Repository: DHI/mikeio
Pull request #491 (2022-12-01 10:54:20)

Fix broken Dfsu.write_header()


Patch:
diff --git a/mikeio/dfsu.py b/mikeio/dfsu.py
index 365d23d8..c5f775cd 100644
--- a/mikeio/dfsu.py
+++ b/mikeio/dfsu.py
@@ -1,4 +1,5 @@
 import os
+from typing import List, Union
 import numpy as np
 import pandas as pd
 import warnings
@@ -889,22 +890,23 @@ class _Dfsu(_UnstructuredFile, EquidistantTimeSeries):
 
         Examples
         --------
-        >>> msh = Mesh("foo.mesh")
-        >>> n_elements = msh.n_elements
-        >>> dfs = Dfsu(meshfilename)
+        >>> import numpy as np
+        >>> import mikeio
+        >>> meshfilename = "tests/testdata/north_sea_2.mesh"
+        >>> outfilename = "bigfile.dfsu"
+        >>> dfs = mikeio.Dfsu(meshfilename)
+        >>> n_elements = dfs.n_elements
         >>> nt = 1000
         >>> n_items = 10
-        >>> items = [ItemInfo(f"Item {i+1}") for i in range(n_items)]
+        >>> items = [mikeio.ItemInfo(f"Item {i+1}") for i in range(n_items)]
         >>> with dfs.write_header(outfilename, items=items) as f:
-        >>>     for i in range(1, nt):
-        >>>         data = []
-        >>>         for i in range(n_items):
-        >>>             d = np.random.random((1, n_elements))
-        >>>             data.append(d)
-        >>>             f.append(data)
+        ...     for _ in range(nt):
+        ...         # get a list of data
+        ...         data = [np.random.random((1, n_elements)) for _ in range(n_items)]
+        ...         f.append(data)
         """
 
-        return self.write(
+        return self._write(
             filename=filename,
             data=[],
             start_time=start_time,
@@ -932,8 +934,54 @@ class _Dfsu(_UnstructuredFile, EquidistantTimeSeries):
             full path to the new dfsu file
         data: Dataset
             list of matrices, one for each item. Matrix dimension: time, x
+        dt: float, optional
+            The time step (in seconds)
+        elements: list[int], optional
+            write only these element ids to file
+        title: str
+            title of the dfsu file. Default is blank.
+        keep_open: bool, optional
+            Keep file open for appending
+        """        
+        if isinstance(data, list):
+            raise TypeError(
+                "supplying data as a list of numpy arrays is deprecated, please supply data in the form of a Dataset"
+            )
+
+        return self._write(
+            filename=filename, 
+            data=data, 
+            dt=dt, 
+            elements=elements, 
+            title=title, 
+            keep_open=keep_open
+            )
+
+
+    def _write(
+        self,
+        filename,
+        data,
+        start_time=None,
+        dt=None,
+        items=None,
+        elements=None,
+        title=None,
+        keep_open=False,
+    ):
+        """Write a new dfsu file
+
+        Parameters
+        -----------
+        filename: str
+            full path to the new dfsu file
+        data: list[np.array] or Dataset
+            list of matrices, one for each item. Matrix dimension: time, x
+        start_time: datetime, optional, deprecated
+            start date of type datetime.    
         dt: float, optional, deprecated
             The time step (in seconds)
+        items: list[ItemInfo], optional, deprecated
         elements: list[int], optional
             write only these element ids to file
         title: str
@@ -944,15 +992,14 @@ class _Dfsu(_UnstructuredFile, EquidistantTimeSeries):
         if self.is_spectral:
             raise ValueError("write() is not supported for spectral dfsu!")
 
-        if dt:
+        if dt and not keep_open:
             warnings.warn(
-                "setting dt is deprecated, please supply data in the form of a Dataset",
+                "argument dt is deprecated, please supply data in the form of a Dataset",
                 FutureWarning,
             )
-
-        if isinstance(data, list):
-            raise TypeError(
-                "supplying data as a list of numpy arrays is deprecated, please supply data in the form of a Dataset"
+        if keep_open and not dt:
+            warnings.warn(
+                "argument dt missing, should be provided when keep_open=True"
             )
 
         filename = str(filename)
@@ -970,7 +1017,7 @@ class _Dfsu(_UnstructuredFile, EquidistantTimeSeries):
                 zn_dynamic = data[0]._zn
 
             # data needs to be a list so we can fit zn later
-            data = [x.to_numpy() for x in data]
+            data = [np.atleast_2d(x.to_numpy()) for x in data]
 
         n_items = len(data)
         n_time_steps = 0
@@ -1116,23 +1163,25 @@ class _Dfsu(_UnstructuredFile, EquidistantTimeSeries):
             self._dfs.Close()
             os.remove(filename)
 
-    def append(self, data: Dataset) -> None:
+    def append(self, data: Union[Dataset, List[np.ndarray]]) -> None:
         """Append to a dfsu file opened with `write(...,keep_open=True)`
 
         Parameters
         -----------
-        data: Dataset
+        data: list[np.array] or Dataset
+            list of matrices, one for each item. Matrix dimension: time, x
         """
 
         deletevalue = self._dfs.DeleteValueFloat
         n_items = len(data)
-        n_time_steps = np.shape(data[0])[0]
-        for i in trange(n_time_steps, disable=not self.show_progress):
+        has_time_axis = len(np.shape(data[0])) == 2
+        n_timesteps = np.shape(data[0])[0] if has_time_axis else 1
+        for i in trange(n_timesteps, disable=not self.show_progress):
             for item in range(n_items):
                 di = data[item]
                 if isinstance(data, Dataset):
                     di = di.to_numpy()
-                d = di[i, :]
+                d = di[i, :] if has_time_axis else di
                 d[np.isnan(d)] = deletevalue
                 darray = d.astype(np.float32)
                 self._dfs.WriteItemTimeStepNext(0, darray)
diff --git a/notebooks/Dfsu - Write huge file.ipynb b/notebooks/Dfsu - Write huge file.ipynb
new file mode 100644
index 00000000..691bd43d
--- /dev/null
+++ b/notebooks/Dfsu - Write huge file.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dfsu - Write huge file\n",
+    "\n",
+    "The Dataset.to_dfs() method is the primary way of writing dfs files in MIKE IO. In some case, however, the data we want to write to a file is larger than the system memory and we will therefore not be able to store it in a Dataset before writing to disk. \n",
+    "\n",
+    "This notebook shows how to write larger-than-memory data to a dfsu file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import mikeio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Flexible Mesh Geometry: Dfsu2D\n",
+       "number of nodes: 1296\n",
+       "number of elements: 2259\n",
+       "projection: LONG/LAT"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mesh_file = \"../tests/testdata/north_sea_2.mesh\"\n",
+    "msh = mikeio.open(mesh_file)\n",
+    "msh.geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time = pd.date_range(\"2018-1-1\", end=\"2018-2-1\", freq=\"D\")\n",
+    "dt_seconds = (time[1] - time[0]).total_seconds()\n",
+    "xy = msh.geometry.element_coordinates[:,:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the \"data\" to be written to file\n",
+    "item1 = mikeio.ItemInfo(\"Item1\", mikeio.EUMType.Water_Level)\n",
+    "def func1(t, x, y):\n",
+    "    t = np.atleast_1d(t)\n",
+    "    data = np.zeros(shape=(len(t), len(x)))\n",
+    "    for j in range(len(t)):\n",
+    "        data[j,:] = 0.01*t[j]*np.cos(0.2*x)*np.sin(2*y)\n",
+    "    return data\n",
+    "\n",
+    "item2 = mikeio.ItemInfo(\"Item2\", mikeio.EUMType.Water_Level)\n",
+    "def func2(t, x, y):\n",
+    "    t = np.atleast_1d(t)\n",
+    "    data = np.zeros(shape=(len(t), len(x)))\n",
+    "    for j in range(len(t)):\n",
+    "        data[j,:] = 0.01*(3-t[j])*np.cos(0.4*x)*np.sin(0.8*y)    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Option 1: Dataset with one timestep and append()\n",
+    "\n",
+    "1. Create first a Dataset ds only containing the first timestep\n",
+    "2. Use dfs.write(..., keep_open=True) to write the header and the first time step to disk\n",
+    "3. Write additional timesteps with dfs.append(...)\n",
+    "4. close with dfs.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data10 = func1(t=0, x=xy[:,0], y=xy[:,1])\n",
+    "da1 = mikeio.DataArray(data=data10, time=time[0], geometry=msh.geometry, item=item1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Zd2sh7LnmvhMwX+N0WTg7TjpgybVyhwj7Js7N0X4FiM17UunQSgKQdry4OKabP5P7SyAFCJVto0N4J5CeG0XeZZN2X+z/rg2GonHsPvYEQln5fz0ToKGhN99sq/qt8PSxwxCOoSXz0XpkHR7ecwCdQEBzKpSIzJcpkDqRcVoDSktIwVCaEEiVEBKlgCAR8OZOprQwAtgKO9NeQwCINUGzAEGDISBIIxDpHVQzoFgmYxow6mGczEcpMvORClJk777NVgApGWGgPCFshTkDsSIIYf4ym/FroUYcm+2wJCOKASmMEFZKgoghhVkTZqATm6+LILPNEBBACJ0Vb5ZgpRLTrpVHiFinG7R2BAMQ9txYw0gqXybb02btCEqOR7A9Jo+8gPRJBKf9pm3NvCnf1u3Pt0+IAmXmk29X2MewkhZdCQcAQzhKBHrSWFtC4vXhyCTl+3Ln5EiGv5/tWH5bJ8BF2kbkzORMAHW5HyfExp6mm48WMIJeA0LbPtKlgK55cw4AEaUfGQDLtC0HuXZ2DBVk552cM8zYFNvPbpslXf7pN7Qx52hdCZxnAhQz1BQo7aeizx0RO4xr9fK99wEBGFzXgc7dvJ1GIFaiIHzcE7W2mgfXViVtrWD3BChgtB2ElGyY7ijZp/z7lxMSmVsdIYqlEc6MRPPBuSfpWBnNSKwEtCavO7b7hdW8IHkK1UxGyIOgNCXaAkGOpJg7NDMlffpr5jQmbp5ubdz6KM6uBUDJOqbtUoEPj5wkpMLOufA798mKLnKJrnANu2ksMm25SCzy/RRAKb/aEmzugZnK3zMxuNsoZJbSfC39b0FJ3+S939ycNtfGH9fNU3pKD58pINVKdOuPg+KYWnr9k+GVeWUUyGg0Et4pJ5g7mTlmtuXvhq4NJnFdK2xXaPCUvSo8fewwhOMFBzwPADD0ZBvFu1x6Z1Q6JR0MI4gZqcB1wj9SAu1YohNJNNuBMR9YxIrQbAUYbwXoxBJKkzFneHdMxcJoNnQqjPPCVbMw+705ZgQ/A53I3Z0JnShIyIOZp+vfJyKmr1gROpFEFAVot2UiY7VGhkg5cqJURurlFPaUIzvpWvnnbMxUgFaUbqdcG02FdfA/6wyxsyTFm1cBbLUlvlrdm1tChNzLqdyLPW0ZupGOfIdujbOnUt6WAAaDibNkwZ5HRq9ChpQQCFpkWZPrQ0uGCi1xyWtrctozOHKQvfzJS1tiwcKMCyA1oWTUT/YS5AW9d94EY0pxQj4/h0T7EaKg3fKhA/Mq9JH/nJ9jrp32SNOz3NXtWQMNhpqCV0U4tg12GJPKTvPmo9UrMWN9JyNYnHpfBvZmyYQoEuh0apBBnJgvnCCOlUCs7B1RAa4zrRmBVNBaJNqIWFPO9uybSQiRIow3GxDEaDSi9KnfQxxLCMrqeTsdM77O3TEZhHYk0agpaE1odQIIAes7gkxbpSSYhdVWSDRbToNDxkRCSOYZKwGljD9LSsCy83Qkh31iFpnHUBnqpC/VEWAtQFJ1pbusCTp258YQVkCxMwVklxSsAcoLqXR3SjrsGhABWZOs92WYCGW7qXx7ql+gTNNkv51Y+d7iGCzYU/lnVQbspLnbKlMVsJYAKU60AnltQ0F7EgAUM4TTpDlCKHLmkLB8orqN1PcoJ9CdAM+75jgFV9RjtBuiU7IajhA7nwx7CoWfDCHx/SiZXmHcgm8IAXEfQLk5VCaVZwamShtREY5tgx2GcADArKVLML7qQYQjGq0+c1eKo8DetSJ7cyWo2NyZVRxAiQ4YhDgW0NY8QSIrRpgBrQTijoAIPOdSJjCyzqadyBqNrXYDMFqEVjtAGGZvao4kNJs11BspUWk3A0RRCCkVwkac6V9riVYbiKIAcRyAwBCBBgdpuzhOddpuDJ/saC0hhALYmn+saScItXezJ+94II6F3WbPSblHV0Js7e++jwUrAQQ6S4TYE8GcSi0da4gQ0G0AgjJPt8wEKMcfqOhn6vr3tRvJMlO2HWfULblOKCuBCs28DT4JEelu9nmB8HpmSxrcZ5E9CWY2hMqaCVgxKLY6JmEcHwsC2j+1br9ysk/uAoAmoxVwxK1jNSIhGY0D+9fQzLlAHMCGiFiizQC45lgKgeu2oQbI+m64BYh7KBmbBQo+ISyAuJ7jMGUaCjORxDVmIjAYxmXKI+1OW9MLkAK4nWdoFaYzKh+O6Y0dinC8cMWh+OGqBzFjfRuP1gcykQVRJzRREgB8fW8UhSBh7n6JL4aGkxLQWhjh6o5RClJ6ksXKKkdKXEZCXxtgDiMISEhLOlgDUSQRdwxBabcJQpovvdOgKCWhxgTCegwZpM6drXYN7PmNqFhCxcbhFDBmiXzUjZsrA2DN0EoAJDK+H3FECGoKRAT3wKcjAWW1Ea5Pp1WARx7YSYEEVtsBNk/EbEmKAhLvwQQCqmX+2pAesLSTdWtvtU3uCTp5ko4zlzO9JgWygdy1947R3oX0t/ukovSJOtueiUFM6dMSGW1FMh0/aiQ/h5wGg2M2gtGuA5VFySAlMlRyXkZbwNB1gJqcEB3jH8FQNU4cO4ko0XS47zPlnUcDd+3NtSZlxwgAxGyjUChx1IwCR0ZyXM5FzFhoAbDVbLAGpE0mmddusF0bHZoPIkYS+eKvg9GU2PPWxtG0dO0EoGwkfSVvnhnQyCkvt2G/FZ4+dijCccxBz8N/feM7mLWhhUcX5GOprQDLSWGtCBxl9cdaA4gEUNPIZzTWytwtnfDVSgAqvd1lH3NTIcZKojkWotbXArNxAPUdUVUkQELZcNSs4I7aIVSsENaMFoM9dbh/eiqW0E57QwwSsQl9lZYQxQI6spEndVXyhEiIOwJBTSNuOfd+X6sBQ8RiAQQ5QpNXXTOg2zbUQLJJO8/eTn/+LmIlOZiA2K1jdi2cJgHM9uGZEsmU+ixQwZzT9WmYN9ui2JG7rPm4YyJo3w+DrYbBJ0COmNj5lkanEKBrnDkHrRhCZdvqwDPDxOVz1c4FKEQmIiXuYaBuF0DDKACVt44CUMKeo4CNbkk1VSBknT+lvYSRudauj4Roxd5a2XVj2GgV/1oJQNVhwlqZofoYctywS+W3JXssAxQzdN2Yi+SY16fTZghOCFveoVXYL8CmVrtkAStMNzifi6not8LTxw5FOPr7+4HZA5j11CjSRA4OlAopT2iwJrDOSafItu8IcF3nOApBKwmtGERsBDybR1eSDJYaQjovRiQ8xwn6TrMGWcuZGmC0IdG4RFDX1oie2Q3VkVDNALLexTeC3BhuXAJHAeJxAdHXgVYB/EdGVgRyWoRkozG3dMasJKG8CcisiblTK2MGcF10JOB8Odj07whCKqD9ybK1FXDG0yEdzKkxSs4VMBdRZReJbChFMow3bo4aFPty7TPbc++9y9rVhyB/DMFqEfwZcBq4o7igvWDL9TL+B9bUAph9GWHvh4b6/ciUtHAAQKX5PTj0pioArjM4YmNeKQk1JQ1DJpIwX3s+vnaJAK6Zdhl/krymImDzpBp4JCZdGXAIcI9xeuUeGOdYIlBMyOcr4RoDPUjGUz05EkcwDrQ2b46IRGb/jNY4AOD5B+6PCtMfis1rKvqt8PSxQxEOAKgvXgBafx9mPtXCU7N6cnvtzdbmedCxAEUwT3q+IEkEHYHbRtPhJ+liBqBEklsiaasIrAR0bG7soj+y40jj0wAYW3fZlzsmoBUiVrG5annpaMdQbQkKNUSQlaLGaTInoK22Q7fDgqMdxwJKK6AjQT3akoRcuELa2pCNyJOoMaUP+DGAyMQ1shUOGR2lL6jdGsfCEiAuhjfCnoun4t9yGKU6K3Mc5U0YBa3MJDrPh3omtqVcH3nh756qdW4bYIgEmfkKUCmPce05TLUP+cgVHTBEZH2KQNBhVkOSmUOeGLnTCb22MPOCBEiT0RTUjDaBvLwhuVQqZi4h27W3e3LXXzcMiaDYamaYTHRNwMkcmDhxXNUNQ3g4ZKP5sPk12I7jR2mzy7/hzsmSEufnooUGaQJ1DHmZPT4GHYY4YK89u618hWmEyqQyvbFdw2IvvvhiEFHmtWDBgmT/61//+sL+ww477GmNecHJrwQALFw9DtWS5tV2L4IaFlDNADqS1sdDZL9tnD4pGxC4IxJHRHbCssysARji0JHgjoQaD6CjwJpAUlHCilCQLO7m3Jbpk6UPSt9wJKGjbE4RjkX2GL8PVSZUCWgGQBSAh0NwM0Ahm2YSkULmvPyTdRoIRYZsANl1yc0lmSvb+fhJHMp+7WyFalnkir8/t831mZltgWRsAzgBXpxUd4LjduRJVOJnzBnhWYBAORm1+7QwGgpV06W/fOODYcJmy8fIbS/R2HBofCg0WT+bkn4o76NDyX9GoyXTvlSDEdcV4n4N3ZcLDS6ZC4cM1aeh6rqcjEpLytzXMEjJBgiJBodrjFrUwUCnjdk7L62qwT5DoEFQU/DShS9Sha3BdtdwLF++HDfddFPyWcpsfOOLX/xifPWrX00+P916KEsXLYacOwvz1z2FINaIAy+jEFuSwb5gsKp9YbeVOucZ0oFQp4IWQEayMIxQjrwbV0cCDZ30kfSlROKomhwbeQK4LYB63tMOWY1GbEiQqNufitO2uDaq5Ng8tCUpZAlFDLMOgU60DqwpRxC8NWEXTeHtU2TzNeSkhbbrm28PQ/BYe0/kHpkgePsmIg6b1VaUXLOyJpMhJInWoMS8wN77/NTKppl8Tb2+yubitBQl+7jG4DqbFN5xySDWnwGiyzoVL7HrObtTMnQvADLXn1T2wrAAdENDNIXRjjgtlgJ0PTdxgolukZkeMp/9tWDi5K7GHT8FvNfWKVYkF7Q2EABr40A7IzLmlN323KN8PSpMO2guhuxvq34rPH1sd8IRBEFGq5FHvV6fcP/W4PDDD8fPfvAjLNgwhkfnWedRRlKzIhFgTkgpexNzT99lYAKNBODAqnrz8qtDRbKiuz29p9k/oQFuy6xAV8I42dXSX0FSHyPXvx4LAKmtU6YntHOZUaFRfOrNcyF3XEfaJ1G2Nokua6KR+pukMzVkJ79ZwSr7y/siTenTbaFHe80SwlJCQEqOSqVUbnuJI+dWw5EO1iirDVOcUokWxIIt6SWbLTWfR4Jd0i5CYjrx9iYmBgQwviH5jLtgoME2bLU4V3Y5Pfx18bUT3omw1MYZOWSoCBAd4xuhpSE+CBi6XwMdAsWAGlCgMQIhq4ljUUIugKI5CMXPJqdIcb8J87XrUQIXBdRqmIHb1o+jwvSH00hMRb8Vnj62u57wvvvuw8KFC7Fs2TK88pWvxJ///OfM/pUrV2LevHnYY489cPbZZ2Pt2rUT9tdutzE8PJx55XHaMcdCC8KiJ0fSjZ72wt1sKa/Wz5sk8lAE0ZYQ48KGd9rtHWEFb8mXtiOKfTGMb4gGuJUjG/5xKm2PMjOMfRKmcWk0JE6T4fw5/HZ5ItXxCEmpyYIKTpkFdCVnuc8xQG1pxpzoSUIhnX+uP4qt3V0TSAuQKjEhbW4eCfISaqK2WwDr65BxGCkqctJtXcZKHChDR/RykyzTlri9vs8GWW1HjnARyBDrGqfkxYdAQpoyZoncXFgYQpGcTwjoPg0VKkM2yBFCsy66YXU3vdlxXSKyAvJJ3rqRD4ny85CpH0kpLEkc7jOJQx548MEuDStMN0yFOWWqSMyOiO1KOA499FBcddVVuP766/HFL34Ra9aswRFHHIH169cDAE488UR885vfxM0334x/+Zd/we23344XvvCFaLe7h6h95CMfwdDQUPJasmRJoU1vby9m7rYMM0db6GvaoH7tEQwu0Twoa1boJnhyWgAaF0CLjPmjS44E17Ywln1S59FuBnnbqO2Zg0r9PgBqCxCEEcYtacwi3SqbJn0hSxa6CVwmQxKiEkLmzqNsnNiSFQUzH2tGIQjTV5d1Tnw2Mn3ZV5lzg8LWeZF1M79sLelwU3ORxOCCFSLTroyMODj+GTAgdUoa8lP2L5/kopC2mo5MW98/opbN2Oj8OnwtSsEsZPvgvLnPtQ0pe35uLXzNTN2ck4tGKV2jEqUZy5J5AcV6Kcm5d3P+sZCMOJAYq4fYtOaJ7u0qTCtopil7VXj6IJ5GRQLGxsbwnOc8B+9617tw/vnnF/Y//vjjWLp0Ka6++mqceuqppX202+0MIRkeHsaSJUuwadMmDA6muTd+dted+PrnPo8/LZyB+xbPBmLy1MjGXJBRSTNDjgTQdQ1ucPqEZ29wpAiilXXJN3kQPMnSZaWNx73dqQCKjPaCNJmbt02+VNR0MFDTRvi0Zda27chGXm3uLrdTVfvCLbCComNJkj/fLkKQIjOvpJZGIkyMCSE7PoMiSs4jMREoyvGFnBD1TCmuTkj5euTm6/rSME/mBUHlScxt+ivwzQ7lT9KcyOSyRbV/89k2nX+C65IBdOxYiSC2B8eWoJENDXWnmL0coHZqxmLnm+Og0/26lpp6KKI01brrRxlfDP/7mknB7ifHk1YDAgAdyoTnOhJJSpSbxcpMf24OkSUY0tsIDYqCTEMO2PhAaZgHArd+fhv7bsWfVmPuyDiuvPLKgn/Z8PAwhoaGCveWCn95uGtx628XoX9g2z9Hj45oHL3vY9W1fprY7j4cPvr6+rDffvvhvvvuK92/0047YenSpV33A8bno16vd93vcMQBB+JrPXUsenIE9y2alXvqp+ITqLLq+ojADSP0/KdLahW/5KQALmTNzMF1ERui4QtoAoHaAjr/xOjPsy2BloaITdZO3WeTfJeQDTAglHHi04IzOQ6oAwAyIVpsnfsSVXzZ06zVfBAA0oB2zoYMmJzRnP2G5UnCRCYNG1NLOSlpZB5vuW7ORRSVaH8SJ4gudVgmRE5AlUvzfDvv8HwYbGm/ORS0FAz0eBoCF0bMDEgCR5T1JyoZiyVbp878mprP3KvBLULqf0IZc0rSmqwgL6ylJRc+gU3miyKpIICCrl+Mic8lWW87ZqBBmgHW1heE0/EcQZMaaPqTsOdBDKkYs8aawOwZBbJRYXpCQUBNgeJebb5JhS3Advfh8NFut7Fq1SrstNNOpfvXr1+PRx55pOv+yUAIgd0PPACNSGHOxia6OSs6nwHRNEslYgK1KKOqT4R77j5JmkCbS1CoAGoZ34/SrJIgiHa5nwcAIAbkiLCESIDGCHJUQrRLntoVkoiBzFgaELb+RboO5slWRMJqJVAU3Jn2JfPTSP0ugIlNSwUZ07Xn7iaeknakCe5f/nhyUmqy2o082UjMBJxqTRLzSbepcZLlkoUJ9yyUns+ZKgoLIjgrsH1B2tBAr7KahM0LcCOQtXmFymjO6to4JvcpoEcBDQU0YpPAreY5EIUa3BenWrp8/xlSwZn5lvlYUGjnkZ93/iEgcxCDehTQF5l5Om1dyKDBCNQbl5erFwANxKC6MmvcUCCrpdlpwyikZhxxxBHd16/CtAJPkTmlrLBmhcljuxKOCy64ALfeeiseeOAB/PKXv8Tpp5+O4eFhvO51r8Po6CguuOAC3HbbbXjwwQexcuVK/PVf/zXmzJmDU045ZZuM/4pjjwcAzH0q54UeM8INEtQy6mNqCogk2oKMJmJcQIwKiHFhCEgZGBCxsNqD7HZoQLQIsiUgI9E9n4QdM+MnYdXHclQgaEoILZNkRjKSxoFSE4TvhBkjk/ra9/MTcXJmxXkCSV/k+1YwCgW2hH8OTqD7hCN/VhMJ+7LtPlHYnF9Gme9GRpZ7Ui9vPtoaELIC3ycBBXDazr2cKUAw8g6hDC6WbPdNYnk4IS5gBK8lHcWmbC8agB5tCINLh+4TBDc2mb7lUAdiIAICBfQqUG/cXSvhnEd934+80M9NTNQUwqEOgpltoK8D9MRAPbZzLf5QRG8bwYw25ECEjB+KR27IOan644IhwxhhTwzRE5lx3LkKxqJ1I1CCcPoLjys/twrTDpXT6PTGdjWpPProo3jVq16FdevWYe7cuTjssMPwi1/8AkuXLkWz2cS9996Lq666Chs3bsROO+2EY445Bt/5zncwMDCwTcZfumgxdBhi5kgLokmJjVq0hXm6H5cmFTOM70MipKzQE20C2WqSqlHyBOrusR2rhrZFpSgikxHRHkAgiA5M5EBexWyLS8lYQNuMiBR5vhFOtsQEJTn7EMgAtWHSVOcLbbn/nCbC6wsw92bW8ISaKcYFZc0sXUqA58NzCQSOOfVDKbTPaR6cn0CxaRFlWTHdmnd7IuEu+/xcKxMhr92YJDK+P2WwCb6g2aQar8GQTT9Mmzwzij+vvDuWG0PAaCoifyMbLYVLtFUWzQGbjt8RVWLImq3no2BCaGGtLdJpZyglhb6fh2BQENsnxe6LLEKVZO0lADLQpqifhNG8uDVXhihyLECeZkWGGqqTPkdl6ucJDVapk1NQj01GXsCWEzBZf81xjEYnggolGltgoq0wPaBYQPEUmFSe7gNJBQDbmXBcffXVXff19PTg+uuvn9LxiQj9CxaAHnkEgWYoCJNO2d4QBROUze/gP/2TFdL+NtEBdP6+ZAWsUASxiaEHADlm7OE6Z1snkCkJXk+FnuhYcmHbyCbAARWedpP9EUHlnjQJZGpcxMhkobRy3VTULBMAVoNRHIsgxg35YAGbNZJTWQhTm8JVgIUy5hpm85TJ+cgI72Myc58AdZFPZAUb+7lF3LyLzZPeC9GkGXK3GdJRbvHaPCglrATP1yBf3M3vM2DjcxDC1LQh73wnGrfM9OI+h2yr5zJEzfr6CEDXbMK6knkkNfU8sgEgLRAITq57Nj271ZYQQwYKokchCBjtkdDU7YHXzl59UdMJAXDjCwmw5oxKmwigmkJYU4jjLEMnwSCpTRp+8rpP5mf6D3tiUO7LENQUOrY/kgqPL+rDrg8M4/u3/g9OfeHxxfWpMO2gQdBToLjXW/FwUaGIaeXDsT2w+267gQAMjTUN2cjdeGVMBdMBMRUqc5LOmQ+c+j82xEFCIhyREFpAKIJsU6GGhNOauO1GC5IbN/bGyIF0iUC1cxMxQbbI1qawO3Jmh6Saat50kTsnEVnnU2cS0dah1jqkipbRrFAnbSdg23RQbmZxmhZtSJCIzVwp6tLeWzMXYkvKifQuzMCxookwwVhZP5bJ3YDIVa3rZgZxAzg/EMEgJ5e9/BkkUBCU2eMnmgRDNGKIukqIHJER0mV9kTDfBxJZspGzTaVaJZl+mSjUCBsRaj0RZKAh7BhBTwwRKDsmI2xEABiyrrJkw4MohLAyQjufslxqQd18ySlHvsy5anMuGWLJdp8bCxAB8MiSfihBuOGGGzCNgvkqTIDpZFK58sorsWzZMjQaDaxYsQI//elPJ2x/6623YsWKFWg0Gth1113xuc99LrP/i1/8Io466ijMnDkTM2fOxLHHHotf/epXk57X9sQOTziOOOAAAMDM8S6Oo77JwW1iLi2wJj1VrjGFEGRUnj2TGJBtFMqGEwjUAkSzxNHRHpchQPl55PaJDiA7aV8ismRHIWPWcWPbNym5aKX9UgyINgrmnPz4QSQQtEqiZOwYQlni4eSIgin4FRsy45uanCajFE6DogSEsn4wmifLBbL9+eMl5KJb2wm6yn1BXKEzCCskSzkR2yd07t4GsKaKMvtUl4mShggUZE1DCFiThTk5hv/Z60q4OTBEWKyI7D77Qp2kRqO/bcbqjYxmxs7LBTAJyZCNGEFvhNpAGz19HQzMGkf/YBOUZ/beaQmZqr3Cmn/7z82bACEAEXaJ7BLoYj4ykDWTvIUIiEOJ1Qv7wE+NYOWvf9n1mArTB86kMhWvyeA73/kO3v72t+O9730v7rrrLhx11FE48cQT8fDDD5e2f+CBB3DSSSfhqKOOwl133YULL7wQ5557Lq655pqkzcqVK/GqV70Kt9xyC2677TbsvPPOOP744/HYY489rTX7S2Ja5eGYCmwuVj6OY/ztP/wDWDN+sXhXxKIY/sZAMXxPld/PVMN4uFMbCEpCZcugAk7vyFaDUHhQ16mMYQDKVbDV2funSVkNqAYgO4YcdHuwN3k7KLNP26dMio1fiVBAXGOQpGQeTi4wIZsJ0mp0ku4IUDUuju3OkU2hrwmdLC3B0pKTsuxJmzJzENl23hxK+0VO4eFNk10mzHwfeeTbFfrn9DqWPY2ztaqw6YuCkrVynz0ThhPkybFun5/zxbaVtZwaze3R5nj3ddcxeWYSY9qQgbZWn2yfxoeDoCIJYROCCaHR29uBEIy4TYh0APYWV8r0S6qU8frvaUSJ5sO5rYyP18vXCoCKTI2hWl1l+tJe+nwShlRpxVCd0BAmYbQdDGMKooCRrcXGGQKlYqeRAurNGIf9/AnouQP48ocuy8ynysMxfeCuxTW/2QN9A9s+hHlsROG0A/64xdf60EMPxXOf+1x89rOfTbbtvffeeNnLXoaPfOQjhfb/+I//iB/84AdYtWpVsu2cc87Bb37zG9x2222lYyilMHPmTHzmM5/Ba1/72q04q788dngNRxAEOO6kv0JNKyx7al2xwSTpGDmzQGczKjhnbmkD4QhBjpv3RoOQG5eLphKyqv9kFA2Ipjk+aBFqG6m8QFfmvLrs19Z/xN7XpdeP/xCanyflSRgb7Up+DclmdTVht+i+xp52iTSZ0N0YxvmxW2B8Vsanc9yMqWTyCtOJD0rCWyeq6WLV+JBsXt3Ihm/C8dilMwNQoCHqEUSgQUIb7YRgo00oCy9FqmVJXEIEQwQKQS1CvTdGrZGaXUqn7kWdhGGMvr52Qh5qdYWeRicZN08eBDGCQGdNQ1YL0tfbPY5cBApBmL3w+b7dZyEZc2aPYs6sMQwMtFBrxKjVUy1PCs70UQti9PdGkFJBSg3VL7B+QQPB2hE8vPqZ8yS5o0LbPBzb+uX8QvJlM8qyXnc6Hfz617/G8cdn/X6OP/54/O///m/pvG+77bZC+xNOOAF33HEHoigqPWZ8fBxRFGHWrFlbs1TbBTs84QCAM058MeTMmVg08hT6OvYLZAUVxYBsAXLcvkYAOWZMDSKyT/TePT0YI/SsJoSjsFk4vYE8kiFa1qRhNb+iAwTjQNBKyURyLGX7IaShrNCmHzlutBHSOWjCajg6KMobqzUImuX7RDtHHsqjETPrhC7+IwXSwcgQoQLpyJkzEgVFYlKyxMP5kJQhT3Dsq9v8u1kiNosJ/SWwxb8u8kwUE4/DJe0YItAQEghqGmFDmc8BQ4aGfAShKpgrnC6E7MIEtQi1RozAOfzCaC1KZ+s0EKTRaHTQ0xNl50WAEIx6zX1JixckDOLiudh+pSxnk0N9Lczoa0J459LNn0V620OpEUhjJtkcsSQya1wPLSEixpNLegAA19w6tU7sFZ4+ptqksmTJkkzpjDJtxbp166CUwvz58zPb58+fjzVr1pTOe82aNaXt4zjGunUlD8IA3v3ud2PRokU49thjt2aptgumVabR7QUhBP72da/HlZd/Enuvexy/mbcYioPUv8LdpRQgPbKZLyzFsRXUZKI4hLJyUyCj5i/c9LQnm9j4dggBxD0okA0fFNm2eadW/9ysf4Su2fmy+Szbpl0wBqg+eJETLnIl2x/FxfN18wWhEHabbyM6xnyS79v0TxARFyJ3yshQhpCUSg8yuvls8sj0uFxq7AkFkBujbKyJtBuZXBDedi4zFXB5ZEyp/0FxQUSQ968wREFrabQddl8QamjNULE5eSITqi0EIwhjY53jrESWgkGkM5EgZFVGtVBh1mALmgUiVfxiEAG1UCFWAvkImN56x5hQyhgZATWp0NLC+GOQcTgVxBCkEQYaM/qaGG3V0Ynz41rnTzB669mnwppUYCYo6p7EqR5EkNYsRcQIpIbSAhtn1xDVBH5/1z3gV2Yz9FaYXohYIiokrdkW/Zq/jzzySMakMlFW6/z3hHni705Z+7LtAPCxj30M3/72t7Fy5Uo0Go3Nzn+6oCIcFgfuvRdqu+2Jwfv/gOetfhC/m7EIo2FPKvMYkDkHz4wgYiDowKQy977vBKRlxMu+a5YA5FXvpI2mQffAEJ02UtOK02pEQNSP4pN0biyn7dA2+lF2PDnKRmOjehksKeO06SPRuvi/ZbakagtMEsQE0eLCeWb2Rwxt857AERNfcOe0PIn3UaE/SsIffYcScwxniczWyI4tIRuuqWe2KGntkY3cRct9t0B57YZHNih3DAHO8dGHEAwKFbQmaCUQ1uLEt6I4R0NGBBhKCbufUQtjq7lgBMLYqiLVPfSmHkZoR7XMtr5aB4IYw+2G5+dh50gaQ/1Gy9iMwsx+p9kgAgZ62mh1Yoy168mJO21Nbz3KmU3MMfUgRhwL6CQcmZNjesIIYaCtz7HpSwo23xcirNupgZ0eGsePbrsFLznihaXnWmH7Y+pSm5sfyODg4GZ9OObMmQMpZUGbsXbt2oIWw2HBggWl7YMgwOzZszPbL7vsMnz4wx/GTTfdhP3333+yp7JdUZlUPHzmnefjhS89BSErHLjhYcxvbkz2kSq5KXufRad8uzu2K9nQnmDNQcQwUR8tQzhkx4akxkAQGVkVtHLjsffKgeKUbGS2MxBsBChmiGbJPG0b2TFEh2L7stVeu0ZpuvkoQ56kjdop1VwAmbBZEZmIGtGx22IU/TYm8Mkwmhqyx2RNOEkY7ebMJpbcZeARmOz4XCAbSUvPJJL8Fbrcb6MwDuwF0ol2AUAhtNMnHkQmqqPsYcqYLBgyKOahQEa4c2I6CYMYg31NDA00Ua/FyXb3khkVW7ZPp00gMEIZo7feSfruDYv2vppnTglyqjvKta2HCjP6mgjtMUSMRhgjkGWmIKd1iZFdXEZvrYMwKJ6D+b0bYvLEsgbikPD9b/w7/veeO0r7r7D9oVlM2WtLUavVsGLFCtx4442Z7TfeeGPXNPmHH354of0NN9yAgw8+GGEYJts+/vGP40Mf+hB+/OMf4+CDD57EykwPVIQjhzP+6sV4yz/8AxDWsOfwGjxn+AmQ5qJDJJDeK53wdcg1JNXd5JBoDsr2MRBuzJEZIJNvg1Ruv9OmlBGOLvKNFBBEhJ51XQgBbG4MbbQ4IjaaDaEsGWqhkKskMd1ERjPk5CHZ8NoC2JAM2bL5QnySYPOeSJfePT3VdK5uHW0UjasZI0rSlifhtt3SoyfrZ2fdTZPC7g9jMwk0kxBTo7oqOryUhaa63kWoIaUJ9TSvGDIwTqHdvjwu7Uc3BNKYRbqOac03gjT6GhECyQhE6ugpfB8Jkf3y+0RHSo3eeht9jQ4aNQXpmXlCqXPHMmoiza8R5Bxeg9z5Oi3ErMFx9NQ6CKRCLZi4zFbghdcSGH31ju3Xzd77nxhhEKG31oGYzXjgyH4wAV/9/Jdw+x9/M+E4FbYPpsJhdGu0Jueffz6+9KUv4Stf+QpWrVqF8847Dw8//DDOOeccAMB73vOeTGTJOeecg4ceegjnn38+Vq1aha985Sv48pe/jAsuuCBp87GPfQwXXXQRvvKVr2CXXXbBmjVrsGbNGoyOjm6bxfsLoCIcJTho773x0Q99ELVZc7G4+RQO2PAw+uNWQcXhBLvM10rJQVjNQpnmo6uMsgJb5NMJsOcwaiGjnBC3cq1U81Eyjq/1EJHRYmSQl4/egzrZ+cimNftYQiFb6BqBIlSRdLiEZNYaUAoGp9oV94qQaEDI1ouh/Jp16Y+AQvSKCWXNsYeugpvK06t3ay0ACqxTZinzc4OlC+znwHA+IEI62y6sSSRrmCDS6Kl30Kh3uue2sHk2RElOCiLjM1ELYtTDNOGXQEo0BKW5aKToTnwCodEbpk6l+dPuDaNEcyGIM/MRdh5uLQKhuy5bf08bfY32xM63MJEwgjSk0Ojvaee0MwCg0QgiABr1IEZNpqas5qwADx/SCxEzbrz9JxMPVGG7QANQTNv8tbnSTXmcccYZuPzyy/HBD34QBx54IH7yk5/guuuuw9KlSwEAjz/+eCYnx7Jly3Dddddh5cqVOPDAA/GhD30IV1xxBU477bSkzZVXXolOp4PTTz8dO+20U/K67LLLCuNPV+zweTgmglIKH/z0v2LNqt8BAMZEHWvrg3iyNoiOsGou+6TvwISsD4eGybopARUC2qUX16kDJQPFfBYqzcfh11dxWgMHbfcxgLgXJrGU3c9+KnNr+vDHYRSdTrX18dOhTdVuCQVx9jiWSB1jCQl1ZTt3YnO+3byEGDYHhyxZQwAclh9EsGtokeQEEfDSsBdVOTpI55j14chqJ7pqKvy6IN6EzPXupqLyzCkue6inZmJtG4ETDQcnBWxQknDLHC88IQiGl8vDPMEHgYIgk/uCGYiVRCfK+lmEQZzsj2IBZgKzyU3eqMUIS0wygTCW7I6SCKWyPhxm/HYcINYSBJ80MAZqLQgCWlGImCUIGj2hxzYZiLTAWFRDI4hRl9nolUgJdFSAQCg0whhKl6eunlkfx3gUYqTjHOjK1FGEUMQQrEFQICkz1UA1A4P1FnpChU3tELEOEOsAqa8H0PNUhN1vHsUBxx2G1xx/WpWHY5rA3ec/e+ch6Onf9q6JzdEYf/fc26tr/TRROY1OACklPvD2c/GLu+7Bdf9zCx7/0x+xrPkkdmk+iU1BL56sDWIdDYK7KYo4FfSAIRA6YIBNrgwnqBPHQss+yBZsI3jbbX957YYDwYS5xj3pZ/9YJ9B90ejMJGV9SRuyywJdnxoTM5HXqV9XzNVb6RKMYNaDUTTHuD67Pa16a+WPOyFz9qNT2P5HngLdHT/RmGWbJshamaybH4VC2f3GKdHsF5bssTL1R7jEY7YQ2mlJDWvjnyCs5oKRakQCqRBIhU4UmKgRz2DlTBdRLCGFQk8t6poyHHAaAobMnUcoFWItvWVi9AQRpF3zehhBdURpKGwoNUKlUJPFUNlAaHQUI/D8NPK+Mw0ZoyY1JHXQE8QYjWoYj1xGOaPR6AkiOx9zcEsVPa1DoRAKQ8J6AgUNjeG2Y/y2jf3SB3LbR0JUePqYuuJtlTFgW6AiHFuAww7aH4cdtD/iOMaNP7sN//OTn4Ifexgz4nEspvW4v74TRmSvaZw3m3ipCIiAcBhGuHTYhiaaJ3NusH2wNRk+82YUECb09wAA2WbINqBDMk/8IUy126yWPhHWojyfTAKX/Es1kKkBQjACzpehTob7BIZgjtddtAaO2PgaEgBJJE7X6LYSwpGcYxdPUNI5i1hip6DkmISkuQ/5yeZJUDenGH9imSyq+S9HWq9EWAkuCKbWCZCk0E+dF21NksKYjP6eDpTKqFSSebhN9VqMQBM6ncBGDhuTRRiYxWnUJlIcp0/5QcFeZ0NovW2SGI0gTow9gkzYaaRcVb/sSfhmFx99QRsBIihbWKbssgzU2nYMhhSMoXobM+tN67DKRrNS+EFlK/6FQiP0ySMRBICGjNFSNRAYNRknyiwpq1vndIQp3rYF9s2t6LfC00f1q5kEgiDAiS84Cie+4CiMjo7hX772LTx6z6+xb+thPBbOxiPhHBMWa+H7aBiBzAhs/guXjZkAUMxobLRmAWFK0BvTC2WFeUkOi3QstiXuyQhxGN+Mdpj6U3AA86QvUfRzQE6EKG+8ljnW5RPhwDqqWiLh5lfqJ2JNLF1zeHQ9n+6EQ2hDYrreAsp2sDmnMrNIRv6ValYmmGihvWd6SQhJNxURIEM25dfdJo+UGE0Dm3Bmp5ApmZvxSTACP/YWTWvn45EeJAWjUTd+E43QaCCYgUBGiFVoBXNxvv6WfMgp4PpQiJUEwOivtQshwXUZY2Y9QiA01rd64ZOiBb0jCEWMR8dmJqMRGAv7hgEitOIAGzsNjEaOQZs2g9Zk468HQKjLOHFAHe7UM+tCMGvlyJAhGynZ8oULOzMMWXOYDbeOsBm2XmG7oNJwTG9UhGMr0d/fh/e/9Wz88YHjcPlnrsTi0fWYqUbxh/pCNGGSweTNHz4BIUaaD8lqBtg+1bNNLc6Sk1ojpLuKV0NkmlYboRiQlJgJasNI65tEJjxVNYCy3w/BzkkXyQ3Bmj4UQC4ZqzB+GE6wlCamtMdxLuGWO28oACX+Gm68rr/zMg6Qf3D1thPMWEUfjayqJCMk82aQjD2KE8LlD5RxCCV4B3ZjQcZxM0kxXtJMEGzIbfakCQwpdGJuEMIQT/YmYARrFokTKKWfQ0moy45JjsUCsZKJ4A2EStKadAOREdyxpozpwp1nXSj0BlHSdqDWNn4jIEgoDIRtgAiz6uPocAABRn/YgqmBQmgEMRYEo1CasLHTwFPtXkhi1L0w2jQyx5hH3Hk2ZIzROCUckhiSNGKWCEhlyAaQJUmDtRZ6gwijUQ2KBXqtWlB089upsF0xdXk4KsKxLVCt4tPEHst2wac/9hHsueJQ9Oo2Dmw+iIXtDYDiQuVWPzTWlx3Ov8KRBCfHkrDTNkPE+RRJKaSXilxGyNwx88RBaCAcn8Cc4vmPlM013zZJ316m3ci1TcBI8nhMpADIpFPvZr3wUeb/nGzyKs92Ue1keAbndmY+dpnMhBMszo0STYjTbJSbS0AMIVWiWRBkfBrCQEH6DqRwuSuypo0ytVPgCeR0+qavQGg0wgiNIEJv2MacnjH0JzkzuqixYIhTXcSYWR9HTcRJQOFA2EFfmPUNqUmFeqDQCCLMbown5z2j3kJdxgilQn/QKSyHFIxZ9SZ27t+IuT2jhVwiBGPy8bUeNaky6dAFWRMKRaiV5OzQloQ2ZITewMy7LpWZs/0iz+6ZUTiuwvaHcwKeileFp49Kw7ENIKXE+X/7Btzxm0Px+S99EctaayFJ4wk9O42eYCo6fLrUtd49T+R4CpgRjgFxH0oFGmlO0pQDVovv0pB3+40oNiaWerYBdzHbEDwNBWe3kwZ0jGwGUiA9CenmaTQjOkQhHDgxn+QFYBc/DrbnWPj2lhGJnLZCaIIuezotJSKc9cGYCJTWIEmPJ5THE1Nagt7vgpzpJBXgrraHSZBqtBnODJKaHlKTi0kHzibywvZZ5lBbkzHyzxtENrNmYmpQ6As7NklX5EWXGIHNAGItELOA0gKKCbNrLfTZ5GCsNWIOUOZU7ZQRdREjFDr5uhAYDWFipPOJvvx1qskY/WEbHSWxKepJIlcMmVCZ68kM9MgIY3E96XdGOI5NSe0A7wqxmUOPjG3eEbM9EDoTHh5WPhzTEnqKNBylafgrTBrVKm5DHHzAcnz64x9FJOpYpNejHscIOkDYBGRTl/s3lOTZyPgc2qyitREGKS48xctWUR52i2Rx/cvIzCkY5axTq+quRemSziHZV3Bj6KS5R5JcHbGJpMn31TUpGpBWi1VGmyI7tu/IOrXabKzs6qc4JAXncnYPRo7RAcUV9LQc3TQZmctQVlTNduATNMn2VZIF1LI3IUyV0iDQScQJgCTJVSCt9stXw+TSnvtaDeZsITMAqIk0pNWHFJy0FaQTsuHGzxAmYggCalKjN4gxUOtgVr2JGfVWQpjMPl8zkjtdMHqTJDaUjGM0C50JomWMSQQwpGh2fRQNkaraQpFltETmnAU0CIzBsI2hWhvz6yOYEY6jT7ZRt8czgL4gKlkfuzb2uxMGZXHbFbY3pkOm0QrdUdH0bYxGo4FXnH46rv33b2KhXIeH4wUgbQQl1zlzByXO5p9IYAUdWU0EYNqFIwxVJxM1UuZX4PqFiYLR9eLdWkScyEuhARo3TqpxzRgdCnOI2GgZBE1sNrEC3mk9HFmQzrnUToxgyEei+bHbSrUclrjohjXz2IkTw2hOtDFLkHUidf4n0IAreOQpAtL1ceG6/lBl/h+JyqHkfD3/jK6JvJIZUNJuogyghmR06yfVfCTaGgCay0mi0XK4Qm0AsfPtYNSDGOnqpF8kgklTrhUyZKMMAho6V1gnFLHxI7HcOiSNUBjnzbG4ho7OCumGKJpMAKP1qIsITVvNj6BzWhKCtBnXXJjwUK2JugowGtVyTqTeeDJCf9BGjy2KVJcKoVDJEzGz0QyNxD1mNRgQ64DgCUCuIdA4Q4ybvoKgunVORygQ1BapJCffb4Wnj+pXMwU44YVH4b9/fANmb3oS69UMNHUDggFu26qt9qE7UYXnjhfaCBLfVAJY+3QHkBEj6gE4LCuzlvbBsSnIBiDRYJRVlqUYqDXNLV0lETJ2jjaDJwuGUjZZlzcqWQdTWC6VScoFI9x1jkg4UsKWZLCwan9fftl08AKAGEPRZOOQynMIV+COKB2zbIE4e1yiJ/fV8Pn+S8edyOTCJiGYa1dSfK0ISvJMlO0VOU0FKKfpcJsJkGBbpMzsD4VGRweoidhm16SMqYWQcuG6jEuFdkJU7dO+5uziuDTlgjQUSxMlYj8PhG10dIyxqJ6op2VhTVw/MfqDDhADTV0rKpTACEgjzi1+XcQIa84JND3CtZA1kyvEr77pEy8ioE9G6JExNka9oNslGvfaFgRQn0C9v46ehb1YsedBxYlX2O6YKm1EpeHYNqgIxxSAiHD2G16LKz75L1hYexJ/bi022xkQbVMR1REBKqtZzowg6iLHAJBmNNYzol6rc+7SUESAEqZ/Z9YoBadBF9JmNy3zpxAd6xsCNoXW8gXQnLlC5LZ1EdpEpl+nplbaEhqrxfAUI4mvZt6HBDbkNnEIdecTA0qi+zfcpSS3EzEaGc5kdc1okTyNRvq/3VJyCeHMAfm+Mv2W2Njc/jLNVWENTZSKyDuqWLiS7r21yERlqAg9YQQG0FaB9dkwx/n+EuZ081oFp/BhSFfvhNOIGN9RU4DBUAlBcsSmJhTC2jiaqoamCm2oYVHF50IQ+2QHMQt0cjHVEibNeayzLJQIaAhDGMr8h4mAcRbwdUK+xklY05gkRk/cAa/qgZwpceYrX4uD9jqwUAZ8eHi4OEiF7QpTl3EqNBwVtgUqwjFF2HfvPTBv8e6gx+7DgBjFiOoHYE0KHZNxlEMCKUbQyqo5KDZmDK6Vs2pShiDUxhlRL8okUTpWZEJr85EnpYEGlJIOFaJAEgQAtOz862VS1ph+tNjMNkcccu2kMi9FKA2V7YpuWoiydmzJicwKWIMyE0p+G6VVYUkYluRFmzjnD8r7ACTalPKpUUbl4oR7dn+63IZohNap0UzPxBDHVn3mtByNIEqqrvbXOkl7GcRoxhIdm0M+IQdgDNbaRoMBoFXyRXB+HpIYsZ1j6LFZIkNAktovYHvDNvPqDTqoi8idaW4lGAGppJ/BoIUNnV5r9rDkRigIMnPNGpQYPTZBDJV9N6EgSSZrlK6t8d6QnnOR/IOAigknvvjFOPzAwwp9VZieqDQc0xvVKk4h/uHvXg8NgYX1J5ERa2zMIqKpE7OJS7NAiiFVMadFquJniE56qw7a3ZwqDESHEQ4zKJdIIfPAnQu5dZk+C2BG0GRIbQu1dSvD080vZaLPDnoCv5Yux3RzOk0cR51fh+MJE4xPTOX7fBtLRl45QUib0eaU2M48ArHZvA7WEVQKhUYQJ2TD9A00QoXeegcD9TZ6a23UZcc4dnpfJHeVXSRLbxAnzpJEJnpjMGyZHBoE1IRGf9DOCGKA0RAdNESEPtkCgSGg0SfbEN6XxidIZRyrJkzYK5BdbgkTWusv0YywlWnl5pOf14ygicGg5TmQIrO/IWLMDUdQc5UCvbma/qz2SgP6tyFEL+HEI08qmX2F6QqX+GsqXhWePqpVnELMnTsH+z/3MDREhDnhRrORGSLSCMYZ9VHOluJghnT32hJfC8CmRPe2C2W2dXVE7ABhS9vKrSWNGKURKCJ7TzZza6ZESDjfjRKU5u3wzB35Cq0JbKXZUsKxOeQJCVunWFVqIZoQ5JtbzJakb2uB6HYkwFxOLgrny14OCf+911tiLWMEUmeIRqGom6cZCQRjsN7Ggv6RTLRFPgWYCXeN0Rd0IEhhsNbKJewyPiK9soMeG3LUIyPMqo9jMGyhP+hgZjiK2bVRDIVtzK6NY1Y4hgHZQk3EcHUhy6x+dRmhIWM0RLbUcl/QNin/nXWJDLEYCppJm4BMtMmANNsENGYHYzacFqhR7JmZUtQohiTGzHDcakLsGEjNKQBAsQYiArc1br39erN2zFi/YR1+cfdPsGHjukLfFaYHGJSkN9+Wr+7xexUmg8qkMsX429e/En/3619hfm09hkf7oWOZPGGbkhSpacIv2W4KuDF0SNltvjC2T9OyYyJN/LTqThMirTYkaDFiosQh1N2OXdRKHqZuaAoZmbFNThHTh4hgclrkPQz1BLYD3cVFgZFmRLXvXdTKZp79AaCYCp3TvtgRiKz2fQITB5lzmHR9LgZKa51Yp0PbxmkYnNZCJLk2uOgPQubgsCRRl4OkYlVZKWzYqlDoWA9aLuQFMW0DodEvOpmCbD5cdEfMAgRtHDZtfZKhsJ1xZpXEECJGHUBTpclgjFnFvdcILWkYDJrgDqOJBurUwYBsocNF34xeGaOjO2jpALOCMQwF4xBWGyLAmYgZ076NUS+cK4DxMXHrOxS2EMQaI6oBAiWJwQJS6O1pY+i0Ntb+YC7+/d9+gP++6b/Q3ADE4+Y8d95vLt77tkvKF6vCdkWV2nx6o1rFKUa9XscpJ5+CgDQW9Kz3tehGBjqprrPaDuPrwaCIU82EKvELsH9ky2sHo30Im+xkmjG/NBlCOUc+BhR3zyLqQUQMoZA6eXrjBiWmFdGxRCZiY66JbQ4RldPEeFqPhGy4U1Pe2mzBwwUBaWbSfG0x9zm3vhmtS76/Yn2yzYDdo3K5pYnNf1JwUoBNCFNC3iUMK/odmOe1QKiC5sFvk49eIaQc0JGObnaihowxs9a0KcKLZKQ/aGEgbCepy3tljLpnkihLzuWcNwfDduqPkS4ChoImZoZjmBWMYkbYxKL6U+gTLSyuP4UZQRPzwlHUCio2c1y/aGJmMJZEuMwJx6yJxK6XPW9JnDGd1EWaVt1t6ws66JPGXNMnmpgVjmBWOIaGjFGfF2GnV6xBfV4bI49r0EAbvfuOQTQ0nlq7qexCVJgGqDKNTm9UGo6/AF5y0ovwPzevBPOTeKo5iFbcSGSoyQnBpeYJgtFeKCITiTGBABQ2JbmuAYg1wjGruSCANIPZOOwF1tGUBVAf1dCBDSEti4O0PiXJvb+LIBWxC5c1/iBSAdpFu1jIyAhiXbOhuiKRwQWyAdhzVTbohZEUjpvIPkKeBsY1cU+0bIvWmSE94jOBMibb+UTtslqNbJIse7AmiNDPkFYeEmrmqxFKBpFGIDRCqbs4WJptMufwEwid0ZQISxbYY0IhxegJouSy10ghZkaURIQwZobNTEpwAImfRo1UIXrEHVcj4xtCROgVJtKkqUIwgHFdw+xwBH5a1jBg7BJsyPRSpxiayZIpo5pocoihsFVYt37ZRiuuFeYxKMdRpxhtdhExlKyiW4mBoIO54Qg6KKbm7R9qYe7fPIB1UT80mS/PkxsCjD9VPadNV1S1VKY3KsLxFwAR4S1vfgMuu+yfsWDgSTz41GLjnAgkqcm7ZSsimFoqigERaXRNv8gM2WaACbKVM5NoJFeaAIRjRvAZHweGhCE9qibAofVDiBm1MaN1UT05aesJaYIlOsL4nyQmnxJB7kJzXcl1ZUNoC/LZfnZ9Q9p+lelTC1vyPh/54h1Xto4u74dhILBhnSg1t5SlWjdNqZjVNDneIxH2MhkZX/S7SMIx/W1gSJuFVAhzDZ3Ww2kI8gawMuty0cRiHSLJ5K9o2LTdPhiE3iBCR2s0lYtcKZpqyM49hEJUUDOb/ntlBy6klsgkAOuTY+gRbVNVlhhjXEP5lTJoiAj9MiUXQ2IMAWmsV/2F4wQxZshxbFS9IGj0iQ76RBuh1a5E3AYgsC7uT0rWO9rRJ9oYEC2sU/2ZKrEERg+ZOiozwzFsiAfAIMgBhc5qoN1ud517he2HqdJGVBqObYOKtv2FsMceu2LX3fdHb62NocaI2cgM2dGQbZ1K4RIIxahvihGMa8i2IQPQOimVbUrTM8IRhdqIRibgwZkYPJgMp+kzszPtiEhDtDVk05CXQBvtgq/dKCUHYATj2aRiJa4Cmb8Ek+q82xfQHS8YhVwfpExK9yT6xCYJy/hqUto+sykj5MnkE4mQRtZ00yJR7i0n/5XLTbfLEg1Zy16ErGOpCXOVXspz5myiL9dP3oRRRgryqcz9efcGHQQlXrmOttSEwkDQwkBQ1CRkHCsJ1uyR3d8ni8cRGD2iDUHGoZXICPNui22EfTvV0ECjQRFCUuihdulxvbKDftHETsFGDInxhGwARhtTpwhzgxHjL2O3h1AYEGa+g6KZ6a9BaXXDgBgDchwAQw6Y67h23ZrSuVfYvtAQU/aq8PRRreJfEG/7u9eDEWL+wHoIxBAdbfwjYoYcUybaxA9f1QzRUpBtnYTNipgRtDSCUY1wJEawKULQ1CbM1u4v+FQA2Xu0S1PuISEdLqrD7mcUo0Yo378CCkU38yYYshoLXxMxUX0Wz/ekUHsF1glTWy0Np9sy46NIkMpkcZI0rGu73EEJV8ixoYygpS7vAafxMHtMBIpfN8X0nPbrP12lpMPsL0SVdDHVAIy+wDh4hiVxxIw0MkSQ8W/IQ+YuROATEOvvkSrq0nn1inZhNSQ06laoZ6fL6BOtTD2YPtFK9g6IdiECxVGI+eGmRAPjw22rUYw5wTAEFAiMGcFYml1VKDSoA+c307DaDYeaUOgVHQQDhmQ9uvahwvpU2P5QTFP2qvD0URGOvyD6+/tw/AknIRAKc/s2QEROqptbpogZsqkhWgrUUZAtnWoNcjJPaEtUYktSXISD4uwd1xEYn8hYh9EMMcloEdKnQILtM6818eVsUiiNs9tKCIDRuHBqhehGOjh3TB5UoljIEZxkW34eBf7QxQ6TG690m1/1NaeK4fyaedoB42OhIKUCUdmkKEtA/PUm4wTZW8iRgSQ1uhvUOG1q9AUdo12A8fEIqOg05K66gEZYErucF/TkXaB+2SrVrIRkaqn4ScvcOdRII0R2Hr2ikziEmu+IRi+ldV0EMYZkVhuRmkeiUjKVtCKj7ZgXjmCGHEeQm++AJTY9VCRbANAjOqgPmH1/evzuruNU2H6onEanNyrC8RfGqS97MTT6MWNwGELam2PuIVkoQMZ5P4ys9oMywp1TbQAjk+Qrqffl31sVW0KQlWjdNA4mYqQLmfBNNvl7fV7glvU9gZZjouPzmos8r+g6XrexMr8EzvwpHMSOpFDh2nX7xN5mV3ZeCJhiZ+QuRZ54mPwaRNom8DKfQxGjJ4zRG8aoy2I0B8gI+qGwicFaC32yY+uLpAiFTiJI3FjuptqQpghbkEusJez8ahRhUI5jdjgCAY0eahd8Qsw0GL2iXHg71BAnK9WgToYwEBi9BeHPaKCDmkdUGJyYQMw55YgRZd8PiBYGRJ60GM3OLDGMOhVDkJ2WZO7QRgDA2MhTE55Xhe0DnqJKsVyFxW4TVE6jf2EIIXDQAQfhN7/5KWq1CK2mtI+vucdZxUCYIwRsnRk5q3Eg7RTwtn1sj3X+cUCaG8MSC0JuWG1u8ByUm1syEj03tv+Az2XH5Pvz6q2Q7uq6khxfmGt+Pg6+zKP0T/5Y0sjk7EicOyk9OKkzQiiOA8qerJtQNzZDPnnkhGAwE5izfhGmiUYtiBFmytiTqcZKRovhtAlGa6HQ9nJtDITNDMEIpYJbcH+KASkwAGU9ZDXI5siwRdgswWEQahRjwGbxFMTJ8i+obQKB0eYACmmSFuO30QFBQ8KQm5CUSW9izUICDCE1FDfxVNzrhbe6M1boF81cuLC5NjOCMayNB5MzciaQADpHArMEbo4YQb9sY208gA1qIDOegMbccBybVA86nI96MSSu1/pwxOOF3RWmAapqsdMb25W2XXzxxSCizGvBggWlbd/85jeDiHD55Zf/ZSc5Bdhrz2UAgFotSqVt3ncjn3PDPfxyVtPh9vkaDBF7wi+v4SBKtRVuDEdguNhX0mdhTDfvrqeZ0V50dW/YnB+H/z6vTShTRHQjL/6BJW0yD/NJXRFMwIZQWOMC3NqL7ER9ElGGQGrUgrJkXkiShvn7pDD5NACGBiEsaCbKbFuwESQKMql0wqjLOEOAAlKYHQxjYX0TekTHq7mS7WNItjAkxo3gh8Y8uRE71zZgUbgRC8JhzJZj6BERekWEuohMZVehIK1PybxgGHUR27LzZoxl4TosCjehLNdHSBr9wkWKUDIvQVnTT2oaZMySo+iX5piZcsyeNyf7B6XxQRmSLfSLZmatBBR6qYOwx5Ci0dGxwpwqbH/EWiDWcgpelYZjW2C7aziWL1+Om266KfksZTG94/e+9z388pe/xMKFC/+SU5sy7LnXrgCAMIzSB2NO3QTJOkJCc+Lgmci2nMkkgZ94QTEQx9bcYX8oDCDQ6Xs4zQhlCA41zRMi18jo+23fogOYZBYGOhCA4GKmUoEkxDdRAngJxthpVwLbBkBSASwfGtwl7NUd6DQVBKQOo16+jcyxJWaRhKHkSYhPJLqWn/caM09I3c1lMboCQdlsopqp4PsQaypkHM1n88xDkEk7XhPZpCYCQCCjkiJsRtDXhEIQKIyoOjQL6zgpEu2GEDrhud0ynfrz6qc25tIIekUL0juACOWVkWGcSiUxGLa2CgMBFAbs9prNpZHHgGhiXNegQVAQCXEKSaHNAu6iCmjMkqMZDUoAjXnBJqyLBxGBMCBa8OvI9ogIAoxh3WPnaKofEgFBT4yxkRYqTD+4VORT0W+Fp4/tTjiCIOiq1QCAxx57DG9729tw/fXX46/+6q/+gjObOixYMA/MQOhrOBLThwYpY2cQLWPiYDAoMpEqiIwGQzeC9OZtzSSi2Um2BU1YAZ7axPW42aZ6ZOp/oE36c+GEvjbkIJLCVEUFQJE2yb90WoVTB6aUe2CPY9gcGzEj6rWP4jED0iQvc0/ZDJNbo9NnE4DBpk2PAR1mBZIWQFKB3JGkZCeS2itaeNxIAdybXe+ET1jLAgHZMvRu7ZUlH3nyoEu2cfZt2VO4MXF42gjLH7OuM7k2YNQDnSMi7D21c863wmyTZHw7+mtZnwcinWgRAEav7KAhYlOPhFLetSAYSeqLeC69kFDol21oBoz+ocis3DegRrHNXcGoC4UY2WQmEtpug7dNISRtFV12TNLopzZcfZk6RRnC4eYsCJgtN2FJuAnjOoAkE6UQihBtm0ukRjFmyjEIMmuYWMnIOJDOCUyJ+dlyHJt0A02uJ/OoiwizrNmpR0QY0T0ACLXeGM2xiX1TKmwfTFVESRWlsm2w3QnHfffdh4ULF6Jer+PQQw/Fhz/8Yey6q9EAaK1x5pln4p3vfCeWL1++Rf212+1MUp7h4eEpmffTgZQSWtdQC2OI8cjmumDomjDJvXJQgYCM0+3GfzAG12ViZiFLFNz9vSD+2IakagY3lTGkM8BtQPhPovZA2WJwwJltQnGimQiaGn7xjUR1rUwKdQgzL3SM9sElpXSy0vRvp0aeOSSn/acYiR+HVshoL5IxfZ8QNqQj0w+VvOXslmR8RkK0AJSTDbfdJ0Nd/Tc0fNtQsUgbeYczalJZRU9W5UKeKcMnJwKcqRybBSdTD4XCzGC81BkSyGowfCuSKzMvCAg5BhPZDKMpiyKwTbRlLm7qA6IzNU4EAZKV9fUwxza80FhHXPqonVxzAOgTHatpyH45+qiN+XIcIYBBodCBRECEmo6xAf2oIcYsOerl88hq4wIypKifTC6OBiLUKcY4amizREgaUpike5JssjMEkKFGc1OXyoUVtiuq8vTTG1tEOK644opJd3zWWWdhYGBgwjaHHnoorrrqKuyxxx544okncMkll+CII47A7373O8yePRv//M//jCAIcO65527xuB/5yEfwgQ98YNLz/UsjFAOQ9fWohxGilrkMVEI2ABTJhm2bCFz/adtJJOeXUSKJZMwmU6fz55BFSSM0Q+X8F4yDJ6fakS4qcqFs/9782Hpgus1CpyVOHFEyad69fhjgGLYwnR0/Z+Jwwto5eApYwuFZkkjYvn1Tg4IpZJf0Y8UdsZmYbWsql3Zx0vCJRpflJuFvK7++WhOE1KhJDZmcv2/nSQWlSzVO0AiFzlih8mGeGaJCgCAF7lKRztfQEFInZD9rKRGsA2gncRJtUIQQkad1MZoOIkAwF864gQhj1rm0BpUzD2n0UlQgBhKMAdHEiE5VV0OiiT7RMd8oZktmTIE4EsAuwZMIhcYY1701zK5PYNVkjiglOTmQzcHhfh79ooUHfjcfI2t6sXiPSgBNR2hMUabRyqSyTbBFhOPtb387Fi9eXOpfUYZHHnkEL3nJSzZLOE488cTk/X777YfDDz8cz3nOc/C1r30NRx99ND71qU/hzjvvRLGoVXe85z3vwfnnn598Hh4expIlS7b4+L8U3nj2Gfjil67EwPxxbHhoEIB9UqecEPfrdHsgAIh1iY4eydNhIbLD65NiBgcCyN7vs/3kjiekQp+4KPyTdlaT0i1de9q/PVdKj2OfxJDZpq0vBQGASrUlbk7snTfcHFOrQGZe7K1PQTNBhhKx03JM9LUTZnKUjEOJdsTXQMiAM4cA8EwopkppII3l2U/i5TQe5r2tMGs1GpoZvUGRvNSSENl04qkpxhhDVBd1jJ/Pg+yXh2F8KfwFYhh/kQbFqFMTM+Q4xlWaplxAp46lhS8hQ0JDQkFBJIm/3PmGUKhlwmLT+fRSBx0K0eYQIeIkXNatDWAIhLLmICkYofeF9tv551lnz0nWGC8RkgnVbXPgrSdhbEMDv71hVzT6Nd72pksLa1hh+8OVp5+Kfis8fWyxSeWOO+7AvHnztqjt5ohGN/T19WG//fbDfffdByEE1q5di5133jnZr5TCO97xDlx++eV48MEHS/uo1+uo1+ul+6YTVhy8P7721QH0zxnFxkf7oZV9YvI1DkBO153rxCb8MskcYNo5Qe/IS0lWUTBMFVqtLOlI1fU+KD8XAKTYaiEo1TiUQChA++QpZ5ooE/ilJCA/J4bxXfU0GGau3ly82jEF2P7ZHeM4dGbJ2cmY7CB5OLNKRsWUnghlTB3sRXi4qApLNggIS6rBBkJbQcno9QqtlU/I+HZwZm9WGAsBgLUNg01BKKk4SwBxUUiz9UvpEZ0kAVedIrRhnFJDylIaY1ZJmWkgNHbCRoxxHSop4Gb21dDdTCEIGBLjWK/6rf9Gsa6MO88Y0pwv0rBeAS4o5BhI1gzszxnoExF6dIRRDsEQ6EQBfvW9vaAigXPOeS1mDs2ZlubaHR1VLZXpjS0iHO9///vR39+/xZ1eeOGFmDVr1qQn0263sWrVKhx11FE488wzceyxx2b2n3DCCTjzzDNx1llnTbrv6QYiwtFHvwA33vxf6J/TxPATfUgqmvlai7KoCwdtubwyT3LsntBjAgLrsFem5rCZRE0SSvtUKIs/KNKpY2dynAakYujQKr67mFUIlnRIs78smWY+HwZQNKuwHZOFpwnxzSP+0Fbrksmr0c0BNA1iABcmlusXjsjl2oDAnHue8pxASTAy0jfhjQxmRlgIfU0RyDjJpRGQyiiLyg5x2Tx9648/3Yb004hntQ6hKCnXCwBUFNKAIRh+7ZGaiBFCoalrCCmbsUAga1bpoQ7qQqPO42AGxlBHmw09qJFGAI2oxGnGkCLCbDmKXorQgMY4ZOZ8iJxZhRHY9PH9aGGEe0pTvhuObq6RUbSZ904rRAT0kKkaeNdNu2PTk/34f8fOxv7LjyxZrArTAZUPx/TGFhOOyeA973nPFrW74IIL8Nd//dfYeeedsXbtWlxyySUYHh7G6173OsyePRuzZ8/OtA/DEAsWLMCee+45qflMV7z0lONx403XYWD+OIaf6PVMIb5Ww/oUyJymosSvgOyjGilDXFgK47QXeLYPFwUjUhUBNSMglEAoUHgEzGlY3F4ZabAAFAlj4vC1MFYzQgxIBShpBIIRxGn/ZVlGSduEWyLtT2iCjtlUsrVnwrZYm/NfZCD1vSDzPk9mktPytS05PsbgcoLnmZg47ytRIqyJOPH/cLkz/AOk6J5KKBAKtZLMnf5w7sndIRQq2ZdofcgRljjx7zB+GJzxuq9RjBrFXn/pXzNK2raXWhiSzYLwDkhjphiBJonIi04xmpJ0TnVnMiEgIKCHI/RYH5AAxodFMaHFMvNdlGBoMAZFhMBGnNRZ2bEAn3QErDBgk6QpBnpZIyDGJh0iLsRapyvmfDyM1kOgNR7itpv2ggw07v/NIizdrYHXnHZJ1+tSYfuj0nBMb0w6SqXZbIKZ0dtrHLgeeughXHvttdhnn31w/PHHT6qvRx99FK961auwbt06zJ07F4cddhh+8YtfYOnSpZOd1jMStVoNe+95IFbd92v0zGij+ZQ1BcVWmroQTp0Tgs4htMxPwiMIpDSE0tB9dQAMilWJecUQFKFiaATgWjqQGRtmbG2r1PqRKRqQbY04DFJilI/qYBvyGpRpWpASJ0ZSoE67ejAu3bqw2hAv+sRl60xMKRKpn4cNDCl7KEkCLNgThH7QRZlfSqLN8H023L48+TCpyMmW7DXWGfaiU0xui27uUIIUGkHUVfOR9EGpaYTAhZoq7lQARkPEcI6gjqw4E8yAbKJunTwVk8llQabovWZCQMavImZpNAaiWA3WzakuFEKKoZkwxjV07O1FQENBlibkMqYeRpjLM9JAjBZCOFIgCJiFtol4giEgdTLnPaIFAmgwESIm9BAnPwtJQIMUGgB6ZAdPqsDzhzGR2y5U1yjEDJmJNOGW7x2A1Q+ah56e/ghv+dsPQ4jqSXc6o8rDMb0xacJx8skn49RTT8U555yDjRs34tBDD0UYhli3bh0+8YlP4O/+7u+2uK+rr756UmN389t4JuPVrz0VF130awzOH0dzfQjECiI2eTg4kEDNXiLt1PP2jhvn6j04ElLiqEmdGIgUBDNYCtOvzViaSU3eMqp1tiYdFjDC24bclroyMCDaChwzSAqwKDpYEQzpUGDzWOtv7xT9RMA2BBep6YRAVlsCQyzsdmF5DueIjmBAdEz7JEu1p6VI2mmAO4Cu2eM1QJFHTPKnKzw1DzhjQgEAEhoyzK6UiXJxjp8mM2jRVGH8KPLVX4FSi5V1yDQ5NvKRJO5CERmy4Sx05B1bQ4xZwVim/5AUGrmS8/2iiRo5MwehQTFUIY7EEIA02ydjgNrocIQxrlvRnmpYMudhi3/nbV8CxmE1BKNBGg0y3x/XKomQJmBQmtBdBUaDONObU3ppmK/eXBmj7Y1DzGgzowmBQdJowRTTu+OW3bD6wdlYceh8HHjQCiycvwdmDGU1rhWmHyoNx/TGpOn6nXfeiaOOOgoA8B//8R+YP38+HnroIVx11VVbFT67o2PevDmYM2MJeoY6qIlxUKTSp/4oBqLYZGiMtblba23Iht+Jr/HIgZgh2hGEcwxVGujEoE5s3vttAZvkS0O0FURTQTbj8symSf+AbGlINqG0Il+FNtc2Efps2gmFTHsGIESWlGS0ILkqtLabRLNRACPReEz4kOIRG9KUxuzmO8toQAjQeVNXUT0SSEYgLRckQOl83VVACA2yjpATLLfp0Qr2HhFhbn0Mg7KFOsXG18ObeAijcTAUMLs4wqXa8shTtpibaSNNkR0EdiFdlEl2VciOk0WNNGZQE32inWhVfATQGBQaAwKoQ6OWkA+2x8YYEAoNYcNeQQhgiIZElrwYEgWEngGo7HJLmHbJZwL6hcIMUhiSGjNkjI2PDeGe23bFgsUxXv+ai/C8g07G4oV7l/RWYbphOlWLvfLKK7Fs2TI0Gg2sWLECP/3pTydsf+utt2LFihVoNBrYdddd8bnPfS6z/3e/+x1OO+007LLLLnimlvmYNOEYHx9PolBuuOEGnHrqqRBC4LDDDsNDDz20zSe4I+CvX3YSAGDWHi34N2ViBjXbQLMDdCJLQBzZYPipxqHzz5wwkjguUbUrDRGVmFcAUKyTGiuJ9aCbBGTOkAEzZ9jqtd52SxQCmyVVxGwr0Np93SuKu1ml73JEgOBZNcrOh2BJTTq/orRHVjqRIR5wWh12J1E+tbyiafMgr1365G8EqPGvyO8vg7sHBtJoF0LSqAuFOsUQ0JhdG0OPjNEQsVUiMQLE6BFtzAibhSRk2RBYl0/DrIOg1BFT5sgDWSfNMgSkMUeMY4l4CnPFiB1HowaFHi+PiHQ+HQT0E6OfgDoBdd/nB+n3UlI2q4YAsGlTDx55eBaiVmC0Z1S8ZESmrqFT7Tpvk4Yrhgdg1b2LAABnvfatqNWKRdwqTF9MF8Lxne98B29/+9vx3ve+F3fddReOOuoonHjiiXj44YdL2z/wwAM46aSTcNRRR+Guu+7ChRdeiHPPPRfXXHNN0mZ8fBy77rorPvrRj06YnXs6Y9Imld122w3f+973cMopp+D666/HeeedBwBYu3YtBgcHt/kEdwQcfsRzceuNe+EB/B6Du3Qw/KD15XC+GMxGG6E65ibrNB0AUDOP7oVcJTbihezbNCiBPQFd5lfBoEiBwzT9OSlDbjjIhI/kZKHtixmkKM2VkZfTzGA/9wZgcmvYaJbEhy9nbsiqNbIfk2iXnMlE59KXJ7k5vDaJ2aZkTCKkidSS9cqB3H8TEAOnhcn0745hhNLXWFHOKkagXN8T3/oYg0HLHJdr2EOtJLGYS/PtIKGTaGqHfPXWECpx/A1YWwdMSqM6Mq0VemxiLyJCj1ToYQUFoM+ajZyyyp8HwZAMQZQkngsS/Ux6Tp2OxNe/dShGR+vo6+0gkBoPPjQ7mcWMGeM44fjfYZ8916ANRpjT8TgLmovsSdZcC/zx9wsxd67G0p0P6rLGFaYrptqkkg+F7paG4ROf+ATe+MY34k1vehMA4PLLL8f111+Pz372s/jIRz5SaP+5z30OO++8c6K12HvvvXHHHXfgsssuw2mnnQYAOOSQQ3DIIYcAAN797ndvs3P7S2LSGo5/+qd/wgUXXIBddtkFz3ve83D44YcDMNqOgw6qfqBbi/Pe/RYE3IOhXduoDdobvfvdKC8ZEjMojkGRfY23QEqlUm2sCTTbxuziEwv7yvgW6txTqZ+Pw9NqEJwpRiW+H0XnUyT9y5gRjqmMf0imXQ4EU18l+axtMyoeQwDy2nv78J78Tf7kSEnW9OH1R5RZF+6izSDOJUoTJaGWXrhtso1SK1IZgly+jonpi3dciaNoQCbCpTeIMtsFFEJh8lI4DuVPPcypmaSXwMuhV3TQJzoIKc6YVQSy868jxiDF6JFsXXbSMUMyfhiS4MWyuH6y83J/f3vvYnzvewfii186Cpd/+oX4wpeOxJe/+v/wyMOzQRxi48YhPProTOy1F3DSSYvx3OcOoNkM8eMf74saCwyJAA2Ryz1CQMNO2k9V89BDszA+VseBB+02qYSDFaYHjOGPtvnLfT+WLFmCoaGh5FVGHjqdDn79618XgiiOP/54/O///m/pvG+77bZC+xNOOAF33HEHoigqPeaZiElrOE4//XQceeSRePzxx3HAAQck21/0ohfhlFNO2aaT25FQr9dx7tvfhn/55McxZ98WHv9lH9gXrE5aKVU0ccTK5OLQ2vh7KPuSAklNc5cUbDNwFT1JafN0L0SSYCxghhrTJpIlyHJVQ1JyfUU6E/VizgPdaa4rWuH+doFgy8E8DQa5DKReAjA/syhgSI0WKQEAbFtfM5LNRVUC29DPKOqZP4oExO3M0gjXLMg5kKYKFd8ZtXjdBLikaqyJFCm7yjUbNivB5l/OZOOKubmD6yU+GU0OUacYNVIIhYLWAhGM+SKARoNiCDACpx1il5DVEA7n6e+W2vli3HnPYqzf0Ie4E6DTCbBowQj23+8xPProEH7/xwX41R27AAAGBiL09cUYGe3F+FiIPXaPcd75XymNHPnef34M/339n3Df/Quw715PosOmqoujSYH1B+kAqEHYy064/487AQAOfV51L3smYqo1HI888khGk1+m3Vi3bh2UUpg/f35m+/z587FmzZrS/tesWVPaPo5jrFu3DjvttNPTPYVpga0q3rZgwQKMjo7ixhtvxPOf/3z09PTgkEMOqZ4Inib23Gc3HHX4i/CzX/4PZu3ZwvrfNdKdLilY2WOyp8EA4GlGtE0kJqynXO76eOXnkYR8UtpFrNPcF7ZryYCOTFQAB5TopJkBymcl1bYPSXl7Qm7+hiwYh007tSZDh4AOPXbghQE751FmpKnW2SMTvsmG03EQIfEa9O9LBEt0NDKp0wvwOsybSRy50JqTGirknXreaiNI26drP19HytrY/lcTMWrS+HZoBog0ajLusqRl5MQ4ezJgI2VSjYTgNJupT4pSJ1AG2xJsvRRDgyAtoeilNjaxRI0UerzoFp+HOQ0SYAirTz8DIqxd248ffP+5mfneBeCH/71vZtsrTn8eXnTcG8tOuBTPf8HrcP0NF+G2Xy/Bvns9idDWKx6HQghCYM2QvVZZ5+5djz0+hJ5GhMWLd9/isSpMH0w14RgcHNxi14G8PMznIdqS9mXbn8mYNOFYv349XvGKV+CWW24BEeG+++7Drrvuije96U2YMWMG/uVf/mUq5rnD4DVnvRy/vfe3wE5PoLk+wPijsJlEVUoQfDh/jnzCrow5QYNbGhQG8KqDWV0ypxlLcx6QBACd2BAGL3FEInMjq8C0/hy6BqOZ8BOFaRhCYpODlSbjcgwhP7bL60FIyZSV8i4cllwTkYbITqRFSbQeKLZxjqJ5TURGhPtrmwgrAKxTkiMIUqa2Ha0JQpibjWaXI0InuTOYKSErwvOAJTD6gzYatm6KAAMCqFMHkgr5UQEAHR2gR3YyZCikLDnxS93PFKPWp8bcqAnCJi0zdU8GRQstDoz2gpAQDsBoZ2bo8UworfQr31lTigDQGq/h/+5ZjHXrBrB+Qx/a7RAH7vcoVv3eOL+98pUvwAEHnIAgCLDylivw8MMPYNddd8bee78CcTyGZbseWHK23TFr1nwsXtjE6jW2ThGAOgIIEGpkvoTKEidl/Vm0BtasGcTSnRvPqpv8joTpEBY7Z84cSCkL2oy1a9cWtBgOCxYsKG0fBEEhAeYzGZMmHOeddx7CMMTDDz+MvfdOQ8XOOOMMnHfeeRXheJogIvzjRefh3e+8ELP2bKL1mIRW3qNyhlhoINJAGOZ66WI64TQCBYB52gdAjoSUOJ46WZpNPGabMxuyYTcJbVi5IxdJnkoGEGnomsiSJvswTygK9kTtHhszCHvKGDiTgK1rT64T2AqzzqySv0dY6edkepnPrLYJ1xIfWJTAP4bZ+HKEWbNIpk9tEnw4EqIZaAjtKZg4TYduz14So1d2UPeLtDkCIXXi9JkSC0NQaiLKzIDAOV8PhZAUFAg1UpZHWpuSUWuZzKOI0WOJSgMRRPLdIBBMJk6CxgwZeVoxtnVg04UNAKz63UL8948OQLttvqf1ugKRxvU3LgeBcdihvTj66DMS08hLT76obNUnjRlDAR5dHWLtk33YaW4TRIS6d8uTDAQUoskdKADrN/ShEwVYvHjLakZVmH6YDoSjVqthxYoVuPHGGzNuBjfeeCNOPvnk0mMOP/xw/Nd//Vdm2w033ICDDz4YYeH+/szFpAnHDTfcgOuvvx6LFy/ObN99992rsNhthFmzZ2LvZQfh94/+GrUZjNYTVhOhVKppiOP0UT3gIlnIhGIwiLXJGeE/6XbMkykLMqnNneEdyDiGkivwliE7KdkAUlMGMUAxm9LvnPYnGEDH+IVoYtCWVB52gkyn5MApOwgAe+nOM4e5arH+ajhW40iB77fhtSEiY1ZxxCPdW6I1cdso21dhSgzphYAK0ggCf+3sxNgk8ZLCaBiMfwelDMn2ldQFYSAGIWBDKnplG6HQiL0Uq7WcdqNOCiFphOCCo6ibvIROyAaARJtC0GiQSSauoRFa04zba3w3zHm45Rjf2IMf/uAgNBoRXvGK47D//n+FgYEBNJvjuHXlJ7DXnsdj2XMOLZnH08cJJ74J9666Ct+6dn+c98bbIaWjSVlVdQMhmogxvN6E+8+a0YWwV5j2YCbzm5mCfieD888/H2eeeSYOPvhgHH744fjCF76Ahx9+GOeccw4AU/7jsccew1VXXQUAOOecc/CZz3wG559/Ps4++2zcdttt+PKXv4xvf/vbSZ+dTgf/93//l7x/7LHHcPfdd6O/vx+77bbbNjrTqcWkCcfY2FiS1tzHunXrnhFVWp8p2G/F7vj9o7/OZq1kBseGJPjCHp0IqHmP2FobQqIZqNXMZ0FmWxAUyAkxwO0YCDlrcnFQ2jqgWm9t1iZiw0MmHoUB6hjfDfKiA4QGqKmhGiIbslqGvHnFE+iJXPfNI/7+HJkoK1WfHOO/zbSjtGAbo6S+ivHT8Dhdl9MxqcxTxY75nIdmQuC1k9YpNavFyPpfOOdSSbEJrbUKsAAKkkyVVj+Zl4TySrZTl3BdRh86Bf4aQKGXTMp1bcmFMwkFcJk/zaKvXT2EX922GxqhxvJ9ViOOJWbOaOPII1+V9Nfb24cTT3pf6YptK+y2x5E49vnfwY23DuGmn++Ck57/sDXBZYkWEaEXIXZdMoJAKvz6zsdw/ElTOrUKU4Tpktr8jDPOwPr16/HBD34Qjz/+OPbdd19cd911SdmOxx9/PJOTY9myZbjuuutw3nnn4V//9V+xcOFCXHHFFUlILACsXr06Ewl62WWX4bLLLsPRRx+NlStXPr0TtPjBD34w6WOOO+449PT0bFHbSROO5z//+bjqqqvwoQ99CID5sWqt8fGPfxzHHHPMZLur0AWz5phquyJ/hTrWDhJ6O5gNmQhDQy4iL8JAqaxE0daJ1JdaVlvBnRhomDLjvjAmwBSDs3k9IADOE5PEbJ8eKJihC56VgIwMI9ABZQR24Ted85fw4Xw1BFsrURlBQRfTimvHiTEhRzbSIYUiaKlz88iSDeOoQUZHXzbZnMEoH27qkPh0gJJMoMZQkZpaRK6CqySFUDIYAooFAjYRJMZPI5vIq09ka7TEkGBEmdMOoEAFzkmQiYOr8dFIM3ayLUzP2LSxF/993f548M+pSeI39ywBAOy223NKz3mq8dLT/wW/XfVG/Hjlrjhwzw1YvGAMYEKrLXDbnYvwk18uwYyhNs4+4x7UA43B/g4eerSBdrtdPUA9AzEdTCoOb3nLW/CWt7yldN+//du/FbYdffTRuPPOO7v2t8suuySOpFOFl73sZZNq7/txbgkmTTg+/vGP4wUveAHuuOMOdDodvOtd78Lvfvc7bNiwAT//+c8n212FLpi7wDgKiVr6GMra2haYwZpBfoYopQGKi7Iuk9UIKeFQVogKkZAEY6bQNiS1pES489co1cRb84kb3+WetpVjs3MyfYnYEAJTu4XhFU7J/kVWm8EASDhtS0ocMogB3ozpk9IYSdOv1785pdx8AFukJbfN0QEbnZLZY7UQjiQIYmc9yeSucLk4zHZOStObeTmzCuVCYU0YbHrKAr0UJ/ksALK+HoQ+apeE0RI6HKJBneQcRMmzHAOoIdVmhKSh3NeKnfJM4D+/ewieeGIQ+x/Yj5f81Zvwvz/7Cu6//wmcdtqbsM/y52N7oFar4fVvuAD//LHP4N/+cx+c86p7cevtO+HndyxCsx2ip6GwfmMvPvnlgzFvdhMbNvXg5JfsV5GNZyimi0nlmYw1a9Zg3rwt82NyWce3FJMmHPvssw/uueceXHnllZBSYmxsDKeeeire+ta3PmtihacDhmYY73oRwmgX8kXZlCqqP+KcNgMo8e0AEEWw8ZVGU+IJVlIazCL7zXB6fR/a2hDICn6dSlC/JWnORqYk7QGS1t9CM1SNkmqxTChEs5AyL20VMHa6+aCSZPwkUMYvWZ9fGneoZ4phlBGKLYAjYxrQLACpIYM0L4exSjGEzBWDsZBekRhha6o4R9L0fWpmIQANEZlKr3YB6qKDhjAZzlyKL0EKIUzFV8M9tV0KjZBimwbdQAPoQacwNwIsWTG9tlsBfvyDgzBj1hgOPvgBRO0Ad9y+DE88MYTjjtsNp5/+TgDA0ld/fJKLODXYZdkBOPH4vfGjH/8B77vcJCqcP6eJU0/5fzjs/52Jn9z0cfzH9x/CE+v7cNDyFk58yVu384wrbC2mk4bjmYjXve51W2weAYDXvOY1k8owvtV5OD74wQ9uzaEVthC9vb1gDQQDMJqNto0GcM6WTttREllSNKHkk295Etr363DSW9niI1J6kjvXb0eZ5F8BGXOMH31iiYgZPz2Wvd3QbMJt7WfZYZPXA7D1WNx0jHh1fpW+IgR2uoBV7hAAiTRVuZuy1Y6wR1aSwwskhECKs6XtffMSe+uRbjRz8/0/kF0uZgmlNepWraI51Vb41V4ZjJrQYBAU2BZNs+YdOKFvQl17Rb6MfcqqHEFgEAIvI6qEKRJXpxiCNGpeBIuEQiAYacBx2rkZH1j/5AB+/j9748E/mfC+X/8iNZUsXtLGKaecj+mIv3rpP+ChB96MKAJedPzLsd8BxyVRMcee9F7MmvN1/Paen+Hlf/PZKiT2GYxKw/H08NWvfnVS7T/72c9Oqv1WEY6f/vSn+PznP48///nP+O53v4tFixbh61//OpYtW4Yjjzxya7qskEMQBJjRmIONc59E0FCII5inXK8Nx8rk1nDwEmMlbZQCAmn9GjhNHuY8DBnG5yOQSchq4rMBZEmHRdJXrA1xIGTydBQe4J1ZpYQopJ+zdgbSAMW271xNlHw/5P11BgZC1i+EYP0+8gSDs+2AonYlUYX4Th8ZvxQNEfgnxJBB/gSBWqiScRQThNVaSM98Iok9Hw8BQCEUGqGtQOf8MIrmEXjaj1Q7IpEtrKYhULfOn+yZegCgRipZiAAKHRXgtz/fBQ/+br6JnAFj44Z+AMDe+yoc/fxX4uc//zLmzN4dz3nO87DfvsdCbkn00XaAlBJ/f/6Xuu5/7vPOxHOfd+ZfcEYVpgI8RRqOHYVwOMRxjEajgbvvvhv77rvv5g/YQkyacFxzzTU488wz8Td/8ze488470W63AQAjIyP48Ic/jOuuu26bTW5Hx0tedhK++e9XoX8ZYeM9KDFr5LQczEaqWs0BK2WSeskSG0G+r1iZb4OnqSA2pAZhvuqF3Q8k6dLZ12J4wtiQF4b25JAGTMlONxWkLiA+QRAK0LXUVwNkk3t1ewJ1ZKBLavNEa+I1TyfptnH2M3EiwFPNTVHYZ9KTl0ShEDQCqTNbIi1QE7ElGKbfmsg6yCgm9JAhGwSG1obbKRbo2CyfbrrCixVi6ygioGy6erNdgyBskjJmQnO0hrgVoH+oCdFjzktBoPlUHT/9wX5Yt3oIfYMt1Bp1qLiF5QdEOPH4t2H33Ux20IMOOLZwrhUqbC+UWX+3Vb87EoIgwNKlS6FUmcPe0+h3sgdccskl+NznPofXvva1uPrqq5PtRxxxRGVm2cY48pjD8a1vfQt9uzI23qtAztvQg9NyJNoP9giAK87WibO+GsnBefMLZ31FXSpepQ3D9yNTmBOtBsH6fkhRXhPc3QU8B9VSApNz/kxMI3kNh6fJ8X0w0g3ufJAJZ82TGlBRE8LCHufGLNOsZDQiZi6pwcgSL3c9rPpFSi4sNwAIwfYQRiCiQlSyr9NKS6+ZCSiWMMGvtjianYOOCe21NTQfaGD0D32IRgPUBiLUByLUBiLoFqG9qY7mcA2s0gEbfR0MzBpH31ALj/5xDuJOgCOPWYZXnvaOZ1XyoQrPXmhjFJ2Sfnc0XHTRRXjPe96Db3zjG5g1a9Y26XPShOMPf/gDnv/8osf54OAgNm7cuC3mVMFCCIFdd9odf3pyFXoXE8YfYbBS2aRZWnvkwtd05LQfscqG0uaRSx2e30dMJoJFGNFGgFE3JCYQY/vnEn8RIjKF3KRITB+sOI1kSdpiAsGewicmieuJWzOdDQZ1fThikpSyh9e3T0zKFRhpO7ITyE0mWTbrL6E1JWYWIVwxMwJ5qctDqQzBIAKRRk9Q1IzUhUvAZUNl/UStNuZXdQC1OkT7kQbaa+rorAtNmC6A+gBj/m4S7ZEQYxsijDxGEIFG/wzCwl0F5s2Zj6AxhvFNEuvWDuOpJwM8+cgM9PRH+Nu/fRUO2O8FXRajQoXpB6WFSU08Ff3uYLjiiitw//33Y+HChVi6dCn6+voy+ycK4e2GSROOnXbaCffffz922WWXzPaf/exnWxyLW2HL8abzXot3v/s9GNhDYvyRGIlO3QO32kAcg4PAaCQiWA2El28jyVSaz1blazgsochlFE2cILMfjQ+HFCaChk1pepP3w84vkcvGh4MSwmI2sdMkOBMIUtOM+6zz/hWA1UBwukEhLermfB6FN19GWhUWpr2fn4O00T64CBiCKcCGTEQMAZrALjpH2sHc6WjDVFK+RaaIm+eTkfEzRZpyXDEZbw0mG0liI1IyJhbTj7YhstwGot+HiB+tI16dEoygl7Fw95lYussS7L37Pjhk+Qsy1VQ7nQ7CMOzqGMnMGBsbQ6PRQBBslYtXhQrbDS6Z3VT0u6Nhsjk5tgSTvqO8+c1vxj/8wz/gK1/5CogIq1evxm233YYLLrgA//RP/7TNJ7ijY9bsWdhpaDHWiEcRziR01sNErQjhOW/aRF/5X4WyRd2cUIyVTT3tiphQSjp8TYjz5XD7vHopBY2D0kb7IaXxGbC5NXwwYHKG2JThZE0OienC65Q0Q7sEDyCIyAh1XTPzZQFAploORzDyY/qDe2VAMj4iqXHHhOSyF8KbRMPkz1fY/1yxNm+/CHSBvwVBWqo9NYcwalJlysoYnwsCk0lkVhfGWdTxLec+wiDoByTin9WBcQGSwNxlQ9hn+T448qBjsGSnnSeMsqjVal33AQARob+/f8I2FSpMV1RRKtsO73//+7d5n5MmHO9617uwadMmHHPMMWi1Wnj+85+Per2OCy64AG9729u2+QQrAC8/8zR8+spPYWB3gfVPRkaLUK8bgRlFacMS7Yfzy2A2KgWO4rRYmzO9EGULwMXKaBCCfMW2Lk/Fms0xUlrhmtOcJM6WACmApXHMSEwrdi4kCC7XZkGr4rlIZECU0bwYcuPNuJRgFLsx5MVqMIQhIay5mNLcHlxIZS6sycTr2Qh+AW0zpSV8UHMuqsXMwAXzCOhE++FaCdJAC+Cf1RA/IEE14JhTj8Ipx7xisySiQoUdBRXh2LbYuHEj/uM//gN/+tOf8M53vhOzZs3CnXfeifnz52PRokWT7m9ShEMphZ/97Gd4xzvegfe+9734v//7P2itsc8++1RPRVOIfQ/YBzwaoH8XxlN3MHSLgU4ndcR0YFd11PtxKJWNHGGTRIzyZhOlTD4O/zM42WaEb3lGLGtMSM0azoG0y5M2adg04LaSrLJaAAIEkXE/ER7B8QmLH2mymXuAf4/I1F2hIuFwagShyVSMLWElLFKWQZqAwE5GOmLkH8RJyfmUpQBRTKhJoKMkajBaDgGNemAqsIJVJvO8ZlNuLIgA8d8haJ3A/L3m4tzX/wPmzJw78QJUqLCDQTOluXK2cb87Gu655x4ce+yxGBoawoMPPoizzz4bs2bNwrXXXouHHnooKTw3GUzKE0ZKiRNOOAGbNm1Cb28vDj74YDzvec+ryMZfAEcf+XyQJAzsbkmBl+Z8s1Aq206XHOP6Q66dDa0tdSbNYDP9e+OXTtn3/swL+kSFkQR9IP2QPcb5avjmH7Z95LKCZ8cQ3nYuee+Px5Z8COOukkxDAyo2/h/kHEXtAVJokDBaD4IjVqbUu586hYmgIWzmeVN1NVQK8gZDNg44Zn988LxLKrJRoUIJ3C1xKl47Gs4//3y8/vWvx3333YdGo5FsP/HEE/GTn/xkq/qctOvtfvvthz//+c9bNViFrccr//Z0UCQwsFeYE7S5X4ILhbX7uKwda5QijrOf/ciVyDqsutcEv0Dyj4U1LXB+PzzikGovnKdDpr2nOGDLJjz64R1r4bQgvu+GTPeZ0Nqu0zdmHztvVyyJfSdVV7pe5wkYAVoCWmQuEeWSdCVZP0XqUJpyNHZnaSxdDzHkf9ZAawT2PmIv/N0Z5cWgKlSo4MgBTcFre5/ZXx6333473vzmNxe2L1q0CGvWrNmqPidNOC699FJccMEF+OEPf4jHH38cw8PDmVeFqYGUEgcsfy6CXoG+pROHtyZCEv7Tt0cSOPkvC62NL4Zrl3PDgFKpA2M36u+2KSfRXbv8PL1XiaaCNIwUts4SJlO3pRllGg7/hBm5fCLe6ZJnobFWI985NKFovjYjrxoJuUiaHIghQ50sBZG2USfp8eQO9jQgZP9K9z4m1H9O6LlZgtrA8mP3wbln/kOVdrtChQkwNWRjavxCpjsajUapTP/DH/6AuXO3TsM6aafRF7/4xQCAl770pZmbn/Md2NaZySqkePUbX4G7LrgdA3uHGHswLjZwwp7I+HZoDSbPX8NVihVUrDab7wdAXqKTdQxNPvvRKznyQdqWuw9tcjCtTSRLcixS1wY/JbsLoEEaiZIE0mhAuW8so5DKXVviYMgRkm83wSp1/Czt7q8G2PsVGCMHp6G6ZA0b/lIkeTvSFOIOaaQKOU5RsEQRMYTLRsoEIdJjiABSjP6bgXANYWDnQbzr796JebO2rHpjhQo7MsqeAbZVvzsaTj75ZHzwgx/Ev//7vwMw98KHH34Y7373u3HaaadtVZ+TJhy33HLLVg1U4eljaMYQxFgd9bltmLJctmQ9cmaMODbCnUqcQ5NwjhLNRP6zHxrrH1+WsVTphFwk8yHjkwAga8IgQ1aSLJpJeGzSoTemi2xBQlBI20gULxxWB8h8m4Uy1o3UyTNHLMqUPe60rG9sou3ILA2ZxELS2m00AYLN9Sjxk1VKgAOVaDUE2C6jAMPUVlEsELBtEzFmrNSorQEWr9gF733TP2byaFSoUKE7qiiVbYfLLrsMJ510EubNm4dms4mjjz4aa9asweGHH45LL710q/qcNOE4+uijt2qgCtsGS3ddggfX/QmyB1DjpnhaaTgsM6iboNJGYrPg7ir6xKSQIxgudjPftMxqQiLbJjYRMv7xDACB9znsMh+FxA8jCQZxzqREBZ8MAgqpzcvAItWkZAiH69+N5Zt+pPeBAY5gzCyFqTPCMPa2k81DYk+JBUKoJJRWQGHopxq1NYze/WfhorPfXZlQKlSYDCoVxzbD4OAgfvazn+Hmm2/GnXfeCa01nvvc5+LYY7e+ftKkCcc999xTup2I0Gg0sPPOO6Ner2/1hCpMjNlzZuPBdX9C0ENQo6mULYTDFhJFWGQyj5rw1VKhxt5f33TRTctRKhdzzpK2L5ZZJ1K/PxEztEc6PH/RTLVX/zRARuEQK2TqriSaEddPDPONd6Ycvz9GmvmUkGpd2JpVnFdnULKunjYmv5nZVHx1kCJNDiatf4cQRpfCCqg/xuCFIT7xlg9XZKNChcliqvwtdkANx1VXXYUzzjgDL3zhC/HCF74w2d7pdHD11Vfjta997aT7nDThOPDAAye8EYZhiDPOOAOf//znM6E0FbYNFiycD/wekH0EPOntYI0kpThgnUeLvCDzqK5tyMbmUlg7p09fypeSlHS7MaX4zg2eUGaXbdQKZV8ToZEtngbzW2cyZhR2DqH2NIQGNLHJLZLTsjgFSCZaxTef5KaoQ07GJYJJBKbYmGJ8ApaesKcFMf4crAGlCUGok5oq6bJohCLtIEyyjRI0A0O/NyWi+hbNqMhGhQpbgSq1+bbDWWedhRe/+MWYNy/rPzYyMoKzzjprqwjHpI3D1157LXbffXd84QtfwN1334277roLX/jCF7DnnnviW9/6Fr785S/j5ptvxkUXXTTpyVTYPBbvYrK7BX3F/OFc5oeRR1mOjC3RF2rvl6xzv+okLXo2XJYSouI19YdL+kOGCLgw2kQ7mpADSp1NfWWO9eUgGK1G5lhLYHzikQTN5GW6q+vib3cakHJ1kfeebYbTYgcm3wZDCluzxSqmWnGYXI7+PyvMvFtBzqrjfa94R8lYFSpU2ByqKJVth4LW3OLRRx/F0NDQVvU5aQ3HpZdeik996lM44YQTkm37778/Fi9ejPe973341a9+hb6+PrzjHe/AZZddtlWTqtAdN/3oZgBG/V4IKc37ciiVzURaBiKwq7liP1PeHyTpv4TQeF9IgtVWkBW+REndl+xx2bdGy2E2cr4KumMo+aqynsJEMOCqrCdzsKaTxL/EP1yi3LcjqQbr+uYsFbPaCNamjkqqUDJF2/yVyPvaJhVjrXqGmROljxzWmPurDuI+wgcvuAgzB2eWTK5ChQqbhVOHTkW/OwgOOuggk6CQCC960YsyRRyVUnjggQeSaNXJYtKE495778XSpUsL25cuXYp7770XgDG7PP7441s1oQrdcc3Xv4c/PfFHNNdEGP59OxHcGfNEF1aawIWyunYJiXCdie6Oln55ez/aRXpkxXaVKjqyJIUBEzIrbEipJQNJFRIXGOIJfvKiUZJCa3CfbTisR0KS08+1YwY4cOvkT8i8FbDah8Tvg3I5OLzO/I+SC9uzac7JlbSxBMvsE6TBTJh1bwRi4DWvew3mz67CXytU2FpUJpWnD1cl9u6778YJJ5yQySReq9Wwyy67/OXCYvfaay989KMfxRe+8IWkaFQURfjoRz+KvfbaCwDw2GOPYf78+Vs1oQrl+J/vr8SPV14H3Was/Z+xNCrDM6U4YZ4QACHALi8K5UiBXyE2gxKnUCprn3dQze4n5kRh4DQoLChTHTZxf/A1NUIYMe0pRTJZRO1n9kmRsA6igTWl5JN+5UwwyXZl/UJs+GzGp8T1kTtlThhS6iNDgkEi27lRNrnB2RAQz57kNDv1jQoDDysM7joPxxxwJCpUqPA0kNhSp6DfHQSuSuwuu+yCM844Y5v6Yk7ah+Nf//Vf8cMf/hCLFy/Gsccei+OOOw6LFy/GD3/4Q3z2s58FAPz5z3/GW96y+RTMF198caK6ca8FCxZk9u+1117o6+vDzJkzceyxx+KXv/zlZKf8jMeG9U/h29/9FoiAJ24cgRov//aTFfqJL0cmnTknL9a6PKjEL3nv/3XVxPy06f4vUHPhB5mQCEdw/CxY5D/pG5NIElWjOU0jnumw9G167mx9OfzcG669+6yy+wSnPh/5sRiMjJLCb0R+7wCr/GoahpNkGnXVZ5ky2heAMfs3ERjAG8+YvANWhQoVsqh8OLYdXve616HVauFLX/oS3vOe92DDhg0AgDvvvBOPPfbYVvU5aQ3HEUccgQcffBDf+MY38Mc//hHMjNNPPx2vfvWrMTAwAAA488wzt7i/5cuX46abbko+S89/YI899sBnPvMZ7Lrrrmg2m/jkJz+J448/Hvfff/9Wp1Z9pkEphQ9deClkH+HJn4yi/aQLsczr9UsOzvt4eE4F+V1JkrBE+HsOCEl/7BwSigPmdI4Fp8zCgOVzJlc2Nu+3kT/OWXQcienWZ9m4Pqyzqe+4mvCM/GnmP7s5aBTGEZKTz5oJkthbWrv+MaNvjUJnUR177bL7BJOsUKHCFmMH0kZMJaaiWuykCQcA9Pf345xzztmaQ4sTCIKMVsPHq1/96sznT3ziE/jyl7+Me+65By960YtKj2m322i328nnZ3p9l0ve+RGMYwSb/q+FkT900h0+KaBUW2D2oUS4Z00gVGYy8Y/34Y9V4gTKgC1d70FkTSwscyaZXHPfTCLYupb4At4NT9np+a4YSfiq40plppT8qeUJhKeZAWBCY8GpmSbfHqat1pzyMJvci5ksycjNmE0+Dk0mG+qA7Jl4khUqVNgiVJlGtx3OO+88vP71r8fHPvaxRJkAmGqxedm8pdiqnMlf//rXceSRR2LhwoV46KGHAACf/OQn8f3vf3/Sfd13331YuHAhli1bhle+8pVdK9F2Oh184QtfwNDQEA444ICu/X3kIx/B0NBQ8lqyZMmk5zRd8F///iOsHnsErTUR1t82ntub9Y6iEAiHJOpzBXoWSfTuIjGwZ4Ch5RKDewfoWyrQmCcQDBAon82Tcsm/8pqFXAXa5K8T6LWgQDiM1sATtN3qtpgzgc5lLxXIMgt2H136cD+U1hIF4vS9O6YsxLU40axGpqCd8T/nnEjZmkZYI4lpEVInB2k3cdYIyHi1GDMLgUki6hUYe2qkZGIVKlSYNHgKXzsY7rjjjm1eLXbSGo7Pfvaz+Kd/+ie8/e1vxyWXXJIUa5s5cyYuv/xynHzyyVvc16GHHoqrrroKe+yxB5544glccsklOOKII/C73/0Os2fPBgD88Ic/xCtf+UqMj49jp512wo033og5c+Z07fM973kPzj///OTz8PDwM5J0/PH/7sN/Xf8DqI7GEzeNTlhOvWdhgPnH9UEEW87CdcSIm0A07F6MaBiIR5H6bDj4ZpZ8fRXy7BseSLNJZR6YYnFQnElhXiACCeFI+/KjUUSboXrTAxMziJdJ1FlhmFGMcpHIpkR3o+U0KHBRMH6mVv+YrB0KgLbhsZT4flCujXJV5cAgYgTSdQq0ZgkMPRTjiSfXYv7cKkKlQoWnhy1Qa251vzsWpkW12E9/+tP44he/iJe97GX46Ec/mmw/+OCDccEFF0yqrxNPPDF5v99+++Hwww/Hc57zHHzta19LSMMxxxyDu+++G+vWrcMXv/hFvOIVr8Avf/nLQvYzh3q9/oxPrT46OopPfvxyIGA8cdMoVLPUTdL4XQTA3KN6AQZ2X7AXhmYOIW4qLN5tEebOn43BmYNoNdt4/JE1ePjPj2L9ug3oqDY2rN8ADpoIFzFocdqrVkA0EmH4TwGaT/jpPj3CkZhXONWRJQLaN5ukWgrj1MnZjKO+RqEbODlViBjQwQS+HRN15MazJIWBjIOp0Wqk/ZLKaUg41WDkO05DktOw4OQgAkzJNkKkgbrwM8CmpKbKLFqhwjbAVGkjdkANx7SoFvvAAw/goIMOKmyv1+sYGxvbqkk49PX1Yb/99sN9992X2bbbbrtht912w2GHHYbdd98dX/7yl/Ge97znaY01nfHpD18JXYvx1B1NtJ8oKUPvxWDOXNFA0C9w1Iqj8Zq/ncCu9rzyzZ1OB3+67wH87q5VeOBPD2L146sx2r8Jc1dEaK4VeOr/AsTjIiUXZZVikwJqjoBQ4gdBMESDJCUZPjNuEDn/EXZ9umOdY6YtspZPe56uSe6ty93hOI+N0SU2pCrRePgH+X1o67vhkYKEVPknIbOaDc6RDUpOAkjjddguD6G2SSPuJcybs2M4QVeoMKWoCMc2w7SoFrts2TLcfffdheRf//3f/4199tlnqybh0G63sWrVKhx11FFd2zBzxin02Yi999sLD678Ewb2qGPkj22oMacmyPpJ1OZIDC2vo6578eo3vXKrxqrVath7+Z7Ye/meybb16zbgio9/FmvwMBqzO9j0pwDDf5aF8QFYBwwueANxiUmGQNAa4ICMsM/5dTgfDR8u7UWiCeDUZJLXSmSOcx26Y/36KwxwDKgg18YjElpaspFocOxfnR0gDXMlz5zikQ2YLKMAI7R5OVgTKDAdBm1Gva9yGq1QYZugyjS6zTAtqsW+853vxFvf+la0Wi0wM371q1/h29/+Nj7ykY/gS1/60qT6uuCCC/DXf/3X2HnnnbF27VpccsklGB4exute9zqMjY3h0ksvxUtf+lLstNNOWL9+Pa688ko8+uijePnLXz7ZaT+j8LJXvRTN8SZW/upmLPyrQaz+4XAx9wYBc480guqtb/87iM2lMJ8EZs+ZhQ/883vxi5/djq9/7RuYsUcLfYsUnvqjRGtjSRrSWBvyIGX6IOCRCQKgNQPSunNIgYKGAsgkBXOBuwTj/pH363DaCxOmW+zMHZKQmJwGg2VKCJi9/mCKwUFm24MB8kxEGR8Prw3DRKb4V4PIbbXKjoTEaMg2Y3xGiRarQoUKk0aVaXTbI18t9ulg0oTjrLPOQhzHeNe73oXx8XG8+tWvxqJFi/CpT30Kr3zl5J6yH330UbzqVa/CunXrMHfuXBx22GH4xS9+gaVLl6LVauH3v/89vva1r2HdunWYPXs2DjnkEPz0pz/F8uXLJzvtZxxe9cYzwABu/dXNWPiSQaz+4QhUM90/tG8d9TkB9l6yL/ZcvseUzOGwIw/BwYcdhK9+9pu4/Tf/i3kHjeOp+zRGHs1mnsunCuea9fOwMlkLAgIBZg0WMjV5OLJAORJh+9FW6Du/VGbrd+ERiNTBMyUDcH157dj2mfhs5HlT4sth/S44p7Up80UrcUJNLDC+ecbm3VAMSFdNl4GejRpCAQt2Wphf+goVKmwNKpPKNsWvfvUrrFy5EmvXroXW2ciFT3ziE5Pub6sei88++2w89NBDWLt2LdasWYNHHnkEb3zjGyfdz9VXX43Vq1ej0+ngsccewzXXXJOYZRqNBv7zP/8Tjz32GNrtNlavXo3vf//7OOSQQ7Zmys9IvPqNZ2DPucsRDkns9JIB1GYJyF5CbZbAzBUNiCjA373zb6d0DkEQ4Oy/fx3e/a4LodsBZu7eQv/ittVoCPMSNhKFOQ2HFQRIMuGugSEgJLJSnqwUZmaQ4kwYrZHvbHwknAaDnIzP/voZgBKpf0UmUSp5/bnt3bSj2uhU8qnUAQCSszed0qihlEz4sxMEgEWOExH61xjNxsmHH9dlQhUqVJgUnEllKl6TxJVXXolly5ah0WhgxYoV+OlPfzph+1tvvRUrVqxAo9HArrvuis997nOFNk5G1ut17LPPPrj22msnPa8txYc//GEcdthh+OpXv4o77rgDd911V/K6++67t6rPp6WHnzNnTtdokQrbBudfci6ef/ALUBuSWHzqIJa+egiLTx2ECAivOfM1f7GInF33WIoPf+wDENzArN2b6NtpAj+a/FNGPvqDTdpwFgAkQRCZ9Oa62E3y3vlhuP4obaMDMiG3Ij2mNFrX7RTIpNMgANTJjk/azDXbKHeeLheI7ygqdHYs9v+QdRc16F8TgwPCIXsfiAoVKjx9kJ6612Twne98B29/+9vx3ve+F3fddReOOuoonHjiiXj44YdL2z/wwAM46aSTcNRRR+Guu+7ChRdeiHPPPRfXXHNN0ua2227DGWecgTPPPBO/+c1vcOaZZyZRm1OBT33qU/jKV76CVatWYeXKlbjllluS180337xVfRLz5q1TrlztluDOO+/cqolMFYaHhzE0NIRNmzZhcHBwe09nq/GdL/4H/viH+yBrAhvXDWNGz0xc+KnJhSFvCzyx5klc/L4PQYs21v+hD2NP1I0JIki1FxxKU6hNCluwLeW1WgK67jlQuGMA6JDAXq4OLQAORYZsaAJ0jdKHDjIOqrrGmT51YKvI2jL1yRiB2cYAOPTGtwRIB5yYYlhwYnphoYGa68f+ZAQb50+bV4NsCXoRGCZCZJKABVKDwKiFyhRskzFkzNj7B5swuOt8XPauD27FlaiwvfFsubc8G+CuxZJPfgiiZ9sVG3PQzf/f3nnHSVHf///5mdly/eA4uOPoIIggSlGaGjAqwRgNGnsBS0yIBdGvNWpARfipEf0mVozfiIlGE0uiPlTAhhULYg0gKh2Oo94d13Z35vP7Y8rO7O7BteXu4PP0sXI785nPfGbvdue171rL+qtvZf369b7fdX1lGEaNGsXw4cPd/mIAhxxyCJMmTWLOnDlJ42+44QZeeuklli9f7m6bOnUqX375JR999BEAZ511FhUVFbz22mvumIkTJ9KxY0f+8Y9/tMh1eunatSvvvvsu/fu3XNuFBsVwOO1qAWpra3nooYcYNGgQY8aMAWDJkiV8++23DWrYpmgaZ116emsvAYCi4s7cOvP33D7zTgoGVCElVG+13nBSdwIrLN+BNKWvAqkEf/aKB5nC1iZMWzRo8eMdK4Y3y0UAGCLpr1kIkIZ9TGL2C36Xi3sGryXDkwVr1VS3s08Q8QyWenW4VVE0oNmZKUBdNEAwEMXUIG9rDCGhZ/8+9U2gUCgaS5pjOBKLSM6YMYOZM2f6tkUiEZYuXcqNN97o2z5hwgQ+/PDDlNN/9NFHTJgwwbftZz/7GY8//jjRaJRgMMhHH33E1VdfnTTm/vvvb/z1NICrr76aBx98sEXnb5DgcNrVAvz6179m2rRp3HHHHUlj1q9f32ILU7RdSroVc8sfbmLW7bPpdHAVpqlRXa7HLRlODQ1NxAWGbZEQQkBU2tYF4av06a0sCrY1QsRVgcD2YqRwb3jbyruCBI9gsMWBN44i3tHWfu512yQgnC68nvVCgtdGOuu1/hWaZ7wTfWL3esjcacVvHH/Y2NQnVCgUjSfNgiOVhSORbdu2YRgGRUVFvu1FRUX1lgQvLS1NOT4Wi7Ft2za6du1a75imlhnfG9deey0nnXQS/fr1Y9CgQQSDQd/+F154odFzNjqG41//+heTJye30j7//PN9/ibF/k33HiX8/pYbkaYgv0eNPzXVKXRl2O9+zb6Z29YOJ7DUzQgRgBBu0S8JxDQs90rCB4gpRGI7E+KTAqad3eLBF05ix12424RzmPS7WFJdtOkJCE0QHmAJCVNaC7ZiSPwBJEJYAaQmgoxyEymgf8++qc6kUCiagkzjA6s2hfexpxi6xDAEmdg0swHjE7c3ds7mcOWVV/L2228zYMAAOnXq5OtRlp+f36Q5G50Wm5mZyfvvv5/k13n//ffJyGh535mi7dKzV3eKO/eibMcaAmGTWCyV38L7syfOwmul0OKbTGG5ZqwbM/64DGHrGtPyoDh9VLyN3SDZUpJUg0NaxyZk0vrX6sSH4Pzr2DJMT/qu9B+DQAhhFfpKnNuZUoCUgoxyg0iunvStQaFQNIM2UPirsLAQXdeTLA9lZWVJFgqH4uLilOMDgYDbV6y+MfXN2VyefPJJnn/+eU466aQWm7PRFo7p06fzu9/9jiuuuIK///3v/P3vf+eKK67g8ssvT/IvKfZ/xh97FABZnWrjG21l7rSkt27awhUPprDcLYklzY2AsJq9+dwQFiYk185wsl0SBEVSRHmi+8XRDh5F4O1O6xTnqvczxq3vIaziq55xesD0PY/GNKQJmohXJRVRk1CVSbRDqJ4TKBSKpiBk+h4NJRQKMWLECBYtWuTbvmjRIsaOTe1CHTNmTNL4hQsXcsQRR7hfSuobU9+czaWgoIB+/fq16JyNFhw33ngjTz75JMuWLWPatGlMmzaNZcuW8cQTTyQFySj2f35y7BikqZHdsda33RIBmv3F3yM2ABn0iw33mD3Y2yyR4DEtOsfoqQNC97ShvhQ3x90St7gkmCkcN4kbBEJSzQ1/9XdLCRkJYzJqrAX06doz9UIUCkXTSLNLpaFcc801/OUvf3HTSq+++mrWrVvH1KlTAauruTc0YerUqaxdu5ZrrrmG5cuX83//9388/vjjvoaoV111FQsXLuSuu+5ixYoV3HXXXbzxxhtMnz69cYtrIDNnzmTGjBlUV1e32JyNdqkAnHnmmZx55pkttghF+yUYDJKhd0LkbEUPGsSiuls91IrHEL4AS9ev4MW2OAi7qVpcTciUwgTwZ6kk6II9WT8lqYWN9GbCeK0ldo8Ut7KpL3hUxBWHsGI0zJhAD/o/neyaaAhhogkIVVuCY2BJ7/oXqlAo2i1nnXUW27dv5/bbb2fz5s0ceuihvPrqq24Pss2bN/tqcvTp04dXX33VzQwpKSnhT3/6k68r69ixY3nmmWe45ZZbuPXWW+nXrx/PPvsso0aNSss1/OlPf+KHH36gqKiI3r17J7l/m1ICo0mCQ6Hw0qtvT777YSta0BMcgX1z1xOsGSn6ngDWDdzbKM3j8pD4rRgSrAqk3sNlXCykSrF1MHVrid5zOMcJhNXZLXGJ9mXFA1Ptcc7Bzlo1kKawA7l8q8WUgoBtlw3ZFo7unbvWv1CFQtFoBI1zfzRm3sZy2WWX1Vsq4oknnkjaNm7cuL3exE8//XROP33flEjwlsNoKRokOAoKCvjuu+8oLCxs0KQ9e/bkvffeS+ooq9g/6ZCfA4AIAjGP2Aj47/xu4oZzk7ZxBYWTTusd7z3O9IiJVNYPGY/nEFi9S4SeMIfX2oItVDxxGCJGcnpsYsyHjM8gnEYyntKmpiHQ7W6w3hUaUqAjCdiCo3dRNxQKRQvSBoJG9xe85TBaigYJjl27dvHaa681OBVm+/btGIax94GK/YJwhpUaJjRPlxNNWDdt3ROMid9lYVkqsIMw/fEZ0pMA4qTNCqzqo/GaHX7l4hznrashQ8SFiD0o7trxHOgsWwqMxOK7CU8FVot5y8rhntndJhCYprTLktgBtFKzFyfJKo8hBZR0KU56LRUKRTNoQrxFg+dVNJsGu1SmTJmSznUo2jEZruAAb5+TuNkAnDCIRBwriCUWLEuBxCMQ7Loc0ikSltBRNslcQvyzwe1i67WmJA9PONbzySLAlGZyq3qwGr0lZbRaJ5RS2lrEE+uBRNcNkJKcHTFCRfmEQipLRaFoUZTgaBbp9mY0SHAktqVVKLw4DclE4o058efEm32iR8S0Azo1r1CRdrv75GMEtgskYAsVpyW9vVNoKQSGYI+CI3GfJjRLdPjmkVb32KTjrEqilsYQCEy/S8XUyKmKEIhKuvdV7kaFoqVpbAprY+Y9EEi3N0MFjSqazYb1VjEa00zMP00x2Gtt8MRJmIAZFggZt5AIrMqivgJhCQJCkxDzBHU6lhFfjIbn9JoJpqcEuiuEnG0i2TVjuVAk6LYFJFFsJHytkqZA2o3c3NobAkxTI2dbFIAjDjk8xYujUCiahbJwNJt0ejOU4FA0m3Xrf8Q0BbVV8TK/iYW9IEXqqme/BhiQLFI8lpFUPVRMx6ohkg9JLO5lHQCaYVcxtUWKHYHhFvxyxiUGjsYbtlmBINK0624If1aKpgmrr0sSgtyyKFLAmMHDUg1QKBTNQQmOZpFub4YSHIpmUV1dTVXNNmp2Z1iBkeDGYCR6M6QAqdtmCid40xETTkt44uZLr2jwBpu6cyUEm/o+E/xemfixjttH+sc6+TGO1UUA0pB+MeMTPMLKmrHFhpOoEi/85QlmtccIU5K/LUqoqAPZ2dkpX0+FQtF0lEulbaMEh6JZLFrwPkJATWWGe9OVurCERULQppWV4rlr2zdzU9jbTWy3BW68peERIs6/bhqrLTaEtC0WibU6nGMcK4anzodMEQiaVL/Dtp74rCW+eBCnsqiGFUXqj4yNxXQ0zSRgp8jm7oygG5KDBh68h1dUoVA0GZUW26ZRgkPRLD788FMAqnZnWjdxSbzSqB38ad2wHdURz0RxRYPXquBYPJwTOGmwgIxhdXRNKB7ms344AibgOR58tTakZ7tvHq9gEdK/rsSiZPifC81+eK0wpobpqaPecVsdAMeNGJN8coVC0XyUS6VN0+heKgA//PADt9xyC+eccw5lZWUAvP7663z77bctujhF28Y0TXZVbCZSGyAqg6BrdvM1kbKiqFsbi2R3iLURn3AwQvExPm+G581vYme2JGaieMam/G4i6/kZT6yGd4B03C4JBwn/U/+JTYQQmPa+jlvriAU1Bh+kLBwKRTpoC83bFPXTaMGxePFihgwZwscff8wLL7zA7t27Afjqq6/SUplM0XZZseJ7IEpVVWZyLEUKLWHW06/EX8HTQuAf5LhS3PgOJ65Dx9+MTXqW4hUDiaoj4QPEdDJgvIGj7jQifo56PnikmayfNF262SkZ5VFyKmLI7h3RtCbpfIVCsTdkGh+KZtOkbrGzZs1i0aJFvsJFxx57LB999FGLLk7RtnnxeatVcnVVVnyjsOIjTGHdxA0NjLCGGRRWbAcprBs+8WG9s10B4B3jZpRgVzFN1i0pPxcSLRi6HdwpPcfYgaPxxmz1zGHaj6QxwtM5VoJm+i6xZK3VcfGKSeelWqFCoWgJ0mXdUIKjRWi04Pj666859dRTk7Z37tyZ7du3t8iiFO2D739cgWEIamo86bBgx3B4H1hBoZotOjTbbeE5yNQFZsCeIOGmb2IJEAkYgng5dBufRcOJ3Ui0ltgWCm9qrfAN8CLcZViWGem6bURia1rnZMKyckgJUlqBJEbUmjgQlRRtqkErzGPYwYNSvZQKhaIlUBaOFuGhhx7i+OOP58wzz+Stt97y7du2bRt9+/Zt0ryNFhwdOnRg8+bNSduXLVtGt26qGdWBwsqVPxAKRdhdmY1zx5bgxnAIe4Pzr/OPGRBITSC9Bb1cESEwAwLD02HWCIAMCdAFpi4gJFIIBHt+r5gw8X1ICIN4nQ3v8U73WGec6ThznGuSKV0suPPHTyJNgRmLj3JOVLJhN7opGTd+vCVYFApFelCCo9n86U9/4rrrrmPgwIGEw2F+/vOfM2fOHHe/YRisXbu2SXM3Okvl3HPP5YYbbuBf//qXFRBnmnzwwQdce+21TJ48uUmLULQ/XnjxNQAqKnLiG7XUwaLum1XgKz1uCoHpqdop7cwWNysk8U3uWj8SslTq+TAQpuU+cd04zhTebBVnrMTTv4W4i8VVTfHtzs9CCKtame69QMc+Yjl/9JhJzzW7iWQGOG3cCakXqlAoWgRVh6P5PProozz22GOce+65AFx22WVMmjSJmpoabr/99mbN3WjBceedd3LhhRfSrVs3pJQMGjQIwzA499xzueWWW5q1GEX7wDAM1qxeQSQSoLYu7MZVyECy2HDiI0zADCQYJ9xqWfFxvgMTgzAB01P63CskTK+Y8eBWFPW2oHdO4Ut9wRURXnePJjXMmGm9U7y6wj1WgF323MUzZ4+1uwlGTY75xYmqWZtCoWjzrF69mrFjx7rPx4wZw1tvvcVxxx1HNBpl+vTpTZ670YIjGAzy1FNPcfvtt7Ns2TJM02TYsGH079+/yYtQtC/efucjIEpFZQcQGlKTqdNc7U1SE8gAyKCAmN9g4E9nbbi7QWJbL3TcgmH+ExOvveHU0/BaMeqbN+GrjEQiTE8wqbQn0n0HJcWdAARjMXqsrcTIyeCcE05q8LUpFIomki73xwFk4SgsLGT9+vX07t3b3TZ48GDeeustfvrTn7Jx48Ymz93kwl/9+vWjX79+TT6xov3yxqJ3kBIqK3MB7HgMqy27E6NgCQKBifR0f7WEBzGZeG9OSUpx4MSDOGIDj7vEyWLxiA0RP8QVOO5nR1KqrEzZIE4miBmnMVvSuhLEykHflxOIScZOPB5dT1RECoWipVEuleZz9NFH8/zzz3PMMcf4tg8aNIg333yTY489tslzN0hwXHPNNQ2ecO7cuU1ejKLtU1FRyY4dG6ipySAWs/58pA4yYOWoSju1Q2rCaitvmtaN3xt/4VUBHnwCQ8brdnj3C2FlqnhFgLCndoWHNzBUxk9lesuZOy4R1+rhtVDEYzDcuaTwfer4Q0lcpxLCXleHXbV021xFuFsXJh//i3pfT4VC0YJI3Iy0Fp/3AOHGG29k6dKlKfcNHjyYt99+m+eee65JczdIcCxbtsz3fOnSpRiGwcEHWxUTv/vuO3RdZ8SIEU1ahKL98PenXkYISUWlFSxqxVF4lIIQloUhaG+zi1z5tIQu7J7yxO/tCTEYVuyF5apwLSd7yakSEsuCkoqErrJJ8agJqbY4kRwJlgwpbKVhWmLEb+nQkIaJZpoMXLkDUxNc/evfqswUhWIfoSwczeewww7jsMMOq3f/4MGDGTx4cJPmbpDgePvtt92f586dS25uLvPnz6djx44A7Ny5k4suuijJBKPYv1i/fhOff/4+oFFelxO/2XoEh8QWG6lush5xIXUrVdW5oZs6CXM4+4QVI+LsS7BuJM0PccsFfmuHMBICRVNlnzjbHUOMd58JBD2DvHEb9rmFFAxctYOc6ig5RwyiT0n3eharUChaHBXD0aLU1tby1VdfUVZW5mtdL4Tg5JNPbvR8jY7huPfee1m4cKErNgA6duzIrFmzmDBhAv/zP//T6EUo2j67d1cx5//9EU0YbNxWjBQ6Mij9b0Rpi4H6vtF7x2p2doeUloXEK1pEfA4p7OwXO+JT1pONkko0CPxuFAFIJ8BU+IY26PPElyLrLtTzCSehx6YKSrZUkd2rhHsuubIBsyoUipZCWThajtdff53Jkyezbdu2pH1CCAzDaPScjS78VVFRwZYtW5K2l5WVUVlZ2egFKNo+pmlyzXW3Y8Sq2LqrgKqabGtHQpO2elNjBcRCIqUbQ5oprBvemhmOOHBcKnvwTqTSOcJMMSYeouE/McmiwlfW2FuszDnMKQKiQcfyGg5evQOZm8kd069TPVMUin1NqoJdLfU4wLjiiis444wz2Lx5M6Zp+h5NERvQBMFx6qmnctFFF/Hcc8+xYcMGNmzYwHPPPccll1zCaaed1qi5Zs6ciRDC9yguLgYgGo1yww03MGTIELKzsykpKWHy5Mls2rSpsUtWNJN75z6KMHexa3cuOys7+PZJLMFgBqz4CbuqN6awrRPCLkseFFZMhhchLNGhiXgztkSXRyPCH1JmtSQKBGdbYmCZ93mC9cPnqkk6qfW/jNoYQ1eUYeqC66ZNJzsrK8VghUKRVpTgaDHKysq45pprKCoqarE5G+1SeeSRR7j22ms5//zziUaj1iSBAJdccgn33HNPoxcwePBg3njjDfe5kz5YXV3N559/zq233srhhx/Ozp07mT59OqeccgqfffZZo8+jaBrP/PNlvl/1BdV1GWzZ0ZnEu67U8QsJj0jwxl0AoFkWDdcRISRmyBMMKq1mb+7+FHJYYHliUt383Yqj3v0ei4YEt36GP11W+mt0mAmxHt7E2hi2pSVuCdGkydAVWwjFTE46/xz69+ydvDiFQpF2lEul5Tj99NN55513WrT8RaMFR1ZWFg899BD33HMPP/zwA1JKDjroILKzs5u2gEDAtWp4yc/PZ9GiRb5tf/7znxk5ciTr1q2jZ8+eTTqfouFs2lTKW2+8QtQIWHEbbocRDx5RkOo96bpFwOPPsLaZeorYDR2ksAJF6zNvCBLESD3fQLxBoynFiD1ZPPXV8k1KU0LQnlKzBwmJ0OxUWe9CNBiwZif5VREKRxzGKceMT7lmhUKxD1BBoy3GAw88wBlnnMF7773HkCFDCAaDvv3Tpk1r9JxNLvyVnZ29x9SZhrJq1SpKSkoIh8OMGjWK2bNn19uJrry8HCEEHTp0qHe+uro66urq3OcVFRXNXuOBygv/WYgQsGVnIYapW2mgjutBWm6UpDbziX1O7KZs9iFW9okk2b2CE+DpBIsKd0rfGC2F5UPYLh3d803EERb11OZIykrREp4n4nyQ2amwumFgIuhUXkOv0goCRZ2449e/S3GgQqHYZyjB0WI8/fTTLFiwgMzMTN555x1fer8QYt8IjmOPPXaPdQUSW9nuiVGjRvHkk08yYMAAtmzZwqxZsxg7dizffvstnTp18o2tra3lxhtv5NxzzyUvL6/eOefMmcNtt93W4DUoUiOl5L/ffEHM0KiqteMRhOdeLfAFd5qA0BKSOJzaF84YW3xIUggDgb/xm8RyfzjuDXs+mZBh4p7HW3VU2udK7G9S35+tb9H2Opyqo95BdqBJdm0dR3273j0sqmvMuOIqFSSqULQyyqXSctxyyy3cfvvt3HjjjS322dZowTF06FDf82g0yhdffME333zDlClTGjXXiSee6P48ZMgQxowZQ79+/Zg/f76vumk0GuXss8/GNE0eeuihPc550003+Y6tqKigR48ejVqXAr5b9SNGrIrKmjycO7IUYIaEfVOXyJB1R4+7LSSGbb3QotJOPxXxeAmnp4k9l/dNbCSWFLcFgql7rBOCJOuE9Agf9zy2a8YrdpLLmKfY5tmZsgOtaR0TjhrWoR1yqcsIcfHpZ1LSpeUCqxQKRRNRFo4WIxKJcNZZZ7XoF6lGC4777rsv5faZM2eye/fuZi0mOzubIUOGsGrVKndbNBrlzDPPZPXq1bz11lt7tG4AhMNhwuFws9ahgL8/8woAFbW57r3ZDOKmhvq6obhWCmHd6HWBKSSajLtSTN2OyvQICieDRQjsqqKeBXjFwB7+3r1WFB97yW6R4C/q5W5PsGzYWShed4whrAEFAw5iziVT93wihUKxz1AWjpZjypQpPPvss/z+979vsTmbHMORyPnnn8/IkSP54x//2OQ56urqWL58uVux1BEbq1at4u23305ysyjSg2mabNn0A7FYgKpoBkaGIBCRHrGB3/1h49uua5hYVg8zAI79wQ07teMh0LBKnceHWNYOkSI41BliCwUzhXvFnXcvWFYW6ctG8YoNJx3WrcvhCiWBYbsU83R/EJVCoWhllIWjxTAMg7vvvpsFCxZw2GGHJQWNNqVvWosJjo8++oiMjIxGHXPttddy8skn07NnT8rKypg1axYVFRVMmTKFWCzG6aefzueff84rr7yCYRiUlpYCUFBQQCgUaqmlKxL4/ItvELKOXZGOGGHdKtyly/pVvr09Mb5CAmZAeNwhKawYiRYGxx0iUlsgnIlN59hU6bEpmr4lLlcGSJFhI0CTya4dB1sIGbaoWr9ta4rFKRSKVkMJjhbj66+/ZtiwYQB88803vn1N7Q/VaMGRWNxLSsnmzZv57LPPuPXWWxs114YNGzjnnHPYtm0bnTt3ZvTo0SxZsoRevXqxZs0aXnrpJSA5buTtt99m/PjxjV26ooG8+poV+Lsrmhv/9q8DMX8ohYsAw/63vqBRZ5zpBJ6aJJVBlzhum/jzVIJEQvwvN0UAaVIBsMSYj4CzLX4lbiCs6aTkenY7WSz2PLWhADWhAMEf17J91046deiIQqFoffYUG97ceQ80vD3UWopGR4Pk5eWRn5/vPgoKChg/fjyvvvoqM2bMaNRczzzzDJs2bSISibBx40aef/55Bg0aBEDv3r2RdqfQxIcSG+lj165y1q39jlojRK3piYVxqoIKy+XhCAewAzvDHtcI+OtveJABgQwJzEyRlEKb6CIRAjBw62m4HV2djBRwgzutQmLxAFNT2gXC3MmIu4MS8X5KuSVPnTXE1Y8eMzn8x80c9+VqMiMxAobJ8x+8m2pGhULRGsg0PtLEzp07ueCCC9x76gUXXMCuXbv2eIyUkpkzZ1JSUkJmZibjx4/n22+/9Y2ZN28e48ePJy8vDyHEXuesj++//54FCxZQU1PjnrupNNrC8cQTTzT5ZIq2zVuLP+KZfzyNLgy21BXg3oWde64miGXgxkmIqPWzDGGpA+fmX8+N3WexcMSB6cxtPYTHneG1ePgsJ878jvAwPVaLBAtIklumvk6z7rIsX44TK+pWEzVMhq/eRMHuGuoyw+g1Vq2XM485ds8TKhSKfUZ7DBo999xz2bBhA6+//joAv/nNb7jgggt4+eWX6z3m7rvvZu7cuTzxxBMMGDCAWbNmccIJJ7By5Upyc3MBq1r3xIkTmThxIjfddFOj17V9+3bOPPNM3n77bYQQrFq1ir59+/LrX/+aDh06cO+99zZ6zkZbOPr27cv27duTtu/atavegl2Ktkt1dTUL3nqPS6bdwrNPP4FpxlhXVcz2ug7uGLfehlPES7OsE0YIZNhjqRBgBCAWBqOeeEqfu8OO1zBCYIaAgF+smJ7UVvd4Zz22wHDjMbxuGOeYVO4VIMk+6lpJJKaQSbEdwpQMW72Zgt01dB96GI/8v7sJFHbkiJ8dT4e9ZE0pFIp9SJotHBUVFb6Ht8hkU1i+fDmvv/46f/nLXxgzZgxjxozhscce45VXXmHlypWpL1FK7r//fm6++WZOO+00Dj30UObPn091dTVPP/20O2769OnceOONjB49uklru/rqqwkGg6xbt44sT2+os846yxVHjaXRFo41a9ak7BRXV1fHxo0bm7QIxb7DMAy+/GY5i9//hBUrliPrKhDC+kOojGWyoaaYqBn0FeV0/jUT4yZ0gTT89283SFSXlktDxo9x3CKJHVy98RFSswWEgc8aYjo6wAkqdXaJuKhwrR6JomYPDliBQBrSddcI71psS07v7bso3F1N1kF9uWXqZQghePDO/1f/pAqFovVIozUisabTjBkzmDlzZpPn++ijj8jPz2fUqFHuttGjR5Ofn8+HH37IwQcfnHTM6tWrKS0tZcKECe62cDjMuHHj+PDDD/ntb3/b5PV4WbhwIQsWLKB79+6+7f3792ft2rVNmrPBgsMJ4ARYsGAB+fn57nPDMHjzzTfp3bt3kxah2HfMnD2Xsg3fA2BIjd1GDpVGFuUii4gRRJPC9Y44gZRmwBdaCTiWBIEU0g0m9btMhNuszevugHhgpzRJ/Rco7O2mNakT6GmSbLHYU4Bo4nOZQnxIZHJhMSAUi9GlcjeFVVV0qaiiNhjgwav/p8nR2QqFIv2k26Wyfv16Xy2o5tZ8Ki0tpUuXLknbu3Tp4mZlpjoGSOriWlRU1GQhkIqqqiqfZcNh27ZtTb7uBguOSZMmAVYQXWJF0WAwSO/evZvk01HsW8rLyzGl4Iea7lSbGXbPEuG6NAxNosUsK4SQEAuBmaGhRczkFFGwgkk16StB7iVVx1fTdok4sUe+wxIyQ6SMB4Im9UPxnidpooT9TqZJqvXpnkFCMmhTKSW7Ktyl1AUCXPzrSwgEWiyLXKFQpIM0p8Xm5eXttfgkWIUw99Zi49NPPwVSp5hKKff65SZxf0OOaQw/+clPePLJJ7njjjvc85mmyT333MOxxzYtdq3Bn6CmadnB+/Tpw6effkphYWGTTqhoXTRNQyKoNjPtb/wJEZ5CWK4TTSJjdmwFYAYFmseT5hMSHneJu5+EQFDPdvcG73Fd1Pc2cV0mexETSRkxCed0i5zaYsYt8pVk8YBOu6uIBHR++avTmDjmKDIzM+s/uUKhaDO0laDRK664grPPPnuPY3r37s1XX33Fli1bkvZt3bo1yYLh4HRXLy0tpWvXru72srKyeo9pCvfccw/jx4/ns88+IxKJcP311/Ptt9+yY8cOPvjggybN2eivbKtXr27SiRRtA03TEdbt1teDxMWJ/9QEsUzilUOF5T4RTtaoR0mbdr0N4TFxmAEsYeH5xpEY4Ok9n5Q4hUx9lhKfQElcp/QEke7NleJYS5DxuhqpEBDVdTQpOfWnx9czSKFQtEnSbOFoKIWFhQ36Uj5mzBjKy8v55JNPGDlyJAAff/wx5eXljB07NuUxffr0obi4mEWLFrmFuSKRCIsXL+auu+5q3EL3wKBBg/jqq694+OGH0XWdqqoqTjvtNC6//HKf0GkMDRIcf/rTn/jNb35DRkYGf/rTn/Y4tiktaxX7DiEEAvtrgNDcm7Z/ECmbl5k66DGSsjhkSNhFs0DE8Ad2OhYKaae44osF9blCJHYFUQ/SUwgsyWqheywbgvo/FDwnFNhxJ76T4IsxiQZ0cmqbF32uUCj2PW3FwtFQDjnkECZOnMill17Ko48+Clhpsb/4xS98AaMDBw5kzpw5nHrqqQghmD59OrNnz6Z///7079+f2bNnk5WVxbnnnuseU1paSmlpKd9/b8Xsff311+Tm5tKzZ08KCgr2urZ169bRo0ePlK6hdevW0bNnz0Zfb4MEx3333cd5551HRkZGvc3bwLqZKcHRdolGo2zfsYMQHpeIsIt4aaAlZo94g0fBaspmxNNGpfBXBsV2oSSlsuqeGArPOdyMM29mSMCzE3yWCtMWF0J6UmYT6mzIhH+TkUnHuJMKCEejZNVFD8hmTQpFu6eNWDgaw1NPPcW0adPcrJNTTjmFBx54wDdm5cqVlJeXu8+vv/56ampquOyyy9i5cyejRo1i4cKFbg0OgEceecQnFn7yk58A8Ne//pULL7xwr+vq06cPmzdvTgpq3b59O3369EmZrbo3hGxO2bB2QEVFBfn5+ZSXlzco2Gd/RUrJjP83ly1rvmOL0YENZpH7JjJCliUhUIPrMjGCgAGat/S4DjEkQcNyo5gBK6MlqXS48N/P3QqijhXCk3niFBFzLS2J7hNPi3kzGD+ehHgR53ymnrDPGSuxglu92TJ41qVbL8Zh6zZRVLmbUSf9nItP+eXeXlbFAYz6bGk7OL+Lwy6cjR5qXE+vhmBEavnqid8fUL9rTdPYsmULnTt39m1fu3YtgwYNoqqqqtFzNjqG4/bbb+faa69NSpepqanhnnvu4Q9/+EOjF6FIP3+Y+zBla75jF9ms04usdvB4gj0FxDIkWtS2XITt4hfRuOXD1IGQIGrIeDv5xDocjhXCqSAK/loYToBpYtyFvb2+2FCZMIcXkWpMwk4pUkemBo0YhVVVZEcjZNfV0aWyit0FHZTYUCjaIe3NpdIWueaaawDLY3Hrrbf67vWGYfDxxx8n9TdrKI0WHLfddhtTp05NEhzV1dXcdtttSnC0QZ5/dSFl331JlQjzveyGc+d14iC8tTPMgMfdolnpsiLisTCAK1ZckWAmB4Sa9naEJUKEbZkQJLhQvNYQzbZWeMSKU+o8VV8WB1dopFIrTkZKin2hWIyR69aSGYu559udGebaiy+p/2QKhaLt0g5dKm2NZcuWAZZV/Ouvv/Z1Zg+FQhx++OFce+21TZq70YKjvlzfL7/8skGBKIp9y4effcHr/3mBKAFWBLsTQ0OLWPtSZoykcJHEwrawSJFCagYsQWLqCfttoeFU8JT2AfU58KTnOEfEuB4YnyhKOCYx4yTV/hRopsnQTRvJjMXoPnIk559wAt27diUYrKcmu0KhaPMIUyLMllcH6ZizreJ0ib3ooov43//93xZ1ITVYcHTs2NHKcBCCAQMG+ESHYRjs3r2bqVOnttjCFM1nx65dPP6XeQgEK4PdiYqglS0SsK0IqQpheeMbPO4Wp+qnb6xdwCumgzCSzY7Sm60CqUWDjtURNoV7RUqSqpT6SHDLJLlUXEOMkwgcv7ZeO3eQX1tL98MO49ZLlEVDodgfUC6VluOvf/1ri8/ZYMFx//33I6Xk4osv5rbbbvOVNg+FQvTu3ZsxY8a0+AIVTecvT/2TgDRYpZdQrdmBVE5NCo8rw23rTgqrgVeAxJ9iOj1P7I1SB2nEB0pI+uty3C7CjvtwAkelXTrdmyUjhZ0S62S2eCJDHTG0p9ob8XPKZMGiwc7sLOT27axcuYLdVVXkZGcnH6xQKNoXyqXSbE477bQGjXvhhRcaPXeDBYdTzrxPnz6MHTtWmZ7bOLFYjO+//oIIAbZruf6dnhu3ICGzI0XKqJM9IqUlFkyd5JLlAgw7VkMYKdw1YIkEzUrDdS0sCS4QVwR5OsU64shNefW4d5xpnOJjbpyIc53OObxCSMDOrCy+69KZg8u2cuvce5l7y62qT4pC0c5RFo7m4zUmtDSNjuEYN26c+3NNTQ3RaNS3/0BJGWrr6LpOOLcDomI7HWQVu8ixdtixFaZ9IxamdXN3rQuprARO9oiwxIJZTzaJY62wgjusf4XnG4ev9kcGSV1j7VYmvr4n3roa3r4n0kmj1ezSHrYVQ9r5tdIrNnD2eSYUsK6gI4VVVbBhI5WVlepvV6Fo7ygLR7NJhyvFob4Cz/VSXV3NFVdcQZcuXcjJyaFjx46+h6JtIITghqunEUPjoNgmsmUNRtCqr+HGZWjxuAfv+2mP7y3HfZJY3Avi8tW52etWLxYjC4wM6k1rdecIWGLGCHliSWzXjRMv4gtsdc7jNHdDWpYY55F4Hh2/xBaCgGES0XVfwRyFQtE+cSwc6Xgomk+jBcd1113HW2+9xUMPPUQ4HOYvf/kLt912GyUlJTz55JPpWKOiiXQvKea4X56OBhwSWU+uWZ1800/VwMzz3EyxH92KrzAD8fFJaasiIcNESxAyIlkQyKBl+cAWC75YjYT53X3ucxkf6xMhyetCQEYkSr+tW8mtraU6J1u5UxSK/QGZxoei2TTapfLyyy/z5JNPMn78eC6++GKOOeYYDjroIHr16sVTTz3Feeedl451KprIuT8/jt5FBfz1sXkMrl7Pf7O6UxGwAyQF6NIgbMQIGTHCZoygGSOE9e/mYEcqwla9lVgYRBT/jV8HQ9gdZU3QE10k3jgOW5QIbzVcb2xFkuixBUR9UavCu0vWH0Sa8EGRGYlwUNk2iioqEUBdMMiJP/tZildOoVC0R5Q1ou3SaMGxY8cO+vTpA1jxGjt27ADg6KOP5ne/+13Lrk7RIowdMYxA4DIee/hhBlVvYHlWdwyhURLZQadoZb1mrgwZ5cuMXkhhFwALgojF39ASW2w4EyRkmSRNrIH0ipIUKazu8e6xEoFIFhOeRNc9VShFgDBMOtbW0Lmqkm47y9GA6o4duHDSqRw7ciSa1mhDn0KhaItISb3Ffpo7r6LZNFpw9O3blzVr1tCrVy8GDRrEP//5T0aOHMnLL79Mhw4d0rBERUsw8vAhBK+8goceeIDB1evdG3QkuwP9+vWlW5cu5ObmcXCv7nQr6sx9f50P360gW6+lPJxpDbbTWInZxgNvQS7NdrGY9iNFZVCp2S4TO6hUs7dZwaMJ6TFODIaJXXTHqqXhq6kR2PPnQGYkQt9t2ymqrES3Bxp5eUw57zzGNrE0r0KhaLuoLJW2TaMFx0UXXcSXX37JuHHjuOmmmzjppJP485//TCwWY+7cuelYo6KFGDZ4EFdOu4r7HnqEzl1L+PXZv2JA374px07+1an8cc4culXtoDzcLe6ecGpnyOS4DafQlxkAp0+au48UAkWCZnhqZbhjZdw6YmfUSKSdwWKnp3jrhDiFyWwyIxF679hO14oKNKAyO4sxI47g+FGjGNivn4rXUCj2V1SWSpum0YLj6quvdn8+9thjWbFiBZ999hn9+vXj8MMPb9HFKVqeww45mL/++b69juvfuzfhwiIKt20haEaJaJ66K85NPkXNDqdtvCmseA2nyFdSOIW90dSsHi1OKXNIyICRuC3v3bRWezLhmUtqgCnpu30HfbZvQwOCnTtz3hlnMEb9XSoUBwTCTE63b6l5Fc2n2c7rnj17ctppp1FQUMDFF1/cEmtStBFOmjgBDehWtStpn7T/821zskmcYE7b2pE8MoEAyBDx1FZv9klildB6UmtDRozhGzfQb/s26jIzOP83v+GBWbOU2FAoDiRUlkqbpsWi5Xbs2MH8+fNbajpFG+CEo8ZihjMp2b0LgeHbZwZtkeAhVUdXqUuMDDAD0nKHOMfbPhBTl3FrR0CmbFufEk/Ka8eqakavXkOnqmqKBg3isbvu5pgRIxpzqQqFYj9A1eFo2zTapaI4cNA0jU4HHcTOb78mx6xlV0aWZVo0cINCzZBEi1p3fylMhEfDSiFdi4fUsRSCYQkPGQKj1kRoHkXhuEbsTU5r+pS+G6ww0r7bttNn+3ZMTWPC6afzqxNOaPkXQqFQtA9UlkqbRgkOxR4JSsuyURMMxuMovFmqAsygBFNi6vFaHCYmQk9IZ7WtGK7bJdVfn7d8OSClZQFJ/IaRU1PLoZtLya2rozozk5n/8z/069GjRa5ZoVC0T1SWSttGCQ7FHtlWWkpU06gL2H8qAhKTPKSQmFmABDMmreZtYcCUCE9FL6f0uBtBaremNx1LSAyfxcM7XtrNUzQDem/fTt9t2wEYMGY0V51/AYGA+lNWKA54VJZKm6bBn9J7a1m7a9eu5q5F0cYwTZPYrl1UhjNACMvdIRMitoUVzxEPFJVWW3lhWSHjRcL8waDOeDMYj+HAJB44CknN1zKiUYZs2EzH6lqqMsJcf/kVHDZgQDouXaFQtEOUhaNt02DBsbeWtfn5+UyePLnZC1K0DXZVVPDoc/9EM00qwmFfbIVTgAsBhiMYHLw/a2Ai463oExu+CQm2RUMCQrfFRorW9UXllQzauIWgadJ96GHccMmlhEIJUasKheLARsVwtGkaLDjS2bJW0XbYvLWMGx95kNDGLWhSUhfQ2dQxz6rkJYVrjZBS2taJ5DgNf/8U2+IBbh8VkSK3VXif6vEw0ZyaOgZs2Urh7mpiwQCnT5nMCaPHpOXaFQpF+0ZZONo2yvGtACAajfLIc//ky3ffJ8M02ZmdwbpO+WzplIPUBBim3TrW4+4wSW7Gloi3LocduyGRrq/VER9uMKo9fXZtHX227aRkVwUAoV49uWPqVLoUdGrhK1coFPsNKoajTdOqgmPmzJncdtttvm1FRUWUlpYC8MILL/Doo4+ydOlStm/fzrJlyxiqemC0GFJKVvz4A/9+5y1WfPkVGXVRasNBVvQsYltetpX+6rhBNGxzJfE+Kb6iXPHiXnERsQd3i/OzCaFYjKzaCNmRCNm1EfJraulYXWvtL+zEFRdeyJD+KlZDoVDsGWXhaNu0uoVj8ODBvPHGG+5zXY9Xj6qqquKoo47ijDPO4NJLL22N5e23xGIxfn3rTYR3WBYEgjrflRSwpktHZEAkl74QgC4h5q+b4SA1kEH7XWkQFyepzB5SklMboUtFFV3Kd5NfU+fbbQhBuFdPLjjlFI4YfKjqfaJQKBqGaaXop2VeRbNpdcERCAQoLi5Oue+CCy4AYM2aNftwRQcGjz7/LOEdFZQWZLOmKJ9dOWEw9biSd9rLe4WHIzriXU8sTREwISDcMVJKhO1ycZqqaaZJQVUNheXVFFZUkR2JAmAEdGq7duGIgw9hYJ8+HNqnL0WdO6uW8QqFovEol0qbptUFx6pVqygpKSEcDjNq1Chmz55N33o6mDaEuro66uri35grKipaYpn7FZu3lvHFO+9TGw7wdf/OGG4KivQX2UgZk2FbLkwgICHkxHaQdFxWXYTCXdUUVlRTsLvGbRFfFwyQN7A/vzhmPEcPG+azaikUCkVTEaTJpdLyUx6QtKrgGDVqFE8++SQDBgxgy5YtzJo1i7Fjx/Ltt9/SqVPTggPnzJmTFBei8HPDg3PJNE2W9+mCqWmWeHBiM0yPBUMzreyUqMfcETbt8SJewMs5Rko6VNZSvHM3ncurya6zrBimALOgI4cceignjhnLgN59lJtEoVC0PCottk3TqoLjxBNPdH8eMmQIY8aMoV+/fsyfP59rrrmmSXPedNNNvmMrKirooUpeu7z52RIyN+9kS0EW2zpmWRsF1htKs4WDXYCLoGnVxhCAtMWHoz2c4l/Ccpf027STrjt2kxmJARANhygYNIDRQ4dzwsjRZGVm7uMrVSgUBxoqaLRt0+ouFS/Z2dkMGTKEVatWNXmOcDhMOBxuwVXtP0QiEZ75xzNITbCyf0ecAA1hV/fUQraKkCClcL0rIiCRhsfd4mmoll0TYejKLeTURIlmhOh55HBOHX8ch/Trp6wYCoVi36JiONo0bUpw1NXVsXz5co455pjWXsp+g5SSVWtX85/33mT5l18T3F3Hqr75RLICaKaJGbHiL7SQGS++JUAaJASLYpsV4yKiaPtuDv1+K7opOfy4cUw9/WwV7KlQKFoNYUhEGswRwlCKoyVoVcFx7bXXcvLJJ9OzZ0/KysqYNWsWFRUVTJkyBYAdO3awbt06Nm3aBMDKlSsBKC4urjezRWGJjJWrf+SxRS+y87t1BHdbQbQyqLG+ZzbreuYCIDRLaCAlesjEiHp7w+PLUBEi7sYUpqT/2h302VSBEQpwwcUXc8ywEfv0GhUKhSIRISUiDfEW6ZjzQKRVBceGDRs455xz2LZtG507d2b06NEsWbKEXr16AfDSSy9x0UUXuePPPvtsAGbMmMHMmTNbY8ltFtM0+XTlt7z18fus/mYlorLG2h7S2NA9m61FmZR3sHqiaEgMK9QCoUv0gIEQWNscUSH86a/C7nsiTTh01VZKtlahFXbktqv/h+LCzvv6chUKhSIZ5VJp07Sq4HjmmWf2uP/CCy/kwgsv3DeLaYdUVVXx7yVv8/U337D9x/VotZaKiIQ1tvXMZltxmB15GZYpw0YIaVsqrHeQIzaQoAcSrBxY47SAiabZLeKRZEZiGLrggZl3EAwG99HVKhQKxV5oh1kqO3fuZNq0abz00ksAnHLKKfz5z3+mQ4cOe1iO5LbbbmPevHns3LmTUaNG8eCDDzJ48GDA8g7MmDGDhQsXsn79egoLC5k0aRJ33HHHXhuxppM2FcOhaBi3PzePzV9/j7Gl3I2ers3VKe+eyc6iMNtyMpEIdF2iGWCa/uM1DQwkmi7j0kKApkkIGhgxDacXvR4046U5hETTJHXZGnq5JBaLKcGhUCjaDO0xS+Xcc89lw4YNvP766wD85je/4YILLuDll1+u95i7776buXPn8sQTTzBgwABmzZrFCSecwMqVK8nNzWXTpk1s2rSJP/7xjwwaNIi1a9cydepUNm3axHPPPZe+i9kLSnC0M55/+zU2LlqKoQvKu4TZ2SXEzs5hzGyBsNNag4ZBNGb9ajVNYpr+YE+QhEIxKwNW+oM8dd0kFDKJxSAW032ZJkJIhIDaTGvujWWlHNSrT5qvWKFQKBpImi0ciYUkm5sVuXz5cl5//XWWLFnCqFGjAHjssccYM2YMK1eu5OCDD06xFMn999/PzTffzGmnnQbA/PnzKSoq4umnn+a3v/0thx56KM8//7x7TL9+/bjzzjs5//zzicViBAKtc+tXKQXtiDWb1vP6c/8hEhZ8/tNOrDyyA2W9sohm6eha3FoR0CVCWGYN4VgukGiaQSgYIxyKEQ4bBHRvq1dJRjhCIGBtCwQgHDbceay5rH21mVZl0BUb1qb7khUKhaLBCDN9D4AePXqQn5/vPubMmdOs9X700Ufk5+e7YgNg9OjR5Ofn8+GHH6Y8ZvXq1ZSWljJhwgR3WzgcZty4cfUeA1BeXk5eXl6riQ1QFo52xQNPPY4Wk6wdlQeZxItvuTEZcWtEMGAQM+JWjsxwBEmiNQPCImrV/BImAR10TRI1BKapIQQEgwaaiBKLBQgEYkRiGoZmnae0Ykfar1mhUCgaTJotHOvXrycvL8/d3NyaT6WlpXTp0iVpe5cuXdyu6amOAauzupeioiLWrk39JXD79u3ccccd/Pa3v23WepuLsnC0I5yeI7V5lkVDc2S3SHyPSXLDdWQE6gBJUI8RDhikCrUWQhIOGAQD0n4OQd1A06x658GAQTBgkpUZIRSEjGCMDnaa7U8PVamwCoWiDSHT+ADy8vJ8j/oEx8yZMxFC7PHx2WefAaQskCil3GvhxMT99R1TUVHBSSedxKBBg5gxY8Ye50w3ysLRjhh+2FDeWrmBvLIoO3qGCeomkZhAInzVPwECmiQnHEXXQNesLJPMYJTaaBDX+SIhqFlBod64Ukd0mBpowt/PTddNcndGMYMavbupkvEKhaLt0FbqcFxxxRVuGYf66N27N1999RVbtmxJ2rd169YkC4aDU4OqtLSUrl27utvLysqSjqmsrGTixInk5OTw4osvtnqQvxIc7YgJRx7DW/96hbwtEXb0DCMEBPQYUSOA9DV6Fe7zjGCMmGmVD9WEJTrqYgEMKQjoJsJ0DkxWxnoK+5cmJTnlUcIlnVRVUYVC0bZoI2mxhYWFFBYW7nXcmDFjKC8v55NPPmHkyJEAfPzxx5SXlzN27NiUx/Tp04fi4mIWLVrEsGHDAKttxeLFi7nrrrvccRUVFfzsZz8jHA7z0ksvkZGR0ahrSAfqjtGO6JjfgUhBiNwtUTBNdGGSGYzSMbOaoG4inNoaIp7KqguDjEAUxyYoBIQDUfLDNXTMrCE7o46sUA0FGVVowqrjIZDowiDeRjZOsNZEM8HMC+2jq1YoFIoGIrE+tlr6kaa02EMOOYSJEydy6aWXsmTJEpYsWcKll17KL37xC1+GysCBA3nxxRcBy5Uyffp0Zs+ezYsvvsg333zDhRdeSFZWFueeey5gWTYmTJhAVVUVjz/+OBUVFZSWllJaWophGCnXsi9QFo52Rt+B/djw4XIKK2uo66LjNlHTIkRiGjUxjZxQrRsgqgkrayWgRagzAsRMjZBuENTjWSxhzUTTJAWZtVRHg5hSc0uZRwwdU8atH9EMDSkgO6JK7ykUirZFW3GpNIannnqKadOmuVknp5xyCg888IBvzMqVKykvL3efX3/99dTU1HDZZZe5hb8WLlxIbq7VtmLp0qV8/PHHABx00EG+uVavXk3v3r3Tdj17QgmOdsYpo4/noQ+Xk11qUNdFQ0Ni2h1fM0NROmRYAZ27oyFwbR62eyUQw5ROmxRLRAgkQc3EsF0u2cEodUaAqKlbYkSPURvzxH1oEMnS2L2jImltCoVC0apI0uRSafkpHQoKCvj73/++59MnXJMQgpkzZ9bb4mP8+PFJx7QFlEulnTGk/yBkWCN7cwwNiS5MNNvmF7RdKUJY/VLAcq940QRkBiJuhktIs/7VPC6XjECMsG67YQRkBiM4odpB3SCSo2GU12AmljBVKBSK1sSJ4UjHQ9FslIWjnaFpGqGeOYhVFXQya5BZlpWi1tAxpI4T/CmE9SYJaCYm8aDQkBYjrJmEtDoqoyECdvXQxFoeQc1EExFMCWFNEtZiRAwdA51IjobYEmPXrl0UFBS0wqugUCgUKTBJFf/eMvMqmo2ycLRDxg23opeDGy2xIQRk6AZBzam1IQnrMbL0OjQhCdjWjAw9SqYesywgAsJaPHjIaxVxngc0SUbAtGt1mGSHomQGoq7VpDUr1ikUCkUiTgxHOh6K5qMERzvkZ0cehwQCG+LbhIgL+7Aeo0O4ls5ZVeQFa8gP1dApXEV2IEJiXRjh6UqkJfV2FgSEiWaP0YTVpj5gB4xmZ2e3+LUpFApFk1EulTaN+oraDsnLzSOraw7Vmyqptb0gAklusI6YAVKzslcEENQMgpplBTElVMXi7efDumG7SQA78FTIuOSw0mOtfJeItObTkOh1EhnS3MqnCoVC0SZoI3U4FKlRFo52SkG/AkRUECiTZGhRsvUIQS1miw1LGFiuE+laNQQQsmttgGUVyQ7UodsWjJAWI+C6Waz4D6f5m2s90QwCdSayeS0EFAqFouVRFo42jRIc7ZRfjZkEQOZG0y5PLj1JsLhZKJqnz4oQENLjPVUEEk2DnEAdQREjrMcoCFWj2WGmOYFawlqUoGZlrYT1GJnBGHoE9Exl3VAoFG2MdBT9ch6KZqNcKu2UQ/oegpalYWzQwO5srCHJ0WsQWF1fdWFimoJaGSSevWKlyhpSdwWKEJJO4SpiWCIiP1SDRBD2tK/P0CBmCqJmEFELHbrm78vLVSgUir3SHgt/HUgowdFO0TSNbv27sv7LjbBbkp9fTUAziaK7lUKtcQZmTBCRzq/aCgQ1pO5aRXICdWhIDFMg0XxCw8JyqWQEJHptFGEGyczJ3IdXq1AoFA1AxXC0aZRLpR1z8KD+AIQ3mlYvFWFJg3gmilU/o0t4N4XBSjK1CDrxrBMdg9xALbod5xEWqWvs60i3kVsgYtkWM7NavxGQQqFQ+DBl+h6KZqMERztm4hG/ACGR61Mbqqx8FOuNEtYMsvQo+cEaCvRKMvU6ioPlrvgASUAYvjgQwNrmETCBWmu/zIihUCgUbQoVNNqmUS6VdkxuTi4dumdTvgGkAZrufVNIQiLqK7oXEAZRGSArGCM7tAsA04gRMa0YD02ThIlRawYAgS4MAsIkKq0026Aw0aoFMeDQksH76jIVCoWigaRLHCjB0RIoC0c7Z+DggciogM2CsIiQp1UjMAiJmK/IlxDYFUdN3/ZMLer2YhFIOgSqCQqDsIiSo9XhvNGCtviQu60/mZLCkn12jQqFQtEglIWjTaMERzvn6KHjAZDrg+QF6igJl9MxUOXW1kgkJAz/e0dApl5HWIuSH6ghpBl0DVfQKbjbcsEEasnUIgTs+WSVpVa6de6RzstSKBSKxqNiONo0yqXSzjmo9wBCObB7TSah8VEAMkUEU+jUyQC5eg1hLUZM6sSkhmEKomhoCDQh0TCJiAAG/roawjaDaFKSq9dRaWQAAlmpAZKOHTru4ytVKBSKvWAaQOrg9+bPq2guSnC0c4QQ9D64K98t3YxRrhPsYBLSTIJaNaa0HCKana0SkhARAcKYSInrWpGmaXeaTTW/ZU4Ma1aQaHRbJhmdggSDwX10hQqFQtFATEla4i2UhaNFUC6V/YARh1uVvyrW5AAg7TecJoi3n8cu+uXEa3jiODK0CDlaDfE3qrQb1UtCwiComVaH2FqQlTpFPTrvk+tSKBSKRqFiONo0SnDsB4wcMg6hSXatzgEkuk/h+xV/QCSns2pIMrQYWVodmaKOrsFdBEU0HngqIKxFMbZaVpA+vfqm9XoUCoWiSUjSJDha+8L2D5RLZT8gKyuLLj0z2bpOYsY0grrhukh0YWK68RmSgJCY0iBq/+oF8R4sAWGQo9ehCeikV4EQRKROpZmBJsBcax1z2ICh+/oSFQqFYu+oSqNtGmXh2E8YdOghmDGNyg2Zdvqrnc7qC6AS6JiEtSgaBjqG1ahNWKNDIoZmu1p0u6V9WDPI0WrQowZ1KzLI7RJkUL8h+/ryFAqFYu+YZvoeimajBMd+wuihJwBQvjoXISBXq6FQryCAvzus1bxNUhDYTedgBcJugygw3dLmUuIrGJahmURXhpFRjWPGHeVmsCgUCkWbQsVwtGmUS2U/oVf3voRzo5SvzkEnRpdgBabU2EoewpRI27phYRK24zOytTpqZYgMLeK6XoSwS6Lb7zGjVmPnsnz0sMnPjj61dS5QoVAo9oZyqbRplIVjP0EIwcBB3andGSZcYRAQVnt6DdOuJAoaptWwTat13SiZepR8vZpsrc7XR8X52YwJ1rxUQmRXiJHHHUJGhmraplAo2iiq8FebRgmO/Yjhh48FYNuP+e62Ar2STBFFYJKj1ZGj1br+EoGVJhsUBpqATC3iHieQmDFY90ox1RszGXb0QUw55ep9eTkKhULRKKQ00/ZQNJ9WFRwzZ85ECOF7FBcXu/ullMycOZOSkhIyMzMZP3483377bSuuuG1z+KBxaLpJ6Y8F7rZMEaN/cAt99DJCWswWG/EYjAwtSkhYFUqDwiCIlTZr1GiseaEblatz6H1YLr85739U7IZCoWjbyDRZN5RLpUVodQvH4MGD2bx5s/v4+uuv3X133303c+fO5YEHHuDTTz+luLiYE044gcrKylZccdslMzOTrr0kZWs7EotqCCQZIoamQcdgLXlaNSTU6AgSI0PE0O1slkwtQl1FgBXP9KFqYxbDj+7LDb+7G01r9T8VhUKh2DMqaLRN0+pBo4FAwGfVcJBScv/993PzzTdz2mmnATB//nyKiop4+umn+e1vf5tyvrq6Ourq6tznFRUV6Vl4G+XwISPY+OMXlK3tQO+DtvjSTbJEFBNBNWFCxAgKAymsShxZRKiWYXZXZLDy2d7UVQQZ/4tDOefkK1vvYhQKhaIxmCaINLg/lEulRWj1r62rVq2ipKSEPn36cPbZZ/Pjjz8CsHr1akpLS5kwYYI7NhwOM27cOD788MN655szZw75+fnuo0ePA6ur6ZFDTwRg8w+d0JG+9FYNSUA3ydbqCGommogX/RICZLlg6T8Opq4iyK/OPU6JDYVC0b5QFo42TasKjlGjRvHkk0+yYMECHnvsMUpLSxk7dizbt2+ntLQUgKKiIt8xRUVF7r5U3HTTTZSXl7uP9evXp/Ua2hpdu/Yip0Mdm38oIDHiwuqlIu2slXhtDinhh8+78tZfh1FbEeL0845nwriz9vnaFQqFojlI00zbQ9F8WtWlcuKJJ7o/DxkyhDFjxtCvXz/mz5/P6NGjAZICFaWUewxeDIfDhMPh9Cy4HSCEoHd/yTefZlK+PYsOhdXuPomVlSIRmGgYUY0NKzvz3efd2Lkpj+w8k3OmHM/RR5zZehegUCgUTUVK0tL4RFk4WoRWj+Hwkp2dzZAhQ1i1ahWTJk0CoLS0lK5du7pjysrKkqweCj9HHXEe33z6PBt+LKRD4Tp3u2kIytZ1YMumfMo2daRsTUdiUR2hmQwf24nJZ95CZmZWK65coVAomoEpQSjB0VZpU4Kjrq6O5cuXc8wxx9CnTx+Ki4tZtGgRw4YNAyASibB48WLuuuuuVl5p22bwIeMIBP7Fpu8L6X3wFraX5rFuVRfWfteFSG0QACEkhd1qGTN6HD8ZfTq5ufl7mVWhUCjaOFIC6QgaVYKjJWhVwXHttddy8skn07NnT8rKypg1axYVFRVMmTIFIQTTp09n9uzZ9O/fn/79+zN79myysrI499xzW3PZbZ5wOEzvvhl8/10h/3xwnLu9czEcclgHRgw5g769DyMUCrXiKhUKhaJlkaZEpsHCIZXgaBFaVXBs2LCBc845h23bttG5c2dGjx7NkiVL6NWrFwDXX389NTU1XHbZZezcuZNRo0axcOFCcnNzW3PZ7YKf/WwSUr5A5y4FdOtWwqCBx9K928DWXpZCoVCkD2mSHguHChptCYTcz6VbRUUF+fn5lJeXk5eX19rLUSgU+wnqs6Xt4PwuxotTCYhgi88fk1HekS+q33UzaVMxHOnA0VMHWgEwhUKRXpzPlP38O1u7Iibr0mKNiBFt8TkPRPZ7weGUQT/QCoApFIp9Q2VlJfn5Kui6NQmFQhQXF/N+6atpO0dxcbGKe2sm+71LxTRNNm3aRG5ubptvPlZRUUGPHj1Yv379fmO2U9fUPlDX1HiklFRWVlJSUqJ6DbUBamtriUQiex/YREKhEBkZGWmb/0Bgv7dwaJpG9+7dW3sZjSIvL2+/+dB3UNfUPlDX1DiUZaPtkJGRoQRBG0fJcoVCoVAoFGlHCQ6FQqFQKBRpRwmONkQ4HGbGjBn7VS8YdU3tA3VNCoUi3ez3QaMKhUKhUChaH2XhUCgUCoVCkXaU4FAoFAqFQpF2lOBQKBQKhUKRdpTgUCgUCoVCkXaU4GhDPPTQQ/Tp04eMjAxGjBjBe++919pLajJz5szhyCOPJDc3ly5dujBp0iRWrlzZ2stqMebMmYMQgunTp7f2UprFxo0bOf/88+nUqRNZWVkMHTqUpUuXtvaymkwsFuOWW26hT58+ZGZm0rdvX26//XZMU3X7VChaGyU42gjPPvss06dP5+abb2bZsmUcc8wxnHjiiaxbt661l9YkFi9ezOWXX86SJUtYtGgRsViMCRMmUFVV1dpLazaffvop8+bN47DDDmvtpTSLnTt3ctRRRxEMBnnttdf473//y7333kuHDh1ae2lN5q677uKRRx7hgQceYPny5dx9993cc889/PnPf27tpSkUBzwqLbaNMGrUKIYPH87DDz/sbjvkkEOYNGkSc+bMacWVtQxbt26lS5cuLF68mJ/85CetvZwms3v3boYPH85DDz3ErFmzGDp0KPfff39rL6tJ3HjjjXzwwQft2pKWyC9+8QuKiop4/PHH3W2/+tWvyMrK4m9/+1srrkyhUCgLRxsgEomwdOlSJkyY4Ns+YcIEPvzww1ZaVctSXl4OQEFBQSuvpHlcfvnlnHTSSRx//PGtvZRm89JLL3HEEUdwxhln0KVLF4YNG8Zjjz3W2stqFkcffTRvvvkm3333HQBffvkl77//Pj//+c9beWUKhWK/b97WHti2bRuGYVBUVOTbXlRURGlpaSutquWQUnLNNddw9NFHc+ihh7b2cprMM888w+eff86nn37a2ktpEX788UcefvhhrrnmGn7/+9/zySefMG3aNMLhMJMnT27t5TWJG264gfLycgYOHIiu6xiGwZ133sk555zT2ktTKA54lOBoQwghfM+llEnb2iNXXHEFX331Fe+//35rL6XJrF+/nquuuoqFCxfuNx0pTdPkiCOOYPbs2QAMGzaMb7/9locffrjdCo5nn32Wv//97zz99NMMHjyYL774gunTp1NSUsKUKVNae3kKxQGNEhxtgMLCQnRdT7JmlJWVJVk92htXXnklL730Eu+++y7du3dv7eU0maVLl1JWVsaIESPcbYZh8O677/LAAw9QV1eHruutuMLG07VrVwYNGuTbdsghh/D888+30oqaz3XXXceNN97I2WefDcCQIUNYu3Ytc+bMUYJDoWhlVAxHGyAUCjFixAgWLVrk275o0SLGjh3bSqtqHlJKrrjiCl544QXeeust+vTp09pLahbHHXccX3/9NV988YX7OOKIIzjvvPP44osv2p3YADjqqKOSUpW/++47evXq1Uoraj7V1dVomv9jTdd1lRarULQBlIWjjXDNNddwwQUXcMQRRzBmzBjmzZvHunXrmDp1amsvrUlcfvnlPP300/znP/8hNzfXtd7k5+eTmZnZyqtrPLm5uUnxJ9nZ2XTq1KndxqVcffXVjB07ltmzZ3PmmWfyySefMG/ePObNm9faS2syJ598MnfeeSc9e/Zk8ODBLFu2jLlz53LxxRe39tIUCoVUtBkefPBB2atXLxkKheTw4cPl4sWLW3tJTQZI+fjrX//a2ktrMcaNGyevuuqq1l5Gs3j55ZfloYceKsPhsBw4cKCcN29eay+pWVRUVMirrrpK9uzZU2ZkZMi+ffvKm2++WdbV1bX20hSKAx5Vh0OhUCgUCkXaUTEcCoVCoVAo0o4SHAqFQqFQKNKOEhwKhUKhUCjSjhIcCoVCoVAo0o4SHAqFQqFQKNKOEhwKhUKhUCjSjhIcCoVCoVAo0o4SHAqFQqFQKNKOEhyKVmXNmjUIIfjiiy/SMr8Qgn//+99NPv6dd95BCIEQgkmTJu1x7Pjx45k+fXqTz9WWGD9+vHvd6frdKBSKAwslOA5gLrzwwr3eRNNNjx492Lx5s9uPxLnB79q1q1XXlcjKlSt54oknWnsZ+4wXXniBTz75pLWXoVAo9iNU8zZFq6LrOsXFxa29jL3SpUsXOnTo0NrLIBqNEgwG036egoICKioq0n4ehUJx4KAsHIp6Wbx4MSNHjiQcDtO1a1duvPFGYrGYu3/8+PFMmzaN66+/noKCAoqLi5k5c6ZvjhUrVnD00UeTkZHBoEGDeOONN3xuDq9LZc2aNRx77LEAdOzYESEEF154IQC9e/fm/vvv9809dOhQ3/lWrVrFT37yE/dcixYtSrqmjRs3ctZZZ9GxY0c6derEL3/5S9asWdPo16aqqorJkyeTk5ND165duffee5PGRCIRrr/+erp160Z2djajRo3inXfe8Y157LHH6NGjB1lZWZx66qnMnTvXJ2xmzpzJ0KFD+b//+z/69u1LOBxGSkl5eTm/+c1v6NKlC3l5efz0pz/lyy+/9M398ssvM2LECDIyMujbty+33Xab7/c3c+ZMevbsSTgcpqSkhGnTpjX6dVAoFIqGogSHIiUbN27k5z//OUceeSRffvklDz/8MI8//jizZs3yjZs/fz7Z2dl8/PHH3H333dx+++3ujd40TSZNmkRWVhYff/wx8+bN4+abb673nD169OD5558HLBfG5s2b+d///d8Grdc0TU477TR0XWfJkiU88sgj3HDDDb4x1dXVHHvsseTk5PDuu+/y/vvvk5OTw8SJE4lEIo15ebjuuut4++23efHFF1m4cCHvvPMOS5cu9Y256KKL+OCDD3jmmWf46quvOOOMM5g4cSKrVq0C4IMPPmDq1KlcddVVfPHFF5xwwgnceeedSef6/vvv+ec//8nzzz/vxlOcdNJJlJaW8uqrr7J06VKGDx/Occcdx44dOwBYsGAB559/PtOmTeO///0vjz76KE888YQ7/3PPPcd9993Ho48+yqpVq/j3v//NkCFDGvUaKBQKRaNo5W61ilZkypQp8pe//GXKfb///e/lwQcfLE3TdLc9+OCDMicnRxqGIaW02rMfffTRvuOOPPJIecMNN0gppXzttddkIBCQmzdvdvcvWrRIAvLFF1+UUkq5evVqCchly5ZJKaV8++23JSB37tzpm7dXr17yvvvu8207/PDD5YwZM6SUUi5YsEDqui7Xr1/v7n/ttdd853r88ceTrqmurk5mZmbKBQsWpHwdUq2nsrJShkIh+cwzz7jbtm/fLjMzM9129d9//70UQsiNGzf65jvuuOPkTTfdJKWU8qyzzpInnXSSb/95550n8/Pz3eczZsyQwWBQlpWVudvefPNNmZeXJ2tra33H9uvXTz766KNSSimPOeYYOXv2bN/+v/3tb7Jr165SSinvvfdeOWDAABmJRFJet5TJvxuFQqFoDiqGQ5GS5cuXM2bMGIQQ7rajjjqK3bt3s2HDBnr27AnAYYcd5juua9eulJWVAZaVokePHr4YjZEjR6ZtvT179qR79+7utjFjxvjGLF26lO+//57c3Fzf9traWn744YcGn+uHH34gEon45i8oKODggw92n3/++edIKRkwYIDv2Lq6Ojp16gRYr8+pp57q2z9y5EheeeUV37ZevXrRuXNn33Xs3r3bncehpqbGvY6lS5fy6aef+iwmhmFQW1tLdXU1Z5xxBvfffz99+/Zl4sSJ/PznP+fkk08mEFAfCQqFIj2oTxdFSqSUPrHhbAN82xMDGIUQmKZZ7xxNRdM09/wO0Wg0aW2Ja/FimiYjRozgqaeeShrrvaHvjVTnSsQ0TXRdZ+nSpei67tuXk5PjzlPfa+wlOzs7ae6uXbsmxYMAbvyHaZrcdtttnHbaaUljMjIy6NGjBytXrmTRokW88cYbXHbZZdxzzz0sXrx4nwSlKhSKAw8lOBQpGTRoEM8//7zvpvjhhx+Sm5tLt27dGjTHwIEDWbduHVu2bKGoqAiATz/9dI/HhEIhwPo27qVz585s3rzZfV5RUcHq1at96123bh2bNm2ipKQEgI8++sg3x/Dhw3n22WfdQMumctBBBxEMBlmyZIlr6dm5cyffffcd48aNA2DYsGEYhkFZWRnHHHNMynkGDhyYlHr62Wef7fX8w4cPp7S0lEAgQO/evesds3LlSg466KB658nMzOSUU07hlFNO4fLLL2fgwIF8/fXXDB8+fK9rUCgUisaigkYPcMrLy/niiy98j3Xr1nHZZZexfv16rrzySlasWMF//vMfZsyYwTXXXIOmNezP5oQTTqBfv35MmTKFr776ig8++MANGq3P8tGrVy+EELzyyits3bqV3bt3A/DTn/6Uv/3tb7z33nt88803TJkyxWc5OP744zn44IOZPHkyX375Je+9915SgOp5551HYWEhv/zlL3nvvfdYvXo1ixcv5qqrrmLDhg0Nfs1ycnK45JJLuO6663jzzTf55ptvuPDCC32vy4ABAzjvvPOYPHkyL7zwAqtXr+bTTz/lrrvu4tVXXwXgyiuv5NVXX2Xu3LmsWrWKRx99lNdee22vVqHjjz+eMWPGMGnSJBYsWMCaNWv48MMPueWWW1zB8oc//IEnn3ySmTNn8u2337J8+XKeffZZbrnlFgCeeOIJHn/8cb755ht+/PFH/va3v5GZmUmvXr0a/DooFApFo2it4BFF6zNlyhQJJD2mTJkipZTynXfekUceeaQMhUKyuLhY3nDDDTIajbrHjxs3zg2SdPjlL3/pHi+llMuXL5dHHXWUDIVCcuDAgfLll1+WgHz99dellKkDE2+//XZZXFwshRDuXOXl5fLMM8+UeXl5skePHvKJJ57wBY1KKeXKlSvl0UcfLUOhkBwwYIB8/fXXfUGjUkq5efNmOXnyZFlYWCjD4bDs27evvPTSS2V5eXnK16i+INbKykp5/vnny6ysLFlUVCTvvvvupNcjEonIP/zhD7J3794yGAzK4uJieeqpp8qvvvrKHTNv3jzZrVs3mZmZKSdNmiRnzZoli4uL3f0zZsyQhx9+eNK6Kioq5JVXXilLSkpkMBiUPXr0kOedd55ct26dO+b111+XY8eOlZmZmTIvL0+OHDlSzps3T0op5YsvvihHjRol8/LyZHZ2thw9erR84403fOdQQaMKhaIlEVI2wCGtULQQH3zwAUcffTTff/89/fr1a+3l7JV33nmHY489lp07d+6Twl+XXnopK1as4L333kv7ufbGmjVr6NOnD8uWLWPo0KGtvRyFQtHOUTEcirTy4osvkpOTQ//+/fn++++56qqrOOqoo9qF2PDSvXt3Tj75ZP7xj3+06Lx//OMfOeGEE8jOzua1115j/vz5PPTQQy16jqZw4okn8u6777b2MhQKxX6EsnAo0sqTTz7JHXfcwfr16yksLOT444/n3nvvTUrpbKvU1NSwceNGwIrdaOky7GeeeSbvvPMOlZWV9O3blyuvvJKpU6e26DmawsaNG6mpqQGgZ8+ebjCvQqFQNBUlOBQKhUKhUKQdlaWiUCgUCoUi7SjBoVAoFAqFIu0owaFQKBQKhSLtKMGhUCgUCoUi7SjBoVAoFAqFIu0owaFQKBQKhSLtKMGhUCgUCoUi7SjBoVAoFAqFIu38f6E6yc9edNyVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data20 = func2(t=0, x=xy[:,0], y=xy[:,1])\n",
+    "da2 = mikeio.DataArray(data=data20, time=time[0], geometry=msh.geometry, item=item2)\n",
+    "da2.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mikeio.Dataset>\n",
+       "dims: (time:1, element:2259)\n",
+       "time: 2018-01-01 00:00:00 (time-invariant)\n",
+       "geometry: Dfsu2D (2259 elements, 1296 nodes)\n",
+       "items:\n",
+       "  0:  Item1 <Water Level> (meter)\n",
+       "  1:  Item2 <Water Level> (meter)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = mikeio.Dataset([da1, da2])\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs = mikeio.Dfsu(mesh_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn = \"bigfile1.dfsu\"\n",
+    "with dfs.write(fn, data=ds, dt=dt_seconds, keep_open=True) as f:\n",
+    "    for tj in time[1:]:\n",
+    "        t = (tj - time[0]).total_seconds()\n",
+    "        ds[0].values = func1(t, x=xy[:,0], y=xy[:,1])\n",
+    "        ds[1].values = func2(t, x=xy[:,0], y=xy[:,1])\n",
+    "        ds.time = tj\n",
+    "        f.append(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Option 2: Dfsu.write_header()\n",
+    "\n",
+    "The older option is to use dfs.write_header() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs = mikeio.Dfsu(mesh_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn = \"bigfile2.dfsu\"\n",
+    "with dfs.write_header(fn, start_time=time[0], dt=dt_seconds, items=[item1, item2]) as f:\n",
+    "    for tj in time:\n",
+    "        t = (tj - time[0]).total_seconds()\n",
+    "        data1 = func1(t, x=xy[:,0], y=xy[:,1])\n",
+    "        data2 = func2(t, x=xy[:,0], y=xy[:,1])\n",
+    "        f.append(data=[data1, data2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove(\"bigfile1.dfsu\")\n",
+    "os.remove(\"bigfile2.dfsu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.8 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "7aec4f91c09090e98e6ae8203c38529831bb4a3ce54cd1b69639b53cb01a6aa9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}


Test patch:
diff --git a/tests/test_dfsu.py b/tests/test_dfsu.py
index 484deb89..19131eab 100644
--- a/tests/test_dfsu.py
+++ b/tests/test_dfsu.py
@@ -479,6 +479,8 @@ def test_write(tmpdir):
 
     assert dfs._source.ApplicationTitle == "mikeio"
 
+    dfs.write(outfilename, ds.isel(time=0))  # without time axis
+
 
 def test_write_from_dfsu(tmpdir):
 
@@ -575,14 +577,13 @@ def test_write_big_file(tmpdir):
 
     ds = Dataset(das)
 
-    with pytest.warns(match="dt"):  # TODO should dt be handled in a different way
-        with dfs.write(outfilename, data=ds, dt=3600, keep_open=True) as f:
-            for _ in range(1, nt):
-                data = []
-                for _ in range(n_items):
-                    d = np.random.random((1, n_elements))
-                    data.append(d)
-                f.append(data)
+    with dfs.write(outfilename, data=ds, dt=3600, keep_open=True) as f:
+        for _ in range(1, nt):
+            data = []
+            for _ in range(n_items):
+                d = np.random.random((1, n_elements))
+                data.append(d)
+            f.append(data)
 
     dfsu = mikeio.open(outfilename)
 
@@ -1029,3 +1030,30 @@ def test_interp_like_fm_dataset():
     dsi = ds.interp_like(geometry)
     assert isinstance(dsi, Dataset)
     assert isinstance(dsi.geometry, GeometryFM)
+
+def test_write_header(tmpdir):
+    meshfilename = "tests/testdata/north_sea_2.mesh"
+    outfilename = os.path.join(tmpdir, "NS_write_header.dfsu")
+    dfs = mikeio.Dfsu(meshfilename)
+    n_elements = dfs.n_elements
+    nt = 3
+    n_items = 2
+    items = [ItemInfo(f"Item {i+1}") for i in range(n_items)]
+    time0 = datetime(2021,1,1)
+    with dfs.write_header(outfilename, items=items, start_time=time0, dt=3600) as f:
+        for _ in range(nt):
+            data = []
+            for _ in range(n_items):
+                d = np.random.random((1, n_elements))  # 2d
+                data.append(d)
+                f.append(data)
+
+    # append also works for data without time axis
+    outfilename = os.path.join(tmpdir, "NS_write_header2.dfsu")
+    with dfs.write_header(outfilename, items=items, start_time=time0, dt=3600) as f:
+        for _ in range(nt):
+            data = []
+            for _ in range(n_items):
+                d = np.random.random((n_elements))  # 1d
+                data.append(d)
+                f.append(data)
\ No newline at end of file