{ "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/analysis3-25.09` (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. We can swap temperature with any other scalar variable. We can also, in principle, swap latitude with another scalar.\n", "\n", "#### Adapting for MOM6\n", "\n", "|Variable | MOM5 diagnostic | Equivalent MOM6 diagnostic | \n", "|:--------|-----|------|\n", "| Temperature | `temp` (conservative temperature) | `thetao` (potential temperature) | \n", "| Cell volume (m3) | `area_t * dzt` | `volcello` | \n", "| Lat, lon | `geolon_t`, `geolat_t` | `geolon_t`, `geolat_t` | \n", "\n", "Note that the available MOM6 experiments from the COSIMA community are from a PanAntarctic model and thus limited to the Southern Ocean." ] }, { "cell_type": "markdown", "id": "c9a344ed-1adf-4308-b68c-721565f14a8d", "metadata": {}, "source": [ "## MOM5" ] }, { "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 " ] }, { "cell_type": "code", "execution_count": 2, "id": "44ded25b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Client

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Client-7ab748ae-dd37-11f0-8d52-00000088fe80

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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Cluster Info

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LocalCluster

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3b3c1179

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\n", " Dashboard: /proxy/8787/status\n", " \n", " Workers: 48\n", "
\n", " Total threads: 48\n", " \n", " Total memory: 188.56 GiB\n", "
Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-ea569dfc-369b-4306-98aa-23986ac9e251

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\n", " Comm: tcp://127.0.0.1:39161\n", " \n", " Workers: 0 \n", "
\n", " Dashboard: /proxy/8787/status\n", " \n", " Total threads: 0\n", "
\n", " Started: Just now\n", " \n", " Total memory: 0 B\n", "
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Workers

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Worker: 0

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\n", " Comm: tcp://127.0.0.1:33315\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44657/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38559\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-mn_msrmf\n", "
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Worker: 1

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\n", " Comm: tcp://127.0.0.1:45145\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45847/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46123\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-oeapf1xs\n", "
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Worker: 2

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\n", " Comm: tcp://127.0.0.1:45961\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40741/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40817\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-xd6mg3mz\n", "
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Worker: 3

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\n", " Comm: tcp://127.0.0.1:32949\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41539/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34849\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-c0akb_6t\n", "
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Worker: 4

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\n", " Comm: tcp://127.0.0.1:34591\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42715/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40545\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-0h13dgb4\n", "
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Worker: 5

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\n", " Comm: tcp://127.0.0.1:40291\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:41601\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-utsqzdl4\n", "
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Worker: 6

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\n", " Comm: tcp://127.0.0.1:39905\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:33731\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-52bl613m\n", "
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Worker: 7

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\n", " Comm: tcp://127.0.0.1:46033\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:39053\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-65waudb4\n", "
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Worker: 8

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\n", " Comm: tcp://127.0.0.1:37469\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:34291\n", "
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Worker: 9

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\n", " Comm: tcp://127.0.0.1:36753\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45299/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46399\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-e_bylrp9\n", "
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Worker: 10

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\n", " Comm: tcp://127.0.0.1:45533\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:33231\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-u__gtnoz\n", "
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Worker: 11

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\n", " Comm: tcp://127.0.0.1:39423\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/37555/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40189\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-dg96rhft\n", "
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Worker: 12

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\n", " Comm: tcp://127.0.0.1:39823\n", " \n", " Total threads: 1\n", "
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\n", " Nanny: tcp://127.0.0.1:44209\n", "
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Worker: 13

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\n", " Nanny: tcp://127.0.0.1:41959\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-uq1g3yyl\n", "
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Worker: 14

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\n", " Nanny: tcp://127.0.0.1:37891\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-e9ox18_b\n", "
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Worker: 15

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\n", " Comm: tcp://127.0.0.1:45117\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35603/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46755\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-vxpadx5w\n", "
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Worker: 16

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\n", " Comm: tcp://127.0.0.1:42699\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38719/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42949\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-022yfur4\n", "
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Worker: 17

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\n", " Comm: tcp://127.0.0.1:44547\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/35907/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44183\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-uj3ps8f4\n", "
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Worker: 18

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\n", " Comm: tcp://127.0.0.1:35829\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41191/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46301\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-4hqkr2ir\n", "
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Worker: 19

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\n", " Comm: tcp://127.0.0.1:41289\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39719/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44465\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-b2fghzzj\n", "
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Worker: 20

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\n", " Comm: tcp://127.0.0.1:36673\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45243/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46713\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-2lboj15r\n", "
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Worker: 21

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\n", " Comm: tcp://127.0.0.1:45453\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33641/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34039\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-jga0y894\n", "
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Worker: 22

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\n", " Comm: tcp://127.0.0.1:38599\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/39525/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34047\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-es_1o_vh\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://127.0.0.1:39481\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33075/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34429\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-r_9ztvo4\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 24

\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://127.0.0.1:34597\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44821/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45001\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-ieis8afv\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 25

\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://127.0.0.1:44553\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44879/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38319\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-is0xcvqt\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 26

\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://127.0.0.1:39083\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40317/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45869\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-9ea9r9_0\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 27

\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://127.0.0.1:40165\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/34045/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43571\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-qbnxl7v3\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:46339\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/41447/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39663\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-e9v7e83g\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 29

\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://127.0.0.1:43573\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38859/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37127\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-vh0kpmj_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 30

\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://127.0.0.1:41463\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40609/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35515\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-wdrjraqh\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 31

\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://127.0.0.1:45713\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40051/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:34279\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-vr82n4kj\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 32

\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://127.0.0.1:43569\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38937/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:46665\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-amrz5nzo\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:42785\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38915/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37473\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-m9852un5\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:40551\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40311/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:41819\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-1vhilotu\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:40561\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43237/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35837\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-vt9c05cc\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:35203\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/44437/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:45397\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-7yj8c270\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:36297\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36449/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42905\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-xi54_rnj\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:43377\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40053/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33293\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-4vvnkn32\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:37037\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/40017/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:35393\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-rs6qc0hb\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:36513\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/43075/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40397\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-qi23u7gr\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:42111\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/33045/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:37011\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-93z4qmvp\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:42927\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/42701/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42955\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-dnih9khx\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

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://127.0.0.1:44313\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45721/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:38495\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-4o2a4pt3\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 44

\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://127.0.0.1:35615\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/36383/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44469\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-j0ahta7h\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 45

\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://127.0.0.1:33771\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/38215/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39651\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-evh6tbuw\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 46

\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://127.0.0.1:46253\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/32771/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39567\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-wv1fbk6t\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 47

\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://127.0.0.1:33931\n", " \n", " Total threads: 1\n", "
\n", " Dashboard: /proxy/45883/status\n", " \n", " Memory: 3.93 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42165\n", "
\n", " Local directory: /jobfs/157096073.gadi-pbs/dask-scratch-space/worker-vdlo6oum\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "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 9 source(s) across 19 rows:

