{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen\n", "\n", "In this lesson, we cover some more advanced aspects of xarray." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception reporting mode: Minimal\n" ] } ], "source": [ "import numpy as np\n", "import xarray as xr\n", "from matplotlib import pyplot as plt\n", "%xmode Minimal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolation\n", "\n", "In the previous lesson on Xarray, we learned how to select data based on its dimension coordinates and align data with dimension different coordinates.\n", "But what if we want to estimate the value of the data variables at _different coordinates_.\n", "This is where interpolation comes in." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (x: 11)>\n",
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10
" ], "text/plain": [ "\n", "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])\n", "Coordinates:\n", " * x (x) int64 0 1 2 3 4 5 6 7 8 9 10" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we write it out explicitly so we can see each point.\n", "x_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", "f = xr.DataArray(x_data**2, dims=['x'], coords={'x': x_data})\n", "f" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f.plot(marker='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We only have data on the integer points in x.\n", "But what if we wanted to estimate the value at, say, 4.5?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "4.5", "output_type": "error", "traceback": [ "\u001b[0;31mKeyError\u001b[0m\u001b[0;31m:\u001b[0m 4.5\n", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mKeyError\u001b[0m\u001b[0;31m:\u001b[0m 4.5\n" ] } ], "source": [ "f.sel(x=4.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpolation to the rescue!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray ()>\n",
       "array(20.5)\n",
       "Coordinates:\n",
       "    x        float64 4.5
" ], "text/plain": [ "\n", "array(20.5)\n", "Coordinates:\n", " x float64 4.5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.interp(x=4.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpolation uses [scipy.interpolate](https://docs.scipy.org/doc/scipy/reference/interpolate.html) under the hood.\n", "There are different modes of interpolation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(20.5)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# default\n", "f.interp(x=4.5, method='linear').values" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(16.)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.interp(x=4.5, method='nearest').values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(20.25)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.interp(x=4.5, method='cubic').values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can interpolate to a whole new coordinate at once:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_new = x_data + 0.5\n", "f_interp_linear = f.interp(x=x_new, method='linear')\n", "f_interp_cubic = f.interp(x=x_new, method='cubic')\n", "f.plot(marker='o', label='original')\n", "f_interp_linear.plot(marker='o', label='linear')\n", "f_interp_cubic.plot(marker='o', label='cubic')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that values outside of the original range are not supported:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.5, 2.5, 6.5, 12.5, 20.5, 30.5, 42.5, 56.5, 72.5, 90.5, nan])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_interp_linear.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note}\n", "You can apply interpolation to any dimension, and even to multiple dimensions at a time.\n", "(Multidimensional interpolation only supports `mode='nearest'` and `mode='linear'`.)\n", "But keep in mind that _Xarray has no built-in understanding of geography_.\n", "If you use `interp` on lat / lon coordinates, it will just perform naive interpolation of the lat / lon values.\n", "More sophisticated treatment of spherical geometry requires another package such as [xesmf](https://xesmf.readthedocs.io/).\n", "```" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Groupby\n", "\n", "Xarray copies Pandas' very useful groupby functionality, enabling the \"split / apply / combine\" workflow on xarray DataArrays and Datasets. In the first part of the lesson, we will learn to use groupby by analyzing sea-surface temperature data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we load a dataset. We will use the [NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5) product, a widely used and trusted gridded compilation of of historical data going back to 1854.\n", "\n", "Since the data is provided via an [OPeNDAP](https://en.wikipedia.org/wiki/OPeNDAP) server, we can load it directly without downloading anything:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time
" ], "text/plain": [ "\n", "Dimensions: (lat: 89, lon: 180, time: 708)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", "Data variables:\n", " sst (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n", "Attributes: (12/38)\n", " climatology: Climatology is based on 1971-2000 SST, X...\n", " description: In situ data: ICOADS2.5 before 2007 and ...\n", " keywords_vocabulary: NASA Global Change Master Directory (GCM...\n", " keywords: Earth Science > Oceans > Ocean Temperatu...\n", " instrument: Conventional thermometers\n", " source_comment: SSTs were observed by conventional therm...\n", " ... ...\n", " license: No constraints on data access or use\n", " comment: SSTs were observed by conventional therm...\n", " summary: ERSST.v5 is developed based on v4 after ...\n", " dataset_title: NOAA Extended Reconstructed SST V5\n", " data_modified: 2021-11-07\n", " DODS_EXTRA.Unlimited_Dimension: time" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'\n", "ds = xr.open_dataset(url, drop_variables=['time_bnds'])\n", "ds = ds.sel(time=slice('1960', '2018')).load()\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do some basic visualizations of the data, just to make sure it looks reasonable." ] }, { "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds.sst[0].plot(vmin=-2, vmax=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that xarray correctly parsed the time index, resulting in a Pandas datetime index on the time dimension." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'time' (time: 708)>\n",
       "array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',\n",
       "       '1960-03-01T00:00:00.000000000', ..., '2018-10-01T00:00:00.000000000',\n",
       "       '2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 80992.]\n",
       "    _ChunkSizes:      1
" ], "text/plain": [ "\n", "array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',\n", " '1960-03-01T00:00:00.000000000', ..., '2018-10-01T00:00:00.000000000',\n", " '2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000'],\n", " dtype='datetime64[ns]')\n", "Coordinates:\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", "Attributes:\n", " long_name: Time\n", " delta_t: 0000-01-00 00:00:00\n", " avg_period: 0000-01-00 00:00:00\n", " prev_avg_period: 0000-00-07 00:00:00\n", " standard_name: time\n", " axis: T\n", " actual_range: [19723. 80992.]\n", " _ChunkSizes: 1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.time" ] }, { "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds.sst.sel(lon=300, lat=50).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the plot, the timeseries at any one point is totally dominated by the seasonal cycle. We would like to remove this seasonal cycle (called the \"climatology\") in order to better see the long-term variaitions in temperature. We will accomplish this using **groupby**.\n", "\n", "The syntax of Xarray's groupby is almost identical to Pandas.\n", "We will first apply groupby to a single DataArray." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m\n", "\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mrestore_coord_dims\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Returns a GroupBy object for performing grouped operations.\n", "\n", "Parameters\n", "----------\n", "group : str, DataArray or IndexVariable\n", " Array whose unique values should be used to group this array. If a\n", " string, must be the name of a variable contained in this dataset.\n", "squeeze : bool, optional\n", " If \"group\" is a dimension of any arrays in this dataset, `squeeze`\n", " controls whether the subarrays have a dimension of length 1 along\n", " that dimension or if the dimension is squeezed out.\n", "restore_coord_dims : bool, optional\n", " If True, also restore the dimension order of multi-dimensional\n", " coordinates.\n", "\n", "Returns\n", "-------\n", "grouped\n", " A `GroupBy` object patterned after `pandas.GroupBy` that can be\n", " iterated over in the form of `(unique_value, grouped_array)` pairs.\n", "\n", "Examples\n", "--------\n", "Calculate daily anomalies for daily data:\n", "\n", ">>> da = xr.DataArray(\n", "... np.linspace(0, 1826, num=1827),\n", "... coords=[pd.date_range(\"1/1/2000\", \"31/12/2004\", freq=\"D\")],\n", "... dims=\"time\",\n", "... )\n", ">>> da\n", "\n", "array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,\n", " 1.826e+03])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31\n", ">>> da.groupby(\"time.dayofyear\") - da.groupby(\"time.dayofyear\").mean(\"time\")\n", "\n", "array([-730.8, -730.8, -730.8, ..., 730.2, 730.2, 730.5])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31\n", " dayofyear (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366\n", "\n", "See Also\n", "--------\n", "core.groupby.DataArrayGroupBy\n", "core.groupby.DatasetGroupBy\n", "\u001b[0;31mFile:\u001b[0m /srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/common.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.sst.groupby?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split Step\n", "\n", "The most important argument is `group`: this defines the unique values we will us to \"split\" the data for grouped analysis. We can pass either a DataArray or a name of a variable in the dataset. Lets first use a DataArray. Just like with Pandas, we can use the time indexe to extract specific components of dates and times. Xarray uses a special syntax for this `.dt`, called the `DatetimeAccessor`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.time.dt" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'month' (time: 708)>\n",
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01
