From 420af6defeca433fb618bd34ea5d6a5f09777263 Mon Sep 17 00:00:00 2001
From: Andrew Kiss <31054815+aekiss@users.noreply.github.com>
Date: Tue, 23 Apr 2024 14:59:41 +1000
Subject: [PATCH 1/8] mainly notebook tweaks - NOT TESTED!
---
demos/access_om2-forced.ipynb | 732 +++-------------------------------
demos/reanalysis-forced.ipynb | 94 +++--
docs/index.rst | 3 +-
docs/installation.md | 2 +-
4 files changed, 117 insertions(+), 714 deletions(-)
diff --git a/demos/access_om2-forced.ipynb b/demos/access_om2-forced.ipynb
index f6cd66eb..6264009d 100644
--- a/demos/access_om2-forced.ipynb
+++ b/demos/access_om2-forced.ipynb
@@ -4,19 +4,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Regional Tasmania forced by JRA-55 reanalysis and ACCESS-OM2 model output\n",
+ "# Regional Tasmanian domain forced by JRA55-do reanalysis and ACCESS-OM2-01 model output\n",
"\n",
"### Note: This example requires access to [NCI's Gadi HPC system](https://nci.org.au/our-systems/hpc-systems)\n",
"\n",
- "**Ensure you have access to the relevant NCI projects to access the data listed below**\n",
+ "**Ensure you have access to the relevant NCI projects to access the data listed below. If needed, apply via [mancini](https://my.nci.org.au/mancini).**\n",
"\n",
"## What does this notebook do?\n",
"This notebook is designed to set you up with a working MOM6 regional configuration. First, try and get it running with our default Tasmania case, then you can clone the notebook and modify for your region of interest. \n",
"\n",
"Input Type | Source | Location on NCI\n",
"---|---|---\n",
- "Surface | [JRA55 surface forcing](https://climatedataguide.ucar.edu/climate-data/jra-55) | `/g/data/ik11`\n",
- "Ocean | [ACCESS-OM2-01](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description) | `/g/data/ik11` \n",
+ "Surface | [JRA55-do surface forcing](https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/) | `/g/data/ik11`\n",
+ "Ocean | [ACCESS-OM2-01](https://github.com/COSIMA/access-om2) | `/g/data/ik11` \n",
"Bathymetry | [GEBCO](https://www.gebco.net/data_and_products/gridded_bathymetry_data/) | `/g/data/ik11`\n",
"\n",
"Additionally, you'll need access to `/g/data/x77/` if you want to use the same executable using the latest FMS build (a good idea for troubleshooting)."
@@ -26,13 +26,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To use your own version of regional_mom6 package, clone the entire github repository\n",
- "on your machine and set the regional-mom6 path using the `os` library, like shown below:"
+ "To use your own version of the `regional_mom6` package, clone the entire [github repository](https://github.com/COSIMA/regional-mom6)\n",
+ "on your machine and set the regional-mom6 path using the `os` library, as shown below:"
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -56,463 +56,11 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"client = Client()\n",
"client"
@@ -520,13 +68,15 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
"source": [
- "## What does this package do?\n",
+ "## What does the `regional_mom6` package do?\n",
"\n",
"Setting up a regional model in MOM6 can be a pain. The goal of this package is that users should spend their debugging time fixing a model that's running and doing weird things, rather than puzzling over a model that won't even start.\n",
"\n",
- "In running this notebook, you'll hopefully have a running MOM6 regional model. There will still be a lot of fiddling to do with the MOM_input file to make sure that the parameters are set up right for your domain, and you might want to manually edit some of the input files. *But*, this package should help you bypass most of the woes of regridding, encoding and understanding the arcane arts of the MOM6 boundary segment files. "
+ "In running this notebook, you'll hopefully have a running MOM6 regional model. There will still be a lot of fiddling to do with the `MOM_input` file to make sure that the parameters are set up right for your domain, and you might want to manually edit some of the input files. *But*, this package should help you bypass most of the woes of regridding, encoding and understanding the arcane arts of the MOM6 boundary segment files. "
]
},
{
@@ -539,13 +89,13 @@
"\n",
"* Avoid any regions with ice\n",
"* Avoid regions near the north pole\n",
- "* Although the default configuration is meant to be RYF, I've not fixed up the calendar and encoding to run longer than a year just yet\n",
+ "* Although the default configuration is meant to be repeat-year forced (RYF), the calendar and encoding will need fixing to run longer than a year\n",
"\n",
"\n",
"Input Type | Source\n",
"---|---\n",
- "Surface | JRA \n",
- "Ocean | ACCESS OM2-01\n",
+ "Surface | JRA55-do\n",
+ "Ocean | ACCESS-OM2-01\n",
"Bathymetry | Gebco"
]
},
@@ -558,7 +108,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -575,20 +125,20 @@
"source": [
"## Step 1: Choose our domain, define workspace paths\n",
"\n",
- "To make sure that things are working I'd recommend starting with the default example defined below. If this runs ok, then change to a domain of your choice and hopefully it runs ok too! There's some troubleshooting you can do if not (check readme / readthedocs)\n",
+ "To make sure that things are working I'd recommend starting with the default example defined below. If this runs ok, then change to a domain of your choice and hopefully it runs ok too! If not, check the [README](https://github.com/COSIMA/regional-mom6/blob/main/README.md) and [documentation](https://regional-mom6.readthedocs.io/) for troubleshooting tips.\n",
"\n",
- "To find the lat/lon of the domain you want to test you can use this GUI and copy paste below"
+ "You can log in and use [this GUI](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/download) to find the lat/lon of your domain and copy paste below."
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "expt_name = \"tassie-glorys\"\n",
+ "expt_name = \"tassie-access-om2-forced\"\n",
"\n",
"latitude_extent = [-48, -38.95]\n",
"longitude_extent = [143, 150]\n",
@@ -607,9 +157,8 @@
"## Directory where ocean model cut-outs go before processing\n",
"tmp_dir = f\"{gdata}/{expt_name}\"\n",
"\n",
- "for i in [run_dir, tmp_dir, input_dir]:\n",
- " if not os.path.exists(i):\n",
- " os.makedirs(str(i))"
+ "for path in [run_dir, tmp_dir, input_dir]:\n",
+ " os.makedirs(str(path), exist_ok=True)"
]
},
{
@@ -625,12 +174,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**NOTE: this is hardcoded for the year of 1990, which corresponds to output files `1077`-`1082`. If you want to modify, you'll need to choose the right path to the year of your choice, or use the COSIMA cookbook to locate your data files**"
+ "**NOTE: this is hardcoded for the year of 1990, which corresponds to output files `1077`-`1082`. If you want to modify, you'll need to choose the right path to the year of your choice, or use the [COSIMA cookbook](https://cosima-cookbook.readthedocs.io/) to locate your data files.**"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -675,14 +224,12 @@
"metadata": {},
"source": [
"## Step 3: Make experiment object\n",
- "This object keeps track of your domain basics, as well as generating the hgrid, vgrid and setting up the folder structures. \n",
- "\n",
- "\n"
+ "The `regional_mom6.experiment` contains the regional domain basics, and also generates the horizontal and vertical grids, `hgrid` and `vgrid` respectively, and sets up the directory structures. "
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -706,28 +253,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "After running you can have a look at your grids by calling `expt.hgrid` and `expt.vgrid`\n",
+ "We can now access the horizontal and vertical grid of the regional configuration via `expt.hgrid` and `expt.vgrid` respectively.\n",
"\n",
- "Plotting the vertical grid with `marker = '.'` option lets you see the spacing. For example, you can use `numpy.diff` to compute the vertical spacings, e.g.,\n",
+ "Plotting the vertical grid with `marker = '.'` lets you see the spacing. You can use `numpy.diff` to compute the vertical spacings, e.g.,\n",
"```python\n",
"import numpy as np\n",
- "np.diff(expt.vgrid.zl)\n",
+ "np.diff(expt.vgrid.zl).plot(marker = '.')\n",
"```\n",
- "and then visualise this with the package of your liking.\n",
+ "shows you the vertical spacing profile.\n",
"\n",
"### Modular workflow!\n",
"\n",
- "After constructing your expt object, if you don't like the default hgrid and vgrids you can simply modify and overwrite them. However, you'll then also need to save them to disk again. For example:\n",
+ "After constructing your `expt` object, if you don't like the default `hgrid` and `vgrid` you can simply modify and then save them back into the `expt` object. However, you'll then also need to save them to disk again. For example:\n",
"\n",
"```python\n",
- "new_hgrid = xr.open_dataset(input_dir / \"hgrid.nc\")\n",
+ "new_hgrid = xr.open_dataset(input_dir + \"/hgrid.nc\")\n",
"```\n",
- "Modify `new_hgrid`, ensuring that metadata is retained to keep MOM6 happy. Then, save your changes\n",
+ "Modify `new_hgrid`, ensuring that _all metadata_ is retained to keep MOM6 happy. Then, save your changes\n",
"\n",
"```python\n",
"expt.hgrid = new_hgrid\n",
"\n",
- "expt.hgrid.to_netcdf(input_dir / \"hgrid.nc\")\n",
+ "expt.hgrid.to_netcdf(input_dir + \"/hgrid.nc\")\n",
"```"
]
},
@@ -737,35 +284,16 @@
"source": [
"## Step 4: Set up bathymetry\n",
"\n",
- "Similarly to ocean forcing, we point our 'bathymetry' method at the location of the file of choice, and pass it a dictionary mapping variable names. This time we don't need to preprocess the topography since it's just a 2D field and easier to deal with. Afterwards you can run `expt.topog` and have a look at your domain. "
+ "Similarly to ocean forcing, we point the experiment's `setup_bathymetry` method at the location of the file of choice and also mappings to variable names. We don't need to preprocess the bathymetry since it is simply a two-dimensional field and is easier to deal with. Afterwards you can run `expt.bathymetry` and have a look at your domain. After running this cell, your input directory will contain other topography - adjacent things like the ocean mosaic and mask table too. This defaults to a 10x10 layout which can be updated later."
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Begin regridding bathymetry...\n",
- "\n",
- "If this process hangs it means that the chosen domain might be too big to handle this way. After ensuring access to appropriate computational resources, try calling ESMF directly from a terminal in the input directory via\n",
- "\n",
- "mpirun ESMF_Regrid -s bathy_original.nc -d topog_raw.nc -m bilinear --src_var elevation --dst_var elevation --netcdf4 --src_regional --dst_regional\n",
- "\n",
- "For details see https://xesmf.readthedocs.io/en/latest/large_problems_on_HPC.html\n",
- "\n",
- "Aftewards, run the bathymetry method again but set 'make_topography = False' so the computationally expensive step is skiped and instead the method ensures that only the metadata are fixed.\n",
- "Regridding in parallel: True\n",
- "Regridding finished. Now excavating inland lakes and fixing up metadata...\n",
- "Reading in regridded bathymetry to fix up metadata...done.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"expt.setup_bathymetry(\n",
" bathymetry_path='/g/data/ik11/inputs/GEBCO_2022/GEBCO_2022.nc',\n",
@@ -785,34 +313,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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2ogz9gy5fdpG05CpebKB443KHiLxMflbSTSt698jtjtyec2PjxiU3VNGRVlw1mtsSNEEUIpjBFBy0cp8e5LAGkYWFhYWFxTxFiAghpm8RzeTYgw3WIGoA3YrL5DGJAgp+06wI5/sKu+WVpsW8xn3Vu6d13Nn+n6dtex+0CaZVequeo2jf9Lv6DPM5pZcjfDmmg9yebrlvRc5dLrW9qnxFZKZo3ktuixrRMRGHLxFN5lE7pCGJ2r/M7DmG2KPII8rPJYqtJ3Y/hRXi6Apy/At6JDW2suvltO27cp+8idMT+zr1Qc01zTaL/R/zgwSfJ3Bcp+4vCqM6A4g/RxSmf2Jf0/7zHbrvbemy/RdnZy+Wf/6fa/8s5hg0fzT1pzvO2DU9w/wXxX9RuZL+iW2IIriVIP3zSvLPrUXyrxr/efTnhEj//HH559Toj/aB+DON36E/N0r/sn4t/Vu59GWsXPoyomyU/mW7K+nf0Qt2pH/dXjn9y7nV9M91QoUum28I2/C/VrBu3To4jqP8DQ0NpZ9HUYR169Zh2bJlKBQKOOOMM/DLX/5S6aNcLuPqq6/GokWL0NXVhfPPPx9btmxR9tmzZw/Wrl2Lvr4+9PX1Ye3atRgeHp72dWoHrEFkYWFhYWExTxFE0Yz/WsWxxx6LrVu3pn9PPvlk+tktt9yCz33uc7jtttvw2GOPYWhoCGeddRbGxsbSfa699lp873vfw913342HHnoI4+PjOO+88xAQi3LxxRfjiSeewL333ot7770XTzzxBNauXTuzizVDWMqsARp5dhyPc0P3b8rJeoMOYBAXwdSu4iUS+5honP0pOLMVypr31XxH5bmYj9egGTqO90mSPJwsRT6T+J9ToSyzqtzHrdH8FjrJf0Gf85hoGHz5mB4L6z+vZeU/OPuMs2vLROP155LA8KI8eVClAHGixjg42lOkFOrpr0DJqAvrtgcHuC8hk8koXiGBKIrwhS98AR/96Efx1re+FQDwjW98A0uWLMFdd92FK6+8EiMjI/j617+Ob37zm3jjG98IALjzzjuxfPlyPPDAA1izZg2efvpp3HvvvXjkkUdw8sknAwC+9rWvYfXq1XjmmWdw9NFHd+7LEqxB1Ebsj0aQxcEHxYhv6cAmXgINYlw6ZlDoDBtj2Ed740EaXd+mFh7TvU4GI4gzXYWKQDg2nm5zacxORRoXDo3VrZJGUNIdxw15Ff33Cj1D9qrmMkX69SUiihuK6KQLsnGMUN+CiXRblbQAVhRk+nwQ6e/JUGwnwyikc4QarRP+fLbRrqDq0dFRZXsul0Mul9Mdgt/+9rdYtmwZcrkcTj75ZNx00014xStegU2bNmHbtm04++yzlX5OP/10bNiwAVdeeSU2btyIarWq7LNs2TIcd9xx2LBhA9asWYOHH34YfX19qTEEAKeccgr6+vqwYcOGOTOIDmwz18LCwsLCYj9GiAjBDP6EQbR8+fI0Xqevrw8333yz9nwnn3wy/umf/gk/+tGP8LWvfQ3btm3Dqaeeil27dmHbtm0AgCVLlijHLFmyJP1s27ZtyGazWLBgwT73GRwcxN4YHBxM95kLWA/RAQ5Lg1nsC/dV7krbZ2cvnnmHjbxIRg9G/X26v3pclWzTjp98mp6lisy0CvOSMnOr9BvRz5Edj7fXSKcoyJFXhd8sSrYYbU4uk0K7kQp1wDpELElErqOJWjzW1UufT7eV6eSuwt3JE+mCqEPyIHEWGnuDBFUW7oe+hM2bN6O3tzf9t8k7dO6556btVatWYfXq1XjlK1+Jb3zjGzjllFMA1AtSRlHUUKRy7310+zfTz2zCGkQWjdFC6RGL/QttMYLagDk1fhrEEB0QUDLUkmvNgrF5+XJ0ahQMxGUyKIYoCSFStrGxwzSZEjfENo6g7iiUKSiQAUYCi8iTkGJWGm+jlVhk8sS+F9JtkySTbVJpVo2fuD/fkd97rsUYGe2izHp7exWDqFl0dXVh1apV+O1vf4sLLrgAQOzhWbp0abrPjh07Uq/R0NAQKpUK9uzZo3iJduzYgVNPPTXdZ/v27XXn2rlzZ533qZOwbzWL6aFRWnCn+rDYf+G4zf/NJg7We8915F8Ypn/OyIT8q9TkXxClf5HnxNpAEfR/Dv0ROJU+BaXgO5H8gxelf14uSP/6CqX0rz83hf7cFMaDfPrnu7X0z0Wk/0tS7V0nhIcIHiIEcNO/auSlf2Hkpn/l0E//OoW5yDJjlMtlPP3001i6dClWrlyJoaEh3H///ennlUoF69evT42dE088Eb7vK/ts3boVTz31VLrP6tWrMTIygkcffTTd56c//SlGRkbSfeYC1kNkYWFhYWFhAQC44YYb8OY3vxmHHXYYduzYgU996lMYHR3FJZdcAsdxcO211+Kmm27CkUceiSOPPBI33XQTisUiLr449jb39fXh8ssvx/XXX4+FCxdiYGAAN9xwA1atWpVmnR1zzDE455xzcMUVV+ArX/kKAODd7343zjvvvDkLqAasQaTCcWYnO6ZRBe1ZRCtK2/vohA/e974H2yrbwmI/g3j+3Zyc/qOyLHyKQD7DTlchbbs1Sj/PxscqoTg01fB2psy4LUJ9goIh1Z5iiFid2qPOF2XjTLnJQMY9sbK0T1oAOsVpBqfXcwwRw4+Soq6KxsDsooF+ZVPHt4ItW7bgL/7iL/Dyyy9j8eLFOOWUU/DII49gxYoVAIAPfOADmJqawnve8x7s2bMHJ598Mu677z709PSkfXz+859HJpPBRRddhKmpKZx55pm444474FE247e+9S1cc801aTba+eefj9tuu20G33TmcKJohv60AwCjo6Po6+vDH+UuQsbJNj6gHeiQ4TBrQdWzOX4bn2TRLKZbYPVgNtwdjQ4RGUGgRZS7VMZz1Ib60/bUYBynoxg4WQo+pnatINvVoty/moSzVHrJIOqShojTJY2OJYtkyvjSbtk+tDAMACh4UkepyG2qLMsGkS6omg0iLufBgdkiJmlqvIZrTnwEIyMj04rLaQbivfTLpwfR0zP9OXFsLMSxx+yY1bEeKLAeIgsLCwsLi3mKIMIMq923bywHOqxBNFewq1ULi5mjFYp7P33OZq3oMmeT8XXktGeSEHDIi+Qmb9lyt6RAOFA6pCQtE2UmHC8Kk0WdRCF5nKidIe9OoMkiY08Qf24KgxaeIVafZhFH1xZyPWhgDaK5giId34ICsIWFhYRBy0gYDrNmTLSIdo1juhS4cs70mjWRWs7xRFXS5knS6ksLiF6jovGRySDiU4qvwpeD8vJdT567RCVEdpdlXFO3H5fuKHry5CXKAGPjyKM4JJdOKmKSOBW/SteGDSWhSVQKO3cvdTqG6GCGNYgsLCwsLCzmKUI4Wk9YK8dbNAdrEM0VZjFweFbVqa2nymJvtOrtbOf5CPcH/1y37Szvz+iwufMWmc5nGlMnFObVc2uEGwG16Ct5iARl5pXkrqXF3LlsBnnZ1sQyqwKMTVBmpZr0AE3W4uDuJTlZaZ3hmeg18gaVEjLNo0GzV6hKApaij3IHa5lZdA7WIGK4Tsyf82Skm8jmohyGeNFMR/xwJuebSR/TRTPVuy3mDzrwu3A5DJNxcbb/53Xb5mP5D5OxM6dldvg3pHFEVNLDmZSp+f5YEn80JF8hbBxVu2W7Rmn1Eb1xMpPxb+OQGjZYqZqMoHJZGkFBQY5DZItxNhlDl01mAqtTc7wRG1Jin2oH58Qwmtkrx1Zvah7WILKwsLCwsJinCGZImc3k2IMN1iBiOImHaD46IhqsSJTVpWHflgpoWmrMYh7B8ZoIAJ7H9+z+WmQ5YsqMstIiUauMpw/+iizSSNtDEluskSZRiqz8DT1q5/PSU5XNyHEMaqgypsmYBuNg65D2EVtNGWnsORKB12E0H18SFjOFNYgIju/DcXxlEmgr2hxrYTKCGlbbpnPPlywcC4sDBY0WHvPeODLNA0SfZUbiOdIJqSgs26McdVCR/bESdTiQzLMlaXB4eWnsFAtyHu4rTqXtFd170rafxD5x4LBvqGofGmKIdLQaG0ehZt9WqLiZwnqIOgdrEFlYWFhYWMxThJGTpvtP93iL5mANIoKzcAEcNwfseDndFtU0NWt4BdWO1V6bXf33h9/e5+dnuW+T/9hfvUXimtlAa4t5gEZen3nvFTJAoSpd+aw5ouITO6npUeTMsjBLXqECz3XxHOMvkEHS2aycb7vzFMRtqC0mMsCqjhwnB1gzZWaqZSb2MXmFLA4eWIOIEPk+Is8HfLosgn7ikm/TndwMmVutGCK8b1Sr7mNPM9hg4rRk03nmjXE0j2NELA4OmAwbhRLTUNbNPHOdhvJcm55xMoKQaRDHRZfG4XUkl4esUH9+/DxXp+R86/sUp0Sejf6cTGHLeVSw1RUUFokuGiiznLvv+bJKMUZeg7pnnYSlzDoHaxBZWFhYWFjMUwRwFV2k1o+3aBbWICJMLe9Fxs+jGNKK4MVtAJoIVG4R7XChN6LGmuqDxOz2K2+RQKdFAS0OOtxXvTttN/OM8H0onq+58AoZn1Uxviael6hKOkQ1mgOTOdKh7+2SbpA/LncNZKUNJeXMGY9fP5Ent015OejgOD3a7QWN18enuXogQwOhy8GV7auJOJLJE+Shfq5uIuexbYhmGEMU2RiipmENIkKUcRBlHJSW96fb8mMT8WfDI3JHnkeaMWw08S7TzjohA4BjgdphHDVjXIixzjvDCLCCjhazAjZmjNRYo3tsfzXcIwNFmKhWZ2SYD2pk+HC4TnaEYnMmqUZYPondoXijqCavTS2Q+5Zr8lW1pyRPJAyF/qzMQitnSCySjJxuVo7UQGf4WBxcsAaRhYWFhYXFPIWNIeocrEFEiNz4L8zRCs6Z5s3UQgDwvPS2NECrlN+cfsf9dXVu0RpmqdxMM14hXR01hhJUzVme5pPK9ny5f8lb5JTjwObMJI2tX3p0XAqqjkjWzZOOnFSfqNbF3082w0D+oxbI710O5GtrssYSivUYyknPPmeOheTmLyWUWTMeImFcdDIuJ4hcBDPIegus46tpWIOI4ETxX+DTU9nTFX82MppuaiqeqM0TVyfSdo2T9ixNwvM6Nsli/0CnMw/b8Cw0ZRxN83tN+zky1W/kBSFl30a5xIgoy3FmSkR3GRaSlMiV0moOGT5hjdpkBJUq8sCMRwrWmTiGKEtp+f3+pBwycXelSP+606Xjs/GkKFgn+wZznHlmMTuwBpGFhYWFhcU8RQhH8Wi1frx1ETULaxARyn0ealkP/iRp/fixG9jp65Xbdu+pO7ZtmMsq821Go9XqnHiF5gv9YLHfwFQipy1Znq1Sae2A+A6cfWR6Fjioelx6XoQwoxPKeZFpMkPxeQSkSSRqmXmTJA7bQzpElLXmUHZajmqZdfvxiVZ2STHdpVlJk7F3R82F2X+efRtD1DlYg4iQmQyRqYZgUdQoG1+ilm+pVl68B5ARtF/Bql3v/2glu6sdp2umyOw0YTKwFEOpwT3bFhraRJ9xLFVvHEqQmZTGSWZSf20CMmwCpsySr8Lq1V5eTr6OS3Qc0WQebS8Fs/8K49gi0bYZaQcmrEFkYWFhYWExTzHzoGprvDULaxARQt9B6DtwKSy/vCjWvChsb3G1pVvBmVar01zFtkV7qA3Y7wOiLY1m0STme02yVp5FxZvUjOOLAqWd8VjTx8tRuYuq5MNC0kvkd7lXJQor2cedomDs3bIPp1d2MkX6RFymAwnt9sLkwnQTU2bVSH4xf5rlOHhfmWXWuTkvjiGaQXFXS5k1DWsQEULfQZB1FFExwYVHfV3pNiXjjIu/Nposm3nZtmActV2YsQXs90aQxcGBdtPRsymMakDDYs0kHNlMrbWWQP2FY1L12fVjQ0gINAKqarXD8Uks4k1za2ZK7Cu3uTUSY8zLa+1kZJtT8CuJYGOfL/P5/7s8kLYXkWQ2BxfrDCKOK2qkWm0pswMT1iCysLCwsLCYpwhnWMvMZpk1D2sQEYQwoyJXkaysInINu4OL5DEvbWvDiQ2UTaMVbZvpHZP20EHlDTqA6DNTeZhpl42xUDGH90fbdcJave81OkPelOwjzNA9RvdbwKXKRHw4OdkVSSD2LFG9M67NJeizLqoh4jmzd08Lz1ErlNtMYWOIOgdrEBGcUGY+CNRyiZpqr3ySsy/u7OSwZhUdS/W16DhMxo41gtqDRurUHcNcGPGVOJbAmZL59S4VMwupnpiicM0ijMmUyrXMKgN61WrHpUwvyjjrycWxTFsmF6TbXtnd2vwsqLJmUvSFt6WTafshXKtD1CHs30tgCwsLCwsLC4s2wHqICLW8gyjrKDpEjm413U0B1iXpqo0qpEbWyircQJNpV/LKCrDNFXX2c4qo7TiA6DOL9mC+ZHYqmO69qVDyLeorJR6gsJs4MEXAkjZTKSQu3SEcSiGJNUY5CqR29XMo6xCJkh2L82PptiKpQpqoLZ2HxxQozX2IrLVqB2moIHIQRNMPW5jJsQcbrEFEEJQZPyuCCw99cpv2StewOy4nBMUg6gCmG9vDWSn2RW9hMT2Y6OaOG01tMNzVGDNDf2zwlBPKjLLMwhwZVTQ1cW3IahdtT6iyKKM3pEAGUcaX5+EYIqFUPZiVBpE3zfgeNnxMlNicFHedYVB1YCmzpmHfhhYWFhYWFhYHPayHiBB58V9IV8VLFiNBjsTDeijAuiC9RRiVq5R2QOcBUmg0Wr2x14eDPXXZKEr5AQNFd1BllllY7APGZ2EOvatzUgON4SVzSUmKJypzZEFPk+k0AtlDFFE2mZfRB1hnfZmWVkyyy3ZVutNtQzkpzGgCB1DrqLJGn3cSYeTOKIg7tFlmTcMaRIQwAzgZKEUPvUyDmylPyqoFmS4RTUihsLQG0Cxm9/CkzcaRtvaSIZ3fGkH7gI0nsgBwtv/nadskX6AzUOY69qjdi51oKs7uwqAUQax2y+eiNECq1qxdS/FCohhsZrc8rkxDC30ShSTjKE/FXYWhME5VY01xQ2zksIHhJbn+yjYygljtupPp9gKWMusc7MxuYWFhYWFhcdDDeog08Kq0mtIsCCJP2pFBr/QKebvl5WQxsnZ4hhpqx7CQoilhROcZandpA1PfjcZhMS9hRRzNMF6PDnsQjfTZbI5DQ8PU8iTASJ4ghyWJNEPySrQv6RRFJLCYIQ/RYFGW4xiuxCELx/TqBXJboZpM9cl0ooid1SGaWaaYnW2bhzWICE4U/4UeuVaTK1TLu8p+Ah4VJkRfr2yPT1DPyQEmI6kJA0G8mFp+KTXq2xonFvtAM7WxDlZDqZlrMG/EGwlirCbqrJmMszQ8oEZp8opciWyXZN3VtH4ZANSK8Xmyw/q6Zy4ZQYt6pRH0yu6X03bOiWOYip7M8DVmiBm2CxqMj2OarBpSgdgkzb/WwXlz5sKMlghqFvZKWVhYWFhYWBz0sB4iQugldBO5e0MR+FfSHoKgIC9hhqg0l7LPQuEt4lUkr846KWqxDxgDL9tdX03ss796p2yA9UHrFWoK++N93eI9LeY0h4Kq3Zq+RAfPndVuCpROgqYrJ0lverStmLbzeZnBVsjIdrdnmIw1CJSyG1yag4KmExqA9Yv4OA5oziF+ITgdDFSeeS2zg3OOmg6sQUSI3DjtHjV1G6By4p4Up4ZDXHqUlZfT8Vq4CdsdxzPNPozZJ+2e4PfHF4YJ4rscpIbRvEen7jU6z1xnlHUESXHXoIcWfiTAyJQZCUcjw0Vhk0emMiZlTBa+crf2dHlPTso+cXPTfdnrjJ8S6QOEFLPD5/OT1LjApZfELCOEoxhz0zneojlYg8jCwsLCwmKewnqIOgdrEBEiN/EIMVuk8Yzy/RXRiifKaIIOARlgHczianW6NM6B5K0xwApOWswGjCKplOl1QHmLlLki/r5RhmioKk+W+kBp3hxm4w8iotcmpqS36Jgl29P2CX2b0zZ7bHRohibTGQkcSO1RbhafLy9ElRqMwWL/hDWIGA7qlFTFcxNR5hm32U0c+ZTv7rM8qwbtjsFoxgiaS+OnlZR/2h4FgXYf8aJRRCgNxk5HjCAbV3RQoKnYKY1x1CnDqJUU/JksFJxsHEPgBGwU8kqSdjY8Dk413snpkc9OLitjhfKebCsUVwsUEBs+LhkxjWgkl1bCeVeOYywpwFYKqnXHzBZmLsxo56NmYQ0iQuTEf1y6w62KB4dVU8kI4tRbjhuiycFJjKMooOCjuUCjF3WnDSZD2ZD7g7ua7kIpU8JFaw3QpUpbr5HFrCF55vYrr5EyD3DBVtY6M4mdJV0oXnTZ5rk1SoKq/aI0LjKePHct1CtEV8PmX1tsSHlK8VbqO5nblVghavO5+zKTAIBcpoMxRJGjxDRN53iL5mBNRwsLCwsLC4uDHtZDpAPT38lCQlnx0OIozOqptCgrd3J64sKDUYXSLeZjCn4TFFY7vUhRi+KUTXQo2wZvmE0Xt2gVbbln5oJGbcM5jSKNiTfcrcgJy6F9FaV+nk95KnETDy3RU+MUQxT2yQMnA7md0SieyBQ3xG0/ocTYg8ReIa5rJsQbO6tUPTPKzAozNg9rEBHCHODkAJeesSAJBSIaWWkzNRZSUHWUoRT8rJ/8V+buRyWizwzBmXMa8zOH5QcYxjiIWTLMLH1mMdtoB31mrHDfwnPb8r3ONFk+NlCq/dJQqRZ5cSh3rcnMfIR5nuuS48pU8qgqx19ZLDsx0T6hU7+djR3PUIyVt79cjResh+Vkyr9qEHH8klv3+Wxj5tXurUHULOyVsrCwsLCwsDjoYT1EjDD+45g9oQfGLmD+XGlTxllYkFlmTiXJyEioM2Af9NmBhE54uNrsNWqmdlcTnfCBMxyRxbQwn+UkOAuNEgGaqXum9Qx16B5zctIbFPX3AAACX5673MfJJvI41jCMSvX8WUhp968+QqbXH1bck7Y5JZ4zwFxNkVn2JnmGx5aptMHsaNKvvGd6XKmG7TvyC4yFsbur1sG0+wBOS5l1uuMtmoM1iBhJ2j3f68LgIRFT5QEPcvTwlUn/IktuW5GOn6VOFFdvE1Wzkwm+1aKajfZv5kWv1VuZwQtH9NfMC6AVSsFII7QBllazmFeYgRE03fuXtdVEeEBQoLR2mjerNNWVB+UH3gTF8fTGE+mixWPptgplluVInVrVCOKYnvi7mGKJqqHekCpRf37Szjv6zDHPqT9fJzO3LGXWOdgrZWFhYWFhYXHQw3qICGEGcDKAQ3XLnJQy4/24Zg9lMVDGWYZcyUEx7jBTltHYbpcsYpgWfwWmTZ+14jlqWYRN1/d8Cf4mtCJKZ2HRCJ3OSDzb//PG59Z5aJu4v6ftFeJAavJw13pj+oxji70p2Y5kzVf4e2Qf1SVyDlx2SBzEvKx7NN22MCvnwnJAhbPdBurUtLbnIGhGmeIbOPNKeHuE6CKg0melyK9rlzvoIQowM9rLamo3jzl9U9x888147Wtfi56eHgwODuKCCy7AM888o+wTRRHWrVuHZcuWoVAo4IwzzsAvf/lLZZ9yuYyrr74aixYtQldXF84//3xs2bKl5fGE2TjTTJTwSMt4OHFxV/EX+vKP941cJ/0L8p7868og6Mogyso/dHXJP8eRfyY4bt3E57hO+tfK9pmgYR9RKP/mC3hMnR7ffLweFk2hHc/Lvvrdu+8ojNK/JjrRzgkzGJT2z/Ez6R98P/0Lch6CnIfSAjf9C3JI/5xI/oXZKP1zxr30b9vOPmzb2YfnRxakf1unetM/ETvTrhgY14nSP0YIFyFcTIbZ9G+kVpR/QSH9mwxyyV/WcJb2Q1BmM/mzaA5zeqXWr1+P9773vXjkkUdw//33o1ar4eyzz8bEhFwl3HLLLfjc5z6H2267DY899hiGhoZw1llnYWxM8s7XXnstvve97+Huu+/GQw89hPHxcZx33nkIAmsbW1hYWFjsvxDFXWfyZ9Ec5pQyu/fee5V/33777RgcHMTGjRvxh3/4h4iiCF/4whfw0Y9+FG9961sBAN/4xjewZMkS3HXXXbjyyisxMjKCr3/96/jmN7+JN77xjQCAO++8E8uXL8cDDzyANWvWND+gKP7jAGqxOGF3MMdAh1zXjBYNtZBotaT8R9AjXbKZigzgc/t6ZR/DI9S5ZqU4D6kqBaYVK421E0HJRvqsDZhucLrFLGM+Pg8atErFtZpI0VYwZZaRbcEYTS2SY5sapMQDyhyLMvox+/l4Dsx6VGOMM8RMySYNwPQZB1tz2bWMJgibz11uoTyIxYGDefWrj4zExsDAQExAb9q0Cdu2bcPZZ5+d7pPL5XD66adjw4YNuPLKK7Fx40ZUq1Vln2XLluG4447Dhg0btAZRuVxGuSyFEUdHYw47LERAPoJDxgyE3cJi0iyaytu5DikZBoL+dumJ9LIk3EjprMzZR2Hn6uW0jFZTy00FWzuAljLVmqiHpoPNQpsjtNkImjWDo9Vx8vMyW2MyPLd8/zokMBuR8GytGLeL2+XYKv3yuMqhJCsyzkZV/Xc5tFsuAhfmxhsOO+D4neQ7+JCxSZyRVoW+gv3etFncl2yaaqCJ2KLA7dzcHMFpWIy20fEWzWHe+NKiKMJ1112H173udTjuuOMAANu2bQMALFmyRNl3yZIl6Wfbtm1DNpvFggULjPvsjZtvvhl9fX3p3/Lly9v9dSwsLCwsLGYMS5l1DvPGQ3TVVVfhF7/4BR566KG6z5y9go2jKKrbtjf2tc+HP/xhXHfddem/R0dHsXz58rTaPcvOi8UIe4U8rrpB9FpQo5UVeYOEFAaX9gi6ZYdMnzm9PfLce4bpRPNMvLHVYM4OU2bTxmyKKlrBxoMWrZbomK6nkmF8zlq595gmI++1Px57ecv9si+FhSKvUNch0uszOU4TaTKlbZ2Ucx57iFylOr38Lj7VVHMTrw/TZOwhypG2EO/janKvFE+Qga4TNJ6n/dRif8e8MIiuvvpq/OAHP8B//Md/4NBDD023Dw0NAYi9QEuXLk2379ixI/UaDQ0NoVKpYM+ePYqXaMeOHTj11FO158vlcsjlNMUCvQjwIkRkSIlMTE8Kl4KyMwGKLXINCta1RLxMLX8jA464QKIbkOFQkieNJjiISewwd/FELcc1aMbaqkpvJ6DEHrXhpWSENY6mj07TZDP4rebLfd0IRuPJJYOHVKHFXMYxlGq9R3ncxKicMIcGJT1W8GNa7fAeriEmz5EjWopjepgyE8PmzzluiA0p7k8nrOgZKDM2zETGWydJ/zByZiQE2UkRyf0dczoTR1GEq666Ct/97nfx//7f/8PKlSuVz1euXImhoSHcf//96bZKpYL169enxs6JJ54I3/eVfbZu3YqnnnrKaBBZWFhYWFjsDwiSavcz+bNoDnPqIXrve9+Lu+66C9///vfR09OTxvz09fWhUCjAcRxce+21uOmmm3DkkUfiyCOPxE033YRisYiLL7443ffyyy/H9ddfj4ULF2JgYAA33HADVq1alWadNYvIifWEat1yRZAZFxL1cj+XKDN2nSrZaUogW7zq4WBtbld7pbfKp1WYG9LKtBqvbqIKLcMsZhed8uKI81hPkRlNeIVaCWxvxqup8+40JfjZBg8Wn7sVT2Vb6Gj+XiTGyEHVwlvEXu/S4pD2petLGWc7dkt6LOPHfpZDu6TXKGQvDlNcuiDoJmAq6cEQfTNN1slq9hbzB3NqEH35y18GAJxxxhnK9ttvvx2XXnopAOADH/gApqam8J73vAd79uzBySefjPvuuw89PfLB+vznP49MJoOLLroIU1NTOPPMM3HHHXfA81pjesNCABQCeBPyuKAQPySZCZpoaL4oUyx3ZlK2s1J8FWGiYB0qNdD0k2noS8srSxOQlxhC0W5Z8BCB4YU93+kzufMsjKZ9aCbuI31BGrKCbFHYJjFfssUajKPVWKB2YLrGkQLN/aTcm/y55+q3E6pd8RxZ7pfb3DL1R21/u5xPpw6Vk+CCQ2K9ua6MXGH6BkPEMxhEwmgyfa72wX27ddt9MsYaGVJBBw0mS5l1DnNOmen+hDEExAHV69atw9atW1EqlbB+/fo0C00gn8/j1ltvxa5duzA5OYl77rnHZo5ZWFhYWOz3EEraM/mbLm6++eaUqRFoV/WIPXv2YO3atWm299q1azE8PDztsbYD8yKoet4gKdMR5sh1Wkq8O0SH6bLQ9t7Oyu5u4jkKco52X84+i2jV5lInbl9Xso0CBk0ijrPoym8H0mr3c7DabjfEdzCt3K23aB9ow/3Ybo2eeX9PaujVtmSTKedgBUPKMtPoEGUo2YTfu+ES6fVxu8gDVMnQ7vF5dpW7020ruijAmnSDGr3UXYPGkJJxBvIAUQS4SEtnIci8Q3UnNdXuI2cea8S1CY899hi++tWv4vjjj1e2i+oRd9xxB4466ih86lOfwllnnYVnnnkmZW6uvfZa3HPPPbj77ruxcOFCXH/99TjvvPOwcePGlLm5+OKLsWXLllSg+d3vfjfWrl2Le+65p7NflGANIoKTieBkIkQ0UQsDpdZVn2EBAF6JFVn1Bk81qePqUfhP6FMmGxWFjVwSbyxTAcKuXHI+SakpxtEYCZpN9yXRzASqeYk19VKi4/aXzJtWYPpOHHMShY2F8LRoxTiaa0OqBSOnlSLExn0bnW+a9/R8hc5g46KwzYgtaqHQZ3Tdff0rIkjmL5YjCfLyOmbz0mDoK0qradFCWZZJKFQP5WV8gS6DrBnsrnal7Z0laWAtLchFI9NgC3w5DjaadGBBR5GJVmsiNqldCCJHFaOcxvGtYnx8HG9/+9vxta99DZ/61KfS7e2qHvH000/j3nvvxSOPPIKTTz4ZAPC1r30Nq1evxjPPPIOjjz562t93JjhAl54WFhYWFhb7P0QM0Uz+gFhvj/+4WsPeeO9734s3velNdYlJjapHAGhYPQIAHn74YfT19aXGEACccsop6OvrS/eZC1gPkQZKfHI2WZnm5IogmpIrCrdiCDqU5cmQHY7/y9pELB7qVfTbReAiAGQmY87OLUgazYnkqsipyRVZNEV+bB2V1uqKeB5SG/sLTBQMU2yteEo64s2YRS9TU56e5JxKiZcmxjHv6a5ZgkLLNpNH0uBauj3Sw8I0WZiTE5hXjc9ZWqhPNmGaqTsrX7zdvmwvyMaxBBlX/s4cHN1M7Mt4LZ4PX5zs034+xbEL5BXSIaDM4MlQur5yRK8JL1MnU9mjGVasj5Jj946rvfHGG7Fu3bq6/e+++2787Gc/w2OPPVb32b6qR7zwwgvpPo2qR2zbtg2Dg4N1/Q8ODhorTHQC1iAiuF4ANxMgpGR6tye2VoISTQZMZWXlA1yT9gmyw0SJJc8We2ZZ6JHBcUZMzVV74vO7FSoQWyMRx16ywKpkHDVK02/iRdNKHIzxc5vpoKCVzKG2xCEx2p2R2A4a9SA1ZmYT007BL0haPqKai5HH1H7cZtX+Khe9JlmRqZoMwOQXuzAumskQYyzIEN2V0NDdGbmqrNE5OH3ed1mwsX7eq9KKdZImazaUim4lOcf+t8DbvHkzeuk9oRMn3rx5M973vvfhvvvuQz6fr/tcoB3VI3T7N9PPbMJSZhYWFhYWFvMUAZwZ/wFAb2+v8qcziDZu3IgdO3bgxBNPRCaTQSaTwfr16/F3f/d3yGQyqWdoby+OqXrEvvbZvn173fl37txZ533qJKyHSAMvR7XFktVLwKufHGU/FOUHmT0kL0/3mlh4MDXGuo2cwcZFlJU6aUngdZCX53B6pQXvccmPPvIWUSZaJDxHLdIguuBWpSL2fK5Nth+gFQHAaXuLGK14hVr0IKXjs96fWQV7FZu6D1p55kmHiCmzarece6YGkuxbYqSiLNOe+q7ZeyO8NIHB29KX0ZQrgkph9SZpbtX8mDxHKL07BWXS3Tc4uJpLgbB+kfBqVTvoIQqjmWkJtRKtcOaZZ+LJJ59Utl122WX4vd/7PXzwgx/EK17xirR6xAknnABAVo/49Kc/DUCtHnHRRRcBkNUjbrnlFgDA6tWrMTIygkcffRQnnXQSAOCnP/0pRkZG5rTChDWICF4ugJcLUiMIAMrj8RPveJyKSmn5UzR5+HI7Py9OUvSVn02OJ2JqLOD0fnIdekmsUrVHPrROQCmsSmwRvTTZOBqNJ42ZqF23w/gRk/mBmG3WLrQj9mi6aOY3Vs5tjZ+5xXTjvHhRQ96CqEsutJQFGP3kuZH4H7WC7KMiy02ihzLLIsPLnI0OOSR5L5VoRSioKgCYDOo9G70ZfQwCZ4iZzhMkhpDO8AHMhV4PRPT09NTp/HV1dWHhwoXp9nZUjzjmmGNwzjnn4IorrsBXvvIVAHHa/XnnnTdnGWaANYgsLCwsLCzmLcIZBlXP5Fgd2lU94lvf+hauueaaNBvt/PPPx2233dbWsbYKJ4r2w+iwNmN0dBR9fX149Xeuh1fMYWxSrpBq5fgHDCsUEU1ZZsL7E/+DOqWrKiTt/XH9vh55hlnegj1KoixIdpy0Pkblzg5xevmtUpPImaSox8n4ROEI1RUJDJTILJZSENlD1kPUPuhKiLQlW8zUh/UKzSlM2kPGchw6sAjsgv60HRy6KG3XeqTnmbNeJxfHa+nh36Pneqn00vCpu7vlBLeoSwZEH9Ydx5gUSKCNxRE5+4w9NjrdoFwTZTfYA8S0m6Tu9Ncrr8kyK43X8JGTHsTIyIgSqNxOiPfS2gf/AtnubOMDDKiMV/DNN/zvWR3rgQLrISIEoQuELkIqRhjWkoevyjmlshkW6IXBoorjTG0l4o5FuStnZyiFY8kIovkgjUkKqUZQuV+eg42nIC9v+q7fyRgiJzF+3C45kHCCCrCZjKM2QE0dtzFH7UYrRokSnzTNPiwOMPRyqj1lWPlysguysl1JnAFKSa9ttJDslZNXOSuNlWpB9l1JKDM2iBgcN1OO5KuqkZBiSKEGnP6vo+gAPSXGdc1sodeDB9YgsrCwsLCwmKeYC6XqgxXWICJEUfzHMgiOF68OIvZYMt2V4fIZcie+B4OuuA9vSm6skVcoQ9t5EcOaYmIRUy2yThGXCiHvlCtXcuWlktfNbUkyxIjucFmmf1K6tiPSMtqfShtYNIb1BB0AaIdgJk90FDnBekOmbFixPTOhf9kqdc96KVMtkN6drsStrQQwk0eH9YmqVPamQpNkIaGzxmrSO5Ul707R4H0KFK2i+PzsFfIMgdQihT0wpdHNAuZbDNGBDGsQETJeCM8L4fskbCiyiMngKBYl3zU2wnwXqawWyeiYjCeBWpfclpkgV3SeJgEyjjiZQswZVaLdXLZTlCqzhs2FeEZzaoY6PKR2DVYJDg1xKTq0W/TPwsKiHm2JD6OJguhyNoi8Esl5UDq7CKvxORyRdPycKs1jVT3FNZWk1HL8D8BxPnJ7xqN4Ipr4hKHUQ1lmzQg9shEmDCE2gtjgCTQUXdXObQckrEFkYWFhYWExTxHCmZkOUQe9Wfs7rEFEyHk1ZDIeKlV5WXw/Xkn0FGTw8Z4x6aZhzSK3R7pna+MkV1+M+3Co5EdQ0HuFaiz0qGxPzkcLEy7/4VMWV6ZE7m8Kioz8eKUT5cn3TQGUymPD5T8UzxHqMc0VKmvq2IwzC4vmcV/17rStZJyhmWJmGlTl3MUZq2Fe/2ynTBSHBjDTxokpJcpOq8i5Z9tUTOdzUDUHMPN2zvQKItYIiudqU6C1Sx4n9gqZNIdSEM3EfYvstKCDgdYRnBkZNZE1iJqGNYgI+UwVmYyLICcfhp5cbHVsH5OxOGwEZYvyQa1Myofdo+0iZT+iumegbDGm0rhYbI2NplK8P2ee8TMZZmV/Cq1GFHpQiH/ukIykzCilu/ly/M4CKpa4h9SuWxEDNBlKjlC7tm5nC4s5A8cQ5SU/zwaRsjvbDbpdqDsWmQ5HKUNsoTQuRkpxuMFOX2a45ckI4vpkLqhNAyknrzBWpw6V+mWg45iaq4cSN8R90D7COOokZcYV66d7vEVzsNFWFhYWFhYWFgc9rIeIUMjU4GdcxaIer8QrJ5afLxZIRn5KpoL5BbkCUQIJk2Brx6fARTqv4umhf7hc9yeX9FHSZ6Rx1hqLRdYKRJm58VonO0Lj7JLj54wz10SlJd4iptGcDFXErhlWYRrPEnubLH1mYdFhcFC1UoqF27LpUTl7txa3I9IXy5CkGSc2VaVzHTXKMpsox3PPlkh6o/sLJOKYlSKOocdBzrLzqSQVlzWGmHZzncZlitIsLAMNppbxSLLMOkiZ2SyzzsEaRISsF8D3AoCeIZEG2p2X1NJ4SbqXRYwRsNc8QinxTj5+eGolKhjIlBobT5Pk+qUisk5yU3Pqa016mgEpTg2XstOmiP4r7ErGQYqzSkYJp9tS2+XaaInR5HCM0agsrNgMUpFGMjJnqy6XhcWBCKW4qzfNuCECizEymD5zy5QlW4rnFX7XRiQOSzqKCKnGI2pcqDgJA6AQhCnK8B2uylVeV4aofYIwhNgg8jmuAI0NIpFRxmYDxw15Tn06viktfzZgKbPOwZqOFhYWFhYWFgc9rIeIUKplENQyKGY46yFeCYyWSPiLVjGsiTE6JV0zWZKrr1Tiy5zvlkuoGq2U0vIgANBFq5sSraaSXRTNIgrMZtOWS4RwcGNamdphGk1+Hnk+tSnwmjxErvDksHCjY1iBGCqiW2+QhcU8gKOfP9TY4kjbFjHMyvzBcxB5qSPydIdVntPi/qrk4QpI82y0KufcRTn5qso1KKvBpThMmkS6umXsSzLVQxPn62Q5j3CGWWY27b55WIOIkHFDZPbKfComHHQ1S6mXnnwgd012pe3uvDR4pii9NJ+L+3Cp74BUF/0CpeuXic7yuaZafGxmjCYPmnQYSmYZZ6UlqbD8LDtB/QQFAFGGzp2VFlauEn93Z5LpNTLoai0oXCtFKW3GmYXFtKA8Z9OkzwyLGo4RYqo9SLJaFQpfToVK3BBy+mfb9eq31wI9acFZZAyRms9xMhlXb8wwdVSl6yQMBo7ZVAxEFnFM0vg5nX+2YSmzzsFSZhYWFhYWFhYHPayHiNDnTyGbDTBckcF8lWRl0uPLoL6RinTlFrNEg1EGRd6XbppcQrGNTMp+mVLjVVGWSn6UR8mDknh3aj1ytZIZl+dTaqPx4kXnRWpiNeiPS29RvkSB47mk/AfrlpTktWmGDnPc+vOT1prNOLOw6AAcnylyzoo1eBQ0jzZnulZ6ZbvaTyKIOWpT7cdM0s7TXDhQlKlqAzmZZcZeHx0N1utLldpyoH+tBbz+J6+ayB9haqnKQdqe3uPUKVgPUedgDSKCkEjP0sMnKLQacdsFijHi7YwueshLtXjiYSNJ5bDlzzA1yRVdJaLepL8JzkLjDBCiuFhJjE4jPL9UBxGcVK8cx6MjpVm3HO/k0jiMqfYNoBhPlj6zsOgMEoPH6ZO8llLQNdIvalyNYCO/a33KdFWKuxbI2KK5x0sos4JfH7MJqDQYv9R1BhFXdDel4Oc8/XkagccRJPNU2EFyxRpEnYOlzCwsLCwsLCwOelgPESHjBMi4AS9iUlft7jIFT/skzFiTbhXOTqvRqsJLPB5dWel5mqhIT1BImkUcaOhQ6Y4g0TDijI2I6wVxVgf9qs4EB0eLk8jPSxQImd8l216ZAqxp/1DUQ6MVoJMl1zvRZ0rpDk11bvYEmag2rtPE9ZssLCxmCBN1Ts82J1cEfv3+XKg+Yv22CnucaA6iOUuUQJqqUskjmhN2OzKZo0JB1X2+TJ3lemdym5yfWU9oMpRz7iJyZ5nqoAkETKWlpTusDtGBCGsQEXwnhO+E6M1KPno4KQzGHPVkIB+sXootYg6aDaWuxIDiB58pM9elNNEMGwk0kSSGUkRxSqJoLKAWjvUotZU5fnFKZsQzkqYHJ3KkKfoAMqSOLSbIKCs7drJE83lsBLESrsYZaaDJorml7C0s5j8MmWW8sFCoZ11dQYobYiMopDbXdawVac5K6DOW/uCFGPSKGwDFS5YT+p3tMkehzPTzKWecLc7HorCcJs8Ulyl9Xpd2r6brzx/aPsLMUuetyEnzsAYRoccvI+eHmAzkwydWG/yQMf/MHPU46WZwYULxMFdDVj8lbpt0jXIUxzNRkoaGk6TEe3m5rygaGw9ENkPSKuI4IycxsDLj+rghXihxgHWVJ8JEvj+kgSplPjxDbBEHajYKvDYUhbXB1hYWbURWrzumxBNxk2wEIe0R0VzIFe45RMehhV1EU0KYnFPotAGqt5w1iQKq0sqeeGE08aIShpR4jj0aZ9mTxGgqkmcpR9olOlVqq1R9YMLGEFlYWFhYWFgc9LAeIoKLEC5CFDkbIVltMM/cBUmTjVHKVlHxCknvjuC/syTo6BkyqUamZH9s1+fy8ZhqpGodkto1iD4D00/j5AJKv5beza3GCtHJJVuYrvwiWrGBKDOmzyJWhWT6TJfW2wRNpkvXt7A4KGHwojZ1aOLFjXw9ZcYeopBiiLiumVhKZ4gyY+kPRe2aFfdpnhIxkCHRaAF5ethbVKS4zSylwQvVapMitQns5Rd9MI3W78n0f6bSRBxSzZleZu10YD1EnYM1iAiuE8V/9Hb2EoOI5eLZ3VpwOZWeJhIyOoSLl2/MEmllTFalEZEjnQ5+8AXdFlEfLpUC4UmlWuKoatnUFT1mip3jjUztSk/cSYaDtYvyeriTpE9UZn0iMgCFcdRMCY9GatcWFhaNoSQ1JHGAbATpY6CVtjYchwyLjLJw0r+EmT5D0ua1EscQ+Ya5MNMgvsdkHGUM8URiruZ4o91UObuPjaOk71bS9mcKaxB1DpYys7CwsLCwsDjoYT1EhG6vhLwXaDMQFI+PKz0zZUdyS+OUfZYn2i1MqB6m0XiFwVSaT6KQnLov6C6vIFdHJaqXVmXPEWWqsWMmpbs4eJpZLfYK0T7VIilYT8SdBDkKEKc2fL26o+PSajTYNz/G1FjE2Wm0yj3LfRsA4P7w2/vsy8LigESr9csczfNH6V0KBe7pPQpKdnqyCydfRJwuxg5hQy2z9HPyGvGZmaridpa89SLZJSA3k8lDxJ4Snn+F950VqTlZZpI0TcT2mk27PyBhDSJCnHYf7LUt/reiVUFzBxs5Pj1EPmUsTCVZa6yAzQ+kyQXMtJrYv0pp9wG5vKscW0Rp98jQg1uLH4yAFa6rGp0iQIkB4HCnSnfiXqbirkFejtPLGQyiHumCjoZHko55AjVoFimdUJp+8t1t5pmFhR5KCr7GZopcDhpsokN+XBOjSVk70ufZMdkuGeYjYQjx65rNjImynJDGstIoWZSXWiGpRhBn8NICM1CMIDQNzirm9Lq5KO4aRY4SKjGd4y2ag6XMLCwsLCwsLA56WA8RYWFmDIVMBmNhvu6zQNHHIGVV8vR0ZWQQ8RRpGQm16yLty8qr7Dni7TqY3J+O4iZmN7bcKjI8vFH5s9dIDZt1RPjGCKWzK3WR13K0rxJgTbRgf588bpwUIIVrvUW3s0qlRXXbLCwOGswgy8ztisVmA+6CHbT8SBnUrFPKTNEuo48pCYsd4I2eeE4OYX02BhffFvMhK1bnOATB0XvldWAWgCmzufYahHBmJMw4k2MPNliDiJBzasi7kfJgNJJ1L3A8EaVjhS67ahMxQ5ppfJfOYTCCuHCs6IPFyjgtlScS1yfVVvbsVpLChBSHxJ5fb4rVag3ijRmxjQzELKlkk4J1tEAWj8TIKOrAky3POsRa6owgtQ95oC3zYWFhANHQ4WhctsKpkTyHyfCJ9r3Q4nmCQihRpUdfQQOmiUsXMdWzdbdcXFX7aDHZHSTd0gKNFpgLMjJDLDCYNmK7S99VpcyCun1tcdcDE3Nt/FpYWFhYWFhYzDmsh4jQ5ZZRdD0lEG8sjN2z7Hrt8aTgBnuQlCwGj4S/Er163rcW6pdKHKTNEN4iRYeIRMx8ci8HpGsElrTPx99ByUIbpX0N0Y3sIRKrQPYQhVTvKChQjbOSHJPihU+COSPDNTAVhdXRY6aisDbY2sKCoKHYnDIXRiVVRQNl1sjRwIwUa5dxodcgSxS9oN0Mnm5i6hVH1Y7dvWlbZIYtKchirYrXyrDm12Wczaf6ZQwbVN05WIOIUHDKKDoeSpRKn03I8JAmhhLk55xTxbVwdKn7XG25VTemeGgzlBqapT7KVA8ok6Wir1T7TDwY1Sn62clwU+KNOAVfYxxVqfiry1kkdEu5JYqj6pOTmMgyM6biK4YP0Yk8EF0mmqFYrDWOLA44KEKLJqkLfqDrC706Y5JOwjJ6Ppmm5rWJYbvcKJukVwtvigwUsruEUjWPM6jpDRhe/FVI+XrLjgUAgL5D5SK1ZlikMs3FWWJiscuLXm53sm6ZDpYy6xysQWRhYWFhYTFPYT1EnYM1iAg1eKjCQ57LcehcrrTYqtIl5FUF02OlsH4FF2qXWKpIIwcKCiufrf0yuXGyRJnlCnIcrOVRE+5oXjiyh8iX7UB5huQ/ql3JvvS5Ry7xTIl1jcgD1NMle0uosmhMurmN2SymTDSxyuW6bexBMnmLEkFHwIo6WuzHaCLLTNEh0tHNlPmpeIfZK8QOWprGdDpESi1EYuKDPHt2SYRRjE9x/O47qw0AHJ6zkonqxTEZdL20IBM4VF25+uBohpJZZqDPxLxtmr8t9m9Yg4gQRC6CyN3LnRobGjl6YbP7tqRkJpDhwG5b1BsznAmRoYcr43A6PqWdJr+UKXWUVwETFTkbcRFZId7IlFpA/XHtRqdMBgW5sV2Nim2Qpe9NqtYe1VTzeqWUgVeJDU6nSn71KenyVmKLjJloTVSD1WEG6coWFvsTGkpSsMHERgkdx/E4JqMp7YNtCGXRxds185fhkVTrQct/9PROpe2BYkz7Le2SRtCKwq60zfMwhzEEmmGYMsvmGtEMKTPrIWoe1iCysLCwsLCYp4jQsmRb3fEWzcEaRATfqSHrRErdHBGIF5JnIcsBdxSsOBbUCzrG/SbCjBR0PUmiHaFhJec6RIklHqWQPDQs4liuUXYXu5dpZZXPxh4ZxZtUper0XOaDqC9dDSPFJU6BkrUaeYu6SeisJv3t7kR8sDNGA83QrVjh/BIDxO9hyEIzZZ9ZIUeLgxLsGRXPgBJ0TU2mvlh40TN4axMEcipR6DVO3FCOE90ZyrLxPMXtDGkV9WRjMdxFWUn/sVdIEWaE3psv2uxBUvTnNMHWJkrNYv+GNYgIvhPAd9QHQ1BmAVkZVYrdYXqNY4/YxVtOZodS1Phy+0RxcU2ycjImxZBSMihkO+/LcXDBwqlqPI7JEqlJcwp+VU4C/Lyzl7tWTBSiWbmbs3e7aeKSwt2KeKNQs/aKRepYI9y4N3T0WTM10AywBWItDghoMsiagUPPnykWiNssvCgWRLzsKC+kzw2FopWir0nMorJEYdpe4fH2Hn3SncicNcT0lJSqAhQKQSELQhhXCXOg+c1j6ZI5oNJCOHCsUnVHYA0iCwsLCwuLeQqbZdY5WIOI4CGK/3gVIBYHvFJSvBJEC9EyxtMEZpsC/Hg710BjiJVQnmr21DL6VdEkCTMKrxAAlBKtohoFSdfKVGqD6C7k5fg5aNpNNEDYPc6Xi1hBdUWpCDnG53QL1Mkufd2i6UKlzwxeJBtgbbGfgvW0uGRNUxB0clbODUbtIaLJ2MEt9qkRXU5VMtJsVAAgZwzCAntb9vovAI8SPvi5dTNyfsiQF12EDbSa9ZVjoaQEypxMbi1Fdy6Z7GyW2YEJaxARYsos2ktU0Uv+vzEdY1I61aV4Mt3FBWJ96qNM4xAUXIlicZQYokD+lJUaGV5hPQ/vGCYgryAnicoUTZZl6q87mZhodsxMclaK7DtgI8inFHwxyZJB55BwIxLhRmAfatYWFhYA1Hg512/eGxB1N6FObUi8EpszVLM5pPUNJ8gatBblYtNUdJW257L6MAAxL+bohKbakBwCxWKLOmFGptqYcgqSdtBBGiqMHDhWmLEjsAaRhYWFhYXFPEUUzTDLzKaZNQ1rEBEEZcYRxcIzVI24nIdmqQR1VaHzCvEKpKhwS7JZNdA4qTCjRqyx7ntQEGBG8WzFqygWcaxU5S1QI8+STyU/arRSi5LA6zDLlBrRhvS1lPIfSjvJ3MtRwGNGfysq5T103iLOlGkiwDpqVP7DwmI/Q8uZk8n+1QXSQ8QiqgbWXh/YbNAmonKPcIiKj7h0YuK9dn19hXvWe6sFeq9PfzY+kSnri+dhrmZvCrYW4AQZtV6lFWY8kGENIg10dciaAVNfIRkiIjW/mYeWHzQ2ftJaZtRHnlWtFQpO7pNlMbLEYOAYKDaeSjT+KhlKinpsNZnEON5Il0oLqBOoItSWqNwSjYa8nCmdfqk6GxF9piCtDNna8sfWMrM40KAoUuvtBi0cOk5ZWzXTTg+UTU/JKpVtLu4aUmyROCdnt/pE23uUXu9naDHpy1WXmPc4O1edTyk7GJ52u8g408UVAWq9yrmADaruHKxBZGFhYWFhMU9hDaLOwRpEBMeJ4DqREkAtPDa8jdcRpu0MsWIJDZllJuor43CJjfosM/Y4ceZFjTxA3BZB2FMUmD1FnqCQ9g0Dg5dMDJWKnSlJeaaSZOwhSsoBRBRUXeuXmigZKuOhnLoRfabuTP+QA7SV7y0ONBgpMwP97iYe2Bofx89nE23hGeJ4aIcCqZkiZ89RrUKJFIJ2VxI/KUCc5jSlBBHRZ8KLznNlM0HEivCiZipRBB3nWITRBlV3DtYgImQRIItoL9dw/JRXFPVTisGJZGoFP2QTlHIxkkg5j9fy2n3LNHuwwFioCEQmtBtlU+RcNrCkoTRalbEBJco+E5lok1TrrFyRxhGn4zMTFdF2J6HMTHMET5Bq3BC5zT2xjWq4lWk29Vgllmfk6UUHKin486dEkYWFUmy4GQgRUT7OybRG6kTl2ELhzE+lZpkxBb++LzZ2FHVqfT1XeFOUsZXsr1DyfJzhRV6L6imxmiJ/0tiACYlWE9m8ipwKNXNWlfqggTWILCwsLCws5ilsllnnYA0igisoM8o+Ei7ZrEGjQjmeViZZ8iIVk9Qrj2qFTVIxMHbPsp4Gr2J0q55R8jiVatIjxTQZu0uVFVACDlxUSoGwTiI/UJpVm7KK5I8NwdZiNaqIvnnkncpRRCbDacH1azPILPYHNCMOqruXZyAq6gwuAqB6hZTTNVHGQzzP7DVS2oY3S5StF2Z0iH7necIx6RMRxJzFOmwFyuANlQBreVzOqS+zxMkoPMfz/Cxc4510NMcG0UxiiNo4mAMc1iAihJGDMHKULAWdAJeSxUWPRmji7BNeXDV85APJxpEJI7U4xkZxDdOEkSU52JCyz5R4omQ7CzcyN1+j8fNXcbg4Y9JWCj0qGWQ0aIPbPN2mTLC0Aws25qWhFxlii1qBLe5qMZ/Q3P0oHyqdKrXSh8lQon2E3EVEIo7VLhZRlYcpoTb0thDTjRJjZKDXlLYmJsnr0kdfshFQpTmrKysNnjQukgbdxUUUyZjk+VKRRUn3kdt6SDdAl3Vs0+4PTFiDyMLCwsLCYp7CZpl1DtYgagARzBzQSsNnh6mJImrA2DClpmSqka+5RG1Bu7E3yTP8fEx9cQBiJXErs7Q9r7xqpAcSspgaZ5SJoSo0mnYYynYlaFPoELn12+Lt5CHyqYTIBBVLmi5s/TKLOQZnOk7XY+l4LQgOAWo5jlx8LJfWUcvsQNtmT09mPDmOynVwW/EWUaiAqHAPyCwzdg6HPNcYKPKpSr2oIlNmDNdAuylzZKI01O1Kr1BVSaKhbF8RgN1BD1EE8xTb7PEWzcEaRIQADgI4ioEiat6E9HCGiruVOqDnN2gQ78KGDz+0HCuUBwXyJKf06UFl1zCnhrIR5Nb2TcdFhrxaLqzohE5dm2l1vgY8/yhxBEoMkfhcn9nClBmIMms3RKaOyNyxsJiX0BjxrRpS7qKBtF1NiivXihRfYzB8FAqchiGKuvL0USPRxZAMH6UPr36yUEKkaN7kb1gjGRCf5g2xyJsMeKGoX40qUiccFpF8CY4bKpLkftVQwNviwMOcLpf/4z/+A29+85uxbNkyOI6Df/3Xf1U+v/TSS+E4jvJ3yimnKPuUy2VcffXVWLRoEbq6unD++edjy5YtHfwWFhYWFhYWswNBmc3kz6I5zKmHaGJiAq9+9atx2WWX4cILL9Tuc8455+D2229P/53Nqh6Pa6+9Fvfccw/uvvtuLFy4ENdffz3OO+88bNy4EV6LbuW0lhlBeG+YJgs01Y8BKJ6SqsPaQvs+L9NnDEUAMqrPhFD1i/RCjxxULbLMQvL4mIfWQNbfIOlvFHVTslXqs8wUTRSfVmT0Gyr0WYWKpllYHGgwULsteYZ4X5do8u543qj00DxGjlguFm/yHHkJe13tp89pyEGe5o8se4uIMksmCH5hKzpmrE9Ea3euv5jPxHPnJInNjtOXKXj6gO2AzplmqtF8mjeU8ZgTWM6sY5hTg+jcc8/Fueeeu899crkchoaGtJ+NjIzg61//Or75zW/ijW98IwDgzjvvxPLly/HAAw9gzZo1LY2n6AYoupGSnlmBUKqWG03tqrbYj3y4XPINe5QLycZMYKhlJqAqqDa+01nBWqSxctFERXzWkF7PumSizdsapebuDbGPLq4IgFLjLCqQAUyp+dOtZabAxhNZHMigiSxc2JO2q13xfMPPqiKimtVv5+c5pceaWADBZR4d+nYCz5fzG6fdu5p5jMGLwIkmDCKeR0V2MMchjdSkuC3TZwKVsINWxky9PNZD1DTm/Rvhxz/+MQYHB3HUUUfhiiuuwI4dO9LPNm7ciGq1irPPPjvdtmzZMhx33HHYsGGDsc9yuYzR0VHlz8LCwsLCwuLgxbwOqj733HPxtre9DStWrMCmTZvw8Y9/HH/0R3+EjRs3IpfLYdu2bchms1iwYIFy3JIlS7Bt2zZjvzfffDM+8YlPGD9nGkysPHibEgTNOhe05OHyHiKAWgn2o8/ZfcuUmC7TQTe2vdu1UE+fCc2hZjxLCA2rCo0gm1LLjFea5G5nl7zQnlQqYudonGW958hlyqxEWiMtQKEc7MrJokPgzDKGUql+uhpZJnrNlw9jpV8+gJXepNyF1HVFQG1eJhs0aBVKTLvN0XuFHI0H2THsy7UVlTnXrT83hwZUDfMf96Fsh5gXyYNv0izax7bZglWq7hzmtUH0Z38mJ5HjjjsOr3nNa7BixQr827/9G9761rcaj4uiyJiyCQAf/vCHcd1116X/Hh0dxfLly+EmMUTDkXxT5xPDJTC8PE0ijbrsM1fJbJBtzzAhMIQhFCqUmr7uGT/4GUcvTNbgdEo2iJIFkkxGSsq84VIrkyxrO1aS70LebDZ8gixdmyzdopxxNjZuGnnzsJSZxRyDCwyr6fhKxdP6A5sQYMQrlqdNfqbCJMW+URYoAIQ5/dwkjo3IOFFoMs5UM4i8OolKPm9T4olcvRHEYrL5TDyJdPuS1urN6NPnc7QI5blQiN36rj47raYp4lbroBK+1SHqHParN8LSpUuxYsUK/Pa3vwUADA0NoVKpYM+ePcp+O3bswJIlS4z95HI59Pb2Kn8WFhYWFhYWBy/mtYdob+zatQubN2/G0qVLAQAnnngifN/H/fffj4suuggAsHXrVjz11FO45ZZbpn2efkWDIrauOTQvNFjcai0z+kAsRmgb10vjvpl241WMCMLmFU+Va5ZR5xVyGXO7Gnh14+dzOIormv3cnK1S/zEHTYYk8MaJGhyoKagy/jzI8XHkSctRxllft2wH8fULd+2GhcW8RxMeBfYWnZ29OG1PW7xxUtLKtWJf2hbPn6nUBnuIFE0inh8Edc5DIwFGZCkImpM4yAvjZuI211NksCco55OQrYb6Usty0FyiiNTq506d59xrJqygU4icmdH71kPUNObUIBofH8ezzz6b/nvTpk144oknMDAwgIGBAaxbtw4XXnghli5diueffx4f+chHsGjRIvzJn/wJAKCvrw+XX345rr/+eixcuBADAwO44YYbsGrVqjTrrBXUIic1gATEw8WcMWcoKBQ7Hcoq0xWNy7UZmozT8YUwJGc8jDgyE2IqkCMxxRYJmPh4pdCry35u1LVZfZaNMZ78IhZZIzZRxCWw8aTGE5EoZI/8Xm5Fji+zM+nQ0VmezUG8aIRA496wgo0W7UKn7iW3W6ojllZKMcZKN80JyXRkFGDkR0qhy2l/8Zwrwo1MfVF3Tr0hBci5JyDRRZNxVKZUeycrV1KlJN1+0pMTTIUsOhbU9TXZYoCMucxQPUhe3OqKbOuKbc8WOh1D9OUvfxlf/vKX8fzzzwMAjj32WPzlX/5lmhEeRRE+8YlP4Ktf/Sr27NmDk08+GV/60pdw7LHHpn2Uy2XccMMN+N//+39jamoKZ555Jv7+7/8ehx56aLrPnj17cM011+AHP/gBAOD888/Hrbfeiv7+/ul/2RliTimzxx9/HCeccAJOOOEEAMB1112HE044AX/5l38Jz/Pw5JNP4i1veQuOOuooXHLJJTjqqKPw8MMPo6dHppB+/vOfxwUXXICLLroIp512GorFIu65556WNYgsLCwsLCwOdhx66KH4m7/5Gzz++ON4/PHH8Ud/9Ed4y1vegl/+8pcAgFtuuQWf+9zncNttt+Gxxx7D0NAQzjrrLIyNjaV9XHvttfje976Hu+++Gw899BDGx8dx3nnnIQik0XnxxRfjiSeewL333ot7770XTzzxBNauXdvx78twosjGoI+OjqKvrw+P/3IJuntcrfAie1oCg8BOoAQ5y31KUbyKmaBgbS7dMRHmaLs05FhKfncQr/x2VaUxOFqVUct7KsW0PVyR2wVNBkidjQpt41pmLHjGq7ZaRe4TiXZJX77ErdBqirLFKM4RopA0e42yY9yWHeb2SC9ZdkSuDDPPvQQACEfpQNYGaSHoMaKHlLN+mMKwsOgk1uTfvu8dOKiaKDV38cK0PXns0rQ9sVQ+21OLkjAAyUCrAoxce8zkAUpOHyklOogay9Dzx0PNkM6Q2J8pfPIQeYY2e7j7ilMA1OSRFT1qTKlAvy9rIfI8K7Tk2ENk0ntLRRzHq/i7130fIyMjsxaDKt5LK772cbjFfOMDDAgnS3jhir+a0VgHBgbwmc98Bu985zuxbNkyXHvttfjgBz8IIPYGLVmyBJ/+9Kdx5ZVXYmRkBIsXL8Y3v/nNNDHqpZdewvLly/F//s//wZo1a/D000/jVa96FR555BGcfPLJAIBHHnkEq1evxq9//WscffTR0/6+M8F+FUPUKeytVg1gLwVD2TQbR/XblSKuhu18XNGVMQBPTcSuxh1laRBxfBArtXIKaq1BEUJ2Z/Okw9vZZK6JWmY9ZESM8mxKfXMKLU+sybPtTRk+Z7CxNUUu71pQvy+DXxgNjCO1UGaDfi0sZglNxQ1pssvcBf3yHxmilui2D1n4NLndXaaxc/XGTrwvbc9oqC/exEYQU2MZfTHsSMwlHGNkEGNUMssonqgmFnccA2Uobr27IulEFmwMxYUy0IP+HM8J7coy21tvL5fLIZfbd63IIAjw7W9/GxMTE1i9ejU2bdqEbdu2Kdp/uVwOp59+OjZs2IArr7yyoT7gmjVr8PDDD6Ovry81hgDglFNOQV9fHzZs2GANovkKYRyZdIhM6fgMcaypMKCif0FPIq9ijuuK67M9Fq5Mt704KQMl2Qhi8MpJxPRweqkpeJB5eI4zyuTiyahWIiPIUACSCzw6rFtUStJ+myBsleFxxW6dkcMvEfYWtWAc2VR8i9mASYeo5ar1OpTlQiE4dFHarvTIvmtF2r/RlNWM2rwwjpSAaT5Ov52r2YugalcTrL03MrRYU+KTRBwSzX/DFRlbmffIeKKJig0lMUcqHqJQnq/gyesr5E0qwfQNlLnC8uXLlX/feOONWLdunXbfJ598EqtXr0apVEJ3dze+973v4VWvelUqeLx3FveSJUvwwgsvAEBT+oDbtm3D4OBg3XkHBwf3qSE427AGkYWFhYWFxXxGGwJbNm/erFBm+/IOHX300XjiiScwPDyMf/mXf8Ell1yC9evXp5/vrfPXSPtPt49u/2b6mU1Yg6hJKDXLDK4NJc6IhRdTL5OEksUQSW9LYOojWcX8XtfWdNvOkgwCKNUa/5R+sgLyaSVUIqqNV2FhqKfPyiWRs0sdM8fukweGapV5U+TdEbXMKLNMUbg2iLpFGYpPSh5mRbGal47T9BZNWy3YwkIDkcHYjCdo2vdel/SIVHvlS46lLNiRLTI6WT1erUcYadu6FBxFaNHVU2ZhzdFuF4PiMNaIPC+BUz//AWpWazmZ93iO2l2S7rChLhljyHNk1tN4iOgi1JSKAZxhHG+vhA08zW1EuyizVjT3stksjjjiCADAa17zGjz22GP44he/mMYNbdu2LZW/AVTtP9YHZC/Rjh07cOqpp6b7bN++ve68O3fu3KeG4GzDGkQE14n/9k69B1TKrEKu1+aCrZu/mTnYmh98YRwtzozVHQMAIyU5KRZ8yY9nyWWcTVJTmV4TSq8A4AT622F8Ss6c6dzFXFaO3NkUq+CUqUgrGUdiLlFS8Q0FYjk1P8xSIKRILx5psQ6dYiiJyFD95Ha2/+dp+77q3a2dx8KCoJToaAdNxkrx/TKukJ+dWp7mD4rJFe96YoLUeazIixq9cZTaKqapjfMbOGja1VBf/PBzDBEvtOhENUr4EEkhvJjjMIBdU9I46svJzI4SzXXCIHJpJZalRSMbSpWk72rn7CHMh2r3URShXC5j5cqVGBoawv33359mh1cqFaxfvx6f/vSnATSnD7h69WqMjIzg0UcfxUknnQQA+OlPf4qRkZHUaJoLWIPIwsLCwsLCAgDwkY98BOeeey6WL1+OsbEx3H333fjxj3+Me++9F47j4Nprr8VNN92EI488EkceeSRuuukmFItFXHxxnBTQjD7gMcccg3POOQdXXHEFvvKVrwAA3v3ud+O8886bs4BqwBpEDaGjx0xK1co+qKfMqi14ivaGSOnnQOvj+15M2yNluQQcpTZ7gMSKiwMNOfvEtArjDI8omxSZZRd8ha8Ry3HLppKtIhwzLnt/eF/ZDtmz5JNnrjv2WrldcgUYTsi0WiN9ZmHRQQhBRlNQdTsQFohyz5P0Bz1TSnJC0s5MyE0cdM3SGSF7T7mumfAWGUQXI0NxaKVWmRiIhkYD1EBpPo6lQkQBWPbqR0rYgX5+yynB1nEf7BVisJddJMY47XC7NA0HjSPhGx3fPLZv3461a9di69at6Ovrw/HHH497770XZ511FgDgAx/4AKampvCe97wnFWa877776vQBM5kMLrroolSY8Y477lD0Ab/1rW/hmmuuSbPRzj//fNx2220z+J4zh9UhQr0OUYneyI3orqphX6a+xHbet0KfT5IOUYniiXT7K6n79Pl/l6X+yAMvSgu7Ly9z23uzcbwNu6JZk4hT9ycqOe0+U+V4n1qFvh/rFFX1lJnD2SXJPt4kqcgSE5gjFiw7Sum2uykLZCT293sj8vs5L8syHkbjSIdQ/7miT1SThqVVsLaYCVouy6HJfHQKctETHn1Y2p44VFo2UwvlcZND8thqd6K7Q3F9tQLRU1S1XtEZorT6dEgs1eExT6b9Jtp3s2uIQwppUdbfJ5/niZK09HSGF6fxF3LyueXYSS4FIsAZuT1ZGZvY7cu2yDKrTlTwvbNu74gO0fIvr4NbmIEO0VQJm//Hulkd64ECm2NsYWFhYWFhcdDDUmaEAE5TKtTG45sR1kkQGvrj7DNfMxQ+B3t6uj25illQmNTuU8zEXhVFY4g+z1I9oGpGrqDY7Szos8Dl1SLTZOTmztIykVSrRXdKwVdaAIXk3AmyvIplj1PsqXJqFEy5myOzDcVpdd4iQ0ZaW4JfLSxmAc7Q4rRd6SOPiSE5QaGzkse/vNAgOKgoVbP6tMYDxAlknJ1Gnhu1aPS+4ZEXKqiS95q9QkyDhU7dNs5Iq1AfVRJDK1PG2UAxnnCqBqFbXYhEtVq3afYwD4KqDxZYg4jgIYKHSFGR1hlCv63ItMAxepP/Xm5r3b6A3lBS0us53kgp+kpy9ck+Js3UHk9mULyyZ1faLpPxI1zGIVWTr5E7nl3G7F6uOJSRkbjIwwxnizBPz8OniYTS8aOE94/oy3B2GtWphUupw1MLaXLrjvtQ9OYCKUrnkmJvSFRaw3gik3FE2232mcWcIVlw1BbIO1+JxaNVVGSQr0gLNOdpUZPRvzUdg3CrEFXkl23Glw90rapfTDiKARXWbeO5JF/UF2NlQymsiYBE2sEQ45mhBR9/r4lKNvlcXo/JqjTAypqMtFq1g1aGrXbfMVjKzMLCwsLCwuKgh/UQEUajLMLI1QpxMQ73d6btKnkiXKVWWf0KiQOpGWotM4PoY7KsMxWQZapvWX5YjoPcwLuqsXZP3tP7e0t0O+QVysyldqMgc315jYjVFhMqLarpV7OVftmuUQFKpfbZRBKYvUCO2SvLQHCnIg90SKsoqtCqsxElptMsApTMGpE9ZAvBWuwL7FVsioo1FW/ti4NiAxIpjXy9d7VGNLTiIRLJXfT8OTnpPfF8vR9aoaUSb4oiGGigz1oBe24UoVhFy4iLrSZjo+czDFggUh9i4Lk1asfb+ykBZceEnD9cCu4OkjHVgs7VN4uixnkhjY63aA7WICJkESCLSEnP1PHHgWIw6Q0HRdl6mimTTJ+JAoN8jpCMLj5f0aXaRjyZJv0Jw2hvcE21PMcqgScgp25snB4b0mTKGbshxQJFSVovZ7BU+/QZKt4UTfw84SanKfcS5Uf11ZyaFKrM9Mp0UHD2WTKpRSbVWcNMorzQki/JadXWOLKYNfTF93KtQPRxkV7YRDErMXqUMh8KqizLCxZ92rpvMo6S/3L6uadkiFF/ND9wFpmrMZp4plSKu/LahMYqMtFCiiVURCFZ+dqtF3QEZOzkMInbMnhRmS5MjdWoZwE2hqhjsAaRhYWFhYWFxX6J4eFhPProo9ixY4dScgoA3vGOd7TUlzWINPCppoSOwspSaHMl4tUDrdS47IamD/awcHwe02fct25sVc3ndeMnmk5sLxJlxp4lDsCeCOTyMkMrtUKmnm5jR8oksnWfAwCXMxK9hVwKgAK9HdIyChUdFC4vEP+Xr90krdqcmhyHN94nt1Nl8CgJUHWq0svH2kNKtCf/hJraaLzytd6i+QlRVwyAVttnb7Tzt1NLdxh2Mo2J7kMhwhhm5b7sJa1SlgFnbqrih+K/RIFliULK6D2mHlFYwtscGAQYleBp1iqiZ97VBGwzZVYj/TPel/cR9B4nPaSB1lA9X9USJVpwFpwQrPXryxwBwCRTZom3i73isw4bVG3EPffcg7e//e2YmJhAT09PXfFYaxC1AWwkiMeimZplJqR0FxtMzIlz4UKloKsm44zf0Y6eZmJDqRxy4di47wwbVQ5TcPrvpShbJ+Pm68ETFwuesQt9MqifsJSqxjzhmVRuaQ4S9hoxlqh2U0baInlrO4GMByhU5AHOWCzVGwWSRlPOzEUneeWhhBOJI/gt17n4AosW0IQRxGhnfBj30ZQwI213fBJr7Ynj5Djjkou0ksYrqj369PkoocqcjIG+oiZTZrrwAY+OCwyp9qa4IJH1ZVKhrlI2maJwTX3LOYbmMZoTQjKCeKFVI4NyMhmfqeZaqDF+gqBzBpET7aU0Po3jD1Rcf/31eOc735mWD5kprEFkYWFhYWExX2FjiIx48cUXcc0117TFGAKsQaRge9CN8cBD0ZG0Sj7JRmCvkM6DtDcaiTQqlFnIVBsHZtdnSxiqTBihUGwJpWSqxcaeowz59WuoD0B0DUHV/F14taewT0LLiD1BiqIcrQbp+5KzC8KjrQSO0uWq9FIfdKBXkdL12VribmdP0Ng4nYTGL7fuRasJHRQuYSCPs5pF8x+Nyme0mwJVztcETeb0yaSAam98L1d65OflAXmYwqKzUGIPeUaT/3pZeR+7iufGQJm5+u00aO2+3J+fkecUc4nqbSaNNPYQcekf0jRz/PoJUfEmVeg44u1Duu61cvwarCoeMwospzFXqvG+QbDve8aiM1izZg0ef/xxvOIVr2hLf9YgIizNjKE7o05Qk8nLlGuTcRSNi0aThATHGI0SwZ8lQ8SUgq9Ld+d9c64cVTXYdwZEqKTu69s5SktlkcapJK+3QrdOjlL0WcSsShMJu5iFqBtz/coqhl3s48xP1X8XfgEoMRPcXx9PiqR468Rvktyml+UpKBMlonR9BbrYIlNMgSZFH7CxRXONpmqITWPfGcFrbBzVkuKtFRkWB3r0QQLLCAuURUbPmleMD2A6zCjAaDCCHM0l8WhhYVq46RZjHINYM8xdSsFWqp0YlEXbQLOT+CQL0kJDEfIcpRhpSjHZJGTAIDw5K7AxRAp+8IMfpO03velNeP/7349f/epXWLVqFXyil4G4YGwrsAaRhYWFhYXFfIWlzBRccMEFdds++clP1m1zHAdBi3pR1iAiVPcSZWSw3hDDRI1xPR2xT56ouIpLqw6lPhmX6yAdjrR0B2Vp0OqtHEnLuOjKumY/GT4ibQ/lY49HjbKxchQwnXEb3zyCVhsDebjIK9RNVaLZ/V2uUoZHVbOq8xQXkbbJSB12XP6D92VJEdpeIk+OmyynvVJ/ui3zoix74hRlJ9EkqULWNPeCQWzP4UEdYCu1/Q1KYDNRmUZo6CwTBar0pznuvspd+nNw8LSib0XPQ15GSrtBvJ0dOqUlLPhF91iF7vV+Ofd4viiZoQ925nlFcYZqMr3YcxOxN4nDAJQkCbldZLIGmoyvunPT+JRz1kQZIH7O6HR8ar5otL8I0nYMgeVM7adlhzqZZWahYO/U+nbCGkQNIFLfmWZqhibjOCMkRg6/MvOO9HNPRpQaovTBwmRhcm6m0aQRxLFCPqWMnrHgmbT9yGjMsxYo7d5VaDeKKXDY/U2xAcl2TtFn8L6mrDV5aSjGwRBOVOmniZoKxDrJhKaUaFJqo9GYKM6I1XunBuKj3aq8/sVJGWPkDo/RzrJWHL/wHD++DhGl7pupD7ndxhbNU2iMGZUykzfWmvzb5T5szGhUptcU1sptpG7Px0Wm1axfb2xT6ULluQjyZFCQEcQGgzA6OLNMl1LP+wKqQKswViKe51y+dmxUGeis5KHPGIwdNoJ48gw4rb6uoWapcnadMh0V6q81f9dQUb6up/aj2Xsn18N6iIz4p3/6J/zZn/0Zcjn1HVqpVHD33Xe3nHZvzVwLCwsLC4v5iqgNfwcoLrvsMoyMjNRtHxsbw2WXXdZyf9ZD1ACTUexe6HFL2s9NlJkizJjswx4TptSY4mLwPhOJwIhJp8gUxJiniMvju18EAGyvSi8Ie5YYvL1MAeUiuJuDrlnEbKQiXTC8osxSgHWUlBeosiudMjwUhxmXAODFXineHukXpWpWGItCarLSQhJ8rCyW6ZtZqhfgkocoqmmEHE2Bt5ypRr9XZKWKWgIHpPPy/P7w200fZwyONmR6afc3ZYU1Crw2fG7yCnFwP9/46b3K7A57V7vJy0seID9PSRLJs8jZU5wVpvMExV+hfpJR5jT2Cnn83JLXWKO/Zsp6NQV6K9SWs9d/oV4Pp0EgNY8vMlHarexr0VFEUaT1QG7ZsgV9fX2aI/YNaxARArgI4GKM1M0We+PpZwLKA2wQVWwFJjqOM9vSyUZ508smG0+lSI20F1iUGU361ce7sBHHgo6q0GP9LcMTaF9WGg6cjq/UYBPXj+iraln2q0w2zOXzqZN0W5fTag0vCVVsUTZFsViu38vGUa1X3gf+oMxtdgty4NHuYTFouY05boPaNccWCfqMX46NXvQHAxQjiNGCwOKcGkEmNFNtk2i1sFveb2FSyJUfYa9Ei69Jik1kaQwaqhBP5YUMw0SZ6fbhmUQRrzUUQ2bIy0AUtDHbjeks3kfE/9AWXmjxIqRBaGKkqZG293bRjgzisbMCm2VWhxNOOAGO48BxHJx55pnI0PMSBAE2bdqEc845p+V+rUFkYWFhYWExT2GVqushMs2eeOIJrFmzBt3dshpBNpvF4YcfjgsvvLDlfqdlEN1xxx246KKL2qYOOV8gqt33EIUlPEO8+mE6Kc8ijZSJxl6VSrKOEvQb0Fi4Md6n3rJnL5RJp8hEgwkcmt2dtsdIvGckkL8ne32KngzOFH2bvExTVAOtxmOlFaPIPqvCQCdxmQEOBuUknEyyD8nx8yV1ORFMv2BEphT/gyuEc0AmC1JiUVfa9IpUJ000xmX5D5Tl/WP0FhlEHy3aCyUTjEpmzBsYAo6dbnm/lReRhyi5bdirqdxAnsHDQgHKnkZ40RhIzXXDNJpEirfc4Ikw9S08wezxUS6HKXGMn2evPtvN8QxeIY2nB5DepYDoNSWTLdQNyj61c4kbb7wRAHD44Yfjz/7sz5DP5xsc0RymZRB9+MMfxjXXXIO3ve1tuPzyy3Hqqae2ZTDzBVy8VdBIHr/LoJ8w+KkNaHtXkm5fCiQN1UVGF1NcTJMxdAViTWBjhdvCCGumKCxPoLr9y4GelqtQSr8ai1A/EZrnFI4R0OfQCqMpzNJvQfQZxxO50p5DlhLHBCvIE2WQ1QclBXwN+CdfGlNp3qRcoTjbpNAjZ6cZC8cK44heDFbEUaWvuDhqK9dDiSEySSN0SnhRB6LPHHL7g2jZGhnsgjaeWiKPq/Wy0c39UVwQG0SJYeMbKDOTQcQQYq2m+B8GG1KKwGIk5laeG/THKfNHA7qqGUZSsbCSRVdomBe1Qck2y2xe4JJLLgEAPP7443j66afhOA6OOeYYnHjiidPqb1pBL1u2bMGdd96JPXv24A1veAN+7/d+D5/+9Kexbdu2aQ3CwsLCwsLCwqIVvPjii3j961+Pk046Ce973/twzTXX4LWvfS1e97rXYfPmzS33Ny0Pked5OP/883H++edjx44duPPOO3HHHXfg4x//OM455xxcfvnlePOb3wzXnV6Q8VzBdaL6FVHyz2Yq3Pvkx/ZpCTGReID6PSnuN8npTkofkutRNYf2fS3DFvbl72jyFjEUj1jS5uDvGvXRSmC5Gvyo30eJZ+XfxhHBpRRsSdlpnqJZJLdzZfDUn86rdF5s0/hcpY/6DCCHIr49ojuUVf+odE/pqDTFU0Er6YPNW3SW+zYAqkdnut97Rt4fXTD1dPszUWNMnQ4uStvlw/rTdkTnDLOJV4WSXmvydoO3kNyhBBFIDQBdWf0+AibPboYz0ZLtSiA1PfuRwcvEtQ4beZc4kLopr0+6s2GzLjuN+nYMwdimRJZOwcEMY4jaNpL5h8suuwzVahVPP/00jj76aADAM888g3e+8524/PLLcd9997XU34yDqgcHB3HaaafhmWeewW9+8xs8+eSTuPTSS9Hf34/bb78dZ5xxxkxP0TFkESIL1fgxigs2ABsRgoKbIGqsYqKtiK7TxQKVQFQVU3Sc5k/GkUKDJQfwOZ4YOyxt9/pylp0kSmx5XsYcCXCs0ATVB9tdlurOU1RYaaoq24HO5a0kjhhSlMn4SfegGCPOQuO6Zo6eUYAjhsRpwXR5OQ6JJ1NO84x8EWNGJ18ki3F6o77+uD3DqIMxrsjm6HdMyLKFDLbm+qu/l90uir3sl6nB1SEph1HtIsFGurXSGCK6JdwyLYYoW7N3wUTaZvFDYYhkHD3v08gI4j5aTZlXikJrPudxstJ9wzR3Q+yRApPBk56DPjb1J/o4ADO39kf853/+JzZs2JAaQwBw9NFH49Zbb8Vpp53Wcn/Tfvq3b9+Oz372szj22GNxxhlnYHR0FD/84Q+xadMmvPTSS3jrW9+a8nsWFhYWFhYW04BIu5/J3wGKww47DNVqtW57rVbDIYcc0nJ/0/IQvfnNb8aPfvQjHHXUUbjiiivwjne8AwMDUqelUCjg+uuvx+c///npdD9ncJ34L4h4W/KPJlyWnmGnvMbTMwHSuDF4AFhbqMuJg7C5kj0HY2fpHKHBzvU0K8LX98nSHv9n1/Fpe7yqLycymEQl9/syq2p3Va54TStKnzgnseI1VbY2CagpQbbiA588N+QtCnL67aonqn4be6/YQxTWTH0k2zNMo9Hqvl9eG4f0ZBT9FuEtUtJnWAdFDuSgoM80XprIEHBuCrxOP28mkLqRV6hVmkzjFXKyRJFTKEHQJ++P0iLK0CzIfaoFDvoXHVLn5Dn1svI5Yw9Lb0F6f8Vz2YwXh1Gh51V4bEylPdizbvIiifNoPcZ7Qcn64owyUVONaT7+Ofk42mxgMPWgaVNogtrSHfMDt9xyC66++mp86UtfwoknngjHcfD444/jfe97Hz772c+23N+0DKLBwUGsX78eq1evNu6zdOlSbNq0aTrdzxnCqF7xuZksCh10cyhPGGycmAwp9dikHhqnwSru59ZqrQmwIbWiKKmxJ4eXpe3Hd0ha7ZCeEQDAkd070m19voyN4kw6hSZjYcYkjsA18PRKRompLJh4CbCYmk/GGItCkm2nS1fmYbARxIl0ilGlwNV0IptRngysKp38kIVp00viiaKxcXkc+e9dKu4ZVfYd/8EQsTh1oDfGvDaquGZc41A3OJq0cPXzFp/lVvY3xQglv52aQSZp5fISKiBM56vRPcv0r2jzPR3SooAV4bO+bHMRZx1MCxmGGi/kJP/VP6DTVXJmyoylOlRjRjNvGE6nyHaYplmxADPQ9gqVlsw3TieFGS2MuPTSSzE5OYmTTz45FWes1WrIZDJ45zvfiXe+853pvrt314d+7I1pGURf//rX8e///u/4yEc+gh07dtRVn/3Hf/xHOI6DFStWTKd7CwsLCwsLC8B6iPaBL3zhC23tb1oG0Sc/+Ul84hOfwGte8xosXbrUWM14f0MAJ/0TMK2WBJrx7oh9eF+udm8cj1K3LDY6h0k8URGIdKTnoEo/ayP9ItcQWMku7xqt1LZN9NTtu7Qwkraz5GJhnRMuEyBc5C6tiHN5eT2qVcpaC/TjDxMRNUV4jf/BhxEFF5DHRlw+9gS5Bq8QxZBDe8no1CFVJ3er5O3iumweDVBozpCHSPE4GFJsONBYHkdelQwH4Df2Gmr7IyiUVAv1xJoBn1vQXKzb5Pr6qUoZUysB0ZqK9C2jmXmvktzXPVKnKlwg08ICn56zPN8f1NYE+LJXCF2G+mVEU7uarC/T3Jal4xRdMQ6qTkMJGl8DU0kPcVs3Q6+xJpHiDU88Skp5jZqhbIjJyzjPDQarVG1Gu+OUp2UQffnLX8Ydd9yBtWvXtnUw8wXNGDnT2denO7OH1AKrBvVp3XkO96XoX4nSTyqRwQhq4WE4ofhC2v7t+GDaLvjSWBmdil/eOwxPWTFTobY8rhzQ+JLJy5RRwi5xjjOqVWhG07msMzRhKx+QgUvpyqkwI6fUs0FkyE7TKohTDTQ2glRXPn1HjjPK1UswcHFPTtFXjBxGJF4MeqNFF18D7KXenBgJprgchbZqgywAn1uJ9RHj4G0tBm2I7zBtg8nYcYvGUzb5vYgyK1MBYVZJDw0/LW8XlFlIMXLZLlKSZ8osozeIRHaZzkgCmgsTEPsztdtqRq7YPzLQU4xqlWQtaN4QPwcXfFXoNTYcuanEFTYY9wFsUBwIeO6553D77bfjueeewxe/+EUMDg7i3nvvxfLly3Hssce21Ne0ZohKpXLAqVNbWFhYWFjMO0Rt+DtAsX79eqxatQo//elP8d3vfhfj47GX/Re/+EVa3qMVTMtD9K53vQt33XUXPv7xj0/n8AMW7HnXeY5UbSLSBVE0hPbdBws3soeIg7TzkJ4ZRbeoAbjvV3btTNu7puSKVqzOhidlMCivKAfyMvuMV51KUGfiEClR0DXr/NSaWWimQdWGlSHvSxeV5Z9E5XBVjFG2lXEoq0g6p1PfhwLTQpTp0J44QtZdeaj8/PkX9f0x1aYsp+MvpgSk14jWIs+N4plpQBeZPo9CfcBzSn01UU3elAGm9QYZvTvyx9X1ZwzGboYmm2YoAHv3RDB1VJRR0LUiUcLkWeQ2B00r92zyyEc9JOBKv3khK5/93px0h+Y1QdXTTRhpBmr2mQTPb35Cg3GWWY3mNL69M+TtCjUeJcXjw7XalFprhsE2Mhh0fXTSyLAxREZ86EMfwqc+9Slcd9116OmR4RxveMMb8MUvfrHl/qZlEJVKJXz1q1/FAw88gOOPPx6+r750P/e5z02n23kNjitqhSYD9G64CitLt+Bq9g1vXiV1n7oT6foAUNL45E0U3Sndz6btMike/vjFI+JtJAA3CpkGY0q1N6X4NgIfx27xtE00WcRhWWw38MuPap8JFs8xvRhoyDUWeuQJOc1U48w4eukratdEL/gc5xDv49OLlN/jRvqMDAC5XX+d1+Tfrj1OgTBEmqCWVPqM70mhHDgDeqqFNHjHnbG27F7nnrkRpPSRFMCuLqAFhM9GkNyVDR+mblmJOigk8Yh5Up4uyGecn79uoq91cTpsWJgos9k0moQh1Eyh65aEGfn5NEpuaPozzFGO7rgDWNtnf8KTTz6Ju+66q2774sWLsWvXrpb7m9Zs8otf/AK///u/DwB46qmnlM8OlABrCwsLCwuLuYYNqjajv78fW7duxcqVK5XtP//5zzsnzPjggw9O57B5j70zzAC5QlLLeSjCFLJNXgJKEknJMZMniL00jVZkHlFtPRQhPEGFvHgf1j6qOpqfu4lg1df1/iZt/2YsDrb+9ciQPHdZ7+buzus1c1JBNiX7pHFmn+KGF9XuefxMJ5FYnbLAo/OIRD+mKjga29FrU4LJBy9ZnLtVPd3BAdZ8nmyFPD2J58ibkCt9U3SpyVskT0jfL6uvl8eIqvLbaD1HTWVgMafTYPbl/kz7drr6fDsWcdSHU5TeIISxl7TaK10+nE0WZDnLTB6mlOvgkjS98e9VzOp1hRYWJrTbGWKOGanIE1Zq8iR9+am6ffduCyjPJP2cpswxpXSHmFs5Q8wgwBgaqC+mbuWgqEnzACpca63+MKPXJ9K0O0qZOTPzSB3A3qyLL74YH/zgB/Htb38bjuMgDEP85Cc/wQ033IB3vOMdLffXZn/z/o0wchBGjvaFHCgUF22nm01RpNY8MEEL6q1775MaETSO31akUTKUGdb2xxOTVrCR6AlTfTXGyQPPAwCe/p0UbkRZHjcZyUmWJzTOftHRZyELKRpEzxzNnKhklygzsqPd7pDh4k0kqfusdl3m4+h8/ILSJK6wAcyFYE2ZN3wJhKEUdElLKkP1rqJJ+YJSCtFqjCM9jbYXlD544G2YOFvpQ1GL5h+3jW+bWfRYKzQZG5NU3Ld8WKzgz8rTahybvq0ILxLNK75OLqsv1sqZnY0yxwq077ZhWUctmyHpDL6ZNVCMliaof362BVXGP1EztdEixeJJZwIaFB9Im7kWIom4irlCEXHkAMJQ07eNIZoX+Ou//mtceumlOOSQQxBFEV71qlehVqvh7W9/Oz72sY+13J81iCwsLCwsLCz2O/i+j29961v4q7/6K/zsZz9DGIY44YQTcOSRR06rP2sQaaAIiWlWPfw5ewYUPSGNF6TaRPAgo5EoJGNXICPsF3ujaZtXbbpaZlUSXgsNekhMwR1b2AIAGFo6nG7b9uwi2eGEvKWmWFSxX3o5GgVYK7WKTDuJekxKgC2tIvncBsFGsfh1KuQVUoKFZZNpC94uElr4uCoxVR4FeueGacVON46bBNnWeqSLwBvoT9tc9iEakb8tQ/FWNAIvyalv0YfRs9QKmvHykPAidHQdu+JmMSts2jDQZMFCKcJY7Ym/F9Nk/NvzlMBeofIA32RExRfjG4q1wfi5zhg8OiE950JskeexQk56mXaNSw/Xof3Dso8Wgq1N1Jd+X33beI5GwdGu/t5zMnwi+kDnkeZtSsC2fkizCRtDpOK6667b5+ePPPJI2m41wcsaRNOArynWCgAViqXIRqTMrHmtN1KQBtQJQZxzLJSUVL8nU9x7XKJVGIoRVv+i2zB+VNr+g67n5fgUo1B+Ly8Z9/985QPptq9mX5+2f/cUBbKNy+OmHDnuYmIchYZaRa3AGALF7nFTHn9ybEZeRtQo/IONHK5xprjhk6/AStYehU5lpjjejI5jCi5x1Tucdr2ACubu2qMdvpJxpjGIGsYb7aO/GaMZ44Tre+kMqPloBPGpSX0avbJd6ZeWTbU7Ud1mjUnOJlOoMdqHaNwwL3+XBd1x3CAbGf25+sKtAFBTFjv7vk6D3eP7/LwZVA3Fmk3nFttNBhNvDblmoRJzJBq0s8nY4XgiTR9KvULl5Jr+OlnLzFJmCn7+858r/964cSOCIMDRRx8NAPjNb34Dz/Nw4okntty3NYgsLCwsLCws9gtwUtfnPvc59PT04Bvf+AYWLFgAANizZw8uu+wyvP71rzd1YYQ1iDTQaQ55FEpdUTwmobbdTq9Q3I777idPUCv6RYDMOGMa7WTSG9pZk4GV5lpriauEgrEvX/6TtP2xJ2QZB48ClKukFzPpxt6ifLd0pSgu9mZWX6kAEG0zedjZW0SrwGpfUsKgKr9LhhxtVdJ/4VU96VdKjz0vjunScWaZw2UwlKDvZF+ua9VFoptDkpJ0XtwhOy/LrLR2eHd0lFmrXqaGqFFWG2XBcbabki04TbST/lO8b+zVypAHpiDpzmqP3Ec4aZRsMun8U7xCHFTN92ympz5bsysrf3sul8OokXe4FbqrGape9MHnUDLIdNlf0AdeN9QYgkqH64KcjX3wdr4VdB4l7pdJAKbwQ/W/HcEMKbMDzUPE+Nu//Vvcd999qTEEAAsWLMCnPvUpnH322bj++utb6s8aRBqw+GErwmSm9PlGE0wjIwiQ8Ue8TRFjJFTZYNM9uTSJseEzSbNznoJfqprsM89gMB1z4vNp+9n1K9N2bhdlsyUzf4UyPfycfCFyFokSX6CbZE2ucoJjMJqiJKagtERex8wYFbPkpEFWV9DQHx7VSFOG5zZup3Qb05Q5ul5U+yobLEzbzp4x2a7Gv0c0JQdiMgZaiTdqK42GfUgB6BS4DXSYafy6sbayb92xSVyTMua85LiiXmkxTx4mY/iUzDGhLE3DICZLoWir/fKGi+jZKBakwSNihxSajG78VoygVmIU9+5DJ6bInytxgoY5NAjc+n0NqxolrpCZrWQYvNiIeGwKTUb9cUaZMIQMsUkdNX50sJSZEaOjo9i+fXtdzbIdO3ZgbGzMcJQZ1iAiuE5knCRUr5G+8vLefe29v0nhOnC4b/0+YjtPOjspkNpTCsdO1R2393cQyJK7g40jTzHGvLq2RxYCG3F/vvTRtH3jK6UsQPYZOfNn98TXrOzKl0u5l7w0Ob3GChpNsorhw0GptD1HL52kKjZ7jcI8Taxk83kcw0AvPDfZR4kJ4oxeeuGxV4gncDcZuFKugYPF6atUF8qXcHaq3jPAAdgYJ02adqay733OBgaW4v0x7Nuwj2YMmBb6aEb92xHGDwVMhwPymSstowr2HP9Fnj5RtZ41hgKWKWL7kD2ZWfI8UykKUY6jSIFqStxQoI8bUuQ3RLV7mhtMukGNjKrIsPAzLSR5f/G9mrk1TdXu08B7DnCnfcUzDqieZ0fxBom4ILnpQAtEPlDxJ3/yJ7jsssvwt3/7tzjllFMAxEHV73//+/HWt7615f6sQWRhYWFhYTFfYT1ERvzDP/wDbrjhBvx//9//h2riJc9kMrj88svxmc98puX+rEFE8BDBQ6R4UnSqys31xbFF+74jTfXJlOOS03Pq/nJfZh89WZKFQfuzVGBVIc5T/zKdQ2JxRroYWfmaofMQ8TnYU3Xp8TL98c7n35C2/ZH4v9lRcu9nODNHtoMjKF2fV34t/R769G0nI9LM5MdBhr7XFHkLyIuUIXpM/BwKJUIeJM4488q0gmYvgpAQYA0HDn0gMUm3Rn30SCrNFStl+l2cArklWLCxDZ6jVmg3x5/5NNOSrECrfbNXiDLHogWxNyjok9e50id/XFVlWu9BFF4/plkrveS17aJ7mjxELsXM9OblDdftxzcUe2MqxMExZVYjitmd5luxUciAY4gbUtPu9ceK7ZxtynSYScy1Ss+loL6UuEMlxsiwnYUZDdlx8wU27d6MYrGIv//7v8dnPvMZPPfcc4iiCEcccQS6uroaH6yBNYg00NFMvK0C/QPERpDJyGl0PhPFlVJmXIqDU289+ZJj6ovVp6XhIicgrnD/UnWBdruOFqwQR8STbZaKyR6d35q2h05+KW3vXB+rXLuU6esSPZUdke3xXdI4cvqIJsjUP+VKLALPiYZK9eJrRRV6IbKS9aT83rlheRjHfYhbQSlvynMtUSLlfjZs5HYRyK0EYNNbhF+2FSoBkaH9PT8xVPfQRTXE6zikpKzo/whjcYS4d47tqZgC7VtAlqwFptKmaZiZ6K6UmqPxO1ympE8mECjfkQpVVwYTg4iq07MRVC2SQa8oThN9lvwENcrQj0h5WqHJqN3do48RErF9bKiY0utNRlNWp1WkhNq0RoNNF8L44ec2Q4sepv94OyMUemPG0h50Pu6ixPeKsMzIiDNoFhklPCzmFF1dXTj++ONn3M/sLbssLCwsLCwsLPYTWA+RBrrgY14pseiiaR/dSq2VzDNA7y3iseXJi3OIoZZZK+dgVNkDpPF28XfhrLZSJJfKHGz99kNlsPUtr1oDAAieo0Br8grx6bLD0mYv50hVuSvxVjSxYFMUrDUrSYdoMhjShavd2s3pWJX6VPRUuVX9dh53LRmTepmZUpNb/Qna7rt1bVZJFkVjAcDlwrHs6dFkcjkLpacQnLVm8hDpBBY5yFWnQg0g4u01QyB9Azjk0XHY+5RLPItd5M7j79Inr1NAFG2tmyixfHxNlYDpHAdMy675Nwo0lFm1i+5BvsUMiQBZX16PHNUWyyauRcUrZHgIeI7Je81fXyXYugVFakZoygqjvsVl4C7YK8ROw2pNbu/ukvfysCgszRlkpuBpXSA1pNcnMvwWc+4VsjFEHYM1iAgihojRitZPO/Q9dDQZAJSS2ffFWn+6bdCT1AYbH5wVliduRqTV8zm4zccphhc0kyl9VVeh8XQUHVB05SR2ze//PwDA58tnyeN65a3Y/YK+ECaX2Aiz8QeurzdOmX5QUmx1RSI5NTcv+6t1UfmSEhmzVZ5wk74MMUQGUXPFlS9oFXP2Mf1GPKYyZd7kM8nYDCrqC2Q8kVeW+7hl+dt6Iwl3VybDhzKslJgkjkNixWZBVZFGkmIwkVHCL5qIy3jo0u65IC0VvmWKK+qRVGBlKKa7IjJmOHNPsYt5H4rjEtliYZa3yeN01Fjcn2wHySULivSDkwo1KJvMJcN8oCDjAPuz9ZoONUMZoGbmGLFPzWD8TzfeyDQOhcqmc1aSNhtVtUA/JlOhaNREH7xz47Y2ooHnAdNzq4kZnG3YGKLOwVJmFhYWFhYWFgc9rIdIA581hJKlh+IpYi9sE4GGjbLMTPtyKaeehB5jamyYRG48WiK5DdSzWdAxUAq6suYI2crsDWpAnynilKRmzecZysT82KpXbEm3/XLz0rQ97pD4Ha/ks4ZVZ7qtblNdH8oqMc3uIq+RRgUXAEI6t7I6FHSXaUXJTxg52hQvkrikdGk5cJuTz8oZyhyqyc798SDplwOzqT8ODM6z54soiiS4mL1GINot8sn7VyCXCGv6ZOMxOdRvdbH0IGW3Sa9mRN8FL5ECdxBfCIdEEFGg4qmDMiC61qMPHA+Tvtn7U6PvzXQi398cNC3ajQKmAVVDqtpD7e74PBHVI2MK16EIfI8yn3oVJWrytmlg0hNq5C1SApjJc2PyPrWCjEeeVgMNJmqSVapEhaP+872hZLCJOoA8LTVDcWnmBLfCcx7d9wqV1rjrWYH18nQE1iAiBHAQwNG6jBsJHAJqZlmjTDVTHwqdxRx6Mkk9V12cbuNssn6XKpQSOH1eiC1ORnImLzoyc2uAMtW4iCxDGFhs+FQj/W3ExR7zFEwj4oz+dGhjui3r/X7a3lhZkbadMVY8pM4FDabE5egNJpcspZCE2tIYInpBeURbVJnaoElWMaCS8zuG6tiKXWlSrdaVA+BbkAUbyfBS0ruTlz0LPjJYLDAk2o3e/3CTOI3Q178QgzyreFPZDbLcRSV3r0wyCkSHhn5f2vZH5b3nHL5M9j2ZbKe3JxtVYU4fkxRkyTBLDCGlsjz/Lj7/g/vQGERk+CgCi/S7BLS9Smn1YU5wqjRQw0s1m5WGKC8yWMqikfxHpoXsVpMY43ThKs+cITYxZOMoid2hz3lBEhB9ljFR4+JgKr9jvASGryj6CIm+ZDpXebaT0Xa6dIeNIeoMLGVmYWFhYWFhcdDDeogIE5EPJ3KVIGJZ3JUyQJiGMvA0FY2taQqYbgbC+zQSyIDShVlJM3CpDV2dIT5nYDj3qvzmtL1h8si0/TKlWC3yY50bHXUW9010DPRjEh44HvPqBc+l7Sd7JH0WbZXLc/aEh4lOCAdM8yrSpD2kZJxpXPIKFUcrRtBq1eFyALpLzRImnH1m+smTfVxDIhDrxnEFi/FlpJO0J+68ZwvVoOsi2qqLvAFEIzFN4CZ6TCY6iQOOq3l2fdV7VWp07vGlct/urbQv9eGPUS27vtjdwlpMgcErxGPl+m8i4Dwgbxh7i2oFDraW/XEmoBDVrFIMN5fdqPTVewoB8gphLz0b3fhdvVdFaWuSFpjSVo/jOoz7Xu8as16n6VFwDGMO6DnjbLFqNW4HhkBq7o+f1IlJdplOb6zKM+ruY9teH4ghdZI5s0HVnYM1iAjlMINM6KKLCptKI6LxI1Cit5+JPmuERsKMry88V3fM3pig2XmhI5WexxLff4k+5zR5NmCOzklRxQdKsnCeMIhGqGR3D1U25Uk4JOMo0LS7SMSR6bqFPbI9vlMaY2WaOCsJbcXqvkqsk0HV2uXYhnL8e7FRxZqb2R45vjJxIhGFdHhJ5psaVwQ9DE+bUE9QMuoM1JgpE63anRgiHCuUqzdUAPXlXe4jo2QqTM6nv9f53JVeOo9C3dVfDz5fpYcM2BLTHGxRiN/WxHHQrhSHpKTHJ/Qe03ms8s0K4mwsMvVVSUKVFMqMjCPFCMobOBRdnBob8V3yh877ct7JGqxjYcSYFiSqcUT3eqg3KLV9TNPKMGaWEUzGTwp+Vom+NvUnd9Abp0qqvUG41TAQ2eJgzqhumLMPS5l1DHNKmf3Hf/wH3vzmN2PZsmVwHAf/+q//qnweRRHWrVuHZcuWoVAo4IwzzsAvf/lLZZ9yuYyrr74aixYtQldXF84//3xs2bIFFhYWFhYWFhbNYk49RBMTE3j1q1+Nyy67DBdeeGHd57fccgs+97nP4Y477sBRRx2FT33qUzjrrLPwzDPPoKcnTuW49tprcc899+Duu+/GwoULcf311+O8887Dxo0b4RkE4UwY8KbQ7ak2os4zZPL4dCnlLvZNj5m0gHyjq7z5JUnekPJUTAKb2SvE3iIYvDtTFEA7GdZn9Zjc9GqAOHvPgmSb3h6/aPnP0vZX+s6V56FFsyirERSJJqPVYD5P5Usoi4V/CT8fd1iZktejRmU8xOcAUFwog9YnId0ETig8EbJfl/SSTEyELoDaYaeAKWOOzsPeDHF5JxfR+KfoXiJqqVrQdz4xGB+bHaOVPvF8tQL9tlySpLe+P20WHVSvFVNY7N3xSiJwtZ6qAPYKjmZtIaa+kr6VwOccu61kkzP6Ao03iG/5MEcB0+wV4vFRtpjwDDm0LZuTP3Q+J+/ToW6ZgdfnS89uLdr3PGYKYOb5I0PlOlIvUxNziqlv0Qd7bpgCL9Vojqn6dccBlEVmGIdS4d6gmZSCEiC4O9MzxZ5lR1PtXgHT7JrjZxuWMusc5tQgOvfcc3HuuedqP4uiCF/4whfw0Y9+FG9961sBAN/4xjewZMkS3HXXXbjyyisxMjKCr3/96/jmN7+JN77xjQCAO++8E8uXL8cDDzyANWvWzHiMjegu1zCnVKJ6g4cLs5qMFo4zUUUT3bptbMywcdFLIoglTQZYRWOc7I1tFZkNNEFviRfL/QCAwaycvKsNJmxgLyotuTascM3jGMjIelwr3/B82n56k8xESi8DZaE5XVTrjCYxVv1l4TdRVJINn+qU7K9WlePL0YsrPyBfVuVcfG28HZR1xXEjNb1xxJfdS34ufvEaKThDBpt48U8upX1ZLZh1D+nn4hptlT6RZk6G5R5KzSZDig0HRXWbKCUdqvTS8TibXBOL50+Q8ZHRGz7VIlNwcv/SwnrqTpEyKOm3KwZUIaHufB6H3sLl39zhfZL7MFuQ9w8bGT15eRHGq/Ki8iLEZ2MmudicTdZqvTGdkdPI8DHtz58r4omeISuMqa0GMU4KvUbD4FjBsBg/uxEbTCScakiAVQzUVCyS78FGFLilzA5IzNsss02bNmHbtm04++yz0225XA6nn346NmzYAADYuHEjqtWqss+yZctw3HHHpftYWFhYWFhYWDTCvA2q3rZtGwBgyZIlyvYlS5bghRdeSPfJZrNYsGBB3T7ieB3K5TLKVFpgdHQUQExX+XutlPLJv0u0+qG4UHicYUNZMR6JHwqrs6pQS8bhUR/sUoiPHQ5keQLWIaqSbfvfNXk9el25FBaeoVCh8/TnYy/NZE2uVruSaNRur76cALB39su+M9F4/KHB9fHnSx9L26Ul0vX+4z1HAwB2TMmg6xd2Dsgu6NJlSPyOPUeiNFdIaVyur9+XXfZdeemJyiXep9FROTYuJs5lPtjNXmMaJvHIZEhKSqmTZXDA6QTj2HPDddSMQc699ftz3baIKGSTt0XnwVIoM6ItdIXW4/PItoivrnTrz837stdKBJYzFO9PXt9WvleRg3Ojus+VB8at9wQBUFyBXjb+wuxtVOhcyrrqzclnqovEGDkgWtBgKgWmzzhj7w17lITwIvfBntNm9IR0nqOaYRyc4OBAczPz9aJn1ZgRSs184hWu0vUNTLXzKJg9Imo83ar8hnwgt536z2cb1kPUMcxbg0jA2cuVHkVR3ba90Wifm2++GZ/4xCfqtlcjJ/mTD/ZYcuf3UACLTwp7nqJgTbW7qN90KwvNGeo0mSCMlcWUjcXj3BVKQ0nh8plW0zgEWWKA6biFnjSIFubkOQuJQdSMyCRDF2dUobE1YxyxuOOZA08DAEr05n0w93tp++cvHCrHXJQGTFdOtsVlZ2PHMxhPSv0kDbwyGZlTZCRTzAmFbiHidOty/fVTDJtW6p4aDB9TSjEbBsIQ4fOx6rIvWVLVoODhJ33z+Pn1pKS1B/rtQkSSkj3hj5MhSzRZVd720KwfjIZlkNW//Jj6Sq+T2+obhV68yYtclYSQe3qu3oBh8LMjjJhGKfW87977C6OqFVXr+jHV768UfKZFBn9HNo48QYPyvck0L/S/UUCyFzVNLBO3Q6bduM3Glu6yK4FI1BQ/Xsv3xPRhY4g6h3lLmQ0NDQFAnadnx44dqddoaGgIlUoFe/bsMe6jw4c//GGMjIykf5s3bzbua2FhYWFhMWeI2vDXAm6++Wa89rWvRU9PDwYHB3HBBRfgmWeeUYfUpgzwPXv2YO3atejr60NfXx/Wrl2L4eHh1gbcRsxbD9HKlSsxNDSE+++/HyeccAIAoFKpYP369fj0pz8NADjxxBPh+z7uv/9+XHTRRQCArVu34qmnnsItt9xi7DuXyyGXyxk/Z+iCqstUqpwtSlOSQs4RHhG5YuN+wyY8LMILw96YCXI59DjS3c7ByhxsXUz2GYvk8t4nPqNIK1HOJuunjJdGpQMaZZbxPhyMzRpI7OEyeYvEKpf7/cOB36TtJzYfkrZLU/K7cBBrMUsuCHE+8hbxipdXncWs9DKNixIVdJv4o7KdpT6mBuk37+Isl+S/dAOxl4YDfdlLw/un1JCSSUNtDb0GqDXaRLkCFn/kfZN4+rpz8wpUeIaYHmQNJIrVR0Y6HlXvTtJm2k0J6DaUzOAHML192bPAXiHOSOMSLcp1iuq38ZCNXoJ6qofrcrEXpEr32xRlZk3U5Jfszchnu5ZebH5e9OMwPavCu8NZaErbJBCpBETHbc4EKweUzcnZZMpzJMchjlU8tK5+FlVqoJGnR1xf1ixSD1TcO7LJXjzN2CI+ITeFU6uDHqJOY/369Xjve9+L1772tajVavjoRz+Ks88+G7/61a/Q1RW7ZNuVAX7xxRdjy5YtuPfeewEA7373u7F27Vrcc889c/Ld59QgGh8fx7PPPpv+e9OmTXjiiScwMDCAww47DNdeey1uuukmHHnkkTjyyCNx0003oVgs4uKLLwYA9PX14fLLL8f111+PhQsXYmBgADfccANWrVqVZp1NBzpRRZUikqjwg2roL0iOqBpmVs5UaxRbxCrZeTJmSvRTMo3ExoUwlJieUr+rbA+HkvTLKbFKibqs4bvwdWKDp0zGjDBi6s2R5HO6wln6jmwcCbrNMxSyXdwvKb9tzy9M27sz8nsNLYgtlx5Kny5XGz8SugwfjhXibLGMtCWRf5mMWXrBVxbE/dUq/LbQU2o1EhHkGCGkE7XcpLzo9SEuanxSVN+H1kAA1Ay2sH6z0i9TGKxaTHScAjEO+ilcw83CRpNaq2zfD5LpOyovOrf+cxP/4DR4QUaGbCw2BipUrNcovJhqNGi2Ya/4QGWs9ZR1U0aQwb0gDBeuTdZMtpunCKa6dceZBBg5I00xXMT5avw59cfn4wNpEtdFVyjbNN/F6SS30uEYImGcCNx+++0YHBzExo0b8Yd/+IdtywB/+umnce+99+KRRx7BySefDAD42te+htWrV+OZZ57B0UcfPYMvPT3MqUH0+OOP4w1veEP67+uuuw4AcMkll+COO+7ABz7wAUxNTeE973kP9uzZg5NPPhn33XdfaoECwOc//3lkMhlcdNFFmJqawplnnok77rijZQ0iCwsLCwuL+YZ2xRCJ5CGBZpmSkZERAMDAQJy00igD/Morr2yYAb5mzRo8/PDD6OvrS40hADjllFPQ19eHDRs2HHwG0RlnnKG6JveC4zhYt24d1q1bZ9wnn8/j1ltvxa233jrj8VQjF9XIVbwmfupeligp7mC53eQ1cZPVmeo9Ic0ORaBG37euhAiPcyzUByhXNVkdPvYdIAyoXqajCjKO69nSkmT8+6bDAJWu4/2DZHnF3qsy5PlM2kg6z1fOZW+XPG6yIvd1yPNSmZTumz3Z2FvUV5RunN68pCeYAuBVLK9yFxTi1LDdPTKzz6XK29RUaLDuFyhw/6j4A/YsOWWiBfRefy0NxlSV0QsCA0Q5FPauaOq9xTsZaCaNpyQyPBc6zxIgPUNM53ENMc5aM2bgievA/erzHxrTHy2VfNjrUDF/GAKL+b7KZuQX212W0eLdlBSQ0aTpqfMKU756HR/pLdJnhIYG8R4eay2sf4bVSvb643giFYe6Rpqs8cVuXNKDm/RsKJH+DTLHQs3vvx9SZsuXL1f+feONN+7z3QrE1OF1112H173udTjuuOMAtC8DfNu2bRgcHKw75+Dg4D6zxGcT8zaGaC4wmRR3rZJx0ZOkrecNL/1GwmXKNsN5Sw2KsZqgFGc0EnYSwigxGRy6LDRANWxEpleZjJMq3UbssmcDRlGoTYw+FpwzGVUmCm48CSTxHWlFLPBlUMqJQzJQ/uc0eY0MS8qstxD/tj1ZGVdkErxjZGgCryTZNK981Yvptj1T8hy7d1EO+ziJSLJgo+iOi4JS2xuR31GhiPjlkgzVHyXjr58ojJzhXmLbx9d8ztQdizs2elcZaCZ1/PvuhBW/1XEa4js4QywxphRFYv1hKjQfmBK6FHE/+sKOpr4eU2oFEvksUP2yLl8aPpWQ73X5+3M8kUAzGWcK9eWI4+SmLKfgG7QRQs28Z5r/HIMhwiPNJGmGHLenCKeabjKFEov3UdbVTdgqjkZ92niccnkbGE+zgTZRZps3b0Zvr9TZaMY7dNVVV+EXv/gFHnroobrP2pEBrtu/mX5mC/M2y8zCwsLCwuJgh6DMZvIHAL29vcpfI4Po6quvxg9+8AM8+OCDOPRQKWPSrgzwoaEhbN++ve68O3fu3GeW+GzCeogI1chDNfKwi8QPhY5PDwkc+oaARw7qZYt+c9BVt+9Cb6pu274gvCY6sUZAdV2zR4fpMeEB4jplJfKGcckPdqG/XJMxWyNJOlORyoWbSnfw9ciT6IwY3yStfMs0Dt/gQs+QF0l4b/jcI1Q3YmVhV9peslymZj2Ue0XanqzE5896kqroJ3E81miqGVbhvYl3abgs0526c/I6ZgflmCf65Pcde5EiipPFkEtaNRy0WV0hxxSRAB3KzMcl10PGjwMsRJclwbtAv/pKNee4FhtnRykaRwZ3i2jTML1xCnjlMTG9xxlgyTn5MfMm5XEZCmCvdVMfGurOaabmlJGHTD7mfyj90XVi8UHyPmQSYcYC6V+xV2hxQXo1ixm5z4BPKp0EnTfISNUb5inxQ7KHlumujKZUCCBpsrhvjQfcmJEm93EU3aX4nEx7MbUIA62paDeJ+0nhXOkeY2cXe60MNJ48kJpKllyYfI/GHvn9FVEU4eqrr8b3vvc9/PjHP8bKlSuVz9uVAb569WqMjIzg0UcfxUknnQQA+OlPf4qRkRGceuqpnfq6CqxBpIFK38RPCxdEDegpG/QmaV/54AyH0voWcTxdjpzwmoFuomOWoUQTVNXA+3PdsqyGKgubcBLuqEo36zhLLCdQjCOaWHn7joo0AESdpufG5Nt7xxh9XpLXOkPptMcskauJpYU40K+bCmKxUTWF+nECwGsX/Xfa/u/JOEgwS8E9RvrBMP+VkjgjptFcRSabmmQUVpfI61Qaj++VYJLlnSm2pFdex5BoGkWRN03vok0ev7D5BWAwEhJDxJuieK9uwxd36w0Y0+dBnl549AhkSMCySpSe7vWuGD6GOCkWx0zT/9n4ozHpaK34gPrtLolyKnEthhe9MIIAqWSepd+ejSBF9NSUStcAvEgypuArX1KoVurvdTZmMoooZL0chmIk8TgUg0FPgwmDjOMcOaaULzX3x8ZMTRzLx+mUQgFEeoZTGmFsxPE9zRITyT4NY5faiTZRZs3ive99L+666y58//vfR09PT+oJ6uvrQ6FQgOM4bckAP+aYY3DOOefgiiuuwFe+8hUAcdr9eeedNycB1YA1iCwsLCwsLOYvOmwQffnLXwYQJz0xbr/9dlx66aUA0LYM8G9961u45ppr0my0888/H7fddlvr37FNcKJ9pXkdJBgdHUVfXx/+7y9WoqvH1Qob9hPFlSUayhRszaKJIvOjX6lCT7WNDEHOOphKZnB7kiJX2UMkvEG8jb9XF2WnvViTXqF/3P76tH1I4pl5caov3XZ0t/TcMF3HNN5/7nxl2h4uxbTbQEF610wBzNvIc8Qrv5ULdgMADitKjjrn6WtcTAa6aGHpCRytSbqL60aV6DhFA4nKEogVL1NqJmrh+Z3SI9ZVlPeCeAJN+jT8hNao9pUi9pfsr+jJKZ8b0q00hciims5Hs5f+C6+aNRScEswcGrwqRPlxkDka0FymWFsORE/pQu6rENR/DiiUCdOWnkbsz+QZYA+R8AoBUgiUPSP5jHzOmKJtxltkpMEagD2fQerd4bmBvHW0ne/rCmVdiqBvDv7m+55Ld3B/gUb4lJ8nRYDRIJKqe2Opzws9RzX2EOlvnLSsCjuFgvpnixFOlvD85X+FkZERJVC5nRDvpWPecxO8XL7xAQYE5RKe/vuPzOpYDxRYDxFhSWYS3RlXm0rPhkiFZtAJpR6XnEzHwvqAtVJU0+7bDERqPmekKWrXhreEEi+UjJUFHauG1NwuMt44A2Vnubtu266KzKTqJSXC/55alLZ54i8mLwSeTHn83PdQj4z/2ToqH+b/+l2cQvrrbpm2ubRP6my8aoEM+GNabbgq44xGklxuJQPOUKDSZOSIYxWagakF6vuwRbvTNlOEok5ataanMBQKgO0GemEH4ieN9EYQv8j5feJ69S9YhUbl+k8GeQgt88Lj0MT2AAAobkjJKBPqzqyibbCR2PBi6i410ujF5o7Si7mPC6nR7099CyOHr7NJRNBkKA1PxvcY06UTZUnnTlTkPNFNBV276J5tBRx/V9W8yAE5b4Sq9gD1wdeGD9SLpAq4hvmI9+XrJIwYJrdrypiJJjMIW0oKSx4VcKaaEsukf47E76zGislmpDNCNc/NbMHBzJLa5iZfa/+ENYgsLCwsLCzmKzpMmR3MsAYRIYziP6afxhK1vB6KBi0ZApgDWtEMUbV4garBu8MeJyMllqxuuHQHf84rV65hxPpEwjOkiDtqvwmwkLLqluZH0vYLSSDyK4ov0/eSy6mXypJK+6+dy+T3opWr0BoZ7JbXiFeUk1TTSan7RHSRCHQt75KKfZuGpVt5y67+tN3XI71WrxqQ9F5PoukyRdluFVr4KZSZoTaTDorYHq22+XvpYKIFMrQa5evh6bZzcCm7/XmVzit5uqZyI7VdfX8K9UUelMiQwSb3Zf6PthNlFhaSTB723PiNV+Tch8hKUvrg4O4JupeYnRohCqgnKVNT5DoPspnJkygoUW1M30wlQqB8XTig2+3VZ5s2UwZjuhC0G9+NCkHXhLco7yVeXujFS7ldCmSbqTRdPodCh7lE87Ezi+e35Hd2aZxqQLccn55Ql1A8fvyBW79P1EkPUWT2kDZ7vEVzsAYRIYBTl9nFhpAAx9qwkcOxO7qYGJ7YOMYor9Tr2vfdWyJfrinuxpSa34yAmw6HZiXVs3lqAQB1onyJ4omeG5U0Gce4hFzzKJnERkrSmPE9/QuF1Xv7itJIm0wMoqCLMtwoJb1WIWG7KUlL/HZ4sewvH7+M+rNUPDPSxy00eilxNo4ST0TtYfq+5Yp89MZGEimDHjmO7rw+IzFL8SkVqrsmXgIcY6TETJiGTxll7mQ81qCnMZ3LRhA0rJqSrKUkONE/cpR1t5BeRklskWMo7McxS4rBw8aWqxmHkoFnMDj4pZ6k90ejNE3SrjUyqmoG6s5NMgT9gvzd+MXL98GzI/LZGVgs44kaGUemxZC6T/0LnLexcRQYzqemxMfjdj05F1ZC/euE5SsUxffkOQ8pdbbqymvKtd1Ck7RHct9zdh1Pc7yY4AVVYFJgF+Pkf2hkAZqSc7DY72ANIgsLCwsLi/kKS5l1DNYgIpQiD5nIxQQVlRICiqYyGuxqNnlsxCrLVOeLK9Wz6CNns4nt3AfTZ5y1xuPQ1i0jyo/1iwJIrwR/36NzW9P2jyrHxvsW5fhHq9Lzwas6U6ZUJhl3wKJK5ErnrBOuScYrVFGVXlkd0wqQhQ1r5Dl6eUSKZG7b2g8AyPXKANbDBmTWWp6DSBt4iEzCjYxFRbnq5ww73Tn4e001QSGKYzOkmVMtEZ1BK+WQt5N2j4jzd6dMtSpkM2J9Ir7tk5Wz0U2vBLNS0Cx5nEIn+V4mAUkOeDV4kaKEYnNIiymqsOuAPApUCkSh3TT10JRrwJl07DFgTarke+WzcqbIkdeTM9JqQWPvr2l7o329Bl4kl0VPlYBoyhyj/YWHODAET5vGoSt7Y3p2XPpdlOec5g0h0ugY6LqQ0iH53IEmSLsZalL0HfiNCLg2wxo1HYE1iAi1yEM1chWxRYEivYyrdHNWWIws0vPK6ZzMBU75c+qDjSDd5Jc1KAQqWWuaLDk+j0eUnyld3yOqkCe6sSQrxjR5VA1GkFKvKPkvT2KTRP9EBqqK968kIoaKcjNDUaLVzybZ7vg78nUep6yf0NcbKIyMRrHWZNiYqI/Jar1iNheWfXlUZvHlqQ5WqVIfk8RUAFNw5TLtSy99Nn5SfUpWpGYDhrPWM3Tdi/xyiA9wDHFPSpwGKw5Thl2mKyl2y8YJZx9xbBQbTboirBx7xG3FyCHjqIHIJF8Qhwwsr0g1yUhSQaTdm1Lte30ZQ9TttSbc2ggmkUbTdt3ngfIQ1+/rKpoF8j4wUshEnYv4Oo6tMx3nGQQiRVZaYKC6FVOLjuNZQzyXpjlNNx8505Q/sJjfsAaRhYWFhYXFPIUNqu4crEFE6HYr6HZdxRvkJ0tJdhdXDR4MU7V4UdOLPQRcxoOPY49NXqHM6u9q3pfH5Bv0RUoNFClMFeeXZ0bq9i1TUDhTXGOTMtMrUoKq2UsQj4m9FgEFQSv0DnmAdIKBDlc+11T0rtvMJSUSyom3lWv1gcrAXtk2BF3Vb5MnSOdNAqTXhIXvilT5/JAFw2n7hZ0Dsu+g/n5TApipzRlpAdFFtQG5qveSDCuvRPdVL/0WBbqQefJkkudF/OYul9cI9b8LX3cO8BVOCe6Xs4iCKnkDAoM7Kx2b/ndTqrEzxaXxJvK146B29np256VXiH87QbsWyUPENcsKFJScMdxjylgEZWPwZjTy/jBcUz0aBnPPLtNq8bGcYeqSh5wTOHzlRqSElMRbxKVzxmvSQ5thusvgORLXlb1MQain7Rm6Z9sz6IexsGQ6hkwHKTMbQ9QxWIOI4DsRfCeCRw+weETG6MHieJ2qIXaHqS+Rps8K0ewCZgPGlHbf44htlM4aOtp9ldR8jiNI3jQ69WpAdTsPZSRtyO+zkxa9AEBNVefJiCeVSpUmqVJ99pNS6JPpE5qAnBJROuy9T1KzOYtOERk0GEQKdEYmX1PDZKpm9STjacLdXjXEu+gUtpm6Y7plxWKZ8bdzXFJpo3uk4GQ6NqJ0ct16OsYhukt8FyV0h4vCUpwPGzOKYZP8Boo8gM+/rT5rSXk1J8cy9cRU25Qr772AVipKnFpST4xjdHyKrzIVQ+btnsY46srKMXGMWZ5ekHl66YuXfY5e+tyeLloxfIAmjZ9Gxzn1zwPHKJrijTI01iJdG2HUiZqCgHrt2KiquXpKTFBmfP2rBsFXBps44t5S6hHyXE61K9MFk6XMDkhYg8jCwsLCwmKewlJmnYM1iAg5J0LeicDuhVKy7FRpLarvw9XkabU0GtVXu89TMLNSoRp6bxFDjIOpMbUPCjjWlOsAgIkoqxlHvXDj3ufJ05jE6pY9Y4MFKbDIWjtTY0Sf6cpSkCvCo+BepaA468VwqYd8PA4/Tx4AokeqBj2eUFPOQqG4WFSPPFxcrVxHiRl1dwi8D3sfhCfKM6xQ2dvCukwDRenFO6QvpjXHyLM0Mkm6RxS0Di5Fwdc3CWZmbwwLN+aL0ssUGVbe6XGGEiKewYtXG5NeH+Fxiopyh96CDETm720qk6JNSCDKh3/PRnQnb2O6i0tVdFHZDd5f5w0yei3a/OZi706j7LSmMqwaeIvYz6foGmkEZhkLszL7sqxoGcnfyKgPlniO1EBq8qJ7jekzcW2YluP7o0RUdsGP586ar681NyuwlFnHYA0igg8HPhytc7mfJtNh2mEMZPiQkbDQlZN2BfUcdCnkVHt97I5ukmqmuKu6vxysGJPJ6GrmPEfl4xT8rdUF6bYdZVmXa4qzmYjuUtKZkyKc/kL5EvEMyq81MqSYYikW4hdTaKC42LhgA7ZRcrwulR0wGzxiu0KvMLVnetlq6LOI4pdqFBvDBsqeKWnkTJLgZHU8Nih6F0njtCtHopX0vXr75b2pywALDHXbTGJ22jpeXJiTw3zYOOK0e+ra70peOpoYKUCluFz6YZjCEi+0jJKdRAYRx8NoDP54e/334t+TnyNTYWE5Zv19MF0jqFUKrJGx2Ew6v2p0CBkQfciAKY2fs8yE8WNK12cUNFRbPKb6e4RpN1abVwvV1o9bWfTQQ9ydqc+4rXYyhsiiY7AGkYWFhYWFxTyFpcw6B2sQEXzHhe+4SvmMSrICnaSVLdNQ7AlS+2L3scjI0Isxcn8+ZZ/pPDkmr5BJODJLNJig0jgovIs+N3qceBWeFH4qkWbR8sKetP1LfyhtT+ZZG0n2JwJ8uQK4CRmumqB4WJy6bUp/LN4X6b1Iocbjwf2xh4IraLs67SEOMtZU9AbUFTRTWIXEk2OiobhEh5K1RkHClYR+HN0thSejBfLeZP0i1avGHiKn7nOm1NQgY/39JvoLDN4dzgrjdq5PUmLiOnApFvb08PVtFNjcZdD2YW8RPzu6EhcKDM4M9lroRBBnQoe14g1qRbjRM/RrypbV0Yns3WaExj4oQDnxEJky5pTj6DfSZeOZyhLlDNpeFbf+1Vck2rNR35U2a0btE5Yy6xisQUQoRyGykUqrZMVLgu6qXlJ0ZrFlZkE4TV70N2LIZuHYHYaOBjPFDXFbl6IPyKw0nvCGQ0m7FF3O8OAJXk5AC5Oitf9dkpkhq7q3pO3ePNUbK8q4EKbExAuNs350NFTc1r8IdcbDVImKtFLRV1FPCgB6qNBrLbGaFJrMoI7MdF1AE6uId6nRHG1UGabvGxAVWPXi83PdpdFhmTXm7aT4GqIea5Q+7w7GkzlTUjWDEafScXJ84rrzOFSDSfu11Jp1Ub2hqmScZfQvUEUtIDnWo7R7zhzitHa1kG6trs0vT1NtOpOkQitxN+2OC2q3EWQyfnQwGTllTa0yNUuV6UQuiKs3Vnr9eK4Yr8n7m8/B9KWJ5m9owBIKNL+NU5ZsI0FM3TzsZ2wM0YEIaxARAkQIEKFCN9BY4k1hFed+t0xt+bDs5qrONDkIjwwXdC1R2+Td0U0CvrJKM30PvedIbGdPFZfu6CFDr9og2oYLUR7b9VLafmXvy2l7+4iMLapQEcugnOj/jMproBTs5PmYLkFA5SK8BfFv4FGAcHlEGnfKfLxVBnePv0SB3kk6eP6wsXRblSZkLhBb4/GxN0iUquAUcv6NyPio0jXwNMYge40yL8nv4tHClWN0axR0LDwyXf3S4DPF6OSztGqmc1ZFm1LVS5PyxcHfxVUCs8mASq6ZoiBO1yZL6f9qsLvcXRjPRSpwy542rpje7etfZsIQ4ZgVk1fIBF1QtQktldSYZgp8M+dpxfBppV9ANXKqmmKrbBROUfkjDjjXGY69GbmIGq7KhYApRivQxCQpv63xuzQf82XCZFCvDm9x4MAaRBYWFhYWFvMUNoaoc7AGkQa8xhoOYo/CSzWZVTXgyUyeUiTbRVql7AzytE+8qghMBUBNVIQmM0RZ2Trssoe2vTuU49C5wotUs6ykWfXtDaGwzRTG1kpf2j62W3qLHsbKtB28LMchlJDJOQV/lLwdcnGpXJvMGAm1JbIGYa/edZ3dTZ4BCvNSvmJy/YJhOf7q4fJ6OAZlaa6rlTJ6TJFyHS9Dyj+rLYvis8GkvCBdI/J87CHi6xFIJxKipD+Oo+KYH6bJeJHuaygsphC5aGlEWYM8fu4h9fQRnafU1iPPkccq0gpNGvfBsVMupTlzthCnRHNKvECrXiGTynijfU3IJM9cM2nt00WrXiHduJnC52dbpRNZfTreznUdXZqP/GmKTyoCluTFybB3Kqz30kyQwrWg4uIRNy89YMJ2yqIV9Foz91LbYCmzjsEaRASRdj/G6sJJnAlTXNtq8gV6uC/fXGyIKFXkkwmc00tNPD27iXkSzaaB2Y0nVkXh2q1/SeQV1zelolIkMssC5OklK+jCgby0MraXe9P2Ybldct8uSd9sy0hXeMgGjxiHFF2GRxXYWcHaUQp5Jp8z7dZNAeRLZNvZJr9LVsZ/a8fj/pbiGRaTYdMv++MSIuIdwIZPlCMjggO32ShhA1YEiLPxRMaOL2VaFNSKdG1ygTIewJy2zgh1BhuNjSnJgH6LSPNbAJCGkEFviA0pU8kMXRq/KTW7xkWD6UXuauJCmok3aZSebqoab6JpaokF3gxNZnph6wyeZgKfpwtTTJWnXL/4/D70+yrHGfSLdEZiweOkEr32F8cjcgkhHZTjpmkc8W8rDOygiTIrFvsfrEFkYWFhYWExT+FEEZxo+obuTI492GANIsJoGAd35jUryWdLS9L2SV3PpW32Ck2QV4hXhL0iVd2wmuHVCgtAcv00k/CiQDMuXEGJ5Q0rL/YWKLWeNEHay7ukq2VnSbqUeVX32sX/nbb/7enF8jxJMLNpwR6Qh0XrfQDgluNro9RAo9pd7E2qdfG1Ia/biNhXfsqZ+8Ut5NHZKj1HE4drVoes+Fyix4o9HzR+5bsn3wXkPcmwV4jpJKYZx+R3KS+sH5Ip5Z/BGWBpunuZsugcHj8NmmpLRXTdxe/hsOCjq/cWKZmCmqw0E82kCgA2nwnG5zZRY436ayZFvBU04xXSCikaPDNeG2psuYa5hL108jwsetrai7fRtVYFLFnNn37/5GXPlJrp2pmujRi36fNXFnek7cmEp/Y9m2V2IMIaRISCG6HoRtgZ1GeDLSFqbCHFEO0IJBWUNdBgOmOmxy1p9lSNoOmCjSOOCxJu9gmDDL5ShoS+C2fYITHuBrMyM+vFyf66cwDAUI7oxH6aQCbFefSZZWzM+PJSo0b1S4VRFcnQJJVSM0wCPOeJr+Ua5jZWQ3AojqfnN5R9loxpail/ARoGlRuJqvummRyO4+jR3wfVHu1mJesr3WZQmXYcNoLk/jpdJi7dweN3uBQLpcenWXcG5XHFwDK8EKXGlF5CgLPMOM6kpmRPxu2CknbP97321IpOjg7tKKo6XSPIPKYm6Dh6IHT0YzNGECMNAzAYiLwwMmWntRJXxbGOPJ+KYqt8jkaGj2mfHE0EppCGPi8OA5jKWqXqAxHWILKwsLCwsJinsFlmnYM1iDRYTO7Qn5Xi7LIxytZiD0yPJz09rDg9SArWQqro+aoMxh4OpXBgvyuDjznIWVugEnrXMaPUIFC6Stt6SFNJyQaiVZhOOLKbvjerBZfJm8QrLkWLZjgJNKW4VyUphYN6yZHG+5cHnPqdWSuopvc+eRrHHOvNcRaaMnyDF0lkgOV2UY2mhURJTpKHhTOvuC0UvTOkXbWUAsT7ybNRJP0ioqr6euN7yBQuwPRUaCi2GiTeoIiDsXkhbaC4lPOI684ZlfTbu+RN4kDqSOed0tB5gOotUtSHAxLyy4Z1fSgB0QbPh6vL7JzFN4rJE9EI7OFomapqYX/TviK5gz3C/F3y9MDwPDAZ1Be9Vuh5g3jtGGXtMkQAuDGDV9mXlMxpTtMF26sFt+u9T4FBTHdWYCmzjsEaRIQgiv/48Ti5sAkAsJuoMTZU2Ajih0it8Bz/97CMpJBY4Xoy1Ke79ygPYvxfJa6oCZdzF1W2z2vEyIZpouEJSBV0lOfMJ7ELr87L+KDHKL2ewRPJABUdHd8cT4r8LuD5jNs8V9WkDSk/ZxqnargefJ00kwPPtUoGHKcUk0GkHROdwqP0dEHtxf/g7DmimTRj4ngdh+6PXJ80YEWBWwAY/u/Y2C4M6VPSAiXDjcZB+4g0eIVGI3FKHqfyGzFVmdF9GTqHiUpz9z1rM73GcgI9vrweXJxTKB53Gcsx6O8VXizkkpdeq3FDbEAJJWemznieaCZ+qZUYJ5NxYdqn0b6m44QhwllmbPiwwdHjyQXfcn932n62vKTu3CbKshGV6btc/JXmK9rOhk+fJxes4ntNUGqnakjPPC7LYv+ANYgsLCwsLCzmKSxl1jlYg4gQAnUhkF3JyqQnI4OIx4hX4awwqtyBsmZVWSFPS5ZqdPVxDSYKJB3mAGsh76IpxdEsdJlovUSZcWkR00pUnHOIrkfe07vHeZV17MJtafvRME6J0ukRAarniAOpOfNKLNpcFnQk7R6OA+evwt4gfzQ5h6yHqnqFyKHGC+hACeSu/9woOWPSVEqug5KNxU7DQX0Afqlcn7U4NU4Cdf1yFRxo7iUAqFE5kVRQUknKM9B8NFRtIhd9V65J5hhoMiVzLLkOnqJNRKemEw6XpdswLMntItj6kG7plR3w5fUwFQPVBVubir82I+IovEVKgDvNAyZvUSOYgqT5tmnFs9GqF0R4f03B0zwPcEA0e2wOy8ZlfrZUZV3EVqGb03gcXIR6GYmQ9XtyMhG/Bwd8c0ZwhViAbCepMgFLmXUM1iAilCMXfuQqD9kT5UMAqBPhoCeNgTCS7mCmz1TKLH7gltMbXcm6polwkl21GuOnmQr3JoxG2eQ4/eRnEoUsmgJoEizJjaZtnjB44j+sIF3lPxWbafjVPhJBzDMnRVlw/fQCLdeLGWbkT6GGsFDsEWetpRpwnOXPmeX0tTMGccSJQ6K6cbCdqhOT3Ht8SDK2IqbJHL3hUJmUE3W2Sw7QSRS7nZelQVTrkYZUjuqTlckI0sUcqbU4iV4zxBM5Wc4yi9sRZ6EZvouSBk/GYF6TwaMobVPXO0elomf0W9muHRp/967D5I+fUYwZvtcpDoYWOKnQo7LoIRXnJkQJdWhGWZoNKF0MC7/o84bn0/Qd2wk2fDwDLciUI49b0Il5Zc4wGJYN5jr+rhzHuMiXczXTZHzNmCoT4GvO85iYy5uhGNsF6yHqHDr3q1pYWFhYWFhYzFNYDxFhIvLhRC5+XR5Kt4kSHD6tYnZALwYzwCsQDkBMVjeTM1hVTLd2zjCtfsSq0+Qa5sDPkLNHInaL169uVxU3p22W0h/ISHfMb0vymlZEFpan/04RbVdWN5w5lngaeHHMNdB4mHzZOaOsJjz17LkhzoFj3ZXAa+oj/7JT9zlTcCykyCKSIK9K+iUNWVyKds8e+XsGOfm7iFIa/jh5dH4msxrxmuG0yYHNqjBjcg5FSJHvWXbp8A9TP1aXxqZkk9H5uO4aQ3gnJyYpyJU8ROys6+mRbsHCH2xP24Iyq9CPOFqVPxLTvOw5GqN9FmQnlPEAe9NkHAzc3irzjSisvCGImBEonq96r1sz42/kWZok3ruPgqfZqzJJc1C5QdC3aZ5rdH3zlDzST9Qon6OXsnmZBuMySwLsIWLNOPEemDTcu7MCS5l1DNYgIvS7FXS7Lg6jTAgBduWyQTFK6fgsbMgxNuJxasaoma7hw/FJTN2xwVNMCrNWFINI7ssPvijiujfCqJ66G6LsuZ1BL+0rx3Ry97Np+ztdJ8QN/qpMpSj1v4gipBgR8T7geZJjjzg+iQ0YFjYU25WyVxwmQ550jmViI0x8RX7fFLY7dZ/H55HXfeQo6iOhCBtlWsVjImNmXP62XrFWfz6iEMu/6E/bmePk7+UTlVZLBA8Doru4UG3o6ekuBWK74UXP5wsNIqSTpfjHq47Tj0h0Io9pckruw5l0NT9ud2WpeLFL8VI8PkMh0p1JUc/FOabIDdegiUwp7bkJplR6z0D16cAGgElUsdm+mt1HQM2ydfXtiA0lQeHrY7RaKVqrZLSScG4XxUjyXMhzeFVT1JqPK1JbhAR4Ha5lZmmvzsBSZhYWFhYWFhYHPayHqAGEB4UD7zwl4FGuLnlVMUYuCqEFZBJ4YzFGKCukfS8LTAHWakC0XCGL80xGcmwmYTglQFwjlqboGznsvpfn7iJagq+TGCoH3iq6O1TTi032kGucJV4k9u5wOyudIEr2GUN4i0zJPXxpWM6m1k0UUOI1ibjMB19SJRBZNrtekKvSyUOTPvr0ngql5leeqKhx+RuFST00csCgKp11yvWY+p38IHO4XE17CVUWkUiW4hHhrC+iNYV+kWnMLARZC/SaW0qSnqD/uuUPWiPvGpcTCWgc5aq8Hn4mHhMLNzL1WCPvVMYlL2qmnkqbqMkbqKDUsNLXaGs0qfI15SButbp78wHRbgseJIYpY9U07zTKat0dSK54MXnIef7QCdxOl25kMHX3LIU8HJ1/KW2/VF2QttkbLkIFfH7MyA3MiSK67LpZRxSZFVebPd6iKViDiFCNHFQjBzsCyauMJIKMymRlytLKyH0GyW0rJhJOkm4mm6KipOdGdZ+bJrQeMoJ6KB5kW2IZKCqtSjaIgcIgA6rfiXkYjiXi1H1GP6l1s4H1+qNj+uw//+touTNdHH4He1NkNNFlz0zWb1PeTzQH6NSpAcmUVPoMnxuyzDJTlDWzqO7UmFoiT54dIfqMS8Kx8TYcf8dyNwsm0vcmisihFPaI4pCEInaVjDUeJ9N/+R1y+0SX5AKLS+J7VolZYgFJMpRcJSMOGujjhiKlphqn98lmmBjKbPio2gmUwVkmJXDaPlyJv1cmS7EgRXkjFHwy1sko4XiiMPm+inFiSBVkg79GcUthcixn0THzyNQYUzc5Q6yNaJtoNCUjzTA/CAPLNQXaERRji7aLvnX9AmqMzkigUVQFEKYrI/m9lTEZ7C8dNcfn5sw3jg9iKk0HlxZ2PC8qCtzJPkEHxRptllnnYCkzCwsLCwsLi4Me1kNEyDkh8g5wbFaKCAqxwp2B1DjhFRu7U3nVxjXEsoleialOGe87TKsppqIGkqJZLBzXjDDj7oCzKdy643RZYwAwHErPAWuHiJVfj7sr3VakFXYzdZ9O7vsdAOCnAyvk2Eqc/qUvtcH1yUSQc2aSPmfBQVqU+uTd4The4SFiDxJni3HWGgs96mqchYYnqdLHwbt6L0ckyntQyQ/2/nCiF1NVEQdYJ5RZZkJfh4w9/NwubKEabP3xl8wQBRaRl8YkqhgE+74PefxBVe9lUiinxKtToXNHZb23yOEsPvJEZfPV+nHSj8/UHXuIOCtNPKPs8WEKLkelcIw1CDWeFyVTTXkW5Ti45lcrpTtCJRhb9sceFOGJKir6Ro0pIB21peqR6sfGtD1DjIPH9qr8i3JMdAP/15ScK5geE3OxknVH13SUHmjOnGUPkDiW51tlXmdPW6pDpP1KswObZdYxWIOIsCvIoxS4CuXUm2RbHZKVmWc7aTJlY+YQCmIZpolYGFUlw1szNGSIMc8t6p3xhGiizxgB7Z9HLTlHvcLx3hjypNgiZ+e8WIt5+J0UL7CC4gV6XE69ldeAabplfqwYW6vSC6/KLzz9mHT1xEyUmZI4RNtDrhGpuWRK2n2WY5bo3Nn6TCOuWZaZpJcw9eFSbBTHOwVJf9xHwHXPSCHaoXRfl+igMJdM1GQ0+hV6kRoy6ZTMvB3xxYmWkaJzrf67AlTEda/tIgOMDZGQi+6SURVQXJCbJxo32adAwpJsFPDtUaM4tIDuoWo5kcvIcVZb4wUEQ5yzRi9sboOMo4DoRF4UZJIL7NKz7yuGCsVDkeVYNswVQgwyZJ7SlD7PfWuEYhmmOB42ShrXUeOYwXqaqRlsq/anbTYWuw28txi3STGbwZSkS3NuF+LFZlYJJaAFq5KFFrdLUediiJxwr3luGsdbNAdLmVlYWFhYWFgc9LAeIsJLtT4Uax6WJx4MQK4S+yhweDGv0g3y/SVaVYg+uJK2KYCZVze8oskb3M46GDNGNEsF1g3Kk/bQLkW4TI5DZNKpIo4SQxnpWWLXNgedLkzqCIVj0lPllDl4mvWGyDOgu9S86Ge2iJxgnGWmaLCJmE6mpNizRJSZIrDIgpJO/baIVu8cVG0qLZLqedJxQV62K+SJCClzzOOMs+54Fe6OGDxtHGROC3a+TmJMgcFbp5QTUTwU1B8HQmsQKd4iopinSCA0odJYb4jFHZVK5ES7ZViryK2/1x1H34ei3UP1SVIPEVFtFebo2A3JXkhF4yj5D2X68HORczmDieoA0s2caUBnsSelSp6lcJquAdWDZUr+qO/bMwSf8/UVSSqAWkpDYHtVZjhw7TH2MnX5ci4Wc5PJQ8RZr8t9SfPrdJ7Yc2664uK7lDtYusNSZp2DNYgIR2V3ojvrKsbMz0orAQCP0MOyKi+VmYcoc4FT7Zl2e666GACQVdzI8nOTi5cNG6E43W/I6GKwETRKGWK7k8loOQkp8vhLGoGyeBzy4e9PvleepQc4DsKT49vM6fqcTZP8l40Ij+gdJdGE512mZir120xQ6pPxHJwMTxFu7DXMHrxZc0424pR6qGQEcbyTwrwIg42PI4bArTGlShZMgeighMb1avoLohiCZFvzO13EH9U8ec843fSSJuFDJRVQd8kMqtsq7aZ/qYQJPRxyqVKl0CvdN2S0uso+iZQBU22GF2U1YCqFRQLjdoZe/mwcuTS+kOiuDMUkuYmBxYYFxyQxvTZSy2i3q2KQ4qaFFiZay3frU8dNMMUs6Ywgnhu2VvrT9sY9h6Xtl8alFa/L7puoyBXLrmEZq9ndLR+e4xbLuM7T+qXIq6Dox0IZNPjTkVekbY4J6yHtjMVZSfOf2ftLAKoYrYlirCavTFO81GzAZpl1DtYgsrCwsLCwmK+wOkQdgzWINNhFAcNCbIwzJXZRxhlXvGY3dxFyJTToydWIAGdxVchNr4gccuBfC5QZYze5qIeT78IrxFcQxeXTUqKHFqUldvcLHRTF5Sz3fakmo5ZZvJGDz8WhnH1kcE6B3Q9MtwiqxyilwhQROdU4iyzVMqLjMhO0kqdMNQ4GZppJnMclD5eS+Ubjq3KNM/q+ovZZrYsC4HfWZ9QBQNhHfBcFJWMiCSKmW43Hyd+FvUIcqO4nzkIRoB239dldqp6Q3CWth8bbFJpMbleWrizGqZu/uT8KMlc8NpQdJ36vjKd3pbAniL1IVfbehKHYWR5HzwJ7HzJE0bkRe5QSz51hme7y+Gkf9mAF9Oyk842j965x9pNCYSl1zZoPclb6pgshvEj/tmNVuu1Xzx6StnMvER1OYxrTOQ55zPRGGuuSD+tP9siH5+XDZLs3oc842P2wokyAWUgppkfkZa27fk9u1yWkmEINikkAdtRJYUaLjsEaRATfieA7kSKq+AeF55PP5APQT5lUjAmizLi4q9h/wpDdxe7e0NXXIhJ1n6qGjA2mUnh8nKlWSugzNrr+k9JZGQupMOsYSQGISZEnFO6Px7E8Myw7pPllIhmHM66nRDJjdA04xoWNJnFpAv3nPF9xWAsbA5VEuDYgBWyHja68oeAlK2Yn42bF5OoAZddtlY9YeaEcVETZZ7rarhNklKCbZbDp3CWiepIYLDbAOJMtMrBdrAQg3pNuma4BSQGArgdfJyWmKlL+U3fCiHUjyGpSWLWKm4yZOUQ2YGhf6CG2K0ZGoDciQgP/xMaRDhlDjI6rUH3Jfzidn75XWck+04stsjHjJdfaFLtoio3SCkAqYrMSbPgg4jHJPrZUBgAAz+1YJMf/snxYs3KdpS5UNHFtvNZjAVFFMZ0Om6rJ85w08AL2Bl/TFbmX0/ZCWpjy9RD0mClTVxW47DwsZdY5WIPIwsLCwsJivsIGVXcM1iAiDIdZ1EIXy2lpXUHMfyw0BDO/WJPu27FIunj7I+mZEXQRU0ieJwObRx15XNbgihVeH9cQbGkKplxIHiIRkP3jSVlqnTNe1nT/UttHlTSJdOhy9DW4uPQIC9cJoceIdHdA2WQc2GwSFBSLQM5CUzSECvrrwXSLLkMMXmuzR3qsEnRNHoDl8l5imgmsM5S6M8gLQqKLwmMCAKA2f3evlGREEb3G5UaU24OHQR448TNyIDjTZwH9XrzQZ8HM9Gc2XEbHJOLI2xv8BEofdD1CpjgTYcaawSvEZXaUumuaNgs6KlBVCbVoVHGeRSFNgo6Nan0pY6ZBsafE8+o9Tp7huWWYvEWHJrpsJx4qE0ye7x1I21tfWpC2Mzspg7BS/zubPBjM7HmkuXXCwJa0vTQ7DAD47/LCdNshOZmdppQmou/CGX26MiRGb1HyW3hW3OeAhDWICFHkIIwcPFOVMUIiC6uHqKyJUD4Mr/RlwMhwqBclFOKOE5Tqw5MfG0Gm7AYh2CjEFYG94nIILO7I7eeqgwCA1xd/m27jzDgGGzCba3KiE/XJmCYbVWquyWvDhhJTaYsT1/XhR8rMkee3LE7bEafgc5vuVvESZvpKcc27+u2KurMj+qVt/LI1vcs0ho1TNezMooomY0Cc3jUYBUxxsdIzUVvi5aFkfHNWOF+7/7+9N4/SrKruv/cz19A1dPVU3dDdNAZklBCQwWBARQxRiTErThFIYgZiFJAYxeB6RRcC0aVhJYoJxFdN0OCbpWaZxB+xSRDlh4I2tDIJDTR003R19VRzPfN9/3juueez6zmnn6en6q6q812rV9+6z7n3njudu8937/3dnrE8eQyZlVd0ZDiJSL1T5ZnbJq6vG1d50rid18bjsUp322dPxX/l7QkbI0bVZcM+KvCj0ligO86oWdN14wOFEpWr21c52AG6cZgyn8b+TAyTWqcMqdZCionasqcum+pTym1QmPecGV/n9NmHbMegzSxjcVyOK9OxFPxTe1bYdWXEHqH/py2xrq9Xdtlxw6AdYUZXltz+wipVhyyz+YhgEAUEBAQEBBytCFlms4ZgEAE7a4tkspZxltIYiywTxDIUKkPCVz4jnol1YOZSZGxs5L4NDIjucNDbeeH+3OwTa7Adl9spIiJT8JNwv77ZLEUVjYw9Z5fdELahdkzNI99vgkeXdNprurUAlowTV7hpIgb4Zh3X2rVOvJNfHVHsgCt7SmQGm1FzrOM+wOjUPWyR0TCKPKwWGR1fiRBzi+hmUEHonqBql84TXWbcroo/FOvG627Oi9fWxwpV3IxYsj/lLoXrDqlIGWgxsRSMYX0ipSyJ4FiKf+JcGEjtCrZWDJhHjFG5sGJ2ie8kg66VC7xm30vWScs6Qnl1XS63201VhccNS4hA30NG+F4exy0lm3QsSh3V8sxOa97fq3s3J8sUbuT++jLuRBbX8cgQ9Wb2PYYSzBjOq7E617S+ErLM5iWCQQRkUnXJpFI6RbXFg08KmGJdruKuejuk0rbxco3F6RdMv6/4c9UTLElPNq3joMl97EahLypYDyJbzBg2FRhgu+vdTb+L6I9OtyM7bl23VY59LLsyWWb9rIgfYRY8dYn61d0fWPVxbsEfp2iAVZqNFhGZ8aFONf3uG3cpmqjjbhrHTKmPt6d/dGchO8dIC/hquPkyfZStkm5uyzgkbQTBtcu4q/2oF+bTbjT3LgMDuDYANxmOV8dzkHLMhJVYoyc+jIVeM0rh2sTauFP0q8ooYRsYP/G25RYZayJa0JF10ug6LMVZel2QG696xoEqNqRRZcYbvvsVj+vcV0PMPAw0WroRZ7mnaidiPE4BFnt/rFRNSZMBPHA1T2yUEkpMJmgYs3GuXanWQrauYxRnU4m6BYLLbPYQDKKAgICAgICjFSHLbNYQDCKgEmWlEmXUzMTQpbm6nXUsgQT8uBJQczMiPfGscwqzZ2oSaWl492zPuONGkErDmddxYHG21Xqc+3AxSkWU9hgBXU2mhzpJpiJ3xSUWN+MYlMJ3MWas0cRK5HomzyBou48oDoRWTJHPBeYpHZFykRm+wYMuMeodGUbGU2JE3KeiMuZcE3wySKpsBVkmR4kTxfh4SAkGWCtGyTAzmFQrzSJm/RTJcIHFM+6uqJkBm4lWM9datyfYne7GHNugH/HOI89BdEkPutLAOMU3qerJMiObpI6D9aYsCFnZqkeMUQWz4x2oOTLfKvVmloS/i4gsypad603QN8cdMjcluNTr+5HtxvGjWHdrrrlcZgTPtYQxsgxG2uXa9wWQq5pqbWSRuUBdN46/AfMPwSAC0lKXtKSkHzSxSdsknboTcsesWcbMD9YFq8QD9QDiAiYjygXbxUkok7n8/rq/9qV+pmIF0pgSyn2Y9TwGffZ03XVggFQDcmxFcB+kyrm+CwYR/fdmYFpTsC6z3kX2mu/dY11wdagxKwMm/shG+5k5knJ9nH3uKXx4Vbow1xu3msr0wfWiAKMvuys2cugxUTE1niynGrwc5vLS80EDxidf4BRA9xhV/A7RkxJl+MF29LPQvE7Efz2S68vfaZR4RK2VcbQf2V00xt3b8cbA6MLBlSK26mtjfRluubrnfqYRi9Wq9hkVs/k7i8XSaCpQSdthPCjDpw1jwbzDNG9oSPlCAnTh1cbWlP7g+MH1LFrL8c0Ycj0pxApx7EIPmaG7P6ARZL4H7VyjQ4XgMps9BIMoICAgICDgaEU9cs809mf7gLYQDCKgGOUkHWWkI7IzjO54xsXAaLId1PFRlegdk8AiZn0+McMKxHZaUbzMQhtHQHQZszO2MSzXpNhZ2ApUvqebzCcQabLZ+PsLZashZAIlG23tjJGZaGb2eFLh5WTdykVW/HHPLhuQqVgaVF5PXCgspKbcU57IYZc7y+fiUm4fu5ouJXMZ6CHIeDK9fCKSyYSXLjBHkHFjJ+7lmoPJ5ziY8e2buzPn0lx2Tv0+s6+s3VaL70sNwphpRwB5Yx8et2C8qDxV6n6iLWlDBHqn4oua9rjr2tEHMqU+0iqoGiwOmJ4Ma93xODEz5GOFiDQKkWlNInc5H1dbJnnQaUWGezoeszKK5mtmkmeilfvM5yYjY5NTTHG6absJsO9ToEDJFpFlMtJHHOeYXcfkjxozXT01KFthKg4xmIpmMcssxBDNGoJBBPSkp6U7nVEDgkkRL4KypUHEl3MM8TjdqkhrYxDrx6BYjEixQ8AQ+16FAcu46VjviLQ0jZllqDNGg81kkVU8af5KbBEGFmMDDGXfA7/6Mbm9djsMaM+Xl9t9F7bZ5djI7EABXBbHzHexKCwMIgromWtJo4XfWsYWqfgexhOZbZXjxS52QM2YHyW4P0yqP+UBVK0zT4p72pNy7oIyYFqMw8o15olfIlT8ktnW50JkoiAztlmLKv62qXpoVCTPeYxTR3YaPMxqH+rxpWFTb943XWCqyGwbfgTTwifiyItKl1g+03yTap64Ibq+isgsU8ViaRzVUk3rmJ2W8d28GmU5GvuuICbPF4NDw8c1KaORRCOTrjtf7JFp74sroruLbkFmz3Vlmn2+nBDmEJqwv5m9LpgwgCgoVc9LBIMoICAgICDgKEVKDjKG6JD1ZP7jqDaIbrzxRvnkJz+p1q1YsUKGhhrS7VEUySc/+Um54447ZO/evXLuuefKF7/4RTn11FMP6HilKC+ZKKNEE82siDVxKILImQbrnVUwWzI09iQy1Sp4TKn/Q7wMLQ/rPmsWOJyJrdW+ZJlUssn66sg21/HRx9CzNrrHdsdCj1Me3ZICaATOyB6afkWyfEbHlkY/0HZRzl678gT2jWwblXkTu2GUYCLcFqx95IWZoVbdLFNUocYNslV67KzT1Bnzle5QooqeMiO5CUNn2HXM/lL6Py3kbFyZZ40dYpneWhIsmeZ1Ko6dteSwnoxN4h1R0k98rnANfCO1uRxkoVz10kR0iZZaMxtEloHPRx2sTxqlW1oxRxRxZJ00tZyB2yqumUZGR4s08v2zmBD7DuRx4Q37VAZjTTdap0efqK60e5pdgYW0J+BYiU82lxMpRW7mK+fZXyVqZqoKHpcax1YlMol7arYlq08XGIO0VbYs+v2K/LCzr64+1Y6EPlFQqp41HNUGkYjIqaeeKvfee2/ydyZjX/LPfOYz8vnPf16++tWvyoknnig33XSTvPGNb5Snn35aenrcqef7Qi1KNf7hhdpda2Q8MZOqwngiYXo6BgSHscJwl4rD6JqJZilDkZG6dV/x5VwS11xrHNv2b9xhbI1hYOhN71sBdibMwMRjD2TtsTnw9sDlN1q2/d4TG1WDiF/ihyHfg3T9PYiNghus0NfYty99Ou1JpVbZW5ExqpA5xLR2xp9ASTtVcezPm2rvTpOnYVPpMUEzdl1u3G6Xgzgi43tcNqk39sjjFfTKBbTaBzfbj33QlaaMO3oQY8OXhpZyBfI6urskUfKswM1KdWoY15FH8sEly0ARR6Uy7RGkNNlnStdTpfa7n1MqZqfzCFpz2PlVj9uqA8aRy/hh22moZDPeiW41wsQFaWOGBoxnTKNIY3wj8zA4uD8KNvpkAcwxxxFEtyJnxxUe78WSzcQ9o2tLsmzGVIo4chLoCjGoh8CceYmj3iDKZrMyODjYtD6KIrntttvkhhtukLe//e0iIvK1r31NVqxYId/4xjfkz/7sz2a7qwEBAQEBAYcUIe1+9nDUG0SbNm2SVatWSaFQkHPPPVduvvlmOf7442Xz5s0yNDQkl1xySdK2UCjIhRdeKA8++OABGURGmNGnwWPgm/3QlVZTUvn1eDvM+rGPLhyPs7acqqeTbTp2DVNYMkc5h0w/t62B7VLMDNpyfRfSqrribZXMPWZszCwbR4A1GaWd1QZ7typrg7E7VWqWXWS1+wi0Wn1RTPsjyJVaMEo0j/XEcA9MXLtigrioiqqR6XEE75JZqDb/PmMXmlWJlxloTfanjrhRVa4Dl8xo/fiO0VYMaatgA94XH0sT70O5/FiRAoQl+6QC0c3vHreht09KvDGuVK/Ek9xCioiXl3qN7I3jePy6YH81uOBcukaFvNuFVPckBRCTKfswdMbubj7HHYg+pytNMVh0d7UICPZlk6n9xf3mu6/1f8AyebLP0vELaMaDRluEI+AhYp9c5UQGwJCTFdpRseEDmyZtksd9wyfYfmfjJA9cx/MXP58sr83vSpbJas8aQpbZrOGoNojOPfdc+ed//mc58cQTZceOHXLTTTfJa17zGnniiSeSOKIVK1aobVasWCEvvvjiPvdbKpWkVLIf+bGxRsp3R7oiHem6ok6LcfIq3Wg0Pti2N+2u+9MTj/w5jLzjGJP6ESMwQqpcpdPGxoyKZXLH8UxF1m+eQ8BIxpFqxLZ7QJunVTabfUxMdhmzyTgQDqQnmtqKuF132u/vicGhkdALgy3+GOWzcGXxI8d4BrQp48NlUrZrZXehL34ohcKMrtRxqmvTIPLE9LiUqH0zuWoXmuLbQoPCFGTFLdyH0COWKRcQG1u+GmOtao+pZZyrkgRQrjF3n5I2Phdd1f2sqH2Y+0GpA7qWePnRWRpErcB0eJ1NSOX5eB0uWB7XvMpHyedKc1iDnVlmYroz3CYq9t1m1hpdaQYZZey4rwGz2UymF8eJsap9x6ue2m18z834RqmAJXlqONhFGkEMXzAxixn1oNo+bS0OJMubRqw8yO5HrXFkToGZjM+caNv+4Yk/Tpb74wzeYhuyDXMVP/zhD+Wzn/2sbNiwQbZv3y7f+c535G1ve1vyezuxu6VSST784Q/Lv/7rv8r09LS84Q1vkNtvv12OPfbYpM3evXvl6quvlu9+97siInLZZZfJ3//930t/f/9snWoTjp4Kdg5ceuml8ru/+7ty+umny8UXXyz/9V//JSIN15hBasY0LoqipnUzccstt0hfX1/yb/Xq1Ye+8wEBAQEBAQeJVBQd9L/9weTkpJxxxhnyhS98wfm7id39whe+ID/96U9lcHBQ3vjGN8r4+HjS5tprr5XvfOc7cvfdd8sDDzwgExMT8pa3vEVqNWvlvuc975GNGzfKPffcI/fcc49s3LhRLr/88gO7SIcIRzVDNBPd3d1y+umny6ZNmxKLdWhoSFautJXSh4eHm1ijmfjYxz4m1113XfL32NiYrF69WibrBYnqGemH0pxxI5U9Ghac8VC7Zzko3D0xZUwxrw7MaMgKKWE1B11Ngcgiq2pjfbezHoPNllDuNWZ1gDJWdchw7oZRYp2hqaqdiXa0EWRpzoXM04V9v0yWf5iyGWnU98l02v515BvHoZuMQalKkwizObrSEjbAF2RccTM9SlQxWYnfUYJBuXFU5hVWG7rCE/hc97yljHc1cd7Km0DdI54XvDcsq+Ga9Po0LX26l+baqP3SJZVzXxuyPrn41akgLyKFhAVFYPD68gKaDqY8NxcZDrUSaTWH+5TXwH071Q1VDFzcjxKezSo+CnUKj1KcEstlEDpGE6mUsw9Fdx41y5CKofSLcJPysSuqCDqRrBFdToopdrBnZJOqHmaJ/aBr/MTuBstPFqcTYxcZ5GPze5JlNa64ypAIGSc7Do9N24dS3aN4ueukkWTdG1Y/kywzG9bUVKvMJkNUF6+OWNvb7wcuvfRSufTSS52/tRO7Ozo6Kl/+8pflX/7lX+Tiiy8WEZG77rpLVq9eLffee6+86U1vkqeeekruuece+clPfiLnnnuuiIjceeedcv7558vTTz8tr3zlKw/8fA8Cc8ogKpVK8tRTT8lrX/taWbdunQwODsr69evlzDPPFBGRcrks999/v/zN3/zNPvdTKBSkUGgusJSRumQkpQwb83LR/UPhQw409Jv30f8d/z+OL1sXXvZl+FrtwcBPI6IeGzxlfAVV5ptHbJEwbZakbf9Zp8cVszRz/UCqMcAsK1hlaZOKPxNMcz25Y1vTeh7juJz10xtjR0Sk1IV4KKRHG/dCRwEK2MgAqqnsM2f3kowhpl3XPNlCSohQ5ZyblVjniDHaF0yIloq74Yc3616v+N24jVLG9ihLK21KV2yRJw5JXRqP68vVTxpB2SkYAIyHUrXRGv937LTrql1wneLVVYKNjN2J96GUsdlnZhPCHZpyFHKlgdsOVPvY8KrRoPN8oPjspZRxZNtU4+w5pvkzI61YtUZOZ85eYLrSxsqN8c3EzswE0/x97rNEVNHhApuJbNr1kIk8OrZGREReHF+crBsesVZwLme3u3D1s8ny2YtesG0cDzDXrSvYh+iCYzcnyw/IumTZqIwv6YagLdx/jJ16ttRI8CmWqyLypOu0jlqY0BAD33dwX2gndnfDhg1SqVRUm1WrVslpp50mDz74oLzpTW+SH//4x9LX15cYQyIi5513nvT19cmDDz4YDCIXPvzhD8tb3/pWWbNmjQwPD8tNN90kY2NjcuWVV0oqlZJrr71Wbr75ZjnhhBPkhBNOkJtvvlm6urrkPe95zwEdL5eqSi4VqZmJCRrcVrEvLWdQQ9X+ZPn0wkvO/falG+0HMLZMqeBpNytEFqnoGFNoMGUwi2FgMys1j8VfEh/b5YM2zBp9ZSBkTqWoQtUXX+GtlSXJsplxTabALIGdWtFjqdeJCRinSLs3Hw8VK0SFYBhHGYdysIhIZ09jAJws2Vis8Yo14upkekixKPXj+HcyFYgnqlMKgDEiJPEMQcS4Fn6rqNiMsDHXLFcZTziGiofy3fJWxpuHjFGMWaT/F5kRLI7+uYwgEWsg8npQeqCIcDSVxo99J/eO1wvdTyFuLE0ZBYexwnvL4Hl/zj+WjeHl0NBq7hQWaciheyaGrASDqEbjKAe2Gc9bZ86OD0n8EZ8xz5cg61OZbvGw0IggizOK9+tnLzYMouqkPXh+2I5dJbx//108OVl+cqnNOj5naSNe9KTO7c6+MaiaOHX5jmTZGGyLEb+0PI8xCPGSB6pwfTA4ELfXzO1FpCk05BOf+ITceOON+7WvdmJ3h4aGJJ/Py+LFi5vamO2HhoZk+fLlMhPLly9P2hwJHNUG0UsvvSTvfve7ZdeuXbJs2TI577zz5Cc/+YmsXbtWREQ+8pGPyPT0tLz//e9Pgru+//3vH5AGUUBAQEBAwFGHQ5RltnXrVunt7U1W7y87RBxI7O7MNq727ezncOKoNojuvvvuff6eSqXkxhtv3G8r14eBzKR0ZzIq68TMztZkrQ+bLrXjc1bl9LmKzUx4AftYHdf6OgHZTpN4wJmiT+/DuKP+UQZ8e7eHiu5RhVTditP2eMxas7OzsscFZzLbmHU3iYARuskouDaKVKMt1QZbdE73c8k6XvNju0eS5c0ZyyxVIdJY72jsm3FDZIiUK9NTR8qAcUicmaehdk3BvjoVrI2IXcUVWDQDZHQcXjXFcPjYGJIL6eY2TO5J8xaWmtvOhNm3cpO1kXbBx8qcA91han8I16nA05ofae6HIuVwvI7ddnl6hS8VzbGqiBgzpf4NNqNIOiZS/WnaL2OgXDFhYl12qo4a3WG15rZNx3GwS3weK9wHVcHRD7rYavnG8iIIPrKwLLE/6frMZJuqQmkb7O9Dj9n4wPzOxsPSYcN8FKqdtv+lon2Yl3SSyWm4gXzFXynouAJu/t6cpSezcXsKQfY4UvtFbFhErR1f+KHCIVKq7u3tVQbRgcBoAu4rdndwcFDK5bLs3btXsUTDw8Pymte8JmmzY8cOmYmdO3e2jAE+nDiqDaLZxlSUbwqI6Ek1Xgy+ZIPZcXHhmKzl9Ttg5Zrg58fK3Vjn1ubgvjscYi9USB33DAI1pRGEorSmqCoMlRHQwXSNUUKAxspgptE/Ghbsx86afeH603bgWoUCsP93/EQR0bL6SxCX1Zu1x17Wb0fLoWFrVJWm40B15DAPoCgs1XbVR4LuLofbrbPbHZBOt0QZr41J/696PpQyzeAjxJFk2L/4ZxoWTEn3jb0ed0vyMw7NGpi+UiAub4D60HvW071nPJU+HSLdQbuIKjWSM6+Ax6jidl3bU67VUu6P+1Nwu7tSnlgxfRxjIWIVziVd9twYfLxS8caRJ9ZJ9cMjjaCC8Y171dP/KgxfGkR1x0OkFNw7rGud73YVY4mv4Kxru0UwOJ7YbT+cfFGM/ZHyuIRrnTBUoRQ/Vrbjxs/HGm6gU3petm3RNxpEK2F1D1fsOGXOhbpHvjJGZn3FFwg2z9FO7O5ZZ50luVxO1q9fL+94xztERGT79u3y+OOPy2c+8xkRETn//PNldHRUHn74YTnnnHNEROShhx6S0dHRxGg6EggGUUBAQEBAwFGK2VaqnpiYkGeftUHsmzdvlo0bN8rAwICsWbOmZexuX1+fvO9975O//Mu/lCVLlsjAwIB8+MMfTuRzREROPvlk+c3f/E35kz/5E/nHf/xHERH50z/9U3nLW95yxAKqRYJBpDBe65RaTTNEJvD3pIyl97oVG2NnEiPwB3CW0hW7ts5AeuyOumVPhqC814N9TzlmJjzeJIQZOds7Jmup4f5sM+NBlexusFoE00q31WxM1mTMbNFNxjR+uuC4nkHpufh6MEuOOHuRzQb5xd5VybISaYwDMacL9hpEnXaWy5pTqsYZjmOCL3mvKOKoCr1iOV+wM0mzusZ0/rJ9rbzEOtmiVDMToTLBPC4zVzYYGQ4QbZp98rAPZtkrwEiwf8xmNwHRdN1V3ctKwBiPqSE1MblX/VeZ8WTSMJoZ5QzuI8raE8tQ/4+aoYyvNvvmuYAV8tVDJUNoriVdZvU8nsey+4aSzSKbaK5TvUDVbXfqIUkkdtU8FmROo8iyr5U86o0hEy1bt8fsyjaLO7pYKBHNHHWutONNeboxrtTB4tWgWF7tsze9r9vtwiIzlPQZN52uNCX94WC4qJJN0U2XMGalfhAWyv5ilou7/uxnP5PXve51yd9GoubKK6+Ur371q23F7v7t3/6tZLNZecc73pEIM371q19VtUi//vWvy9VXX51ko1122WVe7aPZQiqKQincsbEx6evrk//3kTOlqycjIzX7IZ+K3To9GfuxvbDLxr64qtqLuGNV6AJbgQCP7TU3Vcvlqfjrsbtu3W7Ux1gC9xTT2ZlRVolHdralyi2NmY4U451QmiMeNHo9xgzB7YaqfVjuFxFtEK3O2cAQxiHd9sIbkuUXnrDGUSZ2RVVX2n0cM2jdcjnEV5VqKKlS50DXuL6q4rgnzbgChWsum49AtYR1JXyZEZPCbCsad2a9L4FFeU59RlNS/sOuY1xOtoiYE6awuwrE1tzL3vR/h+sucnsKtZ4Tp2N0wZnMPaXsjaa+orCOrDtvvJRbnNzpqlTrPMV1PdJfzuvRyk3ZdBxedxOTRGOSKtMwKFLdtCjtYiZOZ89AbqKQQ4FVGEGdkMDoZhp/fJN8bjSXGraIyK6SHb9eGukXEZHJSTu5YnwV+7es17rOj++1Y8WazkZsZ1/Wjs80iJj1qpYd4l4Vl37EDJgM5NJERW49///I6OjoQcfl+GC+Sxee/3HJZpuV/ttFtVqU+39802Ht63xBYIgCAgICAgKOUqTqM2LbDmD7gPYQDCKgL84yY90twwytQFG/EUXDQtSPwcxgJfrjjAXqCo1GnEHZ6RtZpKKDrSArRFBgkdkS7J8JDiRzQ90jzpAmeY7URor9BNtqlvFhgUdqjuxGpCyp612xhtHK3EiyjswMg7HJtEVwE3RsabSfWLHvwFGRGfpEQLli6tRZ+PhSZum4mqTcxFLLzDIRcRYubYcVIpI2+L1iJ+OSphvHw8yY5ZSD8Wk+oKeJK0Ccu+N2HpdTcu6+fjj6PLNNUo/Y87vXbUiXmeO+KFaI/edHx3UcMmae8/YxcGT90nFQco3Ff5E9nVaB1Pa9TC1CwkG1+cSYKEAWtS7u92txYbppHduyyCzZooGCfbcXLW2MU3t67NjFYy9GoHcZLvClBcsWLc41u/z9YpLQa4qaWV6CY7muAdlY72OSDwtm2WW2kBEMIiCXqkkuJdKDDCvzYtCNtoSVlfHiULGZ8vHjUcPAKmK74+CD7wBTuwcfErrgTGZYURkzLNwK2ryFrDwNvi4oBLoMHxFdLsQYUzmsq6QwmOLYyxDLRDeYcUNy0KkwUAPv76uXbEmWn33eCrIlSSIl98DEMgI+cTlTxkOX/LD9Zzo+r2kVKfbOshsE3U+tssLcoSDaFvDM9hJ7iPEu+JB6am0qwyDxMvoy2em28hlVpq3nXH3uM90pR1uHS03E74oy5+7N3PMoj3O98RxVUZyWgpp1WtIeY8sWDkVbT/wVv1u+fptscHjwpYb0dB6HmZE1uOijTuOTtG1po6lsvQoyKvHLRLphkfUX7Fip3hFcBF+JkGp8gwcK087tuhD/uAgijc9PLE2Wp+N9H99ple55UTMed3hdhTo0vxwcv2nomXGv1RgbMDcRDKKAgICAgICjFZH4J1ztbh/QFoJBBByXHZeebFp6EOw7HrMZqkwGlrdUbWHCUbBIhRQD+7LxdiwJYmc/J+SmsJ3dN+JgE2ZmAOkxnKUwkFoFZjvdbqyzBuFG0Bnc93NVOyMzrA5rp3GGRfcZGaBSPdfUhv3kdt0QzTm502aRrF5rZ4Ev5RrXPQL9T7dWJt3acW5mvJES1bGLDJ4uT8H9wAygmCFSYnzU9aQgH4vFthikvNlkrTLEPCwIXSzKjcf9xY+FL+bAG9Dd4ly8MQytBuo2juHRJk2uWarupqrYp3QV99NBOGanwcBgxEx7rrUOgm78T6+tcodhvdqO+3awRUo3EGyRYvGQfRZNwUVk1nWRVYbYKPrH7EmOJWZ5CrXTvGU+sEPWNevKNsZZMkhZT12ZKTzAZdCdm8YaYrh0rb+yy5Z+4Jg7kLUMPZn2baVGBizHPO4vi0Bvk6xR9z54hx6HqnRHQGsEgwjYVu2W7mpaloCPHsw1XloKuT5Ssq6bJ6aPTZb7sF0tDZdZ7KJiVlUtA0o5sn7wKY9v2hhhFFKkMePKJmtsx8wxI8xYcW7HNH6KJup+NPaXSVEN210gVsUvYVDsigM8lJGEY+dSNl6rHwYgM8e6ehtfhOkJ28981j1IkYZnPJFxj1UgFkdDiuNITx9T+kGnx+62CtxoKiaJKsOo2ZQpOj7OHmE+5YJp4abhN6mKGKJ8tbltW/DECnmzzAx8rj1f/I+rjc8g8hSqVaFiZr2SJoABkHOnz7vdZ5RIcLdl5l7KUW8ucnuE1TVVkgQ8d8cx657MN5/EQVTiThobKA3KTvp2sY+Me8JRqTW7mZiFRuOCBgyNpqrPjxtjeNpOMLm/F5602ab1uPjz2Eo7Lj7dYWtkDXZZodtz+qychxZvbFyoyaodS9J40XKO5doRqGkWcPgRDKKAgICAgICjFSGoetYQDCIHWIqiGPPVPZhu/WrBUrKPT9sKwj4dC0NHMzCb7qQdacsQkaqtOKbyJkBbRGQwY4OWOeMZVsHdmKnF/SMrxN+XIeib+9tds1SDySJjQDfPxVUvbWZ74277+dQae14sIYIyBwNpd6Ejw8xwNjuBqvV9ne5gT9+yga4FRTcC9F04g44ZpRR9WVTEY90zFTTbLN7nK4fRTnZUElTtYW5YN4xanE5XlIeZqXuO3cr1lfIwOr42ziBtz+/KHbAfbjyC1yxTbr6omRJYjU53PbQsZLmYWGBeNeVS8+go+c7RpenE26wS3MiYeVxp9dh1S4amxv5hf1X4BalVNF1pLj1URHIF9Yn4nk2IfUeNy7rsYYr2giGi+zrK2313vtjox/hWW0dyBAzdsyssGz60zgoHvmXw8abj0Z1XwAtIhsjUc2tV1+2QIhJ/AkK72we0hWAQARVJS0UyytWzKs6K6ErZF7IvbT/eF/fYF+v/jJ3h3G9f7Pah4fBSecDZ9oR8c8E7EZH+2N3WDXdXF15gSgHklagi3WBxhhh+Z4o7DTotEAmXk1TifVl6mfE/41S8xf54zI44BYg+fWa4sXgua6od12ML7BrD5YUJOxCqjBjUQaq7UrrEfrhY88mXicKvpsoGMklm/B3GThpZcCkWPHOlYXkEAlMuV9CM9omfzpP84nXBuQZaj1Gl3Ek+b4cjQ8znJvO6wYzqdjvGjqd/rn4qw6yd7L8kLgsuVRhMtbx7Pfdd7TBWlV2n7EqP+9Knxp2sa0OKgfXrXCKeKVVTzR4EGqoSQRyRRkk+No6myvbd5zvQh5R5nzCqK8tzsmzHq4lpO8bQGEuVYZTGXfLdz45+O350ZNx6ByZTjXFUNIh848dsIcQQzR5mUUwhICAgICAgIODoRGCIgKWZKVmUSataZZl4djBe5zrLj59XsIxId9/PkuXvjP1astwVszubplck6+gaW4oK99QnGkftoG1xhCz1gSj+SNAlRsbDuLsY3E1mhqwQXWbLM7Z/JpuNWRoUY5zCTJN6R2SIzHoySGnMYqYiOzPsT9mZ5m8utmzco4W1IiIyVrLHmJiy2zE42lf1Ox9H4XKGWkHWGrejyyxy+KXSmNKrLLSce3ZGFskE+yomgIyTZ4KacrAIPtE/L/vkYlV8GW4OFsfbph0Wx7PeXA+ve83H7rhYJG85FM99oS5TfC1VVhgy0tJujVRJqevbaE82iderjvpqvnIiKoDaeIrJ+OFUkLAlTLRUdeFMNqG6jrzpGD9yeI9Qw8+wsXwvqNs1WbFMD91rOXTKxRARLIdTw3uZXmZZn2pMENdG7fFSYGVXdNvx49UDLybLdNGXHGU8GGDNEiJmHPOJPx4WRHKQMUSHrCfzHsEgArZV+6SrmpFXF/Ym63rj4q7TUJbeUWNxV+tyWpe1L8nHlz6RLJfibdPIciD21lkXDNlYCFZZHQd+7MCL7Is3osHTK3bwMGrWNJIYk+TDbhR3Ne5EusxoVPlqBw1kkEkXu/eGE3VFkWWd1uhiDMY4jjOAGKdTO18SEZENXTaGa3zSngsHWQrKkf421y+P9CSK2fEb4YsnMsdJ7U/QiuzD5ZQ0gIHl2bVabdL/uV+PiyvjGcuT47ThdmvlPttflxlpfesya2O7ms9qSjX97jXoCNd1V4anO3VfGacwtozR4TMg+S2m4aOy4Byn6KsV5xPPdGUnKvuLsWI4NjcrTSITtKNxoAyMoOmS231GV1sN7U3WKMcjtqXRVytS78B2tnNRY6ybhrot7avuvB0L6dqv4qGdjlP6O+FjpJFUcUwwg1L1/ERwmQUEBAQEBAQseASGCBipdUu5lpH/mOhP1hm25cLObcm6pZhi05VWZzkLVLDvi5vvgHYH2Z0O5YGx61WgtJhsN+r/2H5MMXgaywya7k836GO6u1qJOIqIrM7a6tJm234wPmSQVuUsu8ZyHdy3OfcuzMjI/ugyKfY4nK32xsp0/XmUWQGtXsF0m+4zXnezVtU6Q4YKt5tEBptr9qsCsJW4DNxnvLx885LIbKwiA+PL0lK+nMYPKgDUwxCQiXAF+Pq0jlQ5iVY6SfuRheZt0wYr5GN6ErdbzcH47GPfrYKVVfy1ul84DjeoNbeN0mB/Ku7ngyU4eL/MflzijyI6cL/u0EMSQeYbu4n9VbtxLmMo+YFagtX4ecNQIhnso1j2fVpYJifuJyidsmKI8N6yhhxYJvMuZneg9iKENHcN2gzZPT122ZVRRtaIrPJ0jRppje3KHpfrYUFdvKxt29sHtIVgEAHTUU6knk2ywkSsi+hF+JRplOTg1mLxVui0yZ7YncF0+D1YZszPMTAoTs9bI2FrPFAw2bVHpYZaw2GZ2OUuurPit6oeQSCSdXrQ/270fwSxNJWI7r0GjsuOJMtpugMwZnDf4/FoPgRDivXjpuAmG0YbGncmHX9XEeJtSHHfO2mNseU99jq6apypbDhPIViXm0zE963nRw4fF37EHIOUUrJuI1WdPyQfUMoDIG6EQpDU3IyQiWQUkb0CjB4xQAVXX9tILW854NOlg7ibDAwKuouS9HSfkemZhKQdbbxZaHj3U4gtUi+B89huI03F9MCQy+A4xhDSxinT/N2dpYFlzkvHrNnF7CSP53af1TMOq4pH9nxZEBYpubgNjaCao/CsiI7jqUzak0m/FLu79jZtIiIipQ02m/cX59kH5BX9VvV+IK4UMI2LxIkFx0jj7i/VD8ZC2T+ELLPZQ3CZBQQEBAQEBCx4BIYISEukqFIRkcHsqIiIrIby2iSa7KnZ6faqrGU59mAG8ctyQ0qeLqQXS7Y+2IrcaLJ8Ss4GF+/BlKw/ThOpqIBj6O6QLUCbUTAi5XiKz5plOUxdub8hJU7ZPHMqI2KTjA6DrX06RCYI+5lpWwLlDb02CN1XNmQJ3GqmbEkXsvIilSGGkgMOd52IZYOqHleh1kxRDgYcJ2bduCEZEU82UMpFj3iED9UEz5FZNvOYCTxurawl2nRtLvOIe3SIPLJMblLI12efS8qxE19gtmZSPG0MPamO4WFmPGxcsg8yPuo6e1ghsDtps0xGIe8O3PedI49j+qpYIRdTOKN7LlFQluOC/I96PlgDjwHlSfkPpc+FZVBBGZbUwS5YCsS1D6JahCtt3D60Jv8l53mmM5YIkh2b7Jg7tdZeqAuOeb7peEzsoIBlIe1L4zyMCEHVs4ZgEAG5VFVyKf3hNYKIOxH/04eXgkZQXwqp6GhzTOxSKkfW2CFe27k5WX6x6s4iy0fNrpwyfBhMEWfxVhZ9NYYNxg4v6MZj7TNTJ40uLrqyRupdzvXMZjP7W5a1Sts+0BijoWT2ceHSTcm6p7ZZA0uJLcKwyUKczVxfnzFJMJNO0txfYz0HcmbSqP35rKYYyr2m0vLZxtk92zXUrMrAu6lSxDE+UrzPCZ9nQKWiY3WrsbcNT0Pi7nKk4u8LLrkAX41fLdK475ikiIaUr/80UJhK7+g2U/fVcdq41sm9i9w/66K1drmO56mebWxchhRADoLwGIJUn7JTeMbj/fF4PEat0/1QMCvNlaGZzdlxjllthKlfJiJSKzT2gWFYoWrnoJLfg+K0qzFhig0enxFUx6ymM/Yx+8aJw4JgEM0agsssICAgICAgYMEjMEQOLAGzMRQHP5sMLREbnCwiMgrmaAj25ZOl45JlwwBtrdoA4RPyth7aY2Ur2MiaXzm6ZuJjqhIYYG7ICo2BSamjT4b1YcAgt0uzOj0eDbI7JltsZ91qCLG0xxRcbTukr2k7nsMvp1cm6361e0uyfFx+Z7K8s2qPw0D043INLnxdYThZd8Yamwn4i5dsRWyyN5yNluN7l/ZMtyMP3VFzCDaqWmcIbE2p6Gl3vTMTCKsyhCDomPZlsLGvMbtUQ8mS3Li77paPNUnaeo5BHHApJ18Q7v5MYg9wH+1oI6n2hjnKkF1BhliZ9Aj33bxDxdY4ssZERITMki8Q3TBfYLVq1CwC+xQptsiur3Q3Hob8GPYBtsgnEEnYwGw0YNA93ddgPtOqJolZ6T5GJm/HOiWMWuC702CQy3aYkOJS27a62F74Qp+lTJctspSYydadQuIMGaCuLDN7GydZdWVFHC4EhmjWEAwi4NjsXunOZZQrqid2mTGjaycLkaqiqtbg6YI44qbKYhHRH14aPh0p+8KNIc7IlTLPjAcqQe9Eqj3354rdKcJgojFD4TKqT9NQMnFQNHy4XSVyP1KuYrBrCkjnxz52w/BhOv6j08cly6tzjW1Zd25Nl6119mTOGpnTqHHWkfXICzvgK+iq4pPiOAiltqtiKdzBNuqDZz4uNFo8Ke6+lH7nh8tnPPlCYlxtuV6ld7s2bA2fGKPLKPEKQfpqsbUhZtkK2iUZu4WY8YVlZQzsB5TLjLtgxlzKYxw5tqOxpa8Nj0NXavPF0arnngfEEfam3IMUloQ0CfvPcn7pOHNMPd50ryGzzKcUX31VI6OWqfgZvHPdBdR+zNvlzpxdnqg0xtGpKrLXGK4AN3s5DlAq12fRIApp97OGYBAFBAQEBAQcpQhp97OHYBABi9IlWZROyyPFtcm6tbldTe3IgtRgfjMAWLEtcZsiWJwp6PmQgTkuZ1mTIbiLTFba8XnrImJg82DKBmxPss6Yg8Fi9hddamSTeC7Loctk1hcw46R2DwOfGZzegajenbHrkFl3FQSNj9fs+jz6xJpviYQ++t+LyErWVfIGwrZAug2awZT6UCU/vGUhsJxqnr1r8T53W5+IoCvGs7QUZRJGSR24u2dWO4iAxjKZCB+JkHIcop3xmBu0qq8G0NWTm9o349TWsXnMtDk2jjEJphPlIlpWn/f1x+fG4x8I2DZlNZS7jvsAu6Ndn2BeYoaImWpetyHdw47kT5fgY6MxAphRD00qdOM2Dkr3FJkefV7uDjJIO9mMCX+ekjvFqh3Dy7VmUa0s/MpT1ebg7spsCjMGzBqCQQSUoqxko7TTzbQH6slMOc/jK6GKpmJUN8ZADh99bsfjqRicqo3BMcdkTM0J+R3JMkUfe9PulAtjoHQj9mg3ssIYO0W34QgMFGPoVVTave0z1++MrAuRBtZE7HKcgOtxMD+SLC+D4bMsYzPRao6vIlP+KV9QyNnry8GtkLXrS9X2H39VQJPxP3GfWKy1DqG5CD4CnTnW/DGK0h5rx/fdamGwKUVkCjCqWA/Hvnk8j5HmFStshTZid9yWGa8dDMSq2wg6UJeZM/0fx6t2MgCI95ZG68F/LOtpz77NPeCxeQnY1ndfMsYAZLS2ZAAAMRtJREFU97gYPYY2DcPEI6bi2BiHBANsEhOwgl1v3soUC8jCOMkg48wn2GiGKRo7OaT5Z1mnkIrYkObOOILqOBmq0kUeB4AxC+2wI8QQzRqCQRQQEBAQEHC0oh4dhIUvumRAwD4RDCKgGGUlE2WU22drZYmIiAyiPAWzu5ix5QsuNswQmZshsD87kX1GlqOLJULifZBpea6yLFkmO8Xg7rojhWM32Cvq6zxTWZ4s8xwZ3G1Ki/AakcniLGwUrBqvTVcskDMF1Te6/8iYMXD89IJlxDZVGpL8DLruQSZgTekQ2dkc6XEzM1SUvWfsoDCjylozNZ3IGnlYIQXXlFzdKo/7xxfk6sjYyUzAPcH6ZYwr5ww/bqMCun3HO0C0w5443W5twF1TzX08RcD56s2YRR87VXH7RlM1ppzt+2RUALNjs5kwAdn1jKdPHjpRna9h1TLum+vVplIuuOYOapcZAp+7sF4RK40/IrjRJEtmBowUMs5cbjLlceW7SNYK14PvfCpeJivk0yZLx+Nl+oDTLAOOZgSDCNhSWSqd5ayKsTGGgcrGgrFDw4AfZH7gjdE0ho87DRu6iLTbbRrt803Ho9HCtPZi1By/pAD/fh3b5R2uQhGR52EoucBj9yPeiIbN3qotrDgVq3svgnLgnpr9nXFSFHoUGbF9KjX6xGKyxIkDNvbr8R1WsDFqEeyR8szESL1nHcUl06D9VWYZYzpoHDE2w4zUPlthf+omKcFBu+wrxqpguuFI824XyeXzuK+ilOO8Z7RPMph82WTSev1sgMZMuoqYNZXdte/t6A6LPC44l4J52nG9Gr9zf2jicj/S3Zh1G0fMYMtOo4Vpzmy4jOP3GR1Uz6FR3eZXSL07nneK6uoO2Yu0Z/LCyatS1Y6XM5gJ5LHciwLSKzsaLvxSrf1s1YNGcJnNGoJBFBAQEBAQcNTiIA2iQ0HtLhAEgwgwpTvI0hhqdAQMBoN7N5cse/LKju3JckfGziCM/hBZF+oUqaw1TKHY3rBPSs/H05a1ylyByLurNgC7JzPd9LuIdnGR6VmSGW9ax2wxHm9HxbrS2L4QawGNgkvvEHu9piLLgvWCdePE21wz3pclWes+o3aIosf3wxcfKaocLgXMXE2l7sjD4qSgpaLqXSFI1JWVpoKuSQeQZXIdku4CuB84O1aLYADMYVTNNeoN+dxnjiBc32VWrJCPfWp1i9q4hcZLqrKkam1s6KlJ5kJK+2DcjRwfspTHJekNgnYFenuuo2KZ0u715uaooYGVgTxMFR9DU9qRQpUZ5nKQ4FKaVXBlxxmC9Tye0068DMjcVHJTeKfSceA1Xdr7846L2LGiv2BPYFGW3wC7vx8PHyciItVJ1MUJmDcIBhGQk7rkUzXpxZvdlWo8+C9XFyfraCxcuOiXyfKyzGSyvNuROr67bj/eNIJogOWVYGOzijQFE1UtM6pPw4igsWKUqpkCX1cK0vZ4qnYX+jocZ7m5rpGIqMGZKtIvlZfYPsXZZT3YxzgyzsawvLU2kCyvQu2zc7ueFRGR+yZOSdbRuBuetkZfR95Nb5uBrp26REp40YEUs2rycJ8gg6Wuis/ioxNfNOVBotq1zwhyjfuMfUCadBrHU8UvHULaLBCqsv99YoyHwIBRRprpUzvbHWhmWTuxUa5sN183IESYqlvrIh3HGSmDhMVaPZlZXuvIPCTKVeV2Q9JI5/MZGcFJtV8cGgKR9Zx73xK/8iwOXe1QQUZ2157CwsYwrDBxr4LtaNHBOCpzMhG/5l2dNvAp7QsE9KASW8+7i8gkzrmzyLpyjeNUc62KAB5CBJfZrCEYRAEBAQEBAUcr6pEclNsrZJm1jWAQAc+UBqWQy8mZXS8m60yWEwOtGcjL4GkXKySi3UXJfsGC9INZSnse/O4446zoKY3BWmukeHvSDFzukpmoeQoJ6RIclhowQd3MjBPhsjS1bRzHzvzWFhoBzyPozx4EXZcQFH7fnpOSZWoOvW7RUyIiMly2x6Zw4yt6bFD15mHLTi3upAtu38wQWTcyRNQtMS1y0EzhHaR+SkrRLc0uD5UhxN/JEPm0ihzBzOmiR4yRsd1kg2IiTVeZFycUywQCzsnStMqME3HKLul6Y2BVVN0wd//MvtupWeZFi/ZkfdLwe0ZKSLFxDzLTbnqtDiaCgdlR1s1QmAy2eh7bFeEi77Q3RjE6yiVmNnQeQgWFR2mwMdnm98VVBmQmqp24TvQ0mXuE57vahXeuA0wV3GopZKVVRxpjQhGMamfBsjdZR0aayMx6hPH+lBgjMoaRpWrG1mqteUwPmPsIBhHw6q7npLs7o1xVu+MYFSpW5x0GjsjMGCH7lXi+0lCZpgEwgHgXFnHlPmqOIAtS1JN4UZkyzzR5lXHmiIdi3TOCqtp06Rl3GzPq2Gcda2UHLmbEGWOqxK+xByctskVwn5q02WKj1YbxuatsXWPFTru/RSjISFfVZNme16JC+3EANIJGp6wx6/ocqNgjGDB1REKk8ebVjc+ABoInhkjFFjlTs9xp1+3UMjOPU4ZijT6byhMHk8QQ8WOr2tJV6Mky29e6/cX+ugtazKZ5rmkaDso4orszjtdR9cFwDVATi2439kNn48VZVVV3bJoyqtQNY/aW7tvM/jOl32fApmO3mooh8oh/FkbshjW41YxnkSoFWjqBExJ0f7r5oSxX7WRUbBlD5S5XxhGFFx31CKupDLZD/GP8cmQyPv/xYUBUdwca7s/2AW0hGEQBAQEBAQFHK0IM0awhGERALlWTXEq7pYxLjO4Tusl2omRGF9gWuqdMQDRFBCuRmxLnerqzdsaCgnSNkXU5JmvdeGSFOKszweBkhXo8ZT6Y1sEgcrMtS4gQLOPhchWKWMFJCk8WwKjxOuU67T6en1qaLL9camSwsc7QYHY0WX59tw12/17XyclyzSvCs2+w9hEZoPJ049pkIRzXVpaLmr3H7ZXuDnVSPPtwufyox4JJLF0OKuDVpZPTxiVSm7ncMZ79qVh9NE4rtqJ5V17Xl0fLqNUtOFDhX5+mkhIcdJAHiv1Rt4IZXR5lRgZKO1xp3C7KeEpKKCrQ0Q829RB3KkvPuLuUG9guUrOIQqBI3pLi4sYG+XH8ju3olqXbjckC1a5GR9Il25FK0V2Wg+8la74ZhqjuqXuWBp2bjR/gqqP+2WFDiCGaNQSDCJiod0q9nlHuIFPfqyzuF2AwY99mxu7kYVGY7C2feCKxGyrTzxYt92sUrAehZJ12iS6KVqcuw8AyIo2MD6JiNl2BSohSubYaoxHdZDRgiD0wFvcg1b8r3eyq2lWx5706tydZZqwSpQyM67A3Zw26lys2E5B13k5dbpdfmrDn64IScsNyBfXJqo66SowVonHEZ6LGGmeMYYjjHyJlP3p8XBzbWggoemzuGfFLjg15ejRgfMlu7F5a/y+i3Ws+sL3RxEu5VKMPBzzHsSKTnt9ZN8zz0VGusqQtG7jjddQ+cvhQO6QAIo/xJGm3Cy5TblxguuLSJTz3KFpbp4HlsKmVYemRKUhDuZ211rp2xG43FOhVRlCBGW52PXNGM7G7kKdaRoyRql9W4aSmucZZFZlsvGsFuN2MgRU56p8FzH0EgyggICAgIOBoRXCZzRqCQQSUo4xko4wKcp6MmZwMpkfjkQ3gYxZXERWUn/fUBTMgA0NGilpADLw2jBIZE7IgDMBWAcw169oyQd1klkoepopsUUe6uf90jdF9RuaImXTsn2lD5mm6Zpd3g1naW7FB2p1giPaWG+eyNG8z9LTYpZ3Z/unKHyTL/8+m306WDRvkc3FNV2yfpqbt+VYn7bKJV8x02fvGIEwyTqzeTf0ZE/QdebLMUq6o5cYG+0S12z2LzUxRMwc/GHYnal43s60KLkbzlgyRJ9BbHCwTA3azRffJplyMjsj+fQQO0L12oFXttUuSAdZtbByzQV63po8tIuJnr640kMjS4Pnw7CJxazqeHxFRbBFZLd5Tc0z+nvGwiYpFgsusvDhmbHI4HoQb66qW4L6ZNh2DDuYOD/tUpfHuVyuzaGREcpAG0SHrybxHMIiAdbldsiiXlm4KIsbvQg5PVV0gKAiO98HiryTL//7yGcnyyf0Nw+XsRS8k62j40MVFQ6QPdcGMUcLf08otx+yzgrONcW3RXdebsudClWnG/zDd3Sh3d2Hwo2HDTDpmpw2kreFijCkaWjR2KE553qJnk+V/evk3ZCayHVas0eeG5DVb3mWNTCPElsU1YortxLQ9b35n8r32vHLZxr59xSA5GrFNKts8StWhUMe6Z8rpwi+UUlV2+DA8CWmMJ6KbLsnqYdcomkfPEUNiuA/X7544E11rrflDrrrvMbB8bjxXn9tR2nZ9PFodo3kfzS7JFiX0Gm3o4nTsQ0SseKNHuLG94zT3Se3Pdy6uA9Xdz6N+Vjx9jYdOGknKZYaMtKqdW6k2yb5ybmtdiaEitisDRWxj/PiMILrSJkuNsatWDlbGfEQwiIDt1T7pqmZUVXrDtvyyvDJZR2OBGkJcf/GKp5NlEzxMA4FsBg0iGjOuSvU0pPbULZNCw4cMEZWqyUQZ0IBhYVYaJeNi95GLI0bHYQSRLeKxadCxyGxPHDnJtrx2J+SHsJ0deAYK9loX4ohhMktE2RNAs6zDGkSbdjWCtDsLrQs1Vst2f4t67PPhYpf0RNn7CU1QNwOut2JnG3D0g8rHSu2acBkunm4ow4frealNG4ae1B2/zzh2yhEP5WVmfPE66psef/SpCh152vqq3bcIRq0ztkel4NuTdxXuVcdmej1S5tW+aXPE8T8M0mZbZTx5qAGzPyV1lYfekMdQcsV0eR9vT6yVktEyhpkdPqSGuKEajSC85jSI02XTHnFPlFtDqr0Svma8kEPZOu0xjkwsYc0RR3jYEFxms4ZgEAUEBAQEBBytqNdFPAk07W8f0A6CQQSckt8lPfm0MDFkayz49bPxdcm6NKazp3e/lCyvzu1Olo/L70yWDetTbyOfWdUTw0sw6lCZpiuI7iK6uMgKmfVpT5FZskxlT601w3gM5kaSdYrVwjkqtWucVyXmvEcie05kqpj5RqHHtZ02+8wwSjyX4bKNZSoWmgUpRUSW5i1D1JFv9MlHj9ex3NHFWknNA0zkmSpzvUoQqzmehXYmcj7/jYPd8bJC3KzkaOMLWXKLlzvZHZWYpYKMPB1RrJRx6bjdf3TBKNaihetLwdeWcTWGSWmDkVLuy1b9UEKEzDb0xIfhYhqFauWG8sUNedxgSfIc2R91w7C+QvXslKuJbVuNnG2VsjjYnXrNQTN52qowRibPxc9vPee5R5H72jDjM5M1aY1oKu7ltI+2DJgXCAYRMBFlRKK09IDXf7J0jIjooGCO7yy1MYjio0MINDZGwqQjFkdEZBwlP6h3RJ2e4/ONQqk/mzo+WfcrBetaonFE7WkaSsb4oSvO59Kpe9LuTTD4nop11/G8GMxMNxiNOzOo0NVG1eqtYktt0BhbV7BGpqtvz03ZQPaKuNOF39y7MVkeWdG47pvGliXrhsZw32C0UFqGircmhqgK44MxRJF3uelUdAHOdjxmrgAZbqeMFncUdL0Ad0a5+aD8mPmKwurYl+bt+LvLePK28RhBwsK3njIj1v3X2sho5bprJ1DZL0mw77gstQ+Pm1Sl98fHV6n2bUgcRI6YI62e7d5OPMasMRJdsgKN47l37XquMwxQxu/ULCr14d3hs2JsGcbcMc0fxWnTCLbOKNXq5j7VPc+NyeJvpyD0IUNwmc0agkEUEBAQEBBwtCIYRLOGYBABA+m69KRF/mPixGTdk1OrRESkiuyjRTk7daE7pgLfQK9DAdqVvi6i3Ws+8UYTHH1m5wvJOrJJZGN8KtiGzVLsiecR6ErBTYbZ5SRcWEk/0GcuZzwB1oZ9YuB2MWXP9f/bdlayfOFym2V2Ysf2pmPzmpYgezBU6U+Wl0C+gIyTqXf2ih7r6uxAjaI9ndaNt3vMnnfKkVFGATiKOPpcN04XG2fVYEF8xV11RlG8vuqeKavDePwBxu3AIGOQlM66ZzO6ZOtk0QOjAmndx27lifAyDmAoSIJli433sgaGwBV0PXO97lSqeV0bwe7OYOZ2sr88bXiOLhZJ1STzMDZEklbfxocyQgq+DraO95Vr/YzRZVp31HTTw6LdsMKisGijSr7FJHNUJIOEPhfcStWqxmB8YkoslQ/1LJJBAUcWwSACnir3Snc5I6d3bE3WGRcRlZS7Mih2io8+jRmXijQzn+jiYiYY4XJFldPNytMiIkPV/mT5FdAnokvPGEK6kr0FDSm6zGh05OLcVf5OY8e3P8Y1GSOy5ok3Wtll1b93oniriM3068k0DM7tZRtv9PRe6zLLpu31/a2BXzj7tzzXcHGOVOz1X7vIxikdv8gaSs922LIhu6escVSOJfzrGExrTJ8nM+8JITMDsSroyi8KUvS9WdBmswrjQtDA46pyaQtFGc8HlvvwZZGZpp5QFrWLFh+aqPnb2VjvcRGlHBliUHOQWt5jBHliiJL9Ma7IU5BWey/3fWLteFv82VuubEJP4xap+SnPl95l+IiIvg6uTVkg1ie7oA4UG+CqYknr2COWpDEGezrHdXgXUdKjhrIn6pk07x1jp/BQ03iKHMbTYUco3TFrCAZRQEBAQEDAUYooqkt0EBXrD2bbhYZgEDnQAz/B6YUGW7Q1YwN9GcjL4Gi6uOhKM2wKt5sSy5i4lKwb620/MvGtUsHTYGCY4Ua2iGrRq/N7mvara5Z1Yj3Oq96s9aPZHXdBWgZKM3vOXCe61xi0PlFBwdaO0WRZ1RaLj/l/h2yQ+e5Ry9zs6rbMEvtPFexXxi64Z6ZszThiGtv152223njZ3rtqPBX2BVlSGI6z7XSmmcqPfFNzCjq24u8p+MgZtmKLPPuOXXOKIfIpVZMBcGSfKTasHRcRf0g5VnoYGNVX9K/W0ehAuupmdLwq2exHwoi46a6UoyBt807itu4krrbaOEUO95egcJy7csX5jue7Tg5WysssedCyAK8vAN/1TDJGHgxRNI2xCc+Ko/7ujGOzHlqzK61e9kTGHw5E0cGxPCGGqG0EgwjIpOqSSaVUtfuRWOVrR8W6Zmhk0H1WRLX13kxzDBHXMbuLLim6yQhjUNAIoiFFF9bLiJ95ZtJ+7NO9jRdjMDviPMakw63VOHZz6krGU2dgqubOOCOMEUkjiG27srge2N/inHXNbSv1i4hICfE6tRJckhm3S5Lik/1x4d5u3EOCsWI9WXvvhqas+7QUF4xU30Pf+MMK9mnHV5i/7yclnyQz0b1G1YOy56vv+Dqn4XZT+6M7qUXBVm/pDnbDJejo6Zq3rXLN0A9isrHsKmYzMbbIaxylXCvdVpU2OB1uLa8LDIs+l6rD2FLGabaNZ8WhKF33fNPb+Xy6XWae330uxBYfap/KOPttrhljjHzuywiFatW1TgwlbMhrE2KIFgyCQRQQEBAQEHC0IjrIGKLAELWNYBAB3amKdKdqshNulV9MrxERkWenrFYNM86WFmwGk6/+l1lPMcNaxPpf3Vjvno5k4mkPXU8Es8JO67BikWvz1pXGDCt7bJtJpbLW4G9heRLjKuP5lTGdUqKPDjeZiCTTM7JJZNoW5Swbs7tkrw2ZnHIsijNdgu5/0fZj88hAsjzaz/Il9nxNJuApXS8n6345bQO3ybrx3q5ZNJIsm+uxY9SyRgS1T3StMgTgx1pGataqgoXdLiIicrFMZHc8mkScWSc6QyRPqOni0+shDFPlCapNedx4qr3DK+R33bnP1wSXc7vMpL0ItZwd+loGOatj816gjS+K3KzyMUhtMEeuNoo98zBL3ntkjt9GRlrLAHFVp661C05vax4WdwPVf9833cS94zmOHIkCIiJpZKLV0uh4nF3JUjeRz6cW3//6dAuK9FCiXt/HzWwDIYaobQSDCKhJSmqSUinbxjUzUrYf0qzn4aQ75pSC/cgaA4tZYT7XGGOEaKyY2By6yZiqzqr1/HhTidq42xi7s7NqP+RD5f5kuRNxVEx3N+49Gjh7qtaAdMUKzYRp0wMXIt2QizL22GMpa8wMl2xfO+L0oVX91k25ecRe00LWPaJlmHq7H3L4vKbHduxNlncVGwbb7ow13CJUtacirs8HYGKHMnm7XbWKV1OJKuKjTyMiETPEoA71XlUsFnYxH+Vs/PhWIYruqybvLd6apLs5+iYzChDgj4zDDaZddHR3oSlusxK+NNeJ6gW+2mMe1edkd+pj7DFIfTW/DHjiHhXntgwllxHjMZ7aKkpr2noKs6o2Lteixwiisag8tA4DypdBpgQp6+7+mevnM5hVuj5V2WEQRSbOTCl0s092vRmyaqXw6ZyPCHc1ICAgICDgaEVwmc0agkEE7Kl1S7GWkZfK1t1iqq0vQaV1avuQcdCZV80aOz8vrkrWMQNradbq7jDAmsuGzXC5vURE8ihFTreVKyuNzI0pCTJzmaBGUG+6uVI9z5v9Iws2XrcMkGFbTNabiMhWXPOnEQjem7PZXUVQA1PVxr5XdttyKZ0n2D4xMLsDrBUz7IwuE8/7uaLVMmLdoi6wVmSLsnFds54Oe97jyCCsVjzRmYpuiVehRlqK2WJMQlSzdFfqkJtNilSZcTdfYGb1eXtJBRVa1Ew+cgS2+qAqlfuEGbE6OQ5ZKEUzuPuhkpKS7CO4STohIAqmhZloym1imBxFa3kCrH1BxA7GzFc2RGWwtZHp1XSMmcvshqsOWYtaZ03rHefoTYz0lG6psx/xubOGm3pOU81tRfRzaFhSJhBEnn4oAUj1njQzVSrPgixjR3N/Djeiel2ig3CZhbT79hEMImC41iud1azKoDotLt66C66lqZqNW9mBgqI7cnaZhoEpVsraZD1QsuZ6upwYw2KMkkk820owkUVhlZFmX3xTM40ZXTRadNYaPhgYEVxuMPaDhg/T3XlMYwxuKVkpA6br58GbT1Ts/gY77Jd6Ir4H/D0PMcapqlsaod8hIsl7peKGxCO6if2Z4zDtvsbCrW24WxLVX1fB15nwZYiZuBtP3IWKJ/K50uLLhxJzkrX2qNTcXl7dPfORa0cIkkaVK0tIxcl4vth1t3FhDB4+u3XPBzZddBs29VjIkTXc6NLxBh+5bnPdY6nUPEZVC/u1ne9jq0wvb/d9j6Hj+nldpz44Yp+UwaSUxd3xWs5z95Um431WbrDmfhBZDBNV6OZWeuK0e08x2YC5jWAQBQQEBAQEHK0ILrNZQzCIgCcmj5FCKqe0b0ZzjekB3STL4VM4sdNWnPcJLBo2yMe6MIuLIGti9j2lynlQaAYZNHj+ydKY4/vEEzmb6klbaoCMk2GitKiiOxuLkvfbi/3JciGenhUwTRur2WkYs/h4zZ4Zt5l+vbFGUEeGbjLLtHWjVPYUKA/eI+NKy7Ux3VbuS2bSxa45Zib6qt37KAAjzJhFMHa57I5KVVlOjkHSm91Dhsjn7moxbqpMHrotsJ1T28bzO26Xc8auXHRet5H7hM03QF1/fBh4y6twpfky2JJ1zPLzXS+HgCWrsSuBTrVzz7IT+2YbG8tuRswZ+J5u0Va0WypZR0Yt62Z0vDDMqCfbTTFHGfd6QwqnPUlfqoSIx3ttGEAyo0wsUC7fuv5/VlCP/JHu7SAYRG0jGETAULFPcpm8+gh3xl+BlZ22AOsgBBgHMjbtnq42Zohl4mywiuvNEpE9+Jjq+CQUjk013yq6wzLKKLFfl7wq+pqN29r9FjwFZ12yASIi41FH0zqC166MEfSYgs3MMgYK3WiLU9YoHMtYN9hw0V6boXHrthzLN9os7bTbVTGSn73YxifVPVy+MYSG4A5Nq2vjzlRz7Y9CkJUsssXSTLXHBg5/BQtKRhV8pD0fShVnFDkacDMoZteZns5H0hTb9HxcfCnzulFzN3zxHz4jJ3FR4ZFXOqeOFH0RbfzYDDFmFXqMJ4/riFlO7gaefjgeN29qPA1LtvelhRlDj8KSre7FzNXNSVX+47WIISLoWlQq2CpOyrE/j6uQUM8kr1m87DVQGIqHeDj1TLYywJkNaVL3i74LFjCXEQyigICAgICAoxVRJLIfEiHu7QPaQTCIgEKmIvlMSnogDLi2c1fj/7xliOhCIiMyAqaHDIsJXCYjUgEHzADrMoOLHRV3yGAwALiGqVAH+tcB15dx+4xB88dVhV5EBxHT5WTcYwyudgVMi2gWiWU6zPmyLXWIyMzQ/VSt2eNMxe6WYt4+wqNF63bLDIBda8HfrwHjtwjpJTkPQ0SXWW/8rAyn7b1XyUdpH73DNk2rdIZYHowOXTa+2gYOpCAQGZEdqTVfG+Xa5TjsE2Z06cJ4XHdqps8g6GbdTu2i85VS8F2OeLnuGeFSnowu58ejDQZDaxk52vsCmOkKUtfRnW2VVIj3MHDt6AmZTvnrlLm30omRUdPxeG/ruFBsU/fdR/M7GUvqE3kCom1bvCOea5OdRj8YKB8fJ6I+l+c5Nfk0NXey72FBVI/UO7vf2weDqG3MG4Po9ttvl89+9rOyfft2OfXUU+W2226T1772tfu1jxO7d0jHopxKgz8uNoS6kK5NY+D5sk3TpnExkIUrLTZiKJ6o0tZpBHmUqM1HPe1xjRGqThqNnLgf2pByZ0QxZqY/ZVMuzDGLkTWS6B5chmvHDDYaVeY60bBYkbNxWTvEZutl8UXOwi3Vla80/Z7B8tMTNnWf2WmMFzLXpoLrNYSswSU5647rcdSmExE5vWebiIhsnehP1kUeXwRVq7OFZr+UCvnpxvMxha+ER6k6UewdhQIzb22PfVYo6FjvYJHZuCAqd0tRyDYmqeZD4hAmFxH7QRGZGcdjl3Pxq8OPlhLj42PfyliJ+BGE+8zngqFgZtqxQ8baeIy+ustF5HM9FTyuL1fqPvvtS6+nQeQrCBp3wJvx18I1xjYuhfGZffJlGRooYxjrC2NuI6da2LfRp+4L7m224r4eyTl4tlNJgXHmYQ3xb4cdUV0OjiEKafftYhbVFA4fvvnNb8q1114rN9xwgzz66KPy2te+Vi699FLZsmXLke5aQEBAQEBAwBzAvGCIPv/5z8v73vc++eM//mMREbntttvkv//7v+VLX/qS3HLLLW3v51cKO6SrI+Ou+VW3LMizpcFk+YuP/UayPDhgmYjXrdiULA9kG0wDGR1miBU8Iocu5ojMDYOjy5jiFQUuLkxXTcYZGR26k1SgNCYVk9IsQMNjk9ViVhuh9h0vMzi5iOvBtr2oOL8dM7X+joYrUAWhgyGiJlEJVMNQ1TJA+awN9DY4v+fZZPmZoq1rlvZEbZq+Lum0LNrwuNt9RoaIoo8myyzF6S4uYx0lJyLSD8pt1bg42TEETzNrphdNOVHOsB/x72ALah1gVcpu94jqhytA1RdU61lvtqU4Hs+Fo5Z6ZOnSS6nuNI5Xd9NJqo0no8/VT19QcsvMPVcA+Qx4swVdekLegG2PG6/VMTxt9M6bf9dZYW4mTe0iPqYK1sc+yj1IGmHuh+NVJDOW8TBB3jp6SeaYezuljZTR/88Ggsts9jDnDaJyuSwbNmyQ66+/Xq2/5JJL5MEHH3RuUyqVpFSyo+3YWMOQ6UlPS3c6o7KjRuqNGlXPFq0Lhh/YM4/dliyv6hxJlo+FCrMRA6SBQ4OCH1stZmiPYwwhpsPT9VVGLAj3MQXjyLiwptTXxcKXOUaDx1wbCjD65AZ8tcwMJhA3RMOG15fXZlHB3jOT7s7taGQw7X6yag26HbFIpojIcbnGPdpa7U/WMYNwS3qps98ZHNMYuecvfi5Zx7inX7xk1ckpQ5DJ7JvGzuL3Ej/M/MhV+TUyB7GrVAwO2ipvDNP408Ywo9sFnfJ89NX3Lprxv+wjS44fJezElNTL7/W0baNwrGmuuk9DJOW2BnTavaP/0vy7yD7cRTM7NBPtxCe1MKC8Boevf+Z3j4JAy2K3MiNmKgYVv8Xj7tw/aQELn+FonnFvhhigbhGMnyQh0eN6rHbY5ak4QmI2Y4iCy2z2MOcNol27dkmtVpMVK1ao9StWrJChoSHnNrfccot88pOfbFo/NdF4qxhTMlVvrCuW7NeljJepMmmdyaWabTNdR+p7PJ1m4LPPICrh2Jzo1OM3PoOpOQMXeTy1HYaBYhzRWHTNfEUkjWjFSClfk4ky+0o72xKlFiMrr1eKBhEUmytFe32rk3YUqkSN9TSIKjX2E/cLxsA0ir5OVBvtp6ooxYGAkuK03UeEa0ODqBqfu7pveCbqU4g9gpGTzrU/SNWhFi1Us3YYRLVitmldYx+41tyMStWGAUKKviL2qh5DiTPvFjEuXoPIwXLwo9NO/Lgr0FuxAp7lqOo2iJJ9HGGDSDVvETPja9vKIGpnf6qNS6MJ17HmKQviMojUo+R6lmQffXUZRFV3Y1awrzvYIF/sVA0MkXkm66XGez0b7EtVKvtlPDq3D2gLc94gMkjNoJKjKGpaZ/Cxj31MrrvuuuTvbdu2ySmnnCKX//pzzvYB8wd3O9du97Te5FnfCv97gNsFBATMJYyPj0tfX1/rhgeAfD4vg4OD8sDQ9w56X4ODg5LPuz0DARZz3iBaunSpZDKZJjZoeHi4iTUyKBQKUihYN8qiRYvkySeflFNOOUW2bt0qvb29zu3mOsbGxmT16tXhHOcw5vv5iYRznA+Y7+cXRZGMj4/LqlWrWjc+QHR0dMjmzZulXD74lLZ8Pi8dHR2tGy5wzHmDKJ/Py1lnnSXr16+X3/md30nWr1+/Xn77t3+7rX2k02k55phjRESkt7d3Xr7ARDjHuY/5fn4i4RznA+bz+R0uZojo6OgIhswsYs4bRCIi1113nVx++eVy9tlny/nnny933HGHbNmyRa666qoj3bWAgICAgICAOYB5YRC9853vlN27d8unPvUp2b59u5x22mnyve99T9auXXukuxYQEBAQEBAwBzAvDCIRkfe///3y/ve//4C3LxQK8olPfELFFs03hHOc+5jv5ycSznE+YL6fX8D8RCoKqk0BAQEBAQEBCxzzonRHQEBAQEBAQMDBIBhEAQEBAQEBAQsewSAKCAgICAgIWPAIBlFAQEBAQEDAgkcwiGLcfvvtsm7dOuno6JCzzjpLfvSjHx3pLh0QbrnlFnn1q18tPT09snz5cnnb294mTz/9tGoTRZHceOONsmrVKuns7JSLLrpInnjiiSPU44PHLbfcIqlUSq699tpk3Xw4x23btsl73/teWbJkiXR1dcmv/uqvyoYNG5Lf5/I5VqtV+fjHPy7r1q2Tzs5OOf744+VTn/qU1FHMaq6d3w9/+EN561vfKqtWrZJUKiX//u//rn5v53xKpZJ88IMflKVLl0p3d7dcdtll8tJLL83iWewb+zrHSqUiH/3oR+X000+X7u5uWbVqlVxxxRXy8ssvq30c7ecYsIARBUR33313lMvlojvvvDN68skno2uuuSbq7u6OXnzxxSPdtf3Gm970pugrX/lK9Pjjj0cbN26M3vzmN0dr1qyJJiYmkja33npr1NPTE33rW9+KHnvsseid73xntHLlymhsbOwI9vzA8PDDD0fHHXdc9KpXvSq65pprkvVz/Rz37NkTrV27NvqDP/iD6KGHHoo2b94c3XvvvdGzzz6btJnL53jTTTdFS5Ysif7zP/8z2rx5c/Rv//Zv0aJFi6LbbrstaTPXzu973/tedMMNN0Tf+ta3IhGJvvOd76jf2zmfq666KjrmmGOi9evXR4888kj0ute9LjrjjDOiarU6y2fjxr7OcWRkJLr44oujb37zm9Evf/nL6Mc//nF07rnnRmeddZbax9F+jgELF8EgiqLonHPOia666iq17qSTToquv/76I9SjQ4fh4eFIRKL7778/iqIoqtfr0eDgYHTrrbcmbYrFYtTX1xf9wz/8w5Hq5gFhfHw8OuGEE6L169dHF154YWIQzYdz/OhHPxpdcMEF3t/n+jm++c1vjv7oj/5IrXv7298evfe9742iaO6f30xjoZ3zGRkZiXK5XHT33XcnbbZt2xal0+nonnvumbW+twuX0TcTDz/8cCQiyeRyrp1jwMLCgneZlctl2bBhg1xyySVq/SWXXCIPPvjgEerVocPo6KiIiAwMDIiIyObNm2VoaEidb6FQkAsvvHDOne9f/MVfyJvf/Ga5+OKL1fr5cI7f/e535eyzz5bf+73fk+XLl8uZZ54pd955Z/L7XD/HCy64QP7nf/5HnnnmGRER+fnPfy4PPPCA/NZv/ZaIzP3zm4l2zmfDhg1SqVRUm1WrVslpp502J89ZpDH+pFIp6e/vF5H5eY4B8wfzRqn6QLFr1y6p1WqyYsUKtX7FihUyNDR0hHp1aBBFkVx33XVywQUXyGmnnSYikpyT63xffPHFWe/jgeLuu++WRx55RH760582/TYfzvH555+XL33pS3LdddfJX//1X8vDDz8sV199tRQKBbniiivm/Dl+9KMfldHRUTnppJMkk8lIrVaTT3/60/Lud79bRObHPSTaOZ+hoSHJ5/OyePHipjZzcSwqFoty/fXXy3ve856kwOt8O8eA+YUFbxAZpFIp9XcURU3r5ho+8IEPyC9+8Qt54IEHmn6by+e7detWueaaa+T73//+PitBz+VzrNfrcvbZZ8vNN98sIiJnnnmmPPHEE/KlL31JrrjiiqTdXD3Hb37zm3LXXXfJN77xDTn11FNl48aNcu2118qqVavkyiuvTNrN1fPz4UDOZy6ec6VSkXe9611Sr9fl9ttvb9l+Lp5jwPzDgneZLV26VDKZTNPsZHh4uGk2N5fwwQ9+UL773e/KfffdJ8cee2yyfnBwUERkTp/vhg0bZHh4WM466yzJZrOSzWbl/vvvl7/7u7+TbDabnMdcPseVK1fKKaecotadfPLJsmXLFhGZ+/fxr/7qr+T666+Xd73rXXL66afL5ZdfLh/60IfklltuEZG5f34z0c75DA4OSrlclr1793rbzAVUKhV5xzveIZs3b5b169cn7JDI/DnHgPmJBW8Q5fN5Oeuss2T9+vVq/fr16+U1r3nNEerVgSOKIvnABz4g3/72t+V///d/Zd26der3devWyeDgoDrfcrks999//5w53ze84Q3y2GOPycaNG5N/Z599tvz+7/++bNy4UY4//vg5f46//uu/3iSX8Mwzz8jatWtFZO7fx6mpKUmn9fCTyWSStPu5fn4z0c75nHXWWZLL5VSb7du3y+OPPz5nztkYQ5s2bZJ7771XlixZon6fD+cYMI9xpKK5jyaYtPsvf/nL0ZNPPhlde+21UXd3d/TCCy8c6a7tN/78z/886uvri37wgx9E27dvT/5NTU0lbW699daor68v+va3vx099thj0bvf/e6jOp25HTDLLIrm/jk+/PDDUTabjT796U9HmzZtir7+9a9HXV1d0V133ZW0mcvneOWVV0bHHHNMknb/7W9/O1q6dGn0kY98JGkz185vfHw8evTRR6NHH300EpHo85//fPToo48mGVbtnM9VV10VHXvssdG9994bPfLII9HrX//6oyolfV/nWKlUossuuyw69thjo40bN6rxp1QqJfs42s8xYOEiGEQxvvjFL0Zr166N8vl89Gu/9mtJmvpcg4g4/33lK19J2tTr9egTn/hENDg4GBUKheg3fuM3oscee+zIdfoQYKZBNB/O8T/+4z+i0047LSoUCtFJJ50U3XHHHer3uXyOY2Nj0TXXXBOtWbMm6ujoiI4//vjohhtuUB/OuXZ+9913n/Pdu/LKK6Moau98pqenow984APRwMBA1NnZGb3lLW+JtmzZcgTOxo19nePmzZu94899992X7ONoP8eAhYtUFEXR7PFRAQEBAQEBAQFHHxZ8DFFAQEBAQEBAQDCIAgICAgICAhY8gkEUEBAQEBAQsOARDKKAgICAgICABY9gEAUEBAQEBAQseASDKCAgICAgIGDBIxhEAQEBAQEBAQsewSAKCAgICAgIWPAIBlFAQEBAQEDAgkcwiAICAgICAgIWPIJBFBCwwHHRRRfJ1VdfLR/5yEdkYGBABgcH5cYbbxQRkR/84AeSz+flRz/6UdL+c5/7nCxdulS2b99+hHocEBAQcOgRDKKAgAD52te+Jt3d3fLQQw/JZz7zGfnUpz4l69evl4suukiuvfZaufzyy2V0dFR+/vOfyw033CB33nmnrFy58kh3OyAgIOCQIRR3DQhY4LjoooukVqspFuicc86R17/+9XLrrbdKuVyW8847T0444QR54okn5Pzzz5c777zzCPY4ICAg4NAje6Q7EBAQcOTxqle9Sv29cuVKGR4eFhGRfD4vd911l7zqVa+StWvXym233XYEehgQEBBweBFcZgEBAZLL5dTfqVRK6vV68veDDz4oIiJ79uyRPXv2zGrfAgICAmYDwSAKCAjYJ5577jn50Ic+JHfeeaecd955csUVVyhjKSAgIGA+IBhEAQEBXtRqNbn88svlkksukT/8wz+Ur3zlK/L444/L5z73uSPdtYCAgIBDimAQBQQEePHpT39aXnjhBbnjjjtERGRwcFD+6Z/+ST7+8Y/Lxo0bj2znAgICAg4hQpZZQEBAQEBAwIJHYIgCAgICAgICFjyCQRQQEBAQEBCw4BEMooCAgICAgIAFj2AQBQQEBAQEBCx4BIMoICAgICAgYMEjGEQBAQEBAQEBCx7BIAoICAgICAhY8AgGUUBAQEBAQMCCRzCIAgICAgICAhY8gkEUEBAQEBAQsOARDKKAgICAgICABY9gEAUEBAQEBAQsePz/LOUhC/oBD7oAAAAASUVORK5CYII=",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "expt.topog.depth.plot()"
+ "expt.bathymetry.depth.plot()"
]
},
{
@@ -823,35 +330,18 @@
"\n",
"This cuts out and interpolates the initial condition as well as all boundaries (unless you don't pass it boundaries).\n",
"\n",
- "The dictionary maps the MOM6 variable names to what they're called in your ocean input file. Notice how the horizontal dimensions are `x` and `y` in the GLORYS reanalysis example, vs `xh`, `yh`, `xq`, `yq`. This is because ACCESS-OM2-01 is on a `B` grid, so we need to differentiate between `q` and `t` points. \n",
+ "The dictionary maps the MOM6 variable names to what they're called in your ocean input file. Notice how the horizontal dimensions are `xt_ocean`, `yt_ocean`, `xu_ocean`, `yu_ocean` in ACCESS-OM2-01, vs `xh`, `yh`, `xq`, `yq` in MOM6. This is because ACCESS-OM2-01 is on a `B` grid, so we need to differentiate between `q` and `t` points. \n",
"\n",
"If one of your segments is land, you can delete its string from the 'boundaries' list. You'll need to update `MOM_input` to reflect this though so it knows how many segments to look for, and their orientations. "
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INITIAL CONDITIONS\n",
- "Regridding Velocities...Done.\n",
- "Regridding Tracers...\n",
- "Done.\n",
- "Regridding Free surface...\n",
- "Saving outputs... done setting up initial condition.\n",
- "Processing south boundary...Done.\n",
- "Processing north boundary...Done.\n",
- "Processing west boundary...Done.\n",
- "Processing east boundary...Done.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Define a mapping from the MOM5 B grid variables and dimensions to the MOM6 C grid ones\n",
"ocean_varnames = {\"time\": \"time\",\n",
@@ -895,109 +385,13 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running GFDL's FRE Tools. The following information is all printed by the FRE tools themselves\n",
- "NOTE from make_solo_mosaic: there are 0 contacts (align-contact)\n",
- "congradulation: You have successfully run make_solo_mosaic\n",
- "OUTPUT FROM MAKE SOLO MOSAIC:\n",
- "\n",
- "CompletedProcess(args='/home/157/ahg157/repos/mom5/src/tools/make_solo_mosaic/make_solo_mosaic --num_tiles 1 --dir . --mosaic_name ocean_mosaic --tile_file hgrid.nc', returncode=0)\n",
- "cp ./hgrid.nc hgrid.nc \n",
- "\n",
- "NOTE from make_coupler_mosaic: the ocean land/sea mask will be determined by field depth from file topog.nc\n",
- "mosaic_file is grid_spec.nc\n",
- "\n",
- "***** Congratulation! You have successfully run make_quick_mosaic\n",
- "OUTPUT FROM QUICK MOSAIC:\n",
- "\n",
- "CompletedProcess(args='/home/157/ahg157/repos/mom5/src/tools/make_quick_mosaic/make_quick_mosaic --input_mosaic ocean_mosaic.nc --mosaic_name grid_spec --ocean_topog topog.nc', returncode=0)\n",
- "\n",
- " ===>NOTE from check_mask: when layout is specified, min_pe and max_pe is set to layout(1)*layout(2)=100\n",
- "\n",
- " ===>NOTE from check_mask: Below is the list of command line arguments.\n",
- "\n",
- "grid_file = ocean_mosaic.nc\n",
- "topog_file = topog.nc\n",
- "min_pe = 100\n",
- "max_pe = 100\n",
- "layout = 10, 10\n",
- "halo = 4\n",
- "sea_level = 0\n",
- "show_valid_only is not set\n",
- "nobc = 0\n",
- "\n",
- " ===>NOTE from check_mask: End of command line arguments.\n",
- "\n",
- " ===>NOTE from check_mask: the grid file is version 2 (mosaic grid) grid which contains field gridfiles\n",
- "\n",
- "==>NOTE from get_boundary_type: x_boundary_type is solid_walls\n",
- "\n",
- "==>NOTE from get_boundary_type: y_boundary_type is solid_walls\n",
- "\n",
- "==>NOTE from check_mask: Checking for possible masking:\n",
- "==>NOTE from check_mask: Assume 4 halo rows\n",
- "==>NOTE from check_mask: Total domain size is 140, 249\n",
- "\n",
- "_______________________________________________________________________\n",
- "\n",
- "NOTE from check_mask: The following is for using model source code with version older than siena_201207,\n",
- "Possible setting to mask out all-land points region, for use in coupler_nmlTotal number of domains = 100\n",
- "Number of tasks (excluded all-land region) to be used is 98\n",
- "Number of regions to be masked out = 2\n",
- "The layout is 10, 10\n",
- "Masked and used tasks, 1: used, 0: masked\n",
- "1111111111\n",
- "1111111111\n",
- "1111111111\n",
- "1111001111\n",
- "1111111111\n",
- "1111111111\n",
- "1111111111\n",
- "1111111111\n",
- "1111111111\n",
- "1111111111\n",
- " domain decomposition\n",
- " 14 14 14 14 14 14 14 14 14 14\n",
- " 25 25 25 25 25 25 25 25 25 24\n",
- " used=98, masked=2, layout=10,10\n",
- " To chose this mask layout please put the following lines in ocean_model_nml and/or ice_model_nml\n",
- " nmask = 2\n",
- "layout = 10, 10\n",
- "mask_list = 5,7,6,7\n",
- "\n",
- "\n",
- "_______________________________________________________________________\n",
- "\n",
- "NOTE from check_mask: The following is for using model source code with version siena_201207 or newer,\n",
- " specify ocean_model_nml/ice_model_nml/atmos_model_nml/land_model/nml \n",
- " variable mask_table with the mask_table created here.\n",
- " Also specify the layout variable in each namelist using corresponding layout\n",
- "\n",
- "***** Congratulation! You have successfully run check_mask\n",
- "OUTPUT FROM CHECK MASK:\n",
- "\n",
- " CompletedProcess(args='/home/157/ahg157/repos/mom5/src/tools/check_mask/check_mask --grid_file ocean_mosaic.nc --ocean_topog topog.nc --layout 10,10 --halo 4', returncode=0)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "cp: './ocean_mosaic.nc' and 'ocean_mosaic.nc' are the same file\n",
- "cp: './hgrid.nc' and 'hgrid.nc' are the same file\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "expt.FRE_tools(layout = (10, 10))"
+ "expt.FRE_tools(layout = (10, 10)) ## Here the tuple defines the processor layout"
]
},
{
@@ -1006,26 +400,17 @@
"source": [
"## Step 7: Modify the default input directory to make a (hopefully) runnable configuration out of the box\n",
"\n",
- "This step copies the default directory, and modifies the `MOM_input` and `SIS_input` files to match your experiment. If you use Payu to run mom6, set the `using_payu` flag to `True` and an example `config.yaml` file will be copied to your run directory. This still needs to be modified manually to work with your projects, executable etc.\n",
+ "This step copies the default directory, and modifies the `MOM_layout` files to match your experiment by inserting the right number of x,y points and cpu layout. If you use Payu to run MOM6, set the `using_payu` flag to `True` and an example `config.yaml` file will be copied to your run directory. This still needs to be modified manually to work with your projects, executable etc.\n",
"\n"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "demos/premade_run_directories/jra_surface\n",
- "Number of CPUs required: 98\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"expt.setup_run_directory(surface_forcing = \"jra\", using_payu = True)"
]
@@ -1036,7 +421,7 @@
"source": [
"## Step 8: Run your model!\n",
"\n",
- "To do this, navigate to your run directory in terminal. If you're working on NCI, you can do this via:\n",
+ "To do this, navigate to your run directory in terminal. If you're working on NCI, you can run your model via:\n",
"\n",
"```\n",
"module load conda/analysis3\n",
@@ -1055,13 +440,22 @@
"\n",
"Hopefully your model is running. If not, the first thing you should do is reduce the timestep. You can do this by adding `#override DT=XXXX` to your `MOM_override` file. \n",
"\n",
- "If there's strange behaviour on your boundaries, you could play around with the `nudging timescale` (an example is already included in the `MOM_override` file). Sometimes, if your boundary has a lot going on (like all of the eddies spinning off the ACC), it can be hard to avoid these edge effects. This is because the chaotic, submesoscale structures developed within the regional domain won't match those at the boundary. "
+ "If there's strange behaviour on your boundaries, you could play around with the `nudging timescale` (an example is already included in the `MOM_override` file). Sometimes, if your boundary has a lot going on (like all of the eddies spinning off the ACC), it can be hard to avoid these edge effects. This is because the chaotic, submesoscale structures developed within the regional domain won't match those at the boundary.\n",
+ "\n",
+ "Another thing that can go wrong is little bays creating non-advective cells at your boundaries. Keep an eye out for tiny bays where one side is taken up by a boundary segment. You can either fill them in manually, or move your boundary slightly to avoid them"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:analysis3-24.01]",
+ "display_name": "Python [conda env:analysis3-24.01] *",
"language": "python",
"name": "conda-env-analysis3-24.01-py"
},
diff --git a/demos/reanalysis-forced.ipynb b/demos/reanalysis-forced.ipynb
index be01403c..2f24ef37 100644
--- a/demos/reanalysis-forced.ipynb
+++ b/demos/reanalysis-forced.ipynb
@@ -4,32 +4,43 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Regional Tasmania forced by GLORYS and ERA5 reanalysis datasets\n",
+ "# Regional Tasmanian domain forced by GLORYS and ERA5 reanalysis datasets\n",
"\n",
"**Note: FRE-NC tools are required to be set up, as outlined in the `regional-mom6` package [documentation](https://regional-mom6.readthedocs.io/en/latest/).**\n",
"\n",
- "For this example we need a copy of the [GEBCO bathymetry](https://www.gebco.net/data_and_products/gridded_bathymetry_data/), access to the [GLORYs ocean reanalysis data](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description), and [ERA5 surface forcing](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). \n",
+ "For this example we need a copy of the [GEBCO bathymetry](https://www.gebco.net/data_and_products/gridded_bathymetry_data/), access to the [GLORYS ocean reanalysis data](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description), and [ERA5 surface forcing](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). \n",
"\n",
"This example reads in the entire global extent of ERA5 and GEBCO; we don't need to worry about cutting it down to size. "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What does the `regional_mom6` package do?\n",
+ "\n",
+ "Setting up a regional model in MOM6 can be a pain. The goal of this package is that users should spend their debugging time fixing a model that's running and doing weird things, rather than puzzling over a model that won't even start.\n",
+ "\n",
+ "In running this notebook, you'll hopefully have a running MOM6 regional model. There will still be a lot of fiddling to do with the `MOM_input` file to make sure that the parameters are set up right for your domain, and you might want to manually edit some of the input files. *But*, this package should help you bypass most of the woes of regridding, encoding and understanding the arcane arts of the MOM6 boundary segment files. "
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What does this notebook do?\n",
- "This notebook is designed to set you up with a working MOM6 regional configuration. First, try and get it running with our default Tasmania case, then you can clone the notebook and modify for your region of interest. \n",
+ "This notebook is designed to set you up with a working MOM6 regional configuration. First, try to get it running with our default Tasmania case, then you can clone the notebook and modify for your region of interest. \n",
"\n",
"Input Type | Source | Subsets required\n",
"---|---|---\n",
"Surface | [ERA5 surface forcing](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) | Data from 2003; whole globe or subset around our domain\n",
- "Ocean | [GLORYs reanalysis product](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description) | Boundary segments & initial condition; see section 2 for details. \n",
+ "Ocean | [GLORYS reanalysis product](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description) | Boundary segments & initial condition; see section 2 for details. \n",
"Bathymetry | [GEBCO](https://www.gebco.net/data_and_products/gridded_bathymetry_data/) | whole globe or subset around domain"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -46,12 +57,14 @@
"source": [
"## Step 1: Choose our domain, define workspace paths\n",
"\n",
- "You can use the graphical user interface (GUI) by Copernicus to find the latitude-longitude extent of the domain of your interest."
+ "To make sure that things are working I'd recommend starting with the default example defined below. If this runs ok, then change to a domain of your choice and hopefully it runs ok too! If not, check the [README](https://github.com/COSIMA/regional-mom6/blob/main/README.md) and [documentation](https://regional-mom6.readthedocs.io/) for troubleshooting tips.\n",
+ "\n",
+ "You can log in and use [this GUI](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/download) to find the lat/lon of your domain and copy paste below."
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -68,15 +81,14 @@
"## Directory where you'll run the experiment from\n",
"run_dir = Path(f\"mom6_run_directories/{expt_name}/\")\n",
"\n",
- "## Directory where fre tools are stored \n",
+ "## Directory where fre tools are stored, e.g. on NCI Gadi /home/157/ahg157/repos/mom5/src/tools/\n",
"toolpath_dir = Path(\"PATH_TO_FRE_TOOLS\") ## Compiled tools needed for construction of mask tables\n",
"\n",
"## Path to where your raw ocean forcing files are stored\n",
"glorys_path = Path(\"PATH_TO_GLORYS_DATA\" )\n",
"\n",
- "for i in [run_dir, glorys_path, input_dir]:\n",
- " if not os.path.exists(str(i)):\n",
- " os.makedirs(str(i))"
+ "for path in [run_dir, glorys_path, input_dir]:\n",
+ " os.makedirs(str(path), exist_ok=True)"
]
},
{
@@ -85,13 +97,13 @@
"source": [
"## Step 2: Prepare ocean forcing data\n",
"\n",
- "We need to cut out our ocean forcing. The package expects an initial condition and one time-dependent segment per non-land boundary. Naming convention is \"east_unprocessed\" for segments and \"ic_unprocessed\" for the initial condition.\n",
+ "We need to cut out our ocean forcing. The package expects an initial condition and one time-dependent segment per non-land boundary. Naming convention is `\"east_unprocessed\"` for segments and `\"ic_unprocessed\"` for the initial condition.\n",
"\n",
"Data can be downloaded directly from the [Copernicus Marine data store](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/download) via their GUI (once logged in).\n",
"\n",
"1. Initial condition: Using the GUI, select an area matching your `longitude_extent` and `latitude_extent` that corresponds to the first day in your date range. Download the initial condition and save it with filename `ic_unprocessed.nc` inside the `glorys_path` directory.\n",
"2. Boundary forcing: Using the GUI, select the Eastern boundary of your domain (if you have one that contains ocean). Allow for a buffer of ~0.5 degrees in all directions, and download for the prescribed `date_range`. Download and name `east_unprocessed.nc`.\n",
- "3. Repeat step 2 for the rest sections of the domain."
+ "3. Repeat step 2 for the remaining sections of the domain."
]
},
{
@@ -99,9 +111,7 @@
"metadata": {},
"source": [
"## Step 3: Make experiment object\n",
- "The `regional_mom6.experiment` contains the regional domain basics, as well as it generates the horizontal and vertical grids, `hgrid` and `vgrid` respectively, and sets up the directory structures. \n",
- "\n",
- "\n"
+ "The `regional_mom6.experiment` contains the regional domain basics, and also generates the horizontal and vertical grids, `hgrid` and `vgrid` respectively, and sets up the directory structures. "
]
},
{
@@ -130,31 +140,26 @@
"source": [
"We can now access the horizontal and vertical grid of the regional configuration via `expt.hgrid` and `expt.vgrid` respectively.\n",
"\n",
- "By plotting `vgrid` with `marker = '.'` option allows us to inspect the vertical spacing. Alternatively,\n",
- "\n",
+ "Plotting the vertical grid with `marker = '.'` lets you see the spacing. You can use `numpy.diff` to compute the vertical spacings, e.g.,\n",
"```python\n",
+ "import numpy as np\n",
"np.diff(expt.vgrid.zl).plot(marker = '.')\n",
"```\n",
- "shows you the vertical spacing profile."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
+ "shows you the vertical spacing profile.\n",
+ "\n",
"### Modular workflow!\n",
"\n",
- "After constructing our `experiment` object, if for some reason we are not happy with the grids, then we can simply modify them and then save them back into the `experiment` object. However, in doing so, we also need to save them to disk again. For example:\n",
+ "After constructing your `expt` object, if you don't like the default `hgrid` and `vgrid` you can simply modify and then save them back into the `expt` object. However, you'll then also need to save them to disk again. For example:\n",
"\n",
"```python\n",
- "new_hgrid = xr.open_dataset(inputdir / \"hgrid.nc\")\n",
+ "new_hgrid = xr.open_dataset(input_dir + \"/hgrid.nc\")\n",
"```\n",
- "Modify `new_hgrid`, ensuring that _all metadata_ is retained to keep MOM6 happy. Then, save any changes\n",
+ "Modify `new_hgrid`, ensuring that _all metadata_ is retained to keep MOM6 happy. Then, save your changes\n",
"\n",
"```python\n",
"expt.hgrid = new_hgrid\n",
"\n",
- "expt.hgrid.to_netcdf(inputdir / \"hgrid.nc\")\n",
+ "expt.hgrid.to_netcdf(input_dir + \"/hgrid.nc\")\n",
"```"
]
},
@@ -164,7 +169,7 @@
"source": [
"## Step 4: Set up bathymetry\n",
"\n",
- "Similarly to ocean forcing, we point the experiment's `bathymetry` method at the location of the file of choice and also pass a dictionary mapping variable names. We don't don't need to preprocess the bathymatry since it is simply a two-dimensional field and is easier to deal with. Afterwards you can run `expt.topog` and have a look at your domain. After running this cell, your input directory will contain other topography - adjacent things like the ocean mosaic and mask table too. This defaults to a 10x10 layout which can be updated later."
+ "Similarly to ocean forcing, we point the experiment's `setup_bathymetry` method at the location of the file of choice and also mappings to variable names. We don't need to preprocess the bathymetry since it is simply a two-dimensional field and is easier to deal with. Afterwards you can run `expt.bathymetry` and have a look at your domain. After running this cell, your input directory will contain other topography - adjacent things like the ocean mosaic and mask table too. This defaults to a 10x10 layout which can be updated later."
]
},
{
@@ -211,7 +216,7 @@
"\n",
"This cuts out and interpolates the initial condition as well as all boundaries (unless you don't pass it boundaries).\n",
"\n",
- "The dictionary maps the MOM6 variable names to what they're called in your ocean input file. Notice how for GLORYs, the horizontal dimensions are `x` and `y`, vs `xh`, `yh`, `xq`, `yq` for ACCESS OM2-01. This is because for an 'A' grid type tracers share the grid with velocities so there's no difference.\n",
+ "The dictionary maps the MOM6 variable names to what they're called in your ocean input file. Notice how for GLORYS, the horizontal dimensions are `latitude` and `longitude`, vs `xh`, `yh`, `xq`, `yq` for MOM6. This is because for an 'A' grid type tracers share the grid with velocities so there's no difference.\n",
"\n",
"If one of your segments is land, you can delete its string from the 'boundaries' list. You'll need to update MOM_input to reflect this though so it knows how many segments to look for, and their orientations."
]
@@ -224,8 +229,8 @@
"source": [
"# Define a mapping from the GLORYS variables and dimensions to the MOM6 ones\n",
"ocean_varnames = {\"time\": \"time\",\n",
- " \"y\": \"latitude\",\n",
- " \"x\": \"longitude\",\n",
+ " \"yh\": \"latitude\",\n",
+ " \"xh\": \"longitude\",\n",
" \"zl\": \"depth\",\n",
" \"eta\": \"zos\",\n",
" \"u\": \"uo\",\n",
@@ -275,19 +280,19 @@
"source": [
"## Step 7: Set up ERA5 forcing:\n",
"\n",
- "Here we assume the ERA5 dataset is stored somewhere on the system we are working. \n",
+ "Here we assume the ERA5 dataset is stored somewhere on the system we are working on. \n",
"\n",
- "Below is a table showing ERA5 characteristics and what needs to be done to sort it out\n",
+ "Below is a table showing ERA5 characteristics and what needs to be done to sort it out.\n",
"\n",
- "**Required ERA data**:\n",
+ "**Required ERA5 data**:\n",
"\n",
- "Name | ERA filename | era variable name | Units\n",
+ "Name | ERA5 filename | ERA5 variable name | Units\n",
"---|---|---|---\n",
"Surface Pressure | sp | sp | Pa \n",
"Surface Temperature | 2t | t2m | K \n",
"Meridional Wind | 10v | v10 | m/s \n",
"Zonal Wind | 10u | u10 | m/s \n",
- "Specific Humidity | na | na | kg/kg, calculated from dewpoint temperature\n",
+ "Specific Humidity | - | - | kg/kg, calculated from dewpoint temperature\n",
"Dewpoint Temperature | 2d | d2m | K\n",
"\n",
"\n",
@@ -317,9 +322,7 @@
"source": [
"## Step 8: Modify the default input directory to make a (hopefully) runnable configuration out of the box\n",
"\n",
- "This step copies the default directory, and modifies the `MOM_layout` files to match your experiment by inserting the right number of x,y points and cpu layout. If you use Payu to run mom6, set the `using_payu` flag to `True` and an example `config.yaml` file will be copied to your run directory. This still needs to be modified manually to work with your projects, executable etc.\n",
- "\n",
- "You also need to pass the path to where you git cloned the mom6-regional code. This just ensures that the funciton can find the premade run directories."
+ "This step copies the default directory, and modifies the `MOM_layout` files to match your experiment by inserting the right number of x,y points and cpu layout. If you use Payu to run MOM6, set the `using_payu` flag to `True` and an example `config.yaml` file will be copied to your run directory. This still needs to be modified manually to work with your projects, executable etc."
]
},
{
@@ -345,6 +348,13 @@
"\n",
"Another thing that can go wrong is little bays creating non-advective cells at your boundaries. Keep an eye out for tiny bays where one side is taken up by a boundary segment. You can either fill them in manually, or move your boundary slightly to avoid them"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -363,7 +373,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.6"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/index.rst b/docs/index.rst
index 1b1fcba0..9d8acf14 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,8 +1,7 @@
Regional MOM6 Documentation
===========================
-Python package for automatic generation of regional configurations for the `Modular Ocean Model version 6`_ (MOM6).
-
+[Regional-mom6](https://github.com/COSIMA/regional-mom6/) is a Python package for automatic generation of regional configurations for the `Modular Ocean Model version 6`_ (MOM6).
In brief...
-----------
diff --git a/docs/installation.md b/docs/installation.md
index 0fad0d1f..6562a145 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -39,7 +39,7 @@ The above installs the version of `regional-mom6` (plus any required dependencie
## "*I want to live on the edge! I want the latest developments*"
-To install `regional-mom6` directly via GitHub using `pip`, first install `esmpy` as described above. Then:
+To install `regional-mom6` directly from the [GitHub repository](https://github.com/COSIMA/regional-mom6/) using `pip`, first install `esmpy` as described above. Then:
```bash
pip install git+https://github.com/COSIMA/regional-mom6.git
From e3337ed11295758d816acc6fb26653b2618dec23 Mon Sep 17 00:00:00 2001
From: Andrew Kiss <31054815+aekiss@users.noreply.github.com>
Date: Tue, 23 Apr 2024 15:04:23 +1000
Subject: [PATCH 2/8] remove regional_mom6_path doc for setup_run_directory
(apparently not used)
---
regional_mom6/regional_mom6.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/regional_mom6/regional_mom6.py b/regional_mom6/regional_mom6.py
index 6b791c1d..5e5e2ed4 100644
--- a/regional_mom6/regional_mom6.py
+++ b/regional_mom6/regional_mom6.py
@@ -1399,8 +1399,6 @@ def setup_run_directory(
existing files in the 'rundir' directory for the experiment.
Args:
- regional_mom6_path (str): Path to the regional MOM6 source code that was cloned
- from GitHub. Default is current path, ``'.'``.
surface_forcing (Optional[str]): Specify the choice of surface forcing, one
of: ``'jra'`` or ``'era5'``. If not prescribed then constant fluxes are used.
using_payu (Optional[bool]): Whether or not to use payu (https://github.com/payu-org/payu)
From bff865ddd5f48283b2076d7990476e29e0d78797 Mon Sep 17 00:00:00 2001
From: Andrew Kiss <31054815+aekiss@users.noreply.github.com>
Date: Tue, 23 Apr 2024 15:06:29 +1000
Subject: [PATCH 3/8] fix bathymetryraphic typo in bathymetry standard_name
---
regional_mom6/regional_mom6.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/regional_mom6/regional_mom6.py b/regional_mom6/regional_mom6.py
index 5e5e2ed4..a84b6a56 100644
--- a/regional_mom6/regional_mom6.py
+++ b/regional_mom6/regional_mom6.py
@@ -1167,7 +1167,7 @@ def tidy_bathymetry(
{"depth": (["ny", "nx"], bathymetry["elevation"].values)}
)
bathymetry.attrs["depth"] = "meters"
- bathymetry.attrs["standard_name"] = "bathymetryraphic depth at T-cell centers"
+ bathymetry.attrs["standard_name"] = "bathymetric depth at T-cell centers"
bathymetry.attrs["coordinates"] = "zi"
bathymetry.expand_dims("tiles", 0)
From 2dc8c2603769b3baa7952dfcc17ee392eabbb20c Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou"
Date: Tue, 23 Apr 2024 08:17:57 +0300
Subject: [PATCH 4/8] homogenize installation instructions
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 516dff15..ef7684dd 100644
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ The above installs the version of `regional-mom6` (plus any required dependencie
#### "*I want to live on the edge! I want the latest developments*"
-To install `regional-mom6` directly via GitHub using `pip`, first install `esmpy` as described above. Then:
+To install `regional-mom6` directly from the [GitHub repository](https://github.com/COSIMA/regional-mom6/) using `pip`, first install `esmpy` as described above. Then:
```bash
pip install git+https://github.com/COSIMA/regional-mom6.git
From b337d0f8ab093c840b75818109e667ec5f377654 Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou"
Date: Tue, 23 Apr 2024 08:19:09 +0300
Subject: [PATCH 5/8] package branding name is lowercase :)
---
docs/index.rst | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/index.rst b/docs/index.rst
index 9d8acf14..75d8359e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,7 +1,7 @@
Regional MOM6 Documentation
===========================
-[Regional-mom6](https://github.com/COSIMA/regional-mom6/) is a Python package for automatic generation of regional configurations for the `Modular Ocean Model version 6`_ (MOM6).
+[regional-mom6](https://github.com/COSIMA/regional-mom6/) is a Python package for automatic generation of regional configurations for the `Modular Ocean Model version 6`_ (MOM6).
In brief...
-----------
From 605ea1cea3bcd6b599b604cc2340070334c1b66b Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou"
Date: Tue, 23 Apr 2024 10:23:39 +0300
Subject: [PATCH 6/8] move access_om2-forced.ipynb to cosima-recipes
---
demos/access_om2-forced.ipynb | 471 ----------------------------------
1 file changed, 471 deletions(-)
delete mode 100644 demos/access_om2-forced.ipynb
diff --git a/demos/access_om2-forced.ipynb b/demos/access_om2-forced.ipynb
deleted file mode 100644
index 47c9b3e1..00000000
--- a/demos/access_om2-forced.ipynb
+++ /dev/null
@@ -1,471 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Regional Tasmanian domain forced by JRA55-do reanalysis and ACCESS-OM2-01 model output\n",
- "\n",
- "### Note: This example requires access to [NCI's Gadi HPC system](https://nci.org.au/our-systems/hpc-systems)\n",
- "\n",
- "**Ensure you have access to the relevant NCI projects to access the data listed below. If needed, apply via [mancini](https://my.nci.org.au/mancini).**\n",
- "\n",
- "## What does this notebook do?\n",
- "This notebook is designed to set you up with a working MOM6 regional configuration. First, try and get it running with our default Tasmania case, then you can clone the notebook and modify for your region of interest. \n",
- "\n",
- "Input Type | Source | Location on NCI\n",
- "---|---|---\n",
- "Surface | [JRA55-do surface forcing](https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/) | `/g/data/ik11`\n",
- "Ocean | [ACCESS-OM2-01](https://github.com/COSIMA/access-om2) | `/g/data/ik11` \n",
- "Bathymetry | [GEBCO](https://www.gebco.net/data_and_products/gridded_bathymetry_data/) | `/g/data/ik11`\n",
- "\n",
- "Additionally, you'll need access to `/g/data/x77/` if you want to use the same executable using the latest FMS build (a good idea for troubleshooting)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import regional_mom6 as rmom6\n",
- "\n",
- "import os\n",
- "import xarray as xr\n",
- "from pathlib import Path\n",
- "from dask.distributed import Client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Start a dask client."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "client = Client()\n",
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true
- },
- "source": [
- "## What does the `regional_mom6` package do?\n",
- "\n",
- "Setting up a regional model in MOM6 can be a pain. The goal of this package is that users should spend their debugging time fixing a model that's running and doing weird things, rather than puzzling over a model that won't even start.\n",
- "\n",
- "In running this notebook, you'll hopefully have a running MOM6 regional model. There will still be a lot of fiddling to do with the `MOM_input` file to make sure that the parameters are set up right for your domain, and you might want to manually edit some of the input files. *But*, this package should help you bypass most of the woes of regridding, encoding and understanding the arcane arts of the MOM6 boundary segment files. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## What does this notebook do?\n",
- "\n",
- "This notebook demonstrates how to set up a regional domain using the package. By the end you should have a running MOM6 experiment on the domain of your choice. To make a stable test case:\n",
- "\n",
- "* Avoid any regions with ice\n",
- "* Avoid regions near the north pole\n",
- "* Although the default configuration is meant to be repeat-year forced (RYF), the calendar and encoding will need fixing to run longer than a year\n",
- "\n",
- "\n",
- "Input Type | Source\n",
- "---|---\n",
- "Surface | JRA55-do\n",
- "Ocean | ACCESS-OM2-01\n",
- "Bathymetry | Gebco"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 0: Your personal environment variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "scratch = \"/scratch/v45/nc3020\"\n",
- "gdata = \"/g/data/v45/nc3020\"\n",
- "home = \"/home/552/nc3020\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1: Choose our domain, define workspace paths\n",
- "\n",
- "To make sure that things are working I'd recommend starting with the default example defined below. If this runs ok, then change to a domain of your choice and hopefully it runs ok too! If not, check the [README](https://github.com/COSIMA/regional-mom6/blob/main/README.md) and [documentation](https://regional-mom6.readthedocs.io/) for troubleshooting tips.\n",
- "\n",
- "You can log in and use [this GUI](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/download) to find the lat/lon of your domain and copy paste below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "expt_name = \"tassie-access-om2-forced\"\n",
- "\n",
- "latitude_extent = (-48, -38.95)\n",
- "longitude_extent = (143, 150)\n",
- "\n",
- "date_range = [\"2003-01-01 00:00:00\", \"2003-01-05 00:00:00\"]\n",
- "\n",
- "## Place where all your input files go\n",
- "input_dir = f\"{scratch}/regional_mom6_configs/{expt_name}/\"\n",
- "\n",
- "## Directory where you'll run the experiment from\n",
- "run_dir = f\"{home}/mom6_rundirs/{expt_name}/\"\n",
- "\n",
- "## Directory where fre tools are stored\n",
- "toolpath_dir = \"/home/157/ahg157/repos/mom5/src/tools/\" ## Compiled tools needed for construction of mask tables\n",
- "\n",
- "## Directory where ocean model cut-outs go before processing\n",
- "tmp_dir = f\"{gdata}/{expt_name}\"\n",
- "\n",
- "## if directories don't exist, create them\n",
- "for path in(run_dir, tmp_dir, input_dir):\n",
- " os.makedirs(str(path), exist_ok=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2: Prepare ocean forcing data\n",
- "\n",
- "We need to cut out our ocean forcing. The pipeline expects an initial condition and one time-dependent segment per non-land boundary. Naming convention is `\"east_unprocessed\"` and `\"ic_unprocessed\"` for initial condition. The following provides an example for cutting out the necessary forcing files from an ocean model. It's hardcoded to pull data from a Repeat-Year Forced ACCESS-OM2-01 database, but you should be able to recycle parts of the code to cut out data from a dataset of your choice"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**NOTE: this is hardcoded for the year of 1990, which corresponds to output files `1077`-`1082`. If you want to modify, you'll need to choose the right path to the year of your choice, or use the [COSIMA cookbook](https://cosima-cookbook.readthedocs.io/) to locate your data files.**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "## Cut out 3 months of forcing from 2003\n",
- "om2_input = xr.open_mfdataset(\n",
- " f\"/g/data/ik11/outputs/access-om2-01/01deg_jra55v13_ryf9091/output1077/ocean/ocean_daily*\",\n",
- " parallel=True, chunks='auto')[[\"u\", \"v\", \"salt\", \"temp\", \"eta_t\"]].sel(\n",
- " yu_ocean = slice(latitude_extent[0] - 0.2, latitude_extent[1] + 0.2),\n",
- " yt_ocean = slice(latitude_extent[0] - 0.2, latitude_extent[1] + 0.2)).isel(time = slice(0, 5))\n",
- "\n",
- "## Cut out initial condition and save\n",
- "ic = om2_input.isel(time = 0)\n",
- "\n",
- "## `longitude_slicer` handles seams in longitude and different grid and ensures that the output matches our 'longitude_extent'\n",
- "ic = rmom6.longitude_slicer(ic, [longitude_extent[0], longitude_extent[1]], [\"xu_ocean\", \"xt_ocean\"])\n",
- "ic.to_netcdf(tmp_dir + \"/ic_unprocessed.nc\")\n",
- "\n",
- "## Cut out East and West segments. Does lat slice first then uses `longitude_slicer` for lon slice\n",
- "eastwest = om2_input.sel( \n",
- " yu_ocean = slice(latitude_extent[0] - 0.2, latitude_extent[1] + 0.2),\n",
- " yt_ocean = slice(latitude_extent[0] - 0.2, latitude_extent[1] + 0.2)\n",
- ")\n",
- "rmom6.longitude_slicer(eastwest, [longitude_extent[1], longitude_extent[1]], [\"xu_ocean\", \"xt_ocean\"]).to_netcdf(tmp_dir + \"/east_unprocessed.nc\")\n",
- "rmom6.longitude_slicer(eastwest, [longitude_extent[0], longitude_extent[0]], [\"xu_ocean\", \"xt_ocean\"]).to_netcdf(tmp_dir + \"/west_unprocessed.nc\")\n",
- "\n",
- "## Cut out North and South segments\n",
- "northsouth = rmom6.longitude_slicer(om2_input, [longitude_extent[0], longitude_extent[1]], [\"xu_ocean\", \"xt_ocean\"])\n",
- "northsouth.sel(\n",
- " yu_ocean = slice(latitude_extent[1] - 0.2, latitude_extent[1] + 0.2),\n",
- " yt_ocean = slice(latitude_extent[1] - 0.2, latitude_extent[1] + 0.2)\n",
- ").to_netcdf(tmp_dir + \"/north_unprocessed.nc\")\n",
- "northsouth.sel(\n",
- " yu_ocean = slice(latitude_extent[0] - 0.2, latitude_extent[0] + 0.2),\n",
- " yt_ocean = slice(latitude_extent[0] - 0.2, latitude_extent[0] + 0.2)\n",
- ").to_netcdf(tmp_dir + \"/south_unprocessed.nc\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3: Make experiment object\n",
- "The `regional_mom6.experiment` contains the regional domain basics, and also generates the horizontal and vertical grids, `hgrid` and `vgrid` respectively, and sets up the directory structures. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "expt = rmom6.experiment(\n",
- " longitude_extent = longitude_extent,\n",
- " latitude_extent = latitude_extent,\n",
- " date_range = date_range,\n",
- " resolution = 0.05,\n",
- " number_vertical_layers = 75,\n",
- " layer_thickness_ratio = 10,\n",
- " depth = 4500,\n",
- " mom_run_dir = run_dir,\n",
- " mom_input_dir = input_dir,\n",
- " toolpath_dir = toolpath_dir\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can now access the horizontal and vertical grid of the regional configuration via `expt.hgrid` and `expt.vgrid` respectively.\n",
- "\n",
- "Plotting the vertical grid with `marker = '.'` lets you see the spacing. You can use `numpy.diff` to compute the vertical spacings, e.g.,\n",
- "```python\n",
- "import numpy as np\n",
- "np.diff(expt.vgrid.zl).plot(marker = '.')\n",
- "```\n",
- "shows you the vertical spacing profile.\n",
- "\n",
- "### Modular workflow!\n",
- "\n",
- "After constructing your `expt` object, if you don't like the default `hgrid` and `vgrid` you can simply modify and then save them back into the `expt` object. However, you'll then also need to save them to disk again. For example:\n",
- "\n",
- "```python\n",
- "new_hgrid = xr.open_dataset(input_dir + \"/hgrid.nc\")\n",
- "```\n",
- "Modify `new_hgrid`, ensuring that _all metadata_ is retained to keep MOM6 happy. Then, save your changes\n",
- "\n",
- "```python\n",
- "expt.hgrid = new_hgrid\n",
- "\n",
- "expt.hgrid.to_netcdf(input_dir + \"/hgrid.nc\")\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4: Set up bathymetry\n",
- "\n",
- "Similarly to ocean forcing, we point the experiment's `setup_bathymetry` method at the location of the file of choice and also provide the variable names. We don't need to preprocess the bathymetry since it is simply a two-dimensional field and is easier to deal with. Afterwards you can inspect `expt.bathymetry` to have a look at the regional domain.\n",
- "\n",
- "After running this cell, your input directory will contain other bathymetry-related things like the ocean mosaic and mask table too. The mask table defaults to a 10x10 layout and can be modified later."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "expt.setup_bathymetry(\n",
- " bathymetry_path='/g/data/ik11/inputs/GEBCO_2022/GEBCO_2022.nc',\n",
- " longitude_coordinate_name='lon',\n",
- " latitude_coordinate_name='lat',\n",
- " vertical_coordinate_name='elevation',\n",
- " minimum_layers=1\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Check out your domain:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "expt.bathymetry.depth.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5: Handle the ocean forcing - where the magic happens\n",
- "\n",
- "This cuts out and interpolates the initial condition as well as all boundaries (unless you don't pass it boundaries).\n",
- "\n",
- "The dictionary maps the MOM6 variable names to what they're called in your ocean input file. Notice how the horizontal dimensions are `xt_ocean`, `yt_ocean`, `xu_ocean`, `yu_ocean` in ACCESS-OM2-01 versus `xh`, `yh`, `xq`, and `yq` in MOM6. This is because ACCESS-OM2-01 is on a `B` grid, so we need to differentiate between `q` and `t` points. \n",
- "\n",
- "If one of your segments is land, you can delete its string from the 'boundaries' list. You'll need to update `MOM_input` to reflect this though so it knows how many segments to look for, and their orientations. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Define a mapping from the MOM5 B grid variables and dimensions to the MOM6 C grid ones\n",
- "ocean_varnames = {\"time\": \"time\",\n",
- " \"yh\": \"yt_ocean\",\n",
- " \"xh\": \"xt_ocean\",\n",
- " \"xq\": \"xu_ocean\",\n",
- " \"yq\": \"yu_ocean\",\n",
- " \"zl\": \"st_ocean\",\n",
- " \"eta\": \"eta_t\",\n",
- " \"u\": \"u\",\n",
- " \"v\": \"v\",\n",
- " \"tracers\": {\"salt\": \"salt\", \"temp\": \"temp\"}\n",
- " }\n",
- "\n",
- "# Set up the initial condition\n",
- "expt.initial_condition(\n",
- " tmp_dir + '/ic_unprocessed.nc', # directory where the unprocessed initial condition is stored, as defined earlier\n",
- " ocean_varnames,\n",
- " arakawa_grid=\"B\"\n",
- " )\n",
- "\n",
- "# Now iterate through our four boundaries \n",
- "for i, orientation in enumerate([\"south\", \"north\", \"west\", \"east\"]):\n",
- " expt.rectangular_boundary(\n",
- " tmp_dir + '/' + orientation + \"_unprocessed.nc\",\n",
- " ocean_varnames,\n",
- " orientation, # Needs to know the cardinal direction of the boundary\n",
- " i + 1, # Just a number to identify the boundary. Indexes from 1 \n",
- " arakawa_grid=\"B\"\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 Run the FRE tools\n",
- "\n",
- "This is just a wrapper for the FRE tools needed to make the mosaics and masks for the experiment. The only thing you need to tell it is the processor layout. In this case we're saying that we want a 10 by 10 grid of 100 processors. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "expt.FRE_tools(layout = (10, 10)) ## Here the tuple defines the processor layout"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7: Modify the default input directory to make a (hopefully) runnable configuration out of the box\n",
- "\n",
- "This step copies the default directory, and modifies the `MOM_layout` files to match your experiment by inserting the right number of x,y points and cpu layout. If you use Payu to run MOM6, set the `using_payu` flag to `True` and an example `config.yaml` file will be copied to your run directory. This still needs to be modified manually to work with your projects, executable etc.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "expt.setup_run_directory(surface_forcing = \"jra\", using_payu = True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 8: Run your model!\n",
- "\n",
- "To do this, navigate to your run directory in terminal. If you're working on NCI, you can run your model via:\n",
- "\n",
- "```\n",
- "module load conda/analysis3\n",
- "payu setup -f\n",
- "payu run -f\n",
- "```\n",
- "\n",
- "By default `input.nml` is set to only run for 5 days as a test. If this is successful, you can modify this file to then run for longer."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 9 and beyond: Fiddling, troubleshooting and fine tuning\n",
- "\n",
- "Hopefully your model is running. If not, the first thing you should do is reduce the timestep. You can do this by adding `#override DT=XXXX` to your `MOM_override` file. \n",
- "\n",
- "If there's strange behaviour on your boundaries, you could play around with the `nudging timescale` (an example is already included in the `MOM_override` file). Sometimes, if your boundary has a lot going on (like all of the eddies spinning off the western boundary currents or off the Antarctic Circumpolar current), it can be hard to avoid these edge effects. This is because the chaotic, submesoscale structures developed within the regional domain won't match those at the boundary.\n",
- "\n",
- "Another thing that can go wrong is little bays creating non-advective cells at your boundaries. Keep an eye out for tiny bays where one side is taken up by a boundary segment. You can either fill them in manually, or move your boundary slightly to avoid them"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python [conda env:analysis3-24.01] *",
- "language": "python",
- "name": "conda-env-analysis3-24.01-py"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
From 6bedb63ba0d769cab25cfffa02a712332b88ae77 Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou"
Date: Tue, 23 Apr 2024 10:39:10 +0300
Subject: [PATCH 7/8] remove ref to NCI
---
demos/reanalysis-forced.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/demos/reanalysis-forced.ipynb b/demos/reanalysis-forced.ipynb
index 5c71a4d1..4c8643bf 100644
--- a/demos/reanalysis-forced.ipynb
+++ b/demos/reanalysis-forced.ipynb
@@ -98,8 +98,8 @@
"## Directory where you'll run the experiment from\n",
"run_dir = Path(f\"mom6_run_directories/{expt_name}/\")\n",
"\n",
- "## Directory where fre tools are stored, e.g. on NCI Gadi /home/157/ahg157/repos/mom5/src/tools/\n",
- "toolpath_dir = Path(\"PATH_TO_FRE_TOOLS\") ## Compiled tools needed for construction of mask tables\n",
+ "## Directory where compiled FRE tools are located (needed for construction of mask tables)\n",
+ "toolpath_dir = Path(\"PATH_TO_FRE_TOOLS\")\n",
"\n",
"## Path to where your raw ocean forcing files are stored\n",
"glorys_path = Path(\"PATH_TO_GLORYS_DATA\" )\n",
From 9c0fac84c4a28de3aeb05adfffdba13991f0212d Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou"
Date: Tue, 23 Apr 2024 10:44:14 +0300
Subject: [PATCH 8/8] fix rendering issue
---
demos/reanalysis-forced.ipynb | 10 +++++++---
1 file changed, 7 insertions(+), 3 deletions(-)
diff --git a/demos/reanalysis-forced.ipynb b/demos/reanalysis-forced.ipynb
index 4c8643bf..82607591 100644
--- a/demos/reanalysis-forced.ipynb
+++ b/demos/reanalysis-forced.ipynb
@@ -6,11 +6,15 @@
"source": [
"# Regional Tasmanian domain forced by GLORYS and ERA5 reanalysis datasets\n",
"\n",
- "**Note: FRE-NC tools are required to be set up, as outlined in the `regional-mom6` package [documentation](https://regional-mom6.readthedocs.io/en/latest/).**\n",
+ "**Note**: FRE-NC tools are required to be set up, as outlined in the [documentation](https://regional-mom6.readthedocs.io/en/latest/) of regional-mom6 package.\n",
"\n",
- "For this example we need a copy of the [GEBCO bathymetry](https://www.gebco.net/data_and_products/gridded_bathymetry_data/), access to the [GLORYS ocean reanalysis data](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description), and [ERA5 surface forcing](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). \n",
+ "For this example we need:\n",
"\n",
- "This example reads in the entire global extent of ERA5 and GEBCO; we don't need to worry about cutting it down to size. "
+ "- [GEBCO bathymetry](https://www.gebco.net/data_and_products/gridded_bathymetry_data/)\n",
+ "- [GLORYS ocean reanalysis data](https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description), and\n",
+ "- [ERA5 surface forcing](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5)\n",
+ "\n",
+ "This example reads in the entire global extent of ERA5 and GEBCO; we don't need to worry about cutting it down to size."
]
},
{