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modeldescriptionrealmfrequencyvariable
name
025deg_jra55_iaf_era5comparison{ACCESS-OM2}{0.25 degree ACCESS-OM2 global model configuration with JRA55-do v1.5.0\\ninterannual forcing (1980-2019)}{ocean}{fx, 1mon}{area_t, temp}
025deg_jra55_iaf_omip2_cycle1{ACCESS-OM2}{Cycle 1/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean}{fx, 1mon}{area_t, temp}
025deg_jra55_iaf_omip2_cycle2{ACCESS-OM2}{Cycle 1/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean}{fx, 1mon}{area_t, temp}
025deg_jra55_iaf_omip2_cycle3{ACCESS-OM2}{Cycle 3/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean}{fx, 1mon}{area_t, temp}
025deg_jra55_iaf_omip2_cycle4{ACCESS-OM2}{Cycle 4/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean}{fx, 1mon}{area_t, temp}
025deg_jra55_iaf_omip2_cycle5{ACCESS-OM2}{Cycle 5/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean}{fx, 1mon}{area_t, temp}
025deg_jra55_iaf_omip2_cycle6{ACCESS-OM2}{Cycle 6/6 of 0.25 degree ACCESS-OM2 physics-only global configuration with JRA55-do v1.4 OMIP2 interannual forcing (1958-2019)}{ocean}{fx, 1mon}{area_t, temp}
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}{area_t, temp, dzt}
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}{area_t, temp, dzt}
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "catalog.search(name='.*025deg_jra55.*', variable=[\"temp\", \"dzt\", \"area_t\"])" ] }, { "cell_type": "code", "execution_count": 5, "id": "3408bb29-c81f-4f28-993f-6b233cfce241", "metadata": {}, "outputs": [], "source": [ "experiment = '025deg_jra55_ryf9091_gadi' # any experiment that includes the required variables\n", "variable = 'temp' # any scalar variable for which volume-weighted average makes sense\n", "\n", "xarray_open_kwargs = dict(use_cftime=True, chunks={\"time\": -1}, decode_timedelta=False)" ] }, { "cell_type": "code", "execution_count": 6, "id": "9eded697-29d4-4101-ba05-fe3d0509fd67", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'temp' (time: 396, st_ocean: 50, yt_ocean: 1080,\n",
       "                          xt_ocean: 1440)> Size: 123GB\n",
       "dask.array<concatenate, shape=(396, 50, 1080, 1440), dtype=float32, chunksize=(2, 10, 216, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "  * st_ocean  (st_ocean) float64 400B 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n",
       "  * time      (time) object 3kB 1904-07-02 12:00:00 ... 2299-07-02 12:00:00\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    units:          K\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_conservative_temperature
" ], "text/plain": [ " Size: 123GB\n", "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " * st_ocean (st_ocean) float64 400B 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n", " * time (time) object 3kB 1904-07-02 12:00:00 ... 2299-07-02 12:00:00\n", "Attributes:\n", " long_name: Conservative temperature\n", " units: K\n", " valid_range: [-10. 500.]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT\n", " standard_name: sea_water_conservative_temperature" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "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]\n", "variable_to_average" ] }, { "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 1.57 s, sys: 214 ms, total: 1.78 s\n", "Wall time: 1.87 s\n" ] }, { "data": { "image/png": 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XOTk50uVySQCm6ZHffvtt2bt3b5mSkiLj4+Nl8+bN5d///nf58ccfB6ltn+LiYjlt2jR50kknyYSEBNmxY0f59ttvy9GjR8vmzZuHVVd2xxWqvM2bN5cXXnhhQD49e/Y0zRymz6i1YcMGUzr9NVCnEZZSytGjR8ukpCTTstLSUvnoo4/KM888UyYkJMj69evL1q1by+uvv17u3LnTSPfTTz/J/v37y+TkZGOK5FDHFs65GqyerHUY7HwJdvzhvNZ79+6Vl1xyiWzYsKFMTk6WAwcOlFu3bg04V4PtQy+Teo4REdUWQkopox08ERERERERVQanXyYiIiIiohqHgQwREREREdU4DGSIiIiIiKjGqVOBzLPPPovc3FwkJCSgU6dO+Oyzz6q7SEREREREVAF1JpB5/fXXMXnyZMyYMQNff/01/va3v2HQoEHYvXt3dReNiIiIiIgiVGdmLevatSs6duxouup1mzZtMGzYMOTl5VVjyYiIiIiIKFJ14oKYJSUl2LRpE+644w7T8v79++OLL76w3aa4uBjFxcXGc6/Xiz///BPp6enGVbyJiIiI6MQhpcThw4eRk5MDl6vmdTy6uLEbv/4lse5PD39vhqFOBDK///47PB4PsrKyTMuzsrJQUFBgu01eXl7Iq2gTERER0Ylpz549aNKkSXUXIyIHDx7E8gNelEnggw8+wIUXXljdRTrh1YlARmeNbKWUQaPd6dOnY8qUKcbzwsJCNGvWDHv27EFKSkr4Oz32R4XKWuNILwAJSO1mPNaXewEpIaVX38C0PGB747lHWaY8hzdgmZQeJQ+P7+b1+tLq91ICXg/gLQM82r30aMv059J3X1bmK4fXA3hLfY89pb71+n1ZmXk7T6nveVmJb58ePT9feqnlKT1eSO0e0gvp9Wj3UnsMSK9v3/5lElLbj/R64VsktedSqzYJr9frqyJIeDzaeinh9UpAAl6vhPQo6bT1EoBXq74yX1J4JeCRAl6lA6pH+pZ7ob0UlmXQtoO23gvAK4XxWCrpPMpjrSgB9/o6j5Knvsxrk04vqr5Pa1o9D3U7r2W5WnYoz6l66B/TLu2xUJa7lMfCso0A4BaB64WQEPA915e7LPmqy4Ryry9zwZ/GrZXD7ZLave+5S/i20dcbafXjERJu0/aAC/6duVzC9x3lgv+x8D32lVNAaMt9N8DlcmnL4Lt3uXzLY1yAywUB7V4IiBjfTwDhjvGli4mBcLl9t9hY37K4eEC4gZhYwOUC3LHaQcUCLrfv3h0DuN2+e+ECYuJ861xxvmUul+854FvvcvvuhUupcKGl0ZZLrz+NS/gfC5f/hNDzE8JXRt/BaJXr8uXlcvteKeF7xX3f+S7/PvUzy3iulFU9o1wuIw/TvqVX26f0rxNCKafwr5Ne/zLfi6KfhVq59LSWfRvlFP66M53t+lP1mJQ81O3rgKLDh9H01DORnJxc3UWJ2IUnpyMnQeDU+gJXDx+MX4+WIiamTv1Uj1idqJ2MjAy43e6A1pcDBw4EtNLo4uPjER8fH7A8JSUlskAmpiSistZsemDi1T6LLYGKFpxII9CxBjFKcALzNtqvdmW5x7dLS6Ai9UDFCIDUe+WxNZjRAglfgKIHL1og5C3zLzcCmRJ/ICO1gMXrATxu7T5Gu9cDnRglnRfSU6aV1RzQ6EGKGuyYlpWVKcGLFtx4PP5AxghqpBbUaI8l4PX4AxePxwtIt39dme8nuykt9EBGCUygPxc2y8yBilS288IciOjBjRZnmQIgj00wYQ1w9LTqMrU86nqvFMZyNfBS05oCITW4UoIXa1o1yLHmQeWzBh0ql2W5NXCxBjOAP2DR0/sDGRmwDBD+gEQJLAKCFmW9njZW/w0qpFGWWOEPQoQA4l2+9DEuaQRTLiVY8eUvjEDE5fJFPm5tpR6U6EEIoAUvarBiCnb8aYUQEG7fj2jhcvuDltg4LRASWuDi+1EstOdwacvdMRButy8Yccf4g5LYeN8PaHeMEsC4gJh4383l9v0wFwKIq6elifMHPHZBifGCKT/6Tcv1kFF/Yeyea2eMKWCB79i1oC0gUDFOLONMCww+1HKYAgRrsGChbq/naXw4eP3LtDQiYJ/WTxDL/oRbORZL4GMNnOq4mtYt66effsKGQxJjm7nRKB5YfxC4uEk83i3wVHfRTmh1IpCJi4tDp06dsHLlSlx88cXG8pUrV2Lo0KHVWLLaRv9XTABCD1DcMP3kk14IoX2uC5e2Tvn3TU9rrPdtA5cWIJiWS993mpYFvIAQXkiXW8vG49u/8Pqe6zuWyr9qwgV4XABKAKnmqwUtLi/gtfxDqJZHv3e7fUGR/lyUaPcCkDFay4y2nZQQXg+E9AIejxF8mQIUIOzgxh/M6I/LtMda64vW8uLVmlukhBbI6IEOIGO9RiBkbKf9oje33GjBDqTReqMHKWogoQc6aktIYKAjfS0/luXW1hvAfh/WIAfKY3OQI21bY/R8gMBgRw2G1HTGaawsMM5nS7pgAY+VXQ9uuxYiY3/Kfq3bBttOysDfXtYWJrWc1lYOtUXEVBZLoKEuCyZUy5bp95peFkvQ4gsolOBESafe6+V222yvLlfzjxH+etWDFbfyPEbfTg9KtAAl1hKwxLoAl9BbUGxaVlz+FhRTAOISAa0tRrm19FBaXwKCFi2xNVCBywVXTIz/ufZj2rfM5UsvhNbq4vbd9Md6IBMT5w9k9NYXVwwQm6AFMtpy4fIHNrH1fPnorTCA/4e4ejYZJ5L6Ix1B0liCD22Z0dJijImwCV6MFiBLEGXdh4myHymVOEEYn7+m7aReJuUdpQcr0utb79KO1dhGapkqQYl/pW+xXn/G95CWTP/zTt9WbR1SXuvAfOlEM+SMlmibLJCd4Hud+jRy4cP9Xhw9ehRJSUnVXLoTV50IZABgypQpGDVqFDp37ozu3bvjhRdewO7du3HDDTdUd9FqHyGUgMML/88N7SYB/6ew/itCX678qjCeW4IHNW/pMgczUkDA4w9mhPR/cXihfbm4te9H6Q9eEOf/t0zvWgb4vzSE8AUqgPalUxL4pahvq39h6V9uXo85L3U/Wi3AowdZ/i9FIbSgxSsBoQVlLi+EFrgIlzSCGgjhW+5VHksv3F4vpEdASgmhBDJGUbUgRXqF0RoDLa0e1AivgNSaMPxd1CRc+o93KQNaVjzSHDzorS9e7eXwSsCrx7vS/1zf1txKI/wxq16lejwKGPuGtl5K3w9KdV9u4Q9SIPwtMvpp5hbSH4wIBAQ4xumoUX8O6D+q1bTW9Kbl0nyqBMtLDaq8NplZf+Bb9xGqDOpro6ZX81b3oZZTDSJM9zZBiJG/TblCxDPmwEh9rAQf+jpri4w1aLF2LdOPyS5ocSn5qcGLHvi44AtS9Hz0QMffSqOtc7v8AYlbBHQLMwU4Ln9Qora62AUuvuMQ2seiEsAIXxcyXwKti5je+qKvd8cYP2p9AZDb1/IiXL57l9vf2mK9qV26bLt2aQGEK8a8TL2ZWimUH/H6iwNlnfWEtraOGC+oS3uoBkCWFhujy5mlO5Z+Mpp2pUcp8H9e62kDghn9g0d5Mxnl1r/f4M8LLsubH4GM/Zi/CwLXwffhaZRX+RCE+u+9Xs9eSx3SiWbTpk347ojEhFz/e6NNfYG1fwKDm6dg1e9slQmmzgQyl112Gf744w/cd9992LdvH9q1a4cPPvgAzZs3r+6i1ULC/0FvPBbKB6kXAi7fj3C1pcPUOqPnZdMSov8DJdwAPOZgxvgxowczHv8XmRbo+L8ktNYi/ctX/yEqlX/SpPLhL1xAmb4+zh/sqPcx0t8yA1i+AK33yhemXnQpfUEN4AtepAtwSQjp9tWX1kojXC5IrxdCG1cDIXzd4FwSwiUgPdq2wgsID4SUEB6vP1iBLwARwuWrTuGFlEILdPwRhtS/cF16oCMgPHr3QPiXeZVl0BqyhNKiIXyBj9FSo63TgxM1cNG39QcnWvCltNS4lW2EVnn6D35rgKMGMfo6IwjTX2br7w6bR8ZpAOsPe/u0XqmuMy/XqYGCmf910rva2QUnalns9mXdp11wZD1C9ce+9ce/8SPf8mNf305dZ7dvwP+6WVvSwmUXMKkBiVoOI+CwSRcjpOm5fjz6uBaXkl5vaRHwtb6oeetjXPwtLL6uYsKtBCRqsOL2dwvzNZYEdhOztrroB2sEKy5/lyR9mS8o0QIVPcgRLl+LjRaQCC3a0oMb/zJr4KIsUwMXvWtYMDLID2ZTq0A5LSDWIMn/xLzeaIXR87euV4Iau+3VMhv7koHr9ADIepIaQYwyRsaILZRAxdjOaC+1HLAakOifwfp3gnGGw/SnoBFvKd2xje8t9fi09OoXpBCWNFTdpJT4+3ld0LWhQGqs/7URQqBfIzcW7/WgoKAA2dnZ1VjKE1edCWQAYPz48Rg/fnx1F6P2UwMBndEC4f8gFhDaD+UwghnjQ96jfS67YXRdE1JpkZHQ/4USkL44RP83zFc4AB7fv4d6dzMvfMGMXm6v8uVnCmoE4Paag5uAY9d/fSuBjBYgBAYwShOAXgdSm4BC/edO+rqbCX17rfVECAHpFb5qK4ORh4T240lKeMvKlPw8WmuOrz6k1wsp9Lxc2q7UIMVXNhe8xne2MA5H2w6++lMDIaPuofyI1f/EFP6WGq8UWnDjD2y88L80ekuNHqiIIC015qDHv0+jVUZ5aezGvthRf9iqz9Ufr9Z1+tmrPjb9jLL57aC3AtitN45JOVb9ubG9dq8PMreWW0+vH7vabc+6D7UceiuG9ce+/kNfbeWw7k9ltx8pfZNJmFrS9PTaOW5qMbM5bnWf1hYUwN9qZA1g1MdqwGJNowYq/tdcWsbIaOtMXcV8mYTqPuZyWwbnmwIbc/Ai3ErrgnJvtLJoLSu+Qvu7jflbYdz+oMZtCX70lhi7oKW8VhiVMZbRukz4l+sfAKYgRc1Pyd9oqrTpDhWsDGo+dowgRflsNcoqw8jXSGy6My03/nCT/uP3F8C/L/2plt73+WvJ3yia9nkq9H2o2wrlu8WrLNPzsAQ/xn61vBjQnDA++OAD7C+WuCzHHbCuWaJAyySBwa1zsPFQeR2V66Y6FchQVVF/mCu/Towmd3WZSvi3VQMd/Zer+oFr/GPu9gcz+s9FCd8Xr9F1TM9bC5YCftYJGAGQdPnufZn4Ahq1b7O1lUYPeLxeLXCy3HvL/F/CLmjdyrR/RI0f+nrLkte/D2vAo325Cil9+1LLoM24JD0efwAUo00uYPS90gINl+9Lzhd0AFJ6fT+myjyQevDi9fp+JOqBivR9AeqBiQcuXzm0wNPr1apMey6F719l35+DvkDIpb2eavczoxuZHmAEaaVRu56p/96rrTe+Q/QHXPqPdOM0hP2PaDvl/VuvrrObhQoI/XNA/THsX6YHfOHlAS0wtGMKroQ/H7uBrlILdO3y0Lcxfly71R/iNt2b4P+hpbbyacX1jcPSzgl1fJZX+oLZUq95YgffeeA7TnU8lBqIqfsyym4JptTDtvtZq9a3Xd3rXcb05daB+i69HpSgwxWjd3EyByv+Vhf/Y7fbFTTIsQtchLBbpj92mQIYoxUmJsafTh/QrwQ/ppYY072/9cZYFiqIUXmV7rTSq71wXu0zUe9e5jJXthFEaH8Q2e5H+WFuBAL+88/0x4+ej07vSmz8UaYEItLjD5b058ZsZcoPfVOLi/IpYvz5pN6kPw+pf9dI+Lu1SSVP//PA65JbAxpf+YReh/5/7nzlNnUvE5Y8XPB1N9PLIPzLww7gqKqUlZXh6uGD0TPdhQS3/evRt5ELz//kwXfffYc2bdpEuYQnvhB/LRMRERERUVUY3iQeHgl0bhA8qEyPE+iQKnDx2W2jWLKagy0yVEHWvh/WVhnYLNPTSfM2BqWFxvgnS/m3zWihUbueKa0yQsAYF6Lc+zfVt5UAPMq/UVoTvZEfAJfyz56QlhYfl9I1TU+j3KvpjXE6AqaWGaOlBv7nwm1ukTF6BQitNcXfY1pvYfH94+n/p9ZoTRIuX54xvhYko5uZnncMAI9H65XnMrqP6S+LCy7o42nc2r6lVjbfwHht3Ar8y6VL+tN51X/rfctdUp31DMrUzso1bKC00JiWBf4zb5xa0P+99z0Jp/G9vLEQ+ixU1nEWepcil/JPpv7vvJXxL7uWudqi4Vvvn5UKlizUvv/WrO1aJKzjMAKfa2n18RTacmPchfJvvjHDlfqvv6kAvn+epdff2qdPI+7vCumFp9Tje728+lTg+sQSvhYaT5lv4g51hjy9Rc80nThgmiHPbuKDirIb7mHuohbYAmNcKVzvQmapawjrWBcY42CESx8jY77Oi6n+zSeCdmd+LYR+8qrjYVxubbnL391MGyMjjHR6NzNrS0yQLmame5f5nFDPC6N7k/55rHez8vrGDArp+0w1WjrcgSeyno9Q85Hm+tD3rX9YCWF8rBtdhfXPQAkY4yfh0lpglFYdqY+f1FpchMvXeqSPI/F6fVNH699FxjXM9LIqLTGA/3Pe6F8a42/1MX1H6l3AlG1NLTYw78fUcuPVOiwIf+u//r4zqtDrP1+MViS1FQbKvtQWHIq2o0ePYtXvXgzKcsFdTutYz3QXntrlwZo1a3DeeedFqYQ1AwMZCoM1aLFZF7I7mfILWfm1LO3Wl0f9IjK6oelfUvqXnxZMGPfaeBI9ojG6DKhBjzQHR3pAAvjS6F3FpBdG1zBACyL0L3itaxoAuDwwxr7o3/fGF6jygaVfH0c/LkvXMuO5cGnTNetjUwB1MgCjv5bLv52QLnjLynxplO5evi5pwkjnltK4nqc+BbSvC5fQHuuBi6/c0usPUIzDkuaxNfqMZ2o6r3LFzNA/WKXxg9Ua4AD2P2Ttzhzjq9v4USqN5XbX9dC7/7hcwuhW5dIiGWPMghI0GPtRfuia9q8GFcqPWrVLkZG/MeOUUH68BjaYS7Vri2lfvgDEGDOh/8jVugsJyw9YU5cj40evum//sfi7i/nv9XKYpgg3zl/twq5lpZBej2/KcO2aTFLrlim9XnjKvEZQY3Q7U4IddRY9o3sa/OefWiRTOa3DNaT5fDXXm/2PBz34BGCaDhnq62cEH7CZbcwayPjPLVeM2/SaGK8HYAoOjGDFv0Aph/Jaudz+19UY+xI4HsbYj1sJVtQuZcZz5V4PYsJhGk+ofZ56Pf7PRHXmLiOggRKsKF1t4YHxDpaAufOI8uPfiA18wZLQP9d9FQX/1Pv656KehV5Wj//eLrAK+iNfCwCkcjFkl8v/XQL41pm6efmDL9++tPrRu7wZSdXvQvX7zhexSen1zUAJ5XhM36f+c910euv7NLpV63mGOk6qSoObp6BBrG92svIkxQicm+bCFQN6Ys9Rj/8PFWLXMiIiIiKiaNm/fz+++NOLfo3cQf9QsereUOBwGfDGG29UcelqFrbIUAg2rS2m1dKcVqqPg7TG2E09aV0fqnXG9l8z+P/lEpYuBEL9V0+l90VQttdbdgDtX0kvjIth6v+yuWK0Jn2p9U/SBv9LfRKAMv8/mnp5A2Yr0/7lVLtf6P9gAjCuO6Nc8FIdSCqM524I7Z9F6fVqM5tJSLcL0P7RdrtjYFxwU7uYpojxXTRTb52RUsLlMppkIL1euJSWErfaJUz6j8P/B719C401jf+U8Kf3as0tUtmH2sXIml8w+mrT94H+rzj8/5LrXbz0lhBz1yDL9Lk2rSj6v9outU+S3qJi6RJk/cfdtqVE6Q5kPQDrl1tAi4Jy0OpFDvUpdn2r1O5Aejqtm5FL+cddKOekZR8CsF0OQGtlsbbYaNN8e8p856HXf7FWKBd1jfF4jOe+Vhy9y5pHmz7cfF4EttKY6yXY+WfXEmNHTWY+b/wL1BYX47llIgSjVc/IBEpLmAsiJtbUNUzfTwC71jmjFcbfgiO0Afrq1MpGKwxg/1pbW2QCWmOEOb1RJstzU3mV/0XVNAHT7AOmwf1qK4XRWmHNW23tUfJQB+vDCwkXhLVrmXG9FfUzWW+J0T/3JYzZSNS6N7qGBenm5i3TWmW8AGJgzKBpfO+orUf695tWOCEs7ysBf8uI0A/AX2alXqWWhxD+GSwD6ttoAdKy0e+NaZjhLw9bZaLuwtMao2WSQLPE8Os91iXQO8OFG0ZdjmHDhiE+Pr4KS1hzsEWGiIiIiCgKvv/+e3xTJNG3UeQ/wc9MEYgTwJCmiRFv+9xzz+GMM85ASkoKUlJS0L17d3z44YfGeiklZs6ciZycHNSrVw+9evXCtm3bTHkUFxdj4sSJyMjIQFJSEoYMGYK9e/dGXBYnMZAhIiIiIoqCYV1OR4dUgfS4yFvBXEKgX6YLa/7w4uDBgxFt26RJEzz00EPYuHEjNm7ciPPPPx9Dhw41gpXZs2djzpw5eOaZZ7BhwwZkZ2ejX79+OHz4sJHH5MmTsXTpUixZsgSff/45jhw5gsGDB8Pj8QTbbZUTMtx29zquqKgIqampKCwsREpKSvgbHvu96gpVpSzdw4zFlu5kpodqM3hgdxO125jtQP+Ae2U2Frt1ahm9Hv8+vR74BlJqA/O1rgTS6JKlDbLUZ6GR6mMlX28ZjFl39AGd6r1XW66uLyv2baPfe7T16uBXo4zwL7ftWuYxH5+01onXsl7b3qMfq6+bjtFlzOOvA6l369G6/hhdhLwe/zK9e46ah9ervLT+19/azceue0hgzyi77mb68sC8I2HXBcjUTUztSia063cY3cD8A+d9XXcs3YC0LmHqzuy6h5m69+jdftQ06iBvU+HNXXTsrwdjrhS9q5q6/4C81HVqFyO7dNaB28HWWdfbdZdUzkdjljPA3/VMOa/Vc9D33Kucd17LeWw+19RZ03xFsT9XgxbdJo3dtXOCXutFfU3V2cX0tNbJF/Rt7aZOMwpgOacs3Rd9SbTB/qZzNkjXQX07oxuZMsjfeu0Y04xmSh76jHauGO15HOCO86WLiTV3U4uJ98385YrRJhnQtjF1UbN2X3Obji/gMaz1r3dbUyetULvs2bwvjK5llu5wUsI/i5nwl01fbu1e5i3zZef1QMTE+/NzxWjHqta/PouZzfV5jFng9LIrfcGM71O9q6FyTvkLj4DvXED7nBLm/ejPhdKF0JRnze9iVlR0GKmNW0b+ey0K1qxZg369e2JSrhtJMRWrayklFu31Iise+OLPcObpDC4tLQ2PPPIIrrnmGuTk5GDy5Mm4/fbbAfhaX7KysvDwww/j+uuvR2FhIRo1aoRXXnkFl112GQDg119/RdOmTfHBBx9gwIABlSpLRbFFhoiIiIioCkkpccWAnjg3zVXhIAbwBej9Grmw4ZDEjh07UFRUZLoVFxeXm4fH48GSJUtw9OhRdO/eHbt27UJBQQH69+9vpImPj0fPnj3xxRdfAAA2bdqE0tJSU5qcnBy0a9fOSFMdONi/TjJG9wVfb22NCdkSEySdXYtO2EWMYBvTv8eV+HdCCHNRjX8wJYypndV7l7Y//c95b5nv30fj3jK1sldq15GRlskAgrSyWAf6W1turI+VZUJ7LvSWGu2fblPLi9JqEzCNrs2/4sY6y3ULfEW1/CtvW78h/jcp53ULGNCq5Bfwj7by76N6PRbTwGm1FUUog+XV7Vyh/mnX/3m1maLW9O9nZMdp2k7PR2390ZOUt025+7D5p96unKYWVRuh3qfKtkI5V0Wwc13fRj9H1RYc6T8XpccbsL116mc9L2lzrtqVXdqVCbC0Crhg1yJitMgoy2xbWYK1rti9ZnoZLOdSwHvApbQgumNgml5Z387u33/TdMtqC02QFhljO8u5Heo8MtJY7oMJNtA/5DZqnpbvNekf/O+bsl7YtLp5jLTC5TbWC3i07LQWDa83sHx6Kz+gtRSWQhhTTQv4rpUDaBcU89Wd6WtUeY31a70Yr69Q0mjnttCeC7e2DP4yGeuEkp+1XoQ/jdHypLXQ8HoyUVFSUoJf/wIuP6nydZ2dIJAZD/zzn//Ev//9b9O6e+65BzNnzrTdbsuWLejevTv++usv1K9fH0uXLsXpp59uBCJZWVmm9FlZWfj5558BAAUFBYiLi0PDhg0D0hQUFFT6mCqKgQwRERERURRo4XGluQBcfPHFmDdvnml5qNnMTjvtNGzevBmHDh3Cm2++idGjRyM/P99YbzdbZnnTQ4eTpiqxaxkRERERUQ0TGxtrzEKm30IFMnFxcTjllFPQuXNn5OXl4cwzz8STTz6J7OxsAAhoWTlw4IDRSpOdnY2SkpKASQbUNNWBgQwRERERURToPQAre3OClBLFxcXIzc1FdnY2Vq5caawrKSlBfn4+zjnnHABAp06dEBsba0qzb98+bN261UhTHdi1rFYIt3+r3TgX63Y2acodHxNqPxURML2V5bm3/H0E9IV2wegPDVhmZxHwXWRNvXCbtgv9wpj6Rdhc8D+W+uxMdmMMtHE1en7eMsBlGdPitRvjEsFzu3ELocbXaLOhCW0MjH7vn8VNajO9wT9LFNTxB8rsUN7A+g9nAsSgzc/KmAIR0Nc+cFyCf3yBy7zM5TKntY4NCLXcur9Q661jUoKNM7HO6AWUPw4gWHnVPAK2sRmjE1Aey9gPu/wjLbddedSyqjOY2aU39dn3r/ePo5EQ4Y4RU8fWaHmZngc5BmnMFqiMAQvG5nU2ndPBZiCzeb/YnfMyyL5FiNdXnx1PxMT4z2t3rD+tMs7LOIZgM5O5LDNqGbNpaeVV8zFmM9PH27jMz00X2Qwyjg1q2VzKR7Ld+KEQ/7saYz00xnnhGyfjOxf0dfpYReX8BGCaPc8Y6wT/d4NlyKc6syPUWTb1Mnv1CzPDN3ub6YKgymvjL7Q/czWN9fzVx8NYx76YLkqt1puWRqj38JcN0lLPHC9T1VxwphUh0lfpzjvvxKBBg9C0aVMcPnwYS5YswerVq7F8+XIIITB58mTMmjULrVq1QqtWrTBr1iwkJiZi5MiRAIDU1FSMHTsWU6dORXp6OtLS0jBt2jS0b98effv2deCIKoaBDBERERFRLbZ//36MGjUK+/btQ2pqKs444wwsX74c/fr1AwDcdtttOH78OMaPH4+DBw+ia9euWLFiBZKTk408Hn/8ccTExGDEiBE4fvw4+vTpgwULFsDtDpyUJloYyBARERERRYFjXcMizMM6KUBAdkJg5syZQWc8A4CEhAQ8/fTTePrppyPbeRViIFPjqe3dobqJqYvVpmR9u9DdL8Iqg+12FelmZilPhablDLKNcAV2sZEuQEjtXpsqU3r9UyULl2+aEa9LuwCaMg2zK8bfjUG4gDLYd60xutgoU8dau5rp7LqhWIUzjW9A1xvlopnWC2yq0zQD/u48gHm6XGv+FWXXJStYl65QXa3CfRxsX5GwPZ9s8imvW2CwvKx5ljddrXrehVMufb+WKYVt05dX5kin2A53WvRgXdpCTjnuf+7vhuZLJ4Kksy1fkKnF9e6WAPxdF73hHU/ABWKt9WCpL6F2c4Kl21qQ19noUmm6qKXSZcvufWDtQhaqa5l6QVghLBe11G7uGH8+gHZBSC1Pt5I39DRq106tW5mpjErXM+VYTVMWW7uTqduZXkPldTfWey330p9OmdpYej3ad4P/dTOmadanTIb/sZQeCOhT7OvH4vZ9vrqUujMdg3q8du9bYV5sdK0W/q5mxrbqd5vy+oRimgba2s2MqoLasZ0qj4EMEREREVE0ONUiQwA4axkREREREdVAbJEhIiIiIoqC6pq1rLZiIFNjlTc+wa4fuHUaRxGYVp1OMuJ9OijUuBhlykzfO1mZHlOoj/XxL9qUrhC+vs16n2yjb7DW31gIXzdpo1q0aZhdMdr0yV4AypgYdRpmAJDC947SpjkOPUZGmp/r6QHfeJyI6spl/9iab9Bpay1jA4JNv2s8j+A8sGs/txuXEWpaUqOvuQiezq4OyhtfoArnGNV6DHeMkmk7h98/XqUffkXZ1VGw8tqN3VLrwe41C7VtuEKNNbJbbh3rYjfOJ1ge6jgyu7TW6aSt21vKLOzeU8HGyVjH+lgFO4f190FMnH8KZH2sSsAUvzCPg7Gbftk6JkZ9j5mmVHb7Phtj433TPevP9emf9XV2Y2OM6ZZDTLNs+9mhHk+I/jmmKb3VsTHBvlPU9Za00gPju0+4bKboVupKauMmXS7fvZFEHVMkAFesso1lnKs+TbIxVse/e/N4GPWxMh2z9NqMubHWk2WMbEA9cgrmquTYYH8CwECGiIiIiCgqONjfWRwjQ0RERERENQ5bZIiIiIiIooBdy5zFQIaIiIiIKAqc6lrGWMiHgUyNFGzgrGVkYNDBxcrFtACbgfWVuTimleUiXdoy3y71gYaWogv4B13qj4WwpNEH/OsXC7MM+JfSGORvDPiXLiMb3wXL3MpFzfSLmOn7div14vUP3BRlvkH9xiB+5SJiUvrWq4M4PWX+wf6AdkFNWAbclzPQvLy/bkIN9g91YUTTc2sZQgxmL2/AbLCyWZ+HGowf7JiCThBgHdBseR7qAoZ2x2q7TB247TW/rsG2UbcrL124Kru9HWv9lXfxzvIuiBnqnDW9XyL4KrarR3XiD7VcdhMWBDsmr/q6ljPxhV1ewfYTzkB/6/bhpAUC3xPGYH9l8L5dWj29vt5tHewf439N9McBA9W1wezuWN/zGG2wv769SwDuOO15bIjB/UHOFVN5QwzmD3XOBtvGbj+m97H19VYfK9urkwIYA+Zd2sVJY3zHrU9yoF+4U61HbZ0Qwsgm8CKoAtKYZECav68DD87m+1yZAMDYztoUoH7HWvOxHDM5xiUqP08L+XGMDBERERER1ThskSEiIiIiigLOWuYsBjJERERERFHg2GB/RkMA2LWMiIiIiIhqILbIEBERERFFAWctcxYDmRrJZmYyocygEjDLTjkzj4Wamcw0o5ndDGSRUGZIsV2u7sK6zG7mMrfvgfQGzlzmcvtmF4PHt50XxoxkAm5IFwCvR5mtRpvlTAqbx8I385n0+mbn0Wcngxvw2Mxe5ooBPJZZmTweXzp9tiC7WcvKmwkq1PJgs5UF27a8vMNhnRlM3W/QcpUzq1q56YPVRZizswHlz24UdGYpZTaqcGc6C6c8dqyzrLlcoWdeC1c4M2FZ0wSr80hmT3NqpjW7+tNf+6AzlJUzI591NrpQeVmX2c2cZs3Tmp8+S5o6bZF15rRg+wX8s4dZxcYps48ps5a5LF/z6kxkejo9jUvJW5+1zB2nPXf77/UZygAgRpmhy5SfS/uMsMxOZpxPIWYkU4/VWsf67GCmDiVeQLiNmb+MZWo+wWjrhBCQxneI+p2nlEudlU9NI4QyY1mMVr9ufz3r+3G5AeGGMNbpP2llYG1IqVWFVGYVk8b+Ame3lNrxS3+x9e2CzVgWQJnlTJ3JjBwlhOSsZQ5iIENEREREFAUc7O8sjpEhIiIiIqIahy0yRERERERRwDEyzmIgQ0REREQUBY5Nv0wAGMgQEREREUUFx8g4i4FMtbGbdcx6akcwQ1i4syFVCXV2L2n/3JitTFhWqzO/QJk0RZ+tRllmmrlMXaE/VmYPk16Y6tMF04xkwuubiAyANnuZDDJjGQDp0R67fY8FtJlopH9GGq/bN/OQ1+2fnUx6fXlLL+D2BpmpTDv+8makisYMYaGWm2ZIC5GPcIVOE6qMwaZxqejMasFmffJG+F6xmwHLdiarSuQbalm0BCt/qL8Owznm8maBi0R5s66ps36F2kafCcu6Ptz3Y3llsUsTMMOkNa00z5wGaJ9NNunV2bD09DHxvmVud+BMWep2QODsZsZy5SeBS/hnJ1O/n4zZtpT3unCb/2bWZ+WyfqfZzKYogp5fev56GYV/ecA2bmWWLwDwajOQhdqHNkOZsi8hvP6ZywDTzJPC5Ta2kcZ3kL4DfZYy7eaK0e61OtDrzBXje27MbKbkoc5yqS4L93eAlBCm3xVSKZc+a1l4WVkyhuXLmeiEwkCGiIiIiCgKXAj+vx1FjoEMEREREVEUcLC/szj9MhERERER1ThskSEiIiIiigLHZi1jkwwABjJERERERFHBWcucxUCGiIiIiCgKhHBmsD+DIR8GMtVCBk6raZ3O0VhuMyWjvjzcfQVdLpT1QlkeirKNurmxOtgUzJZ1ympAn4bZZgpmSJubWhYoy9Upk7VpTKW+A3VqVi8gJIR0afXrhm9KTamkUadKdvmPQZ+zWUrfzaWld8XAeF0BwFumTcdcpqVXpoUNNgVzKJWdatmaprwplgHzJ22wbQOmWw62jTJ1annlDLYu4umNy0sfwfS/al620+6GMw1xiDShpii2e63U9BWZnroy0zyr+4t0CmKVV/rOMX067HC+2cOZOttuimy7aZBDbadP5axyup7t6lGfrt362WA9D4QInH7ZHeef4lef/l2fPlmvZ3VKZcB/r06bHFBOZaplY9/qa6VP46xOv6xPZ2x9TUNM417ecmNbmynepfa5D32ae1fwbEwrLMcrXcoUzFp+wqUdR/DPT+M4hdCmtnb7XyNjqmzf//DCNKW1moldWYHA77xQnyM26Y3XS/1sFqE/j+wzJjohMZAhIiIiIooCdi1zFgMZIiIiIqIocGywPwFgIENEREREFBVskXEWryNDREREREQ1DltkiIiIiIiigF3LnMVAhoiIiIgoClxwpjsUYyEfBjJERERERFHAFhlnMZCJNv26IoB5HveA+eL15XbXYQlHOOlkkMchyuBb4E9vPFSu6WJ7LRl1U8s1Y4xd6Mv1FZZrxwjt2i1Cv7aCdTn8y4V+7Rf12i1aOv2aLlK75ov0AFJqL4F2nRn9OjEAAC+kfn0Ya57GdR485nuv17Jcv56Eej2S8q5NgiDXYjEWhE5vu00513EIN731OhIB6+3+b3Lqk7sS1zQI9/opQd9nEVz7J6x0Icpj+nwQgctqkopcKylSwT4fTcu8geus781g78uAayY5Rfu8k9p1p6TXd80X41pYNueIcW0S+NPExmvXJ3H560II/zVNAAAu868o/Toy6nvY7ho66n6NMusPBYRLvx6LpY7sPieU7Yx79TuuXHbXwNLfl/p3Tqj3uXKNmIAyeX2XjtGuSSOhXEPGeA9aD8PmGjPqtWOM+he+tPrNZXPNHlWo94z1XDde7yBpbb8r+EuaagcGMkREREREUcBZy5zFQIaIiIiIKApcwnerLDaq+XD6ZSIiIiIiqnHYIkNEREREFAXsWuYsBjJERERERFEgHOpaRj4MZIiIiIiIooDXkXEWx8gQEREREVGNU62BzJo1a3DRRRchJycHQgi8/fbbpvVSSsycORM5OTmoV68eevXqhW3btpnSFBcXY+LEicjIyEBSUhKGDBmCvXv3mtIcPHgQo0aNQmpqKlJTUzFq1CgcOnSoio+OiIiIiMhPvZxQZW7kU61dy44ePYozzzwTV199NS655JKA9bNnz8acOXOwYMECnHrqqXjggQfQr18/7NixA8nJyQCAyZMn491338WSJUuQnp6OqVOnYvDgwdi0aRPcbt8Fp0aOHIm9e/di+fLlAIDrrrsOo0aNwrvvvhu9gzWoV4a0rpJhXqTwRGB3UUzlie1FMZULYULbxnQdTV9a+wtjIvCxcXFN+C+KqT8GzBcMM13IUntsvUimaRvzRS99xVQvgGm5yJ5puVc7BgTP1yiXzQX6dNbXPeAik0HOC9tzKPA/CxH0vCrvopqhllf0v5EwL1QJOHNRyIjyCCybtL3wopZOvVBhRPuO8Lis+4uG8i4oai1LuBcgLS+f8DbSd2qzTGW6Oq9lVQTvRyfpF+JVL6SrXrTXWjbhMpdHSu1imNoFGNULTKoXbDQuQulW1vvr2n9xR/++pJSWzwqbzwc1P/XeLr11vSOvtdtXP8Z3gtdcBzrTd6zNfqVQFrsg9NfDpb+npW+5ccFNu4sBu/31qt2M+tOXudywPzfVQ4xw/Qn5O4HssGuZs6o1kBk0aBAGDRpku05KiSeeeAIzZszA8OHDAQALFy5EVlYWXn31VVx//fUoLCzEvHnz8Morr6Bv374AgEWLFqFp06b4+OOPMWDAAHz33XdYvnw51q1bh65duwIAXnzxRXTv3h07duzAaaedZrv/4uJiFBcXG8+LioqcPHQiIiIiqmPYouKsE3aMzK5du1BQUID+/fsby+Lj49GzZ0988cUXAIBNmzahtLTUlCYnJwft2rUz0qxduxapqalGEAMA3bp1Q2pqqpHGTl5entEVLTU1FU2bNnX6EImIiIiIqIJO2ECmoKAAAJCVlWVanpWVZawrKChAXFwcGjZsGDJNZmZmQP6ZmZlGGjvTp09HYWGhcduzZ0+ljoeIiIiI6jaXkI7c2LfM54Sfftnajz+wv24gaxq79OXlEx8fj/j4+AhLS0RERERkj2NknHXCtshkZ2cDQECryYEDB4xWmuzsbJSUlODgwYMh0+zfvz8g/99++y2gtYeIiIiIiGqGEzaQyc3NRXZ2NlauXGksKykpQX5+Ps455xwAQKdOnRAbG2tKs2/fPmzdutVI0717dxQWFmL9+vVGmi+//BKFhYVGGiIiIiKiqsbpl51VrV3Ljhw5gv/+97/G8127dmHz5s1IS0tDs2bNMHnyZMyaNQutWrVCq1atMGvWLCQmJmLkyJEAgNTUVIwdOxZTp05Feno60tLSMG3aNLRv396YxaxNmzYYOHAgxo0bh7lz5wLwTb88ePDgoDOWERERERE5TZkQnRxQrYHMxo0b0bt3b+P5lClTAACjR4/GggULcNttt+H48eMYP348Dh48iK5du2LFihXGNWQA4PHHH0dMTAxGjBiB48ePo0+fPliwYIFxDRkAWLx4MSZNmmTMbjZkyBA888wzUTpKIiIiIiJfIONEiwobZXyqNSjs1asXpJQBtwULFgDwDdKfOXMm9u3bh7/++gv5+flo166dKY+EhAQ8/fTT+OOPP3Ds2DG8++67AVMlp6WlYdGiRSgqKkJRUREWLVqEBg0aROkoiYiIiIiqT15eHrp06YLk5GRkZmZi2LBh2LFjhynN/v37MWbMGOTk5CAxMREDBw7Ezp07TWl69eoFIYTpdvnll0fzUExO+FnLarzEDODY7757ADiyzz6d1K9UryzTQ/aIr2RekSufq7G9zfZC2JRDKbCp7NoTo/zwP5fSvF69yLaRzLde6mmFstLYQN2v/li7l1o66zqpbANlHaT5sVENXptldusRsL3Q1wXUWQRXew/5l005/0GE83dPNK8IH0qkV3+P+P0QTAWvOi+l/T9h6pXEQ5YxxH4dO7YosZbXehX1qlDZvzJNV3d3MK1KPQes54NdflL/DPL6Pve8HgD6fZD81fev9BpXlBcuty8P6dWuKu8O3K/QOrfYXh3eBfUcFVIGlj9gO30fepmEP21A2S2fO5HUr1qn6nPA/zmif/5HRPtOkl5AquVR3qtCANBeD+myqU8o9aDVkcutfZ9pr5cQgCsG/C+9bnNq1rJI5efn46abbkKXLl1QVlaGGTNmoH///ti+fTuSkpIgpcSwYcMQGxuLd955BykpKZgzZw769u1rpNGNGzcO9913n/G8Xr161XBEPgxkiIiIiIiiwCV8t2hbvny56fn8+fORmZmJTZs24bzzzsPOnTuxbt06bN26FW3btgUAPPvss8jMzMRrr72Ga6+91tg2MTHRmF24up0gf8kSEREREdVuTs1aJgCUlpYawyb0W3FxcVjlKCwsBOAbfgHA2C4hIcFI43a7ERcXh88//9y07eLFi5GRkYG2bdti2rRpOHz4sAM1UzEMZIiIiIiIapilS5ciNTXVdMvLyyt3OyklpkyZgh49ehhjz1u3bo3mzZtj+vTpOHjwIEpKSvDQQw+hoKAA+/b5h0VceeWVeO2117B69WrcddddePPNNzF8+PAqO8bysGsZEREREVEUODlG5uKLL8a8efNMy+Lj48vdbsKECfj2229NLS2xsbF48803MXbsWKSlpcHtdqNv374YNGiQadtx48YZj9u1a4dWrVqhc+fO+Oqrr9CxY8dKHlHkGMgQEREREUWBYxe0FL7gIyUlJaLNJk6ciGXLlmHNmjVo0qSJaV2nTp2wefNmFBYWoqSkBI0aNULXrl3RuXPnoPl17NgRsbGx2LlzZ7UEMuxaRkREREQUBfrcgJW9RRoLSSkxYcIEvPXWW/j000+Rm5sbNG1qaioaNWqEnTt3YuPGjRg6dGjQtNu2bUNpaSkaN24cYYmcwRYZIiIiIqJa7KabbsKrr76Kd955B8nJySgoKADgC1r06ZPfeOMNNGrUCM2aNcOWLVtw8803Y9iwYcYF5X/88UcsXrwYF1xwATIyMrB9+3ZMnToVHTp0wLnnnlstx8VAhoiIiIgoCqpr+uXnnnsOgO+Clqr58+djzJgxAIB9+/ZhypQp2L9/Pxo3boyrrroKd911l5E2Li4On3zyCZ588kkcOXIETZs2xYUXXoh77rkHbrc7WodiwkCGiIiIiCgKtEukRp0M4yLFkyZNwqRJk4Kub9q0KfLz850sVqUxkImGxAzlSQWuYlxljEvc2ywPcqX5gDIpaU2bqcuFf7l1lyGe+66I7FvhewOqI+TUcsgQ9+oVqbXHxtWhlefWl0VK/1WarXURUAc2V5e2WxeK3Wsd0WjAE+FK0RU8Xyt7ngfUeUXzkUHO8ZAb2SwL8v4JmU2IY6jo1eWjJdhV66v88ytSXviHhTp0zqjCfp2CDU31Al6v753s8gBSQro8til9n43K1eWlBFwuY5nQlwHacst+rVeity5XScvnqP5Zo6Z1uRHwGSSCHafN9pFQ36dGFtbPYKl8zoc4J63rpBeQbiU/jyWNtl/ra20qk/YzVWh1oteDy62t59BkIicxkCEiIiIiigKnupadwH9vRRUDGSIiIiKiKKiurmW1FQMZIiIiIqIoYIuMs9hZk4iIiIiIahy2yBARERERRYFLSGemX2aTDAAGMkREREREUcExMs5iIENEREREFAWCY2QcxTEyRERERERU47BFhoiIiIgoCti1zFkMZIiIiIiIosCp6ZfJh4EMEREREVEUuODMuA7GQj4MZGoNCf9pLbTn4bJ7O0SSh5LWtJmer/SNbpPSn0AIf7EtSe2eC+HPWEr1WKHlJQM3gtR2J83LrctMz/U8Lc/V9cE+PaQEhFtdECRhLWStv0gE/TRWT4ZK5C+9Fd82eKZVkGewXSnvF6fyU+nvzXD2IY03ZGBexnu8ilWkLuzKZT0vrMcWbDvfihA7i6B81n25pK9cMgaAF8JbFmQ7t6UetJ9FLjeM94t+fPpnkppeuAKX6WXX10mvlo+al0vZRijLXOY81ONz+vy17D6A6TUL83yU2veAUW/adq4Y/zFILyCVupEeAK7AetXrSAgI4fIVVGidiYSWJxE5hu8oIiIiIqIoEMLZ2L6uYyBDRERERBQFHCPjLE6/TERERERENU5YLTJFRUURZ5ySkhLxNkREREREtZVT0y+zUccnrECmQYMG2mDr8Agh8MMPP6Bly5YVLhgRERERUW0iICL6TR08ozo0oVAIYY+R+fe//420tLRy00kpccEFF1SqUEREREREtQ4H+zsqrECmefPmOO+885Cenh5Wpi1btkRsbGylCkZERERERBRMWIHMrl27Isp069atFSoMEREREVGtxfmXHeXIrGWHDh1yIhsiIiIiolpLj2MqeyOfiAOZhx9+GK+//rrxfMSIEUhPT8dJJ52Eb775xtHCERERERHVFkIIh27VfSQnhogDmblz56Jp06YAgJUrV2LlypX48MMPMWjQINx6662OF5CIiIiIiMgq7FnLdPv27TMCmffeew8jRoxA//790aJFC3Tt2tXxAtYpUioTgzsVauv5WKfpCyd/Ebid/heAtMtPBtmltk4IbTslgZFfkOIKYV4ppTZtoX//0pSnuo22P2FXB9J8bySxSxNsvSWd9e8R27S1VGVO1/LqyThvKki47XZa8fwCsgry+lvXhxLqGEWIvCsiVHlN75cQ2+qsSavrH0Kn6ifU+9u0yBs6H+EKnk64fMuF5X9E6fXtS0rfYynh+6/Rq9zD99jlhlHZ6rELl5avBLwe7bm+Xtjs21JvAWmlb4pX6VH2p2wrhHJvPU6b8tnt0y5NsHNUfX3094z1vSOAct/fwc5jKW1eF70OtNdHr1ep1IPL7X+thUv7zFHqy3gd+Bc6+VtkHMjJgTxqvohbZBo2bIg9e/YAAJYvX46+ffsC8P2Y9Hg8zpaOiIiIiKi2cDl0IwAVaJEZPnw4Ro4ciVatWuGPP/7AoEGDAACbN2/GKaec4ngBiYiIiIiIrCIOZB5//HHk5uZi9+7dmD17NurXrw/A1+Vs/PjxjheQiIiIiKg2cK5rGQERBjKlpaW47rrrcNddd6Fly5amdZMnT3ayXEREREREtYpj0yczFgIQYS+72NhYLF26tKrKQkRERERUazk1/TL5RDxc6OKLL8bbb79dBUUhIiIiIiIKT8RjZE455RTcf//9+OKLL9CpUyckJSWZ1k+aNMmxwhERERER1RqcidtREQcyL730Eho0aIBNmzZh06ZNpnVCCAYyREREREQ2nOoaxljIJ+JAZteuXVVRDiIiIiKiWs2xaxwzkgFQiUvqlJSUYMeOHSgrK3OyPEREREREROWKOJA5duwYxo4di8TERLRt2xa7d+8G4Bsb89BDDzleQCIiIiKi2oCzljkr4kBm+vTp+Oabb7B69WokJCQYy/v27YvXX3/d0cIREREREdUa+oVkKntj3zIAFRgj8/bbb+P1119Ht27dTBHh6aefjh9//NHRwlFFSNhPieHwCS8EIKXNPmSQp9oT/ZyRejlhWW5TXHU3Qnmg7d93HvoTSTVvoewDyr6NbZQdSaUctjvXD8N63DbpAo7BbpsTVZTLGs4/SxX59ylknTv4figvq3B3Fa3vpFD7cepYaqqqOD7reWj97DR9Jkrl3gu43IDXE5iny628J/TPVrf5uUv/DHT5lgntf0vhhvHZJ0J9/gktKy/Mn6eWz1ZTOSzHZD5wm2U2adVyhPo8FkLJMpLPLGlfFKkvl5bXR1kPCYgYGK+PXqfSq5TZBbjcEC43jHoXLl8+rtgIykm1lWMXxCQAFWiR+e2335CZmRmw/OjRo2zqIiIiIiKiqIg4kOnSpQvef/9947kevLz44ovo3r27cyUjIiIiIqpFnBojw6YDn4i7luXl5WHgwIHYvn07ysrK8OSTT2Lbtm1Yu3Yt8vPzq6KMREREREQ1n1NdyxjJAKhAi8w555yD//znPzh27BhOPvlkrFixAllZWVi7di06depUFWUkIiIiIiIyibhFBgDat2+PhQsXOl0WIiIiIqLai6P9HRVxi4zb7caBAwcClv/xxx9wu902WxARERERkWOzLxOACrTIyCBTmhYXFyMuLq7SBSIiIiIiqo18gQgjEaeEHcg89dRTAHyV/9JLL6F+/frGOo/HgzVr1qB169bOl5CIiIiIiMgi7EDm8ccfB+BrkXn++edN3cji4uLQokULPP/8886XkIiIiIioFtCnT3Ygo8rnUQuEHcjs2rULANC7d2+89dZbaNiwYZUVioiIiIiotnFqjAvDGJ+IB/uvWrUKDRs2RElJCXbs2IGysrKqKBcRERERUe3C0f6OijiQOX78OMaOHYvExES0bdsWu3fvBgBMmjQJDz30kOMFJCIiIiIisoo4kLnjjjvwzTffYPXq1UhISDCW9+3bF6+//npEeeXl5aFLly5ITk5GZmYmhg0bhh07dpjSSCkxc+ZM5OTkoF69eujVqxe2bdtmSlNcXIyJEyciIyMDSUlJGDJkCPbu3WtKc/DgQYwaNQqpqalITU3FqFGjcOjQocgOnoiIiIioggTYIOOkiKdffvvtt/H666+jW7dupsFKp59+On788ceI8srPz8dNN92ELl26oKysDDNmzED//v2xfft2JCUlAQBmz56NOXPmYMGCBTj11FPxwAMPoF+/ftixYweSk5MBAJMnT8a7776LJUuWID09HVOnTsXgwYOxadMmY1KCkSNHYu/evVi+fDkA4LrrrsOoUaPw7rvvRloFUSIRfg9IaXPv1FkulHytqwQQMB23Jb1eDGl5op87UgZZbslSfaLvU6j78i8XynL/dOFKJsZ6ZZmUNp8MNvVom85aYOuqYNtEwonXNEQZYanL2iCSwwgyrXzVOcHruiLna9h1WJG6VssTbPtgn1XVXMd2uxdAwDkgpPZZoS2WEpAey2cltF8x7sCMhVCWKZ8X+nLTa6qug+WxTYEFAj/HhMt8H1CWYMp5PUJtG2ydKO/8COPzTT9GwPdaGNkpy9TXR2Wc+xJwaa+NcPleJ+HSXrOI/zemWsqxwf4EoAKBzG+//YbMzMyA5UePHo34hdGDCt38+fORmZmJTZs24bzzzoOUEk888QRmzJiB4cOHAwAWLlyIrKwsvPrqq7j++utRWFiIefPm4ZVXXkHfvn0BAIsWLULTpk3x8ccfY8CAAfjuu++wfPlyrFu3Dl27dgUAvPjii+jevTt27NiB0047LdJqICIiIiKKiGMtKoyFAFSga1mXLl3w/vvvG8/14EUPDCqjsLAQAJCWlgbAN1NaQUEB+vfvb6SJj49Hz5498cUXXwAANm3ahNLSUlOanJwctGvXzkizdu1apKamGkEMAHTr1g2pqalGGqvi4mIUFRWZbkREREREdGKIOJDJy8vDjBkzcOONN6KsrAxPPvkk+vXrhwULFuDBBx+scEGklJgyZQp69OiBdu3aAQAKCgoAAFlZWaa0WVlZxrqCggLExcUFTAdtTWPXipSZmWmksTtOfTxNamoqmjZtWuFjIyIiIiKqrlnLwhmXvn//fowZMwY5OTlITEzEwIEDsXPnTlOacMalR1PEgcw555yD//znPzh27BhOPvlkrFixAllZWVi7di06depU4YJMmDAB3377LV577bWAddYua1LKcruxWdPYpQ+Vz/Tp01FYWGjc9uzZE85hEBERERHZ0sfIVPoW4X71cenr1q3DypUrUVZWhv79++Po0aMAfL+Jhw0bhv/9739455138PXXX6N58+bo27evkQbwjUtfunQplixZgs8//xxHjhzB4MGD4fF4HKyl8EU8RgYA2rdvj4ULFzpWiIkTJ2LZsmVYs2YNmjRpYizPzs4G4GtRady4sbH8wIEDRitNdnY2SkpKcPDgQVOrzIEDB3DOOecYafbv3x+w399++y2gtUcXHx+P+Pj4yh8cEREREVE1Km9c+s6dO7Fu3Tps3boVbdu2BQA8++yzyMzMxGuvvYZrr702rHHp0VbhaTQOHDiArVu34ttvvzXdIiGlxIQJE/DWW2/h008/RW5urml9bm4usrOzsXLlSmNZSUkJ8vPzjSClU6dOiI2NNaXZt28ftm7daqTp3r07CgsLsX79eiPNl19+icLCQiMNEREREVFVcrJnWWlpacB47uLi4rDKYR2Xrm+nXlrF7XYjLi4On3/+OYDwxqVHW8QtMps2bcLo0aPx3XffKVPb+gghImpauummm/Dqq6/inXfeQXJysjFeJTU1FfXq1YMQApMnT8asWbPQqlUrtGrVCrNmzUJiYiJGjhxppB07diymTp2K9PR0pKWlYdq0aWjfvr0RLbZp0wYDBw7EuHHjMHfuXAC+6ZcHDx7MGcuIiIiIKCocm35ZCCxduhRXXnmlafE999yDmTNnhtzUblx669at0bx5c0yfPh1z585FUlIS5syZg4KCAuzbtw9AeOPSoy3iQObqq6/Gqaeeinnz5iErK6tSL8Zzzz0HAOjVq5dp+fz58zFmzBgAwG233Ybjx49j/PjxOHjwILp27YoVK1YY15ABgMcffxwxMTEYMWIEjh8/jj59+mDBggXGNWQAYPHixZg0aZIRRQ4ZMgTPPPNMhctORERERBQR9ZJPlXTxxRdj3rx5pmXhDIvQx6XrLS0AEBsbizfffBNjx45FWloa3G43+vbti0GDBpWbXzhj16tKxIHMrl278NZbb+GUU06p9M6tLTp2hBCYOXNmyOgyISEBTz/9NJ5++umgadLS0rBo0aKKFJOIiIiI6IQSGxuLlJSUiLYJNi4d8A3X2Lx5MwoLC1FSUoJGjRqha9eu6Ny5M4DwxqVHW8RjZPr06YNvvvmmKspCRERERFRrCZfLkVukyhuXrkpNTUWjRo2wc+dObNy4EUOHDgUQ3rj0aIu4Reall17C6NGjsXXrVrRr1w6xsbGm9UOGDHGscEREREREtUYFrwNTWeWNSweAN954A40aNUKzZs2wZcsW3HzzzRg2bJgxLCOccenRFnEg88UXX+Dzzz/Hhx9+GLAu0sH+RERERER1hlOBTIR5hDMufd++fZgyZQr279+Pxo0b46qrrsJdd91lSh/OuPRoEjKcgSqKFi1aYPDgwbjrrruCXoOlNioqKkJqaioKCwsj7o9ocqScWR1MJ2a4J6ndS+hktB/iFLE9fWyWySBPTNuHsTzCbYKf3qHyDJImaLogacvdJhISlX9NQ5VDlLO+lnPkNYpoh9p99QyOLFdFvmTDrsOK1LVanmDbBzuHT9A6DjgHpLkOpQSkx3fTnwPajyA3Ao5LqCOIpTk9gvxwsn2dLcuk179/Nb1wme/LzTdI/o4Ldg6Ecd4Z9W/3vSKDn+Nqmth6gHBDuGN9r5Nwaa+Zy76uyHFFRYeR2rhl5X+vOay4uBgJCQn437WNkFGv8udCnzf+xK1PLcall17qQOlqrohbZP744w/ccsstdSqIiZpqmvGhfCG+BISw+XBXvkxts1DW68csLT/UQ/1uCbbOZrlvFg1zBtK6L1/C0BmafhSUt3OY6yTo6xrJDzonzo3y8jhRz78oEKiGYOYEVJnPIOMtVJF6jCSwKy8gD1c09xUOqdz0XcQAUvvBI5V1xg9i9XNLDVYEAK85rTXQUbc3bWfhciv7V9IKV/mvdVjnU3lBRqR/soggn7/qd02wTS31ExC8qcGMZR9SasGKG0K7h3ABLpdv3wxiyCB85wg5IuKaHD58OFatWlUVZSEiIiIiqr2cvCImRd4ic+qpp2L69On4/PPP0b59+4DB/pMmTXKscERERERERHYqNGtZ/fr1kZ+fj/z8fNM6IQQDGSIiIiIiO2xRcVSFLohJRERERESREUJo43crnw9VIJAhIiIiIqIK4Ax2jgqrJqdMmYKjR4+Gnen06dPx559/VrhQREREREREoYQVyDz55JM4duxY2Jn+v//3/3Do0KGKlomIiIiIqNYRLuHIjXzC6lompcSpp54adn+8SFpviIiIiIjqBMcG+zOYAcIMZObPnx9xxrxgJhERERGRwqkxMoxjAIQZyIwePbqqy0FERERERBQ2zlpGRERERBQFTk2/TD4MZIiIiIiIooEXxHQUAxkiIiIiomgQ4GB/BzGQOaFJhHeiCi1tVdLLYbMf/Q0press5QrIQlmgvqmlDHOd+aGpaKZqE6ay+Zp0/c+lDLqhsthuR5a0aj62H1J29ROGgHqt5ax1F63jr/J/yPRzN4zzrbrZ1YWU4b025daj3esZST1Es87CrIfytglJ/Tzx+rcXsNSvhHEOBexf30Zd7vJvL4SynQCk1//YlE2QfPV1pvX682DvzzDqwchPhD6XIv0ICPX62H6WWxgviXJ8+nePsbnle0mvH+nVBnNrz4ULYdUFEVUIAxkiIiIioigQcEFw1jLHRBzIHD16FA899BA++eQTHDhwAF6v17T+f//7n2OFIyIiIiKqNThGxlERBzLXXnst8vPzMWrUKDRu3JgzLxARERERUdRFHMh8+OGHeP/993HuuedWRXmIiIiIiGol4RIQrso3Agj2LQNQgUCmYcOGSEtLq4qyEBERERHVXsKlTQJBToi4Ju+//37cfffdOHbsWFWUh4iIiIiodjJmtKvsrboP5MQQVotMhw4dTGNh/vvf/yIrKwstWrRAbGysKe1XX33lbAmJiIiIiIgswgpkhg0bVsXFICIiIiKq3YQQnCjLQWEFMvfcc09Vl4OIiIiIqHZzbPrlmhkMeTweLF26FN999x2EEGjdujWGDRuGmJiKXdoy4q1atmyJDRs2ID093bT80KFD6NixI68jQ0RERERkpw4P9t+6dSuGDh2KgoICnHbaaQCAH374AY0aNcKyZcvQvn37iPOMuCZ/+ukneDyegOXFxcXYu3dvxAUgIiIiIqLa7dprr0Xbtm2xd+9efPXVV/jqq6+wZ88enHHGGbjuuusqlGfYLTLLli0zHn/00UdITU01nns8HnzyySfIzc2tUCGIiIiIiGo7X88yB7qF1cCeZd988w02btyIhg0bGssaNmyIBx98EF26dKlQnmEHMvqAfyEERo8ebVoXGxuLFi1a4LHHHqtQIYiIiIiIaj2X8N0qreZFMqeddhr279+Ptm3bmpYfOHAAp5xySoXyDDuQ8Xq9AIDc3Fxs2LABGRkZFdohERERERHVLbNmzcKkSZMwc+ZMdOvWDQCwbt063HfffXj44YdRVFRkpE1JSQkrz4gH++/atSvSTSgcNWYGC30f0maVAKR1uU16Yd3ckkavCylhPiZprifrektS22LYrPc18docj7Gb8jIzMgpRAJvtAvIN4oSaptH6mkTBCXX85Qn1mgrLvXVdOCdtuHUR5rkVyWtp9zoEfW3CqQc9qenNGF5ZrO8d02dGGELtp7w8IqqHsAoTvAz67EZSAkL609t+1gbJVgj/NsJlvz81jSkDablH4LHaftSFWx/KZ3+obSKtX7tzSpb3/WJTLK1o1qIGHLRaPm8p4IqBvz5r0ucXRYOAC6KODvYfPHgwAGDEiBFG9zr9N9ZFF11kPBdC2I7Ht1Ohuc4++eQTPP7446ap0yZPnoy+fftWJDsiIiIiotrPsemXa55Vq1Y5nmfEgcwzzzyDW265BX//+99x8803A/A1C11wwQWYM2cOJkyY4HghiYiIiIhqPKcCmRoYC/Xs2dPxPCMOZPLy8vD444+bApZJkybh3HPPxYMPPshAhoiIiIiIAvz111/49ttvceDAAWP8vW7IkCER5xdxIFNUVISBAwcGLO/fvz9uv/32iAtARERERFQXCCGcmX65BjbJLF++HFdddRV+//33gHWRjItRRTzaaMiQIVi6dGnA8nfeeccYqENERERERBbC5cytBpowYQIuvfRS7Nu3D16v13SrSBADVKBFpk2bNnjwwQexevVqdO/eHYBvjMx//vMfTJ06FU899ZSRdtKkSRUqFBERERFRrVOHB/sfOHAAU6ZMQVZWlmN5RhzIzJs3Dw0bNsT27duxfft2Y3mDBg0wb94847kQgoEMERERERHh73//O1avXo2TTz7ZsTx5HRkiIiIioihwaoyMM+NsouuZZ57BpZdeis8++wzt27dHbGysaX1FGkAqdB0ZACgpKcGuXbtw8sknIyamwtkQEREREdUNLgG4auYYl8p69dVX8dFHH6FevXpYvXq1KRiraE+uiGvy2LFjGDt2LBITE9G2bVvs3r0bgC+KeuihhyIuABERERFRnaCPkansrQb65z//ifvuuw+FhYX46aefsGvXLuP2v//9r0J5RhzITJ8+Hd988w1Wr16NhIQEY3nfvn3x+uuvV6gQRERERERUe5WUlOCyyy6Dy8EWqYhzevvtt/HMM8+gR48epiah008/HT/++KNjBSMiIiIiqlUcm3655rXKjB492vFGj4gHt/z222/IzMwMWH706NEaOfCIiIiIiCgqnOoaVgN/cns8HsyePRsfffQRzjjjjIDB/nPmzIk4z4gDmS5duuD999/HxIkTAfhnTXjxxReN68oQERERERHptmzZgg4dOgAAtm7dalpX0caQiAOZvLw8DBw4ENu3b0dZWRmefPJJbNu2DWvXrkV+fn6FCkHBRPKiRjs0FwCkzWIBSJvl1vR6caU1jbJQPamlhPkYZYj1MvDfDmuRAqorWLn1N5f9Ot+ugx1vOWzftMH3c2KogX8BRVVl6qe8bSvweSBt3ktRYXlvBrxX1aQVKFuwbQLeq5XJO5z3osP1KlyB9QUAwnJM0hv6WO3qWwhfGiHg61WufGaa1qnC6H1ebhWEU0eR1GMYr6+wPpFaVxz1OyjItsZ3iXZvOh307UOXV5b9BRGXVGOvvk5VzVVnz41Vq1Y5nmfENXnOOefgP//5D44dO4aTTz4ZK1asQFZWFtauXYtOnTo5XkAiIiIiolrBsVnLau4fi//973/x0Ucf4fjx4wCC/SEcngpdAKZ9+/ZYuHBhhXdKRERERFTniLrbIvPHH39gxIgRWLVqFYQQ2LlzJ1q2bIlrr70WDRo0wGOPPRZxnmHVZFFRUdg3IiIiIiIi1S233ILY2Fjs3r0biYmJxvLLLrsMy5cvr1CeYbXINGjQIOxBOB6Pp0IFISIiIiKq1erwrGUrVqzARx99hCZNmpiWt2rVCj///HOF8gwrkFEH5/z000+44447MGbMGGOWsrVr12LhwoXIy8urUCGIiIiIiGo9IRzqWlbzIpmjR4+aWmJ0v//+O+Lj4yuUZ1iBTM+ePY3H9913H+bMmYMrrrjCWDZkyBC0b98eL7zwAkaPHl2hghARERER1WpOtcjUQOeddx5efvll3H///QB8s8J6vV488sgj6N27d4XyjHiw/9q1a/H8888HLO/cuTOuvfbaChWCiIiIiIhqr0ceeQS9evXCxo0bUVJSgttuuw3btm3Dn3/+if/85z8VyjPitq2mTZvaBjJz585F06ZNK1QIIiIiIqJaz7Hpl2ue+vXrY/PmzTj77LPRr18/HD16FMOHD8fXX3+N2NjYCuUZcYvM448/jksuuQQfffQRunXrBgBYt24dfvzxR7z55psVKgQRERERUa3n2PTLNS+Yyc3Nxb59+3Dvvfealv/xxx9o0qRJhSYMi7gmL7jgAuzcuRNDhw7Fn3/+iT/++ANDhw7FDz/8gAsuuCDiAhARERER1QlOtchEGMfk5eWhS5cuSE5ORmZmJoYNG4YdO3aY0hw5cgQTJkxAkyZNUK9ePbRp0wbPPfecKU2vXr0ghDDdLr/88rDKEOzCl0eOHEFCQkJkB6Sp0AUxmzRpggcffLBCOyQiIiIioujJz8/HTTfdhC5duqCsrAwzZsxA//79sX37diQlJQHwXedl1apVWLRoEVq0aIEVK1Zg/PjxyMnJwdChQ428xo0bh/vuu894Xq9evZD7njJlCgDf4P67777bNHOZx+PBl19+ibPOOqtCx1WhQIaIiIiIiCLkWNeyyFgvODl//nxkZmZi06ZNOO+88wD4JvQaPXo0evXqBQC47rrrMHfuXGzcuNEUyCQmJiI7OzvsfX/99dcAfC0yW7ZsQVxcnLEuLi4OZ555JqZNm1ah44p+TSqee+45nHHGGUhJSUFKSgq6d++ODz/80FgvpcTMmTORk5ODevXqoVevXti2bZspj+LiYkycOBEZGRlISkrCkCFDsHfvXlOagwcPYtSoUUhNTUVqaipGjRqFQ4cOReMQiYiIiIh8HBzsX1paiqKiItOtuLg4rGIUFhYCANLS0oxlPXr0wLJly/DLL79ASolVq1bhhx9+wIABA0zbLl68GBkZGWjbti2mTZuGw4cPh9zXqlWrsGrVKowePRoffvih8XzVqlX46KOPMHfuXLRq1SqSWjRUayDTpEkTPPTQQ9i4cSM2btyI888/H0OHDjWCldmzZ2POnDl45plnsGHDBmRnZ6Nfv36mCps8eTKWLl2KJUuW4PPPP8eRI0cwePBg04ChkSNHYvPmzVi+fDmWL1+OzZs3Y9SoUVE/XiIiIiIiJyxdutT4k16/hXNxeiklpkyZgh49eqBdu3bG8qeeegqnn346mjRpgri4OAwcOBDPPvssevToYaS58sor8dprr2H16tW466678Oabb2L48OFhlXf+/PlISUmJ/EBDEDLYyBsbUkrs3r0bmZmZ5faHq6i0tDQ88sgjuOaaa5CTk4PJkyfj9ttvB+BrfcnKysLDDz+M66+/HoWFhWjUqBFeeeUVXHbZZQCAX3/9FU2bNsUHH3yAAQMG4LvvvsPpp5+OdevWoWvXrgB8s6x1794d33//PU477bSwylVUVITU1FQUFhZW7kU4UmB+HnIKvZowI0WQ0yfkaRVsmzDSBeTrVJoQaUOmL2c7UxZhv9WIarEovQ+krPwUpaHes1Ux/WlYnxFKGun1b2Mqj/6PrbJMXW/dj7GuJnznRIO0/x6R0lfnGtGoTehsjv3ufNHKk5hxYpSjGhUVHUZq45aV/73msOLiYiQkJKDgicFolFyxq9irzn1wFSbe/zQGDx5sWh4fH4/4+ND533TTTXj//ffx+eefo0mTJsbyRx99FC+++CIeffRRNG/eHGvWrMH06dOxdOlS9O3b1zavTZs2oXPnzti0aRM6duxY6eOKVEQtMlJKtGrVKqDrlhM8Hg+WLFmCo0ePonv37ti1axcKCgrQv39/I018fDx69uyJL774AoCv8kpLS01pcnJy0K5dOyPN2rVrkZqaagQxANCtWzekpqYaaewUFxcHNNcREREREVWcc7OWxcbGGsMz9Ft5QczEiROxbNkyrFq1yhTEHD9+HHfeeSfmzJmDiy66CGeccQYmTJiAyy67DI8++mjQ/Dp27IjY2Fjs3LnTsRqKRESBjMvlQqtWrfDHH384VoAtW7agfv36iI+Pxw033IClS5fi9NNPR0GBr+UiKyvLlD4rK8tYV1BQgLi4ODRs2DBkmszMzID9ZmZmGmns5OXlmZrqeLFPIiIiIqoUIfwD/itzi7AFVUqJCRMm4K233sKnn36K3Nxc0/rS0lKUlpbC5TKHBm63G16vF8Fs27YNpaWlaNy4cUTlcUrEY2Rmz56NW2+9FVu3bnWkAKeddho2b96MdevW4cYbb8To0aOxfft2Y72wNN9LKQOWWVnT2KUvL5/p06ejsLDQuO3ZsyfcQyIiIiIiOmHcdNNNWLRoEV599VUkJyejoKAABQUFOH78OAAgJSUFPXv2xK233orVq1dj165dWLBgAV5++WVcfPHFAIAff/wR9913HzZu3IiffvoJH3zwAS699FJ06NAB5557brUcV8TTL//jH//AsWPHcOaZZyIuLi5grMyff/4ZUX5xcXE45ZRTAACdO3fGhg0b8OSTTxrjYgoKCkxR3oEDB4xWmuzsbJSUlODgwYOmVpkDBw7gnHPOMdLs378/YL+//fZbQGuPKpw+hkREREREYVNmHYsm/cKW+tTKuvnz52PMmDEAgCVLlmD69Om48sor8eeff6J58+Z48MEHccMNNwDw/Wb/5JNP8OSTT+LIkSNo2rQpLrzwQtxzzz1wu93RPBxDxIHME088UQXF8JNSori4GLm5ucjOzsbKlSvRoUMHAEBJSQny8/Px8MMPAwA6deqE2NhYrFy5EiNGjAAA7Nu3D1u3bsXs2bMBAN27d0dhYSHWr1+Ps88+GwDw5ZdforCw0Ah2iIiIiIiqXDVdRyacCYeys7Mxf/78oOubNm2K/Px8J4tVaREHMqNHj3Zs53feeScGDRqEpk2b4vDhw1iyZAlWr16N5cuXQwiByZMnY9asWWjVqhVatWqFWbNmITExESNHjgQApKamYuzYsZg6dSrS09ORlpaGadOmoX379sbsCm3atMHAgQMxbtw4zJ07F4DvAj+DBw8Oe8YyIiIiIqJKq6YWmdoq4kBGdfz4cZSWlpqWRTLV3f79+zFq1Cjs27cPqampOOOMM7B8+XL069cPAHDbbbfh+PHjGD9+PA4ePIiuXbtixYoVSE5ONvJ4/PHHERMTgxEjRuD48ePo06cPFixYYGriWrx4MSZNmmTMbjZkyBA888wzlTl0IiIiIiKqRhFdRwYAjh49ittvvx3/+te/bGcvUy9EWZvwOjLB8DoyIbczZcHryBDxOjIV3J8/kfKQ15GpGryOTE12wl9H5v/9HY1SEiqd37n3LseUWc/i0ksvdaB0NVfEnfRuu+02fPrpp3j22WcRHx+Pl156Cffeey9ycnLw8ssvV0UZiYiIiIhqPkeuIcM/HXQRdy1799138fLLL6NXr1645ppr8Le//Q2nnHIKmjdvjsWLF+PKK6+sinISEREREREZIm6R+fPPP42L6KSkpBjTLffo0QNr1qxxtnRERERERLWFExfDrMAFMWuriAOZli1b4qeffgIAnH766fjXv/4FwNdS06BBAyfLRkRERERUe7BrmaMiDmSuvvpqfPPNNwCA6dOnG2NlbrnlFtx6662OF5CIiIiIqFYQwqFWmeo+kBNDxGNkbrnlFuNx79698f3332Pjxo04+eSTceaZZzpaOCIiIiIiIjsRBzIvv/wyLrvsMsTHxwMAmjVrhmbNmqGkpAQvv/wyrrrqKscLWSuV2yxYU0JtAdvpVPXjs51OVD826xSg1sXWOrBMpyqlJY3dNKT2xfOlsZuW2SZtqPT+BEqZgmUR6jWt3JS0nNqZqkro89Yv/HMwSp9tle56EWr65io6hrA+IyzTLMNrs61d1xNh+7DmfNdUjBCiAp+PQqsW9XNfALKs/CmXq4PdlMvW9XVsCuYTGruGOapCXcsKCwsDlh8+fBhXX321I4UiIiIiIqp1ONjfURG3yEgpbf+h27t3L1JTUx0pFBERERFRrSME4GIQ4pSwA5kOHTpACAEhBPr06YOYGP+mHo8Hu3btwsCBA6ukkERERERERKqwA5lhw4YBADZv3owBAwagfv36xrq4uDi0aNECl1xyieMFJCIiIiKqFZwaI8NGHQARBDL33HMPAKBFixa4/PLLjcH+REREREQUBmOMS6UzciCPmi/imjz//PPx22+/Gc/Xr1+PyZMn44UXXnC0YEREREREtQoviOmoiAOZkSNHYtWqVQCAgoIC9O3bF+vXr8edd96J++67z/ECEhERERERWUUcyGzduhVnn302AOBf//oX2rdvjy+++AKvvvoqFixY4HT5iIiIiIhqB8emXyagAtMvl5aWGuNjPv74YwwZMgQA0Lp1a+zbt8/Z0hERERER1RZCOBOIsHsZgAq0yLRt2xbPP/88PvvsM6xcudKYcvnXX39Fenq64wUkIiIiIiKyijiQefjhhzF37lz06tULV1xxBc4880wAwLJly4wuZ0REREREZMGuZY6KuGtZr1698Pvvv6OoqAgNGzY0ll933XVITEx0tHBERERERLWGY7OOsWsZUIEWmfvuuw/5+fmmIAYAGjVqhOeff96xghERERER1SpskXFUxDUxc+ZMDBo0CHPmzDEtP3LkCO69917HCkZERERERBRMxF3LAODll1/GhAkT8O233+KFF15AXFyc0+WqvepnA0f3h0hQE5sKBQAZZJV2PNJufZDt9CoIWGVZoTbNSgn7upPBm3BtixTiWCCCHId1+/J2EiTv8oTYtwjaTB3u/unEFeqcrI59WdP4ngc/B81kee+hE8aJ9llsUx7180q4YP6MDFX+0McW7mtpZX1tK5pPuPnr+5DSev6JgDRaDqa8QpVPGl8zbv/CjFMiK3Bihv/xsd99z4/9HlkeofKkmoktKo6qUE327t0b69atw/r169GrVy/s3x/qhzkRERERERljZJy4UeSBjP4Pxsknn4x169YhJSUFnTt3xsaNGx0vHBERERFRrcExMo6KuCbUpt2UlBR88MEHuPjiizFs2DAny0VERERERBRUxGNk5s+fj9TUVOO5y+XCU089hQ4dOmDNmjWOFo6IiIiIqPYQDrWosGsZUIFAZvTo0bbLr776alx99dWVLhARERERUa3ErmGOYk0SEREREVGNU6Hpl4mIiIiIKEJOzTjGnmUAGMgQEREREUWHY13LGMkADGSIiIiIiKKDY2QcFVYgU1RUFHaGKSkpFS4MERERERFROMIKZBo0aGBcCDMYKSWEEPB4PI4UjIiIiIioVnG5fLdKY9cyIMxAZtWqVVVdDiIiIiKi2o2D/R0VViDTs2fPqi4HEREREVHtJpy6ICYBYQYy3377bdgZnnHGGRUuDBERERERUTjCCmTOOussCCEgpQyZjmNkiIiIiIiC4PTLjgorkNm1a1dVl6NuScoCju63LDyBT0i9L2fIQFYvf5A0QgTZXj1uGXyVabXNNtb+psa+QtRr0FXBymqzn2DHG2wnUoaoi/KyCnWOBMuvnPNKL4/+mE48QiDo6yiV89+x1y+czyJrmvA/v+xOY/VPsnAmlgmW1vpnm11e+sQ05YmkTNEQ9I9E4weRv4yiSrqtBKuDaNSTABDsdROBy+1PMpjqqJzB1kI/roa5EZU0qMQM//2x3/33kWzrxP7D3SdVHafGyBCAMAOZ5s2bV3U5iIiIiIhqN6daZBgLAQAqVJOvvPIKzj33XOTk5ODnn38GADzxxBN45513HC0cERERERGRnYgDmeeeew5TpkzBBRdcgEOHDhljYho0aIAnnnjC6fIREREREdUSLn+rTGVubJIBUIFA5umnn8aLL76IGTNmwO12G8s7d+6MLVu2OFo4IiIiIqJaw4kghtM3GyKuiV27dqFDhw4By+Pj43H06FFHCkVERERERBRKxIFMbm4uNm/eHLD8ww8/xOmnn+5EmYiIiIiIah991rLK3ghAmLOWqW699VbcdNNN+OuvvyClxPr16/Haa68hLy8PL730UlWUkYiIiIio5hOC15FxUMSBzNVXX42ysjLcdtttOHbsGEaOHImTTjoJTz75JC6//PKqKCMRERERUc3H6ZcdFXEgAwDjxo3DuHHj8Pvvv8Pr9SIzM9PpchEREREREQUVcSCza9culJWVoVWrVsjI8F9tdufOnYiNjUWLFi2cLB8RERERUe3g2BgXNskAFRjsP2bMGHzxxRcBy7/88kuMGTPGiTIREREREdU+nH7ZURHXxNdff41zzz03YHm3bt1sZzMjIiIiIiIwkHFYxDUhhMDhw4cDlhcWFsLj8ThSKCIiIiIiolAiDmT+9re/IS8vzxS0eDwe5OXloUePHo4WjoiIiIio1nCqRSbCcTZ5eXno0qULkpOTkZmZiWHDhmHHjh2mNEeOHMGECRPQpEkT1KtXD23atMFzzz1nSlNcXIyJEyciIyMDSUlJGDJkCPbu3VvpaqmoiAf7P/zww+jZsydOO+00/O1vfwMAfPbZZygqKsKnn37qeAGJiIiIiGoFl/Ddoiw/Px833XQTunTpgrKyMsyYMQP9+/fH9u3bkZSUBAC45ZZbsGrVKixatAgtWrTAihUrMH78eOTk5GDo0KEAgMmTJ+Pdd9/FkiVLkJ6ejqlTp2Lw4MHYtGkT3G531I8r4kCmbdu2+Pbbb/HMM8/gm2++Qb169XDVVVdhwoQJSEtLq4oy1k5JWf7HR/dXLA81GpeycuUpL/+w9ycABFlnzS8gD+sbW9qvlnYLLSvC+afCtpgy/O31/UdS93q+Iki5Kyyc47XZj1qOsI5ZIuRrTBVQiS800+tX+ZJERcCpIyEi+GfRnNb8oRCYT+C56ktT/v4iKVPVs9aR/7GUXt+SIOvDzb9i24XapjJ5RrIfWD677NJJJU155dE+4xq2iKRwkUnMMN9HU6T7PPZ78G1CrVPTkJmDF8QsLS1FUVGRaWl8fDzi4+MDUi9fvtz0fP78+cjMzMSmTZtw3nnnAQDWrl2L0aNHo1evXgCA6667DnPnzsXGjRsxdOhQFBYWYt68eXjllVfQt29fAMCiRYvQtGlTfPzxxxgwYIADxxWZiGty9+7daNy4MWbNmoX3338f//73v3H33XcjLS0Nu3fvrooyEhERERGRYunSpUhNTTXd8vLywtq2sLAQAEyNED169MCyZcvwyy+/QEqJVatW4YcffjAClE2bNqG0tBT9+/c3tsnJyUG7du1sZzSOhohbZHJzc7Fv376Ai2D+8ccfyM3N5YB/IiIiIiJbzs06dvHFF2PevHmmZXatMVZSSkyZMgU9evRAu3btjOVPPfUUxo0bhyZNmiAmJgYulwsvvfSSMQa+oKAAcXFxaNiwoSm/rKwsFBQUOHBEkYs4kJHSvhvAkSNHkJCQ4EihiIiIiIhqHaemTxZAbGwsUlJSIt50woQJ+Pbbb/H555+blj/11FNYt24dli1bhubNm2PNmjUYP348GjdubHQlsxMsNoiGsAOZKVOmAPD1w73rrruQmJhorPN4PPjyyy9x1llnOV5AIiIiIiKqvIkTJ2LZsmVYs2YNmjRpYiw/fvw47rzzTixduhQXXnghAOCMM87A5s2b8eijj6Jv377Izs5GSUkJDh48aGqVOXDgAM4555yoHwsQQSDz9ddfA/BFXVu2bEFcXJyxLi4uDmeeeSamTZvmfAmJiIiIiGoDISKeOjlIRhGlllJi4sSJWLp0KVavXo3c3FzT+tLSUpSWlsLlMrcWud1ueL2+SUU6deqE2NhYrFy5EiNGjAAA7Nu3D1u3bsXs2bMrcSwVF3Ygs2rVKgDA1VdfjSeffLJCTVlERERERHVXeDMnOu2mm27Cq6++infeeQfJycnGmJbU1FTUq1cPKSkp6NmzJ2699VbUq1cPzZs3R35+Pl5++WXMmTPHSDt27FhMnToV6enpSEtLw7Rp09C+ffuQXc+qUsRjZObPn18V5SAiIiIiqt2cGiMTIf3ClvrUyrr58+djzJgxAIAlS5Zg+vTpuPLKK/Hnn3+iefPmePDBB3HDDTcY6R9//HHExMRgxIgROH78OPr06YMFCxZUyzVkgApMv1xV8vLyIITA5MmTjWVSSsycORM5OTmoV68eevXqhW3btpm2C+cKowcPHsSoUaOMqelGjRqFQ4cOReGoiIiIiIiql5TS9qYHMQCQnZ2N+fPn45dffsHx48fx/fffY8qUKaaB/AkJCXj66afxxx9/4NixY3j33XfRtGnTajginxMikNmwYQNeeOEFnHHGGabls2fPxpw5c/DMM89gw4YNyM7ORr9+/XD48GEjzeTJk7F06VIsWbIEn3/+OY4cOYLBgwebpoEeOXIkNm/ejOXLl2P58uXYvHkzRo0aFbXjIyIiIiIyxshU9lZjroJctao9kDly5AiuvPJKvPjii6YZEKSUeOKJJzBjxgwMHz4c7dq1w8KFC3Hs2DG8+uqrAGBcYfSxxx5D37590aFDByxatAhbtmzBxx9/DAD47rvvsHz5crz00kvo3r07unfvjhdffBHvvfceduzYUS3HTERERER1kcuhGwEnQE3cdNNNuPDCCwMGCe3atQsFBQWmq4fGx8ejZ8+extVDw7nC6Nq1a5GamoquXbsaabp164bU1NSQVyEtLi5GUVGR6UZEREREVGFOtciwQQZABQb7O2nJkiX46quvsGHDhoB1+mwKWVlZpuVZWVn4+eefjTTlXWG0oKAAmZmZAflnZmaGvAppXl4e7r333sgOiIiIiIiIoqLaWmT27NmDm2++GYsWLUJCQkLQdNYrhYZz9VBrGrv05eUzffp0FBYWGrc9e/aE3CcRERERUUgCDo2RIaAaA5lNmzbhwIED6NSpE2JiYhATE4P8/Hw89dRTiImJMVpirK0mBw4cMNapVxgNlWb//v0B+//tt98CWntU8fHxSElJMd2IiIiIiCrOqTEyDGaAagxk+vTpgy1btmDz5s3GrXPnzrjyyiuxefNmtGzZEtnZ2Vi5cqWxTUlJCfLz83HOOecAMF9hVKdfYVRP0717dxQWFmL9+vVGmi+//BKFhYVGmmqXlAUkZfpu4bJG45WN0iON9kOmEcqtonlY8xH2i0OmL6cMtscbLI8Qt0r/s+Iy3ypShrDKqe+jsmWNYplr281aZ5V+PZRbyH3Dch+NWxARlTvcfZR33CHKViP+2QxefiGE1rsgjLovN/9QSYKdd5XI05q/3b6sy4zHwT5/QpWlJrzWJ5jEDOe3r2yeRIpqGyOTnJyMdu3amZYlJSUhPT3dWD558mTMmjULrVq1QqtWrTBr1iwkJiZi5MiRAMK7wmibNm0wcOBAjBs3DnPnzgUAXHfddRg8eDBOO+20KB4xEREREdVp7BrmqGod7F+e2267DcePH8f48eNx8OBBdO3aFStWrEBycrKRJpwrjC5evBiTJk0yZjcbMmQInnnmmagfDxERERHVYQxkHCWklLK6C1ETFBUVITU1FYWFhVU7XubogfDShXoTVOQlreibqtx9hVGWsMtrky6sTcPMv8JvBYffQlX+lqyC/PkxEh71fSals19mIV8DCV+3Gv0+Gqr6fecQIaq/DJWil72KX9dg56pTdae+Dtb3ibrMeN9U1fFq75EGzaso/1rk2O/hdRM79rv5eWJG4DIHFRUdRmrjllX/ey1CxcXFSEhIQMF/XkajtNRK53fu5bdhyoz7cemllzpQupqr2q8jQ0REREREFKkTumsZEREREVGtwa5ljmIgQ0REREQUDcZse5XNp/JZ1AYMZIiIiIiIosKpMV6MZACOkSEiIiIiohqILTJERERERNHAMTKOYiBDRERERBQVwpkxMgSAgQwRERERUVQIISAcaZFhqw7AMTJERERERFQDsUWGiIiIiCgqXGA7gnMYyBARERERRYNTg/3ZswwAQ0IiIiIiIqqB2CJzoknKDL3+6IHyI/ny1ksZftryqNur+foT6CsrkYealwxcVE72YSYKXhchy6TmX1kydDkcI8I4psizpAiZXmf93C6vIiVs3wcB+enJ9XTCch9Omex2H8l5EyqvSn4GySDvFbvyCVH+Z14kZbDLK9x60cuibhfOvq35mz4zbZaF2lZNG6rc4dZJuJ/f1mMPd5/WZdGY7alB86rfR22QmFHxdPqyY7/bp9GX6+vU5zUdp192FAMZIiIiIqKocGqMDIMhgIEMEREREVF0sEXGURwjQ0RERERENQ5bZIiIiIiIooEtMo5iIENEREREFBUCHCPjHAYyRERERETRwBYZR3GMDBERERER1ThskSEiIiIiigbhcuZ6SGzUAcBAhoiIiIgoSgSciUIYyQAMZIiIiIiIooNjZBzFMTJERERERFTjsEWGiIiIiCganBojQwAYyBARERERRYdjXcvYPQ1g1zIiIiIiIqqB2CJDRERERBQVTs1aRgADmZonKdP/+NhviOzNIH13VTVbhpqvlNaV5jJUKA89n3LyCJ655XmY+ThRX7bHErCjSDMNsk0Y+4r0mOzKL4SyvDJ1ZJdHsGNQX/9g6Sv4OlfZF0uw16ki+xfKfUXfB8Gytu7fph7LfX+GvTObZaFewxDlUNMH28zJzzy7vCLJX00b7nah0pWXR2W2DdzA8txyDoRVloq+FuG+j6hWSMwI/TyUsjhny+I0XkfGUQxkiIiIiIiihlGIUzhGhoiIiIiIahy2yBARERERRQVnLXMSAxkiIiIioqjgYH8nMZAhIiIiIooGx64jQwDHyBARERERUQ3EQIaIiIiIiGocdi0jIiIiIooGdi1zFFtkiIiIiIioxmGLDBERERFRVDg1axlbdQAGMkRERERE0cGuZY5iIENEREREFBW8joyTOEaGiIiIiIhqHLbIEBERERFFg1Ndy9ioA4AtMjVbYiMgMcN/A/z3toTNrYrob9SAN2tl9i+1m+WpDJK8/ELalKWK6ketD7tbxTK1PK9UZZSzqyBlduS47Ora7ly1vkbB0gfLv7xbVQkj7wrVYQWOxy6/gP2EU48VLXMoIcpuyjfUsdodRzj1U1XnQLjnnt1xhFPeqjxv7fYXbHlFjyPSMjikQXP7ZQ2aAw1aOLcfCk/I3y21UaSfC858Z+Xl5aFLly5ITk5GZmYmhg0bhh07dphLJoTt7ZFHHjHS9OrVK2D95ZdfHnF5nMJAhoiIiIgoGsr7AzDcW4TBTH5+Pm666SasW7cOK1euRFlZGfr374+jR48aafbt22e6/d///R+EELjkkktMeY0bN86Ubu7cuU7UTIWwaxkRERERUS22fPly0/P58+cjMzMTmzZtwnnnnQcAyM7ONqV555130Lt3b7Rs2dK0PDExMSBtdWGLDBERERFRVDjXtay0tBRFRUWmW3FxcVilKCwsBACkpaXZrt+/fz/ef/99jB07NmDd4sWLkZGRgbZt22LatGk4fPhweIdeBRjIEBERERFFg4BDXcuApUuXIjU11XTLy8srtwhSSkyZMgU9evRAu3btbNMsXLgQycnJGD58uGn5lVdeiddeew2rV6/GXXfdhTfffDMgTTSxaxkRERERUVQ4N1HHxRdfjHnz5pmWxcfHl7vdhAkT8O233+Lzzz8Pmub//u//cOWVVyIhIcG0fNy4ccbjdu3aoVWrVujcuTO++uordOzYMcIjqDwGMkRERERENUxsbCxSUlIi2mbixIlYtmwZ1qxZgyZNmtim+eyzz7Bjxw68/vrr5ebXsWNHxMbGYufOnQxkiIiIiIjIWVJKTJw4EUuXLsXq1auRm5sbNO28efPQqVMnnHnmmeXmu23bNpSWlqJx48ZOFjdsDGSIiIiIiKJAv/aKAzlFlPqmm27Cq6++infeeQfJyckoKCgAAKSmpqJevXpGuqKiIrzxxht47LHHAvL48ccfsXjxYlxwwQXIyMjA9u3bMXXqVHTo0AHnnntu5Q6ngjjYn4iIiIioFnvuuedQWFiIXr16oXHjxsbN2n1syZIlkFLiiiuuCMgjLi4On3zyCQYMGIDTTjsNkyZNQv/+/fHxxx/D7XZH61BM2CJDRERERBQVzg32j4SUMqx01113Ha677jrbdU2bNkV+fr6Txao0BjJERERERNGgTJ9cuXwqn0VtwECGiIiIiCgqqqdFprbiGBkiIiIiIqpx2CJDRERERBQNTnUtIwAMZIiIiIiIosSprmUMhgAGMrVLYob5Xnfs9xAb2b0RpLbcboaLcN44+nYixC4saaz7Km92jaCry5uVQ9g+DFgggyaqAL0+AzJXdh1kPyHrIbwZSJxRXj0EOWfC/ddJSn/aMGdWOaFYjzPUMYT9T5xWn5HkHTI77cvT5apcPkbZgIjPQfVYytu/XT2VV3cy1GdXQGYhlkVyXE7+mAiVV7B1wZaHOgZrHanPwzgeIcyvn/7cuK9Er/Vovf8bNI/Ofig06++VuoAtMo7iGBkiIiIiIqpxqjWQmTlzpnGFU/2WnZ1trJdSYubMmcjJyUG9evXQq1cvbNu2zZRHcXExJk6ciIyMDCQlJWHIkCHYu3evKc3BgwcxatQopKamIjU1FaNGjcKhQ4eicYhERERERBrh0I2AE6BFpm3btti3b59x27Jli7Fu9uzZmDNnDp555hls2LAB2dnZ6NevHw4fPmykmTx5MpYuXYolS5bg888/x5EjRzB48GB4PB4jzciRI7F582YsX74cy5cvx+bNmzFq1KioHicRERER1XF617LK3gjACTBGJiYmxtQKo5NS4oknnsCMGTMwfPhwAMDChQuRlZWFV199Fddffz0KCwsxb948vPLKK+jbty8AYNGiRWjatCk+/vhjDBgwAN999x2WL1+OdevWoWvXrgCAF198Ed27d8eOHTtw2mmn2ZaruLgYxcXFxvOioiKnD52IiIiIiCqo2ltkdu7ciZycHOTm5uLyyy/H//73PwDArl27UFBQgP79+xtp4+Pj0bNnT3zxxRcAgE2bNqG0tNSUJicnB+3atTPSrF27FqmpqUYQAwDdunVDamqqkcZOXl6e0RUtNTUVTZs2dfS4iYiIiKiucaprGVtlgGoOZLp27YqXX34ZH330EV588UUUFBTgnHPOwR9//IGCggIAQFZWlmmbrKwsY11BQQHi4uLQsGHDkGkyMzMD9p2ZmWmksTN9+nQUFhYatz179lTqWImIiIiIGMM4p1q7lg0aNMh43L59e3Tv3h0nn3wyFi5ciG7dugEAhKUfoJQyYJmVNY1d+vLyiY+PR3x8fFjHQUREREQUHkYiTqn2rmWqpKQktG/fHjt37jTGzVhbTQ4cOGC00mRnZ6OkpAQHDx4MmWb//v0B+/rtt98CWnuIiIiIiKhmOKECmeLiYnz33Xdo3LgxcnNzkZ2djZUrVxrrS0pKkJ+fj3POOQcA0KlTJ8TGxprS7Nu3D1u3bjXSdO/eHYWFhVi/fr2R5ssvv0RhYaGRhoiIiIioyjk1axkbdQBUc9eyadOm4aKLLkKzZs1w4MABPPDAAygqKsLo0aMhhMDkyZMxa9YstGrVCq1atcKsWbOQmJiIkSNHAgBSU1MxduxYTJ06Fenp6UhLS8O0adPQvn17YxazNm3aYODAgRg3bhzmzp0LALjuuuswePDgoDOWERERERE5j4NcnFStgczevXtxxRVX4Pfff0ejRo3QrVs3rFu3Ds2bNwcA3HbbbTh+/DjGjx+PgwcPomvXrlixYgWSk5ONPB5//HHExMRgxIgROH78OPr06YMFCxbA7XYbaRYvXoxJkyYZs5sNGTIEzzzzTHQPloiIiIjqNl4HxlFCSimruxA1QVFREVJTU1FYWIiUlJTqLk5kjv0e4QYSvn8L7E6NcN58+nbC8lxNYl0my1lvSRP0rC3vdBa2DyPOJiJ6fVYg85Bvz2i+dct73UOdM2GQ0v/BXhM/kqxfSqGOIewvsCD1GXb9SHNaof0L6Gg9B8kjWN7qsZe3/4p80Rt5Wt9zEWcUQdoT9QdJqGOwnlvq8zCOR4jAc0t/D6vv5YqIxvu/QfOq3wdVmxP191pxcTESEhJw4KftaNQoo9L5de89CFOm3Y5LL73UgdLVXNV+QUwiIiIiorrAF6tX/g+QmvifX1U4oQb7ExERERHVNrGxsTjl5Fws+2B5pfP6efcefLt1O1q3bu1AyWo2BjJERERERFXI5XIh76HZuPv+h3H06NFK5TVj5ixcevFFaN++vUOlq7k4RiZMJ2qfyyoR8ZiaiihvTIz0Lzf16Zem1f6u3ZY+8dLrf2w3JsClxvB2TbxhjPkIZ1hIiGFC5awIs91YPe5gj8MRQf/4iIVxHGr/eqdVRb7hjAWw22dA+nDGHlWC9PrLIVyRlzeyndnnFcl4CaOswbYJYxyW8TkR6blsff8EE+YYkpC7qmRdRzLWyJ8Qjry/q3qQstPv1QbNgUM/m59TrXYi/16TUuKcbmdjUP8+uHv6tArlsenrb/C3fhdhx44daNq0qcMlrHnYIkNEREREVMWEEHh0zhN45IlnsH//gYi3l1Li1jtnYtKN1zKI0TCQISIiIiKKgnPPPRf9zu+FmbMeiXjbDz/6GN9u247pd99fBSWrmRjIEBERERFFyUOPzMGCRUvw/Y6dYW9TVlaG2/55H+6+YypSU1OrsHQ1CwMZIiIiIqIoOfXUUzF29Ejcftd9YW+zYNFrKC4pxg2TKja2prZiIENEREREFEV335eHVWs+x5rPvyg37dGjR3H3/Q8j76HZiIuLi0Lpag4GMkREREREUZSZmYnbp0zEtDtnorwJhOc8/TyaN2uKSy65JEqlqzkYyBARERERRdktt/0Tv+4rwL/efDtomv37D2D240/j0TlPQFT19Oc1EAMZIiIiIqIoS0xMxP0PPIjp9zyI4uJi2zQzZz2Cfuf3wrnnnhvl0tUMDGSIiIiIiKrBVVddhfpJSXj2hfkB677fsRMLFi3BQ4/MqYaS1QwMZIiIiIiIqoHb7cbsRx/D/Q8/hoMHD5nW3X7Xfbjmqitw6qmnVk/hagAGMkRERERE1WTAgAHodNaZyHv0SWPZms+/wKo1n+Oe+x+qxpKd+BjIEBERERFVEyEEHpnzBJ6ZOw8/794DKSVunXEvbp8yEZmZmdVdvBNaTHUXgIiIiIioLjvrrLNw6cUXYcbMWbjogv7Y+8uvuOW2f1Z3sU54DGSIiIiIiKrZ/XmPoHXr1vh4VT5m5T2ExMTE6i7SCY+BTJj0ixUVFRVVc0mi4NjhKOzEcvGngItBSf9yfd50KZXlWjKhP5baE31zr/+xaXuNS+1VaTcvu55fiItUhZHEyDpomlD5h75AlrkQoR6HQ61Qp4VxHPrrHNYxR6gq8tXzDDWnv90+A9KXV9+VLLf0+sshXJGXN7Kd2ecVyXUPjLIG26a8NxyUz4lIz2Xr+yeYMPIt75grW9dq/mHnVZE6KWffVcHp96qrCCg6bH5OtZr+O628i0yeiJo1a4Y7b70Z+Z+txejRo6u7ODWCkDXxla4Ge/fuRdOmTau7GERERERUjj179qBJkybVXQyqYgxkwuT1evHrr78iOTkZQggUFRWhadOm2LNnD1JSUqq7eCcc1k/5WEehsX7KxzoKjfVTPtZRaKyf8p1odSSlxOHDh5GTkwOXi3Na1XbsWhYml8tlG9mnpKScEG/cExXrp3yso9BYP+VjHYXG+ikf6yg01k/5TqQ6Sk1Nre4iUJQwVCUiIiIiohqHgQwREREREdU4DGQqKD4+Hvfccw/i4+OruygnJNZP+VhHobF+ysc6Co31Uz7WUWisn/Kxjqg6cbA/ERERERHVOGyRISIiIiKiGoeBDBERERER1TgMZIiIiIiIqMZhIENERERERDUOA5kK+OGHHzB06FBkZGQgJSUF5557LlatWmVKs3v3blx00UVISkpCRkYGJk2ahJKSkmoqcfS9//776Nq1K+rVq4eMjAwMHz7ctL6u14+uuLgYZ511FoQQ2Lx5s2ldXa2jn376CWPHjkVubi7q1auHk08+Gffcc0/AsdfV+tE9++yzyM3NRUJCAjp16oTPPvusuotULfLy8tClSxckJycjMzMTw4YNw44dO0xppJSYOXMmcnJyUK9ePfTq1Qvbtm2rphJXr7y8PAghMHnyZGMZ6wf45Zdf8I9//APp6elITEzEWWedhU2bNhnr63odlZWV4Z///KfxudyyZUvcd9998Hq9Rpq6XkdUTSRF7JRTTpEXXHCB/Oabb+QPP/wgx48fLxMTE+W+ffuklFKWlZXJdu3ayd69e8uvvvpKrly5Uubk5MgJEyZUc8mj49///rds2LChfO655+SOHTvk999/L9944w1jfV2vH9WkSZPkoEGDJAD59ddfG8vrch19+OGHcsyYMfKjjz6SP/74o3znnXdkZmamnDp1qpGmLtePlFIuWbJExsbGyhdffFFu375d3nzzzTIpKUn+/PPP1V20qBswYICcP3++3Lp1q9y8ebO88MILZbNmzeSRI0eMNA899JBMTk6Wb775ptyyZYu87LLLZOPGjWVRUVE1ljz61q9fL1u0aCHPOOMMefPNNxvL63r9/Pnnn7J58+ZyzJgx8ssvv5S7du2SH3/8sfzvf/9rpKnrdfTAAw/I9PR0+d5778ldu3bJN954Q9avX18+8cQTRpq6XkdUPRjIROi3336TAOSaNWuMZUVFRRKA/Pjjj6WUUn7wwQfS5XLJX375xUjz2muvyfj4eFlYWBj1MkdTaWmpPOmkk+RLL70UNE1drh/VBx98IFu3bi23bdsWEMiwjsxmz54tc3Nzjed1vX7OPvtsecMNN5iWtW7dWt5xxx3VVKITx4EDByQAmZ+fL6WU0uv1yuzsbPnQQw8Zaf766y+Zmpoqn3/++eoqZtQdPnxYtmrVSq5cuVL27NnTCGRYP1LefvvtskePHkHXs46kvPDCC+U111xjWjZ8+HD5j3/8Q0rJOqLqw65lEUpPT0ebNm3w8ssv4+jRoygrK8PcuXORlZWFTp06AQDWrl2Ldu3aIScnx9huwIABKC4uNjVV10ZfffUVfvnlF7hcLnTo0AGNGzfGoEGDTM3Ldbl+dPv378e4cePwyiuvIDExMWA968issLAQaWlpxvO6XD8lJSXYtGkT+vfvb1rev39/fPHFF9VUqhNHYWEhABjny65du1BQUGCqr/j4ePTs2bNO1ddNN92ECy+8EH379jUtZ/0Ay5YtQ+fOnXHppZciMzMTHTp0wIsvvmisZx0BPXr0wCeffIIffvgBAPDNN9/g888/xwUXXACAdUTVJ6a6C1DTCCGwcuVKDB06FMnJyXC5XMjKysLy5cvRoEEDAEBBQQGysrJM2zVs2BBxcXEoKCiohlJHz//+9z8AwMyZMzFnzhy0aNECjz32GHr27IkffvgBaWlpdbp+AF8/4jFjxuCGG25A586d8dNPPwWkqet1pPrxxx/x9NNP47HHHjOW1eX6+f333+HxeAKOPysrq9Yfe3mklJgyZQp69OiBdu3aAYBRJ3b19fPPP0e9jNVhyZIl+Oqrr7Bhw4aAdawf3/fWc889hylTpuDOO+/E+vXrMWnSJMTHx+Oqq65iHQG4/fbbUVhYiNatW8PtdsPj8eDBBx/EFVdcAYDnEVUftshoZs6cCSFEyNvGjRshpcT48eORmZmJzz77DOvXr8fQoUMxePBg7Nu3z8hPCBGwDyml7fKaINz60Qf+zZgxA5dccgk6deqE+fPnQwiBN954w8ivttUPEH4dPf300ygqKsL06dND5lfb6ijc+lH9+uuvGDhwIC699FJce+21pnW1rX4iZT3OunTswUyYMAHffvstXnvttYB1dbW+9uzZg5tvvhmLFi1CQkJC0HR1tX4AwOv1omPHjpg1axY6dOiA66+/HuPGjcNzzz1nSleX6+j111/HokWL8Oqrr+Krr77CwoUL8eijj2LhwoWmdHW5jqh6sEVGM2HCBFx++eUh07Ro0QKffvop3nvvPRw8eBApKSkAfLMHrVy5EgsXLsQdd9yB7OxsfPnll6ZtDx48iNLS0oB/K2qKcOvn8OHDAIDTTz/dWB4fH4+WLVti9+7dAFAr6wcIv44eeOABrFu3DvHx8aZ1nTt3xpVXXomFCxfWyjoKt350v/76K3r37o3u3bvjhRdeMKWrjfUTroyMDLjd7oDWlwMHDtT6Yw9l4sSJWLZsGdasWYMmTZoYy7OzswH4/jFu3Lixsbyu1NemTZtw4MABo+szAHg8HqxZswbPPPOMMcNbXa0fAGjcuLHpOwsA2rRpgzfffBMAzyEAuPXWW3HHHXcYn+Ht27fHzz//jLy8PIwePZp1RNWGgYwmIyMDGRkZ5aY7duwYAMDlMjdmuVwuozWie/fuePDBB7Fv3z7jDb1ixQrEx8ebvkxqknDrp1OnToiPj8eOHTvQo0cPAEBpaSl++uknNG/eHEDtrB8g/Dp66qmn8MADDxjPf/31VwwYMACvv/46unbtCqB21lG49QP4pkLt3bu30aJnfb/VxvoJV1xcHDp16oSVK1fi4osvNpbrXV7rGiklJk6ciKVLl2L16tXIzc01rc/NzUV2djZWrlyJDh06APCNM8rPz8fDDz9cHUWOqj59+mDLli2mZVdffTVat26N22+/HS1btqzT9QMA5557bsCU3T/88IPxnVXXzyHA99vH+jnsdruN3z2sI6o21TLFQA3222+/yfT0dDl8+HC5efNmuWPHDjlt2jQZGxsrN2/eLKX0Tw3bp08f+dVXX8mPP/5YNmnSpM5MDXvzzTfLk046SX700Ufy+++/l2PHjpWZmZnyzz//lFKyfqx27doVdPrlulhHv/zyizzllFPk+eefL/fu3Sv37dtn3HR1uX6k9E+/PG/ePLl9+3Y5efJkmZSUJH/66afqLlrU3XjjjTI1NVWuXr3adK4cO3bMSPPQQw/J1NRU+dZbb8ktW7bIK664ok5PC6vOWiYl62f9+vUyJiZGPvjgg3Lnzp1y8eLFMjExUS5atMhIU9fraPTo0fKkk04ypl9+6623ZEZGhrztttuMNHW9jqh6MJCpgA0bNsj+/fvLtLQ0mZycLLt16yY/+OADU5qff/5ZXnjhhbJevXoyLS1NTpgwQf7111/VVOLoKikpkVOnTpWZmZkyOTlZ9u3bV27dutWUpi7Xj5VdICNl3a2j+fPnSwC2N1VdrR/d//t//082b95cxsXFyY4dOxrTDdc1wc6V+fPnG2m8Xq+85557ZHZ2toyPj5fnnXee3LJlS/UVuppZAxnWj5TvvvuubNeunYyPj5etW7eWL7zwgml9Xa+joqIiefPNN8tmzZrJhIQE2bJlSzljxgxZXFxspKnrdUTVQ0gpZXW0BBEREREREVUUZy0jIiIiIqIah4EMERERERHVOAxkiIiIiIioxmEgQ0RERERENQ4DGSIiIiIiqnEYyBARERERUY3DQIaIiIiIiGocBjJERERERFTjMJAhojrpp59+ghACmzdvBgCsXr0aQggcOnQo6DZCCLz99tuV3rdT+dhp0aIFnnjiiQpvr9eLEAJnnXVWyLRjxozBsGHDKryv2kY/h4QQrBcioihgIENEJ7xevXph8uTJ1V2MiM2cOdM2GNi3bx8GDRoEIDCgOlF8/PHH+OSTT6q7GCeEMWPGQAiBhx56yLT87bffhhDCeH7OOedg3759GDFiRLSLSERUJzGQISKKsuzsbMTHx1d3MUJKT09Henp6dRcDHo8HXq+3uouBhIQEPPzwwzh48GDQNHFxccjOzka9evWiWDIiorqLgQwRndDGjBmD/Px8PPnkk0a3nZ9++gkAsG3bNlx44YVISUlBcnIy/va3v+HHH380tp0/fz7atGmDhIQEtG7dGs8++6yjZbv99ttx6qmnIjExES1btsRdd92F0tJSAMCCBQtw77334ptvvjHKvWDBAgDmrmW5ubkAgA4dOkAIgV69egGwb4UaNmwYxowZYzw/cOAALrroItSrVw+5ublYvHhxQBkLCwtx3XXXITMzEykpKTj//PPxzTffRHysHo8HU6ZMQYMGDZCeno7bbrsNUkpTGiklZs+ejZYtW6JevXo488wz8f/bu9eYJq8/DuBfuRQr0FYMyMUCbiCOKQPcVEhoFWGQ+MK5mehkijdmnFjidBKmkW3GgVuYd51hDpFswUVpNGKccUwlMhRq6/BWBFsgBKfGTcIGltLfXvDnCY8tiv+UObLf591z7uf0RXNynt95jh49Kipz4sQJhIeHQyqVYubMmSguLha90nfo0CEoFAqcPHkSkZGR8PDwQFNTEywWCzZs2ICgoCB4enpi2rRpOHfunKjtqqoqqFQqSKVSKJVKaDQa/Pnnn0L+vn37EB4ejpEjR2Ls2LGYN2/eoOeflJQEf39/5OXlPd/CMcYYGzJuL3oAjDH2NDt37kR9fT0mTZqEzz77DADg6+uL1tZWqFQqzJgxAxUVFZDJZLh48SKsVisAoLCwELm5udizZw9iYmKg1+uRkZEBT09PpKenO2Vs3t7eOHToEAIDA1FXV4eMjAx4e3tjw4YNmD9/Pq5du4bTp0/j7NmzAAC5XG7XxuXLlzF16lScPXsWr776KiQSyaD7X7JkCVpaWlBRUQGJRAKNRoN79+4J+USE2bNnw8fHB6dOnYJcLseBAwcwa9Ys1NfXw8fHZ9B9FRQU4Ntvv8XBgwcRGRmJgoICaLVaJCYmCmU2bdqEsrIy7N+/H+Hh4bhw4QLee+89+Pr6Qq1Ww2w2Y968ecjKysKKFSug1+uxfv16u77++usv5OXl4ZtvvsGYMWPg5+eHpUuXwmw2o7S0FIGBgdBqtUhNTUVdXR3Cw8NRV1eHlJQUbNmyBQcPHsT9+/eRmZmJzMxMFBUVoba2FhqNBiUlJYiPj8fDhw9RWVk56Pm7urri888/x8KFC6HRaDBu3LhB12WMMTZEiDHG/uXUajVlZWWJ0nJycmj8+PFksVgc1lEqlfT999+L0rZs2UJxcXFERGQymQgA6fV6IiL6+eefCQD9/vvvA44DAGm12gHzv/jiC5oyZYrwnJubS6+99tpT23lyHH0czXnOnDmUnp5ORERGo5EAUHV1tZB/8+ZNAkDbt28nIqKffvqJZDIZdXV1idp5+eWX6cCBAw7nMNB4AgICKD8/X3ju7u6mcePG0Zw5c4iIqKOjg0aOHElVVVWiesuXL6d3332XiIiys7Np0qRJovyNGzeK1r2oqIgAkMFgEMo0NDTQiBEjqLW1VVR31qxZlJOTQ0REixYtovfff1+UX1lZSS4uLtTZ2UnHjh0jmUxG7e3tDuf9NOnp6cI8p0+fTsuWLSMiIq1WS47+RvuXZ4wxNnT4RIYxNiwZDAYkJCTA3d3dLu/+/ftoaWnB8uXLkZGRIaRbrVaHpyL/r6NHj2LHjh1oaGhAR0cHrFYrZDKZ09p/mps3b8LNzQ2vv/66kDZx4kQoFArhWafToaOjwy7WpbOzU/QK3rM8evQIbW1tiIuLE9L6+qb/vV5248YNdHV1ITk5WVTXYrEgJiYGAGA0GvHGG2+I8qdOnWrXn0QiQVRUlPB85coVEBEmTJggKvf48WNhbjqdDg0NDaLX64gINpsNJpMJycnJCAkJwUsvvYTU1FSkpqZi7ty5GDVq1KDXAQC2bduGxMRErFu37rnqMcYYcz7eyDDGhqWnBVT3BYcXFhZi2rRpojxXV1en9F9dXY0FCxbg008/RUpKCuRyOUpLS1FQUOCU9l1cXOxiUPribwAIef1vzXqSzWZDQECAXSwJANGGxxn61ry8vBxBQUGivL6LDYjIbrxPzhHo/W37l7PZbHB1dYVOp7P7/by8vIQyK1euhEajsWsvODgYEokEV65cwblz53DmzBls3rwZn3zyCWpqap5rLVQqFVJSUvDxxx+L4pUYY4z983gjwxj715NIJOjp6RGlRUVFobi4GN3d3XanMmPHjkVQUBDu3LmDtLS0IRnTxYsXERISgo0bNwppTU1Nzxz3k/piYp4s5+vri7a2NuG5p6cH165dw8yZMwEAr7zyCqxWK2pra4VTDaPRKPoOTmxsLO7evQs3NzeEhoY+9xz7yOVyBAQEoLq6GiqVCkDv6ZZOp0NsbCwACIH5zc3NUKvVDtuZOHEiTp06JUqrra19Zv8xMTHo6enBvXv3kJCQ4LBMbGwsrl+/jrCwsAHbcXNzQ1JSEpKSkpCbmwuFQoGKigq8/fbbzxxDf/n5+YiOjrY7IWKMMfbP4lvLGGP/eqGhobh06RLMZjMePHgAm82GzMxMtLe3Y8GCBaitrcXt27dRUlICo9EIoPcbLnl5ecJlAXV1dSgqKsJXX33llDGFhYWhubkZpaWlaGxsxK5du6DVau3GbTKZYDAY8ODBAzx+/NiuHT8/P0ilUpw+fRq//fYbHj16BABITExEeXk5ysvLcevWLXzwwQeiTUpERARSU1ORkZGBS5cuQafTYcWKFaKTqqSkJMTFxeGtt97Cjz/+CLPZjKqqKmzatGlQG4j+srKykJ+fD61W63A83t7eWL9+PdauXYvi4mI0NjZCr9dj7969KC4uBgCsXLkSt27dQnZ2Nurr6/HDDz+IbnIbyIQJE5CWlobFixejrKwMJpMJNTU12LZtm7Axys7Oxi+//ILVq1fDYDDg9u3bOHHiBNasWQMAOHnyJHbt2gWDwYCmpiYcPnwYNpsNERERz7UOADB58mSkpaVh9+7dz12XMcaYE73A+BzGGBsUo9FI06dPJ6lUSgDIZDIREdHVq1fpzTffpFGjRpG3tzclJCRQY2OjUO+7776j6OhokkgkNHr0aFKpVFRWVkZEzgn2/+ijj2jMmDHk5eVF8+fPp+3bt5NcLhfyu7q66J133iGFQkEAqKioyGE7hYWFpFQqycXFhdRqNRERWSwWWrVqFfn4+JCfnx/l5eWJgv2JiNra2mj27Nnk4eFBwcHBdPjwYQoJCRGC/YmI2tvbac2aNRQYGEju7u6kVCopLS2NmpubHc5xoGD/7u5uysrKIplMRgqFgj788ENavHixKKjdZrPRzp07KSIigtzd3cnX15dSUlLo/PnzQpnjx49TWFgYeXh40IwZM2j//v0EgDo7O4moN9i//xr2sVgstHnzZgoNDSV3d3fy9/enuXPn0q+//iqUuXz5MiUnJ5OXlxd5enpSVFQUbd26lYh6A//VajWNHj2apFIpRUVF0ZEjRxyuwZMcBe+bzWby8PDgYH/GGHuBRhA5eEGZMcbYf5LZbMb48eOh1+sRHR095P1t3boVX3/9NVpaWoa8r3/KkiVL8McffwjfCmKMMTY0+NUyxhhjduLj4xEfH+/0dvft24eamhrcuXMHJSUl+PLLL532XZ8XrbKyEl5eXg4/TMoYY8z5+ESGMcaYwGq1wmw2A+i9bUypVDq1/bVr1+LIkSN4+PAhgoODsWjRIuTk5MDN7cXcPdPc3IzIyMgB82/cuIHg4OBBtdXZ2YnW1lYAvbep+fv7O2WMjDHGHOONDGOMsf+s/hs3R0JDQ1/YJosxxtjT8UaGMcYYY4wxNuxwjAxjjDHGGGNs2OGNDGOMMcYYY2zY4Y0MY4wxxhhjbNjhjQxjjDHGGGNs2OGNDGOMMcYYY2zY4Y0MY4wxxhhjbNjhjQxjjDHGGGNs2PkbTWwebnDFfsAAAAAASUVORK5CYII=", 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" ] }, "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 let's 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": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'geolat_t' (yt_ocean: 1080, xt_ocean: 1440)> Size: 6MB\n",
       "dask.array<open_dataset-geolat_t, shape=(1080, 1440), dtype=float32, chunksize=(540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    geolat_t  (yt_ocean, xt_ocean) float32 6MB dask.array<chunksize=(540, 720), meta=np.ndarray>\n",
       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "    geolon_t  (yt_ocean, xt_ocean) float32 6MB dask.array<chunksize=(540, 720), meta=np.ndarray>\n",
       "Attributes:\n",
       "    long_name:     tracer latitude\n",
       "    units:         degrees_N\n",
       "    valid_range:   [-91.  91.]\n",
       "    cell_methods:  time: point
" ], "text/plain": [ " Size: 6MB\n", "dask.array\n", "Coordinates:\n", " geolat_t (yt_ocean, xt_ocean) float32 6MB dask.array\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " geolon_t (yt_ocean, xt_ocean) float32 6MB dask.array\n", "Attributes:\n", " long_name: tracer latitude\n", " units: degrees_N\n", " valid_range: [-91. 91.]\n", " cell_methods: time: point" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coord = 'geolat_t' # can be any scalar (2D, 3D, eulerian, lagrangian etc)\n", "\n", "var_search = cat_subset.search(variable=coord, frequency='fx')\n", "variable_as_coord = var_search.to_dask(xarray_open_kwargs=dict(use_cftime=True))[coord]\n", "variable_as_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', frequency='fx')\n", "yu_ocean = var_search.to_dask(xarray_open_kwargs=dict(use_cftime=True))['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": [ "Now load the thickness and the area of the t-cells and from those compute the volume of each t-cell." ] }, { "cell_type": "code", "execution_count": 10, "id": "7ce8c76f-02b7-4c6e-869e-7accafe4e1b8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/intake_esm/source.py:308: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n", " warnings.warn(\n" ] } ], "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 t-cells\n", "\n", "var_search = cat_subset.search(variable='area_t', frequency='fx')\n", "area_t = var_search.to_dask(xarray_open_kwargs=dict(use_cftime=True))['area_t'] # area of t-cells\n", "\n", "dVt = dzt * area_t # volume of t-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(dVt).where(~np.isnan(dVt)), bins=[bins], weights=dVt, dim=['yt_ocean', 'xt_ocean'])\n", "histTVolCoordDepth = histogram(variable_as_coord.broadcast_like(dVt).where(~np.isnan(dVt)), bins=[bins], weights=dVt * 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": [ "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/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 14.63 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.14 s, sys: 443 ms, total: 6.58 s\n", "Wall time: 6.65 s\n" ] }, { "data": { "image/png": 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", "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/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 14.83 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.56 s, sys: 622 ms, total: 7.18 s\n", "Wall time: 7.36 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 `dVt` as the weights and we only do the comparison for year 2000." ] }, { "cell_type": "code", "execution_count": 14, "id": "bf7b4fbe", "metadata": {}, "outputs": [], "source": [ "variable_to_weighted_average = variable_to_average.copy().sel(time='2000').mean(dim='time')\n", "variable_to_weighted_average = variable_to_weighted_average.weighted(dVt.sel(time='2000').fillna(0))\n", "meanweighted_y2000 = variable_to_weighted_average.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/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/distributed/client.py:3363: UserWarning: Sending large graph of size 14.83 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\n", "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.09/lib/python3.11/site-packages/dask/_task_spec.py:764: RuntimeWarning: invalid value encountered in divide\n", " return self.func(*new_argspec)\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." ] }, { "cell_type": "code", "execution_count": 16, "id": "d66429bb-0dcd-4974-aafb-9fed2a44d093", "metadata": {}, "outputs": [], "source": [ "client.close()" ] } ], "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:analysis3-25.09]", "language": "python", "name": "conda-env-analysis3-25.09-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": "3.11.13" }, "thumbnail_figure": 1 }, "nbformat": 4, "nbformat_minor": 5 }