2.52351583e-06],\n", " [-2.57763872e-06, -3.84479017e-06, -2.77097206e-06, ...,\n", " 6.42505483e-06, 4.72095462e-06, 3.74518891e-06]]]],\n", " dtype=float32)\n", "Coordinates:\n", " * time (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n", " * YC (YC) float64 -3.917 -3.75 -3.583 -3.417 ... 9.417 9.583 9.75 9.917\n", " * Z (Z) float64 -1.0 -3.0 -5.0 -7.0 -9.0 ... -68.5 -76.0 -85.0 -95.0\n", " * XC (XC) float64 210.1 210.2 210.4 210.6 ... 249.4 249.6 249.8 249.9\n", " rA (YC, XC) float32 3.425e+08 3.425e+08 ... 3.382e+08 3.382e+08\n", " Depth (YC, XC) float32 4.65e+03 4.491e+03 ... 3.44e+03 3.665e+03\n", " dxF (YC, XC) float32 1.849e+04 1.849e+04 ... 1.825e+04 1.825e+04\n", " dyF (YC, XC) float32 1.853e+04 1.853e+04 ... 1.853e+04 1.853e+04" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time \n", "\n", "# calculate the divergence of the flow at all depths\n", "u_transport = ds.UVEL * ds.dyG * ds.hFacW * ds.drF\n", "v_transport = ds.VVEL * ds.dxG * ds.hFacS * ds.drF\n", "\n", "div_uv = (grid.diff(u_transport, 'X') + grid.diff(v_transport, 'Y')) / ds.rA # calculate the divergence of the flow\n", "div_uv.compute()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "del ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### With Dask" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# spin up our Dask client\n", "from dask.distributed import LocalCluster, Client\n", "cluster = LocalCluster()\n", "client = Client(cluster)\n", "client" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Y Axis (periodic, boundary=None):\n", " * center YC --> outer\n", " * outer YG --> center\n", "Z Axis (not periodic, boundary=None):\n", " * center Z\n", "T Axis (not periodic, boundary=None):\n", " * center time\n", "X Axis (periodic, boundary=None):\n", " * center XC --> outer\n", " * outer XG --> center" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.open_dataset('TPOSE6_Daily_2012.nc').chunk({'time':1})\n", "grid = xgcm.Grid(ds, periodic=['X','Y'])\n", "grid" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'THETA' (time: 366, Z: 22, YC: 84, XC: 240)>\n",
       "dask.array<xarray-THETA, shape=(366, 22, 84, 240), dtype=float32, chunksize=(1, 22, 84, 240), chunktype=numpy.ndarray>\n",
       "Coordinates: (12/14)\n",
       "    iter     (time) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time     (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n",
       "  * YC       (YC) float64 -3.917 -3.75 -3.583 -3.417 ... 9.417 9.583 9.75 9.917\n",
       "  * Z        (Z) float64 -1.0 -3.0 -5.0 -7.0 -9.0 ... -68.5 -76.0 -85.0 -95.0\n",
       "    drF      (Z) float32 dask.array<chunksize=(22,), meta=np.ndarray>\n",
       "    PHrefC   (Z) float32 dask.array<chunksize=(22,), meta=np.ndarray>\n",
       "    ...       ...\n",
       "    rA       (YC, XC) float32 dask.array<chunksize=(84, 240), meta=np.ndarray>\n",
       "    Depth    (YC, XC) float32 dask.array<chunksize=(84, 240), meta=np.ndarray>\n",
       "    hFacC    (Z, YC, XC) float32 dask.array<chunksize=(22, 84, 240), meta=np.ndarray>\n",
       "    maskC    (Z, YC, XC) bool dask.array<chunksize=(22, 84, 240), meta=np.ndarray>\n",
       "    dxF      (YC, XC) float32 dask.array<chunksize=(84, 240), meta=np.ndarray>\n",
       "    dyF      (YC, XC) float32 dask.array<chunksize=(84, 240), meta=np.ndarray>\n",
       "Attributes:\n",
       "    standard_name:  THETA\n",
       "    long_name:      Potential Temperature\n",
       "    units:          degC
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates: (12/14)\n", " iter (time) int64 dask.array\n", " * time (time) timedelta64[ns] 00:01:12 00:02:24 ... 01:10:48 01:12:00\n", " * YC (YC) float64 -3.917 -3.75 -3.583 -3.417 ... 9.417 9.583 9.75 9.917\n", " * Z (Z) float64 -1.0 -3.0 -5.0 -7.0 -9.0 ... -68.5 -76.0 -85.0 -95.0\n", " drF (Z) float32 dask.array\n", " PHrefC (Z) float32 dask.array\n", " ... ...\n", " rA (YC, XC) float32 dask.array\n", " Depth (YC, XC) float32 dask.array\n", " hFacC (Z, YC, XC) float32 dask.array\n", " maskC (Z, YC, XC) bool dask.array\n", " dxF (YC, XC) float32 dask.array\n", " dyF (YC, XC) float32 dask.array\n", "Attributes:\n", " standard_name: THETA\n", " long_name: Potential Temperature\n", " units: degC" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.THETA" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time \n", "\n", "# calculate the divergence of the flow at all depths\n", "u_transport = ds.UVEL * ds.dyG * ds.hFacW * ds.drF\n", "v_transport = ds.VVEL * ds.dxG * ds.hFacS * ds.drF\n", "\n", "div_uv = (grid.diff(u_transport, 'X') + grid.diff(v_transport, 'Y')) / ds.rA # calculate the divergence of the flow\n", "div_uv.compute()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "client.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See [this page](https://docs.xarray.dev/en/stable/user-guide/dask.html#best-practices) for a more detailed discussion of best practices with Xarray and Dask." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE** Take a look at all of the cool [Xarray related projects](https://docs.xarray.dev/en/stable/user-guide/ecosystem.html)! Most of them are for geoscience applications. There is also an extensive set of [Xarray tutorials](https://tutorial.xarray.dev/overview/get-started.html)." ] } ], "metadata": { "kernelspec": { "display_name": "scicompute", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }