From 7091407d5f98ea4e5696134cfd9a09d42e69378d Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Wed, 8 Sep 2021 14:23:09 -0500 Subject: [PATCH 01/23] fixed yaml --- .readthedocs.yaml | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index eb789b5..f2debee 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,15 +1,18 @@ version: 2 -# Build PDF & ePub -formats: - - epub - - pdf conda: file: docs/environment.yml + python: version: 3.8 install: - method: setuptools path: package + sphinx: fail_on_warning: true + +# Build PDF & ePub +formats: + - epub + - pdf From ea39c36507013ceeab52bd6d2d1ce811de27dca5 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Wed, 8 Sep 2021 14:26:15 -0500 Subject: [PATCH 02/23] fixed yaml --- .readthedocs.yaml | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index f2debee..d83fa78 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,13 +1,7 @@ version: 2 conda: - file: docs/environment.yml - -python: - version: 3.8 - install: - - method: setuptools - path: package + environment: docs/environment.yml sphinx: fail_on_warning: true From e8a8e3316ebbce933d06e938821257f44b3a8ca2 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Wed, 8 Sep 2021 15:16:11 -0500 Subject: [PATCH 03/23] mock imports to try to get docs to work --- docs/conf.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 79b990a..31ab8e0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -61,6 +61,10 @@ "sphinxcontrib.srclinks", ] +# packages that I don't want to install for docs but package depends on +autodoc_mock_imports = ['cf_xarray', 'cmocean', 'dask', 'jupyter', 'jupyterlab', + 'matplotlib', 'netcdf4', 'numpy', 'pip', 'requests', 'xarray', 'xcmocean', 'xesmf'] + # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] From bbdda38abb5548adcd5af943dacd187402837497 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Tue, 14 Sep 2021 10:50:05 -0500 Subject: [PATCH 04/23] hopefully fix for docs --- extract_model/extract_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 10ad7b9..db3bd37 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -67,8 +67,8 @@ def select( ------- DataArray of interpolated and/or selected values from da. - Example - ------- + Examples + -------- Select a single grid point. From 69a1b95682357d3fab862c421ca1d4c0545ef504 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Tue, 14 Sep 2021 10:54:18 -0500 Subject: [PATCH 05/23] precommit changes --- docs/conf.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 31ab8e0..a69f64a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -62,8 +62,21 @@ ] # packages that I don't want to install for docs but package depends on -autodoc_mock_imports = ['cf_xarray', 'cmocean', 'dask', 'jupyter', 'jupyterlab', - 'matplotlib', 'netcdf4', 'numpy', 'pip', 'requests', 'xarray', 'xcmocean', 'xesmf'] +autodoc_mock_imports = [ + "cf_xarray", + "cmocean", + "dask", + "jupyter", + "jupyterlab", + "matplotlib", + "netcdf4", + "numpy", + "pip", + "requests", + "xarray", + "xcmocean", + "xesmf", +] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] From d56919c58730dead8866db74d1a651ed215c8c04 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Tue, 14 Sep 2021 11:03:08 -0500 Subject: [PATCH 06/23] try to fix docs env in case that is problem --- docs/environment.yml | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/docs/environment.yml b/docs/environment.yml index c55e4bb..d3e48fd 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -1,26 +1,27 @@ name: extract_model_docs channels: - conda-forge + - defaults dependencies: - python=3.8 # If your docs code examples depend on other packages add them here - extract_model # These are needed for the docs themselves + - numpydoc + # https://stackoverflow.com/questions/68642540/nbsphinx-causes-build-to-fail-when-building-jupyter-notebooks + - sphinx==4.0.2 + - sphinx_rtd_theme + - ipython + - nbconvert + - nbformat + - ipykernel + - pandoc + - recommonmark - pip - pip: - docrep<=0.2.7 - - ipython - - ipykernel - - jupyter_client - - nbconvert - - nbformat - nbsphinx - - numpydoc - - pandoc - - recommonmark + - jupyter_client - sphinx_pangeo_theme - sphinx-copybutton - sphinxcontrib-srclinks - # https://stackoverflow.com/questions/68642540/nbsphinx-causes-build-to-fail-when-building-jupyter-notebooks - - sphinx==4.0.2 - - sphinx_rtd_theme From 1a8e0e057f362242a270722b05494b1f7c210791 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Tue, 14 Sep 2021 11:31:22 -0500 Subject: [PATCH 07/23] maybe this will help --- docs/conf.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index a69f64a..5240192 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -62,21 +62,21 @@ ] # packages that I don't want to install for docs but package depends on -autodoc_mock_imports = [ - "cf_xarray", - "cmocean", - "dask", - "jupyter", - "jupyterlab", - "matplotlib", - "netcdf4", - "numpy", - "pip", - "requests", - "xarray", - "xcmocean", - "xesmf", -] +# autodoc_mock_imports = [ +# "cf_xarray", +# "cmocean", +# "dask", +# "jupyter", +# "jupyterlab", +# "matplotlib", +# "netcdf4", +# "numpy", +# "pip", +# "requests", +# "xarray", +# "xcmocean", +# "xesmf", +# ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] From bd9a335c66f4749dc643ce484a18067eedb58dd1 Mon Sep 17 00:00:00 2001 From: Kristen Thyng Date: Tue, 14 Sep 2021 12:16:44 -0500 Subject: [PATCH 08/23] readthedocs yml revert --- .readthedocs.yaml | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index d83fa78..dd5bd02 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,12 +1,9 @@ -version: 2 - conda: - environment: docs/environment.yml - + file: docs/environment.yml +python: + version: 3.8 + install: + - method: setuptools + path: package sphinx: fail_on_warning: true - -# Build PDF & ePub -formats: - - epub - - pdf From af0decc42cc9dfb35facc91ebc5d5383a9703ff1 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Tue, 22 Feb 2022 18:03:06 -0800 Subject: [PATCH 09/23] Add rough support for Pyinterp for systems without xesmf. - No current support locstream - Refactor of this rough code to match xesmf in progress and will be deprecated soon. --- docs/models.ipynb | 1475 -------------------------------- environment.yml | 3 +- extract_model/__init__.py | 4 +- extract_model/extract_model.py | 386 ++++++++- setup.cfg | 6 +- tests/model_configs.yaml | 67 ++ tests/test_em.py | 165 +--- tests/utils.py | 21 + 8 files changed, 475 insertions(+), 1652 deletions(-) delete mode 100644 docs/models.ipynb create mode 100644 tests/model_configs.yaml create mode 100644 tests/utils.py diff --git a/docs/models.ipynb b/docs/models.ipynb deleted file mode 100644 index 8f182b9..0000000 --- a/docs/models.ipynb +++ /dev/null @@ -1,1475 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "47162460-42fa-4f36-8e91-5ce45c976fcb", - "metadata": {}, - "outputs": [], - "source": [ - "import cf_xarray\n", - "import numpy as np\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "import xcmocean\n", - "import cmocean.cm as cmo\n", - "import extract_model as em" - ] - }, - { - "cell_type": "markdown", - "id": "1e0af68b-a8da-4c43-8d6a-3db9a5e24be3", - "metadata": {}, - "source": [ - "# Generically access model output" - ] - }, - { - "cell_type": "markdown", - "id": "1ed06a35-45ab-4ee7-8039-ff11f164caed", - "metadata": {}, - "source": [ - "## MOM6" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eecc74e2-adb0-4808-b568-0654b9cd82a0", - "metadata": {}, - "outputs": [], - "source": [ - "url = '/Users/kthyng/Downloads/20111231.ocean_daily.nc'\n", - "ds = xr.open_dataset(url)\n", - "ds = ds.cf.guess_coord_axis() # setup for success with cf-xarray" - ] - }, - { - "cell_type": "markdown", - "id": "579965a6-086c-4a52-8e40-a1ed321b2198", - "metadata": {}, - "source": [ - "### grid point" - ] - }, - { - "cell_type": "markdown", - "id": "3a1eea02-288d-40b4-bed5-9b60051f27ed", - "metadata": {}, - "source": [ - "#### horizontal interp, vertical and time isel" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "99e93b53-7c2a-42ba-ac1d-451a8870a1dc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'uo' # this name should match the custom_criteria for xarray (see __init__)\n", - "\n", - "# sel\n", - "longitude = float(ds[varname].cf['X'][0])\n", - "latitude = float(ds[varname].cf['Y'][0])\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds['uo'], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.sel(sel).cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "e833f054-c0ac-4866-81b8-36ac2db7d822", - "metadata": {}, - "source": [ - "#### horizontal and vertical interp, time isel" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b6023361-33ca-49cb-97f0-42d650c4d84c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "longitude = float(ds[varname].cf['X'][0])\n", - "latitude = float(ds[varname].cf['Y'][0])\n", - "Z = float(ds[varname].zl[0])\n", - "sel = dict(longitude=longitude, latitude=latitude, Z=Z)\n", - "\n", - "# isel\n", - "T = 0\n", - "isel = dict(T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, Z=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.sel(sel).cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "a139e03b-3219-4283-9863-745b59890ff1", - "metadata": {}, - "source": [ - "### not grid point" - ] - }, - { - "cell_type": "markdown", - "id": "292eab46-3d23-4c85-8d45-f8cd22c72675", - "metadata": {}, - "source": [ - "#### inside domain" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4de34458-2864-41f0-b7cc-1b9b19ed9d17", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "longitude = -155\n", - "latitude = 54\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "fig, ax = plt.subplots(1,1)\n", - "dacheck.cmo.plot(ax=ax)\n", - "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n", - " vmin=dacheck.min(), vmax=dacheck.max(), cmap=cmap, edgecolors='k')" - ] - }, - { - "cell_type": "markdown", - "id": "513df367-84bd-450b-b7d3-fa258b2bc75f", - "metadata": {}, - "source": [ - "#### outside domain" - ] - }, - { - "cell_type": "markdown", - "id": "441707a7-d6dc-44e1-9272-8a02e44a60fa", - "metadata": {}, - "source": [ - "Don't extrapolate" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5aa7cdd9-f6a0-4500-9dba-dc0fb37b180f", - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "the input longitude range is outside the model domain", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvarname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlongitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlatitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miZ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/projects/extract_model/extract_model/extract_model.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(da, longitude, latitude, T, Z, iT, iZ, extrap, extrap_val, locstream)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mextrap\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlongitude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlatitude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0massertion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"the input longitude range is outside the model domain\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m assert (longitude.min() >= da.cf[\"longitude\"].min()) and (\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"longitude\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m ), assertion\n", - "\u001b[0;31mAssertionError\u001b[0m: the input longitude range is outside the model domain" - ] - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "longitude = -166\n", - "latitude = 48\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False)\n", - "\n", - "em.select(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "id": "d7ed6e64-1ce8-419d-9f18-b6810010b2cc", - "metadata": {}, - "source": [ - "Extrapolate" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b1cbf4ba-4e39-4963-9ddd-0119ff5e24d6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "(-167.0, -143.0)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "longitude = -166\n", - "latitude = 53.5\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=True)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "fig, ax = plt.subplots(1,1)\n", - "dacheck.cmo.plot(ax=ax)\n", - "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n", - " vmin=dacheck.min(), vmax=dacheck.max(), cmap=cmap, edgecolors='k')\n", - "\n", - "ax.set_xlim(-167,-143)" - ] - }, - { - "cell_type": "markdown", - "id": "4531d5bd-7746-4ade-843e-bc0be3a52433", - "metadata": {}, - "source": [ - "### points (locstream)\n", - "\n", - "Unstructured pairs of lon/lat locations instead of grids of lon/lat locations, using `locstream`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2fbb0086-fd12-4072-b270-81c9314ca4f3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "# this creates 12 pairs of lon/lat points that \n", - "# align with grid points so we can check the \n", - "# interpolation\n", - "longitude = ds[varname].cf['X'][::20].values\n", - "latitude = ds[varname].cf['Y'][::24].values\n", - "# selecting individual lon/lat locations with advanced xarray indexing\n", - "sel = dict(longitude=xr.DataArray(longitude, dims=\"pts\"), latitude=xr.DataArray(latitude, dims=\"pts\"))\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, locstream=True)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.isel(isel).cf.sel(sel)\n", - "\n", - "assert np.allclose(da_out, da_check, equal_nan=True)" - ] - }, - { - "cell_type": "markdown", - "id": "e0940537-0911-4f77-92d5-5bf3b81ea055", - "metadata": {}, - "source": [ - "### grid of known locations" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "658798af-6f51-4297-bce1-b83b2b1ecdb3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "longitude = ds[varname].cf['X'][:5]\n", - "latitude = ds[varname].cf['Y'][:6]\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.sel(sel).cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "9158bffd-4a87-4db4-927e-0a5d8dc7370c", - "metadata": {}, - "source": [ - "### grid of new locations" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "affa4fe0-c2ab-4a12-a199-aa39c1725b79", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'uo'\n", - "\n", - "# sel\n", - "longitude = np.linspace(ds[varname].cf['X'].min(), ds.cf[varname].cf['X'].max(), 30)\n", - "latitude = np.linspace(ds[varname].cf['Y'].min(), ds.cf[varname].cf['Y'].max(), 30)\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False, extrap_val=np.nan)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "\n", - "fig, axes = plt.subplots(1,2, figsize=(10,4))\n", - "dacheck.cmo.plot(ax=axes[0])\n", - "da_out.cmo.plot(ax=axes[1])\n" - ] - }, - { - "cell_type": "markdown", - "id": "01ed35fe-cd31-44b0-90d2-423dc14f3393", - "metadata": {}, - "source": [ - "## HYCOM" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9870855d-79a3-48e0-82ff-bfbc5c6e563b", - "metadata": {}, - "outputs": [], - "source": [ - "url = '/Users/kthyng/Downloads/hycom.nc'\n", - "xrargs = {'decode_times': False}\n", - "ds = xr.open_dataset(url, **xrargs)" - ] - }, - { - "cell_type": "markdown", - "id": "c79341d1-492e-4896-8c49-aa8db06a7aba", - "metadata": {}, - "source": [ - "### grid point" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8c796884-ee95-4410-8a6c-fbc455041932", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "longitude = float(ds[varname].cf['X'][100])\n", - "latitude = float(ds[varname].cf['Y'][150])\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = None\n", - "isel = dict(Z=Z)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.sel(sel).cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "40f3ca75-f2af-43bf-9f8c-0818b7072d2a", - "metadata": {}, - "source": [ - "### not grid point" - ] - }, - { - "cell_type": "markdown", - "id": "caf72205-aec3-42dc-b77f-b12a931be41c", - "metadata": {}, - "source": [ - "#### inside domain" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ea47f0dc-a5ed-46f4-81c0-2044856c3f2b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RXvDNp9b44NmE2eAKjd4WhTjDOEy5NZ6RZDmOrnNupUkx/SxJOKLRO4uq2SScJslKHF1BKa8zO7xCUWSomkVz9RKH/hJxVpBkOUkufx8FIQfzoH7eZpmcyE84Bve3m6iKgiYEjq7Rtiz6no2pqdiGgq3vUxTr+HHOcBYRphkPnrp4kzdpRVGydOqNlXf3XzxaerPf93vB3nKHsrCyLMdCiE8hFWwPhBBrVXSyhlSkfbXP/DPgnwE88uhD5cmOw+M3DrB1ndPdc7in72c4S3huMOTyF56lZehydakqOLrOmfbXs3bft/CB+/8ga0/+CF+8/Ul+/XDIJLV4uJ2y0jiJ6S4zy0qCJOPGaMrNqc9BGHOu5XIcyYnyXFMSuVcbDnE+4/HBBFcLWPdcPNMgyXJURWDrGllUcLbdoi1+k+nBZfzxVVqGx8l3/CX8OGEUxXQsk/3ZmGmS0jR0Li336DVMbEPFKJ5ndniF6WSb7vpjOG6fcOrj9s9jN9c59JfYHSW4pk6QzlAVgR8ntWMzVTkRDsKIcZqzaslboKGpqEKwbFlggaYIbvgzNEXBVBQ2mxpt26r2dwLLHVEWGYqiyYlg5RLY72QwjXEilVb2Iq4NcZ6jKYKVhkOS54yimGmSMowTpknKNE5YdR3yosCPE5awURXBwSxAq5zwJIwZRxFZUeKZBg+un8MPM4Ikw1AVPMMgSAO2Wh6GqmJqKhdX2+jpM+Spxd5knSTLCdOM88sX0JIEW9dQheD+pQ5+nLA99TFVlVwIro+m5GVB37b4+LrOcZwwjBP2Q+kIo7xgkuQ0dIW2oZIVcDuIyMqSLU2lY5mc77cZ+CFJluOaBisNB9fUOdlxKKKnybMQRdFYO/ct5OajhEnO6gaMw4Sfv32EqR3RNHQGs4CjMCJIU7kqL0uO44Qr0whLFWw2TN69skTbktfG1FTiTDroOM+ZxQlBmjFNEjqWhWca7I1DVEVho+OSVc50x58zSVM0IcjKkigv0YQgzgu+eDTBrRy6pSrM0oKWoXIYRLww6LG19DGSsiSMcwxN5ZETXcr8Wu1g2m6feD5gPryG0z5Frktnkkx+nSQcoZkelt3BdPs8v+/hxz55UXAURhwGMmoJsoybs4jbQUqQlhiqoGXKY5omKX3b4mTLo2WbrHgmWfAEhtGlEGcI01VMdRvPBEPbYBbdLYUjZKrgntX2Vhfl+0BaORMbSQ78u8A3AMM7ivLdsiz/8uvt67HHHih/6Kd+ip944TovjFNGQU4UFViWwpKrcjTL8f0cXRcYhrwJTnY0Hu7aPLTU5YNnVymmn2V2fJUkGgHgdrZQet9KkGQ8sz/kh6/skBWQ5DJt1DQFu9Mcf56z1tV5/4rDumNzzZ9zEKWccAx6plFHK8uejZV8Cf/oCrrpYbdPoeo2ZZ6RhCMOeTfPHh5zGEQEWYajaTQNuXLWFMF93TabPRdTVdC1PQ6vfoo8C+meeh83/c16Zb/Ze1mpZDhLCNOMzV4DgHGQoimCXmOfID3B//nsDQZRzEdOrnNp5RhVtzj0lxj4IT9//RYHYcrFtssfe8dZTFUhzgvyKspXFcgLGAcJwyDE0XXOL4+IZwOw38nOKCBMsmpbmVJKshw/Sbjtz5in0tlsNl2mSYqpqniGjp+kjCI5kSzZNm1b8vY0oXzFfgCCNMVPUo6CkCgvCLIMtUp1DOMEV9d4tN/FUFVURaHnWORlWUeaALauMYsTVEXBjxMcXWMUxRyGEfMsp6nr5KVMmUzSlNtBwo2JPK8lR+FDK23WGjaGqrLiOpzte2jFiwTDa8wn2yiKhlC0+lqbjT5C1fH65/ncdXm+AE8fHXMQpliq4IzXQFMEcZ4zr851q+URZzlBlnGp32Wt2aDt6BjcAKAoUhRFZ5KcYjiPuDX2OQrDemybhs665xKmKVlRYmoqbctkGIS104nzgmmSMk5S5llOlJdYqqgjl42GxrmmS9PQ6/Suo8tFiWcadBsWSw0D8hdIZodopodmeiThMaPbT6AbHo3uWYSqUeYZqm6jWA8B4Mc5T+wM2J8HTJOU20HEOCnQFNCEqKOSJVOlZWhYqsqaY3HCc/EMGRm2bBPHUDE0tbrf5f1vqAqqojAMQsI044PnH3miLMvHXneC+iqm23q5dObVBKp/q+0/d/Cmv+/3gr3VEcoa8CNVHUUBfrIsy/8ghHgc+EkhxJ9Fyn//sa+2o3ks+FfPX+fFSUJewJKrshMVzOfSsfRaGg1TIS/KalICV5eO5ep4yhceP8LVu6w561zq98iLksOywPNDwiTj6aNjto8ypqMUp6lhmQrTSGDpgqKUTubmPEZTFFZskwe7LeI8p2kYvOfUKvn+TxIOj8Fbx2lvEs8HzA5fwLA76HaHMk/ptSw6vsn+PGCcpGRVvvk4TojynJ1ZwNbEI85zVEXhTPsPAHBrkAExfpwQZBm2rtF2TLmC1xQMzSBMCtLdH2Ow9zl0zUbd+CDwJf7Q6XUOCymRVBQpB899krJIWba6fP/5deL5gHg+YPh8iKLoqJrN6oVv4cBfYRImDGZzDoOIpmmwvOwAp5iJVeKZjJIAOekYckLPi4JWYmKoKuMoZn8ecBhEmJpKkErHMM8yhlVEdWsW0NA0upZB0zBoWyYH84AlxyZIM3qOxbppsO41+MKuDGTzsuR8p8WHvAZ5UXL5cMgJr4EB7PlzbF0jLwqMKlpzdJ21ZgPP0giTnKwoOfTD2sFMkpTTTZesLNk9GDJLC1ZdFVdTWLF1LFXm7Nu2yaX1OZPdzzKrUoFFkaEYHlZjmSQcIVQNe+lj3BoF/Oqzx+RFyt5cnvMkyfj69SXWvQZJJutDB/OArKp/jKKYs+0my67DsmdjaAJNCG6OVhhHMQDDICLO94iznI5lcqrpkVT3i2cYGJp0AH6ckJcltyY+8zRjmqR1esxUFc56DfmcCIUb/oyWodHSpROJ85zDMCLINLKirFOYfdtkNXA4ntuoyjquuUkelyRBTl70WTv9KJ6pUubXSDhNlBSoiiCPZQTvR/IY5mnGOElpaGodEY1TeWyaEDQ0lWXLYq1hs+65uKZep0rDNGMyiTmYBwSV40zynCgvyMuCSeUo74YJBOIuRShCiB9CqkkPyrL8LWn/ChH7j4BvRQp1fl+lNo2QzfD+EVJE9Z+XZfl37spBfQ32ljqUsiyf5mVp7jtfHyKbHr1hm6cZLUPlXMvg6cOY4SSj4aikWUlRPZAP9gy+fq1fPxCmKtMTixSFbagkWcmN4yl5dSM6uk6c51z1Y7K85P4tm0koV0r+PGcOfGTL4YVxykGQs2SmRHmOqar8wYtnsPJnoByin/kgeXGSIC1omLcRzQ9hFM+TKPdj6beJZwOujWbE1c0+z+Tq3VQVZlXhuWcqPHF4zDDO0ITg07tHAKzYOu9bW8ar8vd7/pxZnNKyTdZbA/z9ywz3vsTB6FnyMkfPTPLtX5GTnaLhNeTfV1OfZmuL7sn3Ydhdjrc/x+T4eRRFRzc8ehvvIzIe44lbE5J8QlwdY8cyaVf1nt1JVD/YizRWkEpgwFrTJUyzOrpoWyZxnrPjz4mLgqYuJ+d5ltMzZVFWrtKLKs8/JcpL3rvc4bYvwYErDaeuwbx/Q+ob9homVvRZsljWue473eeleY84z3FUFdfU8SydvndEUWSMwxZ2/iTZdAbTXZQi5Ux7k4vnv6E6n4IboynkOd9+9iSmqvL88JjDUE7ihqqS5Dm3Jj6Dmcq69342Vl2WysvEswHx/JCiyOieeh97s3UOBlMZCWlanQ6UqaIZ8+FVstinVFPc1Qs8PzzBURAyimLysuDaWH42TDJsQ5P1lVym84I0w1QV2pZZRyGGpuKZsh5nagrDeVxHJAA7szmTJGUYp1iqwrJl0tA0lhy7jvTuX+rgVvdWkuf4ccI4jDE1lZ5jy/ShIupr65oGszjhucExAHlVuB7M5tX2KxhawixOyYsCW9fwLJ28KHCqxYOmiHryd3WFnqWhVcez7shoMM5zro8n8v6zLPwkIS8K9ucBL05mTJKcozhjFH1lVG2or9ll+Ldvr92x+LdrP4yUd/rR13j/W5BdVc8hZWf+CVJ5WkU2P/s4sAN8UQjxs2VZPnu3Duy3Y291hHLXbIEeuTyMWW+qWB2dCy2bNceiY1n8yu0D/ui5Tc4vt3hhMGHdczFVlawsZEisqRSjX8bUbU6238ONY18WeIuCJM9591KTJVNlP8po6KIuxG64Dp8fHPPd9y1zGEb1xNi3LR6/sUdWdllpOJhjFUObsdFxeGHQIcl81lrneOFwiB8rGOoJNCUkyXMamnx4NCGqlaN8GlShcDtIGQQSlTUNC5q2gqsrjKKYU02PtX6nflClncLu+PQA3fDIsxCqv53WJprpMT++yvHwMtePn8YeP8+FIsXtbNXOxGtvYW/8cb506xBVOcbWNWxFk8VyVWEwC2pn5hgaQZLVRfXDIKpWyIIkz7m40qXvHXFz2GEWJyzZFmY1OeRFwQ1/TkNT2XAbzKtJz9TUaixk3eUwjNjwGqx7DbJSIp16jsWq9pv4R1dgrlPYHeL5AG/pAmk44mL/gJj7AEgyGaUWRcZ8eI18+hnmqkYW+wSzXXTDgzGk4U+z1Fim0T+LoXV5cu9QTuaK4Ey7RdOQUUyc5/XqftmVziEvCpJoRKO3hb30McZhym/s+oyjY46CkFMtj/WmS5LlrLUc2rZONHmB6eAZjoaXaXmnsLx1+p5d10Zu+zOahi6L50EIAdWiRyNIM7KylEVp22RrqUW3MSAJ94gmu4hMp9H6MH4kHU3bMglSGQnenCUMgpxHlywaukbHMqt6hMEsThnOQ/KipGWbuKbOsmcDL6eTwjTDNXXatrwH/CglSDOOghBDVWmaBmpVj3npeEzHMuk5Nqqi0KrSmX4k034rrgSCLFBpy7ZVOxLPNOoaKEBWlNyezes5YLFwMVUZ2eR6SVaqlUMpsXWFpinqzMSbNgFod6ezQlmWnxZCnH6dTf4Q8KOVtP/nhBDtqr58GnipLMtrAEKIn6i2vedQ3oxFecHNWcK3n27zQK+NaxqEaVav+D9yYoXLh8ec7jZZD36al7Z/gc8Pn0YTKl9/8pvIV/4eQ+X97I1mqMq4TonIycrmg67Dg2HMrj9jbx6SlyV92+RMu8WZdgtDU7F1jSTPuXI04sZ0xvY85KwnC86OrmOqMoXy1OBIrr73BJtNKVp8MA+41O+x7Kpkx2M+fGKlSgPJSTXOCzaqvPqLkxn7UUbHUthwdGZpwU9e3cfSDlgyVc41Xc53W3QbFnl6BaFoeKuXcHpnURSdsNgEwNFvMxtcIY6Ocew+D21+G2niYzaW0UyPtbPfSGy9j6tHE8xDn2W3gWvqrDg3iaYSFdfofZhZnHJr7LPiOmx0XExNqSOTURzXDvFSv0evsU88GwEd8moFTZJyczrjdhBxwrFoGhLJdmm5h1FNHkleEKQpYZrRtsw6V1+mz5JFPmk4YjS4SlzVv5rqBQxbInAMt08W+5iOQpk+i2PLMS/zDKd9ijJPuXrlJ9n1rzOMjnE0m67V41TvEdbOfiMgJ7j3nlzBNuSEduDHX+EIx1FMVkqkWss26DUMcvsbCPOC4SxiMJNoNIALSx2JaNM1zi+PyOLbpL6Pf3QFs7HMmd4FdKeDYXdpozMJE1Qhndjiu47CEFUoJHnO1YlPlOdYqiyU3/ZnLNk2602Plt3D69wvI++hjFyd6j69PZvzxUFIkpdseJqMDtOMrAhpWyZJllcpMp2WbdJ2pMMY+GE1QWu4pl5HpHvTmHEU14uZxeTuVACIIM1Y91x6joWqKDiGimvJVJUfCRl1VQ5RU0QdoQZpxjzNOAwiLFXhUr9Ht2HJiKhK52VFyTzN8EyDM+0Wx1GCpWaYqnS0wygnzmDJ1O6iQxGgvOEIZUkI8aU7/v9nFajojdoJvrKnzU712qu9/t7fxn7vqr1tHIqqCC60bM53WrimwTCIuDqeoimCvm2x0nD45C0f86VtVht/gHd+/ffxQfOAtEqLXB2F3Jr4vDCacDuI0ITgXf0Oj6wts96ySP3Ps65m3O9kGHaHokgx3QZ5ekAwvIblriNUDVWzOdsBMJgk99VFxufHU0DmgCdJJlE0CuwGEWe8BptNl75ns9Ya0mus8PlbB7Rtk42Wh2foPHN0zCiK+NjWSb4uyfjlGzsM4wRNCHqKQstQycuS/TDj53dG/NrBmA1H58FukxOeK2sPM522bbLsSsSQ6YYIVefkO/4Sw3mCoalY+TMURYqq2wyT+3hmR9Yl2pbJWsuh1zCACzR6UgB4HGZsdFxsQ6NtGzLFpz2Ioamsey5785BxEqNVaLMDf4W8WGYWS+e8M5vX9ZKGJifrpiFRUeeXPeLZl2rnZTgdNNMji32KJCMMU4Sqo2oW3uol1N43YqoKqnKLvDiJqtziePtzNLpnGeYP0jz6RbLEJ09lwT9NfBRFQzM9VpYfo+WeYjLb5tnhk3x5+CxfOrrMB8bPs9q+n+7KI3R77yNMVwnSAsfQMDWHgR9y+XDIdX+OJuQ5eqZB09BZdhsyoklTRlFMnOd0LIvBLABkNPP5nQywcfQmTeM0y1aDXsOk6R2RZmvMwrQGIhzMA0ZRzCCUx5+VpYw2qqgFoGnodCyzro/cGBU1v2Yx0c/TjP0g5OYs4qGeybmmRCNqQuCZhlwYVc4kLyTEfGfiszeVk+eC+xOkKcdRwiCKuDlLmCRy8fbOnsNJ12HJltBdkHWtk20P19TRFFE5pZennySTnBKJZtTIC4dZnBKkaV3r6ljmy/urHNrpjnSyB7OA/UBG+GfaTd612udgHvDs8ZhZWtTH8PRhTMu+i8isN+5Qjt5kUf7Vvqh8ndffEnvbOBRDkcW6m9MZXz4cMs9yWobOWqPBSsPhwfUuR2HEFw6G7AURXzw44tF+D1Pr0LZMNjoOvYbM6d+cRaw0DPbmIe9SBOQvEPm7PHvlJzgM9rE1h47dZ73/Lpq9C0RzOenqdocw3EbRbbT21+NHsUQVFQWDMOLmXObcx7FErmx5Ju+qEEiGqrIznnHoO6hKyJkKWz8MQtq2xWrD4TCIuHk85cH1Ln/mXRf4oSeusD0LaeiqrLkI6VS1tqjz39f9OS9OZqzYJqsNp+qXCO9Y65AFPobdIUhlIXo4CWjZ96MpgrGfkGQRpztN8qKsCp8qirJLGvukwYgs9kn9Xcoi49SJxzDdPtDny9eP8ZMUU1WwVIVelaq47c/59d0Dorxg3ZFpk90g4sokZsszeLDb5P5et65x5EWJYXfJ4hnj/SeJ947J8oiikFGbZXborDyC6fbJ0xBHv02ZZ+Rpim7uEaYbdE99F/tX/inDw/8fkd2n1b2fPAspigzd8EiiY9LERzc82v1LdFYeYX39g4SzXUbTa5i6h2l1aye0QMmpikKQVMTKosTVNExVkZBiX6ZhRlWhPKvrcRqjKKrTmJ/eOyQrYJzmLJmqjM6mM064DU6GTdp2im2oqIrkooyimOv+nJ0g5TgscHRB21TQFIFVobjysuAoDDmOEkxVZZqmzNIMV9dYtmSdK84L3rHU4UMbFudWmqjZc+TpCLu5TlEsk6dXJPpKSQnTVcZByqEveSD784A4z+vrdjgriJOCJC3JK/7KOCxwzYCWKbkhlipwNYWGrpIVJZaq0DVNNptuPZaLOlTLNlhqGBzNE4I0ZVzVqZZdp67LjaMIP0mqDIKM2K5OfMYVHP04TthsujiaxgPdNqaiMIhizOpe7JgmP3m3Jp7fOdjwDl/ZyG8D2cLAeI3X3xJ72zgUkDWGvCy4r+Wx7rns+jOmScqSI3PmHz13giCVD/Pjgwm3gz16psb97aYk0LkG33R+g7woOAwjHlzqAjBOztBegfXDd2BpDebJhDSPOR5dwXKWSaIR127+POv9d9LuS4DGcJbUsNR1T8J4o/yYtqHTt00u9XtcXJ0QTXawWuvcGrUJ04xlz6ZlbBNNd8nTEHRIohHn738fu5OTXBuOeer2kPP9Nn/6YsSXBut8ZvcAS1W4MpEP34Nd6QSivGBZVdlqe2x2Wpzs2OTpFcoig/wQu7lOmq0RhjJiCdKU/3j1JsM45YRj8acePk/LllHc8fbnuLr3OQbzbVa9s9hmB9tZZmnzQ6T6g1weTGBExblRWPcaBFUKYqXhYGoqy65DxzL5zeGIX9kb0zYlgudCy+ThXpv7um3WWg3yosC1NIroabIiRVE0mr0LtSNRNUtOeLpVn4Ou7TEfXiMJR6iaRaTqaOYuuemxeuFbWDr9QfI0lKoJRYaiW2SxLOwn0TGa6WE2llF0C6d9iq7+PpaDEfH8EFWz0EyPoshY8e7szrvOYCZXzisNh7ZtMosTbk5nxBWL36lqEiBXyHGe8+x4XvM9nj1KKErY1/P6tTiX0OqOZbLZebk32wm3gSYELSPmMgGTuGB3lhPlMRuOTkOTE3acyxrbAn3l6rImF+UFcZLTty0A+p5NNv40SSod7OzwBQDMRh/d6aAoOrYLM2WpVjU4jhOGUcJ+lOHHJXFSoCgCxxZkWUmWlRxPc4bVtTJNBddWaNsKPUsi43qmQUNXa4eb5HkdhbQtk7aj41kaN49zjsKwTpvFWc7BPCDKixoMAnBzOuOqH3IU5YyjAkONOeWHbLoWXdOga0no/gIA0a7O/02b4LcTobxZ+1ngL1Q1kvcCk4qndwicE0KcAW4je8T8yd+pg3qlva0cStcy+PDWBjbPkwa3eHD9A1w79MmLkp1RQNs2uNTvYWgqJ9wGeSmLk4DM55oHHPpLvOfkKsfziJZtsmxdRdVshHqBZu8CuuGRJj4v7P86zxxfJt75JRzNZN1ZYa3IaK0/zM8/VxDnB5xsefQcm5Mdmwdp07ZMxlHMuzaWUUf/gXC0zG7+CHvXZtyc3sbRNPKiQ+5t0G6dAWRaQylKdicpszil59jEec6t8YzT3Uu858RVNjvn+OTVW7QMndvzGFUonGo3SPKcLx4ccXwQ88WDI9YdG0e3KjBCiaMJTO2QZdeR6YNuk//mpIwAoskug+f/HnvxCMvs0F17jHNL38sDToeY+xhMI3aDiE+/OObLw6f5utVuzTa/uNytFAAkUODB9TZp8BSa6XFCH/Dh+9b51Esdrk2mmKrKO1f7XFydMNn9NEbUQXc65GGI7j6GH+cYtsBTd1AUjTBdlc4vyQmSnGSQA3NsvUuvuUqzeaPmYwhV3t55cZJcK8kVuYJOshLPVLGbezR6H0ZRdgmnu9jNdWbxCiiC/WlMkp8g10patsGKd8B8eI0s9smzEFWzsZsDHlx/J4OpJG22bQPbUNmctdiZ+FXhWUjuTSwXGE3D4HzTQVNEzfOYJDmmuiAUSscSpNlXOCVbl+lKzzQ4lee8oyf5KIdBxKAifU6SFO2OFXPXNFhtOHiGzlEYEWc5Dd1mmiTsz2Xa7f2nv4Fg8PPkWViPV5bINLDVWqcoMqk2UBXNs6IgykuirERVoOupOLrgQkumzlRF8DM3jxhXSMg7C+Erlk6cF1zzQ56dzKv9waqtcbIhx2QcxTy1k+AnaX2Mi/vKT2T0YaoKa3YDQ1V57njMU6M5k7hgFBQoQqK4XE0hKySnpmsaOKYEdqiKgq3fpWlP6vrcpV2JHwc+jKy17CAbvukAZVn+IPCfkJDhl5Cw4f9r9V5WdRH9BSRs+IfKsnzL2iK/bfqhXHjwgfKZJ36R/ef+EePJVbzGOr2N99PofVhOSpqE4A7nSS2/YmgKe9M54zDm8f1DAM61XL7p/CaO8pLM1adSUyv0d9nd/Qym7uHYfdzOFmZjGVWzEO57CJOCndGMJ/YP6VoGqqKwPw9YbTicano8tBFwebfBrYnPyZZHXpS8eDzm84MRWVnSMzV6pkFcFDQ0lbOtJiuuw2bPRcweZ3Z8lSgYkGcRjruOYXfQDI8s8UnCEe7p78OPMpKs4PM7ezJdpKo1v2CSpkySjJahcanbpmno3JzOeHEyI8rlPeDqCve3PU54Lhstj27xeaLpbl1T6Z56H4qi8bnrukTxVKS51YaDJkQtLXJ9PCUvCjqWyTtP9lHjL6PqMsVl2B3SbI0seII0GJFEI+L5oE47uZ0tNNPD7nyQccWGX+TSF8ifYQWjXdQQ4lymN+/rtis+iV6TPONMch08S6tJmEmeszed8exwDEDL0HloZYmLqy3mw09hNdc4CDarFEtZp2FS//NE/i5JOGI6voqiaNjOMu3VR6SGGpCGI4SioTsdZuIheo19yjxjb7bO8TyS6CwkYmmt2cDUFMZhQl6UDGZzeb0qDonk2qQ1MENTBD3HptuwWPFMFGW3hn7vTXrcPJ7y+f1Dblep1RVbSrKc8Bq8e3OZwTTmyb0BcZ6zZNuc73fqsVIVgaO8RBpIBQS780F2JzLN51k64yDh6nDMtcmUYZxwEErZl0UK66FOg7WGjaPreIZOXpbc9uX9tdCuW0DAZ2nBLCtktGJpxHlBzzQ4X0Vj55fkcf3iS9vkRcmplsea12AwC3huOOY4jqvxULg9j9kPM4K0pGkKtjwTV9foVnWsBeFxIVlzwpME3/efe/jNExs9q1x656k3tO3+p1/8fUFsfNs4lHe96+HyZ//Ft9FZfwyhahRphNVcpxBniPOCMMlxLY3BNObqcFxDEJ/YP+TWPODyKGYUFOgqfOtmi1XHplGtZHqOLTkViYTjfvTcCfzdn8YfXUU3mvTPfT+/8MIOXzgYcsq1UYXCcRzzR86fYcO9gdAfkJpW1WSxH4TcDiJmWcELw5Sj45SygMDPCY8z7K7G/RdsHuvbfPTkGhf0z3F4+3G5Qo9HMoLIZhiqRcs9RZaFLK09Br0/yMAP2Vpy0bU9jufL3DiekmQ5bduqJ+UFOgmgbUuS4TAIGQYRjq5xttdixTuQKTegLDI08xKKssvOqMuv3bjNcSyRR+/od2XBOctZdR08w+Dy4RCApapOskD5GJWwoq1rbLYPiGcDyiIji32EomG11plk5xiHkufgx0ldhA2q/HlcETxvBxFRXnLCMer6wblOC880cE2D5aaJJgRHc8l3WNSU9vw5+7OAnXnAQRhTka9ZsXUe7fc43+/Q5Ukif7dOsZV5Rp6FpImPqtkYdocyz5hPb9YaVKbVJQoGJImP29ykrKDXhtvHsLvEswGqbsvoqzgpCX3pFRRFpxBnyMqyVhaI85yeY7HctDBVhSCVSLG8LFlr2RjF82imhx+fAMDVbhLPBrUza/S2+Nwtl2vjKVlR0rUMHllb5mTnkDjf4PHrB/hJgmcYeKYhnYmu1yv32R3coQXjfEG0fOl4XAMANCFJt7MsY57mmOrL9RxNCMaVAoCU0JGvAzia9hWvL9Jdfcdis9NiEsa1FM7F5S4rnimBFmlIppzjE1du8cThiKM4Z9M1aOl6fU8P4wRNEbiadCqeadTqAgu48TRJ+XPv/8jdcSiPbb6hbfc/9cLvC4fytkl55UXJiUvfwbXDFmEo4YfLhVWLwS3gpwszVZVxFPGbI58rxwmuqXD/ko51R8qg59isNSVrOS8lxHWt2aCInuZo70vMo0M6zbMo5XXesbJGkKZMk5TjWDLmf/PgCNu4j1b0eb5uc5nP3XL50sERVyZhnQd2TQXfVAjmOWVRojsKqgprDRmxGJqKomgYhoeq2TjuCYLZbQhhMN8mSmdYukt445O0hlfouOsMJxlFkWE1+tzvraM7HTLlXC2SN5wnxJlEKnUbA9LYZ711njgvMIrnydNtyryLn23Jia8ouXpryLLb4eZoLAucisKG16glVCxVYX8W4OsJ791YZWfiM4pizrRbNcTz2cGQnVlAlOesOzYda4N1T6LbJmHC4PacOD+qrmdBVspi9jzNaOga0yRhL4i4MgmZpQWWJrg5j1ky5W1825/TiGI808DQFJYaRsV+z0hySaCzdU2y+vO8Vs1taBLeGmQyGlpZei+a9Szx7JA0PCZNpTNJq1RQFmsYdgfbXSeJRiiKRhQMCMJD0iwkG0XkRcrR8DJLvUvY3jqqZjMdXCaY7WJaMrpUFB2nfQpFP8awu5zunsGPcwa+lAfxo4ykQlrZhoZjaCRZyTjZgggcQ6buijyVisSKRhKNCK79ClvAo2cuERmPcfVoQl4UBOkJTF7kG87o7EzXmMVJTYpMspzBLGeaSESaJgQXljq0bFPCeisk3mKCbpoGpqrQqcipN/w5B6GMFuK8pKHLgvxiwm/oWp1+80yDYRDWCgB+kuDoEhXXcw2W9cushJeZT7cpE4+BItGTeRYSRz/Jtz/6vXSurdZZhZahyxpLnrPWsGuFg3mWVT95zWVZ6IHdFRNQ3j1i49vC3jYRyvlLF8sf/Hc/xu0qbWCqCuc7LUmECyKmacppr8Eja8ssN032xiHPHh5zdSLFHk84Fhe7bU62Pc4tT5mEK+RFSduWxfGj6DTP7A8xVQXXNDjdbeKZKjeO51wdjut8+a/t7BPlOW61CttqeYyimEmSEhcFwyhhJ0iZp2WF9DJYdyz6tkXTNGr+i6YIbF1na6mFHnwGp30KVbc4uvEZ5pObaJpNmsiVvW40GY+u4EeHpHmMKlR01cK1+/T6j0i5D2+dRu8sAMfz5brwLVdt23L1F/sYtgQihMUmO6OgRtEAeIZBnOc8c3hM35GFzTsnmnWvwbLrsN6S/JdFulCzPBLlfp7ZPeYolMzzaZLW+e37um0pYJl8pYClV43HzemMQRgxjFOO4hxLFbL2EBckeYmjC0xVcKltc6HT4ky7ybJn16rM8DJRTlVETcjbnc4IsowgTVGFQt+xsHW9qllo2IZ0Rqqi4Fk6rnKFPJUSNLrzMONQRk2G/0uEVUQTzG7jB3tE6Ywkj1EVjYbRQhEauiYjNj+SzHnXWqLffwTbW8fpnUU3PSbhCgM/5MZoyiiKOI7kxJiVJZe6Eriw0XHqNGiW+LVzEqqGomjkaVTLvtjeOq21h/GzrVp5168kf1L9Qa4d+vW4+3HCYRixPQs4iFLausqKbXK+06prjQfzAD9OasJinOc4muS1LCKXM+0mmz0XNf4yB1c/SRAeYugu7f6DqJqNZnp4/fP1fehHqYSdOzpa8SJlkRHPDhnuPE4Uj1CETC0WRcp8vkeahZy9+Mc5Vt7LzdGErJBy/KaqsOw2aNlGneIc+CFXh+NaXubXDsZcH2Z86s9855uPUJpW2XvP6Te07cEvXbkXofxeshK4PZuzPQu46ssJ8OnRjKyAd/ZcHltZqpnveVHSdkw+dGadj1a1lSy+TBpI2YskPIWjr0mI5WyA5ryLvemIoyBkpeFwa+Lz+O0DnhrNuTHK+OAJm5ahE+U58zSvHcQi5922TF4cTSCV/JMPrbTZbLqsuA7394ccvPRJppOrrC9/nEZvC4BJcoo4y2kZ22CcYpZtkicl3VPfRatSnVWz58ginyyW9YdWdMx8tksYj4iyGaPZLRqNdSxnmWC8DSAneC6jqzpZBIrdoTDfwbgquq7pQ4oiI8+qyTdMqlSTdHTLrsPFSj3A1nXWvUYttDiLJVS15xrYpsdiHaiZHhX6UzKdDZ1H1pbpuZVUzCSsVZnjPGcYhFyt2gOsOjZ922J7FqAJwTu7LmsNG1NV65rNAqG2ILnFuVzlu6aBZ+l4poqu7ZHGPmWR4bQewI9zXFPneB4xjqKKJ1IQ55KcF+c5cZZzf7VKD5KMQXyKJMvZ9WfE+U2WHJtl16HX+jiqV6ACZ6wdgvE2wfgm4WyXJPGJU58gGQPQ8k7h2H2ieIShS2SZlPu/wCzOK5WBkmmlxixrahp922Ldk5Olax4wGfooio5meHV9amELPpSmaOiGJ1F9SDCCrcs6VjTdRTN9Njrvq1WR40ymrVZsk0mSc2WScGWScHkcsGpL2HG/UpxO8pxplZKU6SQVQwjWmy6bzhWm25eZHF/h5vFTJLm8+PbwSfruJl5jnWB8E6uxjO32sZihFhamug6qTZ6GGHaH/qkPyUi7uUamnMNUFdLgKfavfoK9a5+gs7TL19/3YWbxinyGS6mQTP4CwxufQdVserrFidV1Cufd7IwCttoeWVHyqbsy6wjK3zmU1+8Je9s4FK1iy571GtwOUvbmGZaqVGiPshbv67kWevoM870nGQwvE8YjbLODpspmRkWZcUL/ViJL9nXwzPt5dnubX90bsuXZrDQcycwtS5ZMlcgr+dIgIkpDFAEfP+Wy1W5yutNko+NIFnGS10VKTRF8fOskneJLHO9+iRe3pQNI8oj0pZ/lVPGteKuXAGg7Okl4DPY78aOE43nEjeOixuP3nA26DQunpdFbvk0ajOikIfH8kGBykzAYkGcheRbS6G6h6BZ5GpJEI5orlzDsDmG6ys3hnCCV07+pLdNtDPAjSSjrNaSmU5IX5EVR9WPxuDYcs+vPyIqSaSK5GYSS0S95BS0MrSsjgxhAroLXvUYN0TaK54lnh6wUKbrTQdVtQu7nuf0Cczbn2bEkPfZMg4eXOqx7DU62XbrWDtH0Fkk4Ik+kDLzudtFMl3H5Dg59WfuRdaGQnmPTc1dw9IVsuai1p7KyqBV2p0lay72sNWQxOy9KJpU3XAgpjuKYrCg5jhKOAim/soC2tq0em71NOm4fZ3ZImacEk21+89q/5zN7jzNNM7bcLg/0Hma9tYXVXEOxHiLOCxktWloNidUUQR+JcjrZ8uREfXCZG9d2sZxlDLuDoXXqwrxQNISqo5kuht1FqGfr5yOfJ5IwWEWNQtGI5wNc5wp27yJhUtBtWLRnMsW12YzZ8edc9+fsRxn7ofxx/ZCWodaF77ysFIBtiwfXuyjBF5keXCFLfBruOo8sPYiqWxIpWQEXhKpj2B0U3aJII0kutaR6QRb7lLm8Bnbng9wahcyGCXkxrQAKy5w/9V9xxrnJeOcJ9q/8nFQzVjQppTO5SZr4eO2t+tznx9dQ/V1O2F1OvIzCfvMmoLxLKK+3i71tHIqtS74FwPe0vDpVAnB/r1PLPXjmbcY7EjGlqRaes0aSzlB0HdtZxu1syXB8mJNkOf/7lRd5cSIL0LNsjqNpnO+2OF2hsd7R0Wol04amcV+3LdVgmybl7AuIIqPfO0ucdVnzJALJij7Lc8/8CNN4SMdeodM8i1A0yiKr+2RIVrOCqtskRVkLKpqqSliF7yD7jUgdrRN4zim0/BrBZJs08RnPbnFj8gJxkdAze5xoncM2O5hWt5JXuUSYZLRsAz9O2PVnjMOYD51dY70lGM4THGT6qdfYJw1GaIZHo/Rpn5Tpku2pj1VpdcV5gSpk35dFQ6wFLyUvCpZdp24Q5poHxLMMzXTJ4hnRdK/S3hrx4Pr76wL+AsV1deLjxwmaUDC0TXLrFF5LJfU/Tzw/JJ4PUDULx9KqvH9R9Q8pasnyZW+dtr0LbOOZp8gLi7womanS2R1X6btZmjGp6mF925KoKEfCbVWh0LctgjRDXciCZBkd08RUFW5NfII0Y9k9y2a/w3x4DaFoLDnr3J/HzNIZhmIwigY4oysoigY8RVwRLIsiQ9NsHl15hNZ9386N4Zy2Y9JtDJjsHpJnUZ02Wvw23D6Zco5xkBImGXlUQgS2LlFapqZU0kD7FMVZhHua0ioxFFCVm5A9R9u+hG0o9FyjRn0tmnAtgCs7gUR2aYosqEuZFpW1hs271o8Z3vhZZlOZjrWcZRpd+SyF6Wp17yoEaU6Y5Lw4kk24PNPgbN9DFC/K+8v0KJSUIo3I4sv03PvZm87Ynvo1IXQUxZzp9Dm/8ccpRr9MlvhE80Nm05uYVoe1s99I5rxfsukNBTV7jjSQaMLp8ApZpWf3pk1Aec+ffIW9bRyKIOZ9J4bMigvEWc4kTOrcapimqIqczOLZgCQcEUcjijLDMju4TSmS2D31Pg78FYIJqErJr97e51Mvzul3NTZbGllZ8uJ0Rl4WNA2dDVdi4b98OGTZtjjhSakNU1XxLB2/eJgwzWAfTplPMj2+TBAdszu5BsBq8z7anQt4SxewmmukgdShmoQr+FFEnBXAFj1XcLo3RFE0Xhw0JWolTbk68cmqlMjLkE2Phzf+KLb3WSxnGVOXpEpT9zAMj2bvAq21R3hx2Cffn2BoKr2GxVpTypWvNRvo2h7BaBtXlT08NNOjzDOi6S6zkeTl2N4uW7bHxX6fmPsYzhJm1YS82RuRZmuMw1RGNmnGWqtBy9iuxSmLwpOIp4psGIxvysL2jV36pzIeOfFhQEYFWVFyWMmNSESSUqew7uSFCPc9jKcy/bZwwC/3UBEM5xF5sYqhqRiaXFnLFJJO27IqIqxMNU2SlN0gZJ5mbLWb2LrGezfWahl+oO6KOI5i7Eq3KpsFHMwDxlHM3lTHM9/B8up7uH/lEqcrp5km8pijeMTN258iSueEWUCYBRiqwZKzRhSPOLz9OFkecS0ecQ3odx/AcpZlzaV9Ct3pEOcbHMwSwjSoHcGCAwSy10vPscmKkoHfZDg/rImEvYaNra/L+yNLcC0Ngxsk8TFZPKOhWVzsdzi3co69ccjOxOfx/UNuBwlZkXHGa/DQSp+tzi63n/33+HNZR7JNCVhQNYs0WyMvJMrymi8jxhdGE54dz2kbKicrpeW11n24bkUaTaFUM9JghOm+xMXV+8jLEqdCE2qKYDAL0ITCg+sX8Pcl7aK7IutRducU47AkznL8KKXn3o/ZUrA7AlX7uRpc8WathHspr1fY26Yo/+hDp8snnvg37E6W2ZvOZc8M2+JgHnCm3aLv2bQdHVPdwd+/zHyyTXv1YXRHCgjemJyoi3f7QcjPXJuweyuiv2byjlWDrJAkw0WXvGXbqiGZC6lvR9fZ6Lh0GwNZoE1PMJhGqIrCctPE1vfJ06hueQoQzweYjWXc/nmC4r6a/5Kbj5IXsDcJKshmSl6WeIZsYrTesijza8yyTXZGs1p4EEBVFM7321VqSIIKhKpT5lL7ymqucRSdxrM0TF5isvcUezu/xvb4OU61L9L0TqFpNm5nC9/+CNeGY+K8YKvb4mRzh8Nrn0LVLPIsIgoGWM4yuuHVxLhGV6ZaFEVH0a2qYHwW2CYcbcu8eOcbyMoSW99nEq7gajeZD68yObxcrW7PUjjv5uZwRlCJALYtk/PLTZTyOgmnATDVHeJ8gzApiLOcWSxX6YsU3mLMFlpjrqXV22qKoG3rFNHTKLpFUNwnxzqRPJVxhRjzDOl0TjZ3mOw9xfjwGebhIQ27j9fewlu6gFClJthBsFnffzv+nLwsaRo6Z9vN+ji2llyiyeeI/F3Gh5eZhwOCZMw8mWCoFg2jhW12sMyOVD4GNMPDsLsyzVVFJLMoY+CHXB9Pv6KVQFwJaYIEUpi1Jpd0onElQT+vah8dy6Rp6LimUTHVTTxTrZuptWw50afZGgd+LLlbUcxKw+Fi/wD/4DLR/JBgdpskneF5p2i0TuH2L5BrFxnOZLr2+njCE4cjfvM4JkxKbENwsWPwzafWaFtW5VgcPFOtOTZwiiDN8aMMTZHQ8yTLGc7jSipf9kLRFFFzjoB6W4CbQx9DU/HjBFWRfKmttXNvukiute2y9eGtr74hcPwzl+8V5X8vmaIaZLHPJGxxUDVtWjRHCtKM95qS/d5trLDczzDcPoP0Evm0YBan/NyN68zTnBVbPlwfPdXgsqeyasvIZJzm7Ps5Gy2pm7UbhEySDM0XfOykyXLTrVI9gqd3HJ4aHDEIn+PmXHIdLrUd3rHUYbOzxnL/NLa+TzyTsh56xT4fBwljTrHmXEMtnifXLlb8F5mr35kFzLKMZcvifLfF15210Gdf5P7+MmGxSZjIB+2nXrjOf7xxm4ttj0v9ByTXAEGvZWHrUnFXGfwE0yyiLFLms11M3eNc/z20uhcw7C5lkTIbXcUDPnj2W1CUXWaxRcx9rNynE0130Z0O4Wib0N9lPr1JloVyoj18Bre5SZr4WI4sOmfx5XpVnachWVkSJgVhssyN4wlh6rLmfYDN+7ekE8wijPQZLq4+TJDmDGeyJ7xSXmeWbRIkaUVOXWEWS6ebF2XdE6OGjqYZsZWzhI1ryu6Gpq3V4pG1g9PX8MzbYELbCFlunmMwjZmEcRXRqKi6TaN7VtZsRlclhybxOdz+NRRFx7A6rJwCz7oPc6jW6MLjOMH0Z5yoJHhujUKWm+/BVn8DgDayZrYADeRZWJFmbclxcaX6s1DPEucFflIwnAcMZgFBmkl1ZstEFZJpfmM6Y5KmWKpKQ4tQhSIh0Vl2RysEQV6WVfOqMQAbjk7PNFityJBApSbRkLU6o2DFk9Io4yBlvTUgT6HR3SKaH6IbHl57i/baI+jOw/hxzngq+6/M4oS9echRnJMXkOYlaVjygkhZsY9paCoNTePxHXl8lqqw5NiY6j5tS2qOLVSeP3tzj8MwQlMEZ1tN1psuy56NZ6k1mm+BHsuLks2e9xVO0Y/vToMtmfK6F6HcaW8bh4KQaKJF+1tNCPbmIdf8kBU740u7B+wFEV3T4ITboG2fJclm+EnKC6MJN2cJlipYr/qQtHSdh7vyZonygtvzjKYle4+YqkJLlzIS4yTnU7cPODOdoQnBIIq5MgnZm2d4hoKlCZZMlYvdNmd7bTY6x+RpRJieJtdX0J0vE463aXR1ZvFpKbvP/XimyrWBX6cuzrRb3Ndts+vPcXSNZdfhc9dDVOUcXmywtaSgWqJmLn/+IOS5kVTBXXcsGppWQz17zhonV7+bFe+IJBzRiXys1jrD+SpXjyaQykLwgxen7D33sxzc+qs03HU6649ROO9mEJwA7QQOGq31DsWOjLbGoyv48ZDBfJt88AWSIiYvcta9Tdb778Ltn8fPtjBMgQo1kz3Jcm77M54bjnhwqcuD6+/HLJ4HQFF2cU3IixUMTTDLNhnOIgxN1pIc42WexIKR7VkWLdtkEsrOfTenM0ZRjJ8kzOJlXFMnySIcYxXbPUFRQKYIlPwGSXgsnYN5g+XmaZI8lzwWVSFIT6DaG7Tdx2h0nyKeDWqlAgDd9Iime1h2xLmVd9SINZDkzgWBDyBMCgzzUbz1d6GU16UqQ5HWWmMLUqm99DEO/BhSyIuw6jMTVUiznEWf+QXPYp5ltSrC7SzB1aRAZ5QXFbMdLFUKNmpCME5zjsOFCnGMKhI0ZcaGM67b7PZtk44vFbv7nk2YZLQdkzjfIKfE1F+itXwJu3OKID3B9VHA+PCwjiYWEaamyD5CAHtKRpiUhGnBVT/EqoAuB2FGnIGpvQypX2vYXB9POI6S2jFKTTOdozAkyDJujCZ1KnJhC6fpGTq9RrNq9Tyroe53w+7VUL7S3jYpr4cf3Cg//XN/A7tzikS5n6duD9me+Hxy95glU8NSBc+OEkZBzpKrcqFlMMsKLFWQFTDLCrY8mcpaKLou+jFsNgzyspTqxY5Vr/IAVhuO7PWQSeTV4jML/SFTVVhrumwtHct6wR3s6yQcYbp9hKKRBiMi4zGCJGPFMxmHKcNKQuPOrnbDeYyqCJY9m52RnFRmFS+gbVv0GmbNNH/m8JgvH0/JCmgbkmi2eEAXZlZKx0meM00Snh/L/HLL0NlqeTyy1mfZusr04DLdU9/FrVHIJIzrPiCqovDO9QlXvvgD7E9f4qa/zV40AaBrOKw7y7SMDiut+zjz4PegWA/VfIUkKxkHCTdHk7rvuUTRiar/hk5eyML+wA/Z9SUabcmxOdtr4RgaO6OZjB4qJr6qiJrIuBg7kEz7BRzYVNWvmGwWYoGLcTVSKRWjajZhscneRE7ui3GSTbEatO1dyVCv5EoAqTRdoayOow2pN1almO7sqpgVJZ5pcH65jWdKRzOcJ7VmlqGpDGYBszjBrpjgi2ZwoyiuU1ZxXjDLMlxNo2nIe243COumWaoCeSEjDZCT7Lqr0tbVuufIOM0ZRjm745z5PCdNCoqsRLcUbFtlpa2x7Mho/f62x3s31ui5BoNpxCxOax7LQjFhmqTkZUHftnB0/Stk8xd8leM44fY85uYsRVOkpleYliRZSVFKzUXb+MrVv6MLepbKiqXj6todHT4zJklagWOkKOpR/HLL4Lah0DKkI1u2LDabLh994F1vPuXVtUv34/e9oW0nP/nMvZTX7yUL42P2tn+FM83vIUeqlh4FIaddg6eHMcuOiqrAZJIxmWQczaRjOdGQzmbVlqux6/6cy6OY3eOMMMxZ6upYquCBVoOuZeDoOqsNHVvXa+jpouETyI6FLfuAJByRBiNCfxcCGM7/MC8cysZd7zsxZLz3JOFMqkyvbH0cp3MKPVOrbncFvUbVpCm9gn9wGU3xaFhbZMUJWSOomhG1bJO1ZoOd8azuy72ASG82XW7NA47ijEaVT9YUwecHx+zMZU+LcVgQpXKy0VXBqbbGlmdy2mtwpt1kvWUxHx7SPfVd3BjOawb8ggdycbVNnu3j2H1WgVO9R1g++SHszin8bIsgyVhvDZgPr+EfXUHVtnH75zkINtkZywhs2XU422tjagoDPyTOc47nUc0RuTr2+fLxlFla4OoKD3VkevGdJyPE5DLN5Usc+ku0bZ0yfZZwvs1sdJU08UkSnyyPONm7xDvPfoSd6VrtCBeaXuOq4H8wD7g1NtjqXeKke4i/f5miuMzW+sPsTpaZxSnjMGIcLdJgJ3Bc6h4tWTxDUSSQQahnibOohnjP4qTuXrkbhMyzHFUInhoc1Wi2W/OA/VCSGDcbBqdcB1NVyItSkhsrkImjaTR0lY5p1vd/UK3aF31N5LVOiPKSeVowDiu9NlOSQvfzrJ5sXU1hphY4piDLFNKkIA4LonlBbBcUhYwkXE3h/l4XQ1MYB2mtnnwUhFXXSgm/dirntkD3Gars1dPQNRpVpHbCbWAqE2ZZwSDIaZp3EFALiNMSf56jaQLXVnBNhTiDgyCv7oOUJTPl/rbHhttgtSzZnwfsBhH7YcbuNCfNS4oCTF3QslNONLRKIPXuTHvlPR7Kb7G3jUPRFIM0C1F0Cwpk4Tp1ZYc4c8DlccAkLLBtlaIomc/lCqZlKoDCUZzy0lSmTY5mOYoCJ1cN3rficH+7yX1d2QVysUJd9mya4jmi6S50vhE/SllzdwnG2+TaKcLRdq391Nl4F//h2QPJ4zAMJtk5+lsSOVUUab261bU92s4KqgLz4adIw2PMxjLeyiXKQm671hoyCWX6Z60lNbPCdJUwseoUwwJttdDB0oRUtp1lBcM4Y9uX37dYtVq6IC9kv+3Vqnf3cZRwMAu4/vwUPz7J/MYVskJOCr92MJYkw57LRttjvXUJ7fQWfU0hzgpuhTHXr07Z8V+ib1ucmnqc7n6AdkfHj3OujkIGszGGqnJ+SYIiJmHMS8djbvhzmlV09+J0xs1Zih+XfMN6g0f7PRxdo9ewadsGh75Le+lj5Ol1rOiz7N94skbwFEVKlkeE8YiD2S2uHz+N+tK/xTWadBsbbJz9FjLvIxiqWkUBGp5pEKQZe9MZSd5lo/9NpMNfIp4NMLVVrg3nxLmUhHl2MOTqUCXOSxzdo20t1Qx/QgjSw7p9QVZIKfm8LNgNQr5wGDCLC4K4ZD6foiggFEHLVek1FFZs2T0xK0o0UTKIYq76IS8MU+LK+TumYMVV2fJMPrS+zFrTldD2WDbkeghZfB9Xab/dIERTFExFqZWOJ0nKUZzVemauKSfu0FOZxwUNU2HJUbjUtuuGWX6SEI9llNdtWLimVBY4mAUyrSVEXRiPsxyzembiXPbcMTUVKgJq37Y4ESfs+BmaovBQV3JvhlHGOM2ZpzJFpykQZ5DkJUlcsj8uMA2FpiX3uV6pNjR0jUudFicbKdeduVRFLgqsqgHdoifQ4rq8absHG/4t9rZxKIbR5NTZb+UoOs0kDDBUhWXXwTHkQ56VBwyjnDAuCGvHAodBDg5EWcm+L2+0ReTyQLvBHzh/mlbxZfyjTzG5dYVpsMc0HnIk5NAZqsl6/xmavfuZ+Clp7KOZLs7yNxOkBUGSce0wo2n4L3NHMqmr5Oi3oUjRzEuk2SL1WDCYRpztn2e6f5ms6iiZhCOs5hqqfgEj/RLpZITR20LV5cO03JS/xwFV5BEzimKiagI8igsmcVE7joYumMbQthcPv3xgs7JkkmRc9WPysuA9ayt0G1aN7AnSlEtth6yQKY0wzfjCTamp5Og6cZ6zPZHnOowTJmnKKI4J05SNlkdYpZ5GFUooyQuuHB3z1HDMUSyVBPZnOYfjjG5T5ULX4NtOtrjU79WNt1xLQ82eIxxtE/jg9M5iun2avQsIRSPPJFmuKDKCyU2m0ZBJMiLMQl7yb5AfPsnS/q/x4Qvfy+al7+DmsMPeVKbOPFNycvw44dphzqX1SxRFxt5QalwFaUaUF7w4nXEUy0mvZcg+H5oiu05aqoKlqjiaRB+BTH3O01yq9MYFaQ5FVUBOkxKhlIBKQ1doGypd06Shq6hCOoElU8NdUWhoKm1DZ7PpsmRbtG2Lli25I6amcLK5U0u4qAqESc6K73AiCOuo+s7WvuMwIkgz/CTh+fFUAk2EoKGrddMwVcgo6WAe1P3gJdJwQJlnLDXO1PeIqihcrbqT9m2rFvRcLEbujA4ausaKbfJgL6/SsTaaIlh3Fosg2fN+0eE0ykuujVJmfs6MnGMFDqcZ/WbCuqtyoenQrMZmq+0RpBnTJKlT1FlR8uzxuNZwuxt2z6F8pb1tHEqpthloH+Lq7qFksdtWlZaQ+lLLlslWM0ZT4MWwIJjnrC7rqEIQZSWnXZ37mgaqELyr32Wr1+Z06zZXv/zf8xtHv8EgPOI4mTFKMo4TBVMtMZWSuBCs7X+BS+0znOu/h9MPfQ9Cf4ADP5akuWr1e3Gli22oiNnjZKGP7V4AzqLq2zXZaxFheJbOob+E2/soprrDfHit5oJk8WVMd5lx+Q4+f20oV+zOvK6dSGmQOUdByHGcyCKsIntDNHRZL/KTgu3DlLIoEdWEZ+iCrqfK7XUpA74bRPzKrV0e6LbZ6rZwdI1hIBsezdMMzzAYzsNa7M9PUnqOxaXlHiDrFy8dj5lWSDtVUWjZMk0ziqTEyTiKeX485ctHEbcHKXlW0u5onF3ReU/fZavl8UC/y5r5HKPbT+AHA3xkBAIQxiPUG7qUkrf7OO4JzEYfd/lC3XYgLzNszUEVcnERFzk350f8xJP/kPfufpp3vfcvMzPPVYTLl1V3VUVwPG/gRyk7E1kUPo5jJknGC+OUMJWOYSgKDE3QthTa5suF7xVbx9UkSvD2PGaWFUR5wWZLr9toHAQLwqrsGeJq0mk0dJWOJeVWPtCwWGoYRKNfJZrukkTHxOEIQtAND81ZRq90vCK7S8Qeefg50Gxct0+756Gu2pR5QFGk6KZHmsmmWWV+SBJK3sk3n+6gO48ynCcESV6n6wxNZdmzsfJnmB8/JeVi3HUUxaIAiuBpVryHCZOMYRAxqZrYLVo7Lzo0mlUDMFOTdbusKNlselzsdeqa1qJn0P5MOq+Hl2Xktz2VxNZNN+CyG708fjM5flFecnMWMcsyeqbBZtOTsjjFywX9SZpy1Y+JsrtUN74XofwWe9s4FD9J+Mz2Hs+NJTv+sX6LU03ZdySpmM4nqhu8t6XywjDF1hWWnaqLnGVw2mtwsuVxutvEj1JuTTfYfNff4byqoCi75GmEUDXC0TaTwWXSZEqeReiGh+VU0M7KmYRpRts2WG81UZVbRJNdykQW4rPYR9UtjucZw3mXMPFRFcFGx617tYDkk8yKVVRnHdtQpPT+rXGVwniRrCxZd2yOgpD7CpmSy4uSHX/OIIoYRhlHcVb1iFB4ehhjanCuZfCxE02iKvSfZ7mU1dAUznp23TrWVGXDp6vjKW3LxNE1KZvi2BzMApnP13X8OME1DVwWRX6FnmuhKuBWzsYzDdZbFkp5nY2ORZzL3jCHYcQXByHTsKDf1fj6Ew0e7nU42fK4uDohHN0knn+R48Eu89kug+lLHEeHdK0+umoSpfP6HsjHz9M0e7h2n6VIjvNsepOOvYIiNIJkjCo0hvGQIIsYJQGfH/wG+eN/mw98+H9B1e1a/DGeDTDdZQbBCcLKGTYNvZb979oZx4CtU6PL4lzCcC1VYZYVjCcRWQFBKmsQqiLwTMGqrdSRzJan1DUPVQgaVYfHlYZDz7E42dxhdvgCh7u7teCjbnj0T30Iw+7UMOyiujfTYESehuh2l2i6y/jwGfIswrQ6GFZX6ob1LnA0j2XDtsYpPDMlDUaM954kS36tTht6FQemvfoIWi7rRN7KJdlgLcgwNZW2vYvuPMxz+xNuTXz25iFxXrAfZgyCnK4tnWSUF/Qdi3ZV83MMjYUAeJjkjMOEwSzgYBbQtk0eWl2S17RqP/3QRkBR6OSpgaI0EKrG7mSZn3/pZi1bs0jjbc/kMaxWUvYvTmZMEpny3Z/lzOO7F6G8fWbQu2Nvm+EYRhk/ff2YvCj5+hMNlmyJZJLS9WotHvdIr8WSbXPQD3hu7BPlUvX35kzKomu7R6xYOmc92Yv+ZRSL1Glq2SYrbki+KyXNNU2igQBCf5d4/kOsn/s4u5NlgkTmiltGhmZ5RJNdJofP0F17jFm8wnAe8uxgSJDKQuw4ilnzGrQdE0Or2N2zjGf2B4yimGv+nIMoRROCgzBDFYKbswRNgV/aPcJSJKx5lsoHZpbJInacF/Qsg3cvK5xqOCxVhUmJHtNJKsSWoco0SN+zyYuSz9/al9FFJvtgeKbBI2t9euoznO956KbHLPZIspKBH0o4pgprbVsWbYsSQ1Nq/a3Z4ApJOEKoGo+c+q6Kxa7wzSclWm6l4bDWbNT9Lw6v/grDw6fIi5Q4nZHmEUke4epyopvGx6hCpWFIgaZ5MmEaD2VK0r9BmM2xtQbLzfskd0SzK6kT0MSYvCxQhcJN/wYnnv4x2v0HURStaiMse5dItWEpJ9PQNZJcSrFHecGSKWX777SdIJWdF7MSPymYV21yiwI0TXYMnGUFLUNj2bbqdrwL6K+qKPW1GQYRqnIKtbFJbkv0YF6UzNOMwyiFCBhBmMp9yPv8BGsth15jH810MRt98iwiCY8pilTK8QdP4VkPYmoqnnaVcLRNEslmZ0WRUhRZLU+iGx6hv4see7j9Czx/2MOPjwnSjJNtjxvHLoPZDlfGU4ZRtWCzDIkS9AomSUZDV+lVYIG8WjC1HSnKqii7jIMu4zCu+5YQSmTfov+Qo2uY2hIt7UXC0TbRfIDVWGZjXeN8pyX5PklKVkhk5iwr2BnOcKcBS6bGuHImUVYSpbJQf1dMlKDcnWhHCPHNwD9Cdl3852VZ/p1XvP+XgO+p/tWAi0C/LMtjIcQNwAdyIHsr0WRvG4eSJgWD45Q/cqnFu5eX6v7ew3mIoUnWb9eUKC1H17jU73Gp3+PF43EdwSwgpOeXPebDT9U6U0ZbKvIuWsf6+9fw/W3yIqVpeLSWL8mGR0dXuHH95zg8fJIH3vsX+eKOxyTUaa+f5eD5f0gYDMjyiC4SyjkOY3ZmAeMk5SBK2Z4FtSNbdLebxgk3/BkHYcp+KJFZQVoSJiVpLsEFRQFxIH+bjkKrpdG0FfIClhy5QpwkKQ1NJVqIEALLbgNb1yTrPn1WjmMwIjjaptHo84HNr2dnPKtqHhHvPblCI/sNDq7+iiQDOsu19tiZzQ+Rdh5kOEuw9X20xhpxXuCasid9nmX1tZLs72363gq2ofHhrXWM4nmy6DZlkZH6GrP5IYqi0+1dIk184uiYLI9oVUKIk2CPMJvXEQmApbkEyZj9+W38dIanu3SsZWxHkgR73jonq+t0ePgkw+A2SZ6QlxlD/zpJOkNTLdzmJqYpJeEXZqqyvuLoGkGacVytfhe5f0NVMTWVc3HCNX9e104mScY4kakuS1VoGwpnPZnr71hWvb84TdEqOLNaobVURcHQlHpfWaXpFqQpw2DRBiD5Cmn+RZ+fttWk21im3dFx9dtEE0kWBSrl6W00zSJEtvwt86xm5edZiJpZ6EYTq9GnKCoicHCCXX9YCZNKPsquP+fqxOezBzOOZjmWLvjAmsK6Y1fnKFNdCyUJ29BqnbAZMA6bPDs4qBdVmhDEmewlrwrZb8U1jWpxdoH26iXs+DKa6bEz6nLbl83expVAac8yaBUFmpDPy41JxGPLFj1TY17J8SxqpXfF7gLISwihAv8Y+DiwA3xRCPGzZVk+u9imLMu/D/z9avtvB/5iWZbHd+zmI2VZHr35o3lz9rZxKLal8H2PLPHhzRN1M61FgfXWxOeGP6/CYTlpX1jqsLXU4tKKzB/Hs2tkiU8+j7h9ecB8vouhe/jzXbzGOpbzOdzOWfwiI5oPaLXO1nIYIHP6htWh2zzLc3u/yo/9y/8LjWp0/4MQbNgdzrUvsNJ5ALtzit1ZwsE84InhjFEkRRXHccHtIKVtTGkZsjXqgpB2EOQkeck8LusJ5OggIZoXJLOcIitRDUEwEoR+jt/S6LY12obKOzpNVEVwwnNrbSfP0vG0q7IN7yQjnkuNs2C2i+NKfaelhoFjtAkS6Qy61g77L3yJy7d/ieuzHaZpjCYUOobDR6MRGw9orLce4MVBE1UErLVtimKdccVTUFsXsHuCJCtRlAFrrSGzeEWqP4+3awDCQu/LaZ1CMz00yyOeHdbtiBVFx5716WcRk9k2t0aXCbOAvMhJisUqucuSs8bK0sO0+pdorl7i5rDDesuiSENsb5377I4858lNrtz6BGkes+SdlvImtkSf9VyLcSD3uew6gFw9O7pWQ2TXmy69holraQxnCR8oSgazgGcOj1l3pNNZNFRzNI37lzp1ncazdLKirLkzqiKktpij12oKC8uLkJYGJzo20/ZFbhzL8Vr0ns+LEtNQKzZ9QlYWHM8VDK2DofZq4l+vb5AXEoUlnZbANQ/I00hGKKl0PIpuURYZht3h5rDD1eExcZ7X/U72JpJoeRDGJHlZowW/fBQx9DK2PJu8LFiybZYcW0q+zKSSwf484NYs4HaQ8NI4JclKWrbCQ12LU65D37ZoW2alxZcRphnDIKxQaxaTJCAurnF7HhMVEtL8jk6zbhd8uieVom+NAj5x7RYgU709M+DhLnzybkw6gLg7NZT3AC+VZXkNQAjxE8AfAp59je2/G/jxu/LNd9neNg6lbeh84MQqZ9xrZLFPo3eWL940eel4zPY84IuDkCQvWXUlJv44Ttj153QsF0Nt0XPuQ20orLcsUv/zLOt2rU0Vzw4lzyDxsdtSo2ihTZUGI6LpLmnVazxJfbaW3sm6d4aD2S3yMkMVGmveGdZPfKjmYPzmwSGf3T+qi4pSak4BJCprP8wYRQXTsMDSBT1HZRYXHI9S0qig2dExHRWnqVEWJVFQ1EX2Xl+nYSpseBpbno1X9dfeaHlSXNHQsA0FBdmUyT+6gj++itfe4vSj38twvsooybk1mFbtaG1WzRc4uvEke4MnuD7bYT+MuBWYxIUK5Hzm8F/xwas/yR989L/l9Jn/gqyUjm8YSbLenj/nheMJQZax6th87NxJPFNFjb9Enss2utPh80z9bbI8wtQ9NNPDdJfrCV4zXcpcpg8b0VmCybZsupRMmCU+qqLSNXo0zS6O0WZ55TG8pQu1XtvJjkxNuv0LsqVvILst2t46D93/pyoJeL36Lo+iSHG1m7R6FkWxTp5eIYt9THe5br9r3NFPJx4f0q8aVyV5znvWlwEqOX+p+OsYmjxvRTAOszpVKPvLaJxsuzX/KJz6hONt0tgnCga1GjGA7SzzwPpjTNvv5pn9YY0+W/SRMVVZ81lwWO6UXKnhzUDXMlhpOLRtD/DqnjQLC9O04s/s4ugaH9hco9fYR1HmQLOCNgs8QyHXZS1JcrpkC+BFNOwnCcdRwnV/zpVJwtEsJ4oK0rTENBVsS2Ewzfnl2ZzVZsSWZ3Cx7dUdIl84njBNU9lxcS7HQFPgQsvkYsNlw2uw1W1xtj/BP/wie889I2tBRcYfcDxsbx195Tv5teu7NdHyTZugQue9IVsSQnzpjv//WVmW/6z6+wRw6473doD3vupXCuEA3wz8hTteLoFPCCFK4J/esd/fcXvbOJRRnPK/fvlFvn61yWqjy+H1Odf9AZoiOIozZnHBsiuRJUex7O1gqi8XRMM0ZdltcODHmNo7MVS1mix25CRmuuTmo+xNIwbDgAfXc2wlq0UBRdXMyO2crYu7W1D3+QDQzEsc+DFP7g347P4Rv3EQc3ScEvo5mi5wmvJylIsUhiqwLKmWO44kATHPSjRDIQxzTFOh19J4sGdwqmK/x0XBsmXWKbyNtodjaDVaB8AzbzMbXGEy2SaOjmm0Njl58Ts5TC/wiStHXJ1cJStLmrpO09CZxQm9kw9iewNcu8+q1cNUpvTNGFMVzLOYnTDn2WnCyRd+nI+sPUIoLhEkGcfziO2pz44/p29bdC0DU1XZGc1o2SaO8QieeZXhzue4Nfgin97/IocxmErJ6Vu/gKOabHqneeQdfw4A3e6gmZeAy5jZMm0exHFPcKZIa/0rkE2k7M4HifOCoyDleBjhx4cchRF+XBLlBg19nWbVV32z08QxNLKiIANcU0NXd6qGa8dk8QtksY+iaJU0y0Z97wVpjlMdUxb5OI5EMUkZGL2C6Cq1xP/RPCHJCiZhzOXDYS1//+haH9tQge3qHLqkwYj5ZJvx5BpRNkMVGqbuYmQydddes2hbZ8iLgsMw4uYsIioKxnFBXNUHVSGwNIlmlIoPkvWuCsF1f85sb1jXEhfPg6WKCpVWsmppPNxr856Tq7jl05R5l0l8gsFsKHvDVGnYJJcNvBby9poi2A0iXpgEuLrCS9OEF3Zj/FEma0q6wHQUikLWmbKsZDrKuH2j4EktYGk1oNVQsXVBkr+srgDQc1Qe7tp885mTnO17aMWLpME2/mFEPB8QR3JxpwiNLAsJgwEcPMlHL34H12bn7sqcIwDljUcoR69T23i1xNlreapvB379FemuryvLclcIsQx8UgjxfFmWn37DR3YX7W3jUDYbGd92qsen9kZcyHKiPOcoznE1hWEkJSiCtJSkwVSmj64ZAXFe4GgaeVlw259zwmvUrXcNbhLPpLaT6S6zO0m5MZqiKYJxkLKXyhWorW+BKVMhYZqRh5JBvGBBbzalvliQ7hDnOZ8fHPPMMGE8zYiDgiSQBcPQz1ENhXiaIRSB09GIHYXIVChK6WgURWBYCpoqMCppinFSkBUyTbSQNfFMgxXdYc3dZbL3JEXsQ0Mer4+cmBvdLcx4Gad9iv34PJ/dvs0nd4/rFNyyo2Kpgrah0nNs7msu02is07dXsBcTt2oyS3y6xpSszAmzQLYHMKTOVI2KsgyCTPZxycqy7qzYtkxcV9ZFGkaLNauFJnxMRSUrc4I85jg6YnTwJLrh0dQfJEhzVO0iWvsiWhusSrY9TApKJFz5KEwId8dfwYvZDUJuzpNau0pV4ERD44RjsD3x6VgmqqLQtswKHHCSPH2CLJ5RVF0EDVfK9YdRVqeqVEXB1xRM7QLdzoCigJ5roFY1Fl3boyhkGlETgiQr6n7ui+vV0LWqB4yComgE3EeSlLR6oOo2zd4FKXufhlI40+5gNpYx3WXakUmYpsynGVG1+nZ1hSVLsuIHQc44KupU6XBecG0kYddpXhJFEkafJSWGpWDZCl5DxTYEZ5o6H1pf5nSnCcDN4ALDo5C8OOTmdMY1P2BnnjEK8krJ92XtsP0wYxjJ65/kJbcHKXGQYzVUVpd1TjS1WpASIMpzfnlnxu3dhPkoY3iQMHNULFvBMBSiuEBTBY9umHxwpSProOtzgtFzzOaHdcq0KNIaeWnYHco8q1sGHN/6HGdP3qVJR4Cm3pWi/A5w51FtALuvse2f4BXprrIsd6vfAyHETyNTaPccypuxnfEtXnr6o/zA9/wq/+43ZT75jNcgzgsutstKHTinZUi45u0gYZwU9MycrZbHyZZXk7cMtWAcxATJOqpyAlOotNG4ORoQZBkdy+SFw1HdwGuBFgqyjIMw5sokYTCVbHtLF6y4Ujxwnr588/mhRP40O1XKys8RiqDISpyORhIWJGFBGhfoKwZ5VpImBWlUEM1zFFXgtmWBM8oLLrWlppimCDabHj3HIkhTfv2mQ8/5KG7DYJrnNUjhotvG4QZCPcvVoxmf27nN44NJLVmhqwJHF7iabP2qKgq68zBLmymKonE0vEyaR3VBPM1C1KqIHYxv0tvoozo2nnWKtWZDEhpDqTw7TzNOd5o1t2H7mZ9iHh6iCI37OxdZjg4ZJxM2vTMsN++rkXRxJNOLTfsZpuVF/Citc+uy8VJEnBdoQmCoatWQKWc3iIhyif5ZMlUutZ06ilsUthemCsEwCEnynElocn75Xaj6swhFIyju42AaoSpSBn0Wp9i6xmZvxCxeYTCN2Js0aNlRDYsNi4I8XCJIpMOXKUcVMDA0lXeu9gnSDKfSapNjuUaYpIzDhKtHLRy9R16UtDyTtmPgmSpBmjMOUvaP5LgutNCWTK1GOr00XiDtBKoChqowiwsOj1KSSKZILVdF0wS6oVBkOUVRkuclrqnwUM/k/rZsVveZ7b26e+M8LQjSl+t5WSaRU4pSMpjBOIqI0pIwKtA0gWPK2kq3rWEv6/QdlQtNi5ahV62N3Vqr7N0rCTfPzfjycMoXb0UURUnDUWlaCuurBheaDu8/scLJtkvfOyKslKkVRaO5IsExixrcIlrNM7kYKIp14tmdWac3ZzJCuSsO5YvAOSHEGeA20mn8yd/yfUK0gG8A/tQdrzUApSxLv/r7G4G/cTcO6muxt41DEaJkmpb8+P/xnXzre/4618uvQxMKmz2vFt878GOeuD3gMIjommYtFBhXUhAbHYeNjowmVEVgaLKA7FTQ27hCidz2Zzw5nHAUS6XWUYWwSpKCKCxQNUG3rfHOFUniGycFk0TKdjdNSWBr6IJ5U61D+cWDnS1C+hUD21RI85IkKTF0QbuloVfpb0OTq0dNCKKi4OYswtUVeqbBURC+3CHRNBhHMbv+HFNV6kLyiwdT4ryFH+/hJwm7lSruevPl/PqqrXGu6dJ3LNkYaQC9xkMsbcpe5vPpds1v0EwPRdFkp8jDZ7i18ylM3WXj9Dey3DtL7l6sBC01dv05rqmjBY9z69onSNIZttnBMDzwIS9z8jLn7MZHaa8+TFlkjPeflCv32W3Zb3y9T1b0GEdyMl3oOO0EElZtqQK3Yr/NMlm0XbFN7mt5PLDc43S3QRE9Dci2s3kaojsdTPcxXhj4DGZznj444hdv3pYsb62kb9/i0nKPlqHV/IgVz6zvl+WmVQsmhmkmoa8V92khc2NXStGOoaJmEkbbtk1sXZONsQIpIunHsni9PfVRhUR6Zccvs80X3Atb1wlTeU03vAbTNGUYJWRlyTyWaSTXVrjQlVFA5pY81DeZZQV784yjSY5tKegqpC0NRcCptsbH1ruSj5SkfHrvkJ15RpyXde1jOspqh2TbKh3v5fvG1gVLjkK7b1QcFCmBsmRqtAydKM8rzohcgAGcbHmsNRtoista0+Vcp8WWd8TnD2f4SUHTFGx5Nu9Y6nB+uVW1j84w3cdIdDluxOCUai3IaVSN7jxTZRIW+FEEPFg77jdt4u44lLIsMyHEXwB+AQkb/qGyLC8LIf589f4PVpv+EeATZVnO7/j4CvDTQqYqNeBfl2X587/dYxBC/H/fwGbTsiz/2utt8LZxKJ6mc8FrE2QRn3/qH/Po+WPSpT9OkGQ4ykuMd54gCwZ8ZP2xOre+sLwocXTZ1CetwmbJsVippNFzxkHM7dmcaZIS5XktQDcPC9K0oNFQ2VjSeU/f4VK3LZn23Qa7k4irwzFfHBxxO0ho66ps1KUouLrUGTrT1InykmEknY6jC/Z9KRPj2gpLrnxYG7pgw9HRFMEsleivE47Go16zlgM5CGNmWcY1f876PGSz6daFzawoMVWVozBkFMVoiuAwjOqx2PJMJklVW1IE9zU9TE0lqCasIE1hDqp3iZVzffzDF5gOnkEzPZorl6R+VtUTXNNsonjE5PAZyiLFbIwQachyeMiG1yGfhBwcXibLIzTVIk59psEeR8EeeZnRtfoyXVFkBONtfL/qW2J2yLOQye5TLPXOcuivSM5GnDBLZQQS5SXtheROUfJAu8Fm0+P8Uoe+8hR5dp0skJDwYHyT4eFTbI+eRVVUOtYyG+sf5P4L38Ll3SWePjhinsmeI5eWe2wtuZC/UNfFAI7n6/hVm4G1to2p7kj5efU8R3PZXGoh4Z4XL6P0FqZW+ld7k4BxJDkVh0FU15umiXSSjq4xTzP25iFrDZt2w8GuVHQlzFgQpCnLFQn1GzekQ3U0TUaETQtbkY3M4vkhht0hb32cL2zvM6qOP8oLGrrkI+3PAw7CmGeGEpo8Cwsmo4wskfdLp2+w0tY439Y54VjMsqyGSbuawomGSUNTmSQpmqKw5lg0DYM4z2sZoqahc7LlcbLt0rZ1dG0P21iRDjaXqeunjkPmacnlsTyeF0YTmoZdOdqbtQjlIr26qIupQjCYLXg9Za2KfGcbgTdjghL1LvFQyrL8T8B/esVrP/iK/38Y+OFXvHYNePguHMIfAv76V9nmrwC/PxxKw2zzjqVHOZjfYpb63Nr5FOe9dZqrl0iz8whV9u2+9ty/wTY/Ufe9VjUbw+7gI/WykugYp7VJa/3b8aO47naXl5JQdhDGdV8HTRHMG7LecF/T4FKnxUe2Nqq+5S5+LFdKnmmwbMkOj24FF40qIb0F9j4rS5ZMScDa9jPmQU6el2iaYLOlsmTK/Hrb0OnbJqaqchhGmKrKqZZMS2iK4DhOaGiyCdg0Tbk9m+NU57EouOZFya1ghnXHg7V4b4GsWajFBmmGo2msuE4tjilhxMt4TZ8yT6XUu26BnCMwG32c9ini+QB/dFUqLkOtNJBnIXkq0xSLVFlRZESZbIXbMttsLD1akwyT6Jg48SnKjLxIUUcWptVF0W02OluMowhX05irOeO0GteipGcZrDkW93XbXFxtkxz+AlEWYrdPkcwOefHKT3J1/Bw354dM05ymrtLUbxBlc4Sisbz63VwqehWU12CjuUcweoJgfJMkGpEmskjf7F2gXaVXRi9tkyZTNM1GMz6HZXc521zDbq5XrZ1TGblERR21LFbMQSojreMoYVBJ7bcMvZ58F1wWoHYAPUei+BbSO55h0LHMug7oKleYHlxmtnOV7fAQPzpkFB4yjIfkRc6G+08423mQ/on3M298nJ2xz21/zv48qBcabUshL2XqTFEESVJwp0/UKu6Mq0lh0Z4p2x90KrmV1QZ1y4CFrTZ02pZJz7HouVaVRdgmzU6SZDmmpuCaBiddh2Gc8qX9mOt7SSXNE9FxVNZdtVYBVyv87nGUsBdI2LOpKDR0rUaa5UVZM/bvlqlvH7Hhf1CW5Y+83gZCiM5X28nbxqGousPpM99C8+BJBtOXmCcT4vkh8+E1Gj1obfwxmiuX/v/c/XmUbVl+1wd+9pnPuXPcGF7EG/O9yqEqq5RIVZpAZmwJi0WjBgMGDAvjgbbdXm0vtwfsXqt7tdttBG2zmjZ2YxmDwWCM3QiDsAALjBBiElWSasjKyqx8mfmmeC+GG3HHM+9z+o/fPjviZYmsV5VPquraa+XKF8O9cYdz92//ft+JxeFnmZ38PKv0Mb1yRVGJvTmA50aE/gDl+PSrN/GcFwBY5RWzTcZWFBizOsFiPjoeUGjNUVYwNlkUP//ohEfrksP08yxKcXKdRoZ+rBv6PuxGod204cKN9iTN7egp8YXZNVvUfPrdjFeuhXz/1QnfcWWHa5M+ifM2D5bXyKraJEW6XBsNeLhYUeiGQeBbs0PdtMzSjN1+Qj/0WWQl7tEJD9YpuW54ZTzkSj8xAUSlzRu/s91HGEc30NWbtE2NF75qvJ5qAu8jOJOPUDdQ1Q6u+8CKAetihXJ8ivycefEO/uIdkniHuH9A3dTk6TF1nVmKsG4qIq/HyzufYrrzGls3vgfXfxldvYnrRSa9UKjcrRbaaJWdEQwV10YDCt2wE+ec5SVFIyLWm8M+t6djBtnfZvneMYPtlxmMfpDZu/8d9+7/BG+ef4FSV0yChNv9IbEXE7gywlqcvcn1vc+x1fs4ICB7mZ1RZWfk6THzxTusihlPNo8o3v1REjcm1RmrKiV0fBIvYiua4iqXYThl3L9OnOySDA44uPZbmGc187TAdUUdX5gc+E2lKZrGMhFfHSccZQWnhQD4ddNS6JbQVdzqB1zvJXx8Z8tG/o7j0Mb4joL76BoG2y8T9nYZFSuuGqeAbHXIenmPTXZC09acPPoHXP/oPozl+epWiskwENeIxPfY7gScBu/47NGMs6LgIIn51NU9rk0SyaTnFg/PJVGyix5eFhKOlXgeVwc9bm4N7WFtlVes8ookOMB15O8GnhBBPFMo5uU5r5cNRw8LTu/meInDmxOPwzua791LuN5PGAYBZ7nkrHgONlCscy7wHEmlTC+JbD/MUs9p5PXNsNq2/X89j9/5likoSrkkoxu0uiaMJug6J10/IuztcLbZ5a3jUwbhNV66JjTfxckXWKzu83j1LgCvHPwqesObBPGE/s7LPFzuA5qDkc/jRc2jlYwtXzN26082Kcuy4uawz83hgIerDe+t1vzPD044XGrrU6QbOBjW3OoH1G3Lurq4mBPPM0l+8vWd8UDYUFVt1fLnecEkChlHEXHgmQyUI8qs5voksVbh86xmnPhcnyQcLnICz2HL/RKbs7u4XszN7R10+O1Gye7x3df2ebWQUUZntTKIfBLnbapUSAT5QgpDmf9D5idfIE52GUwP2bnyKmebXWbr0li9a3b7PfrhLtP+NXrhe+TLQ4rNMU1bM0sfcZbPOC+X6LahbjU3evvEXkLs9Rgk+/TiXa7sfy9bN76Hh8t93jgpCLwVrnOVOLyJe0VRNC3jxCcwaY5BPOFs0xJ4LtdHAwaBz3Ysr28nNtwZnJIywZ3+AO/O1txMZCwy7l/nV26/ZplAXjiwPmtVKbqPB2/8KMPxZ0lGN6gKKHPJt8nMJuy7IbEXc7qZ8yg946zUnJUeI3/DjsHt+n6HyXmU1Zoil9c2Ht9gPP0UaSVjznVR2ZFNbYR699aiu7jaC6nbgk8/KawrdRIqQLrReV5YbYXrOLx9NqfQjT18BM3Pkc7viQuB6aq6Ym6xK2B98hZ7Nz9pg8A623npVMVt+SNbY6up+aGt4VOfQU8pMeQ8+Z8YFiui/NwC5EG0RTCc4Br7G7+ZkPRusMj2KAyFel1U3Nnu4zoP0M11gqGQEAAmUcR3bm/48o01f+9+xtHDgvS85u17Oeep5rsPKn7znRskvseiqiTOO/KtHX7ttRZHC93no0ZUQOg+R1+wb4KllPrDwH8EZMBfR8Zp/2bbtn/2WW7/DS0oSqnrwJ8BrgANIvb5o0qpLeAvALeA94Df3rbt+QfdV03Mk/oT3L5zg+WT11mcvE48OCCe3ODueWbn14frG1w/eFmEinXGK4MbxP0Dhrd+D8fLglXT8Nl7K2JfTOoKLWFMoecSujKX3esnfGxni4cLGQ+c6IbPn684zTXvnlTMZ2Y+PPHYnXhciQ0YmRVsagEk15WkzPV8z2ZI3BgK2wxkJj6OA0bel3n4xT/D0Xtf5L3sCbrR/L2mJK1zEi/iSnLAVu8qW5OXyR2fqlySZScs64yFF1vcIUp2cZzPEjkefjjACwbs9idk7i8j8BRB8yWy8/ucbY5NJzKwFNWqXJL0rxL1dqiNcWC3gR1tUh5vMlITNHa28dkZXGVvchsv/CyD1SFXmppSl5wWc+q2o5GWuMol9nqE0RZ+MGB87ZP844cD5rnkvIyj0PyuhDd1KZGhK87Hse+z268IPIdRHDylPt/qHVMVh1QFLJxv5+GTOSBGhFs3vof+zkuE/U9JjLPxHUsmHv3AwSk+z+bsLk8e/jR/7tN/kPOyJnRh6AWErs8kGNhCoVvNdjgmdDa4ak3PE4+1xPOpDblAt5q8WlPqnFV+QlmtGKwO6U9OGB28xslqm9j3+MTeNo9XG96ZL9kKQ6ZhwZvLnL2m4ZdNR6zrcz57WFAUDaF/YY3fFaKTNOcfnCyIXMXLw0TCy+qY2I/xTcH0g4GMeYHe6CaAtfp3vZi2+iL7o1d4vFxzmpU2hqDveewAnzs6sYSAG0MZj3Zr5rm46ha7e68w9h9RrI8p1ycSNleuLL7m+hF6lZEu7uN6Mft7rxJ611jl4lKcOJIN5ANOccKrewO21gdcHw24uVyzF59zdEOwneOzCt9VPMlq3p0v+O5r+3x8f5vPPDwGYFmU7EbiRzc3o8PntZRqbUf1LbR+oG3bf1cp9ZsROvNvA/428M1fUIAa+D+1bfuzSqkB8Bml1E8A/zzwt9q2/WGl1B9AwKB/74PuSBu7i8DbYn96B13n9HdeouAjQMo0iQUPCAR8H+69yvSF30VaaVZ5zT9494hH6w2bqubOeCjU4axg2ouIfY+P7WzZ8KJRHHIwOub6MOO9xVU+9+RUlMG5APSOA2XRMntSsjyvOZ7UvHat4WrisxeHLMqKz55lXOtpbvQkA+JKL2F/2Ofm9JzVyVs8uvvj/NUH/wt/8yjn08e7zNYBReHjBy6B7xH5ETeGG0bBXULnTa73/ioAhVa4quVX7R5wpSdq7qg4p1+Jylob878wGOAojzD622xf+16KpiZbHXJ0JLTKnrGCF68uH9eLaJqawfbL3JtNyCqZ4YeuS8/3uLtYMTueEboOr26N2Y4jpr073PpIRH/2DoPBDfbOXuc0PWRtilLsJQzCKVGyy/SF7+PB+Q6llmzw0HVsTkehtR1l1Y04CfQ9j51YRofjJLGKdV29SVPlZEvZPLzkk5xtltYA8/EiJS33SYJrzFeFvX4GkW+7Sj+ZEFUHbE1f5drR3+e8fMJxrli6JZOgotAVSbUh8SISN8ZVLokXEbg+nnJldOaEosnRAgSXukC3mlKXLIszJtkJ4/QYP54wHojvmW58o6p3yaqKl5wRW+HC0tJ/1ZUp3zYp+JnTFU/WmrxumRWCu3RsxV9zZUJiHIs/vj8lqH6O9Ymo7VUnyjS2Mn48wYtElAtQrk9EmDl6ROz3OMtLZkVpkhlL1iY1MnIdep7HpprbGGHdXsRfT8KQSRQyCF9gEL5Mf8u3nWW+OCRd3Ge9vCePIRB24NYOwDWyUuM614idJ1TFijI7Z3X6JjvbL7Mbeby0+yqJ7/HGbM4oyPlcmBGaxNVlWfF4ueZgtM0nr+3y3pnksgzDQHJdHId/+PjYan+ex3LVt8bI69LqKHC/AfjzxnzymW/8DS0obds+Bh6bf6+UUm8gNgQ/BPxq82t/GvhJvkpByetaNCSei272GUxugOviti13tvsWYIydeyyym9w9j5g9OmSaRITG2G8njtiJ5WSsm8aewq9PYmtf31l26GqHcn3CFfWYbOuXcV4UYt7YeCx9h3SjWc0qCCHPGj77MOfhqObWqOL79iZ85962DTy6tTXET3+av/cP/2X+q+Mv8e4m4qyIKLTLph6RVS79uCYJFevcJS9cNhuP83shqmlpPIUzaZgOSwZBxUeGC+5tjtCtpu/3OctPYP0egRNKLojjkdeioamWb7Nc3cc33UzT1niugJZtU4nrbjBgdPAa6+ZlHqUF8zwT3yizidVNy7V+wif3trm5NWQavE2V3iNbHXLv3hfYZCfMsyNOsiNW1Yazcs3QjwncQCzl/Qi4wbpYWFpz4LqklQRaHaYZr88zzvOGrBSNROIrbvULPnWwxyCU0/DZ2TtsFveoyhXT/e8kGd8AYBSHT7F8utl+N9a57PQ7CHzubF9hq18zaGq++9YP8crqPrP0EYtijuu4xF7ClcFtBr0Da6jYmERNx/HRdUZZrjjfPCCvNpRNgW40gRsSOCGucqlMmuTm7B1G0cAo7cF1ApJAqK9J4HJzMmSRFSZLRrqBH3pFUdQNb52c84YZbw0Dn1emO3zy+pS6eJ188RaLt19/OhHUdCfr8zPiwQGBu4PyP4bvPZbnUOW2i7i1NWBVlLx1vuCLiwuWat8TgH5jWIMdJTjXrS02V+KcnueyG0fcHg9Jq4qsCrg2+SjxxEO5vqWZu16MFw4o1sfsTCIW2R6eUjRNTWuMQB3HM4aW4HqHfPzg1xJ4LsfrlFfGQ2v4ep4X6LblreMlr+zMuD4WN4PQlTwX13G4ORnymUdHz75JfcBS8NxYXt9E68eUUl9CRl7/mlJqB/G1fqb1je5Q7FJK3QK+HfhHwJ4pNrRt+9hYCvxCt/n9wO8HmOztUmjNLM2ZZwV7/YStgw318pDWj8F5hX54xCK7yWceHPNoveG13W2uTfpWp/LgPLXzY922JpnumGJ9YjcL5XroKuPknZ8k6u3gBQOuTfrW6fXd1YbPzQreyzTJyEM5YiDnOIq8anmwqvmCv0Q3rRjZJT3Gscd8dsb+4AV+rRvw1uIuP3u+5qwI6fkeaeWhW0XgNnxkUphC4zEfywbTXdSJVxO6mp5XUzSaZbWxoVKrasNG50yCAbvxLpHXw3cjIq/PIpMP2CjeYzK8DcjmM5i+zPDKqyyyPR6nJUnQmIItGTOroqRuWraigE/sbV+ipR5SbE5sfgdALxgZXzOXwPVJ3JjYSxgl+wz2XuXu6Zp3TdJfZyQIQvHcCkNu9TXbYWOV4HcGMR/bGjNOQnSVsTx6ncPDn0Y3Nb4b0d8cCzbC59jvD0iCG8xTyakpS3ns53nBpq65v874+dOCvGrZ6Tv8bw4mvLY35aNXbrF7y2Na57xg2GzK8VCuZ1lboiM5JF8cUmXn9vnqOqO3OcD1YsLeLm1TWfNFx/EtlpGMb+B6sdi3+C4hb+OrnHFfirrbi9mNRCfT6to6BieOx7cFh9yJBRvxvQGrx/f5W18Uo0xXuRwMhFTiKI8wGNA0NUk/pj+5YzZ0H/RbVMa6y/EjuyEUdcPBsC8Cz6qyqYl3l5UkU/oOkStOAJHj4JmT+jhweXHYJ3BdhmHAOAoJPPcitMyP6U1v05v+aosftQ7EvmvwoYZ5VlE3e3jOFdzIYTBycdp3L5TwjrJkk91+ws3pOVBzb7ZNqTWuUjTqBfqRfIan/UBe13DAVg/2BjeeeV/6oKXAPu//f19Kqf22bR+3bfsHlFJ/CNGcaKVUihzwn2l9UxQUpVQf+IsI+LN81hbLmKD9CMDeSy+2rnKYJpHN2WiaAxznRGJhG3jnZMSPvf0mPc/l49tbvLg3lDFJ0+K073JzDFlzk+NlLiyTfgCscPyIfCZhRUn/Ksn4hlBeC8mMH8eHFPUev/LmVb6zqvmBLOfnr8742bO1ZeWsipbYV1ztedzox7iOYlWU7PYTq3/J6zULgzNcT2r2o5rQVfymqw23BjfYTg7w3Ii0nHOenXCcHZPqgk1dsKxqPAcS12MnHBK6MtboskP6/sCcrntEXo+drY/RNjWrzSGR1yP0+4xHd+iNbhCPb+AlnyStGsrsS1RHP8324IC4933cPZXOpqi1ORE2hIbZVdYtYfgCri+uwN2mORrcoK4z+vEOU/O98egOo51X6e++zN3TLd47X7IqSq4OesKc20gX0fM9rvQTvv/OddkY3IfADVr9Dun8LVb3f4zSnHRH/RtssmN8L2Z+/ia6zuhP7hD0dxiEFVs7YnnejZUC12VVltxfZ9YjqqhhWVU8WKzQTcs0+QQA40FI7N2lqXIcP+Jss8sqLxlEkhbpRQOrTemKxtaN387RqmBZN+wOQ2L/CQAnq218hMnUOqBcl75/yGb2jiUGdMmKZSbQYdjbNeaV3lMamO49LOZrTtPHZHXKVrRN5PfQTY1rcmAGgxti1dLbJRru4zg+jXqBo1VBaVyHp/0AfPCQcdSqKMVyp6p5uEklgz7XFLplUzXc7EV8fGtkzCWlAEo3IhlE3WbfabvausbxJ5xtdpmnG7QxENWN4JSuo5htMuZ55w8m2NYgDHh5e5dxfI04cMlyEY3uD3uM6n/I2f0zosEB0/4eq7ymrKUo7QxO2R1eIXQfklYf4ei84mAU8ZwweZRqCd3naIX/jV1/0tCCfxIB438awIgoNx9wu6fWN7ygKKV8pJj8ubZtf9R8+6irmEqpfeD4q91PWrb8vaMzSbszFNhVoUmib2NVaD7z8IjPnZ5xlFV8amdE4vvM1hdZErBvsie0Dbjymi9z/vAtspWk5UleeUbT1EyufsqemFotDCtPKdrqi7T9mu+44vEb849z73zJaZrxJXP69pQYBG4qTehq9kcxyyf/gM3SGAK6AZNgwMfGL7HTv8mgd8Bw+jLx+AaBSeFbn93lSnrMq+aku8mOJcnPi9FNRVrObfjUNLmK78UEfp/R1ivWQbnzNxp7MUE0IR4c0N/9fg4XObgecfoZjt74UZq2lmjd0Q1WhXx4jjap1Sl8dGvM9fGAeS4snX7osz/+5YzDAcF8i2Ijb50XDux75QcDBld+kPdmG754L+VoM6NuWgu0l1pbV4LEF2+t3egu68dvcr45ER1LneF6MfHgwJAHMgbjOyT9A/L0mDQ7YbF4B4CB41EXa+pwxTjZIwk8dgfxRSgYULdn3FsbMoUvOR6l1nzx5Ex0OFXC3L+GbiWTRDdryWfXGt1s4zq7uK66wHKalgfnGYusYFWWJvVxaCjcc3TT0Dcn+N1hRIiA1sXmmDw9xjdOBJ3OJeztkExuiH2IAcW98LME0YRocZ+qXDFM98mKc8JggOdG+MGAMNoiHhwwuPKDHK0KzosKd6nMBl6I+3HkCzV+XTJOApR/m73kTeAmj5cbhmHAVl2Ta82tvs+8Eo+8V8ZDDgZ9xmakKGPi1haTg1HE8snrtquKhvs04asUtVwraVXJyNF0vHXbcm+5Zl5WLErRFI19l5v9iL1eQllLTHbgunz0yojs/KeZH3/BdoGuc+GTVmqXtJyQVRtcZ0uMLPOCt048Qu95CRu/dUZebdv+oFIqQqCG3wz8J0qp+0hx+ett295/lvv5RrO8FPBfA2+0bftHLv3orwC/F/hh8/+//NXuq64bfuqtDY82D9hLXMa+eyFoals+NxMA9p/a7zOJxOfq3UcL0qq2zrylUfAOA5/dfo9XrogvUDw4YHzlNaLRAfPsgHfP1xSpgLzTXsw1ErJSMJeRh5337k7h4JY4DCe+b1lKhdY2L9wtfg4nHLD/4g8SPphw3fGJBwdEw33C/i6FvsY6r3mcV1DALB2xau9wrgpoYTII2dtPGIYye5/2njB796cpczEjTUY3CXs7RKPvYbYpacHmhc+dnFJrbk/H7E3OWD75Cdbv/QTvrd6j1Dk7/ZsMBzcYTl+hSb6T40XGe+dLfurwlHmleXkYyaZ7PMN1xLnZ3Tg8XKwI3AMB5a/0qNvWWtjUbcs6r/nJtx9ztBEdzFYUcHXQY9oTnYPriLYEMM8pYH38Myxnb7JYyWsbBgMm/QOSkYwvlOsR9neFtnp+n3h1aNlq2eoQv5vZ83PEyOglDe/Ysd2Lwz63Bw0L4/8llu2aTV3jKod3FksO01yce01GehcgBQNi3zMUbg8hLMrqskqK5RrPUaRVbUOxJpco5NcnLwHvEPZ2iQcHrM/vEiW7jHZflfycxS5fepCimxlZVbE/7HNt8glCPi+ZJeY67TAJ14uIRgccrfa4u9ww+/Khvf4S32cQ+Ex7sRVVTvuB7dJAAPot55zB7nfy2Ucn7MQizH2gU8a+y4ujPi9MhnjKYZ4VdtSk29YWk85RwPVjHD8yY0JtX5dVWbEsSs6LwjIf721KNlVDUcNB32UvFsfrVVESui790Cdc/M+8845oyQbJPlt7v8xMIVp737M048kmtfb9ncWL5yheGPS+2nbyTEsp8NS3DsurbdscU0AAjLfYDwJ/TCl1pW3b7/pq9/GN7lB+BfB7gM8rpX7efO8/QArJ/6CU+hcRZd1v+2p3pJQES332Cxv80CFMHHTdEkYOYejQjx2uDj16ngDJA2SDP84L7m9SFmWNpxTzSpPrlpeHEYfLKYPw1zFbZTx8vOGL81PgFE8pPAdGgcc0DHhpOWKaxPTDgDh4hf7Oxby4izz96O4Woecwz+REPI4D4sAlDg/IqitkZUN95VUCz+G0qHjwZCUhSc19dhL5MD9ab/jyYs3cDL2vJgEvtBKROopDxrFPXaxsfkjQ32HefoKjrOTw7ce8s1hylBXc25S8NunxT13b55PXt6lW/4jzhyeU2Rm93gFROKGuM5L+VXqjGyTT28wN7nCaZngOXIk8ZkXF3cWKnThEV7XdfO+MByS+L95UWUU/EqPEB+cpj1cbAcRrMUTc6wXcmgwZJyFbPelmyuycYn3C5uwuVVNz4kUWWB70DnC9iN7oJsnoBmF/l5JbcvLfaIq6gfAmbqwYRB6xc4/zR59GV7kt9GFvh/jKAYvzgpVJXuz5QgufhCG6lVHbMAxwjRXKopQEQGMPxlFWsRfLIaTzjfIcRez7jOOQfuizOwwJPIdw5XJ/uTJGlBXHmZzYa7P5AhwucsaTH2ASS8Ll6OA1qnqfo1XBm49Sjten1K0QIQaBzzwrOF6nuEoKdxxLQavAMq++9GjDLJ1xnuecZAW51jaS94VBT8SwkwSv+TI0EIYvo5uWtNLEwwOK9TFB9XO8uPMJFllJ4ntsRZJ6ejDoWbLDuhCzzMT36Yc+B6NjivWXCOIt2v73kpXShczOW1ynNhR8ETWeFwVvLVJjlS+R1XcGMbtRxEenY+5sjxgH7zJ778dZP77H3Te+xEn6hH4wYDs5YOfq9xJPbuCFrzLPaotlHpaVMQcVN3GQg+Unp1tMk4uR4Ydbz8965ZttKaWGwAL4781/62e53QcWFKXUb3mG+8iND83XvNq2/Wn+ySGav+5ruS/XhXjgsprVNFpyQ+qyJYzgytizSXBd11JeChH63FnGl+7JCaYzdny0XPPj765YrTS6blEO9Houo57LduJwJfboex5XegmBK+KyLm/E9eWD6TiP0Q3cnJ7LbDxf0Y8E0AVomgM+9zAhrWbM84L3lmurxM+bxthYeDzcpKyrmk0ttiJXE8nn7pkPymmWM+3FFNqnb/JC5lnNvVXG68ePOS8KjrKCf3SUMY4cfs3+mDtj6QDeO9vQD7+dZOqx7d1jefS6FaP5wYCgv4Pj+PSN2n9Ti1fTcSrizc52YxT49LzulG6Uzr2QaS/AccR6JYu2ySrRllwbDcSu3XMYqjfIF4c8eu+uVcRXhlrc5Y/oprbC03hyg9J5hUVek81rsmp1kWKZZqxKOc3u9hNubd0giO6yPn+HtqnoT+7Q3/1+Xj9c8GCxYlPV6LZhEkX2hN1ZhCS+xzSJOF6nDA1D7CirTEGXDfAwzThMs6dsbLZC03EZqvqqKHmySc17WzMK5NoptOb1EzOm7SUEnsMq32JdDEym/JHtlEAICud5wRfPSo6ygsh12I1C7oyH9rreN4D4LM2YpTlPNilvzFfcXVZ09nXDULEoax6nOcfrlN3+Drv9hHEiILluWkpuMB7dRldvsucfkQSShHptNGDaEQYcY4pJaMwyj0jP7zN/eEjY26VJXuCNJ2esipJBGPDS7phRLASQ2UZEkXUjLLF1Jfkt1xKXG72El7cnfPTKGLKfZX3ymDw9pqzWjOI9tge3GE9eZnrr+zjNb/HwPAcWBJ5LVtYUWrMdR3hKWSfpDuvpSCXPYwko/63ToQAopf73iFtxxkUmS9u27e1nuf1X61D+K2Tc9EEo+a/kfaZm34jluYrrVwLOei5FIW/y3q7Hbt/l5VHIa9sThoHPSZqzKkoerjbM7OnUIem51Fos4o+OS+qypSoalKPEhr7n0o8lBfHVccJ37e/y8YMJAe+h3JZW1yh3l1a/Q3Z+KLTH/g7F+i0Wm2OCaEJpVNJNlQvLJtq34VP3NxlvzStma7EQj0OHfugwLzV9X8wBPaXoBbJx5LoBashlA5hnhTldRhyuHghobk5/T7Ka47XmpanPr7oyNWMaSdFblSWrwsNTDv1wj1sHt6mL12l1LeLPYi2dliMAaueZFHoQuuJBtqlr9nsxie+jm8ZmnwO2mFT1PjsDMf4r65Y4cMwmeJ/s/CKJsMglf8Yx2gjlRRJL29QMxjfs+Ofxcm5s62VMualq8VYzf/