" ], "text/plain": [ "\n", "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5,\n", " 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", " 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3,\n", " 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1,\n", " 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,\n", " 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", " 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4,\n", " 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", " 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2,\n", " 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7,\n", " 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", " 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5,\n", " 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", " 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3,\n", " 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1,\n", " 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,\n", " 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", " 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4,\n", "...\n", " 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7,\n", " 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", " 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5,\n", " 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", " 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3,\n", " 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1,\n", " 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,\n", " 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", " 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4,\n", " 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", " 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2,\n", " 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7,\n", " 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", " 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5,\n", " 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", " 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3,\n", " 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1,\n", " 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n", "Coordinates:\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.time.dt.month" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ds.time.dt.year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use these arrays in a groupby operation:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DataArrayGroupBy, grouped over 'month'\n", "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12." ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = ds.sst.groupby(ds.time.dt.month)\n", "gb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Xarray also offers a more concise syntax when the variable you're grouping on is already present in the dataset. This is identical to the previous line:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DataArrayGroupBy, grouped over 'month'\n", "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12." ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = ds.sst.groupby('time.month')\n", "gb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the data are split, we can manually iterate over the group. The iterator returns the key (group name) and the value (the actual dataset corresponding to that group) for each group." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'sst' (time: 59, lat: 89, lon: 180)>\n",
       "array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2018-01-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]
" ], "text/plain": [ "\n", "array([[[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " ...,\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " ...,\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " ...,\n", "...\n", " ...,\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " ...,\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan]],\n", "\n", " [[-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " [-1.8, -1.8, -1.8, ..., -1.8, -1.8, -1.8],\n", " ...,\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan],\n", " [ nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * time (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2018-01-01\n", "Attributes:\n", " long_name: Monthly Means of Sea Surface Temperature\n", " units: degC\n", " var_desc: Sea Surface Temperature\n", " level_desc: Surface\n", " statistic: Mean\n", " dataset: NOAA Extended Reconstructed SST V5\n", " parent_stat: Individual Values\n", " actual_range: [-1.8 42.32636]\n", " valid_range: [-1.8 45. ]\n", " _ChunkSizes: [ 1 89 180]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for group_name, group_da in gb:\n", " # stop iterating after the first loop\n", " break \n", "print(group_name)\n", "group_da" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Map & Combine\n", "\n", "Now that we have groups defined, it's time to \"apply\" a calculation to the group. Like in Pandas, these calculations can either be:\n", "- _aggregation_: reduces the size of the group\n", "- _transformation_: preserves the group's full size\n", "\n", "At then end of the apply step, xarray will automatically combine the aggregated / transformed groups back into a single object.\n", "\n", "```{warning}\n", "Xarray calls the \"apply\" step `map`. This is different from Pandas!\n", "```\n", "\n", "The most fundamental way to apply is with the `.map` method." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshortcut\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Apply a function to each array in the group and concatenate them\n", "together into a new array.\n", "\n", "`func` is called like `func(ar, *args, **kwargs)` for each array `ar`\n", "in this group.\n", "\n", "Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n", "to stack together the array. The rule is:\n", "\n", "1. If the dimension along which the group coordinate is defined is\n", " still in the first grouped array after applying `func`, then stack\n", " over this dimension.\n", "2. Otherwise, stack over the new dimension given by name of this\n", " grouping (the argument to the `groupby` function).\n", "\n", "Parameters\n", "----------\n", "func : callable\n", " Callable to apply to each array.\n", "shortcut : bool, optional\n", " Whether or not to shortcut evaluation under the assumptions that:\n", "\n", " (1) The action of `func` does not depend on any of the array\n", " metadata (attributes or coordinates) but only on the data and\n", " dimensions.\n", " (2) The action of `func` creates arrays with homogeneous metadata,\n", " that is, with the same dimensions and attributes.\n", "\n", " If these conditions are satisfied `shortcut` provides significant\n", " speedup. This should be the case for many common groupby operations\n", " (e.g., applying numpy ufuncs).\n", "*args : tuple, optional\n", " Positional arguments passed to `func`.\n", "**kwargs\n", " Used to call `func(ar, **kwargs)` for each array `ar`.\n", "\n", "Returns\n", "-------\n", "applied : DataArray or DataArray\n", " The result of splitting, applying and combining this array.\n", "\u001b[0;31mFile:\u001b[0m /srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/groupby.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gb.map?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aggregations\n", "\n", "`.apply` accepts as its argument a function. We can pass an existing function:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'sst' (month: 12)>\n",
       "array([13.659641, 13.768647, 13.76488 , 13.684034, 13.642146, 13.713043,\n",
       "       13.921847, 14.093956, 13.982147, 13.691116, 13.506494, 13.529454],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
" ], "text/plain": [ "\n", "array([13.659641, 13.768647, 13.76488 , 13.684034, 13.642146, 13.713043,\n", " 13.921847, 14.093956, 13.982147, 13.691116, 13.506494, 13.529454],\n", " dtype=float32)\n", "Coordinates:\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.map(np.mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we specified no extra arguments (like `axis`) the function was applied over all space and time dimensions. This is not what we wanted. Instead, we could define a custom function. This function takes a single argument--the group dataset--and returns a new dataset to be combined:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)>\n",
       "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
" ], "text/plain": [ "\n", "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " ...,\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan]],\n", "\n", " [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", "...\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan]],\n", "\n", " [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n", " -1.7996519, -1.7995045],\n", " [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n", " -1.7998358, -1.7996247],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " ...,\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan]]], dtype=float32)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def time_mean(a):\n", " return a.mean(dim='time')\n", "\n", "gb.apply(time_mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like Pandas, xarray's groupby object has many built-in aggregation operations (e.g. `mean`, `min`, `max`, `std`, etc):" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)>\n",
       "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n",
       "         -1.7996519, -1.7995045],\n",
       "        [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n",
       "         -1.7998358, -1.7996247],\n",
       "        [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n",
       "         -1.8000009, -1.8000009],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
" ], "text/plain": [ "\n", "array([[[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " ...,\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan]],\n", "\n", " [[-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", "...\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan]],\n", "\n", " [[-1.7995025, -1.7995973, -1.7998415, ..., -1.7997988,\n", " -1.7996519, -1.7995045],\n", " [-1.7995876, -1.7997634, -1.8000009, ..., -1.8000009,\n", " -1.7998358, -1.7996247],\n", " [-1.8000009, -1.8000009, -1.8000009, ..., -1.8000009,\n", " -1.8000009, -1.8000009],\n", " ...,\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan],\n", " [ nan, nan, nan, ..., nan,\n", " nan, nan]]], dtype=float32)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this does the same thing as the previous cell\n", "sst_mm = gb.mean(dim='time')\n", "sst_mm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we did what we wanted to do: calculate the climatology at every point in the dataset. Let's look at the data a bit.\n", "\n", "_Climatlogy at a specific point in the North Atlantic_" ] }, { "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sst_mm.sel(lon=300, lat=50).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Zonal Mean Climatolgoy_" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sst_mm.mean(dim='lon').transpose().plot.contourf(levels=12, vmin=-2, vmax=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Difference between January and July Climatology_" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "(sst_mm.sel(month=1) - sst_mm.sel(month=7)).plot(vmax=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Transformations\n", "\n", "Now we want to _remove_ this climatology from the dataset, to examine the residual, called the _anomaly_, which is the interesting part from a climate perspective.\n", "Removing the seasonal climatology is a perfect example of a transformation: it operates over a group, but doesn't change the size of the dataset. Here is one way to code it." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan
" ], "text/plain": [ "\n", "Dimensions: (lat: 89, lon: 180, time: 708)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", "Data variables:\n", " sst (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def remove_time_mean(x):\n", " return x - x.mean(dim='time')\n", "\n", "ds_anom = ds.groupby('time.month').apply(remove_time_mean)\n", "ds_anom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{note}\n", "In the above example, we applied `groupby` to a `Dataset` instead of a `DataArray`.\n", "```\n", "\n", "Xarray makes these sorts of transformations easy by supporting _groupby arithmetic_.\n", "This concept is easiest explained with an example:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan
" ], "text/plain": [ "\n", "Dimensions: (lat: 89, lon: 180, time: 708)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", " month (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n", "Data variables:\n", " sst (time, lat, lon) float32 9.537e-07 9.537e-07 9.537e-07 ... nan nan" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = ds.groupby('time.month')\n", "ds_anom = gb - gb.mean(dim='time')\n", "ds_anom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can view the climate signal without the overwhelming influence of the seasonal cycle.\n", "\n", "_Timeseries at a single point in the North Atlantic_" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_anom.sst.sel(lon=300, lat=50).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Difference between Jan. 1 2018 and Jan. 1 1960_" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "(ds_anom.sel(time='2018-01-01') - ds_anom.sel(time='1960-01-01')).sst.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grouby-Related: Resample, Rolling, Coarsen\n", "\n", "\n", "### Resample\n", "\n", "Resample in xarray is nearly identical to Pandas.\n", "**It can be applied only to time-index dimensions.** Here we compute the five-year mean.\n", "It is effectively a group-by operation, and uses the same basic syntax.\n", "Note that resampling changes the length of the the output arrays." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (time: 13, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2020-12-31\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -0.0005707 -0.0005493 ... nan nan
" ], "text/plain": [ "\n", "Dimensions: (time: 13, lat: 89, lon: 180)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2020-12-31\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", "Data variables:\n", " sst (time, lat, lon) float32 -0.0005707 -0.0005493 ... nan nan" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_anom_resample = ds_anom.resample(time='5Y').mean(dim='time')\n", "ds_anom_resample" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_anom.sst.sel(lon=300, lat=50).plot()\n", "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "(ds_anom_resample.sel(time='2015-01-01', method='nearest') -\n", " ds_anom_resample.sel(time='1965-01-01', method='nearest')).sst.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rolling\n", "\n", "Rolling is also similar to pandas.\n", "It does not change the length of the arrays.\n", "Instead, it allows a moving window to be applied to the data at each point." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan
" ], "text/plain": [ "\n", "Dimensions: (lat: 89, lon: 180, time: 708)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", " month (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n", "Data variables:\n", " sst (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_anom_rolling = ds_anom.rolling(time=12, center=True).mean()\n", "ds_anom_rolling" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEiCAYAAADwEwVaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACfUUlEQVR4nOydd5wU5f3H389su97vgOOAA6RIB0FFUVAUsffeSzQaNTH52RM1JpYYTSyxxG6MXYm9F1RsKAoKIr0d7Xq/2zLz/P6YndnZ3dm7PbjjDm4+rxfc7pRnnmd25tuLkFLiwIEDBw56H5TunoADBw4cOOgeOAzAgQMHDnopHAbgwIEDB70UDgNw4MCBg14KhwE4cODAQS+FwwAcOHDgoJfCYQAOtglCiLVCiIO6ex49AUKIGUKIsu6ehwMHHYXDABx0OYQQUgixWxeMe44QQhVCNFr+zbDszxNC/E8I0SSEWCeEOK2d8a4QQmwRQtQJIR4XQvg6e847AkKIUUKI74QQNeF/HwohRln2CyHE34QQVeF/dwghhGV/qRDiEyFEsxDil7YYfXtjOejZcBiAg50dX0kpMyz/5lr23Q8EgD7A6cCDQojRdoMIIQ4BrgFmAqXAEODPXTnxLsQm4AQgDygAXgeet+y/EDgGGA+MA44ALrLsfw74AcgHrgdeFkIUJrhWe2M56MFwGICD7YYQYk8hxFdCiFohxGYhxL+EEN7wvs/Chy0KS+gn76A5pQPHA3+SUjZKKeehE8IzE5xyNvCYlHKJlLIG+AtwzjZee3chxNzw/VgihDjKsu9JIcT9Qoi3hBANQohvhBBDt+U6iSClrJVSrpV6mr8AVMCqgZ0N3CWlLJNSbgTuIrxWIcRwYBJwo5SyRUr5CvAT+r20Q8KxHPR8OAzAQWdABa5AlzanokvRlwBIKfcPHzM+LKG/EHuyEGJamFgm+jetjWtPFEJUCiGWCyH+JIRwh7cPB1Qp5XLLsYsAWw0gvH1RzLF9hBD57aw9di0e4A3gfaAIuAx4RggxwnLYqejaRS6wEriljfHaui/XtDOXWqAVuA+41bLLbq2jLftWSykbEuyPRVtjOejhcLd/iAMHbUNKucDyda0Q4t/AdODuJM+fB+Rsw6U/A8YA69CJzgtACLgNyADqYo6vAzITjBV7vPE5E6jqwJz2Do91u5RSAz4WQryJTvRvCh8zR0o5H0AI8Qzwj0SDSSlzOnDtuHPDmtDZ6PfIgN1aM8K2+0T3rX+CyyQcSzqFxno8HA3AwXZDCDFcCPFm2IFajy5tFnT1daWUq6WUa6SUmpTyJ+BmdNs3QCOQFXNKFtCAPWKPNz4nOj4RioENYeJvYB3RBHSL5XMzOhHtEkgpm4CHgP8IIYrCm+3W2hgm2J1x3xod4r9zwGEADjoDDwK/AMOklFnAdei256QghNgvJpIn9t9+SQ5l2LwBlgNuIcQwy/7xwJIE5y4J77ceu1VK2RHpH3QH7AAhhPXdGghs7OA4ALRzX65LchgFSCPChOzWusSyb4gQIjPB/li0NZaDHg6HATjoDGQC9UCjEGIkcHHM/q3oUTW2kFJ+HhPJE/vvc7vzhBCHCiH6hD+PBP4EvBYeswmYA9wshEgXQuwLHA08nWAa/wHOD4dQ5gJ/BJ60XOtJIcSTCc614hugCbhKCOEReljqkURH4SSNdu7LrXbnCCEOFkJMFEK4hBBZ6CamGmCpZa2/F0L0F0IUA38w1hr2mSwEbhRCpAghjkWP7nklwRQTjuWg58NhAA46A/8HnIZuJngE3RZvxU3AU2HH5UmdeN2ZwI9CiCbgbXSCbyWKlwCpQDl6aOPFUsolAEKIgWEpeiCAlPJd4A7gE3STzTrgRstYA4Av2puQlDIAHAUcClQCDwBnSSl/2Y51dhQ56OutA1ahRwDNllK2hvf/G91R/ROwGHgrvM3AKcBkdKZxO3CClLICItqa5dj2xnLQgyEcU50DB20jHNK6CBgnpQx293wcOOgsOAzAgQMHDnopHBOQAwcOHPRSOAzAgQMHDnopHAbgwIEDB70UDgNw4MCBg16KnaoUREFBgSwtLe3uaThw4MDBToUFCxZUSinjKrruVAygtLSU7777rrun4cCBAwc7FYQQ6+y2d5sJKJxlOF8IsShcMndnrb3uwIEDBzslulMD8AMHSikbwyV05wkh3pFSft2Nc3LgwIGDXoNuYwCWyoMAnvA/JyvNgQMHDnYQutUHIIRwAQvQa5XcL6X8pqNjBINBysrKaG1tbf9gBw6SQEpKCiUlJXg8nu6eigMHXYpuZQBSShWYIITIAf4nhBgjpVxsPUYIcSF631EGDhwYN0ZZWRmZmZmUlpYinF7UDrYTUkqqqqooKytj8ODB3T0dBw66FD0iD0BKWQvMBWbb7HtYSjlZSjm5sDC+L3Vrayv5+fkO8XfQKRBCkJ+f72iUDnoFujMKqDAs+SOESAUOQm8qsi1jdeLMHPR2OM+Tg96C7tQA+gGfCCF+BL4FPpBSvtmN8+lRqK2t5YEHHjC/z507lyOOOML22BkzZjj5EQ4cdBCb61oYe9N7LN/adtdPTZO8vmgTqrbrxah0GwOQUv4opZwopRwnpRwjpby5u+bSExHLABw4cNC5eG/xFhpaQ/z3a9scKRMvfreBy5/7gae/WrtjJrYD0SN8ADsz1q5dy8iRI7ngggsYM2YMp59+Oh9++CH77rsvw4YNY/78+QBUV1dzzDHHMG7cOPbee29+/PFHAG666SbOO+88ZsyYwZAhQ7j33nsBuOaaa1i1ahUTJkzgyiuvBKCxsZETTjiBkSNHcvrppxPby+Gxxx7jiiuuML8/8sgj/P73v4+b88UXX8zkyZMZPXo0N94YaXpVWlrKjTfeyKRJkxg7diy//PJLu3M/++yzmTVrFqWlpcyZM4errrqKsWPHMnv2bIJBp3eKg50flY1+AMob/N08k87HTlUKoj38+Y0l/LypvlPHHFWcxY1Hjm7zmJUrV/LSSy/x8MMPM2XKFJ599lnmzZvH66+/zq233sqrr77KjTfeyMSJE3n11Vf5+OOPOeuss1i4cCEAv/zyC5988gkNDQ2MGDGCiy++mNtvv53Fixebx8ydO5cffviBJUuWUFxczL777ssXX3zBtGnTzHmccsopjBs3jjvuuAOPx8MTTzzBv/8d353vlltuIS8vD1VVmTlzJj/++CPjxo0DoKCggO+//54HHniAO++8k0cffbTNua9atYpPPvmEn3/+malTp/LKK69wxx13cOyxx/LWW29xzDHHbPdv4MCBg66BowF0AgYPHszYsWNRFIXRo0czc+ZMhBCMHTuWtWvXAjBv3jzOPPNMAA488ECqqqqoq6sD4PDDD8fn81FQUEBRURFbt261vc6ee+5JSUkJiqIwYcIEc2wD6enpHHjggbz55pv88ssvBINBxo4dGzfOiy++yKRJk5g4cSJLlizh559/Nvcdd9xxAOyxxx5Jzf3QQw/F4/EwduxYVFVl9mw9kMu6dgcOdgXsirEBu5QG0J6k3lXw+XzmZ0VRzO+KohAKhQDizDUQiTaxnu9yucxz2rpOouMuuOACbr31VkaOHMm5554bt3/NmjXceeedfPvtt+Tm5nLOOedEhTwa17COn8zcFUXB4/GY261rd+BgV8Cu2D3X0QB2EPbff3+eeeYZQDfnFBQUkJWVlfD4zMxMGhrajk6ww1577cWGDRt49tlnOfXUU+P219fXk56eTnZ2Nlu3buWdd97p9Lk7cLAzINlw3105LHiX0gB6Mm666SbOPfdcxo0bR1paGk899VSbx+fn57PvvvsyZswYDj30UA4//PCkr3XSSSexcOFCcnNz4/aNHz+eiRMnMnr0aIYMGcK+++7b6XN34GBngJ1m29sgdqabMHnyZBkb77506VJ23333bppRz8QRRxzBFVdcwcyZM7t7KjstnOdq18ejn6/mr28t5aypg7j56DEJj7v/k5X8/b1lXDxjKFfPHrkDZ9h5EEIskFJOjt3umIB2IdTW1jJ8+HBSU1Md4u/AQTvwh7TunkK3wzEB7ULIyclh+fLl3T0NBw52ChgMYFfM8E0WjgbgwIGDXokH564EINCLNQGHAThw4KDXodEfIqjqkn9vNgU5DMCBAwe9DlazT7IawE4UL5M0HAbgwIGDXgfNwgD8IbUbZ9K9cBhAJ6C0tJSxY8cyYcIEJk+Oi7Tq9SgtLaWysrK7p+HAgQnVIs5/sqyC/+yClT6TQe+LAvrxRfjoZqgrg+wSmHkDjDtpu4f95JNPKCgo6IQJJo9QKITbbf8TqqqKy+XaofNx4GBngRYT+XPDa0s4a2pp90ymG9G7NIAfX4Q3Loe6DYDU/75xub69C9HQ0MDgwYPN8sj19fWUlpYSDAZZtWoVs2fPZo899mC//fYzSzC/8cYb7LXXXkycOJGDDjrILBB30003ceGFFzJr1izOOuusqOvMnTuXAw44gNNOO80sznbllVcyZcoUxo0bZ1YG3bx5M/vvvz8TJkxgzJgxfP7550DbZaKvu+46pk6dyuTJk/n+++855JBDGDp0KA899JB57f33359jjz2WUaNG8etf/xpNi7et/ve//2XPPfdkwoQJXHTRRahq71W/HXQf1A4Y9HfhShC7mAbwzjWw5afE+8u+BTWmpnewBV67FBYkKG/QdywcenublxVCMGvWLIQQXHTRRVx44YVR+zMzM5kxY4ZZHvn555/n+OOPx+PxcOGFF/LQQw8xbNgwvvnmGy655BI+/vhjpk2bxtdff40QgkcffZQ77riDu+66C4AFCxYwb948UlNT4+Yyf/58Fi9ezODBg3n44YfJzs7m22+/xe/3s++++zJr1izmzJnDIYccwvXXX4+qqjQ3NwNtl4keMGAAX331FVdccQXnnHMOX3zxBa2trYwePZpf//rX5rV//vlnBg0axOzZs5kzZw4nnHCCObelS5fywgsv8MUXX+DxeLjkkkt45pln4hiZAwddjd4c+2/FrsUA2kMs8W9ve5L44osvKC4upry8nIMPPpiRI0ey//77Rx1zwQUXcMcdd3DMMcfwxBNP8Mgjj9DY2MiXX37JiSeeaB7n9+tzKSsr4+STT2bz5s0EAgEGDx5sHnPUUUfZEn/QS0Ybx77//vv8+OOPvPzyywDU1dWxYsUKpkyZwnnnnUcwGOSYY45hwoQJgF4m+uGHHyYUCrF582Z+/vlnkwEcddRRgF7mubGxkczMTDIzM0lJSaG2tta89pAhQwA49dRTmTdvXhQD+Oijj1iwYAFTpkwBoKWlhaKioo7fcAcOthM2ymm7kOx6TGPXYgDtSOr8c0zY/BOD7AFw7lvbfNni4mIAioqKOPbYY5k/f34cA9h3331Zu3Ytn376KaqqMmbMGOrr68nJyTGbq1hx2WWX8fvf/56jjjqKuXPnctNNN5n70tPTE87Fuk9KyX333cchhxwSd9xnn33GW2+9xZlnnsmVV17Jfvvtl1SZaGu5a+O7UfY5tmpi7HcpJWeffTa33XZbwvk7cLAj0BET0K6M3uUDmHkDeGIkZ0+qvn0b0dTUZJZtbmpq4v3332fMGPvCUmeddRannnqqWac/KyuLwYMH89JLLwE6gVy0aBGgS+v9+/cH2Obqm4cccggPPvig6XtYvnw5TU1NrFu3jqKiIn71q19x/vnn8/33329TmehYzJ8/nzVr1qBpGi+88EJUtzKAmTNn8vLLL1NeXg7orSbXrWu7H6sDB9uKupYgpz/6NYs31sXtc0xAOnoXAxh3Ehx5ry7xI/S/R967XVFAW7duZdq0aYwfP54999yTww8/3OyKFYvTTz+dmpqaqDr9zzzzDI899hjjx49n9OjRvPbaa4Du7D3xxBPZb7/9tjm66IILLmDUqFFMmjSJMWPGcNFFFxEKhZg7dy4TJkxg4sSJvPLKK/z2t7+NKhN93nnnJVUmOhZTp07lmmuuYcyYMQwePJhjjz02av+oUaP461//yqxZsxg3bhwHH3wwmzdv3qa1OXDQHh6ft4YvVlbxvx82xu3TOuIEZtf1AjvloHcgXn75ZV577TWefvrp7p5Kp2Pu3LnceeedvPnmm909lU7BzvRcObDHH15cxCvfl3Hz0aPjQjyXbq7n0Hs+j9q29nb7nhsPzl3F3979hYumD+HaQzv+TJz52DeU5qfzl2MSl5zuaiQqB71r+QB6MC677DLeeecd3n777e6eigMHvQJKWHAPqfFC7jaZgLZRVv58RSWfr6jsVgaQCA4D2EG47777unsKXYoZM2YwY8aM7p6GgyQx7qb3OHXPgVx72K6r5SjhIISQTchPh0xAu64FqJf5ABw4cABAfWuIf3+2urun0aVQwtQtZCPtd0QD6CwreUNrsHMG6kQ4DMCBAwe7JIwwZNXGBNQRDaCzUN8a2uHXbA8OA3DgwMEuCcNyE7TVADowTieZgJr8DgNw4MCBgx0Cg+6rNj6A7sgDaHQYwK6J8847j6KiorgEsCuvvJKRI0cybtw4jj32WLNkwo7A2rVrefbZZ83vTz75JJdeemmnjW8t8ZyRkQHApk2boko/OOiZ2JlCv7cHxjrtfACGCcilJC/eb+9dczSAXRTnnHMO7777btz2gw8+mMWLF/Pjjz8yfPjwHVoCIZYBbAs6WqmzuLjYrDvkoOeit2TBGutMFAYq1RDKDqzv4zAAC4QQA4QQnwghlgohlgghfttdc9le7L///uTl5cVtnzVrllmvf++996asrCzumLlz5zJ9+nROOukkhg8fzjXXXMMzzzzDnnvuydixY1m1ahUA69atY+bMmYwbN46ZM2eyfv16QGc+l19+Ofvssw9DhgwxCfA111zD559/zoQJE/jnP/8J6BL67NmzGTZsGFdddZXtWkpLS7n55puZNm0aL730Es899xxjx45lzJgxXH311W3eh7Vr15pa0JNPPslxxx1ne73HHnuM4cOHM2PGDH71q1/ZaiY33XQTZ599NrNmzaK0tJQ5c+Zw1VVXMXbsWGbPnm2Wt1iwYAHTp09njz324JBDDjEzix955BGmTJnC+PHjOf74482Kp4nuV2+CnUS8KyJiAopfb0trC2UPnEPZk1d0+nXv+2gFP2+qj9ve6O95pc+7Mw8gBPxBSvm9ECITWCCE+EBK+fO2Dvi73/3OtrDa9mDChAncfffd2z3O448/zsknn2y7b9GiRSxdupS8vDyGDBnCBRdcwPz587nnnnu47777uPvuu7n00ks566yzOPvss3n88ce5/PLLefXVVwG9vv+8efP45ZdfOOqoozjhhBO4/fbbozJzn3zySRYuXMgPP/yAz+djxIgRXHbZZQwYMCBuPikpKcybN49Nmzax9957s2DBAnJzc5k1axavvvoqxxxzTFJrtruey+XiL3/5C99//z2ZmZkceOCBjB8/3vb8VatW8cknn/Dzzz8zdepUXnnlFe644w6OPfZY3nrrLQ4//HAuu+wyXnvtNQoLC3nhhRe4/vrrefzxxznuuOP41a9+BcAf//hHHnvsMS677LKE96s3ofcwAH2dQRuP78Z1a9Gaa2ltriVQsZaUotKE43TEB6xpkrs+WM49H61g5a2HRe3riRpAtzEAKeVmYHP4c4MQYinQH9hmBtBTccstt+B2uzn99NNt90+ZMoV+/foBMHToUGbNmgXopZc/+eQTAL766ivmzJkDwJlnnhklUR9zzDEoisKoUaPMxjF2mDlzJtnZ2YBel2fdunW2DMBgVN9++y0zZsygsLAQ0GsZffbZZ0kzALvrVVZWMn36dFNjOvHEE1m+fLnt+Yceeigej8dsbmPUWBo7dixr165l2bJlLF68mIMPPhjQTVbGfVy8eDF//OMfqa2tpbGxMaoiarL3a1eFXVjkrgiDAdhpAGXr15qfm37+FG9haadcU23D79AT8wB6RCawEKIUmAh8Y7PvQuBCgIEDB7Y5TmdI6p2Np556ijfffJOPPvoorjyygdjyytbSy0ap5VhYx7Ke35aDz3qcy+VKOLZRUnp7nYV21+vImNb74PF4zDUb90VKyejRo/nqq6/izj3nnHN49dVXGT9+PE8++SRz5861nVdvcYhaYZcZ25246/1lHLR7H8YPyOnUcU0fgA0x3hhmAL6iUlpWLyB3+tkJxzHOTuZZsWM2itDNUd+vr233/B2NbncCCyEygFeA30kp4wxnUsqHpZSTpZSTDUl0Z8G7777L3/72N15//XXS0tK2a6x99tmH559/HtAriMaWWo5FZmamWaZ6W7HXXnvx6aefUllZiaqqPPfcc0yfPn27xtxzzz359NNPqampIRQK8corr2zzWCNGjKCiosJkAMFgkCVLlgB6G85+/foRDAZ55plntmvOuxp6khO4JaBy38crOfWRrzt9bINeh2xMQGuWL0VJzSJn1DSC5aupnZc4YKIjMkJsgpmU0vRF2JWl7m50KwMQQnjQif8zUso53TmX7cGpp57K1KlTWbZsGSUlJTz22GMAXHrppTQ0NHDwwQczYcIEs3XituDee+/liSeeYNy4cTz99NPcc889bR4/btw43G4348ePN53AHUW/fv247bbbOOCAAxg/fjyTJk3i6KOP3qaxDPTv35/rrruOvfbai4MOOohRo0aZZqKOwuv18vLLL3P11Vczfvx4JkyYwJdffgnAX/7yF/baay+zQ5uDCHqSD6CyUe+A53N3PilqSwNYvWwJ3qIh5I3eD4C6L55NGPXWkazhWOZq/dqTGK+BbisHLXR9/imgWkr5u2TO2dnLQTvQ0djYSEZGBqFQiGOPPZbzzjsvrndAd2NXfq42VDez3x26bylRCeQdhYUbajnm/i8YXJDOJ/83o1PHvvA/3/H+z1s5dExfHjxjj6h9mTm5MGQfRh5/BYsfu4bWNQvYtGmT6UOy4l8fr+DO95fzq/0Gc/3ho9q8Zl1zkPE3vw/o99YfUhnxRz1EPDvVw6IbZ3XS6jqGROWgu1MD2Bc4EzhQCLEw/O+w9k5ysPPjpptuYsKECWbjmGSdyg46B3ZRMd2FqrAGkJ3q6fSxI1FA0UJuKBSisa4WV1o2LiHInKAHF2zatMl2nEQy8tNfr+P9JVuitsW2mrS6W3qiBtCdUUDz6FiElYNdBHfeeWd3T6FXoycRoqrGANBVDED/G1sKYv4vehtSV1oOLkXgytAj0l7/cjHXf97E25fvh2LJEE50u/706mIgWouKdbAbDEERPc/5Dj3ACezAgYMdi57kA2gK6JFoKZ4d4wNYVdHICf/UTTJKWjaHjumLK1Nvufrgm9/wy5YGAjEakqFJJGMtj6Xxxhw8LqVHMV4DuwQD6I2hfA66Drv689STCJFhjuqKvrsG4baWgtha14rarEfjuNKyuHD/IcyYMAzF4yNYq5tzYhmkjPkLiZ+ReBOQ/t3rVnoU4zWw0zOAlJQUqqqqdvmX1sGOgZSSqqoqUlJSunsqXYaeRIgM+3xXdN2ySwTTJGgteni0kpKJoghy0n2k5vUjUKP7AGLDRg3aYo0G8ofszTlazL01GILPrSBl/P7uRo9IBNselJSUUFZWRkVFRXdPxcEugpSUFEpKSrp7Gl0Gu/LI3YVAmJB2CQMILzNoWa8mJTLQAoDiS8MlBC5F4MvrS2N1uT4nCwOQUvLI56vDnyNj1yfI6o0LAzU0AJcua4c0ibcDFUi7Gjs9A/B4PAwePLi7p+HAwU4Du+qY3QXDMdoVZinVRgNQpUQLMwDhSUERApcQuFOz0Fr0wovW+7OhuoXWoD5Hq5WhIUF3r1gTkPHdG85z6EnmN9gFTEAOHDjoGHqiCagrmJK0CQP1B7WIBuBNQ1H0siqu1EzU1sa4uVjNPtbblpABxJqAtGgG0NMigRwG4MBBL0NPYgCGCagr5mQQX2veQ2tQRQu2gOJGuD16GKgCrpRMZKAFqYaiTEaKsIaDRuaYqLJnvAlI/+tzu2z3dzccBuDAQS9DT/IBGMS5KyRjg9YGQtEMQAZaULypgG6bdykCJVXvaqe1NlJW08Kd7y1D0ySKEj8eEBcqaiCWwBvrimgAPYsB7PQ+AAcOHHQMPckHYDKALpiTIbFbGUBLUEULtCC8qXx97UzcLgVFCJSUTP2c1gYue/Z76ltDHDSqD4WZPsuIkTkGQ9GOYqNSbWzdIOO74QR2NAAHDhx0K3oSETIIf2dLxvNWVJq2f6u0bjAAxZtqZh+7FIFI0Uuga62N1Ift+yFVi3L8WpWUKL+ChRnE+wD0vz3VCexoAA4c9DL0JDNEQO24D2BdVRM5ad6E5SPmLivnnCe+Nb9bpfXWgIr06wzAyD62agCGIxjCfYMt07JK91a/gj+okeKxt/HHOoF7GgNwNAAHDnoZDCLUE8LRIyag5H0A0/8+lyPvm5dwf2W4vpABv2XszXWtpgnIbDAkBMIX9gG0RHpoaDI+icyAVaswPlc3BTj/qehqxbEmoJ7EfMFhAA4c9Dp0pL59V2Nbw0DXVzcn3OeN6S0QCEVMOe8u2RLlBAbITHETcOvfNYsGoEkZda+s5qCAjdnniS/WUNcSnSAWrwH0HAc8OAzAgYNeh55khuiKKCCvK161CaoSKSVN/lBYA4h06BveJxO8hg/AqgHIKKk/kQkoNvnLCtXRABw4cNCT0LM0gM7PA4jVAEA304Q0naDLQDOKL6IBDOuTgVBcCF+6WSgODB9AZF6vLtxkSS6zMIA2tBctRgPoSRFY4DAABw56HXqSENpRE1AyRR9dig0DCGkRU1CwlXOmR9qEGs5kX9/daP5lHlLTW0PGagAAb/64GSllVBRQW9qLnRO4vKGVMx/7xmyH2Z1wGIADB70MHTUBSSn5qaxrGpp31AQU293LDnYVNwMhDX9IQ4YCaJpKv4Jcc59hnkkbsS9aSz1qU214nPh7ddlzP/DEF2vjfAAPzl3FfR+vjLuutRoo6JrOf79ez+crKvnPl2vbXUtXw2EADhz0MnTUBDTn+40c+a95vBfT/rAzYBDSZJlSMu0s7cxJOgNQzTpAmZmZ5j5DOndl5OtzaazS/8Y4gQ0s3FAb5wP427u/2M7FWNeWVYtpXPIJqibpn6OXGt9Y29ruWroaDgNwsEOwtrKJleUN7R/ooMvR0Zr0y8O/25rKpk6fS6ReT3JzSsZUZGUmRqx/QFXxBzW0oE50bRlApsEAqs1x7Hhlus8VxQASzSkY9jsA/PPSE6l68y6CqkpumheAzXUt7a6lq+EwAAc7BDPunMtB//gs6eOllHy92mn00xUw6JVIsgi/0a2rK34Ks21jknkAwSRMRaomCZSvoeLV2/EEdaYVCEkCqoYM6OGjVgbgDidEGL2BDQYQVDVbDSDN645iWIm0l+aASkiVSDUSGlpVGelbUtHg+AAcOLDFu4u3cMrDX/Ps/PXdPZVdDoYGoGqSP7+xpNPG/X59DaXXvMX6qsQx+rEwJORko4CSMQGpUlL90cM0L5tHzfxXAT0KyB/UzF4AVgZgMEJXWjYIhVCDbgIKqfYmoHSvy0z+OkqZx8jn92G17zTmeS/nKCWSoNYaVAmpGqH6SnNb2fp1ZkWhtsJHdxQcBuCgR2Jjrf6irq7ofLNDb4eVqD3xxdp2jzcUBUnbBOvFbzcA8MWqyjaPs8LauP3Hslq21LVtF0/GBBRSVYIV6wDwl+t/TR+AP54BGBCKC1d6rukDCGmabcRUus9NMKRxlDKP2z2P4mvaiCKgRKnkds+jJhNoDqiENEmobqt5btm6dRGttvvpv8MAHPRMGFJZT0pa2lXQVZLntpSYsHYEO+pfX7D/3z9p8/hEZZitqNi6Ba2lHoDW6o36eeEwUDsNwIrsgiLUsAYQTKABuBRBQNW4yv0iaSJSdkKTkgvm1PLzy/ciNZVlW+oJaRqhcK9hgIfe/NLsMNYT4DAABz0SRjJnT0pa2lXQUSdwsvTcGFbpQIPfWHoeSNBs3UAyGsCWMt1s6CkspblyI1JTdSdwEgwgK78PoXrdTh9Utah7dd6+eutZTUqCqkax0DWdldV6fsFD3wV5bnGIj1Y0M6L+Gy7573e8/sxjVL//AADCm0bjkk/5dq3uY+gJT7bDABz0SLgU+/rqDrYf26pUtfdTGL9VxxhAx6ThZHwANVU6YU4dOhktGCBYsbbNMFArBo0cR6i6jFBDZdgHENl31ewR4TnDpNoPEMDicpVh9zVywyd+nlwY0QZuCNzNp66LeeofNwFw1PEn4UrLJli1npqtZUDPeLYdBuCgRyJiAurmiezkWFPZxHEPfBFVpMxqVstM6byK8IYj121Ti6e9c5JFVPx9gnNrq3QJPm23vQDwb/wFfzgRTLOJArJi1KSpAAS2rCKoRfcDqK+rJVixhsmr7+eCittYKYt5f42+76+fB/h2k8ahw/Ss4ltqZnH1vBSkhL8c4OOlsZ9w7Uw9yqhh7U9A10RVdRQOA3DQI2FqAI4PYLvwr49X8v36Wt5bHEnishK1DF/7DCBZgX7bNABphmEmAyvDsDMXfb++hnUbNwPg7TuMtJwC/Jt+0TWAcEN4X0oKbrf9uotL+uvzaqohGIpoAK1lS+lTWMCmxy9jyvrH+DR9Nseod/DIxsFR5zcNmg7AN/WFvF45AIDFk65DnXAGxw7S/RJTFt3Iy96bODU4ByqWJ732roDDABz0KHyxspKgqpmOxJ6gJu/MyEnTJdLaloh5QpUSlyI4efKATpVCNW3bGIDPpnhbIlibu8QyAH9I5bgHvuS7X9ahpGQiXG6KdxtNoHwNAVWjKRBCbaknLy8/4fgjB5fo82qqCUcB6Wvqs+JV85i3iy7i4ZzfIdxe1lSFKCkdau5bVnAguNyUpvpxt9aw29jJfO3eA3X2HRzjvh/F7eHj1pH4CHBx8D9w/xS4bw94/4+w7isI1yHaUXAYwE6EBeuqaQ3u2AdkR+KH9TWc/ug33PneMpOI9IRY6Z0ZOeFCZ9EmIHAJgaIkx2AjiWBtH2uM5eoAVVE1aXbTag+Pz1vDaY9+Y373h6LfhQXravR5tNSjpOomnpyCPqhNtQRDml4KurmOosLChNcYVJiNkpKpn6NKVCkZLdZSvvRrDGvZR679CWl6fZ9gzSZGTdrbPN+VVYArI5+W+ir8tRVkF/YB9GQzIRREWi4/+vtxZOBWjvU9AofdCTkD4euH4InZcOcwePUSWPomBMIh0D++CP8cAzfl6H9/fDGp+5UMupUBCCEeF0KUCyEWd+c8dgasrWzi+Ae/4qbXOy9xp6ehukmXUleUN5omIIf+bx+yDQ2gOcIApJQoiu5n6YiFrb1jDfN8shqAlJJQBzSAt37aHPXdH9J488dNXDtHt6mX1+uZtZq/CSVF7/CVk1eA1lLPx68/z6ZNm9Fa6ujTpyjhNbwuBVd6LlpTDaqmkb/hfZ7gJrY0qBw4fT8A3n78H6iaRAk0orXU483vb57vysjHnZFHc005/voKsvP7ApZs49RMmutr9fmKAtjzV3Dm/+CqVXDCEzD0QPjlTXjhdLhjCDy0H7x2CdRtAKT+943LO40JdLcG8CQwu5vnsFPAkOB+3lzfzTPpOgiL2cdgAE4ewPbBIMW1MU5gRQgUkVx5ZUOyb69cQ7I+gG9WV/HZ8gq9Nr+UrHz+L1S+dbdZhllKPUP5u3C4pIG+2SlR3/0hjUuf/YHn5q8npGrUNOsChNbahOLTG7zk5ueD1Hj6jut49cFbkC31FNpoAJ9eOYP3r9gfj0vBlZGD2lTDpA1PMXbepXxcqZuMjjn5TADWLf1e7y1QpzOkb6p89DvvX+QecD6Kx4crI4/K9SuRoSBZBboGYDzPSmoWLQ215jpNpGTDmOPg+EfhylVw1uuwxzmwdQmo0V3GCLbARze3eY+TRbcyACnlZ0B1uwc66BUwIn80aYkCsiFQUsqE8eKPfLbajLN2YK23H1290iUEihBJmdgMx2ugnRj8iAmobQZw8sNfc9bj86lvbKTqnXuo+PFTmhZ/SONPH+nz0yRPfLGWEx76KvrEmMtbTUCbalupCWs5Vg0gLz9C7JsbGwg11lBUFK8BDMpPZ3ifTLxugSc9h5zmdRy65SHmp+3PZSumADBr1izyphxJS0Mdn9x+Lv4KPfPZk1uMt7CUrD2P1b8XDKK5XjdHZeT3weMSkf7DqZkEmurtlhOBywNDpsOhfwOZgOnWlSU6u0Pobg3AQZLoDXKwQTaklKZD0U5C/cNLixj+x3dsx7jl7aWcGEs4ejEMDcqqSEkJiqIzALsoq+fnr+eTZeXm92Qbt3dUW3v9tddp+ulD83vz0rn6OAmYUmwWsD+kUZDhA2BtVRO1hgbgb0RJ0TWAEWMnmsevXzIfLehn5MiRJIIvUMMRWctpbGzm7tBxnFx9IY3rlzJ89zEMGjQIt1fXQurLVlC5ZB4IBXdO36gxsqYcbX7OyOtjMsR3frsfrtRMgk16b4WkzJvZJR3b3kH0eAYghLhQCPGdEOK7ioqK9k/YxdGBLPudDg9/ttr8bEiTdkRlzvd6en95fffXU+/pMKpnWhmpbgLSTTV2ROiaOT9x7hPfmt+N7Nv2krCM3yzZyK2q6oimlrrbXgSrwglSCS4TVDXG9s/mv+fr8f2BkEaaV3cgNwdCpp9DNwHpGkDfkoEU7ncaA0ZOQKq6xjB27Fj7C5T/Qr+XjmBMej1NQfhH8+GAIFhdxrAw01As3ca2/jQPd1Yhwu2JGma3kiIuuv1JUgaOJbf/EDzhc3bvl0VKRg5qaxNSU9utrQTAzBvAkxq9zZOqb+8E9HgGIKV8WEo5WUo52c5252DXwZer9BosmpQm4bejOSW5+guxK/tDOgtGv1orHzXCQBURT6wNKdoKo15PsB0J307baAvr168HxcXJ932Er99w1MZqtGBrYg0gpOFzK6SGib4/pEU9JzXNAbRAK6hB0wTkUgQDDj6H2b+OEMzx48fHD77yQ3jsYJRgE6+kHKOP2VSrN3Gp28qQocMA6D/jNGaefw2p+cUAuHOLzSHuOWUCi26Yxbu/3Z+Re0ylz6m3ITwpUYlxvoxsQKK1NianAYw7CY68F7IHAEL/e+S9+vZOQOelATroUuzMdfGTmbv1GCkjhMnu3OxUD2U1LVGhjQ7sEdTiNSkpw05gJT4KaLVN0xfDjxBsp06PMVayz+qGDetxZxWSmZVlmlFCtVsSmpKCqobHpZhRQ/6gGqV11DYHURv0MhBGcxePS+B1K2QXD2G3Q84hPz+ftLS06IHnPwLvXA2FI2k89r+ULXgG0DuDVb15FwD7HXAQAL60DMbPOoXFixbSUrUJT8FAc5jMFLcZdeUJx8K2BFXclrhYb3q2Pt+WeiSJo5GiMO6kTiP4sejuMNDngK+AEUKIMiHE+d05n50CHUiy6SlIJt0/mkBFiImdNGi8UC2B6DhwJ2s4HobdPraEgiIEwkYDqAubUawZwsYY7f2OWgc1gLING3BlFeJzK1HduBL9jgFV4nUrZpcvqwagSUlNcwDRrGuR7izdWuBSFFyKQNUkQ2efy7hDTokMqIbg7Svh7f+DYQfD+e/hzh+IK13vF9y6QY9O9w0Ywx5T9tTHdQlUTaPPXkcBkDFmpu1cjT7D81ZURjFEXQMAtaWhR4Q4d3cU0KlSyn5SSo+UskRK+Vh3zsdB1yCpNn6WtyHaBGTDAMJOtZaYpLiO1pWx4pUFZV3S87a7EWm5aGUAhE1AIo4BNAVCQKRNIkS0iPZKMasxWpuUklcWlFHfaq+plW3YgDurkBSPy9KPtyahCSgYMjQA3QQUCGlxGkCqX4++cWUWAOBRhM4Aws+UaY5prYNnT4L5D8PUS+GUZ8GXqYeBpufoh6z7EYDcAy8wQ1tdQhDSJCnFw/jd8z/g7TPEnJ/Vd2Fcp7zBT2VjxKyWkmFoAA30hNCOHu8DcLDzI5k2ftZDok1A8ccmZgDbXjnuDy8t4qKnF2zz+T0VhvnGGsJpJIK5bBLBmsNalcditzY0AH87deyNsYy/7y3Zwh9eWsS/P10Vd6zUVLZs3oQ7M6wBZOhSt9pYZasB1DUHWbx2C98+/09qq/QIJasG4A9qNPpDBDb+jPCm4g4zAFeYAYQ0aWo+VK+BRw+GNZ/q9vRDbgFFZypuRaCkZoFQCFbqzWRc6bkRBhDWJuxKWFiZqSdBOnSEAdQ7GsCORpM/xMryxu6eRru47n8/8XZM1mMPeFa2GWoM8bGD9eWRyKi2hbEwrGCxJqDt0QC6Cp8ur2CuJaRyR8Mot2zNm1BlJBEsVgNoMRlAhDQYGlxs6YVYLNpQC0TG/HS5HrWXneqJO1ZtqkXTNFyZ+aR4XCieFIQ3LVyDJ/53/P2LC6n97Cl+fPdZXn35BUDXaoznw8gBqF7xPalDJiNcugnLpQid0Wl61vHw1p/gkQOhcauegbvH2VHXEULoncHSstGa9XBNJSXDfOYMBhDSZJSWtNfgPPbdrcD87k3AANKywoyupb5HvNO9ygl83pPf8s2aatbefnh3T6VNPPvNep79Zr3tPHc+D0C0BqDJSLMXK6xqv5SRxuV25gCDIMUxgCRMTTsaZz8+H6DbnrmgrQlITwQT4TBQKaWZqGSagFzxJqC2NIBlWxrMzwYDaGjVx0q3qTiqNoVNNem5piTtSs9Gba6zZfpb6ltRG/VzVq9cCYVj0GSkWmdLUEVKSUtdFem77WueJ0SEaM8KfsQl6x+E/FI47UXIHxp3HQNKei5qUw3C7dWze8Nap+4DkKiqxK0oPHrWZMaVZFOUFZ2lnKgkti8tDRQ3WktDjwjs6FUM4Js1etyxpkmUjvStc7BdsL7QmpS4bNiYFnNMW31TDWLWmSagXRWqTQy/NRHM+B6rVRkEr7Y5QEuYKbSnAVjHh0hJCDuCbjKAjDyzGJySkoXW0mCbR5DmdaE26u/vM08/RfbhhaiH724e6w+paP4m1FAQV1oOoDdsn/r6//F282Za61JJk82sSJ/EsAvmQGpue6vQ/w8FotZi+ABCYX/CQaP62J5tzYa+/MDdzM8elwtXamY4Cqj70atMQAZ6oqmgPfQAYWGbYZXME4X4RUUBWb7bJcsYdu04BtADNYDuRtDOBBROBJNqEC3ojyK4hg/AeEcm3PwB367ViXVbvWytDMYYz2Aqdr+LIc270nMZlK+HZRqE0e4ZSfG4UJtqyBm2B3379qNpySe6pmg4qEMamslUcs2G7anNm1CQpMlmQij8kHtoEsQfguVror4rFhOQJiUhTWuz5IV134i+WeZnj0ugpGaitjpRQN2GnbHGfE9QF7cVdsQhFtFRQG2XgTY1gBgTkFM4Lh52WbyGD+DJW66g7F9nElQj97E5LO3bZf22pQH4Q9FmPoiYK62/yz0frtCPMU1AOWbIqZKWhdpSH+0PCn9OCzMAX9FgJu2xB/4NS/D7Wy0agGZqFUpaTlzDdgA3GgdvfTThGqwoOPJKAHL20wvAGSYyt6KwprKJoNp2IxvrPqs7wK0oKKlZPcYE1CsZwM6oAZgvVYJn7qOlW3tsYpQdcYhFlPVGSlM6sntHEpmAkukX29tgEN/KxgAry3U7vZQSBY1Fn7+PDDRz7z33mMebGoCN1N6WBmDVMAzCFtvTQUrJPz/UO2CpTTUovnTdxq4IslM9KCmZaC0NUdnfZqN5fwMyFECk5zNu3Di01gZuOPNgs7DagvfnsPW56wDw5PYzG7bHIieYnEM+fdR0Bl39Jtn7nBxei77dpQjKalrC60l8vrUiqvWz2yXw5A/AlZbtmIC6CzujpNiW1lLXEuT8p77j/Ce/TXhMd8IfY36wg3V9Rsge2Ec/BRM5gXfC37WrYWWKN4Z7SaiapKVyk7n944/0KpzPfLOO1xbq2+38KW1pAIEYHwNY+zpHpHQDamM1rgy9R64/qFKU6cOVnoMMtlJfHynxYTwXrbVbARgzcjeuvOoqsvc7g5qtm2jZ+AsAaxZFCgAelrM24TzrvPY2+/ZgDQM10OgPJTzeFaUBRD57XAr5h/yGwmOu7RGhfb2SAfTkjNGEYZJtzNkfloS/C3dE6mnwWyR1a5XP95ZsMU0OVsagWhmAzf0wiFpsdzTHBxAP630tytQjVZrqa/n+qZsAUHzpVNfoztXr/xfpyxRUZdwzl6wGkMgHEMUAmmpRwhm3XrdCYaYPd04/ANavi9jf61qClF7zFm9+sQiAv555IGmpqWSM0UszqPV6qGlDjf731t8cy8O+e1gvi2iR3qg5tkgvn5b8OuEa2oIRNGIl5vVtaNzW46wBJ1bTUE94WnslA+jJkmJCE0mMXdWKtl7MngC/DXF4fdEmLnp6Af/9el3UdtB/H4Pwx96PeSsqKW/QOz81x2kAPfs+dAesBdyKc3QG8N0Ld1Nfptvivf1HUl0V3z8hpGpxfhh/SE0ooET7eaL3GbkIVkFAbarGlZ7LCxfuzQEjijh1z4F4woXVVq5caR5XFc6iDdXpppsRuw1BEbqjV1FchMK1fxorNrHfmP5cW/ARb6h7c0jgDq4OXkBrWjESQblSxHXqr1hWdGiiW2XiqfP2jNtm0G0rAU+U4QzRDMAdowEYcHwA3YSe7AROxkQSi2TD87oL/pgkJIAPftZV+tRwCGCcBpCgtPC9H60wP3dmKYhdFSFVY/IgPdbeuD9NVVvN/Z6cvtSENQBr9m9Qk3EalSYT32M7DcAs6R3++9EvOhGXoSChunI8OX3Za0g+QgiOHF/MXb86BFwe3nvvfXMsg5CG6rbiTs0gJycHIQSKy0V2QR/U+gqyglU0VW/hwPwK1oy+lMuDl+LHy+vaNOp+/QPnDXqfC/Ke4FV133ab1QBMHx5fddgwASlRGkAbJiCL3d8V4wMw0BOe1l7JAHqyDyARobfb/u3aam57Z+lOoAFYTUCwz20f8eaPeqZzpAuYvj6PS7Cmson7P9HLB8T+VlmWrNKuNAFZm9JY8eoPGym95q0e63CPRUiVZgVNI5HLH3acTtzvYJSUTGpralBVFbdizf7VbDWqpgR2bzsnsPHIGkzD6N0brNkIUouqpAmw9/D+pA3fh28/izSJMeag1m0lNS/SeEURgrzigSjVa7i94U9ICd/kHMrmiVcgUUj3ulh7++H0yUrBpQiCqh5YkAwDsINiRgFFzt+9X2bC4xObgCL3uCfQIYcB9DB0RAM48aGv+Penq3u8BhArHW6qizRyia37H5tCH88AIrmL8ZnAnccIz33yW4Zc93bc9gfm6uaJLXU9oxmNZjGX2SGganjcCj6PyzThNFZuZMi+R3DpLfejpGYipaSuri5KOtVk9O82vI9eX//jX+yjaPyWe//Gos1UNPgjv20MYw5s1Zm7p3BQ1PZ+2Sl48vrTWFuJDOkM1mDqofpy0iwMwCUEpYXpULWOjdV6eZdFeTPJTvOw95A87j99UuRYRRAIvyNthW62BWspCAPXHb57wuMTO4Gt97j76ZDDAHoYEsW/G8KYsIkDbQr0bAbQVhSQYTs2tnvdCtUfPEj9d6/ZHp+VEtEAmoPRNunONAHNXWbffc7Qtrzurn91lm1pO1a8Nagy5Lq3ueejFSxYV0Nloz/umKCq4XUJUjy6BlBeXo7qb6Fw0Ai8Hi9Kii7FVlVVxRUwa7X8blOH6NU611c3287Fyizmr63mvCe/jTSTj3Umr12IkpYdpwH43Aqe7CKQklCDfv9f+V7vEqY2VpOWGzHNnKh8zJGuL2kISP68Ue8Q5s4qwud28fyFU5kxIlJr36UI8xnc1goAsRrAgLxUsyqp7fFWDSCBCagnkKHeyQB6AOcF+Gx5BfNWRMcrJ+oB3Za00FY0Qk9AVBRQzDoM4mBsdwuNhu/fouajR9ACLQl/q4umD0HKaOaSjBNY0yTPfrM+YVP5WMQSYMPsFLuOF75dz2mPfJ3UmMngy1WVHHL3ZzzzzfqExxga0BNfrOX4B7/k6H99EXdMUNVwK3oJZX9IY8kSPRS0cEApihC4UnUG8J9PFkcRKog2sXlcCopILDzF3s+NtS2mCWjusvKo+xisXI+3727ceeKEqHOEEGQU6pFAhtP3iS/WIkNBtNZG0nIKQFPhveu5xfUwWr5eYqFqyxYUjw/hS4+SsA0oQpjz21YNwJDiDcJuNeXYwZ3ACdzTTEBJ1QISQgyWUq5pb9vOgp5w4wHOsikUllADaGPKRtGtnoq2NADDbGM6DusiNfnVhkpULT/q+JaASp8sH33Dxbdag6pZSyYZH8DrizZx3f9+Ymt9K1ccPByI7UYmo7Ss5oAaVczMWEvsta5+5Sfb87cVa8KduZZsar/tpUGoN9a2xO0LqlI3AbkVWoMqX3+tM6lBI8brfYHDGsDDHyzC01xBxQ8fUnjs9aCp1DU0IaVG48J3eWX+ehp9g1CnX2o7h1gGIIj8pmurmqNMR6HaLUyaNIUTJw+IGyenUI8EUusjx6vNenhzTnY2vHAGLHubZ+UhzCudCfyaQPlqPJm6M9ltU4XTrQgzT8HVDuFOBOMXNYh5e74EKzNNZAKC7q9LlmwxuFeASTHbXgb26Nzp7BjsSAbw2fIK/vDSIj678gCzl2lbSNYHsMbSuq+tcLSegLYygY2kLrNxSbgxOECooSrufrSGdIJvRA+1BFVyjOOT+F2Nnrc1lt63sRFIVjW9oTUUwwDU8LztNYgGf8g0U3VOvkniMQxhwd+GNhMIaXhcQncChzS+/vprfAUDyM3Va9wrYQ1AbW2k/O0HANj8+KVorQ1cvHgGmxYvI1S7GSNQVL3sN/bXUePNkNb7aoTuqq2NaP4m8vqW2I6TU9gXhGJqABCpG3Q+c2B5NRx6B7e9U8rIDD2zWWuuw12om5NsNQBl+zQAtyIseQBKUuO4EpqA4nsIKN1Y47dNdiiEGCmEOB7IFkIcZ/l3DpDS1rk9GTuSAdz69lIqGvysrkyuD0F7UUDGo3LAnXPNfQ07kAFIS7euZI//xwfLze/xJqBoDUBtqjX3qQ3xzUFaAiqpHpcp9VsdwcmUgjBGs75yaht+hFjmamoAMccZ73ttU+T4ZBrh2OG5+eujkrISIRkGo/sAFFI8LlqDIb7++mtSikeS6nUhBCYDMGrzpPQditaqE9b58+YSqo3uS/GX357DBU/FZ5zHagBVTQHe/zkSbmqEQoZqdQ0vv589A8hKS8GVkUeoLnJuSdNSAIan1ullnPe6SK/zH+7cBaCk61nFHhsJ370NPoCTJkfmZzxrEKnr054G4E7kBI45r7vN0e3pQyOAI4Ac4EjLv0nAr7p0Zl0IOwL2yoIyvlpVFbVNSsld7y9jxdaGuOOThbXkbuzY7c3t0c9XU3rNWzT5Q22agNqKR+5sPPr5GoZe97YpSbeH+hjzVLwJKDoKKNQYSUoK1W6Oe0FaQxo+j8t8qQ6861Pe+Wkz36yu4uHPVrc7n9gyBbFzimUisczVDG2MOS4zLPVXJ9AsOpL0Y2WYbSEZ4hHSImGgTY0NVFRUIPJK9CYsQqD40kFxE6hYC8Ckg48jPSMS3ujOK2HAb5/n/+5/CYCWlfN566PPba+TqAlK1HE1eqmJgv6DbPen+9x48gcQrNR9H7OV+ZzZ+hwAr4+4Ve/di/5eSW8awpsKgAhnFXtsnPNGPwBIXgO444TxZKbomp/Rg1g/PzkNQIliAJHtsRpAd4cTt/mLSSlfk1KeCxwhpTzX8u9yKeWXO2iOnQLrC2j34vzhpUWcGuPEq28Jcd/HK+O2dwSJzMGJInesROOJL9YCUN0UaFPa66gG8MP6GlZVbFtntOe/1V/Miob4iBM7bK7T7dKTB+kvaCInsLHuUFMtSmoW7txiglUb4k1AARXFX8/vTplF3TdzAJjzw0ZOfvjrpOzldrBew2BIaWFzndW/snRzZPxgjA/AqGhpNS1Zj+mI0umNyhZNbt6JYO2jW1epS9/uzAJSw0xUKC58xSNoXqa/zpnZecz5+Bv6nnkXu43fk/xDf4uSksFuoydStO8J+phVG2znEhXi6G+K2m9oQ6EaXaMoLI6OADKQ7nPj7TOEQOU6LmQOf2z9B//6TgMErX0mmscpQhBQpVn736grZEeYE4VktgdDeLNG+7iS9AFEJYJZtJLYRjGPJCG0dCWS9YgcK4TIEkJ4hBAfCSEqhRBndOnMOhnWdyVZ26xBrGJf9m1B7IuciPPbmYCEaDsKyMpM2pM0y2qaOfaBL/nd8wvbPC4R7CTotrA5HC8/ZbD+gsZaRQxJ2ph3sKEaV3oOnoKBBCttGEBIpXHjClb/soTauY8TrNzA7n0TJ+TEzd9mm3VOBqGyK2VcbmF6sRFHBsOwRmRZtYTyhuTzBhJ1k4pFMhYmPQ9A4HUrNIYzgF1ZhaR6FFM48fXfHRnU55eRk8OQQQPwFY/gzL88QUrJ7uE5KZQcdA6gm+ZioTdI0clJ7efPsOHuk2ktW2ruN308NZtxZeSRmppuO980r4u0okGghji89gUOednDivIWQJLi85nHKQJ+WF+LO1sPDTWaytv14k2UldseDBofrQEkFwUUxXQs14w1UW2r0NJZSJYBzJJS1qObg8qA4cCVXTarLkBsrZkdBdMEFEN6EknticwGbZWDttrB25MKjVK2izfVtXlcIhj3MVlBqqJeJ5rF2brLKFb7MmrVGNvrNq/BndNXNwPUbCQUDEYxbH9Qo7UqUskyULXeNvIjEYx7ar2PVmIeG91jZf7W+xx7nPHN+pxZf4vZd8ebTRLBKsW2Ra+SMQEZPgCXImiu0qVvd2YBqV6X+Wx68vqbx2dn55rJdlZtxuMSeH0pKCmZhBqr2BCTD6CGM47rv3uNui91k03ruoXmfjOhq3YT7tzihM9PtlbP3SUfAPC3jRNZVq+b1lIGT8Ljjnes5h10MTn7n0Xq0Clm+8dYtCWBtwXjGlYfgF1RODtEZwJHtluvf/peA1m+tbFbawIl++YY2TeHAc9JKeOrR/VwWF/MjmoA2xPVZ5wbe0krAWmvYYoQwrZJhgGjoia0z9yMtXdEEoo6P4EGsGBdtVnYzQqDiORn+MLn24eBqppEbaqlfst6fCWj9CQhTaV68waGXPc2b4VLRwRVjaaqiGMyVLPJNqa/vZfKGplhJaRGSKWxPmuZY2tcfOx9Nq4X9btajumIrdeTpAno1reXJt6Jfk9bt6xi5YJ5zJ/zKKtevYe+xf1xZRWaPgAAt4UB5BX1JWflq8zzXs4/ls5gnvdyjlLmhfMABK7MfNSGKva745Poa0ndBFT3xXPmtsDm5eE1SN569mGCNZsJ1WzGndPP3hlbsZzL117MkfkbcbvdvFGWidZcT9rwfSg6/oao+2IQWE/BALKnnoTiSzMjw2JhlQ86YgISNgzA1ADaYSSJzE5WYeWqQ0by1bUHdkrY8LYi2TDQ14UQvwAtwCVCiEKgZ+TCJwnri5SsBmBIcNvz8xjnxhI+69fWoGo+3FbfoilVapG6NN+urWHwtdElCqxVMdtLLzeWvq3RB4YmE3ud4x/U67GfsXe0c6+mOYjXpZDhcxMoX8M7nyoEazaZlR8NgqlJScvaHwBIGTjOvEHBmk14CgbwzuLNHD6uHwFVo6F8IwMG78bG8kqC1ZuiiLQ5T2nPuE0TlmWb1ZQSW2Du8ud+oDWosnvfrKh9sU5gWw1gG02HdmaMiTe/z7Rhuunm2kN3JzfdaxbUS4Sauno2P/lbHrJs+921N/JgmcDndplMy2spybAv3+N9+2+UKLqmWKJUcrvnUZZsGYTbNQIlNTPOvg/hdyXUgtbaSM6McwhsXkmgPFzPqamGOQ/ejvClI/1NeAsGxCWdsXouvHgWKZrCGaE/IvL+RePCdwDI3ONIhMsd5Rux+21TEjIAC+PYThOQmRDWzjgJS0FYPmeneehuJMsAvgceBDYC1wL7A7/vqkl1BaJezCSJX6eEaCVojG2dT0tQNaNIoiX9yLFt8SwrYWrPBBTpztT+1O1gEMtktaja5gA5aR5CQT+bn7iMPz6hb+93/gN4CwZGTEAaBDb9gjc1A2/f3VAbdCVTbdL/GpJXUNVoqNjEoJKBlAe9CTUANUF8tcHAEpmATA3Acs5VL/8IwA1HjDK3BWPXb0QHWSOKtjEM1E66rGkO8sYi3fTlcSnccuzYdsdZ9ONP5udBY/ck9+jrmH30dB68/ws8LoHx2Ci+iD3+wI0PQTA6oSxNBBj/061Md13MCp9GfV0dWTQSCqm4ww7SkCYJ1enlG9xZRWj+FpqXzaNl9QKEV+/5K8OMw1s8kpEV78I/j4W6MkjNgZZaKBzJw4V/4fvvA3hy+xEsX03qbnviGzDGXLcBO0m+usk+Mm1bNQBbJ3B4W3t8JHE10J5VfCFZBvAnKeU4IcQ04BDgTuAfwF5dNrNOxrY4gQ3pdHtUNOPM2PBCK6H/enU1R40PZ0BafQBEImTakuybO+AD2N7kJNPUkWAcIxO2ORAizeumpjlAbpqXRd9EB40Ftq7CWzAw2gTU0kB6Th5CKOEYb4EaDgs1pLiQKqkrL6N46t4sqVVoXb0Af0jDrYioOSW6X3ZObCudnreiirH9c2zPfesni+kpkQYQk1S2LbA6Cu2W0RxQkyp8t3GjzjCuuudpXCXjeG/JVvMe6c3NI8f2Oe12EAqZ/ltsx/IG67g5eDtrU1uYVxHix5QLkbdeAmkFkF7Ab+q9FG1u5DvglNxfqMr08txX0PrJg/SfejiGrqKk53BccTkHLP8PaGEjQksNCAX2vpiGTcXAWnKmn0PaiGmkjdjH/K0S1ddpD1EaQIcYgP43SgNI0oegJNIAOuCD2BFIlh0ZFOZw4EEp5WuAt43jexySdQJHhYt2grPYeE7VsBnn5jd+ZlVFY9R8Ln/uB/NaiTSAtmzaHXECJ1uB0B9SeWDuyjjp2jQhJbhOUJW8v2QLo254j0UbaqltDpKd5mHlL3piU/+LdRVgt/QgI/tmRtUC0loaSMvMAUC43Chp2WYWqKEBNJRvoLWpgcHDRuDJLUZtqqGhvj6uOJuVqC/f2sDj89YA9lFAVk3vnx8u5+/v/WJr91tg6bgW5wS2YYzbzADcbROJJn8oqQKAGzfpDKB02EjcLgVVSpNxGLV9DKQMGEP/kv5owt6MEkjtw28y7uYb9x5s9qfwl+AZNE66WI/LzxmIV2uhfMNq3Ar8rf8nPLvb+9w8w0d95RaOrn7aHOeU4SHuTn0MjxZjQZYafPZ3U8r35PYjfff9EEpkPta70hGZzCqBDynMSPo80wfgjvcBtHd5d5QT2KIBbGMpiq5CsrPZKIT4N3AS8LYQwteBc3sErEXWYqVg63erPdkgDNvDsw1JJaRKVlY08vgXa/jNM9/HmXSabFojGp9UrW1iYnVOtme2SpYoPfr5Gu54dxlPxzh2zYzdBOO0hlQ+W6GbAhaV1dIa0kj1uFi9fCmuzAJcmQUIt48cmvC4lKhaQFprA+lZ2eZYrow81EY95NAwi9SvXgTAXvsdgDvsR6jYtA5fLAOw3Icj7pvHzW/+jJTS1gegxphqlm9tP0ci1rxjxxi3lQHEEonYcTbWtjD+z+/THjZv3gxCIS+/EEXoyVAGg3IrIkqK7ksVL3pv1h86ly9qnGbpZfOe17HOtxtbvANo9Qd5NHQoW6ZcDUf/C059jluL7+O/G/qh9BnORPUp9m69j//l6JHij63MMceq9RTiIQHzqivb5nr9bcF4dgoyfAwusA8/tYPxM/iiMoGTI3uJwkA7EoW0I5AsET8JeA+YLaWsBfLYicNA40MRIy9zsz9emt6uKCDjGqpmNuRQhIhjQkajDTsNQDcBJb5GR6TOZDUAQ6uIbQDSnhPZyowEOnN1KYKN69fiyStGCIErI5eaqnLcLhGVCKa2NJCRnWue787pY5YOMCSqQF05QlHoVzLQDF+s2rguznFqnZ+hxaiajITjWn7TWGuKnRM2FvFhoPGMsSN+lkBIMyvDxpoJYrWwleVtM6jfv7CQyX/9kM2bN+FKzyHF69ZNPpo0TZFuVyQPYKDYykvemykUtcyd+hgc/S/qvH3RpKBMK+Ca4AX4Rx2PQKD40kALgRqM0kIa62qoXfczqUOn4MfLFvLZmj8ZgIqtkbo+i3IPZqMssJ94dkmbGbbW97Ajpsz0cI5Guq/9WlxWGAzS67IS846dC7uACUhK2SylnCOlXBH+vllK2b4I0oOQKD4bol/mKA3APG77f7SQJs2CWV63Eq8B+A0NwLrVYh5J1nHdnhM4Sb+kobbGX7dtDcAf051M1SSKEGzdvAlXZjhpJ7OAys1leBTFJEjBUAi1sZpMCwPw5PQjWLsFKTVciqITsIZqMnPy8Xo8ugaguKhYvxwRbEaqEWZlV1bb6AoFICy/aexavG4R94tLTSVUX05mOOM3Pgw0PFZU7kbkc6avbXfbHe/+whmPfcMP62viNIDYzmdtFX8DPTO6stHP1i1bcGXk4QnnAaiWOk6GBjBMlPGS98+kixZOC1xPXeFkGHcSj01+nSH+Z5gWuJfXtWmkelx64bKwQ1fzN0cJBzWb1oCU+PoNN7e58/vjDtfrGTDlYPIPu4L++x7PHaGTCCkxpcQ8qTDzhjY1AOuv0pFcnrxwCHJHNTIzT8LqfA5/bs8vmKgW0M5qAuoSCCFmCyGWCSFWCiGu6cprWX/7OAYQk2gUe9z2aACmCUiT5ovrdStxNv3GsOZhJ0G25wS2or3Ak2THMZ7ZOHNZOz4AvetU+Es4f0HIEJXlW3Bn6pKfJ7+E1Ut+oGnrWpP5fv7uG6AG2W3UeHMsd05fUIOoDdW4FPjXJysJNVSRXdAHlyJQPD48BYPYunIxC/52Gpsej1SqtFunVdOz0hlrUp5UQ6z97pO4c2vnPsnGB8+DSt2XEOcENu6LRZiwMoOxJbpp68eyWl74Nr7Gv1Gao6Y5EFXPRiJtCX7t5/+l6t374rZbUb41wgB0rTOS2OZ2CTKrF/OC92YEcHLgBhbLIRGpN8akluJxoUlQUnQTitbaSKOFAdRt1au4unP0rl39slMQQmHPEy/BUzCQ0gNPJ2PsTHw+D69r0/hy1A2QPQAQ+t8j74VxJyWtAdg9f787aJjtefnpuruyowEQxvWsv0eyPgBlJzEBJRsF1OkQQriA+4GD0bOLvxVCvC6l/LkrrteWc9f6MlvL2nZK1VARuYahyvvcSpwJpdnGBGTOo40wULWlgda1C0kbuS9CxI8bi2QZgPHQxl43kQ9ADyuUtAa1qIqbqibx11ehaRqurLAGEK7f8tWjN7DfNU8CsHDBfIQ3lSOPO4EPX9DDLt054eYgtZtxKQr/+GA5amMVucOGmATc22co5T9/okv/rU1ITUUoLtv7YNX0rMTk0ud+MD/XfPIY7yx4gz6n3goIqt69F6F4CFbpRHvta/8g/4x/JkwEs17XrlvZUeGmLSdPsa+FA9Gx4qqGbcvPui+fB8CVnovUVHKnnx13TPnWLbhKJuNxCVyKPjcj5DWz/DuKPzqLLaRweuA61sm+UfclngHoQouSmqXPq6UuSgOoL9+IEArubL0T156D83ht4SZGHXAsm/vug7sgHSqaTBPIupIj4IT40tKGjd3rVmx7DETuix0DGB63DSA/Q2cAHQ3rNjUAG2LeoYziKBOQowEY2BNYKaVcLaUMAM8DR3fVxZLVAKwN1o3tnZEIFgoTR9DjimOf30Z/YiewFqMBaEE/aoteQ6Rizl+pfP1v1H3xfPj86JemvD462iJZpmY8tLEvjSFFxTMA/VGKJVaqlDRW6zZgd6ZeryVj4mH6WKGgyXxXLV1Eat+h5KZHTAPuXJ0BBGs2m5KX2lhNXlFfUwX35PePMv2ozXX62DHzPkqZR/ZDE7nk08nM816ux6GHYY2ialqiS/9qUy3VHzxIqGazSfwBmreuQ0oZF9ar2jDGtp65RNKolNHEN6RpUc9kLOq+fJ76r1+KS86SaoiaqkpcGbl6KQjDCaxKpik/UfLmaQRTCznRf6NJ/MHe7AG6BiAluNJ0TUZrrouaV2PFRtLyihAuPZ/FIJSK0P0NreF7bCRrJRL0jd851qkfC+N+jh+Q0+ZxADmp3vDYHSN3xjNkvRdbw+9TaX7yzmQrtrUjWVehOxlAf8BaVrAsvK1LYCUIsb1TrQzA3gew7TAkqpAmaQnqhMrnVuIIlBkFZGNDVjUZ5VDc+uw1lN17mj7fcBnfui+eRfM3R9n4v1pVxZ63fmSWUdDHTG7eiqkBJLB1x9nN9UepNahFzVXTJM3VkSJkAO6MPA487RLqt26gpaGOUCjEltXLyC8dGfWyubMKw81BtuB2Cb01YEs9+YV9LXVsortKGYXKrHzwKGUet3sexdVQhkBSolQye/Vt8OOLgC6tjuybSe3n/0Vr1U0xgfLVBKs3kjn5aFMTSRu+D1ooAK31cU7gSFnreE3T61biTEZNgcQlvK33IKTKOKaqBeMrsTb+9FH0fQj3VXBl5ONxKyZDL9j4AY95/k4oZyjLDn2RzUR3XEtkAvK49GdWSdUZgNpcbzZaB50BZBZGaugb11OEwKMIM1mxMFO3xydqyelqgwFEJ+/p9/a8fUttx7GiT5aP86cN5rFzJrd7rBUGk7aagIz+ELv3y+rQWAYcDSACO1YYR56EEBcKIb4TQnxXUWHfqDsZWAd+d8mWqH3Wl7OzfQCG4yqkaWbCltUHcPbUQQA0hX0AVrOBGb0io5uwBLasAMKEwCr9NkZ30FoSLvj2/foac1uyiUp2PoAfy2ppCGsqsSYQI00/KgpI6HNvNjWASPTH4HFTkVKjYtkClixZghr0M2DY6CjCIxSXXnumvkKXYMNZwXlFfcz5WQuZAagNeiSNlXFd5X6RNBGdJerRWuGjm805e4KNplkFoP7rl0FTcef0pejEmyg46irSRk0HwNVcHeeYjW1ub52Dz6XE3a9Gvz0DiK38GlSjNQC1tZHq9x80bhBZex2Pt99wmhbHMIBw+KwrI1d3AgvBMco89v72Cn6WpVSd8AoyvZBYmKa18O85ok8m864+wFyPK00nfFpLfZSw1FS5kcyiyG9hagCK3qbRYAB9MnUNL1HWrmEiaq+3gPFcJtODQAjBn44Yxci+HSPa1pBZA785YDeumj2CI8OJmx1FT/MBdCcDKAOs4lsJsCn2ICnlw1LKyVLKyYWF8Q9ssrASspZA7MvbtgYQHxOSPAzmEVSleV2vSzEl1AN37wNYi6JFzjUbkGsRoiBDkRcnsHkZMmQpUdxYnXQpCH1OiU0LisUH8GNZLQvW1Zj2a32e0dfpG672+cuW6OY5mgaN1VtJT89AWEoOXHv2kXhTM1j/+Ss8/tR/AJix//5xL7Q7q5BQfQWahFCYuGflFUUKmeX0jTo+WLMxMj9NgzWf019U2i+ybgMEW2gNqjSWLQOgz6m3kbnHUeYhvuKRePL6k777/iaz0arWxiVixfY1MNYOugRpbE+36TMQi1jTkVUDqHrzLpoWfwhA/18/Su6Mc0kZNJ5AxVq0oD/iiwh3+HKl5+FxCcZueYV/eB5kvjaSMwLX4krPjYpOOXiU/hyKGA0gK9VNSW64lAMg3F6ENxW1uc58b7Zs2UKgsZbc4sHmeFYNwK0Ik4n1ydI1gMrGRGUbFPOeJdoHkfvdlRK1VYMzkJPm5ZIZuyWVr/DbmcP47/nRxRJ6WhRQtzmBgW+BYUKIweg1hk4BTuuqi8mYl8oK63d/B5KqkoFpArI4gb0WJ7BB8GwJiGFusTiBQ/URLajm06cAyD/sd1S9fTdqY3WMEzJ+PlGahKolLKBlDQM95v4v4kxHsffQiLT4Zk01/XNSoo5rrN5K3+JiQuGbcc4+pQztm83QydNZ+vlb3PvPn3DnlXDA5NFxpgdXViH+jb+gSkmwQk9KKxq4m8kAhMvyCAuFYFUZJaKczK/vguUvQe06JAKRqLfuHUP5HZO4O2yM9BQNJm/gWPIOuhAZCiLckYJdnoJBpGbn07JmIU3+06OGMX4/u3IUuoNc/+3TfG6aAmpCBiBltBYY1CK+Iy3QQsuaH/AWjyBt6J54souQEnzFI0BTCWxdzc/hpjURBpBL0Y//ZvSK2/hIm8glwd/ix4tHUaKI2F6D8/jg560MyteJvfFc2lUmdaVmoTXXmdFJCxYsAKDv0NFsDS/LOE33AUSu0z9X7+DlD8Y7tq2IJezHTerP2fsMMr8bz7kdo+gsqNvJZK44ON4pvVPmAXQFpJQh4FL0BLOlwItSyiVddT1raee4RLCoKCCrBqCZ51hR3tCaVC0WsJqAZFQBMbMSozuy3zrPqLlrkWqgVgYQ2LQMV2YhacOn6vOtr7DVAKzTjyIubcSTW01AdkpFnHM4/PWz5RW8vKAsfF099ry+YhPF/SP2YcO+e9C5kVzC3Olnk53mibc955Wg1pXTVF9HoHw1SkoG2YV9o2qsD/nNE0w476+UDh5E6ppPeV/5Ldnz74K8wXDcI1wZ/BXNMrpySUD4YNrvYfzJjAn9SN8N75ObIvh35qMcqXxJGq1RxB906bhoyCiat66OKsENRGU0R+5JRII0fhejc1iifhChGIf/zx++yOJF3wPg37AEtBA5084gb9op5u/qDcfe13z8KIffO0+/douuif028wMKv76FVUWzuCh4Bf5wBReXS0QxgPOnDebzqw4wbdsG0bP+HsYalLTssAagr3ndOp0x51naPEacwCKqTPMho/ty8YyhXHvY7rbrN965WKJ767FjSfNGmH2kpEXXEdSOtpFMBtbCcj0B3aqPSCnfllIOl1IOlVLaV6HqJJjSmKLExconygOIdfSB7gTa85aPuKWdWuwGIhqANB9uq0TvdbkIVpVRWx1vuzagatIkuP5NvwBQeNwfyZt9GX3P+DuKLx1XRj7B6jLbYnKxYxloq9OZS4mYgOwQG20UbbeOMNvyb95g6+qlUYkzxsudlZtnRo34+o8k1eOKeqFz0zz4+u8OSJYv/FqvJZ/Xn5BmmKgke4ql3JY7h3kD7uWxaVupbAwy7bUilh7/MZz1Gow7iVe0GXo2a3p/JHp262sDroGDboQj/snR3sf4tHUkxX3ymaws5z7vv/jedxH/9vyDo5V5ZBBpflIwYChN5Ruob45EV0nL7/nVqio+WrqV5+avjzAAiw/AIIbNCWr5BFUNTUL/nFSmDMxkwXN38fvT9KipltXfgcuDr2QUfzt+XMQMlpGH4kvXTYJa2GzY2oDX7eLK1DdoGn0qn469jZBF4fcoSkzjGcGAvDTze8iGEBsOUFdqlu4DCAsQdXW6ryk1LdKZzdAghcA0IYFOAK+ePdJ0BsevP97sAvFEOPL+dB0JM36z2LlsD4yQ1J6CnmWQ6kIYD4zHJeLa+VmJmVUDMLUGy7Fbwy0OP1uenEPaGMMfUiNmHjUi5TXV1bDp0V/zt4uOjZqnFSEt4gRuXPQ+ruw+pA6dQub4Q3Bn6Y5Vb0EJwaoyew3AmkBj2d2WD8Ag2InMYLGn2mouUlL+zWsAnHL6meZ244VyKwojrniavz7yMq70XL0frOVle/r8vUgpGY0rq4iv33qeUH057qwiUhrLyP/un3zqvYIXfX/hMOUb3mMf7u93E+7cfvywZBUvvPlx1Fxe16bxw/Gfc924z5kWuJcfcg829zWHoLyumeGT9mNv/7840X8Dz6kHMl5ZxT3eB1jg+zWPeO7kOOUziov7ItUQVVs2WtYZuc6K8kbOf+o7rp3zkyloeN0u83cxGGuiTNZQ+NkQAgK1kXr/rWVLaPzxA9JHTkPx+EixtHQEyAnnAajNdShoTPIvoDBV4/HQbJoO+QeKEi15ul3RtYBiEVAjzMtAbbPOAAwNwMoAhMtDSkrE9GdoAC4hGJgfYQDtOUGN++SLIeyJbO5pXjePnDWZxzsY4ZMMzCigTmQyThRQN8EwfbhdShzxskrCfpuuT1bp1ejslJWaXDMH44FuDWpmlmjIYlbZsFZvmlFbsTlqnlZoUo/flqEgan05GWNmRlVJBEgtHERg83I+ee9NUyKzo92JCt/FwniBE1UhjdMAbIZSNYnqb2H8AUdx/Eknm9sjDEAgUrMpHau/vGleVxTBGZCbhnB78PUbTu3WMmR9OcfkruL8BUeT/+1dlMlCfhe4hCn+B/g//wUsYBTFF+jtT9atXRs3n5AaqYZpvQ+tQZWGqq0MGDAADYVv5Uj+HDqbqf77OM5/E0+rBzNKWcc/vA9xWfP9AOy26U1orjbXaYeIBiDMZ8ygY9+srmJTbaTuvvGMBVU9jFYRgubKSExE3bxnkSE/6bvrkUgpbldUcIISjs8PrPyaP7XcSY5/E1pqNjeHzsTrdscRULci2nRIGuZBOxOLKz0HtbmWQDisua6uDldKunmN3YoyohqnDLRoFu2ZU4xsbasgcNH0IQlLL2SmuDl4VB8OHNmnzXG3BXZRQLsautMJvENh1QBiiVf7tYAiMBhAdkcZQEg1iZsmJaGQihZoZeOGiJT305rNcRFKxhialIQawg03suMf9rSigVQD1//mHJo2Xsctt0QsalG1763ZqW2agCLX9rqUOGaRjAYQUvUCb2lZOdGNscODu10KIUt0VKrXFfWyZfpcTBa/0JBRxvvL9GSscfkhOOB6Ngw4mtMfXhm5mIRT9xzAXoPzOfGxEjaVxZdbCGqa6Xy1/rbNTY34mxvp378/VFuHVPheDqdk9AxuWVTGeLGa04Z8AzzHPhWvwJ3vwODpiBFHkksK+yk/cpX7RYpFJZtkAVXrrgb6h30A4UKA4fU98816XvqujOW3HBp9z8K/tSKguVLXMtweL63rFgHgLdbt/T6PEqWaGglaFe89wG3fKgTT+1Dty6cvQi8FEUPEhBC0FZBilK44bGy/uH3unH6ghqjaugmYoDMAXxouRbDoxln43Ar3fLQivF4osph72quhYwhJVsZz7aH2/gKA9HZqLG0PTCdwJzuaCzJ8Uf2WuxO9iAFE1LlAKMaBZ2EIdnkAVhhqcFZKRzUA1SSCIU1y/83/R9m7b/DqtGnmsbNveQ1PQSQyNtRYjeLx6cRf0whW6qEq7hydARylzDMJzjNZaZwVPq+sTHfC2pH3bQkD9brtGIB9LRwr/H4/WqCF9KycKHODId15XIKgplFS9ibzvPeQeXsVZPfnbOUAMkUryr+u52XfGv6YEcKoPPjFuL/A9AuRVc3AyqjrpXvdOhPJLmJj2QZiEQxp1LYEou5DUNUI1Ov+l5KSkigGYI7rcyNRWCh3Y9L46cBznPNaK83DDuPi6pV43v4d3/kABC6hm+tKlEr6LvgTRynnUeU+2pQmrffBTgMzfACKEDRWbETxpjBi0lSWfP0JvgFjcIVLMcS2NszMyTWbrqyo1lBaW0gZmAvoZhe7VohtaQDD+2Sy6tbDojSH+0+bxG+e/d4Mh928djlwGPX19ShhBmAIRpHOWYKCDHt7vx06Gt7Z0QqfHUFspF5nwcir6AnoNSYgzTQBiTg7u1USttpmIyagyLEG585KTY53GgzAH9TM8Vqbm/j49ReRgRY+/fgDs8JiKJy8Y2Dj/Wex6bHf6KYUTdKy6luENxVfvxFmdmuJUoki4IxhTTx/UibDB5ewecvWhIlGyZqATAagSZNgB6s3ImV8whPYawB1tXooYnpWTpQEapTXdSsKR4p5HLjiFkqUSgQSUVfGTZ6n+T/PS5BdAsc8xHMZZ5nnFpWOAOyT81xhQufOKmKzDQMIadJk4MZ9aGwNEQpnD5eUlMSdo88zcrH0tFQOPV+PXrrmCzdcvpDGcz6miVRcQnL1B63k/q0Bf0hywKNVvPnQ31l0z6+QKz/n6a/XRTWVsUNQlWz85QdaqjbSULGR1Lx+TD/l1/j6DCX3wAvM44SIGICyaOSF/H/z0klpjBw9Wl9fcy3eIj0u36Motvb+9kLSY81Gh4/rxz5D8/EUliI8Pr5+8QFUVaWurg7Fl2bbBEURJHT42sGYZ7IadldG1XRFFBDo5TAShV/vaPQaBmDQJ4+ixDmBrd+jE3niGYCRsZua5A8YsmgAhtRcWbYagNwDL+CxZ16k9NQb9TkGmuPOVxsqUTVJMKTRsmo+qaUTEW5PXHarEIKTdxcM8lTz6aKVjLnxPfsG6FYncDtlhfXjdROQ2lzHpkcuourtu6PWZT0uFg11OrHLyM6JIiamD8AluMr9It6Y7lBCwBaZC+e8CRNORcmNZF2mZegScKxJA8IljhVwZxdRU11FU1OTOR7o0rVhwjPmv6aqySwf0b+/fSWSP8yKxHO7FMF+x5+Lt89QApuWgxCoRePIoAVNSu74MkBDAD5bpzJvvcqaGpUlixez5JlbqLtjCg96/smvXa8zVVlCJvG/d11NNf+7+Xx+eOj31G3dgC+vmMLBoxn26/vx9d0t6h4JAfnU8bz3FsaINXww9Erqp14Wuc99hpr3yi4ibFuSkqQEV2omeQf9muoNK/jmm2+oqKjAlZqVsPl6RzSAC/YbzPnTBnPTUaM7PLeuQlfmGnQ3dt2VxcAo4uSxZOEa8fxWYraprsV0FBrbKxr85jYjpb3css16jVjJeHM4amje03/nnrOm0fDD26xZqWedpg7Zg9mHHUFanp7NqgUiTkEtECGKmpRUbVyH2lhNypA9AChOkN1anBo0C6KZzeKF7szdUtcaV/doS11rlFYgpWRzXUukuJnUCU2oRndSNy3+OFxzSB/PcBJrEqYOyY9Sl2sq9BIQWXmFUcW/DPU+ldaEWbpF1Jqfs0sjDdBdFskyFmurmlGEMGsOLViyIsqJ7Q9p5u9hMKxV5Y1m+YhYBjCuJJsLpg0mJ83LpQfsFr5uWMvILyFYu1n/zaVkkyxgaUXkeXhjua6BnTQ+0oLwsv9VUNy6hms8z/Oc9xZ+SrkA7psMcy7koPr/MVGs4JvP9Cxff2059Vs3omT35ckv18YljvXPSaUv1bzovZnBYjMXBP+P97QpZgE9AF9JhIjaKXt2ZqFk4es/EoCvFv7M2nXrcGUVxTRf1/9KGUkSTAbpPjd/OmIU6T43x08q4Vf7DW7/pC5GV4aadjd6hQ9ga30r5z/1HUC4C5XG1vpW9rr1I/YbVsDxkyKq/5zvN1LVGOCp8/Y0iURrUOOSZ77n4bMm0xJOAHpt4Saa/CqPnq1HsKyvamb/v3/C7NF9eehMnUivqWyiuimADAVYNe8N1EArrWsXsrZxIAgFd45e1Kw2qGsTmj8iEarNtebnBfM+5n9/uwEwpDpJA2lk20iQWWletDADuDfsiAP4sayOo+//IurY+WuqOfOx+Zw/bTB/OmIUoGfynvLw16bUpvsfZFQCWuuGxXyxahC3vfMLF88YytWzR6JJic+jcPCoPmbz9MqtuhMzv0//aB+AS4H133D8/PMT/WRskvkYv4ovPYsDzr2GCm9fkwHYES9/UNOLj4XLQ5xwxxzu+kMOesYAzPm+zDzWYNRVTQFCDZUUFBREhTEC/Oe8PclJ04lXRor+qqT73ARVDXd2H5p/mceef32fPx89lu9DJ1G0/F7z3Kd/DJsKZ5zHPsP8fPnyvwGYteE8+pbuxjhlNePEas5JqaJw9aec1riF03xwy/wgL4bHCAX9FGZ68KOioj8jRynzuCvvNTz3buJDRRDAxZmBa/lO6gRZKC6KTvwzobqtKN4U9h9eaP6OsUi2wbkVew3J46vVVRT3H8Am4I7/vo2/tZW0jIIou72hocnw53SvK6k+xlbcddL4hPte+vVUU6jrKpTmp7G2qplUb88w13QFegUDsBaecrsUjhDzyH34Slb7NrJpXQFl+f8HROqzfxqO8bf6Bj5ZpkuzLZYw0Q+XRiJ4KsIVRq2F5oyqo4GKdahhiT7UUIFwe/XeuC4PigDh1dPjZVgDaFz8MVVv/cMc5++/P9f8/NjlR7FxznVki2ZCUsEtLOYrBMVpIaQaZFBoLevcpYCekRtbARVgU60+J6tduqymJWruWtj/UCDqMWT1YNV6ttTpx81fo3tN9cgVwZwHb0XtN4a0oVP0WHmhkFfYxyTcXoKMWHwX/PIoSkpf7gsdw4Wed0ghMr9WfCwYdpnJABQFxh9yMl+uqow05LBhALcdN5blWxvwFAzS51mxjlUVkTLJW8LSf1aK25SImwMqoZpNDB4cLWlaiT/o5StKclPZf3ghj32+BndWEWgqamM1KysaeaVyMJXzVCb197Fwk5/aVhg3ejgfpMzimqObTQagNddRRwafa+P4nHH0GzuGM/YexOX/fovWdd+yuPoZYCVF6Qp1rRr/LX2D3X0fsliW0ihT2Ff5GU+jLoS4hSQkXRSLSiYPzKWhNcSyrQ2khrVEiNT4sWpCJeFyDNti2778wGEcM6E/N76+hB8zcqhZ+YM+VlYhaRZCGcugv7xmpplI1hmYUprXaWMlwnMX7s2iDXUM6UAf4Z0Nu65uY4HVLHOA/xNu9zyKt3EjioASpZLJP97EUcq8uPOsTlLD2dRWBmcsDAaSFtQJbEHpSEL1FYTqtpqRPIoiEG4vKC5aVs5HbWnQK1HGYOYlt7Dnb+7msA138Sv32zwROoQ/BC+iTCtAk4JgRn/+4vot73tmAPC4+kcucL2FgqabcGwimoxic9Z3tb4l+iXVcxA0skJ1CF86row8WlYvoCX8Mpu9jDXQggG2fPEKFS//GS3QQsXG9bgy8vF6vShCMEqs5TXvHxm09GGYeAbvTJvDP9STuMN7CRVKEUZ3qJTj/sXRZ/7OnINbUcLJcNHOxVgUZvoQAhRfGhn5/QhU6rX7DWax5Pk7qPvyBYqyUkyJuCUQIlS1gdGjo23OseV+UzwujhhXTFaKB5cizN4GakMVldU1bHr8NwT8ftaNuwjCDW9cpXohsCl7TDLHMfo4mN81yePz1vD6GnhP3YNv1zaRMmg8e/zlA/7+zFs8mX85z6ozAZiu/IhXRJuCUkSQq9wvkpniZr9h8b12jeJzxs8/MC+NVy7eJ3wPO84AFEVQWpCOx6WQPWwywXA5cnduP1ItpRoMhm/wnew0T1Sm8c6AftmpzB7Tt93Q1Z0ZvYIBRNX6+eReNtW0RqnEbq2Vq9wvxp1njdU16tfElgG2u4YBs5pouIxx0dBxaE21BKvKzFh+RQjzAfNv+oWye08lVLcFXB4KjrnWHCu/eBB39nsPfniae0LH8ufQWbym7ce0wL0M8T/D5nO/48uMA/nJNxGAj5qH8UfPM7zsvYm8lrW2DUgMbcb6eMdKaaqmE4/6yi26lDdyP/zrf2LVO48CEYaoSUlzTUT7aV7xNZtW/Yy3zxBcUkX5/E5e9f6JfNHA8pmPwVH3gU+3j/8vtA9XD3wWbqqFKxbDuJOi5qAIQxPRTKk1EfEyJM/0wmLU2q2Rpjr+Jiq/f5faz5+mpWypyRArq6oINVYzatSoqHHasvu6FGEmXqkt9Sz55lNQQ+QeeAHpYw9CuHTNwZulM4ni4mLO/7eemaw1RzOAoKpx85t6E7zA1tWEajeTPuZA3B4vjZmDeV3bl7+EzmTZYfFCgYFiUUVmisc2i9wIVjCexQNGFNInSzd1bU90i9ctyBo93fzuyS2O0gAM4SmjC8M0HWw/egUDMB7+YOUGrnurgmH3NXLp29H2w2JRFXdebVOEGBoMIJEGYNfgwoguko1VCJeHzAF6z1KttcHCAMIHa5FxZShA5qTD8eZHzFKXtv6b/YJfwKy/MuOiu4ltp+DzKOSkelFSdKJ6Q+NxXB64lMFiC+f+eCaDlj2KQvQcIxpAZKxYZ6MM5yDUV27GnVVI7gHn4eu/O1vnv4HUVLMompTQXBVhAMGtq6natJY+/fpy7MLzEJ/8lfe1Kczy/42W0oOASLJPfWuoTTurSwl3s9JkVIapHQwNIS23L6H6cqQMl1TetNw8pnLxPDMia91Svf3k5MnRpQQ87sTEURHCbI2oNdexZc0yUFxk7nEkQijmb2loCYoQZGXnoqRmxWkAUU75TXpwQMqAsQghzLIJI/tmcsbeg9gk4yV80P0lw/tk2Nr5jYirSDHE+FDNbYFbUXD30Z9n4fEh3N4oBlATNrvmpScfAeRgx6NXMAAzuaRgAB9fMgCvC+atjybk5WTHnWfVAIwXqWUbNIA8VwtKeg5rWlLNfUYd+1j10ttvBAVHXknOfmeipEXMEFOVRdyfeTnscxnjB+Rw4MiiqPN8boX8jAgD0FqbeF3bh1n+O1idM5VRi+/kFe9NDBWRGjbGWpR2TEANqxeyadVSZk4ZzV+PHUf2pEPR/M34Ny0z8w00KWmsinQea17xFUjJP0o+Ibt5PRz/GJcGL6eWzEgmcDhsUNUkaW2E1SpCryqqadLSaCTxsQDVShZqYw2BQDjxKxzpg+KmuWqTWZph0xqdMUycODFqnLYSkVxKJPM2WLWBlR88A0JwxHjda5ExUc/u9eSVmHNyKQJXWk5kHmHc/s4vZpz8KbungMuDK0uPmjLujy98b+4InRRX1VR6Uqnc62ounrGbLQMw1mGWcu6kmHa3SxD0ZlJ4wo30v0jXBq2x7VVhBtCRCCAHOx69gwFYnLnLd7+Ey/dOZWmlZha8kkAuDZzomouRPytlJGkIIj4Au1INECmeFXVdo5pgqAlXapbe4jAMTzi23Xgf+51zD+ljD6bvGXeQPmo6isdHYarOVIrSBQ/kX8fc9EjZgNgaPSkeVzQDCJcDriCHV4f/jfl7/J1BYgtve6/jDP+LqBVraGrRHa/WmjJxJiAJm569DoBzTzySM6eWkj5oHAB1X70QqVUvJY2VW3C5XEyZNJ5Qra4NyD678+rUl2HsCeaYBjO1NhVvK6XfpQg0QwNwtaMBhDe7MgsAScVWnSmpjboZLm3gaJoqN9LkD6FqkrqqrbhT0sjJyYkapy3ziCIEwqMT6+blX4VvVIhDxuhMPWefUyi59Gl8fYaYc3K7FLx9hhDYsiLut6to8HPOPqU0NzWgpKTrZRqEsHTH0v++rk3jmuAFyOwSDH+JOPJeJhx+oX6P2mAAxr7OYgAeRS9xnTZ0Cq50PePYqgFUmxqAwwB6MnoHA7Akev2QO4sFWdMJabClEcq0Aj4dciXfqsP57Kl/kPLMmWTSTEtQjdIADJNAQg3AYgKKbQ/YVF+LOy0Ld3ZEavfk6yUfzOiYPkMZf9o1ZpG3E3aD19NvZcvVefzp1tv5SNm3zZfX61LIT/fhStVL8hq9bQGEIljb71Bm+f/Ok+XDuO2e/1D2+GVsfvOf4QMi41iZnr4GDcIlm4844ggAfDl9SB99AIEteimG/e74GCmhoXIT/QtzGKHp4ad5uVn83vdHAmnRtYsM+/pkSyRHuyagcFvM9nwAxnZDQq+q0iXuUGM1SkomKYUDaazYyOKNdVz+3A80VFeQmh1vWmnL8edSdL+NKy2bUK3OYPqceltU5IsrPdc0F+oagB47rzZWo1pCag3kpXupr6tDCXdNM5gGRGsjr2vTkL9dbOsvsYv1N/pNqKYJKOGyOgQ7E5mVAcwapTPD0f23rXeugx2DXsEArFFAHkXwk0ePL967/na9NHC/kzi57BTmb9RYVlbL6+5r8K/5JsrebwhXiZpZW01AhmRrbKutriYtKwfh8uiSI3qkCkQTMoMIDhabuaP+Kko8DVzuup47N41mUVldmwxAUQQFGV6ENw2EgmphAKA7USvJ5k9bDzSdhfU/vM3Frtdxycg6Y2OrG1pDICUHnfIrvN5wMxFF4C0ajNZch9pSz4bqFtzVK1j11bsMT6tleKmu3Xj7DAfiyw4bGsBuRRlmSGJ7JqD6lqCuAZg1ZuyPNX0E4ebl63/+AS3Qglpfjisjj7T8fgRbmtBaG3jrp80011SQkduxVqORa0SIm7ffMBQBfz5qNP85b08A0z+ihCtveov1WP3Kt/5h5nz4N/5C+f9uobmuiob6OvO5EOFWihBhBAYSrd2qWeSne9l/eCFj+meH9+nbtyXyxw52WcRWE9Cpew5g2V9n0y87Ne44Bz0HvSIPwFru2eNSzJfMeAlDmoa/7GfzmMqmEKXPH8kp4hT+waFIIh2dEtVxtzKAlqBKDhHGU1tTTf7A8ahA/4seRbOUfLC+jx6Xwu5iHf/x3gZBD5z9Bj89tAVUnUC72kndT/e5dfNBSka0BoCI9CJoqgWgaMphrP/2bX4Veo6aqgVQ/jSycCRbYhhAVVWV3oUqPyLFKwLcYRNWqHYrh6YtpeqNO5FS8vc//Z6tfWbAq4czZP9j2Ei82cFa6dEwz7VVG8WlCDNXweiqloiQGZuN5uULX7oHb583CVaVkTZ8Kqn5/cPz3oIrNYuWukoKd5+Q8Np2sGoZQfQ8DsWTgqIIzt6nNBx6ikUD0E1K3qLBKOk5+DcspvHHD/CXLSFQvoZQ7WauPV7v6pZSOtE8x+zKFZOwlUg7sQo6I/pmmowIYOpQ3SE9Y/i299W2wq5MtLVjlxCix3W/chCPXqcBuF2KqWZLv54kFFIlam2k9voplb+iccihXKY9wwupf6OQGnOMRLXfrT4AK7OQapC62hry8vQXcI+RpRy6T8ThaCVkY9SlPO/9C0HctJzxJhRPiKqL3l7YnkEwdAYQ05zd2ixcCOQAPTb9/M3HUaCWw7/3x//x3wkGo01AVeW6LT+vIGK+UhRhmljOCzzD0G/vYM7iFqYfcTwTzrmDWbMPZe/rXqDfhAOA+KQguw5LbTUKsZ5vOKmtt6Io08fMsFPczBROzzH3B7auQob8ePsNJ70wzABqNiOlxF9fSWZetEO9PcRqAMa9sFbA9LmVKBPQoPw0hOKi5Df/AaDm40doXv6laUIyYNUM3ZaiecnA+mjGCiqTBuay+tbD2GtIftLrbAuxWgnoCXYOdi70CgZg9QG4FWEyAM3fhBDQ0uqnadUCUPQHuL6hiY0HPcCftAuZKJbzcfp1TA5+q4+VoIKmVQOwMotQnZ5B3H9gKaATq6GFkcxCgwFMU37iL/V/pFpmcqL/RijQC5BZ49Hbc+AZDEBtqqF56WdmTSB93hEG4EnPNrNlP6/M4eo+D8PIw0n5/Bb+572BESJSS7+2Sp9/bmGESLqEYM90nTEMbfmZm7+E1IISJpx6DaATwNS8IvOexIYbWiVFo0hZW8zNam+uDTMA672Yf/1BPHbOFP1aRpSQL53MSUeQ2m830sceTP6hl5MxbhZZRXpkTrBmEy0rvkaGgmTnd5ABmBqAzgDcuf3j5mTVaFyK4PS99PsthELG+EOixsvc40hmHn+2vt+Taq7DaBgzIC85M4rVBGSX97E9YZ+x8NiM5Th8dz70DgZgkc6FAOHTXygt0AKaymPXnEmwdgupg3XJXK2vIKTB8+oBPDX2KerdBdzW8heqXv49ihbdyMF46axO4JBFAzCiYYYO0SNC/CEtSgJWBByifMtjnr+zxV3MSYEb2UhhhJBZpF8rkbTTQwy13Cgp4d+83FyzoQEEtq4itaBEbyUpFEJ15TS6cuHEJ9lw0EMUiyre8F7Ppa7/4SZEqEE3veQXhU1AgSaukY/ydO7DANxWvi/NLX4KpxyOxxMhAIqI74JlwI6RtUWcrAyjLuykTmQGsd6vvIN/TfE591Bw2G/JGDcLxZuCLyWF9Lw+1M//HxX/05vmZObZx9cnghKjARg9HKxrSLGYP4TQ9xl1efJmXRK9vuFTmXXKBcycOZO0YXua55Tm64LC0RPsq5TG4upDR9I3nOSVyFTZWbALk92VM2Z3VfQKBhDVKF0SpQGo1RvZtGIx+XvMJv+w3yE8KYQaKgmqGkFVUp8xlHsGP8iToVnkL36MZ8QfGSwiartB5KwagCF9qapmMoARw/TSvNVNATOpDEAseo4HPHezRJZyc/4dVJCjbw+/S/5QxEGbrAZQcuYd+jyadOItwvdA8zcR2LySvGGTES4Prox8QvVbEQLWVjZx6aKBHOy/g3e1Pfk/z0v8z3sDk5v1EhkX/3A0/H0Y3DOeE7V3eVE5FOH2sHGjXg/Jm1ccFZuviIjmlUzoYVsaQLolusRo6JIIscPEEkK3opCWWxRVejszO7fd+Vlh0L6McbNIG7kfGWP1xDarqSrFY2XyBjPXvwvFRfqYAwHof/ETpAwcR1G//nz44YdkjdzXPOf4SSV898eDTEdue+iTlcIDZ+imPbuQ0M6EnQnIwc6HXvErBi0mIE1KhCcFRXGh+ZtNG2zfKUfgSsvGlVmAWl9hhnt6XQLNlcJNoXO4IPAHiqnkTe91nOD6FJAm4Q/YNJUJaZJAxVqyc3IYvVspoMd8GxrAOa534dWLaS3ZF3nmq/g9kagSg5a0BqPNV7G499SJ/PAnvcG5wQB8/XTzUagx0t4qpEmCNZsBSXaJzozc2YWE6spRNclVL//Iog211JDF5cHLuCjwOwaJLQxrXkSmFzK8QFM5NFXyhHI8f1XPwp2ahVquh3yK7OiaKYoQpubVVuRJJEEp8aOYZskR+MOsEQmP08dph0m6FaacdCmewlJKfvM0g868nWET92nznFgY63Fn5lN49NV4C0vD2y1zttbFsdHm8g+7ggG/f8XMDTG1CkutIz2yq2OZtIbJsK12n50BqxP40bMms/CGg7v0eg66Br2CAcQ6boUQeNPSkf6mMFHUa8cAePKKCVaXmQlfDVVbuOukCbSsXsCH2h7M9t/OT3IId3r+zT2e+wk06XZ2Ox9ASJMEtq5m7Nix9M/VnXtbG1rxKoLLXHO4yfMfGHkE6ee8wh7DBkQRQSM5q9WiAdiZSTJ9bnLDtlcj5lu6PKRk5qDWR7JONU0SqtEd3ZlhO7grLQetuR5NyqjCd4oa4N3gRBplGqtrNIbmWR8TyaHyMwDcqekEWlsAgZbRJ4oAKkKYjNcgyi/9eiqfXjkjbg2QnAaQl+7lgBFt2+sTMRsjRt2jCPoMn0jxef/ClZGLb+C4KHPGNYeO5NfTh7Z5jURMxvr7ZKZYI2KMuWHZJlA88b1y7ZhFRxBp5dnFGoBlMX2zU6IqpzrYedArGIBVGjI+eVMz0PxNhOor8KSkkZqZA4CncDDBqjLqGnUTwbqfFwJQ/tKNNC39nK3kcVrgeu4MnsjhytdkPXUAlC2IYgBmI5nNGwlsXsbMmQfRN1u3zUop2WvlP/mD52VeUfeDE58CT3xxLuOj9T1uLwrIiBaRUpJbPJhAxRp9h9DDQI2mLtlhBmBEC2mapWZM0M+Gf/+KjQ9fSKGs4vN1IYbnRz8mRVJnLJ5w0pkrq5CQcEcRLUUhTgOYUprHoPzo0rpm2GYSPoBkSGIiX0JOuMVgbLSRqsmobb+ePpRrDh3Z5jVcIv53gug1ZIZ7RuuduyLRQQnnHXMfttWebqwlWR/AyL6ZHDcpOR+DFdYuWW1FcDno2egdDMBiAjIIqjc1Xe9s1VRNWk6BadP0FpaC1Fi5XC/MVVsRCQ+tfP1vqK2NaCj8Sz2WkwN/QmoheHwWUzc/jSA6A3j1koUAHHnEEaR4XCho/Cf/aXZf+x+eCB3C/wUvAld8CV2wJwDt5QFE0v6haMgoguVrkKEgAl0iDNZs1pOh0sLZpikZqK2NqOGSzwCt6xYRaqhCra/gnLegIQCjC6PjucuF7jT1poejYCxFzwwoQuAPte8DMH6PtphbRtgE1FYP48h17bcbEqrHpURFy+gF5jr2GkT1N3bH2/ohEhKZiFnEzzvaT7CtATvGvU4UrhyLd3+3P/84aUKHr+Ox3LNtaS3poGegV/xyhjQ0e3Rfs4iaJzUDLdCM2lhDanZ+JOkm3Ej737dey5b/XskL999OalYe2VNPBqB27pNsee5aZCjAAjmCFce9AyMO49AtD/Efz+1ROQMb1ywDoTB69CgI+Vk+4UX2b3qXpcN/zZ9DZyFjbr+dBpBov52G77WES5aM2RMZCtCybqF5D0K1m3Hn9DPDKpXUTFCDBFpbzSqgathxDPDsDw2M7ePi91Mt6r0nlce8Z+rXS8sMjxPu0yuiGZhRD2a3okhbxERoUwMIlxT2B5NhAPbjGE3GPS4lrmyyXUhjW7ASdU+CMF3DBBTLFNsbs71qp+3B3UEGsK2wSv12SWEOdg70DgYQNkX867SJZn10d2o60t9MqLEGLSWbgeFYa6On6uqfF+LfuBSA0TOPJ3vfU1FSs2hc9C7+9T+x4b7TCdZsok5mwEn/4bk+/8dkZTnv+K4lY4Ne+33rhtV4cvqSoqjw3Km4f3kdZt3CytG/xc6g0b4GYPOiWTZZJbHSsXsBgkA4FFQLh6S6c/pGJYwB+JvrTYJhFJEzupT1nXIote5CZLj4GEfey1zfDEBv1QhWBhCZi/E5L91LaX77jUDaMiOke5PXABJV8TRMFh6XiAuh7WhrxKgG99Y2iJZhDBMQUfck8XWMXTXhMNeFG2o7NCcDRjmRZHMHthXWKCAnImjnRa/45QwNwKUIU2VXvGlo/iaCjdW0eLLMDlBGMTYrhkyZiXC5ydnvDHwleuMQGWhh08MX0tgaAiH4NOMwjgz8lQqZzaiPz0c+fQLeNXPZP2cr/K0UVn2kN0HZ59KEElOk3aH9OqzmBts8AMt+r8+HKyOXUNgRHNIkWmsjrtSsCAMIN2TxN9aZxcK0lnoUt5eSS54id+avWDbifKYF7mXe6SvM4mMGAUzL1k0/ImzGsppGzMYsPleb9mxjHW2ZYawhle0hN81ju92Q8t0xJiB937a/BlF9cEW8BmCtHWW9DedPGxw1TixziC3JkSyKMlN4+Mw9uP+0Se0fvB1QLWbVjmpQDnoOegUDMDpJCSHICRMI6U0nVF+BDLTgSs+lODuV0/bSG7AYEm3x+Q/yuz/fRXZ/PYkrc+Jh9D39DvqeeZc59rqyDYAeBbRSlnBM4C98po5BrPqAjTV+BmUroAbA5QW34ey1v+1mI+0E2nuKTQkF66tnZSxuReDKLEStr0BKScAfQIb8iJR0U2p1pevx5S31llIXLQ1407NQfGlkTT4a4dbvl7Wui0Gscvvp98uocS9spN1kiWubmcAJJMxrDx3JCxfuHbVNCMHHf5ged6wRSjogN14b2R4nppUpWzUDw+lvhXFPZo/uy5+OiO5Alsxt2j/JOj6zRvft8qgco580OBrAzoxu+eWEECcKIZYIITQhxOT2z9g+hNRIJymDAWiZfczOTa70PNwuwa3HjuWLaw6kz6m3MfuiG/AUDOCok87QzR8W+IpHUHj8DQAs+FqvBx9QNRQBfrwMEVsIqJItjZKB2eFbrAbgo5uBaEndijnfb4zb9vbl+5kVM31tFEyDaGLrUgTurEJCDRVoEpoa9U5Uii/dZBSuDF2Cb6mtiDCA+gpSMuITj6zJa8ZlhoyaCEDa8Pges8bHthqrWNFmqesE9+ui6UNta9sMKYz3ORgSeXaqJ47Bbl9rRHsGMNRmDqaD12Y5ydj8nzp3CmtvP7zjk+wCGMXlYNsd1g66H93FuhcDxwGf7YiLhTRpEqKcVF0yCmT0M/e7MnLNl9ejCLyFgxgx4xj9u43JACB1yB4It5dlixcBugZg+BeKRSUb6vQqNwOzLW9HXZl5DTuk29TEH1WcZdZW9yUghAY8MREprqwC1PpKVE2juUm37Su+dFNii2UAgYp1tK5bSEFpfBikz2KGMcw7xSX9+Wr5FtLDmbCxeQD6nNqmDslEAbXVn7c9HLR7EUdPKObcfUrpk+XjiHH94mLkt0eCtcv4BRhSmB53rLHfziSWTNhnTyq1MGlgLu/9bn/OnzbYdLA72PnQLQxASrlUSrlsR11PtfSS9boVMnxu3HmR2GdXem6kTWH4r1HJ0eMSthEVQnHhzsynMlwtM6hKUsPOyk2ygHV1uo10UI7lFmeXRF0jFkZBs/j562O1Xw002gTkzipEhvw01dfS2hhhAKYk6k1B8aXTUr2V5sqN1H72FAiFaaf9Nm7sKBNQeICsFA8+n9ckTLF5APo8tl8DSKQxJYNHz57CPadMZFifTL657iAG5KXFaQDb0yWrT2bE1GMdJ83rJjOmy5lx34yjrHb62Iqpz1yw1zbPaUdhRN9M/nTEqB7FmBx0DL2ifqtLEWYsOUBuuocGS3tGd26x+fIa9mCjFITbJmzw7pMncNT4YrKey6e2Uq+FE1Q1Uxq8I3QSo2r/BaD7AAA8qTBTNxslcgIXZdqn/ZtO7HYkVasJSFEE7kx9jTUVm2is18M7FV9aVGKct+9Q6tf/TOWKBQSqNpIxbha5BYWwPtocZdU+jKS3rFRPFIG3C3lMVnpvi1Fsiwbw2NmTE1an7JMVfZ87GsYoLS74oqwIA4jlI19fN5OqxkDcfuPeHD6uH5c+F65PFXOuEZTgwEFXossYgBDiQ6Cvza7rpZSvdWCcC4ELAQYOHLhNc/nTEaOinG5ZKZ6oaB/F4zOla4OIWpOY4k0GAkURpOUUUB/Otg2EdBOQ5m/mdd80vqn+HMEX9M9S9PDJmTeY7fsS2cUTSaKGBtKeBmCNwnErAleYydVs3cyqBV+Ay42nsDSq05lv4DjqPv8vABnjZ5N3yG9sm7NYGUCTXz8/K9UTNWc7BtC+g7X9XrWJfABtYebufRLu+8sxY9ha7+er1VX6HLcjCqhftpUBRK8h3eeO6nUcm+wFujYgiTfvOLH1DnYEuowBSCkP6qRxHgYeBpg8eXKnZLcYL2XfM/5u9rt1myYg/cXzhzUAlxAJnYbpuQWULZvPwg21rK1qQvv2eTa89x9KLv0vS2pTUTLyeGD/L/l9TAGzREQxkSPQ1AAs++38EgCFmT4u3G8IlU1+veQzUFu+mbULvyS1dCKu1MyovsZZU47B11pN3bqlZE89Ua+TZMOgrA7oxnDLy6wUd9Ra7PIAknUCt8UotscHYIfMFA9nTR1kYQDbTmzHWHretmdKsisJIYQAKeM0gGTvmwMH24NeYQKKhWGb9fXf3dxm9l8N/21LAzBi1rPz+7DW38xR//gAxZfGuvf+A0D5y39GC7TgyiqkOCc+IWdbNYBkbNXfXq/z3Tve/QUlLRtcHlYu/JLaLevJmTETiG5sX5yfA8f8nprmgLlmO4Jo1QCM2PZUjytaA7DRBpKVZLvKB5AI1gqj2+oETvO6OHBkRNNo7/cxy0HHaAD6vlgNwGEADroe3RUGeqwQogyYCrwlhHhvR14/3RfP9yJJWHozbisDiHUCG8fmhtskhurLCVSsNfcHtqwgVF2GO6vItjhZotj4dk1AFmJ645Gj2G9YAXsnaPGn5z0ouDMLWDZ/Lr70bNJHHwBAa1DllYunctXsERwwshBVyigmZzcPK1Mw6gZ53ErUdrcNM2iPkHV1FFAiWCOutlUD2GtwXtT39kI57Zr8GIi959vjmHbgIFl0iwYgpfwf8L/uuDZAhk3v0ujaJgqthglIiU/MMl7OgiLdxbH58UvNfUpqFlINkjZiGpnjZ8VFd0Di0MhE9MNOA9itKJOnz08cKWJG6ux5LIW1Sxl64MksJQ9NQktAZY9BeewxKI/r/vcTUsqo6pF2xMdqtjCO9bmUqGPdUVmx8dvaQlvEc1t8AO3BWq+/s6pZtpexbFfozVi2E0jjoDvQK01AdvH2VkdgikehKWznVoTgioOHc+ojX1uO1d/WvsXxZXTzD/stabtFCLO9NJ1AA0joA0guDNRurMyJh3Hy1IvZUNNCU0Uja6uaOXXPgVHHhTSJlHDJjKFcNH0oj36+us2xDYbkdStRa/FE+QM6ZgLakT4A0EtUGOiotL1boV4E79Ax/aK2Z9hollaYPgBLYqEIu4G3tfibAwfbg15paKxo8ANQnG0fw53qcdEUUM3tU4fmc4Mlisg4tt+gwfQ9/joG/O5Fc1/KgLFR17IjLomIYiJCNHuMrmmM7Jt8aKC1wJkmdaKdneph7e2Hc56lDo1LEWY/41SPi+xUT7tmm5CFAURpADYhockS786OAmoP1ryGjtrbB+anseyvszlpyoCo7e3Fw5smIOvlYkJDHTjYkeiVDCAYJmCjiiME1UoEUrwuU8o1XsxZoyPOPkNa9bldpA7bB8WXRt8z/s6xV9yO4ouuNWPPANquBRSLYyeWsOyvsyktiM8uTQSzGiXw9NfraPKH7JuxW5u3W5LlkoG3DR+AQc+SNa+0WSq5C+zhrgTzThZWBpIsIk5gqwYQvc+Bgx2JXskAbjxiFDcdOYpJg3LNbbEaQOx2u3h3txJJEvP13529Zh0dd622HH5xjr82iGBHCU5hRnQS1HframwJqUuJlFk2CGGyUrvXpURpGlZib5bWaM8JnNSVOh/RWdM75jWIzQS2wsmmddAd6JUMoCgrhXP2HRxFcN3tMAArITeJW4wzN8WGSNsRXZ9b4fCx/Xjq3D3bPXZbYddMPJEGELs/2bBLjytGA7ApjdweAzCyn9tifl2BaOf1jrl2pDdwvKbkRP046A70SiewAeuLaCUCqd740sd2Wa6x4Zx2USB29E8Iwf2nx9dr70w7cNIMwEaz8SWpAfhifAAeGxNQe07gJ8/dk89WVJiN7dvCyL6ZSc0rGVgZ0/YkggFcd9hI2/sdC/tM4PhtDhzsKPRuBpDAgWmvARC3LZa42VXr7AhR70wiUGhTV8iu6YrLhgl2zAdgXw7ZIGztmVf6Zqdw0uQBbR4DMP/6me1G2XQEicJXtwUX7j80uWvaZgITt82Bgx2F3s0ALO9cIg0gtler9XMs4bAznXTEvtyZZoB0n5ufbprF2Jvej4xvM7ydBtARBmCdslWqNhLLOiuCpygzvsHK9iCR87or0XYmsP73rcun0WKp1eTAQVeilzOA9n0ABv2OJpT631hnqZ29uyP+xc6WAq2RQNC+BmCWzO6AE1gkMKMlW8Cuu5Bo3jvimlF5ADEmxtHF8c14HDjoKvRKJ7ABJYEZIKUdJ3CiSpd2JqAd7dxsC3Z03TZyJ0mpPVYDsmo7BgPYGWra7LAoINMEFL/PcQI76A70/LezCxFlArJ8sTIA46W1k5RjiZsdsetJL7YdobOu21jjtmbeemw0gJ2hrPGONgFZS4sYV+5BcoKDXoRezQDsiDrEEEUlXmpLVObA3gTUc95su7lYpfiOOoFjYb2HqtyJNIAdxKRsnwWb0FAHDnYUev7b2YVI5AOIMou04QSO1wDiX+IeZQKymYrbxgncXu/haw8dGVUH30CUE9gxAcXB7lGIOIF7znPioPegVzuBo6IxEjADxcYHkCgKyM500pNMQHZOYDsfQHsawEXT9aJxbY0Vsilh3VOxwzSA8DNkbSkZcQLvkCk4cBCFni+edSESEWc7QmlXwjfOBGTnBO5Bb7adMO6xieOPldqPn1SS1PhWSdoMA90pNIAd6wOwwskDcNCd6NUagEHw0mLKQ9uZcqwvqEvYE8qe7gS2DQO1cwJbGNmiG2fZls+2g/W+GY3ntzfJakdgRxHfHV3wzoGD9tCrGcDwPnppgeaYxJv2XsaO+AB6km032TDQLEvDnOxUT9w5ice3cwL3nPV3NwxGY40CsisP4cDBjkLPF8+6EAYDOGJcdGOP9kwChl8g9jivS+G13+zLbw6I2Md7kmRn55C2K+UQm0CWLHZWJ/COgqFJ5aRF7q/jBHbQnejVGoBLEfzwp4NJ87litrdNtOxMJaATu/EDMinNT+f+T1ZFHdsTYFd62U4D6Cg8Lr2ngJWIhRwGEIfj9yihX04qkwbmmNuclpAOuhO9mgEAtlUo24sKifQDsHcC+yxVQV3dbALZe0geX6+uBohrbg/Ra7D6Qt757X7UtwSTusbDZ03m7g+WR0m2hhN4Z4gC2lHwuBSmDy+M2RofZebAwY5Cr2cAdmjfBKT/TY1xjhrnWePou1sDeP7CqTzxxRr+/MbPJlG2wuqk7Zedan7evV/y7ScPGFHEASOKoraZmcA7KMZ+Z0dPMhU66D1w3k4btOsEDhP1NE80/7QL6esJ9M8ww9hpANYa/nYlpLcVxrUcwtY2HMHfQXeiB5Cnnof27NYGUYvVAOzaNna3BgCRWPxw58co2GU4dwYcBpAcjLtjo5w5cNDlcBiADdojWsLGCfzkuVNsQyZ7AgE05tCeCagzYYSB9oT192QY8oHstu7IDnozHAZgg23JDI137unoCQTQmEN7TuDOhKMBJAejN4CjATjoDjgMwAbbQrQSZZP2BAJo5C2othpAFzOAHmACS4S9BueZTem7Cz6bPtIOHOwoOFFANuiM2PUDRhTyybKKHVZpsi0YRFiz1QD0+XU2nc5L91JW09JpLSG7Ai9cNLW7p8AT50xhzvcb6ZfduS0vHThIBg4DsEFnSO0PnrEHZTXNPYIAGvzM1gQU1gBSbBzY24NHzprMR0vL6esQtjYxpDCD/ztkRHdPw0EvRfdTpx6IzrCLp3hc7FaU2Qmz2X4YSUZ2TmAjTr+zTRF9slI4ba+BnTqmAwcOOheOBmCDjkTGHD6uH9WNgU67ts+tdHr5hLacwIaFqrM1AAcOHPR8dAsDEEL8HTgSCACrgHOllLXdMRc7dEQDuP+0SZ167R9vmmVGhnQWIk7g+H3B8EbHGenAQe9Dd731HwBjpJTjgOXAtd00D1u4bEo67Cj43K5O9xu05QTuk6VHwVzp2KEdOOh16BYNQEr5vuXr18AJ3TGPRDA0gKwO1MLvyRhSmA7AIWP6xu1L87pZe/vhO3pKDhw46AHoCT6A84AXunsSVhiCsrUxys6Mktw0lt48mxTHzOPAgQMLuozCCSE+BOJFTrheSvla+JjrgRDwTBvjXAhcCDBw4I6JKgmGi+YMLkjfIdfbEYitW+TAgQMHXcYApJQHtbVfCHE2cAQwU8rEifBSyoeBhwEmT568QxLmRxdncdORozh2YnLN0B04cOBgZ0R3RQHNBq4Gpkspm7tjDm1BCME5+w7u7mk4cODAQZeiu4zC/wIygQ+EEAuFEA910zwcOHDgoNeiu6KAduuO6zpw4MCBgwicsBAHDhw46KVwGIADBw4c9FI4DMCBAwcOeikcBuDAgQMHvRS7RqrrDsKiG2fZ1tNx4MCBg50RDgPoAOyavjtw4MDBzgrHBOTAgQMHvRQOA3DgwIGDXgqHAThw4MBBL4XDABw4cOCgl8JhAA4cOHDQS+EwAAcOHDjopXAYgAMHDhz0Uog2erH0OAghKoB123h6AVDZidPpLjjr6Flw1tGz4KzDHoOklIWxG3cqBrA9EEJ8J6Wc3N3z2F446+hZcNbRs+Cso2NwTEAOHDhw0EvhMAAHDhw46KXoTQzg4e6eQCfBWUfPgrOOngVnHR1Ar/EBOHDgwIGDaPQmDcCBAwcOHFjgMAAHDnoBhBCiu+fQGXDW0bnY5RhAT7mx2wtnHT0Lu8A6dpVmFs46OhG7BAMQQowWQswAkDuxU8NZR8/CrrAOIcRUIcRLwJ1CiFFCCFd3z2lb4Kyji+azkz7XAAghFOBfwIHAeuAb4DUp5XdCCEVKqXXrBJOEs46ehV1oHUXAO+hrGQD0B76TUj4ihBA7C1Nz1tF12Nk1gFwgE9gdOB2oAv4ghMjYWV7SMLLZNdaxq/weOUAGO/86xgPLpZRPAHcBc4CjhRDDpZRyJzJrjWHXWEeP+z12OgYghDhBCHFJ+GsWMBVIk1JWAK8A1cBvwsf22AdDCHGcEOKf4a/57LzrmCSEGB7+mg3sw865jsFCiJTw1zx2wnUIIU4VQvxZCHFUeNMPwGQhxFApZRPwLfAdcBH0XLOWEGK6EGIvy6ZF6OsYspOt4xghxHVCiMPDmxbSw36PnYYBCCEyhBCvAP8H1Agh3FLKNcAXwO/Ch21G56oThRDFPfHBCNv9ngX+BPw2PM+VwFfsXOsYLIR4C7gfeFoIcbCUcjXwJTvXOkqFEO8AjwLPCCFGhX+Pz4Dfhw/r0esQOn4NXAWsBf4uhLgAaAT+A/w2fGgt8CGQJoTo1w1TbRNCiEwhxBzgf8BFQohcACllFfACcHn40Fp69joKhRCvoj8/1cATQogTLMLEZeFDa+nmdfRoBhAjaQ0Atkop95ZSPgeo4e1PAvuGpYMQsBVoBVJ36GTbgLEOIcT+wCPA11LKicA9gCHpPIa+jsE9fR1h/B+wUEo5FXgNOC+8/XF2vnV8I6WcCXwC/FkIMQr9udq7Jz9XBsIMaSpwe9i88BtgBjATeBvYTQhxUNh8VYVue67rpum2hQDwMXAGsAk40bLvFWCkEGLmTrCOocAXUsr9pZQPAX8Argjve44etI4ezQCAFMvncUAJQNgEdKMQYhrwM7rUeSeAlHIxMAjw79iptgmDaPwM/H97Zx+rZV3G8c/XwwFGTSoDYfYC85RRmiORpOGINcrNrRczpjkra7NyZWy1XufGMEdSsVMQWy5fUEJL09ooKVlEw5pzh2RHY70gLelFrKklFIJc/XFdD+fhzNN5zuF4fr+H5/ps957f/fbs+u5+uX7X9bvu+367mX1T0kSgB2jklHcCO4BVUK2OyXD0BrofOBTLTwZ2SerBI7IHqft4NHRMiPlHAMxsLTAfuAS/AT1IpcdD0gciVfKyWLQLOC0i4y3Aw8B5wBPARqA3js/bAAETS9g9mCYdLzGzg3gktgX4PZ4uOSM23QncQd063ippCtCHR17Iq3x+GxNAP67jGzXoqNIBSFoi6T5glaRLY/EO4G+SbsJ7O08BXwLeBawGpktaK+lh/JsBT5fO1Q7ScYmZ/cPM9kuabGbP4ifDZQBm9iSwAniFpDWV6viqpKXR49wOvEbSb4ALgC78RrMIWAnMrPh4NHQcxkP0uZLOlnQ2fuOcjev5MhUdj0j1zJS0Ffggfu6skXQy8BgwHe9UgN9kzgROMbMNwHeBz+PO7bNm9tR4299gCB3fkvRyM/tvXBu/BvYBSwHM7IiZ3QLcBnyBOnW8H4/wp5rZ45K6zOw5vJhgKhyj41ZqOB5mVtWEn8AP4Df2ufiJ+2lgAj5y3gd0x7aXAzdEezo+cPfO0hqG0LEB+GKsa9i/KJZPa9pvWuU6NgKfiXVnAHc3bXsNsDbap1au43bgKrxq6RpgE+7U5oXGZTXpALri97XAhmhPANYB6/EHi26Ka2JqrL8FuK7pPyZWcByG0rEG+MGgbd8T+nqAFwEntYmOuwdtcyuwNNozajoejRC4KPK6a8xzYm8G+szsR7Hu5/iN/0Y81zwXzw1uxMPC98prs/fhPYZitKBjtaTvhK3gF+1U4MnGf5gPFD0xroYPYhgdW3Adt+G958ckzTGzXXgOfZkkmdnjeN68GC3o+Dpwp5ldG7n+R2Pd/QykevaFliJEmmoF0CXpJ3i67TkAMzss6RP4IPXr8Wvi3XiqdCWeXvxV47/Me9ZFaEHH1cBfJS0ys22x/B5Jc4DNeFnuYmBXu+nAB+P3SFoBXCTpAjPbW1JHg+IpIElXAHuBa2NRP3CppFkxPwHYA6wys18CvXhN9ufwMHd7/E/p9MJwOrqB3URuHMA8VzsP72FWQYs6Ho31/8ZLJq+W9Cng23j+tjgtnle7gUYp7p7Y70rgI3jKEYuuWgkkLcIj3pcCf8S1HAIWS5of9h3Bb0jXx/l0A7BQ0gOx3y8KmH4MLeowXMfypv3eh6d5twJvjE5GMUajI8YAPgzchTuLxWa2d9yNH4rCYdSLgR/iZWo7gNfF8l48RL8fT5GchVczzIj15+K1swtKh1Cj0PHjJh3dwJXArNIaRqHjXjwsn4OXta0HziutYZTH49RYvwwf+D23tIaw53zg8qb5dcDHgQ/h0Qx4J24GcGfjPMIfZDuttP2j1PF9YHbTfueXtv84dLwarwjqBd5U2v7n1VTcAHhV/H4F+F60u/Ce5cKYfyWez5xc2t4x0HEzMKm0vWOgYz0V5DDH6LyaFPNTSts9SMMUYBIDueTLgJXRfgj4ZLTnAbeXtjd1HKPjjtL2tjIVTwGZ2Z+j2QvMlvQO85Hzp81se6z7GHCAgbLD6hiBjv8AhwuY2BIj0LGfgWcxqmOE59Xh2OfAuBv6fzCzA2Z2MOwGWMLA+NAVwBxJm/CoZkcJG1uhQ3X0QfnU9LCU9kCDPOxHgW1N8/Pxgd+j6Z92mFJHXVO768Ajl5PwtFtPLOvBUz0LqSjdkzraR4eZ1fM20KjkOSLpLryq4SA+oPgHM9td1rrWSR11cSLoiF7kRPwhqXvwQcV/4imHf5W0bSSkjvqoogwUvJpB/hTddPwx9hVmtrmsVSMnddTFiaDDzEzSXDznPBu42cxuLGzWiEkd9VGNAwiuwnOAS8wfC29XUkddnAg69uIlkavbWAOkjqqoJgUEA+F6aTuOl9RRFyeKjiQZa6pyAEmSJMn4UbwMNEmSJClDOoAkSZIOJR1AkiRJh5IOIEmSpENJB5Akz4OkUyQ9FNPfJf0l2s9IWlfaviQZC7IKKEmGQdJy4Bkz+9pw2yZJO5ERQJKMAPl3XzdFe7mk9ZJ+JulPki6StEpSv6TNkrpju3MkbZPUJ+mnkmaWVZEkTjqAJDk+TgcuxD81uQHYamZn4W99vTCcwBrgYjM7B/9s43WljE2SZmp7FUSStBv3mtkhSf34WyIb7xnqB2bh300+E7gv3gzchb+ULkmKkw4gSY6Pg3D0pXOHbGBQ7Qh+fQl4xMwWlDIwSYYiU0BJ8sLyO2CapAUAkrolvaGwTUkCpANIkhcUM3sWuBi4XtJO/NOBbylqVJIEWQaaJEnSoWQEkCRJ0qGkA0iSJOlQ0gEkSZJ0KOkAkiRJOpR0AEmSJB1KOoAkSZIOJR1AkiRJh5IOIEmSpEP5H4CZurGcdhozAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_anom.sst.sel(lon=300, lat=50).plot(label='monthly anom')\n", "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o', label='5 year resample')\n", "ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label='12 month rolling mean', color='k')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Coarsen\n", "\n", "`coarsen` is a simple way to reduce the size of your data along one or more axes.\n", "It is very similar to `resample` when operating on time dimensions; the key difference is that `coarsen` only operates on fixed blocks of data, irrespective of the coordinate values, while `resample` actually looks at the coordinates to figure out, e.g. what month a particular data point is in. \n", "\n", "For regularly-spaced monthly data beginning in January, the following should be equivalent to annual resampling.\n", "However, results would different for irregularly-spaced data." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (time: 59, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-06-16T08:00:00 ... 2018-06-16T12:00:00\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/38)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-11-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time
