diff --git a/DocumentedExamples/True_Zonal_Mean.ipynb b/DocumentedExamples/True_Zonal_Mean.ipynb new file mode 100644 index 00000000..c1e585a1 --- /dev/null +++ b/DocumentedExamples/True_Zonal_Mean.ipynb @@ -0,0 +1,2861 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e3c7ab35", + "metadata": {}, + "source": [ + "# True Zonal Mean\n", + "\n", + "Calculate the *true zonal mean* of a scalar quantity regardless of the horizontal mesh. \n", + "\n", + "Specifically, we calculate the volume weighted mean along all grid cells whose centres fall within finite latitude intervals rather than the arithmetic mean of cells along the model's curvilinear grid. The method presented can also be used to re-grid models onto the same latitudinal grid and the general principles can be used to define any multidimensional sum or average using the `xhistogram` package.\n", + " \n", + "**Requirements:** \n", + "Select the `conda/analysis3-22.04` (or later) kernel.\n", + "This code should work for just about any MOM5 configuration since all we are grabbing is temeprature and standard grid information. You can swap temperature with any other scalar variable. You can also in principle swap latitude with another scalar." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b8cf50e7", + "metadata": {}, + "outputs": [], + "source": [ + "import intake\n", + "import matplotlib.pyplot as plt\n", + "import cmocean as cm\n", + "import xarray as xr\n", + "import numpy as np\n", + "from dask.distributed import Client\n", + "from xhistogram.xarray import histogram #This package has only recently been added to conda/analysis3-20.01" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "44ded25b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Worker: 20

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:45825\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/40037/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:43171\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-r4ckcqtw\n", + "
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Worker: 21

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:44703\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/38159/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:38197\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-gt5yq6rc\n", + "
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Worker: 22

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:39819\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/39715/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:43367\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-kmll47qd\n", + "
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Worker: 23

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:44663\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/35999/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:34199\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-757va698\n", + "
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Worker: 24

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\n", + " Comm: tcp://127.0.0.1:35043\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/39273/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:45945\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-eyp4kkqj\n", + "
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Worker: 25

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\n", + " Comm: tcp://127.0.0.1:36277\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/45803/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:41647\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-6rm06dt5\n", + "
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Worker: 26

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\n", + " Comm: tcp://127.0.0.1:41827\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/35435/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:43487\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-se6tlqg4\n", + "
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Worker: 27

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\n", + " Comm: tcp://127.0.0.1:38317\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/33693/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:33649\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-hbhvpfu2\n", + "
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Worker: 28

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:41055\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/40795/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:40875\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-jw1op2vm\n", + "
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Worker: 29

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\n", + " Comm: tcp://127.0.0.1:40021\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/34607/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:45023\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-589saxr0\n", + "
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Worker: 30

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\n", + " Comm: tcp://127.0.0.1:43169\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/34453/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:35233\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-fqhhzj8h\n", + "
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Worker: 31

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\n", + " Comm: tcp://127.0.0.1:35967\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/41649/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:34967\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-ngfgut_j\n", + "
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Worker: 32

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\n", + " Comm: tcp://127.0.0.1:45871\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/44241/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:43883\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-9dz4xjx3\n", + "
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Worker: 33

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:35175\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/37849/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:38253\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-1c15m76a\n", + "
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Worker: 34

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:36423\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/46081/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:36173\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-_lqwsu8r\n", + "
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Worker: 35

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:34959\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/42005/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:40165\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-rlszz02t\n", + "
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Worker: 36

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:37491\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/42345/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:37573\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-_h4xv9vc\n", + "
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Worker: 37

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:35553\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/44069/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:38237\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-1cubn5fs\n", + "
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Worker: 38

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:38015\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/38363/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:42851\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-9hhdbs_8\n", + "
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Worker: 39

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:35909\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/37301/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:35263\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-qey27q2g\n", + "
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Worker: 40

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:40753\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/36261/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:38537\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-lsbyb5bj\n", + "
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Worker: 41

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:45747\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/36329/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:39305\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-ey21rknv\n", + "
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Worker: 42

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:34469\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/36777/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:39287\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-owvynbrx\n", + "
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Worker: 43

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "
\n", + " Comm: tcp://127.0.0.1:42399\n", + " \n", + " Total threads: 1\n", + "
\n", + " Dashboard: /proxy/44185/status\n", + " \n", + " Memory: 3.93 GiB\n", + "
\n", + " Nanny: tcp://127.0.0.1:39847\n", + "
\n", + " Local directory: /jobfs/122373923.gadi-pbs/dask-scratch-space/worker-u4q00_c7\n", + "
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Worker: 44

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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "client = Client(threads_per_worker=1)\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "196737d8-0467-4078-8878-bcf3840564e5", + "metadata": {}, + "source": [ + "Open ACCESS-NRI's default catalog:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f073f448", + "metadata": {}, + "outputs": [], + "source": [ + "catalog = intake.cat.access_nri" + ] + }, + { + "cell_type": "markdown", + "id": "00372e86", + "metadata": {}, + "source": [ + "Choose the experiment and the variable we want to average. This example uses temperature but you can choose any scalar, 3D variable. The variables `dzt` and `area_t` are also required so we can only use experiments that save those:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9088c3ef-3e46-401b-a3e5-5cfddebbc7b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Intake dataframe catalog with 2 source(s) across 5 rows:

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modeldescriptionrealmfrequencyvariable
name
025deg_jra55_ryf9091_gadi{ACCESS-OM2}{0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.3 RYF9091 repeat year forcing (May 1990 to Apr 1991)}{ocean}{fx, 1yr, 1mon}{dzt, temp, area_t}
025deg_jra55_ryf_era5comparison{ACCESS-OM2}{0.25 degree ACCESS-OM2 global model configuration with JRA55-do v1.4.0\\nRYF9091 repeat year forcing (May 1990 to Apr 1991)}{ocean}{fx, 1mon}{dzt, temp, area_t}
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "catalog.search(name='.*025deg_jra55.*', variable=[\"temp\", \"dzt\", \"area_t\"], require_all=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3408bb29-c81f-4f28-993f-6b233cfce241", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = '025deg_jra55_ryf9091_gadi' #Choose any run (that includes the required variables)\n", + "variable = 'temp' #any scalar variable where volume weighted averaging makes sense\n", + "\n", + "xarray_open_kwargs=dict(use_cftime=True, chunks={\"time\": -1})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9eded697-29d4-4101-ba05-fe3d0509fd67", + "metadata": {}, + "outputs": [], + "source": [ + "cat_subset = catalog[experiment]\n", + "var_search = cat_subset.search(variable=variable, frequency=\"1yr\")\n", + "variable_to_average = var_search.to_dask(xarray_open_kwargs=xarray_open_kwargs)[variable]" + ] + }, + { + "cell_type": "markdown", + "id": "643ddcbe", + "metadata": {}, + "source": [ + "First we show the standard approach, which is to take the arithmetic mean of all grid cells along the quasi-longitudinal coordinate. For MOM5's tri-polar grid this approach is in principle \"okay\" for the southern hemisphere, where grid cell areas are constant at fixed latitude. It doesn't though, take into account partial cells.\n", + "\n", + "The `xarray`'s method `.mean(dim='dimension')` applies `numpy.mean()` across that dimension. This is simply the arithmetic mean.\n", + "\n", + "For some scalar $T$ the arithmetic mean, e.g., across dimension `i`, is given by\n", + "\n", + "$$ \\left_{j,k} = \\frac{1}{I}\\sum_{i=1}^{I} T_{i,j,k},$$\n", + "\n", + "where $i$, $j$ and $k$ are the indicies in the $x$, $y$ and $z$ directions respectively of the curvilinear grid and $I$ is the number of indicies along the $x$ axis. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4bb61f00-4c6a-4ba9-a6e4-737a378f860d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.8 s, sys: 389 ms, total: 2.19 s\n", + "Wall time: 2.54 s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "x_arith_mean = variable_to_average.groupby('time.year').mean(dim='time').mean(dim='xt_ocean')\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "x_arith_mean.sel(year=2000).plot(yincrease=False, vmin=273, vmax=300, cmap='Oranges')\n", + "plt.title('x-coordinate arithmetic mean');" + ] + }, + { + "cell_type": "markdown", + "id": "1e55737f", + "metadata": {}, + "source": [ + "The main issue with this average is that the 'latitude' coordinate may be meaningless near the north pole, particularly when comparing to observational analyses or other models which can have either a regular grid or a different curvilinear grid. Even different versions of MOM might have different grids! \n", + "\n", + "Let us consider what the true zonal average looks like. That is consider a set of latitude 'edges' $\\{\\phi'_{1/2},\\phi'_{1+1/2},...,\\phi'_{\\ell-1/2},\\phi'_{\\ell+1/2},...,\\phi'_{L+1/2}\\}$ between which we want to compute an average of $T$ at $\\{\\phi'_{1},\\phi'_{2},...,\\phi'_{\\ell},...,\\phi'_{L}\\}$ such that\n", + "\n", + "$$ \\overline{T}(\\phi'_\\ell,\\sigma) = \\dfrac{\\iint_{\\phi'_{\\ell-1/2} < \\phi \\leq \\phi'_{\\ell+1/2}} T(\\phi,\\lambda,\\sigma)\\frac{\\partial z}{\\partial \\sigma}(\\phi,\\lambda,\\sigma)\\,\\mathrm{d}A}{\\iint_{\\phi'_{\\ell-1/2} < \\phi \\leq \\phi'_{\\ell+1/2}}\\frac{\\partial z}{\\partial \\sigma}(\\phi,\\lambda,\\sigma)\\,\\mathrm{d}A},$$\n", + "\n", + "where $\\lambda$ is longitude and $\\sigma$ is an arbitrary vertical coordinate. \n", + "\n", + "In discrete form this average is\n", + "\n", + "$$\\overline{T}_{\\ell,k} = \\frac{\\sum_{i=1}^{I}\\sum_{j=1}^{J}\\delta_{i,j}T_{i,j,k}\\Delta Z_{i,j,k}\\Delta \\mathrm{Area}_{i,j}}{\\sum_{i=1}^{I}\\sum_{j=1}^{J}\\delta_{i,j,k}\\Delta Z_{i,j,k}\\Delta \\mathrm{Area}_{i,j}},$$\n", + "\n", + "where $\\delta_{i,j} = 1$ if $\\phi'_{\\ell-1/2}<\\phi_{i,j}\\leq \\phi'_{\\ell+1/2}$ and $\\delta_{i,j} = 0$ elsewhere, $\\Delta Z$ is the grid cell vertical thickness and $\\Delta \\mathrm{Area}$ is the grid cell horizontal area.\n", + "\n", + "For our purposes we will use the edges of the models `xt_ocean` coordinate to define $\\phi'_{\\ell+1/2}$ so the number of 'bins' $L$ will be the same as the length of the quasi-latitude coordinate ($J$). \n", + "\n", + "Fortunately, as you can see below, the two sums are weighted histograms (one for $T$ times volume and the other for just volume) and these can be rapidly computed using `xhistogram`.\n", + "\n", + "First lets load the scalar variable (latitude) we want to use as our coordinate then define the bin edges." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4a23fc10-5c4e-4929-b355-18fe7616dce9", + "metadata": {}, + "outputs": [], + "source": [ + "coord = 'geolat_t' #can be any scalar (2D, 3D, eulerian, lagrangian etc)\n", + "\n", + "var_search = cat_subset.search(variable=coord)\n", + "var_search = var_search.search(path=var_search.df.iloc[0].path) # Work-around to get only the first file\n", + "variable_as_coord = var_search.to_dask()[coord]" + ] + }, + { + "cell_type": "markdown", + "id": "267ebeed-af2d-4c33-b2a1-31f7085f2c37", + "metadata": {}, + "source": [ + "Now we want to define the coordinate bins as the latitude edges of the t-cells, adding the first edge (0) at latitude -90:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "338680da-7b90-41eb-9e3d-a2e19d939e4d", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the coordinate bins as the latitude edges of the T-cells\n", + "var_search = cat_subset.search(variable='geolat_c')\n", + "var_search = var_search.search(path=var_search.df.iloc[0].path) # As above\n", + "yu_ocean = var_search.to_dask()['yu_ocean']\n", + "\n", + "# make numpy array (using .values) and add 1st edge at -90\n", + "bins = np.insert(yu_ocean.values, 0, np.array(-90), axis=0) \n", + "\n", + "# Alternatively we could just use some regular grid like this \n", + "# bins = np.linspace(-80, 90, 50)\n", + "# or use a grid from a different (coarser) model." + ] + }, + { + "cell_type": "markdown", + "id": "6df5a3af-c1f5-4201-85ce-34700a0cc193", + "metadata": {}, + "source": [ + "Get the thickness, area a volume of cells" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7ce8c76f-02b7-4c6e-869e-7accafe4e1b8", + "metadata": {}, + "outputs": [], + "source": [ + "var_search = cat_subset.search(variable='dzt', frequency=\"1yr\")\n", + "dzt = var_search.to_dask(xarray_open_kwargs=xarray_open_kwargs)['dzt'] #thickness of cells\n", + "\n", + "var_search = cat_subset.search(variable='area_t')\n", + "var_search = var_search.search(path=var_search.df.iloc[0].path) # As above\n", + "area_t = var_search.to_dask()['area_t'] #area of cells\n", + "\n", + "dVol = dzt * area_t #Volume of cells" + ] + }, + { + "cell_type": "markdown", + "id": "511fec74", + "metadata": {}, + "source": [ + "Now let's compute the numerator and denominator of the equation above using `xhistogram`, then the time mean and then the zonal mean." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fa09dd3a", + "metadata": {}, + "outputs": [], + "source": [ + "histVolCoordDepth = histogram(variable_as_coord.broadcast_like(dVol).where(~np.isnan(dVol)), bins=[bins], weights=dVol, dim=['yt_ocean', 'xt_ocean'])\n", + "histTVolCoordDepth = histogram(variable_as_coord.broadcast_like(dVol).where(~np.isnan(dVol)), bins=[bins], weights=dVol * variable_to_average, dim=['yt_ocean', 'xt_ocean'])\n", + "coord_mean = (histTVolCoordDepth/histVolCoordDepth).groupby('time.year').mean(dim='time')" + ] + }, + { + "cell_type": "markdown", + "id": "164c8deb", + "metadata": {}, + "source": [ + "Now we can plot the results which thankfully retain all the data-array info on variables and axis etc." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0a28d65a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.28 s, sys: 627 ms, total: 3.9 s\n", + "Wall time: 4.18 s\n" + ] + }, + { + "data": { + "image/png": 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gRbtvQ7CiXW8MUnzRBTKaQEcXyGjKp+xHG4SY7ccrkIHrDaWCBi9mylq3rEOHDmHrGYkRdeyoGQlsOQ0MrB2JT7IchW9cgVWIQCYiIgKtW7fGmjVrMHDgQHX5mjVr0L9//1IsGfmn+RVLCUQgoPm6CEjpWi3hflN3ArC507t/pVLe2CXcH4AOaL4CuR5LGyCcmmV2CDggbXb37hwA7K7NnHDl6XTqPzTC4P7W7A4ypdP9Ae0OSpQPHuUXRUceIMNdgYmSxm53tcCo2+S59+XeXkrXOumEsLuCEOH0tLAorS6Q0tWi5NQEK7rgRttqowRCrqBIavLTBjdO9b5UW2pcrTeAtsUGEp77TqhBD9xBjpQS0t16o7TO6AMaV30aAxM1Pdx56h7rAxxlGaBvHVLyMbak+GqpcUhpHrSoZRK69NqyQpNOYdZCU1iwY8bY69tfAGX8cu9ru8KCKW3ZteXT5mnTfKcy7ssYbGiXFbY/I933Oe2+NeuVAEVJow1AtOl9tawo9aUEMcp22lYUV1AidY91gQxcj+3uoCbMnZfSumK3KV9goQYrQgjYdK0sNk+wYvMEJMZABnAFKa7jd7fE2D1f1oX7y7Gw2VyBis3u2s5mgy0szPNYCWyEu5XGHewgLNwVsNjD3K0p7gBGaWnRtr7YwoDwKE3Q4m5tUIKb8Equ/JT3RwhPy4T2zNEGGupYB8N69b7hS71X4AJNAGT3Diy88jLL35BWSvVjRv2cUoMJN6l8hgGAwzvQkEItl9pLRPn8Uluh3H8kPPWk/ICn7B9w/8CmPRbhvT+6ZPVrUR9XVhVIinI9X91q2vDFMSfOnz+PypUrl3LpLl0VIpABgAkTJmDo0KFo06YNOnTogNdffx2HDx/GAw88UNpFo0Ipb+jaYMYdEKgfEMpy5RuHMKRzby+hSaPkqQQ33v/1wYz700ITL0Eq3w7d64QTQAQgCjz7dTo8v7ApgUwBXOmk5iuh9sNT+RZnD9OUH54PSSk9eQkB2DwBDYQSnNgAmwScAnC6fnGWThuElJBOO6TDAWFzBzzufIWUkA4bpNPpTudwtfzY7BBOT8uMcLrK4FCKKqV7vfa+dMeaEsLdJKMNamwSsLsDBaXlxhWMeJa5YkN3641wH477kKTUPwbcgYcmUHFKAaHkraZR1rnyVJZJ6frCqQYl7v2pLTLCO9ixC+kJHpRTEfoAR3MWq5Qv2Eo6hb9gQvv9yJiXr5Yj6NLpv+gb8/a3f7NWKIU2OLALY7DgCSR0/02OQy2LSXl8lUv4uq8JTpR1/pZpgzxt4GJsKbEJT9cwQN8lTDl2pbVF2T7c5nrthQlX8OLanycoUbqG2dw71ba22GyelhVjK4ynhUXpjuQJaCDgaVWxeVpXIGzuFpkw/TrtY5u7a5nN1SKhBD26AEa5KYGIEJ7/6pPgbtGwabt/mdzU93dD9zDloJQgRglClLzNzgRNcCLUYEV48vZqfdFsB2jeX53e+zC+D6vLlWBCeoIL4wtfCXS06X1SNtZ8DmjTa/ctpOejT7u98oaldm+zGRPRJWb79u345ZzEmFTPed6kisCmv4C+dWOw7iRbZXypMIHMbbfdhlOnTmHmzJnIzMxEs2bN8Pnnn6Nu3bqlXTQKmDZQUR67ogoBm+sLufrlXvkQ0rTQqMGQHa43eIe6vWcZ4AlmlC9h7mBGbeXRbCbcAYNwt9ZIzYefNkjRfmGVNtcrr8C9XEnnLPD8D5NwRQlOdwuM9Pzap81baj4cASi/uMKp6SrnDnRcebuCGggHhE0JWFwtL9pWJqF0XXN/uZFOB2wO1699TiEhhBNSM3ZFSgklYpBSaO67Hguhua+MsQF0QY00BjVCCTak+kVaOAWk8AQhapDirlqb0xPkuA5XuoMdoS5Tn3Hh+YIuDFWpBkrCE9TAXTYl2FGrXfvUmtwDPF+WoUnu+h7qL3TxUIIS45dw7Z7Nur8ZW1rMvtD72p/2GI0BnDY/pVxKntqWDe0+bfBVB97HqZRd27KmDXACYWy10QYrxlYYpSzalhQlcLEbyq600ijp7O7lSkCjBC66rmbK/tTAxB28hCkBhnudttVFDVhs7h/VPeNdlMBEDV6UerV5vrB7BTB2d2DiboURyjKbp3XGE/zYNC022sBFOXDtuBZP8OBFO05ReW+GXZ9Gm4/pk6e8z2vS655Z7ZOqdM+CJk+zAMmkVUc90bTBjKGs6ueIkp/yC4cm5DYGQeqbhLscxpNYTe8JYKS7p4HnM0R49ik05ZQ2TzADGF6c2hes9K4DumRIKfF/17VFu+oCseGe50kIgR417XjnqANZWVlISkoqxVJeuipMIAMAo0aNwqhRo0q7GGSZ5pcuHeG5CUC4PwA8H5jKB6xJMCPh/pBSf7uH4WcveAIaQLg/CKRT0y1N2yKjxizubmOAd/Ci3ncntktPwOF1yMo3cbuPQEYY/it5eLqxCSk93d+U7QH1A05KdyuNFK5qKYD6pUI6na4vBO7xNEqXE0gJW4EDUtg0n/vGYEV6iiRd5VAatIQ7KJDS9VzZhYBnbI0oNKgRNmW5q5VGCUCU7mI2m358jRrqCk9LjdAENdpWGqeharXBgNm4FiPt9wRfX5iVL8va9NqvZU7DY7PvHkqrgHG9seuXtpudtly+Wha025p11YNmuXb/2nx0g9mV4zakMe5Py2s8lPTuPghoWrwMZVPy0DKWVbkPmNelzZDWNcDeu66U59QuPI/t7sBH7X4mlO5jnqBE6T7migf03cZ0LTWGNAB03cxcb2+GL+nC04VMCWaEzXNft1wTtCgD+10BjitQUdZ7AhhP0OM1ON9VOH3Fqy3STlfXMt24RKchyLHrjsH72VPWqc+WJ62xK5dfJkGN1/bGXyeU9UrQYgxwjG8IynutWchv+K/82KRppVEv7af7IUyzqfazULpfjUrQot2n8kYmpKdMvgJOKlWff/45juVK3JZs91pXJ1qgfmWBvo2Tse1MYZ2OK6YKFchQWSI930y0gYw0PNYRnmXCHeAov4YJeD+W0HwIG38p0wY2yieM9gMD7g8fp2a58rVZ6j/gAf2HktKSo/Z1dn/I2+D57yzQf3lwOjwBjZqHNHy4aavP8wGvBDRSCnfgItUQUCp9w6TTNfOQ0+GpD6f2w9zp2U8Y3C02rg9RZXIApUiulhqnGph4HruL7lSq0dW64+rmLeCaS8GpBldmQY3SlUxppbFpup1p/ystKtovxOoyNZDzVKVdG9xoq1F6fzFWGL8g2w1f4gHNTFQC3l/wfeWrfP/Q3Hct1w9IV9L4JjV/PbQBldLVyXUc+tyUSTTMtlfSu05Rz+xZ2sHonh/GhSY/TSzubuoxG08lnRJOqR9HpYyXcgWxnnPCa4IItfyGcgv912KzIE5h9hwIw3ql25y2G5kNrhnHtGNWlO5jgL7VRQh9YKINYHyOkbFr3q+UfQizZa73Dn+tMGrAogl+PF3NNC0w6n+bSRATQABhbIVxKjM+qs+UIX0ggYkSVKgnk7qNZ5yJJh+1xdr9fq2cJdquYtL4Shee90No61jZRjtnovDcV7oSa99Llc8Dp8MdBGmP2b1vJzR1KnXnrxpHAVCboXX7NXZ5Uz7nhDugMeutQJeCgoIC3DOoL7rUsCHKbn7ed69pw8JDDvzyyy9o0qRJiEt46eMZTUREREQUYoNqR8IhgTbVfAfvNSIEWsYKDLzmyhCWrOxgiwxdQjS/rimPdS0xxmXKr1K+WmgAXSuNcl8Iz49ZutZ/93JofnUzzGgmYHc3Cin5aFpjlHRSs73QtOII969hSsuLkO7WFye8umco26mtMUq9KIP7ldYcp+6XWM+YGqWlRd8yI+FqTYG7S5dQtlFaX4QAUOB6rC2LdHq6mQGQ0tX1zFbggBM2V+sJPE+DDTYoUzEbH9vVLmjuX7uVbkI2CTjd+3T/Oq92OYNrYgC7U9/lrEDbBQ2eLmfaZerUzz5+rXd1KZOFzhQGmHeXssH1q7xrLIR+fIS2i5GAvtVDaJtblGVqa4bQdUGCsq0w5GFo9fD8Iu/J06xlwt90veo+lXJotjEdRK7pvmTavQhwtd4pU6U73dd/kq5xWE6H9LTquVtjHA6nZrpvp3sslfu6RbpzA+p04Nrzwqk+r8VnNpZI29XMbqgzpVVFSeh3ymTDQH6htshorvdi90w1LLStINrWF4V7ML9r355ZyYS2dUYdR+NaJpQB/co6peXFq0VGO7jf2Doj1P2btkQr713aCVAcNvdQRHcrhrGbnJqH9LSkSE0rh9mzor63wdOrWGhaZpTHSuu51Ayglg5POqm0nGhbOqS727Dyfu/Ub6tN58j3vB68uoppW1OUzy5l35rPM/UQNWNmlP16tcho60tpbdLuRvnsg+/tKOTOnz+PdSed6JNog72QVsguNWx4+aADGzZswHXXXReiEpYNDGSolBiDFl/LlSBGu9wQzChdYLwCIM12/qhjajSffupm7oBCDVJc3RakcH8QKl3QpDadJqgSNrhGoNsMQY0SHLm/ODjd+Sn5KuNsbMpUzPAEKNr6UYIm9UPS3SddGSuj/HcHNEqXC+lwuHvbuQM1zexmrmw0dajMgCacENLmviCn5wPa7u5m5hTKTGXS3Y3LvS8JOLWTA7j3oQY67uORmq5knqdRSeu6bzd8iQ33+hIr1S+xxgAHMP9y6yv8BfRdiJQARXtxQgGoX07tds/4B+0MVJ4gQOi+fynb6farCWQ8wQT0X26VgEKZjlbN27yBXXp1mQG8puJVvtgqX3zV4MTmCWgMA8K1ZTd2H9O9rqVrZjwlmFGea+U89Ez37bnGkXQUQDsFuNrdzKEEPMZpv6Hm61TOL82TrS+O+4HTZJnhvvbYvOvQPNj0TIHseay7hos2QLQJr3TCppn+WH1uNYGCsn/t+aPO1KUJctznhHBfTE8NVjTPoVBnFHPvQ+lSpg1SlDTGAf/KfhS+Zo/QfeGX0HUrc0rX+6MQridEuvep7QoGeAckEvDuUKJ0n1J26wCEhICmS67uRyfp2ZdJFzXA4eniqxsIqUQIJpRrg8E9jkYZS2MzbKP74U16trVp8zfWpyfwUbp9Cu32yueAdjZPY1ZSqSfvsRgUen3rxqBauGt2ssJUDhPoFGfDHb264Mh5h2u8HQFg1zIiIiIiopA5duwYvv/LiR417T5/LDHqUF3gbAHw/vvvl3Dpyha2yFCImbS4qKukSTrjNtL7vzF/3Xrot7VCuLse6H4hFCbZaH+tU9IoTfnKr5meVg1Xi4synbO7K5jNfb0Y6f7vLPD8Ciocmq5kmhYZ9ddEd1cDZTIAm/LYpk8HqANQhU0ZcGuHcLq7/7h/FRd2O9SLadrt6i+ArpYYCWkPc/13OiALXF3GRJiEcLjXO52wKRfPVH55l9quZvpWFs9TpG+hMabRb2voVqSk07TYGLfxRfcjrOY51LaOaH95180q5TW7lL4lRXutD7U1RdmFcrFC7XkDQDttrq61xG7XtKQIz6/zZl2MNHWqO1D12Nxp1e5HSh7mrQBqlyNtFyNA/2s74Oq2aNICBLVLmf7177kwqwPKxVul0wkorTPS6dlOOiELCqBe8FXZ1uHUnQPacwKac0pqzh21CCbr/dEmUatQnQ1A6B9DaUnTt8Aoy71a19xdwNQLVGrr3+yLjllLnHY/yrliV1pkbLrugK50mudVe00Y0/uadLoubobuZP5oj0M7a5naauIAzKYqVgfpa/PSnmdKC7Gmm67y3mPTvDaUlmQhNfvUdn+DZ//Sodm/3fVY/e1XaSlyQnehTkeuOy93VzltlzE1f6VFRNNkom0l1M4uoXtzkvpzVDohISC0A/nd6bxbY5QWGun5z+5lpeqmK2qhfmWBOtGBPw/hNoHr4214YOjtGDBgACIjI0uwhGUHW2SIiIiIiELg119/xc4cie41rX8FvypGIEIA/VKiLW/72muvoUWLFoiJiUFMTAw6dOiAL774Ql0vpcSMGTOQnJyMSpUqoWvXrtizZ48uj9zcXIwdOxbx8fGoXLky+vXrh6NHj1ouSzAxkCEiIiIiCoEBbZuiZaxAjQjrrWI2IdAjwYYNp5w4ffq0pW1r166NOXPmYNu2bdi2bRtuuOEG9O/fXw1W5s6di3nz5mHBggXYunUrkpKS0KNHD5w9e1bNY/z48Vi1ahVWrFiBjRs34ty5c+jbty8cDoev3ZY4IQNpTyfk5OQgNjYW2dnZiImJKe3iABdOlnYJikDbvG4yKFi33Mc2xvvuQfreA/01Xa7MuptpH2uvByAN2zkdcF/gxJ3EAUiHuxuTw5NGKjd3fso1BLQXf3O4B4JKp3tQqLt7lyPftVxdL4GCXNe6glzXeimBggJXnup1GLRlhWGfmnXa5cpxGNMa81W68zgdnu5hmu4/ShcydbC2+zGcDlfXHqemW4bUb+N56jVdfKBfZjwvjKeJeXcz6LqVmW1XGN2gbZMB90Lou4mp3cOU7jpKlyxl9ihtFx5A7dIDQJ1FSrtj9Urq6n1N1yElD2M3I7OuRYY0pgPYbfp9e+WlqxBDtzJttx9A003I+EQZupn528b9GtGdd4BnljPtuaekcSpdF52a88szI5py7mr3IzXnvFQ+fP10L/N17imMXcn8TsRg03QT084wppwz2nPJvdxsdjvPDvXdE/UzmLnyVLqp6Qb2a7osqv8B75nKtIP8vWYyM3Q/sytpw1yPwyJc9+3hQFi4votaWKRruS3MNcGAPRyAMByPtvua0D9Wl+meCf2xKDOkGWaEFOrgfS1DNzUlf3WWNHfXMluEfrm265f7vV/pBinCIj1lsoVpJnCxA0KZ/QxQu3gpz7nN3a1QeM4B6EbrC89nivv4hFLngObzTHi21dWD+3h0+ZYvOTlnEVur/qXzfU1jw4YN6HF9F4xLtaNyWNHqX0qJ5UedSIwEvv8rkPk2fYuLi8Nzzz2Hf/zjH0hOTsb48ePx6KOPAnC1viQmJuLZZ5/F/fffj+zsbNSsWRNvv/02brvtNgDAn3/+iZSUFHz++efo1atXscpSVGyRISIiIiIqQVJK3NGrCzrF2YocxACuYLxHTRu2npHYt28fcnJydLfc3NxC83A4HFixYgXOnz+PDh064ODBg8jKykLPnj3VNJGRkejSpQu+//57AMD27duRn5+vS5OcnIxmzZqpaUoDB/tTEPkbQOivNcb4k6fXHe/7RWlINGuNMU6BqUtfjF861KsoK4MsNQP+AddgS+PAf+XHeul0/ULpLHD/UglXa4nyanVofwHUtKhoW2KM66WmJcYsjbF+pFMduK0O4FYHV3vyUCYI8PdruatomnXaMgO6aXJ91rmvwcQBPEfqlb4NeXn90q1tfYDy67pQl3sNtFdbXwyDspVWFO2v4O78PYPsbZ5fYLX7NjvWQM5Dr8HX+mPzelUaWoIKz9/k13uz8mnPRSN/r1nNdrrzTpuX8VxVJwFwn4vqfc10z8r22oH+6rmrac3xVUavlkHv89f43Bmv6WM8f1zJzAbv61vWvPIxlstwvrqSatLaPOeaUAb9242D9g2tIMYWF2Pri9l6YXPtS+jPdc+kAMZWPpv//7q0wv95Y1Yv2gH/2rpSrmkE9yB8n9wToriv3wLlml8AhHRPRuA0nN9SQkqH/txwKpO5hLlaSaR7gL+0GV6Q0lAHEroB+VLCM2Bfua98hjk9x6a2sCij/A37UFtz3HnrPq7LZ8vMpSYvLw9//g3cflnx6zspSiAhEnjiiSfwn//8R7du+vTpmDFjhul2u3btQocOHfD333+jSpUqWLVqFZo2baoGIomJibr0iYmJ+P333wEAWVlZiIiIQPXq1b3SZGVlFfuYioqBDBERERFRCNgRnNDRBmDgwIFYvHixbrm/2cyuuOIK7NixA2fOnMEHH3yAYcOGISMjQ11v1i25sOmhA0lTkti1jIiIiIiojAkPD1dnIVNu/gKZiIgINGzYEG3atEFaWhquuuoqzJ8/H0lJSQDg1bJy/PhxtZUmKSkJeXl5XpMMaNOUBgYyREREREQhoO21XJxbMEgpkZubi9TUVCQlJWHNmjXqury8PGRkZKBjx44AgNatWyM8PFyXJjMzE7t371bTlAZ2LSMfDDOq+E1jXGbcxmSsS6DjY7TjWHzNcFYidFNpGdYZ+lgrfZOVC15qL3ypzCyjzjAjPVkAmj7/mvEzDmgugKmkD/PMeKaMqVH7YpuMldFuaxwDYDbmQHfoJmNulNnP1DEM7nEMjgL9es3FNdU+6ZqxC67HrhmovHdb+PNp2nytmzXKx6xbmvtC7b+v2c5uMl7FOCZEO45AWe9nP17r/T1W+HruCqsbs3L6uGil37Kr5TDszzizVVHK668c2teJr/PSZD+ecTQSwni+mpXN19gaXTqzc1MZY6YZ5+WL4Xk1HbdiZPJ6MDuvpcl+vS6IqRs/45oBTdhs7pnDTMa3aL8VqTNn+RkjoxyjbnyMfqyY7qKZNpthmWb2MtNxasbz2L3MdMYy5didnuW+xtwoz6uA9/OnrTNlETTno3rRTIdraIrmHFHH0igzQUJ5n1PG2tgAKaDOJuYs8MxgpswsZnwejM+jel//Xqo+Fsp/aPI1GwenHXcj9HWiv0MlyIbgtCJYfbYef/xx9OnTBykpKTh79ixWrFiB9evXIz09HUIIjB8/HrNnz0ajRo3QqFEjzJ49G9HR0RgyZAgAIDY2FiNGjMDEiRNRo0YNxMXFYdKkSWjevDm6d+8ehCMqGgYyRERERETl2LFjxzB06FBkZmYiNjYWLVq0QHp6Onr06AEAmDx5Mi5evIhRo0bh9OnTaNeuHb766itUrVpVzePFF19EWFgYBg8ejIsXL6Jbt25YunQp7NofA0OMgQwRERERUQgErWuYxTyMkwJ4ZScEZsyY4XPGMwCIiorCK6+8gldeecXazksQAxkqRGFdxaBpnhbm67VpCttVoduUZHcyLUM3BrOyKFMr6x67u49pu5dBQm1ItjkB2F3dx5T1QpmiU+keYQMKoO8aoO0KoL3IJgDYTLrRmHVVMQpkWl9fUzcbpmTW3Qc83Xzgrkmz7jhFmUIbMHQxMeuGYlhu1qXL2BXLV7css+2t8HWMxny06cy6CfrLy6ycpmXRnF/+ymIsl7G+Cyu7WVkL695WWFqzcpnt01e3SUP3MvX8dDo8r/TCuscZXwda2ul4lYthGqfoNT0MQxdW47Ebu6lpu2vC0G3Nx/Mq7O4uYfYwQ9cxH90mtRe6NC7TXSjT2C3K5r7wo9ItTdu9THOxR+WimXbtvpT9GbpYabuZ6c4747mjfU2bdVcDPN2qnN5dq4yU7mRK12b11HDo0wjh6YKmzd+VWF8G9flxdzMTYfr60a7XlVu57+v1L92zRnnKafpNWds9TTdFs1kXM8DHAgoCd2d0ChIGMkREREREoRDEwfrEWcuIiIiIiKgMYosMEREREVEIlNasZeUVAxkyKGzcgvTdv9hrrIxhecDjW4I1Dka4uwJryqWWHfp1Sp9hpZ+2dLj7LDvdafXTYQq4+0ZDmWZZuse8KHlBM07G7kqnTqXpdC1S7quHrUxFK1yvTKU/tjp9s3GMjLbfvzYfTf4BV5XN/L42T3Vfxulspflys+0Ba2NjjOeS2ZSi/qY1tmna8H2Oe7F5rzfL20xhx2XsL1/Y+A+r0y5b4ZS+p/8NlL+pnbVlNRujpRszoIwZMylPIOO7zPibdtxsuW7KXAvbu6ch95tWu87nY6dn+mglX7O8jHWtjPExS2Okjl2xu6dfNk67bHjtFDb9st0wBbfXWBjNGJjwSPc24a7HQnjuh0d6piDWjZGB6762TK5EJsuMx+oeH+Ozz45mKuPC6PLQjCvx+izzVxb3f6Ecn/Z9RvMc2MM9dagtm+nnqGG8j3G9MByncmkAr+PRbqeZuloa8uW0zCUiaIP9CQADGSIiIiKikOBg/+DiGBkiIiIiIipz2CJDRERERBQC7FoWXAxkiIiIiIhCIFhdyxgLuTCQIQ3Dxdl0PxkUMnBVO/iwsAtZFmsAs+FCXu79usYpusugJtGk0w3sN+SnG0yrnRgAngGYykB+ZQC/tEEIp/tiaNI1YNUJzeB+hzsP4b5vd6dz56FeXE8zUFY7iYIo8Cx3KIP9bZqB/iYXTfQ38Lywn398DfYv7GKJusdO8/uFbedvX14X+QtgQH5hx+Ivvc2wzt9FDU0HeBc2wNy43kf+gVxQMlDF2daMv8kgTPdfyIU8/W1nmGQjIGZ1J0xeQ8o6X3VtNnmB9mKw/ia30OZjzMu43NdFPM2OydfFUo2ME1vY7EBYhH6gv5JOO3GL2QUv1cH+Yfr8AMOAfZvropv2cPcg9gj9YH/ANchf2ABbuOZnaZOLYPqa5MOzQKkYz2Ozi8IWdu772o9yrqgXxTT7LNRM3qJNoxnML2xhnsH+Nrt7ubuObcp7v6vuhHBfTNkHqf0MNZRb+Hp96F47hUzCYzZ/gTLRjemFsamobKL4866QB8fIEBERERFRmcMWGSIiIiKiEOCsZcHFQIaIiIiIKASCNtif0RAAdi0jIiIiIqIyiC0yREREREQhwFnLgouBDPmmzngiPY/1CUweB/DS0s6So1uuZOm+o6YzPtbkYZqXe5YyCE+RpGa5up12HTQzGynH4HSts9nds4+5Zy9zwtWW6U4qbQCcDn06lQOuWcw09SUASId79h73DEoOzb6V8tnCAIfQzLQU5ppFq7BZy8xm6jGbacq43Gs2L5NZgALNNxDGGcKUffrL39+Mav7Smj32lY/C6sxh/mYx8zXTVKAzihU205/Z7Go2m/9Z1wrdZwCzYhnT+DsfAj7WIMyyZvr+IrzX+5ppz9fscwDgLPCdh26bQp5r4yxlSn5Ozb6UqY2Ms6b52q9xtjJtPmERnpnH1OWGrwDqrGZmM5PZPOttdndehmXK7GYAEBbuIy+b+7VvmIFQLa/Z7IW+Plc8y4XNDvWNWX2Dtrtns4R7uUld6XYjXOmFzXVfyUd3Pinl1daxNo2r35AQ7jqy2V2zuQltfQm1/oTNrs5c5v2h5Caluwo0n8XGz0NlBk5dXZn0YSrsI1qXpzTMXBZoJuSPEJKzlgURAxkiIiIiohDgYP/g4hgZIiIiIiIqc9giQ0REREQUAhwjE1wMZIiIiIiIQiBo0y8TAAYyREREREQhwTEywcVAptyR3jP2eM3OUsgMSLqkvmYGs5CHXyYztBSaTjuLmeGhduYYdZYy90Mos8JI77zURJr9KMctne68pGtWMhvc/10z2ggnALuAdDr06bR1J90znnktd7oL736OnHbXDEVOu2udze6aEU2dqczp2c44I1YgM1QZZworzixhZukDWabNx9f+fKXxdd9sCpjizKhmNiOU0+I5bzaLnL/ZsIpSpkDWlQRf7wv+fmYM5Fj9zfxmha9ttLOtFTYrnXTqz6GivN6szHSnmy3KLK3xde/0PA9a6mtIc6y2MMAeDtjt+vXKbGLKY+MsZNp81NnLTGY2082YpZ05Tbhn5FJm+lL2byyzcfYx7Xu5azYxD8PMZMoyNY3d81+d7cvpnoFMu0vzevYst0EI93YCXvXtVU7je5bN5jpem3vGMpvdU7fC5qkLNY1htjFX7p5FuvPVZHYzbb3qPveMeWrKqp0V1LiB9twyO8+ILhEMZIiIiIiIQsAG89/eqGgYyBARERERhQAH+wcXp18mIiIiIqIyhy0yREREREQhELRZy9gkA4CBDBERERFRSHDWsuBiIENEREREFAJCBGewP4MhFwYy5Ykyla9xqkTjNKLqcmM6H9Ojek376LcQARZWSzvlsXJXN6eyJqkwTBnpTqtJ7iqyMqWxdrplp3uGSWFYruwP0E9DKTXTbmqnPLVppm91uqdbdkAIu7pMqlOluvPTplemY9bet7nv28Jc6Z0O1/6cBYDDAdPplo3T+xY2va2VqZaLlN7wWPtOXdgUyuq+TLbxmnrVR/l0ZTGsszLNcaHpLUwHbHyu9CuLVw5fUxWbvtY1aYsyLXVRp3c2m77Y6n6Uaa9twnU/kG8ASjrjlNnKtk7DVMbG/ft8L9S85oTNe3urdeuvTnxN/ewsMJ8+Wret0E+/LGxAWKRnymRlmS3MUyfqtMDKFMx2T1666Y2hX67bp+G16jX9ss2d1Pgcer+u9bMR+0pv0y/z+rwAXO+zAkL5DPCxP1PSNQUzAPf0za56E5pjV6do1hVXU2e2MH2daupZKOt102D7eO/yWUzt56D2/dNwx/Q4lc/YEE/fThREDGSIiIiIiEKAXcuCi4EMEREREVEIBG2wPwFgIENEREREFBJskQkuXkeGiIiIiIjKHLbIEBERERGFALuWBRcDGSIiIiKiELAhON2hGAu5MJAhIiIiIgoBtsgEFwOZ8kQ6oMboXnPpm1xzIJBryOh3UPSyFSqQa8mYPBYw3HdnIdzXktEuV68roxyLklhq5upXrvuh+S9smvqR+uv1KNdzkA7dtWGEml5/3Riv68sA+mtCSIf+v9OpycPpKYOW32uVKMet3De+e5q8m/pND/i9BkMA14fQ3/e13vh7VbDe9Yt4Dgd0DRRfeTsDSBNIPib5+drO8mv7EmL1ekhWCMP7o/ZaWrr9Gp4z42tPm9Z4jZ6Ayqe9DlaAabXXkNFeB8fs3FSuVaKUT9iA8EhP+ZTj1l7jBTbAZjO/DxRy3SYlreE1brjmiud6MhbeJ4zXO/NJ2YfyWLn2jvK54YTv38FtML/GjOsaNJDSVRQYrhFjcq56jlfJT3iu66NcxwfClU6461l7HRmvDP0ftesYlTqy+NpQnw+z/Zex9w2qsBjIEBERERGFAGctCy4GMkREREREIWATrltxsXuaC6dfJiIiIiKiMoctMkREREREIcCuZcHFQIaIiIiIKAREkLqWkQsDGSIiIiKiEOB1ZIKLY2SIiIiIiKjMKdVAZsOGDbj55puRnJwMIQQ++ugj3XopJWbMmIHk5GRUqlQJXbt2xZ49e3RpcnNzMXbsWMTHx6Ny5cro168fjh49qktz+vRpDB06FLGxsYiNjcXQoUNx5syZEj46IiIiIiIP5YKYxb2RS6l2LTt//jyuuuoq3HPPPbjlllu81s+dOxfz5s3D0qVLcfnll+OZZ55Bjx49sG/fPlStWhUAMH78eHzyySdYsWIFatSogYkTJ6Jv377Yvn077HbXRZ6GDBmCo0ePIj09HQBw3333YejQofjkk09Cd7AlzZlf2iWwxvRiYmYXidNcBBPKasNj5QVtuK+7zp1Q8oFnv/4uhKmsMy5X72u2NbtIpjadup3T+0KZuryNyzQX0DSWwZCvjq8LIwKBXWjS1zuk17bKxd+MTH4fMUsX6LYBCeCClUBwLg4ZcB76Mklf2ykXLNQ+DnifFo5HydfkeSwRxgtFGo/LWI5ALjpqZOlYzM43zYV2deuVehWGx7D2evNbHJM68UVzsV3PBXgd3ueF9lxSLswoHe5O+WHwupCjTdPJRblAo3LM6mPlofmxSSk16wwXttRePFItE7zTGtcV+RzVfobYPe/LyoUtjfvSXWzTZJ+6i2mq//TfJKWxbrR1YPdchFS9SKnmYphwX5S0uJ2ESuRbLb8plxR2LQuuUg1k+vTpgz59+piuk1LipZdewtSpUzFo0CAAwLJly5CYmIh3330X999/P7Kzs7F48WK8/fbb6N69OwBg+fLlSElJwddff41evXrhl19+QXp6OjZv3ox27doBAN544w106NAB+/btwxVXXGG6/9zcXOTm5qqPc3JygnnoRERERFTBsEUluC7ZMTIHDx5EVlYWevbsqS6LjIxEly5d8P333wMAtm/fjvz8fF2a5ORkNGvWTE2zadMmxMbGqkEMALRv3x6xsbFqGjNpaWlqV7TY2FikpKQE+xCJiIiIiKiILtlAJisrCwCQmJioW56YmKiuy8rKQkREBKpXr+43TUJCglf+CQkJahozU6ZMQXZ2tno7cuRIsY6HiIiIiCo2m5BBubFvmcslP/2ysV+uvj+uOWMas/SF5RMZGYnIyEiLpSUiIiIiMscxMsF1ybbIJCUlAYBXq8nx48fVVpqkpCTk5eXh9OnTftMcO3bMK/8TJ054tfYQEREREVHZcMkGMqmpqUhKSsKaNWvUZXl5ecjIyEDHjh0BAK1bt0Z4eLguTWZmJnbv3q2m6dChA7Kzs7FlyxY1zQ8//IDs7Gw1DRERERFRSeP0y8FVql3Lzp07h//+97/q44MHD2LHjh2Ii4tDnTp1MH78eMyePRuNGjVCo0aNMHv2bERHR2PIkCEAgNjYWIwYMQITJ05EjRo1EBcXh0mTJqF58+bqLGZNmjRB7969MXLkSCxatAiAa/rlvn37+pyxjIiIiIgo2NyTklOQlGogs23bNlx//fXq4wkTJgAAhg0bhqVLl2Ly5Mm4ePEiRo0ahdOnT6Ndu3b46quv1GvIAMCLL76IsLAwDB48GBcvXkS3bt2wdOlS9RoyAPDOO+9g3Lhx6uxm/fr1w4IFC0J0lERERERErkAmGC0qbJRxKdWgsGvXrpBSet2WLl0KwDVIf8aMGcjMzMTff/+NjIwMNGvWTJdHVFQUXnnlFZw6dQoXLlzAJ5984jVVclxcHJYvX46cnBzk5ORg+fLlqFatWoiOkoiIiIio9KSlpaFt27aoWrUqEhISMGDAAOzbt0+X5tixYxg+fDiSk5MRHR2N3r17Y//+/bo0Xbt2hRBCd7v99ttDeSg6l/ysZeRDdLzr/4WTrvvnMv2nN7v6dDCucm5Ke3Vlkytja8vhqwy6zTT5CaHJSrsPqV8n3H+k9D5cob2StzTkpfkvlI209wEI99W1dRcCl55tpSEv3dW7jWkM6w3bCmWdaT0FeAV4nz/9+PkdI9Cfi0J1lXhfrF4NPijnfBGuQC9l4b+e+S2bn32W2Ou4BITyfehS70RuLJ+U5su166QDUkrA6QCg/PeRt3LleOU9xxamztQp3fkJYdfvU3uVe+0y6X5/Vd8z3Oej0+k5r8067qv5K9sZ96NNq3kvCfS5M55PxnNJOvXvzQHR7Fs63Nsrrz+noWwOAHZN/Whp69DmqgthgxA2/fNT2u+hFHLBmrXMqoyMDIwePRpt27ZFQUEBpk6dip49e2Lv3r2oXLkypJQYMGAAwsPD8fHHHyMmJgbz5s1D9+7d1TSKkSNHYubMmerjSpUqlcIRuTCQISIiIiIKAZtw3UItPT1d93jJkiVISEjA9u3bcd1112H//v3YvHkzdu/ejSuvvBIA8OqrryIhIQHvvfce7r33XnXb6OhodXbh0safAoiIiIiIQiBYs5YJAPn5+eqwCeWWm5sbUDmys7MBuIZfAFC3i4qKUtPY7XZERERg48aNum3feecdxMfH48orr8SkSZNw9uzZINRM0TCQISIiIiIqY1atWoXY2FjdLS0trdDtpJSYMGECOnfurI49b9y4MerWrYspU6bg9OnTyMvLw5w5c5CVlYXMTM/whTvvvBPvvfce1q9fj2nTpuGDDz7AoEGDSuwYC8OuZUREREREIRDMMTIDBw7E4sWLdcsiIyML3W7MmDH4+eefdS0t4eHh+OCDDzBixAjExcXBbreje/fu6NOnj27bkSNHqvebNWuGRo0aoU2bNvjxxx/RqlWrYh6RdQxkiIiIiIhCIGgXtBSu4CMmJsbSZmPHjsXq1auxYcMG1K5dW7eudevW2LFjB7Kzs5GXl4eaNWuiXbt2aNOmjc/8WrVqhfDwcOzfv79UAhl2LSMiIiIiCgFl/r/i3qzGQlJKjBkzBh9++CG++eYbpKam+kwbGxuLmjVrYv/+/di2bRv69+/vM+2ePXuQn5+PWrVqWSxRcLBFhoiIiIioHBs9ejTeffddfPzxx6hatSqysrIAuIIWZfrk999/HzVr1kSdOnWwa9cuPPTQQxgwYIB6QfkDBw7gnXfewY033oj4+Hjs3bsXEydORMuWLdGpU6dSOS4GMkREREREIVBa0y+/9tprAFwXtNRasmQJhg8fDgDIzMzEhAkTcOzYMdSqVQt33303pk2bpqaNiIjA2rVrMX/+fJw7dw4pKSm46aabMH36dNjt9lAdig4DGSIiIiKiEBCw3i0sGGQAFx8eN24cxo0b53N9SkoKMjIyglmsYmMgU9ZFx2seFOFqyEEn/NwPZL+GdOpDJS+puYKz0C2GIZnnsSu9dnCdWgVC2YFxQ5P/QrOhUK60bfLY+DTo6lv6WG5c5zRfXhizK6cH5FK4CnoRzsvinMu6Oi4GqT0nLW2ouR/g60M6PVcCD1b5Q8ns6vUl+n5UHKVZv2bDV52ADIOQErA7AacT0ubwSiXUq8kbfh0VdsDmylc43cfmfqxLq15p3vCeoFyNXjnvpARsUn9OQjOKWbmCvTEv0yvZm+zLKq/3XuU92am5LwPIW/NadGpf1xIQTkMduNebnddCADa7e5mr7oR6ARDNciIqFgYyREREREQhEKyuZQyDXRjIEBERERGFANvigouBDBERERFRCLBFJrh4HRkiIiIiIipz2CJDRERERBQCNiGDM/0ym2QAMJAhIiIiIgoJjpEJLgYyREREREQhIDhGJqg4RoaIiIiIiMoctsgQEREREYUAu5YFFwMZIiIiIqIQCNb0y+TCQIaIiIiIKARsCM64DsZCLgxkyg0/p7SUrtFlRSILzz/g8ghNfsoi4Sqfv3S6h+4H6naax2bF1T327EsI13ZSzUNTHt2GhfwX0JRfGh4reWoeS8NxmZESEHbtAh8JQ8XkeQs2r3MgQD5PS+3zWoyyS2fRtzXPMMj5+dpNcV7zJnkZBZK3WRmUvIyve9P3gWIqyvEby2B8/rXl97WNZ4WvggVeHq/9SFeZpASEA0KafR2yubYTAp6vS07AFu7Zt3C4/7vfZ2x2fRamdSdc6aUD6rE5HYCwuW7qdso+NMu1eRS6nyLyl5U0vn8bE5s8V1ICdrv7HDCeF5rjV58TBzx1b3NvJ9TnQih1o60Xe3jAh0dE5hjIEBERERGFgPo7AwUFAxkiIiIiohDgGJng4vTLRERERERU5gTUIpOTk2M545iYGMvbEBERERGVV8GafpmNOi4BBTLVqlVzD1QLjBACv/32G+rXr1/kghERERERlScCwtJ3at8ZlfZEQJeGgMfI/Oc//0FcXFyh6aSUuPHGG4tVKCIiIiKicoeD/YMqoECmbt26uO6661CjRo2AMq1fvz7CwzmtIBERERERlYyAApmDBw9aynT37t1FKgwRERERUbnF+ZeDKiizlp05cyYY2RARERERlVtCBOdGLpYDmWeffRYrV65UHw8ePBg1atTAZZddhp07dwa1cERERERE5YUQIki30j6SS4PlQGbRokVISUkBAKxZswZr1qzBF198gT59+uCRRx4JegGJiIiIiIiMAp61TJGZmakGMp9++ikGDx6Mnj17ol69emjXrl3QC0jBokzTV9QQXrq3NeZjdfo/bR7KIgFIYz6GdLqH7gfqdpqyKD9R+Cqm9vAlNFMgevYltXkKZcfGdNryGpYJmByPZh3M1mvTGJ4j07ShVsI//RT5tDSpG+35ZHpuWSDsxh0WPS+rCiu3v2MTCG7fAynNz0t/+zB9bnzcN3scbMGqD1+vbd0iZyFl0fyGaEwrfPy+KJ2ufUnpui/DAOnwLo8Q7jw072FSAja7e7kEpN2zDvCc59Kp39aYrzLdkrJP7eXJle3UevbT/8VreaDpfCjs9V7YNLX+zlUpoXuOpfTkp+xPOr3rVMnAFuapX2Fz34TnMVU4SotKEHIKQh5ln+VXUfXq1XHkyBEAQHp6Orp37w7A9eXP4XAEt3REREREROWFLUg3AlCEFplBgwZhyJAhaNSoEU6dOoU+ffoAAHbs2IGGDRsGvYBERERERERGlgOZF198EampqTh8+DDmzp2LKlWqAHB1ORs1alTQC0hEREREVB4Er2sZARYDmfz8fNx3332YNm0a6tevr1s3fvz4YJaLiIiIiKhcCdr0yYyFAFjsZRceHo5Vq1aVVFmIiIiIiMqtYE2/TC6WhwsNHDgQH330UQkUhYiIiIiIKDCWx8g0bNgQTz/9NL7//nu0bt0alStX1q0fN25c0ApHRERERFRuuGcyp+CwHMi8+eabqFatGrZv347t27fr1gkhGMgQEREREZkIVtcwxkIulgOZgwcPlkQ5iIiIiIjKtaBdr5iRDIBiXFInLy8P+/btQ0FBQTDLQ0REREREVCjLgcyFCxcwYsQIREdH48orr8Thw4cBuMbGzJkzJ+gFJCIiIiIqDzhrWXBZDmSmTJmCnTt3Yv369YiKilKXd+/eHStXrgxq4YiIiIiIyg3lQjLFvbFvGYAijJH56KOPsHLlSrRv314XETZt2hQHDhwIauHIgipJwLmsEOzI+MIJ0gtJOZekNMlbeh5Kwzo1idSnV/MzFlN49iE0GWp2KzTLpS5f7TZm+WjrQnscJsuE8Vj9bKc7xrIiRGX19auUMD5nFvit5xB+cASyq1AVx2w/he27vH7GBvu4Cntd694bpSe9dADSDjgdrjTKcpvdvZ0NnvclCQi7pvAOTRrh/g/v/+YF8rx/KfvVfqkShv9e758m+fk7boXu/dZIs87y+6X0/ZxKCQjpua8trpSuelL3Y9ifdLrT213pdP8B2Cx//aJyImgXxCQARWiROXHiBBISEryWnz9/nk1dREREREQUEpYDmbZt2+Kzzz5THyvByxtvvIEOHToEr2REREREROVIsMbIsOnAxXLbZlpaGnr37o29e/eioKAA8+fPx549e7Bp0yZkZGSURBmJiIiIiMq+YHUtK6ORzMyZMzFp0iRER0frll+8eBHPPfccnnzySUv5WW6R6dixI7777jtcuHABDRo0wFdffYXExERs2rQJrVu3tpodERERERFVAE899RTOnTvntfzChQt46qmnLOdXpNFmzZs3x7Jly4qyKRERERFRxVTBR/tLKU3H1O/cuRNxcXGW87McyNjtdmRmZnoN+D916hQSEhLgcDgsF4KIiIiIqLyrqHFM9erV1fE9l19+uS6YcTgcOHfuHB544AHL+VoOZKSPKQ1zc3MRERFhuQBERERERBWBK5CpeJHMSy+9BCkl/vGPf+Cpp55CbGysui4iIgL16tUr0qRhAQcyL7/8MgBX5b/55puoUqWKus7hcGDDhg1o3Lix5QIQEREREVH5NWzYMABAamoqOnXqhLAw/yHInDlz8MADD6BatWp+0wUcyLz44osAXC0yCxcuhN1uV9cpkdTChQsDzY6IiIiIqEJRulcFIaPi51EKunTpElC62bNnY/DgwcELZA4ePAgAuP766/Hhhx+ievXqgW5KRERERFThBWuMTNkMYwLnayiLkeXpl9etW4fq1asjLy8P+/btQ0FBgeXCERERERFVOEokU9wbAShCIHPx4kWMGDEC0dHRuPLKK3H48GEAwLhx4zBnzpygF5CIiIiIiMjIciDz2GOPYefOnVi/fj2ioqLU5d27d8fKlSst5ZWWloa2bduiatWqSEhIwIABA7Bv3z5dGiklZsyYgeTkZFSqVAldu3bFnj17dGlyc3MxduxYxMfHo3LlyujXrx+OHj2qS3P69GkMHToUsbGxiI2NxdChQ3HmzBlrB09EREREVEQCbJAJJsvTL3/00UdYuXIl2rdvrxus1LRpUxw4cMBSXhkZGRg9ejTatm2LgoICTJ06FT179sTevXtRuXJlAMDcuXMxb948LF26FJdffjmeeeYZ9OjRA/v27UPVqlUBAOPHj8cnn3yCFStWoEaNGpg4cSL69u2L7du3q5MSDBkyBEePHkV6ejoA4L777sPQoUPxySefWK2CMk4i8J6VgfVPtE4UPW+l6FK7QHpe1VLqE6nLTfLQLheGMkllsX651OYvtIXRZCql4V1G3YmfNMa0Zor7fFh57q2UoZBjLWusFD3APrzBEYznrwQU9RM1oLqzWr9m56LZeuO6UqxXn7s2vJaEdL9vKKsFIJ2AsHmnFzbvjIXJ75bKMuWbkfY58Xpejfm592ncRt2PyYH5PFcCqH9/55nZOtP3YKv71h6b+75yvGbHr24mAeGeEEnYIGx2wGZ31Y3w8fxQhRG0wf4EoAiBzIkTJ7wuhgkA58+ft/zEKEGFYsmSJUhISMD27dtx3XXXQUqJl156CVOnTsWgQYMAAMuWLUNiYiLeffdd3H///cjOzsbixYvx9ttvo3v37gCA5cuXIyUlBV9//TV69eqFX375Benp6di8eTPatWsHAHjjjTfQoUMH7Nu3D1dccYXVaiAiIiIisiRoLSrlPBa69tprUalSpULTWe5a1rZtW3z22WfqYyV4UQKD4sjOzgYAxMXFAXDNlJaVlYWePXuqaSIjI9GlSxd8//33AIDt27cjPz9flyY5ORnNmjVT02zatAmxsbFqEAMA7du3R2xsrJrGKDc3Fzk5ObobEREREREVjd1ux/Hjx72Wnzp1Sndpl88//xy1atUqND/LgUxaWhqmTp2KBx98EAUFBZg/fz569OiBpUuXYtasWVazU0kpMWHCBHTu3BnNmjUDAGRlZQEAEhMTdWkTExPVdVlZWYiIiPCaDtqYxqwVKSEhQU1jdpzKeJrY2FikpKQU+diIiIiIiEpr1rJAxqUfO3YMw4cPR3JyMqKjo9G7d2/s379flyaQcen++JpWOTc3FxEREZaPy3LXso4dO+K7777D888/jwYNGuCrr75Cq1atsGnTJjRv3txyARRjxozBzz//jI0bN3qtM3ZZk1IW2o3NmMYsvb98pkyZggkTJqiPc3JyGMwQERERUZEFa4yM1RwKG5cupcSAAQMQHh6Ojz/+GDExMZg3bx66d++uG7seyLh0My+//LKr3ELgzTffRJUqVdR1DocDGzZsQOPGjS3Xg+VABgCaN2+OZcuWFWVTU2PHjsXq1auxYcMG1K5dW12elJQEwNWiom1eOn78uNpKk5SUhLy8PJw+fVrXKnP8+HF07NhRTXPs2DGv/Z44ccKrtUcRGRmJyMjI4h8cEREREVEpKmxc+v79+7F582bs3r0bV155JQDg1VdfRUJCAt577z3ce++9AY1L9+XFF18E4GpEWLhwoS7oiYiIQL169bBw4ULLx2W5a5ni+PHj2L17N37++WfdzQopJcaMGYMPP/wQ33zzDVJTU3XrU1NTkZSUhDVr1qjL8vLykJGRoQYprVu3Rnh4uC5NZmYmdu/erabp0KEDsrOzsWXLFjXNDz/8gOzsbDUNEREREVFJCmbPsvz8fK/x3Lm5uQGVwzguXdlOe2kVu92OiIgItbdUIOPSfTl48CAOHjyILl26YOfOnerjgwcPYt++ffjyyy91Y9kDZblFZvv27Rg2bBh++eUXr35uQgg4HI6A8xo9ejTeffddfPzxx6hatao6XiU2NhaVKlWCEALjx4/H7Nmz0ahRIzRq1AizZ89GdHQ0hgwZoqYdMWIEJk6ciBo1aiAuLg6TJk1C8+bN1WixSZMm6N27N0aOHIlFixYBcE2/3LdvX85YRkREREQhEbTpl4XAqlWrcOedd+oWT58+HTNmzPC7qdm49MaNG6Nu3bqYMmUKFi1ahMqVK2PevHnIyspCZmYmgMDGpRdm3bp1AR5gYCwHMvfccw8uv/xyLF68GImJicV6Ml577TUAQNeuXXXLlyxZguHDhwMAJk+ejIsXL2LUqFE4ffo02rVrh6+++kq9hgzgaq4KCwvD4MGDcfHiRXTr1g1Lly7VNVu98847GDdunBpF9uvXDwsWLChy2YmIiIiILBEI2tTJAwcOxOLFi3XLAhkWYTYuPTw8HB988AFGjBiBuLg42O12dO/eHX369Ck0v0DGrmsdPXoUq1evxuHDh5GXl6dbN2/evIDzAYoQyBw8eBAffvghGjZsaHVTL75mLtASQmDGjBl+o8uoqCi88soreOWVV3ymiYuLw/Lly4tSTCIiIiKiS0p4eDhiYmIsbeNrXDrgGq6xY8cOZGdnIy8vDzVr1kS7du3Qpk0bAIGNSy/M2rVr0a9fP6SmpmLfvn1o1qwZDh06BCklWrVqZelYgCKMkenWrRt27txpeUdERERERBWZsNmCcrOqsHHpWrGxsahZsyb279+Pbdu2oX///gACG5demClTpmDixInYvXs3oqKi8MEHH+DIkSPo0qULbr31VsvHZblF5s0338SwYcOwe/duNGvWDOHh4br1/fr1s1wIIiIiIqJyr4jXgSmuwsalA8D777+PmjVrok6dOti1axceeughDBgwQB2WEci49ML88ssveO+99wAAYWFhuHjxIqpUqYKZM2eif//+ePDBBy0dl+VA5vvvv8fGjRvxxRdfeK2zOtifiIiIiKjCCFYgYzGPQMalZ2ZmYsKECTh27Bhq1aqFu+++G9OmTdOlD2Rcuj+VK1dWZ0hLTk7GgQMH1OmeT548aemYgCIEMuPGjcPQoUMxbdo0n9dgISIiIiKiS0Mg49LHjRuHcePG+U0TyLh0f9q3b4/vvvsOTZs2xU033YSJEydi165d+PDDD9G+fXvL+VkOZE6dOoWHH36YQUyZJlG0KTOKup0Z4c7PuNidv+4Fp+xT6hdJk/W67U2WG7LRH47w7FdodqDdrTYbtYyGOhHG8uo28s7IV1ptetNtjNsWJhjPXWF5hL65vFQJGM7VCqS4vygWqe58vE78pvW180CEaj+BkJp9SQA2QNgA6XQvMr5XaN+HjL8AC006TXqhWe/1/Pp6n/OTxpdCzx2T93yv9VbOHeN7u4F0f0YYz0dhqEOvMhmfE8120vC5I+zu2arczxtVYAKiAp8D8+bNw7lz5wAAM2bMwLlz57By5Uo0bNhQvWimFZYDmUGDBmHdunVo0KCB5Z0REREREVVYpTRG5lJRv3599X50dDReffXVYuVnOZC5/PLLMWXKFGzcuBHNmzf3GuxfWJMUERERERFRcRVp1rIqVaogIyMDGRkZunVCCAYyRERERERmKmCLTPXq1QO+YOZff/1lKe8iXRCTiIiIiIisEUIE/KW+sHzKipdeeqnE8rYcyBARERERURFUwAkfhg0bVmJ5B1STEyZMwPnz5wPOdMqUKZabhoiIiIiIqHxzOBz44IMP8Mwzz2DWrFlYtWpVka9DGVAgM3/+fFy4cCHgTP/5z3/izJkzRSoQEREREVF5JGwiKLey6r///S+aNGmCu+++Gx9++CH+85//4K677sKVV16JAwcOWM4voK5lUkpcfvnlAffHs9J6Q0RERERUIQRtsH/ZDGbGjRuHBg0aYPPmzYiLiwPgukblXXfdhXHjxuGzzz6zlF9AgcySJUssF5QXzCQiIiIi0gjWGJmyGccgIyNDF8QAQI0aNTBnzhx06tTJcn4BBTIlOUiHiIiIiIjKv8jISJw9e9Zr+blz5xAREWE5v4o1bQIRERERUSlRpl8u7q2s6tu3L+677z788MMPkFJCSonNmzfjgQceQL9+/Sznx0CGiIiIiCgUlDEyxb2VUS+//DIaNGiADh06ICoqClFRUejUqRMaNmyI+fPnW86P15EhIiIiIgoFgQo92L9atWr4+OOPsX//fvz666+QUqJp06Zo2LBhkfJjIFMRFPkFIwDIYJYk8PyFAKRxnSG9cljSZIFyzFJC/2KX+vrQ7cLHOl31ecrladr1ZCJ1ZTapd2Eoi1labR6mz51ZvRTCqy5LgOlzFmJm9VXSZQrJL2OmJ+OloajHL2UA2yqv36IcfzDryvu17nc/fo/Narm0xy6h1omA4dz2tU/3Mu17orB5thXCnZ8NPt+TtNv7Xefr/c2kPL4Y34ON7yvqcfjPxm++vtYJH59JZtUiNc+DboV7nc3mvVEFvBAikS9169aF0+lEgwYNEBZW9HCErygiIiIiohAQsEGI4t8uxd+1AnHhwgWMGDEC0dHRuPLKK3H48GEArmmZ58yZYzk/y4HM+fPnMW3aNHTs2BENGzZE/fr1dTciIiIiIjJRwcfITJkyBTt37sT69esRFRWlLu/evTtWrlxpOT/LbTn33nsvMjIyMHToUNSqVatMz5xARERERESh8dFHH2HlypVo3769LoZo2rQpDhw4YDk/y4HMF198gc8++6xIF60hIiIiIqqohE1A2IrfCCDKaN+yEydOICEhwWv5+fPni9Q4YrlrWfXq1XVX4yQiIiIiogAokz4U91ZGtW3bFp999pn6WAle3njjDXTo0MFyfpZbZJ5++mk8+eSTWLZsGaKjoy3vkIiIiIioQgrWGJey2SCDtLQ09O7dG3v37kVBQQHmz5+PPXv2YNOmTcjIyLCcX0CBTMuWLXXNPf/973+RmJiIevXqITw8XJf2xx9/tFwIIiIiIiIq3zp27IjvvvsOzz//PBo0aICvvvoKrVq1wqZNm9C8eXPL+QUUyAwYMMByxkRERERE5CGEqPATZTVv3hzLli0LSl4BBTLTp08Pys6IiIiIiCqsoE2fXDaDoZycHNPlQghERkYiIiLCUn6WRwvVr18fp06d8lp+5swZXkeGiIiIiMiXCj7Yv1q1aqhevbrXrVq1aqhUqRLq1q2L6dOnw+l0BpSf5cH+hw4dgsPh8Fqem5uLo0ePWs2OiIiIiIgqgKVLl2Lq1KkYPnw4rrnmGkgpsXXrVixbtgxPPPEETpw4geeffx6RkZF4/PHHC80v4EBm9erV6v0vv/wSsbGx6mOHw4G1a9ciNTXV4uEQEREREVUMrp5lFXfWsmXLluGFF17A4MGD1WX9+vVD8+bNsWjRIqxduxZ16tTBrFmzghvIKAP+hRAYNmyYbl14eDjq1auHF154IdDsiIiIiIgqFptw3YqtbEYymzZtwsKFC72Wt2zZEps2bQIAdO7cGYcPHw4ov4A72TmdTjidTtSpUwfHjx9XHzudTuTm5mLfvn3o27dvoNkREREREVEFUrt2bSxevNhr+eLFi5GSkgIAOHXqFKpXrx5QfpbHyBw8eNDqJlTmlWTUr+QtTVYJQBqXm6QXxs01aZTmWzUf7bEY1/ta56MImqSeInsVxpPM57EYFxvK4Z3AmLF5Pj7zLEF+92P2HITAJT/NZQDPn2mdGc+14hxnUctQDH5eK977LOa+ldeI9lzwt0zdvXG/ftL62q7Y5592eyf8vof5Oxa1LMpxa97zhM29zvB+KTT1r11vmq+B6VtXIHUhvO/qtnMfg9V6NXu+TdOYrNfWm/FtTGoXmpTXkQfY7K5bGR6gTcEjYIOowOfC888/j1tvvRVffPEF2rZtCyEEtm7dil9//RX/+c9/AABbt27FbbfdFlB+lgMZAFi7di1efPFF/PLLLxBCoHHjxhg/fjy6d+9elOyIiIiIiMq/oE2/XDb169cPv/32GxYuXIh9+/ZBSok+ffrgo48+Qr169QAADz74YMD5WQ5kFixYgIcffhj/93//h4ceeggAsHnzZtx4442YN28exowZYzVLIiIiIqLyL1iBTBmOherWrYu0tDS/aUaNGoWZM2ciPj7ebzrLgUxaWhpefPFFXcAybtw4dOrUCbNmzWIgQ0RERERERbZ8+XJMmjSp0EDGcie9nJwc9O7d22t5z549fV6tk4iIiIioohNCBOVWpptkAuA9rtic5UCmX79+WLVqldfyjz/+GDfffLPV7IiIiIiIKgZhC86NABSha1mTJk0wa9YsrF+/Hh06dADgGiPz3XffYeLEiXj55ZfVtOPGjQteSYmIiIiIyrIKPtg/2CwHMosXL0b16tWxd+9e7N27V11erVo13bzQQggGMkREREREVCJ4HRkiIiIiohDwjHEpfj5UhDEyiry8POzbtw8FBQXBLA8RERERUflkE4DNVvxbOXfXXXchJiam0HSWW2QuXLiAsWPHYtmyZQCA3377DfXr18e4ceOQnJyMxx57zHppiYiIiIjKO46Rwd9//42ff/4Zx48fh9Pp1K3r168fAOC1114LKC/LgcyUKVOwc+dOrF+/XjcNc/fu3TF9+nQGMkRERERE5CU9PR133303Tp486bVOCAGHw2EpP8ttUx999BEWLFiAzp076/rnNW3aFAcOHLCaHRERERFRxRC06ZfLZqvOmDFjcOuttyIzMxNOp1N3sxrEAEVokTlx4gQSEhK8lp8/f54Dj4iIiIiIfAlW17Iy+pX7+PHjmDBhAhITE4OSn+UWmbZt2+Kzzz5THyvByxtvvKFeV4aIiIiIiEjr//7v/7B+/fqg5We5RSYtLQ29e/fG3r17UVBQgPnz52PPnj3YtGkTMjIyglYwCpJiR/2hCvmV/UjDYgFI6ZXaK73p5pqF2npQ8/O33sc6kyJ6V5F5mV1Bv9mxKLv1d5x+mD7HvvdTesroz0clrjj1Eqw6deejnIMha133sx/T10NRd2OyH/X1KPyn85tnIGUMYl0Km/45EtA81iaUmpuhHOqvwf6OW/u+p2xn8XfPQg87kHrxlcakvIVtU+hza/Jeb7YL3WeNcaGPTfMvQkRUKWT/VHHYrL+eypEFCxbg1ltvxbfffovmzZsjPDxct97qNSgtBzIdO3bEd999h+effx4NGjTAV199hVatWmHTpk1o3ry51eyIiIiIiCqGoM1aVjZ/HHz33Xfx5ZdfolKlSli/fr1uWIoQouQDGQBo3ry5Ov0yEREREREFQFTsFpknnngCM2fOxGOPPQZbEK6HE1Agk5OTE3CGgVy8hoiIiIiIKpa8vDzcdtttQQligAADmWrVqgU8I1lRpk4jIiIiIir3KvisZcOGDcPKlSvx+OOPByW/gAKZdevWqfcPHTqExx57DMOHD1dnKdu0aROWLVuGtLS0oBSKiIiIiKjcESJIXcvKZiTjcDgwd+5cfPnll2jRooXXYP958+ZZyi+gQKZLly7q/ZkzZ2LevHm444471GX9+vVD8+bN8frrr2PYsGGWCkBEREREVCEEbbB/2bRr1y60bNkSALB7927duqJcj9LyYP9NmzZh4cKFXsvbtGmDe++913IBiIiIiIio/NP28goGy21bKSkppoHMokWLkJKSEpRCERERERGVO0qLTHFvBKAILTIvvvgibrnlFnz55Zdo3749AGDz5s04cOAAPvjgg6AXkIiIiIioXAja9MtlN5jZunUr3n//fRw+fBh5eXm6dR9++KGlvCzX5I033oj9+/ejf//++Ouvv3Dq1Cn0798fv/32G2688Uar2RERERERVQzBapGxGMekpaWhbdu2qFq1KhISEjBgwADs27dPl+bcuXMYM2YMateujUqVKqFJkyZ47bXXdGm6du0KIYTudvvttwdcjhUrVqBTp07Yu3cvVq1ahfz8fOzduxfffPMNYmNjrR0UinhBzNq1a2PWrFlF2ZSIiIiIiEIoIyMDo0ePRtu2bVFQUICpU6eiZ8+e2Lt3LypXrgwAePjhh7Fu3TosX74c9erVw1dffYVRo0YhOTkZ/fv3V/MaOXIkZs6cqT6uVKlSwOWYPXs2XnzxRYwePRpVq1bF/PnzkZqaivvvvx+1atWyfFxFCmSIiIiIiMiioHUtsyY9PV33eMmSJUhISMD27dtx3XXXAXBN6DVs2DB07doVAHDfffdh0aJF2LZtmy6QiY6ORlJSUpHKceDAAdx0000AgMjISJw/fx5CCDz88MO44YYb8NRTT1nKL/Q1qfHaa6+hRYsWiImJQUxMDDp06IAvvvhCXS+lxIwZM5CcnIxKlSqha9eu2LNnjy6P3NxcjB07FvHx8ahcuTL69euHo0eP6tKcPn0aQ4cORWxsLGJjYzF06FCcOXMmFIdIREREROQSxMH++fn5yMnJ0d1yc3MDKkZ2djYAIC4uTl3WuXNnrF69Gn/88QeklFi3bh1+++039OrVS7ftO++8g/j4eFx55ZWYNGkSzp49G/Dhx8XFqekvu+wydQrmM2fO4MKFCwHnoyjVQKZ27dqYM2cOtm3bhm3btuGGG25A//791WBl7ty5mDdvHhYsWICtW7ciKSkJPXr00FXY+PHjsWrVKqxYsQIbN27EuXPn0LdvXzgcDjXNkCFDsGPHDqSnpyM9PR07duzA0KFDQ368RERERETBsGrVKvVHeuUWyMXppZSYMGECOnfujGbNmqnLX375ZTRt2hS1a9dGREQEevfujVdffRWdO3dW09x555147733sH79ekybNg0ffPABBg0aFHCZr732WqxZswYAMHjwYDz00EMYOXIk7rjjDnTr1s3C0bsIKaUMNLGUEocPH0ZCQoKl/nBWxMXF4bnnnsM//vEPJCcnY/z48Xj00UcBuFpfEhMT8eyzz+L+++9HdnY2atasibfffhu33XYbAODPP/9ESkoKPv/8c/Tq1Qu//PILmjZtis2bN6Ndu3YAXLOsdejQAb/++iuuuOKKgMqVk5OD2NhYZGdnIyYmpkSOvdjOZekf+52e71Ke7cLHKenzVPWVPoB0XnnKQtYXMZ9C8wxwW7heh0TlXwjOcymLP42pv9djSUyRquxPyVu3f2lIJw3HKDTbaspmLKdxH9ptKyx3naj17f7v9PxoKmo28b35hZMlUyzykpNzFrG16l9y39dyc3MRFRWFrJf6ombVyGLn12nWOox9+hX07dtXtzwyMhKRkf7zHz16ND777DNs3LgRtWvXVpc///zzeOONN/D888+jbt262LBhA6ZMmYJVq1ahe/fupnlt374dbdq0wfbt29GqVatCy/3XX3/h77//RnJyMpxOJ55//nls3LgRDRs2xLRp01C9evUAjt7DUouMlBKNGjXy6roVDA6HAytWrMD58+fRoUMHHDx4EFlZWejZs6eaJjIyEl26dMH3338PwFV5+fn5ujTJyclo1qyZmmbTpk2IjY1VgxgAaN++PWJjY9U0ZnJzc72a64iIiIiIii54s5aFh4erwzOUW2FBzNixY7F69WqsW7dOF8RcvHgRjz/+OObNm4ebb74ZLVq0wJgxY3Dbbbfh+eef95lfq1atEB4ejv379xd65AUFBfjkk09gs7nCD5vNhsmTJ2P16tWYN2+e5SAGsBjI2Gw2NGrUCKdOnbK8I1927dqFKlWqIDIyEg888ABWrVqFpk2bIivL1bqQmJioS5+YmKiuy8rKQkREhNeBG9MkJCR47TchIUFNYyYtLU3XVMeLfRIRERFRsQjhGfBfnJvFVlIpJcaMGYMPP/wQ33zzDVJTU3Xr8/PzkZ+frwYZCrvdDqfT6TPfPXv2ID8/P6AZx8LCwvDggw8GPI4nEJbHyMydOxePPPKIOjinuK644grs2LEDmzdvxoMPPohhw4Zh79696nphaPKWUnotMzKmMUtfWD5TpkxBdna2ejty5Eigh0REREREdMkYPXo0li9fjnfffRdVq1ZFVlYWsrKycPHiRQBATEwMunTpgkceeQTr16/HwYMHsXTpUrz11lsYOHAgANeMYzNnzsS2bdtw6NAhfP7557j11lvRsmVLdOrUKaBytGvXDj/99FPQjsvy9Mt33XUXLly4gKuuugoRERFeY2X++usvS/lFRESgYcOGAIA2bdpg69atmD9/vjouJisrSxflHT9+XG2lSUpKQl5eHk6fPq1rlTl+/Dg6duyopjl27JjXfk+cOOHV2qMVSB9DIiIiIqKAaWYdCyXlwpbK1MqKJUuWYPjw4QBcF6ucMmUK7rzzTvz111+oW7cuZs2ahQceeACA6zv72rVrMX/+fJw7dw4pKSm46aabMH36dNjt9oDKMWrUKEycOBFHjx5F69at1WvYKFq0aGHpuCwHMi+99JLVTSyRUiI3NxepqalISkrCmjVr0LJlSwBAXl4eMjIy8OyzzwIAWrdujfDwcKxZswaDBw8GAGRmZmL37t2YO3cuAKBDhw7Izs7Gli1bcM011wAAfvjhB2RnZ6vBDhERERFRiSul68gEMlFQUlISlixZ4nN9SkoKMjIyilUOZXKucePGqcuEEGpPKe2sw4GwHMgMGzbM6iY+Pf744+jTpw9SUlJw9uxZrFixAuvXr0d6ejqEEBg/fjxmz56NRo0aoVGjRpg9ezaio6MxZMgQAEBsbCxGjBiBiRMnokaNGoiLi8OkSZPQvHlzdXaFJk2aoHfv3hg5ciQWLVoEwHWBn759+wY8YxkRERERUbGVUovMpeLgwYNBzc9yIKN18eJF5Ofn65ZZmeru2LFjGDp0KDIzMxEbG4sWLVogPT0dPXr0AABMnjwZFy9exKhRo3D69Gm0a9cOX331FapWrarm8eKLLyIsLAyDBw/GxYsX0a1bNyxdulTXxPXOO+9g3Lhx6uxm/fr1w4IFC4pz6EREREREZEHdunWDmp+l68gAwPnz5/Hoo4/i3//+t+nsZVabhMoKXkcmlHgdGb9reR0ZqhB4HRm/++N1ZEKM15EpKy7568j88/9QMyaq2Pl1eiodE2a/iltvvTUIpQud1atXmy4XQiAqKgoNGzb0mlHNH8stMpMnT8a6devw6quv4u6778Y///lP/PHHH1i0aBHmzJljNTsiIiIiooqhgnctGzBggDomRks7TqZz58746KOPArqujOXRRp988gleffVV/N///R/CwsJw7bXX4oknnsDs2bPxzjvvWM2OiIiIiIgqgDVr1qBt27ZYs2aNeomTNWvW4JprrsGnn36KDRs24NSpU5g0aVJA+Vlukfnrr7/UJp+YmBh1uuXOnTvjwQcftJodEREREVHFELRZy8pmq85DDz2E119/XTdzcLdu3RAVFYX77rsPe/bswUsvvYR//OMfAeVnuSbr16+PQ4cOAQCaNm2Kf//73wBcLTXVqlWzmh0RERERUcWgdC0r7q2MOnDggOnYpZiYGPzvf/8DADRq1AgnTwY2rsxyIHPPPfdg586dAIApU6bg1VdfRWRkJB5++GE88sgjVrMjIiIiIqoYhPC0yhTrVtoHUjStW7fGI488ghMnTqjLTpw4gcmTJ6Nt27YAgP3796N27doB5We5a9nDDz+s3r/++uvx66+/Ytu2bWjQoAGuuuoqq9kREREREVEFsHjxYvTv3x+1a9dGSkoKhBA4fPgw6tevj48//hgAcO7cOUybNi2g/CwHMm+99RZuu+02REZGAgDq1KmDOnXqIC8vD2+99Rbuvvtuq1lSMBXa3FgWQngB06lXhfAx1amv9MbF2mP3NYWpIY3ptKQmdei1yFdZlX36m1rWz7ZwzezhW9GnrOW0zlRc/s9Nj8DOtRC8VwWje4ZpFiVYdmOZde8nQjPdstTclHQ+pl/22ofXnUKK5EoXjPcQs9mMgsHs3LS2H23duTnz/U+5rBUdX/JTMEfHu/5zqudLWxnvGlZcV1xxBX755Rd8+eWX+O233yClROPGjdGjRw/YbK6OYgMGDAg4P8uBzD333IPevXsjISFBt/zs2bO45557GMgQEREREZmp4IP9AdcPC71790bv3r2LnZflmlTmeDY6evQoYmNji10gIiIiIqJySQjAFoRbGZaRkYGbb74ZDRs2RKNGjdCvXz98++23Rcor4BaZli1bQggBIQS6deuGsDDPpg6HAwcPHgxKZEVEREREROXP8uXLcc8992DQoEEYN24cpJT4/vvv0a1bNyxduhRDhgyxlF/AgYzSX23Hjh3o1asXqlSpoq6LiIhAvXr1cMstt1jaORERERFRhRGsMTJltFFm1qxZmDt3rm7ysIceegjz5s3D008/XXKBzPTp0wEA9erVw+23364O9iciIiIiogBU8DEy//vf/3DzzTd7Le/Xrx8ef/xxy/lZrskbbrhBN/fzli1bMH78eLz++uuWd05EREREVGFU8AtipqSkYO3atV7L165di5SUFMv5WZ61bMiQIbjvvvswdOhQZGVloXv37mjWrBmWL1+OrKwsPPnkk5YLQURERERE5dvEiRMxbtw47NixAx07doQQAhs3bsTSpUsxf/58y/lZDmR2796Na665BgDw73//G82bN8d3332Hr776Cg888AADGSIiIiIiM0HrWlY2Pfjgg0hKSsILL7yAf//73wCAJk2aYOXKlejfv7/l/CwHMvn5+er4mK+//hr9+vUDADRu3BiZmZmWC0BEREREVCEIEZxApgx3Lxs4cCAGDhwYlLws1+SVV16JhQsX4ttvv8WaNWvUKZf//PNP1KhRIyiFIiIiIiKi8qV+/fo4deqU1/IzZ86gfv36lvOzHMg8++yzWLRoEbp27Yo77rgDV111FQBg9erVapczIiIiIiIyULqWFfdWRh06dAgOh8NreW5uLv744w/L+VnuWta1a1ecPHkSOTk5qF69urr8vvvuQ3R0tOUCEBERERFVCEGbdaxsdS1bvXq1ev/LL79EbGys+tjhcGDt2rWoV6+e5XwtBzIzZ85E586dccMNN+iW16xZEy+88AIH+xMRERERmSnjLSpFNWDAAACAEALDhg3TrQsPD0e9evXwwgsvWM7Xck3OmDEDffr0wbx583TLz507h6eeespyAYiIiIiIqPxyOp1wOp2oU6cOjh8/rj52Op3Izc3Fvn370LdvX8v5Wm6RAYC33noLY8aMwc8//4zXX38dERERRcmGgs1vU2XZaoJ0lVeaLHYfhzSu0x6fLHSxZ4XU56umlTCtM1/VaFJUV55mKzQZeR2HdtvCduAnX1987Q+uX0n8bBjg/qn0ac9tw3ke9P0Ulq8xjeux/3PNRfo5V0NFCBFAOS6F91ZNGZS6Vd9/nK71ln4BNj+mwp43X+ullGpdBvLcB5LGLG/f2/s7Hn+fJbo9ea+PbxxwOQEA0fHeyy6ctJaHv7y064qaL5W8Ctoiozh48KDXsjNnzqBatWpFyq9INXn99ddj8+bN2LJlC7p27Ypjx44VaedERERERBWGMkYmGLcy6Nlnn8XKlSvVx7feeivi4uJw2WWXYefOnZbzsxzIKL90NGjQAJs3b0ZMTAzatGmDbdu2Wd45EREREVGFUcFnLVu0aBFSUlIAAGvWrMHXX3+N9PR09OnTB4888ojl/Cx3LdM238bExODzzz/H+PHj1UE8RERERERERpmZmWog8+mnn2Lw4MHo2bMn6tWrh3bt2lnOz3Igs2TJEt2UaTabDS+//DJatmyJDRs2WC4AEREREVHFYHXMmp98yqDq1avjyJEjSElJQXp6Op555hkAroYSs+vLFMZyIGOcMk1xzz334J577rFcACIiIiKiCqGMdw0rrkGDBmHIkCFo1KgRTp06hT59+gAAduzYgYYNG1rOr0izlhEREREREVnx4osvol69ejhy5Ajmzp2LKlWqAHB1ORs1apTl/BjIEBERERGFQrBmHCubPcsQHh6OSZMmeS0fP358kfJjIENEREREFApB61pWdiKZ1atXo0+fPggPD8fq1av9pu3Xr5+lvBnIEBERERGFQgUcIzNgwABkZWUhISHB7yzHQgjLA/4DCmRycnICzjAmJsZSAYiIiIiIqHxyOp2m94MhoECmWrVq6oUwfZFSFimSIiIiIiKqEGw2163Yyk7XMqO1a9di7dq1OH78uC6wEUJg8eLFlvIKKJBZt26dtRISEREREZFeBR/s/9RTT2HmzJlo06YNatWqVWhDSWECCmS6dOlSrJ0QEREREVV4IlgXxCybFi5ciKVLl2Lo0KFByS+gQObnn38OOMMWLVoUuTBERERERFQ+5eXloWPHjkHLL6BA5uqrr4YQAlJKv+k4RoaIiIiIyIcKOP2y1r333ot3330X06ZNC0p+AQUyBw8eDMrOqIRVTnT9P39Ms/ASPdGVPpF+g2Ol7CZp/G7vYztjVUjjQk16bZ9N3T581KcQ5uWE8H+Mws8xGvMxU8iPC77354uv/CycR1IG+PxS0OmeX1/33YLy3ARyXhjTBHYumZ2q2h/TAulXraQ3pvWXj3Gb4vbfDhVlwh3tY0DpwuJZXjLHY5andx0Hvm9/74vKe62Sp2uZJ2vDPgrdp4UySQlUTw0wvQXR8cCFk4GntZIu0HwpdII1RqYMmTBhgnrf6XTi9ddfx9dff40WLVogPDxcl3bevHmW8g4okKlbt66lTImIiIiIyCBYLTJlKBb66aefdI+vvvpqAMDu3bt1y4vyQ0uRLoj59ttvY+HChTh48CA2bdqEunXr4qWXXkJqair69+9flCyJiIiIiKicKcnZjy2HhK+99homTJiAG2+8EWfOnFHHxFSrVg0vvfRSsMtHRERERFRO2DytMsW5laUmmRJkOZB55ZVX8MYbb2Dq1Kmw2+3q8jZt2mDXrl1BLRwRERERUbkRjCCmAk/fbGS5Jg4ePIiWLVt6LY+MjMT58+eDUigiIiIiIiJ/LAcyqamp2LFjh9fyL774Ak2bNg1GmYiIiIiIyh9l1rLi3ghAEQb7P/LIIxg9ejT+/vtvSCmxZcsWvPfee0hLS8Obb75ZEmUkIiIiIir7hAhS1zAGM0ARApl77rkHBQUFmDx5Mi5cuIAhQ4bgsssuw/z583H77beXRBmJiIiIiMq+Cjj9ckkq0vTLI0eOxMiRI3Hy5Ek4nU4kJCQEu1xEREREREQ+WQ5kDh48iIKCAjRq1Ajx8Z4rzO7fvx/h4eGoV69eMMtHRERERFQ+BG2MC5tkgCIM9h8+fDi+//57r+U//PADhg8fHowyERERERGVP5x+Oags18RPP/2ETp06eS1v37696WxmREREREQEBjJBZrkmhBA4e/as1/Ls7Gw4HI6gFIqIiIiIiMgfy4HMtddei7S0NF3Q4nA4kJaWhs6dOwe1cERERERE5UawWmQsjrNJS0tD27ZtUbVqVSQkJGDAgAHYt2+fLs25c+cwZswY1K5dG5UqVUKTJk3w2muv6dLk5uZi7NixiI+PR+XKldGvXz8cPXq02NVSVJYH+z/77LPo0qULrrjiClx77bUAgG+//RY5OTn45ptvgl5AIiIiIqJywSZctxDLyMjA6NGj0bZtWxQUFGDq1Kno2bMn9u7di8qVKwMAHn74Yaxbtw7Lly9HvXr18NVXX2HUqFFITk5G//79AQDjx4/HJ598ghUrVqBGjRqYOHEi+vbti+3bt8Nut4f8uCwHMldeeSV+/vlnLFiwADt37kSlSpVw9913Y8yYMYiLiyuJMpJVlRNd/88fL9r22ihfyuKXx1feAe9LAPCxzu/2xjcK6b1aGheYpA3kVw8pTfbnziegX02EJh8LvPIu7vMV6LH6y0KY3/edYQBpyhMf51mh20jN/WAUQ8nnEq1/k2KZn06+6kNCqBsIH8u166R7H2bbBJuv94ui52c8Jtdjqb5cXY+Lss+inne+n5fA8/KVzuz5M84EFaz6dZe3Wt0g5WcQHa//X1L5F+bCyaJt4yutNj/SC+IFMfPz85GTk6NbGhkZicjISK/U6enpusdLlixBQkICtm/fjuuuuw4AsGnTJgwbNgxdu3YFANx3331YtGgRtm3bhv79+yM7OxuLFy/G22+/je7duwMAli9fjpSUFHz99dfo1atXEI7LGss1efjwYdSqVQuzZ8/GZ599hv/85z948sknERcXh8OHD5dEGYmIiIiISGPVqlWIjY3V3dLS0gLaNjs7GwB0jRCdO3fG6tWr8ccff0BKiXXr1uG3335TA5Tt27cjPz8fPXv2VLdJTk5Gs2bNTGc0DgXLLTKpqanIzMz0ugjmqVOnkJqaygH/RERERESmgjfr2MCBA7F48WLdMrPWGCMpJSZMmIDOnTujWbNm6vKXX34ZI0eORO3atREWFgabzYY333xTHQOflZWFiIgIVK9eXZdfYmIisrKygnBE1lkOZKQ0NmG7nDt3DlFRUUEpFBERERFRuROs6ZMFEB4ejpiYGMubjhkzBj///DM2btyoW/7yyy9j8+bNWL16NerWrYsNGzZg1KhRqFWrltqVzIyv2CAUAg5kJkyYAMDVz3batGmIjo5W1zkcDvzwww+4+uqrg15AIiIiIiIqvrFjx2L16tXYsGEDateurS6/ePEiHn/8caxatQo33XQTAKBFixbYsWMHnn/+eXTv3h1JSUnIy8vD6dOnda0yx48fR8eOHUN+LICFQOann34C4Iq6du3ahYiICHVdREQErrrqKkyaNCn4JSQiIiIiKg+EcWKKImdkKbWUEmPHjsWqVauwfv16pKam6tbn5+cjPz8fNpu+tchut8PpdAIAWrdujfDwcKxZswaDBw8GAGRmZmL37t2YO3duMY6l6AIOZNatWwcAuOeeezB//vwiNWUREREREVVcRZ1BsHhGjx6Nd999Fx9//DGqVq2qjmmJjY1FpUqVEBMTgy5duuCRRx5BpUqVULduXWRkZOCtt97CvHnz1LQjRozAxIkTUaNGDcTFxWHSpElo3ry5365nJcnyGJklS5aURDmIiIiIiMq3YI2RsUi5sKUytbJiyZIlGD58OABgxYoVmDJlCu6880789ddfqFu3LmbNmoUHHnhATf/iiy8iLCwMgwcPxsWLF9GtWzcsXbq0VK4hAxRh+uWSkpaWBiEExo8fry6TUmLGjBlITk5GpUqV0LVrV+zZs0e3XSBXGD19+jSGDh2qTk03dOhQnDlzJgRHRURERERUuqSUpjcliAGApKQkLFmyBH/88QcuXryIX3/9FRMmTNAN5I+KisIrr7yCU6dO4cKFC/jkk0+QkpJSCkfkckkEMlu3bsXrr7+OFi1a6JbPnTsX8+bNw4IFC7B161YkJSWhR48eOHv2rJpm/PjxWLVqFVasWIGNGzfi3Llz6Nu3r24a6CFDhmDHjh1IT09Heno6duzYgaFDh4bs+IiIiIiI1DEyxb2VQve0S1GpBzLnzp3DnXfeiTfeeEM3A4KUEi+99BKmTp2KQYMGoVmzZli2bBkuXLiAd999FwDUK4y+8MIL6N69O1q2bInly5dj165d+PrrrwEAv/zyC9LT0/Hmm2+iQ4cO6NChA9544w18+umn2LdvX6kcMxERERFVRLYg3Qi4BGpi9OjRuOmmm7wGCR08eBBZWVm6q4dGRkaiS5cu6tVDA7nC6KZNmxAbG4t27dqpadq3b4/Y2Fi/VyHNzc1FTk6O7kZEREREVGTBapFhgwyAIgz2D6YVK1bgxx9/xNatW73WKbMpJCYm6pYnJibi999/V9MUdoXRrKwsJCQkeOWfkJDg9yqkaWlpeOqpp6wdEBERERERhUSptcgcOXIEDz30EJYvX46oqCif6YxXCg3k6qHGNGbpC8tnypQpyM7OVm9Hjhzxu08iIiIiIr8EgjRGhoBSDGS2b9+O48ePo3Xr1ggLC0NYWBgyMjLw8ssvIywsTG2JMbaaHD9+XF2nvcKovzTHjh3z2v+JEye8Wnu0IiMjERMTo7sRERERERVdsMbIMJgBSjGQ6datG3bt2oUdO3aotzZt2uDOO+/Ejh07UL9+fSQlJWHNmjXqNnl5ecjIyEDHjh0B6K8wqlCuMKqk6dChA7Kzs7FlyxY1zQ8//IDs7Gw1TblVOUF/C4Qxyi/OrwBWt/WbJoAZOgrdl4BXPsJ8sfeKQvbv81h95ePjVuxfaWyB3dT9GR4HVEZtXkEss9W6KrM37flSyHHr6jmYde6+mZ6jRThvi1sfZnzO0uNne93r3qTOfR6vv+UlIdj5+z4mIWwQojhfevzUR5F+MQ6gHEU+n0P1/JVD0fGeW7DyIwqBUhsjU7VqVTRr1ky3rHLlyqhRo4a6fPz48Zg9ezYaNWqERo0aYfbs2YiOjsaQIUMABHaF0SZNmqB3794YOXIkFi1aBAC477770LdvX1xxxRUhPGIiIiIiqtDYNSyoSnWwf2EmT56MixcvYtSoUTh9+jTatWuHr776ClWrVlXTBHKF0XfeeQfjxo1TZzfr168fFixYEPLjISIiIqIKjIFMUAkppSztQpQFOTk5iI2NRXZ2dtkdL3P+eOFpAnlxBXrKFPWF6jd/C6drUfIpNPtg7T8I+ReHUjblOSry20AQy1uR3oq0rw1/x13SH3am+5YIbdecIL6mhPCdxt86KprCzs+i1neRz/uSOG/dr4dqdUsg7zLuwkn/XcgunAxdWTRycs4itlb9S+77Wm5uLqKiopD13VuoGRdb7Pw63T4ZE6Y+jVtvvTUIpSu7Sv06MkRERERERFZd0l3LiIiIiIjKDXYtCyoGMkREREREoaDO1FncfIqfRXnAQIaIiIiIKCSCNUU4IxmAY2SIiIiIiKgMYosMEREREVEocIxMUDGQISIiIiIKCRGcMTIEgIEMEREREVFICCEggtIiw1YdgGNkiIiIiIioDGKLDBERERFRSNjAdoTgYSBDRERERBQKwRrsz55lABgSEhERERFRGcQWmYqkcoLvdeePB/4Lgb90Uhb/lwbt9lIaV2p3Vng+Xttr8zFZ52Nxkfdv5LM8ZvkXlUShB2IsW1Gfs8IOx0omFXI6ShHgU272fAZaXxbOA6mcO4EUKYB0hZ7vCHx/CPC9RdigHrPx/Ui5ry2X2ftNcc7FgI7ZRLD2afac+srbWFbT88HP+sL4yk95bw44PyWdNNw3rqeQio4vfroLJwPbxlc6s20KIgIrV2nh9MtBxUCGiIiIiCgkgjVGhsEQwECGiIiIiCg02CITVBwjQ0REREREZQ5bZIiIiIiIQoEtMkHFQIaIiIiIKCQEOEYmeBjIEBERERGFAltkgopjZIiIiIiIqMxhiwwRERERUSgIm/t6V8XNp/hZlAcMZIiIiIiIQkIgOFEIIxmAgQwRERERUWhwjExQcYwMERERERGVOWyRISIiIiIKhWCNkSEADGSIiIiIiEIjaF3L2D0NYNcyIiIiIiIqg9giQ0REREQUEsGatYwABjKkqJzguX/hBIr2IpPBn4lDm5+UxpWufQayvde2PgSYzLN/ixsGo34KPRZh+G95B4EnLerxSO25IgJ/fgLfgcmy4tS9LOb2/gSab1H3r2wXpDo2fc7Nlpm8HxTreTYeh7/6EIb9K+8Vwv+mwXh9lsZsRP72WdR1gaz3JDQ89vE8a/MTRfkyJ3zcL2HV6oZuXxVNdHxw05UFvI5MUDGQISIiIiIKGUYhwcIxMkREREREVOawRYaIiIiIKCQ4a1kwMZAhIiIiIgoJDvYPJgYyREREREShELTryBDAMTJERERERFQGMZAhIiIiIqIyh13LiIiIiIhCgV3LgootMkREREREVOawRYaIiIiIKCSCNWsZW3UABjJERERERKHBrmVBxUCGiIiIiCgkeB2ZYOIYGSIiIiIiKnPYIkNEREREFArB6lrGRh0AbJEhM9E1geh4101dFu87vUoYbkGmvPh1bwDGfRZhv1Jz85mg0MIV42aRth783SzzWwnBZSyfv2PQpg34uIJQz175lbJAn3efz7/F8zCgfP3VrUnexT5H/ezTK2/j/o3vG8YyFlYvJXEOFOd9o4TeX4pUdn/rSqNsFlWr67qZLq8X8uKUG8bvEeRWOq/ttLQ0tG3bFlWrVkVCQgIGDBiAffv26UsmhOntueeeU9N07drVa/3tt99uuTzBwkCGiIiIiCgUrP4o5fNHJWvBTEZGBkaPHo3NmzdjzZo1KCgoQM+ePXH+/Hk1TWZmpu72r3/9C0II3HLLLbq8Ro4cqUu3aNGiYNRMkbBrGRERERFROZaenq57vGTJEiQkJGD79u247rrrAABJSUm6NB9//DGuv/561K9fX7c8OjraK21pYYsMEREREVFIBK9rWX5+PnJycnS33NzcgEqRnZ0NAIiLizNdf+zYMXz22WcYMWKE17p33nkH8fHxuPLKKzFp0iScPXs2sEMvAQxkiIiIiIhCQSBIXcuAVatWITY2VndLS0srtAhSSkyYMAGdO3dGs2bNTNMsW7YMVatWxaBBg3TL77zzTrz33ntYv349pk2bhg8++MArTSixaxkRERERUUgEb7KLgQMHYvHixbplkZGRhW43ZswY/Pzzz9i4caPPNP/6179w5513IioqSrd85MiR6v1mzZqhUaNGaNOmDX788Ue0atXK4hEUHwMZIiIiIqIyJjw8HDExMZa2GTt2LFavXo0NGzagdu3apmm+/fZb7Nu3DytXriw0v1atWiE8PBz79+9nIENERERERMElpcTYsWOxatUqrF+/HqmpqT7TLl68GK1bt8ZVV11VaL579uxBfn4+atWqFcziBoyBDBERERFRCCjXXglCTpZSjx49Gu+++y4+/vhjVK1aFVlZWQCA2NhYVKpUSU2Xk5OD999/Hy+88IJXHgcOHMA777yDG2+8EfHx8di7dy8mTpyIli1bolOnTsU7nCLiYH8iIiIionLstddeQ3Z2Nrp27YpatWqpN2P3sRUrVkBKiTvuuMMrj4iICKxduxa9evXCFVdcgXHjxqFnz574+uuvYbfbQ3UoOmyRISIiIiIKieAN9rdCShlQuvvuuw/33Xef6bqUlBRkZGQEs1jFxkCGiIiIiCgUNNMnFy+f4mdRHjCQISIiIiIKidJpkSmvOEaGiIiIiIjKHLbIEBERERGFQrC6lhEABjJERERERCESrK5lDIYABjJUmOh48/sAcOGka9mFkz42LsEXmb+spdcdz68f/mbtEPpNdHlIpz4f7UZWDtN098WtJx/HZCyr6bEHNouJJtMi5hHoMRryUo5BeyxmxyWE/+e2KEoiT2P+Rmb78/nLnfaEVdKYnPOB7EPJQ8BTnzD51bDI9WFSPp9JfdSLr+fD13EW9otnwIcifNwvLINL5UtGYW+WZueRcX2guyrBYy7J16KiWt2S3wcRW2SCimNkiIiIiIiozCnVQGbGjBnqFU6VW1JSkrpeSokZM2YgOTkZlSpVQteuXbFnzx5dHrm5uRg7dizi4+NRuXJl9OvXD0ePHtWlOX36NIYOHYrY2FjExsZi6NChOHPmTCgOkYiIiIjITQTpRsAl0CJz5ZVXIjMzU73t2rVLXTd37lzMmzcPCxYswNatW5GUlIQePXrg7Nmzaprx48dj1apVWLFiBTZu3Ihz586hb9++cDgcapohQ4Zgx44dSE9PR3p6Onbs2IGhQ4eG9DiJiIiIqIJTupYV90YALoExMmFhYbpWGIWUEi+99BKmTp2KQYMGAQCWLVuGxMREvPvuu7j//vuRnZ2NxYsX4+2330b37t0BAMuXL0dKSgq+/vpr9OrVC7/88gvS09OxefNmtGvXDgDwxhtvoEOHDti3bx+uuOIK03Ll5uYiNzdXfZyTkxPsQyciIiIioiIq9RaZ/fv3Izk5Gampqbj99tvxv//9DwBw8OBBZGVloWfPnmrayMhIdOnSBd9//z0AYPv27cjPz9elSU5ORrNmzdQ0mzZtQmxsrBrEAED79u0RGxurpjGTlpamdkWLjY1FSkpKUI+biIiIiCqaYHUtY6sMUMqBTLt27fDWW2/hyy+/xBtvvIGsrCx07NgRp06dQlZWFgAgMTFRt01iYqK6LisrCxEREahevbrfNAkJCV77TkhIUNOYmTJlCrKzs9XbkSNHinWsRERERESMYYKnVLuW9enTR73fvHlzdOjQAQ0aNMCyZcvQvn17AIAw9AOUUnotMzKmMUtfWD6RkZGIjIwM6DiIiIiIiALDSCRYSr1rmVblypXRvHlz7N+/Xx03Y2w1OX78uNpKk5SUhLy8PJw+fdpvmmPHjnnt68SJE16tPUREREREVDZcUoFMbm4ufvnlF9SqVQupqalISkrCmjVr1PV5eXnIyMhAx44dAQCtW7dGeHi4Lk1mZiZ2796tpunQoQOys7OxZcsWNc0PP/yA7OxsNQ0RERERUYkL1qxlbNQBUMpdyyZNmoSbb74ZderUwfHjx/HMM88gJycHw4YNgxAC48ePx+zZs9GoUSM0atQIs2fPRnR0NIYMGQIAiI2NxYgRIzBx4kTUqFEDcXFxmDRpEpo3b67OYtakSRP07t0bI0eOxKJFiwAA9913H/r27etzxjIiIiIiouDjIJdgKtVA5ujRo7jjjjtw8uRJ1KxZE+3bt8fmzZtRt25dAMDkyZNx8eJFjBo1CqdPn0a7du3w1VdfoWrVqmoeL774IsLCwjB48GBcvHgR3bp1w9KlS2G329U077zzDsaNG6fObtavXz8sWLAgtAdLRERERBUbrwMTVEJKKUu7EGVBTk4OYmNjkZ2djZiYmNIuzqXhwkkgOt71P+T8nLbqKW2SRne6S30yYbaJksb93+vNx2Lzbom82gLM1PSlbrVAZgcbSB6BVlIRKkhK1/MS7LeyksjTmL+R2f58fuBpT1glTQDl9XtM0lOfMPmwLXZ9GLYP9Hj9PcdF/UIQ7GPxUha+qEiYn0fG9QEqyS9nJflarFa35PKmkLtUv6/l5uYiKioKxw/tRc2a8cXOr8P1fTBh0qO49dZbg1C6sqvUL4hJRERERFQRuGLy4gf9bIZwuaQG+xMRERERlTfh4eFo2CAVqz9PL3Zevx8+gp9370Xjxo2DULKyjYEMEREREVEJstlsSJszF08+/SzOnz9frLymzpiNWwfejObNmwepdGUXx8gE6FLtc1lmhGQcTWH9783WS9/jYszyUfqAa5fZlN8DAmkqLuTlVuRXYyBjhsqycnJ8gYwhMDvfPAsK29hqiQybOz37Kays/uq90PFFRRwrE5AAxwsV6bwxjinxt3+zVRaOqSjlC3TcldU8C8sjFAOXS+p1zvEx5c6l/H1NSomO7a9Bn57d8OSUSUXKY/tPO3Ftj5uxb98+pKSkBLmEZQ9bZIiIiIiISpgQAs/PewnPvbQAx44dt7y9lBKPPD4D4x68l0GMGwMZIiIiIqIQ6NSpE3rc0BUzZj9nedsvvvwaP+/ZiylPPl0CJSubGMgQEREREYXInOfmYenyFfh13/6AtykoKMDkJ2biyccmIjY2tgRLV7YwkCEiIiIiCpHLL78cI4YNwaPTZga8zdLl7yE3LxcPjCva2JryioEMEREREVEIPTkzDes2bMSGjd8Xmvb8+fN48ulnkTZnLiIiIkJQurKDgQwRERERUQglJCTg0QljMenxGShsAuF5ryxE3TopuOWWW0JUurKDgQwRERERUYg9PPkJ/JmZhX9/8JHPNMeOHcfcF1/B8/NeggjFVOdlDAMZIiIiIqIQi46OxtPPzMKU6bOQm5trmmbG7OfQ44au6NSpU4hLVzYwkCEiIiIiKgV33303qlSujFdfX+K17td9+7F0+QrMeW5eKZSsbGAgQ0RERERUCux2O+Y+/wKefvYFnD59Rrfu0Wkz8Y+778Dll19eOoUrAxjIEBERERGVkl69eqH11Vch7fn56rING7/Hug0bMf3pOaVYsksfAxkiIiIiolIihMBz817CgkWL8fvhI5BS4pGpT+HRCWORkJBQ2sW7pIWVdgGIiIiIiCqyq6++GrcOvBlTZ8zGzTf2xNE//sTDk58o7WJd8hjIEBERERGVsqfTnkPjxo3x9boMzE6bg+jo6NIu0iWPgUyAlIsV5eTklHJJyqgLZ0OwE8MFpbwuMGW2Xnot9puPMoe7dplN6aEZyPzu/i96VdjqIm1YyIW2yoZycnyBXAPA7HzzLChsY6slMmzu9OynsLL6q3chCnleCnutIrC6Mt+5+T68ilCUupLu/P1t66fcVo6pKOUzy7+4r49Cn0sf+w22knqd2/iZXt4o39MKu8jkpahOnTp4/JGHkPHtJgwbNqy0i1MmCFkWn+lScPToUaSkpJR2MYiIiIioEEeOHEHt2rVLuxhUwhjIBMjpdOLPP/9E1apVS/zKqjk5OUhJScGRI0cQExNTovsqS1gv3lgn5lgv5lgv5lgv3lgn5lgv5i6lepFS4uzZs0hOTobNxjmtyjt2LQuQzWYLeWQfExNT6m8IlyLWizfWiTnWiznWiznWizfWiTnWi7lLpV5iY2NLuwgUIgxViYiIiIiozGEgQ0REREREZQ4DmUtQZGQkpk+fjsjIyNIuyiWF9eKNdWKO9WKO9WKO9eKNdWKO9WKO9UKlhYP9iYiIiIiozGGLDBERERERlTkMZIiIiIiIqMxhIENERERERGUOAxkiIiIiIipzGMhcYn777Tf0798f8fHxiImJQadOnbBu3TpdmsOHD+Pmm29G5cqVER8fj3HjxiEvL6+UShwan332Gdq1a4dKlSohPj4egwYN0q2viHWiyM3NxdVXXw0hBHbs2KFbV9Hq5dChQxgxYgRSU1NRqVIlNGjQANOnT/c65opWLwDw6quvIjU1FVFRUWjdujW+/fbb0i5SSKWlpaFt27aoWrUqEhISMGDAAOzbt0+XRkqJGTNmIDk5GZUqVULXrl2xZ8+eUipx6KWlpUEIgfHjx6vLKmqd/PHHH7jrrrtQo0YNREdH4+qrr8b27dvV9RWxXgoKCvDEE0+o76/169fHzJkz4XQ61TQVsV6olEm6pDRs2FDeeOONcufOnfK3336To0aNktHR0TIzM1NKKWVBQYFs1qyZvP766+WPP/4o16xZI5OTk+WYMWNKueQl5z//+Y+sXr26fO211+S+ffvkr7/+Kt9//311fUWsE61x48bJPn36SADyp59+UpdXxHr54osv5PDhw+WXX34pDxw4ID/++GOZkJAgJ06cqKapiPWyYsUKGR4eLt944w25d+9e+dBDD8nKlSvL33//vbSLFjK9evWSS5Yskbt375Y7duyQN910k6xTp448d+6cmmbOnDmyatWq8oMPPpC7du2St912m6xVq5bMyckpxZKHxpYtW2S9evVkixYt5EMPPaQur4h18tdff8m6devK4cOHyx9++EEePHhQfv311/K///2vmqYi1sszzzwja9SoIT/99FN58OBB+f7778sqVarIl156SU1TEeuFShcDmUvIiRMnJAC5YcMGdVlOTo4EIL/++msppZSff/65tNls8o8//lDTvPfeezIyMlJmZ2eHvMwlLT8/X1522WXyzTff9JmmotWJ1ueffy4bN24s9+zZ4xXIVOR60Zo7d65MTU1VH1fEernmmmvkAw88oFvWuHFj+dhjj5VSiUrf8ePHJQCZkZEhpZTS6XTKpKQkOWfOHDXN33//LWNjY+XChQtLq5ghcfbsWdmoUSO5Zs0a2aVLFzWQqah18uijj8rOnTv7XF9R6+Wmm26S//jHP3TLBg0aJO+66y4pZcWtFypd7Fp2CalRowaaNGmCt956C+fPn0dBQQEWLVqExMREtG7dGgCwadMmNGvWDMnJyep2vXr1Qm5urq7Zu7z48ccf8ccff8Bms6Fly5aoVasW+vTpo2uqrmh1ojh27BhGjhyJt99+G9HR0V7rK2q9GGVnZyMuLk59XNHqJS8vD9u3b0fPnj11y3v27Invv/++lEpV+rKzswFAPTcOHjyIrKwsXT1FRkaiS5cu5b6eRo8ejZtuugndu3fXLa+odbJ69Wq0adMGt956KxISEtCyZUu88cYb6vqKWi+dO3fG2rVr8dtvvwEAdu7ciY0bN+LGG28EUHHrhUpXWGkXgDyEEFizZg369++PqlWrwmazITExEenp6ahWrRoAICsrC4mJibrtqlevjoiICGRlZZVCqUvW//73PwDAjBkzMG/ePNSrVw8vvPACunTpgt9++w1xcXEVrk4AVz/k4cOH44EHHkCbNm1w6NAhrzQVsV6MDhw4gFdeeQUvvPCCuqyi1cvJkyfhcDi8jjkxMbFcHm8gpJSYMGECOnfujGbNmgGAWhdm9fT777+HvIyhsmLFCvz444/YunWr17qKWif/+9//8Nprr2HChAl4/PHHsWXLFowbNw6RkZG4++67K2y9PProo8jOzkbjxo1ht9vhcDgwa9Ys3HHHHQAq7vlCpYstMiEwY8YMCCH83rZt2wYpJUaNGoWEhAR8++232LJlC/r374++ffsiMzNTzU8I4bUPKaXp8ktVoHWiDCKcOnUqbrnlFrRu3RpLliyBEALvv/++ml95qBMg8Hp55ZVXkJOTgylTpvjNr6LVi9aff/6J3r1749Zbb8W9996rW1de6sUK47GV9+P1Z8yYMfj555/x3nvvea2rSPV05MgRPPTQQ1i+fDmioqJ8pqtIdQIATqcTrVq1wuzZs9GyZUvcf//9GDlyJF577TVduopWLytXrsTy5cvx7rvv4scff8SyZcvw/PPPY9myZbp0Fa1eqHSxRSYExowZg9tvv91vmnr16uGbb77Bp59+itOnTyMmJgaAa6ahNWvWYNmyZXjssceQlJSEH374Qbft6dOnkZ+f7/UryKUs0Do5e/YsAKBp06bq8sjISNSvXx+HDx8GgHJTJ0Dg9fLMM89g8+bNiIyM1K1r06YN7rzzTixbtqxC1ovizz//xPXXX48OHTrg9ddf16UrT/USiPj4eNjtdq/Wl+PHj5fL4y3M2LFjsXr1amzYsAG1a9dWlyclJQFw/apcq1YtdXl5rqft27fj+PHjatdlAHA4HNiwYQMWLFigzupWkeoEAGrVqqX7zAGAJk2a4IMPPgBQMc8VAHjkkUfw2GOPqe/FzZs3x++//460tDQMGzaswtYLlS4GMiEQHx+P+Pj4QtNduHABAGCz6RvKbDab2jLRoUMHzJo1C5mZmeobxVdffYXIyEjdh9GlLtA6ad26NSIjI7Fv3z507twZAJCfn49Dhw6hbt26AMpPnQCB18vLL7+MZ555Rn38559/olevXli5ciXatWsHoGLWC+CaNvX6669XW++Mr6fyVC+BiIiIQOvWrbFmzRoMHDhQXa50Y60opJQYO3YsVq1ahfXr1yM1NVW3PjU1FUlJSVizZg1atmwJwDW+KCMjA88++2xpFLnEdevWDbt27dItu+eee9C4cWM8+uijqF+/foWrEwDo1KmT19Tcv/32m/qZUxHPFcD1HcX4fmq329XvJxW1XqiUlcoUA2TqxIkTskaNGnLQoEFyx44dct++fXLSpEkyPDxc7tixQ0rpmTq2W7du8scff5Rff/21rF27drmeOvahhx6Sl112mfzyyy/lr7/+KkeMGCETEhLkX3/9JaWsmHVidPDgQZ/TL1ekevnjjz9kw4YN5Q033CCPHj0qMzMz1ZuiItaLMv3y4sWL5d69e+X48eNl5cqV5aFDh0q7aCHz4IMPytjYWLl+/XrdeXHhwgU1zZw5c2RsbKz88MMP5a5du+Qdd9xR4aaO1c5aJmXFrJMtW7bIsLAwOWvWLLl//375zjvvyOjoaLl8+XI1TUWsl2HDhsnLLrtMnX75ww8/lPHx8XLy5MlqmopYL1S6GMhcYrZu3Sp79uwp4+LiZNWqVWX79u3l559/rkvz+++/y5tuuklWqlRJxsXFyTFjxsi///67lEpc8vLy8uTEiRNlQkKCrFq1quzevbvcvXu3Lk1FqxMjs0BGyopXL0uWLJEATG9aFa1epJTyn//8p6xbt66MiIiQrVq1Uqcdrih8nRdLlixR0zidTjl9+nSZlJQkIyMj5XXXXSd37dpVeoUuBcZApqLWySeffCKbNWsmIyMjZePGjeXrr7+uW18R6yUnJ0c+9NBDsk6dOjIqKkrWr19fTp06Vebm5qppKmK9UOkSUkpZGi1BRERERERERcVZy4iIiIiIqMxhIENERERERGUOAxkiIiIiIipzGMgQEREREVGZw0CGiIiIiIjKHAYyRERERERU5jCQISIiIiKiMoeBDBERERERlTkMZIiIimn48OEYMGDAJZOPmRkzZuDqq68uVh5du3aFEAJCCOzYscNnuvXr10MIgTNnzhRrf+VJvXr11LpjvRARBQcDGSK6ZHTt2hXjx48v7WKUuEOHDpkGA/Pnz8fSpUvVx5difYwcORKZmZlo1qxZaRel1CkBW7NmzeBwOHTrqlWrpnsut27dig8++CDEJSQiKt8YyBBRmSKlREFBQansOy8vr0Tzj42NRbVq1Up0H8UVHR2NpKQkhIWFlXZRkJ+fX9pFAAAcOHAAb731lt80NWvWRFxcXIhKRERUMTCQIaJLwvDhw5GRkYH58+erXXAOHTqk/ur95Zdfok2bNoiMjMS3336LAwcOoH///khMTESVKlXQtm1bfP3117o8c3NzMXnyZKSkpCAyMhKNGjXC4sWL1fV79+7FjTfeiCpVqiAxMRFDhw7FyZMn1fVdu3bFmDFjMGHCBMTHx6NHjx4BHUt6ejo6d+6MatWqoUaNGujbty8OHDigrk9NTQUAtGzZEkIIdO3aVa0DpWuZr/pYunSpV7Dz0UcfQQihWzZnzhwkJiaiatWqGDFiBP7++2+vci5ZsgRNmjRBVFQUGjdujFdffTWg4zP6/PPPcfnll6NSpUq4/vrrcejQIa8033//Pa677jpUqlQJKSkpGDduHM6fP///7d1/TJXVH8Dxd0AMkh+BKYPCyBgDAiWoIW6IIutOHSOYWWKhQyTLHygrMBKBArTZNK0tkAZoSeiGsSyKRMUhasAdEKPbBQSMDLOUXBBu2T3fP5x3PvwwKP0m6/Pa2Ljn53MO7O757DnnPOb83t5eFi1ahK2tLY888gglJSV4eHjwzjvvmMvcc8895OXlERUVxaRJk8jOzgbg8OHDBAUFYWNjw/Tp08nKytIEu1euXCExMZGpU6fi4OBAeHg4zc3N5vzm5mbmzZuHvb09Dg4OBAUF0dDQMObxr1u3joyMjBHnWAghxJ0jgYwQ4q6wa9cuQkJCzEuXent7cXd3N+enpKSwdetWDAYDM2bMoL+/n4ULF1JVVUVjYyM6nY7IyEi+//57c524uDhKS0vZvXs3BoOBvLw87OzsgOs3zmFhYQQEBNDQ0MCXX37JTz/9xJIlSzTXtXfvXqysrKitrSU/P39MYxkYGCA5OZn6+nqOHj2KhYUF0dHRmEwmAOrq6gCoqqqit7eXQ4cOjXs+buXgwYNkZGSQk5NDQ0MDrq6uw4KUgoICXn/9dXJycjAYDOTm5pKens7evXvH1McNPT09xMTEsHDhQpqamkhISGDTpk2aMi0tLeh0OmJiYvjmm284cOAAJ0+eZO3ateYycXFx/Pjjj1RXV1NWVsaePXu4ePHisP4yMjKIioqipaWF+Ph4Kisref7551m/fj3ffvst+fn5FBcXk5OTA1x/grdo0SIuXLhARUUFer2ewMBA5s+fz+XLlwFYtmwZDz30EPX19ej1ejZt2sS999475jnYsGED165d47333hvX3AkhhPiHlBBC3CXCwsJUUlKSJu348eMKUOXl5X9Z39fXV7377rtKKaWMRqMC1JEjR0Ysm56erp566ilNWk9PjwKU0Wg0X09AQMBf9rt8+XIVFRU1av7FixcVoFpaWpRSSnV1dSlANTY23rKdkeajqKhIOTo6atI++eQTdfPXeUhIiFq9erWmTHBwsJo5c6b5s7u7uyopKdGUefPNN1VISMio4xjpel577TXl4+OjTCaTOS01NVUBqq+vTyml1AsvvKASExM19WpqapSFhYUaHBxUBoNBAaq+vt6c397ergC1c+dOcxqgNmzYoGknNDRU5ebmatI+/PBD5erqqpRS6ujRo8rBwUFdvXpVU+bRRx9V+fn5Siml7O3tVXFx8ajjHs2N/82+vj6Vl5ennJ2d1a+//qqUUsrR0VEVFRWNWl4IIcQ/J09khBATwhNPPKH5PDAwQEpKCr6+vtx///3Y2dnx3XffmZ/INDU1YWlpSVhY2Ijt6fV6jh8/jp2dnfnH29sbQLMMbGi/Y3H27FliY2OZPn06Dg4O5qVkNz8tupMMBgMhISGatJs///zzz/T09LBy5UrN+LOzszVjH2tfs2bN0ixtG9q3Xq+nuLhY05dOp8NkMtHV1YXRaMTKyorAwEBzHU9PT5ycnIb1N/TvodfreeONNzRt33iK9fvvv6PX6+nv72fy5MmaMl1dXeaxJicnk5CQQEREBNu2bRv3HACsXLmSBx54gLfeemvcdYUQQvw9//5uTSGEGINJkyZpPr/66qtUVlby9ttv4+npia2tLYsXLzZvyLe1tb1leyaTicjIyBFvPF1dXUftdywiIyNxd3enoKAANzc3TCYTfn5+t+WwAAsLC5RSmrTxbnq/scStoKCA4OBgTZ6lpeW42hp6LaP19+KLL7J+/fphedOmTcNoNI657aF/D5PJRFZWFjExMcPK2tjYYDKZcHV1pbq6elj+jb1GmZmZxMbG8vnnn/PFF1+QkZFBaWkp0dHRfzm2G6ysrMjOzmbFihWaJXNCCCHuHAlkhBB3DWtr62HH2I6mpqaGFStWmG82+/v7NZvM/f39MZlMnDhxgoiIiGH1AwMDKSsrw8PD47aewHXp0iUMBgP5+fmEhoYCcPLkSU0Za2trgL8c60jzMWXKFH777TcGBgbMN/VDj3H28fHhzJkzxMXFmdPOnDlj/t3FxYUHH3yQzs5Oli1bNr4BDuHr60t5ebkm7ea+4Ppct7a24unpOWIb3t7eXLt2jcbGRoKCggDo6OgY0/tWAgMDMRqNo7YdGBjIhQsXsLKywsPDY9R2vLy88PLyYuPGjSxdupSioqJxBTIAzzzzDNu3bycrK2tc9YQQQvw9srRMCHHX8PDw4Ouvv6a7u5tffvnF/ORgJJ6enhw6dIimpiaam5uJjY3VlPfw8GD58uXEx8dTXl5OV1cX1dXVHDx4EIA1a9Zw+fJlli5dSl1dHZ2dnXz11VfEx8ePOZgaiZOTE5MnT2bPnj10dHRw7NgxkpOTNWWmTp2Kra2t+YCBK1eujHk+goODue+++0hLS6Ojo4OSkhLN+0oAkpKSKCwspLCwkLa2NjIyMmhtbdWUyczMZOvWrezatYu2tjZaWlooKipix44d4xrv6tWrOXv2LMnJyRiNxhGvJzU1ldOnT7NmzRqamppob2/n008/Zd26dcD1QCYiIoLExETq6upobGwkMTERW1vbYaexDbVlyxb27dtHZmYmra2tGAwGDhw4wObNmwGIiIggJCSEp59+msrKSrq7uzl16hSbN2+moaGBwcFB1q5dS3V1NefOnaO2tpb6+np8fHzGNQ83bNu2jcLCQs2JbEIIIe4MCWSEEHeNV155BUtLS3x9fZkyZcot95Ts3LkTJycnZs+eTWRkJDqdTrPHAuD9999n8eLFvPzyy3h7e7Nq1SrzDaabmxu1tbX8+eef6HQ6/Pz8SEpKwtHREQuLv//VaGFhQWlpKXq9Hj8/PzZu3Mj27ds1ZaysrNi9ezf5+fm4ubkRFRU15vlwdnbmo48+oqKiAn9/fz7++GMyMzM19Z599lm2bNlCamoqQUFBnDt3jpdeeklTJiEhgQ8++IDi4mL8/f0JCwujuLjYvJ9nrKZNm0ZZWRmHDx9m5syZ5OXlkZubqykzY8YMTpw4QXt7O6GhoTz++OOkp6drlvDt27cPFxcX5syZQ3R0NKtWrcLe3h4bG5tb9q/T6fjss884cuQITz75JLNmzWLHjh08/PDDwPUjmysqKpgzZw7x8fF4eXnx3HPP0d3djYuLC5aWlly6dIm4uDi8vLxYsmQJCxYs+NtPVcLDwwkPD//X3nUkhBD/JfeosSxwFkII8Z83d+5cAgICNO92uVN++OEH3N3dqaqqYv78+Xe8v/+H6upq5s2bR19f313/4lMhhJgIJJARQggxJnPnzuXUqVNYW1tz+vRp/P39b1vbx44do7+/H39/f3p7e0lJSeH8+fO0tbWN650ud6vHHnuMzs5Orl69KoGMEELcJrLZXwghxJjs37+fwcFB4PqSstvpjz/+IC0tjc7OTuzt7Zk9ezb79+//V4OYBQsWUFNTM2JeWloaaWlpY26roqLCfLqcg4PDbbk+IYT4r5MnMkIIIcQIzp8/bw7chnJ2dsbZ2fn/fEVCCCFuJoGMEEIIIYQQYsKRU8uEEEIIIYQQE44EMkIIIYQQQogJRwIZIYQQQgghxIQjgYwQQgghhBBiwpFARgghhBBCCDHhSCAjhBBCCCGEmHAkkBFCCCGEEEJMOP8DRLNQC4Z6DQ8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "coord_mean.sel(year=2000).plot(yincrease=False, vmin=273, vmax=300, cmap='Oranges')\n", + "plt.title('True zonal mean');" + ] + }, + { + "cell_type": "markdown", + "id": "f995e265", + "metadata": {}, + "source": [ + "Since we used the same bin edges as the standard `yt_ocean` coordinate we can take a difference between the arithmetic mean along the model's x-axis and our mean along grid cells within latitude bands. The main differences are near the North Pole where the grid is furthest for being regular. There are also differences near the Antacrtic Shelf suggesting partial cells also matter." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c47e9e8c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.75 s, sys: 380 ms, total: 4.13 s\n", + "Wall time: 4.47 s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "\n", + "zonal_minus_x_mean = coord_mean.sel(year=2000) - x_arith_mean.sel(year=2000).values\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "zonal_minus_x_mean.plot(yincrease=False, vmin=-0.8, vmax=0.8, cmap='RdBu_r', extend='both')\n", + "plt.title('True zonal minus $x$-coordinate arithmetic mean');" + ] + }, + { + "cell_type": "markdown", + "id": "0a2e3978", + "metadata": {}, + "source": [ + "`xarray` has a new `weighted` functionality which allows it to do weighted means instead of arithmetic mean.\n", + "\n", + "Let's see how that works out... We chose `dVol` as the weights and we only do the comparisson for year 2000." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bf7b4fbe", + "metadata": {}, + "outputs": [], + "source": [ + "variable_to_average_weighted = variable_to_average.copy().sel(time='2000').mean(dim='time')\n", + "variable_to_average_weighted = variable_to_average_weighted.weighted(dVol.sel(time='2000').fillna(0))\n", + "meanweighted_y2000 = variable_to_average_weighted.mean(dim='xt_ocean').groupby('time.year').mean(dim='time').sel(year=2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5b427451", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "zonal_minus_x_mean = coord_mean.sel(year=2000) - meanweighted_y2000.values\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "zonal_minus_x_mean.plot(yincrease=False, vmin=-0.8, vmax=0.8, cmap='RdBu_r')\n", + "plt.title(\"True zonal minus xarray's $x$-coordinate weighted mean\"); " + ] + }, + { + "cell_type": "markdown", + "id": "6b2ab410", + "metadata": {}, + "source": [ + "South of 65N, where complications of the tripolar grid don't matter, `xarray`'s weighted mean does the job! But in the region of the tripolar we need to be more careful." + ] + } + ], + "metadata": { + "extensions": { + "jupyter_dashboards": { + "activeView": "grid_default", + "version": 1, + "views": { + "grid_default": { + "cellMargin": 10, + "defaultCellHeight": 20, + "maxColumns": 12, + "name": "grid", + "type": "grid" + }, + "report_default": { + "name": "report", + "type": "report" + } + } + } + }, + "kernelspec": { + "display_name": "Python [conda env:analysis3-24.01]", + "language": "python", + "name": "conda-env-analysis3-24.01-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "thumbnail_figure": 1 + }, + "nbformat": 4, + "nbformat_minor": 5 +}