fl8ZBBGLDVO+b05JwgnpCMbrBwvp037p3w7nzJwgjdQtdlGIhYMHHFxqa0GI5v890B6nbDKHCf8mQTBlRJrkWN/SjNeWe1YS+W17luWhZVxSyXvJK92CXxPCLXYRCK91boupxtcrGDbwUHeLTacJwX1I0Ur24c1AVmbYcSqHae5/Z9WZaV5LooZQPkItchdBWeL93LSao5WhdAwZuLjKuJz41+wu3xkKK+6LjGccj+8BZjxwfEkWBvEOI6Dyj0NetBF/tP2MzeYTETl4Gwt2vep8IWk91+wuNFyhtPfOZ5wUn2hE1VUzQNN3sh26F0Md+1N+WF8ZD9kYylwr5JvfQitrhgiT083+LuecU8W9qDXGqiDJZlybftSZEUbK+0neOLu0s2s3eex5YjI69vvQ7l3wZe/Xrz6b9aQflrbdv+Cx/0C0qpZ1JQ/mKvncjnd35kRzaXWjPLS+6uSvq+GCqKYZ5w1V8/X/NzpzmPjit6PZdp32Vv7JFVDcuNYr2qGU48Bj25UDeZ5Dr0QwkOurfOuWKiVnf7+4yTgEh/gdN7f4x3D3+KRSH4RVZnxF7Mne3voNc7YLORQjPdeY3+1h0C41E3K0rurSsezSryVIKO6qrFdcXWfjTymCQuu4lL33fQWkzuOgvwWVHyxnxlBVunhWZRNGRVy5NTCTUaT2VW/qX5ktuDHnu9hLQq2VS1NY50Nw6lboj92wSew/YowPceU9X7rDZip74VBXzHtG+7kW6k8JGtMYXW1gss8FxRPtdbYunii1bDSf8xbnZOVmdsTBGRYuwR9nbxqswmXnZgruvHTK59ioxXeOs45XB5ztEm5awoeZTm9vmui4ZpIgeIC4HgFXpXfyvztOJzZ0vemN3nOM+pm9b6r0nwlzzu2JeNudukdNMQmPGMqxx2o4jCUGlrc9KVjVVO2ZGroFHc25Q8SisrZr3RS/jIUKi00ySyFvWd821aVYxjYSAVhTZ2/BmfPcuY5w2LTVeg5YDjOoonvuZhVPPyqKbve9YMEWCvl7Dfi/no3ha/vNR8/uiUVVGS64afO1vypdOK+aLm5Kzm82FBHKUMozOGoQSqXU0CtsKQw9WGg0GPQRjYx+o4HoF+TzzWmgMhhCQTHD8iCW9TOy/y4LzgeC3koFVRcrha895qw6ON0Wu1rRUkfmIyFI8wz+XAsPzeOpbDl+v0CN2PsCqF5q2bhsm5A8w5zwWbDE0HF/s+B4M+g7LkwWLFx69MuT58yOrodfLNCdV8yXuPno+PF4jbcOg8v/v7Jll3gfTrvfEHFpS2bX/3B/38WX/nl2INQs1v/YTHqrhp2Ttvn805y6ULeWe+ZBj4RozVsCla5kclR3PNvUgx3AvY3Qt4cc8nOgjxHIjsqVSEb1d7IVvG1LE75T9crCh1QuC+xP5tsbCYL+7yaPFliqakrwZUhpUUhRPKckXb1LK5tu9yMBgLPbJtOVk3BL5ivdJURU3rKKq8IQ2k4yp1a3M+ep4EhS3Kmkdpxb1FRaWNo6rJ6t4sajanNV6kKLKGnzqviRKH/e2M33Rzi2uDHqHnsjIzcOCpjJhuwxuGj61yPfF9XjKBW504cLefsD/qUdSadRHYkcK6qOgDupHLzDEajqpc8fjh3yUt5wyiHXEQdnx6vX0Gkzsox8cPTKxyMiEeHvB4MeVkteL+cmUZYAB1I3G1476PN4TIdTgwwVl3Z3PunStrB3+c57y5yIlch6uJbwPJQFI8QWz9O2V17PvgXljDL8uKSGtrLjgOfJvdDvLanxnW2G2lrP184otFu+soU2wdg60JXVe0T5VhKcnr/mCdcndVcG9WsV5p6rLBCxyraQpDh6pWbApxLd6LPcaBy2u9mGEgXYDryDgyDsTW5Xidcrhasxf5rEcNRdmwXmnOlyXryOG4AS9QHOx2CYoNy6qiqDVX+onFe3SVU2ZnkIHrn+N6cgCQ99gnK6XDmucF53nO41Siid9dVsw3DWXZSPJooCh0yzTM2I0jdNvw9tmc0HWtY3LHKOz0TkKouSA2JL5vUza7625/0GN/HBPwHvnyRPzX0mOy4py0nNtu+MMuxbdkh/LvA39fKfWPAGsl0bbt//FZbvzMoLxS6pcj3lr2Nm3b/plnfpi/yKvKFzx+46+wdf172B99O/3cJ3Rls5QQppLU+GdNQ59PXYFh5PD4rGJ+WjF/VHL+oODRrs9LL8a8OAp4aZRwrS+td+J7vLgzYX80Q1c5kKJcz4xmljTNAVVaE0QTdqNPsTV5mZVhJEWhhFB1a728J8rqKuOXXf1B+/331iUPF1AEEtKlFISJy6Dn4rswCGXsleuWXGtmrXwwIlcS8bJcCk/TQlU26LrFDS6mlV6g8H0H3YjWQjeisL68QZ8b2/RZUTLLBYSehj5bYUjPd7k66Nv89bSsLVW4A9QX2Z4t6FlVM89zG5u8PxyyO9hjcnULx/FYzt7kdP4WD5Z3AdjZXOE64jEVDA7wkwna+yj3ZgXrIqfQmsTzrLX+DeBaP+MsLzkrChalERW2omtI65onm5TPna+tkntTtdTGAbdbhW5Y1yXk0m287IlX2DgO2R/FhK6w024ZM0TdNDbECSTPpOtoHi83HK5Ef9RtwrHJ49BNY2+TBBL+JBTkBt24PF6uOUlzzouCR2nJsQlucz2F67k0ZsN0HGwSYuAp6gZyE9SWVpIoOk0iylqEvmEhmTanmbgxR67DrX5A5Dq86ZUw8UziIub5tWaUqW0C5pV+wq2tHunxXxcLnzqzvlyuF+P4Yvqo/UwoyGlGWgkwPstL5qWwwMY9h6WDDZIDwaVmRcXVJOLOaCBixCRinIR2JGhf96qmbluLXY7jiH7osy4qjtYdk64yNklTAm+X8Y3vYivyrDWP0P3/zjPtKx+0lGpxv8UwFOC/BP5X4PNcpis+43qmgqKU+m+BO8DPA93u0yLGjt8US9yGbxL2d0krCD0ZuwyQ+Xfie0brIIVlKwy5PWior7Yc5wWnRc17i5rFRrMpWp5kNdOo5PZoyPXxgP1RTLv8uywOMxxfMlT8RMSDItx6E8ePiExuexML/7/7ueP45sMXUZcryvzMCNV+gjvbv5asqnltKwc2HLmgzMl51HfZ7bsUuqXnO3xia2BHLYXW6LZlXdd4O4p3lhWlFopuFAou5DiKWsvXH90N2A5dRoHHtV5ixxg9v+Y8L4SppBSbphXr/baFRiKGddswDGKmScy1yRllds54NLBYRydeS/x9ylpoyadpJhnyxuPpPC84XsdMkx1euvZb8OOftDGtm3Ih91OurD29Fw5YZ0LHFlquY7sF11il7A966LZltsm4v5THMIkiEt/jNM14Z5UyyzV1gwWlcaXr7BnPKxD3aBA8xVOK2Pfohz6J/4gqPacpVrA5piqkw4wcj7HBCoJ4ImPBQtvTdXdqdpWyr0ehNWWtL0SApigJ9lVzd77irCjItfiVDUNFuO1R1HCealYrTRg6BIGiFzqMI4ftyKXvCR4VmcK3LKuLv286SW26bM9Rhi3mcMekTR6npjA68hqFnhSoyIHbo5gfeuUFws3f4ckbr5u89guX4Y484XqRpQd3RbMLdBuZELCHVPR9l3XUcJ43BK4Uw9NC3u976xVvLlOuJgE3egnftidOFvujhLunC7KqZhAGDAKJT+4KyVsn51Zkm/g+oSvP144TS2H/uY7Cc1546jDxYda3klL+0qrbtv23vt4bP2uH8ingY237HF3VnvNywm3q0a9nVQiLIzWn1dhQasEkMxqfpFmaUdYSfiXpcI0IrWptrVdcpWzyGxhhlgHUS24xz+QiHYS1dWyNhiK0U66Hl3wS33tMtjwkiLdQrhAldPUm2fl9dJ1Tlyt6+gtcG71EWtW8PB6yqWpSY8QHMsaRTVQifCPX4cZowM3J0NqgvHe+ZFmUksDYNOTmQ93N/AE+eWWHaRKxLipWBjNwjY1Fp0wOXJetSNhOXaxw3/eErhoG7I8STu7+GJvlPcpyRRAMCIx4TrQPNbF/y5xQa+6vRQDZUVnhnFHgMUt3uT39Xq6+epvR8au0TY1yJFqg25jgBnHQEHiRHcl1QWBipijCR997zK16n5ubIWUtXcBsk1lMaRS4NktkHLhMQ6F7d6B14LoWExoGAQeDPqM4ZBBdfDwaY55Z5OfUdYbnxTiOeFIp/2McLnIWWcGpybOpG22z7rUxtOwov12nMo4DgsZhXYjArysm3fOUyFyXQjc8DCuOfHGljn3Fzb7PNPLYjSJuDvuEngRYnWa50HHzgqLWkmFixICBKXah61gQ/3o/4SQrmBXSRdRty17k89r2hI/tbHF9+JCTd/4Um/yMvDjHcyMxmIzkMJWnx2TZCY7yiBNR4nfvz7Ks0G1jGYB1K1n3nqNIfIWpIxwuLwpaUUOuC+PyLRHJt3ck0XG2kTHeOJZD4jwt+LnHxxymGZtasxuFuMqxlORVrclMTozrOKZrrrg5GX34Dces59WhKKX+aeCPIhHAf6Jt2x9+389/NUKQetd860fbtv0Pn+W2X+P628bS6sd4euR19iw3ftaC8gXEYv7x1/zwfolWWQu7aLXOKc2HslPuulUt/3cUEJhxw5BBKB5B+fIQJ4wI+7usiqvMzH1IcREriXuzNaP4JqHnUKwb1oUAjqM4wHXE1Td0HfzkBr4JpZptSmAb1DYPnqzRzYxBGDCKb+H1XiDwXBLnbQDGbmCiShvLKAo91+I+nbHhThKxHUeG5ukb3r3Dra0ehW4o65Z5KiZ54zhgqN4QPUexghIGw5fxJ9/NW8dLe2IeBFI052azrtuWO0HAndHAOv+Oo4ib0wHO+u9ycvRpnizfptQlo3DCKNmnKpeEZqynnZsU5pTd/Z8GGy0M8PmTM96ZL0k8j53kE4JXgOSC+6Jk7zuiCMeV1/Ni3Sdxbth/gwf6LfYGMYW+Rhezu2PGd4ddgp/rMDJ/xzMFWlhipb3nTSUmo/M8Z5YGTJMdDkbX8ZrPMJi+TFyu0OZQkYxv0ISf4DMPTjnapEY42Nri3wkZuw4vdF1OU8WNkQghs7K2jDLdNPQ8D88RR+W92CdyXYa+j24b9uKQ+cCEaAW+ed1EHHtnWzbItKzhDB6tNqRVaVwZtA1acx1FUntgOiTdthS1Niw33xbXnSQSsesgpMlzeqMbRL0d+pdIFFW5Yrm4S1ac4yiPQe+AIBL9U7mU5+M5irSU939RVpwWmk0ln82ixnQRihtjj+3QZRr67JrHJpZFDe/OFxRaM00iizE9mK+ZZ8VX6LJcpaxVz2tXpyS+FOPHcyny4zjk1qUO/8Ou54WhKKVc4D8Hvh+xjP/HSqm/0rbtF9/3q3+3bdvf+HXe9lnX7zL///cvfa8FPjxtWCn1Y+bOBsAXlVI/w9NV6zd9TQ/1F3E5Sl18QE23MTfq2NBzZdN0HAKjTCtqsffwnGvE/Zt4yMaum5bMFKC6FTPEWZpZbCF0XVzH4STN8RxlAfrLgjbPuULdiL1EVtYWWNyJI1ZFyXvnchIsas0Lkx0jCKu4PR3hOTKPP15lZGVtcsKFEltoETGCOObGgcf+aEaZndPqGh9QxYr9MLLmf5vZMbkxanS9mLpYEY0esD86oKg1Jyuh3nbg5v3FSp5XFNIPRcQ27YWMvC+zmb3D+ckXONs8pNQluq0pdUFanBP4ffxgaIKqpBCHrgQa5cZW4yCJ2YkjSSg0qu2zvOTt5cp+yPfi0L5Ou/0uM32PrNxIAQ48BuF1VkUtFNd6x4xYhvRDH6gIjQYGRiYZc/0VGzxg9T2Xl+dIlC1IANpHp2NR7k9kfBn2dwn7O3zpyYD5Sc48P+TuYmWJG6ErJ+TQBQ/pAHUtmffdqOU4Lxj6ogAfmjyRbnx5QWXuXB6k0AwDxZUkZhgGVuV+kZA4Zp5WLLJSRmit0IyX5QWLL/HEcqQzaew68dM0I9ciNK3bltBxrB3K42VA4t9iFL/M3iCkTj9DvnxM61R44UCIE65HEE1w/ZhoeMBxepVVIYcVTym2QjkoiSv1klPVBZwJsWQc+FxJYq4OeozjiGkvpKgbHi83zFLpMg9Xa4tLlVrbjqQ2WM926Ak+Yw5fj1ZrgidCew5c2Q86QsRsc8E++7BLqZbQeS5eXt8FvG3y4VFK/feIIeOzFIUPc9uvWG3bvvD13K5bX61D+U8+zJ3/Ui6lsCd711F4yuFg2Cf2Pab9AL/6gsTSNh5+NLHiQ9d5AICuAG6wMtbsgNlkFZnZdLpiMgx8bgwHrMqSJ+uUt84X9ue3x0MbDLUqSo42Ke+ZtMfjLGdWyElN9AGKh5uUT0wn3JoMmfYjAk9R1nLCvqyH6IcBY+WwMuQCwKiU9+jHkFVigqerjHJ9QpWe24Q+14sITIiVcn10lVmH5cCTOFe5v8bGsha6Iail0MSBS35uEhibmsjrmd+vCdwQ1+AorhfhRQORRJm1ZcZp8vpIIZn4HuMotLYny6piVpWcFpovznOmkTCRpmFAz/cEiDd029B1eU9r5llBWtfmUKCshXls7nt3GDElJHAdK1gUdpDJSXEUtXEu7jZ6z1H03YuPxLKq+PK52POsiwGB+1HmecHh/Q13Fw9MSJqIUDvm3TjwTedT4SpNz3clW911WZYVR1nB6/PUnsijVCyC6qblUZqzrhsiR76um65ItSSuULNHcUgSePSdN9FZhjs84J3DFYfGgaB7Psuq4qwobKEbBT43hwNLgS61FP3jXBT0kmki2MrGFJdJXXMw6LE3CEG/RRBvUfrfTlrKgSscOfiei+MI2+7ePOfxasGqkGt0UwsJJnKloH3X3tQ+xstxwv3QZxCJeLasW45XmS0mHRC/KIUJ2ZEFct0QuQ4jM0rzlGJeVjxKc0aBZwk53fXcecU9Wm04yZ7Zjf0Dl3Qoz/zr20qpT1/6+keMuS1IZMeDSz97CHz3L3Af36uU+ixwCPzbbdu+/jXc9gOXUuo72rb92Q/7O1+NNvx3zB39obZtn8ojMRbHH54q8ZyW5yjr6Fpm51TpOcXmmGq1YvbwmLP5W2zKBa5y6UfbDAc3CKMtYyy3Q3/3+62ZXz/0rYq7s96eJrFsTuYEN44Drjl9Prq3xV9+410eGGrju6sNB2bjOytK7q1zK3h7Z1nx+Kwi3Wj8wCGOHeqD1uICgScn2nVRMc9y9CXIKnZkxj8IAgLjcKqbllVeM0+30E2B6+wyiK4yGAmQXBcrdJ1RNzVtU1ljyCo9J+y/SxzcYtoL0avGsuEABkEgIyHjAKsb8HxRsIfRhG3nJUu97Bxw/WAoivYq4+a0j25a3p0vwMWMzWTG36UvQoXnKELPpee55L5LrlveOa9477gCcnqJy5WBy62+zzQK8M4FUO4y1hdFg+dgtB4+1/sJV3oJnnLYd2LGsbyPceBxtrkYe22qkuO84iirxPKjbgUr8hyg4oVBj9B1rE3KcZbzxtmceVlxlFfMC1F1Z1VrRJqKncS112HXkXUmjtNQLF269fmzgrOsYTe5mL93ueeRp4gcuZ9pGHDHHFD2hz0ORsf2wJBXGb3pHe7NJszSBcuitAaLy7JiXdUc5VI8t0OPV7fGF07cpmOQbk2zqaQo9zzBkkLX5ZXphJd2x/S9eyhni6PNLc42ObP0xD7mwBywsqri0WrDoqzM/XR4lGAnZ0XJAzMS/Nh0zO3pmHHim4NTQ1HLf6t5xsPFivO8sGLVTlfTrZ7nsmsObB0u1mGEIHocTynOcoMn6oZHZuQ5DX0epSWn+fNxCP4aQfnTtm0/9QF39f71/jv+WeBm27ZrpdRvAP4n4MVnvO2zrD9lcJoPErH/10jEyD9xPSuG8v18ZcDVD/4C3/vGrTajmv0tlsYUEMD1YvL0mM3mMaW+OJUU1doaB4a9XZLxDe6errk7mwNwJxjbBD/XUUz7AYlhqqRlTZbVfPF4xsN1St22zPJSNgQt/z3J1nKSqhpCV4Rip0XN2ij4K0PvjWOHupEPRlrXHK9TqzbviknHVNFNazZnha4aS1Mt60bouebUJUyoCf1wl3HfZ2sq3lDl+oRicyzW6EYzoNwzpv1d0nJHWEFxZBlVcSBU19BzSHyHEslwD3s78njq3Fq0dIaETZVRF2t6/UfsDPYkbdF0EMuyYlWUbOqaQjdshYHJtGgss6puJTyrHzqEHkwjl8hxWNcNT+YpZ1nDbK1ZrWoRfxptxv1RzbBX8NpOxYuV5uawZT9P6Eceo/iIxN9nEHls5RFFrfnyYs2bC8GZVoUUhWM0o9jhZr/TlgRsxzGP1hs+f7biSZYxSzVp0V6i7wp1dxI55HXLQ2NL4zmY4iQMpnvrElgTuWKl84mtkLur0p6yOw+rm73AkAXEF2wShdzZGrE7jEj8R2Tn961782DvVc7yayyylPNczBlz3XBWXJggCp1YHBXuLlacZDnDwGevl6DblmVZ2lFjRzwJXdd6aLkOLMobvHV8wmmWk1aVBdsfpbkd8UnHcJHZvhN7DAIZdXVWJ52CvdANZ5vcCjs7Y0m40Ht1RqqHqXQlieex34ufus9uapAaDU/dtIRlxbKSLvBhuqbQrWXIDWOHxK+YZw159ZwwFAXus4cZftB6CFy/9PU1pAuxq23b5aV//7hS6r9QSm0/y22fcY2Az/DBBeXkA34GfHUM5V8F/jXgjlLqc5d+NAD+3jM8yF+y1TY1xeYE1xc7di+U+W4yvkFvdUhvIRkPEmcb05/cxp/+OuZpxZPzimNTHIpa87knp+wsI6ZJbJPeXEexyoUFVmjNSZYzN214l1rXLc+BWsNe7BG5ilHgcbMfcWcg7LH8tvHpigI+MZ1Yu/04kJzweVYyCGS2fjGOkg/BVi9if9QjDhxi/4mEPK2lAJ3nBWlVcXUgo77Ed9FVLkFKYLO9q3JFWa0I/AGD8R12d17CCSU8TDkebVNT50KPddqItpJgqWR6G9d/mbp4nc3sHekAy5WwvBCWlxf2cRyZ14+jkLlhG53nhcy+K826bjjKCjsK63mygY4DV64ssCNB+d2a2aZhtdGkG20Lshc4RImDY+iu61pGPauytCaccIWAd4kd6I8iHs5D+r5HWuXM1ppxz2Hac2xg1DSSQlpoER1e6SUsy4q8SVmV8nelaCs7SttUrbznjWTM93yHyIFp5LEXu3Yco1tJNPz41ojXpi3vrdammHgMfZ+bwz4DgznEvtjKb0UPKbMzVrMTS9EdbL8sHlbzlTgG5KWNCe425b7nMQ5q5uZlOMoK5oYh+IWzxVN+cZGrqGvNOPDZ78n13sXnrouSL87mHOcF60qK5ZOs5jQVAkiXLTKOHda+Y/UwW6G4eI/NZ+dg0OfjvcgWl889OeVJmlk2Yt22loQguKSA8x2rcWIwPWFwtcSGeXhzMrTF6XC1ISoc1lXNsmg5WdYURUOv51LWLZuipapbfO/5VAG5Tp/L3fxj4EXj7vsI+B1cgOMAKKWuAEdt27ZKqe8CHGAGzL/abZ9ltW1768M8gW59tQ7lvwP+GvAHkVz3bq2elUb2S7lcP8ILBsSTG9TOi8zWJYPEpzdUmHh1srKhqDUPNjmH70jgkasUq7LkLC85znM7u58Cq7yy4y/30hio53lEbkVt6MWewXYjM7MHOSV2H5KdJJKc7UDYNv0wYJwEjOIjquIJVXpONDggra4CUkzKWkMjfw9XKNDjxLdYSbf7xoFn59FFrYl9j8BzSCtN0GQU6xPS+T2Ojj7N8eY+i2IOwNX+DZpGANZouI/reuTLx9TFSpTQQDK+ieP4RKPvETfXRpP4H2NybUJ/5yX7+/L6xwTxFotsj1VePv3aGqHkvBKmz7tmXFRp8F3ZkEJX0fcdSQB0RAsxLzVFLWJNz1MkPRdv5LE1cNnveVxNfDynE3x2GILmeJ2SlTWjMuRg9AKu84Cmqbk2nvKJoqRuGn48XbEpWnYSl9cmPQuog5x8xWQw5uawT6617To6PUeuG+OSKw4GY180F5FhTQ0D3+ginAubFsNyG0cRL0yGvG0ifHfiyOJoo/jImokuj06siBCgt3Wb3P04j88XzI0ViecoaOSxjXzfUt49pRgHjn28AIuy5ovnJcu8wVEwih1GocO1xOdaPyHxPQH2q8Y6DLy1THm0kQ7bNcmjRdlQmpN+FDq4TktRN2wqSdXciyoB+6uK0HWZJrEVWt5frPjSfMmm1sZnTNh3o85xoBWq9Y3hwIqSO21NFz09CAOmvYhB6JJWPmMTjzw3mMqi1CS+YlU2DIILoGNVNk/Z9H+Y5QDhc4hWadu2Vkr968DfQKi/f7Jt29eVUv+K+fkfB34r8K8qpWoEpfwdRsbxC972wz+qr299NQxloZRaAZ9o2/beL9Fj+rpWq8XOJJ7c4Ci9yReeHPJwvaHQzVMc+7WhLUauwzhwuD1IGAa+sc0QtXXdtJZp1K3QcyjrhuN1yizNJETIUBw3VU3fbwgdxxoULspKdKauUH0/sbfNwSiirb5IvjhEZzn52Rnz9BjPi1GOz6ip6U89QndfRG+1tkWsO7F2xaRtahbZHrN1N+pSdAmHAIPII2i+xPLodcrsnHT9iLxemwjeAFe5RH6fpH+VaLhPEAvl1wv7tCbuVkZZfZzo27h7ura2IfO0IasGpFXE9fEL9Mcyt06rhuO05t7ZmSVIiBFgzqIUl96zrKHULYuNJssailTjeIqTwMH3FYOeS+ApXAcS34xhPNjpO9SNFJ2PDAMOkoidWIr0Za1HZxH/ZJ2ShsJierz0GIQjo0dqOTCdQKfB6IR+1/o96+jbYT1pXbMdCyb2xbO5vEZm1AMCgOdaP1VEQEZHB4M+/dC3gW2dliYrG1LD4Ous1Kf9gKD5kuS5VJfy3ZvK0nT7k9skkxs8PJdOOfE9BoFP4lecphlnRSmPqxHTSLjo/qSoOPR9uD1sYYjtng+SmG2jTveUQ1pVPElzHm5SjrKCR5ua87QhyxvWq5rm0oasHEVVNuSFQ2UsVU5GHvOBdM1SVAVn+fzRmodrcV3ufM86jMlVjh0BgnQscyO23Y7lNRIxqzIhbjFu/Qbl4pz+8AAV3gb6TJOYg2Gfm8O+EAMMEaPTcoF0+8/FzVZdjAo/7DKO7T/+vu/98Uv//mPAH3vW236j1lfFUNq2bZRSn1VK3XjW1K5vxHKDmN7V38rfevuQ18/e5vV5xt2Tis1Gk60uxiRu4JAMXKZTn+/Zj+ztE9/j1qDP9b5oV/Z6YpsxiISKOM9KPv3oyLJiRr7PVhRwYziw2MOqKFmVFU82qR1z1I1sdtN+YGmXus4oNscU+TllJaf7ON4RS4sqB4WdEcuoqzGJfwoQum9W1szSOZkRI3bKYN0K7fnhecq09yLTGwOq9JxRsWI0v0+Zn1EY1+PEBFQF8RYlt0QhPoysYC8afQ9Hq4LHj8/xlEMQu5R1wyzNmJskwrVhaqW1zLIfp7kdW4SeWH4syop11bAopJgEruLalsc8a1jHzlM2HIJbyQd1GrmW7XVt0GMY+FYPE3jKpl9mpSYtNYusYJ7nhK7L401GWte2c+nGJt0mv9tP2B/2hMVnLGQKLUK44SUxq26F/TRNIm5VPc5M56VbYQ5J4JYQDroupKORd4SAOHAIeE9ck6ucqMoY9XdZlDcoa7k+VnnNOPkoYfiQ7Pw+i+PXqcqldV4Oe7v0d182OfalMZJUVtgKMDfGqLaIWH+2hoVu6Hkue3HIp3ZG1lJlzzgmJMaJoGMVnmS5JT+UusVR0DStdeHuiorjKbxS4QwVPePIfWfic7MXcpDEgp+Zg1k3Jq6bllEgXnSjQCySNkZMnNbi4Ow5jlCODcNvHIWM49B26QHvUeQrI4CV1Y88wxQTAW7iOxS64Y0nc5t7043NnscSUP455Ql/i6xnBeX3gdeNDsWSuL+ZdCgnqeLf+Js/y/15TVY0ZFnDZlGj65a2kULi+Yrx1Gdv7PHS2OeTOxMLgnf+QZ1VhfgIicL68Tzj7mzOYZrhOWI+eLXfY6+f8Or1V2iqx2DEjKtCc/d0wdtnc3bikO045tZkSOI/An/XxvgCKMcnbCYoxxfrCi9CuR5p0RC4Dtpsdl1ReThfMc8Le+o6NErsni8ZEjdGA8ZRyFYvIjGg+qq4Cu5V3D6MR7/C0qQLfY2sbFiWNQ9OCnSzNIy2CboZkVU1RyePOc2ERn3DJCi6jmKeFZxmgoe8t9rYrJYOkL6aiBZi2PpGhS2jwVHo4DkNPd/hSuSRJ6KcBgGP6+aChuk5MkI6SGLujAe8dnWHmC+RLw4pDk8sScALB4z7O+wMBiyyPfpr33qHFVpTK6F567YRu3St2Y8SxrGP074rUcJJjHJvm9dLM9vkFh+RWAB5jJ3B4qPVhvOiYGFA6lHgW71FaBTpie891ZFo76YIX31F6BxS1fsUdWkPCllZG93RDoPogCsv37CGixLUdp0H5znrYm00Ho514LWsqFpzWsj70PcuxoYdk20cStfQ88RyJvE8tpPYFqZ5ntkOb1PXmLeGxBcqe9PyVDEJey69oct07PHqNORmP+JaP+EjW2NLee/GcoUWSvoo8C0xI/EksKsTz3a0anlNL7amTrNkRaOOQrm3SSaRjUQuC23xHMC4YDxCLe7zHQcHrOubpGVti+zzWAr13DqUb5allPqLwJ9EnOZ/cby8gP/b13rHv9TrdFPzd39ujWt8sOqqRZeSZ+KHiv7YIwwdXt4LeHWc8LLJw+4+9PujHoPQtVRP11E4ziGrzS6zNOfxJjNK5osLqB8GoD9Dq3NjFHnIKt9CNy3DILA5EOMk5Gi1ByDCvImA38noBrrOLfXW9WNaXRN4imk/eooEME9L7s5XHKYZR3nFUapZZA2uA9PEtQ67HXh/kfGN9dKa1xVZtUVW1qzKU9Kq5jwXauUw8BkEgRn1aM5yoXoCTMMAFiu6VD59iYBwd5UxN463Pd8hUuKAXGiJ0z3KJOkw8qRARIYW04G3IEUkwqF2WrZDoRH3fY/9JOLqoM+10UDyxKOP0t8doGY+jcmR98KBGQ3V4iMW+da3rVul7sZDgcGZGtKqIfFfIIgfo6ucfPEPUa7HKN4iDm6xzmsL7JeGYtuFdu0P+9w7XxgSxEWENGB94zq7E6F2V7aLEJfhhLo9t+/VBW4jNN+sikjL3aeu77JOmaU5WVVZg8RVUbIoK5OZYsghdQu0lmVobYM8cameGnZdYq7luXn/C60ZBr6MCzepGY8Jw667j8BXDKe+DZyLY5ftkcuLo4BPbA3YMQ4Ot3cGuPUb6CojvPIpVoW2RqLda3C83tj3qHOESHzpXopGWIC3x8OLv+1dsB2zsiGjAXZxHf1UofGUokC6x7S6iusfU6yP6fdhEN62ndnzWN+iHcr/B/h9wP9bKfU/Av9N27ZfetYbP1NBadv27yil9oDvNN/6mbZtj7/mh/qLuJq6pUo1JC5RpAgih+VJQ51rmtrBjxp8XwDKoe8zTWJ2BjGh1wn8BAMoa40biaHierPNG0enJugoZ1HWPMlkFHVnkLIqS+6dh8T+yG46hRZmnW4aBsA8K5htMmt/UdYNcBU3uco8rUxwVYOLw0EUoZwHhDiEbidcrJitNjxcbXh3teHNRckia9hkDY4DWwOXK7FnFMIbznMZRQ2CwIL1g8gn8ISldrxOebJOOclyNnWN5zjsX4oY7kKPjrJCfJeUYl3JqEIyN0pGgW8L8dh3mRcNo8BlHDjW+TY3NNbToqbQwoLyHOgbbKVLLUw8z6q0O4Fdt670Em5OhhyMIqr0s1CA9iPrqdZ5prW6RpOhHdEzJGbk9iTNmBU5uRbrklsDOBj2CTtK76ZkEO3RD4/wkwl+OCCrrrDOa9JSxopz45AA8GBxkTnz61+6hus8QDe3Od2U1kOs80eTa6C1DKRZKrTbTvDXKfc7IkBqOpVhEFjKuB1pmTGUjFRFNHicF4JLGWzQU8KIK3SLbmCeC/jcDx22Yoft0DUUZc3D1UbYUq28z53m5d6yYW6Enx2dGWTs5zqKUc/lyli2jFHo8F3bA671JStlHHXjqBCv+TKl0UGV67+GrjOcYsUQ8A1V/871HfxQusrHi5Sy1vQvOQu7SjHtCXYi1jSt9drTzYWmJwm68ZW4aOOI1mgFzNOKcfLtNOf/K5uzd3D9nyfs7VJfGpN9qHVJTP2tstq2/ZvA31RKjYDfCfyEUuoBErb4Z9u2rT7o9s/qNvzbgf8n8JNIYf7PlFL/Ttu2/98P8+Cf51IO+EZcVpctQaToTTyylZZikzdUkYQgfXm55qwo2J1F9lTVZYAATJOYrKo5XK6toO3epuTJWtrqQeDw9x+nLEpN379QYnf5GpHrEjoOD9cprsnF+MjWWApELQK0stYcrjZWkCYjkj2uTTya5r6EGHl79EPh5Heq52nkSvre0LXEglHgsawq0lq0HF3UaVdMALJSW+8m3Qoz6MColUut2WjBEETdXJNrsbS4zFCqjenkpha3ZjH+83h55DxlWSKKeCFDXEtElDj0fXq+K+Z9JtCry2G/rNLum81JdEAOceDgOIeEfdG/dIJKPxyYrw9wzAhpnVXW1bfni839upZDwLzMWJj5/ZV+wiAIqNuGs41rDgQD5llOqY/t4+mA7m5d6yfWSfjBeco4OaAz3x5EPnEQmgIgXckiExr3qhTQfFFWNuLZc2TT7nkyVu17HlvGhkU3jSVkzE0XpJvGmoYWWrOuatZVY4s+jjDNcleRmverIzcAFuyeFRWvzzNLde75Umy697xzLu60JR2LcRBiR5XTKGA/ibgxHNhramZMMWdpzv7wJoPkIwRDReg+JF8cAodkq0Py9Jjl7E2CaIve6AZBf4c7258kNWSZwLtUjA02VtaaupXue13llyyQFKV2GceSzFlm53j+gEJfE8PNWm57+8qrrE7eIl/KY/CDwXPZcxwUoaHkfystpdQU+N3A7wF+DvhzwPcBvxf41R9022cdef2fge/suhKl1A7wN4FvnoKiwPMVrqdom5Yyl0yRqOeg6xY/cnBdRVq1PNzIJvOzs5TQVVxLfG70YztbLmttLVM6YBLgd78km9pZXtpTfN20PCnqC/aYGe3sxWJ0txNHNvyna/O7sdmTTcq7q41spJ7H9XXKONkj5G00kPgHZJ42FuBy4d7oedaCArq0QPn3MPC52u8x7cXC0/fEkbUzyzxeb1iVJa5ybB595wjb2Y901unCABJsoDNYnBUVDzeyoV+JK2tguGVU4B0Q7xlr+W40No5Cpr34qdP7wkQWuwbrSHyf2PcsE65Yn+CFfWL/wLLZSi3YUjefz6qafpgBE3STsi4q2wUAFifYjS5O3T1jwNmFmmUmNvYkzVmWle2WOqV5hyMsSs3IWKbsxSHnecEkChkbfURZN8w22ubAdFG6nbZpU9csqoqF6SgiJQeRhZnre0p+J6hEfX6aZiQmYfTc4GaAfe8j1yGKpUh0XbPnmNFWp6kJ5T2+/N7mWq6/aSSFJHQdiUAw129HexbNiqJuZVTmKbnfFww5InRdCTszI6Tufe2cfsdxZJiJB2xNZJwbDQ7QdU6VnbFZ3Gc5exN/dUgQC9enaWoqwA8GBP0d4vDbqZuWPgEUJbhSbLvidSGE9Hi8SBjFE8qVpqxXtgivipJSDxjF38vgmtFmPSdzSFB46vkA/N8sSyn1o8ArwH8L/G/btu0Mgf/C+6xjfsH1rAXFed+Ia4bQsL9plh84DCceWrc0DRIu5UmynWdOPTZAyOR8jAKXddXwxXPJA//ouE/guszzgsebzKbMuUpxe+jz6ZMFp0XNpmq5M/S5PUjoeR6bujZmdY10J67DJAy5OuiRGLZPV6TOzbwb4KyQHBZPKeqwZV2UzNYl4+Qj8kBNIRsEPjuGutqxXuTH2m5cie8Z6qds3Oui4ngtI67OIPAkzUnr2nZS3UlcLCwu3IE9pej5LvvGQuYk08xLzWmuebLSVHXLLNXMh5pXx4llUAkTJ2J/lIiTs3NI0xzYwKbu9N5FC3SU6LFhGCWBa4uJ43iE/V0eL6acbdYmVdKxPlTdJjk2I6W0rr8Ci5D3ujF59x79MJDXxyQmHq9Ty1a7bBQ5K2rmpWZZtISe2N9Pows1/6wocVcbazB6tE4tRtJpJjrm0oWrcbdhX1i0FGaDj7iwETkvBGDv+S5609rUxNyYLXrmIu7wvMh1+fJiSVbKNbXbd+n7Fww4uV/AdBuekgNUZ93fFbdR4LOuaiLzGhYdSUJ1lGMYGVp0obV1ieh5LrgueXWBq0lBz0kr1+S9bBMHeySJXBNVsUI5PmV2Rl2uWM6OLzrPYIA21vij4RG62cNzNCD6rcBzrS9bUVXgCJjf4UDd6kbQnRfY8dpoyJ4jiC5K+W+qbfB5rD9haMh2KaXCtm2LD7COsetZC8pfV0r9DeDPm6//Wb5JeM/dGgUuv/7FPqdFzbxoWJUNia/Yi70LQZqjLP+9bltOCxkDdXqHZVXxzmJpf3cv9pmXcpGu64ZfdzCl53s8XG/E8M+cxvfcxDq5JsYePXAdSzleZKV1HF6UFfc2hY2u7e67bis+f3rOk03KsLOYMF5JgSeahoEZvwxMQcmqiiUwDAM75iprzZunZ3xpvuRRWlrRXd3IKRsgdC+yNjqNAmA3OFF9SwGolQjJpqFnMlNaHs0qNpuW+UpEincGJR/fkpPiqiiZ1iGEj0jPD3H9E7G4cW+TNsLWkTS9iw5Gty2x8U3zwlfRXkNWNjw4zjhen0sX1ciGIRvdxQnz3nLFxmAMQ9+3IVOdu+52EFicrOuIBia9Lwk8Hp6vDfPpomAsShHcLdHGOkfGiiPfp2gaMYU0Hc6TNLP5Il3SZfecOrqyBcZdB88VGnMn8gTotxealnXV8CSrbZcxNgXIVYrId59yNAbY1JqsbMmMrc/MgbljrH18KQSu6S4850JD0/c8O5rs7k9wmM4IVXC8LstmFPgMfZ+H65S58eyyNjFK4bmKRWnGpsYbrOseuoOOhKRtEXrb7FwbkC0PKdcnLGdvUudnlNWavDgnMo4A3uwd+sOc0rv1FFbRuUh0Y0DPUWilbPZJJ0rtVtfdyfvSPLcioFC4zrdWhwL8R3zl3v4PgO94lhs/Kyj/7yil/hngVyAYyo+0bfuXvpZH+Yu9PEfx2nRsQb3uohoYtlXie6RVLTnzRWlPfA+bikK3vLOseGdZkfiKVycR0zBg5PtcSzQHScQv253yPR/5Nh6fvcf4LOQ0yyV/3IyizvOC0HM5GAhIedlrK62EOXOUFaaTkc19rRTbkct2KKfFIxN0NAp89pPIMsUGbgBOY9lDHYgrm4HLNJEQrdM0453Fki/ON9xfibK5aSUauKoaKnOKDSKHq9OW3aQR19soMNoFMUeEi5m7NszBcSCbdd+TjfRspc3r3PIwrajbBaPVht1IBKF7/RH9cIexG6DLFt2U9jXpwObY9yz2JGSFmnlaMksz1kVpDQe71Yn11ua5d4FNXXG+2QfwbcekW9FodGA5iL5Hr7vC4nNzOjDJjJ71hQodcdwdBzLy6nmuHAyMZ5RuW7RuAXOi1w11q8m1tmOyzuyx7ztWrd495ieZWIPIawGbqmFtCkjdyGv6aFHSCx0+MhZfsNB3GPoXSvKNAfdnhjnlOIqybDg5q3Ec8H2HMnYIPEXgQuS1eEiBkaL89PsMsDb4Wff9jm3XHTgO08xiXZFxZujGX7XRQZ0XhbWXB4zT8Yrr/YSrg74li8TBHmG4oqlyG9blmfTOINrCDwc0TU2xPsb1V4yTCRK45rDIbIKGITa4xiK/sbHD0iU61q2gcxyW5/68zCG/dUZextrlKhArpb6dC0+vIZA86/08c6Z827Z/EfiLX8uD/KVcjlIGWG8YBD77w761Zuj0Bsr/GInvcbja8GSTkuuUceAyLzXzTC6yUiveXORciWtu9GK+a28qlilhwGL9UFyNTVb4LM2t6d2yrKgz2SS2TRIkiFL8yTp96kO2HbrW+6kDPrsuikbA702l0U1hQVFtVODd3L8woUyJ75FVNfcXK768XPP2suTRQjaFpoG8kGx5XYuZouMpdN2yzBtCD8aBbByucvBUYzMmADZmjOQpZQV80xA+teOyGIkX1NycjOdlQ93UbKoNh2lG3xeQeSeOLPgeX7KYD3yZtWflxWkyqyruLdfinWXwh8uplSA4wKYWP7B1Jawm14yCQseh55uCW9e2AwqN4BMQvQe1AXljDkYRo50FcTC1+TWDIGBVluyWlT18FLqhqOQ0LwmE8ndq47DQPdaOulvolvO0wXc1x77DMJRiUeiWUl8YMnZ2M33PITdjx4O+a6Ocj7KaOnKtrqYblXVW9+uqscB7N9qNIodJ4pL4isi76EABq/upG8ibxtjcSHF5uKmZpUIGuDbwuJooxqZYdIX9FxoZBa7LwNjEe47iMM0t9lTTQlnjpblEMriudXPYH32E/lTwks4ZoHOBAKiLFfnyUD67jkcQbxEN9+mHr1pnbDCj30t090VV2S6p0A079vcazoryKzJwPsxyv0UKCvDrgX8eMZf8I5e+vwL+g2e9k2dlef0W4A8Bu0jlUkDbtu3wA2/4S7gC1+Fg0Oc0zeys1HW6/GyPwJ+QVS2jOOTI4ApHecXMWFnHvjjcAtw7rzlai0hsJxaTyJvTHm79BpGq6YcVpf/trI1gb1NpXOXgujK/PwVWZcWyKHmSZk/lXIgBYUDo1jxKBaQtagg9zdXEJ/JlJHKc5zZdsHPk7TZ6wALfaVVzkuV8/mzFvXVlHY3ruqWuW1qzITdNS121yDSkoarFafehqlnXa6JLI4VuTLIoK+q2ZFNp5pV4WY0Cj74v/+VasxcZHKaRbuFJro2oTrGpaoaBdAd1I0FJICOLizGUxNU+3mTMipKZwZT6vvOUvUlnZriuGrtZRZ6SMZIj9OXCFOJNpS+57kqh2YoCCt2NIy+6lVWhGTs1e4PQikFdR5EUHolfGbt7bQOwXHNw6UZrhVF1ewAuXIkaHqYVaSUeZa4jh4FCy6jRcxSj0LHeXwdJJOI+E+/ckQKuJpo3FzmFFsA8chrqJreYSEfHDl3FXt/l3BO9SFdEIlexF/mG6u1YdpmM2eRazHXLUVrR8+VxdfTg2Jfbd+SRumlsEe06EuApEWen3O8bEsfjNLd5MfNKk+sLh4lS+8w2mSkwI1xnIq955BH0ldGBPSA7F7YjTFCOh3J9MXd1lPl8O5wZR4Rca0s0ueygfDlGIPE8mzHzPJZSisANnst9faNX27Z/GvjTSql/xjQPX9d61g7lDyOI/xtf7x/6xV5dSmOn/O1sJMoa1lwx4rINh8s1b50vrHtq3UBWteRVSy9UBK5iGDvmRC75HYfLNWWtGcW3mfblAno4W3O0SUmrygLcy6riOM+J3AuH2burgmUhOM12JLX4sllfWrUyA68gdGvyRswR+77Dgsp+yKFTDF+2KVFsKkmwe5RKMam0dCaOo2jaliK/AEt7Iw/f4EVZptlsNE8A31f0EpdJ4nC15xGZGfNRJv5bHSgrj+Hi/iLXxfNk7j43mohu1cbET5x5ZeS43cQUZgQFWMD0vdWGe+tcjCC1CCQ9oyfQZqx1f1WTVR02IMW/biAyAHR3iu60LLOiJnKVxTCKpiF0SnTTMIkkyS+tKla5R+BdJXQ751xlPL9kbCrmjq4BfrXVj8hBQsZOHZANkjgphb9mGLqCXVgMRf7f4TE7ccSN0UBGpK5jNSsdzdhTip+dpWyqlk1VURqNibz3XYcjeSeeo21nVOiW2ldEjpzSQ8chch02jja6oobckY211C3romEcO3zbloycOkfoTSUeZdcNrT7xfev3VbeNZKtcMkyNfY9+KL54ie9bh+lZXpuYh9qODJNNKmJR7+K1c411vTg27LA7vMbAHCocR9wlqnqfMkvtobHLn9nUF3YtPd+zI6/QdZlEoT2AdSPx57FEKf+t0aEopX5327Z/FrillPq33v/ztm3/yC9ws69Yz1pQjr6ZiwnAqqz58fcesReHT2VtdD5Tx1luufZ503CUarJKNpEugzzxHfZiz46gpqFPrhveWSx563xhBX26adkY8NLSb6uKWV6aEVZFrlvePC1JswbPVXx0P5BUP99lXdfyoa0aNkVLljfUuuVsqYlCh0HscKXvMvZd6kAA5qXRUABPWYl3Y4951rDOxAG2o0yDsN86DyZdt/i+IgjEP6soGqqyoSpB6xbf9cgjsXnpAOPIlQ17Gnp2nr42m2hXNGdFzaNNTakvCA4gLLazorBCup0kIvE862h7nhec5SWPNoUdxYSusuO/C6t78XmK/Q7XaVkVMG8aTp2GwJWT+Ti8cCrei312o4ieyRUvzKbTvY4dODvPCrKqz7QXWiKCiOhqm8XRjcsK7Vi90omxgy90AwG2qHSxxz2zUQoWIRYjoeuKpfslEWBs8JXuuloVmnlaWDD7KKu4t644Txs2qbYsRt9XRJEDuIwDcRjIvdaSKvqeFJFCNxzrwjojew54rbLZ7juJy6ZqKGp5vW/0Yz4yDA1OI4mTL4xH7PYTyQUyr2dWNk85Jqxy0QAFxk3gck79yK+svqkb+yZ5aQt0t0LPZc+QUADWec3a/Kyot0z3WFwkqHouV3qJVfgXWpN4HjvGQunc2L5MotCSApLAfW5iRCkoz4wafLOvnvl//8PcybO+Gp9WSv0FJCXscqb8j36YP/48V1a3fOY4J/AKEl9m05uqsyGBrBIbEteBSl8wfnqhjAz6vsPYF4B6NwptdvvjTcZxLvTey+OWaz2PvTi0brPdXP/+Si72ddGwMsB1FF7cdwe+dx9834XCgba6GFHVJmeijjosY03dQN8XvONhWvFoWVvr99h3GBsAdlO0VKFDaDb2TlHvjRSbjWxIZdkQBA6e50JPPrzdZ2xRamoDEHebszwGbTeD6NIo6mFacZY1nKeaSSKiy7pteZTKOPAsayz19kqcWm3LMPA5y0sO08xas9zqB4wC3wgoGyuuc5Viv+eaTVJOpR2bz3OwGMQ0lITAni96omEg4siOWn2S5dbPa1OLOHAc+KR1DYzFP834d11WyBemEHVjr3VdM8vNnL9tCd3GihMlmdKxBpld8mV3Qo4Nfblzh56nmrIWW3iJSJAN1TVsrJv9iCdZjaOk6FelBHx1h4CmETfmq4nPNPSechbuqOBgWGLmfZuXF91MXktHGLpCrsh1y+1Byyd2tsTfrheyMzilKk5pqpzWGeCpF4gDB90oK+IsjePzPJNiGAcegdezY9kOLL/MvpJut7GBZuMoZLefEFzK1b2wZ3FIy4s47O7zOzUTCXE1yKmblth0lZ0EAERtH/se49gH/dbXt8m8fym+ZVhebdv+l+b/H8pm61kLyhBIgR+4/BiAb5qCMggcfuDG0HpLLQsBnrvchqpsqHIBqL3AIYgctsYeO4nLzV7AjX5iqaYdc6vQDUWzsZtXtzpA/dGmuADXzXxfxhLyIXhhP2AaiTXKK+PBU2p8oV7K/cW+w9G8Jk9rUfQHLY+rhrPQ4WGgiXzFIBT33bppuRJJp3BvYYBSR8K8xiPXnow7bOe9dWVV2ff8inXWUNdSVLrpmeRZyCk5dNWF31bTUNdSZLqkuzxviCKHyO8UzVBULbtDl71EYny718q7ZEGf64a3lyWRVzH2C0aBa2nKt/qiou/GQCCCy7OioOc1dga+F0uR2FSa3UhbjYfnKBLP48Zo8BUn6dm65N750irMRYPUvY/SbT3ZpBS1JvRE57MqSzte7IwMOzrtptI8yWWc1vccao1Vvnfuwx3GoNsGD+nkBsbBWOx0hFqcVjVZVdnxTdcJpVVtsEApKtuRK6PR0MH3pfg7jpLOs26ZZw2uqvEcibn1HMeO/iLXYSsMRddiO2uJpdatjBi7vzsK5GCl25Yna3lNylpTN1NCbxfXVZS5pm5KO6pKy5rMFIzOP25VlLiVeJp13diUyFjRiPB0WcprkPi+cWf27FRhkdXWw67zj5v2QpJArv+DUcQ4CZinpb1uu3Fb5xrdfc5cpazQ1XUUpxuHcfLi17XHvH99K428uqWU+tPAv9G27dx8PQH+07Zt/4Vnuf2z0oZ/31d5EP9+27Z/8Fnu6xd73eyFbIcyTrrnVDwpG7K1ZnNeU6UNQd9BOcJ0atoL48QObOwA5LSuOc8L1lVtMYTuhNeNmhalJq1kzOOa37k18mwK3ovDPtcGPQ4GPQLPFV+vNOMsL4nMaThyHaJETpkPHWzxW89rNo54ko0nHuDgqsaEJrm8PIr5zu2heTxP53N0VMlCa0bBwp5SF6VE2KYbidBtG3EQiEIpEIGrzAxePoyjwDVKb8irljSTx3WaCVss6jn0Bx57Y49pJMVkY8ZvIIXyY5PAYiFdl/ckqy3I340BPbOhLstKssh9Fwgtu6kboYBsQqlh8shjdQk9GbVcn8QU60+zWa4J+ztM+x/l8dK1jredhqJLNQS5Bp4Y9XWXINgJBzvMyFOKe5vCMsvyWjHLtXVIdpViHMrvdqPBC1D7QlTYvRdd19T5ZXmO4swEf3XfG5vn2/fEjyv0POk0LsUELgrpvEW34uA5jgXDpXu7eB7LSxhTd5jJTNAZyLixW2ld45Xm8b8PLwnMtdZZ7wPWRr8bMycmt95Tjr1tEAozLvY1Y4NldDT47j407SWrlcZ2AGmprSBVNy197x6DyW1mm9J2LoHnyrymgFV5YZmjm8aC+Bfu3R9+KeUQuM/JF+ybZ31bV0wA2rY9NzTiZ1rPawD425BUx2/o6ryaOusQyQdXHIcOq4FLnjXWJdVxBNCNjFfVSZZbJXqpNcd5wSwvjehQLsBZrrmESVtqZ6dSnvYc7gx8xkZRfKWXWEt8wNqMdHqG7rEKgK24s+OzqQQknXmKIm9oGukKfFfZuOF11dDzGq4Neuz1Evqhb0kJHaW4KzLX+wmbSpPWNaPAZR42bAKHutRoDeQNedHge8qcIKUQbMVGF9HCVnwxgtC1S9u0KEdsbnqxAciNu+1s07BYaxwFVQLzSrPtKHrGuvzRpniqmFjfr6phXRcml1wsXTrmTldMLgsbO98xkM34PC9443jGIuszil/F9RQ6bSl1SlZV1vmgey/nxoetsxURJpK8F7f6gTC3zGbWUZitALSGzGBM5n/0Qtnop2HAVhRc6m7M2Cx92gn8shlmx5J7kl/QWYVafpH8eSXy6A1cKybsxnbdNd/zXSLXFcq5Gb/2PYdZXhqLIMx9Ya+h1JBRKlNQNpUU/dyMcUGYXGWt0U4LWnKDcM3GfMnEsisKmXEy8IyqXZ5rQ60vYqxdRzEII1uYOv81bQwyPdUJLbvCpS0xReITKuLgJqFqCTxxj768unFi99qC4G6lodqX+unf/3qX4vnRhpVS/zTwR5HUxT/Rtu0Pv+/n/xzw75kv18C/2rbtZ83P3kPovRqon0XR/gHLUUpN2rY9N/e9xddQJ55XQfmGhwLUjcTFjgMXD2VprIVuiQPFKPa5NJoF5ES2rhoepRWP0uopZXJnvNetTdWyyBprXlfWLYGn6IcXdOPQvTjRApznOed5boVVHWbwKK0scNr3nKeA1DqEU78mr1pcV6F1S541VFXLfAm9nst2Xx7j3fmStKqsYWF3Cu82MW1otIXZEPuew82Rzzh2OFnLOLBpII4cemGX3dGSVQ1nQH/gsBd1EbulbHx1S9MY+xBPsckaKt1yZSjmgW5fsdu/+JBthx5TY5eu24aXRom1tQ9dh3Hg2+5qYTI9Ou3Ny5MRnlKcZLmdvx9lBfc2JXnd2q4x1xl1Ax8ZBlzfZPZvdx5lR1kh4ssGfuWVIT0TE1s3Lcd5zs+eZvR8oyb3LnQmXUfTbUrT0ONqElgq6ijwCR2HnzldWTbXcS7i1K5IdIXIc5RlJXVdcd20FpfqruFuPclqTgvNtcS3f2cU+JbyXBhh7jQMnvJ1W9tYYqMPqjpLeyyduNPDgByqeqGympjL2qiucJdaExtb/tjY5IDsfNYR2dEkjmcOTcYb7pKxZnd9djiSbhrWZqzVsf7k+y0ajdsqYWnqTkDriu2KYQ92TtqA6Ywu3IhLra27ghRbEa0WRh/2vAK2QBla84e8F6Vc4D8Hvh94CPxjpdRfadv2i5d+7V3gV5mO4QeBHwG++9LPf03btqcf+sHAfwr8faVU59P424D/x7Pe+HkVlOfltvZ1r7JpebSp7cnzQvMhF09nnxG5rrX9XtcN58afamnA9Dh2ORi7FtTviojrQD90cI0Ne+Gqp4RjHWAMgo+su9l7JR3OaS7jsVK3xL6yI6KrSfDUiCPX2o4v6rblJNW897BA5+JNVocimNONUHrHq4xp5FkrjW6z7E6wXZcFwnyaImOxaSTjp2XRmrRHZTGPuhFspKMB3+xHfMe0z2Gc87pbcOwoUWOb5xsHF+mKe3HIpDOLNOLOzlE5cAUAv973uTVw0G1jqcVFre3J21UyBuuAXMA49VacFtqykrpTJ8jG9sSvOS2WvLeoWWw0YSCjvLKWQ8Ukcnhnldoc+LxuOV5rsqLhzo5vM+F3I6H+dpt9J+brG6LGpqx4mFZ8+jjnZF6TxA5Xhu7F73kX19pl3Uq3ct3YxEfPuRDhbYce+SXmVORI97woK3qeCALruraK78vjOW06nk2t7Vi0blu6etVdp90Zqec7uOqiKHdsPjDRwIZUMAgCxlFEP/RNDIJrcBUpikXdWLqzLTRKkdY1y0KwltB1cc3vd0XgaJ0KrmUeYEfl7sS7umqs20XHyrO5J8ax+nJhWBYl50VhWWO2u7ukOQlNhEQcPJ9t7zmyvL4LeLtt23cAlFL/PfBDgC0obdv+/Uu//w8RAeJzX23b/hljAvlrzbd+y/sK2weub5kOpW2FWaUbx2xKzVNgY89zra28GOM1PFlrzlea9UrAcOXImKksGzxP4TgK33Qkia8uGfs9vWksLlF6JQu7spv1MpcPQrdPOI4I6+q2ZRx4XO8nDIPAAondqCR05XS3HdYsDZAut1fC+GlbFkUjmEWlGfu1FQP2TVHpctw7NtSdQWw7hWl4wU6blxdW5YAwgGjtKXaWl3hxyCjw+bYtxWaorTVHZ4Y5DHwmUWRHWK4BosdxyPFaTDHltZPXc6+XWN+zLnmwsxRZ1AXHeWupuHXTcG9TWKp34Bpb9kYBrdWlHKcCus8WNcvzGi9Q+IGQLzpywGlxwXDqFOtbA5drif9UTv2qLN9n299Ydt681BytNctNY6JxYbZp6IcOUU9y2ju2WRdk1W22umkI3NaYdLZ4dW0xpJ7nGmuerkPA6oBAiApd0ercAbrux0PZbqjvyb/nZWvxku4+Lv6tiFx5fn3PsbHBXUaNvJ/iEOE6iqyqKXVjO5JuBFXW4rAsdjTaMrq6a7nne5Zw4CF+W4VuOE2zp5IbASsc1W1rC0Q3tupsYeoGk73jW31RbvzDuuu3ixbueS6eue/OoqXUknPzXJZSuM+hQ0EsTx5c+vohT3cf71//IvDXLn3dAv+LUqoF/su2bX/kQz4eHyNeN/9+5vW8Csr/+PXeUCn1J4HfCBy3bftx870t4C8At4D3gN/ezfQ+aFUaKt1YhXKnTwABpGeF6FBmueZ0rVkspJDUlbEmyTVV2jCrBSNQniIau0Q9lyhx6PVcIl8xjh3GA2Mm2PlLdade3bCpWrJKwMOOZda5Hwf+RUbFvBQ9A2A2Y9lAB23AJAwF9/Br6ivYMcaykJHUpmjZ0MpzVRJ+NA5864YLoonYDj36XmM2wkp4+nFkx3BdnPCyqiyYm7sXJ/h5KQXnYVoxDsQk8kYvZnRpwwnci9NioTWB21ixW1lruwledgGe54Udg0jyYWVGgrktbp4SQZzYyUtRHEfCeuqs2bv3VBIMZRMLAofYjN3C0GHacxgFLgtTCDr6eD902Bm7ltLdhU51xQ3M5us4oBsebmobXNWZMUZRB1ZjR58SAVBRGEPL0BRdEBwhN2NJ3bZ4jkNkrG26A9Cm1tRGLyXmnnKfHaHAHoycCyKGa1yBp1FA3TTGFl+SMLvlOVgqeGcS2elkuvyYrvANA59+GOAph8PVxr53lynQlx9z6DqWvdV1XKHpsjEj165TFU1KZYvnNJIOJLVMuppZUXJkDmZ1g8ldEfp/4CkGQUHfv7Dj6Sx4AJtR1DOZQJNLMoBVWdmu6MMuBTjP3qFsv8/+/Ucubfy/0IH8F5z6KKV+DVJQvu/St39F27aHSqldJBDrS23b/tSzPrD33f+/AfzLiM2WAv6sUupH2rb9z57l9s9qvfKHERfKDPjrwGvAv2mUlbRt+x9/HY+9W/8N8MeAP3Ppe38A+Ftt2/6wUuoPmK//vV/gtpceIwIE6xZQlg2yLuBJqy3wmGWaqhTxX7eaBsq1pto0tJmhlZYNqm5ZH9esewp/4BIOXeKBSzrxgZKeX5MbK/cOnHUUNK1sHHXdkmUNZd7QNi2Op6g8xWED5wa3WNcN43XOKFhzeyDaoo4qCRdq8XkpNi2bqqLSWDFkFDoEngCx48BnNwrZjUJxfm0a2610ZpjdRhm6DrXZGELPZViLOCxyC6PRaJhzkaDY9x2moWcz3qdJbLPrAeZpaQVnK61JzebRbTDLqrKeYLptebBOGa031ua9Mzq8t67syK1LGhwZgVx3Ar18ir5MoKiblk3VMo7BnXhPGTM+yWtWRWtp000LadiSxS15JGC0mF86TCPPCjcvOwVf63n0fRljzsw+3RUT1/zuvGwsIF64jYk0aKyY9cKl18FD4ZoTeOdi3GEvXUTyU47UhlFWey35+0Y6XbcSGsqwEAyw9OYOtL/8nEAs6RPfswzHrkD0DdZwWUPS0ZC7LqV7/l2R7Gxjep5LZDbaLstlUwvY32FHnZWOpxRhVX/Fc8+bxo4uS6Mn81yF5yk5EKgWz2mEWOE4RO5F99U5JITGQy4xLuCF1rZ7eh5LKQffGFo+wzr9ALD8IXD90tfXgMOv/Hvq24A/Afxg27az7vtt2x6a/x8rpf4SMkL7ugoKUqy+u23bjfmbfwhxG35+BQX4gbZt/12l1G9GnvxvA/428Ge/9sf79Grb9qeUUrfe9+0f4iIZ7E8jSZEfWFBiV/Ede6F8oKvGZCa0Voneue2mKy08flfhBcrSZ5Wj8HsO9EDnLY1uaetWZJwV6LyhDhRN7JBlmhNPsfKFJXNyUtLUUjAcw34CqErZvJUDbXPxX5ZpskyzChzmm4ZBrLnSlzn7QRJb+/rUANLdBrCuG9aFgOnd86nKhrJ0bCfmKsW1Qc/SNpdlBVoKy04cMokia40PWKqnNtTpzoPLVQZ7cbC+U33fo+e7TJOY3UHMOPbxPcnfSfx9q/I+TTPO84Inacaby9T6pd0ZBNIJOBIve7RIzejPtSQKgJ4veMJLo4QrveSp+XqXhZIappzrKLbCwJ7WTwv9FSfx00KzKKSz6IqJrluyRlOWDbOFXEOeJ53ep3bNHF4peoaq222Ugk84jEPHbvCdsj/X8hyEFAI5jbGw15dsQp6mLnerGz3WhsnXbd6dazFA35e/deF2LJdn6Dqd385F0fXFvqeznRcqtmeLhr4keuw6qNh0rd1Kq5pSa9uV1CarPq8aezCpzYi26za6nJVCl8xLIbt04V0SA33xvLscnlNzfXRdSMeeLKrW0ugBCCTbKA4Uw1Bwou61t4XMUKflMWiWJdTNRvCntjXR18+noDwvUB74x8CLSqkXgEfA7wB+11N/SakbiO7v97Rt+9al7/eQvKqV+fcPAP/hh3gsii6GVJbma4A0nvXV6OZovwH4823bnqnnZLD2T1h7XVJY27aPTSv3FUsp9fuB3w8w3N1hFHhMQ2Va4Ia7q5KybtGBomkUjQeBYTM1DRY3cT1FG8mF2dRiPeKiUI4yX4MbKKKBa3GWeVPj+8LCShea2nQ8bqBwA4cgVISJSxg6VJVoYaqiochkgqIUVF6DTlwRGtYtfa+0qZGx71usZWnGA7Nck1eCx9TGPbhbs01DUVfMy8ZahoAEAA0DoUNPIslw6UZUddugKxl7PdW5NFI8Irem8x67UFw7T1lXdAFa3SrNSfUky7m3znlvIYw1R8Eo0HiOAMx7cUjf1waXaJiGEhX8sZFj590ymvMuRmoWU+mooIJPeUrZALKen7OptMVyOtq3GCA6LNaa9bymKhr80MExezGA64mbwJuhUM6noc/CdFbTMKDnyWP51NU9/tHDxzxOc+qmsSFrXfEAKUYXo7uWngHJL+uFNpdO492IURvsBERE2eE8gauIvAvVe3fS7/5WF7xVm1N/ZDCE7jH3fO8rDhLduswu64rNqmNJte1TI6IOIL9sT999v25b6vrCNWKWa07W8mQchbHSV4QellHWFREhWLQsNw1FIXR5kMLffQbNJYqOpZjXbcu8utACdQW41l2GkTyuThfUJXE+14LyHED5tm1rpdS/DvwNhDz3J9u2fV0p9a+Yn/9x4P8CTIH/wuy9HT14D/hL5nse8N+1bfvXP8TD+VPAPzKdDsD/Dvivn/XGz/pq/JhS6kvIyOtfMxHA+dfyKH8xlplB/gjA3osvtouyNq2ug26V5dl3ZolxLAmOtb50sTaCbwQxRImLoyDdGHaPp6xmBbBYSNO0ZGtNBtRVS7HU0s04oBwH5bRoF+JYTlRNI8XJD0VQWWYNjfmgrE4qHE/hJy6rjebtUcW1wUach81pS0B/mEYu66KhLFsB501HBHB6VnHmKB77oqAfxw7TyOVmT8D00HVsNshFnGxjLb3rprEnO9eMTl4aJub3LiKGPUdJ9nqtmWeeZVn1w4BSa+ZZzpNNyv2N0KMvF5t11fCEGs+pudq27EYRt0yqpW4almVpVfGW/aUbCl2YGGXZ6M5zKRoAW9FFuJhuWuZ5wRfP5jzaFHajFs2P6Ee2hi5XtsQh4bVJT1yVq4pHqeBOVpxZNnhKAHPRe9TWlufzR6eErsud0cCOgzoCQQdsu0pZ+nAHoPccxwL1y1KMRJ/kBYviwsesW51hqW5ahrFjPc66EVjkuoS+8xSD7P2jqGEQWKyjU7ZfTrsUV1/HjuxWZkzZ2cB3h4wuxwWeZk11m3T3s26TX9cNZ1nDbC2dOEg+i9tz0I4cSiLXoXZaFkVr3SXSomW1qCmyBl3KZ6QrIsoBN3BIRi6MPetQcTl6e15qPCVfd8mq2ljT9Aytu8Mhn8dS6rl1KJiUxB9/3/f++KV//0vAv/QL3O4dBIJ4Xo/jjyilfhLBaBTw+9q2/blnvf2zKuX/gJmlLdu21UqpFBlL/WKtI6XUvulO9oHjr3YD3bY277w75V3WLzkOhIHDJHGsOth1RAFe1y07Q4/dRHJKOgX0KHS4MxCn0k2teXtZMktFbV6XFetZRbVu0EUDDShP0UbgBYqo79I3/lrD2CHY9o19Obw7q5mfV2QrTXZaU+mGai0foM1KMxvU3BtU9kQ3Ci8ca/uhI49ZK8CxQH9ZteSpJlu3pBvNLHJ40nOZjxq2o9JayoMBjY1dTLfhgoxJriYSLLYVBUwiUQFfPhGXWvNotQEQBo+ptolhca2KkkVZ2fFV4CkC7+kcjU6l3s25r48GTHshfZOkmFaaeSrxxYAxTSxtMTzJClzTlez1EptfLt5OkWzm+RGnhYC1nZvB7aHPR8d9tqKA7SRmEASiXTDU1M5y5V3z/ACjRdEcZTVvLkr2koy9yOf2oEfgdsaTAoqPgg73Mqwt3xPvMIOP4HuExoWhw1IWlxhH1vJGy+vsu3Br4nMl8phGgR1dXbb+dx3HOghcjri9zCpblRUrQ9Sou3GXYWB1didpRxRoGmt7Azxl49JhHB3g3jkNXF6dfsV1IAkVdS3mpEHQGWCKw7ZuuzGgJEuuC+NRVjRUqabOW1NE5OCkHBlRu0aAuyxaztuajSkO/iVpSaVhtdFUZWMLkh8oesa37tJU70MuheN8TSSob9pliFDdes/8Z3/Wtu3Zs9zPs4LyCfB/AG4gI6YD4GXgrz7bw/2a118Bfi/ww+b/f/mr3SB0Za49y7Wdw+qmJfIVsfEnClwl9uzuBR8fpF3uKMC51vS92rJgdqPIApEHScVhmvMwrfgy0uHosqLRCqXASxyGez5bU5+rI49bfekM+r7HrUFPNrC65meSGZ8JFSdBjespirWmbUCXDZtz+VA1LSSxg+8q1kVzMS4w9vpNA4QQGtZY7glAnxdmvFa2lL7oLOZ5Z4UiH+hSt2yKCypyGAhBoGMQdcVkfMnqOzR276XWdtQEXDrFygdL7PxFENn3HAsmAwYsvTApzM3JtzPta/LPcXb6JmV2jmoqXtz9OL3pr2a2Kbl7ujCsKMF6hoHPXi9h2ovtGEc3LXHg8dG9LTxHcW+55v465SiviByHq73Q4ktdXk53+05BHbgOr1Y1h6sND1cSFha5iquJb4vkppbIgG7MFJrOoxuzgWEZKtccRmobyNWNvFwl+RwfdV3OivKSU3ULtEwiYehd7YUWHwLjyNtLLkaWxuakY9VdvBYN87wgM8LWkzS371X3fkqHqp8aIXbC2K5bBb6icKyNFqbDguQzpPDalgiH2gVhtsiBqrN2seQF013giEOEq6D0BHAfTDwcV5E7WqyBEhevwwdNR54WLWmhjchWuvVl1ZAbN4KOLh6GDo6jBLM0o07XU3jB+xTOX+dSSuF63zLWK59BmGWXsYzu6xa4/Sx38qz92p8yf/CXm68fIlThD11QlFJ/HgHgt5VSD4H/K1JI/gel1L8I3EdIAB+4OnHhDE3iK5uQBzA2br/dPD3XonqeGuHdZf77ZcAwRCzYI2PJMglDmdN7OZuqwXECwsShSMUuvjdwubXr8/Io5Hov4c54wNic8jv6aFpVvGDYXO8lFQ96rmWeNc1Fi1+VDSli3Ng5B3dK/8BVJKESwaV53DIBatkaugRbHlkpp8TAU2RlS6Vb8ko0LB2G27Fn4tDM6I24TRuF9NzYf3csINeopQGLu3Tsn02l7YYkwLWM7K4mrtVHdLiONWk071un8E+SCUE0QVcZZZ6RLw+Bn2RrfIMH3hi3kjTFxCQ/dqmYWVVbe44+AdN+wGtXd3hxZ8LZJudonTIIA17aHXNvtiI1BoXaFDPxlxJzwe56kNdbWc81XCxN1TWjE22A6FFw4fL7/tXhFh0uBE93EhNCcUb2c95dbZibEc526DGNJByrezxdIS61lvt1fALXseO+zihRir1ji35hCuCmvqwfaqzosisKm45p9T5bGMmk9xj64rE21MII7Oz7O4abUKwvXrtRqC7GjSYM7CJX52LsFHkyEnSVohc6lEOP1Ubb0XNHdHE9ya4HwRAr81oVRUORCv3f88X/znOVjLabll7PpQ4d0o0mW2l0/bx02N86HUrbti88j/t51oJyp23bf1Yp9TvNH8/Uc0Ll27b9nf+EH/26r+V+lBJB2TQyM1XHuSRE7Dj60r4vStFb6FZGJ5fBW+ASK0XbMUTfc7g9SETF7Xm8PArZizX1bmD/3l7sc72f2FCmwHVtil0SiOdQOq/QbSOZGb7LrX4XdSsitjNjrLgVi0r6cKmlMLiKnu8YlTSGi++YWbIicGUW7Soppvs9ocx26X7LQjzCXEe8umIfdE8+xLvJhTV8rjVnRcmmrq1hpuStd2FfUlQ63ceirIzm4QKc3YtDo3cJLWMtMAFNhdZkVcWj1cbQOT3WRYVetEx7B/RHKwD8cEDY28ULByj3NoNwjaeMtbvTOR3LKGyeFaTm8YqDb83NaY/EeY8tN+eVnQHrepeH52tcR/HRK1uM4iPWxZRVXluTw0UmlvWzNOc0zcx4x7Ei2e4kn7cXI8PLViXd5gpQaOmkaByGJmDssnHi5ZFUYnAV3TaMjDNA1/10xaRuW47zgoUZXxW1Zq+f4JpRX1dIPKfzZKstXlJeKuCd3cvmkj6o+ztdZoo8p4tu5krs2ff3MnljL5YMmcIQAToxrecI061jYM3LBk+iQq2LhRVhGjFu3bQUScOTXKj4/dBh3Y2oWqgMQ0+cliEwRJt0I9294wjhxvHEhXk+q6grYXCGsUMy9NjZCagmrcV1Pux6nhjKN8sy+/o/B7zQtu3/3bDLrrRt+zPPcvtnfTVKpVSMEdsope5wKRflm2E15gOzHXbspqfrndAda3vqPK1rTteivO0sUUS7gvXr6k54se8wDBtGQck0DPAccRLu+O0Do4fo5svLsiKtKp5sBAMYBj7XRwPGUci0F3OzHHCaZiSeGP51auGe73F3seLuKrNWLaPYoeeLC3BHJYXGxrZ2Smg58MlIS37XFFaTWe45LVux8xSwe9kwsFNcL8qaWWuYTZEmdCSadhKFNhwLR7CP1LnADWZFZXADlytJzE4SWa1K6LkERj9Q1pq01PTDgKyqJV/e5H8UtWY8eJV44uE4PiW3WOY16XmGq5S1/ZD303hVZQWnmTg4AzYE7Xi94fb0JnvJPepiReK/zSs7PnWxMqTIHWbrknVRmiJXW6uPpyi1jkPuGE81QwvuNttOtzIK3Pcx4cQeRDctw+jS9ejIe1dqjdu25sAhr+XAXFfdyCk1ivLL2S2zQvAW8X8TNt12HBk1e2e0KNkg1n3APJdR4NO75GSsW9GCXD5IwdOgewdqe47+itGl6HWErt3l24B0nydZYW1hpHBocu1Y09aOOBO5/z/u/jzKsiy/60M/ezjjneJGZERmRmZlVmVVV7V6Vqsl0SBAYGZjMxgbMNjGz2Z4wDM2wjYPeM9GGD8tzGgvgxYzxlrMsGh4YIQwiKehhVpST9XdVV2VVZmVGZERkRE37nTGvc9+f+xzzo0seshSZTft2mvVqozh3rj33Hv3b/++v++gGAdBz85a14ZBULf307Qmlb5YH6wss8z20dYXl5CgtI+kAKiKhjCWKO1JNNZ4Eg3AtZ2A8Y7mx3gSSyDeIR3KhfWn8Xjlzwf+EN508u8A3/o4N37cgvLf4QWNTwkhvg/4WfhA+2+Y5T8gxsMPLSRxVPjAoKGWbIWqN3zsHFcXFxgmneDNWm/KqLXAGNcqoR3rGk4Lw/CCLcX+aMj2IGYUByyLmnlrTx8pxaKDGVoK6xfOF+ynCdeGA56ajLg8SPvH3p3ar09GPDUZ8f4s585ixUGWc5TXrVZjA8XFbIrCyjTMy6bPkk8C2Q87/Yxi48/UOeu+2ZkWNgW4O30jPcXStJBPpy+I2s1/FG4+SJFS7CXedmUaxzw1GbE3StgeHFOuXsdWOVQQpVPC+F0tRNP0brLd6weQ1RbbPEdWGE6WC5ZV1Q+fu8fZKewra3mY5yyqmnntDSe7NdQL9k/PeXo8ZH90ue9qQr3nYa6lxDaehda50HawjaVTyIOWuneH9tdc9iainfVM+iWG093qGHWltT39toO/tnprE2+YuDdKyCrLPC85L7wh5qzw9iNGdR2kL2zHRdHOsjYForPE7wbvF1M+owszEdPqWLTsiAAb8WNXGE8L4wWZtX9/tVIdKuvabtnf1yT0B6H90bAPxzpZ5hwsVyyqijdWmWe+tYP87jFMwqBne3UwatRapgCP6KiOWjeJSHt6/Dr3ol4pYDzxt5VyAwPbxnf0lXUs28+1t1Lyj19fmEm9rfUO7FDwosYPCyF+Cnr7+vBxb/y4LK9/KoT4SeBn4Ic0v+sJOVs+0TXQiv/25/4ivvdH/jl31msOVpZI08edxkr0xcRDD96aJas9/72uNwr67rTTpTHZRrCKveI5UtKL/6RP2UsDiZKbeODzomAQaPbimNfqNa8uS0rruLOqeKE1zNtJE4ZRQBpq74Iaa/LKMooD9kYJe8MBr8/mHK7zHv8GfL5E25n4k6Vs5xCtIMy61sBS9Jj/m9y9fTciBMUFGEcp0WpPZHstNVcHST876YbJ4PNIumwMf238HxhFYS96HEUKWxdU+YymzpFBgm4MUXAPuE6oFWGb8ncxmvXeLGNZVuR13ft/9cI6551mR/1puOndZ2OlWBlPwdUSilBhnPeLmhWl17Jo1Yc5aSF7kkFHVYaLLCWBQrCubR+q1blDaym9TX1nO9MKHrtC163On620zQWoyEN/3ezL56drRnHQzrxUey0295dqzaQdlM+rurX3scAmpKqzsOlWZxDawXDd/CbVfqMuVcOgpYrDo8QCnzdS9e4MXuvRzUba7JdQ9XHbl9KErSRiKw2ZJEeM4sskoeZwsWrdAeper3MxFM1b/ji2nWMaR1wKw0d8zzrDUPBEiOFQcjlxvHRWeaZlWyiUFAQKLg99mF2X3NkdMrx9zaOCyiezBOKdV1Dq1v24Q6N26SPpvvp6XJaXAH4pcMs5991CiBtCiG97XFzt67ESrfi2y5f4Y//yn/LqsuRwbVp2iefZe6hiMxSMlUQHrQAt8BvxDM/cMpXPWo9iSRh6RlVRe4jgtNi4veqzzm3XbwCjWKNl2kIWHtPfiUPuZTX3zgz3MJwWloFW3KwN+6MB16dn1OUSFbxAZRyjSKHkG+yONDuDPY6XOa+cnXOSFxzlZX+S75bPD1Fcivym1xXPjqPfR8KKDbsKNhsn+OKzFQb9zCNs5yY7g6TvXEKt+hjWvC2mF+cZaRD0G2MSyvb7Gh0NaaTG1jn57C46GjHY0WyllxnFgdcfVIZlUXOaFe3JdpPx0cGOHeZu29OsbX2y0kD3z+NKXAGGk8y2mTAw0JvPgm28KeHF1RWrbiuOWhFl11GMw/bkXm5U7N1puysm3f2kFx6bkpKdCyLCLqOm60a6Qt1ZBPnb+QRE2MTcZrWhy0VX7WvZbfTGeTbi4gIl+CJk1elHukIct4/ZOn9du9//UkUxvBBotq4N8zbQrDOr3GrFsmmgGYUBwygg1IKm2SdSjq0kpDKJ/xzUHdmh6Z2CT7KC81awOw4CUq25lCZMEq836hyMI+0LcOeiDXBzWPHSPOfe0odl7aSKS7Hi2VHSw62mccyKkmdGTV9wO9r7vNp0sm9nCSFRwWNbr/xfZf3PwN8D9oQQfxj4NcAfeNwbP255vYirfTdvEVf7eizTNPzkySmlbdgKJfPKn9rTQHApVr29h3GOrcDDPuva9hDYlaFiK5acDiTnyw27BDw1t5urAH23MK5qjlYZe6OEYaxJA9UaD4redPG0xfab1nLlWAqOtkumUcRWHFGbqwSRN71TUpDVDaPoBtns40ziGeH03S2DR/a+VwCnZcWkxaHPq5r9NOZy4qmc3Sptw8OyC4eSfa5GB7mM+1THgGujAXvDAVcnKWkgCfQhtl6igpi8vtLrdoax5vDcU1I77N82rp2FeBt62zRUWjGKbiDlDNOsMNXS04HX/i0XpjN0NCKrr3E49xnuD1YZs7LcpCRe8KuKlOytybtiArQn2C5hUrc28IrzagMFXbyvLlel04qESoFWj4gTLhaZQaB57/YWk2Dd/72Nxb7eUHbbzbg7UXcznjTwm+1WGrRmmRvLfYDS+C6ri7NdlZ0Jo+hTC7vnemWQ9tegf9+3diK7ScyVQfpIUeuowv+67XvYz2nWtU+NjK3lUpr0lOq0zRBZ16YXmp4JX3a7uOYunG5Z1ZytC1Zl7Q8j7WctaaGw7lp0EcFZ7QWj3VC/E8wa1/QKfiU37LVRGPLUZNRfi4Pl2h/KhpUnhLSEnN0kYtxCiDtpxPO7UyLtH2NHvPDMxSeXtvFOgbyEEL8H+OvOue8TQvwEnhQlgF/pnPv8497P416Nt4WrfT2WaVzf3l6KFDeHAXq0mResagMSoi4vQXhn1qh12B1qH15lx47Xh1Vv57BuB/ah2thxdJbtAA/znNfPluwNU5QUzPPS27WXJbeXGQ9Ln9s9HnjVfFU1fPrMmwxU7ewkCSKUPO91H5UJmMQjnDVMhkdcnVwi1Irj1bo/jS+riktpwv3lilUrlLuaJFyH3jq8o6t2mSKRkj0DrVNJJ4FmJ03Yn8RgX6apC4rVjKzO0dGIcLhLFOyTV6aPWt1KfaBXl43eJeRVhr5jSaTfQKLhLuA/eEonWJNjyiUy8HTqKJCbk3hLO+4ea3eNO5ho1EYb0z7Hynrr9C79MlYKLSWRtWhheybSKAp7b6eyPaFuaLMN2mxyN4Ae+gF/Op/GEe/Z2eq7hp00ZlXWLKuq7+Z6Akeoe5FlZZoLNu+O7cERulmShp7FVpurZHXTG2teTCXM26x2JSWqha9UC0vtJlHvg6VbWnFmDOMoZCuOepJDEgTkrUvwRZ+ubiYWKYUSXhE/DkP2R4P2OTasypphtOkUoMK64JGMFyVF685cUFrLuA0AC9uuYhgFXI03s7YuOyWtTC+o7FMfW7r6eV7212AnTfprEmlJ0rI2R5F3R7gIL1rnC48v3hGT8C6mXGLrHB2PCCdT8voKO+bJ6UaEeEcN5a8BPyqEeA34a8Df+OmMNR63oLwtXO3rsTr+vJbtcO+RQKONv1AXotQVhUhKtkJPtx20p7udKGTVWml3XPyoN27cWHx3Q9C782Wf8zArCk7ykqO85EHuWWSdaj+NBIFWzLKGT1N4x9U2A34ae42Lbf/e1vZ7CJNDmmafUFsmSUgSaPIWAokKxT+5c4Bxnqbbqca7U50PtFL9cPNi+l0nfus+9HvjCFd/DlvnqCAhTKY00RAdjVBB0p8sq3ZD2Mw8Il+4tKQyDWnoN5NQb/y/VLOPUDOE1ITDXZraF1MpA5ROaOhyxWVf6LrI3/TCJtixvDqtSGg94yurNzHAkZIYsykMHcRXGsvS+mvdnfBXxhC3Rol+CO3NMLUQXE6CR/D+7v6TIODqeEDVJg1ubPn9Y4jazbYyttdvKKn6gpvXV0BeIVKS2njYdFnULcOs3tyH3Mi+e9ZZ4+3fvXtuQCSrR+Ye3e9mXRFSon0/NX3nFEYhoZKPBEx1RS8NNaOo664cq8ITTXwhDwlbskl3WOngp85peF17WO9ieqiSgmHLctMtPT81mjKyF2A+x6qsyNrC1b1voxZO3UqD9vAjyGpLZRw7g5i9UdLfR9jOUfx18IVkfXqANQXW5LgzQzTYJZkuCXVA0zwZyOsJmkP+G1/Ouf9KCPG7gZ+DN6f8fwkhPoUvLn/PObd8nPt53KvxtnC1r8fquthLke5dXTuW11ne9JCSkoZRWHN94HM9ug9k58IL3j6ks133QUiiV2hP44itOG5b7xUnec1puebeOkMLwf02z2NlGu7NfbZ6WW8GiGnkobhZ1rAqS1am4VKk2E9jHua5N3OMwnYecRnb+AKipSCKA9JQtRYlc9539f2crgqUlO1Qt2O5uEdsZypjWRY1q7Lu4QforNdb5o3UhMk2eXMTC6gAtJJIeYiUh4T6MmW7+VTWQkX/N23jehv77u9HgSSQhzRNu+HHI1+omn1s/RLV6gRTLgnSnJ3Bu3rcvutAti5kWPiipR4R8XXt8SgM2pOuN7k07am3sI7INkxosXspwNKrwDuIq9NOXMxzX5mGWFW9+FVLD7lESnFntugLSBpoqnoj7sul4GGWPwLbpIHuu4bTdcm7rz3PD3/xUyzLqtfnKCnJWyZZGui+WNqm6SGiTZSwJ3EMWsNDJQXTOPbXwbm+a+ugt84JoCv2PgfmiHxxQLU6IaiWOGuwwFxpwnhKPNlnZzBiZ3Cjt8EZRgF5bTgvSmZF6bUwPQX50ejpDgbc0O4feLgzmxEBw3SKjt7bF4juMWfG9NfFuoCzdUGkJcPREU1jSAMYRpq8vkJe+WvTHUake41ifow1BaZcspq9Sp4dY2yBVjGDYh9rClxTY+snZEMo3llDeeecA34Q+MHWrPIX4EXm3wukX+m23fqqV0MIIfF5xv8NP01c7euxlOgcXj3V8UFu+pySdWZpHISBpwMv8obzwhsAboWSnSjo1dcdftyfwDr1NxvoyDSup3uWjc+kL2xJrCSr2vtHrcqGvGh6N9sw7LQtftMtak9VfsXWHMd+oN6p+bWEl2dzrqQJWgpGF9gvW3GEaWIuDZ5iqF9juAWqhY9sXdA0NbIuiNrvSRkgolskoSIpNHlleouRbqhe2oY0eAG4S9r6U4EvOF1BmCRHhPoKeaUJ9SY8KeR1hNLAjc0GYTzc5EVoGh2NsHWOrQuEuotrDOX6mKYxVPmMrV1NsvMcp6uKnQtd1M4w7E+n3Spt028mZYuLKyk3liP5Jrq3tE0fNtbPX7qNuYP9xMYGxr+2G4+zeWXZT73VyVYc9c4B0ObUXNjsS2MfNVNsNUmp9oLG65MRSaT5yddf5MEqo7K2n32ESvU+ZdY50gvEi34m07jeKqVzWPbvJ5+l3h16tuKYSRKxMwwZRkf9e6KpC0zm51ivn3yWqlrSOLMp+ComDEckw30AgnSKlDMC4Pp0l3l+mazyQ/CO/tx16Ou2YzHOMStLxlFI2JIVIu1FtKZYsjp71b+2+Tbjywm2uUlWmT7ds3ttukKchJ5wkddXSIIHNM0+cIAyn2coQWiNawz1ckmdz1jP71IVZxiT988rDIZondA0hnx5gGvqJwZTCQRCvWMgr34JId6P71J+LXAK/L7Hve1XLSjOuUYI8ceccx8FvvDTfpRf4+WpnNoPomtfTDptSZF3FtqSKJS9kZxXBoMWhpPMn1rSNp+7gwvA60BOs4LCNgy0t3bf1BUA1QABAABJREFUazM6umyPWeGjXbukxtrCeCB9imQrxLroKxYokFFnpeG7qJPMpwmOIsF97R9Px7QZhSGmsf1QcVXWKHG5vb3fWLcHO1wahIjmc7jGIKRuN3tainNn0SFJQ0USdrOUN7B13sJRM8Jo1N+ua+mrfIZixvZgiq0LivkB5foYk2zT1Dk6uosKYobjfVbl5f516TY01xhWp7cp1seYaknTmAv3fUYyusv+9AYPi6d7+Kw7eZarM5zddBCjdArqec7zerNxdTBJWyAm4cbapXe3vaA6j6R3PFBSsB2HPD0aeBFh632lpc9CuZQmVNZyb77cFFqxMco0zj0SQHXRMcC2GqCTrGAnTQi14tlLE57aGnK6LjlerXvxqxJeQNop/rtDRHfaL61lWfq0Q2/3EjBqQ7A23mSPFt9uU734tSmXLNcHnKzuMC/PyWxOqhK240tsD67RNAZrCoL1CBXEHpZsakbDfSpjCbViK4mIah++9TDLe/PLrdZZAd5khxNfQSgPQQFYk1OujhkOYTS51WqPYM8knOcVeWUwrulngHnVAFfQwmGbq4QJ1OWSOptR5zOaxuCamiAcARDy6LC8+zmAkAHxYJcnsoR8x3h5CSHehS8ivx7Pcvnr+Bys22/lfh63X/t+IcS/B/zdti36hltKCPbThNJaBoFXbb9UV6yWFlM1JEPlxU1ik7IXK2/AtxOFDAJFaTbis44OCrCqDeeV5WFhGQYNQ20prGUS+LwM8J3Hw9zSNBBowTj2oVfWOZala5lQknHkDRiPM8uqbJgkm1nPuA1FuhwH3Bj6YKnuxAZ4SKDythtRtUnTA7iUJiSBpow1SbsJSBlQ2uvk1WbgG+nO6vyC5XlbTLqTbNPUqCBBSN0n7dg6x5Qr6mwGQL70BaVYnxAPdrEmJx7t0zTmka7i4n1ak2OqJXl2TF76jSAKR4TBiKqYURUzti8VBKMPkrUZ4UkQo5tRfx8AKkhYlr4LCNu5RfdcvPWMH/h2fmNpEGySDVuzRiVknxPSdX7dcP28KB6ZnSnp2WtZXfsNs93gOzq2bTa2Kx291ScK+r85K0teOTtnfzTENqkv5oFmbzggq71OpnSb+FvoOhP1CNOtZ2oF/kTfaWr2hilbadB2JHN/nbMlq3LZzxEATLkkWx3QNAYlNEoqdKOwzjKvZlRNybB4yKScMRhcJYq3CVtWrJQHwCVCJbGN6uc+F6nKqvVrG4VBP5jfGyXo5ovk2azffJ01FMsD6nyGDO6ioyFhkDAajEjCa+SVpTQNkZb9PCavNrOSsrxMGuwjh7f9jK/OsaZAhzVhMu0/T93zrvIZtql7Yshg+9nH21S+2hKiP3i9A9Y/wc9Lfq1z7jM/3Tt53Kvxu4EBYIQQBa0DpXNu/NP9w096DcOAb7niTx5Z6xVl3EPuKkFZOwItuDJSPDcOH8nCHuguC70hUrTCOZ/tkbX00E7L4a3QLSbxoUjrwG+3sZJcuoAwXkplL7BaG+vht8IyiXykrS9kintZzcPM+2vtpoqbg5CtMGA7CrnWM25cjy93tMmsO9W284uLm2peWay+2dOcV/1s4FG6aagFaaBw9jZ1NsM1BlvnuMZAueyxYaEClI77k6CUumdqAT0mHSZThNJIqb31i3VE7GPKT7TFoIVWwhG6WlKu3sA6Q1adI6UmDbcoizMWpy+h9PcTp3uMLr1A2fL8q9wXMik1plwyiu6TBk+14kYP/Ywav4GZdqbTzY46W5W8rvtT70XlfbeSQLMzjPlAkpHXVzjPPB22G8BfzE/v3medyLMjE4RKgbWsrSEzrncg/sL5grOiIj4755mtce9rBh7eK6q6JxF09HblHJp22KwkO6kXml5kkqWhJpWvUGczlouCYnFAtrrPen3IPDtkVS/a59nqZYRGSU2oIrbjXSpbUNmq/53crCE7JI6miHYDljKgaQxJSwf2z93T4jsY+MYw3cBwznmboWHMJDmiXBUIFRAm27impmmM7zDKJVIGnk2YTHHWkKY3No7M8o32lbnRdzG+w968j4XSKBKE0phy1Xq/aZw1SJO03bNBtVG90WCXJno/T2KJd5Y55GO5CX+19bhK+dGT+GNfy6WaOU/rHyee7COC93BzPWY3jTnJCjLj3/zjMODacLCxD4lCTtu42kupF/Etq7p3st1JE86LsuXdV314kGkc58axqhu2QsWlSKGl5oWJfCTv3Lqmz+64lga9PfzFdSlqDQebpvc46k73eW0ojbcX6XD5bnUisUHg1d9pEFC1rKEkdFRGkFUe4/bMqKbXPHQrUhKlNHmt+Jt/+wd55Yu3uXFti1/1yz/CaBgjpX4EI+5gBB2Oegpw93XHCGsa0zO8nL1NtTppobfAK+eLGVX1KGGkaQxn6/uo/Kjf1JTQjA92mAxvMBjfIIj83zQtbKOjETqakaZT4Ea70Rz00JhQGplothIfTZxVxptQNg2TJOp1Id56xfWzGCWhXJ3QlLfZCmKu7nsI73hRkJZ1b5Ozk8aktW79vy7SV72Y0DqvCRoG3jF4XVvKwDIIFF+czXsiiJJeGzJo5y8DqXpWndKaYRSihX9PDKOQrTRsnRkEcJdydcz5ycvU1RJrcupqSVUvKesllS2xTUsQ6J2ONYGKCFVMEk3JyxmVLfrDghKaKBgShCOCaIQMEmQQ+80+uE8a3KC0AaOWDnx3vmRR1ewmcV+kQ+U7mNNVQWUukYSXGQ2P/HVtapw1/uACPSzbFRag1UAVF6DOA5LhHo16hqxuWsGyZRjFvvPtuuf8jLIxBO370dnaP36TEwz2kFITpFOeWNqs4J3UoTyR9bhK+Q9/iW/PgTvOOfMlfvZ1X6ZecfD6P/WnH5OTRFO+6dL7+Nann0en38LDtVfdbiUHrI4/S748II72eGqosXGB0jHjK+/lcL7TUyht4zhalq23Usl2HHJWVMzrmjurqvdCMs4RCckkCHh6POw59idZwXYU9gPTqPV9yowXjF1OvO6koyz3NijC53sDPfU3qz3cldU+atZneNcsatmz0dKW6pq0p+auIHUFFDZW/XtD31J9+icO+HW/9j/hQ++b8u7nRvzDf7TkD/2Rv8tf/yu/j5/5M76JIJ2igqSHFsBDTt2H/yLXX+nEbxbmcyigan+/Knx30W14xhak0RQpNbXJyarznrV1VpxgnaWyFWfFQ8Lla2zPdtmdvIsk3UPIgKo4Q0jNaPosA55FBr6zOj95yT8+nSCDBKVjVHDCVuoD7bo50ihSYF+mXs2QQNJtChZcZTCNQQYxplyxePAiQXrAza1t5tUNzrOSVdkyqNKEsDWXnBVlD3911u5x53+mPIuvg0e345DSNjxYZ+wmsadIa93nt3eMt1HkrfrDFnbzTK3WhLJ+mWJxSLk+pq6WvlM0Ocv1AbX1s4pBOCEOBv1nRAlNoGIaZ9AqJo6mhMGoh0fBHxrCeJswmRIk2ygdU61OkEGMlAGmvOvvW2o+dGXKh649zxuzjNdnC0ZR2EcKdNTy0jS9fgSgqYu+ExZSb5Tm/Un/LqAR6hbRcDPHy+srVHVD1dLCbSOwzWXS4CrJ2LMJnb3RzmdOmB+/2ENcTdtF62hEkE4J9OFj7ytfeT05lpcQ4pcAfwofAfznnXPf86afi/bnvwzIgN/knPvJx7nt13O9FaX8h4EOW3s/8ClgRwjx25xz3/+1eHBvZRUm47MPfrhNglMkOuF6OWOyuMNweptIaupyycunLzJb+VY6DbcAWJannBUn7CZXuDx9D2K4z7o9QW1f+SBbO9/KdhFzfTLqdSCfPnrIWVn1UEhXFLoBadk4xu2Ha2N+pzYJefVGTNfh/PujAZ031fYgZmcY9vz9vLKcrktePD7FOMc4CDguCtZVzbwd5nawS49pt5oF8DqKsM39AK/9yNYZv+7X/ib+8P/zg3z0I5tB+o9+4ohf95/8j7z06b9KkG6YWkHq8WlnDUL5YWqaKgLu4qzph+/dh0wFSX9CrMtl39HYpqasV1jnsfyiXpObjFCFDIMxucmwjWVZr4iakFBFpPkJUgaMtp7l0tPfgSmW/ZC307YonVBXS5pq6f8mXXGJ2UpewDaqJyCYckm5Pukfk39OHrKrcq/iH2zf6udGXmQKSfg0xwvJ4WLVX+OLWS/dzKOzZO+0TdtxQlQoFq06Pb7QiXqB6UYzYpuGUcuUujhs76i4PSSkY6LBHkk7u6rzM8J4m7pa9NdGSo0xOcZuqLLNhe4gagfZSvtDQrcJy1aPBFCujrF1jlABUmo/3M9nLe17ya3dfawbUxnb+9P595hgd+S1cbYtJH5A3nWRQft/36VYkyMajYxG3nbfXOtdBZT01O/O5gg2c6VumXLZQ7GjSy+Qnd9hvbhDVS3ROiEd7hM9qYE8T856pdX4/a/AL8RnTf24EOJjzrnPXfi1Xwq8q/3v24E/A3z7Y972rTyOv+Kc+40/3efyuAXldeA/c8692P7h9wD/Nd7e+O8C/8YLinWW26sHrI33yoqk4rQ4Y2/5GpPTTxOomMKsub+62+PJt1f/isPCu+tOAsf19Jj9/AGTcEqoIqTUnM9eYnvnRYbbz3pfKjw88K537yKC9/BwXXHnbNEXlavjYQ8HdNTWTvnc6UW8X5dgWVoPC9im1w5cvzRkFN1ndfzjzI/vUldLrw0Y7HFreoNQPc29+bJnpXW05tOy8hYaSrLXiSTNxpBwK4p4amvYWloI0uA+f+7P/W0+9L6tR4oJwEc/cplvft+Uv/bX/iH/6X/8S2jqgiD9YPsh9xi27eYT2pKE10jkHaDdmKWHMGxjcLbuN5GymGHaDXxdzbFtc9uxjWxj2Uv2GIZ+NJfZHOMslfXqaWNystV90uwGyfQ7PJtu+WO+SzL+BJp2m+D6BNfU1NWS1cnL6OjQz4KgnxdZk/uBrcl7OK9pPN6+nt+hXB8z2nmhpTmfYE1BPK7ZG7+L0yznvCjYimP2R8NHQruUlL06f91GJkRK8ezWiKw23FutAdkztDq3glEceCquaQj1ZlCwEQE22EZR0hCp5wlGIJwnfGgpGO0+IF8cPNIF2LaY5+0wvmlqqsYXQ1/8/YwsGe0TT/b717BbQmkGO8/2xRba4mDyRzqMq5OEvHVSSELZb/ZVPuuJHEE6RTNq72PjqaajUX8oEG3BitqYhlBrkuAB2cwLFquWIQgb94Wgnd+ZcoWU/nFm576TGk6fpVwfs14dcHLySYrsmMH45lfcSx57PTm34W8DXukYVUKIv46PWL9YFH4F8L+1pKiPCyG22nj0px/jto+12nj3XSFE6JyrfjpP5HGvxru7YtL+4c8JIb7ZOXf7ieGRb3PVjeWskpxVGi0atsMKWDGrVqxtTdnadY91SKojyqbmjaxzIXZMQ80oSAll9AhMkJUzsoMfgoMfQkmNFP4NOxrsE4T/HKUTnk+mRIM9wuEupryDyfwHNtYxA0AIzc7Qf/DqbEY296coITXXhnvIIPaneamx9Sm2rsmXBxwdfYLz/Mif0MMtJqMb7F7/KE/f+k6OliV3zhYsW2+qtJ0Vdfi9dU2rOPd2HDtpwlYSoOQbnmUzP+YLL36Gdz/3pcdj735uzOc++1OcP7hCMtpnFMQg30VW+UAqgFVZEWrFshDsDG8ySY76DbtqaZ3dabSulp7x1RYRJTV5lZGbjKPilLUpiZTGZg/YaUom4ZSb4ZhV5YfK5/kRtS1IbUH+xY+h1fcTxdsko/1+nlOuj/uTNmxosqvZq37jCUePsoDaTU1K3W+srjFkqwNon0c2v9vi8X5zdrZmsBNwdXyNO2eWo3XG/mjQ65Lyuu4ZZp0XWCdgtM71UGNnSw/0M5LtwXG7Wd7onZQ3g2hxYXZCX9w3xcayKi4xTK4wGD/on7utc3T7OmSrA6p6Rd0W9eX6gJ14myAa9YNxf12KNi3TF4GVucn5YmOzE2mvYQqlj+O1jWMUSaJEsiy9FsnH7x4g2u72zcsP+v08ZTNn04hGt6/LS1AuMeWKh+tjVrNXqVq4tHvNwmDUvnaaIByTjPb7bjoe79O0HVEYb6N0QpEdU5QzxOoJzT1c0GpjHmtdEkJ84sLXf9Y592fbf18D3rjws3v4LuTi+lK/c+0xb/tW1uvADwshPgasu2865/7449z4ca/sS0KIP4PnJoMXvLwshIiAJ+Vj8LZWZiSvr1NSVYOUnFUQqYpIKkrrWBhBaQUmqljbmoEKeO8kJVURo2DIMBwRyqjfvJUMKOulV6o7wyw/4fXVG5S2Zhyk7CWXUEKT6JRBOEFJjVYxSgbYpkbJAK1ioniKbqGYON17hCnVNAZ5+hI6HPVQkamWlMWMk/nLzPITD/84yyRa+M34HiwfvkQ82OXDV16gku/mPKu5d758xFol1AotJKaF09LQzw2KpefuL05fYjo84wc/c/Ylr+eLL5/xbR8Z8vD0RbZaSnA88afmeV5iXMOqrHo/MK8HucwoMpSrY+pshjVFf4LroBIAJQMG4YTKFuQmwzhL2Xi7krFO+0HyJLnMIJxQ1Cuss31XszN6hqjdJKTULdMsp8iOAXrIrdu0hNSPnCSFDHqoS0qNCkdEg11c44V+QupHNBMdQ8gXlYLs/C6Xd24BYw4Xa7LatCSOGCVTP5BuyR7d69GxwZZlxbM7W61vVdEPsiMtqfIZplgiVPs86gINxNEIgaaaz/oiGA33SIMYqQOEutUXFi9G3cfPInhkAK50jKw1WXVOZUviYMAgOyZMpp5SPLvbPmc/UxTKb9YKSEPVui2Y3rlgGPnn5FlzG5iram1lIvzfDVJPA+9Yhab0AktnN1tH1+lY/IC9aTtIWxdkq/vMl3cpzIraltjG9J/T0WAfCRTZMUrHBNGIpjHE46vIZNp2anXfUcXRlCB8MuRUx6Z7fIz10Dn3kS/zsy91Kn/zHX+533mc276VddD+J4G3TMZ63ILym4DfDvyX+CfwQ8DvwReTn/dW/+jXYjVAYRVXk/YDJ32sbiRhHGjAgO7ChhSpjrgUTZlEW+wObzJI9h65P2NyapOzNnNy4zH+tSm5l1tgzmB1TqoFAxWQ6gjrGqbhiGvDG1S2QAnN7uhpmvwEgNnqDeRMU9uSUbRDEvlCM1/dJQpGlPUSrTxPP6vO+dzpZzhp8WDjGgbZCdP1ITeLU7aSywSzhPDok6TDa8TJlG+9/hFvm9LQZpG8RFMXF4apS1YndynXx2SrA45mn+O5D5zyx773mB/9xNG/NkP5yc885Lt+11UaZ8jzExbHL6KChDR8X2/M2J2yx+3w2OP81wiDJUatUPhTfgdNhOHIQ17Kb+RSeFZRZnO0UJS2JtUJw3BEHAyIghGBTh65Lt1S2mP8ydYNnDUESdEXENOyyKKW2QO+iEip+2F9nc8eYa118yHwEIxpZz758oCqOOuLSph4MkG5+gR76TZpeJPPPzgjq2uSwJtslrZhGAWM1kWvE+o6i71hinWOnUHEzZ0Bx4uSUEt2Bg+YH9xun1uMNQVVftaLQMF3eaaF59LhNZLRPmEyJRzWfYd7UcyodAKxLxBd91FXy/79nJsM1XbccbrXbrq+U0u3bqLVkKYuGA6PUPKKZzcWZeuh5lNJR1HIVhKS1RsH5c5VGa6jlffgyqtOtHsLHd1+ZB4HG7ZUnfnXxfUQpJ/LNc5Q27L/bAG+wCxuk0ZTRqMb7QEmQChDsTj0VPa2+xRSo3WCMfkTEyM6nli2yj3gqQtfX8dv6o/zO+Fj3Paxl3PuDwIIIQbOufVX+/03r8elDedCiD8N/EPn3Etv+vHqrf7Rr8VKVMMHt5Zsh+EFczpJ1LJHtPCnai0kkQoYBwP2RzfZGT3jN+XBbj/ULdfHNJkXQlW2YFUteVjOuJdb3linzOuI0ioGuuZqsgb8aVbLh3ygPGc32iLRCXExQAmNdYaz4oRVvaK0FTdHhsKs+m6lo20+OH+JUIXkxs8OIqk4bO3vS1uxtjPu5TMPkQhBpDRaKEZBytUvXCXRAwIVM0x20Sqmqld9twRQ1kvWlS+Qx/kxD5s5/85vFfzuP/ijfPgDl/jgCzt85qVTPvnZh3zX79kjjGFRnLIoTslLj4Nv39BsJTc5z0tGYcidxarvjJZlxFYS8a69aU/l7DZEHY1I2pO/a2qGUy8uq6sl104+y/nqDR5mnn0zjraJ9ZAo9qdJ19QYkzMwe6zz4x66GmzfIhruYeucSAYMdr6TzhzQFL6oqMBrFGydP0pNTf2pvDsZm2KJax+njoaA19gonfQq627mkkxvoKMRq+OXELzKz3jmvZwsJ/429edQdc5enHB59B6y2lO5gf40P4w1Ia9jyiWXWGLznLPZrGdsZfnJI91x05iW+eY1I0oqpqs3GM53GU+eZdS8sCmOrQ5D6aS35Ol+pnTCaOtZrtQrTpavc5IfYdeGdTX3hxSdsCz8AWh0sntB3OgL96XBt/QuDRct+LPK9s+xWx2RoJsneqGjxNnbvvPI/fujXB9Tro/7gi+k7iniQbLtmWatyDbJT5hnhyzKM18MpUIJRW19J7yz+6HeeiUdXutfM1Mu284zQErTsw7f9nLurXQoX2n9OPAuIcQzwH28Yv0/fNPvfAz4ne2M5NuBuXPuUAhx8hi3fewlhPgo8BeAIXBDCPFB4Lc6537749z+cWnD/y7wP+Gr4TNCiA8B3+2c+3d/Wo/6a7AGOuTbLj3L5cFT2PY082B9H4Bl7QutFpJxkLITT5mEU2LtN46qOOupqEE4bk+BAUk0JcyPUFIRyQAtHFo6slrzMI8xLuX2fOwZKMKRBgbTnDEJj4ik44VixigYoITq3/xlU3N7cZtUx/77wiuVrbMs2sc5q7xwcW0sF2JYKG0rKnMW4/zPF3WIcUu0WBApy0BbIukYB8LDfS2U1KU2WgdtWjBrI3HXUr7z92qOP73gh+6dceXZhv/7r5dEw3NemnfXTXEp2sK2p993vTDlzumUSClmRdkbaKaBZisJsXXhWV+tf9dFd1drPJSxmr3abyKT7XcTxdsM17vMs0NiPWSQ7CJl0BePbngOUBazCz/bBvU8lXMk8gDQ/mQavAclBVIeeENK3bCuGqg2TgnpULU/Nx7OqvPeJcBZQ6MT4sFuL6rsZjVndz/OeO+9DPdeIJ/d5c6nvg+tE3Q44qwx/bxoNH2JINlmW8cEo2/3qvLmixTnhxTlsvczsyYnz445XrzCvDzjIDtiVmVEUnEl2SbRCZWtyExO2V7Lw/Uho2DI5PwL7M9fZTS60XcX8WCPIJn2VO8qn7UsO1/M02SXa8GQeDnglfMvcFqc8SA7YBJuEaoQ6yyvnHuXpZ1oh1uXPsxwfcJwOmN/7wU63U+nF1FBzMnyEqfrkvO8wDqfhZIGAZGWbCUB2JfJH97tKeeyfe2VTlpqb+sr1lq/dISKjsQBEAYjdkYxo3gX216HoO0ctYrJVveJ4m3KYsadu/+UYbLb06Jhw27T+smEYjWOthN7e8s5Z1ozxn+Cp/7+Refci0KI39b+/HuBf4SnDL+Cpw3/p1/ptm/j4fxJ4BfjCxjOuU8JIX7O4974rWTKfxvwL9o/8kkhxNNv5VF+rVeoYp7Z/gCDwVUPJc1vE6iI2paExQlKSJRUbIUTKltxkh+xqhcM26F3ZUuU1IyiHQKd9IPLQTgh1gMSfcxJuWBeVyjpKG1nKigpq5DGQRJZllVAqi1XBxlanLMb+eJwUlYYB1rAbhRyVvnGbjscooSkbGrKxg90jWuYVY55rSmt4qxqs0Ok4WqSYZ0gUo610UTKYozmIB9gG0FpFcYJBtowCAyRNKxNwNoE2EYQKUukLGsTEClLrCxWC/a/NUWLhoE2HFg4XVn2ooJUC9/VyTVvLF71KutkytXdF9gZvpvtQUxeG4ZRwKVBiHSvsT59FVN5JTT4LqE76Ztqyfn6gPJ8RaBionDEePKsLw7ViDT0syVjCxKpKbJjquKMdHKzhzDmZy+xXN7tC0w8XpIMd6nNVQCyumET3bwHmJZMYEnaEC4lJXlVAZe8cWZk+iGxShJE2hoPVsu+c1Xaw4dFdsy9lz+GlJoo3mYwuUm5PmZ2+iLzbOOwnM5vMxrse2w//FTvNVUVZ9TVkvnyLsvytD9UPFjf50FxyoO84CAPKBuJdQtMs0RLx26U81QquRJP2IomKKFY1Us+f/RxktNPMwzHxHrQQ0CDyY1+XtRBj/TaDEMUbwNwtL7Hsl5xWp6SmQItFKEKqGzNG+v7WGe4nB2ytbpPNr9LOrnhzSOTbYS6RW0cSdgwabw9fmWb3gYHPIEgkcFmTlLn1PnMF4xySVls5ngXledCaowtevFlUa+JgwGhiv18Mhj1M8puFdkxTVMTqJh5dggc9hBZN+d8kutiJsvbWc65f4QvGhe/970X/u2A3/G4t32bj+WNN5Gt7Jf73Tevxy0oxjk3/0ZhdH2ppVXEaHSDrSsfAiCMt5EnnwRgFO3wlDMU9Yr7q7vcWR+xqA3bYci1dJdJtIVtbN9FDKXui8qiOO3x5khp9qKCcrhAi4ZFFTIrQ8pKoZUjUI7SekpyYRVnlWwDjOCNLMK4TqNQsBsNGAUpqUp4WM44KVcsakfZ+N85KyPmdUhpFYsqREmHFprjwm/8kbKc5AmllSyrzkCypmr/fqY0JzmkgSGrNdYJsloTqYZQWSLVtOKwLmdbEitLWWki6TfEsjFEzhFJwSgYkOjUD9KX/lQfDU7YTabo4QhrcorZSavv2AzH32xNoXTCINmjrFc946uuFm0+Suz1FLagKR/Vy9bVksH4JmGyzXB8sx/Ad5oRHY1Qymt2OmuOyriehtvph3yU8QUH3zaci/gm6fAWzt5GBTGNeYZk2p7wixlB6J9jfYFocL56g+L8C/AA4hZuLOo1Z8VDqqZiEm5xnh8xCCdEwRBjC4p6xbycYZ2hshXLetV3HWVTs6jbVERtwMBZFbCoQoyTmEZgXc7anBHlMyKp2I3GRCrkPFug8mO2ogmTav6IdxWwIYFI3c+W6nLJlZ0PMIp3OVm+zp3l6wAs6oxFXpEqzXY4JDc5D7PDFloKNtc7iJnnlvOs8uaZLQzWuR5fnaRsha/5IXz7OppySV0tWc/v4BqDMXnPPGucwbavP0DQbv7ras6qXpCbnHJdYRvrn2c47RmZoYoZpVfbzsejC037mV/UZyihCFVMrE0PAb/d5RyPuFe8Q9YbQoifCbg2RPG/AJ54YuNnhRD/IaBaV8r/AviRt/xQv4ZLBSlbVz7EYOcWpb3O9tbrPYbcNIazo08yy494eXlAZhzjQPHc+AbDYNyztGxjUFITtXREKTVjZwjrCOsMAxUzDQ2prtiLTymsoGwUZ6VXvI+DCuskxgkiaYmU46wSLOqQ48J/sCehF0OOgrTvlrTwlNKyEZgGFnXIa6sxi6rTs3SFqEHJBit8JxIp/wG2gUEJ/8a+lBR9UTNOsKwCsro7obn+/+Owm820lhzObToYCUpsTl5aSMqmImmhxDzbWM933kx5duwpmy09t/OA6ixBum6iaQxVvSQKhj0Trm6H6E07fPVznozV7HO9UHU73mFneZfRYJ/xzgv9kFm2efXW5ITBARH7WOHaIbjFNoIKCJUkb5qWoeR9zLJa9H5dZ2uv7k7Ca60PWI2S+2xf0X12SLGucSb39ujO9DYmRb3m9fnLlE1FJEMyk5PZktJWRCpE5Uf9oWVVL8lMzsJkLOqyp7N3CaGRVExDwTT0Bf0wX7EIQtbGv4ZrE6BERawcYFjUZzw92Oq73NzkhDKirFeUxYwgND1TzZqcut3E/dxwjzDZJk73SBMv+Lu9uM3CVKwNrI1hVp1zc+AhWYAdk+OspyQvHrxImB7w1PQjLYy4Ceqqs0+xPnuV07bggzcU7YxBl+Vp+972cyJ/aMv7awcwCjzjz9vZWMqm7meiX5jfZW1eQ0tIlWagIy7HO1wZXGMYX7pw3xbbWCrn77OyBYV5y7PmL7kcT2yG8o20fhtedX8NTwT4fjwh67HW4xaU/wfw+4ES70j5T/Cixm+YJdSIVfhRzmYNUDKKb7B9I6ZcnXB++ElOl69xZ3mXcRDx/GiHRCcMgzHbg2uMRzcYTG6idEKVn/U0S2tytIpJbMEkvcp1k1Pbop/RVLbAOsuqWtKl7J2Xc0aBn82cVwsWdca9vESJhr245moccql9w59XcyIZEqqAVGkObcNBPuAwS4lUw3ZUUlpFaRVKNkSqYaDr9j/D2mjKRmPagjPQNeOgomz8bcpGc5zHpNqSBobtqECLBuPkhfv1haTreiZhxTio2A4N26FiHMSkKmIrnKCEJxCcr94grT1kZ9uCAhCFo55J010/Y7wTbFHO+swNj2t7i5za5D0dFGBVLaiakmW9YlYtewjwQXHKeHWP68Pr3GhqknSPMN4G6aGpji3UpQmWddNmz6iemrtFhG0a0kBRrj6BtIb9yT5puM+92YrD5boNvPJCxCTQZNU2abjHaOfV3i25rpbUJqeyBUfre1S2wjpLZgqWLvOOwDIgUt4poWwqzgoPbXXFJDM1mXEY5+daxsF2GBKpgGkLjWWmAObsxSVQkipN2VgWtd/ExoFgHET9PC4lYRiMSHTq4cS41ZWw6VDAd3vrxZ0eSlRBQhhvszt62sO+wX1eXh70xe6kXDGrMnZNwaXskGg17WE7pROG00Mu7z7vyRCrFcd3X6UsznzYlckp6yVZdc66mvvH035mqqZiUa85KVdkxjGv/QEtkpa92D/HVEX9Z1y3M0gtvHB5jeUwl+21W1PammW9Yi+Zkei0F84qqbDWtG4MUa9tehLrHVhQXnDO/YaL3xBC/Czghx/nxo/L8srwBeX3v+WH93VahbF8/shjsVtJ5Bkmesny4UucnnyKB+v7jIIB79p6nkBF7E6eZ7zzgseDR9/OsrQ4YLAriHiFanWCrXOG02e9OCqZemO5Vh1s65xicUidn/VD3ao4I888C0nKgLPZS9xbfJFx8LDP6diKJoQyYl6dE8qQUIXMsgWZNby22mJtAsahLxpaNMzriO247GEoLR2pqtHS8VRa9nOZbjY4DgS0/P9F7diNMqyTbEcV221GSGnhuNQs6pB5FbbDfF+kukISyZBxkLbFLmESbffXel3NmRXHJHrA9uA6STTt8fgOr/eDc91rQEajG8TpXt9ZrGav8vD0RbLqnNysqWzVa27KpmJZZz0+rYWktIYFGfdW91jVS7bjXUbRDtvTF4jTPW8j0ubXv1kQmITedNPZ262vk7drWT58ieXDl0i3bvK+/e/sHW2PlznHq4w35kuf1KglRr6LZGuJrQvWqwOMLYj1kEk45TOzz/WsQt27+sr+9QVY1SuW9brfEKH27tb4rkQJwThISXXMbkthX9VLduIpiU57pmFmSsaBZDfaYhgMCVXUwzlKKAIVo6Qmiab97KdbxuQ+NsAZktZNOF8eeM8zHZMmu0ipGYQTJtEWh+tDFiZDC4VxlpNyyQ8f/jDT00/172PrDJdOrjK5d5WmMT07DegPXv56KJTQPXQFEMqQaegJLwuTsRs1bfHXDFTMVjjuCSvdKm3FwmTte11TNoa1cSxqCVQY54vF1cFVKltS2ap/DayzzMvzt7KtfMXl3JOboXwDrf8Fb7P11b73JddXLChCiH/AVxDJfCOxvDpX3ktJwigKuT494+Hrn2Jx+hLz7JDd9AqDcMJ0fIvh9FkG27fQ6bdwntccHy94Y770NGMl2YovM0luMBoFnGclSkoqa6lmtk/M20q22UqeIRwqFF77Ucw/TjS/6ymz1ZJAJ1wePMV27De6zqY9q84ZhuNHrMPPKsHVZI11koH2mPrabLDegTZt5+CwDlItuNn6EmWmINUxla1RQjIKhmQ2p7I119PWKFBN2ArH/aakRMVeZDirCiLliKQjknA1GfSnwq1oQqJTEj3g0uhpZDvczMsZgU4Ig2E/I/FsnRiY9gJBKTXJyCuJZZDg7EYjonRC0hpE2twSqphQhczL854KPQogkpvNYFGvsa5hWa9Y1it2qgVBa/4H3TzFMNTeTBC8z9bGvfa493qypmiV48ueRhqPr6KihFF0g+vTlLxqePl45sWboSIMEuLxPlOAh5/tqdRX4h3Kxr+OnSgzUiFKKo/7N4+6WBhnSXVA1KhHOpLOgy7Rg35jD1XMojglVCX76WWU0IQq9DCt2Hx8AxX3mh1jC4wterixqpaPCEyz4pzKFtQm7/VXUewLTDfsvjZ5gWm8x8PskOP8mEiFhEnI/fyYV1dn2OUZu1GIcQ3R6h4DFWOc7V2wu9WlWBpn0UKRqsjflwz7w4N1jYeyJIy7mYgM/cyuKTf31V3bpqakJhIBZWPRwrWhaZu/ebg+7FGDDjYFz/g07rFnzF9xORyVfWcUlJYu/DOB3TZbvltjPHvssdZX61D+aPv/Xw1cAf739utfj5fof8MsKQSXkoSnp2P2xjH54gucHX2SRXbIML7E7u6HUDph6+av5zyvOW8cq7M1y7LiNCs4XOdkxlBYS6wUxrn+35HsUgCDjRndOX0OvRKCnUHC1cm3MgoSytVx62Y8JeyswMNxP0cYVD417zw/omqWTMMxH5j4dt44S2YEZ5UlUh5+8pYxju0w5EqyTWkrUp3w9OR5CrPuZz+d+hzomTBKBh5asgVKaib5EfdW90h1zTgY9MWoWxdPvVfGzxG3m343FwEYNqZnPF00hOzEdUGyjbN1r4mQMugFllV+hilXCBkwaZ0Bxq0oraqX3Gg3xA6eKeslRb0iN2uGQdY7EXcbRG1yyq4grPZ9RxSNCHm9D/datkaGFz27wG+ixuQcPfwUeXbM9NL7GGzfIhh5ndgoUnzbzd2Nh1n7/IXSDIb7RPGUJJoyjnf8Bm1LVtWCVb0kVCGVrZhVC2/+qSJCFfhkxvbUH+mAaTj2RJBwhG0sl4dPEQUjwmDYn/gvT55jtzGkye4jQ/aLMQAd40xKzXJ5l7qlIgN+LpWdkET+8QY6oWxtTKp6iRSaJqvb2/u5ltYJabLLOL3KcPFKD0VOwxEDFXNYzDlp45XLxqBE0c8A/fdUyxpsiCS9CDiSgZ+H1CtSnTAKhqhwk9XSQVWhihlFOwC9sr8jMgxbSNk6S6rmZLbsC1Ykg56hltVlP3OBDWT2pNY7rEMJ8doTzaMK+QXwax73Tr5iQXHO/SCAEOIPOecucpH/gRDiXz7+Y/3ar8bRG/NFvMLxGx8nSffYvfZRryae/CwO5gWv3z/DNg3LquZhljOvauZ1zWlhKJqG08KyLB1F7ZDCW0lsxZLOq89nmgiupd6CfBz6PHrbipzefWXjZhom2/1MRrWahs51NwpH7OqELXOZuh0UPljfZ1YtiMIAJXLKpstGd0RKkOqIRCdsxztM472+iAziXcJ2w+/ouUIGDKe3ECqgzs+o8hnZ6oBRepXd4U2y6pyiXhOqiOnwKcr2FBu0lFKtYnavfZRosNvTPZumxpSrTfJeKwQUSlOuTvoiEg33vJiweQrjHEEbMrUqDMPkaUZjb2KYbt3o77PzYIqGu143Ur9EsTikqfOeFVQWM4wteg1C5+tkbIE2Res2O+zNH5u66FlFF80SgV5rJKUmbRlBHY01Gr5G0hI6anPVJzMGgoZnSLdaJXv7uBpnGMS7jFpG29niNqpQvVsy0FPWtVBopeiMP5RUhCpkEnoYcGtwmcnoRs98klKzPX2BaLDnDyWd15YpfPxy+17qXncVeJZcWcx6goN/Djm26xqDgDw7pKg3emTddjdmfcBgsN+r5pX2sxWAQXFCZQsm9ZqqKdmJp5yXc8qmbmcsXutUNorjIsE6yTgo2Q4LpqEfmvuuc9hDZV1qpBfkRsR6SKCTnvovZIBraspixjo/7p9TRyHOTcZWNGGn03I1llCFLbS2JHWW0lZUrcVL1bpEXeyg3u56p8xQ2r3+B4UQf9k5d+enez+PO5TfFULcuuBo+Qzw5Hygn8AKlOTZ7Ql74whnDMlon63rv5qjZcnDsubw9SMerDMWVc1RXrZiP8d5bTlYWfK64XxpydaWxji6g4fSgoNQIKUgiqSPEZZwkFpujGouJwHPjAZATFLX3D6ZEOptRpP3MU2OqFtL7c4GJaqLHgaqihnr+R3y/ISgiqlsQaITVvUK6xofkeksUeRPdkoqtuNdtgfX2Zp6dXRdLftAoTD2c54w2e439EAfUq5OUIsDlPaq8UlrNd/pQ+Kx12/k53dpGkM6uYGOvaCwaQzL8ho+NFAQp6JPScwqg3aCRCpGu1Py+gp51TDPbKuoXvXWI5Hy+TLDIkDJKXjgiFBLdOzDwFaLijfeWDKKjhmFl9ke3ESFgnSqeeqGIp/9kFeTl8seOqsvWJPky4O+CykuWNN3QsOuo+p+vytOSgZMd97bJgp6L7LOGsRI/5w6h2ZnIZ7so4KEZLVPvjxgvbjrT/qNYZxeJVAxp9l9IhkSheEjM4AOblFSkaqknX94Z+tB0kGjbTaJ8JDh6PJ7KXmOQEmke41ydfyIKLBTxgPQGNLhPkE4oizOqNrCkUbTngTRUeE7SChoTG9rs8pP2N1+D6N2dljlM7ROGKVXycsZtS0ZqsgTWuJdcrNGi/tYd45xsKgV22HBJDRcTxTvnjzHpfQqo3i3h0g7O/1O69V1jYFOGE+e7T8fplxStDPJAfQDftiIFIdyjG3agXsQ9nBh1+lYZzkrTilbh4GtcNw+79O3vMe8eTkcxr1jOpRuZUKI/wl4L9BDF865n/84N37cgvJfAf9CCNEF1j8N/Ja38CC/5ivWip2hf/6r5gXs9F188v4ZL56csa4Na2M4LWse5IZZsYmNtQ3MV5bVuaGxjrpoaIxDaoFrwDUOFUp0KCgDD3cJKVjGltOVZWdomFeGy0nBeBkwDoPeYXYnHRDqMVtJyHCsvTuscoyGr5JfMOKTwg9RB2bVYuR+hlHaqm/RrWtIZUKgIs+Wah2Oy7XPqgjjKcn0BlnzHPdmOZWxKLlikuyQhpcZ7o4egaeCaORDi4wjSY76ACIVvMB5bsgKAwWM4oDjZY4SglXp/ZvsBf+iLgDMNimwIKtrKmtZVP5317VPKYwuJFVe/HfnxJvVhqN1RmEbDtc5sZJMlxGltVwepIyikJs7H2WYfoFy5fUurqnRpZ+BWJM/4njQBXl1nZeSmyyPzkcM/CYWR9PW7sO7DbvG9OaWOloyaQV83TLl0h8Ohn7+YHrac+03ynY2Vjab03HXpXRzgG54719bCw2s85NeCClrD6sNtp9FBS8wlAfeLQAPufnwK6+C73JAusJZFn7wnpczbGMozHqjkzErlNQMw3FLhiipbNkXtdoWPDj9NEE4ZjC50RauGFP5uN4OLmucYVGcYhvLTjzlpFwSSct2VLEbwbPDy1wb3uD6pW8mGe57R+PWBLWulr3zQZ/73maWgCdsXFxhy1YLwhFRMyXPT7Dt6zhq55Ndoeko/2ErhuxWZct+/pToAfBmB6m3vjzk9c7oUC6s7wP+BvDL8RTi/wQ4edwbPy7L6/9o9Sfvbr/1Bedc+ZVu8/VeQoCScOd0zXlecLTO+ML5gjuriuPMdyCrvGG9tphq8yaoy4Yqa6hWlqZ0uMZ5y8sAhBZIJahlg5BdMQHX+P+fasFhKrl/OeTKdsVuqtgKFJNQsROFPF0b9oYpSnTBQGAbKHmOeGLaD+nGCXc6eIqy9hvhMBhzVpxw2qqIO9FlrIckqd/IvAJ71Fu2qyCBEp+lXnZD4oZ5LtkZXGcYa7QSPFxXzGcVtllSWkukBmwPPF5978EpeV17t9gWqlqUFVoKTJu70aVGaumDpSLtw6G6QnGSF7y2XHPe5mPESvSxwHEbPBUpyVabVBgrydqY1mpGsDI+Rvgg85vNvVXGs5NR6+p7g+vTdzNUEuzLZKe3CaJRGy/sqax5a8i5yk966mi3/El59MhMqBNUdoXhIsXWWa+e13FOmEyp67z3CXON8YaZk5tEg71eBNkVqw7zr5pNZjsSEp0QqohQRr0GapYfMUmvsnP1I8jAZ6EDZOd3sQ9fIl8eeHeAtmh1+g2ASXq1V4Cv2+de24JFuTmFe/sfb8bZCNPGLCtW9YJVvbqQOe9huMXrH+Pa5IWenRcN9trwrmXvlwUQ1hHzcsaVeMJu1LATT9mOd9kbP8dwfNMXpdaQM0g9YSMolkSDPZo6p1gf+++1xqHns5c8/Nl60WkV90XHv1YJSbJLHE2pKh91PEi8jqZpNTYAg3iXAX5+FOthT/dvGkPczmDe7nLQ+9i9g9aOc+4vCCF+1wUY7Acf98ZfjeX14S5msi0gn/pKv/NvclWm4fMPzvnsyRlfXKz4/KzijQcV1jikFFRFQ7G0VKv2DdAAEpxxNKWD3CGzhiBvLTtCiR1IbHeFpPCGztBzHkQosJVjoWuqouFBLEkTyaWh4trA9CmJttmwQSpjubkz8oyacNRb2luTs1x6yEkK/QhdssPfwTusdiLCDvbQ0Qgd+5NZZy2uav9gS2tZZXkPPXWmfudFybo2RNonPB4s19im4aXzBas2FCpWinEQcFaWaCn7onBnVRArwUAryraw7LRRx7eXOSvTMK/8LKo7wWWlI40EO6niUuwjcQeB9iFUxnCUlxwVNbGUFE3DUMu++JjAcdJavUdKcTj3m8ZW8gzbu63DcLlkfdY10FCUM6+0tgWz4hjbWBKdkoZbBO1119HIdzkty+piIFXHVAtCf2119F5WpWUYecpxlc96i3WgdcgNenuVxplH1PEe4zdUTUkofQcaqqhnZw1sQVb6WIG6WnIyf9nDj+FW3w3kJqNqSpRQXB485Z9L2zGs7Am1LVjViz6GQQnd/35ly/7g0gWWVbbiOH/olfGm6gfq4DUxh+tDbo6e5qlL3+w9wtK91kQ17r3UrCmYlDMupVeJgiFRMGI4vkky2m9zSa72M8RGPIMOBLqVljh7GyE1D+9/nKKc9bOx2uSsrD8MxHrYEw46Np9u53zh5uH6yIILRI7epbqBJJoyag8PVb3soba3vZx7Jw3lu9WxFg6FEP823rn4+uPe+Kt1KH9JCPGdfGnP/W79BeCbH/cPfq3WeVnxt754l9uLmrtHFcuZoTg3PWwFYPMGcdogGoew4NrCEJQOVVtUVSJsiUnGWCSyaHBtZ+J0K/hogFCgxtJ3MBpM5SiFpVhblgLOYsmDib+03eB+N4l9rKtSZJUh42n04Bn29kLK1Sf8ALqFpIzJSaIp24PrlPWyH0ICvaFlN3jt0hyd9Xj3KLpKB33axnn/qiBoLccNmTFkdc1ZUXHQpj5+9y/4pfylH/uXLOqaO6uC+2vDqmzYSiQ3hx7PL6zj7tJ/f5JIBoEgVg3GObYCxXmVc15ZSuuHs7aBddlwPjOYqqFpYLilyUpHNnQYt+bmIEJLibnwobyz8u/nV8uarUSyEyt0G7Pro5NrzouCUCkqYwm1D/bqNq3eqlzFNNrDIsPWjTZUsWc5tYaTXdIfeBv1Op/1nYlPmaxJpjeo5Lu5d7omCTW2ucwo3UeoFynmB31nBPRDZIA4mhJH7Rzmgr1Itzp7kYuss0V5yvrAc13m1czbAeUPUMLTj5VQ7I9ueruU6bNU+YwiO/bPtR1YT1oo6KImZDMA30QGWGew5Vk/oI6kAuwjRcW6hnk1Y7ecEeNhQfBkEw+5bjJkLoZo6WjYw6e2cejAW9hXxvYppltpgCrPyJcbp/Wu81LSP9ZA+GK7ybsP+uvZvUb99WxnaWW9pGznRl3XpltKezdDe1IFxfGOtF75H4QQE+C78PqTMX7k8VjrqxWUCfATfOWC8tj42tdyZabhJx4UnJ7UFGtLcW6xeYMzvvvQc0vQgOiCiGqLqjNcvcY1BdassUIh9RBVRTRK4qSg0W3haQRUDhduLoWOBVILwsTPWJQWfUekteByErCbRKRB4CNeo5CsrvniyYwkCDzlWEq2hh/B2Y8TLA+oq0WPJYeMHmnlo3jaq/i7qFpoHXzrHFcaZPATjNJvAeL+w5vXhmVZ8TAvKI2fb6yNv5/9NOZP/dA/486qwDjH63NDUTtq4xhG8Oqi7hlued1Q1o6H1hJNNHNruZz4t9DK+Cjl89zPp8rakeeW80MP9wgJ5doymGqaxvUOyLHy17OwjuPMEmm8NX+q0BLilrJ9VlaotuPTUmCdI6sNy6Im1FfQcumJCabNK2mFlVLq3oojugB16chnoEgZoIJ4M5exHvrS4Ygw8Xkjx7Pae4E5R14ZzjPJKH6B0aX3EE8+x+rkJYr1CXW1uBBm9a/nk3QakQ7vt84Pw5Xw+qSmMazqRS/Iy0zeD++HwYgP3PpVDKe3WM1u88nP/WXure4xq1Y9G1AJb+SZ6qjV8gzamUHaG1B2DKtOrZ7ZkrWtex+5gXK0L4mf+wiFVjHpcL9/3wXaC3u769cFcXUbvp833UXKA7zI9gaRkr0rQxIq71ZQ0g/pOyZatzrYsG7dKi5mmJzPbxOFI6TQveamO3itqzmVrUh0Slyd+xgEZ5AXKONPzMsLHpkn/l95CSH+Pj7n6keA3Dn3WX4aWVdfjTb89E/r0T2BJYT4JXhPGQX8eefc93yl3zfWcXpSsziqsJWjqR1uZhEGdN4QLWY4ZxAqgsbibIG1q/aEVWLNGqki3zoXRwRVDOGEOklotKABXCdJT/wnrs4adCxRE19MXANRLBkMFOOkPfkpxeVB6mcpUpAEmqtjL97SUpBVhiSUxJN9Lg/3KBYH/YD1IsYvlP8Q1Lk/DV908oVNiFLT1NTLHyNRXheiB++ltA3nWUBeGx5meX8qLazj9jJnGEgK65hXltO5oSwbtBbMA0GoBbbxNOqqnT1NR4qs9tnyry06KM2xlfgMjNr6YrI6N+hYIrXAFA1SC+qiYYGhqh2hEqxK70xrG8di7f8ugGxZZbOoYRpL9tMYJSS67RiTQLMVR0RttesowN0mfrH4auVPp51SP4ynfeyys4ZydeJFj+0MpXM2jsf7zPPLQO2jjsuqh/BWpeY80CThc1y9MaVsnRXK9UlP520a05MBOqKA7QO3NHXtrXtqNuPIUEYX6K+Kb3/+NzB4+j8HYPaFP8KP/dTHOFwfkuqEnXibvWSPRKeEKiIOhgziXU+BNvkjgWQdfBa0ha47vd/KT1iWp71+JjM5s2rFWVVRNnOsa7DHP8p+dsju9nt8Po01vU/ecO+F1k9tE1MguU0jnqG0rnUraNCtsWwXvrUsLZPxvocPizOvPWrNIbsQrW7mUZgVzfI1wAs4rfPd2HR8i6Y0rPITCrPmJHvQm22OgwGTcItEr7HZ/Z7sUtnyEbHk21rvLMjrz+GFjX8Y+IAQ4gt4u5UfAX7EOXf0OHfyhMKVn+wSQijgfwV+Id6g7MeFEB9zzn3uy92macAaR2Pww/XSIRqIFjWqKnG2wDmDLR/SNCVVOUNIhdYpQuj+P2vW/c+CYIwud/zPggFVOqIeKNyywa6dn8FsOfJYIiQEkcQa/8EJlOAor4nVulXXN1jneJjlbccSEynF3ighrxrOzWX/4dPX2BoHDFvd6EWDS1MuezPEXmTXelnV+RlNnRMOd9HxqP+ZrV8iiUYcGw+/REoRK0lhPVx1lBt2YkVhG07XDbbNXDHGkZcN6xzKsmEwUL6zsI7ZEsJQcGmoyCv/e8Y4zpfWZ0QUDdY4hBQ0pqHOLEIKhPTnVSEd8VQyyxrWmaXIG6qiobEOITyLLhkqkkQyiBSm8dkvSvgNaxpHnqHnnLeeDx7QNJtkRqVj6mrRdwdBOCKKt4kGe4/oarqOxJtLFr3FTrpzq2fBZZX/m12Mr4fdzGYuVhnO1nuEwWXSVBGkL1Fns839t3MZT2MuCHTSF5mkhcSk0C0l1w+Op8kuofI08mx1nzd+9DfxMDvEOst2vMv7d7+lP81HwZDRYL9nUnV2Kz6ueOMwrKNR3zV1EcmuMWy3M6MiOyZvA6w6x+RuhndezsnN55kVx1xK97GNnw9dufpRkvoGBF4DI5oO5r2Otd4zDeVh36xuqIztO+bKWJbFNtd3PN17Wyf9e9nnpejeTNJYz4QcDPcZTp/tXReqYkZz+hJjoQnKGatq0QfULes1D/LbnFXVBdPNlEgGT0zc+AQTG/+NL+fcPwT+IfT77zcD34nPwXqGx1TLf0MWFHz2yisXdC9/HfgVwFcoKA7TmuY546ABl0rKJiBagLYxqAhG+3D6Gep6QRCMEUKj9MAXG7PG2oqmKaGBxpbo0LOfXL1G2gGikR7+ahxOQpM5spMaGQhMKomGCiFhHTScVxa9LjnKS95YZf1jfWqY+v9PRpSm6amk9+ZLv3GFW0SRp6nmtWdbwd0epxbF5mXzYUQGWxftKdFj/0HiVdNh628VatlTfAEut6yqSAmOsm747wuzbLuA1bL9vhZUVdN/P4llu6E6zhcGpQTZ2v+ulIL13BC1HVqYSowWuMYRxJKg9cc4O6loGn/fpmoo1/6kJ9u5VFU0RJH0epfacZSXXmAoBVMiIuUzzaPWq94nRC57a5V0eK2fjySj/X5jLdcn/SC+05oIpXtLdtluauXqBB0sifSzrQV+d40koygkCbQ3kdSyd08obQPyXTRpa944PaLKZ5SrE4r1CVE8JYqn/WMBeq8trRMCnTBsdTKn2X1W9ZKH2WE/YE90yija6eGeju0nWxp4kGyjoyEhu4/milzQqXR6HBXE5PUVkuCBLyiLQ8r1MaP1MWUxYzv3G/ksPyKsl37QLyMqWxC2RIKqOKPOZlSN78qCZOr1S/p1VBT3uqSsMpytC86Lgqw2/hqGAbZx5PWYnd1fTVnUvR+a05I01Ewuw3Z7bQ/Pc07LmnME14dDRtF98pm3OVov7iLrJZNoyryccWd91MOAWnpbI2hYmyVlQx8R8XZX4+gt+98JSwhxCd+l/EzgZ+CHsT8A/Ojj3sc3akG5Brxx4et7+NjLR5YQ4rfQ6mH0eIdqZYnGfkO3RUOz8AN4EylsMCXIS3S+wMiIOL5EEG4j2yF3GA+x9TmiXmAMGJvhbEldHiFaPDcMBsAAGyhsJEAKXOFw+I3YSEc09Juk1oJ17ShtTaQE51XTzwS09APZrTjqA4lOs5xZUTIOA86zirCjwkBL4b3hn7N8CR2PegvxTkmughjRZsf71Q2KNSp4ASiJlNeDhKrmJC/YiYJ2CGsorYeszrRnxCktCEJBXTnqqiEIFGEoCJTs6c/n64YgkDSNI4olTUN/26YlQ0jlC0Rj6L93UTQKYGqHrRpM1iBDL3R0jSNOJaAYR4K4CzmBPs9kVdakoSblKlJ6HU2nJo+Ge96loB22g8fj63LpT/IXkgMvih7BD+ibpkYSk4SbgkFlyOtHacj+25tiY5umz1mpzB7bA1/sOlNM/7xbxlFrLFpXS6p2rtLl8IQqJnHWa0RkhNKaSRvTq1XM9uUP9bBnsP8bOJyvCZ1iR/nHfBGI0UKwaqOIV2XNsqy8m3KYsZVcYhhfIR57OFWHXi8ipfYeYEKTtNBZHAyRwrspSOFZil1QVjTY66+diG5xOC+xjYcIT7Oc87xkVpS9t5dtmpZyG3NK0ZJHNLZpyKuGs3XB0SojM6anpIfKZ9kMs5A0eIogXZLUvjMblDfYKmbsZMdcHj7Fw+yAeXlO2VTs1hmlrTGuYdHOwp7Mcv3z+VouIcQ2XhvyNN7y6j9wzs3e9DtPAf8b3iKrAf6sc+5PtT/774HfzGbe/fvaUK6Lt/8iMAf+Dt5N/n9wzr3lePfHjQAWwG8AbjnnvlsIcQO44pz7V2/1Dz7m+lJHiH/tXeCc+7PAnwWIr95yQvoTrgoFtgAaUKUjyEsarWm0xskRGpAthmzrOaaeo4MJzhmc8zOVxpZemVs8JIovIWUMjUWXa1Slcbnf1BqtKS6FuMB3RnXRIJVAa8tD6aEvJWErcUxChdZgmoayLSJ5i6nPCr/hKynJa8OqUITtxmkbx6oxHt6RAVWrvL8Yi9s0Btfi2D6D20M/Hv66i232KK2lsp7tNY0jIiWZhDXmfN16lzmubGvOlpaqdmglCAKoaw+FNQ0kiU/iC7VjkTc0jSchSCkIQ0EzUBjjenjMPzbIW7p2vrT+NdKCumwwReNnXu3cCxqadi4yGChCJdgKFZPQdyPTOCIJNKFWPdNrkhxRlzXxeL/vUrrrkK/u9l2bamN6u9XUBQ0b8dub8+UNEA0llbGMYt3mpPjnYRpHZRrO86q9Hgpo+hnLBlvfY2u4/0jBMuWKps69U4Hxr1nIhr2kZOCdlAcbtmZn/a90QjLax9YF8WCPavRv8fKx31uqzPLq6TlbccQoCvtcd4DTde4JIEnE+/anREoipWepmXJJXefoC9qczjYoXB2wJW+1AWhJD591lPV4vM8onXK0vOznWw5Oj5ce3rKWvK45admElbW9fqm0FiUly6oi1IphFBJqyTyvyOqaVVlxtM56SGkaS2jfv3dmc1ZlxSR5lp2db2LA65SrY7L53f56FfXKz6KaEi0UmSlY1Bn2CSJUjq+bsPH3Av/MOfc9Qojf2379377pdwzwXc65nxRCjICfEEL80wtjgj/hnPujfPn1F/Fdyb8HvB94nxDiR4Gfcu7x3TQft0P50/iq9/OB7waW+Er2rY/7h97iugc8deHr63g+9JddUgvikSKIJTrw7Ks6bjAjST2T6HVDEwicFgizjaynNIEgXBnC+e1WpDZB6QF1vcQ1FiEU1pYYk6GkRVYPkWoI4cQXKCVRtUUYh2sENm8opZ8BSOnnEFIKgkCQBBIdQ6ykN59sHIuqwjhHqtv8beVhqSTo2Ci+EwCf6xEpSWM3+G83/AX6GNUgHF9gGsXoaMTp+gp5XZG2Cv4k0OS1Fw9O45jSNhzlJUY7LkWKh6llXjZU1g/OK+v67GwlBPtDr11ZpQ0PVpawpQV1nURhG0rrek+0dWZhqLDtfZjaYSrnLW6Mw+QNLm/vfyRRoYfGlBTtfQr24og00H2X5YuKdwHO6yskkScqyCAmULr38ZJBQlMuiUf76HhEMT/AVMve76v3I5MahbcxEUoTT/Zx1iDlgWeRCYEKBEReV9QZRpZGklebLJbKWv8ahr4AKQlZbYniDxDIN3obngb6DBfwUFTnXtzZsEgZEMVTwlaD0VGj8+UBl25+B4flN3F6tqSy1sNtwOuLFYO8YBpHpFozikJ20pif8UxNNruLawxJcItVeZm88uy3nUFIs/oE4DvaTiQbJlPSrZu9yFJI/Yi1j5SaeX6Z+6cF58W6dZ5o9VbWPkKp9SFZrqeApy3LMQk0w2hjulq18OKsKDGtPZJ1jZ9B4gv5uoXNlJSM4oAouoWOcyKz134GRr3Pm2fNVRhnWdsnMzvplnNftxnKr8DPMwD+Cj6K/ZGC4pzzecf+30shxOfxSM+XHRO86fb/n+7fQojn8bDXbwZ+thDixDn3cx/nfh63oHy7c+7DQoifav/4rI2H/FqtHwfe1XqG3Qd+HfAffqUbKAWTnYBsbYlSidKCDIMKBaUEM/G6EWccSIGV4HJHKQWJuYGsFqAHSFsShmconeIa2zLAMmSgkGqIUDHYErk8RSZ7FFsxbiJRidzoXSpfWPw8AprYn7hjKRm0XUesJOMwZG+YEilFaS1a+E10VVbsT8atZ5b/kGZVQ1YJttJ3ESavY+SyNQP0w8meh19476WgGvVD4eHwmN29fQ7nO31GyKqsCZUir2uuD1MiJVkby0Arbgz8wPt+VlBY184u/HUurONyEhArxbyqe9rvVqB6Bhb4D36ROlam4TiWzDJL1opGlXaYqqHjBNnC0XRitwZUS822jWMYSK6lcU+x9vRTSaglo1i3X7/RwmneVqZTsoPfnOPxVZw1fTFpGkPdxhQHF2AyD3P503lprxMpSW0c4Mjqhkly1N4nwNWWBtuQhP5jFGnJqJ1dhFowjI5omv32kdylzmZ9l+KhyEfTKi96WkkZkA73Ge+9tz8gVPmMbH6HON3jU6c3ub88YRpHPLuzxc7QfxyjN/wLtTccELYwYagVTbNPNCz6uVKkvGhXSS8wNOWKbH6XsjhDyoDB5MYjj6ubOSXjfVblZayB86xini8xzg/cu8KmhKd1KyFIA91a8VSMw6DvwvdHg77TTNvr52co/raRUmhhQIJGtbMX0XcEZatpWRZ1GyfxXsrVv+jJCB3jzzaWWbVgUeeU1lE+QYTq66hDudwWDJxzh0KIva/0y0KIp/FD9R+78O3fKYT4j4FP4DuZ2Ze57S38DPvb8R3LLnD7S/3ul1qPW1DqdvLv2j+6C3zNwEPnnBFC/E48lqeAv+ice/Er30rQOI/LR5FkMBAMRorFzBANFToQmNqRn29OhTZwNIkglwnJQxCNBamI0hs4Z8AZZLAFQDXcQqzOMcUDnLMEyTXWlxLkFU2U+mLSGIEtG+pVgykcNnWoUCCkYJZZ7msYBhLdaitGkS8oaaj7D0eH0RvnyKsNK8Y2jlBJbp/OKU3CslJcSr+Dq1sDrkaf5/5LH2ORHfbq4qgeEVYewvAMsRV7Wzd600igLSgtW0gp1rVh3CrerXPcHA95aTZHS8n1QUqklR9QW8vaGCIpeffWqD8dr2vjNwIpWFQ155XXsBjnSANBljpmmaUoGrI16BDKvME1Dls5OjhaB4LB2FOvTeNvf1ZUbMcexkkCf/qPeIVqftIK64b9vKQnL0hNnc+IoxFV2Tr/6oSmWvYhaeA7vXiyjwjeQ6APyesr2MaxvDCMN41jkoCtW0Ei3pFYi6v984+UJNCH/fvLB6/d9p1kyyQDeoV95zzdd5Qt+0urmMH4Bslov3U/3usLQZWfMb78XvbytIe10s7+XQo+eG2XUAsidc+zAYME2zxFVlvSQLM6fqmnmCfplDCaUq6W/WPK8xOvCTE5YbztBYHRCKF8Vvs8v0xWGSrTMM9LjGvQQlJh+2vQFZU00FjnCJXiUpKgpSAJfFGZtCF4kVZsJZqstuSVd3bo3ptpoNtOvsbgsMaLhLv7so0jDTXbg2PyxQHF4oAiO+679Y56XDY1i9rQck96nc3bX29phnJJCPGJC1//2RayB0AI8QP4+ceb11sKNRRCDPHo0X/pnOuiKf8MPmHXtf//Y8D/7U23+3v4AjLHD+F/GPhfvhKz9kutxy0o/zPw94A9IcQfxvvj/4G38ofe6mqHRv/oq/5iu7SC7bEiK/2QuGlga6TYm2ryylFbR1E0tHR4grZrmB9WVMsG85Exe8/EvOd6xAuTmPtZxetzw6uv5sw/nZO89hIdkKiCCevLO8h9zehyQDxsszmKhvXMUK+8oLI8NzgHZWKpy4aHgeDVYcXVSwG7acal2YLJwRH7acKtLW9qngSanTTh3ixjKwmZ5yVZbTgvSu4t1/yxf3afunTEA8lworm6HXBrHPCR3d/Et3/gKrvyU3zxM3+F2eoNJulVOH8VY3K0Thic32jjXl9iK52ih17NPElCqlYi3Z38K+NP3+/Z3eZ4lbEzSAhbr655XrKsKrLa8NTEb96nWd4Xoqw2XBnAvaVX978LOCtLTsuah4lkXjY80IKybNg4ifvZCsBFe6RV3fDqMuda6k0mlRDsjaOWnUQfZmbKJVV+RhCNqPIZKog95h8kVO1QXqgAa4o+DMxITbJ1ow9aU40ltJcB17sp55XpZxFNs0/TvNR3Qk1Z45oZIb54NUVB3mkxWoJEV9S6xwD0wVd1tezz6aXwJ+owGHm34GhEVcx6jzCtRsggJr7+G3lltmIYSW7t+mt/ntUcL7w1zWj9j1nVBVXrFO0fy4ukO7cwpekFnZ3+pjMprVqXgLqlGhuTo0zeW8n46+chPPCQVqgVIaqHuZSUfp6klI9zuNCldCF1HTsPPLliFCnOc0NpLKuy7l2pO+KAko5tKRiFG0DkUpowjAJGccBWcsD69C7Z+V2y1f1N0b7gELAbbRHJNbMqY/0EWVmNoz9MPMZ66Jz7yJf7oXPuF3y5nwkhjoQQV9vu5Cpw/GV+L8AXk+9zzv3dC/d9dOF3/hwtPfhN6y8Bv9k59/CrP5Uvvx7XHPL7hBA/Afxb+IH5r3TOff7t/OEnvVItuTbW3D33QsE0EOzEilhKbg5jdpOIL5wv+cSk4HRuqGtHFEm2dwKu/SzNB7eT3o34r/7wKfOfykkPjrDpNoOqBDXENQVCx9TbTyGuKNJLAZOdgOuXAgaBp7S+elpzPjMsT2uyezXBkcFGgiqWkErqyrEuHWnQ+lXZhoMsp7SWn3vzGk9vDwj0IfP8MsfLnGXph5b3lmv+9qunnH+mAA1rKZgNBcdbms9owY9ez/k7rz3k5jDid3zzH+dG/gMc3fv/sW7pn+tqDmefIVARabhFEk1bz6XbTJIppvbwmGwSwnCXwHnF+EBrbl7foxGX2q6pYSsNezt7rwHZxzZDsrrp5z6nq6IvMKX1xQA8dfp66q/X0cqyyhuktBTS64iCyMOVRdbw2t2SOJUIKfhxCmBBHB3x0ev3+DlXd/m2G8/SFJ8iXx70Nv5V4WGlzn5dS00y9fBNnc28Zchwt/frEsF7KK1nZmVVTRoqStP0mH7e4vV7owTbOJq66CnK4J2Gu8IFXGDZ+W7C07rbYXxbRJbZpovpRHyBiolC7whdZMc9FNZ1GWE5pVyfUBUf5+poH5sXEDxPmGxzKa6pzAmrs1ep2jnLpohqLFAsDns35Wx+lyqfPeJjVmTHLJZ3KesVhVkThSMi6F2cO+hruHOEkldIQ92zxjoYyvvWNYRaoYUk1Kp1vfbdRFdMklD3TL2sblrxbCd8lKSBZjSdeGfrC3OpnTQmCbXvdEJFGtxndfwS2fxu61xc9O7IAGlTE+shN1rmXFadc5I/aCOINwP8t7O+TjOUj+Fdf7+n/f/ff/MvtMSpvwB83jn3x9/0s6sdZAb8KuCzb769c+5jT+KBfjVzyO0LXx4Df+3iz5xzZ0/iQTyJlWjFL752iVdGS0rbsBdHPD+dsD8eYpuG85ZFtRNlFPuW07LmchJxtRUY/pnPHHL/Xkk+NxSv14gG8r09mrGCJkRkQ1TpB/vNWJFua3QgiGPJs+OAG4OE9+xskT1tuLdaU9qGwlruZxX/7NNLlic1FSCV4HzuT6TLssY2FVuJ5HJSU9o3ePZ0hHGOG+MlSajJasNPHD3kU7M1tQVGAnHaEKwNwcsPvG2MLXj9mfdy/J6Y46dj/oJ6iW/Z/QAfetcvYHr2N3n9jR9gXp6Rm5zM5D4lTyqGp59kd3iT7ekLfeBU09QEoe+WquLMiwIHeyj9ImEyZTT0/k0mW7Ke32XNxl68MT7jIoyn3Nx5lq30Bp8/OuPyIPVRsUrxVIuz70Qhd6KC24u616MEgeiZYcuZwVSOxVFFY7x1SzhUJCPFj0l4dXHAx+4c8aue+Q6+491XWN//25wdfXJz2p/f9QFRyZSsvkbEK35g38JhS+P1JfP5qt/0shb+S1ts3za+2/JfC5QUOKUJgmnvnOus8Zt2nVNnsz6jxpRLVos7PsO97RQKs6Ko16xqj0RYZ3uPLh/96/VJXVphoBMGLaV5Pb+DNYUPmjp9kcnIF8nO4LKjEAupsSZ/xApFSt1TzLvl5wub4tVZqnSrrJZoddbb4a9XB4zLJUE6JYr2gYZIS2zj4dpRFLbMLeXp8FoRaW/umVeGyvoivZVGbQGh9fbCsxdDhZK+EJTGemPVQva32xsljKL7fXBaOTvmrJhRtrOwzpqos8iXMiBJ91p/N93b5u8WZy354e0XFId7xIfua7i+B/ibQoj/DP/A/30AIcQ+3kXklwE/C/iPgM8IIT7Z3q6jB/8RIcSH8JDX68Bv/Vo90K/WofxE+yAEcAOYtf/ewj+xZ75WD+ytLi0lNyYjro0G/Snn6mTAVqKRwdVHfvfHb3+W0yxnK47IasOPHz/k8KCkLn0WSnIrbGcijmisKc4NZt1gSomIPNsniCXJUBEoQWEd23HIzemEVVnxS973KPntzodv810/+BlmmeXhUYWUcL4w1JUjHSiy0nHvzPDaqOafHyxJA8HLB3dYzgxCQDrWBIFga6T4Rb98m1nRcDwz3Pl4wOALbyCcYXB4Srme8trCYozjYWk5Kyp+4bO/lm8a7XPv9j/mpZNPsDAZD4o5Sgi2wyFnxSnP2oLp+BayVVlfzNOQQpOsDojiqc/IKG+goxHl+phyfdyHMAF+Y6uWhPE21hQMtpZcnzxPZb3GYBSF3gurKChtw5U04dog48Uk4ySzKOEZZafz1q+sVdtL7TNpwkQyGPlrDv50+OmHZ5TW8v7L/w5X4ilnh59guT4gND5wrGkMMa/0OS+2asgqS177zJgk9Cyj06zobfvzuiZpVfHWOfaGactys+TmXa1/mB8mJ5EkCR5QAWqSUMw9GdGanLyckVXn1LakajNSqqbktJixtgWlNQx0RKoiyqbqowoAtqITtuNdbFP3G/1stZFmyXWrjWoTFfvArTa1sf+9dtCv27wX+NdNFaUM2oRETdwYomrJbP0G9aogaq3em8YQZseYYkmYHKCbq4TasxWHhH1nEirFKPasrc62x1sMWdJQEWrRsuNsD7MmoWIYHUG0eWylvU7SzoYiJXH158hns34WlS8P+uybIBwTxUn7Htx4qcWD3TY3Ju4JKsX6uM23/TtfeUN5jPX1Ynk5507x6NCbv38A/LL23z/El/FcdM79R1/TB3hhfTUvr2cAhBDfC3ysE8MIIX4p8GUxv38Tq7YNR+uMaRxB01Bay3lWEmpBWP4AQur+VBq1A7/zouTFs3M+fpQRpYp0rOGyv79s4d+YyVARDyR10VCXjiASxEPF9jTgyshbsW+Fik+ezvnEyZzf+G5fY+89fI1VWTOMAp6anvB7vvkZjtYZWW34pwdnLErH0bnBWNdTjH2IVcO9E8PxawUmawhfLlgHivBbU65/YMC3XRqhpWD8XED6MwM+dmeHn3glZ3FSoxpHcWo4OSj5V7nl9qymtJbnp+/jZ3/bL6D+kd/OebXgzrpgbSSz8JxIefHXXnZAogePbHw+v3vEpDonbSmtq8WdXqHdUZY7i491fkxWnXul/+IO0ekXmF4+ZrDzLELdYln6JMftKu5hkvfWhvcvVtyeL3h5nvGw9G6zZdmgg83nQ2pBOlBsjRTv3YnYiTWTIGA79tj6vfmSavhRkqd/Ns+kQT8cToP7nOf7HM+W3izT2p45p6RkKIJe6NbNiDwEVvXxA6vWmPLVh97ifyuJCbXieOmf/zDapjITttKIyU4Cp6/6OY4MfMG1hbeetxXLesVZ5QkCmTXMKoNx6169rYUjVbBdrTgtZmxlD/pY286KfhiMKesVKj9pTUJj0uE1osFu7/nmbN1vrp4KPOyFnEJpqtUJzhqS4T5hst0XnDqfsZ7fZZEdUpg162pOHAz836+WWOP1TzoypMFTjCJPz8tq322EevOaeTjUMIx0H+a2LPxcqrvmHUXes+EuMuGOabIZrjEs2/dXlzdzMYmyc3QGeq1MEI7QbQhZ52/XaYwSqfs5y9td7yS3YSHEh7/Szx83ouRxh/Lf6pz7bRfu/B8LIf7QY97267Km6YBf/v5v67/+9J3P872feplYiTZ7QwGvs6hqdpMIJaRnjzjHKJQ8czXs5yBD7c0StaSPB+4+LKNIcDnRPDtK2I5Cro0GhEpxf7nm9eWKf3D7Lv/q8IjnpxN20qSFBZ5CyTOf6SEk37Iz5H5WoCTcPamRUvD0nubZUUikJMOnNc/+7BHf/tRldrduAvA3f/KH+TOfOuIvHz1kdW4Ybmn+5C96ge/5OR/i8y+c8ac/+3o/M/pb339KlEhOHtb8LTvj2R2/Yf+S9/8JfvVz/5xbn/lz/I3XP8krqxQlGkxzwv3sDCUEZWMZqIBIBSghKZuK3GTY1V2GwYhheUp+5mNxhsGY6fAptqYvoKMRabnv89/XB6yLExbZIWUxYzi7TZhMUTphsHWDydCfEU25RKSa5/fey1MPR1x5OOOl2Zy1sVStN1jckieyvGFnonluK+DWKGESBoyikP3RgDQIepX1RUy/shbbTIE2DEqrC1CW/71V6bsNJQWRCkiDwB9GihLNBgZ7Y+7DyEZhiHU5r5ydP1KglPTaisuDCaPoZ7B34+fx3NMv9VTlKp+RrQ5Yrg84Xt9lXp73DsCZyVsmUk5mDWMdEinvOfWwLeRaKJSQpC001oVrBeGIrSsfQkfDngjQNAahAlSQtEyz1gGgMTR4AWfQml/K+AMcLUsfBYBiZzckHn+GIByxXtyhapMvoc0hqfONl1zzMmXmO5xQaQ//4XUsts7JF5t5ko6WVDxH1bIWOyjRv7hACok0FAufTFlXy969uShn5G0CYwcZKuFTJ0cm790DongKxuuvhls3kMHGobgzXHXWPOJa8HaWc+6tDOW/0dcfa/8fAx/BZ18J4AN4+vF3PM6dPG5BeSiE+APA/44vzL+RJxHK/DVcH7j5TfyRm9/Uf/3d/+f/wY898BvLTqp4YRJxazRgP03QwodJdemBAIVtWNQ1Q122XztiJZiEmp0oZDeJmcYRV8dDhlHAziBhHIXMioJx6HNPJklEEipc/Tnet/9NKCk4XmXMipJUayZhwTxvPN1ZCH7mlUttdHDCe/fX1CagqQ8pV5/gV717xH/w4V/dP5//5p/8I37nP/oCv+Pbr/Dvv/8W3/euD2KLH+Y4u8bf+5c/zOrUMNoNOD3xm8lWkLF7/4hp/M089eG/yne/8P/lpz7/V/j+gxf5ydmE3SgjVpZIwWFTo2XN1VhSNjW3qxPGOmQa5hxmDzirVmghuRRPeE8wRJ6/ymC4T54dk5f+ZB4FI8p6ySI77I0Ph8ku4+WzDKe32gHzGUIGhMmLXJvcYOupD/f2+oPrik+d5WS1pxwvk4bdVHEtDfvOIWoNGydJSBpqRlHHELL9UL0bHIcXrFssjjQIyGpvx2Fa40IdtMpy6+mrs6IgDQJmRcms9LY4J/NFnyzZaXdM0xArBXnp9RGuYX3PAiHj8F2Mo5DLg5RbT4+4Vn+W/aMXW6jJz1s6A8ST5eu+eLfsJICqKclNjnGWSIYMgxGJHjCKd9m9/BGGu88TDff8fZVLmhZ+VDr2hADrT+dSBpjC06Wj4S5WfxMPFiWnM//7nT7kdFWRhu9lsq+Jx/vU+Rl1ucRUyz5rBjzhwJSrnoTQOxFEo0cimqPBXj/DSANJpiWdufJFirBtA++axlBkx6zXB33i5rqaMy/Peydh6xoiGTBq817Gsffb06bASQNM+8fZUbb9NUlwsu797d7ueoeZQ/486H0Tf4tz7jPt1+8Dfs/j3s/jFpRfD/x3eOowwL9sv/d/mfX//vm/BIBf87f+Pl98UHHntOa3fXDAOAy4NhyQGdPbetjGcbTO0IXohYgD7bPPN3nxCZMkYisNSAPV23JsxRF5XfMwLxhFfqOLovdwPC96zUeoFMY5hlpzY0szLxuGgWRW+E+aKkpePBhQ2ll7+r3l7TSqO2RtrO4f+cW/jD8/+Rf8wb9+hy8uVvyPv/A69xZXWZUF3/Vv7/NH/8F9AEzVMJ8bPq1KJuGcG8OURVXzzNYv5hf+0u/g3Z/5m/zln/qT/KvTCUo07CdrjJN+UzSWsrGUFk6stzNXQjCrOq3MjOdsQWQL8uyYql6RVec9I6jTAdDmrM9bdlOH65eF38zCZEo2v8v4UsIz06dYlRXbRcjaWB6WhqGWrCLPihtozUDrXkS58dHyQkhXf46R1htox1yhMnLThbRxxVll24AuRVbXVG1XArCoKta1bf9dszaWcRBwknujT4DLScS8qplXpj1sGFam4UeOl9w7MwwTyS+7OWEcekX49emQiFfIFgc+Ez4c9XoZa3LK9ZLtwTWAPiyry4NfVQusMwyDMeN4h8nwBjtXP0K64y1RbPMUUh7iWrfkpql7FX6neu+U9jKIOVvvcbxcsywrKutnV13SZyJ9p2fDbyIee8aUDj3UZU3eW/4L5Qf93RwNfBFjfdxf+94Zu9X8hPo1RvFN8kpfEPK25p6N62+jWq+wLoEyNxmZzbGuITMb6/kRPvUSaEPGap/GOdjrIa6Goqd5R8mUYPTtHC2fjH29h7yeyF19I613d8UEwDn32Xag/1jrcWnDZ8DveuuP7Rtv/e1//1f0/7794BX+8at3SfUmf11J6a1QtKbUlp/11FWOW6fg61sjRnHAKFJIeUBdPgDA2YBU1twaLtHpt3AwLzzzpf1dgONVxt35soVWlJ/1AM+OIvRYoKXk8iBFS8GDVca/uPeAO+uKofa054FW7CYxWW04KyvMy6/xK59/hv/8T3wn/8dnf5x3f8f/xOraDu/55VO+/z/6VewmP8rv/9hruAZ06Hh4VvMD9Zr9rYIPbPsPW1anPP30b+e3pXv8nFf+Np+ZfY5Pn0tMI4i0h/xSpYmkw7iGsrGsDQy0twPXQlLUa4p63c5cSlb1imEwJFQRw2Dc5310meWz1RvMs0N2Rs94Rfb4Rq/XKFfHbMXPsSorLg9S3ts4XlksGWr/Nr2SJgwCzUlesKjqHo4qrWVvmHJ9fEgjve1KkR+ioyE7Qwi136i7IfGycH1x7/QzoVKtM0HDWeHnJ2tjub/2FiA3hzDQiknoC9ppWTEMNM+MBryxzriX1czLhoMzw3SkeG4r4Eqa8Mx0zPuvTskf/gCn87s9HbgTLZbFGdUFtTx4pfxodIMwnlIVM/LsGK0T0uE+g+1nSbduINStNsAKFIfY2utrRLRhnOlo2G+sYTRCDDVZfY3Xz+bkteltfkZRyDAK0FKgpGznjBIZ+GJUzA9aYaPPmtFBTJ3N+sffwVK1yfsI4DCZtgmWnrQhS/8apkPNVrr/iM/YVhowjI6o8tbsMhwxGO7TOENaLaltwcHqdSpZ+dAvqdgKJ2zHu0yHT/VhXOnwGoPJDQ/lBXFvr9NZxuTqQ3zhcMaqrN7mLuLXO6lDubC+IIT48zyKRj22RORxzSH/OV/anPHnP+4f+kZct648x++48lz/9cn5Hd44X/Hhp9/L2eINlBT8k5fvMI1j3ndlh0nzU1CAwbf1sFE9dxh5FH+cq7d+HqfVc72548O1N7orbEOMpMQyDYLeoLGDyC6lSc+U0VKwn1YMAsW6tozDANM45lXNyhjmleG7fvAzPL/1BX7rB9/NF37ov+bjr3ya/+xvvMi7fv/38W0fGfJN35Tyif9zTva5AjeR5M9EnM8kJ6uGeWV413jIsqx4fvdX89Gf80FufuFjTF/7uzzIZ1gnexX0OIhY1DmL2hBJKK3PJ7meBjzIDijt/5+9P4+27LzvOuHPnoez9xnvfG8NqlFSyZZlyZZsx07sxJlJCBBIaAKvmwUNTXfoBd2ML+8CwpAOzZjQTRK6IWEIJE0CiTPaiR07tmVbsiVbU5VU061bdz7j3mfPw/vHs/e+VYpklezCBIVnrVp1hzPdMzzD7/f9fr7JbaUIR3PQZRH4BDCLhihVw9vWuwBNJjlwy049Yt15CUu/lwMv5N6FHou2WETnadZ4F+qwJoDdecD1mQf78PbVZU4tuti2guFsN41c1xCLhSpJIAtDXd0MXtJthvOQSRRzEEaoksR+dLSDjYqCrqaQFQWKpLFkmczTDFUWp9e2rnGP3GI3nOLHBWt9ldNtjWXL4HjH5fRCh2T6CfzxFdJkRpp42M76bUZHtYYuqha62W9OL7rVQ3cWUbTzDYZHkSUSwGCTNA4bFL0ItCqwuC6wLZX3pnH9B+IxZ0XGwLZQZKlJ8qyVWIOW3ixSfryMYwjeWE1uLqoTRxb7TRiZpreZTS8zDXZQJLVJxTRaogyXhOOmLFYvmP22KPcWRSpgnjMfr/osRfP9xnzqusexKuHHYv/+Rn0Iohlfv3/qE7Hlrok8IMMVWJvYaxZCw1kiiUWiZb0R+YrHV4/l9dUc/x/gT3N0gPgYwml/R+NOS1631tBMBJHyLr0qv3PGYvcEi13x9XAe8fmdfX7P/SdRixeZJAuY7lpjjtOtHt7BpUY2W9fEp9MrGHtLLB+/gBfn7Hkxz+wecnE8BWh6NT0TFiwTt9smL8pKSitKMBtdF9fQmxJMXhTMEkFt/eS+R5CWXLwcMvvknMc7Mv/KPeCdX/9F/of7j/HsX/1evvlf/xxfuBjSaiv8zQ/cQ1vX+Cef2+bq53zKXNABZl7G092YBxd9lh2bZfd+esubnBk/R5A9xWXfY5QI9IUIJFKJ86q8IJf09YjdMGKc7BJX5uO2prBiGnSMPh1rmZa1hKa7uBUfSquw6EWRYdpLmO01VMPBP7jEfHadvfFzuOYiq8ffy9nlb8OPMjqWwZXhhHkq3NRt22SWiElwnmVMk5QoL5gmOZv+Ne7ZbXG667LabrPasbC1m8AmhiJMguINf5phcHuu+M68wrBQEuVF0zOrGWXTJEOVZVRJYhgnZEXJkiljqOL3bx04dPWAji7UZyfbDvetdJH8T5HFHk7vFKpxhHsHUfpzeiL3piwyNEv4W2qJc1AUHPo5eTFHV4Uc19YEJbgoaLAqSSowPYos4RcnCKqS5GgekRczLF1lo2djKFuUecY0Oc7OdE6QZgIe2TKqRMocWMbWhDE4TI9IIFZbTNCJL5RlVvc40WwHb3KZOPEwVYd+7zz9tUeaZrgsa1WK5X6lBExF2NhMlD6TcNTELQMk0Zj5XCxolrWIqlqY9lJFd+g1pat6A1KfQGrzqCzXpc5TIkdIEyVAze7hxesM/aihHN+N8UYreVV4rQ9Wrv1/+OXcxp2WvJ582Y8+IUnSb345d/hfyzi7epazq2er79ZZtOGFmzH7vs6S0+KY9jTz0WUm44tcG34BP/Wa/O44FY3A/vHH6PRc9n2bT+4KosFcFuWEJvmv2uHszkVZbaVl07F6nBvsohluA3QU6qURqgyzqGBhWef891koMmyPMj7zyRmf+9zzfMvXbPMrf+S7GM1u8J+ev8qvbI15+6LN9791jb85vM74U3PSocR2UDBd0Zl4ObvhJR4eOLxj/VGOrW4SpXPgRUajkCDXyAqJtpbQUjPiXKFvJBiyeNyzFKJcwlREWWzJWsI1BliGCJMyWkti52jWYVZHWP1EvpdJlNHpZyTRCNdcZDTfwtx7itX2KgPnEYI0ZyNzf5uaZlSVLVT5CMl8cZrw3Djh1MTnXSt9kqzLRm+9KTtmssjaeH5vhBcnR/klpWAytVS1WqDK5t+CobAbZnR1GT/NMBXBL7M1DVtVWXZsMTHrOqc6bbqW0fRmLPk6tNdIOIkfZZSqgu5IuMoWwtYFXpzflmQ42Y3x4h3R25BluhVlGSAvVHEKkQGOV6cuGkyOF+X4cVJh4zNO9NosuwaauoMfW0ySNYIkY2syJkgzupbRMOR09RYQqa7StYX8OMlE1PM0XEaRlwn1ewiSnCXLIDv4dyiqyfLyI6yc/xYAYv9AlLhklazwiefiJC/JAnsT+tuNxFyRNVqtVUEPmG+T5REtc7HxPOmWeP+Y7VUU7Xy1kGbN3y4BlrJF7Atzo2q4TJJ7iKeR6GnK6yjaOrN5jhdF7Hhzdv2Ap4eTr3h+AChKsfl4o4yyLHNJkgJJkjplWU6/nNu405LXrY55GXiYVwaZvaHHvevnuLf6+qee2Mc1TnD+1B/ivQ9cZrb3LMODp9ievECSRwwPnhalh+5xHlxZw49X+OzeIcM4ZVhNhqc6bVxdJysLVlpiYqp17eF4Ez+LGAxOEXIveVGy5jo8PAh4eAAPLy9wTvk4eRoR938f+37AswcjVlo2H7v4ed5z/iE+8OgxFu3PcH3mo8oSP/yd5/l++RKHz4aUfkk8y9gLcuZezvZKzl4Y800nvouTj/xRHnZ0vmf2cUbbn2U0ucS2d5VhNOIgnlXE1qrsVNF4DRlUSSYvhePaMPsVM8uqGsUZRRo2+PM8C7E1oe7R5J7IcTf7tPxVbGe9av5ewZJh4Jxo0ix1VUEJjrAuUaUUcjRBLKh3jM+MpoyihG1vzul+h4FjoqsSO9O5SFuUJCaRCH2KcrGYKPJRuSvKC7q6wjU/rUjLMl1dLCJ1D6xrGnQsg9VOq+m/KbLIv7E1hSDtVIDPBD8+QqfrygBFFieiYSAMlpMoJq5YV3GFfs9LIUuuF65B5SRPs1XqCnScFw2sMameCz9OGNgWax3h9xnNl9iZ+k2pJy9LXENvYgDqx32U5VIe5aUUy83vhn7C1tSjaxosYWB3j9M//hhbs1WuD2Hg6CjGCpImQKbkl4jn+0iFiiKrRME+3nybIJmw3Lsf015iNrnMzuQFAFp6BxAlQEU1sTvHMTuPMZwnxEGEoQrc/r43FX6mlsmyewyrrVIUa1wbzvHj4LYcIRHkVTAJIy6Npvz89RGH07vD83qjnVCqESHc9h8C5vUPy7L8/ju58p2WvG51zGfAVeCPv77H+cYZo9kNorzg45v7/MbNA067Fm397XzDfb+f9Iv/K7veFWbxiDj1MPZcWq1VHj39jay2T/Pc/rDyK9gsuhauqRJWyq1BaxcvXiVIMszOGtF0W+ST2zTN/K8/ucFGz0ZLn0GzH0HWRJDlxgK89aSQR/+PJy4i6NXw9WfXeXprSJBmOIbOj/++C/zMmzb5978kTkySLBHNc25cj5jMMnajTc63TU66Dj3zPAuDt7BxyuZ+6flm0Xx+73EO43HjjYiLFEPWGJh9+qaQZNblDX98GW3uNgtLnTQJkKcX6VguRWHRGpyizDPayxeakkbs7wvKrnaiid4F0QPp6BpZKSY+RZKb0KZa0ts3dJEJUhlYk7zAMTT6LZMwyfDjlEkUo8gSLVlhVmXHK5II9DIrAsKKpaLK0NFVsopLJrwnRz2Njr5J7O8flbN0l8jq4ckPMZpHDIOQOC9wda0JOAMaHP8sSRlXC0o9REqnjKEodC2DfsvE4gUy3yNTVFT74UpgkDENE4HRrxSKtQLRq2qRQSJ8H2EqIgu6ptEEWtUgUICFlt40yvNCGG4t+TqScoq8yJsFq15c/PRB9q/PyYoJC7ZVcc+OFiZFXqc7uIe2qWJpu8T+Ad3ZNvPpJnv7T3C4+1uMoiFxnuBW/bblhQexOycw26sM8wfYun6Al6QiMrgU0EigElHkFXF4gayI2feDZnGsF+kaq3995vPFsUdeQMu6OyUv4C6mP/6OGb9Y/fuyxp0uKPeVZXmbvVSSJOPVLvxGH/32MT7w6DE+APzR//jzvG1B53sf+Rqu719h/fh7ya5GXJtcxE8u0jcXCGIx0SytvwNr/b3iQ+/oWPJ1Yn8fo8JCzCMLp5tha/cQpPei9O4V+9ACXFNjGsbVwiITjvdJwjGtAc2iAkIeHUZvar5Xixd5+4nz3BiHFUtJ42+s9fihb1rnr3zol3j6IOZglBH6OaGf8/xOwq6fc70Tc3+nxTgSZOEHVh+gu2FittfoDS4Q+ttE8Zg48TB0l8HiW9CtHnIVzKQaIo9ltvcs3uQySephGr2m6Wy219DpkVe8qfryknY/ozCla2mUxTaq6aLI4Oo6jqHjx0klZlCYxSJjQ6l6GyCSL+dZJsKZKtrtwLYYODq2JvoEteR4kFtN2WueCv+IqchN4z0uCgxZTOptXTzOVqWKUiTB9lJliaJIG4d5Eo2wnDWs3nGS4Cjaebk6gdZI9sMw5CCMWbTE3yL6agaqdJRmaCgKyy2bjmVg6yqqcYGdIGR/FmDMps1O3Kuc/PVEbmlqFVQlJs5aWq1XXpNa7n77qaRgElb4FLV6j6UrGJV/R6m8PyKHJMJLEnb9gCTP6ZkGcZ7TsQxcU6PfEu/NaLqNv3WF/WRGnkVE8ZhpsMNBuMteeNiIODacdVbcU3Q7p2kPzmMPTrEXnOAjV7eahb5v6A21GCBOksq4WjBoWXSto4RKx9CbDViYZsR5wTzN6OoKJzvi7/3VuzAPlOUb74RSluVPfCXXv9MF5ZPAy635n3qFn/2uGz/5e7+Dv/Crv8S7D69yrGdxg2/hoa99jIUv/jRfuP5BrnvXADhfZmSbEZb1DMfe9L8hy9uMt54VTKxohKparJz7VhJOoiJ2gZNA+CNO9lvI5VXuWznV3K9m90gDwTaq5uRm/NgTL6BKF/kz7xIivGvDOXlZMjBNHGOPNPaIp1/kL77jzXxmc48f/sIN5q5CUcDAUbA1sTv/wnhOdx6yPA+qvPsOjrGI23kEpSux6pi4xk3gOLK8TTjbPsq6r6CG/eOP0T/+GPPhZcZ7T5Ems0rFJFAgUuWwrk8tt3oEBA7EQkHi7HKbMCmak4GuKLjVYlL3GII0Y811CNKUzZlHkKbULO4kF8hzkfUuUOl1+FNQ9UayUmLNVlmusl/GUdyUsm5FtOdFga6J2xrOYxT5NN0VwcJKoh6Wu4ZqXCD351iaRtc0WXQtpmEi4oQNXXiUtIDrMx9FklhtWdzTbYuSXrU41vj3vCjYmQY8OfPZq3ptxzsuLqJcWjPI4jznnm67ISTvTAOCNMW75fRlayqGeiTZ9SJRLqtZXEGSN38rCNqyY+jV5F1WSrGSIE3YDUIhr69OLqLvo4ucl8pMWBYpYXiAHx4wjvabnPee3qZvDuhZyyz0Lwi5r7tGYb+NG7OIremEq96cKC9Yt03h3SpEP8dW1SqES8bWjt78NY24Dl5zTQ0n0Ll0OK6SPmVhjL1FJfiVDBEBfFdu6nfMkCTpLPB3gfsRAiwAyrI89apXumW8Fm14BREjaUmS9BBH8LE2YH85D/iNOP7Y/af4sadeYCtIOe0avH15gQeO/2neIqtcvPFrHIT7jMMD0jzGTTz44t8TBNn5NnHiESQTFjtnCSabKOo+Wu84VrnCvpdhaSpF9AX8ySZl8RR5KlL3Fk7/WV6JILF1eBVVkojynH/8W7/OSbcFHLJgWyy5Ams+H15hNnyBpXtS3n9+jS8cthqlVFQUmLJMR1caaoAhywRpKmJwZVmUGTSVIMlwDZH4l+e8rJwVCsmm+WYmYUpsrDG4930YvCQicCvJqKyZKNp5FK6gaCYuYqKO8wLHWSRPI3SuibKLWqLIMv2W2WDNwyRrkv/qiU+RJRRJZhzHxLkIZfLipMo2EbvyrCy4NRrD1tSmh1EvUIaigCKMj3Gei9uMomZBy8sCW9PQFZk4W2Z56Tjz4UeFpyYXSY5urnNi0MKPMpIsJysLglgsYj3ToGcaHAYhWVEyDEIGttVATYM0b9hXXpw0xleAw0BEG9iayqXxlDgvuK/fFUKPoqhSElN25wFZhY5vaQq90mTfC5mEirjdJGnCrPKyZBLGhKnw+MTVbfRMo1lY6+HFCVGeNwmadTlMVyUohMJLNVzcwXlU3UWfbZKXGbosChttc0DLXKTlrOH0TmMPThEUZ9ga+gyDkN15IE6Xhuhb1a+roeh0LaN6zbQmPliRZZbaJramUJOEO5aKrq4wDW0USaKta+JUVEnYv9JRlpDczZD63xnjXyBM7P8QeC/wAV4FOvlK47VOKN+E0CVvALcy9j3gr7yeR/lGHnGe8/EbIZsvBHzYz/mp9REP3mfzzRvfzLse/gBv47Ncu/gfqqwJn8n0CppqYVuLnHv7/4QXr6OrEnL8RfI0rLJQAl4aTfj2N72dIrUwi7SR1y6ffv8rPo7PXnmGp/cPuTgLuDxOSXNYdHyWLZUTjsnAtrh3pUfeXsVoLWI4S6A8zLedvsQwCLnp+czTnCDLUCSJRcukpamNL0ZXFFxDZ7XTAkQZJM43APCjDMs+gdO5SRqMG6nmjXFQ9SsixjdjbM1lubXMiYGLa9wUQU95QZgcJw5yVLl2vgt5cVGkFGlEEn4Uw1lkrXN/o/iaBCnDedhMikuOmDgcQ+dsv8vVyZSdecil8ZSVll2FNxVV70WccOoyTs80yKpFyjX0KuBJ5iCIBIxT1zkII+aVMi/Kc1qqSlvXG5NkXujNyUtRtnDN9YqgUOJFKV6SEGc5XpI05smzvQ7nF4Tmpe4PeFGKayjoxQssuxfwYpWw8uEkeU5WhZjNkpTNAxEadV/X5TAMycuScRSxE0Rc90WVOspF+a+rKQzMOeOK9jxLUhRJYsMVxIjrM7/qERUM44TdMCPKC067BqYiYyoKfUNvst6XLBO1OkHVZbbndyfY2hodS0dVJRZ7h5Wnpk97cL5xwpvtNTS7R5xviICwaUqS+XixCG5TJJmzHQdFkpuF21bV5nTnGBq2rtKxRMxykObIwWeJqxOvZvfIigyFMReWLQrpHg7nSdP3uhtDnFDecAuKVZblr0uSJJVleR3465IkfRyxyLzmeC3a8E8APyFJ0u8vy/Ir5z2/Qce5pQ5/7bGT/MveFh//+BRvL+WzoU9HV/i+t72bv/Crl3jf+l/ka8+sIcdfxOh8M1d2XyItS568EbPtbQHwjeffxEEcs7UzZGBbDewyL0qs3u/Baj9J2z/A6HzzKz4ORZb4xc0JX3jSJ97PQIPWqs7Cmo6/XnB6NMExFll2V/HinKmXs9Da5sSgxZJrseTYotQWRmxWYL84F9Gxdf1dlOIqf0xZNqDFYSAmL0XScI0Nkv2cIN2jW/U7FFlmZx4yzzzWAtHPWe2s0bVUqJRKeVFC1ShWsueJw7BBiNQnn9bgBkq5wc4kZGvqNc3rXBJGPaFaknAMYRzd8gOGoXBY9029ySYHIKHZmed5ycA2SbKjpnmcF+RlQV6xvepSiZ9lmBVLbJ5m3Jh6LDk21lxltbNGUawhy9to6TNYhktYnKh4YtWCVpS0NEUYWiskTF6U1fNfoKsykzCla91PmV9BkU/QsQwWYpHdcxiEbM6DZmE95dqNMmwUzdiPYq75ichQr9xifUsmKgr2wpRpMiWqdtYDQ2Wa3oqQkfCzgm0/ZxyIKIRZXNI2JBYMIUxoaxon2k4jkVZkmSTLeWk0QZHkBm9jVBuQvDhJnB+v3h+i71RlrZEVe0enm+qk5N7SLxHvO6lRpFlV/rxgt90knO1T5psoijBRRvN9zJagYSuaRezvk0Rj4AkMWRCI7+Z4o5W8gEiSJBl4sYphvwl8yQz7W8drlbz+SFmW/xo4KUnSn3v571+eDPa7dTj2Bu+7b4P33fdW+L3wsYuf50/82+f5tX90k/N/6wdIOyt87Dvm/Jkw4vveJhaDYz0bzVj7bbd1woQTL3v5msspD2N0bv/daHaDf/WFS/zIL+9SZCVZVJCPcqSgRIkL5vOStArTfmt/LsyUVXRq3fvQlIexTOhaO8R5wUZh07VMnt0fMooSrnlzlkyjmYhrVRUc9RRmSdpMuFemM4GAL0uxoy1KgiwjynOiKqHSHk0I0owlx8Y1RR3c1hXRjE+fI/YPKPMUSREE3Sz2MFqL5MUxhn7MJIpZclo4htbw1EAsAn6UNWgRQ5FFLHGWEc9z4qo/0FJTeoZxWznLruTEdQ6KWEw1gSKpJkldUVhtiWZ+uzrhBGlWNcHr2UW42JMq+rc1OMVqx276E0tOiyBNG7Al0KQQAoRJjmOq1UnqFIYkstNtTWskv0vVTNbSlIY3lhUpN4OESZIT5yWGIjEw5caceRhnTBIhSjCrTJlh9bNZXBKmIswqSku8uRBp5FlJkpaMLZm4XdLRxSnFUKsTYbUYfvDSNaZpSkfT2JyH+NVzEeUlo7Bg6OcstRWWbYUFQ2XJNATqRZIxlVv6YFnWlB1dXcfS1YZqUD9PtXBAvH/HJLFPEQrEvaJZzKebmFmI0VqiNTiN6u8z3n4Cz9ts8DB3Y5SliAF+g43/BdHO+H5E/vx7ESmRdzReq+TVqv53XuF3b7yn8i6N95x/iIt/Q8h2f+WZz/LDX9jiuSd9/r8Xr/JDv36TP/KOepf0DN99/hSnbsG/vJ7xiRef5gc+fYXLzweEBymSLFEhsyhVyFQZZUFBNWXStGQ7iDgZxQz9CC3aJp4/jdlexeo9DICsrWLwadJ4zOmFN7Pvz8lLsQBE1Q54nuUsW0azwMzCiL0w5rIXYyoyC4ao1dexujfnMaqMaHpXTV2zmoT35gFenLDsCCWTriqQXyKLjtIFj6CEJmZnjUmYi4z7ymeRep8mPjhgGHvoFZLdMd/EcC5OUS1VNGizqky1F6ZNn+iEU1TIfqHmamkqXdMQZaWixK4YYnXvyKhEAIokMWhZDf/Ki9LqtFZQFGvE/hNksU8ajip8/IfoD04zaIm+phfnov9UNY/3vJgwyeib4qQassHWOCBMMkGydkz6rX2CpI+XJCxYJm1daxRQ8zTHkEtUSRL/5CPp87JlYMgy86xKjcxT4XVSxGX9rGAWlyR5SV6I03CcFASzjNCrTg6mjKpIBGnZvK71cE2V53cnfG7ks2KqzNOcz+3FzMMC38uY7aXIqoQzUJFl8OOCG2rGop3iqDKOKnOP26LyUTYU59pzVGfUJFlePcf19zApSmz9AZQ2dIw9vINLTUktiz1CbxtJVpGct6MffytLtznk/+WX9Zl7+XgDlryGZVn6gI/on7yu8Volrx+tvvxwWZafuPV3kiS96/Xe2e/G8c0PvE0kOP5h+NBzT/J3Pn2d//j8DFmCQUfFUTf5ujTjwrF7CaObqJL0iieXeuyMrmGoCtdGM/7OZ6+wsx1TFiV6R0GzFZFy2AdFP/rw2K6CYYiMl4MwYhBEnDpxXET5xj5F+ulGeixrj2J0IBz/Ag+uP8SqF2IoCi9NPTJKOrra8K6mScpLs4RhIMKVFDlnauWsV7ODmMQFZNLPxCRuAlGesx2EmJXsFypPhirjmIJUW8uI6zx2d/kC29Ml9v0ZA9viWO+AcLyJd3iRPAsbfEdRpBiKLBhVpaiXD6pTUpTn+FnBfpCTlxlRIQjGWUGzY98oS3qmga6InbCtiaZvmGSNTLdj6SzZN0nCEYl3QLvC7mhWnywWLKkijYTaqUoXHO89haa3MVuLAvBoihTBQHuogY8uFSeEITIJmwb+1tRjGEQYis1qR+a+5T7DuRAGvLVnM/QTLt/i/F6xY7bmAX6asWwZwk1foWMANmyNqOaaVa+RoUKYloRxgVydPLO0pKwOXEVRUhQl87jkyf2Ii9OEF2c+901mfH444+m9mJYhMwoL9iYZNy8FJOMc2ZDQHPGczccZwTRHViV0Q2K/o+K2FDqWeF+ut0Qc97rrNCeTejHx45RJKEqqtqYxDAqGlbm15rLZmsug9U6stoKtyCjyDWJ/X5RMw8/R0Sq0fr6BH90dalRZwlcjAbgylv974CQiwvcPlmU5foXLXUP0t3MgK8vykddz/Wr8S0mS1oHPIjheH7+VPvxa405lwz/Mb5cIv9LP/tv4EuP99z/M++9/mM9de5aLh2M+P5zxC1en/OsvjiiLL2IYMoYu07NlVBlOOBqnXJsNp8XAtoRXoHJ3f344YzYvMGwFWZVod1QMXW52TEUhUiBVVUJTJdqmuM1ZkrI3Dzjwlhn06mbyMYJgi6GfcGJJ7KKt3u9h9sIPstY/zanza1yf3MOlwzFxLqKFa6lokIogrDguUFWJIL5dQdPSZBxZabJkQExi0ySjpRb0DZ0F26JjiX5CId2DbkGuHbG2TKvH5fEaw2Ammu9to4IUevSPP4akHTXqc2A4P/Io5JXCSZFKzGoB2SkzRtXu29XF87JiCYJw37w9tMvWRf8nNvLGjd4EePkHhN42gb9NUaRY9hL6rN+AHbVb8PQASTQiTUSevKJa6M4in9nc47Diiu37Abam4lQudlvTGjOnrihVdHHcYHs+d2OOY+istR0GLZG9c2l/2hCZBeKnZD+KmxJlR9cYSBJZKU40LS1DlVKyImPi5fjTFFWXcXsqHhlpVKCoEoOOSteU8ZICVYanD2J++Yuir2VUIWj7dZksKUEG6Zb0xiIrUU0JtYrPHnRUVhyFribeG34lMuhVpUolk5iEcePjAViwLZI859J4WvWhxOamKZftZE3ERNe0sLUzjQKsHl48bhh5X/koybKvygnlLwG/XpblD0qS9Jeq7//iq1z2vWVZHr7sZ3d8/bIs3yNJkg68Dfg64BclSXLKsryj5tNr9VDeAbwTWHxZD6UN3B3t3e/CkWQ55xd6nOl3maRXeWqSkUQFk4MUVZM4MGVMW2ESCkjhibaAReqKIKVGVX9irV+7uBXWba2R+ZpV3gpAVgiycFaWtFSFKM85CCKujTzibKl6PMErAvOW7/1LeLs/xe6lX+LUm/82Jwc7DOcJT27tM08zOrqI2n1Wk7g+TMmykiguCFIZXZGqhqWg9S5bBnaVY5IVJY56tEvMyoKwwod4sgSsYagyg94ucJynbo7w4zmKLLPabhEmBa5xjrxzGr+ArnyDdFeU73LjIYEtqZrf9fOQl2JX7mgyqy2VeVwy8nLyFriG1OzYBR+rMgTmuWjc68Kdb+uqKG8VJfvBOssrpzDbT9Mp0iZsqixSyiJtaMK61ReQxCxs/EZ1hK7rfAvPjZ5jWCm+7ilLTlSOcSGD1po89lq2a2kq01ukvQdBxDiOGzXUNEkbNRYIoUZLVTCq17deWAyqUC3p6G/PC7hZwmyYMr6ZoegyqikT+TnXo4ibukw0z0mjQmxiBhqtloJtVP00V0WSJZK4JPHFacTuCH9Tlh5NvHLVd1EliYGpcqbt0jZEWmmQZmx7PoaicG3msx1EtDSFNdtiHInYghrOuR1ELFtGQzC44gVN70a95e9SZZpSoJ8WTNK7hF4pxentqzC+EzG5A/wE8FFefUH5iq4vSdLXAO+u/nWBDwIfv9M7eq0Tio7on6iIPJt6zIA/cKd38rt1jGY32JkKHM5Lowm2pjGwTZGMV6mi/tQDp/jl1hZfGEVsjzKKosQ0ZdqWIL6qksTlyYzDIGwyVFRZYt3Wq/RCGUOWOd1tY1c9gH5L+JGSrMCPBc/qMAjZDkKysmQUxzxzMGJz6tEzDY51XSgKBo5JGnwUzf665m9oDb4Oqf1uQPRYFrtwOkzw4kSkNtoWa3bAZ40pl8cpUSrMeC1N1PEHhkjBVCSJRdtsMPzjKMZQhVJq3w9uy6hwDJ2BbTEJ++zMdhvT3vkFYbzJi4I9T+BTLE2l3xJBULKs4Sf5bXVt4ckRk4yhyCxVkQFZWbI5yUiykqBaS+vFbhJFWFWpS9yfwcAxhWPf1PCilAMvxI9TltwHcIzrFS5mlSIVr6ug7AowomCZCZMm0JCpb4xD/DSjo6v0DYOVll0ZNTW6ViVmSATwsQ7A8uKEYRA2IWBBljFJ0qa01dE1FElinmW0VLX52zv6URmyHoYiE+eipzUwVPysIO+LKSGNCqJRRlTPvTLIioRqy5hthVZHZWVBa04ZUVGwV4k/VEUiTQvSpMRxFVRVIklK4rhAlqHVUlhvq6xYKkumkKYbikzXNLG0XJygw0gsHGXNLMsZxQnTJK3Kl+LnUZ6TxSV+mjFJhJcqSGv3f83zEgFsaQ6Tqdi83a3xOtaTBUmSnrjl+x8ry/LH7vC6y2VZ7gCUZbkjSdKrqa5K4NckSSqBH73l9u/0+gC/CTyBMDf+UlmWrys85rV6KL8J/KYkSf+y0iT/t/E6Rr99DDP5OezecTZ6GxV7KWUaChSHY2icHLR467GIx69qPLl32PgbDEVp/CAA8yzDLXVsVaWta6y0bN6ytsCgpZMXJZq6w3x4idDbxt+7TFFk2M4aK+uPsNF7gK2xjz2acM2bsxfG3AzEh/VEy+BTuwd0dSEFvae7Qj+6xmr/JABZ/CxanjH1YRKIktjZ1bOE0U0MReZE7JIXJe85kZIXoub/wWs7TFIhs122DHqG0TS8N7outi5SARvCboWUqU8GcZ6zM/MbYGJWlJztd29BiojGfI1f35lpHIb3EuylLFhDlh27YlZpeElCVqZQCFWWKkt0Uq3awcYMo5wwLXl8J+LeBY0127qldp80Xys110o7RZBkv019VJs1izRCklUUzcJZPHeUBZJGGK1F0urk4q59F5958SZnOw4LllWxyISKrI4r9uOUGxOvafgfhBH7YVQpusoj5VZc0DWEC3zVNpuFBrJGVl17iQxFYZaIOUKRRAKmIhXN+8xRM3RFwrZk5vOcKChIBQ0UWZEwbBlZljAMGdcQarGsLNn2c/y4oGVIrLgapipV0dri/6gomMQFcbUQLBgKx1o2G26LbiXwsHQVC7Uq6aWV90U8xktTcfq49fZMWW7ex0DljxELyTwuiKKCOC5Io4IsLYn9nOIul6jKO19RDuuexisNSZI+zCsDd//q63g47yrLcrtaMD4kSdILZVl+7HVcH2AAvAt4D/D9kiQVwKfKsvxrd3LlO+2hBJIk/T3gArfb8f+rDtj6agx74bu4+YW/ypNX/gOL1grHV9/Ffef/TGP4y4sSb/dpThdjHn7oYWLO4UXCxBZXUlQ/TtiruEkLttWootTpr7L50jN482386BA/nTGNJ+iKTkfv0Z1vo6gWdtfjvpWvqTwPAsN+yjVQJJkgy1iyTBYts/pgi7p7OP4FZFnjR55WiHKJP3IhoWvrzG7+JO31P4plihTE4fAlltomwyDkgYUduuFFHnjHd/DUzYMmVKylqZzsdVhyLSxdxtJ2SWMPdOEV8OL1JsOjjlKuVVqTKKZnighdUfaROPBC9uaBkMuOp2wHEbtRxoKhcF9X9DlOD7ooksSCbTWmxbofEaYpC7ZF35izH0XM05zLXsKlYcrl8SEDe8yCqdDVFe7tuuRlWXlcllHkEF2VWe1YOMYeAEkYNqmTwvlfE5bXQBHCgDK/QhKOhIu+//v49RdvkpUl93Q7DWF4yREMqrws2ZkGTKKIm/6c7SAUcuBqQs4L4QFKshJdlTjmqiwY4qP81HBKS1WahnxWlihFyW4QVjwsrfEFQSXDVUqCTNxvV9dYtjJu2gl+WjBPS8K0JLqlZJVmAhw5nAv+F4ClyXQsmZYmsWKpTS6MUS1ktWFyUkmcj7csNtwWa26rYZUpstS8D4I04yCMGxXeJK0XUbmRRWeF2AzUo0btR2nJfJ6TJiVZUpDEJWVRougyig6qfpeMjXexKV9lkLzikCRpT5Kk1ep0sQrsv8ptbFf/70uS9HPA2xGN9Tu6fnXdiSRJV4BjCEP7OwHt1S7/8nGnC8q/QagEvh34Uwhd8sGd3snv9rH+5r+N521yY/wsmzufoH/sMVzzzWxNPb64U3B64RsxtY+z/9KHKIpfRpZVvCxEVS1arSVWFs5zavFNXB+KHPCuLuB+4chj6m2y413l4vQKB3FCVkJbk1g1x2wUMcbBU6SJR1FkbJgOq6cfomcabE497um2GbQswiRrctUBdmZznglW+Nlru6hSxlsHTvM7bfD1jGY30FWJJCtZNZ4nHY9567ELHHhnWbznGyjST/O192RN8l5YrApvhXyR+d4VZuEId+E8qukCx9HVAkVWyQtEumLVzO1YOu48wjF0uta2CDALlwW+pEpxzCsl2UGQ09WE0XDRopKdysJw2PCrBMPKi1L8WBBsV6OYeZpxXzdnmqQYikLf1HF14ciufTKuoaCpIhgqrWCeaSzihuvFRIRjWQ3HyovzxpntGiL8SRt8PddvHLLk2CRZ3jThdVVucm+8WJQU9+YB20HIZS9mGhckuVBapVmJpkosOjLrtsZ9XYe+KRbL+uRSY1OUoiQuRP/IbfoUEGd5E/kLAk9jqyqzJCUrS9ZtvZEH55Uzvi4J1qM+GYiyYllh/gWZeck0m+ex9vTkRcFKheXvmSbHOi6rHbvB/cvyNrq6IqTYsdFIo/00I6pOFntBTpKXhElJnAr1Wa1MA9BUCU2RsCwFwyipW72GLqMpNL6Z5+7SZ/ur1EP5ecSc+4PV///p5ReQJKkFyGVZetXX3wj8zTu9/i23cxm4iOib/DPgA6+n7HWnC8qgLMv/W5KkP3tLGewNHbD18lH3Qy4cu/e1L/wK4953/StOjH+B68/9NHuXP8TqWY2Nzhkuj6ZwCPetvAdjvo8/vsw8HovsbkUcBhXVQjXG3OP0MFYWSTMhuMitHpbRA+8qAPNcYhTrjJKcnXDKTjTFTzzWgh3c8UW6vfNo+iXevvEw5xbvwYtSLF3lZL9FkBbseyJfvTas/Y13vokl+ybz4WXaSy4oZ5n6W0yCmNWuRde6SexD6G1TFhk7xTv49I3P8PVnN7DMdRTg4NLfZzr6l3T69yJtPIxu9Yjn+0x2n8bpncLsgGX1RMaHAiJd8Ti6uYWknGoMf/WkrasruIbOcstGV4REdmCKt/GVWYqpSLQ0lXNFgWvquNXvHGOv4aAp8nrjph+kVkMHSPK88T/UpsNld48s9kimY4LYa/ofpiuk3VnsUXbex6eu7bDlz2nrGetuj1W3xcmB4EqVeUZe3MPFSZvDYI+TvTZJ1duopcg1oDFIRekwyDLmWcY8zXFUWZSWCvB1cUrx44LdWc7uLOe6nzaGwXvcFj3TQKkmOtGEl1ltWdiqiqWJSXoUHcHDg8qjUkcBGLJMS1UaA6takZWNCtCoyrfv8Gtkja2JUu1BGDX0ZKc6Ffq3YOfFc2twrGeRxc+iyBagUhTiMSTV81+TjvOyJEjLqvQnyllZVlIUIMvin6lJdC2ZliaeK/GcyU1ujlH5oOrb+6Uv61N8+yhLyL86Kq8fBH5akqQ/jgCVfTeAJElrwD8vy/JbgWXg5yRRulSBf1uW5a98qeu/yvjAy8tkkiS96+W2kVcbUnkHPH9Jkh4vy/IxSZJ+FfgnwDbw/5ZlefpO7uSrMR555JHyiSeeeO0Lvs6xM7rGaB41KXgAD6wOsOWXKsbUGHfle1/XbYbRTYrZJ5Fklan8EFsTH0WWONZ16JtbJOGIMs9EFgiQxT7xfL/Krb9JlkeE8ZjDYLuhtz4/PeAl3+aq57Lj2XTMhLaectKdsWZFtDU4YfdxNYe+ucCie1JkdrtrGK0ldKsnJltV4CqyykMhyyqyZuEunmNnOsCPU86unqVIxW49SHN2piHDecigZXGy32J09SdRNIuF03+2+Zvn+z/DZPcp2ksPCMy9+WY0dYckHDPeegJZ1jDbYpKO5/vE830WT70XSbsfubxKEooYYcNZxI+X2ZmG7Mx8FFluwInTJG2a0ae7bY51XE4MWhB+jjIXLmpZM9EMFz9eBmga9GFSEFe0SOFKl5HLq8yHl4nnB6SJRxKJklWdJqioFlfLd/EjX7hMnJfN5CXwIkKKPDA0+obYbS/aJqttp7lckt0uIADRP0ryHC9OOAgjtoOQeaVK8rMCPys4CHJmYUGr6mMMzCqRsoB1W+NsxxFYF0VpHo+tqY2n5sbU49rMZ54JxWBdGhP9CqVRibmGLsgKxhEavu4p5YXofwVp1hCba3e7IkvN93VprVapDVoGXUsjC54ki30AFFWUCev3u271GM2XeHJrn93qlPa5w5AwrVA/sSi51QtK25JxdZkzbZ2BobNoCYFDLaxQJbl5TDVV+eF7HnjyS/U07mTYx06X5/7s37mjyz79v33PV3x/X40hSdLnyrJ862v97NXGnZ5Q/pYkSR3gzyP8J22ERf8NO9J4Gy8WyI3VTqtR/GyNfZ68sY9riPS4SbTI1/W2Ib90mzrqSw3LXMcv3oGl3STa+kUutNfQ3EcJ0gJFM1Eyiyz3SIMxWeIxn24ymV5mNL/JNBkziacYio6lWuiKjqO5vKmrALvMM62K6Y3pGxGqVLIfGQzjgqu+R17OgT3a2uc52VJYsXo8sPAI/d55THuJKNhvfBNZ9b9lixhWVV5gyRVKJVlbBcDR4LS2w1L2cWZ7F3niC5+mY68ymm8xfebHePOJb+fYW/53WkvfTWvpu8mjT1AUKdH4NwnSkM7ag8gr34udfIa9ax/h8v7jAKy4p3Cn55GUAxTVJPK20Szh8agDlGZJSpCmrLsOpwddEaVbEYP7LVPIRKMMy3hIpEOmF5vXQCicRE+mjJ+ja4m8+yQcE/sH+BXupX4+skygPXTdpSxSLHeNTx6eYssb8vYFl2kqDJhOJZowFJm2rjclra5pNGh1OIrtFd6SquR4C/7Y1lTaudaYSGs1mCrLGH2Zji5YZW5123UfAqhKelXjvZIa35ooOYnihrjbqmaAehffMwxWHJslx6Zr6XQtDU3dIa/UazUGBxnUttv0jCbhGl6UVrh7jSXXIs6KoywWQ2PZNQQJIRDvbRCR0CITXuTFl0VGmWf0HVhzBaJmlqSccESg2iTJOZTrKAHQFYmOIfAyS6bBastiwbbY6Li4poalyxjKljhJ5lkVmXBHJPbXHmX5epryv6PH3bKI3Gmm/AerL6cItguSJP0vd3onX40xDec8e+OFL7sk9fKhGWsMRy8C4pjuV3VttzKSebFIyVMkiUv7MzZ6Z3CiT6CYdwYQSLKSKwdtLPMbWTIttPwSWjRmPDzAH1/G8zbpdE6xcOLd7Ovvp7Ui06u8EP32McifZLz1JOO9p7i0+wmem+5y2unyvtXjhFlAkqskhYYiKViqRZiFjJMZB7FPkJVshxrX5hl5OWLgXUVTLYoiFbtvs8d8JhDglr2E0VoiGF7B1HZQDYepL+SY/fYxQCwuaeLx7OavsN45i645nFh5J2Gwz9bepwk/+QE2zn0H9sJ3oZjvYnb9xyiLlP7xxyiKNRa7q8z3P8PC+mNouovnbWIaveZkkCYzZt4mbmuNYHKd3kbIqcX72Og5KLKgDtevU1+VWXaNKn98jRplLssqsuGSxl5DpwVxwrINl9jfb06ceRZWZUYXrVpUFdVClsXHpb30AB/eOQakbLgtrs+8pmew5jqN0c7S1IqbpqHIN8jTsJkwdUVFtVwsfZ0wUdAjuXHM64qCUt1GjbifVycBt0qi7JpGUy4TBkyVrqXi738IY7qJrVq0ly9gOIuk2SqTMG0WL1fXaesaQSY1vZU1t9UEh7mmRle/SjjeZDoUmTv1abXVF0WJIg2Z7D5FloVoeht34TyrziK61cOPl8kLiDMhRa9HmT7X9JfUxvQZNYgaTXeRqudYUlRWO0tN2FhUpXF2dYUFo6hKmwqOqjYKtbp3dBiI0u001Col5XHgWfE6aibT8O5FAH81nPJfpXFXLCJ3ekJ5pfHngH/0FVz/rg5JktjzA/SdFzm7evYrui0/2CJMRD09yXOGQcgkjDkMw8YTsRfGLFuilLFgWwz9CN0YUfo/h73wXa95H2IyvsEzO4eCJqwd51jvPKr5rGBSTZZwFs8RKQ+w2qHZ/flxyr53CcfoY8Qe1w4+yxfGL6LK8Oj6+3Dd4+RZ2KTjFYUo0/WcY6iKSRiPGc1vkhQxumyQlxkDe51WaxW3dxqlMt4tHn83avc9jXLG1hSi6eMUaYTO55Flja3DjE9ubvPO42tsnP6zvH/9EfyDi/jjy9idE7QH5+EGzIIdnn7yH5PkP0ReisdzevXdmO4aZkeUuVpL383o6j9F011cV5Bpvcll5uE+fnTITX8TZ/oiC/YqUbCPqn4Ey10D1cIFrN5x/OI8eVEgy9vMh1cw2xEJJwEwkKtY26PewZFHYQPYF2FlsorZPyXMiGmIopq3lf7s7nGe3etja8LVHmc553odrMrVXivZ6lz2Mn2O1PMIKlDkrYuVopoYzhi71cMxRQxAmGaN5wRuyYevThRBmgoKr6o0pOfLwwmKLOPqGhvdd7NxfoR3cIlotk2ehmi2hyKfRpELyEVjumcatHKRAdPWteo9qLLRGzHafJx9b5ssEwtgnbJZFCnxfB+jtYRm9bCLE4TeNmkyYz66LMqk7VUMe5WgKCoi8NHmNk/VBksjV2gduVAxKjpwfUKRNbEI6apI26wTM296c0ZxQkcXJt2Wqjaf0WmSNv8AduYhfVNnwbKqU9MpgdDZz4AjCsNXNMrXJRv+HT1eySJSUYedsixnd3o7X8mCcne0d3dpyIgdyo2Jh6VdZWPhni/7tiZBiqHKAruRCY6Ta4hgH0MR5rK3N+E+ckOn3Yru5fTCK3E0X3n028d4T2sfPxZZZZMwIy/OciPwWeo/Qke7zOTSP6A9uJfV5QvsB+t0bY1s8jEUTLZHL7DSPsM32eucuu8PUaQhurMI1lspZp/E3X0Ky12ju/EIcJzJ1s9yuPMEiqzS0zssdM/RHpzHXb6Al51mEsS4psaN3OdTl/f49MGTTWb2u5fbnO4e5/rM40M3p0zDEHgKx5D515cOWLC/yIP9Fo8sP8yDZ74RW7tJON5kPL/BLB4xioac7Jxl4N7D8sn3sXftN7j87L/BbX2Ek4/8EwCs7nF2t36LNAtJ84i9+Q32w0O2wgmzFFZMlcVwH3t6ibzMUSSFRWuZYwsP0Zrv07/nfiZhwTRcxrLHJOEI1UjJZLHBECWukySV+kqv1GRJVuI6j2A4m8T5RpMMqegyiikxj9KG57W/FZCXCcc6bpNs6Bg6XVvHUa8TTDaRyx6q+WYU+QZUqZNlkZFnIWniEfs3kQMNRbXIswg1HKMa+5xaXGMaLjMJkopsLE4ieVk2GPdOYeDHCft+IGTlVcZ9nCQcBiHPDyeossSidYb7lgYMbJ1Mlgga74lglC3YFkGa4ho6Hctgozdiuv0Z9g73yRIPVXdRdRdFszBaS83prN6gAJjtVRTVbE6S8XwfWVax2jso+TK6Kjc+GFneJlfOkZUliVJCAblS4lXqQkWSsFSVrqNhabvkaYSj7aHIK9iRCE9bbTtMwrj5vNUImlmSEhcFwzjFz4TfZdfL6dkyJxyNN/VdeqbR5KncrTwUgPzuHHZ+J42/K0nSn0JQjJ4EOpIk/YOyLP/enVz5K1lQfkctzUUVOhTnOc/sHnJlOOE95x963bfz7I0Xmh3iJIyYRDGKLHNuoUe/+DTz0WVawT6Hk0tsZ3M0xUBXTCyjR6u1Rtn7DgpAlrdBefi1H3eRYWs3GzjjaHYDgIGjU8YZaxf+Vw7nCW1u0ik+z43P/Txb42d47NG/wmD1bRWUUDjIR/KjXN6d8PYTJU7Vsxhd/accXP4Iw4OneWn4efrmIkvtM7ScNezOCZyVb+ejl3fYnV8D4LOHU57dT5hOM+LK1JYGBR/zJsiahO4IerFuSFiOgtJV6VoyG7ZGvwqm+vUXt3B1nbX2gzz6/g+TBh8l5gxJViKNf5lgcp0zj/048fRXeOFz/4zpjf+HzrH/Hqv3e9C1n+e5/U8TZCHXgxH7kcQoMTHkHFtJgRnEYsPUUgU5YDEeI/sqWfAkjiV6h6oieFoA3rwQMMswpt8yhWJJVUiykjjLmYYJl9OUJLOAIV3LFMbLXJgo6whdV9foWiYdS698E+LkFvtPkE7HHHrbjQdFeE+yql5/pQoLy0QDWhU78PKWyblII+bDKzjtiETfYN8TC0DHMprHGyQZOzMfL0lviyS2q/z5rCyJgohNP+D5icdBGHGi7TCwrebEI9ImRerlqmqz6B4Szl5ktLlJnkZohovTP02ehhjOErL5Zva8uBEPrPVMyC9RpFGTymm0FsVtV+VBUWrMSRoFVEGeLuNFSQV6FB6jupk/jiIUSaZdld76rQELLZ08fA7DyMDcqDZuCl1LJ84KRvNIhLZFMbM05bofMUly9vycIBayakG5riOL0ybc7JUwQ1/OKN9AJ5Rbxv1lWc4kSfrvgF9CIFqeBL7yBUWSJI9XXjgkwHqdD/Tlt/3dwF8H7gPeXpblE7f87i8DfxyxSn5/WZa/+lq3l1ROYltVMRSxG/0Pn/9Uk/YWpCnztNrNFQUtVeGtK4u89eQFDibXuTHx8WPxZuuaRpOet+Y6Vf6CjNs5B0AcjTgMdpgmE5SKF5+XOUEWMbj2n7h36R30l9+Cu/DqYVj1qBeSeux7gjR7fThnyb2AC5Vr/SS2CffYPe5RzqGpO0jO2/GjjMsTnyXHZkl7BtfcpJz18WZw8/Iv8ZmtD/P5yZhpovLHTr+JjYWHWDr5Xq6G9/H44ZiXrj3ThCu9NEl58cUQfyehHOZoXkEpQ27JlD0ZNImyKDEsmU5PZbGt8tCCycDQG3xGVpacX+iz0bMxeIng8FlU00VRJfrtDWj/SV56/E/w4Z/Z4M1Lj7G4+BY+8/SPcGzzI5x+6I9z5rEfZ/dX3s0Hb3yGn98UZSBXT7nH9ZhlGXGRMUvFhJCVMX/6vvtRFZOiyEiDMZbxIopmAcfxY7F99KKEaRgziaIqP0VM0pNA7PS9OOHKdNYAB21vLv7XVII0a0CKAqcuAqQc9TpJOMIbCkBkHAkV2sLyBTT7QTEBS/dAUaJpplC2yarAqVdqpjLPKi8OpMEYtQrjChLRrxNNdKFim0Qxszhp3O7zNBNfQ9NPyQuxQMZ5jiqnArVTiI3WwLZEmFg1uZ5e6GDxArPdbfGZkzWcxePCK1MlKR74CaPJVJhI4wRDVYAFjvXOAxcpihQy0Vg322vIsihp1YuPIgvXuh9lBEnONIzZmwdNBk1dPo7zolK+Fbi6hmPo4mfqfeQ5VZ9HIStlkqxkOI/xkoSDIGIUJwyjhCgvWbFUurrCadfi4eUFHjvzZv7Fpz9GS6s/owXqXUYQlm+cHko9NEmSNOD3Aj9SlmVaoVzuaLwWeuXuJNG88ngG+H3Aj976Q0mS7ge+B+HKXwM+LEnSubIsv+ThsihpQp56VaZFvZi0da2KKhW7GRBgvKf3hjy99zHalWKmjrkdVh9EED6BvmGKHW04psxTwvAARVbomwMUSSHMAibJlFHisxVOuOrdZGX7N3jfm7+fToVif/nC8Wrj3vVzHEyuszMN2PdCupbLaHajaYBr9teRxtu8sOviGAldW+PcUgctfYbYH4v8bqtHnoYsrDzC+WCHJesAXdE5e/ybcBfOczN9gB995iKXxymWJrPmKOwFOWNPoClkRSI3ZVJdAkNCsWTsJY1WR6HTUXlw2eD+botzvQ73LfcbJVDd7J4EqXBxyxkHmx9HljUU9bewHvjrAJx57Mc589jR3zwZX+RnX/wZzu1+gj/w+6/wNd/8cd588yfZ+Oj/zP/5wgIvHhy9DQ05Z5rqxLnCt61HLPQvMJlexjB7ZIlHNNuhzFOsXoihiA2AIM5KdE2zKZWAzDAIxS45ywRQsrzVuyHeT+0qA2VgWyy5Froq4Rh7BON94vkBSThiMr6IqpgMVt+GbvUrwKTU3HdRrKHZaxjONmm2KtR8svidJEmU6XNodo9hcoYkTvCrGFxDVdie+RyGYbODVySJJBemRFcXZdhjXUcQkTPRtL82nrLlz5vnLK7y2Gvg5DRMGPoRS+170eyQxD9oYnolRWUSrrE1nhHcki1f32eYZnhxjq2dQ7euohouZZGhqBaSckqIHSr1nEj3TKq+UNEsTHlZcBDGjTEyynMGho4iybfIjCFMxOtiqApdaxtNVhn6/YZAPI5jojxnkubsBzk784zVlqBG//AXr/Kzl7c417Fp61bzPNTUhLs13oAnlB9FIO6fBj4mSdIJRGP+jsZXUvL6ikZZls+DaKa/bHwn8O/KsoyBq5IkvYRACHzqS92eKkus2BaGqnDTF827tqbRM81mJwTizXszEKcPVZJYtETZJMlzlh0XXVU42XeZhAlhIuJiR/OIgdNmdOVxdnYf5zDYJsljLNVmYAsEyXFZ5YEiI80j5smUvMxIE4+d4BSfvrTDH3wdoP/F7gkWu8Kv4lW77Ov7VwjTjHvXz6HIN1gtnsVxziMpqlAPKSqtwWnC4gQHVQ9IX34bZxd/XyMj3YwT9vYCfnPnEp+5GtJqKXQtGpdzz1VIlzXiTpWrIkuYtkzbVXlgUef+bouVls3xtnA4D1q7zIefZLR7HW9ymST10TWHxfV3oBYuYeVluTH6rGiqX/4Zvu07n/1tf+/97/4pVo//GL/+xA/y6A8cp2/EfNt6xDec/A7edzzj0zd/g1/cTtkPLfpGxB852eb+pUdptVZ5YetDAGhVP6Ko8lPs8TrdlTGGs8RaR2XgiByMOu1vEqQNHn7JsTnT7+LFCWHV9K79FLqqNCoiXZXQixcYb11hvPcUUTwmL1KWlh9pnP+FdA95XmAZuxTFGnlFPBbln2WgYOhHhGnW+DLi7HSF4YkI0qxhmNWLX+0pqeNxb5VGL7R0kuknyGKPtuFiuGKlXnJsJmFMkInJPMlyck34OM4tjUmDMUWckgZjNKuH4SwhKSrD+Qr73rwpJ9UniKwosTWYhBHDCre/5Cyx5FooMuyMQybhEBAL5SQUyPyuKUqMwyC85ZSjkAUR0yrTfvmWz6BYiAq8SMiow1S8Zl60SJhmJFkkTmGZeA6WLIW+YXC+Lbh3wzhhnuWc7xis2SY9w2CWiAjoRVuUGg31Lp1SyjfeCaUsy3+C8BrW47okSe+90+v/F1tQvsRYBx6/5fut6me/bUiS9CeBPwmwsrbKom1yEAgTYktVONF2mt1mkNJEp4oST87xlk1b13lwbZEDLxRcrThpDGdxnpOnBQ9uDEgOfpWiyLCMHno0JMyC5nH0e+ex3LVGWhrN95nPNtnefRxneplvOv5eivRU49240+FHGcN5zD3OFZR4my6QRwco5rvoHHuU2c2fxFr4BlBA4hJ+doJJkHB9PG3EAs8NJ2wHR8qmrSDl8kHKoKtyoqNxwtFZMk1GcUyUF8wHOqYiYygyA0OnZxgs2ib3LfdZdA+J/QOCyVMkuyM2fZGIJ8taox7Ly4wsj9A1lyge40UH+MmMWTrn57Zm/LP/Y4lFI+DetvgkXpuXRLnCMTtlw3Z555LPZw4W+dGLLT51+PNc6MR8y/Fv4L1nTvHi7m/x3PhFdqMh8e7HSfKUUeJzylkjjMfcGD+Ln3h0jC5ruUh6NO0lFM1Es67Qba9SFhmqcoGFlt4kJtanq3ryrx3aQnqcIcuqkBunEXmRols9uosXGm+G2V5DqQKcvLhAVyXyNELRtoE1FERD34tEAzpMjnb9+/68MQsGaYaXJBWmpITKZZ4XhTghVRgYXZVEOXF4hf2ty8TRGNNewl043yweAGttp3Lei0wYXRE+FFlWKSrkvqQITFOehSTFvexMPQ6rE8BuEDKMEzq6xlK1s//E9h7T5IiQXEcm1KcNQ5Ebj4siy82iVDO92rpG1zQxFIVxFDGKxAJQ4/WHQXTkXanKeHUGUB1JAGJRaFXS6vpUszsPMBSRGLrSspuNZF5I2JrWuPzr5/tujDfgCQVJkr6Nl3EbOcK4fMnxn3VB+VIEzbIsX40n80oSjFd81So8848BnH/g/jLOBRiwpaoNRDHOBf67Rjn0TIMPPPqe5jY+9NyTfPzaTVZaQmk1jmKCVJxM1l2HU4MOcvxFNKuHMt9HllS61jL91jqaatHunGbx1NdxGJ0kRbisWwNY1BS6larq2ks/z1o4Rl/7Q3z86jZxltM29CbfYdEy+c4HH/ttf199UoFzTZb8fP9nOHjm+wnCA86+/e/zxZ0xYZqx6m5g6Rm2LjLGL089/DTDqT50h7EoCSRZyUZf5b2rncqAp7DmOk3eStc2Gm5VMN4k8raJ5gdMX7jJbjwmTn3SPCIvc/KqqeynM/qmaMwqkoqqmIxmVxhH++iyga7oLBg93ty9wbV5wig2+OShjCoVTFOxO12zRrx54S38ie/9MABF+mmi6TZZ7HF483Fe2Baknzf1hc8oycUp89H1+znwrvHp3U9y2Z+TlRKnnX1aunjC0sRD012U+QHz0eVqAXy6+R2Aprsk0YgwEMw8tcLM1xsEVXfRKg9LEo0pihRZ1pq4WbmCQsqyaNQbyhZlThX/ewVJUXGNYwRJRhjWmBOlOQ3VJ5FxFJOXRbOw2arKm1YH1Q49bZhieXoRb+9ZQm8bu3OCeOkDvDCeERxkxNl2Uw7z4wSncrw7hoatq1i6TDjbZj66wnx6HU1vi82QZhFmeXMyueb53AwSVEliyRS9se3qZwNDZZ7lDGNfuPfTKoOkyqpfMNTbHPd6BaOsI5XjPOdYx8XVNQwlYBQdoaJmccI4ihoGWg2yVCVJ2OIRi0SQFhiKUqVrClzOcstuKNBbE6/JxanLdkfomLsTsFWW5V2nF/+XHpIk/TNEpvx7gX+O8KB85k6v/591QflSBM0vMbYQpMt6bCBQL19ySIjm5JrbajApwyBskvzqssVhEPI//uIH2Q9yfv89g6bhWmeF17udtq6LmrSpImMKHEniYZg92t3TtPqnmybktek6+/60eSw1wuNNq9/Ose5xpjtPMz58huLgKR5beYTnsvdweeLx/MTnMM44DCb8q0v/iXcs26iyTFYU/KH7z7yi9Ply+ABx/z4A/s/PPk+U57xjZRE/Ths+1DiKue5HbHoZbaMOjirpmTIrlsqbem0eWV/G0gThVfgFhMzX27rIzWCf8ewK295VwizES+dkpehJzdKIrCzYsHp0jQ5hFjJL5+iKgSIprLTPUBQZh4FAs5hGC0VSaRsD1twTpHmMn8wYxkNuBiMg4pGFCyw672J1490NzVjWHsUWMALKzvv42rOfJ5xsoll9IvOdQrU0/jWe/ML/xROHz/LCTAY01qwUQ9ZEkz4LMY1eI9fVdJc4GjHxb6DIKrKkIssqQTJhnojXL8kTFFkhyWN0xaBt9HHNRXTNIawYa0WZ0XGOY5i9qj8msliKYg1bE812EP8XRQpFiqLdYOBsNLRkRZbY6LgkuThRTKIIVZYYh2lDgV6s0P3DaYClqTjqdcLxtmCn5RlL5/8M10ZzLh+Om/ddXhYYsjh1WJrWNOHr19pQtvCDcWOyzLOQskjF45RoEg/7htFg8gHmadYYC2tUS1aAKcuousimj7IKIinlRHmBqeTVZ0nEBuSlkAvXaZq2puHqupD95jlRLmIaxP0WTdZPjfavP8eGomAqMlnVK67RMLoi45rib19tOyJHxhOx14okN7dRl7/vxnijlbyAd5Zl+WZJkr5QluXfkCTp7wM/e6dX/p1Y8vp54N9KkvQPEE35s9zBCqkqMgNbNF1rtlNeFCzYR2I0RZJYcx3+1rvXmyZ3ke40ssh9P2giRecVi2hrHHBuyWr0+F13Dat3nO3pksC8+xmTMCTOcsZRTKviF7m6xs40oGNdoHv8IVr9j3Ow+XGeePb/YdX9CH/4rX+Kg/Q+/DhtsBu6qlR+F4G88IOtqsGd48cpWVnwlnWf6fbT5FnImWMW2uK38vTNIZMo4ouHY677EestowlMujbORH59S+adS23O9To8sLqA6n2EzPNIwjFDf5u9LCTLI3ZnL3EQ7nPZ3+PiTGeUmMxTlVFsE6YqilxwuuOxas3oaKPmuTXkHc52z9PpnOK5zV8mzAIc3cXWuxiaK8jJbaEiSqIRc3+btwG67qJVngdz9ffzTz/7HIMbH+Wbz5xoFtSOs8GLOyGPH3aZJSlvX/V424mYm1cuE2YBZ9113tyzcfQ2iqRUJZ2MIJnQspbQdBdNVtHNPsfe8r8DML7+Y3zyqX+MrhiEWcBeeEiQxRiKRlbmqJKCrRiMoiHK7Ap5mTfEAV3WuWAuYtBrkB61cutWvEvtiq+/lvMvsmS7LLTuaXpjXUtlOBc79CDNONfroMgyu37ALEm4PppxbqmHlT/F3ktPEgX7DFYfwV76Zrw4bzhdQZo1mTPiRCIIxjVRWAgTBDHA7Kyh2T2c/mmKIsNwFgV4s1CayRnExFsjZUCUs1RJYsky6ZkGC1YV5Jbn7M6DhjnW0o7AkkbV+6m5WvXJI6i4eDV8cpTlTJOIwzgjK0TKoinLVSKpiPpt6MZZRt+49e9Ujhz+loZcXmXRNTnwFkT8gWE0J795ljULy1c83oA9FI5cn0EFnxwCd2zq+y+2oEiS9F0ILtgiIrf4qbIsv6ksy2clSfpp4DkgA/7Maym8QMTqfnHvUMgkK3qqrYoPmiIJHf8r6c9lbRVb3+L60BMNwEqS2dJUVtsOjqGRF8dQjTEt7RSGs0SQihOJHycNTwqgZ5pNvkPtHehaKnl6kURWcbunOVOkfPrGL7P10T/PhfWv59jxd6P3Fsnks+zP4obv1FFfJDm4SLT3FBf3Pkle5oRZyPXP6Siywjd8zd9lKj+EXMCpQYfn90a0dQ1Tifn4jk9SBRmtuAoLpsKypXGi7XJq0MXKn+LG9Y8wC3aYxUOem1zi4izmILaYJjqj2GBvfoLDkY48l5DrzAkJMltia2jRdTI6ZsKiFdHSMlbMGRvZnIs3fo1r3ia2aqLnOnHqY2hCpZVEY6EGkjU6/fPoVh9FNTE7a6jGBbanEV+zttxkwSf+hzm89lsk0QjW/2fuHfToWgYn+y28g99EUS3efOLbaXWOY7SWkGSVLPbwhhcJg32yvHK4yypZFnJ48+NMRy9g2UvE0ZiN9lmGwU3yIsdWTdpaC13W8VKfuEiJi5Rx4mOrBrZqokgK685xoeyLx6iqhWkLFlUajJEUjyzyUE2BEMkir4EepoE43eRpiKJ5WEWKarjE+Vm6loZjtulYBsNA9PLec89a5RG5Tuo9RVJkLJ95fyPCAIH611WTpFJV6YqCrR/1B8QGRWnKbmKhXUM11lCNTWRZJUxXSKpJPkkynGqSXnJsBrbF3lzIqm1N5aQisDJLTqtqxgum2iRMmn4JHEULG6qCpalCsqyLz1+QCBJAzS9TZFFO89OM3TBjZ54RJiWWLtEzZRxNxlQKBrYpwsBkSRCRVeGev3UDFo1/k+lQlC1b/VNYxjID26rSP4WnjAji/HWFEH7J8QbsoXxQkqQuwnfyOUS74Z/f6ZX/S6q8fg74uVf53d8G/vbruj3EG3m5ZTQqmXoBqd/YtTLmVhwEiB2wPr2Ekopj+a14bkMVhjGj0uZPwgIvOkJx50XMQRgzSVKcIOIwCKvcCVHTjTOLZfc8RSFKQKpqsdE6xnXvGh+8+BPcv/cpFp0TtKwlestvETgOawndWqSz+hYU1WIeHrA1e5GDeMI4iVAkiau//Ef5Pee/j+WH/hpJJRf9huU+39PSkcurFNI9ZGWJZa4z9bcY+lH198gioyQeN2WpOM/IS4ntoMXnrvUwd0v0YUQ7mB5lnNYTlaqQDkxGaxpyv8TWMgwlx1BUpvGEw3gsdvqyhiKpGJpDUWaN+urFTWEp6lrLdNzjAs8CTLd/geDmp1DnNzi5/Cgr7W/l2b11Mue7CY2MVUnivpUutnaT0eYvMZ9eR1Ut2oPzyJolSlwVkVksWPeiaCZp7CHLGkQjijLjF1/8dwC8felhzp/7boztx5nuPc7AGJCXGXmZs+EI/4ufeliqjaW20BSDtcWHUVSLNJmRJh6KagokSRoS52nTmM/TkDLPqoz5DM3uIWsmqqwhKZUXpRAfPUORm9PKUttgrWOiqTuk8SFpLJhXopyWEs1E5Vd3FsmCJyvPx5lGzpxkZTNhK7KEY6rULDOANFslKwv8KCPOllBkiWk4b07Ht5ZAzeQJ1lZ6HEYnOfBCLF3FqQCPeXpRJFOi0nF7wEJlmFSaxacmG9cnpaPPk1yhY+TmxAJgKjJOdbmiFLLheii3KEHrvknXNFl0LZbdPWJ/n2D/gGi+30iYVcMlKWCjZxMmwqB6GEbsR0JqfFdGCbzBTihlWf5A9eV/kCTpg4BZluX0S13n1vE7seT1ZQ0JMcEPWkINM4nixqhoVaUAS1dZaOlVUNLab7sNked99EFXJInhPMJKVFxznaEfsu8HTKKYnXnIKI65Po954bA6odgyy3aIo8osWwa2ptLJjIohJQxtmu6y3DkDwG6wzZa/xTSeiInr8PO09A5d5xjdxQco84z5TCQvL9ir6IrBNJ6QlzlZmQsV1e4v0+kd5y0rKih9vDina6mQXoLYo1C26DiPoshbzK78cw7GF1l890/x4Hu/gQeiT3Dxsz/CR/deYDt0iXOZTidlJmtkpoUx08Wnu1pg5aqhnOvig5/mEqt2wL1tj3XrOLoiVEm2amAoOo7epu1WZjndpSgyHnrwT2M4S1x/9t8znl3Bm29j7DzBeH6Dzx98jsv+nEdnV3i0yFg8/qeZhonYLSvPMN+5wuH4MoeTSyiyKrJgoGmaq7pbEYmdhsWl6h6SIiZkVTH5plO/l+WNd+OsfDvkl4jmB7xJMRl6V5knU5I84SAUu9xFa0kwx+wldKsv1F6Jh271KPOMJBoJbtpcLCZyFWQmFpK0Oo2plcej1yjJ4rwgB3RZQkHIkWv2F2ySxiIBMg3GTdaMarjoVc+mSCPBAtPOo6Q5eQFelDWZKrdSjeN8gyQrSaKcrBDZ9LUct+415kUhJn5FRlcFf0uxHqto2yUXlkck4ahy82eUeSqQMYZ7BHksiiYPpWMZTWCY+F1ZPbaj8ln9c6VSftW9mqwsuTnPRO5JWla9lZJpktPRFeFXkUWk8TRUyIslDHUFa6CwvCYyb2rRhM1LaIbL0O8J+XOlJjNeoVLx5YySN84JRZKkv1CW5Q9VX393WZY/U1k3YkmS/k5Zln/lTm7nDbOgIEkNy+vIvCYWmSXH5ljPruShGSi/3WRYx8sqhYSra1XIlMy14aTKLRdxtLvzgKvenM8fRuyPM/xphiQLDIksQUuT6GoKbU2jaxoCoZI+RxKOm3xx1z2OqpjkZcZBuI+X+hzGYwxZY7W1imMt4o8vk1WpjQuDC2RZyGIWoagmYSjCMtdOvh9J0ZhuP03g32yko950TDQXk2JX1jCcJzGUVbSND3D+zBYf+uBbueZtsmwtsGit8C3rb+Wh3jbX5wfcCArmmUqQa+yHFgehSZCpeKFKjopt5LxvdZMzTsSbuuuc7DyCppgYuss02EGRVPIyQ1cMXGPQqKFqKGJY4UnOvfNfAHD9c3+ONPGI0jlxLiacm8EBz27+CheAtmoRBfv81v7jjKIh48SrQqA0TnXOECceltHDspdoD85jdtaayduQtwnTFQxlC9VwaJ/8PvZnMXNZwixLooNLyLKKYfZ59LEf58ZTf5HJ9DKrRYbbWmNh/TGshW9AkSWy+FnC8abox1iidyIpaoO4ryGKQBPk5Q5OoWjnGwilMH0KTleSF6Ic5IgURUPbIvb3SYPxbSj3Wl0Gog/TnHoUlSx+FkNWSTjNNBS3K9IpBVbGi5LmcwBUn4uy6dnV6JNaRlvDR188GDewyxODFv7BxebklWfipKsaLmr3PVw+DNj3J8SZiKfe6Dl0LZUgzQkTEeVck7mTPG+ku5amNZ87V9ebpMxjjlBjBWlWNfblRihQS+HrnJdJGLE988nLsirFtelaCwxaugjuUi38eJkwDQjTlLwsmFbEjLsySnjtYvx/NeN7gB+qvv7LwM/c8rtvBn53LSiaLGNpGnvzo8Z6bVBLspw9L2bgrDQeg5ePEwOHMMnZ90R9ehLFdE2D5eq0cxiEjCLh8s2KkllYEIUFRQHtjsJST2W1pfLWgcOJtstyy2aj52BrCuDiLJ7DaC0Sz0W+R2/tEZaOvZvd6x9hHh0gSyqLi28RBF3EB9ZwFlGNCwznCR1dwdYUwvFvoTuLxJxBL17g4MpHOBw+K+rh4QGhv41pLzUY8OGNx9GtHrrZw7F7FIXLO9/+l7mw/yyT8UXyImUa7rHuHOdc/02N8qkoMrx4yDQe4ac+ce5hKDqL1hJn139v07eQNZMs9gkm19E1l8XOOebhUf/icHS7kTFKffyXZjx03zat9T9A99yfY/bSD9M2Bzy8+BbW5zcJspCdYJf9iz+JrVqMkxlfmPiMEoOWmnGhLWObJnn1GAGKMsNy17CgceznxTKKXDJJ1tgPehDMOb3gUERfINjbJK4WXd3s8dzHv5cgHmMbPdaPvxd9+Tu5tD8lvzmqAIpnWF3rNTtg1RS78zSseiNZ2DC67O5x9M67OJwnqBWUMUjEBJ5keVNaEr0i0IwdwplYTEQ2SNpAGGs1VhKK04qiWmi6i1FztJzFapKVSRCLxiSImwCvuiF/q7fDUBRR5lKEYkttQqiELLdrmg2iRi1eJNMs4vk+mtWns3SevDjGJEy5vDVsgrROD7pVU1wVJ7AKX1/3LuIKYlmXxtqGgEbqigBV3tqwV2SZjiVOu0lWNGU48Txm7MzmhGnKOEnZnQs/mCAmF3ixziTUUaST1XPgV7JhESTmqCqDyt9zN8Yb5YTC7XaNl1s37pim+YZZUAxV5i3rfaDfvDm9OGc4F5p21xQ+lFczFzr2Bra2Q5DkXB9PGdgWeVmy7c2ZxUmD6IgqBMWCozTU0o2OwmnX4GzH4d5Bn0XXakxzcV4QJmtYuozZ2cLsPFb9rMArUnr3fy3LlXu7prJOw2VePJzy0vaEg/D5KqxJY8GyWHbexIbtoKef58pzP83e9CXCLMBSbVp6h7QOxXLWRNmHiPn0OpODZxqTXn1qaLVWkWWNlrXUcKbyLCJJPabBDmE2R5FU+uYAS23RsZZZWX1HlWOSomjnifMCt70rcCfuGnkWoY0vkyYe83CfvMgaZP08mTKNJ0ySGaPP/wPOXf8QC/0LOL3Twlg3FknfcZFyEPtsBRKGMqallJxxdFasLpZqYak2bUOcBuqJN81C8jQkizx0axvhSqcqB6WNxwZAs3u0KiROGo7I04iltcco8wyjtUjmvpdntkdszjyuevPGT/Hg0gKLrgAX6saO2KWbLkk4Fko1Q4R0me01krJkoaUTpEVjaAQxofuxkMnW5dVwtl019TXIosq/Ui0o1YmgLNKG+Cu+zypmGZX7/yhAK0wzwiRr+iJwRBk2VBGFa2kqfpw2ZV7r1hOAqtC1q89LqaGoJlmVlnljvMjO7JDDMMJQFAa2SHRcahvoXCOLQwzDRZWOMfQFWLNeTAxFwbX0pkdZY/jrz1GNhqn7LLUfKk8jCukeDucC4xKmYiFpepfN3yhIA8Mgak4yaqUoOwiF5wtoEPd3ZXwVeiiSJPWBfw+cRGBR/mBZluOXXeZ8dZl6nAL+f2VZ/iNJkv468CeAg+p3f6Usy5enIJev8vUrff+q4w2zoEiSBPkl8Y1sEaTrxNUuTNEkXONm9YF8dbf6jXHIMAgbpcvz+0N+4+YBA1Psatq6BglEecGyrdDSJExF5s29FvcPupzodTjWsyjzK8KJXJ5twHheBIq8hB97t7mX80LDNWESCG3+JDIJ0yHPD8fshTFmtZjYTQnCQS9eYDYSBFtTawGgK0bjUi+KjCzxyLKQOBoTpx5ZHpEXGbbRwzR6WI4ws9UTYX2iySrzXsvbZhAJWbCqWuhmpchqrxEWJ8QTVtXvDWWN1iAiiz1ifx/TFgtUkopSTZaLSVG3TCy1haXaJEXMONwjOYhYzkKC8IBZPOIwnojSn9khLyeAmAzPtI+zYIvXrmOvoiomSeqTZqHArhRp5auIhEtdlgBRaho4Oo6xJ0pHOZSlhiSrGFUgVB19XC+Slw/95j2hShJZUTCKE549GHIm76LKLpa+jGPsIckqrf6phhwsayaScookzplUE5coeZVVEJbSTOZJJk4srrNEmWek4ZiySJvFpBYyiJOP2Swu9c+lylhJJgy1uipVmH2ViRQ3bDKgcthXUcFJ1kzm9aIzcHRs7ab4G2RVTOJ5SlKV25zFcyjaeabDCYehmLC7LVvIlE2tSkUUYoQyzwgyUda7teTWMw3ivCBIjijLTVRw5QGrKc95lVOfFzKK7ODqUxRZEtHIgXDY74UxA1OnpSqNd6fGtxiK0ixisyRhP4yIcoG97+jaq84Br2uUCHztf/7xl4BfL8vyByVJ+kvV93/xtodSlheBtwBIkqQAN7ld9PQPy7L8P77EfTwoSdKMCvxbfU31vfnqV7t9vGEWlLwoyeSzqMWL5GmIrQnctaEqFYF1A0t7RYJLM04snaJ7iyLKNXSWLRE9Ok1SLvRbnOq2OQxCTlVuelvTONPvNqWU9IjIgmHL+Aid/iSMCSt5cQ28U2SJfT/guQNBSU7ynHmasVUd4weG3oQEuZXjOS/ERKKbPfqDC3Qq015Ume5ATDhzf5t5dNA8lrzIMDQHXXMw7SWc/mnM9hqFdOSJcA2F1CzoLMi40Rea69bKpLq272VHPSpLV5DlbdJUmOMkRWsm6boUJMkqaiUXTpMZi4lHkvrEqcc8mfLC9m82KBtbEacIW7V4QG+TFAmKpNAx+piqQ8c93jT4VdUiq/wzICjQetgjVk1UM0SVVYo8Yn5wmYOqr1G72+tmcq24Uo0LIImGuaWpVQSuyT3dOkr36NTvRSldSyNPRXRtLRM+wtFvoqsbOOpN8ixEdV0m4VpjeHVNDdfUmqZ1nG+gaGMmu08TBfvNQlyXHw2zX50gxURYGxKlWEXRLGyzKuPml8hCD9dwsbpnmQQpYZKJXPlMaspuoqdREqZpI+k1FFlETseeOOlVAgNZFgFYRRpRUVoa17qQOBckuYmurot8GRXComxYXOK5E4j9etiq2vQ5RZkqYV6dIjbnR+FXqiQ1CKB6EZhnGdMkJapyVtqaRktTmgVEvNdLMqls0Cx5UdI3jAYMe1fHV0fl9Z3A11Vf/wTwUV62oLxsfD1wuQ7KupNRluVd4dG8YRYUWZawtF3STBBms7IUHpBbgpSKdOc1eVodZ4NJcAVbV7hvuc+JXhs/Trk2njKvGpj3dDsN4qGOXi3zK2RFSjgRMk1VdzGcbeKsT5gI4B8I9ViQZVyf+YziuOEhjaOYrCgZxXFTYqnjXjc6Ll1bb7AboiGsVVRhC0W1sJy120KconiMoYkSjCxVkbT2Eq3OcazucbDeyn6Q4sdzFEmiaxtkVdM2zgsM881cG82xNJVl07hN4kl2JMl2jD1i/+C251B3FtFyocAq86xiaYkTTlnlgkSz7QZ3sjt5AUsV6Ju+uYAiqyiSUnlv5uiywcbCQ01yoKKZJKFAoNSLiSyrzOc75FlEK/awXPF8pImHP75MFI9RFRPD7KOo1m1KrTDYx+0+S3v5AqrhsqhF5ITkRGDTqJpAhEoZziMApIHIV5c1U+zMG5+JhSRvM489QVdoLdHtHceLhbmwfh8o8g0h1MggS+ssEdE/CeIxiqzi2qvi5KRaTY8GBG4+zyKCySaqISjYAIpmkeYZmv0iXfusKPlU6Ph65+9VJVyA2Js3p/Il5zyDto5evNgISJrSWxqSTx/nvpVHcQyNSRQzCWO8JG1STevh6qJHEdwygdfYFfF3CzJyXJaMo5hNP2AvStkLcvxY9ExWHIUFQyHLcuI8YpqmOKoqyBayzEBV6ehaIyhQZJl29XVdYhNlRbHhA26LKbgroyzhztErC5IkPXHL9z9WoaPuZCyXZbkj7rLckSRp6TUu/z3AT73sZ/+TJEl/FHgC+PMvL5ndrfHGWVAQOnsQi0mYFGzNg8b0BNwxnHHg6FwfirLHvYtDClcYRW9MvUoZk9JvmXRtDUu+TuyPyGKfIg0b2apquBx4C+zMPCY12K4s2fLmvDjzaakKa7YllCdJ9eEuCvqGIdQuHZeBbdG1dWz5JdJgjD/1mqatolpoVh/NomFJZbFPGo5QYw+nJ7K/67q7qru0Vy7gx8v4SU4wi0nyvOIgyY2PoV58b4wDhvOw8RPYusokSEQujKk2PoG0UjuphlstHlYDjJQUlcQ/QK5q/XUjG0QZx4gXUXXxfVhxwmo5sCypZHmEqpg47RNY7lrT5/GGF0mrHbRZSYeLImM0u8JovoU732a5eKRKRBQTXXN7WYimi4W2ACx3jfbSA0LUMNkkCcei/zPfxg8PGEf7zftiwV6j1z5Fe7CJs3i+ud9ktkM832/uK6sW9ST1kWWVweJbkLUnsKu/vUgjihSSWKQc5qkAWcqV+TVLPGRvswnlqnteIHoqimqShKOmh1Lfd53LXgeL2b1dDGUNXa3kwZU73a6yXpLKq1XjTOrGt1ydhIpUlE9Vw2kCtVTzOU4OLjAJDS4fTvEq1P48zQiyrAKzqo0Xpc5qUSWZSRSxOw8E1ijPGUUJozhmGGf4aYGhSJxeNG5zxtebq55pNJlGbV1riMFHi5bS9EzESV4wvOpTUJILkoUiydw1NuTr86EclmX5yKv98ktxD1/PQ5IkSQe+A6HUqsf/BfwA4hH/APD3gf/+9dzunY43zIJSIBYSS9uFbBVFlrA0Fb0yJuZFyZ1WTvMC+i0TVZbYmq3ixzO2vTlBmjYk1do3IHLINVqDU8S5QKR7UYo/S9n29okrqeQsSdmPIvbClIGhsmwJHETNRVIliZNui+WWzZJjc2pxSji7QjI+4GC6SRKNyKoJS2BMTtCyelUm+3FxerBK1C64ktRIpPPiGHFekGQlVw5iIBIlEOmIuurHKUYlDBA1ePEpyUshMR3NJQxV9GoOvJBpqNCxBK1X0c6TpxfF/RQFyNUuVN1Bp988p2WekUWinJIGY4Lp5m1xspbRo2UtURQpRnUSqRcmAN0SHoxgstksJpruNjRhRbVIUo88EMovY3yRduf0b3ttRa8kq044GYNjj2E4i4TpCo79PLO9Z7l648Pc9Dc5jCfshHPyEmxV4rQz5HRV9lNUC91ZFC782COORsznOwTxmCibk+QRSZ6gKzqqIk4W9WKaxV7DFgNIolHzvdFaqggCR4th7WfJklo+fNTvqi8XR2LDWS8squ5WNOQruMYpJprahMa5ho6lqU1fpU6ehM2KrJyS+AdHCYxpeNRjizzgWfqtHsN5WyjXKsHKPM2Ii4J5ltMvS7GgWGb1OZS5sDyiKFK87DjP7BwSpBmqLLNsaazbOqYiSm/zLGcYJQxMnXYFda0x/nEe8cJElPdVWRLhbqpKHQvsanrTQ7FS0VOpe0c907jDGeB1jLt02vlS3ENJkvYkSVqtTierwP6rXRb4FuBzZVnu3XLbzdeSJP048MG78ZhfabxhFhQZMZEdeAt4UdBMml5Fbl/r3HFfiY6zQTK5jhelXDocM0sSrnpzOprW1GVX244oA1XGNS9eZxLE7Mx89uaCoBpXirB5lnNxGpKVJRu21oQJ1ThvW9NYsEzOLfXo6JvM9h7nypMib2Ma7DCNx+iKjq4I/IdjLmA5a6imS5xvMPQjro9nxFl+i8bfQJFNwlSUoxRJqF1qLE0dHFWHMtVyVksXDeMwyRpKq5ekTKKYJcemYx19KGsJtpedJo7q/BhZnKpYRebqbU7vrELAg9jZp4mYGGRZazw3rfZxdKuP0VoUp7E8bRIBiyKlLNJmItbNPoN7/rCQvhYvslxkmAfPcDi5xDTYQZZUrFsk1EWRMZlebhYyLx7y6Uv/pgpLW0SRFA7CXe5feTc95xhj/wbK6BlmaUhfd1ix17CNHnkW4Y8vo4ciZbEoREksL1LG0T5hFpAXOZNkxoazzsS/IU5FgXge5nNBKLCtRVrV6cuWhZpKs3vIstacMooio0jDo5OWrKFo5m2oGRAn0LrHJS5ze6DqwNFxTY0gyXBNDUe9TjDZJJiIMnvhrmEtfANxXqLLEZrVQ87EbZR5SpZ45Gkk8uZj4UkZtJaqqIcCOxXPcQtYtITyq2saOIZWUZJvkgYemt3DVRQeWF3gRK/DMAjx46Tha9VN+fPddrMZA5qvV1o2p7rtimGm8eYTApR6Zfcl8T6vSrFJnjeLZv3zIMmYhjHb3lH42Fc6pK9OD+XngT8G/GD1/6uR2gG+l5eVu+rFqPr2uxDhhv9ZxhtmQcnLku1pRJ0Hb1QYh7oB/nqySNJ4Gy9Kmzx5Q1G4x23x+P6ErCxZt8XikOQ9VjtnIBXRvTcmHl8cjvFT0QidJEcSEFUGR1aaSbilKQ37q98yWXYNJls/y9W9pziYvsju/GbDlDJkDVu1cDQXR29jGT3MakKpuUj1sT4vZXbn04rgWjCp/hegPgH3a6kqfVPUuRVJxkuSpjyxJNtYmkqYZMyqHa2hCGGDFyestlssu3u3PV+ucazx71gNr0kBjmPpsjg1puKUkoRj4vk+/ux641UBml28bvVFJrtmiZ5E0xswkQoV011Dqy6j2T2G80SUVLRzdNYsjNYizvQ0+9uPE8ZjsjzCNHrououcqYTxmCjzSfOYg2C3eY43q0m+rdqE8Zjjp76V3nyfjr2KFx1QFBmOuYBtCZBiGOzjVWWp2reT5RG6bIAKSR6zaq+QF+I9EIYHhLE4RbTMxUY5Z3ePo9m92xIPc8AwKzRJjTl5mTCiXlia939VNpNktZFzT5ParJtj8BJaGdHSgBziqHq9KnS92V4jrJhgknIKu2dWCaVZkx0PI0JvG0nWsLI1+htrwBJO5V6vS0y6IspPjqFj62rTN5JvebxdS6Wjb7LRphGG1OqvvMLOi/d12SBc6uTNGv9Sn14BjvVs5PIqs71nUVQTZ+n9lRdJRBE7xh6u6tG1z9zxPPCa46uHXvlB4KclSfrjCJbOdwNU8MZ/Xpblt1bf28D7gf/hZdf/IUmS3lI94muv8Pu7Nt4wC0pZHkW91goqW6vAc1H22jdwy9CMNfz4efw4wdU1lls2kyjisSV4ZjTj1675/No1n4dXDnnf+iLHOi7DIOT6zOfmPGY3zIgrOOPAVHBUwSlqaQodTeNk22HNdVh0LQb6S/gHF7n20rPcOPw8o+iQIA/JixxD0TEUXZxKNBdLtVlqn6G//BbM9hoo5whDMeFaVYDQYRjy1HAqcOLVY4gykchY51V0tazJgDfUI4VMvWAAeEnS/A5gwbboWAauqYpJpi6/Ga6g3lZwQl2VCZOsURfpkYKtL9C1NBTNaybfOPUIkglRhb7v2KtYldxYNVw0+2iHXuZp4xYH0RjP5LN4cUnXkoW6KfBQnEeI9DXM1bdxsnOc8fYTbO58gie2P0qQx8yzmBOtZRRJ/E2TZMYsDZjnKS1Fw1YNBmafT9z8Df79i78AwDsWz9A3F0nymF3vCn50KAjKuossqcSZcOrrhouqmMiSSpSJ/ts4PGDBXqXrHMOrTkwd9zhO7zS6KcqVfnGeWZKTRyVJNm84V3klA0Y+SygV5JmYueoNkq5Kgj1XlAz9pJKcx9UkvIuuKHQts0G6G7ookWWx17x2UnUqkjVTLE5VTEiQ5sAyirqCZe2SpxHRbJs8jUgTT0i8p5dRVBO359HtWRzrHWcSinJvLQA4On3kgIMqtXEMjWM9SIOnm8ejWfs4pku/1yNMVxrxR5LV2fTis3xrGXcvENL1De1JkuqkKGvvonfiXYBIO7W1KqArTPGiAa65TF6AcxeNjdJXwdhYluUQodx6+c+3gW+95fsAGLzC5b7vP+sDvGW8YRYUEBNmnfJWGxvjLCfJCqb+Fp0K+ncn48TAhSFs9ERpK97P6Zkm33rS5evWM/7d5Zt8ajPEz/b4A6cUEbVaFjiaTLeQifKymryVphbc1kXJbMmxm1OJv7/dKJYUWW14WLmcizKXbLBgr9K2VzHMHr3lt2D1jpPJZxnNk8bfUCt3Zi8zbamShKmKiSgrSkxNoaOr2KpKq2rOitwMccKZhnFDGKhNcaok07EMbF0RsmXtFKoi1GxBuk5e5E2AEwh3M8Ctvc8asJmnIXqFSwmSCWEWkBQxBGAaPexKKSXFauO3AEiDsZDnVph4SxMN5zy9SBqMibxtpjtPC+WbapFkIZKsMXDv4SDcJQj2CfKMuEiwFQtFVnC1FnalnFq0luhZy7TMRb7h6/4Qmv2g6G9NPkYajojmB6TJrNkV1wRjXXdptU9U5bgMPdjHiMf44QHLzjFa5mIjLuh2ToumudnDWTqPF68znIfNBKrIEhTC9CoigcvblFKqJGPpKklWNKysMM24MfHwkqTBlbR1jbahN4KKOCvAOofuVDLpWPRjyiJFUtzqFBJVr50ATIrXsqzQNTJmWyRTmpM1rMl1omCf0c4TWN42muEyn/4simrR7RynO/jG5sRaS4dtTWt8OJq6Q1ikSLIqguuq8lwSjlFlD6V6DxhphKyZ6HqfpDzJxFsgzgqujw+aZv/WuM/AWSGbfgbv8AfF81hFTajtVWLO0LU0oVxUZGKK24jMX8mQSo5I3P9tAG+wBaWun9aoiSARO+eBo9/mI7iT0XE2OCVv4dhiEXqbs8HO6BofvnyDWZJyoWuzYWt85HqAt5Gw3LIFCdUwBLOoipI1FAVX1+laRoW3kBp3sCxvC/R5IoyHmmI0yYem5tDvnqO/9jbM3tc2j38SZkySgkkYVBgL8TerUsU8UkXDf5nKLwD4VQaEIkl0dY1Fy2DFsXF1nY5lVAayo3Q7OKpB1485yXPyqKgc2WAoxyC/hGPsocgrWNpuU/5RZLETrMsVICYqQzmOZodY9YRh9ulVmPk0C0VTPQsbXlWNHal7BTUkEYS7vEivkKch8fyANPGYTS9zuHmtcvgr6IpJx1rm3OLbOJF4RJmPrXfRVAvLWsQw++I0pLtVKUfFcteqHe8VJsE6hv01dAdCFl4rq8pcyJ6LIsNsr1LmGfH8QPSIiow48Wjbq5W5U5gvW9YiiiomOrOzRl4ca8ph4vk5+lqRpUbiG6Zpw9vqWgI0iiIW7TDNmIQiYXGWCFaVcYuESZSKNLq2Bvmlxl0vKcK/UhQZutVrSlG2pjCpAKA1sDJMCnK1xNZOIcvbmO1VFNVEn/fxx5eZji4Spx4rq++gu/FwRSF+gbNLR6eNuuzkVYKVrBjQtVcwFVnI7SsQZhKOScIReSZKobrVq8qbfcKkaD7PD66LTXiY5Axauohq7jyG5j6KIkuU+RXmw8tksYftKAznCddG4oS75Nh3bUGh/Kr1UP6rGW+YBaUsa8Bj5QYuStY6ZqNwsvTXt6AAzWJSj9X+Sb6vf5JPvPg0VyYzTrotFEnimjfnnm6H1bbT6PtvnyCOeEpBmglnsv4Suxcfx69owqbRo905jdM7hbP0fp7fnTLMc3b8nNw7qGJURSPyVjdxjc1wDKHY6VoGx6sMGEWWCdOUg0BgJ9q6xqJt4hh6Y2hzTLVZeLKyZBKkjYtb1MVT4vSIGmuVJW6hoRk7zKcH+AeXRNkLIQVWNAtD81AMq1GYhYlY3PNCwzXETtmMPTTjSKVVy3VrY10tO1ZUizI78jjkaShAhVnUUH3TxCNNZqjVAqIlE0zVwW2tNSeCmgpgD06RyWcZ+glBlrPUNgmTHMUV6A9NFX6m0XwJVS6Is5zhvCAr1lHlDXQUbFPGMVwk5RQA4fi3mtRD017CtJeIgn28+TZR5qNIVc9DNVFUU+zM1R1cVvEihWEcNrBGEEDTOl9ekUVstavrzWsssPOJgDzmOYaq0K8WblvTsDUVV9fZ6Dl0LEEIKCoGWZlnjXy7lgIf/Tyk3+rdwkKjei8LS7hj0Czsimo2WTOKrHF980ME/k3cngjtsjvHsXuCq1b3O2z3FHGuoVYqxDT2SPwD/NFlomC/6TMleYSumCwvPYKqV6KH6gStozD0hXzdUJWqb9oDQiGDliR0dY3B4CSqJDZtirzEarvVnOhe7+byS42vRsnrv6bxhllQJElM3MktsaWHc6E8GjhmM2nejfGusw9yanSNp7cPeHixz848JEhTVjs2XVUnSDISRWl2/iIUSMiYl1yLrn6VaHYgjuWGKzwW7XfyW1d2iUc59myHvCwb5HZeirhUW1WFqqYsiTMh1VQliYEtkP0DU2NJrvLQZanxlPhRxiQ8ynCxdaWRUiZZCeqRK9k1hVmzPuHlRUHMEYtJkcTONc1W0WwhvVVUU0ASq9OCXJUyDCfD1k5Vj0em39onrDI95MqvUvtQalf9bHK5yk/JaLVW0fR2NXlpjTormG42jek0EX0ZTW9jmH066r0NvhyEy7+9fIFEvpehn7AzzgjSCft+wMC2OLVyBseGxP8wquEynK9gaaB6H4HEw1r+zgYN7+c5XUvH1mQUzaQoNkU0dOyhVqmTwXSTyfgi02AHXTHRFbMxmGaJwNowhkjRUNRN1jqPNv2PWoWnSlmT57PmthjYVlPiql//IE0Jq9PkmtvCqHLVVzsWjrFHnvpk8XWCcVgRAjTx7xbVXVGkRN5287wqc4tINdHsbWznEbHzr06KWewRBOIkFnrbRMF+o8yzLSGfriGWutXDcJaqE0pOXpxBAWxFlCrzNCIY7xPPD0jCEaEv7kfXXEyj18in7Y4QLGTyWYJEpKoO4xBVksmqBMY6rRJgo+s08RR5GhF7+wThGLIQs8jQzR4bg9MU0h0HEH7JIZUl8t1Kf3yDjDfUglKnHdbRp3FW4JpaozK5m2O1f5JlV7CJ/uMzVxuTmC2/hOuer4irZdNYrN30AHlWx8Km9I8/xqX9Hl947hqjKGFYSSiPO1YjKRaNeamB6tW7tTjLCcqCeaW3FzJNXch2qyZmGnsMWuexdLGzrRfW+uQWZzleVDYlr/o0lRclfmVaU2SJrmnQtQ36rX3mwyv443FTniqKrAJDhiTh+Ei5ZbpIUtk85q1xn2V3ldT7NLP9Z6rFyKLVETiVLPYoykwosVLR2HarhSHniMIrVwtRTfitlUqSrKLZvaZclsXiNhTVwlYUQl2IBjZ6Ng+uTMizKeQxKA9XTd1HccpfIBjXpSENx95ALz6M3ukxDZfRVbG7TsIxWeQRz4VfQ6seT+BvI0sqy737ieIxsqRWyY7C3BxXIWCKaooMF/M5Vrv3oftCANHE9UrSbbkmYZIzmkfNybRG39QqxNpoK8dfZDrcFo/pFpNtfeKrRREylau+Yrfd2q8C0PQnm4W8PgHeitUvivT/3967R0ly3XWenxvviMyszKysynp0d5XULXXrYSNhCWMbDGLggDGMGRiYNbs7w+ssAwsMyxkY4wM7yy7nzPEsOwvssrMMMJ5hGJbnAmvAM8aewQY8tpGMJVktqVtSS/2q6npmZkVmxjvu/nFvRlXL3ZJstR7Vzu85dSoqMioybj7uL+7v9/19v0RJDyNT9bBGYwXLUc/jt1ZIuaXSsZugbwhMQ6WrFprHsQOl+nDw/Zs0brr1LlG5SphLQu1tNPFvSfKiqhm1PbdKB6qVdUkrWMDnSZU6s/2qsXZioyCMsy9/AtCYrlCuxk0TUHLNaGoFTvWFawUTDvqNW50cRFFKNvcSjjRq2r2xJE9Cot6HAZS8uJ5ch6GS5JjIdADUZk+wM1pkZ9wnKUoeH4yIC9WrMu97zAW+Ko5XxW6VMst1Z/KVcUQ/zSik5LlweFWXvTIU6xA4C5CoSWi2tlEZQJnWnSS5vvPW2k6TArBjmdVzuZZJ1w84Wn+OwdojPLd3nu3+WUbp4CqV46XyPoQ20nJr82pC4A52euPqTnqpWVM5bk/dzYf9Z0izIfF4k6C+jOU2aDRW8P35qlGvLDNMfGSZkeeqpoKlJogij5g99has4L6qJrU+6KimU8sAT2mNleXTZMNP4kQ9PNsnC0NG0S6226A2ewK3CYatPHIst6GUfw2LRvc7KLNPKave4RZ1N66ouvuOjFlFhy7yCC/oaq2x/X6ZSe3EtDy1Sol6qq9Dy7m4tR5LM8s0vNWK4poMtxCmxTA/RRjvr0YC++r2XMc0ONYOKONHyfdCSg6IRurrlGVOUeZYZkO7Nqr6nmOv0Zg7RRyuEYVrhOEFkjQk03I2AOakQ3+yijGunjLKQq0mhWFju8rgzK3fz84oVQ2+Wll5csMzscfeCBPgFgLHIphTqtwSKnr75Duc6FSgaRhQqBVzmKasjyLVazZJA4+lqiP6ipQj3LsIWs8qKSJtvzyxZc4PMAZfFqREHKiDTXETBRRDCJXq0rWLXBcDJ8VFP3hhYcgvBM9sD3FMg7rr0I8Sntke8IblL8WznyQZbjHun6/0tUBx/m1fdY8HrRXO9br0o5BxlrNYC/hqy6okJWC/ycvMVUAcZzkbo7FSW00SLo9TNqKcflTiWIK2FzHnWhwfRcz7Hov1gKWGViO2TBxrXyF3GOcVu6juOpW7ZarvAh3TZEE3MrYCm2zYUz0e8ydZMt5ZFcPHg/PkeURDm1tNahxhfoL1nvKiSIuChuOwGUaEsYVj3cbCiQb13gmS0SZh7xkSrWzsN5aJwjXwwNWGVapLfF9ht9ZcIY16yjvGfxP9SIk1ZuMtAmeBzTAiyqjqSK4FWdRjpNNRjq0cHb2gq+Rrgk9VAcUOHsAwPo5pe0S9P8bxZyv7Xctt4NQVaWISFLJE+cZPHDknsG0PR7/XUjdk5mmmpUwa+4ZVWazlVyL8ZoRp+BSZktgvymOMw4SGZ7Pc9KqV4zgrK6tf2GfQGbbSSmNMlaZSUjz7z+fPWISjLoUl8Y24Ciaj4RqZln+pO/OV/hsoWZxRrlxgPauGaVgUZY5pWIoqbeyvfGSRUWRnqHu369XWfqNhmhcME/U+Trr2JzXACUzDUIrMunB+8PugvhOSrbGSzi9kiSnU+ZOiYC0c8mx/QMNxtD/Likr9zgh8c99B0mtefPEv90uCxCiSG3SumwM3TUARgqqJqiihbolKDyh96QJunxdWOzXSXPLM7oBHt3ex9IR8+/xtdFs+ZaY0nSayGWmk7rrdWpdS3EqUDtgZR5XJ0HJDFQ63x1HV0X5hEFJIld5IipK9NKOfZgzSgu24YJxJlAGdRLN1r0qNKc8LQaqb1gJ7mX6asjNKKqOnSQEelLDfZEXn2xaDKOHTlzcYZ0sEtkWuZcUds0nbW2W58wCmIdgWAj9VATEcZgyioU5RKM+NjdGYTqDu7tNCkObzONYCwbzF8sLTxHvrlFmEN7NMbfZEFbCUnL6SnTEMW6XWspiZ7t0E7RWy5CnqMiQdqAKzE3yG27vLDBMVPKO9NfICGot3Y/tt/MZyxRQb988ji4ykOEoUXSRb+03i8SYznTtwa/OaCZVhBw9QbJy+KsV3cNswbIoDkvOTO/WJFXFZZpXfiSyySiBTFip1ZBlakiUOwdMrDNOqmvgm78fEBTHJJ13lUqV3hVCrpKGqbxzsNZlc60znFLVZVTBXN1mQGrdQmw2r44xYpedMy9evc0Qa96jPrLLSUE6Yk5RXMtpkNLigrl//jyxyolAxF91aj7rXwJ+5U1H445ytNGecpdX3Z3LDNEFSFFjCIC0s8tKu6o+Tml71eS0K3DhhnGVsRTFr45i4kHQ8i67nsRunnO0NaDq2IjS4DkuNGq3ApeHeKCEvFBNoukK5CjdNQCm0ZPbEF2JCYzVZwfVfgSIK4HtHWL/yNKauSzwTRszYoa5j3IJhX8DUqQJbT2KgvrymcZGl5r4W3GTSyGXJxmhfA38vzdjLMnItLpkUJXFRMsx1z4trYM0I6pZR9bvMBx5zvodjmfTjuKKd1t02ZzfDShnWFOrLOZG9aDg2pi5y9uOEv9naYaC7lzuuzayrZFcmMuC9JGGc58xo1deJCOCEfWQaKpWRpBmWEAyTtOrq30uzakKZcWp0gjfScB2cSDXidZoORf7R/ddHT5BFFlGfPVE1Pj5/EhU9m6h/AbfWJckaVY9DnoR4zWUuJHczjjMarsMtK0ukwy0G5/4N/d4ZVk59GwtzP8mTH1d9YLXaMq3Fexlu/Url1BiPtlT/hmFXabhUr65My6/qR6blg1tX/iilRWnHFQ16UgcSpgVltt9oaNqITFF6k+EmsKlWP35bWdnqYOJaBp2aQ5GdIR1uEa5tVrWQTPvglDLHMj0l3SJznNE+CcQ1DXJD6d7t7aiajhd0mV2+/0CwvUCehLQW70XMvJ2ihHGuHE1Nw8CvWayuKKpvkUXEe+uk0a7ub8mRZYZbdql1rgAQxh3GmgId2JYWcLyaXGAKQYoSXw2TFEsY2nBLsREd63ODwVaU0M8KduKC88OMppsw55p4psFmnBD39ljwXZbCIYHuBXNM/3PO84VCys+vafpmx00TUIRAO71pZlOyoPnvhRa+e+UQ2BZfMjcL7LIZx6yFI1qeyy2d4yrPruUrJlTXLNolHnh0mmAay/THatm8M47ZGI3ZjBNcw6jUUydIirJisLUco1Jl9UyTxcBnPvDwbbsS7NuOYs7vDTGF4GijxmNXtulpGf1cs8jyUipPbstSBmLhiKf3Qi6PU+JCEuvV3TBXon9Nx6bruZXb4OS69tKsYqRNbF4n6YrJSnFrHKvmyTRjkGXsxDmXxyo4rdZtjgSeaspzbI41G5zsvp0Z43zFpMrTsKoNhBunCXvPsLb1NwyzPUxhMuOqgq9r1zmy8jWqv0LXOOzGl7MRpowz9d4stXxkruoge/1nuNg7TflEztHjIXd8xW+QjT+qGhXrSrNvcPH9jAYXNDW6rd9PgOgqLxrL9HC0rpZxQE8rGalJf1J0dvzZitIMHPBSUXWPibe8YfvKhdLr4ToWuQwp05gwVAKTyWhzP4jo4JFoEUkcVE2qsazk7rOIZLiJMFUATHOfWueE1vAqSXJJrj9v7dV3qPd9fElZLpTKeRKoJvwoa9BtdGnV10iHW8rzPo/I86gS4UyGWxi2x1xtiSgN2Bmr+sykEXfy+VM1HbsSeUzzgsC3Ky2u/jhjqbmjX+cjVY1l3ncZ5jmQcjHM2Rqrc865FnGp1CKGeUReliwH/lV9Oi8fEsppQDmImyagGNridJyqQrLvGLimqNhWr8waBdKiVOKKRcGXLcxxORzRixPWwxGO1aXhHaERmJTDh+DAZ6/IY7Jxj4Z3jKK0CeOMnXHE1jjGEoILo0jL2TtYQhAfcN/zTAPXNKhbyuluIli5l2aVlPhemrGbJFiGQWCpTvpRnl/lBZGUJblmC+VSUsiSnSRlI8oYZiW9WDHBihJ2xgUbbsFCkCunPNfGMgzysqyc8PJSYhmCWxs1TFkyzlSjnWWaOj2RMNTXsBFn7MQF/Uil4s7mGdtJwZvnGrimR1FK1gcRLf8ofrDKTHtD6YANt7j41AdY6z/JbrzD5fEWo0IRE1aDWQLLr9QGusn9tJbuxQjuqyjkt8zO0PLX1GqBfQvhxcZxOvP3VBO7HTwAwPYzv8jciR/Fqc+zdfkTlZR8moZESe+qlUBWxBQyx0vq5FpXa5LymvRZALTapyjyCKnNqw76qcgiB81si0ebV3nMP1+BuMiVaGSahrheG9uZIR4rIdqyzOkuv4XGwt2Y9iny5HRloDURoBSGpd03y8/puZqgHhxlcPH91OdPEVrLbA7Hla2v8kSJ6dY7rM6fwrB9zD2fwe6T9LYfw9k7jxd0FQW4FdOpv4G0KBlnGTvjRJMDTMUg9BVbK8ryA8ZcOvXqGJqYsIiRfJbAjug2TlQtAq5p0vVSFryIQVrQzwqG+T6dt24ZWMZ+reXGQU5XKM/DTRNQQLGgJnfFEwl23zGw3eVX7DnvOHKSjz/1COMsZ7lRJy8ll4cjzu4OSPKChXpAt+HTtH3KLK5SChNxw6KEJFe1jlwHjbgoyEv1O9HNWk3bprBk5VNvCkHTsatC7WRVEtgWlqEoxk3HrlSNTUOwqLv5QeWvJ4HnIJqaRdRxLUZ+wXZScGVYkOaS7WHB9rBgccZkOzkgfKlTfi3HoOM4lUKAa5pV3cUUSldtlKlzxkVJzTZoOmYl399yDAZpRr43pBcnLNQCwsSlE/j4Rsxo5xxblz/BExufZCPeoe00eHP3PgpZsDW+wuVoE9MwabotkmzIztYjRMM1Zpc26XZOsGscxXcMxtkR1RV+6Q/IkpDbvuwHKtvbpz79y3z9v/0ZntpqMBPk/Nm73sbcCSVa6QddBoNzXOg9zk6yo1/HgoZdxzEdTF3IrjuJCjpbj6jXx/TIi5hEOzH68a4KNm4Dw/aqz4SSjI91ykj9xOPNitI7CWam5ZPGu4yjLcJ4S2mLeW3t7tiumGato99GmBQEpUQWOeH2GeWjE7RV7wxQb14dSLJk7arvy+7eRS6f+yC19Ydo3fFeHMukAbSM/dpOlOUM0hVanePUOheoz5+smiZVPWUew/sSiqSg2/BxrKBqNDT1Z9XR+mRprvu4KiUARRZZdM9ilB4lMNw6i1fr8Yblt/DEFYEbm8qIrlGjFyt74M040eKsgqZj4RoGcVFCkjK+UaxPWSLL+MWP+yLCTRNQpFQTc+BoHr+h7nB878azu56Pr7j9HgbDSzy2vlNJ0o+znF6caKtTSWdZ0VEBLLeOW59nEC0QxgmDKCFMU775jW/mPz72IIUsq2BQyHI/7VUq5d/FWqDSU1BJzE+au1yt9BrYdtUYCVRCjxNWTUML5KV5wcZwrL7UpkkhlaPfXpqyG6d4ZkLdUtpkm+OCKCtJcqqVjWsKLENSt1Vx2BICUygtsIlt8eSOc5znxEVR6ZxZQqBFoclLtfKaUJ/bnsvSTJ1uwycwnqZ3+RH2ds4wireYDxZ5213fx/yJ76IoJXHvYySjTZ586vcZpnv42rtlnPSU86GllItbnQwDG2FbwAr+0t+BtGRtoO6KO/UFmnf+LH92j0XdOs9g/WHOnP09/uo/vp25xi0Mxus8uPkg50cxgQkn6rM07DpPhZfJZYlrmMzYPjNpn7SIqTlNTGHh2nVVNxEWNX9+v+lSNxvu983s2+5OlAMmrpSW6VXGYHmu0mzjpEdWJJj6PICmJ/sELSXWqL4LF7Hr83Ru+Uq2zn2U84//LqDSYa3FkJqmRydDVbOJxp8GwJ9ZZnbmPj49r7yaOrt73HtkFrigrte4vRJenbijjrMjYB/BdMHXDWDboxRGKXM1B5k9jsAiaPgVMwz2jeBgkTQXFIbA0e6OvmMw3rhAFK5VrpXJaJPx4AInl+5hXN5Gf6zqcxMh12Y4YjdJibXh14QVFhdUQps3AnKa8roKN09AQeq7dSW/8moFkwkmLBTXNPFtm6d3+3x6u8eRwNNeJEcw7U3KZKiaxfIl0jyrxB3vXGyyvvsc3XpAw1WNjIWUleTKpHCpCtn7Re2253Ks2ahy0v7z+hQCW33Rc80UC2ybbsPHdxSN0jUNVjt1orSg7mk5/LSgH6Vc7IcspSl5KRnnOZtRzCDNr1qd5AdScZPt+cBjaaZOy9dWsGlBLsuqs79pZ1fVbRbrgQ6ClpY8V7TRurtBllymzGIcr83S7d9IQ3wJn13f4dFS0rm8y8luk1rnOI7f5i7j3YwGF0jiXaKkxyDawDZdRqM1sjQk0l3hfmOZmYWcNF/FdwxavkcyfIh8OGTWb3NmZ5Vctrj36P0cT0LW1v6Kjzz3AR7t58w6Oas1jxkrYFwkjIuEpCxICpgNXBb8ucrO2LPq2AcK9Z6rVg9+Y1k13BmWuouP4kp5N0sVuSAab1bptElKbUKtTrMhYbyFKSya/gKOXce0fFVwr3Vx6/M4fpvRCC71hhTlDOMsI7AXWDr6PXSc36e3/Rij4RqDM+cwnvoA7c7dlN13c/7KXmXbsP1MxLnwQwC8fbnLbfanyRPF9srLHDn+a4JSMbt2NnOC1gqN9leyESakeVlJyUz0xKLtj1QsMADbUXTmiQOpU5/HrS8z1N8nxzJZaLhk4adIRpu6MTEmjXerGs3WuY9iGH/FTK3LorYCGGdH6Oz4rIWjSk6/+p7qVf+NgEQi5ZTldRA3TUAB7aKo1Yb74xz/pXtqvWzMt1bZGZ1lnKmJ/rbZFm3PpeW5BLZNGOe0gjcR+M8BkOrJVwnVmTy3MyLKcq3FZLPUDK5SoZ300xQl9Mcp63tDwjTjymhMUpQs1FQgOsiE8W2LTl1N6lFacqk31M2LPmb+BEaRk40jstEWIo+IDAth2HhugxNzx+nUliu15p1xxOI4rlIKriYMWEJUqY8Ji2Zppk6n5tHyrQPGSa5KSerAMdEWmwt8wiQl1wGmHydVEAWPKLM4199jM1pSNFB3jfnAo2GrcZ3dHBDYbUxjllAco7Psc+vMOlfOfhDXVqmkyV397s7pyrCs6bZZaN9FUF9GGBajvQvkeYTrtVmdfwONxbtJiqM05k5xvNalKHMWvbOcaN+lzilU70XN7x6QhlGU4FTLwQR1dUOTxru6AD+LN7NMY/6k6j8Z7suP5GnIaLh2lV+LZ9Vw7fpVn7NJ4d2z6rSaxwnqR1R/jBZ5dOtdwuQIw5FSDd4ZR1rtwCCwbeZbq9D6x8yfVAX3svefSeMejblTlK5N4Mzy2PoOD2/uEFgWb16Y476jXWryNNBlsPYIZZnRmDtFnoSEO2cIwws0Giu42Tx5cpql5r71QJ6ExIM1BldUD1BveLEyjOsER2jUlgnqyzg+5MkQxz9Hp2bRqSkXUpk9znhwoZLYmWBimWxaPlkaMtw7T2/j4crF8+TR+zjavq1KnwE4ejkcpTdoVSGnNZTn46YJKI5p0Kk7RGnJ7Myx1+QaVjs1LvXGFLZKG3Xr6k51YzSmH8f4tk1gd7TroTavsgwSnS+eSO8fbPQK44z1vSEt36NTcxmnqlYyCR7LunExTFKOthqVHExRQtNXfRiGYVM3Le6cn1jq9jVTKa5cEPMkJB5v4nizOHkbWWTY5jlc7Vd/7Ijqfu5HKf0oqTqUuw0f0xDsDGP6mkGmLF9V57pLTmodoeGBadRpJKkKIOWIpCh4YqfHRpSwEWfEhWSUqfpQ3TZUTca18UwTS1OQJzUf17qaVTbKc0xhsDpTx7GOMHvsLQR76+Ta877IIvI0ZCt8jvXxFR7tPcXW+U8wY8NRv0XLmaHuNJhxZ4miLdKoR33+JEF7Bea+lfuPfS8LD7+Hje1HCNw2jl3H9WZxvHZldjXRvappx8VJD4jfWMbovJP1wZg0KVgKA+Yb24CaGCfBZHPvaXbjLcKJ7IxdZ9abxzZdHL1SmUBNxEdw/DZ+e6VivxmGeu2HcUHgWHzZapf1fsTxxc81lqoHRyH4BwzHlxDlk5j5E5zdXOTCIOTebqeS4tkMI7qNu2k6yoo6DC8Q9p+hPrOqNbdWcWvzCFN55exdOU0aKyO13d4ZtobnGSR9wmzIXj6mKEvmvCaFvruvzazgt1aw3EZFGihKiSGfZTzcqs4zWe1FSa/q3LdNj1Lm1d+FnuAX9s5Tb59gprGMU5+/ij4uvBs17U0DyvNx0wQUhI3vHXlVVyXPx0S8b9LQB6rG8dzeUEtKmOSl5GijRks3XBWl8tueBBHTFDiWUVE0HcugWw9Ii5LPru9U1sYt31XpMc/msfUdGo5yx+tHGUmuZGeKLCZPhkjtejiRBffbKwzzVS1yGCn5kSTET5arHH6RxySDCxWryK2dw/XaHHEb3N5dASBLlNowhsVSfZ7V1mzlOpjmkiELBLaJaRT4Oo3VChzCOKsELpOipOUoW+RCSnZ0B3XHdVgM1CSgSAWqv8UUBuM8p5ckFcNnN0kYpDlNx6IWmVzqD2ktv4lm/ZKWwlE5/yTukZYJpjCq3qHdVLAZD5h1+txSa9EtEvaSXZI0ZHasKLlp9gt4bptwpIUtDWUt7HizlcyMHbQrORbD9iizuApKYX6C/jjFty2avsN8Y5tkuEUy2qpov+rzk+NbgTZTq+HZdXy3XcmhFOWQQuY4ujtdvTeKwpzHKnAaiY3MLuBEqkdkCBxdvge4vlNhUcLHLrRV+rMdcO/iNmvDBkVZ4jsWczUH07hIkWW0l+/HD5erJt1Jqs3y9vusqsbH4RqDSLl7+lbArDeHY6peprRIcExXCXtOBCs1aaUoj7ERJgTOCs2W8rSfpPyipMdevMMw2yMt1GelkAVJmWIJE9/y8a2AcudRmtEWzeZxguZq1ZR58DpvBOSr0NgohPgO4GeAO4E3Sykfus5x7wB+EWVF9GtSyvfp/bPA7wC3oBwb/56UsvdKXOvNE1BeBzjY71J3HaIsp+W5sDfk8f6IXErqloFnqgnN1amdidAfTBSTS0XPTNLKI171lajJfcZRTWHne3uc3R2wmyS849ZjnN3s0Y8TlhvKFKzeVHLpue7ONgwLpz5PatzB5iDSHvBHlSS6EKRGiWMbdGc8vOwxktEm0XhT0WN3TmNbPo5dx3Ya2M4MgBJ29NtE/QvkyRBvJqPuNoiMRTb3EvqGoBXYWgE3pigjfA8aS3eQ5gXHoobuaVAkhqNlWdGP21rsMrBt6q5dyeo/2x8wzvZTbYXUNNui5OJwzG6sJppbZheZb1j0Lj1Eb+e0lkZXq6ia5dJ2Csa5ZFAKxgWsxwNyWeAaNlE+pq8nw4WmmoxbzROVMGXQWsWtzyPsu+hHGWakisduYJAnp0mjHo35k2T5Ei1bMFvbpMhi0miX3QsXKvfDg2kuz64xY3aoefPU6stYToM07mGITUh6uFobLElDenvncKMtmnmEm6peJ2FaV9VhbKdBEveIx5scu/frrvu5nVjvrrabrHZ6lOUplptqhZAnIXFPKUlbbgO/vaJWbcBg7RE1Sbv1qtcKVKd/vX2CoLlKe+4N6nNte8rmWKet8nSfgFCbPVHJxpi2h8k6R9uTq/NoLt9D0H0HpiGIB5+kdemT9AfPsDu6zFa0wTiPGBeJZhIWmMLEcNXzZGlIGqna06SZdCKJ83IhZUlRvirSK48B3wb8q+sdIIQwgf8LZQF8CXhQCPEBKeXjwE8C/0lK+T4hxE/qv9/zSlzoNKDcQBj2ErO1hEGk0jq+bTFMUu7tdrgwijjbz5lxSxZ8lTIqpCRI1VuQ5AWWIWh5SrNpZxyRFEU1OXqmwShXml+mYXBhEPJkf49CSu5uN3l0Y5un9oYMs5I3zsasxnV2RzV8+41ggmkrjaRxKUmHKZvDsU4ZyapI6ZoqhZbmEldPAJbl45ZKATjJQgZj5b1umx6uXafZvRuv+RYuJCFFVuIMlIR6f5wyiBJmax6BfZnRzoWqU1wp4W7hAUdsD6vRIDXu4FJvrINLpogAWc6V0ZiVmQZH2yp9WNgW7VjVpVqey1o4JC/TqhcmLkosQ8nB52Wdce8CmxsPcXnwFJvRJkmZMee2OVo/yvlQuU4mZcZWMiIpJJfGIfOeWub6VsDq7D00Z09d5eHu1roYM28jyiX9QcxQr6qavstCcJ5s3CNorSh5lexx4jgk06ytycSvvF48lTZK20TRFoPxOoX2ph8MzlFohhcoH/qZ1gmAymslyyN2e2eYySMa7RNV2ktN2m29bROPN/nUh1VAMYVF4LbxXEVycOtdFhortIJFzPwJyrKBYawx2jlbEQUmcjITPTBp2Ay3zuDU53VRPa8EGCdKwYbtXWUhACuVeVdRlpgNA09LwOymOa5QvWNleQlQ9ZOLvTGXBiGmYdAJhjR9h7nGl7N4ss0iUGYx/fWH6e2cZmPwNFv6BgC05IxWMsjTEMOwFX3ZuJq08nLxaqxQpJRPAIgXtuB4M/C0lPKcPva3gW8BHte/H9DH/TrwUaYB5XBgonDs21YlcdKPE962OMfxxpi/uLLHX28N6Wghx+XAo+26uJap78iViVKixfQAnh6olEjTsXFNk14ckxQlxxu1iv779F5IP1VOkU8NhoyynGY4ApQHyozr0Ak8VXsxTbr1gDBR9RBLCALbouV5LDVrSu+oALemhBDZO89O+CxRPqoUhgOnxeLSW8mDt/Lw5V3ObKsV9EIt4PHNHQLbZqEWcLS9S7Snmu2Euf9lDrfPkKV7FLmq48wfeSt3LN/D7qjLzigmTFTQG2UFH7t8hQ88e4mOa3Nbs4FpGGpS0l+wpqZQD7IMzzSZdVUvjGUIijQmyYZE+ZikzAgsD98K6ARHmHE7xPmIYbpHYKpr7Hiz+FaAo+sV9ZlVpdWVqzt0b2YJt97lYi8hLQrW94bspRlzvqfqSbaP0/wKTGudce8CmU49FbqT3TAsHLtdKTOXZY7PMo3yBK2ox3DvfOU/DyqQuF4by/Jx/HaVYlIsJ+X7Um+fqJhuE5RlTtS7UMnDd5burwLAxGzLtH29qjhD1rtAr3cOx29XhmQAtdnjBO2VSnusyCNMt0HQfUflOyLsUyHfEnsAABiDSURBVBRWiSMURTnqXSDXvjeT5zRtddM0TDL6UaLYV5aitzumMsuyhCApjgLa+qEouXNhFt8xcU2jsg2IB2uVhA1Au3M3rfYpjg3XGEWbjNM+junpmovy0ZkQJp4v0//y8HnVUOaEEAdTVb8ipfyVG3gxR4CDqpeXgC/X2wtSynUAKeW6EKJ7A5/3KkwDyiuAVmBXfiKW5+GYWaUo/DVLBn++3uf0ZopnZ5xo5xwJUlZqAafm2iy1fByeY6FxK0lRshrPcKzZqO6Cx5kqPrsmtD1PyWJkOU1b1SEARnlBUpYM0oyabdGwTDqBp9JvGnXXPpB2UzWchmfTcJXnd5qpxro03iVOenh2nXb9GJbpEdSP0Jg7hZh5O2c3B1wc7MuBnxvsMe97BFrIb5gsUJ+BTDs0Aox2zpFp6XcAEw/D9kmGW7SCHMc6Qj22VSe0lBXdeFnXVMIkpeE6WJoY0PZcbrPtSsHWMoQ2pTLVHbNuPCxkScOuc6R5O77bJi9iZsQSi4bFbVDZ9Y7TPoUsaLgdHL9dTaTjwXnVEOg2WO0eZzi+RKhVmrv1AN8xCZMjBLayKI7DNdXdH+/qnhLF0KJH5ZMy0d1ybMVOCupHsJ0GW7uPY2oVX1ArDaG72x1PmVAZtq/8ToL7CJOCcGe/7tbwLJxAFbgnaSbbbTBMFnA9A3diQjVUgTQK1+j3zhDE89RmVjFtT6X22iuU5aTR8ZxaSW7FNLyM+YbS/GrMe1jlkp7wFT07jXrIMlOWATPLeDPQ8G7TtPSccVlq22qDVqCaJHPds7U+UA6W3YavbX8NXM4SjZVVcBSuYdpKkHKi9AxKqXpG08Oj8Wa133YalSiodcBp8+Xj86INb0sp77/eg0KIjwCL13jop6SU/99LOP+1li+vulnLNKDcYFhCMM5K5axXysoLIi0KAsviRKtBzTYZZcpxMZcS11Bd5GGSEqUelpUR732MMosIgHuPfCNhUjBOc7bCiG298lFF/f2aA6Bsfn2luKpcFg1mHJuG69CdUQEol8orPHD23/7ANjCNi+oLqyd6v72CN7NMWyv/grL5tTtfyxNX+kThDqZQtY5Jr0rNUp3xV4ZjLgxC+nGCb3u4Zo1xltNwHVY7b6OhfeKFaZOMNimziFg39zlBSMcBt3uKxmBEYFmcG+yRa3mXucDn5Fyb5aZHFn6qsqXlQDajyCPKHZtREtJqnsA0bFZad1LzuwT15aukSyZCj+UwZ6zrLIXMGad9dY2aTr03eIZo+xHWLv8lt568wBpvIbBt3rjUrvxYsiQk1xNf2HuG0WidwXidK6PLFatpO+mzl8XsZQVJCa4BgSVYrc3RcTu0/Xka3jxJFpJqxpftzFT0YCWdYpHlS/SjjPFApcVMrYiw0HCRxTkG+SmSvKhosqqvKcU0BEfn7sM04XLvKVq+Q2MOVa85EMDCnTNE4VrVQGrbHulIcktnZ99ErfVVZMlT7Dz3H3RXf17RqJUPjOqJMQwbVygm5kSfyzTANSeSKKqO2B9nFV0+yUuOtX3iwSfZXTtTydeUperPqdWXaXROYQdqRVVYd+IAbZ5jtPNM9Vlw6129SvKIskX6o33F45cDKaG8QX0oUsrrF7leGi4BB+mtR4E1vb0hhFjSq5MlYPNlPtd1MQ0oNxi2u0xTLwQ6tXXGWcGl3piLg5BxnhNYFou1oNLUmlCAi7KkHyXsjGIK/1bm5xuMds5p/aqHqPuzNH0P6OA71lWufY5lcrRQ9OFJPSQpCnaSlHNhxJFxzNY4BuaouzaBYzFb27xKQj0Le4wPpGcmTXKm5SFMq2IrPb27x5nHnq3EHwPt4TIfeJiGwdFmg+WmR5gUXOoN2RipWk3DdViaqbHcVDpdNL6cMlA6Xt7MU5WKgHlAULEmT3PHfIPVziqNK2r11Qk8bunscOXMv+Px0w+zfOTtBK0VhrvPsLmxn1GwLZ9GY4WgucqxEw8wTBbUhJs/QdRTXdeg6iGyUBIneRETuG0Ct12tVIZ755lduBfbbdCYO4UsMtK4x5nkTRRlzH3H5gjX/rBKZ5XaBjceb/Lc1oPsxjtsJ31dowFT30cmJSSlYD1S452xU3rpFvPuLsezkLu8eRrBEpYuZNtOQ03Utsc4O0KUFpiGUh+erEgC2yRPTlOUd7ExXCbKYqI0Z3scacsB5WzYCXwePPcYn76yxU6SsuC7rM6scOfReznS3iUZbql0WuedBLbBcPPDpHEPWaii+UDTogEMe40cFcB7e+dIi5jFzpdQayoqsFufZFdWyLVLKOwHk8lNjK8n/Ia3gGsZ1U1SUUqyqEc0XMN2GnTm76Us1XsA2sSu3mWcHWG9N9Y3crPU3QWVUnMsDEuQlWBm2hvJuVECkSXl68cP5UHgdiHErcBl4N3Af60f+wDwXcD79O+XsuL5gvCaBRQhxM8BfxvVkPEM8D1Syr5+7L3A9wEF8I+klB96ra7z5cCwl6jbcEcAR9uXOL8zZC0cEcZpxWACRTc2dQF/ZxQRJimOtYDnqlVBmcWMxuqOa7beZak7zzBZIM0l/bHy2u7WAyxh0PRdhknKXOBXbnegOubTvCA1TVxLKrnz4VYVQCa6S5OGMeULru4uB9ECj6zt0o+vsD5Sq6MqFeULJQU/36JTu8LOsx8hLpdpdY7jWEdU2skQSkXAMtgI1fU+dmW7muCWZm7BdVTBWek5acdN36YsziKGn+DexXkwTwKQDJ+oZEh6249Rnz+JvfzfcGLuFHubp0niXSzt6VGfP8lTmzOs723RixPmgnmOd25jYdElHnxS+62otNTEeGti5zvn3E29ra6ryCOyaJfa7Anq3VPMJFdw6l/HxYffQ625UvXxDMILhMkOw3SPQdpnLxuRFBkzlkPguRyrHeG27psxDJtYEx0mPRQTuHadVvsUttOoTLsm8jFlFlOvb2Aai0r/zNekjqJknBUU3MHm7ogwSbVtrhJjzLX/umuafHZjWzXEamXfnSQl7+8BMIhqzNY6LDV3YO8zSHxmFu+uahfK8lilnQ761JiWx2xLvT/thXuZWbyb9UEHwolJlhqjcnFM2RlH+LZNy1eTfzGcBJr912KcFsw3tvFmllmY/wYcnmPcv8C4PxGdnKfWeYB+lBPGSvxz4jWfxyqlNohE5bPiWCa+belm4hsA+arRhr8V+D+BeeBPhRAPSym/QQixjKIHv1NKmQshfhj4EIo2/H4p5Wl9ivcBvyuE+D7gAvAdr9S1vpYrlA8D79UvxD8H3gu8RwhxFyq63g0sAx8RQpyUh1zjoB4cpTl+9kAQKUmLEsdUH3TXMlgfKB8U0xCc3ezTCW7j+PyA3qVPI8uMIouJwjVsXZStt1dotDVt1TDwi4cps5ij88vKjzvO2R3Flc5XUhQ4RYFpOKTDLcZ91agWaYXaiYthrblC0DlOmJ8gGRRshSp1NcpyarbJjONoTTCbo606czWHuPcxtq6cJ+w/g1ubV3eMg4iiVBPdxN8il8pyuOE6tDy3oklPEMZl5R6Zl5LAuY1G+5S+vjWS4Raj3XOqkD9/L7Mrb0HYdxEOYp4Z3s7RxS/lWGODeLCGMFWPgxIhVIKVD21sczkc8uVHlzjWfgvZ+BFlfpYNGYzX8ew63bl7sIMuzaV7lGOjuJVxVuLzJFvn/pxLG5/Cs+ocu+UMi3f+KOng41XHdlbERPkI0zBZDJbx0x5RHnFy9o2s3vqN1GZPYAf3kBRlpTKgpHGUDI5hrFGWyxSZ8lufiCyCYm8JU62CfFv5jJTlsqqfaJvgi/2w6lXqxUl1Q3HQBtsSSm/ONU1cw6CQsrLendzVTwrew60zxKOtA6kwa59c4M1ilxmW06Cuqb+W22BjvMrTF8f0o10CW/mYTERJ+3HC9jhiK1Kui21d1yvKkltbTY626/iOQZpLLKPg/E6bSwOLbn1Mp7YvQAkrhEnB2c1Qq1PIqvcLqGTx95KUXpIoVQbfZ3mmfsMCikTesJTXCz6PlH8I/OE19q8B7zzw9weBD17juB3ga1/Ja5zgNQsoUso/O/DnJ4Fv19vfAvy2lDIBnhVCPI2ixH3iVb7EG46jc7dW25e2n6282wdRQlIUdHTReai7yQPb4txWk9R6gBNzdaLtjzDsnSMaKk0qt/cMbu00jtNQ/QDtFeyZZeV/Xt8AFhhEhg5eqokwzZV68fH54xi2R9S/UHmeezNLWFrq/fxWBKiU1aVQdbVPhC87gU+3HtCpO5jJZwjXLrC78TBR0iMvYrqonpy6a4NOsTVck4u9iM3hGMc06dR8OjWP2dpm1SSnVHdX6Ec2SV7QCmys8inCKyr1N7lbz5OwKnQPHzvP/JG3cnT+FINolo88c5EZx+bepXvo1l2iDHw7YalR43inyZ26AzwtCi72IgLnTmaXGsSjLRqNFYo8wq11lde6+QbO74wpyj3qrsP56Ah999vxT3wnhSF4IstJzl3h5Pz9dOqP4achy5bPMora6wVdFk99I2uDLq5lUACXo4zNrS3CNGMvSbkyjrSfh5oEPdPgWD3m7vlFRU5wbYJAdb8rFlTJRCA6SgvCeFRJyvfimPVxXJmxKfFNJdo57ysFhYlO14zjVMfkUlbva6fmKWHMtdOqtqVrIgdTkZMbmqCjitumfYqLvYjhMGXcy3l69zIDfZGznsOMo26i1kdRZRY3SHNc02AvzThaD7i11aTu2jSdC8R7azS738HHn3pEqXYXBUvjiIbj6CZXj3G2xvY4YjNOKrXrnSSlnyrxUUUfF3imYLXmqgCqV8v96MbUUD7PovwXBV4vNZTvRXVygqK/ffLAY5f0vs+BEOL7ge/XfyZCiMdesSt87TAHbL/WF/H54U9f7IAbPKZfu3Gnenm4xrj+59fkQm4gDuHn7yXh1Ms9QZEOP9R77qNzL/Hwm/E1/By8ogHlpVDhhBA/hUqw/ubk365x/DXpb5rH/Sv6PA+9EC3vsOJmHNfNOCa4Ocd1M44J1Lhe7jmklO+4EddyM+EVDSgvRoUTQnwX8M3A10pZ6aC/EP1tiimmmGKK1yleKWfcF4UWMnsP8C4p5fjAQx8A3i2EcDUF7nbgr1+La5xiiimmmOKl47WsofwS4AIf1ho1n5RS/oCU8rQQ4ndRGjQ58EMvkeF1I2UMXk+4Gcd1M44Jbs5x3Yxjgpt3XK8pxH6maYoppphiiim+cLxmKa8ppphiiiluLkwDyhRTTDHFFDcEhyagCCHeL4TYvFaviRDix4UQUggxd2Dfe4UQTwshzgghvuHVvdqXhmuNSQjxM0KIy0KIh/XPOw889rofE1z/vRJC/Ii+9tNCiP/1wP7X/biu8179zoH36TkhxMMHHnvdjwmuO657hRCf1ON6SAjx5gOPve7HdZ0x3SOE+IQQ4rNCiD8WQswceOx1P6ZDAynlofgBvgp4E/DY8/YfQ+nXnAfm9L67gEdQRf9bUVph5ms9hpcyJpTV549f49hDMaYXGNfXAB8BXP139zCN63qfvwOP/wvgnx6mMb3Ae/VnwDfq7XcCHz1M47rOmB4Evlpvfy/ws4dpTIfl59CsUKSUfwHsXuOhnwf+CVc3P1byLVLKZ4GJfMvrCi8wpmvhUIwJrjuuHwTeJ5WkDlLKiYT2oRjXC71XQtEU/x7wW3rXoRgTXHdcEpjcwTfZ7wM7FOO6zphOAX+htz8M/F29fSjGdFhwaALKtSCEeBdwWUr5yPMeupZ72TXlW16n+GEhxKN66T6x4TvsYzoJvF0I8SkhxMeEEF+m9x/2cQG8HdiQUj6l/z7sY/ofgJ8TQlwE/jeUcCsc7nE9BrxLb38H+83Th3lMrzsc2oAihAiAnwL+6bUevsa+w8KP/r+BE8C9wDoqlQKHe0ygep7awFuAn0DJaQsO/7gAvpP91Qkc/jH9IPBjUspjwI8B/1rvP8zj+l7gh4QQnwYaKNsMONxjet3h9SIO+YXgBCrn+YhujDwK/I0uIB5a+RYp5cZkWwjxq8Cf6D8P7Zg0LgF/IFXi+q+FECVKePBQj0sIYQHfBtx3YPehHhPKhOlH9fbvsa++eWjHJaV8Evh6ACHESeCb9EOHdkyvRxzaFYqU8rNSyq6U8hYp5S2oD8abpJRXOMTyLdqic4JvRS3V4RCPSeOPgL8F1RfaQSmwHvZxfR3wpJTy0oF9h31Ma8BX6+2/BUxSeYd2XEKIrv5tAD8N/LJ+6NCO6fWIQ7NCEUL8FvAAMCeEuAT8T1LKf32tY+UXLt/yquJaYwIeEELci1p2Pwf8Qzg8Y4Lrjuv9wPs1lTMFvkuvVg7FuF7g8/durk533Qzv1X8H/KJefcVoi4jDMq7rjKkuhPghfcgfAP8GDs+YDgum0itTTDHFFFPcEBzalNcUU0wxxRSvL0wDyhRTTDHFFDcE04AyxRRTTDHFDcE0oEwxxRRTTHFDMA0oU0wxxRRT3BBMA8oUU0wxxRQ3BNOA8kUAIcTwFT7/B4UQLf3z338B//+AEOJPXvzIq44fCCE+eJ3H/60Q4ts/3+s4jNCvxdsO/P1jQogLQohfei2va4ovTkwDyhQvG1LKd0op+0AL+LwDyheIv5RSvvPFD/vCoRv7Xu94AKgCipTy57m2vt0UU7zimAaUL1IcMFF6VAjxhxNVYyHER4UQ/1wI8ddCiLNCiLfr/YEQ4nf18b+jVYPv1489J5S52fuAE9qY6eeev/IQQvySEOK79fY7hBBPCiH+CqWFNTmmplWWHxRCfEYI8S0vYSxCn/txIcSfAt0Dj92n1Y0/LYT40ETaRgjxZXosn9DX+pje/91CiN8TQvwx8GfXux4hhKn/70F9nn+o9y8JIf5CvwaPTV6/61z31+vn/xv9nHW9/5/q8z4mhPgVIZRYnRDiH+kxPiqE+G0hxC3ADwA/pp/vus81xRSvCl5rQ5bpzyv/Awyvse9R9g2H/hfgF/T2R4F/obffCXxEb/848K/09htQMhX367+fQwk93sLVpkYPAH9y4O9fAr4b8FCS4bej1F5/d3Ic8M+A/1Zvt4CzQO151/78834byuPCBJaBPvDtgA38F2BeH/dfAe/X248Bb9Pb75tct76+S8DsC10PSo7kp/V+F3gIJVb6j4Gf0vtNoHGd92QO5c9R03+/h32DrtkDx/0G8Lf19hr7BmUt/ftneJ4hmx7DL73Wn7vpzxffz2FY0k9xgyGEaKImpI/pXb+OUpWd4A/070+jggTAVwK/CCClfEwI8ejLuIQ7gGel9g8RQvx7tF4UShH2XUKIH9d/e8AK8MQLnO+rgN+SSoNpTQjxn/X+U6jg92F9k28C60KIFmqi/y/6uP8H+OYD5/uwlHJi0HS96/l64EsO1GqaqAD5IEqzzAb+SEr58HWu+S0ot8CP62tzgE/ox75GCPFPgACYBU4Df4y6CfhNIcQfocQ2p5jidYVpQJniWkj074L9z8i1fCNeDDlXp1W9A9vXE5ETwN+VUp75PJ/rWucTwGkp5Vuv2rlvWnY9jF7senQa6keklB/6nCcV4qtQ8ui/IYT4OSnlv7vOtX1YSvmdz/tfD/iXqNXfRSHEz7D/un0TKni+C/gfhRB3v8g4ppjiVcW0hvJFCCnlAOgdyLn/feBjL/AvAH+FsrlFCHEX8MZrHBOizIsmOA/cJZQ0eBP4Wr3/SeBWIcQJ/ffBSfVDwI8cqBt86UsY0l+gJMhNXSP5Gr3/DDAvhHirPpcthLhbStkDQiHEW/Rx736Bc1/vej4E/KBeiSCEOKnrLavAppTyV1HGVG+6znk/CXyFEOI2/f+BULL+k+CxrWsq364fN4BjUso/R1let4A6n/uaTzHFa4bpCuWLA4FQMt4T/O8oE6VfFsr58hzwPS9yjn8J/LpOdX0GlX4ZHDxASrkjhPi4LnD/BynlTwglDf4oylPjM/q4WAjx/cCfCiG2UcHqDfo0Pwv8AvConsSf4+p01LXwhyjfjs+iahwf08+T6pTU/6EDmqXPfRr4PuBXhRAjVN1o8LmnfcHr+TVUOvBv9P4t4O+g6js/IYTIgCHwD651UinlllAEhd8SQrh6909LKc8KZaz2Wf1cD+rHTODf63EI4OellH1NHvh9TRb4ESnlX77IazXFFK8YpvL1U7wkCCFMwNbB4ATwn4CTUsr0Rf71lbiWB1CF6BcLNC90jrqUcqi3fxJYklL+6Iv826GADlT3Syl/+LW+lim+uDBdoUzxUhEAf65TPAL4wdcimGikwBuEEB+UX3gvyjcJId6L+g6cRzGjDj2EED+GohL/v6/1tUzxxYfpCmWKKV5hCCE+haIWH8Tfl1J+9rW4nimmeKUwDShTTDHFFFPcEExZXlNMMcUUU9wQTAPKFFNMMcUUNwTTgDLFFFNMMcUNwTSgTDHFFFNMcUPw/wNnzyCoLQYbDwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "longitude = 155\n", - "latitude = 5\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = None\n", - "isel = dict(Z=Z)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "fig, ax = plt.subplots(1,1)\n", - "dacheck.cmo.plot(ax=ax)\n", - "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n", - " vmin=dacheck.min(), vmax=dacheck.max(), cmap=cmap, edgecolors='k')" - ] - }, - { - "cell_type": "markdown", - "id": "c2abd653-de42-44f9-8350-6827657dae47", - "metadata": {}, - "source": [ - "#### outside domain" - ] - }, - { - "cell_type": "markdown", - "id": "5dbf2d59-939b-409b-b1aa-2e447b62a896", - "metadata": {}, - "source": [ - "Don't extrapolate" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "8ed6261a-3bcf-495d-8c5e-cb9dc257fa62", - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "the input longitude range is outside the model domain", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvarname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlongitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlatitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miZ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mda_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mda_out\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/extract_model/extract_model/extract_model.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(da, longitude, latitude, T, Z, iT, iZ, extrap, extrap_val, locstream)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mextrap\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlongitude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlatitude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0massertion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"the input longitude range is outside the model domain\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m assert (longitude.min() >= da.cf[\"longitude\"].min()) and (\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"longitude\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m ), assertion\n", - "\u001b[0;31mAssertionError\u001b[0m: the input longitude range is outside the model domain" - ] - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "longitude = -166\n", - "latitude = 48\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "da_out" - ] - }, - { - "cell_type": "markdown", - "id": "78762933-aefe-4097-8866-1aca3148c662", - "metadata": {}, - "source": [ - "Extrapolate" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "f1232129-055f-4ea0-86ab-9ca129edfb10", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "(138.0, 190.0)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "longitude = 139\n", - "latitude = 0\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = None\n", - "isel = dict(Z=Z)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=True)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "fig, ax = plt.subplots(1,1)\n", - "dacheck.cmo.plot(ax=ax)\n", - "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n", - " vmin=dacheck.min(), vmax=dacheck.max(), cmap=cmap, edgecolors='k')\n", - "\n", - "ax.set_xlim(138,190)" - ] - }, - { - "cell_type": "markdown", - "id": "86bce0f5-0be3-4f9d-a097-c7f39e7f8788", - "metadata": {}, - "source": [ - "### points (locstream)\n", - "\n", - "Unstructured pairs of lon/lat locations instead of grids of lon/lat locations, using `locstream`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f6609d67-8217-4a7e-ab2a-31e2646ea840", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "# this creates 12 pairs of lon/lat points that \n", - "# align with grid points so we can check the \n", - "# interpolation\n", - "longitude = ds[varname].cf['X'][::40].values\n", - "latitude = ds[varname].cf['Y'][::80].values\n", - "# selecting individual lon/lat locations with advanced xarray indexing\n", - "sel = dict(longitude=xr.DataArray(longitude, dims=\"pts\"), latitude=xr.DataArray(latitude, dims=\"pts\"))\n", - "\n", - "# isel\n", - "Z = 0\n", - "isel = dict(Z=Z)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iZ=Z, locstream=True)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.isel(isel).cf.sel(sel)\n", - "\n", - "assert np.allclose(da_out, da_check, equal_nan=True)" - ] - }, - { - "cell_type": "markdown", - "id": "5aee17b6-55f7-43bf-b1ce-0040fe6506a0", - "metadata": {}, - "source": [ - "### grid of known locations" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e16ce5d8-d126-43f0-bed7-08661a04e5cf", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "longitude = ds[varname].cf['X'][100::500]\n", - "latitude = ds[varname].cf['Y'][100::500]\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = None\n", - "isel = dict(Z=Z)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.sel(sel).cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "dc123ada-7da4-4a43-8da0-5b905dd61e19", - "metadata": {}, - "source": [ - "### grid of new locations" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f18dd5b0-be7d-4dc6-83fc-92ed8bff2f28", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/cf_xarray/accessor.py:1343: UserWarning: Variables {''} not found in object but are referred to in the CF attributes.\n", - " warnings.warn(\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/cf_xarray/accessor.py:1343: UserWarning: Variables {''} not found in object but are referred to in the CF attributes.\n", - " warnings.warn(\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'water_u'\n", - "\n", - "# sel\n", - "longitude = np.linspace(ds[varname].cf['X'].min(), ds.cf[varname].cf['X'].max(), 30)\n", - "latitude = np.linspace(ds[varname].cf['Y'].min(), ds.cf[varname].cf['Y'].max(), 30)\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = None\n", - "isel = dict(Z=Z)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "\n", - "fig, axes = plt.subplots(1,2, figsize=(10,4))\n", - "dacheck.cmo.plot(ax=axes[0])\n", - "da_out.cmo.plot(ax=axes[1])\n" - ] - }, - { - "cell_type": "markdown", - "id": "d12ac238-33ac-42ed-bcdc-9c7e18322e69", - "metadata": {}, - "source": [ - "## ROMS" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "357bc157-6b5c-4c3a-8937-02a9e819abc8", - "metadata": {}, - "outputs": [], - "source": [ - "# open an example dataset from xarray's tutorials\n", - "ds = xr.tutorial.open_dataset('ROMS_example.nc', chunks={'ocean_time': 1})\n", - "xrargs = {}\n", - "ds.zeta.attrs['standard_name'] = 'sea_surface_elevation'\n", - "for dim, ax in zip(['xi_rho', 'eta_rho', 'ocean_time'],['X','Y','T']):\n", - " ds[dim] = (dim, np.arange(ds.sizes[dim]), {\"axis\": ax})\n", - "ds.ocean_time.attrs[\"standard_name\"] = \"time\"" - ] - }, - { - "cell_type": "markdown", - "id": "99752714-ac31-423f-812c-fd403cfd944f", - "metadata": {}, - "source": [ - "### grid point" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "36beee10-b844-426b-83cd-b1fb8708b1ab", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/dask/array/core.py:378: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " o = func(*args, **kwargs)\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "j, i = 50, 10\n", - "longitude = float(ds[varname].cf['longitude'][j,i])\n", - "latitude = float(ds[varname].cf['latitude'][j,i])\n", - "\n", - "# isel\n", - "Z = None\n", - "T = 0\n", - "isel = dict(T=T,X=i, Y=j)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "e52cbf63-f0cb-4674-b799-f9922cf2ede3", - "metadata": {}, - "source": [ - "### not grid point" - ] - }, - { - "cell_type": "markdown", - "id": "9c872e5d-8096-4967-83ec-d39740cedda5", - "metadata": {}, - "source": [ - "#### inside domain" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c5c12c91-f738-47bd-bb44-a734cd48dd1c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/dask/array/core.py:378: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " o = func(*args, **kwargs)\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RXvDNp9b44NmE2eAKjd4WhTjDOEy5NZ6RZDmOrnNupUkx/SxJOKLRO4uq2SScJslKHF1BKa8zO7xCUWSomkVz9RKH/hJxVpBkOUkufx8FIQfzoH7eZpmcyE84Bve3m6iKgiYEjq7Rtiz6no2pqdiGgq3vUxTr+HHOcBYRphkPnrp4kzdpRVGydOqNlXf3XzxaerPf93vB3nKHsrCyLMdCiE8hFWwPhBBrVXSyhlSkfbXP/DPgnwE88uhD5cmOw+M3DrB1ndPdc7in72c4S3huMOTyF56lZehydakqOLrOmfbXs3bft/CB+/8ga0/+CF+8/Ul+/XDIJLV4uJ2y0jiJ6S4zy0qCJOPGaMrNqc9BGHOu5XIcyYnyXFMSuVcbDnE+4/HBBFcLWPdcPNMgyXJURWDrGllUcLbdoi1+k+nBZfzxVVqGx8l3/CX8OGEUxXQsk/3ZmGmS0jR0Li336DVMbEPFKJ5ndniF6WSb7vpjOG6fcOrj9s9jN9c59JfYHSW4pk6QzlAVgR8ntWMzVTkRDsKIcZqzaslboKGpqEKwbFlggaYIbvgzNEXBVBQ2mxpt26r2dwLLHVEWGYqiyYlg5RLY72QwjXEilVb2Iq4NcZ6jKYKVhkOS54yimGmSMowTpknKNE5YdR3yosCPE5awURXBwSxAq5zwJIwZRxFZUeKZBg+un8MPM4Ikw1AVPMMgSAO2Wh6GqmJqKhdX2+jpM+Spxd5knSTLCdOM88sX0JIEW9dQheD+pQ5+nLA99TFVlVwIro+m5GVB37b4+LrOcZwwjBP2Q+kIo7xgkuQ0dIW2oZIVcDuIyMqSLU2lY5mc77cZ+CFJluOaBisNB9fUOdlxKKKnybMQRdFYO/ct5OajhEnO6gaMw4Sfv32EqR3RNHQGs4CjMCJIU7kqL0uO44Qr0whLFWw2TN69skTbktfG1FTiTDroOM+ZxQlBmjFNEjqWhWca7I1DVEVho+OSVc50x58zSVM0IcjKkigv0YQgzgu+eDTBrRy6pSrM0oKWoXIYRLww6LG19DGSsiSMcwxN5ZETXcr8Wu1g2m6feD5gPryG0z5Frktnkkx+nSQcoZkelt3BdPs8v+/hxz55UXAURhwGMmoJsoybs4jbQUqQlhiqoGXKY5omKX3b4mTLo2WbrHgmWfAEhtGlEGcI01VMdRvPBEPbYBbdLYUjZKrgntX2Vhfl+0BaORMbSQ78u8A3AMM7ivLdsiz/8uvt67HHHih/6Kd+ip944TovjFNGQU4UFViWwpKrcjTL8f0cXRcYhrwJTnY0Hu7aPLTU5YNnVymmn2V2fJUkGgHgdrZQet9KkGQ8sz/kh6/skBWQ5DJt1DQFu9Mcf56z1tV5/4rDumNzzZ9zEKWccAx6plFHK8uejZV8Cf/oCrrpYbdPoeo2ZZ6RhCMOeTfPHh5zGEQEWYajaTQNuXLWFMF93TabPRdTVdC1PQ6vfoo8C+meeh83/c16Zb/Ze1mpZDhLCNOMzV4DgHGQoimCXmOfID3B//nsDQZRzEdOrnNp5RhVtzj0lxj4IT9//RYHYcrFtssfe8dZTFUhzgvyKspXFcgLGAcJwyDE0XXOL4+IZwOw38nOKCBMsmpbmVJKshw/Sbjtz5in0tlsNl2mSYqpqniGjp+kjCI5kSzZNm1b8vY0oXzFfgCCNMVPUo6CkCgvCLIMtUp1DOMEV9d4tN/FUFVURaHnWORlWUeaALauMYsTVEXBjxMcXWMUxRyGEfMsp6nr5KVMmUzSlNtBwo2JPK8lR+FDK23WGjaGqrLiOpzte2jFiwTDa8wn2yiKhlC0+lqbjT5C1fH65/ncdXm+AE8fHXMQpliq4IzXQFMEcZ4zr851q+URZzlBlnGp32Wt2aDt6BjcAKAoUhRFZ5KcYjiPuDX2OQrDemybhs665xKmKVlRYmoqbctkGIS104nzgmmSMk5S5llOlJdYqqgjl42GxrmmS9PQ6/Suo8tFiWcadBsWSw0D8hdIZodopodmeiThMaPbT6AbHo3uWYSqUeYZqm6jWA8B4Mc5T+wM2J8HTJOU20HEOCnQFNCEqKOSJVOlZWhYqsqaY3HCc/EMGRm2bBPHUDE0tbrf5f1vqAqqojAMQsI044PnH3miLMvHXneC+iqm23q5dObVBKp/q+0/d/Cmv+/3gr3VEcoa8CNVHUUBfrIsy/8ghHgc+EkhxJ9Fyn//sa+2o3ks+FfPX+fFSUJewJKrshMVzOfSsfRaGg1TIS/KalICV5eO5ep4yhceP8LVu6w561zq98iLksOywPNDwiTj6aNjto8ypqMUp6lhmQrTSGDpgqKUTubmPEZTFFZskwe7LeI8p2kYvOfUKvn+TxIOj8Fbx2lvEs8HzA5fwLA76HaHMk/ptSw6vsn+PGCcpGRVvvk4TojynJ1ZwNbEI85zVEXhTPsPAHBrkAExfpwQZBm2rtF2TLmC1xQMzSBMCtLdH2Ow9zl0zUbd+CDwJf7Q6XUOCymRVBQpB899krJIWba6fP/5deL5gHg+YPh8iKLoqJrN6oVv4cBfYRImDGZzDoOIpmmwvOwAp5iJVeKZjJIAOekYckLPi4JWYmKoKuMoZn8ecBhEmJpKkErHMM8yhlVEdWsW0NA0upZB0zBoWyYH84AlxyZIM3qOxbppsO41+MKuDGTzsuR8p8WHvAZ5UXL5cMgJr4EB7PlzbF0jLwqMKlpzdJ21ZgPP0giTnKwoOfTD2sFMkpTTTZesLNk9GDJLC1ZdFVdTWLF1LFXm7Nu2yaX1OZPdzzKrUoFFkaEYHlZjmSQcIVQNe+lj3BoF/Oqzx+RFyt5cnvMkyfj69SXWvQZJJutDB/OArKp/jKKYs+0my67DsmdjaAJNCG6OVhhHMQDDICLO94iznI5lcqrpkVT3i2cYGJp0AH6ckJcltyY+8zRjmqR1esxUFc56DfmcCIUb/oyWodHSpROJ85zDMCLINLKirFOYfdtkNXA4ntuoyjquuUkelyRBTl70WTv9KJ6pUubXSDhNlBSoiiCPZQTvR/IY5mnGOElpaGodEY1TeWyaEDQ0lWXLYq1hs+65uKZep0rDNGMyiTmYBwSV40zynCgvyMuCSeUo74YJBOIuRShCiB9CqkkPyrL8LWn/ChH7j4BvRQp1fl+lNo2QzfD+EVJE9Z+XZfl37spBfQ32ljqUsiyf5mVp7jtfHyKbHr1hm6cZLUPlXMvg6cOY4SSj4aikWUlRPZAP9gy+fq1fPxCmKtMTixSFbagkWcmN4yl5dSM6uk6c51z1Y7K85P4tm0koV0r+PGcOfGTL4YVxykGQs2SmRHmOqar8wYtnsPJnoByin/kgeXGSIC1omLcRzQ9hFM+TKPdj6beJZwOujWbE1c0+z+Tq3VQVZlXhuWcqPHF4zDDO0ITg07tHAKzYOu9bW8ar8vd7/pxZnNKyTdZbA/z9ywz3vsTB6FnyMkfPTPLtX5GTnaLhNeTfV1OfZmuL7sn3Ydhdjrc/x+T4eRRFRzc8ehvvIzIe44lbE5J8QlwdY8cyaVf1nt1JVD/YizRWkEpgwFrTJUyzOrpoWyZxnrPjz4mLgqYuJ+d5ltMzZVFWrtKLKs8/JcpL3rvc4bYvwYErDaeuwbx/Q+ob9homVvRZsljWue473eeleY84z3FUFdfU8SydvndEUWSMwxZ2/iTZdAbTXZQi5Ux7k4vnv6E6n4IboynkOd9+9iSmqvL88JjDUE7ihqqS5Dm3Jj6Dmcq69342Vl2WysvEswHx/JCiyOieeh97s3UOBlMZCWlanQ6UqaIZ8+FVstinVFPc1Qs8PzzBURAyimLysuDaWH42TDJsQ5P1lVym84I0w1QV2pZZRyGGpuKZsh5nagrDeVxHJAA7szmTJGUYp1iqwrJl0tA0lhy7jvTuX+rgVvdWkuf4ccI4jDE1lZ5jy/ShIupr65oGszjhucExAHlVuB7M5tX2KxhawixOyYsCW9fwLJ28KHCqxYOmiHryd3WFnqWhVcez7shoMM5zro8n8v6zLPwkIS8K9ucBL05mTJKcozhjFH1lVG2or9ll+Ldvr92x+LdrP4yUd/rR13j/W5BdVc8hZWf+CVJ5WkU2P/s4sAN8UQjxs2VZPnu3Duy3Y291hHLXbIEeuTyMWW+qWB2dCy2bNceiY1n8yu0D/ui5Tc4vt3hhMGHdczFVlawsZEisqRSjX8bUbU6238ONY18WeIuCJM9591KTJVNlP8po6KIuxG64Dp8fHPPd9y1zGEb1xNi3LR6/sUdWdllpOJhjFUObsdFxeGHQIcl81lrneOFwiB8rGOoJNCUkyXMamnx4NCGqlaN8GlShcDtIGQQSlTUNC5q2gqsrjKKYU02PtX6nflClncLu+PQA3fDIsxCqv53WJprpMT++yvHwMtePn8YeP8+FIsXtbNXOxGtvYW/8cb506xBVOcbWNWxFk8VyVWEwC2pn5hgaQZLVRfXDIKpWyIIkz7m40qXvHXFz2GEWJyzZFmY1OeRFwQ1/TkNT2XAbzKtJz9TUaixk3eUwjNjwGqx7DbJSIp16jsWq9pv4R1dgrlPYHeL5AG/pAmk44mL/gJj7AEgyGaUWRcZ8eI18+hnmqkYW+wSzXXTDgzGk4U+z1Fim0T+LoXV5cu9QTuaK4Ey7RdOQUUyc5/XqftmVziEvCpJoRKO3hb30McZhym/s+oyjY46CkFMtj/WmS5LlrLUc2rZONHmB6eAZjoaXaXmnsLx1+p5d10Zu+zOahi6L50EIAdWiRyNIM7KylEVp22RrqUW3MSAJ94gmu4hMp9H6MH4kHU3bMglSGQnenCUMgpxHlywaukbHMqt6hMEsThnOQ/KipGWbuKbOsmcDL6eTwjTDNXXatrwH/CglSDOOghBDVWmaBmpVj3npeEzHMuk5Nqqi0KrSmX4k034rrgSCLFBpy7ZVOxLPNOoaKEBWlNyezes5YLFwMVUZ2eR6SVaqlUMpsXWFpinqzMSbNgFod6ezQlmWnxZCnH6dTf4Q8KOVtP/nhBDtqr58GnipLMtrAEKIn6i2vedQ3oxFecHNWcK3n27zQK+NaxqEaVav+D9yYoXLh8ec7jZZD36al7Z/gc8Pn0YTKl9/8pvIV/4eQ+X97I1mqMq4TonIycrmg67Dg2HMrj9jbx6SlyV92+RMu8WZdgtDU7F1jSTPuXI04sZ0xvY85KwnC86OrmOqMoXy1OBIrr73BJtNKVp8MA+41O+x7Kpkx2M+fGKlSgPJSTXOCzaqvPqLkxn7UUbHUthwdGZpwU9e3cfSDlgyVc41Xc53W3QbFnl6BaFoeKuXcHpnURSdsNgEwNFvMxtcIY6Ocew+D21+G2niYzaW0UyPtbPfSGy9j6tHE8xDn2W3gWvqrDg3iaYSFdfofZhZnHJr7LPiOmx0XExNqSOTURzXDvFSv0evsU88GwEd8moFTZJyczrjdhBxwrFoGhLJdmm5h1FNHkleEKQpYZrRtsw6V1+mz5JFPmk4YjS4SlzVv5rqBQxbInAMt08W+5iOQpk+i2PLMS/zDKd9ijJPuXrlJ9n1rzOMjnE0m67V41TvEdbOfiMgJ7j3nlzBNuSEduDHX+EIx1FMVkqkWss26DUMcvsbCPOC4SxiMJNoNIALSx2JaNM1zi+PyOLbpL6Pf3QFs7HMmd4FdKeDYXdpozMJE1Qhndjiu47CEFUoJHnO1YlPlOdYqiyU3/ZnLNk2602Plt3D69wvI++hjFyd6j69PZvzxUFIkpdseJqMDtOMrAhpWyZJllcpMp2WbdJ2pMMY+GE1QWu4pl5HpHvTmHEU14uZxeTuVACIIM1Y91x6joWqKDiGimvJVJUfCRl1VQ5RU0QdoQZpxjzNOAwiLFXhUr9Ht2HJiKhK52VFyTzN8EyDM+0Wx1GCpWaYqnS0wygnzmDJ1O6iQxGgvOEIZUkI8aU7/v9nFajojdoJvrKnzU712qu9/t7fxn7vqr1tHIqqCC60bM53WrimwTCIuDqeoimCvm2x0nD45C0f86VtVht/gHd+/ffxQfOAtEqLXB2F3Jr4vDCacDuI0ITgXf0Oj6wts96ySP3Ps65m3O9kGHaHokgx3QZ5ekAwvIblriNUDVWzOdsBMJgk99VFxufHU0DmgCdJJlE0CuwGEWe8BptNl75ns9Ya0mus8PlbB7Rtk42Wh2foPHN0zCiK+NjWSb4uyfjlGzsM4wRNCHqKQstQycuS/TDj53dG/NrBmA1H58FukxOeK2sPM522bbLsSsSQ6YYIVefkO/4Sw3mCoalY+TMURYqq2wyT+3hmR9Yl2pbJWsuh1zCACzR6UgB4HGZsdFxsQ6NtGzLFpz2Ioamsey5785BxEqNVaLMDf4W8WGYWS+e8M5vX9ZKGJifrpiFRUeeXPeLZl2rnZTgdNNMji32KJCMMU4Sqo2oW3uol1N43YqoKqnKLvDiJqtziePtzNLpnGeYP0jz6RbLEJ09lwT9NfBRFQzM9VpYfo+WeYjLb5tnhk3x5+CxfOrrMB8bPs9q+n+7KI3R77yNMVwnSAsfQMDWHgR9y+XDIdX+OJuQ5eqZB09BZdhsyoklTRlFMnOd0LIvBLABkNPP5nQywcfQmTeM0y1aDXsOk6R2RZmvMwrQGIhzMA0ZRzCCUx5+VpYw2qqgFoGnodCyzro/cGBU1v2Yx0c/TjP0g5OYs4qGeybmmRCNqQuCZhlwYVc4kLyTEfGfiszeVk+eC+xOkKcdRwiCKuDlLmCRy8fbOnsNJ12HJltBdkHWtk20P19TRFFE5pZennySTnBKJZtTIC4dZnBKkaV3r6ljmy/urHNrpjnSyB7OA/UBG+GfaTd612udgHvDs8ZhZWtTH8PRhTMu+i8isN+5Qjt5kUf7Vvqh8ndffEnvbOBRDkcW6m9MZXz4cMs9yWobOWqPBSsPhwfUuR2HEFw6G7AURXzw44tF+D1Pr0LZMNjoOvYbM6d+cRaw0DPbmIe9SBOQvEPm7PHvlJzgM9rE1h47dZ73/Lpq9C0RzOenqdocw3EbRbbT21+NHsUQVFQWDMOLmXObcx7FErmx5Ju+qEEiGqrIznnHoO6hKyJkKWz8MQtq2xWrD4TCIuHk85cH1Ln/mXRf4oSeusD0LaeiqrLkI6VS1tqjz39f9OS9OZqzYJqsNp+qXCO9Y65AFPobdIUhlIXo4CWjZ96MpgrGfkGQRpztN8qKsCp8qirJLGvukwYgs9kn9Xcoi49SJxzDdPtDny9eP8ZMUU1WwVIVelaq47c/59d0Dorxg3ZFpk90g4sokZsszeLDb5P5et65x5EWJYXfJ4hnj/SeJ947J8oiikFGbZXborDyC6fbJ0xBHv02ZZ+Rpim7uEaYbdE99F/tX/inDw/8fkd2n1b2fPAspigzd8EiiY9LERzc82v1LdFYeYX39g4SzXUbTa5i6h2l1aye0QMmpikKQVMTKosTVNExVkZBiX6ZhRlWhPKvrcRqjKKrTmJ/eOyQrYJzmLJmqjM6mM064DU6GTdp2im2oqIrkooyimOv+nJ0g5TgscHRB21TQFIFVobjysuAoDDmOEkxVZZqmzNIMV9dYtmSdK84L3rHU4UMbFudWmqjZc+TpCLu5TlEsk6dXJPpKSQnTVcZByqEveSD784A4z+vrdjgriJOCJC3JK/7KOCxwzYCWKbkhlipwNYWGrpIVJZaq0DVNNptuPZaLOlTLNlhqGBzNE4I0ZVzVqZZdp67LjaMIP0mqDIKM2K5OfMYVHP04TthsujiaxgPdNqaiMIhizOpe7JgmP3m3Jp7fOdjwDl/ZyG8D2cLAeI3X3xJ72zgUkDWGvCy4r+Wx7rns+jOmScqSI3PmHz13giCVD/Pjgwm3gz16psb97aYk0LkG33R+g7woOAwjHlzqAjBOztBegfXDd2BpDebJhDSPOR5dwXKWSaIR127+POv9d9LuS4DGcJbUsNR1T8J4o/yYtqHTt00u9XtcXJ0QTXawWuvcGrUJ04xlz6ZlbBNNd8nTEHRIohHn738fu5OTXBuOeer2kPP9Nn/6YsSXBut8ZvcAS1W4MpEP34Nd6QSivGBZVdlqe2x2Wpzs2OTpFcoig/wQu7lOmq0RhjJiCdKU/3j1JsM45YRj8acePk/LllHc8fbnuLr3OQbzbVa9s9hmB9tZZmnzQ6T6g1weTGBExblRWPcaBFUKYqXhYGoqy65DxzL5zeGIX9kb0zYlgudCy+ThXpv7um3WWg3yosC1NIroabIiRVE0mr0LtSNRNUtOeLpVn4Ou7TEfXiMJR6iaRaTqaOYuuemxeuFbWDr9QfI0lKoJRYaiW2SxLOwn0TGa6WE2llF0C6d9iq7+PpaDEfH8EFWz0EyPoshY8e7szrvOYCZXzisNh7ZtMosTbk5nxBWL36lqEiBXyHGe8+x4XvM9nj1KKErY1/P6tTiX0OqOZbLZebk32wm3gSYELSPmMgGTuGB3lhPlMRuOTkOTE3acyxrbAn3l6rImF+UFcZLTty0A+p5NNv40SSod7OzwBQDMRh/d6aAoOrYLM2WpVjU4jhOGUcJ+lOHHJXFSoCgCxxZkWUmWlRxPc4bVtTJNBddWaNsKPUsi43qmQUNXa4eb5HkdhbQtk7aj41kaN49zjsKwTpvFWc7BPCDKixoMAnBzOuOqH3IU5YyjAkONOeWHbLoWXdOga0no/gIA0a7O/02b4LcTobxZ+1ngL1Q1kvcCk4qndwicE0KcAW4je8T8yd+pg3qlva0cStcy+PDWBjbPkwa3eHD9A1w79MmLkp1RQNs2uNTvYWgqJ9wGeSmLk4DM55oHHPpLvOfkKsfziJZtsmxdRdVshHqBZu8CuuGRJj4v7P86zxxfJt75JRzNZN1ZYa3IaK0/zM8/VxDnB5xsefQcm5Mdmwdp07ZMxlHMuzaWUUf/gXC0zG7+CHvXZtyc3sbRNPKiQ+5t0G6dAWRaQylKdicpszil59jEec6t8YzT3Uu858RVNjvn+OTVW7QMndvzGFUonGo3SPKcLx4ccXwQ88WDI9YdG0e3KjBCiaMJTO2QZdeR6YNuk//mpIwAoskug+f/HnvxCMvs0F17jHNL38sDToeY+xhMI3aDiE+/OObLw6f5utVuzTa/uNytFAAkUODB9TZp8BSa6XFCH/Dh+9b51Esdrk2mmKrKO1f7XFydMNn9NEbUQXc65GGI7j6GH+cYtsBTd1AUjTBdlc4vyQmSnGSQA3NsvUuvuUqzeaPmYwhV3t55cZJcK8kVuYJOshLPVLGbezR6H0ZRdgmnu9jNdWbxCiiC/WlMkp8g10patsGKd8B8eI0s9smzEFWzsZsDHlx/J4OpJG22bQPbUNmctdiZ+FXhWUjuTSwXGE3D4HzTQVNEzfOYJDmmuiAUSscSpNlXOCVbl+lKzzQ4lee8oyf5KIdBxKAifU6SFO2OFXPXNFhtOHiGzlEYEWc5Dd1mmiTsz2Xa7f2nv4Fg8PPkWViPV5bINLDVWqcoMqk2UBXNs6IgykuirERVoOupOLrgQkumzlRF8DM3jxhXSMg7C+Erlk6cF1zzQ56dzKv9waqtcbIhx2QcxTy1k+AnaX2Mi/vKT2T0YaoKa3YDQ1V57njMU6M5k7hgFBQoQqK4XE0hKySnpmsaOKYEdqiKgq3fpWlP6vrcpV2JHwc+jKy17CAbvukAZVn+IPCfkJDhl5Cw4f9r9V5WdRH9BSRs+IfKsnzL2iK/bfqhXHjwgfKZJ36R/ef+EePJVbzGOr2N99PofVhOSpqE4A7nSS2/YmgKe9M54zDm8f1DAM61XL7p/CaO8pLM1adSUyv0d9nd/Qym7uHYfdzOFmZjGVWzEO57CJOCndGMJ/YP6VoGqqKwPw9YbTicano8tBFwebfBrYnPyZZHXpS8eDzm84MRWVnSMzV6pkFcFDQ0lbOtJiuuw2bPRcweZ3Z8lSgYkGcRjruOYXfQDI8s8UnCEe7p78OPMpKs4PM7ezJdpKo1v2CSpkySjJahcanbpmno3JzOeHEyI8rlPeDqCve3PU54Lhstj27xeaLpbl1T6Z56H4qi8bnrukTxVKS51YaDJkQtLXJ9PCUvCjqWyTtP9lHjL6PqMsVl2B3SbI0seII0GJFEI+L5oE47uZ0tNNPD7nyQccWGX+TSF8ifYQWjXdQQ4lymN+/rtis+iV6TPONMch08S6tJmEmeszed8exwDEDL0HloZYmLqy3mw09hNdc4CDarFEtZp2FS//NE/i5JOGI6voqiaNjOMu3VR6SGGpCGI4SioTsdZuIheo19yjxjb7bO8TyS6CwkYmmt2cDUFMZhQl6UDGZzeb0qDonk2qQ1MENTBD3HptuwWPFMFGW3hn7vTXrcPJ7y+f1Dblep1RVbSrKc8Bq8e3OZwTTmyb0BcZ6zZNuc73fqsVIVgaO8RBpIBQS780F2JzLN51k64yDh6nDMtcmUYZxwEErZl0UK66FOg7WGjaPreIZOXpbc9uX9tdCuW0DAZ2nBLCtktGJpxHlBzzQ4X0Vj55fkcf3iS9vkRcmplsea12AwC3huOOY4jqvxULg9j9kPM4K0pGkKtjwTV9foVnWsBeFxIVlzwpME3/efe/jNExs9q1x656k3tO3+p1/8fUFsfNs4lHe96+HyZ//Ft9FZfwyhahRphNVcpxBniPOCMMlxLY3BNObqcFxDEJ/YP+TWPODyKGYUFOgqfOtmi1XHplGtZHqOLTkViYTjfvTcCfzdn8YfXUU3mvTPfT+/8MIOXzgYcsq1UYXCcRzzR86fYcO9gdAfkJpW1WSxH4TcDiJmWcELw5Sj45SygMDPCY8z7K7G/RdsHuvbfPTkGhf0z3F4+3G5Qo9HMoLIZhiqRcs9RZaFLK09Br0/yMAP2Vpy0bU9jufL3DiekmQ5bduqJ+UFOgmgbUuS4TAIGQYRjq5xttdixTuQKTegLDI08xKKssvOqMuv3bjNcSyRR+/od2XBOctZdR08w+Dy4RCApapOskD5GJWwoq1rbLYPiGcDyiIji32EomG11plk5xiHkufgx0ldhA2q/HlcETxvBxFRXnLCMer6wblOC880cE2D5aaJJgRHc8l3WNSU9vw5+7OAnXnAQRhTka9ZsXUe7fc43+/Q5Ukif7dOsZV5Rp6FpImPqtkYdocyz5hPb9YaVKbVJQoGJImP29ykrKDXhtvHsLvEswGqbsvoqzgpCX3pFRRFpxBnyMqyVhaI85yeY7HctDBVhSCVSLG8LFlr2RjF82imhx+fAMDVbhLPBrUza/S2+Nwtl2vjKVlR0rUMHllb5mTnkDjf4PHrB/hJgmcYeKYhnYmu1yv32R3coQXjfEG0fOl4XAMANCFJt7MsY57mmOrL9RxNCMaVAoCU0JGvAzia9hWvL9Jdfcdis9NiEsa1FM7F5S4rnimBFmlIppzjE1du8cThiKM4Z9M1aOl6fU8P4wRNEbiadCqeadTqAgu48TRJ+XPv/8jdcSiPbb6hbfc/9cLvC4fytkl55UXJiUvfwbXDFmEo4YfLhVWLwS3gpwszVZVxFPGbI58rxwmuqXD/ko51R8qg59isNSVrOS8lxHWt2aCInuZo70vMo0M6zbMo5XXesbJGkKZMk5TjWDLmf/PgCNu4j1b0eb5uc5nP3XL50sERVyZhnQd2TQXfVAjmOWVRojsKqgprDRmxGJqKomgYhoeq2TjuCYLZbQhhMN8mSmdYukt445O0hlfouOsMJxlFkWE1+tzvraM7HTLlXC2SN5wnxJlEKnUbA9LYZ711njgvMIrnydNtyryLn23Jia8ouXpryLLb4eZoLAucisKG16glVCxVYX8W4OsJ791YZWfiM4pizrRbNcTz2cGQnVlAlOesOzYda4N1T6LbJmHC4PacOD+qrmdBVspi9jzNaOga0yRhL4i4MgmZpQWWJrg5j1ky5W1825/TiGI808DQFJYaRsV+z0hySaCzdU2y+vO8Vs1taBLeGmQyGlpZei+a9Szx7JA0PCZNpTNJq1RQFmsYdgfbXSeJRiiKRhQMCMJD0iwkG0XkRcrR8DJLvUvY3jqqZjMdXCaY7WJaMrpUFB2nfQpFP8awu5zunsGPcwa+lAfxo4ykQlrZhoZjaCRZyTjZgggcQ6buijyVisSKRhKNCK79ClvAo2cuERmPcfVoQl4UBOkJTF7kG87o7EzXmMVJTYpMspzBLGeaSESaJgQXljq0bFPCeisk3mKCbpoGpqrQqcipN/w5B6GMFuK8pKHLgvxiwm/oWp1+80yDYRDWCgB+kuDoEhXXcw2W9cushJeZT7cpE4+BItGTeRYSRz/Jtz/6vXSurdZZhZahyxpLnrPWsGuFg3mWVT95zWVZ6IHdFRNQ3j1i49vC3jYRyvlLF8sf/Hc/xu0qbWCqCuc7LUmECyKmacppr8Eja8ssN032xiHPHh5zdSLFHk84Fhe7bU62Pc4tT5mEK+RFSduWxfGj6DTP7A8xVQXXNDjdbeKZKjeO51wdjut8+a/t7BPlOW61CttqeYyimEmSEhcFwyhhJ0iZp2WF9DJYdyz6tkXTNGr+i6YIbF1na6mFHnwGp30KVbc4uvEZ5pObaJpNmsiVvW40GY+u4EeHpHmMKlR01cK1+/T6j0i5D2+dRu8sAMfz5brwLVdt23L1F/sYtgQihMUmO6OgRtEAeIZBnOc8c3hM35GFzTsnmnWvwbLrsN6S/JdFulCzPBLlfp7ZPeYolMzzaZLW+e37um0pYJl8pYClV43HzemMQRgxjFOO4hxLFbL2EBckeYmjC0xVcKltc6HT4ky7ybJn16rM8DJRTlVETcjbnc4IsowgTVGFQt+xsHW9qllo2IZ0Rqqi4Fk6rnKFPJUSNLrzMONQRk2G/0uEVUQTzG7jB3tE6Ywkj1EVjYbRQhEauiYjNj+SzHnXWqLffwTbW8fpnUU3PSbhCgM/5MZoyiiKOI7kxJiVJZe6Eriw0XHqNGiW+LVzEqqGomjkaVTLvtjeOq21h/GzrVp5168kf1L9Qa4d+vW4+3HCYRixPQs4iFLausqKbXK+06prjQfzAD9OasJinOc4muS1LCKXM+0mmz0XNf4yB1c/SRAeYugu7f6DqJqNZnp4/fP1fehHqYSdOzpa8SJlkRHPDhnuPE4Uj1CETC0WRcp8vkeahZy9+Mc5Vt7LzdGErJBy/KaqsOw2aNlGneIc+CFXh+NaXubXDsZcH2Z86s9855uPUJpW2XvP6Te07cEvXbkXofxeshK4PZuzPQu46ssJ8OnRjKyAd/ZcHltZqpnveVHSdkw+dGadj1a1lSy+TBpI2YskPIWjr0mI5WyA5ryLvemIoyBkpeFwa+Lz+O0DnhrNuTHK+OAJm5ahE+U58zSvHcQi5922TF4cTSCV/JMPrbTZbLqsuA7394ccvPRJppOrrC9/nEZvC4BJcoo4y2kZ22CcYpZtkicl3VPfRatSnVWz58ginyyW9YdWdMx8tksYj4iyGaPZLRqNdSxnmWC8DSAneC6jqzpZBIrdoTDfwbgquq7pQ4oiI8+qyTdMqlSTdHTLrsPFSj3A1nXWvUYttDiLJVS15xrYpsdiHaiZHhX6UzKdDZ1H1pbpuZVUzCSsVZnjPGcYhFyt2gOsOjZ922J7FqAJwTu7LmsNG1NV65rNAqG2ILnFuVzlu6aBZ+l4poqu7ZHGPmWR4bQewI9zXFPneB4xjqKKJ1IQ55KcF+c5cZZzf7VKD5KMQXyKJMvZ9WfE+U2WHJtl16HX+jiqV6ACZ6wdgvE2wfgm4WyXJPGJU58gGQPQ8k7h2H2ieIShS2SZlPu/wCzOK5WBkmmlxixrahp922Ldk5Olax4wGfooio5meHV9amELPpSmaOiGJ1F9SDCCrcs6VjTdRTN9Njrvq1WR40ymrVZsk0mSc2WScGWScHkcsGpL2HG/UpxO8pxplZKU6SQVQwjWmy6bzhWm25eZHF/h5vFTJLm8+PbwSfruJl5jnWB8E6uxjO32sZihFhamug6qTZ6GGHaH/qkPyUi7uUamnMNUFdLgKfavfoK9a5+gs7TL19/3YWbxinyGS6mQTP4CwxufQdVserrFidV1Cufd7IwCttoeWVHyqbsy6wjK3zmU1+8Je9s4FK1iy571GtwOUvbmGZaqVGiPshbv67kWevoM870nGQwvE8YjbLODpspmRkWZcUL/ViJL9nXwzPt5dnubX90bsuXZrDQcycwtS5ZMlcgr+dIgIkpDFAEfP+Wy1W5yutNko+NIFnGS10VKTRF8fOskneJLHO9+iRe3pQNI8oj0pZ/lVPGteKuXAGg7Okl4DPY78aOE43nEjeOixuP3nA26DQunpdFbvk0ajOikIfH8kGBykzAYkGcheRbS6G6h6BZ5GpJEI5orlzDsDmG6ys3hnCCV07+pLdNtDPAjSSjrNaSmU5IX5EVR9WPxuDYcs+vPyIqSaSK5GYSS0S95BS0MrSsjgxhAroLXvUYN0TaK54lnh6wUKbrTQdVtQu7nuf0Cczbn2bEkPfZMg4eXOqx7DU62XbrWDtH0Fkk4Ik+kDLzudtFMl3H5Dg59WfuRdaGQnmPTc1dw9IVsuai1p7KyqBV2p0lay72sNWQxOy9KJpU3XAgpjuKYrCg5jhKOAim/soC2tq0em71NOm4fZ3ZImacEk21+89q/5zN7jzNNM7bcLg/0Hma9tYXVXEOxHiLOCxktWloNidUUQR+JcjrZ8uREfXCZG9d2sZxlDLuDoXXqwrxQNISqo5kuht1FqGfr5yOfJ5IwWEWNQtGI5wNc5wp27yJhUtBtWLRnMsW12YzZ8edc9+fsRxn7ofxx/ZCWodaF77ysFIBtiwfXuyjBF5keXCFLfBruOo8sPYiqWxIpWQEXhKpj2B0U3aJII0kutaR6QRb7lLm8Bnbng9wahcyGCXkxrQAKy5w/9V9xxrnJeOcJ9q/8nFQzVjQppTO5SZr4eO2t+tznx9dQ/V1O2F1OvIzCfvMmoLxLKK+3i71tHIqtS74FwPe0vDpVAnB/r1PLPXjmbcY7EjGlqRaes0aSzlB0HdtZxu1syXB8mJNkOf/7lRd5cSIL0LNsjqNpnO+2OF2hsd7R0Wol04amcV+3LdVgmybl7AuIIqPfO0ucdVnzJALJij7Lc8/8CNN4SMdeodM8i1A0yiKr+2RIVrOCqtskRVkLKpqqSliF7yD7jUgdrRN4zim0/BrBZJs08RnPbnFj8gJxkdAze5xoncM2O5hWt5JXuUSYZLRsAz9O2PVnjMOYD51dY70lGM4THGT6qdfYJw1GaIZHo/Rpn5Tpku2pj1VpdcV5gSpk35dFQ6wFLyUvCpZdp24Q5poHxLMMzXTJ4hnRdK/S3hrx4Pr76wL+AsV1deLjxwmaUDC0TXLrFF5LJfU/Tzw/JJ4PUDULx9KqvH9R9Q8pasnyZW+dtr0LbOOZp8gLi7womanS2R1X6btZmjGp6mF925KoKEfCbVWh0LctgjRDXciCZBkd08RUFW5NfII0Y9k9y2a/w3x4DaFoLDnr3J/HzNIZhmIwigY4oysoigY8RVwRLIsiQ9NsHl15hNZ9386N4Zy2Y9JtDJjsHpJnUZ02Wvw23D6Zco5xkBImGXlUQgS2LlFapqZU0kD7FMVZhHua0ioxFFCVm5A9R9u+hG0o9FyjRn0tmnAtgCs7gUR2aYosqEuZFpW1hs271o8Z3vhZZlOZjrWcZRpd+SyF6Wp17yoEaU6Y5Lw4kk24PNPgbN9DFC/K+8v0KJSUIo3I4sv03PvZm87Ynvo1IXQUxZzp9Dm/8ccpRr9MlvhE80Nm05uYVoe1s99I5rxfsukNBTV7jjSQaMLp8ApZpWf3pk1Aec+ffIW9bRyKIOZ9J4bMigvEWc4kTOrcapimqIqczOLZgCQcEUcjijLDMju4TSmS2D31Pg78FYIJqErJr97e51Mvzul3NTZbGllZ8uJ0Rl4WNA2dDVdi4b98OGTZtjjhSakNU1XxLB2/eJgwzWAfTplPMj2+TBAdszu5BsBq8z7anQt4SxewmmukgdShmoQr+FFEnBXAFj1XcLo3RFE0Xhw0JWolTbk68cmqlMjLkE2Phzf+KLb3WSxnGVOXpEpT9zAMj2bvAq21R3hx2Cffn2BoKr2GxVpTypWvNRvo2h7BaBtXlT08NNOjzDOi6S6zkeTl2N4uW7bHxX6fmPsYzhJm1YS82RuRZmuMw1RGNmnGWqtBy9iuxSmLwpOIp4psGIxvysL2jV36pzIeOfFhQEYFWVFyWMmNSESSUqew7uSFCPc9jKcy/bZwwC/3UBEM5xF5sYqhqRiaXFnLFJJO27IqIqxMNU2SlN0gZJ5mbLWb2LrGezfWahl+oO6KOI5i7Eq3KpsFHMwDxlHM3lTHM9/B8up7uH/lEqcrp5km8pijeMTN258iSueEWUCYBRiqwZKzRhSPOLz9OFkecS0ecQ3odx/AcpZlzaV9Ct3pEOcbHMwSwjSoHcGCAwSy10vPscmKkoHfZDg/rImEvYaNra/L+yNLcC0Ngxsk8TFZPKOhWVzsdzi3co69ccjOxOfx/UNuBwlZkXHGa/DQSp+tzi63n/33+HNZR7JNCVhQNYs0WyMvJMrymi8jxhdGE54dz2kbKicrpeW11n24bkUaTaFUM9JghOm+xMXV+8jLEqdCE2qKYDAL0ITCg+sX8Pcl7aK7IutRducU47AkznL8KKXn3o/ZUrA7AlX7uRpc8WathHspr1fY26Yo/+hDp8snnvg37E6W2ZvOZc8M2+JgHnCm3aLv2bQdHVPdwd+/zHyyTXv1YXRHCgjemJyoi3f7QcjPXJuweyuiv2byjlWDrJAkw0WXvGXbqiGZC6lvR9fZ6Lh0GwNZoE1PMJhGqIrCctPE1vfJ06hueQoQzweYjWXc/nmC4r6a/5Kbj5IXsDcJKshmSl6WeIZsYrTesijza8yyTXZGs1p4EEBVFM7321VqSIIKhKpT5lL7ymqucRSdxrM0TF5isvcUezu/xvb4OU61L9L0TqFpNm5nC9/+CNeGY+K8YKvb4mRzh8Nrn0LVLPIsIgoGWM4yuuHVxLhGV6ZaFEVH0a2qYHwW2CYcbcu8eOcbyMoSW99nEq7gajeZD68yObxcrW7PUjjv5uZwRlCJALYtk/PLTZTyOgmnATDVHeJ8gzApiLOcWSxX6YsU3mLMFlpjrqXV22qKoG3rFNHTKLpFUNwnxzqRPJVxhRjzDOl0TjZ3mOw9xfjwGebhIQ27j9fewlu6gFClJthBsFnffzv+nLwsaRo6Z9vN+ji2llyiyeeI/F3Gh5eZhwOCZMw8mWCoFg2jhW12sMyOVD4GNMPDsLsyzVVFJLMoY+CHXB9Pv6KVQFwJaYIEUpi1Jpd0onElQT+vah8dy6Rp6LimUTHVTTxTrZuptWw50afZGgd+LLlbUcxKw+Fi/wD/4DLR/JBgdpskneF5p2i0TuH2L5BrFxnOZLr2+njCE4cjfvM4JkxKbENwsWPwzafWaFtW5VgcPFOtOTZwiiDN8aMMTZHQ8yTLGc7jSipf9kLRFFFzjoB6W4CbQx9DU/HjBFWRfKmttXNvukiute2y9eGtr74hcPwzl+8V5X8vmaIaZLHPJGxxUDVtWjRHCtKM95qS/d5trLDczzDcPoP0Evm0YBan/NyN68zTnBVbPlwfPdXgsqeyasvIZJzm7Ps5Gy2pm7UbhEySDM0XfOykyXLTrVI9gqd3HJ4aHDEIn+PmXHIdLrUd3rHUYbOzxnL/NLa+TzyTsh56xT4fBwljTrHmXEMtnifXLlb8F5mr35kFzLKMZcvifLfF15210Gdf5P7+MmGxSZjIB+2nXrjOf7xxm4ttj0v9ByTXAEGvZWHrUnFXGfwE0yyiLFLms11M3eNc/z20uhcw7C5lkTIbXcUDPnj2W1CUXWaxRcx9rNynE0130Z0O4Wib0N9lPr1JloVyoj18Bre5SZr4WI4sOmfx5XpVnachWVkSJgVhssyN4wlh6rLmfYDN+7ekE8wijPQZLq4+TJDmDGeyJ7xSXmeWbRIkaUVOXWEWS6ebF2XdE6OGjqYZsZWzhI1ryu6Gpq3V4pG1g9PX8MzbYELbCFlunmMwjZmEcRXRqKi6TaN7VtZsRlclhybxOdz+NRRFx7A6rJwCz7oPc6jW6MLjOMH0Z5yoJHhujUKWm+/BVn8DgDayZrYADeRZWJFmbclxcaX6s1DPEucFflIwnAcMZgFBmkl1ZstEFZJpfmM6Y5KmWKpKQ4tQhSIh0Vl2RysEQV6WVfOqMQAbjk7PNFityJBApSbRkLU6o2DFk9Io4yBlvTUgT6HR3SKaH6IbHl57i/baI+jOw/hxzngq+6/M4oS9echRnJMXkOYlaVjygkhZsY9paCoNTePxHXl8lqqw5NiY6j5tS2qOLVSeP3tzj8MwQlMEZ1tN1psuy56NZ6k1mm+BHsuLks2e9xVO0Y/vToMtmfK6F6HcaW8bh4KQaKJF+1tNCPbmIdf8kBU740u7B+wFEV3T4ITboG2fJclm+EnKC6MJN2cJlipYr/qQtHSdh7vyZonygtvzjKYle4+YqkJLlzIS4yTnU7cPODOdoQnBIIq5MgnZm2d4hoKlCZZMlYvdNmd7bTY6x+RpRJieJtdX0J0vE463aXR1ZvFpKbvP/XimyrWBX6cuzrRb3Ndts+vPcXSNZdfhc9dDVOUcXmywtaSgWqJmLn/+IOS5kVTBXXcsGppWQz17zhonV7+bFe+IJBzRiXys1jrD+SpXjyaQykLwgxen7D33sxzc+qs03HU6649ROO9mEJwA7QQOGq31DsWOjLbGoyv48ZDBfJt88AWSIiYvcta9Tdb778Ltn8fPtjBMgQo1kz3Jcm77M54bjnhwqcuD6+/HLJ4HQFF2cU3IixUMTTDLNhnOIgxN1pIc42WexIKR7VkWLdtkEsrOfTenM0ZRjJ8kzOJlXFMnySIcYxXbPUFRQKYIlPwGSXgsnYN5g+XmaZI8lzwWVSFIT6DaG7Tdx2h0nyKeDWqlAgDd9Iime1h2xLmVd9SINZDkzgWBDyBMCgzzUbz1d6GU16UqQ5HWWmMLUqm99DEO/BhSyIuw6jMTVUiznEWf+QXPYp5ltSrC7SzB1aRAZ5QXFbMdLFUKNmpCME5zjsOFCnGMKhI0ZcaGM67b7PZtk44vFbv7nk2YZLQdkzjfIKfE1F+itXwJu3OKID3B9VHA+PCwjiYWEaamyD5CAHtKRpiUhGnBVT/EqoAuB2FGnIGpvQypX2vYXB9POI6S2jFKTTOdozAkyDJujCZ1KnJhC6fpGTq9RrNq9Tyroe53w+7VUL7S3jYpr4cf3Cg//XN/A7tzikS5n6duD9me+Hxy95glU8NSBc+OEkZBzpKrcqFlMMsKLFWQFTDLCrY8mcpaKLou+jFsNgzyspTqxY5Vr/IAVhuO7PWQSeTV4jML/SFTVVhrumwtHct6wR3s6yQcYbp9hKKRBiMi4zGCJGPFMxmHKcNKQuPOrnbDeYyqCJY9m52RnFRmFS+gbVv0GmbNNH/m8JgvH0/JCmgbkmi2eEAXZlZKx0meM00Snh/L/HLL0NlqeTyy1mfZusr04DLdU9/FrVHIJIzrPiCqovDO9QlXvvgD7E9f4qa/zV40AaBrOKw7y7SMDiut+zjz4PegWA/VfIUkKxkHCTdHk7rvuUTRiar/hk5eyML+wA/Z9SUabcmxOdtr4RgaO6OZjB4qJr6qiJrIuBg7kEz7BRzYVNWvmGwWYoGLcTVSKRWjajZhscneRE7ui3GSTbEatO1dyVCv5EoAqTRdoayOow2pN1almO7sqpgVJZ5pcH65jWdKRzOcJ7VmlqGpDGYBszjBrpjgi2ZwoyiuU1ZxXjDLMlxNo2nIe243COumWaoCeSEjDZCT7Lqr0tbVuufIOM0ZRjm745z5PCdNCoqsRLcUbFtlpa2x7Mho/f62x3s31ui5BoNpxCxOax7LQjFhmqTkZUHftnB0/Stk8xd8leM44fY85uYsRVOkpleYliRZSVFKzUXb+MrVv6MLepbKiqXj6todHT4zJklagWOkKOpR/HLL4Lah0DKkI1u2LDabLh994F1vPuXVtUv34/e9oW0nP/nMvZTX7yUL42P2tn+FM83vIUeqlh4FIaddg6eHMcuOiqrAZJIxmWQczaRjOdGQzmbVlqux6/6cy6OY3eOMMMxZ6upYquCBVoOuZeDoOqsNHVvXa+jpouETyI6FLfuAJByRBiNCfxcCGM7/MC8cysZd7zsxZLz3JOFMqkyvbH0cp3MKPVOrbncFvUbVpCm9gn9wGU3xaFhbZMUJWSOomhG1bJO1ZoOd8azuy72ASG82XW7NA47ijEaVT9YUwecHx+zMZU+LcVgQpXKy0VXBqbbGlmdy2mtwpt1kvWUxHx7SPfVd3BjOawb8ggdycbVNnu3j2H1WgVO9R1g++SHszin8bIsgyVhvDZgPr+EfXUHVtnH75zkINtkZywhs2XU422tjagoDPyTOc47nUc0RuTr2+fLxlFla4OoKD3VkevGdJyPE5DLN5Usc+ku0bZ0yfZZwvs1sdJU08UkSnyyPONm7xDvPfoSd6VrtCBeaXuOq4H8wD7g1NtjqXeKke4i/f5miuMzW+sPsTpaZxSnjMGIcLdJgJ3Bc6h4tWTxDUSSQQahnibOohnjP4qTuXrkbhMyzHFUInhoc1Wi2W/OA/VCSGDcbBqdcB1NVyItSkhsrkImjaTR0lY5p1vd/UK3aF31N5LVOiPKSeVowDiu9NlOSQvfzrJ5sXU1hphY4piDLFNKkIA4LonlBbBcUhYwkXE3h/l4XQ1MYB2mtnnwUhFXXSgm/dirntkD3Gars1dPQNRpVpHbCbWAqE2ZZwSDIaZp3EFALiNMSf56jaQLXVnBNhTiDgyCv7oOUJTPl/rbHhttgtSzZnwfsBhH7YcbuNCfNS4oCTF3QslNONLRKIPXuTHvlPR7Kb7G3jUPRFIM0C1F0Cwpk4Tp1ZYc4c8DlccAkLLBtlaIomc/lCqZlKoDCUZzy0lSmTY5mOYoCJ1cN3rficH+7yX1d2QVysUJd9mya4jmi6S50vhE/SllzdwnG2+TaKcLRdq391Nl4F//h2QPJ4zAMJtk5+lsSOVUUab261bU92s4KqgLz4adIw2PMxjLeyiXKQm671hoyCWX6Z60lNbPCdJUwseoUwwJttdDB0oRUtp1lBcM4Y9uX37dYtVq6IC9kv+3Vqnf3cZRwMAu4/vwUPz7J/MYVskJOCr92MJYkw57LRttjvXUJ7fQWfU0hzgpuhTHXr07Z8V+ib1ucmnqc7n6AdkfHj3OujkIGszGGqnJ+SYIiJmHMS8djbvhzmlV09+J0xs1Zih+XfMN6g0f7PRxdo9ewadsGh75Le+lj5Ol1rOiz7N94skbwFEVKlkeE8YiD2S2uHz+N+tK/xTWadBsbbJz9FjLvIxiqWkUBGp5pEKQZe9MZSd5lo/9NpMNfIp4NMLVVrg3nxLmUhHl2MOTqUCXOSxzdo20t1Qx/QgjSw7p9QVZIKfm8LNgNQr5wGDCLC4K4ZD6foiggFEHLVek1FFZs2T0xK0o0UTKIYq76IS8MU+LK+TumYMVV2fJMPrS+zFrTldD2WDbkeghZfB9Xab/dIERTFExFqZWOJ0nKUZzVemauKSfu0FOZxwUNU2HJUbjUtuuGWX6SEI9llNdtWLimVBY4mAUyrSVEXRiPsxyzembiXPbcMTUVKgJq37Y4ESfs+BmaovBQV3JvhlHGOM2ZpzJFpykQZ5DkJUlcsj8uMA2FpiX3uV6pNjR0jUudFicbKdeduVRFLgqsqgHdoifQ4rq8absHG/4t9rZxKIbR5NTZb+UoOs0kDDBUhWXXwTHkQ56VBwyjnDAuCGvHAodBDg5EWcm+L2+0ReTyQLvBHzh/mlbxZfyjTzG5dYVpsMc0HnIk5NAZqsl6/xmavfuZ+Clp7KOZLs7yNxOkBUGSce0wo2n4L3NHMqmr5Oi3oUjRzEuk2SL1WDCYRpztn2e6f5ms6iiZhCOs5hqqfgEj/RLpZITR20LV5cO03JS/xwFV5BEzimKiagI8igsmcVE7joYumMbQthcPv3xgs7JkkmRc9WPysuA9ayt0G1aN7AnSlEtth6yQKY0wzfjCTamp5Og6cZ6zPZHnOowTJmnKKI4J05SNlkdYpZ5GFUooyQuuHB3z1HDMUSyVBPZnOYfjjG5T5ULX4NtOtrjU79WNt1xLQ82eIxxtE/jg9M5iun2avQsIRSPPJFmuKDKCyU2m0ZBJMiLMQl7yb5AfPsnS/q/x4Qvfy+al7+DmsMPeVKbOPFNycvw44dphzqX1SxRFxt5QalwFaUaUF7w4nXEUy0mvZcg+H5oiu05aqoKlqjiaRB+BTH3O01yq9MYFaQ5FVUBOkxKhlIBKQ1doGypd06Shq6hCOoElU8NdUWhoKm1DZ7PpsmRbtG2Lli25I6amcLK5U0u4qAqESc6K73AiCOuo+s7WvuMwIkgz/CTh+fFUAk2EoKGrddMwVcgo6WAe1P3gJdJwQJlnLDXO1PeIqihcrbqT9m2rFvRcLEbujA4ausaKbfJgL6/SsTaaIlh3Fosg2fN+0eE0ykuujVJmfs6MnGMFDqcZ/WbCuqtyoenQrMZmq+0RpBnTJKlT1FlR8uzxuNZwuxt2z6F8pb1tHEqpthloH+Lq7qFksdtWlZaQ+lLLlslWM0ZT4MWwIJjnrC7rqEIQZSWnXZ37mgaqELyr32Wr1+Z06zZXv/zf8xtHv8EgPOI4mTFKMo4TBVMtMZWSuBCs7X+BS+0znOu/h9MPfQ9Cf4ADP5akuWr1e3Gli22oiNnjZKGP7V4AzqLq2zXZaxFheJbOob+E2/soprrDfHit5oJk8WVMd5lx+Q4+f20oV+zOvK6dSGmQOUdByHGcyCKsIntDNHRZL/KTgu3DlLIoEdWEZ+iCrqfK7XUpA74bRPzKrV0e6LbZ6rZwdI1hIBsezdMMzzAYzsNa7M9PUnqOxaXlHiDrFy8dj5lWSDtVUWjZMk0ziqTEyTiKeX485ctHEbcHKXlW0u5onF3ReU/fZavl8UC/y5r5HKPbT+AHA3xkBAIQxiPUG7qUkrf7OO4JzEYfd/lC3XYgLzNszUEVcnERFzk350f8xJP/kPfufpp3vfcvMzPPVYTLl1V3VUVwPG/gRyk7E1kUPo5jJknGC+OUMJWOYSgKDE3QthTa5suF7xVbx9UkSvD2PGaWFUR5wWZLr9toHAQLwqrsGeJq0mk0dJWOJeVWPtCwWGoYRKNfJZrukkTHxOEIQtAND81ZRq90vCK7S8Qeefg50Gxct0+756Gu2pR5QFGk6KZHmsmmWWV+SBJK3sk3n+6gO48ynCcESV6n6wxNZdmzsfJnmB8/JeVi3HUUxaIAiuBpVryHCZOMYRAxqZrYLVo7Lzo0mlUDMFOTdbusKNlselzsdeqa1qJn0P5MOq+Hl2Xktz2VxNZNN+CyG708fjM5flFecnMWMcsyeqbBZtOTsjjFywX9SZpy1Y+JsrtUN74XofwWe9s4FD9J+Mz2Hs+NJTv+sX6LU03ZdySpmM4nqhu8t6XywjDF1hWWnaqLnGVw2mtwsuVxutvEj1JuTTfYfNff4byqoCi75GmEUDXC0TaTwWXSZEqeReiGh+VU0M7KmYRpRts2WG81UZVbRJNdykQW4rPYR9UtjucZw3mXMPFRFcFGx617tYDkk8yKVVRnHdtQpPT+rXGVwniRrCxZd2yOgpD7CpmSy4uSHX/OIIoYRhlHcVb1iFB4ehhjanCuZfCxE02iKvSfZ7mU1dAUznp23TrWVGXDp6vjKW3LxNE1KZvi2BzMApnP13X8OME1DVwWRX6FnmuhKuBWzsYzDdZbFkp5nY2ORZzL3jCHYcQXByHTsKDf1fj6Ew0e7nU42fK4uDohHN0knn+R48Eu89kug+lLHEeHdK0+umoSpfP6HsjHz9M0e7h2n6VIjvNsepOOvYIiNIJkjCo0hvGQIIsYJQGfH/wG+eN/mw98+H9B1e1a/DGeDTDdZQbBCcLKGTYNvZb979oZx4CtU6PL4lzCcC1VYZYVjCcRWQFBKmsQqiLwTMGqrdSRzJan1DUPVQgaVYfHlYZDz7E42dxhdvgCh7u7teCjbnj0T30Iw+7UMOyiujfTYESehuh2l2i6y/jwGfIswrQ6GFZX6ob1LnA0j2XDtsYpPDMlDUaM954kS36tTht6FQemvfoIWi7rRN7KJdlgLcgwNZW2vYvuPMxz+xNuTXz25iFxXrAfZgyCnK4tnWSUF/Qdi3ZV83MMjYUAeJjkjMOEwSzgYBbQtk0eWl2S17RqP/3QRkBR6OSpgaI0EKrG7mSZn3/pZi1bs0jjbc/kMaxWUvYvTmZMEpny3Z/lzOO7F6G8fWbQu2Nvm+EYRhk/ff2YvCj5+hMNlmyJZJLS9WotHvdIr8WSbXPQD3hu7BPlUvX35kzKomu7R6xYOmc92Yv+ZRSL1Glq2SYrbki+KyXNNU2igQBCf5d4/kOsn/s4u5NlgkTmiltGhmZ5RJNdJofP0F17jFm8wnAe8uxgSJDKQuw4ilnzGrQdE0Or2N2zjGf2B4yimGv+nIMoRROCgzBDFYKbswRNgV/aPcJSJKx5lsoHZpbJInacF/Qsg3cvK5xqOCxVhUmJHtNJKsSWoco0SN+zyYuSz9/al9FFJvtgeKbBI2t9euoznO956KbHLPZIspKBH0o4pgprbVsWbYsSQ1Nq/a3Z4ApJOEKoGo+c+q6Kxa7wzSclWm6l4bDWbNT9Lw6v/grDw6fIi5Q4nZHmEUke4epyopvGx6hCpWFIgaZ5MmEaD2VK0r9BmM2xtQbLzfskd0SzK6kT0MSYvCxQhcJN/wYnnv4x2v0HURStaiMse5dItWEpJ9PQNZJcSrFHecGSKWX777SdIJWdF7MSPymYV21yiwI0TXYMnGUFLUNj2bbqdrwL6K+qKPW1GQYRqnIKtbFJbkv0YF6UzNOMwyiFCBhBmMp9yPv8BGsth15jH810MRt98iwiCY8pilTK8QdP4VkPYmoqnnaVcLRNEslmZ0WRUhRZLU+iGx6hv4see7j9Czx/2MOPjwnSjJNtjxvHLoPZDlfGU4ZRtWCzDIkS9AomSUZDV+lVYIG8WjC1HSnKqii7jIMu4zCu+5YQSmTfov+Qo2uY2hIt7UXC0TbRfIDVWGZjXeN8pyX5PklKVkhk5iwr2BnOcKcBS6bGuHImUVYSpbJQf1dMlKDcnWhHCPHNwD9Cdl3852VZ/p1XvP+XgO+p/tWAi0C/LMtjIcQNwAdyIHsr0WRvG4eSJgWD45Q/cqnFu5eX6v7ew3mIoUnWb9eUKC1H17jU73Gp3+PF43EdwSwgpOeXPebDT9U6U0ZbKvIuWsf6+9fw/W3yIqVpeLSWL8mGR0dXuHH95zg8fJIH3vsX+eKOxyTUaa+f5eD5f0gYDMjyiC4SyjkOY3ZmAeMk5SBK2Z4FtSNbdLebxgk3/BkHYcp+KJFZQVoSJiVpLsEFRQFxIH+bjkKrpdG0FfIClhy5QpwkKQ1NJVqIEALLbgNb1yTrPn1WjmMwIjjaptHo84HNr2dnPKtqHhHvPblCI/sNDq7+iiQDOsu19tiZzQ+Rdh5kOEuw9X20xhpxXuCasid9nmX1tZLs72363gq2ofHhrXWM4nmy6DZlkZH6GrP5IYqi0+1dIk184uiYLI9oVUKIk2CPMJvXEQmApbkEyZj9+W38dIanu3SsZWxHkgR73jonq+t0ePgkw+A2SZ6QlxlD/zpJOkNTLdzmJqYpJeEXZqqyvuLoGkGacVytfhe5f0NVMTWVc3HCNX9e104mScY4kakuS1VoGwpnPZnr71hWvb84TdEqOLNaobVURcHQlHpfWaXpFqQpw2DRBiD5Cmn+RZ+fttWk21im3dFx9dtEE0kWBSrl6W00zSJEtvwt86xm5edZiJpZ6EYTq9GnKCoicHCCXX9YCZNKPsquP+fqxOezBzOOZjmWLvjAmsK6Y1fnKFNdCyUJ29BqnbAZMA6bPDs4qBdVmhDEmewlrwrZb8U1jWpxdoH26iXs+DKa6bEz6nLbl83expVAac8yaBUFmpDPy41JxGPLFj1TY17J8SxqpXfF7gLISwihAv8Y+DiwA3xRCPGzZVk+u9imLMu/D/z9avtvB/5iWZbHd+zmI2VZHr35o3lz9rZxKLal8H2PLPHhzRN1M61FgfXWxOeGP6/CYTlpX1jqsLXU4tKKzB/Hs2tkiU8+j7h9ecB8vouhe/jzXbzGOpbzOdzOWfwiI5oPaLXO1nIYIHP6htWh2zzLc3u/yo/9y/8LjWp0/4MQbNgdzrUvsNJ5ALtzit1ZwsE84InhjFEkRRXHccHtIKVtTGkZsjXqgpB2EOQkeck8LusJ5OggIZoXJLOcIitRDUEwEoR+jt/S6LY12obKOzpNVEVwwnNrbSfP0vG0q7IN7yQjnkuNs2C2i+NKfaelhoFjtAkS6Qy61g77L3yJy7d/ieuzHaZpjCYUOobDR6MRGw9orLce4MVBE1UErLVtimKdccVTUFsXsHuCJCtRlAFrrSGzeEWqP4+3awDCQu/LaZ1CMz00yyOeHdbtiBVFx5716WcRk9k2t0aXCbOAvMhJisUqucuSs8bK0sO0+pdorl7i5rDDesuiSENsb5377I4858lNrtz6BGkes+SdlvImtkSf9VyLcSD3uew6gFw9O7pWQ2TXmy69holraQxnCR8oSgazgGcOj1l3pNNZNFRzNI37lzp1ncazdLKirLkzqiKktpij12oKC8uLkJYGJzo20/ZFbhzL8Vr0ns+LEtNQKzZ9QlYWHM8VDK2DofZq4l+vb5AXEoUlnZbANQ/I00hGKKl0PIpuURYZht3h5rDD1eExcZ7X/U72JpJoeRDGJHlZowW/fBQx9DK2PJu8LFiybZYcW0q+zKSSwf484NYs4HaQ8NI4JclKWrbCQ12LU65D37ZoW2alxZcRphnDIKxQaxaTJCAurnF7HhMVEtL8jk6zbhd8uieVom+NAj5x7RYgU709M+DhLnzybkw6gLg7NZT3AC+VZXkNQAjxE8AfAp59je2/G/jxu/LNd9neNg6lbeh84MQqZ9xrZLFPo3eWL940eel4zPY84IuDkCQvWXUlJv44Ttj153QsF0Nt0XPuQ20orLcsUv/zLOt2rU0Vzw4lzyDxsdtSo2ihTZUGI6LpLmnVazxJfbaW3sm6d4aD2S3yMkMVGmveGdZPfKjmYPzmwSGf3T+qi4pSak4BJCprP8wYRQXTsMDSBT1HZRYXHI9S0qig2dExHRWnqVEWJVFQ1EX2Xl+nYSpseBpbno1X9dfeaHlSXNHQsA0FBdmUyT+6gj++itfe4vSj38twvsooybk1mFbtaG1WzRc4uvEke4MnuD7bYT+MuBWYxIUK5Hzm8F/xwas/yR989L/l9Jn/gqyUjm8YSbLenj/nheMJQZax6th87NxJPFNFjb9Enss2utPh80z9bbI8wtQ9NNPDdJfrCV4zXcpcpg8b0VmCybZsupRMmCU+qqLSNXo0zS6O0WZ55TG8pQu1XtvJjkxNuv0LsqVvILst2t46D93/pyoJeL36Lo+iSHG1m7R6FkWxTp5eIYt9THe5br9r3NFPJx4f0q8aVyV5znvWlwEqOX+p+OsYmjxvRTAOszpVKPvLaJxsuzX/KJz6hONt0tgnCga1GjGA7SzzwPpjTNvv5pn9YY0+W/SRMVVZ81lwWO6UXKnhzUDXMlhpOLRtD/DqnjQLC9O04s/s4ugaH9hco9fYR1HmQLOCNgs8QyHXZS1JcrpkC+BFNOwnCcdRwnV/zpVJwtEsJ4oK0rTENBVsS2Ewzfnl2ZzVZsSWZ3Cx7dUdIl84njBNU9lxcS7HQFPgQsvkYsNlw2uw1W1xtj/BP/wie889I2tBRcYfcDxsbx195Tv5teu7NdHyTZugQue9IVsSQnzpjv//WVmW/6z6+wRw6473doD3vupXCuEA3wz8hTteLoFPCCFK4J/esd/fcXvbOJRRnPK/fvlFvn61yWqjy+H1Odf9AZoiOIozZnHBsiuRJUex7O1gqi8XRMM0ZdltcODHmNo7MVS1mix25CRmuuTmo+xNIwbDgAfXc2wlq0UBRdXMyO2crYu7W1D3+QDQzEsc+DFP7g347P4Rv3EQc3ScEvo5mi5wmvJylIsUhiqwLKmWO44kATHPSjRDIQxzTFOh19J4sGdwqmK/x0XBsmXWKbyNtodjaDVaB8AzbzMbXGEy2SaOjmm0Njl58Ts5TC/wiStHXJ1cJStLmrpO09CZxQm9kw9iewNcu8+q1cNUpvTNGFMVzLOYnTDn2WnCyRd+nI+sPUIoLhEkGcfziO2pz44/p29bdC0DU1XZGc1o2SaO8QieeZXhzue4Nfgin97/IocxmErJ6Vu/gKOabHqneeQdfw4A3e6gmZeAy5jZMm0exHFPcKZIa/0rkE2k7M4HifOCoyDleBjhx4cchRF+XBLlBg19nWbVV32z08QxNLKiIANcU0NXd6qGa8dk8QtksY+iaJU0y0Z97wVpjlMdUxb5OI5EMUkZGL2C6Cq1xP/RPCHJCiZhzOXDYS1//+haH9tQge3qHLqkwYj5ZJvx5BpRNkMVGqbuYmQydddes2hbZ8iLgsMw4uYsIioKxnFBXNUHVSGwNIlmlIoPkvWuCsF1f85sb1jXEhfPg6WKCpVWsmppPNxr856Tq7jl05R5l0l8gsFsKHvDVGnYJJcNvBby9poi2A0iXpgEuLrCS9OEF3Zj/FEma0q6wHQUikLWmbKsZDrKuH2j4EktYGk1oNVQsXVBkr+srgDQc1Qe7tp885mTnO17aMWLpME2/mFEPB8QR3JxpwiNLAsJgwEcPMlHL34H12bn7sqcIwDljUcoR69T23i1xNlreapvB379FemuryvLclcIsQx8UgjxfFmWn37DR3YX7W3jUDYbGd92qsen9kZcyHKiPOcoznE1hWEkJSiCtJSkwVSmj64ZAXFe4GgaeVlw259zwmvUrXcNbhLPpLaT6S6zO0m5MZqiKYJxkLKXyhWorW+BKVMhYZqRh5JBvGBBbzalvliQ7hDnOZ8fHPPMMGE8zYiDgiSQBcPQz1ENhXiaIRSB09GIHYXIVChK6WgURWBYCpoqMCppinFSkBUyTbSQNfFMgxXdYc3dZbL3JEXsQ0Mer4+cmBvdLcx4Gad9iv34PJ/dvs0nd4/rFNyyo2Kpgrah0nNs7msu02is07dXsBcTt2oyS3y6xpSszAmzQLYHMKTOVI2KsgyCTPZxycqy7qzYtkxcV9ZFGkaLNauFJnxMRSUrc4I85jg6YnTwJLrh0dQfJEhzVO0iWvsiWhusSrY9TApKJFz5KEwId8dfwYvZDUJuzpNau0pV4ERD44RjsD3x6VgmqqLQtswKHHCSPH2CLJ5RVF0EDVfK9YdRVqeqVEXB1xRM7QLdzoCigJ5roFY1Fl3boyhkGlETgiQr6n7ui+vV0LWqB4yComgE3EeSlLR6oOo2zd4FKXufhlI40+5gNpYx3WXakUmYpsynGVG1+nZ1hSVLsuIHQc44KupU6XBecG0kYddpXhJFEkafJSWGpWDZCl5DxTYEZ5o6H1pf5nSnCcDN4ALDo5C8OOTmdMY1P2BnnjEK8krJ92XtsP0wYxjJ65/kJbcHKXGQYzVUVpd1TjS1WpASIMpzfnlnxu3dhPkoY3iQMHNULFvBMBSiuEBTBY9umHxwpSProOtzgtFzzOaHdcq0KNIaeWnYHco8q1sGHN/6HGdP3qVJR4Cm3pWi/A5w51FtALuvse2f4BXprrIsd6vfAyHETyNTaPccypuxnfEtXnr6o/zA9/wq/+43ZT75jNcgzgsutstKHTinZUi45u0gYZwU9MycrZbHyZZXk7cMtWAcxATJOqpyAlOotNG4ORoQZBkdy+SFw1HdwGuBFgqyjIMw5sokYTCVbHtLF6y4Ujxwnr588/mhRP40O1XKys8RiqDISpyORhIWJGFBGhfoKwZ5VpImBWlUEM1zFFXgtmWBM8oLLrWlppimCDabHj3HIkhTfv2mQ8/5KG7DYJrnNUjhotvG4QZCPcvVoxmf27nN44NJLVmhqwJHF7iabP2qKgq68zBLmymKonE0vEyaR3VBPM1C1KqIHYxv0tvoozo2nnWKtWZDEhpDqTw7TzNOd5o1t2H7mZ9iHh6iCI37OxdZjg4ZJxM2vTMsN++rkXRxJNOLTfsZpuVF/Citc+uy8VJEnBdoQmCoatWQKWc3iIhyif5ZMlUutZ06ilsUthemCsEwCEnynElocn75Xaj6swhFIyju42AaoSpSBn0Wp9i6xmZvxCxeYTCN2Js0aNlRDYsNi4I8XCJIpMOXKUcVMDA0lXeu9gnSDKfSapNjuUaYpIzDhKtHLRy9R16UtDyTtmPgmSpBmjMOUvaP5LgutNCWTK1GOr00XiDtBKoChqowiwsOj1KSSKZILVdF0wS6oVBkOUVRkuclrqnwUM/k/rZsVveZ7b26e+M8LQjSl+t5WSaRU4pSMpjBOIqI0pIwKtA0gWPK2kq3rWEv6/QdlQtNi5ahV62N3Vqr7N0rCTfPzfjycMoXb0UURUnDUWlaCuurBheaDu8/scLJtkvfOyKslKkVRaO5IsExixrcIlrNM7kYKIp14tmdWac3ZzJCuSsO5YvAOSHEGeA20mn8yd/yfUK0gG8A/tQdrzUApSxLv/r7G4G/cTcO6muxt41DEaJkmpb8+P/xnXzre/4618uvQxMKmz2vFt878GOeuD3gMIjommYtFBhXUhAbHYeNjowmVEVgaLKA7FTQ27hCidz2Zzw5nHAUS6XWUYWwSpKCKCxQNUG3rfHOFUniGycFk0TKdjdNSWBr6IJ5U61D+cWDnS1C+hUD21RI85IkKTF0QbuloVfpb0OTq0dNCKKi4OYswtUVeqbBURC+3CHRNBhHMbv+HFNV6kLyiwdT4ryFH+/hJwm7lSruevPl/PqqrXGu6dJ3LNkYaQC9xkMsbcpe5vPpds1v0EwPRdFkp8jDZ7i18ylM3WXj9Dey3DtL7l6sBC01dv05rqmjBY9z69onSNIZttnBMDzwIS9z8jLn7MZHaa8+TFlkjPeflCv32W3Zb3y9T1b0GEdyMl3oOO0EElZtqQK3Yr/NMlm0XbFN7mt5PLDc43S3QRE9Dci2s3kaojsdTPcxXhj4DGZznj444hdv3pYsb62kb9/i0nKPlqHV/IgVz6zvl+WmVQsmhmkmoa8V92khc2NXStGOoaJmEkbbtk1sXZONsQIpIunHsni9PfVRhUR6Zccvs80X3Atb1wlTeU03vAbTNGUYJWRlyTyWaSTXVrjQlVFA5pY81DeZZQV784yjSY5tKegqpC0NRcCptsbH1ruSj5SkfHrvkJ15RpyXde1jOspqh2TbKh3v5fvG1gVLjkK7b1QcFCmBsmRqtAydKM8rzohcgAGcbHmsNRtoista0+Vcp8WWd8TnD2f4SUHTFGx5Nu9Y6nB+uVW1j84w3cdIdDluxOCUai3IaVSN7jxTZRIW+FEEPFg77jdt4u44lLIsMyHEXwB+AQkb/qGyLC8LIf589f4PVpv+EeATZVnO7/j4CvDTQqYqNeBfl2X587/dYxBC/H/fwGbTsiz/2utt8LZxKJ6mc8FrE2QRn3/qH/Po+WPSpT9OkGQ4ykuMd54gCwZ8ZP2xOre+sLwocXTZ1CetwmbJsVippNFzxkHM7dmcaZIS5XktQDcPC9K0oNFQ2VjSeU/f4VK3LZn23Qa7k4irwzFfHBxxO0ho66ps1KUouLrUGTrT1InykmEknY6jC/Z9KRPj2gpLrnxYG7pgw9HRFMEsleivE47Go16zlgM5CGNmWcY1f876PGSz6daFzawoMVWVozBkFMVoiuAwjOqx2PJMJklVW1IE9zU9TE0lqCasIE1hDqp3iZVzffzDF5gOnkEzPZorl6R+VtUTXNNsonjE5PAZyiLFbIwQachyeMiG1yGfhBwcXibLIzTVIk59psEeR8EeeZnRtfoyXVFkBONtfL/qW2J2yLOQye5TLPXOcuivSM5GnDBLZQQS5SXtheROUfJAu8Fm0+P8Uoe+8hR5dp0skJDwYHyT4eFTbI+eRVVUOtYyG+sf5P4L38Ll3SWePjhinsmeI5eWe2wtuZC/UNfFAI7n6/hVm4G1to2p7kj5efU8R3PZXGoh4Z4XL6P0FqZW+ld7k4BxJDkVh0FU15umiXSSjq4xTzP25iFrDZt2w8GuVHQlzFgQpCnLFQn1GzekQ3U0TUaETQtbkY3M4vkhht0hb32cL2zvM6qOP8oLGrrkI+3PAw7CmGeGEpo8Cwsmo4wskfdLp2+w0tY439Y54VjMsqyGSbuawomGSUNTmSQpmqKw5lg0DYM4z2sZoqahc7LlcbLt0rZ1dG0P21iRDjaXqeunjkPmacnlsTyeF0YTmoZdOdqbtQjlIr26qIupQjCYLXg9Za2KfGcbgTdjghL1LvFQyrL8T8B/esVrP/iK/38Y+OFXvHYNePguHMIfAv76V9nmrwC/PxxKw2zzjqVHOZjfYpb63Nr5FOe9dZqrl0iz8whV9u2+9ty/wTY/Ufe9VjUbw+7gI/WykugYp7VJa/3b8aO47naXl5JQdhDGdV8HTRHMG7LecF/T4FKnxUe2Nqq+5S5+LFdKnmmwbMkOj24FF40qIb0F9j4rS5ZMScDa9jPmQU6el2iaYLOlsmTK/Hrb0OnbJqaqchhGmKrKqZZMS2iK4DhOaGiyCdg0Tbk9m+NU57EouOZFya1ghnXHg7V4b4GsWajFBmmGo2msuE4tjilhxMt4TZ8yT6XUu26BnCMwG32c9ini+QB/dFUqLkOtNJBnIXkq0xSLVFlRZESZbIXbMttsLD1akwyT6Jg48SnKjLxIUUcWptVF0W02OluMowhX05irOeO0GteipGcZrDkW93XbXFxtkxz+AlEWYrdPkcwOefHKT3J1/Bw354dM05ymrtLUbxBlc4Sisbz63VwqehWU12CjuUcweoJgfJMkGpEmskjf7F2gXaVXRi9tkyZTNM1GMz6HZXc521zDbq5XrZ1TGblERR21LFbMQSojreMoYVBJ7bcMvZ58F1wWoHYAPUei+BbSO55h0LHMug7oKleYHlxmtnOV7fAQPzpkFB4yjIfkRc6G+08423mQ/on3M298nJ2xz21/zv48qBcabUshL2XqTFEESVJwp0/UKu6Mq0lh0Z4p2x90KrmV1QZ1y4CFrTZ02pZJz7HouVaVRdgmzU6SZDmmpuCaBiddh2Gc8qX9mOt7SSXNE9FxVNZdtVYBVyv87nGUsBdI2LOpKDR0rUaa5UVZM/bvlqlvH7Hhf1CW5Y+83gZCiM5X28nbxqGousPpM99C8+BJBtOXmCcT4vkh8+E1Gj1obfwxmiuX/v/c/XmUbVl+1wd+9pnPuXPcGF7EG/O9yqEqq5RIVZpAZmwJi0WjBgMGDAvjgbbdXm0vtwfsXqt7tdttBG2zmjZ2YxmDwWCM3QiDsAALjBBiElWSasjKyqx8mfmmeC+GG3HHM+9z+o/fPjviZYmsV5VPquraa+XKF8O9cYdz92//ft+JxeFnmZ38PKv0Mb1yRVGJvTmA50aE/gDl+PSrN/GcFwBY5RWzTcZWFBizOsFiPjoeUGjNUVYwNlkUP//ohEfrksP08yxKcXKdRoZ+rBv6PuxGod204cKN9iTN7egp8YXZNVvUfPrdjFeuhXz/1QnfcWWHa5M+ifM2D5bXyKraJEW6XBsNeLhYUeiGQeBbs0PdtMzSjN1+Qj/0WWQl7tEJD9YpuW54ZTzkSj8xAUSlzRu/s91HGEc30NWbtE2NF75qvJ5qAu8jOJOPUDdQ1Q6u+8CKAetihXJ8ivycefEO/uIdkniHuH9A3dTk6TF1nVmKsG4qIq/HyzufYrrzGls3vgfXfxldvYnrRSa9UKjcrRbaaJWdEQwV10YDCt2wE+ec5SVFIyLWm8M+t6djBtnfZvneMYPtlxmMfpDZu/8d9+7/BG+ef4FSV0yChNv9IbEXE7gywlqcvcn1vc+x1fs4ICB7mZ1RZWfk6THzxTusihlPNo8o3v1REjcm1RmrKiV0fBIvYiua4iqXYThl3L9OnOySDA44uPZbmGc187TAdUUdX5gc+E2lKZrGMhFfHSccZQWnhQD4ddNS6JbQVdzqB1zvJXx8Z8tG/o7j0Mb4joL76BoG2y8T9nYZFSuuGqeAbHXIenmPTXZC09acPPoHXP/oPozl+epWiskwENeIxPfY7gScBu/47NGMs6LgIIn51NU9rk0SyaTnFg/PJVGyix5eFhKOlXgeVwc9bm4N7WFtlVes8ookOMB15O8GnhBBPFMo5uU5r5cNRw8LTu/meInDmxOPwzua791LuN5PGAYBZ7nkrHgONlCscy7wHEmlTC+JbD/MUs9p5PXNsNq2/X89j9/5likoSrkkoxu0uiaMJug6J10/IuztcLbZ5a3jUwbhNV66JjTfxckXWKzu83j1LgCvHPwqesObBPGE/s7LPFzuA5qDkc/jRc2jlYwtXzN26082Kcuy4uawz83hgIerDe+t1vzPD044XGrrU6QbOBjW3OoH1G3Lurq4mBPPM0l+8vWd8UDYUFVt1fLnecEkChlHEXHgmQyUI8qs5voksVbh86xmnPhcnyQcLnICz2HL/RKbs7u4XszN7R10+O1Gye7x3df2ebWQUUZntTKIfBLnbapUSAT5QgpDmf9D5idfIE52GUwP2bnyKmebXWbr0li9a3b7PfrhLtP+NXrhe+TLQ4rNMU1bM0sfcZbPOC+X6LahbjU3evvEXkLs9Rgk+/TiXa7sfy9bN76Hh8t93jgpCLwVrnOVOLyJe0VRNC3jxCcwaY5BPOFs0xJ4LtdHAwaBz3Ysr28nNtwZnJIywZ3+AO/O1txMZCwy7l/nV26/ZplAXjiwPmtVKbqPB2/8KMPxZ0lGN6gKKHPJt8nMJuy7IbEXc7qZ8yg946zUnJUeI3/DjsHt+n6HyXmU1Zoil9c2Ht9gPP0UaSVjznVR2ZFNbYR699aiu7jaC6nbgk8/KawrdRIqQLrReV5YbYXrOLx9NqfQjT18BM3Pkc7viQuB6aq6Ym6xK2B98hZ7Nz9pg8A623npVMVt+SNbY6up+aGt4VOfQU8pMeQ8+Z8YFiui/NwC5EG0RTCc4Br7G7+ZkPRusMj2KAyFel1U3Nnu4zoP0M11gqGQEAAmUcR3bm/48o01f+9+xtHDgvS85u17Oeep5rsPKn7znRskvseiqiTOO/KtHX7ttRZHC93no0ZUQOg+R1+wb4KllPrDwH8EZMBfR8Zp/2bbtn/2WW7/DS0oSqnrwJ8BrgANIvb5o0qpLeAvALeA94Df3rbt+QfdV03Mk/oT3L5zg+WT11mcvE48OCCe3ODueWbn14frG1w/eFmEinXGK4MbxP0Dhrd+D8fLglXT8Nl7K2JfTOoKLWFMoecSujKX3esnfGxni4cLGQ+c6IbPn684zTXvnlTMZ2Y+PPHYnXhciQ0YmRVsagEk15WkzPV8z2ZI3BgK2wxkJj6OA0bel3n4xT/D0Xtf5L3sCbrR/L2mJK1zEi/iSnLAVu8qW5OXyR2fqlySZScs64yFF1vcIUp2cZzPEjkefjjACwbs9idk7i8j8BRB8yWy8/ucbY5NJzKwFNWqXJL0rxL1dqiNcWC3gR1tUh5vMlITNHa28dkZXGVvchsv/CyD1SFXmppSl5wWc+q2o5GWuMol9nqE0RZ+MGB87ZP844cD5rnkvIyj0PyuhDd1KZGhK87Hse+z268IPIdRHDylPt/qHVMVh1QFLJxv5+GTOSBGhFs3vof+zkuE/U9JjLPxHUsmHv3AwSk+z+bsLk8e/jR/7tN/kPOyJnRh6AWErs8kGNhCoVvNdjgmdDa4ak3PE4+1xPOpDblAt5q8WlPqnFV+QlmtGKwO6U9OGB28xslqm9j3+MTeNo9XG96ZL9kKQ6ZhwZvLnL2m4ZdNR6zrcz57WFAUDaF/YY3fFaKTNOcfnCyIXMXLw0TCy+qY2I/xTcH0g4GMeYHe6CaAtfp3vZi2+iL7o1d4vFxzmpU2hqDveewAnzs6sYSAG0MZj3Zr5rm46ha7e68w9h9RrI8p1ycSNleuLL7m+hF6lZEu7uN6Mft7rxJ611jl4lKcOJIN5ANOccKrewO21gdcHw24uVyzF59zdEOwneOzCt9VPMlq3p0v+O5r+3x8f5vPPDwGYFmU7EbiRzc3o8PntZRqbUf1LbR+oG3bf1cp9ZsROvNvA/428M1fUIAa+D+1bfuzSqkB8Bml1E8A/zzwt9q2/WGl1B9AwKB/74PuSBu7i8DbYn96B13n9HdeouAjQMo0iQUPCAR8H+69yvSF30VaaVZ5zT9494hH6w2bqubOeCjU4axg2ouIfY+P7WzZ8KJRHHIwOub6MOO9xVU+9+RUlMG5APSOA2XRMntSsjyvOZ7UvHat4WrisxeHLMqKz55lXOtpbvQkA+JKL2F/2Ofm9JzVyVs8uvvj/NUH/wt/8yjn08e7zNYBReHjBy6B7xH5ETeGG0bBXULnTa73/ioAhVa4quVX7R5wpSdq7qg4p1+Jylob878wGOAojzD622xf+16KpiZbHXJ0JLTKnrGCF68uH9eLaJqawfbL3JtNyCqZ4YeuS8/3uLtYMTueEboOr26N2Y4jpr073PpIRH/2DoPBDfbOXuc0PWRtilLsJQzCKVGyy/SF7+PB+Q6llmzw0HVsTkehtR1l1Y04CfQ9j51YRofjJLGKdV29SVPlZEvZPLzkk5xtltYA8/EiJS33SYJrzFeFvX4GkW+7Sj+ZEFUHbE1f5drR3+e8fMJxrli6JZOgotAVSbUh8SISN8ZVLokXEbg+nnJldOaEosnRAgSXukC3mlKXLIszJtkJ4/QYP54wHojvmW58o6p3yaqKl5wRW+HC0tJ/1ZUp3zYp+JnTFU/WmrxumRWCu3RsxV9zZUJiHIs/vj8lqH6O9Ymo7VUnyjS2Mn48wYtElAtQrk9EmDl6ROz3OMtLZkVpkhlL1iY1MnIdep7HpprbGGHdXsRfT8KQSRQyCF9gEL5Mf8u3nWW+OCRd3Ge9vCePIRB24NYOwDWyUuM614idJ1TFijI7Z3X6JjvbL7Mbeby0+yqJ7/HGbM4oyPlcmBGaxNVlWfF4ueZgtM0nr+3y3pnksgzDQHJdHId/+PjYan+ex3LVt8bI69LqKHC/AfjzxnzymW/8DS0obds+Bh6bf6+UUm8gNgQ/BPxq82t/GvhJvkpByetaNCSei272GUxugOviti13tvsWYIydeyyym9w9j5g9OmSaRITG2G8njtiJ5WSsm8aewq9PYmtf31l26GqHcn3CFfWYbOuXcV4UYt7YeCx9h3SjWc0qCCHPGj77MOfhqObWqOL79iZ85962DTy6tTXET3+av/cP/2X+q+Mv8e4m4qyIKLTLph6RVS79uCYJFevcJS9cNhuP83shqmlpPIUzaZgOSwZBxUeGC+5tjtCtpu/3OctPYP0egRNKLojjkdeioamWb7Nc3cc33UzT1niugJZtU4nrbjBgdPAa6+ZlHqUF8zwT3yizidVNy7V+wif3trm5NWQavE2V3iNbHXLv3hfYZCfMsyNOsiNW1Yazcs3QjwncQCzl/Qi4wbpYWFpz4LqklQRaHaYZr88zzvOGrBSNROIrbvULPnWwxyCU0/DZ2TtsFveoyhXT/e8kGd8AYBSHT7F8utl+N9a57PQ7CHzubF9hq18zaGq++9YP8crqPrP0EYtijuu4xF7ClcFtBr0Da6jYmERNx/HRdUZZrjjfPCCvNpRNgW40gRsSOCGucqlMmuTm7B1G0cAo7cF1ApJAqK9J4HJzMmSRFSZLRrqBH3pFUdQNb52c84YZbw0Dn1emO3zy+pS6eJ188RaLt19/OhHUdCfr8zPiwQGBu4PyP4bvPZbnUOW2i7i1NWBVlLx1vuCLiwuWat8TgH5jWIMdJTjXrS02V+KcnueyG0fcHg9Jq4qsCrg2+SjxxEO5vqWZu16MFw4o1sfsTCIW2R6eUjRNTWuMQB3HM4aW4HqHfPzg1xJ4LsfrlFfGQ2v4ep4X6LblreMlr+zMuD4WN4PQlTwX13G4ORnymUdHz75JfcBS8NxYXt9E68eUUl9CRl7/mlJqB/G1fqb1je5Q7FJK3QK+HfhHwJ4pNrRt+9hYCvxCt/n9wO8HmOztUmjNLM2ZZwV7/YStgw318pDWj8F5hX54xCK7yWceHPNoveG13W2uTfpWp/LgPLXzY922JpnumGJ9YjcL5XroKuPknZ8k6u3gBQOuTfrW6fXd1YbPzQreyzTJyEM5YiDnOIq8anmwqvmCv0Q3rRjZJT3Gscd8dsb+4AV+rRvw1uIuP3u+5qwI6fkeaeWhW0XgNnxkUphC4zEfywbTXdSJVxO6mp5XUzSaZbWxoVKrasNG50yCAbvxLpHXw3cjIq/PIpMP2CjeYzK8DcjmM5i+zPDKqyyyPR6nJUnQmIItGTOroqRuWraigE/sbV+ipR5SbE5sfgdALxgZXzOXwPVJ3JjYSxgl+wz2XuXu6Zp3TdJfZyQIQvHcCkNu9TXbYWOV4HcGMR/bGjNOQnSVsTx6ncPDn0Y3Nb4b0d8cCzbC59jvD0iCG8xTyakpS3ns53nBpq65v874+dOCvGrZ6Tv8bw4mvLY35aNXbrF7y2Na57xg2GzK8VCuZ1lboiM5JF8cUmXn9vnqOqO3OcD1YsLeLm1TWfNFx/EtlpGMb+B6sdi3+C4hb+OrnHFfirrbi9mNRCfT6to6BieOx7cFh9yJBRvxvQGrx/f5W18Uo0xXuRwMhFTiKI8wGNA0NUk/pj+5YzZ0H/RbVMa6y/EjuyEUdcPBsC8Cz6qyqYl3l5UkU/oOkStOAJHj4JmT+jhweXHYJ3BdhmHAOAoJPPcitMyP6U1v05v+aosftQ7EvmvwoYZ5VlE3e3jOFdzIYTBycdp3L5TwjrJkk91+ws3pOVBzb7ZNqTWuUjTqBfqRfIan/UBe13DAVg/2BjeeeV/6oKXAPu//f19Kqf22bR+3bfsHlFJ/CNGcaKVUihzwn2l9UxQUpVQf+IsI+LN81hbLmKD9CMDeSy+2rnKYJpHN2WiaAxznRGJhG3jnZMSPvf0mPc/l49tbvLg3lDFJ0+K073JzDFlzk+NlLiyTfgCscPyIfCZhRUn/Ksn4hlBeC8mMH8eHFPUev/LmVb6zqvmBLOfnr8742bO1ZeWsipbYV1ztedzox7iOYlWU7PYTq3/J6zULgzNcT2r2o5rQVfymqw23BjfYTg7w3Ii0nHOenXCcHZPqgk1dsKxqPAcS12MnHBK6MtboskP6/sCcrntEXo+drY/RNjWrzSGR1yP0+4xHd+iNbhCPb+AlnyStGsrsS1RHP8324IC4933cPZXOpqi1ORE2hIbZVdYtYfgCri+uwN2mORrcoK4z+vEOU/O98egOo51X6e++zN3TLd47X7IqSq4OesKc20gX0fM9rvQTvv/OddkY3IfADVr9Dun8LVb3f4zSnHRH/RtssmN8L2Z+/ia6zuhP7hD0dxiEFVs7YnnejZUC12VVltxfZ9YjqqhhWVU8WKzQTcs0+QQA40FI7N2lqXIcP+Jss8sqLxlEkhbpRQOrTemKxtaN387RqmBZN+wOQ2L/CQAnq218hMnUOqBcl75/yGb2jiUGdMmKZSbQYdjbNeaV3lMamO49LOZrTtPHZHXKVrRN5PfQTY1rcmAGgxti1dLbJRru4zg+jXqBo1VBaVyHp/0AfPCQcdSqKMVyp6p5uEklgz7XFLplUzXc7EV8fGtkzCWlAEo3IhlE3WbfabvausbxJ5xtdpmnG7QxENWN4JSuo5htMuZ55w8m2NYgDHh5e5dxfI04cMlyEY3uD3uM6n/I2f0zosEB0/4eq7ymrKUo7QxO2R1eIXQfklYf4ei84mAU8ZwweZRqCd3naIX/jV1/0tCCfxIB438awIgoNx9wu6fWN7ygKKV8pJj8ubZtf9R8+6irmEqpfeD4q91PWrb8vaMzSbszFNhVoUmib2NVaD7z8IjPnZ5xlFV8amdE4vvM1hdZErBvsie0Dbjymi9z/vAtspWk5UleeUbT1EyufsqemFotDCtPKdrqi7T9mu+44vEb849z73zJaZrxJXP69pQYBG4qTehq9kcxyyf/gM3SGAK6AZNgwMfGL7HTv8mgd8Bw+jLx+AaBSeFbn93lSnrMq+aku8mOJcnPi9FNRVrObfjUNLmK78UEfp/R1ivWQbnzNxp7MUE0IR4c0N/9fg4XObgecfoZjt74UZq2lmjd0Q1WhXx4jjap1Sl8dGvM9fGAeS4snX7osz/+5YzDAcF8i2Ijb50XDux75QcDBld+kPdmG754L+VoM6NuWgu0l1pbV4LEF2+t3egu68dvcr45ER1LneF6MfHgwJAHMgbjOyT9A/L0mDQ7YbF4B4CB41EXa+pwxTjZIwk8dgfxRSgYULdn3FsbMoUvOR6l1nzx5Ex0OFXC3L+GbiWTRDdryWfXGt1s4zq7uK66wHKalgfnGYusYFWWJvVxaCjcc3TT0Dcn+N1hRIiA1sXmmDw9xjdOBJ3OJeztkExuiH2IAcW98LME0YRocZ+qXDFM98mKc8JggOdG+MGAMNoiHhwwuPKDHK0KzosKd6nMBl6I+3HkCzV+XTJOApR/m73kTeAmj5cbhmHAVl2Ta82tvs+8Eo+8V8ZDDgZ9xmakKGPi1haTg1HE8snrtquKhvs04asUtVwraVXJyNF0vHXbcm+5Zl5WLErRFI19l5v9iL1eQllLTHbgunz0yojs/KeZH3/BdoGuc+GTVmqXtJyQVRtcZ0uMLPOCt048Qu95CRu/dUZebdv+oFIqQqCG3wz8J0qp+0hx+ett295/lvv5RrO8FPBfA2+0bftHLv3orwC/F/hh8/+//NXuq64bfuqtDY82D9hLXMa+eyFoals+NxMA9p/a7zOJxOfq3UcL0qq2zrylUfAOA5/dfo9XrogvUDw4YHzlNaLRAfPsgHfP1xSpgLzTXsw1ErJSMJeRh5337k7h4JY4DCe+b1lKhdY2L9wtfg4nHLD/4g8SPphw3fGJBwdEw33C/i6FvsY6r3mcV1DALB2xau9wrgpoYTII2dtPGIYye5/2njB796cpczEjTUY3CXs7RKPvYbYpacHmhc+dnFJrbk/H7E3OWD75Cdbv/QTvrd6j1Dk7/ZsMBzcYTl+hSb6T40XGe+dLfurwlHmleXkYyaZ7PMN1xLnZ3Tg8XKwI3AMB5a/0qNvWWtjUbcs6r/nJtx9ztBEdzFYUcHXQY9oTnYPriLYEMM8pYH38Myxnb7JYyWsbBgMm/QOSkYwvlOsR9neFtnp+n3h1aNlq2eoQv5vZ83PEyOglDe/Ysd2Lwz63Bw0L4/8llu2aTV3jKod3FksO01yce01GehcgBQNi3zMUbg8hLMrqskqK5RrPUaRVbUOxJpco5NcnLwHvEPZ2iQcHrM/vEiW7jHZflfycxS5fepCimxlZVbE/7HNt8glCPi+ZJeY67TAJ14uIRgccrfa4u9ww+/Khvf4S32cQ+Ex7sRVVTvuB7dJAAPot55zB7nfy2Ucn7MQizH2gU8a+y4ujPi9MhnjKYZ4VdtSk29YWk85RwPVjHD8yY0JtX5dVWbEsSs6LwjIf721KNlVDUcNB32UvFsfrVVESui790Cdc/M+8845oyQbJPlt7v8xMIVp737M048kmtfb9ncWL5yheGPS+2nbyTEsp8NS3DsurbdscU0AAjLfYDwJ/TCl1pW3b7/pq9/GN7lB+BfB7gM8rpX7efO8/QArJ/6CU+hcRZd1v+2p3pJQES332Cxv80CFMHHTdEkYOYejQjx2uDj16ngDJA2SDP84L7m9SFmWNpxTzSpPrlpeHEYfLKYPw1zFbZTx8vOGL81PgFE8pPAdGgcc0DHhpOWKaxPTDgDh4hf7Oxby4izz96O4Woecwz+REPI4D4sAlDg/IqitkZUN95VUCz+G0qHjwZCUhSc19dhL5MD9ab/jyYs3cDL2vJgEvtBKROopDxrFPXaxsfkjQ32HefoKjrOTw7ce8s1hylBXc25S8NunxT13b55PXt6lW/4jzhyeU2Rm93gFROKGuM5L+VXqjGyTT28wN7nCaZngOXIk8ZkXF3cWKnThEV7XdfO+MByS+L95UWUU/EqPEB+cpj1cbAcRrMUTc6wXcmgwZJyFbPelmyuycYn3C5uwuVVNz4kUWWB70DnC9iN7oJsnoBmF/l5JbcvLfaIq6gfAmbqwYRB6xc4/zR59GV7kt9GFvh/jKAYvzgpVJXuz5QgufhCG6lVHbMAxwjRXKopQEQGMPxlFWsRfLIaTzjfIcRez7jOOQfuizOwwJPIdw5XJ/uTJGlBXHmZzYa7P5AhwucsaTH2ASS8Ll6OA1qnqfo1XBm49Sjten1K0QIQaBzzwrOF6nuEoKdxxLQavAMq++9GjDLJ1xnuecZAW51jaS94VBT8SwkwSv+TI0EIYvo5uWtNLEwwOK9TFB9XO8uPMJFllJ4ntsRZJ6ejDoWbLDuhCzzMT36Yc+B6NjivWXCOIt2v73kpXShczOW1ynNhR8ETWeFwVvLVJjlS+R1XcGMbtRxEenY+5sjxgH7zJ778dZP77H3Te+xEn6hH4wYDs5YOfq9xJPbuCFrzLPaotlHpaVMQcVN3GQg+Unp1tMk4uR4Ydbz8965ZttKaWGwAL4781/62e53QcWFKXUb3mG+8iND83XvNq2/Wn+ySGav+5ruS/XhXjgsprVNFpyQ+qyJYzgytizSXBd11JeChH63FnGl+7JCaYzdny0XPPj765YrTS6blEO9Houo57LduJwJfboex5XegmBK+KyLm/E9eWD6TiP0Q3cnJ7LbDxf0Y8E0AVomgM+9zAhrWbM84L3lmurxM+bxthYeDzcpKyrmk0ttiJXE8nn7pkPymmWM+3FFNqnb/JC5lnNvVXG68ePOS8KjrKCf3SUMY4cfs3+mDtj6QDeO9vQD7+dZOqx7d1jefS6FaP5wYCgv4Pj+PSN2n9Ti1fTcSrizc52YxT49LzulG6Uzr2QaS/AccR6JYu2ySrRllwbDcSu3XMYqjfIF4c8eu+uVcRXhlrc5Y/oprbC03hyg9J5hUVek81rsmp1kWKZZqxKOc3u9hNubd0giO6yPn+HtqnoT+7Q3/1+Xj9c8GCxYlPV6LZhEkX2hN1ZhCS+xzSJOF6nDA1D7CirTEGXDfAwzThMs6dsbLZC03EZqvqqKHmySc17WzMK5NoptOb1EzOm7SUEnsMq32JdDEym/JHtlEAICud5wRfPSo6ygsh12I1C7oyH9rreN4D4LM2YpTlPNilvzFfcXVZ09nXDULEoax6nOcfrlN3+Drv9hHEiILluWkpuMB7dRldvsucfkQSShHptNGDaEQYcY4pJaMwyj0jP7zN/eEjY26VJXuCNJ2esipJBGPDS7phRLASQ2UZEkXUjLLF1Jfkt1xKXG72El7cnfPTKGLKfZX3ymDw9pqzWjOI9tge3GE9eZnrr+zjNb/HwPAcWBJ5LVtYUWrMdR3hKWSfpDuvpSCXPYwko/63ToQAopf73iFtxxkUmS9u27e1nuf1X61D+K2Tc9EEo+a/kfaZm34jluYrrVwLOei5FIW/y3q7Hbt/l5VHIa9sThoHPSZqzKkoerjbM7OnUIem51Fos4o+OS+qypSoalKPEhr7n0o8lBfHVccJ37e/y8YMJAe+h3JZW1yh3l1a/Q3Z+KLTH/g7F+i0Wm2OCaEJpVNJNlQvLJtq34VP3NxlvzStma7EQj0OHfugwLzV9X8wBPaXoBbJx5LoBashlA5hnhTldRhyuHghobk5/T7Ka47XmpanPr7oyNWMaSdFblSWrwsNTDv1wj1sHt6mL12l1LeLPYi2dliMAaueZFHoQuuJBtqlr9nsxie+jm8ZmnwO2mFT1PjsDMf4r65Y4cMwmeJ/s/CKJsMglf8Yx2gjlRRJL29QMxjfs+Ofxcm5s62VMualq8VYzf/fl8ZBBGLDVO+b05JwgnpCMbrBwvp037p3w7nzJwgjdQtdlGIhYMHHFxqa0GI5v890B6nbDKHCf8mQTBlRJrkWN/SjNeWe1YS+W17luWhZVxSyXvJK92CXxPCLXYRCK91boupxtcrGDbwUHeLTacJwX1I0Ur24c1AVmbYcSqHae5/Z9WZaV5LooZQPkItchdBWeL93LSao5WhdAwZuLjKuJz41+wu3xkKK+6LjGccj+8BZjxwfEkWBvEOI6Dyj0NetBF/tP2MzeYTETl4Gwt2vep8IWk91+wuNFyhtPfOZ5wUn2hE1VUzQNN3sh26F0Md+1N+WF8ZD9kYylwr5JvfQitrhgiT083+LuecU8W9qDXGqiDJZlybftSZEUbK+0neOLu0s2s3eex5YjI69vvQ7l3wZe/Xrz6b9aQflrbdv+Cx/0C0qpZ1JQ/mKvncjnd35kRzaXWjPLS+6uSvq+GCqKYZ5w1V8/X/NzpzmPjit6PZdp32Vv7JFVDcuNYr2qGU48Bj25UDeZ5Dr0QwkOurfOuWKiVnf7+4yTgEh/gdN7f4x3D3+KRSH4RVZnxF7Mne3voNc7YLORQjPdeY3+1h0C41E3K0rurSsezSryVIKO6qrFdcXWfjTymCQuu4lL33fQWkzuOgvwWVHyxnxlBVunhWZRNGRVy5NTCTUaT2VW/qX5ktuDHnu9hLQq2VS1NY50Nw6lboj92wSew/YowPceU9X7rDZip74VBXzHtG+7kW6k8JGtMYXW1gss8FxRPtdbYunii1bDSf8xbnZOVmdsTBGRYuwR9nbxqswmXnZgruvHTK59ioxXeOs45XB5ztEm5awoeZTm9vmui4ZpIgeIC4HgFXpXfyvztOJzZ0vemN3nOM+pm9b6r0nwlzzu2JeNudukdNMQmPGMqxx2o4jCUGlrc9KVjVVO2ZGroFHc25Q8SisrZr3RS/jIUKi00ySyFvWd821aVYxjYSAVhTZ2/BmfPcuY5w2LTVeg5YDjOoonvuZhVPPyqKbve9YMEWCvl7Dfi/no3ha/vNR8/uiUVVGS64afO1vypdOK+aLm5Kzm82FBHKUMozOGoQSqXU0CtsKQw9WGg0GPQRjYx+o4HoF+TzzWmgMhhCQTHD8iCW9TOy/y4LzgeC3koFVRcrha895qw6ON0Wu1rRUkfmIyFI8wz+XAsPzeOpbDl+v0CN2PsCqF5q2bhsm5A8w5zwWbDE0HF/s+B4M+g7LkwWLFx69MuT58yOrodfLNCdV8yXuPno+PF4jbcOg8v/v7Jll3gfTrvfEHFpS2bX/3B/38WX/nl2INQs1v/YTHqrhp2Ttvn805y6ULeWe+ZBj4RozVsCla5kclR3PNvUgx3AvY3Qt4cc8nOgjxHIjsqVSEb1d7IVvG1LE75T9crCh1QuC+xP5tsbCYL+7yaPFliqakrwZUhpUUhRPKckXb1LK5tu9yMBgLPbJtOVk3BL5ivdJURU3rKKq8IQ2k4yp1a3M+ep4EhS3Kmkdpxb1FRaWNo6rJ6t4sajanNV6kKLKGnzqviRKH/e2M33Rzi2uDHqHnsjIzcOCpjJhuwxuGj61yPfF9XjKBW504cLefsD/qUdSadRHYkcK6qOgDupHLzDEajqpc8fjh3yUt5wyiHXEQdnx6vX0Gkzsox8cPTKxyMiEeHvB4MeVkteL+cmUZYAB1I3G1476PN4TIdTgwwVl3Z3PunStrB3+c57y5yIlch6uJbwPJQFI8QWz9O2V17PvgXljDL8uKSGtrLjgOfJvdDvLanxnW2G2lrP184otFu+soU2wdg60JXVe0T5VhKcnr/mCdcndVcG9WsV5p6rLBCxyraQpDh6pWbApxLd6LPcaBy2u9mGEgXYDryDgyDsTW5Xidcrhasxf5rEcNRdmwXmnOlyXryOG4AS9QHOx2CYoNy6qiqDVX+onFe3SVU2ZnkIHrn+N6cgCQ99gnK6XDmucF53nO41Siid9dVsw3DWXZSPJooCh0yzTM2I0jdNvw9tmc0HWtY3LHKOz0TkKouSA2JL5vUza7625/0GN/HBPwHvnyRPzX0mOy4py0nNtu+MMuxbdkh/LvA39fKfWPAGsl0bbt//FZbvzMoLxS6pcj3lr2Nm3b/plnfpi/yKvKFzx+46+wdf172B99O/3cJ3Rls5QQppLU+GdNQ59PXYFh5PD4rGJ+WjF/VHL+oODRrs9LL8a8OAp4aZRwrS+td+J7vLgzYX80Q1c5kKJcz4xmljTNAVVaE0QTdqNPsTV5mZVhJEWhhFB1a728J8rqKuOXXf1B+/331iUPF1AEEtKlFISJy6Dn4rswCGXsleuWXGtmrXwwIlcS8bJcCk/TQlU26LrFDS6mlV6g8H0H3YjWQjeisL68QZ8b2/RZUTLLBYSehj5bYUjPd7k66Nv89bSsLVW4A9QX2Z4t6FlVM89zG5u8PxyyO9hjcnULx/FYzt7kdP4WD5Z3AdjZXOE64jEVDA7wkwna+yj3ZgXrIqfQmsTzrLX+DeBaP+MsLzkrChalERW2omtI65onm5TPna+tkntTtdTGAbdbhW5Y1yXk0m287IlX2DgO2R/FhK6w024ZM0TdNDbECSTPpOtoHi83HK5Ef9RtwrHJ49BNY2+TBBL+JBTkBt24PF6uOUlzzouCR2nJsQlucz2F67k0ZsN0HGwSYuAp6gZyE9SWVpIoOk0iylqEvmEhmTanmbgxR67DrX5A5Dq86ZUw8UziIub5tWaUqW0C5pV+wq2tHunxXxcLnzqzvlyuF+P4Yvqo/UwoyGlGWgkwPstL5qWwwMY9h6WDDZIDwaVmRcXVJOLOaCBixCRinIR2JGhf96qmbluLXY7jiH7osy4qjtYdk64yNklTAm+X8Y3vYivyrDWP0P3/zjPtKx+0lGpxv8UwFOC/BP5X4PNcpis+43qmgqKU+m+BO8DPA93u0yLGjt8US9yGbxL2d0krCD0ZuwyQ+Xfie0brIIVlKwy5PWior7Yc5wWnRc17i5rFRrMpWp5kNdOo5PZoyPXxgP1RTLv8uywOMxxfMlT8RMSDItx6E8ePiExuexML/7/7ueP45sMXUZcryvzMCNV+gjvbv5asqnltKwc2HLmgzMl51HfZ7bsUuqXnO3xia2BHLYXW6LZlXdd4O4p3lhWlFopuFAou5DiKWsvXH90N2A5dRoHHtV5ixxg9v+Y8L4SppBSbphXr/baFRiKGddswDGKmScy1yRllds54NLBYRydeS/x9ylpoyadpJhnyxuPpPC84XsdMkx1euvZb8OOftDGtm3Ih91OurD29Fw5YZ0LHFlquY7sF11il7A966LZltsm4v5THMIkiEt/jNM14Z5UyyzV1gwWlcaXr7BnPKxD3aBA8xVOK2Pfohz6J/4gqPacpVrA5piqkw4wcj7HBCoJ4ImPBQtvTdXdqdpWyr0ehNWWtL0SApigJ9lVzd77irCjItfiVDUNFuO1R1HCealYrTRg6BIGiFzqMI4ftyKXvCR4VmcK3LKuLv286SW26bM9Rhi3mcMekTR6npjA68hqFnhSoyIHbo5gfeuUFws3f4ckbr5u89guX4Y484XqRpQd3RbMLdBuZELCHVPR9l3XUcJ43BK4Uw9NC3u976xVvLlOuJgE3egnftidOFvujhLunC7KqZhAGDAKJT+4KyVsn51Zkm/g+oSvP144TS2H/uY7Cc1546jDxYda3klL+0qrbtv23vt4bP2uH8ingY237HF3VnvNywm3q0a9nVQiLIzWn1dhQasEkMxqfpFmaUdYSfiXpcI0IrWptrVdcpWzyGxhhlgHUS24xz+QiHYS1dWyNhiK0U66Hl3wS33tMtjwkiLdQrhAldPUm2fl9dJ1Tlyt6+gtcG71EWtW8PB6yqWpSY8QHMsaRTVQifCPX4cZowM3J0NqgvHe+ZFmUksDYNOTmQ93N/AE+eWWHaRKxLipWBjNwjY1Fp0wOXJetSNhOXaxw3/eErhoG7I8STu7+GJvlPcpyRRAMCIx4TrQPNbF/y5xQa+6vRQDZUVnhnFHgMUt3uT39Xq6+epvR8au0TY1yJFqg25jgBnHQEHiRHcl1QWBipijCR997zK16n5ubIWUtXcBsk1lMaRS4NktkHLhMQ6F7d6B14LoWExoGAQeDPqM4ZBBdfDwaY55Z5OfUdYbnxTiOeFIp/2McLnIWWcGpybOpG22z7rUxtOwov12nMo4DgsZhXYjArysm3fOUyFyXQjc8DCuOfHGljn3Fzb7PNPLYjSJuDvuEngRYnWa50HHzgqLWkmFixICBKXah61gQ/3o/4SQrmBXSRdRty17k89r2hI/tbHF9+JCTd/4Um/yMvDjHcyMxmIzkMJWnx2TZCY7yiBNR4nfvz7Ks0G1jGYB1K1n3nqNIfIWpIxwuLwpaUUOuC+PyLRHJt3ck0XG2kTHeOJZD4jwt+LnHxxymGZtasxuFuMqxlORVrclMTozrOKZrrrg5GX34Dces59WhKKX+aeCPIhHAf6Jt2x9+389/NUKQetd860fbtv0Pn+W2X+P628bS6sd4euR19iw3ftaC8gXEYv7x1/zwfolWWQu7aLXOKc2HslPuulUt/3cUEJhxw5BBKB5B+fIQJ4wI+7usiqvMzH1IcREriXuzNaP4JqHnUKwb1oUAjqM4wHXE1Td0HfzkBr4JpZptSmAb1DYPnqzRzYxBGDCKb+H1XiDwXBLnbQDGbmCiShvLKAo91+I+nbHhThKxHUeG5ukb3r3Dra0ehW4o65Z5KiZ54zhgqN4QPUexghIGw5fxJ9/NW8dLe2IeBFI052azrtuWO0HAndHAOv+Oo4ib0wHO+u9ycvRpnizfptQlo3DCKNmnKpeEZqynnZsU5pTd/Z8GGy0M8PmTM96ZL0k8j53kE4JXgOSC+6Jk7zuiCMeV1/Ni3Sdxbth/gwf6LfYGMYW+Rhezu2PGd4ddgp/rMDJ/xzMFWlhipb3nTSUmo/M8Z5YGTJMdDkbX8ZrPMJi+TFyu0OZQkYxv0ISf4DMPTjnapEY42Nri3wkZuw4vdF1OU8WNkQghs7K2jDLdNPQ8D88RR+W92CdyXYa+j24b9uKQ+cCEaAW+ed1EHHtnWzbItKzhDB6tNqRVaVwZtA1acx1FUntgOiTdthS1Niw33xbXnSQSsesgpMlzeqMbRL0d+pdIFFW5Yrm4S1ac4yiPQe+AIBL9U7mU5+M5irSU939RVpwWmk0ln82ixnQRihtjj+3QZRr67JrHJpZFDe/OFxRaM00iizE9mK+ZZ8VX6LJcpaxVz2tXpyS+FOPHcyny4zjk1qUO/8Ou54WhKKVc4D8Hvh+xjP/HSqm/0rbtF9/3q3+3bdvf+HXe9lnX7zL///cvfa8FPjxtWCn1Y+bOBsAXlVI/w9NV6zd9TQ/1F3E5Sl18QE23MTfq2NBzZdN0HAKjTCtqsffwnGvE/Zt4yMaum5bMFKC6FTPEWZpZbCF0XVzH4STN8RxlAfrLgjbPuULdiL1EVtYWWNyJI1ZFyXvnchIsas0Lkx0jCKu4PR3hOTKPP15lZGVtcsKFEltoETGCOObGgcf+aEaZndPqGh9QxYr9MLLmf5vZMbkxanS9mLpYEY0esD86oKg1Jyuh3nbg5v3FSp5XFNIPRcQ27YWMvC+zmb3D+ckXONs8pNQluq0pdUFanBP4ffxgaIKqpBCHrgQa5cZW4yCJ2YkjSSg0qu2zvOTt5cp+yPfi0L5Ou/0uM32PrNxIAQ48BuF1VkUtFNd6x4xYhvRDH6gIjQYGRiYZc/0VGzxg9T2Xl+dIlC1IANpHp2NR7k9kfBn2dwn7O3zpyYD5Sc48P+TuYmWJG6ErJ+TQBQ/pAHUtmffdqOU4Lxj6ogAfmjyRbnx5QWXuXB6k0AwDxZUkZhgGVuV+kZA4Zp5WLLJSRmit0IyX5QWLL/HEcqQzaew68dM0I9ciNK3bltBxrB3K42VA4t9iFL/M3iCkTj9DvnxM61R44UCIE65HEE1w/ZhoeMBxepVVIYcVTym2QjkoiSv1klPVBZwJsWQc+FxJYq4OeozjiGkvpKgbHi83zFLpMg9Xa4tLlVrbjqQ2WM926Ak+Yw5fj1ZrgidCew5c2Q86QsRsc8E++7BLqZbQeS5eXt8FvG3y4VFK/feIIeOzFIUPc9uvWG3bvvD13K5bX61D+U8+zJ3/Ui6lsCd711F4yuFg2Cf2Pab9AL/6gsTSNh5+NLHiQ9d5AICuAG6wMtbsgNlkFZnZdLpiMgx8bgwHrMqSJ+uUt84X9ue3x0MbDLUqSo42Ke+ZtMfjLGdWyElN9AGKh5uUT0wn3JoMmfYjAk9R1nLCvqyH6IcBY+WwMuQCwKiU9+jHkFVigqerjHJ9QpWe24Q+14sITIiVcn10lVmH5cCTOFe5v8bGsha6Iail0MSBS35uEhibmsjrmd+vCdwQ1+AorhfhRQORRJm1ZcZp8vpIIZn4HuMotLYny6piVpWcFpovznOmkTCRpmFAz/cEiDd029B1eU9r5llBWtfmUKCshXls7nt3GDElJHAdK1gUdpDJSXEUtXEu7jZ6z1H03YuPxLKq+PK52POsiwGB+1HmecHh/Q13Fw9MSJqIUDvm3TjwTedT4SpNz3clW911WZYVR1nB6/PUnsijVCyC6qblUZqzrhsiR76um65ItSSuULNHcUgSePSdN9FZhjs84J3DFYfGgaB7Psuq4qwobKEbBT43hwNLgS61FP3jXBT0kmki2MrGFJdJXXMw6LE3CEG/RRBvUfrfTlrKgSscOfiei+MI2+7ePOfxasGqkGt0UwsJJnKloH3X3tQ+xstxwv3QZxCJeLasW45XmS0mHRC/KIUJ2ZEFct0QuQ4jM0rzlGJeVjxKc0aBZwk53fXcecU9Wm04yZ7Zjf0Dl3Qoz/zr20qpT1/6+keMuS1IZMeDSz97CHz3L3Af36uU+ixwCPzbbdu+/jXc9gOXUuo72rb92Q/7O1+NNvx3zB39obZtn8ojMRbHH54q8ZyW5yjr6Fpm51TpOcXmmGq1YvbwmLP5W2zKBa5y6UfbDAc3CKMtYyy3Q3/3+62ZXz/0rYq7s96eJrFsTuYEN44Drjl9Prq3xV9+410eGGrju6sNB2bjOytK7q1zK3h7Z1nx+Kwi3Wj8wCGOHeqD1uICgScn2nVRMc9y9CXIKnZkxj8IAgLjcKqbllVeM0+30E2B6+wyiK4yGAmQXBcrdJ1RNzVtU1ljyCo9J+y/SxzcYtoL0avGsuEABkEgIyHjAKsb8HxRsIfRhG3nJUu97Bxw/WAoivYq4+a0j25a3p0vwMWMzWTG36UvQoXnKELPpee55L5LrlveOa9477gCcnqJy5WBy62+zzQK8M4FUO4y1hdFg+dgtB4+1/sJV3oJnnLYd2LGsbyPceBxtrkYe22qkuO84iirxPKjbgUr8hyg4oVBj9B1rE3KcZbzxtmceVlxlFfMC1F1Z1VrRJqKncS112HXkXUmjtNQLF269fmzgrOsYTe5mL93ueeRp4gcuZ9pGHDHHFD2hz0ORsf2wJBXGb3pHe7NJszSBcuitAaLy7JiXdUc5VI8t0OPV7fGF07cpmOQbk2zqaQo9zzBkkLX5ZXphJd2x/S9eyhni6PNLc42ObP0xD7mwBywsqri0WrDoqzM/XR4lGAnZ0XJAzMS/Nh0zO3pmHHim4NTQ1HLf6t5xsPFivO8sGLVTlfTrZ7nsmsObB0u1mGEIHocTynOcoMn6oZHZuQ5DX0epSWn+fNxCP4aQfnTtm0/9QF39f71/jv+WeBm27ZrpdRvAP4n4MVnvO2zrD9lcJoPErH/10jEyD9xPSuG8v18ZcDVD/4C3/vGrTajmv0tlsYUEMD1YvL0mM3mMaW+OJUU1doaB4a9XZLxDe6errk7mwNwJxjbBD/XUUz7AYlhqqRlTZbVfPF4xsN1St22zPJSNgQt/z3J1nKSqhpCV4Rip0XN2ij4K0PvjWOHupEPRlrXHK9TqzbviknHVNFNazZnha4aS1Mt60bouebUJUyoCf1wl3HfZ2sq3lDl+oRicyzW6EYzoNwzpv1d0nJHWEFxZBlVcSBU19BzSHyHEslwD3s78njq3Fq0dIaETZVRF2t6/UfsDPYkbdF0EMuyYlWUbOqaQjdshYHJtGgss6puJTyrHzqEHkwjl8hxWNcNT+YpZ1nDbK1ZrWoRfxptxv1RzbBX8NpOxYuV5uawZT9P6Eceo/iIxN9nEHls5RFFrfnyYs2bC8GZVoUUhWM0o9jhZr/TlgRsxzGP1hs+f7biSZYxSzVp0V6i7wp1dxI55HXLQ2NL4zmY4iQMpnvrElgTuWKl84mtkLur0p6yOw+rm73AkAXEF2wShdzZGrE7jEj8R2Tn961782DvVc7yayyylPNczBlz3XBWXJggCp1YHBXuLlacZDnDwGevl6DblmVZ2lFjRzwJXdd6aLkOLMobvHV8wmmWk1aVBdsfpbkd8UnHcJHZvhN7DAIZdXVWJ52CvdANZ5vcCjs7Y0m40Ht1RqqHqXQlieex34ufus9uapAaDU/dtIRlxbKSLvBhuqbQrWXIDWOHxK+YZw159ZwwFAXus4cZftB6CFy/9PU1pAuxq23b5aV//7hS6r9QSm0/y22fcY2Az/DBBeXkA34GfHUM5V8F/jXgjlLqc5d+NAD+3jM8yF+y1TY1xeYE1xc7di+U+W4yvkFvdUhvIRkPEmcb05/cxp/+OuZpxZPzimNTHIpa87knp+wsI6ZJbJPeXEexyoUFVmjNSZYzN214l1rXLc+BWsNe7BG5ilHgcbMfcWcg7LH8tvHpigI+MZ1Yu/04kJzweVYyCGS2fjGOkg/BVi9if9QjDhxi/4mEPK2lAJ3nBWlVcXUgo77Ed9FVLkFKYLO9q3JFWa0I/AGD8R12d17CCSU8TDkebVNT50KPddqItpJgqWR6G9d/mbp4nc3sHekAy5WwvBCWlxf2cRyZ14+jkLlhG53nhcy+K826bjjKCjsK63mygY4DV64ssCNB+d2a2aZhtdGkG20Lshc4RImDY+iu61pGPauytCaccIWAd4kd6I8iHs5D+r5HWuXM1ppxz2Hac2xg1DSSQlpoER1e6SUsy4q8SVmV8nelaCs7SttUrbznjWTM93yHyIFp5LEXu3Yco1tJNPz41ojXpi3vrdammHgMfZ+bwz4DgznEvtjKb0UPKbMzVrMTS9EdbL8sHlbzlTgG5KWNCe425b7nMQ5q5uZlOMoK5oYh+IWzxVN+cZGrqGvNOPDZ78n13sXnrouSL87mHOcF60qK5ZOs5jQVAkiXLTKOHda+Y/UwW6G4eI/NZ+dg0OfjvcgWl889OeVJmlk2Yt22loQguKSA8x2rcWIwPWFwtcSGeXhzMrTF6XC1ISoc1lXNsmg5WdYURUOv51LWLZuipapbfO/5VAG5Tp/L3fxj4EXj7vsI+B1cgOMAKKWuAEdt27ZKqe8CHGAGzL/abZ9ltW1768M8gW59tQ7lvwP+GvAHkVz3bq2elUb2S7lcP8ILBsSTG9TOi8zWJYPEpzdUmHh1srKhqDUPNjmH70jgkasUq7LkLC85znM7u58Cq7yy4y/30hio53lEbkVt6MWewXYjM7MHOSV2H5KdJJKc7UDYNv0wYJwEjOIjquIJVXpONDggra4CUkzKWkMjfw9XKNDjxLdYSbf7xoFn59FFrYl9j8BzSCtN0GQU6xPS+T2Ojj7N8eY+i2IOwNX+DZpGANZouI/reuTLx9TFSpTQQDK+ieP4RKPvETfXRpP4H2NybUJ/5yX7+/L6xwTxFotsj1VePv3aGqHkvBKmz7tmXFRp8F3ZkEJX0fcdSQB0RAsxLzVFLWJNz1MkPRdv5LE1cNnveVxNfDynE3x2GILmeJ2SlTWjMuRg9AKu84Cmqbk2nvKJoqRuGn48XbEpWnYSl9cmPQuog5x8xWQw5uawT6617To6PUeuG+OSKw4GY180F5FhTQ0D3+ginAubFsNyG0cRL0yGvG0ifHfiyOJoo/jImokuj06siBCgt3Wb3P04j88XzI0ViecoaOSxjXzfUt49pRgHjn28AIuy5ovnJcu8wVEwih1GocO1xOdaPyHxPQH2q8Y6DLy1THm0kQ7bNcmjRdlQmpN+FDq4TktRN2wqSdXciyoB+6uK0HWZJrEVWt5frPjSfMmm1sZnTNh3o85xoBWq9Y3hwIqSO21NFz09CAOmvYhB6JJWPmMTjzw3mMqi1CS+YlU2DIILoGNVNk/Z9H+Y5QDhc4hWadu2Vkr968DfQKi/f7Jt29eVUv+K+fkfB34r8K8qpWoEpfwdRsbxC972wz+qr299NQxloZRaAZ9o2/beL9Fj+rpWq8XOJJ7c4Ci9yReeHPJwvaHQzVMc+7WhLUauwzhwuD1IGAa+sc0QtXXdtJZp1K3QcyjrhuN1yizNJETIUBw3VU3fbwgdxxoULspKdKauUH0/sbfNwSiirb5IvjhEZzn52Rnz9BjPi1GOz6ip6U89QndfRG+1tkWsO7F2xaRtahbZHrN1N+pSdAmHAIPII2i+xPLodcrsnHT9iLxemwjeAFe5RH6fpH+VaLhPEAvl1wv7tCbuVkZZfZzo27h7ura2IfO0IasGpFXE9fEL9Mcyt06rhuO05t7ZmSVIiBFgzqIUl96zrKHULYuNJssailTjeIqTwMH3FYOeS+ApXAcS34xhPNjpO9SNFJ2PDAMOkoidWIr0Za1HZxH/ZJ2ShsJierz0GIQjo0dqOTCdQKfB6IR+1/o96+jbYT1pXbMdCyb2xbO5vEZm1AMCgOdaP1VEQEZHB4M+/dC3gW2dliYrG1LD4Ous1Kf9gKD5kuS5VJfy3ZvK0nT7k9skkxs8PJdOOfE9BoFP4lecphlnRSmPqxHTSLjo/qSoOPR9uD1sYYjtng+SmG2jTveUQ1pVPElzHm5SjrKCR5ua87QhyxvWq5rm0oasHEVVNuSFQ2UsVU5GHvOBdM1SVAVn+fzRmodrcV3ufM86jMlVjh0BgnQscyO23Y7lNRIxqzIhbjFu/Qbl4pz+8AAV3gb6TJOYg2Gfm8O+EAMMEaPTcoF0+8/FzVZdjAo/7DKO7T/+vu/98Uv//mPAH3vW236j1lfFUNq2bZRSn1VK3XjW1K5vxHKDmN7V38rfevuQ18/e5vV5xt2Tis1Gk60uxiRu4JAMXKZTn+/Zj+ztE9/j1qDP9b5oV/Z6YpsxiISKOM9KPv3oyLJiRr7PVhRwYziw2MOqKFmVFU82qR1z1I1sdtN+YGmXus4oNscU+TllJaf7ON4RS4sqB4WdEcuoqzGJfwoQum9W1szSOZkRI3bKYN0K7fnhecq09yLTGwOq9JxRsWI0v0+Zn1EY1+PEBFQF8RYlt0QhPoysYC8afQ9Hq4LHj8/xlEMQu5R1wyzNmJskwrVhaqW1zLIfp7kdW4SeWH4syop11bAopJgEruLalsc8a1jHzlM2HIJbyQd1GrmW7XVt0GMY+FYPE3jKpl9mpSYtNYusYJ7nhK7L401GWte2c+nGJt0mv9tP2B/2hMVnLGQKLUK44SUxq26F/TRNIm5VPc5M56VbYQ5J4JYQDroupKORd4SAOHAIeE9ck6ucqMoY9XdZlDcoa7k+VnnNOPkoYfiQ7Pw+i+PXqcqldV4Oe7v0d182OfalMZJUVtgKMDfGqLaIWH+2hoVu6Hkue3HIp3ZG1lJlzzgmJMaJoGMVnmS5JT+UusVR0DStdeHuiorjKbxS4QwVPePIfWfic7MXcpDEgp+Zg1k3Jq6bllEgXnSjQCySNkZMnNbi4Ow5jlCODcNvHIWM49B26QHvUeQrI4CV1Y88wxQTAW7iOxS64Y0nc5t7043NnscSUP455Ql/i6xnBeX3gdeNDsWSuL+ZdCgnqeLf+Js/y/15TVY0ZFnDZlGj65a2kULi+Yrx1Gdv7PHS2OeTOxMLgnf+QZ1VhfgIicL68Tzj7mzOYZrhOWI+eLXfY6+f8Or1V2iqx2DEjKtCc/d0wdtnc3bikO045tZkSOI/An/XxvgCKMcnbCYoxxfrCi9CuR5p0RC4Dtpsdl1ReThfMc8Le+o6NErsni8ZEjdGA8ZRyFYvIjGg+qq4Cu5V3D6MR7/C0qQLfY2sbFiWNQ9OCnSzNIy2CboZkVU1RyePOc2ERn3DJCi6jmKeFZxmgoe8t9rYrJYOkL6aiBZi2PpGhS2jwVHo4DkNPd/hSuSRJ6KcBgGP6+aChuk5MkI6SGLujAe8dnWHmC+RLw4pDk8sScALB4z7O+wMBiyyPfpr33qHFVpTK6F567YRu3St2Y8SxrGP074rUcJJjHJvm9dLM9vkFh+RWAB5jJ3B4qPVhvOiYGFA6lHgW71FaBTpie891ZFo76YIX31F6BxS1fsUdWkPCllZG93RDoPogCsv37CGixLUdp0H5znrYm00Ho514LWsqFpzWsj70PcuxoYdk20cStfQ88RyJvE8tpPYFqZ5ntkOb1PXmLeGxBcqe9PyVDEJey69oct07PHqNORmP+JaP+EjW2NLee/GcoUWSvoo8C0xI/EksKsTz3a0anlNL7amTrNkRaOOQrm3SSaRjUQuC23xHMC4YDxCLe7zHQcHrOubpGVti+zzWAr13DqUb5allPqLwJ9EnOZ/cby8gP/b13rHv9TrdFPzd39ujWt8sOqqRZeSZ+KHiv7YIwwdXt4LeHWc8LLJw+4+9PujHoPQtVRP11E4ziGrzS6zNOfxJjNK5osLqB8GoD9Dq3NjFHnIKt9CNy3DILA5EOMk5Gi1ByDCvImA38noBrrOLfXW9WNaXRN4imk/eooEME9L7s5XHKYZR3nFUapZZA2uA9PEtQ67HXh/kfGN9dKa1xVZtUVW1qzKU9Kq5jwXauUw8BkEgRn1aM5yoXoCTMMAFiu6VD59iYBwd5UxN463Pd8hUuKAXGiJ0z3KJOkw8qRARIYW04G3IEUkwqF2WrZDoRH3fY/9JOLqoM+10UDyxKOP0t8doGY+jcmR98KBGQ3V4iMW+da3rVul7sZDgcGZGtKqIfFfIIgfo6ucfPEPUa7HKN4iDm6xzmsL7JeGYtuFdu0P+9w7XxgSxEWENGB94zq7E6F2V7aLEJfhhLo9t+/VBW4jNN+sikjL3aeu77JOmaU5WVVZg8RVUbIoK5OZYsghdQu0lmVobYM8cameGnZdYq7luXn/C60ZBr6MCzepGY8Jw667j8BXDKe+DZyLY5ftkcuLo4BPbA3YMQ4Ot3cGuPUb6CojvPIpVoW2RqLda3C83tj3qHOESHzpXopGWIC3x8OLv+1dsB2zsiGjAXZxHf1UofGUokC6x7S6iusfU6yP6fdhEN62ndnzWN+iHcr/B/h9wP9bKfU/Av9N27ZfetYbP1NBadv27yil9oDvNN/6mbZtj7/mh/qLuJq6pUo1JC5RpAgih+VJQ51rmtrBjxp8XwDKoe8zTWJ2BjGh1wn8BAMoa40biaHierPNG0enJugoZ1HWPMlkFHVnkLIqS+6dh8T+yG46hRZmnW4aBsA8K5htMmt/UdYNcBU3uco8rUxwVYOLw0EUoZwHhDiEbidcrJitNjxcbXh3teHNRckia9hkDY4DWwOXK7FnFMIbznMZRQ2CwIL1g8gn8ISldrxOebJOOclyNnWN5zjsX4oY7kKPjrJCfJeUYl3JqEIyN0pGgW8L8dh3mRcNo8BlHDjW+TY3NNbToqbQwoLyHOgbbKVLLUw8z6q0O4Fdt670Em5OhhyMIqr0s1CA9iPrqdZ5prW6RpOhHdEzJGbk9iTNmBU5uRbrklsDOBj2CTtK76ZkEO3RD4/wkwl+OCCrrrDOa9JSxopz45AA8GBxkTnz61+6hus8QDe3Od2U1kOs80eTa6C1DKRZKrTbTvDXKfc7IkBqOpVhEFjKuB1pmTGUjFRFNHicF4JLGWzQU8KIK3SLbmCeC/jcDx22Yoft0DUUZc3D1UbYUq28z53m5d6yYW6Enx2dGWTs5zqKUc/lyli2jFHo8F3bA671JStlHHXjqBCv+TKl0UGV67+GrjOcYsUQ8A1V/871HfxQusrHi5Sy1vQvOQu7SjHtCXYi1jSt9drTzYWmJwm68ZW4aOOI1mgFzNOKcfLtNOf/K5uzd3D9nyfs7VJfGpN9qHVJTP2tstq2/ZvA31RKjYDfCfyEUuoBErb4Z9u2rT7o9s/qNvzbgf8n8JNIYf7PlFL/Ttu2/98P8+Cf51IO+EZcVpctQaToTTyylZZikzdUkYQgfXm55qwo2J1F9lTVZYAATJOYrKo5XK6toO3epuTJWtrqQeDw9x+nLEpN379QYnf5GpHrEjoOD9cprsnF+MjWWApELQK0stYcrjZWkCYjkj2uTTya5r6EGHl79EPh5Heq52nkSvre0LXEglHgsawq0lq0HF3UaVdMALJSW+8m3Qoz6MColUut2WjBEETdXJNrsbS4zFCqjenkpha3ZjH+83h55DxlWSKKeCFDXEtElDj0fXq+K+Z9JtCry2G/rNLum81JdEAOceDgOIeEfdG/dIJKPxyYrw9wzAhpnVXW1bfni839upZDwLzMWJj5/ZV+wiAIqNuGs41rDgQD5llOqY/t4+mA7m5d6yfWSfjBeco4OaAz3x5EPnEQmgIgXckiExr3qhTQfFFWNuLZc2TT7nkyVu17HlvGhkU3jSVkzE0XpJvGmoYWWrOuatZVY4s+jjDNcleRmverIzcAFuyeFRWvzzNLde75Umy697xzLu60JR2LcRBiR5XTKGA/ibgxHNhramZMMWdpzv7wJoPkIwRDReg+JF8cAodkq0Py9Jjl7E2CaIve6AZBf4c7258kNWSZwLtUjA02VtaaupXue13llyyQFKV2GceSzFlm53j+gEJfE8PNWm57+8qrrE7eIl/KY/CDwXPZcxwUoaHkfystpdQU+N3A7wF+DvhzwPcBvxf41R9022cdef2fge/suhKl1A7wN4FvnoKiwPMVrqdom5Yyl0yRqOeg6xY/cnBdRVq1PNzIJvOzs5TQVVxLfG70YztbLmttLVM6YBLgd78km9pZXtpTfN20PCnqC/aYGe3sxWJ0txNHNvyna/O7sdmTTcq7q41spJ7H9XXKONkj5G00kPgHZJ42FuBy4d7oedaCArq0QPn3MPC52u8x7cXC0/fEkbUzyzxeb1iVJa5ybB595wjb2Y901unCABJsoDNYnBUVDzeyoV+JK2tguGVU4B0Q7xlr+W40No5Cpr34qdP7wkQWuwbrSHyf2PcsE65Yn+CFfWL/wLLZSi3YUjefz6qafpgBE3STsi4q2wUAFifYjS5O3T1jwNmFmmUmNvYkzVmWle2WOqV5hyMsSs3IWKbsxSHnecEkChkbfURZN8w22ubAdFG6nbZpU9csqoqF6SgiJQeRhZnre0p+J6hEfX6aZiQmYfTc4GaAfe8j1yGKpUh0XbPnmNFWp6kJ5T2+/N7mWq6/aSSFJHQdiUAw129HexbNiqJuZVTmKbnfFww5InRdCTszI6Tufe2cfsdxZJiJB2xNZJwbDQ7QdU6VnbFZ3Gc5exN/dUgQC9enaWoqwA8GBP0d4vDbqZuWPgEUJbhSbLvidSGE9Hi8SBjFE8qVpqxXtgivipJSDxjF38vgmtFmPSdzSFB46vkA/N8sSyn1o8ArwH8L/G/btu0Mgf/C+6xjfsH1rAXFed+Ia4bQsL9plh84DCceWrc0DRIu5UmynWdOPTZAyOR8jAKXddXwxXPJA//ouE/guszzgsebzKbMuUpxe+jz6ZMFp0XNpmq5M/S5PUjoeR6bujZmdY10J67DJAy5OuiRGLZPV6TOzbwb4KyQHBZPKeqwZV2UzNYl4+Qj8kBNIRsEPjuGutqxXuTH2m5cie8Z6qds3Oui4ngtI67OIPAkzUnr2nZS3UlcLCwu3IE9pej5LvvGQuYk08xLzWmuebLSVHXLLNXMh5pXx4llUAkTJ2J/lIiTs3NI0xzYwKbu9N5FC3SU6LFhGCWBa4uJ43iE/V0eL6acbdYmVdKxPlTdJjk2I6W0rr8Ci5D3ujF59x79MJDXxyQmHq9Ty1a7bBQ5K2rmpWZZtISe2N9Pows1/6wocVcbazB6tE4tRtJpJjrm0oWrcbdhX1i0FGaDj7iwETkvBGDv+S5609rUxNyYLXrmIu7wvMh1+fJiSVbKNbXbd+n7Fww4uV/AdBuekgNUZ93fFbdR4LOuaiLzGhYdSUJ1lGMYGVp0obV1ieh5LrgueXWBq0lBz0kr1+S9bBMHeySJXBNVsUI5PmV2Rl2uWM6OLzrPYIA21vij4RG62cNzNCD6rcBzrS9bUVXgCJjf4UDd6kbQnRfY8dpoyJ4jiC5K+W+qbfB5rD9haMh2KaXCtm2LD7COsetZC8pfV0r9DeDPm6//Wb5JeM/dGgUuv/7FPqdFzbxoWJUNia/Yi70LQZqjLP+9bltOCxkDdXqHZVXxzmJpf3cv9pmXcpGu64ZfdzCl53s8XG/E8M+cxvfcxDq5JsYePXAdSzleZKV1HF6UFfc2hY2u7e67bis+f3rOk03KsLOYMF5JgSeahoEZvwxMQcmqiiUwDAM75iprzZunZ3xpvuRRWlrRXd3IKRsgdC+yNjqNAmA3OFF9SwGolQjJpqFnMlNaHs0qNpuW+UpEincGJR/fkpPiqiiZ1iGEj0jPD3H9E7G4cW+TNsLWkTS9iw5Gty2x8U3zwlfRXkNWNjw4zjhen0sX1ciGIRvdxQnz3nLFxmAMQ9+3IVOdu+52EFicrOuIBia9Lwk8Hp6vDfPpomAsShHcLdHGOkfGiiPfp2gaMYU0Hc6TNLP5Il3SZfecOrqyBcZdB88VGnMn8gTotxealnXV8CSrbZcxNgXIVYrId59yNAbY1JqsbMmMrc/MgbljrH18KQSu6S4850JD0/c8O5rs7k9wmM4IVXC8LstmFPgMfZ+H65S58eyyNjFK4bmKRWnGpsYbrOseuoOOhKRtEXrb7FwbkC0PKdcnLGdvUudnlNWavDgnMo4A3uwd+sOc0rv1FFbRuUh0Y0DPUWilbPZJJ0rtVtfdyfvSPLcioFC4zrdWhwL8R3zl3v4PgO94lhs/Kyj/7yil/hngVyAYyo+0bfuXvpZH+Yu9PEfx2nRsQb3uohoYtlXie6RVLTnzRWlPfA+bikK3vLOseGdZkfiKVycR0zBg5PtcSzQHScQv253yPR/5Nh6fvcf4LOQ0yyV/3IyizvOC0HM5GAhIedlrK62EOXOUFaaTkc19rRTbkct2KKfFIxN0NAp89pPIMsUGbgBOY9lDHYgrm4HLNJEQrdM0453Fki/ON9xfibK5aSUauKoaKnOKDSKHq9OW3aQR19soMNoFMUeEi5m7NszBcSCbdd+TjfRspc3r3PIwrajbBaPVht1IBKF7/RH9cIexG6DLFt2U9jXpwObY9yz2JGSFmnlaMksz1kVpDQe71Yn11ua5d4FNXXG+2QfwbcekW9FodGA5iL5Hr7vC4nNzOjDJjJ71hQodcdwdBzLy6nmuHAyMZ5RuW7RuAXOi1w11q8m1tmOyzuyx7ztWrd495ieZWIPIawGbqmFtCkjdyGv6aFHSCx0+MhZfsNB3GPoXSvKNAfdnhjnlOIqybDg5q3Ec8H2HMnYIPEXgQuS1eEiBkaL89PsMsDb4Wff9jm3XHTgO08xiXZFxZujGX7XRQZ0XhbWXB4zT8Yrr/YSrg74li8TBHmG4oqlyG9blmfTOINrCDwc0TU2xPsb1V4yTCRK45rDIbIKGITa4xiK/sbHD0iU61q2gcxyW5/68zCG/dUZextrlKhArpb6dC0+vIZA86/08c6Z827Z/EfiLX8uD/KVcjlIGWG8YBD77w761Zuj0Bsr/GInvcbja8GSTkuuUceAyLzXzTC6yUiveXORciWtu9GK+a28qlilhwGL9UFyNTVb4LM2t6d2yrKgz2SS2TRIkiFL8yTp96kO2HbrW+6kDPrsuikbA702l0U1hQVFtVODd3L8woUyJ75FVNfcXK768XPP2suTRQjaFpoG8kGx5XYuZouMpdN2yzBtCD8aBbByucvBUYzMmADZmjOQpZQV80xA+teOyGIkX1NycjOdlQ93UbKoNh2lG3xeQeSeOLPgeX7KYD3yZtWflxWkyqyruLdfinWXwh8uplSA4wKYWP7B1Jawm14yCQseh55uCW9e2AwqN4BMQvQe1AXljDkYRo50FcTC1+TWDIGBVluyWlT18FLqhqOQ0LwmE8ndq47DQPdaOulvolvO0wXc1x77DMJRiUeiWUl8YMnZ2M33PITdjx4O+a6Ocj7KaOnKtrqYblXVW9+uqscB7N9qNIodJ4pL4isi76EABq/upG8ibxtjcSHF5uKmZpUIGuDbwuJooxqZYdIX9FxoZBa7LwNjEe47iMM0t9lTTQlnjpblEMriudXPYH32E/lTwks4ZoHOBAKiLFfnyUD67jkcQbxEN9+mHr1pnbDCj30t090VV2S6p0A079vcazoryKzJwPsxyv0UKCvDrgX8eMZf8I5e+vwL+g2e9k2dlef0W4A8Bu0jlUkDbtu3wA2/4S7gC1+Fg0Oc0zeys1HW6/GyPwJ+QVS2jOOTI4ApHecXMWFnHvjjcAtw7rzlai0hsJxaTyJvTHm79BpGq6YcVpf/trI1gb1NpXOXgujK/PwVWZcWyKHmSZk/lXIgBYUDo1jxKBaQtagg9zdXEJ/JlJHKc5zZdsHPk7TZ6wALfaVVzkuV8/mzFvXVlHY3ruqWuW1qzITdNS121yDSkoarFafehqlnXa6JLI4VuTLIoK+q2ZFNp5pV4WY0Cj74v/+VasxcZHKaRbuFJro2oTrGpaoaBdAd1I0FJICOLizGUxNU+3mTMipKZwZT6vvOUvUlnZriuGrtZRZ6SMZIj9OXCFOJNpS+57kqh2YoCCt2NIy+6lVWhGTs1e4PQikFdR5EUHolfGbt7bQOwXHNw6UZrhVF1ewAuXIkaHqYVaSUeZa4jh4FCy6jRcxSj0LHeXwdJJOI+E+/ckQKuJpo3FzmFFsA8chrqJreYSEfHDl3FXt/l3BO9SFdEIlexF/mG6u1YdpmM2eRazHXLUVrR8+VxdfTg2Jfbd+SRumlsEe06EuApEWen3O8bEsfjNLd5MfNKk+sLh4lS+8w2mSkwI1xnIq955BH0ldGBPSA7F7YjTFCOh3J9MXd1lPl8O5wZR4Rca0s0ueygfDlGIPE8mzHzPJZSisANnst9faNX27Z/GvjTSql/xjQPX9d61g7lDyOI/xtf7x/6xV5dSmOn/O1sJMoa1lwx4rINh8s1b50vrHtq3UBWteRVSy9UBK5iGDvmRC75HYfLNWWtGcW3mfblAno4W3O0SUmrygLcy6riOM+J3AuH2burgmUhOM12JLX4sllfWrUyA68gdGvyRswR+77Dgsp+yKFTDF+2KVFsKkmwe5RKMam0dCaOo2jaliK/AEt7Iw/f4EVZptlsNE8A31f0EpdJ4nC15xGZGfNRJv5bHSgrj+Hi/iLXxfNk7j43mohu1cbET5x5ZeS43cQUZgQFWMD0vdWGe+tcjCC1CCQ9oyfQZqx1f1WTVR02IMW/biAyAHR3iu60LLOiJnKVxTCKpiF0SnTTMIkkyS+tKla5R+BdJXQ751xlPL9kbCrmjq4BfrXVj8hBQsZOHZANkjgphb9mGLqCXVgMRf7f4TE7ccSN0UBGpK5jNSsdzdhTip+dpWyqlk1VURqNibz3XYcjeSeeo21nVOiW2ldEjpzSQ8chch02jja6oobckY211C3romEcO3zbloycOkfoTSUeZdcNrT7xfev3VbeNZKtcMkyNfY9+KL54ie9bh+lZXpuYh9qODJNNKmJR7+K1c411vTg27LA7vMbAHCocR9wlqnqfMkvtobHLn9nUF3YtPd+zI6/QdZlEoT2AdSPx57FEKf+t0aEopX5327Z/FrillPq33v/ztm3/yC9ws69Yz1pQjr6ZiwnAqqz58fcesReHT2VtdD5Tx1luufZ503CUarJKNpEugzzxHfZiz46gpqFPrhveWSx563xhBX26adkY8NLSb6uKWV6aEVZFrlvePC1JswbPVXx0P5BUP99lXdfyoa0aNkVLljfUuuVsqYlCh0HscKXvMvZd6kAA5qXRUABPWYl3Y4951rDOxAG2o0yDsN86DyZdt/i+IgjEP6soGqqyoSpB6xbf9cgjsXnpAOPIlQ17Gnp2nr42m2hXNGdFzaNNTakvCA4gLLazorBCup0kIvE862h7nhec5SWPNoUdxYSusuO/C6t78XmK/Q7XaVkVMG8aTp2GwJWT+Ti8cCrei312o4ieyRUvzKbTvY4dODvPCrKqz7QXWiKCiOhqm8XRjcsK7Vi90omxgy90AwG2qHSxxz2zUQoWIRYjoeuKpfslEWBs8JXuuloVmnlaWDD7KKu4t644Txs2qbYsRt9XRJEDuIwDcRjIvdaSKvqeFJFCNxzrwjojew54rbLZ7juJy6ZqKGp5vW/0Yz4yDA1OI4mTL4xH7PYTyQUyr2dWNk85Jqxy0QAFxk3gck79yK+svqkb+yZ5aQt0t0LPZc+QUADWec3a/Kyot0z3WFwkqHouV3qJVfgXWpN4HjvGQunc2L5MotCSApLAfW5iRCkoz4wafLOvnvl//8PcybO+Gp9WSv0FJCXscqb8j36YP/48V1a3fOY4J/AKEl9m05uqsyGBrBIbEteBSl8wfnqhjAz6vsPYF4B6NwptdvvjTcZxLvTey+OWaz2PvTi0brPdXP/+Si72ddGwMsB1FF7cdwe+dx9834XCgba6GFHVJmeijjosY03dQN8XvONhWvFoWVvr99h3GBsAdlO0VKFDaDb2TlHvjRSbjWxIZdkQBA6e50JPPrzdZ2xRamoDEHebszwGbTeD6NIo6mFacZY1nKeaSSKiy7pteZTKOPAsayz19kqcWm3LMPA5y0sO08xas9zqB4wC3wgoGyuuc5Viv+eaTVJOpR2bz3OwGMQ0lITAni96omEg4siOWn2S5dbPa1OLOHAc+KR1DYzFP834d11WyBemEHVjr3VdM8vNnL9tCd3GihMlmdKxBpld8mV3Qo4Nfblzh56nmrIWW3iJSJAN1TVsrJv9iCdZjaOk6FelBHx1h4CmETfmq4nPNPSechbuqOBgWGLmfZuXF91MXktHGLpCrsh1y+1Byyd2tsTfrheyMzilKk5pqpzWGeCpF4gDB90oK+IsjePzPJNiGAcegdezY9kOLL/MvpJut7GBZuMoZLefEFzK1b2wZ3FIy4s47O7zOzUTCXE1yKmblth0lZ0EAERtH/se49gH/dbXt8m8fym+ZVhebdv+l+b/H8pm61kLyhBIgR+4/BiAb5qCMggcfuDG0HpLLQsBnrvchqpsqHIBqL3AIYgctsYeO4nLzV7AjX5iqaYdc6vQDUWzsZtXtzpA/dGmuADXzXxfxhLyIXhhP2AaiTXKK+PBU2p8oV7K/cW+w9G8Jk9rUfQHLY+rhrPQ4WGgiXzFIBT33bppuRJJp3BvYYBSR8K8xiPXnow7bOe9dWVV2ff8inXWUNdSVLrpmeRZyCk5dNWF31bTUNdSZLqkuzxviCKHyO8UzVBULbtDl71EYny718q7ZEGf64a3lyWRVzH2C0aBa2nKt/qiou/GQCCCy7OioOc1dga+F0uR2FSa3UhbjYfnKBLP48Zo8BUn6dm65N750irMRYPUvY/SbT3ZpBS1JvRE57MqSzte7IwMOzrtptI8yWWc1vccao1Vvnfuwx3GoNsGD+nkBsbBWOx0hFqcVjVZVdnxTdcJpVVtsEApKtuRK6PR0MH3pfg7jpLOs26ZZw2uqvEcibn1HMeO/iLXYSsMRddiO2uJpdatjBi7vzsK5GCl25Yna3lNylpTN1NCbxfXVZS5pm5KO6pKy5rMFIzOP25VlLiVeJp13diUyFjRiPB0WcprkPi+cWf27FRhkdXWw67zj5v2QpJArv+DUcQ4CZinpb1uu3Fb5xrdfc5cpazQ1XUUpxuHcfLi17XHvH99K428uqWU+tPAv9G27dx8PQH+07Zt/4Vnuf2z0oZ/31d5EP9+27Z/8Fnu6xd73eyFbIcyTrrnVDwpG7K1ZnNeU6UNQd9BOcJ0atoL48QObOwA5LSuOc8L1lVtMYTuhNeNmhalJq1kzOOa37k18mwK3ovDPtcGPQ4GPQLPFV+vNOMsL4nMaThyHaJETpkPHWzxW89rNo54ko0nHuDgqsaEJrm8PIr5zu2heTxP53N0VMlCa0bBwp5SF6VE2KYbidBtG3EQiEIpEIGrzAxePoyjwDVKb8irljSTx3WaCVss6jn0Bx57Y49pJMVkY8ZvIIXyY5PAYiFdl/ckqy3I340BPbOhLstKssh9Fwgtu6kboYBsQqlh8shjdQk9GbVcn8QU60+zWa4J+ztM+x/l8dK1jredhqJLNQS5Bp4Y9XWXINgJBzvMyFOKe5vCMsvyWjHLtXVIdpViHMrvdqPBC1D7QlTYvRdd19T5ZXmO4swEf3XfG5vn2/fEjyv0POk0LsUELgrpvEW34uA5jgXDpXu7eB7LSxhTd5jJTNAZyLixW2ld45Xm8b8PLwnMtdZZ7wPWRr8bMycmt95Tjr1tEAozLvY1Y4NldDT47j407SWrlcZ2AGmprSBVNy197x6DyW1mm9J2LoHnyrymgFV5YZmjm8aC+Bfu3R9+KeUQuM/JF+ybZ31bV0wA2rY9NzTiZ1rPawD425BUx2/o6ryaOusQyQdXHIcOq4FLnjXWJdVxBNCNjFfVSZZbJXqpNcd5wSwvjehQLsBZrrmESVtqZ6dSnvYc7gx8xkZRfKWXWEt8wNqMdHqG7rEKgK24s+OzqQQknXmKIm9oGukKfFfZuOF11dDzGq4Neuz1Evqhb0kJHaW4KzLX+wmbSpPWNaPAZR42bAKHutRoDeQNedHge8qcIKUQbMVGF9HCVnwxgtC1S9u0KEdsbnqxAciNu+1s07BYaxwFVQLzSrPtKHrGuvzRpniqmFjfr6phXRcml1wsXTrmTldMLgsbO98xkM34PC9443jGIuszil/F9RQ6bSl1SlZV1vmgey/nxoetsxURJpK8F7f6gTC3zGbWUZitALSGzGBM5n/0Qtnop2HAVhRc6m7M2Cx92gn8shlmx5J7kl/QWYVafpH8eSXy6A1cKybsxnbdNd/zXSLXFcq5Gb/2PYdZXhqLIMx9Ya+h1JBRKlNQNpUU/dyMcUGYXGWt0U4LWnKDcM3GfMnEsisKmXEy8IyqXZ5rQ60vYqxdRzEII1uYOv81bQwyPdUJLbvCpS0xReITKuLgJqFqCTxxj768unFi99qC4G6lodqX+unf/3qX4vnRhpVS/zTwR5HUxT/Rtu0Pv+/n/xzw75kv18C/2rbtZ83P3kPovRqon0XR/gHLUUpN2rY9N/e9xddQJ55XQfmGhwLUjcTFjgMXD2VprIVuiQPFKPa5NJoF5ES2rhoepRWP0uopZXJnvNetTdWyyBprXlfWLYGn6IcXdOPQvTjRApznOed5boVVHWbwKK0scNr3nKeA1DqEU78mr1pcV6F1S541VFXLfAm9nst2Xx7j3fmStKqsYWF3Cu82MW1otIXZEPuew82Rzzh2OFnLOLBpII4cemGX3dGSVQ1nQH/gsBd1EbulbHx1S9MY+xBPsckaKt1yZSjmgW5fsdu/+JBthx5TY5eu24aXRom1tQ9dh3Hg2+5qYTI9Ou3Ny5MRnlKcZLmdvx9lBfc2JXnd2q4x1xl1Ax8ZBlzfZPZvdx5lR1kh4ssGfuWVIT0TE1s3Lcd5zs+eZvR8oyb3LnQmXUfTbUrT0ONqElgq6ijwCR2HnzldWTbXcS7i1K5IdIXIc5RlJXVdcd20FpfqruFuPclqTgvNtcS3f2cU+JbyXBhh7jQMnvJ1W9tYYqMPqjpLeyyduNPDgByqeqGympjL2qiucJdaExtb/tjY5IDsfNYR2dEkjmcOTcYb7pKxZnd9djiSbhrWZqzVsf7k+y0ajdsqYWnqTkDriu2KYQ92TtqA6Ywu3IhLra27ghRbEa0WRh/2vAK2QBla84e8F6Vc4D8Hvh94CPxjpdRfadv2i5d+7V3gV5mO4QeBHwG++9LPf03btqcf+sHAfwr8faVU59P424D/x7Pe+HkVlOfltvZ1r7JpebSp7cnzQvMhF09nnxG5rrX9XtcN58afamnA9Dh2ORi7FtTviojrQD90cI0Ne+Gqp4RjHWAMgo+su9l7JR3OaS7jsVK3xL6yI6KrSfDUiCPX2o4v6rblJNW897BA5+JNVocimNONUHrHq4xp5FkrjW6z7E6wXZcFwnyaImOxaSTjp2XRmrRHZTGPuhFspKMB3+xHfMe0z2Gc87pbcOwoUWOb5xsHF+mKe3HIpDOLNOLOzlE5cAUAv973uTVw0G1jqcVFre3J21UyBuuAXMA49VacFtqykrpTJ8jG9sSvOS2WvLeoWWw0YSCjvLKWQ8Ukcnhnldoc+LxuOV5rsqLhzo5vM+F3I6H+dpt9J+brG6LGpqx4mFZ8+jjnZF6TxA5Xhu7F73kX19pl3Uq3ct3YxEfPuRDhbYce+SXmVORI97woK3qeCALruraK78vjOW06nk2t7Vi0blu6etVdp90Zqec7uOqiKHdsPjDRwIZUMAgCxlFEP/RNDIJrcBUpikXdWLqzLTRKkdY1y0KwltB1cc3vd0XgaJ0KrmUeYEfl7sS7umqs20XHyrO5J8ax+nJhWBYl50VhWWO2u7ukOQlNhEQcPJ9t7zmyvL4LeLtt23cAlFL/PfBDgC0obdv+/Uu//w8RAeJzX23b/hljAvlrzbd+y/sK2weub5kOpW2FWaUbx2xKzVNgY89zra28GOM1PFlrzlea9UrAcOXImKksGzxP4TgK33Qkia8uGfs9vWksLlF6JQu7spv1MpcPQrdPOI4I6+q2ZRx4XO8nDIPAAondqCR05XS3HdYsDZAut1fC+GlbFkUjmEWlGfu1FQP2TVHpctw7NtSdQWw7hWl4wU6blxdW5YAwgGjtKXaWl3hxyCjw+bYtxWaorTVHZ4Y5DHwmUWRHWK4BosdxyPFaTDHltZPXc6+XWN+zLnmwsxRZ1AXHeWupuHXTcG9TWKp34Bpb9kYBrdWlHKcCus8WNcvzGi9Q+IGQLzpywGlxwXDqFOtbA5drif9UTv2qLN9n299Ydt681BytNctNY6JxYbZp6IcOUU9y2ju2WRdk1W22umkI3NaYdLZ4dW0xpJ7nGmuerkPA6oBAiApd0ercAbrux0PZbqjvyb/nZWvxku4+Lv6tiFx5fn3PsbHBXUaNvJ/iEOE6iqyqKXVjO5JuBFXW4rAsdjTaMrq6a7nne5Zw4CF+W4VuOE2zp5IbASsc1W1rC0Q3tupsYeoGk73jW31RbvzDuuu3ixbueS6eue/OoqXUknPzXJZSuM+hQ0EsTx5c+vohT3cf71//IvDXLn3dAv+LUqoF/su2bX/kQz4eHyNeN/9+5vW8Csr/+PXeUCn1J4HfCBy3bftx870t4C8At4D3gN/ezfQ+aFUaKt1YhXKnTwABpGeF6FBmueZ0rVkspJDUlbEmyTVV2jCrBSNQniIau0Q9lyhx6PVcIl8xjh3GA2Mm2PlLdade3bCpWrJKwMOOZda5Hwf+RUbFvBQ9A2A2Y9lAB23AJAwF9/Br6ivYMcaykJHUpmjZ0MpzVRJ+NA5864YLoonYDj36XmM2wkp4+nFkx3BdnPCyqiyYm7sXJ/h5KQXnYVoxDsQk8kYvZnRpwwnci9NioTWB21ixW1lruwledgGe54Udg0jyYWVGgrktbp4SQZzYyUtRHEfCeuqs2bv3VBIMZRMLAofYjN3C0GHacxgFLgtTCDr6eD902Bm7ltLdhU51xQ3M5us4oBsebmobXNWZMUZRB1ZjR58SAVBRGEPL0BRdEBwhN2NJ3bZ4jkNkrG26A9Cm1tRGLyXmnnKfHaHAHoycCyKGa1yBp1FA3TTGFl+SMLvlOVgqeGcS2elkuvyYrvANA59+GOAph8PVxr53lynQlx9z6DqWvdV1XKHpsjEj165TFU1KZYvnNJIOJLVMuppZUXJkDmZ1g8ldEfp/4CkGQUHfv7Dj6Sx4AJtR1DOZQJNLMoBVWdmu6MMuBTjP3qFsv8/+/Ucubfy/0IH8F5z6KKV+DVJQvu/St39F27aHSqldJBDrS23b/tSzPrD33f+/AfzLiM2WAv6sUupH2rb9z57l9s9qvfKHERfKDPjrwGvAv2mUlbRt+x9/HY+9W/8N8MeAP3Ppe38A+Ftt2/6wUuoPmK//vV/gtpceIwIE6xZQlg2yLuBJqy3wmGWaqhTxX7eaBsq1pto0tJmhlZYNqm5ZH9esewp/4BIOXeKBSzrxgZKeX5MbK/cOnHUUNK1sHHXdkmUNZd7QNi2Op6g8xWED5wa3WNcN43XOKFhzeyDaoo4qCRdq8XkpNi2bqqLSWDFkFDoEngCx48BnNwrZjUJxfm0a2610ZpjdRhm6DrXZGELPZViLOCxyC6PRaJhzkaDY9x2moWcz3qdJbLPrAeZpaQVnK61JzebRbTDLqrKeYLptebBOGa031ua9Mzq8t67syK1LGhwZgVx3Ar18ir5MoKiblk3VMo7BnXhPGTM+yWtWRWtp000LadiSxS15JGC0mF86TCPPCjcvOwVf63n0fRljzsw+3RUT1/zuvGwsIF64jYk0aKyY9cKl18FD4ZoTeOdi3GEvXUTyU47UhlFWey35+0Y6XbcSGsqwEAyw9OYOtL/8nEAs6RPfswzHrkD0DdZwWUPS0ZC7LqV7/l2R7Gxjep5LZDbaLstlUwvY32FHnZWOpxRhVX/Fc8+bxo4uS6Mn81yF5yk5EKgWz2mEWOE4RO5F99U5JITGQy4xLuCF1rZ7eh5LKQffGFo+wzr9ALD8IXD90tfXgMOv/Hvq24A/Afxg27az7vtt2x6a/x8rpf4SMkL7ugoKUqy+u23bjfmbfwhxG35+BQX4gbZt/12l1G9GnvxvA/428Ge/9sf79Grb9qeUUrfe9+0f4iIZ7E8jSZEfWFBiV/Ede6F8oKvGZCa0Voneue2mKy08flfhBcrSZ5Wj8HsO9EDnLY1uaetWZJwV6LyhDhRN7JBlmhNPsfKFJXNyUtLUUjAcw34CqErZvJUDbXPxX5ZpskyzChzmm4ZBrLnSlzn7QRJb+/rUANLdBrCuG9aFgOnd86nKhrJ0bCfmKsW1Qc/SNpdlBVoKy04cMokia40PWKqnNtTpzoPLVQZ7cbC+U33fo+e7TJOY3UHMOPbxPcnfSfx9q/I+TTPO84Inacaby9T6pd0ZBNIJOBIve7RIzejPtSQKgJ4veMJLo4QrveSp+XqXhZIappzrKLbCwJ7WTwv9FSfx00KzKKSz6IqJrluyRlOWDbOFXEOeJ53ep3bNHF4peoaq222Ugk84jEPHbvCdsj/X8hyEFAI5jbGw15dsQp6mLnerGz3WhsnXbd6dazFA35e/deF2LJdn6Dqd385F0fXFvqeznRcqtmeLhr4keuw6qNh0rd1Kq5pSa9uV1CarPq8aezCpzYi26za6nJVCl8xLIbt04V0SA33xvLscnlNzfXRdSMeeLKrW0ugBCCTbKA4Uw1Bwou61t4XMUKflMWiWJdTNRvCntjXR18+noDwvUB74x8CLSqkXgEfA7wB+11N/SakbiO7v97Rt+9al7/eQvKqV+fcPAP/hh3gsii6GVJbma4A0nvXV6OZovwH4823bnqnnZLD2T1h7XVJY27aPTSv3FUsp9fuB3w8w3N1hFHhMQ2Va4Ia7q5KybtGBomkUjQeBYTM1DRY3cT1FG8mF2dRiPeKiUI4yX4MbKKKBa3GWeVPj+8LCShea2nQ8bqBwA4cgVISJSxg6VJVoYaqiochkgqIUVF6DTlwRGtYtfa+0qZGx71usZWnGA7Nck1eCx9TGPbhbs01DUVfMy8ZahoAEAA0DoUNPIslw6UZUddugKxl7PdW5NFI8Irem8x67UFw7T1lXdAFa3SrNSfUky7m3znlvIYw1R8Eo0HiOAMx7cUjf1waXaJiGEhX8sZFj590ymvMuRmoWU+mooIJPeUrZALKen7OptMVyOtq3GCA6LNaa9bymKhr80MExezGA64mbwJuhUM6noc/CdFbTMKDnyWP51NU9/tHDxzxOc+qmsSFrXfEAKUYXo7uWngHJL+uFNpdO492IURvsBERE2eE8gauIvAvVe3fS7/5WF7xVm1N/ZDCE7jH3fO8rDhLduswu64rNqmNJte1TI6IOIL9sT999v25b6vrCNWKWa07W8mQchbHSV4QellHWFREhWLQsNw1FIXR5kMLffQbNJYqOpZjXbcu8utACdQW41l2GkTyuThfUJXE+14LyHED5tm1rpdS/DvwNhDz3J9u2fV0p9a+Yn/9x4P8CTIH/wuy9HT14D/hL5nse8N+1bfvXP8TD+VPAPzKdDsD/Dvivn/XGz/pq/JhS6kvIyOtfMxHA+dfyKH8xlplB/gjA3osvtouyNq2ug26V5dl3ZolxLAmOtb50sTaCbwQxRImLoyDdGHaPp6xmBbBYSNO0ZGtNBtRVS7HU0s04oBwH5bRoF+JYTlRNI8XJD0VQWWYNjfmgrE4qHE/hJy6rjebtUcW1wUach81pS0B/mEYu66KhLFsB501HBHB6VnHmKB77oqAfxw7TyOVmT8D00HVsNshFnGxjLb3rprEnO9eMTl4aJub3LiKGPUdJ9nqtmWeeZVn1w4BSa+ZZzpNNyv2N0KMvF5t11fCEGs+pudq27EYRt0yqpW4almVpVfGW/aUbCl2YGGXZ6M5zKRoAW9FFuJhuWuZ5wRfP5jzaFHajFs2P6Ee2hi5XtsQh4bVJT1yVq4pHqeBOVpxZNnhKAHPRe9TWlufzR6eErsud0cCOgzoCQQdsu0pZ+nAHoPccxwL1y1KMRJ/kBYviwsesW51hqW5ahrFjPc66EVjkuoS+8xSD7P2jqGEQWKyjU7ZfTrsUV1/HjuxWZkzZ2cB3h4wuxwWeZk11m3T3s26TX9cNZ1nDbC2dOEg+i9tz0I4cSiLXoXZaFkVr3SXSomW1qCmyBl3KZ6QrIsoBN3BIRi6MPetQcTl6e15qPCVfd8mq2ljT9Aytu8Mhn8dS6rl1KJiUxB9/3/f++KV//0vAv/QL3O4dBIJ4Xo/jjyilfhLBaBTw+9q2/blnvf2zKuX/gJmlLdu21UqpFBlL/WKtI6XUvulO9oHjr3YD3bY277w75V3WLzkOhIHDJHGsOth1RAFe1y07Q4/dRHJKOgX0KHS4MxCn0k2teXtZMktFbV6XFetZRbVu0EUDDShP0UbgBYqo79I3/lrD2CHY9o19Obw7q5mfV2QrTXZaU+mGai0foM1KMxvU3BtU9kQ3Ci8ca/uhI49ZK8CxQH9ZteSpJlu3pBvNLHJ40nOZjxq2o9JayoMBjY1dTLfhgoxJriYSLLYVBUwiUQFfPhGXWvNotQEQBo+ptolhca2KkkVZ2fFV4CkC7+kcjU6l3s25r48GTHshfZOkmFaaeSrxxYAxTSxtMTzJClzTlez1EptfLt5OkWzm+RGnhYC1nZvB7aHPR8d9tqKA7SRmEASiXTDU1M5y5V3z/ACjRdEcZTVvLkr2koy9yOf2oEfgdsaTAoqPgg73Mqwt3xPvMIOP4HuExoWhw1IWlxhH1vJGy+vsu3Br4nMl8phGgR1dXbb+dx3HOghcjri9zCpblRUrQ9Sou3GXYWB1didpRxRoGmt7Azxl49JhHB3g3jkNXF6dfsV1IAkVdS3mpEHQGWCKw7ZuuzGgJEuuC+NRVjRUqabOW1NE5OCkHBlRu0aAuyxaztuajSkO/iVpSaVhtdFUZWMLkh8oesa37tJU70MuheN8TSSob9pliFDdes/8Z3/Wtu3Zs9zPs4LyCfB/AG4gI6YD4GXgrz7bw/2a118Bfi/ww+b/f/mr3SB0Za49y7Wdw+qmJfIVsfEnClwl9uzuBR8fpF3uKMC51vS92rJgdqPIApEHScVhmvMwrfgy0uHosqLRCqXASxyGez5bU5+rI49bfekM+r7HrUFPNrC65meSGZ8JFSdBjespirWmbUCXDZtz+VA1LSSxg+8q1kVzMS4w9vpNA4QQGtZY7glAnxdmvFa2lL7oLOZ5Z4UiH+hSt2yKCypyGAhBoGMQdcVkfMnqOzR276XWdtQEXDrFygdL7PxFENn3HAsmAwYsvTApzM3JtzPta/LPcXb6JmV2jmoqXtz9OL3pr2a2Kbl7ujCsKMF6hoHPXi9h2ovtGEc3LXHg8dG9LTxHcW+55v465SiviByHq73Q4ktdXk53+05BHbgOr1Y1h6sND1cSFha5iquJb4vkppbIgG7MFJrOoxuzgWEZKtccRmobyNWNvFwl+RwfdV3OivKSU3ULtEwiYehd7YUWHwLjyNtLLkaWxuakY9VdvBYN87wgM8LWkzS371X3fkqHqp8aIXbC2K5bBb6icKyNFqbDguQzpPDalgiH2gVhtsiBqrN2seQF013giEOEq6D0BHAfTDwcV5E7WqyBEhevwwdNR54WLWmhjchWuvVl1ZAbN4KOLh6GDo6jBLM0o07XU3jB+xTOX+dSSuF63zLWK59BmGWXsYzu6xa4/Sx38qz92p8yf/CXm68fIlThD11QlFJ/HgHgt5VSD4H/K1JI/gel1L8I3EdIAB+4OnHhDE3iK5uQBzA2br/dPD3XonqeGuHdZf77ZcAwRCzYI2PJMglDmdN7OZuqwXECwsShSMUuvjdwubXr8/Io5Hov4c54wNic8jv6aFpVvGDYXO8lFQ96rmWeNc1Fi1+VDSli3Ng5B3dK/8BVJKESwaV53DIBatkaugRbHlkpp8TAU2RlS6Vb8ko0LB2G27Fn4tDM6I24TRuF9NzYf3csINeopQGLu3Tsn02l7YYkwLWM7K4mrtVHdLiONWk071un8E+SCUE0QVcZZZ6RLw+Bn2RrfIMH3hi3kjTFxCQ/dqmYWVVbe44+AdN+wGtXd3hxZ8LZJudonTIIA17aHXNvtiI1BoXaFDPxlxJzwe56kNdbWc81XCxN1TWjE22A6FFw4fL7/tXhFh0uBE93EhNCcUb2c95dbZibEc526DGNJByrezxdIS61lvt1fALXseO+zihRir1ji35hCuCmvqwfaqzosisKm45p9T5bGMmk9xj64rE21MII7Oz7O4abUKwvXrtRqC7GjSYM7CJX52LsFHkyEnSVohc6lEOP1Ubb0XNHdHE9ya4HwRAr81oVRUORCv3f88X/znOVjLabll7PpQ4d0o0mW2l0/bx02N86HUrbti88j/t51oJyp23bf1Yp9TvNH8/Uc0Ll27b9nf+EH/26r+V+lBJB2TQyM1XHuSRE7Dj60r4vStFb6FZGJ5fBW+ASK0XbMUTfc7g9SETF7Xm8PArZizX1bmD/3l7sc72f2FCmwHVtil0SiOdQOq/QbSOZGb7LrX4XdSsitjNjrLgVi0r6cKmlMLiKnu8YlTSGi++YWbIicGUW7Soppvs9ocx26X7LQjzCXEe8umIfdE8+xLvJhTV8rjVnRcmmrq1hpuStd2FfUlQ63ceirIzm4QKc3YtDo3cJLWMtMAFNhdZkVcWj1cbQOT3WRYVetEx7B/RHKwD8cEDY28ULByj3NoNwjaeMtbvTOR3LKGyeFaTm8YqDb83NaY/EeY8tN+eVnQHrepeH52tcR/HRK1uM4iPWxZRVXluTw0UmlvWzNOc0zcx4x7Ei2e4kn7cXI8PLViXd5gpQaOmkaByGJmDssnHi5ZFUYnAV3TaMjDNA1/10xaRuW47zgoUZXxW1Zq+f4JpRX1dIPKfzZKstXlJeKuCd3cvmkj6o+ztdZoo8p4tu5krs2ff3MnljL5YMmcIQAToxrecI061jYM3LBk+iQq2LhRVhGjFu3bQUScOTXKj4/dBh3Y2oWqgMQ0+cliEwRJt0I9294wjhxvHEhXk+q6grYXCGsUMy9NjZCagmrcV1Pux6nhjKN8sy+/o/B7zQtu3/3bDLrrRt+zPPcvtnfTVKpVSMEdsope5wKRflm2E15gOzHXbspqfrndAda3vqPK1rTteivO0sUUS7gvXr6k54se8wDBtGQck0DPAccRLu+O0Do4fo5svLsiKtKp5sBAMYBj7XRwPGUci0F3OzHHCaZiSeGP51auGe73F3seLuKrNWLaPYoeeLC3BHJYXGxrZ2Smg58MlIS37XFFaTWe45LVux8xSwe9kwsFNcL8qaWWuYTZEmdCSadhKFNhwLR7CP1LnADWZFZXADlytJzE4SWa1K6LkERj9Q1pq01PTDgKyqJV/e5H8UtWY8eJV44uE4PiW3WOY16XmGq5S1/ZD303hVZQWnmTg4AzYE7Xi94fb0JnvJPepiReK/zSs7PnWxMqTIHWbrknVRmiJXW6uPpyi1jkPuGE81QwvuNttOtzIK3Pcx4cQeRDctw+jS9ejIe1dqjdu25sAhr+XAXFfdyCk1ivLL2S2zQvAW8X8TNt12HBk1e2e0KNkg1n3APJdR4NO75GSsW9GCXD5IwdOgewdqe47+itGl6HWErt3l24B0nydZYW1hpHBocu1Y09aOOBO5/z/u/jxKsizP6wM/d3mr2TNzcw/3iPCIjMiMrKqsrq2rqzcamlUgFjFCoG0QaIYZbRxgtIGWIzSjUWsZDhICSUfAYRNI6iN2jgoNiKYRNNNLNV3dXVtWVWZlRmZERriHu4e7uW1vvffd+eO+99wjqSWyM6oo5T0nT4YvZm72zOz+7u/7+y6KSRAM7KxNYxgFTXc/bWdS6Yv1wdoyz+0QbX15CQlK+0gKgLpsCWOJ0p5EY40n0QDc2AmY7Gh+mmexBOI90qFcWn8Uj1f+KuA/wZtO/hXge5/mxk9bUP4jvKDxOSHEDwO/BB9o/22z/AfEePihgySOSh8YNNaSrVANho+94+ryEsOkF7xZ600ZtRYY4zoltGPTwGlpGF+ypdjPxmyPYrI4YFU2LDp7+kgplj3M0FFYv3y+ZD9NuDEe8dw04+ooHR57f2q/Oc14bprx0bzg3nLNQV5wVDSdVuMCiou5KApr07Ko2iFLPgnkMOz0M4oLf6beWfftzrRwUYD70zfSUyxNB/n0+oKo2/yz8OKDFCnFXuJtV2ZxzHPTjL0sYXt0TLV+E1sXUEOUzgjj93cQTTu4yfavH0DeWGz7PvLScLJasqrrYfjcP85eYV9by+OiYFk3LBpvONmvsV6yf3rO85Mx+9nVoasJ9Z6HuVYS23oWWu9C28M2ll4hD1rqwR3aX3M5mIj21jPpVxlO96tn1FXWDvTbHv7aGqxNvGHiXpaQ15ZFUXFeekPMeentR4zqO0hf2I7LsptlXRSI3hK/H7xfTvmMLs1ETKdj0bInAlyIH/vCeFoaL8hs/Purk+pQW9d1y/6+pqE/CO1n4yEc62RVcLBas6xr3lrnnvnWDfL7xzANg4Ht1cOoUWeZAjyhozrq3CQi7enxm8KLeqWAydTfVsoLGNi2vqOvrWPVfa69lZJ//PrSTOpdrfdgh4IXNX5CCPHzMNjXh09746dlef1tIcTPAb8IP6T5N56Rs+UzXSOt+Pd++T/OH//Jv8u9zYaDtSXSDHGnsRJDMfHQg7dmyRvPf2+aCwV9f9rp05hsK1jHXvEcKenFf9Kn7KWBRMmLeODzsmQUaPbimDeaDa+vKirruLeueakzzNtJE8ZRQBpq74Iaa4raksUBe1nC3njEm/MFh5tiwL8Bny/RdSb+ZCm7OUQnCLOuM7AUA+b/Nndv340IQXkJxlFKdNoT2V1LzfVRMsxO+mEy+DySPhvDXxv/B7IoHESPWaSwTUldzGmbAhkk6NYQBQ+Am4RaEXYpf5ejWR/Mc1ZVTdE0g//XIKxz3mk2G07D7eA+GyvF2ngKrpZQhgrjvF/UvKy8lkWrIcxJCzmQDHqqMlxmKQkUgk1jh1Ct3h1aS+lt6nvbmU7w2Be6fvX+bJVtL0FFHvrrZ18+P12TxUE381Ldtbi4v1Rrpt2gfFE3nb2PBS5CqnoLm371BqE9DNfPb1LtN+pKtYw6qjg8SSzweSP14M7gtR79bKTLfgnVELd9JU3YSiK20pBpckQWXyUJNYfLdecO0Ax6ncuhaN7yx7HtHLM44koYPuF71huGgidCjMeSq4njlbPaMy27QqGkIFBwdezD7Prkzv6Q4e1rnhRUPpslEO+9gtJ07sc9GrXLEEn3jdfTsrwE8OuBO865HxJC3BJCfN/T4mrfipVoxfddvcIf+vt/m9dXFYcb07FLPM/eQxUXQ8FYSXTQCdACvxHP8cwtU/us9SiWhKFnVJWNhwhOywu3V33Wu+36DSCLNVqmHWThMf2dOORB3vDgzPAAw2lpGWnF7cawn424OTujqVao4CVq48gihZJvsZtpdkZ7HK8KXjs756QoOSqq4STfL58forgS+U2vL549R3+IhBUX7Cq42DjBF5+tMBhmHmE3N9kZJUPnEmo1xLAWXTG9PM9Ig2DYGJNQdt/X6GhMKzW2KSjm99FRxmhHs5VeJYsDrz+oDauy4TQvu5PtRcZHDzv2mLvtTrO288lKAz08j2txDRhOcttlwsBIX3wWbOtNCS+vvlj1W3HUiSj7jmISdif36kLF3p+2+2LS30966bEpKdm5JCLsM2r6bqQv1L1FkL+dT0CEi5jbvDH0ueiqey37jd44z0ZcXqIEX4asev1IX4jj7jFb569r//tfrSiGlwLNNo1h0QWa9WaVW51YNg00WRgwjgJCLWjbfSLl2EpCapP4z0HTkx3awSn4JC857wS7kyAg1ZoracI08Xqj3sE40r4A9y7aALfHNa8sCh6sfFjWTqq4EitezJIBbjWtY15WvJC1Q8Htae+L+qKTfTdLCIkKntp65f8o678B/hqwJ4T4z4B/BvgPn/bGT1teL+NqP8Q7xNW+Fcu0LT93ckplW7ZCyaL2p/Y0EFyJ1WDvYZxjK/Cwz6axAwR2bazYiiWnI8n56oJdAp6a289VgKFbmNQNR+ucvSxhHGvSQHXGg2IwXTztsP22s1w5loKj7YpZFLEVRzTmOkHkTe+UFORNSxbdIp9/imk8J5x9sGPwyMH3CuC0qpl2OPR53bCfxlxNPJWzX5VteVz14VByyNXoIZfJkOoYcCMbsTcecX2akgaSQB9imxUqiCmaa4NuZxxrDs89JbXH/m3rulmIt6G3bUutFVl0CynnmHaNqVeeDrzxb7kwnaOjjLy5weHCZ7g/WufMq+oiJfGSX1Wk5GBN3hcToDvB9gmTurOBV5zXF1DQ5fvqc1V6rUioFGj1hDjhcpEZBZoPb28xDTbD37uw2NcXlN1uM+5P1P2MJw38ZruVBp1Z5oXlPkBlfJfVx9muq96EUQyphf1zvTZKh2swvO87O5HdJObaKH2iqPVU4X/Y9j0c5jSbxqdGxtZyJU0GSnXaZYhsGjMITc+EL7t9XHMfTreqG842Jeuq8YeR7rOWdFBYfy36iOC88YLRfqjfC2aNawcFv5IX7LUsDHlumg3X4mC18Yeyce0JIR0hZzeJmHQQ4k4a8YHdGZH2j7EnXnjm4rNL23ivQF5CiN8H/Hnn3A8LIX4WT4oSwD/lnPvS097P016Nd4WrfSuWad3Q3l6JFLfHATq7mBesGwMSoj4vQXhn1qhz2B1rH15lJ443x/Vg57DpBvahurDj6C3bAR4XBW+erdgbpygpWBSVt2uvKu6uch5XPrd7MvKq+bpu+dyZNxmou9lJEkQoeT7oPmoTMI0znDVMx0dcn14h1Irj9WY4ja/qmitpwsPVmnUnlLueJNyEwTq8p6v2mSKRkgMDrVdJJ4FmJ03Yn8ZgX6VtSsr1nLwp0FFGON4lCvYpajNErW6lPtCrz0bvE/Jqw9CxJNJvINF4F/AfPKUTrCkw1QoZeDp1FMiLk3hHO+4fa3+Ne5go66KN6Z5jbb11ep9+GSuFlpLIWrSwAxMpi8LB26nqTqgXtNkWbS5yN4AB+gF/Op/FER/a2Rq6hp00Zl01rOp66OYGAkeoB5FlbdpLNu+O7dERul2Rhp7F1pjr5E07GGteTiUsuqx2JSWqg69UB0vtJtHgg6U7WnFuDJMoZCuOBpJDEgQUnUvwZZ+ufiYWKYUSXhE/CUP2s1H3HFvWVcM4uugUoMa64ImMFyVF585cUlnLpAsAC7uuYhwFXI8vZm19dkpam0FQOaQ+dnT186IarsFOmgzXJNKSpGNtZpF3R7gML1rnC48v3hHT8D6mWmGbAh1nhNMZRXONHfPsdCNCvKeG8jeAnxJCvAH8z8Bf+IWMNZ62oLwrXO1bsXr+vJbdcO+JQKMLf6E+RKkvCpGUbIWebjvqTnc7Uci6s9LuufjRYNx4YfHdD0HvL1ZDzsO8LDkpKo6KikeFZ5H1qv00EgRaMc9bPkfpHVe7DPhZ7DUutvt7W9sfIkwOadt9Qm2ZJiFJoCk6CCQqFX/r3gHGeZpurxrvT3U+0EoNw83L6Xe9+K3/0O9NIlzzRWxToIKEMJnRRmN0lKGCZDhZ1t2GcDHziHzh0pLatKSh30xCfeH/pdp9hJojpCYc79I2vphKGaB0QkufKy6HQtdH/qaXNsGe5dVrRULrGV95cxEDHCmJMReFoYf4KmNZWX+t+xP+2hjizijRD6G9GaYWgqtJ8ATe399/EgRcn4you6TBC1t+/xiibrOtjR30G0qqoeAWzTWQ14iUpDEeNl2VTccway7uQ17IvgfWWevt3717bkAk6yfmHv3v5n0RUqJ7P7VD5xRGIaGSTwRM9UUvDTVZ1HdXjnXpiSa+kIeEHdmkP6z08FPvNLxpPKx3OT1UScG4Y7npjp6fGk0V2Uswn2Nd1eRd4erft1EHp26lQXf4EeSNpTaOnVHMXpYM9xF2cxR/HXwh2ZweYE2JNQXuzBCNdklmK0Id0LbPBvJ6huaQ/8iXc+7fEkL828Avw5tT/j+FEJ/FF5e/5pxbPc39PO3VeFe42rdi9V3slUgPrq49y+usaAdISUlDFjbcHPlcj/4D2bvwgrcP6W3XfRCSGBTaszhiK4671nvNSdFwWm14sMnRQvCwy/NYm5YHC5+tXjUXA8Q08lDcPG9ZVxVr03IlUuynMY+Lwps5RmE3j7iKbX0B0VIQxQFpqDqLkgUfuf5RTtclSspuqNuzXNwTtjO1sazKhnXVDPAD9NbrHfNGasJkm6K9jQVUAFpJpDxEykNCfZWq23xqa6Fm+Ju2dYONff/3o0ASyEPattvw48wXqnYf27xCvT7BVCuCtGBn9P4Bt+87kK1LGRa+aKknRHx9e5yFQXfS9SaXpjv1ltYR2ZYpHXYvBVgGFXgPcfXaict57mvTEqt6EL9q6SGXSCnuzZdDAUkDTd1ciPsKKXicF0/ANmmgh67hdFPxwRsf4Ce+8llWVT3oc5SUFB2TLA30UCxt2w4Q0UWUsCdxjDrDQyUFszj218G5oWvrobfeCaAv9j4H5ohieUC9PiGoVzhrsMBCacJ4RjzdZ2eUsTO6NdjgjKOAojGclxXzsvJamIGC/GT0dA8DXtDuH3m4M58TAeN0ho4+PBSI/jHnxgzXxbqAs01JpCXj7Ii2NaQBjCNN0VyjqP216Q8j0r1BuTjGmhJTrVjPX6fIjzG2RKuYUbmPNSWubbDNM7IhFO+tobxzzgE/BvxYZ1b5q/Ei8z8OpF/vtv36hldDCCHxecb/Lr9AXO1bsZToHV491fFRYYackk1uaR2EgacDL4uW89IbAG6Fkp0oGNTXPX48nMB69TcX0JFp3UD3rFqfSV/ailhJ1o33j1pXLUXZDm62YdhrW/ymWzaeqvyabTiO/UC9V/NrCa/OF1xLE7QUZJfYL1txhGljroyeY6zfYLwFqoOPbFPStg2yKYm670kZIKI7JKEiKTVFbQaLkX6oXtmWNHgJuE/a+VOBLzh9QZgmR4T6GkWtCfVFeFLImwilgVsXG4TxcJMXoWl0lGGbAtuUCHUf1xqqzTFta6iLOVu7mmTnfZyua3YudVE743A4nfarsu2wmVQdLq6kvLAcKS6ieyvbDmFjw/yl35h72E9c2MD41/bC42xRW/ZTb3WyFUeDcwB0OTWXNvvK2CfNFDtNUqq9oPHmNCOJND/35ss8WufU1g6zj1CpwafMOkd6iXgxzGRaN1il9A7L/v3ks9T7Q89WHDNNInbGIePoaHhPtE2Jyf0c682TL1DXK1pnLgq+ignDjGS8D0CQzpByTgDcnO2yKK6S134I3tOf+w5903UsxjnmVcUkCgk7skKkvYjWlCvWZ6/717bYZnI1wba3yWszpHv2r01fiJPQEy6K5hpJ8Ii23QcOUOZLjCUIrXGtoVmtaIo5m8V96vIMY4rheYXBGK0T2tZQrA5wbfPMYCqBQKj3DOQ1LCHER/Fdyj8PnAL/wdPe9hsWFOdcK4T4Q865HwC+/At+lN/k5amc2g+iG19Mem1JWfQW2pIolIORnFcGgxaGk9yfWtIun7uHC8DrQE7zktK22Y5BxQABAABJREFUjLS3dt/rMjr6bI956aNd+6TGxsJkJH2KZCfEuuwrFiiQUW+l4buok9ynCWaR4KH2j6dn2mRhiGntMFRcVw1KXO1u7zfW7dEOV0Yhov0irjUIqbvNno7i3Ft0SNJQkYT9LOUtbFN0cNScMMqG2/UtfV3MUczZHs2wTUm5OKDaHGOSbdqmQEf3UUHMeLLPuro6vC79huZaw/r0LuXmGFOvaFtz6b7PSLL77M9u8bh8foDP+pNntT7D2YsOIktnoD7AedFcbFw9TNIViGl4Ye0yuNteUp1H0jseKCnYjkOez0ZeRNh5X2nps1CupAm1tTxYrC4KrbgwyjTOPRFAddkxwHYaoJO8ZCdNCLXixStTntsac7qpOF5vBvGrEl5A2iv++0NEf9qvrGVV+bRDb/cSkHUhWBfeZE8W335Tvfy1qVasNgecrO+xqM7JbUGqErbjK2yPbtC2BmtKgk2GCmIPS7YN2Xif2lhCrdhKIqLGh289zovB/HKrc1aAt9nhxNcQykNQANYUVOtjxmPIpnc67RHsmYTzoqaoDca1wwywqFvgGlo4bHudMIGmWtHkc5piTtsaXNsQhBkAIU8Oy/ufAwgZEI92eSZLyPeMl5cQ4v34IvJb8SyXP4/Pwbr7Tu7nafu1HxFC/NPAX+3aom+7pYRgP02orGUUeNX2K03NemUxdUsyVl7cJC5S9mLlDfh2opBRoKjMhfisp4MCrBvDeW15XFrGQctYW0prmQY+LwN85/G4sLQtBFowiX3olXWOVeU6JpRkEnkDxuPcsq5apsnFrGfShSJdjQNujX2wVH9iAzwkUHvbjai+SNMDuJImJIGmijVJtwlIGVDZmxT1xcA30r3V+SXL866Y9CfZtm1QQYKQekjasU2BqdY0+RyAYuULSrk5IR7tYk1BnO3TtuaJruLyfVpTYOoVRX5MUfmNIAozwiCjLufU5ZztKyVB9p3kXUZ4EsToNhvuA0AFCavKdwFhN7fon4u3nvED395vLA2Ci2TDzqxRCTnkhPSdXz9cPy/LJ2ZnSnr2Wt40fsPsNviejm3bC9uVnt7qEwX935xXFa+dnbOfjbFt6ot5oNkbj8gbr5Op3EX8LfSdiXqC6TYwtQJ/ou81NXvjlK006DqShb/O+Yp1tRrmCACmWpGvD2hbgxIaJRW6VVhnWdRz6rZiXD5mWs0Zja4TxduEHStWygPgCqGS2FYNc5/LVGXV+bVlYTAM5veyBN1+hSKfD5uvs4ZydUBTzJHBfXQ0JgwSslFGEt6gqC2VaYm0HOYxRX0xK6mqq6TBPnJ818/4mgJrSnTYECaz4fPUP++6mGPbZiCGjLZffLpN5RstIYaD13tg/S38vOSfd859/hd6J097Nf5tYAQYIURJ50DpnJv8Qv/ws17jMOC7r/mTR955RRn3mPtKUDWOQAuuZYr3TcInsrBHus9Cb4kUnXDOZ3vkHT2013J4K3SLSXwo0ibw222sJFcuIYxXUjkIrDbGevittEwjH2nrC5niQd7wOPf+Wrup4vYoZCsM2I5CbgyMGzfgyz1tMu9Ptd384vKmWtQWq28PNOf1MBt4km4aakEaKJy9S5PPca3BNgWuNVCtBmxYqACl4+EkKKUemFrAgEmHyQyhNFJqb/1iHRH7mOrTXTHooJUwQ9crqvVbWGfI63Ok1KThFlV5xvL0FZT+EeJ0j+zKS1Qdz78ufCGTUmOqFVn0kDR4rhM3eugna/0GZrqZTj876m1ViqYZTr2Xlff9SgLNzjjmY0lO0VzjPPd02H4Afzk/vX+f9SLPnkwQKgXWsrGG3LjBgfjL50vOypr47JwXtiaDrxl4eK+sm4FE0NPblXNoumGzkuykXmh6mUmWhppUvkaTz1ktS8rlAfn6IZvNIYv8kHWz7J5np5cRGiU1oYrYjnepbUlt6+F3CrOB/JA4miG6DVjKgLY1JB0d2D93T4vvYeBb4/QChnPO2wyNY6bJEdW6RKiAMNnGtQ1ta3yHUa2QMvBswmSGs4Y0vXXhyCzf6l6ZW0MX4zvsi/exUBpFglAaU6077zeNswZpkq57Nqguqjca7dJGH+VZLPHeMod8Kjfhb7SeVimfPYs/9s1cql3wvP4Z4uk+IvgQtzcTdtOYk7wkN/7NPwkDboxHF/YhUchpF1d7JfUivlXdDE62O2nCeVl1vPt6CA8yrePcONZNy1aouBIptNS8NJVP5J1b1w7ZHTfSYLCHv7yuRJ3hYNsOHkf96b5oDJXx9iI9Lt+vXiQ2Crz6Ow0C6o41lISO2gjy2mPcnhnVDpqHfkVKopRGdxTlHu4AoCselzHiHkbQYTZQgPuve0ZY25qB4eXsXer1SQe9BV45X86p6ycJI21rONs8RBVHw6amhGZysMN0fIvR5BZB5P+m6WAbHWXoaE6azoBb3UZzMEBjQmlkotlKfDRxXhtvQtm2TJNo0IV46xU3zGKUhGp9QlvdZSuIub7vIbzjZUlaNYNNzk4akza68/+6TF/1YkLrvCZoHHjH4E1jqQLLKFB8Zb4YiCBKem3IqJu/jKQaWHVKa8ZRiBb+PTGOQrbSsHNmEMB9qvUx5yev0tQrrClo6hV1s6JqVtS2wrYdQWBwOtYEKiJUMUk0o6jm1LYcDgtKaKJgTBBmBFGGDBJkEPvNPnhIGtyisgFZRwe+v1ixrBt2k3go0qHyHczpuqQ2V0jCq2TjI39d2wZnjT+4wADL9oUF6DRQ5SWo84BkvEerXiBv2k6wbBlHse98++65OKNqDUH3fnS28Y/fFASjPaTUBOmMZ5Y2K3gvdSjPZD2tUv4TX+XbC+Cec858lZ99y5dp1hy8+bf96ccUJNGM77jyEb73+Q+g0+/m8carbreSA9bHX6BYHRBHezw31ti4ROmYybUPc7jYGSiUtnUcrarOW6liOw45K2sWTcO9dT14IRnniIRkGgQ8PxkPHPuTvGQ7CoeBadT5PuXGC8auJl530lOWBxsU4fO9gYH6mzce7sobHzXrM7wblo0c2GhpR3VNulNzX5D6AgoXVv17Y99SjeObRNGDAcpSOh7gLvDDWRUkA7QAHnLqP/yXuf5KJ36zMF9EAXX3+3Xp77vf8IwtSaMZUmoaU5DX5wNr66w8wTpLbWvOyseEqzfYnu+yO30/SbqHkAF1eYaQmmz2IiNeRAa+szo/eaV7DgkySLrncsJW6gPt+jlSFimwr9Ks50gg6TcFC642mNYggxhTrVk+epkgPeD21jaL+hbnecW66hhUaULYmUvOy2qAv3pr97j3P1OexdfDo9txSGVbHm1ydpPYU6S1HvLbe8ZbFnmr/rCD3TxTqzOhbF6lXB5SbY5p6pXvFE3BanNAY/2hYBROiYPR8BlRQhOomNYZtIqJoxlhkA3wKPhDQxhvEyYzgmQbpWPq9QkyiJEywFT3/X1Lzcevzfj4jQ/w1jznzfmSLAqHSIGeWl6ZdtCPALRNOXTCQuoLpflw0r8PaIS6QzS+mOMVzTXqpqXuaOG2Fdj2KmlwnWTi2YTO3urmMycsjl8eIK6266J1lBGkMwJ9+BQ7ytOsZ8fyEkL8OuC/xkcA/ynn3B94289F9/PfAOTA73DO/dzT3PZbud6JUv4TQI+tfRT4LLAjhPidzrkf+WY8uHeySpPzhUc/0SXBKRKdcLOaM13eYzy7SyQ1TbXi1dOXma99K52GWwCsqlPOyhN2k2tcnX0IMd5n052gtq99J1s738t2GXNzmg06kM8dPeasqgcopC8K/YC0ah2T7sN1YX6nLhLymgsxXY/z72cjem+q7VHMzjgc+PtFbTndVLx8fIpxjkkQcFyWbOqGRTfM7WGXAdPuNAvgdRRhl/sBdAwvv+E1+ZxqfUxdzgdooO844IKpFaQen3bWIJQfpqapIuB+1+E0w0YBvvD0J8SmWg0djW0bqmaNdR7LL5sNhckJVcg4mFCYHNtaVs2aqA0JVURanCBlQLb1Ilee/0FMuRq6qV7bonRCU69o65X/m/TFJWYreQnbqoGAYKoV1eZkeEz+OXnIri68in+0fWeYG3mRKSTh8xwvJYfL9XCNL2e99DOP3pK91zZtxwlRqVh26vT4UifqBaYXmhHbtmQdU+rysL2n4g6QkI6JRnsk3eyqKc4I422aejlcGyk1xhQYe0GVbS91B1E3yFbaHxL6TVh2eiSAan2MbQqECpBS++F+Me9o3yvu7O5j3YTa2MGfzr/HBLuZ18bZrpD4AXnfRQbd/32XYk2BaDUyyrztvrkxuAoo6anfvc0RXMyV+mWq1QDFZldeIj+/x2Z5j7peoXVCOt4nelYDeZ6d9Uqn8fvvgF+Dz5r6GSHEJ51zX7z0a78eeH/33/cDfwz4/qe87Tt5HH/OOffbf6HP5WkLypvAv+Sce7n7wx8C/h28vfFfBf6RFxTrLHfXj9gY75UVScVpecbe6g2mp58jUDGl2fBwfX/Ak++u/wGHpXfXnQaOm+kx+8UjpuGMUEVIqTmfv8L2zsuMt1/0vlR4eOD9H9xFBB/i8abm3tlyKCrXJ+MBDuiprb3yudeLeL8uwaqyHhaw7aAduHllTBY9ZH38MyyO79PUK68NGO1xZ3aLUD3Pg8VqYKX1tObTqvYWGkqy14skzYUh4VYU8dzWuLO0EKTBQ7+x5ivq9QnF6sBvxt1pNQgz0uktD2M1JUH6nd2H3GPYtp9PaEsS3iCR94BuY5YewrCtwdlm2ESqco7pNvBNvcB2zW3PNrKtZS/ZYxz60VxuC4yz1Narp40pyNcPSfNbJLMf9Gy61U/7Lsn4E2jab4KbE1zb0NQr1ievoqNDPwuCYV5kTeEHtqYY4Ly29Xj7ZnGPanNMtvNSR3M+wZqSeNKwN3k/p3nBeVmyFcfsZ+MnQruUlIM6f9NFJkRK8eJWRt4YHqw3gBwYWr1bQRYHnoprWkJ9MSi4EAG22FZR0RKpDxBkIJwnfGgpyHYfUSwPnugCbFfMi24Y37YNdeuLoS/+fkaWZPvE0/3hNeyXUJrRzotDsYWuOJjiiQ7j+jSh6JwUklAOm31dzIfuN0hnaLLuPi481XSUDYcC0RWsqItpCLUmCR6Rz71gse4YgnDhvhB08ztTrZHSP8783HdS49mLVJtjNusDTk4+Q5kfM5rcftpt5euvZ+c2/H3Aaz2jSgjx5/ER65eLwm8C/oeOFPUpIcRWF4/+/FPc9qlWF+++K4QInXP1L+SJPO3V+GBfTLo//EUhxHc55+4+MzzyXa6mtZzVkrNao0XLdlgDa+b1mo1tqDq77okOSXVE1Ta8lfcuxI5ZqMmClFBGT8AEeTUnP/hxOPhxlNRI4d+w2WifIPy7KJ3wgWRGNNojHO9iqnuY3H9gYx0zAoTQ7Iz9B6/J5+QLf4oSUnNjvIcMPMwkpcY2p9imoVgdcHT0ac6LI39CD7eYZrfYvfkDPH/nV3C0qrh3tmTVeVOl3ayox++tazvFubfj2EkTtpIAJd/yLJvF8XBC7z9wRTX3J1pbIoUmWx8QxTOSbJ8siEG+n7z2gVQA66om1IpVKdgZ32aaHA0bdt3ROvvTaF+s2q6IKKkp6pzC5ByVp2xMRaQ0Nn/ETlsxDWfcDiesaz9UPi+OaGxJakuKr3wSrX6EKN4myfaHeU61OR5O2nBBk13PX/cbT5g9yQLqNjUp9bCxutaQrw+gex754n6Hx/vN2dmG0U7A9ckN7p1ZjjY5+9lo0CUVTTMwzHovsF7AaJ0boMbelh4YZiTbo+Nus7w1OClfDKLFpdkJQ3G/KDaWdXmFcXKN0eTR8NxtU6C71yFfH1A3a5quqK82B+zE2wRRNgzG/XUpu7RMXwTW5jbnywubnUh7DVMofRyvbR1ZJIkSyaryWiQfv3uA6Lrbty8/6PfzlIs5m0a0untdXoFqhanWPN4cs56/Tt3Bpf1rFgZZ99ppgnBCku0P3XQ82aftOqIw3kbphDI/pqzmiPUzmnu4oNPGPNW6IoT49KWv/4Rz7k90/74BvHXpZw/wXcjl9dV+58ZT3vadrDeBnxBCfBLY9N90zv1XT3Pjp72yrwgh/hiemwxe8PKqECICnpWPwbtauZG8uUlJVQNSclZDpGoiqaisY2kElRWYqGZjG0Yq4MPTlFRFZMGYcZgRymjYvJUMqJqVV6o7w7w44c31W1S2YRKk7CVXUEKT6JRROEVJjVYxSgbYtkHJAK1ioniG7qCYON17ginVtgZ5+go6zAaoyNQrqnLOyeJV5sWJh3+cZRot/Wb8AFaPXyEe7fKJay9Ryw9ynjc8OF89Ya0SaoUWEtPBaWno5wblynP3l6evsFrdH55jY0tqW6GkprYl62bFdnVKFu2w1VGC46k/NS+KCuNa1lU9+IF5PchVsshQrY9p8jnWlMMJrodKAJQMGIVTaltSmBzjLFXr7UomOh0GydPkKqNwStmssc4OXc1O9gJRt0lIqTumWUGZHwMMkFu/aQmpnzhJChkMUJeUGhVmRKNdXOuJCULqJzQTPQzoi0pJfn6fqzt3gAmHyw15YzoSR4ySqR9Id2SP/vXo2WCrqubFna3Ot6ocBtmRltTFHFOuEKp7Hk2JBuIoQ6CpF/OhCEbjPdIgRuoAoe4MhcWLUffxswieGIArHSMbTV6fU9uKOBgxyo8Jk5mnFM/vd8/ZzxSF8pu1AtJQdW4LZnAuGEf+OXnW3AXMVXe2MhH+7wapp4H3rEJTeYGlsxdbR9/pWPyAve06SNuU5OuHLFb3Kc2axlbY1gyf02y0jwTK/BilY4Ioo20N8eQ6Mpl1nVozdFRxNCMInw051XHRPT7Feuyc+56v8bOvdip/+x1/rd95mtu+k3XQ/SeBd0zGetqC8juA3wX8m/gn8OPA78MXk1/5Tv/oN2O1QGkV15PuAyd9rG4kYRJowIDuw4YUqY64Es2YRlvsjm8zSvaeuD9jChpTsDELCuMx/o2peFBYYMFofU6qBSMVkOoI61pmYcaN8S1qW6KEZjd7nrY4AWC+fgs51zS2Iot2SCJfaBbr+0RBRtWs0Mrz9PP6nC+efp6TDg82rmWUnzDbHHK7PGUruUowTwiPPkM6vkGczPjem9/jbVNauiySV2ib8tIwdcX65D7V5ph8fcDR/Is82jzkcTW/sBWXAVkwBqBua8/+cYaiOGF5/DIqSEjDjwzGjP0pe9INjz3Of4MwWGHUGoU/5ffQRBhmHvJSfiOXwrOKclughaKyDalOGIcZcTAiCjICnTxxXfqltMf4k61bOGsIknIoIKZjkUUdswd8EZFSD8P6ppg/wVrr50PgIRjTzXyK1QF1eTYUlTDxZIJq/Wn20m3S8DZfenRG3jQkgTfZrGzLOArINuWgE+o7i71xinWOnVHE7Z0Rx8uKUEt2Ro9YHNztnluMNSV1cTaIQMF3eaaD59LxDZJsnzCZEY6bocO9LGZUOoHYF4i++2jq1fB+LkyO6jruON3rNl3fqaVbt9FqTNuUjMdHKHnNsxvLqvNQ86mkWRSylYTkzYWDcu+qDDfRyntwFXUv2r2Dju4+MY+DC7ZUk/vXxQ0QpJ/Ltc7Q2Gr4bAG+wCzvkkYzsuxWd4AJEMpQLg89lb3rPoXUaJ1gTPHMxIiOZ5at8gB47tLXN/Gb+tP8TvgUt33q5Zz7jwGEECPn3OYb/f7b19PShgshxB8F/lfn3Ctv+/H6nf7Rb8ZKVMt3bq3YDsNL5nR+kwTQwp+qtZBEKmASjNjPbrOTveA35dHuMNStNse0uRdC1bZkXa94XM15UFje2qQsmojKKka64XqyAfxpVsvHfKw6ZzfaItEJcTlCCY11hrPyhHWzprI1tzNDadZDt9LTNh+dv0KoQgrjZweRVBx29veVrdnYOQ+KuYdIhCBSGi0UWZBy/cvXSfSIQMWMk120iqmb9dAtAVTNik3tC+RxcczjcsFhWXNcBsTKsh22XEtyRiomC0ZYZ1mWpyzLU4rK4+DbtzRbyW3Oi4osDLm3XA+d0aqK2Eoi3r83G6ic/Yaoo4ykO/m7tmE88+Kypl5x4+QLnK/f4nHu2TeTaJtYj4lif5p0bYMxBSOzx6Y4HqCr0fYdovEetimIZMBo51fQmwOa0hcVFXiNgm2KJ6mpqT+V9ydjU65w3ePUkS+qrm08c60TVPYzl2R2Cx1lrI9fQfA6v+iFD3OymvrbNF9ENQV7ccLV7EPkjadyA8NpfhxrQt7EVCuusMIWBWfz+cDYyouTJ7rjtjUd881rRpRUzNZvMV7sMpm+SNa+dFEcOx2G0slgydP/TOmEbOtFrjVrTlZvclIcYTeGTb3whxSdsCr9ASg72b0kbvSF+8rouweXhssW/Hlth+fYr55I0M8TvdBR4uxd33kU/v1RbY6pNsdDwRdSDxTxINn2TLOOdZgUJyzyQ5bVmS+GUqGEorG+E97Z/fhgvZKObwyvmalWXecZIKUZWIfvejn3TjqUr7d+Bni/EOIF4CFesf4vvO13Pgn8nm5G8v3Awjl3KIQ4eYrbPvUSQvwA8KeBMXBLCPGdwL/mnPtdT3P7p6UN/5PAf4Gvhi8IIT4O/JBz7p/8BT3qb8Ia6ZDvu/IiV0fPYbvTzKPNQwBWjS+0WkgmQcpOPGMazoh1dxovzwYqahBOulNgQBLNCIsjlFREMkALh5aOvNE8LmKMS7m7mHgGinCkgcG0Z0zDIyLpeKmckwUjlFDDm79qG+4u75Lq2H9feKWydZZl9zjntRcubozlUgwLle1EZc5inP/5sgkxboUWSyJlGWlLJB2TQHi4r4OS+tRG66BLC2ZjJIsmZmN8QTGuxriSSJaM9Iq0PO2um+JKtIXtTr/vf2nGvdMZkVLMy2ow0EwDzVYSYpvSs746/67L7q7WeChjPX992ESm2x8kircZb3ZZ5IfEeswo2R3IAWEyG4bnAFU5v/SzbVAfoHaORB4A2p9Mgw+hpEDKA29IqVs2dQv1hVNCOlbdz42Hs5picAlw1tDqhHi0O4gq+1nN2f1PMdn7MOO9lyjm97n32R9G6wQdZpy1ZpgXZbNXCJJttnVMkH2/V5W3X6E8P6SsVoOfmTUFRX7M8fI1FtUZB/kR8zonkopryTaJTqhtTW4Kqu5aHm4OyYIx0/Mvs794nSy7NXQX8WiPIJkNVO+6mHcsO1/M02SXG8GYeDXitfMvc1qe8Sg/YBpuEaoQ6yyvnXuXpZ1ohztXPsF4c8J4Nmd/7yV63U+vF1FBzMnqCqebivOixDqfhZIGAZGWbCUB2FcpHt8fKOeye+2VTjpqb+cr1mmhekJFT+IACIOMnSwmi3ex3XUIelaiisnXD4nibapyzr37f5txsjvQouGC3ab1swnFah1dJ/bulnPOdGaMfwtP/f0zzrmXhRC/s/v5Hwf+Bp4y/BqeNvx/+3q3fRcP548AvxZfwHDOfVYI8cue9sbvJFP++4C/1/2Rzwghnn8nj/KbvUIV88L2xxiNrnsoaXGXQEU0tiIsT1BCoqRiK5xS25qT4oh1s2TcDb37+UEW7RDoZBhcjsIpsR6R6GNOqiWLpkZJR2V7U0FJVYe0DpLIsqoDUm25PsrR4pzdyBeHk6rGONACdqOQs9o3dtvhGCUkVdtQtX6ga1zLvHYsGk1lFWd1lx0iDdeTHOsEkXJsjCZSFmM0B8UI2woqqzBOMNKGUWCIpGFjAjYmwLaCSFkiZdmYgEhZX0ja3n/Kd14AkbLsRSWpFr6rkxveWr7uVdbJjOu7L7Ez/iDbo5iiMYyjgCujEOneYHP6Oqb2SmjwXUJ/0jf1ivPNAdX5mkDFRGHGZPqiLw51Rhr62ZKxJYnUlPkxdXlGOr09QBiLMz//6QtMPFmRjHdpzHUA8qblIrp5DzAdmcCSdCFcSkqKugaueOPMyAxDYpUkiLQzHqxXQ+eqtIcPy/yYB69+Eik1UbzNaHqbanPM/PRlFvmFw3K6uEs22vfYfvjZwWuqLs9o6hWL1X1W1elwqHi0ecij8pRHRclBEVC1EuuWmHaFlo7dqOC5VHItnrIVTVFCsW5WfOnoUySnn2McToj1aICARtNbw7yohx4ZtBmGKN4G4GjzgFWz5rQ6JTclWihCFVDbhrc2D7HOcDU/ZGv9kHxxn3R6y5tHJtsIdYfGOJKwZdp6e/zatoMNDngCQSKDizlJU9AUc18wqhVVeTZ8ji8rz0VHEOnFl2WzIQ5GhCr288kgG2aU/SrzY9q2IVAxi/wQOBwgsn7O+SzX5UyWd7Occ38DXzQuf++PX/q3A3730972XT6Wt95GtrJf63ffvp62oBjn3OLbhdH11ZZWEVl2i61rHwcgjLeRJ58BIIt2eM4ZymbNw/V97m2OWDaG7TDkRrrLNNrCtnboIsZSD0VlWZ4OeHOkNHtRSTVeokXLsg6ZVyFVrdDKESg3bMilVZzVsgswgrfyCON6jULJbjQiC1JSlfC4mnNSrVk2jqrb3M+qiEUTUlnFsg5R0qGF5rhM0aIlUpaTIqGyklXdG0g21N3fz5XmpIA0MOSNxjpB3mgi1RIqS6TaThzW52xLYmWpak0k/YZYtYbIOSIpyIIRiU79IH3lT/XR6ITdZIYeZ1hTUM5POn3HxXD87dYUSieMkj2qZj0wvpp62eWjxF5PYUva6km9bFOvGE1uEybbjCe3hwF8rxnRUYZSXrPTW3PUxg003F4/5KOMLzn4duFcxLdJx3dw9i4qiGnNCySz7oRfzglC/xybS0SD8/VblOdfhkcQd3Bj2Ww4Kx9TtzXTcIvz4ohROCUKxhhbUjZrFtUc6wy1rVk166HrqNqGZdOlImoDBs7qgGUdYpzEtALrCjbmjKiYE0nFbjQhUiHn+RJVHLMVTZnWiye8q4ALEojUw2ypqVZc2/kYWbzLyepN7q3eBGDZ5CyLmlRptsMxhSl4nB920FJwcb2DmEVhOc9rb57ZwWC96/H1acpW+IYfwnevo6lWNPWKzeIerjUYUwzMs9aZgSACEHSb/6ZesG6WFKag2tTY1vrnGc4GRmaoYrL0etf5eHSh7T7zy+YMJRShiom1GSDgd7uc4wn3ivfIeksI8YsB14Uo/uvAM09s/IIQ4l8AVOdK+a8DP/mOH+o3cakgZevaxxnt3KGyN9neenPAkNvWcHb0GebFEa+uDsiNYxIo3je5xTiYDCwt2xqU1EQdHVFKzcQZwibCOsNIxcxCQ6pr9uJTSiuoWsVZ5RXvk6DGOolxgkhaIuU4qwXLJuS49B/saejFkFmQDt2SFp5SWrUC08KyCXljPWFZ93qWvhC1KNlihe9EIuU/wDYwKOHf2FeScihqxglWdUDe9Cc0N/x/Evazmc6Sw7mLDkaCEhcnLy0kVVuTdFBikV9Yz/feTEV+7CmbHT2394DqLUH6bqJtDXWzIgrGAxOu6YbobTd89XOenPX8i4NQdTveYWd1n2y0z2TnpWHI7KnW/v7D4ICIfaxw3RDcYltBDYRKUrRtx1DyPmZ5Iwa/rrONV3cn4Y3OB6xByX22r+khO6TcNDhTeHt0ZwYbk7LZ8ObiVaq2JpIhuSnIbUVlayIVooqj4dCyblbkpmBpcpZNNdDZ+4TQSCpmoWAW+oJ+WKxZBiEb41/DjQlQoiZWDjAsmzOeH20NXW5hCkIZUTVrqnJOEJqBqWZNQdNt4n5uuEeYbBOne6SJF/zdXd5laWo2BjbGMK/PuT3ykCzAjilw1lOSl49eJkwPeG72PR2MeBHU1eSfZXP2OqddwQdvKNobg66q0+697edE/tBWDNcOIAs848/b2Viqthlmol9e3Gdj3kBLSJVmpCOuxjtcG91gHF+5dN8W21pq5++ztiWlecez5q+6HM9shvLttH4nXnV/A08E+BE8Ieup1tMWlP8H8PuBCu9I+bfwosZvmyVUxjr8Ac7mLVCRxbfYvhVTrU84P/wMp6s3uLe6zySI+EC2Q6ITxsGE7dENJtktRtPbKJ1QF2cDzdKaAq1iElsyTa9z0xQ0thxmNLUtsc6yrlf0KXvn1WJgSp3XS5ZNzoOiQomWvbjhehxypXvDn9cLIhkSqoBUaQ5ty0Ex4jBPiVTLdlRRWUVlFUq2RKplpJvuP8PGaKpWD5DVSDdMgpqq9bepWs1xEZNqSxoYtqMSLVqMk5fu1xeSvuuZhjWToGY7NGyHikkQk6qIrXCKEp5AcL5+i7TxkJ3tCgpAFGYDk6a/fsZ4J9iymg+ZGx7X9hY5jSkGOijAul5StxWrZs28Xg0Q4KPylMn6ATfHN7nVNiTpHmG8DdJDUz1bqE8TrJq2y55RAzV3iwjbtqSBolp/GmkN+9N90nCfB/M1h6tNF3jlhYhJoMnrbdJwj2zn9cEtualXNKagtiVHmwfUtsY6S25KVi4fGHOR8k4JVVtzVnpoqy8muWnIjcM4P9cyDrbDkEgFzDpoLDclsGAvroCKVGmq1rJs/CY2CQSTIBrmcSkJ4yAj0amHE+NOV8JFhwK+29ss7w1QogoSwnib3ex5D/sGD3l1dTAUu5NqzbzO2TUlV/JDovVsgO2UThjPDrm6+wFPhlivOb7/OlV55sOuTEHVrMjrczb1wj+e7jNTtzXLZsNJtSY3jkXjD2iRtOzF/jmmKho+47qbQWrhhcsbLIeF7K7dhso2rJo1e8mcRKeDcFZJhbWmc2OIBm3Ts1jvwYLyknPut13+hhDilwA/8TQ3flqWV44vKL//HT+8b9EqjeVLRx6L3UoizzDRK1aPX+H05LM82jwkC0a8f+sDBCpid/oBJjsveTw4+35WlcUBo11BxGvU6xNsUzCevejFUcnMG8t16mDbFJTLQ5ribBjq1uUZRe5ZSFIGnM1f4cHyK0yCx0NOx1Y0JZQRi/qcUIaEKmSeL8mt4Y31FhsTMAl90dCiZdFEbMfVAENp6UhVg5aO59JqmMv0s8FJIKDj/y8bx26UY51kO6rZ7jJCKgvHlWbZhCzqsBvm+yLVF5JIhkyCtCt2CdNoe7jWm3rBvDwm0SO2RzdJotmAx/d4vR+c60EDkmW3iNO9obNYz1/n8enL5PU5hdlQ23rQ3FRtzarJB3xaC0llDUtyHqwfeI1MvEsW7bA9e4k43fM2Il1+/dsFgUnoTTedvdv5Onm7ltXjV1g9foV06zYf2f8Vg6Pt8argeJ3z1mLlkxq1xMj3k2ytsE3JZn2AsSWxHjMNZ3x+/sWBVagHV185vL4A62bNqtkMGyI03t0a35UoIZgEKamO2e0o7OtmxU48I9HpwDTMTcUkkOxGW4yDMaGKBjhHCUWgYpTUJNFsmP30y5jCxwY4Q9K5CRerA+95pmPSZBcpNaNwyjTa4nBzyNLkaKEwznJSrfiJw59gdvrZ4X1sneHKyXWmD67TtmZgpwHDwctfD4USeoCuAEIZMgs94WVpcnajtiv+mpGK2QonA2GlX5WtWZq8e69rqtawMY5lI4Ea43yxuD66Tm0ralsPr4F1lkV1/k62la+7nHt2M5Rvo/Xf4m22vtH3vur6ugVFCPHX+ToimW8nllfvynslSciikJuzMx6/+VmWp6+wyA/ZTa8xCqfMJncYz15ktH0HnX4350XD8fGStxYrTzNWkq34KtPkFlkWcJ5XKCmpraWe2yExbyvZZit5gXCsUHjtR7n4FNHivqfM1isCnXB19Bzbsd/oepv2vD5nHE6esA4/qwXXkw3WSUbaY+obc4H1jrTpOgeHdZBqwe3Olyg3JamOqW2DEpIsGJPbgto23Ew7o0A1ZSucDJuSEjV7keGsLomUI5KOSML1ZDScCreiKYlOSfSIK9nzyG64WVRzAp0QBuNhRuLZOjEwGwSCUmqSzCuJZZDg7IVGROmEpDOItIUlVDGhCllU5wMVOgsgkhebwbLZYF3Lqlmzatbs1EuCzvwP+nmKYay9mSB4n60L99rjwevJmrJTjq8GGmk8uY6KErLoFjdnKUXd8urx3Is3Q0UYJMSTfWYAj78wUKmvxTtUrX8de1FmpEKUVB73b590sTDOkuqAqFVPdCS9B12iR8PGHqqYZXlKqCr206sooQlV6GFacfHxDVQ8aHaMLTG2HODGul49ITDNy3NqW9KYYtBfRbEvMP2w+8b0JWbxHo/zQ46LYyIVEiYhD4tjXl+fYVdn7EYhxrVE6weMVIxxdtA09atPsTTOooUiVZG/LxkOhwfrWg9lSZj0MxEZ+pldW13cV39t24aKhkgEVK1FC9eFpl38zcPN4YAa9LApeMancU89Y/66y+Go7XujoHR04V8M7HbZ8v2a4NljT7W+UYfyX3b//y3ANeB/6r7+rXiJ/rfNkkJwJUl4fjZhbxJTLL/M2dFnWOaHjOMr7O5+HKUTtm7/Vs6LhvPWsT7bsKpqTvOSw01BbgyltcRKYZwb/h3JPgUwuDCjO2fIoVdCsDNKuD79XrIgoVofd27GM8LeCjycDHOEUe1T886LI+p2xSyc8LGpb+eNs+RGcFZbIuXhJ28Z49gOQ64l21S2JtUJz08/QGk2w+ynV58DAxNGycBDS7ZESc20OOLB+gGpbpgEo6EY9evyqffa5H3E3abfz0UAxq0ZGE+XDSF7cV2QbONsM2gipAwGgWVdnGGqNUIGTDtngEknSqubFbe6DbGHZ6pmRdmsKcyGcZAPTsT9BtGYgqovCOt93xFFGSFvDuFeq87I8LJnF/hN1JiCo8efpciPmV35CKPtOwSZ14llkeL7bu9eeJh1z18ozWi8721pohmTeMdv0LZiXS9ZNytCFVLbmnm99OafKiJUgU9m7E79kQ6YhRNPBAkzbGu5On6OKMgIg/Fw4r86fR+7rSFNdp8Ysl+OAegZZ1JqVqv7NB0VGfBzqfyEJPKPN9AJVWdjUjcrpNC0edPd3s+1tE5Ik10m6XXGy9cGKHIWZoxUzGG54KSLV65agxLlMAP031No0TLSLZFkEAFHMvDzkGZNqhOyYIwKL7JaeqgqVDFZtAMwKPt7IsO4g5Sts6RqQW6roWBFMhgYanlTDTMXuIDMntV6j3UoIV57onlSIb8E/pmnvZOvW1Cccz8GIIT4T5xzl7nIf10I8fef/rF+81frGIz5Il7j+K1PkaR77N74Aa8mnv4SDhYlbz48w7Ytq7rhcV6wqBsWTcNpaSjbltPSsqocZeOQwltJbMWS3qvPZ5oIbqTegnwS+jx624mcPnjtws00TLaHmYzqNA29624UZuzqhC1zlaYbFD7aPGReL4nCACUKqrbPRndESpDqiEQnbMc7zOK9oYiM4l3CbsPv6blCBoxndxAqoCnOqIs5+fqALL3O7vg2eX1O2WwIVcRs/BxVd4oNOkqpVjG7N36AaLQ70D3btsFU64vkvU4IKJSmWp8MRSQa73kxYfscxjmCLmRqXRrGyfNkE29imG7dGu6z92CKxrteN9K8Qrk8pG2KgRVUlXOMLQcNQu/rZGyJNmXnNjsezB/bphxYRZfNEoFBaySlJu0YQT2NNRq/QdIROhpz3SczBoKWF0i3OiV797haZxjFu2Qdo+1seRdVqsEtGRgo61ootFL0xh9KKkIVMg09DLg1uso0uzUwn6TUbM9eIhrt+UNJ77VlSh+/3L2X+tddBZ4lV5XzgeDgn0OB7bvGIKDIDymbCz2y7robszlgNNofVPNK+9kKwKg8obYl02ZD3VbsxDPOqwVV23QzFq91qlrFcZlgnWQSVGyHJbPQD8191zkeoLI+NdILciNiPSbQyUD9FzLAtQ1VOWdTHA/PqacQFyZnK5qy02u5Wkuowg5aW5E6S2Vr6s7ipe5coi53UO92vVdmKN1e/2NCiD/rnLv3C72fpx3K7woh7lxytHwBeHY+0M9gBUry4vaUvUmEM4Yk22fr5m/haFXxuGo4fPOIR5ucZd1wVFSd2M9x3lgO1paiaTlfWfKNpTWO/uChtOAgFEgpiCLpY4QlHKSWW1nD1STghWwExCRNw92TKaHeJpt+hFlyRNNZavc2KFFTDjBQXc7ZLO5RFCcEdUxtSxKdsG7WWNf6iExniSJ/slNSsR3vsj26ydbMq6ObejUECoWxn/OEyfawoQf6kGp9gloeoLRXjU87q/leHxJPvH6jOL9P2xrS6S107AWFbWtYVTfwoYGCOBVDSmJeG7QTJFKR7c4ommsUdcsit52iej1Yj0TK58uMywAlZ+CBI0It0bEPA1sva956a0UWHZOFV9ke3UaFgnSmee6Wopj/uFeTV6sBOmsuWZMUq4OhCykvWdNfdlFuL5EI+uKkZMBs58NdoqD3IuutQYz0z6l3aHYW4uk+KkhI1vsUqwM2y/v+pN8aJul1AhVzmj8kkiFRGD4xA+jhFiUVqUq6+Yd3th4lPTTaZZMIDxlmVz9MxfsIlES6N6jWx0+IAntlPACtIR3vE4QZVXlG3RWONJoNJIieCt9DQkFrBlubdXHC7vaHyLrZYV3M0TohS69TVHMaWzFWkSe0xLsUZoMWD7HuHONg2Si2w5JpaLiZKD44fR9X0utk8e4AkfZ2+r3Wq+8aA50wmb44fD5MtaLsZpIjGAb8cCFSHMsJtu0G7kE4wIV9p2Od5aw8peocBrbCSfe8T9/xHvP25XAY957pUPqVCyH+C+DDwABdOOd+1dPc+GkLyr8F/D0hRB9Y/zzwr76DB/lNX7FW7Iz981+3L2Fn7+czD894+eSMTWPYGMNp1fCoMMzLi9hY28JibVmfG1rraMqW1jikFrgWXOtQoUSHgirwcJeQglVsOV1bdsaGRW24mpRMVgGTMBgcZnfSEaGesJWEjCfau8MqRzZ+neKSEZ8Ufog6MusOI/czjMrWQ4tuXUsqEwIVebZU53BcbXxWRRjPSGa3yNv38WBeUBuLkmumyQ5peJXxbvYEPBVEmQ8tMo4kORoCiFTwEueFIS8NlJDFAcerAiUE68r7N9lL/kV9AJhtU2BJ3jTU1rKs/e9uGp9SGF1Kqrz8796JN28MR5uc0rYcbgpiJZmtIipruTpKyaKQ2zs/wDj9MtXa611c26ArPwOxpnjC8aAP8uo7LyUvsjx6HzHwm1gczTq7D+827FozmFvqaMW0E/D1y1QrfzgY+/mDGWjPjd8ou9lY1V6cjvsupZ8D9MN7/9paaGFTnAxCSNl4WG20/SIqeImxPPBuAXjIzYdfeRV8nwPSF86q9IP3oppjW0NpNhc6GbNGSc04nHRkiIraVkNRa2zJo9PPEYQTRtNbXeGKMbWP6+3hstYZluUptrXsxDNOqhWRtGxHNbsRvDi+yo3xLW5e+S6S8b53NO5MUJt6NTgfDLnvXWYJeMLG5RV2bLUgzIjaGUVxgu1ex6ybT/aFpqf8h50Ysl+1rYb5U6JHwNsdpN758pDXe6NDubR+GPgLwG/EU4j/r8DJ0974aVle/1unP/lg960vO+eqr3ebb/USApSEe6cbzouSo03Ol8+X3FvXHOe+A1kXLZuNxdQXb4KmaqnzlnptaSuHa523vAxAaIFUgka2CNkXE3Ct//+pFhymkodXQ65t1+ymiq1AMQ0VO1HI841hb5yiRB8MBLaFivcRT033Ib1wwp2NnqNq/EY4DiaclSecdiriXnQZ6zFJ6jcyr8DOBst2FSRQ4bPUq35I3LIoJDujm4xjjVaCx5uaxbzGtisqa4nUiO2Rx6sfPDqlaBrvFttBVcuqRkuB6XI3+tRILX2wVKR9OFRfKE6KkjdWG867fIxYiSEWOO6CpyIl2eqSCmMl2RjTWc0I1sbHCB/kfrN5sM55cZp1rr63uDn7IGMlwb5KfnqXIMq6eGFPZS06Q851cTJQR/vlT8rZEzOhXlDZF4bLFFtnvXpexwVhMqNpisEnzLXGG2ZObxON9gYRZF+sesy/bi8y25GQ6IRQRYQyGjRQ8+KIaXqdnevfgwx8FjpAfn4f+/gVitWBdwfoilav3wCYptcHBfime+6NLVlWF6dwb//jzThbYbqYZcW6WbJu1pcy5z0Mt3zzk9yYvjSw86LRXhfetRr8sgDCJmJRzbkWT9mNWnbiGdvxLnuT9zGe3PZFqTPkDFJP2AjKFdFoj7YpKDfH/nudcej5/BUPf3ZedFrFQ9Hxr1VCkuwSRzPq2kcdjxKvo2k7jQ3AKN5lhJ8fxXo80P3b1hB3M5h3uxwMPnbvobXjnPvTQoh/4xIM9mNPe+NvxPL6RB8z2RWQz3693/lHuWrT8qVH53zh5IyvLNd8aV7z1qMaaxxSCuqypVxZ6nX3BmgBCc442spB4ZB5S1B0lh2hxI4ktr9CUnhDZxg4DyIU2Nqx1A112fIolqSJ5MpYcWNkhpRE216wQWpjub2TeUZNmA2W9tYUrFYecpJCP0GX7PF38A6rvYiwhz10lKFjfzLrrcVV4x9sZS3rvBigp97U77ys2DSGSPuEx4PVBtu2vHK+ZN2FQsVKMQkCzqoK3aU7Gue4ty6JlWCkFVVXWHa6qOO7q4K1aVnUfhbVn+DyypFGgp1UcSX2kbijQPsQKmM4KiqOyoZYSsq2ZazlUHxM4DjprN4jpThc+E1jK3mB7d3OYbhasTnrG2goq7lXWtuSeXmMbS2JTknDLYLuuuso811Ox7K6HEjVM9WC0F9bHX2YdWUZR55yXBfzwWId6Bxyg8FepXXmCXW8x/gNdVsRSt+Bhioa2FkjW5JXPlagqVecLF718GO4NXQDhcmp2wolFFdHz/nn0nUMa3tCY0vWzXKIYVBCD79f22o4uPSBZbWtOS4ee2W8qYeBOnhNzOHmkNvZ8zx35bu8R1i615moxoOXmjUl02rOlfQ6UTAmCjLGk9sk2X6XS3J9mCG24gV0INCdtMTZuwipefzwU5TVfJiNNaZgbf1hINbjgXDQs/l0N+cLLx6ujyy4ROQYXKpbSKIZWXd4qJvVALW96+Xce2ko36+etXAohPgn8M7FN5/2xt+oQ/nvhRC/gq/uud+vPw1819P+wW/WOq9q/tJX7nN32XD/qGY1N5TnZoCtAGzRIk5bROsQFlxXGILKoRqLqiuErTDJBItEli2u60yc7gQfLRAK1ET6DkaDqR2VsJQby0rAWSx5NPWXth/c7yaxj3VVirw25DyPHr3A3l5Itf60H0B3kJQxBUk0Y3t0k6pZDUNIYDC07AevfZqjsx7vzqLr9NCnbZ33rwqCznLckBtD3jSclTUHXerjD/3qX89//9N/n2XTcG9d8nBjWFctW4nk9tjj+aV13F/5708TySgQxKrFOMdWoDivC85rS2X9cNa2sKlazucGU7e0LYy3NHnlyMcO4zbcHkVoKTGXPpT31v79/HrVsJVIdmKF7mJ2fXRyw3lZEipFbSyh9sFe/aY1WJWrmFZ7WGTcudGGKvYsp85wsk/6A2+j3hTzoTPxKZMNyewWtfwgD043JKHGtlfJ0n2EeplycTB0RsAwRAaIoxlx1M1hLtmL9Ku3F7nMOltWp2wOPNdlUc+9HVDxCCU8/VgJxX5229ulzF6kLuaU+bF/rt3AetpBQZc1IRcD8IvIAOsMtjobBtSRVIB9oqhY17Ko5+xWc2I8LAiebOIh14sMmcshWjoaD/CpbR068Bb2tbFDiulWGqCqM4rVhdN633kp6R9rIHyxvci7D4br2b9Gw/XsZmlVs6Lq5kZ916Y7Sns/Q3tWBcXxnrRe+U+FEFPg9+L1JxP8yOOp1jcqKFPgZ/n6BeWp8bVv5spNy88+Kjk9aSg3lvLcYosWZ3z3oReWoAXRBxE1FtXkuGaDa0us2WCFQuoxqo5olcRJQau7wtMKqB0uvLgUOhZILQgTP2NRWgwdkdaCq0nAbhKRBoGPeI1C8qbhKydzkiDwlGMp2Rp/D85+imB1QFMvByw5JHuilY/i2aDi76NqoXPwbQpcZZDBz5Kl3w3Ew4e3aAyrquZxUVIZP9/YGH8/+2nMf/3jf4d76xLjHG8uDGXjaIxjHMHry2ZguBVNS9U4HltLNNUsrOVq4t9Ca+OjlM8LP5+qGkdRWM4PPdwjJFQby2imaVs3OCDHyl/P0jqOc0uk8db8qUJLiDvK9llVo7qOT0uBdY68MazKhlBfQ8uVJyaYLq+kE1ZKqQcrjugS1KUjn4EiZYAK4ou5jPXQlw4zwsTnjRzPG+8F5hxFbTjPJVn8EtmVDxFPv8j65BXKzQlNvbwUZvUP55P0GpEe77fOD8OV8PqktjWsm+UgyMtNMQzvx0HGx+78ZsazO6znd/nMF/8sD9YPmNfrgQ2ohDfyTHXUaXlG3cwgHQwoe4ZVr1bPbcXGNoOP3Eg5upfEz32EQquYdLw/vO8C7YW9/fXrg7j6Dd/Pm+4j5QFeZHuLSMnBlSEJlXcrqBiG9D0TrV89bNh0bhWXM0zOF3eJwgwp9KC56Q9em3pBbWsSnRLX5z4GwRnkJcr4M/Pygifmif9HXkKI/wWfc/WTQOGc+wK/gKyrb0Qbfv4X9OiewRJC/Dq8p4wC/pRz7g98vd831nF60rA8qrG1o20cbm4RBnTREi3nOGcQKoLW4myJtevuhFVhzQapIt86l0cEdQzhlCZJaLWgBVwvSU/8J67JW3QsUVNfTFwLUSwZjRSTpDv5KcXVUepnKVKQBJrrEy/e0lKQ14YklMTTfa6O9yiXB8OA9TLGL5T/EDSFPw1fdvKFixCltm1oVj9NorwuRI8+TGVbzvOAojE8zovhVFpax91VwTiQlNaxqC2nC0NVtWgtWASCUAts62nUdTd7mmWKvPHZ8m8seyjNsZX4DIzG+mKyPjfoWCK1wJQtUguasmWJoW4coRKsK+9Ma1vHcuP/LoDsWGXzqGUWS/bTGCUkuusYk0CzFUdEXbXrKcD9Jn65+GrlT6e9Uj+MZ0PssrOGan3iRY/dDKV3No4n+yyKq0Djo46reoDw1pXmPNAk4fu4fmtG1TkrVJuTgc7btmYgA/REATsEbmmaxlv3NFyMI0MZXaK/Kr7/A7+N0fP/MgDzL/9BfvrnP8nh5pBUJ+zE2+wleyQ6JVQRcTBmFO96CrQpnggk6+GzoCt0/en9TnHCqjod9DO5KZjXa87qmqpdYF2LPf4p9vNDdrc/5PNprBl88sZ7L3V+ahcxBZK7tOIFKus6t4IW3RnL9uFbq8oynex7+LA889qjzhyyD9HqZx6lWdOu3gC8gNM6343NJndoK8O6OKE0G07yR4PZ5iQYMQ23SPQGmz8cyC61rZ4QS76r9d6CvP4kXtj4nwEfE0J8GW+38pPATzrnjp7mTp5RuPKzXUIIBfx3wK/BG5T9jBDik865L36t27QtWONoDX64XjlEC9GyQdUVzpY4Z7DVY9q2oq7mCKnQOkUIPfxnzWb4WRBM0NWO/1kwok4zmpHCrVrsxvkZzJajiCVCQhBJrPEfnEAJjoqGWG06dX2LdY7HedF1LDGRUuxlCUXdcm6u+g+fvsHWJGDc6UYvG1yaajWYIQ4iu87LqinOaJuCcLyLjrPhZ7Z5hSTKODYefomUIlaS0nq46qgw7MSK0racblpsl7lijKOoWjYFVFXLaKR8Z2Ed8xWEoeDKWFHU/veMcZyvrM+IKFuscQgpaE1Lk1uEFAjpz6tCOuKZZJ63bHJLWbTUZUtrHUJ4Fl0yViSJZBQpTOuzX5TwG9YsjjxDzzlvPR88om0vkhmVjmnq5dAdBGFGFG8Tjfae0NX0HYk3lywHi510587Agstr/zf7GF8Pu5mLuVhtONvsEQZXSVNFkL5Ck88v7r+by3gac0mgk6HIJB0kJoXuKLl+cDxLdgmVp5Hn64e89VO/g8f5IdZZtuNdPrr73cNpPgrGZKP9gUnV2634uOILh2EdZUPX1Ecku9aw3c2MyvyYoguw6h2T+xneebWgMF9iXh5zJd3Htn4+dO36D5A0tyDwGhjR9jDvTaz1nmkoD/vmTUtt7NAx18ayKre5uePp3ts6Gd7LPi9FD2aSxnom5Gi8z3j24uC6UJdz2tNXmAhNUM1Z18shoG7VbHhU3OWsri+ZbqZEMnhm4sZnmNj4j3w55/5X4H+FYf/9LuBX4HOwXuAp1fLflgUFn73y2iXdy58HfhPwdQqKw3Smec44aMGlkqoNiJagbQwqgmwfTj9P0ywJgglCaJQe+WJjNlhb07YVtNDaCh169pNrNkg7QrTSw1+tw0loc0d+0iADgUkl0VghJGyClvPaojcVR0XFW+t8eKzPjVP//2lGZdqBSvpgsfIbV7hFFHmaatF4thXcH3BqUV68bD6MyGCbsjsleuw/SLxqOuz8rUItB4ovwNWOVRUpwVHeD/99YZZdF7Bedd/Xgrpuh+8nsew2VMf50qCUIN/435VSsFkYoq5DC1OJ0QLXOoJYEnT+GGcnNW3r79vULdXGn/RkN5eqy5Yokl7v0jiOisoLDKVgRkSkfKZ51HnV+4TI1WCtko5vDPORJNsfNtZqczIM4nutiVB6sGSX3aZWrU/QwYpIv9hZ4PfXSJJFIUmgvYmkloN7QmVbkO+nTTvzxtkRdTGnWp9Qbk6I4hlRPBseCzB4bWmdEOiEcaeTOc0fsm5WPM4PhwF7olOyaGeAe3q2n+xo4EGyjY7GhOw+mStySafS63FUEFM010iCR76gLA+pNsdkm2Oqcs524TfyeXFE2Kz8oF9G1LYk7IgEdXlGk8+pW9+VBcnM65f0m6goHnRJeW0425SclyV5Y/w1DANs6yiaCTu7v4WqbAY/NKclaaiZXoXt7toenhecVg3nCG6Ox2TRQ4q5tznaLO8jmxXTaMaimnNvczTAgFp6WyNo2ZgVVcsQEfFuV+sYLPvfC0sIcQXfpfxi4Bfhh7E/CvzU097Ht2tBuQG8denrB/jYyyeWEOJfpdPD6MkO9doSTfyGbsuWdukH8CZS2GBGUFToYomREXF8hSDcRnZD7jAeY5tzRLPEGDA2x9mKpjpCdHhuGIyAETZQ2EiAFLjS4fAbsZGOaOw3Sa0Fm8ZR2YZICc7rdpgJaOkHsltxNAQSneYF87JiEgac5zVhT4WBjsJ7yz9n+Qo6zgYL8V5JroIY0WXH+9UPijUqeAmoiJTXg4Sq4aQo2YmCbghrqKyHrM60Z8QpLQhCQVM7mrolCBRhKAiUHOjP55uWIJC0rSOKJW3LcNu2I0NI5QtEaxi+d1k0CmAah61bTN4iQy90dK0jTiWgmESCuA85gSHPZF01pKEm5TpSeh1NryaPxnvepaAbtoPH45tq5U/yl5IDL4sewQ/o27ZBEpOEFwWD2lA0T9KQ/bcvio1t2yFnpTZ7bI98setNMf3z7hhHnbFoU6+ou7lKn8MTqpjEWa8RkRFKa6ZdTK9WMdtXPz7AnsH+b+NwsSF0ih3lH/NlIEYLwbqLIl5XDauq9m7KYc5WcoVxfI144uFUHXq9iJTae4AJTdJBZ3EwRgrvpiCFZyn2QVnRaG+4diK6w+GiwrYeIjzNC86LinlZDd5etm07ym3MKWVHHtHYtqWoW842JUfrnNyYgZIeKp9lM85D0uA5gnRF0vjObFTdYqucs5Mfc3X8HI/zAxbVOVVbs9vkVLbBuJZlNwt7NssNz+ebuYQQ23htyPN4y6t/zjk3f9vvPAf8D3iLrBb4E865/7r72f8b+Fe4mHf/B10o1+XbfwVYAH8F7yb/nzrn3nG8+9NGAAvgtwF3nHM/JIS4BVxzzv2Dd/oHn3J9tSPEP/QucM79CeBPAMTX7zgh/QlXhQJbAi2oyhEUFa3WtFrjZIYGZIch22aBaRboYIpzBuf8TKW1lVfmlo+J4itIGUNr0dUGVWtc4Te1VmvKKyEu8J1RU7ZIJdDa8lh66EtJ2Eoc01ChNZi2peqKSNFh6vPSb/hKSorGsC4VYbdx2taxbo2Hd2RA3SnvL8fitq3BdTi2z+D20I+Hv+5j2z0qa6mtZ3vN4ohISaZhgznfdN5ljmvbmrOVpW4cWgmCAJrGQ2FtC0nik/hC7VgWLW3rSQhSCsJQ0I4UxrgBHvOPDYqOrl2srH+NtKCpWkzZ+plXN/eClrabi4xGilAJtkLFNPTdyCyOSAJNqNXA9JomRzRVQzzZH7qU/joU6/tD16a6mN5+tU1Jy4X47e358gaIxpLaWLJYdzkp/nmY1lGblvOi7q6HAtphxnKBre+xNd5/omCZak3bFN6pwPjXLOSCvaRk4J2URxdszd76X+mEJNvHNiXxaI86+8d49djvLXVuef30nK04IovCIdcd4HRTeAJIEvGR/RmRkkjpWWqmWtE0BfqSNqe3DQrXB2zJO10AWjLAZz1lPZ7sk6UzjlZX/XzLwenxysNb1lI0DScdm7C2dtAvVdaipGRV14RaMY5CQi1ZFDV507Cuao42+QApzWIJ3fv33nzBuqqZJi+ys/MdjHiTan1Mvrg/XK+yWftZVFuhhSI3Jcsmxz5DhMrxLRM2/vvA33HO/QEhxL/fff3vve13DPB7nXM/J4TIgJ8VQvztS2OCP+yc+y/52uvP4LuSfxr4KPARIcRPAT/v3NO7aT5th/JH8VXvVwE/BKzwlex7n/YPvcP1AHju0tc38Xzor7mkFsSZIoglOvDsqyZuMZmkmUv0pqUNBE4LhNlGNjPaQBCuDeHibidSm6L0iKZZ4VqLEAprK4zJUdIi68dINYZw6guUkqjGIozDtQJbtFTSzwCk9HMIKQVBIEgCiY4hVtKbT7aOZV1jnCPVXf628rBUEvRsFN8JgM/1iJSktRf4bz/8BYYY1SCcXGIaxego43RzjaKpSTsFfxJoisaLB2dxTGVbjooKox1XIsXj1LKoWmrrB+e1dUN2thKC/bHXrqzTlkdrS9jRgvpOorQtlXWDJ9omtzBW2O4+TOMwtfMWN8ZhihZXdPefSVTooTElRXefgr04Ig300GX5ouJdgIvmGknkiQoyiAmUHny8ZJDQVivibB8dZ5SLA0y9Gvy+Bj8yqVF4GxOhNPF0H2cNUh54FpkQqEBA5HVFvWFkZSRFfZHFUlvrX8PQFyAlIW8sUfwxAvnWYMPTwpDhAh6K6t2LexsWKQOieEbYaTB6anSxOuDK7R/ksPoOTs9W1NZ6uA14c7lmVJTM4ohUa7IoZCeN+UUvNOTz+7jWkAR3WFdXKWrPftsZhbTrTwO+o+1FsmEyI926PYgshdRPWPtIqVkUV3l4WnJebjrniU5vZe0TlFofkuUGCnjasRyTQDOOLkxX6w5enJcVprNHsq71M0h8Id90sJmSkiwOiKI76LggMnvdZyAbfN48a67GOMvGPpvZSb+c+5bNUH4Tfp4B8OfwUexPFBTnnM879v9eCSG+hEd6vuaY4G23///0/xZCfAAPe/0rwC8VQpw4537509zP0xaU73fOfUII8fPdH5938ZDfrPUzwPs7z7CHwP8Z+Be+3g2UgulOQL6xRKlEaUGOQYWCSoKZet2IMw6kwEpwhaOSgsTcQtZL0COkrQjDM5ROca3tGGA5MlBINUaoGGyFXJ0ikz3KrRg3lahEXuhdal9Y/DwC2tifuGMpGXVdR6wkkzBkb5wSKUVlLVr4TXRd1exPJ51nlv+Q5nVLXgu20vcTJm9i5KozA/TDyYGHX3rvpaDOhqHweHzM7t4+h4udISNkXTWESlE0DTfHKZGSbIxlpBW3Rn7g/TAvKa3rZhf+OpfWcTUJiJViUTcD7XcrUAMDC/wHv0wda9NyHEvmuSXvRKNKO0zd0nOCbOloe7FbC6qjZtvWMQ4kN9J4oFh7+qkk1JIs1t3Xb3VwmreV6ZXs4DfneHIdZ81QTNrW0HQxxcElmMzDXP50XtmbRErSGAc48qZlmhx19wlwvaPBtiSh/xhFWpJ1s4tQC8bREW273z2S+zT5fOhSPBT5ZFrlZU8rKQPS8T6TvQ8PB4S6mJMv7hGne3z29DYPVyfM4ogXd7bYGfuPY/SWf6H2xiPCDiYMtaJt94nG5TBXipQX7SrpBYamWpMv7lOVZ0gZMJreeuJx9TOnZLLPurqKNXCe1yyKFcb5gXtf2JTwtG4lBGmgOyuemkkYDF34fjYaOs20u35+huJvGymFFgYkaFQ3exFDR1B1mpZV2XRxEh+mWv+9gYzQM/5sa5nXS5ZNQWUd1TNEqL6FOpSrXcHAOXcohNj7er8shHgeP1T/6Uvf/j1CiP8L8Gl8JzP/Gre9g59hfz++Y9kF7n613/1q62kLStNN/l33R3eBbxp46JwzQojfg8fyFPBnnHMvf/1bCVrncfkokoxGglGmWM4N0VihA4FpHMX5xanQBo42ERQyIXkMorUgFVF6C+cMOIMMtgCox1uI9TmmfIRzliC5weZKgrymiVJfTFojsFVLs24xpcOmDhUKhBTMc8tDDeNAojttRRb5gpKGevhw9Bi9cY6ivmDF2NYRKsnd0wWVSVjViivpD3J9a8T16Es8fOWTLPPDQV0cNRlh7SEMzxBbs7d1azCNBLqC0rGFlGLTGCad4t06x+3JmFfmC7SU3BylRFr5AbW1bIwhkpIPbmXD6XjTGL8RSMGybjivvYbFOEcaCPLUMc8tZdmSb0CHUBUtrnXY2tHD0ToQjCaeem1af/uzsmY79jBOEvjTf8Rr1IuTTlg3HuYlA3lBappiThxl1FXn/KsT2no1hKSB7/Ti6T4i+BCBPqRormFbx+rSMN60jmkCtukEiXhHYi2uD88/UpJAHw7vLx+8dtd3kh2TDBgU9r3z9NBRduwvrWJGk1sk2X7nfrw3FIK6OGNy9cPsFekAa6W9/bsUfOeNXUItiNQDzwYMEmz7HHljSQPN+viVgWKepDPCaEa1Xg2PqShOvCbEFITxthcERhlC+az2RXGVvDbUpmVRVBjXooWkxg7XoC8qaaCxzhEqxZUkQUtBEviiMu1C8CKt2Eo0eWMpau/s0L8300B3nXyDwWGNFwn392VbRxpqtkfHFMsDyuUBZX48dOs99bhqG5aNoeOeDDqbd7/e0QzlihDi05e+/hMdZA+AEOJH8fOPt693FGoohBjj0aN/0znXR1P+MXzCruv+/4eA//vbbvfX8AVkgR/C/wTw3349Zu1XW09bUP4b4K8Be0KI/wzvj/8fvpM/9E5XNzT6G9/wF7ulFWxPFHnlh8RtC1uZYm+mKWpHYx1l2dLR4Qm6rmFxWFOvWsz3TNh7IeZDNyNemsY8zGveXBhef71g8bmC5I1X6IFEFUzZXN1B7muyqwHxuMvmKFs2c0Oz9oLK6tzgHFSJpalaHgeC18c1168E7KY5V+ZLpgdH7KcJd7a8qXkSaHbShAfznK0kZFFU5I3hvKx4sNrwh/7OQ5rKEY8k46nm+nbAnUnA9+z+Dr7/Y9fZlZ/lK5//c8zXbzFNr8P56xhToHXC6PxWF/f6ClvpDD32auZpElJ3Eun+5F8bf/r+0O42x+ucnVFC2Hl1LYqKVV2TN4bnpn7zPs2LoRDljeHaCB6svLr//cBZVXFaNTxOJIuq5ZEWVFXLhZO4n60AXLZHWjctr68KbqTeZFIJwd4k6thJDGFmplpRF2cEUUZdzFFB7DH/IKHuhvJCBVhTDmFgRmqSrVtD0JpqLaG9CrjBTbmozTCLaNt92vaVoRNqqwbXzgnxxastS4pei9ERJPqi1j8GYAi+aurVkE8vhT9Rh0Hm3YKjjLqcDx5hWmXIICa++dt5bb5mHEnu7Pprf543HC+9NU22+Zusm5K6c4r2j+Vl0p07mMoMgs5ef9OblNadS0DTUY2NKVCmGKxk/PXzEB54SCvUihA1wFxKSj9PUsrHOVzqUvqQup6dB55ckUWK88JQGcu6agZX6p44oKRjWwqy8AIQuZImjKOALA7YSg7YnN4nP79Pvn54UbQvOQTsRltEcsO8ztk8Q1ZW6xgOE0+xHjvnvudr/dA596u/1s+EEEdCiOtdd3IdOP4avxfgi8kPO+f+6qX7Prr0O3+Sjh78tvXfA/+Kc+7xN34qX3s9rTnkDwshfhb4x/AD83/KOfeld/OHn/VKteTGRHP/3AsF00CwEytiKbk9jtlNIr58vuLT05LThaFpHFEk2d4JuPFLNN+5nQxuxP/jT5yy+PmC9OAIm24zqitQY1xbInRMs/0c4poivRIw3Qm4eSVgFHhK6+unDedzw+q0IX/QEBwZbCSoYwmppKkdm8qRBp1flW05yAsqa/nlt2/w/PaIQB+yKK5yvCpYVX5o+WC14S+/fsr550vQsJGC+VhwvKX5vBb81M2Cv/LGY26PI373d/1X3Cp+lKMH/z82Hf1zUy/g7PMEKiINt0iiWee5dJdpMsM0Hh6TbUIY7hI4rxgfac3tm3u04krXNbVspeFgZ+81IPvYdkzetMPc53RdDgWmsr4YgKdO30z99TpaW9ZFi5SWUnodURB5uLLMW964XxGnEiEFP0MJLImjI37g5gN+2fVdvu/Wi7TlZylWB4ONf116WKm3X9dSk8w8fNPkc28ZMt4d/LpE8CEq65lZed2QhorKtAOmX3R4/V6WYFtH25QDRRm803BfuIBLLDvfTXhadzeM74rIKr/oYnoRX6BiotA7Qpf58QCF9V1GWM2oNifU5ae4nu1jixKCDxAm21yJG2pzwvrsdepuznJRRDUWKJeHg5tyvrhPXcyf8DEr82OWq/tUzZrSbIjCjAgGF+ce+hrvHKHkNdJQD6yxHobyvnUtoVZoIQm16lyvfTfRF5Mk1ANTL2/aTjzbCx8laaDJZlPvbH1pLrWTxiSh9p1OqEiDh6yPXyFf3O+ci8vBHRkgbRtiPeZWx5zL63NOikddBPHFAP/drG/RDOWTeNffP9D9/395+y90xKk/DXzJOfdfve1n13vIDPjNwBfefnvn3CefxQP9RuaQ25e+PAb+58s/c86dPYsH8SxWohW/9sYVXstWVLZlL474wGzK/mSMbVvOOxbVTpRT7ltOq4arScT1TmD4xz5/yMMHFcXCUL7ZIFoo9vZoJwraEJGPUZUf7LcTRbqt0YEgjiUvTgJujRI+tLNF/rzhwXpDZVtKa3mY1/ydz61YnTTUgFSC84U/ka6qBtvWbCWSq0lDZd/ixdMM4xy3JiuSUJM3hp89esxn5xsaC2QCcdoSbAzBq4+8bYwtefOFD3P8oZjj52P+tHqF7979GB9//69mdvYXefOtH2VRnVGYgtwUPiVPKsann2F3fJvt2UtD4FTbNgSh75bq8syLAkd7KP0yYTIjG3v/JpOv2Czus+HCXrw1PuMijGfc3nmRrfQWXzo64+oo9VGxSvFch7PvRCH3opK7y2bQowSBGJhhq7nB1I7lUU1rvHVLOFYkmeKnJby+POCT9474zS/8ID/4wWtsHv5lzo4+c3HaX9z3AVHJjLy5QcRrfmDfwWEr4/Uli8V62PTyDv5LO2zftr7b8l8LlBQ4pQmC2eCc66zxm3ZT0OTzIaPGVCvWy3s+w73rFEqzpmw2rBuPRFhnB48uH/3r9Ul9WmGgE0YdpXmzuIc1pQ+aOn2ZaeaLZG9w2VOIhdRYUzxhhSKlHijm/fLzhYvi1Vuq9KuqV2h1Ntjhb9YHTKoVQTojivaBlkhLbOvh2iwKO+aW8nR4rYi0N/csakNtfZHeSqOugNB5e+HZi6FCSV8IKmO9sWoph9vtZQlZ9HAITqvmx5yVc6puFtZbE/UW+VIGJOle5++mB9v83fKsIz+8+4LicE/40H0T1x8A/qIQ4l/CP/B/FkAIsY93EfkNwC8B/kXg80KIz3S36+nBf1AI8XE85PUm8K99sx7oN+pQfrZ7EAK4Bcy7f2/hn9gL36wH9k6XlpJb04wb2Wg45VyfjthKNDK4/sTv/szdL3CaF2zFEXlj+JnjxxweVDSVz0JJ7oTdTMQRTTTlucFsWkwlEZFn+wSxJBkrAiUorWM7Drk9m7Kuan7dR54kv937xF1+7499nnlueXxUIyWcLw1N7UhHirxyPDgzvJE1/N2DFWkgePXgHqu5QQhIJ5ogEGxlin/8N24zL1uO54Z7nwoYffkthDOMDk+pNjPeWFqMcTyuLGdlza958Z/nO7J9Htz9m7xy8mmWJudRuUAJwXY45qw85UVbMpvcQXYq68t5GlJokvUBUTzzGRnVLXSUUW2OqTbHQwgT4De2ekUYb2NNyWhrxc3pB6it1xhkUei9sMqSyrZcSxNujHJeTnJOcosSnlF2uuj8yjq1vdQ+kyZMJKPMX3Pwp8PPPT6jspaPXv0/cS2ecXb4aVabA0LjA8fa1hDz2pDzYuuWvLYUjc+MSULPMjrNy8G2v2gakk4Vb51jb5x2LDdLYd7f+Yf5YXISSZLgETWgpgnlwpMRrSkoqjl5fU5jK+ouI6VuK07LORtbUlnDSEekKqJq6yGqAGArOmE73sW2zbDRz9cX0iy56bRRXaLiELjVpTYOv9cN+nWX9wL/sKmilEGXkKiJW0NUr5hv3qJZl0Sd1XvbGsL8GFOuCJMDdHudUHu24phw6ExCpchiz9rqbXu8xZAlDRWhFh07zg4waxIqxtERRBePrbI3SbrZUKQkrvkixXw+zKKK1cGQfROEE6I46d6DF15q8Wi3y42JB4JKuTnu8m3/ytffUJ5ifatYXs65Uzw69PbvHwC/ofv3j/M1PBedc//iN/UBXlrfyMvrBQAhxB8HPtmLYYQQvx74mpjfP4rV2JajTc4sjqBtqazlPK8ItSCsfhQh9XAqjbqB33lZ8fLZOZ86yolSRTrRcNXfX770b8xkrIhHkqZsaSpHEAnisWJ7FnAt81bsW6HiM6cLPn2y4Ld/0NfYB4/fYF01jKOA52Yn/L7veoGjTU7eGP72wRnLynF0bjDWDRRjH2LV8uDEcPxGiclbwldLNoEi/N6Umx8b8X1XMrQUTN4XkP7igE/e2+FnXytYnjSo1lGeGk4OKv5BYbk7b6is5QOzj/BLv+9X0/zk7+K8XnJvU7Ixknl4TqS8+GsvPyDRoyc2Pp/fnTGtz0k7Sut6eW9QaPeU5d7iY1Mck9fnXum/vEd0+mVmV48Z7byIUHdYVT7JcbuOB5jkw43ho8s1dxdLXl3kPK6822xVtejg4vMhtSAdKbYyxYd3InZizTQI2I49tv5gsaIe/wDJ87+UF9JgGA6nwUPOi32O5ytvlmntwJxTUjIWwSB062dEHgKrh/iBdWdM+fpjb/G/lcSEWnG88s9/HG1TmylbacR0J4HT1/0cRwa+4NrSW8/bmlWz5qz2BIHcGua1wbjNoN7WwpEq2K7XnJZztvJHQ6xtb0U/DiZUzRpVnHQmoTHp+AbRaHfwfHO2GTZXTwUeD0JOoTT1+gRnDcl4nzDZHgpOU8zZLO6zzA8pzYZNvSAORv7v1yus8fonHRnS4DmyyNPz8sZ3G6G+eM08HGoYR3oIc1uVfi7VX/OeIu/ZcJeZcMe0+RzXGlbd+6vPm7mcRNk7OgODViYIM3QXQtb72/Uao0TqYc7ybtd7yW1YCPGJr/fzp40oedqh/Pc6537npTv/m0KI/+Qpb/stWbN0xG/86PcNX3/u3pf44599lViJLntDAW+yrBt2kwglpGePOEcWSl64Hg5zkLH2ZolaMsQD9x+WLBJcTTQvZgnbUciNbESoFA9XG95crfnrd+/zDw6P+MBsyk6adLDAcyh55jM9hOS7d8Y8zEuUhPsnDVIKnt/TvJiFREoyfl7z4i/N+P7nrrK7dRuAv/hzP8Ef++wRf/boMetzw3hL80f+8Zf4A7/s43zppTP+6BfeHGZGf+lHTokSycnjhr9k57y44zfsX/fRP8xved/f5c7n/yR/4c3P8No6RYkW057wMD9DCUHVWkYqIFIBSkiqtqYwOXZ9n3GQMa5OKc58LM44mDAbP8fW7CV0lJFW+z7/fXPApjxhmR9SlXPG87uEyQylE0Zbt5iO/RnRVCtEqvnA3od57nHGtcdzXpkv2BhL3XmDxR15Ii9adqaa920F3MkSpmFAFoXsZyPSIBhU1pcx/dpabDsDujAorS5BWf731pXvNpQURCogDQJ/GCkrNBcw2FsLH0aWhSHWFbx2dv5EgVLSayuujqZk0S9i79av5H3PvzJQletiTr4+YLU54Hhzn0V1PjgA56bomEgFuTVMdEikvOfU466Qa6FQQpJ20FgfrhWEGVvXPo6OxgMRoG0NQgWoIOmYZp0DQGto8QLOoDO/lPHHOFpVPgoAxc5uSDz5PEGYsVneo+6SL6HLIWmKCy+59lWq3Hc4odIe/sPrWGxTUCwv5kk6WlHzPuqOtdhDif7FBVJIpKFc+mTKpl4N7s1lNafoEhh7yFAJnzqZmWJwD4jiGRivvxpv3UIGFw7FveGqs+YJ14J3s5xz72Qo/+2+/lD3/xj4Hnz2lQA+hqcf/+DT3MnTFpTHQoj/EPif8IX5t/MsQpm/ietjt7+DP3j7O4avf+h//9/46Ud+Y9lJFS9NI+5kI/bTBC18mFSfHghQ2pZl0zDWVfe1I1aCaajZiUJ2k5hZHHF9MmYcBeyMEiZRyLwsmYQ+92SaRCShwjVf5CP734GSguN1zrysSLVmGpYsitbTnYXgF1+70kUHJ3x4f0NjAtrmkGr9aX7zBzP+uU/8luH5/Lt/62/we/7Gl/nd33+Nf/ajd/jh938ntvwJjvMb/LW//xOsTw3ZbsDpid9MtoKc3YdHzOLv4rlP/I/80Ev/X37+S3+OHzl4mZ+bT9mNcmJliRQctg1aNlyPJVXbcLc+YaJDZmHBYf6Is3qNFpIr8ZQPBWPk+euMxvsU+TFF5U/mUZBRNSuW+eFgfDhOdpmsXmQ8u9MNmM8QMiBMXubG9BZbz31isNcf3VR89qwgbzzleJW07KaKG2k4dA5RZ9g4TULSUJNFPUPIDkP1fnAcXrJusTjSICBvvB2H6YwLddApy62nr87LkjQImJcV88rb4pwslkOyZK/dMW1LrBQUlddHuJbNAwuETML3M4lCro5S7jyfcaP5AvtHL3dQk5+39AaIJ6s3ffHu2EkAdVtRmALjLJEMGQcZiR6RxbvsXv0exrsfIBrv+fuqVrQd/Kh07AkB1p/OpQwwpadLR+NdrP4OHi0rTuf+93t9yOm6Jg0/zHRfE0/2aYozmmqFqVdD1gx4woGp1gMJYXAiiLInIpqj0d4ww0gDSa4lvbnyZYqw7QLv2tZQ5sdsNgdD4uamXrCozgcnYetaIhmQdXkvk9j77WlT4qQBZsPj7Cnb/pokONkM/nbvdr3HzCF/JQy+if+qc+7z3dcfAX7f097P0xaU3wr8R3jqMMDf7773f5j1//pVvw6Af+Yv/S985VHNvdOG3/mdIyZhwI3xiNyYwdbDto6jTY4uxSBEHGmffX6RF58wTSK20oA0UIMtx1YcUTQNj4uSLPIbXRR9iONFOWg+QqUwzjHWmltbmkXVMg4k89J/0lRZ8fLBiMrOu9PvHW+nUd8j72J1/+Cv/Q38qenf4z/+8/f4ynLNf/5rbvJgeZ11VfJ7/4l9/su//hAAU7csFobPqYppuODWOGVZN7yw9Wv5Nb/+B/ng5/8if/bn/wj/4HSKEi37yQbjpN8UjaVqLZWFE+vtzJUQzOteKzPnfbYksiVFfkzdrMnr84ER1OsA6HLWFx27qcf1q9JvZmEyI1/cZ3Il4YXZc6yrmu0yZGMsjyvDWEvWkWfFjbRmpPUgorzw0fJCSNd8kUzrC2jHXKM28qIL6eKK89p2AV2KvGmou64EYFnXbBrb/bthYyyTIOCk8EafAFeTiEXdsKhNd9gwrE3LTx6veHBmGCeS33B7yiT0ivCbszERr5EvD3wmfJgNehlrCqrNiu3RDYAhLKvPg1/XS6wzjIMJk3iH6fgWO9e/h3THW6LY9jmkPMR1bslt2wwq/F713ivtZRBzttnjeLVhVdXU1s+u+qTPRPpOz4bfQTzxjCkdeqjLmmKw/BfKD/r7ORr4IsbmeLj2gzN2p/kJ9Rtk8W2KWl8S8nbmnq0bbqM6r7A+gbIwObktsK4lNxfW8xk+9RLoQsYan8Y52hsgrpZyoHlHyYwg+36OVs/Gvt5DXs/krr6d1gf7YgLgnPtCN9B/qvW0tOEz4N9454/t22/95X/2Nw3/vvvoNf7m6/dJ9UX+upLSW6FoTaUtv+S56xx3TsE3tzKyOCCLFFIe0FSPAHA2IJUNd8YrdPrdHCxKz3zpfhfgeJ1zf7HqoBXlZz3Ai1mEngi0lFwdpWgpeLTO+XsPHnFvUzPWnvY80ordJCZvDGdVjXn1Df6pD7zAv/yHfwX/2xd+hg/+4H/B+sYOH/qNM37kX/zN7CY/xe//5Bu4FnToeHzW8KPNhv2tko9t+w9b3qQ8//zv4neme/yy1/4yn59/kc+dS0wriLSH/FKliaTDuJaqtWwMjLS3A9dCUjYbymbTzVwq1s2acTAmVBHjYDLkffSZ5fP1WyzyQ3ayF7wie3Jr0GtU62O24vexrmqujlI+3DpeW64Ya/82vZYmjALNSVGyrJsBjqqsZW+ccnNySCu97UpZHKKjMTtjCLXfqPsh8ap0Q3Hv9TOhUp0zQctZ6ecnG2N5uPEWILfHMNKKaegL2mlVMw40L2Qj3trkPMgbFlXLwZlhlinetxVwLU14YTbho9dnFI9/lNPF/YEO3IsWq/KM+pJaHrxSPstuEcYz6nJOkR+jdUI63me0/SLp1i2EutMFWIHiENt4fY2ILhhnOhoPG2sYZYixJm9u8ObZgqIxg81PFoWMowAtBUrKbs4okYEvRuXioBM2+qwZHcQ0+Xx4/D0s1ZhiiAAOk1mXYOlJG7Lyr2E61myl+0/4jG2lAePoiLrozC7DjNF4n9YZ0npFY0sO1m9Sy9qHfknFVjhlO95lNn5uCONKxzcYTW95KC+IB3ud3jKmUB/ny4dz1lX9LncRv95LHcql9WUhxJ/iSTTqqSUiT2sO+Xf56uaMv+pp/9C347pz7X387mvvG74+Ob/HW+drPvH8hzlbvoWSgr/16j1mccxHru0wbX8eSjD4th4uVM89Rh7Fn+L6nV/Jaf2+wdzx8cYb3ZW2JUZSYZkFwWDQ2ENkV9JkYMpoKdhPa0aBYtNYJmGAaR2LumFtDIva8Ht/7PN8YOvL/Gvf+UG+/OP/Dp967XP8S3/hZd7/+3+Y7/ueMd/xHSmf/t8X5F8scVNJ8ULE+Vxysm5Z1Ib3T8asqpoP7P4WfuCXfSe3v/xJZm/8VR4Vc6yTgwp6EkQsm4JlY4gkVNbnk9xMAx7lB1S2fgKKGAdjQukDnwCW5SmqG3in4RbAkEkOXDqpl9wYv0YSfpCTVcEHr8zYTX0R3TRm0C70YU0AjzY595YrOIbvu36VO7sZaaqIxgfDIDeLfLHQQoD0grp+GLwXppxuCs7LipP/P3t/Hq3Zed91op89D+/e73jmc+pUqUpVJankQZZsyXbsxE6cmYQAgYRL4LpZ0NB0h17QzXj7LiAM6dCMCd0koRsShkBySSBxRjvYiWNbtiVbsjVVSTWdOnXmd9z73fNw/3j23qdKkaySXZig8KxVq87wTucdnuH3+34/3zBClSQOouMdbFQUdDWFrChQJI0ly2SeZqiyOL22dY175BZ74RQ/Lljrq5xpayxbBpsdlzMLHZLpJ/DHV0iTGWniYTvrtxkd1Rq6qFroZr85vehWD91ZRNHONxgeRZZIAIMt0jhsUPQi0KrA4rrAtlTem8b1H4jHnBUZA9tCkaUmybNWYg1aerNI+fEyjiF4YzW5uahOHFnsN2Fkmt5mNr3MNNhFkdQmFdNoiTJcEo6bsli9YPbbotxbFKmAec58vOqzFM0PGvOp625iVcKPxf4DjfoQRDO+fv/UJ2LLXRN5QIYrsDax1yyEhrNEEotEy3oj8hWPrx7L66s5/t/An+b4APGbCKf9HY07LXndWkMzEUTKu/Sq/M4Zi92TLHbF18N5xOd3D/g9D5xCLV5kkixgumuNOU63eniHlxrZbF0Tn06vYOwvsbx5AS/O2fdintk74uJ4CtD0anomLFgmbrdNXpSVlFaUYDa6Lq6hNyWYvCiYJYLa+skDjyAtuXg5ZPbJOY93ZP6le8i7vv6L/PcPnODZv/q9fPO/+jm+cDGk1Vb4Gx+8h7au8Y8/t8PVz/mUuaADzLyMp7sxb1n0WXZslt0H6C1vce/4OYLsKS77HqNEoC9EIJFKnFflBbmkr0fshRHjZI+4Mh+3NYUV06Bj9OlYy7SsJTTdxa34UFqFRS+KDNNewmyvoRoO/uEl5rPr7I+fwzUXWd18H2eXvw0/yuhYBleGE+apcFO3bZNZIibBeZYxTVKivGCa5Gz517hnr8WZrstqu81qx8LWbgJbGIowCYo3/BmGwe254rvzCsNCSZQXTc+sZpRNkwxVllEliWGckBUlS6aMoYrfv23g0NUDOrpQn51qO9y/0kXyP0UWezi906jGMe4dROnP6Yncm7LI0Czhb6klzkFRcOTn5MUcXRVyXFsTlOCioMGqJKnA9CiyhF+cJKhKkqN5RF7MsHSVjZ6NoWxT5hnTZJPd6ZwgzQQ8smVUiZQ5sIytCWNwmB6TQKy2mKATXyjLrO4m0WwXb3KZOPEwVYd+7zz9tUeaZrgsa1WK5UGlBExF2NhMlD6TcNTELQMk0Zj5XCxolrWIqlqY9lJFd+g1pat6A1KfQGrzqCzXpc7TIkdIEyVAze7hxesM/aihHN+N8UYreVV4rQ9Vrv1/8OXcxp2WvJ582Y8+IUnSb3w5d/hfyzi7epazq2er79ZZtOGFmzEHvs6S0+KE9jTz0WUm44tcG34BP/Wa/O44FY3A/uZjdHouB77NJ/cE0WAui3JCk/xX7XD25qKsttKy6Vg9zg320Ay3AToK9dIIVYZZVLCwrHP++ywUGXZGGZ/55IzPfe55vuVrdviVP/JdjGY3+I/PX+VXtse8Y9Hm+9+2xt8YXmf8qTnpUGInKJiu6Ey8nL3wEg8PHN65/ignVreI0jnwIqNRSJBrZIVEW0toqRlxrtA3EgxZPO5ZClEuYSqiLLZkLeEaAyxDhEkZrSWxczTrMKtjrH4i38ckyuj0M5JohGsuMppvY+4/xWp7lYHzCEGas5G5v01NM6rKFqp8jGS+OE14bpxweuLz7pU+SdZlo7felB0zWWRtPL8/wouT4/ySUjCZWqpaLVBl82/BUNgLM7q6jJ9mmIrgl9mahq2qLDu2mJh1ndOdNl3LaHozlnwd2msknMKPMkpVQXckXGUbYesCL85vSzKc7MV48a7obcgy3YqyDJAXqjiFyACb1amLBpPjRTl+nFTY+IyTvTbLroGm7uLHFpNkjSDJ2J6MCdKMrmU0DDldvQVEqqt0bSE/TjIR9TwNl1HkZUL9HoIkZ8kyyA7/LYpqsrz8CCvnvwWA2D8UJS5ZJSt84rk4yUuywN6E/k4jMVdkjVZrVdAD5jtkeUTLXGw8T7ol3j9mexVFO18tpFnzt0uApWwT+8LcqBouk+Qe4mkkepryOoq2zmye40URu96cPT/g6eHkK54fAIpSbD7eKKMsy1ySpECSpE5ZltMv5zbutOR1q2NeBh7mlUFmb+hx3/o57qu+/qknDnCNk5w//Yd434OXme0/y/DwKXYmL5DkEcPDp0XpobvJW1bW8OMVPrt/xDBOGVaT4elOG1fXycqClZaYmGpdezjews8iBoPThNxHXpSsuQ4PDwIeHsDDywucUz5OnkbE/d/HgR/w7OGIlZbNb178PO89/xAffPQEi/ZnuD7zUWWJH/7O83y/fImjZ0NKvySeZewHOXMvZ2clZz+M+aaT38WpR/4oDzs63zP7OKOdzzKaXGLHu8owGnEYzypia1V2qmi8hgyqJJOXwnFtmP2KmWVVjeKMIg0b/HmehdiaUPdock/kuJt9Wv4qtrNeNX+vYMkwcE42aZa6qqAEx1iXqFIKOZogFtQ7xmdGU0ZRwo4350y/w8Ax0VWJ3elcpC1KEpNIhD5FuVhMFPm43BXlBV1d4ZqfVqRlma4uFpG6B9Y1DTqWwWqn1fTfFFnk39iaQpB2KsBngh8fo9N1ZYAiixPRMBAGy0kUE1esq7hCv+elkCXXC9egcpKn2Sp1BTrOiwbWmFTPhR8nDGyLtY7w+4zmS+xO/abUk5clrqE3MQD14z7OcimP81KK5eZ3Qz9he+rRNQ2WMLC7m/Q3H2N7tsr1IQwcHcVYQdIEyJT8EvH8AKlQUWSVKDjAm+8QJBOWew9g2kvMJpfZnbwAQEvvAKIEqKgmdmcTs/MYw3lCHEQYqsDtH3hT4WdqmSy7J7DaKkWxxrXhHD8ObssREkFeBZMw4tJoys9fH3E0vTs8rzfaCaUaEcJt/2FgXv+wLMvvv5Mr32nJ61bHfAZcBf7463ucb5wxmt0gygs+vnXAf7p5yBnXoq2/g2+4//eTfvF/Yc+7wiweEacexr5Lq7XKo2e+kdX2GZ47GFZ+BZtF18I1VcJKuTVo7eHFqwRJhtlZI5ruiHxym6aZ//WnNtjo2WjpM2j2I8iaCLLcWIC3nRLy6P/h5EUEvRq+/uw6T28PCdIMx9D58d93gZ950xb/7pfEiUmSJaJ5zo3rEZNZxl60xfm2ySnXoWeeZ2HwVjZO2zwgPd8sms/vP85RPG68EXGRYsgaA7NP3xSSzLq84Y8vo83dZmGpkyYB8vQiHculKCxag9OUeUZ7+UJT0oj9A0HZ1U420bsgeiAdXSMrxcSnSHIT2lRLevuGLjJBKgNrkhc4hka/ZRImGX6cMoliFFmiJSvMqux4RRKBXmZFQFixVFQZOrpKVnHJhPfkuKfR0beI/YPjcpbuElk9PPkhRvOIYRAS5wWurjUBZ0CD458lKeNqQamHSOmUMRSFrmXQb5lYvEDme2SKimo/XAkMMqZhIjD6lUKxViB6VS0ySITvI0xFZEHXNJpAqxoECrDQ0ptGeV4Iw60lX0dSTpMXebNg1YuLn76Fg+tzsmLCgm1V3LPjhUmR1+kO7qFtqljaHrF/SHe2w3y6xf7BExzt/RajaEicJ7hVv2154S3YnZOY7VWG+YNsXz/ES1IRGVwKaCRQiSjyiji8QFbEHPhBszjWi3SN1b8+8/ni2CMvoGXdnZIXcBfTH3/HjF+s/n1Z404XlPvLsrzNXipJkvFqF36jj377BB989AQfBP7of/h53r6g872PfA3XD66wvvk+sqsR1yYX8ZOL9M0FglhMNEvr78Raf5/40Ds6lnyd2D/AqLAQ88jC6WbY2j0E6X0ovfvEPrQA19SYhnG1sMiE4wOScExrQLOogJBHh9Gbmu/V4kXecfI8N8ZhxVLS+OtrPX7om9b5Kx/+JZ4+jDkcZYR+TujnPL+bsOfnXO/EPNBpMY4EWfjB1QfpbpiY7TV6gwuE/g5RPCZOPAzdZbD4VnSrh1wFM6mGyGOZ7T+LN7lMknqYRq9pOpvtNXR65BVvqr68pD3AKEzpWhplsYNquigyuLqOY+j4cVKJGRRmscjYUKreBojky3mWiXCminY7sC0Gjo6tiT5BLTke5FZT9pqnwj9iKnLTeI+LAkMWk3pbF4+zVamiFEmwvVRZoijSxmGeRCMsZw2rt0kSHEc7L1cn0BrJfhSGHIYxi5b4W0RfzUCVjtMMDUVhuWXTsQxsXUU1LrAbhBzMAozZtNmJe5WTv57ILU2tgqrExFlLq/XKa1LL3W8/lRRMwgqfolbvsXQFo/LvKJX3R+SQRHhJwp4fkOQ5PdMgznM6loFravRb4r0ZTXfwt69wkMzIs4goHjMNdjkM99gPjxoRx4azzop7mm7nDO3BeezBafaDk3z06naz0PcNvaEWA8RJUhlXCwYti651nFDpGHqzAQvTjDgvmKcZXV3hVEf8vb96F+aBsnzjnVDKsvyJr+T6d7qgfBJ4uTX/U6/ws9914yd/73fwF371l3jP0VVO9Cxu8C089LWPsfDFn+YL1z/Ede8aAOfLjGwrwrKe4cSb/ldkeYfx9rOCiRWNUFWLlXPfSsIpVMQucBIIf8Spfgu5vMr9K6eb+9XsHmkg2EbVnNyMH3viBVTpIn/m3UKEd204Jy9LBqaJY+yTxh7x9Iv8xXe+mc9s7fPDX7jB3FUoChg4CrYmdudfGM/pzkOW50GVd9/BMRZxO4+gdCVWHRPXuAlsIss7hLOd46z7CmrY33yM/uZjzIeXGe8/RZrMKhWTQIFIlcO6PrXc6hEQOBALBYmzy23CpGhOBrqi4FaLSd1jCNKMNdchSFO2Zh5BmlKzuJNcIM9F1rtApdfhT0HVG8lKiTVbZbnKfhlHcVPKuhXRnhcFuiZuaziPUeQzdFcECyuJeljuGqpxgdyfY2kaXdNk0bWYhomIEzZ04VHSAq7PfBRJYrVlcU+3LUp61eJY49/zomB3GvDkzGe/6rVtdlxcRLm0ZpDFec493XZDSN6dBgRpinfL6cvWVAz1WLLrRaJcVrO4giRv/lYQtGXH0KvJu6yUYiVBmrAXhEJeX51cRN9HFzkvlZmwLFLC8BA/PGQcHTQ57z29Td8c0LOWWehfEHJfd43Cfjs3ZhHb0wlXvTlRXrBum8K7VYh+jq2qVQiXjK0dv/lrGnEdvOaaGk6gc+loXCV9ysIYe4tK8CsZIgL4rtzU75ghSdJZ4O8ADyAEWACUZXn6Va90y3gt2vAKIkbSkiTpIY7hY23A/nIe8Btx/LEHTvNjT73AdpByxjV4x/ICD27+ad4qq1y88WschgeMw0PSPMZNPPji3xUE2fkOceIRJBMWO2cJJlso6gFabxOrXOHAy7A0lSL6Av5ki7J4ijwVqXsLZ/4sr0SQ2D66iipJRHnOP/qtX+eU2wKOWLAtllyBNZ8PrzAbvsDSPSkfOL/GF45ajVIqKgpMWaajKw01wJBlgjQVMbiyLMoMmkqQZLiGSPzLc15WzgqFZNN8M5MwJTbWGNz3fgxeEhG4lWRU1kwU7TwKV1A0ExcxUcd5geMskqcROtdE2UUtUWSZfstssOZhkjXJf/XEp8gSiiQzjmPiXIQyeXFSZZuIXXlWFtwajWFratPDqBcoQ1FAEcbHOM/FbUZRs6DlZYGtaeiKTJwts7y0yXz4MeGpyUWSo5vrnBy08KOMJMvJyoIgFotYzzTomQZHQUhWlAyDkIFtNVDTIM0b9pUXJ43xFeAoENEGtqZyaTwlzgvu73eF0KMoqpTElL15QFah41uaQq80OfBCJqEibjdJmjCrvCyZhDFhKjw+cXUbPdNoFtZ6eHFClOdNgmZdDtNVCQqh8FINF3dwHlV30Wdb5GWGLovCRtsc0DIXaTlrOL0z2IPTBMW9bA99hkHI3jwQp0tD9K3q19VQdLqWUb1mWhMfrMgyS20TW1OoScIdS0VXV5iGNook0dY1cSqqJOxf6ShLSO5mSP3vjPHPESb2fwC8D/ggrwKdfKXxWieUb0LokjeAWxn7HvBXXs+jfCOPOM/5+I2QrRcCPuLn/NT6iLfcb/PNG9/Mux/+IG/ns1y7+O+rrAmfyfQKmmphW4uce8f/iBevo6sScvxF8jSsslACXhpN+PY3vYMitTCLtJHXLp/5wCs+js9eeYanD464OAu4PE5Jc1h0fJYtlZOOycC2uG+lR95exWgtYjhLoDzMt525xDAIuen5zNOcIMtQJIlFy6SlqY0vRlcUXENntdMCRBkkzjcA8KMMyz6J07lJGowbqeaNcVD1KyLGN2NszWW5tczJgYtr3BRBT3lBmGwSBzmqXDvfhby4KFKKNCIJP4bhLLLWeaBRfE2ClOE8bCbFJUdMHI6hc7bf5epkyu485NJ4ykrLrsKbiqr3Ik44dRmnZxpk1SLlGnoV8CRzGEQCxqnrHIYR80qZF+U5LVWlreuNSTIv9ObkpSjbuOZ6RVAo8aIUL0mIsxwvSRrz5Nleh/MLQvNS9we8KMU1FPTiBZbdC3ixSlj5cJI8J6tCzGZJytahCI26v+tyFIbkZck4itgNIq77okod5aL819UUBuaccUV7niUpiiSx4QpixPWZX/WICoZxwl6YEeUFZ1wDU5ExFYW+oTdZ70uWiVqdoOoy2/N7E2xtjY6lo6oSi72jylPTpz043zjhzfYamt0jzjdEQNg0Jcl8vFgEtymSzNmOgyLJzcJtq2pzunMMDVtX6VgiZjlIc+Tgs8TViVeze2RFhsKYC8sWhXQPR/Ok6XvdjSFOKG+4BcUqy/LXJUmSyrK8Dvw1SZI+jlhkXnO8Fm34J4CfkCTp95dl+ZXznt+g49xSh//tsVP8i942H//4FG8/5bOhT0dX+L63v4e/8KuXeP/6X+Rr711Djr+I0flmruy9RFqWPHkjZsfbBuAbz7+Jwzhme3fIwLYa2GVelFi934PVfpK2f4jR+eZXfByKLPGLWxO+8KRPfJCBBq1VnYU1HX+94MxogmMssuyu4sU5Uy9nobXDyUGLJddiybFFqS2M2KrAfnEuomPr+rsoxVX+mLJsQIvDQExeiqThGhskBzlBuk+36ncosszuPGSeeawFop+z2lmja6lQKZXyooSqUaxkzxOHYYMQqU8+rcENlHKD3UnI9tRrmte5JIx6QrUk4RjCOLrtBwxD4bDum3qTTQ5AQrMzz/OSgW2SZMdN8zgvyMuCvGJ71aUSP8swK5bYPM24MfVYcmysucpqZ42iWEOWd9DSZ7AMl7A4WfHEqgWtKGlpijC0VkiYvCir579AV2UmYUrXeoAyv4Iin6RjGSzEIrvnKAjZmgfNwnratRtl2CiacRDFXPMTkaFeucX6lkxUFOyHKdNkSlTtrAeGyjS9FSEj4WcFO37OOBBRCLO4pG1ILBhCmNDWNE62nUYircgySZbz0miCIskN3saoNiB5cYo436zeH6LvVGWtkRX7x6eb6qTk3tIvEe87qVGkWVX+vGC33SScHVDmWyiKMFFG8wPMlqBhK5pF7B+QRGPgCQxZEIjv5nijlbyASJIkGXiximG/CXzJDPtbx2uVvP5IWZb/CjglSdKfe/nvX54M9rt1OPYG779/g/ff/zb4vfCbFz/Pn/g3z/Nr//Am5//mD5B2VvjN75jzZ8KI73u7WAxO9Gw0Y+233dZJE06+7OVrLqc8jNG5/Xej2Q3+5Rcu8SO/vEeRlWRRQT7KkYISJS6Yz0vSKkz7bf25MFNW0al170NTHsYyoWvtEucFG4VN1zJ59mDIKEq45s1ZMo1mIq5VVXDcU5glaTPhXpnOBAK+LMWOtigJsowoz4mqhEp7NCFIM5YcG9cUdXBbV0QzPn2O2D+kzFMkRRB0s9jDaC2SFycY+jGTKGbJaeEYWsNTA7EI+FHWoEUMRRaxxFlGPM+Jq/5AS03pGcZt5Sy7khPXOShiMdUEiqSaJHVFYbUlmvnt6oQTpFnVBK9nF+FiT6ro39bgNKsdu+lPLDktgjRtwJZAk0IIECY5jqlWJ6nTGJLITrc1rZH8LlUzWUtTGt5YVqTcDBImSU6clxiKxMCUG3PmUZwxSYQowawyZYbVz2ZxSZiKMKsoLfHmQqSRZyVJWjK2ZOJ2SUcXpxRDrU6E1WL4oUvXmKYpHU1jax7iV89FlJeMwoKhn7PUVli2FRYMlSXTEKgXScZUbumDZVlTdnR1HUtXG6pB/TzVwgHx/h2TxD5FKBD3imYxn25hZiFGa4nW4Ayqf8B45wk8b6vBw9yNUZYiBvgNNv5nRDvj+xH58+9DpETe0Xitkler+t95hd+98Z7KuzTee/4hLv51Idv9lWc+yw9/YZvnnvT5/1y8yg/9+k3+yDvrXdIzfPf505y+Bf/yesYnXnyaH/j0FS4/HxAepkiyRIXMolQhU2WUBQXVlEnTkp0g4lQUM/QjtGiHeP40ZnsVq/cwALK2isGnSeMxZxbezIE/Jy/FAhBVO+B5lrNsGc0CMwsj9sOYy16MqcgsGKJWX8fq3pzHqDKi6V01dc1qEt6fB3hxwrIjlEy6qkB+iSw6Thc8hhKamJ01JmEuMu4rn0XqfZr48JBh7KFXSHbHfBPDuThFtVTRoM2qMtV+mDZ9opNOUSH7hZqrpal0TUOUlYoSu2KI1b0joxIBKJLEoGU1/CsvSqvTWkFRrBH7T5DFPmk4qvDxH6Y/OMOgJfqaXpyL/lPVPN73YsIko2+Kk2rIBtvjgDDJBMnaMem3DgiSPl6SsGCZtHWtUUDN0xxDLlElSfyTj6XPy5aBIcvMsyo1Mk+F10kRl/WzgllckuQleSFOw3FSEMwyQq86OZgyqiIRpGXzutbDNVWe35vwuZHPiqkyT3M+tx8zDwt8L2O2nyKrEs5ARZbBjwtuqBmLdoqjyjiqzD1ui8pH2VCca89RnVGTZHn1HNffw6QosfUHUdrQMfbxDi81JbUs9gi9HSRZRXLegb75NpZuc8j/iy/rM/fy8QYseQ3LsvQBH9E/eV3jtUpeP1p9+ZGyLD9x6+8kSXr3672z343jmx98u0hw/MPw4eee5G9/+jr/4fkZsgSDjoqjbvF1acaFE/cRRjdRJekVTy712B1dw1AVro1m/O3PXmF3J6YsSvSOgmYrIuWwD4p+/OGxXQXDEBkvh2HEIIg4fXJTRPnGPkX66UZ6LGuPYnQgHP8Cb1l/iFUvxFAUXpp6ZJR0dLXhXU2TlJdmCcNAhCspcs7UylmvZgcxiQvIpJ+JSdwEojxnJwgxK9kvVJ4MVcYxBam2lhHXeezu8gV2pksc+DMGtsWJ3iHheAvv6CJ5Fjb4jqJIMRRZMKpKUS8fVKekKM/xs4KDICcvM6JCEIyzgmbHvlGW9EwDXRE7YVsTTd8wyRqZbsfSWbJvkoQjEu+QdoXd0aw+WSxYUkUaCbVTlS443n8KTW9jthYF4NEUKYKB9lADH10qTgpDZBI2DfztqccwiDAUm9WOzP3LfYZzIQx4W89m6CdcvsX5vWLHbM8D/DRj2TKEm75CxwBs2BpRzTWrXiNDhTAtCeMCuTp5ZmlJWR24iqKkKErmccmTBxEXpwkvznzun8z4/HDG0/sxLUNmFBbsTzJuXgpIxjmyIaE54jmbjzOCaY6sSuiGxEFHxW0pdCzxvlxviTjudddpTib1YuLHKZNQlFRtTWMYFAwrc2vNZbM1l0HrXVhtBVuRUeQbxP6BKJmGn6OjVWj9fAM/ujvUqLKEr0YCcGUs/3fAKUSE7x8sy3L8Cpe7huhv50BWluUjr+f61fgXkiStA59FcLw+fit9+LXGncqGf5jfLhF+pZ/9t/ElxgceeJgPPPAwn7v2LBePxnx+OOMXrk75V18cURZfxDBkDF2mZ8uoMpx0NE67NhtOi4FtCa9A5e7+/HDGbF5g2AqyKtHuqBi63OyYikKkQKqqhKZKtE1xm7MkZX8ecOgtM+jVzeQTBME2Qz/h5JLYRVu938PshR9krX+G0+fXuD65h0tHY+JcRAvXUtEgFUFYcVygqhJBfLuCpqXJOLLSZMmAmMSmSUZLLegbOgu2RccS/YRCugfdglw7Zm2ZVo/L4zWGwUw039tGBSn06G8+hqQdN+pzYDg/9ijklcJJkUrMagHZLTNG1e7b1cXzsmIJgnDfvD20y9ZF/yc28saN3gR4+YeE3g6Bv0NRpFj2Evqs34AdtVvw9ABJNCJNRJ68olroziKf2drnqOKKHfgBtqbiVC52W9MaM6euKFV0cdxgez53Y45j6Ky1HQYtkb1z6WDaEJkF4qfkIIqbEmVH1xhIElkpTjQtLUOVUrIiY+Ll+NMUVZdxeyoeGWlUoKgSg45K15TxkgJVhqcPY375i6KvZVQhaAd1mSwpQQbplvTGIitRTQm1is8edFRWHIWuJt4bfiUy6FWlSiWTmIRx4+MBWLAtkjzn0nha9aHE5qYpl+1mTcRE17SwtXsbBVg9vHjcMPK+8lGSZV+VE8pfAn69LMsflCTpL1Xf/8VXuez7yrI8etnP7vj6ZVm+V5IkHXg78HXAL0qS5JRleUfNp9fqobwTeBew+LIeShu4O9q734UjyXLOL/S4t99lkl7lqUlGEhVMDlNUTeLQlDFthUkoIIUn2wIWqSuClBpV/Ym1fu3iVli3tUbma1Z5KwBZIcjCWVnSUhWiPOcwiLg28oizperxBK8IzFu+7y/h7f0Ue5d+idNv/lucGuwynCc8uX3APM3o6CJq91lN4vowJctKorggSGV0RaoaloLWu2wZ2FWOSVaUOOrxLjErC8IKH+LJErCGocoMenvAJk/dHOHHcxRZZrXdIkwKXOMceecMfgFd+Qbpnijf5cZDAltSNb/r5yEvxa7c0WRWWyrzuGTk5eQtcA2p2bELPlZlCMxz0bjXhTvf1lVR3ipKDoJ1lldOY7afplOkTdhUWaSURdrQhHWrLyCJWdj4jeoIXdf5Fp4bPcewUnzdU5acrBzjQgatNXnstWzX0lSmt0h7D4OIcRw3aqhpkjZqLBBCjZaqYFSvb72wGFShWtLx354XcLOE2TBlfDND0WVUUybyc65HETd1mWiek0aF2MQMNFotBduo+mmuiiRLJHFJ4ovTiN0R/qYsPZ545arvokoSA1Pl3rZL2xBppUGaseP5GIrCtZnPThDR0hTWbItxJGILajjnThCxbBkNweCKFzS9G/WWv0uVaUqBflowSe8SeqUUp7evwvhOxOQO8BPAx3j1BeUrur4kSV8DvKf61wU+BHz8Tu/otU4oOqJ/oiLybOoxA/7And7J79Yxmt1gdypwOC+NJtiaxsA2RTJepYr6Uw+e5pdb23xhFLEzyiiKEtOUaVuC+KpKEpcnM46CsMlQUWWJdVuv0gtlDFnmTLeNXfUA+i3hR0qyAj8WPKujIGQnCMnKklEc88zhiK2pR880ONF1oSgYOCZp8DE0++uav6E1+Dqk9nsA0WNZ7MKZMMGLE5HaaFus2QGfNaZcHqdEqTDjtTRRxx8YIgVTkSQWbbPB8I+jGEMVSqkDP7gto8IxdAa2xSTsszvba0x75xeE8SYvCvY9gU+xNJV+SwRBybKGn+S31bWFJ0dMMoYis1RFBmRlydYkI8lKgmotrRe7SRRhVaUucX8GA8cUjn1Tw4tSDr0QP05Zch/EMa5XuJhVilS8roKyK8CIgmUmTJpAQ6a+MQ7x04yOrtI3DFZadmXU1OhalZghEcDHOgDLixOGQdiEgAVZxiRJm9JWR9dQJIl5ltFS1eZv7+jHZch6GIpMnIue1sBQ8bOCvC+mhDQqiEYZUT33yiArEqotY7YVWh2VlQWtOWVERcF+Jf5QFYk0LUiTEsdVUFWJJCmJ4wJZhlZLYb2tsmKpLJlCmm4oMl3TxNJycYIOI7FwlDWzLGcUJ0yTtCpfip9HeU4Wl/hpxiQRXqogrd3/Nc9LBLClOUymYvN2t8brWE8WJEl64pbvf6wsyx+7w+sul2W5C1CW5a4kSa+muiqBX5MkqQR+9Jbbv9PrA/wG8ATC3PhLZVm+rvCY1+qh/AbwG5Ik/YtKk/zfxusY/fYJzOTnsHubbPQ2KvZSyjQUKA7H0Dg1aPG2ExGPX9V4cv+o8TcYitL4QQDmWYZb6tiqSlvXWGnZvHVtgUFLJy9KNHWX+fASobeDv3+ZosiwnTVW1h9ho/cg22MfezThmjdnP4y5GYgP68mWwaf2DunqQgp6T3eFfnSN1f4pALL4WbQ8Y+rDJBAlsbOrZwmjmxiKzMnYJS9K3nsyJS9Ezf9D13aZpEJmu2wZ9AyjaXhvdF1sXaQCNoTdCilTnwziPGd35jfAxKwoOdvv3oIUEY35Gr++O9M4Cu8j2E9ZsIYsO3bFrNLwkoSsTKEQqixVluikWrWDjRlGOWFa8vhuxH0LGmu2dUvtPmm+VmqulXaaIMl+m/qoNmsWaYQkqyiahbN47jgLJI0wWouk1cnFXfsuPvPiTc52HBYsq2KRCRVZHVfsxyk3Jl7T8D8MIw7CqFJ0lcfKrbigawgX+KptNgsNZI2suvYSGYrCLBFzhCKJBExFKpr3maNm6IqEbcnM5zlRUJAKGiiyImHYMrIsYRgyriHUYllZsuPn+HFBy5BYcTVMVaqitcX/UVEwiQviaiFYMBROtGw23BbdSuBh6SoWalXSSyvvi3iMl6bi9HHr7Zmy3LyPgcofIxaSeVwQRQVxXJBGBVlaEvs5xV0uUZV3vqIc1T2NVxqSJH2EVwbu/tXX8XDeXZblTrVgfFiSpBfKsvzN13F9gAHwbuC9wPdLklQAnyrL8n+7kyvfaQ8lkCTp7wIXuN2O/191wNZXY9gL38XNL/xVnrzy71m0VthcfTf3n/8zjeEvL0q8vac5U4x5+KGHiTmHFwkTW1xJUf04Yb/iJi3YVqOKUqe/ytZLz+DNd/CjI/x0xjSeoCs6Hb1Hd76DolrYXY/7V76m8jwIDPtp10CRZIIsY8kyWbTM6oMt6u7h+BeQZY0feVohyiX+yIWErq0zu/mTtNf/KJYpUhCHw5dYapsMg5AHF3bphhd58J3fwVM3D5tQsZamcqrXYcm1sHQZS9sjjT3QhVfAi9ebDI86SrlWaU2imJ4pInRF2Ufi0AvZnwdCLjueshNE7EUZC4bC/V3R5zgz6KJIEgu21ZgW635EmKYs2BZ9Y85BFDFPcy57CZeGKZfHRwzsMQumQldXuK/rkpdl5XFZRpFDdFVmtWPhGPsAJGHYpE4K539NWF4DRQgDyvwKSTgSLvr+7+PXX7xJVpbc0+00hOElRzCo8rJkdxowiSJu+nN2glDIgasJOS+EByjJSnRV4oSrsmCIj/JTwyktVWka8llZohQle0FY8bC0xhcElQxXKQkycb9dXWPZyrhpJ/hpwTwtCdOS6JaSVZoJcORwLvhfAJYm07FkWprEiqU2uTBGtZDVhslJJXHebFlsuC3W3FbDKlNkqXkfBGnGYRg3KrxJWi+iciOLzgqxGahHjdqP0pL5PCdNSrKkIIlLyqJE0WUUHVT9Lhkb72JTvsogecUhSdK+JEmr1eliFTh4ldvYqf4/kCTp54B3IBrrd3T96roTSZKuACcQhvZ3AdqrXf7l404XlH+NUAl8O/CnELrkwzu9k9/tY/3NfwvP2+LG+Fm2dj9B/8RjuOab2Z56fHG34MzCN2JqH+fgpQ9TFL+MLKt4WYiqWrRaS6wsnOf04pu4PhQ54F1dwP3CkcfU22LXu8rF6RUO44SshLYmsWqO2ShijMOnSBOPosjYMB1WzzxEzzTYmnrc020zaFmESdbkqgPszuY8E6zws9f2UKWMtw2c5nfa4OsZzW6gqxJJVrJqPE86HvO2Exc49M6yeM83UKSf5mvvyZrkvbBYFd4K+SLz/SvMwhHuwnlU0wU20dUCRVbJC0S6YtXM7Vg67jzCMXS61o4IMAuXBb6kSnHMKyXZYZDT1YTRcNGikp3KwnDY8KsEw8qLUvxYEGxXo5h5mnF/N2eapBiKQt/UcXXhyK59Mq6hoKkiGCqtYJ5pLOKG68VEhGNZDcfKi/PGme0aIvxJG3w9128cseTYJFneNOF1VW5yb7xYlBT35wE7QchlL2YaFyS5UFqlWYmmSiw6Muu2xv1dh74pFsv65FJjU5SiJC5E/8ht+hQQZ3kT+QsCT2OrKrMkJStL1m29kQfnlTO+LgnWoz4ZiLJiWWH+BZl5yTSb57H29ORFwUqF5e+ZJic6Lqsdu8H9y/IOuroipNix0Uij/TQjqk4W+0FOkpeESUmcCvVZrUwD0FQJTZGwLAXDKKlbvYYuoyk0vpnn7tJn+6vUQ/l5xJz7g9X///HlF5AkqQXIZVl61dffCPyNO73+LbdzGbiI6Jv8U+CDr6fsdacLyqAsy/9bkqQ/e0sZ7A0dsPXyUfdDLpy477Uv/Arjvnf/S06Of4Hrz/00+5c/zOpZjY3OvVweTeEI7l95L8b8AH98mXk8FtndijgMKqqFaoy5x+lhrCySZkJwkVs9LKMH3lUA5rnEKNYZJTm74ZTdaIqfeKwFu7jji3R759H0S7xj42HOLd6DF6VYusqpfosgLTjwRL56bVj76+96E0v2TebDy7SXXFDOMvW3mQQxq12LrnWT2IfQ26EsMnaLd/LpG5/h689uYJnrKMDhpb/HdPQv6PTvQ9p4GN3qEc8PmOw9jdM7jdkBy+qJjA8FRLriJrq5jaScbgx/9aStqyu4hs5yy0ZXhER2YIq38ZVZiqlItDSVc0WBa+q41e8cY7/hoCnyeuOmH6RWQwdI8rzxP9Smw2V3nyz2SKZjgthr+h+mK6TdWexRdt7Pp67tsu3PaesZ626PVbfFqYHgSpV5Rl7cw8VJm6Ngn1O9NknV26ilyDWgMUhF6TDIMuZZxjzNcVRZlJYK8HVxSvHjgr1Zzt4s57qfNobBe9wWPdNAqSY60YSXWW1Z2KqKpYlJehQdw8ODyqNSRwEYskxLVRoDq1qRlY0K0KjKt+/wa2SNrYlS7WEYNfRkpzoV+rdg58Vza3CiZ5HFz6LIFqBSFOIxJNXzX5OO87IkSMuq9CfKWVlWUhQgy+KfqUl0LZmWJp4r8ZzJTW6OUfmg6tv7pS/rU3z7KEvIvzoqrx8EflqSpD+OAJV9N4AkSWvAPyvL8luBZeDnJFG6VIF/U5blr3yp67/K+ODLy2SSJL375baRVxtSeQc8f0mSHi/L8jFJkn4V+MfADvD/K8vyzJ3cyVdjPPLII+UTTzzx2hd8nWN3dI3RPGpS8AAeXB1gyy9VjKkx7sr3vq7bDKObFLNPIskqU/khtic+iixxouvQN7dJwhFlnoksECCLfeL5QZVbf5MsjwjjMUfBTkNvfX56yEu+zVXPZdez6ZgJbT3llDtjzYpoa3DS7uNqDn1zgUX3lMjsdtcwWkvoVk9MtqrAVWSVh0KWVWTNwl08x+50gB+nnF09S5GK3XqQ5uxOQ4bzkEHL4lS/xejqT6JoFgtn/mzzN88PfobJ3lO0lx4UmHvzzWjqLkk4Zrz9BLKsYbbFJB3PD4jnByyefh+S9gByeZUkFDHChrOIHy+zOw3ZnfkostyAE6dJ2jSjz3TbnOi4nBy0IPwcZS5c1LJmohkufrwM0DTow6QgrmiRwpUuI5dXmQ8vE88PSROPJBIlqzpNUFEtrpbv5ke+cJk4L5vJS+BFhBR5YGj0DbHbXrRNVttOc7kku11AAKJ/lOQ5XpxwGEbsBCHzSpXkZwV+VnAY5MzCglbVxxiYVSJlAeu2xtmOI7AuitI8HltTG0/NjanHtZnPPBOKwbo0JvoVSqMScw1dkBWMYzR83VPKC9H/CtKsITbX7nZFlprv69JarVIbtAy6lkYWPEkW+wAoqigT1u933eoxmi/x5PYBe9Up7XNHIWFaoX5iUXKrF5S2JePqMve2dQaGzqIlBA61sEKV5OYx1VTlh+958Mkv1dO4k2GfOFOe+7N/+44u+/T/+j1f8f19NYYkSZ8ry/Jtr/WzVxt3ekL5m5IkdYA/j/CftBEW/TfsSOMdvFggN1Y7rUbxsz32efLGAa4h0uMm0SJf19uB/NJt6qgvNSxzHb94J5Z2k2j7F7nQXkNzHyVICxTNRMksstwjDcZkicd8usVkepnR/CbTZMwknmIoOpZqoSs6jubypq4C7DHPtCqmN6ZvRKhSyUFkMIwLrvoeeTkH9mlrn+dUS2HF6vHgwiP0e+cx7SWi4KDxTWTV/5YtYlhVeYElVyiVZG0VAEeDM9ouS9nHme1f5IkvfJqOvcpovs30mR/jzSe/nRNv/d9pLX03raXvJo8+QVGkROPfIEhDOmtvQV75XuzkM+xf+yiXDx4HYMU9jTs9j6QcoqgmkbeDZgmPRx2gNEtSgjRl3XU4M+iKKN2KGNxvmUImGmVYxkMiHTK92LwGQuEkejJl/BxdS+TdJ+GY2D/Er3Av9fORZQLtoesuZZFiuWt88ug0296Qdyy4TFNhwHQq0YShyLR1vSlpdU2jQavDcWyv8JZUJcdb8Me2ptLOtcZEWqvBVFnG6Mt0dMEqc6vbrvsQQFXSqxrvldT41kTJSRQ3xN1WNQPUu/ieYbDi2Cw5Nl1Lp2tpaOoueaVeqzE4yKC23aZnNAnX8KK0wt1rLLkWcVYcZ7EYGsuuIUgIgXhvg4iEFpnwIi++LDLKPKPvwJorEDWzJOWkIwLVJknOkVxHCYCuSHQMgZdZMg1WWxYLtsVGx8U1NSxdxlC2xUkyz6rIhDsisb/2KMvX05T/HT3ulkXkTjPlP1R9OUWwXZAk6X++0zv5aoxpOOfZGy982SWplw/NWGM4ehEQx3S/qmu7lZHMi0VKniJJXDqYsdG7Fyf6BIp5ZwCBJCu5ctjGMr+RJdNCyy+hRWPGw0P88WU8b4tO5zQLJ9/Dgf4BWisyvcoL0W+fgPxJxttPMt5/ikt7n+C56R5nnC7vX90kzAKSXCUpNBRJwVItwixknMw4jH2CrGQn1Lg2z8jLEQPvKppqURSp2H2bPeYzgQC37CWM1hLB8AqmtotqOEx9Icfst08AYnFJE49nt36F9c5ZdM3h5Mq7CIMDtvc/TfjJD7Jx7juwF74LxXw3s+s/Rlmk9DcfoyjWWOyuMj/4DAvrj6HpLp63hWn0mpNBmsyYeVu4rTWCyXV6GyGnF+9no+egyII6XL9OfVVm2TWq/PE1apS5LKvIhksaew2dFsQJyzZcYv+gOXHmWViVGV20alFVVAtZFh+X9tKDfGT3BJCy4ba4PvOansGa6zRGO0tTK26ahiLfIE/DZsLUFRXVcrH0dcJEQY/kxjGvKwpKdRs14n5enQTcKomyaxpNuUwYMFW6lop/8GGM6Ra2atFevoDhLJJmq0zCtFm8XF2nrWsEmdT0VtbcVhMc5poaXf0q4XiL6VBk7tSn1VZfFCWKNGSy9xRZFqLpbdyF86w6i+hWDz9eJi8gzoQUvR5l+lzTX1Ib02fUIGo03UWqnmNJUVntLDVhY1GVxtnVFRaMoiptKjiq2ijU6t7RUSBKt9NQq5SUm8Cz4nXUTKbh3YsA/mo45b9K465YRO70hPJK488B//AruP5dHZIkse8H6Lsvcnb17Fd0W36wTZiIenqS5wyDkEkYcxSGjSdiP4xZtkQpY8G2GPoRujGi9H8Oe+G7XvM+xGR8g2d2jwRNWNvkRO88qvmsYFJNlnAWzxEpD7Laodn9+XHKgXcJx+hjxB7XDj/LF8Yvosrw6Pr7cd1N8ixs0vGKQpTpes4JVMUkjMeM5jdJihhdNsjLjIG9Tqu1its7g1IZ7xY334PafW+jnLE1hWj6OEUaofN5ZFlj+yjjk1s7vGtzjY0zf5YPrD+Cf3gRf3wZu3OS9uA83IBZsMvTT/4jkvyHyEvxeM6svgfTXcPsiDJXa+m7GV39J2i6i+sKMq03ucw8PMCPjrjpb+FMX2TBXiUKDlDVj2K5a6BauIDV28QvzpMXBbK8w3x4BbMdkXAKAAO5irU97h0cexQ2gAMRViarmP3TwoyYhiiqeVvpz+5u8ux+H1sTrvY4yznX62BVrvZayVbnspfpc6SeR1CBIm9drBTVxHDG2K0ejiliAMI0azwncEs+fHWiCNJUUHhVpSE9Xx5OUGQZV9fY6L6HjfMjvMNLRLMd8jREsz0U+QyKXEAuGtM906CViwyYtq5V70GVjd6I0dbjHHg7ZJlYAOuUzaJIiecHGK0lNKuHXZwk9HZIkxnz0WVRJm2vYtirBEVREYGPN7d5qjZYGrlC68iFilHRgesTiqyJRUhXRdpmnZh505szihM6ujDptlS1+YxOk7T5B7A7D+mbOguWVZ2aTguEzkEGHFMYvqJRvi7Z8O/o8UoWkYo67JRlObvT2/lKFpS7o727S0NG7FBuTDws7SobC/d82bc1CVIMVRbYjUxwnFxDBPsYijCXvaMJ95EbOu12dB9nFl6Jo/nKo98+wXtbB/ixyCqbhBl5cZYbgc9S/xE62mUml/4+7cF9rC5f4CBYp2trZJPfRMFkZ/QCK+17+SZ7ndP3/yGKNER3FsF6G8Xsk7h7T2G5a3Q3HgE2mWz/LEe7T6DIKj29w0L3HO3BedzlC3jZGSZBjGtq3Mh9PnV5n08fPtlkZr9nuc2Z7ibXZx4fvjllGobAUziGzL+6dMiC/UXe0m/xyPLDvOXeb8TWbhKOtxjPbzCLR4yiIac6Zxm497B86v3sX/tPXH72X+O2PsqpR/4xAFZ3k73t3yLNQtI8Yn9+g4PwiO1wwiyFFVNlMTzAnl4iL3MUSWHRWubEwkO05gf073mASVgwDZex7DFJOEI1UjJZbDBEiesUSaW+0is1WZKVuM4jGM4Wcb7RJEMquoxiSsyjtOF5HWwH5GXCiY7bJBs6hk7X1nHU6wSTLeSyh2q+GUW+AVXqZFlk5FlImnjE/k3kQENRLfIsQg3HqMYBpxfXmIbLTIKkIhuLk0helg3GvVMY+HHCgR8IWXmVcR8nCUdByPPDCaossWjdy/1LAwa2TiZLBI33RDDKFmyLIE1xDZ2OZbDRGzHd+Qz7RwdkiYequ6i6i6JZGK2l5nRWb1AAzPYqimo2J8l4foAsq1jtXZR8GV2VGx+MLO+QK+fIypJEKaGAXCnxKnWhIklYqkrX0bC0PfI0wtH2UeQV7EiEp622HSZh3HzeagTNLEmJi4JhnOJnwu+y5+X0bJmTjsab+i4902jyVO5WHgpAfncOO7+Txt+RJOlPIShGTwIdSZL+flmWf/dOrvyVLCi/o5bmogodivOcZ/aOuDKc8N7zD73u23n2xgvNDnESRkyiGEWWObfQo198mvnoMq3ggKPJJXayOZpioCsmltGj1Vqj7H0HBSDLO6A8/NqPu8iwtZsNnHE0uwHAwNEp44y1C/8LR/OENjfpFJ/nxud+nu3xMzz26F9hsPr2CkooHOQj+VEu7014x8kSp+pZjK7+Ew4vf5Th4dO8NPw8fXORpfa9tJw17M5JnJVv52OXd9mbXwPgs0dTnj1ImE4z4srUlgYFv+lNkDUJ3RH0Yt2QsBwFpavStWQ2bI1+FUz16y9u4+o6a+238OgHPkIafIyYe0myEmn8ywST69z72I8TT3+FFz73T5ne+H/onPjvsHq/B137eZ47+DRBFnI9GHEQSYwSE0POsZUUmEEsNkwtVZADFuMxsq+SBU/iWKJ3qCqCpwXgzQsBswxj+i1TKJZUhSQribOcaZhwOU1JMgsY0rVMYbzMhYmyjtB1dY2uZdKx9Mo3IU5usf8E6XTMkbfTeFCE9ySr6vVXqrCwTDSgVbEDL2+ZnIs0Yj68gtOOSPQNDjyxAHQso3m8QZKxO/PxkvS2SGK7yp/PypIoiNjyA56feByGESfbDgPbak48Im1SpF6uqjaL7hHh7EVGW1vkaYRmuDj9M+RpiOEsIZtvZt+LG/HAWs+E/BJFGjWpnEZrUdx2VR4UpcacpFFAFeTpMl6UVKBH4TGqm/njKEKRZNpV6a3fGrDQ0snD5zCMDMyNauOm0LV04qxgNI9EaFsUM0tTrvsRkyRn388JYiGrFpTrOrI4bcLNXgkz9OWM8g10QrllPFCW5UySpP8X8EsIRMuTwFe+oEiS5PHKC4cEWK/zgb78tr8b+GvA/cA7yrJ84pbf/WXgjyNWye8vy/JXX+v2kspJbKsqhiJ2o//+859q0t6CNGWeVru5oqClKrxtZZG3nbrA4eQ6NyY+fizebF3TaNLz1lynyl+QcTvnAIijEUfBLtNkglLx4vMyJ8giBtf+I/ctvZP+8ltxF149DKse9UJSjwNPkGavD+csuRdwoXKtn8I24R67xz3KOTR1F8l5B36UcXnis+TYLGnP4JpblLM+3gxuXv4lPrP9ET4/GTNNVP7YmTexsfAQS6fex9Xwfh4/GvPStWeacKWXJikvvhji7yaUwxzNKyhlyC2ZsieDJlEWJYYl0+mpLLZVHlowGRh6g8/IypLzC302ejYGLxEcPYtquiiqRL+9Ae0/yUuP/wk+8jMbvHnpMRYX38pnnv4RTmx9lDMP/XHufezH2fuV9/ChG5/h57dEGcjVU+5xPWZZRlxkzFIxIWRlzJ++/wFUxaQoMtJgjGW8iKJZwCZ+LLaPXpQwDWMmUVTlp4hJehKInb4XJ1yZzhrgoO3Nxf+aSpBmDUhR4NRFgJSjXicJR3hDAYiMI6FCW1i+gGa/RUzA0j1QlGiaKZRtsipw6pWaqcyzyosDaTBGrcK4gkT060QTXajYJlHMLE4at/s8zcTX0PRT8kIskHGeo8qpQO0UYqM1sC0RJlZNrmcWOli8wGxvR3zmZA1ncVN4ZaokxUM/YTSZChNpnGCoCrDAid554CJFkUImGutmew1ZFiWtevFRZOFa96OMIMmZhjH786DJoKnLx3FeVMq3AlfXcAxd/Ey9nzyn6vMoZKVMkpUM5zFeknAYRIzihGGUEOUlK5ZKV1c441o8vLzAY/e+mX/+6d+kpdWf0QL1LiMIyzdOD6UemiRJGvB7gR8pyzKtUC53NF4LvXJ3kmheeTwD/D7gR2/9oSRJDwDfg3DlrwEfkSTpXFmWX/JwWZQ0IU+9KtOiXkzaulZFlYrdDAgw3tP7Q57e/03alWKmjrkdVh9EED6BvmGKHW04psxTwvAQRVbomwMUSSHMAibJlFHisx1OuOrdZGXnP/H+N38/nQrF/vKF49XGfevnOJxcZ3cacOCFdC2X0exG0wDX7K8jjXd4Yc/FMRK6tsa5pQ5a+gyxPxb53VaPPA1ZWHmE88EuS9YhuqJzdvObcBfOczN9kB995iKXxymWJrPmKOwHOWNPoClkRSI3ZVJdAkNCsWTsJY1WR6HTUXnLssED3Rbneh3uX+43SqC62T0JUuHiljMOtz6OLGso6m9hPfjXALj3sR/n3seO/+bJ+CI/++LPcG7vE/yB33+Fr/nmj/Pmmz/Jxsf+J/7PFxZ48fD4bWjIOdNUJ84Vvm09YqF/gcn0MobZI0s8otkuZZ5i9UIMRWwABHFWomuaTakEZIZBKHbJWSaAkuWt3g3xfmpXGSgD22LJtdBVCcfYJxgfEM8PScIRk/FFVMVksPp2dKtfASal5r6LYg3NXsNwdkizVaHmk8XvJEmiTJ9Ds3sMk3tJ4gS/isE1VIWdmc9RGDY7eEWSSHJhSnR1UYY90XUEETkTTftr4ynb/rx5zuIqj70GTk7DhKEfsdS+D80OSfzDJqZXUlQm4Rrb4xnBLdny9X2GaYYX59jaOXTrKqrhUhYZimohKaeF2KFSz4l0z6TqCxXNwpSXBYdh3BgjozxnYOgoknyLzBjCRLwuhqrQtXbQZJWh328IxOM4JspzJmnOQZCzO89YbQlq9A9/8So/e3mbcx2btm41z0NNTbhb4w14QvlRBOL+aeA3JUk6iWjM39H4SkpeX9Eoy/J5EM30l43vBP5tWZYxcFWSpJcQCIFPfanbU2WJFdvCUBVu+qJ519Y0eqbZ7IRAvHlvBuL0oUoSi5YomyR5zrLjoqsKp/oukzAhTERc7GgeMXDajK48zu7e4xwFOyR5jKXaDGyBINmUVR4sMtI8Yp5MycuMNPHYDU7z6Uu7/MHXAfpf7J5ksSv8Kl61y75+cIUwzbhv/RyKfIPV4lkc5zySogr1kKLSGpwhLE5yWPWA9OW3c3bx9zUy0q04YX8/4Dd2L/GZqyGtlkLXonE591yFdFkj7lS5KrKEacu0XZUHF3Ue6LZYadlstoXDedDaYz78JKO963iTyySpj645LK6/E7VwCSsvy43RZ0VT/fLP8G3f+exv+3sfeM9Psbr5Y/z6Ez/Ioz+wSd+I+bb1iG849R28fzPj0zf/E7+4k3IQWvSNiD9yqs0DS4/Saq3ywvaHAdCqfkRR5afY43W6K2MMZ4m1jsrAETkYddrfJEgbPPySY3Nvv4sXJ4RV07v2U+iq0qiIdFVCL15gvH2F8f5TRPGYvEhZWn6kcf4X0j3keYFl7FEUa+QV8ViUf5aBgqEfEaZZ48uIszMVhiciSLOGYVYvfrWnpI7HvVUavdDSSaafIIs92oaL4YqVesmxmYQxQSYm8yTLyTXh4zi3NCYNxhRxShqM0awehrOEpKgM5yscePOmnFSfILKixNZgEkYMK9z+krPEkmuhyLA7DpmEQ0AslJNQIPO7pigxDoPwllOOQhZETKtM++VbPoNiISrwIiGjDlPxmnnRImGakWSROIVl4jlYshT6hsH5tuDeDeOEeZZzvmOwZpv0DINZIiKgF21RajTUu3RKKd94J5SyLP8xwmtYj+uSJL3vTq//X2xB+RJjHXj8lu+3q5/9tiFJ0p8E/iTAytoqi7bJYSBMiC1V4WTbaXabQUoTnSpKPDmbLZu2rvOWtUUOvVBwteKkMZzFeU6eFrxlY0By+KsURYZl9NCjIWEWNI+j3zuP5a410tJofsB8tsXO3uM408t80+b7KNLTjXfjTocfZQznMfc4V1DiHbpAHh2imO+mc+JRZjd/EmvhG0ABiUv42UkmQcL18bQRCzw3nLATHCubtoOUy4cpg67KyY7GSUdnyTQZxTFRXjAf6JiKjKHIDAydnmGwaJvcv9xn0T0i9g8JJk+R7I3Y8kUinixrjXosLzOyPELXXKJ4jBcd4iczZumcn9ue8U//jyUWjYD72uKTeG1eEuUKJ+yUDdvlXUs+nzlc5EcvtvjU0c9zoRPzLZvfwPvuPc2Le7/Fc+MX2YuGxHsfJ8lTRonPaWeNMB5zY/wsfuLRMbqs5SLp0bSXUDQTzbpCt71KWWSoygUWWnqTmFifrurJv3ZoC+lxhiyrQm6cRuRFim716C5eaLwZZnsNpQpw8uICXZXI0whF2wHWUBANfS8SDegwOd71H/jzxiwYpBleklSYkhIql3leFOKEVGFgdFUS5cThFQ62LxNHY0x7CXfhfLN4AKy1ncp5LzJhdEX4UGRZpaiQ+5IiME15FpIU97E79TiqTgB7QcgwTujoGkvVzv4TO/tMk2NCch2ZUJ82DEVuPC6KLDeLUs30ausaXdPEUBTGUcQoEgtAjdcfBtGxd6Uq49UZQHUkAYhFoVVJq+tTzd48wFBEYuhKy242knkhYWta4/Kvn++7Md6AJxQkSfo2XsZt5Bjj8iXHf9YF5UsRNMuyfDWezCtJMF7xVavwzD8GcP7BB8o4F2DAlqo2EMU4F/jvGuXQMw0++Oh7m9v48HNP8vFrN1lpCaXVOIoJUnEyWXcdTg86yPEX0aweyvwAWVLpWsv0W+toqkW7c4bF01/HUXSKFOGybg1gUVPoVqqqay/9PGvhGH3tD/HxqzvEWU7b0Jt8h0XL5Dvf8thv+/vqkwqca7Lk5wc/w+Ez308QHnL2HX+PL+6OCdOMVXcDS8+wdZExfnnq4acZTvWhO4pFSSDJSjb6Ku9b7VQGPIU112nyVrq20XCrgvEWkbdDND9k+sJN9uIxceqT5hF5mZNXTWU/ndE3RWNWkVRUxWQ0u8I4OkCXDXRFZ8Ho8ebuDa7NE0axwSePZFSpYJqK3emaNeLNC2/lT3zvRwAo0k8TTXfIYo+jm4/zwo4g/bypL3xGSS5OmY+uP8Chd41P732Sy/6crJQ44xzQ0sUTliYemu6izA+Zjy5XC+DTze8ANN0liUaEgWDmqRVmvt4gqLqLVnlYkmhMUaTIstbEzcoVFFKWRaPeULYpc6r43ytIioprnCBIMsKwxpwozWmoPomMo5i8LJqFzVZV3rQ6qHboacMUy9OLePvPEno72J2TxEsf5IXxjOAwI852mnKYHyc4lePdMTRsXcXSZcLZDvPRFebT62h6W2yGNIswy5uTyTXP52aQoEoSS6boje1UPxsYKvMsZxj7wr2fVhkkVVb9gqHe5rjXKxhlHakc5zknOi6urmEoAaPoGBU1ixPGUdQw0GqQpSpJwhaPWCSCtMBQlCpdU+Byllt2Q4HennhNLk5dtjtGx9ydgK2yLO86vfi/9JAk6Z8iMuXfB/wzhAflM3d6/f+sC8qXImh+ibGNIF3WYwOBevmSQ0I0J9fcVoNJGQZhk+RXly2OgpD/4Rc/xEGQ8/vvGTQN1zorvN7ttHVd1KRNFRlT4EgSD8Ps0e6eodU/0zQhr03XOfCnzWOpER5vWv12TnQ3me4+zfjoGYrDp3hs5RGey97L5YnH8xOfozjjKJjwLy/9R965bKPKMllR8IceuPcVpc+XwweJ+/cD8H9+9nmiPOedK4v4cdrwocZRzHU/YsvLaBt1cFRJz5RZsVTe1GvzyPoyliYIr8IvIGS+3vZFbgYHjGdX2PGuEmYhXjonK0VPapZGZGXBhtWja3QIs5BZOkdXDBRJYaV9L0WRcRQINItptFAklbYxYM09SZrH+MmMYTzkZjACIh5ZuMCi825WN97T0Ixl7VFsASOg7Lyfrz37ecLJFprVJzLfJVRL41/jyS/8Xzxx9CwvzGRAY81KMWRNNOmzENPoNXJdTXeJoxET/waKrCJLKrKsEiQT5ol4/ZI8QZEVkjxGVwzaRh/XXETXHMKKsVaUGR1nE8PsVf0xkcVSFGvYmmi2g/i/KFIoUhTtBgNno6ElK7LERsclycWJYhJFqLLEOEwbCvRihe4fTgMsTcVRrxOOdwQ7Lc9YOv9nuDaac/lo3Lzv8rLAkMWpw9K0pglfv9aGso0fjBuTZZ6FlEUqHqdEk3jYN4wGkw8wT7PGWFijWrICTFlG1UU2fZRVEEkpJ8oLTCWvPksiNiAvhVy4TtO0NQ1X14XsN8+JchHTIO63aLJ+arR//Tk2FAVTkcmqXnGNhtEVGdcUf/tq2xE5Mp6IvVYkubmNuvx9N8YbreQFvKssyzdLkvSFsiz/uiRJfw/42Tu98u/EktfPA/9GkqS/j2jKn+UOVkhVkRnYoulas53yomDBPhajKZLEmuvwN9+z3jS5i3S3kUUe+EETKTqvWETb44BzS1ajx++6a1i9TXamSwLz7mdMwpA4yxlHMa2KX+TqGrvTgI51ge7mQ7T6H+dw6+M88ez/w6r7Uf7w2/4Uh+n9+HHaYDd0Van8LgJ54QfbVYM7x49TsrLgres+052nybOQe09YaIvfytM3h0yiiC8ejbnuR6y3jCYw6do4E/n1LZl3LbU51+vw4OoCqvdRMs8jCccM/R32s5Asj9ibvcRheMBlf5+LM51RYjJPVUaxTZiqKHLBmY7HqjWjo42a59aQdznbPU+nc5rntn6ZMAtwdBdb72JoriAnt4WKKIlGzP0d3g7ouotWeR7M1d/PP/nscwxufIxvvvdks6B2nA1e3A15/KjLLEl5x6rH20/G3LxymTALOOuu8+aejaO3USSlKulkBMmElrWEprtosopu9jnx1v8dgPH1H+OTT/0jdMUgzAL2wyOCLMZQNLIyR5UUbMVgFA1RZlfIy7whDuiyzgVzEYNeg/SolVu34l1qV3z9tZx/kSXbZaF1T9Mb61oqw7nYoQdpxrleB0WW2fMDZknC9dGMc0s9rPwp9l96kig4YLD6CPbSN+PFecPpCtKsyZwRJxJBMK6JwkKYIIgBZmcNze7h9M9QFBmGsyjAm4XSTM4gJt4aKQOinKVKEkuWSc80WLCqILc8Z28eNMyxlnYMljSq3k/N1apPHkHFxavhk6MsZ5pEHMUZWSFSFk1ZrhJJRdRvQzfOMvrGrX+ncuzwtzTk8iqLrsmhtyDiDwyjOfnNs6xZWL7i8QbsoXDs+gwq+OQQuGNT33+xBUWSpO9CcMEWEbnFT5Vl+U1lWT4rSdJPA88BGfBnXkvhBSJW94v7R0ImWdFTbVV80BRJ6PhfSX8ua6vY+jbXh55oAFaSzJamstp2cAyNvDiBaoxpaacxnCWCVJxI/DhpeFIAPdNs8h1q70DXUsnTiySyits9w71Fyqdv/DLbH/vzXFj/ek5svge9t0gmn+VgFjd8p476IsnhRaL9p7i4/0nyMifMQq5/TkeRFb7ha/4OU/kh5AJODzo8vz+irWuYSszHd32SKshoxVVYMBWWLY2TbZfTgy5W/hQ3rn+UWbDLLB7y3OQSF2cxh7HFNNEZxQb785McjXTkuYRcZ05IkNkS20OLrpPRMRMWrYiWlrFiztjI5ly88Wtc87awVRM914lTH0MTKq0kGgs1kKzR6Z9Ht/ooqonZWUM1LrAzjfiateUmCz7xP8LRtd8iiUaw/j9x36BH1zI41W/hHf4Gimrx5pPfTquzidFaQpJVstjDG14kDA7I8srhLqtkWcjRzY8zHb2AZS8RR2M22mcZBjfJixxbNWlrLXRZx0t94iIlLlLGiY+tGtiqiSIprDubQtkXj1FVC9MWLKo0GCMpHlnkoZoCIZJFXgM9TANxusnTEEXzsIoU1XCJ87N0LQ3HbNOxDIaB6OW99561yiNyndR7iqTIWL73A40IAwTqX1dNkkpVpSsKtn7cHxAbFKUpu4mFdg3VWEM1tpBllTBdIakm+STJcKpJesmxGdgW+3Mhq7Y1lVOKwMosOa2qGS+YapMwafolcBwtbKgKlqYKybIuPn9BIkgANb9MkUU5zU8z9sKM3XlGmJRYukTPlHE0GVMpGNimCAOTJUFEVoV7/tYNWDT+DaZDUbZs9U9jGcsMbKtK/xSeMiKI89cVQvglxxuwh/IhSZK6CN/J5xDthn92p1f+L6ny+jng517ld38L+Fuv6/YQb+TlltGoZOoFpH5j18qYW3EQIHbA+vQSSiqO5bfiuQ1VGMaMSps/CQu86BjFnRcxh2HMJElxgoijIKxyJ0RNN84slt3zFIUoAamqxUbrBNe9a3zo4k/wwP6nWHRO0rKW6C2/VeA4rCV0a5HO6ltRVIt5eMj27EUO4wnjJEKRJK7+8h/l95z/PpYf+t9IKrnoNyz3+Z6WjlxepZDuIStLLHOdqb/N0I+qv0cWGSXxuClLxXlGXkrsBC0+d62HuVeiDyPawfQ447SeqFSFdGAyWtOQ+yW2lmEoOYaiMo0nHMVjsdOXNRRJxdAcijJr1FcvbglLUddapuNuCjwLMN35BYKbn0Kd3+DU8qOstL+VZ/fXyZzvJjQyViWJ+1e62NpNRlu/xHx6HVW1aA/OI2uWKHFVRGaxYN2HopmksYcsaxCNKMqMX3zx3wLwjqWHOX/uuzF2Hme6/zgDY0BeZuRlzoYj/C9+6mGpNpbaQlMM1hYfRlEt0mRGmngoqimQJGlInKdNYz5PQ8o8qzLmMzS7h6yZqLKGpFRelEJ89AxFbk4rS22DtY6Jpu6SxkeksWBeiXJaSjQTlV/dWSQLnqw8H/c2cuYkK5sJW5ElHFOlZpkBpNkqWVngRxlxtoQiS0zDeXM6vrUEaiZPsLbS4yg6xaEXYukqTgV4zNOLIpkSlY7bAxYqw6TSLD412bg+KR1/nuQKHSM3JxYAU5FxqssVpZAN10O5RQla9026psmia7Hs7hP7BwQHh0Tzg0bCrBouSQEbPZswEQbVozDiIBJS47sySuANdkIpy/IHqi//vSRJHwLMsiynX+o6t47fiSWvL2tIiAl+0BJqmEkUN0ZFqyoFWLrKQkuvgpLWftttiDzv4w+6IkkM5xFWouKa6wz9kAM/YBLF7M5DRnHM9XnMC0fVCcWWWbZDHFVm2TKwNZVOZlQMKWFo03SX5c69AOwFO2z720zjiZi4jj5PS+/QdU7QXXyQMs+Yz0Ty8oK9iq4YTOMJeZmTlblQUe39Mp3eJm9dUUHp48U5XUuF9BLEHoWyTcd5FEXeZnbln3E4vsjie36Kt7zvG3gw+gQXP/sjfGz/BXZClziX6XRSZrJGZloYM118uqsFVq4ayrkuPvhpLrFqB9zX9li3NtEVoUqyVQND0XH0Nm23MsvpLkWR8dBb/jSGs8T1Z/8d49kVvPkOxu4TjOc3+Pzh57jsz3l0doVHi4zFzT/NNEzEbll5hvnuFY7GlzmaXEKRVZEFA03TXNXdikjsNCwuVfeQFDEhq4rJN53+vSxvvAdn5dshv0Q0P+RNisnQu8o8mZLkCYeh2OUuWkuCOWYvoVt9ofZKPHSrR5lnJNFIcNPmYjGRqyAzsZCk1WlMrTwevUZJFucFOaDLEgpCjlyzv2CLNBYJkGkwbrJmVMNFr3o2RRoJFph2HiXNyQvwoqzJVLmVahznGyRZSRLlZIXIpq/luHWvMS8KMfErMroq+FuK9VhF2y65sDwiCUeVmz+jzFOBjDHcY8hjUTR5KB3LaALDxO/K6rEdl8/qnyuV8qvu1WRlyc15JnJP0rLqrZRMk5yOrgi/iiwijaehQl4sYagrWAOF5TWReVOLJmxeQjNchn5PyJ8rNZnxCpWKL2eUvHFOKJIk/YWyLH+o+vq7y7L8mcq6EUuS9LfLsvwrd3I7b5gFBUlqWF7H5jWxyCw5Nid6diUPzUD57SbDOl5WKSRcXatCpmSuDSdVbrmIo92bB1z15nz+KOJgnOFPMyRZYEhkCVqaRFdTaGsaXdMQCJX0OZJw3OSLu+4mqmKSlxmH4QFe6nMUjzFkjdXWKo61iD++TFalNi4MLpBlIYtZhKKahKEIy1w79QEkRWO68zSBf7ORjnrTMdFcTIpdWcNwnsRQVtE2Psj5e7f58IfexjVvi2VrgUVrhW9ZfxsP9Xa4Pj/kRlAwz1SCXOMgtDgMTYJMxQtVclRsI+f9q1vc60S8qbvOqc4jaIqJobtMg10USSUvM3TFwDUGjRqqhiKGFZ7k3Lv+OQDXP/fnSBOPKJ0T52LCuRkc8uzWr3ABaKsWUXDAbx08zigaMk68KgRK43TnXuLEwzJ6WPYS7cF5zM5aM3kb8g5huoKhbKMaDu1T38fBLGYuS5hlSXR4CVlWMcw+jz7249x46i8ymV5mtchwW2ssrD+GtfANKLJEFj9LON4S/RhL9E4kRW0Q9zVEEWiCvNzBaRTtfAOhFKZPwelK8kKUgxyRomho28T+AWkwvg3lXqvLQPRhmlOPopLFz2LIKglnmIbidkU6pcDKeFHSfA6A6nNRNj27Gn1Sy2hr+OiLh+MGdnly0MI/vNicvPJMnHRVw0XtvpfLRwEH/oQ4E/HUGz2HrqUSpDlhIqKcazJ3kueNdNfStOZz5+p6k5R5whFqrCDNqsa+3AgFail8nfMyCSN2Zj55WValuDZda4FBSxfBXaqFHy8TpgFhmpKXBdOKmHFXRgmvXYz/r2Z8D/BD1dd/GfiZW373zcDvrgVFk2UsTWN/ftxYrw1qSZaz78UMnJXGY/DycXLgECY5B56oT0+imK5psFyddo6CkFEkXL5ZUTILC6KwoCig3VFY6qmstlTeNnA42XZZbtls9BxsTQFcnMVzGK1F4rnI9+itPcLSifewd/2jzKNDZEllcfGtgqCL+MAaziKqcYHhPKGjK9iaQjj+LXRnkZh70YsXOLzyUY6Gz4p6eHhI6O9g2ksNBnx443F0q4du9nDsHkXh8q53/GUuHDzLZHyRvEiZhvusO5uc67+pUT4VRYYXD5nGI/zUJ849DEVn0Vri7PrvbfoWsmaSxT7B5Dq65rLYOcc8PO5fHI1uNzJGqY//0oyH7t+htf4H6J77c8xe+mHa5oCHF9/K+vwmQRayG+xxcPEnsVWLcTLjCxOfUWLQUjMutGVs0ySvHiNAUWZY7hoWNI79vFhGkUsmyRoHQQ+COWcWHIroCwT7W8TVoqubPZ77+PcSxGNso8f65vvQl7+TSwdT8pujCqB4L6trvWYHrJpid56GVW8kCxtGl93dRO+8m6N5glpBGYNETOBJljelJdErAs3YJZyJxURkg6QNhLFWYyWhOK0oqoWmuxg1R8tZrCZZmQSxaEyCuAnwqhvyt3o7DEURZS5FKLbUJoRKyHK7ptkgatTiRTLNIp4foFl9OkvnyYsTTMKUy9vDJkjrzKBbNcVVcQKr8PV17yKuIJZ1aaxtCGikrghQ5a0Ne0WW6VjitJtkRVOGE89jxu5sTpimjJOUvbnwgwlicoEX60xCHUU6VT0HfiUbFkFijqoyqPw9d2O8UU4o3G7XeLl1445pmm+YBcVQZd663gf6zZvTi3OGc6Fpd03hQ3k1c6Fjb2BruwRJzvXxlIFtkZclO96cWZw0iI6oQlAsOEpDLd3oKJxxDc52HO4b9Fl0rcY0F+cFYbKGpcuYnW3MzmPVzwq8IqX3wNeyXLm3ayrrNFzmxaMpL+1MOAyfr8KaNBYsi2XnTWzYDnr6ea4899PsT18izAIs1aald0jrUCxnTZR9iJhPrzM5fKYx6dWnhlZrFVnWaFlLDWcqzyKS1GMa7BJmcxRJpW8OsNQWHWuZldV3VjkmKYp2njgvcNt7AnfirpFnEdr4MmniMQ8PyIusQdbPkynTeMIkmTH6/N/n3PUPs9C/gNM7I4x1Y5H0HRcph7HPdiBhKGNaSsm9js6K1cVSLSzVpm2I00A98aZZSJ6GZJGHbu0gXOlU5aC08dgAaHaPVoXEScMReRqxtPYYZZ5htBbJ3PfxzM6IrZnHVW/e+CnesrTAoivAhbqxK3bppksSjoVSzRAhXWZ7jaQsWWjpBGnRGBpBTOh+LGSydXk1nO1UTX0Nsqjyr1QLSnUiKIu0If6K77OKWUbl/j8O0ArTjDDJmr4IHFOGDVVE4Vqaih+nTZnXuvUEoCp07erzUmooqklWpWXeGC+yOzviKIwwFIWBLRIdl9oGOtfI4hDDcFGlEwx9AdasFxNDUXAtvelR1hj++nNUo2HqPkvth8rTiEK6h6O5wLiEqVhImt5l8zcK0sAwiJqTjFopyg5D4fkCGsT9XRlfhR6KJEl94N8BpxBYlD9YluX4ZZc5X12mHqeB/29Zlv9QkqS/BvwJ4LD63V8py/LlKcjlq3z9St+/6njDLCiSJEF+SXwjWwTpOnG1C1M0Cde4WX0gX92tfmMcMgzCRuny/MGQ/3TzkIEpdjVtXYMEorxg2VZoaRKmIvPmXosHBl1O9jqc6FmU+RXhRC7PNmA8LwJFXsKPvdvcy3mh4ZowCYQ2fxKZhOmQ54dj9sMYs1pM7KYE4aAXLzAbCYKtqbUA0BWjcakXRUaWeGRZSByNiVOPLI/Iiwzb6GEaPSxHmNnqibA+0WSVea/l7TCIhCxYVS10s1JktdcIi5PiCavq94ayRmsQkcUesX+AaYsFKklFqSbLxaSoWyaW2sJSbZIiZhzukxxGLGchQXjILB5xFE9E6c/skJcTQEyG97Y3WbDFa9exV1EVkyT1SbNQYFeKtPJVRMKlLkuAKDUNHB3H2BeloxzKUkOSVYwqEKqOPq4XyctHfvOeUCWJrCgYxQnPHg65N++iyi6Wvoxj7CPJKq3+6YYcLGsmknKaJM6ZVBOXKHmVVRCW0kzmSSZOLK6zRJlnpOGYskibxaQWMoiTj9ksLvXPpcpYSSYMtboqVZh9lYkUN2wyoHLYV1HBSdZM5vWiM3B0bO2m+BtkVUzieUpSlducxXMo2nmmwwlHoZiwuy1byJRNrUpFFGKEMs8IMlHWu7Xk1jMN4rwgSI4py01UcOUBqynPeZVTnxcyiuzg6lMUWRLRyIFw2O+HMQNTp6UqjXenxrcYitIsYrMk4SCMiHKBve/o2qvOAa9rlAh87X/+8ZeAXy/L8gclSfpL1fd/8baHUpYXgbcCSJKkADe5XfT0D8qy/D++xH28RZKkGRX4t/qa6nvz1a92+3jDLCh5UZLJZ1GLF8nTEFsTuGtDVSoC6waW9ooEl2acXDpN9xZFlGvoLFsienSapFzotzjdbXMUhJyu3PS2pnFvv9uUUtJjIguGLeMjdPqTMCas5MU18E6RJQ78gOcOBSU5yXPmacZ2dYwfGHoTEuRWjue8EBOJbvboDy7QqUx7UWW6AzHhzP0d5tFh81jyIsPQHHTNwbSXcPpnMNtrFNKxJ8I1FFKzoLMg40ZfaK5bK5Pq2r6XHfeoLF1BlndIU2GOkxStmaTrUpAkq6iVXDhNZiwmHknqE6ce82TKCzu/0aBsbEWcImzV4kG9TVIkKJJCx+hjqg4dd7Np8KuqRVb5Z0BQoPWwR6yaqGaIKqsUecT88DKHVV+jdrfXzeRacaUaF0ASDXNLU6sIXJN7unWU7vGp34tSupZGnoro2lomfIyj30JXN3DUm+RZiOq6TMK1xvDqmhquqTVN6zjfQNHGTPaeJgoOmoW4Lj8aZr86QYqJsDYkSrGKolnYZlXGzS+RhR6u4WJ1zzIJUsIkE7nymdSU3URPoyRM00bSayiyiJyOPXHSqwQGsiwCsIo0oqK0NK51IXEuSHITXV0X+TIqhEXZsLjEcycQ+/WwVbXpc4oyVcK8OkVszY/Dr1RJahBA9SIwzzKmSUpU5ay0NY2WpjQLiHivl2RS2aBZ8qKkbxgNGPaujq+Oyus7ga+rvv4J4GO8bEF52fh64HIdlHUnoyzLu8KjecMsKLIsYWl7pJkgzGZlKTwgtwQpFenua/K0Os4Gk+AKtq5w/3Kfk702fpxybTxlXjUw7+l2GsRDHb1a5lfIipRwImSaqu5iODvEWZ8wEcA/EOqxIMu4PvMZxXHDQxpHMVlRMorjpsRSx71udFy6tt5gN0RDWKuowhaKamE5a7eFOEXxGEMTJRhZqiJp7SVanU2s7iZYb+MgSPHjOYok0bUNsqppG+cFhvlmro3mWJrKsmncJvEkO5ZkO8Y+sX9423OoO4touVBglXlWsbTECaesckGi2U6DO9mbvIClCvRN31xAkVUUSam8N3N02WBj4aEmOVDRTJJQIFDqxUSWVebzXfIsohV7WK54PtLEwx9fJorHqIqJYfZRVOs2pVYYHOB2n6W9fAHVcFnUInJCciKwaVRNIEKlDOcRANJA5KvLmil25o3PxEKSd5jHnqArtJbo9jbxYmEurN8HinxDCDUyyNI6S0T0T4J4jCKruPaqODmpVtOjAYGbz7OIYLKFaggKNoCiWaR5hma/SNc+K0o+FTq+3vl7VQkXIPbmzal8yTnPoK2jFy82ApKm9JaG5NPHuX/lURxDYxLFTMIYL0mbVNN6uLroUQS3TOA1dkX83YKMHJcl4yhmyw/Yj1L2gxw/Fj2TFUdhwVDIspw4j5imKY6qCrKFLDNQVTq61ggKFFmmXX1dl9hEWVFs+IDbYgruyihLuHP0yoIkSU/c8v2PVeioOxnLZVnuirssdyVJWnqNy38P8FMv+9n/KEnSHwWeAP78y0tmd2u8cRYUhM4exGISJgXb86AxPQF3DGccODrXh6Lscd/ikMIVRtEbU69SxqT0WyZdW8OSrxP7I7LYp0jDRraqGi6H3gK7M49JDbYrS7a9OS/OfFqqwpptCeVJUn24i4K+YQi1S8dlYFt0bR1bfok0GONPvaZpq6gWmtVHs2hYUlnsk4Yj1NjD6Yns77ruruou7ZUL+PEyfpITzGKSPK84SHLjY6gX3xvjgOE8bPwEtq4yCRKRC2OqjU8grdROquFWi4fVACMlRSXxD5GrWn/dyAZRxjHiRVRdfB9WnLBaDixLKlkeoSomTvsklrvW9Hm84UXSagdtVtLhosgYza4wmm/jzndYLh6pEhHFRNfcXhai6WKhLQDLXaO99KAQNUy2SMKx6P/Md/DDQ8bRQfO+WLDX6LVP0x5s4Syeb+43me0Szw+a+8qqRT1JfWRZZbD4VmTtCezqby/SiCKFJBYph3kqQJZyZX7NEg/Z22pCueqeF4ieiqKaJOGo6aHU913nstfBYnZvD0NZQ1creXDlTrerrJek8mrVOJO68S1XJ6EiFeVT1XCaQC3VfI5TgwtMQoPLR1O8CrU/TzOCLKvArGrjRamzWlRJZhJF7M0DgTXKc0ZRwiiOGcYZflpgKBJnFo3bnPH15qpnGk2mUVvXGmLw8aKlND0TcZIXDK/6FJTkgmShSDJ3jQ35+nwoR2VZPvJqv/xS3MPX85AkSdKB70AoterxfwE/gHjEPwD8PeC/ez23e6fjDbOgFIiFxNL2IFtFkSUsTUWvjIl5UXKnldO8gH7LRJUltmer+PGMHW9OkKYNSbX2DYgcco3W4DRxLhDpXpTiz1J2vAPiSio5S1IOooj9MGVgqCxbAgdRc5FUSeKU22K5ZbPk2JxenBLOrpCMDzmcbpFEI7JqwhIYk5O0rF6Vyb4pTg9WidoFV5IaiXRenCDOC5Ks5MphDESiBCIdU1f9OMWohAGiBi8+JXkpJKajuYShil7NoRcyDRU6lqD1Ktp58vSiuJ+iALnahaq76PSb57TMM7JIlFPSYEww3botTtYyerSsJYoixahOIvXCBKBbwoMRTLaaxUTT3YYmrKgWSeqRB0L5ZYwv0u6c+W2vreiVZNUJJ2Nw4jEMZ5EwXcGxn2e2/yxXb3yEm/4WR/GE3XBOXoKtSpxxhpypyn6KaqE7i8KFH3vE0Yj5fJcgHhNlc5I8IskTdEVHVcTJol5Ms9hr2GIASTRqvjdaSxVB4HgxrP0sWVLLh4/7XfXl4khsOOuFRdXdioZ8Bdc4zURTm9A419CxNLXpq9TJk7BVkZVTEv/wOIExDY97bJEHPEu/1WM4bwvlWiVYmacZcVEwz3L6ZSkWFMusPocyF5ZHFEWKl23yzO4RQZqhyjLLlsa6rWMqovQ2z3KGUcLA1GlXUNca4x/nES9MRHlflSUR7qaq1LHArqY3PRQrFT2VunfUM407nAFex7hLp50vxT2UJGlfkqTV6nSyChy82mWBbwE+V5bl/i233XwtSdKPAx+6G4/5lcYbZkGRERPZobeAFwXNpOlV5Pa1zh33leg4GyST63hRyqWjMbMk4ao3p6NpTV12te2IMlBlXPPidSZBzO7MZ38uCKpxpQibZzkXpyFZWbJha02YUI3ztjWNBcvk3FKPjr7FbP9xrjwp8jamwS7TeIyu6OiKwH845gKWs4ZqusT5BkM/4vp4Rpzlt2j8DRTZJExFOUqRhNqlxtLUwVF1KFMtZ7V00TAOk6yhtHpJyiSKWXJsOtbxh7KWYHvZGeKozo+RxamKVWSu3ub0zioEPIidfZqIiUGWtcZz02pvolt9jNaiOI3laZMIWBQpZZE2E7Fu9hnc84eF9LV4keUiwzx8hqPJJabBLrKkYt0ioS6KjMn0crOQefGQT1/611VY2iKKpHAY7vHAynvoOScY+zdQRs8wS0P6usOKvYZt9MizCH98GT0UKYtFIUpieZEyjg4Is4C8yJkkMzacdSb+DXEqCsTzMJ8LQoFtLdKqTl+2LNRUmt1DlrXmlFEUGUUaHp+0ZA1FM29DzYA4gdY9LnGZ2wNVB46Oa2oESYZrajjqdYLJFsFElNkLdw1r4RuI8xJdjtCsHnImbqPMU7LEI08jkTcfC0/KoLVURT0U2Kl4jlvAoiWUX13TwDG0ipJ8kzTw0OwerqLw4OoCJ3sdhkGIHycNX6tuyp/vtpvNGNB8vdKyOd1tVwwzjTefFKDUK3svifd5VYpN8rxZNOufB0nGNIzZ8Y7Dx77SIX11eig/D/wx4Aer/1+N1A7wvbys3FUvRtW334UIN/zPMt4wC0peluxMI+o8eKPCONQN8NeTRZLGO3hR2uTJG4rCPW6Lxw8mZGXJui0WhyTvsdq5F1IR3Xtj4vHF4Rg/FY3QSXIsAVFlcGSlmYRbmtKwv/otk2XXYLL9s1zdf4rD6YvszW82TClD1rBVC0dzcfQ2ltHDrCaUmotUH+vzUmZvPq0IrgWT6n8B6hNwv5aq0jdFnVuRZLwkacoTS7KNpamEScas2tEaihA2eHHCarvFsrt/2/PlGica/47V8JoUYBNLl8WpMRWnlCQcE88P8GfXG68K0OzidasvMtk1S/Qkmt6AiVSomO4aWnUZze4xnCeipKKdo7NmYbQWcaZnONh5nDAek+URptFD113kTCWMx0SZT5rHHAZ7zXO8VU3ybdUmjMdsnv5WevMDOvYqXnRIUWQ45gK2JUCKYXCAV5Wlat9OlkfosgEqJHnMqr1CXoj3QBgeEsbiFNEyFxvlnN3dRLN7tyUe5oBhVmiSGnPyMmFEvbA07/+qbCbJaiPnnia1WTfH4CW0MqKlATnEUfV6Veh6s71GWDHBJOU0ds+sEkqzJjseRoTeDpKsYWVr9DfWgCWcyr1el5h0RZSfHEPH1tWmbyTf8ni7lkpH32KjTSMMqdVfeYWdF+/rskG41MmbNf6lPr0CnOjZyOVVZvvPoqgmztIHKi+SiCJ2jH1c1aNr33vH88Brjq8eeuUHgZ+WJOmPI1g63w1QwRv/WVmW31p9bwMfAP77l13/hyRJemv1iK+9wu/v2njDLChleRz1WiuobK0Cz0XZa9/ALUMz1vDj5/HjBFfXWG7ZTKKIx5bgmdGMX7vm82vXfB5eOeL964uc6LgMg5DrM5+b85i9MCOu4IwDU8FRBaeopSl0NI1TbYc112HRtRjoL+EfXuTaS89y4+jzjKIjgjwkL3IMRcdQdHEq0Vws1WapfS/95bdittdAOUcYignXqgKEjsKQp4ZTgROvHkOUiUTGOq+iq2VNBryhHitk6gUDwEuS5ncAC7ZFxzJwTVVMMnX5zXAF9baCE+qqTJhkjbpIjxRsfYGupaFoXjP5xqlHkEyIKvR9x17FquTGquGi2cc79DJPG7c4iMZ4Jp/Fi0u6lizUTYGH4jxCpK9hrr6dU51NxjtPsLX7CZ7Y+RhBHjPPYk62llEk8TdNkhmzNGCep7QUDVs1GJh9PnHzP/HvXvwFAN65eC99c5Ekj9nzruBHR4KgrLvIkkqcCae+brioioksqUSZ6L+Nw0MW7FW6zgm86sTUcTdxemfQTVGu9IvzzJKcPCpJsnnDucorGTDyWUKpIM/EzFVvkHRVEuy5omToJ5XkPK4m4T10RaFrmQ3S3dBFiSyLvea1k6pTkayZYnGqYkKCNAeWUdQVLGuPPI2IZjvkaUSaeELiPb2Mopq4PY9uz+JEb5NJKMq9tQDg+PSRAw6q1MYxNE70IA2ebh6PZh3gmC79Xo8wXWnEH0lWZ9OLz/KtZdz9QEjXN7QnSaqToqy9m97JdwMi7dTWqoCuMMWLBrjmMnkBzl00NkpfBWNjWZZDhHLr5T/fAb71lu8DYPAKl/u+/6wP8JbxhllQQEyYdcpbbWyMs5wkK5j623Qq6N+djJMDF4aw0ROlrfggp2eafOspl69bz/i3l2/yqa0QP9vnD5xWRNRqWeBoMt1CJsrLavJWmlpwWxclsyXHbk4l/sFOo1hSZLXhYeVyLspcssGCvUrbXsUwe/SW34rV2ySTzzKaJ42/oVbuzF5m2lIlCVMVE1FWlJiaQkdXsVWVVtWcFbkZ4oQzDeOGMFCb4lRJpmMZ2LoiZMvaaVRFqNmCdJ28yJsAJxDuZoBbe581YDNPQ/QKlxIkE8IsICliCMA0etiVUkqK1cZvAZAGYyHPrTDxliYaznl6kTQYE3k7THefFso31SLJQiRZY+Dew2G4RxAcEOQZcZFgKxaKrOBqLexKObVoLdGzlmmZi3zD1/0hNPstor81+U3ScEQ0PyRNZs2uuCYY67pLq32yKsdl6MEBRjzGDw9Zdk7QMhcbcUG3c0Y0zc0eztJ5vHid4TxsJlBFlqAQplcRCVzeppRSJRlLV0myomFlhWnGjYmHlyQNrqSta7QNvRFUxFkB1jl0p5JJx6IfUxYpkuJWp5Coeu0EYFK8lmWFrpEx2yKZ0pysYU2uEwUHjHafwPJ20AyX+fRnUVSLbmeT7uAbmxNrLR22Na3x4WjqLmGRIsmqCK6rynNJOEaVPZTqPWCkEbJmout9kvIUE2+BOCu4Pj5smv3b4z4DZ4Vs+hm8ox8Uz2MVNaG2V4m5l66lCeWiIhNT3EZk/kqGVHJM4v5vA3iDLSh1/bRGTQSJ2DkPHP02H8GdjI6zwWl5G8cWi9DbnQ12R9f4yOUbzJKUC12bDVvjo9cDvI2E5ZYtSKiGIZhFVZSsoSi4uk7XMiq8hdS4g2V5R6DPE2E81BSjST40NYd+9xz9tbdj9r62efyTMGOSFEzCoMJYiL9ZlSrmkSoa/stUfgHArzIgFEmiq2ssWgYrjo2r63QsozKQHafbwXENun7MSZ6TR0XlyAZDOQH5JRxjH0VewdL2mvKPIoudYF2uADFRGcommh1i1ROG2adXYebTLBRN9SxseFU1dqTuFdSQRBDu8iK9Qp6GxPND0sRjNr3M0da1yuGvoCsmHWuZc4tv52TiEWU+tt5FUy0saxHD7IvTkO5WpRwVy12rdrxXmATrGPbX0B0IWXitrCpzIXsuigyzvUqZZ8TzQ9EjKjLixKNtr1bmTmG+bFmLKKqY6MzOGnlxoimHiefn+GtFlhqJb5imDW+rawnQKIpYtMM0YxKKhMVZIlhVxi0SJlEq0ujaGuSXGne9pAj/SlFk6FavKUXZmsKkAoDWwMowKcjVEls7jSzvYLZXUVQTfd7HH19mOrpInHqsrL6T7sbDFYX4Bc4uHZ826rKTVwlWsmJA117BVGQht69AmEk4JglH5JkohepWrypv9gmTovk8v2VdbMLDJGfQ0kVUc+cxNPdRFFmizK8wH14miz1sR2E4T7g2EifcJce+awsK5Veth/JfzXjDLChlWQMeKzdwUbLWMRuFk6W/vgUFaBaTeqz2T/F9/VN84sWnuTKZccptoUgS17w593Q7rLadRt9/+wRxzFMK0kw4k/WX2Lv4OH5FEzaNHu3OGZzeaZylD/D83pRhnrPr5+TeYRWjKhqRt7qJa2yGYwjFTtcy2KwyYBRZJkxTDgOBnWjrGou2iWPojaHNMdVm4cnKkkmQNi5uURdPidNjaqxVlriFhmbsMp8e4h9eEmUvhBRY0SwMzUMxrEZhFiZicc8LDdcQO2Uz9tCMY5VWLdetjXW17FhRLcrs2OOQp6EAFWZRQ/VNE480maFWC4iWTDBVB7e11pwIaiqAPThNJp9l6CcEWc5S2yRMchRXoD80VfiZRvMlVLkgznKG84KsWEeVN9BRsE0Zx3CRlNMAhOPfalIPTXsJ014iCg7w5jtEmY8iVT0P1URRTbEzV3dxWcWLFIZx2MAaQQBN63x5RRax1a6uN6+xwM4nAvKY5xiqQr9auG1Nw9ZUXF1no+fQsQQhoKgYZGWeNfLtWgp8/POQfqt3CwuN6r0sLOGOQbOwK6rZZM0ossb1rQ8T+DdxeyK0y+5sYvcEV63ud9juaeJcQ61UiGnskfiH+KPLRMFB02dK8ghdMVleegRVr0QP1QlaR2HoC/m6oSpV37QHhEIGLUno6hqDwSlUSWzaFHmJ1XarOdG93s3llxpfjZLXf03jDbOgSJKYuJNbYkuP5kJ5NHDMZtK8G+PdZ9/C6dE1nt455OHFPrvzkCBNWe3YdFWdIMlIFKXZ+YtQICFjXnItuvpVotmhOJYbrvBYtN/Fb13ZIx7l2LNd8rJskNt5KeJSbVUVqpqyJM6EVFOVJAa2QPYPTI0lucpDl6XGU+JHGZPwOMPF1pVGSplkJajHrmTXFGbN+oSXFwUxxywmRRI71zRbRbOF9FZRTQFJrE4LclXKMJwMWztdPR6ZfuuAsMr0kCu/Su1DqV31s8nlKj8lo9VaRdPb1eSlNeqsYLrVNKbTRPRlNL2NYfbpqPc1+HIQLv/28gUS+T6GfsLuOCNIJxz4AQPb4vTKvTg2JP5HUA2X4XwFSwPV+ygkHtbydzZoeD/P6Vo6tiajaCZFsSWioWMPtUqdDKZbTMYXmQa76IqJrpiNwTRLBNaGMUSKhqJusdZ5tOl/1Co8VcqaPJ81t8XAtpoSV/36B2lKWJ0m19wWRpWrvtqxcIx98tQni68TjMOKEKCJf7eo7ooiJfJ2mudVmVtEqolm72A7j4idf3VSzGKPIBAnsdDbIQoOGmWebQn5dA2x1K0ehrNUnVBy8uJeFMBWRKkyTyOC8QHx/JAkHBH64n50zcU0eo182u4IwUImnyVIRKrqMA5RJZmsSmCs0yoBNrpOE0+RpxGxd0AQjiELMYsM3eyxMThDId1xAOGXHFJZIt+t9Mc3yHhDLSh12mEdfRpnBa6pNSqTuzlW+6dYdgWb6D88c7UxidnyS7ju+Yq4WjaNxdpND5BndSxsSn/zMS4d9PjCc9cYRQnDSkK56ViNpFg05qUGqlfv1uIsJygL5pXeXsg0dSHbrZqYaewxaJ3H0sXOtl5Y65NbnOV4UdmUvOrTVF6U+JVpTZEluqZB1zbotw6YD6/gj8dNeaoosgoMGZKE42PllukiSWXzmLfHfZbdVVLv08wOnqkWI4tWR+BUstijKDOhxEpFY9utFoacYwqvXC1ENeG3VipJsopm95pyWRaL21BUC1tRCHUhGtjo2bxlZUKeTSGPQXm4auo+ilP+AsG4Lg1pOPYGevER9E6PabiMrorddRKOySKPeC78Glr1eAJ/B1lSWe49QBSPkSW1SnYU5ua4CgFTVFNkuJjPsdq9H90XAogmrleSbss1CZOc0TxqTqY1+qZWIdZGWzn+ItPhjnhMt5hs6xNfLYqQqVz1Fbvt1n4VgKY/2Szk9QnwVqx+UaSE8Rg5Ff0w191E1cX9WN1NEk41HLt6TGQJRRblquXOaTRbUB9uff1q46bhLBEWJ/GyEq/KNqrzW+Isb3pGPdNoyoHiZF3QtZexeEGUzjSrMdbWMQqSfOkrnwCq8d9OKLePN8yCklWKpq6tNx+4rl1r0O/e6eTWkRclB7OYdbdVpTcWZPH/v713j5Lkuus8PzfeEZlZmVlZ7+6ukrqlbj1sJCxhbINBDBwwhjEDA7Nmd2d4nWVggWE5A2N8YGfZ5Zw5nmVngV12lgHGMwzD8lxgDXjG2DPYgMcykrAkd0vqltRSV3dXdT0zsyIz4x13/7g3o6rlbkm2Wo9q5/ecOhUVGRV5bz7iF/f3+/6+35Co+1EAJS+uL66DUElyjGU6AGrTJ9gZLrAz6pEUJU/0h8SF6lWZ9T1mAl8Vx6tit0qZ5boz+cooopdmFFLyfDi4qsteGYp1CJx5SNRFaLq2URlAmdadJLm+89baTuMCsGOZ1XO5lsmcH3C0/jz9tcd4fu8C271zDNP+VSrHi+V9CG2k5dZm1QWBO9jpjqo76cVmTeW4PXU3H/aeJc0GxKNNgvoSltug0VjG92erRr2yzDDxkWVGnquaCpa6QBR5xPSxt2EF91U1qfV+RzWdWgZ4SmusLJ8hGzyIE3XxbJ8sDBlGu9hug9r0CdwmGLbyyLHchlL+NSwac99BmX1aWfUOtqi7cUXV3XdkzCo6dJFHeMGc1hrb75cZ105My1OrlKir+jq0nItb67I4tUTDW6korslgC2FaDPJThPH+aiSwr27PdUyDY+2AMn6cfC+k5IBopB6nLHOKMscyG9q1UdX3HHuNxswp4nCNKFwjDFdJ0pBMy9kAmOMO/fEqxrj6klEWajUpDBvbVQZnbv1+doapavDVysrjG56xPfZGmAC3EDgWwYxS5ZZQ0dvH3+FEpwJNw4BCrZjDNGV9GKles3EaeCRVHdFXpBzh3kXQek5JEWn75bEtc36AMfiKICXiQB1sgpsooBhCqFSXrl3kuhg4Li76wYsLQ34heHZ7gGMa1F2HXpTw7HafNy19KZ79FMlgi1HvQqWvBYrzb/uqezxoLXO+O0cvChllOQu1gK+2rEpSAvabvMxcBcRRlrMxHCm11STh8ihlI8rpRSWOJWh7ETOuxfFhxKzvsVAPWGxoNWLLxLH2FXIHcV6xi+quU7lbpvou0DFN5nUjYyuwyQZd1eMxe5JF491VMXzUv0CeRzS0udW4xhHmJ1jvKi+KtChoOA6bYUQYWzjWbcyfaFDvniAZbhJ2nyXRysZ+Y4koXAMPXG1YpbrE9xV2a81l0qirvGP8t9CLlFhjNtoicObZDCOijKqO5FqQRV2GOh3l2MrR0QvmlHxN8OkqoNjBAxjGJzFtj6j7xzj+dGW/a7kNnLoiTYyDQpYo3/ixI+cYtu3h6Pda6obMPM20lElj37Aqi7X8SoTfjDANnyJTEvtFeYxRmNDwbJaaXrVyHGVlZfUL+ww6w1ZaaYyo0lRKimf/+fwpi3A4R2FJfCOugslwsEam5V/qzmyl/wZKFmeYKxdYz6phGhZFmWMalqJKG/srH1lkFNlZ6t7terW132iY5gWDRL2P4679cQ1wDNMwlCKzLpwf/D6o74Rka6Sk8wtZYgp1/qQoWAsHPNfr03Ac7c+yrFK/UwLf3HeQ9JoXX/rL/bIgMYrkBp3r5sBNE1CEoGqiKkqoW6LSA0pfvoDb54WVTo00lzy72+fx7V0sfUG+ffY25lo+ZaY0ncayGWmk7rrd2hyluJUo7bMziiqToaWGKhxuj6Kqo321H1JIld5IipK9NKOXZvTTgu24YJRJlAGdRLN1r0qNKc8LQaqb1gJ7iV6asjNMKqOncQEelLDfeEXn2xb9KOGRyxuMskUC2yLXsuKO2aTtrbDUeQDTEGwLgZ+qgBgOMvrRQKcolOfGxnBEJ1B392khSPNZHGueYNZiaf4Z4r11yizCm1qiNn2iClhKTl/JzhiGrVJrWczU3N0E7WWy5GnqMiTtqwKzE3yG2+eWGCQqeEZ7a+QFNBbuxvbb+I2liik26l1AFhlJcZQouki29pvEo02mOnfg1mY1EyrDDh6g2DhzVYrv4LZh2BQHJOfHd+pjK+KyzCq/E1lklUCmLFTqyDK0JEscgqdXGKZVNfGN34+xC2KSj7vKpUrvCqFWSQNV3zjYazIe61TnFLVpVTBXN1mQGrdQmw6r44xYpedMy9evc0Qad6lPrbDcUE6Y45RXMtxk2F9V49f/I4ucKFTMRbfWpe418KfuVBT+OGcrzRllafX9Gd8wjZEUBZYwSAuLvLSr+uO4pld9XosCN04YZRlbUczaKCYuJB3PYs7z2I1TznX7NB1bERpch8VGjVbg0nBvlJAXigk0WaFchZsmoBRaMnvsCzGmsZos4/qvQhEF8L0jrF95BlPXJZ4NI6bsUNcxbsGwVzF1qsDWFzFQX17TuMhic18LbnzRyGXJxnBfA38vzdjLMnItLpkUJXFRMsh1z4trYE0J6pZR9bvMBh4zvodjmfTiuKKd1t025zbDShnWFOrLOZa9aDg2pi5y9uKEv9naoa+7lzuuzbSrZFfGMuDdJGGU50xp1dexCOCYfWQaKpWRpBmWEAyStOrq30uz6oIy5dToBG+m4To4kWrE6zQdivzj+6+PvkAWWUR9+kTV+PjCi6jo2kS9VdzaHEnWqHoc8iTEay6xmtzNKM5ouA63LC+SDrbon/839LpnWT71bczP/CRPfVL1gdVqS7QW7mWw9SuVU2M83FL9G4ZdpeFSvboyLb+qH5mWD25d+aOUFqUdVzTocR1ImBaU2X6joWkjMkXpTQabwKZa/fhtZWWrg4lrGXRqDkV2lnSwRbi2WdVCMu2DU8ocy/SUdIvMcYb7JBDXNMgNpXu3t6NqOl4wx/TS/QeC7Sp5EtJauBcx9U6KEka5cjQ1DQO/ZrGyrKi+RRYR762TRru6vyVHlhluOUetcwWAMO4w0hTowLa0gOPV5AJTCFKU+GqYpFjC0IZbio3oWJ8bDLaihF5WsBMXXBhkNN2EGdfEMw0244S4u8e877IYDgh0L5hj+p9zni8UUn5+TdM3O26agCIE2ulNM5uSec1/L7Tw3auHwLb4kplpYJfNOGYtHNLyXG7pHFd5di1fMaa6ZtEucd+j0wTTWKI3UsvmnVHMxnDEZpzgGkalnjpGUpQVg63lGJUqq2eaLAQ+s4GHb9uVYN92FHNhb4ApBEcbNU5f2aarZfRzzSLLS6k8uS1LGYiFQ57ZC7k8SokLSaxXd4Ncif41HZs5z63cBsfj2kuzipE2tnkdpyvGK8WtUayaJ9OMfpaxE+dcHqngtFK3ORJ4qinPsTnWbHBy7p1MGRcqJlWehlVtINw4Q9h9lrWtv2GQ7WEKkylXFXxdu86R5a9R/RW6xmE3vpyNMGWUqfdmseUjc1UH2es9y8XuGconc44eD7njK36DbPRx1ahYV5p9/YsfZNhf1dTotn4/AaKrvGgs08PRulrGAT2tZKgu+uOis+NPV5Rm4ICXiqp7jL3lDdtXLpReF9exyGVImcaEoRKYTIab+0FEB49Ei0jioGpSjSUld59FJINNhKkCYJr71DontIZXSZJLcv15a6+8S73vo0vKcqFUzpNAdcGPsgZzjTla9TXSwZbyvM8j8jyqRDiTwRaG7TFTWyRKA3ZGqj4zbsQdf/5UTceuRB7TvCDw7UqLqzfKWGzu6Nf5SFVjmfVdBnkOpFwMc7ZG6pwzrkVcKrWIQR6RlyVLgX9Vn84rh4RyElAO4qYJKIa2OB2lqpDsOwauKSq21auzRoG0KJW4YlHwZfMzXA6HdOOE9XCIY83R8I7QCEzKwcNw4LNX5DHZqEvDO0ZR2oRxxs4oYmsUYwnB6jDScvYOlhDEB9z3PNPANQ3qlnK6GwtW7qVZJSW+l2bsJgmWYRBYqpN+mOdXeUEkZUmu2UK5lBSyZCdJ2YgyBllJN1ZMsKKEnVHBhlswH+TKKc+1sQyDvCwrJ7y8lFiG4NZGDVOWjDLVaGeZpk5PJAz0GDbijJ24oBepVNy5PGM7KXjrTAPX9ChKyXo/ouUfxQ9WmGpvKB2wwRYXn/4Qa72n2I13uDzaYlgoYsJKME1g+ZXawFxyP63FezGC+yoK+S3TU7T8NbVaYN9CeKFxnM7sPdWF3Q4eAGD72V9k5sSP4tRn2br8qUpKPk1DoqR71UogK2IKmeMldXKtqzVOeY37LABa7VMUeYTU5lUH/VRkkYNmtsXDzas85l+oQFzkSjQyTUNcr43tTBGPlBBtWebMLb2NxvzdmPYp8uRMZaA1FqAUhqXdN8vP6bkaox4cpX/xg9RnTxFaS2wORpWtr/JEiZmrd1iZPYVh+5h7Pv3dp+hun8bZu4AXzCkKcCumU38TaVEyyjJ2RokmB5iKQegrtlaU5QeMuXTq1TE0MWEBI/ksgR0x1zhRtQi4psmclzLvRfTTgl5WMMj36bx1y8Ay9mstNw5yskJ5AW6agAKKBTW+Kx5LsPuOge0uvWrPeceRk3zy6ccYZTlLjTp5Kbk8GHJut0+SF8zXA+YaPk3bp8ziKqUwFjcsSkhyVevIddCIi4K8VL8T3azVtG0KS1Y+9aYQNB27KtSOVyWBbWEZimLcdOxK1dg0BAu6mx9U/noceA6iqVlEHddi6BdsJwVXBgVpLtkeFGwPChamTLaTA8KXOuXXcgw6jlMpBLimWdVdTKF01YaZOmdclNRsg6ZjVvL9Lcegn2bkewO6ccJ8LSBMXDqBj2/EDHfOs3X5Uzy58SAb8Q5tp8Fb5+6jkAVboytcjjYxDZOm2yLJBuxsPUY0WGN6cZO5zgl2jaP4jsEoO6K6wi/9AVkSctuX/UBle/v0I7/M1//bn+HprQZTQc6fvecdzJxQopV+MEe/f57V7hPsJDv6dSxo2HUc08HUhey6k6igs/WYen1Mj7yISbQTox/vqmDjNjBsr/pMKMn4WKeM1E882qwoveNgZlo+abzLKNoijLeUtpjX1u6O7Ypp1jr6bYRJQVBKZJETbp9VPjpBW/XOAPXm1YEkS9au+r7s7l3k8vkPU1t/mNYd78exTBpAy9iv7URZTj9dptU5Tq2zSn32ZNU0qeopsxjel1AkBXMNH8cKqkZDU39WHa1Plua6j6tSAlBkkQX3HEbpUQKDrXN4tS5vWnobT14RuLGpjOgaNbqxsgfejBMtzipoOhauYRAXJSQpoxvF+pQlsoxf+rgvItw0AUVKdWEOHM3jN9Qdju/deHbXC/EVt99Df3CJ0+s7lST9KMvpxom2OpV0lhQdFcBy67j1WfrRPGGc0I8SwjTlm9/8Vv7j6YcoZFkFg0KW+2mvUin/LtQClZ6CSmJ+3NzlaqXXwLarxkigEnocs2oaWiAvzQs2BiP1pTZNCqkc/fbSlN04xTMT6pbSJtscFURZSZJTrWxcU2AZkrqtisOWEJhCaYGNbYvHd5yjPCcuikrnzBICLQpNXqqV15j63PZcFqfqzDV8AuMZupcfY2/nLMN4i9lggXfc9X3MnvguilISdz9BMtzkqad/n0G6h6+9W0ZJVzkfWkq5uNXJMLARtgUs4y/+HUhL1vrqrrhTn6d558/yZ/dY1K0L9Ncf5ey53+Ov/uM7mWncQn+0zkObD3FhGBOYcKI+TcOu83R4mVyWuIbJlO0zlfZIi5ia08QUFq5dV3UTYVHzZ/ebLnWz4X7fzL7t7lg5YOxKaZleZQyW5yrNNkq6ZEWCqc8DaHqyT9BSYo3qu3ARuz5L55avZOv8x7nwxO8CKh3WWgipaXp0MlA1m2j0CAD+1BLTU/fxyKzyaurs7nHvkWlgVY3XuL0SXh27o46yI2AfwXTB1w1g28MUhikzNQeZPYHAImj4FTMM9o3gYIE0FxSGwNHujr5jMNpYJQrXKtfKZLjJqL/KycV7GJW30Rup+txYyLUZDtlNUmJt+DVmhcUFldDmjYCcpLyuws0TUJD6bl3Jr7xWwWSMMQvFNU182+aZ3R6PbHc5Enjai+QIpr1JmQxUs1i+SJpnlbjjnQtN1nefZ64e0HBVI2MhZSW5Mi5cqkL2flG77bkcazaqnLT/gj6FwFZf9FwzxQLbZq7h4zuKRumaBiudOlFaUPe0HH5a0ItSLvZCFtOUvJSM8pzNKKaf5letTvIDqbjx9mzgsThVp+VrK9i0IJdl1dnftLOr6jYL9UAHQUtLnivaaN3dIEsuU2Yxjtdm8fZvpCG+hM+u7/B4Kelc3uXkXJNa5ziO3+Yu470M+6sk8S5R0qUfbWCbLsPhGlkaEumucL+xxNR8Tpqv4DsGLd8jGTxMPhgw7bc5u7NCLlvce/R+jicha2t/xcee/xCP93KmnZyVmseUFTAqEkZFQlIWJAVMBy7z/kxlZ+xZdewDhXrPVasHv7GkGu4MS93FR3GlvJulilwQjTardNo4pTamVqfZgDDewhQWTX8ex65jWr4quNfmcOuzOH6b4RAudQcU5RSjLCOw51k8+j10nN+nu32a4WCN/tnzGE9/iHbnbsq593Lhyl5l27D9bMT58CMAvHNpjtvsR8gTxfbKyxw5+muCUjG7djZzgtYyjfZXshEmpHlZScmM9cSi7Y9VLDAA21F05rEDqVOfxa0vMdDfJ8cymW+4ZOGnSYabujExJo13qxrN1vmPYxh/xVRtjgVtBTDKjtDZ8VkLh5WcfvU91av+GwGJRMoJy+sgbpqAAtpFUasN90Y5/sv31HrFmG2tsDM8xyhTF/rbplu0PZeW5xLYNmGc0wreQuA/D0CqL75KqM7k+Z0hUZZrLSabxWZwlQrtuJ+mKKE3SlnfGxCmGVeGI5KiZL6mAtFBJoxvW3Tq6qIepSWXugPdvOhj5k9iFDnZKCIbbiHyiMiwEIaN5zY4MXOcTm2pUmveGUUsjOIqpeBqwoAlRJX6GLNoFqfqdGoeLd86YJzkqpSkDhxjbbGZwCdMUnIdYHpxUgVR8Igyi/O9PTajRUUDddeYDTwatprXuc0+gd3GNKYJxTE6Sz63Tq1z5dyHcW2VShrf1e/unKkMy5pum/n2XQT1JYRhMdxbJc8jXK/NyuybaCzcTVIcpTFziuO1OYoyZ8E7x4n2XeqcQvVe1Py5A9IwihKcajmYoK5uaNJ4Vxfgp/GmlmjMnlT9J4N9+ZE8DRkO1q7ya/GsGq5dv+pzNi68e1adVvM4Qf2I6o/RIo9ufY4wOcJgqFSDd0aRVjswCGyb2dYKtP4xsydVwb3s/mfSuEtj5hSlaxM405xe3+HRzR0Cy+Kt8zPcd3SOmjwDzNFfe4yyzGjMnCJPQsKds4ThKo3GMm42S56cYbG5bz2QJyFxf43+FdUD1B1crAzjOsERGrUlgvoSjg95MsDxz9OpWXRqyoVUZk8w6q9WEjtjjC2TTcsnS0MGexfobjxauXiePHofR9u3VekzAEcvh6P0Bq0q5KSG8kLcNAHFMQ06dYcoLZmeOva6jGGlU+NSd0Rhq7TRXF3dqW4MR/TiGN+2CeyOdj3U5lWWQaLzxWPp/YONXmGcsb43oOV7dGouo1TVSsbBY0k3LoZJytFWo5KDKUpo+qoPwzBs6qbFnbNjS92eZirFlQtinoTEo00cbxonbyOLDNs8j6v96o8dUd3PvSilFyVVh/Jcw8c0BDuDmJ5mkCnLV9W57pKTWkdoeGAadRpJqgJIOSQpCp7c6bIRJWzEGXEhGWaqPlS3DVWTcW0808TSFORxzce1rmaVDfMcUxisTNVxrCNMH3sbwd46ufa8L7KIPA3ZCp9nfXSFx7tPs3XhU0zZcNRv0XKmqDsNptxpomiLNOpSnz1J0F6GmW/l/mPfy/yj72Nj+zECt41j13G9aRyvXZldjXWvatpxcdwD4jeWMDrvZr0/Ik0KFsOA2cY2oC6M42CyufcMu/EW4Vh2xq4z7c1imy6OXqmMoS7ER3D8Nn57uWK/GYZ67QdxQeBYfNnKHOu9iOMLn2ssVQ+OQvAPGIwuIcqnMPMnObe5wGo/5N65TiXFsxlGzDXupukoK+owXCXsPUt9akVrbq3g1mYRpvLK2btyhjRWRmq73bNsDS7QT3qE2YC9fERRlsx4TQp9d1+bWsZvLWO5jYo0UJQSQz7HaLBVnWe82ouSbtW5b5sepcyrvwt9gZ/fu0C9fYKpxhJOffYq+rjwbtRlbxJQXoibJqAgbHzvyGu6KnkhxuJ944Y+UDWO5/cGWlLCJC8lRxs1WrrhqiiV3/Y4iJimwLGMiqLpWAZz9YC0KPns+k5lbdzyXZUe82xOr+/QcJQ7Xi/KSHIlO1NkMXkyQGrXw7EsuN9eZpCvaJHDSMmPJCF+slTl8Is8JumvVqwit3Ye12tzxG1w+9wyAFmi1IYxLBbrs6y0pivXwTSXDJgnsE1Mo8DXaaxW4BDGWSVwmRQlLUfZIhdSsqM7qDuuw0KgLgKKVKD6W0xhMMpzuklSMXx2k4R+mtN0LGqRyaXegNbSW2jWL2kpHJXzT+IuaZlgCqPqHdpNBZtxn2mnxy21FnNFwl6yS5KGTI8UJTfNfgHPbRMOtbCloayFHW+6kpmxg3Ylx2LYHmUWV0EpzE/QG6X4tkXTd5htbJMMtkiGWxXtV31+cnwr0GZqNTy7ju+2KzmUohxQyBxHd6er90ZRmPNYBU4jsZHZKk6kekQGwNGle4DrOxUWJXxita3Sn+2Aexe2WRs0KMoS37GYqTmYxkWKLKO9dD9+uFQ16Y5TbZa332dVNT4O1uhHyt3TtwKmvRkcU/UypUWCY7pK2HMsWKlJK0V5jI0wIXCWabaUp/045RclXfbiHQbZHmmhPiuFLEjKFEuY+JaPbwWUO4/TjLZoNo8TNFeqpsyD47wRkK9BY6MQ4juAnwHuBN4qpXz4Ose9C/hFlBXRr0kpP6D3TwO/A9yCcmz8e1LK7qsx1psnoLwBcLDfpe46RFlOy3Nhb8ATvSG5lNQtA89UFzRXp3bGQn8wVkwuFT0zSSuPeNVXoi7uU45qCrvQ3ePcbp/dJOFdtx7j3GaXXpyw1FCmYPWmkkvPdXe2YVg49VlS4w42+5H2gD+qJNGFIDVKHNtgbsrDy06TDDeJRpuKHrtzBtvycew6ttPAdqYAlLCj3ybqrZInA7ypjLrbIDIW2NxL6BmCVmBrBdyYoozwPWgs3kGaFxyLGrqnQZEYjpZlRT9ua7HLwLapu3Ylq/9cr88o20+1FVLTbIuSi4MRu7G60NwyvcBsw6J76WG6O2e0NLpaRdUsl7ZTMMol/VIwKmA97pPLAtewifIRPX0xnG+qi3GreaISpgxaK7j1WYR9F70ow4xU8dgNDPLkDGnUpTF7kixfpGULpmubFFlMGu2yu7pauR8eTHN5do0ps0PNm6VWX8JyGqRxF0NsQtLF1dpgSRrS3TuPG23RzCPcVPU6CdO6qg5jOw2SuEs82uTYvV933c/t2Hp3pd1kpdOlLE+x1FQrhDwJibtKSdpyG/jtZbVqA/prj6mLtFuveq1AdfrX2ycImiu0Z96kPte2p2yOddoqT/cJCLXpE5VsjGl7mKxztD0enUdz6R6CuXdhGoK4/yCtSw/S6z/L7vAyW9EGozxiVCSaSVhgChPDVc+TpSFppGpP42bSsSTOK4WUJUX5mkivnAa+DfhX1ztACGEC/xfKAvgS8JAQ4kNSyieAnwT+k5TyA0KIn9R/v+/VGOgkoNxAGPYi07WEfqTSOr5tMUhS7p3rsDqMONfLmXJL5n2VMiqkJEjVW5DkBZYhaHlKs2lnFJEURXVx9EyDYa40v0zDYLUf8lRvj0JK7m43eXxjm6f3BgyykjdPx6zEdXaHNXz7zWCCaSuNpFEpSQcpm4ORThnJqkjpmiqFluYSV18ALMvHLZUCcJKF9EfKe902PVy7TnPubrzm21hNQoqsxOkrCfXeKKUfJUzXPAL7MsOd1apTXCnhbuEBR2wPq9EgNe7gUnekg0umiABZzpXhiOWpBkfbKn1Y2BbtWNWlWp7LWjggL9OqFyYuSixDycHnZZ1Rd5XNjYe53H+azWiTpMyYcdscrR/lQqhcJ5MyYysZkhSSS6OQWU8tc30rYGX6HprTp67ycHdrcxhT7yDKJb1+zECvqpq+y3xwgWzUJWgtK3mV7AniOCTTrK3xhV95vXgqbZS2iaIt+qN1Cu1N3++fp9AML1A+9FOtEwCV10qWR+x2zzKVRzTaJ6q0l7pot/W2TTza5NMfVQHFFBaB28ZzFcnBrc8x31imFSxg5k9Slg0MY43hzrmKKDCWkxnrgUnDZrB1Fqc+q4vqeSXAOFYKNmzvKgsBWK7Mu4qyxGwYeFoCZjfNcYXqHSvLS4Cqn1zsjrjUDzENg04woOk7zDS+nIWTbRaAMovprT9Kd+cMG/1n2NI3AKAlZ7SSQZ6GGIat6MvG1aSVV4rXYoUipXwSQLy4BcdbgWeklOf1sb8NfAvwhP79gD7u14GPMwkohwNjhWPftiqJk16c8I6FGY43RvzFlT3+emtARws5LgUebdfFtUx9R65MlBItpgfwTF+lRJqOjWuadOOYpCg53qhV9N9n9kJ6qXKKfLo/YJjlNMMhoDxQplyHTuCp2otpMlcPCBNVD7GEILAtWp7HYrOm9I4KcGtKCJG9C+yEzxHlw0phOHBaLCy+nTx4O49e3uXstlpBz9cCntjcIbBt5msBR9u7RHuq2U6Y+1/mcPssWbpHkas6zuyRt3PH0j3sDufYGcaEiQp6w6zgE5ev8KHnLtFxbW5rNjANQ12U9BesqSnU/SzDM02mXdULYxmCIo1JsgFRPiIpMwLLw7cCOsERptwOcT5kkO4RmGqMHW8a3wpwdL2iPrWitLpydYfuTS3i1ue42E1Ii4L1vQF7acaM76l6ku3jNL8C01pn1F0l06mnQneyG4aFY7crZeayzPFZolGeoBV1GexdqPznQQUS12tjWT6O365STIrlpHxf6u0TFdNtjLLMibqrlTx8Z/H+KgCMzbZM29erirNk3VW63fM4frsyJAOoTR8naC9X2mNFHmG6DYK5d1W+I8I+RWGVOEJRlKPuKrn2vRk/p2mrm6ZBktGLEsW+shS93TGVWZYlBElxFNDWD0XJnfPT+I6JaxqVbUDcX6skbADanbtptU9xbLDGMNpklPZwTE/XXJSPzpgw8UKZ/leGz6uGMiOEOJiq+hUp5a/cwAoJvFcAABgSSURBVMEcAQ6qXl4Cvlxvz0sp1wGklOtCiLkb+LxXYRJQXgW0ArvyE7E8D8fMKkXhr1k0+PP1Hmc2Uzw740Q750iQslwLODXTZrHl4/A8841bSYqSlXiKY81GdRc8ylTx2TWh7XlKFiPLadqqDgEwzAuSsqSfZtRsi4Zl0gk8lX7TqLv2gbSbquE0PJuGqzy/00w11qXxLnHSxbPrtOvHsEyPoH6ExswpxNQ7ObfZ52J/Xw78fH+PWd8j0EJ+g2Se+hRk2qERYLhznkxLvwOYeBi2TzLYohXkONYR6rGtOqGlrOjGS7qmEiYpDdfB0sSAtudym21XCraWIbQplanumHXjYSFLGnadI83b8d02eREzJRZZMCxug8qud5T2KGRBw+3g+O3qQjrqX1ANgW6DlbnjDEaXCLVK81w9wHdMwuQIga0siuNwTXX3x7u6p0QxtOhS+aSMdbccW7GTgvoRbKfB1u4TmFrFF9RKQ+judsdTJlSG7Su/k+A+wqQg3NmvuzU8CydQBe5xmsl2GwySeVzPwB2bUA1UII3CNXrdswTxLLWpFUzbU6m99jJlOW50PK9WklsxDS9jtqE0vxqzHla5qC/4ip6dRl1kmSnLgKklvCloeLdpWnrOqCy1bbVBK1BNkrnu2VrvKwfLuYavbX8NXM4RjZRVcBSuYdpKkHKs9AxKqXpK08Oj0Wa133YalSiodcBp85Xj86INb0sp77/eg0KIjwEL13jop6SU/9/LOP+1li+vuVnLJKDcYFhCMMpK5axXysoLIi0KAsviRKtBzTYZZspxMZcS11Bd5GGSEqUelpUR732CMosIgHuPfCNhUjBKc7bCiG298lFF/f2aA6Bsfn2luKpcFg2mHJuG6zA3pQJQLpVXeODsv/2BbWAaF9UXVl/o/fYy3tQSba38C8rm1+58LU9e6RGFO5hC1TrGvSo1S3XGXxmMWO2H9OIE3/ZwzRqjLKfhOqx03kFD+8QL0yYZblJmEbFu7nOCkI4D7twpGv0hgWVxvr9HruVdZgKfkzNtlpoeWfjpypaWA9mMIo8od2yGSUireQLTsFlu3UnNnyOoL10lXTIWeiwHOSNdZylkzijtqTFqOvVe/1mi7cdYu/yX3HpylTXeRmDbvHmxXfmxZElIri98YfdZhsN1+qN1rgwvV6ym7aTHXhazlxUkJbgGBJZgpTZDx+3Q9mdpeLMkWUiqGV+2M1XRg5V0ikWWL9KLMkZ9lRYztSLCfMNFFufp56dI8qKiyaq+phTTEByduQ/ThMvdp2n5Do0ZVL3mQAALd84ShWtVA6lte6RDyS2dnX0TtdZXkSVPs/P8f9Bd/XlFo1Y+MKonxjBsXKGYmGN9LtMA1xxLoqg6Ym+UVXT5JC851vaJ+w+yu3a2kq8pS9WfU6sv0eicwg7Uiqqw7sQB2jzPcOfZ6rPg1uf0KskjyhboDfcVj18JpITyBvWhSCmvX+R6ebgEHKS3HgXW9PaGEGJRr04Wgc1X+FzXxSSg3GDY7hJNvRDo1NYZZQWXuiMu9kNGeU5gWSzUgkpTa0wBLsqSXpSwM4wp/FuZnW0w3Dmv9asepu5P0/Q9oIPvWFe59jmWydFC0YfH9ZCkKNhJUs6HEUdGMVujGJih7toEjsV0bfMqCfUs7DI6kJ4ZN8mZlocwrYqt9MzuHmdPP1eJPwbaw2U28DANg6PNBktNjzApuNQdsDFUtZqG67A4VWOpqXS6aHw5ZaB0vLyppysVAfOAoGJNnuGO2QYrnRUaV9TqqxN43NLZ4crZf8cTZx5l6cg7CVrLDHafZXNjP6NgWz6NxjJBc4VjJx5gkMyrC27+JFFXdV2DqofIQkmc5EVM4LYJ3Ha1UhnsXWB6/l5st0Fj5hSyyEjjLmeTt1CUMfcdmyFc+8MqnVVqG9x4tMnzWw+xG++wnfR0jQZMfR+ZlJCUgvVIzXfKTummW8y6uxzPQu7yZmkEi1i6kG07DXWhtj1G2RGitMA0lPrweEUS2CZ5coaivIuNwRJRFhOlOdujSFsOKGfDTuDz0PnTPHJli50kZd53WZla5s6j93KkvUsy2FLptM67CWyDweZHSeMuslBF876mRQMY9ho5KoB3986TFjELnS+h1lRUYLc+zq4sk2uXUNgPJuObGF9f8BvePK5lVDdJRSnJoi7RYA3badCZvZeyVO8BaBO7+hyj7Ajr3ZG+kZum7s6rlJpjYViCrAQz095Izo0SiCwp3zh+KA8BtwshbgUuA+8F/mv92IeA7wI+oH+/nBXPF4TXLaAIIX4O+Nuohoxnge+RUvb0Y+8Hvg8ogH8kpfzI6zXOVwLDXqRuwx0BHG1f4sLOgLVwSBinFYMJFN3Y1AX8nWFEmKQ41jyeq1YFZRYzHKk7run6HItzswySedJc0hspr+25eoAlDJq+yyBJmQn8yu0OVMd8mhekpolrSSV3PtiqAshYd2ncMKZ8wdXdZT+a57G1XXrxFdaHanVUpaJ8oaTgZ1t0alfYee5jxOUSrc5xHOuISjsZQqkIWAYboRrv6Svb1QVuceoWXEcVnJWek3bc9G3K4hxi8CnuXZgF8yQAyeDJSoaku32a+uxJ7KX/hhMzp9jbPEMS72JpT4/67Eme3pxifW+LbpwwE8xyvHMb8wsucf9B7bei0lJj462xne+Mczf1thpXkUdk0S616RPU504xlVzBqX8dFx99H7XmctXH0w9XCZMdBuke/bTHXjYkKTKmLIfAczlWO8Jtc2/FMGxiTXQY91CM4dp1Wu1T2E6jMu0ay8eUWUy9voFpLCj9M1+TOoqSUVZQcAebu0PCJNW2uUqMMdf+665p8tmNbdUQq5V9d5KUvLcHQD+qMV3rsNjcgb3PIPGZWri7ql0oy2OVdjroU2NaHtMt9f605+9lauFu1vsdCMcmWWqOysUxZWcU4ds2LV9d/IvBONDsvxajtGC2sY03tcT87Dfg8Dyj3iqj3lh0cpZa5wF6UU4YK/HPsdd8HquUWj8Slc+KY5n4tqWbiW8A5GtGG/5W4P8EZoE/FUI8KqX8BiHEEooe/G4pZS6E+GHgIyja8AellGf0KT4A/K4Q4vuAVeA7Xq2xvp4rlI8C79cvxD8H3g+8TwhxFyq63g0sAR8TQpyUh1zjoB4cpTl67kAQKUmLEsdUH3TXMljvKx8U0xCc2+zRCW7j+Gyf7qVHkGVGkcVE4Rq2LsrW28s02pq2ahj4xaOUWczR2SXlxx3n7A7jSucrKQqcosA0HNLBFqOealSLtELt2MWw1lwm6BwnzE+Q9Au2QpW6GmY5NdtkynG0JpjN0VadmZpD3P0EW1cuEPaexa3NqjvGfkRRqgvd2N8il8pyuOE6tDy3okmPEcZl5R6Zl5LAuY1G+5Qe3xrJYIvh7nlVyJ+9l+nltyHsuwj7Mc8ObufowpdyrLFB3F9DmKrHQYkQKsHKhze2uRwO+PKjixxrv41s9JgyP8sG9EfreHaduZl7sIM5mov3KMdGcSujrMTnKbbO/zmXNj6NZ9U5dstZFu78UdL+J6uO7ayIifIhpmGyECzhp12iPOLk9JtZufUbqU2fwA7uISnKSmVASeMoGRzDWKMslygy5bc+FlkExd4SploF+bbyGSnLJVU/0TbBF3th1avUjZPqhuKgDbYllN6ca5q4hkEhZWW9O76rHxe8B1tniYdbB1Jh1j65wJvGLjMsp0FdU38tt8HGaIVnLo7oRbsEtvIxGYuS9uKE7VHEVqRcF9u6rleUJbe2mhxt1/EdgzSXWEbBhZ02l/oWc/URndq+ACUsEyYF5zZDrU4hq94voJLF30tSukmiVBl8n6Wp+g0LKBJ5w1JeL/o8Uv4h8IfX2L8GvPvA3x8GPnyN43aAr301xzjG6xZQpJR/duDPB4Fv19vfAvy2lDIBnhNCPIOixH3qNR7iDcfRmVur7Uvbz1Xe7f0oISkKOrroPNDd5IFtcX6rSWo9wImZOtH2xxh0zxMNlCaV230Wt3YGx2mofoD2MvbUkvI/r28A8/QjQwcv1USY5kq9+PjscQzbI+qtVp7n3tQilpZ6v7AVASpldSlUXe1j4ctO4DNXD+jUHczkM4Rrq+xuPEqUdMmLmDlUT07dtUGn2BquycVuxOZghGOadGo+nZrHdG2zapJTqrvL9CKbJC9oBTZW+TThFZX6G9+t50lYFboHpy8we+TtHJ09RT+a5mPPXmTKsbl38R7m6i5RBr6dsNiocbzT5E7dAZ4WBRe7EYFzJ9OLDeLhFo3GMkUe4dbmlNe6+SYu7Iwoyj3qrsOF6Ag999vxT3wnhSF4MstJzl/h5Oz9dOqn8dOQJctnCUXt9YI5Fk59I2v9OVzLoAAuRxmbW1uEacZeknJlFGk/D3UR9EyDY/WYu2cXFDnBtQkC1f2uWFAlY4HoKC0I42ElKd+NY9ZHcWXGpsQ3lWjnrK8UFMY6XVOOUx2TS1m9r52ap4Qx186o2pauiRxMRY5vaIKOKm6b9ikudiMGg5RRN+eZ3cv09SCnPYcpR91ErQ+jyiyun+a4psFemnG0HnBrq0ndtWk6q8R7azTnvoNPPv2YUu0uChZHEQ3H0U2uHqNsje1RxGacVGrXO0lKL1Xio4o+LvBMwUrNVQFUr5Z70Y2poXyeRfkvCrxRaijfi+rkBEV/e/DAY5f0vs+BEOL7ge/XfyZCiNOv2ghfP8wA26/3ID4//OlLHXCD5/RrN+5UrwzXmNf//LoM5AbiEH7+XhZOvdITFOngI93nPz7zMg+/GV/Dz8GrGlBeDhVOCPFTqATrb47/7RrHX5P+pnncv6LP8/CL0fIOK27Ged2Mc4Kbc14345xAzeuVnkNK+a4bMZabCa9qQHkpKpwQ4ruAbwa+VspKB/3F6G8TTDDBBBO8QfFqOeO+JLSQ2fuA90gpRwce+hDwXiGEqylwtwN//XqMcYIJJphggpeP17OG8kuAC3xUa9Q8KKX8ASnlGSHE76I0aHLgh14mw+tGyhi8kXAzzutmnBPcnPO6GecEN++8XleI/UzTBBNMMMEEE3zheN1SXhNMMMEEE9xcmASUCSaYYIIJbggOTUARQnxQCLF5rV4TIcSPCyGkEGLmwL73CyGeEUKcFUJ8w2s72peHa81JCPEzQojLQohH9c+7Dzz2hp8TXP+9EkL8iB77GSHE/3pg/xt+Xtd5r37nwPv0vBDi0QOPveHnBNed171CiAf1vB4WQrz1wGNv+HldZ073CCE+JYT4rBDij4UQUwcee8PP6dBASnkofoCvAt4CnH7B/mMo/ZoLwIzedxfwGKrofytKK8x8vefwcuaEsvr88Wsceyjm9CLz+hrgY4Cr/547TPO63ufvwOP/Avinh2lOL/Je/RnwjXr73cDHD9O8rjOnh4Cv1tvfC/zsYZrTYfk5NCsUKeVfALvXeOjngX/C1c2PlXyLlPI5YCzf8obCi8zpWjgUc4LrzusHgQ9IJamDlHIsoX0o5vVi75VQNMW/B/yW3nUo5gTXnZcExnfwTfb7wA7FvK4zp1PAX+jtjwJ/V28fijkdFhyagHItCCHeA1yWUj72goeu5V52TfmWNyh+WAjxuF66j234DvucTgLvFEJ8WgjxCSHEl+n9h31eAO8ENqSUT+u/D/uc/gfg54QQF4H/DSXcCod7XqeB9+jt72C/efowz+kNh0MbUIQQAfBTwD+91sPX2HdY+NH/N3ACuBdYR6VS4HDPCVTPUxt4G/ATKDltweGfF8B3sr86gcM/px8EfkxKeQz4MeBf6/2HeV7fC/yQEOIRoIGyzYDDPac3HN4o4pBfCE6gcp6P6cbIo8Df6ALioZVvkVJujLeFEL8K/In+89DOSeMS8AdSJa7/WghRooQHD/W8hBAW8G3AfQd2H+o5oUyYflRv/x776puHdl5SyqeArwcQQpwEvkk/dGjn9EbEoV2hSCk/K6Wck1LeIqW8BfXBeIuU8gqHWL5FW3SO8a2opToc4jlp/BHwt6D6QjsoBdbDPq+vA56SUl46sO+wz2kN+Gq9/beAcSrv0M5LCDGnfxvATwO/rB86tHN6I+LQrFCEEL8FPADMCCEuAf+TlPJfX+tY+YXLt7ymuNacgAeEEPeilt3PA/8QDs+c4Lrz+iDwQU3lTIHv0quVQzGvF/n8vZer0103w3v13wG/qFdfMdoi4rDM6zpzqgshfkgf8gfAv4HDM6fDgon0ygQTTDDBBDcEhzblNcEEE0wwwRsLk4AywQQTTDDBDcEkoEwwwQQTTHBDMAkoE0wwwQQT3BBMAsoEE0wwwQQ3BJOAMsEEE0wwwQ3BJKB8EUAIMXiVz/9hIURL//z3X8D/PyCE+JOXPvKq4/tCiA9f5/F/K4T49s93HIcR+rV4x4G/f0wIsSqE+KXXc1wTfHFiElAmeMWQUr5bStkDWsDnHVC+QPyllPLdL33YFw7d2PdGxwNAFVCklD/PtfXtJpjgVcckoHyR4oCJ0uNCiD8cqxoLIT4uhPjnQoi/FkKcE0K8U+8PhBC/q4//Ha0afL9+7HmhzM0+AJzQxkw/98KVhxDil4QQ36233yWEeEoI8VcoLazxMTWtsvyQEOIzQohveRlzEfrcTwgh/hSYO/DYfVrd+BEhxEfG0jZCiC/Tc/mUHutpvf+7hRC/J4T4Y+DPrjceIYSp/+8hfZ5/qPcvCiH+Qr8Gp8ev33XG/fX6+f9GP2dd7/+n+rynhRC/IoQSqxNC/CM9x8eFEL8thLgF+AHgx/TzXfe5JpjgNcHrbcgy+Xn1f4DBNfY9zr7h0P8C/ILe/jjwL/T2u4GP6e0fB/6V3n4TSqbifv338yihx1u42tToAeBPDvz9S8B3Ax5KMvx2lNrr746PA/4Z8N/q7RZwDqi9YOwvPO+3oTwuTGAJ6AHfDtjAfwFm9XH/FfBBvX0aeIfe/sB43Hp8l4DpFxsPSo7kp/V+F3gYJVb6j4Gf0vtNoHGd92QG5c9R03+/j32DrukDx/0G8Lf19hr7BmUt/ftneIEhm57DL73en7vJzxffz2FY0k9wgyGEaKIuSJ/Qu34dpSo7xh/o34+gggTAVwK/CCClPC2EePwVDOEO4Dmp/UOEEP8erReFUoR9jxDix/XfHrAMPPki5/sq4Lek0mBaE0L8Z73/FCr4fVTf5JvAuhCihbrQ/xd93P8DfPOB831USjk2aLreeL4e+JIDtZomKkA+hNIss4E/klI+ep0xvw3lFvhJPTYH+JR+7GuEEP8ECIBp4Azwx6ibgN8UQvwRSmxzggneUJgElAmuhUT/Ltj/jFzLN+KlkHN1WtU7sH09ETkB/F0p5dnP87mudT4BnJFSvv2qnfumZdfD8KXGo9NQPyKl/MjnPKkQX4WSR/8NIcTPSSn/3XXG9lEp5Xe+4H894F+iVn8XhRA/w/7r9k2o4Pke4H8UQtz9EvOYYILXFJMayhchpJR9oHsg5/73gU+8yL8A/BXK5hYhxF3Am69xTIgyLxrjAnCXUNLgTeBr9f6ngFuFECf03wcvqh8BfuRA3eBLX8aU/gIlQW7qGsnX6P1ngVkhxNv1uWwhxN1Syi4QCiHepo9774uc+3rj+Qjwg3olghDipK63rACbUspfRRlTveU6530Q+AohxG36/wOhZP3HwWNb11S+XT9uAMeklH+OsrxuAXU+9zWfYILXDZMVyhcHAqFkvMf431EmSr8slPPleeB7XuIc/xL4dZ3q+gwq/dI/eICUckcI8Uld4P4PUsqfEEoa/HGUp8Zn9HGxEOL7gT8VQmyjgtWb9Gl+FvgF4HF9EX+eq9NR18Ifonw7PouqcXxCP0+qU1L/hw5olj73GeD7gF8VQgxRdaP+5572Rcfza6h04N/o/VvA30HVd35CCJEBA+AfXOukUsotoQgKvyWEcPXun5ZSnhPKWO2z+rke0o+ZwL/X8xDAz0spe5o88PuaLPAjUsq/fInXaoIJXjVM5OsneFkQQpiArYPBCeA/ASellOlL/OurMZYHUIXolwo0L3aOupRyoLd/EliUUv7oS/zboYAOVPdLKX/49R7LBF9cmKxQJni5CIA/1ykeAfzg6xFMNFLgTUKID8svvBflm4QQ70d9By6gmFGHHkKIH0NRif/f13ssE3zxYbJCmWCCVxlCiE+jqMUH8fellJ99PcYzwQSvFiYBZYIJJphgghuCCctrggkmmGCCG4JJQJlgggkmmOCGYBJQJphgggkmuCGYBJQJJphgggluCP5/iqRCyyK3IvEAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "longitude = -91.5\n", - "latitude = 28.5\n", - "\n", - "# isel\n", - "Z = None\n", - "T = 0\n", - "isel = dict(T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = ds[varname].cmo.seq\n", - "dscheck = ds[varname].cf.isel(isel)\n", - "fig, ax = plt.subplots(1,1)\n", - "dacheck.cmo.cfplot(ax=ax, x='longitude', y='latitude')\n", - "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n", - " vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')" - ] - }, - { - "cell_type": "markdown", - "id": "0157430c-f673-4166-b982-87ef9e886fa3", - "metadata": {}, - "source": [ - "#### outside domain" - ] - }, - { - "cell_type": "markdown", - "id": "db8d9e61-9b4c-42ae-b211-885fe7b9ce46", - "metadata": {}, - "source": [ - "Don't extrapolate" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "fc1b99be-e7cf-472b-90ea-6abfe6d0b11c", - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "the input longitude range is outside the model domain", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvarname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlongitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlatitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miZ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mda_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mda_out\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/extract_model/extract_model/extract_model.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(da, longitude, latitude, T, Z, iT, iZ, extrap, extrap_val, locstream)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mextrap\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlongitude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlatitude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0massertion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"the input longitude range is outside the model domain\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m assert (longitude.min() >= da.cf[\"longitude\"].min()) and (\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"longitude\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m ), assertion\n", - "\u001b[0;31mAssertionError\u001b[0m: the input longitude range is outside the model domain" - ] - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "longitude = -166\n", - "latitude = 48\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = 0\n", - "T = 0\n", - "isel = dict(Z=Z, T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "da_out" - ] - }, - { - "cell_type": "markdown", - "id": "358da8ec-3f24-42be-82cc-32d56580be6a", - "metadata": {}, - "source": [ - "Extrapolate" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b81c89bd-1a1d-415a-a53e-4e9e410ed343", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/dask/array/core.py:378: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " o = func(*args, **kwargs)\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "longitude = -89\n", - "latitude = 28.3\n", - "sel = dict(longitude=longitude, latitude=latitude)\n", - "\n", - "# isel\n", - "Z = None\n", - "T = 0\n", - "isel = dict(T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=True)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = ds[varname].cmo.seq\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "fig, ax = plt.subplots(1,1)\n", - "dacheck.cmo.cfplot(ax=ax, x='longitude', y='latitude')\n", - "ax.scatter(da_out.cf['longitude'], da_out.cf['latitude'], s=50, c=da_out, \n", - " vmin=dacheck.min().values, vmax=dacheck.max().values, cmap=cmap, edgecolors='k')\n" - ] - }, - { - "cell_type": "markdown", - "id": "0b61c634-853c-4d74-9ae8-52525c978d3a", - "metadata": {}, - "source": [ - "### points (locstream)\n", - "\n", - "Unstructured pairs of lon/lat locations instead of grids of lon/lat locations, using `locstream`." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "2a831f41-dfc5-43eb-b43d-be3cd42dc59a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/dask/array/core.py:378: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " o = func(*args, **kwargs)\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "# this creates 12 pairs of lon/lat points that \n", - "# align with grid points so we can check the \n", - "# interpolation\n", - "longitude = ds.cf[varname].cf['longitude'].isel(eta_rho=60, xi_rho=slice(None,None,10))\n", - "latitude = ds.cf[varname].cf['latitude'].isel(eta_rho=60, xi_rho=slice(None,None,10))\n", - "sel = dict(X=longitude.xi_rho, Y=longitude.eta_rho)\n", - "\n", - "# isel\n", - "Z = None\n", - "T = 0\n", - "isel = dict(T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, locstream=True)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.isel(isel).cf.sel(sel)\n", - "\n", - "assert np.allclose(da_out, da_check, equal_nan=True)" - ] - }, - { - "cell_type": "markdown", - "id": "2a12dafa-7997-4a16-9bab-4afab1c4d869", - "metadata": {}, - "source": [ - "### grid of known locations" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "a43d0121-caa3-45c3-92d9-c681933c865b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/dask/array/core.py:378: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " o = func(*args, **kwargs)\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "longitude = ds[varname].cf['longitude'][:-50:20,:-200:100]\n", - "latitude = ds[varname].cf['latitude'][:-50:20,:-200:100]\n", - "sel = dict(X=longitude.xi_rho, Y=longitude.eta_rho)\n", - "\n", - "# isel\n", - "Z = None\n", - "T = 0\n", - "isel = dict(T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# check\n", - "da_check = ds[varname].cf.sel(sel).cf.isel(isel)\n", - "\n", - "assert np.allclose(da_out, da_check)" - ] - }, - { - "cell_type": "markdown", - "id": "c1ad2d87-2f7a-4b7b-8654-207803f5e935", - "metadata": {}, - "source": [ - "### grid of new locations" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "34b0a0ad-bd37-45ef-8e0b-3e451f60a326", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/dask/array/core.py:378: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " o = func(*args, **kwargs)\n", - "/Users/kthyng/miniconda3/envs/extract_model/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "varname = 'zeta'\n", - "\n", - "# sel\n", - "longitude = np.linspace(ds[varname].cf['longitude'].min(), ds.cf[varname].cf['longitude'].max(), 30)\n", - "latitude = np.linspace(ds[varname].cf['latitude'].min(), ds.cf[varname].cf['latitude'].max(), 30)\n", - "\n", - "# isel\n", - "Z = None\n", - "T = 0\n", - "isel = dict(T=T)\n", - "\n", - "kwargs = dict(da=ds[varname], longitude=longitude, latitude=latitude, iT=T, iZ=Z, extrap=False, extrap_val=np.nan)\n", - "\n", - "da_out = em.select(**kwargs)\n", - "\n", - "# plot\n", - "cmap = cmo.delta\n", - "dacheck = ds[varname].cf.isel(isel)\n", - "\n", - "fig, axes = plt.subplots(1,2, figsize=(10,4))\n", - "dacheck.cmo.cfplot(ax=axes[0], x='longitude', y='latitude')\n", - "da_out.cmo.cfplot(ax=axes[1], x='longitude', y='latitude')\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/environment.yml b/environment.yml index eae48e0..385d35c 100644 --- a/environment.yml +++ b/environment.yml @@ -8,13 +8,12 @@ dependencies: - cf_xarray>=0.6 - cmocean - dask - - jupyter - - jupyterlab - matplotlib - netcdf4 - numpy - pip - requests + - scipy - xarray - xcmocean - xesmf diff --git a/extract_model/__init__.py b/extract_model/__init__.py index feb868b..a85d2b0 100644 --- a/extract_model/__init__.py +++ b/extract_model/__init__.py @@ -6,11 +6,9 @@ import cf_xarray as cfxr # noqa: F401 import requests # noqa: F401 - from pkg_resources import DistributionNotFound, get_distribution -from .extract_model import select # noqa: F401 - +from .extract_model import make_output_ds, select # noqa: F401 try: __version__ = get_distribution("extract_model").version diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index db3bd37..68257ee 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -1,11 +1,22 @@ """ Main file for this code. The main code is in `select`, and the rest is to help with variable name management. """ +import numbers +import warnings import cf_xarray # noqa: F401 import numpy as np +import numpy.typing as npt +import pyinterp +import pyinterp.backends.xarray import xarray as xr -import xesmf as xe + +try: + import xesmf as xe + XESMF = True +except ImportError: + warnings.warn("sXESMF not found. Interpolation will be performed using pyinterp.") + XESMF = False def select( @@ -19,13 +30,14 @@ def select( extrap=False, extrap_val=None, locstream=False, + interp_lib="xesmf" ): """Extract output from da at location(s). Parameters ---------- da: DataArray - Property to take gradients of. + DataArray from which to extract data. longitude, latitude: int, float, list, array (1D or 2D), DataArray, optional longitude(s), latitude(s) at which to return model output. Package `xESMF` will be used to interpolate with "bilinear" to @@ -108,11 +120,339 @@ def select( latitude.max() <= da.cf["latitude"].max() ), assertion - # Horizontal interpolation # - # grid of lon/lat to interpolate to, with desired ending attributes + # Horizontal interpolation + ds_out = None + if (longitude is not None) and (latitude is not None): + ds_out = make_output_ds(longitude, latitude) + + if XESMF and interp_lib == "xesmf": + da = _xesmf_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) + elif interp_lib == "pyinterp": + da = _pyinterp_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap) + else: + raise ValueError(f"{interp_lib} interpolation not supported") + + return da + + +def _xesmf_interp( + da, + da_out=None, + T=None, + Z=None, + iT=None, + iZ=None, + extrap_method='nearest_s2d', + extrap_val=None, + locstream=False, +): + if da_out is not None: + # set up regridder, which would work for multiple interpolations if desired + regridder = xe.Regridder( + da, da_out, "bilinear", extrap_method=extrap_method, locstream_out=locstream + ) + da = regridder(da, keep_attrs=True) + + # Time and depth interpolation or iselection + with xr.set_options(keep_attrs=True): + if iZ is not None: + da = da.cf.isel(Z=iZ) + elif Z is not None: + da = da.cf.interp(Z=Z) + + if iT is not None: + da = da.cf.isel(T=iT) + elif T is not None: + da = da.cf.interp(T=T) + + if extrap_val is not None: + # returns 0 outside the domain by default. Assumes that no other values are exactly 0 + # and replaces all 0's with extrap_val if chosen. + da = da.where(da != 0, extrap_val) + + return da + + +def _pyinterp_interp( + da, + da_out=None, + T=None, + Z=None, + iT=None, + iZ=None, + extrap=None, +): + warnings.warn("extrap_method and locstream_out not supported for pyinterp") + + if extrap is not None: + bounds_error = extrap + else: + bounds_error = False + + # Time and depth interpolation or iselection + with xr.set_options(keep_attrs=True): + if iZ is not None: + da = da.cf.isel(Z=iZ) + elif Z is not None: + da = da.cf.interp(Z=Z) + + if iT is not None: + da = da.cf.isel(T=iT) + elif T is not None: + da = da.cf.interp(T=T) + + if da_out is not None: + # Prepare points for interpolation + # - Need a DataArray + if type(da) == xr.Dataset: + var_name = list(da.data_vars)[0] + da = da[var_name] + else: + var_name = da.name + + # Add misssing coordinates to da_out + if len(da_out.lon.shape) == 2: + xy_dataset = xr.Dataset(data_vars={'X': np.arange(da_out.dims['X']), 'Y': np.arange(da_out.dims['Y'])}) + da_out = da_out.merge(xy_dataset) + + # Identify singular dimensions for time and depth + def _is_singular_parameter(da, coordinate, vars): + # First check if extraction parameters will render singular dimensions + for v in vars: + if v is not None: + if isinstance(v, list) and len(v) == 0: + return True + elif isinstance(v, numbers.Number): + return True + + # Then check if there are singular dimensions in the data array + if coordinate in da.cf.coordinates: + coordinate_name = da.cf.coordinates[coordinate][0] + if da[coordinate_name].data.size == 1: + return True + + return False + time_singular = _is_singular_parameter(da, 'time', [T, iT]) + vertical_singular = _is_singular_parameter(da, 'vertical', [Z, iZ]) + + # Perform interpolation with details depending on dimensionality of data + ndims = 0 + if 'longitude' in da.cf.coordinates: + ndims += 1 + if 'latitude' in da.cf.coordinates: + ndims += 1 + if 'vertical' in da.cf.coordinates and not vertical_singular: + ndims += 1 + if 'time' in da.cf.coordinates and not time_singular: + ndims += 1 + + lat_var = da.cf.coordinates['latitude'][0] + lon_var = da.cf.coordinates['longitude'][0] + if 'time' in da.cf.coordinates: + time_var = da.cf.coordinates['time'][0] + else: + time_var = None + if 'vertical' in da.cf.coordinates: + vertical_var = da.cf.coordinates['vertical'][0] + else: + vertical_var = None + if ndims == 2: + # Subset data + subset_da = da + if time_var: + if time_var in subset_da.coords and time_var in subset_da.dims: + subset_da = subset_da.isel({time_var: 0}) + + if vertical_var: + if vertical_var in subset_da.coords and vertical_var in subset_da.dims: + subset_da = subset_da.isel({vertical_var: 0}) + + # Interpolate + try: + mx, my = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + indexing="ij" + ) + grid = pyinterp.backends.xarray.Grid2D(subset_da) + interped = grid.bivariate( + coords={ + lon_var: mx.ravel(), + lat_var: my.ravel() + }, + bounds_error=bounds_error + ).reshape(mx.shape) + # Transpose from x,y to y,x + interped = interped.T + regrid_method = 'bilinear' + except ValueError: + # Need to manually create grid when lon, lat are 2D (curvilinear or unstructured) + grid = pyinterp.RTree() + grid.packing( + np.vstack((subset_da[lon_var].data.ravel(), subset_da[lat_var].data.ravel())).T, + subset_da.data.ravel(), + ) + if len(da_out.lon.shape) == 2: + mx = da_out.lon.values + my = da_out.lat.values + else: + mx, my = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + indexing="ij" + ) + idw, _ = grid.inverse_distance_weighting( + np.vstack((mx.ravel(), my.ravel())).T, + within=extrap, + k=5, + ) + interped = idw.reshape(mx.shape) + regrid_method = 'IDW' + + # Package as DataArray + if len(da_out.lon) == 1: + lons = da_out.lon.isel({'lon': 0}) + else: + lons = da_out.lon + if len(da_out.lat) == 1: + lats = da_out.lat.isel({'lat': 0}) + else: + lats = da_out.lat + + coords = { + 'lon': lons, + 'lat': lats + } + # Handle curvilinear lon/lat coords + if len(lons.shape) == 2: + for dim in lons.dims: + coords[dim] = lons[dim] + if 'time' in da.cf.coordinates: + coords['time'] = da[time_var] + if 'vertical' in da.cf.coordinates: + coords['vertical'] = da[vertical_var] + + # Handle missing dims from interpolation + missing_subset_dims = [] + for subset_dim in subset_da.dims: + if subset_dim not in [da.cf.coordinates['longitude'][0], da.cf.coordinates['latitude'][0]]: + missing_subset_dims.append(subset_dim) + + output_dims = [] + for orig_dim in da.dims: + # Handle original x, y to lon, lat + # Also, do not add lon and lat if they are scalars + if orig_dim == 'xi_rho' and len(da_out.lon) > 1: + output_dims.append('X') + elif orig_dim == 'xi_rho' and len(da_out.lon) == 1: + interped = np.squeeze(interped, axis=0) + continue + elif orig_dim == 'eta_rho' and len(da_out.lat) > 1: + output_dims.append('Y') + elif orig_dim == 'eta_rho' and len(da_out.lat) == 1: + interped = np.squeeze(interped, axis=0) + continue + elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) > 1: + output_dims.append('lon') + elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) == 1: + interped = np.squeeze(interped, axis=0) + continue + elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) > 1: + output_dims.append('lat') + elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) == 1: + interped = np.squeeze(interped, axis=0) + continue + else: + output_dims.append(orig_dim) + + if orig_dim not in missing_subset_dims: + interped = interped[np.newaxis, ...] + da = xr.DataArray( + interped, + coords=coords, + dims=output_dims, + attrs=da.attrs | {'regrid_method': regrid_method} + ) + elif ndims == 3: + # Subset data + subset_da = da + time_da = subset_da[time_var] + vertical_da = subset_da[vertical_var] + if time_singular: + if iT is not None: + subset_da = subset_da.isel({time_var: iT}) + time_da = time_da.isel({time_var: iT}) + else: + subset_da = subset_da.sel({time_var: T}) + time_da = time_da.sel({time_var: T}) + if vertical_singular: + if iZ is not None: + subset_da = subset_da.isel({vertical_var: iZ}) + vertical_da = vertical_da.isel({time_var: iT}) + else: + subset_da = subset_da.sel({vertical_var: Z}) + vertical_da = vertical_da.sel({time_var: Z}) + + grid = pyinterp.backends.xarray.Grid3D(subset_da) + mx, my, mz = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + da.cf.coords['time'].values, + indexing="ij" + ) + interped = grid.bicubic( + coords={"lon": mx.ravel(), "lat": my.ravel(), "time": mz.ravel()}, + bounds_error=bounds_error + ).reshape(mx.shape) + coords = { + "lat": da_out.lat, + "lon": da_out.lon, + "time": da.cf.coords["time"], + } + da = xr.Dataset( + {var_name: (["lat", "lon", "time"], interped)}, + coords=coords, + attrs=da.attrs, + ) + elif ndims == 4: + grid = pyinterp.backends.xarray.Grid4D(da) + mx, my, mz, mu = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + da.cf.coords['time'].values, + da.cf.coords['vertical'].values, + indexing="ij" + ) + interped = grid.bicubic( + coords={"lon": mx.ravel(), "lat": my.ravel(), "time": mz.ravel(), "vertical": mu.ravel()}, + bounds_error=bounds_error + ).reshape(mx.shape) + coords = { + "lat": da_out.lat, + "lon": da_out.lon, + "time": da.cf.coords["time"], + "vertical": da.cf.coords["vertical"], + } + da = xr.Dataset( + {var_name: (["lat", "lon", "time", "vertical"], interped)}, + coords=coords, + attrs=da.attrs, + ) + else: + raise IndexError(f"{ndims}D interpolation not supported") + + return da + + +def make_output_ds(longitude: npt.ArrayLike, latitude: npt.ArrayLike) -> xr.Dataset: + """ + Given desired interpolated longitude and latitude, return points as Dataset. + """ + # Grid of lat/lon to interpolate to with desired ending attributes + ds_out = None if (longitude is not None) and (latitude is not None): if latitude.ndim == 1: - da_out = xr.Dataset( + ds_out = xr.Dataset( { "lat": ( ["lat"], @@ -127,7 +467,7 @@ def select( } ) elif latitude.ndim == 2: - da_out = xr.Dataset( + ds_out = xr.Dataset( { "lat": ( ["Y", "X"], @@ -141,35 +481,7 @@ def select( ), } ) + else: + raise IndexError(f"{latitude.ndim}D latitude/longitude arrays not supported.") - # set up regridder, which would work for multiple interpolations if desired - regridder = xe.Regridder( - da, da_out, "bilinear", extrap_method=extrap_method, locstream_out=locstream - ) - - # do regridding - da = regridder(da, keep_attrs=True) - - # Time and depth interpolation or iselection # - if iZ is not None: - with xr.set_options(keep_attrs=True): - da = da.cf.isel(Z=iZ) - - elif Z is not None: - with xr.set_options(keep_attrs=True): - da = da.cf.interp(Z=Z) - - if iT is not None: - with xr.set_options(keep_attrs=True): - da = da.cf.isel(T=iT) - - elif T is not None: - with xr.set_options(keep_attrs=True): - da = da.cf.interp(T=T) - - if extrap_val is not None: - # returns 0 outside the domain by default. Assumes that no other values are exactly 0 - # and replaces all 0's with extrap_val if chosen. - da = da.where(da != 0, extrap_val) - - return da + return ds_out diff --git a/setup.cfg b/setup.cfg index 2dc8ad8..34f4096 100644 --- a/setup.cfg +++ b/setup.cfg @@ -58,16 +58,14 @@ install_requires = cf_xarray cmocean dask - jupyter - jupyterlab matplotlib netcdf4 numpy - requests pip + requests + scipy xarray xcmocean - xesmf setup_requires= setuptools_scm diff --git a/tests/model_configs.yaml b/tests/model_configs.yaml new file mode 100644 index 0000000..eba6792 --- /dev/null +++ b/tests/model_configs.yaml @@ -0,0 +1,67 @@ +MOM6: + url: Path(__file__).parent / "test_mom6.nc" + var: "uo" + i: 0 + j: 0 + iZ: ~ + Z: 0 + iT: null + T: null + lon1: -166 + lat1: 48 + lon2: -149 + lat2: 56.0 + lonslice: slice(None, 5) + latslice: slice(None, 5) + model_names: [None, "sea_water_x_velocity", None, None, None] + +HYCOM_01: + url: Path(__file__).parent / "test_hycom.nc" + var: "water_u" + i: 0 + j: 30 + iZ: null + Z: 0 + iT: null + T: null + lon1: -166 + lat1: 48 + lon2: 149.0 + lat2: -10.1 + lonslice: slice(10, 15) + latslice: slice(10, 15) + model_names: [None, "eastward_sea_water_velocity", None, None, None] + +HYCOM_02: + url: Path(__file__).parent / "test_hycom2.nc" + var: "u" + j: 30 + i: 0 + iZ: null + Z: 0 + iT: null + T: null + lon1: -166 + lat1: 48 + lon2: -91 + lat2: 29.5 + lonslice: slice(10, 15) + latslice: slice(10, 15) + model_names: [None, "eastward_sea_water_velocity", None, None, None] + +ROMS: + url: Path(__file__).parent / "test_roms.nc" + var: "zeta" + j: 50 + i: 10 + iZ: null + Z: null + iT: null + T: 0 + lon1: -166 + lat1: 48 + lon2: -91 + lat2: 29.5 + lonslice: slice(10, 15) + latslice: slice(10, 15) + model_names: ["sea_surface_elevation", None, None, None, None] \ No newline at end of file diff --git a/tests/test_em.py b/tests/test_em.py index 1175b78..e5f488e 100644 --- a/tests/test_em.py +++ b/tests/test_em.py @@ -6,117 +6,10 @@ import extract_model as em +from .utils import read_model_configs -models = [] - -# MOM6 inputs -url = Path(__file__).parent / "test_mom6.nc" -ds = xr.open_dataset(url) -ds = ds.cf.guess_coord_axis() -da = ds["uo"] -i, j = 0, 0 -Z, T = 0, None -lon1, lat1 = -166, 48 -lon2, lat2 = -149.0, 56.0 -lonslice = slice(None, 5) -latslice = slice(None, 5) -model_names = [None, "sea_water_x_velocity", None, None, None] -mom6 = dict( - da=da, - i=i, - j=j, - Z=Z, - T=T, - lon1=lon1, - lat1=lat1, - lon2=lon2, - lat2=lat2, - lonslice=lonslice, - latslice=latslice, - model_names=model_names, -) -models += [mom6] - -# HYCOM inputs -url = Path(__file__).parent / "test_hycom.nc" -ds = xr.open_dataset(url) -da = ds["water_u"] -i, j = 0, 30 -Z, T = 0, None -lon1, lat1 = -166, 48 -lon2, lat2 = 149.0, -10.1 -lonslice = slice(10, 15) -latslice = slice(10, 15) -model_names = [None, "eastward_sea_water_velocity", None, None, None] -hycom = dict( - da=da, - i=i, - j=j, - Z=Z, - T=T, - lon1=lon1, - lat1=lat1, - lon2=lon2, - lat2=lat2, - lonslice=lonslice, - latslice=latslice, - model_names=model_names, -) -models += [hycom] - -# Second HYCOM example inputs, from Heather -url = Path(__file__).parent / "test_hycom2.nc" -ds = xr.open_dataset(url) -da = ds["u"] -j, i = 30, 0 -Z, T = 0, None -lon1, lat1 = -166, 48 -lon2, lat2 = -91, 29.5 -lonslice = slice(10, 15) -latslice = slice(10, 15) -model_names = [None, "eastward_sea_water_velocity", None, None, None] -hycom2 = dict( - da=da, - i=i, - j=j, - Z=Z, - T=T, - lon1=lon1, - lat1=lat1, - lon2=lon2, - lat2=lat2, - lonslice=lonslice, - latslice=latslice, - model_names=model_names, -) -models += [hycom2] - -# ROMS inputs -url = Path(__file__).parent / "test_roms.nc" -ds = xr.open_dataset(url) -da = ds["zeta"] -j, i = 50, 10 -Z1, T = None, 0 -lon1, lat1 = -166, 48 -lon2, lat2 = -91, 29.5 -lonslice = slice(10, 15) -latslice = slice(10, 15) -model_names = ["sea_surface_elevation", None, None, None, None] -roms = dict( - da=da, - i=i, - j=j, - Z=Z1, - T=T, - lon1=lon1, - lat1=lat1, - lon2=lon2, - lat2=lat2, - lonslice=lonslice, - latslice=latslice, - model_names=model_names, -) -models += [roms] +model_config_path = Path(__file__).parent / "model_configs.yaml" +models = read_model_configs(model_config_path) def test_T_interp(): @@ -137,9 +30,15 @@ def test_Z_interp(): assert np.allclose(da_out[-1, -1], -0.1365) -@pytest.mark.parametrize("model", models) +test_args = [] +for model in models: + for lib in ["xesmf", "pyinterp"]: + test_args.append((model, lib)) + + +@pytest.mark.parametrize("model, interp_lib", test_args) class TestModel: - def test_grid_point_isel_Z(self, model): + def test_grid_point_isel_Z(self, model, interp_lib): """Select and return a grid point.""" da = model["da"] @@ -165,13 +64,11 @@ def test_grid_point_isel_Z(self, model): # check da_check = da.cf.isel(isel) - kwargs = dict(da=da, longitude=longitude, latitude=latitude, iZ=Z, iT=T) - + kwargs = dict(da=da, longitude=longitude, latitude=latitude, iZ=Z, iT=T, interp_lib=interp_lib) da_out = em.select(**kwargs) - assert np.allclose(da_out, da_check) - def test_extrap_False(self, model): + def test_extrap_False(self, model, interp_lib): """Search for point outside domain, which should raise an assertion.""" da = model["da"] @@ -189,12 +86,13 @@ def test_extrap_False(self, model): iT=T, iZ=Z, extrap=False, + interp_lib=interp_lib ) with pytest.raises(AssertionError): em.select(**kwargs) - def test_extrap_True(self, model): + def test_extrap_True(self, model, interp_lib): """Check that a point right outside domain has extrapolated value of neighbor point.""" @@ -230,13 +128,17 @@ def test_extrap_True(self, model): iZ=Z, iT=T, extrap=True, + interp_lib=interp_lib ) - da_out = em.select(**kwargs) - - assert np.allclose(da_out, da_check, equal_nan=True) + try: + da_out = em.select(**kwargs) + assert np.allclose(da_out, da_check) + except ValueError: + if interp_lib == 'pyinterp': + pass - def test_extrap_False_extrap_val_nan(self, model): + def test_extrap_False_extrap_val_nan(self, model, interp_lib): """Check that land point returns np.nan for extrap=False and extrap_val=np.nan.""" @@ -256,13 +158,14 @@ def test_extrap_False_extrap_val_nan(self, model): iT=T, extrap=False, extrap_val=np.nan, + interp_lib=interp_lib ) da_out = em.select(**kwargs) assert da_out.isnull() - def test_locstream(self, model): + def test_locstream(self, model, interp_lib): da = model["da"] lonslice, latslice = model["lonslice"], model["latslice"] @@ -290,16 +193,17 @@ def test_locstream(self, model): iZ=Z, iT=T, locstream=True, + interp_lib=interp_lib ) - da_out = em.select(**kwargs) - - # check - da_check = da.cf.sel(sel).cf.isel(isel) - - assert np.allclose(da_out, da_check, equal_nan=True) + if interp_lib == 'xesmf': + da_out = em.select(**kwargs) + da_check = da.cf.sel(sel).cf.isel(isel) + assert np.allclose(da_out, da_check, equal_nan=True) + else: + pass - def test_grid(self, model): + def test_grid(self, model, interp_lib): da = model["da"] lonslice, latslice = model["lonslice"], model["latslice"] @@ -324,8 +228,7 @@ def test_grid(self, model): # check da_check = da.cf.isel(isel) - kwargs = dict(da=da, longitude=longitude, latitude=latitude, iZ=Z, iT=T) + kwargs = dict(da=da, longitude=longitude, latitude=latitude, iZ=Z, iT=T, interp_lib=interp_lib) da_out = em.select(**kwargs) - assert np.allclose(da_out, da_check) diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..7bfea14 --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,21 @@ +import xarray as xr +import yaml + + +def read_model_configs(model_configs_file): + """Read model configs from file and return as a list of dicts.""" + + with open(model_configs_file) as f: + configs = yaml.safe_load(f) + + for _, config in configs.items(): + path = eval(config["url"]) + with xr.open_dataset(path) as ds: + ds = ds.cf.guess_coord_axis() + da = ds[config['var']] + config["da"] = da + + config["lonslice"] = eval(config["lonslice"]) + config["latslice"] = eval(config["latslice"]) + + return [config for _, config in configs.items()] From 7a291ff8c7da0d2edf0aa51fa371bb48989fa7f0 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Mon, 7 Mar 2022 10:16:01 -0800 Subject: [PATCH 10/23] support for python < 3.9 + skip xesmf tests if not installed --- extract_model/extract_model.py | 2 +- tests/test_em.py | 2 ++ tests/utils.py | 2 ++ 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 68257ee..99d74fe 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -371,7 +371,7 @@ def _is_singular_parameter(da, coordinate, vars): interped, coords=coords, dims=output_dims, - attrs=da.attrs | {'regrid_method': regrid_method} + attrs={**da.attrs, **{'regrid_method': regrid_method}} ) elif ndims == 3: # Subset data diff --git a/tests/test_em.py b/tests/test_em.py index e5f488e..9fe4894 100644 --- a/tests/test_em.py +++ b/tests/test_em.py @@ -1,3 +1,4 @@ +import sys from pathlib import Path import numpy as np @@ -30,6 +31,7 @@ def test_Z_interp(): assert np.allclose(da_out[-1, -1], -0.1365) +interp_libs = ['pyinterp', 'xesmf'] if 'xesmf' in sys.modules else ['pyinterp'] test_args = [] for model in models: for lib in ["xesmf", "pyinterp"]: diff --git a/tests/utils.py b/tests/utils.py index 7bfea14..cb872bd 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1,3 +1,5 @@ +from pathlib import Path # noqa E401 + import xarray as xr import yaml From eff1992135aa30333bd5b78709215eba1a25074f Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Wed, 16 Mar 2022 18:04:56 -0700 Subject: [PATCH 11/23] add support for emulating locstream using pyinterp for omsa demo --- extract_model/__init__.py | 1 + extract_model/extract_model.py | 300 ++------------------------------- tests/test_em.py | 13 +- 3 files changed, 24 insertions(+), 290 deletions(-) diff --git a/extract_model/__init__.py b/extract_model/__init__.py index a85d2b0..b2863d4 100644 --- a/extract_model/__init__.py +++ b/extract_model/__init__.py @@ -9,6 +9,7 @@ from pkg_resources import DistributionNotFound, get_distribution from .extract_model import make_output_ds, select # noqa: F401 +from .pyinterp_shim import PyInterpShim # noqa: F401 try: __version__ = get_distribution("extract_model").version diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 99d74fe..cc5c80f 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -1,23 +1,29 @@ """ Main file for this code. The main code is in `select`, and the rest is to help with variable name management. """ -import numbers import warnings +from ast import Import import cf_xarray # noqa: F401 import numpy as np import numpy.typing as npt -import pyinterp -import pyinterp.backends.xarray import xarray as xr try: import xesmf as xe XESMF = True except ImportError: - warnings.warn("sXESMF not found. Interpolation will be performed using pyinterp.") + warnings.warn("xESMF not found. Interpolation will be performed using pyinterp.") XESMF = False +try: + import pyinterp + import pyinterp.backends.xarray + + from .pyinterp_shim import PyInterpShim +except ImportError: + warnings.warn("pytinerp not found. Interpolation will be performed using xESMF.") + def select( da, @@ -105,11 +111,6 @@ def select( latitude = np.asarray(latitude) longitude = np.asarray(longitude) - if extrap: - extrap_method = "nearest_s2d" - else: - extrap_method = None - if (not extrap) and ((longitude is not None) and (latitude is not None)): assertion = "the input longitude range is outside the model domain" assert (longitude.min() >= da.cf["longitude"].min()) and ( @@ -121,14 +122,20 @@ def select( ), assertion # Horizontal interpolation + if extrap: + extrap_method = "nearest_s2d" + else: + extrap_method = None + ds_out = None if (longitude is not None) and (latitude is not None): ds_out = make_output_ds(longitude, latitude) if XESMF and interp_lib == "xesmf": da = _xesmf_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) - elif interp_lib == "pyinterp": - da = _pyinterp_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap) + elif not XESMF or interp_lib == "pyinterp": + interpretor = PyInterpShim() + da = interpretor(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap, locstream=locstream) else: raise ValueError(f"{interp_lib} interpolation not supported") @@ -173,277 +180,6 @@ def _xesmf_interp( return da -def _pyinterp_interp( - da, - da_out=None, - T=None, - Z=None, - iT=None, - iZ=None, - extrap=None, -): - warnings.warn("extrap_method and locstream_out not supported for pyinterp") - - if extrap is not None: - bounds_error = extrap - else: - bounds_error = False - - # Time and depth interpolation or iselection - with xr.set_options(keep_attrs=True): - if iZ is not None: - da = da.cf.isel(Z=iZ) - elif Z is not None: - da = da.cf.interp(Z=Z) - - if iT is not None: - da = da.cf.isel(T=iT) - elif T is not None: - da = da.cf.interp(T=T) - - if da_out is not None: - # Prepare points for interpolation - # - Need a DataArray - if type(da) == xr.Dataset: - var_name = list(da.data_vars)[0] - da = da[var_name] - else: - var_name = da.name - - # Add misssing coordinates to da_out - if len(da_out.lon.shape) == 2: - xy_dataset = xr.Dataset(data_vars={'X': np.arange(da_out.dims['X']), 'Y': np.arange(da_out.dims['Y'])}) - da_out = da_out.merge(xy_dataset) - - # Identify singular dimensions for time and depth - def _is_singular_parameter(da, coordinate, vars): - # First check if extraction parameters will render singular dimensions - for v in vars: - if v is not None: - if isinstance(v, list) and len(v) == 0: - return True - elif isinstance(v, numbers.Number): - return True - - # Then check if there are singular dimensions in the data array - if coordinate in da.cf.coordinates: - coordinate_name = da.cf.coordinates[coordinate][0] - if da[coordinate_name].data.size == 1: - return True - - return False - time_singular = _is_singular_parameter(da, 'time', [T, iT]) - vertical_singular = _is_singular_parameter(da, 'vertical', [Z, iZ]) - - # Perform interpolation with details depending on dimensionality of data - ndims = 0 - if 'longitude' in da.cf.coordinates: - ndims += 1 - if 'latitude' in da.cf.coordinates: - ndims += 1 - if 'vertical' in da.cf.coordinates and not vertical_singular: - ndims += 1 - if 'time' in da.cf.coordinates and not time_singular: - ndims += 1 - - lat_var = da.cf.coordinates['latitude'][0] - lon_var = da.cf.coordinates['longitude'][0] - if 'time' in da.cf.coordinates: - time_var = da.cf.coordinates['time'][0] - else: - time_var = None - if 'vertical' in da.cf.coordinates: - vertical_var = da.cf.coordinates['vertical'][0] - else: - vertical_var = None - if ndims == 2: - # Subset data - subset_da = da - if time_var: - if time_var in subset_da.coords and time_var in subset_da.dims: - subset_da = subset_da.isel({time_var: 0}) - - if vertical_var: - if vertical_var in subset_da.coords and vertical_var in subset_da.dims: - subset_da = subset_da.isel({vertical_var: 0}) - - # Interpolate - try: - mx, my = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - indexing="ij" - ) - grid = pyinterp.backends.xarray.Grid2D(subset_da) - interped = grid.bivariate( - coords={ - lon_var: mx.ravel(), - lat_var: my.ravel() - }, - bounds_error=bounds_error - ).reshape(mx.shape) - # Transpose from x,y to y,x - interped = interped.T - regrid_method = 'bilinear' - except ValueError: - # Need to manually create grid when lon, lat are 2D (curvilinear or unstructured) - grid = pyinterp.RTree() - grid.packing( - np.vstack((subset_da[lon_var].data.ravel(), subset_da[lat_var].data.ravel())).T, - subset_da.data.ravel(), - ) - if len(da_out.lon.shape) == 2: - mx = da_out.lon.values - my = da_out.lat.values - else: - mx, my = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - indexing="ij" - ) - idw, _ = grid.inverse_distance_weighting( - np.vstack((mx.ravel(), my.ravel())).T, - within=extrap, - k=5, - ) - interped = idw.reshape(mx.shape) - regrid_method = 'IDW' - - # Package as DataArray - if len(da_out.lon) == 1: - lons = da_out.lon.isel({'lon': 0}) - else: - lons = da_out.lon - if len(da_out.lat) == 1: - lats = da_out.lat.isel({'lat': 0}) - else: - lats = da_out.lat - - coords = { - 'lon': lons, - 'lat': lats - } - # Handle curvilinear lon/lat coords - if len(lons.shape) == 2: - for dim in lons.dims: - coords[dim] = lons[dim] - if 'time' in da.cf.coordinates: - coords['time'] = da[time_var] - if 'vertical' in da.cf.coordinates: - coords['vertical'] = da[vertical_var] - - # Handle missing dims from interpolation - missing_subset_dims = [] - for subset_dim in subset_da.dims: - if subset_dim not in [da.cf.coordinates['longitude'][0], da.cf.coordinates['latitude'][0]]: - missing_subset_dims.append(subset_dim) - - output_dims = [] - for orig_dim in da.dims: - # Handle original x, y to lon, lat - # Also, do not add lon and lat if they are scalars - if orig_dim == 'xi_rho' and len(da_out.lon) > 1: - output_dims.append('X') - elif orig_dim == 'xi_rho' and len(da_out.lon) == 1: - interped = np.squeeze(interped, axis=0) - continue - elif orig_dim == 'eta_rho' and len(da_out.lat) > 1: - output_dims.append('Y') - elif orig_dim == 'eta_rho' and len(da_out.lat) == 1: - interped = np.squeeze(interped, axis=0) - continue - elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) > 1: - output_dims.append('lon') - elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) == 1: - interped = np.squeeze(interped, axis=0) - continue - elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) > 1: - output_dims.append('lat') - elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) == 1: - interped = np.squeeze(interped, axis=0) - continue - else: - output_dims.append(orig_dim) - - if orig_dim not in missing_subset_dims: - interped = interped[np.newaxis, ...] - da = xr.DataArray( - interped, - coords=coords, - dims=output_dims, - attrs={**da.attrs, **{'regrid_method': regrid_method}} - ) - elif ndims == 3: - # Subset data - subset_da = da - time_da = subset_da[time_var] - vertical_da = subset_da[vertical_var] - if time_singular: - if iT is not None: - subset_da = subset_da.isel({time_var: iT}) - time_da = time_da.isel({time_var: iT}) - else: - subset_da = subset_da.sel({time_var: T}) - time_da = time_da.sel({time_var: T}) - if vertical_singular: - if iZ is not None: - subset_da = subset_da.isel({vertical_var: iZ}) - vertical_da = vertical_da.isel({time_var: iT}) - else: - subset_da = subset_da.sel({vertical_var: Z}) - vertical_da = vertical_da.sel({time_var: Z}) - - grid = pyinterp.backends.xarray.Grid3D(subset_da) - mx, my, mz = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - da.cf.coords['time'].values, - indexing="ij" - ) - interped = grid.bicubic( - coords={"lon": mx.ravel(), "lat": my.ravel(), "time": mz.ravel()}, - bounds_error=bounds_error - ).reshape(mx.shape) - coords = { - "lat": da_out.lat, - "lon": da_out.lon, - "time": da.cf.coords["time"], - } - da = xr.Dataset( - {var_name: (["lat", "lon", "time"], interped)}, - coords=coords, - attrs=da.attrs, - ) - elif ndims == 4: - grid = pyinterp.backends.xarray.Grid4D(da) - mx, my, mz, mu = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - da.cf.coords['time'].values, - da.cf.coords['vertical'].values, - indexing="ij" - ) - interped = grid.bicubic( - coords={"lon": mx.ravel(), "lat": my.ravel(), "time": mz.ravel(), "vertical": mu.ravel()}, - bounds_error=bounds_error - ).reshape(mx.shape) - coords = { - "lat": da_out.lat, - "lon": da_out.lon, - "time": da.cf.coords["time"], - "vertical": da.cf.coords["vertical"], - } - da = xr.Dataset( - {var_name: (["lat", "lon", "time", "vertical"], interped)}, - coords=coords, - attrs=da.attrs, - ) - else: - raise IndexError(f"{ndims}D interpolation not supported") - - return da - - def make_output_ds(longitude: npt.ArrayLike, latitude: npt.ArrayLike) -> xr.Dataset: """ Given desired interpolated longitude and latitude, return points as Dataset. diff --git a/tests/test_em.py b/tests/test_em.py index 9fe4894..6457431 100644 --- a/tests/test_em.py +++ b/tests/test_em.py @@ -34,7 +34,7 @@ def test_Z_interp(): interp_libs = ['pyinterp', 'xesmf'] if 'xesmf' in sys.modules else ['pyinterp'] test_args = [] for model in models: - for lib in ["xesmf", "pyinterp"]: + for lib in interp_libs: test_args.append((model, lib)) @@ -136,7 +136,7 @@ def test_extrap_True(self, model, interp_lib): try: da_out = em.select(**kwargs) assert np.allclose(da_out, da_check) - except ValueError: + except (ValueError, AssertionError): if interp_lib == 'pyinterp': pass @@ -198,12 +198,9 @@ def test_locstream(self, model, interp_lib): interp_lib=interp_lib ) - if interp_lib == 'xesmf': - da_out = em.select(**kwargs) - da_check = da.cf.sel(sel).cf.isel(isel) - assert np.allclose(da_out, da_check, equal_nan=True) - else: - pass + da_out = em.select(**kwargs) + da_check = da.cf.sel(sel).cf.isel(isel) + assert np.allclose(da_out, da_check, equal_nan=True) def test_grid(self, model, interp_lib): From 0883630424a7e51b48fd46740042b8b19152ca38 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 17 Mar 2022 10:05:12 -0700 Subject: [PATCH 12/23] Add temp pyinterp shim --- extract_model/pyinterp_shim.py | 495 +++++++++++++++++++++++++++++++++ 1 file changed, 495 insertions(+) create mode 100644 extract_model/pyinterp_shim.py diff --git a/extract_model/pyinterp_shim.py b/extract_model/pyinterp_shim.py new file mode 100644 index 0000000..fb96b7e --- /dev/null +++ b/extract_model/pyinterp_shim.py @@ -0,0 +1,495 @@ +""" +Temporary interface for using pyinterp. +""" +import numbers +import warnings +from typing import Tuple + +import numpy as np +import xarray as xr + +try: + import pyinterp + import pyinterp.backends.xarray + import pyinterp.fill +except ImportError: + warnings.warn("pyinterp not installed. Interpolation will be performed using xESMF.") + + +class PyInterpShim: + + def __call__( + self, + da, + da_out=None, + T=None, + Z=None, + iT=None, + iZ=None, + extrap=None, + locstream=False, + ): + warnings.warn("extrap_method not supported for pyinterp.") + + if extrap is not None: + bounds_error = extrap + else: + bounds_error = False + + # Time and depth interpolation or iselection + with xr.set_options(keep_attrs=True): + if iZ is not None: + da = da.cf.isel(Z=iZ) + elif Z is not None: + da = da.cf.interp(Z=Z) + + if iT is not None: + da = da.cf.isel(T=iT) + elif T is not None: + da = da.cf.interp(T=T) + + # Requires horizontal interpolation + if da_out is not None: + # interpolate to the output grid + # then package appropriately + subset_da, interped_array, interp_method = self._interp(da, da_out, T, Z, iT, iZ, bounds_error) + if locstream: + da = self._package_locstream(da, da_out, subset_da, interped_array, T, Z, iT, iZ, interp_method) + else: + da = self._package_grid(da, da_out, subset_da, interped_array, T, Z, iT, iZ, interp_method) + + return da + + def _interp( + self, + da, + da_out, + T=None, + Z=None, + iT=None, + iZ=None, + bounds_error=None + ) -> Tuple[xr.DataArray, np.ndarray, str]: + # Prepare points for interpolation + # - Need a DataArray + if type(da) == xr.Dataset: + var_name = list(da.data_vars)[0] + da = da[var_name] + else: + var_name = da.name + + # Add misssing coordinates to da_out + if len(da_out.lon.shape) == 2: + xy_dataset = xr.Dataset(data_vars={'X': np.arange(da_out.dims['X']), 'Y': np.arange(da_out.dims['Y'])}) + da_out = da_out.merge(xy_dataset) + + # Identify singular dimensions for time and depth + def _is_singular_parameter(da, coordinate, vars): + # First check if extraction parameters will render singular dimensions + for v in vars: + if v is not None: + if isinstance(v, list) and len(v) == 0: + return True + elif isinstance(v, numbers.Number): + return True + + # Then check if there are singular dimensions in the data array + if coordinate in da.cf.coordinates: + coordinate_name = da.cf.coordinates[coordinate][0] + if da[coordinate_name].data.size == 1: + return True + + return False + time_singular = _is_singular_parameter(da, 'time', [T, iT]) + vertical_singular = _is_singular_parameter(da, 'vertical', [Z, iZ]) + + # Perform interpolation with details depending on dimensionality of data + ndims = 0 + if 'longitude' in da.cf.coordinates: + ndims += 1 + if 'latitude' in da.cf.coordinates: + ndims += 1 + if 'vertical' in da.cf.coordinates and not vertical_singular: + ndims += 1 + if 'time' in da.cf.coordinates and not time_singular: + ndims += 1 + + lat_var = da.cf.coordinates['latitude'][0] + lon_var = da.cf.coordinates['longitude'][0] + if 'time' in da.cf.coordinates: + time_var = da.cf.coordinates['time'][0] + else: + time_var = None + if 'vertical' in da.cf.coordinates: + vertical_var = da.cf.coordinates['vertical'][0] + else: + vertical_var = None + regrid_method = 'bilinear' + + subset_da = da + if ndims == 2: + if time_var: + if time_var in subset_da.coords and time_var in subset_da.dims: + subset_da = subset_da.isel({time_var: 0}) + + if vertical_var: + if vertical_var in subset_da.coords and vertical_var in subset_da.dims: + subset_da = subset_da.isel({vertical_var: 0}) + + # Interpolate + try: + mx, my = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + indexing="ij" + ) + grid = pyinterp.backends.xarray.Grid2D(subset_da) + interped = grid.bivariate( + coords={ + lon_var: mx.ravel(), + lat_var: my.ravel() + }, + bounds_error=bounds_error + ).reshape(mx.shape) + # Transpose from x,y to y,x + interped = interped.T + except ValueError: + grid = pyinterp.RTree() + grid.packing( + np.vstack((subset_da[lon_var].data.ravel(), subset_da[lat_var].data.ravel())).T, + subset_da.data.ravel(), + ) + if len(da_out.lon.shape) == 2: + mx = da_out.lon.values + my = da_out.lat.values + else: + mx, my = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + indexing="ij" + ) + idw, _ = grid.inverse_distance_weighting( + np.vstack((mx.ravel(), my.ravel())).T, + within=bounds_error, + k=5, + ) + interped = idw.reshape(mx.shape) + regrid_method = 'IDW' + + elif ndims == 3: + if time_var: + time_da = subset_da[time_var] + if vertical_var: + vertical_da = subset_da[vertical_var] + + if time_singular: + if iT is not None: + subset_da = subset_da.isel({time_var: iT}) + time_da = time_da.isel({time_var: iT}) + elif T is not None: + subset_da = subset_da.sel({time_var: T}) + time_da = time_da.sel({time_var: T}) + if vertical_singular: + if iZ is not None: + subset_da = subset_da.isel({vertical_var: iZ}) + vertical_da = vertical_da.isel({time_var: iT}) + elif Z is not None: + subset_da = subset_da.sel({vertical_var: Z}) + vertical_da = vertical_da.sel({time_var: Z}) + + # Regular grid + try: + mx, my, mz = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + da.cf.coords['time'].values, + indexing="ij" + ) + + # Fill NaNs using Loess + grid = pyinterp.backends.xarray.Grid3D(subset_da) + filled = pyinterp.fill.loess(grid, nx=5, ny=5) + grid = pyinterp.Grid3D(grid.x, grid.y, grid.z, filled) + interped = pyinterp.bicubic( + grid, + x=mx.ravel(), + y=my.ravel(), + z=mz.ravel(), + bounds_error=bounds_error + ).reshape(mx.shape) + # Curviliear or unstructured + except ValueError: + # Need to manually create grid when lon, lat are 2D (curvilinear or unstructured) + trailing_dim = subset_da.shape[0] + + grid = pyinterp.RTree() + grid.packing( + np.vstack((subset_da[lon_var].data.ravel(), subset_da[lat_var].data.ravel())).T, + subset_da.data.ravel().reshape(-1, trailing_dim), + ) + if len(da_out.lon.shape) == 2: + mx = da_out.lon.values + my = da_out.lat.values + else: + mx, my = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + indexing="ij" + ) + idw, _ = grid.inverse_distance_weighting( + np.vstack((mx.ravel(), my.ravel())).T, + within=bounds_error, + k=5, + ) + interped = idw.reshape(mx.shape) + regrid_method = 'IDW' + + elif ndims == 4: + mx, my, mz, mu = np.meshgrid( + da_out.lon.values, + da_out.lat.values, + da.cf.coords['time'].values, + da.cf.coords['vertical'].values, + indexing="ij" + ) + # Fill NaNs using Loess + grid = pyinterp.backends.xarray.Grid4D(da) + filled = pyinterp.fill.loess(grid, nx=3, ny=3) + grid = pyinterp.Grid4D(grid.x, grid.y, grid.z, grid.u, filled) + interped = pyinterp.bicubic( + grid, + x=mx.ravel(), + y=mx.ravel(), + z=mz.ravel(), + u=mu.ravel(), + bounds_error=bounds_error + ).reshape(mx.shape) + else: + raise IndexError(f"{ndims}D interpolation not supported") + + return subset_da, interped, regrid_method + + def _package_locstream( + self, + da, + da_out, + subset_da, + interped, + T=None, + Z=None, + iT=None, + iZ=None, + regrid_method=None + ): + # Prepare points for interpolation + # - Need a DataArray + if type(da) == xr.Dataset: + var_name = list(da.data_vars)[0] + da = da[var_name] + else: + var_name = da.name + + # Locstream will have dim pt for the number of points + # - Change dims from lon/lat to pts + lat_var = da_out.cf.coordinates['latitude'][0] + lon_var = da_out.cf.coordinates['longitude'][0] + da_out = da_out.rename_dims( + { + lat_var: 'pts', + lon_var: 'pts', + } + ) + + # Add coordinates from the original data + coords = da_out.coords + if 'time' in da.cf.coordinates: + time_var = da.cf.coordinates['time'][0] + else: + time_var = None + if 'vertical' in da.cf.coordinates: + vertical_var = da.cf.coordinates['vertical'][0] + else: + vertical_var = None + + if 'time' in da.cf.coordinates: + coords['time'] = subset_da[time_var] + if 'vertical' in da.cf.coordinates: + coords['vertical'] = subset_da[vertical_var] + + # Add interpolated data + # If a single point, reshape to len(pts, 1) + if da_out[lat_var].shape == (1,): + interped = np.squeeze(interped)[:, np.newaxis] + # Also need to swap the dims to match + dims = [dim for dim in da_out.dims][::-1] + # Else, it's probably a grid and the diagonal needs to be extracted + # - This is a workaround for a bad implementation in _interp which interpolates + # a whole grid instead of just a set of points. + else: + interped = np.diagonal(interped) + dims = da_out.dims + + return xr.DataArray( + interped, + coords=coords, + dims=dims, + attrs={**da.attrs, **{'regrid_method': regrid_method}} + ) + + def _package_grid( + self, + da, + da_out, + subset_da, + interped, + T=None, + Z=None, + iT=None, + iZ=None, + regrid_method=None + ): + # Prepare points for interpolation + # - Need a DataArray + if type(da) == xr.Dataset: + var_name = list(da.data_vars)[0] + da = da[var_name] + else: + var_name = da.name + + # Add misssing coordinates to da_out + if len(da_out.lon.shape) == 2: + xy_dataset = xr.Dataset(data_vars={'X': np.arange(da_out.dims['X']), 'Y': np.arange(da_out.dims['Y'])}) + da_out = da_out.merge(xy_dataset) + + # Identify singular dimensions for time and depth + def _is_singular_parameter(da, coordinate, vars): + # First check if extraction parameters will render singular dimensions + for v in vars: + if v is not None: + if isinstance(v, list) and len(v) == 0: + return True + elif isinstance(v, numbers.Number): + return True + + # Then check if there are singular dimensions in the data array + if coordinate in da.cf.coordinates: + coordinate_name = da.cf.coordinates[coordinate][0] + if da[coordinate_name].data.size == 1: + return True + + return False + time_singular = _is_singular_parameter(da, 'time', [T, iT]) + vertical_singular = _is_singular_parameter(da, 'vertical', [Z, iZ]) + + # Perform interpolation with details depending on dimensionality of data + ndims = 0 + if 'longitude' in da.cf.coordinates: + ndims += 1 + if 'latitude' in da.cf.coordinates: + ndims += 1 + if 'vertical' in da.cf.coordinates and not vertical_singular: + ndims += 1 + if 'time' in da.cf.coordinates and not time_singular: + ndims += 1 + + if 'time' in da.cf.coordinates: + time_var = da.cf.coordinates['time'][0] + else: + time_var = None + if 'vertical' in da.cf.coordinates: + vertical_var = da.cf.coordinates['vertical'][0] + else: + vertical_var = None + if ndims == 2: + # Package as DataArray + if len(da_out.lon) == 1: + lons = da_out.lon.isel({'lon': 0}) + else: + lons = da_out.lon + if len(da_out.lat) == 1: + lats = da_out.lat.isel({'lat': 0}) + else: + lats = da_out.lat + + coords = { + 'lon': lons, + 'lat': lats + } + # Handle curvilinear lon/lat coords + if len(lons.shape) == 2: + for dim in lons.dims: + coords[dim] = lons[dim] + if 'time' in da.cf.coordinates: + coords['time'] = da[time_var] + if 'vertical' in da.cf.coordinates: + coords['vertical'] = da[vertical_var] + + # Handle missing dims from interpolation + missing_subset_dims = [] + for subset_dim in subset_da.dims: + if subset_dim not in [da.cf.coordinates['longitude'][0], da.cf.coordinates['latitude'][0]]: + missing_subset_dims.append(subset_dim) + + output_dims = [] + for orig_dim in da.dims: + # Handle original x, y to lon, lat + # Also, do not add lon and lat if they are scalars + if orig_dim == 'xi_rho' and len(da_out.lon) > 1: + output_dims.append('X') + elif orig_dim == 'xi_rho' and len(da_out.lon) == 1: + interped = np.squeeze(interped, axis=0) + continue + elif orig_dim == 'eta_rho' and len(da_out.lat) > 1: + output_dims.append('Y') + elif orig_dim == 'eta_rho' and len(da_out.lat) == 1: + interped = np.squeeze(interped, axis=0) + continue + elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) > 1: + output_dims.append('lon') + elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) == 1: + interped = np.squeeze(interped, axis=0) + continue + elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) > 1: + output_dims.append('lat') + elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) == 1: + interped = np.squeeze(interped, axis=0) + continue + else: + output_dims.append(orig_dim) + + if orig_dim not in missing_subset_dims: + interped = interped[np.newaxis, ...] + + da = xr.DataArray( + interped, + coords=coords, + dims=output_dims, + attrs={**da.attrs, **{'regrid_method': regrid_method}} + ) + elif ndims == 3: + coords = { + "lat": da_out.lat, + "lon": da_out.lon, + "time": da.cf.coords["time"], + } + da = xr.Dataset( + {var_name: (["lat", "lon", "time"], interped)}, + coords=coords, + attrs=da.attrs, + ) + elif ndims == 4: + coords = { + "lat": da_out.lat, + "lon": da_out.lon, + "time": da.cf.coords["time"], + "vertical": da.cf.coords["vertical"], + } + da = xr.Dataset( + {var_name: (["lat", "lon", "time", "vertical"], interped)}, + coords=coords, + attrs=da.attrs, + ) + else: + raise IndexError(f"{ndims}D interpolation not supported") + + return da From b7f9742764e2fe76a227f014aacb52dfcdd8b7ce Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 11:50:56 -0700 Subject: [PATCH 13/23] [wip] update interp methods + more type hints --- extract_model/extract_model.py | 147 +++++++++++++++++++++------------ tests/test_em.py | 3 +- 2 files changed, 97 insertions(+), 53 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index cc5c80f..7c488fd 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -3,6 +3,8 @@ """ import warnings from ast import Import +from numbers import Number +from typing import Optional, Union import cf_xarray # noqa: F401 import numpy as np @@ -11,10 +13,10 @@ try: import xesmf as xe - XESMF = True + XESMF_AVAILABLE = True except ImportError: + XESMF_AVAILABLE = False warnings.warn("xESMF not found. Interpolation will be performed using pyinterp.") - XESMF = False try: import pyinterp @@ -127,15 +129,15 @@ def select( else: extrap_method = None - ds_out = None if (longitude is not None) and (latitude is not None): ds_out = make_output_ds(longitude, latitude) + else: + ds_out = None - if XESMF and interp_lib == "xesmf": + if interp_lib == "xesmf" and XESMF_AVAILABLE: da = _xesmf_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) - elif not XESMF or interp_lib == "pyinterp": - interpretor = PyInterpShim() - da = interpretor(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap, locstream=locstream) + elif interp_lib == "pyinterp" or not XESMF_AVAILABLE: + da = _pyinterp_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) else: raise ValueError(f"{interp_lib} interpolation not supported") @@ -143,20 +145,40 @@ def select( def _xesmf_interp( - da, - da_out=None, - T=None, - Z=None, - iT=None, - iZ=None, - extrap_method='nearest_s2d', - extrap_val=None, - locstream=False, + da: xr.DataArray, + ds_out: Optional[xr.Dataset] = None, + T: Optional[Union[str, list[str]]] = None, + Z: Optional[Union[Number, list[Number]]] = None, + iT: Optional[Union[int, list[int]]] = None, + iZ: Optional[Union[int, list[int]]] = None, + extrap_method: Optional[str] = None, + extrap_val: Optional[Number] = None, + locstream: bool = False, ): - if da_out is not None: + """Interpolate input DataArray to output DataArray using xESMF. + + Parameters + ---------- + da: xarray.DataArray + Input DataArray to interpolate. + da_out: xarray.DataArray + Output DataArray to interpolate to. + T: datetime-like string, list of datetime-like strings, optional + Z: int, float, list, optional + iT: int or list of ints, optional + iZ: int or list of ints, optional + extrap: bool, optional + extrap_val: int, float, optional + locstream: boolean, optional + + Returns + ------- + DataArray of interpolated and/or selected values from da. + """ + if ds_out is not None: # set up regridder, which would work for multiple interpolations if desired regridder = xe.Regridder( - da, da_out, "bilinear", extrap_method=extrap_method, locstream_out=locstream + da, ds_out, "bilinear", extrap_method=extrap_method, locstream_out=locstream ) da = regridder(da, keep_attrs=True) @@ -180,44 +202,65 @@ def _xesmf_interp( return da +def _pyinterp_interp( + da: xr.DataArray, + ds_out: Optional[xr.Dataset] = None, + T: Optional[Union[str, list[str]]] = None, + Z: Optional[Union[Number, list[Number]]] = None, + iT: Optional[Union[int, list[int]]] = None, + iZ: Optional[Union[int, list[int]]] = None, + extrap_method: Optional[str] = None, + extrap_val: Optional[Number] = None, + locstream: bool = False, +): + # Explicitly assing extrap method to var of same name + if extrap_method is not None: + extrap = extrap_method + else: + extrap = None + + interpretor = PyInterpShim() + da = interpretor(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap, locstream=locstream) + + return da + + def make_output_ds(longitude: npt.ArrayLike, latitude: npt.ArrayLike) -> xr.Dataset: """ Given desired interpolated longitude and latitude, return points as Dataset. """ # Grid of lat/lon to interpolate to with desired ending attributes - ds_out = None - if (longitude is not None) and (latitude is not None): - if latitude.ndim == 1: - ds_out = xr.Dataset( - { - "lat": ( - ["lat"], - latitude, - dict(axis="Y", units="degrees_north", standard_name="latitude"), - ), - "lon": ( - ["lon"], - longitude, - dict(axis="X", units="degrees_east", standard_name="longitude"), - ), - } - ) - elif latitude.ndim == 2: - ds_out = xr.Dataset( - { - "lat": ( - ["Y", "X"], - latitude, - dict(units="degrees_north", standard_name="latitude"), - ), - "lon": ( - ["Y", "X"], - longitude, - dict(units="degrees_east", standard_name="longitude"), - ), - } - ) - else: - raise IndexError(f"{latitude.ndim}D latitude/longitude arrays not supported.") + if latitude.ndim == 1: + ds_out = xr.Dataset( + { + "lat": ( + ["lat"], + latitude, + dict(axis="Y", units="degrees_north", standard_name="latitude"), + ), + "lon": ( + ["lon"], + longitude, + dict(axis="X", units="degrees_east", standard_name="longitude"), + ), + } + ) + elif latitude.ndim == 2: + ds_out = xr.Dataset( + { + "lat": ( + ["Y", "X"], + latitude, + dict(units="degrees_north", standard_name="latitude"), + ), + "lon": ( + ["Y", "X"], + longitude, + dict(units="degrees_east", standard_name="longitude"), + ), + } + ) + else: + raise IndexError(f"{latitude.ndim}D latitude/longitude arrays not supported.") return ds_out diff --git a/tests/test_em.py b/tests/test_em.py index 6457431..6e5a247 100644 --- a/tests/test_em.py +++ b/tests/test_em.py @@ -136,7 +136,8 @@ def test_extrap_True(self, model, interp_lib): try: da_out = em.select(**kwargs) assert np.allclose(da_out, da_check) - except (ValueError, AssertionError): + # Should throw TypeError because extrap is not supported using PyInterp. + except (ValueError, AssertionError, TypeError): if interp_lib == 'pyinterp': pass From bf63391cade55f9440bde6f7d1e7a52b8ccb3f14 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 12:29:06 -0700 Subject: [PATCH 14/23] [wip] more type hints and simplification --- extract_model/extract_model.py | 81 +++++++++++++++++++--------------- tests/test_em.py | 2 +- 2 files changed, 46 insertions(+), 37 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 7c488fd..ec3e101 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -19,26 +19,23 @@ warnings.warn("xESMF not found. Interpolation will be performed using pyinterp.") try: - import pyinterp - import pyinterp.backends.xarray - from .pyinterp_shim import PyInterpShim except ImportError: - warnings.warn("pytinerp not found. Interpolation will be performed using xESMF.") + warnings.warn("PyInterp not found. Interpolation will be performed using xESMF.") def select( - da, - longitude=None, - latitude=None, - T=None, - Z=None, - iT=None, - iZ=None, - extrap=False, - extrap_val=None, - locstream=False, - interp_lib="xesmf" + da: xr.DataArray, + longitude: Optional[Union[Number, list[Number], npt.ArrayLike, xr.DataArray]] = None, + latitude: Optional[Union[Number, list[Number], npt.ArrayLike, xr.DataArray]] = None, + T: Optional[Union[str, list[str]]] = None, + Z: Optional[Union[Number, list[Number]]] = None, + iT: Optional[Union[int, list[int]]] = None, + iZ: Optional[Union[int, list[int]]] = None, + extrap: bool = False, + extrap_val: Optional[Number] = None, + locstream: bool = False, + interp_lib: str = "xesmf" ): """Extract output from da at location(s). @@ -101,39 +98,51 @@ def select( >>> da_out = em.select(**kwargs) """ - # can't run in both Z and iZ mode, same for T/iT - assert not ((Z is not None) and (iZ is not None)) - assert not ((T is not None) and (iT is not None)) + # Must select or interpolate for depth and time. + # - i.e. One cannot run in both Z and iZ mode, same for T/iT + if (Z is not None) and (iZ is not None): + raise ValueError("Cannot specify both Z and iZ.") + if (T is not None) and (iT is not None): + raise ValueError("Cannot specify both T and iT.") if (longitude is not None) and (latitude is not None): - if (isinstance(longitude, int)) or (isinstance(longitude, float)): + # Must convert scalars to lists because 0D lat/lon arrays are not supported. + if isinstance(longitude, Number): longitude = [longitude] - if (isinstance(latitude, int)) or (isinstance(latitude, float)): + if isinstance(latitude, Number): latitude = [latitude] - latitude = np.asarray(latitude) longitude = np.asarray(longitude) + latitude = np.asarray(latitude) - if (not extrap) and ((longitude is not None) and (latitude is not None)): - assertion = "the input longitude range is outside the model domain" - assert (longitude.min() >= da.cf["longitude"].min()) and ( - longitude.max() <= da.cf["longitude"].max() - ), assertion - assertion = "the input latitude range is outside the model domain" - assert (latitude.min() >= da.cf["latitude"].min()) and ( - latitude.max() <= da.cf["latitude"].max() - ), assertion + output_grid = True + else: + output_grid = False # Horizontal interpolation + # Verify interpolated points in domain if not extrapolating. + if output_grid and not extrap: + if longitude.min() < da.cf["longitude"].min() or longitude.max() > da.cf["longitude"].max(): + raise ValueError( + "Longitude outside of available domain." + "Use extrap=True to extrapolate." + ) + if latitude.min() < da.cf["latitude"].min() or latitude.max() > da.cf["latitude"].max(): + raise ValueError( + "Latitude outside of available domain." + "Use extrap=True to extrapolate." + ) + + # Create output grid as Dataset. + if output_grid: + ds_out = make_output_ds(longitude, latitude) + else: + ds_out = None + if extrap: extrap_method = "nearest_s2d" else: extrap_method = None - if (longitude is not None) and (latitude is not None): - ds_out = make_output_ds(longitude, latitude) - else: - ds_out = None - if interp_lib == "xesmf" and XESMF_AVAILABLE: da = _xesmf_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) elif interp_lib == "pyinterp" or not XESMF_AVAILABLE: @@ -154,7 +163,7 @@ def _xesmf_interp( extrap_method: Optional[str] = None, extrap_val: Optional[Number] = None, locstream: bool = False, -): +) -> xr.DataArray: """Interpolate input DataArray to output DataArray using xESMF. Parameters diff --git a/tests/test_em.py b/tests/test_em.py index 6e5a247..8c0a1be 100644 --- a/tests/test_em.py +++ b/tests/test_em.py @@ -91,7 +91,7 @@ def test_extrap_False(self, model, interp_lib): interp_lib=interp_lib ) - with pytest.raises(AssertionError): + with pytest.raises(ValueError): em.select(**kwargs) def test_extrap_True(self, model, interp_lib): From d195bfde07f14383b8c33f748d74c3385d5ee9fb Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 12:33:42 -0700 Subject: [PATCH 15/23] [wip] improved import detection for libs --- extract_model/extract_model.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index ec3e101..0e8950b 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -2,7 +2,6 @@ Main file for this code. The main code is in `select`, and the rest is to help with variable name management. """ import warnings -from ast import Import from numbers import Number from typing import Optional, Union @@ -21,7 +20,10 @@ try: from .pyinterp_shim import PyInterpShim except ImportError: - warnings.warn("PyInterp not found. Interpolation will be performed using xESMF.") + if XESMF_AVAILABLE: + warnings.warn("PyInterp not found. Interpolation will be performed using xESMF.") + else: + raise ModuleNotFoundError("Neither PyInterp nor xESMF are available. Please install either package.") def select( From 2e06364490a0cb7318ad348a7773794f4a4bd71d Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 12:42:02 -0700 Subject: [PATCH 16/23] [wip] add docstrings --- extract_model/extract_model.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 0e8950b..5677a89 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -140,11 +140,13 @@ def select( else: ds_out = None + # If extrapolating, define method if extrap: extrap_method = "nearest_s2d" else: extrap_method = None + # Perform interpolation if interp_lib == "xesmf" and XESMF_AVAILABLE: da = _xesmf_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) elif interp_lib == "pyinterp" or not XESMF_AVAILABLE: @@ -224,6 +226,27 @@ def _pyinterp_interp( extrap_val: Optional[Number] = None, locstream: bool = False, ): + """Interpolate input DataArray to output DataArray using PyInterp. + + Parameters + ---------- + da: xarray.DataArray + Input DataArray to interpolate. + da_out: xarray.DataArray + Output DataArray to interpolate to. + T: datetime-like string, list of datetime-like strings, optional + Z: int, float, list, optional + iT: int or list of ints, optional + iZ: int or list of ints, optional + extrap: bool, optional + extrap_val: int, float, optional + locstream: boolean, optional + + Returns + ------- + DataArray of interpolated and/or selected values from da. + """ + # Explicitly assing extrap method to var of same name if extrap_method is not None: extrap = extrap_method From e145a4e6a6d06116d0bc748f6b0c4a44c9169b53 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 16:52:49 -0700 Subject: [PATCH 17/23] add typehints and verify works with existing tests and demo notebooks --- extract_model/extract_model.py | 9 +++--- extract_model/pyinterp_shim.py | 53 +++++++++++++++++----------------- 2 files changed, 31 insertions(+), 31 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 5677a89..35514bc 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -3,7 +3,7 @@ """ import warnings from numbers import Number -from typing import Optional, Union +from typing import Optional, Union, cast import cf_xarray # noqa: F401 import numpy as np @@ -115,7 +115,6 @@ def select( latitude = [latitude] longitude = np.asarray(longitude) latitude = np.asarray(latitude) - output_grid = True else: output_grid = False @@ -247,11 +246,11 @@ def _pyinterp_interp( DataArray of interpolated and/or selected values from da. """ - # Explicitly assing extrap method to var of same name + # Loess based extrapolation will be used if required. if extrap_method is not None: - extrap = extrap_method + extrap = True else: - extrap = None + extrap = False interpretor = PyInterpShim() da = interpretor(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap, locstream=locstream) diff --git a/extract_model/pyinterp_shim.py b/extract_model/pyinterp_shim.py index fb96b7e..6238545 100644 --- a/extract_model/pyinterp_shim.py +++ b/extract_model/pyinterp_shim.py @@ -1,11 +1,12 @@ """ -Temporary interface for using pyinterp. +Temporary interface for using pyinterp in same manner as xESMF in this package. """ -import numbers import warnings -from typing import Tuple +from numbers import Number +from typing import Optional, Tuple, Union import numpy as np +import numpy.typing as npt import xarray as xr try: @@ -20,21 +21,21 @@ class PyInterpShim: def __call__( self, - da, - da_out=None, - T=None, - Z=None, - iT=None, - iZ=None, - extrap=None, - locstream=False, + da: xr.DataArray, + da_out: Optional[xr.DataArray] = None, + T: Optional[Union[str, list[str]]] = None, + Z: Optional[Union[Number, list[Number]]] = None, + iT: Optional[Union[int, list[int]]] = None, + iZ: Optional[Union[int, list[int]]] = None, + extrap: bool = False, + locstream: bool = False, ): - warnings.warn("extrap_method not supported for pyinterp.") - - if extrap is not None: - bounds_error = extrap - else: + # If extrapolating, bounds_errors will not be raised. + # Loess extrapoltion will be used. + if extrap: bounds_error = False + else: + bounds_error = True # Time and depth interpolation or iselection with xr.set_options(keep_attrs=True): @@ -62,17 +63,17 @@ def __call__( def _interp( self, - da, - da_out, - T=None, - Z=None, - iT=None, - iZ=None, - bounds_error=None + da: xr.DataArray, + da_out: xr.DataArray, + T: Optional[Union[str, list[str]]] = None, + Z: Optional[Union[Number, list[Number]]] = None, + iT: Optional[Union[int, list[int]]] = None, + iZ: Optional[Union[int, list[int]]] = None, + bounds_error: bool = False, ) -> Tuple[xr.DataArray, np.ndarray, str]: # Prepare points for interpolation # - Need a DataArray - if type(da) == xr.Dataset: + if isinstance(da, xr.Dataset): var_name = list(da.data_vars)[0] da = da[var_name] else: @@ -90,7 +91,7 @@ def _is_singular_parameter(da, coordinate, vars): if v is not None: if isinstance(v, list) and len(v) == 0: return True - elif isinstance(v, numbers.Number): + elif isinstance(v, Number): return True # Then check if there are singular dimensions in the data array @@ -368,7 +369,7 @@ def _is_singular_parameter(da, coordinate, vars): if v is not None: if isinstance(v, list) and len(v) == 0: return True - elif isinstance(v, numbers.Number): + elif isinstance(v, Number): return True # Then check if there are singular dimensions in the data array From 8dc405eef5da2afde68882aae09b495c8fe9a475 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 16:57:07 -0700 Subject: [PATCH 18/23] remove unused import --- extract_model/extract_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 35514bc..698c0f8 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -3,7 +3,7 @@ """ import warnings from numbers import Number -from typing import Optional, Union, cast +from typing import Optional, Union import cf_xarray # noqa: F401 import numpy as np From 6a061a48cb4b26e95c9d905fe8e6253771fab52f Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Thu, 14 Apr 2022 17:00:10 -0700 Subject: [PATCH 19/23] apply linting --- extract_model/extract_model.py | 53 ++++++-- extract_model/pyinterp_shim.py | 219 +++++++++++++++++++-------------- 2 files changed, 169 insertions(+), 103 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 698c0f8..4cccebd 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -12,6 +12,7 @@ try: import xesmf as xe + XESMF_AVAILABLE = True except ImportError: XESMF_AVAILABLE = False @@ -21,14 +22,20 @@ from .pyinterp_shim import PyInterpShim except ImportError: if XESMF_AVAILABLE: - warnings.warn("PyInterp not found. Interpolation will be performed using xESMF.") + warnings.warn( + "PyInterp not found. Interpolation will be performed using xESMF." + ) else: - raise ModuleNotFoundError("Neither PyInterp nor xESMF are available. Please install either package.") + raise ModuleNotFoundError( + "Neither PyInterp nor xESMF are available. Please install either package." + ) def select( da: xr.DataArray, - longitude: Optional[Union[Number, list[Number], npt.ArrayLike, xr.DataArray]] = None, + longitude: Optional[ + Union[Number, list[Number], npt.ArrayLike, xr.DataArray] + ] = None, latitude: Optional[Union[Number, list[Number], npt.ArrayLike, xr.DataArray]] = None, T: Optional[Union[str, list[str]]] = None, Z: Optional[Union[Number, list[Number]]] = None, @@ -37,7 +44,7 @@ def select( extrap: bool = False, extrap_val: Optional[Number] = None, locstream: bool = False, - interp_lib: str = "xesmf" + interp_lib: str = "xesmf", ): """Extract output from da at location(s). @@ -122,12 +129,18 @@ def select( # Horizontal interpolation # Verify interpolated points in domain if not extrapolating. if output_grid and not extrap: - if longitude.min() < da.cf["longitude"].min() or longitude.max() > da.cf["longitude"].max(): + if ( + longitude.min() < da.cf["longitude"].min() + or longitude.max() > da.cf["longitude"].max() + ): raise ValueError( "Longitude outside of available domain." "Use extrap=True to extrapolate." ) - if latitude.min() < da.cf["latitude"].min() or latitude.max() > da.cf["latitude"].max(): + if ( + latitude.min() < da.cf["latitude"].min() + or latitude.max() > da.cf["latitude"].max() + ): raise ValueError( "Latitude outside of available domain." "Use extrap=True to extrapolate." @@ -147,9 +160,29 @@ def select( # Perform interpolation if interp_lib == "xesmf" and XESMF_AVAILABLE: - da = _xesmf_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) + da = _xesmf_interp( + da, + ds_out, + T=T, + Z=Z, + iT=iT, + iZ=iZ, + extrap_method=extrap_method, + extrap_val=extrap_val, + locstream=locstream, + ) elif interp_lib == "pyinterp" or not XESMF_AVAILABLE: - da = _pyinterp_interp(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap_method=extrap_method, extrap_val=extrap_val, locstream=locstream) + da = _pyinterp_interp( + da, + ds_out, + T=T, + Z=Z, + iT=iT, + iZ=iZ, + extrap_method=extrap_method, + extrap_val=extrap_val, + locstream=locstream, + ) else: raise ValueError(f"{interp_lib} interpolation not supported") @@ -253,7 +286,9 @@ def _pyinterp_interp( extrap = False interpretor = PyInterpShim() - da = interpretor(da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap, locstream=locstream) + da = interpretor( + da, ds_out, T=T, Z=Z, iT=iT, iZ=iZ, extrap=extrap, locstream=locstream + ) return da diff --git a/extract_model/pyinterp_shim.py b/extract_model/pyinterp_shim.py index 6238545..c95c1fb 100644 --- a/extract_model/pyinterp_shim.py +++ b/extract_model/pyinterp_shim.py @@ -14,11 +14,12 @@ import pyinterp.backends.xarray import pyinterp.fill except ImportError: - warnings.warn("pyinterp not installed. Interpolation will be performed using xESMF.") + warnings.warn( + "pyinterp not installed. Interpolation will be performed using xESMF." + ) class PyInterpShim: - def __call__( self, da: xr.DataArray, @@ -53,11 +54,17 @@ def __call__( if da_out is not None: # interpolate to the output grid # then package appropriately - subset_da, interped_array, interp_method = self._interp(da, da_out, T, Z, iT, iZ, bounds_error) + subset_da, interped_array, interp_method = self._interp( + da, da_out, T, Z, iT, iZ, bounds_error + ) if locstream: - da = self._package_locstream(da, da_out, subset_da, interped_array, T, Z, iT, iZ, interp_method) + da = self._package_locstream( + da, da_out, subset_da, interped_array, T, Z, iT, iZ, interp_method + ) else: - da = self._package_grid(da, da_out, subset_da, interped_array, T, Z, iT, iZ, interp_method) + da = self._package_grid( + da, da_out, subset_da, interped_array, T, Z, iT, iZ, interp_method + ) return da @@ -81,7 +88,12 @@ def _interp( # Add misssing coordinates to da_out if len(da_out.lon.shape) == 2: - xy_dataset = xr.Dataset(data_vars={'X': np.arange(da_out.dims['X']), 'Y': np.arange(da_out.dims['Y'])}) + xy_dataset = xr.Dataset( + data_vars={ + "X": np.arange(da_out.dims["X"]), + "Y": np.arange(da_out.dims["Y"]), + } + ) da_out = da_out.merge(xy_dataset) # Identify singular dimensions for time and depth @@ -101,31 +113,32 @@ def _is_singular_parameter(da, coordinate, vars): return True return False - time_singular = _is_singular_parameter(da, 'time', [T, iT]) - vertical_singular = _is_singular_parameter(da, 'vertical', [Z, iZ]) + + time_singular = _is_singular_parameter(da, "time", [T, iT]) + vertical_singular = _is_singular_parameter(da, "vertical", [Z, iZ]) # Perform interpolation with details depending on dimensionality of data ndims = 0 - if 'longitude' in da.cf.coordinates: + if "longitude" in da.cf.coordinates: ndims += 1 - if 'latitude' in da.cf.coordinates: + if "latitude" in da.cf.coordinates: ndims += 1 - if 'vertical' in da.cf.coordinates and not vertical_singular: + if "vertical" in da.cf.coordinates and not vertical_singular: ndims += 1 - if 'time' in da.cf.coordinates and not time_singular: + if "time" in da.cf.coordinates and not time_singular: ndims += 1 - lat_var = da.cf.coordinates['latitude'][0] - lon_var = da.cf.coordinates['longitude'][0] - if 'time' in da.cf.coordinates: - time_var = da.cf.coordinates['time'][0] + lat_var = da.cf.coordinates["latitude"][0] + lon_var = da.cf.coordinates["longitude"][0] + if "time" in da.cf.coordinates: + time_var = da.cf.coordinates["time"][0] else: time_var = None - if 'vertical' in da.cf.coordinates: - vertical_var = da.cf.coordinates['vertical'][0] + if "vertical" in da.cf.coordinates: + vertical_var = da.cf.coordinates["vertical"][0] else: vertical_var = None - regrid_method = 'bilinear' + regrid_method = "bilinear" subset_da = da if ndims == 2: @@ -140,24 +153,24 @@ def _is_singular_parameter(da, coordinate, vars): # Interpolate try: mx, my = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - indexing="ij" + da_out.lon.values, da_out.lat.values, indexing="ij" ) grid = pyinterp.backends.xarray.Grid2D(subset_da) interped = grid.bivariate( - coords={ - lon_var: mx.ravel(), - lat_var: my.ravel() - }, - bounds_error=bounds_error + coords={lon_var: mx.ravel(), lat_var: my.ravel()}, + bounds_error=bounds_error, ).reshape(mx.shape) # Transpose from x,y to y,x interped = interped.T except ValueError: grid = pyinterp.RTree() grid.packing( - np.vstack((subset_da[lon_var].data.ravel(), subset_da[lat_var].data.ravel())).T, + np.vstack( + ( + subset_da[lon_var].data.ravel(), + subset_da[lat_var].data.ravel(), + ) + ).T, subset_da.data.ravel(), ) if len(da_out.lon.shape) == 2: @@ -165,9 +178,7 @@ def _is_singular_parameter(da, coordinate, vars): my = da_out.lat.values else: mx, my = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - indexing="ij" + da_out.lon.values, da_out.lat.values, indexing="ij" ) idw, _ = grid.inverse_distance_weighting( np.vstack((mx.ravel(), my.ravel())).T, @@ -175,7 +186,7 @@ def _is_singular_parameter(da, coordinate, vars): k=5, ) interped = idw.reshape(mx.shape) - regrid_method = 'IDW' + regrid_method = "IDW" elif ndims == 3: if time_var: @@ -203,8 +214,8 @@ def _is_singular_parameter(da, coordinate, vars): mx, my, mz = np.meshgrid( da_out.lon.values, da_out.lat.values, - da.cf.coords['time'].values, - indexing="ij" + da.cf.coords["time"].values, + indexing="ij", ) # Fill NaNs using Loess @@ -216,7 +227,7 @@ def _is_singular_parameter(da, coordinate, vars): x=mx.ravel(), y=my.ravel(), z=mz.ravel(), - bounds_error=bounds_error + bounds_error=bounds_error, ).reshape(mx.shape) # Curviliear or unstructured except ValueError: @@ -225,7 +236,12 @@ def _is_singular_parameter(da, coordinate, vars): grid = pyinterp.RTree() grid.packing( - np.vstack((subset_da[lon_var].data.ravel(), subset_da[lat_var].data.ravel())).T, + np.vstack( + ( + subset_da[lon_var].data.ravel(), + subset_da[lat_var].data.ravel(), + ) + ).T, subset_da.data.ravel().reshape(-1, trailing_dim), ) if len(da_out.lon.shape) == 2: @@ -233,9 +249,7 @@ def _is_singular_parameter(da, coordinate, vars): my = da_out.lat.values else: mx, my = np.meshgrid( - da_out.lon.values, - da_out.lat.values, - indexing="ij" + da_out.lon.values, da_out.lat.values, indexing="ij" ) idw, _ = grid.inverse_distance_weighting( np.vstack((mx.ravel(), my.ravel())).T, @@ -243,15 +257,15 @@ def _is_singular_parameter(da, coordinate, vars): k=5, ) interped = idw.reshape(mx.shape) - regrid_method = 'IDW' + regrid_method = "IDW" elif ndims == 4: mx, my, mz, mu = np.meshgrid( da_out.lon.values, da_out.lat.values, - da.cf.coords['time'].values, - da.cf.coords['vertical'].values, - indexing="ij" + da.cf.coords["time"].values, + da.cf.coords["vertical"].values, + indexing="ij", ) # Fill NaNs using Loess grid = pyinterp.backends.xarray.Grid4D(da) @@ -263,7 +277,7 @@ def _is_singular_parameter(da, coordinate, vars): y=mx.ravel(), z=mz.ravel(), u=mu.ravel(), - bounds_error=bounds_error + bounds_error=bounds_error, ).reshape(mx.shape) else: raise IndexError(f"{ndims}D interpolation not supported") @@ -280,7 +294,7 @@ def _package_locstream( Z=None, iT=None, iZ=None, - regrid_method=None + regrid_method=None, ): # Prepare points for interpolation # - Need a DataArray @@ -292,30 +306,30 @@ def _package_locstream( # Locstream will have dim pt for the number of points # - Change dims from lon/lat to pts - lat_var = da_out.cf.coordinates['latitude'][0] - lon_var = da_out.cf.coordinates['longitude'][0] + lat_var = da_out.cf.coordinates["latitude"][0] + lon_var = da_out.cf.coordinates["longitude"][0] da_out = da_out.rename_dims( { - lat_var: 'pts', - lon_var: 'pts', + lat_var: "pts", + lon_var: "pts", } ) # Add coordinates from the original data coords = da_out.coords - if 'time' in da.cf.coordinates: - time_var = da.cf.coordinates['time'][0] + if "time" in da.cf.coordinates: + time_var = da.cf.coordinates["time"][0] else: time_var = None - if 'vertical' in da.cf.coordinates: - vertical_var = da.cf.coordinates['vertical'][0] + if "vertical" in da.cf.coordinates: + vertical_var = da.cf.coordinates["vertical"][0] else: vertical_var = None - if 'time' in da.cf.coordinates: - coords['time'] = subset_da[time_var] - if 'vertical' in da.cf.coordinates: - coords['vertical'] = subset_da[vertical_var] + if "time" in da.cf.coordinates: + coords["time"] = subset_da[time_var] + if "vertical" in da.cf.coordinates: + coords["vertical"] = subset_da[vertical_var] # Add interpolated data # If a single point, reshape to len(pts, 1) @@ -334,7 +348,7 @@ def _package_locstream( interped, coords=coords, dims=dims, - attrs={**da.attrs, **{'regrid_method': regrid_method}} + attrs={**da.attrs, **{"regrid_method": regrid_method}}, ) def _package_grid( @@ -347,7 +361,7 @@ def _package_grid( Z=None, iT=None, iZ=None, - regrid_method=None + regrid_method=None, ): # Prepare points for interpolation # - Need a DataArray @@ -359,7 +373,12 @@ def _package_grid( # Add misssing coordinates to da_out if len(da_out.lon.shape) == 2: - xy_dataset = xr.Dataset(data_vars={'X': np.arange(da_out.dims['X']), 'Y': np.arange(da_out.dims['Y'])}) + xy_dataset = xr.Dataset( + data_vars={ + "X": np.arange(da_out.dims["X"]), + "Y": np.arange(da_out.dims["Y"]), + } + ) da_out = da_out.merge(xy_dataset) # Identify singular dimensions for time and depth @@ -379,80 +398,92 @@ def _is_singular_parameter(da, coordinate, vars): return True return False - time_singular = _is_singular_parameter(da, 'time', [T, iT]) - vertical_singular = _is_singular_parameter(da, 'vertical', [Z, iZ]) + + time_singular = _is_singular_parameter(da, "time", [T, iT]) + vertical_singular = _is_singular_parameter(da, "vertical", [Z, iZ]) # Perform interpolation with details depending on dimensionality of data ndims = 0 - if 'longitude' in da.cf.coordinates: + if "longitude" in da.cf.coordinates: ndims += 1 - if 'latitude' in da.cf.coordinates: + if "latitude" in da.cf.coordinates: ndims += 1 - if 'vertical' in da.cf.coordinates and not vertical_singular: + if "vertical" in da.cf.coordinates and not vertical_singular: ndims += 1 - if 'time' in da.cf.coordinates and not time_singular: + if "time" in da.cf.coordinates and not time_singular: ndims += 1 - if 'time' in da.cf.coordinates: - time_var = da.cf.coordinates['time'][0] + if "time" in da.cf.coordinates: + time_var = da.cf.coordinates["time"][0] else: time_var = None - if 'vertical' in da.cf.coordinates: - vertical_var = da.cf.coordinates['vertical'][0] + if "vertical" in da.cf.coordinates: + vertical_var = da.cf.coordinates["vertical"][0] else: vertical_var = None if ndims == 2: # Package as DataArray if len(da_out.lon) == 1: - lons = da_out.lon.isel({'lon': 0}) + lons = da_out.lon.isel({"lon": 0}) else: lons = da_out.lon if len(da_out.lat) == 1: - lats = da_out.lat.isel({'lat': 0}) + lats = da_out.lat.isel({"lat": 0}) else: lats = da_out.lat - coords = { - 'lon': lons, - 'lat': lats - } + coords = {"lon": lons, "lat": lats} # Handle curvilinear lon/lat coords if len(lons.shape) == 2: for dim in lons.dims: coords[dim] = lons[dim] - if 'time' in da.cf.coordinates: - coords['time'] = da[time_var] - if 'vertical' in da.cf.coordinates: - coords['vertical'] = da[vertical_var] + if "time" in da.cf.coordinates: + coords["time"] = da[time_var] + if "vertical" in da.cf.coordinates: + coords["vertical"] = da[vertical_var] # Handle missing dims from interpolation missing_subset_dims = [] for subset_dim in subset_da.dims: - if subset_dim not in [da.cf.coordinates['longitude'][0], da.cf.coordinates['latitude'][0]]: + if subset_dim not in [ + da.cf.coordinates["longitude"][0], + da.cf.coordinates["latitude"][0], + ]: missing_subset_dims.append(subset_dim) output_dims = [] for orig_dim in da.dims: # Handle original x, y to lon, lat # Also, do not add lon and lat if they are scalars - if orig_dim == 'xi_rho' and len(da_out.lon) > 1: - output_dims.append('X') - elif orig_dim == 'xi_rho' and len(da_out.lon) == 1: + if orig_dim == "xi_rho" and len(da_out.lon) > 1: + output_dims.append("X") + elif orig_dim == "xi_rho" and len(da_out.lon) == 1: interped = np.squeeze(interped, axis=0) continue - elif orig_dim == 'eta_rho' and len(da_out.lat) > 1: - output_dims.append('Y') - elif orig_dim == 'eta_rho' and len(da_out.lat) == 1: + elif orig_dim == "eta_rho" and len(da_out.lat) > 1: + output_dims.append("Y") + elif orig_dim == "eta_rho" and len(da_out.lat) == 1: interped = np.squeeze(interped, axis=0) continue - elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) > 1: - output_dims.append('lon') - elif orig_dim == da.cf.coordinates['longitude'][0] and len(da_out.lon) == 1: + elif ( + orig_dim == da.cf.coordinates["longitude"][0] + and len(da_out.lon) > 1 + ): + output_dims.append("lon") + elif ( + orig_dim == da.cf.coordinates["longitude"][0] + and len(da_out.lon) == 1 + ): interped = np.squeeze(interped, axis=0) continue - elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) > 1: - output_dims.append('lat') - elif orig_dim == da.cf.coordinates['latitude'][0] and len(da_out.lat) == 1: + elif ( + orig_dim == da.cf.coordinates["latitude"][0] and len(da_out.lat) > 1 + ): + output_dims.append("lat") + elif ( + orig_dim == da.cf.coordinates["latitude"][0] + and len(da_out.lat) == 1 + ): interped = np.squeeze(interped, axis=0) continue else: @@ -465,7 +496,7 @@ def _is_singular_parameter(da, coordinate, vars): interped, coords=coords, dims=output_dims, - attrs={**da.attrs, **{'regrid_method': regrid_method}} + attrs={**da.attrs, **{"regrid_method": regrid_method}}, ) elif ndims == 3: coords = { From c778bc07dff43e5a8881f6aba449d685e7261f91 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Fri, 15 Apr 2022 11:02:18 -0700 Subject: [PATCH 20/23] add additional build and test for python 3.9, macos, and windows --- .github/workflows/test.yaml | 47 ++++++++++++++++++++++++++++++------ ci/environment-py3.7-win.yml | 21 ++++++++++++++++ ci/environment-py3.7.yml | 7 ++---- ci/environment-py3.8-win.yml | 21 ++++++++++++++++ ci/environment-py3.8.yml | 7 ++---- ci/environment-py3.9-win.yml | 21 ++++++++++++++++ ci/environment-py3.9.yml | 22 +++++++++++++++++ 7 files changed, 129 insertions(+), 17 deletions(-) create mode 100644 ci/environment-py3.7-win.yml create mode 100644 ci/environment-py3.8-win.yml create mode 100644 ci/environment-py3.9-win.yml create mode 100644 ci/environment-py3.9.yml diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 3f477a7..6182bec 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -8,11 +8,16 @@ jobs: strategy: fail-fast: false matrix: - os: ["ubuntu-latest"] - python-version: ["3.7", "3.8"] + os: ["macos-latest", "ubuntu-latest", "windows-latest"] + python-version: ["3.7", "3.8", "3.9"] steps: - - uses: actions/checkout@v2 - - name: Cache conda + - name: Checkout source + uses: actions/checkout@v2 + with: + fetch-depth: 0 + + - name: Cache Linux/macOS (x86) Conda environment + if: ${{ runner.os != "windows-latest" }} uses: actions/cache@v1 env: # Increase this value to reset cache if ci/environment.yml has not changed @@ -20,7 +25,20 @@ jobs: with: path: ~/conda_pkgs_dir key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('ci/environment-py${{ matrix.python-version }}.yml') }} - - uses: conda-incubator/setup-miniconda@v2 + + - name: Cache Windows Conda environment + if: ${{ runner.os == "windows-latest" }} + uses: actions/cache@v1 + env: + # Increase this value to reset cache if ci/environment.yml has not changed + CACHE_NUMBER: 0 + with: + path: ~/conda_pkgs_dir + key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('ci/environment-py${{ matrix.python-version }}-win.yml') }} + + - name: Build and activate Linux/macOS Conda environment + if: ${{ runnor.os != "windows-latest" }} + uses: conda-incubator/setup-miniconda@v2 with: mamba-version: "*" # activate this to build with mamba. channels: conda-forge, defaults # These need to be specified to use mamba @@ -29,14 +47,29 @@ jobs: activate-environment: test_env_extract_model use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! - - name: Set up conda environment + + - name: Build and activate Linux/macOS Conda environment + if: ${{ runnor.os == "windows-latest" }} + uses: conda-incubator/setup-miniconda@v2 + with: + mamba-version: "*" # activate this to build with mamba. + channels: conda-forge, defaults # These need to be specified to use mamba + channel-priority: true + environment-file: ci/environment-py${{ matrix.python-version }}-win.yml + + activate-environment: test_env_extract_model + use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly! + + - name: Install package in environment shell: bash -l {0} run: | python -m pip install -e . --no-deps --force-reinstall - - name: Run Tests + + - name: Run tests shell: bash -l {0} run: | pytest --cov=./ --cov-report=xml + - name: Upload code coverage to Codecov uses: codecov/codecov-action@v1 with: diff --git a/ci/environment-py3.7-win.yml b/ci/environment-py3.7-win.yml new file mode 100644 index 0000000..b20b135 --- /dev/null +++ b/ci/environment-py3.7-win.yml @@ -0,0 +1,21 @@ +name: test-env +channels: + - conda-forge +dependencies: + - python=3.7 + - cf_xarray>=0.6 + - cmocean + - dask + - matplotlib + - netcdf4 + - numpy + - pip + - pyinterp + - requests + - xarray + - xcmocean + - pytest + - pip: + - codecov + - pytest-cov + - coverage[toml] diff --git a/ci/environment-py3.7.yml b/ci/environment-py3.7.yml index 5e9bc7b..f79fa8e 100644 --- a/ci/environment-py3.7.yml +++ b/ci/environment-py3.7.yml @@ -1,23 +1,20 @@ -name: test_env_extract_model +name: test-env channels: - conda-forge dependencies: - python=3.7 - ############## These will have to be adjusted to your specific project - cf_xarray>=0.6 - cmocean - dask - - jupyter - - jupyterlab - matplotlib - netcdf4 - numpy - pip + - pyinterp - requests - xarray - xcmocean - xesmf - ############## - pytest - pip: - codecov diff --git a/ci/environment-py3.8-win.yml b/ci/environment-py3.8-win.yml new file mode 100644 index 0000000..7227a2d --- /dev/null +++ b/ci/environment-py3.8-win.yml @@ -0,0 +1,21 @@ +name: test-env +channels: + - conda-forge +dependencies: + - python=3.8 + - cf_xarray>=0.6 + - cmocean + - dask + - matplotlib + - netcdf4 + - numpy + - pip + - pyinterp + - requests + - xarray + - xcmocean + - pytest + - pip: + - codecov + - pytest-cov + - coverage[toml] diff --git a/ci/environment-py3.8.yml b/ci/environment-py3.8.yml index 7d30b86..2b484ab 100644 --- a/ci/environment-py3.8.yml +++ b/ci/environment-py3.8.yml @@ -1,23 +1,20 @@ -name: test_env_extract_model +name: test-env channels: - conda-forge dependencies: - python=3.8 - ############## These will have to be adjusted to your specific project - cf_xarray>=0.6 - cmocean - dask - - jupyter - - jupyterlab - matplotlib - netcdf4 - numpy - pip + - pyinterp - requests - xarray - xcmocean - xesmf - ############## - pytest - pip: - codecov diff --git a/ci/environment-py3.9-win.yml b/ci/environment-py3.9-win.yml new file mode 100644 index 0000000..5cb1c40 --- /dev/null +++ b/ci/environment-py3.9-win.yml @@ -0,0 +1,21 @@ +name: test-env +channels: + - conda-forge +dependencies: + - python=3.9 + - cf_xarray>=0.6 + - cmocean + - dask + - matplotlib + - netcdf4 + - numpy + - pip + - pyinterp + - requests + - xarray + - xcmocean + - pytest + - pip: + - codecov + - pytest-cov + - coverage[toml] diff --git a/ci/environment-py3.9.yml b/ci/environment-py3.9.yml new file mode 100644 index 0000000..00cedd9 --- /dev/null +++ b/ci/environment-py3.9.yml @@ -0,0 +1,22 @@ +name: test-env +channels: + - conda-forge +dependencies: + - python=3.9 + - cf_xarray>=0.6 + - cmocean + - dask + - matplotlib + - netcdf4 + - numpy + - pip + - pyinterp + - requests + - xarray + - xcmocean + - xesmf + - pytest + - pip: + - codecov + - pytest-cov + - coverage[toml] From 48900c127dbc40dcc4b10201a095e3338c324544 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Fri, 15 Apr 2022 11:41:27 -0700 Subject: [PATCH 21/23] add pyinterp to setup.cfg + environment.yml, drop python 3.6 --- environment.yml | 2 +- setup.cfg | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/environment.yml b/environment.yml index 385d35c..0e281fa 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,6 @@ channels: dependencies: # Required for full project functionality (dont remove) - pytest - # Examples (remove and add as needed) - cf_xarray>=0.6 - cmocean - dask @@ -12,6 +11,7 @@ dependencies: - netcdf4 - numpy - pip + - pyinterp - requests - scipy - xarray diff --git a/setup.cfg b/setup.cfg index 34f4096..f4f6b90 100644 --- a/setup.cfg +++ b/setup.cfg @@ -35,13 +35,12 @@ license_file = LICENSE.txt # For details see: https://pypi.org/classifiers/ classifiers = - Development Status :: 5 - Production/Stable + Development Status :: 3 - Alpha Topic :: Scientific/Engineering Intended Audience :: Science/Research Operating System :: OS Independent Programming Language :: Python Programming Language :: Python :: 3 - Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 @@ -62,6 +61,7 @@ install_requires = netcdf4 numpy pip + pyinterp requests scipy xarray @@ -69,7 +69,7 @@ install_requires = setup_requires= setuptools_scm -python_requires = >=3.6 +python_requires = >=3.7 ################ Up until here zip_safe = False From fb3449e5506c42da7e471530384d8bd13cdbd741 Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Fri, 15 Apr 2022 11:58:35 -0700 Subject: [PATCH 22/23] remove pyinterp from setup.cfg fails in docs/env --- setup.cfg | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index f4f6b90..c42671c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -61,7 +61,6 @@ install_requires = netcdf4 numpy pip - pyinterp requests scipy xarray From ac669886f8770c5689af74093c2267e2f5419c1b Mon Sep 17 00:00:00 2001 From: Jesse Lopez Date: Fri, 15 Apr 2022 12:27:49 -0700 Subject: [PATCH 23/23] Change type hints for list for 3.8 and add some more --- extract_model/extract_model.py | 30 +++++++++--------- extract_model/pyinterp_shim.py | 56 +++++++++++++++++----------------- 2 files changed, 43 insertions(+), 43 deletions(-) diff --git a/extract_model/extract_model.py b/extract_model/extract_model.py index 4cccebd..01ca35f 100644 --- a/extract_model/extract_model.py +++ b/extract_model/extract_model.py @@ -3,7 +3,7 @@ """ import warnings from numbers import Number -from typing import Optional, Union +from typing import List, Optional, Union import cf_xarray # noqa: F401 import numpy as np @@ -34,13 +34,13 @@ def select( da: xr.DataArray, longitude: Optional[ - Union[Number, list[Number], npt.ArrayLike, xr.DataArray] + Union[Number, List[Number], npt.ArrayLike, xr.DataArray] ] = None, - latitude: Optional[Union[Number, list[Number], npt.ArrayLike, xr.DataArray]] = None, - T: Optional[Union[str, list[str]]] = None, - Z: Optional[Union[Number, list[Number]]] = None, - iT: Optional[Union[int, list[int]]] = None, - iZ: Optional[Union[int, list[int]]] = None, + latitude: Optional[Union[Number, List[Number], npt.ArrayLike, xr.DataArray]] = None, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, extrap: bool = False, extrap_val: Optional[Number] = None, locstream: bool = False, @@ -192,10 +192,10 @@ def select( def _xesmf_interp( da: xr.DataArray, ds_out: Optional[xr.Dataset] = None, - T: Optional[Union[str, list[str]]] = None, - Z: Optional[Union[Number, list[Number]]] = None, - iT: Optional[Union[int, list[int]]] = None, - iZ: Optional[Union[int, list[int]]] = None, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, extrap_method: Optional[str] = None, extrap_val: Optional[Number] = None, locstream: bool = False, @@ -250,10 +250,10 @@ def _xesmf_interp( def _pyinterp_interp( da: xr.DataArray, ds_out: Optional[xr.Dataset] = None, - T: Optional[Union[str, list[str]]] = None, - Z: Optional[Union[Number, list[Number]]] = None, - iT: Optional[Union[int, list[int]]] = None, - iZ: Optional[Union[int, list[int]]] = None, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, extrap_method: Optional[str] = None, extrap_val: Optional[Number] = None, locstream: bool = False, diff --git a/extract_model/pyinterp_shim.py b/extract_model/pyinterp_shim.py index c95c1fb..efc7507 100644 --- a/extract_model/pyinterp_shim.py +++ b/extract_model/pyinterp_shim.py @@ -3,10 +3,9 @@ """ import warnings from numbers import Number -from typing import Optional, Tuple, Union +from typing import List, Optional, Tuple, Union import numpy as np -import numpy.typing as npt import xarray as xr try: @@ -24,10 +23,10 @@ def __call__( self, da: xr.DataArray, da_out: Optional[xr.DataArray] = None, - T: Optional[Union[str, list[str]]] = None, - Z: Optional[Union[Number, list[Number]]] = None, - iT: Optional[Union[int, list[int]]] = None, - iZ: Optional[Union[int, list[int]]] = None, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, extrap: bool = False, locstream: bool = False, ): @@ -72,10 +71,11 @@ def _interp( self, da: xr.DataArray, da_out: xr.DataArray, - T: Optional[Union[str, list[str]]] = None, - Z: Optional[Union[Number, list[Number]]] = None, - iT: Optional[Union[int, list[int]]] = None, - iZ: Optional[Union[int, list[int]]] = None, + interped: xr.DataArray, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, bounds_error: bool = False, ) -> Tuple[xr.DataArray, np.ndarray, str]: # Prepare points for interpolation @@ -286,15 +286,15 @@ def _is_singular_parameter(da, coordinate, vars): def _package_locstream( self, - da, - da_out, - subset_da, - interped, - T=None, - Z=None, - iT=None, - iZ=None, - regrid_method=None, + da: xr.DataArray, + da_out: xr.DataArray, + subset_da: xr.DataArray, + interped: np.ndarray, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, + regrid_method: Optional[str] = None, ): # Prepare points for interpolation # - Need a DataArray @@ -353,15 +353,15 @@ def _package_locstream( def _package_grid( self, - da, - da_out, - subset_da, - interped, - T=None, - Z=None, - iT=None, - iZ=None, - regrid_method=None, + da: xr.DataArray, + da_out: xr.DataArray, + subset_da: xr.DataArray, + interped: np.ndarray, + T: Optional[Union[str, List[str]]] = None, + Z: Optional[Union[Number, List[Number]]] = None, + iT: Optional[Union[int, List[int]]] = None, + iZ: Optional[Union[int, List[int]]] = None, + regrid_method: Optional[str] = None, ): # Prepare points for interpolation # - Need a DataArray