" ], "text/plain": [ "\n", "Dimensions: (time: 59, lat: 89, lon: 180)\n", "Coordinates:\n", " * lat (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n", " * lon (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n", " * time (time) datetime64[ns] 1960-06-16T08:00:00 ... 2018-06-16T12:00:00\n", "Data variables:\n", " sst (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n", "Attributes: (12/38)\n", " climatology: Climatology is based on 1971-2000 SST, X...\n", " description: In situ data: ICOADS2.5 before 2007 and ...\n", " keywords_vocabulary: NASA Global Change Master Directory (GCM...\n", " keywords: Earth Science > Oceans > Ocean Temperatu...\n", " instrument: Conventional thermometers\n", " source_comment: SSTs were observed by conventional therm...\n", " ... ...\n", " license: No constraints on data access or use\n", " comment: SSTs were observed by conventional therm...\n", " summary: ERSST.v5 is developed based on v4 after ...\n", " dataset_title: NOAA Extended Reconstructed SST V5\n", " data_modified: 2021-11-07\n", " DODS_EXTRA.Unlimited_Dimension: time" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.coarsen(time=12).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Coarsen also works on spatial coordinates (or any coordiantes)." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_coarse = ds.coarsen(lon=4, lat=4, boundary='pad').mean()\n", "ds_coarse.sst.isel(time=0).plot(vmin=2, vmax=30, figsize=(12, 5), edgecolor='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## An Advanced Example\n", "\n", "In this example we will show a realistic workflow with Xarray.\n", "We will\n", "- Load a \"basin mask\" dataset\n", "- Interpolate the basins to our SST dataset coordinates\n", "- Group the SST by basin\n", "- Convert to Pandas Dataframe and plot mean SST by basin" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (Z: 33, X: 360, Y: 180)\n",
       "Coordinates:\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * X        (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * Y        (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    basin    (Z, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL
" ], "text/plain": [ "\n", "Dimensions: (Z: 33, X: 360, Y: 180)\n", "Coordinates:\n", " * Z (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n", " * X (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n", " * Y (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n", "Data variables:\n", " basin (Z, Y, X) float32 ...\n", "Attributes:\n", " Conventions: IRIDL" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basin = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/dods')\n", "basin" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (Z: 33, lon: 360, lat: 180)\n",
       "Coordinates:\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    basin    (Z, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL
" ], "text/plain": [ "\n", "Dimensions: (Z: 33, lon: 360, lat: 180)\n", "Coordinates:\n", " * Z (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n", " * lon (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n", " * lat (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n", "Data variables:\n", " basin (Z, lat, lon) float32 ...\n", "Attributes:\n", " Conventions: IRIDL" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basin = basin.rename({'X': 'lon', 'Y': 'lat'})\n", "basin" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'basin' (lat: 180, lon: 360)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.],\n",
       "       [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)\n",
       "Coordinates:\n",
       "    Z        float32 0.0\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "Attributes:\n",
       "    long_name:  basin code\n",
       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
       "    valid_min:  1\n",
       "    valid_max:  58\n",
       "    scale_min:  1\n",
       "    units:      ids\n",
       "    scale_max:  58
" ], "text/plain": [ "\n", "array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [11., 11., 11., ..., 11., 11., 11.],\n", " [11., 11., 11., ..., 11., 11., 11.],\n", " [11., 11., 11., ..., 11., 11., 11.]], dtype=float32)\n", "Coordinates:\n", " Z float32 0.0\n", " * lon (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n", " * lat (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n", "Attributes:\n", " long_name: basin code\n", " CLIST: Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n", " valid_min: 1\n", " valid_max: 58\n", " scale_min: 1\n", " units: ids\n", " scale_max: 58" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basin_surf = basin.basin[0]\n", "basin_surf" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "basin_surf.plot(vmax=10)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "basin_surf_interp = basin_surf.interp_like(ds.sst, method='nearest')\n", "basin_surf_interp.plot(vmax=10)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'sst' (time: 708, basin: 14)>\n",
       "array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.6758132 ,  6.504184  , 29.279463  , ..., 10.920228  ,\n",
       "        15.955025  , 29.41976   ],\n",
       "       [-0.7937442 ,  3.0715032 , 27.608435  , ...,  5.4078875 ,\n",
       "        10.673693  , 27.7558    ],\n",
       "       [-1.8       , -0.06063586, 25.881481  , ...,  0.5253569 ,\n",
       "         7.267694  , 26.163145  ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]
" ], "text/plain": [ "\n", "array([[-1.8 , -1.8 , 23.455315 , ..., -1.8 ,\n", " 3.3971915 , 24.182198 ],\n", " [-1.8 , -1.8 , 23.722523 , ..., -1.8 ,\n", " 0.03573781, 24.59657 ],\n", " [-1.8 , -1.8 , 24.601315 , ..., -1.8 ,\n", " -0.26487017, 26.234186 ],\n", " ...,\n", " [ 0.6758132 , 6.504184 , 29.279463 , ..., 10.920228 ,\n", " 15.955025 , 29.41976 ],\n", " [-0.7937442 , 3.0715032 , 27.608435 , ..., 5.4078875 ,\n", " 10.673693 , 27.7558 ],\n", " [-1.8 , -0.06063586, 25.881481 , ..., 0.5253569 ,\n", " 7.267694 , 26.163145 ]], dtype=float32)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", " Z float32 0.0\n", " * basin (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n", "Attributes:\n", " long_name: Monthly Means of Sea Surface Temperature\n", " units: degC\n", " var_desc: Sea Surface Temperature\n", " level_desc: Surface\n", " statistic: Mean\n", " dataset: NOAA Extended Reconstructed SST V5\n", " parent_stat: Individual Values\n", " actual_range: [-1.8 42.32636]\n", " valid_range: [-1.8 45. ]\n", " _ChunkSizes: [ 1 89 180]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.sst.groupby(basin_surf_interp).first()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'sst' (time: 708, basin: 14)>\n",
       "array([[18.585493 , 20.757555 , 21.572067 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.49982  ],\n",
       "       [18.705065 , 20.81674  , 21.902279 , ...,  4.8877654,  5.44638  ,\n",
       "        26.577093 ],\n",
       "       [18.845842 , 20.865038 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.908558 ],\n",
       "       ...,\n",
       "       [19.84992  , 21.960493 , 20.389412 , ..., 17.571943 , 18.184528 ,\n",
       "        29.336565 ],\n",
       "       [19.424026 , 21.722925 , 21.061403 , ..., 13.461868 , 13.863244 ,\n",
       "        28.755905 ],\n",
       "       [19.265354 , 21.512274 , 21.814356 , ...,  9.417906 , 10.607256 ,\n",
       "        27.905243 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0
" ], "text/plain": [ "\n", "array([[18.585493 , 20.757555 , 21.572067 , ..., 6.238062 , 6.889794 ,\n", " 26.49982 ],\n", " [18.705065 , 20.81674 , 21.902279 , ..., 4.8877654, 5.44638 ,\n", " 26.577093 ],\n", " [18.845842 , 20.865038 , 22.031416 , ..., 4.686406 , 5.5322194,\n", " 27.908558 ],\n", " ...,\n", " [19.84992 , 21.960493 , 20.389412 , ..., 17.571943 , 18.184528 ,\n", " 29.336565 ],\n", " [19.424026 , 21.722925 , 21.061403 , ..., 13.461868 , 13.863244 ,\n", " 28.755905 ],\n", " [19.265354 , 21.512274 , 21.814356 , ..., 9.417906 , 10.607256 ,\n", " 27.905243 ]], dtype=float32)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n", " Z float32 0.0\n", " * basin (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basin_mean_sst = ds.sst.groupby(basin_surf_interp).mean()\n", "basin_mean_sst" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Zsst
basin
1.00.019.284992
2.00.021.178225
3.00.021.127054
4.00.019.845881
5.00.08.131749
6.00.015.084384
7.00.028.494108
8.00.026.619698
9.00.00.310854
10.00.01.547191
11.00.0-0.816617
12.00.012.085889
53.00.014.338935
56.00.028.465738
\n", "
" ], "text/plain": [ " Z sst\n", "basin \n", "1.0 0.0 19.284992\n", "2.0 0.0 21.178225\n", "3.0 0.0 21.127054\n", "4.0 0.0 19.845881\n", "5.0 0.0 8.131749\n", "6.0 0.0 15.084384\n", "7.0 0.0 28.494108\n", "8.0 0.0 26.619698\n", "9.0 0.0 0.310854\n", "10.0 0.0 1.547191\n", "11.0 0.0 -0.816617\n", "12.0 0.0 12.085889\n", "53.0 0.0 14.338935\n", "56.0 0.0 28.465738" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = basin_mean_sst.mean('time').to_dataframe()\n", "df" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 Atlantic Ocean\n", "2 Pacific Ocean \n", "3 Indian Ocean\n", "4 Mediterranean Sea\n", "5 Baltic Sea\n", "6 Black Sea\n", "7 Red Sea\n", "8 Persian Gulf\n", "9 Hudson Bay\n", "10 Southern Ocean\n", "11 Arctic Ocean\n", "12 Sea of Japan\n", "13 Kara Sea\n", "14 Sulu Sea\n", "15 Baffin Bay\n", "16 East Mediterranean\n", "17 West Mediterranean\n", "18 Sea of Okhotsk\n", "19 Banda Sea\n", "20 Caribbean Sea\n", "21 Andaman Basin\n", "22 North Caribbean\n", "23 Gulf of Mexico\n", "24 Beaufort Sea\n", "25 South China Sea\n", "26 Barents Sea\n", "27 Celebes Sea\n", "28 Aleutian Basin\n", "29 Fiji Basin\n", "30 North American Basin\n", "31 West European Basin\n", "32 Southeast Indian Basin\n", "33 Coral Sea\n", "34 East Indian Basin\n", "35 Central Indian Basin\n", "36 Southwest Atlantic Basin\n", "37 Southeast Atlantic Basin\n", "38 Southeast Pacific Basin\n", "39 Guatemala Basin\n", "40 East Caroline Basin\n", "41 Marianas Basin\n", "42 Philippine Sea\n", "43 Arabian Sea\n", "44 Chile Basin\n", "45 Somali Basin\n", "46 Mascarene Basin\n", "47 Crozet Basin\n", "48 Guinea Basin\n", "49 Brazil Basin\n", "50 Argentine Basin\n", "51 Tasman Sea\n", "52 Atlantic Indian Basin\n", "53 Caspian Sea\n", "54 Sulu Sea II\n", "55 Venezuela Basin\n", "56 Bay of Bengal\n", "57 Java Sea\n", "58 East Indian Atlantic Basin\n", "dtype: object" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "basin_names = basin_surf.attrs['CLIST'].split('\\n')\n", "basin_df = pd.Series(basin_names, index=np.arange(1, len(basin_names)+1))\n", "basin_df" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "df = df.join(basin_df.rename('basin_name'))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot.bar(y='sst', x='basin_name')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }